NASA/TM–2012-104606/Vol 28

April 2012

Andrea Molod, Lawrence Takacs, Max Suarez, Julio Bacmeister, In-Sun Song, and Andrew Eichmann

National Aeronautics and 

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Technical Report Series on Global Modeling and Data Assimilation, 

Volume 28

Max J. Suarez, Editor 

The GEOS-5 Atmospheric General Circulation Model: 

Mean Climate and Development from MERRA to Fortuna
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Technical Report Series on Global Modeling and Data Assimilation, 

Volume 27

Max J. Suarez, Editor 

The GEOS-5 Atmospheric General Circulation Model: 

Mean Climate and Development from MERRA to Fortuna

NASA/TM–2012-104606/Vol 28

April 2012

Andrea Molod

University of Maryland College Park, College Park, MD

Lawrence Takacs

Science Systems and Applications, Inc., Lanham, MD

Max Suarez

NASA Goddard Space Flight Center, Greenbelt, MD

Julio Bacmeister

National Center for Atmospheric Research, Boulder, CO

In-Sun Song

Next Generation Model Development Center, Seoul, South Korea

Andrew Eichmann

Science Systems and Applications, Inc., Lanham, MD 
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Abstract

This report is a documentation of the Fortuna version of the GEOS-5 Atmospheric General Circulation Model (AGCM). The GEOS-5 AGCM is currently in use in the NASA Goddard Modeling and

Assimilation Office (GMAO) for simulations at a wide range of resolutions, in atmosphere only,

coupled ocean-atmosphere, and data assimilation modes. The focus here is on the development

subsequent to the version that was used as part of NASA’s Modern-Era Retrospective Analysis for

Research and Applications (MERRA). We present here the results of a series of 30-year atmosphereonly simulations at different resolutions, with focus on the behavior of the 1◦

resolution simulation.

The details of the changes in parameterizations subsequent to the MERRA model version are outlined, and results of a series of 30-year atmosphere-only climate simulations at 2◦

resolution are

shown to demonstrate changes in simulated climate associated with specific changes in parameterizations. The GEOS-5 AGCM presented here is the model used for the GMAO’s atmosphere-only

and coupled CMIP-5 simulations.

The seasonal mean climate of the 1◦simulation is evaluated against multi-year climatologies computed from available satellite-based and reanalysis fields. The seasonal mean climate of the Fortuna

version of the GEOS-5 AGCM represents a substantial improvement over the simulated climate of

the MERRA version. The Fortuna simulation generally agrees well with the observational estimates.

The improvements in simulated climate are primarily associated with the increased re-evaporation

of frozen precipitation and cloud ice, resulting in a wetter atmosphere. The largest differences between the Fortuna GEOS-5 AGCM and observations are in the cloud cover and cloud effects, and

indicate a GEOS-5 AGCM climate with excessive cloud cover in the extratropics and tropical clouds

which are too low in altitude. Other biases include a cold tropical tropopause, and an eastward

shift in the boreal summer tropical circulation pattern, both of which exist in many other AGCM

simulations. Recommendations are included to address these remaining errors in mean simulated

climate, which involve improvements in the parameterization of clouds and convection.
Contents

1 Introduction 11

2 Model Description - Transition from MERRA to Fortuna 12

2.1 Moist Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.2 Turbulence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.3 Surface Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.4 Gravity Wave Drag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.5 Land Surface Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3 Climate Simulation Results 18

3.1 Experiment Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.2 Mean Circulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.2.1 Zonal Wind and Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.2.2 Meridional Wind and Mass Streamfunction . . . . . . . . . . . . . . . . . . . 19

3.2.3 Surface Wind Speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2.4 Eddy Height . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2.5 Velocity Potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2.6 Humidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.3 Moisture Budget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.3.1 Evaporation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.3.2 Precipitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.3.3 Net Sources and Sinks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.3.4 Equilibrium Total Precipitable Water . . . . . . . . . . . . . . . . . . . . . . 24

3.4 Top of Atmosphere Energy Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.4.1 Total Atmospheric Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.4.2 Cloud Amount and Radiative Contribution . . . . . . . . . . . . . . . . . . . 27

3.5 Land and Ocean Surface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.5.1 Surface Energy Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.5.2 Land Surface Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4 Attributing Simulation Changes to Differences Between MERRA and Fortuna

Versions 31

4.1 Experimental Design and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2
5 Synthesis and Discussion 37

A Verification Data Sources 38

B Acknowledgements 41

References 113

3
List of Tables

1 Changes in GCM Algorithms from MERRA to Fortuna . . . . . . . . . . . . . . . . 42

2 Experiments to attribute MERRA to Fortuna AGCM simulation changes to changes

in parameterizations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

4
List of Figures

1 The difference (Fortuna AGCM minus MERRA AGCM) of zonal mean specific humidity source term due to all re-evaporation for December-January-February. . . . . 44

2 Critical relative humidity. Black from MERRA AGCM formulation at different

latitudes, green from Fortuna AGCM formulation for 1◦resolution, and red from

Fortuna AGCM formulation for 2◦resolution. . . . . . . . . . . . . . . . . . . . . . . 45

3 Surface bulk Richardson number as a function of sensible heat flux in W m−2in a

single July from experiment 3 (red) and experiment 2 (black). . . . . . . . . . . . . . 46

4 Scatter diagram of surface wind speed (m sec−1) versus ocean roughness (m) in

the MERRA and Fortuna AGCMs. a) diagram focusing on medium range of wind

speeds, Fortuna shown in black and MERRA in green, and b) diagram extending to

high wind regimes, MERRA shown in green and Fortuna in black. . . . . . . . . . . 47

5 Background nonorographic drag from the MERRA and Fortuna AGCM simulations 48

6 December-January-February Zonal Wind Climatology in m sec

−1

from a) GEOS-5

AGCM b) MERRA and c) the difference, GEOS-5 - MERRA. . . . . . . . . . . . . . 49

7 June-July-August zonal mean zonal wind in m sec−1from a) GEOS-5 AGCM b)

MERRA and c) the difference, GEOS-5 - MERRA. . . . . . . . . . . . . . . . . . . . 50

8 December-January-February Temperature Climatology in K from a) GEOS-5 AGCM

b) MERRA and c) the difference, GEOS-5 - MERRA. . . . . . . . . . . . . . . . . . 51

9 June-July-August Temperature Climatology in K from a) GEOS-5 AGCM b)

MERRA and c) the difference, GEOS-5 - MERRA. . . . . . . . . . . . . . . . . . . . 52

10 December-January-February Meridional Wind Climatology in m sec−2 a) from

GEOS-5 AGCM, b) from MERRA Reanalysis, c) ECinterim reanalysis, d) GEOS-5

AGCM - MERRA, e) GEOS-5 AGCM - ECinterim, f) MERRA - ECinterim. . . . . 53

11 June-July-August Meridional Wind Climatology in m sec−2 a) from GEOS-5 AGCM,

b) from MERRA Reanalysis, c) ECinterim reanalysis, d) GEOS-5 AGCM - MERRA,

e) GEOS-5 AGCM - ECinterim, f) MERRA - ECinterim. . . . . . . . . . . . . . . . 54

12 Meridional Mass Streamfunction in 108 kg sec−1 during December-January-February

from a) GEOS-5 AGCM, b) MERRA, and c) EC-Interim, and during June-JulyAugust from d) GEOS-5 AGCM, e) MERRA, and f) EC-Interim. . . . . . . . . . . . 55

13 December-January-February Surface Wind Speed in m sec−1from a) GEOS-5 AGCM

b) GSSTF and c) the difference, GEOS-5 - GSSTF. . . . . . . . . . . . . . . . . . . 56

14 June-July-August Surface Wind Speed in m sec−1from a) GEOS-5 AGCM b) GSSTF

and c) the difference, GEOS-5 - GSSTF. . . . . . . . . . . . . . . . . . . . . . . . . . 57

5
15 December-January-February 300 mb Eddy Height Climatology in m from a) GEOS-5

AGCM b) MERRA and c) the difference, GEOS-5 - MERRA. . . . . . . . . . . . . . 58

16 December-January-February 200 mb Velocity Potential Climatology in from a)

GEOS-5 AGCM b) MERRA and c) the difference, GEOS-5 - MERRA. . . . . . . . . 59

17 June-July-August 200 mb Velocity Potential Climatology in from a) GEOS-5 AGCM

b) MERRA and c) the difference, GEOS-5 - MERRA. . . . . . . . . . . . . . . . . . 60

18 December-January-February Specific Humidity Climatology in g kg−1a) from

GEOS-5 AGCM, b) from MERRA Reanalysis, c) ECinterim reanalysis, d) GEOS-5

AGCM - MERRA, e) GEOS-5 AGCM - ECinterim, f) MERRA - ECinterim. . . . . 61

19 June-July-August Specific Humidity Climatology in g kg−1a) from GEOS-5 AGCM,

b) from MERRA Reanalysis, c) ECinterim reanalysis, d) GEOS-5 AGCM - MERRA,

e) GEOS-5 AGCM - ECinterim, f) MERRA - ECinterim. . . . . . . . . . . . . . . . 62

20 December-January-February Relative Humidity Climatology in percent a) from

GEOS-5 AGCM, b) from MERRA Reanalysis, c) ECinterim reanalysis, d) GEOS-5

AGCM - MERRA, e) GEOS-5 AGCM - ECinterim, f) MERRA - ECinterim. . . . . 63

21 June-July-August Relative Humidity Climatology in percent a) from GEOS-5

AGCM, b) from MERRA Reanalysis, c) ECinterim reanalysis, d) GEOS-5 AGCM -

MERRA, e) GEOS-5 AGCM - ECinterim, f) MERRA - ECinterim. . . . . . . . . . . 64

22 Oceans only zonal mean Latent Heat Flux W m−2from GEOS-5 AGCM (blue) and

GSSTF (black) during a) December-January-February and b) June-July-August. . . 65

23 December-January-February Latent Heat Flux W m−2from a) GEOS-5 AGCM b)

GSSTF and c) the difference, GEOS-5 - GSSTF. . . . . . . . . . . . . . . . . . . . . 66

24 June-July-August Latent Heat Flux in W m−2from a) GEOS-5 AGCM b) GSSTF

and c) the difference, GEOS-5 - GSSTF. . . . . . . . . . . . . . . . . . . . . . . . . . 67

25 Zonal mean total precipitation in mm day−1from GEOS-5 AGCM (blue) and GPCP

(black) during a) December-January-February and b) June-July-August. . . . . . . . 68

26 December-January-February Precipitation mm day−1from a) GEOS-5 AGCM b)

GPCP and c) the difference, GEOS-5 - GPCP. . . . . . . . . . . . . . . . . . . . . . 69

27 June-July-August Precipitation in mm day−1from a) GEOS-5 AGCM b) GPCP and

c) the difference, GEOS-5 - GPCP. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

28 Oceans only zonal mean net humidity flux at the surface (Evaporation - Precipitation) mm day−1

from GEOS-5 AGCM (blue) and COADS (black) during a)

December-January-February and b) June-July-August. . . . . . . . . . . . . . . . . . 71

6
29 December-January-February Total Precipitable Water g cm−2

from a) GEOS-5

AGCM b) SRB and c) the difference, GEOS-5 - SRB. . . . . . . . . . . . . . . . . . 72

30 June-July-August Total Precipitable Water in g cm−2from a) GEOS-5 AGCM b)

SRB and c) the difference, GEOS-5 - SRB. . . . . . . . . . . . . . . . . . . . . . . . 73

31 Zonal mean net shortwave radiation at the top of the atmosphere (incomingoutgoing) in W m−2

from GEOS-5 AGCM (blue) and CERES (black) during a)

December-January-February and b) June-July-August. . . . . . . . . . . . . . . . . . 74

32 December-January-February net shortwave radiation at the top of the atmosphere in

W m−2from a) GEOS-5 AGCM b) CERES and c) the difference, GEOS-5 - CERES. 75

33 June-July-August net shortwave radiation at the top of the atmosphere in W m−2

from a) GEOS-5 AGCM b) CERES and c) the difference, GEOS-5 - CERES. . . . . 76

34 Zonal mean outgoing longwave radiation at the top of the atmosphere in W m−2from

GEOS-5 AGCM (blue) and CERES (black) during a) December-January-February

and b) June-July-August. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

35 December-January-February outgoing longwave radiation at the top of the atmosphere in W m−2

from a) GEOS-5 AGCM b) CERES and c) the difference, GEOS-5

- CERES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

36 June-July-August outgoing longwave radiation at the top of the atmosphere in W

m−2from a) GEOS-5 AGCM b) CERES and c) the difference, GEOS-5 - CERES. . 79

37 Zonal mean net radiation at the top of the atmosphere (incoming-outgoing) in W

m−2from GEOS-5 AGCM (blue) and CERES (black). during a) December-JanuaryFebruary and b) June-July-August. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

38 Zonal mean shortwave cloud radiative effect (outgoing shortwave - clear sky outgoing

shortwave) at the top of the atmosphere in W m−2from GEOS-5 AGCM (blue) and

CERES (black) during a) December-January-February and b) June-July-August. . . 81

39 December-January-February shortwave cloud radiative effect (outgoing shortwave

- clear sky outgoing shortwave) at the top of the atmosphere in W m−2from a)

GEOS-5 AGCM b) CERES and c) the difference, GEOS-5 - CERES. . . . . . . . . . 82

40 June-July-August shortwave shortwave cloud radiative effect (outgoing shortwave

- clear sky outgoing shortwave) at the top of the atmosphere in W m−2from a)

GEOS-5 AGCM b) CERES and c) the difference, GEOS-5 - CERES. . . . . . . . . . 83

41 Zonal mean longwave cloud radiative effect (clear sky outgoing longwave - outgoing

longwave) at the top of the atmosphere in W m−2from GEOS-5 AGCM (blue) and

CERES (black) during a) December-January-February and b) June-July-August. . . 84

