NASA/TM-2000-104606, Vol. 17

)

Technical Report Series on

Global Modeling and Data Assimilation

Max J. Suarez, Editor

Volume 17

Atlas of Seasonal Means Simulated by the

NSIPP 1 Atmospheric GCM

Julio Bacmeister, Philip J. Pegion, Siegfried D. Schubert, and Max J. Suarez

July 2000
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NASA/TM-2000-104606, Vol. 17

Technical Report Series on

Global Modeling and Data Assimilation

Max J. Suarez, Editor

Goddard Space Flight Center, Greenbelt, Maryland

Volume 17

Atlas of Seasonal Means Simulated by the

NSIPP 1 Atmospheric GCM

Julio Bacmeister

Universities Space Research Associates

Philip J. Pegion

General Sciences Corporation, Laurel, Maryland

Siegfried D. Schubert

Data Assimilation Office, Goddard Space Flight Center, Greenbelt, Maryland

Max J. Suarez

Climate and Radiation Branch

NASA Seasonal to Interannual Predication Project

Goddard Space Flight Center, Greenbelt, Maryland

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland 20771

July 2000
NASA Center for AeroSpace Information

7121 Standard Drive

Hanover, MD 21076-1320

Price Code: A17

Available from:

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

Price Code: A10
Abstract

This atlas documents the climate characteristics of version 1 of the NASA Seasonalto-Interannual Prediction Project (NSIPP) Atmospheric General Circulation Model

(AGCM). The AGCM includes an interactive land model (the Mosaic scheme), and

is part of the NSIPP coupled atmosphere-land-ocean model. The results presented here

are based on a 20-year (December 1979-November 1999) "AMIP-style" integration of

the AGCM in which the monthly-mean sea-surface temperature and sea ice are specified

from observations.

The climate characteristics of the AGCM are compared with the National Centers

for Environmental Prediction (NCEP) and the European Center for Medium-Range

Weather Foreacsting (ECMWF) reanalyses. Other verification data include Special

Sensor Microwave/Imager (SSM/I) total precipitable water, the Xie-Arkin estimates of

precipitation, and Earth Radiation Budget Experiment (ERBE) measurements of short

and long wave radiation.

The atlas is organized by season. The basic quantities include seasonal mean global

maps and zonal and vertical averages of circulation, variance/covariance statistics, and

selected physics quantities.

°..

111
iv
Contents

List of Figures vii

1 Introduction

2 Description of the model

3 Description of the integration 4

4 Validation data sets 5

5 Organization and calculations 6

6 Means of Upper Air Fields

7 Sub-Monthly Quadratics of Upper Air Fields

8 Surface and TOA Fluxes 8

9 Summary 9

10 References 11
vi
List of Figures

ZONAL MEAN FIELDS

Zonal mean zonal wind (m s-I) -- Upper panels: Model; Lower panels:

Reanalysis; Left panels: DJF; Right panels: JJA ...............

2 Zonal mean zonal wind (m S -1) -- Upper panels: Model; Lower panels:

Reanalysis; Left panels: MAM; Right panels: SON ..............

Zonal mean meridional wind (m s-1) -- Upper panels: Model; Lower panels:

Reanalysis; Left panels: DJF; Right panels: JJA ...............

7

Zonal mean meridional wind (m s-1) -- Upper panels: Model; Lower panels:

Reanalysis; Left panels: MAM; Right panels: SON ..............

Zonal mean mass streamfunction (109 kg s-1) -- Upper panels: Model; Lower

panels: Reanalysis; Left panels: DJF; Right panels: JJA ...........

Zonal mean mass streamfunction (109 kg s-1) -- Upper panels: Model; Lower

panels: Reanalysis; Left panels: MAM; Right panels: SON ..........

Omega (rob d -1) -- Upper panels: Model; Lower panels: Reanalysis; Left

panels: DJF; Right panels: JJA .........................

Omega (mb d -1) -- Upper panels: Model; Lower panels: Reanalysis; Left

panels: MAM; Right panels: SON ........................

Zonal mean temperature (K) -- Upper panels: Model; Lower panels: Reanalysis; Left panels: DJF; Right panels: JJA .................

10

11

12

Zonal mean temperature (K) -- Upper panels: Model; Lower panels: Reanalysis; Left panels: MAM; Right panels: SON ................

Zonal mean specific humidity (g kg -1) -- Upper panels: Model; Lower panels:

Reanalysis; Left panels: DJF; Right panels: JJA ...............

Zonal mean specific humidity (g kg -1) -- Upper panels: Model; Lower panels:

Reanalysis; Left panels: MAM; Right panels: SON ..............

13 Zonal mean relative humidity (%) -- Upper panels: Model; Lower panels:

Reanalysis; Left panels: DJF; Right panels: JJA ...............

14 Zonal mean relative humidity (%) -- Upper panels: Model; Lower panels:

Reanalysis; Left panels: MAM; Right panels: SON ..............

15 Zonal mean zonal wind bias (m s-1) ......................

16 Zonal mean temperature bias (K) .......................

13

14

15

16

17

18

19

2O

21

22

23

24

25

26

27

28

29

vii
17 Zonalmeanspecifichumidity bias(g kg-1) .................. 30

18 Zonalmeanrelativehumiditybias (g kg-1) .................. 31

GLOBAL MAPS

19 Zonal wind at 200 mb for DJF-- Top: Model, Bottom: Reanalysis. Contour interval: 5 m s-1. Easterlies indicated by dark shading, light shading

-1 indicates westerlies in excess of 40 m s ....................

2O Zonal wind at 200 mb for JJA-- Top: Model, Bottom: Reanalysis. Contour interval: 5 m s-1. Easterlies indicated by dark shading, light shading

-1 indicates westerlies in excess of 40 m s ....................

21 Zonal wind at 200 mb for MAM-- Top: Model, Bottom: Reanalysis. Contour interval: 5 m s-1. Easterlies indicated by dark shading, light shading

indicates westerlies in excess of 40 m s-1 ....................

22 Zonal wind at 200 mb for SON-- Top: Model, Bottom: Reanalysis. Contour interval: 5 m s-1. Easterlies indicated by dark shading, light shading

indicates westerlies in excess of 40 m s-1 ....................

23 Zonal wind at 850 mb for DJF-- Top: Model, Bottom: Reanalysis. Contour

interval: 3 m s-1. Easterlies are shaded .....................

24 Zonal wind at 850 mb for JJA-- Top: Model, Bottom: Reanalysis. Contour

interval: 3 m s-1. Easterlies are shaded .....................

25 Zonal wind at 850 mb for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 3 m s-1. Easterlies are shaded .....................

26 Zonal wind at 850 mb for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 3 m s-1. Easterlies are shaded .....................

27 ;ea-level pressure for DJF-- Top: Model, Bottom: Reanalysis. Contour

interval: 4 mb. Shading indicates pressures in excess of 1000 mb .......

28 Sea-level pressure for JJA-- Top: Model, Bottom: Reanalysis. Contour

interval: 4 mb. Shading indicates pressures in excess of 1000 mb .......

29 Sea-level pressure for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 4 mb. Shading indicates pressures in excess of 1000 mb .......

30 Sea-level pressure for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 4 mb. Shading indicates pressures in excess of 1000 mb .......

31 Eddy geopotential height at 300 mb for DJF-- Top: Model, Bottom: Reanalysis. Contour interval: 40 m. Shading indicates negative values .....

33

34

35

36

37

38

39

4O

41

42

43

44

45

46

°°°

VIII
32

33

34

35

36

37

38

39

4O

41

42

43

44

45

46

47

48

49

Eddy geopotential height at 300 mb for JJA-- Top: Model, Bottom: Reanalysis. Contour interval: 40 m. Shading indicates negative values .....

Eddy geopotential height at 300 mb for MAM-- Top: Model, Bottom: Reanalysis. Contour interval: 40 m. Shading indicates negative values .....

Eddy geopotential height at 300 mb for SON-- Top: Model, Bottom: Reanalysis. Contour interval: 40 m. Shading indicates negative values .....

Omega at 500 mb (mb d -1) for DJF-- Top: Model, Bottom: Reanalyms.

Contour interval: 4 mb d -1. Shading indicates rising motion .........

Omega at 500 mb (mb d -1) for JJA-- Top: Model, Bottom: Reanalysls.

Contour interval: 4 mb d -1. Shading indicates rising motion .........

Omega at 500 mb (rob d -1)

Contour interval: 4 mb d -1.

Omega at 500 mb (mb d -1)

Contour interval: 4 mb d -1.

for MAM-- Top: Model, Bottom: Reanalysls.

Shading indicates rising motion .........

for SON-- Top: Model, Bottom: Reanalysls.

Shading indicates rising motion .........

Eddy stream function at 200 mb for DJF-- Top: Model, Bottom: Reanalysls.

Contour interval: 5 x 10 6 m 2 s -1. Shading indicates negative values .....

Eddy stream function at 200 mb for JJA-- Top: Model, Bottom: Reanalysls.

Contour interval: 5 x 10 6 m 2 s -1. Shading indicates negative values .....

Eddy stream function at 200 mb for MAM-- Top: Model, Bottom: Reanalysis. Contour interval: 5 x 10 6 m 2 s-1. Shading indicates negative values.

Eddy stream function at 200 mb for SON-- Top: Model, Bottom: Reanalysis.

Contour interval: 5 x 106 m 2 s-1. Shading indicates negative values .....

Velocity potential at 200 mb for DJF-- Top: Model, Bottom: Reanalysis.

Contour interval: 5 x 106 m 2 s-1. Shading indicates negative values .....

Velocity potential at 200 mb for JJA-- Top: Model, Bottom: Reanalysis.

Contour interval: 5 x 106 m 2 s -1. Shading indicates negative values .....

Velocity potential at 200 mb for MAM-- Top: Model, Bottom: Reanalysis.

Contour interval: 5 x 106 m 2 s -1. Shading indicates negative values .....

Velocity potential at 200 mb for SON-- Top: Model, Bottom: Reanalysis.

Contour interval: 5 × 106 m 2 s -1. Shading indicates negative values .....

Zonal mean sea-level pressure (mb) Solid: Model, Dashed:Reanalysis ....

Zonal mean zonal wind at 200 mb (m s-1) Solid: Model, Dashed:Reanalysis.

Zonal mean zonal wind at 850 mb (m s-1) Solid: Model, Dashed:Reanalysis.

47

48

49

5O

51

52

53

54

55

56

57

58

59

6O

61

62

63

64

ix
ZONAL MEAN STATISTICS 65

5O Zonal mean eddy standard deviation of u (m s-1) for DJF-- Upper panels:

Model; Lower panels: Reanalysis; Left panels: Transients; Right panels:

Stationary .....................................

51 Zonal mean eddy standard deviation of u (m s-1) for JJA-- Upper panels:

Model; Lower panels: Reanalysis; Left panels: Transients; Right panels:

Stationary .....................................

52 Zonal mean eddy standard deviation of u (m s-1) for MAM-- Upper panels:

Model; Lower panels: Reanalysis; Left panels: Transients; Right panels:

Stationary .....................................

53 Zonal mean eddy standard deviation of u (m s-1) for SON-- Upper panels:

Model; Lower panels: Reanalysis; Left panels: Transients; Right panels:

Stationary .....................................

54 Zonal mean eddy standard deviation of v (m s-I) for DJF-- Upper panels:

Model; Lower panels: Reanalysis; Left panels: Transients; Right panels:

Stationary. ....................................

