GLOBAL ENERGY AND WATER CYCLE EXPERIMENTWORLD CLIMATE RESEARCH PROGRAMMEThe Second Global Soil Wetness Project

Science and Implementation Plan
December 2002
IGPO Publication Series
No. 37

The Second Global Soil Wetness Project
GSWP-2Compiled by

Paul Dirmeyer and Xiang Gao
Center for Ocean-Land-Atmosphere Studies

andTaikan Oki
University of Tokyo

December 2002
IGPO Publication No. 37

EXECUTIVE SUMMARYThe Global Soil Wetness Project (GSWP) is an ongoing environmental modelingresearch activity of the Global Land-Atmosphere System Study (GLASS) and the International
Satellite Land-Surface Climatology Project (ISLSCP), both contributing projects of the Global
Energy and Water Cycle Experiment (GEWEX).
Its goals are to:•

Produce state-of-the-art global data sets of land surface fluxes, state variables, andrelated hydrologic quantities.

•

Develop and test large-scale validation, calibration, and assimilation techniques over
land.

•

Provide a large-scale validation and quality check of the ISLSCP data sets.

•

Compare Land Surface Schemes (LSSs), and conduct sensitivity studies of specificparameterizations and forcings, which should aid future model and data set
development.
GSWP-2 is closely linked to the ISLSCP Initiative II data effort, and LSS simulations in

GSWP-2 will encompass the same core 10-year period as ISLSCP Initiative II (1986-1995).
There are five basic categories of participants in GSWP-2: the operational centers, theland-surface modelers, validators of the model output, those involved in remote sensing
applications, and other users of the model output. An Inter-Comparison Center will collect
results from participating models, perform consistency checks, and basic comparisons.
A major product of GSWP-2 will be a multi-model land surface analysis for the ISLSCP
Initiative II period. This will be a land surface analog to the atmospheric reanalyses, and will
include estimates of uncertainties based on inter-model spread. The science plan also includes
in situ validation with data from field campaigns, observational networks and long-termmonitoring sites. Modeling sensitivity studies will involve re-integrating the LSSs over part or allof the global, 10-year domain to test the response of the models to changes in meteorological
data (including choice of reanalysis products, impacts of bias correction, sensitivity to the range
in observational estimates, and impacts of rain-gauge under-catch) and surface parameters.
Comparisons to land models of simple and intermediate complexity will also be conducted.
A new thrust for GSWP-2 is a stronger connection to applications in remote sensing. In
addition to the classical attempts to validate the typical land-surface state variables using
satellite retrievals, GSWP-2 also intends to expand the validation and assimilation capabilities oficurrent LSSs. This is to be done by the development of algorithms by which LSSs can directly
report brightness temperatures, like those sensed by instruments in orbit.
All data sets will conform to the Assistance for Land-surface Modeling Activities (ALMA)
standards set forth within GLASS.

New Internet data server technologies will be used to

distribute and analyze data, reducing archiving and data management burdens on participants.

ii

TABLE OF CONTENTSEXECUTIVE SUMMARY ..................................................................................................................... i
LIST OF TABLES ............................................................................................................................. v
LIST OF FIGURES ........................................................................................................................... vi
ACRONYMS .................................................................................................................................. vii
1.0 Introduction .........................................................................................................................1
1.1
Overview .......................................................................................................................1
1.2
Background ...................................................................................................................2
1.3
Participants....................................................................................................................4
1.4
End Users......................................................................................................................5
2.0 Science Plan .......................................................................................................................7
2.1
Global continuous multi-model analysis ........................................................................7
2.1.1 Mean seasonal cycle.................................................................................................7
2.1.2 Interannual variability/anomalies ...............................................................................8
2.1.3 Uncertainty in model-derived surface fluxes and state variables ..............................92.1.3.1
Establish the multi-model envelope.................................................................10
2.1.3.2
Uncertainty by variable ....................................................................................10
2.1.3.3
Uncertainty by region.......................................................................................11
2.2
Evaluation....................................................................................................................112.2.1 Field campaigns ......................................................................................................122.2.1.1
Global (ISLSCP) data sets versus local data ..................................................132.2.1.2
LSS output versus in situ measurements ........................................................132.2.2 Observational networks and long-term monitoring..................................................132.2.3 Streamflow ..............................................................................................................142.2.4 Multi-model validation..............................................................................................142.2.4.1
Simple ensembling ..........................................................................................15
2.2.4.2
Optimal ensembling.........................................................................................15
2.3
Sensitivity studies........................................................................................................16
2.3.1 Sensitivity to atmospheric forcing............................................................................16
2.3.1.1
Precipitation data.............................................................................................16
2.3.1.2
Radiation data .................................................................................................18
2.3.1.3
All meteorological data ....................................................................................192.3.1.4
Implications of results ......................................................................................192.3.2 Differences in parameters .......................................................................................20
2.3.2.1
Sensitivity to prescribed surface conditions.....................................................20
2.3.2.2
Implications for future ISLSCP efforts at data synthesis .................................21
2.4
Remote sensing applications ......................................................................................212.4.1 Prognostic brightness temperatures........................................................................212.4.1.1
Microwave/soil wetness...................................................................................222.4.1.2
Vegetation index/Dynamic Vegetation Models (DVM).....................................222.4.2 Validation of classical state variables......................................................................23
2.4.2.1
Skin temperature .............................................................................................23iii

Albedo .............................................................................................................24
2.4.2.2
2.4.2.3
Snow cover......................................................................................................24
2.4.3 Assimilation techniques...........................................................................................25
2.5
Other science efforts ...................................................................................................25
2.5.1 Comparison to simple and intermediate models .....................................................25
2.5.2 Uniqueness .............................................................................................................26
2.5.3 Global Water and Energy Cycles ............................................................................27
3.0 Implementation plan..........................................................................................................29
3.1
Production of the GSWP-2 data sets ..........................................................................29
3.1.1 Input fields ...............................................................................................................29
3.1.1.1
Land surface data............................................................................................29
3.1.1.2
Atmospheric forcing data.................................................................................32
3.1.1.3
ALMA conventions...........................................................................................38
3.1.1.4
Serving of data ................................................................................................40
3.1.2 Initial conditions.......................................................................................................41
3.1.3 Execution and production........................................................................................423.1.4 Output fields ............................................................................................................44
3.1.4.1
Daily fields .......................................................................................................45
3.1.4.2
Global 3-hourly fields.......................................................................................47
3.1.4.3
Fixed fields ......................................................................................................48
3.1.4.4
Local 3-hourly fields.........................................................................................48
3.1.4.5
Recording output data .....................................................................................49
3.1.4.6
Ancillary information ........................................................................................49
3.2
Inter-comparison Center..............................................................................................51
3.2.1 Data submission......................................................................................................51
3.2.2 Quality control .........................................................................................................52
3.2.3 Data redistribution ...................................................................................................52
3.3
Evaluation....................................................................................................................53
3.3.1 Hydrologic validation ...............................................................................................53
3.3.1.1
Streamflow.......................................................................................................53
3.3.1.2
Hydrology standards for ALMA........................................................................53
3.3.2 Field campaign data ................................................................................................54
3.3.2.1
Access .............................................................................................................54
3.3.2.2
ALMA formatting..............................................................................................54
3.3.3 Observational networks...........................................................................................55
3.4
Sensitivity studies........................................................................................................55
3.4.1 Optional input data sets...........................................................................................56
3.4.2 Data submission/distributed analysis ......................................................................57
3.5
Remote sensing applications ......................................................................................58
3.6
Contact Information .....................................................................................................59
REFERENCES ...............................................................................................................................60

iv

LIST OF TABLES
Table 1.

Some field campaigns that overlap the GSWP-2 period........................................12

Table 2.

Soil properties as a function of texture class. Cosby values for silt are
estimated, as they were not provided in the original RhôneAGG data set.............31

Table 3.

Soil parameter data................................................................................................38

Table 4.

Vegetation parameter data.....................................................................................39

Table 5.

Vegetation categories for the IGBP-derived land cover types................................40

Table 6.

Meteorological forcing data (July 1982 - December 1995).....................................41

Table 7.

GDS file names of input fields for the baseline integration (B0).............................43

Table 8.

Potential space requirements per output variable..................................................44

Table 9.

ALMA standard output variables for GSWP-2 (see http://www.lmd.jussieu.fr/
ALMA/ for a detailed discussion of these variables)...............................................45

Table 10.

ALMA variables to be reported at 3-hourly intervals during the IMOP...................48

Table 11.

ALMA fixed fields to report with output data...........................................................48

Table 12.

Requested ancillary information about the LSS and its integrations in GSWP......50

Table 13.

Alternate input files for the sensitivity studies.........................................................57v

LIST OF FIGURES
Figure 1.

The five categories of participants in GSWP-2.........................................................4

Figure 2.

Seasonal eastern tropical Pacific sea surface temperature anomalies....................9

Figure 3.

Regional rainfall indices..........................................................................................10

Figure 4.

Implementation flowchart for GSWP-2....................................................................29

Figure 5.

Timeline for GSWP-2..............................................................................................30

Figure 6.

Example of gauge density for CRU stations for January 1986.
Unshaded areas have no stations within 2° of grid box..........................................34vi

ACRONYMS

AGCM
AMIP
ALMA
ARM-CART
BATS
CF
COLA
CREST
CRU
DIS
DODS
DOE
DVM
ECMWF
EDC
EMC
ENSO
ERA-40
EROS
FIFE
FTP
GEWEX
GCM
GDS
GLASS
GPCC
GPCP
GrADS
GSFC
GSWP
GTS
GVaP
HAPEX-Sahel
ICC
IGBP-DIS
IIS
IMOP
ISCCP
ISLSCP

Atmospheric General Circulation Model
Atmospheric Model Inter-comparison Project
Assistance for Land-Surface Modeling Activities
Atmospheric Radiation Measurement Program – Cloud and Radiation
Testbed
Biosphere-Atmosphere Transfer Scheme
Climate and Forecast
Center for Ocean-Land-Atmosphere Studies
Core Research for Evolutional Science and Technology
Climate Research Unit
Data Sets Information Systems
Distributed Oceanographic Data System
Department of Energy
Dynamic Vegetation Models
European Centre for Medium-Range Weather Forecasts
EROS Data Center
Environmental Modeling Center
El Niño/Southern Oscillation
ECMWF Re-analysis-40
Earth Resources Observation Systems
First ISLSCP Field Experiment
File Transfer Protocol
Global Energy and Water Cycle Experiment
Global Atmospheric Climate Model
GrADS-DODS server
Global Land-Atmosphere System Study
Global Precipitation Climatology Centre
Global Precipitation Climatology Project
Grid Analysis and Display System
Goddard Space Flight Center
Global Soil Wetness Project
Global Telecommunication System
GEWEX Water Vapor ProjectHydrology-Atmosphere Pilot Experiment in the SahelInter-Comparison Center
International Geosphere-Biosphere Programme Data Information System
Institute for Industrial Studies
Intensive Model Output Period
International Satellite Cloud Climatology ProjectInternational Satellite Land-Surface Climatology Project

vii

JST
LAI
LDAS
LMD
LSSs
MODIS
NASA
NCAR
NCEP
NDVI
NetCDF
NOAA
NWP
PILPS
PFT
SCAN
SiB
SMMR
SRB
SSiB
SSM/I
UMBC
USAF
USDA
UTC
WCRP

Japan Science and Technology Corporation
Leaf Area Index
Land-Surface Data Assimilation
Laboratoire de Météorologie Dynamique du C.N.R.S.
Land Surface Schemes
Moderate Resolution Imaging Spectroradiometer
National Aeronautics and Space Administration
National Center for Atmospheric Research
National Centers for Environmental Prediction
Normalized Difference Vegetation Index
Network Common Data Format
National Oceanic and Atmospheric Administration
Numerical Weather Prediction
Project for the Inter-Comparison of Land-Surface Parameter Schemes
Plant Functional Type
Soil Climate Analysis Network
Simple Biosphere
Scanning Multichannel Microwave Radiometer
Surface Radiation Balance
Simplified Simple Biosphere
Special Sensor Microwave/Imager
University of Maryland, Baltimore County
United States Air Force
United States Department of Agriculture
Coordinated Universal Time
World Climate Research Programmeviii

1.0 INTRODUCTION
1.1 Overview
The Global Soil Wetness Project (GSWP) is an ongoing environmental modelingresearch activity of the Global Land-Atmosphere System Study (GLASS) and the International
Satellite Land-Surface Climatology Project (ISLSCP), both contributing projects of the Global
Energy and Water Cycle Experiment (GEWEX) in the World Climate Research Programme(WCRP). GSWP is charged with producing as a community effort global estimates of soil
moisture, temperature, snow water equivalent, and surface fluxes by integrating one-way
uncoupled land surface schemes (LSSs) using externally specified surface forcings andstandardized soil and vegetation distributions. GSWP-2 will produce the best model estimatesof the land-surface water and energy cycles over a ten year period. This project will include anevaluation of the uncertainties linked to the LSSs, their parameters and the forcing variables.
The goals of GSWP are to:•

Produce state-of-the-art global data sets of land surface fluxes, state variables, andrelated hydrologic quantities.

•

Perform large-scale model evaluation, validation and calibration over land.

•

Provide a large-scale validation and quality check of the ISLSCP data sets.

•

Compare LSSs, and conduct sensitivity studies of specific parameterizations andforcings which should aid future model and data set development.

GSWP-2 is closely linked to the ISLSCP Initiative II data effort (http://islscp2.sesda.gov/), and
LSS simulations in GSWP-2 will encompass the same core 10-year period as ISLSCP Initiative
II (1986-1995).

Participation by land surface modelers, remote sensing scientists, fieldresearchers, data collectors and others is voluntary.

The basic operation of GSWP-2 iscurrently supported in the United States by the National Aeronautics and Space Administration(NASA), and in Japan by the Core Research for Evolutional Science and Technology (CREST)of the Japan Science and Technology Corporation (JST) under a project titled "Modeling Global
Hydrological Cycles and World Water Resources Coupled with Human Activities."11.2 Background
GSWP-2 is the follow-on project to GSWP-1, a 2-year pilot phase based on the ISLSCP
Initiative I data set for 1987-1988. GSWP-1 was also an offline land-surface modeling andevaluation effort conducted at a spatial resolution of 1 degree.
The original motivation for GSWP stemmed from the paradox that soil wetness is an
important component of the global energy and water balance, but it is unknown over most of the
globe. Soil wetness is the reservoir for the land surface hydrologic cycle, it is a boundary
condition for atmosphere, it controls the partitioning of land surface heat fluxes, affects thestatus of overlying vegetation, and modulates the thermal properties of the soil. Knowledge ofthe state of soil moisture is essential for climate predictability on seasonal-annual time scales.
However, soil moisture is difficult to measure in situ, remote sensing techniques are only
partially effective, and few long-term climatologies of any kind exist. The same problems exist
for snow mass, soil heat content, and all of the vertical fluxes of water and heat between land
and atmosphere. Even a consistent definition of soil wetness is elusive.
In GSWP-1, the ISLSCP Initiative I data set was used to supply boundary conditions,model parameters and meteorological forcing for more than one dozen LSSs integrated bymembers of the Production Group, which then reported a set of standard output data at thricemonthly intervals to an Inter-Comparison Center, which performed consistency checks andbasic comparisons.

These data were then made available to the Validation Group, which

performed in situ and remote sensing validation, as well as hydrologic validation of LSS runoff
against observed streamflow.

