Final report of the
Joint WCRP/SCOR Working Groupon Air-Sea Fluxes(SCOR Working Group 110)

INTERCOMPARISON AND VALIDATION OF
OCEAN-ATMOSPHERE ENERGY FLUXFIELDS
byMembers of the WGASF

Edited by P.K.Taylor

Section A – Executive Summary,and Chapters 1 to 9

June 2000

Report of the Working Group on Air Sea Fluxes

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TABLE OF CONTENTSPREFACE.......................................................................................................................................................... vii
EXECUTIVE SUMMARY ............................................................................................................................... ix
1 INTRODUCTION............................................................................................................................................1
1.1 The Surface Flux Field Problem....................................................................................................1
1.2 The Joint WCRP/SCOR Working Group on Air Sea Fluxes......................................................1
1.2.1 Creation of the Working Group...................................................................................1
1.2.2 The remit of the Working Group.................................................................................2
1.2.3 The Working Group's Mode of Working....................................................................3
1.3 The variables to be considered ......................................................................................................3
1.4 The flux products assessed ............................................................................................................4
1.5 Layout of the report........................................................................................................................4
2 THE REQUIREMENTS FOR SURFACE FLUX DATA SETS..................................................................7
2.1 Introduction ....................................................................................................................................7
2.2 NWP systems—assimilation and forecasting ...............................................................................7
2.2.1 Introduction ..................................................................................................................7
2.2.2 The needs of data assimilation systems for flux fields...............................................8
2.2.3 The needs of atmospheric forecast models for flux fields........................................112.2.4 Regional observing / nowcasting / forecasting systems ...........................................12
2.3 Oceanic General Circulation Models (OGCM's).......................................................................132.3.1 Introduction ................................................................................................................132.3.2 Parameterisation of the thermal feedback .................................................................142.3.3 Salinity Forcing..........................................................................................................15
2.3.4 Geophysical consistency of the ocean forcing function ...........................................16
2.3.5 Forcing fields..............................................................................................................16
2.3.6 Discussion ..................................................................................................................172.3.7 Conclusions ................................................................................................................18
2.4 Wave Models................................................................................................................................18
2.5 Sea Ice in Ocean Models and Sea-Ice Modelling .......................................................................19
2.6 Air-Sea Flux Fields and Ocean Data Assimilation.....................................................................20
2.6.1 Introduction ................................................................................................................20
2.6.2 Ocean Data Assimilation ...........................................................................................20
2.6.3 Example of Ocean Assimilation requirements - GODAE........................................21
2.7 Evaluation of Climate Models.....................................................................................................21
2.8 Climate change studies ................................................................................................................25
2.9 Conclusions ..................................................................................................................................27
3 SPACE-TIME VARIABILITY OF THE FLUXES ....................................................................................29
3.1 Introduction ..................................................................................................................................29
3.2 Synoptic and mesoscale processes ..............................................................................................29
3.2.1 Mid to High latitude variability.................................................................................29
3.2.2 Variability in the tropics ............................................................................................31
3.3 Climate variability........................................................................................................................31
3.3.1 Introduction ................................................................................................................31
3.3.2 Detection of climate trends........................................................................................32
3.3.3 Interannual to decadal scale variability.....................................................................33
3.4 Summary.......................................................................................................................................36
4 DATA SOURCES FOR FLUXES AND RELATED VARIABLES..........................................................37
4.1 Introduction ..................................................................................................................................37
4.2 in situ data sources .......................................................................................................................37
4.2.1 Voluntary Observing Ships........................................................................................37
4.2.2 Ocean Weather Ships.................................................................................................39
4.2.3 Buoys..........................................................................................................................40
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4.2.4 Platforms, Rigs, etc ....................................................................................................41
4.3 Satellite Data Sources ..................................................................................................................41
4.3.1 Introduction ................................................................................................................41
4.3.2 Non geostationary satellites.......................................................................................424.3.3 Geostationary satellites ..............................................................................................45
4.4 NWP and Reanalyses...................................................................................................................45
4.4.1 Introduction ................................................................................................................45
4.4.2 Fluxes from operational data assimilation ................................................................46
4.4.3 Fluxes from reanalyses ..............................................................................................48
4.5 Residual methods (TOA + model divergences)..........................................................................54
4.6 Ocean Data assimilation ..............................................................................................................54
4.7 Data Sources for Fluxes and Flux-Related Variables over Sea Ice............................................54

5 DIRECT FLUX OBSERVATIONS - STATE OF THE ART...................................55
5.1 Introduction ..................................................................................................................................55
5.2 Radiative fluxes............................................................................................................................55
5.2.1 Introduction .................................................................................................................55
5.2.2 Shortwave radiation ...................................................................................................56
5.2.3 Longwave radiation. ..................................................................................................59
5.3 Turbulent fluxes ...........................................................................................................................61
5.3.1 The Eddy correlation method ....................................................................................61
5.3.2 The inertial dissipation method .................................................................................62
5.4 Precipitation Flux .........................................................................................................................64
5.4.1 Introduction ................................................................................................................64
5.4.2 in situ rainfall using Funnel raingauges. ...................................................................65
5.4.3 Other methods of in situ measurement......................................................................66

6 PARAMETRISATION OF RADIATIVE FLUXES ................................................69
6.1 Introduction ..................................................................................................................................69
6.2 Radiative fluxes from ship data ...................................................................................................69
6.2.1 Parameterisation of SW flux......................................................................................69
6.2.2 Parameterisation of LW flux .....................................................................................71
6.3 Radiative Transfer Models - RTM’s ...........................................................................................71
6.3.1 Longwave radiative flux (LW) ..................................................................................71
6.3.2 Shortwave radiative flux (SW)..................................................................................72
6.3.3 COARE atmospheric soundings................................................................................74
6.4 Radiative Fluxes by Remote Sensing..........................................................................................75
6.4.1 SW and LW fluxes from TOA radiances..................................................................75
6.4.2 Longwave Net Flux from a combination of AVHRR and SSM/I............................76
6.5 Surface Albedo.............................................................................................................................77
6.5.1 Ocean Surface Albedo ...............................................................................................77
6.5.2 Albedo over sea ice....................................................................................................78
6.6 Summary - Parameterisation of Radiative fluxes .......................................................................78
7 PARAMETERISATION OF TURBULENT FLUXES ...............................................................................79
7.1 Introduction ..................................................................................................................................79
7.2 Example of traditional "Bulk Aerodynamic" formulae ..............................................................79
7.3 Determining the Transfer Coefficients........................................................................................81
7.3.1 Traditional bulk formulae ..........................................................................................81
7.3.2 New bulk formulae.....................................................................................................83
7.4 Bulk formulae for light to moderate wind conditions.................................................................89
7.4.1 The TOGA COARE algorithm..................................................................................89
7.4.2 Other Algorithms .......................................................................................................92
7.5 Transfers in moderate and high winds .........................................................................................99
7.6 Summary of bulk algorithms ........................................................................................................99
8 RANDOM AND SAMPLING ERRORS IN FLUX FIELDS. ................................................................. 103
8.1 Introduction ............................................................................................................................... 103
8.2 Random errors and sampling density ....................................................................................... 103

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8.2.1 Introduction ............................................................................................................. 103
8.2.2 Errors and Variability of Monthly Mean Fields .................................................... 104
8.3 Systematic Errors ...................................................................................................................... 109
8.3.1 Introduction ............................................................................................................. 109
8.3.2 Example of a parameterisation error ...................................................................... 109
8.3.3 Determining errors in VOS reports ........................................................................ 109
8.3.4 Method of averaging fluxes .................................................................................... 110
8.3.5 Further discussion .................................................................................................... 111
8.4 Combining Errors...................................................................................................................... 111
8.5 Mapping errors .......................................................................................................................... 112
8.5.1 Introduction ............................................................................................................. 112
8.5.2 Successive correction.............................................................................................. 112
8.5.3 Kriging of monthly means and error estimation .................................................... 113
8.6 Sampling problems for satellite-derived quantities ................................................................ 118
8.6.1 Introduction ............................................................................................................. 118
8.6.2 Sampling errors for precipitation............................................................................ 118
8.6.3 Scatterometer Sampling Errors............................................................................... 119
9 METHODS OF EVALUATING FLUXES AND FLUX RELATED PRODUCTS................................. 121
9.1 Introduction ................................................................................................................................ 121
9.2 Sources of reference data........................................................................................................... 121
9.2.1 The accuracy of research quality measurements: experience from TOGACOARE 121
9.2.2 High quality buoy data............................................................................................. 123
9.2.3 High-Quality Automated Data from ships .............................................................. 125
9.2.4 Data for verification of satellite products................................................................ 126
9.3 Intercomparison of field products.............................................................................................. 127
9.3.1 Comparison to Specialised Products ....................................................................... 127
9.3.2 Comparison of Variability patterns in different flux products ............................... 127
9.4 Integral constraints..................................................................................................................... 129
9.4.1 The global heat and water balance .......................................................................... 129
9.4.2 Meridional heat and water transport........................................................................ 129
9.4.3 Enclosed Seas Used for Flux Validations ............................................................... 133
9.4.4 Ocean Mixed Layer Budgets ................................................................................... 136
10 BASIC VARIABLES - STATE OF THE ART....................................................................................... 137
10.1 Introduction ............................................................................................................................. 137
10.2 Sea Surface Temperature (SST) ............................................................................................. 137
10.2.1 Introduction - Definition of Sea Surface temperature ......................................... 137
10.2.2 in situ SST measurements..................................................................................... 138
10.2.3 Remote sensing of SST......................................................................................... 140
10.2.4 SST Analyses ........................................................................................................ 141
10.3 Sea Surface Salinity (SSS)...................................................................................................... 141
10.3.1 Introduction ........................................................................................................... 141
10.3.2 In Situ measurement of SSS ................................................................................. 142
10.3.3 Remote sensing of SSS ......................................................................................... 142
10.4 Air temperature ....................................................................................................................... 144
10.4.1 in situ air temperature measurement .................................................................... 144
10.4.2 Surface air temperature from the Satellite SSM/I sensor .................................... 147
10.5 Humidity................................................................................................................................. 148
10.5.1 in situ humidity measurement .............................................................................. 148
10.5.2 Remote sensing of near surface humidity ............................................................ 149
10.6 Wind ........................................................................................................................................ 151
10.6.1 Introduction ............................................................................................................ 151
10.6.2 in situ wind data .................................................................................................... 152
10.6.3 Remote sensing of surface wind........................................................................... 155
10.7 Waves ...................................................................................................................................... 158
10.8 Cloudiness ............................................................................................................................... 160
10.8.1 Introduction ............................................................................................................ 160
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10.8.2 in situ cloud data .................................................................................................... 160
10.8.3 Remote sensing of cloud data................................................................................ 160
10.8.4 Combined products ................................................................................................ 160
10.9 Precipitation ............................................................................................................................ 161
10.9.1 Surface-based precipitation data........................................................................... 161
10.9.2 Precipitation by ocean freshwater budgets........................................................... 163
10.9.3 Satellite Remote sensing of precipitation............................................................. 163
10.9.4 Intercomparison projects ..................................................................................... 164
10.10 River inflow........................................................................................................................... 166
10.11 Sea ice.................................................................................................................................... 168
10.11.1 Sea Ice amount .................................................................................................... 168
10.11.2 Flux-related variables over ice ........................................................................... 168
10.12 COADS Monthly Summary Trimmed Groups (MSTG) variables. .................................... 169
11. EVALUATION OF FLUX PRODUCTS ............................................................................................... 171
11.1 Introduction ............................................................................................................................. 171
11.2 Flux products based on in situ data ........................................................................................ 171
11.2.1 Introduction ........................................................................................................... 171
11.2.2 pre-1990 flux studies............................................................................................. 171
11.2.3 UWM/COADS...................................................................................................... 174
11.2.4 SOC ....................................................................................................................... 175
11.2.5 IFM ........................................................................................................................ 179
11.2.6 Comparative assessment....................................................................................... 180
11.3 Flux products based on Satellite data..................................................................................... 185
11.3.1 Introduction ........................................................................................................... 185
11.3.2 Evaluation of HOAPS........................................................................................... 185
11.3.3 US Satellite Radiation products........................................................................... 196
11.3.4 Japan Surface Shortwave and Longwave Products ............................................ 200
11.3.5 Residual method................................................................................................... 203
11.4 Evaluation of the reanalysis products..................................................................................... 204
11.4.1 Introduction ........................................................................................................... 204
11.4.2 Comparisons to in-situ observations .................................................................... 204
11.4.3 Polar regions.......................................................................................................... 207
11.4.4 Ocean Surface Energy Balance ............................................................................ 207
11.4.5 Evaporation ........................................................................................................... 208
11.4.6 Sensible heat flux .................................................................................................. 215
11.4.7 Net short-wave ...................................................................................................... 218
11.4.8 Net long-wave ....................................................................................................... 225
11.4.9 Net heat flux.......................................................................................................... 230
11.4.10 Evaluating the NCEP1 reanalysis by forcing an ocean model.......................... 234
11.4.11 Winds................................................................................................................... 234
11.4.12 Zonal surface stress............................................................................................. 237
11.4.13 Meridional surface stress .................................................................................... 241
11.4.14 Precipitation ........................................................................................................ 244
11.4.15 Conclusions ......................................................................................................... 251
11.5 Flux Products from Operational NWP model's ..................................................................... 253
11.6 Evaluation of fluxes in sea-ice covered areas ........................................................................ 253
11.6.1 Cloud and surface radiative flux data sets in the Arctic ...................................... 253
11.6.2 Precipitation in the Arctic..................................................................................... 255
11.6.3 Need for more assessment .................................................................................... 256
11.7 Evaluation of Waves ............................................................................................................... 256
11.7.1 Visual wave observations ...................................................................................... 256
11.7.2 Altimeter wave data ............................................................................................... 257
11.8 Summary ................................................................................................................................. 258
12. CONCLUSIONS....................................................................................................................................... 265
12.1. Introduction ............................................................................................................................. 265
12.2. Summary ................................................................................................................................. 265
12.3. Meeting the needs ................................................................................................................... 267
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12.3.1. Budget studies ....................................................................................................... 267
12.3.2. Climate variability studies .................................................................................... 268
12.3.3. Climate models...................................................................................................... 269
12.3.4. Ocean general circulation models ........................................................................ 269
12.3.5. Wave modelling .................................................................................................... 270
12.3.6. Sea-ice modelling.................................................................................................. 270
12.3.7. NWP and data assimilation................................................................................... 270
12.4. Perspectives............................................................................................................................. 272
12.5. Specific Recommendations .................................................................................................... 273
ACKNOWLEDGEMENTS............................................................................................................................ 274
REFERENCES................................................................................................................................................ 275
APPENDIX A. MEMBERS OF THE WORKING GROUP ON AIR-SEA FLUXES............................... 303
APPENDIX B. CATALOGUE OF FLUX DATA SETS AND FLUX RELATED PRODUCTS............. 305
B.1 Introduction................................................................................................................................ 305
B.2 Accessing the Catalogue ........................................................................................................... 305
B.3 Preliminary Initial Contents List............................................................................................... 305
B.4 Information to be submitted...................................................................................................... 305
APPENDIX C. ACRONYMS AND ABBREVIATIONS ........................................................................... 307

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Preface

PREFACEThis document is the final report of the Working Group on Air-Sea Fluxes (WGASF)
which was jointly sponsored by the World Climate Research Programme (WCRP) and theScientific Committee on Ocean Research (SCOR). For the WCRP, because the question of
surface fluxes is considered central to a number of its scientific projects, the WGASF reported
directly to the Joint Scientific Committee. For SCOR, the WGASF was SCOR Working Group
110: "Intercomparison and Validation of Ocean-Atmosphere Energy Flux Fields". This joint
sponsorship has impacted the work of the WGASF in a number of ways. On a logistic level, it
resulted in more funding being available for meetings than would otherwise have been the case,which was clearly advantageous. However there have been more fundamental effects. In
particular, although uniform terms of reference were agreed by WCRP and SCOR, theexpectation with regard to the Working Group outcome was somewhat different. Typically a
SCOR working group consists of experts in a particular field, chosen to reasonably represent theinternational membership of SCOR. They are tasked with reviewing a subject in some depth
and producing a monograph which may be formally published in book form; often this may
take several years. The requirements of the WCRP were somewhat different since the resultsof the WGASF were expected to have a direct impact on future WCRP projects. Thus the
WCRP requirement was for an expert group capable of rapidly concluding its deliberations andof producing one or more reports within a restricted time scale. Fulfilling this last requirement
was particularly difficult since the inception of the WGASF coincided with the first
dissemination of the results from the "reanalysis" projects at the European Centre for Medium
Range Weather Forecasting (ECMWF) and the US National Centers for Environmental
Prediction (NCEP); projects which were clearly of major importance with regard to theestimation of air-sea fluxes but which would take time to evaluate. Indeed a first detailed global
intercomparison of the various analyses is presented in this report.
It was clear even from the first meeting of the WGASF that, both to justify any
evaluation of flux products and also to provide a suitable review for SCOR, it would benecessary first to summarise the requirements for surface fluxes, the state of the art with regard
to surface flux estimation, and the methods available for flux validation. The first half of thereport deals with these issues. Two later chapters deal with the evaluation of the basic
meteorological variables (temperature, wind etc.) and of a number of available flux products.
All the chapters have been assembled from many separate sections written by different Working
Group members. Inevitably the degree of detail varies considerably; in some cases acomprehensive review has been undertaken, in other cases a point has been made by quoting
typical examples. Often Working Group members have relied heavily on their own work justifiable because it was directly relevant, but also very necessary because there was not thetime or resources for a more comprehensive review. Thus this report should not be considered
an exhaustive review on air-sea flux research. However the WGASF does believe that thereport represents a reasonably balanced assessment of the present state of air sea flux
determination.
The conclusions with regard to the relative merits of different flux products are lessprecise than might have been hoped or originally anticipated. However it must be emphasisedthat this uncertainty does not stem from a lack of evaluation effort (although more work would
clearly be desirable). The detailed discussions presented in the earlier chapters of the reportemphasise that there are a number of issues where our knowledge remains inadequate. These
include, for example, the question of the correct transfer coefficients for the turbulent fluxes,
and the reconciliation of measurements and radiative transfer modelling for the radiation
balance. Many of these issues have been the subject of research for many years; however there
has recently been significant progress both in measurement techniques and in our
understanding. It is likely that, within a few more years, the absolute values of the surface
fluxes over the earth's oceans will be much better known. It is hoped that this report represents
a positive step toward that aim.

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Executive Summaryix.

EXECUTIVE SUMMARY
The Working GroupThe main aims of the Joint JSC/SCOR Working Group on Air Sea Fluxes1 were toreview the requirements of different scientific disciplines for surface flux data sets, to catalogue
available surface flux data and flux-related data sets, and to review the strengths andweaknesses of each. The three meetings of the Working Group were (1) NOAA Headquarters(Silver Spring, Maryland, USA), 22nd to 25th October 1997; (2) KNMI (De Bilt, The
Netherlands) 14th to 17th April 1999; (3) SOC (Southampton, UK) 6th to 10th December,1999. The Working Group's findings are contained in this report, which has been prepared onan accelerated schedule at the request of the JSC.
The Fluxes Considered
The Fluxes considered by the working group were the transfers of heat, water andmomentum between the sea and the air. The flux products considered were those which: werereadily available to all scientists; were a gridded, derived surface flux or flux related field;
covered major ocean basin scales and upwards; and, were adequately documented. The
Working Group has assembled a catalogue of flux products which meet these criteria. It will beavailable on the Internet2 and described in an appendix to this report. Given the limited timeand resources, it only proved practicable to evaluate a subset of the more recently available flux
products. The Working Group also summarised the State-of-the-Art with regard to the basic
meteorological variables (used in flux calculation), and other important components of theclimate system such as Waves and Sea Ice.
Requirements and data sources
The Working Group has reviewed the requirements for flux fields and thecharacteristics of the various data sources available for flux estimation. The latter include insitu data from ships, buoys, rigs, etc.; instruments on satellites either in polar (or otherprecessing) orbits or geostationary; and models, run either in operational Numerical Weather
Prediction (NWP) or reanalysis modes. The requirements for surface flux fields are many anddiverse. However the Working Groups review suggests that there are four main types ofrequirement.
One class of requirement is the need for surface flux fields on high time and spaceresolution; typically 3 hours and 50 km. Such data are needed, for example, for forcing ocean
general circulation models, for wave modelling and forecasting, and for regional weather
nowcasting and prediction. Such high space/time resolution is only possible at present using
models, however in future, high resolution satellite products will become available. The
quality of the wind fields is particularly important for these applications and operational
satellite-borne scatterometers are expected to make an important contribution.
A second class of requirement is for flux fields on longer space and time scales but withhigh absolute accuracy, typically within a few W/m2. Customers for these data include climate
and sea-ice modellers. At present such needs are difficult to satisfy because of the problems ofquantifying systematic errors in the flux data. Only by comparing flux values obtained using
different data sources and estimation methods are these stringent accuracy requirements likely
to be obtained.
1(SCOR Working Group 110: Intercomparison and Validation of Ocean-Atmosphere Energy
Flux Fields)

2

available from the WG web site: http://www-pcmdi.llnl.gov/airseawg/ (mirrored at
http://www.lmd.jussieu.fr/pcmdi-mirror/airseawg/ and at
http://www.bom.gov.au/bmrc/clch/pcmdi-mirror/airseawg/ )Report of the Working Group on Air Sea Fluxes

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Executive Summaryx.

Climate variability studies define a third class of requirement. Again high absoluteaccuracy is desirable, however consistency and continuity of the data over a suitable time
period (and spatial domain) is the vital need. The longest time data set available is from themerchant ship observations (as assembled in the Comprehensive Ocean-Atmosphere Data Set,
COADS). However the ship data suffers from poor sampling in many regions and timedependent biases due to changing ship characteristics and observational technique.
Comparison with the reanalyses suggests that the COADS data can give useful information oninterannual variability of monthly mean fluxes in the mid-latitude North Pacific and North
Atlantic, and in parts of the South Atlantic; over much of the tropics and the Southern
Hemisphere, north of 40S, the COADS data can only define a long-term monthly mean
climatology. However, the reanalyses also suffer from time dependent biases because of theassimilation of data from the evolving observational system. Thus, whether using models or
observations, the detection of long-term trends in the surface fluxes is difficult if not impossible.
Satellite data has now been available for a significant and lengthening time period. Such data
has the potential to provide a consistent time record but only if great care is taken to intercalibrate sensors on successive satellites.
A final flux requirement is for high quality verification data. For example, NWP
models need independent estimates of the basic meteorological variables and the fluxes forverification of the model physics. Such estimates must be associated with a realistic error
assessment and specification of the true resolution of the data. Similarly Ocean General
Circulation Model development would be aided by air-sea interaction experiments withmeasurements of the fluxes in both atmosphere and ocean, with ocean mixed layer
measurements sufficient to allow budget closure. Such high quality measurements are not easy.
The "flux buoy" systems are an example of instrumentation capable of providing verification
data for both models and satellite - borne instruments.
Flux Variability
For many applications it is important that a flux product contains accurate informationon the variability of the fluxes. In some ways the determination of flux variability is easier thanestimating mean flux values; systematic errors are of less importance, and the main spatial
scales of longer term variability tend to be large. Numerical weather models tend to performbetter with regard to determining the variability than they do in determining the mean; probably
because they are targeted at forecasting the weather - the day to day variations in climate.
However despite these considerations, and the undoubted importance of surface flux
variability, the Working Group considered that a detailed assessment of variability in the flux
products could not be accomplished at this time. The report contains a brief discussion of flux
variability to set the context for the flux field evaluations. It was noted that most previous
studies had concentrated on the variability of basic variables, such as sea surface temperature(SST), rather than the fluxes themselves, and that further studies focused on the fluxes weredesirable. The Working Group concluded that a thorough assessment of the variability ofavailable flux products must remain an important priority for further studies, perhaps by a future
working group.
Direct Flux measurement
Direct measurements of the air-sea fluxes are too few to contribute directly to thecalculation of large scale flux fields. Rather they are important for developing, calibrating, andverifying the parameterisation formula used to estimate the fluxes from the basic variables.
Thus the accuracy of the direct flux determinations represents an accuracy limit for the indirect
estimates. The methods of direct measurement of the radiative fluxes, turbulent fluxes, andprecipitation were reviewed.
Accurate measurement of surface radiative fluxes is not easy, and with regard to the
instrumentation needed there are contrasting views. Experts in radiation measurement assert

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that only sophisticated instruments and procedures, as defined by the Baseline Surface
Radiation Network standards, will provide adequate accuracy. However such measurements are
difficult to implement at sea and will be restricted to very few sites. In contrast, scientists
involved in air-sea interaction studies believe that, by avoiding known pitfalls, consistent
results can be obtained from simpler instrumentation. Much depends on the accuracy and time
resolution sought. The simpler instrumentation should be adequate to highlight the biases in theradiative fluxes in many models (presently a few 10's Wm-2 in some regions).
Estimation of the turbulent fluxes using fast response instrumentation mounted on
research ships or buoys is becoming more common. The techniques used in the eddy
correlation method and the less direct, but more easily implemented, inertial dissipation method
are summarised. When implemented on a ship both methods are prone to errors but of differing
nature. Thus in future it is desirable that both methods be used to fulfil an important
requirement, the determination of the transfer coefficients for flux parameterisation (seebelow).
Rainfall, particularly that from tropical convection, is highly sporadic both in time andspace. For most applications area averaged values are required. These can be obtained by shipor aircraft mounted radars on the local scale, or from satellites in the longer term and on larger
scales. However the sensors and retrieval algorithms need to be verified by reliable groundtruth measurements. Accurate measurement of rainfall at sea presents difficulties in two
respects; there are measurement (instrumental) problems, and sampling problems. New raingauges have been developed for high wind conditions but air-flow distortion by the ship remains
a problem. Even during intensive measurement campaigns the spatial and temporal sampling
may be inadequate. Results of different satellite algorithm intercomparison projects suggest that
presently available in situ measurements and radar estimates are not representative enough toserve as a verification data source.
Parameterisation of the fluxes
Radiative fluxes
The parameterisation formulae for obtaining the net surface shortwave and longwave
fluxes from ship observations are relatively crude, relying on the estimate of cloud amount tocharacterise the effects of cloud on the fluxes. Satellite based estimates use measurements ofthe top-of-the-atmosphere radiation, and radiative transfer models (RTM's) to estimate thesurface value. For clear-sky conditions, the surface LW values from a sophisticated RTM within situ radiosonde data can be used to check the calibration of pyrgeometers. However undercloudy conditions longwave RTM's are limited by the information available on both cloud baseheight and cloud emissivity. This limits the reliability of satellite estimates of surface
longwave; new techniques are being developed but still need evaluation. Knowledge ofatmospheric scattering is essential for shortwave RTM's under both clear and cloudy conditions.
Uncertainty in the diffuse component may cause RTM's to over estimate the surface shortwave
compared to measurements, possibly by a few percent. Despite this, satellite estimates ofshortwave radiation are more reliable than for longwave. Atmospheric models (NWP andreanalyses) use simplified RTM schemes for computational efficiency. However at present thelimiting factor in most models is the representation of clouds and their radiative effects. In
particular, low level stratiform clouds are often poorly modelled.
Turbulent fluxes
The parameterisations for the turbulent fluxes are based on values of basic
meteorological variables such as wind speed, temperatures, and humidity. Since these "bulkformulae" are used for flux estimation from not only in situ data, but also satellites, and mostmodels, they were reviewed at some length by the Working Group. Despite years of research
there is still uncertainty with regard to the behaviour of the various transfer coefficients, inparticular for sensible and latent heat at wind speeds over 10 m/s. For this reason, some newer
parameterisations use wind stress estimates and surface renewal theory to predict the heat
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fluxes. However, the variation of the drag coefficient with sea state remains controversial,particularly in sudden storms, or in light wind, swell dominated conditions. The newer
algorithms also incorporate treatments of other physical processes, particularly light windphenomena such as the diurnal warm surface layer and wind gustiness.
Error Estimation
An important requirement of flux products, which is rarely satisfied, is the realistic
estimate of accuracy. The error estimates should consider as many contributing factors as
possible, both random and systematic. Representative error estimates may be difficult todetermine. The report reviews some theoretical approaches, gives practical examples ofquantifying random, systematic, and sampling errors, and shows that it is possible to estimate
the cumulative errors.
Many applications need gridded flux fields that are complete both in space, and oversome significant time period. The non-uniform sampling of in-situ and remotely-sensed fluxes
necessitates the use of an analysis method to interpolate and, sometimes, extrapolate the data.
Such techniques alter the error budgets of the flux fields. For example, depending on the choiceof method and averaging period, sampling errors in remotely-sensed data may dominate thesystematic biases. The advantages and disadvantages of two examples of mapping techniques
were discussed. The successive correction technique is computationally simple and has been
widely used. However it creates fields with spatially varying resolution, and spreads
information so as to degrade the flux values in well sampled areas. In addition, error estimates
are difficult to determine. Objective analysis techniques which take proper consideration ofobservational and sampling errors, and are capable of producing a posteriori error estimates,
should be employed instead of the sucessive correction method. In particular, state-of-the-art
variational techniques developed in the context of atmospheric and oceanic data assimilation
should be applied to the analysis of surface marine fluxes.
Evaluation of available products
Methods of evaluation
The methods of evaluation for the basic variables and the flux fields include
comparisons to reference data sets (such as direct flux measurements or those from high-quality
sources) and intercomparisons between products. For the fluxes it is also possible touse integral constraints such as the global heat and water balance, oceanic transports, andenclosed basin budgets, as well as comparisons with other indirect flux estimates.
Basic Meteorological Variables
It is important to know the accuracy of our estimates of the basic meteorologicalvariables. Errors in these variables affect the accuracy of the surface flux values. Using thebasic variables to verify model and satellite derived estimates avoids uncertainty due to the useof different flux parameterisation formula. As a rough guide, achieving 10 Wm-2 accuracy in aheat flux estimate requires knowledge of wind speed to a few tenths m/s, air and sea
temperatures to about 0.2K, humidity to about 0.3 g/kg; these requirements vary regionally
being generally more stringent in the tropics.
For most of the basic variables, the Working Group reviewed the accuracy of highquality in situ measurements, standard in situ measurements, and satellite based estimates. The
high quality measurements, as might be obtained from research experiments and "flux buoys"
are of value for verification and for parameterisation development. The standard in situ data,
for example from Voluntary Observing Ships (VOS), represent our only near surface
measurement of many quantities for use both in flux estimation and model assimilation
schemes. As instrumentation and retrieval algorithms improve, the satellite remote sensed data
are increasingly being used to define the spatial and temporal variations of a given variablewhile the in situ data are used for verifying the retrievals.

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The ocean affects the surface fluxes through the sea surface temperature (SST). In situ
observations of "SST" are often made at a few metres depth; the SST difference due to the cool
ocean surface skin can be allowed for, but significant errors may occur if a near surface diurnal
thermocline is present. Satellite estimates are limited by the effects of cloud and biases due tocloud contamination (particularly at night) and the effects of aerosols. The better SST analyses
use combinations of satellite and in situ data. Typical RMS differences between analyses inmost regions are a few tenths K, greater than that in the Southern Ocean, in coastal regions, andin sea ice areas.
Variations of sea surface salinity (SSS) are not significant for air-sea flux calculation(although its mean effect must be allowed for in latent heat flux calculation). However SSS isimportant for flux verification and for the freshwater forcing of ocean general circulation
models. In situ salinity data remain relatively few but will increase significantly due to projects
such as ARGO. The potential for, and difficulty of, salinity measurement from space wasreviewed. Useful data may be obtained in regions of large salinity gradients, however the 0.1
psu accuracy needed by ocean models is unlikely to be achieved by remote sensing in the near
future.
Near surface air temperature is not easy to measure accurately in the marine
environment. Biases due to radiative heating of the sensor (or its surroundings), sea spray, etc.,
are common. VOS data tends to be biased high, probably by a few tenths K on average, but by
several degrees on occasion. New techniques for estimating air temperature from satellite data
have been suggested; further evaluation is needed. Fortunately, except in high latitudes, thesensible heat flux is a minor part of the heat balance. However air temperature is also needed in
all regions for stability corrections during latent heat flux calculation.
The latent heat flux depends on the humidity of the near surface air. Humidity
measurement on ships is in some ways easier than temperature determination. However it islikely that VOS data are biased high, particularly where fixed thermometer screens are used.
Methods of determining near surface humidity data from satellites have improved significantly
and useful accuracy is obtained in many regions. However developing an algorithm which
performs well both within and outside of tropical regions remains a problem.
There is no accurate absolute standard for near surface wind measurement over the open
ocean. Anemometer-based and visual estimates from ships, and buoy measurements, all are
subject to systematic and random errors. "Beaufort scale" values for the visually estimatedwinds have been calculated which, in the mean, result in good agreement between visual andanemometer winds. Importantly, the main reasons for the differences from previous Beaufort
Scales are now understood. Buoy winds may be biased low in high sea state conditions.
Satellite scatterometer winds are considered significantly superior to ship winds but thealgorithms were developed using buoy data so biases may still remain. Passive microwave
derived wind speeds are useful for heat flux estimation. They have the potential to provide
good sampling and an increasingly long time series.
Waves are an important climatic variable in their own right and may, to some still
uncertain extent, modulate the surface fluxes. The amount of wave data from buoys isincreasing but the spatial distribution is poor. Wave data from ship reports is generally
considered to be of poor quality; however it has been used to construct wave climatologies
which are still much in use. One problem is the separate reporting of, but unclear distinction
between, sea and swell. Satellite altimeters provide verified global estimates of wave height;
algorithms for scatterometer wave estimation are still being developed. Wave modellers believe
that, given an accurate wind field of high temporal and spatial resolution ( say 3 hours and 50km), then a good quality wave field can be calculated.
Cloud amount is used in some radiative flux parameterisations and is also important for
the verification of NWP and climate models. Sources of cloud data reviewed included shipreports and the International Cloud Climatology Project (ISCCP). Precipitation also may be
estimated from ship weather reports; however variable satellite based estimates offer better
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xiv.

future potential. The Global Precipitation Climatology Project (GPCP) and the CPC Merged
Analysis of Precipitation (CMAP) have produced precipitation climatologies which merge insitu and satellite estimates.
River inflow must not be neglected as a component of the ocean freshwater budget.
Data are available through the International Hydrography Program (IHP) which published a
Global River Discharge Catalogue, and from the Global Runoff Data Centre (GRDC).
Sea ice is an important climate variable which significantly modifies the surface fluxes
and is also a component of the ocean fresh water budget. At present, sea-ice models canreproduce a realistic annual cycle of the global sea-ice cover. Probably this is despite large but
opposite errors in the fluxes over sea-ice. Now that multidecadal satellite and submarine
records of ice extent and ice thickness are available, the challenge is to understand interannual
variability. This will require a higher level of accuracy in estimates of the radiative forcing of
the ice surface and data on the interannual variability of snowfall. The errors in reanalysis andsatellite-derived surface flux fields must be better characterised and then reduced by morethorough comparisons amongst themselves and with in situ observations. Boundary layer
formulations need to be examined for their applicability in predominantly stable polar
conditions.
Flux field products
Newer products based on in situ data3 have benefited from the valuable new data sets
constructed by the COADS project. For wind stress the newer climatologies are definitely to bepreferred since previous climatologies, such as Hellerman and Rosenstein (1983) or Oberhuber(1988), over estimated the wind stress in most areas. However poor in situ sampling in thesouthern hemisphere, and particularly in the Southern Ocean (where the winds areunderestimated in almost all in situ climatologies), suggests that the lengthening satellite
derived wind data-set is a promising alternative. For the heat fluxes, our inability to balance
the long term mean global heat budget together with the uncertainty in the transfer coefficientvalues, remains a major hindrance to evaluating many of the flux products.
To illustrate the problem: the global heat balance of the SOC climatology was 30Wm-2;
almost exactly the same as the for the UWM product. This was despite the use of corrections
for observation biases in the in situ data. In a tuned version of UWM, shortwave radiation was
decreased (by about 10%) and latent heat flux increased (by about 14%) resulting in a balanced
budget. Previous flux studies have required similar adjustments, often to ensure that the heattransport in the ocean agreed with oceanographic estimates. The satellite estimates of net SW
radiation do not support reduction of the shortwave flux (although they in turn may be overestimated). However by adjusting the other fluxes in various ways a variety of balanced
budgets can be produced. As a result, the various estimates span the range of the values from
NWP models and reanalyses4. The overall conclusion is that for the global balance, and also
for zonal averages, the differences between the various flux products are within the uncertaintyof the observational estimates (with only one or two outlying exceptions).
Interestingly, almost all adjustments suggest that the latent heat flux must be increased,
toward the higher values for latent heat flux predicted by most models. Such an increase would
generally worsen the agreement between climatologies (such as UWM or SOC) and the highquality buoy data; however the latter also depend on transfer coefficients to determine thefluxes. Thus while comparisons of fluxes from ship observations and buoys suggest that3

Here we will refer to in situ climatologies produced at the University of WisconsinMilwaukee (UWM), the Southampton Oceanography Centre (SOC) and the Institut f r
Meereskunde, Kiel (IfM).

4

The reanalyses considered were the ECMWF 15 year reanalysis (ERA15), the NCEP/NCAR
reanalyses (NCEP1 and NCEP2) and the Goddard GEOS reanalysis (GEOS1).

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NCEP1, NCEP2 and ERA15 evaporation may be too large, we can not be certain. This also
reflects the uncertainty in our knowledge of the strength of the hydrological cycle. New satellite
based products may help through better estimates of precipitation (although there is a lack ofverification data) and global estimates of evaporation. With regard to the latter, the HOAPS
climatology5 suggests that in situ climatologies under-estimate evaporation in the Southern
Ocean. Further development of these satellite products is needed, for example, the HOAPS
latent heat flux is biased low in convective tropical regions.
Other significant differences exist between the different products on a regional level andin some cases the cause can be identified. For example, the reanalyses all appear to havesubstantial errors in surface radiation fields, reflecting problems both with regard to clouds andclear sky radiation. NCEP1 had errors in the downward shortwave and the albedo which tended
to cancel. A major shortcoming for ERA15, GEOS1 and NCEP2 appears to be a lack of lowlevel stratiform clouds over the eastern subtropical oceans. Satellite Surface Radiation Budget(SRB) short wave radiation appears to have more realistic patterns than the reanalyses, but may
have a positive bias. Even the estimates from crude in situ parameterisations are in better
agreement with SRB than are the models.
Choice of ProductConsidering the recent, global climatologies based on in situ data, UWM and SOC are
similar. The SOC climatology gives marginally better agreement with buoy measurements andmay be preferred for regional studies. However for a balanced global net heat flux (e.g. forforcing models) then the tuned UWM fluxes must be preferred; these also cover a longer time
period. More use should be made of the UWM sensitivity fields for producing new versions of
the constrained fluxes. In all cases the sampling density in any particular region must be
considered when using these products.
Covering the Atlantic Ocean only, the IFM climatology has used a better mapping
technique, has good error estimates, and implies a realistic meridional heat transport. The latteris due to the choice of radiation parameterisations and transfer coefficients which are notgenerally used for open ocean regions (but are within the bounds of uncertainty). Further
verification of this new product is desirable.
The NCEP1, NCEP2 and ERA15 reanalyses appear to have more realistic fluxes thanthe earlier GEOS1 reanalysis (the GEOS data assimilation system has since been substantiallydeveloped). At present the NCEP1 reanalysis covers the longest period and is the most widelydistributed and studied. It has the most realistic oceanic low-level stratiform clouds. NCEP2
corrected mistakes in NCEP1 and used improved short wave radiation and boundary layer
parameterisations. Its precipitation patterns, downward short wave radiation at the surface, andequatorial wind stress appear superior to NCEP1, but its sensible heat flux over the oceans islower than all other estimates. ERA15 is the highest resolution reanalysis now available. It
appears to produce the most realistic precipitation patterns of the four reanalyses and has more
realistic surface downward short wave radiation than NCEP1. Concerning month-to-monthvariability in surface fluxes, GEOS1 had, in general, the smallest variability, NCEP2 tends tohave the largest.
For most of the world ocean, satellite based products are capable of providing muchbetter time and space resolution, and more consistent accuracy, than fluxes based on in situ
observations. With passing years the restricted time period for which data is available isbecoming less of a disadvantage. The surface shortwave flux fields are superior to the model
estimates; and presumably also to in situ estimates. However on a regional basis the difference
between satellite and ship estimates is similar in magnitude to the difference between different
satellite fields. The satellite longwave estimates remain less reliable. Satellite scatterometerwind data has significantly less scatter than ship observations. Satellite estimates of the latent
5 Hamburg Ocean Atmosphere Parameters and fluxes from Satellite data.

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Executive Summaryxvi.

heat flux have improved substantially but problems still remain in some regions. Satellite
precipitation estimates have not been evaluated here other than to review the various
comparison projects that have been undertaken by others; lack of verification data is a major
problem. For the future, satellite products should exploit combinations of different sensors,preferably flown on a single platform (as for example, the TRMM mission). Combined
satellite and in situ data products have the potential to provide a data set which is independent
from the models.
In summary, all the various flux products that were reviewed in this report havedifferent advantages and disadvantages. However, it is also evident that the various flux
products are complementary rather than being in competition, and that the ensemble of air-sea
flux estimates that they represent cover the time and space scales that ocean science studies areinvestigating.
Specific Recommendations
The main conclusions from the Working Group's endeavours appear throughout the
body of the report, are summarised in the final Chapter, and have been described above. At its
final meeting, the Working Group also adopted the following specific recommendations:❍

Reanalyses should be performed every 5-10 years by more than one centre; adequate
resources should be provided to the reanalysis efforts to improve their surface fluxes, tocarry out and evaluate the reanalyses and to ensure that they are easily available to theentire scientific community. Surface fields should be output every 3 hours .

❍Evaluation of the surface fields and fluxes from global operational NWP systems will
benefit future reanalyses as well as provide critical guidance and product uncertainty
estimates to users of these flux products. The WGNE's plans to archive and evaluate thesurface fields and fluxes from a number of global NWP systems should therefore besupported.

❍

A network of high quality “flux reference platforms” (combination of long-term
moorings and ships) should be established to deliver highly accurate values of stress andall components of the air-sea heat fluxes for, inter alia, verification of surface fields andflux estimates from satellites and models, and the long term calibration of satellite
sensors.

❍

There is continuing need to compare and assess the quality of fluxes derived fromvarious sources, and to evaluate the parameterisations used. Encouragement should begiven to efforts to enhance the reliability of momentum, net heat and freshwater fluxes
by combining the best estimates from these various sources.

❍

Support should be provided for the continuing assembly of flux and flux-related data
sets (in particular Voluntary Observing Ship-based collections such as COADS andother historical data). Continued efforts are needed to remove non-stationary
observational biases in historical data. Basic meteorological variables should be
included as well as uncertainties, error estimates and adequate documentation for all
flux data sets. A catalogue of flux data sets should be maintained on the Internet.

❍

Research and field experiments are needed
to improve boundary layer
parameterisations and bulk formulae, especially in regions where our physicalunderstanding is poor. Adequate resources for complete analysis of the resulting data
are necessary to realise the full benefits of the field experiments.

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1.1 The Surface Flux Field problem
1.2 The Working Group

1 INTRODUCTION
1.1 The Surface Flux Field Problem
The extent to which the earth's climate may be changing, either due to the actions ofmankind or natural variability, is at present a major cause of societal concern. Numericalmodels of the earth's climate system are being used to quantify the possible effects of climatechange. A vital test of such models is to verify that the present day climate is correctly
simulated. An important aspect of that simulation is the transfer of energy between the ocean
and atmosphere, the air-sea fluxes of heat, water, and momentum. These fluxes represent keyprocesses in the earth's climate system; they establish links and feedbacks between its main
components, the ocean and the atmosphere.
In recent years there has been significant progress in our knowledge of the surface
fluxes. Air-sea interaction experiments have refined the formulae used for flux calculation, we
have better understanding of the errors in data and the corrections needed, methods ofobtaining fluxes from satellite data have been developed, and weather forecast models havebeen used to produce a consistent re-analysis of the past state of the atmosphere.
Unfortunately, despite these advances, the observational flux data remains limited and various
attempts to produce climatologies of the flux fields, and to describe their climatic variability,
differ in significant aspects. The major problem is that, while the magnitude of the flux
components is of order a few 100 W/m2, both the inter-annual variability and the mean heat
flux in any region is typically a few 10's W/m2, and the mean net heat flux must balance towithin a only few W/m2. This places stringent requirements on the accuracy of flux estimates
which we are still struggling to meet. For example Figure 1.1 shows recent estimates of theannual mean distribution of the net heat flux. Two examples of fluxes calculated from weather
forecast models, which have been used to reanalyse the past weather, are compared to two
climatological estimates based on ship weather observations ("COADS"). It is obvious thatthe fluxes calculated from the ship data have more areas where heat is going into the ocean
compared to the other estimates. Since unrealistically large ocean heating would result, thesefluxes cannot be correct and various adjustment schemes have been proposed. However,where comparisons with high quality buoy data are available, the ship data often show good
agreement - better than that which the models typically achieve. Thus the bias can not be
uniform but must vary both regionally and temporally. Similarly, while the heating andcooling calculated by the models is better balanced globally, in some regions large errors may
occur both in the flux components and the net heat flux.
Given this situation, the main aims of the Joint WCRP/SCOR Working Group on Air
Sea Fluxes (SCOR Working Group 110: Intercomparison and Validation of Ocean-Atmosphere
Energy Flux Fields) are to review the requirements of different scientific disciplines for surface
flux data sets, to catalogue available surface flux data and flux-related data sets, and to review
the strengths and weaknesses of each. The working group's findings, contained in this report,
will be of particular use for those developing numerical climate models for research andforecasting purposes.
1.2 The Joint WCRP/SCOR Working Group on Air Sea Fluxes
1.2.1 Creation of the Working Group
Proposals for co-ordinated activities in the World Climate Research Programme(WCRP) in the area of air-sea fluxes stemmed from the WCRP Workshop on Air-Sea Flux
Fields for Forcing Ocean Models and Validating General Circulation Models in October 1995
(White, 1996a). This workshop was a landmark in encouraging interaction and dialogue
between the diverse scientific communities involved in producing and using air-sea fluxes;
hitherto it had been limited. Among the principal recommendations from the workshop was
the need to continue to foster interdisciplinary consultations in this area, and to catalogue andReport of the Working Group on Air Sea Fluxes

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1.2 The Working Groupkeep under review available surface flux and flux-related data sets. It was suggested that alimited-life group should be established to carry forward the needed activities.

Figure 1.1 The annual mean net surface heat flux from the atmosphere into the ocean (negative
implies ocean cooling). Shown as examples are values from the NCEP/NCAR reanalysis, the
ECMWF reanalysis, and the climatologies based on ship data in the COADS data set as
calculated by UWM and SOC (see Chapter 11 for a detailed discussion)
The Scientific Committee on Oceanic Research (SCOR) is also very interested in
air-sea fluxes, especially their use in the determination of meridional heat and freshwatertransports - basic features of ocean climate. Therefore a proposal was made to establish a
SCOR Working Group on Intercomparison and Validation of Ocean-Atmosphere Flux Fields.
Since the roles of the WCRP and SCOR groups overlapped and included several common
members, it was considered reasonable to merge the interests of the WCRP and SCOR in thisarea and to create a joint WCRP/SCOR Working Group on Air-Sea Fluxes. T h eestablishment of the joint group, its terms of reference and membership were duly endorsed by
the Joint Scientific Committee (JSC) for the WCRP at its eighteenth session (Toronto, March
1997).
1.2.2 The remit of the Working GroupThe Terms of Reference (TOR) agreed between the JSC and SCOR were:
TOR.1 to review the requirements of different scientific disciplines for surface flux data sets;
TOR.2 to compile a catalogue of available surface flux data and flux-related data sets,
including those becoming available from the various reanalysis projects, and to review, in
consultation with users and producers, the strengths and weaknesses of these data sets;
TOR.3 to inform the scientific community of the work of the group by the use of the World
Wide Web, by the publication of the final catalogue, and by convening, at a suitable time, ascientific workshop;

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1.2 The Working Group
1.3 The variables to be considered

TOR.4 to keep the JSC and SCOR informed of progress in the area and present
recommendations for action as necessary.
1.2.3 The Working Group's Mode of Working
The first meeting of the Working Group on Air-Sea Fluxes took place at NOAA
Headquarters (Silver Spring, Maryland, USA), during the 22nd to 25th October 1997. The
mode of working was agreed and a draft table of contents for the final report was adopted.
Over the following 17 months a draft report was assembled from the contributions of working
group members. This draft was discussed at the second Working Group meeting held 14th to17th April 1999, at KNMI (De Bilt, The Netherlands). Although the Working Group hadplanned to finish its work by summer 2000, in response to a request from the JSC, theschedule was brought forward to produce a completed draft by the end of 1999. Accordingly athird meeting was scheduled to be held at SOC (Southampton, UK) during 6th to 10th
December, 1999.
At this meeting the report was reviewed and the Working Groups
conclusions agreed. The final version of the Working Group report is the result of further
editing following that meeting. Because of the shortened time scale it has not proved possible
to circulate the report within the air-sea flux community for comment and the final version
therefore mainly represents the understanding and beliefs of the Working group members.
1.3 The variables to be considered
The basic set of physical fluxes between the atmosphere and ocean are the transfers ofshortwave radiation (Qsw - wavelength 0.3 to 3 µm), longwave radiation (Qlw - wavelength 3 to 50µm), sensible heat (Q sen), water vapour (E ), precipitation (P ), and momentum (τ - windstress). For many purposes the radiative fluxes are characterised by the downwards componentof the flux, and the upwards shortwave and longwave fluxes are determined from the surface
albedo, A, and the sea surface temperature respectively. Given this basic set of fluxes, otherflux variables which couple the atmosphere and ocean can be determined. Thus for the latent
heat flux Qlat, net heat flux Qnet, and the freshwater flux, Fw, we have:

Qlat ∝ E

Qnet = Qsw + Qlw + Qsen + Qlat

Fw = E − P(1)

The dynamic coupling between atmosphere and ocean depends both on the temporal and spatial
variability of the fluxes. Water masses can form at the surface of the ocean in response to thebuoyancy flux F ρ(Qsw, Qlw, Qsen, E, P) and the mixing by the wind, and surface water is also
carried into the interior of the ocean by Ekman pumping due to curl τ and by spatial differencesin the ocean's response to the buoyancy forcing. The ocean currents that redistribute heat are
driven both at the surface and in the interior by curl τ and the density gradients that result fromthe surface buoyancy flux.
The air sea fluxes can be obtained in several ways. The most straightforward, but also
most difficult, is the direct measurement of the fluxes.
Due to its complexity, direct
measurement of fluxes is limited in space and time. However, it provides valuable data fordeveloping formulae to estimate the fluxes and for the validation of fluxes derived from othersources.
Fluxes with a larger coverage of space and time usually come from satellite
measurements and/or the application of bulk formulae. Bulk formulae are a parameterisationof fluxes in terms of variables that can be measured more easily, e.g., temperatures. Thesevariables are referred to as basic variables in this report (Table 1). Traditionally, they are
obtained from ship observations, which have been collected in the COADS data set (Woodruff
et al 1993), but nowadays also satellite measurements are used. The same bulk formulae thatare applied to measurements are also used in numerical models to calculate fluxes from thebasic model variables.
Because most of the available flux values are based onparameterisations in terms of the basic variables, large parts of this report are devoted toparameterisation formulae and the measurement of those basic variables.

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1.4 The flux products assessed
1.5 Layout of the report

1.4 The flux products assessed
During the first meeting the working group determined that the flux products which
would be considered would be those which:
(i)
(ii)
(iii)
(iv)

are readily available to all scientists either routinely or on request;
are a gridded, derived surface flux or flux related field;
cover major ocean basin scales and upwards;
are adequately documented.

The Working Group has assembled a catalogue of flux products which meet thesecriteria (Appendix C). However, given the limited time and resources, it has only proved
practicable to consider a subset of these products. Nevertheless this subset includes the main
recent global flux field products (Table 2). Other products are included in the Catalogue ofFlux products (see Appendix B) but are not considered in the Report, for example because they
are new products that could not be evaluated before the report was finalised (e.g. J-OFURO,
Kubota 1999).
Table 1. Summary of the Basic Flux-Related Variables
Sea surface temperature (SST)Waves

Sea surface salinity (SSS)

Cloudiness

Air temperature

Precipitation

Air Humidity (specific humidity, or mixing
ratio)

River inflow

Wind (speed and direction)

Sea Ice

Table 2. Summary of the flux products considered in this report.
Type

Product

Referencein situ

UWM (COADS)da Silva et al. (1994)

in situ

SOC (COADS)

Josey et al. (1999)

in situ

IFM (COADS)

Lindau (2000)

model

ECMWF: ERA15

Gibson et al. (1997)

model

NCEP/NCAR Re-analysis 1

Kalnay et al. (1996)

model

NCEP/NCAR Re-analysis 2

Kanamitsu et al. (2000)

model

GEOS1

Schubert et al. (1995)

Satellite

HOAPS

Schulz et al. (1997)

Sat. Rad.

Various US products

Sat. Rad.

New Japanese products

Combined

Residual

Trenberth & Solomon (1994)

1.5 Layout of the report
The different requirements for flux data sets will be summarised in Chapter 2. In manycases the requirements include knowledge of the variability of the fluxes. Indeed, the spacetime variability of the fluxes not only defines a set of accuracy requirements, it also impacts the
sampling requirements, and the accuracy to which the fluxes can be determined. While

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1.5 Layout of the reportdetailed treatment of flux variability was considered beyond the remit of the present working
group, Chapter 3 gives a summary of the state of knowledge of flux variability. The
characteristics of the data sources for either flux values, or values of flux related variables, will
be considered in Chapter 4. We will then summarise the state of the art with respect to Direct
flux measurement (Chapter 5). However most flux fields are based on parameterisation
formula and these will be considered at some length for the radiative fluxes (Chapter 6) andturbulent fluxes (Chapter 7). Sources of error in the flux determinations, such as the sampling
density, are considered in Chapter 8. Chapter 9 discusses methods of evaluating the various
available products for fluxes, and flux related variables. The state of the art for determining
the basic variables will then be reviewed (Chapter 10). The results of the evaluation of the flux
products are given in Chapter 11. Finally the conclusions (Chapter 12) will include suggestions
for future flux related activities. The working group membership is listed in Appendix A,
Appendix B describes the catalogue of flux products , Appendix C lists the acronyms used in
the report.

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2.1 Introduction
2.2 NWP systems

2 THE REQUIREMENTS FOR SURFACE FLUX DATA SETS.
2.1 Introduction
There are many requirements for surface flux values, for example, operationalmodelling, nowcasting/forecasting;climate change studies and diagnostic studies;
atmospheric and oceanic synoptic scale case studies; shelf and coastal seas studies; oceanic,climate and earth observing systems; biogenic cycles, wave forecasting, marine engineering,etc. To consider the requirements of all of these applications would be a large task andprobably not helpful. However, the types of surface flux fields considered by the Working
Group are large scale, non-real time, documented products. The main requirements for thesetypes of flux fields are those for climate models: atmospheric General Circulation Models(GCM's), oceanic GCM's, and, for verifying coupled atmosphere ocean GCM's.
This Chapter will discuss the flux requirements for atmospheric and ocean GCM's on a
global scale, and also the requirements for some specific applications: regional applications andstudies, wave models, and sea ice modelling. The needs of Global Climate Models andClimate Change Studies will also be discussed. Finally an attempt will be made to summarise
all these different requirements for surface flux information.
2.2 NWP systems—assimilation and forecasting
2.2.1 Introduction
Atmospheric computer models used in meteorology are based on the laws of physics as
expressed in the equations of fluid mechanics (e.g. see Kalnay, 1999). For both numerical
weather prediction (NWP) and climate simulations they are integrated in time from an initial
realistic state of the atmosphere obtained through data assimilation. Climate simulations areintegrated long enough (typically months to years) so that the details of the initial conditions areunimportant and are generally run at lower resolution than NWP models. Climate simulations
tend to focus on the atmospheric response to variations in boundary forcing fields; theirrequirements for air-sea flux fields are discussed elsewhere.
NWP provides the basic guidance for modern weather forecasting on time scales fromseconds to years. Successful forecasting requires both realistic representations of atmosphericprocesses and accurate initial conditions from data assimilation. Early NWP models contained
very limited or no parameterisation of physical processes; modern NWP models include
physical parameterisations that attempt to reproduce all important atmospheric processes, allexchanges of radiation with space and all exchanges of momentum, water and energy with theearth’s surface (Pan, 1999). The increasing sophistication and accuracy of such physicalparameterisations has contributed to the tremendous improvement in the skill of weather
forecasts over the last 20 years (Kalnay et al., 1998).
Data assimilation creates an initial atmospheric state for NWP (Zupanski and Kalnay,1999). It is designed to produce the atmospheric state that will yield the best subsequent
forecast. In operational practice it is used continuously in data assimilation cycles. In eachcycle, usually every 6 hours, a new analysis is produced by a combination of observations and ashort-range model forecast from the previous analysis. The short-range model (first guess)forecast interpolates in space and time between observations and also serves as one of several
quality control checks on the observations. The observations used include:
A. CONVENTIONAL
1) rawinsondes providing vertical soundings of temperature, moisture and wind.
2) aircraft observations of wind and temperature at flight level.
3) surface observations of pressure, temperature and humidity from land stations, ships
and moored drifting buoys.
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B. SATELLITE
1) vertical retrievals of temperature and moisture generated by NOAA/ NESDIS1 fromTOVS vertical sounders on NOAA polar orbiters. Some data assimilation systems,
including the NCEP and ECMWF operational systems and the ECMWF ERA
reanalysis, now use the radiances observed by TOVS instead of the derived temperatureand moisture profiles.
2) cloud-tracked winds from geostationary weather satellites.
3) surface winds from scatterometer instruments on polar orbiters.
4) surface wind speed from SSM/I .
Research is underway on the assimilation of precipitation estimates from satellites andother sources. It is hoped that such assimilation would improve the tropical analysis,
particularly the divergence of the wind field. Extensive experimentation with data assimilation
systems is now underway investigating the impact of different current and proposed
observations on numerical weather prediction. These experiments are designed to guide thedesign of the future observational system.
The use in data assimilation of a first-guess model forecast acts to make the initialcondition of the atmosphere more compatible with the time integration of the model, although
two significant errors can develop. One is ‘spin-up’ during the initial stages of a forecast,reflecting an imbalance between the initial conditions which are influenced by both the forecast
model and observations and the atmosphere consistent with the forecast model’s physics anddynamics (White and Saha, 1999). This is often manifested as significant changes in thehydrological cycle during the first few hours or days of the forecast. The second error is climate
drift, a systematic tendency for atmospheric models to drift away from the actual atmosphericclimatology towards their own model climatology (Kalnay, 1999). Several papers document
examples of these errors, including Heckley (1985), Arpe and Klinker (1986), Caplan and White(1989), Arpe (1989, 1991) and Klinker and Sardeshmukh (1987).
One problem with NWP data assimilation has been the frequency of changes in theanalysis/forecast systems over the years. While the changes have produced greatly improved
weather forecasts, they have also caused changes in the analysed fields that can exceed andmask interannual variability. These changes are particularly noticeable in physical fields suchas precipitation and air-sea fluxes (Siefridt et al., 1999). To remedy this problem of apparent
climate changes due to data assimilation or model changes, several meteorological centres haveconducted extensive ‘reanalyses’ of past atmospheric data using a modern, frozen
analysis/forecast system (White and Saha, 1999). The climate change record in the reanalyses
can still be contaminated by changes in the observing network over time, such as theintroduction of satellite observations in the 1970’s (White, 1999).
2.2.2 The needs of data assimilation systems for flux fields
Modern data assimilation systems contain atmospheric models that produce flux fields
determined by physical parameterisations from surface and near-surface model fields oftemperature, moisture and wind. These flux fields are usually generated at each model timestep and accumulated over the length of the first guess forecast or over some other forecast
interval. Thus the flux fields from data assimilation are usually from short-term forecasts.
These flux fields and flux-related variables need to be carefully verified against independent
estimates and observations.
Assimilation systems combine observations with fields from the short-range forecasts
and use the short-range forecasts as part of the assessment of the quality of the observations.
Data assimilation groups need to know how well their analyses and short-range forecasts agree
1 a comprehensive list of acronyms used in the report can be found in Appendix C.

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with the observations and whether their systems draw sufficiently from the information in theobservations. Quantitative estimates of the uncertainties in the observations are used in theanalysis; better estimates of observation error would be useful. Observations not used in thedata assimilation provide an independent check on the analyses. Quantitative estimates of theuncertainties in the independent observations are also useful in order to assess correctly how
well the data assimilation fields fit the independent observations.
Independent flux estimates permit a check on the physical parameterisation used in theassimilation system. If the data going into a parameterisation are close to independent
estimates, but the fluxes calculated by the parameterisation are not, this could reveal a need to
update the parameterisation scheme. Comparison of different flux algorithms could help data
assimilation groups improve their parameterisation. Such a comparison of bulk aerodynamic
algorithms for computing sea surface fluxes has been carried out by Zeng et al. (1998) using
TOGA COARE and TAO data. The study led to a change in the thermal roughness length over
the ocean in the US National Centers for Environmental Prediction’s (NCEP) operational global
model.
High quality observations and flux estimates from field experiments such as TOGACOARE can be especially valuable in evaluating the performance of a data assimilation system.
Comparisons of the NCEP reanalysis to results from FIFE, a land surface field experiment overthe central United States, played an important role in developing a new boundary layer
parameterisation that substantially improved the NCEP operational global model (Betts et al.,
1996; Hong and Pan, 1996). Comparisons of surface fluxes from the European Centre for
Medium-range Weather Forecasts (ECMWF) ERA reanalysis and NCEP reanalysis to surface
fluxes from TOGA COARE and other in-situ observational campaigns have also been made(Klinker et al., 1999; Moyer and Weller, 1997; Hendon and Shinoda, 1999). Recent
development of single column versions of NWP models makes the comparison of model
physics to field experiments easier and more complete. Uncertainty estimates need to beprovided. In data assimilation analysis/forecast systems all the physical parameterisations
influence each other; adjusting one physical parameterisation to fit one independent estimate or
one field experiment too closely may distort other physical fields or the same physical fields inother locations.
The purpose of operational data assimilation at NWP centres is to provide the initialcondition that gives the best forecast, not necessarily the initial condition that fits observations
most closely nor the initial condition that makes maximum use of all available observations. A
few years ago the assimilation of satellite estimates of column-integrated precipitable water was
introduced into the NCEP global operational analysis/forecast system. It was subsequently
found, however, that the use of this data led to excessive precipitation over the tropical oceans
during the first few days of the forecast and, in response, too little precipitation over thetropical continents. The artificial drought over tropical continents amplified with time due tothe interaction of precipitation and soil moisture. While there was some evidence of biases inthe satellite estimates, the main problem was believed to be incompatibility between the model
physics and the satellite estimates of precipitable water. The use of satellite estimates ofprecipitable water in the NCEP global data assimilation was discontinued. Research on how to
use these estimates and how to improve model physics continues at NCEP. This shows how
closely coupled data assimilation and model physics are in modern NWP. The assimilation of
new data, such as satellite moisture fields or precipitation estimates, may require changes in thephysical parameterisations of the model.
Operational NWP serves many users interested in many different phenomena anddifferent forecast lengths. Aviation users of global forecasts are interested in short-range (48 hr
or less) forecasts of upper level winds; marine meteorologists are interested in forecasts ofweather systems out to 5 days and near-surface winds and resulting ocean waves out to 2 days.
Climate Prediction Center meteorologists at NCEP are concerned with forecasts from one week
to several seasons in advance. With many different user communities, NWP data assimilationand modelling groups may not focus on air-sea fluxes and may lack expertise in air-sea fluxes,
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since personnel resources have not kept pace with increases in the complexity of NWP systems.
The focus of the analysis and model developers may be on the effect of changes on the forecast
skill of an atmospheric field and on atmospheric fields directly observed, fields whose accuracy
they know and have confidence in. The developers may not know what independent estimatesof air-sea fluxes are available and how much confidence can be placed in the accuracy of these
independent fields. Advice from experts on air-sea fluxes, such as this working group, would bevery useful.
Operational NWP groups focus on forecast performance and usually on atmosphericfields. At NCEP a change in the operational analysis/forecast system, while beneficial in many
respects, led to too warm low-level temperatures over the summertime Northern Hemisphere
continents. After complaints from operational forecasters, the parameterisation of sensible andlatent heat was changed and a parameterisation of atmospheric aerosols removed to lower thetemperatures; the warm bias was significantly reduced. Operationally, changes in physicalparameterisations are often made to improve observable atmospheric fields of importance toweather forecasters; their effect on the surface fluxes is not always as carefully assessed, partly
because of a lack of confidence in independent estimates of the surface fluxes.
The complexity of modern NWP analysis/forecast systems makes it difficult to establish
with confidence the exact cause of an error. NWP groups often adjust specificparameterisations and measure the success of that adjustment not by whether the parameterised
physics is more realistic but by whether specific atmosphere fields (such as precipitation over
specific land regions, 500 hPa geopotential height, sea level pressure or winds) are better
forecast. This is a reflection not only of the perspective of NWP groups, but of the lack of
independent estimates of atmospheric physics whose degree of accuracy is confidently known
by NWP groups. The problem with such adjustments is that if the true error is not found a newerror may be introduced. The new error may happen to offset an existing error, at least in thetest cases. If groups such as this working group can establish what independent estimates ofatmospheric physics exist and what their accuracy is, NWP groups could evaluate the effect ofchanges on the parameterised physics directly as well on the skill of forecasts of specificatmospheric fields.
Data assimilation systems used in operational forecasting are constantly being
improved, with substantial changes occurring annually or even more frequently. The effects ofthese changes on surface fluxes can be considerable. Independent estimates of fluxes could help
establish whether these changes have a significant impact and whether they have positive,
negative or neutral effects on the accuracy of fluxes. The accuracy of the independent estimateswould need to be established so that model developers could have confidence in the independent
estimates and in how much uncertainty exists in the independent estimates.
The use of independent estimates of atmospheric physics to assess operational NWP
data assimilation is limited by the requirement of timeliness. Many independent estimates ofatmospheric physics are based on observations taken one or more years before that have been
carefully processed. TOGA COARE atmospheric soundings, for example, have only recently
been corrected for moisture biases, more than 6 years after they were taken. NWP systems
undergo frequent changes, sometimes to adjust to changes in the observations. The operational
data assimilation system may be designed specifically for current observations and not forprevious observation systems. A change in satellite data in early 1998, for example, required
significant changes in the NCEP operational data assimilation system. A few months later thesechanges made it extremely difficult to test a higher resolution version of the operational data
assimilation/forecast system even on data from 1997. As a result, biases during the Northern
Hemisphere summer were not uncovered until after the new system was implemented. At NCEP
proposed changes to the operational system are tested for long periods on current observations;a comprehensive archive of output from the tests is available for the last 40 days. To be usefulin these tests, independent estimates of atmospheric physics must be available within a month ofobservation time.
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Reanalysis efforts alleviate this problem. By processing several years of pastobservations with a frozen modern data assimilation system and making the results widely
available, reanalysis provides a much larger sample of the results of data assimilation. The
results can be compared to a much larger range of independent estimates of atmospheric physics
from any time in the last 20 to 50 years by a much larger, more diverse group of scientists. For
example, COADS based estimates of air-sea fluxes take considerable time to prepare and areavailable over many decades; they are more suited for comparison to air-sea fluxes fromreanalysis than from operational NWP. The reanalysis effort needs to be periodically repeated.
The NCEP/NCAR reanalysis was performed with the operational model introduced in 1995.
The NCEP/NCAR operational model has been changed several times since, limiting theapplicability of the results of reanalysis to model development. The reanalysis system has been
run to test proposed changes to the operational system on past periods. The NCEP/NCAR
reanalysis system continues to be run on current data as the Climate Data Assimilation System(CDAS). Comparisons of CDAS to operational analyses and to tests of proposed changes canyield insight into whether known biases in the reanalysis are being reduced or increased.
Global data assimilation systems currently provide air-sea fluxes every six hours,reflecting the length of their assimilation cycle. Users of surface fluxes have requested fields
every three hours. Many fields are output from the systems at intervals of 1 hour or less. The
current resolution of operational global NWP systems ranges from approximately 40 km(ECMWF) to 210 km (NCEP forecasts beyond day 7). For complete verification and validation,
independent air-sea flux fields should have the same spatial and temporal resolution.
Independent estimates of air-sea flux fields at lower spatial and temporal resolutions are also
very useful, as long as adequate information is given on the true resolution of the data and on
any smoothing applied to ensure appropriate comparison.
2.2.3 The needs of atmospheric forecast models for flux fields
Since modern data assimilation uses the forecast model as an integral component, mostof the needs discussed in the last section also apply here. Short-range forecasts of physicalfields can suffer from the effects of ‘spin-up’. Independent estimates of physical fields of known
accuracy could establish at what forecast lengths the most accurate estimates of physical fields
occur and thereby offer insight into the cause of the ‘spin-up’. Longer forecasts suffer fromsystematic model drifts. Independent estimates of physical fields could establish the real error
in the forecast physical fields, rather than the departure of the physical fields from theirestimated data assimilation values which have errors of their own.
All NWP systems have problems, some common to all or most NWP systems and someunique to the particular system. Diagnosing and remedying these problems has led to major
improvements in NWP systems. As an example, problems in the NCEP global model areemphasised here, reflecting the author’s experience with the NCEP model. NCEP's operational
global forecast model has problems maintaining the correct distribution of tropical precipitationin its forecasts. Tropical precipitation increases during the first 24 hours of model integration,then decreases in the convective maximum in the west equatorial Pacific. The diagnosis of thisproblem is complicated by the fact that different independent estimates of precipitation, both
based on satellite data, give significantly different amounts of precipitation in the westernequatorial Pacific (see Sections 10.9.4 and 11.4.14). This makes it difficult to determine at what
forecast length the model’s precipitation is most correct.
In the NCEP reanalysis, surface winds and stresses in the eastern equatorial Pacificwere too weak, even in the analysis phase (White, 1996b). The current NCEP global model has
stronger surface stress in the eastern Pacific; however, the equatorial surface stress in the eastern
Pacific increases with forecast length. Accurate independent estimates of surface fields andfluxes (such as surface winds from the TOGA TAO buoys) have helped to identify these
problems and can help identify which changes to the analysis/forecast system can reduce these
problems.
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Accurate tropical analyses and forecasts are important not only for tropical forecasts,
including hurricanes, but for seasonal forecasts. Seasonal forecasting has received increased
emphasis in recent years at NCEP and elsewhere with successful forecasts of dramatic El Nino
and La Nina events in the Pacific and their impacts on the United States. Seasonal forecasting
often involves the use of an ocean data assimilation and forecast system coupled to theatmospheric model. As a result, at ECMWF, the effects of proposed changes in the operational
atmospheric model on air-sea fluxes (and the impact of these changes on the ocean model usedin seasonal forecasting) have been carefully examined in pre-implementation testing.
Another problem in the NCEP global model is that the tropical upper tropospherictrough over the Caribbean is weakened significantly in the forecasts. This weakening causes
too favourable an environment for hurricanes in the global forecasts, complicating the work of
hurricane forecasters. This weakening of the tropical upper tropospheric trough may reflect
problems in maintaining the divergent flow in the tropics. Accurate independent estimates ofprecipitation and surface fluxes could help identify model problems in these fields and lead to
improvements in the divergent flow.
Surface fluxes over the Gulf of Mexico and the Gulf Stream play a crucial role inaccurate analysis and forecasting of explosive United States east coast cyclogenesis, a major
wintertime concern for forecasters. Modern global models often have considerable success inforecasting such events, implying, as do comparisons with COADS-based fluxes (Section 11.4.2
below), that the models’ surface fluxes are fairly accurate in these areas. In NCEP’s operational
meso-scale model, a comparison of air-sea fluxes over the Gulf Stream with COADS-based
estimates led to changes in the model’s physics.
Some years ago seasonal forecasters at NCEP found large differences in surface
radiation between NCEP global model forecasts and independent estimates in the eastern
subtropical North Pacific in regions of oceanic low-level stratus clouds. Due to excessive
radiative heating rates when cloudiness occurred in thin near-surface layers over land, the NCEP
model did not permit low-level clouds anywhere, even when the vertical profiles of humidity
and temperature over the subtropical oceans clearly suggested that low-level stratus clouds
should be present. Simple changes in the model permitted the occurrence of oceanic stratus
clouds and dramatic improvements in the surface radiative budget over the ocean. Morerecently, the parameterisation of low-level clouds were changed to reduce excessive low-levelclouds over the Arabian Sea; this change also affected low-level stratus clouds in the eastern
subtropical oceans. Recent tests indicate that low-level stratus clouds can be significantly
changed by changes in the vertical and horizontal resolution of the model. Other operationaland reanalysis data assimilation systems have problems with stratus clouds (White, 1995; Whiteand da Silva, 1998; section 11.4.7 below).
Comparison of surface net short-wave radiation from the NCEP/NCAR reanalysis toindependent estimates by da Silva and White (1996) led to the discovery of an error in theoceanic surface albedo. The error has been corrected in the operational NCEP global model andin the NCEP-2 reanalysis.
2.2.4 Regional observing / nowcasting / forecasting systems
Regional operational systems are used for nowcasting and forecasting both themeteorological conditions at the ocean surface and also the dynamics of the upper layer of theocean. They are characterised by fine spatial and temporal resolution, increasing in the nearoffshore regions. Thus, for example, the EUROWAVE operational system for diagnosis andforecasting of waves in European coastal areas is running on a 5 km spatial resolution in the offshore regions and at about 1/4 degree in the open sea. The French operational system for
nowcasting of tides and tidal currents in the English channel uses 12 km spatial resolution in
operational mode, and twice finer resolution in non-operational mode. Operational models for
nowcasting and forecasting of oil spills are running at 0.5 to 2 km resolution in the nearest
vicinity to the source and at 5 to 10 km resolution in areas at least 50 km remote from thesource. The highest priority parameter required for such systems is wind speed. Since theReport of the Working Group on Air Sea Fluxes

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2.3 Ocean GCM's

majority of operational models have a special focus on coastal regions, the wind fields required
have to be provisionally corrected for coastal biases. Further parameters which are very
important for these systems are SST and SSS. These are particularly used in oil spill
forecasting for describing the interaction between the oil and sea water. Systems running in ice
covered areas also require fine resolution sea ice extent data.
Many operational
forecasting/nowcasting systems use measurements at buoys, these being usually implemented
as part of the systems. In this context, a high priority is to provide reasonable estimates of thepossible biases between the buoy measurements and the VOS and satellite data assimilated by
the models over the larger area. At present, most of systems are focused on the use ofdownscaled operational analyses from global and regional meteorological centres together withsatellite observations. Thus, improvements in accuracy of these two sources of fluxes and fluxrelated variables, and increased resolution, would contribute considerably to the developmentof regional operational systems.

2.3 Oceanic General Circulation Models (OGCM's)
2.3.1 Introduction
The ocean receives energy through the air-sea interface by exchange of momentum,
which also represents a source of kinetic energy, and by exchanges of heat and water which
locally modify the buoyancy of the fluid and so act as a source of potential energy generatinginternal pressure forces. Thus ocean models require an accurate representation of the surface
flux fields to define their surface boundary conditions. However this alone is not sufficientbecause of systematic errors in ocean general circulation model (OGCM) solutions which often
result in large inconsistencies between the forcing fluxes and the model circulation. For
example, mean currents in coarse resolution models are far too broad and slow, and if forced
with observed surface heat fluxes they tend to produce unrealistic surface temperatures.
Another difficult problem that ocean-atmosphere modellers are confronted with is theinteractive nature of air-sea exchanges. Variations in one fluid will force variations in the other,and this raises the question whether it is conceivable to drive one fluid without considering its
coupling with the other. Indeed, forcing an ocean model with specified surface fluxes generally
produces SST's which are far from the observed sea surface temperature (Rosati and Miyakoda,
1988). Therefore, it is not possible to force an OGCM without some kind of a parameterisationto account for the feedback to the atmosphere.
This feedback issue is not relevant to the wind stress forcing, for which there is not a
direct local feedback from the ocean circulation. However, the spatial resolution is of particularimportance to the momentum exchange since basin gyres are primarily driven by the wind stresscurl, a first order derivative of the wind components. For heat and freshwater, a common
OGCM boundary condition is to restore the model surface temperature and salinity to observed
climatological values (Cox and Bryan, 1984). The paradox here is that if the model tracer fields
agree with observations, there is zero net tracer flux across the air-sea interface. In general,model solutions differ from observations both because they cannot 'keep up' with the changing
observations, and because advection introduces a systematic bias (Killworth et al., 2000).
Several parameterisations of the feedback of the ocean to the atmosphere have beendeveloped in the past. It is not the purpose of this report to present an overview of the various
way of forcing ocean models. A recent review can be found in Barnier (1998). However, we
present here some details of such parameterisations applied to the thermal forcing since they areuseful in determining which surface variables are required in the definition of the atmosphericforcing.

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2.3.2 Parameterisation of the thermal feedback
A. THERMAL SURFACE BOUNDARY CONDITION.
There are several ways to formulate the forcing of an OGCM based on the primitive
equations. A formulation which remains close to the physics of the air-sea interactions is theflux boundary condition. It is assumed that vertical gradient of the ocean temperature at thesurface is proportional to the flux across the interface. Thus the advection-diffusion equation
for the evolution with time (t) of the ocean temperature (T) is:∂T= Advection + Diffusion + FSOL (z )∂t
(2.1)
which includes a source term, FSOL(z), which represents the depth-dependent heating by the solar
heat flux. FSOL(z) is commonly parameterised by an absorption law with depth (z) (Paulson andSimpson, 1977). The forcing of the ocean temperature by the non-solar heat flux Q N S (theaddition of the net infra-red, latent and sensible heat fluxes) is introduced as a surface (z=0)
boundary condition:

∂T 
QN S

 K v (z )  =∂z  z = 0 ρ 0 C p w
(2.2)
where Kv is a mixing coefficient which depends on the depth z. Other parameters are the density(ρ0) and the specific heat (C pw) of sea water. This type of formulation is commonly used formixed-layer models (Gaspar et al., 1990). The parameterisation of the ocean feedback usuallyoccurs through a model dependent definition of the non-solar heat flux QNS.
B. FEEDBACK PARAMETERIZATION.
There are two methods frequently used to overcome the physical inconsistency due tothe lack of air-sea feedbacks in the formulation of the atmospheric forcing of ocean models; amethod of flux correction, and a bulk forcing method.
Flux Correction Method. This method is based on the pioneer work of Haney (1971) and Han(1984). More recent applications are those of Oberhuber (1988), and Barnier et al. (1995). The
underlying idea is to express the feedback to the atmosphere as a flux correction which depends
upon the model SST. Therefore, at a given time step of the model integration, the non solar
surface heat flux, QNS(t), used in boundary condition (2.2) for the temperature is:

QNS (t ) = Q

C lim
NS

 ∂Q 
+  NS  ∂SST 

C lim

× ( SST C lim − SST (t ))(2.3)

where:

QNCSlim = climatic estimate of the non-solar net heat flux

SST Clim = climatic estimate of the sea surface temperature used to estimate the air-sea fluxes
SST (t ) = instantaneous model sea surface temperature ∂Q N S  ∂SST 

C lim= climatic estimate of the sensitivity of the non-solar heat flux to variations in SST

The second term in the right-hand-side of (2.3) is a feedback term the effect of which isto prevent the model SST(t) drifting away from the climatic value used to estimate the forcing
fluxes. Thus it is there to drive the model ocean toward a climatic state consistent with the airsea fluxes. The sensitivity term ∂Q NS / ∂SST can be estimated from atmospheric surface
variables using a first order Taylor expansion of the bulk formulas (Barnier et al., 1995). This
term calculated from the surface fields provided by the ECMWF re-analysis is shown in Figure
2.1.

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Figure 2.1: Sensitivity term ∂Q NS / ∂SST (Wm-2K -1) estimated with atmospheric surface
variables of the ECMWF re-analysis using the first order Taylor expansion of the bulk formulas
proposed by Barnier et al. (1995).
To apply this parameterisation of the thermal forcing requires estimates of the various
surface heat fluxes and the SST. Therefore, this method allows flux estimates to be directly used
as components of the forcing; the feedback enters the surface boundary condition through thesensitivity term. This only needs to be estimated once, from a climatology of surface variables.
Bulk Forcing Method. In this method, air-sea heat fluxes which enter the flux boundary
condition (2.2) are made model dependent by the direct use of the model-computed SST(t) inbulk formulations (see Large et al., 1997 for a detailed review of the bulk forcing method). For
example, the latent heat loss at a given time QLAT(t) would be computed by the model according
to the bulk formula (with the usual notations)2:

QLAT (t ) = ρ a LE CEU1 0(qa − q s (t ))(2.4)
where qs(t) is the saturated specific humidity estimated with the SST predicted by the model attime t. All the other variables entering the calculation of (2.4) are obtained from a climatology
or from an analysis of surface variables. In addition, the sensible heat flux and longwave
radiation are calculated with the model SST. The application of this method does not require an
estimate of every component of the net heat flux, but rather the knowledge of the net downward
radiation (shortwave and longwave), and of surface variables which enter the bulk formulas.
Thus, the requirements for surface variables and air-sea fluxes are different for the Flux
Correction compared to the Bulk Forcing methods.
2.3.3 Salinity Forcing
The case of salinity forcing is different from that of temperature. The salinity of surface
waters vary because the ocean loses or gains water by evaporation, E, or precipitation, P, river
runoff, R, and sea-ice production and melt, which causes a variation of the salt concentration in
the water. However, the total amount of dissolved salt remains constant. Because OGCM
formulation generally assumes a constant volume, the forcing of the salinity equation is
2 see Chapter 7 for a detailed discussion of the bulk formulae.

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expressed as a virtual salinity flux which is proportional to the balance E-P-R (see Barnier,
1998, for a detailed discussion). The formulation is then similar to the one used fortemperature, and a correction term is generally added in the form of a relaxation toward a
climatic estimate of the sea surface salinity (SSS). However, more OGCM's are now using afree surface formulation, and a more physical formulation of the salinity forcing could be used.
This would consider the change in volume implied by the freshwater flux at the surface (Roullet
and Madec, 2000). As yet, such a formulation presently is far from being used routinely, andadditional restoring to a climatological SSS is still included.
2.3.4 Geophysical consistency of the ocean forcing function
The section above discussed the two methods most used to overcome the physicalinconsistency between the model SST and the forcing fluxes which arise from the lack of air-sea
feedback in the formulation of the forcing.
However, when applying these methods, it is important to consider the geophysicallinks that exist between the various components of the atmospheric forcing. This is commonlyreferred to as the "geophysical consistency" of the model forcing function. Indeed, it isimportant that the inter-correlation existing between the wind field, the turbulent fluxes, theradiative fluxes, and evaporation and precipitation, are correctly represented. Inconsistencies
between the forcing variables may have a dramatic effect on the results of a simulation. This isespecially so since the feedback term, which also works to compensate for inaccuracies in theflux estimates, will generally not compensate for such inconsistencies. For example, in thewestern tropical Pacific the surface salinity of the ocean cannot be correctly calculated if theevaporation (air humidity and wind) and precipitation are not consistent. It is only recently thata bulk forcing which links heat and freshwater forcing through evaporation has been used in a
global ocean simulation (Large et al., 1997). Therefore, when using flux estimates from various
sources (satellites, NWP analyses, COADS), geophysical correlation between the various
components of the forcing should be verified.
The analysis carried out by Garnier et al. (1998) on the 15-year flux climatology
obtained from the 6-hourly forecast ECMWF re-analysis provides an example of theinconsistencies which may arise. Their global budget calculation indicates that the ocean
receives an excess of heat of 8 Wm-2, despite a lack of incident short wave radiation in theintertropical zone which is only partly balanced by an excess of incoming short wave radiation
at mid-latitudes. In the freshwater budget, evaporation globally dominates precipitation, and theocean loses, on annual mean, 1.4 Sv (1 Sv =106 m3s-1) of water (runoff estimates being
included). For the purpose of forcing an ocean model, corrections to the weak solar radiation
reaching the surface in equatorial regions could be sought, but increasing this quantity would
increase the global imbalance of heat of 8 Wm-2. This could be compensated by an increase inlatent heat loss at latitudes where the analysis suggest it could be underestimated, but then theimbalance of the water cycle would be amplified. This contradiction illustrates the difficulty of
producing a globally consistent forcing function.
2.3.5 Forcing fields
Considering both flux correction and bulk forcing methods, and the fact thatgeophysical consistency of the forcing variables should be checked, the basic forcing
components of a prognostic OGCM include much more than the usual air-sea fluxes. A list of
the useful ( if not always necessary) variables to define a forcing function of an OGCM is given
below.
A. FLUXESThe net heat input by solar radiation; the non solar heat flux (the sum of the sensible, latent andinfra-red heat fluxes); the components of the wind stress vector; the wind stirring; theevaporation rate; the precipitation rate; the river runoff and sea-ice melt.

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2.3 Ocean GCM's

B. OTHER SURFACE VARIABLES
The sea surface temperature; the sea surface salinity; the ocean albedo; the surface wind speed,
air temperature and specific humidity at specified height; the mean sea level pressure, the cloud
fraction, the sea-ice coverage and growth (or melting) rate. According to the forcing method
which is used, additional parameters, such as exchange coefficients and all variables entering
the determination of the stability of the air column may also be necessary.
C. RESOLUTION ISSUES
The requirements on air-sea flux resolution for modelling the different features of theocean vary according to the application and the beliefs of the users, from coarse resolution
global scale models run over centuries, to eddy resolving models run over a few decades. For
many applications in forthcoming years, monthly means on a one degree grid will be sufficient.
However, the number of applications which have finer requirements is rapidly increasing as
ocean model resolution increases with available computer power, and these needs areemphasised here.
Considering time scales, it is now well established that the characteristics of the surface
mixed layer are very sensitive to short-term fluctuations of the wind stress which set theamplitude of the vertical shear, and thus determine the intensity of the turbulent vertical mixing.
The inertial frequency needs to be resolved here, and 3 hourly winds are recommended.
Requirements for the heat and freshwater fluxes are not as stringent and six hourly to daily
values, which can presently be achieved by NWP models and geostationery satellites, arelikely to be acceptable.
Considering spatial resolution, again the requirements for the wind are most critical, anda resolution of 50 km, which can already be achieved by scatterometer products, is a strong
requirement to obtain an accurate estimate of the wind stress curl, the primary forcing of ocean
gyres. Requirements for the heat and freshwater fluxes could be somewhat looser, but 50 km
resolution would allow a better resolution of the gradients across the major ocean currents.
D. CONSTRAINTS
The various components of the forcing should meet to some degree of accuracy several
constraints:- a global balance constraint- a global transport constraint- geophysical consistency
2.3.6 Discussion
Having discussed the forcing of ocean models with atmospheric variables, one may
briefly comment upon the feedback from ocean observations and ocean general circulation
models on the validation of air-sea fluxes.
Ocean hydrographic sections (WOCE) provide estimates of the meridional heattransport across ocean basins (Macdonald and Wunsch, 1996, Ganachaud, 1999), which can bechecked against other estimates (derived from satellites, numerical weather prediction centres,or COADS). However, large uncertainties (in excess of 0.2 PW) are still associated with thesetransport estimates.
In specific cases (to be carefully controlled), the feedback term can provide valuable
information on the consistency of the air-sea flux estimates used to force the model.
Assimilation of ocean surface variables (sea surface height from altimetry and SST) in
relation to the heat content of the surface ocean may permit diagnosis of an estimate of the net
heat flux at the ocean surface, independent of other estimates.
Coastal oceanography is also concerned with air-sea fluxes. For such very local
applications where the diurnal cycle generally needs to be resolved, air-sea fluxes are of course
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2.3 Ocean GCM's
2.4 Wave models

important, but the necessary resolution, in particular the high time sampling required for thewind field, are beyond the capabilities of the instrument systems used to observe the global
ocean. Other means could be used, including dedicated instrumentation and atmosphericmodelling, since the studied areas are usually limited.
Finally, it is clear that OGCM’s will always need to include some parameterisation of
the mixed layer. Validation of mixed layer models requires very accurate estimates of the airsea fluxes, combined with measurements of the turbulent fluxes and large scale flow in theupper ocean. Such complete data sets could only be obtained at a few given locations, andwould require specific experimental designs.
2.3.7 Conclusions
While many applications still call for at least medium spatial (1 degree) and temporal(daily to monthly mean) resolution and global coverage, the demand for high temporal (3hourly) and fine spatial (50 km) resolution at global scale is rapidly increasing. Due to thesparseness of observations, these requirements can only be met by numerical or satellite
products. These products, however, have to be carefully checked and validated by comparison
with in situ measurements. The specific requirements for development and validation of newparameterisations of mixing in the upper ocean are calling for local but very accurate flux
measurements.
2.4 Wave Models
Ocean surface waves are generated by the wind blowing over the water. Wave models
thus require the wind vector as input. Several studies (e.g., Cardone et al. 1995, Graber et al.
1995, Sterl et al. 1998) have shown that the quality of the input wind is crucial for the quality ofthe modelled wave characteristics. Empirically it has been found that the significant wave
height for fully developed sea is proportional to the square of the wind speed (Sanders 1976,
Janssen et al. 1984). Thus, although high waves are rarely fully developed, one may anticipate
that errors in the wind speed will be amplified by the wave model. As very high wind speeds
are of limited temporal and spatial extend, a major aspect of "quality" turns out to be the spatial
and temporal resolution of the driving wind field. For example:
Cardone et al. (1995) used analysed wind fields from four Numerical Weather
Prediction Models and from a kinematic analysis to drive the WAM wave model (Komen et al.
1994). They found that extreme wave heights were only correctly reproduced by the model if
the driving wind contained wind-speed peaks of the correct magnitude.
Graber et al. (1995) drove the same WAM model by wind fields that were artificiallydegraded by lowering the spatial and temporal resolution. They found that in regions of"slowly evolving, nearly linear features" a resolution of 1.5° and 6 hours was satisfactory, while
fronts and rapidly propagating jets required 0.5° and less than 3 hours resolution.
Sterl et al. (1998) used the ERA winds to drive a global version of the WAM model.
They found that with a resolution of 3°, mean wave heights were severely underestimated, andthat, even at a resolution of 1.5°, missed peaks in wind speed led to an underestimation of
extreme wave heights, thus corroborating the results of Graber et al. (1995).
The conclusion to be drawn from these studies is thus that the resolution of the windfield for wave modelling studies should be of order of half a degree in space and roughly threehours in time. The spatial extent of the required fields depends, of course, on the desired
purpose and can vary between regional and global.
Two remarks should be added.
1.

The above estimates have been derived from climatological studies (Sterl et al. 1998) orfrom extratropical storms (Cardone et al 1995, Graber et al 1995). Due to their muchsmaller spatial extends, tropical storms (hurricanes) may need even higher resolution.
E.g., a resolution of 0.25˚ in space is used in the case study of the WAMDI group (1988).

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2.4 Wave Models
2.5 Sea Ice

From the fact that the forcing wind is so important for the quality of wave model results it
follows that for forecasting wave heights, the quality of the wind forecast is crucial.
Improved wind forecasts will automatically lead to improved wave forecasts.

2.5 Sea Ice in Ocean Models and Sea-Ice Modelling
Ice-covered and sub-polar oceans play a special role in the global ocean thermohaline
circulation - the vertical overturning of the world ocean. Whereas low-latitude thermohaline
processes are controlled both by surface heating and by net evaporation, at polar latitudes theseprocesses are dominated by surface freshwater fluxes, that is, by net precipitation, and to alarger extent by sea ice melting and freezing; sea ice melt water has the same effect on the ocean
surface as precipitation. In the northern hemisphere, large inter-annual variations of the exportof ice through the Fram Strait into the GIN (Greenland/Iceland/Norwegian) Seas are modelled(Häkkinen, 1993) and observed (Kwok and Rothrock, 1999). It is presently hypothesised that
abnormally high exports of sea ice in the 60's capped the Labrador Sea with fresh water andlimited the production of Labrador Sea Water (Lazier, 1980). This freshening event was called
the Great Salinity Anomaly by Dickson et al. (1988). It appears that variations in freshwater
input, both in the form of ice and somewhat less saline upper ocean water from the Arctic
Ocean, influence convective overturning and contribute to interannual variability in the North
Atlantic circulation.
In the southern hemisphere, the freezing and melting of sea ice controls the amount andlocation of deep and bottom water formation in the Weddell Sea and the Ross Sea which aremajor sources of bottom water of the world ocean. The appearance and disappearance over long
periods of polynyas are thought to force decadal variability in the properties of the flow ofAntarctic Bottom Waters into the North Atlantic. The seasonal melting of sea ice around theAntarctic is responsible for the low salinity of the Antarctic Intermediate Waters, and thesewater masses are found to intrude far into the North Atlantic with the upper branch of theAtlantic overturning cell. So, the importance of the freshwater forcing of the global ocean
circulation in the form of input of sea ice is quite clear.
We have seen in the previous section that some ocean models use as a surface boundary
condition the net surface heating and the net precipitation or ice growth or melt rate. Others
treat separately the penetrating component of solar radiation. In this latter case, the model
requires the ice concentration in order to compute how much radiation penetrates into the water
through open water "leads" (openings within the ice cover). Still others include a full ice model
within the ocean model. In this case, and for modelling sea ice separately from the ocean todetermine growth and melt rates as a function of space and time, the specification of global
ocean surface fluxes takes on a different character over ice covered oceans. One can specify orobserve the momentum, radiative and turbulent fluxes at the top surface of the ice cover, but ofcourse then one needs to "solve" an ice model in order to translate these into fluxes to the mixed
layer of the ocean. Wind stress pushes the ice which has its own mass and resistance todeformation, and these conspire to apply a somewhat modified stress to the ocean surface. The
same can be said of radiative fluxes; some of the flux goes directly into the ocean through leads,
but most of it goes into the heat balance of the sea ice. The ice cover represents an isothermalboundary condition on the ocean, and turbulent heat fluxes at the ocean surface (and theunderside of the ice) are determined by the mixed layer and the vertical mixing at the base of themixed layer. The ice conducts heat away from the mixed layer, and ice melt (or growth) also
absorbs heat from (or deposits heat into) the mixed layer. In order to know these quantities, one
needs an ice model.
To force today's sea ice models, with or without an underlying ocean model, thefollowing fields are specified:❍wind stress, usually taking a bulk drag coefficient as constant;❍downwelling solar radiation (the surface albedo and reflected radiation being
determined within the ice model);Report of the Working Group on Air Sea Fluxes

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2.5 sea Ice
2.6 Ocean data assimilation❍

downwelling longwave radiation (the outgoing being determined within the model by
surface temperature);❍surface temperature being internal to the model;❍

the evaporation and latent heat flux, specified as a function of near-surface wind andhumidity (determined by the air temperature);❍

precipitation.

These fields at the sea-ice surface, then, are the proper subject of this working group.
To date, the practice has been to specify fluxes parametrically: downwelling solar
radiation in terms of solar geometry and cloud fraction, downwelling longwave in terms of
surface air temperature, cloud fraction, and all turbulent fluxes in terms of surface wind and airtemperature. It is fair to say that the subject of surface fluxes over sea ice has been taken prettymuch for granted by modellers, who have found that the mean annual cycle of sea ice can bemodelled satisfactorily with the fluxes specified parametrically years ago by Parkinson andWashington [1979) and have not had cause to examine these inputs much until recently. Now
the focus of research is shifting more to interannual variability and climate change, and this has
sparked recent interest in data on long-term variability of polar surface fluxes.
A study of the sensitivity of an Arctic sea-ice model (Rothrock and Zhang, 1997) toerrors in radiative forcing has shown that an error of +10 Wm-2 in the downwelling shortwave
produced an error of -20 cm in mean ice thickness in a full thermodynamic-dynamic model (and-60 cm in a radiative model with no ice dynamics). The same error in downwelling longwave
radiation produced an error of -50 cm in the full ice model (and -110 cm in the radiative model).
The lesser sensitivity to shortwave was due to the high albedo that caused most of thedownwelling solar radiation to be reflected. The mean Arctic ice thickness is about 2 mpresently, so flux errors of only 10 Wm-2 cause a very significant error in modelling the sea ice
cover.

2.6 Air-Sea Flux Fields and Ocean Data Assimilation
2.6.1 Introduction
Data assimilation (sometimes known as "ocean state estimation") is a very powerfultool which is now becoming the tool of choice for all operational centres as well as ongoing(e.g., WOCE AIMS) and future research programs (e.g., GODAE). As is the case for forward
ocean models, ocean data assimilation (ODA) is dependent on accurate air-sea fluxes forforcing. However, unlike forward modelling, ODA requires fairly comprehensive error
estimates so data-model misfit feedback can be properly partitioned.
2.6.2 Ocean Data Assimilation
In general terms, data assimilation techniques are employed to bring, in an extremely
efficient way, a model into consistency with the observations, which the forward model byitself predicts. From the resulting misfit between the model and the observations one candetermine which uncertain model parameter must be adjusted, and by how much, so as to bring
the forward computation into accord with the data. Changes in those fields - often referred to as"control'' terms - are determined as a best-fit in a least-squares sense over the full data period.
Control terms are usually the model initial conditions, but can likewise include internal model
parameters such as friction coefficients and eddy tracer transfers. It is essential to realise that all
adjustments of control parameters are enforced to stay in a dynamically acceptable range andthat the model equations are otherwise preserved.
Since the observation-model misfits determine the control terms (in a statistical sense),
it is critical to have error estimates of the observations in order to correctly partition the misfit
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2.6 Ocean data assimilation
2.7 Evaluation of Climate models

information into each of the control terms. Additionally, estimates of the ocean circulation
depend critically on the quality of the meteorological (forcing) fields. But error estimates forsurface fluxes of momentum, heat and freshwater provided by NWP centres are generally notavailable. One way to deal with this situation is to incorporate flux error terms as control
parameters into the estimation process. Nevertheless, prior information about error structures isneeded to obtain a statistically consistent solution.
Ocean observing systems are being rapidly developed for the purpose of monitoring theocean and to allow ocean prediction at global scale. These are the major objectives of importantinternational programmes such as the Global Ocean Observing System (GOOS) and the Global
Ocean Data Assimilation Experiment (GODAE). For these purposes, an ambitious ocean float
program is being implemented (ARGO). This plans to release more than 3000 profilinglagrangian floats (of P-ALACE and PROVOR types) which will sample the temperature andsalinity of the upper ocean with a space-time coverage never attained before. Assimilation ofthese data (with additional XBT, SST, and sea surface height observations) into a global model
should permit estimation of the net heat flux into the ocean and its variation. The question
remains whether the accuracy of such estimates will approach the few Wm-2 required for climate
studies.
Information about amplitudes, and spatial and temporal error covariances inmomentum, heat and freshwater flux fields, including those from operational centres such as
NCEP and ECMWF are necessary. A concise review is needed of what the atmosphericscientists believe the error in meteorological centre analyses are. A summary of formalcomparisons among different centre estimates would also be valuable if error estimates cannot
be given.
2.6.3 Example of Ocean Assimilation requirements - GODAE
The requirements which have been stated for GODAE are as follows. For SST,GODAE has a requirement for high-resolution global real-time products. A representative
spatial resolution would be 5 to 10 km, and ideally the diurnal cycle should be resolved(implying 6 hour or better time resolution). The desired accuracy would be 0.1 to 0.2°C. If sea
surface temperature is used as a strong constraint on the ocean models (that is a very small error
value is associated with it), then knowledge of the net downward shortwave radiation would
complete the determination of the thermal boundary conditions. The latter is treated as aninternal heating source within the model.
Such a product will require careful direct
measurements for validation; for delayed-mode products GODAE would be seeking an
accuracy of around 10-15 Wm-2.
The availability of suitable surface wind stress products continues to be a major issue
for oceanography.
Apart from the need for improved accuracy, modern ocean data
assimilation systems are now demanding estimates of the error in this fields; without them, theingested wind data cannot be balanced against other ingested data. Given the GODAE,
emphasis on global, high-resolution, real-time products, the matter of how to define and obtaina suitable wind field is a matter for urgent debate.
2.7 Evaluation of Climate Models
When an Atmospheric General Circulation Model (AGCM) is constrained withprescribed SST's and sea-ice, there is no feedback from the ocean on the simulated atmosphere.
If the AGCM is coupled to an Ocean General Circulation Model (OCGM) and a sea-ice model,
air-sea feedbacks are present in the resulting (AOGCM) simulations. For this reason, coupledmodels are more suitable than AGCM's for studies involving long term variability and offer themost plausible means of predicting anthropogenic climate change. Realistic coupled model
simulations depend critically on interactions between the atmosphere, ocean and sea-ice, via the
air-sea fluxes of heat, momentum and moisture. An important motivation for a better
understanding of air-sea fluxes (and hence the effort of this Working Group) is the need for an
improved means of evaluating air-sea fluxes simulated by coupled models.
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2.7 Evaluation of Climate models

Despite the lack of air-sea feedbacks, studying surface fluxes in AGCM's is often
instructive because many deficiencies are so serious that they can be readily identified even withthe use of prescribed SST's and sea-ice. Moreover, if the SST simulated by a coupled model is
not close to a realistic state, it is difficult to determine if deficient surface fluxes are a direct
result of problems in the way fluxes are parameterised, or simply because the basic state of thesimulated climate is different from that observed. Detecting problems in coupled model surface
fluxes is thus perhaps best done early on in the simulation (before there is much drift in the
SST's). However this is complicated by adjustments resulting from incompatibilities in theinitial states of the simulated atmosphere and ocean, even when they have been independently'spun up.' In short, it is useful to evaluate surface fluxes in both coupled and uncoupled climate
models, even if the ultimate motivation is improved coupled models. Accurate observationallybased estimates of surface fluxes are fundamental in either case.
To put the state of affairs in perspective, we take a first cursory look at AGCM
simulations recently made (1998-1999) as part of the on-going Atmospheric Model
Intercomparison Project (AMIP). Figure 2.1 shows the zonally averaged global ocean net
shortwave radiation annual mean climatology of ten models.
Also shown are sevenobservationally based estimates that are described and evaluated in this report. We see that
where the fluxes are largest (in low latitudes for the annual mean), there are discrepancies
and nearly as much among the different
among models on the order of 50 Wm -2 ,observationally based products. The situation for the latent heat flux is perhaps even moretroubling, as shown in Figure 2.2, although in this case there seems to be separation between
the models and observational products. Gleckler et al. (1994) demonstrated how common
300

250

Watts/m**2200
AMIP Models
SAT (Pinker)

150

SAT (Staylor)
SAT (Li)100

Figure 2.1 Zonal averages of
the Global Ocean net SW flux
from reanalyses, observations(COADS and satellites) andAMIP2 (1998-99)

VOS (SOC)
VOS (da Silva)

50

RNL (ERA)RNL (NCEP)

0
90N 60N 45N

30N

EQ

30S

Latitude (area weighted)g(

45S 60S 90S)

200

Figure 2.2 Zonal averages of
the Global Ocean latent heat
flux from reanalyses,
observations (COADS) andAMIP2 (1998-99)

Watts/m**2

150

100

AMIP Models
VOS (SOC)
50

VOS (da Silva)
RNL (ERA)RNL (NCEP)

0
90N 60N 45N

30N

EQ

30S

45S 60S 90S

Latitude (area weighted)

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2.7 Evaluation of Climate models

deficiencies in modelling the cloud-radiative effects radically altered the implied oceanic heattransport (a common measure of evaluating an AGCM's suitability for coupling). While thiswas believed for some years to be the primary culprit for coupled model deficiencies, more
recently the implied ocean heat transport of the NCAR CCM3 has been shown to be equally as
sensitive to the parameterisation of deep convection (Hack, 1998), which principally altered thelatent heating. Clearly, the suitability of an AGCM for coupling is critically sensitive to all
forms of surface heating, but especially to its surface net shortwave and latent heat flux.
The need for "flux correction" has declined in the last several years (Covey et al. 1999)
as many teams are having increasing success in reducing climate drift in their coupled models.
Encouraging integrations of more than a hundred years with minimal drift have been made by
the Hadley Centre (Gordon et al. 2000) and NCAR (Boville and Gent, 1998). However a stable
simulation with a reasonable SST distribution does not guarantee that surface fluxes areproperly simulated. This can only be verified with suitable observations. In Figure 2.3, the 20
year mean (years 60 to 80 of each simulation) net surface heat flux of four simulations takenfrom the Coupled Model Intercomparison Project are compared with four of the observational
products to be evaluated in this report. While there are encouraging similarities, once again we
see that even qualitatively there are substantial differences among the observations andmethods. The general impression is that the coupled models have large regions where there is arelatively small (less than 20 Wm-2) heat flux into the ocean. In comparison ERA shows ocean
cooling over many of these areas. The coupled models appear similar to the Residual method
results. Compared to the COADS based climatologies they fall somewhere between the SOC
climatology and the tuned UWM climatology. Of course, if more of the ocean has net heating,to achieve a global heat balance the coupled models must have significantly greater cooling in
the regions where the ocean is loosing heat.
Improving our understanding of, and the accuracy of estimates of, the seasonal cycle ofsurface fluxes is perhaps the most pressing need for the evaluation of climate models. We havejust demonstrated how state-of-the-art climate models still have difficulty in simulating many
basic features of this most basic climate variability. Examples include excessive shortwave
heating in marine stratus cloud regimes which remains a problem in AGCM's (and which is notapparent in the zonal means), and excessive evaporation with respect to climatologies in low
latitudes. High quality estimates (i.e. with low and quantified uncertainty) of global ocean
seasonal cycle climatologies of heat, momentum and freshwater are all desperately needed to
guide further development of climate models.
Estimates of interannual variability in as many areas as possible are also becoming
increasingly valuable in the evaluation process as coupled modellers strive to improve simulated
variability in phenomenon such as ENSO, PNA and the NAO. In a recent study by
Timmermann et al. (1999), it was clearly demonstrated that global coupled models that have'reasonable' climatologies are doing a poor job at capturing ENSO variability. While there aremany other measures of variability to evaluate models with, air-sea interactions are the link
between the atmosphere and ocean, and thus there will be an increasing need for reliable
estimates of flux variability.
Estimates of global ocean air-sea fluxes derived from in-situ observations, satellite
measurements, atmospheric analysis, or potentially some combination of each can all be usefulin evaluating climate models. However, climate modellers want to know which is the single
best (most accurate) observational data set, and we may not be able to answer that question withabsolute confidence. While identifying the strengths and weaknesses of these different products
is an important objective of this report, complex uncertainties with temporal-spatial variations
are inherent in each of the observationally based estimates and these are not well understood.
As described in later sections of this report, the problems differ with each class of flux
estimates, and thus the 'best' estimate for the climate modeller may be application dependent.
Developers of observationally based flux estimates are beginning to take advantage of the few
high quality direct measurements available for evaluating their methods. Climate modellers are
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2.7 Evaluation of Climate models

likely to consider this strategy less valuable, as they are more interested in how theirsimulations are fairing on the whole, that is they want maps!

Figure 2.3. The climatological values of the net surface heat flux from four coupled models andfour other flux products. Shown are (from top, left) ARPEGE/OPAICE, HadCM3, ECHAM3 +LSG, NCAR-CSM, the SOC climatology, the ERA15 reanalysis, the tuned UWM/COADS
climatology, and the results from the Residual method (Trenberth and Solomon, 1994)
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2.7 Evaluation of Climate models
2.8 Climate Change Studies

A better quantitative understanding of our global ocean estimates of heat, momentum
and freshwater fluxes is urgently needed for the evaluation of climate models. Accuracy oflong-term means and inter-annual variability with medium spatial resolution and global
coverage is important. Wind stress, latent and sensible heat, short and long wave radiation,
precipitation and evaporation, sea-ice extent and thickness, are all important for long term
climate simulations. Despite the difficulties, for the evaluation of coupled models the time isright for a comprehensive examination of state-of-the-art flux products. While it may be years
before uncertainties in large scale fluxes can be reduced to a desired level (e.g. <10Wm-2 for the
net heat flux) it would be of tremendous benefit if a serious attempt is made to identify thestrengths and weaknesses of available products, and to the extent possible, quantify theiruncertainties (as this Report attempts to do).

2.8 Climate change studies
Ocean-atmosphere fluxes establish the link between ocean surface temperature changes
and atmospheric circulation variability. On the other hand, they provide the mechanism of bywhich ocean variability is forced by the atmosphere. Thus accurate knowledge of the flux
variability is extremely important for understanding climatological variations in the coupled
ocean-atmosphere system. Climate change studies are primarily based on the consideration ofgridded flux products with at least monthly temporal resolution. For these products therequirements can be divided into three groups: (1) accuracy requirements; (2) resolution
requirements; (3) specific requirements with respect to the reliable description of fluxes in key
regions.
With respect to accuracy, the most important requirement of the flux fields is reliable
representation of the temporal behaviour of the anomalies of fluxes, rather than closure of thebalance either for the globe or for a particular basin. Cayan (1992a-c) and Gulev (1995)
stressed that consideration of anomalies avoids a number of errors and uncertainties inherent inconsideration of climatological means. On the other hand, variability patterns derived fromdifferent data sets are largely influenced by time dependent biases which result from changes inobservational techniques and sampling density (for the VOS data) and from the changes in themodel configurations and assimilation input (for NWP products).
If we consider interannual and decadal-scale variability, sensible and latent heat are themain contributors to the net flux anomalies in mid- and high latitudes, explaining 70 to 90 % of
the magnitude of the net flux anomalies (Cayan 1992a, Gulev 1995, Gulev et al. 2000). In thetropics and subtropics, the net flux anomalies are determined by both latent heat and SW
radiation. Evaporation minus precipitation anomalies are largely influenced by precipitation in
low and high latitudes; in mid latitudes and subtropics, anomalies of evaporation are of similar
importance. Rough estimates of the typical magnitudes of the interannual to decadal mid
latitudinal winter signals associated with the North-Atlantic Oscillation (NAO) and PacificNorth American Oscillation (PNA) are from 10 to 50 Wm-2 (Cayan 1992b,c, Iwasaka andWallace 1995, Tanimoto et al. 1997, Alexander and Scott 1997, Gulev et al. 2000). Thus anaccuracy of at least 10 to 20 Wm-2 is required for mid-latitudinal gridded anomalies of fluxes.
This estimate does not have a direct link to the accuracy of the mean flux. In the tropics the
typical magnitude of the interannual variability, associated with the ENSO signal and theAtlantic subtropical dipole, is somewhat smaller, and we can take 7 to 10 Wm-2 as the accuracy
requirements for the tropical anomalies of latent heat and SW radiation.
For secular changes (with respect to a period of about several decades), the largest
observed tendencies vary from 5 to 15 Wm-2 per decade for selected locations and from 1 to 5
Wm -2 for ocean basins, implying a somewhat higher accuracy for the flux anomaly estimates
than is needed for an adequate description of interannual variability. Note however, that allexisting estimates of secular climate changes in sea-air fluxes are influenced by the uncertainties
inherent in the data used. Thus any given estimates of required accuracy should be considered
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2.8 Climate Change Studies

as very tentative. This is particularly important with regard to anomalies of wind speed and thewind stress products used to force ocean models. Wind speed based on Voluntary Observing
Ship (VOS) observations may be affected by trends which are artificial in nature, resulting from
historical changes in observational techniques (such as the relative contribution of Beaufort
estimates and anemometer measurements, changing ship size and macrostructure, etc. Peterson and Hasse, 1987, Cardone et al. 1990, Lindau et al. 1990, Isemer 1995, Kent andTaylor 1997, Gulev 1999). NWP wind (and stress) products can be also influenced by timedependent biases due to the impact of VOS and other data on the data assimilation. Changes ofwind speed of around 1 ms-1 can result, for moderate and strong winds, in an apparent 20 to
25% increase of wind stress, and even larger effects on the mixing parameters (for instance, u*3)
used in ocean modelling. Time dependent biases inherent in the wind fields can strongly affectevaporation estimates, leading to trends in latent heat flux of up to 10 Wm-2 per decade or evenmore. Therefore, accurate evaluation of wind fields free from time dependent biases, would beof great importance for climate research.
The required spatial resolution of the surface flux fields for climate studies isdetermined by the necessity to resolve the main variability patterns. Although multi-year mean
climatologies are available currently at 1˚ latitude x longitude grid resolution (da Silva et al.
1994, Josey et al. 1999) the true spatial resolution is worse, typically by a factor of 3 or more
(Kent et al., 2000).
To require 1˚ resolution for monthly anomalies would therefore be
unreasonable, since it is even more difficult to meet the accuracy requirements for very fineresolution on these time scales. Moreover, taking into account that linear matrix operations,based on SVD and EOF analysis, still constitute the core of most of climate analysis
techniques, it is important to avoid continuous gaps in the anomaly fields. On the other hand,the 5˚ resolution used in the studies of Cayan (1992b,c) and Iwasaka and Wallace (1995) may
lead to an inadequate description of a number of key-processes in mid- and high latitudes; inparticular those associated with flux variability in the vicinity of SST fronts and sea ice margins.
It is probable that the optimal spatial resolution for monthly anomalies of the fluxes is around2˚. That would fit the resolution of the best, present, coupled models but would be hard toachieve from observational data. Furthermore, some key-regions associated with mechanisms
which are crucially important for the description of climate variability require finer resolution of
the anomalies of the flux fields, and high accuracy. The most remarkable examples are theLabrador Sea and GIN Sea regions which are responsible for deep convection driven primarily
by the surface heat and fresh water fluxes. Convection events may occur within quite shortperiods of around several days, so that both, monthly temporal and 2˚ spatial resolutions can be
too coarse for adequate description of the climatic variability of the sea-air fluxes.
Studies of climate variability in ocean-atmosphere flux fields require accurate
description of the sea ice extent. In the presence of ice, the fluxes between the ocean and theatmosphere are changed considerably compared to open water conditions (section 2.5).
Moreover, the flux between the ice surface to the air and the flux lost by the ocean are different
in the presence of ice, and dependant on the ice thickness. In addition the degree of ice coverstrongly affects the ocean-atmosphere fluxes in the vicinity of the ice margins, where thehighest local sea-air temperature gradients may be observed. It is known that the ice cover has
pronounced decadal scale variability (Hilmer et al. 1998). If this is not taken into account (thatis if flux producers do not have a time-dependent ice coverage and use a climatological icemask), large artefacts will affect the computed anomalies of the fluxes.
To summarise, climate studies have some important specific requirements with regard
to flux products, relating primarily to the reliability of the anomalies of fluxes. These may notnecessarily be properly represented by even the best flux climatologies. In this context,
satellite fluxes (when they reach continuity on climatological time scales) will definitely serveas an important data source for reliable anomalies of the fluxes, being expected to have thesmallest time-dependent biases.

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2.8 Climate Change Studies
2.9 Conclusions

2.9 Conclusions
For NWP models there is clearly a need to continue the observations needed for
assimilation into the model. However beyond that, the requirement is for independent
estimates of the basic variables and of the fluxes for verification of the model physics. Suchestimates must be associated with a realistic error assessment and specification of the true
resolution of the data. For regional models the requirement is similar but with increased
emphasis on high space/time resolution which at present may only be achieved by using models(although in future high time resolution satellite products may become available).
For OGCM's high resolution forcing fields are needed. For example 3 hourly winds are
required to simulate the shear in the mixed layer, and a 50km resolution is sought for the windstress curl. A useful development for OGCM's would be air-sea interaction experiments where
measurements of the fluxes in both atmosphere and ocean are obtained and the ocean mixed
layer measurements allow budget closure. Although difficult, such experiments are necessary
for parameterisation development.
For wave models, high resolution in space and time is increasingly found to beimportant, and, for wave forecasting, high quality forecast winds are vital. Time resolution
on the order 2 to 4 hours is presently sought and NWP and reanalysis centres are being asked for
3 hourly fields.
For sea ice modelling resolution of the flux fields on synoptic scales is adequate: daily
and 50 km resolution. The mean flux values must be known to high accuracy; biases of a few
Wm -2 can change mean ice thickness substantially. The fraction of open water needs to be
known to order 1%.
For climate variability studies the fluxes need to be known to a few Wm-2 accuracy but
the requirements on spatial resolution can be relaxed; a factor of two coarser than mean
climatological fields would generally be adequate. However there are certain vital areas where
higher resolution may be required, for example the Labrador Sea. For such areas it is also
important to allow for the variability of ice cover; climatological ice masks are not sufficient.
For variability studies a long time series of observations is vital. At present such studies are
based on the ship data and we must recognise that there are some areas of the ocean,particularly the Southern Ocean, where we really have little idea of what the variability is.
Even in better sampled regions such as the Kuroshio there is scope for further studies.
Climate modellers need a reliable data set to compare with - both basic variables andsurface fluxes. Climate models tend to be very sensitive to the surface fluxes which are needed
to within 10 Wm-2 . They also need information with regard to sensitivity, for example, how
do the fluxes change with changes in the fields of the basic variables?
In summary, for many purposes there is a need for high space time resolution which atpresent can only be obtained from models (although new satellite products show promise for the
future). The models need high quality verification data of known resolution and error
characteristics, and also improved representation of the basic physics of air-sea fluxes. Thus itwill be argued in this report that the flux products from various sources are complementaryrather than being in competition, and that the ensemble of air-sea flux estimates that they
represent cover the time and space scales that ocean science studies are investigating.

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3.1 Introduction
3.2 Synoptic and mesoscale processes

3 SPACE-TIME VARIABILITY OF THE FLUXES
3.1 Introduction
The surface air-sea fluxes link oceanic and atmospheric processes, and knowledge of
their variability on different space-time scales is crucial for understanding ocean-atmosphereinteraction. Accurate quantitative description of the variability of atmosphere-ocean energy
exchange is important for process studies and for the development of all types of oceanic andatmospheric models. In addition, accurate knowledge of sea-air flux variability can help toimprove the flux fields themselves, since it indicates the accuracy which is required to quantify
that variability.
In some respects, determination of the flux variability may be easier than determining
the mean flux values; systematic errors are of less importance, and the main spatial scales of
longer term variability tend to be large. Similarly, numerical weather models tend to performbetter with regard to determining the variability than they do in determining the mean. This isprobably because the models are designed to forecast the weather - the day to day variations inclimate. However despite these considerations, and the undoubted importance of surface flux
variability, the Working Group considered that detailed assessment of the variability of flux
products could not be accomplished at this time. Thus, in this chapter, a brief discussion offlux variability will be presented. The aim will be to set the context of the flux field evaluations
presented in future chapters rather than to present a complete description of the characteristics
of air-sea flux variability. A thorough assessment of the variability characteristics of available
flux products must remain an important priority for further studies, perhaps by a future working
group.
3.2 Synoptic and mesoscale processes
3.2.1 Mid to High latitude variability
A. INTRODUCTION
In the mid and high latitudes, synoptic and mesoscale variability of the sea-air fluxes is
primarily determined, on the one hand by the atmospheric synoptic scale transients and theirassociated high frequency variability, and on the other hand, by mid-latitudinal
inhomogeneities of sea surface temperature. Thus SST fronts, together with coastal andmarginal ice zones, generate sharp spatial gradients of surface properties which vary little on
the time scales of atmospheric synoptic variability. In other words, mid latitudinal synoptic
scale ocean-atmosphere energy exchange is characterised by a highly non-stationary
atmosphere and a strongly inhomogeneous ocean. The contrast is considerable; atmospheric
synoptic variability has spatial scales several times larger than the ocean surface
inhomogeneities, and ocean temporal variability on synoptic space scales is several times
slower than the atmospheric synoptic time scales.
B. MAGNITUDE OF FLUX VARIABILITY
The typical temporal time scale of mid-latitudinal atmospheric variability is from 1 to 7
days. Atmospheric parameters in different parts of propagating mid-latitudinal cyclones arehighly variable. For instance, in winter time, the air temperature in cyclones over the northwest Atlantic and north-west Pacific can vary from 15˚C in the warm sector to strongly negative
temperatures behind the cold front. Each winter, the monthly record of wind speed in midlatitudes shows a range from calm conditions to gale force winds. Thus, mid-latitudinalsensible and latent heat fluxes vary enormously, from heat input into the ocean to very highrates of ocean cooling. As an example, direct measurements during the winter season in the
Labrador Sea (The LabSea Group 1998) showed variations of sensible flux between weakocean warming, of order several tens Wm-2, to 600 to 800 Wm-2 cooling during a 22 day
period.

