NASA/TM-2001-104606, Vol. 20

Technical Report Series on

Global Modeling and Data Assimilation

Max J. Suarez, Editor

Volume 20

The Climate of the FVCCM-3 Model

Yehui Chang, Sieg_'ied D. Schubert, Shian-Jiann Lin, Sharon Ncht_da. and Bo-Wen Shen

August 2001
- ?

Available from:

NASA Center for AeroSpace Information National Technical Information Service

7121 Standard Drive 5285 Port Royal Road

Hanover, MD 21076-1320 Springfield, VA 22161

Price Code: AI7 Price Code: A10

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Abstract

This document describes the climate of version 1 of the NASA-NCAR model developed at the Data Assimilation Office (DAO). The model consists of a new finite-volume

dynamical core and an implementation of the NCAR climate community model (CCM3) physical parameterizations. The version of the model examined here was integrated

at a resolution of 20 latitude by 2.50 longitude longitude and 32 levels. The results

are based on a simulation that was forced with observed sea surface temperature and

sea ice for the period 1979-1995, and are compared with NCEP/NCAR reanalyses and

various other observational data sets. The results include an assessment of seasonal

means, subseasonal transients including the Madden Julian Oscillation, and interannual variability. The quantities include the zonal and meridional winds, temperature,

specific humidity, geopotential height, stream function, velocity potential, precipitaion,

sea level pressure, and cloud radiative forcing.

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Contents

List of Figures

1 Introduction

1.1 The NASA-NCAR Model ............................

1.2 Verification Data .................................

2 Results

2.1 Seasonal Means ..................................

2.2 Interannual Variability of Seasonal Means ...................

2.3 Intra-monthly Variability ............................

2.3.1 Total ....................................

2.3.2 Filtered Fields ..............................

2.4 The Madden-Julian Oscillation .........................

2.4.1 Data processing ..............................

2.4.2 Results ..................................

3 Conclusions

References

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List of Figures

Seasonal Mean Zonal Means 17

Seasonal means of zonally averaged u-wind for the model. The contour intervals are =t=5, 10, 15, 20, 25, 30, 40, 50 m s-1. Negative values are shaded. 18

Seasonal means of zonally averaged u-wind for the reanalysis. The contour

intervals are 4- 5, 10, 15, 20, 25, 30, 40, 50 m s-1. Negative values are shaded. 19

Seasonal means of zonally averaged u,wind difference between the model and

the reanalysis. The contour interval is 2 m s-1. Negative values are shaded. 2O

Seasonal means of zonally averaged v-wind for the model. The contour interval is 0.5 m s-l. Negative values are shaded ................. 21

Seasonal means of zonally averaged v-wind for the reanalysis. The contour

interval is 0.5 m s-1. Negative values are shaded ................ 22

Seasonal means of zonally averaged v-wind difference between the model and

the reanalysis. The contour interval is 0.2 m s-1. Negative values are shaded. 23

Seasonal means of zonally averaged temperature for the model. The contour

interval is 5°K. Values less than 220°K are shaded ............... 24

Seasonal means of zonally averaged temperature for the reanalysis. The

contour interval is 5°K. Values less than 220°K are shaded .......... 25

Seasonal means of zonally averaged temperature difference between the model

and the reanalysis. The contour interval is 2°K. Negative values are shaded. 26

10 Seasonal means of zonally averaged specific humidity for the model. The

contour interval is 2 g kg -1. Values larger than 8 g kg -1 are shaded ..... 27

11 Seasonal means of zonally averaged specific humidity for the reanalysis. The

contour interval is 2 g kg -1. Values larger than 8 g kg -1 are shaded ..... 28

12 Seasonal means of zonally averaged specific humidity difference between the

model and the reanalysis. The contour interval is 0.5 g kg -1. Negative values

are shaded ..................................... 29

13 Seasonal means of zonally averaged mass stream function for the model. The

contour interval is 2× 101° kg s-1. Negative values are shaded ......... 3O

14 Seasonal means of zonally averaged mass stream function for the reanalysis.

The contour interval is 2× 10 l° kg s-1. Negative values are shaded ......

31

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15 Seasonalmeansof zonallyaveragedmassstreamfunctiondifferencebetween

themodelandthe reanalysis.Thecontourintervalis 0.5×10l° kg s-1. Negativevaluesareshaded.............................. 32

Seasonal Mean Global Maps

16 Seasonalmeansof 50mb u-windfor the model.Thecontourintervalis 5 m

s-1. Negativevaluesareshaded.........................

17 Seasonalmeansof 50mb u-windfor the reanalysis.The contourintervalis

5 m s-1. Negativevaluesareshaded.......................

18 Seasonalmeansof 200mb u-windfor the model. Thecontourintervalis 5

m s-1. Negativevaluesareshaded........................

19 Seasonalmeansof 200mbu-windfor the reanalysis.Thecontourintervalis

5 m s-1. Negativevaluesareshaded.......................

20 Seasonalmeansof 850mb u-windfor the model. Thecontourintervalis 5

m s-1. Negativevaluesareshaded........................

21 Seasonalmeansof 850mbu-windfor the reanalysis.Thecontourintervalis

5 m s-1. Negativevaluesareshaded.......................

22 Seasonalmeansof 300mb eddyheightfor the model. Thecontourinterval

is 40m. Thezerocontouris omitted.Negativevaluesareshaded......

23 Seasonal meansof 300mbeddyheightforthereanalysis.Thecontourinterval

is 40m. Thezerocontouris omitted. Negativevaluesareshaded......

24 Seasonal meansof 200nabstreamfunctionforthemodel.Thecontour interval

IS 5× 10 6 m _ s-1. Negative values are shaded ..................

25 Seasonal means of 200 nab streamfuction for the reanalysis. The contour

interval is 5×10 6 m2 s-1. Negative values are shaded .............

26 Seasonal means of 200 mb velocity potential for the model. The contour

interval is 2×106 m 2 s -1. Negative values are shaded .............

27 Seasonal means of 200 mb velocity potential for the reanalysis. The contour

interval is 2×106 m2 s-1. Negative values are shaded .............

28 Seasonal means of sea level pressure for the model. The contour interval is 5

rob. Values larger than 1015 mb are shaded ...................

29 Seasonal means of sea level pressure for the reanalysis. The contour interval

is 5 mb. ValUes larger than 2015 mb are shaded ................

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39

Seasonal meansof precipitationforthemodelduring1980-1995.Thecontour

intervalis 2 mmday-1. Valueslargerthan4 mmday-1 havelight shading.

Valueslargerthan 8 mm day-1 havedark shading..............

Seasonalmeansof precipitation for Xie-Arkin data during 1980 1995. The

contour interval is 2 mm day -1. Values larger than 4 mm day -1 have light

shading. Values larger than 8 mm day -1 have dark shading .........

Seasonal means of shortwave cloud radiative forcing for the model during

1985-1991. The contour interval is 20 W m -2. Values larger than 80 W m -2

are shaded .....................................