55 Zonal mean eddy standard deviation of v (ms -1) for JJA-- Upper panels:

Model; Lower panels: Reanalysis; Left panels: Transients; Right panels:

Stationary .....................................

56 Zonal mean eddy standard deviation of v (m s-1) for MAM-- Upper panels:

Model; Lower panels: Reanalysis; Left panels: Transients; Right panels:

Stationary .....................................

57 Zonal mean eddy standard deviation of v (m S -1) for SON-- Upper panels:

Model; Lower panels: Reanalysis; Left panels: Transients; Right panels:

Stationary .....................................

58 Zonal mean eddy kinetic energy (m 2 s-2) for DJF-- Upper panels: Model;

Lower panels: Reanalysis; Left panels: Transients; Right panels: Stationary.

59 Zonal mean eddy kinetic energy (m 2 s-2) for JJA-- Upper panels: Model;

Lower panels: Reanalysis; Left panels: Transients; Right panels: Stationary.

60 Zonal mean eddy kinetic energy (m 2 s-2) for MAM-- Upper panels: Model;

Lower panels: Reanalysis; Left panels: Transients; Right panels: Stationary.

61 Zonal mean eddy kinetic energy (m 2 s-2) for SON-- Upper panels: Model;

Lower panels: Reanalysis; Left panels: Transients; Right panels: Stationary.

62 Zonal mean standard deviation of w (mb d -1) for DJF-- Upper panels:

Model; Lower panels: Reanalysis; Left panels: Transients; Right panels:

Stationary .....................................

66

67

68

69

7O

71

72

73

74

75

76

77

78

x
63 Zonal mean standard deviation of w (mb d -1) for JJA-- Upper panels:

Model; Lower panels: Reanalysis; Left panels: Transients; Right panels:

Stationary. .................................... 79

64 Zonal mean standard deviation of w (mb d -1) for MAM-- Upper panels:

Model; Lower panels: Reanalysis; Left panels: Transients; Right panels:

Stationary. .................................... 80

65 Zonal mean standard deviation of w (mb d -1) for SON-- Upper panels:

Model; Lower panels: Reanalysis; Left panels: Transients; Right panels:

Stationary. .............................. , ..... 81

66 Zonal mean standard deviation of T (K) for DJF-- Upper panels: Model;

Lower panels: Reanalysis; Left panels: Transients; Right panels: Stationary. 82

67 Zonal mean standard deviation of T (K) for JJA-- Upper panels: Model;

Lower panels: Reanalysis; Left panels: Transients; Right panels: Stationary. 83

68 Zonal mean standard deviation of T (K) for MAM-- Upper panels: Model;

Lower panels: Reanalysis; Left panels: Transients; Right panels: Stationary. 84

69 Zonal mean standard deviation of T (K) for SON-- Upper panels: Model;

Lower panels: Reanalysis; Left panels: Transients; Right panels: Stationary. 85

70 Zonal mean standard deviation of Z (m) for DJF-- Upper panels: Model;

Lower panels: Reanalysis; Left panels: Transients; Right panels: Stationary. 86

71 Zonal mean standard deviation of Z (m) for JJA-- Upper panels: Model;

Lower panels: Reanalysis; Left panels: Transients; Right panels: Stationary. 87

72 Zonal mean standard deviation of Z (m) for MAM-- Upper panels: Model;

Lower panels: Reanalysis; Left panels: Transients; Right panels: Stationary. 88

73 Zonal mean standard deviation of Z (m) for SON-- Upper panels: Model;

Lower panels: Reanalysis; Left panels: Transients; Right panels: Stationary. 89

74 Zonal mean eddy momentum transports (m 2 s -2) for DJF-- Upper panels:

Model; Lower panels: Reanalysis; Left panels: Transients; Right panels:

Stationary. .................................... 90

75 Zonal mean eddy momentum transports (m 2 s-2) for JJA-- Upper panels:

Model; Lower panels: Reanalysis; Left panels: Transients; Right panels:

Stationary. .................................... 91

76 Zonal mean eddy momentum transports (m 2 s-2) for MAM-- Upper panels:

Model; Lower panels: Reanalysis; Left panels: Transients; Right panels:

Stationary. .................................... 92

xi
77 Zonalmeaneddymomentumtransports(m 2 S-2) for SON-- Upper panels:

Model; Lower panels: Reanalysis; Left panels: Transients; Right panels:

Stationary ..................................... 93

78 Zonal mean eddy meridional heat transports (m s-1 K) for DJF-- Upper

panels: Model; Lower panels: Reanalysis; Left panels: Transients; Right

panels: Stationary ................................ 94

79 Zonal mean eddy meridional heat transports (m s-1 K) for JJA-- Upper

panels: Model; Lower panels: Reanalysis; Left panels: Transients; Right

panels: Stationary ................................ 95

8O Zonal mean eddy meridional heat transports (m s-1 K) for MAM-- Upper

panels: Model; Lower panels: Reanalysis; Left panels: Transients; Right

panels: Stationary ................................ 96

81 Zonal mean eddy meridional heat transports (m s-1 K) for SON-- Upper

panels: Model; Lower panels: Reanalysis; Left panels: Transients; Right

panels: Stationary ................................ 97

82 Zonal mean eddy vertical heat transports (mb d -1 K). for DJF-- Upper

panels: Model; Lower panels: Reanalysis; Left panels: Transients; Right

panels: Stationary ................................ 98

83 Zonal mean eddy vertical heat transports (rob d -1 K). for JJA-- Upper

panels: Model; Lower panels: Reanalysis; Left panels: Transients; Right

panels: Stationary ................................ 99

84 Zonal mean eddy vertical heat transports (mb d -1 K). for MAM-- Upper

panels: Model; Lower panels: Reanalysis; Left panels: Transients; Right

panels: Stationary ................................ 100

85 Zonal mean eddy vertical heat transports (mb d -1 K). for SON-- Upper

panels: Model; Lower panels: Reanalysis; Left panels: Transients; Right

panels: Stationary ................................ 101

86 Zonal mean eddy meridional moisture transports (m s-1 g kg -1) for DJF--

Upper panels: Model; Lower panels: Reanalysis; Left panels: Transients;

Right panels: Stationary ............................. 102

87 Zonal mean eddy meridional moisture transports (m s-1 g kg -1) for JJA--

Upper panels: Model; Lower panels: Reanalysis; Left panels: Transients;

Right panels: Stationary ............................. 103

88 Zonal mean eddy meridional moisture transports (m s-1 g kg -1) for MAM--

Upper panels: Model; Lower panels: Reanalysis; Left panels: Transients;

Right panels: Stationary ............................. 104

xii
89

9O

91

92

93

94

95

96

97

Zonal meaneddymeridionalmoisturetransports(m s-1 g kg-1) for SON--

Upper panels: Model; Lowerpanels: Reanalysis;Left panels:Transients;

Right panels:Stationary............................. 105

Verticalmeantransienteddykineticenergy(m2 s-2). Solid: Model, Dashed:Reanalysis.

106

Square root of the vertical mean [(w02] (mb d -1) Solid: Model, Dashed:Reanalysis.

107

Square root of the vertical mean [(T') 2] (K) Solid: Model, Dashed:Reanalysis. 108

Square root of the vertical mean [(Z') 2] (m) Solid: Model, Dashed:Reanalysis. 109

Vertical mean [u-'_V](m 2 s-2) Solid: Model, Dashed:Reanalysis ........ 110

Vertical mean [v-7_T_] (m s-1 K) Solid: Model, Dashed:Reanalysis ...... 111

Vertical mean [w'T'] (mb d -1 K) Solid: Model, Dashed:Reanalysis ..... 112

Vertical mean [v-_] (m s-1 g kg -1) Solid: Model, Dashed:Reanalysis .... 113

GLOBAL MAPS OF SELECTED STATISTICS

98

99

100

101

102

103

104

115

interval:

interval:

interval:

2]

interval:

interval:

interval:

interval:

at 200mb for DJF-- Top: Model, Bottom: Reanalysis. Contour

2 m s-1. Shading indicates values exceeding 6 m s-1 ........ 116

at 200mb for JJA-- Top: Model, Bottom: Reanalysis. Contour

2 m s-1. Shading indicates values exceeding 6 m s-1 ........ 117

at 200mb for MAM-- Top: Model, Bottom: Reanalysis. Contour

2 m s-1. Shading indicates values exceeding 6 m s-1 ........ 118

at 200mb for SON-- Top: Model, Bottom: Reanalysis. Contour

2 m s-1. Shading indicates values exceeding 6 m s-1 ........ 119

at 200mb for DJF-- Top: Model, Bottom: Reanalysis. Contour

2 m s-1. Shading indicates values exceeding 6 m s-1 ........ 120

at 200mb for JJA-- Top: Model, Bottom: Reanalysis. Contour

2 m s-1. Shading indicates values exceeding 6 m s -1 ........ 121

at 200mb for MAM-- Top: Model, Bottom: Reanalysis. Contour

2 m s-1. Shading indicates values exceeding 6 m s-1 ........ 122

xiii
105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

V_(V') 2] at 200mb for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 2 m s-1. Shading indicates values exceeding 6 m s-1 ........ 123

_L + (v') 2] at 200mb for DJF-- Top: Model, Bottom: Reanalysis. Contour interval: 30 m 2 s-2. Shading indicates values exceeding 120 m 2 s-2. . . 124

1⁄2[(u') 2 + (v') 2] at 200mb for JJA-- Top: Model, Bottom: Reanalysis. Contour interval: 30 m 2 s-2. Shading indicates values exceeding 120 m 2 s-2. . . 125

1_tr(u,)2 + (v,)2] at 200mb for MAM-- Top: Model, Bottom: Reanalysis. Contour interval: 30 m 2 s-2. Shading indicates values exceeding 120 m 2 s-2. . . 126

1⁄2[(u') 2 + (v') 2] at 200mb for SON-- Top: Model, Bottom: Reanalysis. Contour interval: 30 m 2 s-2. Shading indicates values exceeding 120 m 2 s-2. . . 127

Vf_W_) 2] at 500rob for DJF-- Top: Model, Bottom: Reanalysis. Contour

interval: 20 mb d -1. Shading indicates values exceeding 120 mb d -1 ..... 128

V/[(w') 2] at 500rob for JJA-- Top: Model, Bottom: Reanalysis. Contour

interval: 20 mb d -1. Shading indicates values exceeding 120 mb d -1 ..... 129

V_(W_) 2] at 500rob for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 20 mb d -1. Shading indicates values exceeding 120 mb d -1 ..... 130

V/[(w') ] at 500rob for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 20 mb d -1. Shading indicates values exceeding 120 mb d -1 ..... 131

v_(T') 2] at 850rap (K) for DJF-- Top: Model, Bottom: Reanalysis. Contour

interval: 1 K. Shading indicates values exceeding 4 K ............. 132

v_(T') 2] at 850mp (K) for JJA-- Top: Model, Bottom: Reanalysis. Contour

mterval: 1 K. Shading indicates values exceeding 4 K ............. 133

_at 850mp (K) for MAM-- Top: Model, Bottom: Reanalysis. Contour interval: 1 K. Shading indicates values exceeding 4 K .......... 134

V/[(T') 2] at 850mp (K) for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 1 K. Shading indicates values exceeding 4 K ............. 135