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3.2 Synoptic and mesoscale processes

Extremely high variability of mid-latitudinal fluxes occurs in cold and warm air
outbreaks, associated with the propagation of cyclones over SST fronts or ice margin areas.
This variability can be further enhanced in rapidly intensifying cyclones (“meteorologicalbombs” - Rogers and Bosart, 1986), which are characterised by extremely high winds andtemperature gradients. Thus, in 12 hours during the development of a rapidly intensifying
cyclone in the CASP experiment, Yau and Jean (1989) reported changes of sensible heat flux
from -70 Wm-2 (ocean warming) to 500 Wm-2 (ocean cooling), and changes in the latent heat
flux from 30 to 1100 Wm-2. Neiman et al. (1990) reported about 1000 Wm-2 spatial change inthe latent flux between the warm and cold sectors of an extra-tropical cyclone propagated along
the North American coast. Gulev and Tonkacheev (1996) demonstrated that during a cold air
outbreak latent heat fluxes over the Gulf Stream front increased from 20 Wm-2 to 500 Wm-2 in a
distance of 12 km. During the GALE experiment, Bane and Osgood (1989) measured similar
spatial gradients of sensible and latent heat fluxes during a cold air outbreak over the Gulf
Stream. While the Gulf Stream and Kuroshio are major features, less pronounced frontal
zones in the mid-latitudinal and subtropical ocean can be associated with strong local anomaliesof surface fluxes (quantified in such experiments as FASINEX and SEMAPHORE).
Temperature gradients at the FASINEX front in the subtropics of about 3˚C per 20 km wereassociated with surface latent flux changes from 150 to 300 Wm-2 (Friehe et al. 1991, Halliwell
and Cornillon 1990). Moreover, local anomalies of the turbulent fluxes showed spatial
structures with typical horizontal scales of 30 to 60 km.
C. EFFECTS ON OCEAN AND ATMOSPHERE
The synoptic variability of the fluxes in mid latitudes plays an important role in ocean
dynamics and atmospheric synoptic variability. High sensible and latent fluxes from the ocean
to the atmosphere, together with strong winds, are responsible for cooling and mixing in the
Labrador Sea, Greenland-Iceland-Norway Sea, and in some areas of the Southern Ocean The
deep convection events which result are crucially important for the thermohaline circulation of
the ocean. These events are associated with flux anomalies on daily to weekly time scales (theLabSea Group 1998), and their description requires accurate knowledge of energy exchange at
the ocean surface on those time scales. In the mid-latitudinal open ocean, and in thesubtropics, synoptic and meso-scale sea-air flux variability contributes to ventilation andconvergence processes in the ocean. These may result in SST and mixed layer temperaturechanges, and so form long-lived anomalies of upper ocean temperature.
Synoptic variability of mid-latitudinal sea-air fluxes strongly affects the development
and propagation of atmospheric synoptic systems, modifying low-level static stability andcontributing to local vorticity anomalies. High extremes of the synoptic scale fluxes in midlatitudes can be directly responsible for the intensification and even local generation of marine
cyclones. In particular, most rapidly intensifying cyclones with deepening rates more than 1
mb per hour are considered to be directly associated with the highly variable sea-air interaction
processes in mid latitudes. Strong spatial gradients of the sea-air fluxes in the vicinity of SST
frontal zones can diabatically modify extra-tropical cyclones and thus affect their life cycle(Neiman et al. 1990).
D. MEASURING FLUX VARIABILITY
Accurate quantitative description of synoptic and meso-scale variability of the oceanatmosphere fluxes cannot be done on the basis of Voluntary Observing Ship (VOS) data, since
the sampling is generally insufficient for this task. Ocean weather ship (OWS) data provide a
valuable source of information for the analysis of short-period flux variability, if only in a few
locations. These data have been analysed in a number of studies (e.g. Josey et al. 1995, Gulev
1997a) which quantified synoptic scale variability in the surface fluxes. In this context datafrom NDBC and JMA buoys located in mid latitudes are also a valuable data source.
It should be noted that, for a number of key synoptic and meso-scale processes such as
exchange in the vicinity of sharp SST fronts and ice margin zones, the use of standard bulk
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3.2 Synoptic and mesoscale processes
3.3 Climate Variability

formulae (Chapter 7) may not be valid due to the very strong non-stationarity and intensive
local advection effects. Thus new experiments (like FASINEX, GALE, ERICA, CASP, andLabSea) are very desirable to provide direct measurements of the fluxes in regions of strong
gradients of surface temperature and highly variable atmospheric processes.
The high
resolution flux fields now available from reanalyses also give an excellent opportunity for
global-scale analysis of synoptic variability in the ocean-atmosphere fluxes. Satellite data also
may be used. For example, Miller and Katsaros (1992) demonstrated the possibility of using
SSM/I data for the diagnosis of surface latent heat fluxes in a rapidly deepening marine cyclone
in the Atlantic. Even if the absolute flux values derived from satellites are biased, remotelysensed fluxes may realistically depict the space-time variability on synoptic and mesoscales.
3.2.2 Variability in the tropics
Although synoptic and mesoscale sea-air flux variability in the tropics has smaller
magnitude in comparison to mid- and high latitudes, it may play an important role in some key
processes in the tropical boundary layer. Large synoptic variations of tropical evaporation canaffect cumulus convection, contributing to the instability above the 850 mb level. Note alsothat in the tropics, the diurnal cycle in the sea-air fluxes becomes important, especially undercalm conditions and considerable insolation (Slingo and Delecluse 1999, Weller and Anderson
1996). This enhancement of convection, and the reduction of the magnitude of the diurnal
cycle, is responsible for the transition of tropical convection regimes from primarily
continental to marine type. These effects were studied explicitly in the TOGA-COARE
experiment (Slingo and Delecluse 1999). In general, TOGA-COARE contributed considerably
to our knowledge of synoptic and mesoscale sea-air flux variability in the tropics particularly
through the extended buoy observational program. It has been found that tropical synoptic
variability can be characterised by 2-3 days dominant time scales. Weller et al. (1999) showedthat large variations in sea-air fluxes are associated with a family of convective systems ofdifferent origin. Mesoscale convection can result in 60 and 250 Wm-2 peaks in the sensible andlatent heat fluxes respectively. Peaks associated with the sub-mesoscale systems are somewhat
smaller, but also pronounced (20 and 150 Wm-2). Esbensen and McPhaden (1996) analysed
TAO moorings and IMET buoy data in the TOGA-COARE experiment and found meso-scalevariability in the surface heat fluxes, with characteristic ranges of 0-50 Wm-2 and 0-300 Wm-2
respectively for sensible and latent heat.
3.3 Climate variability
3.3.1 Introduction
Climate variability covers time scales from several years to centuries. Although thenumber of studies dealing with climate variability in sea-air interaction processes is quite large,
these studies are (to date) primarily based on the consideration of anomalies of SST andassociated changes in the atmospheric circulation characteristics. There are very few studies
which directly consider the variability of the ocean-atmosphere fluxes. However, it is throughthe exchange of heat, moisture and momentum that the atmosphere interacts with the ocean.
Thus it is not SST itself, but these energy fluxes to and from the ocean, that actually force theatmospheric circulation and, at the same time, control the ocean temperature. Reviewing oneof the latest results on the mid-latitudinal links between SST and atmospheric circulation(Rodwell et al. 1999), Kushnir (1999) argues that we need “to understand the mechanisms bywhich the atmosphere ‘feels’ the SST anomalies”. Although it is obvious that sea-air fluxes
provide more valuable information on these mechanisms in comparison with say, SST,
researchers tend to avoid consideration of the climate variability of the fluxes. This may be
explained by the following reasons: (i) flux fields contain more uncertainties and errors(including time dependent biases) than individual variables; (ii) flux fields are characterised byworse sampling in comparison to say SST, and only shorter time series in well sampled ocean
basins are available. Nevertheless, some results for a number of basins have been obtained
during the past several years.
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3.3 Climate Variability

3.3.2 Detection of climate trends
The best studied ocean basins are the North Atlantic and the North Pacific, since these
are characterised by better sampling.
Bunker (1982) computed linear trends in themeteorological variables and sea-air fluxes over the Atlantic Ocean from 1948 to 1972 on thebasis of the Bunker (1976) data set. He found significant positive tendencies in winds andevaporation in the tropics and mid latitudes. However, in the 1980's and 1990's, there has
been considerable debate on the reliability of wind trends derived from VOS data (Ramage
1987, Peterson and Hasse 1987, Cardone et al. 1990, Lindau et al. 1990, Ward 1992, Bigg
1993, Isemer 1995, Ward and Hoskins 1996, Gulev 1999). Changes with time of the fractionof anemometer measurements, growing ship size and other reasons were considered as possible
sources of unrealistically high (0.1 to 0.5 ms-1 per decade) trends in the wind speed derived
from VOS data. This makes all estimates of secular tendencies in the turbulent fluxes
questionable, since any wind speed bias propagates into the heat flux results through the bulk
formulae. Thus, Wu and Li (1995) found a significant increase in evaporation in a number ofthe World Ocean basins in the decade of the 1980's, the same period which showed a sharp
increase in wind speed in comparison to earlier decades. Trends in the sea-air fluxes can also
be affected by time-dependent biases in variables other than wind. For example, the changing
fraction of SST bucket measurements and engine intake readings could result in an artificial
SST trend.
Thus, at present, estimation of secular trends in the fluxes is more uncertain comparedto other scales of climate variability, and time dependent biases in the basic meteorologicalquantities constitute the main contribution to this uncertainty. Any future efforts in this area
should include corrections to the observations (as recommended for example by Kent et al.,1993b; Josey et al., 1999). However, such corrections require that additional information beavailable. Since that information is not available for every observation, the sampling errors
may be considerably increased, and this can also affect the estimates of trends. Now that thereanalyses have become available for periods of several decades, there is an alternative
opportunity to estimate secular changes in the flux fields. However, although the performanceof the models has been kept frozen for the reanalyses, the assimilation input unavoidably
changed with time. Thus, estimates of secular changes from the reanalyses may also be
seriously influenced by the changes in the data assimilation input. White (1999) reported thatthe significant increase of satellite data available in the late 1970's could result in artefacts inestimated secular tendencies in SST and precipitation, especially in the Southern Hemisphere(where the relative role of satellite data in the total amount of observations is very large). In
addition, time dependent biases in the COADS data may propagate into the reanalyses flux
products resulting in additional uncertainty of the reanalyses-based trend estimates. Inspectionof linear trends in evaporation and net flux, derived from the VOS measurements and from
NCEP/NCAR reanalysis, give comparable trend estimates for well sampled areas (e.g., upward
tendency of about 15 Wm-2 per decade in the North Atlantic mid latitudes and negative trends
matching 10 Wm-2 per decade in the Gulf Stream area in the subtropics). However in otherareas there are strong disagreements. In the tropics the VOS fluxes indicate significantly large
positive trends, in disagreement with the NCEP reanalysis fluxes. In high latitudes (e.g. theLabrador Sea) the NCEP reanalysis fluxes demonstrate strongly positive secular changes which
are not supported by estimates based on VOS observations.
Satellite data sets are already becoming comparable in time span with the reanalyses.
In future these will give another alternative data source for estimating secular tendencies, whichmight reasonably be considered less biased in comparison to VOS and NWP fluxes. In thissense, satellite data have an advantage because, even if there are biases in the flux products,
these should not be of time-dependent nature and thus should not affect estimates of seculartendencies. As an example of the application of these data, White (1999) reported that evenfor the period from late 1970's to late 1990's (which is considered to have more homogeneous
data assimilation input) precipitation trends derived from the NCEP/NCAR reanalysis andsatellite data set of Xie and Arkin (1997) have different signs.
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3.3 Climate Variability

3.3.3 Interannual to decadal scale variability
A. MID AND HIGH LATITUDES.
We can expect better progress to be achieved when interannual to decadal scalevariability is analysed, since the uncertainties present in the mean fields and time dependent
biases can be avoided. Cayan (1992a-c) used COADS MSTG products to compute the sea-air
flux anomalies for the period 1946-1986 in the North Atlantic and North Pacific and toinvestigate the links between the flux variability patterns and associated changes in the SST andatmospheric circulation. Iwasaka and Wallace (1995) and later Tanimoto et al. (1997) madesimilar studies of the variability in the ocean-atmosphere fluxes derived from COADS data.
All these studies emphasised that the major portion of variance in the surface net fluxes in the
North Atlantic and North Pacific is explained by dipole patterns made by subtropical andsubpolar centres of action. These patterns mirror corresponding SST patterns on interannual todecadal time scale, and can be linked to the sea level pressure patterns associated with theNorth Atlantic Oscillation (NAO) in the North Atlantic and with the North Pacific Oscillation
and Pacific-North American pattern in the North Pacific. Cayan (1992a) found a typical winteranomaly magnitude of about 30 to 50 Wm-2 in the mid-latitudinal North Atlantic and asomewhat smaller anomaly in the North Pacific. Sensible and latent fluxes were primarily
responsible for these mid latitudinal anomalies in the net flux.
Although the mid latitudinal variability patterns in the flux fields reported by different
authors are quite comparable to each other, careful inspection of the results shows that the
spatial features and the flux behaviour in time (usually described in terms of the leading
empirical orthogonal functions, EOF's and the corresponding principal components) show
differences in different flux products. Whether these observed differences are significant
depends on the scientific task considered. For describing the surface flux signal affecting thelarge scale atmospheric circulation (represented by sea level pressure or 500 mb heightpatterns), one can be satisfied with similarity of the time series of principal components
corresponding to the first EOF, which typically accounts for 20 to 35% of the variance. On
the other hand, analysis of relationships between the surface flux variability and behaviour ofthe mid-latitudinal storm tracks requires consideration of the flux patterns associated with thehigher order EOF's. These can reflect differences between products, and may not necessarily
be clearly separated one from another because of the errors and uncertainties which again canbe different in different data sets. From the view point of forcing the general circulation of theocean by surface fluxes, even the apparent comparability of the first EOF's in different flux
products has to be viewed with caution. For example, for the ocean circulation in the North
Atlantic, it is crucially important whether the local flux anomalies in the Labrador Sea,
primarily caused by sensible flux, are depicted explicitly. If we consider, for example, theCayan (1992a) pattern, the Labrador Sea will be characterised by weak anomalies. These arecorrelated with the anomalies in the centre of the subpolar gyre, which may be partly generated
by the spatial interpolation procedures during periods of poor sampling in the Labrador Sea.
However, the NCEP/NCAR and ECMWF reanalyses show a pattern characterised by strong
anomalies explicitly located over the Labrador Sea and not centred on the subpolar gyre in
general (Gulev et al. 2000). Exactly this pattern was also found recently by Alexander and
Scott (1997) who studied surface flux variability from the atmospheric GCM simulations. The
time behaviour of these patterns is highly correlated, however only one of the two products
can provide realistic production of Labrador Sea water.
Recently Gulev et al. (2000) analysed interannual variability in VOS products derived
by using different calculation schemes in comparison to the NWP fluxes taken from the
NCEP/NCAR reanalysis.
First, the EOF's of the VOS fluxes derived using different
parameterisations were computed. They were very comparable to each other. The use ofdifferent schemes resulted, as expected, in different magnitudes for the anomalies, but did notaffect the estimates of the long-term tendencies and interannual variability. In order to achieve
full comparability of the spatial patterns, the anomalies of fluxes derived from different
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3.3 Climate Variability

schemes were projected on the EOF's of a reference product - for example, the Large and Pond
(1982) scheme for the turbulent fluxes. The correlation between the temporal behaviour of the
projected fluxes (i.e. the quasi-principal components) and the principal components taken as
reference, showed that different parameterisations did not lead to any qualitative differences inthe temporal behaviour of the flux anomalies. The correlation between different projections
was close to one and always over the 0.98 limit. On the other hand, the standard deviation of
the variability obtained using different parameterisations was quite different, and the selectionof which scheme to apply was important in order to estimate the forcing magnitude (forexample for model forcing fields).
This is illustrated in Figure 3.1 (Gulev et al. 2000). Figure 3.1b shows the first EOF'sof the net heat flux derived from fluxes computed using individual COADS reports and theobservation corrections and parameterisations recommended by Josey et al. (1999) - except thatfor turbulent heat fluxes the Large and Pond (1982) scheme was used instead of Smith (1988).
Also shown (Figure 3.1a) are those from the NCEP/NCAR Reanalysis data for the period from1958 to 1997. On the first glance, at least in mid latitudes, the patterns are quite comparable
and indicate a north-south dipole made by centres of action in the subpolar gyre and in the Gulf
Stream area. But if we consider the NCEP/NCAR fluxes, the northern centre of action iscentred exactly on the Labrador Sea, and for the other products we have somewhat moresmooth picture with the local maximum located east of Newfoundland. More comparability isachieved in the southern centre of action.
The observed difference between the VOS and NCEP/NCAR can be influenced by inadequate
sampling in the VOS data set, which has a climatological dependence and can affect both longterm trends and interannual variability. Figure 3.1c shows the first EOF in the modified
NCEP/NCAR net flux, which was derived from the original 6-hourly snap-shots by matchingthem to the dates and hours of the COADS reports used for computing the VOS fluxes.
Remarkably enough, this “undersampled” NWP product demonstrates a spatial pattern which isvery similar to the original VOS net flux. In this case, the importance of good sampling inorder to describe variability justifies the use of COADS MSTG products (see also Section
10.12), at least for investigating climate variations in the flux fields. Figure 3.1d shows thefirst EOF in the fluxes derived from the enhanced COADS MSTG, which were taken as
trimmed statistics of the basic variables; the same parameterisations as for the VOS-type ofdata were applied on a monthly basis. In comparison to the NCEP fluxes, the variability over
the Labrador Sea is more similar in amplitude when derived from the MSTG rather than fromthe VOS. The reason is that the MSTG product is less influenced by undersampling comparedto VOS products computed on an individual basis. This suggests that MSTG can be
successfully used for climate studies, especially if some ad-hoc corrections are applied toremove, at least partially, the time dependent biases inherent in the MSTG. Figure 3.2 shows
the time behaviour of the 1st normalised principal components of different net fluxes incomparison to the NAO index. The time coefficients for all four products are highly correlatedwith each other and show quite impressive correlation with the NAO index.
B. TROPICS
The interannual and decadal variability of the fluxes for tropical oceans is significantly
influenced by variations in the radiative fluxes in addition to variations of the turbulent fluxes,
and this considerably increases the uncertainty of the variability estimates. Moreover, themagnitude of flux anomalies in the tropics is smaller in comparison to mid latitudes. Also,
sampling frequency in the tropics is lower than in mid latitudes; fortunately the smallersynoptic variability in these regions does not require the same sampling as in mid latitudes foradequate description of the anomalies (Weare and Strub 1981). If we consider the tropicalAtlantic Ocean, we can expect that the flux variability should reflect the dominant mode
associated with the Atlantic subtropical dipole, characterised by a pronounced decadal cycle in
the SST anomalies (Mehta 1998). In the tropical Pacific the dominant variability is associatedwith El Niño events. Although the fluxes are crucially important for understanding the El Niño
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3.3 Climate Variability

3

3

2

2

1

1

0

0

-1

-1

-2

NC EP (R =0.85)
N CE P /us (R=0.71)-2-3

C O AD S/ind (R=0.76)-3

1st norm alized P C of the net flux

NA O index

Figure 3.1 First EOF of the net heat flux derived from (a) (top left) NCEP/NCAR reanalysis;(b) (top right) COADS ship reports; (c) (bottom left) the reanalysis sampled at COADS report
positions; (d) (bottom right) using COADS MSTG data.

C O AD S /M S TG (R =0.85)
-4

-4
55

60

65

70

75
80
Y E A RS

85

90

95

Figure 3.2 Time series of the NAO index (red line) and of the first normalised principal
component of the net heat flux from the analyses shown in Figure 3.1. (NCEP/NCAR
reanalysis; NCEP/us is sampled at ship data positions; Individual COADS reports; andCOADS MSTG data.)
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3.3 Climate Variability
3.4 Summary

life cycle on an interseasonal scale, there is no indication that they can serve better than SST
as predictors on longer time scales. Variability in the Indian Ocean is also poorly quantified.
Jones et al. (1995) made an EOF analysis of 30-year surface fluxes over the Indian Ocean,using COADS, and found increasing latent heat flux from the 1960's to 1980's ranging fromseveral Wm-2 to several tens of Wm-2.
C. SEASONAL VARIABILITY
The interannual variability of flux fields should be considered in the context of seasonalvariability since this affects the observed climate changes. Summer flux anomalies can be
primarily driven by net flux components different from those which are crucially importantduring wintertime. In particular, radiative fluxes may play a more important role during thewarm season. Tanimoto et al. (1997) noted differences in the summer and winter variability
patterns in the mid-latitudinal North Pacific. However, this problem is still has to be fully
explored.
3.4 Summary
To summarise, our knowledge of the climatic variability of ocean-atmosphere fluxes isstill poor. Results obtained over the last several years have suggested that studies of thevariability of the fluxes can potentially tell us much about the surface climate signals on
different time scales. However in practice the signals which have been derived have notproved significantly more valuable for understanding climate variability compared to thoseobtained by studying the variability of basic variables such as SST. Nevertheless, development
and improvement of different flux products, available from VOS, NWP and satellites, may
improve this situation in the immediate future.

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4.1 Introduction
4.2 in situ data sources

4 DATA SOURCES FOR FLUXES AND RELATED VARIABLES
4.1 Introduction
This Chapter will consider the characteristics of the different sources of data for thefluxes and flux related variables. Each have their advantages and disadvantages. The main
sources of in situ data are from ships and buoys. Merchant ships in the Voluntary Observing
Ship programme are the traditional source of flux estimates providing weather observations
from which the fluxes can be estimated. However the sampling is restricted to shipping lanes
and many parts of the ocean are rarely observed. Likewise, intensive field campaigns byresearch ships (e.g. TOGA-COARE) are too restricted in both sampling time and space, tocontribute significantly to the global flux database; however, the resulting high-quality
measurements of fluxes and related basic variables are invaluable for verification of systems
with broader coverage. Satellites provide globally distributed data but for a restricted range ofvariables. Models can be used to assimilate the various information available, and produce
regular gridded fields of flux values. There is continuous effort, using results from the intensive
field studies referred to above, to improve the performance of model parameterisations andhence the accuracy of the fluxes. Flux values can also be estimated from what are effectively
budget methods, the "residual technique" and assimilation into ocean models. Finally in this
Chapter the data sets for determining the fluxes in sea ice regions will be discussed.
4.2 in situ data sources
4.2.1 Voluntary Observing Ships
Our present understanding of the climate over the global ocean (e.g. Budyko, 1963;
Esbensen and Kushnir, 1981; Oberhuber, 1988; da Silva et al., 1994; Josey et al. 1999) isbased on the meteorological reports from the Voluntary Observing Ships (VOS) of the World
Weather Watch. These merchant ship observations constitute the only quasi-global data set
which extends over a number of decades. In addition, the range of observations is such that it
has been possible to develop parameterisation formulae with which to calculate all the required
fluxes.
A major disadvantage of the ship observations is that adequate coverage is obtained
only in areas that are well covered by shipping lanes (Figure 4.1). For example, in July there
are very few observations which enable a latent heat flux estimate to be made south of 40°S.
In contrast the North Atlantic and North Pacific are well covered by observations. Inevitably thecharacteristics of the ship observations (biases, precision etc.) have changed with time. There
have been, and will continue to be, changes in instrumentation, for example, sea surface
temperature buckets (of varying types) are increasingly being replaced by engine room intake
readings. The increasing use of anemometers to estimate the wind may results in errors due toairflow disturbance and errors in calculating the true wind velocity. However the major change
has been in the size and type of the ships with modern ships typically larger, faster, andtravelling on different routes compared to those in past decades.
In recent years ourunderstanding of the resulting biases in these observations has increased significantly (Chapter
10 ) allowing the possibility both of applying correction schemes and implementing improved
observational methods.
Because there are about 7000 VOS, with ships continually leaving the programme andnew ships being recruited, the instrumentation provided by the Meteorological Agencies has
generally been basic and inexpensive. Kent and Taylor (1991) described a subset of 46
Voluntary Observing Ships (VOS) operating in the North Atlantic, and the meteorologicalinstrumentation with which they were equipped. Typical of this subset would be a container
vessel of about 210m length, travelling at about 9 ms-1, and loaded with cargo to about 10m to
20m above the main deck. As an instrument platform, this represents a large object capable ofsignificantly altering the environment to which the meteorological sensors are exposed. Sea
temperatures were measured near the surface using a SST bucket, or, more likely, at depths ofReport of the Working Group on Air Sea Fluxes

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38

4.2 in situ data sources

3m to 9m using engine intake or hull contact sensors.
Air temperature and humidity
observations were taken at about 20m to 30m above the sea using a thermometer screen or hand
held psychrometer. If carried, the anemometer was mounted at about 30m to 35m above sea
level, but, in the North Atlantic, two-thirds of the ships reported visually estimated winds.
80
250

Latitude

60
40

100

20

25

0

10

−20

5

−40

2

−60

0

−80
60

120

180

−120

−60

0

Longitude

Figure 4.1Global distribution of the number of latent heat flux estimates per 1˚ x 1˚ square forthe period 1980 - 1993. White indicates that there were no observations over the entire period
considered (from Josey et al. 1999)
For some time there have been suggestions that improved instrumentation might beplaced on a subset of the VOS e.g. (Taylor, 1984).
An example is the Improved
Meteorological System, IMET (Hosom et al., 1995) developed at Woods Hole Oceanographic
Institution (WHOI) which has been installed on a number of the U.S. Research Vessels and is
now being placed on U. S. VOS. IMET uses sensors chosen for accuracy, reliability, and theirability to stay in calibration during unattended operation. The present set of IMET modules
includes wind speed/direction, air temperature, sea surface temperature, relative humidity,
precipitation, incoming shortwave radiation, incoming longwave radiation, and barometricpressure. A data set of automatically recorded meteorological data has been assembled fromthe research ships operating during the WOCE Hydrographic Programme (1991 - 1998). These
ships used IMET or similar systems. Often these ships were away from the main shipping
lanes, so these data are potentially of value for flux verification (see Section 9.2.3).
Recently there has been an initiative by the World Weather Watch to establish a VOS
Climate Project. The initial aim would be to assemble a set of chosen VOS and to document
carefully the instrumentation in use. Data from these ships would undergo detailed analysis inorder to estimate any biases on a ship by ship basis. The Working Group considered that suchan initiative was to be encouraged since, in time, the resulting data set will form an important
asset for verification of both basic variables and flux estimates.
4.2.2 Ocean Weather Ships
The array of Ocean Weather Ships was originally subsidised by the commercial
aviation sector, and maintained at different sites for varying periods during the years 1945 to
1989 (Table 4.1 and Figure 4.2).
Apart from surface and upper air meteorologicalmeasurements, the weather ships provided, during various periods, time series of surface
radiation budget, surface waves, ocean profiles, and other variables. Valuable climate data
were obtained, for example the long time series of data from ocean station Papa. However theReport of the Working Group on Air Sea Fluxes

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39

4.2 in situ data sources

weather ships were implemented for weather forecasting rather than for climate observations
and this has implications with regard to the accuracy of the data. Indeed, it should not be
considered that the Weather Ship data were immune from systematic errors. For example,
Isemer (1994) demonstrates that deriving a long time series of consistent weather ship windobservations is not a trivial task given the many changes in ship type and observing practise.
Nevertheless the weather ships form a valuable data resource where a long time series of data isrequired from specific sites.
M
I

A
B
P
X

Longitude

C
D

J
R
K

E

N

T

L

Figure 4.2 Distribution of Weather Ship Stations
Table 4.1 Summary of the Weather ship sites (from Isemer, 1994)
Position

start

end

Country

Position

start

end

CountryA

62N 33W

4/45

4/74

US,F,N,
NL,UK

L

57N 20W

7/75

12/89

F,NL,UK

B

56.5N 51W

1/46

4/74

US

M

66N 2E

1/49

12/89

F,UK

C

52.5N 35,5W

11/45

12/89

US,
USSR

R

47N 17W

7/75

12/85

F

D

44N 41W

1/46

6/73

US

N

30N 140W

6/46

4/74

US

E

35N 48W

9.49

6/73

US

P

59N 145W

10/49

6/81

US,C

I

59N 19W

9/47

6/75

NL,UK

T

29N 135E

9/48

10/81

J

J

52.5N 20W

8/47

6/75

NL,UK

V

34N 164E

3/55

1/72

US

K

45N 16W

6/49

6/75

F,NL,UK

4.2.3 Buoys
Buoy data does not allow global estimation of the surface fluxes; however buoys are agood source of verification data for other observing systems. Many countries now operatemoored coastal buoy arrays, for example the NDBC (Gilhousen, 1998) and AES (Axys, 1996)
buoys off North America, and the ODBS buoys off Japan. Taking as an example the NDBC
buoys, these range in type from the very large 12m and 10m discus designs, through the 6m
Nomad buoy to the 3m Discus buoy. Over 20 buoy locations have been maintained in both the
Atlantic and Pacific with most time series dating from the mid 1970's, or early 1980's, to theReport of the Working Group on Air Sea Fluxes

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4.2 in situ data sources

Longitude

present. In addition to standard surface meteorological data, spectral wave data and current
profiles are available from some locations. Although primarily established for weather
forecasting and now-casting purposes, these buoys have also proved valuable for thecalibration of remotely sensed data from satellites.
Operational buoy arrays have begun to be established away from coasts in the open
ocean. Foremost of these was the TOGA TAO array (McPhaden et al., 1998). A major
responsibility for the western part of the array is now to be assumed by the TRITON
programme (Kuroda et al., 1999) and a similar array (PIRATA) is being implemented in the
Tropical Atlantic (Servain et al., 1998). The relatively simple and inexpensive Autonomous
Temperature Line Acquisition System (ATLAS) buoys which are used for these arrays measurea basic set of the meteorological variables. In contrast "Flux buoys" have been developed,
principally by a group at WHOI as part of the IMET programme, to measure all the variables
required to estimate the heat, momentum and radiative fluxes. They have been used in the
Subduction experiment in the North Atlantic (Moyer and Weller, 1997), in the west Pacificduring the TOGA COARE experiment (Weller and Anderson, 1996), in the Arabian Sea(Weller et al., 1998) and elsewhere. These buoys are a valuable source of data for verifying
flux estimates. Equipped with fast response sensors, research buoys can also be used todetermine the transfer coefficients.

IMET

Operational

TAO/Triton, Pirata

Figure 4.3 Positions of Meteorological buoy arrays (past and present). (circles: standard
meteorological buoys; triangles: TAO/Triton and PIRATA arrays; black diamonds: Flux buoysites(IMET) ). Note that not all of these buoys are still in operation.
Drifting buoys are deployed in significant numbers and are well distributed over the
global ocean. However they measure a too restricted range of variables to allow flux
estimation. Sea temperature is normally measured, air pressure may be measured, and, onsome buoys, wind speed may also be estimated.
4.2.4 Platforms, Rigs, etc.
Fixed platforms, drilling rigs, etc. generally tend to be poor instrument platforms.
Their large size is likely to result in poor instrument exposure. Instruments, such as
anemometers, are often mounted several tens of metres above the sea. If the platform is
involved in oil or gas production then there may be strict controls on the type of instrumentationthat can be installed and the facilities for access for maintenance. However, because some
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4.2 in situ data sources
4.3 Satellite data sources

platforms report meteorological observations on a very frequent basis it may be necessary toensure that potentially biased observations from a platform do not dominate the climatology ofa region. This is also true of coastal observation stations, such as lighthouses, where the data
may be significantly influenced by the position of the platform.
An exception to these negative judgements are platforms designed or implemented
specifically for meteorological observation, however these are very few in number andconfined to coastal regions.

4.3 Satellite Data Sources
4.3.1 Introduction
Many algorithms have been developed deriving basic state variables like SST,
atmospheric surface temperature, wind speed, atmospheric humidity, cloud liquid water, rain
rate, longwave and shortwave radiation etc. from satellite data using instruments measuring atdifferent wavelengths. Whereas the radiative fluxes at the surface are derived by measuring
radiative fluxes at the top of the atmosphere and using a radiative transfer model (Chapter 6),
turbulent fluxes are estimated from the basic state variables using bulk formulae (Chapter 7).
Today most of the basic state variables necessary for estimating surface fluxes can be derived
from satellite data. These quantities may be directly derived using radar backscatter or fromvisible, infrared or microwave brightness temperatures. The exception is atmospheric surface
air temperature. This variable is important to estimate sensible heat flux, and the transfer
coefficients within the bulk formulae, but estimation from satellite data is still troublesome.
Previously, some of these retrievals have been used to derive flux climatologies, however
mostly they have only been used to derive exemplary fields of the basic state variables overshort time periods. However, there has been a remarkable development of new algorithms
which is gradually changing the situation.
In this section the major instruments used for flux determination which are presently
orbiting are introduced. The orbit and instrument characteristics of the different satellites are
identified and discussed. Major demands on the instruments utilised are that they must deliveraccurate and relatively stable measurements over a long period. This is only attainable by using
on-board calibration, applying algorithms that consider the ageing of the particular radiometer,
or by applying ongoing calibration with high quality in situ data. A further requirement, for
instruments on polar orbiters, is that the swath width must be large enough (over 1000 km) to
sample the earth's surface during a couple of days.
4.3.2 Non geostationary satellites
The classical instruments that meet the above requirements are the Advanced VeryHigh Resolution Radiometer (AVHRR) and the Special Sensor Microwave/Imager (SSM/I).
Measurements of sea surface temperature are best performed in the infrared range where the sea
surface has a high emissivity. On the other hand, this high emissivity dominates the signal andprevents the extraction of wind speed or boundary layer water vapour content with highaccuracy from infrared measurements. For these parameters the microwave spectral range ismuch better suited since the ocean surface emissivity is relatively small in the microwave range
compared to the infrared. A disadvantage of these considerations was that, before the TropicalRainfall Measuring Mission (TRMM), it was not possible to obtain energy fluxes from
instruments installed on one satellite. Thus, any estimate of a parameterised quantity like latent
heat flux will suffer from being derived from estimates of basic variables obtained fromdifferent satellites at different times. Fortunately, in general it is expected that this error isrelatively small since the sea surface temperature obtained from AVHRR data varies relatively
slowly compared to the atmospheric variables obtained from SSM/I measurements. To derive
fields of wind speed and rainfall, active instruments like scatterometers and radars may also be
used
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4.3 Satellite data sources

A. AVHRR
The AVHRR instrument has been flown on the NOAA satellite series since 1979 andused to construct global fields of sea surface temperature, for example the optimallyinterpolated SST dataset of Reynolds (Reynolds and Smith, 1994) or the NOAA/NASA Oceans
Pathfinder Sea Surface Temperature dataset (Brown et al. 1993). Flight periods for thedifferent NOAA satellites are shown in Table 4.2.
The AVHRR instrument measures in five channels, two in the solar spectral range, one
in the near infrared, and two located within the infrared atmospheric window. The infraredwindow channels are best suited for estimates of sea surface temperatures in cloud free cases.
Despite the broad swath of approximately 3000 km, a complete coverage of the Earth surface isonly achieved within one or two weeks depending on the actual cloud coverage. Some
problematic regions, with almost persistent cloud coverage, are the extended stratocumulus
fields to the west of Africa, the ITCZ, and Arctic and Antarctic regions during winter.
Therefore the quality of an estimation of sea surface temperature is highly dependent on thequality of the implemented aerosol and cloud detection scheme. A description of errors
occurring in sea surface temperature, due to the different quality of cloud detection during day
and night, can be found in Reynolds (1993). If measurements of sea surface temperature arehampered for weeks by persistent cloudiness (or long periods of darkness as over polar regions)the estimates might become biased due to the more difficult cloud detection in the infrared
range. What consequences this has for the combination of AVHRR and SSM/I has not been
fully explored.
Table 4.2: Temporal coverage of AVHRR (lighter shading = partial coverage of the year)
78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
TIROS-N
NOAA-6
NOAA-7
NOAA-8
NOAA-9
NOAA-10
NOAA-11
NOAA-12
NOAA-14

B. ATSR
The Along-Track Scanning Radiometer (ATSR – Zavody et al., 1994) is a visible andinfrared radiometer with a built-in calibration system. It provides views of the sea surface at
two different incidence angles and hence has the potential to estimate atmospheric transmission
effects. In comparison with buoy data, rms scatter better than ±0.25°C has been claimed (seesection 10.2.3). However the recent workshop on SST (Arkin, 1999) considered that the ATSR
had not yet fully fulfilled its promise for a number of reasons. There had been a failure of one
channel on the first instrument (flown on ERS-1) and problems with the cloud clearance
algorithm. Because the ATSR was not part of the core payload for the satellites the data were
not widely available in operational mode and this had limited their exploitation. A further
limitation is that the ATSR has a narrow swath, only 500km, and therefore provides many
fewer cloud-clear views of the sea surface compared to AVHRR.

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4.3 Satellite data sources

C. SSM/I
One of the most advanced sensors for monitoring flux related atmospheric variables
and ice cover from space is the SSM/I aboard the satellites of the Defense Meteorological
Satellite Program (DMSP). The SSM/I is a passive microwave radiometer measuring emitted
radiation from the earth’s surface and the atmosphere at four frequencies located at 19.35,
22.235, 37.0, and 85.5 GHz at two polarisations (with the exception of 22 GHz which is only
measured at vertical polarisation). The SSM/I instrument scans conically at a constant earth
incidence angle of 53.1° resulting in a swath width of nearly 1400 km, only half of that of the
AVHRR. However, cloud coverage is less of a problem within the microwave spectral range,
so the determination of flux related variables is only hindered in cases where rain occurs.
SSM/I data are sampled every 25 km (A-Scan) at the five lower frequencies and every
12.5 km (B-Scan) at 85.5 GHz. Most of the retrieval schemes rely upon the A-scan data anddon’t consider the effects of different ground resolutions at different frequencies. A moredetailed description of the instrument can be found in Hollinger et al. (1987).
SSM/I's have been flown on different DMSP satellites since July 1987 as shown in
Table 4.3. These satellites have different orbits resulting in different local observing times andthis can have implications for the construction of a satellite-derived climatology (see section
8.6). If the data from only one satellite is used (as has been the case to date) sampling errors
can result, at least in all quantities that have a distinct diurnal cycle, for example, precipitation.
D. TRMMThe Tropical Rainfall Measuring Mission (TRMM) satellite, dedicated to performing
measurements relevant to tropical precipitation and the resulting latent heat release, waslaunched in November 1997. It is a low latitude, precessing satellite carrying five instrumentsof which three are suitable for the precipitation mission. These are the TRMM Microwave
Imager (TMI), the Visible and Infrared Scanner (VIRS), and the Precipitation Radar (PR). The
TMI is an instrument similar to the SSM/I but with key differences: an additional channel withvertical and horizontal polarisation at 10.7 GHz, and a shifted water vapour channel at 21.3
GHz instead of 22.235 GHz used for the SSM/I which is the centre of the water vapour
absorption line). The latter change should avoid the signal saturation at high atmosphericwater vapour contents found within SSM/I measurements.
The VIRS has the same
wavelengths and bandwidths as the AVHRR (see above). The major differences between
VIRS and AVHRR are that the VIRS has a post launch calibration unit and a larger
instantaneous field of view at nadir of 2.11 km (compared to 1.1 km for the AVHRR). The PR
is an active system working at 13.8 GHz that provides, for the first time, information on thevertical distribution of rainfall. Since the TMI and VIRS are very similar to SSM/I and
AVHRR, in addition to measuring precipitation they may be used to obtain most of flux related
atmospheric variables including SST using almost the same algorithms developed for the lattersensors. A comprehensive description of the different features of all three instruments can befound in Kummerow et al. (1998).
Table 4.3: Temporal coverage of SSM/I (lighter shading indicates partial coverage of the year)
87 88 89 90 91 92 93 94 95 96 97 98 99
F-8
F-10
F-11
F-13
F-14
F-15
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4.3 Satellite data sources

Due to the orbit characteristics, TRMM measurements are only available within ±40°
latitude so no global datasets can be derived. In addition, TMI, VIRS, and PR have very
different swath widths ranging from 215 km for the PR, 720 km for the VIRS, and 759 km for
the TMI, respectively. Thus more time is needed to achieve full coverage compared to the
SSM/I and AVHRR. In coming years it is expected that TRMM and SSM/I, together with the
new Advanced Scanning Microwave Radiometer (AMSR), will be used to improve andcontinue satellite-derived products.
E. TOVSThe TIROS Operational Vertical Sounder (TOVS) consists of two components. The
High-resolution Infrared Radiation Sounder (HIRS) is the infrared part with 19 channels
between 3.7 and 15 µm and a single day-time cloud-detection band in the visible spectral
domain. The HIRS is the main vertical sounding component with sixteen temperature andthree water vapour channels. Additionally there is the Microwave Sounding Unit (MSU) withfor channels arranged between 50.31 and 57.95 GHz. These channels are used for sounding
temperature profiles with coarser vertical and horizontal resolution but the ability to obtain data
in cloudy cases.
The TOVS package has been flown on the NOAA satellite series, being used to invert
temperature and humidity profiles for assimilation into NWP models. Temperature andhumidity from the lowest levels of the HIRS derived profiles could be useful for flux
calculations but so far have not been used for that purpose. Over ice surface, TOVS data havebeen used to determine the drag coefficients as well as cloud fractions (see Section 10.11.2).
F. SCATTEROMETERS (ERS-1,2; NSCAT; QUIKSCAT)
Satellite borne scatterometers have the unique capability of returning estimates of windspeed and direction over the ice-free zones of the global oceans. This is achieved throughtransmitting microwave signals aimed off-nadir and analyzing backscattered energy from theocean surface. The magnitude of the backscattered energy indicates wind magnitude while
numerous looks at the same ocean surface location provides the wind direction information.
The wind direction information is critical for determining ocean-relevant forcings such as windstress curl. Additionally, wind divergence is an important diagnostic variable indicative ofboth ocean and atmospheric processes, and can be calculated only with directional information.
Since the first such system was launched aboard SeaSat (1978), there have beensignificant advances in technology and instrument design to improve the quality of the winds
from these instruments. The European spacecraft ERS-1 (replaced by ERS-2) has provided
continuous wind measurements over the ocean since 1991. In its scatterometer mode, the ERS
platforms provide wind vectors from a single but relatively narrow (500-km) swath with winds
provided every 50-km within the swath.
Near coastal regions and in ice zones thescatterometer was turned off to facilitate SAR imaging thus limiting wind vector coverage.
The NSCAT mission (Oct 96 - June 1997) featured dual swaths of 600-km width each with 25km grid spacing for the wind retrievals. Continuous scanning over the globe resulted in windvectors reported over more than 90% of the world’s ice-free oceans every two days. A special
issue of JGR-Oceans (May 1999 Volume 104, No. C5) contains a number of papers describing
the NSCAT mission and scientific results. The next mission, QuikSCAT, was launched inJune 1999 and features a radially scanning scatterometer providing a single 1800-km swath ofwind vector data covering 90% of the earth’s oceans every day.
4.3.3 Geostationary satellites
Geostationary satellites like GOES-E, GOES-W, GMS, and METEOSAT typicallyscan the earth using three bands in the visible, infrared window, and water vapour absorption
band at 0.5 – 0.9 µm, 10.5 - 12.5 µm, and 5.7 – 7.1 µm, respectively. These data have beenused in combination with data from microwave polar orbiting instruments to enhance thesampling rate - one of the largest error sources for rainfall measurements from space. Since
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4.4 NWP and reanalysis

rainfall estimates from geostationary satellites are inaccurate (because they rely on cloud top
temperature) they have been calibrated with the more accurate microwave estimates to develop
rainfall products like GPCP or CMAP where the sampling is only improved within the field ofview of the geostationary satellites (see Section 10.9).
4.4 NWP and Reanalyses
4.4.1 Introduction
If one was to design a system to produce the best estimates of air-sea fluxes, the system
would use all available observations of the atmosphere and ocean, their exchanges of radiation
with space and the exchanges of energy, water and momentum between the atmosphere and theearth’s surface. It would include the effect of data from different observation times and fill ingaps in the data by using the laws of physics as expressed in the equations of fluid mechanics tointerpolate in time and space. The fields actually observed, and not derived fields, would be
used; thus radiances observed by satellites would be used rather than derived temperature andmoisture fields to ensure consistency in physical parameterisation. This is a data assimilation
system.
Such a system would not be identical to a data assimilation system designed fornumerical weather prediction, but might well be a modified version of an operational NWP data
assimilation system. As discussed in Section 2.2, data assimilation for NWP is designed to
produce the best forecasts, not necessarily the most complete or the most realistic depiction of
the atmosphere. NWP has important timeliness requirements. Data assimilation for the most
realistic estimates of air-sea fluxes could have a longer data cut-off time and in fact mightchoose to use observations occurring after the analysis time, using a computer model tointerpolate both forward and backward in time. Observations could be used even if theirprocessing took significant time and they had little effect on weather forecasts. More complete
and complex assimilation of satellite measurements could be done than is desirable inoperational NWP. The selection and improvement of parameterisations would be designed togive the more realistic and complete depiction of the atmosphere and its boundary conditions
rather than the best forecast. The system would benefit from a close association with anoperational NWP data assimilation effort.
Such a system should not provide the only estimates of air-sea fluxes. Global estimatesof precipitation by Xie and Arkin (1996) (CMAP) include separate estimates of precipitationfrom each source they use - rain gauges, different satellite methods and data assimilation. A
similar range of estimates for air-sea fluxes could give valuable insight both into the uncertaintyof the estimates and also the primary sources of information for the best estimate.
Today, data assimilation for operational NWP produce complete global analyses of theatmosphere and its interactions with space and with the earth’s surface every few hours on aregular grid at fairly high spatial resolution (from 40 to 105 km). These analyses use large
amounts of observations from a wide variety of sources and combine them with highly accurate
short-range forecasts. The analyses are widely distributed a few hours after observation timeand are critically scrutinised by many diverse users, including marine forecasters. Operational
data assimilation and NWP modelling groups are made aware every day of the limitations of
their analyses and forecasts; they are constantly seeking to improve all aspects of their analyses
and forecasts. Data assimilation systems and NWP models have greatly improved over the lasttwo decades, but the physical parameterisations used are still relatively crude and the physicalfields produced by analysis/forecasts systems need to be critically examined to see how close
we are to the future system described above.
4.4.2 Fluxes from operational data assimilation
A. INTRODUCTION
Physical fields from operational global data assimilation systems, such as surface fluxesof moisture, energy and momentum, are generally accumulated during the short-range six-hour
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4.4 NWP and reanalysis“first guess” forecast and during longer-range forecasts as well. Meteorological fields such as
near-surface winds, temperature and moisture are analysed from a combination of theobservations and the first guess forecast and are available at 6 hour intervals as analyses and as
6 hour forecasts. Users of fluxes from data assimilation have requested fields every three hours
and global NWP systems may well move to produce analyses every three hours in the near
future. Special files for individual points can be prepared in which data is output every hour ofthe forecast. Model errors can affect the analysed meteorological fields as well as the physicalfields through the first guess forecast, especially in data-scarce regions over the oceans. White(1995) compared precipitation and surface fluxes from four operational global NWP
analysis/forecast systems: NCEP, ECMWF, the United Kingdom Meteorological Office(UKMO) and the Japanese Meteorological Agency (JMA). Section 11.5 of this report describes
a Working Group on Numerical Experimentation (WGNE) proposal to conduct a more
extensive comparison of surface fluxes of operational global NWP systems and to establish an
ongoing centre for archiving and comparing such fluxes.
There are many operational NWP systems. Pan (1999) describes the parameterisationof subgrid-scale processes in atmospheric models. Phillips (1994) describes the atmosphericmodels of several centres. This section considers NCEP and ECMWF as examples ofoperational systems.

B. THE NCEP OPERATIONAL GLOBAL MODEL
Formulation of the Model: The home page of the NCEP global modelling branch(http://sgi62.wwb.noaa.gov:8080/research/global2.html) contains information on theformulation and performance of the NCEP operational global analysis/forecast system. A
description of the NCEP global analysis/forecast system can be found in Kanamitsu (1989). As
an example of an operational global model, the present operational NCEP global model issummarised below (http://sgi62.wwb.noaa.gov:8080/research/mrf.html):
a)
b)
c)
d)
e)
f)
g)

h)

i)

j)
k)

T170 spectral model (equivalent to a horizontal resolution of 75 km).
42 vertical unequally spaced sigma levels. For a surface pressure of 1000 hPa, its lowest
level is about 996 hPa.
physics calculated on a Gaussian grid of 512 by 256, approximately 1˚ resolution;
Shortwave radiation is based on work by Chou (1992). Surface solar absorption isdetermined from the surface albedo. Ocean surface albedo depends on zenith angle.
Longwave radiation follows Fels and Schwarzkopf (1975), Schwarzkopf and Fels (1991).
Long-wave emissivity is prescribed to be unity for all surfaces.
Cloud fractions are diagnosed from the relative humidity based on RTNEPH
observations.
Over the ocean the Charnock (1955) formula is used to update the momentum roughness
from the estimated surface stress. Thermal roughness is based on TOGA COARE
observations (Zeng et al., 1998).
The lowest model layer is assumed to be the surface layer (sigma=0.996) and the MoninObukhov similarity profile relationship is applied to obtain the surface stress and sensible
and latent heat fluxes. The formulation was based on Miyakoda and Sirutis (1996) andhas been modified by P. Long in the very stable and very unstable situations. A bulk
aerodynamic formula is used to calculate the fluxes once the turbulent exchange
coefficients are obtained.
Boundary layer turbulent mixing is a bulk Richardson number based non-local mixing
scheme (Troen and Mahrt, 1986; Hong and Pan, 1996). Free atmosphere vertical
diffusion uses a local mixing scheme.
Shallow convection by Tiedtke (1983).
Deep convection is a simplified Arakawa-Schubert scheme originally developed by Grell(1993) and modified by Pan and Wu (1995) and Hong and Pan (1996).

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4.4 NWP and reanalysis

Large-scale precipitation is caused by super-saturation removal and by convective
adjustment when the sounding is conditionally unstable and supersaturated. Falling rain
is evaporated based on Kessler (1969).

Formulation of the operational analysis: The analysis seeks to obtain the best fit tothe observations and to the six-hour background or first guess forecast (Parrish and Derber,
1992; Derber et al., 1991; http://sgi62.wwb.noaa.gov:8080/RTPUB). The analysis is performed
as a series of iterative problems in the model’s vertical coordinate system and uses thefollowing observations:
a)
b)
c)
d)
e)
f)
g)
h)
i)
j)
k)
l)

rawinsondes and dropwindsondes—winds, temperatures, specific humidity, surfacepressure
aircraft reports—winds and temperaturessatellite cloud tracked winds
surface marine observations—winds, temperatures, specific humidity, surface pressure
surface land observations—specific humidity, surface pressure
SSM/I wind speeds over water
Australian sea level pressure bogus
satellite microwave and infrared radiances
hurricane bogus winds
profiler winds
ERS-2 wind speeds and directions over water
SBUV ozone profiles

Observational error statistics vary with each observation type and can vary withlocation. They contain both instrument error and representativeness error. Background error
statistics are used to weight the first guess field and are nearly homogeneous around a latitudeband.
C. THE ECMWF OPERATIONAL SYSTEM
ECMWF currently uses a spectral T319 (about 40 km) model with 60 levels in thevertical. It uses a 4D variational (4DVAR) analysis and processes raw TOVS/ATOVS radiancedata. A fully coupled wave model produces surface fluxes dependent on sea state. It currently
blacklists ship humidity data; ship wind data makes use of WMO tables for height assignment.
A seasonal forecast system, based on a coupled integration of a T63 and 31 level (as ofOct. 1998) version of the ECMWF atmospheric model and the HOPE ocean model, is run
daily. However the model drift tends to be comparable in magnitude to the seasonal anomaly
being forecast. Tests of revised physical parameterisations appeared to reduce the drift in SST.
Beljaars (1997) described the parameterisation of atmosphere-ocean exchange in the
ECMWF model. Klinker (1997) examined the ECMWF model’s surface fluxes. Siefridt et al.
(1999) examined the effect of parameterisation changes of ECMWF operational fluxes during
1986-95. ECMWF publications can be obtained from the ECMWF Library, also see
http://www.ecmwf.int/pressroom/publications.html .
4.4.3 Fluxes from reanalyses
A. INTRODUCTION
In reanalysis a frozen NWP analysis forecast system is used to process data over
several years of past data. The advantages of a reanalysis over operational NWP data
assimilation is that it offers several years of fields from an unchanging system and that thefields can be more easily obtained. The disadvantage is that by the time the complete reanalysis
record is available the system used is lower resolution and uses older, less developed physicalparameterisations than the latest operational NWP systems. Several years of results areavailable from four reanalyses:a)

ERA15 - ECMWF’s reanalysis: 1979-1993.

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c)
d)

48

4.4 NWP and reanalysis

GEOS1 - Goddard Earth Observing System March :1980-Nov. 1993 (Data Assimilation
Office (DAO), Goddard Space Flight Centre (GSFC), National Aeronautics and Space
Administration (NASA))
NCEP1 - NCEP/NCAR reanalysis: 1948-present (ongoing as Climate Data Assimilation
System (CDAS))NCEP2 - A rerun of the NCEP/NCAR reanalysis introducing more up-to-date physics
and correcting known errors in NCEP-1 (also known as the NCEP/AMIP-II DOE
Reanalysis): 1979-Jan. 1996 (as of Nov. 30, 1999). Plans are to continue this up to thepresent. Years before 1979 may also be done.

An earlier pilot reanalysis was run by the Centre for Ocean-Land-Atmosphere Studies (COLA).
It covered the period April 5, 1982 to Nov. 30, 1983 and is documented at
http://grads.iges.org/res/proj5/proj5.html.
The NCEP-1 reanalysis is currently being run on current data as the Climate Data
Assimilation System (CDAS). The Climate Prediction Centre at NCEP is currently considering
whether to run NCEP-1 or NCEP-2 or both as the operational CDAS. ECMWF is preparing to
run a new reanalysis (ERA-40) at higher resolution and with improved parameterisation from1958 to the present. GEOS has spent several years improving its system from what was used
in GEOS-1 and plans to do reanalysis again in the future.
A workshop on the GEOS-1 reanalysis was held in March 1995 (Schubert and Rood,
1995). The 1st WCRP International Conference on Reanalyses was held in Oct. 1997.
Proceedings of the conference are available as WCRP Report 104. The 2nd International
Conference on Reanalyses was held in Aug. 1999 and the proceedings are now available(WCRP, 2000).

B. NCEP1 SYSTEM FORMULATION
Kalnay et al. (1996) and Kistler et al. (2000) describe the NCEP1 reanalysis and further
information is available from a number of web sites (Table 4.4).
Table 4.4 Web site addresses for the NCEP/NCAR reanalysis
Contents
Official sites
reanalysis

URL
for

NCEP/NCAR

http://wesley.wwb.noaa.gov/reanlysis.html
http://www.cdc.noaa.gov/cdc/reanalysis

Reanalysis problems

http://wesley.wwb.noaa.gov/reanalysis.html#problem
http://www.cdc.noaa.gov/cdc/reanalysis/problems.shtml

Reanalysis studies and problem reports
by Robert Kistler (technical manager of
the NCEP/NCAR reanalysis)

http://lnx21.wwb.noaa.gov/

Examples of the observation and forecast
errors used in the reanalysis.

http://lnx21.wwb.noaa.gov/oberr/reanl-obs.html
http://lnx21.wwb.noaa.gov/oberr/fcsterr.html

The NCEP/NCAR reanalysis analysis/forecast system was identical to the NCEP global
operational model implemented in Jan. 1995, except that it had a horizontal resolution of T62(about 210 km). The vertical resolution was 28 sigma levels, the same as the current
operational model. The physical parameterisations were done on a Gaussian grid of 192 x 94,
roughly 2o x 2o latitude. The radiation grid was a Gaussian grid of 128 x 62. A time step of 20
minutes was used for computing dynamics and physics, except that the full atmosphericradiation was calculated every 3 hours with corrections at every time step for diurnal variationsin short wave fluxes and in surface upward long wave flux. Mean orographic heights wereused. The radiation used in reanalysis was an older short wave radiation (Lacis and Hansen,
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4.4 NWP and reanalysis

1974) than that used in current operations. An incorrect, too high, surface albedo was usedover the oceans. An older boundary layer model, than that currently used in operations, wasused. The equation for roughness length for heat in the NCEP bulk flux algorithm used in thereanalysis has been found to be inappropriate under strong wind conditions and to overestimate
latent heat flux (Zeng et al., 1998). It has been changed in the operational model.
The NCEP1 and NCEP2 reanalyses used the following data (Ebisuzaki et al., 1998):
a)
b)
c)
d)
e)
f)
g)
h)
i)

radiosondes, dropsondes, pibals
conventional aircraft winds
ACARS aircraft winds and temperatures
Marine winds, temperature, specific humidity in surface layer
Land surface pressure
Satellite cloud-track winds
NESDIS temperature retrievals
PAOBS
SST and snow-cover analyses

The following data was not used:
a)
b)
c)
d)
e)
f)
g)
h)

precipitation
radar, profilers
SSM/I winds and precipitable waterLand winds, temperature, specific humidity in surface layer
Most cloud information
Radiances from satellite
Satellite humidity estimates
Soil/albedo/snow depth.

The analysis system used was the spectral statistical interpolation (SSI or 3D
variational) analysis (Parrish and Derber, 1992; Derber et al., 1991). Nonlinear normal-mode
initialisation was not needed. Additional details can be found in Kalnay et al., 1996.
Several problems have been found in the reanalysis. Among them are:a)

Bogus sea-level pressure observations generated by Australian analysts over the Southern
Hemisphere oceans during 1979-92 were misplaced by 180 degrees. (Reruns of 1979
and Nov.-Dec, 1992 corrected this.) The problem has little effect north of 40S and little
effects on monthly means. The error is comparable to the difference between ECMWF
and NCEP analyses.

b)

The initial attempt to use high-density vertical soundings over the TOGA COARE region
from Nov. 92 to Feb. 93 included a failure to convert temperature to virtual temperatureand resulted in a cold bias over the TOGA COARE region. The problem has beencorrected, the period rerun, and the archives corrected, but the annual CD-ROM's for1992 and 1993 include this error for the 4 months. The effects are limited to the TOGACOARE region and are strongest near the surface.

c)

The snow cover for 1973 was used for 1974-94 as well.

d)

Due to an incorrect parameterisation of the horizontal diffusion of moisture, snow overland tended to fall in valleys.

e)Discontinuities in radiation fluxes at the International Date Line due to cloud tuning werediscovered.

In other instances, portions of observational data sets did not make it into the NCEP1
reanalysis, such as poorly encoded surface observations between 1948 and 1967 andradiosondes in particular regions in particular periods (http://lnx21.wwb.noaa.gov/). A
comparison of ERA and NCEP/NCAR reanalyses for a few months revealed that eachcontained observations not in the other. The two sets of observations are being merged.
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4.4 NWP and reanalysis

While the data assimilation system was not changed during the reanalysis (except forsome very minor modifications after 1986-89 were processed), the data did change dramaticallyduring the 50 years, as can be seen in Figure 4.4. Satellite temperature soundings became
available in significant numbers in 1979, the major change in the data record in the last 50
years, and had a noticeable effect on atmospheric temperatures (Fiorino, 2000). An increase indata in the late 1960's is also evident, as is an increase in the late 1990's due to an increase insatellite winds. In recent years conventional observations have declined, as can be seen in
figure 4.5. The number of ship reports increased until the late 1960's and decreased from thelate 1980's to 1994. Data from the TAO buoy array in the tropical Pacific in the 1990's isincluded. White (1999) examined global trends in precipitation and air-sea temperature contrast
that could be related to changes in the observational network. It should be noted that the fourdimensional data assimilation systems used in reanalysis can transport information from datarich to data sparse regions; therefore observations can influence regions outside the 2.5o box
they were taken in. Ebisuzaki and Kistler (2000) examined the effect of changes in data on
reanalysis by comparing fields from NCEP1 and NCEP2 to a rerun of 1998 without PAOBS,
aircraft or satellite data.

Figure 4.4. Zonally averaged
number of all types ofobservations available to the
NCEP/NCAR reanalysis per
2.5o latitude-longitude box per
month from 1946 to 1998. A
twelve-month running mean has
been applied. Shading indicatesmore than sixty observations permonth.

Figure 4.5: Zonally averaged
number of ship reports per 2.5obox per month. Oceanic buoys
are included.

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4.4 NWP and reanalysis

C. NCEP2 SYSTEM FORMULATIONThe NCEP2 reanalysis is documented at http://wesley.wwb.noaa.gov/reanalysis2/
index.html. Papers on the NCEP2 reanalysis were presented at the 2nd International Conference
on Reanalyses. Kanamitsu et al. (2000) gave an overview of NCEP2 . Hnilo et al. (2000)
compared NCEP1 and NCEP2. NCEP2 was done with the same data and the same vertical andhorizontal resolution as NCEP1. It corrected the five mistakes listed above as well as the ocean
surface albedo. The following model changes were introduced:
a)
b)
c)
d)
e)
f)
g)
h)
i)
j)

assimilation of five-day mean estimated rainfall from Xie and Arkin to adjust soil
moistureelimination of a nudging of deep layer soil moisture to climatology
a smoother orography than NCEP1
new non-local boundary layer parameterisation of Hong and Pan (1996)
a change to the convective parameterisation
a fix to cloud-top cooling in radiation
updated cloud parameterisation and RH-cloud relationshipradiation code run every hour and on full Gaussian grid
improved desert albedo
improved sea-ice SST fields

NCEP2 did not change the roughness length for heat as suggested by Zeng et al. (1998).
D. ERA15 SYSTEM FORMULATION
An introduction to the ECMWF reanalysis can be found at http://www.ecmwf.int/
research/era/index.html. Reports from the ERA15 reanalysis are available by anonymous ftp at
www.ecmwf.int in the /pub/era/era15 directory. Gibson et al. (1997) described the ERA15
reanalysis system, including initial experiments they did. They tested the effects of a new
prognostic cloud parameterisation (Jakob 1994; Teidtke 1993) for one year of data assimilation
together with changes in ocean surface albedo and shallow convection. Compared to an older
diagnostic cloud parameterisation similar to the one used in the NCEP reanalyses, the newphysics produced quite different surface energy fluxes. It produced more low clouds over theoceanic storm tracks and near the equator and reduced stratus over the Arctic, allimprovements, but decreased low-level stratus over upwelling ocean currents, adisadvantageous impact. The new scheme increased downward longwave radiation into theocean by 20-30 Watts/m2 in the latitudes of the storm tracks, decreased short wave radiation
into the ocean at low latitudes, and decreased evaporation locally by as much as 30 Watts/m2.
The changes, used in the full reanalysis, increased energy input into the ocean in the tropics
and over the high latitude storm tracks. These results indicate the sensitivity of reanalyses tochanges in the physical parameterisation.
The ERA-15 data assimilation system included:
a)
b)
c)
d)
e)
f)
g)
h)
i)
j)

spectral T106 resolution with 31 vertical hybrid levels (Gaussian grid resolution 1.125o orabout 125 km)
Intermittent statistical (optimum interpolation) analysis with 6 hour cycling
One-dimensional variational (1D-Var) physical retrieval of TOVS cloud clearedradiances below 100 hPa.
NESDIS humidity retrieval below 300 hPa over oceans
Diabatic, non-linear normal mode initialisation
3-dimensional semi-Lagrangian advection
Prognostic equations for cloud water and ice content and cloud cover. Clouds tuned to
ISCCP clouds.
ECMWF operational radiation parameterisation with prescribed concentration of aerosols
varying geographically (Morcrette, 1991)
ECMWF operational planetary boundary layer parameterisation based on similarity
Mass flux convection scheme by Tiedtke (1989)

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4.4 NWP and reanalysis

Mean orography with a parameterisation of sub-grid scale orography

Kallberg (1997) examined the 15-year mean climate of the reanalysis. He documented
the sensitivity of precipitation and surface fluxes to “spin-up” during the first 24 hours offorecasts in the ERA15 reanalysis. He also found evaporation over the ocean to be very
sensitive to the observations. Near surface winds observed by ships were generally stronger
than either analysed or forecast winds; near surface winds reported by isolated island stations,affected by local effects and not representative of the surrounding oceans, were generally
weaker than the analysed and forecast winds. These mismatches generated large increases in
evaporation during the first 24 hours of forecasting, locally up to 40 Watts/m2 (the NCEP1
Reanalysis also found suspicious features near isolated island stations; experiments did not finda solution to the problem). ECMWF concluded that the fluxes from 12-24 hour forecasts were
better than the fluxes from 0-6 hour forecasts and recommended the use of the former.
ERA15 assimilated TOVS cloud-cleared radiances from a long series of NOAA
satellites that suffer from the effects of drift and sudden changes in instruments. In spite ofextensive monitoring, quality control and bias correction, the use of radiances resulted insudden jumps in the time series of analysed temperatures and humidities in the lower
troposphere, especially in lower latitudes. The worst jump occurred in Nov. 1986 with an
unrealistic large-scale warming and moistening of the Inter-Tropical Convergence Zone
(Gibson et al., 1997; Fiorino, 2000).
E. ERA40 SYSTEM FORMULATION
Plans for the next ERA40 reanalysis were discussed by Uppala et al. (2000) at the 2nd
International Conference on Reanalyses. ERA40 is still under development. Current plans are
to process data from mid-1957 to 2001. In addition to the data in ERA15, this reanalysis will
include:
a)
b)
c)
d)
e)
f)
g)

conventional ship and upper air data before 1979 from NCAR,
declassified U. S. Navy data;
full VTPR and TOVS 1b data from 1972 on;
SSM/I radiances;
ERS-1 and ERS-2 data;
updated COADS;
new SST data from UKMO and NCEP with compatible sea ice.

The projected production system will use
a)
b)
c)

d)
e)
f)

spectral T 159 resolution with 60 hybrid vertical levels;
3 dimensional variational (3D-VAR) analysis incorporating physical retrieval of satellite
radiance data;
coupling with a 1.5˚ wave model to produce ocean surface fluxes. Analysis of ERS wave
altimeter data will be included. The fluxes will be sea-state dependent. (Janssen andViterbo, 1996);
the Integrated Forecast System (IFS) version of the ECMWF forecast model;
varying carbon dioxide and aerosols;
assimilation of ship winds at 25 m rather than 10 m.

Experiments with the proposed system indicate fewer problems with the near-surface
winds over the open ocean and islands, a weaker and slower “spin-up” and more rainfall overthe northern hemisphere storm tracks.
F. GEOS1 SYSTEM FORMULATION
Overviews of the NASA/GSFC DAO GEOS1 reanalysis can be found in Schubert et al.
(1995) and at a number of web sites (Table 4.5) Higgins et al. (1996) compares the NCEP1
and GEOS1 reanalyses for the period 1985-93. Bony et al. (1997) compared the two reanalyses
over the tropical oceans.
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4.4 NWP and reanalysis
4.5 Residual Methods
4.6 Ocean data assimilation

The GEOS1 data assimilation system is described in Takacs et al. (1994) and
Pfaendtner et al. (1995).
It employed an optimal interpolation (OI) analysis scheme at ahorizontal resolution of 2o latitude by 2.5o longitude at 20 sigma levels. It did not include
initialisation and used an Incremental Analysis Update (IAU) procedure by which a fraction of
the data increment are added each time step during a rerunning of the forecast (Bloom et al.,1996).
The radiation parameterisations follow those of Harshvardhan et al. (1987); a relaxedArakawa-Schubert scheme (Moorthi and Suarez, 1992) is used for deep convection. The
planetary boundary layer is explicitly resolved in a 2 to 4 layer region. Wind, temperature andhumidity profiles, in an extended surface layer up to 150 m thick, and the turbulent fluxes atthe surface are obtained from Monin-Obukhov similarity theory by selecting similarity
functions that approach the convective limit for unstable profiles and agree with observations
for very stable profiles. Surface roughness lengths are taken as a function of surface stressover water (Schubert et al., 1995).
The SST data set used in GEOS1 used monthly mean blended sea surface temperature
analyses of Reynolds (1988) and Reynolds and Marsico (1993); NCEP used the improved
weekly global optimum interpolation SST analyses of Reynolds and Smith (1994). The majordifferences between the SST analyses are in regions of high SST gradients.
Table 4.5 Web site addresses for the NASA/GSFC DAO GEOS1 reanalysis
Contents

URL

Overviews of the GEOS1 reanalysis

http://dao.gsfc.nasa.gov/experiments/assim54A.html
http://www.cdc.noaa.gov/cdc/reanalysis

Technical reports on the GEOS-1
reanalysis

http://dao.gsfc.nasa.gov/subpages/tech-reports.html

Strengths and weaknesses of GEOS1

http://dao.gsfc.nasa.gov/experiments/assim54A/assim_pr
od_evalutaion.html (sic)Problems

http://dao.gsfc.nasa.gov/
experiments/DATA_PROBLEMS.html.

4.5 Residual methods (TOA + model divergences)
In the residual method (Trenberth and Solomon 1994; Keith 1995) the top of theatmosphere radiation budget and the divergence of the atmospheric energy transport from annumerical model is used to infer the net surface heat flux. Used with the atmospheric moisturebudget the net freshwater flux can also be obtained (Trenberth and Guillemot 1998), although
the accuracy is crucially dependent on the calculated wind and moisture divergence. A major
limitation of this method is the lack of estimates of the individual fluxes and the reliance on
atmospheric model data, thus restricting use for model verification.
4.6 Ocean Data assimilation
Inverse techniques such as ocean data assimilation can also provide estimates of thesurface fluxes of heat, momentum, and freshwater. The approach is straightforward. One
constrains (in a least-squares sense) an ocean model (admittedly imperfect or is missing
physical parameterisations of ocean processes) to observed temperatures, salinity, or othersuitable observations (which have their own errors possibly specified in the inverse
methodology) using the flux fields as dependent model parameter fields. Such an approach
was demonstrated using a relatively simple tropical Pacific ocean model forced byclimatological winds and SST (Yu and O'Brien, 1995). The resulting monthly net heat flux
fields had features similar to existing in-situ based climatologies outside the equatorial zone
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4.7 Fluxes over Sea Ice

except the magnitude is not as strong. Similarly, for the Atlantic, seasonally varying ocean
temperature and salinity data provided necessary information to estimate surface heat andfreshwater flux estimates (Yu and Malonotte-Rizzoli, 1998). Estimates of surface fluxes
produced by these inverse methods will improve as ocean modelling evolves.
4.7 Data Sources for Fluxes and Flux-Related Variables over Sea Ice
Like other uninhabited and barely frequented areas of the globe, the ice-covered oceanspresent special problems for observing global phenomena and for estimating global fields. The
situations in both polar regions have similarities: a few permanent manned or unmanned
stations provide sparse, continual data, but large areas are sampled directly only occasionally.
Interpolation is asked to provide information that man did not have the resources to observe.
Winds are specified from pressure fields which in the Arctic have had the benefit since
1979 of a network of drifting buoys, but in the Antarctic have only sporadic buoy data. In the
Arctic the buoy and station surface pressure data are assimilated into the NCEP and ECMWF
reanalysis fields, making them the best gridded pressure fields. In the Antarctic there is no
standing network of buoys, and the reanalysis pressure fields are of lower accuracy but still thebest available. Similarly, air temperature fields are recently available from buoys in the Arctic(for example the IABP/POLES 2-m air temperature data set (Rigor et al. 2000)), and again rely
on more sparse data in the Antarctic. The surface air temperature fields in the reanalyses are notbased on surface temperature observations, but use only the first level temperatures fromradiosondes, and model physics to estimate surface temperature.
In addition to drifting buoys, an important Arctic data set on surface fluxes, cloudamounts, winds, air-surface temperature differences, snow depths, and radiosondes has been
provided by decades of Russian "North Pole" manned drifted stations. Data from these stations
have been assembled into long-term climatologies of monthly means (the Arctic Global
Radiation (AGR-1 and AGR-2) Data Sets. There have been a number of drifting camps (e.g.,
Arlis, T-3, AIDJEX, CEAREX, LEADEX, and presently SHEBA) and ship expeditions(Polarstern cruises, and the Arctic Cross-Section), all with observations crucial toparameterising and validating large-scale estimates of surface fluxes.
Satellite data sets are just coming into use. Satellites provide indirect observations ofsurface variables and fluxes, and much effort goes into trying to make good use of these data.
New satellite-derived data products promise good accuracy, better spatial resolution and theresolution of interannual variability. These include the Surface Radiation Balance estimatesfrom ERBE and AVHRR, the ISCCP cloud and albedo data and radiative fluxes derived fromthem, and Polar Pathfinder data sets from AVHRR and from TOVS.
And of course, global numerical weather prediction models provide fields of many
desired variables, some of which are of more value than others. Surface wind and flux fields
are available from both the NCEP and ECMWF reanalyses.
As for data on the sea ice state, the most comprehensive record is that of gridded sea ice
concentration at both poles are available from 1978 to present, from SMMR and SSM/I passive
microwave sensors. Substantial records are available from scatterometers (Ezraty and Cavanié,1999), ERS-1 and -2 (1991 - 1999), NSCAT (9 months) and QuikSCAT (beginning September1999), but these are not yet presented as data sets of sea ice variables. Synthetic aperture radar
has been used to track ice and infer the ice thickness distribution for ice up to 2 m thick and forridged ice. These data are produced for the Arctic Ocean by the RADARSAT GeophysicalProcessor System (RGPS) and will eventually span several years from 1996 on.

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55

5.1 Introduction
5.2 Radiative Fluxes

5 DIRECT FLUX OBSERVATIONS - STATE OF THE ART
5.1 Introduction
In general, direct measurements of the air-sea fluxes are too few to contribute directly
to the calculation of large scale flux fields. Rather they are used for developing, calibrating,and verifying the parameterisation formula used to estimate the fluxes from the basic variables.
The accuracy of the direct flux determinations thus represents a limit for the accuracy of theindirect estimates. Methods of direct measurement will therefore be reviewed in this Chapter
for the radiative fluxes, turbulent fluxes, and precipitation.

5.2 Radiative fluxes
5.2.1 Introduction
The radiant energy flux to and from the ocean surface comprises the solar (orshortwave) component in the wavelength band 0.3 to 3 µm, and the terrestrial (longwave or
infrared) component from 3 to around 50 µ m. A good illustration of the sources which
contribute to each of these wavebands is given in Figure 1 of Katsaros (1990). For direct
measurement of the radiative fluxes at sea, the best compromise between the competing needsof accuracy and practicality is provided by instruments which have been in existence for aroundhalf a century, with little modification over that time; namely the pyranometer for shortwave
radiation and the pyrgeometer for longwave. The two instruments are similar in form, bothmeasuring the radiation flux through a hemispherical dome of appropriate spectral
transmissivity. Both experience particular problems in use at sea, contamination of the domes
by salt, motion of the ship or buoy and, because it is impossible to screen the sensing element,
vulnerability to the many sources of electromagnetic interference aboard ships (e.g. Lind andKatsaros, 1986). Perversely, to avoid shadowing of the instruments they usually have to belocated at the highest place on the ship along with the radio antennae.
Only the top of the range instruments from the various manufacturers are potentiallycapable of providing radiation data to the accuracy required to close an ocean mixed layer
budget, or to validate coupled models or remotely sensed products. Their use and performance
will be discussed in this section. However, instruments of lesser quality are available, and often
used when cost is a factor. More accurate devices, such as the broadband Normal Incidendence
Pyrheliometer (NIP), are sometimes used as calibration standards for working instruments, but
for reasons given below are generally unsuitable for use at sea.
At the same time, the situation is changing rapidly with the introduction of new
knowledge and technology, and the urgent needs of climate research. Post et al. (1998)
describe instruments, several at prototype or research stage of development, which were
deployed together during a NOAA-sponsored Combined Sensor Program cruise to the central
and western Pacific ocean in March-April 1996. They included sophisticated radiation
instruments, such as the Fourier Transform Infrared Instrument (FTIR) and Atmospheric
Emitted Radiance Interferometer (AERI), both of which measure infrared spectra at highresolution over a broad spectral range, and a sun-tracking photometer to obtain solar flux atseveral filtered visible and infrared wavelengths. The technologies developed to date mostly
serve to retrieve variables that can be used to compute fluxes with a radiative transfer code,
rather than to observe the fluxes themselves. For example, the humidity field from AERI can beused as one of the inputs needed to compute downwelling LW flux, and the aerosol spectral
optical depth from a photometer can be used in the computation of the downwelling broadband
SW insolation. Comprehensive suites of sophisticated and well-maintained radiation
instruments are already installed operationally in various climatically important parts of theworld (e.g. Pacific islands, Alaska), at some of the Atmospheric Radiation Measurement

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5.2 Radiative Fluxes(ARM) program’s Cloud and Radiation Testbed (CART) sites, and it is likely that ruggedised,affordable versions of such sensors will be in fairly routine use within the next decade.
Determination of radiative components in very high latitudes presents particular
problems, partly due to the effects of low temperatures on the instruments, but also because of
spatial inhomogeneity of the surface. Sea ice, snow and open leads greatly affect radiative
exchange through spatial variability of surface albedo and by selectively insulating the cold
atmosphere from relatively warmer water below the ice. To further complicate the issue, it is
not usually meaningful to discuss the radiation fields in polar regions without simultaneousconsideration of clouds and the various cloud-surface feedbacks. A comprehensive overview ofcloud and radiation characteristics in the Arctic, together with discussion of the boundary layer
features, is given by Curry et al. (1996). In the following two sections, which deal separately
with the general features of shortwave and longwave radiation, and in section 6.5 on albedo, we
include comment on the specific problems presented by sea ice.
5.2.2 Shortwave radiation
Downwelling shortwave (SW) radiation at the surface (referred to as shortwave
irradiance) has a component due to the direct solar beam, and a diffuse component scatteredfrom atmospheric constituents and reflected from clouds. Upwelling shortwave radiation comesfrom reflection at the surface and the re-emergence of radiation backscattered as it is absorbed
through the upper ocean (together referred to as exitance). The terms exitance and irradianceimply hemispheric, broadband radiation, and for the shortwave their ratio is the surface albedo,
discussed more fully in section 6.5.
Both irradiance and exitance can be measured with the pyranometer, facing either
skyward or towards the sea surface. Modern pyranometers are still based on the MollGorczynski design (Moll 1923) in which radiation falls on a blackened horizontal receiving
surface bonded to a thermopile and protected by two concentric precision hemispheric glass
domes. The most important factors affecting the accuracy of these instruments are; reliability
and stability of calibration, dome temperature effects, cosine response and detector temperaturestability. Manufacturers claim temperature stability of 1% over the range –20°C to +40°C, andthe same figure for cosine response between 0º and 70º solar zenith angle. The reliability oftransfer of the World Radiation Reference (WRR) calibration standard to working instruments
is estimated to be 1%. Combined, these sources of error give a basic accuracy for the
instrument as 2%, the figure usually quoted. However, Bush et al. (2000) have demonstrated
that variability in the temperature of the dome used to shield the detector can affect theobservation by several Wm-2 . The most common manifestation of the impact of dome
temperature on the pyranometer is the “night offset”; negative values (typically 1-10 Wm-2 andvariable with time) for the SW insolation at night. In the ARM Enhanced Shortwave
Experiment (ARESE) a first order correction for the dome effect was made by adjusting the
diffuse component of the daytime insolation by approximately 5 Wm-2 , being largest in clear
skies (Alberta and Charlock 1999).
Another source of error, particular to pyranometers used at sea, is caused by theplatform motion. For correct measurement the receiving surface must be horizontal, but bothships and buoys can roll through several degrees, or take on a systematic lean caused by windforce or poor trim. For given platform angle the severity of the error depends on several
factors; cloudiness, latitude, season and time of day. Katsaros and DeVault (1986) calculatethat it can be as large as 10-20% in the daily average for clear skies poleward of 45° in winter if
the instrument has a 10° tilt. This is an extreme example, and they point out that the error isconsiderably less at lower latitudes and that there can be compensating errors, which they havenot accounted for, when averages are taken over a whole day. MacWhorter and Weller (1991)
also consider the problem. Their experiments simulating symmetrical rocking of the platform,
without any mean tilt, show that for 10° amplitude the largest error was 2.1% (and for 20°,
5.1%; 30°, 7.8%; 40°, 12%). These figures were for zenith angle of 51° and suggest that for
low latitudes and modest seas the effect of platform roll may not be too serious. Even at mid-

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5.2 Radiative Fluxes

latitudes, more severe seas causing greater roll tend to be associated with bad weather andcloudiness, reducing the absolute irradiance (which is also mostly diffuse and therefore lesssusceptible to tilt error). However, as MacWhorter and Weller (1991) show, a persistent tilt
even of only 5° will produce errors between 5% and 16%. Even if the record of ship or buoy
motion and all other factors were available, it would be a daunting task to correct the radiation
measurement a postieri. Both Katsaros and DeVault (1986) and MacWhorter and Weller(1991) recommend that pyranometers be set in gimbals.
During TOGA-COARE a field comparison between several pyranometers on ships,
aircraft and a mooring on a clear day, revealed systematic differences up to 3% for the firstclass instruments and 8% for one of lower quality, well outside manufacturer’s specification.
The cause was assumed to be calibration error, subsequently confirmed by laboratoryintercalibrations. The instrument’s calibration can change through deterioration of the receptor
surface (flaking of the paint for example) or of the glass dome (scratching), or some electricalfault within the thermopile system. Manufacturers recommend re-calibration once a year
against a WRR standard but hitherto this seems to have been seldom observed. Given the
COARE experience, it seems prudent to calibrate before and after each field deployment, and if
possible to intercompare instruments in the field.
The issues described above highlight the extreme difficulties of making accurate
shortwave measurements at sea, compared with over land. The WCRP has established stringent
procedures to apply at each continuous observing site to comply with the Baseline Surface
Radiation Network (BSRN) standards for climate monitoring (Gilgen et al., 1995). For
measurement of the direct beam these call for the deployment of an NIP, which requires a sun
tracking system; rigorous calibration procedures are employed for measurement of the direct
beam at stations meeting BSRN protocol. The diffuse component is obtained with a shaded
pyranometer; however, there are at present virtually no routine measurements of the diffuse
component of downwelling SW insolation whose calibration protocols are sufficiently rigorous
for clear sky conditions.
Hence, the only measurement system with the prospect of meeting WCRP observing
standards for long-term monitoring of SW insolation at sea would be one deployed on a rigid
platform. Neither the NIP sun tracker nor the diffuse system are compatible with unsteadyplatforms such as ships and moorings. Over land, or on a fixed platform at sea, the diffuse
component of short-wave radiation is measured by fitting the pyranometer with a “shadowband”, set to shield the sensing element from the direct solar beam as it tracks across the sky.
The position of the band is changed manually to accommodate the variation of solar elevation
through the annual cycle. Floating platforms obviously preclude the use of a fixed shadowband.
Nevertheless, the needs of the modelling community to distinguish between the direct
and diffuse components, and to parameterise appropriately, has stimulated efforts to improve
the situation. Recently, measurements of the diffuse component have been made fromshipboard using a rotating shadow-band which, whatever the relative position of the sun, casts a
shadow on the radiation sensor once per revolution. A fast response sensing element, such as aphotocell, is required and the diffuse signal determined from the time series and approximate
timing. The signal produced by the shadow-band is unambiguous under clear skies, but inconditions of broken cloud can be difficult to recognise. Measurement accuracy iscompromised by the poorer quality sensor, although a reference measurement of the directcomponent is usually provided by a high-quality pyranometer alongside. Descriptions of such
instruments are given by Reynolds (1998) and on the ARM web site1..
The effect of the above sources of error in terms of absolute energy exchange depends
on the latitude and season. Table 5.1 shows the daily averages of downwelling irradiance1:http://www.arm.gov/docs/instruments/static/mfrsr.html (mfrsr refers to multi-filter rotating
shadow-band radiometer).

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observed during field experiments at various latitudes. In terms of the basic 2% accuracy of thepyranometer, instrumental error is seen to be less than 7 Wm-2 on daily average, even in thetropics where shortwave irradiance is a dominant component of air-sea energy transfer.
However, the dome effect, tilt error and poor calibration can seriously degrade this.
Furthermore, at noon on a clear day irradiance in the tropics often reaches 1000 Wm-2, andinstrumental uncertainty alone increases to 20 Wm -2 . Such an uncertainty could be
unacceptable when trying to model processes in the oceanic and atmospheric mixed layers on
the diurnal timescale. Two initiatives by the NASA-Langley group will improve the accuracyof measurement of the marine SW radiation field; Haeffelin et al. (2000) have begun a program
to study the physics of the pyranometer; a BSRN-quality measurement site has been established
at Chesapeake Lighthouse (36.9N, 75.71W).
Table 5.1 values for the daily average downwelling shortwave irradiance observed during four
field experiments.
Experiment

Dates

Lat.

SW (Wm-2 )Reference

JASIN

July-Sept. 1978,

60ºN;

40 to 270

(Lind et al., 1984);

STREX

November 1980,

50ºN;

10 to 60

(Lind and Katsaros, 1986);

FLIP

February 1974,

35ºN;

65 to 169

(Simpson and Paulson, 1979);

COARE

Nov.1992-Feb.’93,

2ºS;

63 to 330

(Fairall et al., 1996a).

While the role of air-sea transfer in forcing global climate has long been recognised intropical regions, the importance and vulnerability of polar regions has been acknowledged only
relatively recently. This has led to increased effort to measure fluxes in situ in support of seaice modelling and the validation of remotely sensed products. Some intensive measurement
expeditions to the arctic, and pointers to data sets are given in section 4.7. The recent Surface
Heat Budget of the Arctic Ocean (SHEBA) field experiment2 involved a year-long deployment,from October 1997, of a comprehensively instrumented ice camp within the perennial Arctic
Ocean ice pack. Radiation instruments were included, to augment those on the nearby ARM
CART site3 .
Simplicity and availability result in the pyranometer being used as the front-line
instrument for measurement of shortwave irradiance in most ocean environments, including theice-prone waters of the Arctic and Antarctic. However, these regions present three instrumental
uncertainties additional to those listed above; whether the instrument calibration holds intemperatures below those specified by the manufacturer; cosine response degrades for solar
zenith angles greater than 70°; and frost formation on the dome. The first two problems do notappear to have received much attention, but the third is alleviated in the most recent
deployments by blowing air, sometimes heated, across the dome.
Within the Arctic and Antarctic circles (latitudes > 66.5° N and S) shortwave irradianceis either continuous or completely absent for significant periods of the year. There are areas ofvery high albedo, but variable in space and time. Shine (1984) shows that ignoring multiple
reflections between these high albedo surfaces and clouds can lead to errors in shortwave
surface flux of 30% over bare ice and 50% over new snow. Although the solar zenith angle isalways large, the magnitude of the surface flux is by no means trivial, the average observed forJune being around 300 Wm-2 . Pinto and Curry (1997) point out that this compares withmodelled values ranging from 85 to 185 Wm-2, ascribed to over-prediction of cloud. They note2
3

http://sheba.apl.washington.edu/about/about.html
http://www.arm.gov/docs/sites/nsa/nsaaao.html

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5.2 Radiative Fluxesthat these models may embody parameterisations for clouds and radiative processes which weredeveloped for regions with quite different climatic conditions from those at the poles. The
unique features of the shortwave radiation environment in very high latitudes cause problems ofsimulation and emphasise the need for high-quality validation datasets.
5.2.3 Longwave radiation.
Downwelling longwave radiation (longwave irradiance) originates from the emission
by atmospheric gases (mainly water vapour, carbon dioxide and ozone), aerosols and clouds.
The major component of longwave exitance is emission from the sea surface which depends on
the surface emissivity and the skin temperature, augmented by a small contribution due toreflection of the longwave irradiance. Longwave absorption and emission take place in about
the top 0.5 mm of water, depending on wavelength.
The pyrgeometer is of similar construction to the pyranometer, but the single dome ismade from silicon or similar material transparent to the longwave band, coated on the insidewith an interference filter to block shortwave radiation. The longwave irradiance passing
through the dome, which we wish to determine, is only one component of the thermal balanceof the thermopile. The remaining components come from various parts of the instrument (e.g.
Fairall et al. (1998) - Figure 1). To isolate the geophysical component, Q↓lw , the manufacturers
provide an equation derived by Albrecht et al. (1974):

Q ↓lw = V / s + ε pσTc4 + Bσ (Tc4 − Td4 )(5.1)
where V is the thermopile output, s the radiometer sensitivity (the calibration factor displayedon the side of the instrument, usually µV/Wm-2), σ the Stefan-Boltzmann constant, B is the ratioof emissivity to transmissivity of the dome material, and Tc and Td the respective case and dome
temperatures (K). Modern instruments are provided with thermistors in the dome and case toenable equation (5.1) to be calculated. εp is the emissivity of the thermopile surface which is, infact, included incorrectly in this equation (see below).
For completeness, we note that the most common pyrgeometer in use by geophysicists(the Eppley precision infrared radiometer, or PIR), includes a battery-powered resistancenetwork which generates a voltage in series with the thermopile that is the electrical analogue ofthe second term in (5.1). Thus, providing the observer is prepared to ignore the third (domeheating) term or estimate it using a scheme such as that of Alados-Arboledas et al. (1988),
longwave irradiance may be recorded with a single output signal, as from the pyranometer.
This procedure obviously trades accuracy for simplicity of operation; this is discussed further
below.
The performance of pyrgeometers, and particularly the Eppley PIR, has come underclose scrutiny recently. Philipona et al. (1995) evaluated the thermal balance of the instrument
and developed a more complete version of (5.1), retaining three parameters to be determined
empirically. To obtain the highest measurement accuracy, they stressed the need to characterise
individual instruments and they describe an apparatus designed to obtain the pyrgeometer
calibration factor and their three empirical parameters, one of which is closely equivalent to B
in (5.1). They also measured significant temperature gradients over the dome, and suggest thata single dome thermistor is inadequate to specify Td .
Fairall et al. (1998) also re-examined the thermal balance of the pyrgeometer anddiscovered that the factor εp appears erroneously in (5.1). The reason is that Albrecht et al.
(1974) make the approximation εp = 1 (εp is usually quoted as 0.98), but do so inconsistently.
Since the second term of (5.1) is usually dominant, the error can be significant. For the
COARE data, where Tc was around 300K, it is easy to see that the correction increases thecalculated longwave irradiance by about 9 Wm-2. Fairall et al. (1998) used a radiative transfer
model with atmospheric soundings launched from the ship in clear-sky conditions, to calculate
longwave irradiance at the surface; the results agreed with simultaneous pyrgeometermeasurements within 2 Wm-2 in the mean, supporting their revision of (5.1). Persson et al.
(1997) calculate the impact on various climatically important parameters, of removing theReport of the Working Group on Air Sea Fluxes

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erroneous εp, for example the disappearance of an apparent diurnal cycle of sensible heat flux
transfer coefficient.
Like the thermopile calibration factor, the dome heating correction factor, B, isinstrument-specific and must be determined empirically, yet a value is not normally provided
by the manufacturer. Values from about 2.5 to 4.5 are in common use, their origin usually
obscure. The best published determinations are probably those by Philipona et al. (1995), using
their new calibration system, who obtain values for different instruments ranging from 3.15 to
4.27. Russell et al. (1999b) intercompared five Eppley PIR's during SHEBA and reduced theuncertainty in downwelling longwave by mean-normalising values of s and B for each
instrument. Their adjusted values for B span a very similar range to those quoted above. The
significance of this uncertainty can be tested with a rather severe example from the cruise to theequatorial Indian ocean described by Godfrey et al. (1999). Around midday on August 26,
1005 Wm-2 of solar radiation fell on the pyrgeometer, and Td - Tc was 2°C, so adopting anominal B=3.5, the dome heating term was -46 Wm-2 from a total longwave irradiance of 407
Wm-2. Using B=3.15 the term becomes -42 Wm-2, an uncertainty of 4 Wm-2 or 1%.
In practice, scatter in the apparent values of B may be due to shortwave leakage
through the dome filter. Dickey et al. (1994) added such a term to (5.1):

Q ↓lw = V / s + ε pσTc4 + Bσ (Tc4 − Td4 ) − λRsdown(5.2)
where λ is a shortwave leakage factor and Rsdown the downward shortwave radiation. Pascal andJosey (2000) showed that this shortwave leakage effect was significant with λ varying from
instrument to instrument. There was some indication that instruments with smaller leakage
factors had larger values for B and vice versa. They found that the differential heating of theradiometer was primarily caused by the incident shortwave radiation, modified slightly by thecooling effect of airflow over the dome.
Over the SHEBA sea-ice, Russell et al. (1999a) report frosting of the domes ofpyrgeometers and discovered that an ice coating makes the PIR more sensitive to dome
temperature, leading in clear conditions to an overestimate of downwelling longwave flux by up
to 50 Wm-2. They found that ventilation and 10-25 Watts of heating were needed to keep thedomes ice-free. Similar differences between ventilated and non-ventilated pyrgeometers werereported by Guest (1998) working in the Antarctic. Guest (1998) compares his longwave
observations with the predictions of several empirical formulae, similar to the work of Key etal. (1996) for the Arctic, and draws attention to dissimilarities in the processes of ice formation,
longwave radiation and cloud effect in the north and south polar regions. However, both
Guest’s (1998) observations and the (Arctic) satellite analysis by Curry et al. (1996) show that
downwelling longwave radiation dominates the surface radiation balance, increasing fromaround 200 Wm-2 in winter to 280 Wm-2 in summer. For March and April, Russell et al.
(1999b) report around 260 Wm-2 for cloudy conditions but considerably less (~160 Wm-2) underclear skies, very similar to those of Guest (1998 - Figure 4).
Philipona et al. (1995) conclude that pyrgeometer accuracy can be ±2 Wm-2 withcareful calibration and operation. The error analysis by Fairall et al. (1998) indicates that thebasic precision of the instrument is about 1.5% of the total longwave irradiance or about 5 Wm2. They also find that using the battery-compensation circuit leads to errors of at least 5% or 20
Wm-2. This is unacceptable in many applications nowadays, and also unnecessary. Fairall et al.
(1998) discovered that the limitations of the battery-compensated system are well-known toradiation specialists, but not among ocean and atmospheric scientists who acquire pyrgeometers
without appreciating the subtleties of their design, and therefore fail to take full advantage of
their capability. To do so, however, entails logging three signals and performing regularcalibration checks. For data sets where the separate temperatures have not been logged, Pascal
and Josey (2000), suggest that correction is still possible if the factors B and λ are known forthe specific radiometer and the available data set contains values for the incident solar radiation
and wind speed.

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5.2 Radiative Fluxes
5.3 Turbulent Fluxes

Calibrating facilities for infrared instruments are more complicated and therefore lesscommon than those for solar radiation, and require careful technique. Fairall et al. (1998)
recommend field intercomparisons if several instruments are available (e.g. Russell et al.
1999a,b). They also suggest that calculations from a modern radiative transfer model (RTM),
using on-site atmospheric soundings in clear-sky conditions, would provide an absolutereference for longwave irradiance within about 2 Wm-2; Miskolczi (1994) comes to a similar
conclusion (aspects of RTM's are discussed in Section 6.3). RTM's are not as rigorously
validated for cloudy sky conditions, however. The calculation of the downwelling longwave
flux under a cloud requires knowledge of both cloud base height and emissivity. Cloud baseheight may be measured with active systems such as a MicroPulse lidar (Spinhirne, 1993) or a
cloud profiling radar, although an infrared radiometer (such as the pyrgeometer) would still beneeded to estimate cloud emissivity. The requirement for downward LW under cloudy skiesargues for continued deployment of pyrgeometers on research ships.
The pyrgeometer can be faced downward to measure longwave exitance, but for eithera ship or buoy would need to be mounted at the end of a fairly long boom to exclude theplatform itself from the field of view. There are obviously practical problems to thisarrangement, and it is preferable and probably more accurate, to measure sea temperature anduse the Stefan-Bolzmann law, remembering to include the small component of longwave
reflected from the sea surface. Then:Q ↑lw = εσTs4 + (1 − ε )Q ↓lw(5.3)
where ε is the emissivity of the sea surface, and Ts the skin temperature of the water, as would
be measured by an infrared radiometer. This immediately raises a problem, because accurate
IR radiometers are not usually available, and the sea temperature measured at some depth may
be considerably different from Ts (see Section 10.2).

5.3 Turbulent fluxes
5.3.1 The Eddy correlation method
In a turbulent atmosphere, the instantaneous vertical transport of some atmosphericconstituent is given by the product of its concentration, C, with the vertical wind velocity, W. It
is customary to write these quantities in terms of their mean values over some convenient
period (10 minutes to 1 hour), plus their instantaneous fluctuation from that mean. Thus,
C = c + c ′ and W = w + w ′ ; multiplying and performing the usual Reynolds averaging wehave, for the vertical flux of constituent C:(5.4)
Fc = WC = wc + w ′c ′When the site is horizontally uniform, and atmospheric conditions steady over the averaging
period, it is expected that w is zero, so that the flux is given by the last term of (5.4).
Measurement of the fluctuating quantities w′ and c ′ with fast-response sensors, and calculating
the covariance, is therefore a direct way of obtaining the fluxes. The covariance must embracea frequency range from the turbulence dissipation scale (see below) to a period long enough toinclude all flux-carrying wavelengths. In practice this entails recording continuous time-series
at a resolution of 10-20 Hz. See Friehe et al. (1991) for a discussion of this, and some practicalaspects of the flux calculation.
Referred to as the eddy-correlation method, rapid advances in the development of sonicanemometers and fast-response temperature and humidity sensors have made measurement ofcovariance fluxes over land routine for almost two decades. However, measurements at sea arecomplicated by contamination of the complex sensors by salt and sea spray, by ship motion,
and air-flow distortion. Eddy-flux equipment has been operated from bottom mounted towers
and research platforms, but necessarily these are shallow water sites. Aboard ship, the need tolocate turbulence sensors well clear of obstructions is even more critical than with other

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5.3 Turbulent Fluxes

instruments, because distortion to the wind flow and wakes from even small objects upstream
can significantly modify the turbulent field. More importantly, pitching and rolling of the ship
usually dominates the observed wind signals, and the 3-dimensional motion of the wind sensor
relative to the surface must be recorded and subtracted post-facto from the recorded windsignals (Edson et al. 1998).
Nevertheless, it’s value as a direct method of flux determination to validate indirect
methods such as the bulk and inertial dissipation (see below), has spurred increased effort toovercome these inherent difficulties. Following the pioneering work of Fujitani (1985),
instruments and computational methods have been improved, and several investigators reportsuccessful motion-corrected covariance fluxes from ships (Tsukamoto and Ishida 1995; Fairall
et al. 1996a; Fairall et al. 1997; Edson et al. 1998). The greatest difficulty arises in the case of
momentum flux, or stress τ = ρu ′w ′ where the spurious horizontal and vertical wind signals in
w′ and u ′ are themselves correlated. The sensible and latent heat fluxes,

H = ρCp w ′θ ′ and E = ρLw ′q ′ respectively,
are less susceptible to slight errors in the motion correction, because the scalar concentrations
are only linked to the movement of the sensor as it pitches through the vertical gradients oftemperature and humidity, which are generally small at sensor height.
In the context of a 10 Wm-2 heat flux accuracy goal as specified for TOGA COARE,
the small correction to the fluxes of water vapour (and other trace gases) described by Webb etal. (1980) may be significant. They point out that; “If the heat flux is upwards (positive) thenrising parcels of air are on average warmer than descending parcels, so on the assumption ofzero mean vertical mass flow of air there must exist a small mean upward velocity
component.”. In this case the first term on the right hand side of (5.4) may not be negligible.
This vertical velocity is given by;

w = 1.61w′q ′ + (1 + 1.61q) w′θ ′ / T(5.5)

and the consequent correction to the latent heat flux is:

EWebb = ρL wq(5.6)
Over the COARE 4-month Intensive Observation Period, the “Webb” effect averaged 4 Wm-2.
During strong wind periods, with large sensible and latent heat fluxes, Webb corrections oforder 20 Wm-2 were observed.
Although much progress has been made with the measurement of eddy-fluxes fromships in the open ocean, and the effort continues to make such measurements more common(Fairall et al. 1997; Edson et al. 1998), it is likely that their role will remain as a research orvalidation tool for some time to come. However, the so-called inertial dissipation technique,which employs essentially the same fast-response wind, temperature and water vapour sensors
as the eddy-correlation method, may be more readily developed as an operational facility(Fairall et al. 1990, Yelland and Taylor 1996).
5.3.2 The inertial dissipation method
A. MOMENTUM FLUXThe inertial dissipation method has been favoured for use on ships for two main
reasons. The turbulence measurements required can be made at frequencies (a few Hz) above
those affected by the motion of the ship, and the implied short spatial scale of the turbulence
reduces the errors due to airflow distortion. However since various assumptions are necessary
to determine the fluxes, the inertial dissipation method is less direct than the use of eddy
correlation. The underlying theory will be outlined using, as an example, the momentum flux.

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5.3 Turbulent Fluxes

Based on the Kolmogorov hypothesis, the power spectral density, Suu(n), of the downstream wind component, u, can, in the inertial subrange, be related to the dissipation rate εvia the wave number, n:

Suu (n) = Kε 2 / 3n −5 / 3(5.7)

where K is the one-dimensional Kolmogorov constant. Henjes (1998) has shown that (5.7) isvalid for use in the inertial dissipation method provided the spectrum of the turbulence ishomogeneous and isotropic for wave numbers in the inertial subrange. Using Taylor’s
hypothesis, that is assuming “frozen” turbulence, (5.7) becomes:

Suu ( f ) = Kε 2 / 3 f −5 / 3 (Urel / 2π )

2/3

(5.8)

where U rel is the wind speed as measured by the anemometer, and f is the measurement
frequency. Hence the dissipation rate can be obtained by calculating the mean value of f5/3Suu(f)over an appropriate frequency range.
The wind stress is derived from the dissipation rate using the turbulent kinetic energy
budget (see for example (Busch, 1972)) which for steady state, horizontally homogeneous
turbulence, can be written as:
2
*

u

∂ u'

w' Tv'

+g

∂z

Tv

−

1 ∂ ' '
∂ ' '
we +
wp =ε
∂z
ρ ∂z

(5.9)

or
P

+

B

-

Dt

+

Dp

= ε

(5.10)

where primes indicate fluctuations and < > indicates mean quantities. In (5.10) P is themechanical production, B the buoyant production, Dt and Dp are the vertical divergence of the
turbulent transport and pressure transport terms, and ε is the dissipation. Equation (5.9) can bemade dimensionless by multiplying by the Monin-Obukhov surface layer scaling parameterkz / u*3 giving:

φm −

z
εkz
− φt + φ p = 3 = φεL
u*(5.11)

where the Obukhov length, L, is:L=−u*3Tv(gk T w )'v'

(5.12)and each of the dimensionless profiles φ m , φt and φ p are expected to be universal functions ofζ ( = z/L). Equation (5.11) defines the dimensionless dissipation function φε . If the terms on
0

the left-hand side of (5.11) are known (or equivalently the form of φε (ζ ) ), the friction velocity
can be evaluated from an estimate of the dissipation. Thus :
1/ 3

 2π

kz
f

 Urel (φ m − ( z / L) − φ D ) (5.13)
Unfortunately, the exact forms of φ m (ζ ) , φt (ζ ) and φ p (ζ ) are not well known
1/ 2

fS ( f ) 
u* =  u K 

(Fairall and Larsen, 1986), and previous authors have made various assumptions as to theirmagnitude for turbulence over the sea. The form of φ m (ζ ) will be discussed in Section 7.2;
with regard to the vertical divergence terms, Large (1979) argued that the available evidence
suggested thatφt ≈ −φ p(5.14)
This leads to a balance between dissipation and the sum of mechanical and buoyant production:

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5.3 Turbulent Fluxes
5.4 Precipitation Fluxzφε =  φ m − L(5.15)
However, other authors suggest that there is an imbalance between production and dissipation.
Thus:z
φε =  φ m − − φ D L(5.16)
Deacon (1988) argued that the value of the Kolmogorov constant used in the inertial dissipation
method is not the true value, but rather an effective value which corrects for the φ D term atneutral stability. Edson et al. (1991) and Edson and Fairall (1998) have estimated that thiscorrection might be of order 10 to 20%. By evaluating the friction velocity to wind speed
relationship for different stability ranges Yelland & Taylor (1996) and Dupuis et al. (1997)
evaluated the variation of the apparent imbalance term with ζ. However Taylor and Yelland(2000a) have shown that much of that apparent imbalance was mainly caused by random errors
in the data which caused errors in the calculated stability. The implication is that the true
magnitude of any imbalance may well be smaller than previously estimated, thus giving more
confidence in results from the inertial dissipation method.
As formulated above, the inertial dissipation technique requires assumptions withregard to the values of the von Karman constant, k, the Kolmogorov constant K, and the form
These constants and functions are notof the dimensionless functions φ m (ζ) and φ ε (ζ).
independent and Fairall and Larsen (1986) have suggested a different formalisation of theinertial dissipation method in terms of turbulence structure functions. As implemented by
Edson et al. (1991), this requires determination of φm(ζ) and the dimensionless structure
function parameter fm(ζ) but does not require an explicit Kolmogorov constant value. We havedescribed the above formulation because it follows that of Large (1979) and Large and Pond
(1981,1982) which represent the main verification of the inertial dissipation method over the
open ocean.
B. SCALAR FLUXES
For other variables the inertial dissipation method consists of determining the spectrallevel of the fluctuations in the inertial subrange and then solving the budget equation for thevariance of that quantity. Thus:
1/ 2

 K φ fSγ ( f ) εw' γ ' = βφN γ fSu ( f ) 

u*2(4.17)

where γ signifies any passive scalar and φ Nγ , β are respectively the dissipation function and
Kolmogorov constant pertaining to γ.

5.4 Precipitation Flux
5.4.1 Introduction
The determination of precipitation flux over the oceans is a critical requirement forstudies of the hydrological cycle, ocean stratification and mixing, and for the development ofcoupled ocean-atmosphere GCM's. However, accurate measurement of rainfall at sea presents
difficulties in two respects; there are measurement (instrumental) problems, and sampling
problems. Rainfall, particularly in storms associated with tropical convection, is perhaps the"patchiest" of all meteorological variables. For most applications, over land or sea, single point
observations of rainfall are generally less relevant than area-averaged values or spatial
characteristics. Whereas over land these quantities may be derived from an appropriatelyspaced network of surface raingauges, at sea both spatial and temporal sampling by

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conventional means is usually quite inadequate, even during intensive measurement campaigns(see Section 10.9.1). Remote sensing can provide areal coverage, by ship or aircraft mounted
radars on the local scale, or from satellites in the longer term and globally. The sensors andrecovery algorithms should, however, be verified by reliable ground-truth measurements.
Results of different algorithm intercomparison projects (see Section 10.9.4) have shown that
this is rather difficult because the in situ and radar measurements are generally not
representative enough to serve as a verification data source.
For the TRMM satellite (see Section 4.3.2) an intensive validation experiment(KWAJEX) was conducted around the island of Kwajalein (Marshall Islands) from July to
September 19994. Ground-truth instruments were deployed on the island, on aircraft and shipborne. When available, the results of this experiment will add to our knowledge of theperformance of various instruments in common use to determine precipitation at sea.
Development in this area has been much needed, because, as will be discussed next, there are
significant problems with using conventional funnel rain-gauges on ships.
5.4.2 in situ rainfall using Funnel raingauges.
A. INTRODUCTION
The use of conventional rain-collecting instruments, designed for land use, results inuncertainties which are of the same order of magnitude as the mean precipitation estimates.
Hasse et al. (1998) stressed two main sources of the uncertainties of precipitation measurements
using traditional land rain gauges at ships and buoys: (i) the effect of the flow around the ship’s
overall structure which can lead to undercatch or overcatch depending on the location of gauge;(ii) the effect of the flow in the close vicinity of the rain-gauge, which tends to carry the rain
above the orifice of the gauge and leads to a wind speed dependent undercatch. Corrections ofmeasurements for the wind speed, and proper installation of rain gauges, can slightly improve
the accuracy, but do not resolve the problem in general (Olbrueck 1979, Ruprecht 1993, Hasse
et al. 1998). There is also the problem that conventional tipping bucket raingauges do notwork on board ships or moorings because of the platform motion.
B. FUNNEL DESIGNA serious source of error is loss of catch due to the deflection of raindrops away fromthe opening of funnel-type gauges during strong winds. This phenomenon is well-known infunnel gauges used over land. It was first documented by Heberden (1769) who observed thedifference between rainfall measured in the garden of Westminster Abbey, and that measured
on the roof. Jevons (1861) explained the problem analytically and Koschmeider (1934) devised
a simple empirical wind-speed dependent correction. In about 1980 the WMO Commission for
Instruments and Methods of Observation (CIMO) initiated a series of workshops and amonitoring program designed to standardise international practice in the form and exposure ofoperational gauges (WMO 1985, 1989). A recent contribution to this effort is described by
Yang et al. (1998). Folland (1988) developed a theory for loss of catch based on the pattern of
fluid flow past cylindrical obstacles, and involving droplet size and rainrate. The lighter therainfall and the smaller the drop, the more likely will it be swept past the funnel instead ofdropping in. Folland adopted the drop size distribution (DSD) of Best (1950) rather than the
better-known DSD of Marshall and Palmer (1948) on the grounds that the latter overestimatedthe number of small drops. Folland’s model agrees fairly well with field observations, butcovers rainrates from 0.1 to only 10 mm hr-1 which may be adequate for most rainfall over landin Europe, but is at the low end of rainfall at sea and far short of tropical storms. With a 10 ms-1
wind and 10 mm hr-1 rainrate, the model predicts about 10% loss. But other empirical studies
hint at the possibility of much larger effects; 5-80% loss (Kurtyka, 1953), 50% loss for 1-2mm
drops at 12 ms-1 in a wind tunnel (Mueller and Kidder, 1972), annual loss 20% and“considerably greater” with taller gauges (Rodda and Smith, 1986).
According to Folland’s (1988) model, for the large drop sizes and high rainrates of atropical storm loss of catch would be negligible. However, the problem is much more severe on

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ships because, to avoid obstructions, raingauges are usually mounted aloft where winds can beaugmented by the ship’s speed. Further, the wind flow distortion is over the entire vessel, notjust the gauge itself. The problem has been investigated in respect of the COARE rainfall data,and extended to studies of measurement variability at various locations on the ship during
subsequent cruises. This will be discussed in more detail below, but here we simply observethat even in heavy rainstorms, raindrops were seen to be deflected upward away from thefunnel in some locations. For funnel gauges mounted in more favourable positions,
Koschmeider’s (1934) empirical wind-speed dependent correction, brought the siphon rainfall
values close to the corrected optical raingauge values in the COARE, MCTEX and Indian ocean
cruises (Bradley, pers. comm.; Godfrey et al. 1999).
Hasse et al. (1998) have designed a rain gauge for use on ships specifically to
overcome the wind loss problem, using a "dual collector" arrangement. The top funnel is ofsimilar shape to the “champagne glass” design recommended by Folland (1988), and feeds to acalibrated orifice from which drops are counted. Rain blown against the side of the instrument
is trapped, funnelled down into a separate collector, and its contribution weighted according towind speed measured by a co-located anemometer. This instrument has been tested for someyears on land against standard gauges, and on ships against an optical disdrometer achieving an
accuracy of about 3% for 6-hourly sums. (Grossklaus et al. 1998). Tests continue on various
ships, including a 2-year operational pilot project on ship lines in the Baltic Sea. Its
performance must be judged eventually against a consensus of experience with other
instruments, all of which carry some measure of uncertainty when used at sea. Taking into
account the relatively low cost of the gauge and computer interface for data storage (inoperational design mode), its operational use on ships would appear feasible.
C. MEASUREMENT METHOD
The most common type of funnel gauge used aboard ships and moorings is the siphon
gauge. This has a reservoir which steadily fills with rainfall to its capacity, when it siphonsautomatically and starts filling again. Some electronic system (e.g. capacitance) is used to keep
track of the level of water in the gauge, and provide an analogue output; a common
specification is capacity equivalent to 50 mm rainfall with resolution 0.01 mm. The siphon
system is relatively, but not completely, independent of ship motion. There is also loss of
rainfall registration while the instrument is siphoning; since this point is frequently reachedduring a period of particularly heavy rainfall, the loss can be significant.
5.4.3 Other methods of in situ measurement
A. OPTICAL RAINGAUGESThe most common optical raingauge (ORG) in use was developed partly to address the
problems of measurement in the remote, marine environment. It has no moving parts, is robust
and completely sealed, and provides an analogue output which is a simple function of therainrate. These ORG's measure rainrate directly by detecting the intensity of scintillations
caused by raindrops passing through a partially coherent beam of infrared radiation. The
instrument, and the theoretical basis of its optical scintillation system are described by Wang etal. (1978, 1979) and Crosby (1994). Some ORG's were provided by the NASA-TRMM office
to ships and moorings participating in TOGA-COARE, in support of the TRMM satellite
validation program.
At the 1994 Toulouse COARE data workshop (Chinman et al. 1995, page 26),
disagreement between various rainfall estimates was found to be worse than a factor of two,
which led to a comprehensive study of rainfall measurement. The methods under scrutiny, andtheir Intensive Observation Phase (IOP) average rainfall estimates were reported at a COARE
Flux Group workshop (Bradley and Weller 1995a, page 14). Leaving aside the broad range ofsatellite estimates, these results seemed to form two groups; the radars, atmospheric budgets
and siphon gauges supporting an IOP-average rainfall of around 5-6 mm day –1 with the ORG's
indicating about twice that value. Being a relatively new and untried instrument, the ORG was

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5.4 Precipitation Flux

suspected of overestimating relative to the more conventional systems. Suggested defects were
errors in calibration, sensitivity to ship vibration, and inappropriate response to the intensity and
DSD of tropical rainstorms.
All of these possibilities were studied on three of the COARE ORG's, using an artificial
rain facility (or rain-tower) in which a uniform distribution of raindrops fell to terminal
velocity, and rainrate could be varied over the range 20-200 mm hr -1. An ORG was subjected
to quite severe vibration in the rain tower, but no spurious signals appeared either with orwithout rainfall. Neither was any dependence on DSD found, agreeing with the conclusions ofNystuen et al. (1996). The calibration slopes of all three ORG's tested agreed with the factory
specification and was linear over the entire range of rainrate, but for two of the three there was aconstant offset of a few mm hr –1. This could lead to large percentage errors at low rain rates.
The only other defect found was an imperfect cosine response, such that the verticalcomponent of rain falling through the ORG beam is overestimated. This can be a significant
effect on board ship, where the relative rainfall angles are accentuated by the ship’s speed.
Given relative wind speed and direction measured on the ship, this can be allowed for; thecorrection lowers the originally reported COARE ORG rain rates, typically by 15%. ORG
rainrates were used to obtain closure of the freshwater budget over the R/V Wecoma COARE
cruise area by Feng et al. (1998) and in the tropical Indian Ocean by Godfrey et al. (1999),
providing further support for their performance. The test ORG's have been deployed on theship and a drifting buoy during several cruises of R/V Franklin, and recently on R/V R.H.
Brown.
B. DISDROMETERS
The primary purpose of a disdrometer is the measurement of drop size, and drop size
distribution in rainfall. Automated designs may also serve to record rainrate continuously and,by integration, total rainfall. Among some precipitation investigators the disdrometer isregarded as the reference instrument. However Post et al. (1998) comment that disdrometers
were used during the Combined Sensor Program cruise and appeared to work satisfactorily, butthat after the cruise, serious concerns about calibration and linearity raised doubts that any of
the rain data could be used quantitatively.
The most usual disdrometer is an acoustic device (effectively a microphone) whichconverts the sound of impact of raindrops hitting a sensor surface into an electrical signal
related to the size of the drop (Joss and Waldvogel, 1967). One commercial instrument has asensor area of 50 cm2 and sorts the signals into 20 drop size categories covering the range 0.3 to
5.5 mm diameter. Improved calibration boundaries by Sheppard (1990) and Sheppard and Joe(1994) improved its response to rainfall which contained a high proportion of large drops. Not
surprisingly, the instrument is sensitive to ambient acoustical noise, including noise from rain
hitting nearby surfaces, so special measures have to be taken to isolate the instrument (Nysteun
et al.,1996). For the TRMM project, improved impact disdrometers are being developed
following the design of Roland (1976).
Optical disdrometers, which measure the light extinction by raindrops falling through asensitive volume, have been described by Bradley and Stow (1974), Illingworth and Stevens(1987) and Grossklaus et al. (1998). The latter, designed particularly for use at sea, has ahigher 128-bin resolution. The incidence of multiple drops and grazing incidence on thesensitive volume are considered. Rainrates are determined from droplet spectra by assuming
terminal velocity according to drop size. The resolution is 0.05 mm diameter.
C. ACOUSTIC METHODS
Another acoustical method of rainfall measurement at sea uses the underwater soundproduced by raindrops impacting on the surface. Rainfall produces a unique acoustic signal
which may be readily distinguished from other common sound signals (breaking waves,
biological sources, etc.), and is typically louder by orders of magnitude than these other sources(Nystuen, 1996). Acoustic signatures have been identified for different raindrop sizes (Medwin

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5.4 Precipitation Fluxet al., 1992), enabling estimation of DSD within the rain and hence rainrate. An "Acoustic
Rain Gauge" employing these principles has been described by Nystuen (1999), and deployedon ocean moorings during SCSMEX and on the TOGA-TAO array (Nystuen et al., 2000).