7
42 December-January-February longwave cloud radiative effect (clear sky outgoing longwave - outgoing longwave) at the top of the atmosphere in W m−2

from a) GEOS-5

AGCM b) CERES and c) the difference, GEOS-5 - CERES. . . . . . . . . . . . . . . 85

43 June-July-August longwave longwave cloud radiative effect (clear sky outgoing longwave - outgoing longwave) at the top of the atmosphere in W m−2

from a) GEOS-5

AGCM b) CERES and c) the difference, GEOS-5 - CERES. . . . . . . . . . . . . . . 86

44 Zonal mean total cloud fraction from GEOS-5 AGCM (blue) and SRB (black) during

a) December-January-February and b) June-July-August. . . . . . . . . . . . . . . . 87

45 December-January-February total cloud fraction from a) GEOS-5 AGCM b) SRB

and c) the difference, GEOS-5 - SRB. . . . . . . . . . . . . . . . . . . . . . . . . . . 88

46 June-July-August total cloud fraction from a) GEOS-5 AGCM b) SRB and c) the

difference, GEOS-5 - SRB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

47 Zonal mean net radiation at the surface W m−2from GEOS-5 AGCM (blue) and

SRB (black) during a) December-January-February and b) June-July-August. . . . . 90

48 December-January-February net radiation at the surface in W m−2from a) GEOS-5

AGCM b) SRB and c) the difference, GEOS-5 - SRB. . . . . . . . . . . . . . . . . . 91

49 June-July-August net radiation at the surface in W m−2from a) GEOS-5 AGCM

b) SRB and c) the difference, GEOS-5 - SRB. . . . . . . . . . . . . . . . . . . . . . . 92

50 December-January-February Sensible Heat Flux W m−2from a) GEOS-5 AGCM b)

GSSTF and c) the difference, GEOS-5 - GSSTF. . . . . . . . . . . . . . . . . . . . . 93

51 June-July-August Sensible Heat Flux in W m−2from a) GEOS-5 AGCM b) GSSTF

and c) the difference, GEOS-5 - GSSTF. . . . . . . . . . . . . . . . . . . . . . . . . . 94

52 Annual mean (over oceans only) net surface heating in W m−2from a) GEOS-5

AGCM b) WHOI and c) the difference, GEOS-5 - WHOI. . . . . . . . . . . . . . . . 95

53 Implied Ocean Heat Transport in PW from GEOS-5 AGCM (black), from

Ganachaud and Wunsch(2000) (green) and from Trenberth and Caron (2001) (blue). 96

54 December-January-February Land Surface Temperature in K from a) GEOS-5

AGCM b) CRU TS and c) the difference, GEOS-5 - CRU TS. . . . . . . . . . . . . . 97

55 June-July-August Land Surface Temperature in K from a) GEOS-5 AGCM b) CRU

TS and c) the difference, GEOS-5 - CRU TS. . . . . . . . . . . . . . . . . . . . . . . 98

56 30-year average December-January-February surface wind speed (m sec−1) from: a)

Fortuna control, b) GSSTF, c) Control-GSSTF, d) Experiment 1, e) GSSTF, f)

Experiment 1-GSSTF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

8
57 Spatial average of zonal wind in m s−1

from 10S to 10N latitude as a function of

pressure level in mb and time from a) Fortuna control, b) Experiment 2 and c)

MERRA reanalysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

58 30-year average June-July-August evaporation fractions. a) Fraction of evaporation

from interception canopy in experiment 1 (Fortuna control), b) same as a) but from

experiment 2, c) fraction of evaporation from transpiration in experiment 1, d) same

as c) but from experiment 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

59 June-July-August averaged evaporation in mm day−1 over land surfaces from a)

Experiment 1 (Fortuna), b) experiment 2 (MERRA AGCM) and c) the difference,

experiment 1 minus experiment 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

60 June-July-August averaged sensible heat flux in W m−2from a) Experiment 3 (Louis

scheme), b) experiment 2 (Helfand scheme) and c) the difference, experiment 3 minus

experiment 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

61 30-year average June-July-August turbulent exchange coefficient for heat in m sec−2

from a) experiment 3 (Fortuna control), and b) from experiment 4. . . . . . . . . . 104

62 December-January-February averaged planetary boundary layer height difference in

m, experiment 3 minus experiment 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

63 December-January-February averaged relative humidity in percent from a) Experiment 5 (MERRA AGCM), b) experiment 4 (Fortuna) and c) the difference, experiment 5 minus experiment 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

64 December-January-February averaged cloud fraction from a) Experiment 5 (MERRA

AGCM), b) experiment 4 (Fortuna) and c) AIRS retreivals . . . . . . . . . . . . . . 107

65 a) December-January-February total precipitable water in mm from experiment 6

(MERRA AGCM), b) same as a) but from experiment 5, c) same as a) but the

difference, experiment 6 minus experiment 5, d) December-January-February specific

humidity in g kg−1from experiment 6 (MERRA AGCM), e) same as d) but from

experiment 5, f) same as d) but the difference, experiment 6 minus experiment 5. . 108

66 December-January-February total precipitation in mm day

−1

from a) experiment

6 (MERRA AGCM), b) GPCP, c) the difference, experiment 6 minus GPCP, d)

experiment 5 (Fortuna), e) GPCP, and f) the difference, experiment 5 minus GPCP. 109

67 December-January-February 300 mb Eddy Height Climatology in m from a) experiment 6 (MERRA AGCM), b) MERRA, c) the difference, experiment 6 minus

MERRA, d) experiment 5 (Fortuna), e) MERRA, and f) the difference, experiment

5 minus MERRA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

9
68 a) June-July-August total precipitable water in mm from experiment 6 (MERRA

AGCM), b) same as a) but from experiment 5, c) same as a) but the difference,

experiment 6 minus experiment 5, d) June-July-August specific humidity in g kg−1

from experiment 6 (MERRA AGCM), e) same as d) but from experiment 5, f) same

as d) but the difference, experiment 6 minus experiment 5. . . . . . . . . . . . . . . 111

69 30-year averaged annual cycle of zonal mean zonal wind, averaged from 70S to 50S

latitude in m sec−2from Experiment 7 in red (MERRA AGCM), experiment 6 in

blue (Fortuna) and MERRA in green . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

10
1 Introduction

The Fortuna version of the GEOS-5 AGCM represents the new generation AGCM developed by the

Global Modeling and Assimilation Office (GMAO) to replace the model version that was used as

part of MERRA (Modern-Era Retrospective Analysis for Research and Applications, Rienecker, et

al., 2011). The focus of the development of the MERRA version of the GEOS-5 AGCM was

on the behavior of the components of the hydrological cycle in MERRA itself. The focus of

the development of the Fortuna version was on the behavior in atmosphere-only and in coupled

atmosphere ocean simulations. Results of atmosphere-only and coupled climate simulations with the

new version exhibit major improvements over the MERRA version. Towards this end, adjustments

were made to several of the parameterizations of physical processes, with the most important

impact from the changes to the moist processes and to the turbulence parameterization. The

major adjustments to the moist processes included the increase of the re-evaporation of grid scale

precipitation, the reformulation of the critical relative humidity criterion for phase changes of incloud and precipitating liquid and ice, and the implemention of a resolution-dependant stochastic

trigger for cumulus convection. The adjustments to the turbulence parameterization included

changes in the behavior of the scheme in the presence of large wind shear.

The major features of atmosphere-only climate simulations as compared to available observationally

based estimates are presented here. A single Atmospheric Model Intercomparison Project (AMIP)-

style 30-year simulation at 1◦x1.25◦x72 levels is used here to describe the mean climate of the

Fortuna version of the GEOS-5 AGCM. In addition, results from a series of AMIP-style sensitivity

experiments (at 2◦x2.5

◦x72 levels) exploring the changes in parameterizations between the MERRA

and Fortuna models are presented to demonstrate the importance of the various changes. Although

the development focused on AMIP-style and coupled climate runs, the resulting model has also

performed well in data assimilation and numerical weather prediction applications. As a result,

this version of the GEOS-5 AGCM is used in the GMAO’s real-time atmospheric analysis and

forecast system, unifying the climate and weather models.

11
2 Model Description - Transition from MERRA to Fortuna

The generation of the GEOS-5 Atmospheric General Circulation Model (GCM) that was used as

part of NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) is

described in Rienecker, et al. (2008). A brief summary of the model’s physical parameterizations is

provided here along with a more detailed description of the differences between the MERRA generation model and the current Fortuna generation. The GEOS-5 AGCM physics includes parameterization schemes for atmospheric convection, large scale precipitation and cloud cover, longwave

and shortwave radiation, turbulence, gravity wave drag, and a land surface model.

Convection is parameterized using the Relaxed Arakawa-Schubert scheme (Moorthi and Suarez,

1992) and includes a scheme for the generation and re-evaporation of falling rain (Bacmeister et

al., 2006). RAS is a mass flux scheme with an updraft only detraining plume cloud model and

a quasi-equilibrium closure. The prognostic cloud cover and cloud water and ice scheme is from

Bacmeister et al. (2006). The scheme includes large scale condensation, evaporation, autoconversion and accretion of cloud water and ice, sedimentation of cloud ice and re-evaporation of falling

precipitation. The longwave radiative processes are described by Chou and Suarez (1994), and

include absorption due to cloud water, water vapor, carbon dioxide, ozone, N2O and methane.

The shortwave is from Chou (1990) and Chou (1992), and includes absorption by water vapor,

ozone, carbon dioxide, oxygen, cloud water, and aerosols and includes scattering by clouds water

and aerosols. The turbulence parameterization is based on the Lock (2000) scheme, acting together

with the Richardson-number based scheme of Louis and Geleyn (1982). The Lock scheme includes a representation of non-local mixing (driven by both surface fluxes and cloud-top processes)

in unstable layers, either coupled to or decoupled from the surface, and an explicit entrainment

parameterization. The original scheme was extended in GEOS-5 to include moist heating and

entrainment in the unstable surface parcel calculations. The Monin-Obukhov surface layer parameterization is described in Helfand and Schubert (1995), and includes the effects of a viscous

sublayer for heat and moisture transport over all surfaces except land. The ocean roughness is

determined by a blend of the algorithms of Large and Pond (1981) and Kondo (1975), modified

in the mid-range wind regime according to Garfinkel et al. (2011) and in the high wind regime

according to Molod et al. (2012). The gravity wave parameterization computes the momentum

and heat deposition into the grid-scale flow due to orographic (McFarlane, 1987) and nonorographic

(after Garcia and Boville, 1994) gravity wave breaking. The Land Surface Model from Koster et.

al (2000) is a catchment-based scheme which treats subgrid scale heterogeneity in surface moisture

12
statistically. The applied subgrid scale distributions are related to the topography, allowing it to

exert a major control over much of the subgrid variability. The catchment model is coupled to the

multi-layer snow model of Stieglitz et al. (2001).

The collection of parameterizations in the Fortuna and MERRA AGCMs are identical with the

exception of the parameterization of the surface layer. The Fortuna AGCM employs the scheme of

Helfand and Schubert (1995), while the MERRA AGCM employed the scheme of Louis (1979). The

changes from the MERRA AGCM to Fortuna that are relevant to the results presented here are

largely in the details of the moist scheme, including the cloud-radiative interactions, the turbulence

scheme, and the gravity wave drag scheme. A comprehensive list of the changes for each scheme is

presented below, listed for each scheme in order of the impact on the simulations.

2.1 Moist Processes

The parameterizations for moist processes did not undergo a fundamental change in any of the

algorithms they contain between the MERRA AGCM and Fortuna versions of GEOS-5. Rather,

the changes were related to the parameters which control the relative strengths of the various

sub-processes, and to aspects of the scheme which are specified a priori.

• Increase of re-evaporation of convective and grid-scale frozen precipitation

• Reformulate critical relative humidity for grid scale condensation/sublimation

• Rework of pressure and temperature dependant autoconversion and effective radius of cloud

drops

• Change in the adjustment of cloud fractions for radiation effects, reducing fractions

• Implement autoconverision of all warm fog

• Include stochastic RAS trigger, more restrictive as horizontal resolution increases

• Cloud base for convection set at PBL depth

• RAS time scale dependence on turbulent eddy diffusion coefficients removed

13
The Fortuna scheme for the re-evaporation of precipitation and suspended cloud water and ice

contains a series of new parameter settings which result in a substantial increase over the MERRA

model in the re-evaporation of snow and ice. The main impact came from allowing the snow

re-evaporation process to proceed all the way to the critical relative humidity used by the large

scale condensation algorithm, and from reducing the assumed mean diameter of snow particles,

thereby increasing the residence time in any grid box and therefore the re-evaporation rate. The

impact of the changes in parameter settings on the water vapor source due to re-evaporation for

the December-January-February average is shown in figure 1. The largest increase in the Fortuna

AGCM is aloft, near 500 mb, where the increase is up to 0.7 g kg−1 day−1. The impact on the

model’s humidity is described in section 4.

The algorithm for large scale condensation, as described in Bacmeister et al. (2006), assumes that

the probability distribution function (PDF) of total water is “top-hat” shaped. The width of the

PDF is associated with the grid box “critical relative humidity” (RHcrit) at which condensation

takes place. The vertical profile as well as the magnitude of RHcrit was changed between the

MERRA and Fortuna AGCMs, and now includes an AIRS-based profile and a dependence on

model horizontal resolution after Molod (2012). Typical RHcrit profiles from the MERRA and

Fortuna AGCM are shown in figure 2, and indicate generally lower values in the Fortuna formulation

except in the boundary layer, where turbulent mixing is sufficient to homogenize the total water

distribution.

