NASA/TM–2014-104606/Vol. 36

March 2015

Ronald Gelaro, William M. Putman, Steven Pawson, Clara Draper, Andrea Molod, Peter M. Norris, 

Lesley Ott, Nikki Privé, Oreste Reale, Deepthi Achuthavarier, Michael Bosilovich, Virginie Buchard, 

Winston Chao, Lawrence Coy, Richard Cullather, Arlindo da Silva, Anton Darmenov, Ronald M. Errico, 

Marangelly Fuentes, Min-Jeong Kim, Randal Koster, Will McCarty, Jyothi Nattala, Gary Partyka, 

Siegfried Schubert, Guillaume Vernieres, Yuri Vikhliaev, and Krzysztof Wargan

National Aeronautics and 

Space Administration 

Goddard Space Flight Center

Greenbelt, Maryland 20771

Technical Report Series on Global Modeling and Data Assimilation, 

Volume 36

Randal D. Koster, Editor 

Evaluation of the 7-km GEOS-5 Nature Run
Since its founding, NASA has been dedicated to the 

advancement of aeronautics and space science. The 

NASA scientific and technical information (STI) program plays a key part in helping NASA maintain this 

important role. 

The NASA STI program operates under the auspices 

of the Agency Chief Information Officer. It collects, 

organizes, provides for archiving, and disseminates 

NASA’s STI. The NASA STI program provides access 

to the NASA Aeronautics and Space Database and its 

public interface, the NASA Technical Report Server, 

thus providing one of the largest collections of aeronautical and space science STI in the world. Results 

are published in both non-NASA channels and by 

NASA in the NASA STI Report Series, which includes 

the following report types: 

• TECHNICAL PUBLICATION. Reports of 

completed research or a major significant phase of 

research that present the results of NASA Programs 

and include extensive data or theoretical analysis. 

Includes compilations of significant scientific and 

technical data and information deemed to be of 

continuing reference value. NASA counterpart of 

peer-reviewed formal professional papers but has 

less stringent limitations on manuscript length and 

extent of graphic presentations. 

• TECHNICAL MEMORANDUM. Scientific 

and technical findings that are preliminary or of 

specialized interest, e.g., quick release reports, 

working papers, and bibliographies that contain 

minimal annotation. Does not contain extensive 

analysis. 

• CONTRACTOR REPORT. Scientific and technical 

findings by NASA-sponsored contractors and 

grantees. 

• CONFERENCE PUBLICATION. Collected 

papers from scientific and technical conferences, 

symposia, seminars, or other meetings sponsored or 

co-sponsored by NASA. 

• SPECIAL PUBLICATION. Scientific, technical, 

or historical information from NASA programs, 

projects, and missions, often concerned with 

subjects having substantial public interest. 

• TECHNICAL TRANSLATION. English-language 

translations of foreign scientific and technical 

material pertinent to NASA’s mission. 

Specialized services also include organizing and 

publishing research results, distributing specialized 

research announcements and feeds, providing help 

desk and personal search support, and enabling data 

exchange services. For more information about the 

NASA STI program, see the following: 

• Access the NASA STI program home page at 

http://www.sti.nasa.gov 

• E-mail your question via the Internet to 

help@sti.nasa.gov 

• Fax your question to the NASA STI Help Desk at 

443-757-5803 

• Phone the NASA STI Help Desk at 443-757-5802 

• Write to: 

NASA STI Help Desk 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320

NASA STI Program ... in Profile 
National Aeronautics and 

Space Administration 

Goddard Space Flight Center

Greenbelt, Maryland 20771

NASA/TM–2014-104606/Vol. 36

March 2015

Ronald Gelaro

Goddard Space Flight Center, Greenbelt, MD

William M. Putman

Goddard Space Flight Center, Greenbelt, MD

Steven Pawson

Goddard Space Flight Center, Greenbelt, MD

Clara Draper

Universities Space Research Association, Columbia, MD;

Andrea Molod

Earth System Science Interdisciplinary Center, 

College Park, MD

Peter M. Norris

Universities Space Research Association, Columbia, MD;

Lesley Ott

Goddard Space Flight Center, Greenbelt, MD

Nikki Privé

Morgan State University, Baltimore, MD

Oreste Reale

Universities Space Research Association, Columbia, MD

Deepthi Achuthavarier

Universities Space Research Association, Columbia, MD

Michael Bosilovich

Goddard Space Flight Center, Greenbelt, MD

Virginie Buchard

Universities Space Research Association, Columbia, MD;

Winston Chao

Goddard Space Flight Center, Greenbelt, MD

Lawrence Coy

Science Systems and Applications, Inc., Lanham, MD

Richard Cullather

Earth System Science Interdisciplinary Center, 

College Park, MD

Arlindo da Silva

Goddard Space Flight Center, Greenbelt, MD

Anton Darmenov

Goddard Space Flight Center, Greenbelt, MD

Ronald M. Errico

Morgan State University, Baltimore, MD

Marangelly Fuentes

Science Systems and Applications, Inc., Lanham, MD

Min-Jeong Kim

Morgan State University, Baltimore, MD

Randal Koster

Goddard Space Flight Center, Greenbelt, MD

Will McCarty

Goddard Space Flight Center, Greenbelt, MD

Jyothi Nattala

Science Systems and Applications, Inc., Lanham, MD

Gary Partyka

Science Systems and Applications, Inc., Lanham, MD

Siegfried Schubert

Goddard Space Flight Center, Greenbelt, MD

Guillaume Vernieres

Science Systems and Applications, Inc., Lanham, MD

Yuri Vikhliaev

Universities Space Research Association, Columbia, MD;

Krzysztof Wargan

Science Systems and Applications, Inc., Lanham, MD

Technical Report Series on Global Modeling and Data Assimilation, 

Volume 36

Randal D. Koster, Editor

Evaluation of the 7-km GEOS-5 Nature Run
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320

National Technical Information Service 

5285 Port Royal Road 

Springfield, VA 22161 Price Code: A17

Level of Review: This material has been technically reviewed by technical management

Trade names and trademarks are used in this report for identification only. Their usage does not 

constitute an official endorsement, either expressed or implied, by the National Aeronautics and 

Space Administration.

Notice for Copyrighted Information

This manuscript has been authored by employees of Universities Space Research Association, 

(Contract #NNG11HP16A); Earth System Science Interdisciplinary Center (Contract 

#NNX12AD03A); Morgan State University (Contract (#NNG11HP16A); and Science Systems 

and Applications, Inc., Contract (#NNG12HP06C) with the National Aeronautics and Space 

Administration. The United States Government has a non-exclusive, irrevocable, worldwide 

license to prepare derivative works, publish, or reproduce this manuscript, and allow others 

to do so, for United States Government purposes. Any publisher accepting this manuscript 

for publication acknowledges that the United States Government retains such a license in any 

published form of this manuscript. All other rights are retained by the copyright owner.
Abstract

This report documents an evaluation by the Global Modeling and Assimilation Office (GMAO) of

a two-year 7-km-resolution non-hydrostatic global mesoscale simulation produced with the Goddard Earth Observing System (GEOS-5) atmospheric general circulation model. The simulation

was produced as a Nature Run for conducting observing system simulation experiments (OSSEs).

Generation of the GEOS-5 Nature Run (G5NR) was motivated in part by the desire of the OSSE

community for an improved high-resolution sequel to an existing Nature Run produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), which has served the community

for several years. The intended use of the G5NR in this context is for generating simulated observations to test proposed observing system designs regarding new instruments and their deployments.

Because NASA’s interest in OSSEs extends beyond traditional weather forecasting applications, the

G5NR includes, in addition to standard meteorological components, a suite of aerosol types and

several trace gas concentrations, with emissions downscaled to 10 km using ancillary information

such as power plant location, population density and night-light information.

The evaluation exercise described here involved more than twenty-five GMAO scientists investigating various aspects of the G5NR performance, including time mean temperature and wind

fields, energy spectra, precipitation and the hydrological cycle, the representation of waves, tropical cyclones and midlatitude storms, land and ocean surface characteristics, the representation and

forcing effects of clouds and radiation, dynamics of the stratosphere and mesosphere, and the representation of aerosols and trace gases. Comparisons are made with observational data sets when

possible, as well as with reanalyses and other long model simulations. The evaluation is broad in

scope, as it is meant to assess the overall realism of basic aspects of the G5NR deemed relevant to

the conduct of OSSEs. However, because of the relatively short record and other practical considerations, these comparisons cannot provide a definitive, statistically sound assessment of all model

deficiencies, or guarantee the G5NR’s suitability for all OSSE applications. Differences between the

observed and simulated behavior also must be judged in the context of basic internal atmospheric

variability which can introduce variations that are not necessarily controlled by the prescribed sea

surface temperatures used in generating the G5NR.

The results show that the G5NR performs well as measured by the majority of metrics applied

in this evaluation. Particular benefits derived from the 7-km resolution of G5NR include realistic

representations of extreme weather events in both the tropics and extratropics including tropical

cyclones, Nor’easters and mesoscale convective complexes; improved representation of the diurnal cycle of precipitation over land; well-resolved surface-atmosphere interactions such as katabatic

wind flows over Antarctica and Greenland; and resolution of orographically generated gravity waves

that propagate into the upper atmosphere and influence the large scale circulation. Obvious deficiencies in the G5NR include a “splitting” of the inter-tropical convergence zone, which leads to

a weaker-than-observed Hadley circulation and related deficiencies in the depiction of stationary

wave patterns. Also, while the G5NR captures global cloud features and radiative effects well in

general, close comparison with observations reveals higher-than-observed cloud brightness, likely

due to an overabundance of cloud condensate; less distinct cloud minima in subtropical subsidence

zones, consistent with a weak Hadley circualtion; and too few near-coastal marine stratocumulus

clouds.
Contents

List of Tables vi

List of Figures vii

1 Overview 1

1.1 Observing System Simulation Experiments (OSSEs) . . . . . . . . . . . . . . . . . . 1

1.2 Model Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Resolved Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3.1 Extratropical Cyclones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3.2 Tropical Cyclones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.3.3 Mesoscale Convective Complexes . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.3.4 Aerosols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.3.5 Carbon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2 Wind and Temperature 30

2.1 Zonal and Time Means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.2 Energy Spectra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.3 Extratropical Synoptic Waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.3.1 Overall Wave Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.3.2 Wave Characterics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.4 Stratospheric Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.4.1 Quasi-Biennial Oscillation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.4.2 Annual Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.4.3 Planetary Waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.4.4 Resolved Gravity Waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3 Humidity and Precipitation 67

3.1 Verification Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

ii
3.2 Mean Atmospheric Humidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

3.3 Cloud Condensate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

3.4 Second Moments of Total Water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

3.5 Precipitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

3.6 NEWS Global and Regional Water Budgets . . . . . . . . . . . . . . . . . . . . . . . 71

3.7 NEWS Global Energy Budget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

3.8 Selected Results on Monsoonal and Diurnal Precipitation Variability . . . . . . . . . 72

3.8.1 Tropical waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

3.8.2 South and East Asia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

3.8.3 Africa and South America . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

3.8.4 North America . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

3.8.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

3.9 Specific Humidity (QV) and Total Surface Precipitation Flux (PrFLX): . . . . . . . 75

3.9.1 PDF of OMEGA500 and Composite PrFLX Anomaly . . . . . . . . . . . . . 76

3.9.2 Composite QV Anomaly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

3.9.3 Covarying Anomaly Patterns of QV and PrFLX . . . . . . . . . . . . . . . . 76

3.10 Precipitation Flux and Condensate . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

3.10.1 Verification Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

3.10.2 Rainy Area Fraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

3.10.3 Probability Distribution of Surface Precipitation . . . . . . . . . . . . . . . . 78

3.10.4 Convective Precipitation vs. Stratiform Precipitation . . . . . . . . . . . . . . 78

3.10.5 Vertical Distribution of Precipitation . . . . . . . . . . . . . . . . . . . . . . . 79

3.10.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

4 Tropical Cyclones and Related Phenomena 113

4.1 Tropical Cyclone Activity and Structure in Previous Nature Runs . . . . . . . . . . 113

4.2 Tropical Cyclone Activity and Structure in the G5NR . . . . . . . . . . . . . . . . . 114

4.2.1 Tropical Cyclones in the Atlantic . . . . . . . . . . . . . . . . . . . . . . . . . 114

4.2.2 Tropical Cyclones in the Eastern Pacific . . . . . . . . . . . . . . . . . . . . . 117

4.2.3 Tropical Cyclones in the Northern Indian Ocean . . . . . . . . . . . . . . . . 118

4.2.4 Tropical Cyclones in the Southern Indian Ocean . . . . . . . . . . . . . . . . 119

4.3 Other Significant Weather Phenomena . . . . . . . . . . . . . . . . . . . . . . . . . . 120

4.3.1 Example of Tropical-Extratropical Transition . . . . . . . . . . . . . . . . . . 120

4.3.2 Ongoing Validation of Other Mesoscale Features . . . . . . . . . . . . . . . . 120

4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

iii
5 Surface Characteristics 153

5.1 Land Surface - Hydrological Processes . . . . . . . . . . . . . . . . . . . . . . . . . . 153

5.1.1 Relationship of G5NR Land Surface Hydrological Data to LDAS Products . . 153

5.1.2 Hydrological Land Surface Forcing . . . . . . . . . . . . . . . . . . . . . . . . 154

5.1.3 Inconsistency Between Land-Only and Total-Grid Diagnostics . . . . . . . . . 155

5.1.4 Singularities in Parameter Estimation . . . . . . . . . . . . . . . . . . . . . . 156

5.1.5 Snowfall Forcing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156

5.1.6 Reduction of Solar Radiation During Rainfall Events . . . . . . . . . . . . . . 157

5.1.7 Evaporation Instabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

5.1.8 Occasional High Levels of Rainfall Forcing . . . . . . . . . . . . . . . . . . . . 158

5.2 Land Surface - Energy Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

5.2.1 Surface Energy Budget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

5.2.2 Land Surface Temperatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

5.3 Ocean Surface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

5.3.1 Ocean Energy Budget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

5.3.2 Ocean Winds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

5.4 Polar Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

5.4.1 Polar Atmospheric Moisture and Energy Budgets . . . . . . . . . . . . . . . . 163

5.4.2 Near-Surface Wind Field Over Polar Ice Sheets . . . . . . . . . . . . . . . . . 165

6 Clouds and Radiation 192

6.1 June 2005 Validation Against CERES SSF: Not Diurnally Averaged . . . . . . . . . 193

6.1.1 TOA Outgoing Longwave Radiation . . . . . . . . . . . . . . . . . . . . . . . 193

6.1.2 TOA Outgoing Shortwave Radiation . . . . . . . . . . . . . . . . . . . . . . . 193

6.1.3 Cloud Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

6.1.4 Albedo vs. OLR Joint Histograms . . . . . . . . . . . . . . . . . . . . . . . . 197

6.2 Diurnally-Averaged Validation Against CERES EBAF . . . . . . . . . . . . . . . . . 198

6.2.1 TOA Outgoing Longwave Radiation . . . . . . . . . . . . . . . . . . . . . . . 198

6.2.2 TOA Outgoing Shortwave Radiation . . . . . . . . . . . . . . . . . . . . . . . 198

6.3 CERES Validation Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199

6.4 Cloud Top Pressure and Cloud Fraction Validation Against CALIPSO . . . . . . . . 200

6.4.1 Data and Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201

6.4.2 Mean Cloud Height . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201

6.4.3 Spatial Distribution of Cloud Fraction . . . . . . . . . . . . . . . . . . . . . . 202

6.5 CALIPSO Validation Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

iv
7 Aerosols and Trace Gases 243

7.1 Aerosols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243

7.1.1 Residence Times of Aerosols . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244

7.1.2 Aerosol Optical Thickness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244

7.1.3 Spatial Patterns of AOT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246

7.1.4 Aerosol Speciation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247

7.1.5 Vertical Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247

7.1.6 Comparison with CALIOP Retrievals . . . . . . . . . . . . . . . . . . . . . . 248

7.1.7 Summary and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . 249

7.2 Trace Gases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250

7.2.1 CO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250

7.2.2 CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252

7.2.3 O3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253

References 276

v
List of Tables

3.1 Comparison of global energy budget terms from the NEWS project and G5NR. . . . 81

3.2 Comparisons of rainy area fraction and mean surface rainrate between TRMM Precipitation Radar data and the G5NR. . . . . . . . . . . . . . . . . . . . . . . . . . . 82

3.3 Comparison of convective and stratiform precipitation fractions between TRMM

Precipitation Radar data and the G5NR. . . . . . . . . . . . . . . . . . . . . . . . . 82

5.1 Comparison of G5NR and the NEWS project annual mean surface energy budget

terms over continental regions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167

5.2 Comparison of G5NR and NEWS project annual mean surface energy budget terms

over oceans. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167

6.1 OLR validation against CERES EBAF for 2005 . . . . . . . . . . . . . . . . . . . . . 204

6.2 OSR validation against CERES EBAF for 2005 . . . . . . . . . . . . . . . . . . . . . 204

6.3 OSR validation against CERES EBAF for 2006 . . . . . . . . . . . . . . . . . . . . . 204

6.4 G5NR global TOA energy balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205

6.5 CERES EBAF global TOA energy balance . . . . . . . . . . . . . . . . . . . . . . . 205

6.6 SCF validation against CERES EBAF for 2005 . . . . . . . . . . . . . . . . . . . . . 205

7.1 Residence times of aerosol species. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245

7.2 Global aerosol scaling factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246

vi
List of Figures

1.1 Flow chart describing OSEs and OSSEs. . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.2 City lights and total precipitable water at the 50-km MERRA resolution. . . . . . . 11

1.3 City lights and total precipitable water at the 7-km G5NR resolution. . . . . . . . . 12

1.4 Visible clouds from MERRA and GEOS-5 at 3.5 km. . . . . . . . . . . . . . . . . . . 13

1.5 Extratropical cyclone locations as detected in the 7-km G5NR for DJF 2005–2006. . 14

1.6 As in Figure 1.5 but for DJF 2006–2007. . . . . . . . . . . . . . . . . . . . . . . . . . 15

1.7 Change in snow mass over North America from January 13 to January 21, 2007 in

the 7-km G5NR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

1.8 50m-wind speeds and SLP, and snow/ice/rain rates and 1000-500 hPa thickness in

the 7-km G5NR at 12Z January 18, 2007. . . . . . . . . . . . . . . . . . . . . . . . . 17

1.9 As in Figure 1.8 at 00Z January 19, 2007. . . . . . . . . . . . . . . . . . . . . . . . . 18

1.10 As in Figure 1.8 at 012Z January 19, 2007. . . . . . . . . . . . . . . . . . . . . . . . 19

1.11 As in Figure 1.8 at 00Z January 20, 2007. . . . . . . . . . . . . . . . . . . . . . . . . 20

1.12 As in Figure 1.8 at 18Z January 20, 2007. . . . . . . . . . . . . . . . . . . . . . . . . 21

1.13 Global distributions of tropical cyclone tracks detected in the 7-km G5NR for the

annual period from June 2005 to May 2006. . . . . . . . . . . . . . . . . . . . . . . . 22

1.14 As in Figure 1.13 for June 2006 to May 2007. . . . . . . . . . . . . . . . . . . . . . . 23

1.15 Clouds and surface winds speeds at 2130Z on September 10, 2006 during the 7-km

G5NR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

1.16 MCCs in AHRR imagery and in the 7-km G5NR. . . . . . . . . . . . . . . . . . . . . 25

1.17 Distribution of MCCs from May–June of 2005 and 2006 in geostationary IR observations and in the 7-km G5NR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

1.18 Global distribution of aerosols in the 7-km G5NR at 12Z on September 23, 2006. . . 27

1.19 Concentrations of SO2 and SO4 in the 7-km G5NR at 02Z on October 27, 2005. . . . 28

1.20 Concentrations of CO2 and CO in the 7-km G5NR at 07Z on January 20, 2007 . . . 29

2.1 Time mean zonal mean of the G5NR zonal wind and streamfunction. . . . . . . . . . 39

vii
2.2 Monthly time mean zonal mean maximum strength of the tropospheric zonal wind

and location of the maximum in G5NR, ERA-I and CFSR. . . . . . . . . . . . . . . 40

2.3 As for Figure 2.2, but for the maximum streamfunction of both Hadley cells. . . . . 41

2.4 As for Figure 2.2, but for the location of the maximum streamfunction of the two

Hadley cells. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

2.5 Time mean zonal mean of the G5NR for temperature. . . . . . . . . . . . . . . . . . 43

2.6 As for Figure 2.5, but for the zonal mean meridional wind. . . . . . . . . . . . . . . . 44

2.7 As for Figure 2.2, but for the tropical upper tropospheric zonal mean meridional

wind maximum and tropical tropopause temperature minimum. . . . . . . . . . . . . 45

2.8 Multi-year seasonal mean horizontal wind speed at 300 hPa. . . . . . . . . . . . . . . 46

2.9 Multi-year seasonal mean zonal wind averaged between 10N and 10S. . . . . . . . . . 47

2.10 Power spectra of kinetic energy on the 250 hPa level. . . . . . . . . . . . . . . . . . . 48

2.11 Multi-year seasonal standard deviation of 200 hPa meridional wind. . . . . . . . . . 49

2.12 Individual seasonal standard deviation of 200 hPa meridional wind along latitudinal

bands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

2.13 Phase-spectra decomposition of 200 hPa meridional wind, contours of power. . . . . 51

2.14 Phase-spectra decomposition of wave packets of 200 hPa meridional wind, contours

of power. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

2.15 G5NR monthly averaged zonal wind component averaged zonally and between 10◦S

and 10◦N. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

2.16 Monthly average 10 hPa temperatures at 90◦N for the G5NR and MERRA. . . . . . 54

2.17 Monthly average 10 hPa zonally averaged zonal wind component at 60◦N for G5NR

and MERRA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

2.18 Daily zonally averaged zonal wind at 10 hPa and 60◦N for G5NR and MERRA. . . . 56

2.19 As in Figure 2.16 but for 90◦S. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

2.20 As in Figure 2.17 but for 60◦S. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

2.21 Meridional heat flux due to stationary waves at 200 hPa and 60◦N for G5NR and

MERRA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

2.22 Meridional heat flux due to stationary waves at 200 hPa and 60◦S for G5NR and

MERRA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

2.23 G5NR absolute value of EPV for 15 July 2005 and 15 January 2006. . . . . . . . . . 61

2.24 G5NR absolute value of EPV for 16 January 2007. . . . . . . . . . . . . . . . . . . . 62

2.25 Longitude-height cross sections of potential temperature, vertical velocity and zonal

wind component over Greenland. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

viii
2.26 G5NR 10 hPa EPV fields during 2005 at 12 hr intervals. . . . . . . . . . . . . . . . . 64

2.27 G5NR horizontal divergence field and vorticity field at 10 hPa. . . . . . . . . . . . . 65

2.28 Mesospheric GW vertical momentum flux on 1 Jan 2006 00 UTC. . . . . . . . . . . . 66

3.1 Seasonal mean specific humidity in G5NR, MERRA and ERA-I. . . . . . . . . . . . 83

3.2 Seasonal mean relative humidity in G5NR, MERRA and ERA-I. . . . . . . . . . . . 84

3.3 Seasonal mean total precipitable water in G5NR, MERRA and ERA-I. . . . . . . . . 85

3.4 Annual mean cloud ice content in G5NR and CloudSat. . . . . . . . . . . . . . . . . 86

3.5 Annual mean cloud liquid water content in G5NR and CloudSat. . . . . . . . . . . . 87

3.6 Subgrid scale variance of total water and critical relative humidity in G5NR. . . . . 88

3.7 Seasonal mean total precipitation in G5NR, MERRA and ERA-I. . . . . . . . . . . . 89

3.8 Seasonal mean total precipitation over land only in G5NR, MERRA and ERA-I. . . 90

3.9 Graphical depiction of the Global Water Cycle for G5NR and NEWS. . . . . . . . . 91

3.10 Monthly hydrological quantities in G5NR and NEWS. . . . . . . . . . . . . . . . . . 92

3.11 Graphical depiction of the NEWS constrained merged satellite energy balance and

G5NR energy components. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

3.12 Hovmøller diagrams of daily mean precipitation for G5NR and CMORPH observations. 94

3.13 Northward progression of the Indian monsoon precipitation in G5NR and CMORPH

observations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

3.14 Monthly mean precipitation maps for G5NR and CMORPH observations. . . . . . . 96

3.15 Northward progression of the East Asian monsoon precipitation in G5NR and

CMORPH observations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

3.16 Wind vectors and speed at 850mb over southern Asia for G5NR and CMORPH

observations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

3.17 Precipitation over Africa for G5NR and CMORPH observations. . . . . . . . . . . . 99

3.18 Wind vectors and speed over Africa for G5NR and MERRA. . . . . . . . . . . . . . 100

3.19 Precipitation over South America for G5NR and CMORPH observations. . . . . . . 101

3.20 Wind vectors and speed over South America for G5NR and MERRA. . . . . . . . . 102

3.21 Simulated and observed monthly mean precipitation for June-August 2005. . . . . . 103

3.22 Simulated and observed 850mb winds for June-August 2005. . . . . . . . . . . . . . . 104

3.23 Time mean simulated diurnal cycle of precipitation over North America. . . . . . . . 105

3.24 Time mean observed (CMORPH) diurnal cycle of precipitation over North America. 106

3.25 Probability distribution function of diurnal mid-tropospheric vertical velocity composite precipitation flux anomaly for CFSR and G5NR. . . . . . . . . . . . . . . . . 107

3.26 Composite specific humidity anomaly for CFSR and G5NR. . . . . . . . . . . . . . . 108

ix
3.27 Leading CCA patterns of specific humidity and precipitation flux anomaly for CSFR

and G5NR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

3.28 Colocation of TRMM Precipitation Radar data and G5NR precipitation. . . . . . . 110

3.29 Comparisons of probability distribution of surface rain rates between the G5NR and

TRMM PR data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

3.30 Comparison of vertical distribution of precipitation between the G5NR and TRMM

Precipitation Radar data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

4.1 Zonal and meridional vertical cross-sections of winds and temperature for the

strongest Atlantic tropical cyclone in the ECMWF NR. . . . . . . . . . . . . . . . . 122

4.2 Vertical meridional cross section of zonal wind at 0◦longitude in G5NR and MERRA.123

4.3 Hovmøller plots of meridional winds at 700 hPa in the G5NR. . . . . . . . . . . . . . 124

4.4 Hovmøller plots of meridional winds at 700 hPa in MERRA. . . . . . . . . . . . . . 125

4.5 Tropical cyclone activity over the Atlantic in 2005 in the G5NR. . . . . . . . . . . . 126

4.6 Same as in Figure 4.5, but from the NHC, 2005 season (observed best tracks). . . . . 127

4.7 Same as Figure 4.5, but for 2006. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

4.8 Same as Figure 4.5, but from the NHC, 2006 season (observed best tracks). . . . . . 129

4.9 Zonal vertical cross-sections of winds and temperature for an Atlantic hurricane in

the G5NR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

4.10 Same as in Figure 4.9, but for an Atlantic hurricane at an early development stage. . 131

4.11 Same as in Figure 4.9, but for an Atlantic hurricane at a mature development stage. 132

4.12 Tropical cyclone activity over the Eastern Pacific in 2005 in the G5NR. . . . . . . . 133

4.13 Same as Figure 4.12, but from the NHC, 2005 season (observed best tracks). . . . . 134

4.14 Same as Figure 4.12, but for 2006. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

4.15 Same as Figure 4.12, but from the NHC, 2006 season (observed best tracks). . . . . 136

4.16 Hovmøller diagram of equatorial low-level zonal wind in the G5NR and MERRA. . . 137

4.17 900 hPa wind in July 2006 across the Indian Ocean in the G5NR. . . . . . . . . . . . 138

4.18 Same as Figure 4.17, but for MERRA. . . . . . . . . . . . . . . . . . . . . . . . . . . 139

4.19 Vertical meridional cross section of the Somali Jet in July 2006 in the G5NR. . . . . 140

4.20 Same as Figure 4.19, but for MERRA. . . . . . . . . . . . . . . . . . . . . . . . . . . 141

4.21 Tropical cyclone activity over the Northern Indian Ocean in 2005 in the G5NR. . . . 142

4.22 Same as Figure 4.21, but from the JTWC, 2005 season (observed best tracks). . . . 143

4.23 Same as Figure 4.21, but for 2006. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

4.24 Same as Figure 4.21, but from the JTWC, 2006 season (observed best tracks). . . . 145

4.25 Tropical cyclone activity over the Southern Indian Ocean in 2005-2006 in the G5NR. 146

x
4.26 Same as Figure 4.25, but from the JTWC, 2005-2006 season (observed best tracks). 147

4.27 Same as Figure 4.25, but for 2006. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

4.28 Same as Figure 4.25, but from the JTWC, 2006-2007 season (observed best tracks). 149

4.29 Zonal vertical cross-sections of winds and temperature for a Southern Indian Ocean

tropical cyclone in the G5NR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

4.30 Near-surface wind speed at various stages of a tropical cyclone’s life-cycle in the G5NR.151

4.31 Extratropical transition of a tropical cyclone in the G5NR. . . . . . . . . . . . . . . 152

5.1 Dryness index distribution as determined from observations and the G5NR. . . . . . 168

5.2 Illustration of a situation that leads to an inconsistency in the 0.5 degree output

diagnostics of the G5NR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

5.3 Locations where the output diagnostics for land surface runoff and precipitation are

found to be inconsistent in G5NR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

5.4 Amount of snow reaching the surface during a 16-day period in May. . . . . . . . . . 170

5.5 Time series of various quantities at a grid cell in northern Mexico. . . . . . . . . . . 171

5.6 Downward shortwave radiation and precipitation in MERRA and observations near

Gainesville, Florida. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172

5.7 As in Figure 5.6, but for a representative high resolution grid cell in the G5NR. . . . 172

5.8 Time series of precipitation, downward solar radiation and latent heat flux in a G5NR

high-resolution grid cell. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173

5.9 Locations where evaporation instabilities are found in the G5NR during a representative 14-day period during summer. . . . . . . . . . . . . . . . . . . . . . . . . . . 174

5.10 Locations where evaporation instabilities are found in two offline land surface model

simulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

5.11 Locations for which the maximum of half-hourly precipitation totals during a representative 2-week period in summer exceeds 4 cm. . . . . . . . . . . . . . . . . . . . 176

5.12 Monthly mean T2m and locations of ISH T2m stations. . . . . . . . . . . . . . . . . . 176

5.13 Monthly mean T2m from the G5NR, ISH station observations and ERA-I, by continent.177

5.14 G5NR minus ERA-I mean temperature differences. . . . . . . . . . . . . . . . . . . . 178

5.15 Annual mean net surface shortwave and longwave radiation in G5NR and SRB data. 179

5.16 Annual mean latent and sensible heat flux in G5NR and GSSTF data. . . . . . . . . 180

5.17 Implied ocean heat transport in PetaWatts. . . . . . . . . . . . . . . . . . . . . . . . 181

5.18 CCMP 10 meter wind rose from May 2005 to May 2007. . . . . . . . . . . . . . . . . 182

5.19 Same as 5.18 but for the G5NR 10 m winds. . . . . . . . . . . . . . . . . . . . . . . . 183

xi
5.20 Zonal wind speed, meridional winds speed and wind speed from the G5NR, SCOW

and CCMP. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184

5.21 Average wind stress magnitude from the G5NR and SCOW. . . . . . . . . . . . . . . 185

5.22 Net surface energy flux components for the North Polar Cap from the G5NR and

atmospheric reanalyses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186

5.23 Comparison of G5NR 2m temperature and corresponding station temperature for

the South Pole and Barrow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

5.24 Average annual cycle for net surface moisture flux from G5NR and corresponding

values from reanalyses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188

5.25 G5NR surface mass balance for Greenland and Antarctica. . . . . . . . . . . . . . . . 189

5.26 G5NR 10 m wind vectors and contours of vector magnitude over the Antarctic. . . . 189

5.27 G5NR 10 m wind vectors and contours of vector magnitude over the Ross Sea. . . . 190

5.28 G5NR near-surface wind field over Greenland. . . . . . . . . . . . . . . . . . . . . . . 190

5.29 Daily-averaged G5NR 10 m wind field over Greenland and surrounding area. . . . . 191

6.1 Zonal average OLR validation against CERES SSF . . . . . . . . . . . . . . . . . . . 206

6.2 Global map OLR validation against CERES SSF . . . . . . . . . . . . . . . . . . . . 207

6.3 Zonal average OSR validation against CERES SSF . . . . . . . . . . . . . . . . . . . 208

6.4 Global map OSR validation against CERES SSF . . . . . . . . . . . . . . . . . . . . 209

6.5 Zonal average cloud fraction validation against CERES SSF . . . . . . . . . . . . . . 210

6.6 Global map cloud fraction validation against CERES SSF . . . . . . . . . . . . . . . 211

6.7 Frequency distribution of cloud fraction . . . . . . . . . . . . . . . . . . . . . . . . . 212

6.8 Cumulative frequency distribution of cloud fraction . . . . . . . . . . . . . . . . . . . 213

6.9 Global map cloud top pressure comparison against CERES SSF . . . . . . . . . . . . 214

6.10 Zonal average in-cloud cloud optical thickness validation against CERES SSF . . . . 215

6.11 Zonal average corrected in-cloud cloud optical thickness validation against CERES

SSF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216

6.12 Global map corrected in-cloud cloud optical thickness comparison against CERES

SSF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217

6.13 Zonal average condensed water path validation against CERES SSF . . . . . . . . . 218

6.14 Global map condensed water path validation against CERES SSF . . . . . . . . . . 219

6.15 Zonal average cloud fraction validation against CERES SSF (native G5NR resolution)220

6.16 Global map cloud fraction validation against CERES SSF (native G5NR resolution) 221

6.17 Zonal average in-cloud condensed water path validation against CERES SSF . . . . 222

6.18 Global map in-cloud condensed water path validation against CERES SSF . . . . . . 223

xii
6.19 Joint histogram of TOA Cloud Albedo vs. OLR (Tropical oceans) . . . . . . . . . . 224

6.20 Joint histogram of TOA Cloud Albedo vs. OLR (Mid-latitude oceans) . . . . . . . . 225

6.21 Joint histogram of TOA Cloud Albedo vs. OLR (Higher-latitude oceans) . . . . . . 226

6.22 Joint histogram of TOA Cloud Albedo vs. OLR (Global oceans) . . . . . . . . . . . 227

6.23 Zonal average OLR validation against CERES EBAF (June 2005) . . . . . . . . . . 228

6.24 Zonal average OLR validation against CERES EBAF (September 2005) . . . . . . . 229

6.25 Global map OLR validation against CERES EBAF (September 2005) . . . . . . . . 230

6.26 Zonal average OSR validation against CERES EBAF (September 2005) . . . . . . . 231

6.27 Global map OSR validation against CERES EBAF (September 2005) . . . . . . . . 232

6.28 Zonal average TOA albedo validation against CERES EBAF (June 2005) . . . . . . 233

6.29 Global map TOA albedo validation against CERES EBAF (June 2005) . . . . . . . 234

6.30 Zonal average SCF validation against CERES EBAF (June 2005) . . . . . . . . . . . 235

6.31 Zonal average normalized SCF validation against CERES EBAF (June 2005) . . . . 236

6.32 Global map average normalized SCF validation against CERES EBAF (June 2005) . 237

6.33 Cloud Top Pressure Validation against CALIPSO . . . . . . . . . . . . . . . . . . . . 238

6.34 Cloud Fraction Validation against CALIPSO (all clouds) . . . . . . . . . . . . . . . . 239

6.35 Cloud Fraction Validation against CALIPSO (high clouds) . . . . . . . . . . . . . . . 240

6.36 Cloud Fraction Validation against CALIPSO (mid-level clouds) . . . . . . . . . . . . 241

6.37 Cloud Fraction Validation against CALIPSO (low‘clouds) . . . . . . . . . . . . . . . 242

7.1 Time series of global monthly mean aerosol optical thickness. . . . . . . . . . . . . . 256

7.2 Annual-mean AOT from the MERRAero aerosol reanalysis and the G5NR simulation.257

7.3 Seasonal-mean AOT from the MERRAero aerosol reanalysis and the G5NR simulation.258

7.4 Seasonal variations of aerosol speciation. . . . . . . . . . . . . . . . . . . . . . . . . . 259

7.5 Zonal mean mass concentrations of OC, BC and sulfate aerosols. . . . . . . . . . . . 260

7.6 Zonal mean mass concentrations of sea salt and dust aerosols. . . . . . . . . . . . . . 261

7.7 Vertical profile of attenuated backscatter coefficient over the African dust region. . . 262

7.8 Vertical profile of attenuated backscatter coefficient over the southern Africa biomass

burning region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262

7.9 Global distribution of aerosol optical thickness on 8 Aug 2005 00UTC. . . . . . . . . 263

7.10 Map of surface stations used in CO and CO2 evaluation. . . . . . . . . . . . . . . . . 264

7.11 Time series of simulated and observed surface CO at select sites . . . . . . . . . . . . 265

7.12 Monthly mean simulated and observed CO at all surface stations. . . . . . . . . . . . 266

7.13 Simulated and observed mid-tropospheric CO for April and October, 2006. . . . . . 267

7.14 Simulated and observed zonal mean CO from MOPITT TIR and NIR measurements. 268

xiii
7.15 Time series of simulated and observed surface CO2 at select sites. . . . . . . . . . . . 269

7.16 Monthly mean simulated and observed CO2 at all surface stations. . . . . . . . . . . 270

7.17 Simulated and observed XCO2 for February and August. . . . . . . . . . . . . . . . . 271

7.18 Simulated and observed zonal mean XCO2. . . . . . . . . . . . . . . . . . . . . . . . 272

7.19 Ozone and potential vorticity at 100 hPa on January 1, 2006. . . . . . . . . . . . . . 273

7.20 Monthly mean difference between G5NR ozone and OMI climatology. . . . . . . . . 274

7.21 G5NR and MLS ozone for June. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275

xiv
Chapter 1

Overview

William M. Putman

High-resolution global atmospheric models provide a unique tool for resolving the regional features of weather within the global climate system. While global models like the Goddard Earth

Observing System, version 5 (GEOS-5) model are regularly applied in seasonal-to-decadal climate

simulations at coarse resolutions (50 to 200 km), GEOS-5 is also uniquely adaptable for application

as a global mesoscale model in pursuit of global cloud resolving applications at horizontal resolutions even finer than the highest resolution global weather models used around the world. These

global mesoscale simulations with GEOS-5 represent multiple scales of regional weather from clusters of deep convection and mesoscale convective complexes, to hurricanes and large midlatitude

storm systems, all within a cohesive simulation of the global circulation. This chapter provides

an overview of a two-year global mesoscale simulation with GEOS-5 intended as a Nature Run

for conducting observing system simulation experiments (OSSEs). Subsequent chapters address in

detail the climatology and variance of this simulation.

1.1 Observing System Simulation Experiments (OSSEs)

The global mesoscale capability of GEOS-5 enhances our ability to maximize the impact of satellite

observations in climate, weather and atmospheric composition prediction. Exploring the impact of

new observations is a key element of this mission. OSSEs (e.g., Errico et al. 2013) are powerful tools

for estimating the usefulness of various configurations of envisioned observing systems. Their utility

stems from their being conducted in an entirely simulated context, with realistic simulated errors

1
included in the observations and a known truth with which to evaluate any algorithm or observation

impact. For OSSEs, a high-resolution model simulation that is validated against nature, usually

referred to as a Nature Run, produces the most useful simulated observations

OSSEs are designed to mimic the process of data assimilation in which real imperfect observations

are used to produce estimates of global atmospheric states, or analyses, at sequences of time. Like

observing system experiments (OSEs) in which real observations may be added or withheld from

a data assimilation system to assess their impact on the analysis or forecast (left side of Figure

1.1), in an OSSE, simulated observations can be added or withheld from the assimilation system

to assess their impact on the estimated Nature Run states (right side of Figure 1.1). Moreover,

since the OSSE deals entirely with simulations, it is not restricted to using only observations that

already exist, but also proposed future observations.