The Production Group members also individually performed

assigned sensitivity tests to determine the impact of changes in model parameterizations anddata sets on the results from the LSS simulations.
The pilot phase of the GSWP revealed that the quality of simulated land surface
quantities, particularly in the hydrologic cycle, is a strong function of the availability of in situ
observations feeding into the analysis stream of meteorological forcing data (Oki et al. 1999).
Where forcing and parameter data are of good quality, the participating LSSs performed well.
LSSs were found to have some variation in the partitioning of precipitation between runoff andevaporation, but much larger differences were found among the soil moistures simulated by the
LSSs (Entin et al. 1999). The data sets have also been used in a number of coupled landatmosphere climate modeling studies, which have shown the impact of high-quality soil moisturedata, and land surface variability on climate simulations (Mocko et al. 1999; Douville andChauvin 2000; Dirmeyer 2000, 2001; Douville et al., 2001; Douville 2002). Participation in
GSWP-1 gave land surface modelers a global testbed for improving their LSSs, and many of theparticipants have used it for that purpose.
2

A special issue of the Journal of the Meteorological Society of Japan (Vol 77, No. 1B;
1999) was published containing the preliminary results of GSWP. An overview article was also
published (Dirmeyer et al., 1999). Subsequent to that, other papers have been published — acomplete bibliography is maintained on the GSWP web site (http://www.iges.org/gswp/).
More recently, the Rhône Aggregation Experiment (Rhône-AGG; Boone et al., 2001,
2002) has been completed. The entire Rhône model domain size contains the Rhône river
basin in France, and is on the order of that of a coarse-resolution Global atmospheric Climate
Model (GCM). However, the atmospheric forcing, the soil and vegetation parameters, and theobserved river discharges are available at a significantly higher spatial resolution. This isaccomplished through use of the Rhône modeling system, which is comprised of a distributedhydrological model, an analysis system to determine the near-surface atmospheric forcing froma combination high resolution monitoring and model assimilation, and a LSS interface.
It was the interest of Rhône-AGG to examine how the simulations from a wide range of
LSSs (used in GCMs, Numerical Weather Prediction [NWP] models, mesoscale atmosphericmodels or hydrological models) are impacted by changing the spatial resolution over the domain(8km, 0.5°, and 1°). The main goals of the Rhône-AGG were to examine how various state of
the art LSSs are able to simulate the river discharge over several annual cycles when inserted
into the Rhône modeling system, and to explore the impact of the various scaling or aggregation
methods on the simulation of certain components of the hydrological cycle (such as snow coverand surface runoff). A limited number of multi-year simulations (August 1985 through July 1989)
were performed and studied by approximately 20 LSSs.
The conclusions from Rhône-AGG are that overall all LSSs simulated monthly dischargewell, but only 9 of 15 simulated good statistics at the daily scale. Parameterization of surface(sub-grid) runoff was critical: at the gauging station at Viviers, seven of nine LSSs with sub-grid
runoff parameterizations had significant skill, while only two of the six remaining LSSs showedskill. This same trend was found at other stations.

The sub-grid runoff and drainage

parameterizations were greatly impacted by scale. Surface runoff was generally reduced as
resolution decreased.

Drainage response was mixed among the LSSs, owing to different

evaporation and baseflow responses. The most consistent response was in terms of snowpack:
explicit snow schemes generally compare best with observations and discharge in Alpine
basins. Evapotranspiration changes were generally offset by runoff differences as resolution
was changed. LSSs tended to have a slightly wetter equilibrium soil moisture state at low
resolution (less than 5% change for most LSSs). Grid resolution greatly impacted the snow
simulation: snowmelt occurred too soon when the LSSs were run at low resolution, primarily
owing to warmer conditions over the snow cover. One model, which included sub-grid altitude3

banding, showed results that were largely unaffected by changes in resolution.
informationisavailableattheproject

website:More

http://www.cnrm.meteo.fr/mc2/

projects/rhoneagg/.

1.3 Participants
There are five basic categories of participants in GSWP-2, and there may be someoverlap as individuals or groups can be listed in more than one category. The categories are
the operational centers for the project, the land-surface modelers, evaluators of the model
output, those involved in remote sensing applications, and end users of the model output. The
relationship among these groups is illustrated in Figure 1.
The members of the first category are the two operational centers for GSWP-2. These
are the Center for Ocean-Land-Atmosphere Studies (COLA) in Calverton, Maryland, USA, andthe Institute for Industrial Studies (IIS) at the University of Tokyo, Japan. These operational
centers generate the forcing data and boundary conditions for the participating modelers, collectand compare the results of the models, and generally oversee the functioning of the project.
Primary responsibility for data set production is at COLA.

The IIS will maintain an Inter-

Comparison Center (ICC) that will collectthe model output and perform basic
consistency checks on the results.

The

ICC will then redistribute the data to thevalidation and remote sensing groups, aswell as generate a multi-model analysis,along with COLA, for broader distribution.
There will be regional mirror sites of input
data, and possibly also for model output
submission, to ameliorate trans-oceanic
Internet connectivity problems.
The second category includes theparticipating land surface modelers (called
the “Production Group” during GSWP-1).
The members of this group will run their
Figure 1. The five categories of participants in LSSs with the provided forcing and
GSWP-2.
boundary conditions, and furnish theresults of this “baseline” integration to the ICC. The modeling group is open to anyone with aunique LSS and an interest in participating. No direct financial support can be provided from4

GSWP to modeling groups for their participation. However, the access to data, analysis tools,
validation, and expertise should make participation attractive, as has been the case in previous
Project for the Inter-comparison of Land-surface Parameter Schemes (PILPS) and GSWP
experiments. Once a modeling group has set up its LSS for participation in GSWP, it has aready testbed for offline testing of changes and improvements to the LSS. Those who havepreviously participated in the Rhône-AGG or PILPS 2(e) experiments should find the transition
to GSWP-2 very easy, because of the adherence to the ALMA data standards. The modelinggroups will also participate in sensitivity studies that are designed to help elucidate the workingsof both the models and the land surface component of the climate system.
The evaluation group (called the “Validation Group” in GSWP-1) is involved withcomparison and validation of the model results with in situ observations. Largely this means
local validation on time series of model output at a single grid point that corresponds to a field
campaign or long-term monitoring site. For hydrologic validation, basin-scale runoff will bevalidated against stream gauge measurements.

This group will also include a multi-model

evaluation, that will feed back upon efforts of the operational group to produce multi-model
analyses, and those involved in other research efforts within GSWP. Specifically, the otherresearch efforts will be comparisons of the model results beyond that of the ICC, including
comparisons to simple and intermediate models, and examination of any LSSs’ ability tosimulate other LSSs.
The remote sensing applications group is a new focus for GSWP, concerned with thelarge-scale use of satellite data for validation of LSSs, and ultimately assimilation of satellite
data into LSSs. Studies of model performance that can be evaluated by satellite observations,such as snow coverage or radiative skin temperature, will be performed in this group. This will
also involve the development and application of algorithms by which current LSSs can provide
as output the brightness temperatures that are directly observed by space-based platforms. By
performing validation and calibration of observable quantities, rather than converting observable
quantities to model state variables, a more direct comparison can be achieved.

1.4 End Users
Finally, there is the group of users outside the project, a.k.a. the “End Users.” This
group includes those with the potential to take advantage of the GSWP-2 model output data, as
well as the unique atmospheric forcing and boundary condition data sets. These people are, bydefinition, not contributors to GSWP-2 science goals, but are scientists, engineers, and otherclients who may use the data for their own unique purposes.

5There continues to be a strong need for global data sets, and the model products of
GSWP-2 will expand upon those data sets provided by ISLSCP Initiative II by supplying
enhanced versions of ISLSCP Initiative II data sets in ALMA format, as well as estimates of land
surface fluxes and state variables with global coverage. End users may have interest in local,
regional or global data, mean diurnal and annual cycles or synoptic, seasonal and interannual
variations.

Hydrologists, engineers, biogeochemists, agronomists, botanists, ecologists,

geographers, climatologists, and educators may all have an interest in the products of GSWP-2.

6

2.0 SCIENCE PLAN
This section outlines the science plan for the global element of GSWP-2. Details of theexecution of the project are described in Section 3 — the Implementation Plan. There are fiveparts to the science plan: surface model analysis, validation, sensitivity studies, remote sensing
applications, and basic model investigations.

2.1 Global continuous multi-model analysis
A major product of GSWP-2 will be a multi-model land surface analysis for the ISLSCP
Initiative II period.

This will be a land surface analog to the atmospheric reanalyses, but

encompassing an ensemble of different LSSs. There will be a monthly seasonal cycle data setof monthly values, and a larger data set for the entire series. Using the results of multiple LSSs
will provide a model-independent result — compilation of a single analysis from an ensemble of
LSSs may be a simple or complex exercise, and is discussed in Section 2.2.4. Of particularvalue, uncertainty estimates can be put on all of the fields, based on inter-model spread.
Additional uncertainties regarding forcing data can be quantified, based on the sensitivity
studies described later in this plan.
In addition to publication of the analysis data sets themselves, a journal article will be
written describing the seasonal cycle, interannual variability and anomalies, including thesignals of significant climate events. This effort will deliver on an unfulfilled goal of GSWP-1.
The multi-model analysis will be the principal data product of GSWP-2. Based on thereception of the GSWP-1 and ISLSCP Initiative I data sets, it is anticipated that a wide range ofearth scientists, engineers, educators and social scientists will find the multi-model analysis
useful in their work.

2.1.1 Mean seasonal cycle
The fundamental time interval for climate is the annual cycle. A multi-model climatologyof the annual cycle at 1° resolution will be produced for all of the standard output variables, andwill be served to the community at large. The purpose of this climatological data set will be toprovide a best analysis of basic land surface state variables and fluxes at high spatial resolution.
Many of the users cited above could make use of such a data set as a benchmark or boundary
condition for other model simulations, or for ecological, geophysical, or economic calculations.
For users who do not need information on interannual variations during the 10-year period of
GSWP-2, this data set will be smaller and easier to use than the complete 10-year data set. It
will consist of 12 monthly means for all of the output fluxes, surface state variables, and7selected subsurface state variables.

Because different LSSs have different vertical

discretizations of soil layers, and different LSSs have different operating ranges for soil wetness(Koster and Milly 1997), soil wetness will be represented in terms of a few column integrated
deviations from the annual mean calculated over standard depths.

2.1.2 Interannual variability/anomalies
A multi-model analysis for the complete 10-year period of GSWP-2 will serve as a landsurface analog to the atmospheric reanalyses. As the coverage in space and time of land
surface observations is meager, an analysis using state-of-the-art models driven by the bestpossible forcing data (based on atmospheric observations) offers the best proxy to a global
observational network.
The analysis will contain 10 years of complete annual cycles of state variables andfluxes over the land surface at 1° resolution. As with the climatological analysis, monthly means
will be provided. Soil moisture will be in terms of anomalies from the mean annual cycle. The
data set can be used to drive ecological, biogeochemical, agricultural, meteorological or
economic models. It can also be analyzed in studies of climate variability at the land surface on
seasonal-interannual time scales. However, the 10-year time series will be too short to be used
for the detection and analysis of trends, such as those associated with global warming. Thirty toforty years of data, at the very least, are necessary for such an analysis.
The period of GSWP-2 encompasses the 1986-1987 and 1991-1992 El Niño events, the1994-1995 weak warm event, and the strong 1988-1989 La Niña. Figure 2 Shows the seasonal
progression

of

the

Niño

3.4

SST

anomalyduringthe

GSWP-2

period(http://www.cpc.ncep.noaa.gov/data/indices/index.html).
In addition to the cycle of El Niño/Southern Oscillation (ENSO), there were a number ofsignificant regional hydrological anomalies during the GSWP-2 period. The late 1980s andearly 1990s were a period of considerable variability in rainfall over many regions that rely
heavily on seasonal precipitation regimes.

For instance, 1986 and 1987 were consecutive

years of very poor monsoon rainfall over India, as indicated in the all-India rainfall index(http://tao.atmos.washington.edu/data_sets/india/parthasarathy.html).

Figure 3 (top panel)shows this, along with the very wet year in 1988. There are smaller but still relevant variations
during the 1990s. Over the Sahel region of Africa, the decades-long drought that began during
the

1960s

persistedthrough(http://www.cru.uea.ac.uk/tiempo/floor2/data/sahel.htm).

the

GSWP-2

periodFigure 3 (middle panel) shows thewet-season anomalies relative to the 30-year climatology period of 1961-1990 — itself aseverely dry period during the century. Thus, the near-zero anomaly years during the later part8

of the period are in fact dry relative to the long-term record. The droughts shown in 1986, 1987
and 1990 were particularly severe. The only truly wet year in the period is 1994 – 1988 was
actually near normal when compared to the rest of the 20th century.

Figure 2. Seasonal eastern tropical Pacific sea surface temperature anomalies.
The final example shown in Figure 3 (lower panel) is for the wet-season over the
Nordeste region of Brazil (http://tao.atmos.washington.edu/data_sets/brazil/). The span of theanomalies between the wet year of 1986 and the dry year of 1993 is nearly as large as any
anomalies measured during the 20th century.
These examples show that there was indeed interesting variability in the ocean andatmospheric components of climate during the period of the GSWP-2 experiment. There isgreat potential for the GSWP-2 experiment to enhance our understanding of the variability of theland surface component associated with these and other climate variations.

2.1.3 Uncertainty in model-derived surface fluxes and state variables
There is no doubt that the different LSSs participating in GSWP-2 will not all produce
identical estimates of surface fluxes and land surface state variables. There is no doubt that thedifferent LSSs participating in GSWP-2 will not all produce identical estimates of surface fluxes
and land surface state variables.

The range of estimates calculated by the participating LSSs

is one measure of our uncertainty in these terms, lacking complete coverage of observational9data for validation. This is a part of the intercomparison effort, although there will be lessemphasis on comparison of standardized soil
wetness indices than in GSWP-1, as that was
found to be an unsuitable means of comparison(Saleem and Salvucci, 2002).
2.1.3.1 Establish the multi-model envelope
GSWP-2 will establish the envelope ofmodel certainty for globally complete estimatesof surface and sub-surface temperature andwater storage, and fluxes of energy and water
between land and atmosphere.

This will bedone for the ideal scenario that the forcing data
are well known (i.e. identical for each LSS) andonly the models themselves differ.

The

uncertainty in estimates of these quantities byuncoupled LSSs is measured by the spread of
estimates among all LSSs (or, to mute theimpact of problematic extreme outliers, somestatisticalmeasurebasedonstandard

deviations or interquartile ranges). Incertitude
due to errors or uncertainty in forcing data will
be addressed in the sensitivity experiments(Section 2.3).
2.1.3.2 Uncertainty by variable
It is quite likely that different variables

Figure 3. Regional rainfall indiceswill have different relative and absolute ranges
of inter-model spread, and thus different levels of uncertainty associated with them.

For

instance, it was found that in GSWP-1, given the same precipitation forcing, different models
produced nearly identical skin temperatures, similar time series of evaporation, but drasticallydifferent mean levels of soil moisture. The variables of the National Centers for Environmental
Prediction/ National Center for Atmospheric Research (NCEP/NCAR) reanalysis were assigned
letter designations based on how model-dependent, and thus potentially how observationally10consistent, each variable was (Kalnay et al., 1996).

This letter designation served as anindicator of confidence in each variable. A similar ranking may be applied for the multi-model
analysis, based on the clustering of the values of each variable. Those that are strongly tied to
the forcing data should show the least spread, and those that are the result of parameterizations
removed several computational steps from the forcing data may show the most variation.
2.1.3.3 Uncertainty by region
Similarly, there may geographical dependence to the inter-model spread. For instance,
in a hot desert region where moisture is in short supply but radiative energy is in abundance,
one would expect that all models would partition nearly all of the specified precipitation toevaporation, and little or none to runoff, exhibiting a high level of inter-model consistency. Yetover a temperate mid-latitude region with seasonal snowpack, there may be a large range ofsimulated runoff and evaporation among the same models. Thus, a blanket assertion regarding
the confidence interval for a given variable in the multi-model ensemble may be misleading.
Spatial variability will be assessed as well.

2.2 Evaluation
PILPS, in its Phase 2, has conducted and continues to craft local land surface modelingexperiments built around nearly-complete sets of forcing and validation data at a single location(Henderson-Sellers et al., 2002). It is not the intent of this in situ validation program to duplicatethat effort. Rather, using the global forcing data sets, local validation may be performed when
and where such data are available.
Observations of land-surface state variables are sparse; in several cases individuals
have gone to great effort to collect related data sets from locations around the globe. These
data are invaluable for LSS validation, and their participation in GSWP-1 helped make that pilot
project a success. With a 10-year period of coverage, validation efforts can now span more
observational data sets, and examine interannual variations.
Much of the data described here is not sufficiently complete (in temporal coverage, or interms of the breadth of quantities measured) to be the basis of a stand-alone PILPS-2
experiment. With incomplete data it is not possible that closure of local and water energybalances can be obtained — it is unlikely that even all of the contributing terms to thesebalances would be measured in sufficient totality at a 1° scale. Furthermore, budgets from insitu measurements rarely come within a few percent of closure, due to instrument error,
miscalibration, and other real-world hindrances. It is the goal of the in situ validation effort of
GSWP-2 to provide measurements of key variables and fluxes of sufficient coverage and11accuracy to provide an estimate of the ground truth, when and where available, for the LSS. Of
course, this is far from ideal. The intent is to make the best use of the information available, as
incomplete as it is.