_/[(Z') 2] at 200 mb (m) for DJF-- Top: Model, Bottom: Reanalysis. Contour

interval: 20 m. Shading indicates values exceeding 100 m ........... 136

V/_(Z_) 2] at 200 mb (m) for JJA-- Top: Model, Bottom: Reanalysis. Contour

interval: 20 m. Shading indicates values exceeding 100 m ........... 137

V/_(Z_) 2] at 200 mb (m) for MAM-- Top: Model, Bottom: Reanalysis. Contour interval: 20 m. Shading indicates values exceeding 100 m ........ 138

xiv
121 _/_Z') 2] at 200mb (m) for SON-- Top: Model,Bottom: Reanalysis.Contour interval: 20m. Shadingindicatesvaluesexceeding100m........ 139

122 _/_X_)2] at 200mbfor DJF-- Top: Model, Bottom: Reanalysis.Contour

interval: 2 ×106m2 s-1. Shading indicates values exceeding 8 ×106 m 2 s-1. 140

123 V/_(X') 2] at 200mb for JJA-- Top: Model, Bottom: Reanalysis. Contour

interval: 2 ×106 m 2 s-1. Shading indicates values exceeding 8 ×106 m 2 s-1. 141

124 V/_(Xr) 2] at 200mb for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 2 × 106 m 2 s-1. Shading indicates values exceeding 8 x 106 m 2 s-1. 142

125 V/[(X_) 2] at 200mb for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 2 xl0 s m 2 s -1. Shading indicates values exceeding 8 ×10 6 m 2 s-1. 143

126 [u-_v_] at 200 mb for DJF-- Top: Model, Bottom: Reanalysis. Contour interval: 20 m 2 s -2. Shading indicates negative values ............... 144

127 [u_v _] at 200 mb for JJA-- Top: Model, Bottom: Reanalysis. Contour interval: 20 m 2 s -2. Shading indicates negative values ............... 145

128 [u_v _] at 200 mb for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 20 m 2 s-2. Shading indicates negative values ............ 146

129 [u_v _] at 200 mb for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 20 m 2 s -2. Shading indicates negative values ............ 147

130 [v--7_T_] at 850 mb for DJF-- Top: Model, Bottom: Reanalysis. Contour

interval: 5 m s-1 K. Shading indicates negative values ............ 148

131 [v-7_T_] at 850 mb for JJA-- Top: Model, Bottom: Reanalysis. Contour interval: 5 m s-1 K. Shading indicates negative values ............... 149

132 [v_T _] at 850 mb for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 5 m s-1 K. Shading indicates negative values ............ 150

133 [v--_-TT _] at 850 mb for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 5 m s-1 K. Shading indicates negative values ............ 151

134 -[w-_-TT_] at 850 mb for DJF-- Top: Model, Bottom: Reanalysis. Contour

interval: 50 mb d -1 K. Shading indicates downward heat transport ..... 152

135 -[w--T_T_] at 850 mb for JJA-- Top: Model, Bottom: Reanalysis. Contour

interval: 50 mb d -1 K. Shading indicates downward heat transport ..... 153

136 -[w_T _] at 850 mb for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 50 mb d -1 K. Shading indicates downward heat transport ..... 154

xv
137

138

139

140

141

-[w'T'] at 850 mb for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 50 mb d -1 K. Shading indicates downward heat transport ..... 155

[v--_] at 850 mb (m s-1 g kg -1) for DJF-- Top: Model, Bottom: Reanalysis.

Contour interval: 2 m s-1 g kg -1. Shading indicates negative values .... 156

[v'q'] at 850 mb (ms -1 g kg -1) for JJA-- Top: Model, Bottom: Reanalysis.

Contour interval: 2 m s-1 g kg -1. Shading indicates negative values .... 157

[v-_] at 850 mb (ms -1 g kg -1) for MAM-- Top: Model, Bottom: Reanalysis.

Contour interval: 2 m s-1 g kg -1. Shading indicates negative values .... 158

[v--_] at 850 mb (ms -1 g kg -1) for SON-- Top: Model, Bottom: Reanalysis.

Contour interval: 2 m s-1 g kg -1. Shading indicates negative values .... 159

GLOBAL MAPS OF PHYSICS DIAGNOSTICS 161

142 Total precipitation (ram d-l). The comparison is for the entire 20-year period

of the run. -- Upper panels: Model; Lower panels: Reanalysis; Left panels:

DJF; Right panels: JJA ............................. 162

143 Total precipitation (mm d-l). The comparison is for the entire 20-year period

of the run. -- Upper panels: Model; Lower panels: Reanalysis; Left panels:

MAM; Right panels: SON ............................ 163

144 Total precipitable water (kg m-2). The comparison is for the period July

1987 to February 1992. -- Upper panels: Model; Lower panels: Reanalysis;

Left panels: DJF; Right panels: JJA. Contour interval: 10 kg m -2. Shading

indicates values in excess of 30 kg m -2 ..................... 164

145 Total precipitable water (kg m-2). The comparison is for the period July 1987

to February 1992. -- Upper panels: Model; Lower panels: Reanalysis; Left

panels: MAM; Right panels: SON. Contour interval: 10 kg m -2. Shading

indicates values in excess of 30 kg m -2 ..................... 165

146 Outgoing longwave radiation at the top of the atmosphere (W m-2). The

comparison is for the ERBE period: January 1985 to December 1989.. Solid:

Model, Dashed: ERBE .............................. 166

147 Net downward shortwave radiation at the top of the atmosphere (W m-2).

The comparison is for the ERBE period: January 1985 to December 1989..

Solid: Model, Dashed: ERBE ........................... 167

148 Net downward radiation at the top of the atmosphere at the top of the

atmosphere (W m-2). The comparison is for the ERBE period: January

1985 to December 1989.. Solid: Model, Dashed: ERBE ............ 168

149 Longwave cloud radiative forcing (W m-2).. Solid: Model, Dashed: ERBE. 169

xvi
150

151

152

153

154

155

156

157

158

159

160

161

162

Shortwave cloud radiative forcing (W m-S). The comparison is for the ERBE

period: January 1985 to December 1989.. Solid: Model, Dashed: ERBE. 170

Net cloud radiative forcing (W m-S). The comparison is for the ERBE

period: January 1985 to December 1989.. Solid: Model, Dashed: ERBE. 171

Longwave cloud radiative forcing (W m-S). The comparison is for the ERBE

period: January 1985 to December 1989.-- Upper panels: Model; Lower

panels: Reanalysis; Left panels: DJF; Right panels: JJA ........... 172

Longwave cloud radiative forcing (W m-2). The comparison is for the ERBE

period: January 1985 to December 1989.-- Upper panels: Model; Lower

panels: Reanalysis; Left panels: MAM; Right panels: SON .......... 173

Shortwave cloud radiative forcing (W m-S). The comparison is for the ERBE

period: January 1985 to December 1989.-- Upper panels: Model; Lower

panels: Reanalysis; Left panels: DJF; Right panels: JJA ........... 174

Shortwave cloud radiative forcing (W m-S). The comparison is for the ERBE

period: January 1985 to December 1989.-- Upper panels: Model; Lower

panels: Reanalysis; Left panels: MAM; Right panels: SON .......... 175

Net cloud radiative forcing (W m-S). The comparison is for the ERBE

period: January 1985 to December 1989.-- Upper panels: Model; Lower

panels: Reanalysis; Left panels: DJF; Right panels: JJA ........... 176

Net cloud radiative forcing (W m-S). The comparison is for the ERBE

period: January 1985 to December 1989.-- Upper panels: Model; Lower

panels: Reanalysis; Left panels: MAM; Right panels: SON .......... 177

Outgoing longwave radiation bias (Model - ERBE) (W m-2). The comparison is for the ERBE period: January 1985 to December 1989 ........ 178

Net downward shortwave radiation bias (Model - ERBE) (W m-2). The

comparison is for the ERBE period: January 1985 to December 1989 .... 179

Net radiation bias (Model - ERBE) (W m-2). The comparison is for the

ERBE period: January 1985 to December 1989 ................ 180

Zonal component of the surface wind stress (dynes cm-2). -- Upper panels:

Model; Lower panels: Reanalysis; Left panels: DJF; Right panels: JJA.

Results from the ECMWF re-analysis are for the 10-year period December

1979 to November 1989. Model results are for all 20 years ........... 181

Zonal component of the surface wind stress (dynes cm-2). -- Upper panels:

Model; Lower panels: Reanalysis; Left panels: MAM; Right panels: SON.

Results from the ECMWF re-analysis are for the 10-year period December

1979 to November 1989. Model results are for all 20 years ........... 182

xvii
163

164

165

166

167

168

169

170

Meridional componentof the surfacewind stress(dynescm-2). -- Upper

panels:Model; Lowerpanels:Reanalysis;Left panels:DJF; Right panels:

JJA. Resultsfrom the ECMWF re-analysisarefor the 10-yearperiodDecember1979to November1989.Modelresultsarefor all 20years...... 183

Meridional componentof the surfacewind stress (dynes cm-2). -- Upper

panels: Model; Lower panels: Reanalysis; Left panels: MAM; Right panels: SON. Results from the ECMWF re-analysis axe for the 10-year period

December 1979 to November 1989. Model results are for all 20 years ..... 184

Curl of the surface wind stress (10 -6 N m-3). -- Upper panels: Model; Lower

panels: Reanalysis; Left panels: DJF; Right panels: JJA. Results from the

ECMWF re-analysis are for the 10-year period December 1979 to November

1989. Model results are for all 20 years ..................... 185

Curl of the surface wind stress (10 -6 N m-3). -- Upper panels: Model;

Lower panels: Reanalysis; Left panels: MAM; Right panels: SON. Results

from the ECMWF re-analysis are for the 10-year period December 1979 to

November 1989. Model results are for all 20 years ............... 186

Surface sensible heat flux (W m-2). -- Upper panels: Model; Lower panels:

Reanalysis; Left panels: DJF; Right panels: JJA. Results from the ECMWF

re-analysis are for the 10-year period December 1979 to November 1989.

Model results are for all 20 years ......................... 187

Surface sensible heat flux (W m-2). -- Upper panels: Model; Lower panels: Reanalysis; Left panels: MAM; Right panels: SON. Results from the

ECMWF re-analysis are for the 10-year period December 1979 to November

1989. Model results are for all 20 years ..................... 188

Surface latent heat flux (W m-2). -- Upper panels: Model; Lower panels:

Reanalysis; Left panels: DJF; Right panels: JJA. Results from the ECMWF

re-analysis are for the 10-year period December 1979 to November 1989.

Model results are for all 20 years ......................... 189

Surface latent heat flux (W m-2). -- Upper panels: Model; Lower panels: Reanalysis; Left panels: MAM; Right panels: SON. Results from the

ECMWF re-analysis are for the 10-year period December 1979 to November

1989. Model results are for all 20 years ..................... 190

xviii
1 Introduction

The mission of the NASA Seasonal-to-Interannual Prediction Project (NSIPP) is to use

remotely-sensed observations to enhance the predictability of E1 Nino/Southern Oscillation

(ENSO) and other major seasonal-to-interannual signals and their global teleconnections.

Fullfilling this mission requires state-of-the-art general circulation models of the coupled

ocean-atmosphere-land system that can be used to assimilate observations and to demonstrate the utility of those observations through experimental prediction.