A non-hydrostatic 7-km global mesoscale simulation with GEOS-5 has been used to produce over

two-years of high-resolution weather and chemistry data at 30-minute intervals for the period May

2005 to June 2007 (Putman et al., 2014). This simulation is intended to serve as a Nature Run,

referred to as the 7-km GEOS-5 Nature Run or 7-km G5NR, for conducting OSSEs at NASA,

NOAA and partner institutions such as the Joint Center for Satellite Data Assimilation (JCSDA).

In addition to standard meteorological fields, the 7-km G5NR includes a suite of aerosol types (dust,

seasalt, sulfate, black and organic carbon) and trace gas concentrations (O3, CO and CO2), with

emissions downscaled to 10-km using ancillary information such as power plant location, population

density and night-light information. The 7-km G5NR develops its own weather around the Earth

in an environment constrained only by surface boundary conditions for sea-surface temperatures

and sea-ice, daily volcanic and biomass burning emissions, as well as high-resolution inventories

of anthropogenic sources. The output from this computation totaled nearly 4 Petabytes and was

produced in just over 75 days of dedicated computation on the Discover cluster at the NASA Center

for Climate Simulation (NCCS).

1.2 Model Aspects

At 7-km global resolution, GEOS-5 executes on more that 12 million horizontal grid cells. Compared

with the Modern-Era Retrospective Analysis for Research and Applications (MERRA, Rienecker et

al., 2011) performed at 50-km global resolution, the 7-km G5NR includes 64 additional horizontal

grid cells within each 50-km MERRA cell. This increase in resolution effectively moves the resolved

scales from the synoptic scale to the local scale. A snapshot of city lights across the eastern United

States from the NASA black marble image highlights this increase in resolved scales. While the

50-km MERRA resolution coarsely resolves only the largest cities (Figure 1.2), the 7-km G5NR

resolves the local towns connecting these larger cities (Figure 1.3).

At 50 km, cloud processes and vertical motions are very coarsely resolved (Figure 1.4, left) and

GEOS-5 is highly dependent on the statistical estimation (parameterization) of these processes over

the large grid cells. In the 7-km G5NR these parameterizations are relaxed, but not completely

2
eliminated. Even at 7 to 3.5 km, local updrafts and deep convection cannot be explicitly resolved.

However, the mesoscale organization of these systems is resolved including cloud features such as

the broken marine stratocumulus in the Pacific Ocean, shallow convection over the Amazon, and

organized convection throughout the inter-tropical convergence zone and within large-scale midlatitude cyclones (Figure 1.4, right). Specific details of clouds and their radiative properties in the

7-km G5NR are presented in Chapter 6.

Atmospheric models with horizontal resolutions in the range of 15 to 1 km are very much in the

“gray zone” of parameterizations. The gray zone defines a region where physical processes such

as deep convection are being resolved to some degree by the explicit algorithms of the model, but

not completely, thus still requiring some level of parameterization. This is uniquely challenging in

the case of global models that not only must accurately represent the processes occurring in the

midlatitudes, but at the same time they must resolve the very different convective regimes of the

equatorial tropics while propagating these small scale processes correctly into the global circulation.

Computational capability has increased dramatically in recent years, but remains at a level that

will require global models to execute in this gray zone for several years to come. GEOS-5 has

been designed to automatically adapt to this regime through scale-aware dynamics and physical

parameterizations, as described below.

The hydrostatic formulation of the finite-volume (FV) dynamics (Lin, 2004) has been extended

to the fully compressible non-hydrostatic flow (essentially the unapproximated Euler equations

on the sphere). To maintain the advantages of the vertically Lagrangian discretization of the

hydrostatic system, an explicit sound wave solver based on the conservation of Riemann invariants

was developed. Due to the vertical Courant-Friedrichs-Lewy (CFL) condition imposed by the

vertically propagating sound waves, the Riemann solver for the non-hydrostatic FV dynamics is

consistently Lagrangian, such that the time step is not severely limited by vertically propagating

sound waves. The non-hydrostatic dynamics becomes suitable for use at resolutions of 25 km and

finer.

Convection is parameterized in GEOS-5 using the Relaxed Arakawa-Schubert (RAS) scheme of

Moorthi and Suarez (1992). RAS is a mass flux scheme with an updraft-only detraining plume cloud

model and a quasi-equilibrium closure. As resolution is increased, the convection parameterization

is restrained using a stochastic, resolution-dependent limit on deep convection (Tokioka et al., 1988).

As resolution increases, approaching cloud resolving scales in the gray zone, the large-scale moist

processes begin to explicitly resolve some of the deep convection. This stochastic limiting scheme

essentially restrains the deep convection parameterization from stabilizing instabilities within the

column and restricts RAS to act as a shallow convection scheme.

The prognostic cloud cover and cloud water and ice scheme is from Bacmeister et al. (2006),

with the total water probability distribution function (pdf) of Molod (2012). The critical relative

humidity within the pdf increases (approaching 1.0) as resolution increases to account for the

shrinking region being parameterized. This accounts for the decreased area being parameterized

and requires a larger fraction of the grid cell to become saturated before vapor is converted to

3
liquid.

The gravity wave parameterization computes the momentum and heat deposition into the gridscale flow due to orographic (McFarlane, 1987) and non-orographic (after Garcia and Boville, 1994)

gravity wave breaking. Mountain waves are forced by the sub-grid orographic variability, where the

variance of the orography is scaled down with increasing resolution to account for the better resolved

topographically induced gravity waves due to increased resolution in the dynamics. This variance is

adjusted within the model as resolution increases to account for the increase in resolved topography

and the explicitly resolved gravity waves being generated by the high-resolution dynamics.

1.3 Resolved Features

The 7-km G5NR is capable of resolving robust weather systems and high-impact events critical for

simulating observations. Because the 7-km G5NR is a free running simulation, meaning there are

no constraints to the observed meteorology, it will not produce the specific weather events of the

period 2005–2007. However, it must produce similar weather events with realistic variability and

climatology representative of nature. The variability and climatology for the 7-km G5NR must fall

within a statistically acceptable level of variance for the climate of Earth.

The 7-km resolution of the G5NR is 16-times finer than the highest resolution operational version

of GEOS-5 used within the GMAO to date. This means that weather features will be resolved to a

level not typically produced in standard GEOS-5 products. As a result, we can closely examine the

fidelity of significant weather events that would be of importance to the OSSE community. These

include, but are not limited to, extratropical cyclones, tropical cyclones and mesoscale convective

complexes.

In addition to increased resolution, this Nature Run is unique to the OSSE community with the

inclusion of radiatively active aerosols and trace gases. A version of the Goddard Chemistry,

Aerosol, Radiation and Transport model (GOCART, Chin et al., 2002) is run online, with coupling

to the GEOS-5 radiation code (Colarco et al., 2010). GOCART treats the sources, sinks, and

chemistry of dust, sulfate, sea salt, and black and organic carbon aerosols. The inclusion of carbon

monoxide (CO), carbon dioxide (CO2) and sulfur dioxide (SO2) provides a distinct capability to

examine potential observing systems for these important trace gases.

1.3.1 Extratropical Cyclones

Large midlatitude cyclones are significant drivers of local weather around the globe. These extratropical cyclones are well resolved by global models even at much coarser resolutions than the 7-km

G5NR. However, fine scale regional and local details of these storms due to improved topographical

4
resolution and better-resolved mesoscale properties contribute to the improved representation of

these significant synoptic events in the 7-km G5NR.

The 7-km G5NR output has been searched for cyclones, and a catalogue of all instances at 30-

minute intervals throughout the two-year period has been made including locations and intensities

(in terms of sea level pressure field (SLP), 850 hPa winds, 10-meter winds and precipitation rates).

The detection algorithm follows that of Murray and Simmonds (1991) and Simmonds et al. (1999)

by first applying a Gaussian smoothing on the SLP. The Laplacian of this smoothed SLP (LSLP)

field is then searched for any local maxima with a threshold value greater than 2.5 Pa m−2. All

locations of LSLP maxima are searched for a local SLP minimum which, if found, is marked and

classified as a closed-low cyclone. If no local SLP minimum is found surrounding the LSLP maxima,

then a search is performed to find the minimum pressure gradient and the cyclone is marked as

an open system. This methodology detects both tropical and extratropical cyclones; a separate

methodology is used to distinguish tropical cyclones by searching for additional characteristics

unique to their warm-core structure.

The results of this cyclone detection scheme for the two northern hemisphere December-JanuaryFebruary (DJF) seasons are displayed in Figures 1.5 and 1.6. Intensities of extratropical cyclones

are shown in these plots based on SLP minimum: points marked in shades of blue range from

1005–975 hPa, and shades of yellow-to-red range from 975 to <950 hPa. These plots clearly show

the regions of primary cyclogenesis off the coast of Japan and the eastern United States. In several

cases these cyclones rapidly deepen into major winter storms with minimum pressures in the range

1005 to <975 hPa before making landfall along the Siberian Peninsula or the Canadian Maritimes.

The highest concentration of very intense extratropical cyclones is observed throughout the North

Atlantic Ocean and into the Greenland and Norwegian Seas where storms that intensify over the

North Atlantic eventually dissipate or make landfall in northern Europe or Scandinavia. The

Bering Sea and Gulf of Alaska is another region of high concentration for these mature extratropical

cyclones.

Several of these extratropical storms occur off the US east coast in the 7-km G5NR, with some

developing into major Nor’easter winter storms having significant impacts on the mid-Atlantic

and Northeast US. From January 17–21, 2007 in the 7-km G5NR a low-pressure system formed

along the Gulf coast, drawing large amounts of moisture from the Gulf of Mexico, then tracked

off the Carolina coast where it rapidly intensified into a major Nor’easter. This storm produced a

wide swath of heavy snow from the southern Appalachians through the mid-Atlantic and into the

northeast US (Figure 1.7), and is a classic representation of a major winter storm in this region.

A series of snapshots at 12-hour intervals show the evolution of this major winter storm over a

two-day period from 12Z January 18, 2007 through 12Z January 20, 2007 (Figures 1.8–1.12). This

storm forms from a surface low moving along the gulf coast with a central pressure of 1005 hPa,

digging southward along a deep trough in the central US (Figure 1.8). An intense line of storms is

present along the frontal boundary through the Gulf of Mexico feeding large amounts of moisture

into the system. By 00Z January 19 as the storm moves across the southeast, this moisture begins

to override the cold air filling into the Appalachians, producing heavy snows along the mountains

5
and rain along the coastal regions and into the mid-Atlantic (Figure 1.9). The storm begins to

intensify as it moves off the mid-Atlantic coast at 12Z January 19 as cold air has pushed into this

region now producing heavy snows along the eastern seaboard through the major metropolitan

areas from Washington DC to New York City into the northeast US (Figure 1.10). Lines of 1000-

500 hPa thickness less than 5400 m (blue) indicate a deep layer of cold air in this region of falling

precipitation supporting heavy snowfall rates up to two-inches per hour in many locations. A band

of icy mixed precipitation, typical of winter storms in this region, develops in areas where the

surface is at or below freezing while the upper levels have enough warm air intruding to prevent the

column from supporting snow. By 00Z on January 20 the storm reaches a central pressure of 975

hPa as it moves off the New England coast. This rapid intensification is known as “bombogenesis”

as the pressure drops by 25 hPa in less than 24 hours (Figure 1.11). Heavy snows and frozen

precipitation continue across New England with gale force winds in excess of 40 knots wrapping

around the storm. Strong winds behind the storm lead to lake-effect snow bands across the upper

midwest, New York and the mid-Atlantic. As the storm moves into the Canadian Maritimes at 12Z

January 20 with a central pressure of 968 hPa, lake-effect snows continue across the Ohio Valley

and into the northeast (Figure 1.12). Meanwhile, a strong extratropical cyclone moves into the

Pacific Northwest with a central pressure of 984 hPa. These tightly wrapped cyclones are typically

heavy snow producers throughout the Cascades of northern California, Oregon and Washington.

1.3.2 Tropical Cyclones

Improved resolution in the 7-km G5NR may have the most significant impact on the ability to

develop very intense tropical cyclones. These synoptic scale features rely heavily on the development

of mesoscale convection within favorable large-scale environments. Thus, it is critically important

that parameterizations in the model adapt to this increased resolution to permit feedback between

small-scale convective features and developing synoptic-scale storms.

The global distributions of tropical cyclone activity in the 7-km G5NR for the two annual tropical

seasons from June–May (2005–06 in Figure 1.13, and 2006–07 in Figure 1.14) highlight the realistic

variability between seasons and within individual basins across the globe. The very active 2005

Atlantic hurricane season is complemented by a less active 2006 season, while the east Pacific

sees increased activity in 2006. Not only are tropical cyclone tracks and seasonal variability well

represented, but also the ability to produce major hurricanes and super typhoons categorized as

category 3 and higher based on near-surface wind speeds in excess of 112 mph using the SaffirSimpson scale.

Multiple tropical cyclones in multiple basins are resolved simultaneously in the 7-km G5NR (Figure

1.15). In September of 2006 a major hurricane makes landfall along the US Gulf Coast while another

major hurricane moves northward in the east Pacific basin. A weak tropical storm is dissipating

in the north Atlantic, and the surface circulation of the remnants of another east Pacific storm

spins northward off Baja California. The diagnostic cloud optical thickness as represented in the

7-km G5NR highlights the intense banding structure of the mature tropical cyclones feeding into

6
the deep convection surrounding the well defined eye-walls with their strong surface wind speeds.

Specific details of the tropical atmosphere in the 7-km G5NR are presented in Chapter 4.

1.3.3 Mesoscale Convective Complexes

A mesoscale convective complex (MCC) is a large thunderstorm complex easily observed as cold

cloud tops in infrared (IR) satellite imagery or outgoing long wave radiation from models. These

large clusters of convection can be identified as continuous areas of cold IR brightness temperatures

cooler than 225 K (-48 C) or outgoing longwave radiation (OLR) less than 150 Wm−2, with

surface areas larger than 100,000 km2. In North America, these complex nocturnal clusters of

thunderstorms typically form over the eastern slopes of the Rockies and travel eastward across

the Midwest maintaining their structure and intensity over long distances. Their development is

driven by temperature and moisture advection from a strong low-level jet, typically forming along

a weak shortwave trough or surface front. These systems commonly produce heavy rainfall, strong

winds, frequent lighting, hail and often tornadoes. Over the central US, MCCs are responsible for

a significant fraction of the May-June-July rainfall.

While MCCs occur globally, primarily over land, they are well known to be severe weather producers

across the central United States during spring and summer months. An observed MCC on April 28,

2014 at 02Z, as seen by Advanced Very High Resolution Radiometer (AVHRR) imagery, shows the

classic large circular cloud shield of an MCC (Figure 1.16, top). This MCC produced tornadoes in

the region of the coldest overshooting cloud tops, seen as white to purple shades over southeastern

Missouri. A similar storm system is observed from OLR in the 7-km G5NR on May 19, 2006 at

04Z (Figure 1.16, bottom). This merging pair of MCCs in the 7-km G5NR also produces the classic

large circular cloud shield, and similar overshooting tops are observed over southern Nebraska and

south-central Oklahoma.

The 7-km G5NR and composite geostationary IR brightness temperature observations have been

sampled for occurrences of MCCs. MCCs detected over the US from May–July for both seasons

2005 and 2006 are plotted with circles representing the relative size of the MCC (from 100,000 km2

to >500,000 km2), and colored by the local solar hour of their occurrence (Figure 1.17). Not only

does the 7-km G5NR produce a realistic number of MCCs, but the timing of their formation in

the late afternoon and evening hours (hours 18 to 23) over the eastern slopes of the Rockies and

growth during the nighttime hours (hours 00 to 06) over the central Midwest is very well represented.

While MCCs in the 7-km G5NR tend to be slightly smaller than observed ones, several large MCCs

occur over Kansas, Nebraska, Missouri and Iowa. This is a substantial improvement in capability

for global models, which typically struggle to resolve these systems leading to substantial rainfall

deficits in spring and summer totals from May–July across the central US.

7
1.3.4 Aerosols

Aerosols play an important role in both weather and climate. They are transported around the

globe far from their source regions, interacting with weather systems, scattering and absorbing

solar and terrestrial radiation, and modifying cloud micro- and macro-physical properties. They are

recognized as one of the most important forcing agents in the climate system. Several missions in the

Decadal Survey portfolio, such the Aerosol-Cloud-Ecosystem (ACE) and Geostationary Coastal and

Air Pollution Events (Geo-CAPE) missions, as well as the Cloud-Aerosol Transport System (CATS)

mission which will fly on the International Space Station (ISS), aim to gather new information on

aerosol distributions and properties. The specific properties of aerosols are discussed in detail in

Chapter 7, while brief highlights of the global distribution of aerosols from snapshots of the 7-km

G5NR (Figures 1.18 and 1.19) are presented here.

Seasalt is lifted from the ocean surfaces around the globe within the strong winds of surface lows

moving through the Southern Ocean, in the marine boundary layer within the stratocumlus regions

of the east Pacific and southern Indian Oceans, and highly concentrated within the strong surface

winds of tropical waves and hurricanes in the north Atlantic.

Dust from Africa fill the regions surrounding these developing Atlantic tropical storms, and can

play a significant role in the limiting intensification of tropical waves in these regions while also

providing an indication of where dry air may be intruding into these moist tropical cyclones.

Organic and black carbon species in smoke plumes are included from the Quick Fire Emissions

Dataset (QFED) that has been downscaled to 0.1 degrees; this produces realistic individual fire

plumes over southern Africa and Australia as well as large-scale features from fires in the Americas

and Indonesia.

Sulfur dioxide (SO2), produced during the burning of fossil fuels and from volcanic eruptions, is a

short-lived gas which can act as a pollutant near the surface with detrimental health and acidifying

effects. With a mean lifetime of just a couple of days in the troposphere, emitted SO2 is quickly

converted to sulfate aerosol (SO4) through oxidation by OH or by reaction with H2O2 within clouds.

The resulting SO4 exerts a direct radiative effect on the atmosphere. The 7-km G5NR includes

these important constituents and produces a realistic distribution of their concentrations around

the globe. A snapshot of SO2 and SO4 as seen at 02Z on 27 October 2005 (Figure 1.19) highlights

the fidelity of these emissions and the transport within the prevailing meteorology of the 7-km

G5NR. A large ash plume can be seen in the eastern Pacific Ocean emitted from the eruption of

the Sierra Negra volcano on the Galapagos Islands beginning on 25 October 2005. Meanwhile,

volcanic emissions can be seen throughout the ring of fire around the north and west Pacific and

from active emissions in the Hawaiian Islands. Large concentrations from fossil fuel emissions are

evident throughout Asia and from industrial sources over North America. Details of the aerosol

simulation in the 7-km G5NR are presented in Chapter 7.

8
1.3.5 Carbon

Carbon exists in many forms—e.g., carbon dioxide (CO2), carbon monoxide (CO)—and continually

cycles through Earth’s atmosphere, ocean and terrestrial ecosystems. CO2 variations are largely

controlled by fossil fuel emissions and seasonal fluxes of carbon between the atmosphere and land

biosphere. CO2 concentrations are enhanced by carbon sources, mainly from human activities.

During Northern Hemisphere spring and summer months, plants absorb a substantial amount of

CO2 through photosynthesis, thus removing CO2 from the atmosphere. This annual cycle of CO2

production and absorption, as well as the two-year trend, is well captured as observed from surface

concentrations sampled from the 7-km G5NR at Mauna Loa, Hawaii (Figure 1.20, inset). The

global distribution of CO2 varies substantially with high surface concentrations near local emission

sources, and column concentrations distributed within the prevailing large-scale circulation. Figure

1.20 shows high column concentrations of CO2 over North America, Europe and Asia, with local

maximums wrapped up along frontal systems and mid-latitude cyclones in these regions. In this

snapshot, a strong cold front has moved off the east coast of the United States while a deep cyclone

drifts up the eastern seaboard into Canada. The gradient in CO2 concentrations along these

synoptic features pushes higher concentrations off-shore behind the front wrapping up into the

cyclonic circulation of the storm, while lower concentrations are lifted out of the tropical Atlantic

ahead of the front. Similar features are seen throughout the northern hemisphere. Details of the

trace gas and constituent properties of the 7-km G5NR are presented in Chapter 7.

9
Figure 1.1: Flow chart describing the process of performing OSSEs by replacing observations of

the real atmosphere as used in an OSE with simulated observations from a Nature Run simulation

like the 7-km G5NR.

10
Figure 1.2: City lights over the eastern United States from the NASA black marble image and total

precipitable water (colors) at the 50-km MERRA resolution.

11
Figure 1.3: City lights over the eastern United States from the NASA black marble image and total

precipitable water (colors) at the 7-km G5NR resolution.

12
Figure 1.4: Western hemisphere view of visible clouds at 18z on 27 July, 2009 from MERRA at 50 km (left) and G

km (right).

13
Figure 1.5: Extratropical cyclone locations as detected in the 7-km G5NR for December-JanuaryFebruary (DJF) 2005–2006. Intensity of these cyclones is denoted based on SLP minimum; points

marked in shades of blue range from 1005–975 hPa, and shades of yellow-to-red range from 975 to

<950 hPa.

14
Figure 1.6: As in Figure 1.5 but for DJF 2006–2007.

15
Figure 1.7: Change in snow mass over North America from January 13 to January 21, 2007 in the 7-km G5NR show

snow accumulation along the US east coast from a major Nor’easter that developed and intensified over this perio

16
Figure 1.8: 50m-wind speeds and SLP (hPa, top), snow/ice/rain rates and 1000-500 hPa thickness

contours (labeled as meters, blue ≤5400 m, red >5400 m, bottom) in the 7-km G5NR at 12Z

January 18, 2007.

17
Figure 1.9: As in Figure 1.8 at 00Z January 19, 2007.

18
Figure 1.10: As in Figure 1.8 at 012Z January 19, 2007.

19
Figure 1.11: As in Figure 1.8 at 00Z January 20, 2007.

20
Figure 1.12: As in Figure 1.8 at 18Z January 20, 2007.

21
Figure 1.13: Global distributions of tropical cyclone tracks detected in the 7-km G5NR for the annual period from 

May 2006. Weak surface circulations, tropical depressions and tropical storms are denoted in light blue, tropical distinct warm cores and surface winds in excess of 74 miles per hour (mph) are colored yellow, major tropical cyclon

in excess of 112 mph are colored in red.

22
Figure 1.14: As in Figure 1.13 for June 2006 to May 2007.

23
Figure 1.15: Clouds and surface winds speeds showing tropical cyclone activity in the east Pacific

and north Atlantic at 2130Z on September 10, 2006 during the 7-km G5NR.

24
Figure 1.16: A large MCC is observed from AVHRR 15-micron IR brightness temperatures (degrees

C) over the central US on April 28, 2014 at 02Z (top). A similar MCC is observed from OLR

(Wm−2) in the 7-km G5NR on May 19, 2006 at 04Z (bottom).

25
Figure 1.17: Distribution of MCCs from May–June of 2005 and 2006 as detected from composite

geostationary IR observations (top) and in the 7-km G5NR (bottom). The size of the circles

indicates the relative size of the detected MCC ranging from 100,000 km2to >500,000 km2. The

color of the circles indicates the local solar hour at which the MCC was detected; shades of blue

represent overnight hours (00–08), shades of green represent morning to midday hours (09–16), and

shades of orange to red represent later afternoon to evening hours (17–23).

26
Figure 1.18: Global distribution of aerosols in the 7-km G5NR at 12Z on September 23, 2006.

27
Figure 1.19: Concentrations of SO2 and SO4 in the 7-km G5NR at 02Z on October 27, 2005.

28
Figure 1.20: Concentrations of CO2 and CO in the 7-km G5NR at 07Z on January 20, 2007

29
Chapter 2

Wind and Temperature

Nikki Priv ́e, Lawrence Coy and Ronald M. Errico

In this chapter, the wind and temperature fields of the G5NR are described. The general circulation

and time mean fields are discussed in Section 2.1, energy spectra are featured in Section 2.2, synoptic

extratropical waves are reviewed in Section 2.3, and dynamics of the stratosphere and mesosphere

are addressed in Section 2.4.

2.1 Zonal and Time Means

The general circulation of the G5NR is observed in the zonal and time mean fields of wind and

temperature. Time mean fields are examined on a monthly or seasonal mean basis to reveal the

annual cycle of the atmospheric state.

Seasonal mean zonal mean tropospheric zonal wind and streamfunction are shown in Figure 2.1 for

the combined two years of the G5NR. Overall, the strength and position of the tropospheric zonal

jets are realistic, with the strongest westerly jet in the winter hemisphere midlatitudes, a weaker

westerly jet in the summer hemisphere midlatitudes, and upper level easterlies in the deep tropics.

In the winter hemisphere, a secondary westerly maximum is seen closer to the pole, this being the

lower edge of the stratospheric polar jet.

Ideally a month or season selected from the G5NR would not be distinguishable from a similar

time period selected at random from the real world. The westerly tropospheric jet strength and

30
locations for individual months from the G5NR are compared to monthly means from the ERAInterim (ERA-I) and CFSR reanalyses in Figure 2.2. The G5NR jet maxima amplitudes generally

fall within the envelope of the reanalysis amplitudes. One exception is the April to June weakening

Northern Hemisphere jet, which is consistently weaker in the G5NR than in the reanalyses. The

location of the jet maxima also shows overall good agreement between the G5NR and reanalysis,

although the JJA Northern Hemisphere jet tends to move further poleward than seen in reanalysis.

In the Southern Hemisphere, the strong stratospheric polar jet sometimes results in a more poleward

westerly maximum than the tropospheric midlatitude jet, a result seen in both reanalyses and the

G5NR.

In Figure 2.1, the zonal mean streamfunction displays the expected Hadley cells, with the strong

‘winter’ Hadley cell dominant in the June-July-August (JJA) and December-January-February

(DJF) seasons, and with transitional weaker cells in the September-October-November (SON) and

March-April-May (MAM) seasons. Ferrel cells are observed in the midlatitudes.

The shape and strength of the Hadley cells differ somewhat from the circulation found in reanalyses. Figures 2.3 and 2.4 show the strength of the maximum streamfunction of the Hadley cells in

each hemisphere along with the locations of the streamfunction maximum, compared with reanalyses as in Figure 2.2. Compared with reanalysis, the G5NR circulation tends to be on the weak

side, particularly in the SON and MAM seasons. The CFSR Northern Hemisphere cell (positive

streamfunction) is weaker in the boreal summer months than the same circulation cell in the ERA-I

reanalysis; the G5NR is in closer agreement with the CFSR.

The streamfunction maximum is displaced further poleward compared to reanalyses, and a pronounced ‘jump’ occurs in the cross-equatorial Hadley cell where the low-level flow crosses the

equator. This type of behavior is commonly found in GCMs, and is often associated with a ‘doubleITCZ’ feature (Chapter 3). The Northern Hemisphere (positive streamfunction) Hadley cell shows

better agreement between the G5NR and reanalyses; as seen in Stachnik and Schumacher (2011),

the tendency for low-level cross-equatorial flow to jump at the equator is common across numerous

reanalyses for this cell, particularly in DJF. In contrast, the JJA cross-equatorial cell (negative

streamfunction) shows minimal low-level equatorial jumping behaviour in the reanalyses, but a

strong jump in the G5NR. This results in a weakened cell with poleward shifted streamfunction

maximum in the G5NR compared to reanalyses.

Seasonal mean zonal mean meridional wind and temperature are shown in Figures 2.5 and 2.6. The

zonal mean meridional wind shows the expected pattern of upper tropospheric tropical maxima in

the upper branch of the Hadley cell(s), with return flow at low levels. The equatorial jumping

behavior is most visible in the DJF low level flow, where the southward meridional flow reaches a

maximum near 10N, and is near zero at low levels at the equator, with weak southward flow near

800 hPa. A weakening of low-level meridional flow is seen at the equator in other seasons as well.

The zonal mean temperature fields also show the expected broad tropical maxima in the middle

and lower troposphere with sharp poleward-negative gradients in the midlatitudes. There is also

a temperature minimum at the tropical tropopause, and the cold upper atmosphere of the polar

vortex at the winter pole, which will be discussed further in Section 2.4.

31
The magnitude of the meridional flow in the upper tropospheric leg of the cross equatorial Hadley

cell is compared to reanalysis in Figure 2.7. While there is agreement between the ERA-I and

CFSR reanalyses for the northward maximum associated with the Northern Hemisphere (positive

streamfunction in Figure 2.1) cell, there is less agreement for the Southern Hemisphere cell, particularly in the boreal summer period when this cell is dominant. The G5NR is in better agreement

with the weaker meridional cross-equatorial flow in the ERA-I than the stronger flow in the CFSR.

The temperature minimum at the tropical tropopause is also shown in Figure 2.7, where this minimum is warmer in the G5NR compared to reanalyses for all months. This may impact stratospheric

moisture levels, as ascent in this region is the main source of stratospheric moisture, and the cold

minimum acts to limit the amount of moisture transported into the stratosphere.

Figure 2.8 shows the jet structure at 300 hPa for both JJA and DJF, comparing long term means

from the reanalyses to the two-year mean from the G5NR. The jets in the G5NR show good

agreement with the reanalyses. There is more eastward extension of the Northern Hemisphere jet

across the Atlantic Ocean and into northern Europe in DJF in the G5NR compared with reanalyses.

In JJA, the weaker jet branch over the south Pacific reconnects to the main jet near South America,

a feature not seen in the reanalyses.

The Walker circulation is examined in Figure 2.9, showing the seasonal mean zonal wind averaged

within 10◦ of the equator. For JJA, the strong easterly outflow from the upper level anticyclone

over the Asian monsoon region is observed between 0E and 120E. Over the tropical Pacific, the

Walker circulation is weak during JJA, with minimal evidence of upper level westerlies. During

DJF, the easterly flow over Asia is weaker than in boreal summer, and the two Walker cells over the

central/eastern Pacific and Atlantic basins are seen. The Pacific Walker cell is somewhat weaker

aloft in the G5NR compared to reanalyses, with a stronger cell observed in 2006–7 compared to

2005–6. The Atlantic Walker cell is weak for DJF in the G5NR in 2006–7, but closer to the reanalysis

climatological mean in 2005–6. Upper level easterlies over Africa during DJF are considerably

stronger in both years of the G5NR compared to the reanalysis climatologies.

2.2 Energy Spectra

Power spectra P for the wind field at 250 hPa were determined at one time per month during the first

year of the G5NR. Results for one such time are presented in Figure 2.10. The spectra are presented

separately for rotational and divergent wind as functions of spherical-harmonic, two-dimensional

length scale L = a/n, where a is the earth’s radius and n is the two-dimensional dimensionless

wavenumber. Lines indicative of theoretical ranges of 2-dimensional and 3-dimensional turbulence,

respectively having P ∝ n

−3 and P ∝ n−5/3 are also presented. The spectral characteristics noted

for this single time are common to all times examined.

Common to almost all atmospheric models and observations (Augier and Lindborg (2013), Burgess

32
et al. (2013), Blazica et al. (2013), Hamilton et al. (2008), Tulloch and Smith (2006)), and first

suggested by Charney (1971), the rotational component of power follows a n

−3

law for 100 <

L < 1000 km. It strongly dominates the divergent component at the same wavenumbers. For

L < 100 km the slope of rotational power becomes less steep, but does not approach the n

−5/3

behavior thought to be indicative of the mesoscale (e.g., Tulloch and Smith, 2006). For L < 10 km,

it approaches a n

−8 behavior, steeper than that seen in the previous ECMWF NR (Errico and Priv ́e,

2014) and some other models (Burgess et al., 2013), which has been attributed to unrealisticallystrong model damping at the smallest resolved scales.

The ratios of powers of the rotational and divergent winds for L < 100 km (the smallest ratio

being 4) appear somewhat larger than those seen in the previously mentioned papers at the same

elevations and horizontal scales. In fact, the ECMWF model shows the divergent wind spectrum

dominating the rotational (Burgess et al. (2013), Errico and Priv ́e (2014)), although this ECMWF

result is uncommon. The G5NR result is likely due to the fact that horizontal diffusion is primarily

applied to the divergent, rather than rotational, wind in this model.

Qualitatively, the spectra for L > 100 km look like those of other models, theory, and inferences from

observations. For smaller scales, model behaviors are more varied, the theory is more uncertain,

and the representativeness of observations more suspect. Aside from the weakness of the spectrum

of divergent wind compared with that of the rotational wind, the GEOS-5 results fall within the

ranges of other reported results. Along with all other reported results, at very small scales the

GEOS-5 spectra decrease much more rapidly with decreasing scale than either theory suggests or

observations reveal. Thus, the true resolution of the G5NR should be considered as several times

coarser than its grid spacing of 7 km. Unfortunately, without a better understanding of what the

shape of the real atmospheric spectra is and what criteria should be used, it is difficult to define

an unambiguous “true” resolution. Also, the significance of the weak spectrum of divergent wind

in the G5NR is unclear.

2.3 Extratropical Synoptic Waves

2.3.1 Overall Wave Activity

One measure of wave activity is the variance of metrics of interest. For extratropical synoptic waves,

the variance of instantaneous upper tropospheric meridional wind (v) is commonly chosen. Figure

2.11 shows the standard deviation of 200 hPa v over different seasons, comparing two seasons from

the G5NR to multiple seasons from reanalyses.

For JJA, the overall magnitude and pattern of wave activity in the G5NR is similar to that seen in

the reanalyses. One notable difference is the area of large standard deviations near 60S from 180W

to 60W; these large values are found in both years of the G5NR. The G5NR standard deviations

are also somewhat low over the tropical Pacific basin.

33
In DJF, the G5NR again has similar patterns of wave activity compared with reanalyses, with

some differences in the magnitude of activity. There are uniformly higher standard deviations in

the G5NR compared to reanalyses from 40S–60S, and also over the North Atlantic. The tropical

Pacific again has lower standard deviation in the G5NR compared with reanalyses.

In order to put the two seasons of G5NR wave activity into perspective, individual seasons from

the reanalyses are compared to the G5NR in Figure 2.12. The seasonal mean standard deviation

of 200 hPa v is calculated for each season in the G5NR, and for the CFSR (1994–2010) and ERA-I

(1994–2013) reanalysis, with the areal mean calculated over the region of strongest wave activity for

both JJA and DJF in the two hemispheres. Ideally, the G5NR values would fall within the envelope

of the reanalysis values, indicating that the G5NR is indistinguishable from the reanalyses.

In JJA, the G5NR standard deviations of v are within the envelope of the reanalysis values, although

one year of the G5NR has weak activity over the North American region. However, for DJF the

G5NR values are relatively high in both years, particularly in the eastern hemisphere, with localized

excursions above the envelope of reanalysis results.

2.3.2 Wave Characterics

Synoptic scale extratropical waves are further evaluated in terms of the spectra of phase speed

and wavenumber. This type of cross-spectral analysis is familiar from Wheeler and Kiladis (1999),

using the method of Hayashi (1971) to calculate the spectra. For these calculations, the 200 hPa

meridional wind is averaged over a latitude range, and used in an unfiltered state as there is

relatively little noise in the meridional wind field. Eastward progressing waves are assigned positive

wavenumbers, while westward progressing waves are assigned negative wavenumbers. In Figure

2.13, the entire two-year G5NR period is used, in comparison to longer periods for ERA-I (1994–

2013) and CFSR (1994–2010). The shorter G5NR period will tend to yield a ‘spottier’ appearance

to the phase-spectra diagrams.

In the Northern Hemisphere, the phase spectra are quite similar for the G5NR and reanalyses,

with greatest power at wavenumbers 5–7, and phase speeds up to 10 m s−1, with higher speeds for

higher wavenumbers. The Southern Hemisphere also shows similar properties of synoptic waves for

the G5NR and reanalyses. Wavenumbers 4–6 are dominant, with phase speeds from 3 to 13 m s−1,

again with faster speeds for higher wavenumbers. These values are in agreement with previous

studies (Chang and Yu (1999), Chang (1999), Dell’Aquila et al (2005)).

The technique of phase spectra decomposition can also be used to estimate group velocities. The

method of Zimin et al. (2003) allows the wave packet magnitude to be calculated around a latitude

band. These wave packets can then be decomposed using the method of Hayashi (1971) to find the

group velocity and wave packet size spectra. These calculations are performed for the same regions

as the phase speed spectra previously shown, filtering for wavenumbers 1–20 to capture packets of

synoptic and planetary waves only, with the results illustrated in Figure 2.14. The “wave packet

34
wavenumber” is not the wavenumber of the component waves, but instead the “wavenumber” of

the entire wave packet.

As expected, the group velocities are faster than the phase speeds, and the wave packet sizes are

larger (smaller “wavenumber”) than the wavenumber of the component waves. In the Northern

Hemisphere, the G5NR group velocities are similar to the group velocities of the CFSR, with group

velocities up to 15 m s−1. ERA-I has greatest power for somewhat higher group velocities, up to

20 m s−1. The group velocities are roughly twice as fast as the phase speeds, which results in

the observed downstream development of synoptic waves, and which is consistent with theoretical

properties of Rossby waves.

In the Southern Hemisphere, there is relatively good agreement between group velocities for the

G5NR and both reanalyses. Again, the group velocities are higher than the phase speeds, with

greatest power for group velocities of 15–30 m s−1. The G5NR does have some local power maxima

at slow group velocity (wavenumbers 4–5, 3–12 m s−1

) that is not seen in the reanalyses.

2.4 Stratospheric Overview

2.4.1 Quasi-Biennial Oscillation

The current configuration of the GEOS AGCM produces a tropical lower stratospheric QuasiBiennial Oscillation (QBO) that is generated by a combination of resolved global scale waves and

parameterized gravity waves. Figure 2.15 (upper panel) shows the tropical zonal winds with a

downward propagating QBO signal dominating in the lower stratosphere. At 10 hPa the easterlies

are stronger in magnitude that the westerlies (35 vs 20 m s−1) in agreement with observations.

With only two years of the G5NR it is difficult to analyze the period precisely, however, the lower

panel in Figure 2.15 shows the difference in corresponding months between the two years, a process

that removes annual and semi-annual cycles that repeat between the two years, highlighting the

QBO signal. If the QBO period in the G5NR was exactly two years then the two halves of the

figure would match at the middle time (by definition the left and right halves are the negative of

each other). The discontinuity at the middle of the figure (where the contour lines jump up) shows

that the G5NR QBO period is slightly less than two years. The observed QBO periods is close to

27 months, so the G5NR descends a bit too quickly.

2.4.2 Annual Cycle

Figure 2.16 compares the two annual cycles of the G5NR mid-stratospheric 90◦N temperature

with the corresponding monthly averaged temperatures from MERRA. The winter (Dec–Mar)

polar temperatures in the G5NR are colder than typical MERRA winters while the spring (May

35
and June) temperatures are warmer. The G5NR realistically captures the annual cycle of polar

temperature change and are generally within the range seen by MERRA.

Figure 2.17 shows that the mid-stratospheric jet generally follows the polar temperatures by being

stronger than most of the MERRA years during the winter season (Nov–May). In particular the

G5NR 2006 December winds are ∼10m s−1stronger than is seen in any of the MERRA Decembers.