2.2.1 Field campaigns
There have been a large number of field campaigns during the span of the GSWP-2
period that are of relevance to climate over the land surface. A partial list is presented inTable 1. These campaigns covered a broad range of ecological and climate regimes, andprovide in situ observational data of a higher quality, better resolution (spatial and temporal),and greater range of variables than is otherwise available.
Table 1. Some field campaigns that overlap the GSWP-2 period.
Name

Location

PeriodReferences

Boreal Ecosystem-Atmosphere Study (BOREAS)

Central Canada

1993-1996 Sellers et al. (1997), Hall (1999)

GEWEX Continental-Scale International Project(GCIP)

Mississippi River basin,USA

1995-2000

Baltic Sea Experiment (BALTEX)

Baltic Sea basin

1994-2001 Raschke et al. (1998)

Mackenzie GEWEX Study (MAGS)

Mackenzie River basin,
Canada1994

Anglo-Brazilian Climate Observation Study(ABRACOS)
European International Project on Climatic andHydrological Interactions between Vegetation,
Atmosphere and Land Surface (ECHIVAL) Field
Experiment in Desertification Threatened Areas(EFEDA)

Manaus, Ji-Paraná andMarabá, Brazil

1990-1995 Gash and Nobre (1997)

Southeastern Spain

1991-1995 Bolle et al. (1993)

First ISLSCP Field Experiment (FIFE)

Central Kansas, USA

1987-1989

Hydrological and Atmospheric Pilot Experiment Modelisation du Bilan Hydrique (HAPEXMOBILHY)

Southern France

1985-1987 Andre et al. (1989)

Coughlan and Avissar (1996),
Lawford (1999)

Stewart et al. (1998)

Sellers et al. (1992), Hall andSellers (1995)

Hydrological and Atmospheric Pilot Experiment inWestern Niger
the Sahel (HAPEX Sahel)

1991-1993 Goutorbe et al. (1994)

Hei Ho River Basin Field Experiment (HEIFE)

Gansu Province, China

1992-1993 Wang et al. (1993)

Central Sweden

1994-1996 Halldin et al. (1999)

Murray-Darling basin,
Australia

1994-1995

Southwestern France

1995-1997 Calvet et al. (1999)

Northern Hemisphere Climate Processes Land
Surface Experiment (NOPEX)
Observation at Several Interacting Scales(OASIS)
Monitoring the Usable Soil Reservoir
Experimentally (MUREX)

http://www.clw.csiro.au/research/environment/interactions/oasis/

These data provide a unique validation opportunity for the various LSSs participating in
GSWP-2. Many LSSs experience limited validation because of the effort necessary to collect12

and apply the relevant data sets from multiple observational campaigns. Thus, validation isoften limited to one or two locations. With the help of ALMA, these data may be synthesized
into a more useful standard and applied to the suite of LSSs (see Section 3.1.1.3).
2.2.1.1 Global (ISLSCP) data sets versus local data
Many of the field campaigns included a nearly-complete sampling of local near-surface
meteorological variables.

Some even include direct measurements of radiation.

This

information should be compared to the ISLSCP Initiative II based forcing data before any
validation of the LSSs with other components of the in situ data.

If there are significant

differences between the NCEP or ECMWF reanalyses and the local meteorologicalmeasurements, then the interpretation of the results from any attempt at in situ validation mustbe altered accordingly.
To some extent, this comparison is a validation of the reanalyses themselves. However,differences, even systematic differences, do not necessarily reflect badly on the reanalysis
products. Because of the differences in spatial scales, and the possible existence of regional or
local microclimates in the domain of the field campaigns, one should not expect complete
agreement between field campaign meteorological measurements and reanalysis data.
Nonetheless, differences in the meteorological variables may go a long way toward explaining
apparent failings in the simulations of the LSSs in these locations.

Likewise, differences

between GSWP and actual local soil and vegetation conditions should be considered whencomparing results to local measurements.
2.2.1.2 LSS output versus in situ measurements
The principal validation of the LSSs will be between output variables from the LSS andthe corresponding field measurements. The variables that can be validated will fluctuate among
the field campaigns, but some measure of validation should be possible.

2.2.2 Observational networks and long-term monitoring
The broadest available archive for in situ soil moisture measurements is the Global Soil
Moisture Data Bank (Robock et al., 2000).

It includes station data from Russia, China,

Mongolia, India and the United States covering between six and ten of the years of the GSWP-2
period. Some of these data formed the basis of the in situ evaluation for the pilot phase of
GSWP, and it is expected that they will again play a major role in the validation effort.
Soil wetness data are also available for 19 of the USDA Soil Climate Analysis Network(SCAN) sites that typically start in October 1994. The data content includes soil moisture and13

temperature profiles (5, 10, 20, 50, 100 cm depth), meteorological data, and some flux data
(surface water, radiation and heat), sampled at 6-hour intervals. LSS output for 1995 will bereported at a 3-hour interval (see Section 3.1.4.2), and can be compared to the high temporal
resolution SCAN data.
Snow cover and depth data for the entire 10-year period are available over the Northern
Hemisphere from the USAF Daily Snow Depth Analysis (Fennessy and Schlosser 2002). This
analysis is a blend of in situ data and empirically-based estimates.
There exist other potential sources for in situ flux measurements (e.g., FluxNet, ARMCART) and surface state variables (e.g., Oklahoma Mesonet, SnoTel).

2.2.3 Streamflow
Runoff fluxes from all participating LSSs will be routed with common river routing
schemes to compare with streamflow measurements across a large portion of the globe, as an
assessment of the simulation of annual, seasonal, and interannual variations in surfacehydrology. Modeled streamflow will be assessed on several timescales. Climatological annual
mean model-routed streamflow will be compared to observed discharge for basic water balance
checks, bias audits, and consideration of impacts of irrigation withdrawals. The interannual
variation of annual runoff can be used to assess the unbiased performance of the LSSs insimulating climate variability in the surface water cycle, as well as the drift in model statevariables over the period. Monthly or seasonal discharge will be used to assess the basin-scaleseasonal water balance, the simulation of snow melt, the monsoon signal, and the reddening of
the precipitation spectrum in the LSSs. Daily discharge without missing data over the entire 10year period exists for at least 25 stations, with basin sizes greater than 2,500 km2, so it may bepossible to examine more detailed hydrographs in these areas for flood and drought frequencies
and extremes. Flow duration curves can be examined, and the ratio of surface/sub-surface
runoff may be examined by “traditional” separation of the hydrograph both for LSS outputs andobservations.
Similarly, large basin comparison of model water storage change with observed
atmospheric moisture flux convergence minus discharge will be performed. This evaluation
may also uncover problems in the forcing data and models at large basin scales.

2.2.4 Multi-model validation
Finally, we propose an effort for multi-model validation. That is, we propose a validationof the ensemble multi-model estimate of surface fluxes and state variables. It is the experienceof the meteorological community that even simple averaging across models helps ameliorate
14

the systematic errors of individual models, and usually produces a superior estimate to thatgiven by any individual model, although more sophisticated ensembling methods may yieldfurther improvements (Kharin and Zweirs, 2002; Krishnamurti et al., 1999; 2000). This will be
pursued further, by trying to create an optimum ensemble, based on the assessed strengths
and shortcomings of the results of the various LSSs.
This element would feed back on production of the multi-model analysis. If a clearlysuperior method of ensembling other than simple averaging of the results of the participating
LSSs, the multi-model analysis described in Section 2.1 would be conducted with the bettermethod.
2.2.4.1 Simple ensembling
The simplest approach to ensembling is to take an arithmetic mean of all ensemble
members. This is commonly done for multiple simulations with the same atmospheric generalcirculation model (AGCM) in weather and climate prediction.

It can be shown that for areasonably large ensemble of simulations with similar statistics, the mean-square error of an
ensemble mean forecast will converge to half the value of the mean-square error of anindividual ensemble member. This does not guarantee a better simulation of anomalies by theensemble mean, as measured, for instance, by a spatial anomaly correlation.

Individual

members may still outperform the ensemble mean in a given forecast. However, when thesame model is used to produce each ensemble member in each forecast, there is no reason to
expect one realization to consistently outperform the ensemble mean.
When we average together the results of different models, which may havefundamentally different statistics, it is mathematically possible that one or more members would
consistently outperform or underperform the ensemble mean, in terms of mean-square error,
correlation, or other metrics of skill. However, it is likely, based on experience with atmosphereand ocean models, and more importantly our experiences in the PILPS experiments, that noindividual LSS will exhibit clear superiority of skill in the GSWP-2 simulation. Thus, our default“best analysis” for GSWP-2 will be a simple arithmetic mean of the participating LSSs.
2.2.4.2 Optimal ensembling
Kharin and Zweirs (2002) have found that for climate prediction of atmospheric 500 hPa
geopotential height over the tropics, a bias-removed multi-model ensemble mean performs bestwhen the ensemble size for AGCMs is larger than a half dozen or so, and a regressionimproved ensemble mean performs best for small ensembles. Over the mid-latitudes, the biasremoved ensemble mean performs best, but only slightly better than climatology. This result15was obtained after comparing seven different approaches, including regression-improved multimodel ensembling like that of Krishnamurti et al. (1999, 2000). A similar comparison can bemade for LSSs in an offline analysis mode to determine whether there is method of ensemblingthat is superior to a simple average.

However, the physics of land surface models is

fundamentally different than the fluid dynamics of atmosphere or ocean models, so it is not clear
whether such approaches will yield the same results in this context.
Even without global fields for validation and assessing skill, the exercise of multi-model
ensembling with offline LSS simulations is a useful one, to assess, for instance, what may begained by multi-model participation in land-surface data assimilation (LDAS). One participating
LSS can be arbitrarily chosen to represent the truth, and the ensemble of the remaining models
could be validated against the truth simulation. This would give a practical measure of thevarious ensembling techniques.

2.3 Sensitivity studies
The default meteorological forcing data set for GSWP-2 will be the ISLSCP Initiative IIregridding of the NCEP-DOE reanalysis of 1986-1995 (Kanamitsu et al. 2002), with corrections
to the systematic biases in the reanalysis fields made by hybridization of the 3-hourly analysis
with global observationally-based gridded data sets at lower temporal resolution (see Section
3.1). The original ISLSCP Initiative II data sets will not have hybrid correction, but will contain
only the uncorrected reanalysis products. Radiation forcing will come from the 3-hourly Surface
Radiation Balance (SRB) product directly without hybridization with reanalyses estimates.
Modeling sensitivity studies will involve re-integrating the LSS over part or all of the
global, 10-year domain to test the response of the models to changes in forcing data andsurface parameters. Each participating modeling group will be encouraged to participate insome or all of the proposed studies. Several likely studies are described here — more may beconceived during the course of the project.

2.3.1 Sensitivity to atmospheric forcing
2.3.1.1 Precipitation data
Global observational data sets of quantities such as precipitation are probably superior
to reanalysis estimates of precipitation, but they are far from perfect.

Incomplete gauge

coverage, difficulty in collecting observational data, and problems with the instruments
themselves can lead to very uneven coverage and quality of observational data. Oki et al.
(1999) showed that in the pilot phase of GSWP LSS runoff was systematically underestimated16

over high latitudes where snow is a significant contributor to annual precipitation. Motoya et al.
(2002) has examined this issue in detail and provided an algorithm for gauge correction that isbeing applied in GSWP-2 (see Section 3.1.1.2).
Often remote sensing is employed to fill gaps and synthesize data across space. But
remote sensing estimates of physical surface fluxes such as precipitation require the applicationof empirical algorithms to retrievals, and many such algorithms exist and are used (Adler et al.
2001), leading to variations in estimates.

In addition, multiple spaceborne platforms are

combined, sometimes to provide complete spatial coverage (e.g., in the case ofgeosynchronous satellites) or temporal coverage (e.g., polar orbiters with different timings ofascending and descending trajectories). Lastly, over long intervals like the 10-year ISLSCP
Initiative II period, satellites come and go, each with different instruments that must be crosscalibrated, and few capable of performing usefully over such a long period. The instruments
also drift in their calibration while in orbit.
All of these problems complicate the efforts to produce consistent long-term
observational records with global coverage. As a result, there are many ways to go about
producing such a global data set, each with its own strengths and weaknesses. In the ISLSCP
Initiative II data set, there are at least five different interpretations of global precipitation,
including the reanalysis model products. One set of sensitivity studies will compare the impactof the range of estimates of precipitation on simulation of the land surface climate, and inparticular the hydrologic cycle.

In some cases it may be possible to show precipitation

estimates to be clearly incorrect (e.g., over a basin where observed streamflow is consistently
greater than estimates of upstream rainfall). But in most cases, the range of estimates will be
simply another degree of uncertainty, and its impact on the surface simulations can beappraised.
The following list describes the studies involving LSS sensitivity to uncertainties in the
precipitation data. Tantamount to the precipitation sensitivity studies will be evaluation of the
impact on simulated river discharge, by the same methodology described in Section 3.3.1. The
baseline integration uses a hybrid precipitation product composed of NCEP/DOE reanalysis 3hourly rainfall scaled to agree with gauge-corrected monthly means from GPCC, which aresupplemented with merged data from GPCP where gauge density is low (see Section 3.1.1.2 for
a complete description of the precipitation data set).
P1

Hybrid precipitation derived using the ERA40 in place of the NCEP/DOE reanalysis.
This integration, compared to the baseline, will show the consequence of the choiceof reanalysis rainfall (atmospheric model physics impact on temporal distribution of17

precipitation) on the simulation of the surface water balance. By the hybridization
process, the monthly mean precipitation will be the same as in the baseline
experiment, but the distribution of rainfall within the month, and particularly thediurnal cycle, may be quite different.
P2

Hybrid precipitation as in the baseline simulation, but without the relaxation to GPCP
(satellite-estimated) precipitation in regions of low gauge density. This experimentwill show the impact of satellite rainfall products on simulation of the terrestrial
surface water balance in data-sparse regions.

P3

Hybrid precipitation as in P2, but without correction for gauge undercatch.
Comparison between this case and P2 will show the hydrologic impact of neglecting
the effect of wind on raingauge accuracy.

P4

NCEP/DOE precipitation without hybridization. This data set would be the pure NWP
model product, without any observational data used for adjustment. Comparison to
the baseline simulation would show the impact of adjustment of reanalysis
precipitation to agree with observed time means on surface hydrologic simulation.
Comparison to P3 would show the impact of unadjusted gauge correction alone.

P5*

Using the experimental 0.5° GTS-based daily global precipitation product of Xie forhybridization of daily, rather than monthly, totals in the NCEP/DOE reanalysis.
Gauge undercatch adjustment would be performed as in the baseline case, but withdaily wind analyses. Comparison to the baseline simulation would show the impact ofpotentially incorrect synoptic (sub-monthly) variability of precipitation on thesimulation of the surface water balance.

* This sensitivity test may be dropped from the list, depending on interest and required effort.
2.3.1.2

Radiation data
The 3-hourly SRB radiation product will be used for the baseline simulations. But there

exist 3-hourly downward shortwave and longwave radiation fields from each of the reanalyses.
Sensitivity studies can be performed using in turn NCEP/DOE or ERA-40 estimates of
downward radiation. The recommended studies are:
R1

Using NCEP/DOE downward shortwave and longwave radiation instead of SRB.
Comparison to the baseline simulation will show the impact of the systematic errors
in the reanalysis radiation on simulation of the surface energy and water balances,and provide an indication of the effect of representative downward radiation errors on
the land surface in coupled land-atmosphere models.
18

RS

As in R1, but substituting only for the downward shortwave radiation. This sensitivity
experiment will isolate the impact of shortwave radiation errors on the surface energy
and water balance in LSSs.

RL

As in R1, but substituting only for the downward longwave radiation. This sensitivity
experiment will isolate the impact of longwave radiation errors on the surface energy
and water balance in LSSs.
The motivation for looking at the shortwave and longwave effects separately comes fromthe finding that the spatial and temporal structure of errors in these two terms in atmosphericgeneral circulation models is typically very different, and may have very different effects on theland surface (Dirmeyer, 2002).
R2

Using ERA40 downward shortwave and longwave radiation instead of SRB.
Comparison to the baseline simulation and R1 will show the differences andsimilarities in the radiation errors in the reanalyses, and their impact on simulation of
the surface energy and water balances.