Seasonal means of shortwave cloud radiative forcing for ERBS data during

1981-1991. The contour interval is 20 W m -2. Values larger than 80 W m -2

are shaded .....................................

Seasonal means of longwave cloud radiative forcing for the model during

1985-1991. The contour interval is 20 W m -2. Values larger than 40 W m -2

are shaded .....................................

Seasonal means of longwave cloud radiative forcing for ERBS data during

1985 1991. The contour interval is 20 W m -2. Values larger than 40 W

m -2 are shaded ..................................

Seasonal means of outgoing longwave radiation for the model during 1980

1995. The contour interval is 20 W m -2. Values less than 220 W m -_ are

shaded .......................................

Seasonal means of outgoing longwave radiation for NOAA data during 1980

1995. The contour interval is 20 W m -2. Values less than 220 W m -2 are

shaded .......................................

Seasonal means of total precipitable water for the model during Jul 1987 Jan

1992. The contour interval is 5 kg m -2. Values larger than 40 kg m -2 are

shaded .......................................

Seasonal means of total precipitable water for SSM/I data during Jul 1987-

Jan 1992. The contour interval is 5 kg m -2. Values larger than 40 kg m -2

are shaded .....................................

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57

Interannual Variability of Seasonal Means 59

40 Standard deviation of 50 mb u-wind for the model. The contour interval is

1 m s-1. Values larger than 4 m s-1 are shaded ................ 60

41 Standard deviation of 50 mb u-wind for the reanalysis. The contour interval

is 1 m s-1. Values larger than 4 m s-1 are shaded ............... 61

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42

43

Standard deviation of 200 mb u-wind for the model. The contour interval is

1 m s-1. Values larger than 4 m s-1 are shaded ................ 62

Standard deviation of 200 mb u-wind for the reanalysis. The contour interval

is 1 m s-1. Values larger than 4 m s -1 are shaded ............... 63

44 Standard deviation of 850 mb u-wind for the model. The contour interval is

1 m s-1. Values larger than 2 m s-1 are shaded ................ 64

45 Standard deviation of 850 mb u-wind for the reanalysis. The contour interval

is 1 m s-_. \Zalues larger than 2 m s-a are shaded, 65

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47

48

49

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Standard deviation of 300 mb height for the model. The contour interval is

20 m. Values larger than 40 m are shaded .................... 66

Standard deviation of 300 mb height for the reanalysis. The contour interval

is 20 m. Values larger than 40 m are shaded .................. 67

Standard deviation of 200 mb streamfunction from the model. The contour

interval is lxl06 m 2 s-1. Values larger than 4x106 m 2 s-1 are shaded .... 68

Standard deviation of 200 mb streamfunction for the reanalysis. The contour

interval is 1×106 m2 s-l. Values larger than 4x106 m 2 s-1 are shaded .... 69

Standard deviation of 200 mb velocity potential for the model. The contour

interval is 0.5x106 m 2 s-1. Values larger than 2x106 m 2 s-1 are shaded... 7O

51 Standard deviation of 200 mb velocity potential for the reanalysis. The contour interval is 0.5×10 6 m 2 s -1. VMues larger than 2×106 m 2 s-1 are shaded. 71

52 Standard deviation of outgoing longwave radiation for the model. The contour interval is 5 W m -2. Values larger than 10 W m -2 are shaded ..... 72

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57

Standard deviation of outgoing longwave radiation for the reanalysis, during

1979 1995. The contour interval is 5 W m -2. Values larger than l0 W m -2

are shaded ..................................... 73

Standard deviation of precipitation for the model during 1980 1995. The

contour interval are 1 mm day -1, Values larger than 2 mm day -1 are shaded. 74

Standard deviation of precipitation for Xie-Arkin data during 1980 1995.

The contour interval are 1 mm day -1. Values larger than 2 mm day -1 are

shaded ....................................... 75

Standard deviation of sea level pressure for the model. The contour interval

is 1: rob. Values larger than 4 mb are shaded .................. 76

Standard deviation of sea level pressure for the reanalysis. The contour

interval is 1 mb. Values larger than 4 mb are shaded .............. 77
Zonal Mean Intra-monthly Variability 79

58 _ for the model. The contour interval is 50 m. Values larger than 100

m are shaded ....................................

59 _for the reanalysis. The interval is 50 Values than contour m. larger

100 m are shaded .................................

60 Zonal mean [(u'u'+ v'v')/2] for the model. The contour interval is 20 m 2

s-2. Values larger than 60 m 2 s-2 are shaded ..................

61 Zonal mean [(u'u' + v'v')/2] for the reanalysis. The contour interval is 20 m 2

s-2. Values larger than 60 m _ s-2 are shaded ..................

62 Zonal mean [_ for the model. The contour interval is 10 m 2 s-2. Negative

values are shaded .................................

63 Zonal mean [u'v I] for the reanalysis. The contour interval is 10 m 2 s-2.

Negative values are shaded ............................

64 Zonal mean [vT] for the model. The contour interval is 5 m s-1 K. Negative

values are shaded .................................

65 Zonal mean [v_-_ for the reanalysis. The contour interval is 5 m s-1 K.

Negative values are shaded ............................

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87

Global

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Maps of Intra-monthly Variability 89

zv_z _ at 300 mb for the model. The contour interval is 20 m. Values larger

than 100 m are shaded .............................. 90

67 zv_z _ at 300 mb for the reanalysis. The contour interval is 20 m. Values

larger than 100 m are shaded ........................... 91

68 (u_u _+ v_v_)/2 at 200 mb for the model. The contour interval is 30 m 2 s-2.

Values larger than 120 m 2 s-2 are shaded .................... 92

69 (uluP-t-vlv_)/2 at 200 mb for the reanalysis. The contour interval is 30 m 2

s-2. Values larger than 120 m 2 s-2 are shaded ................. 93

70 u_v_ at 200 mb for the model. The contour interval is 20 m 2 s-2. Negative

values are shaded ................................. 94

71 u_v_ at 200 mb for the reanalysis. The contour interval is 20 m 2 s-2. Negative

values are shaded ................................. 95

72 vT at 850 mb for the model. The contour interval is 5 m s-1 K. Negative

values are shaded ................................. 96

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73 vT at 850 mb for the reanalysis. The contour interval is 5 m s -1 K. Negative

values are shaded ................................. 97

Filtered Intra-monthly Variability 99

74 _ at 300 mb for the frequency ( 2.5 < T < 6 days ) for the model. The

contour interval is 10 m. Values larger than 40 m are shaded ......... 100

75 _ at 300 mb for the frequency ( 2.5 < T <: 6 days ) for the reanalysis.