The changes related to autoconversion, effective radius and cloud fractions seen by radiation were

designed to improve the cloud forcing. In general, the changes increased autoconversion and particle

effective radii for low-level, warm clouds, thereby reducing the radiative impact, and had the reverse

effect on low-level cold clouds. The implementation of the stochastic RAS trigger is resolution

dependant, and has its main impact on limiting deep convection at higher resolution (half degree

or higher). The remaining changes had a smaller impact on the Fortuna climate simulations.

2.2 Turbulence

The fundamental change that took place in the evolution from the MERRA to the Fortuna AGCM

turbulence parameterization is in the balance between the turbulent diffusion from the local and

non-local schemes. The change was designed in part to improve the simulated diffusion just above

14
the surface layer, and to incorporate the effects of wind shear on atmospheric turbulence.

• Remove restrictions on maximum allowed eddy diffusion coefficients from Louis scheme

• Reformulate turbulent length scale in Louis scheme (make it smaller in general)

• Reduce effect of Lock scheme in the presence of wind shear

• Scale back cloud top entrainment at top of shallow Lock plumes

• Increase turbulence due to small scale variations in topography

The first two items took place in tandem, and together constitute an enhanced diffusion from the

Richardson-number based Louis scheme. The estimate of the turbulent length scale for the Louis

scheme was reformulated in the Fortuna AGCM, still based on Blackadar (1962) but now written as

a function of the boundary layer height as estimated from the combined Lock and Louis schemes. In

addition, the Lock scheme “surface plumes” are more inhibited in the presence of strong wind shear

in the current Fortuna version than in the MERRA version. The scaled back cloud top entrainment

and the increase of orographic drag had little impact on the mean climate and were implemented

based on improved atmosphere-ocean coupled simulations, and on improved short term forecasts,

respectively.

2.3 Surface Layer

The parameterization of the surface layer in the Fortuna AGCM uses Monin-Obhukov (MO) similarity theory and is based on the surface layer scheme of Helfand and Schubert (1995). The scheme

was implemented to replace the paramerization of Louis (1979), to maintain a closer consistency

between the local stability and the surface layer fluxes.

• Replace Louis surface layer with Helfand and Schubert Monin-Obukhov formulation

• Remove viscous sublayer over land surfaces

• Change equation that relates ocean roughness to surface stress for middle and high wind

regimes

15
The implementation of the MO scheme included the use of the MO stability functions, the replacement of the algorithm for the viscous sublayer (the laminar layer that can act to impede the flux

of heat and moisture), and the replacement of the functional relationship between ocean surface

wind and wind stress. The effect of the laminar sublayer in the MO scheme is implemented by

augmenting the non-dimensional gradients of moisture and humidity rather than by modifying the

heat and moisture roughness lengths. In addition, the stable surface layer stability function in the

Helfand scheme differs from the functional dependance of fluxes on Richardson number in the Louis

scheme, and results in an increased turbulent heat exchange (of both signs) under stable conditions.

Figure 3 shows a scatter diagram of the sensible heat flux as a function of surface bulk Richardson

number under conditions where the monthly mean air temperature exceeds the monthly mean skin

temperature. The black points are from a simulation with the Helfand surface layer, and the red

are from a simulation using the Louis scheme. The larger values of sensible heat flux in the Helfand

simulation are apparent, and even more apparent when the monthly mean sensible heat flux is

downward.

The laminar sublayer in the new scheme was removed over land surfaces, due in part to the ambiguity of defining a viscous sublayer between the vegetation canopy and the atmospheric surface

layer, and due also to the inclusion of some of the viscous sublayer effects as part of the land surface

model.

The functional relationship between the wind stress and the ocean surface roughness for medium

surface wind speeds from the MERRA AGCM (green) and the Fortuna AGCM (black) is shown in

figure 4a, where the increase based on Garfinkel et al. (2011) is apparent. The decrease of roughness

at strong tropical cyclone strength winds from the MERRA AGCM (green) to the Fortuna AGCM

(black) is shown in figure 4b, based on Molod et al. (2012).

2.4 Gravity Wave Drag

• Changed profile of background drag (increased in tropics)

• Added intermittency of drag

The latitudinal profile of background nonorographic drag in Fortuna was modified to include a

source related to tropical precipitation in addition to the local maxima related to storm track

16
precipitation. The use of this structure is justified based on the physical argument that major

sources for nonorographic gravity waves (e.g., convective and frontal systems, see Richter et al.

2010) often accompany precipitation. The background nonorographic drag profile is shown in

figure 5, where the MERRA AGCM tropical profile is shown with the dashed line, and the Fortuna

AGCM profile is shown with the solid line.

The intermittency factor used in the standard Lindzen-type method is a factor used to reduce

the strength of the gravity wave drag computed from linear theory. In the MERRA AGCM the

factor is 0.125 for both orographically and nonorographically excited gravity waves. It is increased

for orographic waves gradually as a function of latitude in the Fortuna AGCM, reaching a value

of 0.3125 south of approximately 40◦S. This behavior is based on observational evidence of strong

gravity waves from isolated small mountains in the Antarctic peninsula (Alexander and Teitelbaum

2007) and the South Georgia islands (Alexander et al. 2009).

2.5 Land Surface Model

A small set of parameter changes in the land surface model was implemented, designed to compensate for an error in the simulated climate in both the MERRA and Fortuna versions of the GEOS-5

AGCM.

• Reduced fraction of grid box wetted by rainfall, and capacity of interception reservoir

The change in parameters was implemented to prevent the immediate evaporation of almost all

the incident rainfall, particularly from the canopy interception reservoir. By reducing both the

capacity of the interception reservoir and the fraction of surface precipitation incident on it, the

precipitation is used to wet the soil. The results of this change are described in detail in section 4.

17
3 Climate Simulation Results

3.1 Experiment Configuration

The 30 year GEOS-5 AGCM simulation to be used as a basis of comparison with observations

was initialized with January 10, 1979 conditions. The horizontal grid is a lat-lon grid with global

resolution of 1◦ x 1.25◦. The vertical grid is an eta coordinate grid with 72 levels, spaced to increase

the resolution near the surface and near the tropopause. The simulation was forced with observed

sea surface temperatures (Reynolds, 2002).

3.2 Mean Circulation

The seasonal variation of GEOS-5 AGCM mean state is presented and compared to the MERRA reanalysis. The comparisons will generally entail examination of December-January-February (DJF)

and June-July-August (JJA) means from reanalysis and from GEOS-5 AGCM simulations. The

examination of the mean seasonal state includes the zonal mean and seasonal mean zonal and meridional wind fields, mass streamfunction, temperature and relative humidity. The velocity potential

at 200 hPa is shown as in indicator of the divergence aloft associated with diabatic heating, and

the eddy height at 300 hPa is shown to assess the stationary wave pattern. Additional comparisons

are made to the European Center Interim (EC-I) reanalysis (Dee et al., 2011) for quantities that

show relatively lower correlations between MERRA and EC-I reanalyses (Rienecker et al., 2011),

presumably due to the greater influence of the underlying AGCM. The meridional wind field, particularly in the tropics, and the humidity field show much lower MERRA to EC-I correlations than

the zonal wind or temperature, and so the comparison of AGCM and MERRA meridional wind

and humidity fields will also be augmented with comparisons against EC-I reanalysis.

3.2.1 Zonal Wind and Temperature

The zonal mean zonal wind fields for each season are shown in figures 6 and 7. In each figure, the

top panel shows the model result, the middle panel shows the MERRA validation, and the bottom

panel shows the difference. The MERRA zonal wind field is well constrained by observations

(Rienecker et al. 2008), and differences between GEOS-5 AGCM and MERRA fields are attributed

18
mainly to GEOS-5 AGCM errors. The pattern of the GEOS-5 AGCM fields matches well with

MERRA fields in all seasons in the troposphere. The subtropical jet is stronger in GEOS-5 AGCM

by up to 4 m sec−1in the winter hemisphere in both seasons, and weaker by up to 4 m sec−1in

the summer hemisphere with a slight poleward displacement. The surface winds are also generally

weaker in GEOS-5 AGCM by up to 2 m sec−1in both JJA and DJF with the exception of the

southern hemisphere polar region in DJF, where the winds are stronger in the AGCM by up to 2

m sec−1. The consequences of the strong surface winds on the surface fluxes and wind stress will

be discussed in section 3.5. The stratospheric jets are weaker in the GEOS-5 AGCM simulation

than in MERRA in the winter hemisphere by up to 4 m sec−1in both DJF and JJA.

The zonal mean temperature fields for DJF and JJA are shown in figures 8 and 9. In general

the temperatures in the troposphere are within 1K of MERRA temperatures, as seen in figures 8c

and 9c. The exceptions include up to a 3K warm bias in the AGCM in the northern hemisphere

extratropical continents in JJA (geographical distribution not shown). GEOS-5 AGCM tropopause

temperatures are also colder than MERRA in both seasons by up to 2K in the tropics and 5K in the

southern hemisphere high latitudes in DJF. The cold tropical tropopause is typical of many AGCMs

(Solomon, et al., 2007), and is consistent with strong subtropical jets based on the thermal wind

relationship. The GEOS-5 temperatures are warmer than MERRA in the high latitude stratosphere

in wintertime by up to 7K in JJA, which is consistent with the weak stratospheric jets discussed

above.

3.2.2 Meridional Wind and Mass Streamfunction

Figures 10 and 11 show the comparisons among the meridional wind fields from the GEOS-5 AGCM

and each of the two reanalyses. There is good general agreement between GEOS-5 AGCM and

either reanalysis estimate, in terms of magnitude and location of wind maxima in both seasons. In

DJF the poleward v-wind in the extratropics is weaker in the GEOS-5 AGCM by up to 0.5 m sec−1

as compared to either reanalysis, as shown in figures 10d and 10e. The model-reanalysis difference

in the diverging v-wind aloft in the tropics is comparable in magnitude to the difference between

the MERRA and EC-I reanalysis shown in figure 10f. This makes the characterization of model

error there difficult, but the comparison with EC-I (figure 10e) suggests that the region of diverging

v-wind extends further down in the atmosphere in EC-I than in the GEOS-5 AGCM simulation.

In JJA in general the GEOS-5 AGCM v-wind pattern aloft (11a) shows a closer resemblance to the

19
pattern seen in MERRA (11b) than to the pattern seen in EC-I (11c) . As compared to the EC-I

estimate, the GEOS-5 AGCM v-wind aloft is stronger by up to 1.2 m sec−1, and does not extend

as far down into the atmosphere. The Hadley cell strength is revealed by the mass streamfunction,

shown in figure 12. The general agreement between GEOS-5 AGCM and both reanalyses in DJF,

along with the stronger GEOS-5 AGCM mass streamfunction in JJA relative to EC-I, is consistent

with the discussion of the meridional wind.

3.2.3 Surface Wind Speed

The surface wind speed in the GEOS-5 AGCM is influenced by the winds aloft and by the description

of the wind stress at the land or ocean surface. Comparisons between the GEOS-5 AGCM simulated

surface wind speed over the oceans and the satellite estimates from Goddard Satellite-based Surface

Turbulent Fluxes (GSSTF) are shown in figures 13 and 14. In general, the surface winds are

underestimated in the GEOS-5 AGCM in tropical and subtropical regions, and overestimated at

higher latitudes. The comparison with GSSTF is consistent with the evaluation against MERRA

described in section 3.2.1 over mostly ocean covered regions. The overestimate of surface winds in

the northern hemisphere extratropical oceans was not apparant in the zonal means shown above.

3.2.4 Eddy Height

The northern hemisphere wintertime stationary wave pattern of GEOS-5 AGCM is evaluated with

the DJF eddy geopotential height field at 300 hPa, shown in comparison to the MERRA field in

figure 15. The dominant feature is the Pacific-North-America (PNA) pattern. The location and

magnitude of the Pacific trough and ridge are well captured by GEOS-5 AGCM, although the

intensity of both trough and ridge are weaker than the MERRA eddy heights by up to 50 m.

3.2.5 Velocity Potential

The 200 hPa velocity potential (χ) field is shown in figures 16 and 17. The gradients of χ represent

the divergent part of the flow and reflect the response to diabatic heating aloft due primarily to

convection and radiative cloud forcing. The DJF velocity potential pattern in MERRA (figure

16b) is dominated by the outflow associated with the Australian monsoon, and is well captured

20
by GEOS-5 AGCM. The AGCM χ field (figure 16a), however, has a second region of outflow over

South America which is not present in the MERRA (or in the EC-I reanalysis field, not shown). The

JJA velocity potential pattern (figure 17) in both the GEOS-5 AGCM and MERRA is dominated

by the outflow associated with the Asian monsoon and the Pacific warm pool. The GEOS-5 AGCM

χ field, however, is shifted approximately 10◦ of longitude to the east, and represents an error in

the tropical climate during JJA that is present in other AGCMs (eg., Neale et al, 2010). This

eastward shift is also shown in sections 3.3.2 and 3.4 to be manifest in many other aspects of the

AGCM simulated climate.

3.2.6 Humidity

The specific and relative humidity (RH) fields from GEOS-5 AGCM, MERRA and EC-I are shown

in figures 18, 19, 20 and 21. The differences in specific humidity between the two reanalyses

(figures 18f and 19f), particularly in the tropics near the surface, are comparable in magnitude to

the differences between the AGCM and either reanalysis, which makes an assessment of the model

error in that region difficult. The general trend is for high GEOS-5 AGCM specific humidities

as compared to either reanalysis throughout most of the atmosphere in both JJA and DJF. The

humidities are higher in GEOS-5 AGCM by up to 1 g kg−1in the tropics near 500 mb, and in the

extratropics in both hemispheres near 850 mb. The tropical near surface humidity in the GEOS-5

AGCM is slightly wet as compared to MERRA (figures 18d and 19d) and dry as compared to EC-I

(figures 18e and 19e). The relative humidity differences between the reanalyses and the AGCM

are, in general, consistent with specific humidity differences, and so are governed primarily by

water content rather than by temperature. The RH is low as compared to either reanalysis near

the tropical tropopause (figures 20d,e and 21d,e). This dry bias exists despite a cold bias in the

tropopause temperatures (shown in section 3.2.1), implying an upper tropospheric humidity that

is dry.

