/

NASA Technical Memorandum 104606, Vol. 10

Technical Report Series on

Global Modeling and Data Assimilation

Max J. Suarez, Editor

Volume 10

Dynamical Aspects of Climate

Simulations Using the GEOS

General Circulation Model

Lawrence L. Takacs and Max J. Suarez

April 1996

o

DATA ASSIMILATION OFFICE

NASA Technical Memorandum 104606, Vol. 10

Technical Report Series on

Global Modeling and Data Assimilation

Max J. Suarez, Editor

Goddard Space Flight Center

Greenbelt, Maryland

Volume 10

Dynamical Aspects of Climate

Simulations Using the GEOS

General Circulation Model

Lawrence L. Takacs

General Sciences Corporation

Laurel, Maryland

Max J. Suarez

Goddard Space Flight Center

Greenbelt, Maryland

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland

1996
This publication is available from the NASA Center for AeroSpace Information,

800 Elkridge Landing Road, Linthicum Heights, MD 21090-2934, (301) 621-0390.
Abstract

The general circulation simulated by Version 1 of the Goddard Earth Observing

System (GEOS-1) general circulation model is compared with the five-year reanalysis

recently completed using the GEOS-1 data assimilation system. Emphasis is on the

comparison of dynamical quantities, such as heat and momentum fluxes. The results

indicate that, while many features are well simulated, the model exhibits a number of

severe biases. These include: a cold bias at both poles and an associated westerly bias

at upper levels, a strong low-level westerly bias during northern hemisphere winter, a

dry tropical boundary layer and excessive moisture at all levels outside the tropics, too

much poleward heat and momentum flux by transient eddies, and too little heat flux

by stationary eddies.

The effects on these biases of increasing resolution and order of accuracy, of including a parameterization of gravity wave drag, and of increasing the vertical extent of the

model are also examined. The main impacts of increasing resolution and accuracy are

found to be on the transient eddy statistics. The higher-order and higher-resolution experiments are in closer agreement with observations in the southern hemisphere, where

the fluxes are primarily determined by the transient flow; however, in the northern

hemisphere, where the fluxes due to the stationary flow are important, the more "accurate" simulations are systematically further from the analysis. As in several other

studies, gravity wave drag is found to have a beneficial effect on both the time-mean

flow and the transient statistics in the northern hemisphere, while little impact is seen

in the southern hemisphere. Increasing the vertical extent of the model significantly

improves the cold pole and westerly bias in the southern hemisphere.

.°°

111

Contents

List of Figures vii

List of Tables

1 Introduction

2 Model Description

2.1 GEOS GCM ...................................

2.2 GEOS DAS ....................................

3 Comparison of GEOS-1 Simulation and Analysis

3.1 The Zonal Mean Circulation ...........................

3.2 The Stationary Flow ...............................

3.3 Eddy Statistics ..................................

ix

2

2

3

5

5

8

9

Resolution and Order-of-Accuracy Studies 10

4.1 The Zonal Mean Circulation ........................... 11

4.2 Eddy Statistics .................................. 13

5 The Effects of Gravity Wave Drag 15

6 Summary 19

List of Figures

Five year average of Northern Hemisphere winter zonal mean u-winds (m

sec -l) for the GEOS-1 GCM, the GEOS-1 DAS, and the u-wind and temperature difference (GCM-DAS) ......................... 25

Five year average of Northern Hemisphere summer zonal mean u-winds (m

see -t) for the GEOS-1 GCM, the GEOS-1 DAS, and the u-wind and temperatnre difference (GCM-DAS) ......................... 26

Five year average of Northern Hemisphere winter and summer se,_sonal means

of the zonal mean v-wind (m sec -1) for the GEOS-1 GCM, the GEOS-1 DAS,

and the unitialized ECMWF analysis ...................... 27

Five year average of JJA se,'_sonal mean of the zonal mean a) specific humidity

(g kg-') for the GEOS-1 GCM, b) specific humidity difference for the GCMDAS, c) specific humidity difference due to temperature: d) specific humidity

difference due to relative humidity, e) blended relative humidity using GEOS1 DAS and SAGE, f) relative humidity from the GEOS-1 GCM ....... 28

Five year average of Northern Hemisphere winter seasonal means of the 200-

mb u-wind (5 (msec -J) contours, with negatives shaded) and 300-mb eddyheights (60 m contours, with light shading < 60 m and dark shading > 60