The contour interval is 10 m. Values larger than 40 m are shaded ...... 101

76 (uru'+ v'v')/2 at 200 mb for the frequency ( 2.5 < T <: 6 days ) for the

model. The contour interval is 10 m 2 s-2. Values larger than 40 m 2 s-2 are

shaded ....................................... 102

77 (u'u'+ v'v')/2 at 200 mb for the frequency ( 2.5 < T < 6 days ) for the

reanalysis. The contour interval is 10 m 2 s-2. Values larger than 40 m 2 s-2

are shaded ..................................... 103

78 utv _ at 200 mb for the frequency ( 2.5 < T < 6 days ) for the model. The

contour interval is 10 m 2 s-2. Negative values are shaded ........... 104

79 u_v_ at 200 mb for the frequency ( 2.5 < T <: 6 days ) for the reanalysis. The

contour interval is 10 m 2 s-2. Negative values are shaded ........... 105

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81

vT at 850 mb for the frequency ( 2.5 < T <

contour interval is 2 m s -1 K. Negative values

v't' at 850 mb for the frequency ( 2.5 < T < 6

contour interval is 2 m s-1 K. Negative values

6 days ) for the model. The

are shaded ........... 106

days ) for the reanalysis. The

are shaded ........... 107

82 zv_7_z_ at 300 mb for the frequency ( 10 < T < 30 days ) for the model. The

contour interval is 20 m. Values larger than 80 m are shaded ......... 108

83 zv_Tz _ at 300 mb for the frequency ( t0 < T < 30 days ) for the reanalysis.

The contour interval is 20 m. Values larger than 80 m are shaded ...... 109

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(u'u'+ v'v')/2 at 200 mb for the frequency ( 10 < T < 30 days ) for the

model. The contour interval is 20 m 2 s-2. Values larger than 40 m 2 s-2 are

shaded .................. ..................... 110

(u_u'+ v'v')/2 at 200 mb for the frequency ( 10 < T < 30 days ) for the

reanalysis. The contour interval is 20 m 2 s -2. Values larger than 40 m 2 s-2

are shaded ..................................... 111

u'v' at 200 mb for the frequency ( 10 < T < 30 days ) for the model. The

contour interval is 10 m 2 s-2. Negative values are shaded ........... 112

xii
87 u_v J at 200 mb for the frequency ( 10 < T < 30 days ) for the reanalysis. The

contour interval is 10 m 2 s-2. Negative values are shaded ........... 113

88 vT at 850 mb for the frequency ( 10 < T < 30 days ) for the model. The

contour interval is 2 m s-1 K. Negative values are shaded ........... 114

89 vT at 850 mb for the frequency ( 10 < T < 30 days ) for the reanalysis. The

contour interval is 2 m s-1 K. Negative values are shaded ........... 115

The Madden-Julian Oscillation 117

9O 200 mb velocity potential variances (20-90 days) a) for the model, b) for the

reanalysis. The contour interval is 2× 1012 m 4 s-2. Values larger than 4 × 1012

m 4 s-_ are shaded ................................. 118

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The first complex EOF of the 200 mb velocity potential filed from the model

(left panels) and the reanalysis (right panels). The numbers in ther upper

right correspond to the phase angle. Negative values are dashed ....... 119

Time-frequency representation of the modulus of the the complex Morlet

wavelet coefficients based on the first complex EOF of the 200 mb velocity

potential filed from the model. The left ordinate is frequency in octive, and

the right ordinate is period in day ........................ 120

Time-frequency representation of the modulus of the the complex Morlet

wavelet coefficients based on the first complex EOF of the 200 mb velocity

potential filed from the reanalysis. The left ordinate is frequency in octive,

and the right ordinate is period in day ..................... 121

94 Composite anomalies of OLR and 200mb velocity potential at times of amplitude of the first component of 200mb velocity potential is larger than one

standard deviation. The contour is for the velocity potential and the negative

values are dashed. Shading is for OLR. Units are W m -2 ........... 122

95 Composite anomalies of TPW and 200mb velocity potential at times of amplitude of the first component of 200mb velocity potential is larger than one

standard deviation. The contour is for the velocity potential and the negative

values are dashed. Shading is for TPW. Units are km m -2 .......... 123

96 Composite anomalies of OLR and 200rob streamfunction at times of amplitude of the first component of velocity potential at 200mb is larger than one

standard deviation. The contour is for the streamfunction and the negative

values are dashed. Shading is for OLR. Units are W m -2 ........... 124

.,,

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1 Introduction

The NASA-NCAR GCM is part of the next generation Data Assimilation System (DAS)

currently being developed within the Data Assimilation Office (DAO) of the Goddard LaboJ

ratory for Atmospheres. It is the follow-on to the GEOS GCM development - see the DAO's

Algorithm Theoretical Basis Document (DAO 1996) for a description of the GEOS-2 DAS.

The main characteristics of this new model-analysis system are described in da Silva and

Lin (2001).

The NASA-NCAR GCM is being developed jointly by the DAO and the Climate and Global

Dynamics Division (CGDD) at NCAR. The model is based on the finite-volume dynamical

core developed at the DAO (Lin and Rood 1996, Lin and Rood 1997, Lin 1997, Lin and

Rood 1998), with physical parameterizations from the NCAR CCM (Kiehl et al. 1996).

The main features of the model are summarized in the next section.

This document describes the climate of the initial version of this model (internal version

number 1.1.2d) consisting of the finite-volume dynamical core and an implementation of

version three of the NCAR climate community model (CCM-3) physical parameterizations.

The model was integrated at a resolution of 2 ° latitude by 2.5 ° longitude and 32 levels. The

results are based on a single simulation for the period 1979-1995 forced with observed sea

surface temperature and sea ice (Reynolds and Smith 1994). All output from the simulation

was saved every 6 hours as instantaneous fields. All quantities presented here are based on

daily mean values (the average of the four times daily values).

1.1 The NASA-NCAR Model

The finite-volume dynamical core has a horizontal discretization builtupon the Flux- Form

Semi-Lagrangian (FFSL) transport algorithms (Lin and Rood 1996; Lin and Rood 1997).

The vertical structure is based on the Lagrangian control-volume concept of Lin (1997) and

Lin and Rood (1998). This physically based dynamical core allows circumventing many

of the problems associated with sigma, pressure, or isentropic coordinates, increasing the

physical integrity and computational efficiency of the model. The novel attributes of the

dynamical core can be summarized as follows:

Terrain-following Lagrangian control-volume vertical coordinate with a monotonicitypreserving and mass-, momentum-, and total energy-conserving mapping algorithm

to the "fixed" Eulerian reference coordinate.

Two-dimensional conservative flux-form semi-Lagrangian transport between two bounding "horizontal" Lagrangian surfaces that define the finite control-volume.

Accurate representation of the terrain. Accurate and physically consistent finitevolume integration of pressure gradient force for the terrain-following Lagrangian

control-volume.
• Highlyadaptableandcomputationallyefficientalgorithmdesignfor vectoror RISCbasedparallelcomputers.

The vertical coordinateconsistsof a hybrid sigma-pressurecoordinate.The approximate

pressurelevelsof themodelusedherearegivenin Table1. Wenotethat, whilethis version

of themodelhas32levels,morerecentversionsof themodelhaveadditionallevelsto better

resolvethe stratosphere.