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6.1 Introduction
6.2 Radiative fluxes from ship data

6 PARAMETRISATION OF RADIATIVE FLUXES
6.1 Introduction
Shortwave (SW) is considered as radiation whose source is the sun. Longwave (LW) is
considered as radiation whose source is the emission of thermal infrared by the atmosphere andsurface. SW (LW) is generally below (above) 3 µm in wavelength; the maximum overlap at
the surface is well below 10 Wm-2. LW forcing to the climate system by concentrations of tracegases can be computed more confidently than it can be directly measured; the trace gases canbe measured accurately and their spectroscopic characteristics are fairly well known.
Unfortunately the exception is water vapour – which is the most important radiatively. SW
forcing to the climate system by aerosols (direct scattering, absorption, and some thermal
emission; indirect through clouds) and changes in land use (surface albedo) is uncertain andcannot be measured in a straightforward manner; theoretical calculations of SW forcing could
be validated by comparing a time history of computed and measured fluxes (provided that themeasurements are sufficiently stable). Because at present the computed and measured SW
fluxes do not match, the uncertainty in SW forcing is a significant problem for IPCC
assessments of anthropogenic impacts on climate.
6.2 Radiative fluxes from ship data
6.2.1 Parameterisation of SW flux
Because the radiation community has generally considered that acceptable accuracy
would not be obtained, merchant ships are not equipped to measure the incoming shortwave
radiation. Instead the insolation must be estimated from information on the ship's position andthe cloud information visually estimated by the ship's officer. Such an estimate has to beconsidered relatively crude and it might be argued that the deployment of pyranometers (andpyrgeometers) on at least a subset of the VOS would be advantageous.
In general, the short-wave radiation flux on the sea surface may be parameterised as:(6.1)
Q↓sw =Qt TF
where Qt is the SW radiation at the top of the atmosphere, equal to Qt =S0 cos h, where S0 is thesolar constant, and h is solar altitude. TF represents the transmission factor of the atmosphereand has to be parameterised in terms of the cloud cover and thermodynamic parameters of theatmosphere. There are few parameterisations which use this very general approach (see forexample the reviews of Lind et al., 1984; Dobson and Smith 1988). However, the accuracy ofthis approach is not very high because it requires consideration of the radiation transfer in thewhole atmospheric column. Parameterisations based on ship data are usually poorly determinedbecause of the very complicated and uncertain dependency of the transmission factor on thesurface parameters available from marine data. To avoid this very large uncertainty, it is more
helpful to parse the transmission factor into two terms. One represents the modification ofshort-wave radiation under clear sky conditions (astronomy, temperature, humidity, andaerosols are the main agents of these modification). The other is the cloud modification of theclear sky radiation. In this case, the general formula for the short-wave radiation becomes:(6.2)
Q↓sw = Q0 F(n, T, h)where Q0 is clear sky solar radiation at sea surface, F(n, T, h) is the empirical function of thefractional cloud cover n, air temperature T, and solar altitude h (in general case). Clear sky solar
radiation is assumed to be a function of the astronomy and of the transmission for the clear sky
atmosphere. In most schemes, these two factors are parameterised through the purely
astronomical characteristics (latitude and solar altitude) and empirical coefficients which
account for the atmospheric air transparency under clear skies (Seckel and Beaudry 1973; Lumb
1964; and others). There are only a few parameterisations which directly include surface
atmospheric parameters into the clear sky radiation formula. Thus, Malevskii et al. (1992)
suggested to use for Q0 the parameterisation:
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Q0 = c(sinh)d

6.2 Radiative fluxes from ship data

(6.3)

where c and d are empirical coefficients, which depend on the atmospheric transmission for the
optical mass number 2P 2. P2 itself is a function of atmospheric surface humidity, although
Malevskii et al. (1992) also gives formulae using air temperature, when humidity measurements
are not available.
Quantitative estimation of the cloud reduction factor F is given in many works,
reviewed by for instance Dobson and Smith (1988). The most frequently usedparameterisations are the result of a compromise between the complexity of the radiationtransfer in the cloudy atmosphere and the availability of data to describe this complexity. Thus
it is obvious that a universal parameterisation of the cloud modification of radiation should bebased on the consideration of cloud types and heights. Against that it is often considered thatthe only reliable parameter in the VOS data is the amount of cloud cover. Attempting to fit thelimited data set of direct measurements using this parameter obviously leads to a dependency of
the fits on the region and time of the year, and results in the different parameterisations
suggested by different authors. As a good example, Dobson and Smith (1988) foundpronounced differences fitting their formulae using the data from OWS Papa and Sable Island.
However for this flux there is a further potential problem; most ship's report at most 4 times per
day and the most reports occur at the standard reporting hour closest to local noon. To avoid
introducing bias, the Reed (1977) formulae for the daily mean net shortwave flux QSW uses thecalculated monthly mean values for the fractional cloud cover n, and the noon solar elevation indegrees, φ. Hence:

Qsw = Qsw0 (1 − cn n + 0.0019φ )(1 − α )(6.4)
where cn = 0.62 the cloud attenuation factor, and α the albedo (Payne 1972). Qsw 0 is theshortwave insolation at the surface under clear skies (Seckel and Beaudry 1973). The Reed
formula (6.4) should only be used for 0.3 ≤ n ≤ 1 , and Qsw = Qsw 0 should be used for n < 0.3(Gilman and Garrett, 1994).
Being based only on the mean monthly estimated cloud amount, (6.4) is a crude methodof estimation. However it was recommended by the comparative studies of Frouin et al.
(1988) and Dobson and Smith (1988) who found a site dependant long term bias of -1 to +12
Wm -2 and a monthly mean rms error of about 8 Wm-2. In contrast, using comparisons withdata from several air-sea interaction experiments, Katsaros (1990) and Godfrey et al. (1991)
found the Reed formula to be biased high by about 20Wm-2.
Malevskii et al. (1992) suggested formulae for the use of the low and total cloud coveras available from the VOS reports (although the quality of the low cloud cover estimates can bequestioned). Their two level scheme uses the low cloud amount and total cloud amount, while
their one level scheme just uses the total cloud amount. Intercomparison of theseparameterisations with Reed (1977) and Dobson and Smith (1988) as well as with instrumental
measurements shows that the two-level Malevskii et al. (1992) scheme gives the least biases atleast in the mid latitudes (Niekamp 1992).
An important problem of the validation of the SW schemes is that the most ofcontinuous instrumental measurements were undertaken in mid latitudes. However the tropicalcloudiness is characterised by very different transmission characteristics. An effect in highlatitudes, still poorly understood, is the secondary reflection of radiation from the cloud
margins under low declinations and small cloud cover. Note here, that the Dobson and Smith(1988) and Malevskii et al. (1992) schemes assume the possibility for the cloud “reduction”
coefficients to be greater than 1, partially accounting for this effect.
We should note that the atmospheric radiation community generally avoids the use of
expressions such as (6.4). This is because the optical depth (optical thickness) in (6.4) is
implicitly constant. In a formal radiative transfer model (RTM - Section 6.3) the perturbation
to TOA albedo and surface insolation induced by overcast cloud (n =1 in 6.4) over a dark ocean
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6.2 Radiative fluxes from ship data
6.3 Radiative Transfer Models

can vary widely, depending primarily on the optical depth of the cloud. Thus, a thin veil of
cirrus may increase the albedo by 0.10, while thick altostratus may increase the albedo by 0.60.
For similar reasons, remote sensing of cloud cover n and cloud optical depth with satellite data
are equally challenging problems. Nevertheless, expressions such as (6.4) will continue to be
useful for some applications, since they allow changes in the surface insolation and TOA albedo
to be simply estimated from changes in cloud cover n noted by routine visual observation.
One of the more difficult issues faced by the RTM community is indirect aerosol
forcing: the changes to cloud optical depth and reflectivity that are caused by increases in theconcentration of cloud condensation nuclei (CCN). A group of radiation experts (Hanen et al.,1998) has used historical records of cloud cover n over the US in this century to estimate thechange in radiative forcing (i.e., the output of an RTM using optical depth as input) due toincreases in CCN.
6.2.2 Parameterisation of LW flux
As for short wave radiation, the incoming longwave radiation is not measured but mustbe estimated from the visually estimated cloud amount. Other parameters are the airtemperature and humidity and, for determining the net longwave flux, the sea temperature. For
example (Clark et al., 1974):

QLW = εσ TS4 (0.39 − 0.05e 0.5 )(1 − λn 2 ) + 4εσ TS3 (Ts − Ta )(6.5)

where ε (= 0.98) is the spectrally integrated emittance, σ (= 5.67 x 10 Wm-2K -4) the StefanBoltzmann constant, e the water vapour pressure, n the fractional cloud cover, and Ta and Ts are
the air and sea temperatures in K. The cloud cover coefficient λ varies with latitude. Equation(6.5) is essentially a simple parameterisation of the RTM's for clear sky conditions (Section
6.3).
-8

The latitude-based adjustment λ accounts for the variation of cloud based height andemissivity, both of which may vary enormously within a latitude zone. Thus it is perhaps not
surprising that the global validity of (6.5) and similar formulae has recently been questioned.
For the Mediterranean, Gilman and Garrett (1994) found that this class of formulae
underestimated Qlw by about 17 Wm-2 and, based on Mediterranean data, Bignami et al. (1995)
proposed a new formulae which, for mid-latitude regions, gives Q lw values some 25 Wm- 2
greater than (6.5) (see Section 9.4.3B). In contrast the review of Katsaros (1990) found that
(6.5) underestimated the mean by only 5.5 Wm-2 and comparisons with ship measurementsfrom the Atlantic and Southern Ocean (Josey et al. 1997) have shown agreement typically towithin 5 Wm-2 on timescales of order one month. In the tropical western Pacific, Godfrey et al.
(1991) found (6.5) to be the best of four such formulae, showing no systematic bias comparedwith pyrgeometer measurements.
6.3 Radiative Transfer Models - RTM’s
6.3.1 Longwave radiative flux (LW)
In a cloud-free atmosphere, the net longwave flux density at any height may be
calculated from an atmospheric temperature profile, with knowledge of the thermal absorption
and emission characteristics of the atmospheric gaseous constituents, via an appropriate
radiative transfer model, RTM (see e.g. Paltridge and Platt 1976, chapter 7). RTM's require as
input the vertical concentration profiles of the atmospheric gases, the most important for
infrared transfer being water vapour, carbon dioxide and ozone. Most exact are the Line-ByLine Radiative Transfer Models (LBLRTM) which, as their name implies, compute transfer of
each constituent for each emission and absorption spectral line at many levels throughout the
profile. Their computational burden is therefore large, which makes them unsuitable for routine
use in numerical models Over the years therefore, many broadband RTM's have beendeveloped, increasing in accuracy and efficiency with improved parameterisations and increased
computer power. Such models are widely applied in climate modelling, and in flux retrieval
from direct and remotely sensed atmospheric variables.
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Longwave irradiance at the surface may thus be obtained by integrating the calculations
from an RTM through the atmosphere. Dutton (1993) used LOWTRAN7 (Kneizys et al. 1988)
to determine longwave irradiance from two radiosonde launches per day at each of four sites
spread from the South Pole to the tropics, over the course of a year, selecting the clear-sky
occasions. His purpose was comparison with simultaneous pyrgeometer readings, and theyagreed overall within 5 Wm-2. Dutton (1993) was careful to stress that the result demonstrated
internal consistency of the two methods, and claimed absolute accuracy for neither, but his work
illustrated convincingly the potential value of RTM's for obtaining accurate global flux fields
from routine meteorological observations.
Miskolczi (1994) also used an RTM to compare with pyrgeometer measurements and,as noted in Section 5.2.3, Fairall et al. (1998) used the Rapid Radiative Transfer Model (RRTM)
developed for the Atmospheric Radiation Measurement Program (ARM) science team by
Mlawer et al. (1997) for comparison with the Moana Wave pyrgeometer measurements. Thisparticular model allows for the use of up to 35 molecular species, defaulting to the seven mostimportant, H2O, CO2, O3, N2O, CO, CH4 and NO. Mlawer et al. (1997) verified RRTM against
a LBLRTM (Clough et al. 1992), and relied on LBLRTM verifications against measurement.
They claim that RRTM and LBLRTM longwave calculations differ by less than 1.0 Wm-2 at any
altitude and, based on a large number of validations with well specified water vapour profiles(the SPECTRE data), that LBLRTM has an accuracy of 2 Wm-2.
The parameterisations used in RTM's steadily improve as new observational technology
provides better validation data. For example, in collaboration with COARE, the ARM program
operated the Pilot Radiation Observation Experiment (PROBE) which was equipped with a
Fourier Transform Infrared Radiometer (FTIR, Shaw et al. 1991) (see section 5.2.1). One of the
primary goals of PROBE was to compare radiance spectra measured with the FTIR with thepredictions of a LBLRTM based on radiosonde observations of temperature, water vapour andpressure. In the water vapour window region (830 to 1250 cm-1), the calculations were lower
than measurements by about 5%, which enabled revision of the water vapour continuum
formulation used in the model (Clough 1995). Post et al. (1998, their Figure 10) illustrate theeffect of this improvement. Tobin (1999) notes that recent refinements to the parameterisationof the water vapour continuum (Clough et al., 1992) improve accuracy by 1-2 Wm-2. However,the continuum itself is not rigorously understood in terms of basic physics.
The computation of LW flux with the best high spectral resolution codes under clear
conditions is at an advanced state. Further advances will depend upon developments in sensor
technology in the far infrared (wavelengths exceeding ~ 20µm). Accurate computation of LW
under clear skies requires a treatment of surface emissivity, to account for scattering as well as
absorption and emission. Some rapid codes (e.g., Fu and Liou, 1993) include this. For cloudysky conditions, however, RTM's are not well validated. The calculation of the downwelling
longwave flux under a cloud requires knowledge of both cloud base height and emissivity.
Cloud base height may be measured with active systems such as a MicroPulse lidar (Spinhirne,1993) or a cloud profiling radar, although an infrared radiometer (such as the pyrgeometer)
would still be needed to estimate cloud emissivity. RTM's designed for GCM calculations (e.g.,Morcrette, 1991; Chou et al., 1995a; Fu and Liou, 1993) are compared with higher spectral
resolution codes in the Intercomparison of Radiation Codes in Climate Models (ICRCCM,
Ellingson et al., 1991; Ellingson and Fouquart, 1991).
6.3.2 Shortwave radiative flux (SW)
Knowledge of atmospheric scattering is essential for shortwave RTM's under both clear
and cloudy conditions. Recent studies with SW RTM's under clear skies have indicatedsignificant discrepancies when compared with measurements (e.g., Wild et al. 1995; Charlock
and Alberta, 1996); computed insolation values are too high, suggesting that the real atmosphereabsorbs more than is predicted by theory. A thorough study by ARM (Kato et al., 1997) has
demonstrated that under clear skies, the discrepancy is in the diffuse component. Zender et al.
(1997) used alternate radiometers (Valero et al., 1982) and do not regard their excess inReport of the Working Group on Air Sea Fluxes

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computed versus observed flux as “significant”, although their discrepancies are of order 10
Wm -2. One recent study (Wild, 1999) found no discrepancy for clear skies at Payerne; but
personal communication with one of the authors revealed that a “model” aerosol profile was
employed. The aforementioned ARM studies used measured spectral optical depth. Someocean studies (Chou and Zhao, 1997; Waliser et al., 1996) have also reported no discrepancies
for clear skies, but their measurements were made with unshaded pyranometers. Similarly,
many measurements of surface shortwave irradiance (i.e., most of those in the Global Energy
Balance Archive (GEBA), have been made with pyranometers only. Under clear skies, theerrors with a pyranometer can be quite large (DeLuisi et al., 1991); for the most accurate results,
the correct procedure is measurement of the direct beam with a normal incidence pyrheliometer(NIP) and the diffuse component with a shaded pyranometer (see Section 5.2.2). A strictmeasurement and calibration protocol has been developed under WCRP to guide the Baseline
Surface Radiation Network (BSRN, Gilgen et al., 1995). At present, no ship-borne system
subscribes to the BSRN protocol.
The importance of such a protocol is illustrated with the following results from thecomprehensive ARM Program (Stokes and Schwartz, 1994) in Oklahoma under clear skies in
October, 1995. ARM measurements for aerosols, cloud screening, soundings, and otherparameters were thorough.
Table 6.1. Results from the Atmospheric Radiation Measurement (ARM) Program
Mean incoming at TOA for SW

794 Wm-2

Surface SW Insolation:
Pyranometer only

549 Wm-2

NIP + shaded pyranometer

571 Wm-2

RAMS (Valero radiometer)

585 Wm-2

Theory (Fu-Liou code)

617 Wm-2

The theoretical calculation with the Fu-Liou code is 32 Wm -2 larger than themeasurement with the sophisticated RAMS radiometer (Pilewskie and Valero, 1995). A similarbias has been found by many teams in ARM, with most of the discrepancy in the diffuse
component. The NIP + shaded pyranometer set-up approximates BSRN protocol; the NIP has
already been corrected by over 2% following special cavity radiometer measurements(Michalsky et al., 1997). The measurement with the pyranometer only (which would be typicalfor an ocean measurement but without the additional errors listed in Section 5.2.2) is the outlier;the bias depends upon the angular distribution of the radiation and is not constant. There areseveral possible reasons for this. Taking the NIP + diffuse measurement as a calibrationreference, it may be that the particular pyranometer here was poorly calibrated by the
manufacturer, or had changed subsequently. In Section 5.2.2 we discuss this question and notethat systematic differences of 3% were found among first-class pyranometers used in COARE.
GCM's use fast RTM's to compute SW under clear and cloudy skies. Well regarded
GCM-type codes include Morcrette (1991), Chou et al. (1995a), and Fu and Liou (1993). It is
well known that faster codes, which employ techniques such as 2-stream, delta-Eddington, and4-stream, are not as accurate as the higher order plane parallel codes such as discrete ordinate(Stamnes et al. 1988) and adding-doubling; errors of several per cent can be expected (Liou,
1992). But few clouds are plane parallel. With finite geometry, more computationallyintensive routines like Monte Carlo and the ingenious spherical harmonics discrete ordinate(Evans, 1998) are more appropriate for estimating an instantaneous flux. Finite geometry isappropriate for some LW applications, too (e.g. Takara and Ellingson, 2000). Finite cloud
geometry often has a dramatic effect on the albedo at the top of the atmosphere (TOA) and thetransmission to the surface; Stephens and Greenwald, (1991) have pointed out that cloud
macrostructure (i.e. finite geometry) can be more important than cloud microphysics. But theReport of the Working Group on Air Sea Fluxes

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surprising general result is that when the TOA albedos of plane parallel and finite cloud RTM's
agree, the surface insolation in the RTM's often differs by only a few Wm-2. While finite cloud(2-D or 3-D) geometry can either increase or decrease the atmospheric absorption relative toplane parallel for particular cases, ensemble means generally show a small effect - at least in
RTM's. Cloud finite geometry appears to be much more critical for the retrieval of the physicaland optical properties of clouds than for the computation of time-mean radiative flux.
But if theoretical 3-D and 1-D clouds with the same albedos also have approximately
the same atmospheric absorption, do 3-D effects account for the strong “anomalous SW
absorption by clouds” which has been reported? The existence of such absorption is a matter of
debate. For example, Cess et al. (1995), Ramanathan et al. (1995), and Pilewskie and Valero(1995) have found significant discrepancies between computed and measured SW radiation forcloudy skies, as was noted earlier by Stephens and Tsay (1990). From Cess et al. (1995), wewould infer that if a plane-parallel (GCM) code produced an albedo at the TOA that matchedobservations, it should have a global mean error of approximately 25 Wm-2 for the atmosphere(and hence also for the surface). Over Oklahoma, matched aircraft observations by Valero et al.
(1997; 2000) found a strongly absorbing cloud, corroborating the finding of Pilewskie andValero (1995) in the Pacific Ocean; both studies used the broadband RAMS instrument, rather
than Eppley or Kipp and Zonen radiometers, and were based on a few hours of measurement.
The brief aircraft-based results of Valero et al. (1992) have been seconded (Charlock et al.,1998) with narrow band (GOES-8) satellite data (Minnis et al., 1995) but only for short periods;
when the domain is extended to several weeks, observed absorption by the clear and cloudy
atmospheres both exceed theoretical values by approximately the same amount (i.e. there is alittle anomalous absorption – but in both clear and cloudy skies). Cess et al. (1995) and
Ramanathan et al. (1995) used longer records of broadband Earth Radiation Budget Experiment,
ERBE, data (which is more reliable than GOES) at the TOA and broadband radiometers at thesurface to infer anomalous SW absorption by clouds; most of their surface observations,however, were not based on separate measurements of the direct (NIP) and diffuse (shaded
pyranometer) components, as recommended by BSRN. An intensive study (Charlock et al.,1999) with the newly released broadband CERES data and separate direct and diffuse surface
measurements at a score of ARM Southern Great Plains (SGP) sites for January-August 1998
found no support for the level of cloud forcing to atmospheric absorption reported by Cess et al.
(1995) and Ramanathan et al. (1995). A similar study by Trischencko et al. (1999) also foundno support for anomalous absorption by clouds over long time periods in either ERBE, CERES,
or ScaRaB (Scanner for Radiation Budget) data that was matched with surface data.
6.3.3 COARE atmospheric soundings
Clearly, however good its internal physics and parameterisations, the accuracy withwhich an RTM can deliver radiative fluxes depends directly on the reliability of the atmosphericsoundings used as input. Following COARE, several research groups, came to suspect that the
majority of COARE soundings were in error, in particular that the humidities seemed about 1 g
kg-1 too low (Bradley and Weller 1997; Zipser and Johnson 1998). This was initially based on
anomalous north-south gradients of humidity and Convective Available Potential Energy(CAPE), which did not correspond with any observed surface meteorology or SST gradient.
Other signals appeared to reinforce this view; a step-function was evident in humidity timeseries when the brand of radiosonde was changed; budget calculations based on the soundings
predicted that some visibly rainy regions ought to have been cloud-free; during thepyrgeometer/RRTM comparisons described in Section 5.2.3, only two sets of independent
soundings known to be reliable could be used, while the others clearly underestimated the
longwave irradiance. Working with the radiosonde manufacturers, NCAR-ATD have found thesource of the problem: calibration drift due to contamination of the radiosonde by its packaging
material. A time-dependent correction algorithm has been developed (Cole and Miller 1999),
and a schedule has been worked out for recalculation of the entire COARE soundings dataset.
Guichard et al. (2000) confirm that the impact of this bias on the calculation of convective
available potential energy (CAPE) in the tropics can be quite large. Meanwhile, ECMWF and
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6.4 Remote Sensing for Radiation

NCEP have postponed their planned high-resolution reanalysis of the COARE period pending
this process. The consequences may well reach beyond COARE into other areas of climate
research, reanalysis and data assimilation, which make use of historical atmospheric soundings.
The impacts of these humidity biases are likely to be enormous for numerical weather
prediction models, which attempt to predict wind fields modulated by convection, responding tothe observed thermodynamic profile. The observed errors would seriously distort theconvection field in space and time, and produce unrealistic ocean-atmosphere coupling. An
incorrect moisture field would certainly affect the dynamics of cloud-resolving models. The
impact would also be of first order for the calculation of moisture budgets, particularly during
light rainfall periods. However, the effect on longwave irradiance computed from RRTM using
the COARE data (see Section 5.2.3) was not large; with humidity in the lowest 300m of the
profile increased by the full 1g kg-1, downwelling longwave was increased by only 1.5 Wm-2.
This calculation was for a tropical atmosphere, of course; in a drier atmosphere, the same
increase of specific humidity would have a larger effect on the flux.
A similar problem has also recently been identified by ARM scientists with their
CLASS radiosondes. They find that the new generation of high technology sensors, such as the
microwave radiometer, can be used to detect radiosonde problems. At the ARM Southern Great
Plains Central Facility, data from a Microwave Water Substance Radiometer (MWSR) is now
used to scale radiosonde moisture profiles.
6.4 Radiative Fluxes by Remote Sensing
6.4.1 SW and LW fluxes from TOA radiances
The determination of the surface radiation budget (SRB) from satellite data involves
measuring the radiative fluxes at the top of the atmosphere (TOA) and accounting for the effectsof the atmosphere using a radiative transfer model (RTM). An obvious problem is allowing forthe effects of clouds. It has proved easier to model the scattering and absorption of SW
radiation than to model the effects of clouds on the LW radiation. The problem for LW is thatthe surface budget is dependent on the height of the lowest cloud, a quantity not easily
determined by satellite. As a result there are significantly more products for surface SW thanthere are for LW.
The different algorithms differ in the cloud information used and the sophistication of
the RTM. While various methods have been proposed that use the narrow band (visible)
satellite radiances directly together with an RTM (e.g., Tarpley, 1979; Gautier et al., 1980;
Dedieu et al., 1987; Stuhlmann et al., 1990; Pinker and Laszlo, 1992) no extensive datasets
have been produced.
Most data sets are based on algorithms which have used cloud
information from ISCCP. These cloud data have been retrieved from the narrow band satellite
radiances using a method based on a radiative transfer model. Examples are the SW products
from GEWEX SRB-1 (Whitlock et al., 1995), and Bishop et al. (1997), and the SW and LW
products of Rossow and Zhang (1995), Gupta et al., (1999). While each of these products has
used the ISCCP C1 data (280 by 280 km equivalent area grid boxes; 2.5 by 2.5 degree at theequator; number of grid boxes changing with latitude), the GEWEX SRB Version 2 will bebased on ISCCP DX (pixel scale) data with correspondingly high resolution.
The goal of the CERES SRB is to reduce the error in the GEWEX SRB by a factor of
two. In addition to the CERES broadband scanning instruments, the CERES data will beprocessed with simultaneous cloud imager data (VIRS on TRMM and MODIS on EOS-AM1);
3-hourly GEOS 2-3 meteorology (Schubert et al., 1995); and 3-hourly operational
geostationary satellite data. SRB retrievals will be validated and released 2-3 years after launch.
Gridded products (1x1 degree) will eventually be available 3-hourly, daily, and monthly.
Instantaneous SRB at the individual “footprints” of the broadband CERES scanner will typicallyhave diameters of 10-50 km. One set of CERES retrievals of the SRB will be based on fast“surface-only” algorithms for SW (Li and Leighton, 1993) and LW (Inamdar and Ramanathan,Report of the Working Group on Air Sea Fluxes

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1997; Gupta et al. 1992); a second set will be based on “full-physics” radiative transfer (Fu andLiou, 1993), balancing CERES broadband TOA measurements (Charlock et al., 1997).
More details of each of these various satellite based radiation products will be given in
Section 11.3.3 where the products are evaluated.
6.4.2 Longwave Net Flux from a combination of AVHRR and SSM/I
This section describes a method to compute the longwave net flux, RL, at the sea surface
from a combination of AVHRR-derived sea surface temperature and SSM/I-derived
atmospheric back radiation. For sea surface temperature TS:

RL = ε RL↓ − εσTS4(6.6)

where ε is the spectrally integrated surface emissivity (which is close to 0.98, Gardashov et al.,1988) and σ is the Stefan-Boltzmann constant (see also equation 5.3).
Schlüssel et al. (1995) described a retrieval scheme of the atmospheric back radiation
under clear and cloudy conditions from SSM/I measurements using three channels at 22, 37, and85 GHz. This retrieval is based on the following consideration: about 80 % of the downwelling
irradiance is emitted from the lower 500 m of the atmosphere (Schmetz, 1989) and is mostly
emitted in the water vapour bands near 6.3 µm and above 20 µm, and to a lesser extent in theCO2 band at 15 µ m. As shown by Schulz et al. (1993) the SSM/I measurements contain
information on the boundary layer water vapour content. Schlüssel et al. (1995) conclude thatthe irradiance emerging from the lower 500 m should be proportional to the product of the mean
layer temperature and the absorption by the columnar water-vapour content of this layer, since
the emitted flux density is proportional to the fourth power of the temperature according to the
Stefan-Boltzmann law. Radiative transport calculations then showed that functions f1 and f2 arein fact closely correlated:f (T22 v , T37 h ) = σ (T224 v − T374 h ) ∝ f2 (TS , wl ) = σTS4 (1 − e − wl )(6.7)

where T22v and T 37h are the brightness temperatures for the 22Ghz vertically polarised and 37
GHz horizontally polarised channels respectively.
The RMS error for the retrieval scheme has been theoretically estimated to be 30 Wm-2.
It has been partly verified using simultaneously obtained pyrgeometer measurements,
radiosonde data, and cloud observations from the TOGA-COARE and CEPEX experiments.
The radiosonde data and the cloud observations have been used within radiative transfer
simulations as another means for comparison. Considering hourly means, Schanz and
Schlüssel (1997) stated a bias of -3 Wm-2 and a standard deviation of 14 Wm-2 for thecomparison of pyrgeometer measurements and satellite retrieval and a bias of +3 Wm-2 and thesame standard deviation for the comparison of radiative transfer simulations with satellite
retrieval. For monthly averages a RMS error of around 10 Wm-2 is expected.
Combining the back-radiation retrieval with AVHRR derived sea surface temperatureusing equation (6.6) gives the net flux at the sea surface. This scheme has been used within
HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data) to retrieve
longwave net fluxes with the same sampling as evaporation and precipitation.
An alternative method estimating net longwave fluxes R L directly from SSM/I
measurements using neural networks has been published by Liu et al. (1997). The neuralnetwork technique has been used to better account for the non linearity between R L and themeasured brightness temperatures. Validation attempts were restricted to 5 clear sky and 14
cloudy cases during the International Cirrus Experiment (ICE) in October 1989 over the North
Sea. The comparison of the technique with pyrgeometer measurements revealed small errors
for the clear sky cases but a large standard error (24 Wm-2) for the cloudy cases. Liu et al.
(1997) claimed that this error is probably caused by the spatial inhomogeneity of clouds in thefield of view of the SSM/I. However a likely further source of error was the use by Liu et al.
(1997) of an algorithm which excluded the 85 GHz channels (due to the malfunctioning of these
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6.4 Remote Sensing for Radiation
6.5 Surface Albedo

channels during 1989). However those channels are the most sensitive SSM/I channels to cloudparticles and act as main predictors for the cloud signal in the Schlüssel et al. (1995) algorithm.
A comparison of the monthly mean of RL over the Atlantic for October 1989 reveals thebest agreement with climatological values from Lindau (2000), but large discrepancies with theIsemer and Hasse (1985) climatology, and also with an ISCCP derived dataset (Darnell et al.,1992), especially in regions with quasi-persistent maritime stratocumulus over the Atlantic westof the African coast. It can be argued that factors such as the solar breakup of stratocumulus
during the day, which is not considered in ISCCP, account for a certain amount of thisdifference.
6.5 Surface Albedo
6.5.1 Ocean Surface Albedo
As mentioned in Section 5.2.2, sea surface albedo is the ratio of shortwave exitance toirradiance. The broadband albedo can be measured with a pair of pyranometers, one facing
upward and the other downward, but as with upwelling longwave the latter must be mounted ona boom so that it does not “see” the platform. This presents obvious difficulties for ships on the
open ocean. The seminal study of sea surface albedo is that of Payne (1972), who madecomprehensive measurements from a platform in Buzzards Bay, MA (41°N), expressing theresults in terms of only two parameters, solar altitude and atmospheric transmittance. The latteris the ratio of solar irradiance actually measured at the surface to that incident at the TOA,
which can be simply calculated from knowledge of the solar constant, date, time and location(Paltridge and Platt 1976). Solar transmittance is affected by absorption or scattering fromatmospheric constituents, mainly water vapour, ozone, aerosols and clouds. Thus, Payne’s(1972) parameterisation actually relates to the varying ratio of diffuse to direct shortwave
radiation. The Fresnel laws predict (and common observation confirms) that reflectivity at awater surface increases toward glancing angles of incidence. Generally speaking then, for highsolar altitude and clear skies the albedo is small, but any increase in the diffuse component due
to cloudiness will reduce the average angle of incidence and increase the albedo. For low solar
altitude, the addition of cloudiness has the opposite effect.
Katsaros et al. (1985) confirmed Payne’s albedo results during GATE at 7°N and
JASIN at 60°N (both during summer), and their Figure 1 provides an excellent illustration of theeffects of diffuse radiation, solar altitude and surface roughness on surface albedo. Albedo isexpected to depend on wind speed through changing sea roughness, but Payne (1972) showed
theoretically that the effect is small excepting for solar altitudes less than 30° with a clear sky.
Within this restricted regime, Katsaros et al. (1985) combined Payne’s observations with theirown, and those of Simpson and Paulson (1979), to estimate that albedo decreased by 3.6% per
ms-1 for winds less than 12 ms-1. At wind speeds above about 15 ms-1, albedo increases due tothe presence of whitecaps (Monahan and O’Muircheartaigh 1987).
Payne’s (1972) empirical relationships are widely used in ocean-atmosphere studies,and to parameterise broadband albedo in numerical models. They were confirmed over the deep
ocean with direct albedo measurements from a boom mounted forward of the bow of R/VFranklin over 20 days during COARE in the vicinity of 2°S. To avoid undue bias from the verylarge albedos at low sun elevations, daily average values were calculated as the ratio of total
upwelling to downwelling radiant energy. For the typical conditions of predominantly lightwinds and relatively smooth seas, a constant albedo of 0.058 was found to apply to daily
averaged air-sea heat flux with negligible error over using the diurnally variable values. This isthe value incorporated into the COARE bulk flux algorithm (Section 7.4.1).
As the broadband albedo of the sea is a ratio of upwelling to downwelling flux, it is not
an appropriate input for the spectrally resolved theoretical radiative transfer calculations madein GCM's or satellite-based retrieval algorithms. Rather, a radiative transfer model uses aspectral reflectance as an input, and the broadband albedo is determined from the ratio of its
output fluxes. For example, the CERES retrievals of surface fluxes use the Fu and Liou (1993)
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6.6 Summary

code and assume, following Bowker et al. (1985) and Payne (1972), spectral reflectances for
diffuse SW: 0.0878 (0.2 to 0.7 µm), 0.0505 (0.7 to 1.3 µm), and 0.0163 (above 1.3 µm).
Under clear conditions, these are adjusted with solar zenith angle. A more rigorous treatmentof the spectral bi-directional reflectance function (BRDF) of the sea has been developed by Hu(2000, pers comm.) using the Cox-Munk wind-speed wave distribution and a modified discrete
ordinates scheme with 100 streams; this is to be validated at the Chesapeake Lighthouse withmeasurements of upwelling spectral radiance.
Using data from aircraft-mounted instruments during the JASIN experiment (flight level
50m and solar altitude about 50°), Katsaros et al. (1985) determined the sea surface albedo inthree spectral bands.
In the shortest waveband (0.28 to 0.53 µm) albedo was almost
independent of cloudiness at around 0.07, whereas in the remaining visible (0.53 to 0.70 µm)
and near-infrared (0.70 to 2.8 µm) bands albedo increased with cloudiness. Observations thatthe broadband surface albedo under clouds exceeds the clear-sky value are widely reported.
There are two main reasons; the change to the diffuse component referred to above, and theselective absorption of solar radiation by clouds at wavelengths greater than about 0.7 µm(Lindsay and Rothrock, 1994; Curry et al., 1996).
6.5.2 Albedo over sea ice
The variability of albedo, including its spectral dependence, take on particularsignificance at high latitudes. Lindsay and Rothrock (1994) tabulate values of albedo withinpack ice ranging from 0.06 for smooth water, through 0.32 – 0.68 for various forms of melting
ice, to 0.87 for fresh snow. The effective albedo over a given region will depend on the relative
areas of each type of surface present. At these predominantly large solar zenith angles the
proportion of ice to water is particularly critical. It creates obvious problems for both
representative measurement and modelling, leading Lindsay and Rothrock (1994) to devise anequivalent “potential open water fraction” based on surface temperature and albedo. Drawing
on a large number of previous studies, Ebert and Curry (1993) developed a surface albedo
parameterisation which considers both spectral variation and solar zenith angle dependence.
They find that the annual cycle of Arctic ocean broadband surface albedo ranges from about
0.48 during summer to 0.83 in winter.
Several authors (e.g. Shine, 1984; Lindsay and Rothrock,1994: Curry et al, 1996)
emphasise the particular importance over high albedo surfaces of multiple reflections between
the surface and cloud base, which contribute substantially to the downward flux. Shine (1984)
presents a calculation method which takes account of this and finds that, for reasonable cloud
thickness, neglecting multiple reflections can incur errors in the surface net flux of 30% overbare ice and 50% over new snow. On the other hand, neglecting spectral variability of albedo
seems to generate errors no greater than 1.5% in surface flux. Pinto and Curry (1997) note thatmultiply reflected shortwave radiation from the high albedo surface will change the heating rate
within the cloud layer, as well as modifying the flux at the surface itself.

6.6 Summary - Parameterisation of Radiative fluxes
In summary, parameterisations of both SW and LW radiation result in wide range of
estimates of radiative fluxes. More in-situ observations of radiative fluxes are needed indifferent regions, especially in the tropics to verify the parameterisations. Although cloudcorrection is the most uncertain point of SW parameterisations developed for use with VOS
data, large uncertainties also result from the formulation of clear sky radiation. Use of twolevel cloud cover can improve parameterisations of SW radiation, but there are still relatively
few VOS reports which provide both total and low level cloud cover information. LW radiation
estimates computed with different parameterisations largely depend on the data used to produce
particular formula. These data are affected by regional variations in cloud conditions.

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7.1 Introduction
7.2 Traditional Bulk Formula

7 PARAMETERISATION OF TURBULENT FLUXES
7.1 Introduction
The parameterisation formulae are used for determining the turbulent fluxes from thebasic variables such as wind speed, air temperature, etc. The same "bulk formulae" areapplicable whether the basic variables have been obtained by in situ measurement or by remotesensing, or have been calculated by a numerical atmospheric model. Thus these formulae arefundamental to flux estimation and therefore they will be discussed in some detail in this
Chapter.
Until recently there was general agreement as to the form of the bulk formulae which,at the simplest, could be written: F ∝ u z Cz (∆ sz) where F is the flux, uz is the wind speed
measured at height z, and ∆sz is the difference in value of the appropriate basic variable between
height z and the sea surface. Written in this form, most of the physical processes have been
parameterised by the transfer coefficient Cz which will vary with height, and stability (andpossibly wind speed and other factors). Since the form of the stability correction is thought tobe reasonably well known, Cz is normally represented by C10n, the 10m neutral value (defined asthat which would give the same flux given measurements at 10m height obtained under neutral
atmospheric stability). Depending on which flux is to be calculated (and the state of ourknowledge), C10n may be taken to be a constant value, to have a simple wind speed
dependence, or to have a more complicated variation with sea state and other factors.
While the standard bulk formula is designed to model turbulent transfers near a roughsurface, in recent years experiments such as TOGA COARE have resulted in formulae whichattempt to also take into account other physical processes. Now many versions of the formulae
exist and new formulae are constantly being developed. At the same time, many models havefor many years used the older versions of the formulae, partly because "that is what they havealways used" and there is the possibility that other processes in the model may have become
tuned to the resulting flux values.
To try to bring some order to the present situation, in this Chapter we will first describe
the derivation of a "traditional" bulk formula for the surface fluxes. We will then summarise thepresent state of knowledge with regard to the roughness lengths (which determine the transfer
coefficients). Recent algorithms for the bulk formula will then be described. Although many ofthe modifications have attempted to deal with low wind phenomena, these new formulae arebeing applied more generally. Finally we will consider transfers in strong wind conditions.
7.2 Example of traditional "Bulk Aerodynamic" formulae
The aim of the stability dependent bulk formulae is to calculate the air-sea flux of
momentum, sensible, or latent heat from mean values of the meteorological variables (windspeed u, air temperature t, and humidity q) measured at some height z above the surface,
together with values of the sea surface temperature, ts. Although often considered to be purely
empirical, the formulae used can be derived from the dimensionless profile equations (which
can themselves be derived using dimensional considerations and "similarity theory", see forexample Geernaert (1990) for a more detailed treatment):kz ∂ < u >
= φm
u* ∂z
kz ∂ < t >
= φt
t* ∂z
kz ∂ < q >
= φq
q* ∂z

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7.2 Traditional Bulk Formulawhere k is the von Karman constant, and the dimensionless profiles φx are each functions of thestability parameter ζ (= z/L ). Here L is the Monin-Obukhov length, defined by (e.g. Stull,1988 p.180):

L=

Tvu*3
gk w' Tv '(7.2)
where Tv is the virtual temperature of the air. The dimensionless profile functions have been
determined experimentally. The correct form is critically dependent on the assumed value for k(Frenzen and Vogel, 1994). Edson et al. (1991) review several formulations for φ m (z/L),
modified where necessary on the assumption k = 0.4. The best performing formulae could besummarised by:

φ m = (1 − α ( z / L))−βφm = 1 + γ ( z / L)ζ<0(7.3a)

ζ>0(7.3b)

where values of α ranging from 16 to 28 all fitted the data to a similar degree and a value α = 20
was chosen because it gave the best overall fit with γ = 8. In most formulations β is 1/4 but
some authors have suggested β = 1/3 as theoretically more consistent. For temperature andhumidity (referred to by many authors as the scalar variables), it will be assumed that:φt = φq = φ m2

ζ<0

(7.4a)

φt = φ q = φ m

ζ>0(7.4b)

where the φ t relationship follows Paulson (1970).
The assumption that φ q = φ t seems
reasonable but is based on very little evidence. In (7.1) the scale variables u *, t* and q* aredefined, using dimensional reasoning, by reference to the surface fluxes:

u*2 = u' w' =τρ

(7.5a)

H
u*t* = t' w' =
ρc p

(7.5b)

E
u*q* = q' w' =
ρLv

(7.5c)

Remembering that we need the formulae to allow the fluxes to be determined frommeasurements at some height z, (7.1) is integrated between the surface and the measurement
height to give:uz = u0 +
u*   zu 
ln
−
Ψ


m

k   z0 t  z 
tz = t0 + *  ln t  − Ψt 
k   zot 

qz = q0 +
q*   zq ln  − Ψq k   zoq 

(7.6a)(7.6b)(7.6c)

where u o, to, and qo are the surface values and the stratification functions Ψi represent thestability corrections to the profiles and are the integrals of the dimensionless profiles φi (Paulson,1970):

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7.3 Determining the Transfer Coefficients 1 + φ m−1  1 + φ m−2 π−1 −1Ψm = 2 ln+ln − 2 tan φ m +
 2 
 2 
2

ζ<0

(7.7a)

Ψm = 1 − φ m

ζ>0(7.7b)

1 + φ 
Ψt = Ψq = 2 ln

 2 

ζ<0

(7.7c)

Ψt = Ψq = 1 − φtζ>0(7.7d)−1tNote that in neutral stability φi = 1 and Ψi.=0 Using Equation 7.6(a-c) to substitute for u*, t* andq* in 7.5 (a-c) we can derive the bulk formulae:

u*2 = CD (uz − u0 )2

(7.8a)

u*t* = CH (uz − u0 )(tz − t0 )(7.8b)

u*q* = CE (uz − u0 )(qz − q0 )(7.8c)

which define the transfer coefficients as the expressions: z 
CD = k  ln u  − Ψm 
  z0 −2

2(7.9a)
−1

−1

−1

−1

 z   z CH = k 2  ln t  − Ψt   ln u  − Ψm 
  z0 t    z0   z  z 

CE = k 2  ln q  − Ψq   ln u  − Ψm    z0   z0 q (7.9b)(7.9c)

From (7.9) it can be seen that the 10m neutral drag coefficient is:  10  CD10 n = k  ln    z0  −2

2(7.10a)which emphasises that the drag coefficient is directly related to the roughness length; the two
are interchangeable. The transfer coefficients for heat and water vapour (the Stanton and Dalton
numbers, respectively) depend on both the momentum roughness length and the heat andmoisture roughness lengths:−1  10     10  
CH10 n = k  ln    ln    z0 t     z0  

−1

2

−1

  10     10  
CE10 n = k  ln    ln    z0 q     z0  
2(7.10b)−1(7.10c)

7.3 Determining the Transfer Coefficients
7.3.1 Traditional bulk formulae
A. INTRODUCTION
The above discussion suggests that the transfer coefficients could be determined byobtaining, in some way, the various roughness lengths (7.10). However in the traditional
approach the transfer coefficients have been obtained by measuring the surface fluxes, using oneof several techniques, together with measurement of the mean physical variables required (7.8).

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7.3 Determining the Transfer Coefficients

Early work on air-sea exchange tended to focus on the momentum transfer between
atmosphere and ocean. Synoptic methods, such as the slope of the surface or the geostrophic
departure of the wind field, were used to estimate the wind stress on the ocean and obtain a dragcoefficient. Later, application of classical boundary-layer laws relating the momentum flux (orstress) to the vertical wind gradient (e.g. (7.6a)), allowed a drag coefficient to be generated
entirely from measurements of the vertical wind profile. Semi-empirical expressions for the
diabatic flux-profile relationships established over land sites (e.g. Businger et al. 1971) enabledcorrection of the measured drag coefficient to its equivalent neutral value, and also to a standard
reference height of 10m above the water surface, as set out in the previous section.
Experimental studies on wind/wave interaction and the ocean drag coefficient proliferated, sothat by the time Garratt (1977) published his comprehensive review of the subject, he was able
to quote five previous reviews between 1951 and 1969 (his Table 1). Garratt (1977) goes on tolist seventeen determinations of CD10n between 1967 and 1975, most of which obtained the stressby the direct method, covariance (see Section 5.3.1).
For the scalar (sensible and latent heat) fluxes, Pond et al (1974) examined results forCH10n and C E 1 0 n determined from profile measurements, while Friehe and Schmitt (1976)
compiled data from nine experiments all using covariance fluxes. However, it was the muchquoted comparison of ten common bulk transfer coefficient schemes by Blanc (1985) whichdrew attention to the substantial differences between these schemes, and quantified theconsequent uncertainty in flux estimates. This review also highlighted the limited wind speed
range over which many of the schemes had been verified. There were virtually no
determinations above 14 ms-1, where exchange of heat and moisture begin to be complicated byspray droplets, or below 4 ms-1, a common situation in the climatically important region of thetropical oceans. Improved parameterisation of the fluxes in both of these regimes was badly
needed for numerical climate models.
Some of the more frequently used results from recent traditional determinations of theexchange coefficients are discussed briefly below. It is common practice to plot the neutral
exchange coefficients against the 10m wind, as Blanc (1985) does, although the possibly of
dependence on other factors should be borne in mind.
B. WIND STRESSFor wind stress, the drag coefficient values used in many past studies were thoseobtained by Large and Pond (1981, 1982) or Smith (1980, 1988). Although these studies usedboth eddy correlation and inertial dissipation stress data, the bulk of the Large and Pond results
were obtained with the latter technique (see Section 5.3.2). For 10m wind speeds u10 between 3
and 10 ms-1 they found a constant value (103CD10n = 1.12 ± 0.2), and for u10 between 10 and 25
ms-1 suggest a linear increase given by (103CD10n = 0.49 + 0.065u 10). Other schemes suggest a
linear relation over the entire range of validity; Garratt (1977) gives (103CD10n = 0.75 + 0.067u10)
valid from 4-21 ms-1 and Smith (1980) gives (103 C D10n = 0.61 + 0.063u10) from 6-22 ms-1
(Figure 7.1).
These upper wind speed limits are supported by very few observations, and are best
regarded as extrapolations from the lower wind regime where data is more plentiful and morereliable. However Yelland et al. (1998) used a large data set from the Southern Ocean,obtained using the inertial dissipation technique, to effectively confirm the Smith (1980) eddy
correlation derived formula for open ocean wind stress for wind speeds between about 6 and 20
ms-1. Above 20 ms-1 the Smith (1980) formula possibly underestimates the wind stress, theSmith (1988) formulation (a reinterpretation of the 1980 results based on the Charnock formula,and presented in tabular form) under-estimates significantly at higher wind speeds.
As the wind decreases below about 2 ms-1, Smith (1988) suggested that CD10n would
increase due to viscous effects. However, the significantly greater CD10n values for winds
below about 5 ms-1 predicted by Wu (1994) appear to be a better fit to the data of Bradley et al.
(1991) and Yelland and Taylor (1996) (however see discussion in section 7.4.2B).

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7.3 Determining the Transfer Coefficients

3.0
Large & Pond 1982
Smith 1980

CD10n(x1000)

2.5

Figure 7.1 Examples forthe Drag Coefficient, CD10n,plotted as a function ofwind speed, U10n

Smith 1988
Yelland et al. 1998
Smith 1992

2.0

1.5

1.0

0.5
0

5

1 0

u10n

1 5

2 0

2 5

(m/s)

C. SENSIBLE AND LATENT HEAT
Unlike the drag coefficient, traditional estimates of CE10n and C H10n over the ocean tend
to support a fairly constant value over a wide range of wind speed. Instrumentation problems,
partly caused by exposure to the salt environment, have limited the number of reliable
determinations and there is still at least 10% uncertainty in their values and hence in the derived
fluxes. Friehe and Schmitt (1976) recommend a constant Dalton number (103 CE10n =1.32 ±0.07) on the basis of several turbulence-based datasets where the highest wind speed was 4 ms-1.
After a critical assessment of previous studies, Smith (1989) also suggested a constant“consensus” value (103 C E10n =1.2 ± 0.1) for winds between 4 and 14 ms-1. The Humidity
EXchange Over the Sea (HEXOS) experiment results (DeCosmo et al., 1996) also suggest anear constant value (any increase being less than 15 to 20%) with (103CE10n , = 1.12 ± 0.24 ) for
winds up to 18 ms-1.
For the Stanton number, Friehe and Schmitt (1976) obtained slightly different values forunstable and stable conditions (103 C H10n = 0.97 and 0.86 respectively), and proposed that thedissimilarity with their Dalton number was partially an effect of the classical ratio of the thermal
to species diffusivities (Prandtl/Schmidt numbers), CE/CH = 1.16. Smith (1988) suggested 103
CH10n = 1.0 which is consistent with this ratio and his value of CE10n . In contrast DeCosmo etal. (1995) suggest that ( CE10n ≈ CH10n) for the high wind HEXOS results, to the accuracy of theirdetermination.
Given that the drag coefficient, and hence the roughness length, increases with windspeed, a constant value for the Dalton number, implies that the humidity roughness length
decreases with increasing wind speed (see (7.10b,c)). In explanation, Liu et al. (1979)
suggested that, whereas momentum is transferred by pressure differences as well as bymolecular forces, heat and moisture are transferred only by molecular diffusion which becomesless efficient as the roughness increases and sheltering occurs between the wave troughs.
For the low wind speed regime, the transfer coefficients, CE10n and CH10n, have beenfound to increase as the wind decreases below about 2 ms-1 (Bradley et al., 1991) as predicted
by Liu et al. (1979) (see section 7.3.2C).
7.3.2 New bulk formulae
A INTRODUCTION
The remaining uncertainty in the specification of transfer coefficients, and theconsequent limited accuracy with which fluxes could be calculated from bulk formulae, became
unacceptable in the context of climate models and particularly in attempts to couple ocean andReport of the Working Group on Air Sea Fluxes

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7.3 Determining the Transfer Coefficients

atmospheric models. Along with improved instruments and further experimental studies, newbulk formulae have been developed aimed at incorporating better physical descriptions of thetransfer process. These newer bulk formulae differ from the traditional approach with regard to
the transfer coefficients in several ways. Typically, the momentum roughness length, zo , isobtained from a formula which takes sea state into account, and the roughness lengths fortemperature and humidity are calculated from zo by considering the transfer of heat through theinterfacial boundary layers, so-called “surface renewal theory” (Liu and Businger 1975;
Brutsaert 1975). They also seek to extend the wind speed range over which the algorithm isapplicable, particularly into the climatically important low-wind, convective regime. In thisrespect, improved convective flux/gradient relationships have been incorporated, and steps
taken to overcome the fundamental singularity in the bulk formula as the wind speed approacheszero.
B MOMENTUM ROUGHNESS LENGTH, Z0
The aerodynamic roughness length, zo , appears as the integration constant in (7.6a); it
is the height at which the wind velocity extrapolates to zero on the logarithmic wind profile
under neutral conditions. Considerable efforts have been made in the disciplines of fluid
mechanics, engineering and micrometeorology to relate zo to the geometrical properties ofvarious natural and fabricated surfaces and some empirical generalities have emerged (e.g.
Figure 4.1 of Garratt 1992). Over most rigid land surfaces zo may be obtained from the neutral
wind profile (and its value is nowadays cross-checked by measuring all the other quantities in(7.6a)). Panofsky and Dutton (1984) give values of zo for various surfaces, from which we notethat the sea is very “smooth”. The associated small velocity gradient make both z o and u *extremely difficult to measure accurately using the profile method, especially from shipboard.
Furthermore, the sea surface geometry and roughness alter continuously with varying
windspeed. It has therefore become common practice to specify the roughness length
independently, drawing on the methods of classical fluid dynamics (Schlichting, 1968, Chapter
XX).
Kraus and Businger (1994, Chapter 5) describe in detail the application of this work to
the air-sea interface, so we discuss only the basic issues here as they relate to current flux
algorithms. It is common to characterise the surface and the wind flow regime in terms of theroughness Reynolds number:
Rr = u* z0 / ν(7.11)
where ν is the kinematic viscosity of air. When Rr < 0.13, aerodynamically “smooth” flow exists
when the roughness elements lie entirely within the viscous sublayer at the surface. The
concept of a viscosity dependent transfer regime is important in the context of surface renewal
discussed below. In smooth flow, classical experiments show that:z0 = 0.11ν / u*(7.12)
With increasing windspeed the flow becomes aerodynamically “rough” when Rr> 2.5,
as the stress is dominated by pressure and viscous forces around the surface roughness elements.
In this regime over a rigid surface, zo (and CDz) is independent of windspeed, but a water surface
responds to the varying wind stress continuously by changing its roughness. Charnock (1955)
argued for proportionality of the two quantities, and on dimensional grounds proposed a relation
for rough flow over the sea:z 0 = α c u *2 / g(7.13)
where αc is referred to as Charnock’s constant, for which values between 0.010 and 0.035 cantypically be found in the literature (Garratt 1992, Table 4.1). Fairall et al. (1996a) present
values of α c as a function of R r and conclude that the “best” value to use in the COARE bulk
flux algorithm (see below) was 0.011. However in the open ocean, the near linear increase of
CD10n with wind speed (Yelland et al. 1998) implies that α c increases to around 0.018 at about
20 ms-1 (Hare et al. 1999).

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7.3 Determining the Transfer Coefficients

Roughly speaking, flow over the ocean is smooth below a 10 m windspeed of about 2
ms-1 and fully rough above 8 ms-1; between these limits there is a transition regime. Smith(1988) therefore proposed:u*2νz0 = α c+ 0.11gu*(7.14)

valid for the entire smooth to rough flow regime, a scheme which is being increasingly adopted
in bulk flux algorithms (Fairall et al. 1996a; Zeng et al. 1998). Kraus and Businger (1994,
figure 5.3) illustrate the form of this relationship.
The wide range of empirical values found for αc have led to suggestions that it is not a
constant, but dependent on what may loosely be called “sea state”. The complicated topographyof the ocean surface depends strongly on the interaction between wind and waves, which has
been an active field of study and controversy for the past half-century. Claims have been madefor relationships between the drag coefficient (or equivalently z 0) and various wave
characteristics and influences. For example, several sets of observations over shallow watersites appear to give higher drag coefficient values than those over the open ocean (Geernaert
1990, Figure 8). This has prompted modification of Charnock’s relationship to make α cdependent on wave parameters, particularly the wave age:
wa = c p / u*(7.15)
where cp is the phase speed of the dominant waves at the peak of the locally wind-driven wave
spectrum (Melville 1977, Nordeng 1991, Donelan et al. 1993). The notion is that younger
waves, developing as a result of limited coastal fetch, limited duration, or steepening due toshallowing depth, travel slower than the wind and absorb more momentum, therefore appearing
to be rougher.
Wu (1968) rationalised Charnock’s expression as an “equation of state characterisingequilibrium interaction between the wind and waves with gravity waves as the roughness
elements”, and suggested that capillary waves contribute significantly to the stress atintermediate wind speeds, implying a dependence on surface tension and viscosity of the water.
On this basis Wu (1968) proposed an expression for z0 due to capillary waves, and subsequently(Wu 1980) a further refinement to the Charnock relationship for separating flow behind “shortwaves superimposed on the dominant waves in the ocean”.
During the HEXOS experiment near the coast of Holland, Smith et al. (1992) found therelationship:

0.48 u *2
z0 =
wa g

(7.16)
effectively modifying the Charnock “constant” to accommodate the range from very young
waves (wa ≈ 5 ; α c = 0.096) to mature waves (w a ≈ 30 ; α c = 0.016). Komen et al. (1998)
reviewed various formulations for the Charnock parameter and concluded that, of thoseavailable the HEXOS relationship (7.16) had the advantage of fitting data sets ranging from theopen ocean to the short fetch Lake Ontario data of Donelan (1982). However reanalyses of the
HEXOS data by Janssen (1997) and Oost (1998) questioned the validity of (7.16). For
example, Oost (1998) suggested that at least some of the enhanced stress values were caused byshoaling waves. When those data were excluded, Equation (7.16) was valid only for the
youngest waves (wa < 13). Similarly, Yelland (2000) found little difference in stress values forthe open North Atlantic and the region of the HEXOS experiment. However data collectedfrom the shallower North Sea regions did show higher stress values.
Bourassa et al.(1999) proposed an air-sea interaction model which, for zo, used the rootsum-of-squares of Wu’s (1968) capillary wave parameterisation and the wave-age modified
Charnock expression of Smith et al. (1992):

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7.3 Determining the Transfer Coefficients
0.48 u*2 2 bσ 2) 
z 0 =  (β c 2 ) + (β g
wa g 
u* ρ w


1/ 2(7.17)

Here σ is surface tension, ρw the water density, b is Wu’s parameter (modified by Bourassa etal.), and βc, βg are weights for capillary and gravity waves respectively, based on physicalfactors. Capillary waves are the dominant roughness for windspeed < 7 ms-1 and short gravity
waves for wind > 7 ms-1. Other aspects of the bulk flux model of Bourassa et al. (1999) will be
discussed below (section 7.4.2B).
Wave age is not necessarily a function of fetch alone; in the open ocean a suddenchange in windspeed due to passage of a frontal system, for example, can produce a nonequilibrium wave/stress situation. Indeed, the effects of wave age as predicted by (7.16) can besignificant. For example Gulev and Hasse (1998) estimated sea state dependent wind stress, bycomputing wave age from the wave periods reported by Voluntary Observing Ships. They used
the sea period, arguing that, by definition, swell is not related to the local wind, and that it is
the interaction of the local wind with wind sea that determines the part of the wind stress caused
by waves. Computing the drag coefficient from (7.16) they found differences between waveage-based and traditional estimates to be quite pronounced in the mid latitudes and tropics, withwave induced stress contributing up to 20 % of the total stress estimate in wintertime.
The occasionally large C D10n values predicted by wave age based formulae have led
Yelland et al. (1998) to question whether the roughness depends on wave age. Their large
dataset obtained during three cruises to the Southern Ocean showed no evidence for wave-age
dependence of the wind stress nor of the high values predicted for storms. Furthermore, using abuoy mounted sonic anemometer system, Taylor et al. (1999b) found no variation of roughness
for wave ages from 10 to over 30. Janssen (1999) suggested that this was because the data
were obtained using the inertial dissipation method, but this was disputed by Taylor andYelland (2000b) who emphasised that Janssen's arguments, if true, would have led to an
enhanced wave age dependence in their data set. Examining the published data from a numberof field experiments, Taylor and Yelland (2000c) proposed a different roughness length
formulation:

H zo= A  SHS
 Lp 

B(7.18)

where H s and L p were defined as the significant wave height and peak wavelength for the
combined sea and swell spectrum. The suggested values for the coefficients were A = 1200, B= 4.5. The character of the CD10n values derived from (7.18) are significantly different fromthose predicted by wave age based formulae. Short duration or fetch do not result inenhancement of the roughness. Thus the CD10n values predicted for a pure wind sea always havea similar relationship to the wind speed. At most wind speeds these pure wind sea values werehigher than is observed in the open ocean. However the typical mean open ocean values werepredicted when (7.18) was applied to wave measurements which were dominated by mixed
wind sea and swell. Thus Taylor and Yelland (2000c) claimed that (7.18) successfully predicted
the observed values of CD10n for the open ocean, coastal seas, lakes, and even wind-wave
flumes. This is a wider range of conditions than those for which wave age based formulae havebeen found to hold. However they were unable to predict the large CD10n values observed byDonelan (1982) at very short fetch on Lake Ontario and questioned whether the upwind
roughness over land had affected these data.
In summary the present position is that, despite decades of work on wind-wave
interactions, the parameterisation of z0 in terms of sea state is still a matter of considerable
controversy. A problem is that, for the wave age based formulae to be valid, there must be awave age dependence of the short waves (wavelength ≈ 10cm). However, at present, there isno direct proof for or against this (Komen et al. 1998). This uncertainty in z0 also affects ourknowledge of CE and CH.
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7.3 Determining the Transfer CoefficientsC SURFACE RENEWAL THEORY
Analogous to the momentum roughness length, surface length scales for the scalarquantities appear in the equations involving temperature and humidity (Section 7.2). The
model of Liu et al. (1979) was one of the first to deal with air-sea transfer in the low wind-speed
regime, and to derive the scalar roughness lengths from the principles of “surface renewal
theory”. This theory was introduced many years ago in chemical engineering applications, andapplied to the air-sea interface by Liu and Businger (1975) and independently by Brutsaert(1975). Here we will briefly outline the development leading to the Liu et al. (1979) bulk
algorithm (hereafter LKB).
Whereas the atmospheric (and oceanic) surface boundary layer transports heat, massand momentum to the interface by turbulent motions, at the surface itself there exists aninterfacial layer of order 1 mm thick, in which molecular diffusion plays a significant role in thetransport. The idea behind surface renewal theory is that, across this interfacial layer, small
eddies of air transfer heat randomly and intermittently between the “bulk” turbulent fluid, of
temperature Tb, and the surface itself which therefore warms or cools by conduction from theeddies. The temperature gradient and the surface heat flux are determined by the heat
conduction equation:

∂T
∂ 2T
=κ 2
∂t
∂z

(7.19)
where κ is the thermal diffusivity. The solution for initial condition T(t=0) = Tb = constant, andsurface temperature T(z=0) = Ts = constant (Carslaw and Jaeger, 1959) gives the surface heat
flux :

H(0, t) = -κρC p (κρC p (Ts − Tb )∂T
) z =0 =∂z(πκt )1 / 2(7.20)
Liu and Businger (1975) define a distribution function to describe the areal fraction ofeddies which have been in contact with the surface for time t, and assume a characteristic time,tc, for which an eddy remains in contact with the surface before breaking away. For constant Ts
and a random distribution of contact duration they determine the time-averaged temperature
profile in the interfacial layer as:(T − Ts )z = 1 − exp
1/ 2 
(Tb − Ts )
(
κ
t
)
 c

and average heat flux:H=(7.21)

κρC p (Ts − Tb )(κt c )1 / 2(7.22)For the scaling length (κtc)1/2 , LKB substitute δ T, the thickness of a layer in which the sameheat flux and temperature difference would be maintained solely by molecular diffusion.
Similar relations to (7.21) and (7.22) can be derived for moisture and momentum
transport:

Q − Qsz= 1 − exp( − ); δ Q = ερ (Qs − Qb ) / E = (εtc )1 / 2
Qb − Qs
δQ(7.23a)U − Usz= 1 − exp( − ); δ U = νρ (Us − Ub ) / τ = (νtc )1 / 2
Ub − U s
δU(7.23b)where ε is the molecular diffusivity of water vapour and ν the kinematic viscosity. With thescale variables, u* , t* , q* as defined in (7.5), the above relations can be written:(T − Ts ) / t * = S[1 − exp(− zu * / Sκ )]Report of the Working Group on Air Sea Fluxes(7.24a)June, 2000

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7.3 Determining the Transfer Coefficients(Q − Qs ) / q* = D[1 − exp(− zu * / Dε )](7.24b)(U − U s ) / u * = C[1 − exp(− zu * / Cν )](7.24c)where “sublayer parameters”, sometimes referred to as inverse interfacial Stanton and Dalton
numbers (Brutsaert 1982) are given by:

S = (Tb − Ts ) / t * = δ T u * / κ(7.25a)D = (Qb − Qs ) / q* = δ Q u * / ε(7.25b)and the inverse interfacial drag coefficient:C = (U b − U s ) / u * = δ U u * / ν(7.25c)Brutsaert (1975) suggested that the renewal time scale tc be proportional to the time scale ofKolmogorov eddies:(7.26)
tc = (νzo / u*3 )1 / 2
which, combined with the relations for sublayer thickness (7.21) indicates that the sublayer
parameters can be expressed in terms of the roughness Reynolds number (7.11)), the Prandtl
number, Pr (= ν/κ) and the Schmidt number, Sc (= ν/ε).

S = GRr 1 / 4 Pr 1 / 2
D = GRr 1 / 4 Sc 1 / 2
C = GRr 1 / 4(7.27)
where G is a proportionality constant determined empirically by LKB as 9.3.
LKB match the exponential sublayer profiles (7.24) smoothly with the slope of the outer
turbulent logarithmic profiles (7.6) via a transition layer. This leads to a relationship between
the scalar roughness lengths (z0T ,z 0Q) and the momentum roughness length via the roughness
Reynolds number. As they show, the resulting continuous temperature and velocity profiles
agree well with laboratory and field experiments; the profile for moisture (and other scalarquantities) is assumed similar to temperature. LKB (their Figs. 6 and 7) illustrate their modelfor the roughness scalar lengths compared with a small number of field measurements, andparameterise the relationship in the form:

zot u*= aRr bνz u
Rq = oq * = cRr dν
Rt =(7.28a)(7.28b)where the values a, b, c and d were determined from field experiments by LKB and given intabular form.
Brutsaert (1982) also matched the sublayer and logarithmic profiles, but assumed
different sublayer constants and velocity behaviour. This leads to significant differences
between his model and that of LKB, as illustrated by Fairall et al. (1996a) for the neutral
Othermoisture exchange coefficients and for the functional dependence of Rq and R r .
variations on the application of surface renewal theory will be discussed below in the context ofrecent bulk flux models.
The physics and thermodynamics of the interfacial sublayers applies at both sides of theinterface. Soloviev and Schlüssel (1994, 1996) have developed models of ocean-atmospheregas transfer, applying surface renewal theory to the sublayer on the ocean side of the interface,otherwise referred to as the cool skin. They explore the sources of convective instability which
control the sweeping and breaking away of the turbulent eddies. In this case, both heat andsalinity fluxes play a role; Soloviev and Schlüssel (1996) point out that under low wind speed
conditions, solar heating can damp the convective instability, increasing the renewal time andreducing gas transfer across the sublayer.

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7.4 Light to Moderate winds

7.4 Bulk formulae for light to moderate wind conditions
7.4.1 The TOGA COARE algorithm
A. BACKGROUND
Process studies such as GATE, COARE, ASTEX, HEXOS etc. are closely focussed
experiments designed to describe, improve our knowledge of, and to quantify certain aspects of
the ocean-atmosphere climate system. While they may be limited in extent and scope in global
terms, they nevertheless provide the basic relationships and parameterisations of physicalprocesses needed within the broader framework of global atmospheric and oceanic studies, andof climate models. Thus, within the overall aims of TOGA, TOGA-COARE was designed toanswer specific questions about energy exchange, convective processes and scale interactions in
the west Pacific warm pool (Webster and Lukas 1992). Various considerations led to therequirement that the net air-sea energy exchange be specified to an accuracy of about 10 Wm-2of which the allowable uncertainty in the turbulent flux components would be 6-7 Wm-2 . This
leads to limits on permitted errors in the mean variables used to calculate bulk fluxes, windspeed = 0.2 ms-1, sea and air temperatures T s , Ta = 0.2 K, specific humidity q = 0.2 g kg-1,
assuming that they are independent.
It is likely that the COARE surface observations have experienced closer scrutiny andmore rigorous quality control than any previous similar data set. Details are given in the various
Flux Group workshop reports (Bradley and Weller 1995a, b; 1997; Bradley et al. 1997) (also on
the COARE Data Users web site: http://www.ncdc.noaa.gov/coare/ ). These observations havebeen used as a benchmark to develop and validate various models of the tropical oceanatmosphere climate system, including remote sensing and surface flux bulk algorithms. The
TOGA-COARE algorithm was developed early in the post-experiment analysis, by Fairall et al.
(1996a), to provide common code for use by the COARE research community
B. ALGORITHM HISTORY
The algorithm was based on the model of Liu et al. (1979, LKB), to take account of thelight wind, strongly convective conditions commonly found over tropical oceans. Some
additional physics has been incorporated and, on the basis of data from COARE and otherrecent field programmes, some empirical constants have been adjusted. Version 1.0 was
released in November 1993, and included various modifications to the basic LKB code.
Following Smith (1988) the momentum roughness length (and hence Cd10n) was specified as thesum of the Charnock formula and a smooth flow limit; i.e. (7.14), with the value αc = 0.011
evaluated from the Moana Wave direct flux measurements. The interfacial specific humidity
was calculated from the saturated vapour pressure at sea surface temperature, appropriate to asalinity of 34 psu; i.e. reduced by 2% relative to the saturated vapour pressure over fresh waterat the same temperature (Kraus and Businger 1994).
Like LKB the Monin-Obukhov stability length, L (7.2), was calculated using virtual
potential temperature, defined in full here to emphasise the point that the contribution ofmoisture to buoyancy is usually of first order over tropical oceans:L=T (1 + 0.61q )u *2
gk [t * (1 + 0.61q) + 0.61Tq * )](7.29)

where the scaling parameters, u* , t* , q* , were defined in. (7.5). However, the dimensionlessprofile functions were given a form that asymptotically approached the proper convective limit
as wind speed goes to zero (Garratt 1992). In this “free convection” regime, the dimensionlessscalar profile functions are expected to follow a (z/L)-1/3 dependence. The form:y = [1 − γ ( z / L)]−1 / 3(7.30)

was adopted for both velocity and scalar dimensionless profiles, with the empirical constant γ =12.87, which leads to stratification functions of the form,

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7 - Parameterisation of Turbulent Fluxes90

 y 2 + y + 1
 2 y + 1 π
ψ c = 1.5 ln  − 3 arctan 
+
3
3
 3 



7.4 Light to Moderate winds(7.31)As stability approaches neutral conditions, the function was blended to a standard Kansas type(7.7) (Businger et al. 1971). For stable conditions, the Kansas form ψ = −4.7( z / L) , which
follows from (7.3b) with γ = 4.7, is used for both momentum and scalar profiles.
Following Godfrey and Beljaars (1991), the wind speed in the bulk expression was augmented
by a gustiness velocity, Wg, such that the wind speed, SU , becomes:

S U = ux2 + u y2 + Wg2 = U 2 + Wg2(7.32)

where ux and u y are the mean wind components (i.e., magnitude of the mean wind vector) and
Wg is proportional to the convective scaling velocity, W* (Deardorff 1970):

W*3 =

g H
E 
+ 0.61Tz .
T  ρC p
ρLV  i(7.33)

where zi is the depth of the convective boundary layer. Thus W g = β W* , and a value β = 1.2
was adopted on the basis of the Moana Wave direct flux measurements in COARE. A
reasonable default value for the depth of the marine boundary layer in the tropics is zi = 600m.
The major modification for Version 2.0 (August 1994) was the inclusion of optionalmodels for the cool skin temperature effect and the diurnal thermocline to correct bulk watertemperatures to true SST. The cool skin model was based on Saunders (1967) with amodification to include the effects of buoyancy flux. This indicates a cool skin of about 0.3˚K
during the night. During the day the cool skin may be reduced or apparently eliminated entirely
by solar heating in the upper layer of the ocean (Soloviev and Schlüssel 1994). A warm layer
model was added to correct bulk water temperature measurements made at some depth, zb. The
idea is that ship intake and buoy temperature sensors at a metre or so depth are unable to resolve
the diurnal warm layer which commonly forms in the upper tropical oceans under light windconditions. This model was based on a simplified scaling version of the Price, Weller and
Pinkel (1986) mixing model. If daytime solar heating is sufficient, a stable near-surface layer isformed causing the surface temperature to increase. Linear profiles of temperature and current
are assumed. The depth is determined by a critical Richardson number and the profile ofabsorption of solar energy in the water (Paulson and Simpson 1981). The physics of both thecool skin and warm layer models are described in Fairall et al. (1996b)
Under very light wind conditions, peak solar warming as great as 4˚K is produced witha warm layer depth of about 0.25 m. Once the warm layer forms, its depth and intensity are
determined by integrating the accumulated momentum and heat input in the layer. Thus, thismodel requires a complete time series of data throughout the diurnal cycle; appropriate time
resolution is from ten minutes to one hour. Both the cool skin and warm layer models can bedisabled if true skin surface temperature is available (e.g. from aircraft or satellites with IR
radiometers).
Calculation of fluxes of momentum (Caldwell and Elliott, 1971) and sensible heat due
to rainfall were included, with raindrops assumed to be 0.2˚K cooler than the surface wet-bulb
temperature (Gosnell, Fairall and Webster, 1995). The so-called Webb correction to latent heat
flux, which arises from the requirement that the net dry mass flux be zero (Webb et al., 1980), is
also calculated in the algorithm.
The last major modifications to the algorithm were made at the COARE Air-Sea
Interaction (Flux) Group Workshop in Honolulu, 2-4 August 1995 (Bradley and Weller, 1995b).
Transfer coefficients were adjusted by six percent to give better average agreement withcovariance latent heat fluxes from several COARE ships. At the Woods Hole workshop, 9-11
October 1996 (Bradley and Weller, 1997) it was agreed that no further development would beattempted to the community version of the COARE Bulk Flux Algorithm, and that a version
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2.5b bulk algorithm "package" would be made available, consisting of the Fortran source code
and a test data set. This was released at a meeting of the Flux Group at NCAR, 14-16 May
1997 (Bradley et al., 1997)1.
The formalism of the COARE algorithm is fully described in Fairall et al. (1996a). It
has been used with success in subsequent field observations from ships and aircraft, both inequatorial and mid-latitude regions; a sample list of publications in which the COARE
algorithm has been used appears in Bradley et al. (1997). During an ocean mixed layer heatbudget experiment by R/V Franklin in the Indian ocean in 1996, using the COARE algorithm
for the sensible and latent heat fluxes, closure was obtained to within the equivalent of about 3
Wm-2 over a 7-day period (Godfrey et al. 1999).
The major limitation of the COARE algorithm is that it has not been verified above
about 12 ms-1 windspeed. In it’s published form there are two factors which could impede its
use in numerical models. Although the iteration around exchange coefficients, stability andfluxes normally converges within 5 iterations, except for very stable conditions, the code stillretains the original 20 loops. Also, the use of complete functional forms for parameters such asg, LV and ν , and the inclusion of all minor flux corrections in pursuit of the COARE 10 Wm-2
accuracy goal, may be regarded as superfluous at this stage.
The authors have released2 for trial a new version 2.6 (Bradley et al. 2000) which
addresses these limitations and introduces other recent analytical developments, as follows:1.

The empirical constants in the convective portion of the profile functions have beenchanged for improved matching to direct profile observations (Grachev et al., 2000)

2.

The Kansas stable profile functions have been replaced by those from Beljaars andHoltslag (1991) based on new profile data taken over the Arctic ice cap.

3.

A fixed value of the Charnock parameter (α c= 0.011) has been replaced by aformulation with a simple wind-speed dependence above 10 ms-1 based on data fromYelland and Taylor (1996) and Hare et al. (1999).

4.

The Liu et al. (1979) scalar roughness relationship [fx(Rr)] has been replaced with asimple analytic form which fits both the COARE and HEXMAX data bases.

5.

The stability iteration loop has been reduced from 20 to 3 using bulk Richardson
number parameterisation for an improved first guess (Grachev and Fairall, 1997).

6.

The latent heat flux has been reformulated in terms of mixing ratio instead of water
vapour density to eliminate the need for a Webb et al. (1980) correction.

7.

Gustiness velocity has been expanded to include the effects of mesoscale variability
associated with convective precipitation using the approach of Jabouille et al. (1995) or
Redelsperger et al. (2000).

For a later release, two options are being considered to make the COARE algorithm
more useful for applications to more limited data sets and for implementation in numericalmodels. The first is the addition of simple parameterisations to furnish downward IR and/or
solar radiative fluxes when they are not available by direct measurement (e.g., COADS data).
These would be standard parameterisations available in the literature (see Josey et al. 1999 forexamples or refer to Section 6.2). The second is the addition of a parameterised form of thewarm layer effect, probably based on Webster et al. (1996). This has the great advantage that

1

Available at: http://www.ncdc.noaa.gov/coare/catalog/data/atmosphere_large_scale/bulk.html
or by ftp from the NCAR site:ftp://ncardata.ucar.edu/datasets/ds606.1/flux_algorithm/

2 The new algorithm is available at ftp://ftp.etl.noaa.gov/pub/et7/users/cwf/bulkalg/

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7.4 Light to Moderate winds

the fluxes would be a pure function of a single record of data, rather than an integral of theprevious time series. A major addition is being considered to account for the effects of waves
on the fluxes. This would allow the algorithm to be applied in coastal/shallow water areas suchas much of the Gulf of Mexico. The new model from Taylor and Yelland (2000c) is a primary
candidate with the surface roughness parameterised in terms of the significant wave height andthe peak wavelength (or, equivalently the wave period) both of which are observable fromsatellites.
7.4.2 Other Algorithms
A. ZENG, ZHAO AND DICKINSON (1998)
The algorithm described by Zeng et al. (1998, ZZD) takes essentially the same path as
the COARE algorithm, but uses different stability functions for the velocity and scalar profiles,
and a different form for the scalar roughness lengths. The various empirical constants are given
values which ensure reasonable agreement with observation over the wind speed range 0 -18
ms-1. The observations used are the COARE Moana Wave dataset for low to moderate windspeeds (0-12 ms-1) and the HEXOS data up to 18 ms-1.
Like Fairall et al. (1996a), ZZD use the Kansas expressions for near-neutral
atmospheric stability and the stable case (with γ = 5 in (7.3b)) but, as stability increases beyond
z/L = 1, the expression of Holtslag et al. (1990) is adopted :φ m = φt = φq = 5 + z / L(7.34)

which prevents φ from becoming too large in very stable conditions. For convective conditions
ZZD use the expressions of Kader and Yaglom (1990, see their Figure 1):z
φ m = 0.7k 2 / 3  − 
 L

1/ 3

z
and φt = φq = 0.9k 4 / 3  −  L

−1 / 3(7.35)

which lead to simpler integrals than those given in (7.30) and which are therefore more suitablefor incorporation into numerical models. ZZD match the near-neutral to convective forms at z/L= -1.574 for momentum and z/L = -0.465 for the scalars.
They also use the functional form of Smith (1988) for the momentum roughness length,
but their analysis of the Moana Wave COARE data suggests a value for the Charnock constant
αc = 0.013. For the scalar roughness lengths they use the expression given by Brutsaert (1982):

 z 
 z 
ln 0  = ln 0  = b1 Rr 1/ 4 + b2
 z0T  z 0Q (7.36)

where the constants determined empirically by ZZD (b1 = -2.57 and b2 = 2.67) differ slightly
from those given by Brutsaert (1982). This is partly because they are optimised to provide acontinuous fit to both the COARE and HEXOS datasets.
Other similarities with the COARE algorithm include allowance for the 2% reduction insaturated specific humidity over sea water, which ZZD find decreases latent heat flux by 20% at
14 ms-1, and the “gustiness” correction at low wind speeds. However, they adopt a value β =1.0, rather than the value 1.2 which Fairall et al. (1996a) prefer. ZZD does not include thecontributions to sensible heat and momentum due to rainfall, nor models for the cool skin anddiurnal thermocline, so input data for the algorithm must be either measured or derived skinsurface temperature. Zeng et al. (1999) have developed an hourly dataset from the Pacificocean TOGA-TAO mooring array for use with their algorithm, which includes such a derived
surface skin temperature field.
ZZD compare their algorithm with COARE 2.5b, and with those embodied in several
research and operational numerical climate models, NCAR’s CCM3, the ECMWF forecast
model, the NCEP medium range forecast model and the GEOS DAS model.

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7.4 Light to Moderate winds

B. BOURASSA-VINCENT-WOOD (1999) FLUX AND SEA STATE MODEL
The main innovation of the Bourassa et al. (1999, BVW) model is a scheme to relateroughness lengths (z 0 , and by implication the neutral exchange coefficients) to the various
constituents of “sea state”, particularly drawing the distinction between the effects of capillary
waves and gravity waves. BVW also note that, particularly under low to moderate windconditions with swell, the wind, stress and current directions are not necessarily parallel, andtheir algorithm allows the cross-wind component of stress to be calculated. BWV contend thatwith the co-existence of these different wave types, and interactions between them, it is unclear
what the proper coordinate frame of reference should be for the wind and water velocities, andfor z0, and question whether the surface current is the appropriate reference velocity (see Section
10.6.2). They argue that wind parallel to the crests of swell will not “feel” the swell as
roughness, citing the observations of Donelan et al. (1997), who measured much larger dragcoefficients in the presence of counter and cross-swells, than would be expected over a purewind sea.
BVW distinguish between aerodynamically smooth and rough regimes with a Smith(1988) type roughness length expression (c.f. 7.14) :1/ 2

0.11ν bσu2 + ( sc βc 2 )2 + ( sg β g * )2 
z0 = sν
u*
u* ρw
g 


(7.37).

Each term includes a binary switch, sx , to exclude any surface type which is not contributing to
the roughness at that time. Thus for smooth flow sν =1 and sc = sg = 0, the criterion for whichis that the calculated phase speed, cp , for the dominant wave be less than the minimum phasespeed for surface waves:c p min = (4 gσ / ρ w ) 1 / 4(7.38)

which is approximately 0.23 ms-1. Above this windspeed surface waves are assumed to exist,
and s ν becomes zero. This is the basis for the BVW claim to distinguish between
aerodynamically smooth and rough flow. Capillary waves appear above a windspeed U 10 ≈ 1.8
ms-1, referred to as the “capillary cutoff”. BVW re-evaluate Wu’s (1968) parameter, b, on thepremise that Wu failed to take account of a zero-displacement correction when using the profile
method for z0 and u*, and Wu (1994) failed to correct for the increase in drag coefficient for the
low wind, strongly unstable regime of the Bradley et al. (1991) observations which he used toverify his value of b for field conditions. Their re-evaluation reduces b from Wu’s 0.18 to 0.06,
which agrees with their mean value of 0.05 using the wave age and phase speed data fromNOAA’s San Clemente Ocean Probing Experiment (SCOPE) on R/P FLIP (Kropfli and
Clifford, 1994).
The weighting parameters, βc and β g , account for the two effects referred to above, namely thecorrect frame of reference for velocities and z0 and the non-parallel wind and stress vectors. Not
much detail of the structure of these weights is provided, although two examples are given; thecorrection to u0 (7.8a) for the velocity of capillary waves superimposed on a gravity wave field,
and for gravity waves propagating in different directions relative to the wind. Then :β c = exp(−κU c c p / u * )(7.39)

for simplicity not written here in the vector form used by BVW. The shift in velocity, Uc , is anempirically determined fraction of the orbital speed (Uorb = πHs/Tp), where the significant wave
height, Hs , and the wave period, T p , are estimated from the “3/2”relationship of Toba et al.
(1990) :(H = B( gu ) T )
1/ 2

s

*

3/ 2
p

(7.40)

with B = 0.62. For the gravity wave roughness length zog , BVW use the weighting β g=0.48/(cP/u*) (the HEXOS result, see equation 7.16) rather than the form which may be a better
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7.4 Light to Moderate winds

βg = exp(-κΨcP/u*)(7.41)
because of the lack of data to determine the scaling parameter
Ψ. This shortcoming in thegravity wave parameterisation leads to an overestimation of stresses for rising seas, and a verysmall underestimation of fluxes for falling seas (e.g. those examined by Fairall et al. (1996c)).
However data from a directional Waverider and a buoy mounted anemometer (Dobson et al.
1999; Taylor et al., 1999b) showed that the BVW model significantly overestimated stress forconditions of growing seas, with the larger errors occurring for U10 > 12 ms-1 (which presumably
corresponds to larger departures from local equilibrium).
BVW go on to express the wind profile, roughness length components, and wave age invector form, so that their working form of (7.37) is written in terms of parallel and crosswind
components of stress. For aerodynamically smooth or rough flow, the BVW scalar roughness
lengths are adopted from the forms of Brutsaert (1982, Section 7.3.2C) :z 0T =

0.40ν
u*

;

z 0Q =0.62ν
u*(7.42)

and, citing Smith (1988, 1989) apply them to both smooth and rough flow conditions.
For the profile stability functions, BVW depart from tradition in respect of both stableand unstable forms. We detail these here because Clayson et al. (1996), whose algorithm is
described in the next section, use the same functions. Beljaars and Holtslag (1991) point out
that the Webb (1970) stable relation ( ψ m = ψ t = ψ q = −5z / L see (7.3b, 7.4b)) is fairly
consistent with most data for 0 < z/L < 0.5 , but produced unrealistic results when used in aweather forecast model (Louis 1979). On the basis of several sets of very stable data they
propose the following :−ψm = azz  bc z c
+ b −  exp − d  + L d
LL d(7.43)

and2 az − ψ t = −ψ q =  1 +


3 L

3/ 2

z  bc z c
− 1 (7.44)+ b −  exp − d  + L d
L dvalid over the range 0 < z/L < ~10 , with a = 1 , b = 0.667 , c = 5 and d = 0.35 in both
expressions.
In place of the standard Kansas expressions for the unstable case, BVW and Clayson etal (1996) adopt the expressions of Benoit (1977) : (ζ 02 + 1)(ζ 0 + 1) 2 − ψ m = ln  2+ 2[tan −1 (ζ ) − tan −1 (ζ 0 )]
2 
(
ζ
+
)(
ζ
+
)
1
1


 λ + 1− ψ t = −ψ q = 2ln 0 λ + 1(7.45a)(7.45b)whereζ = (1 − 16z / L) 1/ 4

; ζ0 = (1 − 16z 0 / L) 1/ 4

λ = (1 − 16z / L) 1/ 2

; λ0 = (1 − 16z / L) 1/ 2

(7.46)

and(7.47)
which have the correct analytical limit as z/L tends to zero and are stable numerically. BVW
adopt the “gustiness” correction for low wind speeds, using the same value of β = 1.25 as in the
COARE algorithm.
There are few open ocean data for low to moderate wind speed, including wave
measurements, with which the BVW model can be compared. The model results show minima

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7.4 Light to Moderate winds

in the neutral drag coefficient (CDN) for windspeed just below the capillary cutoff (~1.8 ms-1).
For aerodynamically rough surfaces there is a local minimum in CDN(U10) for cP/u* = 5. This isqualitatively in accord with measurements by Bradley et al. (1991), Dupuis et al. (1997) and acomposite result by Large et al. (1995). In other words, if capillary waves exist, below 4 ms-1
the drag coefficients are much larger than those for an aerodynamically smooth surface. On theother hand, Grachev et al. (1998) have suggested that the traditional stability formulae overestimate the drag coefficient at wind speeds below about 2 ms-1 and Taylor and Yelland (2000a)
suggest that many low wind speed data may be biased high. These studies suggest that theminimum drag coefficient occurs at lower wind speeds, possibly at 3 ms-1 as predicted by Smith(1988).
The BVW comparison of modelled stress with the bulk of the COARE observations is
not unreasonable, most of the serious disagreements being underestimates on the part of themodel. The SCOPE observations are nearly all underestimated. BVW speculate that this is due
to lack of wave information to put in the model, and the probability of large angles between thewind direction and that of wave propagation. The model predicts that the stress (and dragcoefficient) are least when these directions are parallel, in accord with the findings of Donelan
et al. (1997).
The BVW model predicts that the influence of capillary waves on surface fluxes is verysignificant for winds less than 7 ms-1 down to the capillary “cutoff” around 2 ms-1. On the basisof the Franklin (Bradley et al, 1991) low windspeed observations they calculate that, near thecutoff, capillary waves contribute about 10 Wm-2 to the latent heat flux compared with only 1
Wm -2 due to “gustiness”. The model flux predictions are compared with data from SCOPE,
COARE (R/V Moana Wave) and R/V Franklin, with and without capillary waves in the model.
Inclusion of capillary waves increased the mean modelled fluxes of momentum (by 0.004 Nm2), sensible heat (by 1 Wm-2) and latent heat (by 6 Wm-2); and in all cases, the modelled results
were closer to observation. BVW argue that the overall effect of capillary waves is three to six
times that of gustiness, and is therefore much more important than convective processes.
Unfortunately, the comprehensive dataset including meteorological variables, air-sea
fluxes and the various sea state parameters which could validate this algorithm are rare. Even
observations of the angle between the wind and direction of wave propagation are uncommon.
While the anisotropic z0 aspect is qualitatively consistent with the observations of Donelan et al.
(1997) and appears to be of reasonable magnitude, this aspect of the model has yet to bequantitatively validated.
C. CLAYSON, FAIRALL AND CURRY (1996) FLUX ALGORITHM
The Clayson, Fairall and Curry algorithm (Clayson et al. 1996, CFC) is based on
surface renewal theory (Brutsaert 1982) as set out in section 7.3.2.C, following the general
pattern established by LKB and Fairall et al. (1996a). The main differences from theseantecedents are: a new surface renewal time-scale parameterisation, the BWV sea state surface
roughness model, derivation of the scalar roughness lengths solely from surface renewal theory,
and a new skin surface temperature model.
Following Wick (1995) CFC identify Brutsaert’s renewal time-scale (7.26) as a sheardriven timescale, and define a convective time-scale also: νρ w C p t *c = Const .  αgQN 

1/ 2(7.47)

where α is the thermal expansion of sea water and QN is the sum of the turbulent and radiative
fluxes. The shear-driven and convective forms are combined and the contribution of eachdetermined by a Richardson number criterion. This composite time-scale is then used to form
expressions for the interfacial Dalton and Stanton numbers after the method of Brutsaert (1975).

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For z0 , CFC employ the parameterisation developed by BVW, but with somedifferences. The capillary and gravity wave terms are simply added (not root-mean -squared)
but without any weighting, and there is a clear distinction between smooth and rough flow on
the same basis as BVW:zo ( rough ) =0.48 u*2bσ+,
u*2 ρw
wa g

0.11ν
zo ( smooth ) =u*,

for u* wa > c p min(7.48)

for u* wa < c p min

CFC use a value of Wu’s b for capillary waves of 0.019. i.e. smaller even than the
BVW value. Referring back to BVW (their Fig. 2), we find that this is the value found initiallyin their re-evaluation of Wu’s data before applying the frame of reference correction. In otherwords, CFC adopt the simplest form of the BVW parameterisation, ignoring the features which
account for anisotropic wind/wave propagation directions and frame of reference adjustment.
Unlike BVW, they do not include a “gustiness” term, relying on their capillary wave
parameterisation and surface renewal theory to obtain correct fluxes at low wind speeds.
The turbulent boundary layer profiles used by CFC are those of Beljaars and Holtslag(1991) for stable conditions, and Benoit (1977) for unstable conditions, with the interfacial
layers of height, h, matched to these assuming continuity of T and q as before. Algebra leads to
Brutsaert-like expressions for the scalar roughness lengths: u  uh1 
1 
zot = zo exp k  h −  ; zoq = zo exp k  −  u* Pr St0    u* ScDa0  (7.49)

where Pr and Sc are the turbulent Prandtl and Schmidt numbers (0.71 and 0.60 respectively),
and St0 and Da0 the interfacial Stanton and Dalton numbers. CFC take (uh/u*) = 5 after Brutsaert(1975). They have a system of eight equations which they solve iteratively for Da0, St0, u*, t*,q*, z0, ,zoT and z0Q. Because this algorithm was developed with the calculation of fluxes fromsatellite data in mind, they incorporate a model of the ocean cool skin (Wick et al. 1996) toreduce bulk SST observations to true surface skin temperature, and adjust surface humidity forthe 2% reduction of vapour pressure over saline water. However, they do not correct SST forthe diurnal thermocline; the data set which they use for verification (Fairall et al. 1996a) has sea
temperature measured at 5 cm depth, so only the cool skin adjustment is needed. However,lack of a warm layer model would be of concern in the tropics particularly, with bulk watertemperatures measured at customary depths on a ship’s hull.
D. ZHANG AND MCPHADEN (1995)
Zhang and McPhaden (1995, ZM) studied the relationship between SST and latent heat
flux in the equatorial Pacific ocean, using data from the TOGA-TAO moored array. In
developing a flux algorithm for this region, they faced the same difficulties as the previous
models described here, namely the need to take account of occasionally very low wind, strongly
convective conditions. Their approach was to incorporate the minimum of complexity
consistent with this, and we describe their model here briefly because it represents possibly thesimplest of its type and is computationally economical, and because it was included in thecomparison by Chang and Grossman (1999) referred to in Section 7.4.2F.
ZM employ the Kansas forms (Businger et al. 1971) for the stability-dependent wind,temperature and moisture profiles (7.6), and the Paulson (1970) integral expressions for the Ψfunctions (7.7). For the value of α in (7.3a) they use 16, and for γ in (7.3b) they use a value of
7. They adopt the Smith (1988) Charnock plus smooth form for the momentum roughness
length (7.14), but set the temperature and moisture roughness lengths equal at a constant 2 x 105
m (Geernaert 1987).

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7.4 Light to Moderate winds

E. BELJAARS (1994)
Beljaars (1994) has developed a parameterisation for the case of free convection in theatmosphere, for use with bulk transfer models. It is based on the idea that normal MoninObukhov similarity theory can be used in the surface layer without modification for freeconvection, except for inclusion of the convective scaling velocity, w*, as an extra windcomponent in the bulk equation. This derives from the analysis of Godfrey and Beljaars (1991),
and is given by (7.32) and (7.33) as used in the COARE algorithm.
Beljaars (1994) follows the standard development set out in Section 7.2, leading to theusual form of the bulk exchange coefficients (7.9). The “Kansas” expressions are used for thedimensionless profiles, with the Paulson (1970) integrals for the stability functions, ψm , ψt,q.
These do not have the 1/3-power law dependence expected for free convection. However,
Beljaars (1994) argues that the behaviour of the stability functions for large −z/L is not too
critical because vertical gradients are small in the well-mixed regime, and contribute little to the
air-surface velocity, temperature or moisture difference.
The problem is simplified by writing asymptotic forms for the profile stability
functions, leading to expressions for the stress and heat flux in terms of z0/L, z0t/L, βw* and (t0 −tm) the temperature difference between the surface and the mixed layer; i.e. they are
independent of height, z. Beljaars (1994) shows that the resulting heat flux transfer coefficientis strongly dependent on roughness length, indicating that the surface is still being “felt” in thefree convection regime, through coupling between convective motions and the rough surface.
Over a smooth surface, Beljaars (1994) adopts the classic smooth-flow form (7.12) for z0, andthe same expressions as BVW for the scalar roughness lengths, (7.41) from Brutsaert (1982).
He compares his parameterisation with convective data taken over land by Stull (1994), and on
the basis of large eddy simulation data by Sykes et al. (1993), estimates the empirical constant,β, to be about 1.2, close to the value measured over the western Pacific by Fairall et al. (1996a)
during COARE.
For air-sea transfer over the ocean, Beljaars (1994) uses the Smith (1988) interpolation(7.14) for z0 with a Charnock constant of 0.018, and applies the Brutsaert (1982) smooth-flow
scalar roughness length parameterisation to both smooth and rough flow. His justification forthis is tentative, on the grounds that empirical evidence points to an almost constant moisturetransfer coefficient over the entire low to high wind speed regime (Smith 1989; DeCosmo et al.
1996). Approaching zero wind speed, his transfer coefficients for both heat and moisture follow
quite well the increasing trend shown by the low wind data of Bradley et al. (1991).
F. STULL (1994)
A quite different approach from the foregoing models is the so-called convective
transport theory (CTT) by Stull (1994), specifically aimed at prescribing the surface fluxes in
the limit of free convection. Unlike Beljaars (1994), Fairall et al. (1996a), Zeng et al. (1998),
etc., who deal with the approach to zero wind speed by introducing a horizontal “gustiness”
velocity based on the convective scale w*, Stull (1994) focuses on the role of convective plumes(thermals) in transporting heat and mass vertically from the surface into the mixed layer.
Like Beljaars (1994), Stull (1994) notes that a crucial factor controlling the turbulent
fluxes at the surface is the total difference in velocity, temperature or moisture concentration(i.e. ∆u, ∆t and ∆q) between the surface and the atmospheric mixed layer. This total is the sumof the individual differences across the three layers discussed above, the interfacial layer, thelogarithmic surface layer and the mixed layer. The lowest two layers respond fairly rapidly tochanges, whereas thermal circulations are relatively slow. Stull (1994) suggests that thisseparation of time-scales creates a reservoir of air in the surface layer which has been modified
by the surface, and that the removal of this air and its transport into the mixed layer by thermals
is the bottleneck in the system. Thus, Stull (1994) argues that, apart from their role inmaintaining the reservoir of air, processes in the lower two layers can be ignored, and thattransport by convective plumes is the governing process.
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7.4 Light to Moderate winds

Against this background, Stull (1994) goes on to parameterise the surface fluxes interms of mean mixed layer variables rather than surface-layer variables. This leads to thesurprising proposition that the surface fluxes should be independent of surface roughness, incontrast with the Beljaars (1994) result. Furthermore, provided the mixed layer is deep enoughthat variables may be defined in a mid-region well above the surface layer, Stull (1994) suggeststhat its depth and the shape of the surface layer profiles are irrelevant also.
Stull (1994) writes the vertical transport of an entity, χ, across an interface as the
product of a vertical transport velocity, w T, and the concentration difference, w ′χ ′ = wT ∆χ .
This is in the form of the usual bulk transport equation where vertical transport is caused byshear-driven turbulent eddies in the surface layer; i.e. wT appears in place of CD∆u. By analogy
Stull (1994) proposes that, in free convection, thermal eddies transport air aloft with a transport
velocity which scales with w* :wT = C* w*(7.50)

where C * is a mixed-layer transport coefficient to be determined empirically. He therefore
writes equations for the surface fluxes in the usual form, but in terms of mixed layer variables:

u*2 = C*D w* ∆u
w ’t ’ = C*H w* ∆t(7.51)

w ’q ’ = C* H w* ∆qwhere C * D and C*H are mixed layer transport coefficients for momentum and scalars
respectively. (To properly include all buoyancy effects, Stull (1994) works throughout with
virtual potential temperature (see (7.29)), and also expresses moisture as the mixing ratio. Here
we describe his formalism, but for simplicity and consistency with previous discussion, retain t
and q).
Because the fluxes appear on the RHS of (7.51) through w*, Stull (1994) converts theseequations to explicit form by defining a buoyancy temperature difference:∆t B ≈ ∆t (1 + 0.61q s ) + 0.61t s ∆q

(7.52)

and a buoyancy velocity scale:g
w B ≡  z i ∆t B 
t


1/ 2

(7.53)

which as Stull (1994) points out, unlike w * does not contain the turbulent fluxes. Finally hewrites a set of equations which form the mathematical basis for the application of convective
transport theory:

w ’t vs’ = b H w B ∆t B
w ’t vs’ = b H w B ∆t B
w ’q ’ = bH w B ∆q

(7.54)

u*2’ = bD w B ∆uwhere bH ( = C*3H/ 2 ) and bD ( = C* DC*1H/ 2 ) are simplified convective transport coefficients for
scalars and momentum, respectively. These must be determined experimentally, and Stull(1994) quotes values: bD = (1.83 ± 0.60) × 10-3 ; bH = (5.0 ± 1.9) × 10-4 obtained from the BLX83
experiment in Oklahoma (Stull and Eloranta, 1984).
The theory is tested against the independent Koorin experiment over land in Australia(Clarke and Brook, 1979). The agreement between predicted and observed surface stress isgood on average, but very scattered; the plot of kinematic heat flux has much less scatter, but apronounced difference in slope and an offset. Stull (1994) argues that the overall agreement

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7.4 Light to Moderate winds
7.5 Moderate to high winds

between transport coefficients measured in Oklahoma and Australia confirms that neither
Coriolis parameter nor Ekman layer depth are relevant for bulk transfer in free convection. For
the continuity from low to high wind speed conditions, Stull (1994) proposes linear
superposition of the convective transport and bulk aerodynamic formulas, suggesting that in thesurface layer free convection takes over when z/L < -3.
Sorbjan (1997) argues that the results embodied in CTT can be obtained within theframework of Monin-Obukhov similarity theory, and suggests that the lack of dependence on
surface roughness and surface layer processes could lead to errors in situations other than thoseunder which CTT was tested. Stull (1997) disagrees, pointing out that Sorbjan’s analysis isbased on surface layer, rather than mixed layer equations, and that his definitions of mixed layer
to surface temperature difference and of transport coefficients are not equivalent to those in
CTT. Fairall et al. (1996a) also suggest that as the mean vector wind approaches zero, agustiness velocity, w* , appearing in the standard bulk equation will yield a result equivalent to
the Stull (1994) scaling theory. Chang and Grossman (1999) included CTT in their comparisonof several algorithm’s using COARE data, but needed to “tune” the coefficients of CTT for use
over the tropical ocean.
7.5 Transfers in moderate and high winds
There is now a fairly large body of reliable momentum flux measurements at windspeeds in excess of 12 ms-1 (Yelland and Taylor, 1996; Yelland et al., 1998; Hare et al., 1999).
These show that the drag coefficient increases steadily (and more or less linearly) to around 25
ms-1. However, the fluxes of heat and water vapour have not yet been measured in the field forwinds over 20 ms-1. In fact, the most widely quoted dataset for the high wind regime, from theHEXOS experiment (DeCosmo et al. 1996), contains fewer than a dozen data points above 16
ms-1. Furthermore, the HEXOS site was over water only 15m deep. More flux data in highwinds over the open ocean are badly needed.
The evaporation of spray might be expected to modify the heat fluxes at high windspeeds. Thus Andreas (1992) and Andreas et al. (1995) suggested that, whereas the turbulent
fluxes are proportional to wind speed, spray production should increase at roughly the third
power of the wind speed. This implies that for wind speeds up to 15 m/s the spray contributes
no more than 10% of the total heat flux. At 20m/s and above the spray contribution would be ofsimilar magnitude to the surface turbulent flux. However consideration of the feedback on theparameterised surface evaporation due to changes in the mean surface humidity profiles causedby the evaporating spray was not fully included. This may substantially modify the estimatedflux (Katsaros and de Leeuw, 1994, Andreas, 1994).
7.6 Summary of bulk algorithms
In the introduction to this chapter we outlined the traditional concept of bulk flux
formulae, particularly drawing attention to the notion that many of the physical processes
involved in air-sea transfer of heat or momentum became embedded within a transfer coefficientfor the particular entity. The formalism commonly used to construct these transfer coefficients isgiven in section 7.2. The best-known algorithm employing this methodology is probably that ofLarge and Pond (1981,1982) who supported their analysis with some direct (covariance) flux
data which in turn validated their much more extensive inertial dissipation data set.
Nevertheless, while transfer coefficients were known to be a function of wind speed, height andatmospheric stability, more often than not the available test data justified use of only a constantvalue for the scalar fluxes, or a simple wind-speed dependant value for stress.
Such simple expressions have become inadequate for several reasons, mostly connectedwith the burgeoning requirements of climate research, and recognition of the sensitivity ofnumerical climate models to small changes in air-sea flux values, particularly when attempting
to couple ocean and atmospheric GCM's. Initially, the focus was on the climatically important
tropical oceans where bulk flux theory was inadequate to deal with the prevailing low winds andReport of the Working Group on Air Sea Fluxes

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7.6 Summarystrong convection. Attention was directed toward an understanding of transfer mechanisms at
the surface itself, including development of surface renewal theory, and considerable
experimental effort was made to obtain supporting data in this regime. Most modern bulk
algorithms for the tropical regions are based on the pioneering work of Liu et al. (1979), and thedata obtained during the 1992-93 TOGA-COARE experiment. The basis of this approach is
given in section 6.3.2. In a somewhat curious twist, the exchange coefficients have now
become embedded in the analysis, which is an iterative process between the fluxes and thesurface roughness, and are often only calculated post facto for diagnostic purposes. This isindicative of the connection between stress and wave state formulated in its simple form byCharnock (1955), and now receiving closer attention.
We have described several recent bulk flux algorithms in some detail. The list is not
exhaustive or even possibly up to date, because air-sea flux determination by a variety of means
is a growth industry responding to the immediate needs of forecasting and climate research.
Those described have in general been developed to serve some particular area of this research.
Common features tend to be corrections for thermal stability, exchange processes in light winds
and sophisticated representation of surface roughness lengths for velocity, temperature andhumidity. Differences between algorithms tend to lie in the choice of parameterisations, forgustiness, for surface renewal parameters (timescale, thickness), and for the roughness lengths.
Direct measurements of most of these parameters over the ocean are virtually non-existent.
Similarly, direct covariance flux data sets against which to test the various algorithms are very
limited, although some well-validated inertial dissipation measurements are available.
For research purposes, perhaps the most widely used of the modern bulk algorithms isthe so-called COARE algorithm (Fairall et al., 1996a), developed specifically as a common
resource for COARE scientists. Originally, it adhered strictly to the LKB model as regards
stability corrections and scalar roughness parameters, but gradually introduced modifications forgustiness, convection and surface roughness which were actually derived from shipboard
observations during COARE. It was scaled against direct (covariance) measurements of thelatent heat flux. Thus there were no arbitrary assumptions. However, from the perspective ofmodellers it was inefficient because of long iterative loops in the code, one of which included
corrections to the measured SST for cool skin and the diurnal thermocline. At the same time,
being structured around the basic physical processes involved, the results of new theory andfresh data are readily incorporated. With much additional direct flux data available in both
tropics and mid-latitudes since COARE, an improved version of the "COARE" algorithm is now
available, including more efficient computation.
The algorithm by Zeng et al. (1998) is designed to suit the needs of numerical model
code, being a simplified version of the COARE algorithm. It is tuned to the same data set as the
COARE algorithm for low to moderate wind speeds (the Moana Wave COARE observations by
Fairall et al., 1996a). However, there is very little of this data with wind speeds in excess of 12
m/s, so for higher wind speeds Zeng et al. (1998) extend their algorithm using the HEXOS dataof DeCosmo et al. (1996). This is the best-known of flux data sets at stronger winds, but should
be used with caution, because these are shallow water coastal measurements, and the authors
show that they do not conform to open ocean exchange coefficients in the overlapping windspeed range.
The Bourassa et al. (1999, BVW) bulk model is notable for its attempt to relate surface
roughness lengths, and hence the exchange coefficients, to various aspects of sea state, swell,gravity waves and capillary waves. They draw on, and extend, published representations ofwave structure in their analysis, but adequate validating data is not yet available to test thepotential of this algorithm. The Clayson et al. (1996) algorithm, developed to obtain surface
fluxes from satellite observations, make use of the BVW sea state model, but otherwise follow
the surface renewal theory and stability correction path.
Two of the algorithms which we describe depart from the conventional approach to freeconvection, by Fairall et al. (1996a) or Zeng et al. (1998) for example. Beljaars (1994)

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7.6 Summary

approaches the problem from the perspective of the numerical modeller seeking economicalsolutions without violating physical reality. He argues that normal Monin-Obhukov similarity
can be used in the surface layer, provided a convective scaling velocity (gustiness) is included in
the bulk equation and that, in the well mixed region, the behaviour of stability functions is not
too critical in any case. His analysis suggests that the surface influences the free convection
regime through coupling between convective motions and the surface roughness. Stull (1994)
takes a similar approach in his convective transport theory, that transport into the mixed layer by
convective plumes is the dominant process, but his analysis leads to the opposite conclusionfrom Beljaars (1994). By parameterising the surface fluxes in terms of mixed layer, rather thansurface layer variables, Stull (1994) finds that turbulent exchange is independent of surface
conditions.
With such a variety of advanced bulk flux algorithms available, it is obviously tempting
to intercompare their flux output using a common set of basic input data, to determine which isthe "best". Using COARE and TAO mooring data, Zeng et al. (1998) have compared several of
the algorithms described here and show that, up to around 12 m/s, the performance of thealgorithms is very similar. Since they derive from essentially the same analysis as given in theabove sections, the only difference will be due to the different choice of parameters orfunctions. As expected, these differences become more prominent at higher wind speeds, but
uncertainty about the quality of the test data at these speeds makes judgement difficult. Zeng etal. (1998) also consider the parameterisations employed in several numerical models. They
conclude that these are seriously defective, and recommend substitution with their own
algorithm. As indicated in section (4.4.3B), the Zeng et al. (1998) algorithm is now used in the
NCEP operational model, achieving some improvement. It is probably fair to comment that
almost any of the modern algorithms, with reasonable roughness parameterisations and stability
correction, would have considerably improved the performance of the models.
Chang and Grossman (1999) have also compared several algorithms, using the Moana
Wave covariance flux measurements from COARE as reference. Adopting a particular set ofcriteria to constitute agreement, they find that most of the algorithms predict latent heat flux
within 8% of the observed value. The predictions for sensible heat and momentum are morevariable. The authors identify the departure from measured values as “a windspeed dependentbias in the model due to the surface flux parameterisation”, but don’t examine this in moredetail. It would have been helpful to identify the sources of disagreement, as a guide to thesensitivity and relative importance of various parameters, and to the accuracy with which theyneed to be determined. To be constructive, any further comparisons between algorithms should
consider not just the output values, but the consequences of alternative choices of parameters
and internal physics.
The main shortcoming in the area of algorithm development is not so much a dearth of
new parameterisations, as a supply of reliable directly measured flux data at high wind speeds
and higher latitudes. Reference data from tropical regions is becoming relatively plentiful.
Since TOGA-COARE, considerable additional low to moderate windspeed direct flux data has
been assembled by the NOAA/ETL group, in tropical and mid-latitudes. As a result, better
relationships between momentum and scalar roughness lengths than those used by LKB (1979)
and Fairall et al. (1996a) have been established, and incorporated into the COARE algorithm.
At the same time, advanced forms of the profile functions and a more accurate first estimate of
the atmospheric stability have enabled convergence of the flux-roughness loop within 3
iterations, making the algorithm more attractive for numerical modelling and remote sensing
applications.

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8.1 Introduction
8.2 Random errors and sampling

8 RANDOM AND SAMPLING ERRORS IN FLUX FIELDS.
8.1 Introduction
This Chapter will consider some aspects of determining the error characteristics of flux
fields. Random errors, and the implications for the required sampling density will beconsidered in Section 8.2. Systematic errors will be only briefly discussed in Section 8.3; morediscussion with respect to the parameterisation formulae was given in sections 6 and 7.
Methods of determining the systematic errors, and error estimates for the various products are
discussed in sections 9 through 11. A potential error source for flux fields depends on themethod chosen for averaging the fluxes. The use of averages of the basic variables in theparameterisation formulae (rather than averaging individual flux estimates) was originallyadopted for computational efficiency, however it also makes fuller use of the available data incases where all the needed basic variables are not available with each observation. As
discussed in Section 8.4, this advantage must be weighed against the possibility of introducing
biases into the flux estimates due to neglecting the correlations between the basic variables.
The problem of combining the estimates of the random and systematic errors, toprovide the overall error estimate, will be discussed in Section 8.4. The calculation of flux
fields involves smoothing with interpolation and extrapolation of the flux estimates. As
examples, the effects of a commonly used method, successive correction, and the advantagesof a more sophisticated method, Kriging, will be discussed in Section 8.5.
Although more generally applicable, most of the discussion in sections 8.2 to 8.5 has
been developed with regard to the application to ship data. Therefore the particular sampling
problems associated with satellite data will be specifically discussed in Section 8.6.
8.2 Random errors and sampling density
8.2.1 Introduction
The sampling density is an important problem with regard to the reliability of oceanatmosphere flux fields derived from VOS and satellite observations. With respect to individual
variables, the sampling problem can be formulated in terms of the similarity of probability
density distributions of meteorological quantities, computed both from the real observations andfrom some kind of “ideally sampled” observations for the selected space boxes and time
periods. The latter are typically one month periods for most climatologies, although for
instance Legler (1991) studied importance of sampling for 5-day wind and temperature means.
Since the distributions of most of the directly observed quantities are not Gaussian, or at least
may be not Gaussian in particular regions, the significance of sampling biases is regionallydependent. Although sampling biases are usually considered with respect to temporal
inhomogeneity of sampling, for some particular regions spatial inhomogeneity of sampling
may be as important as temporal. This is especially important for VOS observations which areusually sampled along the major ship routes. For some regions the arrangement of samples
within the n by n degree box may seriously affect the monthly means of both the individual
variables and the fluxes even though the overall number of observations may be adequate.
Morrissey and Maliekal (1995) found that a linear network, which aligned along the major axisof the spatial correlation function, provided standard errors in wind speed estimates three tofive times higher than a random network. This problem is serious only for spatial boxes
greater than 3-4 degrees, because the ship routes form relatively wide tracks of about 100 to 200
km width.
When the fluxes of heat, moisture and momentum are computed from individual
variables using strongly non-linear parameterisations, even small sampling errors in individual
observations can lead to significant sampling biases in space-time averaged fluxes. Weare andStrub (1981) studied sampling errors in the tropical surface heat fluxes, computed from VOS
observations, and found eleven individual observations per month to be adequate for monthly
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8.2 Random errors and samplingmean flux estimates. However in the mid and high latitudes, this limit can be several times
higher due to the necessity to sample extreme conditions associated with very high sea-air
fluxes. These can seriously affect monthly means. Thus, Cayan (1992a) and Legler (1991)
found 50 to 100 samples per month to be required for unbiased flux computations for a 5 degreebox.
Climatologies of fluxes derived from VOS observations in COADS and similararchives are characterised by adequate sampling in much of the North Atlantic and North
Pacific but to be strongly affected by under sampling in the southern hemisphere. However,even in such well sampled areas as the North Atlantic, there are some regions which are poorly
sampled. Such areas may also be characterised by extreme flux values of crucial importancefor adequate description of regional sea-air exchange processes. A good example is theLabrador Sea, known as the source of Labrador intermediate waters which are formed by theextreme sea-air exchange processes in that limited region (LabSea Group 1998). From the1950's to the early 1970's this region was represented by regular observations at OWS B.
However, since 1973 when OWS B was stopped, only a few observations per month wereavailable. Yet this period, the 1970's - 1990's, was characterised by several dramatic deepconvection events which are therefore poorly represented in the VOS observations.
The flux products which have become recently available from numerical weather
prediction systems are regularly sampled with 6-hourly temporal resolution. These allow thesampling errors in the VOS fluxes to be estimated. Gulev et al. (2000) simulated the sampling
frequency of COADS using the NCEP/NCAR 6-hourly fields, and found that both climate
means and interannual variability patterns may be seriously affected by the poor sampling. It
was found that the absolute value of the sampling error over the North Atlantic domain waswithin a range of ± 5 Wm-2. Thus the meridional heat transport estimates were not seriously
affected by the under sampling. However, locally the effects in such areas as the Labrador Sea
were large, possibly reaching 50 to 60 Wm-2 for climatological means.
Further assessment of the possible impact of under sampling in the VOS observations
should be made using the regular instrumental observations at buoys, OWS, and other in-situ
platforms. However it will be shown in the next section that, if it is the accuracy of, say, amonthly mean that is required, then that is simply determined from the variance of thecalculated monthly means. It is only if natural variability is to be distinguished from variability
due to sampling errors that a more sophisticated analysis is required.
8.2.2 Errors and Variability of Monthly Mean Fields
Monthly mean fields of the air-sea fluxes are affected by two kinds of uncertainties:
systematic and random errors. In general the first influence the mean, the latter the variance ofthe calculated fields. The wind stress is an exception from this rule. Since the squared windspeed occurs in this parameter, random errors are not cancelled out by averaging.
Consequently, they affect not only the variance, but also the mean. The principal aim is todiscriminate the error variance from the true natural variability, and, of course, to determine andeliminate the systematic errors.
Random errors are completely unavoidable, but good-natured compared to systematic
errors. Several statistical techniques are available to quantify them and to eliminate their meaneffects at least. Random observation errors of the basic synoptic parameters, such as windspeed and temperature, propagate through the applied bulk formulae and influence the resulting
flux fields. Due to random errors the true variance is increased. For long-time means, inregions with a high number of observations, the effect of random errors remains small.
However, if not only the long-term mean annual cycle is considered but also the interannual
variability of individual monthly means, the data density even in the central North Atlantic is
not sufficient to neglect the effects of random errors.
Before discussing problems connected with variability and error variance, the different
relevant variances should be clearly defined. The total temporal variance at a fixed point is:Report of the Working Group on Air Sea Fluxes

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8.2 Random errors and sampling

n

1 N k
2
xi − x)(∑∑m − 1 k =1 i =1

(8.1)

where m is the total number of observations and x the total mean, xi denotes the individual
values, N is the number of months and nk the number of observations in the k-th month.
Obviously, the total number m can be obtained by:N

m = ∑ nk

(8.2)

k =1

and monthly means xk are defined as:
n

xk =

1 k
∑ xi
n k i =1

(8.3)

The total variance can be split up into the total apparent variance and the error varianceof the total mean:
n

1 N k
2
xi − x)(∑∑m − 1 k =1 i =1

n

=

n

N k
1 N k
1
2
2
−
+
x
x
xi − x)
(
)
(
∑
∑
∑
∑
i
m k =1 i =1m (m − 1) k =1 i =1

(8.4)

The second righthand term in (8.4), the error variance of the total mean, is mostly very small, if
the total number of observations is large enough. The first righthand term in (8.4), the total
apparent variance, consists again of two parts: the apparent extra-monthly variance and theapparent intra-monthly variance:
n

1 N k
2
xi − x)(∑∑m k =1 i =1

=

1 N
2
n k (xk − x)∑m k =1n

1 N k
2
xi − xk )(∑∑m k =1 i =1

+

(8.5)

The apparent extra-monthly variance gives a mean value for the interannual variability
weighted by the numbers of observations per month. The intra-monthly variance gives ameasure for the mean short time variability within the months. We added the expression'apparent', because the two terms are not equal to the true variability, but both are affected byrandom errors, in other words, true natural variability is mixed with the error variance. In thenext step we will try to separate these two kinds of variance. That will be quite simple for the
extra-monthly but more difficult for the intra-monthly variance. Fortunately, we are mostlyinterested in those outer quantities: the monthly means and their errors and variability.
The apparent intra-monthly variance of (8.5) can be split up into:
N nk

1
2
xi − xk )(∑∑m k =1 i =1n=

1 N nk k2xi − x k )(∑∑m k =1 n k − 1 i =1

n

−

k
1 N
nk
2
xi − xk )(∑∑m k =1 n k ( n k − 1) i =1

(8.6)

In order to interpret the decomposition carried out in (8.6), we have to consider thefollowing: the mean error variance of a sample's mean is given by the variance of the sample
divided by the number of independent observations. Regarding ship observations, the demandof independence is obviously fulfilled, one of the great advantages of such data sets. Thus, theerror variance of an individual monthly mean is given by the inner variance of that month
divided by the number of observations. A weighted average over all months gives then an
estimate of the mean error variance of monthly means. The second righthand term in (8.6) justdescribes these errors. Subtracting this error variance from the apparent extra-monthly
variance finally gives an estimate of the true natural variability.
The first righthand term in (8.6) gives the weighted average of intra-monthly variancewhich is still affected by observation errors. For this expression the separation of the natural
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8.2 Random errors and sampling

variability from errors is possible following Lindau (1995a). Pairs of simultaneous individual
observations are considered. The idea is that natural variability increases with increasing
distance, while errors are independent of separation because of their randomness. The squared
difference between both observations is plotted as a function of distance between the locations.
A linear fit gives an idea of the potential value at distance zero where no natural variability but
only error variance remains. In Figure 8.1, to give an example, the mean random error of VOS
observed wind speed is determined by this technique. For zero distance a variance of 23.5
(ms-1)2 is obtained, representing twice the error variance of merchant ships. By repeating thesame procedure with pairs of VOS-OWS observations, the error of OWS's can be concluded.
Since 11.75 (ms-1)2 is allotted to the merchant ships, 5.4 (ms-1)2 remain for the OWS. But wehave to keep in mind that this procedure has to be applied only when individual observations
are under consideration, which is only necessary when sub-monthly processes are investigated.

Figure 8.1 Determination ofindividual error variance. Meansquared wind speed differencesfrom VOS-OWS pairs (shaded)
are compared to VOS-VOS pairs(total columns).

In contrast to the rather arduous technique to reveal the mean individual observation
error, the situation for extra-monthly errors is quite simple: the intra-monthly variance gives ameasure of the error of the monthly mean. It is not relevant whether the intra-monthly varianceis caused by observation errors or natural variability. The following consideration may help toillustrate that: even with a time series of perfect quality we are not able to derive the trueaverage during the considered month, because of the disturbing natural variability. The higher
this variability is, the higher is the uncertainty of the mean, when a constant number ofobservations is available. An additional random error of the instrument just increases the intramonthly variance and consequently the error of the monthly mean. Thus, if we are dealing withmonthly means, the error of a particular month is easy to determine: just divide the intramonthly variance by the number of observations. The overall effect of several months is thengiven by a weighted average of all such monthly errors as indicated in the second righthandterm in (8.6). As mentioned above, for the estimation of the true extra-monthly variability thisvariance has to be subtracted from the apparent extra-monthly variance.
Summarising the above discussion, the total variance can be decomposed into:
n

1 N k
2
xi − x)(∑∑m − 1 k =1 i =1

n

=
+

N k
1
2
xi − x)(∑∑m (m − 1) k =1 i =1

1 N
2
n k (xk − x)∑m k =1

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n

−

k
1 N
nk
2
xi − xk )(∑∑m k =1 n k ( n k − 1) i =1+

1 N nk k2xi − x k )(∑∑m k =1 n k − 1 i =1

(8.9)

n

(8.10)

The four constituents are (8.7) error of the total mean, (8.8) apparent extra-monthly variance,(8.9) mean error of monthly means, (8.10) intra-monthly variance. In order to give an idea
about the magnitude of these quantities, the variances of COADS derived latent heat flux for
January are given in Figure 8.2 - 8.5. For these calculations only monthly 1o by 1o grid boxes
with at least 2 observations can be taken into account, since errors should be definable, and 2 ofsuch months should exist, to define the extra-monthly variance. These restrictions diminish thedata base especially in the South Atlantic.
In the most parts of the Atlantic the error of the total mean is negligibly small (Figure8.2). All other three components (Figures 8.3 - 8.5) reach their maxima in the Gulf Stream
region, while the South Atlantic shows generally remarkable low variances. Comparing thedifferent kinds of variance, it is not surprising that the intra-monthly (Figure 8.5) dominates: byaveraging over a period of one month, the main part of variance will be cancelled out, so thatthe majority of the total amount is found inside the months and only a minor part remains forthe extra-monthly variability. The prevailing of the inner variance is even increased by theeffects of random errors. They are perceptible nearly entirely as an additional part of the intramonthly variance, while only a small portion, depending on the number of observations,contributes to the error variance of monthly means. However, the intra-monthly variance isobviously not dominated by errors. Errors increase the intra-monthly variance by an amountwhich is equal to the mean individual observation error. In contrast to the extra-monthly case,the size of error effects does not depend on the number of observations so that a spatiallyhomogeneous offset can be expected. Thus, the strong regional differences in intra-monthly
variance are an indication for the dominance of natural variability.
However, the intra-monthly variance plays a minor role in our considerations, and is
only used for the determination of the errors of our main topic, the monthly means. Theircharacteristics are given in Figure 8.3 and Figure 8.4. The apparent extra-monthly variancedoes not only reflect the true natural variability but overestimates the variance due to theincluded errors. These are depicted in Figure 8.4. The first striking feature is that themagnitude of the error variance is comparable to the apparent extra-monthly variance itself.
Thus, the overestimation by the raw data is considerable. Subtracting the error from theapparent extra-monthly variance (not explicitly shown) reveals that the true extra-monthly
variability is extremely small in the South Atlantic.
The strongest errors occur in the Gulf Stream, although this region has a remarkablehigh observation density. Obviously, the very strong internal variability is responsible for thehigh uncertainty. On the other hand, low errors are not only found in the shipping lanes but
also in the South Atlantic, where an extraordinary low intra-monthly variability
overcompensates the effects of sparse data coverage. Therefore, only few observations areenough to give relative accurate monthly means in that region.
One of the grid points with most observations in the central North Atlantic is
considered in more detail (Figure 8.6). Error effects in such well covered regions are expected
to be minimal. The latent heat flux at the selected point has a mean value of 116 Wm-2, with aconsiderable extra-monthly variance. Additionally, the errors of monthly means are plotted,
which depend on the intra-monthly variance and the number of observations. It becomes clearthat the information about the errors is absolutely necessary, since otherwise we are not able tointerpret such variability. In most months the error bar passes over the total mean line,
indicating a reliability of less than 68% for a significant difference from the total mean. Total
variance of the shown time series is about 1300 W2m -4. More than 60% of this is not due toReport of the Working Group on Air Sea Fluxes

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natural variability but due to errors. Remembering that such circumstances prevail in one of
the best sampled regions of the world ocean, we realise the outstanding importance of errors.