This report presents the climate characteristics of version 1 of the NSIPP Atmospheric General Circulation Model (the NSIPP 1 AGCM). This model, which is the atmosphere/land

component of the full coupled atmosphere-land-ocean model, is currently being used in a

wide range of atmospheric, coupled ocean/atmosphere and land/atmosphere simulation and

predictability studies. Subsequent reports will summarize the predictability characteristics

and interannual variability of this version of the AGCM.

The NSIPP AGCM was developed at Goddard. NSIPP 1 is a production version of the

development cycle Aries l_l/Patch 4. We note that the Goddard Earth Observing System

(GEOS) model currently being used by the Data Assimilation Office (DAO) stems from

the same development path. The GEOS model was, however, tailored for atmospheric data

assimilation, while the NSIPP model was developed for climate simulation and prediction.

This difference in application manifests itself largely in the tailoring and tuning of the

physical parameterizations to ensure that certain key aspects of the atmosphere/land system

are faithfully reproduced by the model. For example, in the development of the NSIPP

AGCM, much attention has been devoted to the simulation of wind stresses over the tropical

Pacific Ocean in order to obtain the proper atmosphere-ocean coupling when run in a

coupled mode. Also, the middle latitude atmospheric stationary waves must be sufficiently

unbiased in order to obtain the proper extratropical ENSO response and its variability

(e.g., Schubert et al. 2000). In fact, it is these two aspects of the model climatology that

motivated the recent model development, leading to Patch 4.

Although one may regard most changes in Patch 4 as fairly minor, they led to a much

improved simulation over earlier versions. These changes include an increase in vertical

resolution from 22 to 34 levels, with all new levels added near the surface; a modified

version of the convection parameterization, with a more complete liquid water budget in

updrafts; a modified version of the turbulence scheme, together with the elimination of dry

convective adjustment; the use of filtered topography; and some minor modifications to the

cloud disgnostic scheme. More details are presented in the next section.

The results presented are from a 20-year (December 1979-November 1999) "AMIP-style"

integration of the NSIPP 1 AGCM. Here AMIP indicates that the model was run with

monthly mean sea surface temperature and sea ice specified from observations following the

experimental design of the Atmospheric Model Intercomparison Project (Gates 1992). The

results are compared with the reanalysis performed by the National Centers for Environmental Prediction and the National Center for Atmospheric Research (the NCEP/NCAR

Reanalysis, Kalnay et al., 1995) for the same time period. Other verification data include

the European Center for Medium-Range Weather Foreacsting (ECMWF) reanalysis (Gibson

et al., 1996), Special Sensor Microwave/Imager (SSM/I) total precipitable water, Xie arts
Arkin (1997)estimatesof precipitation,andEarth RadiationBudgetExperiment(ERBE)

measurementsof shortwaveandlongwaveradiation.

The atlas is organizedby season.The basicquantitiesincludeseasonalmeanglobalmaps

and zonaland vertical averagesof circulation, variance/covariancestatistics,and selected

physicsquantities.

Section2 describesthe NSIPP1 AGCM. Sections3 and 4 describethe modelintegration

and validation data, respectively.Section5 givesan overviewof the organizationof the

atlas.The resultsarediscussedin Sections6-8.

2 Description of the model

The AGCM is the atmospheric component of the NSIPP coupled prediction system. It uses

a finite-difference dynamical core based on a C-grid in the horizontal and a standard sigma

coordinate in the vertical. A detailed description of this core is given in Suarez and Takacs

(1995).

Finite differences are second-order accurate, except for advection by the rotational part of

the flow, which is done at fourth order. The momentum equations use a fourth-order version

of the enstrophy conserving scheme of Sadourney (1975). The horizontal advection schemes

for potential temperature and moisture are also fourth-order and conserve the quantity and

its square (Takacs and Suarez, 1996).

The parameterizations of solar and infrared radiative heating rates are described in Chou

and Suarez (1999) and Chou and Suarez (1994). The solar parameterization includes absorption due to 03, CO2, water vapor, 02, and clouds, as well as gaseous and aerosol

scattering. The solar spectrum is divided into eight Visible-UV bands and three near-IR

bands. A k-distribution method is used within each band. The eight VIS-UV bands use a

single k-interval, while the IR bands use ten intervals each. Effects of multiple scattering by

clouds and aerosols are treated using the $-Eddington approximation for the direct beam

and Sagan-Pollock for diffuse radiation. The infrared parameterization includes absorption by water vapor, CO2, 03, methane, N20, CFC-11, CFC-12, and CFC-22, within eight

spectral bands, but in the results prsented only water vapor, CO2, and 03 are included.

From the moist physics parameterizatious, the GCM estimates a cloud fraction at each

level. For the solar radiation calculation, the GCM levels are then grouped into three

regions which axe identified with high (a < 0.56) middle (0.56 < a < 0.77) and low (a >

0.77) clouds. Within each of these regions, clouds are assumed to be maximally overlapped

and the cloud fractions are scaled using a scheme that depends on solar zenith angle and

optical thickness. This leaves us with a single cloud fraction in each of the three regions.

The overlapping between these region is treated "exactly" by assuming random overlapping

and combining the results of full transfer calculations for the eight possible cases.

Turbulence throughout the atmospheric column is modeled using the Louis et al.(1982)

scheme. This is a local "K" scheme with Richardson number-dependent viscosity and

diffusivity. In practice, we found that the scheme contributed to excessive annual mean
stressesover the equatorialpacific, as well as to unrealistic seasonal variation of these

stresses. These deficiencies were alleviated by using a smaller than usual value for the eddymixing length scale A0. We use A0--=20 meters, compared to typical values of 80 to 160 meters

in other implementations of the scheme. We also truncate mixing in the stable Ri regime, so

that for Ri>3.0 vertical viscosity and diffusivity are exactly zero. This eliminates sporadic

patches of significant momentum mixing in the middle troposphere, which we believe have

an adverse effect on the simulation of surface wind stresses. These modifications to the

standard implementation of the Louis scheme do not have noticeable negative impacts on

other aspects of the model climate. We also note here that dry convective adjustment has

been eliminated from the current version of the model.

The model uses the gravity-wave drag parameterization described by Zhou et al. (1996).

The Zhou scheme incorporates only orographically forced gravity waves. Directional anisotropy

of the orographic forcing is ignored. The scheme contains two important "tunable" parameters, the effective wavenumber krnw for the waves, and a maximum surface amplitude for

the waves hrnax. These must be determined empirically. Currently we use krnw = 2.5 x 10 -5

m -1 and hmax = 400 m. The surface amplitude of waves is the lower of hmax and a

local gridbox RMS topographic deviation derived from the GTOPO30 thirty arcsecond

topographic data set (http://edcwww.cr.usgs.gov/landdaac/gtopo30/gtopo30.html). The

GTOPO30 data have been binned by averaging the heights in 5 x 5 squares to produce a

2.5' dataset. It is with these data that the RMS amplitudes are computed.

In addition to the Zhou scheme, the model incorporates enhanced Rayleigh damping above

a=0.05. This damping is formulated as

('0.05- o (0. rJ)0

= "y0\ 0.05

where U is the model horizontal wind vector. The strength _'0 = (60 m s-1)-2(10 d) -1 is

chosen to damp a 60 m s-1 jet in 10 days. This drag is a crude ad hoc representation of

missing gravity wave drag in the middle atmosphere. It is intended primarily to reduce the

strength of the polar night jet in the winter stratosphere in the interest of computational

stability. This drag formulation does not produce realistic simulations of the stratospheric

climate.

We find that our simulation of stationary planetary waves improves significantly when the

topographic elevation data used by the model is first filtered to eliminate high spatial

frequencies. This is accomplished using a 12-th order Coiflet filter. Coiflets are nearly

symmetrical orthogonal wavelets with compact support and exact reconstruction. We filter

by simply removing the highest frequency (octave) of the Coiflet transform of the topography

and reconstructing. The compact support of the Coiflet filter reduces the ringing that

plagues higher-order filtering techniques.

Penetrative convection originating in the boundary layer is parameterized using the Relaxed

Arakawa-Schubert (RAS) scheme (Moorthi and Suarez, 1992), which is a simple and efficient implementation of the Arakawa-Schubert scheme. The version described in Moorthi

and Suarez, RAS-1, is the standard parameterization used at Goddard. It has also been

tested at NCEP, NCAR, and COLA, and has performed particularly well in simulating the

atmospheric response to tropical SST anomalies -- a crucial aspect of the coupled prediction problem. We have recently updated it by including a more detailed condensate budget

3
in the updraft. This version,whichwereferto asRAS1.5,is theoneusedin the NSIPP1

AGCM.

Cloudsare obtained from an empirically-based, diagnostic scheme in which the cloud cover

at each grid point depends directly on the results of the large-scale condensation and convection parameterizations. The scheme defines both large-scale and convective cloudiness.

Large-scale cloudiness is determined in two steps. First, an intial cloud fraction is estimated

using a simple diagnostic scheme based solely on relative humidity (RH). This scheme is

similar to that of Slingo (1987). A high threshhold RH of 95% is used. Even with this high

threshhold value, excessive cloudiness results over tropical and subtropical oceans. Thus,

a second "destruction" step is invoked. We simply use the magnitude of subsidence drying produced by RAS to destroy a fraction of the large-scale clouds produced by the RH

diagnostic,

where C_s is the initial estimate from the RH-diagnostic, Deny is the 3-dimensional distribution of net convective drying from RAS, and Do is a tunable parameter, which we choose

by examining the global radiation budget. In the simulations discussed here, this parameter

has a value of 7 g kg-1 day-1.

The land surface model (LSM) is the Mosaic LSM of Koster and Suarez (1992, 1996),

The core of the LSM is a standard soil-vegetation-atmosphere-tranfer (SVAT) model. The

most distinctive feature of Mosaic is that it subdivides each AGCM grid square into subregions, or tiles, of relatively homogeneous vegetation type and then calculates separate

one-dimensional energy and water balances over each tile, with distinct stomatal control

over transpiration rates. This model has performed well in tests against observations (Chen

et al. 1997, Wood et al. 1998), and has been used in studies of land-atmosphere interactions

(Koster et al., 2000, and references therein).

3 Description of the integration

All results presented here are from a single AMIP-style run begun on 1 January 1979 0Z

and extending to 1 December 1999 OZ. The first eleven months of the run were discarded

as a "spin up" period. We thus analyzed twenty years (December 1979 - November 1999)

of integration.

For this run, the model was integrated at a resolution of 2 ° latitude by 2.5 ° longitude, using

34 sigma layers (Table 1).

Sea-surface temperatures (SST) and sea-ice fractions were specified based on the monthly

Reynolds O-I dataset (Reynolds and Smith 1994). The land surface was fully interactive

and consists of some 13000 tiles distributed over the atmospheric grid boxes that contain

a non-zero land fraction. The tiles represent six different vegetation types, as well as land

ice, bare soil, desert, and lakes. Lakes are treated as a freely evaporating surface (i.e., no

surface, only aerodynamic, resistance) with a heat capacity equivalent to 2 meters of water.
Table 1: Sigma surfaces separating the 34 layers of the model.

L a L a L a L a L a

1 0.000 2 0.005 3 0.010 4 0.015 5 0.025

6 0.050 7 0.075 8 0.100 9 0.125 10 0.150

11 0.175 12 0.200 13 0.225 14 0.275 15 0.325

16 0.375 17 0.425 18 0.500 19 0.625 20 0.700

21 0.750 22 0.775 23 0.800 24 0.825 25 0.850

26 0.865 27 0.880 28 0.895 29 0.910 30 0.925

31 0.940 32 0.955 33 0.970 34 0.985 35 1.000

One peculiarity of this run is that, to avoid running with a sea-ice model, we have specified both sea ice fractions and temperatures. The former vary interannual, but sea-ice

temperatures repeat the same seasonal cycle each year.