Table1: Approximatepressurelevelsof the32-layermodel

mb_-] mb

1 0.7000 2 1.5000

5] 8.1500 6 12.5500

9 32.9000 10 41.0750

13 72.2925 14 85.4390

17 139.1150 18 163.6615

21 266.4789 22 313.4988

25 510.4555 26 600.5238

29 867.1572 30 929.6455

_-_ mb _ mb

3 2.8500 4 5.0000

7 18.3500 8 25.3000

1t 50.i000 12 60.5613

15 100.5145 16 118.2502

19 192.5410 20 226.5135

23 368.8161 24 433.8927

27 696.7930 28 787.7000

31 970.5525 32 992.5550

The NCAR physical parameterizations represent a set of processes with a long history of

development and documentation (Kiehl et al., 1996 for CCM3, Hack et al., 1993 for CCM2).

The components of the CCM3 physics that are implemented in the initial version of the

NASA/NCAR model consist of the following:

• Moist physics: M

- deep convection (Zhang and McFarlane 1995) with updrafts and downdrafts

- mid and shallow convection (Hack 1998)

- large-scale condensation (Sundqvist 1988)

- rain evaporation

• Radiation and clouds: R

- Kiehl et al. 1994;1996

- long wave computation includes CO2,O3,CH4,N20,H20,CFCll,CFC12

- short wave computation includes a background aerosol (0.14 optical depth)

- diagnostic clouds are a generalization of Slingo (1987)

• Land and Ocean surface: S

- Land Surface model (Bonan i996)

±: :: :

- Prescribed sea surface temperature

2
• PlanetaryBoundaryLayer:T

- first orderclosurescheme

- localandnon-localtransport(ttoltslagandBoville1992)

• Gravity wavedrag:W

- McFarlane1987

In the current setupof the modelthe dynamicsand the physicalparameterizationsare

operator-splitasfollows:

Wn+ 1 -_ W(T(S(R(M(D(wn)))))) (1)

where w represents the prognostic variables (the state vector), D the finite-volume dynamical core, M the moist physics packages (cumulus convection with both updrafts and

downdrafts, mid and shallow convection, and the large-scale condensation and rain reevaporation), R the cloud and radiation packages (diagnostic clouds; short and long wave

radiation schemes), oc the surface models (prescribed sea-ice and SST; the land surface

model), T the PBL mixing/turbulence parameterization, and W is the gravity wave drag

parameterization. The model is effectively two-time-level with each operator (from D to W

in alphabetic order) performing instant "adjustments" to the state vector w.

The next generation of CCM parameterizations (CCM4) will include prognostic cloud water, a revised radiation scheme, a new moist convection scheme, and the common land

model (CLM). In addition, to allow higher vertical resolution, an improved cloud overlap

specification scheme will be incorporated.

1.2 Verification Data

The upper air prognostic fields and their statistics are compared with the NCEP/NCAR

reanalysis (Kalnay et al. 1995) averaged for the same period as the model simulation.

The precipitation fields are compared with the combined gauge and satellite estimates of

Xie and Arkin (1997).

Outgoing long-wave radiation (OLR) is compared with the satellite estimates from the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite,

Data and Information Service. The OLR estimates have been computed for June 1974 to

the present (Gruber and Kruger 1984) based on theoretical and empirical relationships between total flux and the 11 #m window radiance measurements. The measurements are

from the scanning radiomenter in the NOAA 2-5 advanced very high radiation radiometer

(AVHRR) instrument on TIROS-N and NOAA 6-10.

Cloud forcing is compared with estimates from the Earth Radiation Budget Experiment

(ERBS) based on measurements from the ERBS, NOAA 9 and NOAA 10 satellites between
November1984andFebruary1990.Welimit our comparsionto theyears1985-1989.More

informationonERBE maybeobtainedfrom

(http:/ /asd-www.larc.nasa.gov/erbe/ ASDerbe.html).

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 model that accounts

for absorption and emission in the atmosphere. The model uses a surface emissivity value

over oceans appropriate for a wind roughened sea surface. The model does not account for

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

rates. No calculation is done over land or sea ice, because of the complexity of the surface

emissivity. These data are available starting in June 1987.

2 Results

In the following we present some selected results from the 17-year model simulation and

compare them with observations and NCEP/NCAR analysis data. The discussion is meant

to serve as a baseline evaluation of this version of the model, and to highlight those aspects

of the simulation that require improvement. For some quantities, our verification data cover

only a subset of the 17 years. In that case, the comparison with the model simulation is

restricted to only those years. This is noted in the Figures.

The first section presents the seasonal mean fields, both as global maps and zonal means.

The seasons are defined by the calendar months December-January-February (DJF), MarchApril-_.'lay (MAM), June-July-August (JJA), and September-October-November (SON).

The quantities consist of the zonal wind, eddy (deviations from the zonal mean) height,

eddy stream function, velocity potential, sea level pressure, precipitation, surface temperature, short- and long- wave cloud forcing, total precipitable water, and outgoing long

wave radiation. The next section shows the interannual variability of the seasonal mean

fields, including the 50rob, 200mb, and 850rob zonal wind, the 300rob height, 200rob stream

function, 200rob velocity potential, sea level pressure, and precipitation. The subsequent

sections deal with variability on sub-seasonal time scales, including all sub-monthly, 2.5-6

day, and 10-30 day time scales. The quantities include, the height field, kinetic energy, and

northward momentum and heat fluxes. The last section of the results examines tropical

variability with time scales of 20-90 days. The analysis includes a complex empirical orthogonal function decomposition and a wavelet decomposition to help assess the ability of

the model to represent the Madden-Julian Oscillation (Madden and Julian 1972).

2.1 Seasonal Means

Overall the model does a good job in capturing the seasonal cycle of the zonal mean zonal

and meridional wind fields (Figures 1-6). The model does show some tendency to produce

too strong westerlies in the Southern Hemisphere and too strong tropical easterlies during

all seasons. The seasonal cycle of the zonal mean temperature and humidity (Figures 7-12)

is also generally reasonable, though the polar regions (especially the southern hemisphere)

4
aregenerallytoocold betweenabout400and 100rob.Themodelis consistentlydrier than

the reanalysisin theboundarylayer,andwetterthanthereanalysisin thetropicsabovethe

boundarylayer.It shouldbenotedthat forthereana]ysisdata,thequalityofboththezonal

meanmeridiona]windandthespecifichumidityis suspectsincethesearehighlydependent

onthe modelusedin the assimilationsystem,and thereforeimpactedby modelbias.The

maindifferencebetweenreanalysisandthesimulatedmassstreamfunction (Figures13-15)

is the tendencyfor the modelto havea strongerlowerand mid troposphericcirculation.