2.3.1.3

All meteorological data
The final set of sensitivity studies proposed is simply the complete substitution of the

NCEP/DOE based meteorological forcing data with the parallel product derived from the ERA40 reanalysis. As with the NCEP/DOE reanalysis, the ECMWF reanalysis may also be provided
as forcing data directly and in a hybridized form corrected by observations. The monthly meansof the two hybrid products would be the same, but the different reanalyses would likely exhibit
somewhat different characters in the diurnal cycle and synoptic variability, which could influence
the simulation of the surface energy and water balances in offline LSSs. This experiment, M1,
will be a test of how the different interpretations of the synoptic evolution of global weather
impact the surface simulation of the energy and water balances. Because of the hybridization
process for precipitation and temperature, the two forcing data sets will be somewhat
constrained, and not as dissimilar as the original reanalyses on time scales of a month or
longer.
2.3.1.4 Implications of results
Since reanalysis products are so widely used as a proxy for true atmospheric conditions,these sensitivity tests with forcing data have important implications for the certitude that should
be applied to scientific results achieved using these data sets. Results from these experiments
have broader implications for the impact of biases in any dynamical atmospheric model on landsurface simulation, particularly in an uncoupled mode. Such a comparison could provide useful19

feedback to the operational meteorological centers, particularly if land-surface validation reveals
distinct differences in overall performance depending on the choice of reanalysis.
Results from these investigations may point to regions where increased or improved
observations may have the biggest impact.

Oki et al. (1999) showed a clear connection

between a drop in the quality of simulated annual river discharge by LSSs in GSWP-1, and alower threshold for rain gauge density within a river basin. Similar results maybe expected forother observable quantities.

2.3.2 Differences in parameters
There are also uncertainties in the parameters that describe the state of the land
surface. Again, quality may vary spatially, as some nations have complete surveys of soil orvegetation, for instance, while others do not.

Also, different analyses may reflect differinginterpretations of the same basic information.
2.3.2.1 Sensitivity to prescribed surface conditions
Just as with meteorological forcing data, there is uncertainty in the data for surface
conditions. For instance, the ISLSCP Initiative II includes global land cover classifications of
IGBP-DIS from the EROS Data Center (EDC), the University of Maryland, and a MODIS-derived
land cover product, which employ somewhat different divisions and definitions of vegetation
types. This leads to ambiguity, and potentially different results for a LSS depending on whichdata set is used, and how it is mapped to the vegetation treatment within the LSS. Similar
vagaries may exist for soil properties, albedo (for which there are also a half dozen versions
with slightly different definitions in the ISLSCP Initiative II data set), Normalized Difference
Vegetation Index (NDVI), leaf area index (LAI), and other vegetation properties.
Sensitivity to different interpretations of surface parameters in LSSs can be tested todetermine the impact of uncertainty in these parameters on estimations of surface fluxes.
Sensitivity test V1 will be concerned with the global specification of vegetation type.
baseline simulations will be predicated on the IGBP vegetation.

The

A global 1° map will be

specified from one of the two other data sources, and used in place of the IGBP map. Impactsof this change on model simulations and errors will be assessed.
A second study will look at the impact of interannual variations of vegetation on thesimulation of surface fluxes and state variables. In this sensitivity study, I1, a mean seasonal
cycle of time-varying vegetation parameters (LAI, roughness length, displacement height, NDVI,
greenness fraction) will be prescribed throughout the 10-year simulation.

20

This study will

elucidate the role of interannual variations on the land surface in an uncoupled mode (without
atmospheric feedback). This experiment will be of particular interest to those modeling groups
whose LSSs are also used in climate models, and who have an interest in land cover change
studies, or interactive vegetation. Comparison of results from I1 and baseline simulations toparallel integrations of the full climate model can isolate the role of land-atmosphere model
coupling and feedbacks in the response of the land surface to phenology variability.
For LSSs that parameterize sub-grid variability in surface parameters, the ISLSCP
Initiative II data sets include, in some cases, information of sub-grid variability at the 1°
resolution, or explicit distributions of surface parameters at ½° or ¼° resolution. LSSs that use
either a tiling approach or a statistical representation of sub-grid variability could conduct
sensitivity experiments into the impact of aggregation/disaggregation on their simulations at the
global scale.
2.3.2.2 Implications for future ISLSCP efforts at data synthesis
GSWP-2 may uncover problems in the ISLSCP Initiative II data sets, as they apply toland surface modeling. Hopefully GSWP-2 will find solutions as well. This information must be
fed back to the ISLSCP data efforts for planning of future initiatives.

2.4 Remote sensing applications
One of the new thrusts for GSWP-2 is a stronger connection to applications in remotesensing. In addition to the classical attempts to validate the typical land-surface state variables
using satellite retrievals, GSWP-2 also intends to expand the validation and assimilation
capabilities of current LSSs. This is to be done by the development of algorithms by which
LSSs can directly report brightness temperatures, like those sensed by instruments in orbit.
The principal goal of the effort in remote sensing applications is to expand validation
beyond those few areas where in situ data are readily available. A secondary goal is to facilitate
efforts to assimilate remotely sensed observations of the land surface into LSSs. Details are
given below. Of course, remotely sensed surface state variables have their own inaccuracies,
so in some sense this is a cross-validation exercise, where consistency between GSWP-2
model estimates and remote sensing may increase confidence in both products.

2.4.1 Prognostic brightness temperatures
The principal new remote sensing element is the direct reporting by LSSs of brightness
temperatures in different radiative bands. Ideally, fairly basic and general algorithms can be
developed and distributed that will allow participating LSSs to be modified without excessive
21

duplication of developmental effort, or even to calculate brightness temperatures in a postprocessing step given key model parameters and state variables.

These algorithms may

include options that could be implemented depending on the complexity of the scheme.
2.4.1.1 Microwave/soil wetness
Certain bands in the microwave spectrum are sensitive to moisture in the top few
centimeters of soil. Microwave satellite instruments such as Scanning Multichannel Microwave
Radiometer (SMMR) and Special Sensor Microwave/Imager (SSM/I), as well as aircraft-borne
instruments deployed for limited-area field campaigns, can sense near-surface soil moisture. In
our application within GSWP-2, we will pursue the more direct problem of estimating theobservable brightness temperature from model soil wetness and vegetation properties.
Relatively simple calculations exist to estimate microwave brightness temperatures, given
surface characteristics such as the profile of near-surface soil moisture, vegetation cover, andsurface topographic characteristics.
There are practical problems in this calculation resulting from different numbers andthicknesses of soil layers in the various LSSs. Microwave instruments are extremely sensitive
to the soil wetness profile in the top few centimeters of soil, with little contribution from deeper
layers. Most LSSs not developed for remote sensing applications have only one or perhaps two
soil layers within the shallow depth range sampled by the microwave instruments.

This

presents a challenge — how should continuous or near-continuous profiles of soil wetness beinterpolated (or between the center of the top soil layer and the surface; extrapolated) from thevery coarse and discrete soil layers of each LSS? Two models may have the same nearsurface soil wetness, but because of differences in layer thicknesses they may yield different
brightness temperature calculations. Some thought is also needed on parameterizations within
the microwave emission models, particularly in terms of vegetation. Vegetation attenuates thesoil wetness signal at a rate proportional to the canopy density, essentially contributing avegetation optical depth through which the soil moisture signal must travel.
2.4.1.2 Vegetation index/Dynamic Vegetation Models (DVM)
Vegetation indices are a special case of shortwave remote sensing.

These usually

involve the ratio of brightness temperatures measured in different shortwave bands or“channels”, and are used to enhance the detection of the state of vegetation. The NDVI is themost common of these indices.
Some LSSs take vegetation indices as an input parameter for determining greenness,
LAI, etc. In fact, the global grids of vegetation cover fraction, greenness and LAI in the ISLSCP

22

Initiative II data set, which can be used as input parameters by many LSSs, are derived fromremotely sensed vegetation indices.
For vegetation models that predict plant phenology, vegetation indices can be used tovalidate their simulation of seasonal and interannual variations in vegetation.

Provided asufficient number of the participating models in GSWP-2 have predictive vegetation
components, GSWP-2 will also explore this area of remote sensing validation.

2.4.2 Validation of classical state variables
2.4.2.1 Skin temperature
Perhaps the most basic and straightforward quantity that is directly observable fromspace is the thermal skin temperature, or more properly surface upward longwave radiation. Of
course, satellites can only measure this quantity during cloud free conditions, and atmosphericmoisture and aerosols attenuate the signal. Nevertheless, most LSSs already report a radiative
skin temperature that is used in the calculation of upward longwave radiation. Relatively robust
retrieval algorithms exist to obtain surface skin temperature from remote sensing observations.
These data can be used to validate the radiative skin temperature simulated by the LSSs.
Model skin temperature is highly controlled by the near surface air temperature, which will be
specified as the same for all LSSs. Nonetheless, there may be variations due to differences inthe specification of heat capacity and thermal conductivity of vegetation and soils among the
LSSs, as well as the simulated Bowen ratios, so identical results are not to be expected. Thereexist International Satellite Cloud Climatology Project (ISCCP) estimates of radiometric skintemperature from the surface that can be applied for validation over relatively cloud-free areas(less than 40% cloud cover). To represent surface radiative temperature, however, surface
emissivity must be known.

Emissivity is a function of soil properties, water content, andvegetation. Nevertheless, skin temperature is one of few quantities where global validation ispossible.
There are complicating factors, however. The satellite instrument does not sample theentire infrared band, and different instruments have somewhat different sensitivity spectra (thisis true for all types of radiative instruments). Also, most LSSs produce a single value per gridbox, or a small number of values for LSSs that include sub-grid tiling. At 1° resolution, thismodel grid box corresponds to hundreds or thousands of individually sensed satellite pixels,
each of which represents the sensor’s synthesis of patterns of radiation at even finer scales thatthe instrument cannot resolve.

How should this gap in spatial scales be bridged?

methods of aggregation are appropriate?

What

Also, the instant of sampling of a location by a23

satellite may not correspond to the time that data are reported by the LSS. Some sort ofwindowing must be applied to assure the model and satellite data are adequately co-temporal.
GSWP-2 will have to address these issues.
2.4.2.2 Albedo
Another fairly direct quantity that is both observed from space and reported by LSSs isthe surface reflection of visible light, or albedo. As with infrared, visible radiation is usuallysensed by orbital instruments in rather discrete bands, so a total shortwave albedo is notdirectly observed. Much of the shortwave spectrum is not truly visible but in the near-infrared.
Many of the same problems that plague retrievals of infrared brightness temperatures also
affect albedo — cloud contamination, clear-sky attenuation, frequency band mismatch, andspatial aggregation. In addition, many LSSs take snow-free albedo as an input parameter, not
as a prognostic variable. These models may only alter the specified albedo in the presence ofsnow cover, or in cases of high soil moisture (soil albedo). Models that perform a two-stream
calculation for shortwave radiation, such as the Simple Biosphere (SiB) family of models, docalculate surface albedo in all situations. Also, models with dynamic vegetation cover calculate
greenness, LAI, and other parameters which then affect surface albedo. For these schemes,
direct validation of albedo against remote sensing products could be a useful tool.
2.4.2.3 Snow cover
Areal snow coverage is routinely deduced from remote sensing, and historical archives
for Northern Hemisphere snow cover exist on daily to monthly time intervals during the GSWP-2
period. This data can be used to assess the large-scale performance of the LSSs in simulatingsnow coverage and the timing and rate of snowpack loss due to melt during the spring.
The daily product comes from the US Air Force (USAF) Daily Snow Depth Analysis,
available through National Oceanic and Atmospheric Administration (NOAA) (K. Mitchell) withadditional quality control for climate applications (M. Fennessy, Center for Ocean-LandAtmosphere Studies [COLA]; C. A. Schlosser, University of Maryland, Baltimore County[UMBC]).

These analyses are a blend of in situ and empirically-based estimates of snow

coverage and depth, and cover the entire GSWP-2 period. Microwave-based retrievals of snow
are also available (Chang, D. Hall, J. Foster, and R. Kelly, National Aeronautics and Space
Administration [NASA]/Goddard Space Flight Center [GSFC]) from SMMR up to 1987, and are
currently being produced for 1987-1995 using SSM/I. Surface freeze-thaw cycles can also be
detected by remote sensing, and compared to the LSS simulations.

24

2.4.3 Assimilation techniques
As mentioned before, some models already take remotely sensed data as input
parameters (e.g., albedo or NDVI). Assimilation implies taking as input observed data that
corresponds to one or more model state variables. Thus, LSSs that predict albedo within theirshortwave radiation calculations could assimilate observed albedo. Already some DVMs aredesigned to assimilate remotely sensed vegetation indices.
Assimilation of remotely sensed surface infrared information is already being conducted(Houser et al., 1998) and could be expanded to other LSSs. There is tremendous interest in theassimilation of soil moisture information (e.g., Salvucci, 1997; Margulis and Entekhabi, 2001;
Crow and Wood, 2002) as soil moisture is the most important land surface state variable forseasonal-interannual climate variations throughout most of the globe (Dirmeyer and Shukla,
1993). In particular, since remote sensing cannot penetrate the surface layers to detect soil
moisture in the vadose zone, a combination of modeling and remote sensing provides the bestsolution for diagnosing soil moisture.
Methods of assimilation can be simple (e.g., relaxation with a fixed damping time scale)or complex (variational or Kalman-filtering methods). It is not the purview of GSWP-2 to explore
the assimilation techniques themselves. Rather, it is to provide a starting point for independent
efforts to probe this area further (e.g., LDAS; http://ldas.gsfc.nasa.gov/).

2.5 Other science efforts
2.5.1 Comparison to simple and intermediate models
Can simple or intermediate models reproduce the behavior of more complex LSSs? The
aim of this investigation is to determine how much of the “signal” in complex LSSs, integrated attime steps of one hour or less, can be reproduced by simple or intermediate models.
The inclusion of the so-called “Bucket” model in previous PILPS and GSWP simulations
is a form of this comparison, although historically the Bucket has been treated like a benchmarkthat other LSSs should be able to outperform. There are other approaches to approximating the
behavior of complex LSSs with systems that have a much more limited number of freeparameters.

Koster and Milly (1997) showed for a particular PILPS experiment that thefundamental behavior of the surface water balance of all of the participating LSSs on monthly
time scales could be described almost entirely by a two-parameter model. Koster et al. (2001)
showed that for coupled land-atmosphere models, the behavior of the partitioning of
precipitation between evapotranspiration and runoff on annual time scales conformed to
Budyko’s (1974) expectation, based on knowledge of only net radiation and total precipitation.
25

Several LSSs of intermediate complexity have been developed to study specific phenomena
such as tropical deforestation (e.g., Eltahir and Bras, 1993; Zeng 1998; Neelin and Zeng, 2000).
How well do simple and intermediate models perform in the GSWP framework, whencompared to the complex LSS? This comparison will quantify this both in terms of skill (where
validation is available), and complexity of response. For example, a simple model with nodiurnal cycle, that must be driven by daily mean data, cannot reproduce variations on those
short time scales, whereas a complex LSS can. This is an extreme example, but there may beother modes of variability (both in time and space) where there are discernable differences.
Perhaps more significant will be the identification and explanation of the situations where there
is no real difference in performance, particularly when the complexity built into one or more
LSSs is justified by the need to better simulate a given phenomenon (e.g., snow cover). The
simple and intermediate models are thus diagnostic tools for understanding the working of bothprocesses in the physical climate, and the more complex LSSs.
Another possibility is to revisit the study of Koster et al. (1999), which compared GSWP1 runoff rates against those produced with the simple Budyko model.

In that study, thecomplexity of the LSSs did not lead to a systematically better simulation of the annual waterbalance. Perhaps LSSs have since improved.

2.5.2 Uniqueness
The question may be asked, “Do we need so many different LSSs?” This is a valid
question, particularly if it can be shown that there is not a significant variation among LSSs intheir performance. Another diagnostic project in GSWP-2 will be to see how well each LSS can“predict” the behavior of the others.
This investigation of uniqueness among LSSs amounts to an exercise in the constructionof transfer functions among LSSs. For each pair of LSSs, a training period is defined (e.g., thefirst 5 years of the baseline GSWP-2 period), over which state variables or fluxes from one
model are regressed upon those of another at each grid point to produce transfer coefficients.
Then, for the test period (the last 5 years), the results of the predictor model are run through thetransfer functions to produce a simulation of the predictand model — a forecast of one LSS byanother LSS. This can be performed for some or all of the participating models, on one or moretime scales (e.g., for annual, monthly, or daily) using the output data reported to the InterComparison Center (ICC).
The null hypothesis is that any LSS can perfectly predict any other. In the absence ofdifferences in forcing data, variations from perfect skill must be due to differences among the26models. The patterns of differences (in space, time, and within the matrix of participating LSSs)
may reveal something about capabilities of the models, reconfirm their original design
applications, and perhaps reveal something new about the family of LSSs. If two or more LSSs
are found to be essentially undifferentiatable, that would also be very revealing and raise theissue of redundancy in the land surface modeling community.