The relatively strong winds seen in May suggest that the seasonal weakening of the polar jet

is somewhat delayed in the G5NR. Note that the January 2006 winds (blue diamonds) are much

stronger in the G5NR than in MERRA, as MERRA reflects the decrease in winds experienced during

the major Stratospheric Sudden Warming (SSW) of January 2006 while no warming occurred in

the G5NR.

More detail of the zonal wind at 10 hPa and 60◦N is seen in Figure 2.18, where the NH winter

G5NR winds are persistently near the maximum values seen by MERRA. There are no mid-winter

stratospheric warming events in the G5NR since the G5NR zonal wind at this location remains

positive until the final spring warming in April.

The winter (May–Oct) Southern Hemisphere in the G5NR (Figures. 2.19 and 2.20) also shows

somewhat cold polar temperatures and strong polar jet winds when compared to MERRA. As in

the Northern Hemisphere, the relatively cold temperatures and strong winds seen in the G5NR

in October indicate a delayed end to winter when compared to MERRA. Despite the wintertime

cold pole and strong winds the overall seasonal cycle is well captured in the G5NR winds and

temperatures.

2.4.3 Planetary Waves

The relatively strong NH wintertime jet and cold polar temperatures in the G5NR are associated

with relatively weak stationary planetary wave activity propagating upward from the troposphere.

Figure 2.21 shows the meridional heat flux at 200 hPa due to stationary waves (calculation based on

monthly averaged meridional wind and temperature fields), near the tropopause. The meridional

heat flux is proportional to the vertical component of the Eliassen-Palm (EP) flux, a useful measure

of the vertical flux of wave activity (Andrews et al. 1987, page 128). For the G5NR the vertical

flux of stationary wave is particularly weak in January and February. Since the stratospheric winds

remain westerly throughout the winter (no warming events) the planetary waves are not blocked

by the presence of easterlies in the stratosphere as often happens after major stratospheric sudden

warming events. Instead the reduction in vertical stationary wave propagation is likely due to

the stratospheric winds being too strong westerly, a configuration that can also reduce vertical

planetary wave propagation.

The corresponding Southern Hemisphere (SH) meridional heat flux at 200 hPa due to stationary

waves is shown in Figure 2.22. Note that in the SH negative values are associated with upward

propagating planetary waves. In agreement with MERRA, the G5NR stationary planetary waves

36
are weaker in the SH than in the NH. The G5NR wave fluxes tend to be strong compared to

MERRA in July during both years and somewhat strong during June as well, but most of the SH

winter/fall season (Aug–Oct) shows G5NR planetary wave forcing to vary somewhat between the

two years in good agreement with MERRA.

Figure 2.23 shows typical G5NR mid-stratospheric EPV (Ertel Potential Vorticity) fields vertically

interpolated to the 850 K potential temperature surface. The SH has a more zonal pattern than the

NH and even though the stationary planetary waves are weak compared to MERRA, the G5NR

shows a high, anti-cyclonic, region over the North Pacific, in agreement with the climatological

location. Note the fine horizontal filaments that are resolved in the G5NR.

2.4.4 Resolved Gravity Waves

The high horizontal resolution∗ of the G5NR enables the detailed examination of resolved gravity

waves (GW). The largest amplitude GW’s in the mid-stratosphere G5NR are directly associated

with orography and these waves are often seen to affect the EPV far downstream of the GW

origin. An example of a large amplitude GW over Greenland that generated large downstream EPV

perturbations can be seen in the 850 K EPV on 26 January 2007 (Figure 2.24). The downstream

EPV developed a strongly perturbed pattern in response to the large amplitude GW over Greenland.

The vertical GW structure in the potential temperature, vertical velocity, and zonal wind are shown

in Figure 2.25. The waves are seen to originate from the east coast of Greenland (the white area

adjacent to the bottom of the panels) and grow in amplitude into the middle stratosphere. There is

evidence of wave breaking in the mid-stratosphere (∼10 hPa) as the potential temperature surfaces

overturn at those altitudes.

The ability of breaking orographic waves to produce downstream changes in EPV can be seen in a

second example (Figure 2.26) where the flow over the Andes on the west coast of South America

generates high and low EPV streaks. In this example the EPV changes near the orography appear

to be advected downstream over hundreds of kilometers. The resolution of the G5NR allows

the development of this vorticity perturbation to be directly associated with the orographically

produced GW divergence field (Figure 2.27). Seen at 3 hourly intervals the mid-stratospheric

divergence first grows over the orography. Later (at 8 Sep 00 and 03 UTC) when the GWs break,

the vorticity perturbations begin to grow. That the GW’s begin to break at that time can be seen

in the local reversal of the potential temperature gradients and an increase in eddy (Kzz) mixing

(not shown). The horizontal resolution of the G5NR is essential for a realistic representation of

these processes.

In the mesosphere rings and half circles (rings partly filtered as they propagate vertically through

changing background winds), characteristic of small scale convective forcing, dominate the vertical

velocity field. To examine the influence on the momentum budget of these smallest scale waves,

the vertical momentum flux has been calculated from the full horizontal resolution G5NR (0.0625◦)

∗See Section 2.2 for a discussion of the effective resolution of the G5NR.

37
and the derived reduced grid (0.5◦

) u and w fields and plotted at 0.128 hPa (Figure 2.28). Results

show that the largest momentum fluxes found at this time (60◦N, Figure 2.28, top panel) are well

resolved at the reduced grid scale and are associated with the underlying orography, so that little is

gained by the high horizontal resolution of the G5NR. In contrast, in the tropics and subtropics the

smaller scales can be seen to contribute substantially to the momentum fluxes, however the overall

magnitude of these fluxes is small compared to that of the northern winter momentum fluxes. The

relatively minor contribution of the smallest scale GWs to the momentum budget in the G5NR

may be a reflection of the reduced effective resolution discussed in Section 2.2.

38
JJA

Latitude

p, hPa

−90 −60 −30 0 30 60 90

200

400

600

800

1000

SON

Latitude

p, hPa

−90 −60 −30 0 30 60 90

200

400

600

800

1000

DJF

Latitude

p, hPa

−90 −60 −30 0 30 60 90

200

400

600

800

1000

MAM

Latitude

p, hPa

−90 −60 −30 0 30 60 90

200

400

600

800

1000

−40

−30

−20

−10

0

10

20

30

40

Figure 2.1: Time mean zonal mean of the G5NR zonal wind (color contours, m s−1) and streamfunction (black contours, kg s−1

). Dashed lines indicate negative (counter clockwise) streamfunction;

dot-dash line indicates zero streamfunction line. Seasonal means include both years of the G5NR.

39
1 2 3 4 5 6 7 8 9 10 11 12

0

20

40

60

Month

Jet max, m/s

Northern Hemisphere

1 2 3 4 5 6 7 8 9 10 11 12

0

20

40

60

Month

Jet max, m/s

Southern Hemisphere

1 2 3 4 5 6 7 8 9 10 11 12

−50

0

50

Month

Jet Latitude

Figure 2.2: Monthly time mean zonal mean maximum strength of the tropospheric zonal wind in

each hemisphere (top two panels), and the location of the maximum (lower panel). Blue circles,

CFSR 1994–2010; green circles, ERA-I, 1994–2013; red crosses, G5NR. Each symbol indicates one

month of one year.

40
1 2 3 4 5 6 7 8 9 10 11 12

−3

−2

−1

0

1

2

3

x 1011

Month

Streamfunction, kg/s

Figure 2.3: As for Figure 2.2, but for the maximum streamfunction of both Hadley cells. Northern

hemisphere cell has positive streamfunction, Southern hemisphere cell has negative streamfunction.

41
1 2 3 4 5 6 7 8 9 10 11 12

−40

−30

−20

−10

0

10

20

Month

Latitude

Negative Streamfunction

1 2 3 4 5 6 7 8 9 10 11 12

−20

−10

0

10

20

30

40

Month

Latitude

Positive Streamfunction

Figure 2.4: As for Figure 2.2, but for the location of the maximum streamfunction of the two

Hadley cells.

42
JJA

Latitude

p, hPa

−90 −60 −30 0 30 60 90

200

400

600

800

1000

SON

Latitude

p, hPa

−90 −60 −30 0 30 60 90

200

400

600

800

1000

DJF

Latitude

p, hPa

−90 −60 −30 0 30 60 90

200

400

600

800

1000

MAM

Latitude

p, hPa

−90 −60 −30 0 30 60 90

200

400

600

800

1000

180

200

220

240

260

280

Figure 2.5: Time mean zonal mean of the G5NR for temperature (K). Seasonal means include both

years of the G5NR.

43
JJA

Latitude

p, hPa

−90 −60 −30 0 30 60 90

200

400

600

800

1000

SON

Latitude

p, hPa

−90 −60 −30 0 30 60 90

200

400

600

800

1000

DJF

Latitude

p, hPa

−90 −60 −30 0 30 60 90

200

400

600

800

1000

MAM

Latitude

p, hPa

−90 −60 −30 0 30 60 90

200

400

600

800

1000

−4

−3

−2

−1

0

1

2

3

4

Figure 2.6: As for Figure 2.5, but for the zonal mean meridional wind, v (m s−1).

44
1 2 3 4 5 6 7 8 9 10 11 12

−6

−4

−2

0

2

4

6

Month

V, m/s

Northward V Maximum

1 2 3 4 5 6 7 8 9 10 11 12

−6

−4

−2

0

2

4

6

Month

V, m/s

Southward V Maximum

1 2 3 4 5 6 7 8 9 10 11 12

185

190

195

200

Month

T, K

Temperature Minimum

Figure 2.7: As for Figure 2.2, but for the tropical upper tropospheric zonal mean meridional wind

maximum, v (m s−1), top and center panels. Lower panel, temperature of tropical tropopause

temperature minimum (K).

45
Longitude

p, hPa

NR JJA

−180−120 −60 0 60 120 180

−90

−60

−30

0

30

60

90

Longitude

p, hPa

NR DJF

−180−120 −60 0 60 120 180

−90

−60

−30

0

30

60

90

Longitude

p, hPa

ERA−Int JJA

−180−120 −60 0 60 120 180

−90

−60

−30

0

30

60

90

Longitude

p, hPa

ERA−Int DJF

−180−120 −60 0 60 120 180

−90

−60

−30

0

30

60

90

Longitude

p, hPa

CFSR JJA

−180−120 −60 0 60 120 180

−90

−60

−30

0

30

60

90

Longitude

p, hPa

CFSR DJF

−180−120 −60 0 60 120 180

−90

−60

−30

0

30

60

90

0

5

10

15

20

25

30

35

40

45

50

Figure 2.8: Multi-year seasonal mean horizontal wind speed at 300 hPa, m s−1.

46
Longitude

p, hPa

NR JJA

−180−120 −60 0 60 120 180

200

400

600

800

1000

Longitude

p, hPa

NR DJF

−180−120 −60 0 60 120 180

200

400

600

800

1000

Longitude

p, hPa

ERA−Int JJA

−180−120 −60 0 60 120 180

200

400

600

800

1000

Longitude

p, hPa

ERA−Int DJF

−180−120 −60 0 60 120 180

200

400

600

800

1000

Longitude

p, hPa

CFSR JJA

−180−120 −60 0 60 120 180

200

400

600

800

1000

Longitude

p, hPa

CFSR DJF

−180−120 −60 0 60 120 180

200

400

600

800

1000

−20

−15

−10

−5

0

5

10

15

20

Figure 2.9: Multi-year seasonal mean zonal wind averaged between 10N and 10S, m s−1.

47
Figure 2.10: Power spectra of kinetic energy on the 250 hPa level. Rotational energy, red line;

divergent energy, blue line; total energy, green line. Black lines indicate -3 and -5/3 slope lines.

48
Figure 2.11: Multi-year seasonal standard deviation of 200 hPa meridional wind, m s−1

.

49
Figure 2.12: Individual seasonal standard deviation of 200 hPa meridional wind along latitudinal

bands, m s−1. Blue lines, ERA-I (1994–2013); green lines, CFSR (1994–2010); red lines, G5NR. Top

left, JJA from 40N–60N; top right, DJF from 20N–60N; bottom left, JJA from 20S–70S; bottom

right, 40S–60S.

50
Figure 2.13: Phase-spectra decomposition of 200 hPa meridional wind, phase speed in m s−1

, contours of power (m2

s

−2

). Positive wavenumbers indicate eastward progression, negative wavenumbers indicate westward progression. Left panels, 40N–60N; right panels 40S–60S. Top panels, G5NR;

center panels, ERA-I (1994–2013); lower panels, CFSR (1994–2010).

51
Figure 2.14: Phase-spectra decomposition of wave packets of 200 hPa meridional wind, contours of

power (m2s

−2

). Wavenumbers indicate ‘wavenumber’ of wave packet, not wavenumbers of component waves. Positive wavenumbers indicate eastward progression, negative wavenumbers indicate

westward progression. Left panels, 40N–60N; right panels 40S–60S. Top panels, G5NR; center

panels, ERA-I (1994–2013); lower panels, CFSR (1994–2010).

52
Figure 2.15: Top panel: G5NR monthly averaged zonal wind component (m s−1

, contour interval:

5 m s−1), averaged zonally and between 10◦S and 10◦N. Bottom panel: Same field as top panel but

filtered by differencing each month of the second year from the first year, and each month of the

first year from the second year to remove annual and semi-annual signals.

53
Figure 2.16: Monthly average 10 hPa temperatures at 90◦N for the G5NR (filled diamond symbols)

and MERRA (unfilled diamond symbols). The colors denote the years 2005 (red), 2006 (blue), and

2007 (green). Note that the symbols for the MERRA 2005–2007 years have been slightly offset in

time, colored, and repeated for clarity.

54
Figure 2.17: Monthly average 10 hPa zonally averaged zonal wind component at 60◦N for the

G5NR (filled diamond symbols) and MERRA (unfilled diamond symbols). The colors denote the

years 2005 (red), 2006 (blue), and 2007 (green). Note that the symbols for the MERRA 2005–2007

years have been slightly offset in time, colored, and repeated for clarity.

55
Figure 2.18: Daily (12 UTC) zonally averaged zonal wind at 10 hPa and 60◦N for the G5NR

(black curve) and the multi-year (1979–2014) averaged MERRA (blue curve). The green filled area

denotes plus or minus one standard deviation from the mean, and the boundary of the light blue

filled area denotes the extreme values seen by MERRA

56
Figure 2.19: As in Figure 2.16 but for 90◦S.

57
Figure 2.20: As in Figure 2.17 but for 60◦S.

58
Figure 2.21: Meridional heat flux (m s−1K) due to stationary waves at 200 hPa and 60◦N. Symbols

for G5NR and MERRA as in Figure 2.16.

59
Figure 2.22: Meridional heat flux (m s−1K) due to stationary waves at 200 hPa and 60◦S. Symbols

for G5NR and MERRA as in Figure 2.16.

60
Figure 2.23: G5NR absolute value of EPV for the SH on 15 July 2005, 12 UTC (left) and the

NH for 15 January 2006, 12 UTC (right). The EPV has been vertically interpolated to the 850 K

potential temperature surface (∼10 hPa) with high values colored red, middle values colored white,

and low values transparent.

61
Figure 2.24: G5NR absolute value of EPV for 16 January 2007, 12 UTC. Same scaling as in

Figure 2.23

62
Figure 2.25: Longitude-height cross section over Greenland showing potential temperature (left),

vertical velocity (center), and zonal wind component (right) on 26 Jan 2007, 12 UTC. All fields are

taken from the G5NR.

63
Figure 2.26: G5NR 10 hPa EPV fields during 2005 at 12 hr intervals: a) 7 Sep 18 UTC, b) 8 Sep

06 UTC, c) 8 Sep 18 UTC, and d) 9 Sep 06 UTC.

64
Figure 2.27: G5NR horizontal divergence field (blue and red filled contours) and vorticity field

(blue and red unfilled contours) at 10 hPa during 2005 on a) 7 Sep 18 UTC, b) 7 Sep 21 UTC,

c) 8 Sep 00 UTC, and d) 8 Sep 03 UTC. The yellow arrow denotes the direction of the prevailing

wind throughout the troposphere and lower stratosphere.

65
Figure 2.28: Mesospheric (0.138 hPa) GW vertical momentum flux (m s−1

) on 1 Jan 2006 00 UTC

for calculations using the full horizontal resolution fields (0.0625◦, black curves) and reduced grid

saved fields (0.5◦, red curves). Note the different vertical scales on each panel.

66
Chapter 3

Humidity and Precipitation

Andrea Molod, Min-Jeong Kim, Jyothi Nattala, Michael Bosilovich, Siegfried Schubert and

Winston Chao

Atmospheric components of the G5NR hydrological cycle are examined here in comparison with

various observationally based estimates. The analysis includes an assessment of monthly mean

vapor, cloud condensate and precipitation, mean surface moisture exchange, and moisture variability at time scales from the diurnal to the seasonal. The G5NR simulation spans 2 years only,

and is forced with observed sea surface temperatures, and so the majority of the comparisons to

follow will assess the climate of the G5NR against observations from the same June 2005–August

2007 time period as the G5NR simulation. Naturally one should keep in mind that the G5NR,

a free-running model simulation, cannot be expected to reproduce observed weather events. This

necessarily limits the attainable agreement between observed and modeled precipitation rates.

3.1 Verification Data Sources

Verification for the mean state variables, particularly temperature, zonal wind and related fields, is

provided by the climatology of the Modern-Era Retrospective Analysis for Research and Applications (MERRA, Rienecker et al., 2011). MERRA has two primary objectives: to place observations

from NASA’s Earth Observing System satellites into a climate context and to improve upon the hydrologic cycle represented in earlier generations of reanalyses. It covers 1979-present with ongoing

reanalyses at 0.5

◦

resolution with 72 vertical levels.

67
ERA-Interim (ERA-I, Dee et al., 2011) is used for a second estimate of model-dependent fields,

including humidity, meridional wind and related fields at a resolution of approximately 0.7

◦ and

61 levels. It is a reanalysis by ECMWF currently produced from 1979 onwards using the ECMWF

forecast model and is intended to bridge from the earlier ERA-40 reanalysis to a future project

that will cover the 20th century.

One of observational estimates for total precipitation is from the Global Precipitation Climatology

Project (GPCP) satellite-gauge precipitation product of Huffman et al. (1995). The precipitation

estimate is a combination of microwave satellite data, infrared satellite data, rain gauge analyses,

and numerical weather prediction models. The first step of the technique used to combine the

various data sources is the development of a multi-satellite product using microwave and infrared

at low latitudes, and microwave data alone at higher latitudes. The second step is the inclusion of

the rain gauge analysis, weighting each field by its inverse relative error variance. Finally, ECMWF

numerical model results are used to fill data voids in the combined satellite-gauge estimate. The

bias estimated from this technique is 3.7 mm month−1.

The other observational estimate for total precipitation is from the Climate Prediction Center

(CPC) Merged Analysis of Precipitation (CMAP) product of Xie and Arkin (1996, 1997). The

merging method starts with the creation of a base period (July 1987–June 1995) by combining IR,

SSM/I, and Microwave Sounding Unit (MSU) satellite data using a maximum likelihood approach.

Next, the possible biases are removed by combining the results of the first step with gauge analyses

via a blending technique. Over land it is assumed that the combined satellite estimates can represent

the structure of the precipitation distribution and that there is no bias in gauge estimates. Over the

ocean, the bias remaining after the first step is removed by comparison with the atoll gauge data

over the tropics and by subjective assumption regarding the bias structure over the extratropics.

3.2 Mean Atmospheric Humidity

The specific (QV) and relative humidity (RH) fields from the G5NR and the comparison with

MERRA and ERA-I reanalyses are shown in Figures 3.1 and 3.2. The differences in specific

humidity between the two reanalyses, particularly in the tropics near the surface, are comparable

in magnitude to the differences between the G5NR and either reanalysis, and so the comparison

against both is provided here. The general trend is for high G5NR specific humidities as compared

to either reanalysis above 700 mb in both JJA and DJF, by up to 1 g kg−1. High specific humidities

relative to both reanalyses are also found near 850 mb in the southern hemisphere near 25-30S in

both seasons by up to 1 g kg−1relative to MERRA and up to 1.8 g kg−1relative to ERA-I. In

these regions these differences are on the order of 15-20% of the total specific humidity. The wet

bias is these two regions is also reflected in the comparison between the relative humidity in G5NR

and either reanalysis shown in Figure 3.2. In in the tropics near 900 mb, the G5NR is dry with

respect to MERRA and wet with respect to ERA-I, and at the surface is dry with respect to both

reanalyses, more so with respect to ERA-I. The difference between the two reanalyses here makes

it difficult to attribute these differences to a bias in the G5NR humidity. The relative humidity

68
field additionally shows a wet bias near 200 mb of up to 30% RH in both seasons and a dry bias

near 300-400 mb of up to 10% RH, accentuated at higher latitudes.

The comparison between the G5NR total precipitable water (TPW) and the estimate from Surface

Radiation Budget (SRB, Pinker and Laszlo 1992) is presented in Figure 3.3 for DJF (panels a, c, e)

and JJA (panels b, d, f). This comparison provides an assessment of model low level moisture bias

in the region where the reanalyses provide different estimates and cannot be used for this purpose.

The G5NR TPW is biased high by up to 0.5 mm (out of approximately 6 mm) in DJF in the region

of the South Pacific Convergence Zone (SPCZ), and high by up to 0.5 mm throughout the northern

hemisphere tropics and extratropics in JJA.

3.3 Cloud Condensate

Zonal means of the annual mean G5NR cloud ice and cloud water are shown in Figures 3.4 and 3.5

in comparison with the climatology of the CloudSat based estimates of Li et al. (2012) and Li et

al. (2013), respectively. Figure 3.4 shows general agreement between the G5NR and the CloudSat

non precipitating cloud ice in terms of altitude and latitudinal distribution, but shows that the

G5NR cloud ice is substantially overestimated (by up to 10 mg kg−1, a factor of 2) near 300 mb

in the tropics. The CloudSat cloud water estimate is considered less accurate than the cloud ice

estimate (Li et al., 2013), so the differences may not all be G5NR bias, but the G5NR cloud water

estimate shows substantial disagreement with the CloudSat non precipitating cloud water. G5NR

cloud water shows the same general altitude and latitude distribution, but is higher than CloudSat

almost everywhere by almost a factor of 2.

3.4 Second Moments of Total Water

As part of the standard output of the G5NR (da Silva et al., 2014), a suite of second moments were

computed at native resolution and aggregated to approximately 0.5oresolution. These fields, along

with the 0.5oresolution first moments, can be used to compute 0.5osubgrid scale (SGS) variances

resolved by the G5NR. For example, the SGS variance of total water (QT , the sum of vapor, cloud

ice and cloud liquid) is written: σQT

2 = ( < QT

2 > - < QT > ∗ < QT > ), where <> refers to

an 0.5o average. The zonal mean σQT

2 as a function of pressure is shown in Figure 3.6a for JJA.

The maximum variance is found near boundary layer cloud tops in the tropics, decreases below

perhaps due to smoothing by turbulent motions, and decreases aloft due to the decrease in mean

total water. The 0.5o SGS variance of total water is related to the “critical relative humidity”

specified as part of the AGCM moist processes, and for the shape of the probability distribution

function used in the GEOS-5 AGCM, can be written as: RHc = 1−

σQT

q

∗ , where q

∗

is the saturation

specific humidity. RHc is shown in Figure 3.6b; it is largest (near 1.0) near the surface in the

tropics and decreases to values of 0.9 towards higher latitudes and towards higher altitudes. RHc

69
computed in this manner can be used as a validation for the values that are specified in the GEOS-5

AGCM when run at 0.5oresolution (in MERRA, for example), and can be used to develop a state

dependant parameterization. Inset as part of Figure 3.6b is the vertical profile of RHc used in the

GEOS-5 AGCM over the oceans when run at 0.5oresolution. The profile is characterized by a tanh

function, which has a value near 1. at the surface, decreases to a value of 0.93 at approximately 850

mb, and remains at that value aloft. Although this profile is a simple depiction of the variations

in the RHc computed from the G5NR SGS variance, the basic character and magnitude are well

captured by the 0.5o AGCM.

3.5 Precipitation

General comparisons between the G5NR and observations of large scale features, trends and seasonal variations in precipitation provide a reasonable sense of the ability to simulate convection

and atmospheric supersaturation-related rainfall. The observational precipitation datasets chosen

for this comparison are from the Global Precipitation Climatology Project (GPCP) estimates of

Huffman, due to the extensive raw data coverage over land (section 3.1) and the blending of several different types of data over oceans, and from the Climate Prediction Center (CPC) Merged

Analysis of Precipitation (CMAP), which is also a merged gauge-satellite product but which has a

different mix of input satellite data (Yin et al., 2004).

Figure 3.7 shows the G5NR precipitation as compared to both GPCP and CMAP in DJF and

JJA. Northern winter G5NR precipitation in the tropics is underestimated in the region of the

South Pacific Convergence Zone (SPCZ), a region which has been shown (Hurwitz, et al., 2011)

to be important for poleward transport of energy in the stratosphere. In addition, the Pacific

Intratropical Convergence Zone (ITCZ) precipitation is overestimated by up to 5 mm day−1relative

to both GPCP and CMAP estimates. The central feature of the oceanic precipitation in the G5NR

in JJA is the ”double ITCZ” structure, common to many high resolution AGCM simulations (e.g.,

Bacmeister, et al. 2014), and the apparent shift eastward of the region of maximum precipitation

in the warm pool, common to many AGCMs at any resolution (e.g., Schmidt et al. 2006).

Land-only precipitation is shown in Figure 3.8 using a different color shading to accentuate the

behavior over land. There is generally good pattern agreement between the G5NR and both the

GPCP and CMAP precipitation in DJF, although the G5NR precipitation is low over central South

America by up to 4 mm day−1relative to both observational estimates. The G5NR precipitation

is low relative to GPCP but in good agreement with CMAP over Europe. In addition, the DJF

precipitation is overestimated over the Andes. In JJA, the magnitude of the precipitation in the

G5NR is in generally good agreement with both observational estimates, but there are shifts in

pattern in several locations. The Asian monsoon precipitation is shifted to the north in the G5NR,

resulting in an overestimate in the northern monsoon regions and an underestimate in the southern

monsoon region. This is tied to an overestimate of precipitation over the slopes of the Tibetan

Plateau. In North America in JJA there is a general trend towards overestimating precipitation

in the north and west, and underestimating precipitation in the south and east. Again, though,

70
recall the limitations associated with comparing only two years of precipitation data, given that

the G5NR is a free-running simulation.

3.6 NEWS Global and Regional Water Budgets

The NASA Energy and Water Cycle (NEWS) Program has produced a climatology of the water

and energy budget data derived from EOS satellite data, retrieved level 4 data products and

reanalyses (limited usage to variables and places where observed systems are weak) (Rodell et al.

2014, L’Ecuyer et al. 2014). The data providers also included uncertainty estimates. Using an

optimization routine and the uncertainty estimates, the water and energy cycle data were balanced

constrained by known physical conditions and relationships. We present both versions (i.e., with

and without the applied constraint) for completeness and a better assessment of uncertainty. The

observed data are generally available from 2001-2010, to approximate the early 21st century climate.

The constrained balance of the observations can allow some reasoning of long term global and

regional bias in the G5NR.

Figure 3.9 shows the water cycle of the G5NR compared to the NEWS assessment of observations.

Over the ocean, too strong water cycling occurs with excessive precipitation and evaporation in the

G5NR. In addition, too much water is transported from ocean to land, consistent with excessive

continental precipitation and runoff. Clear biases in the G5NR water cycle are apparent. Yet,

land evaporation is very close to the NEWS estimate, despite the overestimate of precipitation.

One possibility would be that the evaporation may have regional biases that cancel in the global

average.

NEWS processed regions concurrently with the global data. The regions consist of major continents

and ocean basins, though some smaller regions have also been considered, producing mean annual

cycles of all water budget terms in each (Figure 3.10). In North America, cold season precipitation in

the G5NR matches closely the NEWS merged data. In warm seasons, the excessive precipitation is

generally matched by surface runoff. Evaporation is close to the merged data in magnitude; however,

there may be a subtle phase shift of the surface evaporation. Eurasia generally follows the seasonal

cycle of North America, though the water storage amplitude is somewhat damped. In Africa,

evaporation is underestimated in the early part of the year, and precipitation is overestimated

later in the year, leading to runoff overestimates all year. The comparison of South American

precipitation is reasonable when integrated for the continent. However, sub-continental biases do

seem to be canceling out (see Figure 3.7). Mainland Australia experiences an overestimate of their

wet season precipitation and also a noticeable phase shift of the G5NR’s annual cycle of the water

budget. The Oceana continent (or Australasian and Indonesian Islands of the tropical west Pacific)

systematically both receives too much precipitation and produces too much runoff. When taken

as a whole, the global land evaporation compares well with NEWS estimates, while precipitation

and runoff are biased high (the global land annual cycle tracks closely with North America and

Eurasia).

71
3.7 NEWS Global Energy Budget

In the global sense, there are many flux terms that are simulated within reasonable range of the

NEWS energy budget results (Figure 3.11, Table 3.1). There seems to be too much shortwave

radiation reflected at the top of the atmosphere (though the G5NR OLR is close to the NEWS

OLR). This deficit in the energy seems to be compensated for in net surface imbalance. The G5NR

has 9.4 Wm−2 net energy leaving the ocean surface to the atmosphere (this term is 6.4 Wm−2in

the global average). The global estimate from balancing observations is approximately 1 Wm−2

directed into the surface. The main areas of bias in decreasing importance are the North Atlantic,

Indian and South Pacific oceans. While such errors can occur in models, here we document the

bias for the G5NR users. Spatial considerations are also very important, for example, the OLR has

regional bias that may be important for any individual use of the data (see sections 5 and 6 for

specific examples).

3.8 Selected Results on Monsoonal and Diurnal Precipitation

Variability

Here we examine the verisimilitude of the precipitation associated with selected tropical wave,

monsoonal, and diurnal variability produced in the G5NR for the period June 2005–July 2006.

The results include Hovmøller diagrams of daily means to highlight subseasonal propagation, maps

of monthly means to highlight the basic patterns and the seasonal development of the various

monsoons and related low-level winds, as well as maps of the mean diurnal cycle of precipitation

over North America

Comparisons are made with the high-resolution CMORPH (Joyce et al. 2004) precipitation estimates. The CMORPH or CPC MORPHing technique produces global precipitation analyses at

very high spatial and temporal resolution. The technique uses precipitation estimates derived from

the passive microwaves aboard the DMSP 13, 14 and 15 (SSMI), the NOAA-15, 16, 17 and 18

(AMSU-B), and AMSR-E and TMI aboard NASA’s Aqua and TRMM spacecraft, respectively. We

use here fields from the years 2005 and 2006 from an updated CMORPH dataset that has recently

been reprocessed and bias corrected by NOAA (Pingping Xie, personal communication) to produce

an hourly, 16-year record at a horizontal resolution of 8 km and covering the globe from 60S–60N.

3.8.1 Tropical waves

Figure 3.12 provides an overall view of the tropical wave activity as it is reflected in the daily mean

precipitation field averaged between 10S and 10N. Overall the G5NR appears to do a good job of

simulating the basic eastward and westward propagating tropical systems (e.g., easterly waves).

72
Several things stand out as being somewhat unrealistic. The very regular and strong eastward

propagation simulated over the Pacific appears to be associated with overly active Kelvin waves

propagating eastward from the Pacific warm pool. On the other hand, there is insufficient eastward

propagation from the Indian Ocean into the warm pool, with the latter deficiency appearing to

contribute to an underestimate of the warm pool precipitation and northward propagation (linked to

the eastward propagation) of precipitation associated with Indian monsoon breaks and resurgences.

Some evidence for the latter is shown in Figure 3.13, which highlights the northward propagation

that often occurs over the Indian monsoon region at subseasonal time scales but is absent in the

G5NR.

3.8.2 South and East Asia

Figure 3.14 shows the monthly (April - July 2006) evolution of the precipitation and 850mb winds

with the focus on the south and east Asian monsoon regions. The model reproduces the basic

evolution quite well, including the monsoon onset over India. Other key features include the

northward movement of the precipitation over Australasia, and the persistent (but slowly northward

moving) east-west rain band known as the Meiyu front over eastern China, Taiwan, Korea (Changma front) and Japan (Baiu front). The latter northward movement is highlighted in Figure 3.15,

which shows the observed and simulated northward propagation of the precipitation during June

and early July that is quite prominent in the simulation though it is less regular in the observations

during that year. A key component of the monsoon development over southern Asia is the seasonal

evolution of the Somali jet. Figure 3.16 shows that the model does a very credible job in reproducing

the observed timing of the initial development, magnitude and overall horizontal structure of the

jet, keeping in mind (as discussed above) that there is likely some unforced (by SST) interannual

variability in the jet development.

3.8.3 Africa and South America

The development of the west African monsoon rains are tied to seasonal northward progression of

the Atlantic ITCZ and the associated southwesterly winds that bring moisture into western Africa

in late June and move back south by October. Figure 3.17 shows that the model captures that

development quite well, including the abundant precipitation that falls over much of central Africa

from July into September. The observations show a much stronger land-sea contrast than the

simulation with an abrupt drop in precipitation in going from ocean to land in western Africa that

is not apparent in the simulation. Whether this in part reflects a problem with the CMORPH data

is unclear and requires further investigation. Figure 3.18 shows the evolution of the low level winds,

highlighting the development of the southwesterlies during June and July. The development in the

model is much stronger than the observed (MERRA) with the southwesterlies near 10N already well

developed and extending into Africa by July. This contrasts with the rather weak southwesterlies

over the tropical Atlantic during that time from MERRA and these do not extend into Africa.

73
The evolution of the South American summer monsoon is characterized by an onset during October,

followed by a 3-month mature phase, and demise in April. During the peak of the monsoon,

convection over the southern part of the Amazon Basin contributes to the establishment of the

Bolivian high: an anticyclone that is responsible for the near-surface easterly moisture flux into

the tropical and subtropical Andes over northern Brazil, as well as the southward extension of the

moisture fluxes into Bolivia and regions further south. Figure 3.19 shows that the model does

quite well in reproducing the basic evolution of the monsoonal precipitation, including the onset

of strong precipitation over the Amazon basin in December, and the southward movement of the

Atlantic ITCZ precipitation as it impinges on the continent in December and January, though the

model appears to rain too much over the Andes. The model also does a remarkably good job of

simulating the basic seasonal evolution of the low level winds over the South American continent

(Figure 3.20).

3.8.4 North America

Figure 3.21 shows the development of the North American monsoon from June through August

of 2005. The observations show the typical seasonal development and northward progression of

monsoonal precipitation over the Sierra Madre Occidental along the west coast of Mexico. The

simulation shows a realistic placement of the maximum precipitation in that region though perhaps

starting somewhat earlier than observed (but again keeping in mind that there is likely substantial

year to year variability in that development). Both the model and observations show the signature

of an active hurricane season especially in the August precipitation field. The major deficiency in

the simulation in this region is the large summer precipitation bias over the eastern Pacific Ocean,

Mexico and Central America south of 20N. The simulation of the low-level wind field (Figure 3.22)

is overall quite good with a realistic low level jet over the Great Plains seen here as a westward

extension of the easterlies associated with the Bermuda High. The model does produce excessive

westerlies in the eastern tropical Pacific and this is likely linked to the excessive precipitation in

the region (cf. Figure 3.21)

The diurnal cycle is a key component of the mean climatology over a number of regions of North

America, reflecting important regional circulations and weather systems phasing to the diurnal solar

forcing. As shown in Figures 3.23 and 3.24 the G5NR does a credible job of capturing key diurnal

variations over Mexico, Central America and the Caribbean: these appear to be associated in part

with land-sea breeze circulations. The model also captures some aspects of the diurnal cycle over the

United States including the afternoon maximum over the southeast (though somewhat too weak),

and the late afternoon maximum over the Rockies, with some propagation to the east into early

evening. The model, however, fails to capture the nocturnal maximum over the northern Great

Plains. Further analysis (not shown), suggests that the model is capturing some of the initial

eastward propagation into the western parts of the Great Plains associated with late afternoon

eastward propagating weather systems but, despite having what appears to be a very realistic low

level jet, does not generate the distinctive nocturnal maximum over the Great Plains which in the

observations is tightly coupled to the evolution of the low level jet.

74
3.8.5 Summary

The G5NR generates a wide range of tropical wave, monsoonal, and diurnal variability that is in

many ways quite similar to the observed. This includes a range of eastward and westward moving

tropical weather systems, the northward propagation of the east Asian monsoon, the onset of the

south Asian (Indian) monsoon, the basic seasonal development of the African, South American and

North American monsoons and related low level circulations. The analysis of the diurnal cycle was

limited to North America, but here too the model reproduces many of the basic regional and other

small scale features of the diurnal cycle that appear to be linked to land-sea breeze circulations.

There are a number of aspects of the simulation that are unrealistic but it should be noted that these

are for the most part similar to those found in coarser resolution simulations with earlier versions

of this and other state-of-the-art climate models. The unrealistic aspects include the lack of slow

eastward-moving Madden-Julian (MJO)-type convectively coupled waves especially over the Indian

Ocean and Pacific warm pool (presumably impacting the ability to simulate subseasonal variability

of the Indian monsoon) and the lack of a nocturnal precipitation maximum over the northern US

Great Plains in summer, despite the presence of a rather realistic low level jet. Other deficiencies

include excessive precipitation over high topography (especially the Andes) and excessive summer

precipitation over the eastern Pacific, Mexico and Central America south of 20N.

3.9 Specific Humidity (QV) and Total Surface Precipitation Flux

(PrFLX):

In this section, we compare the vertical structure of QV (kg · kg−1) and PrFLX (kg · m−2· s

−1

)

simulated by the G5NR against that obtained from reanalysis (NCEP CFSR (Saha et al. 2010) &

MERRA (Rienecker et al. 2011)) for the months of June to September (JJAS). Daily anomalies

of QV and PrFLX (for JJAS 2005 and 2006) for G5NR and analysis are computed at a specific

time (1330 hrs) by removing the monthly mean diurnal cycle. For many models (e.g., Neale et al

2008) the simulated PrFLX in the vicinity of the tropical central and eastern Pacific Intertropical Convergence Zone (ITCZ) region (30oS − 30oN, 120oE − 80oW) tends to be in disagreement

with observations/analysis. We therefore focus on the simulated PrFLX and QV in this region.

Firstly, convective regimes of deep and shallow convection are identified using daily large-scale

(LS) mid-tropospheric vertical velocity (OMEGA500 (hPa · day−1)), following Bony et al., 2004.

Using OMEGA500 as a proxy for convective and subsidence regimes (i.e. OMEGA500 < \ > 0

respectively), QV and PrFLX are then analyzed in specific regimes by compositing the anomalies

into equal bins of width 10 hPa day−1.

75
3.9.1 PDF of OMEGA500 and Composite PrFLX Anomaly

Figure 3.25 shows the probability distribution function (PDF) of OMEGA500 and the composite

PrFLX anomaly in the various circulation regimes. The PDFs peak at about 25 hPa · day−1,

consistent with widespread subsidence associated with clear-sky radiative cooling in the tropics

(Bony et al. 2004). The normalized peak grid point average is about 0.08 in the analysis while

the area of LS descent is relatively larger in the G5NR (peak value of 0.1). As a consequence, the

area of LS ascent is higher in the analysis relative to G5NR across the convective regimes. The

composite PrFLX anomaly in the analysis peaks at above 0.025 kg ·m−2·s

−1

in the deep convective

regime (OMEGA500 < -80 hPa · day−1), while the value actually lies below the diurnal monthly

mean value in the G5NR in this regime. A general reduced contrast is seen across the regimes in

the composite G5NR PrFLX anomaly relative to analysis.