4 Validation data sets

For the upper air fields and their statistics, we compare with the NCEP/NCAR Reanalysis

(Kalnay et al. 1994) averaged for the same period as the model simulation. For the

moisture field and various physics diagnostics we compare with various satellite and in

situ measurements described below.

Precipitation is compared with the combined satellite,gauge, and model estimates derived

by Xie and Arkin (1997). These data are available for the entire period of the simulation

from ftp: / / ftp.ncep.noaa.gov /pub /precip /cmap /monthly/.

Estimates of total precipitable water (TPW) are those generated by Wentz (1992) from the

Special Sensor Microwave Imager (SSM/I) measurements. The radiative transfer algorithm

uses three channels of microwave measurements (22V, 37V, 37H) and a scheme that accounts

for absorption and emission in the atmosphere and uses a surface emissivity value over

oceans appropriate for a wind-roughened sea surface. The scheme does not account for

scattering by raindrops or by frozen hydrometers and is, therefore inaccurate for high rain

rates. No data is produced over land or sea ice, because of the complexity of the surface

emissivity.

To validate the top of the atmosphere radiation budget, we compare with the Earth Radiation Budget Experiment (ERBE) data collected by the ERBS, NOAA 9 and NOAA 10

satellites between November 1984 and February 1990. More information on ERBE may be

obtained at (http://asd-www.larc.nasa.gov/erbe/ASDerbe.html). We limit our comparison

to the 5-year period from 1985 through 1989.

Surface fluxes are compared with the first 10 years of the ECMWF reanalysis (Gibson et

al., 1996).
5 Organization and calculations

Unless otherwise noted, quantities presented in this report are averaged over the 20 years

of the integration. We will concentrate on seasonal means. Instantaneous values of the

simulated upper air data were saved four times daily at 0Z,6Z,12Z, and 18Z. Surface and

top-of-atmosphere (TOA) fluxes, precipitation, and cloudiness were accumulated at each

time step and saved once daily.

Results axe presented as zonal means and global maps of climatological means and submonthly variance/covariance statistics. For selected quantities we also show the zonal

mean bias (departures from NCEP/NCAR reanalysis), and/or line plots of zonal means

at selected pressure levels or of vertical means. The zonal means are computed at the following pressure levels (1000, 925, 850, 700, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30,

qnd 10 mb). While results are presented up to 10 mb, the model was not tuned to produce a very realistic stratosphere. In fact, the current values of Rayleigh friction produce

a rather strong damping on the stratosphere, resulting in unrealistically weak high latitude

westerlies and covariance statistics above about 50 rob.

The variance/covariance statistics are divided into "transient" and "stationary" components. The "transient" statistics are computed from 6 hourly deviations from monthly

means of each year and each calendar month. Products are then taken and averaged for

each season and for the twenty years of the analysis. The "stationary" statistics are computed from zonal departures of monthly means, these are then averaged for each season

and for the twenty years of the analysis. We will use overbars to denote a calendar monthly

mean, so that u _ = u - _ is the monthly deviation of u, and square brackets to denote

a zonal mean, so that u* = u -_ is the zonal departure of u. Then the mean quadratic

quantities are defined as:

TRANSIENT(u,v) = {u-_vl}, STATIONARY(u,v) = {[_-'9-'1},

where the braces represent an average over the three months of each season and all years.

For all squared statistics, other than kinetic energy, we plot the square root, which is taken

after all averaging.

Section 6 discusses the climatological means and bias of upper air fields. Section 7 discusses

sub-monthly quadratic statistics, and Section 8 selected physics quantities.

6 Means of Upper Air Fields

The model produces a very good simulation of the general circulation of the troposphere,

including the zonally asymmetric flow and stationary eddy patterns. In the following discussion, we emphasize deficiencies that remain in the simulation.

The model simulation of the seasonal mean zonal mean winds is generally quite good.

Notable deficiencies include, weak high latitude stratospheric winter westerlies, an easterly

bias in the upper troposphere/lower stratosphere of the tropics, and a westerly bias in the
middlelatitudes of the SouthernHemisphere.At 200mb, the modelproducesa westerly

nodein the easterntropical Pacificthat is too strong.During JJA, the 200mb Asian and

North Pacificjets aretoo weak. Thereis a tendencyfor a westerlybias at 200mb over

the tropical westernhemisphereduring DJF and MAM. The modelfails to capture the

separationof the 200mb AfricanandEastAsianjets duringMAM. At 850mb, the model

generatestoo strongtropical easterliesovertheeasternPacific,andtoo strongwesterliesin

the SouthernHemispheremiddlelatitudesandthe Asiansummermonsoonregion.

Theseasonalcycleof the HadleyCell is quite realistic, though the maximum rising motion

during JJA occurs substantially lower in the atmosphere (below 500 rob) than the estimates

from the NCEP/NCAR reanalysis show (400 mb). The model has a consistent cold bias

throughout most of the stratosphere, the Southern Hemisphere high latitude upper troposphere, and the tropical upper troposphere. A substantial warm bias (greater than 8 degrees

C) occurs during the winter in the stratosphere of the southern high latitudes. During all

seasons the tropical and subtropical troposphere below 800 mb is too dry (maximum bias

near 925mb), while between 700 mb and 500 mb the tropics are too wet. The moisture

bias is reflected in the relative humidity (RH) bias, though the latter also show that the

boundary layer relative humidity is too high, while away from the polar regions the upper

tropospheric relative humidity is too low.

The North Pacific and North Atlantic surface highs tend to be too strong, especially during

JJA. The North American upper tropospheric west coast ridge is too strong during DJF.

The east Asian/west Pacific trough is too weak (and has a noticeable hump) during DJF,

and it does not extend far enough into the eastern Pacific during MAM.

The model shows excessive noise over mountains in the 500 mb omega fields. Compared

to the NCEP reanalysis, there is insufficient rising motion over the tropical eastern Pacific

in the region of the ITCZ. The eddy stream function at 300 mb shows tropical/subtropical

stationary waves that are too weak in the eastern hemisphere, while they are too strong in

the western hemisphere during all seasons. The seasonal evolution of the 200 mb velocity

potential is quite good.

7 Sub-Monthly Quadratics of Upper Air Fields

The model produces good tropospheric transient and stationary zonal and meridonal wind

variances. However, the transient variances in both wind components (especially v) tend to

be somewhat weak in the Northern Hemisphere. This leads to a substantial underestimate

of the transient kinetic energy in the Northern Hemisphere troposphere during all seasons.

The stationary zonal wind variance is too strong in the upper tropospheric tropics. The

wind variances are much weaker than observed in the high latitudes of the stratosphere

during winter.

There are large differences in the variance of the omega field, with the model showing considerably larger variance than the NCEP/NCAR reanalysis in the tropics and extratropics.

It should be noted that the quality of the reanaIysis are suspect for this field. The model

produces very good geopotential height variances in the troposphere. Similar to the wind

7
variances;however,the heightvariancesareweakerthan observedduring thecold seasons

in the highlatitudesof the stratosphere.This isespeciallysofor thestationarycomponent

during DJF in the NorthernHemisphereand during SON in the SouthernHemisphere.

At 200mb, the seasonalcycleof the heightvarianceis quite good,thoughthe varianceis

somewhatweakerthan observedin the NorthernHemispheremiddleand highlatitudes.

The modelproducesexcellentmeridionalfluxesof zonalmomentum.Exceptionsaxethe

too strongstationary fluxesbetween200 mb and 100 mb during JJA, and a tendency

to overestimatethe southwardtransientfluxes in the SouthernHemisphere.The model

producesreasonableheatfluxesin thetroposphere,thoughtransientsouthwardfluxesin the

SouthernHemispherearesystematicallyhigherthanin the reanalysis.In the stratosphere,

thestationarymeridionalheatflux ismuchtooweakat highlatitudesduringDJF, whilethe

transientcomponentis too strong.The modelgeneratesveryrealisticmeridionalmoisture

fluxes.

8 Surface and TOA Fluxes

The model's global precipitation distribution is much improved from that produced by

earlier versions. In particular, its tendency to produce unrealistic double ITCZs in the

central and eastern Pacific has been greatly lessened. A strong vestige of the problem,

however, remains in the MAM season. One of the more intractable problems with the

precipitation distribution is a "gap" in the eastern Pacific ITCZ and an associated "bull's

eye" in precipitation over Central America. This problem is apparent in all four seasons of

the simulation.

The simulated precipitable water (vapor only) agrees quite well with the satellite estimate

(SSM/I). As might be expected, however, it shows some of the same unrealistic features as

the precipitation fields.

The zonal mean total radiation budget at the top of the atmosphere is simulated well. The

most obvious deficiency is the excessive outgoing longwave radiation at almost all latitudes

and all seasons. This results in a systematic "cold" bias in the net radiation, which is

otherwise extremely well simulated. The "cloud radiative forcings" (CRF) highlight better

the model's performance. In the OLR-CRF, the model does surprisingly well in the tropics--

a result of our improved distributions of convective activity. In the middle latitudes, the

model consistently underestimated the OLR-CRF, implying too little, or too low cloudiness

in these regions. The solar CRF is extremely good, the main problem being too weak

forcing in middle latitudes of the southern hemisphere during MAM. Aside from this, little

differentiates it from the ERBE data.

The global distributions of CRF show clearly that some of the agreement in the zonal mean

results from a compensation of errors along latitude circles, but they also show that much of

the agreement is due to the model's improved distribution of tropical (convective) cloudiness

and of the marine stratus and stratocumulus regimes. The latter is best seen in the solar

CRF distributions.

Because of its importance to the ENSO problem, we have devoted considerable attention to
the simulationof tropical surface stresses, particularly the seasonal cycle in the equatorial

Pacific. As may be seen from the global distributions shown, both the zonal and meridional

stress compare quite well with the ECMWF reanalysis. In fact, even the more difficult and

oceanographically important curl of the wind stress is very close to the reanalysis.

The same cannot be said of the sensible and latent heat fluxes, both of which the model

seems to overestimate very significantly, at least over oceans.

9 Summary

The atlas presents a very good simulation of the mean seasonal cycle of the tropospheric

general circulation. The model is shown to have very good skill in simulating the horizontal

and vertical distribution of both mean fields and variance/covariance statistics.

The results also identify a several deficiencies. Some of these, like the problems in the

eastern Pacific ITCZ, may require increased horizontal resolution. Others, however, are

things that we feel can clearly be improved within the current framework.

Nevertheless, we feel this is an acceptable model for NSIPP's purposes and have frozen it

in the form presented here. A number of other experiments have already been conducted

with it and many more will follow. These experiments address the model's sensitivity to

sea-surface temperatures, its teleconnection patterns and modes of natural variability, the

nature of its land-atmosphere interactions, and its performance in coupled integrations. In

all of these areas, the model appears to be performing quite well, and results will be reported

in the near future.

9
10
10 References

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Chou, M.-D. and M. Suarez, 1994: An efficient thermal infrared radiation parameterization

for use in general circulation models. NASA Technical Memorandum, 104606, 10, 84pp.