m) for the GEOS-1 GCM and the GEOS-1 DAS ................ 29

Five year average of Northern Hemisphere winter and summer seasonal means

of Sea-Level Pressure (4 mb contours, with light shading < 988 mb and dark

shading > 1020 mb) for the GEOS-1 GCM and the GEOS-1 DAS ...... 30

7 Five year average of DJF seasonal mean of the eddy Sea-Level Pressure (4

mb contours, with light shading < -4 mb and dark shading > 4 mb) for the

GEOS-1 GCM and the GEOS-1 DAS ...................... 31

8 Five year average of DJF and JJA seasonal means of the vertically integrated

momentum flux (m 2 see -2) , and kinetic energy (m 2 sac -_) , due to the

transient and stationary flow for the GEOS-1 GCM and the GEOS-1 DAS. 32

9 Five year annual mean average of horizontal momentun flux (m 2 see -2) and

kinetic energy (m 2 sac -2) due to the transient flow for the GEOS-1 GCM

and the GEOS-1 DAS ............................... 33

10 Five year annual mean average of horizontal momentun flux (m 2 see -2) and

kinetic energy (m 2 sac -2) due to the stationary flow for the GEOS-1 GCM

and the GEOS-1 DAS ............................... 34

vii
11 Fiveyearaverageof DJF and JJA seasonal means of the vertically integrated

heat (m sec- I deg), and moisture (msec -1 g kg- 1) fluxes due to the transient

and stationary flow for the GEOS-1 GCM and the GEOS-1 DAS ....... 35

12 Five ye_ annum mean average of meridional heat flux (m sec -1 deg) due to

the transient and stationary flow for the GEOS-1 GCM and the GEOS-1 DAS. 36

13 Five year annum mean average of meridional moisture flux (m sec -1 g kg-1)

due to the transient and stationary flow for the GEOS-1 GCM and the GEOS1 DAS ........................................ 37

14 Two ye_ annual and zonal mean u-wind (m sec -1) in the southern hemisphere for the Control (H22) run, experiments L22, L42, L44, H44, and the

GEOS-1 DAS .................................... 38

15 Two year annum and zonal mean u-wind (msec -1) in the northern hemisphere for the Control (H22) run, experiments L22, L42, L44, H44, and the

GEOS-1 DAS .................................... 39

16 Two year annual and zonal mean temperature (deg K) at 100 mb for the

Control (H22) run, experiments L22, L42, L44, H44, and the GEOS-1 DAS. 4O

17 Two year annual and zonal mean differences in precipitable water (gcm -2)

between a) experiments L22 through H44 and the Control (H22), and b)

between L22 through H44, the Control (H22), and the GEOS-1 DAS ..... 41

18 Vertically integrated poleward momentum flux for the Control (H22) run,

experiments L22, L42, L44, H44, and the GEOS-1 DAS, due to transient and

stationary flow ................................... 42

19 Vertically integrated poleward heat flux for the Control (H22) run, experiments L22, L42, L44, H44, and the GEOS-1 DAS, due to transient and

stationary flow ................................... 43

2O Vertically integrated poleward moisture flux for the Control (H22) run, experiments L22, L42, L44, H44, and the GEOS-1 DAS, due to transient and

stationary flow ................................... 44

21 Five year Northern Hemisphere winter seasonal averages of momentum torques

for (a) the GEOS-1 DAS, and (b) the GEOS-1 GCM. Also shown is the two

year DJF seasonal average for (c) the GEOS GCM with Gravity Wave Drag

(L44 w/GWD). (Units are in 106 N m-J ) ................... 45

22 Two year DJF seasonal mean difference of momentum torques from the

GEOS GCM with Gravity Wave Drag (L44 w/GWD) and the control (L44).

(Units are in 106 N m -1 ) ............................ 46

Vlll
23

24

25

26

Vertically integrated annual mean momentum and heat flux due to transient

and stat, ionary flow for tile GEOS-1 DAS and experiments L44 and L44

w/GWD ......................................

DJF SeasonM mean Sea-Level Pressure and 300 mb Eddy Heights from the

GEOS GCM w/GWD and the GEOS-1 DAS ..................

Two year average of DJF zonal mean u-winds (m see-') for the GEOS GCM

w/GWD and the Stratospheric GEOS GCM w/GWD, and their temperature

diffe.rences (GCM-DAS) ..............................

Stencil showing the position and indexing of the prognostic fields u, v, and

h., together wit, h t,he vorticity, (, and the scheme parameters oe, fl, 7, _, v,

and p.........................................

47

48

49

50

ix

List of Tables

GEOS-1 Sigma Level Distribution .......................

Sensitivity Expcrimcnts Summary .......................

4

11

xi

1 Introduction

Th,_ GE()S general circulation model (GCM) is part of the data assimilation system (DAS)

lining developed aS Goddard's Data Assimilation Office (DAO) for use by NASA's Earth

()bs__rving System program. The prinlary objective of this system is to produce research

quality datasets from She satellite and surface me,asurements of the earth system that will

be available at the turn of the century.

Although both the model and the analysis algorithms are still under development, a preliminary version of the DAS (the GEOS-1 DAS) has already been used in a number of

applications. Perhaps the most important and visible of these hms been the production of

a gh_bal reanalysis of atmospheric data (Schubert et al. 1993 and Schubert et al. 1995),

which is n_w generally availaMe to the atmospheric research comnmnity. This version of

the DAS has also been used for shorter assimilations in support of the Coupled OceanAtmosph,,,re Response Experiment (COARE), of subprojects of the Global Energy and

Wa{;.,?r Cycle Experiment (GEWEX), and of the Airborne Southern Hemisphere Ozone Experiment (ASHOE) mission. In addition to these data assimilation applications, the GEOS

GCM is also being used for climate simulations, and results fl'om a low-res,)lution version of

the model have been submitted to the Atmospheric Model Intereomparison Project (AMIP)

(Gates, 1992).