3.9.2 Composite QV Anomaly

The composite QV is shown in Figure 3.26, along with the difference values between the analysis

and G5NR. While the range of composite QV across the regimes is similar in CFSR and G5NR, the

vertical structure in the convective regime (OMEGA500 < -60 hPa · day−1) is rather different. In

particular, there are two distinct peak composite QV values in the G5NR (centered near the surface

and at about 800 hPa·day−1) across the deep convective regime, in contrast to analysis which shows

a single peak value centered at this pressure level. The general drying of the atmosphere in the

strong subsidence regime (OMEGA500 > 60 hPa · day−1) is also absent in the G5NR, unlike that

in the analysis.

3.9.3 Covarying Anomaly Patterns of QV and PrFLX

In order to examine covarying patterns of QV and PrFLX anomalies, the canonical correlation

analysis (CCA) is performed on these fields. The CCA finds linear combinations of two variables

in independent data sets that have maximum correlation in time (Delsole et al. 2003). The EOF

analysis method is used to compress the co-varying fields and apply the CCA in a lower-dimensional

subspace such that the dominant modes of variability in time are captured (Delsole and Tippett,

2007). In this study, following the method of Delsole and Tippett, the reduction of space is obtained

by projecting the original time series onto the leading principal components (PCs).

Figure 3.27 shows the leading physical patterns (also called canonical loading vectors) corresponding

to a maximally correlated set of distinct time series (called canonical variates) of QV and PrFLX

that are linear combinations of elements of QV and PrFLX such that the two linear combinations

have maximum correlation. The PCs and canonical variates are normalized to unit variance, and

hence the loading vector absorbs the actual units of the field. The panels on the left and center

show zonal and meridional cross-sections of the leading QV pattern, while that on the right shows

76
the leading PrFLX pattern. The analysis PrFLX exhibits increased positive anomalies across

the Pacific and west of the dateline, extending in a diagonally-oriented manner across the South

Pacific Convergence Zone (SPCZ). This pattern is maximally correlated with positive anomalies of

vertical QV west of 130oW across 20oS - 20oN in the vicinity of the warm pool region. In contrast,

two longitudinally distinct centers of positive G5NR PrFLX anomalies are seen to extend across

the Pacific at about 10olatitude. This structure is maximally correlated with vertical positive

anomalies of QV in regions on either side of the dateline (also over the cold tongue region, unlike

that in analysis), with a distinct vertical center of negative QV anomaly centered at 5oN to the

immediate east of the dateline. It can be speculated that spurious drying north of 5oN and to

the immediate east of the dateline is partly responsible for the spurious vertical peaks of stratified

positive QV anomaly noted earlier in the deep convective regime.

In summary, the general trend of the simulated QV and PrFLX in the G5NR is to exhibit increased

areas of subsiding motion in the G5NR across the equatorial Pacific and spurious vertical drying

centers across the dateline presumably in the deep convective regimes. This is correlated with

reduced PrFLX anomalies to the west of the dateline and in the SPCZ, unlike that in the analysis,

resulting in the classic double-ITCZ precipitation error.

3.10 Precipitation Flux and Condensate

This section focuses on the validation of precipitation fields in the G5NR. By comparing with

TRMM Precipitation Radar (PR) Level 2 product (2A25), we examine several aspects of the

G5NR’s precipitation fields in terms of precipitating area fraction, average surface precipitation

rate, probability distribution of preciptation rate, convective and stratiform precipitation area

fraction and precipitation rate, and vertical distribution of precipitation. For the purpose of this

study, the 2005 June - August (JJA) months of the G5NR and TRMM PR data are considered.

3.10.1 Verification Data

The level-2 precipitation profiles from TRMM PR, 2A25 Version 6 (Kozu et al, 2001, Iguchi et al.

2000) are the verification data used in this study. The TRMM PR can estimate quantitative rainfall

over land as well as ocean. The TRMM satellite has an orbital domain extending from 35◦N-35◦S

and has a precessing orbit so that it samples the full diurnal cycle. The algorithm 2A25 retrieves

profiles of the radar reflectivity factor, Z, with rain attenuation correction and rain rate for each

radar beam by the combination of Hitschfeld-Bordan and surface reference method [Iguchi 2000].

Owing to power constraints, the TRMM PR has a relatively low sensitivity of 17 dBZ (which

corresponds to a precipitation rate of approximately 0.5 mm/hr). Therefore, this study uses only

data ≥ 17 dBZ to avoid the ambiguity of sampling at lower reflectivities. To collocate the data, the

G5NR precipitation is projected from the model domain to the observation scale considering the

similarity of spatial resolution between the G5NR and the TRMM PR.. The PR data are nearly

77
continuous in time while the G5NR data are only available every 30 min. Therefore, a nearestneighbor-in-time sampling of the G5NR data is performed to collocate the data rather than linearly

interpolating (Figure 3.28). More than 200,000 collocated data were used in this study.

3.10.2 Rainy Area Fraction

Fractions for rainy area and non-rainy area are compared between TRMM PR and the G5NR. As

mentioned in the previous section, PR is not sensitive to the reflectivity below 17 dBZ which is

approximately corresponding to 0.5 mm/hr rain rate. Therefore, data points with precipitation

rate > 0.5mm/hr (≤ 0.5 mm/hr) were considered rainy (non-rainy) in the comparisons. Results

are summarized in Table 3.2. About 2.8% of collocated TRMM PR data were rainy and 3.5%

of collocated G5NR data were rainy so that the latter has 0.7% larger rainy area. The maximum

surface precipitation rate that occurred during the period considered in this study was 265.3 mm/hr

for PR and 298.4 mm/hr for the G5NR. Mean and standard deviation of PR surface precipitation

rate were 2.34 mm/hr and 25.47 mm/hr, respectively. Mean and standard deviation of the G5NR

surface precipitation rates were 2.64 mm/hr and 36.9 mm/hr. That is, the G5NR presents about

10% larger averaged precipitation rate than the TRMM PR observations.

3.10.3 Probability Distribution of Surface Precipitation

To evaluate the G5NR surface precipitation for different precipitation intensities, probability distributions of surface precipitation rates in TRMM PR observations and the G5NR data were

calculated for each 0.5 mm/hr precipitation rate bin, up to the precipitation rate of 200 mm/hr

(Figure 3.29). Comparisons show that G5NR exhibits larger frequency than TRMM PR observations for light precipitation rates (< 1 mm/hr) and for heavy precipitation rates (> 20mm/hr)

while it has smaller frequency for precipitation rates ranging 1 mm/hr and 20 mm/hr compared to

the TRMM PR observations.

3.10.4 Convective Precipitation vs. Stratiform Precipitation

Precipitation is generally considered to be of two clearly distinguishable types: stratiform and convective. Houze (1982, 1989) demonstrated that the relative proportions of convective and stratiform

rain amounts can be used to infer the vertical structure of latent heating in a tropical precipitating

system. Higher fractions of stratiform rainfall are associated with an upward shift in the level

of maximum heating and an increase in the vertical heating gradient in the upper troposphere.

Horizontal variations in the vertical heating profile associated with tropical precipitating systems

can affect the large-scale dynamical response because the generation of potential vorticity in the

tropics is directly proportional to the local vertical gradient in the heating profile (Hartmann et al.

1984, Haynes and McIntyre 1987, Mapes and Houze 1995).

78
In the PR 2A25 products, all observation points are classified as convective, stratiform or “other”

based on the vertical profile of reflectivity and the horizontal variability of the radar echo. Data in

the “other” category are considered unreliable and are ignored here. The GEOS-5 moist physics

schemes allow for the simultaneous presence of stratiform and convective precipitation, and the

ratio of one to the other is highly dependent on model configuration and resolution (Molod et

al., 2014). For the present comparison, the total precipitation from the G5NR is categorized as

convective if the convective source precipitation generated by the model is greater than 0.5 mm/hr,

and stratiform otherwise. The total precipitation must exceed 0.5 mm/hr to exceed the trace limit

of TRMM.

Table 2 summarizes the comparisons of convective and stratiform precipitation fractions from

TRMM PR and the G5NR data. Results show that convective and stratiform rainy area fractions

for the nature are remarkably consistent with the PR observations: In the nature run, convective and stratiform rainy area fractions are 22.9% and 77.1%, respectively while convective and

stratiform rainy area fractions for TRMM PR are 23.3% and 76.7%, respectively. Mean (standard

deviation) surface precipitation rate for convective precipitation is 153.0 (347.2) mm/hr which is

slightly smaller (larger) than TRMM PR average rain rate of 164.2 (205.5) mm/hr for convective

precipitation. Average surface precipitation rate for stratiform precipitation is 50.9 (82.8) mm/hr

for the G5NR which was slight smaller than 58.7 (81.5) mm/hr for TRMM PR observations.

3.10.5 Vertical Distribution of Precipitation

The benefit of an active instrument like radar to measure precipitation is that observations can

provide the vertical structure of precipitation. It should be noted that the units for precipitation

fields in the G5NR are generated in kg/kg to be easily converted to the input units for radiative

transfer models while precipitations in TRMM PR 2A25 products have unit of mm/hr. Therefore,

it is not possible to make quantitative comparisons for the vertical distribution of rainrates directly

but still qualitative comparisons of the vertical structures of TRMM PR and the nature precipitation

can provide useful information for the validation. As shown in Figure 3.30, the vertical extension

of precipitation is consistent between TRMM PR and the G5NR over the tropical and subtropical

regions that TRMM PR observations cover. In addition, the latitudinal location of Intertropical

Convergence Zone (ITCZ) characterized by the intense precipitation rates in TRMM PR is very

close to the location where the main ITCZ in the G5NR exists. It should be noted that another

weak precipitation maximum exists in the G5NR between the equator and 5◦S which is not present

in the TRMM PR observations.

3.10.6 Summary

The G5NR presents about 10% larger averaged surface precipitation rate than the TRMM PR

observations. The G5NR shows larger frequency for light precipitation rates (< 1 mm/hr) and for

heavy precipitation rates (> 20 mm/hr) compared to the TRMM PR observations. In contrast, the

79
G5NR shows smaller frequency for precipitation rates ranging 1 mm/hr and 20 mm/hr compared

to the TRMM PR observations. The convective and stratiform rainy area fractions for the G5NR

are remarkably consistent with the PR observations. Mean precipitation rates for both precipitation types were similar but the standard deviation of precipitation rates in G5NR for convective

precipitation type was about 70% larger than that for the TRMM PR observations.

80
Table 3.1: Using EOS observations, merged data products and reanalyses, NEWS has developed

an energy budget for the globe and continental basins. This table shows the global energy budget

for the combined original data, and a constrained budget. The constrained budget was developed

with physical realism, a simple error model and rigorous error estimates of the contributing data.

NEWS data include the EOS period from 2001-2010, while the G5NR data are for Jun 2005 - May

2007. Units are W m−2

.

Budget Term Original Constrained G5NR

Incoming Solar F 340 ±0.5 340 ±0.5 340

Outgoing Shortwave OSR 100 ±5 98 ±2 106

Outgoing Longwave OLR 238 ±2 239 ±2 240

Downwelling LW at SFC LW↓sfc 344 ±7 339 ±4 334

Downwelling SW at SFC SW↓sfc 190 ±6 187 ±3 184

Surface Emitted LW↑sfc 398 ±6 400 ±4 394

Surface Reflected SW↑sfc 22 ±2 22 ±1 23

Sensible Heating SH 21 ±5 23 ±3 21

Precipitation LH LvP 77 ±7 80 ±4 87

Evaporation LH LvE 75 ±7 80 ±4 87

Surface Heat Storage Net Abs. 0.6 ±0.4 0.91 -6.44

81
Table 3.2: Comparisons of rainy area fraction and mean surface rainrate between TRMM Precipitation Radar data and the G5NR.

TRMM PR G5NR

Rainy 2.8 % 3.5 %

Non-rainy 97.2 % 96.5 %

Rainrate Avg. 2.33 mm/day 2.62 mm/day

Rainrate STD 25.5 mm/day 37.0 mm/day

Maximum rainrate 265.3 mm/hr 298.4 mm/hr

Table 3.3: Comparison of convective and stratiform precipitation fractions between TRMM Precipitation Radar data and the G5NR.

TRMM PR G5NR

Convective precip area 23.3 % 22.9 %

Stratiform precip area 76.7 % 77.1 %

Convective rainrate Avg 164.2 mm/day 153.0 mm/day

Stratiform ainrate Avg 58.7 mm/day 50.9 mm/day

Convective rainrate STD 205.5 mm/day 347.2 mm/day

Stratiform ainrate STD 81.5 mm/day 82.8 mm/day

82
Figure 3.1: Seasonal mean specific humidity in g kg−1

from a) the G5NR in DJF, b) the G5NR

in JJA, c) MERRA in DJF, d) MERRA in JJA, e) the difference G5NR - MERRA in DJF, f)

the difference G5NR - MERRA in JJA, g) the difference G5NR - ERA-I in DJF, h) the difference

G5NR - ERA-I in JJA.

83
Figure 3.2: Seasonal mean relative humidity in % from a) the G5NR in DJF, b) the G5NR in JJA,

c) MERRA in DJF, d) MERRA in JJA, e) the difference G5NR - MERRA in DJF, f) the difference

G5NR - MERRA in JJA, g) the difference G5NR - ERA-I in DJF, h) the difference G5NR - ERA-I

in JJA.

84
Figure 3.3: Seasonal mean total precipitable water mm from a) the G5NR in DJF, b) the G5NR

in JJA, c) SRB in DJF, d) SRB in JJA, e) the difference G5NR - SRB in DJF, f) the difference

G5NR - SRB in JJA.

85
Figure 3.4: Annual mean cloud ice content in mg kg−1

from a) the G5NR, b) CloudSat nonprecipitating cloud ice.

86
Figure 3.5: Annual mean cloud liquid water content in mg kg−1

from a) the G5NR, b) CloudSat

non-precipitating cloud liquid water.

87
Figure 3.6: a) 0.5o

subgrid scale variance of total water in (g kg−1)

2

∗ 1000, b) Critical relative

humidity (RHc) in fraction computed from G5NR, inset RHc profile used in 0.5o GEOS-5 AGCM.

88
Figure 3.7: Seasonal mean total precipitation in mm day−1

from a) the G5NR in DJF, b) the

G5NR in JJA, c) MERRA in DJF, d) MERRA in JJA, e) the difference G5NR - MERRA in DJF,

f) the difference G5NR - MERRA in JJA, g) the difference G5NR - ERA-I in DJF, h) the difference

G5NR - ERA-I in JJA.

89
Figure 3.8: Seasonal mean total precipitation over land only in mm day−1

from a) the G5NR in

DJF, b) the G5NR in JJA, c) MERRA in DJF, d) MERRA in JJA, e) the difference G5NR -

MERRA in DJF, f) the difference G5NR - MERRA in JJA, g) the difference G5NR - ERA-I in

DJF, h) the difference G5NR - ERA-I in JJA.

90
Figure 3.9: Graphical depiction of the G5NR Global Water Cycle terms in units of km3 yr−1

compared to that of the NEWS water cycle terms before and after constraint. Note that the G5NR

balance also considers a small total tendency term, -0.12 km3 yr−1 globally. Runoff as considered

here does not include glacier runoff and other approximations, leading to an imbalance between

runoff and the terrestrial data. In addition, terrestrial water storage has also not been included.

91
Figure 3.10: Monthly hydrological quantities in G5NR and NEWS. Dashed lines indicate the G5NR,

solid lines with error bars represent the NEWS Climatology. The full nature run, Jun2005-May2007

were used.

92
Figure 3.11: Graphical depiction of the NASA Energy and Water Cycle Study (NEWS) constrained

merged satellite energy balance (black) and G5NR energy components (yellow). Units are W m−2

(courtesy of Dr. Michael Bosilovich).

93
Figure 3.12: Hovmøller diagrams of daily mean precipitation averaged between 10S- 10N and 0-60E.

Left panel is for the G5NR. Right panel is for the CMORPH observations. Units: mm day−1.

94
Figure 3.13: Northward progression of the Indian monsoon precipitation during June through

August 2005. Left panel is for the G5NR, and right panel is for the CMORPH observations. Units:

mm day−1.

95
Figure 3.14: April - July 2006 monthly mean precipitation maps focusing on the Indian Ocean

and western Pacific warm pool regions. Left panels are for the G5NR. Right panels are for the

CMORPH observations. Units: mm day−1.

96
Figure 3.15: Northward progression of the East Asian monsoon precipitation during June through

August 2005. Left is for the G5NR, and right panel is for the CMORPH observations. Units:

mm day−1.

97
Figure 3.16: April through July 2006 wind vectors and speed at 850mb. Left is for G5NR, and

right panel is for the CMORPH observations over southern Asia. Units: m s−1

98
Figure 3.17: June through September 2005 precipitation over Africa. Left is for the G5NR, and

right panel is for the CMORPH observations. Units: mm day−1.

99
Figure 3.18: June through September 2005 wind vectors and speed over Africa. Left is for the

G5NR, and right panel is for MERRA. Units: m sec−1.

100
Figure 3.19: November 2005 through February 2006 precipitation over South America. Left is for

the G5NR, and right panel is for the CMORPH observations. Units: mm day−1

101
Figure 3.20: November 2005 through February 2006 wind vectors and speed over South America.

Left is for the G5NR, and right panel is for MERRA. Units: m sec−1.

102
Figure 3.21: Simulated and observed monthly mean precipitation (June-August 2005). Units:

mm day−1.

103
Figure 3.22: Simulated and observed 850mb winds (June-August 2005). Units: m sec−1

.

104
Figure 3.23: Time mean (JJA 2005 and 2006) simulated diurnal cycle of precipitation over North

America. Units: mm day−1.

105
Figure 3.24: Time mean (JJA 2005 and 2006) observed (CMORPH) diurnal cycle of precipitation

over North America. Units: mm day−1.

106
Figure 3.25: Probability distribution function (PDF) of diurnal mid-tropospheric vertical velocity

(OMEGA500) hPa · day−1 and composite precipitation flux anomaly kg · m−2· s

−1

from CFSR &

the G5NR in JJAS.

107
Figure 3.26: Composite specific humidity anomaly kg · kg−1

from A) the CFSR in JJAS B) the

G5NR in JJAS C) the difference CFSR - G5NR in JJAS

108
Figure 3.27: Leading CCA patterns of specific humidity kg · kg−1 and precipitation flux anomaly

kg·m−2·s

−1

from A) the CFSR averaged between 10oS - 20oN B) the CFSR averaged between 120o

- 190o C) the CFSR D) the G5NR averaged between 10oS - 20oN E) the G5NR averaged between

1200- 190o F) the G5NR

109
Figure 3.28: For comparisons with TRMM Precipitation Radar (PR) data, the G5NR precipitation is projected from the model domain to the PR observation points. Nearest-neighbor-in-time

sampling of G5NR precipitation fields was performed to collocate data.

110
Figure 3.29: Comparisons of probability distribution of surface rain rates between the G5NR and

TRMM PR data.

111
Figure 3.30: Comparison of vertical distribution of precipitation between the G5NR and TRMM

Precipitation Radar data.

112
Chapter 4

Tropical Cyclones and Related

Phenomena

Oreste Reale, Deepthi Achuthavarier, Gary Partyka and Marangelly Fuentes

Among the requirements of a nature run for use in OSSEs is “a realistic climatology consisting of

realistic weather patterns” (McCarty et al. 2012). Different Instrument Science Teams may have

different requirements for features that can be considered realistic. For example, the Science Team

for Cyclone Global Navigation Satellite System (CYGNSS) could benefit from realistic OSSEs, as

long as the nature run adopted can resolve wind fields on the scale of 10 km or less, particularly

around high-impact weather features such as tropical cyclones (TCs) and intense extratropical

disturbances. This chapter focuses TC activity in the G5NR over various basins (Atlantic, Eastern

Pacific, North Indian and South Indian Oceans), as compared with observations. Elements of

the circulation that are important in the formation of TCs, or in controlling their evolution, are

also discussed. Finally, an example of an extratropical transition is provided and future work is

discussed.

4.1 Tropical Cyclone Activity and Structure in Previous Nature

Runs

A previous nature run that has served the OSSE community for several years was produced by

the European Centre for Medium-Range Weather Forecasts in 2006 (hereafter referred to as the

ECMWF NR), and is documented, among other places, in Reale et al. (2007), Masutani et al.

113
(2010) and McCarty et al. (2012). It was produced at a T511 wave truncation, corresponding to

a horizontal resolution of about 40 km at the equator. The ECMWF NR was considered one of

the first free-running simulations, forced only by prescribed 2005 SSTs and sea-ice, which produced

a realistic depiction of tropical cyclone activity. The activity was considered realistic because:

1) the average climatological factors that are conducive to cyclogenesis were present, and 2) the

frequency, distribution, life cycle, and track of TCs were within observed climatological ranges.

The evaluation of the ECMWF NR demonstrated not only that TCs were present, but that they

originated out of realistic and very specific weather patterns and underwent realistic evolution

and decay, including dissipation, landfall or extratropical transition. Moreover, individual TCs

displayed an overall realistic structure in terms of vertical alignment, presence of a warm-core,

winds in excess of 50 m s−1 and an eye-like feature (i.e., a virtually windless column), as shown in

Figure 4.1. While the eye-like feature was broader and more diluted than a real TC eye because

of the relatively coarse resolution of the simulation, the ECMWF NR nevertheless has been an

invaluable tool for the OSSE community for several years.

4.2 Tropical Cyclone Activity and Structure in the G5NR

4.2.1 Tropical Cyclones in the Atlantic

It is important to demonstrate that TCs occur in the G5NR not as sporadic or localized events,

but as a realistic consequence of realistic large-scale forcings. The preliminary step is to verify that

the climatology of the main dynamical players over the African Monsoon region and the tropical

Atlantic is well represented. Figure 4.2 shows a meridional vertical cross-section of zonal wind

at 0◦, comparing the G5NR in the July, August, and September (JAS) months of two different

years with the corresponding MERRA years. It is worth stressing that a comparison of monthly

means cannot be interpreted as an actual seasonal forecasting validation. Since the G5NR is a

free-running simulation constrained only by SSTs, and in which the memory of initial conditions

is removed by the sufficiently long spin-up, a strict correspondence with observed means should

not be expected. It can only be noted that the G5NR represents the basic features of the African

Monsoon circulation, namely: a) the Tropical Easterly Jet (TEJ), an upper-tropospheric jet located

at about 100–200 hPa, b) the African Easterly Jet (AEJ) at about 600 hPa and peaking at about

12◦N–16◦N, c) the low-level westerly monsoonal flow confined below 800 hPa, and finally d) the

low-level easterly flow (also known as Harmatthan flow) at about 27◦N. The overall depiction of

the AEJ in the G5NR is about 20% weaker than in MERRA, but it should be remembered that the

AEJ depiction is affected by very large uncertainties, with differences of 20% in speed even among

state-of-the-art reanalyses such as ERA-40, NCEP-R2, JRA-25 and MERRA, as discussed in detail

in Wu et al. (2009) and in Wu et al. (2012).

Aside from the intensity, the position of AEJ, TEJ and low-level monsoonal flow is very important

because the cyclonically-sheared southward side of the AEJ (in which horizontal shear dominates)

is conducive to barotropic instability at about the jet level, while the lower levels below the AEJ

114
(in which vertical shear dominates) are conducive to baroclinic instability. African Easterly Waves

(AEWs) arise out of a combination of mechanisms: the presence of localized triggers, which can

be convective in nature and may alter the vorticity and thermal profile of the atmosphere, and the

favorable large-scale environment in which barotropic-baroclinic instability of the Charney-Stern

type can occur (e.g., Kiladis et al. 2006; Hall et al. 2006; Thorncroft et al. 2008; Wu et al. 2012).

Moreover, the presence of the TEJ, which is responsible for strong easterly shear and is generally

unfavorable for development of vertically aligned structures, is an important player that confines

the potential development of TCs to a narrow latitude range (just few degrees south of the AEJ

and north of the Inter Tropical Convergence Zone). The presence of all the fundamental elements

of the African Monsoon region atmospheric circulation is a good preliminary assurance that the

model may be capable of producing realistic weather patterns.

The next logical step is to verify whether the G5NR is able to produce realistic AEW activity. This

is a complex issue because at least three types of AEWs are currently known: the 2.5–6 day waves

developing to the south of the AEJ at about the jet level, the low-level baroclinic wave developing

below the AEJ, and less known 6–9 day waves developing at the AEJ level to the north of it. For a

comprehensive discussion of various types of AEWs, see Wu et al. (2013). The AEWs that are more

relevant to TC development are the 2.5–6 day waves developing to the south of the AEJ at about

the jet level. In addition, the tracking or definition of AEWs may involve sophisticated objective

methodologies (i.e., Berry et al. 2006) or the use of spectral techniques such as the Hilbert-Huang

transform (Wu et al. 2013). However, a very simple way of detecting them is to plot a latitude-time

Hovmøller diagram of the meridional component of the wind at or slightly below the jet level, and

at a latitude south of the jet.

In Figure 4.3 the 700 hPa Hovmøller obtained from the G5NR for the months of August 2005 and

2006 is plotted for the latitude of 15◦N and for a longitude range spanning from 40◦W to 40◦E.

These plots can be compared with those in Figure 4.4 where the same quantities are computed

from MERRA data. The comparison reveals that in both the G5NR and MERRA, the amplitude,

frequency and propagation speed of the AEWs are the same. In particular, waves occur at a

given longitude approximately every 3–6 days, and they propagate westward at a speed of about

5

◦–8◦ d−1

. Other features of the AEWs produced by the G5NR and present in MERRA include:

a discontinuity at about 15◦W corresponding to the African coastline, where the transition of

disturbances from land to ocean is observed; a pronounced diurnal cycle (evident by the horizontal

lines near the easternmost side of the panels); occasional higher wind speeds, indicating the tendency

of some AEWs to develop into TCs; upward curvatures indicating acceleration; the disappearance

of disturbances either through dissipation or movement to the north of the affected latitude. Aside

from the higher detail present in the G5NR due to the much higher resolution, the similarity

between the AEW activity as produced by the G5NR and the reanalysis is remarkable. Comparison

with other months (e.g., July and September) reveal a similar correspondence between G5NR and

MERRA (not shown).

The next step is to investigate TC number, tracks, distribution and life cycles. Figure 4.5 shows

the tracks of TCs in the G5NR for 2005 and their center pressures. Only ‘pure’ TCs are plotted,

and their extratropical transitions are not followed. A caveat should be inserted. The storm

115
detection algorithm involves parameters such as center pressure and presence of warm core. The

use of ‘thresholds’ can modify the number of detected storms. In particular, it was found that

less stringent thresholds in terms of warm-core intensity allow many more (weaker) depressions to

be detected as TCs, especially over the Indian Ocean. For clarity, it was decided to concentrate

on stronger storms, and use the same more stringent criteria throughout all basins, which led to

a slightly lower total number of TCs. Individual researchers can alter these criteria according to

their needs, and may be able to detect a slightly higher number of TCs than the 17 TCs shown in

Figure 4.5 by including some weak systems at a tropical depression intensity level. The choice of

thresholds in the TC-detecting algorithm also affects a hurricane’s life span: a system undergoing

transition can be categorized as extratropical (or still tropical) with a more (or less) stringent

threshold. Since we have consistently used a stringent definition of TC, it should be noted that

some TC tracks could be prolonged if less restrictive tracking choices were to be adopted.

Figure 4.6 shows the observed 2005 TC tracks and center pressures for the Atlantic, obtained from

the National Hurricane Center HURDATA2 best track database available at

http://www.nhc.noaa.gov/data/#hurdat

which contains 6-hourly center pressure, winds, and location (Sampson and Schrader 2000). Retrospectively classified storms and non-developing depressions are not included in this plot. While the

TCs produced by the G5NR are less than observed (17 versus 27, with the latter being an all-time

record), the track distribution in the G5NR is very realistic. The majority of the TCs are of the

Cape Verde type, tracking across the Atlantic and recurving north. Two TCs originate in the Gulf

of Mexico, leading to an overall very realistic partition between Gulf and Atlantic systems (e.g.,

Asnani 2005). One system (TC 17) originates in the westerlies, as typical for late-season hurricanes.

It is particularly noteworthy that four TCs reach central pressures of less than 945 hPa, in good

agreement with the observations for that year.

The comparison with the 2006 season, which was much less active (Figures 4.7 and 4.8), shows that

the G5NR produces only 10 TCs in 2006 (against 9 observed). It should not be expected that a

free-running model forced only by SST produces the same number of cyclones as in observations,

since there are many factors controlling TC frequency other than SST. Moreover, as previously

said, the choice of the detection algorithm affects the TC number. However, the fact that the

model-generated natural variability compares well with the observed variability, is remarkable. For

reference, it should be noted that the ECMWF NR produced only 12 TCs with 2005 SSTs (Reale

et al. 2007).

While, as noted before, a rigorous correspondence between observed and modeled storms cannot be

expected, the fact that the interannual variability in the G5NR has the same sign as the observed

one suggests that SST alone, as is reasonable to expect, exerts some control on TC number. As

noted for 2005, the distribution of tracks is realistic, with a majority being Cape Verde systems,

and four storms forming in the Caribbean or in the Gulf.

116
In both years the TCs produced by the G5NR display a life time spanning from a few days to

less than two weeks. Individual tracks reveal singularities (i.e., sharp curvature changes) as well

as binary interaction (i.e., two cyclones rotating around a common center with the stronger one

moving at a lower speed, not shown), all features that are well known to forecasters and that

frequently occur in nature.

The final step in the investigation consists of examining the individual structure of the most intense

storms taken at representative times.

Figure 4.9 shows a zonal cross-section of wind and temperature across TC 2 (from map in Figure 4.5)

taken at 1800 UTC 17 September 2005. The expected features of a mature hurricane can be noted:

a perfectly aligned structure, with wind speeds in excess of 65 m s−1, a strong warm core (greater

than 12◦C), a spatial scale on the order of few hundreds of km, a radius of maximum wind on the

order of about 40–50 km, and a distinct eye-like feature with a very clear windless column. The

overall structure is very realistic and represents an improvement with respect to the hurricane seen

in Figure 4.1, which was the best produced by the ECMWF NR.

The very realistic representation of TC 2 is not an isolated occurrence in the G5NR. The subsequent

Figures 4.10 and 4.11 display two different snapshots of 2005 hurricanes in the G5NR: TC n. 9 and

TC n. 12, taken at an early development stage, and at a mature stage, respectively. Both display

the same realistic features: perfectly calm central column, vertical alignment, scale on the order

of hundreds of km, radius of maximum wind on the order of tens of km, and pronounced warm

core. Interestingly, the snapshot depicting the strongest and most mature hurricane (Figure 4.11),

whose warm core temperature anomaly exceeds 14◦C, is also the one characterized by the tightest

and most narrow eye-like feature, in agreement with the well-known fact that TCs reduce their

scale during intensification, with the strongest systems displaying the most narrow and confined

eyes (e.g., Asnani 2005).

4.2.2 Tropical Cyclones in the Eastern Pacific

The seasonal activity and presence or interannual variability of TCs have been investigated in

the G5NR for other basins where global models typically have more difficulties, even in assimilation mode. Figures 4.12 and 4.13 compare the Eastern Pacific TC activity for 2005, while

Figures 4.14 and 4.15 depict the corresponding activity for 2006. Given the same caveats about the

TC detecting and tracking algorithm previously noted, and the fact that by choosing a less restrictive definition of TC, more weaker TCs and longer tracks could be detected, the results suggest

that interannual variability is reasonably reproduced by the G5NR, with more TCs in 2006. In

fact, the number of observed TCs in 2005 and 2006 were 14 and 18, respectively, as compared with

8 and 19 in the G5NR. As for TC genesis, the region between 90◦W and 120◦W, and between 10◦N

and 15◦N, is the favorable location in both the G5NR and observations, with a predominant TC

motion towards the west-north-west. However, the presence of outliers and TCs displaying erratic

or retrograde motion with respect to the easterly flow (known to forecasters), is not captured very

117
well by the G5NR, with TCs displaying less track variability than observed.

While the investigation of the Western Pacific tracks is ongoing, an overall assessment of the dynamics over the Pacific seems to suggest that this basin is more penalized than others by prescribed

SSTs and the resulting inability of surface fluxes to respond and adapt to the atmospheric forcing.

However, in spite of the absence of air-sea interaction which could be represented only by a coupled

model, it should be noted that some important atmospheric circulation elements which were missing

in previous nature runs (see Reale et al. 2007) are represented in the current one. Notable among

these are features resembling westerly bursts. Figure 4.16 compares the equatorial zonal wind at

approximately 950 hPa in the G5NR and MERRA for August 2006. Ideally, a time-longitude plot

of unfiltered equatorial wind across the Pacific reveals, in the midst of predominantly easterly flow,

two sets of lines, representing propagating anomalies: peaks of increased easterly speed which travel

within the easterly flow, moving from east to west, and regions of decreased easterlies (or westerlies) which travel against the easterly flow, moving from west to east. When these anomalies are

stronger than the mean flow they appear as eastward-propagating westerly anomalies and are aptly

named ‘westerly bursts’. Westerly bursts are associated with the Madden-Julian oscillation (MJO,

Madden and Julian 1971, 1972) but are strongly controlled by other factors, first and foremost

the phase of the ENSO. Transitioning ENSO can affect the clarity of an MJO unfiltered signal.

The 2005 and 2006 summers were not particularly representative in terms of the MJO signal, with

the ENSO phase transitioning from positive to negative (2005) and then from negative to positive

(2006). Nevertheless, evidence of westerly bursts (i.e., eastward moving areas of westerly wind) is

clear in Figure 4.16 particularly to the west of the date line. While the G5NR underestimates the

intensity of the westerlies, it is important to note that these bursts have not been detected by this

team in other non-coupled global simulations.

4.2.3 Tropical Cyclones in the Northern Indian Ocean

The Northern Indian Ocean is arguably the most difficult basin for TC forecasting. Even objective

analyses can provide poor representations of existing TCs in this region, as in the 2008 case of

Nargis discussed by Reale et al. (2009). Free-running models examined by this team, including

but not limited to the ECMWF NR, have produced results ranging from seasons without a single

storm in the Northern Indian Ocean to seasons in which up to 40 TCs were simulated. The latter,

unrealistically large number of TCs is probably due to extreme model sensitivity to small changes

in the circulation. In fact, the SSTs over the Indian Ocean are extremely warm (often more than

30◦C) but the environment is not generally conducive to TC development because of the very

strong shear. Moreover, during the summer, the low-level circulation is controlled by the Somali

Jet (SJ), with southwesterly flow peaking at about 900 hPa in connection with the Indian Monsoon

(e.g., Krishnamurti et al. 1976, Halpern and Woiceshyn, 2001), while the upper level circulation is

controlled by the TEJ, with easterly flow peaking at about 150 hPa (e.g., Chen and van Loon 1987,

Nicholson et al. 2007) creating zonal shear values of up to -40 m s−1 or more. In spite of the huge

fluxes consequent to the very warm marine temperature, and the environment being extremely

conducive to convection, cyclonic circulations cannot generally overcome the vertical shear except

in very rare situations when the shear relaxes and very sudden development can occur. Other cases

118
show TCs which cannot fully overcome the shear and present shallow structures which keep being

eroded above 300 hPa by the upper-level easterly flow. In this environment, which essentially has

surplus energy available but hostile dynamical forcing, small errors in the representation of the

shear can lead to large errors in the depiction of TC activity.

With this in mind, it is easier to appreciate the representation of TC activity in the G5NR over

the northern Indian Ocean. Figures 4.17 and 4.18 compare the representation of the Somali Jet

in 2006 (2005 does not offer a significantly different situation, and is not shown). It can be noted

that the predominantly easterly flow over the southern hemisphere is deflected northward and then

northeastward by the combining effect of the Indian monsoon low and the orography of eastern

Africa, in agreement with observations and other modeling studies (e.g., Chakraborty et al. 2009).

It also is worth noting that the higher resolution of the G5NR allows a sharper depiction of the

SJ than MERRA, including the well-known bifurcation caused by the ‘Horn of Africa’. Most

interesting is the SJ vertical structure. From aircraft measurements acquired during campaigns

such as Monsoon Experiment (MONEX 79; e.g., Holt and Sethuraman 1985) it is known that the

SJ is a very shallow feature, peaking at about 10◦N–15◦N and about 875 hPa and disappearing

at about 600 hPa. These features are clearly represented in Figure 4.19 and are confirmed by the

reanalysis (Figure 4.20).

Probably because of the overall realistic rendering of the mean SJ in the G5NR, the representation

of the Northern Indian TC activity has improved with respect to previous long simulations. Figures. 4.21, 4.22, 4.23 and 4.24 show a total of four TCs in 2005 in the G5NR versus seven that were

observed, and six for both the G5NR and observations in 2006. However, given the fundamentally

erratic nature of TCs over that basin, the spatial distribution of TCs and their tracks vary significantly. As noted previously, very small lapses in the shear can very quickly trigger a TC genesis

process, making it extremely difficult overall to predict TCs in the northern Indian Ocean. In terms

of vertical structure, the G5NR displays a significant number of non-fully developed systems, or

systems fighting against shear, in agreement with climatology (not shown).

4.2.4 Tropical Cyclones in the Southern Indian Ocean

Over the Southern Indian Ocean, the G5NR produces very realistic activity (Figures 4.25, 4.26,

4.27 and 4.28) not just in terms of overall number, but in terms of track distribution. TCs generally form between 5◦S and 15◦S (except for a few originating to the west of Madagascar) and

track southward or westward, gradually recurving eastward under the westerly flow influence, but

displaying frequent singularities in their tracks, such as loops, sharp recurvatures and binary interactions. The G5NR exhibits a very convincing spectrum of TC tracks over this basin. Finally, a

zonal vertical cross section of wind and temperature across a mature TC reveals the high degree of

consistency between TC structures produced by the G5NR over all basins (Figure 4.29): the system

is very symmetric, with a pronounced warm core, winds in excess of 65 m s−1, a very well-defined

eye, and a radius of maximum wind on the order of 45 km.

119
4.3 Other Significant Weather Phenomena

4.3.1 Example of Tropical-Extratropical Transition

Important weather phenomena that may be of interest to scientists developing OSSEs for future

instruments are TCs that undergo extratropical transitions (ETs). During ET, a warm-cored

cyclone evolves into a larger scale baroclinic system undergoing a number of transformations that

involve, among other things, a change in its primary energy source from latent heat to baroclinic

energy conversion processes (e.g, Sinclair 1993; Kyle and Bosart 2014). This evolution typically

takes on the order of two to three days and is marked by an expansion of the low-level wind field, the

development of frontal structures (particularly a warm frontal-like band poleward and downstream

of the vortex), and a more asymmetric distribution of precipitation about the cyclone.

A representative case of a deep warm-core tropical cyclone that undergoes ET in the G5NR is

highlighted in Figure 4.30. This figure illustrates the four primary stages of such an event from

incipient tropical genesis, maximum warm-core peak intensity, early ET, and finally mature ET.

In Figure 4.31 we see the tropical cyclone just east of 150◦E on the northern fringe of supportive

SSTs. Upstream of the cyclone, a deep mid-latitude trough is found in a favorable position to

induce the cyclone to undergo ET. The upstream trough is neither too close to the tropical cyclone

to quickly absorb it, nor too far upstream to have limited baroclinic effects on the cyclone prior

to its reaching colder SST’s (and thereby leading to rapid decay). Figure 4.31b provides further

evidence of a fully mature ET, with a classic frontal “T-bone” structure in the precipitation and

wind fields, especially sharp and concentrated about the warm-frontal feature along 52◦N. Also

noteworthy is the asymmetric distribution of rainfall near the southwest quadrant of the vortex.