The zonal wind is well simulated at both 50rob (Figures 16-17) and 200rob (Figures 18-

19). At 200mb during DJF, the main deficiencies are a somewhat too strong westerly

saddle in the eastern tropical Pacific, and a North American jet that is too strong. During

the transition seasons, the east Asian jet is retracted compared with the reanalysis. Ill

the Southern Hemisphere, the stronger zonal mean westerlies mentioned earlier, manifest

themselves at 200rob as a more zonally elongated jet in the South Pacific, compared with the

reanalysis, especially during JJA and SON when it extends eastward across South America.

At 850rob (Figures 20-21) the Southern Hemisphere westerlies are generally too strong,

and the east Asian westerly summer monsoon flow does not extend far enough to the east

across Indo-China. Over the Pacific, the tropical easterlies tend to be too strong during all

seasons.

The model produces realistic stationary wave patterns during DJF and MAM (Figures 22-

23), though the amplitude of the east Asian trough tends to be somewhat weaker than in

the reanalysis. During JJA the high latitude wave one pattern is stronger than that in the

reanalysis. The 200rob eddy stream function (Figures 24-25) is generally well simulated.

The main difference from the reanalysis is the tendency for the model to produce weaker

anticyclones in the eastern hemispheres northern subtropics.

While the model generally reproduces the main features of the velocity potential field (Figures 26-27), the minimum over the Indonesia and the western tropical Pacific during JJA is

much too weak compared with the reanalysis. This would suggest a weak east Asian monsoon, and is consistent with the retracted low level monsoon westerlies mentioned earlier.

This problem is also evident during SON.

The simulation reproduces tile basic seasonal evolution of tile sea level pressure (Figures

28-29). During DJF the Aleution low is, however, somewhat weak and shifted to the west.

Also, the North Pacific and North Atlantic anticyclones during JJA tend to be too strong.

In the Southern Hemisphere, the climatological high pressure belt near 30 ° is somewhat too

zonal and has maximum pressures higher than those found in the reanalysis.

The simulated and observed precipitation (Xie and Arkin 1997) are shown in Figures 30 and

31, respectively. During DJF the simulated precipitation over the Indian Ocean and western

Pacific tends to lie north of the equator, while the observed maximum precipitation tends

to lie south of the equator. The model also appears to have excessive precipitation over

the Andes Mountains between the equator and 30°S during DJF. During JJA the model

has insufficent precipitation over Indochina, and much of the Pacific warm pool region,

consistent with the weaker velocity potential field noted earlier (this problem continues into

the next season, SON). Also, the heaviest east Asian monsoon precipitation extends too

far north across eastern China. During MAM the model produces a somewhat exaggerated

5
split in the Pacific InterTropical Convergence Zone (ITCZ).

The overall distribution and seasonal evolution of the simulated shortwave cloud radiative

forcing is quite realistic (Figures 32-33). The model appears to capture the short wave

cloud forcing associated with the west coast marine stratus clouds. Some of the main

regional deficiences include insufficient DJF cloud forcing in the South Pacific Convergence

Zone, a weak Pacific ITCZ during MAM, and too much shortwave cloud forcing over the

subtropical anticyclones. The model longwave cloud forcing distribution is also quite good

(Figures 34-35). Tile main deficiencies are the weak cloud forcing over the Pacific warm

pool, and excessive cloud forcing over the western Indian Ocean during JJA. Similar results

are obtained from a comparison of the simulated and observed outgoing longwave radiation

(OLR) in Figures 36-37.

Figures 38-39 compare the simulated total precipitable water (TPW) against SSM/I for

July 1987 through January 1992. The model tends to be dry throughout much of the

tropics. The unrealistic split in the Pacific ITCZ during MAM mentioned earlier for the

precipitation is also evident in the TPW field.

2.2 Interannual Variability of Seasonal Means

In this section we present the global distribution of the interannual variability of selected

seasonal mean fields. At each grid point the unbiased estimate of the variance of a quantity

x is computed as

N

8]A= 1/(N- 1) (2)

i=1

where x is the mean over the N years.

Figures ,10-41 show the interannual standard deviation (s) in the 50rob zonal wind. Ttle

model shows no evidence of the relatively large tropical interannual variability evident in

the reanalysis during all seasons. At 200mb (Figures 42-43) the model produces realistic

DJF variability over the Pacific Ocean, though the region of maximum variability over the

North Pacific is displaced somewhat to the north and west of the reanalysis values. Also

for DJF, the model produces excessive variability over the eastern United States and the

Gulf of Mexico. During JJA, the model produces greater than observed variability over the

tropical eastern Pacific and Atlantic Oceans. At 850rob (Figures 44-45), tile model produces

excessive JJA zonal wind variance over the Indian Ocean, and unrealistically large variance

over and just west of central America. : :

The comparison of the 300rob height standard deviation (Figures ,16-47), indicates that the

model produces a reasonable spatial distribution and seasonal evolution of the variance.

The main discrepancy with the reanalysis data is the larger variance over the North Pacific,

North America and the North Atlantic regions during DJF. Similar results are obtained from

the comparison of the standard deviation of the 200rob streamfunction (Figures 48-49).
Theinterannualstandarddeviationof the velocitypotentialisshownin Figures50and51,

for the modeland reanalysis,respectively.The modelhasmuchlargervariancethan the

reanalysisoverthe easternPacificandwesternIndianOceanduringJJA and SON.Also,

the reanalysisshowsvariancemaximaoverIndonesiaduringDJF, MAM andSON,that

aretoo weakin the model.The OLR variancefields(Figures52-53)showthat the model

tendsto producetoo muchvariabilitythroughoutthe tropics,duringall seasons.

Figures54-55comparethesimulatedandobservedinterannualstandarddeviationin precipitation. Thegeneralpatternof highvariabilityin the tropicsis reasonablywell reproduced

by the model. DuringDJF and MAM the model,however,tendsto extendthe regionof

hightropicalPacificvariability toofar east.Also,duringJJA themodelproducesexcessive

variabilityin the IndianOceansouthof the equator,whilethereistoo little variability over

the Pacificwarm pool.

Figures56-57 show the simulated and observed interannual standard deviation in sea level

pressure. The basic regions of high variability are well simulated by the model. There is

some tendency to produce too much variability over the North Pacific and North Atlantic

during DJF.

2.3 Intra-monthly Variability

In this section we focus on sub-monthly variability. We begin by examining the total

variability with time scales less than one month (but greater than 1 day). The subsequent

subsection isolates the contributions to the sub-monthly variability coming from time scales

between 2.5 and 6 days, and time scales between 10 and 30 days. These frequencies are

isolated by applying band-pass digital filters similar to those of Blackmon (1976) to the

daily data (see also Schubert 1986).

2.3.1 Total

The total sub-monthly variability is computed for each season as

where

S2SM = 1/N _ 1/3 s2(j ,

i=l j=l ] i

(3)

M

s2(j) = 1/(M - 1) _-_(xk(j) - g(j))2.

k=l

(4)

Here xk(j) is the kth daily mean value for month j, g(j) is an average of x for month j, M

is the number of days in the month, and the subscript i indicates a sum over the N years.