3.3 Moisture Budget

The vertically integrated atmospheric moisture budget is given by:

21
∂W

∂t = −

1

g

Z p=top

p=bot

∇ · πVqdσ + E − P (1)

where W is the total precipitable water, π is the surface pressure, q is the specific humidity, E is

the evaporation and P is the precipitation. The first term on the right hand side represents the

divergence of the moisture transport, E is the source term, and P is the sink term.

In the simulation the long term (monthly or seasonal time scales or longer) vertically integrated

moisture convergence almost balances the sources and sinks, and the small residual gives rise to

the seasonal cycle. A careful examination, then, of the sources and sinks individually and together,

along with the mean W, provides a summary of the climate of the atmospheric hydrological cycle.

3.3.1 Evaporation

The evaporation over the oceans is assumed to depend chiefly on the surface wind magnitude,

the air-sea moisture and temperature gradients and the surface roughness. Larger gradients will

enhance the evaporation, as will stronger surface winds (up to a point) or enhanced roughness.

Because of the similarity in technique used to estimate evaporation by the AGCM and the GSSTF

algorithm (see Appendix A), any differences between the AGCM and the GSSTF verification are

primarily due to differences in estimates of the near surface gradients of humidity or winds and

surface roughness. The oceanic zonal mean (figure 22) evaporation in the AGCM and GSSTF

observational data has a maximum over the subtropical oceans, where precipitation is low and the

air above the surface is relatively dry due to atmospheric subsidence. There is a local minimum in

evaporation at the equator, due to weaker local winds and cooler sea surface temperatures associated

with the regions of oceanic upwelling. The winter hemispheres have a higher evaporation rate

associated with higher surface winds in winter. The AGCM simulated evaporation corresponds well

to the latitudinal and seasonal changes seen in the GSSTF data, but overestimates the magnitude

by up to 20 W m−2, in the subtropics, corresponding to approximately 0.75 mm day−1. The GSSTF

estimates at high latitudes are limited by the sampling and quality of the satellite retreivals at those

latitudes.

The spatial pattern of GEOS-5 AGCM and GSSTF evaporation fields are shown in figures 23 and

24. In general GEOS-5 AGCM evaporation values are higher than the GSSTF estimates by up

22
to 40 W m−2

in the tropics, and lower than GSSTF by up to 30 W m−2 at higher latitudes.

The underestimate of the evaporation is larger in the winter hemisphere in both DJF and JJA,

as indicated by the larger difference in the northern hemisphere in figure 23c and in the southern

hemisphere in figure 24c. These errors in the AGCM evaporation are not consistent with the errors

in the surface winds (shown in section 3.2.3 to be underestimated in the tropics and overestimated

elsewhere), but are consistent with the errors in the near surface humidity (shown in section 3.2.6

to be dry in the tropics near the surface and wet elsewhere) and so in the near surface humidity

gradient.

3.3.2 Precipitation

Precipitation is a sink term for atmospheric moisture, and the ability to simulate it has far reaching

implications for atmospheric temperature and humidity profiles as well as cloud-radiative interactions. Precipitation is highly variable, both in nature and in a GCM, and is also difficult to measure

accurately, particularly over oceans. General comparisons between the AGCM and observations of

large scale features, trends and seasonal variations provide a reasonable sense of the ability to simulate convection and atmospheric supersaturation-related rainfall. The observational precipitation

chosen for this comparison is from the GPCP estimates of Huffman, mainly because the extensive

raw data coverage over land (appendix A) and the blending of several different types of data over

oceans.

Zonal mean plots of GEOS-5 AGCM and GPCP precipitation from DJF and JJA are presented in

figure 25. All show a maximum in the Intertropical Convergence Zone (ITCZ) and the seasonal shift

from a split ITCZ straddling the equator in DJF to a stronger ITCZ in the northern hemisphere

in JJA. The tropical values correspond well, except for a stronger ITCZ in the GEOS-5 AGCM

simulation north of the equator in DJF. GEOS-5 AGCM and GPCP also all show a minimum

of precipitation over the subtropics, where atmospheric subsidence suppresses convection. The

magnitudes of GEOS-5 AGCM and GPCP observations correspond relatively well here, although

here the GEOS-5 AGCM overestimates the precipitation in JJA by approximately 1.5 mm day−1.

GEOS-5 AGCM and GPCP also show a secondary smaller maximum over the extratropics, where

the excursions of the polar front initiate precipitation. GEOS-5 AGCM zonal mean precipitation

in the extratropics matches the GPCP estimate in both JJA and DJF, with the exception of a

poleward shift of the frontal precipitation in the northern hemisphere in JJA.

23
The lat-lon maps of precipitation, figures 26 and 27, show some pattern differences between the

AGCM and GPCP even at latitudes where the zonal means agree. In DJF the difference pattern

(figure 26c) shows that the GEOS-5 AGCM precipitation is excessive over the Andes, exhibits

a northward shift of the ITCZ precipitation over the western Pacific and Indian ocean regions,

and is excessive over southern Africa. In JJA in the tropics, where the zonal means showed good

agreement, the AGCM pattern exhibits a shift to the east in longitude of about 10◦. This shift was

seen in the velocity potential pattern shown in section 3.2.5. The GEOS-5 AGCM overestimate

the precipitation over high terrain is manifest over the Tibetan plateau in JJA.

3.3.3 Net Sources and Sinks

Evaporation minus Precipitation (E-P) is the atmospheric measure of the total net source of moisture from the continents and oceans. The verification data for E-P is available over oceans from

the COADS data set. There are some discrepancies between the COADS precipitation estimates

and the GPCP fields used for comparison above, and between the evaporation estimates and the

latent heat flux from GSSTF, but the differences are chiefly in magnitude and not in pattern.

Discrepancies between COADS and GSSTF estimates of evaporation are largely in high latitude

regions. The oceanic zonal-mean plots of E-P from GEOS-5 AGCM and COADS, figure 28, both

show positive maxima over the subtropical oceans, especially in the winter hemisphere, reflecting the role of evaporation over the subtropical oceans as the major global source of atmospheric

water vapor. The negative minima in the GEOS-5 AGCM and COADS over the equator and in

the extratropics reflect the major sink regions of the ITCZ and frontal storm tracks, respectively,

where precipitation is highest. Differences between zonal mean GEOS-5 AGCM and COADS E-P

are largely found over high latitude regions, where the discrepancies between COADS and GSSTF

estimates of evaporation make assessment of model errors difficult.

3.3.4 Equilibrium Total Precipitable Water

The total precipitable water (W) field represents the balance between the net sources and sinks

which is achieved by the moisture transport. The 5-year mean DJF and JJA W fields from GEOS-5

AGCM and from SRB are shown in figures 29 and 30 and are relatively zonal, with a maximum in

the tropics and a decrease poleward, corresponding to the atmosphere’s temperature-related ability

to hold water. The major departures from zonality are near the western coasts of the continents,

24
where W is sensitive to topography and to cold ocean currents in regions of oceanic upwelling. The

general agreement between GEOS-5 AGCM and SRB is good, in that GEOS-5 AGCM captures

the general behavior and major departures from zonality and the seasonal shift of the maximum

W with the ITCZ. The difference plots (bottom panel for each season) show that W in GEOS-5

AGCM is generally wetter than the SRB in both DJF and JJA. The wet bias in the AGCM is in

the tropical regions in DJF, and is generally less than 1 g cm−2. In JJA the wet bias is present in

the northern hemisphere extratropics as well as the tropics, and is as large as 1.5 g cm−2. The wet

bias is related to the re-evaporation of rainfall simulated by the AGCM, and is discussed in section

4.

3.4 Top of Atmosphere Energy Balance

The net radiation flux at the top of the atmosphere (TOA) is the balance between the net incoming

solar and outgoing terrestrial radiation, and can be expressed:

Fnet = Fsw↓ − Fsw↑ + Flw↓ − Flw↑ (2)

where Fsw terms are the upward and downward shortwave fluxes, Flw terms are the longwave fluxes,

and Flw↓ = 0.

The net radiation at any location depends largely on the orientation of the earth with respect to

the sun, on the surface reflectivity, on the amount and height of the clouds, and on the surface

temperature.

3.4.1 Total Atmospheric Radiation

The earth is assumed to be in approximate radiative balance, meaning that the globally averaged temperature is nearly constant during the year. The global net radiation at the top of the

atmosphere, therefore, which represents the balance between the net incoming solar and net outgoing terrestrial radiation, is close to zero. In the GEOS-5 AGCM simulation the balance is held

to within approximately 1 W m−2. The horizontal distribution of the different components of

25
the radiation budget have important implications for the atmospheric general circulation patterns.

The net cooling due to longwave radiation and the net warming due to shortwave radiation will

be examined separately and together to provide the detailed description of the total atmospheric

radiation budget.

The distribution of the net shortwave radiation at the top of the atmosphere depends chiefly on the

earth’s distance from the sun and orientation, on the surface albedo and on the cloud amount and

optical properties. The distribution and amount of water vapor and ozone, the primary atmospheric

shortwave absorbers, also affects the net shortwave radiation. As can be seen from the zonal mean

plots of the net shortwave radiation at the top of the atmosphere, figure 31, the general character of

the latitudinal distribution is determined by the incident shortwave, with a maximum in the summer

subtropics and a minimum at the winter pole. This general behavior is certainly captured by the

GEOS-5 AGCM, although in both DJF and JJA we see that the summer maximum is shifted

slightly poleward relative to the CERES climatology. In addition, the tropical net shortwave is

underestimated in the GEOS-5 AGCM by up to 15 W m−2. The lat-lon maps of GEOS-5 AGCMCERES outgoing shortwave differences, seen in the bottom panel of figures 33 and 32, show that in

DJF the poleward shift is present at all longitudes, while in JJA there is a large overestimate of net

shortwave radiation over cold ocean regions near the west coast of the North and South American

continents. This will be shown in section 3.4.2 to be related to the absence of low level cloud cover

in those regions.

The outgoing longwave (OLR) component of the net radiation at the top of the atmosphere depends

chiefly on the surface temperature and cloud height and amount. Higher OLR values are associated

with warmer surface temperatures, low cloud heights or clear skies, that is, with warmer radiating

temperatures. The zonal mean plots of OLR from CERES and from the GEOS-5 AGCM, figure

34, show a smaller seasonal and latitudinal variation than the net shortwave radiation. The OLR

maxima are in the subtropics, where the cloud cover is low and the surface emissions escape to space,

with a local minimum near the equator, corresponding to cold radiating temperatures at cloud

tops, and smaller minima. The GEOS-5 AGCM zonal mean OLR matches quite well with CERES

everywhere except for the summertime extratropics, where the AGCM OLR exceeds CERES values

by up to 20 W m−2. The separate examination of the cloudy components in section 3.4.2 will help

distinguish between errors in the outgoing longwave due to errors in cloud amount and errors due

to surface temperature or atmospheric constitution. These will show that the overestimate of OLR

in GEOS-5 AGCM is almost entirely due an underestimate of cloud forcing. The spatial pattern of

26
the OLR and the comparison with CERES is shown in figures 35 and 36. From the bottom panels

of these figures we can see clearly that the overestimate of OLR in the GEOS-5 AGCM summertime

extratropics is present at almost all longitudes, and that the close correspondence between AGCM

and CERES zonal means in the tropics is the balance of positive and negative differences of up to

40 W m−2locally. The northward shift of the ITCZ in the AGCM in DJF and the eastward shift

in JJA that were shown in the precipitation field in section 3.3.2 are also apparant in the OLR

difference fields.

The distribution of the net TOA radiation, in particular the latitudinal gradients, is associated

with the net driving energy for the atmospheric and oceanic general circulations. The zonal mean

cross sections, figure 37, show a pattern that follows the net shortwave, which is a positive maximum in the summertime subtropics, decreasing towards the summer pole and towards the winter

hemisphere, crossing zero in the winter subtropics, descending to a negative minimum at the dark

winter polar latitudes. The strongest north-south gradients, which imply the strongest north-south

heat transports, are in the winter hemisphere. The figure shows that GEOS-5 AGCM matches the

general distribution of the net radiation quite well. The errors in the shortwave and the errors in

the longwave compensate and the resulting net radiation matches the CERES estimate.

3.4.2 Cloud Amount and Radiative Contribution

The net TOA radiation can be decomposed into a part due to the presence of clouds and a part

due to the presence of all other atmospheric absorbers and emitters. The Cloud Radiative Effect

(CRE) is defined (c.f., Charlock and Ramanathan, 1985):

CRE = F

net − Fnet

clearsky (3)

The general effect of clouds in the shortwave portion of the spectrum, where the suspended liquid

water is highly reflective, is to reduce the net shortwave available to the atmosphere by reflecting

incident radiation back to space. The amount of cooling due to clouds in the shortwave band

increases with increasing cloud amount and cloud optical thickness. The change in radiative flux

associated with this cooling is seen in the zonal mean shortwave CRE, figure 38. GEOS-5 AGCM

and CERES both show a local maximum cooling near the equator and another in the summer

27
hemisphere extratropics which is largest in the southern hemisphere summer. The GEOS-5 AGCM

matches the CERES summer extratropical shortwave CRE in the southern hemisphere in both DJF

and JJA, and is lower than CERES in the northern hemisphere extratropics by up to 15 W m−2

in JJA. The GEOS-5 AGCM also overestimates the tropical shortwave CRE relative to CERES

by up to the same amount. These CRE errors account for most of the error seen in the net TOA

shortwave radiation of figure 31. The lat-lon plots of shortwave CRE and GEOS-5 AGCM-CERES

differences, figures 39c, and 40c, indicate that the zonal mean CRE patterns are largely present at

all longitudes. The excessive TOA shortwave seen off the coasts of the North and South American

continents in figure 33c appears here in figure 40c and is attributed to an underestimate of shortwave

cloud forcing there.