Becausv of tile wide dissemination of these results, particularly of the dat.a assimilation

products, we felt it was important that the behavior of the atmospheric GCM used to

produc,: them be thoroughly doeume, nted. The behavior of physical processes in the model,

such as moist convection, radiative heating, surface fluxes_ and the hydrological cycle are

the f;wus of Molod et al. (1996).

In this paper we concentrate on the behavior of the mode, l's circulation statistics, emphasizing those biases in the simulated climate that may affect the analysis through systematic

errors in tile model provided first guess. This information is necessary both for a critical

ewduation of the analysis products and to identify model deficiencies that need to be addressed in flltm'e versions of the system. A second purpose of the paper is to preview the

effects of some of the modifications to the model that will be included in fllture versions of

the system and to begin to assess the impact of increasing horizontal resolution.

In section 3 we compare the climatology of a five-year simulation with that of the fiveyear GEOS-1 DAS reanalysis. An extcnsiw _. comparison is made of both primary fields

(wind, temperature, moisture, and pressure) and second moments (transient and stationary

momentum, heat, and moisture fluxes). We believe that this is the first time in which hmgterm climatologies from a simulation and an unchanging data assimilation system using the

same general circulation model have been directly compared.

In section 4 we examine the sensitivity of the GEOS GCM to horizontal resolution and

order-of-accuracy. A number of studies have considered the impact of increasing horizontal resolution (for example: Mahlman and Umseheid (1987), Boer and Lazare (1988):
Tibaldi et al. (1990), Boville (1991), and Boyle (1993)). As mentioned in Boyle (1993),

the rationMc for these studies is to determine the coarsest (and presumably most efficient)

resolution which adequately simulates the atmospheric climate signal in question. The

impact of order-of-accuracy within dynamical systems h`as also been examined by several

investigators. Kreiss and Oliger (1972) were among the first to discuss the merits of using

higher-order accuracy to reduce space truncation errors and improve the phase characteristics of wave propagation. Other examples include Kalnay-Riva.s et al. (1976), in which

the impact of fourth-order over second-order accuracy in short-range weather prediction

w`as examined, and Russell et al. (1986) who compared the ability of several shallow water nmnerical schemes of second- and fourth-order to simulate a Rossby-Haurwitz wave.

While the advantages of higher-order accuracy for passive tracer advection and short-range

forecasting are readily apparent from the studies just cited, the climatological response of

a GCM to such changes h`as not received similar attention. In this study we examine the

ilnpact of order-of-accuracy on the climatology produced from long-term GCM simulations,

and colnpare them to both the GEOS-1 GCM benchmark and the GEOS-1 DAS analysis.

For this sensitivity study, the GEOS GCM w`as run using two horizontal resolutions (4 ° x

5° and 2 ° x 2.5 ° ) and two higher-order versions being evaluated for implementation in the

DAS.

In section 5, we present results from a version of the model that includes the gravity wave

drag parameterization of Alpert et al. (1988), as modified by Zhou et al. (1996). As has

been noted many times since the studies of Palmer et al. (1986) and McFarlane (1987),

the inclusion of gravity wave drag in the troposphere and lower stratosphere can have a

strong impact on the zonM mean circulation, the stationary wave pattern, and even on the

model's transient statistics. We find all of these impacts to occur, and in ne_ly Ml cases to

have a strongly beneficial effect on many of the most egregious bi`ases noted in thc GEOS-1

sinmlation. We conclude section 5 with an examination of the impact of increa.sing the

vertical extent of the model by including a well-resolved stratosphere up to 0.1 rob.

Wc begin, in section 2, with a brief description of the GEOS-1 GCM aim the GEOS-1 DAS.

2 Model Description

2.1 GEOS GCM

We describe first the GEOS-1 GCM, which is the version of the model that wa.s used in

the GEOS-1 reanalysis, and which will be the focus of much of our attention and serve ,as

a b,aseline or "Control" for our sensitivity studies.

The GEOS-1 GCM uses the second-order version of the Aries/GEOS dynamical core described in Suarez and Takacs (1995). This core is a modular, Eulerian, finite-difference

dynamics package used for many global modeling applications at Goddard. The equations

arc finite-differenced on an Arakawa C-grid in the horizontal and a Lorenz grid on a standard
cz coordinate in tile vertical. Tile horizontal finite-differencing of the monlentum equation is

b;used ,-m the second-order energy and potential enstrophy conserving scheme of Sadourny,

;us described in Burridge and Haseler (1977). In the thermodynamic and moisture equations the horizontal differencing is hased on the standard, square-conserving, second-order

scheme. The vertical finite differencing is *.hat of Arakawa and Suarez (1983).