Figure 8.2 Error of the total means of LHF for
January, from COADS 1940-1979.

Figure 8.3 As fig.8.2, but the apparent extra
monthly variance

Figure 8.4. Error variance of monthly mean.

Figure 8.5 Intra monthly variance.

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8.2 Random errors and sampling
8.3 Systematic Errors

Figure 8.6. Example for the apparent extra variability and the error variance of monthly means
for a 1o by 1o grid box in the North Atlantic (29.5o E, 36.51o N)
8.3 Systematic Errors
8.3.1 Introduction
Systematic errors occur in the measurement or estimate of the basic flux observables.
They are also a fact of life within the parameterisations themselves, particularly for climate
conditions that differ from those in which the parameterisation was developed. When such
errors can be identified it is possible to correct their effects, but generally these errors are
difficult to isolate. The key difference between systematic and random errors is that the overallsystematic error is not dependent on the number of observations used to compute the flux.
8.3.2 Example of a parameterisation error
A possible misapplication of the Reed (1977) formula for shortwave radiation serves as
an illustration for a systematic error due to parameterisation. The formula provides mean daily
radiation using as astronomical input only the noon sun altitude. Obviously, such a crude
parameterisation of clear sky radiation is only possible for constant latitudes. Consequently, theapplication of Reed's formula in high latitudes where long day lengths during summer
compensate the relative low sun altitude would cause considerable systematic errors. For windstress, to give another example, possible sources of systematic error are a mean underestimateof CD due to sea state effects or biases in the directly observed wind speed.
8.3.3 Determining errors in VOS reports
As an example of the determination of systematic errors in observational data consider
the accuracy of data from the VOS. As for any systematic errors, determining the typical biases
in the meteorological reports from merchant ships is very difficult. The accuracy depends not
only on the calibration of the instruments, but their siting, the characteristics of the ship, andthe way in which the observations were taken. One attempt to determine the systematic errors
in ship data was the Voluntary Observing Ship Special Observing Programme - North Atlantic(VSOP-NA) project (Kent et al., 1993a). The method used was to choose a subset of 46 VOS,
document the instrumentation used on each (Kent & Taylor, 1991), and ask the ships' officers
to include extra information with each report. Since the ships would rarely pass close to one
another, the output from an atmospheric forecast model was used as a common standard forcomparison. The results were then analysed according to instrument type and exposure, ship
size and nationality, and other factors. By this means relative biases could be determined.
For example it was found that SST values from engine intake thermometers were biased warmcompared to other methods (Kent et al. 1993a), and that daytime air temperatures were too
warm due to solar heating (Kent et al. 1993b). It could be shown that the dew point
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temperature was not biased by the latter error (Kent and Taylor, 1996) but, compared toaspirated psychrometer readings, the dew point was biased high when obtained from fixed
thermometer screens. The VSOP-NA results suggested that correction of measured winds toallow for the height of the anemometer was advantageous. Following the VSOP-NA, later
work by Kent and Taylor (1997) suggested that, for visually estimated winds, the form of theBeaufort scale devised by Lindau (1995a) gave best agreement with the anemometer winds.
A specific problem which affects VOS reports, and which can lead to errors which aresystematic rather than random in nature, is errors in the reported ships' positions. About 2 to
3% of the VOS weather reports in COADS can be identified as having incorrect position
information. Typically the position is incorrect by 10° or is in the wrong quadrant. Often thesedata exist in COADS as duplicates, one report having the correct position (Lander andMorrissey, 1987). Position errors are detected in operational forecast centres by tracking
individual ships, but this is rarely done for climate studies. However position errors arepotentially very serious because the ship might be falsely placed away from the shipping lanes
in a data sparse region where the false report may be given undue weight. For example in
January, 1984, ship reports from near Iceland appeared as a group of erroneous duplicates in the
COADS data set, positioned near Antarctica.
8.3.4 Method of averaging fluxes
A particular problem connected with the issue of sampling, is the difference between
the averaged fluxes computed from individual observations and from observations which havebeen previously averaged in space and time. Again this has the potential to introduce
systematic errors.
The restricted computer power available in the past led to many "classical" flux fields
being calculated from averaged observations rather than individual samples. Hence thisproblem has become known as the "sampling" versus "classical" difference in the flux fields.
The present level of computer power and data availability allows fluxes to be calculated fromindividual observations - the sampling approach. However, reliable estimates of "sampling
minus classical" differences for different flux components are still important for comparative
assessments of historical data sets which are frequently only available as monthly or seasonal
grids. Moreover, climate studies still use monthly averaged meteorological quantities to derive
long-term series of sea-air fluxes. The problem is also important where fluxes are derived fromsatellite data since, in many cases, some variant of the "classical" approach must be used.
Comparisons of sampling and classical estimates of fluxes are given in many articles(e.g. Esbensen and Reynolds 1981, Hanava and Toba 1987, Ledvina et al. 1993, Gulev 1994,
1997b, Josey et al. 1995, Staneva et al. 1995, Zhang 1995, Esbensen and McPhaden 1996).
Different authors report various "sampling minus classical" differences. Thus, for the sensible
and latent heat fluxes, it is generally believed that sampling estimates are slightly higher thanthose computed using the classical method (by about 5 to 15 percent). Gulev (1994) andStaneva et al. (1995) reported larger sampling to classical ratios. Such errors will occur inregions where stronger winds are correlated with cold, dry air and large heat loss from theocean. In contrast, Josey et al. (1995) found that sampling estimates for the latent flux may besmaller than classical estimates in the North East Atlantic due to negative correlations between
the wind speed and humidity gradients. In this region, the stronger winds were often
associated with the moist, warm air in the warm sectors of mid-latitude depressions.
The "sampling minus classical" differences will also strongly depend on the schemes
used for determining the bulk transfer coefficients (Gulev, 1997b). For example, the Liu et al.
(1979) scheme provides negative correlations between wind speed and the sensible and latent
heat transfer coefficients for moderate and strong winds. The Smith (1988) parameterisation is
also characterised by slightly negative correlations between wind speed and the coefficients forunstable conditions. Thus, estimates of "sampling minus classical" differences for these

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8.4 Combining errors

schemes will be different compared to parameterisations which assume coefficient values which
increase with wind speed.
8.3.5 Further discussion
Other examples of systematic errors in meteorological observations and their correction
are described in Section 11.2.4. Systematic errors associated with parameterisations werediscussed throughout Sections 5 and 6. Efforts to determine the total effect of the random andsystematic errors on the total uncertainty is summarised in the next section.
8.4 Combining Errors
Sections 8.1-8.3 have concentrated on the quantification of random and systematic
errors. To further assess confidence in our observational estimates, ultimately we need to know
the combined effect on the observational uncertainty of all random and systematic errors.
Gleckler and Weare (1995, 1997) attempted to formulate a strategy for combining all errors
associated with VOS type flux estimates. They made use of simple sampling theory which isbriefly summarised here: considering a field F, which is a function of x and y, to first order theuncertainty estimate for a single measurement of x and y is:22 ∂F  ∂F  2 2
 ∂F  2  ∂F  2
σ x σ y σ y + 2 ρ xy 
σF =  σ x + 
 ∂x  ∂y  ∂x  ∂y 

(8.10)

where σx and σy are the combined random and systematic uncertainties in x and y, and ρxy is thecorrelation between the error in x and y. If x and y are independent, they are uncorrelated andthe third term in the above equation vanishes. If they are fully dependent (ρxy= 1), then there isa maximum propagation of uncertainty in σ F. In the case of anticorrelaton (ρxy ≤ 1), or thepartial derivatives with respect to F are negative, the correlation term will act to reduce theuncertainty in F.
To estimate uncertainties from a collection of measurements of each observable, than
the first RHS term can be expanded to:222

 ∂F  2 ∂F  2 ∂F  2
 σ x, ran f ( N ) σ x , xyx +  σx ⇒ ∂x  ∂x  ∂x 

(8.11)

where σx,sys and σ x,ran are respectively estimates of systematic and random uncertainties in x.
Random uncertainties are reduced by some function of the number of available observations, N.
This simple treatment for the combination of uncertainties was generalised by Gleckler
and Weare and applied to the same bulk parameterisations (and data) as used by Oberhuber(1988). Estimates of random and systematic errors for each basic observable and theparameterisations themselves were based on previous research, and were assumed to beconstant in space and time. With the assumptions of a valid sampling theory and accurate
uncertainty estimates, Gleckler and Weare were able to construct spatial maps estimating therelative importance of each uncertainty associated with the various heat flux terms. Spatial lag
correlations were used to estimate the degree of spatial dependency from which random errors
could be further reduced as zonal means.
Since the effort of Gleckler and Weare, several possible improvements to their method
have been identified. First, there have been advancements with the parameterisations
themselves in the subsequent work of da Silva et al. (1994) and Josey et al. (1999).
Additionally, the estimation of random error reduction needs to be generalised to that outlined
in Section 8.2.2, as Gleckler and Weare assumed temporal inhomogeneities to be nearly to benearly Gaussian. But perhaps most importantly, significant refinements have been made tosome of the uncertainty estimates used (by Gleckler and Weare) such as in the surface wind.
But in fact the situation is likely to be more complex than merely updating error estimates, as
Josey et al. (1999) have demonstrated, perhaps not surprisingly, that such errors are likely toReport of the Working Group on Air Sea Fluxes

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8.5 Mapping Errors

vary substantially in both space and time. Thus any revisit of the method applied by Gleckler
and Weare should take into account spatio-temporal variations in systematic error of basic
observables and parameterisations. Determining just what these variations are poses a
significant challenge, and adds further argument to the plea for additional buoy deployments.
There has been progress in combining errors and estimating their relative importance.
Quantification of the propagation of error estimates into total uncertainties should be an integral
part of future flux estimates derived from VOS. Such efforts should help to further establish
confidence limits and assist us in determining how to improve them. Clearly, further work isneeded in this area.
8.5 Mapping errors
8.5.1 Introduction
Errors in flux fields may be caused by the need to smooth, interpolate and extrapolate
the data to generate global fields. If not done correctly, significant errors may be introduced
into the analysis. The "successive correction" method will be discussed as an example of afrequently used technique that is likely to introduce errors into a flux field. An alternative
method will then be described, the "Kriging" technique, which is computationally more
expensive, but which has the advantage of providing an error estimate for each part of the field.
8.5.2 Successive correction
Successive correction has been used in a number of studies to produce fields ofgeophysical variables (e.g. Barnes, 1964; Levitus, 1982; 1994). It has the advantage,compared to more sophisticated methods such as optimal smoothing (Kaplan et al. 1998),
Kalman filtering, or optimal interpolation, that it does not require information about thetemporal and spatial structure of the data. In particular, the recent surface flux climatologies ofda Silva et al. (1994) and Josey et al. (1998; 1999) were compiled using this method. Data
within different radii of influence are weighted by distance and averaged in successive passes ofthe smoothing process. The radii used by Levitus and da Silva were 1541, 1211, 881 and 771
km. Josey et al. used 1541, 1211, 771 and 331 km, so this latter data set will contain structure
on smaller scales than those of Levitus and da Silva.
Kent et al. (2000) have investigated the effects on a data set of smoothing by successive
correction. The resulting spatial resolution was estimated using a distribution of ship reports
from a sample month. Although the same characteristic radii were used over the whole globe,
the resulting resolution was shown to be spatially variable depending on the local sampling
density. Thus in data sparse regions large month to month variability of the resolution could
occur with changes in the distribution of the ship tracks. The Josey et al. (1999) climatology,
which was gridded at 1°, was shown to have a typical resolution of 3°. The mean resolution(using SST for January 1980 as an example) was 4.0 ° latitude by 5.8 ° longitude. The
maximum zonal spread of information was 50° and the maximum meridional spread was 19°.
This reflected the choice of a zonal mean as the background field. Thus the resolution was
significantly worse than the grid might suggest and information was smeared appreciable
distances into data sparse areas. An obvious example of the latter effect was the wind speed in
the Southern Ocean in winter. Since there were no data from much of the Southern Ocean,lighter winds observed further north are extrapolated southwards. As a result the climatology
shows significantly lighter winds than, for example, were measured by satellite scatterometersin that region.
Kent et al. (2000) also showed that the successive correction procedure was not
successful in removing all of the noise in data sparse regions. Furthermore, the well-definedintermonthly variability in the main shipping lanes, where there are many observations, wasdegraded by the influence of poorer quality data in the surrounding regions. This typicallyincreased the intermonthly variability estimates in the shipping lanes by a factor of two. In
contrast, the reduction of intermonthly variability in highly variable regions such as the Gulf
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Stream by up to a factor of six, was greater than could be accounted for by noise in theindividual ship reports. This reduction was due to the removal of small-scale variability by thesmoothing process. Removal of coherent and persistent small-scale variability changed thetemporal and spatial characteristics of the data. Kent et al. (2000) concluded that smoothing by
successive correction, although commonly used, is poorly suited to such spatiallyinhomogenous data as those from the merchant ships.
Considering the impact of the successive correction smoothing on variability analysis
using empirical orthogonal functions (EOF's), Kent et al. (2000) found little effect for the mostsignificant modes of variability identified in the Gulf Stream region. This was because thelarge-scale variability occurs in the highest order modes. However, too large a fraction of thetotal variance explained was ascribed to these large-scale modes of variability. If raw data
were used in the EOF analysis, variability with small spatial scales was more likely to besignificant. Finally, little significance should be given to EOF modes with spatial scales
similar to the size of gaps between shipping lanes; a distance which varies from region toregion.
8.5.3 Kriging of monthly means and error estimation
A. INTRODUCTION
On a global scale, monthly means represent the shortest time resolution where shipobservations provide reasonable results. However, even on that relative long time scale, data
density begins to degrade drastically for most regions of the world ocean. In this situation theerror variance of the observations begins to dominate. Therefore, monthly maps of the raw data
show large unrealistic scatter which has to be eliminated. Many smoothing techniques exist,
leading in each case to an acceptable appearance of the maps. But often these techniques areused in a somewhat arbitrary manner by repeating them over again until the result looks
satisfactory. Clearly, we cannot be sure that such results are optimal.
It should be mentioned here, that a widely used technique for eliminating error effects,
that is searching for the best bi-linear or higher order 'surface', has disadvantages. Such fittingof polynomials is used to discriminate the errors from the true natural part of the total variance.
The spatial variability of the fitted 'surface' is considered to be true. Any additional variance,that is deviations of the data points from the fitted polynomial, is identified as observation error.
However, polynomials of higher order are able to follow the structure of the data better andinclude more variance, so that the data appears to be more accurate. Which order ofpolynomial is appropriate is not exactly definable, so that the resulting error estimates are notentirely objective.
In Section 8.2.2 we pointed out that it is absolutely necessary to provide not only themonthly fields of air-sea fluxes but also the error estimates of each field, because in mostregions of the world ocean the magnitude of error variance is comparable to the naturalvariability. Without knowledge of the errors the interpretation of variability is not possible.
Kriging techniques provide both optimum fields of the considered parameters together
with an error map. Therefore these techniques are especially suitable for the inhomogenously
distributed ship data set. In addition, it is possible to produce complete fields of values even inareas with a very low data density. Without an explicit knowledge of the errors, these regions
are often left as blank areas on flux maps because the errors appear to be irresponsibly large.
Kriging provides an error estimate for each point so that users may decide whether uncertain
values should be omitted. Experience shows that many users would fill gappy data on theirown, so that the optimal value, even if very uncertain, is better than blanks.
B. PRINCIPLESIn principle Kriging can be regarded as a prediction of a value x0 at a location P0 where
no measurement has been carried out, by using information from measurements in thesurrounding region, at Pi. That means the following expression has to be solved:
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8.5 Mapping Errorsnx 0 = ∑ λ i (x i − ∆x i )(8.12)

i =1

where x0 is the value which has to be predicted. xi denotes the available measurements and ∆xi
their errors. The task is to determine the weights λi. Obviously, a solution is not possible forone single case. But if a time series of m measurements at each location P i is available, areasonable requirement is that the mean squared deviation between predictions and truth isminimal:
m

n

t =1

i =1

∑ ( x0 − ∑ λ i (xi − ∆xi ) )2 = min(8.13)

Differentiating (8.13) leads to the following expression, where, for the sake of clarity, thetemporal summation is abbreviated by brackets [].:

L[x1x 2][x1x n ]  λ1  [x1x1 ] + [ ∆ x1∆ x1]
 λ 2[x 2 x1 ][ x 2 x 2 ] + [ ∆ x 2 ∆x 2 ] L[x 2 x n ] • 
M
M
MM
  M   L [x n x n ] + [ ∆x n ∆x n ]  λ n [x n x1 ][x n x 2 ] [x 0 x1 ]
 [x 0 x 2 ]
= 
 M

 [x 0 x n ](8.14)
Thus, a linear set of equations results, containing the covariance matrix between the data points
Pi with the error variances on its diagonal, and a vector giving the covariance between thepredicting point P0 and the locations Pi where measurements are available. If these quantities
are known, the solution of the above set of equations leads, via the determination of the weights
λi, to the optimal prediction at P0.
Obviously, the minimised expression in (8.13) is equal to the error of the predictedvalue, often called as Kriging error. Transformation of that expression leads to:
n

n

n

i =1

i =1

j =1

[ ]

n

[ x o x o ] − 2 ∑ λ i [ x o xi ] + ∑ ∑ λ i λ j xi x j + ∑ λ i λ i [ xi xi ](8.15)

i =1

The first term of the transformed Kriging error denotes the variance at P0.
are carried out with normalised anomalies

x norm =xi − x
σx

If the calculations(8.16)

which is advantageous for many reasons: [x0x0] is equal to unity. That means if no information
at all is available, the error is equal to the interannual variability. This is reasonable because
without any information we cannot do more than predicting the long-time mean of this location.
The error of such a crude procedure is easy to realise. If we predict for every month of a timeseries just the total mean, the error defined by the mean squared deviation between truth andprediction is obviously the intermonthly variance itself. This fact is represented by the firstterm in (8.15). A further conclusion of the above discussion is that the maximum error formonthly means is the intermonthly variability of the considered location.
The second term in expression (8.15) contains the covariances between the predicting
point P0 and the data points Pi, and represents that part of the Kriging error which can be called'information'. Its negative sign indicates that the error decreases with increasing information.
But information of the data points Pi are not independent from each other, as two points inclose vicinity carry more or less the same information. That is what the third term represents
which we can call 'redundance'. Covariances between the data points increase the error.
Obviously the fourth term describes the effects of individual errors for the data points used.
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C. NUMERICAL IMPLEMENTATION AND PRESELECTION
If a fixed number of n observations is preselected, the Kriging technique is easy toapply. By solving only a single set of linear equations (8.14) the optimum value at thepredicting point is obtained. That is of course only optimal under the condition that thepreselected data points should exclusively be used. However, the use of an additional data
point Pn+1 could improve the result. On the other hand, also omitting some data points of highindividual error and redundance may lead to an improvement. Thus, a kind of meta optimum
has to be searched for.
The amount of information carried by a certain measurement does not only depend on
its accuracy and its distance to the predicting point, but also on its redundancy in relation to theother points already used (8.15). That makes an a priori order of rank concerning the qualityof data points impossible. Thus, beginning with the trivial prediction, the long-term mean, we
have to select always that data point explaining for the moment the most variability at P0. As
more and more points are included, the Kriging error decreases. The meta optimum is found,when no additional point is able to diminish the Kriging error under the condition of a positive
weight λ+1.
However, as outlined the method is arduous and time consuming. As mentioned abovea preselection of a fixed number of observations would provide only the optimum result for thatspecific combination, but we are searching for the meta optimum. Nevertheless, a kind of meta
preselection is necessary in any case. Considering only the Atlantic, on a spatial resolution of
1o by 1o, about 10,000 possible data points are involved. Solving a huge number of 10,000 by
10,000 matrices becomes a problem even for modern computers. On the other hand the meta
preselection should not be too petty in order not to risk missing the meta optimum. Dealing
with 100 data points in the surrounding of the predicting point P 0 is therefore realistic. The
determination of the second data point (the first is trivial) requires solving 99 2-by-2 matrices,that of the third one 98 3-by-3 matrices, and so on. These are still rather extensive calculations.
When arriving at, say 58 43-by-43 matrices, where the meta optimum may be finally found,
we have to be aware that only one single monthly mean of a single parameter in one specificyear at one point in the world ocean is determined.
A substantial reduction of computing time is attainable, when the potential change ofthe Kriging error for an additionally included data point Pn+1 is considered. Instead of solving
several matrices the potential change of Kriging can be directly computed from:n− 2λ n +1[x 0 x n +1] + 2 λ n +1∑ λ i[x i x n +1] + λ n +12[x n +1x n +1 ] + [ ∆ x n +1∆ x n +1 ](8.17)

i =1

The first two terms are giving information and redundance gain, respectively. The last two
describe natural and error variance for the potential new data point P n+1. The point with thehighest potential Kriging error reduction is actually included, and the exact new Kriging error is
obtained by solving only one single (n+1) by (n+1) matrix. Following the above described
technique, optimum monthly mean fields, always paired with their error fields, are obtainedwith an acceptable expenditure of time.
D. MEANING OF THE SUM OF WEIGHTS
Spatial gradients in long-term mean fields are not treatable with correlation techniques.
This becomes obvious by considering the following example. In the extreme case twoneighbouring points with different total means may well have a correlation up to 1. In that case
we would be able to predict exactly the monthly anomalies of one point from the other, but ofcourse not the supposed difference between their long-term mean, because correlations do notcontain any information about the averages at both locations. That is the main reason for theuse of anomalies instead of absolute values.
Dealing with anomalies, a constraint for the sum of weights:
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8.5 Mapping Errorsn− 2λ n +1[x 0 x n +1] + 2 λ n +1∑ λ i[x i x n +1] + λ n +12[x n +1x n +1 ] + [ ∆ x n +1∆ x n +1 ]i =1(8.18)
is not necessary, moreover, it would not be useful. To illustrate that, the trivial case of Kriging
with only one available data point may be considered. The minimising condition of (8.13) issimplified to:m∑ (x0 − λ 1(x1 + ∆x1))

2

= min

(8.19)

t =1

with the solution:[x 0 x1][x1x1] + [ ∆x1∆x1]

λ1 =(8.20)In the denominator the total variance at P1 appears, split into the natural and the error variance.
The numerator shows the covariance between the predicting and the data point. Analogue tothe slope of a regression line, the entire fraction is equal to the relative variance at thepredicting point, which is explained by the time series at P1. Because of the errors at P1, P0 isin any case not completely predictable. Consequently, the variance at P0 is underestimated, aknown and unavoidable feature of regression techniques. Thus, when Kriging techniques areapplied to anomalies, the sum of weights can obviously be interpreted as the sum of explained
variance. Values less than 1 do not indicate a bias, as for absolute values, but a reduction ofvariance to the explainable portion, which is indeed reasonable.
E. ESTIMATION OF THE TRUE COVARIANCE FROM REAL DATA
According to (8.14), a prerequisite for the application of the discussed Kriging
technique is the knowledge of the spatial covariance matrix. Since complete time series for all
points in the world ocean, as conceived in (8.13), are obviously not available, mean covariances
have to be derived.
Firstly, the covariance of two time series strongly depends on the distance between theobservation places. For many applications it is therefore sufficient to assume a constantcovariance for a given distance. However, on global scale anisotropy has to be taken into
account, additionally. Secondly, spatial covariance strongly depends on the considered timescale: the prediction of the annual anomaly at a certain location is indeed possible withmeasurements from a far distant station, while short-term variability is connected only overshort distances. Since we are aiming at the correlation length of monthly means, covariances
should of course be calculated on the base of monthly means, which show much larger
correlation lengths than individual observations representing a quasi instantaneous snap shot.
Thus, the average covariance of monthly means is calculated separately for eachparameter as a function of distance and direction. Due to the spatial averaging, the covariancesof different regions are lumped together. Therefore it is advantageous to normalise theanomalies according to (8.16). Otherwise regions of high variability would dominate theresults. The normalisation allows to take into account adequately the information from low
variability regions, such as the tropics.
An exponential fit over the covariances, which due to the normalisation are converted
into correlations, conserves the directional information:r = a0 e

−

x2 y2
+
a12 a 22

(8.21)

where r denotes the correlation, x and y the zonal and meridional distances, respectively.
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8.5 Mapping Errors

After the derivation of the coefficients a i in (8.21), we are able to describe thecorrelation as a pure function of the position. However, the coefficients ai are derived by using
real observations, which are of course affected by errors. But for the intended Kriging
technique information about the natural covariance on the one hand and the error variance on
the other hand is needed separately. Therefore, we have to consider to which extent theobserved correlation, given in (8.21), is diminished compared to the actually sought true natural
correlation.
For error affected anomalies the correlation between two points is:

=

rerr

∑ ((x1 + ∆x1)(x2 + ∆x2 ))22∑ (x1 + ∆x1) ∑ (x2 + ∆x2 )(8.22)

Assuming that errors ∆x1 and ∆x2 are random, the above equation can be transformed to:

=

rerr

∑ x1x2(∑ x1x1 + ∑ ∆x1∆x1)(∑ x2x2 + ∑ ∆x2 ∆x2 )(8.23)On the other hand, the true natural correlation, unaffected by errors, is:∑ x1x2∑ x1x1 ∑ x2 x2

=

rtrue

(8.24)The quotient of both is equal to:

rerr
rtrue

∑ x1x1 ∑ x2 x2(∑ x1x1 + ∑ ∆x1∆x1)(∑ x2x2 + ∑ ∆x2 ∆x2 )=(8.25)

which is equal to the quotient of the true variance, excluding errors, and the total variance,
including errors. This becomes clearer when, for the moment, spatially homogenous variances
are assumed, so that (8.25) is simplified to:

rerr
rtrue

=

∑ x1x1∑ x1x1 + ∑ ∆x1∆x1(8.26)

Due to errors, the observed correlation is reduced by the above given expression.
Since errors do not dependent on distance, it is a reduction by a constant factor. The
magnitude of this factor is given by a0 in (8.21), because at distance zero the true correlation isequal to 1, so that here the observed correlation becomes equal to the reduction factor. Thus,
the true correlation is obtained by a simple division of (8.21) by the factor a0:

r=e

−

x2 y2
+
a12 a 22

(8.27)

Besides the true spatial correlation, given in (8.27), the individual error variance of theused data points has to be known for solving the Kriging (8.14). As discussed in section 8.2.2,
the error of an individual monthly mean is obtained by dividing the intra-monthly variance ofthat particular month by the number of available observations. For months with only a single
observation the intra-monthly variance is not attainable. In order not to lose the information ofsuch months, the error variance is assumed to be as high as the total variance at this location.
Applying the above described technique, monthly fields of the air-sea fluxes arederived.
Figure 8.7 shows an example for the latent heat in January 1979, while thecorresponding errors are given in Figure 8.8. In the region south of 40o S the errors are
increasing up to values of 1, which is the theoretical maximum. As mentioned above, aReport of the Working Group on Air Sea Fluxes

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8.5 Mapping Errors
8.6 Satellite sampling errors

normalised error of 1 indicates that no information is available at all. In this case the error isequal to the interannual variability. However, since the interannual variability is extremely low
in the South Atlantic, the absolute error in this region is astonishing small. Thus, we are in a
lucky position. In regions as the North Atlantic, where due to high interannual variability thedetermination of monthly means is very difficult, many observations are available. In theSouth Atlantic the data base is much smaller. But both intra-monthly and extra-monthly
variability are low. The first reduces the individual errors of monthly means, the second
guarantees a limitation of the absolute Kriging error, even if the normalised Kriging error
reaches maximum values.

Figure 8.7 Example of a Kriging result.
Values are normalised by the total standard
deviation at each grid point.

Figure 8.8. As figure 8.7, but for the
Kriging error

8.6 Sampling problems for satellite-derived quantities
8.6.1 Introduction
Temporal sampling limitations of a sensor can be the most important source of bothrandom and systematic error in longer term (i.e. monthly and longer) applications. This isespecially bad for quantities like precipitation that exhibit a highly intermittent behaviour inspace and time and can also have a significant diurnal cycle. The only way out of this dilemma
is to combine measurements of the same kind of sensor at different orbits, or measurementsfrom different instruments like infrared sensors on geostationary satellites and microwave
sensors as done in the GPCP. Since the sampling situation is worst for precipitation the next
section deals exclusively with that variable. For all other satellite-derived quantities, thesampling errors found for precipitation can be regarded as a maximum estimate.
8.6.2 Sampling errors for precipitation
A paper by Salby and Callaghan (1997) concentrates on the under-sampled diurnal
variability using polar-orbiting measurements. They compared the time-mean behaviour of
global cloud distributions to the true time-mean behaviour determined from the high-resolution
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8.6 Satellite sampling errors

Global Cloud Imagery (GCI) according to different orbital and viewing characteristics of one
and multiple platforms. The main conclusion is that the pattern of the time-mean error closely
resembles the pattern of error in the mean diurnal cycle.
This means that precessing
instruments like TRMM can describe the right time-mean behaviour only as accurately as they
can determine the mean diurnal cycle. If measurements are averaged over many months tomake the determination of the diurnal cycle more consistent, the time-mean behaviour iscontaminated by seasonal variations, which is also not desirable.
Another study of satellite orbits has been performed by Shin and North (1988). They
used a stochastic model and data from the GATE I experiment to represent rainfall statistics.
They stated sampling errors for a low inclination satellite at a height of 300km like TRMM of
8-12% for a monthly mean rainrate over a grid box of 5° x 5° in the tropical ocean. This isroughly the same as what is found for instruments in a sun-synchronous orbit like the SSM/I
with heights of around 750km. It should be noticed that for a polar orbiter the maximum
sampling error is located near the equator where the sampling interval is sometimes only 24h.
For a precessing instrument like TRMM the sampling error increases with increasing latitude
because of the uneven sampling if so called flush visits (the whole grid box is seen at one visit)
are considered. This effect is counteracted by the increasing sampling area with latitude whenpartial visits are considered (Shin and North, 1988). Because there can be some doubt about
the validity of GATE rainfall statistics for the whole tropical ocean, Oki and Sumi (1994) usedradar-derived rainfall maps around Japan and found for a 5° x 5° grid box a sampling error of
16% which increases at a box of 2.5° x 2.5° to 24% for the TRMM TMI swath. The study ofSoman et al. (1995) using data from the Darwin radar in Australia also stated high sampling
errors up to 65% for sun-synchronous orbiters with a sampling interval of 24h and much lower
errors (5%-10%) for non-sun-synchronous orbiters. However, the last two studies also include
data over land surfaces where the diurnal cycle of precipitation is much more distinctive, and in
the case of the Japanese data, not only tropical convective rainfall but also midlatitude cyclones
were included in the analysis.
All the above mentioned studies found that any combination of sun-synchronous withsun-synchronous or with non-sun-synchronous orbits reduces the sampling error to less than
10% if an optimal time sampling interval is chosen. With four SSM/Is in space, and two
AMSR's to come within the next year, it should be possible to estimate the time-mean
behaviour of precipitation and all other parameters detectable at microwave frequencies withreasonable quality. But only if all available data are used which is not the case for mostexisting climatologies. Combination of microwave and infrared instruments on low orbit (300750 km) and geostationary satellites remains attractive if an optimal coupling of methods isapplied.
8.6.3 Scatterometer Sampling Errors
The irregular sampling from polar orbiting spacecraft leads to significant errors andloss of equivalent resolution in gridded wind fields created from remotely sensed data. Thus,
although measurements are acquired densely within swaths, the limited swath widths andspacecraft revisit times result in errors in gridded fields constructed from satellite
measurements, owing to variability in the true surface field that was not measured by thesatellite instrument. The magnitudes and types of these "sampling" errors are complicatedfunctions of the instrumental coverage (i.e., swath width and orbit) and the natural spatial andtemporal variability of the true surface wind field.
Sampling errors and degraded equivalent resolution have important negative impacts on
the scientific utility of remotely sensed data. Ocean and climate models respond sensitively towind forcing on a broad range of scales. Modelling studies by Large et al. (1991) and Chin etal. (1997) demonstrate clearly that large-scale, low-frequency upper ocean circulation issensitive to high-frequency, small-scale wind forcing - the large-scale circulation patternschange if the small-scale wind forcing changes. Small-scale, high-frequency wind forcing
information is thus a fundamental requirement for climate modelling and prediction,
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8.6 Satellite sampling errors

notwithstanding the fact that the climate system variables of interest involve inherently large
temporal and spatial scales. Large et al. (1991) suggest that 2-3 day temporal resolution at 2 x
2 degree spatial resolution is required to properly reproduce the intra-annual streamfunction
variance and barotropic Rossby wave response of the ocean in the mid and high latitude
regions. The required temporal resolution should be finer going equatorward, because the
Rossby wave becomes faster.
As an attempt of determining a desirable resolution for scatterometer products, analysis
metrics have been developed by Chelton and co-workers (e.g. Greenslade et al., 1997) toquantify the effective resolution of remote sensing systems and have been applied to present
and planned scatterometer systems by Chelton et al. (1997). The latter analysis showed that asingle polar-orbiting, dual-swath scatterometer with NSCAT sampling (600 km wide swaths
separated by a 329 km nadir gap) yielded an equivalent resolution of 2 x 2 degrees and nearly
20 days at 30 degree latitude. (Resolution metrics are based on a threshold of 0.72 ms-1
sampling-induced random component error. Increased equivalent temporal resolution can beobtained at the expense of lower spatial resolution. These values depend on the selected
threshold value.) In the satellite scatterometer science community, there are many efforts ofconstructing gridded products using different construction procedures and different temporaland spatial resolutions (e.g. Zeng and Levy, 1995; Kutsuwada, 1998; Kubota and Yokota,
1998; Kelly et al., 1999). Continuous swath instruments (such as the one to fly on QuikSCATand ADEOS-2) can achieve smaller spatial and temporal resolutions given the QuikSCAT orbit.
Thus, although wind forcing information from spaceborne scatterometers has greatscientific potential and utility, no single polar-orbiting vector wind instrument can provide thedata required to produce wind forcing fields with complete and proper temporal and spatial
resolution for climate modelling (independent of the measurement accuracy and resolution
within the satellite swath). However, tandem missions, such as QSCAT and ADEOS-2, or
possibly a SeaWinds and an ASCAT instrument, would provide data with sufficient equivalent
resolution to allow accurate climate modelling and prediction.

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9.1 Introduction
9.2 Sources of reference data

9 METHODS OF EVALUATING FLUXES AND FLUX RELATED PRODUCTS
9.1 Introduction
This Chapter will consider methods which can be used for verifying flux fields.
Accurate flux data which can be used as a reference for verification purposes are difficult toobtain. In general there is a trade-off between accuracy and temporal and spatial coverage.
Thus the most accurate flux estimates are obtained from dedicated air-sea interaction
experiments which are mounted in a restricted area for a limited period, perhaps one or twomonths. The accuracy of these research quality flux measurements will be discussed using the
TOGA-COARE experiment as a "State of the Art" example (Section 9.2.1). "Flux" buoys havebeen developed to obtain measurements of high enough quality such that the derived fluxes may
be used for verification purposes. Such buoys have been deployed at a limited number oflocations for typical periods of up to a year, occasionally longer. The data obtained is lesscomprehensive than that from air-sea interaction experiments but covers longer periods andmore locations. The data quality (Section 9.2.2) is significantly better than that obtained fromoperational meteorological buoys. Research ships have been used in "opportunistic" mode toobtain time series of automated meteorological data during cruises designed for other purposes.
As an example of these data, the data set obtained from the WOCE Hydrographic Programme
ships will be considered (Section 9.2.3). This data set is important in that a special effort wasmade to assemble the data into a single data resource to which consistent quality control
procedures had been applied. Such ship data can sample a wider range of geographicallocations compared to the high quality buoy data; however the ships are a poorer instrument
platform, and a long time series from any given area is generally not available. Finally, withregard to sources of flux reference data, the only routinely measured direct flux data is that forradiation, albeit from a limited number of locations. This will be briefly discussed in Section
9.2.4 (for more detailed discussion of direct radiation measurements see Section 5.2).
For many flux field products, suitable flux reference data are not available. In this casemuch can be learnt by intercomparing one product with another and this method will be used
extensively in Chapters 10 and 11. In this Chapter we will consider two specific cases of suchintercomparisons; the comparison with specialised products (Section 9.3.1) and comparison of
the variability patterns in flux fields (Section 9.3.2). While intercomparisons allow for relative
assessment of the flux field products there still remains the problem of deciding which productmight more nearly represent reality. There is also the need to identify and correct for residual
systematic errors in the data. A number of integral constraints are available and have variously
been used for that purpose. These include the global heat and water balance (Section 9.4.1), themeridional heat and freshwater transports (Section 9.4.2), the heat and water balance of
enclosed (or semi-enclosed) seas (Section 9.4.3), and ocean mixed layer budgets (Section 9.4.4).
9.2 Sources of reference data
9.2.1 The accuracy of research quality measurements: experience from TOGA-COARE
The most accurate source of basic observations for calculation of fluxes or validation ofmodels is expected to be that obtained from ships and moorings during the small (butincreasing) number of intensive air-sea measurement campaigns mounted each year in supportof some particular research project (e.g. BOMEX, TOGA-COARE, SOFIA, JASMINE, etc).
The instruments and methodology are usually under the supervision of scientists with particularexpertise in field measurement and instrumentation. However, the harsh marine environment
and limitations imposed by the platform itself, can easily degrade instrument performance
relative to its calibration in the laboratory or exposure on dry land, and in unexpected ways.
The state of the art for individual variables will be discussed at more length in Section 10; here
we summarise the accuracy of research quality observations, as revealed by the TOGA-COARE
experiment November 1992 - March 1993.

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9.2 Sources of reference data

The TOGA-COARE expedition involved 15 ships, 7 aircraft and several moorings, oneof which (the IMET mooring from WHOI; Hosom et al., 1995) was developed specifically toobtain high quality meteorological measurements for flux calculation. Six survey ships were
specially equipped for the measurement of fluxes. The cruise plans of these ships were coordinated to allow 2 days from the experiment total of 120 days to be dedicated to in situ
comparison of individual instruments and, subsequently, the calculated fluxes. This was doneby cruising upwind in close formation in the vicinity of the IMET buoy. Aircraft which wereoperational on those particular days performed repeated low-level fly-overs.
The quality of surface flux measurement achieved during TOGA-COARE is due inlarge measure to these field intercomparisons, carefully analysed during a series of workshops
involving PI's responsible for measurements on the various platforms (Bradley and Weller,
1995a,b; 1997; Bradley et al, 1997). Instrumental faults and calibration errors were discovered
which would otherwise have remained undetected. The usual procedure was to obtain “best”
estimates for each variable during the intercomparison days. In some cases a particular
instrument was identified as being the most reliable, on the basis of proven performance,stability of calibration, or mode of operation. Otherwise, an ensemble average was taken as the“correct” observation. At the end, bias and calibration corrections were obtained for all
instruments; those with obvious calibration or other problems were recalibrated where possible.
The above effort to refine accuracy of the surface measurements was driven by the
TOGA-COARE goal of no more than 10 Wm-2 uncertainty in the total surface energy budget ofthe ocean (WCRP, 1990). As Fairall et al. (1996a) point out, this implies accuracy requirements
for the individual bulk measurements, assumed independent, of about 0.2 ms-1 for wind speed,
0.2°C for both air and sea surface temperature, and 0.2 g kg-1 for humidity. Subsequently, Burns
et al. (1999, 2000) have compared aircraft measurements with the surface platforms, using datafrom the two days and other occasions of coincidence during the experiment. Table 9.1 gives
estimates by some PI's of the accuracy of their measurement following the TOGA-COARE
intercomparison process, as provided to Burns et al. (1999, 2000). Some of these estimatesappear rather optimistic; others seem conservative. Without details of how they were arrived
at, and what sources of uncertainty are included, they can be regarded as a guide only, but
illustrate some useful points (see also section 10).
Table 9.1 Subjective estimates (by the observers) of the accuracy of measurement on some
platforms following TOGA-COARE intercomparison corrections. The last row is an estimateof what might be presently achieved under similar, tropical or subtropical, weather conditions.
Accuracies in higher latitudes are likely to be worse.
Winds
ms-1

Air
Temp.°C

Humidityg kg-1Sea
Temp.°C

Shortwave

Longwave

Wm-2

Wm-2

R/V Moana Wave

±0.2

±0.2

±0.4

±0.2

±4

±2.5

R/V Wecoma

±0.2

±0.2

±3%

R/V Franklin

±0.2

±0.05

±0.15

±0.1

±3

±5

IMET

±2.5%

±0.1

±0.1

±0.1

±3

±10

ATLAS

±0.2

±0.17

±4%

±0.03

Present practice

±0.2

±0.1

±0.2

±0.25
(SSST)±3±2.5

Platform❍

Wind speed: all PI's considered that their wind speed measurements met the COARE goalof 0.2 ms-1 accuracy❍

Air temperature and humidity: on R/V Franklin Ta (and Qa ) were measured with aspirated
psychrometers, mounted on a boom ahead of the ship. Over land, accuracy of one or two
hundredths of a degree in both wet and dry bulb temperatures can be achieved with such

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9.2 Sources of reference data

instruments, but shipboard this degrades for several reasons. Other temperature/humidity
measurements in the table were made with commercial sensors, comprising a Platinum
resistance thermometer and moisture-sensitive polymer, basic specifications for whichbarely meet the COARE criterion. However, the most serious source of error (>1˚C)arose from diurnal heating when the sensors were mounted in simple unaspirated screens.
For this, a correction algorithm was devised (Anderson and Baumgartner, 1998). Then all
air temperature measurements met the COARE goal, but not all humidities.
❍

Sea temperature: higher accuracy is possible for water temperature, using precision
thermistors, because solar heating is generally negligible, the water temperature varies
slowly, and because the heat capacity of water is much higher than air. During the
COARE intercomparisons, night time sea temperatures agreed within 0.1˚C across several
platforms when the surface layer was well mixed, implying good calibration technique.
However, the quoted accuracies are for sensors at various depths, and do not reflect theaccuracy of skin temperature measurement (SSST); this requires extrapolation to thesurface via models of the cool skin and diurnal heating (Fairall et al., 1996a). The physics
of the former phenomenon is well enough understood that it may be calculated to order
0.1˚C. Thus the accuracy of SSST derived from the Moana Wave sensor at ~0.05m depth
is probably ±0.25˚C, whereas the other platforms must add the uncertainty of a mixed
layer model to the quoted accuracy. With the best current instruments, direct radiometricmeasurement of SSST has an accuracy of about 0.2˚C (Barton et al., 1995).

The COARE intercomparison initiative alerted observationalists to several potential sources oferror in air-sea measurement. This, together with some instrument development since COARE,
might be expected to improve the accuracy of measurement of basic flux-related variables insubsequent air-sea interaction studies. The bottom line in Table 9.1 is an attempt to estimate
measurement accuracy currently achievable, using equipment readily available to theinvestigator. A final word of caution is necessary however. TOGA COARE took place in aregion of relatively benign weather. Achieving these accuracies in stormy conditions at mid tohigh latitudes may be significantly more difficult.
9.2.2 High quality buoy data
The establishment of "Flux calibration sites" for verifying the fluxes from climatologies(based on in situ and satellite data), and from models, was recommended by WCRP (1989).
Perhaps the nearest realisation of such sites has been the series of mooring deployments
undertaken by the Woods Hole group; for example, the Subduction experiment in the North
Atlantic (Moyer et al., 1996), the Improved Meteorological System (IMET) mooring in thewest Pacific during the TOGA COARE experiment (see previous section - Weller andAnderson, 1996), and in the Arabian Sea (Weller et al., 1998). Although often referred to as"flux buoys" these buoys only make direct measurements of the downward radiative fluxes.
The turbulent fluxes are determined using accurate measurements of the basic variables and thebulk aerodynamic formulae.
The IMET instrumentation resulted from extensive sensor testing and development
during the World Ocean Circulation Experiment (WOCE). In addition to the surface moorings,
IMET has been installed on a number of the U.S. Research Vessels and is now being placed on
U. S. VOS. The IMET instruments were chosen, following on laboratory and field studies, for
accuracy, reliability, low power consumption, and their ability to stay in calibration during
unattended operation. The actual sensors were combined with front end and digital electronics
to make a module that is digitally addressable (RS- 232 or RS-485), and, importantly, retains its
calibration information. The present set of IMET modules includes wind speed/direction, airtemperature, sea surface temperature, relative humidity, precipitation, incoming shortwave
radiation, incoming longwave radiation, and barometric pressure.
Laboratory calibrations and in situ calibration studies and intercomparisons have beenused to assess the accuracy of the IMET sensors as installed on buoys. Figure 9.1 summarises
the progress that has been achieved in reducing measurement error over a series of air-sea
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9.2 Sources of reference datainteraction experiments. However it must be remembered that the accuracy of the turbulent
fluxes will also depend on the accuracy of the bulk formulae used to calculate them (Section 6).

Error (W m-2)

250
200

Total heat
Shortwave

150

Longwave
LatentSensible

100
50

Bu
LO nke
TU r
LO S8
TU 2
FA S8
SI 4
N
FA EX
SI 1
NE
X2
Su IM
bd ET
CO uctA ion
CO RE
AR lowEh
Ar igh
ab
i
CL an
IV
AR

0

Figure 9.1. The estimatedreduction of measurement error
in the components of the heat
flux and net heat flux associatedwithsurfacemooring
deployments during various
experiments since the early
1980s. The errors estimated inclimatologies of the early 1980s,
such as Bunker’s, are given as astarting point (from Weller andTaylor, 1999)

Future developments of flux buoys could include the addition of instrumentation tomeasure the direct and diffuse solar radiation (e.g. Reynolds, 1998) and to provide turbulent
flux estimates using eddy correlation and/or inertial dissipation.
A particular cause for concern with buoy data is the accuracy of the measurements of
the wind speed. In strong winds these are believed to be biased low (Large et al., 1995; Weller
and Taylor, 1999; Zeng and Brown, 1998). During the Storm Wind Study 2 experiment, SWS-2(Dobson et al., 1999; Taylor et al., 1999b) 10m neutral equivalent winds were estimated using
sonic anemometers on a buoy (at 4.5m) and a nearby research ship (at 17.5m). A preliminary
comparison of the measured wind speed values is shown in Figure 9.2. On average the buoy
appears to under estimate the wind by about 5%, however the data are very scattered.

difference (ship-buoy) % or Error (%)

20
Large et al.
(1995)
Zeng & Brown(1998)
2m

15

1.5m

10

3.0m
5

4.5m
17.5m

0

-5
0

10

20

wind speed (m/s)

30

Figure 9.2. The difference (closed
circles: (ship - buoy) %) between
values of the 10m neutral windfrom buoy and ship data during the
SWS-2 experiment for cases where
the separation was less than 10 km(anemometer heights 4.5m and17.5m respectively). Also shown
are the calculated effect of verticalmovement through a logarithmic
wind profiles for instruments atheights between 1.5m and 17.5m(light dashed lines); the mean error
curve reported by Large et al.
(1995) and the polynomial of Zeng
and Brown (1998). (from Taylor etal. 1999a)

Two possible mechanisms for the under estimate have been suggested. First, assumingthat the mean wind profile is logarithmic, an instrument being moved up and down vertically by
the waves will measure an average wind which is less than the wind at the mean measurement
height. Using the observed wave height to wind relationship for SWS-2, this effect has been
crudely estimated for different anemometer heights (light dashed lines on Figure 9.2). Zeng and
Brown, (1998) noted that there were a lack of high wind speed data in buoy observations used

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9.2 Sources of reference data

for scatterometer calibration. They used surface air pressure data to infer a low bias for buoy
winds at higher wind speeds. Their polynomial relationship (also shown on Figure 9.2) appears
very similar to what might be expected due to the logarithmic averaging for a 3m anemometerheight - not an unreasonable mean anemometer height for the mix of buoy data which they used.
The second mechanism is that the instrument may enter regions where the vertical windprofile is distorted due to the sheltering effect of the waves. Large et al. (1995) have suggested
that this effect significantly biases buoy wind data for wind speeds above some threshold. Their
predicted error for a 5m anemometer height is also shown on Figure 9.2, it is much greater thanthat predicted by Zeng and Brown (1998). The preliminary SWS-2 results shown on Figure 9.2.
appear to be of similar order to the Large et al. (1995) prediction, however the measured friction
velocity values suggested the wind error in the 20 to 25 ms-1 region was 3% to 5% (similar to
Zeng and Brown) rather than 15% or more. More work is needed before the bias in buoy windspeed measurements can be adequately quantified.

9.2.3 High-Quality Automated Data from ships
As mentioned previously, the IMET systems have been installed aboard numerous (sixto eight) US research vessels. Additionally, similar systems are also found on other (i.e. nonUS) research vessels. The WOCE Data Assembly Center (DAC) for surface meteorology,
located at COAPS/Florida State University, has assembled, reviewed, and made available asizeable record of in-situ surface meteorological data from research vessels participating in
WOCE scientific cruises (approximately 1993 to present). Many of these data originate fromIMET (and similar) automated systems that record and report data continuously. These data areconsidered to be of higher quality because of the quality of the instrumentation, complete andcontinuous data collection, and documentation efforts. Because these WOCE data are highly
qualified, extensive quality-control reviews were undertaken and suspect data were flagged (seeSmith et. al., 1996a). For the WOCE period, many of these data are from relatively poorlysampled regions of the southern hemisphere oceans. Finally, many of these observations do notappear in the COADS nor are they reported via the GTS (implying that they are NOT includedin the reanalyses or NWP products). Some of the ships do report surface meteorological values
at synoptic hours, but again the data from the automated systems are not typically reported.
Work is in progress to quantify the differences between simultaneous COADS and theautomated system observational data.
Because of their higher-quality, uniqueness, and data coverage characteristics, theseresearch vessel data are valuable for evaluating various flux products. As is the case for otherhigh-quality reference data, the research vessel data allow an evaluation of flux products onmuch smaller (e.g. synoptic) time scales, rather than climatological or other longer-averaged
quantities. The disadvantage of this approach is the limited spatial and temporal coverage (e.g.
Figure 9.3). While the ships do sometimes make observations in normally under sampled
regions, the research vessel database includes typically few cruises to a region, thus limiting theinterpretation of the results.
The approach of implementing the automated research vessel data is straightforward,
but there are many details (see Smith et. al., 2000). Automated meteorological data werereviewed and those of the best quality selected for the comparison. The observations are firstheight adjusted, and can be compared to gridded analysis values (nominally every 6 hours).
Turbulent flux values (Smith, 1988) can then be calculated and averaged over 6-hour (or longer)
periods (to match the integration period of the analysis). These values are also matched togridded analyses (e.g. NCEP Reanalyses) for further analysis.
Note that data from other research ships and platforms may be utilised for flux
evaluations. These data may not be of the same high quality as those described above, but otheradvantages such as longer time records and unique combinations of observations may qualify
them for addressing other aspects of the flux fields under review. For example, Ocean Weather

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Ship stations (see section 4.2.2) provide time series of meteorological measurements, mostly
obtained by professional meteorologists, spanning about two decades in 11 sites in the midlatitudinal North Atlantic and North Pacific with a time resolution of at least 3 hours. These
data are considered to be of higher accuracy than the regular VOS observations. In addition,
during the period 1981 to 1991, a considerable amount of meteorological measurements werecollected in the Northwest Atlantic by six Russian sister ships (which also operated at OWS C).
The data set contains about 50000 meteorological reports with temporal resolution from 1 to 3
hours (Gulev 1999). All measurements are taken by trained meteorologists using known
instruments and at known observational heights. A considerable portion of the routine
observations (about 30%) are accompanied by direct observations of SW and LW radiation that
increases the value of this regional data set.

Figure 9.3 Map of positions ofmeteorological data obtained fromresearch ships during the WOCE
experiment for the period 1988 to
1998 as held at COAPS, FSU. The
colours of the ship tracks indicate
the data sampling rate:

9.2.4 Data for verification of satellite products
A. COMPARISON OF SATELLITE PRODUCTS WITH IN SITU DATA
A common way to determine the quality of satellite-derived bulk parameters, fluxes,
and precipitation is by comparison to different in situ data sources. There is always a problemof finding in situ data of good quality which are globally distributed. For precipitation someauthors, e.g. Smith et al. (1998) support the opinion that the quality of in situ measurements is
not good enough for validation or calibration of satellite methods. For turbulent fluxes it isclear that only a handful of datasets of direct flux estimates exist and are available to thecommunity of satellite algorithm developers. The consequence is that most of theintercomparisons between satellite derived quantities rely on locally restricted datasets fromscientific experiments or on the more widely distributed observations from merchant ships
transmitted via the GTS. Where comparisons are made with ship data, either through individual
observations or mean values, the errors inherent in the ship data must be taken into account.
Since the ship data can be of doubtful quality, global in situ products like the da Silva et al.
(1994) or SOC climatology (Josey et al. 1999) are sometimes used to avoid the problems
inherent in the individual observations. However, this introduces a further problem that thecomparison is very much dependent on the data analysis procedure used for the production of
the in situ climatology.
A recurrent problem in any comparison of in situ and satellite products on an
instantaneous time scale is the mismatch in time and space between the various measurements;
point measurements are compared to field averages. The effect these two mismatches have on
the result of a given comparison depends on the spatial and temporal scale of the variations in
the variable considered.

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9.3 Intercomparison of field products

B. SOURCES OF RADIATION PRODUCTS
The direct measurement of radiation was discussed at some length in Section 5.
Networks of surface measurements have been implemented for verification. Unfortunately, todate, coverage of the sea is minimal. Available data sets include those from the Baseline Surface
Radiation Network (Gilgen et al., 1995), the Global Energy Balance Archive (GEBA) databaseof surface flux measurements (Gilgen and Ohmura, 1999), the NOAA/Climate Monitoring and
Diagnostics Laboratory (CMDL) data set (Stone et al. 1996). Most GEBA sites are located in
the midlatitudes; a few of them are located on islands.
The CERES/ARM/GEWEX Experiment (CAGEX; Charlock and Alberta, 1996) data isavailable on-line at: http://snowdog.larc.nasa.gov/cagex . CAGEX provides a space-time set of
input data, calculated fluxes, and validating measurements. With the launch of CERES,CAGEX has been extended to the ARM Tropical West Pacific (TWP) site. ARM TWP data is
expected to become a great resource for the study of radiation at the air-sea boundary. The
ARM web page is: http://www.arm.gov . Also for CERES, work on the long-term CERES
Ocean Measurement Platform for Aerosol and Radiation Evaluation (COMPARE) has begun at
the Chesapeake Light, 25 km off the Virginia coast.
Otherwise satellite radiation data has been verified by comparison with other, higher
quality fields. For example, in addition to GEBA data, the GEWEX SRB files provide forverification the monthly TOA net flux by independent, higher quality measurements from the
Earth Radiation Budget Experiment (ERBE; Barkstrom et al., 1989) over most of the globe.
Unlike the narrow-band radiometers on the ISCCP satellites, ERBE provides broadband
measurements with on-board calibration for SW and LW.
9.3 Intercomparison of field products
9.3.1 Comparison to Specialised Products
Operational centres have historically had difficulty producing satisfactory wind fields
over the tropical oceans (McPhaden, et al., 1988). Specialised products were developed to meetthe need for near-real time tropical wind fields. These specialised products include the much
utilised Florida State University (FSU) tropical Pacific pseudo-stress product (Stricherz, et al.,1997) as well as a similar product for the Indian ocean (Legler, et al., 1997), and for the Atlantic
the ORSTOM/IFREMER product (Servain, et al., 1996). These monthly mean fields of pseudostress are based on in-situ data, that is COADS (for real-time production, GTS reports are
utilised; sometime later COADS-type data are utilised in a reanalysis to produce a research
quality product) and extend for many years. The Pacific and Atlantic analyses are available for1964 to present. The Indian Ocean analysis from 1970 to present. The analysis methodology
for these fields is largely subjective for the Pacific product, objective/subjective for the Atlantic,
and objective for the Indian ocean. Continually produced in the same way for more than adecade, these analyses have been very popular, but resolvable spatial resolution (primarily in thezonal direction) is rather coarse, and adjustments to account for observation system changes
have not been considered. Nevertheless these analyses are the longest set of consistently
analysed (and highly referenced/utilised) tropical wind products available to the community.
There is value in evaluating new products such as the NCEP/NCAR (NCEPR) and
ECMWF (ERA) reanalysis surface wind products in the light of these proven products. The
comparison can be based on differences of monthly pseudo-stress fields for the common
analysis period, 1979-1993. Comparison of climatological characteristics as well as anomalousfeatures, particularly those associated with the ENSO phenomena, would be directly useful for
potential users of the reanalysis winds.
9.3.2 Comparison of Variability patterns in different flux products
The task of intercomparison of variability patterns derived from different flux products
is quite different from the intercomparison of climatologies of the mean fluxes. Indeed, even if
there is an agreement on mean values, variability patterns may not necessarily be in agreement
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and vice versa. When the climatological means are removed, the comparability of flux
anomalies can be considered in terms of the general level of interannual variability,
characterised by standard deviations, secular tendencies, and space-time patterns of interannual
variability.
Comparison of variability in different flux products with long-term time series of highquality measurements is highly desirable. The longest time series of high quality measurements(of up to four decades in length) are those from the Ocean Weather Ships (OWS’s). For
example, careful analysis of OWS wind records (Isemer 1995) does not support the conclusion,
based on COADS data (Wu and Li 1995), that winds and evaporation exhibit significant
positive trends in recent decades. The NBDC buoys provide a somewhat shorter time series ofabout one to two decades which nevertheless can be used for the validation of the variability
reported by VOS and NWP climatologies. For some variables (e.g. winds, precipitation)comparisons against coastal and island stations may be useful. Coastal and island stations
provide longer time series than those available over the sea. Bigg (1993) used coastal stationsin the Tropical Atlantic to verify COADS winds and found differences in trend estimatesderived from different sources. However, one has to be very careful in the extensive use ofcoastal and island data for comparison with VOS observations. First, these data may be
influenced by different local effects, which may affect the data over the land and also over thecoastal waters. Thus the comparison may be unrepresentative of the open ocean. Secondly,
historical changes in observational practice may have occurred for both coastal and island data,
and the effects may be of the same order of significance as inhomogeneities in the time series ofocean measurements.
When different flux products are compared to each other, they may indicate different
variability patterns, and these differences may have importance for climate studies. In thiscontext it is important to quantify the common patterns which are present in different analysed
products. Recently Barnett (1999) suggested the use of so-called “common EOF's” for theintercomparison of different space-time fields. The common EOF shows the pattern ofvariability commonly shared by all the fields analysed and illustrates the features which can be
discussed with confidence given the present state of our knowledge. Figure 9.4 shows, as an
example, the first two common EOF's of the latent heat flux from the four flux products whichwere shown analysed separately in Figure 3.1. The first EOF accounts for 28% of the total
variance and the 2nd EOF explains 12% of variance. The common normalised principal
components have similar temporal behaviour and indicate quite pronounced correlation witheach other. For this case, the common EOF's (Figure 9.4) show that the large-scale subpolarsubtropical dipole is present in all the flux products used and therefore can be discussed withconfidence. However, although the Labrador Sea pattern is the outstanding feature of
NCEP/NCAR fluxes, it does not appear in the other flux products.

Fig. 9.4 1st two common EOF's for the latent heat flux for the analyses shown in Figure 3.1.
(These were the NCEP/NCAR reanalysis; COADS ship reports; the reanalysis sampled at
COADS report positions; and COADS MSTG data.)
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9.4 Integral constraints
9.4.1 The global heat and water balance
An additional heat flux of 10 Wm-2 over one year would, if stored in the top 500m of
the ocean, heat that entire layer by about 0.15°C. Temperature changes on a decadal timescale
are at most a few tenths of a degree (e.g. Parilla et al., 1994) so the global mean heat budget
must balance to better than a few Wm-2. At present few global flux products achieve thisbalance, so this is a useful integral test of flux data sets. Using the global water balance as aconstraint is more difficult because of the need to know river run off (section 10.9) and changes
in ice amounts.
9.4.2 Meridional heat and water transport
A. INTRODUCTION
There is a long standing tradition to judge the reliability of global flux fields on thebasis of the implied meridional heat transport (MHT) and meridional fresh water transport(MFWT). These quantities are the integrals of the zonally averaged surface heat (H) and water(W) balances in the meridional direction:ϕ

MHT = ∫ dϕ ∫ Hdλ + H 0
ϕ0

ϕ

(9.1)

MFWT = ∫ dϕ ∫ Wdλ + W0
ϕ0

Here H0 and W 0 are the boundary conditions at the starting point of integration, whichhave to be taken from alternative estimates. For example, at approximately 70°N in the North
Atlantic, the MHT estimate is traditionally taken from Aagaard and Greismann (1975), who givethe value 0.107 PW, although Gulev (1995) and Bacon (1997) give a somewhat higher value,
close to 0.3 PW. For MFWT the boundary condition at the north results from the fresh water
flux through the Bering Strait which is estimated to be 0.78 Sv (Coachman and Aagaard 1988).
The MHT and MFWT estimates, obtained from the heat and fresh water balances at theocean surface, may be compared to oceanographic estimates obtained from hydrographic crosssections. These oceanographic estimates are normally considered to be of higher accuracy thancan be achieved by integration of the surface fluxes. For example, typical uncertainties of MHT
through oceanic cross-sections in the Atlantic are 0.3-0.4 PW (Hall and Bryden 1982, Rago andRossby 1987, Molinari et al. 1990, Koltermann et al. 1999). In contrast, the uncertainty of thesurface flux estimates propagates quickly into large errors in the meridionally integrated surface
heat and fresh water budgets. For the Atlantic Ocean, an error in zonally averaged net heat fluxof 15 Wm-2 results in an uncertainty of MHT estimate of ±0.5 PW at 30°N, and up to ±1 PW at
the Equator. And yet even this level of accuracy for the zonal net heat flux is a challenging
target for any climatology (Gleckler and Weare 1997); the situation is even worse with regard to
the fresh water flux accuracy. The same situation holds for VOS-based, remotely sensed, orreanalysis based products. Thus although MHT is a very important physical parameter, it has
limitations with regard to determining the reliability of surface flux fields.
A better proposition is to use the oceanographic estimates of heat and freshwater fluxes
through chosen hydrographic sections to determine the implied net surface flux between thosesections. This value can then be compared with the mean flux derived from the climatologies.
Since the latter do not need to be integrated the resulting comparison is more likely to be
informative.
B. ACCURACY OF MHT AND MFWT FROM SURFACE FLUX INTEGRATION
Existing estimates of MHT derived from surface fluxes vary in a very wide range evenin such well sampled area as the North Atlantic. Figure 9.5 summarises the sources of theReport of the Working Group on Air Sea Fluxes

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uncertainties inherent in the existing MHT estimates (Gulev et al. 2000). In order to quantify
the possible range of variability of MHT estimates, individual LMR from the COADS Releases
1 and 1a were used for the period 1958-1997, which overlaps the NCEP/NCAR Reanalysis
period. The most frequently used parameterisations for flux components and variablecorrections were applied to the COADS reports. Their cross-application gave the possibility to
produce an ensemble of the net flux and MHT estimates for the North Atlantic Ocean and tosimulate the wide range of approaches used for flux computation by different authors. Of
course not all of these parameterisation schemes are equally valid, so this simulation will
include the worst possible combination, as might be constructed by someone with complete
ignorance of the merits of the different algorithms!
25

20

Fig 9.5. Standard deviations of M H T
computed from the VOS data for the period
1957-1998, corresponding to the variability
due to the choice of different schemes andvariable corrections.
MHT estimatesderived from the NCEP/NCAR Reanalysis
fluxes and from the VOS using Josey et al.
(1998) method are also shown. The latter
authors chose not to adjust their flux
estimates to give global heat balance; hence
the apparent bias in the MHT estimate.

M H T, 10**14 W

15

10

5

0

S T D (variables correction s)
S T D (differen t sch em es)-5

zon al error of 10 W /m *m-10

N C E P /N C A R M H T
Josey et al. (1998)

-15
-20

-10

0

10

20
30
40
LA TITU D E

50

60

70

The application of the different parameterisations and corrections to the variables may
result in a ±4 PW range of MHT estimates at 20˚S. At 25˚N, where we have more or lessconsistent MHT estimates from oceanographic cross-sections, the range is 3 PW. The standard
deviation of MHT estimates with different variable corrections is approximately adequate to 20
Wm -2 zonal error in the net flux, and the standard deviation which results from the applicationof different schemes is somewhat greater, equivalent to about 24 to 26 Wm-2. This is perhaps
not surprising since the worst possible combination of parameterisation schemes has been
included in the set. Analysis of different factors shows that the correction of winds, the choiceof the equivalent Beaufort scale, and the choice of the scheme for turbulent fluxes are crucial
with regard to the variations of MHT estimates.
With regard to the MHT estimates available from NWP systems, Figure 9.5 also shows
values computed from the NCEP/NCAR Reanalysis fluxes for the 1958-1997 period. In termsof comparability to the oceanographic estimates, it looks like quite reasonable, giving
approximately 1 PW northward transport at 25°N.
C. VARIABILITY OF MHT AND MFWT
Recent WOCE results based on sections carried out in different years (Koltermann et al.
1999) show that both MHT and MFWT can vary over a wide range, for example about 30% to
50% for MHT estimates in the mid-latitudinal North Atlantic. Thus Table 9.2 shows estimatesof MHT and MFWT through three North Atlantic cross-sections for the late 1950's, early 1980's
and early 1990's. Large interdecadal variability in MHT and MFWT in the North Atlantic midlatitudes is possibly suggested, with transports in the subtropics being more steady. Model
simulations of the interdecadal variability (Knochel 1999) also show interannual variability of
MHT and MFWT, although in a smaller range of 0.1 PW.
The variability of MHT and MFWT may explain a paradox: transoceanic hydrographic
sections have been employed for some time in inverse calculations to determine the exchange ofReport of the Working Group on Air Sea Fluxes

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9.4 Integral Constraints

heat between the major ocean basins (Macdonald, 1998). However, according to the method
used, heat transport estimates at one individual section may differ by almost 100%, a range far
greater than the error bars provided with the estimates. The WOCE observations show that inmany parts of the ocean, there is a significant divergence of the meridional heat flux with large
seasonal changes and weaker interannual variability. Hence, considering the "instantaneous"character of a section, a large range in heat flux estimates is to be expected. Recent attention tothis issue by models and observations have not yet narrowed the range, but there is optimismthat further analysis of the diverse WOCE data set will provide better estimates of the spatial
and temporal variability of the meridional heat flux, and will narrow the range and produce error
bars of greater significance. As an example, Ganachaud (1999) has used an inverse box model
to provide estimates of the net input/output of heat over large portions of ocean basins, defined
between WOCE sections with an accuracy ranging from 0.1 to 0.5 PW, which makes theseestimates useful for surface flux evaluation (Figure 9.6).
Table 9.2. Meridional heat flux (Pw) and meridional fresh water transport (Sv) in the North
Atlantic derived from hydrographic data at 24˚N, 36˚N and 43-48˚N (Koltermann et al. 1999)
Year

1957-59
1981-82
1992-93

LATITUDE
36° N
MHT PW
MFWT Sv
0.47 ± 0.24
-0.77 ± 0.17
1.29 ± 0.17
-1.26 ± 0.13
0.70 ± 0.15
-1.14 ± 0.14

24° N
MHT PW
MFWT Sv
1.38 ± 0.29 -1.19 ± 0.31
1.48 ± 0.20 -1.22 ± 0.22
1.54 ± 0.19 -1.31 ± 0.21

43-48° N
MHT PW
MFWT Sv
0.27 ± 0.15 -0.87 ± 0.17
0.62 ± 0.11 -1.03 ± 0.13
0.53 ± 0.12 -1.02 ± 0.1360˚
0.56±0.1-0.53+_0.11

INPUT (PW)OUTPUT (PW)

0.5+_0.28

0.8±0.6
0.2±0.3

1.5+_0.39

1.36±0.14-0.056+_0.32-0.039+_0.34

0.6±0.2-0.28+_0.16

0.063+_0.18

0.029+_0.36

0.13+_0.53-0.61+_0.54

300˚

330˚

0˚

0.45+_0.19

1.7±0.3

0.9±0.6

1.3±0.15-0.28+_0.21

0.8±0.2270˚

0.5±0.4

0.31+_0.15-0.48+_0.59-1.4±0.2

0.45±0.15
0.6±0.1-60˚-1.6±0.4-0.33+_0.5
0.94±0.4
0.33+_0.31
0.8±0.2-30˚-0.63+_0.18

0.013+_0.48-1.3±0.8

0˚

0.024+_0.089

0.56±0.2

Uncertainty

1.26±0.1

30˚

1 PW

-0.01±0.05

HEAT

1.4±0.25

30˚

60˚

90˚

120˚

150˚

180˚

210˚

240˚

270˚

Figure 9.6. Estimates of heat transport (PW) across hydrographic sections(black labels), and net
input/output of heat (PW) through the surface between sections (ocean cooling in blue, heatingin red) obtained by Ganachaud (1999) with an inverse box model. Error bars are from a very
careful error analysis and are independent of the model. The net heat input between two
sections does not match exactly the residual of the net transport because it has been corrected
for mass residuals (noise correction).
In general, the quantitative estimate of the magnitude of interannual variations in MHT
is still under debate, although most researchers agree on the qualitative conclusion that it isvariable. Therefore, reasonable comparative assessments should be done for certain years (or at

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9.4 Integral Constraints

least pentads). However this approach leads to more problems with the surface balance
estimates. First, the accuracy of the net flux for the selected years will be reasonably lower incomparison to the accuracy of the long-term climate mean. Second, computing MHT and
MFWT for the selected years using surface heat and fresh water balances, one has to account for
the interannual heat storage and fresh water storage changes. Since these changes take place inthe whole ocean column (in contrast to the seasonal changes which occur primarily in the mixed
layer) their accurate estimation is impossible at the present level of hydrographic data coverage.
Gulev (1995) made an estimate of the possible uncertainty in MHT due to the heat storagechanges corresponding to the decadal heating (cooling) of 0.2˚C, corresponding to physicallyreasonable zonally integrated interdecadal temperature variations. The uncertainty obtained was
0.3 PW at 40°N and 0.5 PW at the Equator in the North Atlantic, and this uncertainty has to becoupled with the random uncertainty of the flux estimates.
D. ADJUSTMENT OF FLUX FIELDS USING INTEGRAL CONSTRAINTS
Flux fields derived from VOS observations tend to show more heat going into the ocean
than can be realistic. For example, despite carefully choosing the parameterisation schemes andapplying corrections for known observation errors, the recent Josey et al. (1999) flux
climatology suggested a net heat flux into the ocean of 30 Wm-2. It is therefore not surprisingthat this climatology shows an unrealistic variation of the MHT (see Figure 9.3). Indeed, Figure
9.3 shows that even an error of 10 Wm-2, which is often considered to be desirable for climate
research, integrates to a significant uncertainty in the net heat flux. This problem has generally
been overcome by adjusting (or tuning) the flux estimates to various oceanographic estimates of
MHT. Based on error estimates for the different variables, reasonable ranges are determined forthe transfer coefficients and other parameters. The inverse problem is then solved to adjust
those parameters, within the allowed range, to give reasonable agreement between the implied
MHT and the known oceanographic estimates. This method was used by Isemer et al. (1989) totune flux estimates for the North Atlantic. Using a similar technique, da Silva et al. (1994)
provided both unadjusted flux values, and flux values tuned to various oceanographic
constraints. Earlier, Oberhuber (1988) also tuned his flux estimates in order to arrive at areasonable M H T in the South Atlantic. He decreased the net shortwave and increased
evaporation by imposing an arbitrary increase of the Charnock constant in his computations.
Recently, Lindau (2000) also used tuning (to a number of oceanographic sections in the
Atlantic) to produce MHT estimates based on the IFM climatology (see Section 11.2.5).
Although this approach appears very attractive, and provides the possibility of basin
scale closure, there are a number of problems. Tuning does not guarantee that the individual
flux components reflect reality (apart perhaps from the tuned net flux). The degree of tuning islimited by the dimensions of the domain, and, for example, tuning of fluxes for the northern
North Atlantic, based on the cross-sections located north of 36°N, requires physicallyunreasonable changes to the variables and coefficients. The observed decadal variability of
MHT suggests that time-dependent tuning may be required, at least for the mid latitudes. Joseyet al. (1999) did not tune their flux estimates to the MHT arguing that to do so on a global scale
would degrade the agreement between their flux estimates and high quality buoy observations.
They argued that regionally varying flux corrections are required and that the number ofoceanographic values was too few to allow adequate regional corrections to be defined.
However the quality and number of such estimates is increasing and further progress on thisproblem should soon be possible.
In addition to matching the MHT, matching the MFWT would provide a further
constraint. However there are only few VOS-based estimates of MFWT (e.g. Wijffels et al.
1992), and they have larger uncertainty than that for M H T due to the large errors in the
precipitation.
In summary, the problem of the reliable estimation of MHT and especially MFWT fromdifferent sea-air flux fields is still open. Although there are obvious uncertainties inherent to theoceanographic estimates, we have to conclude that the accuracy of these estimates is still

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significantly better than that obtained by integrating surface flux estimates. Achieving
convergence between the two kinds of estimates is highly desirable. However, as measures of
the reliability of flux products, the MHT and MFWT result in significant uncertainty. Nor does
compatibility of the estimates of MHT and MFWT guarantee the reliability of the individual flux
components. Thus, the development of flux products should be primarily based on thejustification of parameterisations, variable corrections and interpolation techniques. Direct
MHT and MWFT estimates have to serve as important and independent basin scale measures ofreliability, but should not result in physically unreasonable corrections being made to fluxes orflux related parameters.
9.4.3 Enclosed Seas Used for Flux Validations
A. INTRODUCTION
For the entire world ocean the net energy budget between ocean and atmosphere can beconsidered as balanced, since any long-term temperature changes are small compared to theinaccuracy of ship derived flux fields. However, data coverage, especially in the Southern
Ocean, is too small to calculate a reliable global average. Enclosed seas give an attractive
opportunity to verify ocean-atmosphere flux fields. Usually the heat and water budgets of
enclosed and semi-enclosed seas are considered to be known to higher accuracy than basin-scalebalances from oceanic cross-sections. The Mediterranean and the Baltic Sea, with theirrelatively high number of observations, are most suitable for such a purpose. Moreover, they
represent rather different climate zones. If it would be possible to close the energy and water
budget for these two different seas by using the same set of parameterisations, then credibility
for worldwide application of those parameterisations would increase.
On the other hand, the special nature of enclosed seas, in particular the fact they are
surrounded by land, means that there are a number of problems concerning the heat and fresh
water budgets:❍

river runoff becomes even more important for the smaller scale seas, and it is poorly
known.

❍

many of the enclosed seas (Baltic, Caspian, Black) are characterised by continuous ice
cover over a considerable part of the area and period. The number of observations in icecovered areas is very small, and parameterisations of the flux components are poorly
developed in comparison to the case of open water.

❍

consideration of budgets for enclosed seas has to account carefully for the analysis of thefluxes in the vicinity of coastlines. Local corrections are required for a number ofvariables in the VOS data (first of all, winds). Reanalysis and operational analysis data,
especially those derived from the models with relatively coarse resolution (T62 andcoarser), need to be carefully checked.

❍

the uncertainty of the estimates depends crucially on the size of the domain analysed, andincreases for the large basins such as Mediterranean. On the other hand, consideration ofsmaller enclosed domains is difficult due to under sampling. In this sense, the use of
enclosed seas is characterised by the same methodical problem which is typical for the
MHT and MFWT estimates.

B. MEDITERRANEAN SEA.
Bunker et al. (1982) suggested the use of the Mediterranean and Red Seas as a test area
for the bulk formulae. For this purpose, the parameterisations of Bunker (1976), originally usedin the North Atlantic, were examined. Using individual merchant ship reports, they computed
the net sea surface energy flux for the Mediterranean and the Red Sea. For the Mediterranean, aheat loss of about 5 Wm-2 would be compatible with the measured heat import through the Straitof Gibraltar. However they found a strong surplus of energy at the surface After improving theparameterisation for longwave radiation by using climatological values of upper air humidity, in
addition to surface observations, the surplus decreased from 36 to 20 Wm-2. However, thisReport of the Working Group on Air Sea Fluxes

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value was still in poor agreement with the oceanic observations. The authors speculated that adrastic increase of the transfer coefficient CE up to values of 2 x 10-3 would be the most plausible
way to achieve a near balanced state. Similar conclusions were obtained for the Red Sea.
Gilman and Garrett (1994) also considered the energy balance of the Mediterranean Sea
to detect possible inconsistencies in the bulk flux formulae. As a starting point they used the
COADS derived heat fluxes presented by Garrett et al. (1993) which show an imbalance of 29
Wm -2. Changing both short and longwave radiation, Gilman and Garrett (1994) were able toclose the Mediterranean energy budget. On the one hand, the mean longwave radiation wasincreased by 10 Wm-2 compared to Bunker's results, when the formulae suggested by Bignami(see Bignami et al. 1995) was applied. On the other hand, they reduced the values for the
shortwave radiation by a total amount of 19 Wm-2. This was justified as follows: in order totake into account the effect of both anthropogenic and mineral aerosols which increase theatmospheric absorption over the Mediterranean, the transmissivity of the atmosphere was
reduced by a factor of 5% for one half of the year; this weakened the solar radiation by 6 Wm-2 .
However, the major part of the shortwave reduction (13 Wm-2 ) was achieved by correcting theprevious misapplication of Reed's (1977) formula (5.4). This was particularly effective becauseof the very low cloud amounts over the Mediterranean compared to other ocean areas.
For low cloud fractions the actual radiation Qsw from the Reed formula would exceed
the clear sky radiation value. Following Malevskii et al. (1992), a small increase over clear sky
radiation may be possible due to clouds. However, Reed recommended to neglect the cloud
reduction for cloud covers less than 0.2, that means setting (Qsw /Qswo) to be not greater than 1.
A non-linear relationship between radiation and cloud cover results. After removing thispossible overestimation of shortwave radiation, Gilman and Garrett obtained a balanced energy
budget for the Mediterranean.
However, Lindau et al. (2000) balanced the Mediterranean heat budget by using theparameterisations of Malevskii et al. (1992) and Bignami et al. (1995) for the short andlongwave radiation respectively, and the corrected Beaufort scale of Lindau (1995) together
with the exchange coefficients of Isemer & Hasse (1987) for the turbulent fluxes. They found a
net oceanic energy gain of only 2 Wm-2, which is in reasonable agreement to the measurementsof heat transport through the Strait of Gibraltar (MacDonald et al., 1994), which imply a meanannual heat loss of 5 Wm-2 from the sea surface.
Angelucci et al. (1998) analysed individual variables from the three different NWP
products - the ECMWF operational analyses, NCEP operational analyses, and ECMWF
Reanalysis, merged with the COADS MSTG and da Silva et al. (1994) cloudiness data, tocompute and intercompare the heat budget of the Mediterranean Sea. The Reed (1977), Kondo(1975) and May (1986) parameterisations for SW radiation, turbulent fluxes, and LW radiation
respectively, were applied to different combinations of the data. Angelucci et al. (1998) foundquite a wide range of heat balance estimates, varying from -50 to 50 Wm-2 depending on the data
set used. This range can be considered as the measure of the impact of different data on thebalances examined. Note here, that the use of VOS data would probably make this range evenlarger. The closest to the Bethoux (1979) estimate of about -6 Wm-2 was obtained using
ECMWF operational analyses. However that may have been just a function of the selection ofparameterisations chosen.
Although Garrett and co-workers had checked that their balance for the heat budget wascompatible with the freshwater balance, there have otherwise been few studies of the couplederror in the Mediterranean heat and fresh water balance estimates. There are attempts to deriveclosed budgets from some sub-areas of Mediterranean, for instance Adriatic Sea (Maggiore etal. 1998), although these estimates have a more uncertain imbalance (heat outflow to the open
Mediterranean) to be tuned to.
C. BALTIC SEA
Bumke et al. (1998) made a comprehensive study of the evaporation over the Baltic Sea
on the basis of VOS data. This included an investigation of the importance of corrections toReport of the Working Group on Air Sea Fluxes

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variables for coastal effects, and an intercomparison of several frequently used schemes for thetransfer coefficients. The range of the estimates obtained was from 460 to 620 mm per year forthe annual evaporation; indicating 20 to 30% uncertainty in the latent flux. This emphasised theneed to carefully choose the parameterisation scheme; otherwise the uncertainty for the net flux
may be of the same order as the mean flux values.
A particular problem for the Baltic Sea is the effect of sea ice. The sea ice changes thealbedo of the surface and consequently the net shortwave radiation. Moreover, sea ice
diminishes the effective surface temperature so that outgoing longwave radiation and theturbulent fluxes are reduced. In order to take these effects into account, the equilibrium
temperature of the ice surface has to be iterated under the constraint that the four atmosphericfluxes and the conductive heat flux through the ice are in balance (Maykut & Untersteiner,1971). If that is not possible at temperatures below 1˚C, the surplus of surface energy is used
for melting, which again would affect the albedo.
Using monthly mean ice charts from GISST data and applying the above scheme, theice corrected atmospheric fluxes can be estimated. As an illustration, the mean state for the ice
covered part of the Baltic Sea in the month of January is given in Figure 9.7. Averaging theindividually iterated temperatures for the ice surface, a value of -6.3˚C is obtained, whereas themean reported air temperature for that month is -6.6˚C. A latent heat flux of 15 Wm-2 results.
Although the sum of atmospheric fluxes cools the ice surface, melting occurs due to strong
convective fluxes through the relative thin ice (of on average 24 cm). The oceanic flux at thebottom of the ice is considered to be constant at 5 Wm -2 , so that the freezing at the bottom
prevails over the surface melting. For the month of January a mean ice growth of 36 cm permonth results. Using this ice correction scheme together with the same flux parameterisations
as in the Mediterranean, a long time mean heat loss for the Baltic Sea of 1 Wm-2 is obtained,very close to the observed quasi-balanced state.

Heat into
ceice
heating

3

5

SW

Ocn

net heat loss = 43

Heat outce coolingof ice

0

10

LW

LH

SH

34

15

2

20
30
Flux (W/m2)

40

50

60

Figure 9.7 Modelled mean energy fluxes over sea ice for the Baltic Sea in January (Wm-2 ). The
air temperature was –6.6˚C and the ice surface temperature –6.3˚C. At the top ice surface theheat into the ice from shortwave radiation (SW) was 3 Wm-2 whereas the surface cooling from
longwave radiation (LW), latent heat flux (LH) and sensible heat flux (SH) totalled 51 Wm-2.
Thus the net heat loss to the atmosphere was 48 Wm-2 . This was offset by convective heattransfer upward through the ice of 53 Wm-2 . Thus melting occurred at the top surface.
However since only 5 Wm-2 heat entered the bottom of the ice from the ocean, the net heatbalance for the ice was an overall cooling of 43 Wm-2 and the ice grew downward. The mean
ice thickness was 0.24m and the thickness growth rate was 0.364 m/month.
Thus the combination of parameterisations used by Lindau et al. (2000) provide
reasonable results for both the Mediterranean and the Baltic Sea. Moreover, the meridional heattransport in the Atlantic Ocean is consistent with independent oceanographic data, giving
further confidence in this choice of parameterisations. Nevertheless there are questions such as
to whether the Bignami et al. (1995), which was developed for the Mediterranean, should beapplied to open ocean areas. This will be discussed further below (Section 11.2.6).
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D. OTHER ENCLOSED SEAS
Analysis of the energy balance of the Black Sea has a long history. The first study wasdone by Neumann and Roseman (1954), who used a limited data set based primarily on
observations from coastal stations. They arrived at a 36 cm per year annual imbalance inprecipitation minus evaporation that constituted approximately 30 percent of the mean annual
values. Although during later decades better data were collected and more advanced
parameterisations applied, progress was not too obvious. Efimov et al. (1985) used probably
the most complete collection of data, but found considerable underestimation of both heat andfresh water balances (e.g. about 20 Wm- 2 for the heat) in comparison to those derived fromhydrographic data. Note that Efimov et al. (1985) used the Malevskii et al. (1992) scheme (in aprevious version, which does not deviate strongly from the final version), and that this scheme,
largely used in budget studies, gives relatively high values of the transfer coefficients incomparison to Large and Pond (1982) and Smith (1988).
Investigation of the balances for the Caspian sea is even less developed than for otherenclosed basins. For this, the largest lake on the globe, additional uncertainty results from theestimates of sea level changes, which are influenced by strong interannual variations and still
poorly documented. Moreover, nearly 1/3 of the Caspian Sea (its northern part) has a depth ofabout 1m to 5m and it is unclear to what extend the open ocean bulk parameterisations can beapplied there. In particular, the formulation for the radiative fluxes must definitely bereconsidered. The most advanced study, of Larin and Panin (1985), showed quite large
imbalances and it was pointed out that a reasonable closure requires ad-hoc adjustments of thetransfer coefficients up to values which are greater than all known estimates of the coefficients.
There is a useful integral constraint on the heat and mass balance of the Arctic Ocean.
Nakamura and Oort (1988) have presented polar heat balances. For the Arctic they relate theadvection of heat northward across 70°N to the surface heat balance and the top of atmosphereheat balance. They show a net annual surface heat balance of 2 Wm-2 . This agrees quite wellwith the net ice production of 0.2 m yr-1 or an export of about 10% of the Arctic ice cover's masseach year through Fram Strait. This freshwater export from the Arctic Ocean is crucial indetermining the stability and convective potential of the Greenland Sea; a similar transport
through the archipelago affects convection in the Labrador Sea. There is no similar tidy
constraint on the Antarctic ice cover, although it is true that the ice is generally formed closer to
Antarctica and flows northward carrying fresh water (as ice) to melt further from Antarctica.
9.4.4 Ocean Mixed Layer Budgets
The overall accuracy of air-sea flux measurement can, in principle, be validated throughclosure of the heat (and freshwater) budgets of the ocean mixed layer. This requires accurate
measurement of the various oceanic heat (and salinity) transport processes; and in practice has
never been very successful (e.g. Pollard and Thomas, 1989). However, because of the particularcare with measurement accuracy in COARE, two publications have so far appeared describing
upper ocean budget closure over the 130 km square survey area (Smyth et al., 1996: Feng et al.,1998). These showed that horizontal and vertical advection were major components of both theheat and salt balances, and emphasised the need to determine these components very well forthe technique to be useful. Subsequently, Godfrey et al. (1999) conducted a budget closure
experiment in the equatorial Indian ocean specifically designed to test our ability to determine
the oceanic advective components with sufficient accuracy to validate the COARE bulk flux
algorithm, and the accuracy of the measured input variables. By following a volume of water“tagged” with a drogued buoy over a 13-day period, they succeeded in closing the heat budget
to within 5 Wm-2 and freshwater to 1 mm day-1, again finding that the advective terms were offirst order. The SEMAPHORE experiment was also designed with the aim of closing the ocean
mixed layer budget (Eymard et al. 1996; Eymard 1998).

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Final report of the
Joint WCRP/SCOR Working Groupon Air-Sea Fluxes(SCOR Working Group 110)

INTERCOMPARISON AND VALIDATION OF
OCEAN-ATMOSPHERE ENERGY FLUXFIELDS
byMembers of the WGASF

Edited by P.K.Taylor

Section B – Chapters 10 to 12,
References and Appendices

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10.1 Introduction
10.2 Sea Surface Temperature

10 BASIC VARIABLES - STATE OF THE ART
10.1 Introduction
In previous Chapters, factors determining the requirements for surface flux fields havebeen reviewed (Chapters 2 and 3) and the available data sources have been considered (Chapter4). It has been shown that it is impracticable to determine the flux fields by direct
measurement (Chapter 5) and that parameterisation formulae must be used (Chapters 6 and 7).
This Chapter will consider the present state of the art for determining the basic variables. The
variables considered are those used in the parameterisation formulae and also other variables
which are otherwise relevant to the surface flux problem. The evaluation of these variables has
been delayed until now because the discussion of the random and sampling errors (Chapter 8),
and the consideration of methods of evaluation (Chapter 9), are relevant to fields of the basic
variables as well as to the flux fields (which will be evaluated in Chapter 11).
The main property of the sea surface which is relevant to the surface fluxes is the sea
surface temperature, this will be considered first (Section 10.2). Although variations of sea
surface salinity (SSS) are not significant with regard to air-sea flux calculation, SSS isimportant for flux verification and for the freshwater forcing of ocean general circulation
models; it will be considered in Section 10.3. The difference between the SST, and theimplied sea surface humidity, compared to the air temperature (Section 10.4) and air
humidity (Section 10.5) determines the potential for transfer of heat and water vapour. The
magnitude of the transfers is determined by the wind (Section 10.6) which, of course, is also
important for the momentum flux, the wind stress. The efficiency of the transfers of heat,water and momentum depends on the atmospheric stability, also a function of the air-sea
temperature and humidity contrasts and the wind. It is also likely that these transfers areaffected by the sea state and waves (Section 10.7), which are important climatic variables intheir own right.
Cloud amount (Section 10.8) is used in some parameterisations of the radiative fluxes
and is also an important variable for the verification of NWP and climate models. It is obtained
from ship reports and satellites. Precipitation (Section 10.9) also may be estimated from shipreports; although satellite based rainfall estimates offer better future potential. Other variables
affecting the ocean freshwater budget are river inflow (Section 10.10) and sea ice. Sea ice(Section 10.11) is an important climate variable and the surface fluxes are significantly
modified in sea ice areas.
Finally in this Chapter we will discuss a particular dataset of basic variable values, the
COADS monthly summary trimmed groups, MSTG (Section 10.12).
10.2 Sea Surface Temperature (SST)10.2.1 Introduction - definition of sea surface temperature
Sea surface temperature is the basic ocean variable which influences the magnitude of
the surface turbulent fluxes and also the upward flux of longwave radiation. The state of ourpresent knowledge of SST fields was examined at a recent GCOS workshop (Arkin, 1999); thefollowing sections have made considerable use of the conclusions of that workshop.
First we must consider the definition of sea surface temperature. The sea surface skintemperature (SSST) is the temperature that physically determines the surface heat fluxes. It
may be measured radiometrically from ships and other in situ platforms, and by satellite-borne
radiometers provided the atmospheric effects are properly corrected. The cooling due tosensible and latent heat fluxes and the longwave emission occurs at the skin, whereas theshortwave heating is distributed over a greater depth. Thus, most of the time, the SSST is
colder than the water just beneath the skin, typically by a few tenths ˚C. This difference
increases with increased surface cooling and decreases with increasing wind speed. However

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10.2 Sea Surface Temperatureoccasions can exist (for example with advection of warm, moist air over a colder sea producing
low cloud or fog) when the SSST may be warmer than the water directly below.
If there is a significant net heat flux from the ocean to the atmosphere then the water
below the skin will be well mixed and in situ measurements will record a temperature,normally reported as the SST, which will be warmer than the skin temperature. For traditionalbulk formulae (Section 7.2) the transfer coefficients have been determined with respect to thisbulk SST, so for application of these formulae, this would be the more appropriate temperature
to use. The bulk temperature also has the advantage of varying relatively slowly with time;thus bulk temperature observations may be composited over several days. Indeed, mosttraditional SST products are, in effect, fields of estimated bulk SST.
However there are problems with defining SST as being the bulk value. Newer formsof the bulk formulae (Section 7.3.2) use transfer coefficients derived from surface renewal
theory. For these the skin temperature is the appropriate value. Also, under daytime lightwind conditions (less than, say, 3 ms-1) a diurnal warm layer may form below the surface skin.
Sea temperatures in this warm layer may be a few degrees C higher than in the bulk of theocean mixed layer. Since the layer may not be well mixed and in any case is shallow (say one
or two metres), the numerical value of a bulk temperature measurement beneath the surface
skin will vary with the measurement depth. The COARE bulk algorithm (Fairall et al., 1996a –see Section 7.4.1) incorporates models of both the cool skin and warm layer, and so canestimate SSST from a bulk measurement at known depth. For determination of precise fieldsof SST or SSST the use of such models would appear to be the way forward particularly if flux
estimates are required with high time resolution in tropical regions.
Fortunately, the offset of the surface skin temperature should not be considered too
serious a problem for mean flux determination. Averaged over 70 days during COARE, taking
account of the cool skin increased heat input to the ocean by about 11 Wm-2, while the warm
layer decreased it by about 4 Wm-2 (although the effect can be up to 50 Wm-2 at midday – see
Fairall et al., 1996b). Over much of the mid-latitude and high latitude ocean, assuming asurface skin offset of about 0.3˚C would probably be adequate. Since this is also the order ofaccuracy of radiometric measurements (see below), a large programme of operational in situ
radiometric SST measurements is not justified; although research campaigns to improve ourknowledge of the skin effect may still be encouraged.
10.2.2 in situ SST measurements
A. HIGH QUALITY IN-SITU MEASUREMENTS
The accurate measurement of sea surface skin temperature (SSST) is a matter of wideimportance in climate-related research and simulation, particularly for tropical regions. The
TOGA program specified an accuracy of ±0.3°C for SSST over a 2 x 2 degree region (WCRP
1985) as a target for validation of space-borne radiometers. An error of 0.3°C changes sensible
and latent heat fluxes calculated with a bulk algorithm by 2 Wm-2 and 10 Wm-2 respectively, fortypical climatic conditions in the tropics (winds 3-4 ms-1, air and sea temperatures around 30°C,
75% humidity). However, it would cause an error of only 1.8 Wm-2 in the calculation of
longwave exitance, which is still more accurate than direct pyrgeometer measurement at sea(see Section 5.2.3 ).
In response to this specification, the past few years have seen development of several
high-resolution infrared radiometers (Smith et al., 1996c). Barton et al. (1995) describe
validation of the Along Track Scanning Radiometer (ATSR) on ERS-1 during several cruises of
R/V Franklin. High-resolution, self-calibrating infrared radiometers were mounted on the ship
to measure SSST, using narrow spectral bands similar to the 11 and 12 µm channels of the
ATSR. Barton et al. (1995) quote the absolute accuracy of their instrument as 0.1°C, and the
SSST measurement as accurate to 0.2°C, after correction for reflected sky radiation and surface
emissivity (see below). They conclude from their validation results that ATSR can provide
SSST within its design accuracy of 0.3°C, and foresee an improvement to 0.2°C.

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Measurements of SST from a high-quality commercial CTD, well-calibrated andmounted on a mooring, SeaSoar or hydrographic rosette are extremely accurate, in the order ofa few thousands °C (see for example Table 1 of Hosom et al., 1995). This degree of precision,achieved by using a highly stable platinum resistance thermometer (PRT), is usually far greaterthan necessary when calculating heat and moisture fluxes from a bulk algorithm. The main
value of such accurate (and usually costly) measurement of sea temperature is in thedetermination of small vertical gradients, conversion of conductivity to salinity, and crosscalibrating alternative sensors which may be more convenient for continuous deployment.
These would include lower quality PRT systems (accuracy 0.01°C) or thermistors which arenowadays available with accuracies of 0.05°C to 0.2°C (depending on cost). These figures
represent the basic accuracy of an SST measurement, when the sensor is deployed clear of theship or on a mooring. When used in a research vessel’s thermosalinograph, two otheruncertainties arise; possible changes in the water temperature through contact with the ship’s
hull or (depending on the sensor arrangement) en route from intake to sensor, and uncertainty
about the original depth of the water entering the intake.
The latter is of importance when using a model of the mixed layer to obtain SSST as
noted above. There are few estimates of the accuracy of SSST obtained in this way;comparison with a high-quality infrared radiometer (of the type discussed above) is required.
During COARE, measurements from the R/V Franklin’s thermosalinograph, taking water
effectively from 1m depth, were extrapolated to the surface using the COARE bulk algorithm.
Comparison against an early version of the Barton et al. (1995) IR radiometer showed anaverage bias of –0.11°C with rms deviation of 0.47°C, for a 10-day period during which 3 days
experienced diurnal surface warming greater than 3°C. It is impossible to apportion sources oferror between the model and the radiometer, but we note that the correction for reflected sky
radiation was fixed at the average value of 0.4°C, not measured continuously. Depending onsky conditions and atmospheric water vapour content, this correction can vary from close tozero to at least 1°C; i.e. coincident observations (sky temperature or atmospheric soundings)
needed to make this correction are essential if the final estimates of SSST are to be within thespecified accuracy.
It is possible that improvements in infrared radiometers for industrial purposes may
make them more readily available and affordable for use on ships, particularly enabling
simultaneous seaward and skyward observation. Donlon et al. (1998) describe tests at sea of
one such radiometer (spectral response 8-12 µm, beam half-angle 4.3°, cost ~US$600) by four
separate research groups, with excellent results. Comparison against an 0.1°C accurate
radiometer, of the type described above, showed a bias of only 0.1°C with rms 0.08°C. With
appropriate protection from the marine environment, such an instrument could be used widelyin an SST validation program.
B. VOS
Ship SST data are obtained mostly from Engine Room Intake (ERI) thermometers or(about 1/3 of the modern data) from SST buckets. A small but increasing number of ships use
hull contact sensors which, if carefully calibrated, appear to give the most consistent SST data
(Kent et al., 1993a; Emery et al., 1997). Although the VOS are asked to report temperatures intenths °C, many reports are given to half or whole degrees, probably reflecting the confidence
the ship's officers have in the accuracy of the reading. At the OOPC/AOPC Workshop (Arkin,1999), Taylor et al. (1999c) presented results that suggested that, compared to hull contact
sensors, ERI SST data were warmer under most conditions, on average by 0.35C although there
was significant scatter about this typical value. Individual ships using ERI readings had meanbiases between -0.5°C (too cold) and +2.3°C (too warm). Bucket measurements were foundonly to be biased compared to hull values during sunny daytime conditions when they gave on
average SST values about 0.3°C warmer. This is more likely due to the buckets heating on
deck prior to use rather than to near surface ocean heating. However, at the same meeting,Parker presented data that showed different characteristic biases between the observing

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methods. A plot of the zonal averages of "bucket" minus "non-bucket" SST values for theperiod 1975 to 1981 indicated the non-bucket values to be 0.1 to 0.2°C warmer except in thehigh latitude northern hemisphere (Folland et al., 1993). A more detailed study of the regional
comparisons on which the zonal averages were based suggested that the buckets were biased
cold in some high ocean to atmosphere heat flux areas.
Kent et al. (1999) found that the rms errors for a single VOS SST observation weretypically between 1°C to 2°C. The median rms error was 1.4°C and the mean rms error 1.5°C.
The highest values, up to 3°C occurred in high latitude coastal regions, and higher values
occurred in the Gulf Stream and Kuroshio regions than in other areas at the same latitude.
However it is possible that in these cases the error was being overestimated due to the effect ofvarying SST values on very short spatial scales.
A historic reconstruction of the global SST time series (Folland and Parker, 1995)
assumed that SST data were, on average, biased cold in 1860 by about 0.1°C with this bias
increasing to 0.4°C in 1940 due to the increased use of canvas (rather than wood) buckets. It
was then assumed that wartime conditions resulted in a wholesale switch to ERI SST data; thosebuckets used after that time were assumed to be of insulated construction. Thus, no correction
is applied for the period 1942 to the present. Comparisons of the trend in the corrected SST
with night time marine air temperatures and temperatures over land implied that theseadjustments were successful. However the differences between present day bucket and ERI
measurements suggests that further evaluation is necessary.
C. BUOY DATADrifting buoys provide data away from shipping lanes and are used for adjusting thesatellite calibration. However, the SST Workshop (Arkin, 1999) suggested that we do nothave sufficient knowledge of the characteristics of the various different types of drifting buoys
with respect to SST measurement. The type of each drifting buoy should be known and thecalibration details fully documented. This is not presently the case. Thus the accuracy of buoy
data varies but it is usually better than 0.5°C, significantly better than the ship data (Reynolds,
1999).
The most useful moored buoys are those in the open ocean, for example the JMA
buoys and the TAO and PIRATA arrays. Unfortunately many moored buoys are deployed nearcoasts where high SST gradients are likely. They are of limited value for satellite data
verification and are not routinely used for satellite calibration adjustments.
Research buoys designed to allow flux determination (such as the IMET system) are
important for verification of SST analyses and climatological SST data sets (Taylor et al.,1999c,d). Carefully calibrated sensors may be deployed at various depths in radiation shields(e.g. Weller et al., 1998). Since these buoys are limited in number and deployment duration,
perhaps their best use is as a "withheld" data source which has not been used in, or had asignificant impact on, the various SST analyses.
10.2.3 Remote sensing of SST
Infrared radiometers carried by satellites provide the potential for SSST measurement
over the global ocean on a regular repeat basis. Unfortunately, at any time significant areas ofthe ocean are cloud covered and data from different over-passes must be composited.
Atmospheric water vapour, aerosols, and clouds have all the potential to significantly bias thedata unless adequate correction procedures are implemented. Reliable cloud clearance remains
a problem, as does the effect of sub-pixel cloudiness. The measurements rely upon a small
number of sensors with the possibility of changes in sensor characteristics between satellites.
Accurate operational SST retrievals from the AVHRR carried on the NOAA series ofpolar orbiting satellites have been available since late 1981 (Reynolds, 1999). The error budget
for the AVHRR is dominated by the instrument calibration accuracy and atmospherictransmission effects. Thus the instrument must be calibrated against drifting buoys and theReport of the Working Group on Air Sea Fluxes

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10.2 Sea Surface Temperature
10.3 Sea Surface Salinity

calibration accuracy continually verified. Walton et al. (1998) reviewed the algorithms usedwhich are different depending on whether it is day or night. They found that the daytime rms
accuracy (compared to drifting buoy data) had improved from 0.8°C in 1989 to 0.5°C in 1998,
whereas the night time rms had remained constant at about 0.5°C. The bias was normally
between -0.2°C to +0.4°C. However large errors can occur due to changes in atmosphericaerosol loading. For example the eruption of Mount Pinatubo resulted in a regional cold bias oforder 2°C over much of a two year period. Explicit water vapour corrections using SSM/I
derived vertically integrated water vapour content (or "precipitable water") have been tested andshown to be successful (Emery et al., 1994); however they have not been implemented
operationally.
Compared to the AVHRR, the Along-Track Scanning Radiometer (ATSR) has
improved sensor stability and built-in calibration systems. However there have been problems
in ensuring that clouds do not contaminate the data. For cloud-free pixels, Simpson et al.
(1998) compared ATSR and drifting buoy data and found a mean difference (ATSR - buoy) of-0.24°C ±0.51°C (standard deviation); similar to what might be expected given the typical skinbulk difference. Merchant and Harris (1999) used the TOGA TAO array buoys for verificationand found the mean and standard deviation of the differences to be -0.16°C and ±0.37°C
respectively. Using the TAO data to calculate a skin temperature they obtained an even closer
match: +0.07°C ±0.27°C. They concluded that about 5% of the ATSR retrievals had errors
which they attributed to residual cloud contamination. For the uncontaminated data theysuggested a standard deviation of ±0.19°C. Examining retrievals for the period when theatmospheric aerosol loading due to Mount Pinatubo was decreasing, they found no significant
trend in the error (0.00 ±0.16°C/year), suggesting that the retrieval scheme was robust to
aerosol effects. With regard to the cloud contamination problem, Jones et al. (1996a,b) alsosuggested that 4.65% of the data was cloud contaminated and suggested a scheme for removing
the residual cloud pixels.
Thus the ATSR appears capable of achieving accuracies which are significantly better
than can be achieved with ship data, and apparently better than the drifting buoy data.
However the sampling is limited by the 500km wide swath. If the ATSR retrieval algorithms
are confirmed to be "aerosol proof" then this instrument may be best used both as a source of
SST data and as a source of calibration data for the wider swath AVHRR instrument.
10.2.4 SST analyses
Reynolds (1999) has summarised intercomparisons of different available climate scale
SST analyses, both by Hurrell and Trenberth (2000) and as reported at the Ocean Observing
Panel for Climate SST Workshop (Arkin 1999). These analyses (Table 10.1) typically have
temporal resolutions from weekly to monthly and spatial resolutions from 1° to 5°.. It was
found that differences among the analyses were smaller within the tropics compared to otherareas. Monthly rms differences between analyses were in the range 0.2°C to 0.5°C between
about 40°S and 60°N except in coastal areas. Analyses which did not use satellite data haddifferences greater than 1°C south of 40°S. The largest differences occurred north of 60°N due
to uncertainties near and within the Arctic sea ice. This was because different schemes are used
to determine SST from ice cover or concentration, and different ice cover analyses exist. It
was stressed that the use of satellite data (AVHRR) without bias correction has the potential tointroduce large errors into the analysis.
10.3 Sea Surface Salinity (SSS)
10.3.1 Introduction
The sea surface salinity (SSS) is an ocean variable which enters the calculation of thefluxes only by reducing the saturation water vapour pressure (SVP) over sea water compared to
fresh water. This is normally accounted for by assuming a relative humidity of 98% for the air
in direct contact with the sea surface. Since the variability of SSS is not sufficient to causesignificant variations in SVP (except in regions such as river estuaries) the SSS does notReport of the Working Group on Air Sea Fluxes

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explicitly enter the air-sea flux formulae. However it is well correlated to the freshwater flux,and is essential for an accurate verification of freshwater flux estimates. Furthermore, it has
been until now the most important component of the freshwater forcing of ocean generalcirculation models.

Table 10.1 Summary of different climate scale SST analyses (adapted from Reynolds, 1999).
The columns show the period available, the spatial resolution, whether satellite data is used("Yes","No") or used with additional bias correction ("Corr.") and whether ice data is used todefine SST's in high latitudes. N.B. some parameters may vary between different releases ofsome analyses.
Analysis
BMRC
UKMO GISSTJMALDEOUK MOHSST
NCEP EOF
NCEP OINRLNOAA/NASA
AVHRR Pathfinder

PeriodJul-93 on
1871 on1982 on1856 on1856 on1950-1998
Nov-81 on
1979 on1985 on

Res'n
1°
1°
2°
5°
5°
2°
1°
0.25˚
0.18˚

Sat.
Corr.
Corr.
No
No
No
No
Corr.
Yes
Yes

Ice
Yes
Yes
No
No
No
No
Yes
Yes
Yes

Reference(quoted by Reynolds, 1999)
Rayner et al. (1996)(quoted by Reynolds, 1999)
Kaplan et al. (1998)
Parker et al. (1994)
Smith et al. (1996b)
Reynolds & Smith (1994)(quoted by Reynolds, 1999)
Brown et al. (1993)

10.3.2 In situ measurement of SSS
In-situ measurements of sea surface salinity are far less numerous than in-situ
measurements of sea surface temperature. One major reason is the technology which is far
heavier, more expensive and less reliable for conductivity than for temperature sensors.
Conductivity sensors were added to disposable instruments (XCTD) only 15 years ago, and still
most VOS lines are, mainly for cost reasons, limited to the routine acquisition of SST. In
consequence, our knowledge of the surface salinity has been largely established from acompilation of the available hydrographic sections (Levitus et al., 1994). At present, if an
accurate climatological estimate for the annual mean sea surface salinity is required for the
global ocean, the present data set presents severe deficiencies in large regions of the ocean(mainly in the southern hemisphere in winter conditions). As a result, our knowledge of theclimatological seasonal cycle at a global scale is still largely inaccurate, and investigation of theinter-annual variability has to be restricted to a very few well sampled oceanographic regions oflimited area. The situation is expected to improve in a near future, driven by the emerging
needs of operational oceanography and the rapid development of a global ocean observing
system. With respect to salinity, the ARGO programme with its large number of profiling
floats is expected to be a major contribution.
10.3.3 Remote sensing of SSS
Despite the crucial role sea surface salinity plays in determining the ocean circulationand in understanding the water cycle up to the present it has not been monitored from space.
Levitus et al. (1994) showed that the number of in situ sea surface salinity observations reacheda peak in the late 1960's and has significantly decreased in the following decades. This, and therelatively poor geographical distribution of in situ salinity measurements over the world oceans,
could be significantly improved by measurements from space. In early papers, Klein and Swift(1977) and Swift and McIntosh (1983) gave a detailed description of the physical basis of how,
and at which frequencies, it would be possible to obtain sea surface salinity from space. A
recent paper on remote sensing of sea surface salinity by Lagerloef et al. (1995) recycled thisidea with the aim of proposing a salinity mission to NASA.

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Basically, the ocean radiometric brightness temperature TB is related to the sea surface
temperature TS and the emissivity ε:TB = ε (TS , S,ν ,θ i , pol)TS(10.1)The emissivity is a function of sea surface temperature, salinity, frequency, incidence angle, andpolarisation. The dependence on salinity is expressed through the dielectric constant which isgoverned by electrical conductivity and frequency. The conductivity itself is a function ofsalinity and temperature. Other effects on salinity, not covered by (10.1) but which must beallowed for in a retrieval of sea surface salinity, are wind, foam, and rain, and their impact on
the surface within the field of view of the radiometer.
Additionally, for an accuratecomputation of a signal measured at satellite altitude (10.1) has to be extended by the upwardand downward components of atmospheric emission, and the transmitted and reflected part of
the galactic space emission.
Lagerloef et al. (1995) stated that frequencies of 1.4 and 2.65 GHz seem to be the best
choice to retrieve salinity since the variation of brightness temperature due to salinity is at its
maximum at these frequencies. Another argument is that the relation between TB and TS andsalinity differs at these two frequencies which should allow a simultaneous retrieval of salinity
and TS. However, the brightness temperature variation at 1.4 GHz is not greater than ~5 K
under open ocean conditions (see Fig.2 in Lagerloef et al., 1995) which is small compared tothe dynamic ranges of ~100K caused by variations in water vapour or rain for the SSM/I
channels. Another problem to consider is that the wind speed, or better the surface stress,
must be known at the same time and location where the salinity will be retrieved. However,
Lagerloef et al. (1995) showed that it is possible to measure salinity with the airborne 1.4 GHz
Electrically Scanning Thinned Array Radiometer (ESTAR). They monitored the sea surface
salinity in a transect from seaward of the Gulf Stream to Cape Hatteras. The large salinitychanges from coastal to off-shore waters are clearly seen and are in agreement with frontal
features noted in sea surface temperature maps.
Sea surface salinity is used in the air-sea flux problem for the relaxation of ocean
general circulation models forced by the changing fresh water flux on the surface (Barnier et al.
1995), and for the correction of humidity variables. The latter does not require the salinity
more accurately than 1 psu. Ocean model simulations require at least 0.1 psu accuracy formonthly averaged salinity and it can be expected that the increase of model resolution will
require even higher accuracy for the reliable simulation of ocean circulation. Although thisaccuracy is problematic for the potential satellite techniques, it is also hardly attainable fromthe traditional alternative sources of the salinity data (in-situ STD, XBT and MBT
measurements), except for well sampled ocean regions such as the North Atlantic andnorthwest Pacific.
Thus, progress in the future is likely to be achieved by better
instrumentation and by combining in situ and remote sensing information.
A new study on critical requirements for ocean salinity retrieval by Drange et al. (1999)
addresses this issue in the light of the optimised and threshold requirements for GODAE. Thus
to describe and quantify many important ocean processes, the accuracy target for sea surface
salinity should be 0.1 psu over a distance of 100 or 200 km for a time period of 10 days. Several
retrieval simulations revealed the following results:❍

Measurements of horizontally and vertically polarised brightness temperatures at 1.4
GHz are needed to obtain reasonable retrievals.

❍

Accurate retrieval of salinity requires small or no bias in the measurement of thebrightness temperature.
This has implications on the engineering design andcalibration of a passive microwave salinity sensor.

❍

If wind speed fields with accuracy better than 0.5 ms-1 and sea surface temperaturefields with accuracy better that 0.5K can be obtained, then the error in the retrieved

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10.3 Sea Surface Salinity
10.4 Air Temperaturesalinity will be lower than 0.4 psu. This is not unrealistic because the accuracy
concerns averages over (200 km)2 and 10 days.
❍

The time scale of mesoscale changes in the ocean is such that a simultaneous
measurement of SST should not be necessary.

❍

Polar and subpolar waters are extremely sensitive to changes in sea surface salinity.
Those regions will require retrieval accuracies around 0.01 psu to be fully useful.
Since the accuracy of remotely sensed salinity decreases with decreasing sea surface
temperature, it will be very difficult to obtain reliable salinity measurements at highlatitudes from space.

The study concluded that the simulations have shown that the GODAE requirements can beclosely met; however some aspects of the problem were not tackled in this study e.g.
geometrical effects inherent to a certain radiometer or the effects of rainfall in modifying thesurface roughness. The Drange et al. (1999) study is related to an activity initialised by a groupof European and American scientists. They proposed to the European Space Agency (ESA) asatellite mission called the Soil Moisture and Ocean Salinity (SMOS) Satellite ( http://wwwsv.cict.fr/cesbio/smos/ ) which consists of an L-band (1.4 GHz) 2D interferometric radiometerthat should be able to detect soil moisture over land and sea surface salinity over oceans. The
mission has been selected for further study during the next two years. The satellite launch isplanned for the year 2004.
10.4 Air Temperature
10.4.1 In situ air temperature measurement
A. HIGH QUALITY IN-SITU MEASUREMENTS
Instruments used for measurement of the basic variables at sea are often different fromthose used routinely over land. This is because of the increased demands on ruggedness,waterproofing, and, in the case of those intended for use on buoys, power consumption andlong-term unattended operation. For atmospheric temperature and humidity, the most accurate
instrument in regular use is the psychrometer (wet and dry bulb thermometer) whose
measurements are based on well-established thermodynamic theory.
The most criticalrequirements to attain its potential accuracy are adequate ventilation of air past the sensing
elements (3 - 4 ms-1 flow rate), to ensure the full wet bulb depression, and adequate shielding
from solar radiation. This usually means a double shield with the space between also ventilated.
Basic accuracy depends on the type of sensing element used; for the familiar sling and Assman
psychrometers this is the precision of the particular mercury-in-glass thermometer, 0.1°C at
best.
For automatic recording, 0.01°C is not difficult to achieve in principle using platinum
resistance thermometers, although in practice environmental sources of error appear,
particularly when the instrument is used at sea (salt spray, signal interference, etc). In TOGACOARE the use of aspirated psychrometers aboard R/V Franklin revealed the inadequacy oftemperature sensors on other platforms which were enclosed in radiation screens without forced
aspiration (Bradley and Weller, 1995a, b). Temperature errors during daytime could be wellover 1˚C due to solar heating (Anderson and Baumgartner, 1998). Aboard many research
vessels and moorings, air temperature is measured with PRT's or precision thermistors, often
integral with a commercial temperature/RH instrument (see Section 10.5.1); quoted accuracies
are between 0.1˚C and 0.25˚C (Hosom et al., 1995; Freitag et al., 1994; Burns et al., 2000).
B. VOS DATA - THE VSOP-NA PROJECT
It has been known for a long time that temperature measurements on board merchantships have considerable systematic errors. Their consequences on heat flux calculations had
been discussed by Dietrich (1950). Kent et al. (1999) found that, while a typical RMS airtemperature error for a single temperature observation from VOS was about 1.4°C, there were aReport of the Working Group on Air Sea Fluxes

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10.4 Air Temperaturesignificant number of higher error values representing high latitude coastal regions with mean
air temperatures of 10°C or lower. This may indicate a failure to remove spatial variability
from the error estimate (for example in the Gulf Stream and Kuroshio areas) or it may be thaterrors in air temperature observations are greater in cold conditions.
For example,
psychrometers may not be exposed outside the wheelhouse for a long enough period.
The exposure of thermometer screens on the VOS selected for the VSOP-NA project(Kent et al. 1993a) varied from good (e.g. screens hung on stanchions on the outboard rails ofeither bridge wing) to very bad (e.g. "the screen is made of brown varnished wood and fitted tothe side of the wheelhouse in the 'porch' of the bridge wing on the port side"). The effect on thetemperature readings is illustrated in Figure 10.1. This shows the mean difference between the
VOS air temperature observations and an atmospheric forecast model for sensor exposure
classed as "good", "medium", or "poor". The left part of this figure shows night data plottedagainst cloud cover and the right part shows day time observations plotted against the solar
radiation. The latter was calculated from the ships' position and the reported cloud cover. At
night the better exposed sensor recorded lower temperatures than the model, suggesting a
possible bias in the model. The poorly exposed sensors were about 0.5°C warm. During the
day all the sensors showed increasingly warm readings with increasing solar radiation. For the
better exposed sensors this bias was up to 2°C; for the poorly exposed sensors the mean bias
reached over 4°C.

Sensor Exposure

3.5

Good
Medium
Poor

3.0
2.5
2.0
1.5

night

1.0
0.5
0.0

(oktas)/Radiation

110000 00

Wave

88 00 00

Short

00

- 2 0 70

- 4 0 50

- 6 0 30

Cover

44 00 0Cloud

22 00 00

Total

- 8 0 10-1.0

66 00 00

day

-0.5

-1000

VSOP - Model Air Temperature (°C)

4.0

(W/m2)

Figure 10.1. The effect ofdifferent instrument exposureon air temperatureobservations. Night time data
are plotted against total cloud
cover, day time data areplotted against the calculated
incoming solar radiation. See
text for discussion. (from Kentet al. 1991)

Data obtained from hand held psychrometers showed solar heating effects similar to thebetter exposed screens suggesting that the bias represents a heat island effect caused by the ship.
The bias in data from better exposed sensors (which constituted the majority of theobservations) was found to depend both on the solar heating and the relative wind speed and acorrection scheme was devised (Kent et al., 1993b).
C. VOS - COMPARISON WITH WEATHER SHIPS
The important values for sensible and latent heat fluxes are the small differences
between air temperature Ta and wet bulb temperature Tw , respectively, compared to the sea
surface temperature Ts. To quantify the systematic errors in these quantities, Lindau (2000) has
compared temperature measurements of Ocean Weather Ships, on the assumption that they arecorrect, with reports of merchant ships in the vicinity of the OWS. Data were available from15 Stations, 11 in the North Atlantic and 4 in the North Pacific, operating during different
periods between 1945 and 1989 (see Section 4.2.2). Simultaneous merchant ship observations
within a radius of 300 km were extracted from COADS. In this way, 700,000 pairs ofobservations were obtained. To avoid errors due to an unsymmetrical distribution of merchantships around the OWS, data were interpolated on a two-dimensional linear field for each monthand each station.
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The air temperature measured on merchant ships was higher than OWS reports for all
stations, except OWS M (Figure 10.2). The mean difference (Ta(VOS) – Ta(OWS)) varied
considerably from - 0.00oC at OWS M to 0.69oC at OWS P. Because no relation to climate
zones was obvious, the difference was averaged over all stations giving an overestimation of
0.259oC. The comparison of wet bulb temperatures yielded similar results. Tw was generally
overestimated by merchant ships, the mean bias was 0.235oC (Figure 10.3). For the calculated
specific humidity (or dew point ) of the air to be unaffected by the temperature error, Tw needs
to change by an amount which depends on the air temperature (Hoeber, 1995; Kent and Taylor,1996) but which is about half the air temperature error. Thus compared to the weather ship
data, the ship wet bulb values were too warm, but only by about 0.1oC. For SST, averaging theresults from all stations, Lindau (2000) found a negligible bias (0.005oC) between merchantships and OWS measured Ts (Figure 10.4).

Figure 10.2 The mean difference of air
temperature measurements (Merchant ships
minus Ocean Weather Ships) at Atlantic
OWS's A, B, C, D, E, I, J,K, L, M, R, andPacific OWS's P, N, T, V. The dashed line
shows the mean of all stations.

Figure 10.3. As Figure 10.2 but for wet
bulb temperature.

Figure 10.4. As Fig 10.2 but for sea surface
temperature.