The sum over j in (3) is computed for the 3 calendar months of each season.
ZonalMeans

Thezonalmeansubmonthlyvariability in the heightfield is quite good(compareFigures

58-59). The simulatedzonalmeantransientkinetic energy(Figures60-61)is alsoquite

good. The main differencefrom the reanalysisis a generaltendencyto underestimate

the maximain the northernhemisphere.The zonalmeannorthwardzonal momentum

transport(Figures62-63)is alsowell simulated,thoughthe modeltendsto overestimate

the peakvaluesin both hemispheres.The modelreproducesthe reanalysiszonalmean

northwardsensibleheattransportquite well(Figures64-65).

GlobalDistribution

Thespatialdistributionof thesubmonthlyvariabilityin the300mbheightfieldiswellreproducedbythemodel(Figures66-67).Themodelalsoproducesagoodoveralldistributionof

the 200robsub-monthlykineticenergy(KE, Figures68-69).ThesimulatedKE is, however,

somewhatweakoverthe northernmiddlelatitude oceansand the regionof maximumKE

overAustraliaandthe westernSouthPacificisplacedtoo far to theeastduringthecolder

seasons.Themodelproducesa goodoveralldistributionof thenorthwardzonalmomentum

transportat 200mb(Figures70-71).Themaindifferencefrom the reanalysisis a tendency

for too strongtransportsfromthe sub-monthlytransientsthroughoutthe middlelatitudes

of both hemispheres(seealsothe zonal meansin previoussubsection).The northward

sensibleheatflux at 850mbdueto tile sub-monthlytransients(Figures72-73)is alsogood.

Oneexceptionis the poorlydefinedsignatureof the NorthPacificstormtrack duringJJA

andSON.

2.3.2 Filtered Fields

In thissectionwetakea brieflook at someband-passtatistics.In this case,the variances

arecomputedasdefinein (3), however,the daily fieldsarefirst filteredto retainonly time

scalesbetweeneither 2.5to 6days,or 10-30daysasdescribedabove.

Figures74-75togetherwith Figures82-83showthat tile modeldoesagoodjob of partitioning ttle 300mbheightvariability betweensynoptic(2.5-6days)and low frequency(10-30

days)variability. Onediscrepancyis that duringDJF the modelunderestimatesthe 2.5-6

day variancemaximumassociatedwith theeastAsianstormtrack.

ComparingFigures75-76with Figures84-85,weseethat the modeldoesa reasonable

job in partitioning the 200mbkinetic energybetweenthe synoptic (2.5-6days)and low

frequency(10-30days)time scales.It is interestingthat at 2.5-6days,the modeltends

to underestimatethe KE in theNorthernHemisphere,but it overestimatesthe KE in the

SouthernHemisphere.Wealsoseethat thecoldseasoneastwarddisplacementof the region

of maximumKE overAustraliaandthe westernSouthPacificnotedearlieris primarily in

the lowerfrequencies.

Turningto the zonalmomentumfluxes,the modelha.stoo muchsouthwardtransportin

the SouthernHemisphereat 2.5-6days(compareFigures78and79), though,overall,the

simulatedmomentumtransportsarereasonablein both frequencyranges(seeFigures78,
79,86and87). Thesimulated850robnorthwardsensibleheatfluxesalsoshowa veryrealistic partitioningbetweenthesynopticandlowfrequencysubmonthlytime scales(compare

Figures80-81with 88-89).

2.4 The Madden-Julian Oscillation

The Madden-JulianOscillation(MJO) is the dominantmodeof subseasonal variability in

thetropics,andoccursontimescalesofabout30-60days.TheMJOisclearIy evident in the

velocity potential field as a large-scale (zonal wave number 1) eastward propagating mode,

that occurs in conjunction with eastward evolving convective anomalies in the tropical

eastern hemisphere. The MJO has substantial impacts on short-term climate variability

throughout the tropics and subtropics, and these may extend into middle latitudes.

In this section, we isolate that part of the variability in the 200rob velocity potential field

that is largely tied to the MJO by filtering in both time and space. For the former we

employ a Fourier transform to retain time scales between 20 and 90 days. For the latter,

we employ a complex empirical orthogonal function (CEOF) decomposition (see below).

The time series of the dominant CEOF is decomposed using a wavelet analysis. Various

other fields are composited during a typical cycle of the first CEOF. The composites are

computed over those times when the modulus of the first CEOF of the 200rob velocity

potential exceeds one standard deviation.

2.4.1 Data processing

The data used in this analysis consist of the daily 200 mb velocity potential, 200mb streamfunction, outgoing longwave radiation (OLR) and total precipitable water. A Fourier spectral transform was used to remove time scales shorter than 20 days and longer than 90

days.

The CEOF decomposition is done following (Pfeffer et al. 1990). For a time series, ¢(x, y, t),

we obtain

1

_(_k(Y, t)c -_kx + _*k(y,t)eikx), (5)

k--1

where x is the longitude, t is time, k is the wavenumber, 2I( + 1 is the number of equally

spaced points around the longitude circle at which g) is specified in y, _k (Y, t) is the Fourier

transform of _(x, y, t) and the asterisk denotes a complex conjugate. We then seek orthogonal function expansion of k_k(y, t) of the form

N

• = A,(t)C,(y,k), (6)

p----1

N

in which we impose the condition, E Cp(y,k)C_(y, k) = 5p,q for orthonormality of the

n(k,y)

basis functions. Here, 5p,q is the Kronecer delta. Ap(t) are determined by projecting _k(Y, t)
onto Cp(y, k), then we have

N

Ap(t)= _ q2k(y,t)C_(y,k). (7)

Wc determine Cp(t) by maximizing the variance of the largest component subject to the

eigenvalue problem

N

Z C ,mCp(m)= (8)

rn=l

where Cn,m = qdk(Y,t)_'k(Y,t)is the covariance matrix and the eigenvalue ),p is real and

the eigenvector Cp(n) is the complex empirical orthogonal function (EOF). We may express

_/,(x, y, t) as follows:

1 N

¢(x,y,t) = -_ _-_[Ap(t)B;(x,y) + A*p(t)Bp(x,y)].

p=l

(9)

where

K

Bp(x,y) = _ C;(y,k)e ik_. (10)

k:l

By expressing the complex functions Ap(t) and Bp(y, k) in terms of their moduli and phases,

we may write (9) in the form

N

_(x, y, t) = _ a,p(t)/3p(x, y)cos( Op(x, y) - _p(t) ), (11)

p----1

here Ap(t) = c_p(t)e iCp(t) and Bp(t) = tip(X, y)e i°p(x'_). The expression of the phase angles,

(Op(x) - Cp(t)) determines only to within an arbitrary constant which can be added to

one and subtracted from the other without changing their difference. Each component of

(11) may be thought of as representing a wave with shape cosOp(x, y) and time dependent

phase Cp(t), the amplitude of which is modulated in phase space by I3p(x, y) and in time by

ap(t). At a fixed position (x, y), the number of highs or lows per unit time is a measure of

local frequency dCp(t)/dt. At a fixed time t, the number of highs or lows of this function

per increment of longitude is a measure of the local wavenumber dOp(x, y)/dx. ¢p(x, y, t)

depicts the shape and motion of the wave and the spatial and temporal modulations of its

amplitude, wavenumber and frequency.