4.3.2 Ongoing Validation of Other Mesoscale Features

While a realistic representation of large-scale baroclinic cyclones and their statistics is certainly not

a novelty in long simulations, even at much lower resolutions, (and is not discussed in this chapter)

there are other atmospheric features that, even if not fully tropical, are seldom captured in global

models and are therefore worthy of mentioning as being of interest for OSSE work.

One such feature is the Mei-Yu front, which is a baroclinic area strongly convective in nature,

resulting from the interaction of the Asian monsoon with the midlatitude circulation, and stretching

west-south-west to east-north-east from the Chinese coast into the Pacific. Generally studies of the

Mei-Yu front have been performed with mesoscale models, but our analysis provides evidence that

the G5NR can provide a representation of the Mei-Yu (not shown).

Other interesting features typically absent or misrepresented in lower-resolution GCMs are polar

and arctic lows. These sub-synoptic scale vortices display strong similarities with hurricanes, even

120
if developed at high latitudes. Similarities include some level of vertical alignment, the presence

of an eye-like feature, a prominent role played by convection, and latent and sensible total heat

fluxes that can reach values comparable to hurricanes, even if with a larger contribution from the

latter (e.g., Rasmussen and Turner 2003). A somewhat intermediate category between fully tropical

cyclones and polar lows is represented by Mediterranean tropical cyclone-like vortices (e.g., Reale

and Atlas 2001). All these types of events have been observed in the G5NR and will be the subject

of future detailed investigations.

4.4 Summary

The G5NR has been evaluated with respect to tropical cyclone activity and other tropical weather

phenomena. It was noted that a viable nature run must be able to produce TCs which arise out of

very precise large-scale forcings. The presence in the G5NR of the important dynamical features

over the African Monsoon region and the tropical Atlantic has been confirmed. The production of

African Easterly Waves in the G5NR compares well to MERRA. The investigation reveals that the

G5NR captures the fundamental aspects of TC activity in all relevant basins (Atlantic, Eastern

Pacific, North Indian Ocean and South Indian Ocean), producing realistic numbers of TCs with

realistic tracks and life spans. The Atlantic TC seasons as depicted in the G5NR for 2005 and

2006 show interannual variability consistent with observations, with more storms in 2005. Over

the Pacific, the presence of westerly bursts in the G5NR is confirmed. Over the Indian Ocean it is

found that the G5NR contains a realistic depiction of the Somali Jet in both years. An example of

a TC undergoing realistic extratropical transition was also presented. Our results confirm that the

G5NR is well suited for OSSEs targeting (but not limited to) tropical cyclones and represents a

vast improvement with respect to previous long simulations that have served the OSSE community

for several years.

121
Figure 4.1: Zonal and meridional vertical cross-sections of winds (shaded, m s−1

) and temperature

(black contours, ◦C) for the strongest Atlantic tropical cyclone in the ECMWF NR. The ability to

produce features closely resembling TCs without “bogusing” or insertion of any data was considered

remarkable for a free-running model (e.g., Reale et al. 2007), and has represented the reference for

next-generation NRs.

122
Figure 4.2: Vertical meridional cross section of zonal wind at 0◦

longitude (m s−1) in the G5NR

(left) and MERRA (right), for 2005 (above) and 2006 (below).

123
Figure 4.3: African easterly wave activity in the G5NR: Hovmøller plots of meridional winds at

about 700 hPa (level 55), August 2005 and 2006 (time upward).

124
Figure 4.4: African easterly wave activity in MERRA: Hovmøller plots of 700hP a meridional winds,

August 2005 and 2006 (time upward).

125
Figure 4.5: Tropical cyclone activity over the Atlantic in 2005 in the G5NR. Individual cyclone

track colors indicate center pressure from the 7-km output. Open circles are drawn at the beginning

of tracks and start and end dates are listed in yy/mm/dd hh:mm format.

126
Figure 4.6: Same as in Figure 4.5, but from the NHC, 2005 season (observed best tracks).

127
Figure 4.7: Same as Figure 4.5, but for 2006.

128
Figure 4.8: Same as Figure 4.5, but from the NHC, 2006 season (observed best tracks).

129
Figure 4.9: Zonal vertical cross-sections of winds (shaded, m s−1

) and temperature (black contours,

◦C) for an Atlantic hurricane (TC 2, from Figure 4.5 reference map) in the G5NR. Temperature

anomalies (◦C, red contours) are computed by subtracting a zonal mean temperature for that

latitude, spanning across 20◦in longitude.

130
Figure 4.10: Same as in Figure 4.9, but for an Atlantic hurricane (TC 9) at an early development

stage.

131
Figure 4.11: Same as Figure 4.9, but for an Atlantic hurricane (TC 12) at a mature development

stage.

132
Figure 4.12: Tropical cyclone activity over the Eastern Pacific in 2005 in the G5NR. Individual

cyclone track colors indicate center pressure from the 7-km output. Open circles are drawn at the

beginning of tracks and start and end dates are listed in yy/mm/dd hh:mm format.

133
Figure 4.13: Same as Figure 4.12, but from the NHC, 2005 season (observed best tracks).

134
Figure 4.14: Same as Figure 4.12, but for 2006.

135
Figure 4.15: Same as Figure 4.12, but from the NHC, 2006 season (observed best tracks).

136
Figure 4.16: Hovmøller diagram of equatorial low-level zonal wind (model level 70) in JJA 2006

across the central Pacific in the G5NR (left) and 950 hPa wind from MERRA (right).

137
Figure 4.17: 900 hPa wind in July 2006 across the Indian Ocean in the G5NR.

138
Figure 4.18: Same as Figure 4.17, but for MERRA.

139
Figure 4.19: Vertical meridional cross section of the Somali Jet in July 2006 in the G5NR.

140
Figure 4.20: Same as Figure 4.19, but for MERRA.

141
Figure 4.21: Tropical cyclone activity over the Northern Indian Ocean in 2005 in the G5NR.

Individual cyclone track colors indicate center pressure from the 7-km output. Open circles are

drawn at the beginning of tracks and start and end dates are listed in yy/mm/dd hh:mm format.

142
Figure 4.22: Same as Figure 4.21, but from the JTWC, 2005 season (observed best tracks).

143
Figure 4.23: Same as Figure 4.21, but for 2006.

144
Figure 4.24: Same as Figure 4.21, but from the JTWC, 2006 season (observed best tracks).

145
Figure 4.25: Tropical cyclone activity over the Southern Indian Ocean in 2005-2006 in the G5NR.

Individual cyclone track colors indicate center pressure from the 7-km output. Open circles are

drawn at the beginning of tracks and start and end dates are listed in yy/mm/dd hh:mm format.

146
Figure 4.26: Same as Figure 4.25, but from the JTWC, 2005-2006 season (observed best tracks).

147
Figure 4.27: Same as Figure 4.25, but for 2006.

148
Figure 4.28: Same as Figure 4.25, but from the JTWC, 2006-2007 season (observed best tracks).

149
Figure 4.29: Zonal vertical cross-sections of winds (shaded, m s

−1

) and temperature (black contours,

◦C) of a Southern Indian Ocean tropical cyclone (TC 5, from Figure 4.27 reference map) in the

G5NR. Temperature anomalies (◦C, red contours) are computed by subtracting a zonal mean

temperature for that latitude, spanning across 20◦in longitude.

150
Figure 4.30: Four-panel depiction of near-surface wind speed at various stages of the tropical

cyclone’s life-cycle in the G5NR, highlighting wind intensity and structural changes. Upper left:

initial tropical genesis at 1100 UTC 03 Aug 2005. Upper right: peak tropical cyclone intensity at

08 Aug. 2005, 0600 UTC. Lower left: early extratropical transition at 1100 UTC 13 Aug 2005.

Lower right: mature extratropical transition at 2300 UTC 15 Aug 2005.

151
Figure 4.31: Extratropical transition of a tropical cyclone in the G5NR. Top: 500hpa geopotential

height (contour, m) and sea-surface temperature (shaded, C) showing favorable positioning of

tropical cyclone to upstream mid-latitude longwave trough 13 Aug. 2005, 2300 UTC. Bottom:

30-minute average rainfall rate (shaded, mm d−1) and 10m wind vectors at full ET maturity 2300

UTC 15 Aug 2005.

152
Chapter 5

Surface Characteristics

Clara Draper, Richard Cullather, Randal Koster, Guillaume Vernieres and Yuri Vikhliaev

In this chapter, the surface characteristics of the G5NR simulation are investigated by separately

considering the land (Sections 5.1-5.2), ocean (5.3), and polar (5.4.2) regions. The realism of the

G5NR simulation is tested by comparison to independent estimates, and known problems with the

G5NR model and output are identified.

5.1 Land Surface - Hydrological Processes

Along with its atmospheric output, the G5NR offers a tremendous amount of high resolution,

globally gridded land surface hydrological data. Users interested in these particular data should

acquaint themselves with the issues highlighted in the subsections below, some of which may limit

the data’s usefulness for certain applications.

5.1.1 Relationship of G5NR Land Surface Hydrological Data to LDAS Products

In general, land data assimilation systems (LDAS) consist of a global or continental-scale array of

land surface model elements driven with observations-based meteorological forcing over a period

of years to decades. Output from these systems, which occasionally live up to their name by

assimilating land surface states along the way, consist of hydrological and energy fluxes and states at

153
high spatial and temporal resolution. Numerous LDAS systems are currently operational (Mitchell

et al. 2004, Rodell et al. 2004, Xia et al. 2012, Reichle et al. 2011) and offer data to a wide variety

of users.

For those interested in hydrological studies requiring high resolution land surface data, it is worth

contrasting LDAS products with G5NR land surface products. For certain applications, the LDAS

products have distinct advantages. The first advantage involves biases in the meteorological forcings

produced by the G5NR’s atmospheric model, which will necessarily produce biases in the land

surface model output. LDAS products are much less limited by this problem, given that the

meteorological forcings they use come from reanalysis and, for precipitation, from datasets corrected

with rain gauge measurements. Biases in the G5NR’s precipitation and other surface forcings are

addressed elsewhere in this volume (and briefly in Section 5.1.2); suffice it to say that these biases

can, in certain regions and seasons, be significant.

The second issue involves chaotic atmospheric dynamics. The G5NR is a free-running simulation

rather than an analysis. Thus, even though the nominal dates for the simulation span 2005-

2007, and even though the atmosphere was forced by sea surface temperatures for those dates,

the simulated atmosphere was free to follow any number of diverging trajectories. The nature of

chaotic atmospheric dynamics implies that even if the atmospheric and land models were perfect, a

given simulation would necessarily produce a time series of precipitation fields not seen in the real

world, though not necessarily unrealistic. It is therefore inappropriate to use G5NR land surface

output to examine specific, historical real-world flooding events or droughts. LDAS data are better

suited for such studies.

This said, the G5NR land surface hydrological dataset does have its advantages. These data, for

example, are fully consistent with the rest of the G5NR dataset. During the G5NR, the land

states were allowed to feed back on the atmospheric states, and vice versa; the resulting mutual

consistency, arguably absent from LDAS products, allows an unprecedented joint analysis of land

variables with any number of atmospheric variables – that is, it allows studies of how these variables

evolve together. Another important advantage involves the spatial resolution of the G5NR data,

which is somewhat finer that of standard LDAS products; for example, the north American LDAS

project (Xia et al. 2012) offers data at a resolution of 1/8 degree, and the MERRA-Land global

product (Reichle et al. 2011) has a resolution of 0.5 degree.

5.1.2 Hydrological Land Surface Forcing

To get a first-order handle on the degree to which forcing biases affect the G5NR’s land products,

we turn to Budyko’s dryness index, defined as DI = Rnet/λP, where Rnet is the annual net radiation

at the surface, λ is the latent heat of vaporization, and P is annual precipitation. Budyko (1974)

showed that DI serves as a chief control over long-term surface hydrology, even more so than

precipitation does by itself. The top panel of Figure 5.1 shows the global field of DI derived from

observations (Koster et al. 2006), which is in essence the field associated, by construct, with LDAS

154
products. The bottom panel of Figure 5.1 shows the corresponding plot for the G5NR.

Overall, the G5NR’s simulation of DI looks good, certainly to first order. The locations of energylimited evaporation (low DI) and water-limited evaporation (high DI) are, for the most part, correctly positioned. One must also keep in mind that some of the differences between the two plots

probably result from the short (2-year) averaging period used for the G5NR plot. In addition, the

large differences seen in near-polar latitudes are likely associated with poor precipitation observations in these areas; we do not have a good sense for what the correct values of DI in these areas

should be.

At least in terms of this bulk forcing diagnostic, the G5NR results look reasonable. Forcing biases

at shorter time scales, however, will not be as good. Again, a more complete description of forcing

biases is provided elsewhere in this report (including an analysis of the bulk land surface energy

budget in the G5NR in Section 5.2).

5.1.3 Inconsistency Between Land-Only and Total-Grid Diagnostics

An inconsistency in the production of grid-averaged hydrological fluxes was discovered midway

through the G5NR simulation. The problem is illustrated in Figure 5.2 as it pertains to the

production of the 0.5 degree aggregated product. In the figure, precipitation in the 0.5 degree box

is seen to be large over the land subset of the box. Prior to fixing the problem, the 0.5 degree

runoff product was computed for only the land fraction of the 0.5 degree box; as a result, the 0.5

degree runoff value in this particular example would be high. The precipitation product, on the

other hand, was computed for the entire 0.5 degree box (land plus ocean), and its average value

would be relatively low, diminished by the zero precipitation found over the ocean fraction.

Based on this strategy, it is quite possible, especially along coastlines but also in areas with lakes,

for grid-averaged precipitation amounts to be fundamentally inconsistent with grid-averaged runoff

amounts. Figure 5.3 shows regions where, in the 0.5 degree dataset, surface runoff production

exceeds precipitation – a condition that should be impossible and indeed does not occur on the

G5NR’s native cube grid. While this post-processing problem is especially present in the 0.5 degree

dataset, it must also be implicit in the high resolution gridded G5NR dataset, given that the surface

elements defined by the cube are not the same as those in the high resolution latitude-longitude

output grid.

The problem was corrected midway through the G5NR simulation. For G5NR output dated after

9 May 2006, a new precipitation diagnostic is included, one that provides the precipitation falling

over the land fraction of a grid element.

155
5.1.4 Singularities in Parameter Estimation

A problem with a certain set of land surface model parameters was discovered after the G5NR was

launched. In essence, a curve fitting routine used to parameterize the net results of some complex

calculations produced, in certain situations, an unwanted singularity. During the G5NR simulation,

the singularity manifests itself as an excessive areal fraction of saturated soil, which in turn induces

excessive runoff ratios. In other words, for a given input of precipitation, too much of the rainwater

runs off.

The problem is somewhat rare. Roughly 4% of the land elements in the G5NR are potentially

affected by the problem, but tests show that the actual manifestation of the problem appears in an

even smaller percentage, of order 2%. Though much of the water runs off from the affected tiles,

evapotranspiration from the tiles still proceeds. The impact of the problem on the atmosphere

should be small and should certainly be non-singular.

5.1.5 Snowfall Forcing

A curious feature of the G5NR is the occasional generation of significant amounts of snow during

warm periods in the tropics and midlatitudes. The map in Figure 5.4, constructed from 0.5 degree

G5NR data, shows the total amount of snow reaching the surface during the 16-day period spanning

May 1 through May 16 of the first simulation year. The counterintuitive snowfall at warm latitudes

is likely the result of temporal truncation in the sequence of processes in the moist physics, whereby

one process that results in a strong cooling is followed in the model by a process that produces a

compensating large heating, and the rain/snow determination is made between the two steps.

Figure 5.5 shows time series covering two days in July for a high-resolution grid cell in Mexico.

In most tropical/subtropical areas, unrealistic snow amounts on the surface melt rapidly. Here,

though, even though the near-surface air temperatures remain above 280K, the deposited snow

remains on the ground into the next day, having a strong and unrealistic impact on albedo and

thus on the absorbed radiation at the surface.

Probably the most important impact of having spurious snow on the ground is the need for energy

to melt it. Presumably that energy should have been taken out of the atmosphere, melting the

snow before it reaches the surface. As it is, the energy comes from the land surface energy budget

at the expense of other terms.

An additional technical point should be made about snow budgets in the model. The model

naturally conserves snow, but there will be instances in the G5NR data when time-integrated

snowmelt appears to exceed time-integrated snowfall. This occurs because liquid water falling onto

a snowpack is allowed to freeze into the snowpack, increasing the snow mass.

156
5.1.6 Reduction of Solar Radiation During Rainfall Events

Reichle et al. (2011) identified an issue with the coupled land-atmosphere system used for the

MERRA reanalysis (Rienecker et al. 2011). Overestimates of land surface evapotranspiration,

particularly the interception loss component, were induced by three unrealistic behaviors of the

coupled system, working together: (i) rain tended to fall only during daylight hours, (ii) the rain

tended to fall as drizzle during the day rather than as high-intensity, episodic events, and (iii)

incoming solar radiation at the land surface tended to be high during the drizzle. All three issues

are apparent in the comparison of MERRA results with observations in Figure 5.6, taken from

Reichle et al. (2011). Notice, for example, the significant reduction in downward solar radiation

on June 21 in the observations, during a daytime rainstorm; similar reductions are not seen in the

MERRA data. The MERRA-Land collection (Reichle et al. 2011) was created largely to account

for improved precipitation estimates but also in part to overcome the interception loss deficiency

associated with these three behaviors.

It is reasonable to investigate whether these issues also appear in the G5NR; it is possible that

they are ameliorated by this simulation’s increased spatial resolution and its concomitant changes

in rainfall and radiation behavior. Figure 5.7 shows some corresponding time series taken from the

high resolution G5NR data. These plots indicate that, indeed, the indicated problems are reduced.

Rain does fall at night in the G5NR, and when it falls during the day, it can be more episodic.

Days with significant rain now show a significant decrease in incoming solar radiation (e.g., on day

3), though days with tiny amounts of drizzle still show low reductions. Overall, the G5NR appears

to behave more accurately in this context than MERRA, giving more credence to its calculation of

land surface evapotranspiration.

5.1.7 Evaporation Instabilities

Figure 5.8 shows 3-day time series of precipitation, net radiation, and total evapotranspiration at

a specific high resolution grid cell. Shown at the end of day 2 is a jump in the evaporation rate

that is unrelated to any contemporaneous precipitation or net radiation input. The jump results

from an instability in the land surface model’s energy budget calculation.

The degree to which such instabilities occur in the G5NR over a representative 2-week summer

period in central-north America is indicated in Figure 5.9. The problem, while ugly, is not rampant.

Instabilities are absent over most of the area, and they happen at most about once per day in a

few grid cells in the southern and northwestern U.S.

Furthermore, such instabilities have been analyzed before and are known to be connected, in large

part, to the transfer of information between the atmosphere and land surface. To compute the

surface fluxes, the land surface model requires the atmosphere to provide initial estimates of evaporation and sensible heat flux as well as the derivatives of these fluxes with respect to near-surface

157
air temperature and specific humidity, which themselves depend on the derivatives of aerodynamic

resistance (a strongly nonlinear function) with respect to temperature and specific humidity. For

various logistical reasons, the aerodynamic resistance derivatives are not produced by the atmospheric model, meaning that the land surface model cannot utilize this information. This leads to

potential instabilities.

This is demonstrated here using two offline calculations with the land surface model (i.e., the land

model is not connected to the atmospheric model but is instead driven with meteorological forcings

derived from observations). In the first offline simulation, employing the offline set-up described by

Koster and Mahanama (2012), the aerodynamic resistance derivatives are implicit in the forcing.

The second simulation is identical to the first except for the zeroing of these particular derivatives.

Figure 5.10 shows, for a 15-day summertime period, the relative incidences of instabilities in the

two simulations. The occurrence of instability clearly increases when the derivative information

is not provided. Again, the information regarding the aerodynamic resistance derivatives is also

lacking in the G5NR; indeed, the regions where instability occurs in the second offline simulation

are quite similar to those shown in Figure 5.9.

5.1.8 Occasional High Levels of Rainfall Forcing

Precipitation intensities in the G5NR are sometimes very high. To construct Figure 5.11, a representative 2-week period during the summer was chosen, and the precipitation rates computed at

each high resolution grid cell were aggregated to half-hour totals. The figure shows the locations

for which, during that 2-week period, a full 4 cm of water reached the surface during a single halfhour period. While a comparison with observed rainfall intensities is not provided here, these high

simulated rainfall intensities seem unrealistically common. Users should be aware of their potential

existence when doing hydrological analyses with G5NR data.

5.2 Land Surface - Energy Processes

In this section, the realism of the long term mean land surface energy budget and low-level atmospheric temperatures over land in the G5NR are tested by comparison to independent estimates.

For both, the means of the two-year free-running G5NR simulations are compared to the mean

of the observed climate over a 10-year time period. In addition to being directly relevant to land

surface applications of the G5NR output, these comparisons also provide a useful diagnostic for the

realism of the physical processes modeled by the G5NR, in terms of both the land surface processes

and the atmospheric processes that interact with the land surface.

158
5.2.1 Surface Energy Budget

The G5NR global energy budget was compared to estimates from the NASA Energy and Water

Cycle Studies (NEWS) project (L’Ecuyer et al. 2014) in Figure 3.11. Here we examine the surface

component of the energy budgets in more detail. Recall that the NEWS project has produced a

10-year climatology (covering approximately 2000–2009) of the global and continental/basin-scale

water and energy budget terms. These budget terms are based on satellite observations, with

model products used where observations are unavailable, and have been adjusted to satisfy water

and energy balance constraints, with reference to the estimated uncertainty of each term.

Table 5.1 shows the annual mean continental surface energy budget from the G5NR and NEWS (the

equivalent comparison will be made over the oceans in Section 5.3.1). The continental G5NR energy

budget estimates are quite close to those from NEWS. Compared to NEWS, both the downwelling

and upwelling components of longwave radiation are underestimated by 2.5–2.6 PetaWatts (PW,

17–18 Wm−2), while both components of the shortwave are slightly overestimated by 0.5–0.6 PW

(4 Wm−2). The G5NR turbulent fluxes are very close to those from NEWS, however this is likely

because they are not independent since the NEWS turbulent flux estimates are partly based on

MERRA fluxes.

5.2.2 Land Surface Temperatures

The monthly mean 2 m air temperatures (T2m) from the G5NR are compared to observations from

the Integrated Surface Hourly (ISH) collection, collated by the National Climatic Data Center

(NCDC, Lott, 2004). Approximately 2000 ISH station are included in the comparison (Figure

5.12), and while these stations span the globe, their coverage is sparse outside of North America

and Europe. For each calendar month, Figure 5.12 shows very good agreement between the mean

T2m over the station locations from the G5NR and the ISH data (solid lines). The G5NR monthly

mean T2m is consistently within 2 K of the ISH mean, and with the exception of April, is within

one temporal standard deviation of the ISH 10-year monthly mean.

Also included in Figure 5.12 are the monthly mean T2m from 10 years of ERA-I reanalyses (Dee et

al. 2011). The very close agreement between the ISH and ERA-I mean T2m at the station locations

is expected, since ERA-I includes a surface analysis which adjusts the model land surface state

variables to reduce the errors in 2m temperature and relative humidity forecasts. However, the

high accuracy of the ERA-I monthly mean T2m at the ISH station locations may not extrapolate to

regions that are less well observed by T2m stations (even if the same stations are not used in ERA-I

and ISH, the same regions will be over-represented by both). Consequently, while we use ERA-I

as a global reference for the G5NR T2m, some caution is required in interpreting these results.

Nonetheless, Figure 5.12 does show a very strong agreement between the global mean T2m over

land from ERA-I and the G5NR (dashed lines).

159
Figure 5.13 shows the monthly mean T2m over each continent from ISH, ERA-I, and the G5NR.

In general, the monthly mean G5NR T2m tracks the ISH time series very well over each continent.

The G5NR T2m is within one temporal standard deviation of the ISH 10-year monthly means,

and only occasionally differs by more than 2 K. The exceptions are North America in Dec-JanFeb and in Europe in Apr-May-Jun, when the G5NR T2m is cooler than ISH. Comparing the

dashed and solid lines in Figure 5.13, the poor sampling of the ISH stations (note the number of

stations in the subplot title) is reflected by the differences between the station-only time series and

continental mean time series. Despite this, the difference between the ERA-I and G5NR time series

are consistent between the station-only and continental mean time series, lending some support to

the use of ERA-I to infer the continental-scale T2m biases in the G5NR. As with comparison to

the ISH observations, comparison to the continental mean ERA-I T2m indicates that the G5NR

monthly mean T2m are reasonable. The G5NR monthly mean T2m are consistently within 2 K of

the ERA-I values, except for the two instances identified above, in which the G5NR is cooler than

other estimates.

Figure 5.14 shows global maps of the bias between the ERA-I and G5NR mean T2m for Dec-JanFeb and Jun-Jul-Aug. The cool T2m in the G5NR mentioned above, during boreal winter over

North America and boreal summer over Europe, are both due to the G5NR being consistently

cooler across the continent in question. However, other regions stand out in Figure 5.14 as having

larger differences between the ERA-I and G5NR T2m. Most notably the G5NR is much cooler over

north Africa, and it is much warmer in the northern high latitudes (the latter being associated

with differences in cloud-cover between the two models). Unfortunately, neither of these regions is

well observed by validating observations, and it is difficult to say which model (if either) is more

accurate at these locations.

Finally, since land surface temperature biases are important to microwave forward modeling of

the land surface (e.g., to retrieve soil moisture), Figure 5.14 also shows the difference between

the monthly mean ERA-I and the G5NR mean land surface temperatures. The differences in

the low-level atmospheric temperature and land surface temperature between the two models are

very similar, in terms of both spatial patterns and magnitude. Consequently, the above examination of the G5NR mean T2m also provides an indication of the biases in the G5NR land surface

temperatures.

In summary, while it is difficult to evaluate land surface energy processes at large scales, the

above comparison suggests that the G5NR land surface energy processes are reasonable in terms

of simulating a realistic climate. Globally, the differences between the two-year G5NR results and

the ten-year observations-based estimates for the annual mean surface energy budget and for the

the monthly mean low-level atmospheric temperatures are within the expected variability, given

the short period of the G5NR simulation. The temperatures also appear to be quite realistic when

averaged over individual continents, although there are possible instances of large temperature

biases (> 5K) at the regional scale.

160
5.3 Ocean Surface

5.3.1 Ocean Energy Budget

In the G5NR, the atmosphere is forced by an SST analysis. The net energy budget over the ocean

surface can be expressed as

FSW↓ − FSW↑ + FLW↓ − FLW↑ − FSH − FLH = Q (5.1)

where FSW and FLW are upward and downward shortwave and longwave radiation fluxes, FSH is

the sensible heat flux, FLH is the latent heat flux and Q is the time rate of change of energy storage

in the ocean. Table 5.2 shows the components of equation (5.1) from the G5NR integrated over

the ocean surface along with their estimates from the NEWS project (recall that the continental

component of the NEWS and G5NR energy budget were compared in Section 5.2.1). All balance

terms from the G5NR, except for the sensible and latent heat fluxes, are within the uncertainty of

the observations. Latent heat flux is over-estimated by several Watts per meter squared, and the

net ocean heat loss is 3.4 PW.

The spatial pattern of the net surface radiation from the G5NR compared to GEWEX Surface

Radiation Balance data (Stackhouse et al. 2001) is shown in Figure 5.15. There is an excess of

shortwave radiation along midlatitudes and over subtropical regions near the west coasts of Africa

and America of up to 40 Wm−2, and a lack of shortwave radiation in the subtropical regions of

up to 20 Wm−2. The net longwave radiation error has a zonal pattern with maxima up to 30

Wm−2along midlatitudes in both hemispheres. The bottom row in Figure 5.15 shows the error

in surface cloud radiative forcing. It is clear that the cloud radiative forcing error pattern closely

corresponds to the radiation error pattern with opposite sign, which leads us to conclude that the

bias in the simulated surface radiation is mainly due to cloud radiative effects. Since the G5NR

simulation was only two years long, our estimate of annual mean errors can be biased due to interannual variability of the estimated fields. Gray areas in Figure 5.15 show the 5% significance level

calculated using a two-sided Student t-test.

Figure 5.16 shows the spatial pattern of latent and sensible heat fluxes compared to Goddard

Satellite-based Surface Turbulent Fluxes (GSSTF) data (Shie et al. 2009). Latent heat is determined by evaporation and is greatest over subtropical oceans where the atmosphere above the

surface is dry. Latent heat flux in the G5NR is higher than the GSSTF estimates over most of the

tropical-subtropical oceans by up to 50 Wm−2, and lower than the GSSTF by up to 30 Wm−2in

high latitudes. It is known that in GEOS-5 errors in evaporation and, hence, the latent heat flux,

are consistent with the errors in near surface humidity gradient (Molod et al. 2012). The sensible

heat flux in the G5NR is almost everywhere stronger than in GSSTF, with maximum values up to

40 Wm−2in the north Atlantic, which corresponds to the wind stress error patterns (see Section

5.3.2).

161
Figure 5.17 shows the implied northward heat transport estimated from the G5NR and from observations (Ganachaud and Wunsch, 2000, Trenberth and Caron, 2001). The northward heat transport

at a given latitude is the sum of the fluxes on the left-hand side of equation (5.1), integrated zonally

and from the north pole to that latitude. Poleward heat transport is overestimated in the northern

hemisphere mid-latitudes, and slightly underestimated compared to Trenberth and Caron (2001)

in the subtropics in both hemispheres.

5.3.2 Ocean Winds

In this Section, we look at the 2 year average (from May 2005 to May 2007) 10 m ocean winds

and surface wind stresses of the G5NR. The analysis uses monthly fields that are interpolated on a

0.5 degree grid and are compared to estimates from the Cross-Calibrated Multi-Platform (CCMP,

Atlas et al. 2011) and from the Scatterometer Climatology of Ocean Winds (SCOW, Risien et al.

2008).

As a first step towards understanding some of the climatological features of the G5NR surface

winds, we compare the global wind rose of the G5NR to that of CCMP. The wind roses for CCMP

are shown in Figure 5.18. The eastern sector clearly identifies the southeasterly and northeasterly

trade winds while the strong westerlies are observed in the western sector. Figure 5.19 depicts the

10 m wind rose for the G5NR. The direction of the trade winds is similar to that in the observations

(see eastern sector), except that the trade winds above 15 knots (brown color) are nearly absent.

This problem is directly related to the weak Hadley cell circulation described in Chapters 2 and

6. The prevailing westerlies’ strength, observed in the western quadrant of Figure 5.19, is similar

to the that observed by CCMP. However, the G5NR statistics indicate an unusual northward

component. This northward bias is caused by the strong northwesterlies observed in the North

Atlantic, depicted in Figure 5.20. In the north Atlantic region, the meridional and zonal G5NR

winds (see the two left-most top panels of Figure 5.20) are significantly stronger than those from

SCOW (middle panels of Figure 5.20) and CCMP (bottom panels of Figure 5.20). These strong

north Atlantic northwesterlies are the probable cause of the large bias observed in the sensible heat

flux discussed in section 5.3.1. Figure 5.21 shows the wind stress from the G5NR and SCOW. Note

that in this case, while we highlighted the weakness of the G5NR trade winds, this somehow did

not translate into weak stresses. Within the trades regions in Figure 5.21 the G5NR wind stress

(top panel) is of similar magnitude to the SCOW wind stress (bottom panel), while in the same

region, the G5NR wind speed (top right panel of Figure 5.21) is clearly weaker than in the SCOW

(middle right panel of Figure 5.21). This indicates an issue in the surface wind stress calculation.

Finally, the stress induced by the westerlies is considerably stronger than the SCOW climatology.

This is observed above the circumpolar current in the Southern hemisphere as well as over the Gulf

stream extension in the north Atlantic.

162
5.4 Polar Regions

The G5NR provides an opportunity for the examination of simulated atmospheric phenomena in

polar regions at very high spatial resolution and in the context of a global model integration. A

general validation is performed with a focus on atmospheric moisture and energy budgets, which

are frequently used as diagnostics for understanding the changing climate of the polar regions. A

few topics related to high spatial resolution are of particular interest in polar regions. These include

the simulated surface mass balance over polar ice sheets and the representation of the near-surface

wind field.

5.4.1 Polar Atmospheric Moisture and Energy Budgets

Atmospheric reanalyses have been frequently used to provide a quantitative depiction of energy and

moisture budgets (e.g., Serreze et al., 2006), although their performance in polar regions contains

significant caveats. A comparison of G5NR energy budget components with three contemporary

atmospheric reanalyses for the North Polar Cap (70 ◦N - 90 ◦N) is nevertheless informative. The

North Polar Cap roughly corresponds to the periphery of the Arctic Ocean and is a frequently-used

region in polar studies. The reanalyses used for the evaluation include ERA-I (Dee et al., 2011),

the Climate Forecast System Reanalysis of the National Centers for Environmental Prediction

(CFSR; Saha et al., 2010), and MERRA (Rienecker et al., 2011). First, it is found that the G5NR

net surface energy flux exceeds reanalyses estimates by an average of 19 Wm−2in winter months

of December-January-February and 13 Wm−2in the summer months of June-July-August, while

transitional seasons compare more favorably (Figure 5.22a). These seasonal discrepancies in the

net surface energy flux occur from differing components, with winter differences associated with

turbulent heat fluxes and summer discrepancies associated with the solar radiative flux. There is

substantial disagreement among the reanalyses in the magnitude and even the sign of the Arctic

sensible heat flux (Figure 5.22b), but the G5NR is a clear outlier to these disparate estimates in

winter by an average of 15 Wm−2. In summer, the G5NR net solar flux at the surface is less than

reanalyses values by about 7 Wm−2. Comparisons of surface solar fluxes illustrate some of the

deficiencies in the reanalyses that need to be kept in mind. The MERRA product used a fixed

surface albedo over sea ice that was too low in comparison to observation for the early summer

period (Cullather and Bosilovich, 2012). This accounts for the large differences between MERRA

and other reanalyses shown in Figure 5.22c. Subsequent improvements to the surface representation

over sea ice and grounded ice sheets in GEOS-5 are incorporated in the G5NR simulation, but

discrepancies between the G5NR net solar flux and the other two reanalyses are associated with an

underestimate of the solar downwelling component. This is suggestive of differences in cloud cover.

Discrepancies between the G5NR and reanalyses radiative fluxes at the top of the atmosphere

(not shown) indicate a simulated atmosphere that reflects about 9 Wm−2 more than reanalyses

shortwave radiative flux values in summer. The South Polar Cap (70◦S - 90◦S) encompasses most of

the Antarctic continent. Here, differences among the reanalyses are large and conclusions regarding

the relative performance of the G5NR are less straightforward. While the net surface energy flux

of the G5NR is within the broad range of reanalysis values, the surface downwelling shortwave

163
radiative flux of the G5NR for the south polar region is less than reanalysis estimates by an average

of 30 Wm−2 during summer months. This is consistent with the underestimate of the net solar

flux in comparison to reanalyses for Northern Hemisphere high latitudes. Despite these differences

in energy flux components, there is generally good agreement between the G5NR and station nearsurface air temperatures. For example, Figure 5.23a shows a comparison with Amundsen Scott

(90◦S, 2835m). Over the Antarctic ice sheet, the orography is typically smooth and discrepancies

with the G5NR surface geopotential are negligible at this spatial resolution. Other Antarctic

stations examined show similar agreement with the exception of Halley Bay (76◦S, 26◦W, 30m),

where the G5NR is 5◦C warmer than corresponding station values in winter. This exceeds the

range found in inter-annual variability, and appears to be associated with differences in the local

circulation. For the Northern Hemisphere, comparisons with several stations indicate the G5NR

is too warm in winter. This includes Barrow (71◦N, 157◦W, 40m), where the G5NR exceeds the

station value by 4◦C in winter (Figure 5.23b).

Atmospheric reanalyses have also been frequently used for the evaluation of the polar atmospheric

moisture budget (e.g., Serreze et al., 2006), but these estimates have been substantiated with

studies using rawinsonde observations and with glaciological data. The net surface moisture flux

— equivalent to precipitation minus evaporation (P−E) — is typically evaluated with reanalysis

prognostic fields, however the atmospheric budget computation — vertically-integrated atmospheric

moisture convergence minus precipitable water tendency — has been found to be a more robust

estimate. The budget computation is often referred to as the ‘aerological’ estimate in contrast to

values from prognostic fields. Reanalysis aerological values have been obtained from the Climate

Analysis Section of the National Center for Atmospheric Research (NCAR) and are shown for the

polar caps in Figure 5.24 in comparison to prognostic values and the G5NR. It is seen in Figure

5.24 that aerological values from the reanalyses agree closely for both regions, and that the G5NR

exceeds these values. For the Arctic, the G5NR produces a net surface flux of 265 mm yr−1, which

compares to reanalysis estimates of 205±45 mm yr−1from prognostic fields and 203±6 mm yr−1

from the aerological method. The largest discrepancies are found to occur in transitional seasons.

For the Antarctic polar cap, the G5NR value is 220 mm yr−1 as compared to average reanalysis

values of 186±24 mm yr−1from prognostic fields and 191±12 mm yr−1from the aerological method.

A further validation of the moisture budget may be obtained through evaluation of the surface

mass balance (SMB) of the polar ice sheets. Significant efforts have been made in recent years

to constrain SMB estimates though increased in situ glaciological measurements, remote sensing

methods, and the employment of high-resolution regional climate models. Shown in Figure 5.25 are

computed SMB fields from the G5NR for (a) the Greenland Ice Sheet (GrIS) and (b) the Antarctic

Ice Sheet (AIS). The SMB is computed as the net of annual precipitation minus evaporation and

runoff. Other wastage terms including blowing snow processes are not considered in the G5NR. For

the GrIS, the G5NR produces a mean SMB of 30.4 cm yr−1 water-equivalent. This is somewhat

larger than available contemporary estimates including 27.4±2.4 cm yr−1 and 25.3 cm yr−1from

the MAR and RACMO2 regional climate models (Ettema et al., 2010; Fettweis et al., 2007). The

spatial patterns of G5NR SMB are nevertheless intriguing and agree reasonably well with regional

climate model fields. Of particular interest are the peripheral ablation zones that exceed 2 m

yr−1in the southwest, and localized areas of accumulation above the equilibrium line along the

western GrIS. Similarly, the AIS field shows excellent spatial agreement with the accumulation

164
field as it is known. The G5NR values for interior plateau are generally between 5 and 10 cm yr−1

.

Historical stake measurements obtained from South Pole have found an average accumulation of

about 8.4 cm yr−1(e.g., Mosley-Thompson et al., 1999). The G5NR simulation also correctly

indicates ablation on the western edge of the Lambert glacier adjacent to the Amery Ice Shelf. A

negative surface moisture flux is also shown over adjacent oceanic areas of the southwestern Ross

Sea and to the northeast of the Antarctic Peninsula. While this would appear to be reasonable

for the Ross Sea region, ablation along the northeastern peninsula seems unusual in comparison

to available climatologies and requires further study. An additional area of ablation in the area of

Roi Baudouin that is featured in earlier compilations is absent in the G5NR field. A preliminary

comparison has been made between the G5NR field and observed radio echo sounding observations

in the high accumulation zones of West Antarctica. These comparisons suggest that the G5NR

values are somewhat high, in agreement with the comparison with reanalyses for the South Polar

Cap.