However, the important quantities for determining heat fluxes are mainly the errors forthe air-sea temperature differences. Thus, merchant ships were reporting a too warm
atmosphere, and one which contained too much water vapour. As the values at sea surface
were fairly well determined, the vertical temperature and humidity gradients were too small, sothat the sensible and latent heat fluxes would be underestimated.
Thus the estimatedtemperature biases in the merchant ship observations found by Lindau (2000) were:∆Tw = 0.235˚C∆Ts = 0.005˚C∆Ta = 0.259˚CThis is in contrast to Kent et al. (1993a), who found higher over-estimations for all threetemperatures reported by VOS.
If not only air and wet bulb temperature should beoverestimated by a comparable bias, but also the SST, much of the overall error effect on

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fluxes would be compensated. Thus the mean correction suggested above is not beyond
question.
10.4.2 Surface air temperature from the satellite SSM/I sensor
The near-surface air temperature Ta enters the bulk formula for sensible and latent heat
fluxes in different ways. It is needed directly to determine the air-sea temperature difference inthe computation of sensible heat flux. But it is also involved in the computation of exchange
coefficients, air density, and latent heat of evaporation for both turbulent heat fluxes. Because
there is no means to directly obtain the air temperature from the satellite measurements
currently used for heat flux estimation, some indirect methods of varying accuracy have beendeveloped.
A very simple method is to assume slightly unstable conditions at any location at any
time and set Ta = TS - 1, where T S is the sea surface temperature. The results of Wells andKing-Hele (1990) show that most of the observed air-sea temperature differences in the tropicaloceans are of the order of 1˚C. However if, instead of the assumed unstable conditions, stableconditions occur, the exchange coefficient for latent heat flux will be underestimated at low
wind speeds by ~50% (Schulz et al. 1997). Another simple method is to compute Ta from theretrieved air specific humidity (see Section 10.5.2) assuming a constant relative humidity, e.g.
80% (Liu, 1988), or using a climatological relative humidity. This might be accurate enoughto compute the exchange coefficients but seems to be too rough an estimate to determine thesensible heat flux.
A more sophisticated approach using a relationship between TS and the Bowen ratio atlong time scales (compared to the synoptic time scale) has been undertaken by Konda et al.
(1996). This work is based on the previous work of Hicks and Hess (1977) who establishedthis relationship from experimental data. Their aim was the reconstruction of sea surface skintemperature (rather that the bulk temperature measured by ships) in order to compute long term
averages of the turbulent fluxes more correctly. Adapted from Konda et al. (1996) the
turbulent heat fluxes can be written as: ∂T  ∂q 
H = − ρc p K H  a  ; E = − ρLK E   ∂z 
 ∂z 

(10.2)

where ρ is the air density, L is the latent heat of evaporation, cP is the specific heat at constantpressure, K H and KE are the diffusivity coefficients of heat and humidity, T a is air temperature,and q specific humidity. Assuming that KH = KE we obtain for the Bowen ratio:β=

H c p ∂Ta=E L ∂q(10.3)

If one uses the bulk formulae for the fluxes (for example, (7.8)) the Bowen ratio reads:β=c p CH (T0 − Tz )LCE (q0 − qz )(10.4)

where C H and CE are the bulk transfer coefficients and the indices 0 and z denote values at thesea surface and at the reference level over the surface, respectively. (10.3) and (10.4) togethergive:

LCE (q0 − qz )L ∂q=c p ∂Ta T = T c p CH (T0 − Tz )(10.5)z

If q is written as αqS(T) with α relative humidity and qS(T) denoting the saturation humidity
function (10.5) can be written as:

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CE (q0 − qz )qz ∂qS (Tz )∂α=+ qS (Tz )CH (T0 − Tz ) qS (Tz ) ∂T T = Tz
∂T T = Tz

10.4 Air Temperature
10.5 Humidity(10.6)The second term on the right hand side of (10.6) has been neglected by Konda et al. (1996)
because it is much smaller than the first term and it is not directly determinable from satellite or
buoy measurements.
Using the empirical relationship between total precipitable water, obtained from SSM/I
measurements and near-surface humidity established by Liu (1986), and AVHRR multichannel
sea surface temperatures adjusted to skin temperatures, Ta can be computed from (10.6). The
error of a monthly mean satellite-derived air temperature is then approximately -0.3˚ ± 3.1˚C asfound by comparison to TOGA TAO buoys and buoys of the Japan Meteorological Agency.
This accuracy is not high enough to study sensible heat fluxes on a monthly scale, but can be
useful for analysing interannual variations. Improvements to this technique by using further
developments of the retrieval schemes for T S and q have not been explored. However,comparison with the approach based on the assumption of a constant relative humidity showthat the Konda et al. (1996) method is better in bias and rms error.
A recent paper by Jones et al. (1999) tried to invert monthly means of Ta from SSM/I
measurements of total precipitable water, W, and sea surface temperature, T S, analysis from
NCEP using neural network techniques. The network was trained with data extracted from theSurface Marine Data provided by da Silva et al. (1994). The neural network was used in a sortof double loop where in the first round the network was trained only with W and TS and in thesecond round the bias between the algorithm’s outcome and the truth data set was used as an
third input parameter for the network. From this procedure it was not surprising that the global
mean bias between the T a produced by the network and the da Silva data set was negligible.
The global rms was stated to be 0.72 ± 0.38˚C which is much lower than the results obtainedwith the Konda et al. (1996) method or the method of Jourdan and Gautier (1995) who obtained
a global rms error of 2.6 ± 1.4˚C from a polynomial fit between W and Ta. However, acriticism is that the da Silva et al. (1994) data set was regarded as ground truth. Given the large
differences between in situ climatologies found by Josey et al. (1999), compared toindependent high quality buoy measurements, this may not be appropriate. Since the satellitederived data sets are expected to improve on the in situ climatologies, a real challenge forverification would be to compare with the independent long term buoy data sets like that of the
Subduction experiment (Moyer and Weller, 1997).
10.5 Humidity
10.5.1 In situ humidity measurement
A. HIGH QUALITY IN-SITU MEASUREMENTS
The wet and dry bulb psychrometer is the traditional meteorological instrument for theroutine measurement of temperature and humidity. In general, however, psychrometers are notsuitable for continuous routine measurement of atmospheric humidity at sea in stand-alone or
automatic mode because of their need for attention (e.g. washing salt from the wick,
replenishing the water reservoir). Dewpoint hygrometers are also based on soundthermodynamic theory, measuring the temperature at which a film of dew forms on a cooled
mirror, but are generally too complex to serve as operational instruments. Their main use in
air-sea studies is as a reference standard; accuracy of 0.2˚C in dewpoint is readily achievable(corresponding to 0.2gkg-1 at about 22˚C).
Nowadays, thin-film polymers which absorb or desorb water as the relative humidity
changes are the most common humidity sensors, and recent developments have greatly
improved their accuracy and stability of calibration. The polymer usually forms the dielectricof a capacitance in a circuit which provides an electrical output proportional to relative
humidity. Conversion to mixing ratio, specific or absolute humidity requires the temperature ofReport of the Working Group on Air Sea Fluxes

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10.5 Humidity

the air surrounding the dielectric. Provided this temperature is obtained from a co-located PRTor thermistor, errors in humidity measurement are less severe than those of temperature, water
vapour being a conservative quantity.
However, for accurate measurement thesetemperature/RH sensors should be shielded from solar radiation in a double screen with forced
ventilation in the same way as a psychrometer. At very high humidity, or if the polymer
becomes physically wet, the humidity measurement can become unrealistic (greater than 100%
relative humidity). Earlier versions of these sensors often failed to recover from this, but
recent models seem to have largely overcome the problem and recover, although sometimes
only after several hours.
More sophisticated instruments which measure humidity fields (e.g. Raman lidar) aredescribed by Post et al. (1998). Most of these are still at prototype stage, and are unlikely toimpact the routine measurement of humidity for flux calculation for many years.
B. VOS HUMIDITY DATAIn assessing the accuracy of VOS observations, Kent et al. (1999) examined the random
errors in specific humidity because that is the humidity variable that is used in calculating thelatent heat flux. However they found that the errors in specific humidity reflected the variationof saturation vapour pressure with temperature. Higher errors occurred in tropical regions
where the air is warm. The humidity variable reported by the ships is dew point temperature.
Errors for dew point tended to be about 2°C in colder regions and somewhat less, between 1°C
and 2°C in warmer regions (Figure 10.5)

Specific Humidity Random Error

3.0

2.5

2.0
1.5

1.0

0.5
0.0
0

5

10

15

20

Mean Air Temperature

25

30

Figure 10.5. Random
observational error in specifichumidity (gkg-1) as a functionof mean air temperature (°C).
The upper line is the error in
specific humidity arising froma 2°C error in dew pointtemperature and the lower thatfrom a 1°C error in dew point.
A relative humidity of 80% has
been assumed in thecalculation.
(from Kent et al. 1999)

The VSOP-NA results (Kent et al. 1993a) showed that psychrometers produced lower(and therefore presumably more accurate) dew point readings compared to screens. Since theship may often be a source of heat but is rarely a significant source of water vapour, shipboard
humidity readings may be of better quality than the temperature data (see for example Kent &Taylor, 1996). The humidity data is vital for determining the latent heat flux which, over muchof the ocean, represents the major loss of heat to the atmosphere and which tends to dominate
the interannual variability of the fluxes. However humidity is at present not reported by a large
number of the VOS. Equipping those ships with wet and dry bulb psychrometers andencouraging them to report humidity observations would represent a major improvement to the
VOS data set.
10.5.2 Remote sensing of near surface humidity
A. INTRODUCTION
Because the interaction of the radiation field with water vapour at a distinct level is notmeasurable, all methods for determining the near-surface humidity make use of a verticallyintegrated water vapour content (obtained from a passive microwave instrument such as the
SSM/I) as a predictor for the near-surface specific humidity, q , required for the bulk approach.
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The correlation between both quantities depends heavily on the time scale considered. At any
given time, Schlüssel (1989) showed that the total precipitable water, W , is only weakly
correlated to q. He computed the correlation coefficients between adjacent atmospheric layers
and atmospheric layers separated by a distance of 50 hPa or more. Vertical profiles of thecorrelation coefficient were larger than 0.9 for adjacent layers and values larger than 0.8 forlayers with a separation distance of 50 hPa. However, the correlation profile always exhibiteda significant minimum near the mean height of the atmospheric boundary layer whenever the
distance between the layers was larger than 50 hPa. This indicates a decoupling of the moisturein the boundary layer from that in the free troposphere. Liu (1990) found similar results when
he examined vertical variance profiles of semi-daily and daily radiosonde ascents for two
tropical stations and one station in mid-latitudes. All three variance profiles showed amaximum at approximately 800 hPa caused by varying atmospheric boundary layer height anda varying contrast with the water vapour content above the boundary layer. The variancemaximum as well as the correlation minimum are much less distinct if monthly averages are
considered. This is the reason why Liu and Niiler (1984) had success in establishing thepolynomial relationship between monthly averages of q and W.
B. THE LIU AND NIILER (1984) ALGORITHM
The method of Liu and Niiler (1984) determines the monthly marine surface-layer
humidity with a simple polynomial regression of q versus W . The relation was found by
examining radiosonde data from 11 mid-ocean island stations and weather ships scattered over
the Atlantic and the Pacific ocean. The accuracy stated for the global regression formula was
0.8 gkg-1 (Liu, 1986) and 1 gkg-1 (Liu et al., 1991) depending on the validation data source.
This simple formula can be used with any retrieval algorithm which determines W, for example
that of Schlüssel and Emery (1990):(10.7)
W = k0 + k1 ln(280 − Tv22 ) + k2 (ln(280 − Tv22 ) − Tv37 )-2which has as accuracy of 1.5 kgm for instantaneous SSM/I measurements. Upper indices ontemperature denote the SSM/I channel and the lower index stands for the polarisation. Some
scientists have tried to also use this method for the production of daily values of q but, as may
be expected from the previous discussion, this reduces the accuracy in q so much that the result
becomes more or less useless.
C. THE SCHULZ ET AL. (1997) ALGORITHM
Because of the inability of Liu’s method to determine q for individual situations,
Schulz et al. (1993) developed a new method that first derives the integrated water vapour
content of the atmospheric boundary layer wB (using a somewhat artificial height of 500 m) andthen deduces q with a simple linear regression from w B . Schulz et al. (1993) showed that wB
can be independently retrieved from W and that the correlation between q and w B is muchhigher than that with W. Schlüssel et al. (1995) improved this technique slightly by obtaining
the specific humidity directly from the brightness temperatures, thus avoiding the error
propagation that occurs in the two-step method. The standard error for this globally valid
retrieval was stated to be 1.1 g kg- 1 for an instantaneous SSM/I measurement. Comprehensive
comparisons have been made by Schlüssel (1995) and Schulz et al. (1997), using data frommerchant ships, OWS and scientific experiments. They confirmed the global validity of thismethod and found rms errors not much higher than the stated retrieval error. A large portion of
the error was due to the mismatch of the measurements in time and space as shown by Wentz(1997).
However, a problem was found over tropical oceans where a systematic
overestimation of q occurred if the mid-tropospheric humidity was high. Schulz et al. (1997)
reported that the correlation between the surface q and the water vapour content in the lowest
500 m of the atmosphere was much lower than that for extratropical atmospheres. A reason
might be that a 500m layer does not represent the boundary layer depth in a case of convective
activity and high mid-tropospheric humidity very well.
D. THE CHOU ET AL. (1995) EOF METHOD
Recently, an alternative approach has been reported by Chou et al. (1995b; 1997).
They estimated q from the total water vapour content W and wB, retrieved with the algorithms
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10.6 Windof Wentz (1989) for W and Schulz et al. (1993) for w B, using an EOF (empirical orthogonal
function) method with different EOF's for six classes of W. The accuracy attained for q was
not much different from the Schulz et al. (1993) method since the weights for w B in the EOF
analysis were two orders of magnitude larger than those for W . Some corrections wereintroduced for dry and wet biases occurring respectively at the low and high end of the humidity
spectrum. At the low end, Chou et al. (1997) only used W within the EOF analysis to correct
for an underestimation of q during wintertime over extratropical oceans. The other correction
concerned a positive humidity bias during summer in regions where warm air moves over acolder ocean surface. In that case they constrained the surface air humidity to the saturation
humidity at sea surface temperature. A side effect of this was that, in each case with anoverestimated wB , a positive bias will be automatically corrected.
E. THE JONES ET AL. (1999) NEURAL NETWORK APPROACH
As for the near-surface air temperature (see section 10.4.2) Jones et al. (1999) used aneural network to obtain monthly averages of q. Input to the neural network were W and seasurface temperature extracted from the Surface Marine Data provided by da Silva et al. (1994),
and the monthly averages of SSM/I brightness temperatures on a 1˚ x 1˚ grid. Since the bias
between satellite derived q and in situ data is incorporated in a second loop of the neuralnetwork, again it is not amazing that this method showed no bias when compared to the da Silva
dataset. The global rms error stated was 0.77 g kg-1 with smaller errors in the North Atlantic
and in the North Pacific (0.6 g kg- 1) and larger errors in the southern Indian, Pacific, andAtlantic Oceans (1.2 g kg-1) reflecting the small observation density in the da Silva dataset inthose regions. This method was only compared to that of Liu (1986) and showed the expected
improvement. Comparisons to Chou’s and Schulz’s methods and to independent in situ
measurements are needed to evaluate this method more carefully.

10.6 Wind
10.6.1 Introduction
Accurate wind data are important because wind stress increases roughly as (windspeed)2.7 and mixed layer deepening with (wind speed)4 while accurate high resolution windfields are crucial for accurate wave modelling. Furthermore, the scalar fluxes calculated using
a bulk flux algorithm are directly proportional to the wind speed and the wind stress is an
important factor in determining the atmospheric stability. Thus any error in wind speed will
result in errors in the latent and sensible heat fluxes.
Concerning the wind stress, a correction has to applied because observation errors, even
if random, would cause a systematic bias. There are two opposite effects: the first is anoverestimation of the wind stress due to errors in the wind speed by the factor
1+

∆W∆W
WW

(10.8)
where the second term denotes the relative error variance (Figure 10.6). The second effect is anunderestimation of the true wind stress due to errors in wind direction by the factor cos ∆dwhere ∆d denotes errors in wind direction (Figure 10.7). The errors decrease the steadiness ofreported wind fields and lead consequently to a lower mean wind stress. As discussed in
Section 8.2 the errors can be determined by comparing pairs of simultaneous wind reports as afunction of the distance between the ships (Lindau, 1995a). Considering both the speed andsteadiness error, the latter dominates in most wind climates, so that the stress may beunderestimated by 5% to 10%.
Wind data are available both from in situ observations and by remote sensing. With
regard to in situ data (Section 10.6.2) it will be argued that even the available "high quality"
data is likely to contain biases. This is important since these data have been used to verify theroutine wind data and also to develop and validate remote sensing algorithms. The routine wind
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10.6 Windobservations available from the VOS are obtained either from anemometer readings or by visual
estimate. Each technique will be shown to have advantages and disadvantages. Remotelysensed wind data is available from active radar instruments (scatterometer and altimeter) andpassive microwave radiometers. There have been four scatterometer missions during the 1990s
that provided wind vector information. The passive microwave SSM/I instrument can also
provide wind speed information. Compared to these instruments the altimeter derived data islimited to wind speed values along the sub-satellite track. However it should be noted that there
has been a sequence of altimeter missions over a number of years, allowing these data to be
used as a useful climatological data set for both significant wave height (e.g. Cotton and Carter
1994b) and wind speed.

Figure 10.6 Relative overestimation of thewind stress as a function of squared windspeed. Individual crosses depict monthly 5o by
10o averages in the North Atlantic. A linear fit
was applied, to be used for the correction.

Figure 10.7 Relative error of the winddirection as a function of squared wind speed
WW . Individual crosses depict monthly 5° x
10° averages.

10.6.2 In situ wind data
A. HIGH QUALITY IN-SITU MEASUREMENTS
The accuracy of ship and buoy winds has recently been discussed by Taylor et al.
(1999a), who stress that accurate measurement of wind over the ocean is difficult. It might be
expected that the best data sources would be anemometer measurements from ocean weather
ships (OWS), research ships, or meteorological buoys. However there are biases in each ofthese data types. For example, examining data from the period 1992 to 1994, Taylor et al.
(1995) showed that the wind observations from OWS Cumulus were not properly corrected forship motion when the ship was drifting or hove to. This resulted in errors of up to 1.5 ms-1.
Isemer (1995) has attempted to evaluate the ocean weather ship wind data more generally. As
for any other ship, the wind data from OWS and research ships are in error due to the air flow
distortion caused by the ship (Yelland et al., 1998); errors of order 10% are quite possible. As
discussed in Section 9.2.2, wind speeds from meteorological buoys are believed to be biased
low in strong winds (Large et al., 1995; Weller and Taylor, 1999; Zeng and Brown, 1998).
Two possible causes are the vertical movement of the buoy through a non-linear near surface
wind profile, and the distortion of that profile due to the effect of high waves; again errors oforder 10% may be possible.

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It is often overlooked that the appropriate wind speed for use in bulk flux algorithms isrelative to the ocean surface (see Equation (7.8)). In many cases currents are a small fraction of
the wind speed, and the contribution to error in (uz - uo) also small, but this is not always so.
Also, for light winds with a stable ocean surface layer, the wind-driven current can be asignificant correction (Kudryavtsev and Soloviev, 1990). This introduces another source of
uncertainty, because the surface velocity is very seldom measured. There are two ways inwhich conversion from relative to true wind can take some account of the surface velocity; by
combining the ship motion in earth coordinates (e.g. from GPS) with currents from the ship’s
ADCP; or by using the Döppler-log/gyro which provides the ship’s motion through the water.
Both methods incur additional sources of instrumental error, and furthermore the measured
currents are at considerable depth (of order 10m) where the locally induced wind drift will bevery small. Thus we can assume that, for the bulk formulae, the wind induced surface drift
has been accounted for in the determination of the transfer coefficient. However any ocean
current, other than that locally induced by the wind, should be taken into account.
B. VOS - ANEMOMETER WINDS
For the VOS data, Kent et al. (1999) found that a typical root mean square (RMS)
error for a single wind speed observation is about 2.2 ms-1. However this was after instrumental
observations had been corrected for the height of the anemometer above the sea surface (using
the WMO47 data) and visual observations corrected using the Lindau (1995a) version of theBeaufort scale. For the observations as reported, the errors were about 15% greater - about 2.5
ms-1. This demonstrates that, despite the varying effects of air flow distortion around the ship,correcting the data for anemometer height does reduce the errors. The RMS wind speed errors
appeared to be lower than average in tropical regions, however no significant dependence onwind speed was found.
The VOS in the VSOP-NA project (Kent et al. 1993a) reported the anemometer
estimated, relative wind speed in addition to the calculated true wind speed. Kent et al., (1991)
showed that a major cause of error was the calculation of the true wind speed. Only 50% of thereported winds were within 1 ms-1 of the correct value, 30% of the reports were more than 2.5
ms-1 incorrect. For wind direction, only 70% were within ±10° of the correct direction and
13% were outside ±50°. These are large, needless errors which significantly degrade thequality of anemometer winds.
A similar conclusion was reached by Gulev (1999).
Preliminary results from a questionnaire distributed to 300 ships' officers showed that only 27%of them used the correct method to compute true wind. The problem is not confined to VOS
observations. A majority of the wind data sets obtained from research ships during the World
Ocean Circulation Experiment showed errors in obtaining true wind values (Smith et al., 1999).
Taylor et al. (1995) reanalyzed the VSOP-NA results for wind speed. They found that,having corrected OWS Cumulus data for ship motion and corrected the VOS data foranemometer height, there appeared to be agreement between the OWS and VOS data for winds
below 10 ms-1. For higher wind speeds the VOS winds were biased high - by about 1.5ms-1 to 2
ms-1 at 20 ms-1 wind speed. If this bias is real, the reasons might include mis-reading of the
anemometer dial (gust values rather than mean winds being reported) and air flow distortion.
The use of computational fluid dynamics (CFD) to analyse the air flow over typical VOS shapes
shows that there is a plume of accelerated air above the wheelhouse top (Taylor et al. 1999a).
The shape of this plume depends on the geometry of the ship's accommodation block. An
anemometer mounted above the wheelhouse may be below, in, or above the plume maximum
depending on how high and how far aft it is mounted. Below the plume the wind will besignificantly underestimated, in or above the plume an overestimate will occur.
C. VOS - VISUAL WINDS
Kent and Taylor (1997) reviewed the various Beaufort Equivalent Scales and found thatthat of Lindau (1995a) was most effective at giving similar wind speed distributions for bothanemometer estimated and visual monthly mean wind data. They also confirmed Lindau's
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statistical method by which they were derived. It should be noted that the Lindau scale is moresimilar to the WMO1100 scale used for the observations (and also to the "UWM" scale used byda Silva et al. (1994)) compared to the so called "scientific scale" recommended by CMMIV(Table 10.2, see also WMO, 1970). Derivation of an accurate scale is not simple so the method
will be briefly described.
The Lindau (1995a) scale was derived by comparing wind speed measurements from
Atlantic OWS with visual estimates from passing merchant ships (VOS). To ensure a truecomparison two conditions had to be fulfilled: the error variance and the natural variability must
to be equal in both data sets. Lindau averaged over four individual OWS observations but oversix VOS observations to compensate for their different accuracy. The OWS data wereaveraged over 24 hours and calculated for the VOS data for each location and each season theradius that contained the same variability as for one day. The new scale was derived fromthese averages by the method of cumulative frequencies, which treats the variances of both data
sets in a symmetric manner. As a result, the new scale (Table 10.2) was able to reproduce the
OWS wind speed without systematic errors in either the average or in the variance.
Table 10.2. New 10m-equivalent values compared to the WMO Code 1100. N gives thenumber of data pairs, which consists of daily means for OWS measurements and spatial means
for Voluntary Observing Ships (VOS).
Bft

0

1

2

3

4

5

6

7

8

9

10

11

12

WMO

0.0

1.7

4.7

8.4

13.0

18.3

23.9

30.2

36.8 44.0

51.4

59.4

67.7

New

0.0

2.3

5.2

8.9

13.9

18.9

23.5

28.3

33.5 39.2

45.5

52.7

61.1

N

6

378

2287 8441

17197

11598 8870 4655 2068 597

122

15

1

The definition of the Beaufort scale has changed during time (as have the characteristicsof the ships from which the estimate is made). A time dependent equivalent scale may therefore
be necessary. Lindau (1995b) checked North Atlantic wind observations of the last hundredyears against simultaneous individual pressure differences between the reporting ships.
Assuming a constant relationship between wind speed and pressure gradient throughout theyears a time dependent scale was obtained (Figure 10.8) . Applying this scale to COADS theapparent positive trend in wind speed since 1945 was removed.
D. RELIABILITY OF COADS MONTHLY WINDS
The reliability of the COADS Release 1a 2-degree monthly winds has been tested bycomparison with instrumental measurements in the North-West Atlantic for the period 19811991 (Gulev, 1999). The instrumental data set contained anemometer measurements of highhomogeneity and quality, taken by six research sister-ships with known anemometer heights in
the North West Atlantic.
Care was taken to process the instrumental data to provide
compatibility with the COADS winds. In particular, the error variances for monthly averagesof wind speed were tested to allow a two-way regression comparison. The results showed an
overestimation of the COADS winds in the low ranges and underestimation of the strong andmoderate winds. Such a relationship is different from those obtained in the other validation
studies (e.g. Lindau, 1995b; Kent and Taylor, 1997); indeed, application of alternative
Beaufort scales did not remove the bias, but made it even more pronounced. Thus, theconclusion of Gulev (1999) was that the disagreement resulted mainly from errors in the
anemometer measurements in COADS, primarily from the incorrect evaluation of the true wind
(Section 10.6.2B). Taylor et al. (1999a) suggested that airflow effects, for the class of researchship used, may also have contributed to the differences. In either case a possible conclusion isthat Beaufort estimates may well be less biased in the COADS than anemometer measurements.
Similar arguments to these are used to justify the continuing preference of a number of
meteorological agencies for visually estimated winds rather than anemometer readings.

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Figure 10.8 Time dependent
equivalent scale illustrated by
the temporal evolution of
Beaufort forces 2 to 7.
10.6.3 Remote sensing of surface wind
A. SCATTEROMETRIC SURFACE WIND VECTORSWind vector estimates from scatterometers are based on empirical relationships ("model
functions") relating backscattered energy to wind speed at 10m under neutral conditions.
Geophysical validation over a wide range of wind speeds has been difficult due to a) the
difficulty in acquiring suitable in-situ observations and b) the inherent incompatibility between
scatterometer measurements and buoy observations (Freilich and Dunbar, 1999). However themodel functions have improved over time with the introduction of more sophisticatedtechniques and also as more scatterometer and high-quality in situ data become available to test
the full parameter space of the backscatter-wind relationship. Scatterometer data are available
from numerous sources (each using a different model function). Thus it is important to note theparticular version and source of scatterometer data.
The fundamental scatterometer design results in multiple possible wind directions (socalled ambiguities) from which the most likely solution is determined. This ambiguity selection
process is fairly accurate; for regions with rms wind speed greater than 4 ms-1, the NSCAT
ambiguity removal skill is conservatively estimated to be 95% (Gonzales and Long, 1999). The
sampling characteristics (number and width of data swaths, data coverage) of the scatterometerdata govern the ability to estimate synoptic fields of winds. This issue was addressed more
fully in Section 8.6.3.
The accuracy of the scatterometers is relatively excellent compared to errors for winds
from VOS. Validation of the CERSAT ERS-1/2 scatterometer wind retrievals found an
systematic underestimation of nearly 0.75 m s-1 and an RMS error of ~1.3 m s-1 (Graber et. al.,1996). Directional biases and RMS errors were ~7° and ~22° respectively. Other comparisonsfurther explore such dependencies as wind direction on incidence angle (e.g. Ebuchi andGraber, 1998) and validation in coastal seas (Kent et. al., 1998).
Evaluation of the 25-km NSCAT instrument winds has been much morecomprehensive. Compared to high-quality ship winds (Bourassa et. al., 1997), the NSCAT
winds had RMS differences for speed and direction of 1.8 m s-1 and 14˚. When compared tobuoys, the bias was -0.3 m s-1 and RMS error ~1.3 m s-1 (Freilich and Dunbar, 1999). Other
validations are available and include a wide range of approaches, including gridded productevaluation through direct comparisons to other products (e.g. Atlas et. al., 1999); comparison ofReport of the Working Group on Air Sea Fluxes

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ocean models forced by various products (e.g. Verschell et. al, 1999); and comparisons for use
in air-sea carbon exchange (e.g. Boutin et. al., 1999).
Some numerical weather prediction (NWP) centres have been incorporating
scatterometer data into their operational surface wind analyses. For example, since January
1996, ERS scatterometer winds have been assimilated in the operational ECMWF analysis.
This will likely continue as the QuikSCAT data systems include the operational distribution of
QuikSCAT data by NOAA.
B. PASSIVE MICROWAVE SURFACE WIND SPEEDS FROM SSM/I
Introduction: Thermal radiation emitted by the sea surface at millimetre frequencies isstrongly modified by wind induced sea surface roughness and partial foam coverage. The
classical description of the emissivity of a foam covered rough ocean surface as a function of
frequency ν, incident angle θι, and polarization P is:ε (ν ,θ i , P; u, C f ) = (1 − C f )ε (ν ,θ i , P; u) + C f ε f (ν ,θ i , P)

(10.9)

where Cf is the fractional foam coverage, εrw is the emissivity of a wind roughened surface, andεf is the emissivity of foam. In the algorithm development history different theoretical andempirical models have been used to relate Cf, εrw, and εf to the physical state variables of the sea
surface and to the wind field above. In the material that follows four types of wind speed
algorithms for the SSM/I are described. These are the modified D-Matrix approach by
Goodberlet and Swift (1989), the radiation transport model and regression based algorithm of
Schlüssel and Luthardt (1991), neural network algorithms by Stogryn et al. (1994) andKrasnopolsky et al. (1995), and the wind speed part of the SSM/I all weather algorithm byWentz (1997) and Wentz and Spencer (1998). The selection of these four algorithms does notcover all the available wind speed algorithms but it gives a description of different
methodologies and a brief survey of the history of algorithm development.
The original D-Matrix algorithm as given in Lo (1983) computes the wind speed at areference level of 19.5m from a linear combination of SSM/I brightness temperatures. It uses
11 sets of coefficients representing particular seasons and latitude bands. Goodberlet and Swift(1989) found that the original D-Matrix algorithm did not meet the accuracy criteria of 2 ms-1
when results obtained from SSM/I measurements were compared to NOAA buoys.
Additionally, they found a low bias at high wind speeds and zonal discontinuities due to thecoefficient scheme used. They developed an alternative new D-Matrix algorithm from SSM/I
brightness temperatures and buoy wind speeds using linear regression analysis that met the 2
ms-1 criterion for all seasons and latitude bands. Since this algorithm works only for rain freecases, the D-Matrix algorithm uses a rain flag system to discard affected measurements. Suchan algorithm is quite robust but doesn't point the way to future improvements since it is purely
statistical; no information on the influence on the measurements of the surface emissivities
given in (10.8), or of the atmospheric part of the signal, can be quantified. Goodberlet andSwift (1992) improved their algorithm with a nonlinear version that should account for waterladen atmospheres. As stated by Krasnopolsky et al. (1995) this retrieval has a singularity at∆37 = 30.7 K which may fall within the useful range of brightness temperatures.
The Schlüssel and Luthardt (1991) algorithm is based on studying theoretically theradiative transport within the SSM/I channels for a large set of oceanic/atmospheric situations
with respect to wind speed. The retrieval formula is derived by multivariate regression analysis
from the simulated synthetic measurements. It derives the wind speed mainly from thebrightness temperature difference between horizontally and vertically polarised components atthe same frequency so it is, like the Goodberlet and Swift (1989) algorithm, a linear
combination of SSM/I channels. The theoretical accuracy for the globally valid passive windspeed retrieval is stated to be 1.4 ms-1 under conditions where the satellite measurements are notaffected by heavy rain.

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This retrieval was evaluated by Schlüssel and Luthardt (1991) by comparing satellite
derived wind speeds with objectively analysed in situ wind speeds over the North Sea (Luthardt,
1985) during the period July 1987 to June 1988. They obtained a standard error of 1.9 ms-1
with a small bias of 0.2 m s-1. Schulz (1993a) compared 3403 globally distributed buoy andship measurements with retrieved wind speeds during the period July 1987 to September 1987
and found a standard error of 2.1 ms-1 with the same bias as for the North Sea. The systematic
error was only significant at wind speeds above 15 ms-1 and could have been caused by an
insufficient parameterisation of the emissivity of the ocean surface at high wind speeds. This isdifficult to prove since there are only a few measurements during high wind speeds andmoreover still fewer measurements of the emissivity in the microwave region under thoseconditions. However, this comparison revealed also that this algorithm produces a high bias of
0.81 ms-1 at latitudes between 20°N and the equator, showing that the retrieval scheme issensitive to liquid water in the atmosphere.
Consequently, Schlüssel (1995) modified the algorithm to allow more liquid water in
the atmosphere during the radiative transport simulations and also determined new coefficientsfor the algorithm (using the same channels) if light rain was present. The choice of retrieval
was decided using a rain flag system similar to Goodberlet and Swift (1989). The accuracy forthe light rain retrieval was determined to be 1.6 ms-1.
Neural network approaches are alternative empirical methods to derive wind speed
from passive microwave brightness temperatures which have become popular during the last
five years. Neural networks can be advantageous if:❍

nonlinearities occur in the transfer function, from brightness temperature to the sought
geophysical parameter, that vary over the range of measurement space.

❍

there is no a priori knowledge with regard to an analytical representation of the transfer
function.

Stogryn et al. (1994) and Krasnopolsky et al. (1995) have developed neural network retrieval
schemes that outperform all of the previously described algorithms. The biases and rms errors
as stated by Krasnopolsky et al. (1995) were 0.05 ms-1 and around 1.6 ms-1, respectively, whencompared to buoy measurements in their test data set. Krasnopolsky et al. (1995) introduced a
new rain flag system that was based on liquid water content and which recovered 40% of
measurements rejected by the Goodberlet rain flag system.
However, these algorithms can have limitations because:❍

the training data sets must include enough low and high wind speed cases. Otherwise ahigh bias at low wind speed and a low bias at high wind speed occurs and the varianceof the wind speed distribution is too low.

❍neural networks are more sensitive to sensor-dependent systematic errors (likecalibration). For example, Krasnopolsky et al. (1995) trained their network for the
DMSP F8 satellite and when they apply their coefficients to data of the F11 satellite
they obtain a bias of -0.9 ms-1 and a rms error of 1.85 ms-1 although the brightness
temperature differences between F8 and F11 were only ~1K for all channels. Thiseffect can be diminished by computing coefficients for each satellite but it should be
kept in mind if neural networks are blindly applied.

With respect to equation (10.8), the same arguments hold as for the empirical regression
algorithms if we ask for physical explanations of the change of surface emissivity with windspeed. As for any empirical algorithm, they may give good estimates of wind speed but little
can be learnt about the physics. This could be changed if the neural network were not trainedon buoy-satellite match ups but rather used in conjunction with radiative transfer models thus
giving more control over the situations considered.
The Wentz (1997) all weather algorithm represented a more physical approach inretrieving the wind speed from SSM/I measurements in rain free situations. Wind speed was
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10.6 Wind
10.7 Waves

retrieved, along with columnar water vapour and columnar cloud liquid water, using anonlinear optimisation method.
The wind-induced emissivity of the sea surface wasparameterised by a two scale theory (Wu and Fung, 1972; Wentz, 1975) which was based on aknowledge of the root mean square slope of the large gravity waves, the standard deviation of
the small irregularities, and the correlation length of the small surface structures. In practice,Wentz (1997) expressed the wind-induced emissivity as a monotonic function of wind speed
which consisted of two linear segments connected by a quadratic spline such that the function
and the first derivatives in wind speed were continuous. The coefficients for this model were
derived from collocated buoy and SSM/I observations.
Wentz (1997) also investigated the role of wind direction on the retrieval accuracy andfound that errors of approximately 3 ms-1 could occur, especially if the radiometer looked in the
upwind direction. He developed a correction of this effect which brought the error down to 0.5
ms-1. Additionally, he claimed that the information on wind direction inherent to the SSM/I
measurements can be retrieved if the signal to noise ratio can be enhanced by building averages
over large scales and long times.
Importantly, Wentz (1997) also gave an error estimation that resolved the error budget
in terms of model errors, wind direction errors, radiometer noise, sampling mismatch between
satellite and buoys, and other errors that could not be resolved. This enabled him to subtract
the sampling mismatch error from the total observed error. For his own retrieval he ended up
with a small systematic error of 0.3 ms-1 and an rms accuracy of 0.9 ms-1 ; significantly betterthat all previous algorithms.
In a new all weather algorithm (Wentz and Spencer, 1998) the retrieval methods areextended to rain cases. With respect to wind speed, this simply consists of discarding theretrieval and filling in values from neighbouring pixels, or using a monthly climatologicalvalue derived from SSM/I. This algorithm has been used to compute a 10 year time series ofwind speed from the chain of SSM/I sensors which is, together with other atmosphericvariables, available via the internet under http://www.ssmi.com.

10.7 Waves
Wave information is currently available from measurements on buoys and other in-situ
platforms, VOS observations, satellite measurements (altimeter and SAR), and from modelexperiments.
Wave recorders at NDBC and JMA buoys, light vessels, and OWS's provide
measurements of significant wave height with continuity of records from several months tonearly two decades. These are relatively high quality data which can be used for validation ofother products, but their spatial coverage is poor, since most of the buoys located in offshore
regions are in the subtropical northwest Atlantic, mid-latitudinal northeast Pacific and equatorial
Pacific. Long-term observations from shipborne wave recorders (SBWR's) are available in a
number of locations in the northeast midlatitudinal Atlantic, particularly at OWS L and Seven
Stones Light Vessel. These two records (with a continuity of 12 to 20 years) were used to
identify secular changes in significant wave height (SWH) by Bacon and Carter (1991, 1993).
However, it is unclear to what degree the records from NDBC buoys and SBWR's are
consistent. There are some specific biases in both buoy and ship records and these have not yet
been quantified.
Reports containing visual wave observations appear in COADS in the early 1960's, andfor later years the contribution of reports with wave observations exceeds 40%. These visual
VOS observations were used to produce “Ocean Wave Statistics” (Hogben and Lumb 1967) and“Global Wave Statistics” (Hogben et al. 1986), which contain statistical distributions based onlimited visual wave data from 50 and 104 areas of the World Ocean respectively. These havebeen widely used by sailors and naval engineers. Gulev and Hasse (1998) brought the visual
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wave observations from COADS reports up to date, producing a North Atlantic wave
climatology on a 5˚ grid for the period 1964-1993. They derived heights, periods and directions
for wind sea and swell. SWH was derived using a combined approach of Barratt (1991) where
SWH is assumed to be the square root of the sum of squares of sea and swell heights for thecases when sea and swell directions are within a certain directional sector (found to be equal to30° (Gulev and Hasse 1998)); for other cases it is taken as higher of the two components.
Relatively good agreement was obtained for the resulting SWH in comparison to in situ
observations from NDBC buoys and OWS measurements. Some known biases in the COADS
observations include the overestimation of very small seas and swell (which results from thecoding of all waves smaller than 0.5 m with the unique code figure “1”), general
underestimation of both sea and swell periods by approximately 0.1 to 0.4 seconds, and large
overestimation of swell periods for 1968-1969 due to a change in swell period codes in 1968
which was not implemented simultaneously by all observers.
The only observation-based wave height climatologies available at present are derived
from the altimeters flying on board GEOSAT, ERS-1/2 and TOPEX/POSEIDON. Products
derived from the ERS-1 and ERS-2 scatterometers are still under development.
First
assessments showed that the algorithms used to retrieve Hs from the scatterometer severely
underestimated wind sea, but gave very reasonable estimates of swell (Bauer and Heimbach1999). Due to the limited sampling, altimeter derived wave climatologies are only available asmonthly means. Cotton and Carter (1994a) calibrated these monthly means against co-locatedbuoy measurements. They found the accuracy of the calibrated data to be better than 18 cm.
This made it possible to combine time series from different missions and construct a global
climatology which has continuity over about 12 years (starting in 1985) with some gaps in early
1990's. Bauer and Staabs (1998) compared the statistical properties of altimeter derived Hs
with those of measurements from OWS and in general found good agreement. They also,
however, observed that the algorithm used before 1994 to retrieve Hs from the ERS-1 altimeterproduced an unrealistic high frequency of occurrence of Hs = 2m, a deficiency that had alreadybeen noted by Cotton and Carter (1994a).
By now, wave models driven by winds from NWP systems can produce long-termclimatologies of waves (hindcasts) covering global ocean or individual ocean basins for periodsof several decades. These wave products are not fully independent, because they are largely
affected by the wind forcing used. Sterl et al. (1998) produced a 15 year climatology of waves
by driving the WAM wave model with the ERA winds (see also Section 2.4 of this report).
While the patterns of Hs thus obtained compared favourably with those derived from altimetermeasurements, the absolute wave heights were generally found to be too low. As the mostprobable reason the authors identified the limited resolution of ERA, stressing once more theimportance of the wind input for the modelling of waves. OceanWeather Inc. performed anumber of 40-yr (1958-1997) wave hindcasts using the ODGP-2 1G fully discrete spectral wave
model and ODGP 3G wave model with a grid resolution of 2.5˚ longitude and 1.25˚ latitude(1G) and 1.25˚ x 0.625˚ (3G) (Cox and Swail 2000). These hindcasts were driven by the
NCEP/NCAR 10m winds. Time resolution of this product is 6 hours, and all major wave
parameters, such as significant wave height, sea and swell heights are available. Details of the
OceanWeather Inc. wave hindcasts were described in Cox and Swail (2000).
Recently Young and Holland (1996) and Young (1999) produced a combined wave
product based on satellite remote sensing and NWP wave outputs. This global scale wave
climatology covers a 10-year period from 1986 to 1995 and has 2˚ spatial resolution formonthly means of significant wave height and period. Data from ERS, GEOSAT and
TOPEX/POSEIDON, as well as from the ECMWF operational system (WAM), were used to
produce the climatology. Analysis of the global fields of Young (1999) shows that this productstill requires comprehensive validation efforts.

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10.8 Cloudiness10.8 Cloudiness10.8.1 Introduction
Comprehensive, ocean-basin scale observations of clouds are based on either (1)synoptic reports or (2) satellite data. Some ship campaigns have also used intensive passive
and active remote sensing systems (e.g. ceilometers and cloud radars) for a more precise
characterisation of cloudiness in a restricted region.
10.8.2 In situ cloud data
Considering the VOS observations, the main problem is the quality of the cloud reports
returned by the relatively untrained observers. There have been hardly any studies of thequality of the reports. With regard to these data, Warren et al. (1988) remains the standard
reference climatology. Ship synoptic reports were obtained from the “SPOT” archive of the
Fleet Numerical Oceanography Center (FNOC) and Release 1 of the COADS for 1930-1979.
The painstaking work on this atlas yielded gridded monthly and seasonal cloud cover, cloudtype amounts, frequencies of occurrence, cloud base heights, harmonic analyses of annual anddiurnal cycles, and interannual variability and trends. Warren (personal communication) notesthat since 1988, the analysis methods have been improved; moonlight data is now included.
Work on a new atlas will cover the 44 years 1952-1995. A digital forms of the atlas isavailable. The main problem is the degree of reliability that can be ascribed to visual cloud
observations made by observers with little relevant training and whose view of the sky is inmany cases obscured.
Information on cloud base height and cloud overlap from synoptic atlases are
sometimes used as a cross check on satellite-based retrievals of parameters, such as surface
downwelling LW (e.g. Gupta et al., 1999).
10.8.3 Remote sensing of cloud data
ISCCP has combined geostationary and polar orbiting satellite data to produce ageographically comprehensive cloud record. The ISCCP second generation “D2” data isavailable for July 1983 – December 1998. ISCCP is an officially reviewed WCRP product, and
CD ROM’s of ISCCP clouds are issued by the NASA Langley DAAC. ISCCP cloud retrievals
are based primarily on two imaging channels, the reflected SW and LW in the emission
window. For polar latitudes only, a third channel at 3.5 micron is also used. Pixel resolution
is nominally 4 km. The pixels are sampled every 30 km and 3 hours (yielding the advanced
research product “DX”, which is internal to ISCCP). The main ISCCP D product is gridded in
280 by 280 km equivalent areas. Sunsynchronous NOAA satellites equipped with the AVHRR
are used at high latitudes. Low latitude retrievals are based on geostationary data. The
requirement to produce comprehensive coverage has resulted in the application of fairly
rudimentary, but ingenious, algorithms in ISCCP. Calibration of ISSCP has resulted in a
improved record from the radiometers on operational satellites (Brest et al., 1997), but goodcalibration remains a challenge for ISCCP (for example, Klein and Hartmann 1993). ISCCP
calibration is sufficient to identify the geographical patterns of interannual variability, such asthat due to ENSO. However, no presently operational cloud imager is considered to have thestability to confidently identify a small secular change in global mean cover. That includes the
VIRS imager on TRMM, which is used by CERES to process coarser resolution broadband
data. The CERES broadband radiometers are, however, sufficiently stable to identify changes
in global mean radiation (in Wm-2 rather than per cent cloud cover) to within 0.2%.
10.8.4 Combined products
Another source of cloud observations is the United States Air Force Real-Time
Nephanalysis (RTNEPH, Hamill et al. (1992)). It is operationally produced 8 times daily on
Northern and Southern Hemisphere polar stereographic grids (horizontal resolution 47.625 km
at 60o latitude). Data sources are primarily the infrared and visible channels on the DMSP
satellites, with some information from NOAA polar orbiters. Surface observations are used to aReport of the Working Group on Air Sea Fluxes

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significant extent when available, and a manual bogus procedure is used during each analysis
cycle. The RTNEPH data set consists of total clouds and up to four distinct cloud layers. Eachgrid point contains cloud coverage, geopotential height of the cloud layer bases and tops, timeof observation, and diagnostic information.
The analysis system makes assumptions to estimate the magnitude of lower cloud
obscured by higher clouds. There are rarely more than two distinct cloud layers. Hamill et al.
(1992) note that the intent is to maximise the probability of cloud detection; therefore both clear
and overcast conditions tend to be overestimated. The IR retrieval is a one-channel algorithm;
therefore, if there is no visible channel or surface data available, the analysis tends to miss warm
low stratus and high thin cirrus or to place the cirrus too low in the atmosphere. There are plans
to improve the algorithm by using multi-spectral satellite techniques. Masutani et al. (1999)
have compared several estimates of clouds, including ISCCP and RTNEPH.
10.9 Precipitation
10.9.1 Surface-based precipitation data
A. HIGH QUALITY IN SITU PRECIPITATION MEASUREMENTS
The advantages and disadvantages of different methods of high quality precipitation
measurements has been discussed at some length in Section 5.4 which dealt with the direct
measurement of precipitation as a flux. It must be recognised that the number of in-situ
precipitation measurements over the whole world ocean is still probably no more than several
thousand samples. Also, there is no possibility of covering much more than a few percent of theocean with radar measurements. Therefore these measurements (be they from mechanical or
optical gauges, disdrometers, or radar) are better considered as a means of verifying satellite
measurements and NWP products rather than the basis for the creation of a precipitation atlas.
However, the development of accurate and reliable raingauges, and their introduction into
operational use would provide a sharp increase in the number of open ocean samples within a
few years.
Despite the progress which has been achieved in precipitation measurement, there aresome problems which are still unresolved. In particular, wintertime solid precipitation in midand high latitudinal regions is still poorly determined, and it is unclear at the moment how thiscan be best overcome. Should the existing gauges be improved or completely new instruments
developed specifically to measure solid precipitation?
B. RADAR MEASUREMENT OF PRECIPITATION
Radar meteorology has a long history of application to precipitation measurement, andto the study of atmospheric dynamics from fields of backscatter intensity and Doppler velocity.
Its strength is the ability to display spatial patterns of rainfall within some hundreds ofkilometres surrounding the radar position. Unfortunately, radar-derived rainfall measurement
cannot be regarded as direct; to make the observed returns quantitative a number of error-prone
steps are involved. The fundamental problem is determination of the relationship between radarreflectivity, Z (mm6m -3), and rainfall rate, R (mm hr-1), which has to be established empiricallyfor each situation. The traditional form is:

Z = aRb(10.10)where the constants a and b fall within rather broad bounds, a being typically a few hundred
while b ranges between about 1.2 and 1.8 (Battan, 1973)1. Z - R relationships have rather an
extensive literature; see for example the discussions by Austin (1987) and Sauvageot (1994).
The accuracy of measurement therefore depends on a number of factors; the radar calibration,
signal attenuation due to the rain along the radar path and water on the radome, the vertical
1 Radar meteorologists usually quote the reflected signal in dbZ whereZ(dbZ) = 10.log10 Z (mm6m-3)

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structure of the precipitating system, and knowledge of the scan volume geometry. The latterpresents a particular challenge when the radar is mounted on a ship, because of the motion.
Finally, it is usual practice to adjust the radar Z - R relationship to whatever in-situ gauge array
may be under the radar beam (Steiner and Houze, 1997). Clearly, this presents difficulties over
the open ocean, and often leads to dependence on the nearest island-mounted gauge where therainfall may not be representative.
A recent use of radars for measuring precipitation at sea occurred during COARE when
C-band Doppler radars were deployed simultaneously on two ships for a total period of 90 days.
Each radar antenna was stabilised, using the combination of an Inertial Navigation Unit (INU)
and the Global Positioning System (GPS) to calculate all instantaneous components of ship
motion and their time derivatives so that the antenna appeared to be fixed to a horizontal plane.
The pointing accuracy was estimated to be 0.1 degrees. The COARE radar rainfall data haveundergone close scrutiny in the light of the initial disagreements with shipborne in situ rain
gauges (see Section 10.9.4E), and are well documented (Rutledge et al., 1993; Short et al.,
1997; Petersen et al., 1999). Several sources of error were discovered, and improvements madeto the signal processing and data analysis. These included careful recalibration of the system,
corrections for range attenuation and gain, and separate Z - R relationships for convective andstratiform rainfall. Tokay and Short (1996) showed that, for the same reflectivity, stratiform
rain rates are about half those of convective precipitation because stratiform produces morelarge raindrops than convective rainfall due to aggregation, and fewer small to medium drops.
Tropical stratiform falls from a layer of melting aggregates, which cause the well-known radar"bright band", a region of enhanced reflectivity just below the zero degree isotherm. Short etal. (1997) give reflectivity Z = 323R 1.43 for stratiform and Z = 120R 1.43 for convective rainfall.
For monthly rainfall, however, Steiner and Houze (1997) question the value of using separate
relationships for convective and stratiform rainfall.
COARE demonstrated that the combination of high-quality Doppler radar with precise
pointing and stabilisation has application in both oceanographic and meteorological studies
associated with precipitating systems over the ocean. Research vessels (e.g. the US R/V Ronald
H. Brown and the Japanese R/V Mirai) are beginning to install such systems as part of the ship’s
inventory of equipment, but it is clear from the above that it is far from being a system forgeneral use. At the same time, Lebedev and Tomczak (1999) have described a method ofrecording and analysing the raw reflectivities from a ship’s navigational radar with a view to its
possible use to measure open ocean precipitation routinely from research vessels and ships ofopportunity.
A novel use of Doppler wind profilers for precipitation profile measurements was also
introduced during COARE. The NOAA Aeronomy Laboratory has developed UHF windprofiler technology for tropical dynamics and climate research (Carter et al. 1995). The
profilers, originally intended for the measurement of lower tropospheric winds in the tropics,
have now been applied to precipitation research (Gage et al. 1994, 1996; Ecklund et al. 1995;
Williams et al. 1995, 1999). The UHF profilers are sensitive to hydrometeors and provide ahighly-resolved time-height cross section of precipitating cloud systems. A unique precipitation
data set was collected using UHF profilers at Integrated Sounder System (ISS) sites during
COARE. Preliminary studies show reasonable agreement between profiler and scanning radars
in the statistical partitioning of tropical precipitation. The profiler uses the vertical structure ofhydrometeor echoes, their Doppler velocity, and spectral width, as well as their temporalcontinuity in determining precipitation type (Williams et al. 1995). Since COARE, a networkof wind profilers has been maintained operationally, particularly in the western Pacific region,to observe precipitating cloud systems. Profiler data at Darwin have also been analysed for
rainfall (Cifelli et al. 1996; Ecklund et al. 1997).
C. VOS PRECIPITATION ESTIMATES
Precipitation is not measured on ships but is determined from the "present weather"
descriptions reported in the ships' weather observations (Tucker, 1961). Weather codes which

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indicate that precipitation is occurring are modelled by formulae which describe theprecipitation rate based on the contribution from three continuous rain categories: light,
moderate, or heavy. The relationship between these categories, and the rain rate which they
represent, was found empirically. On a global basis accuracy of any “present-weather” based
precipitation estimate is not likely to be high. Dorman and Bourke (1978) adapted Tucker's
scheme for tropical regions by taking temperature into account. Lindau (2000) proposed a
simple rain algorithm, which needs as input the reported weather code and specific humidity.
The algorithm was derived by using wind-corrected rain measurements of German lightships.
With weather code and specific humidity they were able to explain more than 70% of theintermonthly variance. It has been used to determine evaporation minus precipitation on a
global basis.
10.9.2 Precipitation by ocean freshwater budgets
An integral method of rainfall determination involves calculating ocean budgets fromsurveys of temperature and salinity in space and time over the ocean mixed layer. Typically,
T/S profiles are obtained over an area of ocean from an array of moorings, or from ships making
CTD casts at frequent stations along the cruise track or with a towed CTD (e.g. mounted on a
SeaSoar). It is necessary to analyse heat and freshwater fluxes in three dimensions, because
advective components have been found to be of first order. The closure of freshwater budgets
during COARE by Feng et al. (1998), Smyth et al. (1996) and in the Indian ocean by Godfrey etal. (1999) are examples of the successful integration over space and time of the patchy rainfall
pattern, indicating that with proper care the ocean can be its own rain gauge.
10.9.3 Satellite remote sensing of precipitation
A. INTRODUCTION
This section describes the existing global estimates of precipitation obtained fromsatellites that might be used for model evaluation or creation of freshwater flux data sets. The
order in which the different products are described should not be considered as a ranking of data
sets. The major problem facing satellite estimates of precipitation is the lack of alternative
fields for validation. Early attempts at validation have been restricted to comparisons withclimatologies obtained from ship present weather reports which also suffer from large
uncertainties (Jaeger, 1976; Legates and Willmott, 1990). However in recent years three
Algorithm Intercomparison Projects (AIP) have been sponsored by the GPCP. In addition, theNASA WetNet project has also sponsored three projects called Precipitation Intercomparison
Projects (PIP). The aims and results of both types of project are discussed below (Section
10.9.4).
B. GPCP
Global climatological fields of monthly mean precipitation over both ocean and land,
derived from a combination of satellite and terrestrial observations made over the period July
1987 to March 1998, have recently been produced within the Global Precipitation Climatology
Project, GPCP (Huffman et al., 1997). The data are provided on 2.5˚ x 2.5˚ latitude-longitude
grids. The values were obtained using a combination of passive microwave and infrared
techniques. The infrared techniques rely on a correlation between cloud top brightness
temperatures and rain rate and are more appropriate for use with deep convective systems.
Used within 40˚ of the equator, they exploit the frequent sampling afforded by geostationary
satellites. The microwave technique detects the radiative effects of hydrometeors and has astronger correlation with surface rainfall. It relies on sensors on polar orbiting satellites (toobtain adequate spatial resolution) and hence has a relatively low sampling rate, as long as only
one satellite is in use, but can be employed to higher latitudes in the GPCP analysis. A futureversion of GPCP will deliver a daily precipitation map on a 1˚ x 1˚ grid.
C. CMAPThe CPC Merged Analysis of Precipitation, CMAP (Xie and Arkin, 1997), consist of a
17 year period, from 1979 to 1995, of gridded global (ocean and land) monthly precipitation
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fields with a resolution of 2.5˚ x 2.5˚ in latitude and longitude. Merged into this dataset are avariety of satellite measurements, gauge observations, and NCEP-NCAR reanalysis. Thus its
value for evaluating models is somewhat reduced. However, it might be a very useful dataset
for climate analysis and hydrological research. Many of the data sources used are the same as
for the GPCP dataset. In particular, the Geostationary Precipitation Index, GPI (derived from
infrared data of geostationary satellites) and the SSM/I-derived components - like the Ferraro etal. (1994, 1996) dataset described below. Thus differences between CMAP and GPCP
remained small over tropical and subtropical oceans. Over extratropical oceans significant
differences have been found. These were caused by the additional use of MSU data (Spencer,1993) and the Outgoing Longwave Radiation (OLR) based Precipitation Index (OPI) (Xie andArkin, 1998).
D. PURE MICROWAVE-DERIVED PRODUCTS
In addition to the merged datasets described above, some of the microwave algorithms
established for the SSM/I have also been used to derive global precipitation climatologies.
Two of these are the Ferraro et al. (1994) and the Bauer and Schlüssel (1993) algorithms.
Those datasets have a higher spatial and temporal resolution, typically 1˚ x 1˚ in latitude andlongitude and one to five days. An obvious deficiency of these datasets is that they wereconstructed from data from only one SSM/I at a time; this enhanced the sampling error
significantly.
The sampling error could be strongly reduced if all available SSM/I
measurements were to be used (at least two sensors have been in space simultaneously since1992).
For the construction of freshwater flux fields (like HOAPS) from SSM/I data it might
be essential to use SSM/I only algorithms since it is important that the precipitation is not
sampled in a different manner than the evaporation.
10.9.4 Intercomparison projects
A. INTRODUCTION
The following conclusions from the recent intercomparison projects on rainfall
measurements from space are mostly adapted from two important papers by Ebert and Manton(1998) and Smith et al. (1998) that gave a comprehensive overview of the results of the AIP-3
and PIP-2 projects. The AIP and PIP projects had the common objective of improving satellite
algorithms. The goal of the AIP projects was to validate different satellite algorithms against
ground based gauges and radar estimates, which were treated as truth in terms of bias and rms
error.
The underlying idea was to calibrate satellite algorithms with ground based
measurements and merge them together as was done in the GPCP dataset described above.
Thus the AIP's were focussed on regional areas with different atmospheric conditions. AIP-1
was conducted over Japan and surrounding waters using data from June to August 1989, AIP-2
was located over the British Isles and surrounding waters using data during February to April,
1991. AIP-3 attracted the largest number of algorithms (57) for a comparison over the radar
covered area of the TOGA COARE experiment domain during November 1992 - February
1993. The philosophy of the PIP's was quite different from the AIP's since they rejected thenotion that ground data are representative enough to serve as a final calibration standard (Smithet al., 1998). The PIP's were directed at establishing a passive microwave algorithm thatincorporated the best features of all existing algorithms. While PIP-1 and PIP-3 were focussed
on monthly rain accumulations over global grids, PIP-2 was dedicated to instantaneous rain
rate estimates. The aim was to understand the systematic differences between the algorithms,
when applied to globally distributed test cases, rather than to use the differences from groundbased measurements as an indication of the uncertainty.
Results from all these intercomparison studies are now in the literature with the
exception of PIP-3. AIP-1 results are described in Arkin and Xie (1994), and in more detailedin Lee et al. (1991). AIP-2 results are published in Allam et al. (1993) and Liberti (1995). The
results of PIP-1 are documented in a special issue of Remote Sensing Reviews. Since AIP-3

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10.9 Precipitation(Ebert, 1996; Ebert and Manton, 1998) and PIP-2 (Smith et al.1998) contain the newest findings
their conclusions are summarised below.
B. AIP-3 CONCLUSIONS
Comparisons of satellite algorithms using GMS VIS/IR, AVHRR, and SSM/I data (oninstantaneous and monthly time scales) to Doppler radar data (sampled every 10 minutes andmapped to a 0.5˚ grid) revealed that:❍

Over the whole TOGA COARE experiment the median value of the ratio of satellite
estimated to radar-derived rain rates was 1.3, with considerable variations for single
phases of the experiment. No categorical differences in bias between algorithms were
found.

❍

Infrared, visible/infrared, and mixed infrared/microwave algorithms exhibited slightly
higher correlations at monthly time scales than did estimates based on AVHRR or
SSM/I alone. But the difference in correlation was too small to be statisticallysignificant due to the small number of quasi-monthly samples.

❍On an instantaneous time scale, the SSM/I algorithms outperformed all otheralgorithms having a statistically significant, higher correlation to the radar data. The
success of the SSM/I algorithms was related to their ability to distinguish raining from
non-raining areas.

❍

The coupled infrared/SSM/I algorithms did not perform better than infrared alone
algorithms because for these the best SSM/I algorithms were not used.

❍

Results from NWP models showed similar biases and rms errors as the satellite
algorithms but exhibited much lower correlations because they can not resolve theraining and non raining areas.

C. PIP-2 CONCLUSIONS
PIP-2 examined a number of globally distributed case studies and intercompared thealgorithm results in various ways: to one another, to algorithm category composites(algorithms were divided in categories by their solution method, channel input, and screening
approach), to the overall algorithm composite, and to ground-based data. From this the main
conclusions drawn were:❍

Passive microwave rainfall algorithms applied to SSM/I data performed credibly. The
biases and rms errors found corresponded reasonably to the AIP-3 results.
Nevertheless, it was stated that the differences between algorithms were not larger thatthe uncertainty in the validation data. PIP-2 gave evidence that ground-based gauge
data from reputable sources were not reliable as a calibration standard for instantaneous
rain rates.

❍

The results of the intercomparison were strongly dependent on the algorithms' decision
as to where it was raining and where it was not. This was most important at low rain
rates. Thus algorithm developers were requested to seek a common rain/no rain
threshold and a common rain screening method for determining the area of rainfall. In
the future, rain screening should deserve more attention than the conversion ofbrightness temperatures into rain rates.

❍

Despite the first conclusion it was concluded that, before a calibration model isdeveloped, intercomparison projects like AIP and PIP are useful to improve algorithms.

❍

The final, somewhat contentious, conclusion stated that the point has been reachedwhere ground validation data are no longer effective in determining the precision ofsatellite algorithms. As an alternative validation strategy, a comprehensive calibration
model should be developed consisting of the most complete radiative transfer model
together with a detailed microphysical model.

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10.10 River Inflow

D. BEST ESTIMATEIn the light of the findings of all the AIP's and PIP's it is difficult to say whichclimatological dataset might be the best estimate. All strategies to build long term datasets canbe defended on scientific grounds. However, the user of these datasets must be cautious withregard to the purpose for which the dataset will be used. For example, it might not bemeaningful to use a dataset which contained reanalyses data to verify the reanalyses. It would
be helpful if each dataset were accompanied by error maps giving the size of the systematic andthe random errors. For instance, products derived from one satellite only, like the pure
passive microwave datasets, would have an error map that gave an estimation of the sampling
error as the most important systematic component.
Another conclusion that can be drawn from the intercomparison projects is that not only
should the quality of satellite estimates be improved but also the status of routinely gained
surface based measurements. This is considered important, despite the PIP-2 conclusions,because the development of a calibration model as suggested by PIP-2 is not easy and will be along-term project. A first step in this direction is the attempt to archive high quality groundradar data for comparison with measurements of the TRMM sensors. With this sensor package
a new era of remote sensing of rain has recently begun with infrared, passive and active
microwave instruments specially dedicated to measure rain on the same platform. Thus, for the
first time, synergistic algorithms can be developed.
E. COARE MEASUREMENT RECONCILIATIONAs mentioned above (Section 10.9.1B), serious disagreements between various
estimates of rainfall during COARE led to intensive study into the reasons, and reappraisal of
instruments and methods. When all instrumental corrections had been made to the various
rainfall methods, the IOP-average disagreement was reduced but still too large. Ciesielski andJohnson (1999; see also Bradley and Weller 1997, p.17; Bradley et al. 1997) resolved thesituation through analysis of atmospheric heat and moisture budgets. This technique, describedby Yanai et al. (1973), makes use of atmospheric soundings of temperature and humidity
obtained over space and time, to determine the “apparent heat source” (known as Q1) and the“apparent moisture sink” (Q2) of a volume of the atmosphere. Being an integral method, it
overcomes small-scale variability. An extensive array of sounding sites was distributed over the
COARE domain, within which the budgets for specific regions were determined using theinterpolation technique of Nuss and Titley (1994).
Ciesielski and Johnson (1999) determined the IFA-averaged rainfall rate for the IOP tobe 8.2 mm/day compared to a radar estimate of 4.8 mm/day (because of compensating effects,
correcting the soundings for the humidity bias described in section 6.3.3 increased the budget
estimate by only 2.5% to 8.4 mm/day). However, when their budgets are computed just over
the radar area (which covers about one-third of the IFA) and for the same 101 days that theradar was operational, their rainfall rate is 5.2 mm/day, which is much closer to that of theradar. For these same 101 days but over the entire IFA, their budget estimate is 7.3 mm/day.
These results indicate that (a) the radars were situated in a relatively dry region of the IFA, and(b) the 101-day radar exposure was from a relatively dry period of the IOP. They serve as areminder that, for meaningful comparisons of quantities as variable as rainfall, sample regions
and periods must be strictly coincident.
10.10 River Inflow
River runoff perceptibly lowers the salinity of the ocean. It might be thought that theconsequences on the buoyancy would be very local in comparison to the size of ocean basins.
However salinity anomalies of fluvial origin may be advected rather far by ocean currents. For
example, the impact of major rivers like the Amazon, the Congo or the Saint-Laurent on thesurface salinity of the Atlantic ocean are perceptible several hundred of kilometres away fromthe mouth of the rivers (Figure 10.9). The inflow of freshwater from the rivers is of extreme
importance in the global freshwater balance of the ocean. This balance is the result of the loss
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of water by evaporation, the gain of freshwater by precipitation, and coastal river runoff. The
contribution of rivers to this balance was estimated at 1.26 Sv (1 Sv=106 m3/s) by Baumgartner
and Reichel (1975), nearly 10% of the contribution of precipitation. Neglecting thiscontribution would increase the average salinity of the upper Atlantic ocean (first 50 m) by 1.5
psu after 10 years.
Global river runoff data are available from the International Hydrography Program(IHP) which published a Global River Discharge Catalogue (Vorosmarty et al., 1996), and fromthe Global Runoff Data Centre (GRDC). IHP data available in reports published by UNESCO,are a selection of monthly discharges at 949 stations over six continents (Africa, Asia, North
America, South America, Europe and Australia/Oceania). Several stations may exist for thesame river, and only 219 stations are listed as corresponding to a direct discharge into an ocean
basin. These reports do not include runoff from the Arctic and Antarctic regions. The lengthof the records from which climatic monthly means can be estimated varies from 1 to 100 years,
the average length being 19.3 years. A great disparity exits between continents, Europe andNorth America presenting the longest records. These data do not include the inflow offreshwater into oceans due to underground water.

Figure 10.9. Mean sea surface
salinity (PSU) in the North
Atlantic from the climatic atlasof Reynaud et al. (1998).
Regions of low salinity are inlight grey and correspond toregions near the major rivers(Amazon, Congo, SaintLaurent) and regions ofimportant sea-ice melt
(Labrador Sea).

Using the IHP data set, Boukthir et al. (2000) have computed a monthly mean
climatology of the direct contribution of rivers to the freshwater flow into the ocean. In theiranalysis, these authors excluded the contribution of rivers for which flow does not exceed 800
m3/s for at least one single month in the year, and they also neglected rivers for which nocontinuous records at least 24 month long were found. Thus their calculation used 145 stationsfor 109 river mouths. For the major rivers, the discharge computed by Boukthir et al. (2000)
agreed very well with the analysis of Hagemann and Dümenil (1996) who used data from
GRDC for the purpose of model validation. The cumulative runoff for all the rivers consideredby Boukthir et al. (2000) sums to 0.57 Sv. A previous comparable estimate based on former
UNESCO reports published in 1969 is that of Baumgartner and Reichel (1975) who found 0.73
Sv, a value significantly larger, but which includes all rivers for which records were available(even with very small discharge), and also includes the contribution of regions beyond the polar
circles which they estimated from other sources. Note that in the analysis of Baumgartner andReichel (1975), the direct contribution of river discharge accounts for only 60% of the total

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10.10 River Inflow
10.11 Sea Ice

freshwater runoff which is 1.26 Sv. They obtained this latter value from a global hydrologicalbalance calculation and part of the runoff was estimated as a residual.
GRDC data are in the form of daily and monthly discharge data for several thousand
stations for more than 2,800 rivers. However, many stations are far from the mouth of therivers, and many, being quite recent, do not yet have records long enough for climatologicalestimates. If one is interested in long time series, GRDC and IHP likely provide, at present,
very similar information.
Therefore, the data set gathering carried out by the IHP and more recently by the
GRDC are initiatives that deserve great support. However a considerable amount of work is
still necessary to reach an accurate estimate of the amount of water which reaches the ocean. In
particular, the contribution of underground waters and small streams cannot be measured andhas still to be estimated, often as a residual of the global budget. Furthermore, it is likely thathuman activities, with increase damming and irrigation, have significantly reduced the riverdischarge reaching the ocean in the last twenty years (by 50% for the Nile River for example).
It is therefore important to encourage the type of studies which permit synthesis of theinformation contained in these huge data sets in terms of freshwater flux to the ocean, andwhich quantify recent changes.
10.11 Sea Ice
10.11.1 Sea ice amountSea ice concentration and extent data are extracted from satellites routinely. The firstmultichannel instrument, SMMR, the Scanning Multichannel Microwave Radiometer, waslaunched in 1978 on SeaSat and Nimbus 7. Shortly before the demise of the latter in 1987, a
new series was launched: the Special Sensor Microwave/Imager, SMM/I; this series continues
flying today (see Section 4.3.2). A single-channel instrument the Electronically Scanning
Microwave Radiometer, ESMR, flew on Nimbus 5 from 1973 to 1976; its best application is in
defining ice extent.
The multichannel sensor resolution varies between 12 and 150 km, smaller for higher
frequencies. Data products are produced at 12 and 25 km grid spacings, which are ideal for alarge-scale geophysical view. Coverage is excellent: global except poleward of 84° latitude for
SMMR and 87° for SSM/I. Because the atmosphere is fairly transparent at their frequencies,
microwave sensors are "all-weather" providing data in darkness and through clouds.
Over sea ice the microwave signals are usually interpreted as concentrations of all ice
and of only multiyear ice. The 15% concentration contour is usually taken as the ice edge.
Estimates of the uncertainties in these quantities vary: ice extent, ±12 km; ice concentration,±6%, with possible biases of similar magnitude; and multiyear concentration, ±20%, again withbiases of similar size.
Data sets are available through the National Snow and Ice Data Center (http://wwwnsidc.colorado.edu) for October 1978 to the present, for total, first-year and multiyear ice
concentrations. They are on a 25 x 25 km grid, daily and monthly, for both hemispheres. The
passive microwave record is presented in three atlases: Gloersen et al. (1992), Parkinson et al.
(1987), and Zwally et al. (1983).
10.11.2 Flux-related variables over ice
Air surface temperature in the Arctic is available from buoys and land stations. The
rms error is 2.5˚C (Rigor et al., 2000). In the Antarctic, with far fewer observations, the bestsource is reanalyses, and the rms error of these is likely 3 to 5˚C.
In polar regions, the default source of surface winds is NCEP and ECMWF reanalysis
data sets. The extreme stability of the planetary boundary layer in winter is an importantcomponent for an accurate estimation of surface winds, but stable PBL’s are not handled wellin these numerical weather prediction models. Some ice models estimate surface winds from

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10.11 Sea Ice
10.12 COADS MSTG

surface pressure fields and do not treat the varying stability in the planetary boundary layer,
producing errors up to 50% in speed and 30 degrees in direction. In the Arctic surface winds
from reanalysis (or operational model) surface pressure and TOVS-Polar Pathfinder dragcoefficients and turning angles have errors of ±4 ms-1 in speed and ±5 degrees in direction. In
the Antarctic there are no TOVS-Polar Pathfinder products and there are many fewer pressure
data than in the Arctic.
Arctic cloud fraction data sets are available individually from TOVS and AVHRR,
from the ISCCP project, and both the NCEP and ECMWF reanalyses. The intercomparison ofradiation data by Rothrock and Zhang (1997) shows quite large discrepancies, both in theannual cycle and in five-year means, which vary from a low of 44% for NCEP to a high of 70%
for ECMWF. A comparison of TOVS-Polar Pathfinder and AVHRR-derived cloud fractions
from the ISCCP project (Schweiger et al., 1999) shows reasonable correlations between TOVSderived cloud fractions and surface observations from Russian drifting stations at time scales ofgreater than 3 days. Mean monthly cloud fractions from TOVS-Polar Pathfinder for the area
north of 80˚ agree within 15% with a climatology based on meteorological surface observations(Hahn et al., 1995). Arctic cloud fractions from the ISCCP project (D-series) are do not appear
to capture properly the annual cycle and actually show lower cloud fractions during summer
than during winter in disagreement with climatologies.
Comparisons of cloud fractions from TOVS-Polar Pathfinder, AVHRR-CASPR, andsurface observations (which include standard meteorological reports) as well as a sophisticatedcloud lidar/radar system during SHEBA paint a more complicated picture. Meteorologicalreports (such as those from the north polar drifting stations) appear to under report clouds by asmuch as 30% during winter compared with lidar/radar. Lidar/radar observations also show abetter agreement with TOVS- and AVHRR-derived cloud fractions during winter. Based on
SHEBA data the annual cycle of cloudiness in the Arctic appears much less pronounced thanpreviously thought.
Over the Arctic Ocean, precipitation has been estimated from 1979 to present from
TOVS. The procedure uses TOVS moisture profiles and NCEP reanalysis wind profiles andcomputes the moisture flux convergence, which equals P-E (precipitation - evaporation) at thesurface. Evaporation, E, is computed independently from TOVS-derived surface winds andvertical moisture gradients, satellite ice concentration data, and different transfer coefficients forthe ice-covered portion and the open water portion. Adding E to P-E gives the precipitation P(Groves, 1999). Monthly precipitation fields are computed as the sum of net precipitation andevaporation. These fields are completely independent of any parameterised precipitation
processes and can serve as good tests of such parameterisations in models. Daily fields areavailable; monthly data are believed to have an accuracy of about ±1 mm/month.
10.12 COADS Monthly Summary Trimmed Groups (MSTG) Variables.
The most extensive collection of the basic individual variables presently available is theComprehensive Ocean-Atmosphere Data Set (COADS), which beside the collection ofindividual observations (known as long marine reports - LMR), also provides monthly
summaries (MSTG) which contain basic monthly statistics, computed by the COADS
community (Woodruff et al. 1998). These MSTG data have been widely used by climate
researchers and so will be considered here as a special example of the basic variable data sets.
The MSTG statistics are available at ftp://ftp.cdc.noaa.gov/Datasets/coads1a.enh (coads1b.enh)
for the 1a and 1b releases respectively (see below).
At present, the available MSTG statistics are based on the very latest update of
COADS which was specially reprocessed in support of the NCEP/NCAR Reanalysis project.
The time period covered by this new update is from 1950 to 1995 (with extension to 1997
expected in early 2000). The data set for the period 1980-1995 is known as COADS Release
1a, and MSTG statistics for 1950-1979 belong to COADS Release 1b. Monthly statistics for 2˚and 1˚ boxes were computed for SST, air temperature, specific humidity, relative humidity,

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scalar wind speed and its north and east components, sea level pressure and total cloud cover.
Additionally, MSTG includes pseudo flux variables computed on the basis of individual
observations, for example products of temperature and humidity differences with scalar wind.
These can be used, together with the fluxes derived from the COADS based climatologies (e.g.
da Silva et al. 1994, Josey et al. 1999), for pilot studies and comparative assessments.
COADS MSTG normally provides, as the basic statistics for each variable, monthly means,monthly standard deviations, and the number of observations. Additionally information isavailable about the mean day of the month, mean coordinates, and the fraction of observations
in daylight. Pre-processing of the individual reports used for computation of the COADSMSTG included different quality control procedures and duplicate checks (Woodruff et al.
1998). Trimming in Releases 1a and 1b was performed using both 4.5σ and 3.5σ level limits,which correspond to the so-called “enhanced” and “standard” MSTG products respectively.
Use of the 4.5σ limits is recommended because of the possible impact of trimming procedure
based on 3.5σ limits on some very pronounced anomalies, in particular those associated with El
Niño events (Wolter 1997).
In general, the MSTG products are of use for different multidisciplinary studies dealing
with sea-air flux fields. Of course, the MSTG products still contain the so-called "known"biases in COADS. In particular, visual wind speed estimates in MSTG are based on the
WMO1100 scale, no corrections for measurement height were applied, and there were nocorrections of biases in SST and humidity. Use of MSTG pseudo-flux products can only partly
avoid the averaging effects caused by the “sampling-to-classical” biases in the flux
computations (see Section 8.3.4). Thus, one has to be careful when using MSTG products forclimate research and especially for budget studies. For example, artifacts in the MSTG winds
can lead to very pronounced biases, both climatological and time dependent (Ward 1992, Wardand Hoskins 1996, Isemer 1995, Gulev 1999). However, the MSTG data also have some
important advantages for the climate studies. First, they are easily available to the flux users
from the CDC FTP server. Second, with respect to the advanced climatologies (da Silva et al.
1994, Josey et al. 1999) they have better sampling in some very poorly sampled areas, which
are nevertheless crucially important from the viewpoint of flux variability. Indeed, in someregions of the Labrador Sea, GIN Sea, and Southern Ocean, the sampling density in MSTG isseveral times greater than the sampling density provided by the flux climatologies. That is
because the latter only use reports which contain values for all the basic variables. However it
is not immediately obvious whether biases connected with undersampling (which is the cost of
the better variable processing and flux computations) are smaller than those inherent in MSTG.
Taking into account that in the future COADS Releases, for instance Release 2, expected in
2001, many of variable corrections can be included in the new updates of MSTG, this productwill continue to have an important future for climate research.

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11.1 Introduction
11.2 In situ Products

11. EVALUATION OF FLUX PRODUCTS
11.1 Introduction
In this Chapter we will attempt to evaluate a number of the flux products. These will
include climatologies based on in situ data (Section 11.2), in particular the UWM (da Silva et al.
1994), SOC (Josey et al. 1999) and IfM (Lindau, 2000) climatologies.
Satellite based
products (Section 11.3) will include an evaluation of the HOAPS (Schulz et al. 1997) surface
fluxes and a number of radiative flux products of US and Japanese origin. The Residual
method (Trenberth and Solomon, 1994), which used top of the atmosphere satellite
measurements together with model derived flux divergences, will also be considered in thissection. The fluxes from the reanalysis projects will be discussed in Section 11.4 for ERA15(Gibson et al. 1997), GEOS1 (Schubert et al. 1995), NCEP1 (Kalnay et al. 1996; Kistler et al.,2000), and NCEP2 (Kanamitsu et al. 2000). We have not attempted to evaluate operationalmodels, however the situation for these will be summarised in Section 11.5. The state of ourknowledge for fluxes over sea ice will be discussed in Section 11.6. Sea State and Waves will
be considered in section 11.7. Finally 11.8 will provide an illustration of the difficulty ofproviding a comparative evaluation of the flux products.
Comparison of the various flux products is not easy. Wherever possible, thecomparisons must be done for the same time period, however in some cases the products do not
overlap. In particular high quality in situ data is only available for limited periods and areas.
Variables such as temperature, humidity and winds should be compared at the same height.
However only time-mean values are normally available, and it is not possible to accurately
convert a 2m mean value to 10m or vice versa. Nevertheless it will be shown that there aredifferences between the various flux products which are significantly greater than would be
introduced by the differences in specification for the variables compared.
11.2 Flux Products Based on in situ Data
11.2.1 Introduction
The differences between the various flux climatologies are due to several factors:
whether "sampling" or "classical" flux calculation was used, what wind scale was used tointerpret the visually observed winds, what other corrections (if any) were applied to theobservations, the formula used for each of the radiative flux components, the formula used foreach of the turbulent flux components, and other factors such as the smoothing algorithm, gridscale, and time period of the data set used. Climatologies of the heat fluxes produced prior to1990 include the North Atlantic flux maps of Bunker (1976) and Isemer and Hasse (1987 andIsemer et al. 1989), and the global climatologies of Esbensen and Kushnir (1981), Hsiung(1986), and Oberhuber (1988). More recent climatologies are those of da Silva et al. (1994),
Josey et al. (1999), and Lindau (2000). The characteristics of these various studies is shown in
Table 11.2.1.
The characteristics of the selected pre-1990 studies will first be summarised (based on
the comparison of sensible and latent heat fluxes by Kent and Taylor, 1995). The post 1990
studies will then be described in more detail before a comparative assessment is presented.
11.2.2 Pre-1990 flux studies
A. BUNKER (1976)
Bunker calculated the wind stress and sensible and latent heat fluxes from individual
ship reports using transfer coefficients tabulated as a function of air-sea temperature difference
and wind speed. These were based on his review of experimental studies between 1957 and1974. Based on the results from BOMEX (Holland et al., 1972), Bunker increased the transfer
coefficients by 10% compared to the experimental values to allow for the difference between
merchant ship data and research platforms. This he assumed accounted for the effects of theship on the local environment as well as the anemometer being generally higher than 10m
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11.2 In situ Products(11.2.2 pre-1990 flux studies)height. He assumed that the transfer coefficient for sensible heat was equal to that for latentheat. Fluxes were not calculated if all the necessary parameters were not present for a given
ship report. The fluxes were averaged by 10° square, month and year. Annual and monthly
averages for subdivisions of the 10° squares, called gerrymanders, were presented. The
number and shape of the gerrymanders depended on the number of observations in the region,
with an attempt being made to enclose homogeneous water masses.
Table 11.2.1 - Characteristics of climatological studies of the air-sea heat fluxes. The tableshows the area and time period covered, the spatial resolution, the origin of the sensible andlatent heat bulk formula coefficients, implied Beaufort wind scale, and whether the fluxes were
calculated from the individual samples (“yes”) or the means of the observations (“no”). IH87
calculated their flux values using mean values but then applied a correction.
Study

Area
PeriodGrid Scale

Short
Wave

Long Wave

Sens/Lat Ht

Wind scale

SampleBunker 1976

N Atl.
41-72

1°x 1°(coast) Budyko
2°x 5°
1963(ocean)Budyko 1963

BunkerWMO 1100yes

Esbensen &Kushnir 1981

Globe
46-79

4° x 5 °Budyko1963

Berliand &Berliand1952

Liu et al. 1979

WMO 1100no

Hsiung 1986

Globe
46-79

5 °x 5 °Reed type

Budyko 1963

BunkerWMO 1100noIsemer & Hasse
1987 (IH87)

N Atl.
41-72

1°x 1°

Reed 1977

Budyko 1974

Bunker(modified)Kaufeld
1981

no +
adj.

Oberhuber 1988

Globe
50-79

2° x 2 °Zillman’72 / Reed

Berliand &Berliand 1952

Large & Pond
’82 (modified)

WMO 1100noda Silva et al.
(1994)

Globe
45-89

1° x 1 °Reed type

Rosati &Miyakoda 88

Large & Pond
1982

da Silva etal. 1995

yes

Josey et al.
(1999)

Globe
80-93

1° x 1 °Reed 1977

Clark 1974

Smith 1988

Lindau1995a

yes

Lindau (2000)

N.Atl.
40-79

1° x 1 °Malevskii
et al. 1992

Bignami et al.
1995

Isemer &Hasse 1987

Lindau1995ayes

B. ESBENSEN AND KUSHNIR (1981)
Esbensen and Kushnir used 5° x 5° monthly mean climatological parameters. These
values were transformed onto a 4° latitude by 5° longitude grid using a Gaussian space
averaging technique designed to uniformly suppress features with wavelengths shorter thanabout 1600 km. The data are therefore both presented on a large scale grid and also have verylarge spatial filtering applied. Additional smoothing was applied in low data density areas.
The fluxes were calculated with the skin temperature version of the Liu et al. (1979) transfer
coefficients (Section 7.3.2) although the observed bulk temperature of the ocean surface wasused. This increased the calculated fluxes slightly. For the North Atlantic, Kent and Taylor(1995) estimated an increase of 3Wm-2 and 7Wm-2 for sensible and latent flux respectively.
C. HSIUNG (1986)
Hsiung produced a 5° x 5° global flux product using the Bunker transfer coefficients
applied to monthly means of the basic meteorological variables. Bunker had stated that his
coefficients were determined from data averaged over short time periods and should not be usedwith monthly means of the bulk variables. However Hsiung justified her choice by summarisingstudies which compared the "classical" and "sampling" methods (see Section 8.3.4).
D. ISEMER AND HASSE (1985, 1987)
The atlas of Isemer and Hasse (1987) contains two distinct sets of flux maps, those
calculated using Bunkers original parameterisations (see above), and also fluxes calculated
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11.2 In situ Products(11.2.2 pre-1990 flux studies)using revised parameterisations which had been adjusted using an inverse technique based on aheat budget study. Isemer and Hasse used the monthly mean averages of the basic variables(Isemer & Hasse, 1985) for each of the gerrymanders as calculated by Bunker (1976). They
attempted to update the parameterisations used by Bunker in several ways. To account for more
recent open ocean determinations of the transfer coefficients, they reduced that for sensible heatby 17% and latent heat by 13%. They used an approximate conversion to the Kaufeld (1981)
wind scale. Although applied to the mean values, the correction was weighted according to thefrequency distribution of wind speeds as calculated by Bunker. Thus they assumed that, forthe period up to 1972 used by Bunker, all wind speeds were visual estimates. Stability effects
were calculated using virtual temperature, rather than temperature, resulting in a more unstabledensity stratification and larger fluxes particularly in moist areas such as the tropics.
The Isemer and Hasse study therefore has fluxes calculated using the ‘classical’ method
but with corrections applied in order to approximate fluxes calculated using the 'sampling'method. To attempt to correct their flux values for any errors so introduced, Isemer and Hasse
adjusted their calculated fluxes for each gerrymander by the ratio of [(Bunkers original results) /(fluxes calculated using the monthly mean values with Bunkers original parameterisations)].
The second set of flux maps presented by Isemer and Hasse (1987) were adjusted using
inverse analysis with the oceanographic constraint of 1 PW of heat transport across 25°N (Halland Bryden, 1982). The parameters included in the inverse analysis were the atmospherictransmission factor and cloud cover coefficient for short-wave radiation, cloud cover exponent
in the infrared formula, the transfer coefficients, and the air-sea differences in temperature anddewpoint. The uncertainties in the variables were estimated and the additional constraint
applied that the sum of the changes in the variables normalised by the estimated uncertainty ofeach variable was an rms minimum. The adjustments added just over 16 Wm-2 to the net heat
flux. The main changes were due to a increase of 5.7% in CE (CH by 4.4%) and an addition of-0.07°C to the air-sea and dewpoint-sea temperature differences.
Isemer et al. (1989)
confirmed these changes using a selection of different constraints.
E. OBERHUBER (1988)
Oberhuber used the COADS data set of filtered monthly means of hourly values
averaged onto a 2° x 2° grid as prepared by Wright (1988). Although he stated that the transfer
coefficients used were from Large and Pond (1982), it would appear that this only applied tothe corrections to neutral stability. The neutral transfer coefficients were calculated using theCharnock (1955) relationship (see Section 7.3.2) with the Charnock parameter taken initially as
0.0064. However he found that he needed approximately another 20Wm-2 heat loss from theocean to agree with estimates of the meridional heat transport. He therefore increased theCharnock parameter to 0.032. He suggested that this was necessary to allow for the use ofmean, rather than instantaneous values, in the bulk formulae, in other words, to correct for his
use of the "classical method". However, this seems unlikely to be the case (Section 8.3.4).
The transfer coefficients for sensible and latent heat were calculated assuming constantroughness lengths for temperature and humidity. Since the Charnock formula predicts that zo
increases with wind speed, this implies that all the transfer coefficients increase with windspeed. Oberhuber (1988) also used a constant albedo of 0.06 rather than the varying albedo
used in other studies.
F. OTHER CLIMATOLOGIESHellerman and Rosenstein (1983)
This wind stress climatology is still in widespread use for forcing models or comparing
wind stress products. However the drag coefficient used was essentially that of Bunker (1976)
which resulted in wind stress values some 20 to 30% higher than those computed by, forexample, Harrison (1989) using the more realistic Large and Pond (1982) coefficients (seeSection 11.2.6F). Josey et al. (2000) found a similar difference in comparison to the SOC
climatology (Josey et al. 1999) except at high latitudes where differences were ascribed to thedifferent periods of the climatologies. In contrast, Böning et al. (1991) found that differences

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11.2 In situ Products(11.2.3 UWM/COADS)

from the Isemer & Hasse (1987) climatology were dominated by the visual wind correction usedby the latter authors.
Hastenrath and Lamb (1977, 1978, 1979)
Hastenrath and Lamb (1977) produced a climatic atlas of the Tropical Atlantic andEastern Pacific Oceans which was the basis of the heat budget study of Hastenrath and Lamb(1978). Similarly a climatic atlas of the Indian Ocean was used for the heat budget study ofHastenrath and Lamb, (1980). Hastenrath (1980) considered the heat budget of the tropicaloceans more generally. These studies have not been discussed in detail here since they werelimited to the tropical ocean, a region for which significantly more data has become available inrecent years.
11.2.3 UWM/COADS
A. INTRODUCTIONThe UWM/COADS climatology (da Silva et al. 1994 - http://www.cdc.noaa.gov/coads/uwm.shtml.) was the result of a collaboration between researchers at the Department ofGeosciences of the University of Wisconsin--Milwaukee (UWM) and the Ocean Climate
Laboratory (OCL) at the National Oceanographic Data Center (NODC). The aim was to extend
the COADS data set to include "true" flux values in addition to the "pseudo" flux values
contained in the COADS monthly means. Objective, global analyses of observed and derived
surface marine parameters were based on individual observations found in the Comprehensive
Ocean-Atmosphere Data Set (COADS) Release 1 complemented by an interim release for the
1980's. Results of the analyses are presented in Atlas of Surface Marine Data 1994, a five
volume series depicting 45-year seasonal climatologies (1945 - 89), anomalies, and standard
deviations of various quantities which characterise the surface marine climate (da Silva 1994).
In-depth documentation of the data set is found in Volume 1.
Besides describing allparameterisations, calculations, and analysis methods used in creating the data set, this volume
discusses possible biases the COADS data may contain and explains the bias corrections
employed. Also discussed are the constraining of heat and fresh water fluxes by oceanographictransport estimates (see below), sampling and fair weather biases, surface layer formulation,
equations for astronomical calculations (e.g., altitude of the sun), and NetCDF data accesssoftware. Volumes 2 through 5 represent a graphical representation of the data set. These
consist mainly of seasonal contour plots of climatology, standard deviation, and anomalies of
the analysed quantities organised according to parameter type. Volume 2 contains directly
observed quantities, Volume 3 contains heat and momentum fluxes. Fresh water fluxes can befound in Volume 4, and Volume 5 has an assortment of miscellaneous derived quantities. The
more recent Volume 6 contains the Heat Flux Sensivity to Sea Surface Temperature.
B. DERIVATION
The analyses presented in the Atlas of Surface Marine Data 1994 were derived from theindividual observations found in Compressed Marine Reports--Product 5 (CMR-5) of COADS
Release 1 (Slutz et al. 1985, Woodruff et al. 1987) for years 1945 through 1989. The transfer
coefficients used were from Large and Pond (1981,1982) except that C D10n was modified forwind speeds below 3 ms-1 following Trenberth et al. (1989). The visual winds were adjusted as
discussed by da Silva et al. (1995). Following Hahn et al. (1995), cloudiness observations
were rejected if they were made under dark sky conditions (calculated from the phase andaltitude of the moon and the altitude of the sun). Present weather observations were adjusted
for an error in the version of COADS used by da Silva et al. (1994) so that clear weather
observations were not assumed to be missing. No other corrections were made to the data forobservational errors. Precipitation estimates are based on Present Weather reports using thealgorithm of Tucker (1961) with a variant of Dorman and Bourke's (1978) corrections.
Mean climatological fluxes were calculated by averaging the 45 years of COADS data
into 1˚ x 1˚ grid cells and then applying the successive correction technique described by
Levitus (1982) (Section 8.5.2) with radii of influence equal to 1541 km, 1211 km, 881 km, and771 km on successive passes. Below 40˚S only the first two passes were used. By calculating
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11.2 In situ Products(11.2.4 SOC)

a complete array of atmospheric forcing parameters, and by using the same grid and analysis
scheme as in Conkright et al. (1994) and Levitus et al. (1994), the UWM/COADS data set was
intended to be used as a complement to the surface and subsurface marine analyses found in
Conkright et al. (1994) and Levitus et al. (1994a,b,c).
C. FLUX TUNING
da Silva et al. (1994) found that their fluxes indicated a global imbalance of about 30
Wm -2 . For the period 1981 to 1992, White (2000) found the smaller imbalance of 23 Wm-2;
the difference may have been partly due to different methods of taking ice covered regions into
account. Whereas the individual flux components, evaporation and precipitation provided in
UWM/COADS were not tuned or constrained, the net heat flux, buoyancy flux, and (E-P) fields
were presented, with the requirement that there should be no net, global heat transport through65˚S and that the equivalent freshwater transport should be 0.06 Sv. The main factors wereabout a 10% decrease of SW radiation and a 14% increase in latent heat flux. To balance thefreshwater budget, precipitation was increased by about 12%. However da Silva et al. (1994)
stressed that these values were only an example and that it was to be expected that other users
would need to apply different constraints. To that end sensitivity fields associated with each of
the tuning parameters were presented. Thus users may specify their own values for the error
estimates for the observed quantities and hence calculate new fields of constrained fluxes.
D. VERIFICATIONThe UWM/COADS flux fields have been compared to other climatologies (see forexample 11.2.4E) and have been extensively compared with results from models, bothreanalysis (see Section 11.4) and operational.
11.2.4 SOC
A. CONCEPTThe Southampton Oceanography Centre (SOC) flux climatology (Josey et al. 1999 http://www.soc.soton.ac.uk/JRD/MET/fluxclimatology.html ) was based on the methods usedby Kent and Taylor (1995) for the "best estimated fluxes" which they used to compare previous
North Atlantic flux fields. The idea was that, if the observations could be corrected forsystematic error, then the correct flux values would be calculated using the best estimates of thetransfer coefficients derived from air-sea interaction experiments without any adjustment of
those coefficients. The error correction was based on the results of the VSOP-NA experiment
which identified relative biases due to different instrumentation types (see Section 8.3.3). In
order to apply the VSOP-NA corrections, additional information was needed. This was
obtained from the International List of Selected, Supplementary and Auxiliary Ships ("WMO
47", e.g. WMO, 1990) which is published regularly by the World Meteorological Organisation(WMO). The details of the instrumentation carried on most of the 7000 plus Voluntary
Observing Ships include the method of SST, air temperature and humidity measurement, andwhether the ship carried an anemometer and, if so, the anemometer height. These meta-data
were merged onto the COADS release 1A data set using the ship's call sign on a report by reportbasis. The success rate in matching the COADS and WMO47 data increased with time as theinformation contained in WMO47 became more comprehensive (Figure 11.2.1). Because manyship reports from years earlier than 1980 have not been archived with valid call signs, thismethod limited the range of years for which the SOC climatology could be calculated.
The observations were corrected for biases in SST data from engine room intake
thermometers, in daytime air temperatures errors due to solar heating, and in dew pointtemperatures from fixed thermometer screens. In addition measured winds were corrected forthe anemometer height taking into account the atmospheric stability. Visual winds were
adjusted to the Lindau (1995a) scale. The impact of the various data corrections on thecalculated net ocean to atmosphere heat flux varied from area to area and season to season being
generally being within the range ±15 Wm-2. Fluxes were decreased in the northern oceans in
winter (mainly due to the wind and sea temperature corrections) and increased in the tropicaloceans (mainly due to the temperature and humidity corrections (Josey et al., 1999). These
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11.2 In situ Products(11.2.4 SOC)

corrections for ship observation error were smaller than might have been expected, partly
because some flux errors cancel.
250000200000150000100000

Year

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

1982

1981

1980

50000

Figure 11.2.1 A time series of
the number of reports used in
the SOC climatology for whichmatching WMO47 data wasavailable. The top (grey) line
shows the monthly number ofreports from ships. The lower
line shows the number of theseship reports matched (adaptedfrom Taylor et al. 1999f).

total matched reports
total ship reports

B. VERIFICATION OF HEAT FLUXES
Despite these corrections, the SOC climatology was similar to other studies in that it
showed a net heat flux into the ocean of about 30 Wm-2 . On a year to year basis this imbalance
was remarkably consistent with a standard deviation of ±7Wm-2 (implying a standard error on
the 14 year mean imbalance of < 1Wm-2 ). However, based on comparisons with high quality
buoy data, Josey et al. (1999) did not attempt to correct their fluxes to produce a balanced
product. Figure 11.2.2 shows the difference between the SOC and buoy values for the
Subduction experiment (Moyer and Weller, 1997), FASINEX (Weller et al. 1995), TOGACOARE (Weller and Anderson 1996) and the Arabian Sea (Weller et al. 1998). For theradiative fluxes (Figure 11.2.2a) the comparison varied from site to site, particularly for the SW
flux. At a number of sites the SW and LW differences were of opposite sign as might be
expected if the cloud amount was in error. Site to site variation also occurred for the turbulent
fluxes (Figure 11.2.2b). The largest difference was for FASINEX where the climatology
underestimated the total turbulent cooling by about 50 Wm-2 due to the sea being colder, andthe atmosphere moister, in the SOC climatology compared to the buoys. For the net heat flux
(Figure 11.2.2c), adjusting the fluxes to achieve a global heat balance (following da Silva et al.
1994) did not generally improve the agreement with the buoy values. Furthermore the two sites
where significant improvement occurred were characterised by significantly different biases.
For FASINEX the improvement was due to increasing the SOC latent heat flux (by 13%)
whereas for TOGA this worsened the turbulent flux comparison. At that site the improvement
came through reducing the SOC SW heating values (by 8%). Thus Josey et al. (1999) argued
that regionally varying corrections were required, a conclusion which was supported bycomparisons with flux values derived from ocean heat transport data.
C. TROPICAL HEAT FLUXESThe comparisons with the TOGA COARE buoy were considered in more detail by
Taylor et al. (1999e). The SOC climatological fluxes predicted about 23 Wm-2 greater net heat
flux into the ocean compared to the buoy. For the specific months of the buoy deployment, theSOC values over estimated the shortwave heating by about 24 Wm-2 and under estimated the
longwave cooling by about 8 Wm-2, thus causing the over estimate of the ocean warming.
The shortwave difference could be accounted for if the cloudiness had been under-estimated byabout 1 octa (true value 6.2 octas). In contrast, the SOC turbulent flux values slightly overestimated the magnitude of the turbulent fluxes (by about 8 Wm-2 ). This was surprising; it
was expected that the sensible and latent heat fluxes would be under estimated in the SOC
climatology under the low wind conditions of the TOGA-COARE area, since constant transfer

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11.2 In situ Products(11.2.4 SOC)

coefficients for sensible and latent heat were used, the Smith 1980 drag coefficient (CD10n) was(unjustifiably) extrapolated to zero ms-1, the merchant ship data were unlikely to have detected
any diurnal warm layer, and no gustiness factor was implemented.
(a)

(b)

50

60

40

50

30

40

20

30

10

20

0

10

-10

0

-20

-10

-30

-20

-40

-30

-50

-40
NE Buoy SE Buoy SW Buoy NW Buoy C BuoyFASINEXTOGA Arabian Sea(c)

NE Buoy SE Buoy SW Buoy NW Buoy C BuoyFASINEXTOGA Arabian Sea

Figure 11.2.2 Comparison of the SOC
climatology and data from IMET buoy
deployments: (SOC-Buoy) difference (Wm-2 ),heat into the ocean is considered positive.

60
50
40
30
20
10
0
-10
-20
-30
-40
-50
-60
-70
NE Buoy SE Buoy SW Buoy NW Buoy C BuoyFASINEXTOGA Arabian Sea(a) Radiative fluxes: (striped) LW; (shaded)
SW. (b) Turbulent fluxes: (striped) sensible(shaded) latent. (c) Qnet (striped)
unadjusted; (shaded) adjusted. The net flux
comparisons for FASINEX assume the LW
flux values (which were not measured) would
have agreed with the climatology. (FromJosey et al. 1999).

Taylor et al. (1999e) showed that the over estimate of the turbulent fluxes was, in fact,
due to the extrapolation of the Smith (1980) formulae for C D10n. Using a C D10n formula withincreased drag at low wind speeds (Yelland and Taylor,1996) reduced the calculated flux
significantly. The reason was that the original C D10n formula had resulted in small values forthe friction velocity at low wind speeds, very unstable values for the stability parameter (z/L),
and therefore large values for C E . The use of a larger CD10n gave less unstable conditions andtherefore smaller transfer coefficients for sensible and latent heat. These effects were only
significant below about 5ms-1 and therefore would not be not important for much of the world's
ocean. Bearing in mind that the behaviour of C D10n at low wind speeds is not well known(Section 7.3.2), this example serves to emphasise just how difficult it is to obtain reliable flux
estimates in low wind speed tropical regions using ship data.
No correction for this bias in the TOGA COARE area has been applied in the SOC
climatology. While it might be considered at least a partial source of the apparent global heat
imbalance, it is not clear whether the problem is confined to the highly convective regions of thewest Pacific warm pool and the relatively narrow ITCZ, of whether it is applicable to thetropical ocean in general.
D. VERIFICATION OF WIND STRESS
Josey et al. (2000) have verified the SOC wind stress climatology against the IMET
buoy deployments. A comparison of the mean wind stress components for each buoy
deployment is shown in Figure 11.2.3. The wind stresses were generally in good agreement.
The only consistent difference was for the Arabian Sea were the SOC stress values were greaterthan those from the buoy during the latter part of the deployment. Noting that prior to thereplacement of the anemometer there had beeen good agreement, Josey et al. (2000) suggested
that a poor anemometer calibration might have been the cause. Although higher mean stress
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11.2 In situ Products(11.2.4 SOC)

components were observed at the Subduction site compared to the Arabian sea, this was due tothe greater consistency of the wind velocity. Good agreement was found at that site.

SOC stress component

0.10

0.05

0.00

-0.05

-0.10
-0.10 -0.05
0.00
0.05
0.10
IMET buoy stress component

Figure 11.2.3 Comparison of
the mean wind stress values
between the SOC
climatology and the IMET
buoy deployments. Open(closed) symbols represent
the eastward (northward)
component. Circles:
Subduction buoys (Moyer
and Weller, 1997); Squares:
TOGA COARE (Weller andAnderson 1996); Triangles:
Arabian Sea (Weller et al.
1998); Squares with cross:
FASINEX experiment(Weller et al. 1995). The lineof agreement is shown (datafrom Josey et al. 2000)

E. COMPARISON WITH UWM/COADS
Josey et al. (1999) discussed differences between the SOC and unconstrained
UWM/COADS climatologies. These may have resulted from the effects of those observational
bias corrections included in the SOC analysis but not in UWM/COADS, the use of different
parameterisation formulae, the difference in the time periods sampled, and the different scales
used in the objective analysis. The main variations were found to occur in the sensible andlatent heat flux fields. The radiative flux fields were typically in agreement to within 10 Wm-2.
The major differences in the latent heat flux occurred over the Gulf Stream and the sub-tropicalNorth Pacific. In UWM/COADS, the Gulf Stream region of strong latent heat loss was broader
and appeared to be displaced further south than in the SOC analysis. This resulted in adjacentbands of positive and negative heat flux difference. The narrower region of strong heat loss
found in the SOC analysis may have been due to the choice of a smaller objective analysis scale
relative to that used for UWM/COADS. Similar behaviour was seen over the Kuroshio
although the differences were smaller in magnitude. Over a broad area in the sub-tropicalcentral North Pacific, the UWM/COADS heat loss was stronger by up to 30 Wm-2. It is
unlikely that this feature was a result of the ship corrections as these only resulted in differencesof order 5-10 Wm-2 in this region. It may have been due to the different periods used togenerate the two climatologies; either because of changes in the instrumentation used by ships
in this area , or to an actual climatic change.
Differences between the two climatologies were also seen in the sensible heat flux field
for which the UWM/COADS analysis had losses of order 10-20 Wm-2 greater in the annual
mean over the Gulf Stream and Kuroshio and localised areas of the high latitude North Atlantic
and North Pacific. In contrast, the effect of the ship corrections used by Josey et al. (1999) was
typically less than 5 Wm-2. The difference in objective analysis scales may again have been
partly responsible. The net heat flux difference field was dominated by the effects of thedifferences in the latent and sensible heat fluxes. Typically the difference was within ±15 Wm-2,
although regions of stronger heat loss in the UWM/COADS fields over the Gulf Stream andsub-tropical central North Pacific were up to 50 Wm-2. However despite these regional
differences the global heat imbalance for the SOC and unconstrained UWM/COADS
climatologies were remarkably similar; around 20 to 30 Wm-2 excess heat into the ocean.

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11.2.5 IFM
A. DERIVATIONThe Institut für Meereskunde (IfM) surface fluxes and flux related parameters (Lindau,
2000 - http://www.ifm.uni-kiel.de/me/research/Projekte/WGASF ) were derived from theComprehensive Ocean Atmosphere Data Set (COADS). This raw data set contains several
million individual ship observations from the world ocean since about 1850. However thecomputations were restricted spatially to the Atlantic Ocean and temporally to the period 1940
to 1979, where the best data coverage was available. This was necessary, since the intention
was not so much to describe the longtime climate of the Atlantic but rather to provide data forstudies on climate variability. Consequently, the IfM data set contains not only the meanannual cycle but also the year to year variations of several climate relevant parameters on anominal spatial grid of 1˚ by 1˚. To attain such resolution the appropriate statistical treatment of
the raw data plays an important role.
Since the observations were concentrated along narrow ship routes a kriging technique
was used to obtain completely covered fields. For this aim correlation lengths depending on
the geographical direction were derived for all considered parameters. Using the information
on intra-monthly variance the accuracy of individual monthly means was derived and taken into
account in the further calculations. In contrast to ordinary kriging, Lindau did not prescribe afixed radius of influence, but searched by iteration for that combination of grid points whichgave the lowest errors at the prediction point. This minimum was reached when the potential
increase of redundancy and error variability was higher than the information gain from any
additional grid point. In that way Lindau produced twin fields: each monthly mean map is
accompanied by a map giving an estimation of the error variance.
These pairs of maps show the anomaly and its error not as absolute values, but
normalised by the observed standard deviation of the longtime monthly time series at eachlocation. For the renormalisation, two maps are available for each calendar month andparameter; these give the standard deviation and longtime mean. Lindau decided to leave thefields in that form (as they were originally computed), because although the kriging techniqueused is also applicable for the absolute values, it is optimal only for normalised anomalies.
In deriving the IfM fluxes, much effort was spent in correcting the known biases inwind speed, which is an essential parameter not only for the wind stress but also for the
turbulent heat fluxes. Since the majority of marine wind observations in the Atlantic during
this period were Beaufort estimates, a correct equivalent scale was of fundamental importance.
Wind measurements from Ocean Weather Stations (OWS's) carried out in the 1960's give agood opportunity for calibrating an equivalent scale. However, there are good reasons forassuming a possible drift in the relationship between the reported Beaufort force and true windspeed since definition of the Beaufort scale and observation practice gradually changed during
the times. Consequently, Lindau used a time-dependent equivalent scale, derived firstly for the
1960's when measurements from OWS's were available, and expanded to other decades byusing the spatial pressure differences between the reporting ships (see Section 10.6.2C)
An additional correction was applied to the wind stress calculations, because due to thequadratic dependence of the stress on wind speed, observation errors, even if random, cause asystematic bias (Section 10.6.1). In addition, COADS temperature measurements of air, sea
surface and dew point were compared to the OWS's in the Atlantic and Pacific, in order toremove systematic errors in the turbulent heat fluxes.
B. MERIDIONAL HEAT TRANSPORT
Considering longterm averages and neglecting decadal changes of the mean ocean
temperature, any net heat loss at the sea surface has to be compensated by an oceanic heattransport into this region. These horizontal transports are sufficiently well determined fromoceanographic sections to serve as an independent constraint for the computed net air-sea heat

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flux. As the Atlantic is laterally enclosed only the meridional heat transport (MHT) contributeto a heat exchange with other oceans. The heat transport across 65˚N (where the northernboundary of the Atlantic may be defined) is small. According to Gulev & Tichonov (1989) thenet heat loss into the Arctic Ocean is equal to 0.275 PW, whereas Aargaard & Greisman (1975)
estimated a smaller value of 0.098 PW. The integration of the net air-sea heat flux from 65˚Nsouthward, yields, according to (9.1), the MHT as function of latitude.
Beginning with 0.098 PW at 65˚N, Lindau(2000) found a maximum northward heattransport of 1.09 PW at 23˚N (fig.11.2.4) while an energy rate of 0.78 PW crossed the equator.
Southward of 10˚S the MHT remained at a nearly constant northward flow of about 0.5 PW,
because the heat budget in this region is approximately balanced. The vertical bars in figure11.2.4 give the oceanographic estimate of Bryden & Hall (1980) for 25˚N, that of Wunsch(1984) for the equator, that of Speer et al. (1996) for 11˚S and that of Holfort (1994) for 30˚S.
Since Lindau's results were in good agreement with these independent estimates no additional
adjustment of parameterisations was considered necessary.
Fig.11.2.4: Atlantic meridional
heat transport (PW) comparedto the results of I & H: Isemer& Hasse (1987) and H & L:
Hastenrath & Lamb (1978).
These authors used different
data bases. For comparability,
their parameterisations havebeen applied to COADS.
Results of oceanographic
sections are indicated at therelevant latitude together with
their error bars: 30˚ S: Holfort(1994), 11˚S: Speer & al.
(1996), 0˚: Wunsch (1984),
25˚N: Bryden & Hall (1980).
C. GLOBAL PRECIPITATION PRODUCTFor the derivation of the Lindau (2000) precipitation algorithm the weather andhumidity observations of 24 years from four light vessels in the German Bight were calibrated
against the wind corrected daily rain measurements carried out aboard these ships. In a second
step the quality of the resulting rain algorithm was checked by its application on independent
COADS data. For monthly means a correlation of 0.85 is obtained, for the climatic annual
cycle even one of 0.96.
A comparison with SSM/I derived rain (Ferraro & Marks,1995) showed an acceptableagreement in the global distribution and mean oceanic precipitation for January 1988.
Applying this algorithm for the entire period 1980 to 1993, 81% of the world ocean is covered
with data and a mean annual precipitation of about 1300 mm results (Figure 11.2.5).
Comparison with evaporation shows an imbalance of -298 mm, which can be explained by thewater export on the continents and low data coverage of the Southern Ocean where rainprevails.
11.2.6 Comparative assessment
A. INTRODUCTION
In this section we will compare the characteristics of the various studies describedabove in order to determine the strengths and weaknesses of each. As we have already noted,
the differences between the various flux climatologies are due to a number of factors: whether"sampling" or "classical" flux calculation was used, what wind scale was used to interpret thevisually observed winds, what other corrections (if any) were applied to the observations, theReport of the Working Group on Air Sea Fluxes

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formula used for each of the radiative flux components, the formula used for each of the
turbulent flux components, and other factors such as the smoothing algorithm, grid scale, andtime period of the data set used.

Figure 11.2.5
Precipitation minus
evaporation derived by
Lindau (2000) for theperiod 1980 to 1993 fromCOADS.

B. "SAMPLING" OR "CLASSICAL"
Apart from the major effort of Bunker (1976) and co-workers, the restricted computing
resources available resulted in all the pre-1990 climatologies being calculated from mean values- the "classical" method. Because correlations of the basic variables are neglected, use of theclassical formula is likely to have introduced a bias of order ±5% to ±15% which varies fromone climatological region to another and even with the flux parameterisation used (Section
8.3.4). Isemer and Hasse (1987) attempted to correct for this on the basis of the Bunker (1976)
calculations. Each of the more recent climatologies has been calculated using the "sampling"method. This is to be preferred, at least where observations are plentiful; thus we favour the
post-1990 studies. However it should be noted that the sampling method has the disadvantageof requiring all the basic variables needed for flux calculation to be available with eachobservation. This reduces the number of available data significantly and may increase the noisein many areas. It also makes it more difficult to combine data sets; for example to calculate aflux product based on remotely sensed and in situ data. Thus there are cases whereclimatologies based on the "classical" method still may be preferred.
C. VISUAL WIND SCALE
The wind scale defined by WMO for visual wind observations is known as"WMO1100" (WMO, 1970). The different variations on this "Beaufort" scale were discussed in
Section (10.6.2.C). We emphasise that, not only did Lindau (1995a) devise a scale which
produced good agreement between visual and anemometer winds, he also demonstrated that thedifferences in the older scales were due to the method used in their derivation. Kent and Taylor(1997) confirmed Lindau's results with respect to the VOS. Independently da Silva et al.
(1974) produced their own version of the Beaufort scale (da Silva et al. 1995). Fortunately thisscale and that of Lindau (1995a) are not very dissimilar to WMO 1100. Thus the only
climatology listed in Table 11.2.1 which used a significantly different Beaufort scale was that of
Isemer and Hasse (1987). That study has effectively been replaced by that of Lindau (2000).
D. OTHER OBSERVATION CORRECTIONSWith the exception of Esbensen and Kushnir (1981), most of the pre-1990 studies used
increased transfer coefficients to allow for some degree of observational error assumed tocharacterise the data sets. This was either done initially (e.g. Bunker, 1974) or after some formof inverse analysis (e.g. Isemer and Hasse, 1987, Oberhuber, 1988). The results of recent
investigations into VOS observational errors were too late to be incorporated in the UWM (daSilva et al. 1994) study (although they are mentioned in the documentation). The more recent
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studies at SOC (Josey et al. 1999) and IfM (Lindau, 2000) have applied explicit corrections to
the data (see sections 11.2.4, 11.2.5.). Taking the SOC corrections as an example, the effect on
the heat fluxes varied region by region. In general the heat loss was increased but generally byless than 10 Wm-2 . On a global mean basis the SOC values agreed closely with the unadjusted
UWM values to within a few Wm-2 in the flux components and the net flux, a somewhat
surprising result. Compared to the SOC fluxes, the temperature corrections suggested by
Lindau would have increased the ocean cooling, probably by around 10 Wm-2 or less. The
main difference between the SOC and IfM is likely to be due to the different transfer coefficient
schemes (see below).
E. RADIATIVE FLUX FORMULAE
For shortwave flux, Bunker (1976) and Esbensen and Kushnir (1981) used the Budyko(1963) shortwave parameterisation. Dobson and Smith (1988) considered this formula to bequestionable. Most other studies have used some variation on the Reed (1977) algorithm which
was recommended by the comparative studies of Frouin et al., (1988) and Dobson and Smith(1988). The latter found a site dependant long term bias of -1 to +12 Wm-2 and a monthly
mean rms error of about 8 Wm-2 . We should note that, using comparisons with data fromseveral air-sea interaction experiments, Katsaros (1990) found this formula to be biased high by
about 20 Wm-2. The comparison between SOC and buoy values (Figure 11.2.2) does notsupport this, however it may be that the instrumentation is at the limit of its accuracy (Section
5.5.2). The IfM study (Lindau, 2000) used the Malevskii et al. (1992) algorithm which has
been found to be less biased in comparison to the Reed formula, at least in mid latitudes(Niekamp, 1992).
In a comparative study of algorithms for estimating longwave radiation Katsaros (1990)
found little difference between the models compared. In general the biases were less than 10
Wm -2 and the rms scatter was significantly less than for the shortwave parameterisations. Of
those tested, Josey et al. (1997) evaluated two of the better performing algorithms, that of
Clark (1974) which was based on the earlier Berliand and Berliand (1952) formula and that of
Bunker (1976) which was based on Efimova (1961). In comparison against pyrgeometermeasurements from several cruises they found that the Clark algorithm performed better andhence chose it for calculating the SOC climatology. In contrast they found that the Bignami etal. (1995) formula underestimated the downward longwave flux by around 25 Wm-2 . This
fitted with the Schiano et al. (1993) observations in the western Mediterranean that almost all
the formulae they tested over estimated the downward LW flux by 25 to 40 Wm-2. It was basedon those results that Bignami et al. (1995) had developed their algorithm. Garrett et al. (1993)
suggested that this greater radiational cooling was needed to balance the heat budget of theMediterranean and Gilman and Garrett (1994) suggested that the difference was due to thedifferent atmospheric properties over this land locked sea. Thus the use of the Bignami
formula for the IfM LW flux over the Atlantic may not be an appropriate choice. It will givesignificantly greater cooling compared to the formulae used for the UWM or SOC studies. In
comparison to the buoy data (Figure 11.2.2a) the SOC values showed too little cooling but only
by about 6 Wm-2 (standard error ± 3Wm-2 )F. TURBULENT FLUX FORMULAE
The studies evaluated here all used the "traditional" bulk formulae (Section 7.2) anddifferences caused by variations in the algorithms are likely to be small compared to those due
to different choices of transfer coefficient. Since latent heat is usually the dominant flux wewill discuss here the choice of CE10n and note that the discussion for CH10n would be very similar.
The transfer coefficient for latent heat as used in the different studies is shown in Figure11.2.6. There is clearly a significant difference between the relationship assumed by Bunker(1976) and that recommended in the review of Smith (1989) (1.2 ± 0.1) or the observations of
DeCosmo et al. (1996) (1.12 ± 0.24). Ignoring any error in the ship data, at first sight it would
appear that the SOC climatology which uses the Smith (1989) value should be more accurate
than the IfM climatology which uses the original Isemer and Hasse version of CE10n.

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However it is interesting to note that most of the coefficients that have been adjusted tosatisfy some form of oceanographic constraint (global heat balance or meridional heat transport)
have similar values at typical midlatitude wind speeds of about 10 ms-1. Thus the Oberhuber(1988), adjusted Isemer and Hasse (1997), and constrained da Silva (1994), all have 1000 x
CE10n between 1.3 and 1.4 in that wind speed range. So too does Lindau (2000) whose CE10nvalues were based on the unadjusted Isemer and Hasse (1997). While such values are highcompared to Smith (1989) or DeCosmo et al. (1996) they are not outside the range ofpossibility. At wind speeds higher than about 12 ms-1 there is very little data on the behaviourof C E10n with wind speed. DeCosmo et al. could not rule out a tendency for C E10n to increase
with wind speed and recent determinations of CE10n by Dupuis et al. (1999) actually indicated anincrease of CE10n with wind speed similar to that assumed by Lindau (2000).
The lower C E10n values used in the SOC climatology were justified by Josey et al.
(1999) mainly on the grounds that the fluxes calculated were in reasonable agreement with the
IMET buoy values. However the IMET fluxes were calculated using the version 2.5 (or earlier)of the TOGA COARE algorithm (Section 7.4.1). That algorithm uses a constant Charnockparameter of 0.011 with the result that the values of CE10n and CH10n tend to decrease withincreasing wind speed. Using a Charnock parameter which increases with wind speed as
observed (Yelland et al. 1998, Hare et al. 1999) results in more constant values of CE10n andCH10n (version 2.6 of the TOGA COARE algorithm).
2.0
Bunker 74
Oberhuber 88
Smith, 1989
DeCosmo 96
IH, 97 adj.
DaSilva adj.
Lindau ??

CE10n(x1000)1.8

1.6

Figure 11.2.6. The transfer
coefficient for latent heat as
used in different studies plotted
as a function of the 10m neutral
wind speed.

1.4

1.2

1.0

0

5

10
U10n

15

20

25

30

(m/s)

The conclusion is that, despite all the various air-sea interaction experiments, ourknowledge of the magnitude of the transfer coefficients is still by no means adequate. New fast
response instruments are beginning to become available for use at sea and their use to determine
CE10n and CH10n , particularly at wind speeds above 10 ms-1, is to be strongly encouraged.
With regard to the drag coefficient, CD10n, the present state of our knowledge issomewhat better. One might allow that there is a dispute with regard to the magnitude of CD10n
at wind speed above, say 15 ms-1 (e.g. Janssen, 1999, 2000; Taylor & Yelland, 2000b), andevidence that swell can alter CD10n at lower wind speeds (say below 5 ms-1) (Donelan et al, 1997;
Rieder and Smith, 1998). However there is a growing body of evidence for the wind speed
range 5 to 15 ms-1 that supports the lower C D10n values suggested by the observations of Smith(1980), Large and Pond (1982), and Yelland et al. (1998). Thus the higher CD10n values (Figure11.2.7) used by Hellerman and Rosenstein (1983) or Oberhuber (1988) are not supported by the
majority of the observations. It follows that the wind stress values contained in the latterclimatologies will be significantly too high for most wind speeds. However, in poorly sampled
regions like the Southern Ocean the stresses contained in almost all in situ climatologies are
believed to be severely underestimated, particularly in winter.

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CD10n(x1000)

3

2

1

0
0

OberhuberdaSilva
H&R
Smith 1988
Smith 1980

5

10
U10n

15
(m/s)

20

25

30

Figure 11.2.7 The dragcoefficient CD10n as used indifferent studies plotted as afunction of the 10m neutral
wind speed.