The effect of clouds in the longwave portion of the spectrum, where water drops absorb and re-emit

radiation effectively, is to reduce the emissions to space by radiating at relatively low temperatures.

The amount of this net warming increases with increasing cloud amount and cloud height. The

cloud optical thickness is of secondary importance for the longwave cloud forcing (LWCRE) because

even the thinnest clouds are effective emitters of longwave radiation. The net warming effect can

be seen in the zonal mean LWCRE, figure 41, where the CRE is positive at all latitudes. The

zonal mean longwave CRE has a maximum in the tropics, associated with the ITCZ, and another

maximum in the extratropics which is approximately the same in summer and winter. These basic

features are captured by GEOS-5 AGCM, but the AGCM underestimates the zonal mean longwave

CRE at all latitudes in all seasons, by up to 15 W m−2in the extratropics. This underestimate of

TOA LWCRE in the AGCM is larger than the error in the OLR itself, which suggests the presence

of a compensating error in the clear sky TOA outgoing longwave radiation. The compensating

error in the AGCM longwave radiative fluxes is attributable to the wet bias seen in section 3.2.6,

which is consistent with an overestimate of clear sky outgoing longwave radiation. The pattern of

the GEOS-5 AGCM-CERES longwave CRE (bottom panel of figures 42 and 43) shows that the

underestimate of LWCRE in the AGCM is present at almost all locations, except for the central

equatorial Pacific in JJA where the eastward shift of the ITCZ precipitation is manifest, and is

slightly larger over continental than over ocean regions.

Since the SWCRE is dominated by cloud amount and thickness, and LWCRE by cloud amount

and height, discrepancies between LWCRE and SWCRE errors point to errors in cloud height or

thickness, while similarities point to errors in cloud amount. Since the shortwave CRE in GEOS-5

AGCM matches well with CERES or is slightly overestimated in the tropics, and the longwave

28
CRE is underestimated by 30 W m−2 or more locally, our inference is that the OLR error is due

to the cloud top pressure and not to the cloud amount. In particular, the cloud tops are too low

in the atmosphere in regions of deep convection. Further evidence towards distinguishing between

errors in cloud amount, height, and thickness can be gained by examining the cloud fractions.

The total cloud fraction and its global distribution is an indicator of both the radiative effects of

clouds and the latent heat released during their formation. The zonal mean cloud fractions from

SRB and from the GEOS-5 AGCM, fig 44, show local maxima near the ITCZ and extratropical

rain bands, higher by about 0.1 in the southern hemisphere, and local minima in the subtropical

subsidence regions. The GEOS-5 AGCM captures these general features, but underestimates total

cloud cover by up to a fractional difference of 0.2 poleward of about 30 degrees N and S. The lat-lon

plots of the cloud fraction, figures 45 and 46, show that the extratropical underestimate is present

everywhere, and that the apparant closer match in the tropics represents compensating areas of

negative and positive difference between the AGCM and SRB.

3.5 Land and Ocean Surface

3.5.1 Surface Energy Balance

The net surface energy balance can be expressed by the following budget equation:

∂Ts

∂t = Fsw↓ − Fsw↑ + Flw↓ − Flw↑ − SH − LH − G (4)

where Fsw terms are the upward and downward shortwave fluxes, Flw terms are the longwave fluxes,

SH is the sensible heat flux, LH is the latent heat flux, and G is the net ground heat storage over

land. Ts is the temperature of a thin skin layer at the surface. During AGCM integration the sea

surface temperature (SST) is basically specified from SST analyses, so the net surface heating over

the oceans serves mainly as a bulk diagnostic indicator of the interaction at the ocean-atmosphere

interface.

The net radiation at surface is given by the first 4 terms on the right hand side of equation 4.

The zonal mean net surface heat flux is shown in figure 47. The errors in the seasonal means are

consistent with the errors in the net radiation at the top of the atmosphere (see section 3.4.1).

That is, the radiation is overestimated by the GEOS-5 AGCM in the summertime extratropics, at

29
the surface somewhat more than at the top of the atmosphere, by up to 30 W m−2 at 40◦N in JJA.

The spatial patterns of the surface net radiation, figures 48 and 49, show that the extratropical

excess of net surface radiation is largely zonal, and that the closer match at other latitudes is the

result of a general overestimate of surface net radiation in the AGCM over continents, particularly

in summertime, and an underestimate over the oceans.

The remaining terms on the right hand side of equation 4 are the latent heat flux (which was shown

in section 3.3.1) and the sensible heat flux, which is shown here in figures 50 and 51. The differences

between the GEOS-5 AGCM and the GSSTF sensible heat flux shows higher values in the GEOS-5

AGCM everywhere in both seasons. The tropical differences are on the order of a few W m−2,

where the effect on the sensible heat flux due to an underestimated surface wind (see section 3.2.3)

is offset by the effect due to a small cold bias in the atmosphere. The larger differences between

the GEOS-5 AGCM and GSSTF are in the extratropics in both seasons, where the AGCM sensible

heat flux is larger than the GSSTF by up to 20 W m−2.

The net surface heat flux, then, which is the sum of all the terms on the right hand side of equation

4, is expected to largely follow the pattern of the net surface radiation, given the aproximate offset

of errors in the sensible and latent heat fluxes. This expectation is borne out by examining the

annual mean net surface heat flux, seen in figure 52, here evaluated relative to the WHOI dataset

estimate. The annual mean net heating is the relevant field to examine the potential impact on

the ocean. Figure 52c shows that the GEOS-5 AGCM underestimates the net heat flux at the

surface relative to the WHOI estimate everywhere except in the northern hemisphere extratropical

oceans near the western boundary currents. This error in the northern hemisphere subtropics is

the result of an excessive net surface radiation there, balanced and more than compensated for by

the excess evaporation (and sensible heating) over the Kuroshio and Gulf Stream currents that is

seen in figures 50 and 23.

If the annual mean net surface heating is integrated zonally and from the north pole to any given

latitude, one obtains the implied ocean heat transport at each latitude. Figure 53 shows the

GEOS-5 AGCM implied ocean heat transport, along with observational estimates from Ganachaud

and Wunsch (2000) and from Trenberth and Caron (2001). Although there is a large amount

of uncertainty in the observational estimate, it seems clear that the GEOS-5 AGCM northern

hemisphere peak in poleward transport is slightly too weak. This reflects the underestimate of

net radiative flux at the surface associated with excessive shortwave cloud forcing discussed in

30
section 3.4.2. It is also apparent from the figure that the GEOS-5 AGCM implied transport in the

extratropics of the southern hemisphere is too weak.

3.5.2 Land Surface Temperature

Over land, where an adequate global observational climatology for the net surface energy flux is

not available, any errors in the net flux into the ground will result in errors in the predicted surface

temperature. The GEOS-5 AGCM surface skin temperature over land is shown relative to the

CRU TS observational estimate in figures 54 and 55. In general, the AGCM skin temperature

over land is warm relative to the CRU TS in the summer hemisphere by up to 10K but generally

near 6K, and cold in DJF in the winter hemisphere by up to 12K but generally near 6K. This is

largely consistent with the net surface radiation discussed in section 3.5.1, with the exception of

equatorial Africa and southeast Asia in DJF and southern hemisphere land areas in JJA. Over all

these regions the net surface radiation is high, which should result in warm skin temperatures, but

the skin temperatures match closely with CRU TS estimates. It is postulated that the sensible and

latent heat fluxes in those regions are overestimated and therefore offset the net surface radiation

error.

4 Attributing Simulation Changes to Differences Between

MERRA and Fortuna Versions

Changes in the substance or in the fundamental behavior of several physical parameterization

schemes since the MERRA version of the AGCM resulted in fundamental changes in simulated climate. Section 2 summarized those changes and here we present the results of a series of experiments

designed to analyze the sensitivity to specific changes in parameterizations.

4.1 Experimental Design and Results

The experiments were all conducted at 2.◦ x 2.5◦ horizontal resolution, ran for 30 years, and the

differences among 30-year climatologies will be examined here. The sequence was designed to start

with the Fortuna AGCM and backtrack, one parameterization change at a time or small groups

31
of parameterization changes as a time, to a model which replicates the MERRA AGCM simulated

climate. The experiments are listed in Table 2.

The control simulation uses the Fortuna version of the GEOS-5 AGCM that was used for for all the

coupled and atmosphere-only simulations performed as part of the Coupled Model Intercomparison

Project (CMIP-5) by the GMAO. The version of the Fortuna AGCM used in the control simulation

is also the version currently in use by the GMAO as part of the operational data assimilation system

used for mission support.

Experiment 1 reverts back to the original formulation for the relationship between ocean surface

roughness and stress used in the Helfand and Schubert surface layer parameterization. In addition,

it reverts to the older setting for the topographic drag. The net effect for simulations at 2.◦ x

2.5◦resolution is expected to result in an increase in surface wind speeds in the mid-range of wind

speeds, that is, in the 5 m s−1to 25 m s−1range. This change also is expected to result in a

net decrease of wind speeds in higher wind regimes, but this impact is not apparent at coarse

resolutions. Surface wind speeds from the Fortuna control and Experiment 1 are shown in figure

56. The change in the simulated surface winds is most apparent in the southern hemisphere, where

GSSTF (56b) shows surface winds near 8 m s−1, Experiment 1 (56d) shows surface winds near 12

m s−1 and the Fortuna control (56a) shows surface winds near 10 m s−1. The difference from the

GSSTF estimate (shown in 56c and 56f) shows a reduction from up to 4 m s−1in Experiment 1

to a difference of up to 2 m s−1in the Fortuna control, pointing out the improvement in AGCM

simulated climate due to the change in roughess formulation.

Experiment 2 combines the impact of two changes in parameterizations that impact regions that

are geographically removed from one another and can be examined independently in a single experiment. These are the changes in the gravity wave drag parameterization of background drag

in the tropics, and the changes in the catchment model’s characterization of two elements of the

surface hydrology. Experiment 2 returns to the MERRA AGCM background drag, and is therefore

expected to exhibit a stratospheric wind with no quasi-biennial oscillation (QBO) variability, and

returns to the MERRA hydrological parameters, which is expected to result in a land surface with

a very high fraction of total evapotranspiration due to canopy interception reservoir evaporation.

The impact of the change in background non-orographic wave drag on the AGCM simulation is

shown by examining the tropical (latitude range 10S to 10N) spatial average of the zonal wind as a

32
function of height and time, figure 57. The patterns of large easterly and westerly winds that slant

downwards in pressure as time proceeds indicate the downward propagation of the variations due

to the QBO. The QBO pattern is seen in figure 57a and 57c, which are the results from Experiment

1 and MERRA reanalysis. Figure 57b, however, which reflects the results of Experiment 2 which

uses the old background drag formulation (from figure 5), shows no QBO pattern of variability.

The impact of the change in the surface hydrology parameters that govern the distribution of evapotranspiration over land surfaces is shown by examining the relative contributions of evaporation

from the canopy interception reservoir and transpiration from vegetation elements to the total

evaporation. By reducing the fractional area of a grid box assumed to be wetted by a precipitation

event, and reducing the capacity of the interception canopy to hold water, the Fortuna simulation

was expected to produce more transpiration and less interception loss. Figures 58a and 58b show

the large increase in the fraction of evaporation due to interception loss in experiment 2 (MERRA

AGCM settings) relative to experiment 1 (Fortuna AGCM settings). The compensating decrease in

the fraction of total evaporation due to transpiration that occured when the settings were reverted

to the ones used in the MERRA AGCM is shown in the comparison of 58c (Fortuna) and 58d

(MERRA AGCM). Reverting to the MERRA AGCM settings resulted, in general, in a net increase

of the total evaporation over land, as is shown in the change between 59a and 59b. The net increase

in total evaporation is attributeable to the increase in the evaporation from canopy interception

reservoir, which is not resisted by vegetation and occurs at the potential evaporation rate. The

important exception to this behavior occurs in the central United States, where experiment 1, with

the Fortuna AGCM settings, shows the larger total evaporation.

Experiment 3 adds to experiment 2 the impact of the change in the surface layer parameterization.

Since experiment 1 as compared to the control evaluated the impact of the changes to the surface

layer parameterization over ocean surfaces, we will evaluate the impact of the change from the Louis

to the Helfand scheme over land surfaces here. The two basic changes discussed in section 2 are

the removal of the viscous sublayer over land in the Helfand scheme, and the change in the stable

surface layer behavior. Figure 60 shows the sensible heat flux from experiments 2 and 3 along with

the difference. Figure 60c shows that over most land surfaces the difference in sensible heat flux is

negative, indicating less sensible heating when using the Louis scheme. This sign of the difference

is consistent with the removal of the viscous sublayer over land surfaces in the Helfand scheme,

which would remove some resistance to turbulent exchange that is present over unvegetated land

surface in the Louis scheme. Figure 60c also shows regions where the sensible heat flux is greater in

33
the Louis scheme than in the Helfand scheme. These are regions where the surface layer is stable,

and where the sensible heat itself is largely downward (that is, the air temperature is greater than

the skin temperature). The change in stability functions between the Louis and Helfand scheme,

which allows more turbulent exchange in the Helfand scheme, is consistent with the sign of the

difference in sensible heat flux in regions where the heat flux is downward.