Key to isolating model

differences will be determining whether the models specified their surface parameters in a novel
fashion. This is conveyed by the reporting of ancillary information with the submission of model
output from each LSS integration (see Section 3.1.4.5).

2.5.3 Global Water and Energy Cycles
One of the main research objectives of GEWEX is to “determine the hydrological cycleand energy fluxes by means of global measurements of atmospheric and surface properties.”
To first order, the “rate” of the global water cycle can be quantified by the global fluxes ofprecipitation and evaporation. On an annual basis, these fluxes should nearly balance, as thecapacity for the atmosphere to store the residual difference between precipitation andevaporation is quite small. Currently, there exists no global-wide capacity to directly measure
the fluxes of water and energy over the continental surfaces, and therefore we must rely on thehighest-quality estimates based on model simulations. The GSWP-2 experiment will provide
the most representative collection to date of continental fluxes of water and energy from “stateof-the-art” land models used by the international climate research community.
Therefore, based on the results from the analyses discussed in Sections 2.2.4 and 2.5.2,
the consensus (and scatter) of the GSWP-2 model simulations will be used to supplement a
global water cycle synthesis. The GSWP-2 outputs will be combined with global precipitation
products (e.g., the GPCP data of Huffman et al., 1997) and ocean flux estimates (e.g., Chou etal., 1997) to assess our scientific accounting of the global water cycle. Using this synthesis, notonly can the relative roles of the land and ocean in the global water cycle be quantified, but the
10-year outputs (1986-1995) of the GSWP-2 (which overlap with the global precipitation andocean flux data) will allow for interannual variations (and trends) to be diagnosed and checked
for consistency against the global variations of precipitation and ocean flux estimates. The
residual of these global precipitation and evaporation fluxes will also be cross-verified against
the GEWEX Global Water Vapor Project (GVaP) data to provide a further assessment ofconsistency and confidence in our global observations of key hydrologic states and fluxes. In asimilar manner, the output of radiation and heat fluxes from the GSWP 2 model suite can beused to further supplement (and update) our current depiction of the global energy cycle.
Leveraging off of GEWEX radiation projects, such as the International Satellite Cloud27

Climatology Project (ISCCP), surface, atmospheric and top-of-the-atmosphere radiation andheat fluxes can be combined to assess and quantify variations and trends in the global energy
cycle, and whether consistent linkages exist between these water and energy cycle
assessments (i.e., do variations/trends in observed cloud cover show a consistent association
with our observed/estimated water and energy budgets?).

28

3.0 IMPLEMENTATION PLAN
In order to conduct the proposed scientific program of GSWP-2, a well-defined sequenceof actions must be undertaken. Figure 4 shows a flowchart for the actions, and Figure 5 shows
the proposed timeline. All discussion in this section centers around these diagrams, and theyshould be used for reference throughout. Herewith, a practical discussion of the conduct of the
GSWP-2 experiment is presented.

3.1

Production of the GSWP-2 data sets
The starting point for GSWP-2 is the ISLSCP Initiative II data set.

There are two

fundamental categories of ISLSCP data that are needed by the LSSs that participate in GSWP2: land surface parameters and meteorological forcing.

Land surface parameters include

vegetation classification maps, vegetation properties (which may vary from month-to-month),and soil properties.
twoparallel

There areversions

of

the

data

in

the

meteorological

ISLSCP data set: the National
CentersforEnvironmental

Prediction/Department of Energy(NCEP/DOE)

andEuropeanCentrefor

Medium-range

Weather

Forecasts(ERA-40)

reanalyses.

(ECMWF)The

meteorological data are provided
at a 3-hourly time step for aperiod of 13½ years (19821995), although the complete set
from

ECMWF

will

not

be

available until early 2003.

3.1.1 Input fields

Figure 4. Implementation flowchart for GSWP-2.

3.1.1.1 Land surface data
Land surface parameters for participating LSSs will be specified from the ISLSCP
Initiative II data set. The ISLSCP Initiative II data set includes land cover data from the EROS

29

Data Center (EDC) in several forms, including vegetation types for Biosphere-Atmosphere
Transfer Scheme (BATS) and Simple Biosphere Model (SiB). In addition, information on thefractional distribution of vegetation for each type in each 1° grid box is provided, for LSSs thathave a mosaic or tile approach to sub-grid variations in land cover type.

Time-varying

information on biophysical parameters (e.g., Leaf Area Index [LAI], Normalized Difference
Vegetation Index [NDVI], greenness fraction) are also included. These monthly fields should beinterpolated linearly from mid-month to mid-month. Data spanning 1982-1998 will be included
for these fields. Each participating LSS should use those data sets that are appropriate to its
formulation. Dynamic vegetation models (DVMs) will need to specify initial plant functional
types (PFTs) rather than the vegetation types described here. It will be the responsibility of
DVM groups to derive their own set of PFTs consistent with the baseline GSWP vegetation
distribution.

Another vegetation distribution data from University of Maryland, as well as a

MODIS-derived land cover product, may be used for sensitivity studies. If necessary data for a
LSS is not provided, the modeler should work out an alternative solution with GSWP and report
the choices made when submitting his output (see Section 3.1.4.5).

Figure 5. Timeline for GSWP-2

30

ISLSCP Initiative II soils data come from the International Geosphere-Biosphere
Programme Data Information System (IGBP-DIS) soils CD-ROM.

LSSs use a variety ofmethods to specify soil parameters; GSWP-2 will try to accommodate them. Global 1° maps ofsand, clay and silt fractions are provided for models that derive soil hydrologic and thermal
properties from soil structure. The fractions add to unity. There also exist data on the fractionof organic content in the soil. Models that use this information need to rescale the soil fractions,as the sum of organic plus mineral content will exceed unity. In the original IGBP soils data
provided by ISLSCP Initiative II, there exists points with missing data over some desert areas,
rocky mountainous regions, and at points with deep icepack. In these areas, reasonable values
have been interpolated by GSWP-2 from surrounding points: the missing values of fractions ofclay, sand, silt, and organic (548 points, mainly over Greenland) are also filled by the averaged
value from surrounding land points; the missing values of W_fieldcap, W_wilt, W_sat, andW_sat_hydc (548 points) are filled by averaging the values from surrounding land points; andthe elevation and slope data are aggregated from 0.5° to 1°. Note that the values of field
capacity over Greenland for soil texture class 2 are unusually high. Soil Depth, Albedo_vi, andAlbedo_ir are obtained from ISLSCP-I by using ISLSCP-II land-sea mask to remap them.
Method CEA84 was used to determined soil hydrological parameters from Cosby et al., 1984.
Some LSSs use a soil texture classification scheme to specify soil parameters based on
texture classes from the USDA soil triangle. GSWP-2 provides a global map of texture classes
with a 12-class soil texture table (Table 2).

Missing values of the soil texture class are

determined from the calculated values of percent of sand and clay, as described above. In the
Table 2. Soil properties as a function of texture class.
Cosby values for silt are estimated, as they were not provided in the original RhôneAGG data set.
TextureUSDA
Cosby (RhoneAGG) USDAClass
Sand Silt Clay Wfc Wwilt Wsatb PHIsat Ksat
92% 5% 3% 0.132 0.033 0.373 3.30 -0.05 2.45E-05
1
Sand82% 12% 6% 0.156 0.051 0.386 3.80 -0.07 1.75E-05
2
Loamy Sand
58% 32% 10% 0.196 0.086 0.419 4.34 -0.16 8.35E-06
3
Sandy Loam
17% 70% 13% 0.270 0.169 0.476 5.25 -0.65 2.36E-06
4Loam
10% 85% 5% 0.361 0.045 0.471 3.63 -0.84 1.10E-06
5Silt Loam
43% 39% 18% 0.250 0.148 0.437 5.96 -0.24 4.66E-06
6Silt
7
Sandy Clay Loam 58% 15% 27% 0.253 0.156 0.412 7.32 -0.12 6.31E-06
10% 56% 34% 0.334 0.249 0.478 8.41 -0.63 1.44E-06
8
Clay Loam
9
Silty Clay Loam 32% 34% 34% 0.301 0.211 0.447 8.34 -0.28 2.72E-06
52% 6% 42% 0.288 0.199 0.415 9.70 -0.12 4.25E-06
10
Sandy Clay
6% 47% 47% 0.363 0.286 0.478 10.78 -0.58 1.02E-06
11
Silty Clay
22% 20% 58% 0.353 0.276 0.450 12.93 -0.27 1.33E-06
12
Clay

31

ISLSCP Initiative II soil map, there are no points categorized as silt.

Table 2 closely

corresponds to the RhôneAGG table, and the approach is similar to the Zobler classifications
used in GSWP-1.
ISLSCP Initiative II also provides maps of soil properties calculated from pedon
attributes on the IGBP CD-ROM. These derived parameters may not be consistent with theassumptions built into each LSS (e.g., wilting point and field capacity are derived using the van
Genuchten (1980) relationship, which would not be appropriate to use in a model based on the
Clapp and Hornberger (1978) approach). GSWP-2 will provide these gridded data, but wecaution modelers to carefully check the IGBP documentation (which will be provided on the
GSWP-2 website) before using these fields. Using a look-up table would be safer.
Topographic data comes from the EDC, derived from the ETOPO30 and Hydro1K data
sets. Fields include mean elevation, surface slope, and sub-grid elevation statistics that may be
useful for land surface modeling.
3.1.1.2 Atmospheric forcing data
Atmospheric forcing data will use information from global gridded observational data sets
when possible. In some cases, no adequate global observational data exist, so a pure model
reanalysis product will be used. In most cases, observational data are available globally, but
not at the high time resolution needed to resolve the diurnal cycle, as is necessary to force
LSSs. In these instances, observational data will be combined with data from model-based
reanalysis products.
The 3-hourly meteorological data provided in ISLSCP Initiative II are pure reanalysis
products, and have not been amended by “hybridizing” with observational data, as was done for
ISLSCP Initiative I.

GSWP-2 is undertaking this step, using the observationally-based

precipitation, surface radiation, and near-surface meteorology data also included in ISLSCP
Initiative II. The hybridization process has been developed, tested, applied, and documented for
the NCEP/NCAR reanalysis data (Dirmeyer and Tan, 2001), and can be applied to the otherreanalysis products. The hybridized products will be the primary forcing data sets for GSWP-2;
the original ISLSCP products will be used in the modeling sensitivity experiments. Because thefull 10-year span of the NCEP/DOE reanalysis data will be available before ECMWF’s
reanalysis, we choose the hybridized version of the NCEP/DOE data set for the baseline
simulation by the LSSs.

The 3-hourly data from the NCEP/DOE reanalyses have beenprocessed for inclusion in the ISLSCP Initiative II data set by COLA from hourly data provided
by W. Ebisuzaki at the NCEP Environmental Modeling Center (EMC).

32

Precipitation
The precipitation product for the baseline simulations in GSWP-2 will be a hybrid
product. In the hybridization process for precipitation and radiation, the errors are removed via
a multiplicative scaling factor that is based on the ratio of observed monthly rainfall to reanalysis
estimates, rather than by subtraction of the error:[P ]Y ,M ,D,T =[POBS ] M
[PNCEP ]Y ,M ,D,T
[PNCEP ] M

To get the adjusted forcing data for precipitation, for instance, the value at a grid box ofone of the reanalysis precipitation terms (total or convective) at a given year, month, day and 3hour time interval [PNCEP]Y,M,D,T is scaled by the ratio of the monthly mean observed precipitation
to the corresponding mean value from the reanalysis for that month. This approach avoids
problems of negative values in positive definite quantities with frequent zeroes, such as
precipitation. It provides the best attainable improvement in the reanalysis estimates given thelack a long-term sub-monthly global observationally-based data set.
No attempt will be made to adjust the monthly storm frequency (Liston et al., 1993), as
was done for the 6-hourly precipitation estimates in the ISLSCP Initiative I data set (Mitchell and
Lin, 1994). Nor is any attempt made to adjust the diurnal cycle, which is known to be in error
over some regions. The main constraint is that the monthly mean precipitation should agreewith the observation data, with some small differences introduced as a result of spatial
interpolation.

This preservation of observed monthly means is also in effect for all otherhybridized variables.
Several observational precipitation data sets will be available from ISLSCP Initiative II.
The Climate Research Unit (CRU) (New et al., 1999; 2000) data set from University of East
Anglia is a high-resolution (0.5°) gauge-only product, but relies on only operational data
sources, does not correct for gauge undercatch, and relaxes the data to a mean annual cycle
climatology when in situ data are scarce. The Global Precipitation Climatology Centre (GPCC)(Rudolf et al., 1994) maintains a gridded gauge analysis that contains more stations than the
CRU analysis; these data are also provided to ISLSCP on a 0.5° grid. They do provide aseparate monthly correction factor to adjust for wind-caused gauge undercatch. The Global
Precipitation Climatology Project (GPCP) (Huffman et al., 1997) also provide monthly analyses,that blend corrected gauge and satellite estimates. This data set may prove to be the best forinterannually-varying precipitation data, although it has the lowest native spatial resolution(2.5°), although a 1° version is provided to ISLSCP. GSWP-2 will use GPCC gauge data for the33baseline period, and CRU for the spin-up period, applying the gauge correction of Motoya et al.
(2002) from source code supplied by the author. Where the gauge density is low (Figure 6), the
GPCP product is blended in. In this way, gauge correction can be applied at a higher spatial
resolution, while maintaining the benefit of satellite data where there are no gauges. The next
step is hybridization with the reanalysis rainfall estimates, as described above, to produce a 3hourly precipitation product from GPCC, GPCP and CRU data, which can then be combined
and blended to produce the final product.

Figure 6. Example of gauge density for CRU stations for January 1986. Unshaded
areas have no stations within 2° of grid box.
The process is as follows. Aggregated monthly GPCC, or CRU data are calculated,transforming from 0.5° to 1° (the GPCP data provided by ISLSCP Initiative II is available at 1°).
Due to wind-caused gauge undercatch for precipitation, the Motoya et al. (2002) wind correction
is applied to the unadjusted GPCC or CRU data. First, an “uncorrected gauge” for the reanalysis
precipitation is estimated based on the catch ratio (CR) correction factor calculated by theMotoya algorithm using the daily mean reanalysis wind:

PNCEP _ Gauge =

PNCEP
CR

This is necessary because the precipitation reported by the analysis model is unaffectedby wind, so the corresponding undercatch error must be introduced into the model estimate
before adjustment.

This step ensures that the final wind-corrected precipitation estimates

maintain the same relative storm-to-storm totals as the NCEP/DOE reanalysis.
Secondly, we hybridize this “NCEP gauge” with GPCC or CRU gauge data as indicatedin the equation at the beginning of this subsection on precipitation, so that the monthly total

34

agrees with the uncorrected gauge data. Then we reapply Motoya’s wind correction to thehybrid data:

PWind _ corrected = C R PHybrid _ uncorrected
In regions of low gauge density, this result is combined with the hybridized 3-hourly
version of the GPCP data:

PGSWP 2 = aPWind _ corrected + (1 − a )PGPCP
where
a = 1,

GPCC or CRU gauge density ≥ 2

a = 0.5,

GPCC or CRU gauge density = 1

a = 0,

GPCC or CRU gauge density = 0

The final step is the separation of the precipitation components into rainfall and snowfall,convective and large-scale. The NCEP/DOE reanalysis reports total precipitation rate, as well
as a snowfall rate that is diagnosed at each model time step from the 850 hPa air temperature(or lowest model air layer temperature if the surface pressure at a given grid point is lower than
850 hPa).

If this temperature is equal to or less than 0.0C, then snowfall is designated;otherwise, rainfall is chosen (K. Mitchell, personal commmunication).