G. SMOOTHING ALGORITHM
The disadvantages of the successive correction technique as used for UWM/COADS
and the SOC climatology has been discussed in Section 8.5.2, and the advantages of the
kringing method (used for the IFM fluxes) in Section 8.5.3. The disadvantage of kriging isthat it is computationally expensive and, at this time, global flux fields have not been
computed using this technique. However, with increasing computer power, there is no doubtthat future global flux climatologies will be based on improved analysis methods such as
kriging, reduced space optimal analysis (Kaplan et al. 1997, 1998), or other techniques.
H. CONCLUSION
Much effort has been invested to ensure that each new release of COADS has a morenearly complete collection of the historical data. For that reason alone, the newer
climatologies are to be preferred to those calculated in the past. With regard to theparameterisations used for the heat fluxes one might have hoped to categorically state that the
new climatologies are superior to previous studies. However, at present we can only state that
we believe that to be the case. For wind stress the case is more definite. There is strong
evidence that climatologies such as Hellerman and Rosenstein (1983) or Oberhuber (1988)
have over estimated the wind stress in most ocean regions. Thus, for wind stress the newer
climatologies are to be preferred. However the poor in situ sampling in the southernhemisphere, and particularly in the Southern Ocean, suggests that the lengthening data set ofsatellite derived wind data is a promising alternative.
Of the recent global in situ climatologies it is hard to choose between the
UWM/COADS and SOC products. The SOC product has been found to be in marginallybetter agreement with buoy measurements than the unconstrained UWM/COADS product(e.g. Weller et al. 1998). Thus the SOC fluxes may be preferred for regional studies.
However if global net heat flux must be in balance (e.g. for forcing models) then theconstrained UWM/COADS product must be preferred even though comparisons with buoy
data are significantly worse for that product (Weller et al. 1998, Josey et al. 1999).
Compared to the SOC climatology the UWM/COADS covers a significantly longer period.
Extension of the SOC fields back in time is hindered by the lack of metadata for applying theship corrections. However, perhaps the greatest strength of the UWM/COADS climatology
is the provision of sensitivity fields so that users can apply their own error estimates andoceanographic constraints. It is recommended that more use be made of that facility.
For the Atlantic Ocean, the IFM product has the advantage of a good mapping
technique, good error estimates, and providing a realistic meridional heat transport. Whilethe latter has been attained through the use of radiation parameterisations and transfer
coefficients which have allowed balanced budgets to be obtained for the Baltic andMediterranean Seas, these are not, at present, the generally accepted choices for open ocean

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regions. This is the newest product and further verification is desirable and doubtless will beforthcoming.
Finally we emphasise again that there is still a need for many more measurements ofthe transfer coefficients, particularly in open ocean moderate to high wind conditions.
Whenever possible such determinations should be made using both the eddy correlation andthe inertial dissipation methods. Both methods are likely to have systematic errors whenused on ships. However the nature of those errors is different for the two techniques. A large
data set of consistent transfer coefficient estimates obtained using the two methods by several
research groups working from various ships would give confidence that the systematic errors
had properly been accounted for.
11.3 Flux Products Based on Satellite Data
11.3.1 Introduction
The next section will evaluate a recent satellite based flux product, HOAPS, which
represents one of the first attempts to use satellite data to produce fields of all the surface
fluxes except SW radiation. Satellite radiation products will be discussed separately in
Section 11.3.3 amd 11.3.4 for US and Japanese products respectively. Finally Section 11.3.5
will briefly consider fluxes produced using the Residual Method, a combination of satellite
measurements and model calculations.
For development of flux climatologies from satellite data the consistency of data overtime is of paramount importance for the flux field producer. As an example we will consider
the SSM/I sensor. This has been flown on several DMSP platforms (see Section 4.3.3) andhas been widely used to construct long time series of flux fields and flux related variables.
Differences between the same sensor on different platforms in different orbits can cause
differences in zonal monthly averages. For example, for wind speed differences ofapproximately 1 ms-1 can occur (Jost et al., 1999). Colton and Poe (1999) conducted a studyof sensor-to-sensor differences for six DMSP platforms starting in 1987 and ending in 1997.
Their paper summarises the prelaunch and postlaunch performances found throughcomprehensive calibration/validation activities. Besides several sensor-specific components
two major results of this study were:❍

The remarkable stability of the SSM/I sensor provided the opportunity to quantify theincremental brightness temperature differences to which the SSM/I's can beintercalibrated. The so called "noise floor" has been determined using distributionfunctions constructed from the brightness temperatures. The noise floor is estimatedto be 0.25-0.35K for the five low-frequency channels and 0.45K for the 85 GHz
channels.

❍

The interpretation of sensor to sensor differences based solely on monthly averaged
brightness temperatures is not recommended for multi-year time series but can be
sufficient for limited periods because regressions between different SSM/I are
relatively stable.

From these results it is suggested that flux producers should focus not only on high accuracyof the retrievals but also on constructing consistent time series of satellite data.
11.3.2 Evaluation of HOAPS
A. INTRODUCTION
The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data
(HOAPS) dataset (Schulz and Jost, 2000) is solely based on the use of infrared andmicrowave satellite data within retrievals developed with the aid of radiative transfer theory.
A time period of 10.5 years (July 1987 - December 1996) is freely available for use by otherscientific groups1, and this will be extended in the future. It contains the following variables:
1 from http://sop.dkrz.de/HOAPS

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evaporation [mm h-1], rain rate [mm h-1], E-P [mm h-1], near surface air humidity [g kg-1], 0.98
∗ specific saturation humidity at sea surface temperature [g kg-1], wind speed [m s-1], latent
heat flux [W m-2], sensible heat flux [W m-2], and longwave net radiation [W m-2]. The
following subsections describe the methodology used to derive the fields and give a summaryof attempts to verify the product through a comprehensive intercomparison with a range of
observation types, from merchant ship observations to data from high quality IMET buoys.
B. METHODOLOGY
The evaporation at the sea surface is parameterised using a bulk approach as
described in Chapter 6. The wind speed u and the atmospheric specific humidity ql are
determined from SSM/I measurements using the retrieval of Schlüssel and Luthardt (1991) as
described in Section 10.6.2 and the method of Schlüssel et al. (1995) for q l as described in
Section 10.5.2.
The saturation specific humidity at the sea surface is essentially a function of the sea
surface temperature. Since continuous handling of large data sets like the AVHRR raw datarequires large amounts of computer and man power, it was decided to use the NOAA/NASA
Oceans Pathfinder Sea Surface Temperature product (see Table 10.1). These products areavailable as daily fields with a spatial resolution of (9 km)2 at best.
Because all the needed variables can not be inverted from the available satellite
measurements, some empirical assumptions have to be incorporated into the computations.
The most important one is an assumption about the atmospheric stability, needed to computethe transfer coefficient for latent and sensible heat. For use in the scheme of Smith (1988), it
is assumed that the atmosphere has a relative humidity of 80% at any time and any place.
With that assumption the missing air temperature can be computed from the measured ql. The
errors in the transfer coefficient that occur if this assumption is wrong are between 2% at highwind speeds and strong unstable conditions, and 50% at low wind speeds and strongly stableconditions (Schulz et al., 1997). Other assumptions concerning the surface pressure or air
density are not of great importance.
Measurements of rainfall at the sea surface with radiometers in space have been the
topic of many studies in recent years. In an early stage of the preparation of the climatology it
was decided to use the rain algorithm of Bauer and Schlüssel (1993) which delivers an
instantaneous rain rate. This algorithm has been successfully tested within the Precipitation
Intercomparison Project (PIP) and the Algorithm Intercomparison Projects (AIP) described in
Section 10.9.4.
In addition to the variables needed for computing E-P the radiative longwave net flux
and the sensible heat flux have been included in the climatology. The longwave net flux iscomputed by a scheme described in Schlüssel et al. (1995) (see Section 6.4.2) and thesensible heat flux is determined using the air temperature derived under the assumption of
80% relative humidity.
The rain retrieval is applied first because all other retrievals can only be applied inrain free or light rain cases. If the derived rain rate is below a certain level the other retrievals
are applied to instantaneous SSM/I measurements to obtain the basic state variables u and q l.
Within the bulk approach, u and ql are combined for each instant whereas the instantaneous u
has been combined with the mapped weekly averaged qS computed from the Pathfinder SST.
The latter is bilinearly interpolated to the location of the SSM/I pixel. Weekly averages must
be used for q S because of the cloud cover problem. After applying the retrieval algorithms,
all the fluxes are computed on this quasi-instantaneous basis using the bulk approach of Smith(1988).
The derived, quasi-instantaneous fluxes are mapped to grids with three different
resolutions: 0.5°, 1.0°, and 2.5° . The time averaging is then performed on the maps and thefields are stored as daily, pentad, monthly, yearly, and climatological fields.

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C. LIMITATIONS
Obviously, the present satellite-derived climatology has limitations just as any
climatology has. Since large parts of the climatology are based on measurements from only
one SSM/I at a time, a serious sampling error can be expected, particularly for precipitation.
A case study with data obtained during the TOGA COARE campaign showed that the
sampling error for grid resolutions of 2° by 3° and a sampling time of 3 weeks can be as highas 30% if only one satellite overpass per day were available. If data from two SSM/I's wereused this error is diminished to less than 4%. So a recomputation of the time series using all
the available satellite data is a major task for the future.
Another systematic error that has not yet been fully explored is caused by the use ofradiometers on different platforms (AVHRR on NOAA and SSM/I on DMSP). It is expectedthat this error is rather small because the sea surface temperature obtained from the AVHRR
data is a relatively inert quantity compared to the atmospheric variables obtained from SSM/I
measurements. But this argument holds only if the measurement of the sea surface
temperature is not hampered for weeks by persistent cloudiness or by long periods ofdarkness over polar regions. For the latter case the sea surface temperature could be biased
due to the more difficult cloud detection using infrared data.
Additionally, the climatology is limited by the fact that the river runoff into the ocean
is not implemented. The observed monthly averages of runoff for several rivers are available(Dümenil et al., 1993) and there would be in principle no obstacle to use them to check thelong term mean of the E-P fields.
D. VERIFICATION OF FLUXES
Introduction
The HOAPS data set has been evaluated using a two way validation strategy. As afirst step, each retrieval algorithm used has been validated with in situ data from different
sources such as scientific experiments or data from the GTS. These intercomparisonsfocussed mostly on the quality of the instantaneous retrievals, with the exception of using asample of GTS data to compare on a monthly time scale but with very coarse spatial
resolution. The second step focused more on comparisons of the gridded HOAPS dataset tolong term buoy measurements like the Subduction Experiment (Moyer and Weller, 1997) andon comparisons to global climatologies derived from in situ data, namely that of theSouthampton Oceanography Centre (SOC - Josey et al. 1999) and of the University of
Wisconsin-Milwaukee (UWM - da Silva 1994). Some of these intercomparisons using
different types of in situ data are briefly described below.
Globally distributed comparisons to GTS data
Schulz et al. (1997) described intercomparisons of satellite-derived wind speed, nearsurface air humidity, and latent heat flux with ship data taken from the GTS during a period
from July to September 1987. Table 11.3.1 gives bias and standard deviation between bothdatasets.

Table 11.3.1 Bias and standard deviation for an intercomparison of satellite-derived windspeed, near-surface air humidity, and latent heat flux with GTS data at two different timescales. N denotes the number of match ups for the instantaneous time scale and the numberof 2˚ x 2˚ grid boxes at the monthly time scale, respectively.
Temporal scaleNu (ms-1)

ql (gkg-1)

Qlat (Wm-2)

bias

σ

bias

σ

bias

σ

instantaneous

11108

0.2

2.1

0.06

1.6

3

50

monthly average

136

-0.05

1.3

0.08

1.1

0.5

20

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11.3 Satellite Products(11.3.2 HOAPS)The bias of 3 Wm-2 in latent heat flux was mostly caused by systematic higher latent
heat flux estimates from the satellite south of 40˚S, where only very few in situ observations
contribute to the comparison. All standard deviations must be interpreted with respect toerrors in both the satellite retrieval and the ship measurements. As shown by Wentz (1997)
for wind speed the mismatch of the ship point measurement and an instantaneous SSM/I pixel
can be the largest component in the error budget. If we conservatively assume that at least
10% of the obtained standard deviation is caused by the mismatch and distribute theremaining error equally then the accuracy for an instantaneous estimate of latent heat flux isapproximately 30 Wm-2. This was a great improvement compared to older techniques likethat of Crewell et al. (1991) applied to SMMR measurements over the North Atlantic.
Although they only derived the near-surface humidity from the satellite, and took the windspeed and sea-surface temperature from in situ measurements, a much larger standard
deviation (73 Wm-2 compared to 50 Wm-2) was obtained. So from this intercomparison it canbe concluded that the retrieval of low-level water vapour content and the use of AVHRR
derived sea surface temperature considerably improved the satellite derived fluxes.
Table 11.3.1 also shows values for the bias and standard deviation for monthly
averages of u, ql, and latent heat flux <Q lat>. The numbers in the table were gained from acomparison on a grid with a 2˚ x 2˚ resolution. Such a coarse resolution was necessary
because the ship measurements were widely scattered over the ocean. Another limiting
condition was that to obtain a representative monthly mean each grid box must be covered
with in situ measurements on at least 25 observation days. As a result of this the number ofboxes was decreased to only 136, which indicates that all the numbers should be interpreted
carefully. To estimate the error of the satellite-derived latent heat flux Schulz et al. (1997)
computed the noise of the ship data for the 136 bins by randomly dividing the in situ data ineach bin in two subsets and taking the standard deviation between the subsets as noise.
Applying this procedure the error of the satellite-derived flux has been estimated to be ~15
Wm -2. This error estimate fits very well in the upper and lower bound error estimation ofChou et al. (1995b) and is slightly higher than that stated by Schlüssel et al. (1995). The
larger error could be caused by the fact that many of the grid boxes are located in coastal
areas where ql (which contributes most to the error in <Qlat>) is much sensitive to the landocean contrast (Liu et al., 1992). However compared to earlier attempts to derive < Qlat>
from satellite data this was an significant improvement because phenomena such as
physically unrealistic downward fluxes in the mid- and high-latitude North Pacific and North
Atlantic Oceans during summer were eliminated. Such problems were found by Esbensen etal. (1993) who compared COADS data with satellite data obtained using Liu's (1986) method.
Comparisons to weather ship data
Schulz et al. (1997) also compared HOAPS to data from the weathership M for a one
year (August 1987 - August 1988) time series using the "instantaneous" values. The aim wasto check that the retrieval schemes for wind speed, air humidity, and sea surface temperature
worked well over the seasonal cycle. The results of this comparison showed two major
things. Firstly, most differences between the in situ and satellite-derived quantities weremuch smaller than in the comparison with unfiltered GTS data. For both wind speed and air
humidity, no bias and standard deviations of 2.0 ms-1 and 0.97 gkg-1 respectively wereobtained for the whole time series. Whereas the deviation for speed is similar to thecomparison above the value for humidity is strongly decreased. This indicates that only highquality in situ data are really useful for the assessment of retrieval errors. The second finding
concerns the chosen sea surface temperature dataset which was in this case the MCSST(Multi Channel Sea Surface Temperature) described in McClain (1981). The surface
saturation humidity showed a systematic under-estimation during July, August, and October1987 and also during the first three months in 1988. This resulted in a mean underestimation of the sea surface saturation humidity of 0.5 gkg-1. This bias was caused byextensive periods of cloud cover and/or darkness that led to a shift toward a nighttime bias in
the day/night observation ratio. Cloud detection is more difficult during nighttime thanduring daytime since a combination of tests using the three infrared channels of the AVHRR
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is required, rather than simple reflectance tests. So less measurements, and a deteriorated
cloud detection, can lead to a significant cold bias in the sea surface temperature. However,this problem has been recognised and producers of newer datasets (e.g. the Oceans Pathfinder
Sea Surface Temperature dataset) are aware of such problems.
Comparisons to scientific experiment data
Comparisons to data sets gained during scientific experiments have mostly the aim ofshowing that the satellite algorithms are able to describe the local temporal variability ofenergy fluxes and flux related variables. HOAPS retrievals have been compared to data
gained during TOGA COARE (Bradley and Weller, 1997) and CEPEX as well as during
ARKTIS'93 (Brümmer et al., 1993). Both intercomparisons represent regions with rather
extreme climates. The ARKTIS'93 experiment was conducted in February and March 1993
in the northern part of the Norwegian Sea. The in situ measurements were taken by standard
instruments aboard the German research vessel Valdivia. The major result was that in mostcases the measurements of the bulk parameters and the parameterised fluxes were in good
agreement. The exception was some cases relating to situations where the ship was too close
to the coastline. The errors found were comparable to the weathership M exercise. Anotherresult was that the wind speed algorithm was not able to reproduce high wind speeds (around20 ms-1). The same result was found in tests of other algorithms (Goodberlet et al., 1989 andStogryn et al., 1994). This problem, inherent with most of the existing wind speed retrievals,
can be caused by insufficient accuracy of the emissivity parameterisation during high windspeed events. However, a slightly surprising result was that the retrievals of wind speed andhumidity gave good results even in the case when the ships instrumentation recorded lightrain of up to 2 mmh-1.
The TOGA COARE and CEPEX field phases took place in purely tropicalenvironment. For both experiments, data from the US research vessel Vickers have beenused to make intercomparisons to the retrieval schemes. It was found that precipitable water(both total and of the 500 m bottom layer) and the surface wind speed were retrieved
excellently by the SSM/I during both experiment phases. Errors were not larger than in otherregions of the world oceans. However, the retrieval for near-surface air-humidity showed asystematic overestimation of 1-2 gkg-1 during a 12 day period in February 1993. The failure
was in some way connected to the vertical structure of the water vapour distribution, sinceduring that period the radiosonde data exhibited very high water vapour loadings in the upper
troposphere with relative humidities higher than 90%. When the tropospheric humidity was
lower the retrieval worked quite well. The reason for this malfunction is not yet fully
clarified. On one hand it can be argued that the high water vapour loading leads to a signal
saturation effect so that any variation below this thick water vapour layer can not be detected
well. Against this is that the 500m integrated water vapour was retrieved quite well during
this phase. On the other hand there was a considerable decrease of correlation between the
integrated water vapour amounts and the near-surface air humidity (which is almost constant);thus the assumed linear relationship might not be valid in the Western Pacific warm pool.
Whether this behaviour of the humidity retrieval is typical for tropical oceans can't beanswered definitely because the CEPEX data (which were recorded along 2˚ south and northof the equator) showed that the retrieval worked well shortly after the ship left the warm poolarea. Additional comparisons to data from the TAO buoy array in the equatorial Pacific anddata from the Pilot Research Moored Array in the Tropical Atlantic (PIRATA) are necessary.
However these datasets lack the information on vertical atmospheric humidity structure whichmight be essential for resolving possible retrieval errors.
Comparisons in enclosed seas
Because of the rather coarse spatial resolution of microwave radiometers, only twointercomparison studies over the North Sea region have been conducted. Objectively
analysed and gridded fields with a spatial resolution of (42 km)2 and a time resolution of 3hours have been used as in situ data (Luthardt, 1985). The first study (Schlüssel, andLuthardt, 1991) was mostly aimed at validating the wind speed retrieval. The second study,

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by Schulz (1993b), examined one case study over the North Sea and compared air-humidity
and sea surface temperature, wind speed, and latent heat flux. Concerning wind speed bothstudies found that the wind speed is recovered by the SSM/I with an accuracy of 2ms-1 in the
inner areas of the North Sea but that the accuracy decreases approaching coastal waters due tothe field of view of the antenna including signals from land surfaces. For sea surface
temperature it was found that the in situ measurements were not able to reproduce the finescale structure obtained from the AVHRR data and that in situ measurements were positively
biased by approximately 1K. This can be explained by the possible existence of a cool skinat the surface, and the well known overestimation of the temperature at cooling water intakes
on ships. With respect to air-humidity the same results as in the global comparison havemostly been found. The parameterised latent heat flux in this case study shown in Figure11.3.1 exhibited generally a good agreement but some hot spots of high differences between
satellite and in situ data occur in regions where the coverage of in situ measurements was low.
It should be noticed that the quality of the in situ temperature and humidity fields is not as
good as the wind fields because the fields couldn't be adjusted by a geostrophic constraint.
Hence, the observation density plays a greater role in the comparison of the thermodynamic
components.

Figure 11.3.1: Distribution of latent heat flux on 3. August 1987 in Wm-2 (left), and differences
between in situ latent heat flux at 6:00 a.m. and those derived from satellite data in Wm-2 (right).
The contour interval is 10 Wm-2, negative isolines are dashed.

Comparisons to global in situ climatologies
To establish differences between HOAPS and climatologies derived from in situ
measurements comparisons have been made with the SOC climatology described by Josey et al.
(1999) and the UWM climatology described by da Silva (1994). Since the findings are very
similar for both in situ climatologies the following discussion is restricted to the SOC
climatology. Because HOAPS and SOC do not cover the same time period, only a five and ahalf year climatology from July 1987 to December 1993 was considered to ensure thatphenomena like El Niño were, or were not, included in both. Figures 11.3.2 to 11.3.4 show the
global climatological mean distributions of evaporation E, precipitation P, and E-P from (a)HOAPS, (b) SOC, and (c) the difference (HOAPS - SOC).

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11.3 Satellite Products(11.3.2 HOAPS)In the evaporation distribution (Figure 11.3.2), it can clearly be seen that thedifferences are smallest in the North Atlantic where the in situ data density is at its maximum.
A positive outcome of the comparison is that the Gulf Stream is located at the same position inboth datasets, which is by no means a matter of course. Also, only small differences areobserved in the strength of the evaporation over the Gulf Stream which are most significant
during winter.
Large zonal homogeneously distributed biases occur over the tropical oceans and thewhole southern hemisphere. The differences in the tropics are manifested by a negative bias of
about -1.5 ms-1 in wind speed and a positive bias of about 1.5 gkg-1 in air-humidity. A
possible explanation for the bias in air humidity may be the malfunctioning of the satellite
algorithm, as found in the comparisons with the COARE data. However that would assume
that the atmospheric conditions in the warm pool could be considered to be typical for tropicaloceans in general. This is contradicted by the results from the CEPEX comparison which
cover a much larger tropical area. The wind speed bias is not yet explained; all comparisons ofwind speed retrievals to tropical experiment data show no bias of the magnitude although thenumber of comparisons is rather small. It should be kept in mind that the observation densityof the in situ measurements is quite low in the tropics (see Figure 2 in Josey et al., 1999) so thatwind speed and air-humidity values may be influenced by subtropical conditions resulting inhigher wind speed and lower humidity values compared to HOAPS. This would also partly
explain the zonally homogeneous distribution of these biases.
In the southern oceans, HOAPS shows a higher evaporation than SOC which isexpressed by a zonal homogeneous positive bias in wind speed of about 2 ms-1 whereas thebias in air-humidity is negative in the range of -0.5 to -4 gkg-1 with maximum differences foundto the south of South Africa. These southern ocean biases are surely caused by undersamplingin the in situ climatology with the result that the analysed values are strongly influenced bymeasurements from better sampled regions further north.
Considering precipitation, it is obvious that in situ precipitation estimates can only be
useful where the spatial observation density is high because precipitation has a very intermittent
spatial structure. Figure 11.3.3 shows that the precipitation pattern, in the southern hemisphereat least, appears to be unrealistic for SOC but reasonable for HOAPS. Due to the small
observation density the analysis of in situ data tends to create clusters of high precipitation in
the South Pacific and Indian Ocean with rainfall that is higher than that in HOAPS by a factor of
2 to 3. Whereas HOAPS shows large areas to the west of the continents with minimum rainfallof less than 0.5 mmd-1, those areas are much smaller in the SOC dataset. But even in the well
sampled North Atlantic, substantial differences occur over the Gulf Stream region and to alesser extent over the Kuroshio in the North Pacific during winter time (Figure 11.3.3).
Rainfall amounts in HOAPS are as high as in the ITCZ which seems to be not unrealistic if the
independent estimates from the TRMM Precipitation Radar (Kidd, 1999, pers. comm.) over thesouthern part of the Gulf Stream are considered. So the reason for the difference may lay inthe method of Tucker (1961) for estimating rainfall from actual weather observations which
has an upper limit of 8.13 mm/(3 hours). For tropical precipitation was found that this scheme
underestimated rainfall seriously and an empirical temperature dependent correction wasdeveloped by Dorman and Bourke (1978). Because this correction does not take effect over the
Gulf Stream, in situ climatologies may underestimate rainfall there. An examination of SSM/I
rainfall estimations for single orbits during the winter months of 1992, 1993 and 1997 showsthat SSM/I derived rain rates are on average twice as large as the maximum possible in situ rain
rate of 2.7 mmh-1. In addition, the work of Tucker (1961) showed that the observation
frequency of weather codes related to this maximum is rather low.
The differences in the precipitation fields have large implications for the freshwater
budget, E-P , which is shown in Figure 11.3.4. Whereas the satellite-derived field ischaracterised by a quite thin ITCZ and a somewhat broader SPCZ where the ocean is provided
with an precipitation excess of about 6 mmd-1, on average the ITCZ covers a much broader
area in the in situ climatology. The patterns in the northern hemisphere are characterised by

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Figure 11.3.2 Comparison of evaporation (mm/day)from (top) HOAPS, (middle) SOC,
(bottom) HOAPS-SOC difference.

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Figure 11.3.3 Comparison of precipitation (mm/day) from (top) HOAPS, (middle) SOC,
(bottom) HOAPS-SOC difference.

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Figure 11.3.4 Comparison of (E-P) (mm/day) from (top) HOAPS, (middle) SOC, (bottom)
HOAPS-SOC difference.

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evaporation excess in the trade wind belts and precipitation excess along the North Atlantic andNorth Pacific storm tracks in both climatologies. Because of the differences in the precipitation
fields, the largest differences (even in sign of E-P ) exist over southern parts of the warm
currents. Over the southern hemisphere both climatologies show very different patterns with
the SOC pattern very intermittent due to the structure of the precipitation field. The satellitederived E-P values show a more realistic pattern with a slightly larger evaporation excess in the
trade wind belts compared to the northern hemisphere, and slight freshening near the Antarctic
ice margin. Here the SOC climatology shows values up to 8 mmd-1 compared to a maximumof 2 mmd-1 in the HOAPS climatology.
Comparison to the Subduction Experiment buoys
Because climatologies can not serve as a validation dataset for each other, independent
ground truth must be used for deeper analysis of the errors. Josey et al. (1999) have used thedata from the Subduction Experiment described by Moyer and Weller (1997) for verification.
The same task has been repeated for the HOAPS climatology. Unfortunately, buoy arrays withvery high quality measurements are not available at many locations in the oceans so that thiscomparison can only serve as a tool to identify possible regional biases in either climatology.
A time series of monthly mean values from the Subduction buoys was compared with 1˚
x 1˚ field data from the HOAPS dataset. The time series at all buoys agreed fairly well withcorrelation coefficients greater than 0.95 for all basic state variables and a slightly lesser
correlation for the derived fluxes. The variability of the bulk parameters within one month was
almost of equal size for both datasets for all months during the two year period. That implies
that, even with only two satellite overpasses per day, it appears to be feasible to represent the
intra-monthly variability of basic state variables - at least in the Subduction area. However,positive biases were found for wind speed at both northern buoys over the whole buoy
deployment period. The reason for this bias can only be found through a comparison of thewind speed spectra derived from simultaneously measured buoy and SSM/I data. A possible
reason could be an underestimation of the wind speed at the buoy, because the sensors were
installed at a height of 2 to 3m so that the instruments may be in the wave trough region duringhigh wind speed events (Section 9.2.2).
Negative biases were found for sea surface
temperature at both southern moorings during the first year of the deployment period. The
suggestion that this bias was caused by aerosols from the Mt Pinatubo eruption, withsubsequent deterioration of the cloud screening in the AVHRR dataset, seems to be unrealistic
because this feature should be found at all buoy sites. Estimates of specific air humidity arealmost bias free at all moorings during the deployment period.
The averages and mean differences between HOAPS and all five Subduction buoys are
summarised in Table 11.3.2. The positive bias in wind speed resulted in an overestimation ofsensible and latent heat flux at the North East and the Centre buoy whereas the negative biases
in sea surface temperature cause a negative bias in latent heat flux at both southern buoys.
Although the bias for sensible heat flux equalled zero at three of the five buoys, this should
only be taken as an indication that crude assumptions, like a constant relative humidity of say
80%, may be sufficient to determine a climatological mean value (rather than the annual cycle
which appears somewhat exaggerated in the HOAPS dataset). Estimates of longwave net flux
are not generally biased low or high. Because of the dependence of the downwelling radiationon actual cloud cover, differences in satellite and buoy estimates can be caused by satellite
resolution effects. The satellite instrument can't resolve the cloud spatial and time scales
because of its coarse spatial resolution - only 30 - 70 km and only two measurements per day
per satellite. All the more surprising is that the mean differences in net longwave fluxes
between HOAPS and the buoys are not much larger than differences between buoy data and the
SOC climatology.
Comparison of the mean differences between the HOAPS dataset and the Subduction
buoy measurements, for all fluxes and basic state variables, with the findings summarised in the
study done by Josey et al. (1999) reveals that the satellite dataset appears to be competitive with
the in situ product even in this not too badly sampled region.
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Table 11.3.1 Mean heat fluxes and differences for the Subduction experiment buoy array.
Location

Source

Qsens

Qlat

-2

-2

Wm

Wm

u
ms

qS
-1

gkg

-1

qL
gkg

-1

QLW
Wm-2

NE Buoy, N=23

HOAPS

14 ± 9102±246.7±1.2

15.2±2.4

11.2±2.074±12(33° N, 22° W)

Buoy

9±4

97±20

5.5±0.615.2±2.4

11.1±2.066±8

HOAPS-Buoy

5±9

5±21

1.2±0.80±0.3

0.1±0.78±10

SE Buoy, N=14

HOAPS

7±21

95±53

7.3±0.715.9±1.4

12.5±2.184±26(18° N, 22° W)

Buoy

7±3

103±257.3±0.8

16.6±1.5

13.0±1.647±16

HOAPS-Buoy

0±20

−8±33

0±0.6

−0.8±0.6−0.5±1.037±13

SW Buoy, N=14

HOAPS

5±15

101±436.9±0.918.1±1.3

14.1±1.960±16(18° N, 34° W)

Buoy

5±3

129±256.6±0.5

18.6±1.4

13.7±1.860±11

HOAPS-Buoy0±13−28±280.3±0.7−0.6±0.6

0.4±0.80±11NW Buoy, N=13

HOAPS

7±8

86±23

6.5±1.515.9±2.712.3±2.566±13(33° N, 34° W)

Buoy

7±3

84±20

5.1±1.1

16.0±2.912.0±2.276±9

HOAPS-Buoy

0±7

2±22.

1.4±0.6−0.1±0.3

0.3±0.5−10±9

C Buoy, N=18

HOAPS

16±10

116±276.5±0.8

16.6±1.7

12.1±1.876±12(25.5° N, 29° W)

Buoy

7±3

107±185.7±0.8

16.8±1.812.2±1.866±13

HOAPS-Buoy

9±8

9±18

0.7±0.5−0.2±0.3−0.1±0.6

11±1311.3.3 US satellite radiation products
A. INTRODUCTION
The evaluation of US produced satellite-based global products for surface radiative
fluxes is described here. Japanese radiation products will be described in the next section.
Networks of surface measurements have been used for validation, but coverage of the sea isminimal. Surface measurements and satellite-based retrievals are improving; they are indeed
useful for many ocean modeling studies. However, intensive, local scale studies haveuncovered significant problems in both the measurement and theoretical computation of surface
SW flux. The surface SW insolation retrieved by the satellite-based programs and computed byradiative transfer theory generally exceeds measured values; the discrepancies between
measurement and theory are significant and unresolved. It should also be noted that none of the
LW algorithms described below yet accounts for corrections to SST due to the cool skin (~0.3
K) discussed in Section 10.2.1.
The CERES/ARM/GEWEX Experiment (CAGEX; Charlock and Alberta, 1996)
illustrates SRB issues for both SW and LW CAGEX provides a space-time set of input data,
calculated fluxes, and validating measurements. Following the launch of CERES, CAGEX isbeing extended to the ARM Tropical West Pacific (TWP) site. ARM TWP data is expected to
become an important resource for the study of radiation at the air-sea boundary. The ARM web
page is: http://www.arm.gov/
B. GEWEX SRB
Version 1
The first version of global products by the GEWEX Surface Radiation Budget (SRB)
Project (Whitlock et al., 1995) have been in use for a few years. Access is through the NASA

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Langley Distributed Active Archive Center (DAAC) with the web address:
http://eosweb.larc.nasa.gov/
The GEWEX SRB is an official project of the WCRP, and it is formally reviewed by
the WCRP WG on Radiative Fluxes. The GEWEX SRB uses ISCCP (operational satellite;
Rossow et al., 1991) data and covers 1985-1988, a period for which the adjusted ISCCP
calibration was fairly stable. GEWEX SRB presently issues SW products only. Daily andmonthly-averaged insolation is available in the 280 by 280 km equivalent area grid system fromthe primary algorithm by Pinker and Laszlo (1992); and monthly retrievals from a backup
algorithm due to Staylor (see Darnell et al., 1992); both algorithms are based on radiativetransfer physics. In addition to the surface SW insolation (downwelling), the Pinker algorithm
produces the surface upwelling flux, the surface net flux, the surface albedo, and the top-of-theatmosphere (TOA) net flux.
For validation, the GEWEX SRB files provide:(1) monthly TOA net flux by independent, higher quality measurements from the Earth
Radiation Budget Experiment (ERBE; Barkstrom et al., 1989) over most of the globe. Unlike
the narrowband radiometers on the ISCCP satellites, ERBE provides broadband measurementswith on-board calibration for SW and LW.
(2) measured monthly-averaged surface insolation for a small fraction of the land mass
as compiled by the Global Energy Balance Archive (GEBA; Gilgen and Ohmura, 1999).
Most GEBA sites are located in the midlatitudes; a few of the GEBA surface sites are located on
islands (note earlier remarks on pyranometer accuracy and GEBA in Section 5.2.2). October1985 was a fortuitous month in that the monthly mean bias of the Pinker algorithm relative to
midlatitude GEBA obaervations was only 2.7 Wm-2.
The estimated global and monthly averaged bias for GEWEX SRB insolation is about
15 Wm ; that is, the retrieved global insolation for a 24-hour day is probably too large by 15
Wm-2; most GCM’s produce even larger values for surface insolation. The estimated rms error
for monthly mean GEWEX SRB insolation in a 280 by 280 km grid box is 20-25 Wm-2; a figure
indicated smaller values for European GEBA sites in October 1985. Errors are smaller over the
midlatitudes and larger over the tropics. The largest errors are found in regions with extensive
biomass burning. We do not know if the errors in surface insolation are larger (or smaller) oversea. At the TOA, there is not a significant land versus sea bias in the GEWEX SRB whencompared to ERBE TOA measurements. The ERBE global mean uncertainty for SW is 5-7
Wm -2; this estimate for ERBE uncertainty is based on the imbalance of ERBE TOA SW and
LW observations. The bias in GEWEX SRB surface insolation appears to be due to an
atmosphere that absorbs more SW than is predicted by radiative transfer theory. The estimatederror for the GEBA surface insolation measurements is 3%; this error may have a significant
systematic component.
-2

Version 2
A new Version 2 of the GEWEX SRB will be issued in early 2001
(p.w.stackhouse@larc.nasa.gov). An improved Pinker algorithm will be used for SW. A 2/4
stream version of the Fu and Liou (1993) algorithm will be used in the LW. The new Version 2
will provide a higher spatial resolution of 1 by 1 degree; time spacing will be 3-hourly, daily
average, monthly-hourly (i.e., the monthly mean flux from 0200-0300 UTC, etc.), and monthly
average; covering July 1983 to at least June 1995.
GEWEX SRB Version 2 will be based on ISCCP DX (pixel scale) data, rather than the
ISCCP C1 (280 by 280 km equivalent area gridboxes; 2.5 by 2.5 degree at the equator; numberof gridboxes changing with latitude) used now; this will provide a true enhancement of spatial
resolution, as opposed to a simple interpolation of C1 to 1 by 1 degree (a few suchinterpolations are circulating at present). The pixels in both ISCCP DX and C1 are sampled at
the same ~30 km spacing, however; unlike ERBE and CERES (whose pixels are almost

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contiguous), the pixels in ISCCP samples are quite separated; the sampling produces spatial
noise in a gridbox product.
Both ERBE and ISCCP are liable to large instantaneous errors of up to 50 Wm-2
because of uncertainty in angular distribution models (ADMs) used to invert from directionalradiance (W/sr - energy per unit solid angle measured by a scanning satellite radiometer) to
directionally-integrated flux (Wm-2 irradiance - energy per unit area). ADM errors are reduced
enormously by averaging, but they may be the source of the present 5-7 Wm-2 imbalance
between SW and LW in the ERBE record.
C. LANGLEY SRB
Darnell et al. (1996) and Gupta et al (1999) have released an 8-year CD-ROM"Langley SRB Dataset" for monthly-mean downwelling and upwelling, shortwave andlongwave fluxes.
Monthly, 280 by 280 km retrievals are available from :http://eosweb.larc.nasa.gov/PRODOCS/srb/table_srb.html .
The "Staylor" SW (Darnell et al., 1992) and the Gupta et al. (1992) LW algorithms areused together with the ISCCP C1 cloud products (Rossow and Schiffer, 1991) that provide
information on clouds, the atmosphere and the surface, and the ERBE top-of-atmosphereshortwave and longwave fluxes (Barkstrom et al., 1989). Accuracy with the Staylor SW
algorithm is comparable to that with the Pinker SW. The Pinker algorithm was selected (overthe Staylor algorithm) for official GEWEX SRB processing because, with more comprehensive
physics, Pinker produces a TOA flux which may be compared with ERBE. The Gupta
downwards LW flux was larger than BSRN measurements at the mean of 7 sites for 3 months in1992 by 5 Wm-2; the rms error was 20 Wm-2. In comparison with the GEBA database of
surface flux measurements (Gilgen and Ohmura, 1999), the NOAA/Climate Monitoring and
Diagnostics Laboratory (CMDL) dataset, and the CSIRO, Australia, dataset,the overall
uncertainty in monthly mean downwelling fluxes was estimated to be about ± 15 Wm-2 for both
shortwave and longwave.
For October 1985, the surface net SW flux (absorption by the surface; global mean
160.6 Wm-2) is larger than the surface net LW flux (negative value denotes cooling; global mean
-46.6 Wm-2) and dominates the geographical pattern of surface net total (SW+LW) flux (global
mean 114 Wm-2) over the ocean. The interannual variability for 8 Octobers showed that the net
SW (global mean standard deviation 10.2 Wm-2) dominates the net LW (global mean standard
deviation 6.22 Wm-2) in setting the pattern of the surface net total interannual variability (global
mean standard deviation 11 Wm-2). Patterns of interannual variability are strongest over thetropical oceans. For an individual gridbox, the interannual variability is smaller than the error in
the satellite retrieved value for a given month. Satellite retrievals do, however, demonstrateskill in identifying the geographical patterns in interannual variability. A regional analysis ofinterannual variability in the SW SRB demonstrated that the interannual variability retrieved bysatellites represents the pattern and maginitude of interannual variability in the GEBA groundmeasurements quite well (Alberta et al., 1994).
D. CERES RADIATION DATA
The ADM problem (see section 11.3.3B) is approached by sophisticated Rotating
Azimuth Plane Scanning (RAPS) in CERES (Wielicki and Barkstrom, 1991). The firstlaunches of CERES were in November 1997 on TRMM (tropical and midlatitude coverage) andin December, 1999 on Terra (formerly EOS-AM1 - global coverage). SRB retrievals will bevalidated and released 2-3 years after launch. Gridded products (1x1 degree) will eventually beavailable 3-hourly, daily, and monthly. Instantaneous SRB at the individual "footprints" of thebroadband CERES scanner will typically have diameters of 10-50 km. The goal of the CERESSRB is to reduce the error in the GEWEX SRB by a factor of two. CERES has extensive
documentation of its algorithms and validation activities at http://asdwww.larc.nasa.gov/ceres/ASDceres.html .

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A verification facility (CERES Ocean Validation Experiment COVE) has been
established at the Chesapeake Light, 25 km off the Virginia coast. Measurements of shortwave
spectral radiances reflected by the sea surface are obtained using a southward facing
Spectrophotometer. The measurements are intended to improve classic formulations of ocean
bi-directional reflectance and albedo. The radiance scans are complemented by collocated,
continuous, long-term observations of surface radiation and aerosols and meteorology, wind
waves, and sea swell. COVE measures broadband fluxes, both upwelling and downwelling, andparticipates in the BSRN.

E. BISHOP SHORTWAVE RADIATION PRODUCTBishop et al. (1997) have produced a global dataset of daily-mean downwellingshortwave fluxes at the surface covering an eight year period (July 1983 -- June 1991) at 2.5˚latitude-longitude resolution. This dataset is available by request from Dr. James K.B. Bishop(e-mail: jkbishop@lnl.gov) or from the archives at the National Center of AtmosphericResearch in Boulder, Colorado (dataset reference = DS741.0 SURFACE SOLAR
IRRADIANCE BY BISHOP).
These fluxes are calculated by applying an approximate radiative transfer method(Bishop and Rossow, 1991) to the ISCCP C1 cloud products (Rossow and Schiffer, 1991) thatprovide information on clouds, the atmosphere and the surface. The Bishop et al. (1997)
method uses the cloud properties retrieved from the narrowband satellite radiances (also basedon a radiative transfer model) as input into a calculation of the fluxes that explicitly accounts for
multiple scattering in and between clouds and the surface: it was tested by comparison to a fullradiative transfer model using the same input data. The results were further verified bycomparison with the GEBA database of surface flux measurements over land (Gilgen and
Ohmura, 1999) and an extensive number of buoy-based measurements from a series of ocean
experiments. Overall uncertainty is estimated to be about ± 25 Wm-2 rms for daily-mean values,
due mostly to cloud sampling errors, and ± 15 Wm-2 for monthly mean values, due mostly tocalibration and aerosol optical thickness errors.
A new global product of daily mean surface solar irradiance is now being produced witha spatial resolution of 0.5˚ latitude-longitude based on the revised ISCCP cloud products(Rossow and Schiffer, 1999). This dataset is complete for a couple of years; more information
about this dataset can be found at ( http://www.giss.nasa.gov/data/seawifs/ ).
F. BREON SURFACE NET SHORTWAVE FLUX AND ALBEDO
Breon et al. (1994) have produced a global dataset of monthly-mean surface net
shortwave and albedo covering a two-year period (1985-1986) at 2.5˚ latitude-longituderesolution. These surface quantities are determined from ERBE (Barkstrom et al., 1989)
monthly-mean full-sky and clear-sky albedos, together with the top-of-atmosphere solar
irradiance, by removing the atmospheric and surface scattering and absorption effects using
results pre-calculated with a radiative transfer model developed by Tanre et al. (1990). Thismethod is conceptually similar to that proposed by Li et al. (1993). The results were evaluatedby comparison with surface shortwave fluxes determined from narrowband satellite radiances
by Gautier (1988). The estimated uncertainty of the monthly-mean values is +15 Wm-2 bias
and ±14 Wm-2 rms. This dataset may be obtained by contacting the authors.
G. NASA GODDARD INSTITUTE FOR SPACE STUDIES
Rossow et al. (1995) have produced a global dataset of downwelling and upwelling,
shortwave and longwave fluxes at the top-of-atmosphere, in-atmosphere, and at the surface
covering an four year period (April 1986 -- January 1989, every third month) with a 3-hr time
resolution and a 2.5˚ latitude-longitude spatial resolution. More information about this dataset
and sample statistics can be found at ( http://isccp.giss.nasa.gov/flux.html ) and the wholedataset can be obtained by sending a request to Dr. Yuanchong Zhang, NASA Goddard Institutefor Space Studies, 2880 Broadway, New York, NY 10025, USA.

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The analysis approach follows that proposed by Rossow et al. (1989) and Rossow andLacis (1990). The fluxes are calculated by applying a full radiative transfer model from the
GISS climate GCM (Zhang, et al., 1995) to the ISCCP C1 cloud products (Rossow and Schiffer,1991).
This model treats the full spectral dependence of absorption and scattering byatmospheric gases, including all radiatively important trace species, six different types ofaerosol, clouds and the surface. The results were verified by comparison with the ERBE topof-atmosphere fluxes (Barkstrom et al., 1989), the GEBA database of surface flux
measurements (Gilgen and Ohmura, 1999), several other specific surface site measurements
and/or field experiment datasets, and by comparison with other satellite analyses. Overall
uncertainty is estimated to be ±10-15 Wm-2 for monthly-mean top-of-atmosphere fluxes and±15-25 Wm-2 for monthly-mean surface fluxes.
A new global radiation flux product covering the period from July 1983 through topresent is planned, based on application of an improved radiative transfer model applied to the
new ISCCP D-series cloud products (Rossow and Schiffer, 1999). The model improvements
include a refined treatment of cloud scattering dependence on cloud optical thicknesses andparticle sizes, including for non-spherical ice cloud particles, extension of the aerosol properties
to include two size ranges for each of six types, refinement of the spectral representation of
longwave absorption and the water vapor continuum, and improved specification of surface
albedos and emissivities. Input quantities have been improved by including both ice and liquidwater clouds, a revised aerosol optical thickness climatology to include more absorbing species,
a revised atmospheric temperature and humidity dataset, and treating cloud type dependence
and layer structure more completely. The new product will report, every 3 hr at 2.5˚ latitudelongitude resolution, the cloudy and clear, upwelling and downwelling, shortwave andlongwave fluxes at five levels: surface, 680 mb, 440 mb, 100 mb and top-of-atmosphere.
Cloud-type dependence will also be reported. Production of this new product is expected to
begin early in 2000 and to be available from Dr. Yuanchong Zhang (see above).
H. SUMMARY - US SATELLITE RADIATION PRODUCTS
Figure 11.3.5 shows the climatological zonal mean net surface shortwave radiation fromseven different products discussed in this Section. In the low latitudes the satellite products
suggest that substantially more radiation reaches the surface compared to the VOS estimates andreanalyses. Throughout the low latitudes, the flux in both reanalysis is substantially smaller, as
pointed out in Section 11.4 a likely result of cloud cover problems in the models. Discounting
the reanalysis, the spread among the estimates is still on the order of 25 Wm-2 in the zonal mean.

11.3.4 Japan surface shortwave and longwave products
A. KIZU (1999) SW FLUXES
Kizu(1999) has produced surface shortwave radiation fluxes covering from October
1992 to Sepember 1993 with monthly and 1.0° x 1.0° latitude-longitude resolution. The spatial
coverage is from 80°E to160°W and from 60°N to 60°S. He used the 3-hourly Visible andInfrared Histogram Data from GMS/VISSR with 0.25° latitude-longitude spatial resolution as a
primary data source, and additionally monthly precipitable water from SSM/I (Wentz,1994),
monthly total ozone amount from TOMS/Nimbus7, and seasonal climatology of troposphericaerosol from NOAA/AVHRR (Husar et al, 1997). Long-term degradation of the VISSR
instruments was corrected by Kizu(2000). A simple parameterization model by Iqbal (1983)
was used to formulate fundamental radiative transfer processes in the atmosphere, and the fivedaily GMS imagery data was used to estimate the cloud albedo. The methodology wasdescribed in detail in Kizu(1995). Based on comparison with routine ground measurements ofinsolation, mainly in Japan and Australia, the statistical errors of daily and monthly mean

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Figure 11.3.5 The climatological zonal mean net surface shortwave radiation from three satellite
products, two VOS based products, and two reanalysis products.
values were estimated to be less than 20% and 10%, respectively. The validation procedure iscontinuing by collecting other available in-situ measurements on both land and sea, and the
products will soon be extended over 11 years from March 1987 to September 1999.
B. IWASAKA(1999) LW FLUXES
Iwasaka(1999), using the narrow band model proposed by Goody(1964), has estimatedmonthly products of surface longwave radiation flux in each 1.0° x 1.0° latitude-longitude grid
over the western Pacific and eastern Indian Oceans (80°E-160°W, 60°S-60°N). The primarydata source was Infrared Radiation (IR) Histogram Data derive from Geostationary
Meteorological Satellite (GMS-4), and additionally Multi-channel Sea Surface Temperature(MCSST) data derived from NOAA/AVHRR data and climatological SST data derived fromCOADS were used. The narrow band radiation model proposed by Goody (1964) was used to
calculate atmospheric absorption and emission between the cloud bottom and sea surface. Thismodel covers the H2O continuum, the CO2 15 micrometer and the H2O 6.3 micrometer bands.
The parameters in the model were given by Rogers and Walshaw (1966), Goldman and Kyle
(1968) and Roberts et al .(1976). The spectral range of 0-2200 cm-1 was divided into 23 narrow
bands to calculate the radiation flux.
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Construction procedures were as follows. First, in order to detect cloud coverage ineach pixel, a pseudo sea surface temperature was computed from IR Histogram data andcompared with the MCSST data. A pixel where the difference between the pseudo SST and
MCSST exceeded 3.0K was assumed to be cloud-covered. Next, the height of cloud base wasdetermined under the assumption of constant cloud thickness and the cloud base temperature
was calculated from NCEP reanalyses data. The downward longwave radiation value wascalculated from black body radiation theory using the assumption of the emissivity = 0.9 at thecloud base. Atmospheric absorption and emission between the cloud base and sea surface wereassessed by a narrow band radiation model. On the other hand, in a cloud free area downward
radiation was calculated in a layer below 300 hPa level determined by the NCEP data. Upward
longwave radiation was calculated from the black body theory using the sea surface emissivityof 0.984 based on Konda et al.(1994). Downward longwave radiation values observed at
Kwajalein Island were used to compare with the estimated values in this data set. The results
revealed that the estimates had a tendency to be over-estimated in cloudy conditions and underestimated in a cloud-free conditions. The bias and random error were both 10-20 W m-2 formonthly mean product. These data sets will be available on the Internet in the near future at
http://dtsv.scc.u-tokai.ac.jp/.
11.3.5 Residual method
During the last 20 years various estimates of oceanic meridional heat transport havebeen made by combining satellite and atmospheric data. These 'residual method' calculations
have provided meridional transport profiles which have been compared to estimates derived
from hydrographic transects as well as those implied by net heat flux climatologies derived
from COADS. One disadvantage of this method is that only the net surface heating iscomputed. On the other hand, the only other means to date of estimating the net surface heating
on a large scale has been via COADS type climatologies or reanalysis, and there are clearlylarge uncertainties in both. Thus the residual method has the potential to be a valuable means ofestimating the total surface energy balance of the global ocean.
Subtracting atmospheric flux divergence from the net radiation at the top of theatmosphere, Trenberth and Solomon (1994) were the first to rigorously apply a residual method
to estimate surface net heating locally. Making use of ERBE radiation data and ECWMF
analysis for a single year (1988), Trenberth and Solomon derived the spatial distribution of theannual mean net surface heat flux for the global oceans. Keith (1995) applied similar methods
with four years of ERBE data (1995-1988) and combination of ECMWF and NMC analysis.
One significant problem encountered in these studies was large imbalances over land
(up to + 100 W/m-2 ) in the annual mean net heat flux. Both studies suggest that these
imbalances were due to problems associated with orographic effects in the atmospheric models,and Trenberth and Solomon estimated errors to be on the order of 30Wm-2, on the order of the
global imbalances in the SOC and unadjusted da Silva climatologies. Trenberth and Solomoncorrected for these land imbalances by applying an atmospheric mass correction and also solveda Poisson equation with the constraint of no heat flux normal to continental boundaries. Sinceboth studies, examination of the ECMWF model has revealed that land surface imbalances werelargely attributed to a non adiabatic condition at lowest level of the land surface model, which
has since been rectified.
A map of the annual mean Trenberth and Solomon estimate of annual mean net heat
flux is shown in Figure (11.8.2) along with estimates from the SOC and da Silva climatologies,
and the ERA and NCEP reanalysis climatologies. There are many encouraging similarities withother estimates. The noise is likely attributable to the fact that there is no geophysicalconsistency between the satellite and analysis.
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11.4 Reanalysis products(11.4.2 Compare with in situ)

11.4 Evaluation of the Reanalysis Products

11.4.1 Introduction
Reanalysis has provided a long, consistent record of the interchange of momentum,
energy and moisture between the earth's surface and the atmosphere. This record presents thebest opportunity to date to evaluate the accuracy of air-sea fluxes from atmospheric data
assimilation systems. There is no "ground truth" for most of these fluxes, since they are notdirectly measured over most of the globe and must be estimated indirectly from observations
and sometimes significantly tuned to ensure net energy balance. In this section we compare
surface energy fluxes, winds, wind stress and precipitation from the four reanalyses to eachother and to independent estimates and review other work evaluating the reanalyses.

11.4.2 Comparisons to in-situ observations
Zeng et al. (1998) used TOGA COARE ship data and TAO moored buoy data tocompare six different bulk aerodynamic algorithms, including the ones used in the ERA15,
GEOS1 and NCEP reanalyses. They found that the ERA15 algorithm gave slightly better fits toroughness lengths than the GEOS1 and NCEP1 algorithm and that the GEOS1 algorithm gaveslightly better fits to momentum and latent heat fluxes using TOGA COARE data. The
equation for roughness length for heat in the NCEP1 and NCEP2 algorithm appeared to beinappropriate under strong wind conditions and to overestimate latent heat flux. None of thereanalysis algorithms considered the vapour pressure reduction of 2% over salt water versus
fresh water, which has a significant impact on latent heat flux under strong winds.
Smull and McPhaden (1998) found that annual mean NCEP1 surface winds were
weaker and less divergent (more zonal) than those observed by the TOGA-TAO moorings in thetropical Pacific. Directional differences of 20-30o were common, although locally these were as
much as 90o near the ITCZ. Magnitudes differed by about 1-2 ms-1 in the annual mean, but by
as much as 5-6 ms-1 locally in monthly means. In the western Pacific NCEP1 tended to be tooeasterly. ERA-15 displayed broadly similar biases, but had 10-20% smaller differences with the
TAO observations, and performed well at intra-seasonal time scales. Bony et al. (1997)
examined the NCEP1 and GEOS1 reanalyses in the tropics and concluded that NCEP1
overestimated evaporation in regions of subsidence.
Shinoda et al. (1999) compared NCEP1 surface fluxes in the tropical warm pool of thewestern Pacific and Indian Ocean to surface fluxes estimated from mooring data during TOGACOARE and to independent gridded estimates. The independent estimates were based on
operational wind and surface pressure analyses and satellite observations of rainfall, short wave
radiation, and outgoing long wave radiation. Stress and latent heat flux that depend primarily
on surface wind variations agreed better than surface short wave radiation and precipitation that
depend largely on fluctuations in convection. The intra-seasonal (periods of 25-100 days)
variance of short wave radiation and precipitation in NCEP1 was about half that estimated fromin-situ observations and satellite observations. NCEP1 stress and latent heat variations
associated with the Madden-Julian oscillation (MJO) agreed well in magnitude and phasing
with the independent estimates; NCEP1's composite short wave radiation and precipitation for
the MJO agreed in phase but had less than half the amplitude of the satellite-based estimates.
Klinker (1997) compared latent and sensible heat flux from ERA15 to the TOGACOARE IMET buoy at 156E and 1o45'S and found that ERA15's latent heat flux had a 17 Wm-2
(16.5%) positive bias. ERA15 represented major events with strong latent heat fluxes well,reflecting good analyses of wind speed, but produced too much latent heat flux during low windspeed periods. ERA15 had a drier boundary layer than later versions of the ECMWF model.
ERA15's sensible heat flux failed to increase during convective events, as the IMET buoy's
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sensible heat flux did, and had a negative bias compared to the buoy. ERA15's precipitation did
not agree well with the precipitation observed at the IMET buoy. Planton et al. (2000) foundthat ERA15, compared to in situ observations and independent climatologies, underestimatednet short wave and overestimated latent heat flux in the tropics and overestimated net long wave
in mid-latitudes.
Smith et al. (1999) compared NCEP1 near-surface meteorological and air-sea flux
fields to high-quality observations collected by numerous international research vessels during
the World Ocean Circulation Experiment (WOCE). These observations were all recorded
continuously by automated systems, thus enabling the data to undergo a higher level of quality
control. The data covers a wide range of conditions over the world's oceans, but do not
typically repeatedly sample the same region. The data are first height adjusted to reference
levels of the reanalysis using Smith (1988). Air-sea fluxes are then calculated, again using
Smith (1988). All meteorological and air-sea fluxes are then compared to co-located reanalysis
values. The RMS differences varied with latitude. They found that the reanalysis atmosphericpressure values are too weak, most notably in the tropical regions. Additionally, pressure
extremes were underestimated. The winds are underestimated at all latitudes. Air temperaturesand humidity agree better and indicate no clear pattern of differences over varying parts of the
globe. For the fluxes, the reanalysis wind stresses matched the wind results, i.e. stresses were
underestimated at all latitudes. NCEP1's sensible and latent heat fluxes were surprisingly toolarge. The RMS differences increased with latitude, but the biases in sensible heat flux werefairly uniform (5 W M-2). Reanalysis latent heat flux was too large (~10-30 W M-2). The bias islargest in the Northern Hemisphere, especially in the 20-40°N band. Use of the Smith (1988)
algorithm on NCEP1 near-surface fields led to considerably better agreement for latent andsensible heat fluxes with ship-based fluxes.
Renfrew et al. (2000) compared meteorological surface observations taken aboard the
R/V Knorr during the Labrador Sea Deep Convection Experiment from Feb. 6 to Mar. 13, 1997
to operational analyses by ECMWF and reanalyses by NCEP1. (The ECMWF operational
system used in 1997 was very similar to the system used in ERA15.) They found that theanalyses reproduced the observed surface layer well, with ECMWF performing somewhat
better, perhaps due to its higher resolution. They did find discrepancies in sea surface
temperature, due to the scarcity of satellite determined SSTs and the crude sea ice mask used in
the models, and in relative humidity, where ECMWF was 10% higher than observed and
NCEP1 25%. The reanalyses did not include a marginal ice zone.
Renfrew et al. (2000) also calculated bulk estimates of surface momentum and heat
fluxes from the ship observations, using the bulk flux algorithm of Smith (1988) with neutral
exchange coefficients updated to those of DeCosmo et al. (1996). They compared these fluxes
to fluxes from ECMWF and NCEP1. The time series of heat fluxes had correlations above 0.9;
however, ECMWF had up to 30% more sensible and latent heat flux and NCEP1 up to 100%
more sensible and 50% more latent heat flux during high heat flux events. Averaged over theentire cruise, ECMWF’s heat fluxes were 15% stronger than the estimate from the Knorr, within
an estimated uncertainty of 15% in exchange coefficients (Garratt, 1992). NCEP1
overestimated the mean sensible and latent heat fluxes by 50% and 30%. Discrepancies inmomentum fluxes between the reanalyses and the Knorr reflected mainly discrepancies in the
10-meter wind. Renfrew et al. (2000) found that recalculating fluxes from model surface-layer
data with the Smith/DeCosmo algorithm dramatically reduced the disagreement in fluxes. They
found that the roughness length formula used by NCEP1 (and NCEP2 as well) overestimatedheat fluxes in cases of large air-sea temperature differences and high wind speed. Zeng et al.
(1998)’s bulk formula, a simplification of the TOGA-COARE algorithm (Fairall et al., 1996a),
was introduced in the NCEP operational model in Jan. 1998 and appears to correct these
problems.

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Josey (2000) compared surface fluxes from NCEP1 and ERA15 to those determinedfrom five meteorological research buoys deployed in the north-east Atlantic between 18N-33N
and 34W-22W during the Subduction Experiment between June 1991 and June 1993. The
buoys used radiometers to measure radiative fluxes, while the COARE flux algorithm (Fairall etal., 1996) determined sensible and latent heat fluxes from one minute mean meteorologicalmeasurements. Josey (2000) found that ERA15 and NCEP1 overestimated the mean
evaporation for the buoy by 16 and 11 Wm-2, underestimated net short wave by 14 and 22 Wm-2,
and underestimated the net downward heat flux into the ocean by 32 and 35 Wm-2. Fields fromthe SOC flux dataset based on ship reports (Josey et al., 1998) displayed better agreement withthe estimates from the buoys.
Josey (2000) also calculated the implied ocean heat transport from ERA15 and NCEP1
climatologies for 1980-93 and found reasonable agreement with independent hydrographic
estimates. Trenberth (1998) examined the heat budget of the atmosphere and ocean in the
NCEP1 reanalysis. He adjusted the NCEP1 surface heat flux south of 30S to achieve balance inthe surface energy budget over the ocean; the resulting ocean heat transports were consistent
with independent estimates.
Trenberth et al. (2000a) calculated surface fluxes from the ERA15 and NCEP1
reanalyses using the residual method. A comparison of these derived surface fluxes with thesurface fluxes calculated directly in the reanalyses and with the UWM COADS-based estimatesfound that all the estimates showed the same broad scale features. The reanalyses' surface
fluxes agreed very well with the COADS estimates over the northern extratropical oceans, but
all three were biased and were not suitable for determining ocean heat transports. The paper
found "…no useful common signal in the tropics". The derived surface fluxes from the residual
method were found to produce more reasonable ocean heat transports. The results implied that"…clouds are a primary source of problems in the models' fluxes, both at the surface and the
TOA".
Trenberth et al. (2000b) compared troposphere temperatures measured by a satellite
microwave sounder (MSU) to analyzed temperatures from the ERA15 and NCEP1 reanalyses.
The NCEP and MSU temperatures agree fairly well. The ERA reanalyses has two
discontinuities in the tropics in late 1986 and early 1989 and also displays "... further spurious
interannual variability", indicating too much month to month tropical variability in the ERA15
reanalysis and reflecting at least partly problems with biases in satellite radiances.
Curry et al. (1999) compared satellite-derived surface fluxes to ERA15 over the tropicalocean for TOGA COARE. The satellite-derived stress showed short-lived, high-wind events
that ERA15 did not. They also found that ERA15 surface net short wave flux exceeded thesatellite-derived estimate during cloudy periods and was less during clear conditions. Daily
values of latent heat flux and wind stress were substantially correlated; daily values of surface
radiative fluxes had low correlations.
Wild (2000) compared radiative fluxes from the NCEP1, ERA15 and GEOS1
reanalyses to collocated observations of surface radiation over land (including island stations)from the Global Energy Balance Archive and satellite observations of top of the atmosphere(TOA) radiation from the Earth Radiation Budget Experiment. NCEP1 and GEOS1
overestimate the downward solar radiation at the surface due to a lack of absorption in theatmosphere, especially at low latitudes and even in cloud-free regions; ERA15 shows good
agreement with observations. All the reanalyses underestimate aerosol absorption and show
little difference in vertically integrated water vapour. NCEP1’s excessive downward solar
radiation is offset by too high surface albedo. Wild et al. (1998) found that ERA15
underestimated the downward long wave flux at the surface by 5-10 Wm-2 compared to surface
observations.

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11.4 Reanalysis products(11.4.3 Polar Regions)11.4.3 Polar regions
At the 2nd International Conference on Reanalyses in 1999, a number of papers
identified shortcomings in the reanalyses in the polar regions, including overly simplistic
representations of sea-ice, albedo parameterisation, treatment of snow cover and clouds in polar
regions. The shortage of data over Antarctica in the earlier years of the reanalyses compromises
the reliability of trends or changes in the reanalyses. Data assimilation systems often haveproblems assimilating isolated observations, since drifts in the "first guess" forecast in theabsence of data can cause isolated observations to be rejected.
In NCEP1 summertime precipitation in the Arctic is unrealistic, there is insufficient
cloud cover, solar radiative fluxes are too large and distortions in the moisture fields are evident.
In ERA15 there is a marked two-metre temperature bias and wrong topographic heights overGreenland and Antarctica, with particular consequences notable as a result of an error in theelevation of the Vostok station. In ERA40 a full range of ice concentrations will be represented
and perhaps variable ice thickness. An improved blended ice/sea surface temperature field will
be used, sea-ice thermal characteristics will be better treated, there will be a refined treatment ofsnow cover and much more accurate topographic data sets will be used together with the innate
advantage of higher resolution in representing topography. NCEP2 corrected the valley snow
problem and other mistakes in NCEP1, has more recent physical parameterisations than NCEP1
and has a better treatment of soil moisture that uses observed precipitation (Kanamitsu et al. ,2000).

11.4.4 Ocean surface energy balance
Da Silva et al. (1994) (University of Wisconsin at Milwaukee (UWM)) carefully
calculated oceanic heat fluxes from several decades of carefully corrected COADS(Comprehensive Ocean-Atmosphere Data Set) ship observations. The result did not produce areasonable global heat balance, reflecting at least in part the scarcity of COADS data over muchof the ocean outside the mid-latitude Northern Hemisphere. Other COADS-based air-sea flux
estimates, such as the climatology calculated by the Southampton Oceanography Centre (Joseyet al., 1998, 1999), display similar biases. To obtain a reasonable heat balance, da Silva et al.
(1994) mathematically tuned the fluxes, effectively increasing the evaporation by 15% anddecreasing the net short wave (NSW) by approximately 7% globally. They changed thesensible heat and net long wave (NLW) much less. The tuning was applied uniformly over the
globe and not predominately where ship reports are least abundant.
Table 11.4.1: Global mean ocean surface energy balance in Wm-2 estimated from COADS databy da Silva et al. (1994) (without tuning), from the four reanalyses and from satellite-based
estimates (SRB)
UWM

ERA15

GEOS1

NCEP1

NCEP2

SRB

Sensible heat

10.1

9.8

10.5

10.9

5.4

Latent heat

88

103.4

79.5

92.7

104

Net short-wave

170.4

160.5

197.8

165.9

167.2

173.4

Net long-wave

49.2

50.5

68.0

56.4

50.6

41.9

Net heat flux

23.3

-3.7

40.0

5.8

7.2

Table 11.4.1 shows global mean components of the ocean surface energy balance forthe four reanalyses for 1981-92. It also displays da Silva et al. (1994)'s untuned air-sea fluxes
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averaged over 1981-92 (UWM) and satellite-based estimates of surface NSW by Darnell et al.
(1992) and net long-wave radiation (NLW) by Gupta et al. (1992) averaged over July 1983-June
1991. These satellite-based estimates from the Langley Surface Radiation Budget (SRB)
Project (see section 11.3.3C) are derived from satellite observations of TOA radiation,
International Satellite Cloud Climatology Project (ISCCP) cloud estimates and radiative transfer
codes.
The ERA15 fluxes shown are from twice-daily 12-24 hr forecasts; GEOS1, NCEP1 andNCEP2 fluxes are from the four times daily 0-6 hr forecasts used as the first guess orbackground field in the analysis cycle. The ERA15 hydrological cycle intensifies ("spins up")between the 0-6 hr and 12-24 hr forecasts; the global hydrological cycle and surface energy
budget are in better balance in the 12-24 hr ERA15 forecasts than in the 0-6 hr forecasts. The
system used in the NCEP1 reanalysis appears to have less global "spin-up" than ERA15.
ECMWF recommends the use of 12-24 hr fluxes from ERA15.
ERA15's and NCEP2’s global evaporation disagree the most with the untuned UWM
estimate, but agree the best with the tuned estimate; NCEP2 has the least sensible heat flux. For
NSW, the UWM untuned estimate displays the best agreement with the satellite estimate;
GEOS1 displays considerable higher radiative fluxes than the other estimates. NCEP1, NCEP2
and ERA15 are nearly in balance over the global ocean; GEOS1 and untuned UWM produce a
substantial net heat flux (NHF) into the ocean.
Kiehl and Trenberth (1997) found that climatological estimates of global mean surface
NSW and NLW varied by 20 Wm-2. Estimates of global mean surface sensible and latent heatvaried by 11 Wm-2. Section 11.3.3 implies that the SRB surface NSW may have a positive bias.
Satellite-based estimates of surface NLW display more qualitative disagreement with each otherthan do satellite-based estimates of surface NSW (White, 1996a). Gupta et al. (1992)’s global
mean estimate for NLW is lower than most of the estimates. The global mean net surface
radiation implied by Darnell et al. (1992)’s and Gupta (1992)’s estimates is larger than mostestimates examined by Kiehl and Trenberth (1997) and would require substantially higher
sensible and/or latent heat fluxes for balance than those found in UWM and the reanalyses.
11.4.5 Evaporation
Figure 11.4.1 displays the zonal mean latent heat flux over the ocean averaged over
1981-92. Both the original UWM and the tuned UWM estimates are shown. The GEOS1
reanalysis has the lowest evaporation of all, while ERA15 shows good agreement with the tuned
UWM estimate and NCEP1 lies between the original and tuned UWM estimate. NCEP2 has thehighest evaporation in the subtropics. The large differences between the original and tuned
UWM estimates indicate a significant uncertainty in the UWM estimate (and in COADS-based
estimates in general). Significant differences between UWM and the reanalyses can be seen
near the North Pole. The reanalyses show more evaporation in late spring and early summer
and less the rest of the year. In the polar regions ship reports are more abundant during summer
than winter, perhaps biasing the UWM estimates. The reanalysis estimates over the oceans usedhere include fluxes over sea ice as well as over the open ocean.
Figure 11.4.2(a) shows the geographical distribution of the original UWM estimate oflatent heat flux. ERA15 (Figure 11.4.2(b)) shows more evaporation nearly everywhere, withmaximum differences in the northern subtropical oceans and slightly south of the equator,
particularly in the southern winter, in the mid-Pacific and western Atlantic. All the reanalyses
evaporate less than UWM in the Far East equatorial Pacific. GEOS1 (Figure 11.4.2(c)) has lessevaporation over the Gulf Stream and Kuroshio, especially in the fall and early winter, and in
the eastern tropical Pacific. All four reanalyses display more evaporation than UWM near 60S.
NCEP1 (Figure 11.4.2(d)) has more evaporation in the subtropics but generally less along theequator. Like ERA15, NCEP2 (Figure 11.4.2(e)) shows more evaporation than UWM nearly

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everywhere. ERA15, NCEP1 and NCEP2 appear to have a more tightly defined Gulf Stream
than UWM, perhaps reflecting differences in spatial resolution and smoothing.
Figure 11.4.3 shows the temporal correlation of UWM's monthly mean evaporation
with evaporation from NCEP1 for the years 1981-92. Correlations are highest where shipreports (Figure 11.4.4) are most abundant (such as over the Northern Hemisphere oceans
between 20 and 60N, and off the western coast of Africa). Correlations are near zero or
negative near the poles. Low correlations tend to occur in regions of few ship reports, but canalso occur in regions of low variability such as the South Atlantic.

Figure 11.4.1 Zonal mean latent heat flux over the oceans for 1981-92 in Wm-2.

Figure 11.4.5 compares the zonal averaged correlations with UWM estimates of
evaporation for all four reanalyses for monthly mean values for 1981-92 (Figure 11.4.5(a)), forthe monthly mean annual cycles averaged over 1981-92 (Figure 11.4.5(b)) and for monthly
anomalies from their own climatologies for 1981-92 (Figure 11.4.5(c)). The reanalyses allshow very similar levels and patterns of agreement with UWM. ERA15’s climatology shows
the best correlation with the UWM estimate, while NCEP1 and NCEP2’s anomalies agree morewith UWM than do ERA15 or GEOS1. GEOS1 shows somewhat less agreement with UWM
than the other three reanalyses. In Figure 11.4.5(b) correlations are much higher in the lower
latitudes of the Southern Hemisphere than in Figure 11.4.5(a) or Figure 11.4.5(c), exceeding 0.6
from 65N to 40S. Correlations of anomalies in Figure 11.4.5(c) exceed 0.6 only from 25N to
55N. NCEP2’s correlations with UWM are slightly higher than NCEP1 for the climatologicalannual cycle and slightly lower than NCEP1 for anomalies.

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Fig.
11.4.2(a).

Fig.
11.4.2(b).

Figure 11.4.2:(a) Latent heat flux from the original COADS estimate (UWM) by da Silva etal. (1994) averaged over 1981-92. Contour interval 20 Wm-2,negativevalues shaded(b) Difference in latent heat flux ERA15 minus UWM averaged over 1981-92.
Contour interval 10 Wm-2, zero omitted, negative values shaded.
(c) As (b) but for GEOS1 minus UWM.
(d) As (b) but for NCEP1 minus UWM.
(e) As (b) but for NCEP2 minus UWM.

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Fig.
11.4.2(c).

Fig.
11.4.2(d).

Fig.
11.4.2(e).

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The reanalyses include other sources of data over the ocean than ship reports and caninterpolate and extrapolate other sources of data from land, from other levels in the atmosphereand from previous analyses. Operational forecasts display considerable skill in the Southern
Hemisphere, indicating that modern data assimilation systems produce accurate daily analysesof the Southern Hemisphere. Anomalies in evaporation from the four reanalyses correlate wellwith each other over the oceans except near the poles and the equator. The differences incorrelations between COADS data rich and data poor regions evident in Figure 11.4.3 is notnearly so evident in correlations of the reanalyses with each other or with independent estimatesof fluxes and precipitation based on satellite data (see below). This suggests that the asymmetry
in correlation is due to the relative lack of ship observations outside 20-60N and suggests thatthe usefulness of COADS in defining inter annual variability may be largely limited to theNorthern Hemisphere mid-latitudes and a few other regions. Figure 11.4.5(b) implies, however,that COADS estimates of evaporation are useful in defining the climatological annual cycle in
the lower latitudes of the Southern Hemisphere.

Figure 11.4.3

Correlation of monthly mean evaporation from UWM and from NCEP1 for
1981-92. Contours 0, .4, .6, .8, .9, and .95. Values over 0.6 are shaded.

Figure 11.4.4

Average number of ship reports available to NCEP1/month/2.5o box for 198192. A nine-point smoother was applied to the field. Values over 10 are shaded.

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Figure 11.4.5(a).
Zonal mean correlation between
the reanalyses and UWM ofmean latent heat flux over theoceans for individual months
during the period 1981-92.

Figure 11.4.5(b).
As in (a), but for climatologicalmonthly means averaged over
1981-92.

Figure 11.4.5(c).
As in (a), but for monthly mean
anomalies from each estimate's
climatology for 1981-92.

Figure 11.4.6 shows the root-mean-square (RMS) differences between the reanalyses
and UWM for monthly mean values (Figure 11.4.6(a)), for the monthly mean annual cycle
averaged over 1981-92 (Figure 11.4.6(b)) and for monthly anomalies from the climatology(Figure 11.4.6(c)). The RMS differences in Figure 11.4.6(a) exceed 20 Wm-2 except between

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30 and 60N where ship reports are most abundant and near Antarctica where evaporation is low.
NCEP2 has the largest RMS differences from UWM, reflecting the largest month-to-monthvariability (nearly 30% more than UWM between 20 and 60N) (see Figure 11.4.7 below).
GEOS1’s anomalies are the least different from UWM estimates, reflecting lower month–tomonth variability than the other reanalyses, while NCEP1’s annual cycle generally is leastdifferent from UWM. GEOS1 has the weakest annual cycle and the weakest anomalies of thefour reanalyses, and a weaker annual cycle than UWM (10% weaker between 20 and 60N).
ERA15, NCEP1 and NCEP2 generally have a stronger annual cycle than UWM and theiranomalies tend to be larger than UWM in the Northern Hemisphere mid-latitudes. Note that thelargest differences in the anomalies occur in the Southern Hemisphere mid-latitudes and aretwice as large as differences in the Northern Hemisphere mid-latitudes.

Figure 11.4.6a.
Zonal mean root-mean square(RMS) difference in monthly
mean latent heat flux over theoceans between the reanalyses
and UWM for 1981-92 in Wm-2.

Figure 11.4.6b)
As in (a), but for climatologicalmonthly means averaged over
1981-92.

Figure 11.4.6c. As in a), but
for monthly mean anomalies
from each estimate's 1981-92
climatology.

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Figure 11.4.7(a) compares the standard deviation averaged over 20-60N of monthly
evaporation from UWM and the reanalyses for the period 1981-92. NCEP2 has the largest
month-to-month variability while GEOS1 has the lowest variability of the reanalyses. The
RMS difference averaged over 20-60N of the reanalyses from UWM is shown in Figure
11.4.7(b). By this measure NCEP2 is most different from UWM and GEOS1’s anomalies areclosest to UWM. However, if one normalises the RMS difference of a reanalysis from UWM
by dividing it by the standard deviation of evaporation in that reanalysis, as is shown in Figure
11.4.7(c), GEOS1 now appears the most different from UWM. NCEP1 shows the least
normalised difference from UWM.
Figure 11.4.7(a)Standard deviation averaged
over 20-60N of monthly meanlatent heat release over theocean in Wm-2. Full values
calculated from monthly meansfor 1981-92, annual fromclimatological monthly meanannual cycles averaged over
1981-92, and anomalies fromdepartures of monthly meansfrom the climatological annual
cycle.

Figure 11.4.7(b)RMS difference averaged over
20-60N of monthly mean latent
heat in the reanalyses fromUWM in Wm-2.

Figure 11.4.7(c)Ratio of the RMS difference
from UWM to the standard
deviation for each of thereanalyses.

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Figures 11.4.8(a) and 11.4.8(b) compare anomalies in evaporation from the fourreanalyses over the Gulf Stream (a data-rich area) and the central equatorial Pacific (a data-poorarea). Figure 11.4.8(a) shows that anomalies in the four reanalyses show very good agreementwith each other over the Gulf Stream, even though GEOS1 (Figure 11.4.2(c)) has less mean
evaporation there than the other reanalyses (Figures 11.4.2 (b), (d), (e)). Over the central
equatorial Pacific anomalies from the four reanalyses (Figure 11.4.8(b)) show less agreement
than over the Gulf Stream. ERA15 displays significantly lower evaporation in Figure 11.4.8(b)after 1986 than before, a pattern not evident in the other reanalyses. A similar discontinuity has
been observed in other fields in the ERA15 reanalysis (Stendel and Arpe, 1997). This may
reflect an interaction between the ERA15 analysis/forecast system and TOVS radiances in
response to a sudden, large change in the MSU channels in November 1986 (Fiorino, 2000).
ERA15 was the only reanalysis that used satellite radiances rather than temperature soundings
derived from satellite radiances.

Figure 11.4.8 (a):Time series of latent heat flux
anomalies (Wm-2) from the fourreanalyses for 1981-92 for a
Gulf Stream Region: 35-40N,
65-60W.

Figure 11.4.8 (b):As (a) but for the central
equatorial Pacific: 2.5S-2.5N,
177.5E-177.5W.

11.4.6 Sensible heat flux
Figure 11.4.9 compares the zonal mean sensible heat flux over the ocean from UWM
and the four reanalyses for the years 1981-92. The reanalyses are quite different from UWM
over the poles, but agree with UWM fairly well in the best-sampled region between 30 and 60N.
Near the North Pole UWM shows a substantial upward sensible heat flux except in summer
while the reanalyses show downward fluxes most of the year. NCEP1 has the strongest sensible
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heat flux. ERA15, GEOS1 and NCEP1 have more sensible heat flux than UWM near 60S,
particularly in winter, and less than UWM nearer Antarctica where GEOS-1 and NCEP1 show
sensible heat fluxes into the ocean. NCEP2 has the least upward sensible heat flux and has thestrongest downward flux in higher latitudes.

Figure 11.4.9. Zonal meansensible heat flux over theoceans for 1981-92 in Wm-2.

The 12-year mean regional distribution of sensible heat flux from UWM is shown in
Figure 11.4.10a. ERA15 (Figure 11.4.10b) and especially GEOS1 (Figure 11.4.10c) have lessupward sensible heat flux than UWM over the Kuroshio and Gulf Stream (especially in late fall
and winter), where NCEP1 (Figure11.4.10d) has more than UWM as well as more upward fluxin the subtropical oceans. NCEP1 has somewhat more sensible heat flux into the ocean in thenorthern mid-latitudes in summer than UWM. ERA15, GEOS1 and NCEP1 exceed UWM
estimates in the eastern subtropical oceans. Large differences appear near Antarctica and in the
Arctic Ocean. NCEP2 (Figure 11.4.10e) has more upward sensible heat flux than UWM overthe Gulf Stream and near Japan, but less than UWM elsewhere in the North Pacific and in theSouthern Hemisphere mid-latitudes.
Zonally averaged correlations of sensible heat from the reanalyses with UWM estimates(not shown) display similar patterns and values to Figure 11.4.5. The reanalyses display lessagreement with UWM in the tropics than for evaporation, reflecting small values of sensible
heat flux in the tropics. GEOS1 displays less agreement with UWM than the other reanalyses
do. ERA15 and NCEP2 tend to show the best agreement with UWM, slightly better than
NCEP1.
Figure 11.4.11 shows the root-mean-square (RMS) differences between the reanalyses
and UWM for monthly mean values (Figure 11.4.11a) and for monthly anomalies from theclimatology (Figure 11.4.11b). ERA15 generally shows the smallest differences from UWM.
NCEP2 has the largest RMS differences, reflecting the largest month-to-month variability.
Differences in the climatologies between the reanalyses and UWM are less than 10 Wm-2
between 50N and 50S. The largest differences between UWM and the reanalyses occur near 60oin both hemispheres, where sensible heat fluxes exhibit considerable variability. However,sensible heat fluxes also exhibit more variability between 30 and 45N than elsewhere outside
the polar regions and display relatively small differences there, indicating, as do thecorrelations, better agreement between UWM and the reanalyses there than elsewhere. GEOS1
shows the least variability in sensible heat flux of the five estimates; NCEP1 and NCEP2
display more variability than the UWM estimate between 20 and 60N. ERA15’s climatologicalannual cycle displays the least normalised RMS difference from UWM between 20 and 60N,
where NCEP1’s anomalies have the smallest normalised RMS difference from UWM
anomalies. NCEP2 displays slightly larger normalised RMS differences than NCEP1 or ERA;GEOS1 has the largest.
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Figure11.4.10(a)Figure11.4.10(b)Figure11.4.10(c)Report of the Working Group on Air Sea Fluxes

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Figure11.4.10(d)Figure11.4.10(e)Figure 11.4.10: (a) Sensible heat flux (Wm-2) from UWM averaged over 1981-92, negativevalues shaded.
(b) Difference in sensible heat flux ERA15 minus UWM. Contour interval
5Wm-2, zero omitted, negative values shaded.
(c) As (b) but for GEOS1 minus UWM.
(d) As (b) but for NCEP minus UWM.
(e) As (b) but for NCEP2 minus UWM.

11.4.7 Net short-wave
Here both UWM and reanalysis estimates of surface net short-wave (NSW) radiation
are compared to Darnell et al. (1992)’s satellite-based (SRB) estimates of NSW. The ERA15
reanalysis used a prognostic cloud parameterisation tuned to ISCCP clouds, the clouds used in
calculating the SRB estimate of surface NSW. The NCEP reanalyses used a diagnostic cloud

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scheme tuned to Air Force nephanalyses, which produce lower cloud amounts than ISCCP
estimates.
It has been suggested that satellite estimates of surface NSW may be more reliable thanother global estimates of surface NSW, although satellite estimates of surface NSW can differ
substantially from point surface measurements of NSW (White, 1996a). Figure 11.4.12 shows
the zonal mean surface NSW over ocean for July 1983-June 1991 from the four reanalyses, theuntuned UWM estimate by da Silva et al. (1994) and SRB.

Figure 11.4.11(a)RMS differences in monthly
mean sensible heat flux over theoceans between the reanalyses
and UWM for 1981-92 in Wm-2.

Figure 11.4.11(b) As in a) but
for monthly mean anomalies

Figure 11.4.12 Zonal meansurface net short wave radiation
over the oceans for 1981-92 in
Wm-2.

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Figure 11.4.13(a) shows the regional distribution of SRB NSW. GEOS1 (Figure
11.4.13(d)) has considerable more NSW than SRB poleward of 30o and in the eastern
subtropical oceans, but less in the western tropical Pacific. GEOS1 has very strong NSW in theeastern subtropical oceans where other estimates show relative minimums due to the presence of
low-level stratus clouds. NCEP1 (Figure 11.4.13(e)), NCEP2 (Figure 11.4.13(f)) and ERA15(Figure 11.4.13(c)) have less in the tropics and more in higher latitudes than SRB; NCEP2 and
ERA15 appear to have slightly larger biases than NCEP1 in the tropics and NCEP2 has a larger
positive bias in higher latitudes than NCEP1 or ERA15. UWM (Figure 11.4.13(b)) shows thebest agreement with SRB in the tropics, in spite of the simplicity of the UWM parameterisationof NSW. In higher latitudes UWM and the reanalyses tend to have more NSW in summer thanthe SRB estimate. The time-mean differences of the reanalyses’ TOA NSW from the Earth
Radiation Budget Experiment (ERBE) satellite estimate are similar to their differences from the
SRB estimate of surface NSW. Since satellites observe TOA NSW directly, this similarity lends
credence to the reliability of the SRB estimate of surface NSW.
The time-mean NSW from ERA15 (Figure 11.4.14(c)) and GEOS1 (Figure 11.4.14(d))
shows no evidence of the influence of low-level stratus clouds on the surface NSW in regions
such as the ocean off Baja California in the Northern Hemisphere summer; NCEP1's NSW(Figure 11.4.14(e)) does show the influence of low-level stratus cloud. Over the rest of the
Pacific NCEP1 and ERA15 clearly underestimate surface NSW; GEOS1 overestimates surface
NSW. NCEP2 (Figure 11.4.14(f)) displays less evidence of stratus clouds off Baja California
than NCEP1, but does display higher NSW than NCEP1 or ERA15 (although still less than
SRB) elsewhere in the Pacific. NCEP2 changed the parameterisation of low-level stratus clouds
to require a higher relative humidity threshold than in NCEP1. The UWM summertime surface
NSW (Figure 11.4.14(b)) resembles SRB (Figure 11.4.14(a)) much more than the reanalyses do.
NCEP1's surface NSW benefited from offsetting mistakes. Its ocean surface albedo
was too high, and it had too much downward short wave radiation at the surface (Wild, 2000).
Its cloud albedo was too high and it had fewer clouds than other reanalyses. NCEP2 corrected
the ocean surface albedo and had more cloudiness. Figure 11.4.15 displays the global mean
downward surface short wave for January and July averaged over 8 years. ERA15 and NCEP2
agree with the SRB estimate; NCEP1 is 10% high.
The annual cycle in surface NSW (calculated over 1984-90) in the reanalyses is wellcorrelated with the SRB estimate (Figure 11.4.16(a)), exceeding 0.9 outside the tropics where
NSW has a small annual cycle. The anomaly correlations of the reanalyses with SRB (Figure
11.4.16(b)) (calculated over July 1983-June 1991) are much lower with only UWM anomalies
showing a correlation greater than 0.6 in the Northern Hemisphere. GEOS1's correlations arelower than the other reanalyses; NCEP2’s correlations with SRB are higher than GEOS1 but
lower than ERA15 or NCEP1. UWM NSW anomalies agree the best with SRB anomalies fromthe North Pole to 20S, but show much less agreement south of 30S. Correlation of TOA NSW
from the reanalyses with ERBE TOA NSW yields similar levels of agreement to Figure 11.4.16.
The UWM estimates show the smallest RMS differences from SRB for the full monthly
means (Figure 11.4.17(a)) and the climatologies and between 15N and 50N for the anomalies(Figure 11.4.17(b)). UWM is most different from SRB near the North Pole and for anomalies
in much of the Southern Hemisphere. In mid-latitudes GEOS1 shows the largest differencesfrom SRB in the full monthly means and in the climatologies, reflecting a larger bias and astronger annual cycle. NCEP1 tends to be closer to SRB than ERA15 and has less variability
than SRB. ERA15 has considerably more variability in monthly anomalies in the tropics thandoes SRB. NCEP2’s anomalies are the most different from SRB, reflecting a greater month-tomonth variability than the other estimates; in mid-latitudes the normalised RMS difference ofNCEP2 anomalies from SRB anomalies is the least of the reanalyses. For most latitudes the
RMS differences of the reanalyses from SRB are more than 20 Wm-2 for the full monthly means
and more than 10 Wm-2 for the anomalies.

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Figure11.4.13(a)Figure11.4.13(b)Figure11.4.13(c)Report of the Working Group on Air Sea Fluxes

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Figure11.4.13(d)Figure11.4.13(e)Figure11.4.13(f)Report of the Working Group on Air Sea Fluxes

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Figure 11.4.13: (a) Surface NSW radiation from the SRB satellite estimate averaged over July
1983-June 1991. Contour interval 20 Wm-2. Values over 220Wm-2 shaded.
(b) Difference in surface NSW UWM minus SRB. Contour interval 20 Wm-2 ;zero omitted, negative values shaded.
(c) As (b) but for ERA15-SRB.
(d) As (b), but for GEOS minus SRB.
(e) As (b) but for NCEP1 minus SRB.
(f) As (b) but for NCEP2 minus SRB.

Figure11.4.14(a)SRB

Figure11.4.14(b)
UWM/
COADS

Figure11.4.14(c)ERA

Figure11.4.14(d)GEOS

Figure11.4.14(e)NCEP

Figure11.4.14(f)NCEP2

Figure 11.4.14. Surface NSW radiation from different sources averaged over July 1983-1990.
Contour interval 20 Wm-2. Values over 260 Wm-2 are shaded.

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Figure 11.4.15 Global mean downward short wave radiation at the surface from SRB and from
ERA, NCEP1 and NCEP2 reanalyses for Jan. 1984-91 and July 1983-90 in Wm-2.

Figure 11.4.16 (a) Zonal mean
correlation of monthly mean
climatological surface NSW
over the oceans of thereanalyses and UWM with SRB
for the annual cycle averaged
over 1984-90.

Figure 11.4.16 (b) Zonal mean
correlation of monthly mean
anomalies in NSW over theoceans of the reanalyses and
UWM with SRB for July 1983June 1991. The anomalies arefrom each estimate's
climatology for 1984-90.

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Figure 11.4.17 (a) RMS
differences in monthly meansurface NSW over the oceans of
the reanalyses and UWM from
SRB for July 1983-June 1991 in
Wm-2.

Figure 11.4.17 (b) As (a) but for
anomalies.

11.4.8 Net long-wave
The zonal mean surface net long-wave (NLW) radiation over the ocean is shown in
Figure 11.4.18. Figure 11.4.19(a) shows the regional distribution of the satellite estimate (SRB)of NLW (Gupta et al., 1992). The SRB estimate is lower than the other estimates. UWM(Figure 11.4.19(b)) agrees the best with SRB, showing, as do the reanalyses, the largest
differences from SRB in the eastern subtropical oceans. GEOS1 (Figure 11.4.19(d)) shows thehighest NLW and the largest differences from SRB. ERA15 (Figure 11.4.19(c)) shows larger
differences from SRB in the eastern subtropical oceans than NCEP1 (Figure 11.4.19(e)), but has
smaller differences elsewhere. NCEP2 (Figure 11.4.19(f)) is generally closer to the SRB
estimate than NCEP1. The large differences of GEOS1, ERA15 and NCEP2 from the SRB
estimate in the eastern subtropical oceans may reflect problems in their treatment of low-levelstratus clouds.
Figure 11.4.20(a) displays the zonally averaged correlations of the mean annual cycles
in NLW averaged over 1984-1990 from UWM and the reanalyses with SRB over the oceans.
Figure 11.4.20(b) displays the zonal mean correlations of the anomalies for July 1983-June
1991 from the climatologies averaged over 1984-90. The correlations of the climatologies with
SRB are less in mid-latitudes than for other components of the surface energy balance;
NCEP2’s annual cycle shows the least agreement with SRB. The anomaly correlations are lessthan 0.6 at all latitudes for the reanalyses and nearly all latitudes for UWM. NCEP1’s NLW
anomalies agree the best with SRB of the four reanalyses; GEOS1 displays the worst
agreement.

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Figure 11.4.18
Zonal mean surface NLW
radiation over the oceans for
1981-92 in Wm-2.

RMS differences with the SRB estimate are shown in Figure 11.4.21(a) for the fullmonthly means and in Figure 11.4.21(b) for the monthly anomalies. GEOS1 is clearly the mostdifferent from the SRB estimate and has considerably more variability than the SRB estimate
outside the tropics. ERA15’s and UWM's mean annual cycles are different from SRB by about
11 Wm-2, while NCEP2’s annual cycle is closer to SRB than NCEP1. NCEP1’s anomalies areclosest to SRB’s except in the northern mid-latitudes where UWM is less than 6 Wm-2 different
from SRB. The SRB estimate displays the most variability in the tropics and the least outside
the tropics.

Figure 11.4.19: (a) Surface NLW from SRB averaged over July 1983-June 1991. Contour(Next page:)interval 10 Wm-2. Values greater than 40 Wm-2 shaded.
(b) Surface NLW UWM minus SRB. Contour interval 10 Wm-2; zero omitted,
negative values shaded.
(c) As (b) but for ERA minus SRB.
(d) As (b) but for GEOS minus SRB.
(e) As (b) but for NCEP1 minus SRB.
(f) As (b) but for NCEP2 minus SRB.

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Figure11.4.19(a)Figure11.4.19(b)Figure11.4.19(c)Report of the Working Group on Air Sea Fluxes

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Figure11.4.19(d)Figure11.4.19(e)Figure11.4.19(f)Report of the Working Group on Air Sea Fluxes

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Figure 11.4.20 (a) Zonal mean
correlation of monthly mean
climatological surface NLW
over the oceans of thereanalyses and UWM with SRB
for the annual cycle averaged
over 1984-90.

Figure 11.4.20 (b) As (a) but for
anomalies for July 1983-June
1991 from each estimate's
climatology for 1984-90.

Figure 11.4.21(a) RMS
differences in monthly meansurface NLW over the ocean of
the reanalyses and COADS
from SRB for July 1983-June
1991 in Wm-2.

Figure 11.4.21(b) As (a) but for
anomalies for July 1983-June
1991 from each estimate's
climatology for 1984-90.

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11.4 Reanalysis products(11.4.9 Net Heat Flux)

11.4.9 Net heat flux
Figure 11.4.22 shows the zonal mean NHF into the ocean averaged over 1981-92. The
untuned UWM estimate (da Silva et al., 1994) is predominately into the ocean; GEOS1 has aneven stronger downward net heat flux. The tuned UWM estimate, NCEP1, NCEP2 and ERA15
show a more balanced distribution of fluxes into the ocean near the equator and fluxes out of theocean in the subtropics. The UWM estimates show much more NHF into the atmosphere near
the North Pole. Near 45o in either hemisphere NCEP1 and ERA15 show NHF into the ocean, in
better agreement with the untuned UWM estimate than the tuned. NCEP2 shows more NHF
into the ocean near 45S than ERA15, NCEP1 or UWM. NCEP1, NCEP2 and ERA15 show
good agreement with the untuned UWM estimate between 40N and 60N, where shipobservations are most abundant, but are close to the tuned COADS estimate in the tropics. The
reanalyses show more NHF into the oceans in the Southern Hemisphere mid-latitudes insummer and more NHF into the atmosphere near 60S in winter than the tuned UWM estimate.
Da Silva and White (1995) found that replacing the NCEP1 estimate of surface net short-wave
with an estimate based on satellite data (Pinker et al., 1995) improved the time-mean global
oceanic heat balance.
The tuned UWM estimate of the 12-year mean net heat flux, shown in Figure
11.4.23(a), shows strong heat flux into the ocean in the eastern equatorial Pacific and intense
upward heat flux over the Kuroshio and Gulf Stream and south of Africa. The original UWM
estimate (Figure 11.4.23(b)) shows broad areas where it is more than 40 Wm-2 different from thetuned estimate. All four reanalyses show large positive differences from the tuned UWM
estimate south of Africa and in the Far East equatorial Pacific and Atlantic. ERA15 (Figure
11.4.23(c)) has less downward/more upward net heat flux than UWM over much of the tropicaloceans; NCEP1 (Figure 11.4.23(e)) has less upward heat flux in the mid-latitudes. GEOS1(Figure 11.4.23(d)) has more downward/less upward net heat flux nearly everywhere. ERA15(Figure 11.4.23(c)) and GEOS1 (Figure 11.4.23(d)) show bigger differences in the eastern
subtropical Pacific than NCEP1 (Figure 11.4.23(e)) does, perhaps reflecting differences in thetreatment of stratus clouds. NCEP2’s differences from UWM tuned (Figure 11.4.23(f))
resemble NCEP1’s differences, but are larger in the eastern subtropical oceans where low-levelstratus clouds are important and between 30 and 60S.

Figure 11.4.22. Zonal mean net
heat flux into the ocean
averaged over 1981-92 fromCOADS and the fourreanalyses. Positive values
denote a flux from theatmosphere into the ocean.

Figure 11.4.23: (a) Surface net heat flux into the ocean from the tuned UWM estimate(next page)
averaged over 1981-92. Contour interval 25 Wm-2. Shaded areas indicate(negative) upward heat flux.
(b) The effect of tuning on UWM (untuned minus tuned). Contour interval 20
Wm-2; zero omitted, negative values shaded.
(c) As (b) but for the difference in net heat flux for ERA minus UWM tuned.
(d) As (c) but for GEOS minus UWM tuned.
(e) As (c) but for NCEP1 minus UWM tuned.
(f) As (c) but for NCEP2 minus UWM tuned.
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Figure11.4.23(a)Figure11.4.23(b)Figure11.4.23(c)Report of the Working Group on Air Sea Fluxes

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Figure11.4.23(d)Figure11.4.23(e)Figure11.4.23(f)Report of the Working Group on Air Sea Fluxes

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11.4 Reanalysis products(11.4.9 Net Heat Flux)

The zonal averaged correlations of the mean annual cycles in NHF of the reanalyses
with the tuned UWM estimate resemble the correlations of the mean annual cycles in NSW(Figure 11.4.16(a)). Very high correlations near 1.0 are seen in mid-latitudes, reflecting thegreat variation with season of the net heat flux outside the tropics; near the equator thecorrelations drop to 0.3(GEOS1) to 0.6 (ERA15). The correlations of anomalies in NHF arevery similar to the correlations for latent heat (Figure 11.4.5(c)), with correlations above .6 onlyin the northern mid-latitudes. Correlations for NHF are virtually the same for NCEP1, NCEP2
and ERA15 and a little lower for GEOS1. ERA15's annual cycle in these fields tends to agreeslightly more with UWM than NCEP1's or NCEP2’s; NCEP1's and NCEP2’s anomalies tend toagree slightly more with UWM than ERA15's.
Figure 11.4.24 (a): RMS
differences in monthly mean
NHF over the oceans of thereanalyses from tuned UWM for
1981-92. (left bottom)

Figure 11.4.24 (b) As in 24a,but for the climatological annual
cycle averaged over 1981-92.
(above)Figure 11.4.24 (c) As in 24a, but
for anomalies from theclimatological annual cycle.

The climatological annual cycles in NHF in the reanalyses have RMS differences fromthe tuned UWM estimate of more than 20 Wm-2 at all latitudes (Figure 11.4.24(b)) and the fullmonthly means are more than 30 Wm-2 different (Figure 11.4.24(a)) at all latitudes. Anomalies
in NHF (Figure24(c)) are more than 15 Wm-2 different at all latitudes. GEOS1’s climatology is

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11.4 Reanalysis products(11.4.10 Forcing Ocean Models)much more different from the tuned estimate than the other reanalyses; NCEP2’s climatology ismore different from UWM than NCEP1 or ERA15. Anomalies from all four reanalyses show
very similar RMS differences from UWM; NCEP2’s anomalies are slightly more different from
UWM than the other reanalyses and display more month-to-month variability. GEOS1 has thelargest normalised RMS differences from UWM between 20 and 60N.
11.4.10 Evaluating the NCEP1 reanalysis by forcing an ocean model
Fluxes from NCEP1 and ERA15 reanalyses are now widely used to force ocean circulation
models at global and basin scale. At present, the mean biases in model solutions make it
difficult to assess the quality of the climatological mean of these fluxes. However, model
solutions have shown in several cases a seasonal to interannual variability which compares
qualitatively well with ocean observations. For example, Figure 11.4.25 shows variations of
the maximum mixed layer depth in March in the Labrador Sea during the period 1958-1997,
simulated with a 1° primitive equation ocean model forced with fluxes from NCEP1 (fromKnochel, 1999). This parameter shows a significant decadal variability. This variability
appears consistent with noticeable oceanic events observed in this region. These include weak
convection periods between 1962 and 1972 (related to the Great Salinity Anomaly, GSA,
(Dickson et al., 1988)) and between 1976 to 1982 (related to the lesser GSA (Lazier, 1997)) andparticularly active convection periods in 1972-76 (McCartney et al., 1997), and in the earlynineties (1990- 1995), as described by Dickson et al. (1996).
This indicates that the low frequency variability found in the reanalysis fluxes could
make sense and should be useful to understand climate change over recent times (the last 40-50
years). These results, which however remain qualitative, indicate that the inter-annual
variability described in the reanalysis products make sense.
DYNA97 NCEP E P : Labrador Sea Mixed Layer De th maximum in March

3000

2500

depth in meter

Figure 11.4.25 Variations of the
maximum mixed layer depth inMarch in the Labrador Sea
during the period 1958-1997,
simulated with a 1° primitive
equation ocean model forced
with fluxes from NCEP1 (fromKnochel, 1999).

2000

1500

1000

500
1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

years

11.4.11 Winds
A. COMPARISON OF THE NCEP WINDS WITH INSTRUMENTAL MEASUREMENTS AT OWS
In order to compare surface winds from the NCEP1 Reanalysis with instrumental
measurements for the 1980s and 1990s, Zolina and Gulev (1998) used observations at four midlatitude ocean weather ships (OWS) in the North Atlantic: C, L, M, and R. These observations
are individual three hourly anemometer observations taken at known anemometer heights byprofessional observers. Additionally, wind data from the SECTIONS Programme (Gulev,
1994) were used. These data were collected by six sister-ships that also operated at OWS C
from 1975 to 1990. All wind measurements from OWS and SECTIONS were adjusted to 10-m

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height and neutral stability. For the comparison NCEP1 data at the nearest grid point of theGaussian grid to the measurement location were used.
In general, instrumental wind speed in the North Atlantic mid-latitudes has been foundto be somewhat higher than NCEP1 winds. Annual mean intercepts for the OWS data vary
within the range of 0.1 to 0.35 ms-1; the largest deviations of about 0.7 ms-1 are obtained for the
SECTIONS data. Note that OWS data were assimilated in the NCEP1 Reanalysis; therefore,
the intercomparison is not independent. However, only 25% of the SECTIONS measurements
were included in the COADS archives and assimilated in the NCEP1 reanalysis. This explains
the larger differences obtained for this data sub-set.
The largest overestimation was observed for the winter months for the SECTIONS data
and all OWS, except for the OWS L. In summer the instrumental measurements of the winds
were less than the reanalysis winds by 0.1 to 0.3 ms-1. Estimation of the regression slopes gave
values varying from 0.88 to 0.97, demonstrating a general tendency of overestimating low
winds and underestimating moderate and high winds in NCEP1. Since the assimilation of the
COADS winds in NCEP1 should provide in general an opposite tendency due to the usage of
WMO1100 scale in COADS, this bias can be explained by the impact of the NCEP1 model on
the wind fields. Separate analysis of the wind components shows that zonal components in thereanalysed products and instrumental measurements are in better agreement than meridional
components.
B. ERA15 AND NCEP1 REANALYSIS TROPICAL WIND EVALUATIONSMonthly mean surface wind products from the NCEP1 and ERA15 reanalysis projects
over the tropical regions have been compared to specialised products from Florida State
University (FSU) and Centre ORSTOM/IFREMER (see section 9.3.1). Monthly mean fields
from the reanalyses for the common period 1979-1993 were first constructed. For comparison
purposes, the daily or 4-time per day NCEP1 and ERA15 10-m winds were first adjusted to 20m height (nominal reference height for FSU products) using the BVW parameterisation scheme,
and monthly mean fields of pseudo-stress were produced. NCEP1 and ERA15 products werethen interpolated to the same spatial grids as the specialised products to allow directcomparisons. Comparisons of the respective climatologies of the pseudo-stress fields, thedivergence and curl as well as the variations of these fields resulted in a number of conclusions
and Figures 11.4.26 and 11.4.27.
Comparisons of the climatological reanalysis products to the FSU and ORSTOM
climatologies indicate the Indian Ocean is much more comparable than the Pacific or Atlantic.
During the Indian Ocean southwest monsoon, the ERA15 winds are strongest in the northernbasins, followed by FSU and then NCEP1. The NCEP1 has the strongest trades in the southern
third of all tropical regions. This strength is in sympathy with the strength of the trades (i.e.
even stronger during trade maximum during June–December). Model spin-up problems nearisland stations, particularly in ERA15, results in relatively large gradients in the pseudo-stressvector fields. Not only does this impact the pseudo-stress vectors, but also the kinematic fields– especially the curl where numerous large bipolar patterns are co-located adjacent to manyislands. The NCEP1 Inter-tropical Convergence Zones (ITCZ) in the eastern Pacific andwestern Atlantic are poorly resolved during July-December, i.e. when the southerly trades are
maximum and the ITCZ is also strongest and at its maximal northern location. The NCEP1
ITCZ is too broad (north south) and too weak. This seems to be linked to the presence of
anomalous weak cross-equatorial southerlies. Not only are the NCEP1 southerlies weak, but inthe equatorial region (0° to 5°S), particularly during January-June, the easterlies are notably
weaker (up to 5 m s-1) than the other analyses. Comparisons of the pseudo-stress anomalies
highlight the outlier nature of the NCEP1 fields in the tropics. These comparative results varyover time. For example, Pacific east west winds along the equator for all three analyses
correlate much better during ENSO events than at any other time. Additionally, NCEP1 winds
correlate better with FSU during 1988-1991 and the ERA equatorial easterlies are stronger than
FSU only for the 1979-1986 period, but afterwards, the FSU easterlies are stronger.

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Figure 11.4.26 Zonal mean climatological pseudo-stress components for FSU, ORSTOM (ATL)
and (height adjusted) ERA15, NCEP1 reanalysis products: (a) east-west component for the
Indian Ocean region (30°E-120°E); (b) east-west component for the Pacific Ocean region(120°E-70°W); (c) east-west component for the Atlantic Ocean region (60°W-15°E); (d) northsouth component for the Indian Ocean; (e) north-south component for the Pacific Ocean region;and (f) north-south component for the Atlantic Ocean region.

Figure 11.4.27 Difference (FSU-NCEP1) of FSU and (height adjusted) NCEP1 climatologicalpseudo-stress vectors for the period 1979-1993 with colours indicating magnitude of thedifference of the wind divergence fields. Areas with no vectors are areas where FSU andreanalyses do not overlap.
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11.4 Reanalysis products(11.4.12 Zonal Surface Stress)

Other approaches to flux product evaluation include the use of ocean models andindependent validation. For example, recent work by Hackert et al. (2000) compares ocean
model simulations for the 1997-1998 ENSO event forced with NCEP reanalysis, satellite-based
winds, and other wind products. They found that the satellite wind products perform best
overall, but add that each wind product has its own respective strengths and weakness withregards to simulating observed SST, subsurface temperatures, and sea level anomalies.
11.4.12 Zonal surface stress
Zonal surface stress is compared to the UWM COADS-based estimates. Zonallyaveraged values over the ocean (Figure 11.4.28(a)) are similar, except in the Southern
Hemisphere mid-latitudes where the reanalyses differ in magnitude from each other, but are allstronger than UWM in a region of few ship reports. UWM's weak values near 50S may reflect
interpolation from lower latitudes in the Southern Hemisphere where the zonal stress is less.
ERA15 and NCEP2 tend to have stronger maxima than the others, GEOS1 tends to be theweakest.
Figure 11.4.28(b) compares zonal surface stress along the equator from the fourreanalyses and from UWM averaged over 1981-92. ERA15 appears somewhat stronger than
UWM while NCEP1 is too weak in the Pacific. Comparisons to observations of surface winds
in the east Pacific also indicate that NCEP1 near-surface winds are too weak (White, 1996b).
GEOS1 and NCEP2’s zonal surface stresses are closer in magnitude to UWM in the east
Pacific, but shift the maximum value to the east.

Figure 11.4.28 (a) Zonallyaveraged zonal surface stressover the ocean averaged over
1981-92 from UWM and thereanalyses (right) in Nm-2.

Figure 11.4.28 (b) As (a) but forzonal surface stress along theequator.

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11.4 Reanalysis products(11.4.12 Zonal Surface Stress)

Figure 11.4.29(a) displays the mean zonal surface stress from UWM for 1981-92.
ERA15 (Figure 11.4.29(b)) displays stronger westerly stresses in mid-latitudes and stronger
easterly stress in the tropics than UWM. GEOS1 (Figure 11.4.29(c)) displays weaker stresses
than UWM except south of 30S. NCEP1 (Figure 11.4.29(d)) displays the weakest easterly
stress in the central equatorial Pacific, but stronger stresses than UWM elsewhere. NCEP2(Figure 11.4.29(e)) agrees better with UWM than NCEP1 does in the equatorial Pacific and has
the strongest stresses in mid-latitudes.

Figure11.4.29(a)Figure11.4.29(b)Figure 11.4.29: (a) Zonal surface stress from UWM for 1981-92. Contour interval 0.025 Nm-2,
easterly stress shaded.
(b) Difference in zonal surface stress ERA12 minus UWM. Contour interval
0.01 Nm-2, zero omitted, negative (more easterly) values shaded.
(c) As (b) but for GEOS minus UWM.
(d) As (b) but for NCEP1 minus UWM(e) As (b) but for NCEP2 minus UWM.

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Figure11.4.29(c)Figure11.4.29(d)Figure11.4.29(e)Report of the Working Group on Air Sea Fluxes

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11.4 Reanalysis products(11.4.12 Zonal Stress)

Figure 11.4.30(a) displays the zonal mean correlations of the annual cycles in zonal
surface stress in the reanalyses with the annual cycle in the UWM estimate from da Silva et al.
(1994). The annual cycles are from monthly mean values averaged over 1981-92. All threereanalyses show correlations above 0.6 from 65N to 40S.
The correlations of monthly mean anomalies from the climatologies are shown in
Figure 11.4.30(b). The pattern is similar to that seen for latent heat flux (see Figure 11.4.5(c));
however, correlations are somewhat higher in Figure 11.4.30(b), particularly in the tropics.
Correlations for zonal surface stress are virtually the same for NCEP1 and ERA15 and slightly
lower for GEOS1. ERA15's annual cycle agrees slightly more with UWM than NCEP1's in thetropics but not in mid-latitudes; NCEP1's anomalies agree slightly more with UWM than
ERA15's except in the Southern Hemisphere mid-latitudes. NCEP2 displays slightly lessagreement with UWM than NCEP1, but more agreement than GEOS1. Anomalies from thereanalyses correlate well with each other over the oceans except near Antarctica; correlations
between the reanalyses are lower near the equator than in mid-latitudes.

Figure 11.4.30(a) Zonal mean
correlations of monthly mean
climatological zonal surface
stress over the reanalyses and
UWM for the monthly meanannual cycle averaged over
1981-92.

Figure 11.4.30(b) As (a) but for
anomalies from each estimate's
climatology for 1981-92.

Zonal mean RMS differences between the four reanalyses’ zonal surface stress and
UWM are shown in Figure 11.4.31(a) for the full monthly means and in Figure 11.4.31(b) forthe monthly mean anomalies. NCEP2 displays the largest RMS differences from UWM in
higher latitudes, reflecting at least in part the largest variability in both the annual cycle and inanomalies. GEOS1 shows less RMS difference from UWM than other reanalyses, reflecting atleast in part the lowest variability and the weakest stress of all the estimates. ERA15 and
NCEP1 are more variable in the northern mid-latitudes than UWM. Between 20 and 60N,
NCEP1 has the smallest normalised RMS differences from UWM and GEOS1 the largest.

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11.4 Reanalysis products(11.4.13 Meridional Surface Stress)

Figure 11.4.31(a) RMS difference inmonthly mean zonal surface stress over theoceans between the reanalyses and UWM
for 1981-92 in Nm-2.

Figure 11.4.31(b) As (a) but for anomalies
from each estimate's climatological annual
cycle in Nm-2.

11.4.13 Meridional surface stress
The zonal mean meridional surface stress over the ocean averaged over 1981-92 isshown in Figure 11.4.32. GEOS1 has weaker northward stress in the Southern Hemispheretropics than UWM, particularly in the Southern Hemisphere winter; all the reanalyses have
weaker southward stress in the Northern Hemisphere tropics, especially in the Northern
Hemisphere winter. In mid-latitudes in both hemispheres the reanalyses all have stronger
poleward stresses than UWM.
Figure 11.4.32 Zonally averaged
meridional surface stress over
the ocean averaged over
1981-92 from UWM and thereanalyses in Nm-2.

Figure 11.4.33(a) shows the annual mean meridional surface stress for 1981-92 from
UWM. In the east tropical Pacific, a strong northward stress can be seen at the equator and astrong southward stress near 15N. ERA15 (Figure 11.4.33(b)) shows a similar, but slightly
weaker pattern; GEOS1 (Figure 11.4.33(c)) and NCEP1 (Figure 11.4.33(d)) are significantly

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Figure11.4.33(a)Figure11.4.33(b)Figure11.4.33(c)Report of the Working Group on Air Sea Fluxes

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11.4 Reanalysis products(11.4.12 Meridional Surface Stress)

Figure11.4.33(d)Figure11.4.33(e)Figure 11.4.33: (a) Meridional surface stress from UWM based on COADS. Contour interval
0.02 Nm-2, northerly stress shaded.
(b) Difference in meridional surface stress ERA minus COADS. Contour
interval 0.01 Nm-2, zero omitted, negative values shaded.
(c) As (b) but for GEOS minus UWM.
(d) As (b) but for NCEP1 minus COADS.
(e) As (b) but for NCEP2 minus COADS.
weaker than UWM in the eastern tropical Pacific and in the tropical Atlantic. NCEP2 (Figure
11.4.33(e)) shows smaller mean differences from UWM than NCEP1, especially along theequator. All the reanalyses show significant differences from UWM south of 30S.
Zonal mean correlations between UWM and the reanalyses for meridional surface stressare similar to, but somewhat lower than, the correlations for zonal surface stress (Figure
11.4.30). The annual cycles in the reanalyses agree quite well (0.9 or higher) with UWM
between 55N and 15S; the correlation of the reanalyses’ anomalies with UWM anomalies
exceeds 0.8 from 25N to 55N. NCEP1’s annual cycle has the worst agreement with UWM
between 20 and 60N and ERA15’s agrees the best; GEOS1 has the worst agreement elsewhere.
GEOS1’s anomalies agree the worst with UWM; NCEP1’s anomalies tend to agree the best.

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11.4 Reanalysis products(11.4.14 Precipitation)

NCEP2’s annual cycle is slightly better correlated with UWM than NCEP1; its anomalies areslightly worse.
Zonal mean RMS differences between the three reanalyses’ meridional surface stressand UWM are shown in Figure 11.4.34(a) for the full monthly means and in Figure 11.4.34(b)for the monthly mean anomalies. Even though GEOS1 displays the least variability of the four
estimates, ERA15’s annual cycle is the least different from UWM. The anomalies in GEOS1
are the least different from UWM in high latitudes and have the lowest month-to-monthvariability. NCEP2 tends to show the largest differences from UWM in high latitudes and has
the largest month-to-month variability. GEOS1 has the largest normalised RMS differencesfrom UWM between 20 and 60N; ERA15 has the least.

Figure 11.4.34(a) RMS
differences in monthly mean
meridional surface stress over
the oceans between thereanalyses and UWM for 198192 in Nm-2.

Figure 11.4.34(b) As (a) but for
anomalies from each estimate's
climatological annual cycle in
Nm-2.

11.4.14 Precipitation
Stendel and Arpe (1997) evaluated the hydrological cycle in the ERA15, NCEP1 and
GEOS1 reanalyses. Trenberth and Guillemot (1998) examined atmospheric moisture and thehydrological cycle in the NCEP1 reanalysis and compared NCEP1 precipitation to CMAP
precipitation. Janowiak et al. (1998) compared GPCP precipitation with precipitation from theNCEP1 reanalyses. Arpe et al. (2000) examined the hydrological cycle in the reanalyses andconcluded, "There is not one reanalysis which is superior in all respects and a new evaluation of
the reanalyses has to be made before each application."
Table 1 compares mean precipitation estimates averaged over different oceanic regions
from the reanalyses with two independent estimates by Xie and Arkin (1996, 1997) (CMAP)
and by the Global Precipitation Climatology Project (GPCP) (World Climate Research
Programme, 1990; Huffman et al., 1997). The two independent estimates are based on satellite
estimates of precipitation over the ocean. In mid-latitudes ERA15 and NCEP2 agree the best

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11.4 Reanalysis products(11.4.14 Precipitation)with the independent estimates; NCEP1 and GEOS1 are less. In the tropics and over the whole
region between 50S and 45N NCEP2 and ERA15 exceed the independent estimates, while
NCEP1 and GEOS1 are close to the independent estimates.
Table 1 Mean precipitation in mm/day over different regions of the ocean averaged over 198892 for the four reanalyses and two independent estimates derived from satellites over the ocean.
50S-45N

20-50S

20S-20N

20-45N

CMAP

3.33

2.55

4.13

2.75

GPCP

3.1

2.64

3.54

2.84

ERA15

3.79

2.47

5.13

2.8

GEOS1

3.05

1.88

4.2

2.29

NCEP1

3.15

2.13

4.12

2.54

NCEP2

3.7

2.64

4.8

2.84

Figure 11.4.35 Zonal mean
precipitation over the oceans
averaged over 1988-92 from thereanalyses, CMAP (Xie-Arkin)
and GPCP in mm/day.

Figure 11.4.35 compares zonal mean precipitation over the ocean from the fourreanalyses with CMAP and GPCP for the years 1988-92. Over the ocean ERA15 and NCEP2
clearly have the most rainfall in the tropics, where the two independent estimates disagree witheach other by as much as 1 mm/day. GEOS1 lies between the two independent estimates while
NCEP1 agrees with the lower independent estimate (GPCP). CMAP uses similar data sets to
GPCP; however, CMAP uses rainfall measurements from tropical atolls to calibrate satellite
rainfall estimates over the tropical oceans and GPCP does not. The mid-latitude maxima inprecipitation are further poleward in the reanalyses than in the satellite-based estimate. NCEP2
has more precipitation in mid-latitudes than the other reanalyses.
Figure 11.4.36 compares the standard deviation of rainfall from the three reanalyses andfrom CMAP over the ocean for the full monthly means (Figure 11.4.36(a)), the annual cycle
averaged over 1988-92 (Figure 11.4.36(b)) and monthly mean anomalies for 1988-92 from theannual cycle (Figure 11.4.36(c)). The results suggest that the ERA15 and NCEP2 reanalyses
overestimate variability and NCEP1 and GEOS1 underestimate variability over the tropicaloceans. All four reanalyses and GPCP have larger precipitation anomalies at higher latitudes
than CMAP. NCEP2 has the strongest variability in mid-latitudes.

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11.4 Reanalysis products(11.4.14 Precipitation)

Figure 11.4.36(a) Zonal mean
standard deviation of monthly
mean precipitation over theoceans for 1988-92 in mm/day.

Figure 11.4.36(b) As (a) but formonthly mean climatology
averaged over 1988-92.

Figure 11.4.36(c) As (a) but formonthly anomalies for 1988-92
from the monthly mean
climatology.

Figure 11.4.37(a) shows the five-year mean precipitation estimate from CMAP. GPCP
(Figure 11.4.37(b)) has less rain in the tropics and more near 45o in both hemispheres. The two
independent estimates based on satellite estimates differ by more than 1 mm/day over much of
the tropical ocean. ERA15 (Figure 11.4.37(c)) has more intense tropical convergence zones
than CMAP, less precipitation near 30S and 45N and more near 60N and 60S. GEOS1 (Figure
11.4.37(d)) shows less rain in the east Pacific and Atlantic ITCZ and in the South Pacific andSouth Atlantic convergence zones, but more precipitation than CMAP north of the equator in
the western Pacific and Caribbean. It has the least precipitation over the northern mid-latitude
storm tracks. NCEP1 (Figure 11.4.37(e)) also has weaker convergence zones than CMAP.
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11.4 Reanalysis products(11.4.14 Precipitation)

Figure11.4.37(a)Figure11.4.37(b)Figure11.4.37(c)Report of the Working Group on Air Sea Fluxes

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11.4 Reanalysis products(11.4.14 Precipitation)

Figure11.4.37(d)Figure11.4.37(e)Figure11.4.37(f)Report of the Working Group on Air Sea Fluxes

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11.4 Reanalysis products(11.4.14 Precipitation)

Figure 11.4.37: (a) Mean precipitation from CMAP (Xie-Arkin) for 1988-92. Contour interval
1 mm/day. Values less than 1 mm/day shaded.
(b) Difference in precipitation GPCP minus CMAP. Contour interval 0.5mm/day, zero omitted, negative values shaded.
(c) Difference in precipitation ERA15 minus CMAP. Contour interval 1
mm/month, zero omitted, negative values shaded.
(d) As (c) but for GEOS1 minus CMAP.
(e) As (c) but for NCEP1 minus CMAP.
(f) As (c) but for NCEP2 minus CMAP.
NCEP1 (Figure 11.4.37(e)) and NCEP2 (Figure 11.4.37(f)) have more rain than CMAP off
northeast Brazil and in the east Pacific dry zone between the ITCZ and the South Pacificconvergence zone. In all the reanalyses the South Pacific Convergence Zone tends to have aneast-west orientation rather than a northwest-southeast orientation. NCEP2 clearly has more
rainfall over the tropical oceans than NCEP1 and the independent estimates. ERA15, GEOS1
and NCEP1 have less rain than CMAP in the mid-latitude storm tracks, as can be seen to theeast of Japan and the United States, and more precipitation further poleward.
Zonal mean correlations of oceanic precipitation estimated by CMAP with GPCP and
the ERA15, GEOS1 and NCEP1 reanalyses are shown in Figure 11.4.38(a) for the annual cycle
averaged over 1988-92 and in Figure 11.4.38(b) for anomalies from the annual cycle. The
regional patterns of correlations in the reanalyses are similar to each other. Low correlations
appear over the east Pacific and Atlantic ITCZ's and other convective regions. The reanalyses
also show high correlations along the equator in the Pacific and lower correlations to the northand south. Over the oceans the reanalyses all show high correlations near 30o and low
correlations at higher latitudes in both hemispheres. ERA15 has the highest correlations, while
GEOS1 is somewhat lower than NCEP1 over the ocean. NCEP2 is better correlated with CMAP
estimates than NCEP1 in mid-latitudes, especially for the climatological annual cycle.