Experiment 4 adds to experiment 3 the impact of the changes in the atmospheric turbulence

parameterization. These changes were specifically targeted for the impact in coupled atmosphereocean simulations. The impact of removing these changes inhibits the diffusion from the Louis

and Geleyn scheme and increases the diffusion from the Lock scheme in the presence of strong

wind shear. This direct impact of the change in the turbulence parameterization is an increase in

the turbulent diffusion coefficient in the Fortuna AGCM simulation. Figure 61a, the zonal mean

JJA averaged turbulent diffusion coefficient for heat (Kh) from experiment 3, shows larger values

of Kh near the column maximum than seen in figure 61b. The vertical extent of the Kh values

is also larger in 61a. The combined impact of reverting to the MERRA AGCM behavior of the

turbulence scheme also resulted in a reduced east-west gradient of boundary layer depth across the

tropical Pacific. Figure 62 shows the difference (experiment 3 minus experiment 4) of the planetary

boundary layer (PBL) height, averaged over DJF. The figure shows small differences along the

equatorial latitudes in the eastern Pacific (110W to 90W), and larger differences to the west. This

added horizontal gradient of the PBL height in the Fortuna AGCM simulation was designed to

contribute to an improved simulation of the oceanic pycnocline in the equatorial Pacific and the

reduction of a cold bias in the eastern Pacific in coupled atmosphere-ocean simulations.

Experiment 5 adds to experiment 4 the impact of the change in the relative humidity threshold

used in the prognostic cloud scheme for condensation, sublimation and evaporation of suspended

water/ice and precipitation (RHcrit). Removing this change, which for much of the atmosphere

means a larger RHcrit, should result in a simulation that is generally wetter because the atmosphere

is being adjusted back to a higher relative humidity. The zonal mean relative humidity from

experiments 4 and 5, along with the difference, is shown in figure 63. The difference, experiment

5 minus experiment 4, shows a clear increase in relative humidity in the MERRA AGCM due to

the increase in RHcrit. Relative to available observational verification (shown in section 3.2.6), the

Fortuna AGCM shows a general wet bias, which means that the experiment 4 relative humidity field

is closer to the observed than experiment 5’s RH field. In addition to having a substantial impact on

atmospheric moisture levels, the change in RHcrit also had an impact on the distribution of cloud

34
cover. Figure 64 shows the zonal mean cloud cover from experiments 4 and 5, and an observational

estimate of zonal mean cloud cover from AIRS. The MERRA AGCM experiment, experiment 5

(64a) shows increased cloud cover in the 300-600 mb range relative to the Fortuna experiment, in

particular at high latitudes in both hemispheres. In this regard, the Fortuna experiment result

more closely resembles the AIRS cloud cover estimate (64c). The increased cloud cover aloft in

the MERRA AGCM experiment is also consistent with an increased RHcrit there, since the higher

RHcrit results in an atmosphere which is wetter in the mean and has more cloud. The MERRA

AGCM result also shows smaller cloud cover near the boundary layer at almost all latitudes. At

high latitudes, the Fortuna boundary layer cloud more closely resembles the AIRS estimate, in

the tropics the Fortuna boundary layer cloud is larger than AIRS, while the MERRA AGCM

boundary layer cloud is smaller. The change in boundary layer cloud between the MERRA AGCM

and Fortuna is not consistent with the RHcrit change there, but RHcrit is not consistently higher in

Fortuna throughout the boundary layer, and it is also less of a determining factor for model mean

relative humidity in the boundary layer.

Experiment 6 examines perhaps most crucial parameterization change, which is the increase in

the amount of suspended and falling liquid and ice re-evaporation parameterized in the moist

physics. The removal of this change is expected to result in a drier atmosphere, in particular aloft.

This simulation is expected to resemble in large part the climatology of the AGCM used as part of

MERRA. Figure 65 shows the direct impact of the change from the Fortuna AGCM to the MERRA

AGCM re-evaporation, and, as expected, shows the drying related to the reduced re-evaporation in

the MERRA AGCM. Figures 65a-c show the change in total precipitable water, and the difference

(MERRA AGCM minus Fortuna, shown in 65c), is negative always everywhere. The vertical

distribution of the moisture is shown in figures 65d-f, and the difference plot (65f) also shows an

almost global reduction in atmospheric water vapor. The resulting mean circulation in boreal winter

underwent a substantial change associated with this drying, and represents the most substantial

impact on the simulated climate of all the elements of the MERRA AGCM to Fortuna AGCM

transition. Figure 66 shows the subtantial impact that changing the moisture levels had on the

3-year averaged December-January-February total precipitation. Given the GPCP climatology as a

reference, the Fortuna AGCM (experiment 5, figure 66d) exhibits an Intertropical Convergence Zone

(ITCZ) structure that is properly placed in longitude, a South Pacific Convergence Zone (SPCZ)

that properly slants from the western Pacific to the southeast, and more accurately simulated storm

track maxima. The precipitation field represents improvements in all these areas related to the

MERRA AGCM-like simulation (experiment 6) shown in figure 66a. The change in the tropical

35
precipitation field related to the additional drying in experiment 6 also resulted in substantial

changes in the Pacific teleconnection patterns, specifically the Pacific-North-America (PNA) pattern

in boreal winter. The eddy height field is an indicator of the strength of the PNA, and is shown in

figure 67 in relation to the eddy height from MERRA reanalysis. The Fortuna AGCM simulated

PNA pattern (figure 66d) has a stronger and more properly oriented ridge near the west coast of

North America relative to the PNA as simulated by the MERRA AGCM (figure 66a). This change

has implications for the poleward propagation of heat and momentum. The standard deviation

of the difference from MERRA is also substantially reduced in experiment 5 (16.8 m) relative to

experiment 6 (20.3 m). The direct impact of the change in re-evaporation was also evident in the

boreal summer climatology. Figure 68 shows this both in the total precipitable water fields and in

the specific humidity fields, which exhibit differences between experiments 5 and 6 that are of the

order of the differences seen in boreal winter. The impact on the mean summertime circulation,

however, was minimal, and the errors seen in the MERRA AGCM precipitation and wind fields are

apparent in the Fortuna AGCM as well (shown in sections 3.3.2 and 3.2).

Experiment 7 is the last experiment in the series, incorporating the effect of all the fundamental

changes between MERRA and Fortuna AGCM. The final parameterization change included in Experiment 7 is the removal of the intermittency in the gravity wave drag scheme. The intermittency

factor was increased in Fortuna, and the removal of this change is therefore expected to decrease the

orographically induced drag in the southern hemisphere, thereby depositing less momentum aloft

and increasing the strength of the westerlies. The zero-wind contour in the southern hemisphere

can be used as an indicator of the level and timing of the stratospheric jet breakup. Figure 69 shows

the zero-wind contour from experiment 7, experiment 6, and MERRA. At the 2 degree horizontal

resolution of the simulations described here, the zero wind line is higher in altitude and delayed

in time relative to MERRA in both simulations, reflecting a delayed stratospheric jet breakup.

The decreased intermittency factor in experiment 7, however, delays the jet breakup even more,

showing the improvement in the Fortuna simulation relative to the MERRA AGCM simulation.

At higher spatial resolution (not shown) the increase of intermittency factor in Fortuna is effective

in producing a reasonable evolution of the polar vortex breakdown in the southern Hemisphere.

36
5 Synthesis and Discussion

A summary of the atmosphere-only climate simulations of the version of the GEOS-5 AGCM that

was used as part of CMIP5 simulations has been presented here. In addition, the developments

since the GEOS-5 AGCM version used as part of MERRA that lead to substantial improvements

in simulated climate were characterized. Based on comparisons with available observational estimates of the climate, there are two central remaining areas for development: the cloud radiative

interactions and the surface energy and momentum fluxes over the oceans.

The issues with the clouds and radiation were discussed in sections 3.4 and 3.4.2 and can be

summarized as follows:

• In the tropics: There is too much cloud cover, too much shortwave cloud radiative forcing

(defined in equation 3), and too little longwave cloud radiative forcing. We suspect from the

similarity in error patterns of the shortwave cloud forcing and the cloud amount that the

parameterization of optical depth is adequate, and that a reduction of cloud amount would

reduce both types of error. The behavior in the longwave, however, with too much cloud

cover and not enough cloud forcing, indicates that the cloud tops are too low in the model

atmosphere. Higher cloud tops which radiate at lower temperatures would result in outgoing

longwave radiation minima that are more consistent with observations.

• In the mid-latitudes: There is too little cloud cover and too little CRE both in the shortwave

and in the longwave. We conclude that the cloud properties are adequately simulated, but

the cloud amounts need adjusting/tuning.

• In the high-latitudes: There is too much cloud cover and too little CRE in the shortwave.

From this it is concluded that the optical depths are too thin. An adjustment is needed in

both the optical depths and cloud amounts.

The errors in GEOS-5 AGCM climate in the mid- and high latitudes can be partially addressed

by adjusting several of the thresholds in the parameterization of cloud-radiative interactions. The

tropical excess of outgoing longwave radiation at the top of the atmosphere in GEOS-5 AGCM

simulations is attributed to cloud tops that are too low in the atmosphere. This is connected to the

behavior of the cumulus parameterization and is more difficult to address than the errors in the cloud

37
amount and optical properties. There are other comparisons against observations that support the

conclusion that the vertical extent of convection as simulated by the cumulus parameterization

is not adequate. The detrainment of cloud water by the cumulus parameterization is a primary

mechanism by which water vapor reaches the upper troposphere. Comparison of specific humidity

in the upper troposphere against MERRA indicates that GEOS-5 AGCM is excessively dry. At

200 mb GEOS-5 AGCM values are half of what is estimated by the MERRA reanalysis, and at 150

and 100 mb GEOS-5 AGCM values are almost an order of magnitude too low. This excessively

dry upper troposphere is also evident in the relative humidity field.

More fundamental improvements in the clouds and cloud radiative properties are possible in the

context of the next generation AGCM. The inclusion of a 2-moment description of cloud microphysics along with an algorithm for the interaction between aerosol and cloud formation allows for

a more accurate geographical distribution of cloud effects. In addition, the next generation AGCM

will be run on an enhanced vertical grid, allowing for the possibility of an improved simulation of

boundary layer clouds.

A Verification Data Sources

Many of the model derived quantities which are important to climate studies are quite difficult

to measure, and observational data on the climate scale is scarce. Much of the observational

data is also indirect, and neccesitates the use of additional parameterizations and algorithms to

derive the actual fields used for verification. The uncertainties in these data, therefore, must be

considered when comparing them with the AGCM. The combination of satellite and ground based

observations chosen for verification here are considered as reliable standards by which to judge the

AGCM climate.

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 NASAs 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.

38
ERA-Interim (ECinterim, Dee et al., 2011) is used for a second estimate of model-dependant fields,

including humidity, meridional wind and related fields at a resolution of approximately 0.7

◦ and 61

levels. It is a reanalysis by the 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.

Validating data for ocean surface values of evaporation, windstress, wind speed, and net heat flux

are obtained from the Goddard Satellite-based Surface Turbulent Fluxes dataset (GSSTF version

2b, Shie et al. 2009), which covers 1987 through 2008 and was created for global energy and

water cycle research. The dataset values are derived from a combination of surface wind, total

precipitable water, and microwave brightness fields from the Special Sensor Microwave Imager

(SSM/I) Version-6 satellite product, and sea surface skin temperature, 2-meter air temperature,

and sea-level pressure from the NCEP/DOE Reanalysis-2. These fields are used as input to the

GSSTF bulk flux model, which produces daily global fields on a 1◦x1◦ grid.

The observational estimate for total precipitation is 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.

Data for verifying monthly mean land surface temperatures are obtained from the Climate Research

Unit (CRU) TS 3.0 dataset (Mitchell and Jones 2005). The dataset produced at the Climate Research Unit at the University of East Anglia, and is intended to be used for study of regional

climate variability. It is constructed from ground station records that have been corrected for inhomogenities using the GHCN method modified to handle incomplete station records and neigboring

stations that only partly overlap in time. The results are interpolated to a 0.5

◦

lat-lon grid with

zero-anomaly “dummy” stations inserted into regions of no stations.

Verification data for top-of-atmosphere (TOA) radiative fluxes are from the Clouds and the Earth’s

39
Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) data set. These data were

obtained from the NASA Langley Research Center Atmospheric Science Data Center (Wielicki, et

al,1996). The EBAF product provides monthly mean TOA radiative fluxes constrained such that

the 5-year averaged global net TOA flux is consistent with the best estimate of heat storage in the

Earth-atmosphere system ( 0.9 W m−2). CERES EBAF is primarily intended for studies that use

Earth Radiation Budget (ERB) data for climate model evaluation, estimating the Earth’s annual

global mean energy budget, and in studies that infer meridional heat transports. The CERES

EBAF dataset consists of monthly 1◦ by 1◦regional, zonal, and global averages of TOA longwave

(LW), shortwave (SW), and net (NET) fluxes under clear and all-sky conditions. It is derived

from the Terra SRBAVG-GEO Edition2D-rev1 and SSF Edition2B-rev1 data products. Temporal

coverage for the EBAF Terra Edition1A data set is from March 2000 through October 2005.

The Surface Radiation Budget (SRB) calculations of Pinker and Laszlo (1992) are used as verification for the surface shortwave fluxes and the cloud amount. The SRB algorithms use measurements

from the ERBE and International Satellite Cloud Climatology Project (ISCCP) satellites and a

radiative transfer scheme to produce surface shortwave fluxes. The radiative transfer algorithm

accounts for absorption by ozone and water vapor, for scattering by molecules, aerosols and cloud

drops, and allows for multiple reflections between the atmosphere and the surface. The cloud

fraction is the ratio of the number of ISCCP cloudy sky pixels to the number of total sky pixels.

The radiative fluxes derived from these methods were verified in select locations against land-based

direct measurements and are considered accurate to within approximately 5 W m−2.