Chou, M.-D. and M. J. Suarez, 1999: A solar radiation parameterization for atmospheric

studies, NASA Technical Memorandum, 104606, 11, 40pp.

Gibson,R., P.Kallberg and S. Uppsala, 1996: The ECMWF reanalysis (ERA) project.

ECMWF Newsletter, 73, 7-16.

Gates, W. L., 1992: AMIP: The atmospheric model intercomparison project. Bull. Amer.

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Gruber, A. and A.F. Krueger, 1984: The status of the NOAA outgoing longwave radiation

data set. Bull. Amer. Meteor. Soc., 65, 958-962.

Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, J. Derber, L. Gandin, S. Sara,

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Mo, C. Ropelewski, J. Wang, A Leetma, R. Renolds, R Jenne, 1995: The NMC/NCAR

Reanalysis Project. Bull Amer. Meteor. Soc., 77, 437-471.

Koster, R., and M. Suarez, 1992: Modeling the land surface boundary in climate models as

a composite of independent vegetation stands. J. Geophys. Res., 97, 2697-2715.

Koster, R. and M. Suarez, 1996: Energy and Water Balance Calculations in the Mosaic

LSM, NASA Tech. Memo. 104606, Vol. 9.

Koster, R. D., M. J. Suarez, and M. Heiser, 2000: Variance and predictability of precipitation at seasonal-to-interannual timescales, J. Hydrometeorology, 1, 26-41.

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ECMWF. In: Proceedings, ECMWF Workshop on Planetary Boundary Layer Parameterization, Reading, UK, 59-80.

Moorthi, S. and M. Suarez, 1992: Relaxed Arakawa-Schubert: a parameterization of moist

convection for general circulation models. Mon. Weather Rev., 120, 978-1002.

Reynolds, W. R. and T. M. Smith, 1994: Improved global sea surface temperature analyses

using optimum interpolation. J. Climate, 7, 929-948.

Rossow, W. B., and R. A. Schiffer, 1991: ISCCP cloud data products. Bull. Am. Meteorol.

Soc.,72, 2-20.

Sadourney, R., 1975: The dynamics of finite difference models of the shallow water equations, J. Atmos. Sci., 32, 680-689.

11
Schubert,S.D., M. J. Suarez,Y. Changand G. Branstator,2000:TheimpactofENSOon

extratropical low frequencynoisein seasonalforecasts.Submitted to J. Climate.

Slingo, J. M., 1987: The development and verification of a cloud prediction scheme for the

ECMWF model, Q. J. R. Meteorol. Soc, 113, 899-927.

Suarez, M. J. and L. L. Takacs, 1995: Documentation of the Aries/GEOS dynamical core

Version 2, NASA Technical Memorandum 104606, 5, 58pp.

Takacs, L. L.and M. J. Suarez, 1996: Dynamical aspects of climate simulations using the

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pp.

Wood, E.F., and 28 others, 1998: The project for the intercomparison of land-surface

parameterization schemes (PILPS) phase 2(c) Red Arkansas river basin experiment, 1,

Experiment description and summary intercomparisons, J. Global and Planetary Change,

19, 115-135.

Xie, P. and P. Arkin, 1997: Global precipitation, a 17-Year monthly analysis based on gauge

observations, satellite estimates and numerical model outputs. Bull. Am. Met. Soc., 78,

2539-2558.

Zhou, J., Y. C. Sud, and K. M. Lau, 1996: Impact of orographically induced gravity wave

drag in the GLA GCM, Q. J. R. Meteorol. Soc, 122, 903-927.

12
ZONAL MEAN FIELDS

(DJF, MAM, JJA, SON)

Zonalwind

Meridionalwind

Massstreamfunction

Omega

Temperature

Specifichumidity

Relativehumidity

Zonalwind bias

Temperaturebias

Specifichumidity bias

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GLOBAL MAPS

(DJF, MAM, JJA, SON)

Zonalwind 200mb

Zonalwind 850mb

Sea-levelpressure

Eddygeopotentialheight300mb

Omega500mb

Eddy streamfunction 200mb

Velocitypotential200mb

ZONAL MEANS

Sea-levelpressure

Zonalwind 200mb

Zonalwind 850mb

33
DJF U200

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S I i I

0 60E 120E 180 120W 60W 0

Figure 19: Zonal wind at 200 mb for DJF-- Top: Model, Bottom: Reanalysis. Contour

interval: 5 m s-1. Easterlies indicated by dark shading, light shading indicates westerlies

in excess of 40 m s-1.

34
JJA U200

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S I I I I I

0 60E 120E 180 120W 60W 0

Figure 20: Zonal wind at 200 mb for JJA-- Top: Model, Bottom: Reanalysis. Contour

interval: 5 m s-1. Easterlies indicated by dark shading, light shading indicates westerlies

in excess of 40 m s-1.

35
MAM U200

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S I I i

0 60E 120E 180 120W 60W 0

Figure 21: Zonal wind at 200 mb for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 5 m s-1. Easterlies indicated by dark shading, light shading indicates westerlies

in excess of 40 m s-1.

36
SON U200

90N

60N

30N

EQ

30S

60S

90S

Model

90N

Reanalysis

60N

30N

EQ

30S

60S

90S , , , ,

0 60E 120E 180 120W 60W 0

Figure 22: Zonal wind at 200 mb for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 5 m s-1. Easterlies indicated by dark shading, light shading indicates westerlies

in excess of 40 m s-1.

37
DJF Usso

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S

0 60E 120E 180 120W 60W 0

Figure 23: Zonal wind at 850 mb for DJF-- Top: Model, Bottom: Reanalysis. Contour

interval: 3 m s-1. Easterlies are shaded.

38
JJA Uss0

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S

0 60E 120E 180 120W 60W 0

Figure 24: Zonal wind at 850 mb for JJA-- Top: Model, Bottom: Reanalysis. Contour

interval: 3 m s-1. Easterlies are shaded.

39
MAM Usso

90N

Model

60N

30N

EQ

30S

60S ........

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S

0 60E 120E 180 120W 60W 0

Figure 25: Zonal wind at 850 mb for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 3 m s-1. Easterlies axe shaded.

4O
SON U850

90N

Model

60N

30N

EQ

30S

60S

90S

90N

60N

30N

EQ

30S

60S

90S

Reanalysis

I I

0 60E 120E 180 120W 60W 0

Figure 26: Zonal wind at 850 mb for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 3 m s-1. Easterlies are shaded.

41
DJF SLP

90N

60N

30N

EQ

30S

60S

90S

Model

90N

Reanalysis

60N

30N

sq

30S

60S

90S

0 60E 120E 180 120W 60W 0

Figure 27: Sea-level pressure for DJF-- Top: Model, Bottom: Reanalysis. Contour interval:

4 mb. Shading indicates pressures in excess of 1000 mb.

42
JJA SLP

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S

0 60E 120E 180 120W 60W 0

Figure 28: Sea-level pressure for JJA-- Top: Model, Bottom: Reanalysis. Contour interval:

4 mb. Shading indicates pressures in excess of 1000 mb.

43
MAM SLP

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S I I I I

0 60E 120E 180 120W 60W 0

Figure 29: Sea-level pressure for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 4 mb. Shading indicates pressures in excess of 1000 mb.

44
SON SLP

90N

60N

30N

EQ

30S

60S

90S

Model

90N

60N

30N

EQ

30S

60S

90S

Reanalysis

I I I I

0 60E 120E 180 120W 60W 0

Figure 30: Sea-level pressure for SON-- Top: Model, Bottom: Reanalysis. Contour interval:

4 mb. Shading indicates pressures in excess of 1000 mb.

45
DJF Z*300

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S I ! I

0 60E 120E 180 120W 60W 0

Figure 31: Eddy geopotential height at 300 mb for DJF-- Top: Model, Bottom: Reanalysis.

Contour interval: 40 m. Shading indicates negative values.

46
JJA Z*3oo

90N

60N

30N

EQ

30S

60S

90S

Model

90N

Reanalysis

60N

30N

EQ

30S

60S

90S , , , ,

0 60E 120E 180 120W 60W 0

Figure 32: Eddy geopotential height at 300 mb for JJA-- Top: Model, Bottom: Reanalysis.

Contour interval: 40 m. Shading indicates negative values.

47
MAM Z*3oo

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S I I I I I

0 60E 120E 180 120W 60W 0

Figure 33: Eddy geopotential height at 300 mb for MAM-- Top: Model, Bottom: Reanalysis. Contour interval: 40 m. Shading indicates negative values.

48
SON Z*3oo

90N

60N

30N

EQ

30S

60S

90S

Model

90N

Reanalysis

60N

30N

EQ

30S

60S

90S _ , _

0 60E 120E 180 120W 60W 0

Figure 34: Eddy geopotential height at 300 mb for SON-- Top: Model, Bottom: Reanalysis.

Contour interval: 40 m. Shading indicates negative values.

49
DJF _500

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S I I I I I

0 60E 120E 180 120W 60W 0

Figure 35: Omega at 500 mb (mb d-I) for DJF-- Top: Model, Bottom: Reanalysis.

Contour interval: 4 mb d -1. Shading indicates rising motion.

5O
JJA _500

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S I I I I I

0 60E 120E 180 120W 60W 0

Figure 36: Omega at 500 mb (mb d -1) for JJA-- Top: Model, Bottom: Reanalysis. Contour

interval: 4 mb d -1. Shading indicates rising motion.

51
MAM _500

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S I I I I

0 60E 120E 180 120W 60W 0

Figure 37: Omega at 500 mb (rob d -i) for MAM-- Top: Model, Bottom: Reanalysis.

Contour interval: 4 mb d -1. Shading indicates rising motion.

52
SON _5oo

90N

60N

30N

EQ

30S

60S

90S

90N

60N

30N

EQ

30S

60S

90S

Model

Reanalysis

I I I i I

0 60E 120E 180 120W 60W 0

Figure 38: Omega at 500 mb (mb d -1) for SON-- Top: Model, Bottom: Reanalysis.

Contour interval: 4 mb d -1. Shading indicates rising motion.

53
DJF ¢*9.00

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S ! !

0 60E 120E 180 120W 60W 0

Figure 39: Eddy stream function at 200 mb for DJF-- Top: Model, Bottom: Reanalysis.

Contour interval: 5 × 10 6 m 2 s -1. Shading indicates negative values.

54
JJA ¢*200

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S l I I

0 60E 120E 180 120W 60W 0

Figure 40: Eddy stream function at 200 mb for JJA-- Top: Model, Bottom: Reanalysis.

Contour interval: 5 ×10 6 m 2 s-1. Shading indicates negative values.

55
MAM ¢*200

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S I I I I I

0 60E 120E 180 120W 60W 0

Figure 41: Eddy stream function at 200 mb for MAM-- Top: Model, Bottom: Reanalysis.

Contour interval: 5 x 106 m 2 s-1. Shading indicates negative values.

56
SON ¢'2oo

90N

Model

60N

30N

Eq

30S

60S

90S

90N

Reanalysis

60N

30N

Eq

30S

60S

90S I I I

0 60E 120E 180 120W 60W 0

Figure 42: Eddy stream function at 200 mb for SON-- Top: Model, Bottom: Reanalysis.

Contour interval: 5 x 106 m 2 s-1. Shading indicates negative values.