Figure 11.4.38(a) Zonal mean
correlation of climatologicalprecipitation over the oceans forthe reanalyses and GPCP with
CMAP (Xie-Arkin) for themonthly mean annual cycle
averaged over 1988-92.

Figure 11.4.38(b) As (a) but formonthly anomalies for 1988-92
from the climatological annual
cycle.

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11.4 Reanalysis products(11.4.14 Precipitation)

Precipitation in CMAP and GPCP is derived from raingauges over land and by
empirical techniques from satellite observations over the oceans. The regional pattern ofcorrelation of precipitation anomalies in GPCP and in CMAP is similar to the regional patternof correlation between CMAP and reanalysis precipitation anomalies. (However, thecorrelations between GPCP and CMAP exceed 0.80 in most regions and are considerably higher
than the correlations between the reanalysis and CMAP.) This similarity of pattern suggeststhat a secondary part of the spatial variation in correlation between the reanalyses and CMAP,
such as lower correlations at higher latitudes, may reflect uncertainty in the CMAP estimate ofprecipitation.
RMS differences from the monthly means of CMAP are shown in Figure 11.4.39(a) forthe monthly means for 1988-92, in Figure 11.4.39(b) for the annual cycle averaged over 198892, and in Figure 11.4.39(c) for the anomalies from the annual cycle. Of the reanalyses, ERA15
has the lowest RMS difference over the extratropical oceans. NCEP2 and ERA15 have thelargest RMS difference over the tropics where they display considerably more month-to-monthvariability than CMAP or the other reanalyses. NCEP2 tends to have the largest RMS
difference from CMAP outside the tropics, reflecting greater month-to-month variability inprecipitation than other reanalyses and CMAP. GEOS1 tends to have the largest normalised
RMS difference from CMAP and ERA15 the least. NCEP2’s normalised RMS difference from
CMAP is significantly less than NCEP1’s.
Also shown in Figures 11.4.38 and 11.4.39 are correlations and RMS differences
between monthly means for GPCP and for CMAP, as a measure of the uncertainty in CMAP
estimates of precipitation. Since GPCP and CMAP use similar methods of estimating
precipitation, they should be regarded as a lower estimate of the uncertainty in CMAP.
Correlations between CMAP and GPCP anomalies are considerably higher than the correlations
between CMAP and the reanalyses, but are lower over the extra-tropical oceans than in thetropics. RMS differences between GPCP and CMAP anomalies are as large in higher latitudes
as the RMS differences between CMAP and reanalysis anomalies and are not negligible in thetropics. Satellite estimates of precipitation are often developed and "tuned" for the tropicaloceans. In addition certain satellite estimates use the cloud-top temperature as a proxy for
rainfall rate; this method does not work well far from the equator. Thus satellite estimates may
not be as applicable to the extra-tropical oceans as they are to the tropical oceans. This suggeststhat significant uncertainties remain in the independent estimates of precipitation, perhaps more
so in the magnitude than in the pattern of precipitation, and that a secondary part of thedifferences between the reanalyses' precipitation and satellite estimates of precipitation reflects
uncertainty in the satellite estimates over the oceans.

Figure 11.4.39(a) RMS
differences in monthly mean
precipitation over the oceans
between the reanalyses and
GPCP and CMAP for 1988-92
in mm/month.

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11.4 Reanalysis products(11.4.15 Conclusions)Figure 11.4.39(b) As (a) but forclimatological monthly means
averaged over 1988-92.

Figure 11.4.39(c) As (a) but for
anomalies for 1988-92 from theclimatology.

11.4.15 Conclusions
An enormous effort has gone into the careful calculation of surface fluxes from COADS
data. The best COADS estimates of surface fluxes, however, are still far from a global meanenergy balance (Josey et al., 1999) and it is not at all clear that the imbalance is solely due to thescarcity of ship observations over large regions of the oceans. The adjustments commonly
made to achieve balance include a large reduction in surface net short wave radiation. The
original UWM NSW actually agrees well with the satellite-based SRB estimate; however, theSRB estimate may have a positive bias. The SRB satellite estimate of NLW also appears
questionable. The global mean SRB NLW appears to be quite different in magnitude than otherestimates and the net surface radiation resulting from the SRB NSW and NLW implies a needfor considerably larger sensible and latent heat fluxes than the estimates examined here. This
study has shown significant time-mean differences between the reanalyses and independent
estimates and has not resolved which is correct.
Surface fluxes from the NCEP1, NCEP2 and ERA15 reanalyses generally show very
similar levels of agreement with UWM and SRB estimates; in many cases fluxes from GEOS1
show lower correlations with independent estimates. The annual cycle in ERA15 tends to agreeslightly better with independent estimates than NCEP1's annual cycle; NCEP1's anomalies
agree slightly more in general with independent estimates than ERA15's anomalies. NCEP1
appears to underestimate variability over the tropical oceans; NCEP2 and ERA15 appear tooverestimate variability in the tropics. NCEP2 has more upward latent heat flux and lessupward sensible heat flux than NCEP1; this may reflect a change in the boundary layer
formulation. NCEP2’s sensible heat flux is less than the other estimates. NCEP2 appears tohave fewer stratus clouds and worse short wave radiation in the eastern subtropical oceans than
NCEP1, but corrects two problems in NCEP1: too much downward short wave radiation at thesurface and too high ocean surface albedo. NCEP2 tends to have the largest month-to-month
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11.4 Reanalysis products(11.4.15 Conclusions)variability of the reanalyses; GEOS1 tends to have the least.
The climatologies from the reanalyses show good agreement with the UWM
climatology in the mid-latitudes of both hemispheres; flux anomalies from the reanalyses show
good agreement with UWM anomalies in the Northern Hemisphere mid-latitudes. The results
suggest that UWM is useful for defining month to month variability in fluxes in the northern
mid-latitudes and a few other regions but may be useful in defining climatological fields in theSouthern Hemisphere north of 40S.
The reanalyses appear to have large biases in surface NSW. GEOS1 in particularappears to have quite different surface radiation than the other estimates examined here. Thissuggests that cloudiness needs to be improved in the reanalyses. Whether these biases reflect
primarily problems in the parameterisation of cloudiness or problems in the parameterisation of
the hydrological cycle is not clear. A major problem in the reanalyses appears to be thetreatment of low-level stratus clouds, particularly in the ERA15, GEOS1 and NCEP2
reanalyses.
Comparisons with in-situ measurements have revealed differences in sensible and latent
heat flux due to differences in flux algorithms; the NCEP algorithm is deficient at high windspeeds where it yields too much latent heat flux. This problem has been corrected in theoperational NCEP model, but not the NCEP2 reanalysis. While the ERA15 algorithm appears
to perform better over the Labrador Sea, it and NCEP2 gives even more evaporation globallythan NCEP1 and display no more agreement in general with UWM estimates. It should be
noted that fluxes in NWP analysis/forecast systems reflect the entire physics package and theanalysed near-surface meteorological fields and not just the flux algorithms. One major
question to which the answer is not clear is the magnitude of the hydrological cycle.
Surface stresses from the NCEP1 and ERA15 reanalyses generally show very similarlevels of agreement with da Silva et al. (1994) COADS-based surface stresses; GEOS1 stresses
show slightly less agreement with UWM. The annual cycle in ERA15 agrees slightly betterwith UWM than the NCEP1 annual cycle; the NCEP1 anomalies agree slightly more withCOADS than the ERA15 anomalies. Time-mean stresses from NCEP2 show less difference
from COADS than do NCEP1 stresses. NCEP2's stronger tropical surface stress appears to be
more realistic than NCEP1, perhaps reflecting stronger tropical precipitation than in NCEP1.
The stronger tropical precipitation in NCEP2 may not be more realistic in magnitude than
NCEP1.
The reanalyses appear to have large biases in precipitation. ERA15 appears to be
somewhat better in depicting monthly precipitation patterns than NCEP1 or GEOS1; NCEP1
may be somewhat better than GEOS1 over the oceans. NCEP2 appears more correlated than
NCEP1 with independent estimates outside the tropics and has less normalised RMS difference
from CMAP in the tropics as well as mid-latitudes. ERA15 and NCEP2 exceed the independent
estimates over the tropical oceans. NCEP1 underestimates variability over the tropical oceans;
ERA15 overestimates variability in the tropics and NCEP2 appears to overestimate it nearly
everywhere.
A major uncertainty is the true magnitude of precipitation over the ocean. Smith et al.
(1999) found the NCEP1 reanalysis evaporation exceeded estimates derived from shipobservations using the Smith (1988) algorithm by 20 Wm-2. (Both NCEP2 and ERA15 havemore oceanic evaporation and more precipitation than NCEP1.) This implies that global
precipitation from the NCEP1 reanalysis should also be too high; comparisons with independent
estimates shown above suggest that NCEP1 precipitation is not too high over the oceans but that
NCEP2 and ERA are. Comparisons with CMAP and GPCP over land suggest that all thereanalyses are too high over land by 0.43 to 0.7 mm/day between 50S and 45N and by 0.8 to1.19 mm/day between 20S and 20N. This suggests that too high evaporation over the ocean in
the NCEP1 reanalysis (and other reanalyses as well) may be associated with too much
precipitation over land. In that case the excessive evaporation could be due to errors in the
NWP systems’ parameterisation of convection as well as errors in their parameterisation of
evaporation over the oceans.

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11.5 Operational Models
11.6 Sea-Ice areas

11.5 Flux Products from Operational NWP Model's
Because operational NWP models are constantly evolving, the Working Group on AirSea Fluxes decided that flux products from these models could not be evaluated for this report.
Rather they requested the Working Group on Numerical Modelling (WGNE) to consider thisproblem. Here we summarise the present position.
The increasing interest in seasonal forecasting has resulted in a much greater need for
improved surface fluxes. Additionally, analysis and reanalysis offer some of the best hope for
improved estimates of large-scale fluxes. The WGNE has identified the need for an
infrastructure to assist NWP centres in the evaluation and intercomparison of analysed surface
fluxes. Previous studies (e.g., White, 1995) have studied ocean surface fluxes from single
versions of several operational models. NWP fluxes have been found to be within the range ofdifferences found with COADS-type climatologies, although cloudiness has been identified asa major source of uncertainty. Significant differences from each other and satellite estimates in
net surface short wave and OLR suggest important variations in cloud-radiative effects. Whilevaluable, these "snapshot" studies have not enabled operational centres to monitor how derived
fluxes vary with their evolving systems.
In response to the urging of the WGNE, Peter Gleckler (PCMDI) and Jan Polcher(LMD) have agreed to reinvigorate the study of NWP surface fluxes. There are several
components to the planned study, the first being a reworking of White (1995) with recent
operational data. The project extends previous efforts in two ways. First, fluxes over ocean
and land are to be evaluated. Although different in many ways (e.g. the types of observational
data available), the study of ocean and land surface fluxes will be coordinated to optimise theefforts involved. The second difference from earlier efforts, and more ambitious aspect of thisproject, amounts to a pilot study. The objective of this exploratory initiative is to examine thefeasibility of developing an operational system to evaluate and intercompare NWP surface
fluxes.
Following the data and metadata standards established for AMIP, centres have begun tocontribute 6 hourly fluxes for 1999 at different spin-up intervals. An update to the White(1995) study is expected within one year, and by the 16th Session of the WGNE (Nov 2000)
experience should be sufficient to evaluate the usefulness and practicality of establishing an
operational flux evaluation system.
Examples from the AMIP2 comparison for the period 1998 - 1999 are shown in Figures
11.5.1 and 11.5.2. For latent heat flux (Figure 11.5.1) the results from the operational models
tended to have a similar magnitude or be greater than the ERA15 reanalysis. In comparison the
NCEP1 latent heat fluxes were somewhat less and the unadjusted COADS based fluxes,
significantly less. In contrast for the net surface SW flux (Figure 11.5.2) the reanalyses haverelatively low values and the COADS based estimates are similar to or less than the satellite
estimates. The operational models span the whole range of the other estimates. However the
majority are similar to or even greater than the satellite based estimates.
11.6 Evaluation of Fluxes in Sea-Ice Covered Areas
11.6.1 Cloud and surface radiative flux data sets in the Arctic
A review of downwelling radiative fluxes from models, satellites, and reanalyses shows
large discrepancies among the various estimates of over 50 Wm-2 in monthly means and over
30 Wm-2 for 5-year means in reanalysis flux fields (Rothrock and Zhang, 1997). Large
discrepancies are traceable to different estimates of the cloud fraction, shown in Figure 11.6.1.
The ISCCP satellite data set show an annual cycle out of phase with the Huschke cloud
climatology, which shows the accepted higher cloud fractions in summer. The two numerical
weather prediction models show little annual cycle; NCEP is uniformly low and ECMWF is
uniformly high. Five-year mean cloud amounts vary over a huge range from a low of 44 % forReport of the Working Group on Air Sea Fluxes

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11.6 Sea-Ice areas

NCEP reanalysis to 70 % for ECMWF forecast data. A similar range is seen in 5-year monthly
means.
g

(

)

200

Watts/m**2

150

100

AMIP Models
VOS (SOC)
50

VOS (da Silva)
RNL (ERA)RNL (NCEP)

0
90N 60N 45N

30N

EQ

30S

45S 60S 90S

Latitude (area weighted)

Figure 11.5.1 Zonal averages of the Global Ocean latent heat flux from reanalyses, observations(COADS) and AMIP2 (1998-99)
300

250

Watts/m**2200
AMIP Models
SAT (Pinker)

150

SAT (Staylor)
SAT (Li)100

VOS (SOC)
VOS (da Silva)

50

RNL (ERA)RNL (NCEP)

0
90N 60N 45N

30N

EQ

30S

45S 60S 90S

Latitude (area weighted)

Figure 11.5.2 Zonal averages of the Global Ocean net SW flux from reanalyses, observations(COADS and satellites) and AMIP2 (1998-99)
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11.6 Sea-Ice areas

Figure 11.6.1 Cloud fraction in percent from four data sources. Huschke’s is a spatially uniform
climatology; the others have been averaged over the Arctic Ocean and over the five years from1986-1990.

The mean annual cycle of downward shortwave and longwave for five data sets isshown in Figure 11.6.2. We tend to look on the observations of Marshunova as the best
estimate; the annual average downward shortwave from Marshunova is 100 Wm-2. The extreme
estimates of downward shortwave are from the two numerical weather prediction analyses:
NCEP is high in every month (because of its low cloud fraction), with an annual average of 128
Wm -2, and ECMWF is low through May, with an annual average of 80 Wm-2. The ISCCP
satellite-derived data set is in good agreement with Marshunova's climatology. Althoughsatellite-derived data sets have low cloud amounts in summer, they have compensating highcloud optical depth and low surface albedo that tend to keep the downward shortwave frombeing overestimated.
The annual average downward longwave from Marshunova is 224 W m-2 (Figure11.6.2b). Other means vary from 198 for NCEP to 231 for the PW parametric estimates. Mostestimates are above those of Marshunova by 10 to 20 W m-2 in winter and below by 20 W m-2 insummer and fall. The low summer cloud amounts in the ISCCP data show up here as low
estimates of summer downward longwave.
Biases of 10 W m-2 in downwelling fluxes correspond to rather large errors (10 to 30%)
in ice thickness. The large discrepancies among radiative flux data sets should be understood
better before choosing any of them as the “standard” against which to test or with which to force
sea ice or climate models.
11.6.2 Precipitation in the Arctic
Two recent studies have examined the quality of both the precipitation and evaporation
fields and the atmospheric flux convergence (P-E) in NCEP and ERA reanalyses (Cullather andBromwich., 2000; Serreze and Hurst, 2000). In the central Arctic (north of 75˚N), the spatial
patterns of precipitation are in rough agreement with observations, although both data sets tend
to underestimate precipitation over the Atlantic side. The mean annual values of precipitation
are overestimated by NCEP and underestimated by ERA, each by about 20%. NCEP
precipitation has its maximum a month earlier than the observed August peak, whereas ERA has
its maximum a month after the observed peak. NCEP fields have spectral noise that has been
removed in later versions (see note at proof in Cullather et al., 1999).

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11.6 Sea-Ice areas
11.7 Waves

11.6.3 Need for more assessment
Broader reviews of the polar surface flux literature and further intercomparisons andevaluations of flux and flux-related data sets, many of which are quite new, are sorely needed,
especially for the Antarctic.

/SK

/SK

Figure 11.6.2a (top) Downwelling shortwave fluxes from five data sources. These are five-year
monthly means for the Arctic Ocean.
(b ) As (a) but for the downwelling longwave flux.

11.7 Evaluation of Waves
11.7.1 Visual wave observations
Evaluation of different wave products have been performed on the basis ofintercomparison with in situ measurements at buoys and the other in situ platforms, withobservations at OWS's (which are expected to have somewhat better accuracy compared toroutine visual estimates from merchant ships), and by intercomparison of different products one
to each other.
Visual VOS observations may be biased due to the poor separation of sea and swell inthe VOS reports. Some young swells can be reported as seas, and it is also possible thatobservations of developed seas propagate into the swell domain. Gulev and Hasse (1999)
checked joint probability distributions of visual wave height and wind speed against JONSWAP
curves for known wind durations and found that at least the wind sea was well defined in visual
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11.7 Waves

observations. Estimation of the observational accuracy of visual VOS wave observation (Gulev
and Hasse 1999) shows that the day minus night difference in visual wave estimates is not aslarge as in wind observations. In the North Atlantic it lies within the range several centimetresto 0.2 m and does not have any pronounced spatial pattern.
The main question for evaluation of wave heights obtained from VOS, is a way ofdetermining significant wave height from the separately reported sea and swell components.
Wilkerson and Earle (1990) intercompared visual wave observations at merchant ships withmeasurements at NDBC buoys in the subtropical Atlantic, tropical and mid-latitudinal Pacific.
They selected visual observations within a radius of 25 to 100 km around the buoys location andfound that least biased estimate of significant wave height (Hs) resulted from taking the higherof the sea and swell heights. They also found a random observational error of several tens
centimetres in significant wave height, and large errors for the period estimates. Gulev andHasse (1998) intercompared visual wave observations with measurements at buoys, OWS L,
and Seven Stones Light Vessel in the North Atlantic. They found that an estimate of H scomputed following Hogben (1988):

HS = (hw2 + hs2 )1 / 2(11.7.1)

where hw and h s are wind sea and swell heights respectively, over estimated the observed Hs in
the majority of cases at each site by several tens of centimetres (mean bias of 0.27m). However,there has been a tendency for frequent under estimation especially in mid latitudes of using theHs estimate of Wilkerson and Earle (1990) - the higher of the two estimates. The best method
was found to be a combined estimate, similar to that recommended by Barratt (1991).
Equation (11.7.1) is used when sea and swell are within the same 30° directional sector (Barratt
had suggested 45˚), in all other cases the higher of the two components is used. This combined
estimate gave mean "buoy minus VOS" differences of -0.03m and -0.07m respectively.
Visual estimates of wave periods were found to be systematically underestimated in
VOS observations. The peak of the wave period distribution may be 1 or 2 seconds less for
VOS observations compared to instruments. However the mean difference for the wave periods
is somewhat less. Wilkerson and Earle (1990) reported about 0.2 s buoy minus VOS
differences. Gulev and Hasse (1998) found a mean departure of about 0.26 s with 0.1 to 0.6 s
standard deviation. Dacunha et al. (1984) and Hogben (1988) reported larger systematic biases
for wave periods for the Cobb seamount in the North Pacific. In order to correct these biases
several methods were developed. Ochi (1978) and Dacunha et al. (1984) recommended to fit
joint probability distributions of wave heights and periods to the observed ones, giving thepossibility to obtain corrected mean periods. Gulev and Hasse (1998) developed a method forthe correction of individual observations of periods which has an accuracy of 0.12 s.
11.7.2 Altimeter wave data
Extensive evaluation of altimeter measurements has been undertaken by Cotton andCarter (1994b) who used NDBC buoy measurements to calibrate altimeter wave height
estimates, which then were used by Sterl et al. (1998) to assess the quality of their WAM wave
model. They found that in general high waves were under, and low waves overestimated. For
the periods of overlap of buoy measurements with the altimeter measurements, Cotton andCarter (1994b) applied regression analysis which helped to remove biases between the three Kuband altimeters of GEOSAT, TOPEX/POSEIDON and ERS-1/2. Thus it was possible to merge
the three altimetric products and to create a data set with continuity over about 12 years. Laing
and Reid (1999) performed an evaluation of altimetric significant wave height in the South
Pacific using a limited collection of visual data around New Zealand. They found that H sderived using (11.7.1) under-predicted altimeter measurements by approximately 0.2 m for all
ranges. It is unclear whether this bias has a regional nature or results from the considerable
undersampling by the VOS observations in the South Pacific.
A pilot study by Gulev et al. (1998) performed a triple intercomparison of significant
wave height derived from visual observations, the WAM model, and altimeters. Climatological
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11.7 Waves
11.8 Summary

spatial patterns of significant wave height in all three products were found to be consistent,
although the differences between the VOS wave data, altimeter measurements and the model
hindcast were not negligible; the nature of the biases still has to be precisely studied. The
smallest "VOS minus WAM" and "VOS minus altimeter" differences were found in midlatitudes, where the differences were of opposite sign: VOS waves overestimated the model
hindcast by 0.1 to 0.3 m and underestimated the altimeter Hs by approximately 0.1-0.2 m. In
the tropics, where the waves and winds are smaller, VOS wave height was higher than thosetaken from WAM, or the altimeter, by 0.3 to 0.7 m. An alternative interpretation is that the
WAM H s and the altimeter data fit better to each other in low latitudes, and show the largest
disagreement in mid latitudes where the "altimeter minus WAM" biases are from 0.3 to 0.5 m.
Of the three sets of comparisons, the VOS and altimeter SWH show least scatter, whilst thelargest scatter is obtained for the "VOS-WAM" comparison.
Two long-term 40 year wave hindcasts of OceanWeather Inc. (Cox and Swail 1999)
were evaluated using in-situ and altimeter measurements (Cox et al. 1999). In-situ comparison
was based on the NDBC buoy 44138 measurements and a number of platform measurements.
This comparison showed that the highest waves are usually under-predicted in the model
simulations, and alternatively, smaller waves tended to be over-predicted. Comparison withsatellites was done for four regions: Southern Hemisphere, Northern Hemisphere, Tropics, andGlobal. Charts of differences show pronounced overestimation of hindcasted waves in midlatitudes, and slight underestimation in the tropics. The best agreement has been found in the
North Atlantic Ocean.
11.8 Summary
Here we briefly review some this Chapter's results for the heat flux fields to illustrate
the difficulty of determining the "best" product. Figure 11.8.1 summarises results for the global
heat balance that were also presented in Table 11.4.1. For each flux component, and for the net
flux, the difference is shown between the fluxes from the various analyses (ERA15, GEOS1,
NCEP1, and NCEP2) and the UWM/COADS climatology. Figure 11.8.1a shows thecomparison with the unconstrained UWM product and Figure 11.8.2b shows the difference fromthe tuned values of da Silva et al. (1994). On the latter plot the net heat flux values displayed
assume that, for the period considered, the tuned UWM/COADS product had an exact heatbalance. It is clear the GEOS1 product is far from a global balance and inspection of Figure
11.8.1 in general shows this product to be an "outlier" so this product can be assumed to beincorrect with regard to the surface fluxes. The other reanalysis products (and the tuned UWM
fluxes, which are not shown) all have a global balance within 10 Wm-2.
However, achieving global balance does not guarantee that the regional variation of thefluxes, or the partitioning between components will be similar from one data set to another.
Thus Figure 11.8.2 shows the net heat flux for the VOS estimates, reanalysis, and the residual
calculation described in Section 11.3.5. The two reanalyses shown at the top of the figure areclearly more similar one to another than the two COADS based flux fields (middle plots).
However, this is mainly due to the fact that the COADS products have not been adjusted for the
global mean heat imbalance, which for both exceed 20 Wm-2. The lower left hand "tuned"estimate of da Silva is more similar to the reanalysis than either uncorrected VOS estimate.
Despite the noisiness of the residual method plot, it also shows similarities to the otherplots particularly in regions of excessive heating or cooling such as the western boundary
currents, eastern Indian Ocean and the western tropical Pacific. However more carefulinspection shows that there are significant differences between the different data sets shown in
Figure 11.8.2. These are particularly marked in the Indian Ocean. For example the Residual
method suggests ocean heating north of Madagascar of a magnitude more similar to the untuned
COADS products than to the tuned product; this is not shown by the reanalyses. On the other
hand the ocean cooling west of Australia is more similar to the tuned COADS fluxes. This
cooling is seen in the NCEP reanalysis but is displaced westward in the ERA results. Note that
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11.8 Summary

the time period differs for each of these climatologies, but this unlikely to have been a major
cause of the large systematic differences which are evident.
That there should be regional variations in the differences between the various net heat
flux estimates is not surprising given the likely biases in the individual flux components. If
these biases could be understood and corrected the need to tune the COADS would be removed.
However we presently do not have that understanding. For example almost all schemes to"correct" the COADS data require the net surface SW to be decreased. However satellite
estimates of net SW show more ocean heating compared to the untuned COADS values, not less
(Figure 11.8.3). It is possible that this is caused by a bias in the satellite derived values.
However, tuning the COADS values for net SW results in greater disagreement with bothsatellite and reanalysis estimates (compare Figure 11.8.1 (a) and (b)).
40

more ocean cooling

Difference(W/m2)

30
20
10
0
-10
-20
-30

ERA15
GEOS1
NCEP1
NCEP2
SRB

more ocean heating

-40
sen

lat

SW

LW

Figure 11.8.1 a. Global meandifference between different
flux data sets and theunadjusted UWM/COADS
climatology for each flux
component and for the net flux.
The SRB data are satellite
estimates of net SW by Darnell
et al. (1992) and net LW by
Gupta et al. (1992) averaged
over July 1983-June 1991.

Qnet

Flux
20
more ocean cooling

Difference(W/m2)

100-10Figure 11.8.1 b. As (a) but for
the global mean difference
from the tuned UWM/COADS
climatology. For the net flux
the global balance is shown..

-20
-30

ERA15
GEOS1
NCEP1
NCEP2
SRB

-40
-50

more ocean heating

-60
sen

lat

SW

LW

Qnet

Flux

Tuning also increases the latent heat (or evaporative) flux (Figure 11.8.4). The untuned
VOS estimates (SOC and da Silva) compare qualitatively quite well, as do the climatologies
derived from reanalyses. However, in the sub tropical western ocean basins, there is much more
evaporation in the reanalysis than in the VOS estimates. Thus it might seem that there is scopefor increasing the VOS flux values. However it would be difficult to justify a large enoughadjustment to the latent flux to balance the budget if, on the basis of the satellite data, the net
SW flux were not adjusted. Figure 11.8.4 also shows two other flux maps. The ERA 0 to 6
hour map illustrates (in comparison to the 12 to 24 hour map) how the spin up process in theearly forecast hours affects the hydrological cycle. The satellite derived estimate (HOAPS) falls
into yet another category. In the tropics this estimate is significantly lower than either the VOSor reanalyses, but elsewhere they are all comparable in magnitude - although the patterns vary.
It would appear that further verification and development of the satellite product is required,
particularly in the tropics.
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11.8 Summary

Figure 11.8.2 The annual mean climatology of six estimates of surface net heat flux. Shownare values from the NCEP reanalysis (top left), the ERA15 reanalysis (top right), the untuned
UWM/COADS climatology (middle left), the SOC climatology (middle right), the tuned
UWM/COADS climatology (bottom left), and the Residual method (Trenberth and Solomon,
1994) (bottom right).
In summary, while there are encouraging similarities between the various estimates
there are also large differences. The discrepancies are often most obvious in the low latitudes
but they are not restricted to those regions. Winter/summer maps of either the latent heat or
surface net shortwave clarify that the differences are relative, i.e., wherever the fluxes are large,
so are the differences with the other flux estimates. This is indicative of relative systematic
errors. The maps certainly do not tell the whole tale of the challenges associated with ocean
surface flux estimates. But they do give a glimpse of the difficulty. Each class of estimates has
its own strengths and weaknesses, and the errors associated with them are complex andReport of the Working Group on Air Sea Fluxes

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11.8 Summary

extremely difficult to quantify. Clearly, further analysis is needed in this area before we can get
a better grasp of the relative quality of the estimates.

Figure 11.8.3 The annual mean climatology of six estimates of surface net SW flux. Shown arevalues from the SOC climatology (top left), the untuned UWM/COADS climatology (top right),
the Pinker satellite algorithm (middle left), the Staylor satellite algorithm (middle right), theNCEP reanalysis (bottom left), and the ERA15 reanalysis (bottom right).

Finally the limited evaluation presented here allows the relative merits of the COADS,
model, and satellite products to be considered. COADS based climatologies have given us
much information about the marine climate. However comparison with the model data suggests
that, as might be expected, such products perform best where there is adequate sampling. Thus
the COADS climatologies can define both the mean climate and interannual variability ofmonthly means in regions such as the North Atlantic but, at best, can only describe the mean
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11.8 Summary

climatology for Southern Hemisphere regions. For the Southern Ocean in winter they may bebadly biased. To obtain a global heat balance they must be tuned. Flux fields from the

Figure 11.8.4 The annual mean climatology of six estimates of surface latent heat flux. Shownare values from the SOC climatology (top left), the untuned UWM/COADS climatology (topright), the 12 to 24 hour estimate from the ERA15 reanalysis (middle left), the NCEP
reanalysis (middle right), the 0 to 6 hour estimate from the ERA15 reanalysis (bottom left),
and the HOAPS satellite product (bottom right).

reanalyses are expected to be of more unifom quality from region to region and they typicallyexhibit a smaller global heat imbalance. However comparisons with satellite fluxes, or even
the COADS fluxes, suggests that the magnitude and distribution of the radiative fluxes issignificantly in error; shortwave heating in particular. Similarly, if the comparisons with buoy
data can be trusted, the latent heat flux is over estimated in many regions.

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11.8 Summary

Finally, although the satellite surface SW flux estimates appear realistic there are still
significant differences between products and there may be an overall bias. In comparison
satellite longwave surface flux estimates are less reliable. A more direct use of the satellite flux
data is to define the top of the atmosphere fluxes for use in models or in the Residual flux
method. The latter technique offers a method of defining the surface heat flux independent of
the bulk formulae and could be used as an important constraint on fluxes produced by othermethods. With regard to the turbulent fluxes, we have evaluated one satellite based product,
HOAPS, which attempts to determine the surface latent heat flux from satellite data. While thisproduct shows promise there is clearly a need for improvement, particularly in the tropics.
In short, there is presently no one flux climatology which does not exhibit significant
errors in one region or another in each of the various flux components.

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12.1 Introduction
12.2 Summary

12. CONCLUSIONS
12.1. Introduction
Air-sea interactions play a pivotal role in regulating the Earth’s climate, and there are
now urgent requirements in climate and oceanographic research for quality estimates of large
scale air-sea fluxes of heat, momentum and fresh water. In recent years there has been
substantial progress in our knowledge of ocean surface fluxes. Research has helped us to better
understand and correct the historical record of merchant ship observations, and field
experiments have led to refined formulae for estimating fluxes. Surface fluxes derived fromsatellites are becoming increasingly promising. Weather forecasting and data assimilation
systems have been used to create a consistent record of the recent past atmospheric state fromwhich surface fluxes are derived. And yet despite these advancements, the air-sea flux problem
is extremely difficult, and in many ways remains unsolved. Whether derived from in-situ data,
satellites, or analysis systems, the deficiencies are complex and difficult to quantify. The
optimal product will depend on the application and often will not be easy to identify. The
overall thrust of this Working Group, guided by the terms of reference, has been to present a
coherent picture of the state-of-the-art of our present understanding.
12.2. Summary
The requirements for "well qualified" air-sea flux information are both wide ranging
and compelling. As discussed in Chapter 2, they include climate change and diagnostic studies;sea-ice modelling; atmospheric and oceanic synoptic-scale case studies; shelf and coastal seasstudies; oceanic, climate, and earth observing systems; biogenic cycles, etc. The diverse natureof the requirements makes considering them all impractical. Thus this report focuses on themost common requirements and considers only those products that are non real-time, largescale, readily-available to the scientific community, and documented.
One facet of the flux requirements is particularly interesting. As discussed in preceding
chapters, the absolute accuracies of air-sea flux products still do not meet the desired standards.
However, as demonstrated in Chapter 3, when one considers the requirements for exploring the
spatial and temporal characteristics of the flux variability, requirements are identified that are
new and different from those applicable to the mean, climatological fluxes. This topic of flux
variability is largely unexplored in this report, but is important for many studies and thus is alikely candidate for attention in the future.
The sources of data used to construct flux products were discussed in Chapter 4.
Broadly speaking, there are three primary sources: in situ, remotely-sensed, and model products.
Each of these sources has unique characteristics, which translate into advantages or
disadvantages for estimating fluxes. In situ data have been widely used; however, biases
traceable to the observing techniques, as well as the restricted spatial coverage, limit theirapplicability. Satellites provide global coverage and hold great promise to deliver quality data,
but they have not been able to estimate net fluxes except within very limited domains. Finally
there are model products that synthesise several sources of observations and data using shortrange forecasting systems to produce gridded fluxes. These need to be continually verified to
identify regional and systematic biases. Other techniques for flux estimation, based on residual
and assimilation approaches, have had relatively little application in the past but are expected tobe utilised more in future.
Direct measurements of the air-sea fluxes (Chapter 5) are relatively few and therefore
large scale fields cannot be constructed based solely on these measurements. However, they areimportant for developing, calibrating, and verifying the parameterisation formulae used toindirectly estimate fluxes from the basic variables. Thus the accuracy of the direct
measurements will ultimately limit the accuracy of the parameterised estimates. The quality ofthe direct measurements is related both to instrument design and to our understanding of theReport of the Working Group on Air Sea Fluxes

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12.2 Summary

underlying physics. Carefully designed experimental campaigns can increase the accuracy
achieved.
Without direct measurements, air-sea fluxes are often parameterised using formulae
based on the basic meteorological observations. Radiative fluxes are particularly difficult toparameterise (Chapter 6). The shortwave (SW) flux parameterisation has traditionally been
based on visual observations of cloud amount. Because this method is fairly imprecise, andmoreover due to the limited set of verifying direct measurements, accurate determination of SW
through this type of parameterisation will continue to present a challenge. A similar situation
exists for longwave (LW) radiation. Other approaches to parameterising radiative fluxes are
based on satellite determined top-of-the-atmosphere radiation, coupled with radiative transfer
models to bring these values to the surface. For SW the situation is promising. However forboth SW and LW, difficulties in correctly modelling the presence of clouds and aerosols limits
the accuracy. New techniques to estimate LW from combinations of satellite information may
hold promise, but they have not been fully evaluated. The range of surface albedo for the ocean
and also sea ice over a wide variety of conditions continues to be investigated.
For turbulent fluxes, parameterisations have been developed which are based largely on
measurements of basic variables such as wind speed, temperatures, and humidity (Chapter 7).
These "bulk formulae" are the most commonly used technique to estimate air-sea fluxes. The
formulae are dependent typically on wind speed measured at some height, and the difference invalues of appropriate basic variables between some height and the sea surface. There havebeen a number of bulk algorithms published in the literature. Newer bulk formulae (forexample the TOGA COARE algorithm) are improved to the extent that they incorporate
treatments of specific physical processes, such as the warm surface layer, and wind gustiness.
Many questions remain as to the accuracy and/or applicability of these formulae in regimes
where relatively few direct measurements have been made. This is particularly true ofmoderate to high wind regimes; in effect winds greater than 10 ms-1. Additionally, the role ofocean waves in modulating the turbulent flux estimates remains an open question.
In the early chapters, we considered the errors when estimating individual air-sea flux
values. In Chapter 8, we discuss limitations on the accuracy of flux fields due to the synthesisof these errors. Both theoretical approaches and practical examples of quantifying random,
systematic, and sampling errors, indicate that it is possible to estimate these cumulative errors.
Additionally, for many applications, the requirement is for gridded flux fields that are complete
both in space and over some significant time period. The non-uniform sampling of the in-situ
and remotely-sensed fluxes necessitates an analysis method to fill in the gaps. Such techniques
alter the error budgets of the flux fields. For example, depending on the particular choice of
method and averaging period, it is not unusual for sampling errors in remotely-sensed data to be
dominant compared to systematic biases. What is most important, but rarely satisfied, is for all
flux products to include error estimates. Preferably these error estimates should consider as
many contributing factors as possible. Although good error values are often difficult to
calculate, they are extremely valuable in a) establishing confidence limits; b) assessing
improvements; and c) fulfilling many of the requirements described in chapter 2.
There are a wide variety of flux products available to the scientific community. These
products span a very large range of characteristics in terms of, for example, temporal andspatial coverage, grid spacing, input data, and parameterisations. Evaluation of all suchproducts would be a very large and continuous effort. We have attempted to concentrate only
on those products with basin to global coverage, which are easily accessible to the community,
and well documented. The determination of the "best product(s)" for specific applications isvery difficult due to the problem specific characteristics. Instead we focus on objective
evaluations of the strengths and weaknesses of the selected products. For objective evaluation
we have used the following methods:❍

comparisons to reference data sets (i.e. to direct flux measurements or those from highquality sources);

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267❍

intercomparisons with similar products;❍

integral constraints of heat and water balance and transports;❍

enclosed basin budgets;❍other indirect flux estimates.

12.2 Summary
12.3 Meeting the Needs

As discussed in Chapter 9, each evaluation methodology has its own inherent advantages andlimitations.
For many of the flux estimates the accuracy is limited by the accuracy of the basic
variables that are used in the parameterisation formulae. The state of the art with regards toobserving or estimating these basic variables was considered in Chapter 10. Each variable andmeasurement method has its unique characteristics that must be carefully considered indetermining the usefulness of the data for flux estimation or for verification.
The chosen flux products were evaluated in Chapter 11 including a first detailed global
comparison of the various reanalyses. The following conclusions are based on the results of that
evaluation. The conclusions are grouped according to the requirements for the several types ofactivities which were described in Chapter 2.
12.3. Meeting the Needs
12.3.1. Budget studies
Budget studies of the ocean surface energy and water balance of the ocean are often
used as an evaluation method for the data sets rather than to derive values of the budget. On
longer time and space scales the energy budget can be regarded as balanced, at least incomparison to the uncertainties in the available estimates of net energy flux at the sea surface.
Proper evaluation of budget studies requires error estimates, both for the individual flux
components and for the net flux.
All flux products presently available (VOS-based, satellite, re-analyses) are imbalanced,
that is the fluxes of heat and moisture do not meet known geophysical constraints. Imbalancesof the net heat flux are within several tens Wm-2 . VOS - based climatologies tend tounderestimate net ocean heat loss, and this underestimation varies from 20 to 30 Wm-2. Poor
sampling, insufficient information to correct variables in the appropriate way, and uncertainties
in many parameterisations, are considered to be reasons for these imbalances. In this situation,
an assessment of the reliability of the VOS flux fields has to be considered with respect to thespecific application for which the flux product is going to be used (for example forcing ocean
models, analysis of interannual variations in sea-air fluxes, basin-scale budgets). For some
purposes, flux fields adjusted to give a balanced heat budget may be preferred; however thereis evidence that the available tuned flux fields may have significant regional errors.
Most reanalyses are much better balanced than the VOS-based flux products, they
achieve imbalances within ±10 Wm-2. Possible reasons are a more adequate description of thefluxes in poorly sampled areas (e.g. the South Pacific), and more data types being taken into
account as assimilation input. However, despite the more realistic global balances derived fromreanalyses products, individual flux components are characterised by strong biases. Thissuggests that some sort of tuning, explicit or implicit in the model formulation, may be
responsible.
For both reanalyses and VOS-based flux climatologies, the reliability of the fresh water
budgets is even worse than for the net heat flux. This reflects a lack of direct measurement ofprecipitation over the oceans. Precipitation in NWP systems is sensitive to changes in physicalparameterisations and to “spin-up”. In this sense satellite observations (e.g. SSM/I andTRMM), which already are providing evaporation minus precipitation estimates over an
extending, continuous period, are extremely valuable. To produce a freshwater budget, it isReport of the Working Group on Air Sea Fluxes

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necessary to consider not only the direct exchange between ocean and atmosphere but also theriver runoff from land surfaces.
Meridional heat and fresh water transport estimates still remain very uncertain when
computed from the VOS - based fluxes. Re-analyses flux products (which are better balanced)
provide more plausible estimates at least for MHT. Comprehensive verification of MHT and
MFWT estimates derived from surface balances against oceanographic estimates athydrographic sections should, of necessity, take into account the interannual variability of theocean interior. Time-dependent estimates of MHT and MFWT require knowledge of the longterm storage terms for the ocean, which are nevertheless smaller than the errors in flux products.
12.3.2. Climate variability studies
The variability of ocean-atmosphere fluxes on climate time scales is still poorly known.
Up to the present, the basic meteorological variables (e.g. SST, pressure, winds) have more
frequently and effectively been used for describing the coupled modes of the ocean atmospheresystem. On the one hand, the errors and uncertainties inherent in the mean climatological flux
fields, particularly those connected with parameterisation errors, do not seriously affect theaccuracy of the climate anomalies of the fluxes. On the other hand, the time dependent biases
present in various flux products create considerable problems for the analysis of variability withany confidence. These biases result from historical changes in observational techniques andsampling frequency (for the VOS data), from changes in the model configuration (foroperational models), and changes in the assimilation input (for both operational NWP andreanalysis systems). In this context, satellite data will be extremely valuable for climate change
studies once a climatologically significant length of record has been achieved. Even so, thecontinuity of the satellite data can suffer from changes in satellites and from instrument drift.
At present the SSM/I and AVHRR provide a continuity of flux time series reaching nearly 15
years, the ERS -1 and ERS-2 missions approach 10 years continuity. For sea ice, long timeseries (up to 22 years) are available from SMMR and SSM/I. All these data can be used forstudying short-period variability, for example the interannual variability of monthly meanvalues.
The quickest progress in the application of flux time series can be expected with regard
to mid-latitudinal variability on interannual to decadal time scales (associated with the NAO,
PNA and NPO patterns). This is due to the large magnitude of the anomalies to be characterised(up to 50 Wm-2), relatively high sampling frequency, and the dominance of the turbulent fluxes
in forming the net flux anomalies. Reanalyses flux products better resolve variability in poorly
sampled areas, but they have still to be verified. Climate variability in tropical atmosphereocean fluxes is largely influenced by the anomalies of the radiative fluxes and precipitation.
Both are poorly resolved in the VOS and re-analysis products; precipitation anomalies inparticular. Fortunately the weaker synoptic and meso-scale variability in the tropics reduces the
sampling requirements compared to mid latitudes. The Atlantic subtropical dipole, Pacifictropical variability, and monsoon intensity variations are the main phenomena which have to be
described in terms of surface flux anomalies. Further improvement of the short wave radiationand precipitation from future reanalyses will aid progress.
Reliability of interdecadal to secular changes derived from the ocean-atmosphere flux
fields is considerably worse than that for interannual to decadal scale variability. Beside thecrucial importance of time-dependent biases, the reliable time series of the ocean-atmosphere
fluxes is still restricted to at most several decades. The possible extension of the flux timeseries, at least for a few locations, is very important.
Empirical and model studies of oceanographic decadal variability require estimates of
the climate variability for the basin scale heat and fresh water budgets, as well as quantitative
estimates of the climate variations in MHT and MFWT. Although the uncertainty is large,
estimates for selected latitudinal belts, bounded by the pre-WOCE and WOCE sections in the
Atlantic ocean, can be made on the basis of VOS, NWP and satellite products.

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12.3.3. Climate models
The accuracy of available surface flux estimates limits their value for evaluation ofclimate models. Despite this, it is useful to compare both atmospheric and coupled oceanatmosphere models with available climatologies, because these models are known to havesubstantial systematic errors in their representation of surface fluxes. Errors in both coupled
and uncoupled climate models manifest themselves differently and independent evaluation ofthese models is complementary.
Evaluation of coupled model variability is becoming increasing important, and as notedin this report, in some respects our knowledge of variability is more accurate than estimates ofthe mean climate. Further improvements are anticipated with, for example, improvements insatellite based estimates.
Improved accuracy of mean climatologies remains the greatest need for climate model
evaluation. It is no coincidence that this is a daunting task and is central to the 'surface flux
problem' identified in this report. There are many parallels between observational and model
deficiencies as they are generally reliant on the same techniques and theory. There has beenencouraging progress with the problem of climate drift in coupled model simulations, but
further advancements may be limited by our deficient understanding of the mean surface energybalance. There is substantial model-to-model scatter in simulated basin-scale means. In somecases observational estimates help reveal serious model biases, but in others the disparity
between the climatologies is such that model errors cannot be identified.
As efforts to improve climatologies continue, additional measures can be taken toevaluate models with available products. More emphasis could be given to the basic
observables described in this report such as surface air temperature, humidity and wind speed.
This requires careful vertical interpolation, but it is nevertheless helpful given the uncertainties
in flux estimates. Additionally, as noted throughout this report, comparison of large scale flux
estimates with reference sites (e.g., 'flux buoys') is proving increasingly useful. Following thisstrategy with climate simulations could yield an important advancement in model evaluation.
12.3.4. Ocean general circulation models
The wind field produced by satellite-borne scatterometers is the component which by
far matches best the requirements for forcing ocean general circulation models. Its high spatial
and temporal resolution allows an accurate estimate of the stress vector and its curl, which isthe dominant forcing for the ocean gyres. One problem is the short length of the record, but thesituation is continuously improving. The continuity in the long term is now assured with anoperational scatterometer program being set-up jointly by ESA and EUMETSAT. NWP
forecasts also benefit from scatterometer winds, and, in turn, operational wind products from
NWP centres are challenging scatterometer winds as an alternative forcing for ocean models.
Other satellite products have a large potential for improvement and they may beconsidered the best practicable alternative to NWP products for forcing ocean circulation
models in the future. The greatest expectations concern the radiation fluxes. With theincreasing use of geo-stationary satellites in combination with orbiting platforms, the space andtime resolution could approach the important scales of oceanic turbulence (a few tens ofkilometres, and a few hours). Progress in the estimation of turbulent fluxes and precipitation isslower, and these scales do not seem attainable in the very near future for these variables,
however there are possibilities for improvements.
The long records of NCEP/NCAR and ECMWF re-analyses, their fairly high spacetime resolution, and the reasonable degree of consistency between the various components ofthe forcing (momentum, heat and freshwater), make them extremely valuable to define acomplete forcing function for ocean GCM's. The recent availability of these products is largely
responsible for the rapid development of ocean variability studies in relation to climate change
in recent times. These products are unique for that purpose, and it is essential that re-analyses
be pursued and continuously improved.
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Although covering a similar period, COADS derived flux climatologies have severe
deficiencies in the Southern Hemisphere. In such regions they can not compete with NWP reanalysed products for defining the ocean forcing. However, in particularly well sampled
regions, such as the North Atlantic and the North Pacific, they remain valuable products thatshould be used, for example, to force basin scale ocean models in studies investigating model
sensitivity to air-sea fluxes or to changes in flux parameterisations. In those regions the
COADS SW fluxes are generally superior to the present reanalysis products, and the LW andsensible heat fluxes superior to satellite based products.
It is likely that the most accurate air-sea flux estimates may come from a combinationof various products in which the contribution of NWP and satellite products will be dominant.
It remains the case that for ocean general circulation models, the consistency of fluxes fromvarious space-borne sensors will have to be verified and this will be an additional constraint thatthe combined products will have to satisfy. Acknowledging that future combined flux estimates
will have a higher accuracy, such products would appear to be the best choice for theoperational or pre-operational oceanographic systems under development (GODAE, Mercator,etc.).
12.3.5. Wave modelling
Wave models require forcing at a high spatial and temporal resolution. In particular,
the required temporal resolution of ~3 hours can at present only be met by numerical products.
While reanalysis products have the advantage of being quasi-homogenous in time, their limited
spatial and temporal resolution may be regarded as a disadvantage. However, the planned ERA40 reanalysis will include a wave model forced at the atmospheric model’s timestep, so time
resolution will not be a problem in that case.
12.3.6. Sea-ice modelling
For sea-ice modelling, our knowledge of net surface heat flux in polar regions has
proven adequate to determine a realistic annual cycle of the global sea-ice cover. However, thismean cycle is undoubtedly maintained by surface heat flux components with offsetting errors
easily as large as 50 Wm-2. A principal focus of ice-ocean modellers now is to understandinterannual variability; this objective seems desirable now that we are acquiring multidecadalsatellite and submarine records of ice extent and ice thickness with which to document
interannual ice variability. So the new direction is to drive sea-ice models with flux and fluxrelated data sets that are not simply climatologies, but which incorporate interannual variability.
If this new understanding is to be pursued, it will require a higher level of accuracy in our dataon the radiative forcing of the ice surface; the present 50 W m-2 uncertainty and lack of data on
interannual variability of snowfall will not suffice. Are greater polar cloud fractions enhancing
downward longwave radiation at the expense of downward shortwave? Are air temperature
variations changing the turbulent fluxes? Is enhanced snowfall insulating the ice from the cold
winter air temperatures and reducing ice thickness? These questions cannot be answered with
our present uncertainties both in surface heat flux components and in precipitation. The errors
in reanalysis and satellite-derived surface flux fields must be better characterised and thenreduced by more thorough comparisons amongst themselves and with in situ observations.
Surface wind fields from NWP models and reanalyses are in general satisfactory, but are subject
to errors in magnitude and direction during winter when the planetary boundary layer is stable;
PBL formulations need to be examined for their applicability in polar conditions.
12.3.7. NWP and data assimilation
The reanalyses provide consistent, complete global fields of near-surface meteorology
and surface fluxes over many years. Even when reanalysis fields have time-mean biases, theirvariability can still be quite realistic. Many scientists from a wide range of disciplines havestudied the reanalyses, providing valuable feedback to the data assimilation and modellinggroups producing the reanalyses. The reanalyses are the most evaluated of all the flux products.
It needs to be remembered, however, that the reanalyses are only interpolations of the input

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observational data that have many gaps in space and time. Physical parameterisations areespecially important in regions of little or no data; differences between reanalyses can highlightwhat fields in what regions are uncertain. The reanalyses so far have provided fields every six
hours; many surface flux users desire them at least every three hours.
The GEOS-1 reanalysis was the first extensive reanalysis produced; its surface radiation
fluxes are larger than other estimates. The GEOS data assimilation system has developed
substantially since the reanalysis was conducted. The NCEP-1 reanalysis covers the longest
period and is the most widely distributed and studied reanalysis. NCEP-1 made many fields
available on the Internet and CD-ROM's. It has the most realistic oceanic low-level stratus
clouds of the various reanalyses to date. At the time of writing, NCEP-2 is shorter than NCEP1. It corrected mistakes in NCEP-1 and used improved short wave radiation and boundary layer
parameterisations. Its precipitation patterns, downward short wave radiation at the surface, andequatorial wind stress appear superior to NCEP-1, but its sensible heat flux over the oceans islower than other estimates. It has not been widely distributed and studied, but is available on
the Internet. ERA-15 is the highest resolution reanalysis now available. It appears to produce
the most realistic precipitation patterns of the four reanalyses and has more realistic surface
downward short wave radiation than NCEP-1 and GEOS. It has not been as widely available as
the NCEP-1 reanalysis. The ERA-40 reanalysis is beginning; first results should be available
during 2000. The reanalyses differ not only in time-mean fields but in the magnitude of monthto-month variability in surface fluxes. GEOS has the smallest variability in general, NCEP-2
tends to have the largest.
The reanalyses’ fields of sensible and latent heat and wind stress show generally good
agreement with COADS in the regions where ship reports adequately sample the atmosphere.
The magnitude of latent heat flux varies significantly (by 24 Wm-2 in the global mean),reflecting differences in the complete model physics packages as well as (if not more than) in
the flux algorithms. The exact magnitude of oceanic evaporation appears uncertain, reflecting
uncertainty in our knowledge of the strength of the hydrological cycle. Comparisons withfluxes from ship observations and buoys suggest that NCEP-1, NCEP-2 and ERA-15
evaporation may be too large in many areas.
Comparison with the reanalyses suggests that the COADS data base appears adequate togive useful information on the interannual variability of the monthly means in the mid-latitude
North Pacific and North Atlantic and in parts of the South Atlantic. Over much of the tropics
and the Southern Hemisphere (north of 40S) it appears adequate to give useful information on along-term monthly mean climatology.
The reanalyses all appear to have substantial errors in surface radiation fields, reflecting
problems in clouds and clear sky radiation. NCEP-1 has offsetting errors in its radiation fields.
A major shortcoming for ERA-15, GEOS-1 and NCEP-2 appears to be a lack of low-levelstratus clouds over the eastern subtropical oceans. SRB short wave radiation appears to havemore realistic patterns than the reanalyses, but may well have a positive bias. Radiative transfer
models in general appear to give too much downward short-wave radiation at the surface
compared to actual measurements. Since surface long wave radiation is quite sensitive toatmospheric humidity profiles (that are not well-determined globally) and in particular to cloudbase height, it is more uncertain than surface short wave.
There are significant differences between the hydrological cycles of the reanalyses. The
magnitude of precipitation appears uncertain and varies widely in the estimates examined here.
Twelve-year mean tropical precipitation over the ocean varies from 3.54 mm/day to 5.13mm/day. The difference between NCEP-1 and NCEP-2 in precipitation indicates that data
assimilation systems’ estimates of the hydrological cycle are quite sensitive to changes in thephysical parameterisations. Satellite estimates of precipitation are generally more uncertain inhigh latitudes than in the tropics. All the reanalyses have problems with the time-mean patternof precipitation. Comparisons to CMAP and GPCP over land implies too much precipitation in
all the reanalyses. This may be linked to too much evaporation over the ocean.

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12.4 Perspectives

Reanalyses were produced with unchanging data assimilation systems, but the available
observations change with time. ERA-15 has discontinuities in several fields at the end of 1986
associated with the use of satellite radiances. NCEP-1 has discontinuities calling into question
long-term trends. Before 1979 the observational network needs to be considered in assessing
the reliability of reanalysis. During the period 1948-79 the observation network became moreglobal with time; these changes in coverage affect the NCEP-1 reanalysis. The end of 1978
marked the beginning of the modern satellite era. In recent years the number of satellite
observations have increased while observations from the surface have decreased; theconsequences of this needs to be assessed.
12.4. Perspectives
Because of the length of time needed to do a lengthy reanalysis, fluxes from global
operational NWP systems will much of the time be based on more developed, more complex,
and hopefully better physical parameterisations and therefore will be worth evaluating in
addition to reanalysis results. Such evaluations would benefit future reanalyses as well as
weather forecasting. We applaud WGNE’s plans to archive and evaluate the fluxes from anumber of global NWP systems.
In situ observations and field experiments provide a very valuable check on nearsurface meteorology and flux estimates from both satellites and data assimilation. These need
to be performed in a wide variety of locations and climates. Successful field experiments
require years of analysing and processing the data once the field experiment is actually carriedout.
Funding agencies need to be willing to fund such work for an extended period.
Uncertainty estimates need to be provided in the resulting data sets. In data assimilation
analysis/forecast systems, all the physical parameterisations influence each other; adjusting
one physical parameterisation to fit one independent estimate or one field experiment too
precisely may distort other physical fields, or the same physical field in other locations.
Study of the TOGA COARE experiment has increased our knowledge of air-sea fluxes
and improved our ability to measure them, partly because of a willingness to devote several
years to analysing and processing the data after the actual field experiment. It is not clear thatthe knowledge gained has been completely absorbed yet by the data assimilation/NWP
community. The data assimilation/NWP community has paid much attention recently to theassimilation of new satellite data; increased attention also needs to be paid to the development
and implementation of improved physical parameterisations. Indeed improved physicalparameterisations are needed to take full advantage of new satellite measurements of moistureand the hydrological cycle. Funding needs to be provided for such work and to provide
opportunities for people from the TOGA COARE community to work with operational data
assimilation/modelling groups in the interest of improving future reanalyses as well as weather
forecasting.
The NCEP-1 Reanalysis is continuing as a climate data assimilation system analysing
current data. In the future, reanalysis and climate data assimilation may diverge from data
assimilation for NWP. Such an independent system would be free from the time constraints on
operational NWP and could have a much longer data cutoff time. It could also have more
complex physics, enabling it to process all observations and eliminating any need for separate
retrievals of satellite data for any field. Thus satellite and data assimilation estimates of surface
fluxes might be merged within a single consistent system using as complete as necessary
physical parameterisations.
Satellite products have been demonstrated to have great potential to improve theoverall situation especially concerning coverage, time sampling, and data consistency. In
addition, the time period covered by satellite data is approaching a length where such data
become useful for climate variability studies (at least on the interannual scale). However up to
now it was not possible to construct a product that included the four relevant energy fluxes andthe freshwater flux. This was of high importance given the requirements of the data user

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12.5 Recommendations

community. Thus the following recommendations concerning the future use of satellite data canbe drawn. Firstly, to approach at least a climatological net flux estimate from satellite dataalone, projects must be established on the combination of different sensors. Future satellite
missions should follow the strategy of the TRMM satellite, where instruments for measuring
radiative fluxes, basic variables for heat flux estimation, and precipitation have been installed on
one platform. Secondly, to take full advantage of the satellite data there is a definite need forflux reference sites. These should deliver highly accurate measurements of all the heat fluxes
for both error estimation and long term calibration. This is particularly important for theconstruction of long time series from similar sensors on different satellite platforms. Thirdly,
the use of satellite data in conjunction with in situ data products should be carefully evaluated toestablish a data set that takes full advantage of each data source and which is independent fromthe models. Finally, within mission related programs, international and national funding
agencies should more strongly support activities targeted on the use of the data for generation orcontinuation of user friendly flux products.
The report has shown that all existing flux estimates have deficiencies and that they are
only suitable for some of the described applications. Nevertheless, despite the various problems
with the range of flux products that were reviewed in this report, it is evident that the products
are complementary rather than being in competition, and that the ensemble of air-sea flux
estimates that they represent cover the time and space scales relevant to present ocean sciencestudies.

12.5. Specific Recommendations
The main conclusions from the Working Group's endeavours appear throughout the
body of this report. They have been summarised in the preceding sections of this Chapter which
includes a number of recommendations. At its final meeting, the Working Group also adopted
the following specific recommendations:❍

Reanalyses should be performed every 5-10 years by more than one centre; adequate
resources should be provided to the reanalysis efforts to improve their surface fluxes, tocarry out and evaluate the reanalyses and to ensure that they are easily available to theentire scientific community. Surface fields should be output every 3 hours .

❍Evaluation of the fluxes from global operational NWP systems will benefit futurereanalyses as well as provide critical guidance and product uncertainty estimates tousers of these flux products. The WGNE's plans to archive and evaluate the fluxes froma number of global NWP systems should therefore be supported.

❍

A network of high quality flux reference platforms (combination of long-term mooringsand ships) should be established to deliver highly accurate values of stress and allcomponents of the air-sea heat fluxes for, inter alia, verification of flux estimates fromsatellites and models, and the long term calibration of satellite sensors.

❍

There is continuing need to compare and assess the quality of fluxes derived fromvarious sources, and to evaluate the parameterisations used. Encouragement should begiven to efforts to enhance the reliability of momentum, net heat and freshwater fluxes
by combining the best estimates from these various sources.

❍

Support should be provided for the continuing assembly of flux and flux-related data
sets (in particular Voluntary Observing Ship-based collections such as COADS andother historical data). Continued efforts are needed to remove non-stationary
observational biases in historical data. Basic meteorological variables should be
included as well as uncertainties, error estimates and adequate documentation for all
flux data sets. A catalogue of flux data sets should be maintained on the Internet.

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12.5 Recommendations

Research and field experiments are needed
to improve boundary layer
parameterisations and bulk formulae, especially in regions where our physicalunderstanding is poor. Adequate resources for complete analysis of the resulting data
are necessary to realise the full benefits of the field experiments.

ACKNOWLEDGEMENTS
Sections of this report have been based on contributions written by Working Group
members to recent WCRP sponsored workshops. We wish to thank the following individuals:
Ken Campana for the paragraph on Air Force nephanalyses; Mike Freilich for contributions
regarding scatterometer data; Bill Rossow for contributions to the radiation section; Detlef
Stammer and Ichiro Fukumori for input to the section on data assimilation. The Working Groupgratefully acknowledges the sponsorship received from both SCOR and the WCRP and the help
and support from the officers of those bodies.

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275

REFERENCE
Aagaard, K., and P.Greismann, 1975: Toward new massand heat budgets for the Arctic Ocean. J.Geophys.
Res., 80, 3821-3827.
Alados-Arboledas, L., J. Vida and J.I.Jimenez, 1988:
Effects of solar radiation on the performance ofpyrgeometers with silicon domes. J. Atmos. Oceanic
Technol., 5, 666-670
Alberta, T. L., and T. P. Charlock, 1999: A
comprehensive resource for the investigation ofshort-wave fluxes in clear conditions: CAGEX
Version 3. Proc. AMS Tenth Conf. on Atmospheric
Radiation, Madison WI (June 28 – July 2, 1999),279-282.
Alberta, T. L., T. P. Charlock, C. H. Whitlock, F. G.
Rose, R. DiPasquale, R. Pinker, W. F. Staylor, and
S. Gupta, 1994: Climate observations with GEWEX
Surface Radiation Budget Project data. Proc. AMS
Eighth Conf. on Atmospheric Radiation, Nashville
TN (January 23-28, 1994), 22-24.
Albrecht, B.A., M.Poellot and S.K.Cox, 1974:
Pyrgeometer measurements from aircraft. Rev. Sci.
Instrum., 45, 33-38.
Alexander, M.A., and J.D.Scott, 1997: Surface flux
variability over the North Pacific and North Atlantic
Oceans. J.Climate, 10, 2963-2977.
Allam, R., G. Holpin, P. Jackson, and G.-L. Liberti,
1993: Second Algorithm Intercomparison Project of
the Global Precipitation Climatology Project: AIP-2.
pre-Workshop Report, 133 pp. [Available from
Satellite Image Applications Group, UKMO,
Bracknell, Berkshire, United Kingdom.]Anderson, S.P., and M.F.Baumgartner, 1998: Radiative
heating errors in naturally ventilated air temperature
measurements made from buoys. J. Atmos. Oceanic
Technol., 15, 157-173.
Andreas, E.L., 1992: Sea spray and the turbulent air-sea
heat fluxes. J. Geophys. Res., 97, 11,429-11,441.
Andreas, E.L., 1994: Reply (to Katsaros and de Leeuw,
1994). J. Geophys. Res., 99, 14,354-14,350.
Andreas, E.L., J.B. Edson, E.C. Monohan, M.P. Rouault,
and S.D. Smith, 1995: The spray contrbution to net
evaporation from the sea: a review of recent progress.
Boundary-Layer Meteor., 72, 3-52.
Angelucci, M.G., N.Pinardi, and S.Castellari, 1998: AirSea fluxes from operational analyses fields:intercomparison between ECMWF and NCEP
analyses over the Mediterranean area. Phys. Chem.
Earth , 23, 5/6, 569-574.
Arkin, P. A. and P. Xie, 1994: The global precipitation
climatology project: First Algorithm Intercomarison
Project. Bull. Amer. Meteor. Soc., 75, 401-419.
Arkin, P. A., 1999 (Ed.) : Report of the OOPC/AOPC
Workshop on Global Sea Surface Temperature Data
Sets. (Palisades, N.Y., USA, 2-4 November 1998).
Report GCOS-57, GOOS-79, WMO/TD 978, WMO.
Arpe, K. and E. Klinker, 1986: Systematic errors of the
ECMWF operational forecasting model in midlatitudes. Quart. J. R. Met. Soc., 112, 181-202.
Arpe, K., 1989: Impact of changes in the ECMWF
analysis-forecasting scheme on the systematic errorof the model. Ten years of medium-range weather
forecasting, ECMWF Seminar 4-8 Sept. 1989,
ECMWF, Reading, England, 69-114.
Arpe, K., 1991: The hydrological cycle in the ECMWF
short-range forecasts. Dynamics of Atmospheres andOceans, 16, 33-59.

Report of the Working Group on Air Sea Fluxes

Arpe, K., C. Klepp and A. Rhodin, 2000: Differences in
the hydrological cycles from different reanalyses –which one shall we believe?. Proc. Conf.2nd Intl.
Conf. on Reanalyses, Reading, England, 23-27 Aug.
1999. WCRP-109 (WMO/TD 985) WMO, 193 196.
Atlas, R., & co-authors, 1999: Geophsical valiation of
NSCAT winds using atmospheric data and analyses.
J. Geophys. Res, 104, 11405-11424.
Austin, P.M., 1987: Relation between measured radarreflectivity and surface rainfall. Mon. Wea. Rev.,
115, 1053-1070.
Axys, 1996: Meteorological and Oceanographic
Measurements from Canadian Weather Buoys.
(draft), Axys Environmental Consulting Ltd., Sidney,
B.C., Canada, 35 pp. + Apps.
Bacon, S. 1997: Circulation and Fluxes in the North
Atlantic between Greenland and Ireland J. Phys.
Oceanogr. 27 (7) 1420–1435.
Bacon, S. and D.J.T. Carter, 1991: Wave climate changes
in the North Atlantic and North Sea, Int. J. Climatol.
11, 545-558
Bacon, S. and D.J.T. Carter, 1993: A connection between
mean wave height and atmospheric pressure gradientin the North Atlantic, Int. J. Climatol. 13, 423-436
Bane, J.M., and K.E. Osgood, 1989: Wintertime air-sea
interaction processes across the Gulfstream. J.
Geophys. Res., 94, 10755-10772.
Barkstrom, B.E., E.Harrison, G.Smith, R.Green, J.Kibler,
R.Cess & ERBE Science Team, 1989: Earth
Radiation Budget Experiment (ERBE) archival andApril 1985 results. Bull.Amer.Meteor.Soc., 70, 12541262.
Barnes, S. L., 1964: A Technique for Maximising Details
in Numerical Weather Map Analysis. Journal ofApplied Meteor., 3, 396 - 409.
Barnett, T.P., 1999: Comparison of near surface airtemperature Variability in 11 coupled global climate
models. J.Climate, 12, 511-518.
Barnier, B., 1998. Forcing the ocean. Ocean Modelingand Parameterization, E.P. Chassignet and J. VerronEds., Kluwer Academic Publishers, The Netherlands,
45-80.
Barnier, B., L. Siefridt, and P. M. Marchesiello, 1995:
Thermal forcing for a global ocean circulation modelusing a three-year climatology of ECMWF analyses.
J. Marine Systems, 6, 363 - 380.
Barrat, M.J., 1991: Waves in the North East Atlantic. UK
Dept. of Energy Report OT190545, HMSO, London
Barton, I.J., A.J.Prata and R.P.Cechet, 1995: Validationof the ATSR in Australian waters. J. Atmos. Oceanic
Technol., 12, 290-300.
Battan, L.J., 1973: Radar Observations of the
Atmosphere. University of Chicago Press, 324pp.
Bauer, E., and C. Staabs, 1998: Statistical properties ofglobal significant wave heights and their use forvalidation. J. Geophys. Res. 103, 1153-1166
Bauer, E., and P. Heimbach, 1999: Annual validation ofsignificant wave heights from ERS-1 Synthetic
Aperture Radar Wave Mode Spectra using
TOPEX/POSEIDON and ERS-1 altimeter data. J.
Geophys. Res. 104, 13345 - 13357.
Bauer, P. and P. Schlüssel, 1993: Rainfall, total water, icewater and water-vapour over sea from polarized
microwave simulations and SSM/I data, J. Geophys.
Res. 98, 20737- 20759.

June, 2000

Reference list

Baumgartner, A., and Reichel, E., 1975. The World
Water Balance. Elsevier, New-York, 179pp.
Beljaars, A.C.M. and A.A.M. Holtslag, 1991: On flux
parameterization over land surfaces for atmosphericmodels. J. Appl. Meteor., 30, 327-341
Beljaars, A.C.M., 1994: The parameterization of surface
fluxes in large-scale models under free convection.
Quart. J. Roy. Meteor. Soc., 121, 255-270.
Beljaars, A.C.M., 1997: Air-sea interaction in the
ECMWF model. Seminar on Atmosphere-surface
Interaction, 8-12 Sept. 1997, ECMWF, Reading, UK,
33-52.
Benoit, R., 1977: On the integral of the surface layer
profile-gradient functions. J.Appl.Meteor.,16,859860
Berliand, M. E. and T. G. Berliand, 1952: Determining
the net long-wave radiation of the Earth withconsideration of the effect of cloudiness., Isv. Akad.
Nauk. SSSR Ser. Geofiz.
Best, A.C. 1950: The size distribution of raindrops,
Quart. J. Roy. Meteor. Soc. 76, 16 – 36.
Bethoux, J.P., 1979: Budgets of the Mediterranean Sea:
their dependence on the local climate and on thecharacteristicas of the Atlantic water. Oceanol. Acta,2, 157-163.
Betts, A. K., S.-Y. Hong and H.-L. Pan, 1996:
Comparison of NCEP-NCAR reanalysis with 1987
FIFE data. Mon. Wea. Rev., 124, 1480-1498.
Bigg, G.R., 1993: Comparison of Coastal wind andpressure trends over the tropical Atlantic: 1946-1987.
Int. J. Climatol., 13, 411-421.
Bignami, F., S.Marullo, R.Santoleri & M.E. Schiano,
1995: Longwave radiation budget in the
Mediterranean Sea. J. Geophys. Res., 100, 25012514.
Bishop, J.K.B., and W.B. Rossow, 1991: Spatial andtemporal variability of global surface solar irradiance.
J. Geophys. Res., 96, 16,839-16,858.
Bishop, J.K.B., W.B. Rossow and E.G. Dutton, 1997:
Surface solar irradiance from the International
Satellite Cloud Climatology Project 1983 - 1991. J.
Geophys. Res., 102, 6883-6910.
Blanc, T. V., 1987: Accuracy of bulk-method-determined
flux, stability, and sea surface roughness. J. Geophys.
Res., 92, 3867-3876.
Blanc, T.V., 1985: Variation of bulk-derived surface flux,
stability and roughness results due to the use ofdifferent transfer coefficient schemes. J. Phys.
Oceanog., 15, 650-669.
Bloom, S., L.L. Takacs, A. M. da Silva, and D. Ledvina,
1996: Data assimilation using incremental analysis
updates. Mon. Wea. Rev., 124, 1256-1271.
Böning, C. W., R. Döscher, and H.-J. Isemer, 1991:
Monthly mean wind stress and Sverdrup transport in
the North Atlantic: A comparison of the HellermanRosenstein and Isemer-Hasse climatologies. J. Phys.
Oceanogr., 21, 221-229.
Bony, S., Y. Sud, K. M. Lau, J. Susskind, and S. Saha,
1997: Comparison and satellite assessment of
NASA/DAO and NCEP-NCAR reanalyses overtropical ocean: Atmospheric hydrology andradiation. J. Climate, 10, 1441-1462.
Boukthir M., Barnier B., K. Béranger and E. Garnier,
2000. A monthly mean climatology of river runoff
into Oceans (in preparation).
Bourassa, M. A., M. H. Freilich, D. M. Legler, W. T. Liu,and J. J. O'Brien, 1997: Wind observations from newsatellite and research vessels agree. EOS Trans.
Amer. Geophys. Soc., 78, 597-602.

Report of the Working Group on Air Sea Fluxes

276

Bourassa, M.A., D.G. Vincent and W.L.Wood, 1999: A
flux parameterization including the effects ofcapillary waves and sea state. J. Atmos. Sci., 56,1123-1139.
Boutin, J., J. Etcheto, M. Rafizadeh, and D. C. E. Bakker,
1999: Comparison of NSCAT, ERS 2 active
microwave instrument, special sensor microwave
imager, and Carbon Interface Ocean Atmospherebuoy wind speed: consequences for the air-sea CO2
exchange coefficient. J. Geophys. Res., 104, 1137511392.
Boville, B.A. & P.R.Gent, 1998: The NCAR Climate
Sysems Model, Version one. J. Climate, 11, 1115 1130.
Bowker, D. E., R. E. Davis, D. L. Myrick, K. Stacy, andW. T. Jones, 1985: Spectral Reflectances of Natural
Targets for Use in Remote Sensing Studies. NASA
Reference Publication 1139, 191 pp.
Bradley, E.F. and Weller, R.A. (editors) 1995b: Third
Workshop of the TOGA COARE Air Sea Interaction(Flux) Working Group. Univ. of Hawaii, Honolulu,
USA, 2 4 August 1995. (TOGA COARE
International Project Office, UCAR, Boulder, USA),
34 pp.
Bradley, E.F. and Weller, R.A. (editors), 1997: FourthWorkshop of the TOGA COARE Air–Sea Interaction(Flux) Working Group. WHOI, Mass., USA, 9-11
October 1996. (TOGA COARE International Project
Office, UCAR, Boulder, USA, 61pp.
Bradley, E.F. and Weller, R.A. (eds.), 1995a: Joint
Workshop of the TOGA COARE Flux andAtmospheric Working Groups. Boulder, Colorado,
USA, 11 13 July 1995. (TOGA COARE International
Project Office, UCAR, Boulder, USA), 35 pp.
Bradley, E.F., C.W. Fairall and J.E. Hare, 2000: An old
and improved bulk algorithm for air-sea fluxes:
COARE2.6a. Proc. 14th Symp. Boundary Layer andTurbulence, Aspen, 7-11 August, Amer. Meteor. Soc.
Bradley, E.F., M. Moncrieff and Weller, R.A. (editors),
1997: Joint Workshop of the TOGA COARE Flux andOceans Working Groups, and the GEWEX Cloud
Systems Study Working group 4. NCAR, Boulder
CO, 14-16 May, NCAR/MMM, UCAR, Boulder,61pp.
Bradley, E.F., P.A. Coppin and J.S. Godfrey, 1991:
Measurements of sensible and latent heat fluxes in
the western equatorial Pacific ocean. J. Geophys.
Res., 96, Suppl. 3375-3389.
Bradley, S.G. and C.D. Stow, 1974: The measurement ofcharge and size of raindrops: Part 1. The disdrometer.
J. Appl, Meteor.., 13, 114-130.
Breon, F-M., R. Frouin and C. Gautier, 1994: Global
shortwave energy budget at the Earth's surface from
ERBE measurements. J. Climate, 7, 309-324.
Brest C. L., Rossow W. B., Roiter M. D. 1997: Update ofradiance calibrations for ISCCP J. Atmos. Oceanic
Tech. 14: (5) 1091-1109.
Brown, J.W., O.B. Brown, and R.H. Evans, 1993:
Calibration of AVHRR Infrared channels: a new
approach to non-linear correction, J. Geophys Res.,98, 18257 - 18268.
Brümmer, B., 1993: ARKTIS 1993 - Report on the field
phase with examples of measurements. Berichte aus
dem Zentrum für Meeres- und Klimaforschung, Reihe
A: Meteorologie, 11. [from Univ. Hamburg
Meteorol-ogisches Inst., Bundesstraße 55, Hamburg,
Germany].

June, 2000

Reference list

Brutsaert, W. A., 1975: A theory for local evaporation (orheat transfer) from rough and smooth surfaces atground level. Water Resour. Res., 11, 543-550.
Brutsaert, W. A., 1982: Evaporation into the atmosphere.
Reidel, Dordrecht, 299 pp.
Bryden H.L. & M.M.Hall, 1980: Heat tranport bycurrents across 25o N in the Atlantic Ocean. Science,
207, 884-886.
Budyko, M. I., 1963: Atlas of heat balance of the world.,
Glabnaia Geofiz. Observ.
Budyko, M. I., 1974: Climate and Life. Academic Press,
New York.
Bumke, K., U.Karger, L.Hasse, and K.-P.NIekamp, 1998:
Evaporation over the Baltic Sea as an example of asemi-enclosed sea. Contr.Atmosph.Phys., 71, 249261.
Bunker, A.,F., 1976: A computation of surface energy
flux and annual cycle of the North Atlantic Ocean,Mon. Weather Rev., 104, 1122-1140.
Bunker, A.F., H.Charnock and R.A.Goldsmith, 1982: A
note on the heat balance of the Mediterranean and the
Red Seas. J. Marine Res. 40, Supplement, 73-84.
Bunker, A.F.,1982: Trends of variations and energy
fluxes over the Atlantic Ocean from 1948 to 1972.
Mon. Wea. Rev., 104, 1122-1140.
Burns, S.P., & co-authors 2000: Comparisons of aircraft,ship and buoy radiation and SST measurements from
TOGA-COARE. J. Geophys. Res., submitted
Burns, S.P., & co-authors, 1999: Comparisons of aircraft,ship and buoy meteorological measurements from
TOGA-COARE. J.Geophys.Res., 104(D24) 3085330883.
Busch, N.E., 1972: On the mechanics of atmosphericturbulence. in “Workshop on Micrometeor.”, D.A.
Haugen (Ed.), Amer. Meteor. Soc., Boston, MA, 161.
Bush, B.C., F. P. J. Valero, A. S. Simpson, L. Bignone,
2000: Characterization of thermal effects inpyranometers: A data correction algorithm for
improved measurement of surface insolation. J.
Atmos. Oceanic Technol. 17 (2) 165-175.
Businger, J.A., J.C.Wyngaard, Y. Izumi and E.F.Bradley,
1971: Flux-profile relationships in the atmosphericsurface layer. J. Atmos. Sci., 28, 181-189.
Caldwell, D.R. and W.P. Elliott, 1971: Surface stresses
produced by rainfall. J. Phys. Oceanog., 1 145-148.
Caplan, P. M., and G. H. White, 1989: Performance of
the National Meteorological Center’s Medium-Range
Model. Wea. and Fcst., 4, 391-400.
Cardone, J.S., J.G. Greenwood and M.A.Cane, 1990: On
trends in historical marine wind data. J.Climate, 3,113-127.
Cardone, V.J., H.C. Graber, R.E. Jensen, S. Hasselmann,
M.J. Caruso, 1995: In search of the true surface windfield in SWADE IOP-1: Ocean wave modelling
perspective. Global Atmos. Ocean System, 3, 107150.
Carslaw, H.S. and J.C. Jaeger, 1959: Conduction of heatin solids. Oxford University Press, London, 510 pp.
Carter, D.A., K. S. Gage, W. L. Ecklund, W. M.
Angevine, P. E. Johnston, A. C. Riddle, J. Wilson
and C.R. Williams, 1995: Developments in lower
tropospheric wind profiling at the NOAA Aeronomy
Laboratory. Radio Sci., 30, 977-1001.
Cayan, D., 1992a: Latent and sensible heat flux
anomalies over the Northern Oceans: The connection
to monthly atmospheric circulation. J.Climate, 5,354-369.

Report of the Working Group on Air Sea Fluxes

277

Cayan, D.,1992b: Variability of latent and sensible heat
fluxes estimated using bulk formulae. AtmosphereOcean, 30 (1), 1-42.
Cayan, D.,1992c: Latent and sensible heat flux anomalies
over the Northern Oceans: Driving the sea surface
temperature. J.Phys.Oceanogr, 22, 859-881.
Cess, R. D., & co-authors, 1995: Absorption of solar
radiation by clouds: Observations versus models.
Science, 267, 496-499.
Chang, H.-R. and R.L. Grossman, 1999: Evaluation of
bulk surface flux algorithms for light wind conditions
using data from the Coupled Ocean-AtmosphereResponse experiment (COARE). Quart. J. Roy.
Meteor. Soc., 125, 1551-1588.
Charlock, T. P., F. G. Rose, and T. L. Alberta, 1999: An
observational study of cloud forcing to theatmospheric absorption of SW over Kansas andOklahoma for January-August 1998. Proc. AMS
Tenth Conf. on Atmospheric Radiation, Madison WI(June 28 – July 2, 1999), 262-265.
Charlock, T. P., F. G. Rose, T. L. Alberta, and G. D.
Considine, 1998: Comparison of computed andmeasured cloudy-sky shortwave (SW) in the ARM
Enhanced Shortwave Experiment (ARESE). Proc.
ARM Science Team Meeting, Tuscon, AZ, March 2327, 1998 (available at the URL www.arm.gov).
Charlock, T., & co-authors,, 1997: CERES Algorithm
Theoretical Basis Document. Compute Surface andAtmospheric Fluxes (Subsystem 5.0). Release 2.
NASA Langley Research Center. 51 pp.
Charlock, T., and T. Alberta, 1996: The
CERES/ARM/GEWEX Experiment (CERES) for the
retrieval of radiative fluxes with satellite data. Bull.
Amer. Meteor. Soc., 77, 2673-2683. (see also
http://www-cagex.larc.nasa.gov/cagex/cagex.html )Charnock, H., 1955: Wind stress on a water surface.
Quart. J. Roy. Meteor. Soc., 81, 639-640.
Chelton, D.B., M.H. Freilich, and M.G. Schlax, 1997:
Sampling errors in wind fields constructed fromsingle and multiple scatterometer datasets. (Poster
presentation at NSCAT Science Working Team
Meeting, Maui, HI, 10-14 November 1997).
Chin, T.M., R.F. Milliff, and W.G. Large, 1998: Basinscale, high-wavenumber sea surface wind fields froma multiresolution analysis of scatterometer data. J.
Atmos. Ocean. Tech., 15, 741-763.
Chinman, R., & co-authors, 1995: Summary report of the
TOGA-COARE International Data Workshop.
Toulouse, France, 2-11 August 1994. (TOGA
COARE International Project Office, UCAR,
Boulder, USA)
Chou, M.-D., 1992: A solar radiation model for use in
climate studies. J. Atmos. Sci., 49, 762-772.
Chou, M.-D., and W. Zhao, 1997: Estimation and model
validation of surface solar radiation and cloudradiative forcing using TOGA COARE
measurements. J. Climate, 10, 610-620.
Chou, M-D., A. Arking, J. Otterman, and W. L. Ridgway,
1995a: The effect of clouds on atmospheric absorpionof solar radiation.Geophys.Res.Lett.,22,1885-1888.
Chou, S.-H., C.-L.Shie, R.M.Atlas, and J.Ardizzone,
1997: Air-sea fluxes retrieved from special sensor
microwave imager data. J. Geophys. Res., 102,
12705-12726.
Chou, S.-H., R. M. Atlas, C.-L. Shie, and J. Ardizzone,
1995b: Estimates of surface humidity and latent heat
fluxes over oceans from SSM/I data. Mon. Wea. Rev.,
123, 2405-2425.

June, 2000

Reference list

Ciesielski, P.E. and R.H. Johnson, 1999: Precipitation
estimates for the COARE intensive observing period.
Proc. Conf. on the TOGA Coupled OceanAtmosphere Response Experiment (COARE98),
Boulder CO, 7-14 July 1998, WCRP-107, WMO,
Geneva, 193-194.
Cifelli, R., S.A. Rutledge, D. J. Boccippio, and T.
Matejka, 1996: Horizontal divergence and verticalvelocity retrievals from Doppler radar and windprofiler observations. J. Atmos. Oceanic Tech., 13,
948-966.
Clark, N.E., R. M.Eber, J.A.Renner and J.F.T.Saur, 1974:
Heat exchange between ocean and atmosphere in theeastern North Pacific for 196 -71. NOAA Tech.
Rep. NMFS SSRF-682, U.S.Dept.Commerce, Wash.,
D.C.
Clarke, R.H. and R.R. Brook, 1979: The Koorin
expedition, atmospheric boundary layer data overtropical savannah land. Bureau of Meteor.,
Australian Govt. Pub. Service, Canberra, 359 pp.
Clayson, C. A., C. W. Fairall, and J. A. Curry, 1996:
Evaluation of turbulent fluxes at the ocean surface
using surface renewal theory. J. Geophys. Res., 101,
28503 - 28513.
Clough, S.A., 1995: The water vapor continuum and its
role in remote sensing, in Optical Remote Sensing of
the Atmosphere, 2, OSA Tech. Dig. Ser., 76-78, Opt.
Soc. of Amer., Washington, D.C.
Clough, S.A., M.J. Iacono and J.-L. Moncet, 1992: Lineby-line calculations of atmospheric fluxes andcooling rates: application to water vapor. J. Geophys.
Res., 97, 15761-15785.
Coachman, L.K., and K.Aagaard, 1988: Transports
through Bering Strait: Annual and Interannual
variability. J.Geophys. Res., 93, 15535-15539.
Cole, H.L. and E.R. Miller, 1999: Correction and recalculation of humidity data from TOGA-COARE
radiosondes, and development of humidity correction
algorithms for global radiosonde data. Proc. of
COARE98, A Conference On The TOGA Coupled
Ocean-Atmosphere Response Experiment, Boulder,
Colorado, USA, 7-14 July 1998. WCRP-107,
WMO/TD 940, WMO, Geneva.
Colton, M. C. and G. A. Poe, 1999: Intersensor
Calibration of DMSP SSM/I’s: F-8 to F-14, 19871997. IEEE Transactions on Geoscience and Remote
Sensing, 37, 418-439.
Conkright, M. E., S. Levitus, and T. P. Boyer, 1994:
World Ocean Atlas 1994, Volume 1: Nutrients.
NOAA Atlas NESDIS 1, U.S. Dept. Commerce,
NOAA/NESDIS.
Cotton, P. D. and D. J. T. Carter, 1994b: Interannual
variability in global wave climate from satellite data.
SPIE, 2319, 174 - 180.
Cotton, P.D., and D.J.T. Carter, 1994a: Cross calibrationof TOPEX, ERS-1, and Geosat wave heights. J.
Geophys. Res. 99, 25025-25033.
Covey, C. and co-authors, 2000: The Seasonal Cycle in
Coupled Ocean-Atmosphere General Circulation
Models, PCMDI Report 51, Lawrence Livermore
National Laboratory, Livermore, CA (May 2000 originally released February 1999).
Cox, A. T. and V. R. Swail, 2000: A global wave
hindcast over the period 1958 – 1997: validation andcomate assessment, J. Geophys. Res. (Oceans) –submitted.
Cox, A. T., V. J. Cardone, and V.R. Swail, 1999: On theuse of in situ and satellite wave measurements for theevaluation of wave hindcasts. in CLIMAR 99, WMO

Report of the Working Group on Air Sea Fluxes

278

Workshop on Advances in Marine Climatology,
Vancouver, 8 - 15 Sept. 1999, 130 – 138.
Cox, M. D., and K. Bryan, 1984. A numerical model ofthe ventilated thermocline. J.Phys.Oceanogr.,14,674-687.
Crewell, S, E. Ruprecht, and C. Simmer, 1991: latent heat
flux over the North Atlantic ocean-a case study. J.
Appl. Met., 30, 1627 - 1635.
Crosby,J.D., 1994: Precipitation measurement on oceangoing platforms. Proc. MTS-94 Conf. and exposition
on Oceanogr. Instrumentation, 7-9 Sept., Wash.,
D.C.
Cullather, R.I., and Bromwich, D.H., 2000: The
atmospheric hydrologic cycle over the Arctic basinfrom reanalyses. Part I: Comparison withobservations and previous studies J. Climate 13 (5)923-937.
Curry, J. A., & co-authors, 1999: High-resolution satellite
derived dataset of the surface fluxes of heat,
freshwater and momentum for the TOGA COARE
IOP. Bull. Amer. Meteor. Soc., 80, 2059-2080.
Curry, J.A., W.B. Rossow, D. Randall and J.L. Schramm,
1996: Overview of Arctic cloud and radiation
characteristics. J. Climate, 9, 1731-1764.
da Silva, A. and G. White, 1995: A comparison of
surface marine fluxes from GEOS-1/DAS and NMC
reanalyses. Proc. of the Workshop on the GEOS-1
Five-Year Assimilation, NASA Tech. Memo. 104606,
Vol. 7, GSFC., Greenbelt, MD, 129-136.
da Silva, A. M., C. C. Young and S. Levitus, 1994: Atlasof Surface Marine Data 1994, NOAA Atlas NESDIS
6. (6 Volumes) Available from: U.S Dept.
Commerce, NODC, User Services Branch,
NOAA/NESDIS E/OC21, Washington D.C, 20233,
USA.
Vol. 1: Algorithms and Procedures.
Vol. 2: Anomalies of Directly Observed Quantities.
Vol. 3: Anomalies of Heat and Momentum Fluxes.
Vol. 4: Anomalies of Fresh Water Fluxes.
Vol. 5: Anomalies of Miscellaneous Derived
Quantities.
Vol. 6: Heat Flux Sensivity to SST.
da Silva, A. M., C.C.Young and S. Levitus, 1995:
Towards a revised Beaufort equivalent scale.
Internat. COADS Winds Workshop, Kiel, Germany,
31 May - 2 June 1994, NOAA/ERL, 270 - 286.
da Silva, A., and G. White, 1996: Intercomparison of
surface marine fluxes from GEOS-1/DAS and
NCEP/NCAR reanalyses. WCRP Workshop on AirSea Flux Fields for Forcing Ocean Models andValidating GCMs, Reading, England, 24-27 Oct.
1995, WMO/TD-762, WMO, Geneva, 19-24.
Dacunha, N.M.C., N. Hogben, and K.S. Andrews, 1984:
Wave climate synthesis worldwide, Proc. Internat.
Symp. on Wave and Wind Climate Worldwide, Roy.
Inst. Naval Architects, London, 1-11.
Darnell, W. L., W. F. Staylor, N. A. Ritchey, S. K.
Gupta, and A. C. Wilber, 1996: Surface radiationbudget: A longterm global dataset of shortwave andlongwave fluxes. Eos, Trans. Amer. Geophys Union,
February.
Darnell, W.L., W.F. Staylor, S.K. Gupta, N.A. Ritchey,
and A.C. Wilber, 1992: Seasonal variation of surface
radiation budget derived from International Satellite
Cloud Climatology Project C1 Data. J. Geophys.
Res., 97, 15741-15760.
Deacon, E. L., 1988: The streamwise Kolmogoroff
constant. Boundary-Layer Meteor., 42, 9 - 17.

June, 2000

Reference list

Deardorff, J.W.,1970: Convective velocity andtemperature scales for the unstable planetary
boundary layer and for Rayleigh convection.
J.Atmos.Sci.,27,1211-1213.
DeCosmo, J., K. B. Katsaros, S. D. Smith, R. J.
Anderson, W. A. Oost, K. Bumke and H. Chadwick,
1996: Air-sea exchange of water vapor and sensible
heat: The Humidity Exchange Over the Sea(HEXOS) results. J. Geophys. Res., 101, 1200112016.
Dedieu,G., P.Y.Deschamps and Y.H.Kerr, 1987: Satellite
estimation of solar irradiance at the surface of the
Earth and of surface albedo using a physical model
applied to METEOSAT data. J. Climate Appl.
Meteor., 26, 79-87.
DeLuisi, J., 1991: Second Workshop on Implementationof the Baseline Surface Radiation Network. WCRP64, WMO/TD 453, WMO, Geneva.
Derber, J. C., D. F. Parrish and S. J. Lord, 1991: The new
global operational analysis system at the National
Meteorological Center. Wea. Forecasting, 6, 538547.
Dickey, T. D., D. V. Manov, R. A. Weller and D. A.
Siegel, 1994: Determination of longwave heat flux atthe air-sea interface using measurements from buoy
platforms. J. Atmos. & Oceanic Tech., 11, 1057 1078.
Dickson, R. R., Lazier, J., Meincke, J., Rhines, P., andSwift, J., 1996. Long term coordinated changes in theconvective activity of the North Atlantic. Decadal
Climate variability, Anderson D. L. T., andWillebrand, J., Eds., Springer-Verlag, 211-262.
Dickson,R.R., Meincke,J.,Malmberg,A.,& Lee,A.J.,
1988. The Great Salinity Anomaly in the northern
North Atlantic 1968-1982. Prog Oceanogr.,20,103151.
Dietrich, G., 1950: Über systematische Fehler in den
beobachteten Wasser- und Lufttemperaturen auf dem
Meere und ihre Auswirkungen auf die Bestimmung
des Wärmeumsatzes zwischen Ozean und
Atmosphäre. Deutsche Hydrogr. Zeitschr. 3, 314324.
Dobson, F. W., R. J. Anderson, P. K. Taylor and M. J.
Yelland, 1999: Storm Wind Study II: Open Ocean
Wind and Sea State Measurements. in Proc Symp. On
the Wind-driven Air-sea Interface (Ed. M.L. Banner),
Univ. of New South Wales, Sydney, 11-15 January
1999, 295 - 296.
Dobson, F., and S. Smith, 1988: Bulk model of solar radiation at sea. Quart.J. Roy. Meteor. Soc., 114, 165182.
Donelan, M. A., 1982: The Dependence of the Aerodynamic Drag Coefficient on Wave Parameters. FirstInternat. Conf. on Meteor. and Air-Sea Interaction of
the Coastal Zone, Amer.Meteor.Soc., Boston, 381387.
Donelan, M.A., F.W. Dobson, S.D. Smith and R.J.
Anderson, 1993: On the dependence of sea surface
roughness on wave development. J. Phys. Oceanog.,23, 2143-2149.
Donelan, M.A., W.M. Drennan and K.B. Katsaros, 1997:
The air-sea momentum flux in conditions of wind sea
and swell. J. Phys. Oceanog., 27, 2087-2099.
Donlon, C. J., S. J. Keogh, D. J. Baldwin, I. S. Robinson,
I. Ridley, T. Sheasby, I. J. Barton, E. F. Bradley, T. J.
Nightingale and W. Emery, 1998: Solid-stateradiometer measurements of sea surface skintemperature. J. Atmos. Oceanic Tech., 15, 775-787.

Report of the Working Group on Air Sea Fluxes

279

Dorman, C. E. and R. H. Bourke, 1978: A temperaturecorrection for Tucker's ocean rainfall estimates.
Quart. J. Roy. Meteor. Soc., 104, 765 - 773.
Drange, H., P. Schlüssel, and M. Srokosz, 1999: Study ofcritical requirements for ocean salinity retrieval
using a low frequency microwave radiometer, Finalreport of ESA contract No. 13224/98/NL/MV,[available: http://www.nrsc.no/gcrieber/rwin.html ].
Dümenil, L., K. Isele, H.-J. Liebscher, U.Schröder, M.
Schumacher and K. Wilke, 1993: Discharge datafrom 50 selected rivers for GCM validation, MaxPlanck-Institut für Meteorologie, Tech. Rep., 100,
Hamburg.
Dupuis, H., P.K.Taylor, A Weill and K.B.Katsaros 1997:
Inertial dissipation method applied to derive fluxes
over the ocean during the SOFIA/ASTEX andSEMAPHORE experiments in low to moderate
windspeeds. J. Geophys. Res. 102, 21115-21129.
Dupuis,H., C.Guérin, A.Weill, and D.Hauser, 1999: Heat
Flux estimates by the inertial dissipation method during the FETCH experiment. Proc Symp. On theWind-driven Air-sea Interface (Ed. M.L.Banner),
Univ. New South Wales, Sydney, 11-15 Jan., 297 304.
Dutton, E.G, 1993: An extended comparison between
LOWTRAN7 computed and observed broadband
thermal irradiances: global extreme and intermediate
surface conditions. J.Atmos.OceanicTech.,10,326336.
Ebert, E. E. and J. M. Manton, 1998: Performance ofsatellite rainfall estimation algorithms during TOGACOARE. J. Atmos. Sci., 55, 1537 - 1557.
Ebert, E.E. 1996: Results of the 3rd algorithm
intercomparison project (AIP-3) of the Global
Precipitation Climatology Project (GPCP). BMRC
Report No. 55, 199 pp., BMRC, GPO Box 1289K,
Melbourne, Vic., Australia 3001.
Ebert, E.E. and J.A. Curry, 1993: An intermediate onedimensional thermodynamic sea-ice model forinvestigating ice-atmosphere interactions. J.
Geophys. Res., 98, 10085-10109.
Ebisuzaki, W. and R. Kistler, 2000: An examination of adata-constrained assimilation. Proc. Conf. 2nd Intl.
Conf. on Reanalyses, Reading, England, 23-27 Aug.
1999. WCRP-109 (WMO/TD 985) WMO, 14 - 17.
Ebisuzaki, W., M. Chelliah, and R. Kistler, 1998:
NCEP/NCAR Reanalysis: Caveats. Proc. 1st WCRP
Intl. Conf. on Reanalyses. 27-31 Oct. 1997, Silver
Spring, MD, USA, WCRP-104, WMO/TD-876.
WMO, Geneva, 81-84.
Ebuchi, N., and H. C. Graber, 1998: Directivity of windvectors derived from the ERS-1/SMI scatterometer.
J. Geophys. Res., 103, 7787-7797.
Ecklund, W. L., C. R. Williams, P. E. Johnston, and K. S.
Gage, 1997: Profiler observations of deep convective
clouds in MCTEX. Proc. 22nd Conf. on Hurricanes
and Tropical Meteor., 19-23 May, Fort Collins, CO,
Amer. Meteor. Soc., Boston, MA.
Ecklund, W.L., P. E. Johnston, J. M. Warnock, W.L.
Clark, and K. S. Gage, 1995: An S-Band profiler fortropical precipitating cloud studies. Proc. 27th
conference on Radar Meteor., 335-336, Oct., Vail,CO, Amer. Meteor. Soc., Boston, MA.
Edson, J. B. and C. W. Fairall, 1998: Similarity
relationships in the Marine Atmospheric surface layer
for terms in the TKE and scalar variance budgets. J.
Atmos. Sci., 55, 2311 - 2328.
Edson, J.B., A. A. Hinton, K. E. Prada, J.E. Hare, andC.W. Fairall, 1998: Direct covariance flux estimates

June, 2000

Reference list

from moving platforms at sea. J. Atmos. Oceanic
Tech., 15, 547-562.
Edson, J.B., C.W.Fairall, P.G.Mestayer and S.E.Larsen,
1991: A study of the inertial-dissipation method forcomputing air-sea fluxes. J. Geophys. Res., 96,10689-10711.
Efimov, V.V., N.A. Timofeev, I.V. Kurzeevsky and E.N.
Sychov, 1985: The estimate of the coefficients of theheat and moisture transfer between the ocean andatmosphere. Phys. Atmos. Ocean, Izvestia Academy
Sciences of the USSR, 21, (7), 735-743.
Efimova, N.A., 1961: On methods of calculating monthly
values of net longwave radiation (in Russian),
Meteor. Gidrol., 10, 28 – 33.
Ellingson, R. G., and Y. Fouquart, 1991: The intercomparison of radiation codes in climate models(ICRCCM): An overview. J. Geophys. Res., 96,8926-8929.
Ellingson, R.G., J. Ellis and S. Fels, 1991: Theintercomp-arison of radiation codes used in climate
models: long wave results. J. Geophys. Res., 96,8929-8963.
Emery, W. J., K. Cherkauer, B. Shannon and R. W.
Reynolds, 1997: Hull-mounted sea surface
temperatures from ships of opportunity. J. Atmos. &Oceanic Tech., 14(5), 1237-1251.
Emery, W., Y. Yu, and G.Wick, 1994: Correcting
infrared satellite estimates of sea surface temperature
for atmospheric water vapor attenuation, J. Geophys
Res., 99, 5219 - 5236.
Esbensen, S. K. and Y. Kushnir, 1981: The heat budgetof the global ocean: An atlas based on estimates fromsurface marine observations., Climatic ResearchInstitute Report No 29, Oregon State University.
Esbensen, S.K. and R.W. Reynolds, 1981: Estimating
monthly averaged air-sea transfers of heat andmomentum using the bulk aerodynamic method. J.
Phys. Oceanogr., 11, 457-465.
Esbensen, S.K., and M.J. McPhaden, 1996: Enhancementof tropical ocean vaporation and sensible heat flux byatmospheric mesoscale systems. J.Climate, 9, 23072325.
Esbenson, S. K., D. B. Chelton, D. Vickers, and J. Sun,
1993: An analysis of errors in SSM/I evaporation
estimates over the global Ocean. J. Geophys. Res.,98, 7081-7101.
Evans, K. F., 1998: The spherical harmonic discrete
ordinate method for three-dimensional atmosphericradiative transfer. J. Atmos. Sci., 55, 429-446.
Eymard, L., 1998: Introduction: The SEMAPHORE
experiment. J. Geophys. Res., 103, 25,005-25,008
Eymard, L., S. Planton, P. Durand, and Co-authors, 1996:
Study of the air-sea interactions at the mesoscale:
The SEMAPHORE experiment. Ann. Geophysicae,
14, 986-1015.
Ezraty, R., and A. Cavanié, 1999: Intercomparison ofbackscatter maps over Arctic sea ice from NSCATand the ERS scatterometer, J. Geophys. Res., 104,
11,471-11,483.
Fairall, C. W., E. F. Bradley, J. S. Godfrey, G. A. Wick,
J. B. Edson, and G. S. Young, 1996b: Cool skin andwarm layer effects on sea surface temperature. J.
Geophys. Res., 101, 1295-1308.
Fairall, C. W., P. O. G. Persson, E. F. Bradley, R. E.
Payne, and S. Anderson, 1998: A new look at
calibration and use of Eppley Precision Infrared
radiometers: Part I theory and application. J. Atmos.
Oceanic Tech., 15, 1230-1243.

Report of the Working Group on Air Sea Fluxes

280

Fairall, C.W., A.A. Grachev, A.J. Bedard, and R.T.
Nishiyama 1996c: Wind, wave, stress, and surface
roughness relationships from turbulencemeasurements made on R/P FLIP in the SCOPE
experiment. NOAA Tech. Memo ERL ETL-268,
37pp.
Fairall, C.W., A.B. White, J.B. Edson, and J.E. Hare,
1997: Integrated shipboard measurements of themarine boundary layer. J. Atmos. Oceanic Tech., 14,338-359.
Fairall, C.W., and S.E. Larsen, 1986: Inertial dissipation
methods and turbulent fluxes at the air oceaninterface. Bound.-Layer Meteor., 34, 287-301.
Fairall, C.W., E.F. Bradley, D.P. Rogers, J.B. Edson, andG.S. Young, 1996a: Bulk parameterization of air-sea
fluxes for the Tropical Ocean-Global Atmosphere
Coupled-Ocean Atmosphere Response Experiment.
J. Geophys. Res., 101, 3747-3764.
Fairall, C.W., J.B. Edson, S.E.Larsen and P.G.Mestayer,
1990: Inertial-dissipation air-sea flux measurements:
a prototype system using real-time computations. J.
Atmos. Oceanic Tech., 7, 425-453.
Fels, S. B., and M. D. Schwarzkopf, 1975: The simplified
exchange approximation: A new method for radiativetransfer calculations. J. Atmos. Sci., 37, 2265-2297.
Feng, Ming, P. Hacker, and R. Lukas, 1998: Upper ocean
heat and salt balances in response to a westerly windburst in the western equatorial Pacific during TOGACOARE. J. Geophys. Res.,103, 10289-10311.
Ferraro R.R., F. Weng, N.C.Grody, and A.Basist, 1996:
An eight-year (1987-1994) time series of rainfall,clouds, water vapor, snow cover, and sea ice derived
from SSM/I measurements. Bull.Amer.Met.Soc., 77,
891-905.
Ferraro, R. R., and Marks, G. F., 1995: The developmentof SSM/I rain-rate retrieval algorithms using groundbased radar measurements, J.Atmos.Oceanic Tech.
12, 755-770.
Ferraro, R. R., N. C. Grody, D. Forsyth, R. Carey, A.
Basist, J. Janowiak, F. Weng, G. F. Marks, and R.
Yanamandra, 1994: Microwave measurements
produce global climatic, hydrologic data. Eos, Trans.
Amer. Geophys. Union, 75, 337 - 343.
Fiorino, M., 2000: The impact of observing systemchanges on the climate-scale variability andtemperature in the ECMWF and NCEP reanalyses.
Proc. Conf. 2nd Intl. Conf. on Reanalyses, Reading,England, 23-27 Aug. 1999. WCRP-109 (WMO/TD
985) WMO, 65 - 68.
Folland, C. K. and D. E. Parker, 1995: Correction Of
Instrumental Biases In Historical Sea-Surface
Temperature Data. Quart. J. Roy. Meteor. Soc.,
121(522), 319 - 367.
Folland, C. K., R. W. Reynolds, M. Gordon and D. E.
Parker, 1993: A study of six operational sea surface
temperature analyses. J. Climate., 6(1), 96 - 113.
Folland,C.K., 1988: Numerical models of the raingauge
exposure problem, field experiments and an
improved collector design. Quart. J. Roy. Meteor.
Soc., 114, 1485-1516.
Freilich, M. H., and R. S. Dunbar, 1999: The accuracy of
the NSCAT 1 vector winds: Comparisons with
National Data Buoy Center buoys. J. Geophys. Res,
104, 11231-11246.
Freitag, H.P., Y. Feng, L.J. Mangum, M.J. McPhaden, J.
Neander, and L.D. Stratton, 1994: Calibration
procedures and instrumental accuracy estimates of
TAO temperature, relative humidity and radiation
measurements. Tech. Report No. 1589,

June, 2000

Reference list

NOAA/Pacific Marine Environmental Lab., Seattle
WA. (see also http://www.pmel.noaa.gov/togatao/caldoc/cal.html).
Frenzen, P. and C. A. Vogel, 1992: The turbulent Kinetic
Energy budget in the atmospheric surface layer: areview and an experimental reexamination in thefield. Boundary-Layer Meteor., 60, 49 - 76.
Friehe C.A., & co-authors, 1991: Air-sea fluxes andsurface layer turbulence around a sea surface
temperature front, J. Geophys. Res., 96, 8593-8609.
Friehe, C.A. and K.B. Schmitt, 1976: Parameterization of
air-sea interface fluxes of sensible heat and moistureby the bulk aerodynamic formulas. J. Phys.
Oceanog., 6, 801-809.
Friehe, C.A., 1991: Air-sea fluxes and surface layer
turbulence around a sea surface temperature front.
J.Geophys. Res., 96, 8593-8609.
Frouin, R., C. Gautier, and J. J. Mocrette, 1988:
Downward longwave irradiance at the oceans surface
from satellite data: methodology and in situ
validation, J. Geophys. Res., 93, 597-619.
Fu, Q., and K.-N. Liou, 1993: Parameterization of theradiative properties of cirrus clouds. J. Atmos. Sci.,
50, 2008-2025.
Fujitani, T., 1985: Method of turbulent flux
measurement on a ship by using a stable platformsystem. J. Meteor. Soc. Japan, 36, 157-170.
Gage, K. S., C. R. Williams and W. L. Ecklund, 1996:
Application of the 915 MHz profiler for diagnosing
and classifying tropical precipitating cloud systems.
Meteor. and Atmospheric Physics, 59, 141-151.
Gage, K. S., C. R. Williams, and W. L. Ecklund, 1994:
UHF wind profilers: a new tool for diagnosing
tropical precipitating cloud systems. Bull. Amer.
Meteor. Soc., 73, 2289-2294.
Ganachaud A., 1999: Large Scale Oceanic Circulation
and Fluxes of Freshwater, Heat, Nutrients andOxygen. Ph.D. Thesis, Department of Earth,Atmospheric and Planetary Sciences, Massachusetts
Institute of Technology, Cambridge, USA, 267 pp.
Gardashov, R. G., K. S. Shifrin, J. K. Zolotova, 1988:
Emissivity, thermal albedo and effective emissivityof the sea at different wind speeds. Oceanologica
Acta, 11, 121 - 124.
Garnier, E., B. Barnier, K. Béranger, and L. Siefridt,
1998: Global ocean thermohaline forcing from 15
years of ECMWF reanalysis Proc. of the 1st
International Conf. on Reanalyses, October 27-31,
Silver-Spring WCRP-104 (WMO/TD 876) WMO,
Geneva, 57 - 60.
Garratt, J. R., 1977: Review of drag coefficients overoceans and continents. Mon.Weather Rev.,105,915929.
Garratt, J. R., 1992: The atmospheric boundary layer.
Cambridge University Press, New York, 316 pp.
Garrett, C., R. Outerbridge, and K. Thompson, 1993:
Interannual variability in Mediterranean heat andbuoyancy fluxes. J.Clim., 6, 900-910.
Gaspar, P., Y. Gregoris, and J. M. Lefevre, 1990: A
simple kinetic energy model for simulations of theoceanic vertical mixing: tests at station Papa andLong-Term Upper Ocean Study site, J. Geophys.
Res., 95, 16,179-16,193.
Gautier, C., 1988: Surface solar irradiance in the central
Pacific during Tropic Heat: Comparison between insitu measurements and satellite estimates. J. Climate,
1, 600-608.
Gautier, C., G. Diak and S. Masse, 1980: A simple
physical model to estimate incident solar radiation at

Report of the Working Group on Air Sea Fluxes

281

the surface from GOES satellite data. J. Appl.
Meteor., 19, 1005-1012.
Geernaert, G.L., 1987: On the importance of the dragcoefficient in air-sea interactions. Dyn. Atmos.
Oceans, 11, 19-38.
Geernaert, G.L., 1990: Bulk parameterizations for thewind stress and heat fluxes. In Surface Waves andFluxes, vol. 1, G.L. Geernaert and W.J. Plant (eds.),
91-172, Kluwer, Dordrecht.
Gibson, J. K., P. Kallberg, S. Uppala, A. Noumura, A.
Hernandez, and E. Serrano, 1997: ERA Description.
ECMWF Re-Analysis Project Report Series, 1.
ECMWF, Reading, UK, 77 pp.
Gilgen, H., and A. Ohmura, 1999: The Global Energy
Balance Archive. Bull.Amer.Meteor.Soc., 80, 831850.
Gilgen, H., C. H. Whitlock, F. Koch, G. Mueller, A.
Ohmura, D. Steiger, and R. Wheeler, 1995:
Technical Plan for Baseline Surface Radiation
Network (BSRN) Data Manangement. WCRP,WMO TD-No. 443, WMO Geneva.
Gilhousen et al., 1990 Gilhousen, D.B.,1998: Improved
Real-Time Quality Control of NDBC Measurements.
Preprints of the 10th Symp. on MeteorologicalObservations and Instrumentation, Phoenix, AZ 363366 (available from NDBC, Building 1100Stennis
Space Center, MS 39529, USA).
Gilman C. and C. Garrett, 1994: Heat flux
parameterization for the Mediterranean Sea: The roleof atmospheric aerosols and constraints from thewater budget. J.Geophys.Res., 99, 5119-5134.
Gleckler, P. J. and B. C. Weare, 1995: Uncertainties in
global ocean surface heat flux climatologies derived
from ship observations. PCMDI Rep. 26, Lawrence
Livermore National Lab., Livermore, Ca., 39 pp.
Gleckler, P. J., & co-authors, 1994: Cloud-radiative
effects on implied oceanic energy transports as
simulated by atmospheric general circaulation
models. PCMDI Report 15, Lawrence Livermore
National Laboratory, Livermore, CA., 13 pp.
Gleckler, P., and B.Weare, 1997: Uncertainties in global
ocean surface heat flux climatologies derived fromship observations. J.Climate, 10, 2764-2781.
Gloersen, P., Campbell, W.J., Cavalieri, D.J., Comiso,
J.C., Parkinson, C.L., Zwally, H.J., 1992: Arctic andAntarctic Sea Ice, 1978-1987: Satellite Passive
Microwave Observations and Analysis, NASA SP511, Washington, D.C.
Godfrey, J.S., and Beljaars, 1991: On the turbulent fluxesof buoyancy, heat and moisture at the air-sea
interface at low wind speeds. J. Geophys. Res. 96,
22043-22048.
Godfrey, J.S., E.F. Bradley, P.A. Coppin, L. Pender, T.J.
McDougall, E.W. Schulz, and I. Helmond, 1999:
Measurements of upper ocean heat and freshwater
budgets near a drifting buoy in the equatorial Indian
ocean. J. Geophys. Res. 104, 13269-13302.
Godfrey, J.S., M. Nunez, E.F. Bradley, P.A. Coppin and
E.J. Lindstrom, 1991: On the net surface heat flux
into the western equatorial Pacific. J. Geophys. Res.,96, suppl., 3391-3400.
Goldman, A., and T. G. Kyle, 1968: A comparison
between statistical model and line calculation withapplication to the 9.6 micro-meter ozone and the 2.7
micro-meter water vapor bands. Applied Optics, 3,
203-208.
Gonzales, A. E., and D. G. Long, 1999: An assessment of
NSCAT ambiguity removal. J. Geophys. Res., 104,
11449-11457.

June, 2000

Reference list

Goodberlet, M. A. and C. T. Swift, 1989: Remote sensingof ocean surafce winds with the Special Sensor
Micro-wave/Imager. J. Geophys. Res., 94, 14547 14555.
Goodberlet, M. A. and C. T. Swift, 1992: Improved
retrievals from the DMSP wind speed algorithm
under adverse weather conditions. IEEE Trans.
Geosci. Remote Sens., 30, 1076 - 1077.
Goodberlet, M. A., C. T. Swift, and J. C. Wilkerson,
1989: Remote sensing of oceans surface winds with
the Special Sensor Microwave/Imager. J. Geophys.
Res., 94, 14547- 14555.
Goody, R. M., 1964: Atmospheric Radiations I.
Theoretical Basis. Oxford University Press, 436pp
Gordon, C., Cooper C., Senior C.A., Banks H., Gregory
J.M., Johns T.C., Mitchell J.F.B., Wood R.A. 2000:
The simulation of SST, sea ice extents and ocean heattransports in a version of the Hadley Centre coupledmodel without flux adjustments, Climate Dynamics
16: (2-3) 147-168.
Gosnell, R., C.W. Fairall and P.J. Webster, 1995: The
sensible heat of rainfall in the tropical ocean. J.
Geophys. Res., 100, 18437-18442.
Graber, H. C., N. Ebuchi, and R. Vakkayil, 1996:
Evaluation Of ERS-1 Scatterometer Winds With Wind
& Wave Ocean Buoy Obervations. Technical Report,
RSMAS 96-003, 1996/05/01, RSMAS/University ofMiami, Miami, FL, 78pp.
Graber, H.C., R.E. Jensen, V.J. Cardone, 1995:
Sensitivity of wave model predictions on spatial andtemporal resolution of the wind field. 4th Intern.
Workshop on Wave Hindcasting and Forecasting,Banff, Alberta, October 16-20, 1995, 146-158.
Grachev, A. A., C. W. Fairall and S. E. Larsen, 1998: On
the determination of the neutral drag coefficient in
the convective boundary layer. Boundary-Layer
Meteor., 86, 257 - 278.
Grachev, A. A., C. W. Fairall, and E. F. Bradley, 2000:
Convective profile constants revisited. Boundary
Layer Meteor., 94 (3), 495 - 515
Grachev, A.A. and C.W. Fairall, 1997: Dependence ofthe Monin-Obukhov stability parameter on the bulk
Richardson number over the ocean. J. Appl. Meteor.,
36, 406-414.
Greenslade, D.J.M., D.B. Chelton, and M.G. Schlax,
1997: The midlatitude resolution capability of sea
level fields constructed from single and multiple
satellite altimeter datasets. J. Atmos. Ocean. Tech.,
14, 849-870.
Grell, G. A., 1993: Prognostic evaluation of assumptions
used by cumulus parameterizations. Mon. Wea. Rev.,
121, 654-787.
Grossklaus, M., K. Uhlig and L. Hasse, 1998: An optical
disdrometer for use in high wind speeds. J. Atmos.
Oceanic Tech., 15, 1051-1059.
Groves, D.G., 1999: A new moisture budget of the Arctic
atmosphere derived from 19 years of daily TOVS
satellite moisture retrievals and NCEP reanalysis
winds, Proc. 5th AMS Conf. on Polar Meteor. andOceanogr., Dallas, TX, 107-112.
Guest, P.S., 1998: Surface longwave radiation conditionsin the eastern Weddell sea during winter. J. Geophys.
Res., 103, 30761-30771.
Guichard, F., D. Parsons, and E. Miller, 2000:
Thermodynamical and radiative impact of thecorrection of sounding humidity bias in the tropics.
J. Climate, in press.