The physics package includes a fllll set of sub-grid parameterizations. Penetrative and shallow cunmlus convection arc parameterized using the Relaxed Arakawa-Schubert scheme of

Moor*hi and Suarez (1992), coupled with a Kessler-type scheme for tile re-evaporation of

failing rain (Sud and Molod, 1988). Tile thermal and solar radiation parameterizations

folh)w closely those described by Harshvardhan et al. (1987). Cloudiness is diagnosed

using a simple scheme b,'used on the incidence of cumulus convection and large-scale condensati,m. Turbulent eddy fluxes of momentum, heat, and moisture in tile surface layer

are calculated ,,sing stability-dependent bulk formulas based on Monin-Obukhov similarity

flmctions. Above tile surface layer: turbulent fluxes of momentum, heat, and moisture are

calculated by the Level 2.5 Mellor-Yamada type closure scheme of Helfand and Labraga

(1988): which predicts turbulent kinetic energy and determines the eddy transfer coetllcients used for a bulk formulation. The GEOS-1 GCM does not inchlde a parameterization

of gravity-wave drag. For a more complete description of the model, refer to Takacs et al.

(1994).

The GEOS-1 fiw>year sim, flation presented in section 3 and the corresponding GEOS-1

DAS fiw>ye;u' reanalysis were run using the same horizontal (2 ° x 2.5 °) and vertical (20

layc.rs) re.solution. Tile vertical distribution of the sigma levels is given in Table 1; these are

chose.n so _us to provide enhanced resolution in tile planetary bo,mdary layer and near the

trot)opa, lse.. The top of tile model atmosphere is at 10 1rib, where we assume d = 0.

2.2 GEOS DAS

As previously mentioned, we will compare the simulations with the DAO's five-year reanalysis of March 1985 through Pehruary 1990. The data ,assimilation system used for this

rcanalysis (tile GEOS-1 DAS) is described in detail in Pfaendtner et al. (1995). An overview

of the rcsTdts is presented in Schubert et al. (1995). The GEOS-1 DAS employs the GEOS-1

GCM in toni,motion with an Optimal Interpolation (OI) analysis scheme (based on Baker

ct al. 1987) and the Incremental Analysis Updating (IAU) proposed by Bloom et al. (1996)

and Bhmm et al. (1991).

The OI is a three-dimensional (multivariate in z, u, v; univariate in mixing ratio), statistical

ob.jective analysis scheme employing damped cosine horizontM autocorrelation functions for

model prediction error, and a multivariate oceanic surface analysis incorporating an Ekman

balance fi-_r the sea-level pressure and winds. Observational data for the surface analysis

consists of surface land, ship, and buoy reports. The upper-air analysis incorporates data

fl'om rawinsondes, dropwindsondes, rocketsondes, aircraft winds, cloud tracked winds, and

thicknesses from the Tiros Operational Vertical Sounder (TOVS).
Table 1:GEOS-1 Sigma

Level[ Sigma

1 0.009500

2 0.029000

3 0.049750

4 0.073000

5 0.100050

6 0.132200

7 0.172150

8 0.222750

9 0.283750

10 0.352000

11 0.424750

12 0.500500

13 0.578390

14 0.657113

15 O.734480

16 0.807832

17 O.874136

18 0.929925

19 0.971300

20 0.993935

Level Distribution

A Sigma

0.019000

0.020000

0.021500

0.025000

0.029100

0.035200

0.044700

0.056500

0.065500

0.071000

0.0745OO

0.077000

0.078779

0.078669

0.076064

0.070641

0.061967

0.049610

0.033141

0.012129
The OI is performed every 6 hours using observations from a 4- 3-hour data window centered

on the analysis times ( 0000; 0600, 1200, and 1800 UTC). Following Bloom et al. (1996),

an analysis tendency (OI "After Analysis" minus GCM "First Guess" over 6-hours) is

c(mltmted on the GCM sigma surfaces and used a,s a constant model forcing during the

assimilatioll. With this method the model is not re-initialized at the analysis intervals, and

the entire, assimilation may be viewed as a continuous integration of the GCM in which the

analysis increments act as another of the physical parameterizations. In fact, the interface

between the GCM and the DAS is programmed in just this way.

3 Comparison of GEOS-1 Simulation and Analysis

In this section we analyze the climatology of the GEOS-1 GCM. These results are from a

fiw'_-year simulation that was run in parallel with the five-year reanalysis. Initial conditions

were identical for the reanalysis and the simulation, being generated from the January 1,

1985 analysis from the European Center for Medium Range Weather Forecasts (ECMWF).

Jmluary and February of 1985 were considered a spin-up period. The results presented in

this section are for the five-year period March 1985 through February 1990. All results

are derived from output which was accumulated four times daily from both the model

simulations and the GEOS-1 DAS analysis.