The Comprehensive Ocean-Atmosphere Data Set (COADS) was used by da Silva et al (1995) to

estimate the various terms of the surface energy budget, surface wind stresses, cloud cover, precipitation, and other surface fields over the tropical and extratropical oceans. Observations taken

during 1945-1989 were corrected for systematic biases and subjected to a quality control, and

the derived quantities were interpolated to a 1◦ by 1◦ grid, filling in data gaps using a sinusoidal

interpolation. The wind stresses and surface fluxes were calculated using a similarity theory parameterization that calculates the transfer coefficients according to Large and Pond (1981 and 1982).

The precipitation estimates were made using Tucker’s (1961) regression formula based on “present

weather” observations with the tropical correction of Dorman and Bourke (1978). Surface radiation

estimates were made using empirical formulae to approximate cloud attenuation, surface albedo

and atmospheric transmissivity.

40
The net surface heat flux over the ocean is verified with the OAFlux product by Woods Hole

Oceanographic Institution (WHOI, Yu et al. 2008). This dataset is produced with a bulk flux

model using for input a blend of satellite retrievals and atmopsheric reanalyses and results in a 1◦

gridded daily and monthly mean field.

B Acknowledgements

The GEOS-5 AGCM development in the Global Modeling and Assimilation Office is funded by

NASAs Modeling, Analysis and Prediction (MAP) program under WBS 802678.02.17.01.211 and

802678.02.17.01.25. The authors gratefully acknowledge the support of David Considine, the MAP

project manager. We appreciate the contribution of the many others in the GMAO who participated in various key ways to the development of the GEOS-5 AGCM. The analysis of GEOS-5

AGCM simulations at many spatial and temporal scales was performed by Siegfried Schubert,

Yehui Chang and Myong-In Lee, and the analysis of the GCM performance in data assimilation

and numerical weather prediction modes was performed by Stephen Bloom, Gary Partyka and

Austin Conaty. The feedback from William Putman about the performance of the AGCM at very

high horizontal resolution was critical in the ability to run the GEOS-5 AGCM seamlessly across a

wide range of resolutions, and the analysis of the transport of tracers done by Lesley Ott aided in

the understanding of model errors. In addition, the feedback to AGCM development from GMAO

scientists involved in the development of the other component models of the GEOS-5 GCM, including Yury Vikhaelev, Bin Zhao, J. Eric Nielsen, Arlindo da Silva, Randal Koster, Rolf Reichle

and Sarith Mahanama, was invaluable. In addition, the efforts of Peter Norris in the development

and testing of the satellite simulator component of the GCM are appreciated. The authors also

gratefully acknowledge the contributions to the AGCM infrastructure of Atanas Tryanov, which

made the AGCM tractable and portable to different platforms, and the efforts of Dan Kokron to

implement portability. Both of these contributions made the simulations possible. The simulations

were performed at the the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight

Center and at the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.

The authors gratefully acknowledge the support of personnel at both of those computing centers.

41
Model Changes from MERRA to Fortuna

Module Algorithm Change Comments

Moist Increased re-evaporation of precipitation Fundamental change in model climate

Modified autoconversion Fundamental change in model climate

Modified effective radius of cloud drops Fundamental change cloud forcing

Anvil fractions cut in half Fundamental change cloud forcing

Autoconvert warm fog Important change in coupled simulations

New critical RH with resolution dependance Substantial change in simulated moisture

Cloud base set at PBL depth Remove clouds detraining below PBL height

RAS time scale no longer depends on turbulence

Stochastic RAS with resolution dependance Substantial impact at high resolution

Turb Remove restrictions on diffusion from Louis Increase near surface diffusion

Reformulate turbulent length scale in Louis

Reduce Lock scheme when there is wind shear Impact on marine PBL

Reduce cloud top entrainment for Lock plumes Impact on marine PBL

Surf Implement Helfand and Schubert scheme

Remove viscous sublayer over land surfaces Improve land temperatures

Change ocean roughness for middle wind regimes Reduce wind bias in S. Ocean

Change ocean roughness high wind regimes Increase tropical cyclone intensity

Land Surf Changed parameters for evapotranspiration Impact on ratio of surface to canopy evaporation

GW Drag Changed profile of background drag Substantial impact on QBO

Added intermittency of drag Impact on timing of winter jet breakup

Table 1: Changes in GCM Algorithms from MERRA to Fortuna

42
Sensitivity Experiments

Experiment Description

Control

Exp 1 Back off change in ocean roughness

Exp 2 Exp 1 + Back off gravity wave background drag and surface hydrology

Exp 3 Exp 2 + use old surface layer parameterization

Exp 4 Exp 3 + Back off increase of Richardson-number diffusion

Exp 5 Exp 4 + Back off decrease of critical RH aloft and decrease below

Exp 6 Exp 5 + Back off increase of all re-evaporation

Exp 7 Exp 6 + Back off gravity wave drag intermittency

Table 2: Experiments to attribute MERRA to Fortuna AGCM simulation changes to changes in

parameterizations.

43
Figure 1: The difference (Fortuna AGCM minus MERRA AGCM) of zonal mean specific humidity

source term due to all re-evaporation for December-January-February.

44
Figure 2: Critical relative humidity. Black from MERRA AGCM formulation at different latitudes,

green from Fortuna AGCM formulation for 1◦resolution, and red from Fortuna AGCM formulation

for 2◦resolution.

45
Figure 3: Surface bulk Richardson number as a function of sensible heat flux in W m−2

in a single

July from experiment 3 (red) and experiment 2 (black).

46
Figure 4: Scatter diagram of surface wind speed (m sec−1

) versus ocean roughness (m) in the

MERRA and Fortuna AGCMs. a) diagram focusing on medium range of wind speeds, Fortuna

shown in black and MERRA in green, and b) diagram extending to high wind regimes, MERRA

shown in green and Fortuna in black.

47
Figure 5: Background nonorographic drag from the MERRA and Fortuna AGCM simulations

48
Figure 6: December-January-February Zonal Wind Climatology in m sec−1

from a) GEOS-5 AGCM

b) MERRA and c) the difference, GEOS-5 - MERRA.

49
Figure 7: June-July-August zonal mean zonal wind in m sec−1

from a) GEOS-5 AGCM b) MERRA

and c) the difference, GEOS-5 - MERRA.

50
Figure 8: December-January-February Temperature Climatology in K from a) GEOS-5 AGCM b)

MERRA and c) the difference, GEOS-5 - MERRA.

51
Figure 9: June-July-August Temperature Climatology in K from a) GEOS-5 AGCM b) MERRA

and c) the difference, GEOS-5 - MERRA.

52
Figure 10: December-January-February Meridional Wind Climatology in m sec−2 a) from GEOS-5

AGCM, b) from MERRA Reanalysis, c) ECinterim reanalysis, d) GEOS-5 AGCM - MERRA, e)

GEOS-5 AGCM - ECinterim, f) MERRA - ECinterim.

53
Figure 11: June-July-August Meridional Wind Climatology in m sec−2 a) from GEOS-5 AGCM,

b) from MERRA Reanalysis, c) ECinterim reanalysis, d) GEOS-5 AGCM - MERRA, e) GEOS-5

AGCM - ECinterim, f) MERRA - ECinterim.

54
Figure 12: Meridional Mass Streamfunction in 108 kg sec−1 during December-January-February

from a) GEOS-5 AGCM, b) MERRA, and c) EC-Interim, and during June-July-August from d)

GEOS-5 AGCM, e) MERRA, and f) EC-Interim.

55
Figure 13: December-January-February Surface Wind Speed in m sec−1

from a) GEOS-5 AGCM

b) GSSTF and c) the difference, GEOS-5 - GSSTF.

56
Figure 14: June-July-August Surface Wind Speed in m sec−1

from a) GEOS-5 AGCM b) GSSTF

and c) the difference, GEOS-5 - GSSTF.

57
Figure 15: December-January-February 300 mb Eddy Height Climatology in m from a) GEOS-5

AGCM b) MERRA and c) the difference, GEOS-5 - MERRA.

58
Figure 16: December-January-February 200 mb Velocity Potential Climatology in from a) GEOS-5

AGCM b) MERRA and c) the difference, GEOS-5 - MERRA.

59
Figure 17: June-July-August 200 mb Velocity Potential Climatology in from a) GEOS-5 AGCM

b) MERRA and c) the difference, GEOS-5 - MERRA.

60
Figure 18: December-January-February Specific Humidity Climatology in g kg−1

a) from GEOS-5

AGCM, b) from MERRA Reanalysis, c) ECinterim reanalysis, d) GEOS-5 AGCM - MERRA, e)

GEOS-5 AGCM - ECinterim, f) MERRA - ECinterim.

61
Figure 19: June-July-August Specific Humidity Climatology in g kg−1

a) from GEOS-5 AGCM,

b) from MERRA Reanalysis, c) ECinterim reanalysis, d) GEOS-5 AGCM - MERRA, e) GEOS-5

AGCM - ECinterim, f) MERRA - ECinterim.

62
Figure 20: December-January-February Relative Humidity Climatology in percent a) from GEOS-5

AGCM, b) from MERRA Reanalysis, c) ECinterim reanalysis, d) GEOS-5 AGCM - MERRA, e)

GEOS-5 AGCM - ECinterim, f) MERRA - ECinterim.

63
Figure 21: June-July-August Relative Humidity Climatology in percent a) from GEOS-5 AGCM,

b) from MERRA Reanalysis, c) ECinterim reanalysis, d) GEOS-5 AGCM - MERRA, e) GEOS-5

AGCM - ECinterim, f) MERRA - ECinterim.

64
Figure 22: Oceans only zonal mean Latent Heat Flux W m−2

from GEOS-5 AGCM (blue) and

GSSTF (black) during a) December-January-February and b) June-July-August.

65
Figure 23: December-January-February Latent Heat Flux W m−2

from a) GEOS-5 AGCM b)

GSSTF and c) the difference, GEOS-5 - GSSTF.

66
Figure 24: June-July-August Latent Heat Flux in W m−2

from a) GEOS-5 AGCM b) GSSTF and

c) the difference, GEOS-5 - GSSTF.

67
Figure 25: Zonal mean total precipitation in mm day−1

from GEOS-5 AGCM (blue) and GPCP

(black) during a) December-January-February and b) June-July-August.

68
Figure 26: December-January-February Precipitation mm day

−1

from a) GEOS-5 AGCM b) GPCP

and c) the difference, GEOS-5 - GPCP.

69
Figure 27: June-July-August Precipitation in mm day−1

from a) GEOS-5 AGCM b) GPCP and c)

the difference, GEOS-5 - GPCP.

70
Figure 28: Oceans only zonal mean net humidity flux at the surface (Evaporation - Precipitation)

mm day−1from GEOS-5 AGCM (blue) and COADS (black) during a) December-January-February

and b) June-July-August.

71
Figure 29: December-January-February Total Precipitable Water g cm−2

from a) GEOS-5 AGCM

b) SRB and c) the difference, GEOS-5 - SRB.

72
Figure 30: June-July-August Total Precipitable Water in g cm−2

from a) GEOS-5 AGCM b) SRB

and c) the difference, GEOS-5 - SRB.

73
Figure 31: Zonal mean net shortwave radiation at the top of the atmosphere (incoming-outgoing)

in W m−2from GEOS-5 AGCM (blue) and CERES (black) during a) December-January-February

and b) June-July-August.

74
Figure 32: December-January-February net shortwave radiation at the top of the atmosphere in W

m−2from a) GEOS-5 AGCM b) CERES and c) the difference, GEOS-5 - CERES.

75
Figure 33: June-July-August net shortwave radiation at the top of the atmosphere in W m−2

from

a) GEOS-5 AGCM b) CERES and c) the difference, GEOS-5 - CERES.

76
Figure 34: Zonal mean outgoing longwave radiation at the top of the atmosphere in W m−2

from

GEOS-5 AGCM (blue) and CERES (black) during a) December-January-February and b) JuneJuly-August.

77
Figure 35: December-January-February outgoing longwave radiation at the top of the atmosphere

in W m−2from a) GEOS-5 AGCM b) CERES and c) the difference, GEOS-5 - CERES.

78
Figure 36: June-July-August outgoing longwave radiation at the top of the atmosphere in W m−2

from a) GEOS-5 AGCM b) CERES and c) the difference, GEOS-5 - CERES.

79
Figure 37: Zonal mean net radiation at the top of the atmosphere (incoming-outgoing) in W m−2

from GEOS-5 AGCM (blue) and CERES (black). during a) December-January-February and b)

June-July-August.

80
Figure 38: Zonal mean shortwave cloud radiative effect (outgoing shortwave - clear sky outgoing

shortwave) at the top of the atmosphere in W m−2from GEOS-5 AGCM (blue) and CERES (black)

during a) December-January-February and b) June-July-August.

81
Figure 39: December-January-February shortwave cloud radiative effect (outgoing shortwave - clear

sky outgoing shortwave) at the top of the atmosphere in W m−2from a) GEOS-5 AGCM b) CERES

and c) the difference, GEOS-5 - CERES.

82
Figure 40: June-July-August shortwave shortwave cloud radiative effect (outgoing shortwave - clear

sky outgoing shortwave) at the top of the atmosphere in W m−2from a) GEOS-5 AGCM b) CERES

and c) the difference, GEOS-5 - CERES.

83
Figure 41: Zonal mean longwave cloud radiative effect (clear sky outgoing longwave - outgoing

longwave) at the top of the atmosphere in W m−2from GEOS-5 AGCM (blue) and CERES (black)

during a) December-January-February and b) June-July-August.

84
Figure 42: December-January-February longwave cloud radiative effect (clear sky outgoing longwave - outgoing longwave) at the top of the atmosphere in W m−2

from a) GEOS-5 AGCM b)

CERES and c) the difference, GEOS-5 - CERES.