Report of the Working Group on Air Sea Fluxes

282

Gulev S.K. & V.A.Tichonov, 1989: Interannual
variations of the ocean heat balance and meridional
heat transport. Atmosphere-Ocean-Space, 7, 332-341.
Gulev, S. K. and L. Hasse, 1998: North Atlantic wind
waves and wind stress fields from voluntary
observing ship data. J. Phys. Oceanogr., 28, 1107 1130.
Gulev, S.K. and J. Tonkacheev, 1996: Investigation of
the Ocean-Atmosphere Interaction in the North
Atlantic mid-latitude Frontal Zone. In: The Air-Sea
Interface, Editors: M.Donelan, W.Hui and W. Plant,
RSMAS, Miami, 535-542.
Gulev, S.K., 1994: Influence of space-time averaging on
the ocean-atmosphere exchange estimates in the
North Atlantic mid-latitudes. J.Phys.Oceanogr., 24,
1236-1255.
Gulev, S.K., 1995: Long-term variability of sea-air heattransfer in the North Atlantic Ocean. Int.J.Climatol.,
15, 825-852.
Gulev, S.K., 1997a: Climate variability of the intensity ofsynoptic processes in the North Atlantic midlatitudes.
J.Climate, 10, 574-592.
Gulev, S.K., 1997b: Climatologically significant effectsof space-time averaging in the North Atlantic sea-air
heat flux fields. J.Climate, 10, 2743-2763.
Gulev, S.K., 1999: Comparison of COADS Release 1awinds with instrumental measurements in the NorthWest Atlantic. J.Atmos.Oceanic Technology, 16, 133145
Gulev, S.K., and Hasse, L. 1999: Changes of wind waves
in the North Atlantic over the last 30 years, Int. J.
Climatology 19 (10) 1091-1117.
Gulev, S.K., D. Cotton, A. Sterl, 1998: Intercomparisonof the North Atlantic wave climatology from
Voluntary Observing Ships, Satellite Data andModelling. Phys. Chem. Earth 23, 587-592.
Gulev, S.K., T.Jung, and E.Ruprecht, 2000: Intercomparison of the ocean-atmosphere heat fluxes from thevoluntary observing ship data and numerical weather
prediction system. J.Phys. Oceanogr., (submitted).
Gupta, S.K, W.L. Darnell and A.C. Wilber, 1992: A
parameterization for longwave surface radiation fromsatellite data: Recent improvements. J. Appl. Meteor.,
31, 1361-1367.
Gupta, S.K., N.A. Ritchey, A.C. Wilber, C.H. Whitlock,
G.G. Gibson and P.H. Stackhouse, 1999: A
climatology of surface radiation budget derived fromsatellite data. J. Climate, 12, 2691-2710.
Hack, J.J. 1998: Analysis of the improvement in implied
meridional ocean energy transport as simulated by
the NCAR CCM3 J. Climate 11: (6) 1237-1244.
Hackert, E. C., A. J. Busalacchi, and R. Murtugudde,2000: A wind comparison study using an ocean
general circulation model for the 1997-1998 El Niño.
J. Geophys. Res., submitted.
Haeffelin, M., S. Kato, A. M. Smith, K. Rutledge, T.
Charlock, and J. R. Mahan, 2000: Determination of
the thermal offset of the Eppley Precision Spectral
Pyranometer. Submitted to Appl. Opt.
Hagemann S. and Dümenil, L., 1996: Development of aparameterisation of lateral discharge for the global
scale, MPI Report No. 219, 34pp.
Hahn, C.J, S.G. Warren, and J. London, 1995: The effectof moonlight on observations of cloud cover at night,
and applications to cloud climatology. J. Climate, 8(5) part 2, 1429-1446.
Häkkinen, S., 1993. An Arctic source for the GreatSalinity Anomaly: A simulation of the Arctic ice

June, 2000

Reference list

ocean system for 1955-1975. J. Geophys. Res., 98,
16397-16410.
Hall, M.M., and H.L.Bryden, 1982: Direct estimates andmechanisms of ocean heat transport. Deep-Sea Res.,35, 1441-1450.
Halliwell, G.R., and P. Cornillon, 1990: Large-scale SST
variability in the Western North Atlantic sutropicalconvergence zone during FAZINEX. Part II: Upper
ocean heat budget and frontogenesys. J.Phys.
Oceanogr., 20, 223-234.
Hamill, T. M., R. P. d'Entremont and J. T. Bunting, 1992:
A description of the Air Force real-timenephananalysis model. Wea. and Fcst., 7, 288-306.
Han, Y. J., 1984: A numerical world ocean generalcirculation model. Part II. A baroclinic experiment.
Dyn. Atmos. Oceans, 8, 141-172.
Hanava, K., and Y.Toba, 1987: Critical examination of
estimation methods of long-term mean air-sea heatand momentum transfers. Ocean-Air Inter., 1, 79-93.
Hanen, J. E., M. Sato, A. Lacis, R. Ruedy, I. Tegen, andE. Matthews, 1998: Climate forcings in the
Industrial era. Proc. Natl. Acad. Sci., 95, 1275312758.
Haney, R. L., 1971: Surface thermal boundary condition
for ocean circulation models, J. Phys. Oceanogr., 1,
145-167.
Hare, J. E., P. O. G. Persson, C. W. Fairall and J. B.
Edson, 1999: Behaviour of Charnock's relationshipfor high wind conditions. Preprint vol.: 13th Conf.
on Boundary Layers and Turbulence, Dallas, Texas,
10-15 January, 1999, Amer. Meteor. Soc., Boston,MA.
Harrison, D. E., 1989: On climatological monthly mean
wind stress and wind stress curl fields over the world
ocean. J. Climate, 2, 57-70.
Harshvardhan, R. Davies, D. A. Randall, and T. G.
Corsetti, 1987: A fast radiation parameterization foratmospheric circulation models. J. Geophys. Res.,92, 1009-1016.
Hasse, L., M. Grossklaus, K. Uhlig and P. Timm, 1998:
A ship rain gauge for use in high winds. J. Atmos.
Oceanic Tech., 15, 380-386.
Hastenrath, S. and P. J. Lamb, 1977: Climatic atlas of theTropical Atlantic and Eastern Pacific Oceans,
University of Wisconsin Press, 105 pp.
Hastenrath, S. and P. J. Lamb, 1978: Heat budget atlas of
the Tropical Atlantic and Eastern Pacific oceans,
The University of Wisconsin Press, Madison, WI., 90
pp.
Hastenrath, S. and P. J. Lamb, 1979: Climatic atlas of the
Indian Ocean: Part 1 Surface climate andatmospheric circulation, The University of
Wisconsin Press, Madison, WI., 19pp + 97 charts.
Hastenrath, S. and P. J. Lamb, 1980: On the heat budgetof the hydroshere and atmosphere in the Indian
Ocean, J. Phys. Oceanogr. 10, 694 - 708.
Hastenrath, S., 1980: Heat budget of the tropical ocean
and atmosphere. J. Phys. Oceanogr. 10, 159 - 170.
Heberden, W., 1769: Of the quantities of rain whichappear to fall, at different heights, over the same spotof ground. Phil. Trans. Roy. Soc., 59, 359.
Heckley, W.A., 1985: Systematic errors of the ECMWF
operational forecasting model in tropical regions.
Quart. J. Roy. Meteor. Soc., 111, 709-738.
Hellerman, S. and M. Rosenstein, 1983: Normal monthly
wind stress over the world ocean with error estimates.
J. Phys. Oceanogr., 13, 1093 - 1104.
Hendon, H., and T. Shinoda, 1999: Assessment of warm
pool surface fluxes from NCEP reanalyses. COARE-

Report of the Working Group on Air Sea Fluxes

283

98: Proc. Conf. TOGA coupled Ocean-AtmosphereResponse Experiment (COARE), Boulder, CO. 7-14
July 1988. WCRP-107, WMO/TD-940. WMO,
Geneva, 255.
Henjes, K., 1998: Justification of the inertial dissipation
technique in anisotropic mean flow. Boundary-Layer
Meteor., 88, 161-180.
Hicks, B. B. and G. D. Hess, 1977: On the Bowen ratio
and surface temperature at sea. J. Phys. Oceanog., 7,
141 - 145.
Higgins, R. W., Y.-P. Yao, M. Chelliah, W. Ebisuzaki, J.
E. Janowiak, C. R. Ropelewski and R. E. Kistler,
1996: Intercomparison of the NCEP/NCAR and theNASA/DAO reanalyses (1985-1993). NCEP/Climate
Prediction Center ATLAS No. 2. U.S. Dept. ofCommerce, Washington, D.C., 169 pp. - see also
http://yao.ncep.noaa.gov:2000/atlas_2/toc_1.html
Hilmer R., Harder M., Lemke P. 1998: Sea ice transport:a highly variable link between Arctic and North
Atlantic, Geophys. Res. Lett. 25: (17) 3359-3362.
Hnilo, J. J., B. D. Santer, J. S. Boyle, K. E. Taylor, and C.
Doutriaux, 2000: Research activities at the programfor climate model diagnostics and intercomparion.
presented at 2nd Intl. Conf. on Reanalyses, Reading,England, 23-27 Aug. 1999. (seehttp://wesley.wwb.noaa.gov/reanalysis2/kana/hnilo1.
pdf )Hoeber, H., 1995: Comment on "Correction of Marine
Air Temperature Observations for Solar Radiation
Effects". J. Atmos. & Oceanic Tech., 12, 197 - 198.
Hogben, N. and F.E. Lumb, 1967: Ocean Wave Statistics,
Ministry of Technology, HMSO, London, 263 pp.
Hogben, N., 1988: Experience from compilation of
Global Wave Statistics, Ocean Eng., 15, 1-31.
Hogben, N., N. Dacunha and G. Oliver, 1986: Global
Wave Statistics, Unwin Brothers, London, 661pp.
Holfort J., 1994: Grossräumige Zirkulation undmeridionale Transporte im Südatlantik. Berichte aus
dem Institut für Meereskunde an der ChristianAlbrechts-Universität, Kiel, 260, 96 pp.
Holland, J. Z., 1972: Comparative evaluation of some
BOMEX measurements of sea surface evaporation,energy flux and stress. J. Phys. Oceanogr., 2, 476486.
Hollinger, J. P., R. Lo, G. Poe, R. Savage, and J. Pierce,
1987: Special Sensor Microwave/Imager user’s
guide. Naval Research Laboratory, Washington,
D.C., 177 pp.
Holtslag, A. A. M., E. I. F. de Bruijn, and H.-L. Pan,
1990: A high resolution air mass transformation
model for short range weather forecasting. Mon.
Weath. Rev, 118, 1561 – 1575.
Hong, S.-Y., and H.-L. Pan, 1996: Nonlocal boundary
layer vertical diffusion in a medium-range forecast
model. Mon. Wea. Rev., 124, 2322-2339.
Hosom, D.S., R.A. Weller, R.E. Payne, and K.E. Prada,
1995: The IMET(Improved Meteor.) ship and buoysystems. J. Atmos. Oceanic Technol., 12, 527-540.
Hsiung, J., 1986: Mean surface energy fluxes over the
global ocean. J. Geophys. Res., 91(C9), 1058510606.
Hu, Yongxiang, 2000: Personal communication.
Huffman, G. J., & co-authors, 1997: The Global Precipitation Climatology Project (GPCP) combined precipitation dataset. Bull. American Met. Soc., 78, 5 - 20.
Hurrell, J. W. and K. E. Trenberth, 2000: Global sea
surface temperature analyses: multiple problems andtheir implications for climate analysis, modeling, andreanalysis. Bull. Am. Meteor. Soc., (in press).

June, 2000

Reference list

Husar,R.B., J.M. Prospero and L.L.Stowe, 1997:
Characterization of tropospheric aerosols over theoceans with the NOAA advanced very highresolution radiometer optical thickness operational
product. J. Geophy. Res., 102(D14), 16889-16909.
Illingworth, A.J., and C.J. Stevens, 1987: An optical
disdrometer for the measurement of raindrop size
spectra in windy conditions. J. Atmos. Oceanic Tech.,4, 411-421.
Inamdar, A. K., and V. Ramanathan, 1997: Estimation ofLongwave Surface Radiation Budget from CERES(Subsystem 4.6.2). Earth Observing System (EOS)CERES Algorithm Theoretical Basis Document(ATBD), Release 2.2, June 1997, 19 pp. + apps.
Iqbal,M., 1983: An introduction to solar radiation.
Academic Press, 390pp.
Isemer, H.J. & L.Hasse, 1987: The Bunker climate atlasof the North Atlantic Ocean. Volume 2: Air-sea
interactions. Springer Verlag, Berlin, 252 pp.
Isemer, H.-J., 1994: On the homogeneity of Surface Wind
Speed records from Ocean Weather Stations, Institut
für Meereskunde, Kiel, Germany, 43 pp. + figs.
Isemer, H.-J., 1995: Trends in marine surface windspeed: Ocean Weather Stations versus Voluntary
Observing Ships. Proc. Internat. COADS Winds
Workshop, Kiel, Germany, 1994. Ed. H.Diaz and H.J.Isemer, NOAA-ERL, IFM (Kiel), 68-84.
Isemer, H.-J., and L. Hasse, 1985: The Bunker Climate
Atlas of the North Atlantic Ocean. Vol. 1,
Observations, Springer Verlag, Berlin, 218 pp.
Isemer, H.-J., J.Willebrand, and L.Hasse, 1989: Fine
adjustment of large-scale air-sea energy flux
parameterizations by direct estimates of ocean heattransport. J.Climate, 2, 1163-1184.
Iwasaka, N., 1999: Estimation of longwave radiation flux
at sea surface based on satellite measurements. Rep.
of Sci. Res. on Priority Areas (No.08241111) ofBetter Understanding of Water and Energy Cycles bySatellites (BUWECS), Japanese Ministry of
Education, Science, Sports and Culture, 11-19. (inJapanese).
Iwasaka, N., and J.M.Wallace, 1995: Large-scale air-sea
interaction in the Northern hemisphere from a view
point of variations of surface heat flux by SVD
analysis. J.Met.Soc.Japan, 73, 781-794.
Jabouille, P., J. L. Redelsperger, and J. P. Lafore, 1995:
Modification of surface fluxes by atmospheric
convection in the TOGA COARE region. Mon. Wea.
Rev., 124, 816-837.
Jaeger, L., 1976: Monatskarten des Niederschlags für dieganze Erde. Berichte des Deutschen Wetterdienstes,
Offenbach, 33 pp. and plates.
Jakob, C., 1994: The impact of the new cloud scheme on
ECMWF’s Integrated Forecast Scheme (IFS). Proc.
ECMWF/GEWEX Workshop on Modelling,
Validation and Assimilation of Clouds, ECMWF,
Nov. 1994. ECMWF, Reading, UK.
Janowiak, J.E., A. Gruber, C. R. Kondragunta, R. E.
Livezey, and G. J. Huffman, 1998: A comparison of
the NCEP/NCAR reanalysis precipitation and the
GPCP raingauge-satellite combined data set withobservational error considerations. J. Climate, 11,2960-2979.
Janssen, J. A. M., 1997: Does wind stress depend on seastate or not? - a statistical error analysis of HEXMAX
data. Boundary-Layer Meteor., 83, 479-503.
Janssen, P. A. E. M., 1999: On the effect of ocean waves
on the kinetic energy balance and consequences for

Report of the Working Group on Air Sea Fluxes

284

the inertial dissipation technique. J. Phys. Oceanogr.,29(3), 530 - 534.
Janssen, P. A. E. M., and P. Viterbo, 1996: Ocean waves
and the atmospheric climate. J. Climate, 9, 12691287.
Janssen, P.A.E.M., 2000: Ocean waves do affect the
kinetic energy balance. J. Phys. Oceanogr.,submitted.
Janssen, P.A.E.M., G.J. Komen, W.J.P. de Voogt, 1984:
An operational coupled hybrid wave prediction
model, J. Geophys. Res., 89, 3635-3654.
Jevons, W.S., 1861: On the deficiency of rain in anelevated rain-gauge, as caused by wind. Phil. Mag.,22, 421-433.
Jones, C., P. Peterson and C. Gautier, 1999: A new
method for deriving ocean surface specific humidity
and air temperature: an artificial neural network
approach. J. Appl. Met., 38: (8) 1229-1245.
Jones, C.S., D.M.Legler, and J.J.O'Brien, 1995:
Variability of surface fluxes over the Indian Ocean:
1960-1989. Global Atmos.Ocean Syst., 3, 249-272.
Jones, M. S., M. A. Saunders and T. H. Guymer, 1996a:
Global remnant cloud contamination in the alongtrack scanning radiometer data: Source and removal.
J. Geophys. Res., 101, 12141-12147.
Jones, M. S., M. A. Saunders and T. H. Guymer, 1996b:
Reducing cloud contamination in ATSR averaged sea
surface temperature data. J. Atmos. & Oceanic Tech.,13, 492 - 506.
Josey, S. A., 2000: A comparison of surface heat fluxes
in the subduction region of the North-East Atlantic
from the ECMWF and NCEP/NCAR reanalyses withmoored buoy measurements. J. Climate. (accepted).
Josey, S. A., D. Oakley and R. W. Pascal, 1997: On
estimating the atmospheric longwave flux at theocean surface from ship meteorological reports. J.
Geophys. Res., 102(C13), 27961 - 27972.
Josey, S. A., E. C. Kent and P. K. Taylor, 1998: The
Southampton Oceanography Centre (SOC) OceanAtmosphere Heat, Momentum and Freshwater Flux
Atlas. Report No. 6, Southampton Oceanography
Centre, Southampton, United Kingdom, 30 pp + figs.
Josey, S. A., E. C. Kent and P. K. Taylor, 2000: On thewind stress forcing of the ocean in the SOC andHellerman and Rosenstein climatologies.
J.Phys.Oceanogr. (submitted).
Josey, S. A., E. C. Kent, and P. K. Taylor, 1999: New
insights into the ocean heat budget closure problem
from analysis of the SOC air-sea flux climatology. J.
Clim., 12, 2856-2880.
Josey, S., E.C.Kent, and P.K.Taylor, 1995: Seasonalvariations between sampling and classical mean
turbulent heat flux estimates in the Eastern North
Atlantic. Ann. Geophys. 13, 1054-1064.
Joss, J., and A. Waldvogel, 1967: A raindrop
spectrometer with automatic readout. Pure Appl.
Geophys., 68, 240-246.
Jost, V., J. Schulz, and S. Bakan, 1999: Remote Sensingof the Global Energy and Water Cycle. Vol. II, Proc.
OceanObs 99 Conf., St. Raphael, France, 18-22 Oct.,
CNES.
Jourdan, G. and C. Gautier, 1995: Comparison between
global latent heat flux computed from multisensor(SSM/I and AVHRR) and from in situ data. J. Atmos.
Oceanic Technol., 12, 46 - 72.
Kader, B. A. and A. M. Yaglom, 1990: Mean fields andfluctuation moments in unstabily stratified turbulent
boundary layers. J. Fluid Mech., 212 637 – 662.

June, 2000

Reference list

Kallberg, P., 1997: Aspects of the Re-analysed Climate.
ECMWF Re-Analysis Project Report Series, 2.
ECMWF, Reading, UK, 89 pp.
Kalnay, E., & co-authors, 1996: The NCEP/NCAR 40year reanalysis project. Bull. Amer. Meteor. Soc., 77,
437-471.
Kalnay, E., 1999: Atmospheric models for weather
prediction and climate simulations. Global Energy
and Water Cycles, K. A. Browning and R. J. Gurney,eds., Cambridge University Press, Cambridge, UK,
30-32.
Kalnay, E., S. J. Lord and R. D. McPherson, 1998:
Maturity of operational numerical weather prediction:
Medium range. Bull. Amer. Meteor. Soc., 79, 27532769.
Kanamitsu, M., 1989: Description of the NMC global
data assimilation and forecasting system. Wea. and
Forecasting, 4, 335-342.
Kanamitsu, M., W. Ebisuzaki, J. Woolen, J. Potter, and
M. Fiorion, 2000 An overview of NCEP/DOE
Reanalysis-2. Proc. Conf.2nd Intl. Conf. on
Reanalyses, Reading, England, 23-27 Aug. 1999.
WCRP-109 (WMO/TD 985) WMO, Geneva, 1 - 4.
Kaplan, A., M. Cane, Y. Kushnir, A. Clement, M.
Blumenthal and B. Rajagopalan, 1998: Analyses of
Global Sea Surface Temperature, 1856 - 1991. J.
Geophys. Res., 103, (C9) 18567 - 18589.
Kaplan, A., Y. Kushnir, M. Cane and M. Blumenthal,
1997: Reduced space optimal analysis for historical
datasets: 136 years of Atlantic sea surface
temperatures.Analyses of Global Sea Surface
Temperature, 1856 - 1991. J. Geophys. Res., 102,27835 – 27860.
Kato, S., T. P. Ackerman, E. E. Clothiaux, J. H. Mather,
G. R. Mace, M. Wesley, F. Murcray, and J.
Michalsky, 1997: Uncertainties in modeled andmeasured clear-sky surface shortwave irradiances. J.
Geophys. Res., 102, 25,881-25,898.
Katsaros, K.B. 1990: Parameterization schemes andmodels for estimating the surface radiation budget. In
G.L.Geernaert and W.J.plant (eds.), Surface waves
and Fluxes, Volume II, 339-368, Kluwer.
Katsaros, K.B. and G. de Leeuw, 1994: Comment on“Sea spray and the turbulent air-sea heat fluxes” by
Edgar L. Andreas. J. Geophys. Res., 99, 14.33914,343.
Katsaros, K.B. and J.E. DeVault, 1986: On irradiancemeasurement errors at sea due to tilt of radiometers.
J. Atmos. Oceanic Tech., 3, 740-745.
Katsaros, K.B., L.A. McMurdie, R.J. Lind and J.E.
DeVault, 1985: Albedo of a water surface, spectral
variation,effects of atmospheric transmittance, sunangle, and wind speed. J. Geophys. Res., 90, 73137321.
Kaufeld, L., 1981: The development of a new Beaufort
equivalent scale. Meteor. Rundsch., 34, 17-23.
Keith, D.A., 1995: Meridional energy transport:
Uncertainty in zonal means. Tellus, 47A, 30-44.
Kelly, K.A., S.Dickinson and Z. Yu, 1999: NSCAT
tropical wind stress maps: Implications for improving
ocean modeling. J.Geophys. Res., 104, 11291-11310.
Kent, E. C. and P. K. Taylor, 1991: Ships observing
marine climate: a catalogue of the Voluntary
Observing Ships Participating in the VSOP-NA.
Marine Meteor. and Related Oceanographic
Activities 25, WMO, Geneva, 123 pp.
Kent, E. C. and P. K. Taylor, 1995: A comparison ofsensible and latent heat flux estimates for the North
Atlantic ocean. J. Phys. Oceanogr., 25, 1530 - 1549.

Report of the Working Group on Air Sea Fluxes

285

Kent, E. C. and P. K. Taylor, 1996: Accuracy of humidity
measurements on ships: consideration of solar
radiation effects. J. Atmos. & Oceanic Tech., 13,1317 - 1321.
Kent, E. C. and P. K. Taylor, 1997: Choice of a Beaufort
Equivalent Scale. J. Atmos. & Oceanic Tech., 14, 228- 242.
Kent, E. C., P. G. Challenor and P. K. Taylor, 1999: A
Statistical Determination of the Random
Observational Errors Present in Voluntary Observing
Ships Meteorological Reports. J. Atmos. & Oceanic
Tech., 16, 905-914.
Kent, E. C., P. K. Taylor and P. G. Challenor, 2000: The
Characteristics of a Sea Surface Temperature Dataset
Smoothed by Successive Correction. J. Climate, 13(11), 1845 – 1857.
Kent, E. C., P. K. Taylor, and P. Challenor, 1998: A
Comparison of Ship and Scatterometer-Derived Wind
Speed Data in Open ocean and coastal areas. Int. J.
Remote Sensing, 19, 3361-3381.
Kent, E. C., P. K. Taylor, B. S. Truscott and J. A.
Hopkins, 1993a: The accuracy of Voluntary
Observing Ship’s Meteorological Observations. J.
Atmos. & Oceanic Tech., 10, 591 - 608.
Kent, E. C., R. J. Tiddy and P. K. Taylor, 1993b:
Correction of marine daytime air temperatureobservations for radiation effects. J. Atmos. &Oceanic Tech., 10, 900 - 906.
Kessler, E., 1969: On the distribution and continuity ofwater substance in atmospheric circulations. Met.
Monographs, 10, (32). Amer. Meteor. Soc., Boston,MA, 84 pp.
Key, R.K., R.A. Silcox and R.S. Stone, 1996: Evaluationof surface radiative flux parameterizations for use in
sea ice models. J. Geophys. Res., 101, 3839-3849.
Kiehl, J. T., and K. E. Trenberth, 1997: Earth’s annual
global mean energy budget. Bull. Amer. Meteor.
Soc., 78, 197-208.
Killworth, P. D., A. D. Smeed, and A. J. Nurser, 2000:
The effects on ocean models of relaxation towards
observations at the surface. J. Phys. Oceanogr., 30(1) 160-174.
Kistler, R., & co-authors, 2000: The NCEP/NCAR 50year reanalysis. Submitted to Bullet. of the Amer.
Meteor. Soc. (Prelim. version:
http://sgi62.wwb.noaa.gov:8080/DISTRIBUTION/wd23gw/close/reanl2.htm. ).
Kizu, S., 2000: Degradation of VISSR visible
radiometers aboard three GMS-series satellites from1987 to 1999. (To be submitted to Int'l J. Remote
Sensing).
Kizu,S., 1995: A study on thermal response of ocean
surface layer to solar radiation using sateliite remotesensing. Doc. Thesis, Tohoku University, 100pp.
Kizu,S., 1999: Estimation of surface shortwave radiation
over the western Pacific based on satellite
measurements. Rep. of Sci. Res. on Priority Areas(No.08241111) of Better Understanding of Water andEnergy Cycles by Satellites (BUWECS) by theJapanese Ministry of Education, Science, Sports andCulture, 1-10. (in Japanese).
Klein S.A., and D.L. Hartmann 1993: Spurious Changes
in the ISCCP Dataset Geophys Res Lett 20 (6) 455 –458.
Klein, L. A. and C. T. Swift, 1977: An improved modelfor the dielectric constant of sea water at microwave
frequencies. IEEE Trans. Antennas and Propag., 25,
104 - 111.

June, 2000

Reference list

Klinker, E. and P. D. Sardeshmukh, 1987: The diagnosisof systematic error in numerical weather prediction
models. Proc. Workshop on Diabatic Heating, 30
Nov.-2 Dec. 1987, ECMWF. ECMWF, Reading,UK,, 209-244.
Klinker, E., 1997: Diagnosis of the ECMWF model
performance over the tropical oceans. Seminar on
Atmosphere-surface Interaction, 8-12 Sept. 1997,
ECMWF. ECMWF, Reading, UK, 53-66.
Klinker, E., A. Y. Hou and G. H. White, 1999: The
legacy of COARE for global weather forecasting andre-analysis. COARE-98: Proc. Conf. on the TOGA
Coupled Ocean-Atmosphere Response Experiment(COARE), Boulder, CO. 7-14 July 1988, WCRP-107,
WMO/TD 940, WMO, Geneva, 95-104.
Kneizys, F.X. & co-authors, 1988: Users guide to
LOWTRAN7. AFGL-TR-88-0177, Air Force
Geophysics Laboratory, Hanscom AFB, MA, 137pp.
Knochel, H., 1999. Développement et validation d'unmodèle numérique de circulation océanique àcoordonnée sigma pour l'étude climatique del'Atlantique Nord. Thèse de l'Université Joseph
Fourier, Grenoble, France.
Koltermann, K.P., A.V.Sokov, V.P.Tereschenkov,
S.A.Dobroliubov, K.Lorbacher, A.Sy, 1999: Decadalchanges in the thermohaline circulation of the North
Atlantic. Deep-Sea Res II, 46, 109-138.
Komen, G., P. A. E. M. Janssen, V. Makin and W. Oost,
1998: On the sea state dependence of the Charnockparameter. Global Atmos.Ocean Syst., 5, 367 - 388.
Komen, G.J., L. Cavaleri, M. Donelan, K. Hasselmann,
S. Hasselmann, P.A.E.M. Janssen, 1994: Dynamics
and Modelling of Ocean Waves, Cambridge Univ.
Press, New York, 532 pp.
Konda, M, Imasato, N., and A. Shibata, 1996: A new
method to determine near-sea surface temperature byusing satellite data. J. Geophys. Res., 101, 14349 14360.
Konda, M., N. Imasato, K. Nishi and T. Toda, 1994:
Measurement of the sea surface emissivity. J.
Oceanogr., 50, 17-30.
Kondo, J., 1975: Air-sea bulk transfer coefficients indiabatic conditions. Bound-Layer Met., 9 ,91-112.
Koschmeider, H., 1934: Methods and results of definiterain measurements. Mon. Wea. Rev., 62, 5-7.
Krasnopolsky, V. M., L. C. Breaker, and W. H. Gemmill,
1995: A neural network as a nonlinear transfer
function model for retrieving surface wind speeds
from the special sensor microwave imager. J.
Geophys. Res., 100, 11033 - 11045.
Kraus, E. and J.A. Businger, 1994: Atmosphere-Ocean
Interaction (2nd. Edition), Oxford University Press,
New York, 362 pp.
Kropfli R.A. and S.F. Clifford, 1994: The San Clemente
Ocean Probing Experiment: A study of air-sea
interaction with remote and in-situ sensors,
Proceedings IGARSS '94, Pasadena CA, IEEE, pp2407-2409.
Kubota, M and H. Yokota, 1998: Construction of surface
wind stress fields with high temporal resolution byusing the ERS-1 scatterometer data. J. Oceanogr.,54, 247-256.
Kubota, M. 1999: Introduction of Japanese Ocean Flux
Data Sets with Use of Remote Sensing Observations(J-OFURO), Proc. Internat. Symp. on Remote
Sensing, Kangnung, Korea, 231-236.
Kudryavtsev, V.N. and A.V. Soloviev, 1990: Slipperynear-surface layer of the ocean arising due to daytime
solar heating. J. Phys. Ocean., 20, 617-628.

Report of the Working Group on Air Sea Fluxes

286

Kummerow, C., W. Barnes, and T. Kozu, 1998: The
tropical measuring mission (TRMM) sensor package.
J. Atmos. Sci., 55, 809 - 817.
Kuroda, Y., Y. Amitani, K. Sono and A. Shoji, 1999: Online Oceanographic Data System for Moored Buoys TRITON Buoy Data Stream-.
http://www.jamstec.go.jp/jamstec/TRITON
Kurtyka, J.C., 1953: Precipitation measurements study.
Report of Investigation No. 20, State Water Survey,
Illinois.
Kushnir, Y., 1999: Europe’s winter perspectives. Nature,398, 289-291.
Kutsuwada, K., 1998: Impact of wind/wind-stress field in
the North Pacific constructed by ADEOS/NSCAT
data, J. Oceanogr., 5, 443-456.
Kwok, R., and D.A. Rothrock, 1999: Variability of Fram
Strait ice flux and North Atlantic oscilliation, J.
Geophys. Res., 104, 5,177-5,190.
LabSea Group, 1998: The Labrador Sea Deep convection
Experiment. Bull Amer. Met. Soc., 79, 2033-2058.
Lacis, A. A., and J. E. Hansen, 1974: A parameterization
for the absorption of solar radiation in the Earth’s
atmosphere. J. Atmos. Sci., 31, 118-133.
Lagerloef, G. S. E., C. T. Swift, and D. M. Le Vine,
1995: Sea surface salinity: The next remote sensing
challenge. Oceanography, 8, 44 - 50.
Laing, A. K. and S. J. Reid, 1999: Evidence of mesoscalelows off the west coast of New Zealand. Weather
Forecast 14 (3) 369-383.
Lander, M. A. and M. L. Morrissey, 1987: Unexpected
duplicate ship reports in the Comprehensive OceanAtmosphere Dataset (COADS). Trop.OceanAtmospher Newsletter, 38, 13 - 14.
Large, W. G., 1979: The turbulent fluxes of momentum
and sensible heat over the sea during moderate tostrong winds., Ph.D. thesis, University of British
Columbia, 180 pp.
Large, W. G., G. Danabasoglu, S. C. Doney, and J. C.
McWilliams, 1997. Sensitivity to surface forcing andboundary layer mixing in a global ocean model:
Annual-mean climatology. J. Phys. Oceanogr., 27,
2418-2447.
Large, W. G., J. Morzel and G. B. Crawford, 1995:
Accounting for Surface Wave Distortion of the
Marine Wind Profile in Low-Level Ocean Storms
Wind Measurements. J. Phys. Oceanogr., 25, 2959 2971.
Large, W.G and S. Pond, 1981: Open ocean momentum
flux measurements in moderate to strong winds. J.
Phys. Oceanog., 11, 324-336.
Large, W.G. and S. Pond, 1982: Sensible and latent heat
flux measurements over the ocean. J. Phys.
Oceanog., 12, 464-482.
Large, W.G., W.R. Holland, and J.C. Evans, 1991:
Quasi-geostrophic ocean response to real wind
forcing: The effects of temporal smoothing. J. Phys.
Oceanogr.,21, 998-1017.
Larin, D.A. and G.N.Panin, 1985: The correlation
between meteorological parameters influence on thecalculations of evaporation and heat exchange over
the Caspian Sea. Water Res., 5, 68-75.
Lazier, J. N. R., 1980: Oceanic conditions at Weather
Station Bravo, 1960-1974. Atmosphere-Ocean, 18,
227 - 238.
Lazier, J. R., 1997. The salinity decrease in the Labrador
Sea over the past thirty years. In Decade to century
time scales of natural climate variability, (Irvine
C.A.S. Editor), National Academy Press.

June, 2000

Reference list

Lebedev, I., and Tomczak, M., 1999: Rainfall measurements with navigational radar. J. Geophys. Res., 104,
13697 - 13708.
Ledvina, D.L., G.S.Young, R.A.Miller, and C.W.Fairall:
1993: The effect of averaging on bulk estimates ofheat and momentum fluxes for the tropical Western
Pacific Ocean. J.Geophys.Res., 98, 20211-20217.
Lee, T. H., J. E. Janowiak, and P. A. Arkin, 1991: Atlasof Products from the Algorithm Intercomparison
Project 1: Japan and Surrounding Oceanic regions.
UCAR, 131 pp.
Legates, D. R. and C. J. Willmott, 1990: Mean seasonaland spatial variability in gauge corrected global
precipitation. Int. J. Climatol., 10, 111 - 127.
Legler, D. M., J. N. Stricherz, and J. J. O'Brien, 1997:
TOGA Pseudo-Stress Atlas 1985-1994: III Indian
Ocean. COAPS Report 97-3, COAPS/Florida State
University, Tallahassee, FL 32306-3041, 163 pp.
Legler, D., 1991: Errors in five-day mean surface wind
and temperature conditions due to inadequate
sampling. J.Atmos.Oceanic.Technol., 8, 705-712.
Levitus, S. et al., 1994: World Ocean Atlas 1994 (4Volumes) NOAA Atlas NESDIS 4, U.S. Departmentof Commerce, Washington, D.C., 117 pp. Available
from: U.S Department of Commerce, NODC, User
Services Branch, NOAA/NESDIS E/OC21,
Washington D.C, 20233, USA.
Vol. 1: (Conkright, M. E., S. Levitus, and T. P.
Boyer) Nutrients.
Vol. 2: (Levitus, S. and T. P. Boyer) Oxygen.
Vol. 3: (Levitus, S., R. Burgett and T. P. Boyer)
Salinity.
Vol. 4: (Levitus, S. and T. P. Boyer) Temperature.
Levitus, S., 1982: Climatological Atlas of the World
Ocean. U S Government Printing Department,
NOAA Professional Paper No. 13, Washington D.C.,
173 pp.
Li, Z., and H. Leighton, 1993: Global climatologies ofsolar radiation budgets at the surface and in theatmosphere from 5 years of ERBE data. J. Geophys.
Res., 98, 4919-4930.
Li, Z., H.G. Leighton, K. Masuda and T. Takashima,
1993: Estimation of SW flux absorbed at the surface
from TOA reflected flux. J. Climate, 6, 317-330.
Liberti, G. L., 1995: Review of SSM/I based algorithms
submitted for the GPCP-AIP/2. Microwave
Radiometry and Remote Sensing of the Environment.
D. Solimini, Ed., VSP Press., 297 - 306.
Lind, R.J, and K.B. Katsaros, 1986: Radiation
measurements and model results from R/V
Oceanographer during STREX 1980. J. Geophys.
Res., 91, 13308-13314.
Lind, R.J., K.B. Katsaros and M. Gube, 1984: Radiationbudget components and their parameterization in
JASIN. Quart. J. Roy. Meteor. Soc., 110, 1061-1071.
Lindau R., 2000: Climate Atlas of the Atlantic Ocean
derived from the Comprehensive Ocean Atmosphere
Data Set, Springer Verlag Berlin, 488pp.
Lindau, R., 1995a: A new Beaufort equivalent scale.
Internat. COADS Winds Workshop, Kiel, Germany,
31 May - 2 June 1994, Environmental ResearchLabs., NOAA, Boulder, Colorado, 232 - 252. (also at
http://www.cdc.noaa.gov/coads/kiel/ )Lindau, R., 1995b: Time dependent calibration of marine
Beaufort estimates using individual pressure
differences. Internat. COADS Winds Workshop, Kiel,
Germany, 31 May - 2 June 1994, Environmental
Research Labs., NOAA, Boulder, Colorado, 253 –
269. (also at http://www.cdc.noaa.gov/coads/kiel/ )Report of the Working Group on Air Sea Fluxes

287

Lindau, R., H.-J.Isemer and L.Hasse,1990: Towards
time-dependent calibration of historical windobservations at sea. Trop.Ocean-Atmos.Newslett., 54,
7-12.
Lindsay, R.W. and D.A. Rothrock, 1994: Arctic sea ice
albedo from AVHRR. J. Climate, 7, 1737-1749.
Liou, K. N., 1992: Radiation and Cloud Processes in the
Atmosphere. Oxford University Press, 487 pp.
Liu, Q., C. Simmer, and E. Ruprecht, 1997: Estimating
longwave net radiation at sea surface from theSpecial Sensor Microwave/Imager (SSM/I). J. Appl.
Met., 36, 919-930.
Liu, W. T., 1986: Statistical relation between monthly
mean precipitable water and surface-level humidity
over global oceans. Mon. Wea. Rev., 114, 1591-1602.
Liu, W. T., 1988: Moisture and latent heat fluxes
variabilities on the Tropical Pacific derived fromsatellite data. J. Geophys. Res., 93, 6749 - 6760.
Liu, W. T., 1990: Remote sensing of surface turbulence
heat flux. Surface Waves and Fluxes, G. L. Geernaert
and W. J. Plant, Eds., Kluwer Academic Publishers,
Dordrecht, 293-309.
Liu, W. T., and P. P. Niiler, 1984: Determination ofmonthly mean humidity in the atmospheric surface
layer over ocean from satellite data. J. Phys.
Oceanogr., 14, 1452-1457.
Liu, W. T., W. Tang, and F. J. Wentz, 1992: Precipitablewater and surface humidity over global oceans fromSpecial Sensor Microwave Imager and European
Center for Medium Weather Forecasts. J. Geophys.
Res., 97, 2251-2264.
Liu, W. T., W. Tang, and P. P. Niiler, 1991: Humidity
profiles over the ocean. J. Climate, 4, 1023 - 1034.
Liu, W.T. and J.A. Businger, 1975: Temperature profilein the molecular sublayer near the interface of a fluidin turbulent motion. Geophys. Res Lett., 2, 403-404.
Liu, W.T., K.B.Katsaros and J.A.Businger 1979. Bulk
parameterization of the air-sea exchange of heat andwater vapour including the molecular constraints at
the interface. J. Atmos. Sci., 36, 1722-1735.
Lo, R. C. A., 1983: A comprehensive description of themission sensor microwave imager SSM/I
environmental parameter extraction algorithm. NRL
Memo. Rep. 5199, 48 pp., Nav. Res. Lab.,
Washington, D.C.
Louis, J.F., 1979: A parametric model of vertical eddy
fluxes in the atmosphere. Boundary-Layer Meteor.,
17, 187-202.
Lumb, F. E. , 1964: The influence of cloud on hourly
amount of total solar radiation at the sea surface.
Quart. J. Roy. Meterol. Soc., 90, 43-56.
Luthardt, H., 1985: Estimation of mesoscale surface
fields of meteorological parameters in the North Sea
area from routine measurements. Beitr. Phys. Atmos.,58, 255 - 272.
Macdonald A. M., 1998: The global ocean circulation: ahydrographic estimate and regional analysis.
Progress in Oceanography, 41, 281-382.
Macdonald, A. M., and C. Wunsch, 1996: An estimate ofglobal ocean circulation and heat fluxes. Nature, 382(6590) 436-439.
MacDonald, A., J.Candela & H.L. Bryden, 1994: An
estimate of the net heat transport through the Strait ofGibraltar. Coastal Estuarine Stud. 46, edit. P.E.
Violette, AGU, Wash. DC.
MacWhorter, M.A., and R.A. Weller, 1991: Error inmeasurements of incoming shortwave radiation madefrom ships and buoys. J. Atmos. Oceanic Tech., 8,108-117.

June, 2000

Reference list

Magiorre, A., M.Zavaterelli, M.G.Angelucci, andN.Pinardi 1998: Surface heat and water fluxes in the
Adriatic Sea: seasonal and interannual variability.
Phys. Chem. Earth , 23, 5/6, 561-567.
Malevskii S.P., G.V.Girduk & B.Egorov, 1992: Radiation
balance of the ocean surface, Hydrometeoizdat,
Leningrad, 148 pp.
Marshall, J. S. and W.M. Palmer, 1948: The distributionof raindrops with size, J. Meteor. 5, 165 – 166.
Masutani, M., K. A. Campana, and S. J. Lord, 1999: Noteof cloud cover of the ECMWF nature run used for
OSSE/NPOESS project. NCEP Office Note 427.
NCEP, 4700 Silver Hill Rd., Mail Stop 9910,
Washington, DC 202330-9910, 19 pp + figs.
May, P. W. 1986: A brief explanation of Mediterraneanheat and momentum flux calculations, NORDA code
322, NSTL, MS 39529.
Maykut, G.A., and N. Untersteiner, 1971: Some resultsfrom a time-dependent thermodynamic model of sea
ice, J. Geophys. Res., 76, 1550-1575.
McCartney, M.S., R.G. Curry, and H.F. Bezdek,
1997.:The interdecadal warming and cooling ofLabrador Sea Water. ACCP notes, IV(1), 1-11
McClain, E. P., 1981: Multiple atmospheric window
techniques for satellite-derived sea surface
temperatures. Oceanography from Space, J. Gower,
Ed., Plenum, 73 - 85.
McPhaden, M. J., A. J. Busalacchi, and J. Picaut, 1988:
Observations and wind-forced model simulations of
the mean seasonal cycle in tropical Pacific sea
surface topography. J. Geophys. Res., 93, 8131-8146.
Medwin, H., J.A. Nystuen, P.W. Jacobus, D.E. Snyder,and L.H. Oswald, 1992: The anatomy of underwater
noise. J. Acoust. Soc. Amer., 92, 1613-1623.
Mehta, V., 1998: Variability of the tropical ocean surface
temperatures at decadal-multidecadal time scales.
Part I. Atlantic Ocean. J. Climate, 11, 2351-2375.
Melville, W.K., 1977: Wind stress and roughness length
over breaking waves. J. Phys. Oceanog., 7, 702-710.
Merchant, C. J. and A. R. Harris, 1999: Toward theelimination of bias in satellite retrievals of sea
surface temperature 2. Comparison with in situ
measurements. J. Geophys. Res., 104, 23579 - 23590.
Michalsky, J., M. Rubes, T. Stoffel, M. Wesely, M.
Splitt, and J. DeLuisi, 1997: Optimal measurementof surface shortwave irradiance using current
instrumentation - The ARM experience. Ninth AMS
Conf. on Atmospheric Radiation. Long Beach, CA.
Amer. Meteor. Soc., Boston, MA, J5-J9.
Miller, D.K., and K.B. Katsaros, 1992: Satellite derived
surface latent heat fluxes in a rapidly intensifying
marine cyclone. Mon. Wea.Rev., 120, 1093-1107.
Minnis, P., W. L. Smith, Jr., D. P. Garber, J. K. Ayers,and D., R., Doelling, 1995: Cloud properties derived
from GOES-7 for spring 1994 ARM intensive
observing period using Version 1.0.0 of ARM satellite
data analysis program. NASA reference publication
1366, 58 pp.
Miskolczi, F., 1994: Modeling of downward surface
longwave flux density for global change applic-ations
and comparison with pyrgeometer measure-ments. J.
Atmos. Oceanic Tech., 11, 608-612.
Miyakoda, K., and J. Sirutis, 1996: Manual of the Ephysics. Available from Geophysical Fluid
Dynamics Laboratory, Princeton University, P. O.
Box 308, Princeton, NJ 08542.
Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono,and S. A. Clough, 1997: Radiative transfer forinhomogeneous atmospheres: RRTM, a validated

Report of the Working Group on Air Sea Fluxes

288

correlated-k model for the longwave. J. Geophys.
Res., 102, 16663-16682.
Molinari R. L., E. Johns, and J. F. Festa, 1990: The
annual cycle of meridional heat flux in the Atlantic
Ocean at 26.5N. J.Phys. Oceanogr, 20, 476-482.
Moll, W.J.M., 1923: A thermopile for measuring
radiation. Proc. Phys. Soc. (London), 35, 257-260.
Monahan E.C., and I.G. O’Muircheartaigh, 1987:
Comments on “Albedos and glitter patterns of awind-roughened sea surface”. J. Phys. Oceanogr., 17,549-550.
Moorthi, S., and M. J. Suarez, 1992: Relaxed Arakawa
Schubert: A parameterization of moist convection forgeneral circulation models. Mon. Wea. Rev., 120,978-1002.
Morcrette, J.-J., 1991: Radiation and cloud radiative
properties in the European Centre for Medium-Range
Weather Forecasts forecasting system. J. Geoph.
Res., 96, 9121-9132.
Morrissey, M.L., and J.A. Maliekal, 1995: Standard error
estimation of COADS monthly mean winds. Proc.
Internat. COADS Winds Workshop, Kiel, Germany,
1994. Ed. H.Diaz and H.-J.Isemer, NOAA-ERL, IFM(Kiel), 165-170.
Moyer, K. A., and R. A. Weller, 1997: Observations ofsurface forcing from the subduction experiment: acomparison with global model products and climatological datasets. J. Climate, 10, 2725-2742.
Moyer, K. A., S. P. Anderson and R. A. Weller, 1996:
Observations of surface forcing from the subduction
experiment and TOGA COARE: a comparison with
global model products and climatology. Proc. WCRP
Workshop on Air-Sea flux fields for forcing ocean
models and validation of GCM's, ECMWF, Reading,
24 - 27 October 1995, WCRP-95 (WMO/TD 762),
WCRP, Geneva, 69 - 74.
Mueller, C.C. and E.H. Kidder, 1972: Rain gage catch
variation due to airflow disturbances around a
standard rain gage. Water Resources Research, 8,1077-1082.
Nakamura, N., and A. Oort, 1988. Atmospheric heatbudgets of the polar regions, J. Geophys. Res., 93,9510-9524.
Neiman P.J., Shapiro M.A., Donall E.G., Kreitzberg
C.W. 1990: Diabatic Modification Of An Extratropical Marine Cyclone Warm Sector By Cold
Underlying Water Mon. Weath.Rev. 118: (8) 15761590.
Neumann, J., and N. Roseman, 1954: The Black Sea:
energy balance and evaporation. Trans. Amer.
Geophys. Union, 35, 767-774.
Niekamp, K.-P., 1992: Untersuchung zur Gute derParametrisierung von Malevskii-Malevich zur
Bestimmung der solaren Einstrahlung an derOzeanoberflache. Diploma Msc, Institut für
Meereskunde, Kiel, 108 pp.
Nordeng, T.E., 1991: On the wave age dependent dragcoefficient and roughness length at sea. J. Geophys.
Res., 96, 7167-7174.
Nuss, W.A., and D.W. Titley, 1994: Use ofmultiquadratic interpolation for meteorologicalobjective analysis. Mon. Wea. Rev., 122, 1611-1631.
Nystuen, J.A., 1996: Acoustical rainfall analysis: Rainfall
drop size distribution using the underwater soundfield. J. Atmos. Oceanic Tech., 13, 74-84.
Nystuen, J.A., 1999: “Listening to raindrops”, Solstice
10, No. 2, http://www.imagenet.org .

June, 2000

Reference list

Nystuen, J.A., J. R. Proni, P. G. Black and J. C.
Wilkerson, 1996: A comparison of automatic rain
gauges. J. Atmos. Oceanic Tech., 13, 62-73.
Nystuen, J.A., M.J. McPhaden, and H.P. Freitag, 2000:
Surface measurements of precipitation from an ocean
mooring: The Underwater Acoustic Log from theSouth China Sea. J. Applied Meteor., (in press.).
Oberhuber, J. M., 1988: An atlas based on the COADS
data set: the budgets of heat, buoyancy and turbulent
kinetic energy at the surface of the Global Ocean.
Rep., Max-Plank-Institut fur Meteorologie, No. 15,
Hamburg, Germany, 100 pp.
Ochi, M.K., 1978: Wave statistices for the design of ships
and offshore structures, Proceedings SNAME,
November 1978, New York, NY.
Oki, R. and A. Sumi, 1994: Sampling simulation of
TRMM rainfall estimation using radar-AMeDAS
composites. J. Appl. Met., 33, 1597 - 1608.
Olbrük, G., 1979: Precipitation measurements at sea.
Marine meteorological and related oceanographicalactivities. Rep. 1, WMO, Geneva, 39 pp.
Oost, W. A., 1998: The KNMI HEXMAX stress data - arevisit. Boundary-Layer Meteor., 86, 447 - 468.
Paltridge, G.W. and C.M.R Platt, 1976: Radiative
Processes in Meteor. and Climatology, 318pp.,
Elsevier, New York.
Pan, H. -L. and W. -S. Wu, 1995: Implementing a massflux convection parameterization package for the
NMC medium-range forecast model. NMC Office
Note 409, 40 pp. Available from NCEP, 5200 Auth
Road, Washington, DC 20233.
Pan, H. -L., 1999: Parameterization of subgrid-scale
processes. Global Energy and Water Cycles, K.A.
Browning and R. J. Gurney, ed., Cambridge
University Press, Cambridge, England, 44-47.
Panofsky, H.A. and J.A. Dutton, 1984: AtmosphericTurbulence: models and methods for engineering
applications. Wiley, New York, 397 pp.
Parilla, G., A. Lavin, H. Bryden, M. Garcia and R.
Millard, 1984: Rising temperature in the subtropicalNorth Atlantic Ocean over the past 35 years. Nature,369, 48 - 51.
Parker, D. E., P. D. Jones, C. K. Folland and A. Bevan,
1994: Interdecadal changes of surface temperaturesince the late 19th century. J. Geophys. Res., 99,
14377 - 14399.
Parkinson, C.L. and W.M. Washington ,1979: A largescale numerical model of sea ice. J. Geophys. Res.,84, 311-337.
Parkinson, C.L., Comiso, J.C., Zwally, H.J., Cavalieri,
D.J., Gloersen, P., and Campbell, W.J., 1987: Arctic
Sea Ice, 1973-1976: Satellite Passive-Microwave
Observations, NASA SP-489, National Aeronautics
and Space Administration, Washington, D.C.
Parrish, D. F., and J. C. Derber, 1992: The National
Meteorological Center’s spectral statisticalinterpolation analysis system. Mon. Wea. Rev., 120,
1747-1763.
Pascal, R. W. and S. A. Josey, 2000: Accurate
radiometric measurement of the atmosphericlongwave flux at the sea surface. J. Atmos. &Oceanic Tech., (accepted).
Paulson, C. A., and J. J. Simpson, 1977: Irradiancemeasurements in the upper ocean, J. Phys.
Oceanogr., 7, 952-956.
Paulson, C.A. and J.J. Simpson, 1981: The temperature
difference across the cool skin of the ocean. J.
Geophys. Res., 86, 11044-11054.

Report of the Working Group on Air Sea Fluxes

289

Paulson, C.A., 1970: The mathematical representation ofwind speed and temperature profiles in the unstable
atmospheric surface layer. J. Appl. Meteor., 9, 857861.
Payne, R.E., 1972: Albedo of the sea surface. J. Atmos.
Sci., 29, 959-970.
Persson, P.O.G., D. Ruffieux and C.W. Fairall, 1997:
recalculations of pack ice and lead surface energy
budgets during the Arctic Leads Experiment(LEADEX) 1992. J. Geophys. Res., 102, 2508525089.
Petersen, W. A., R. C. Cifelli, S. A. Rutledge, B. S.
Ferrier, and B. F. Smull, 1999: Shipborne dualDoppler operations during TOGA COARE:
integrated observations of storm kinetics andelectrification. Bull. Amer. Meteor. Soc., 80, 81-96.
Peterson E.W., and L.Hasse, 1987: Did the Beaufort scaleor the wind climate change? J. Phys. Oceanogr., 17,1071-1074.
Pfaendtner, J., S. Bloom, D. Lamich, M. Seablom, M.
Sienkiewicz, J. Stobie and A. da Silva, 1995:
Documentation of the Goddard Earth Observing
System (GEOS) Data Assimilation System – Version
1. NASA Tech. Memo. 104606, vol. 4, Goddard
Space Flight Center, Greenbelt, MD 20771, 44 pp.
Philipona,R., C. Frohlich and Ch Betz, 1995:
Characterization of pyrgeometers and the accuracy of
atmospheric long-wave radiation measurements. Appl
Opt., 34, 1598-1605.
Phillips, T., 1994: A Summary Documentation of theAMIP Models. PCMDI Report 18, Lawrence Livermore National Laboratory, Livermore, CA, 343 pp (see http://wwwpcmdi.llnl.gov/modeldoc/amip/01toc.html )Pilewskie, P., and F. P. J. Valero, 1995: Direct
observations of excess solar absorption by clouds.
Science, 267, 1626-1629.
Pinker, R. T., I. Laszlo, C. H. Whitlock, and T. P.
Charlock, 1995: Radiative flux opens new window
on climate research. EOS Trans. Amer. Geophys.
Union, 76, 145-158.
Pinker, R.T., and I. Laszlo, 1992: Modeling surface solar
irradiance for satellite applications on a global scale.
J. Appl. Meteor., 31, 194-211.
Pinto, J.O. and J.A. Curry, 1997: Role of radiativetransfer in the modelled mesoscale development ofsummertime arctic stratus. J. Geophys. Res., 102,
13861-13872.
Planton, S., G. Caniaux, and H. Giordani, 2000: Surface
fluxes. in Proc. Conf. 2nd Intl. Conf. on Reanalyses,
Reading, England, 23-27 Aug. 1999. WCRP-109(WMO/TD 985) WMO, Geneva, 111 – 114.
Pollard, R.T. and K.J.H. Thomas, 1989: Verticalcirculation revealed by diurnal heating of the upper
ocean in late winter. Part 1: Observations. J. Phys.
Oceanogr., 19, 269-278.
Pond S., D.B. Fissel and C.A. Paulson, 1974: A note on
bulk aerodynamic coefficients for sensible heat andmoisture fluxes. Boundary-Layer Meteor., 6, 333339.
Post, M.J and Coauthors, 1998: The Combined Sensor
Program: an air-sea science mission in the central andwestern Pacific ocean. Bull. Amer. Meteor. Soc., 78,
2797-2815.
Price, J.F., R.A. Weller and R. Pinkel, 1986: Diurnal
cycling: observations and models of the upper ocean
response to diurnal heating, cooling and wind mixing.
J. Geophys. Res., 91, 8411-8427.

June, 2000

Reference list

Rago, T.A., and H.T.Rossby, 1987: Heat transport into
the Atlantic Ocean north of 32N latitude.
J.Phys.Oceanogr., 17, 854-871.
Ramage, C. S., 1987: Secular change in reported surface
wind speeds over the ocean. J. Climate Appl.
Meteor., 26, 525-528.
Ramanathan, V., B. Subasilar, G. J. Zhang, W. Conant,
R. D. Cess, J. T. Kiehl, H. Grassl, and L. Shi, 1995:
Warm pool budget and shortwave cloud forcing: A
missing physics? Science, 267, 499-503.
Rayner, N.A., E.B. Horton, D.E. Parker, C.K. Folland
and R.B. Hackett, 1996: Version 2.2 of the Global
sea-Ice and Sea Surface Temperature Data Set,
1903-1994. Climate Research Technical Note No. 74,
unpublished doc. available from the Meteorological
Office, London Road, Bracknell, U.K..
Redelsperger, J.L., F. Guichard, and S. Mondon, 2000: A
parameterization of mesoscale enhancement of
surface fluxes for large scale models. J. Climate, 13,402-421.
Reed, R.K., 1977: On estimating insolation over theocean. J. Phys. Ocean., 7, 482-485.
Renfrew, I. A., G. W. K. Moore, P. S. Guest and K.
Bumke, 2000: A comparison of surface-layer, surface
heat flux and surface momentum flux observations
over the Labrador Sea with ECMWF analyses and
NCEP reanalyses. Submitted to J. Climate.
Reynaud T., P. Legrang, H. Mercier and B. Barnier,
1998: A new analysis of hydrographic data in the
Atlantic and its application to an inverse modellingstudy. International WOCE Newsletter, 32, 29-32.
Reynolds, R. M., 1998: The ARM project: contribution
to the Tropical Western Pacific Site Scientific Plan.
(unpublished manuscript).
Reynolds, R. W. and D. S. Marsico, 1993: An improved
real time global sea surface temperature analysis. J.
Climate, 6, 114-119.
Reynolds, R. W. and T. M. Smith, 1994: Improved global
sea surface temperature analyses using optimum
interpolation. J. Climate 7, 929 - 948.
Reynolds, R. W., 1988: A real time global sea surface
temperature analysis. J. Climate, 1, 75-86.
Reynolds, R. W., 1993: Impact of Mount Pinatubo
aerosols on satellite-derived sea surface temperatures.
J. Climate, 6, 768 - 774.
Reynolds, R. W., 1999: Specific contributions to theobserving system: Sea Surface Temperatures.
CLIMAR 99, WMO Workshop on Advances inMarine Climatology, Vancouver, 8 - 15 Sept. 1999,
330 - 339.
Rieder, K. F. and J. A. Smith, 1998: Removing wave
effects from the wind stress vector. J. Geophys. Res.,
103 (C1), 1363 - 1374.
Rigor, I., R. Colony, and S. Martin, 2000: Variations insurface air temperatures over the Arctic Ocean from1979 to 1997, J. Climate,13: (5) 896-914.
Roberts, R. E., J. A. Selby and L. M. Bibermann, 1976:
Infrared continuum absorption by atmospheric water
vapor in the 8-12 micro-meter window. Appl. Optics,
15, 2085-2090.
Rodda, J.C. and S.W. Smith, 1986: The significance ofthe systematic error in rainfall measurement forassessing wet deposition. Atmospheric Environment,
20, 1059-1064
Rodwell, M.J., D.P.Rowell, and C.K.Folland, 1999:
Oceanic forcing of the wintertime North Atlantic
Oscillation and European climate. Nature, 398, 320323.

Report of the Working Group on Air Sea Fluxes

290

Rogers, C. D. and C. D. Walshaw, 1966: The computation of infrared cooling rate in planetary atmospheres, Quart. J. Roy. Meteor. Soc. 92, 67 – 92.
Rogers, E., and L.F.Bossart, 1986: An investigation of
explosively deepening oceanic cyclones. Mon.
Wea.Rev., 114, 702-718.
Roland, J.R., 1976: Comparison of two different raindrop
disdrometers. 17th Conf. on Radar Meteor., Seattle
WA, Amer. Meteor. Soc., 398-405.
Rosatti, A., and K. Miyakoda, 1988. A generalcirculation model for upper ocean simulations. J.
Phys. Oceanog., 18, 1601-1626.
Rossow, W. B., L. C. Garder, P.-J. Lu, and A. Walker,
1991: International Satellite Cloud Climatology
Project (ISCCP) documentation of cloud data.
WMO/TD-No. 266, Revised March 1991, WMO,
Geneva, 76 pp. plus three appendices.
Rossow, W.B., and A.A. Lacis, 1990: Global, seasonal
cloud variations from satellite radiancemeasurements. II: Cloud properties and radiative
effects. J. Climate, 3, 1204-1253.
Rossow, W.B., and R.A. Schiffer, 1991: ISCCP clouddata products. Bull. Amer. Meteor. Soc., 72, 2-20.
Rossow, W.B., and R.A. Schiffer, 1999: Advances inunderstanding clouds from ISCCP. Bull. Amer.
Meteor. Soc., 80, (11), 2261–2288.
Rossow, W.B., and Y-C. Zhang, 1995: Calculation of
surface and top-of-atmosphere radiative fluxes fromphysical quantities based on ISCCP datasets, Part II:
Validation and first results. J. Geophys. Res., 100,
1167-1197 (contact wrossow@giss.nasa.gov foraccess to these data).
Rossow, W.B., L.C. Garder and A.A. Lacis, 1989:
Global, seasonal cloud variations from satellite
radiance measurements. Part I: Sensitivity ofanalysis. J. Climate, 2, 419-462.
Rothrock, D.A., and J. Zhang, 1997: Surface
downwelling radiative fluxes: Ice model sensitivities
and data accuracies, Workshop on Polar Processes in
Global Climate, 13-15 November 1996, Cancun,
Mexico, Amer. Meteor. Soc., D(VT)200, 119-121.
Roullet, G., and G. Madec, 2000. Salt conservation, freesurface and varying volume: a new formulation for
Ocean GCMs. J. Geophys. Res., in press.
Ruprecht, E., 1993: Observation and analysis of global
rainfall. Energy and Water Cycles in the Climate
System, E.Rashke and D.Jacob, Eds., NATO ASI
SeriesI, vol. 5, Springer, 165-185.
Russell, C.A., C.W. Fairall, O.P.G. Persson, E.L.
Andreas, P.S. Guest, R. Lindsay, H.A. Eide and T.
Horst, 1999a: Intercomparison of downward
longwave flux measurements during the first twomonths of SHEBA. pp. 314-318, Proc. 5th conf. onpolar meteor. and oceanography, 10-15 Jan. 1999,
Dallas, TX, Amer. Meteor. Soc., Boston, MA.
Russell, C.A., C.W. Fairall, O.P.G. Persson, E.L.
Andreas, P.S. Guest, R. Lindsay and T. Delany,
1999b: Intercomparison and normalization of five
Eppley Pyrgeometer IR fluxes during April-May,
1998 at SHEBA. Proc. 10th conf. on atmosphericradiation, 28 June-2 July 1999, Madison Wis., Amer.
Meteor. Soc., Boston, MA.
Rutledge, S.A., R. Cifelli, C. DeMott, W. Petersen, T.
Rickenbach, J. Lutz, R. Bowie, M. Strong and E.
Williams, 1993: The shipboard deployment of the
MIT C-band radar during TOGA-COARE. Proc.
26th Inter. Conf. On Radar Met., 24-28 May 1993,
Norman, OK, Amer. Meteor. Soc., Boston, MA.

June, 2000

Reference list

Salby, M. L. and P. Callaghan, 1997: Sampling error inclimate properties derived from satellite
measurements: Consequences of undersampled
diurnal variability. J. Climate, 10, 18 - 36.
Sanders, J.W., 1976: A growth-stage scaling model for
the wind-driven sea, Dtsch. Hydrogr. Z., 29, 136-161.
Saunders, P.M., 1967: The temperature at the ocean-air
interface, J. Atmos. Sci. 24, 269-273.
Sauvageot, H., 1994: Rainfall measurement by radar: areview. Atmos. Res., 35, 27-54.
Schanz, L. and P. Schlüssel, 1997: Atmospheric backradiation in the Tropical Pacific. Meteor. Atmos.
Phys., 63, 217 - 226.
Schiano, M. E., R. Santoleri, F. Bignami, R. M. Leonardi,
S. Marullo and E. Boehm, 1993: Air-sea interaction
measurements in the west Mediterranean Sea during
the Tyrrhenian Eddy Multi-Platform Observations
Experiment. J. Geophys. Res., 98 (C2), 2461 - 2474.
Schlichting, H, 1969: Boundary Layer Theory (6thedition), McGraw-Hill, New York, 747 pp.
Schlüssel, P. and H. Luthardt, 1991: Surface wind speeds
over the north sea from special sensor microwave /imager observations. J. Geophys. Res., 96, 4845 4853.
Schlüssel, P., 1989: Satellite-derived low-levelatmospheric water-vapour content from synergy of
AVHRR with HIRS. Int.J.RemoteSens., 10, 705-721.
Schlüssel, P., 1995: Passive Fernerkundung der unteren
Atmosphäre und der Meeresoberfläche aus dem
Weltraum. Berichte aus dem Zentrum für Meeresund Klimaforschung, Reihe A: Meteorologie, 20, 175
pp. [Available from Universität Hamburg
Meteorologisches Institut, Bundesstraße 55, 20146
Hamburg, Germany].
Schlüssel, P., and Emery W. 1990: Atmospheric watervapor over oceans from SSM/I measurements Int. J.
Rem. Sensing 11 (5) 753-766
Schlüssel, P., L. Schanz, and G. Englisch, 1995: Retrievalof latent heat flux and longwave irradiance at the sea
surface from SSM/I and AVHRR measure-ments.
Advanced Space Research, 16, 107-116.
Schmetz, J., 1989: Towards a surface radiation
climatology: retrieval of downward irradiances fromsatellites. Atmos. Res., 23, 287 - 321.
Schubert, S.D., and R.B. Rood, 1995: Proceedings of theWorkshop of the GEOS-1 Five Year Assimil-ation.
NASA Tech. Mem. 104606, Vol. 7, Goddard Space
Flight Center Greenbelt, MD, 201 pp.
Schubert, S.D., C.-K. Park, C.-Y. Wu, W. Higgins, Y.
Kondratyeva, A. Molod, L. Takacs, M. Seablom, and
R. Rood, 1995: A Multiyear Assimilation with the
GEOS-1 System: Overview and Results. NASA Tech.
Mem. 104606, Vol. 6, Goddard Space Flight Center
Greenbelt, MD, 183 pp. (see also
http://dao.gsfc.nasa.gov/experiments/assim54A.html)
Schulz, J. and V. Jost, 2000: A New satellite-Derived
Freshwater Flux Climatology: The Hamburg Ocean
Atmosphere Parameters and Fluxes from Satellite
Data, (submitted to J. Climate).
Schulz, J., 1993a: Fernerkundung des latenten
Wärmeflusses an der Meeresoberfläche. Ph.D.
dissertation, Universität Hamburg, 108 pp. [Berichte
aus dem Zentrum für Meeres- und Klimaforschung,
Reihe A: Meteorologie, Nr. 4.]
Schulz, J., 1993b: Latent heat flux at the air-sea interface
from a combination of SSM/I with AVHRR data.
Proc. 6th AVHRR data user's meeting, Belgirate,

Report of the Working Group on Air Sea Fluxes

291

Italy, 179 - 185. [EUMETSAT publication: EUM P
12, ISSN 1015 9576]
Schulz, J., J. Meywerk, S. Ewald, and P. Schlüssel, 1997:
Evaluation of Satellite-derived Latent Heat Fluxes.
Journal of Climate, 10, 2782-2795.
Schulz, J., P. Schlüssel, H. Graßl, 1993: Water vapour inthe atmospheric boundary layer over oceans from
SSM/I measurements. Int. J. Remote Sens., 14, 2773 2789.
Schwarzkopf, M. D., and S. B. Fels, 1991: The
simplified exchange method revisited: An accurate
rapid method for computation of infrared coolingrates and fluxes. J. Geophys. Res., 96, 9075-9096.
Schweiger, A.J., R.W. LIndsay, J.R. Key and J.A.
Francis. 1999. Arctic Clouds in Multiyear Satellte
Data Sets. Geophysical Research Letters, 26, No.13,
1845-1848.
Seckel, G. R. and F. H. Beaudry, 1973: The radiationfrom sun and sky over the Pacific Ocean (Abstract).
Trans. Am. Geophys. Union, 54, 1114.
Serreze, M.C., Hurst, C.M., 2000: Representation ofmean Arctic precipitation from NCEP-NCAR andERA reanalyses, J. Climate 13 (1) 182-201.
Servain, J., J. N. Stricherz, and D. M. Legler, 1996:
TOGA Pseudo-stress Atlas 1985-1994 Volume I:
Tropical Atlantic. Centre ORSTOM, Plouzané,France, 158 pp.
Servain,J., A.J.Busalacchi, M.J.McPhaden, A.D.Moura,
G.Reverdin, M.Vianna and S.E.Zebiak, 1998: A Pilot
Research Moored Array in the Tropical Atlantic(PIRATA). Bull. Am. Met.. Soc., 79, 2019-2031.
Shaw, J. A., J. H. Churnside and E. R. Westwater, 1991:
An Infrared Spectrometer for Ground-Based Profilingof Atmospheric Temperature and Humidity. Proc.
SPIE Int'l. Symp. on Optical Appl. Sci. andEngineering, July 21 - 26, 1991, San Diego, CA,
1540, 681-686.
Sheppard, B.E. and P.I. Joe, 1994: Comparison ofraindrop size distribution by a Joss-Waldvogel
disdrometer. J. Atmos. Oceanic Technol., 11, 874887.
Sheppard, B.E., 1990: Effect of irregularities in the
diameter classification of raindrops by the JossWaldvogel disdrometer. J. Atmos. Oceanic Technol.,7, 180-183.
Shin, K.-S. and G. R. North, 1988: Sampling error study
for rainfall estimate by satellite using a stochastic
model. J. Appl. Met., 27, 1218 - 1231.
Shine, K.P., 1984: Parameterization of the shortwave flux
over high albedo surfaces as a function of cloud
thickness and surface albedo. Quart. J. Roy. Meteor.
Soc., 110, 747-764.
Shinoda, T., H. H. Hendon, and J. Glick, 1999:
Intraseasonal surface fluxes in the tropical western
Pacific and Indian Oceans from NCEP reanalyses.
Mon. Wea. Rev., 127, 678-693.
Short, D. A., R. A. Kucera, B. S. Ferrier, J. C. Gerlach, S.
A. Rutledge and O. W. Thiele, 1997: Shipboard
radar rainfall patterns within the TOGA COARE
IFA. Bull. Amer. Meteor. Soc., 78, 2817-2836.
Siefridt L., B. Barnier, K. Béranger and H. Roquet, 1999:
Evaluation of operational ECMWF analyses surface
heat fluxes: impact of parameterisation changes
during 1986-95, J. Marine Sys., 19, 113-135.
Simpson, J. J., A. Schmidt and A. Harris, 1998: Improved
cloud detection in Along Track Scanning Radiometer(ATSR) data over the ocean. Rem. Sensing ofEnvironment, 65(1), 1-24

June, 2000

Reference list

Simpson, J.J. and C.A. Paulson, 1979: Mid-ocean
observations of atmospheric radiation. Quart. J. R.
Met. Soc., 105, 487-502.
Slingo, J., and P. Delecluse, 1999: Scale interactions andthe tropical atmosphere-ocean system. Proc.
Conference TOGA coupled ocean-atmosphereresponse experiment (COARE). WMO/TD-No.940,
WMO, Geneva, 59-69.
Slutz, R. J., S. J. Lubker, J. D. Hiscox, S. D. Woodruff,
R. L. Jenne, D. H. Joseph, P. M. Steurer and J. D.
Elms, 1985: COADS, Comprehensive Ocean-Atmosphere Data Set, Release 1. Climate Research
Program, Environmental Research Laboratory,
Boulder, CO 262 pp.
Smith, E. A., & co-authors 1998: Results of WetNet PIP2 project. J. Atmos. Sci., 55, 1483 - 1536.
Smith, S. D., & co-authors, 1992: Sea surface wind stressand drag coefficients: the HEXOS results. BoundaryLayer Meteor., 60, 109-142.
Smith, S. D., 1980: Wind stress and heat flux over theocean in gale force winds. J. Phys. Oceanog., 10,709-726.
Smith, S. D., 1988: Coefficients for sea surface windstress, heat flux, and wind profiles as a function ofwind speed and temperature. J. Geophys. Res., 93,
15467-15472.
Smith, S. D., 1989: Water vapor flux at the sea surface.
Boundary-Layer Meteor., 47, 277-293.
Smith, S. R., C. Harvey, and D. M. Legler, 1996a:
Handbook of Quality Control Procedures andMethods for Surface Meteorology Data. WOCE
Report 141/96, COAPS Report 96-1, WOCE Data
Assembly Center, COAPS, Florida State University,
Tallahassee, FL 32306-2840, 56 pp.
Smith, S. R., D. M. Legler, and K. V. Verzone, 1999:
Quantifying uncertainties in NCEP reanalysis using
high-quality research vessel observations. CLIMAR
99, WMO Workshop on Advances in Marine Climatology, Vancouver, 8 - 15 Sept. 1999, 223-230.
Smith, T. M., R. W. Reynolds, R. E. Livezey and D. C.
Stokes, 1996b: Reconstruction of historical sea
surface temperatures using empirical orthogonal
functions. J. Climate, 9, 1403 - 1420.
Smith, W.L. & co-authors, 1996c: Observations of theinfrared radiative properties of the ocean:
implications for the measurement of sea surface
temperature via satellite remote sensing. Bull. Amer.
Meteor. Soc., 77, 41-51.
Smull, B. F., and M. J. McPhaden, 1998: Comparison of
NCEP/NCAR and ECMWF reanalysed fields with
TOGA TAO buoy observations over the tropical
Pacific. Proceedings of the 1st WCRP Intl. Conf. on
Reanalyses. 27-31 Oct. 1997, Silver Spring, MD,
USA, WCRP-104, WMO/TD-876. WMO, Geneva,
227-230.
Smyth, W.D., D. Hebert and J.N. Moum, 1996: Local
ocean response to a multiphase westerly wind burst –2. Thermal and freshwater responses. J. Geophys.
Res., 101, 22513-22533.
Soloviev, A.V. and P. Schluessel, 1994: Parameterizationof the cool skin of the ocean and of the air-ocean gas
transfer on the basis of modelling surface renewal. J.
Phys. Oceanog., 24, 1339-1346.
Soloviev, A.V. and P. Schluessel, 1996: Evolution ofcool skin and direct air-gas transfer coefficient during
daytime. Boundary-Layer Meteor., 77 45-68.
Soman, V. V., J. B. Valdés, G. R. North, 1995: Satellite
sampling and the diurnal cycle statistics of Darwin
rainfall data. J. Appl. Met., 34, 2481 - 2490.

Report of the Working Group on Air Sea Fluxes

292

Sorbjan, Z., 1997: Comments on “A convective transport
theory for surface fluxes”. J. Atmos. Sci., 54, 576578.
Speer, K.G., J.Holfort, T.Reynaud, G.Siedler, 1996:
South Atlantic Heat Transport at 11˚ S. In: The South
Atlantic present and past circulation, G. Welen & G.
Siedler eds., Springer Verlag, Berlin.
Spencer, R. W., 1993: Global oceanic precipitation from
the MSU during 1979-91 and comparison to otherclimatologies. J. Climate, 6, 1301 - 1326.
Spinhirne, J. D., 1993: Micro Pulse Lidar. IEEE Trans.
Geo. Rem. Sens., 31, 48-55.
Stamnes, K., S.-C. Tsay, W. Wiscombe, and K.
Jayaweera, 1988: Numerically stable algorithm fordiscrete-ordinates-method radiative transfer inmultiple scattering and emitting layered media. Appl.
Opt., 27, 2502-2509.
Staneva, J.V., E.V. Stanev, and N.H.Rachev, 1995: Heatbalance estimates using atmospheric analysis data: A
case study for the Black Sea. J. Geophys. Res., 100,
18581-18596.
Steiner, M. and R.A. Houze, 1997: Sensitivity of theestimated monthly convective rain fraction to the
choice of Z-R relation. J. Appl. Meteor., 36, 452462.
Stendel, M., and K. Arpe, 1997: Evaluation of thehydrological cycle in reanalyses and observations.
Max-Planck-Institut für Meteorologie, Report 228,
Hamburg, Germany, 52 pp.
Stephens, G. L., and S.-C. Tsay, 1990: On the cloudabsorption anomaly. Quart. J. Roy. Meteor. Soc.,
116, 671-704.
Stephens. G. L., and T. J. Greenwald, 1991:
Observations of the earth's radiation budget inrelation to atmospheric hydrology. Part II: Cloud
effects and cloud feedback. J. Geophys. Res., 96,
15325-15340.
Sterl, A., G.J. Komen, P.D. Cotton 1998: Fifteen yearsof global wave hindcasts using winds from theEuropean Centre for Medium-Range Weather
Forecasts reanalysis: Validating the reanalyzed winds
and assessing the wave climate. J. Geophys. Res.,
103, 5477-5492.
Stogryn, A. P., C. T. Butler, and T. J. Bartolac, 1994:
Ocean surface wind retrievals from special
microwave imager data with neural networks. J.
Geophys. Res., 99, 981 - 984.
Stokes, G. M., and S. E. Schwartz, 1994: The
Atmospheric Radiation Measurement Program(ARM) Program: Programmatic Background and
Design of the Cloud and Radiation Test Bed. Bull.
Amer. Meteor. Soc., 75, 1201-1221.
Stone, R., T. Mefford, E. Dutton, D. Longenecker, B.
Halter and D. Endres, 1996: Surface radiation andmeteorological measurements: January 1992 toDecember 1994. NOAA Data Report ERL-CMDL11, 81pp.
Stricherz, J. N., D. M. Legler, and J. J. O’Brien, 1997:
TOGA Pseudo-Stress Atlas 1985-1994: II Tropical
Pacific Ocean. COAPS/Florida State University.
COAPS Report 97-2, 162 pp.
Stuhlmann, R., M. Rieland and E. Raschke, 1990: An
improvement of the IMGK model to derive total anddiffuse solar-radiation at the surface from satellite
data. J. Appl. Meteor., 29, 586-603.
Stull, R. B., 1988: An Introduction to Boundary Layer
Meteor..Kluwer Academic Publishers, Dordrecht,
Holland.

June, 2000

Reference list

Stull, R. B., and Eloranta 1984: Boundary-Layer
Experiment – 1983, Bull. Amer. Meteor. Soc. 65: (5)450-456.
Stull, R.B., 1994: A convective transport theory forsurface fluxes. J. Atmos. Sci., 51, 3-32.
Stull, R.B., 1997: Reply to Sorbjan’s comments. J.
Atmos. Sci., 54, 579.
Swift, C. T. and R. E. McIntosh, 1983: Considerations for
microwave remote sensing of ocean- surface salinity. IEEE Trans.Geosci.Rem.Sens., 21, 480 – 491.
Sykes, R.I., D.S. Henn and W.S. Lewellen, 1993:
Surface-layer description under free-convection conditions. Quart. J. Roy. Meteor. Soc., 119, 409-421
Takacs, L. L., A. Molod, and T. Wang, 1994:
Documentation of the Goddard Earth Observing
System (GEOS) General Circulation Model-Version
1. NASA Tech. Mem. 104606, Vol. 1, Goddard
Space Flight Center Greenbelt, MD, 100 pp.
Takara, E., and R. G. Ellingson, 2000: Broken cloudfield longwave scattering effects. J. Atmos. Sci., 57,
1298-1310.
Tanimoto, Y., N.Iwasaka, and K.Hanava, 1997:
Relationships between sea surface temperature, theatmospheric circulation, and air-sea fluxes on
multiply time scales. J.Met.Soc.Japan, 75, 831-849.
Tanre, D., C. Deroo, P. Duhaut, M. Herman, J-J.
Morcrette, J. Perbos and P-Y. Deschamps, 1990:
Description of a computer code to simulate thesatellite signal in the solar spectrum. Int. J. Remote
Sensing, 11, 659-668.
Tarpley, J.D., 1979: Estimating incident solar radiation at
the surface from geostationary satellite data. J. Appl.
Meteor., 18, 1172-1181.
Taylor, P. K. and M. J. Yelland, 2000a: A note on theapparent "imbalance" term in the Turbulent Kinetic
Energy Budget. J.Atmos.Oceanic Tech.,17, 82-89.
Taylor, P. K. and M. J. Yelland, 2000b: Comments on:
On the effect of ocean waves on the kinetic energybalance and consequences for the inertial dissipation
technique. J. Phys. Oceanogr. (submitted).
Taylor, P. K., 1984: The determination of surface fluxesof heat and water by satellite microwave radiometry
and in situ measurements. Large-scaleoceanographic experiments and satellites, C. Gautierand M. Fieux, Eds., D. Reidel, 223-246.
Taylor, P. K., and M. A. Yelland, 2000c: The dependenceof sea surface roughness on the height and steepnessof the waves. J.PhysOceanogr.,(in press).
Taylor, P. K., E. C. Kent and S. A. Josey, 1999c: The
accuracy of sea surface temperature data from
Voluntary Observing Ships. in Report of the
OOPC/AOPC Workshop on Global Sea Surface
Temperature Data Sets. (Palisades, N.Y., USA, 2-4
November 1998). Report GCOS-57, GOOS-79,
WMO/TD 978, WMO Geneva., Annex III, 51 -54.
Taylor, P. K., E. C. Kent, M. J. Yelland and B. I. Moat,
1995: The Accuracy of Wind Observations from
Ships. Proc. Internat. COADS Winds Workshop,
Kiel, Germany, 31 May - 2 June, 1994, Env. Res.
Labs., NOAA, Boulder, Colorado (also at
http://www.cdc.noaa.gov/coads/kiel/ ), 132 - 155.
Taylor, P. K., E. C. Kent, M. J. Yelland and B. I. Moat,
1999a: The Accuracy Of Marine Surface Winds
From Ships And Buoys. CLIMAR 99, WMO
Workshop on Advances in Marine Climatology,
Vancouver, 8 - 15 Sept. 1999, 59 - 68.
Taylor, P. K., E. F. Bradley, C. W. Fairall, L. Legler, J.
Schulz, R. A. Weller and G. H. White, 1999d:
Surface Fluxes and Surface Reference Sites. Proc.

Report of the Working Group on Air Sea Fluxes

293

Symp. The Ocean Observing System for Climate Oceanobs 99, St Raphael, 25 - 27 October, 1999.
Taylor, P. K., S. A. Josey and E. C. Kent, 1999e: A
comparison of climatological, model derived andobserved air-sea flux values for the COARE area.
Conf. on the TOGA Coupled Ocean-AtmosphereResponse Experiment (COARE), Boulder, Co., USA,
7-14 July 1998, WCRP-107, WMO/TD 940, WMO,
249 - 250.
Taylor, P. K., S. A. Josey and E. C. Kent, 1999f: Observations of Ocean Surface Fluxes: Means andVariability. Seminar on Atmosphere-surface
interaction, Reading, 8-12 September 1997,
ECMWF, Reading, UK, 1 - 31.
Taylor, P.K., M.J.Yelland, F.W.Dobson and
R.J.Anderson, 1999b: Storm Wind Study II: Wind
Stress Estimates from Buoy and Ship. in Proc. Symp.
On the Wind-driven Air-sea Interfaces (Ed. M.L.
Banner), Univ. of New South Wales, Sydney, 11-15
Jan., 353 - 354.
Tiedtke, M., 1983: The sensitivity of the time-meanlarge-scale flow to cumulus convection in the
ECMWF model. ECMWF Workshop on Convection
in large-scale Models, 28 Nov.-1 Dec. 1983,
ECMWF, Reading, UK, 297-316.
Tiedtke, M., 1989: A comprehensive mass flux scheme
for cumulus parameterization in large-scale models.
Mon. Wea. Rev., 117, 1779-1800.
Tiedtke, M., 1993: Representation of clouds in largescale models. Mon. Wea. Rev., 121, 3040-3061.
Timmermann, A., Latif M., Grotzner A., Voss R. 1999:
Modes of climate variability as simulated by acoupled general circulation model. Part I: ENSO-likeclimate variability and its low-frequency modulation
Climate Dynamics 15: (8) 605-618.
Toba, Y., N. Iida, H. Kawamura, N. Ebuchi and I.S.F.
Jones, 1990: Wave dependance of sea-surface windstress. J. Phys. Oceanog., 20, 705-721.
Tobin, D. C., & co-authors, 1999: Downwelling spectral
radiance observations at the SHEBA ice station:
Water vapor continuum measurements from 17 to 26
micrometer. J. Geophys. Res., 104, 2081-2092.
Tokay, A., and D.A. Short, 1996: Evidence from tropicalraindrop spectra of the origin of rain from stratiform
versus convective clouds. J. Applied Meteor., 35,355-371.
Trenberth K.E., and Guillemot C.J. 1998: Evaluation of
the atmospheric moisture and hydrological cycle in
the NCEP/NCAR reanalyses Climate Dyn. 14 213231.
Trenberth, K. E. and A. Solomon, 1994: The global heatbalance: heat transports in the atmosphere and ocean.
Climate Dyn., 10, 107 - 134.
Trenberth, K. E., 1998: The heat budget of theatmosphere and ocean. Proc. 1st WCRP Intl. Conf. on
Reanalyses. 27-31 Oct. 1997, Silver Spring, MD,
USA, WCRP-104, WMO/TD-876. WMO, Geneva,
17-20.
Trenberth, K. E., J. M. Caron, and D. P. Stepaniak,
2000a: The atmospheric energy budget andimplications for surface fluxes and ocean heattransports. Submitted to J. Climate.
Trenberth, K. E., W. G. Large and J. G. Olson, 1989: The
Effective Drag Coefficient For Evaluating Wind
Stress Over The Oceans. Journal of Climate, 2(12),1507-1516.
Trenberth, K.E. and C.J. Guillemot, 1998: Evaluation of
the atmospheric moisture and hydrological cycle inJune, 2000

Reference list

the NCEP/NCAR reanalyses. Climate Dyn., 14, 213231.
Trenberth, K.E., D. P. Stepaniak, J. W. Hurrell, and M.
Fiorino, 2000b: Quality of reanalyses in the tropics.
Submitted to J. Climate.
Trishchenko, A. P., and Z. Li, 1999: On thedetermination of shortwave cloud radiative forcing.
Proc. AMS Tenth Conf. on Atmospheric Radiation,
Madison WI (June 28 - July 2, 1999), 141-144.
Troen, I., and L. Mahrt, 1986: A simple model of theatmospheric boundary layer: Sensitivity to surface
evaporation. Bound.-Layer Meteor., 37, 129-148.
Tsukamoto, O., and H. Ishida, 1995: Turbulent flux
measurements and energy budget analysis over theequatorial Pacific during TOGA-COARE IOP. J.
Meteor. Soc. Japan, 73, 557-568.
Tucker, G. B., 1961: precipitation over the North Atlantic Ocean. Quart.J.Roy.Meteor.Soc.,87,147-158.
Uppala, S., J. K. Gibson, M. Fiorino, A. Hernandez, P.
Kallberg, X. Li, K. Onogi, and S. Saarinen, 2000 :ECMWF second generation reanalysis: ERA40.
Proc. Conf. 2nd Intl. Conf. on Reanalyses, Reading,England, 23-27 Aug. 1999. WCRP-109 (WMO/TD
985) WMO, Geneva, 9 - 13.
Valero, F. P. J., P. Minnis, S. K. Pope, A. Bucholtz, B. C.
Bush, D. R. Doelling, W. L. Smith, Jr., and X. Dong,
2000: The absorption of solar radiation by the atmosphere as determined using satellite, aircraft, andsurface data during the ARM Enhanced Short-wave
Experiment (ARESE). J. Geophys. Res., 105, 47434758.
Valero, F. P. J., R. D. Cess, M. Zhang, S. K. Pope, A.
Bucholtz, B. Bush, and J. Vitko, 1997: Absorption ofsolar radiation by the cloudy atmosphere:
Interpretations of collocated aircraft measurements.
J. Geophys. Res., 102, 29,917-29,927.
Valero, F. P. J., W. J. Y. Gore, and L. P. M. Giver, 1982:
Radiative flux measurements in the troposphere.
Appl. Opt., 21, 831-838.
Verschell, M. A., M. A. Bourassa, D. E. Weissman, andJ. J. O'Brien, 1999: Ocean model validation of theNASA scatterometer winds. J. Geophys. Res., 104,
11359-11373.
Vorosmarty C. J., Fekete B., and Tucker B.A., 1996.
Global River Discharge Data Base (RivDIS v1.0),
IHP-V Theme 1, Unesco 1996.
Waliser D. E. , W. D. Collins, and S. P. Anderson, 1996:
An estimate of the surface shortwave cloud forcing
over the western Pacific during TOGA COARE
Geophys. Res. Lett. 23: (5) 519-522.
Walton, C. C., Pichel W.G., Sapper J.F., and May D.A.,
1998: The development and operational applicationof nonlinear algorithms for the measurement of sea
surface temperatures with the NOAA polar-orbiting
environmental satellites J. Geophys. Res. 103: (C12)27999-28012.
WAMDI group: Hasselmann, S., & co-authors, 1988:
The WAM model - a third generation ocean wave
prediction model. J Phys, Oceanogr., 8, 1775-1810.
Wang, T., K.B.Earnshaw and R.S.Lawrence, 1978:
Simplified optical path-average rain gauge. Applied
Optics, 17, 385-390.
Wang, T., K.B.Earnshaw and R.S.Lawrence, 1979: Pathaveraged measurements of rain rate and raindrop size
distribution using a fast-response optical sensor. J.
Applied Meteor., 18, 654-660.
Ward, M.N., 1992: Provisionally corrected surface wind
data, worldwide ocean-atmosphere surface fields, andSahelian rainfall variability. J.Climate,5, 454-475.

Report of the Working Group on Air Sea Fluxes

294

Ward, N.M. and B.J.Hoskins, 1996: Near surface windover the Global Ocean 1949-1988. J.Climate, 9,1877-1895.
Warren et al., 1988 Global Distribution of Total Cloud
Cover and Cloud Type Amounts over the Ocean (fordigital version see
http://cdiac.ESD.ORNL.GOV/ndps/ndp026.html )WCRP, 1985: Report on the third session of the
JSC/CCCO TOGA Scientific Steering Group.
WMO/TD-No. 81, WCP-107, 26pp.
WCRP, 1989: WOCE Surface Flux Determinations - A
strategy for in situ measurements. Working Groupon in situ measurements for Fluxes WCRP-23(WMO/TD No.304), WMO, Geneva.
WCRP, 1990: Scientific plan for the TOGA Coupled
Ocean-Atmosphere Response Experiment, WCRP
Pub. 3 Addendum (WMO/TD 64 Addendum), WMO.
WCRP, 2000: Proc. Conf. 2nd Intl. Conf. on Reanalyses,
Reading, England, 23-27 Aug. 1999. WCRP-109(WMO/TD 985) WMO, Geneva.
Weare, B.C., and P.T.Strub, 1981: The significance ofsampling biases on calculated monthly mean oceanic
surface heat fluxes. Tellus, 33, 211-224.
Webb, E. K., 1970: Profile relationships: the log-linear
range and extension to strong stability, Quart. J. Roy.
Meteor. Soc. 96 (407) 67 – 90.
Webb, E.K., G.I. Pearman and R. Leuning, 1980:
Correction of flux measurements for density effects
due to heat and water vapor transport. Quart. J. Roy.
Meteor. Soc., 106, 85-100.
Webster, P.J. and R. Lukas, 1992; TOGA COARE. The
coupled ocean atmosphere response experiment.
Bull. Am. Meteor. Soc. 72, 1377-1416.
Webster, P.J., C.A. Clayson and J.A. Curry, 1996:
Clouds, radiation, and the diurnal cycle of sea surface
temperature in the tropical western Pacific. J.
Climate, 9, 1712-1730.
Weller, R. A. and P. K. Taylor, 1999: A Strategy for
Marine Meteorological and Air-Sea Flux
Observation. Preprint volume: 3rd Symp. on
Integrated Observing Systems, Dallas, Texas, Jan.,
1999, AMS, Boston.
Weller, R. A. and S. P. Anderson, 1996: Surface Meteor.
and air-sea fluxes in the western equatorial Pacificwarm pool during the TOGA Coupled OceanAtmosphere Experiment. Journal of Climate, 9, 1959
- 1990.
Weller, R. A., D. L. Rudnick and N. J. Brink, 1995:
Meteorological variability and air-sea fluxes at a
closely spaced array of surface moorings. J. Geophys.
Res., 100(C3), 4867 - 4883.
Weller, R. A., M. F. Baumgartner, S. A. Josey, A. S.
Fischer and J. Kindle, 1998: Atmospheric forcing in
the Arabian Sea during 1994-1995 : observations andcomparisons with climatology and models. Deep Sea
Research II, 45, 1961 - 1999.
Weller, R.A., S.P.Anderson, H.W. Wijesekera,
C.A.Paulson, M.F.Cronin, and L.M.Rothstein, 1999:
Oceanic response to buoyancy flux and wind stressduring COARE. Proc. Conf. TOGA coupled oceanatmosphere response experiment (COARE).
WMO/TD-No.940, WMO, Geneva, 43-58.
Wells, N. C. and S. King-Hele, 1990: Parameterization oftropical ocean heat flux. Quart. J. Roy. Meteor. Soc.,
116, 1213 - 1224.
Wentz, F. J. and R. W. Spencer, 1998: SSM/I rain
retrievals within a unified all-weather ocean
algorithm. J. Atmos. Sci., 55, 1613 - 1627.

June, 2000

Reference list

Wentz, F. J., 1975: A two-scale scattering model forfoam free sea microwave brightness temperatures. J.
Geophys. Res., 80, 3441 - 3446.
Wentz, F. J., 1989: User’s manual SSM/I geophysicaltapes. Tech. Rep. 060989, 16 pp [available from
Remote Sensing Systems, Santa Rosa, CA, USA].
Wentz, F. J., 1994: User's manual SSM/I-2 geophysicaltapes, RSS Tech. Rep. 070194, 44pp. [from Remote
Sensing Systems, Santa Rosa, CA, USA].
Wentz, F. J., 1997: A well calibrated ocean algorithm for
SSM/I. J. Geophys. Res., 102, 8703 - 8718.
White, G. and A. da Silva, 1998: An intercomparison of
surface marine fluxes from GEOS-1/DAS, ECMWF/ERA and NCEP/NCAR reanalyses. 9th Conf. On
Interaction of the Sea and Atmos.,11-16 Jan., Phoenix, AZ. Amer. Meteor. Soc., Boston, MA, 20-23.
White, G. and S. Saha, 1999: Estimation of the global
energy and water cycle from global data assimilation.
Global Energy and Water Cycles, K. A. Browning
and R. J. Gurney, ed., Cambridge University Press,
Cambridge, UK, 55-60.
White, G. H., 1995: An Intercomparison of Prec-ipitationand Surface Fluxes from Operational NWP
Analysis/Forecast Systems. CAS/JSC Working
Group on Numerical Experimentation Report 22.
WMO/TD-723. WMO, Geneva, 33 pp +figs.
White, G., 1996b: Fluxes from operational
analysis/forecast systems and the NCEP/NCAR
reanalysis. WCRP Workshop on Air-Sea Flux Fields
for Forcing Ocean Models and Validating GCMs,
Reading, England, 24-27 Oct. 1995,WMO/TD-762.
WMO, Geneva, 131-137.
White, G., 2000: Long-term trends in the NCEP/NCAR
reanalysis. 2nd Intl. Conf. on Reanalyses, Reading,England, 23-27 Aug. 1999. WCRP-109 (WMO/TD
985) WMO, Geneva, 54 - 57.
White, G., ed., 1996a: WCRP Workshop on Air-Sea Flux
Fields for Forcing Ocean Models and Validating
GCMs. Reading, England, 14-17 Oct. 1995. WMO,
Geneva, WMO/TD-No. 762, 184 pp.
Whitlock, C. H., & co-authors, 1995: First global WCRP
short wave Surface radiation Budget dataset. Bull.
Amer. Meteor. Soc., 76, 905-922.
Wick, G.A., 1995: Evaluation of the variability andpredictability of the bulk-skin sea surface
temperature difference with application to satellitemeasured sea surface temperature. Ph.D. Thesis,
Dept. of Aerospace Engineering Sciences, Universityof Colorado. 146 pp.
Wick, G.A., W.J. Emery, L.H. Kantha, and P. Schlussel,
1996: The behavior of the bulk – skin sea surface
temperature difference under varying wind speed andheat flux. J. Phys. Ocean., 26, 1969-1988.
Wielicki, B. A., and B. Barkstrom, 1991: Clouds and theEarth's Radiant Energy System (CERES): An Earth
Observing System Experiment. Second Symp. on
Global Change Studies, New Orleans, LA, Jan. 1418, 1991, 11-16.
Wijffels, S.E., R.W.Scmitt, H.Bryden and A. Stigebrand,
1992: Transport of freshwater by the Ocean. J.Phys.
Oceanogr., 22, 155-162.
Wild M., A. Ohmura, H. Gilgen, E. Roeckner 1995:
Validation Of General-Circulation Model Radiative
Fluxes Using Surface Observations. J Climate 8 (5Part 2) 1309-1324.
Wild, M., 1999: Discrepancies between modelcalculated and observed shortwave atmosphericabsorption in areas with high aerosol loadings. J.
Geophys. Res., 104, 27,361-27,373.

Report of the Working Group on Air Sea Fluxes

295

Wild, M., 2000: Assessment of surface and atmosphericradiation budgets in the ECMWF/NCEP/NASAreanalyses using collocated surface and satellite data.
Proc. Conf. 2nd Intl. Conf. on Reanalyses, Reading,England, 23-27 Aug. 1999. WCRP-109 (WMO/TD
985) WMO, Geneva, 181 - 184.
Wild, M., A. Ohmura, H. Gilgen, and J. J. Morcrette,
1998: Assessment of the ECMWF reanalysis
radiative fluxes using surface observations. Proc. 1st
WCRP Intl. Conf. on Reanalyses. 27-31 Oct. 1997,
Silver Spring, MD, USA, WCRP-104, WMO/TD876. WMO, Geneva, 69-72.
Wilkerson, J.C. and M.D. Earle, 1990: A study ofdifferences between environmental reports by ships
in the voluntary observing program andmeasurements from NOAA buoys. J. Geophys. Res.,95, 18,225-18,268.
Williams, C. R., W.L. Ecklund, K. S. Gage, E. R.
Westwater, and J. B. Snider, 1995: Comparison of
integrated liquid water content derived from a 915
MHz wind profiler and a dual frequency microwave
radiometer in the tropics, Preprint vol. 27th Conf. on
Radar Meteor., Vail, CO, 279-280, Oct., 1995.
Williams, C.R., W.L. Ecklund, P. Johnson and K.S.
Gage, 1999: Precipitation climatology observed withprofilers during TOGA-COARE. Proc. Conf. on the
TOGA Coupled Ocean-Atmosphere Response
Experiment (COARE98), Boulder CO, 7-14 July
1998, pp. 191-192, WCRP-107, WMO, Geneva.
WMO, 1970: The Beaufort scale of wind force (technicaland operational aspects)., World MeteorologicalOrganisation, Geneva, 22 pp.
WMO, 1985: Instruments and observing methods Report
No. 25. Workshop on the correction of precipitation
measurements, Zurich, Switzerland, 1-3 April 1985
(B. Sevruk, Ed.), WMO/TD – No. 104, 288 pp.
WMO, 1989: Instruments and observing methods Report
No. 48. International workshop precipitation
measurements, St. Moritz, Switzerland, 3-7
December 1989 (B. Sevruk, Ed.), WMO/TD – No.
328, 584 pp.
WMO, 1990: International list of selected,
supplementary and auxiliary ships, World
Meteorological Organisation, Geneva.
Wolter, K., 1997: Trimming problems and remedies in
COADS. J.Climate., 10, 1980-1997.
Woodruff, S. D., R. J. Slutz, R. L. Jenne and P. M.
Steurer, 1987: A comprehensive ocean-atmospheredata set. Bul. Amer. Meteor. Soc. , 68 , 1239-1250.
Woodruff, S. D., S. J. Lubker, K. Wolter, S. J. Worleyand J. D. Elms, 1993: Comprehensive OceanAtmosphere Data Set (COADS) release 1a: 1980-92.
Earth System Monitor, 4, 4 - 8.
Woodruff, S.D., H.F.Diaz, J.D.Elms, S.J.Worley, 1998:
COADS Release 2 data and metadata enhancements
for improvements of marine surface flux fields. Phys.
Chem. Earth , 23, 5/6, 517-526.
World Climate Research Programme, 1990: Global
Precipitation Climatology Project: Implementationand data management plan. WMO/TD-No. 367,
WMO, Geneva, 47pp.
Wright, P.B. 1988: An atlas based on the COADS data
set: Fields of mean wind, cloudiness and humidity at
the surface of the global ocean. Rep. No.14 MaxPlanck-Institut für Meteorologie, Hamburg.
Wu, J., 1968: Laboratory studies of wind-wave
interactions. J. Fluid Mech., 34, 91-111.

June, 2000

Reference list

Wu, J., 1980: Wind-stress coefficients over sea surface
near neutral conditions – a revisit. J. Phys. Oceanog.,
10, 727-740.
Wu, J., 1994: The sea surface is aerodynamically rougheven under light winds. Boundary-Layer Meteor., 69,
149-158.
Wu, S. T. and A. K. Fung, 1972: A noncoherent model
for microwave emissions and backscattering from thesea surface. J. Geophys. Res., 77, 5917 - 5929.
Wu, Z., and K. Li, 1995: Wind speed discontinuity
related to Beaufort wind observations and its
influence on latent heat flux. Proc. Internat. COADS
Winds Workshop, Kiel, Germany, 1994. Ed. H.Diaz
and H.-J.Isemer, NOAA-ERL, IFM (Kiel), 212-231.
Wunsch, C., 1984: An eclectic Atlantic Ocean circulation
model. Part 1: The meridional flux of heat. J. Phys.
Ocean., 14, 1712-1733.
Xie, P. and P. A. Arkin, 1997: Global precipitation: a 17year monthly analysis based on gauge observations,satellite estimates, and numerical model outputs.
Bull. Amer. Meteor. Soc., 78, 2539 - 2558.
Xie, P. and P. A. Arkin, 1998: Global monthly precipitation estimates from satellite-observed outgoing
longwave radiation. J. Climate, 11, 137 - 164.
Xie, P., and P. A. Arkin, 1996: Analyses of global
monthly precipitation using gauge observations,satellite estimates and numerical model predictions.
J. Climate, 9, 840-858.
Yanai, M., S. Esbensen and J.H. Chu, 1973:
Determination of bulk properties of tropical cloudclusters from large-scale heat and moisture budgets.
J. Atmos. Sci., 30, 611-627.
Yang, D., B. E. Goodison, J. R. Metcalfe, V. S. Golubev,
R. Bates, T. Pangburn and C. L. Hanson, 1998:
Accuracy of NWS 8” standard nonrecording
precipitation gauge: Results and application of WMO
comparison. J. Atmos. Oceanic Tech., 15, 54-68.
Yau, M.K., and M. Jean, 1989: Synoptic aspects andphysical processes in the rapidly intensifying cycloneof 6-8 March 1986. Atmosphere-Ocean, 27, 59-86.
Yelland M.J. and P.K.Taylor, 1996: Wind stressmeasurements from the open ocean. J. Phys.
Oceanogr., 26, 541-558.
Yelland, M. J., 2000: A direct comparison of open oceanand coastal wind stress measurements. (unpublishedmanuscript).
Yelland, M. J., B. I. Moat, P. K. Taylor, R. W. Pascal, J.
Hutchings and V. C. Cornell, 1998: Measure-mentsof the open ocean drag coefficient corrected for air
flow disturbance by the ship. J. Phys. Oceanogr., 28,
1511 - 1526.
Young, I.R. and G.J. Holland, 1996: Atlas of the Oceans:Wind and Wave Climate, Pergamon Press, 241 pp.
Young, I.R., 1999: Seasonal variability of the global
ocean wind and wave climate, Int. J. Climatol., 19,
931-950.
Yu, L. S., and P. Malonotte-Rizzoli, 1998: Inverse
modeling of seasonal variations in the North Atlantic
Ocean. J. Phys. Oceanogr., 28, 902-922.
Yu, L., and J. J. O'Brien, 1995: Variational data
assimilation for determining the seasonal net surface
heat flux using a tropical Pacific ocean model. J.
Phys. Oceanogr., 25, 2319-2343.

Report of the Working Group on Air Sea Fluxes

296

Zavody, A. M., M. R. Gorman, D. J. Lee, D. Eccles, C.
T. Mutlow and D. T. Llewellyn-Jones, 1994: The
ATSR data processing scheme developed for the
EODC. Int.J.Remote Sensing, 15, 827 - 843.
Zender, C. S., B. Bush, S. K. Pope, A. Bucholtz, W. D.
Collins, J. T. Kiehl, F. P. J. Valero and J. J. Vitko,
1997: Atmospheric absorption during the
Atmospheric Radiation Measurement (ARM)
enhanced shortwave experiment (ARESE). J.
Geophys. Res., 102(D25), 29901 - 29917.
Zeng, L. and G. Levy, 1995: Space and time aliasing
structure in monthly mean polar-orbiting satellite
data. J. Geophys. Res., 100, 5133-5142.
Zeng, L. X. and R. A. Brown, 1998: Scatterometer
observations at high wind speeds. J. Appl. Meteor.,
37, 1412 - 1420.
Zeng, X. ; Zhao, M. ; Dickinson, R. E. ; He, Y. 1999 A
multiyear hourly sea surface skin temperature data set
derived from the TOGA TAO bulk temperature andwind speed over the tropical pacific J. Geophys.
Res., 104, 1525 – 1536.
Zeng, X., M. Zhao, and R. E. Dickinson, 1998:
Intercomparison of bulk aerodynamical algorithms
for the computation of sea surface fluxes using
TOGA COARE and TAO data. J. Climate, 11, 26282644.
Zhang, G.J. and M.J. McPhaden, 1995: The relationshipbetween sea surface temperature and latent heat fluxin the equatorial Pacific. J. Climate, 8, 589-605.
Zhang, G.J., 1995: Use of monthly mean data to computesurface turbulent fluxes in the Tropical Pacific. J.
Climate, 8, 3084-3090.
Zhang, Y-C., W.B. Rossow and A.A. Lacis, 1995:
Calculation of surface and top-of-atmosphereradiative fluxes from physical quantities based on
ISCCP datasets, Part I: Method and sensitivity to
input data uncertainties. J. Geophys. Res., 100, 11491165.
Zillmann, J. W., 1972: A study of some aspects of theradiation and the heat budgets of the southernhemisphere oceans., Bureau of Meteor., Departmentof the Interior, Canberra, Australia.
Zipser, E. J., and R. H. Johnson, 1998: Systematic errors
in radiosonde humidities: A global problem? Proc.
10th Symp. on Meteorol. Obs., 11-16 Jan., Phoenix,
Amer. Meteor. Soc., Boston, MA, 72-73.
Zolina, O., and S.K.Gulev, 1998: Intercomparison of the
NCEP Reanalysis winds to the instrumental
measurements and VOS observations in the North
Atlantic, In: First International Conference on
Reanalyses, WMO/TD-876, WMO, 298-301.
Zupanski, M., and E. Kalnay, 1999: Principles of data
assimilation. Global Energy and Water Cycles, K. A.
Browning and R. J. Gurney, ed., Cambridge
University Press, Cambridge, UK, 48-52.
Zwally, H.J., Comiso, J.C., Parkinson, C.L., Campbell,
W.J., Carsey, F.D., and Gloersen, P., 1983: Antarctic
Sea Ice, 1973-1976: Satellite Passive-Microwave
Observations, NASA SP-459, Washington, D.C.

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299

Working Group Membership

APPENDIX A. MEMBERS OF THE WORKING GROUP ON AIR-SEA FLUXES
Dr. Sergey Gulev (co-chair),
P.P.Shirshov Institute of Oceanology,
RAS
36 Nakhimovsky Ave.,
117218 Moscow,
RussiaDr. David Legler
COAPS, Florida State University
2035 E.Paul Dirac Drive
R.M.Johnson Bldg. - Suite 200
Tallahassee, FL 32306-2840,
USA

Peter K. Taylor (co-chair),James Rennell Division (254/27)
Southampton Oceanography Centre,
European Way,Southampton, SO14 3ZH,
United Kingdom

Dr. Ralf Lindau
Institut für Meereskunde
Duesternbrooker Weg. 20,
D-24105 Kiel,
Germany

Dr. Bernard Barnier
LEGI
Institut de Mécanique de Grenoble
BP53, 38041, Grenoble Cedex,
FranceDr. E. Frank Bradley
CSIRO, Land and Water,
GPO Box 1666,
Canberra, ACT 2601,
Australia
Dr. Tom Charlock
Atmospheric Sciences Division
Mail Stop 420
NASA Langley Research Center
Hampton, VA 23681-0001,
USA
Dr. Peter Gleckler
PCMDI,
Lawrence Livermore National Lab,
P.O. Box 808, L-264
University of California,
Livermore, CA, 94551,
USA
Prof. Masahisa Kubota
School of Marine Science andTechnology,Tokai University
Orido,
Shimizu,
Shizuoka 424-8610.
JAPAN

Dr. Drew Rothrock
Applied Physics Laboratory,
University of Washington,1013 NE 40th Streeet,
Seattle, WA 98105,
USA
Dr. Joerg Schulz
Deutsches Zentrum für Luft- und Raumfahrt
DFD-US KP
Linder Hoehe
D-51170 Koeln,
Germany
Dr. Arlindo da Silva
Data Assimilation Office
Goddard Space Flight Center,Greenbelt,
Maryland, 20771,
USA
Dr. Andreas Sterl
KNMI,
PO Box 201
3730 AE De Bilt,
The Netherlands
Dr. Glenn WhiteW/NMC2, WWB, Rm.207
National Centers for Environmental
Prediction
5200 Auth Rd.
Washington, D.C. 20233, USA

Dr. Kunio Kutsuwada,
School of Marine Science and Technology,
Tokai University
3-20-1, Orido,
Shimizu, Shizuoka 424-8610,
Japan

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Appendix B.

APPENDIX B.
PRODUCTS301

The Catalogue

CATALOGUE OF FLUX DATA SETS AND FLUX RELATED

B.1 Introduction
This Appendix give details of how to access the catalogue, the catalogue contents,
and the information which should be submitted for each catalogue entry.
B.2 Accessing the Catalogue
The Catalogue of Flux products is available on the Internet at
http://www-pcmdi.llnl.gov/airseawg/
and also mirrored at
http://www.lmd.jussieu.fr/pcmdi-mirror/airseawg/
and athttp://www.bom.gov.au/bmrc/clch/pcmdi-mirror/airseawg/
It is intended that this catalogue should be continued to be maintained following the end of
the Working Groups term of office.
B.3 Preliminary Initial Contents List
Arctic Global Radiation (AGR-1) Data Set ofpoint observations

Japanese Ocean Flux data sets with Use of
Remote sensing Observations

Arctic Global Radiation (AGR-2) Gridded
Data Set

NCEP-1 Reanalysis

Arctic Ocean Radiative Fluxes
Atlas and Ardizzone SSMI-derived winds
CERSAT

NCEP-2 Reanalysis
NMC/NCEP Operational Analyses
OCEANOR wave fields

ECMWF Operational Analyses

ORSTOM Tropical Atlantic Pseudo-stressand Sea Surface Temperature Analyses

ERA-15 Reanalysis

Polar Radiation Fluxes

FSU Tropical pseudo stress analyses

SOC Satellite altimeter significant wave
height global climatology

GEOS1 Reanalysis
Gridded Daily and Monthly Arctic
Atmospheric and Surface Data from TOVS
HOAPS fluxes from satellite
IABP/POLES 2-m air temperature data set
IfM Fluxes and Flux Related Data Set
IORAS/IFM North Atlantic wave and wave
dependent wind stress

SOC Climatology
UWM/COADS Climatology
WAM cycle 4 data obtained with ECMWF
Re-Analysis winds
WAM wave hindcasts using ERA winds
WOCE Surface Meteorology Data

B.4 Information to be Submitted
Table B.1 (overleaf) shows the categories of information which are required for a
product entry and for a verification data set such as an air-sea interaction experiment.

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Appendix B.

302

PRODUCT DATA SET

EXPERIMENT DATA SET

Name:

Area:

Geographical area:

Time Period:

Time period:
Grid: (space/time)

The Catalogue

VARIABLES:
platforms

Resolution:(space/time; note that this may instruments (accuracies)
be less than the grid spacing)
VARIABLES:FLUXES:

Formulae usedmeasured (method)

Data sources

calculated (formulae)

Corrections applied
AVAILABILITY:AVAILABILITY
How/Where

How/where:

Formats

Format(s)References

DOCUMENTATION:

DOCUMENTATION

Product documentation:

Comments

Reference list:
COMMENTS:(including notes on strengths/weaknesses
and results of quality tests where these areavailable)

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Appendix C.

303

Acronyms and Abreviations

APPENDIX C. ACRONYMS AND ABBREVIATIONS

4DVAR
ADM
ADCP
AERI

4D variational assimilation
Angular Distribution Models
Acoustic Doppler Current Profiler
Atmospheric Emitted RadianceInterferometer
AES(Canadian) Atmospheric Environment
Service
AGCM
Atmospheric General Circulation ModelAIDJEX
Artic Ice Dynamics Joint Experiment
AIP
Algorithm Intercomparison Projects ( of
GPCP)
AMIP
Atmospheric Model Intercomparison
Project
AMSR
Advanced Scanning Microwave
Radiometer
AOGCM
Atmospheric Ocean General Circulation
Model (coupled model)
ARESE
ARM Enhanced Shortwave Experiment
ARGO(experiment to deploy drifting buoys for
ocean data assimilation)
ARKTIS'93 Polarstern Arctic cruises
ARM
Atmospheric Radiation Measurement
program
ARPEGE
Action de Recherche Petite Echelle
Grande Echelle - French climate model
ASTEX
Atlantic Stratocumulus Transformation
Experiment
ATLAS
Autonomous Temperature Line
Acquisition System
ATOVS
Advanced TIROS Operational Vertical
Sounder
ATSR
Along-Track Scanning Radiometer
AVHRR
Advanced Very High Resolution
Radiometer
BOMEX
Barbados Oceanographic MeteorologicalExperiment
BDRF
Bi-Directional Reflectance Function
BSRN
Baseline Surface Radiation Network
CAGEX
CERES/ARM/GEWEX experiment
CART(ARM) Cloud and Radiation Testbed
CASP
Canadian Atlantic Storm Project
CASPR
Cloud and Surface Parameter Retrieval
systemCCN
Cloud Condensation Nuclei
CDAS
Climate Data Assimilation System
CDC(US) Climate Diagnostics Center
CEAREX
Coordinated Eastern Arctic Experiment
CEPEX
Central Equatorial Pacific Experiment
CERES
Clouds and the Earth's Radiant Energy
SystemCERSAT
Centre ERS-1 d'Archivage et deTraitement
CFD
Computational Fluid DynamicsCIMO
WMO Commission for Instruments andMethods of Observation
CLASS(ARM radiosonde type)
CMAP
CPC Merged Analysis of Precipitation
CMDL(NOAA) Climate Monitoring and
Diagnostics Laboratory
CMMIV
Beaufort Scale defined by Commision for
Marine Meteorology 4th Session

Report of the Working Group on Air Sea Fluxes

CMR-5

Compressed Marine Reports--Product 5(of COADS)COADSComprehensive Ocean-Atmosphere Data
Set
COAPS
Center for Ocean Atmosphere Prediction
Studies (FSU)
COARE
Coupled Ocean-Atmosphere Response
Experiment
COLA .
Centre for Ocean-Land-Atmosphere
Studies
COMPARE CERES Ocean Measurement Platform for
Aerosol and Radiation Evaluation
CPC
Climate Prediction Center
CSIRO
Commonwealth Scientific and Industrial
Reserch Organisation (Australia)
CTD
Conductivity Temperature and Depth(instrument)
CTT
Convective Transport Theory
DAAC
Distributed Active Archive Center
DAO
Data Assimilation Office
DMSP .
Defense Meteorological Satellite Program
DOE
Department of Energy
DSD
Drop Size Distribution
ECHAM3 European Centre model with HAMburg
physics (version 3)
ECMWFEuropean Centre for Medium Range
Weather Forecasting
ENSO
El Niño / Southern Oscillation
EOF
Empirical Orthogonal Function
EOS-AM1 Earth Observation System AM1 satellite(now Terra)
ERA15
ECMWF 15 year reanalysis
ERBE
Earth Radiation Budget Experiment
ERI
Engine Room Intake
ERICA
Experiment on Rapidly Intensifying
Cyclones over the Atlantic
ERS-1
European Remote Sensing satellite
ESA
European Space Agency
ESTAR
Electrically Scanning Thinned ArrayRadiometer
EUMETSAT European Meteorological Satellite agency
EUROWAVE (system for diagnosis and forecasting ofwaves in European coastal areas)
FASINEX Frontal Air-Sea Interaction Experiment
FIFEFirst ISCCP Field Experiment
FNOC
Fleet Numerical Oceongraphy Center
FSU
Florida State University
FTIR
Fourier Transform Infrared Instrument
FTP
File Transfer Protocol
GALE
Genesis of Atlantic Lows Experiment
GARP
Global Atmospheric Research Programme
GATE
GARP Atlantic Tropical Experiment
GCI
Global Cloud Imagery
GCM's
General Circulation Models
GCOS
Global Climate Observing System
GEBA
Global Energy Balance Archive
GEOS1
first Goddard Earth Observation Systemreanalysis
GEOSAT(altimetric satelllite)
GEWEX
Global Energy and Water Cycle
Experiment

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Appendix C.

GIN
GISS
GISST

304

Acronyms and Abreviations

Greenland/Iceland/Norwegian Seas
Godard Institute for Space Studies
Global Ice and Sea Surface Temperature(project)
GMS[Japanese] Geostationary Meteorological
Satellite
GODAE
Global Ocean Data Assimilation
Experiment
GOES-E/W [US] geostationary meteorologicalsatellites
GOOS
Global Ocean Observing System
GPCP
Global Precipitation Climatology Project
GPI
Geostationary Precipitation Index
GPS
Global Positioning System
GRDC
Global Runoff Data Centre
GSFC
Goddard Space Flight Centre
GTS
Global Telecommunications System
HadCM3
Hadley Centre Climate Model (version 3)
HEXMAX (main HEXOS field experiment)
HEXOS
Humidity Exchange Over the Sea(experiment program)
HIRS
High-resolution Infrared Radiation
Sounder
HOAPS
Hamburg Ocean Atmosphere Parametersand Fluxes from Satellite Data
IABP/POLES International Arctic Buoy
Programme/Polar Exchange at the Sea
Surface
ICE
International Cirrus Experiment
ICRCCM
Intercomparison of Radiation Codes in
Climate Models
IFREMER Institut Français de la Researche sur la
Mer
IfM
Institut für Meereskunde Kiel
IHP
International Hydrography Program
IMET
Improved Meteorology (WHOI
instrumentation package)
IOP
Intensive Observation Phase
IPCC
International Panel on Climate Change
IR
InfraRedISCCPInternational Satellite Cloud Climatology
Project
ITCZ
Inter-Tropical Convergence Zone
JASIN
Joint Air-Sea Interaction Experiment
JASMINE Joint Air Sea Monsoon Experiment
JMA .
Japan Meteorological Agency
JONSWAP Joint North Sea Wave Project
JSC
Joint Scientific Committee (of the WCRP)
KNMI
Koninklijk Nederlands Meteorologisch
Instituut
KWAJEX TRMM validation Experiment aroundKwajalein island
LabSeaLabrador Sea Deep Convection
Experiment
LBLRTM
Line-By-Line Radiative Transfer Models
LEADEX(Remote Sensing of Lead Properties in the
Arctic)LMD
Laboratoire de Mètèorologie Dynamique(Paris, france)
LMR
Long Marine Records
LOWTRAN7 (radiation transfer code)
LSG
Large Scale Geostrophic model
LW
Longwave
MBT
Mechanical Bathythermograph
MCSST
Multi Channel Sea Surface Temperature
MCTEX
Maritime Continent Thunderstorm
Experiment.

METEOSAT [European] geostationary meteorologicalsatellite
MFWT .
Meridional Fresh Water Transport
MHT
Meridional Heat Transport
MJO
Madden-Julian oscillation
MODIS
MODerate resolution Imaging Spectroradiometer
MSTG
COADS Monthly Summary Trimmed
GroupsMSU
Microwave Sounding Unit
MWSR
Microwave Water Substance Radiometer
NAO
North Atlantic Oscillation
NASA(US) National Aeronautics and Space
Administration
NCAR(US) National Center for AtmosphericResearch
NCAR CCM3 NCAR Community Climate Model(version 3)
NCAR-ATD NCAR Atmospheric Technology Division
NCAR-CSM NCAR Climate System Model
NCEP(US) National Centers for Environmental
Prediction
NCEP1/NCEP2 NCEP/NCAR reanalyses
NDBC(US) National Data Buoy Center
NESDIS(US) National Environmental Satellite
Data and Information Service
NHF
Net Heat Flux
NIP
Normal Incidendence Pyrheliometer
NLW
Net Long Wave
NMC
National Meteorological Centre (now
NCEP)
NOAA
(US) National Oceanic and Atmospheric
Administration
NOAA/ETL NOAA Environment Technology
Laboratory
NODC
National Oceanographic Data Center
NPO
North Pacific Oscillation
NSCAT
NASA Scatterometer
NSW
Net Short Wave
NWP
Numerical Weather Prediction
OCL
Ocean Climate Laboratory
ODA
Ocean Data Assimilation
ODBS(Japan) Ocean data Buoy System
ODGP
Ocean Data Gathering Programme
OGCM
Ocean General Circulation Model
OI
Optimal Interpolation
OLR
Outgoing Longwave Radiation
OPAICE(French Ocean and ICE model)
OPI
OLR based Precipitation Index
ORG
Optical Raingauge
ORSTOM (Institut français de recherche scientifiquepour le développement en coopération)
OWS
Ocean Weather Ship
Profiling Autonomous Lagrangian
P-ALACE
Circulation Explorer
PCMDI
Program for Climate Model Diagnosis andIntercomparisonPI
Principal Investigator
PIP
Precipitation Intercomparison Project
PIR
Eppley precision infrared radiometer
PIRATA
Pilot Research Moored Array in the
Tropical Atlantic
PNA
Pacific-North American pattern
PR
Precipitation Radar
PROBE
Pilot Radiation Observation Experiment(FRENCH PROFILING FLOAT)PROVOR
PRT
platinum resistance thermometer

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Appendix C.

QuikScat(NASA Scatterometry Satellite)
RADARSAT (Canadian Scatterometry Satellite)
RAMS(Valero radiometer)
RAPS
Rotating Azimuth Plane Scanning
RGPS
RADARSAT Geophysical Processor
SystemRH
Relative Humidity
RHS
Right Hand Side
RMS/rms
Root Mean Square
RRTM
Rapid Radiative Transfer Model
RTM
Radiative Transfer Model
RTNEPH(United States Air Force) Real-Time
Nephanalysis
SAR
Synthetic Apperture Radar
SBUV
Solar Backscatter UltraViolet
SBWR
Ship Borne Wave Recorder
ScaRaB
Scanner for Radiation Budget
SCOPE
San Clemente Ocean Probing Experiment
SCOR
Scientific Committee on Ocean Research
SCSMEXSouth China Sea Monsoon Experiment
SECTIONS Energetically Active Zones of the Ocean(EAZO) and Climate Variability
experiment programme
SEMAPHORE Structure des Echanges MerAtmosphere, Proprietes des Heterogeneites
Oceaniques: researche Experimentale
SHEBA
Surface Heat Budget of the Arctic Ocean
SMMR
Scanning Multichannel Microwave
Radiometer
SMOS
Soil Moisture and Ocean Salinity
SOC
Southampton Oceanography Centre
SOFIA
Surface of the Ocean, Fluxes andInteractions with the Atmosphere
SRB
surface radiation budget
SRB
Surface Radiation Budget
SSI
spectral statistical interpolation
SSM/I
Special Sensor Microwave/Imager
SSS
Sea Surface Salinity
SSST
Sea Surface SkinTemperature
SST
Sea Surface Temperature
SVD
Singular Vector Decomposition

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305

Acronyms and Abreviations

SW
SWH
SWS-2
TAO
TIROS

Shortwave
Significant Wave Height
Storm Wave Study - 2
Tropical Atmosphere Ocean Array
Television InfraRed Observational
Satellite
TMI
TRMM Microwave Imager
TOA
Top Of the Atmosphere
TOGA
Tropical Ocean Global Atmosphere
TOMS
Total Ozone Measuring System
TOPEX/POSEIDON (Altimetric satellite system)TOVS
TIROS Operational Vertical Sounder
TRITON(West Pacific buoy array)
TRMM
Tropical Rainfall Measuring Mission
TWP
ARM Tropical West Pacific
UCAR
University Corporation for AtmosphericResearch
UKMO
United Kingdom Meteorological Office
URL
Internet address (Uniform Resource
Locator)
UWM
University of Wisconsin-Milwaukee
VIRS
Visible and Infrared Scanner
VISSR
Visible Infra Red Spin-Scan Radiometer(on GMS)
VOS
Voluntary Observing Ships
VSOP-NA VOS Special Observing Programme North Atlantic
VTPR
Vertical temperature Profile Radiometer
WAM
Wave Model
WAMDI
Wave Model Development and
Implementation group
WCRP
World Climate Research Programme
WGASF
Working Group on Air-Sea Fluxes
WGNE
Working Group on Numerical
Experimentation
WHOI
Woods Hole Oceanographic Institution
WMO
World Meteorological Organisation
WOCE
World Ocean Circulation Experiment
WOCE AIMS WOCE Analysis Interpretation Modellingand Synthesis
WRR
World Radiation Reference
XBT
Expendable Bathy-Thermograph
XCTD
Expendable CTD

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June, 2000