Boundary conditions (which include orography, sea surface temperature, ground wetness,

sea ice m_w_r, and surface albedo) were also identical in the two runs. Sea surface t,emperature, and sea ice cover were. taken from yearly varying monthly mean AMIP data (Gates;

1992)_ and the yearly varying lnonthly mean soil moisture from the estimates of Schemm et

al. (1992). Seasonally-wtrying climatological wdues of surface albedo were based on Posey

and Clapp (1964). At every GCM tilne step, wdues of all time-wtrying boundary conditions

were linearly interpolated between the prescribed monthly means. The, surface, orography

was obtained by averaging the Navy 10 minute by 10 minute data.set supplied by the National Center for Atmospheric Research (NCAR). A Lanczos (1966) filter was applied to the

orography in both dimensions; removing the smallest scales while inhibiting the occurrence

of negative values caused by the Gibbs phenomenon. Resulting negative values were not

filled.

3.1 The Zonal Mean Circulation

The simulation of the zonal mean flow suffers fi'om many of the same failings ,_s other GCMs.

Figure 1 shows December-January-February (DJF) zonal-mean zonal wind for the simulation and for the analysis, ms well as zonal wind and temperature differences (simulation -

analysis). Figure 2 shows the same fields for June-July-August (JJA). The most notable

errors in the zonal mean flow are the westerly biases in the middle and high latitudes, particularly above 200 rob, and the inability of the model to produce negative vertical shears

at upper levels and close the tropospheric jets. This is associated with the usual "cold pole"
tn'oblem.Note that the largest westerly bimses tend to be on the poleward flanks of the

subtropical jets, rather than over the jet cores. In northern hemisphere DJF, for example,

this results in an easterly bias at the jet core. Zonal wind errors tend to be less pronounced

in both hemispheres during JJA, when the model actnally closes the jet in the northern

hemisphere; even in this case, however, the model has a cold pole and a strong westerly

shear. We will return to these hiases in sections 4 and 5, where we discuss the effects of the

mmmrics and of including gravity wave drag on thezonal flow.

Below 500 rob, the wind errors follow more or less the same meridional pattern as in the

upper levels, but with little vertical shear. By far the l_gest errors in low-level winds

m'e tile excessive midlatitude westerlies during DJF in the northern hemisphere. In the

southern hemisphere the low-level wind Mso shows a westerly bias in midlatitudes and a

definite e,_sterly bias poleward of 60S. In the southern hemisphere, however, biases are much

snlallcr than in the northern hemisphere and are fairly uniform around the year.

During JJA the zonM-mean flow tends to show double jets at midtropospheric levels in

both henfispheres (30S and 50S; 45N and 70N). The model clearly reproduces this feature

in tile southern hemisphere, but it fails to capture the weak high latitude jet in the northern

henlisphere, which is ,associated with a weak secondary maximum in the vertical shear.

The low latitude easterlies are well simulated. At low levels the model shows the monsoonal

asymmetry of stronger trades ill the winter hemisphere and weaker actually slightly westerly dm'ing JJA in the summer hemisphere. Aloft, the model properly simulates the

stronger easterlies of the summer hemisphere and captures the a.synlmetry between JJA

and DJF. During DJF it tends to produce a westerly bia.s near the equator, peaking just

above 200mb.

The temperature biases shown in Figs. 1 and 2 reveal that the model atmosphere is too

cold everywhere, except for the lower troposphere at high northern latitudes during JJA. As

shown by Boer et al. (1992), who compared results from fourteen models, this general cold

bias seems to be common to many GCMs. In addition to the cold pole bia.s known to occur

ill almost all models at upper levels, they identified a nearly universal tendency to produce

a cold bias throughout the tropical troposphere. In our case, the tropical tropospheric bia.s

is nearly 2°C and quite constant from the surface to about 250 rob. This is comparable in

magnitude to the biases reported by Boer et al. (1992) and is also comparable to the bias

obtained by Kanainitsu et al. (1990) with the National Meteorological Center MediumRange Forecast model run at triangular truncation T40.

The simulated extratropical tropost)here is also too cold in both hemispheres during DJF

and in the southern hemisphere during JJA. Although these bimses are not as uniform ,_s

the tropical biases, they are roughly equal in magnitude (I°C to 2°C), resulting in the weak

w'xtical shears in the lower tropospheric wind biases noted earlier. Froin tile temperature

crr()rs shown in Fig. 2, we see that the faihn'e of the model to produce the weak maximum

in the vertical shear at high northern latitudes during JJA is ,associated with a warm bias

t)oleward of 70N, which reaches 3°C near the surface at the pole. This suggests a problem

with the. model's summertime sm'facc energy balance over tile Arctic.
Figure 3 depicts the zonal-mean meridional velocity. Since the analysis of the divergent.

component of the wind is strongly influenced by the model, we should be particularly

suspicious of agreements in the mean meridional circulations simulated and analyzed by the

same model. For this reason, we have also shown results from the ECMWF analysis averaged

over the same period of March 1985 through February 1990. Both analyses are uninitialized.

The. winter Hadley cell during DJF seems to bear out our suspicion. In the simulation and

the GEOS analysis, the upper branch reaches a speed of only 1.5 m see -l, while in the

ECMWF analysis it is nearly twice as strong. It is hard to say whether the model is biasing

th,; analysis and underestimating the strength of the Hadley cell, or if the ECMWP anMysis

ow;restilnates it. However, results h'om the National MeteorologicM Center analysis (not

shown) are in closer agreement to the ECMWF analysis, with a maximum wind speed of

2.5 m sec -I. For the winter Hadley cell in JJA the GEOS and ECMWF analyses are in

closer agreement, with GEOS having the stronger cell of the two. In this case the simulation

produces a weaker cell than either analysis.