5.4.2 Near-Surface Wind Field Over Polar Ice Sheets

Surface winds over polar ice sheets are a principal feature of the general circulation and climate of

the high latitudes. In the polar night, the near-surface atmosphere cools rapidly, loses buoyancy,

and descends along the topographic gradient. Over ice sheets, these katabatic winds are sustained

over continental scales and become directionally modified by the Coriolis force. The Antarctic 10

m wind field simulated by the G5NR for the month of July 2005 (Figure 5.26) compares closely

with the diagnostic model field of Parish and Bromwich (1991). Of particular note is the excellent

directional agreement over the Antarctic plateau and concurrence in the location of wind maxima

in confluence zones at Cape Denison (67◦S, 143◦E), the southwestern edge of the Amery Ice Shelf

(70◦S, 71◦E), and the Wilhelm II Coast (∼90◦E). A cyclonic circulation feature over the Ross

embayment and the adjacent area of West Antarctica is more robust in the G5NR than in the

diagnostic model, which may be specific to the time period simulated. Southerly winds along

the eastern side of the Antarctic Peninsula are simulated in the G5NR with monthly-averaged

magnitudes of less than 6 ms−1. This contrasts with the prevalence of barrier winds associated

with the area (Parish, 1983), but may also result from inter-annual variability.

A close-up of the G5NR simulated winds for the southwestern Ross Sea (Figure 5.27) finds a

local maxima in the vicinity of the Terra Nova Bay confluence zone, but the magnitude contours

appear to indicate two airstreams that do not converge. Two factors may hinder the simulation

for Terra Nova Bay. First, the area is a confluence zone for airstreams following four glaciers

which have spatial resolutions that are not resolved by the G5NR. Second, the resulting polynya

modifies the atmosphere and induces a local ice-breeze effect that may act to consolidate and

enhance the airstreams over the bay (e.g., Gall ́ee, 1997). For the G5NR simulation, the temporal

and spatial extent of the polynya is not adequately resolved by the ocean surface forcing field.

Coarse resolution models and atmospheric reanalyses typically indicate a maxima that is farther

inland. With monthly averaged contours of greater than 12 ms−1extending into the bay, the G5NR

simulation clearly benefits from the enhanced spatial resolution. Additional features including the

Ross airstream and the influence of Ross Island on the wind field are seen in the G5NR simulation.

165
For Greenland, the near-surface wind field is more directly influenced by synoptic activity. Nevertheless topography also plays an important role here in the directionality of the winds. The G5NR

simulation (Figure 5.28) agrees well with the Ettema et al. (2010) regional climate model in indicating the northeast as the area of maximum wind magnitude, with additional areas of averaged

magnitudes greater than 9 ms−1 along the western and southeastern periphery of the ice sheet. Notably the G5NR also agrees with Ettema et al. (2010) in showing the offshore synoptically-forced

northeasterly winds acting as an inhibiting barrier to katabatic winds along the southeastern coast.

A particular difference with the regional models is the wind magnitude at Summit. The G5NR

shows calm conditions while Ettema et al. (2010) contour values greater than 6 ms−1. Station

observations show better agreement with the regional climate model. Magnitudes of less than 2

ms−1 are also shown at the top of South Dome in the G5NR. While inter-annual variability may

also play a role, these becalmed areas are intriguing and warrant further investigation.

A cursory assessment of variability in the wind field may be seen by examining different synoptic

conditions. In Figure 5.29a, a surface low is located SSW of Greenland and results in enhanced

flow along the southwestern periphery of the ice sheet, and locally reversed, ‘up-hill’ wind flow

along the southeastern coast. In Figure 5.29b, a surface low is located north of Iceland. Here,

enhanced northerly barrier flow is found along the northeastern coast of Greenland which inhibits

katabatic winds along the coastal escarpment. The synoptic condition enhances westerly wind

flow over the southern part of the ice sheet, and particularly along the southeastern coast. A

wedge-shaped feature in wind magnitude contours in Figure 5.29b extending from Cape Farewell

generally resembles the analysis of Doyle and Shapiro (1999) in identifying a tip jet associated with

the steep topography of the southernmost edge of Greenland. It may be seen that in both cases, the

directionality of the wind in the northern half of the ice sheet stays fairly constant, in agreement

with the study of Heinemann and Klein (2002).

166
Table 5.1: Comparison of G5NR and the NEWS project annual mean surface energy budget terms,

in PetaWatts, over continental regions. Courtesy of Michael Bosilovich.

Nature Run NEWS

Sensible heat flux 5.3 5.6 ±0.9

Latent heat flux 5.6 5.6±0.4

Downward shortwave radiation 27.7 27.2±1.1

Upward shortwave radiation 6.8 6.2±0.5

Downward longwave radiation 42.9 45.5±1.0

Upward longwave radiation 52.8 55.3±1.1

Net change in heat storage -0.1 0.0

Table 5.2: Comparison of G5NR and the NEWS project annual mean surface energy budget terms,

in PetaWatts, over oceans. Courtesy of Michael Bosilovich.

Nature Run NEWS

Sensible heat flux 5.6 7.0±1.3

Latent heat flux 38.6 35.6±1.8

Downward shortwave radiation 66.4 67.9±1.6

Upward shortwave radiation 5.0 5.1±0.4

Downward longwave radiation 127.9 128.0±1.7

Upward longwave radiation 148.5 148.0±1.1

Net change in heat storage -3.4 0.2

167
Figure 5.1: Top: Dryness index distribution, as determined from observed precipitation and net

radiation fields. Figure reprinted from Koster et al. (2006); see that paper for details. Bottom:

Corresponding distribution based on two years of G5NR output.

168
Figure 5.2: Illustration of a situation that leads to an inconsistency in the 0.5 degree output

diagnostics, during the first half of the G5NR. Precipitation falls heavily over the land fraction of

the 0.5 degree cell. The runoff diagostic represents the average runoff over the land only and will

be large; the precipitation diagnostic represents the precipitation over the entire 0.5 degree area

and will be relatively small.

180 120W 60W 0 60E 120E 180

90S

60S

30S

EQ

30N

60N

90N

-2.0

-1.8

-1.6

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0 

Figure 5.3: Locations (marked as red dots) where the output diagnostics for land surface runoff

and precipitation are found to be inconsistent during a 16-day period in May, for reasons outlined

in the text. The processing of the diagnostics was corrected in the latter half of the G5NR.

169
180 120W 60W 0 60E 120E 180

90S

60S

30S

EQ

30N

60N

90N

Total snow (mm) in 16 day period

0.0

0.1

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

8.5

9.0

9.5 

Figure 5.4: Amount of snow reaching the surface during a 16-day period in May. Values as high as

67 mm, for example, are seen in the Tropical Pacific.

170
2-meter Air Temperature

0 10 20 30 40

280

285

290

295

300

Precipitation: Blue=Total, Red=Snow. long/lat = -107.56250, 25.812500

0 10 20 30 40

0.00

0.01

0.02

0.03

0.04

Snow Water Equivalent on Ground

0 10 20 30 40

0

50

100

150

Albedo

0 10 20 30 40

0.0

0.2

0.4

0.6

0.8

1.0

Figure 5.5: Time series (in hours) of 2-meter air temperature (K), precipitation (kgm−2s

−1

; total

in blue and snow in red), snow water equivalent (kgm−2), and albedo (-) over a 48-hour period in

summer (centered on the snowfall event) at a grid cell in northern Mexico. Albedo by construct

has a value of zero at night.

171
Figure 5.6: (Gray lines) Downward shortwave radiation and (black bars) precipitation from (top)

MERRA for a grid cell near Gainesville, Florida (centered at 308N, 828W), and (bottom) in situ

observations taken at the US-SP3 FLUXNET site (29.758N, 82.168W) located within the grid cell.

Reprinted from Reichle et al. (2011).

Figure 5.7: As in Figure 5.6, but for a representative high resolution grid cell in the G5NR. The blue

histogram bars show the precipitation, and the red curves show the downward shortwave radiation.

172
Figure 5.8: Top panel: Time series of precipitation, in mm/day, from the G5NR at a specific highresolution grid cell. Bottom panel: Corresponding time series of downward solar radiation and

latent heat flux, in Wm−2.

173
Figure 5.9: Locations where evaporation instabilities are found in the G5NR during a representative

14-day period during summer.

174
Figure 5.10: Locations where evaporation instabilities are found in two offline land surface model

simulations using prescribed, observations-based meteorological forcing. Top panel: simulation in

which information regarding the derivatives of aerodynamic resistance with respect to temperature

and specific humidity is provided to the land model. Bottom panel: simulation in which this

information is not provided to the land model. Results are shown for a representative 15-day

summer period.

175
Figure 5.11: Locations for which the maximum of half-hourly precipitation totals during a representative 2-week period in summer exceeds 4 cm.

J F M A M J J A S O N D

270

275

280

285

290

295

300

Monthly mean T2m [K]

Global (2068 stations/∼1.61e+08km2)

300

NCDC station obs Global (2068 stations) ERA−I GEOS−5 NR

295

global ERA−I global GEOS−5 NR

Figure 5.12: Left panel: Monthly mean T2m from the G5NR, ISH station observations, and ERA-I.

Solid lines show mean across station locations, and dashed lines show mean across global land. For

the station comparison, the error bars give the standard deviation of the 10-year monthly mean.

Right panel: Locations of the ISH T2m stations.

176
J F M A M J J A S O N D

255

260

265

270

275

280

285

290

295

300

Monthly mean T2m [K]

N.America (892 stations/∼3.01e+07km2)

J F M A M J J A S O N D

265

270

275

280

285

290

295

Monthly mean T2m [K]

Europe (791 stations/∼1.93e+07km2)

J F M A M J J A S O N D

255

260

265

270

275

280

285

290

295

300

Monthly mean T2m [K]

Asia (120 stations/∼5.30e+07km2)

J F M A M J J A S O N D

288

290

292

294

296

298

300

Monthly mean T2m [K]

S.America (69 stations/∼1.78e+07km2)

J F M A M J J A S O N D

288

290

292

294

296

298

300

302

Monthly mean T2m [K]

Africa (136 stations/∼3.25e+07km2)

J F M A M J J A S O N D

284

286

288

290

292

294

296

298

300

302

304

Monthly mean T2m [K]

Australia (56 stations/∼8.24e+06km2)

300

NCDC station obs Global (2068 stations) ERA−I GEOS−5 NR

295

global ERA−I global GEOS−5 NR

Figure 5.13: Monthly mean T2m from the G5NR, ISH station observations and ERA-I, by continent.

Solid lines show mean across station locations, and dashed lines show mean across global land. For

the station comparison, the error bars give the standard deviation of the 10-year monthly means.

177
Figure 5.14: G5NR minus ERA-I mean temperature differences, averaged over Dec-Jan-Feb (upper)

and Jun-Jul-Aug (lower), for T2m (left) and land surface temperature (right). Colors are only

plotted where the difference is greater than 2× the standard deviation of the ERA-I 10-year monthly

mean.

178
Figure 5.15: Annual mean of net surface shortwave (left) and longwave (right) radiation in Wm−2

.

Top row shows the radiation from the G5NR, middle row shows the difference between the G5NR

and SRB data and the bottom row shows the difference of cloud radiative forcing between the

G5NR and SRB data.

179
Figure 5.16: Annual mean of latent (left) and sensible (right) heat flux in Wm−2

from the G5NR

(top) and difference between G5NR and GSSTF data (bottom).

180
Figure 5.17: Implied ocean heat transport in PetaWatts.

181
Figure 5.18: CCMP 10 meter wind rose from May 2005 to May 2007, the wind rose is divided in

128 sectors. For clarity, the sector’s boundary are not shown. The radius of a sector indicates the

percentage of data that have the direction given by the corresponding sector. Colors represent bins

of wind speed in knots within a given sector.

182
Figure 5.19: Same as 5.18 but for the G5NR 10 m winds.

183
Figure 5.20: From left to right: Zonal wind speed, meridional winds speed and wind speed. From

top to bottom: 2 year average from the G5NR, SCOW winds and CCMP winds during the same

period as the G5NR.

184
Figure 5.21: Top panel: Two year average wind stress magnitude from the G5NR. Bottom panel:

Wind stress magnitude from SCOW.

185
Figure 5.22: Net surface energy flux components from the G5NR for the North Polar Cap, averaged

for the annual cycle from available years 2005-2007, and corresponding values from atmospheric

reanalyses, in Wm−2. Vertical bars indicate plus and minus one standard deviation of reanalysis

values for the period 1989-2009. Shown are the net surface energy flux (a., positive upwards),

sensible heat flux (b., positive upwards), and net surface shortwave radiative flux (c., positive

downwards).

186
Figure 5.23: Comparison of G5NR 2m temperature and corresponding station temperature for (a)

South Pole and (b) Barrow, in ◦C. Error bars correspond to the plus and minus the inter-annual

standard deviation for the available station record.

187
Figure 5.24: Average annual cycle for net surface moisture flux from G5NR and corresponding

values from reanalyses for (a) North Polar Cap and (b) South Polar Cap, in mm month−1 waterequivalent.

188
Figure 5.25: G5NR surface mass balance for (a) Greenland and (b) Antarctica, in cm yr−1 waterequivalent.

Figure 5.26: G5NR 10 m wind vectors over the Antarctic averaged for July 2005 and contours of

vector magnitude in ms−1.

189
Figure 5.27: G5NR 10 m wind vectors averaged for July 2005 and contours of vector magnitude in

ms−1for the southwestern Ross Sea.

Figure 5.28: G5NR near-surface wind field over Greenland for January 2005, in ms−1.

190
Figure 5.29: Daily-averaged G5NR 10 m wind field over Greenland and surrounding area for (a) 1

Jan 2006 and (b) 30 Jan 2006, in ms−1.

191
Chapter 6

Clouds and Radiation

Peter M. Norris, Will McCarty and Ronald M. Errico

The following is a validation of the G5NR clouds and top-of-atmosphere (TOA) radiation against

CERES (Clouds and the Earth’s Radiant Energy System) observations aboard the Aqua and Terra

satellites and against CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations

aboard the CALIPSO satellite.

Section 6.1 uses CERES Single Scanner Footprints (SSF) data. This is the highest resolution

Level II CERES product, comprised of individual 20 km footprints. Our validation procedure uses

only the G5NR averages at the Single Scanner Footprints from Terra and Aqua (which sample in

the daytime at 10:30 am and 1:30 pm local time, respectively, and 10:30 pm and 1:30 am in the

nighttime.) This validation is therefore valid for the atmosphere averaged only over the sampled

local times — no diurnal temporal model is employed to attempt full diurnal means. This is the

most accurate validation we can make, but is time consuming, so it is performed only for June

2005.

In contrast, Section 6.2 performs full diurnally averaged validation for four months (June, September, December, 2005 and March 2006) using CERES EBAF products. The EBAF dataset (“Balanced Top-of-Atmosphere Fluxes”) is a Level III CERES product primarily used for climate model

evaluation. It constrains the global net TOA flux to the ocean heat storage term and also spatially

interpolates (fills) clear-sky fluxes in non-observed regions. Finally, it employs a diurnal temporal

model to produce full diurnal average estimates from the Aqua and Terra observations. The differences from the validations in Section 6.1 appear small, so the EBAF validation is preferred in

terms of speed and its full diurnal averages. Conclusions from Sections 6.1 and 6.2 are summarized

in Section 6.3.

192
Section 6.4 compares the G5NR cloud top pressure (CTP) and cloud fraction (total and low, mid and

high bands) against multiyear DJF observations from the active lidar instrument CALIOP (CloudAerosol Lidar with Orthogonal Polarization) aboard the CALIPSO satellite. For the G5NR, the

first (pseudo 2005–06) season is compared against the CALIPSO data. Since the G5NR exists at a

lower resolution than the CALIPSO data (7.5 km vs 1 km), a maximum-random overlap assumption

was employed to sample the G5NR at a sub-gridscale resolution and locations consistent with the

lidar data. This overlap assumption was essential to properly compensate for instances where dense

low clouds are only partially covered by higher clouds in a multilayered cloud environment.

6.1 June 2005 Validation Against CERES SSF: Not Diurnally Averaged

This first section uses CERES Single Scanner Footprints (SSF) data for June 2005. This is the Level

II CERES product, comprised of individual 20 km footprints. This validation uses only the G5NR

averages over the Single Scanner Footprints from Terra and Aqua (which sample in the daytime at

10:30 am and 1:30 pm local time, respectively, and 10:30 pm and 1:30 am in the nighttime.) No

diurnal temporal model is employed. All results are monthly averages for June 2005. The notations

“nC” or “nC (masked)” both mean the Coarsened G5NR sampled at the locations of valid SSF

data, while the notations “nN” or “nN (masked)” mean the Native resolution G5NR sampled at

the locations of valid SSF data.

6.1.1 TOA Outgoing Longwave Radiation

Figure 6.1 shows the zonal average OLR validation against CERES. The agreement is very good.

Figure 6.2 shows the corresponding global map comparison. The differences are unremarkable,

except that the G5NR has a distinct bifurcation in the ITCZ in the Western to Central Pacific.

6.1.2 TOA Outgoing Shortwave Radiation

Figures 6.3 and 6.4 show the corresponding comparisons for TOA Outgoing Shortwave Radiation

(OSR). [Note that, like OLR, the comparisons include both daytime and nighttime samples, but for

OSR the nighttime zero values are averaged in.] It appears that the OSR is being overestimated at

almost all latitudes, likely because the model clouds are too reflective (see later). From the global

map comparison, we can make several observations:

• The cloud minima in the sub-tropical subsidence zones is less intense in the G5NR, indicating

a potentially weaker Hadley circulation. At the same time, it is also broader in latitude,

193
with the transition to northern stratus being further north in the G5NR, although after the

transition the stratus is brighter. This gives the feature in the zonal OSR plot where a positive

bias in the northern subtropics switches to a negative bias in northern mid-latitudes, then to

positive again at northern high latitudes.

• The low cloud (marine stratocumulus) shields off the western continental coasts are different

in nature. This is particularly evident off the North American coast, but also off Africa and

South America. It seems that the G5NR has a tendency to produce clear swaths near these

coasts, possibly indicating that the marine boundary layer height is too low near the coast, or

its inversion is too weak, or surface latent heat fluxes are too weak, maybe due to insufficient

strength of the subtropical highs. The SST should not be a factor since it is forced from daily

data based on observations.

6.1.3 Cloud Properties

Cloud Fraction

Figure 6.5 shows the zonal average total column cloud fraction. Ignore the polar regions, where

CERES may not be correct due to ice issues. The agreement is generally good, but the G5NR

has a more pronounced southern branch of ITCZ, and subtropical subsidence zone cloud minima

displaced a little poleward. Clearly, cloud fraction cannot explain the over-bright zonal OSR.

The global map (Figure 6.6) highlights the same problems seen for OSR, and in particular, the

sparsity of low cloud off western continental coasts is especially evident. It looks like the reduced

cloud cover features off the western continental interiors are being “leaked out” to the nearby marine

environments in some way, as though dry subsiding air over the land is moving out over the ocean

and the normal marine boundary layer mechanisms of maintaining a strong elevated inversion are

not strong enough to resist. The arcs of high cloud fraction SW off the Southern US / Mexico and

West of Southern and Central Africa are evidence of this effect.

Figure 6.7 shows the frequency distribution of cloud fraction on the coarse grid over the month

of June 2005. This study was spearheaded by Ron Errico for his work on radiance assimilation.

Note the logarithmic scale necessitated by the strong binary character of cloud fraction (which

becomes more pronounced as the grid size decreases). There is good agreement, but we note these

differences:

• From 30S to 30N the pattern is similar: the G5NR tends to underestimate small cloud

fractions and overestimate large cloud fractions, though the overcast cases are again underestimated.

• The pattern is similar but less extreme for 30N-60N.

194
• For the Southern Ocean region 60S-30S, both small and large cloud fractions are overestimated, overcast is underestimated, and clear is overestimated.

The cumulative frequency distributions (Figure 6.8) show another perspective: In the 30S-30N

equatorial/subtropical region, the G5NR has a deficit of partial cloud fraction, made up for only

by an excess at high and overcast partial cloud fractions. For mid-to-high latitudes, the situation

is reversed: the G5NR has an excess of partial cloudiness across the cloud fractions, especially in

the southern ocean, but made up for by an underestimate of overcast cases.

Cloud Top Pressure

Cloud Top Pressure (CTP) is a quantity that has quite different definitions in the model (layer

optical thickness threshold) and in CERES (IR-based), so we will not put much credence in differing

magnitudes between the G5NR and CERES. Specifically the IR method mixes high thin cloud with

lower thicker cloud, while the model method does not do this as much. Nevertheless, we show the

global map comparison (Figure 6.9) because it reveals some interesting qualitative details. What is

most evident for the low cloud comparison we have been discussing is that the CTP shows a clear

land/ocean contrast, mostly right up to the coast, unlike the cloud fraction just mentioned. This

tends to argue against a marine boundary layer height problem, but rather that the near-coast

cloud fraction / cloud occurence is too low, not the height per se. This argues more for a weak

inversion in the G5NR, and/or reduced surface latent heat flux.

Cloud Optical Thickness

Figure 6.10 shows the total in-cloud visible optical thickness (COT). In this case, the model diagnostic variable for the in-cloud optical thickness for the whole column (TAUTOT) is incorrect, because

it basically adds the in-cloud optical thicknesses for the low (700 hPa and below), mid (400–700

hPa) and high (400 hPa and above) pressure bands (TAULOW, TAUMID and TAUHGH, respectively). This is incorrect, because these are the in-cloud low, mid and high band optical depths, not

the grid-box band means. [Note: the “128” on the ordinate label indicates limiting of the model

values to the CERES maximum value. This is a small effect.]

In Figure 6.11 we use a replacement diagnostic for total in-cloud COT, namely:

TAUTTN = TAULOW ∗ CLDLOW + TAUMID ∗ CLDMID + TAUHGH ∗ CLDHGH

CLDTOT ,

where CLDLOW, CLDMID and CLDHGH are, respectively, the cloud fractions for the same low,

mid and high pressure bands described above, and CLDTOT is the total column cloud fraction

obtained by combining these three cloud fractions assuming random overlap between the bands.

195
This should be a corrected and acceptable replacement for the random band overlap assumed by

the model. COT is nevertheless still overestimated, and likely explains the over-bright OSR.

We also show the global map comparison (Figure 6.12) just to look at qualitative features, since the

magnitude is incorrect. Specifically for the issue of the paucity of low marine cloud, the in-cloud

optical depths are under-predicted in these marine regions, not just the cloud fraction.

In summary, for the stratocumulus issue, there are fewer low clouds over Eastern Pacific and

Atlantic boundaries, and they are thinner, but their height is not so problematic.

Condensed Water Path

The grid-mean condensed water path (Figure 6.13, this time at full native resolution) shows a

significant overprediction of condensed water path at most latitudes, and this may at least partially

explain the optical depth and OSR overprediction. Other factors such as effective radius may also

play a role. The global map (Figure 6.14) shows very clear differences:

• Cloud minima in subtropical subsidence zones are clearly more distinct for CERES. The

G5NR has too much cloud water in these latitudes.

• The ITCZ is more distinct in the observations, partly because of the previous item. Also the

ITCZ appears to bifurcate in Pacific in the G5NR; and

• Western continental coast cloudiness seems to have a clear swath near the coast in the G5NR,

particularly off North America.

These are grid-column condensed mean water paths. We can divide by the total column cloud

fraction (Figure 6.15) to get an idea of the average in-cloud condensed water path. In preparation,

Figure 6.16 shows the global map of total column cloud fraction at full native G5NR resolution.

As discussed earlier, outside of the unreliable polar regions, the comparison is reasonable, except

that the subtropical cloud fraction minima are more distinct and less broad (closer to the equator)

in the observations. Finally, the “in-cloud” cloud water paths (grid-column mean CWP / total

column cloud fraction) are shown in Figures 6.17 and 6.18. We can now clearly summarize the

issue: subtropical and mid-latitude clouds have too much condensed water in them in the G5NR

(except in the waters adjacent to western continental margins, where they do not have enough).

The first effect dominates the climate, giving too much cloud albedo and an over-bright planet.

196
6.1.4 Albedo vs. OLR Joint Histograms

In Figures (6.19)–(6.22) we show joint histograms of TOA solar albedo versus OLR for different

latitude bands. We choose these variables because they are direct radiative outputs from the model

and rather direct observations from CERES SSF, and are therefore less susceptible to varying

definitions (cf. cloud top pressure) between model and retrievals. A drawback of this choice is

that we are folding in clear-sky and surface albedo effects (the pure cloud radiative effect is not

available from CERES SSF). To mitigate this complication, we consider ocean-only results, where

the surface albedo is low and reasonably constant. All results are again for the June 2005 period.

The OLR axis is inverted, so that the y-axis is a proxy for cloud height. Contours are proportional

to probability density. The filled contours are for CERES SSF. The dashed contours are for the

CERES-SSF-masked coarse G5NR (nC). The five dashed (blue, cyan, green, orange and red) contours delineate the boundaries of the four filled color regions. All results are for daytime only. The

results are built from individual hourly averages.

Figure 6.19 is for the two tropical bands. The southern tropics show reasonable agreement, but

with a bright bias in thicker cloud, and some evidence of a low bias. The northern equatorial

region, which contains the dominant ITCZ branch, shows interesting behavior: (1) Two distinct

cloud types are seen: low clouds and high clouds; (2) Both types are biased bright and warm. Note

that if the clouds are gray, not black, a bright bias will, in and of itself, lead to a cold bias. So this

warm bias is likely to be a true low bias.

Figure 6.20 is for mid-latitudes bands (again over ocean only). The southern mid-latitudes have

a pronounced bright and low bias. The northern mid-latitudes have similar bias, but to a much

lesser degree. Note that a distinct low vs. high cloud type bifurcation is not present in the southern

midlatitudes, in the observations or the G5NR.

Figure 6.21 shows the higher-latitudes from 40–60 degrees. The southern branch contains the

Southern Ocean in austral winter. Clouds tend to be thicker and higher in the G5NR. More

precisely, it seems that the low clouds are lower and thicker, while there is a population of higher,

very thick clouds not seen in the observations. It is possible that the latter represents contamination

from the northern edge of the winter Antarctic ice sheet. The northern (summer) higher latitudes

show distinct low bias, and a narrower range of OLR at cloudy albedos, indicating less variability

in cloud height.

Finally, Figure 6.22 shows the global joint probability density. Note: All these joint histograms are

weighted by the cosine of latitude, so this plot avoids an undue polar influence. About all we can

say in general is that the G5NR has a bright bias for clouds (consistent with our other findings)

and somewhat of a low bias. This is for both the low and high cloud branches. It is still possible

some sea-ice influence is showing in the high cloud branch, but this requires further investigation.

197
6.2 Diurnally-Averaged Validation Against CERES EBAF

Next we conduct monthly and diurnally averaged validation for four months (June, September,

December, 2005 and March 2006) using CERES EBAF products described at the beginning of this

chapter. Because the EBAF dataset also uses a temporal model to create a full diurnal cycle, there

is no masking of the G5NR results along the swath in this section, as contrasted with the previous

SSF section. The appearance of “nC” or “nC(unmasked)” in any of the figures for this section both

refer to full diurnal averages of the coarsened G5NR.

6.2.1 TOA Outgoing Longwave Radiation

June 2005 (Figure 6.23) is one of the best validations, with a global mean difference of only 0.1

Wm−2. September 2005 (Figure 6.24) is one of the worst, but is still very good, with a global

mean difference of only 1.4 Wm−2. The corresponding global map (Figure 6.25) evidences the

double ITCZ issue and the concomitant pushing of the southern subtropical subsidence zone (and

paucity of cloud) further to the South. Table 6.1 summarizes the four monthly global mean OLR

differences. Clearly the differences are small.

6.2.2 TOA Outgoing Shortwave Radiation

The OSR comparison for June shows much the same features as above in Section 6.1. December

shows similar behavior: a weaker but slightly broader subtropical/midlatitude cloud minima and

brighter Southern Ocean stratus. A double Pacific ITCZ is somewhat evident in both model and

CERES. As an equinoctial example we show the results for September (Figures 6.26 and 6.27).

Many of the differences have already been noted: tropical differences including the definition and

bifurcation of the ITCZ; the intensity of subtropical subsidence zones; western continental coast

cloudiness; and the brightness of Southern Ocean cloud.

For each month there is a bright bias, as Table 6.2 of global means shows. The almost 7 Wm−2

overestimate of outgoing solar flux is significant considering that a net 2×CO2 forcing is on the

order of 4 Wm−2. The global mean OLR bias of 1.0 Wm−2 adds to this, giving a combined excess

global cooling of about 7.8 Wm−2. A similar bright bias was found for 2006 in the G5NR (Table

6.3). The bright bias appears consistent from year to year, and maps (not shown) show very similar

features, indicating that these differences cannot be assigned to inter-annual variability, but appear

to be climatic biases.

Note that our analysis is reasonably consistent with a NASA Energy and Water cycle Study

(NEWS) analysis of TOA energy balance provided by Dr. Michael Bosilovich (Figure 3.11), bearing in mind that the NEWS values are for a longer period, 2001–2010. For comparison, Table 6.4

198
provides the summarized global TOA energy balance numbers from the G5NR. Table 6.5 provides

the equivalent numbers for CERES EBAF.

What is the cause of this general bright bias, if it is more than just the sum of the noted specific

cloud problems, as it seems it might be? First, we need to discount the possibility that CERES

and the G5NR have different incoming solar radiation (ISR). There is negligible difference, as per

Tables 6.4 and 6.5. So the OSR bias is not due to incoming solar. Still, it is useful to normalize

the OSR by the ISR as an albedo, since the ISR strongly varies with latitude, especially in the

summer/winter seasons. Figures 6.28 and 6.29 show the TOA albedo for June 2005. The zonal

plot shows that the albedo is biased high over almost all the latitudes. The CERES outlier at the

southernmost latitude should be ignored, where the incoming solar goes to zero. The global map

shows all the features we have been discussing very clearly.

The next issue we would like to eliminate is non-cloud affects, such as differences in surface albedo.

Figure 6.30 shows the shortwave cloud forcing SCF = OSRclr - OSR. The definition gives negative

values, meaning that cloudy regions generally reflect more solar energy than clear regions, thereby

acting to cool the planet. Evidently for June 2005, clouds in the G5NR are cooling the planet in

the global mean by an excessive amount, about 7.4 Wm−2extra compared with CERES EBAF.

Table 6.6 shows the numbers for Jun, Sep, Dec 2005 and Mar 2006. The average is an excessive

cooling due to clouds of about 8 Wm−2.

[Note: the global mean longwave cloud forcing, LCF = OLRclr - OLR, for the four month average

is 25.87 Wm−2for the G5NR and 26.24 Wm−2for EBAF, a difference of only -0.37 Wm−2.]

Finally, we scale the SCF by the incoming solar radiation (ISR), in order to remove the latitudinal

variation in insolation. Figures 6.31 and 6.32 show the June 2005 results. All the features we have

been discussing are clearly evident in the G5NR: tropical differences including the definition and

Pacific bifurcation of the ITCZ; reduced intensity of subtropical subsidence zones; more northerly

but more intense northern high latitude stratus; western continental coast cloudiness has clear

swaths near the coast; and excessive brightness and broadness of Southern Ocean cloud.

6.3 CERES Validation Summary

From the validation work in Sections 6.1 and 6.2, we can draw the following conclusions:

• The G5NR outgoing longwave radiation (OLR) at top-of-atmosphere (TOA) is very good.

Differences from CERES are unremarkable, except for the cloud signature of a more active

southern branch of the ITCZ in the Western to Central Pacific. The average global mean

difference was only about 1 Wm−2.

• In contrast, the outgoing shortwave radiation (OSR) at TOA is overestimated by the G5NR at

199
almost all latitudes. The average global mean excess was about 7 Wm−2

, which is significant

considering that a net 2×CO2 forcing is on the order of 4 Wm−2. This problem appears to

be due to excessive cloud optical depth in the G5NR. Excess in-cloud condensed water is at

least partially responsible, although other as yet unknown factors must also exist. An examination of shortwave cloud forcing scaled by the incoming solar radiation for June 2005 shows

these qualitative features: a less well-defined and more bifurcated ITCZ; reduced intensity of

subtropical subsidence zones; more northerly but more intense northern high latitude stratus;

clear swaths in marine cloud near Western continental coasts; and excessive brightness and

broadness of Southern Ocean cloud.

• The total column cloud fraction validation is reasonable to good. The main differences are a

more pronounced southern branch of ITCZ in the G5NR, a sparsity of low cloud off western

continental coasts, and the subtropical subsidence zone cloud minima being displaced a little

poleward. By contrast these cloud minima are more distinct and less broad (closer to the

equator) in the observations. Note that these cloud fraction differences cannot explain the

over-bright zonal OSR.

• An analysis of the cumulative distribution of partial cloud fractions shows that in the 30S30N equatorial/subtropical region, the G5NR has a deficit of partial cloud fraction, made

up for only by an excess at high and overcast partial cloud fractions, while for mid-to-high

latitudes, the situation is reversed: the G5NR has an excess of partial cloudiness across the

cloud fractions, especially in the southern ocean, but made up for by an underestimate of

overcast cases.

• There are fewer low clouds (marine stratocumulus) over the Eastern Pacific and Atlantic

boundaries, and they are thinner, but their height is not so problematic. The most likely

explanation is a weaker capping inversion in the G5NR.

• The in-cloud condensed water path is significantly overpredicted at most latitudes, and this

at least partially explains the optical depth and OSR overpredictions. In qualitative terms,

subtropical and mid-latitude clouds have too much condensed water in them in the G5NR (except in the waters adjacent to western continental margins, where they do not have enough).

In contrast, the cloud water minima in the subtropical subsidence zones are clearly more

distinct for CERES.

• A study of joint histograms of TOA SW Albedo and OLR reveals that not only do G5NR

clouds tend to be biased brighter, but they also tend to be biased lower, both for high and

low clouds.

6.4 Cloud Top Pressure and Cloud Fraction Validation Against

CALIPSO

Many OSSEs are fundamentally built upon a foundation of synthetically-generated observations.

Typically, a forward model (e.g. a radiative transfer model, a lidar instrument simulator) is used

200
to generate the more complicated measurements (e.g. radiances, lidar backscatter) from the core

atmospheric fields modeled in the G5NR. Fundamental to these simulated observations is the inclusion of realistic cloud effects. Clouds have varying scattering and absorptive/emissive effects

through the entire electromagnetic spectrum, from the ultraviolet to the microwave, which has a

significant effect in the realistic relationship between synthetic and real observations. Without the

inclusion of these effects, simulated observations can be unrealistically cloud-free, and this can result in the over- or under-selling of a platform’s scientific impact. This study performed a validation

on the underlying cloud fields that will be sampled in the generation of remotely sensed satellite

observations.

6.4.1 Data and Sampling

The primary data used in the validation of the G5NR clouds is every December-January-February

(DJF) season over the course of the currently-existing data record (2006–07 to 2013–14) of the

Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument onboard the CloudAerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform. Specifically,

the G5NR was compared against cloud top pressure (CTP) retrievals at 1 km from the version 3.3

release, known as the CAL LID L2 01kmCLay-ValStage1-V3-30 data record.

For the G5NR, the first (pseudo 2005–06) season was compared against the CALIPSO data. Specifically, the cloud fraction field was validated. Since the G5NR exists at a lower resolution than the

CALIPSO data (7.5 km vs 1 km), a maximum-random overlap assumption was employed to sample

the G5NR at a sub-gridscale resolution and locations consistent with the lidar data. This overlap

assumption was essential to properly compensate for instances where dense low clouds are only

partially covered by higher clouds in a multilayered cloud environment.

Finally, CALIPSO data were used as reference because, as a visible lidar, the instrument has the

highest sensitivity to thin, high clouds. This was the most accurate observation to directly compare

to the G5NR cloud fraction fields without having to incorporate an instrument model. These active

lidar measurements have significantly higher sensitivity to optically thin clouds compared to other

options, namely observations and retrievals from measurements in the infrared and microwave as

well as for passive shortwave measurements.

6.4.2 Mean Cloud Height

The zonal mean cloud top pressure for the G5NR and CALIPSO DJF seasons is presented in

Figure 6.33. The G5NR clouds were higher than the distribution measured by CALIPSO. In the

southern hemisphere, tropics, and Arctic, the mean cloud top pressure was beyond the spread of

the eight years of CALIPSO measurements. It is only in the northern midlatitudes that the G5NR

neared the spread of the CALIPSO measurements, though the G5NR was on the vertically higher

201
end of the distribution and was above the CALIPSO mean.

6.4.3 Spatial Distribution of Cloud Fraction

In addition to the mean height of the clouds, the spatial distribution of cloud amount was considered.

To compare the sampling of the nadir-sampling CALIPSO data spatially, all measurements over

the DJF period were aggregated into cloud fractions on a 5◦ × 5

◦ grid. The same was done for

the G5NR fields sampled coincidentally along the CALIPSO tracks at the 1 km sub-grid resolution

with the maximum-random overlap. The spatial maps of cloud fraction and their corresponding

difference are shown in Figure 6.34. The G5NR generally has more clouds than the CALIPSO

measurements in the poles and fewer in the extratropics. The tropics appear to have a zonal mean

difference near zero but differ from positive to negative as a function of longitude. There are also

two large minima off the west coasts of Australia and South America. To further understand the

various regions where there was either an overabundance or deficiency in cloud cover, the cloud

fractions were further investigated by height as a function of the ISSCP definitions of high (CTP

< 440 hPa), midlayer (440 hPa < CTP < 700 hPa), and low (CTP > 700 hPa) cloud.

Spatial Distribution of High Clouds (CTP < 440 hPa)

When considering the spatial distribution of high clouds, as shown in Figure 6.35, the largest

agreement between the high cloud differences and the total differences is in the tropics. The areas

of excess cloud overall seen in Figure 6.34 agree in sign and location with the areas of excess high

cloud, though the magnitude of these differences does not fully explain the difference in total cloud

fraction. Both the Arctic and Antarctic have excess high cloud, though not in complete agreement

with the regions of excessive total cloud fraction. An example of this is over the Arctic north of

Eurasia. In total clouds, the G5NR and CALIPSO means differ on the order of 5–20%, while in high

cloud, the difference is in excess of 50%. In contrast, north of Siberia and Alaska, the discrepancy

between the G5NR and CALIPSO data in high clouds is less than the discrepancy seen in total

clouds. Finally, the G5NR does not have a strong deficiency in high clouds in the midlatitudes

corresponding to that seen in total clouds.

Spatial Distribution of Mid-Layer Clouds (440 hPa < CTP < 700 hPa)

The spatial distribution of mid-layer clouds is shown in Figure 6.36. A general lack of clouds in

this region in the G5NR is seen relative to the CALIPSO data through most of the globe. This is

apparent through the tropics where the mid-layer cloud fraction over ocean is generally less than

5% and the general deficit continues, though to a lesser extent, over land. The most spatially

consistent deficiency is over the Southern Ocean. This largely matches the overabundance of high

202
clouds shown in Figure 6.35. This corresponds to a general upward shift of the highest cloud tops

in the G5NR relative to CALIPSO.

Spatial Distribution of Low Clouds (CTP > 700 hPa)

In the spatial distribution of low clouds shown in Figure 6.37, it is once again seen that the model

has a general lack of clouds relative to the CALIPSO data record, with the some exceptions being

regions over northern hemisphere snow and ice. The clearest deficits are in the marine stratocumulus

regions off the west coasts of South America and Australia, and these areas correspond with the

areas of overall cloud fraction deficits in Figure 6.34. A strong deficit is also seen in the Arctic

region north of Eurasia. This is related to a ”shadowing” effect, as there is an over-abundance in

high clouds in this region as shown in Figure 6.35 which obscures the potential low clouds in a

top-downward, highest cloud only, CTP analysis.