The above CEOF decomposition provides an efficient spatial representation of a traveling

disturbance such as the MJO. Since the MJO tends to occur episodically and on a broad

range of time scales, the temporal behavior of the MJO is best represented in terms of a

Wavelet decomposition. The wavelet transform-(e:g. Weng and Lau 1994)is based on the

convolution of a time series, f(t), with a set of functions derived from a prototype ¢(t). If

g,(t) is admissible and f(t) is square integrable, the continuous wavelet transform, IV] (a, r),

is defined as, :_::;:

Iv](a, r) f(t) ¢_,,(t)dt , (12)

10
wherea, r are real and a >0, a is the scale parameter and 7- is the translation parameter.

The ¢*,_(t) is the conjugate of the transformed and dilated "basic wavelet" which is given

by

= 1

where the constant _a is used for energy normalization.

The basic wavelet used in this study was designed by Morlet (1983) and has since been used

in many studies

#,(t) = e i¢°t e -t2/2, (14)

where _o equals 7r(2/In2) 1/2 in order to satisfy the admissibility condition. In Fourier space,

this wavelet is given by,

1 e_(___o)_/2 (15) =

In our application the input function f(t) to the wavelet transform is the complex time

series associate with the first CEOF of the 200rob velocity potential field.

2.4.2 Results

We begin by showing in Figure 90 the variance of the 20-90 day filtered 200rob velocity

potential averaged over all seasons for 1979-95. The simulated variability is considerably less

than that found in the reanalysis, especially west of the dateline over the Pacific warm pool

region and the Indian Ocean. Figure 91 shows the spatial pattern and eastward propagation

of the first CEOF of the 20-90 day filtered 200mb velocity potential from the reanalysis and

model simulation. The figure shows the evolution of CEOF 1 as it goes through one complete

cycle, though note that the zero and positive lags are basically a repeat of the negative lags

with reversed sign. The model produces a reasonably good simulation of the zonal wave 1

structure and eastward propagation. The reanalysis, however, shows a stronger east-west

asymmetry with anomalies over the Indian Ocean and Indonesia that are stronger than

those over the western hemisphere. Also, the first mode explains 3/4 of the filtered variance

in the reanalysis, while the first mode explains about 1/2 the variance for the model.

Figures 92 and 93 show the modulus of the wavelet decomposition of the first CEOF for the

model and reanalysis. The results show clearly the episodic nature of the MJO. The results

from the reanalysis shows, as expected, that the MJO tends to have much of its power on

time scales between 30 and 60 days, though there are clearly times when it has both shorter

and longer time scales. For the model CEOF 1 has less of a preference for 30-60 days, and

tends to have more variability at shorter time scales as is common for many AGCMs.

Figures 94-96 show composites of various quantities using the amplitude of the first velocity

potential CEOF time series as an index. Only time periods when the amplitude exceeds

one standard deviation (the MJO is considered active) contribute to the composites. Figure

94 shows the composite evolution of the OLR. The reanalysis/observations show a strong

coupling between the velocity potential and OLR over the Indian Ocean and wetsern Pacific.

The simulation shows a much weaker coupling. The composite evolution of the TPW

11
(Figure95) shows,for the reanalysis,a clearsignalof enhancedmoistureon the leading

edgeof the regionof risingmotion (negativevelocitypotentialanomaly):this apparently

contributesto theeastwarddevelopmentof theMJO.Themodelresultsshowlessorganized

moistureanomalieslinkedto the evolutionof thefirst CEOF. Finally, Figure 96 shows the

model and reanalysis composite evolution of the 200rob stream function associated with the

first velocity potential CEOF. This shows, for the reanalysis, a substantial Rossby wave

component associated with the MJO, while for the simulation it is rather weak. This is not

surprising in view of the model's weak coupling with convection in the western Pacific and

Indian Oceans associated with the first CEOF (see Figure 94).

3 Conclusions

The results presented in this atlas shows that, overall, the FV-CCM3 model produces a very

good simulation of the seasonal mean climate and its variability. The model does particularly well at simulating the zonal mean and spatial distrution of the subseasonal transients,

the seasonal mean cloud radiative forcing, and the seasonal mean upper tropospheric/lower

stratospheric zonal wind.

The primary deficiencies are in the simulation of the JJA divergent circulation and precipitation associated with the south Asian summer monsoon. Most notable is the deficient

precipitation over the Pacific warm pool region. Like many GCMs, this model has insufficient interannua[ variability in the tropical lower stratosphere, and it is deficient in the

representation of MJO and its coupling with convection over the western Pacific and Indian

Oceans.

Despite these deficiencies, we feel this initial version of the model is a very good system

with which to carry out data assimilation and climate simulation studies. It also serves as

a valuable benchmark for assessing the impact of on-going model improvements such as the

current implementation of the next generation (CCM-4) of physical parameterizations.

12
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14
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15
i

16
SEASONALMEAN ZONALMEANS

Zonalwind

Meridionalwind

Temperature

SpecificHumidity

M[assstreamfunction

17
7

0

0

0

(quJ) aJnssaJd

0 0 0 0 0 0 0 0

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(quJ) gJnssgJd

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(qua) eJnSSgJcl (qm) eJnsSgJcl

c5

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,.=

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r/3

19
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,,.,.,.,=..

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FILTEREDINTRA-MONTHLYVARIABILITY

2.5-6d_s

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116
THE MADDEN-JULIANOSCILLATION

200mbVelocityPotential\rariance(20-90days)

CEOF1 (20-90day200mbVelocityPotential)

WaveletDecompositionof CEOF1

Composite200robVelocityPotentialandOLR

Composite200mbVelocityPotentialand TPW

Composite200robStreamFunctionandOLR

117
90N

60N

30N

EQ

30S

60S

90S

90N

60N

3ON

EQ

30S

60S

90S

__-_ (0) Model

_ '.> ' - , A ,.-

' ._!#.... <-" - , 4-

0 60E 12'0E 180 12bW 66W

0 60E 12'0E I,BO 120W 60W 0

Figure 90:200 mb velocity potential variances (20-90 days) a.) for the model, b) for the reanalysis.

The contour interval is 2× 1012 m 4 s -2. Values larger than 4×10 _= m 4 s-2 are shaded.

118
20N

EQ

20S

20N

EQ

20S

20N

EQ

20S

Model (50.6Z) Reonolysis (74.3Z)

0 60E 120E 180 120W 60W 0 60E 120E 180 120W 60W 0

Figure 91: The first complex EOF of the 200 mb velocity potential filed from the model (left

panels) and the reanalysis (right panels). The numbers in ther upper right correspond to the

phase angle. Negative values are dashed.