57
DJF X2oo

90N

60N

30N

EQ

30S

60S

90S

Model

90N

60N

30N

EQ

30S

60S

90S I I I

Reanalysis

0 60E 130E 180 120W 60W 0

Figure 43: Velocity potential at 200 mb for DJF-- Top: Model, Bottom: Reanalysis.

Contour interval: 5 x 106 m S s -1. Shading indicates negative values.

58
JJA X200

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S ! !

0 60E 120E 180 120W 60W 0

Figure 44: Velocity potential at 200 mb for JJA-- Top: Model, Bottom: Reanalysis. Contour interval: 5 x 106 m 2 s-1. Shading indicates negative values.

59
MAM X2oo

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

3ON

EQ

30S

60S

90S i I I

0 60E 120E 180 120W 60W 0

Figure 45: Velocity potential at 200 mb for MAM-- Top: Model, Bottom: Reanalysis.

Contour interval: 5 x 106 m 2 s-1. Shading indicates negative values.

6O
SON _200

90N

Model

60N

30N

EQ

30S

60S ......... -

90S

90N

Reanalysis

60N

30N

EQ

30S

60S ....... --

0 60E 120E 180 120W 60W 0

Figure 46: Velocity potential at 200 mb for SON-- Top: Model, Bottom: Reanalysis.

Contour interval: 5 xl06 m 2 s-1. Shading indicates negative values.

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GLOBAL MAPSOF SELECTED STATISTICS

(DJF, MAM, JJA, SON)

_200 mb

_200 mb

1⁄2t_-_+ _PJ 200_b

_500 mb

_850 mb

_200 nab

_200 mb

f_-_'J200_b

[v--Z_T_l 850 mb

-t_-_T'J850_b

t.--_Jsso _b

115
DJF

90N

60N

30N

nq

30S

60S

90S

Model

90N

60N

30N

EQ

30S

60S

90S

Reanalysis

0 60E 120E 180 120W 60W 0

Figure 98: _[_7_] at 200rob for DJF-- Top: Model, Bottom: Reanalysis. Contour interval:

2 m s-1. Shading indicates values exceeding 6 m s-1.

116
JJA :] :oo

90N

60N

30N

EQ

30S

60S

90S

Model

90N

60N

30N

EQ

30S

60S

90S

Reanalysis

0 60E 120E 180 120W 60W 0

Figure 99: _ at 200mb for JJA-- Top: Model, Bottom: Reanalysis. Contour interval:

2 m s-1. Shading indicates values exceeding 6 m s-1.

117
MAM 20o

90N

Model

60N

30N

EQ

30S

60S

90S

Reanalysis

90N

60N

30N

EQ

30S

60S

90S I i I I I

0 60E 130E 180 120W 60W 0

F_- _

Figure 100: _/[(u') 2] at 200mb for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 2 m s-1. Shading indicates values exceeding 6 m s-1.

118
SON

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S

0 60E 120E 180 120W 60W 0

Figure 101: _/[(u') _] at 200mb for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 2 m s-1. Shading indicates values exceeding 6 m s-1.

119
DJF

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S

0 60E 120E 180 120W 60W 0

Figure 102: _ at 200rob for DJF-- Top: Model, Bottom: Reanalysis. Contour

interval: 2 m s-1. Shading indicates values exceeding 6 m s-1.

120
JJA

90N

Model

60N

3ON

EQ

30S

60S

90S

90N

60N

30N

EQ

30S

60S

90S

Reanalysis

I I I I

0 60E 120E 180 120W 60W 0

Figure 103: y_v') 2] at 200mb for JJA-- Top: Model, Bottom: Reanalysis. Contour

interval: 2 m s-1. Shading indicates values exceeding 6 m s-1.

121
MAM 2o0

90N

Model

60N

30N

EQ

30S

60S

90S

90N

60N

30N

EQ

30S

60S

90S

Reanalysis

! !

0 60E 130E 180 120W 60W 0

Figure 104: _/[(v') 2] at 200mb for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 2 m s-1. Shading indicates values exceeding 6 m s-1.

122
SON :oo

90N

Model

60N

30N

EQ

30S

60S

90S

Reanalysis

90N

60N

30N

EQ

30S

60S

90S ! I

0 60E 120E 180 120W 60W 0

Figure 105: y/_v') 2] at 200rob for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 2 m s-1. Shading indicates values exceeding 6 m s-1.

123
DJF 1⁄2[(_'):+ (v')_]_00

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S I

0 60E 120E 180 120W 60W 0

Figure 106: 1⁄2[(u') 2 + (v') 2] at 200rob for DJF-- Top: Model, Bottom: Reanalysis. Contour

interval: 30 m 2 s-2. Shading indicates values exceeding 120 m 2 s-2.

124
JJA 200

90N

Model

60N

30N

EQ

30S

60S

90S

90N

60N

30N

EQ

30S

60S

90S

Reanalysis

I !

0 60E 120E 180 120W 60W 0

Figure 107: 1⁄2[(u') 2 + (v') 2] at 200mb for JJA-- Top: Model, Bottom: Reanalysis. Contour

interval: 30 m 2 s-2. Shading indicates values exceeding 120 m 2 s-2.

125
MAM 1 i)2 + 200

90N

Model

60N

30N

EQ

30S

60S

90S

Reanalysis

90N

60N

30N

EQ

30S

60S ........

90S , , ,

0 60E 120E 180 120W 60W 0

1 t2 _t2 Figure 108: _[(u ) + ( ) ] at 200mb for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 30 m 2 s-2. Shading indicates values exceeding 120 m 2 s-2.

126
SON _r_u'):+ (v'):] _oo Lk

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S !

0 60E 120E 180 120W 60W 0

Figure 109:1[(u')2 ÷ (v') 2] at 200mb for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 30 m 2 s-2. Shading indicates values exceeding 120 m 2 s-2.

127
DJF

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S I I I

0 60E 120E 180 120W 60W 0

Figure 110: _/[(w') 2] at 500mb for DJF-- Top: Model, Bottom: Reanalysis. Contour

interval: 20 mb d -1. Shading indicates values exceeding 120 mb d -1.

128
JJA

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S I I I I I

0 60E 120E 180 120W 60W 0

F_ _

Figure 111: _/[(w') 2] at 500mb for JJA-- Top: Model, Bottom: Reanalysis. Contour

interval: 20 mb d -1. Shading indicates values exceeding 120 mb d -1.

129
MAM 500

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S

0 60E 120E 180 120W 60W 0

Figure 112: ¢[(w') 2] at 500rob for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 20 mb d -1. Shading indicates values exceeding 120 mb d -1.

130
SON

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S , ,

0 60E 120E

I

180 120W 60W 0

Figure 113: 9/[(w')2] at 500mb for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 20 mb d -1. Shading indicates values exceeding 120 mb d -1.

131
DJF ([_-_ 2] sso

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S

0 60E 120E 180 120W 60W 0

f

Figure 114: _/[(T') 2] at 850mp (K) for DJF-- Top: Model, Bottom: Reanalysis. Contour

interval: 1 K. Shading indicates values exceeding 4 K.

132
JJA

90N

60N

30N

EQ

30S

60S

90S

Model

90N

Reanalysis

60N ':

30N

EQ

30S

60S

90S

0 60E 120E 180 120W 60W 0

Figure 115: _/_(T') 2] at 850mp (K) for JJA-- Top: Model, Bottom: Reanalysis. Contour

interval: 1 K. Shading indicates values exceeding 4 K.

133
MAM

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S

0 60E 120E 180 120W 60W 0

Figure 116: _[(T') 2] at 850mp (K) for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 1 K. Shading indicates values exceeding 4 K.

134
SON _/[(T') 2] 85o

90N

Model

60N

30N

EQ

30S

60S

90S

90N

60N

30N

EQ

30S

60S

90S

Reanalysis

0 60E 120E 180 120W 60W 0

Figure 117: V/_(T') 2] at 850rap (K) for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 1 K. Shading indicates values exceeding 4 K.

135
DJF _/[_)-2] 2oo

90N

60N

30N

EQ

30S

60S

90S

Model

90N

Reanalysis

60N

30N

EQ

30S

60S ........

90S

0 60E 120E 180 120W 60W 0

/-

Figure 118: _/[(Z') 2] at 200 mb (m) for DJF-- Top: Model, Bottom: Reanalysis. Contour

interval: 20 m. Shading indicates values exceeding 100 m.

136
JJA

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S

0 60E 120E 180 120W 60W 0

Figure 119: _/[(Z') 2] at 200 mb (m) for JJA-- Top: Model, Bottom: Reanalysis. Contour

interval: 20 m. Shading indicates values exceeding 100 m.

137
MAM 20o

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S I I I I

0 60E 120E 180 120W 60W 0

Figure 120: ¢[(Z') 2] at 200 mb (m) for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 20 m. Shading indicates values exceeding 100 m.

138
SON

90N

Model

60N

3ON

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S

0 60E 120E 180 120W 60W 0

f=.

Figure 121: _/[(Z_) 2] at 200 mb (m) for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 20 m. Shading indicates values exceeding 100 m.

139
DJF

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S ...........................

90S , , ,

0 60E 120E 180 120W 60W 0

Figure 122: .V/[(XO2] at 200mb for DJF-- Top: Model, Bottom: Reanalysis. Contour

interval: 2 × 106 m 2 s-1. Shading indicates values exceeding 8 × 106 m 2 s-1.

140
JJA :oo

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S I

0 60E 120E 180 120W 60W 0

F

Figure 123: VI[(X') 2] at 200mb for JJA-- Top: Model, Bottom: Reanalysis. Contour

interval: 2 x 106 m 2 s-1. Shading indicates values exceeding 8 x 106 m 2 s-1.

141
MAM 2oo

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

3ON

EQ

30S

60S

90S

0 60E 120E 180 120W 60W 0

Figure 124: _ at 200mb for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 2 xl06 m 2 s-1. Shading indicates values exceeding 8 ×106 m 2 s-1.

142
SON

90N

60N

3ON

EQ

30S

60S

90S

Model

90N

Reanalysis

60N

30N

EQ

0 60E 120E 180 120W 60W 0

Figure 125: V/_X_) 2] at 200rob for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 2 x 106 m 2 s -1. Shading indicates values exceeding 8 x 106 m 2 s -1.