6.5 CALIPSO Validation Summary

The zonal mean CTP shows a consistent displacement between the G5NR and the CALIPSO

data record. There are three possible ways to explain the displacement between the CALIPSO and

G5NR fields: the G5NR has excessive high clouds, a shortage of low clouds, or a similar distribution

that is displaced vertically upwards. In considering the overall cloud fraction, it is seen that the

distribution varies regionally across the globe. By further comparing the cloud fraction figures as a

function of cloud height layer, it was seen that some areas were simply deficient throughout, some

areas were “shadowed” by excess high clouds, and others still were dramatically lacking low clouds.

It is noted that the methods of this section are consistent with those of McCarty et al. (2012). That

study performed a similar analysis on the ECMWF NR, though with a shorter CALIPSO record and

different version of CALIPSO cloud height retrievals. While both comparisons reveal shortcomings

relative to observations, the G5NR shows better agreement in terms of high-level clouds, which

were found to be deficient in the ECMWF NR. In contrast, the G5NR shows a greater deficiency

in low-level clouds than the ECMWF NR, particularly in areas of marine stratocumulus. The

conclusions of McCarty et al. (2012), however, still hold true. In an OSSE context, the user should

be aware of these patterns, as too few or too many clouds may result in a synthetic dataset that

does not fully represent the observations from a real, space-borne platform.

203
Table 6.1: Global means of OLR for four months of 2005 for the coarsened nature run and CERES

EBAF. The “Avg” field is the average of the four months.

OLR validation against CERES EBAF for 2005

Jun Sep Dec Mar Avg

nC 242.51 243.55 238.41 238.74 240.80

EBAF 242.61 242.12 236.75 237.66 239.79

nC-EBAF -0.10 1.43 1.66 1.08 1.02

Table 6.2: Global means of OSR for four months of 2005 for the coarsened nature run and CERES

EBAF. The “Avg” field is the average of the four months.

OSR validation against CERES EBAF for 2005

Jun Sep Dec Mar Avg

nC 102.78 100.62 114.32 107.41 106.28

EBAF 96.70 94.65 107.03 99.44 99.46

nC-EBAF 6.08 5.97 7.29 7.97 6.83

Table 6.3: Global means of OSR for four months of 2006 for the coarsened nature run and CERES

EBAF. The “Avg” field is the average of the four months.

OSR validation against CERES EBAF for 2006

Jun Sep Dec Mar Avg

nC 101.79 100.73 114.22 107.07 105.96

EBAF 95.75 94.39 108.10 99.45 99.42

nC-EBAF 6.04 6.34 6.12 7.62 6.53

204
Table 6.4: G5NR global mean TOA energy balance summary. All the numbers are for Jun, Sep,

Dec, Mar averages (in W/m2).

G5NR run global TOA energy balance

nC ISR OLR OSR Onet

2005-2006 340.38 240.80 106.28 6.70

2006-2007 340.37 240.59 105.96 6.18

Table 6.5: CERES EBAF global mean TOA energy balance summary. All the numbers are for

Jun, Sep, Dec, Mar averages (in W/m2).

CERES EBAF global TOA energy balance

EBAF ISR OLR OSR Onet

2005-2006 340.30 239.79 99.46 -1.05

2006-2007 340.29 240.10 99.42 -0.77

Table 6.6: Global means of shortwave cloud forcing (SCF) for four months of 2005 for the coarsened

nature run and CERES EBAF. The “Avg” field is the average of the four months.

SCF validation against CERES EBAF for 2005

Jun Sep Dec Mar Avg

nC -52.57 -53.46 -59.70 -55.50 -55.31

EBAF -45.21 -45.55 -52.58 -46.09 -47.36

nC-EBAF -7.36 -7.91 -7.12 -9.41 -7.95

205
Figure 6.1: Zonal average OLR validation against CERES SSF. “nC” means the Coarsened G5NR

sampled at the locations of valid SSF data.

206
Figure 6.2: Global map OLR validation against CERES SSF. “nC” means the Coarsened G5NR

sampled at the locations of valid SSF data. “(masked)” emphasizes that the G5NR output is

averaged only over the locations of valid SSF data.

207
Figure 6.3: Zonal average OSR validation against CERES SSF. “nC” means the Coarsened G5NR

sampled at the locations of valid SSF data.

208
Figure 6.4: Global map OSR validation against CERES SSF. “nC” means the Coarsened G5NR

sampled at the locations of valid SSF data. “(masked)” emphasizes that the G5NR output is

averaged only over the locations of valid SSF data.

209
Figure 6.5: Zonal average total column cloud fraction validation against CERES SSF. “nC” means

the Coarsened G5NR sampled at the locations of valid SSF data.

210
Figure 6.6: Global map total column cloud fraction validation against CERES SSF. “nC” means

the Coarsened G5NR sampled at the locations of valid SSF data. “(masked)” emphasizes that the

G5NR output is averaged only over the locations of valid SSF data.

211
Figure 6.7: Frequency distribution of cloud fraction on the coarse grid over the month of June

2005. “cerC” are the CERES total column cloud fractions binned to the coarse grid, and “natC”

are the corresponding coarsened G5NR values. Note the logarithmic scale necessitated by the

strong binarity of cloud fraction.

212
Figure 6.8: As for Figure 6.7 but the cumulative frequency distributions.

213
Figure 6.9: Global map cloud top pressure comparison against CERES SSF. “nC” means the

Coarsened G5NR sampled at the locations of valid SSF data. “(masked)” emphasizes that the

G5NR output is averaged only over the locations of valid SSF data.

214
Figure 6.10: Zonal average in-cloud cloud optical thickness validation against CERES SSF. “nC”

means the Coarsened G5NR sampled at the locations of valid SSF data.

215
Figure 6.11: Zonal average corrected in-cloud cloud optical thickness validation against CERES

SSF. “nC” means the Coarsened G5NR sampled at the locations of valid SSF data.

216
Figure 6.12: Global map corrected in-cloud cloud optical thickness comparison against CERES

SSF. “nC” means the Coarsened G5NR sampled at the locations of valid SSF data. “(masked)”

emphasizes that the G5NR output is averaged only over the locations of valid SSF data.

217
Figure 6.13: Zonal average grid-column mean condensed water path validation against CERES

SSF. “nN” means the Native resolution G5NR sampled at the locations of valid SSF data.

218
Figure 6.14: Global map grid-column mean condensed water path validation against CERES SSF.

“nN” means the Native resolution G5NR sampled at the locations of valid SSF data. “(masked)”

emphasizes that the G5NR output is averaged only over the locations of valid SSF data.

219
Figure 6.15: Zonal average cloud fraction validation against CERES SSF at native G5NR resolution.

Otherwise like Figure 6.5.

220
Figure 6.16: Global map cloud fraction validation against CERES SSF at native G5NR resolution.

Otherwise like Figure 6.6.

221
Figure 6.17: Zonal average in-cloud condensed water path validation against CERES SSF. “nN”

means the Native resolution G5NR sampled at the locations of valid SSF data.

222
Figure 6.18: Global map in-cloud condensed water path validation against CERES SSF. “nN”

means the Native resolution G5NR sampled at the locations of valid SSF data. “(masked)” emphasizes that the G5NR output is averaged only over the locations of valid SSF data.

223
Figure 6.19: Joint histogram of TOA Cloud Albedo vs. OLR for the tropical ocean. Contours are

proportional to probability density. The filled contours are for CERES SSF. The dashed contours

are for the CERES-SSF-masked coarse G5NR (nC). The five dashed (blue, cyan, green, orange and

red) contours delineate the boundaries of the four filled color regions. All results are for daytime

only. The results are built from individual hourly averages.

224
Figure 6.20: Joint histogram of TOA Cloud Albedo vs. OLR for the mid-latitude ocean. Otherwise,

as in Figure 6.19.

225
Figure 6.21: Joint histogram of TOA Cloud Albedo vs. OLR for the higher-latitude oceans.

Otherwise, as in Figure 6.19.

226
Figure 6.22: Joint histogram of TOA Cloud Albedo vs. OLR for the global oceans. Otherwise, as

in Figure 6.19.

227
Figure 6.23: Zonal average OLR validation against CERES EBAF for June 2005. “nC” is the full

diurnally and monthly averaged Coarsened G5NR (see text). The “gmean” are the global mean

values.

228
Figure 6.24: Zonal average OLR validation against CERES EBAF for September 2005. “nC” is

the full diurnally and monthly averaged Coarsened G5NR (see text). The “gmean” are the global

mean values.

229
Figure 6.25: Global map OLR validation against CERES EBAF for September 2005.

“nC(unmasked)” is the full diurnally and monthly averaged Coarsened G5NR (see text). The

“gmean” are the global mean values.

230
Figure 6.26: Zonal average OSR validation against CERES EBAF for September 2005. “nC” is

the full diurnally and monthly averaged Coarsened G5NR (see text). The “gmean” are the global

mean values.

231
Figure 6.27: Global map OSR validation against CERES EBAF for September 2005.

“nC(unmasked)” is the full diurnally and monthly averaged Coarsened G5NR (see text). The

“gmean” are the global mean values.

232
Figure 6.28: Zonal average shortwave TOA albedo validation against CERES EBAF for June 2005.

“nC” is the full diurnally and monthly averaged Coarsened G5NR. The CERES outlier at the

southernmost latitude should be ignored, where the incoming solar goes to zero.

233
Figure 6.29: Global map shortwave TOA albedo validation against CERES EBAF for June 2005.

“nC(unmasked)” is the full diurnally and monthly averaged Coarsened G5NR.

234
Figure 6.30: Zonal average shortwave cloud forcing (SCF) validation against CERES EBAF for

June 2005. “nC” is the full diurnally and monthly averaged Coarsened G5NR. The “gmean” are

the global mean values.

235
Figure 6.31: Zonal average shortwave cloud forcing (SCF) normalized by ISR: validation against

CERES EBAF for June 2005. “nC” is the full diurnally and monthly averaged coarsened G5NR.

236
Figure 6.32: Global map average shortwave cloud forcing (SCF) normalized by ISR: validation

against CERES EBAF for June 2005. “nC” is the full diurnally and monthly averaged coarsened

G5NR.

237
Figure 6.33: The mean cloud top pressure as a function of latitude for DJF for the G5NR (green),

each CALIPSO DJF from 2006–07 through 2013–14 (gray), and the CALIPSO DJF 8 year average

(blue).

238
Figure 6.34: Spatial distribution of DJF cloud fraction for 5

◦ × 5◦ bins for the G5NR (top), the

CALIPSO 8-year mean (middle), and the difference of the two (G5NR minus CALIPSO, bottom).

239
Figure 6.35: Same as Figure 6.34, but for high clouds (CTP < 440 hPa).

240
Figure 6.36: Same as Figure 6.34, but for mid-layer clouds (440 hPa < CTP < 700 hPa).

241
Figure 6.37: Same as Figure 6.34, but for low clouds (CTP > 700 hPa).

242
Chapter 7

Aerosols and Trace Gases

Lesley Ott, Anton Darmenov, Arlindo da Silva, Virginie Buchard and Krzysztof Wargan

Aerosols and atmospheric trace gases exert strong influences on both human health and climate.

Satellite observations of aerosols and carbon monoxide have long been used by the scientific community to study the transport and fate of air pollution. Increasingly, satellite data are also playing

a role in understanding air quality and its potential health impacts. Carbon dioxide and ozone

observations are used to understand the distributions of these gases in the atmosphere and the

processes governing them. While ozone observations have been available for over 30 years, carbon

dioxide observations from satellites are relatively new. We expect that the high spatial resolution

of the G5NR will aid users conducting studies to both better observe and model atmospheric trace

gases and aerosols. In addition, because aerosols affect the ability to observe meteorological parameters, the realistic aerosol simulation provided by the G5NR should aid users with a wide variety

of research goals. The following chapter provides an evaluation of the performance of aerosols and

trace gases in the G5NR as well as recommendations for users.

7.1 Aerosols

Aerosols in the G5NR are simulated with an online version of the the Goddard Chemistry, Aerosol,

Radiation, and Transport model (GOCART, Chin et al., 2002) and are radiatively active. Emissions

of organic carbon (OC), black carbon (BC), sulfur dioxide (SO2), carbon monoxide (CO) and carbon

dioxide (CO2) from biomass burning are from the Quick Fire Emissions Dataset (QFED) version

2.4-r6. The QFED methodology is based on the fire radiative power (top-down) approach and

draws on the cloud correction approach utilized in the Global Fire Assimilation System (GFAS,

243
Kaiser et al., 2012). QFED, however, employs a more sophisticated treatment of non-observed

land areas (Darmenov et al., 2014). Location and fire radiative power of fires are obtained from the

Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 fire products and the MODIS

Geolocation products. Volcanic emissions of SO2 are prescribed following Diehl et al. (2012).

Parameterization of sea salt production follows the formulation of size dependent number flux in

Gong et al., 2003) but with a wind dependent term equal to u

2.41

∗

, where u∗ is the friction velocity.

Similarly to the work of Jaegle et al. (2011) we apply an independently derived SST correction

term which modulates the strength of the emissions. More details on anthropogenic and biomass

burning emissions and aerosol optical properties used in the G5NR are provided in Putman et al.

(2014).

Evaluation of aerosols in the G5NR includes comparison with the aerosol climatology from the

NASA/GMAO MERRA Aerosol Reanalysis (MERRAero, da Silva et al., 2012), statistics from

the Aerosol Comparisons between Observations and Models (AeroCom) initiative and satellite

observations. Evaluation mainly focuses on basic performance metrics that included residence

times of tropospheric aerosols, aerosol optical thickness, composition and vertical distribution of

the simulated aerosols.

7.1.1 Residence Times of Aerosols

To compare the net effect of aerosol properties and parameterizations of physical processes controlling the aerosol burden to other models, we compute the residence times of the aerosols in the

G5NR simulation. Residence times and wet and dry removal rates are listed in Table 7.1. Also

included in the table are the AeroCom multi-model mean parameters and their standard deviation

reported in Textor et al. (2006). Residence times of sea salt (0.4 days), dust (4.2 days) and BC

(7.3 days) agree very well with the AeroCom statistics. Residence time of OC in the G5NR is 5.33

days which is about one day less than the AeroCom mean value of 6.54 days but is well within

one standard deviation from the reported mean. The residence time of sulfate aerosol is 2.7 days

whereas the AeroCom mean and standard deviation are 4.12 days and 18%, respectively. The

shorter residence time in the G5NR is due to relatively high wet removal rate of 0.34 days−1. In

comparison, the residence time of sulfate aerosol in the MERRAero reanalysis is 3.9 days, which

is at the lower range of the AeroCom results. In comparison, the residence time of sulfate aerosol

in GEOS-5 simulations performed at 0.5◦ horizontal resolution, with replayed meteorology and

precipitation, were 3.4 days and 3.9 days - values which are at the lower range of the AeroCom

results.

7.1.2 Aerosol Optical Thickness

Time series of global monthly mean aerosol optical thickness (AOT) are computed and compared

with aerosol climatology from the MERRAero reanalysis. Monthly varying total and speciated

global mean AOTs are shown in Figure 7.1. Results from the G5NR simulation are plotted as open

244
Table 7.1: The accompanying residence times in parenthesis are the multi-model mean and standard

deviation from the AeroCom initiative reported in Textor et al. (2006).

Residence times and removal rates of aerosols

Species residence time (days) dry removal rate (day−1) Wet removal rate (day−1)

OC 5.33 (6.54 ± 27%) 3.55 × 10−2 1.52 × 10−1

BC 7.31 (7.12 ± 33%) 3.08 × 10−2 1.06 × 10−1

Sulfate 2.67 (4.12 ± 18%) 3.18 × 10−2 3.43 × 10−1

Sea-salt 0.42 (0.48 ± 58%) 1.13 1.24

Dust 4.22 (4.14 ± 43%) 1.55 × 10−1 8.16 × 10−2

circles connected with solid black lines. The gray shaded area represents the range of minimum

and maximum values from the MERRAero climatology. The mean and median values of the latter

are plotted as solid and dashed gray lines.

The time series of the G5NR and the aerosol climatology are similar, which is expected, given the

similarities in the primary emissions. On the other hand, differences in the annual mean AOTs due

to OC, BC, sulfate and dust are clearly present. The observed similarities in the temporal patterns

of the speciated AOTs enables us to derive global scaling factors for each of the aerosol species

and also to assess how well tuned were the global magnitude of the wind driven and resolution

dependent sea salt and dust emissions. Global scaling factors were computed from linear regression

of the G5NR and MERRAero AOTs while constraining the intercept to zero. Because of the small

sample size, we also computed global scaling factors as the ratio of the annual mean AOTs. The

two methods resulted in very similar values. Here we report the scaling factors found from the

linear regression of AOTs.

On a global scale, the AOT due to BC is 1.36 times smaller when compared to the aerosol climatology. Similarly, factors of 1.43 and 1.3 were computed for the OC and sulfate species. In contrast,

dust AOT is overestimated by a factor of about 1.5 which suggests that magnitude of global dust

emissions in simulations at similar horizontal resolutions needs to be reduced by a factor of about

0.7. Sea-salt AOT is underestimated slightly but the results suggest that scaling up the global

magnitude of sea salt emissions by a factor of 1.08 will improve the sea salt burden. Our findings

from the AOT comparison are summarized in Table 7.2.

The underestimated AOT due to BC, OC, sea salt and sulfate aerosols is compensated by the

overestimated AOT from dust. The net effect is that the G5NR and the MERRAero reanalysis

agree very well in terms of total AOT on a global scale (Figure 7.1).

Early in the simulation (Figure 7.1), dust AOT is slightly higher than the aerosol climatology.

As the simulation progresses, the dust AOT increases and then appears to stabilize, following the

seasonal climatology pattern. This behavior suggests that the tuning of the strength of global

dust emissions was performed over an insufficiently long period, which did not allow for the model

245
Table 7.2: Global aerosol scaling factors

Aerosol species Scaling factor

OC 1.43

BC 1.36

Sulfate 1.30

Sea-salt 1.08

Dust 0.67

physics and feedback mechanisms to adjust fully. Tuning of dust emissions in future global mesoscale

simulations need to account for this behavior in order to reduce biases in dust loadings.

7.1.3 Spatial Patterns of AOT

Maps of annual- and seasonal-mean AOT from the G5NR and MERRAero are included in Figures 7.2 and 7.3. Time-averaged values were computed using model outputs for the time period

December 2005 – December 2006. Comparison of the G5NR annual-mean total AOTs with MERRAero reveal very similar pattens of global aerosol loading. The overestimated dust emissions in

the G5NR result in higher aerosol loading over Africa and along the westward transport route of

Saharan dust. Similar observations are true for the regions affected by dust that originate from

sources in the Middle East. In contrast, emissions from Asian dust sources result in very similar

AOT. Over the oceans, the G5NR AOT tends to be lower compared to MERRAero. AOT is also

lower over the land masses in the northern hemisphere.

In the winter months (DJF), dust mixed with smoke from fires results in slightly overestimated

AOT in the Sahel region. Aerosol loadings across North America are similar. AOT in the West

coast of USA is larger than the mean over the continent, mostly due to anthropogenic emissions

in this region. AOT over East Asia are also very similar. In the southern hemisphere, MERRAero

AOT is spatially more uniform in comparison to the G5NR. In the G5NR simulation, regions

affected by strong sea salt emissions are well defined and located between 45◦S and 65◦S.

In the spring (MAM) and summer (JJA) months, there are remarkable similarities in the spatial

patters of AOT from the G5NR and the aerosols reanalysis. Aerosol loadings downwind of major

fire sources in central and southern Africa, and South America are also very similar. In the fall

(SON), the patterns of AOT due to smoke from fires in South America remain in a good agreement.

MERRAero AOT in central Africa, Indonesia and the boreal forests of North America is slightly

higher in comparison to the results from the G5NR.

246
7.1.4 Aerosol Speciation

We used the speciated AOT to compute the fractional contribution of individual aerosol species to

the total AOT. The resulting maps of column integrated aerosol speciation are shown in Figure 7.4.

The panels are organized by species (rows) and seasons (columns). Seasonal variations of OC and

BC follow the active fire seasons. A high fraction of OC is observed over the major sources of fire

emissions in sub-Saharan Africa, South America, North America and northern Eurasia. Maximum

values of about 0.7 are observed in JJA and SON. Patterns of high sulfate fraction are associated

with anthropogenic and volcanic sources of SO2. Regions that are dominated by anthropogenic

sulfate aerosols include the western US, Europe and China. The contribution of dimethyl sulfide

(DMS), released naturally over oceans, peaks in the southern hemisphere during DJF when the

sea salt emissions are relatively weak. The contribution of sea salt aerosol is large in the marine

environments throughout the year especially in the southern hemisphere. In MAM and JJA sea

salt fraction exceeds 0.7 over the regions with high sea salt emissions. Dust is the other major

contributor to natural aerosols. It influences the aerosol burden in the northern hemisphere. It

dominates the arid regions in Africa, Asia and Australia and downwind of dust transport routes.

Patterns of the column integrated aerosol speciation agree well with the major sources and transport routes of aerosol. Information about aerosol speciation (Figure 7.4) is easy to digest and

compliments the global spatial distribution of AOT (Figure 7.3). It can provide guidance for end

users of the aerosol products from the G5NR simulation about the aerosol composition.

7.1.5 Vertical Distribution

Aerosol mass mixing ratios were initialized to zero at the start of the G5NR simulation and therefore

the vertical distribution and loadings of aerosols are not very realistic early in the simulation. A

spin-up period of about two weeks is needed for the aerosols to reach realistic concentrations in the

troposphere. Reaching equilibrium in the stratosphere requires a longer time period on the order

of several months. Analysis of the time series of aerosol burden in the stratosphere (not shown)

indicates that dust reaches equilibrium in approximately two months, whereas four to five months

were needed for OC, BC and sulfate concentrations to stabilize. The short residence time of sea

salt aerosol and effective wet removal processes restrict them to the lower atmosphere and inhibit

vertical transport to the upper troposphere. Consequently the stratosphere is practically free of

sea salt aerosol.

To investigate the model skill in representing the aerosol vertical distribution, we analyze zonalmean aerosol mass concentration in the troposphere and low- to mid-stratosphere. Snapshots early

in the simulation and one year later are shown in Figures 7.5 and 7.6. Mass concentrations plots

are organized by species and time. Results about a month into the simulation are shown in the

left column, and results from one year later are shown in the right column. Except for sea salt,

aerosols exhibit a slight increase in concentration in the higher troposphere and lower stratosphere;

however, there is no indication of excessive collection of aerosols in the mid and high stratosphere.

247
Independently, we also examined the time series of aerosol burden in the stratosphere (not shown)

and reached a similar conclusion.

The vertical distributions of OC and BC share similar features because of the similar patterns of

anthropogenic and fire emissions. Concentrations in the northern hemisphere are higher than those

in the southern hemisphere with a well pronounced collection of OC and BC over the north pole

driven by transport from mid-latitude regions.

The concentration of sulfate aerosol near the surface follows the major sources of anthropogenic

and fire emissions in the northern hemisphere and DMS emissions in the southern hemisphere. A

poleward transport and collection at the poles is also evident. The observed high concentrations in

the stratosphere near the equator one year into the simulation were traced back to volcanic activity.

Compared to the carbonaceous aerosols, the concentration of sulfate is higher in the stratosphere.

Dust and sea salt concentrations are largest near the surface which reflects the injection of freshly

emitted particles in the surface model layer. Concentrations of sea salt reflect the pattern of

strong wind driven emissions along mid-latitude storm tracks and frontal systems in the Southern

Ocean. Deeper boundary layers over source regions and less effective wet removal result in high

dust concentrations in the upper troposphere and low stratosphere driven by vertical transport due

to the general circulation patterns.

7.1.6 Comparison with CALIOP Retrievals

In this section, we analyze the vertical distribution of the simulated aerosols and perform a qualitative comparison with data from the Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP),

carried by CALIPSO as part of the NASA A-Train (Winker et al., 2007, 2010). We use CALIPSO’s

level 1.5 profile product (i.e., ‘expedited’) adapted for forecasting applications, which have been

cloud-cleared (Vaughan et al., 2011).

We focus our analysis on two different specific regions dominated by dust and biomass burning

aerosols. For the dust case study, we chose an area over the Sahara and the North Atlantic Ocean

(60◦W – 20◦E, 0◦N – 30 ◦N) during the month of July. For biomass burning aerosols, we select a

region over southern Africa (10 ◦W – 30 ◦E, 0 ◦S – 30 ◦S) to analyze during the month of July.

Figure 7.7 shows monthly mean comparisons between CALIOP and the G5NR-derived attenuated

backscatter at 532 nm over the Saharan dust region for the month of July 2006 for the model and

July 2011 for CALIOP. For the sake of comparison, the molecular scattering was removed from the

CALIOP profiles. This comparison should be approached as qualitative because of the differences

in the data collection times due to CALIOP’s level 1.5 data availability, and also note that the

G5NR has not been sampled along the CALIPSO orbit track.

248
We can see that the G5NR and CALIOP have a similar vertical structure with a dust plume

that extends up to a little bit above 6 km for CALIOP and around 6 km for the G5NR over the

Saharan region and descends as it travels west to the Caribbean over a shallow marine aerosols

layer. However, when looking at the magnitude of the attenuated backscatter coefficient, we can see

that the simulated signal of the G5NR is higher than the CALIOP estimates for the dust plume.

For the marine aerosols layer, the attenuated backscatter coefficient is generally in the range of

CALIOP values, except at some longitudes where the G5NR values appear to be lower. As seen

previously, these discrepancies in the magnitude of the signal might be explained by an excess

of dust and slightly underestimated sea salt emissions in the model. Applying the AOT derived

factors moderately improved the comparison (not shown).

Figure 7.8 shows comparisons similar to the ones on Figure 7.7 but over the southern Africa biomass

burning region. We can see a similar vertical structure between the G5NR and CALIOP. The smoke

plume extends up to 4 km above a marine aerosol layer that can reach an altitude around 1.5 km

for both the model and CALIOP. While the magnitude of the simulated signal for the smoke plume

is in the range of the CALIOP estimates, the simulated signal for the marine aerosol layer tends

to be weaker. Applying the coefficients suggested above does not improve the comparison (not

shown), suggesting that the modeled sea salt emissions in this region are very weak.

7.1.7 Summary and Recommendations

In this investigation, the aim was to assess the quality of aerosols in the 7-km GEOS-5 G5NR global

mesoscale AGCM simulation with interactive aerosol chemistry. Analysis of tropospheric residence

times, aerosol optical thickness and vertical distribution of aerosols indicates that the skill of the

G5NR simulation to produce realistic aerosols is very good (Figure 7.9). The following conclusions

and recommendations can be drawn from the present evaluation:

• The G5NR AOT reproduced well the spatial and temporal patters of AOT from the MERRAerosol reanalysis. The observed similarities in the AOT patterns enabled us to derive

global factors for each of the aerosol species (see Table 7.2) that can be applied to scale the

predicted aerosol mass mixing ratios and improve the agreement with the aerosol reanalysis.

• The vertical distribution of tropospheric aerosols in the G5NR simulation agreed well with

CALIOP profiles. We found that the aerosol load in the stratosphere tends to increase as the

simulation progresses, but there does not appear to be excessive accumulation of aerosols in

the upper stratosphere during the simulated time period. For simulations involving aerosols

in the high troposphere and low- and mid-stratosphere we recommend to use model output

in the second half of 2006 and onwards.

• Users of the instantaneous aerosol fields at 7-km horizontal resolution need to be aware that

there are indications of unrealistically high aerosol loadings at times in isolated grid cells.

Examination of the instantaneous aerosol loading at 7-km horizontal resolution revealed that

249
in some instances AOT can exceed values of 10. The simulated extreme AOT values were

associated with strong emissions from fires or from anthropogenic sources of sulfate aerosol.

7.2 Trace Gases

7.2.1 CO

Atmospheric carbon monoxide (CO) is an important air pollutant who presence can affect human

health, the production of tropospheric ozone, and the oxidizing capacity of the atmosphere. The 1-2

month lifetime of CO also makes it a useful tracer for atmospheric transport. A simplified version

of CO chemistry, described in Ott et al. (2010), was used in the G5NR to increase computational

efficiency. Detailed descriptions of emissions are given in Putman et al. (2014). Biogenic and

methane sources of CO are taken from coarse resolution chemical transport model simulations

while biomass burning and fossil fuel emissions were produced at 0.1◦

in order to introduce spatial

heterogeneity into the simulations.

G5NR CO mixing ratios were evaluated using a combination of surface and satellite observations.

NOAA’s Earth System Research Laboratory (ESRL) maintains a global network of remote surface

sites that measure CO and CO2 mixing ratios weekly. Figure 7.10 shows a map of sites operational

during the 2005-2007 G5NR period. For comparison to surface stations, the GEOS-5 grid cell

containing each station location was sampled at the time that an observation was collected and

simulated CO mixing ratios then vertically interpolated to the altitude of the observing station.

Observations and pseudo-data are averaged over the course of a day (when multiple observations

are present) and daily simulated and observed CO mixing ratios at six stations representing a

variety of latitudes are shown in Figure 7.11. In general, the G5NR tends to underestimate CO at

extratropical northern hemisphere stations with the largest underestimates occurring during spring

months. This is evident in Figure 7.11 at Ny Alesund, Sweden (ZEP) and Cold Bay, Alaska (CBA).

Underestimates of this magnitude are common in CO simulations as demonstrated by Shindell et

al. (2006) who compared fields generated by 26 chemical transport models using an earlier version

of the EDGAR inventory. Based on an analysis of radon and CO observations over the western

North Pacific, Wada et al. (2012) suggest that the version of EDGAR used in this study may

underestimate emissions over China by as much as 45%.

These underestimates can be significantly improved through application of an empirically derived

bias correction based on latitude and the simulated mixing ratio given by

COcorr(lat, COsim) = COsim − 28.2 − 0.28 × lat + 0.4 × COsim + 0.0032 × lat × COsim

where COcorr is the bias corrected value, COsim is the G5NR simulated CO value, and lat is the

250
latitude of the simulated value. As shown in Figure 7.11 and Figure 7.12, the bias correction

improves the model result at extratropical northern hemisphere stations by reducing the large

negative bias; performance at southern hemisphere tropical and subtropical stations is slightly

degraded as the bias correction causes larger underestimates.

Satellite observations of CO are provided by a number of satellites with the longest and most

complete observational record provided by thermal infrared (TIR) observations from MOPITT

and AIRS, which have peak sensitivity in the mid-troposphere. Newer releases of MOPITT also

include retrievals from near infrared (NIR) observation (Deeter et al., 2012), which have greater

sensitivity near the surface. MOPITT version 6, level 3 monthly mean observations are compared to

simulated CO from the G5NR by first regridding model output from the native 7-km resolution to

the 1◦ MOPITT grid. Model profiles are then averaged over each month, interpolated to MOPITT

pressure-defined layers, and convolved with the MOPITT averaging kernel and a priori profiles

following Deeter et al. (2012).

Comparisons between observed and simulated mid-tropospheric CO for April and October (Figure 7.13) show that the model tends to underestimate CO in the northern hemisphere midtroposphere during spring, consistent with the comparison with surface observations. In the

mid-troposphere, this underestimate is slightly improved with the bias correction derived from

surface model data comparisons, but the improvement is limited. The model succeeds in capturing

the major features of the MOPITT observations, including a strong gradient between northern

and southern hemisphere due to fossil fuel emissions, and biomass burning over Africa and South

America. During October, 2006, model-data discrepancies are smaller in most regions relative to

April, though the model strongly underestimates the influence of Indonesian fire emissions in the

mid-troposphere. This is likely because of an underestimate in emissions, which are particularly

difficult to estimate for the type of peat fires that occurred over Indonesia during 2006 (e.g. Ott

et al., 2010). The QFED emissions used in the G5NR, which rely on fire radiative power, may

underestimate emissions that occur during the smoldering phase of fires or when fires are obscured

by particularly thick aerosol loading.

Figure 7.14 shows comparisons of monthly zonal mean MOPITT observations of CO with those

from the two-year duration of the G5NR. In the mid-troposphere, GEOS-5 reproduces the major

features of the observed distribution with the exception of underestimates in extratropical northern

hemisphere CO during spring. Underestimates of 40 ppbv are of similar magnitudes to surface

underestimates indicating that they are not likely to be a result of transport errors, but are more

likely associated with underestimates in fossil fuel emissions derived from the EDGAR inventory.

Comparisons against NIR column CO observations indicate that the G5NR underestimates northern

hemisphere spring CO by 10%. MOPITT NIR column CO is also characterized by maxima slightly

south (north) of the equator in September (December) caused by biomass burning emissions in

South America (Africa). GEOS-5 reproduces both the timing and location of these biomass burning

features well, though their magnitude is slightly underestimated.

Comparisons with surface and MOPITT observations demonstrate that the performance of the

G5NR in reproducing observed distributions of atmospheric CO is similar to many contemporary

251
chemical transport models. Models in general, and the GEOS-5 G5NR, tend to underestimate

extratropical northern hemisphere CO. This is typically attributed to underestimates in fossil fuel

emission inventories such as EDGAR. The use of archived hydroxyl radical fields used to calculate

CO loss may also contribute to underestimates. As its performance is similar to other chemical

transport models, the G5NR CO fields should be suitable for most uses. For applications particularly interested in northern hemisphere and/or anthropogenic CO emissions, users may wish to

apply a bias correction. The bias correction provided significantly improves model results at remote

surface sites, though its impact is limited in comparisons with MOPITT observations because of

the influence of a priori information.

7.2.2 CO2

Carbon dioxide (CO2) is the greenhouse gas most strongly influenced by anthropogenic emissions.

While contemporary fossil fuel emissions are considered to be reasonably well constrained, land and

ocean carbon fluxes are less well understood leading to large uncertainties in model projections of

climate change over the coming century. In the G5NR, fossil fuel and biomass burning emissions,

and land fluxes are prescribed and provided at 0.1◦resolution in order to introduce spatial heterogeneity into the simulations. Ocean fluxes are computed within GEOS-5 using as input ocean

partial pressure of CO2 (provided at 1◦resolution from NASA’s Ocean Biogeochemical Model;

Gregg et al., 2013) and G5NR-simulated 10-meter wind speeds and atmospheric CO2. Detailed

descriptions of emissions are given in Putman et al. (2014).

G5NR CO2 mixing ratios are evaluated primarily through comparison with surface observing stations. Figure 7.15 shows simulated and observed CO2 mixing ratios at the same 6 stations shown

for CO in Figure 7.11. At northern hemisphere stations, the seasonal cycle in atmospheric CO2

is driven primarily by the seasonal cycle in land biosphere flux, which is characterized by drawdown by plants during summer months and release of CO2 from decaying vegetation during winter.

G5NR CO2 mixing ratios reproduce observations well at these stations, represented here by ZEP,

CBA, and Mauna Loa (MLO). Model performance is also reasonable at southern hemisphere midand high latitude stations, represented by Crozet Island (CRZ) and Palmer Station, Antarctica

(PSA). At these locations, seasonal variations in CO2 tend to be much weaker because they are

farther removed from the bulk of biomass flux and anthropogenic emissions. The largest CO2 discrepancies in the G5NR occur at tropical ocean locations, represented here by Ascension Island

(ASC), located in the Atlantic Ocean between Africa and South America. Here, the simulation

underestimates CO2 by 3-4 ppmv throughout the 2-year simulation. Figure 7.16 shows that this

underestimate is characteristic of the majority of remote tropical stations. These underestimates

are likely associated with a stronger ocean sink in this region than indicated by observation-based

estimates from Takahashi et al. (2002, 2009) and by ensemble-based budget estimates from Le

Quere et al. (2013). Because ocean flux is calculated online in the G5NR using the square of

the 10-meter wind speed, it can be strongly influenced by wind differences between the simulated

and observed atmospheres. As noted in previous chapters, some characteristics of the ITCZ differ

substantially from observations which is likely contributing to the underestimate in CO2 at remote

marine sites.

252
Because of the high degree of accuracy required for useful measurements, the availability of satellite

observations of CO2 is more limited than for CO. Several instruments, including AIRS and TES,

provide information on CO2 variability in the mid- and upper troposphere, but these observations

capture little of the information about surface fluxes that is of primary interest (Ott et al., 2014).

SCIAMACHY provided NIR observations of CO2 from 2003 through 2012 which provided greater

sensitivity to near surface CO2 mixing ratios, but measurements were characterized by scatter and

systematic biases that limited their utility (Breon and Ciais, 2010). The Japanese Greenhouses

gases Observing SATellite (GOSAT, Kuze et al., 2009), launched in June, 2009 provides increased

reliability and sensitivity to near surface CO2, though data are sparse because of an inability to

observe in the presence of clouds. Several different retrievals of column averaged CO2 dry air

mole fraction (XCO2) based on GOSAT observations are currently being produced. In this work,

we use data produced by NASA’s Atmospheric CO2 Observations from Space (ACOS) effort, a

collaboration between the original JAXA GOSAT team and NASA’s Orbiting Carbon Observatory

(OCO) science team (Crisp et al., 2012). These data are produced using a modified version of the

OCO retrieval algorithm applied to GOSAT observed spectra as described in O’Dell et al. (2012)

and have been validated extensively. Data used here are ACOS version 3.4.

Because observations were not available during the 2005-2007 period, we created a monthly climatology to evaluate the ability of the G5NR to reproduce observed spatial patterns of XCO2.

Monthly means were calculated from 2009 through 2013, detrended to remove the annual increase

in global CO2 mixing ratio, and then averaged. The G5NR is compared by calculating monthly

mean fields, detrending, and then applying a global adjustment of 6.5 ppmv to account for the

difference between 2005 and 2009 atmospheric mixing ratios. Figure 7.17 shows comparisons of

simulated CO2 with the GOSAT climatology for February and August. While the G5NR cannot be

expected to exactly match the GOSAT observations, which represents different years and weather

features, the spatial distributions of XCO2 in the simulation and observations are quite similar.

Disparities between simulated and observed tropical ocean CO2 found at the surface are not evident

in the column. Figure 7.18 shows the seasonal cycle of zonal mean ACOS XCO2 and XCO2 from

the G5NR. The simulation also reproduces the major features of the seasonal cycle of XCO2 well.

Comparisons between surface observations and G5NR CO2 show that the model effectively reproduces observed time series at remote locations in the extratropics of both hemispheres. The largest

model-data discrepancies are found at tropical ocean locations and result from the coupled ocean

flux calculation included in the simulation. These are likely caused by differences in near surface

ITCZ wind speeds. While reliable satellite CO2 observations have only recently become available,

comparisons between G5NR XCO2 and a climatology compiled from 4 years of detrended GOSAT

observations shows the model reproduces observed spatial and seasonal variations well.

7.2.3 O3

The G5NR produces 3-dimensional ozone fields subject to advection by the model winds and

relaxation to a prescribed time-dependent zonal climatology with a relaxation time of three days.

253
The climatology used here is described in Cionni et al. (2011).

A typical wintertime ozone map at 100 hPa is shown in Figure 7.19 along with Ertel’s potential

vorticity (EPV) from the model. The figure illustrates that the morphology of the ozone field

exhibits a large-scale structure similar to that seen in EPV. Because of the uniform and relatively

short (especially in the lower stratosphere and troposphere) relaxation, only large-scale features in

ozone can develop in the G5NR. Fourier analysis reveals that, in terms of power spectra, ozone

variability is up to 10 times less than in GMAO’s operational analyses for wave numbers greater

than 10. Therefore, this analysis focuses on assessing the realism of the representation of ozone

fields in terms of monthly and spatial averages.