119
1991 1992 1993 1994 i995 1996 1997

1 2 3 4 5 6 7 8 9

Figure 92: Time-frequency representation of the modulus of the the complex Morlet wavelet

coefficients based on the first complex EOF of the 200 mb velocity potential filed from the

model. The left ordinate is frequency in octive, and the right ordinate is period in day.

120
II)

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O

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c

ob_

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1979 1980 1981 1982

4

5

7

1985

4

i

1986 1987 1988

l

1983

period

(days)

32

64

I

1984

I

1989

128

1985

period

(days)

32

64

i

1990

7 I i i i ,

1991 1992 1993 1994 1995 1996

128

1991

period

(days)

32

64

128

1997

1 2 3 4 5 6 7 8 9

Figure 93: Time-frequency representation of the modulus of the the complex Morlet wavelet

coefficients based on the first complex EOF of the 200 mb velocity potential filed from the

reanalysis. The left ordinate is frequency in octive, and the right ordinate is period in day.

121
20N

EQ

20S

20NEQ20S -

20N

20S

20N

EQ

20S

20N

EQ

20S

20N

EQ

20S

20N

EQ

20S

20N

EQ

20S

20N

EQ

20S

Model Reonalyeis

-,,<...'_.',,_,_ ..o:::< u _2 2,%a2,1Rll IJ)/d / III

"---:-. _- _. _ "---"_ ,, ,' _:'_ - ; __':->.'.': 1_,.%,_)/71,

0 60E 120E 180 120W 60W 0 60E 120E t 80 120W 60W 0

"20-16-14-i2-10 "8-6-4-2 2 4 6 8 10 12 14 16 20

Figure 94: Composite anomalies of OLR and 200rob velocity potential at tin]es of amplitude

of the first component of 200mb velocity potential is larger than one standard deviation. The

contour is for the velocity potential and the negative values are dashed. Shading is for OLR.

Units are W m -2.

122
Model

2osEO ------.-

20N _ ._ :.'-_" "'t = 'K_" "

EQ '- - ,.-, xz_:l - ,, ,i

20S 'F;" ";, I. '.C, ;

l

EQ .__::_- ,::.:

,_l,'_, " s__,__,_, .,,:a -_o

. ::_L'I.

, ,_,.--.-45

'/_.-. ,,_:_Z4,_._____"" _;,_'2 o

20N " _ ..... ' "' "_

20N 'h ' (-' _ .' .--

EQ .,_ _

20S ..... " .....

20N _:_'. j_/i :),';.,-, _ _t 90 o EQ , - . ._....

20S • _-----.---

20NEQ___-'_,,__' ;f_ _ !_'.... --, .-- 2_ . '_

0 60E 120E 180 120W 60W

Reonolysis

_ _._--,,_ _

y -'-> ,'-7

5" i

__,._L"_,_' ' '_ 18 0

, ._ • . ., ; @[!,...

0 60E i20E 180 120W 60W 0

-4. -3.5 -3 -2.5 -2 -1.5-1-0.5-0.20.2 0.5 1 1.5 2 2.5 3 3.5 4.

Figure 95: Composite anomalies of TPW and 200mb velocity potential at times of amplitude

of the first component of 200rob velocity potential is larger than one standard deviation. The

contour is for the velocity potential and the negative values are dashed. Shading is for TPW.

Units are km m -2,

123
20NEQ20S20N -

EQ"

20S

-20-16-14-12-10-8-6-4-2 2 4 6 8 10 12 14 16 20

Figure 96: Composite anomalies of OLR and 200rob streamfunction at, times of amplitude or

the first component of velocity potential at 200rob is larger than one standard deviation. The

contour is for the streamfunction and the negative values are dashed. Shading is for OLR. Units

are W m -z.

124
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

125
Volume 9

MaTvh 1996

Volume 10

April 1996

Volume 1i

May 1997

Volume 12

August 1997

Volume 13

December 1997

Volume 14

August 1998

Volume 15

June 1999

Volume 16

November 1999

Volume 17

July 2000

Volume 18

December" 2000

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. MichaeI 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

Atlas of Seasonal Means Simulated by the NSIPP-1 Atmospheric GCM

Julio T. Bacmeister, Philip J. Pegion, Siegfried D.

Schubert, and Max J. Suarez

An Assessment of the Predictability of Northern Winter Seasonal Means with the NSIPP 1 AGCM

Philip J. Pegion, Siegfried D. Schubert, and Max J.

Suarez

126
Volume 19

July 2001

A Thermal Infrared Radiation Parameterization for Atmospheric Studies

Ming-Dah Chou, Max J. Suarez, Xin-Zhong, Michael

M.-H. Yan

127

Form Approved

REPORT DOCUMENTATION PAGE OMBNo.0704-0188

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1. AGENCY USE ONLY (Leave b/ank) 2. REPORT DATE 3. REPORT TYPE AND DATES COVERED

August 2001 Technical Memorandum

4. TITLE AND SUBTITLE 5. FUNDING NUMBERS

Technical Report Series on Global Modeling and Data Assimilation

Volume 20--The Climate of the FVCCM-3 Model

6. AUTHOR(S)

Yehui Chang, Siegfried D. Schubert, Shian-Jiann Lin, Sharon Nebuda,

and Bo-Wen Shen

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS (ES)

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

Code 971

8. PEFORMING ORGANIZATION

REPORT NUMBER

2001--03504-0

10. SPONSORING / MONITORING

AGENCY REPORT NUMBER

TM-2001-104606, Vol. 20

11. SUPPLEMENTARY NOTES

Y. Chang, S. Nebuda, and B-W. Shen: General Sciences Corporation, Laurel, Maryland.

S. D. Schubert and S.-J. Lin: Data Assimilation Office, NASA GSFC, Greenbelt, Maryland

12a. DISTRIBUTION ! AVAILABILITY STATEMENT 12b. DISTRIBUTION CODE

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)

This document describes the climate of version 1 of the NASA-NeAR model developed at the Data Assimilation Office (DAO). The model consists of a new finite-volume dynamical core and an implementation of the

NeAR climate community model (CCM-3) physical parameterizations. The version of the model examined

here was integrated at a resolution of 2 degrees latitude by 2.5 degrees longitude and 32 levels. The results are

based on asimulation that was forced with observed sea surface temperature and sea ice for the period 1979-

1995, and are compared with NCEP/NCAR reanalyses and various other observational data sets. The results

include an assessment of seasonal means, subseasonal transients including the Madden Julian Oscillation, and

interannual variability. The quantities include zonal and meridional winds, temperature, specific humidity,

geopotential height, stream function, velocity potential, precipitation, sea level pressure, and cloud radiative

forcing.

14. SUBJECT TERMS

model climate, NASA-NeAR model, interannual variability, intraseasonal variability,

seasonal means, Madden-Julian Oscillation

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