143
DJF _ 200

90N

Model

60N

30N

EQ

30S

60S ..................... :.... _ '

90S

90N

60N

30N

EQ

30S

60S

90S

Reanalysis

I I I

0 60E 120E 180 120W 60W 0

Figure 126: [u-%Tv _] at 200 mb for DJF-- Top: Model, Bottom: Reanalysis. Contour interval:

20 m s s-2. Shading indicates negative values

144
JJA [_ 200

90N

60N

30N

EQ

30S

60S

90S

Model

90N

Reanalysis

60N

30N

EQ

30S

60S

90S , , , , '

0 60E 120E 180 120W 60W 0

Figure 127: [u'v'] at 200 mb for JJA-- Top: Model, Bottom: Reanalysis. Contour interval:

20 m 2 s-2. Shading indicates negative values

145
MAM [_ 200

90N

Model

60N

30N

EQ

30S

60S

90S

90N

60N

30N

EQ

30S

60S

90S

Reanalysis

I I I I

0 60E 120E 180 120W 60W 0

Figure 128: [u_-rV-_at 200 mb for MAM-- Top: Model, Bottom: Reanalysis. Contour interval:

20 m 2 s-2. Shading indicates negative values

146
SON [_ 2oo

90N

60N

30N

EQ

30S

60S

90S

Model

90N

Reanalysis

60N

30N

EQ

30S

60S

90S , , , ' '

0 60E 120E 180 120W 60W 0

Figure 129: [u--_Tv '] at 200 mb for SON-- Top: Model, Bottom: Reanalysis. Contour interval:

20 m 2 s-2. Shading indicates negative values

147
DJF _ 850

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S I I I I

0 60E 120E 180 120W 60W 0

Figure 130: [v'T _] at 850 mb for DJF-- Top: Model, Bottom: Reanalysis. Contour interval:

5 m s-1 K. Shading indicates negative values

148
JJA [_ 850

90N

Model

60N

30N

EQ

30S

60S

90S

90N

60N

30N

EQ

30S

60S

90S

Reanalysis

| |

0 60E 120E 180 120W 60W 0

Figure 131: [v'T'] at 850 mb for JJA-- Top: Model, Bottom: Reanalysis. Contour interval:

5 m s-1 K. Shading indicates negative values

149
MAM _-_ ss0

90N

Model

60N

30N

EQ

30S

60S

90S

90N

60N

30N

EQ

30S

60S

90S

Reanalysis

0 60E 120E 180 120W 60W 0

Figure 132: [v--_TT'] at 850 mb for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 5 m s-1 K. Shading indicates negative values

150
SON _ sso

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

I il lilli

60N

30N

EQ

30S

60S

90S I I I

0 60E 120E 180 120W 60W 0

Figure 133: [v-'rT_T_] at 850 mb for SON-- Top: Model, Bottom: Reanalysis. Contour interval:

5 m s-1 K. Shading indicates negative values

151
DJF -[J-_ sso

90N

Model

60N

30N

EQ

30S

60S

90S

90N

60N

30N

EQ

30S

60S

90S

Reanalysis

!

0 60E 120E 180 120W 60W 0

Figure 134: -[w--}-_T'] at 850 mb for DJF-- Top: Model, Bottom: Reanalysis. Contour interval:

50 mb d -1 K. Shading indicates downward heat transport

152
JJA -[w-_ ss0

90N

Model

60N

30N

E0

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S

0 60E 120E 180 120W 60W 0

Figure 135: -[w_T I] at 850 mb for JJA-- Top: Model, Bottom: Reanalysis. Contour interval:

50 mb d -1 K. Shading indicates downward heat transport

153
MAM -[_-_ 850

90N

Model

60N

30N

EQ

30S

60S

90S

90N

60N

30N

EQ

30S

60S

90S I !

Reanalysis

0 60E 120E 180 120W 60W 0

Figure 136: -[w-'r_T_] at 850 mb for MAM-- Top: Model, Bottom: Reanalysis. Contour

interval: 50 mb d -1 K. Shading indicates downward heat transport

154
SON -[_ sso

90N

Model

60N

30N

EQ

30S

60S

90S

Reanalysis

90N

60N

30N

EQ

30S

60S

90S ! !

0 60E 120E 180 120W 60W 0

Figure 137: -[w-7_T'] at 850 mb for SON-- Top: Model, Bottom: Reanalysis. Contour

interval: 50 mb d -1 K. Shading indicates downward heat transport

155
DJF _q_ ss0

90N

Model

60N

30N

nq

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S

0 60E 120E 180 120W 60W 0

Figure 138: [v-_ at 850 mb (ms -1 g kg -1) for DJF-- Top: Model, Bottom: Reanalysis.

Contour interval: 2 m s-1 g kg -1. Shading indicates negative values

156
JJA _ 850

90N

60N

30N

EQ

30S

60S

90S

90N

Model

Reanalysis

60N

30N

EQ

30S

60S

90S I I

0 60E 120E 180 120W 60W 0

Figure 139: [v-_] at 850 mb (ms -1 g kg -1) for JJA-- Top: Model, Bottom: Reanalysis.

Contour interval: 2 m s-1 g kg -1. Shading indicates negative values

157
MAM _ 850

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S I I I

0 60E 120E 180 120W 60W 0

Figure 140: [v-_] at 850 mb (m s-1 g kg -1) for MAM-- Top: Model, Bottom: Reanalysis.

Contour interval: 2 m s-1 g kg -1. Shading indicates negative values

158
SON _ ss0

90N

Model

60N

30N

EQ

30S

60S

90S

90N

Reanalysis

60N

30N

EQ

30S

60S

90S

0 60E 120E 180 120W 60W 0

Figure 141: [v--_] at 850 mb (m S-1 g kg -1) for SON-- Top: Model, Bottom: Reanalysis.

Contour interval: 2 m s-1 g kg -1. Shading indicates negative values

159
160
GLOBAL MAPS OF PHYSICS DIAGNOSTICS

(DJF, MAM, JJA, SON)

Precipitation

Total Precipitable Water

Outgoing Longwave Radiation

Longwave Cloud Radiative Forcing

Absorbed Solar Radiation

Solar Cloud Radiative Forcing

Net Radiation at the Top of the Atmosphere

Net Cloud Radiative Forcing

Zonal Surface Stress (_'x)

Meridional Surface Stress (Ty)

Curl of the Surface Wind Stress

Surface Sensible Heat Flux

Surface Latent Heat Flux

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Previous Volumes in This Series

Volume 1

September 1994

Volume 2

October 1994

Volume 3

December 1994

Volume 4

January 1995

Volume 5

April 1995

Volume 6

April 1995

Volume 7

September 1995

Volume 8

March 1996

Documentation of the Goddard Earth Observing System

(GEOS) general circulation model - Version 1

L.L. Takacs, A. Molod, and T. Wang

Direct solution of the implicit formulation of fourth order

horizontal diffusion for gridpoint models on the sphere

Y. Li, S. Moorthi, and J.R. Bates

An efficient thermal infrared radiation parameterization for

use in general circulation models

M.-D. Chou and M.J. Suarez

Documentation of the Goddard Earth Observing System

(GEOS) Data Assimilation System - Version 1

James Pfaendtner, Stephen Bloom, David Lamich,

Michael Seablom, Meta Sienkiewicz, James Stobie,

and Arlindo da Silva

Documentation of the Aries-GEOS dynamical core: Version 2

Max J. Suarez and Lawrence L. Takacs

A Multiyear Assimilation with the GEOS-1 System:

Overview and Results

Siegfried Schubert, Chung-Kyu Park, Chung-Yu Wu,

Wayne Higgins, Yelena Kondratyeva, Andrea Molod,

Lawrence Takacs, Michael Seablom, and Richard

Rood

Proceedings of the Workshop on the GEOS-1 Five-Year

Assimilation

Siegfried D. Schubert and Richard B. Rood

Documentation of the Tangent Linear Model and Its Adjoint

of the Adiabatic Version of the NASA GEOS-1 C-Grid GCM:

Version 5.2

Weiyu Yang and I. Michael Navon

193
Volume 9

March 1996

Volume 10

April 1996

Volume 11

May 1997

Volume 12

August 1997

Volume 13

December 1997

Volume 14

August 1998

Volume 15

June 1999

Volume 16

November 1999

Energy and Water Balance Calculations in the Mosaic LSM

Randal D. Koster and Max J. Suarez

Dynamical Aspects of Climate Simulations Using the GEOS

General Circulation Model

Lawrence L. Takacs and Max J. Suarez

Documentation of the Tangent Linear and its Adjoint Models

of the Relaxed Arakawa-Schubert Moisture Parameterization

Package of the NASA GEOS-1 GCM (Version 5.2)

Weiyu Yang I. Michael Navon, and Ricardo Todling

Comparison of Satellite Global Rainfall Algorithms

Alfred T.C. Chang and Long S. Chiu

Interannual Variability and Potential Predictability in Reanalysis Products

Wie Ming and Siegfried D. Schubert

A Comparison of GEOS Assimilated Data with FIFE

Observations

Michael G. Bosilovich and Siegfried D. Schubert

A Solar Radiation Parameterization for Atmospheric Studies

Ming-Dah Chou and Max J. Suarez

Filtering Techniques on a Stretched Grid General Circulation

Model

Lawrence Takacs, William Sawyer, Max J. Suarez,

and Michael S. Fox-Rabinowitz

194

REPORT DOCUMENTATION PAGE J FormApproved OMB No. 0704-0188

Public reporting burden for this collection of information is estimated to average I hour per response, including the time for reviewing instructions, searching existing data sources,

gathering and maintaining the data needed, and completing end reviewing the collection of information, Send comments regarding this burden estimate or any other aspect of this

collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations end Reports, 1215 Jefferson

Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188), Washington, DC 20503,

I" AGENCYUSEONLY(LeBveblank) I2"REPORTDATEjuIy 2000 T 3"REPORT COVEREDTechnical TYPEMemorandum ANDDATES

4. TITLE AND SUBTITLE 5. FUNDING NUMBERS

Technical Report Series on Global Modeling and Data Assimilation

Volume 17--Atlas of Seasonal Means Simulated by the NSIPP 1

---Atmospnenc GeM Code 970

6. AUTHOR(S) 971-622-24-47-26

Julio Bacmeister, Philip J. Pegion, Siegfried D. Schubert, and Max J. Suarez

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS (ES)

Oceans and Ice Branch

Goddard Space Flight Center

Greenbelt, Maryland 20771

9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS (ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

8. PEFORMING ORGANIZATION

REPORT NUMBER

2000-03740-0

10. SPONSORING I MONITORING

AGENCY REPORT NUMBER

TM-2000-104606, Vol. 17

11. SUPPLEMENTARYNOTES

J. Bacmeister: Universities Space Research Associates; P.J. Pegion: General Sciences Corporation, Laurel,

Maryland

12a. DISTRIBUTION I AVAILABILITY STATEMENT

Unclassified-Unlimited

Subject Category:46

Report available from the NASA Center for AeroSpace Information,

7121 Standard Drive, Hanover, MD 21076-1320. (301) 621-0390.

13. ABSTRACT (Maximum 200 words)

12b. DISTRIBUTION CODE

This atlas documents the climate characteristics of version 1 of the NASA Seasonal-to-lnterannual Prediction Project

(NSIPP) Atmospheric General Circulation Model (AGCM). The AGCM includes an interactive land model (the Mosaic

scheme), and is part of the NSIPP coupled atmosphere-land-ocean model. The results presented here are based on a

20-year (December 1979-November 1999) "AMIP-style" integration of the AGCM in which the monthly-mean sea-surface

temperature and sea ice are specified from observations. The climate characteristics of the AGCM are compared with the

National Centers for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasting

(ECMWF) reanalyses. Other verification data include Special Sensor Microwave/lmager (SSM/I) total precipitable water,

the Xie-Arkin estimates of precipitation, and Earth Radiation Budget Experiment (ERBE) measurements of short and long

wave radiation. The atlas is organized by season. The basic quantities include seasonal mean global maps and zonal and

vertical averages of circulation, variance/covariance statistics, and selected physics quantities.

14. SUBJECT TERMS

Numerical Methods, Atmospheric Models, Regional Models, Climate Models

Atmospheric General Circulation

17. SECURITY CLASSIFICATION

OF REPORT

Unclassified

NSN 7540-01-280-5500

18. SECURITY CLASSIFICATION

OF THIS PAGE

Unclassified

19. SECURITY CLASSIRCATION

OF ABSTRACT

Unclassified

15. NUMBER OF PAGES

192

16. PRICE CODE

20. LIMITATION OF ABSTRACT

UL

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