The southern hemisphere Ferrel cells during both seasons are considerably stronger in the

simulation than in the analyses. The same is true of the northern hemisphere Ferrel cell

during DJF, with a stronger and deeper upper branch and much stronger low level flow. The

unrealistically strong Ferrel cells are related to the unrealistic transient eddy momentum

fluxes, which we discuss in the next section.

Figures 4a,b depict the zonal mean specific humidity simulated for JJA and its difference

fr_ml th_ analysis. Results are shown only below 300 rob, since no analysis is done above this

lew_l. Near the surface the simulation is systematically drier in the tropics and subtropics

and w(_t.ter in middle and high latitudes. Away from the surface the situation is more

c_mplicated. In the northern hemisphere there is a maximum in the wet, bias between 900

and 950 mb (presumably at or just above the top of the boundary layer) and extending from

15N t._}60N. Just north of the equator, at the position of the ITCZ, the model produces a dry

bi;_s that extends througho, lt the troposphere. This and the upper tropospheric wet biases

in the s,lbtropics are probably associated with errors in the simulation of deep convection.

Finally, the model shows anot, her maxinmm in the wet bias in the lower troposphere over

th,; Arctic. This is the one region where the simulated zonal mean temperature was warmer

than th_ analysis. The model exhibits a similar dry bias at low levels in the tropics during

DJF (n,,t shown). The wet bi,_s in the extratropics, however, is substantially less.

Since the model has a significant temperature bias in the lower troposphere, we wondered

how much of the moisture bias could be explained by temperatures errors and how much

inv(dw;d changes in relative humidity. We separated these two effects crudely by computing

the diff_rence between the analyzed specific humidity and the humidity of air at the simulat.ed temperature, but at the analyzed relative humidity. This is shown in Fig. 4d. The

residual between this field and the total error in specific humidity may be thought of as

the part of the bi,_s due to relative humidity errors. This is shown in Fig. 4e. As we can

s_,. fl'(,m these two figures, most of the wet bia,ses can be accounted for by errors in relative

lmmidity: only the wet bia,s over the Arctic is attributable to temperature errors. In the

tropics and subtropics between 950mb and 700 mb, the two effects tend to compensate; but

t.he. dry biases w_ry near the surface are dominated by the temperature contribution.

7
The simulation of relative humidity is shown in Fig. 4f. Note that these results extend to

20 mb. For comparison, we show in Fig. 4c a composite of observations. Below 300 mb

we use vMues obtained from the analysis; above 100 mb, we form a relative humidity from

the zonal mean specific humidity climatology estimated from the Stratospheric Aerosol and

Gas Experiment (SAGE) data and the anMyzed zonal mean temperatures; between 100

and 300 mb, we blend the two. Results in the troposphere confirm the model's tendency

to produce too high relative humidity almost everywhere away from the surface. But these

errors are small compared to those above 300 mb. In the stratosphere, the model produces

a gross overestimate of the relative humidity, with a high ,and diffuse tropopause distribution. Specific humidities above the tropical tropopause are up to 10 times larger than in

the SAGE data. Since humidity is not analyzed above 300 mb, the analysis (not shown)

reflects the model's bias and shows almost identicM overestimates of stratospheric humidity.

Unfortunately, both the simulation and the analysis used these unrealistic humidities in the

radiation calculation.

Finally, we note that, ,as with the mean meridional circulation, the model ha,s a strong

effect on the moisture analysis. By comparing the analyzed total precipitable water with

that derived from the Special Sensor Microwave/Imager (SSM/I), Molod et al. (1996) find

that the analysis itself has a dry bias in the tropics. This implies that the simulation

probably has an even greater dry bias in this region than that suggested by Fig. 4. In the

extra-tropics, however, the analysis is in closer agreement with the SSM/I values, and we

can have more confidence that the difibl'ences presented in Fig. 4 are representative of the

true bim_ in the simulation.

3.2 The Stationary Flow

The simulation of the upper tropospheric stationary flow is summarized in Fig. 5, which

shows the 200 mb zonal wind and the 300 mb eddy height fields for DJF. The model does a

particularly good .job of simulating the zonal wind distribution (aver the tropics. Most of the

detail in the two regions of tropical westerlies over the e,xstern Pacific and the Atlantic is

correctly simulated, as are the easterly maxima over South America, the maritime continent,

and equatorial Africa. In the simulation, tropical emsterlies tend to be too weak over the

central Indian Ocean and a bit too strong over Africa and the maritime continent, regions

of strong convective activity.