Monthly maps of total ozone (not shown) display a realistic distribution and seasonal cycle with

maximum values in the boreal spring in high latitudes, elevated ozone over Alaska and northeastern Russia, low values over Antarctica in austral spring accompanied by a wave one ozone

crescent feature with the maximum located south of Australia.

Figure 7.20 shows the difference between the monthly zonal mean total ozone from the G5NR and

a climatology derived from the Ozone Monitoring Instrument (OMI) on the EOS-Aura satellite.

The climatology was obtained by averaging the OMI data in each calendar month between 2005

and 2013. In the tropics and in the northern hemisphere the overall agreement is quite good.

The differences are typically less than 3% and never greater than about 10%. In the southern

high latitudes the agreement is also within 10%. However, the Antarctic spring ozone depletion is

underestimated (relative to the OMI climatology) by up to 40%.

We will now discuss the vertical distribution of zonal mean ozone in the G5NR. We use a climatology computed by averaging monthly data from the Microwave Limb Sounder onboard Aura

(MLS) over the period of 9 years from 2005 to 2013. Version 3.3 of MLS retrievals is used. We

begin the discussion with June 2006 as this is when the G5NR departs from the MLS data more

than in other months. Figure 7.21 shows the zonal mean G5NR ozone mixing ratio and the corresponding MLS climatology. The two data sets match each other very closely everywhere except

in the winter upper stratosphere where the G5NR ozone is less than MLS by up to 1 ppmv (15%)

and around the tropical ozone maximum, which is sharper than in MLS and slightly shifted upward. The underestimation in the upper stratosphere is caused by the model meridional circulation

pushing the climatological ozone isopleths downward. We note that the climatological profile (not

shown) is indeed closer to the MLS climatology than the resulting profile in Figure 7.21, indicative

of a downward motion. The Brewer-Dobson circulation is the strongest in winter hemispheres,

consistent with the observation that the upper-stratospheric low bias in the G5NR ozone is most

pronounced during winter. The shape of the tropical ozone maximum in the G5NR is the same as

in the climatology used in the model. We note that the G5NR ozone in other months also exhibits

a low bias in the upper stratosphere in the middle latitudes but the bias is less pronounced. There

is an overall excellent agreement between these data sets in the lower stratosphere and everywhere

in the summer stratosphere.

Finally, we note that, while the vertical structure of the tropospheric ozone is not expected to

254
be realistic in the G5NR, the seasonal cycle of the tropospheric ozone column (not shown) is well

represented with the maximum values of about 60 Dobson Units in northern high latitudes in spring

and early summer.

255
Figure 7.1: Time series of global monthly mean aerosol optical thickness. Results from the G5NR

run are shown as open circles connected by solid black lines. Monthly aerosol climatology from

the MERRAero aerosol reanalysis is shown in gray - the shaded area enclose the minimum and

maximum values, solid and dashed lines depict mean and median values.

256
Figure 7.2: Annual-mean AOT from the MERRAero aerosol reanalysis (left panel) and the G5NR

simulation (right panel).

257
Figure 7.3: Seasonal-mean AOT from the MERRAero aerosol reanalysis (left column panels) and

the G5NR simulation (right column panels). Time-averaged values for DJF, MAM, JJA and SON

in 2006 are organized in rows from top to bottom.

258
Figure 7.4: Seasonal variations of aerosol speciation based on the fractional contribution to total

AOT. From left to right are shown maps for DJF, MAM, JJA and SON, and from top to bottom

are maps for OC, BC, sulfate, sea salt and dust.

259
Figure 7.5: Zonal mean mass concentrations of (top to bottom) OC, BC, sulfate and aerosols early

into the simulation (left column) and one year into the simulation (right column). Also shown in

each panel is the zonal-mean tropopause height as a solid black line.

260
Figure 7.6: Similar to Figure 7.5 but for sea salt (top) and dust (bottom).

261
Figure 7.7: Vertical profiles of attenuated backscatter coefficient only due to aerosols at 532 nm over th

(60

◦W–20

◦E, 0

◦N–30

◦N). On the left are time-averaged retrievals from CALIOP for July 2011. On the righ

G5NR simulation but for July 2006.

Figure 7.8: Same as Figure 7.7 but for the southern Africa biomass burning region (10

◦W–30

◦E, 

262
Figure 7.9: Global distribution of AOT from the G5NR simulation on 8 Aug 2005 00UTC. The image show

of smoke from fires in Africa, South America and Alaska; Saharan dust off the West coast of Africa and Eur

Europe and the USA.

263
Figure 7.10: Map of NOAA surface trace gas observing stations (red). Sites shown in Figures 7.11

and 7.15 are indicated by green dots.

264
Figure 7.11: .

Simulated and observed surface CO (ppbv) at NOAA stations indicated on Figure 7.10. Red crosses

indicate observations while black (green) dots show model results without (with) a bias correction

applied.

265
Figure 7.12: Monthly mean CO (ppbv) at each NOAA surface site for the 2005-2007 period.

Observations are shown in left top panel while simulated mixing ratios without (with) a bias

correction are shown in the middle (right) panel. Bottom plots show the difference between the

simulation, with and without bias correction, and observations.

266
Figure 7.13: Monthly mean mid-tropospheric CO (ppbv) observed by MOPITT TIR measurements

(top) for April (left) and October (right), 2006. Middle (bottom) plots show simulated mixing ratios

without (with) a bias correction.

267
Figure 7.14: Zonal monthly mean CO calculated from MOPITT (left) TIR (top) and NIR (bottom)

observations. Middle (right) plots show equivalent quantities calculated from the G5NR simulation

without (with) a bias correction applied.

268
Figure 7.15: Simulated and observed surface CO2 (ppmv) at NOAA stations indicated on Figure 7.10. Red crosses indicate observations while black dots show model derived values.

269
Figure 7.16: Monthly mean CO2 (ppmv) at each NOAA surface site for the 2005-2007 period.

Observations are shown in left panel while simulated mixing ratios are shown in the middle panel.

Right panel shows the difference between the simulation and observations.

270
Figure 7.17: Monthly mean XCO2 (ppmv) calculated from climatology of ACOS GOSAT observations (top) for February (left) and August (right). Bottom plots show G5NR simulated XCO2

from 2006.

271
Figure 7.18: Zonal monthly mean XCO2 (ppmv) calculated from ACOS GOSAT climatology (left)

and from the G5NR simulation (right).

272
Figure 7.19: Ozone volume mixing ratio (colors) from the G5NR at 100hPa on January 1st 2006

at 12UTC and Ertels potential vorticity (contours). The latitude range is 45◦- 90◦N.

273
Figure 7.20: Monthly zonal mean difference (shaded) between the G5NR total ozone in 2006 and

2005–2013 climatology calculated from EOS-OMI data. Contours show the OMI climatology values.

Gray shading depicts areas where there are no OMI observations.

274
Figure 7.21: June 2006 zonal mean G5NR ozone (colors) and 2005-2013 June climatology computed

from version 3.3 EOS-MLS data (contours).

275
References

Andrews, D. G., J. R. Holton, and C. B. Leovy, 1987: Middle Atmosphere Dynamics. Academic

Press.

Asnani, G. C., 2005: Tropical Meteorology. Publisher: Indian Institute of Tropical Meteorology,

Pashan, Pune-411008, India. 3 vols.

Atlas, R., R. N. Hoffman, J. Ardizzone, S. M. Leidner, J. C. Jusem, D. K. Smith, D. Gombos, 2011: A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bull. Amer. Meteor. Soc., 92, D157, doi:

10.1175/2010BAMS2946.1.

Augier, P. and E. Lindborg, 2013: A new formulation of the spectral energy budget of the atmosphere, with application to two high-resolution general circulation models. J. Atmos. Sci.,

70, 2293–2308.

Bacmeister, J. T., M. J. Suarez and F. R. Robertson, 2006:Rain Re-evaporation, boundary layer

convection interactions, and Pacific rainfall patterns in an AGCM. J. Atmos. Sci., 63,

3383–3403.

Bacmeister, J. T., M. F. Wehner, R. B. Neale, A. Gettelman, C. Hannay, P. H. Lauritzen,

J. M. Caron, and J. E. Truesdale, 2014: Exploratory High-Resolution Climate Simulations using the Community Atmosphere Model (CAM). J. Climate, 27, 3073–3099. doi:

http://dx.doi.org/10.1175/JCLI-D-13-00387.1

Berry, G., Thorncroft, C. and Hewson, T. 2006: African easterly waves in 2004: Analysis using

objective techniques. Mon. Wea. Rev., 133, 752–766.

Blazica, V., N. Zagar, B. Strajnar, and J. Cedilnik, 2013: Rotational and di  ̆ vergent kinetic energy

in the mesoscale model ALADIN. Tellus, 65, doi:tellusa.v65i0.18918.

Bony, S, and co-authors, 2004: On dynamic and thermodynamic components of cloud changes. Cli.

Dyn., 22, 71–82.

Breon, J.-F., and P. Ciais, 2010: Spaceborne remote sensing of greenhouse gas concentrations.

Comptes Rendus Geoscience, 342, 4-5, 412–424.

Budyko, M. I., 1974: Climate and Life. Academic Press, New York, 508 pp.

276
Burgess, N., A. Ehler, and T. Shepherd, 2013: The troposphere-to-stratosphere transition in kinetic

energy spectra and nonlinear spectral fluxes as seen in ECMWF analyses. J. Atmos. Sci.,

70, 669–687.

Chakraborty, A., Nanjundiah, R. S. and Srinivasan, J., 2009: Impact of African orography and the

Indian summer monsoon on the low-level Somali jet. Int. J. Climatol., 29, 983–992. doi:

10.1002/joc.1720

Chang, E., 1999: Characteristics of wave packets in the upper troposphere. Part II: seasonal and

hemispheric variations. J. Atmos. Sci., 56, 1729–1747.

Chang, E. and D. Yu, 1999: Characteristics of wave packets in the upper troposphere. Part I:

northern hemisphere winter. J. Atmos. Sci., 56, 1708–1728.

Charney, J., 1971: Geostrophic turbulence. J. Atmos. Sci., 28, 1087–1095.

Chen, T-C., and H. van Loon, 1987: Interannual variation of the tropical easterly jet. Mon. Wea.

Rev., 115, 1739–1759.

Chin, M., P. Ginoux, S. Kinne, O. Torres, B. N. Holben, B. N. Duncan, R. V. Martin, J. A. Logan,

A. Higurashi, and T. Nakajima, 2002: Tropospheric Aerosol Optical Thickness from the

GOCART Model and Comparisons with Satellite and Sun Photometer Measurements. J.

Atmos. Sci., 59, 461–483.

Cionni, I., V. Eyring, J. F. Lamarque, W. J. Randel, D. S. Stevenson, F. Wu, G. E. Bodeker,

T. G. Shepherd, D. T. Shindell, and D. W. Waugh, 2011: Ozone database in support of

CMIP5 simulations: results and corresponding radiative forcing. Atmos. Chem. Phys., 11,

11267–11292, doi:10.5194/acp-11-11267-2011.

Colarco, P., A. da Silva, M. Chin, and T. Diehl, 2010: Online simulations of global aerosol distributions in the NASA GEOS-4 model and comparisons to satellite and ground-based aerosol

optical depth. J. Geophys. Res., 115, D14207, doi:10.1029/2009JD012820.

Crisp, D., et al., 2012: The ACOS CO2 retrieval algorithm - Part II: Global XCO2 data characterization. Atmos. Meas. Tech., 5, 687–707, doi:10.5194/amt-5-687-2012.

Cullather, R.I., and M.G. Bosilovich, 2012: The energy budget of the polar atmosphere in MERRA.

J. Climate, 25, 5–24, doi:10.1175/2011JCLI4138.1

Darmenov, A. and A. da Silva, 2014: The Quick Fire Emissions Dataset (QFED) - Documentation of versions 2.1, 2.2 and 2.4, Technical Report Series on Global Modeling and Data

Assimilation, NASA.

da Silva, A. and P. Colarco, A. Darmenov, V. Buchard-Marchant, C. Randles, and R. Govinaradju,

2012: Overview of the MERRA Aerosol Reanalysis: Toward an Integrated Earth System

Analysis. 4th WCRP International Conference on Reanalyses.

da Silva, Arlindo, W. Putman and J. Nattala, 2014: File Specification for the 7-

km GEOS-5 Nature Run, Ganymed Release (Non-hydrostatic 7-km Global Mesoscale

Simulation). GMAO Office Note No. 6 (Version 1.0), 199 pp, available from

http://gmao.gsfc.nasa.gov/pubs/office notes.

277
Dee, DP, and coauthors, 2011: The ERA-Interim reanalysis: configuration and performance of the

data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597.

Deeter, M. N., H. M. Worden, D. P. Edwards, J. C. Gille, and A. E. Andrews, 2012: Evaluation

of MOPITT retrievals of lower-tropospheric carbon monoxide over the United States. J.

Geophys. Res., 117, D13306, doi:10.1029/2012JD017553.

Dell’Aquila, A., V. Lucarini, P. Ruti, and S. Calmanti, 2005: Hayashi spectra of the northern hemisphere mid-latitude atmospheric variability in the NCEP-NCAR and ECMWF reanalyses.

Climate Dynamics, 25, 639–652.

Delsole, T., and Chang, P., 2003: Predictable Component Analysis, Canonical Correlation Analysis,

and Autoregressive Models. J. Atm. Sciences, 60, 409–416.

Delsole, T., and Tippett, M.K., 2007: Predictability: Recent insights from information theory. Rev.

Geophys., 45, RG4002, doi: 10.1029/2006RG000202.

Diehl, T., A. Heil, M. Chin, X. Pan, D. Streets, M. Schultz, and S. Kinne, 2012: Anthropogenic,

biomass burning, and volcanic emissions of black carbon, organic carbon, and SO2 from 1980

to 2010 for hindcast model experiments. Atmos. Chem. Phys. Discuss., 12, 24895–24954.

Doyle, J.D., and M.A. Shapiro, 1999: Flow response to large-scale topography. The Greenland tip

jet. Tellus, Ser. A., 51, 728–748, doi:10.1034/j.1600-0870.1996.00014.x

Errico, R. and N. Priv ́e, 2014: An estimate of some analysis error statistics using the GMAO

observing system simulation framework. Quart. J. Roy. Meteor. Soc., 140, 1005–1012.

Errico, R. M., R. Yang, N. Priv ́e, K.-S. Tai, R. Todling, M. Sienkiewicz and J. Guo, 2013: Validation

of version one of the Observing System Simulation Experiments at the Global Modeling and

Assimilation Office. Quart. J. Roy. Meteor. Soc., 139, 1162–1187. doi: 10.1002/qj.2027

Ettema, J., M.R. van den Broeke, E. van Meijgaard, and W.J. van de Berg, 2010: Climate of the

Greenland ice sheet using a high-resolution climate model - Part 2: Near-surface climate

and energy balance. The Cryosphere, 4, 529–544, doi:10.5194/tc-4-529-2010

Fettweis, X., 2007: Reconstruction of the 1979–2006 Greenland ice sheet surface mass balance using

the regional climate model MAR. The Cryosphere, 1, 21–40, doi:10.5194/tc-1-21-2007

Gall ́ee, H., 1997: Air-sea interactions over Terra Nova Bay during winter: Simulation with a coupled

atmosphere-polynya model. J. Geophys. Res., 102, 13835–13849, doi:10.1029/96JD03098

Ganachaud, A., and C. Wunsch, 2000: Improved estimates of global ocean circulation, heat transport and mixing from hydrological data. Nature, 408, 453–457.

Garcia, R. R., and B. A. Boville, 1994: Downward control of the mean meridional circulation and

temperature distribution of the polar winter stratosphere. J. Atmos. Sci., 51, 2238–2245.

Gong, S. L., 2003: A parameterization of sea-salt aerosol source function for sub- and super-micron

particles. Global Biogeochem. Cycles, 17, 1097, doi:10.1029/2003GB002079.

Gregg, W. W., N. W. Casey, and C. S. Rousseaux, 2013: Global surface ocean carbon estimates in

278
a model forced by MERRA. NASA Technical Report Series on Global Modeling and Data

Assimilation, NASA TM-2013-104606, Vol. 31, 39 pp.

Hall, N. M. J., G. N. Kiladis, and C. D. Thorncroft, 2006: Three-Dimensional Structure and

Dynamics of African Easterly Waves. Part II: Dynamical Modes. J. Atmos. Sci., 63,

2231–2245.

Halpern, D., and P. M. Woiceshyn, 2001: Somali Jet in the Arabian Sea, El Ni ̃no, and India

Rainfall. J. Climate, 14, 434–441.

Hamilton, K., Y. Takahasi, and W. Ohfuchi, 2008: Mesoscale spectrum of atmospheric motions

investigated in a very fine resolution global general circulation model. J. Geophys. Res.,

113, doi:10.1029/2008JD009785.

Hartmann, D. L., H. H. Hendon, and R. A. Houze, Jr., 1984: Some implications of the mesoscale

circulations in tropical cloud clusters for large-scale dynamics and climate. J. Atmos. Sci.,

41, 113–121.

Hayashi, Y., 1971: A generalized method of resolving disturbances into progressive and retrogressive

waves by space Fourier and time cross-spectral analyses. J. Met. Soc. Japan, 49, 125–128.

Haynes, P.H. and M. E. McIntyre, 1987: On the evolution of vorticity and potential vorticity in

the presence of diabatic heating and frictional or other forces. J. Atmos. Sci.,44, 828–841.

Heinemann, G., and T. Klein, 2002: Modelling and observations of the katabatic flow dynamics

over Greenland. Tellus, Ser. A., 54, 542–554, doi:10.1034/j.1600-0870.2002.201401.x

Holt, T., and S. SethuRaman, 1985: Aircraft and ship observations of the mean structure of the

marine boundary layer over the Arabian Sea during MONEX 79. Boundary-Layer Met., 33,

259–282.

Houze, R. A., Jr., 1982: Cloud clusters and large-scale vertical motions in the tropics. J. Meteor.

Soc. Japan, 60, 396–410.

Houze, R. A., Jr., 1989: Observed structure of mesoscale convective systems and implications for

large-scale heating. Quart. J. Royal Meteor. Soc., 115, 425–461

Huffman, G., R. F. Adler, B. Rudolf, U. Schneider, and P. R. Keehn, 1995: Global precipitation

estimates based on a technique for combining satellite-based estimates, rain gauge analysis,

and NWP model precipitation information. J. Climate, 8, 1284–1295.

Hurwitz, M. M., P. A. Newman, L. D. Oman, and A. Molod, 2011: Response of the antarctic stratosphere to two types of El Ni ̃no events. J. Atmos. Sci. 68(4), 812–822, DOI

10.1175/2011JAS3606.1.

Iguchi T., T. Kozu, R. Meneghini, J. Awaka, and K. Okamoto, 2000: Rain-profiling algorithm for

the TRMM precipitation radar, J. Appl. Meteor., 39, 2038–2052.

Jaegl, L., P. K. Quinn, T. S. Bates, B. Alexander, and J.-T. Lin, 2011: Global distribution of sea

salt aerosols: new constraints from in situ and remote sensing observations. Atmos. Chem.

Phys., 11 , 3137–3157, doi:10.5194/acp-11-3137-2011.

279
Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces

global precipitation estimates from passive microwave and infrared data at high spatial and

temporal resolution. J. Hydromet., 5, 487–503.

Kaiser, J. W., A. Heil, M. O. Andreae, A. Benedetti, N. Chubarova, L. Jones, J.-J. Morcrette, M.

Razinger, M. G. Schultz, M. Suttie, M., and G. R. van der Werf, 2012: Biomass burning

emissions estimated with a global fire assimilation system based on observed fire radiative

power. Biogeosciences , 9, 527–554, doi:10.5194/bg-9-527-2012.

Kiladis, G. N., C. D. Thorncroft, and N. M. J. Hall, 2006: Three-Dimensional Structure and

Dynamics of African Easterly Waves. Part I: Observations, J. Atmos. Sci., 63, 2212–2230.

Koster, R. D., B. M. Fekete, G. J. Huffman, and P. W. Stackhouse, 2006: Revisiting a hydrological analysis framework with International Satellite Land Surface Climatology Project

Initiative 2 rainfall, net radiation, and runoff fields. J. Geophys. Res., 111, D22S05,

doi:10.1029/2006JD007182.

Koster, R. D., and S. P. P. Mahanama, 2012: Land surface controls on hydroclimatic means and

variability. J. Hydromet., 13, 1604–1620.

Kozu T., T. Kawanishi, H.Kuroiwa, M. Kojima,K. Oikawa, H. Kumagai, K. Okamoto, M. Okumura,

H. Nakatuka, and K. Nishikawa, 2001: Development of Precipitation Radar Onboard the

Tropical Rainfall Measuring Mission (TRMM) Satellite,Proceedings of the IEEE 2001 trans.

Geoscience and Remote Sensing, 39,102–116

Krishnamurthy, T. N., J. Molinari, and H.-L. Pan, 1976: Numerical Simulation of the Somali Jet

J. Atmos. Sci., 33, 2350–2362.

Kuze, A., S. Hiroshi, N. Masakatsu, and H. Takashi, 2009: Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases

Observing Satellite for greenhouse gases monitoring. Applied Optics, 48, 6716–6733,

doi:10.1364/AO.48.006716.

Kyle G. S., L. F. Bosart, 2014: The Extratropical Transition of Tropical Cyclone Edisoana (1990).

Mon. Wea. Rev., 142, 2772–2793.

Le Quere, C. et al., 2013: The global carbon budget 1959–2011. Earth Syst. Sci. Data, 5, 165–185,

doi:10.5194/essd-5-165-2013.

L’Ecuyer, T. S., H. Beaudoing, M. Rodell, W. Olson, B. Lin, S. Kato, C. A. Clayson, E. Wood,

J. Sheffield, R. Adler, G. Huffman, M. Bosilovich, G. Gu, F. Robertson, P. R. Houser, D.

Chambers, J. S. Famiglietti, E. Fetzer, W. T. Liu, X. Gao, C. A. Schlosser, E. Clark, D.

P. Lettenmaier, K. Hilburn, 2014: Observed State of the Energy Budget in the Early 21st

Century. J. Climate, sub judice.

Li, J.-L. F., D. E. Waliser, W.-T. Chen, B. Guan, T. Kubar, G. Stephens, H.-Y. Ma, M. Deng,

L. Donner, C. Seman, and L. Horowitz, 2012: An observationally based evaluation of cloud

ice water in CMIP3 and CMIP5 GCMs and contemporary reanalyses using contemporary

satellite data, J. Geophys. Res., 117, doi:10.1029/2012JD017640.

Li, J.-L. F., S. Lee, D. E. Waliser, B. Guan, G. Stephens, and J. Teixeira, 2013: Assessment of Cloud

280
liquid water in CMIP3, CMIP5, and contemporary GCMs and reanalyses with observations,

J. Geophys. Res., sub judice.

Lin, S.-J., 2004: A vertically Lagrangian finite-volume dynamical core for global models. Mon.

Wea. Rev., 132, 2293–2307

Lott, N., 2004: The quality control of the integrated surface hourly database. 84th American Meteorological Society Annual Meeting, 2004, Seattle, WA, American Meteorological Society,

Boston, MA, 7.8 (7p.)

Madden, R. A. and P. R. Julian, 1971: Detection of a 4050-day oscillation in the zonal wind in the

tropical Pacific. J. Atmos. Sci., 28, 702–708.

Madden, R. A. and P. R. Julian, 1972: Description of global-scale circulation cells in the tropics

with a 4050 day period. J. Atmos. Sci., 29, 1109–1123.

Mapes, B. E. and R. A. Houze, Jr., 1995: Diabitic divergence profiles in western Pacific mesoscale

convective systems. J. Atmos. Sci.,52,1807–1828.

Masutani, M. K., and Coauthors, 2010: Observing system simulation experiments at the

National Centers for Environmental Prediction. J. Geophys. Res., 115, D07101,

doi:10.1029/2009JD012528.

McCarty, W., R. M. Errico, R. Gelaro, 2012: Cloud coverage in the Joint OSSE Nature Run. Mon.

Wea. Rev., , 1863–1871.

McFarlane, N. A., 1987: The effect of orographically excited gravity-wave drag on the circulation

of the lower stratosphere and troposphere. J. Atmos. Sci., 44, 1775–1800.

Mitchell, K.E., and Co-Authors, 2004: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system, J. Geophys. Res., 109, D07S90,

doi:10.1029/2003JD003823.

Molod, A., 2012: Constraints on the profiles of total water PDF in AGCMs from AIRS and a

high-resolution model. J. Climate, 25, 8341–8352.

Molod, A., L. Takacs, M. Suarez and J. Bacmeister, 2014: Development of the GEOS-5 Atmospheric

General Circulation Model: Evolution from MERRA to MERRA2. Geosci. Model Dev.

Discuss. 7, 7575–7617, doi:10.5194/gmdd-7-7575-2014.

Molod, A., L. Takacs, M. Suarez, J. Bacmeister, I.-S. Song, and A. Eichmann, 2012: The GEOS-5

Atmospheric General Circulation Model: Mean climate and development from MERRA to

Fortuna. Technical Report Series on Global Modeling and Data Assimilation vol. 28, NASA

Global Modeling and Assimilation Office.

Moorthi, S., and M. J. Suarez, 1992: Relaxed Arakawa Schubert: A parameterization of moist

convection for general circulation models. Mon. Wea. Rev., 120, 978–1002.

Mosley-Thompson, E., J.F. Paskievitch, A.J. Gow, and L.G. Thompson, 1999: Late 20th Century increase in South Pole snow accumulation. J. Geophys. Res., 104, 3877–3886,

281
doi:10.1029/1998JD200092

Murray, R.J. and I. Simmonds, 1991: A numerical scheme for tracking cyclone centres from digital

data. Part I: Development and operation of the scheme. Aust. Met. Mag. 39, 155–166.

Neale, R. B., and co-authors, 2008: The impact of convection on ENSO: From a Delayed Oscillator

to a Series of Events. J. Climate, 21, 5904–5924.

Nicholson, S. E., A. I. Barcilon, M. Challa, J. Baum, 2007: Wave Activity on the Tropical Easterly

Jet. J. Atmos. Sci., 64, 2756–2763.

O’Dell, C. W. et al., 2012: The ACOS CO2 retrieval algorithm - Part 1: Description and validation

against synthetic observations. Atmos. Meas. Tech., 5, 99–121, doi:10.5194/amt-5-99-2012.

Ott, L., B. Duncan, S. Pawson, P. Colarco, M. Chin, C. Randles, T. Diehl, and E. Nielsen, 2010:

Influence of the 2006 Indonesian biomass burning aerosols on tropical dynamics studied with

the GEOS-5 AGCM. J. Geophys. Res., 115, D14121, doi:10.1029/2009JD013181.

Ott, L. E., S. Pawson, G. J. Collatz, W. W. Gregg, D. Menemenlis, H. Brix, C. Rousseaux, K.

Bowman, J. Liu, A. Eldering, M. Gunson, and S. R. Kawa, 2014: Assessing the magnitude

of CO2 flux uncertainty in atmospheric CO2 records using products from NASA’s Carbon

Monitoring Flux Pilot Project. J. Geophys. Res., in press.

Parish, T.R., 1983: The influence of the Antarctic Peninsula on the wind field over the western

Weddell Sea. J. Geophys. Res., 88, 2684–2692, doi:10.1029/JC088iC04p02684.

Pinker, R. T. and I. Laszlo, 1992: Modelling Surface Solar Irradiance for Satellite Applications on

a Global Scale. Journ. App. Meteor., 31, 331–347.

Parish, T.R., and D.H. Bromwich, 1991: Continental-scale simulation of the

Antarctic katabatic wind regime. J. Climate, 4, 135–146, doi:10.1175/1520-

0442(1991)004<0135:CSSOTA>2.0.CO;2

Putman, W., A. M. da Silva, L. E. Ott, and A. Darmenov, 2014: Model Configuration

for the 7-km GEOS-5.12 Nature Run, Ganymed Release (Non-hydrostatic 7 km Global

Mesoscale Simulation), GMAO Office Note, bf 5. (Version 1.0), 86 pp, available from

http://gmao.gsfc.nasa.gov/pubs/office notes.

Putman, W. M., and M. Suarez, 2011: Cloud-system resolving simulations with the NASA Goddard

Earth Observing System global atmospheric model (GEOS-5), Geophys. Res. Lett., 38,

L16809, doi:10.1029/2011GL048438.

Rasmussen, E. A., and Turner, J., 2003: Polar Lows: Mesoscale Weather Systems in the Polar

Regions, Cambridge: Cambridge University Press, p. 612, ISBN 0-521-62430-4.

Reale, O., and R. Atlas, 2001: Tropical Cyclone-like Vortices in the Extratropics: Observational

Evidence and Synoptic Analysis. Wea. Forecasting, 16, 7–34.

Reale, O., J. Terry, M. Masutani, E. Andersson, L. P. Riishojgaard, and J. C. Jusem, 2007: Preliminary evaluation of the European Centre for Medium-Range Weather Forecasts (ECMWF)

Nature Run over the Tropical Atlantic and African Monsoon region. Geophys. Res. Lett.,

282
34, L22810, doi:10.1029/2007GL31640.

Reale, O., W. K. Lau, J. Susskind, E. Brin, E. Liu, L. P. Riishojgaard, M. Fuentes, R. Rosenberg, 2009: AIRS Impact on the Analysis and Forecast Track of Tropical Cyclone Nargis

in a global data assimilation and forecasting system. Geophys. Res. Lett., 36, L06812,

doi:10.1029/2008GL037122.

Reichle, R. H., R. D. Koster, G. J. M. De Lannoy, B. A. Forman, Q. Liu, S. P. P. Mahanama, and

A. Toure, 2011: Assessment and enhancement of MERRA land surface hydrology estimates.

J. Climate, 24, 6322–6338.

Rienecker, M. M., and coauthors, 2008: The GEOS-5 Data Assimilation System: Documentation

of versions 5.0.1 and 5.1.0, and 5.2.0. Technical Report Series on Global Modeling and Data

Assimilation, NASA/TM-2008-104606, Vol. 27, 92 pp.

Rienecker, M.M., and coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for

Research and Applications. J. Climate, 24, 3624–3648.

Risien, C. M., and D. B. Chelton, 2008: A Global Climatology of Surface Wind and Wind Stress

Fields from Eight Years of QuikSCAT Scatterometer Data. J. Phys. Oceanogr., 38, D2379,

doi:10.1175/2008JPO3881.1.

Rodell, M., and Co-Authors, 2004: The Global Land Data Assimilation System. Bull. Amer.

Meteor. Soc., 85(3), 381–394.

Rodell, M., H.K. Beaudoing, T. L Ecuyer, W. Olson, J.S. Famiglietti, P.R. Houser, R. Adler, M.

Bosilovich, C.A. Clayson, D. Chambers, E. Clark, E. Fetzer, X. Gao, G. Gu, K. Hilburn, G.

Huffman, D.P. Lettenmaier, W.T. Liu, F.R. Robertson, C.A. Schlosser, J. Sheffield, and E.F.

Wood, 2014: The observed state of the water cycle in the early 21st century. J. Climate,

sub judice.

Saha, S, and co-authors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer.

Meteor. Soc. 91, 1015–1057.

Sampson, C. R., and A. J. Schrader, 2000: The Automated Tropical Cyclone Forecasting System

(Version 3.2). Bull. Amer. Meteor. Soc., 81, 1231–1240.

Serreze, M.C., A.P. Barrett, A.G. Slater, R.A. Woodgate, K. Aagaard, R.B. Lammers, M. Steele,

R. Moritz, M. Meredith, and C.M. Lee, 2006: The large-scale freshwater cycle of the Arctic.

J. Geophys. Res., 111, C11010, doi:10.1029/2005JC003424.

Schmidt, G. A., R. Ruedy, J. E. Hansen, I. Aleinov, N. Bell, M. Bauer, S. Bauer, B. Cairns, V.

Canuto, Y. Cheng, A. Del Genio, G. Faluvegi, A. D. Friend, T. M. Hall, Y. Hu, M. Kelley,

N. Y. Kiang, D. Koch, A. A. Lacis, J. Lerner, K. K. Lo, R. L. Miller, L. Nazarenko, V.

Oinas, Ja. Perlwitz, Ju. Perlwitz, D. Rind, A. Romanou, G. L. Russell, Mki. Sato, D. T.

Shindell, P. H. Stone, S. Sun, N. Tausnev, D. Thresher, and M.-S. Yao, 2006: Present day

atmospheric simulations using GISS ModelE: Comparison to in-situ, satellite and reanalysis

data. J. Climate, 19, 153–192.

Shie, C.-L., L. S. Chiu, R. Adler, E. Nelkin, I-I. Lin, P. Xie, F.-C. Wang, R. Chokngamwong,

W. Olson, and D. A. Chu, 2009: A Note on reviving the Goddard satellite-based surface

283
turbulent fluxes (GSSTF) dataset. Adv. Atmos. Sci., 26(6), 1071–1080.

Shindell, D. T., et al. 2006: Multimodel simulations of carbon monoxide: Comparison

with observations and projected near-future changes. J. Geophys. Res., 111, D19306,

doi:10.1029/2006JD007100.

Simmonds, I., R.J. Murray and R.M. Leighton, 1999: A refinement of cyclone tracking methods

with data from FROST. Aust. Met. Mag., Spec. Ed., 35–49.

Sinclair, M. R., 1993: Synoptic-scale diagnosis of the extratropical transition of a southwest Pacific

tropical cyclone. Mon. Wea. Rev., 121, 941–960.

Stachnik, J. and C. Schumacher, 2011: A comparison of the Hadley circulation in modern reanalyses. J. Geophys. Res., 116, doi:10.1029/2011JD016677.

Stackhouse, Jr., P. W., S. J. Cox, S. Gupta, M. Chiacchio, and J. Mikovitz, 2001: The

WCRP/GEWEX surface radiation budget project release 2: An assessment of surface fluxes

at 1 degree resolution. IRS 2000: Current Problems in Atmospheric Radiation, W. Smith

and Y. Timofeyev, Eds., A. Deepak Publishing.

Takahashi, T., et al., 2002: Global sea-air CO2 flux based on climatological surface ocean pCO2,

and seasonal biological and temperature effects. Deep-Sea Res. II, 49, 1601–1622.

Takahashi, T., et al., 2009: Climatological mean and decadal changes in surface ocean pCO2, and

net sea-air CO2 flux over the global oceans. Deep-Sea Res. II, 56, 554–577.

Textor, C., M. Schulz, S. Guibert, S. Kinne, Y. Balkanski, S. Bauer, T. Berntsen, T. Berglen, O.

Boucher, M. Chin, F. Dentener, T. Diehl, R. Easter, H. Feichter, D. Fillmore, S. Ghan, P.

Ginoux, S. Gong, A. Grini, J. Hendricks, L. Horowitz, P. Huang, I. Isaksen, I. Iversen, S.

Kloster, D. Koch, A. Kirkevg, J. E. Kristjansson, M. Krol, A. Lauer, J. F. Lamarque, X. Liu,

V. Montanaro, G. Myhre, J. Penner, G. Pitari, S. Reddy, O. Seland, P. Stier, T. Takemura,

and X. Tie, 2006: Analysis and quantification of the diversities of aerosol life cycles within

AeroCom. Atmos. Chem. Phys., 6 , 1777–1813, doi:10.5194/acp-6-1777-2006.

Thorncroft, C. D., J. Hall, and G. N. Kiladis, 2008: Three-dimensional structure and dynamics of

African easterly waves. Part III: Genesis. J. Atmos. Sci., 65, 3596–3607.

Tokioka, T., K. Yamazaki, A. Kitoh and T. Ose, 1988: The equatorial 30–60 day oscillation and

the Arakawa-Schubert penetrative cumulus parameterization. J. Meteor.Soc. Japan, 66,

883–901.

Trenberth, K.E. and J. Caron, 2001: Estimates of Meridional Atmosphere and Ocean Heat Transports. J. Climate, 14, 3433–3443.

Tulloch, R. and K. Smith, 2006: A theory for the atmospheric energy spectrum: depth limited

temperature anomalies at the tropopause. Proc. Nat. Acad. Sci., 103, 14,690–14,694.

Vaughan, M., C. Trepte, D. Winker, M. Averya, J. Campbell, R. Hoff, S. Young, B. Getzewich,

J. Tackett, and J. Kar, 2011: Adapting CALIPSO climate measurements for near real

time analyses and forecasting. Proceedings of the 34th International Symposium on Remote

Sensing of Environment.

284
Wada, A., H. Matsueda, S. Murayama, S. Taguchi, A. Kamada, M. Nosaka, K. Tsuboi, and Y.

Sawa, 2012: Evaluation of anthropogenic emissions of carbon monoxide in East Asia derived

from the observations of atmospheric radon-222 over the western North Pacific. Atmos.

Chem. Phys., 12, 12119–12132, doi:10.5194/acp-12-12119-2012.

Wheeler, M. and G. Kiladis, 1999: Convectively coupled equatorial waves: analysis of clouds and

temperature in the wavenumber-frequency domain. J. Atmos. Sci., 56, 374–399.

Winker, D. M., W. H. Hunt, and M. J. McGill, 2007: Initial performance assessment of CALIOP.

Geophys. Res. Lett., 34, L19803, doi:10.1029/2007GL030135.

Winker, D. M., J. Pelon, J. A. Coakley Jr., S. A. Ackerman, R. J. Charlson, P. R. Colarco, P.

Flamant, Q. Fu, R. M. Hoff, C. Kittaka, T. L. Kubar, H. Le Treut, M. P. McCormick,

G. Mgie, L. Poole, K. Powell, C. Trepte, M. A. Vaughan, and B. A. Wielicki, 2010: The

CALIPSO Mission: A Global 3D View of Aerosols and Clouds. Bull. Amer. Meteor. Soc.,

91, 1211–1229. doi: http://dx.doi.org/10.1175/2010BAMS3009.1

Wu, M.-L, O. Reale, S. Schubert, M. J. Suarez, R. Koster, P. Pegion, 2009: African Easterly Jet:

Structure and Maintenance. J. Climate, 22, 4459–4480.

Wu, M.-L, O. Reale, S. Schubert, M. J. Suarez, C. Thorncroft, 2012: African Easterly Jet:

barotropic instability, waves and cyclogenesis. J. Climate, 25, 1489–1510.

Wu, M.-L, O. Reale, S. Schubert, 2013: A characterization of African Easterly Waves on 2.5–6 and

6–9 day time scales. J. Climate, 26, 6750–6774.

Xia, Y., and Co-Authors, 2012: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS2): 1. Intercomparison and application of model products. J. Geophys. Res., 117,

doi:10.1029/2011JD016048.

Xie, P., and P. A. Arkin, 1996: Analysis of global monthly precipitation using gauge observations,

satellite estimates, and numerical model predictions. J. Climate, 9, 840–858.

Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge

observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc.,

78, 2539–2558.

Yin, X, A. Gruber and P. Arkin, 2004: Comparison of the GPCP and CMAP Merged Gauge–

Satellite Monthly Precipitation Products for the Period 1979–2001. J. Hydrometeorol., 5,

1207–1222.

Zimin, A., I. Szunyogh, D. Patil, B. Hunt, and E. Ott, 2003: Extracting envelopes of Rossby wave

packets. Mon. Wea. Rev., 131, 1011–1017.

285