Since the snowfall

criterion is based on the atmospheric model state aloft, and not surface conditions, no reestimation of snowfall will be conducted based on the hybrid near surface air temperature.
Rainfall is assumed to be total precipitation minus snowfall.
The NCEP/DOE reanalysis also reports a convective precipitation rate. For GSWP-2, toconform to ALMA standards, a convective rainfall rate is given where:

RainConv =PConv(PTotal − Snow )PTotalLarge-scale rain would be Rain - RainConv, and if necessary, convective snowfall can be
estimated assuming the same ratio as for total precipitation in the equation above.
Some decisions had to be made where inconsistencies were apparent. For instance, inthe bi-linear interpolation of the precipitation data from the reanalysis grid to the ISLSCP grid,
there were instances near coastlines where the interpolated snowfall rate exceeded the total
precipitation rate. In these cases, the rainfall rate was set to zero. Also, during hybridization,
there were instances where one but not both of the observed monthly precipitation or the35reanalysis precipitation was equal to zero. In these situations, it was assumed that precipitation
for the month was zero.
Temperature
There is also more than one choice for near-surface temperature data, although only the
CRU data set from University of East Anglia will be included in ISLSCP Initiative II. The monthly
CRU temperature data are calculated as anomalies from a 12-month climatology that is derived
relative to a fixed elevation model at 0.5°. In order to make a consistent adjustment of nearsurface air temperature to the ISLSCP grid, the CRU temperatures will be corrected for thealtitude difference between the CRU grid and the ISLSCP Initiative II mean altitude derived fromthe GTOPO30 data set (see Section 3.1.1.1). First, the CRU elevation data are recreated byaggregating the GTOPO5 elevation data from 5' to 0.5°.

The monthly CRU temperature data

are then corrected to the ISLSCP elevation:

T1 = TCRU −6 .5(Z ISLSCP 2 − ZCRU )1000

The monthly CRU data are then aggregated from 0.5° to 1°, and hybridized with the 3hourly NCEP/DOE reanalyses 2-meter air temperature data by correcting the differences ofmonthly diurnal range and mean:

Tair = δ(TNCEP − TNCEP ) + T1
where:

δ=

DCRU
DNCEP

constrained so that:

0 .5 ≤ δ ≤ 2 .0
D is the monthly mean diurnal range of the temperature, which is reported for the CRU
data, and has been calculated from the original hourly data from the NCEP/DOE reanalyses.
Limits are placed on the diurnal scaling factor δ to prevent unreasonable extreme temperatures.
Surface Pressure
An altitude correction is applied to the surface pressure data, to adjust from thereanalysis model grid elevations to the ISLSCP Initiative II mean altitude:−g

PS _ Corr = PS _ NCEP e RT

36

( ZISLSCP 2 − Z NCEP )ZNCEP and ZISLSCP2 are the grid box mean altitudes for the reanalysis and ISLSCP
Initiative II respectively, g is the acceleration due to gravity, and R is the gas constant. T is themean temperature between the two altitudes, calculated using the same lapse rate used toadjust temperature:1  6 .5
T = Tair − (Z ISLSCP 2 − Z NCEP ) 
2  1000
Specific Humidity
These adjustments to temperature also affect the estimated saturation specific humidity.
Thus, it is also necessary to adjust the estimates of near surface specific humidity from thereanalysis to avoid incidents of super-saturation. This is done by assuming the same relative
humidity before and after the temperature correction, and then adjusting the specific humidity
accordingly to agree with the adjusted temperature.
Radiation
No hybridization will be performed on the radiation data for the 1986-1995 period,
because three-hourly Surface Radiation Budget (SRB) data produced at NASA/LangleyResearch Center will be available through ISLSCP Initiative II. In the SRB data set there aresome “undetermined” points for high latitude bands where the solar zenith angle approaches
90E. These have been arbitrarily set equal to a small value: 10 W m-2.
For the spin-up period, a hybrid product will have to be produced for the surface
downward shortwave and longwave radiation. To adjust downward radiation, the diurnal cycle isparticularly important. Studies by Dirmeyer and Tan (2001) have shown that the systematic
errors in the NCEP/National Center for Atmospheric Research (NCAR) reanalysis is very
systematic from year to year, but varies substantially across both the seasonal and diurnal
cycles. We will create a hybrid radiation forcing data set from reanalysis estimates by removing
the climatological monthly mean diurnal cycle systematic errors calculated from the SRB andreanalysis data over the 1986-1995 period:[R ]Y ,M ,D,T =[RSRB ]Y ,M ,T[RNCEP ]Y ,M ,T[RNCEP ]Y ,M ,D,T

As with precipitation, a multiplicative scaling is used to adjust the reanalysis.
Wind
The reanalysis wind products will be used as is, with the 10 meter wind speed provided
in the forcing data set.

37

3.1.1.3 ALMA conventions
All of the gridded surface data to be used by GSWP-2 will be converted to the ALMA
data convention, including compression by gathering to reduce the data set sizes by removing
ocean and land-ice points. Some extensions to ALMA are recommended, especially concerning
the formats for land surface properties, to create an updated ALMA version for GSWP-2.
Tables 3 and 4 shows the ALMA conventions for the land surface parameters that are
being supplied to the modeling groups.

These include vegetation, soils, and topographic

information at 1° resolution for all land points excluding Antarctica, essentially cutting off theTable 3. Soil parameter data.
Name

Description

SoilClass

Soil texture class-

SoilDepth

Depth of active soilcolumnm

Clay

Clay fraction

-

Sand

Sand fraction

-

Silt

Silt fraction-

Organic

Organic fraction-

Elevation

Mean grid elevation

m

Slope

Mean slope

CTI

Compoundtopographic index

W_fieldcap

Field capacity

m3 m-3

W_wiltWilting point

m3 m-3

W_sat

Saturated watercontent

m3 m-3

W_bpower

B exponent

Saturated hydraulicconductivity
Saturated matricW_sat_matp
potential
Visible albedo of
Albedo_vi
soil (snow free)
Near-infrared
albedo of soil
Albedo_ir(snow free)
W_sat_hydc

Units

Range
Max = 12
Min = 0Max = 30.0
Min = 0.0
Max = 1.0
Min = 0.0
Max = 1.0
Min = 0.0
Max = 1.0
Min = 0.0
Max = 1.0
Min = 0.0
Max = 9000.0
Min = -400.0
Max = 1.0
Min = 0.01
Min = 0.0
Max = 13
Max = 1.0
Min = 0.0
Max = 1.0
Min = 0.0
Max = 1.0
Min = 0.0
Max = 15.0
Min = 1.0
Max = 0.0001
Min = 0.0
Max = -0.0001
Min = -3.0
Max = 1.0
Min = 0.0

m m-1
-

m s-1
m
-

Max = 1.0
Min = 0.0-38

SourceTime scale

ISLSCP-II

fixed

ISLSCP-I

fixed

ISLSCP-II

fixed

ISLSCP-II

fixed

ISLSCP-II

fixed

ISLSCP-II

fixed

ISLSCP-II

fixed

ISLSCP-II

fixed

ISLSCP-II

fixed

ISLSCP-II

fixed

ISLSCP-II

fixed

ISLSCP-II
CEA84

fixed

CEA84

fixed

ISLSCP-II
CEA84

fixed

CEA84

fixed

ISLSCP-I

fixed

ISLSCP-I

fixedglobal grid at 60°S. Time-varying vegetation parameters are provided as monthly values. The
IGBP vegetation type data are available in three different styles; IGBP categories, SiB, andBATS. Table 5 lists the classifications for each variety. Table 6 is a similar listing for the near
Table 4. Vegetation parameter data.
Name

Description

VegClass

Time
scale

Periodfixed-monthly

19821995

UK

fixed

-

-

Max = 1.0
Min = 0.0

Calculated
from Univ.
of Walesdatamonthly

19821995-

Max = 1.0
Min = 0.0

IGBP; U.
Maryland

fixed

-

-

Max = 1.0
Min = 0.0UK

monthly

19821995-

Max = 1.0
Min = 0.0UK

monthly

19821995

Units

Range

SourceVegetation class-

Max = 21
Min = 0LAI

Leaf area index

m2 m-2

Max = 8.08
Min = 0.0

IGBP, U.
Maryland
UK
Univ. ofWales

vegFracFraction ofvegetation cover-

Max = 1.0
Min = 0.0

grnFrac

Greenness fraction(green LAI / Total
LAI)

classFrac

NDVI
FPAR

Fraction of each
VegClass(not in ALMA
variable list)
Normalized
difference
vegetation index
Fraction ofphotosynthetically
active radiation

Z0SurfRoughness lengthm

Max = 10.0
Min = 0.0UK

monthly

19821995

DisplH

Zero plane
displacement
heightm

Max = 50.0
Min = 0.0UK

monthly

19821995

AlbedoSnow-free albedo-

Max = 1.0
Min = 0.0

CSU

monthly

19821995m

Max = 30.0
Min = 0.1

ISLSCP-II

fixed

-

s m-1

Max = 1000.0
Min = 10.0

Look-up
table

fixed

-

RootDepth
Rs_minRoot depth (mean
50% and 95%
ecosystem rooting
depth)
Minimum stomatal
resistancesurface meteorological forcing fields, all of which are provided on a 3-hour time interval
synchronized with Coordinated Universal Time (UTC; also known as Greenwich Mean Time,
Zulu,

or

“Z”).

A

description

of

the

http://www.lmd.jussieu.fr/ALMA/.

39

ALMA

standards

isavailableat3.1.1.4

Serving of data
The final input data sets for the participating GSWP-2 LSSs will be served over theInternet via one or more Distributed Oceanographic Data System (DODS) servers.

DODS

provides a protocol for remotely accessing and subsetting large data sets (see:http://www.unidata.ucar.edu/packages/dods/).

DODS-enabled clients exist via the NetCDF

protocol for FORTRAN and C, and a growing number of popular graphics and analysis
packages are DODS-capable. In a nutshell, the “open” statement, which typically accesses afile on local disk via its pathname, instead accesses the DODS server via its URL using HTTP
addressing. DODS includes metadata, so that served data sets are self-describing. DODS
reduces strains on local disk resources, and, when properly used, is not taxing on the networkor the server hardware. In order to reduce trans-oceanic networking delays, we anticipate
serving the data from multiple DODS servers, operating in mirror mode, in North America(COLA, USA), Asia (University of Tokyo, Japan), and Europe (LMD, France). If this approach is
intractable to one or more modelers, we will consider alternate solutions, probably involving
shipping of data on magnetic media.
Assistance will also be provided to the modelers to access the forcing and parameterdata sets on the DODS servers via the ALMA Software Bazaar and the information systems
Table 5. Vegetation categories for the IGBP-derived land cover types.
Simple Biosphere (SiB)
Model Land Cover

IGBP Land Cover
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

Evergreen Needleleaf Forest
Evergreen Broadleaf Forest
Deciduous Needleleaf Forest
Deciduous Broadleaf Forest
Mixed Forest
Closed Shrublands
Open Shrublands
Woody Savannas
Savannas
Grasslands
Permanent Wetlands
Croplands
Urban and Built-Up
Cropland & Natural Vegetation
Snow and Ice
Barren or Sparsely Vegetated
Water Bodies
Missing Data

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Evergreen Broadleaf Trees
Broadleaf Deciduous Trees
Deciduous and Evergreen
Trees
Evergreen Needleleaf Trees
Deciduous Needleleaf Trees
Ground Cover with Trees andShrubsGroundcover Only
Broadleaf Shrubs with
Perennial Ground CoverBroadleaf Shrubs with Bare
Soil
Groundcover with Dwarf Trees
and Shrubs
Bare Soil
Agriculture or C3 Grassland
Persistent Wetland
Water
Ice Cap and GlacierMissing Data
40

Biosphere Atmosphere
Transfer Scheme (BATS)
Land Cover
1 Crops, Mixed Farming
2 Short Grass
3 Evergreen Needleleaf Trees
4 Deciduous Needleleaf Tree
5 Deciduous Broadleaf Trees
6 Evergreen Broadleaf Trees
7 Tall Grass
8 Desert
9 Tundra
10 Irrigated Crops
11 Semi-desert
12 Ice Caps and Glaciers
13 Bogs and Marshes
14 Inland Water
15 Ocean
16 Evergreen Shrubs
17 Deciduous Shrubs
18 Mixed Forest
19 Forest/Field Mosaic
20 Water and Land Mixtures
21 Missing Datasupport at the DODS server sites. COLA has tested the capability of running LSS integrations
driven by data served over the Internet via DODS in an environment of typical network
connection speeds. Accessing the boundary conditions and forcing data from a DODS server
across the local network, increased run time of the SSiB LSS by 15% relative accessing thedata on a local NFS-mounted disk. Accessing the data across the Internet from an identical
DODS server at NCAR in Boulder, Colorado added another 8% to the wall-clock execution time.
The GrADS-DODS server (GDS) software allows for dual-mode access to compressed
NetCDF data in the CF convention (compression by gathering) — either as the original onedimensional spatial vector of data (for access by LSSs), or as a repopulated grid (for easy
display purposes).
Table 6. Meteorological forcing data (July 1982 - December 1995).
Name

Description

Tair
Qair
Wind
SWdown
LWdownNear surface airtemperature at 2m
Near surface
specific humidity at
2m
Near surface windspeed at 10m
Surface incident
shortwave radiation
Surface incident
longwave radiation

Units

Range

Source

Time
Scale

K

Max = 350
Min = 190

NCEP, CRU

3 hourly

kg kg-1

Max = 0.07
Min = 0

NCEP, CRU

3 hourly

m s-1

Max = 75
Min = 0Max = 1360
Min = 0Max = 750
Min = 0Max = 113000
Min = 50000

NCEP

3 hourly

SRB (with NCEP
for spin-up period)

3 hourly

As above

3 hourly

NCEP, EDC

3 hourly

NCEP, GPCC
GPCP (and CRU
for spin-up period)

3 hourly

As above

3 hourly

As above

3 hourly

W m-2
W m-2

Psurf

Surface pressure

Rainf

Rainfall rate

kg m-2 s-1

Max = 0.03
Min = 0Rainf_C

Convective rainfall
rate

kg m-2 s-1

Snowf

Snowfall rate

kg m-2 s-1

Max = 0.03
Min = 0Max = 0.003
Min = 0

3.1.2

PaInitial conditions

Once the DODS servers are populated and the input data documented, modeling groups
can begin their simulations. An issue that must be resolved is the method of spin-up that themodelers should follow. GSWP-1 provided a starting point for land-surface state variables (soilmoisture at 75% of saturation, no snow cover, specified soil temperature), and asked modelers
to loop through the first year (1987) until some degree of “stability” in soil moisture was attained.
This method can unduly amplify a year’s anomalies at the beginning of the free integration

41

period. A balance needs to be found between having the maximum number of useable years inthe simulation, and having an adequate spin-up period. For the NCEP/DOE reanalysis, theoption exists to use data from earlier years for the spin-up. Thus, spin-up will be performed
using data beginning 0300UTC 1 July 1982. LSS integrations will loop through the first 12
months of forcing data until the modeler is satisfied that soil moisture has spun up andsufficiently equilibrated. A lesson from the GSWP pilot project was that this spin-up process
overly amplifies the impact of climate anomalies from that year on the land surface statevariables. Therefore, the models will then proceed with their integrations forward from July
1983 – December 1985 so as to converge to a realistic “land climate” at the start of theevaluation period. The 10-year baseline integration, which will be evaluated within the group of
GSWP participants and later released to the community at large, covers the 10-year period from
0000UTC 1 January 1986 up to 0000UTC 1 January 1996.

3.1.3 Execution and production
After spin-up, each LSS will be integrated globally for the 10-year period 1986-1995.
The forcing data are provided at a 3-hour time interval, which adequately resolves the diurnal
cycle. For modelers that prefer to run their LSS at a shorter time step, the data will have to beinterpolated in time. Sample routines for doing the temporal disaggregation will be supplied(see the GSWP web site), but it will be up to each modeler to choose and report the method
he/she prefers.
There should be no adjustment to the spatial grid — all participating LSSs should be runon the same 1° grid. However, though the data subsetting capabilities provided by DODS, it
should be possible for the modeler to choose how he proceeds with the integration (e.g.,running the entire global grid at each time step, or running each grid point individually from start
to finish). The forcing data can be served to the modeler as a sequence of 2-D (or 3-D) global
or regional grids, as a set of time series at specific points, or as spatial vectors (e.g. latitude
bands). This provides added flexibility to the modeler to tailor the method of integration to best
utilize local resources.
Table 7 lists the files on the DODS server that correspond to the input data sets listed in
the previous three tables. This should aid modeling groups in accessing the data interactively,or downloading the data to local file systems. The GSWP data sets on the COLA DODS server
are available at http://www.monsoondata.org:9191/dods/gswp and can be accessed by browser
or via any DODS-enabled client.

There are two subdirectories: grid and vector.