In the extratropics, we see good agreement in the position of the North American and East

Asian jets, and of the subtropical.jet over Arabia and North Africa. A detailed examination,

however, reveals that in the simulation the Atlantic .jet extends too far east into western

Europe. The core of the Pacific .jet is somewhat weak, and is tilted slightly toward the

north rather than the more zonal configuration found in the analysis. Although, as we saw

earlier_ there is a slight zonal mean ea.sterly bias at 30N, most of the weakness in the Ea.st

Asian and North American .jets can be attributed to a weakness in the standing eddies. The

simulated Asian low at 300 nab is half the strength of the observed and the North American

trongh is also weak and displaced eastward and equatorward. We have not tried to analyze
the sourceof thesedeficiencies,but giventhebiasesshownearlierin the zonalmeanflow,

particularlyfor northernhemispherewinter, sucherrorsin the upper-levelstandingwave

patternarenot surprising.

Figure6 depictsthe DJF andJJAseasonalmeansof thesea-levelpressure.We focusfirst

,m theverypoorsimulationof thenorthernhemispherepatternduringDJF.Themaximmn

zonalmeanpressureat 30Nis toohigh by about 5 rob,whilepressuresat the North Pole

are35mbtoolow. This lm'geerrorin themeangradient,whichis ,associated with the lowlevel midlatitude westerly biases shown in Fig. 1, results in a very zonal pattern that m,a_ks

the presence of stationary features, such ,'_s the Aleutian and Icelandic lows. The same,

but much weaker, tendency to produce an unrealistically low pressure at high latitudes and

westerly biases in midlatitudes occurs during both se,asons in the southern hemisphere. The

northern hemisphere during JJA does not suffer from this problem, m_ we saw in Fig. 2.

Setting aside this zonally symmetric bias, we note that most of the observed features are

present, though somewhat distorted, in the simulation. This is seen more clearly in Fig. 7,

which shows only the eddy part of the sea-level pressure during DJF. The Aleutian low is

too weak and positioned too far west, and the Icelandic low has an mlrealistic extension

over northern Europe.

3.3 Eddy Statistics

In this section we present the zonal-mean transient and stationary eddy statistics. We

will restrict our attention to the eddy kinetic energy and to meridional fluxes of westerly

momentum, heat, and moisture. Transient eddy quantities are defined as products of the

departures from the sixty individual monthly means in the five-year period being analyzed.

In computing the transients, the zonal mean is not removed. The stationary quantities are

products of the departures from the zonal mean of the individual monthly means. Once

these monthly-mean statistics are obtained for each of the sixty months, they are averaged

to produce sem_onal (DJF and JJA) and annual means. To summarize the main features of

the eddy quantities, we have chosen to present only the mass-weighted vertical means for

the two seasons (Figs. 8 and 11) and to show only the annual means of their zonal cross

sections (Figs. 9, 10, and 12).

The poleward momentum flux by transient eddies is overestimated for both se,asons in both

hemispheres. The situation is worst in the nort.hern hemisphere during DJF, when the maximum in the poleward flux is nearly twice that of the analysis. This overestimate, together

with a slight poleward shift of the maximum, results in a spurious deceleration of the westerly flow equatorward of roughly 40N and a spurious acceleration poleward of 40N. This is

consistent with the zonal flow biases obtained during this season (Fig. 1). The meridional

cross sections of the annual mean momentum flux (Fig. 9) show that the model does a

good job of simulating the vertical structure with a maximum flux between 200 mb and

300 rob. The momentum flux by stationary waves (Fig. 10) is significant only in the northern hemisphere during DJF. The model does a reasonable job of simulating the maximmn

poleward stationary flux in midlatitudes though it too is somewhat exaggerated but fails
completelyin simulatingthe equatorwardflux northof about50N.

Tile errorsfoundin thenorthernhemisphereDJFsimulationof total eddymomentumflux

arequalitativelysimilarto thosereportedby Tibaldiet al. (1990)fortheECMWFmodelat

spectralresolutionscomparable to and higher than our grid resohltion. I They also found

large errors in the transports due to synoptic scale waves and to long planetary waves,

and these are similar to the errors we obtain for transient and stationary waves: too much

poleward flux by transients in midlatitudes, not enough equatorward flux by stationary

waves in high northern latitudes.

Errors in the transient kinetic energy follow closely those in the momentum fluxes (cf. Figs. 8

and 9). The kinetic energy is slightly overestimated in the southern hemisphere during both

se`asons and very significantly overestimated (about 20%) in the northern hemisphere during

DJF. This result is somewhat surprising since at this resolution we would anticipate the

horizontal diffusion to play an important role in the kinetic energy budget and for the kinetic

energy to be well below its convergent value. This is illustrated in the work of Tibaldi et

al. just cited. Even though they obtain overestimates of the momentum flux similar to

ours, their simulated eddy kinetic energy is everywhere less than the observed, even at

higher (T106) horizontal resolution. The fact that our error in transient kinetic energy is

an overestimate (even with the excessive dissipation `associated with the low resolution we

are using) suggests that it is linked to an excessive generation ,associated with errors in the

zonal mean state.