85
Figure 43: June-July-August longwave longwave cloud radiative effect (clear sky outgoing longwave

- outgoing longwave) at the top of the atmosphere in W m−2from a) GEOS-5 AGCM b) CERES

and c) the difference, GEOS-5 - CERES.

86
Figure 44: Zonal mean total cloud fraction from GEOS-5 AGCM (blue) and SRB (black) during

a) December-January-February and b) June-July-August.

87
Figure 45: December-January-February total cloud fraction from a) GEOS-5 AGCM b) SRB and

c) the difference, GEOS-5 - SRB.

88
Figure 46: June-July-August total cloud fraction from a) GEOS-5 AGCM b) SRB and c) the

difference, GEOS-5 - SRB.

89
Figure 47: Zonal mean net radiation at the surface W m−2

from GEOS-5 AGCM (blue) and SRB

(black) during a) December-January-February and b) June-July-August.

90
Figure 48: December-January-February net radiation at the surface in W m−2

from a) GEOS-5

AGCM b) SRB and c) the difference, GEOS-5 - SRB.

91
Figure 49: June-July-August net radiation at the surface in W m−2

from a) GEOS-5 AGCM b)

SRB and c) the difference, GEOS-5 - SRB.

92
Figure 50: December-January-February Sensible Heat Flux W m−2

from a) GEOS-5 AGCM b)

GSSTF and c) the difference, GEOS-5 - GSSTF.

93
Figure 51: June-July-August Sensible Heat Flux in W m−2

from a) GEOS-5 AGCM b) GSSTF

and c) the difference, GEOS-5 - GSSTF.

94
Figure 52: Annual mean (over oceans only) net surface heating in W m−2

from a) GEOS-5 AGCM

b) WHOI and c) the difference, GEOS-5 - WHOI.

95
−80 −60 −40 −20 0 20 40 60 80

−3

−2

−1

0

1

2

3

Latitude

Implied Ocean Heat Transport (PW)

Figure 53: Implied Ocean Heat Transport in PW from GEOS-5 AGCM (black), from Ganachaud

and Wunsch(2000) (green) and from Trenberth and Caron (2001) (blue).

96
Figure 54: December-January-February Land Surface Temperature in K from a) GEOS-5 AGCM

b) CRU TS and c) the difference, GEOS-5 - CRU TS.

97
Figure 55: June-July-August Land Surface Temperature in K from a) GEOS-5 AGCM b) CRU TS

and c) the difference, GEOS-5 - CRU TS.

98
Figure 56: 30-year average December-January-February surface wind speed (m sec−1

) from: a)

Fortuna control, b) GSSTF, c) Control-GSSTF, d) Experiment 1, e) GSSTF, f) Experiment 1-

GSSTF

99
Figure 57: Spatial average of zonal wind in m s−1

from 10S to 10N latitude as a function of pressure

level in mb and time from a) Fortuna control, b) Experiment 2 and c) MERRA reanalysis.

100
Figure 58: 30-year average June-July-August evaporation fractions. a) Fraction of evaporation

from interception canopy in experiment 1 (Fortuna control), b) same as a) but from experiment 2,

c) fraction of evaporation from transpiration in experiment 1, d) same as c) but from experiment

2.

101
Figure 59: June-July-August averaged evaporation in mm day−1 over land surfaces from a) Experiment 1 (Fortuna), b) experiment 2 (MERRA AGCM) and c) the difference, experiment 1 minus

experiment 2.

102
Figure 60: June-July-August averaged sensible heat flux in W m−2

from a) Experiment 3 (Louis

scheme), b) experiment 2 (Helfand scheme) and c) the difference, experiment 3 minus experiment

2.

103
Figure 61: 30-year average June-July-August turbulent exchange coefficient for heat in m sec−2

from a) experiment 3 (Fortuna control), and b) from experiment 4.

104
Figure 62: December-January-February averaged planetary boundary layer height difference in m,

experiment 3 minus experiment 4.

105
Figure 63: December-January-February averaged relative humidity in percent from a) Experiment 5

(MERRA AGCM), b) experiment 4 (Fortuna) and c) the difference, experiment 5 minus experiment

4.

106
Figure 64: December-January-February averaged cloud fraction from a) Experiment 5 (MERRA

AGCM), b) experiment 4 (Fortuna) and c) AIRS retreivals

107
Figure 65: a) December-January-February total precipitable water in mm from experiment 6

(MERRA AGCM), b) same as a) but from experiment 5, c) same as a) but the difference, experiment 6 minus experiment 5, d) December-January-February specific humidity in g kg−1

from

experiment 6 (MERRA AGCM), e) same as d) but from experiment 5, f) same as d) but the

difference, experiment 6 minus experiment 5.

108
Figure 66: December-January-February total precipitation in mm day−1

from a) experiment 6

(MERRA AGCM), b) GPCP, c) the difference, experiment 6 minus GPCP, d) experiment 5 (Fortuna), e) GPCP, and f) the difference, experiment 5 minus GPCP.

109
Figure 67: December-January-February 300 mb Eddy Height Climatology in m from a) experiment

6 (MERRA AGCM), b) MERRA, c) the difference, experiment 6 minus MERRA, d) experiment 5

(Fortuna), e) MERRA, and f) the difference, experiment 5 minus MERRA.

110
Figure 68: a) June-July-August total precipitable water in mm from experiment 6 (MERRA

AGCM), b) same as a) but from experiment 5, c) same as a) but the difference, experiment 6

minus experiment 5, d) June-July-August specific humidity in g kg−1from experiment 6 (MERRA

AGCM), e) same as d) but from experiment 5, f) same as d) but the difference, experiment 6 minus

experiment 5.

111
Figure 69: 30-year averaged annual cycle of zonal mean zonal wind, averaged from 70S to 50S

latitude in m sec−2from Experiment 7 in red (MERRA AGCM), experiment 6 in blue (Fortuna)

and MERRA in green

112
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115
 Previous Volumes in This Series

Volume 1 Documentation of the Goddard Earth Observing System (GEOS) General 

September 1994 Circulation Model–Version 1 

 L.L. Takacs, A. Molod, and T. Wang

Volume 2 Direct Solution of the Implicit Formulation of Fourth Order Horizontal 

October 1994 Diffusion for Gridpoint Models on the Sphere 

 Y. Li, S. Moorthi, and J.R. Bates 

Volume 3 An Efficient Thermal Infrared Radiation Parameterization for use in 

December 1994 General Circulation Models 

 M.-D. Chou and M.J. Suarez 

Volume 4 Documentation of the Goddard Earth Observing System (GEOS) Data 

January 1995 Assimilation System–Version 1 

 James Pfaendtner, Stephen Bloom, David Lamich, Michael Seablom,

 Meta Sienkiewicz, James Stobie, and Arlindo da Silva 

Volume 5 Documentation of the Aries-GEOS dynamical core: Version 2 

April 1995 Max J. Suarez and Lawrence L. Takacs 

Volume 6 A Multiyear Assimilation with the GEOS-1 System: 

April 1995 Overview and Results 

 Siegfried Schubert, Chung-Kyu Park, Chung-Yu Wu, Wayne 

 Higgins, Yelena Kondratyeva, Andrea Molod, Lawrence Takacs, 

 Michael Seablom, and Richard Rood 

Volume 7 Proceedings of the Workshop on the GEOS-1 Five-Year Assimilation 

September 1995 Siegfried D. Schubert and Richard B. Rood 

Volume 8 Documentation of the Tangent Linear Model and Its Adjoint of the 

March 1996 Adiabatic Version of the NASA GEOS-1 C-Grid GCM: Version 5.2 

 Weiyu Yang and I. Michael Navon 

Volume 9 Energy and Water Balance Calculations in the Mosaic LSM 

March 1996 Randal D. Koster and Max J. Suarez

Volume 10 Dynamical Aspects of Climate Simulations Using the GEOS General 

April 1996 Circulation Model 

 Lawrence L. Takacs and Max J. Suarez 

Volume 11 Documentation of the Tangent Linear and its Adjoint Models of the 

May 1997 Relaxed Arakawa-Schubert Moisture Parameterization Package of the 

NASA GEOS-1 GCM (Version 5.2) 

Weiyu Yang, I. Michael Navon, and Ricardo Todling

Volume 12

 August 1997 

Comparison of Satellite Global Rainfall Algorithms 

Alfred T.C. Chang and Long S. Chiu 

Volume 13 Interannual Variability and Potential Predictability in 

December 1997 Reanalysis Products 

 Wie Ming and Siegfried D. Schubert 

Volume 14 A Comparison of GEOS Assimilated Data with FIFE Observations 

August 1998 Michael G. Bosilovich and Siegfried D. Schubert 

 

 

 

 

 

 

 

 
Volume 15 A Solar Radiation Parameterization for Atmospheric Studies 

June 1999 Ming-Dah Chou and Max J. Suarez 

Volume 16 Filtering Techniques on a Stretched Grid General Circulation Model 

November 1999 Lawrence Takacs, William Sawyer, Max J. Suarez, and 

 Michael S. Fox-Rabinowitz 

Volume 17 Atlas of Seasonal Means Simulated by the NSIPP-1 Atmospheric GCM 

July 2000 Julio T. Bacmeister, Philip J. Pegion, Siegfried D. Schubert, and 

 Max J. Suarez 

Volume 18 An Assessment of the Predictability of Northern Winter Seasonal Means 

December 2000 with the NSIPP 1 AGCM 

 Philip J. Pegion, Siegfried D. Schubert, and Max J. Suarez 

Volume 19 A Thermal Infrared Radiation Parameterization for Atmospheric Studies 

July 2001 Ming-Dah Chou, Max J. Suarez, Xin-Zhong, and Michael M.-H. Yan 

Volume 20 The Climate of the FVCCM-3 Model 

August 2001 Yehui Chang, Siegfried D. Schubert, Shian-Jiann Lin, Sharon Nebuda,

 and Bo-Wen Shen 

Volume 21 Design and Implementation of a Parallel Multivariate Ensemble Kalman 

September 2001 Filter for the Poseidon Ocean General Circulation Model 

 Christian L. Keppenne and Michele M. Rienecker 

Volume 22 Coupled Ocean-Atmosphere Radiative Model for Global Ocean 

August 2002 Biogeochemical Models 

 Watson W. Gregg 

Volume 23 Prospects for Improved Forecasts of Weather and Short-term Climate 

November 2002 Variability on Subseasonal (2-Week to 2-Month) Time Scales 

 Siegfried D. Schubert, Randall Dole, Huang van den Dool, Max J. Suarez,

 and Duane Waliser 

Volume 24 Temperature Data Assimilation with Salinity Corrections: Validation for 

July 2003 the NSIPP Ocean Data Assimilation System in the Tropical Pacific 

Ocean, 1993–1998 

 Alberto Troccoli, Michele M. Rienecker, and Christian L. Keppenne, 

Volume 25

December 2003

Modeling, Simulation, and Forecasting of Subseasonal Variability

Duane Waliser, Siegfried D. Schubert, Arun Kumar, Klaus Weickmann, and 

Randall Dole 

Volume 26 

April 2005 

Documentation and Validation of the Goddard Earth Observing System 

(GEOS) Data Assimilation System – Version 4 

Senior Authors: S. Bloom, A. da Silva and D. Dee 

 Contributing Authors: M. Bosilovich, J-D. Chern, S. Pawson, S. Schubert, 

M. Sienkiewicz, I. Stajner, W-W. Tan, and M-L. Wu and Gregory C. Johnson 

Volume 27

December 2008

The GEOS-5 Data Assimilation System—Documentation of Versions 5.0.1, 

5.1.0, and 5.2.0 

Editor: M.J. Suarez

Contributing Authors: M.M. Rienecker, M.J. Suarez, R. Todling, J. Bacmeister, 

L. Takacs, H.-C. Liu, W. Gu, M. Sienkiewicz, R.D. Koster, R. Gelaro, I. Stajner, 

and J.E. Nielsen

 

 
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30-04-2012 Technical Memorandum

Technical Report Series on Global Modeling and Data Assimilation,

Volume 28, The GEOS-5 Atmospheric General Circulation Model: Mean

Climate and Development from MERRA to Fortuna

Max J. Suarez, Editor

Andrea Molod, Lawrence Takacs, Max Suarez, Julio Bacmeister, In-Sun

Song, Andrew Eichmann

NASA, Goddard Space Flight Center

Greenbelt, MD 20771

National Aeronautics and Space Administration

Washington, DC 20546-0001

NASA/GSFC

TM–2012-104606/Vol28

Unclassified-Unlimited, Subject Category: 47

Report available from the NASA Center for Aerospace Information, 7115 Standard Drive, Hanover, MD 21076. (443)757-5802

This report is a documentation of the Fortuna version of the GEOS-5 Atmospheric General Circulation Model (AGCM). The GEOS-5

AGCM is currently in use in the NASA Goddard Modeling and Assimilation Office (GMAO) for simulations at a wide range of

resolutions, in atmosphere only, coupled ocean-atmosphere, and data assimilation modes. The focus here is on the development

subsequent to the version that was used as part of NASA’s Modern-Era Retrospective Analysis for Research and Applications

(MERRA). We present here the results of a series of 30-year atmosphere-only simulations at different resolutions, with focus on the

behavior of the 1-degree resolution simulation. The details of the changes in parameterizations subsequent to the MERRA model

version are outlined, and results of a series of 30-year, atmosphere-only climate simulations at 2-degree resolution are shown to

demonstrate changes in simulated climate associated with specific changes in parameterizations. The GEOS-5 AGCM presented here

is the model used for the GMAO’s atmosphere-only and coupled CMIP-5 simulations.

Meteorology, Climatology, Modeling

 Unclassified Unclassified Unclassified

Unclassified

117

Max Suarez

(301) 614-5292