The grid

directory serves full 360x150 grids with the ocean points set to the missing data value. See any

42Table 7. GDS file names of input fields for the baseline integration (B0).
Fixed Fields
FileGrid onlyFilesoilclasslandmask
SoilClassLandmasksoildepth
SoilDepthMonthly Fields File
clay
lai
Clay
LAI
sand
grnfrac
Sand
grnFrac
silt
ndvi
Silt
NDVI
organic
fpar
Organic
FPAR
elevation
z0surfElevation
Z0Surfslope
displhSlope
DisplH
albedo_csu
cti
CTI
Albedow_fieldcap
W_fieldcap
w_wiltW_wilt3-hourlyFilew_sat
tair_cru
W_sat
Tairw_bpower_cea84
qair_cru
W_bpowerQair
w_sat_hydc
wind_ncep
W_sat_hydc
Windswdown_srb
W_sat_matp w_sat_matp
SWdownalbedo_vi
lwdown_srb
Albedo_vi
LWdownalbedo_ir
psurf_eds
Albedo_ir
Psurf
vegclass_igbp,
rainf_gswp
VegClass
Rainf_sib, _batsclassfracrainf_c_gswp
classFrac
Rainf_C
rootdepth_50, _95
snowf_gswp
RootDepth
Snowf
vegfracvegFracof the links labeled “info” to see a description of the data set. The vector directory serves thesame fields as land-only vectors (compressed by gathering in the NetCDF CF convention) of
length 15238. The vectors are more compact, and suitable for input into a LSS. The grids are
directly displayable as maps. On the GDS, only one type of data set, the vector data, areactually stored. The GDS automatically repopulates the grid before serving through the griddirectory. Note the land-sea mask is served only as a grid file.
The time-invariant fields are in a subdirectory called fixed. So, for instance, to accessthevector

form

of

the

soil

porosity

data,

one

would

accessthe

URL:

http://www.monsoondata.org:9191/dods/gswp/vector/fixed/w_sat from one’s DODS-enabledclient. We encourage modeling groups who need input files that are not supplied to generate
their own data sets, and to make them available to the GSWP operational centers, so that wemay share them with others who might also need those data sets.

43

3.1.4 Output fields
Output data can quickly grow to unmanageable proportions if careful choices regarding
variables and time intervals
are not made. Table 8 shows

Table 8. Potential space requirements per output variable.
Frequency

10 Years of Output

requirements per variable forMonthly

6.4 MB

10-years of 1° data (ice-free

Decad

19 MB

Pentad39 MB

Monthly 3-Hourly

51 MB

Daily

195 MB

3-Hourly

1.6 GBthelandprojected

pointsuncompressedstorageonly),
single-precision binary, saved atvarious frequencies.

Whilemonthly data for about 100
variables could be saved on asingle CD-ROM, 3-hourly data for a single variable exceeds the capacity of two CD-ROMs.
Monthly data may be adequate for many purposes and most variables. For hydrologic
validation over major river basins, for instance, monthly data may be adequate.

Pentadfrequency adds information on synoptic-subseasonal variations without an inordinate increase instorage demands, and is rather convenient in that there are exactly 73 pentads in a year,
excluding leap years. During leap years, data for 29 February are typically included in the 12th
pentad, making it effectively a six-day pentad.

For GSWP-1, decads were used. Decads areconstructed to occur thrice per month, divided at 0000UTC on the 1st, 11th, and 21st of eachmonth. This means the last decad of a month may have 8, 9, 10 or 11 days. The decad interval
is slightly more awkward to handle than the pentad, but can be easily accumulated into monthly
data.
The daily interval makes sense because it is the only natural interval shorter than yearly.
However, it requires 30 times more storage space than monthly.
intervals represent a compromise between daily and monthly.

The pentad and decad

A balance must be struck

between maximum useful information and minimum data volume.
The highest temporal resolution, 3-hourly, corresponds to the frequency of themeteorological forcing data.

The 3-hourly frequency resolves the diurnal cycle, providing

information that the afore-mentioned intervals cannot. This interval, along with daily, was used
for the Rhône-AGG experiment. Although at much higher spatial resolution, the Rhône-AGG
experiment covered a very small fraction of the globe, and spanned only 3 years (for required
reporting of model output). This frequency may be impractical for a global experiment. An
alternative is the monthly-3-hourly, which represents a monthly-mean diurnal cycle. Much of the44

data in the ISLSCP Initiative II collection is reported as monthly 3-hourly. This interval provides
basic information on the diurnal cycle, and its variation on seasonal-interannual time scales, andrepresents a compromise over full 3-hourly resolution.
3.1.4.1 Daily fields
The full compliment of ALMA output variables are requested from each model on a dailyinterval. Fluxes will be reported as means for the day. Surface state variables are daily means,and subsurface state variables are instantaneous unless noted otherwise. The reporting time is
0000UTC, with the averaging period as the 24 hours preceding the reporting time.

For

example, the daily flux data for 0000UTC 10 November 1993 will represent the mean for 9
November 1993 (UTC). Table 9 gives the ALMA list of output variables to be reported on thedaily interval. The sign convention shown is traditional meteorological convention. ALMA also
allows for the mathematical sign convention, see their website, listed in the table below, fordetails.
Table 9. ALMA standard output variables for GSWP-2(see http://www.lmd.jussieu.fr/ALMA/ for a detailed discussion of these variables).
Variable

Description

Units

Sign (+)

O.1) General energy balance components
Swnet

Net shortwave radiation

W m-2

Down

Lwnet

Net longwave radiation

W m-2

Down

-2

Qle

Latent heat flux

Wm

Up

Qh

Sensible heat flux

W m-2

Up

Qg
Qf
Qv

Ground heat flux

-2

Down

-2

Sol.<Liq.

-2

Sol.<Vap.

-2

Wm

Energy of fusion

Wm

Energy of sublimation

Wm

Qa

Heat transferred to snowpack by rainfall

Wm

Down

DelSurfHeat

Change in surface heat storage

J m-2

Incr.Heat

DelColdCont

Change in snow cold content

J m-2

Decr.Heat

45

Table 9 (continued)
Variable

Description

Units

Sign (+)

O.2) General water balance components
Snowf
Rainf
Evap
Qs

kg m-2 s-1

Snowfall rate
Rainfall rate

-1

Down

-2

-1

Up

-2

-1

Out

-2

-1

Out

kg m sTotal evapotranspiration (all terms)
Surface runoff

Down

-2

kg m skg m s

Qsb

Subsurface runoff

kg m sQsm

Snowmelt

kg m-2 s-1

Sol.<Liq.

-2-1

Liq.<Sol.

-2

-1

Out

Qfz
Qst
DelSoilMoist
DelSWE
DelSurfStor
DelIntercept

Refreezing of water in the snowpack
Water flowing out of snowpack
Change in column soil moisture
Change in snow water equivalent
Change in surface liquid water storage
Change in canopy interception storage

kg m skg m s-2

Increase-2

Increase-2

Increase-2kg mIncrease

kg m
kg m
kg m

O.3) Surface state variables
SnowT

Snow surface temperature

K

Pos.Def.

VegT

Vegetation canopy temperature

K

Pos.Def.

BaresoilT

Bare soil surface temperature

K

Pos.Def.

AvgSurfT

Area-weighted average surface temperature

K

Pos.Def.

RadT

Effective surface radiative temperature

K

Pos.Def.

AlbedoSurface albedo-

Pos.Def.

SWE

Snow water equivalent on ground (3-D)

kg m-2

Pos.Def.

-2

Pos.Def.

-2

kg m

Pos.Def.

SWEVeg
SurfStor

Snow water equivalent in canopy interception
Surface water storage

kg mO.4) Subsurface state variables
SoilMoist

Average layer soil moisture (3-D)

kg m-2

Pos.Def.

SoilTemp

Average layer soil temperature (3-D)

K

Pos.Def.

SoilWetTotal column soil wetness (wilting point = 0,
saturation = 1)-

> wiltingpointO.5) Evaporation components
PotEvap

Potential evapotranspiration

kg m-2 s-1

Up

EcanopEvaporation of canopy interception

kg m-2 s-1

Up

-2

Up

Tveg

Vegetation transpiration

kg m s46-1

Table 9 (continued)
Variable

Description

Units

Bare soil evaporation

Esoil
Ewater

-1

Up

-2

-1

Up

kg m sOpen water evaporation

kg m s-2

RootMoist

Root zone soil moisturekg m

CanopInt

Total canopy water storage

kg m-2

EvapSnow
SubSnow
SubSurf
Acond
CCond

Sign (+)-2

Evaporation of liquid water from snowpack
Snow sublimation
Aerodynamic conductance
Canopy (stomatal) conductance

Pos.Def.

-2

-1

Up

-2

-1

Up

-2

-1

Up

kg m skg m sSublimation of ice from soil and canopy interception

Pos.Def.

kg m sms-1

Pos.Def.

ms-1

Pos.Def.

O.6) Other hydrologic variables
WaterTableD

Depth to water tablem

Pos.Def.

O.7) Cold season processes
SnowFrac

Snow cover fraction-

Pos.Def.

SAlbedoSnow albedo-

Pos.Def.

SnowDepth

Depth of snow layers (3-D)

m

Pos.Def.

O.8) Variables to be compared with remotely sensed data
RadTmax

Maximum daily radiative surface temperature

K

Pos.Def.

RadTmin

Minimum daily radiative surface temperature

K

Pos.Def.

3.1.4.2 Global 3-hourly fields
For the final year of the integrations (1995), a subset of the required ALMA output
variables will be reported globally at the 3-hourly forcing interval. This is requested for two main
reasons. First, this 1-year data set will provide a global picture of the simulation of the diurnal
cycle across all seasons for all models. Second, this set of output data will provide sufficientarchives for the remote sensing evaluations, as 3-hourly data give a closer rendition of theinstantaneous picture that satellite platforms provide.

Reporting of data only once a day

spatially limits the regions of satellite validation, due to the sun-synchronous orbits of mostpolar-orbiting platforms. As with the daily data, state variables will be instantaneous and fluxes
will be reported as the average rate over the preceding interval (3 hours). Variables to bereported during this intensive model output period (IMOP) are indicated in Table 10. In thiscase, all state variables are surface variables, and should be averaged over the previous 3-hourinterval.

47

Table 10. ALMA variables to be reported at 3-hourly intervals during the IMOP.
O1

O2

O3

O5

Swnet
Lwnet
Qle
Qh
Qg
Qf
Qv
Qa
DelSurfHeat
DelColdCont

Snowf
Rainf
Evap
Qs
Qsb
Qsm
Qfz
Qst
DelSoilMoist
DelSWE
DelSurfStor
DelIntercept

SnowT
VegT
BaresoilT
AvgSurfT
RadT

PotEvap
EcanopTveg
Esoil
RootMoist
Acond
CCond

The simplest way to generate this data set will be for each modeling group to save arestart file for 0000UTC 1 January 1995 during the baseline integration. After completing thebaseline run, one can change the output table for the LSS, and rerun the final year with the newreporting frequency and variable list.
3.1.4.3 Fixed fields
To aid in the calculation of soil wetness indices, and the comparison of soil wetness andtemperatures among models or between models and observations, we ask that each model
report a one-time file of soil properties as listed in Table 11. These may be redundant with the
input soil parameter data in Table 3, but there are so many different approaches to soilmodeling, that it will be clearer if each LSS simply reports the three-dimensional grid of global
soil properties used.
Table 11. ALMA fixed fields to report with output data.
Name

Description

Units

SoilDepth

Depth of each soil layer in the column (3D)M

W_fieldcap

Field capacity (3D)M

W_wiltWilting point (3D)

M

W_sat

Saturated water content (porosity × layer depth) (3D)M

3.1.4.4 Local 3-hourly fields
It will be impractical to request many global 3-hourly fields, and it is certainly not withincurrent storage and data transmission capabilities to do so for the entire 10-year period.
However, it may be tractable to request 3-hourly data over specific grid boxes for more48

extended periods, corresponding to locations where comparable measurements have beencollected that can be used for validation. In particular, locations of field campaigns (such as
those listed in Section 3.3.2) or long-term monitoring sites, such as the ARM-CART site,
FluxNET sites, or Valdai. Again, capabilities of the DODS server technology should facilitate
reintegration of the LSSs over single grid points or small subsets of the global domain. Detailsof these point integrations will be decided later in the project.
3.1.4.5 Recording output data
All output data should conform to the ALMA standards, and be written as land-only
vectors of length 15238 single-precision floating-point values. Assistance and sample software
are available from the GSWP and ALMA websites.
Sample FORTRAN code will be made available on the GSWP website to aid in thereporting of requested ALMA output variables from a LSS.

The basis of the code is two

subroutines. The first maps a LSS’s variables to the variables in the ALMA output table througha series of equations laid out in the same structure as the ALMA table. The example given is for
SSiB, where the LSS variables are passed to the subroutine via a FORTRAN-90 data module.
It would be straightforward for another modeler to simply re-map the variables of another LSS
into those ALMA variables. The second set of code includes a subroutine for writing the ALMA
output variables to a file in NetCDF format, and an easily edited ASCII table, structured on the
ALMA output table, that indicates which variables should be reported and the reporting
frequency.

Again, after an initial investment of time to implement such code in a LSS, it

thereafter becomes trivial to re-run the model with different output requirements.
3.1.4.6 Ancillary information
Each modeler should document all non-standard choices made in regard to theintegration of their model and submit that information to the ICC with their final output. This will
help to ensure clean comparisons of the products from the various models and may affect theway multi-model products are assembled and interpreted. Standard reports should also include
specific information about each model, including references to relevant published work (as in
the RhôneAGG experiment). Table 12 lists the information requested in the report.

49

Table 12. Requested ancillary information about the LSS and its integrations in GSWP
Model description:
1. List the most recent/important reference(s) for your LSSs.
2. Give the name of your model, as used in the output file names (see Section 3.2.1)
3. What time step did you use for your LSS?
4. Describe the vertical structure (number and depth of layers, or other methods of
discretization) of your LSS for each of the following:a. Soil moistureb. Soil temperaturec. Snow
5. How were soil properties (wilting point, field capacity, porosity, hydraulicconductivity, thermal conductivity, etc.) assigned?
a. From provided fields (Table 3)b. Other (describe)
6. Does your LSS include frozen soil properties:
a. No change in soil moisture treatment below freezing
b. Binary frozen soil moisture (all or none)c. Fractional water and ice content in the soil
7. Does your LSS include liquid water in the snowpack?
8. What type of sub-grid parameterizations are used by your LSS (what best describes
your LSS)?
a. Effective parameters (and which parameters)b. Tile/mosaic approaches (structure: i.e. multiple columns or many surfaces
over a single soil, etc.)
c. Integration using probability distributions (gamma, normal, etc...and for which
variables)
d. Some combination of the above (describe)
9. What sub-grid parameterizations are used in your LSS? (none, one or several of the
below)
a. Sub-grid runoff schemeb. Sub-grid precipitation interceptionc. Sub-grid saturated fraction for evaporationd. Fractional snow cover area parameterization and how it influences theoverall surface energy budget
e. Other (describe)
10. Is there recharge of soil moisture from below from the water table?
11. What is the surface energy budget structure of your LSS?
a. Single soil/snow/vegetation composite surface energy budget
b. Distinct canopy and soil with composite snowc. Distinct soil, canopy and snow
d. Other (describe)
Continued on next page50

Table 12 (continued)
Data issues:
12. Which soil parameters are used in your LSS?
a. Sand, silt, clay fractions directly (and what method for deriving hydraulic andthermal parameters?)b. Other derived parameters from Table 3 (which ones?)c. Provided Cosby parameters based on soil class (Table 2)d. Parameters derived from provided data (describe how and for what method;e.g., Van Genuchten)
e. Other (describe)
13. What vegetation type data are used by your LSS?
a. IGBP land cover types (Table 5)b. SiB vegetation types (Table 5)c. BATS vegetation types (Table 5)d. Plant Functional Types derived from IGBP datae. Other (describe)
14. Which of the supplied vegetation data sets from Table 4 did you use?
15. Which forcing data from Table 6 did your LSS require?
16. How did you interpolate the 3-hourly forcing data?
a. Used the provided interpolation code from the GSWP web siteb. No interpolation (model time step is 3 hours or longer)
c. Other (describe)
17. What data sets does your LSS require that were not supplied by GSWP? How did
you supply these data?

3.2 Inter-comparison Center
3.2.1 Data submission
Each participating modeling group will submit their model output to the Inter-Comparison
Center (ICC) at the University of Tokyo for quality control, and basic comparisons. Again, data
may simply be served to the ICC via a DODS server at the modeling group’s home site, or by
FTP or HTTP.

Otherwise, the data will be submitted by magnetic or optical media to be

determined.
A

website

has

been

set

up

at

the

ICC

for

deliveryofoutputdata(http://www.tkl.iis.u-tokyo.ac.jp:8080/gswp2/). The web side allows for data to be submitted
through a web browser from local disk to the ICC (pushed), or posted on the modeler’s own FTP
site or DODS server to be retrieved by the ICC (pulled). To facilitate this semi-automated data
retrieval, a convention for file names should be followed. Daily output data from LSSs should be
structured as:
MODEL_expNAME_VERSION_dYEARMONTH.nc

51

where MODEL: model name, NAME: experiment name identifier (such as GSWP2 for baseline
simulations; a list of names for sensitivity experiments is given in Table 13), VERSION: version
number of the run by 8 digits of the run (probably year, month, and date of completion of theintegration), and the year and month of the contents. The letter d indicated daily data, h is usedfor 3-hourly data, and f for the fixed fields.
The 10-year data set will be divided into 120 monthly files containing all of therecommended variables in one file. For example, the baseline run by the gSiB model from IIS
for October 1992 completed in mid-April would be:
gSiBIIS_expB0_20030415_d199210.nc
The version number is crucial since many models will likely re-run and submit their
updated outputs.

3.2.2 Quality control
The ICC will analyze the multi-model envelope and quantify the uncertainty in simulated
land-surface state variables and fluxes as a function of variable and location. It is advised that
before completing the entire baseline simulation, each modeling group should submit a sample
one-month output data set produced by the LSS. A check can then be performed to ensure
units are not obviously incorrect and that all required variables are reported in a defined andreadable manner. ALMA also provides code to perform basic water and energy balances are
satisfied. All modeling groups should check their output before sending it to the ICC. While the
ICC has primary responsibility for basic model comparison, novel ideas for comparisons byother researchers will be welcome.

The establishment of a multi-model consensus or model-

independent analysis will be pursued among the two operational centers (see Section 2.1).

3.2.3 Data redistribution
Once LSS data are submitted to the ICC and are checked and accepted, they will bemade available for evaluation by other GSWP-2 participants.

The primary means of data

distribution will be online access. Submission of LSS output for the baseline simulation is taken
as an agreement by that modeling group to the release of the data to other GSWP-2
participants. These data will be available not only for validation, evaluation, and remote sensing
applications, but also among the modeling groups themselves for cross-comparison if they sodesire. The timeline in Figure 5 shows the working schedule for data release.
The first product to be released to the general public will be the multi-model analysis.
This will occur after the first overview paper, describing the model comparison and production of52

the multi-model analysis, has been written and submitted to a peer-reviewed journal. Releaseof data from individual model simulations to the general public will be made at a later date, after
a critical amount of validation and evaluation has been publicly presented and/or submitted for
publication. Participation in the baseline simulation is taken as an agreement by each modelinggroup to the eventual public release of that data, unless expressly stated otherwise by the
representatives of that modeling group.
Data from sensitivity studies will be released within the set GSWP-2 participants as
those sensitivity studies are completed. Schedules for individual sensitivity studies will be set
and posted on the website as appropriate.

Each sensitivity study will have a primary

investigator who will lead the evaluation, and who will determine if and when individual model
results should be made available to general public, with the opportunity for exception byindividual modeling groups as spelled out above.

3.3 Evaluation
The main thrust of external analysis of the LSS results will be in the form of model
evaluation. There are several types of evaluation that can be performed, but they fall into twobasic categories: in situ validation and remote sensing evaluation. This section is concernedwith in situ validation — remote sensing evaluation is discussed separately in Section 3.5.
In situ validation means direct validation against observations. We envisage two primary
types of in situ validation: hydrologic validation (be conducted at the IIS, University of Tokyo)
and local validation (led from the Hydrologic Sciences Branch of the Goddard Space Flight
Center of NASA).

3.3.1 Hydrologic validation
3.3.1.1 Streamflow
Hydrologic validation is performed on basin-integrated model runoff against observed
streamflow and discharge. Thus, it is an integrated validation of the models’ water balances
over basins, smoothed in time.

Each LSS runoff will be routed through common routing

schemes for validation. For this validation effort, only the runoff terms from the LSSs will be
used.
3.3.1.2 Hydrology standards for ALMA
Currently the ALMA data protocol does not contain variables associated with subsurface hydrology (e.g., parameters such as topographic index or state variables such as water53

table depth) or lateral transport of surface water (parameters such as river flow direction, slope,or channel width, nor fluxes such as streamflow out of a grid box). ALMA does have a surface
water storage variable that can be used for river or lake water mass.

GSWP-2 offers an

opportunity to expand ALMA to include a complete set of hydrologic quantities.

3.3.2 Field campaign data
The local validation will have two thrusts - validation against data from long-runningobserved networks, and validation against data from intensely monitored but short-duration field
programs. The ISLSCP Initiative II period overlaps part or all of a number of field campaigns(see Table 1).
3.3.2.1 AccessComparison to measurements at locations in these field campaigns would be very
useful, but presents practical difficulties as the data are widely scattered, under various usage
limitations, and in a number of different formats.

Many of these campaigns included data

information systems to collect and archive the measurements that were gathered. In somecases (e.g. First ISLSCP Field Experiment [FIFE]), a certain degree of data synthesis was
performed to put the observational record in a more useful form for land surface modelers (Betts
and Ball, 1998).
It would be a task of value to GLASS and ALMA, as well as to GSWP, to make an effort
to gain access to these data.
experiment.

This effort is being pursued in parallel with the GSWP-2

Data sets that are otherwise publicly available, and of use for calibration andvalidation for GSWP-2, will be collected and served along with the other data on the DODS
servers. To date, data from FIFE, HAPEX-Sahel, Global Soil Moisture Data Bank and the Soil
Climate Analysis Network (SCAN) have been collected.
3.3.2.2 ALMA formatting
GLASS and ALMA will further add value by synthesizing field measurements into the
ALMA standard. This consolidation and standardization will be pursued in conjunction with thedata information systems (DIS) personnel of the field campaigns. Surface observational data
sets that are already publicly available and deemed of potential use to GSWP-2 will be
converted to ALMA format and made available to the GSWP-2 participants online for calibration,
evaluation and validation exercises. With consent of the producers and managers of each dataset, the ALMA-format version of the data may be made publicly available, either through
GLASS, or from the originating DIS. Whether served by ALMA or the individual DISs, the54standardized data should be made accessible to the land surface modeling community. We
recommend that this non-trivial task be heartily pursued with substantial assistance from ALMA.

3.3.3 Observational networks
Long-running observational networks that monitor land surface state variables or fluxes
are not as plentiful as stations that monitor near-surface meteorology. They include the various
soil moisture monitoring stations in the Soil Moisture Data Bank, national soil temperaturemonitoring networks (e.g. Germany), snow monitoring networks such as SnowTel, andpotentially a few of the original FluxNet flux monitoring sites that were in place before the end of
the ISLSCP Initiative II period.

3.4 Sensitivity studies
Two categories of sensitivity studies are proposed, that will involve re-integration of theparticipating LSSs.

The first category involves runs to examine the role of uncertainty inmeteorological forcing data in the estimates of surface state variables and fluxes. This will
involve using alternative meteorological analyses (namely, the ECMWF reanalysis, of which thecomplete 10-year data set should be available from ISLSCP in early 2003), and alternative
observational data sets for hybrid correction of the analyses.
The second category involves uncertainty in the specification of land surface properties.
There are three different maps of global land cover classification. The impact of suppression of
the interannual variability of vegetation phenology will be investigated. For LSSs with sub-gridtiling of surface properties, we may examine the impact of parameter aggregation across thosemodels on the global scale.

These tests will determine the sensitivity of LSSs to thespecification of some key observable parameters on a global scale, and thus establish
uncertainties caused by parameter choices.
Participation in the sensitivity studies is optional, and it is expected to be beyond thecapabilities and interests of most modeling groups to participate in all of them. However eachmodeling group will hopefully find one or more of the studies of sufficient interest to agree toparticipate.
The precipitation (P) series is a key study for GSWP-2, and would benefit fromparticipation of at least 6 modeling groups. From this series, the impact of remote sensing dataon estimates of global surface hydrologic cycle, the impact of gauge errors, reanalysis errors,
and the relative merits of the precipitation statistics of the two reanalysis products can be
estimated. With more LSSs participating, the range of inter-model sensitivity to uncertainties inprecipitation can be determined.
55

The radiation (R) series will provide a similar estimate for the impact of radiation errors
and uncertainties on the LSSs. For modelers with a particular interest in the relative impacts ofshortwave and longwave radiation errors, such as those whose LSS is coupled into an
atmospheric GCM where parameterization development is occurring, the RS and RL will be
particularly interesting and useful.
The all-meteorological (M1) study gives the broadest assessment as to the impact ofdifferences from the two reanalyses.

This study could be useful for anyone thinking of

expanding their LSS simulations for other research or applications, and wanting to ascertain thebest reanalysis product to use for their endeavor.
Similarly, the study using different vegetation data sets (V1) may help a modeling groupdecide which global vegetation data set to use with their model. The interannual vegetation (I1)experiment is useful for any modeling group that is not currently using dynamic vegetation orvegetation phenology parameterizations, but is considering using or developing one for their
LSS. This study will show the sensitivity of the surface energy and water budgets to interannual
variations in vegetation, which may be a factor in deciding the usefulness and approach towardmodeling vegetation phenology. For models that have predicted vegetation phenology, thisstudy can be used along with both specified and predicted time-varying observed vegetation tovalidate that component of the models.

3.4.1 Optional input data sets
Alternate data sets of meteorological forcing will be generated and supplied for thesensitivity experiments. These will include non-hybrid versions of the NCEP/DOE reanalysis,
hybrid and non-hybrid versions of the ECMWF ERA40 reanalysis, and alternate hybridizationsof those reanalysis data. The alternate hybrid data sets would use different observationallybased precipitation or near-surface temperature data sets for the hybridization process. These
data sets will all be in ALMA format, and could easily be swapped in and out. Sample software
to query and parse multiple input ALMA files will be supplied on the GSWP-2 website and in the
ALMA software bazaar.
Alternate data sets for land surface parameters will be supplied in a similar manner.
There may be inconsistencies among the ISLSCP Initiative II data sets that will have to beaddressed (e.g., snow-free albedo inconsistent with land cover type). These inconsistencies
may exist for the control case data sets as well.

56

Table 13 lists the alternative data sets that will be made available for each sensitivity
study. Files not listed are the same as for the baseline simulation. Again, the latest information
on changes will be posted on the project web site.
Table 13. Alternate input files for the sensitivity studies.
Exp

Description

B0

Baseline integration

P1

Hybrid ERA-40 precipitation(instead of NCEP/DOE)

P2

R1

NCEP/DOE hybrid with GPCC
corrected for gauge
undercatch (no satellite data)
NCEP/DOE hybrid with GPCC
(no undercatch correction)
NCEP/DOE precipitation (noobservational data)
NCEP/DOE hybrid with Xie
daily gauge precipitation
NCEP/DOE radiation

RS

Field

Files
As given in Table 7

Rainf
Snowf
Rainf_C
as aboverainf_era
snowf_era
rainf_c_era
rainf_gage snowf_gage,
rainf_c_gage

as above

as aboverainf_gpcc, snowf_gpcc,
rainf_c_gpcc
rainf_ncep, snowf_ncep,
rainf_c_ncep
rainf_xie, snowf_xie, rainf_c_xie

NCEP/DOE shortwave only

LWdownSWdownSWdownlwdown_ncep
swdown_ncep
swdown_ncep

RL

NCEP/DOE longwave only

LWdownlwdown_ncep

R2

ERA-40 radiation

M1

All NCEP meteorological data(no hybridization withobservational data)

lwdown_era
swdown_era*_ncepV1

UMCP vegetation class data

LWdownSWdownTair,
Qair,
LWdown,SWdown,Rainf,
Snowf,
Rainf_C
VegClass

I1

Climatological vegetation

LAI,
vegFrac,
grnFrac,
NDVI,
FPAR,
Z0Surf,
DisplH*_clim

P3
P4
P5

3.4.2

as abovevegclass_umcp

Data submission/distributed analysis

Most likely, the ICC will not be responsible for collecting data from all of the sensitivity
studies. It will likely be the responsibility of one or more participants to propose, arrange,57

oversee and analyze the results from each sensitivity study, similar to the sub-projects in AMIP.
This is a situation where the DODS server/client technology could be extremely useful in the
sharing and analysis of the results from multiple modeling groups, provided those groups
choose to install and operate a DODS server at their sites. DODS would allow distributed
analysis of the results of the control and sensitivity studies, potentially making analysis of results
much easier and swifter than previously possible.

3.5 Remote sensing applications
Remote sensing offers the only opportunity for large spatial scale validation of the LSS
state variables. Validation of radiative skin temperature, snow coverage extent, and surface soil
moisture should be possible. There also is a plan to develop code for LSSs to predict surface
microwave radiances as observed from space. This effort ties into the general remote sensing
thrust in GSWP-2 (see Section 2.4).
Forward retrievals for microwave channels sensitive to soil moisture will be calculated aposteriori using the 3-hourly model output fields from each LSS. LSSs that have poor verticalresolution in the soil column near the surface may not perform well in this test, unless a reliablemeans for interpolating soil moisture near the surface can be applied. Potential observational
data sources include 6.6 GHz SMMR products from the 1987-1988 period and 19 GHz SSM/I
measurements.
The application of backwards (i.e., retrieval) algorithms to remote sensing observations
allows for the possibility of direct comparisons between LSS and remote estimates of soil
moisture.

The ERS global soil moisture data set - derived from 5.3 GHz active radarmeasurements from 1992-2000 - provides one potential point of comparisons for LSS surface
soil moisture predictions during the GSWP-2 period (Wagner et al. 1999).
Developing high-quality retrieval algorithms for surface soil moisture often requires a
great deal of ancillary information concerning land surface states.

One challenge forinterpreting high frequency (> 5 GHz) microwave and radar observations of the land surface isisolating temporal soil moisture signals from simultaneous seasonal variations in vegetation
cover and surface conditions. Independent predictions of seasonal soil moisture trends from
LSSs during the GSWP-2 period may aid in the filtering of vegetation and surface roughness
effects from SSM/I- and ERS-derived soil moisture products.
Radiometric skin temperature will be compared to remote sensing estimates of skintemperature. Daily maximum and minimum radiative temperature are to be reported from eachmodel for the 10-year period, along with daily mean skin temperature, and the radiative58

temperature based on the daily mean upward longwave radiation. This will allow sufficient
coverage to compare to available remote sensing data.
Snow and freeze/thaw coverage comparisons will also be performed using the dailysnow cover and LSS predictions of soil thermal states.
Models which predict vegetation phenology will asked to report NDVI as an output. This
will be compared to the monthly observed NDVI (which is provided as an input for those LSSs
that require it.

3.6 Contact Information
The latest information on GSWP-2 and the latest version of this plan can be found on the
project website: http://www.iges.org.gswp/
Email inquiries should be addressed to: gswp@cola.iges.org.
At the GSWP operational centers:
Paul A. Dirmeyer or Xiang Gao
Center for Ocean-Land-Atmosphere Studies
4041 Powder Mill Road, Suite 302
Calverton, Maryland 20705-3106 , USA
Phone: +1-301-902-1254
Fax: +1-301-595-9793
E-mail: dirmeyer@cola.iges.org
Taikan Oki
Research Institute for Humanity and Nature
Kyoto 602-0878, Japan
Phone: +81-075-229-6180
Fax.: +81-075-229-6150
E-mail: oki@chikyu.ac.jp

Institute of Industrial Science, University of Tokyo
4-6-1 Meguro-ku, Komaba, Tokyo 153-8505, Japan
Phone: +81-03-5452-6382
Fax.: +81-03-5452-6383
E-mail: taikan@iis.u-tokyo.ac.jp

ACKNOWLEDGEMENTS:
Many pairs of eyes have looked over this plan, and their owners have provided many
useful comments and suggestions – we thank them all. In particular, substantive contributions
have been provided by Randy Koster, Mei (Maggie) Zhao, Wade Crow, Adam Schlosser, andJan Polcher.
We would also like to than everyone who participated in the GSWP-2 kickoff meeting in
Calverton, Maryland, USA on 30 September – 1 October 2002 for their help in crystallizing thisproject. Support for this project is provided in the United States by National Aeronautics andSpace Administration Grant NAG5-11579, and in Japan by the Core Research for Evolutional
Science and Technology (CREST) of the Japan Science and Technology Corporation (JST).
59

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