The notion that the simulation h,as overly vigorous transients is reinforced by the heat and

moisture fluxes shown in Fig. 11, 12, and 13. Midlatitude transient fluxes are consistently

overestimated. The biggest errors are in the northern hemisphere during DJF, where transient heat fluxes reach twice the analyzed values. In this c,ase, however, the stationary

transport is nearly half that analyzed, producing almost complete compensation in the total heat flux. Interestingly, errors in moisture fluxes are significantly smMler than in heat

flux. This is partly due to the fact that they peak at a lower latitude, where the simulation

of the transients appears to be better. Moisture fluxes due to the stationary flow are also

very well simulated during both se,asons.

4 Resolution and Order-of-Accuracy Studies

In this section we analyze the sensitivity of the GEOS GCM climatology to resolution and

order-of-accuracy. For this study, four separate experiments were performed and compared

with the GEOS-1 simulation (refercd to ,as the "Control" or H22) and GEOS-1 DAS anMysis

described in section 3.

In the first of these experiments, we simply run the second-order GEOS-1 model at the

lower resolution of 4 ° x 5° . We will refer to this version ms L22. We again note that this

'We _ssume. the dynamical behavior of T63 to b_' comt)arabl(" to that of a 2 (h'grc_ grid point mo(H

(Hchl and Suarcz 1994).

10
Table2: SensitivityExperimentsSummary

Experiment Resolution Vorticity 0 and q

Advection Advection

Control(H22) 2x2.5 2nd-Order 2nd-Order

L22 4x5 2nd-Order 2nd-Order

L42 4x5 4th-Order 2nd-Order

L44 4x5 4th-Order 4th-Order

H44 2x2.5 4th-Order 4th-Order

was the model and resolution used for the DAO's participation in AMIP. In the second

simulation, we keep the low resolution but increase the order-of-accuracy by using a fourthorder schelne in the horizontal differencing of the momentum equation (L42). This was the

finite-difference scheme used in the intercomparison of dynamical cores presented by Held

and Suarez (1994). For the third simulation, we add to the preceeding case a fourth-order

scheme fin' the horizontal advection of potential temperature and moisture (L44). The

fl,m'th simulation repeats the last case but at the higher (2 ° x 2.5 ° ) resolution (H44). The

experiments are summarized in Table 2.

The higher-order momentum scheme is a fourth-order version of the Sadourny scheme.

This scheme wins derived in Suarez and Takacs (1995). A brief discussion of the scheme

and the final form of the finite-difference equations are presented here in the Appendix.

The fimrth-order horizontal advection scheme is the same ,as the scheme used in the UCLA

GCM (Arakawa, personal comm, mication).

Like the Control, all experiments were initialized from the January 1, 1985 ECMWF analysis, and were then run for a period of 26 months. We analyzed the 1,_st two years of these

rlnls.

4.1 The Zonal Mean Circulation

We begin our analysis by examining the effects of resolution and order-of-accuracy on the

annual mean climatology of zonal wind and temperature. We will focus our attention on

two aspects of these simulations; 1) how the low-resolution, low order-of-accuracy simulation

compares with the higher resolution and/or higher order-of-accuracy runs, and 2) how the

model simulations compare with the GEOS-1 DAS analysis. Figure 14 shows the two-year

average of the annual and zonal mean wind in the southern hemisphere at various pressure

levels for the Control run, for experiments L22 through H44, and for the GEOS-1 DAS. It

is evident that L22 exhibits a behavior which is distinctly different from that of the other

experiments. For example, at 200 mb L22 has a narrowed jet with a single maximum that

is shifted toward the equator to about 35°S. The L42 experiment, which adds fourth-order

acc,lracy to the advection of vorticity, accurately maintains the breadth of the jet, while

11
L44 captures the double-jet structure that was simulated in the Control run and which is

evident in the analysis. All simulations, however, produced stronger jets than the analysis.

It is evident from these results that ,as order-of-accuracy and/or resolution are incre,ased, the

climatology of the southern hemisphere zonal wind is systematically converging to profiles

which are similar to that of the analysis, but higher in magnitude. Note that near the

surface, at 950 nlb, M1 simulations except that of the second-order, low-resolution run had

stronger surface winds than the DAS.

Figure 15 shows the two-year averaged annual and zonal mean wind for the northern hemisphere. Here we see a somewhat different, result than in the southern hemisphere. At all

levels, ,as order-of-accuracy and/or resolution are incre,ased, the climatology of the northern

hemisphere zonal wind moves systematically filrther, in both shape and strength, from that,

of the analysis. In this c,ase, the low-resolution (4 ° x 5° ) run is the closest, to the analysis.

On the other hand, if we restrict our attention to the simulations and compare only model

generated results, we see that the low-resolution fourth-order simulations, L24 and L44, are

much closer to the higher resolution Control simulation (H22).

This large difference between the low order-of-accuracy 4° x 5° and 2° x 2.5 ° climatologies is

consistent with resolution comparisons described by other authors. Boville's (1991) analysis

of NCAR's CCM1, ,as well ,as results from Williamson et al. (1994) of NCAR's CCM2,

showed that the climatology produced using a triangular trmlcation of T21 wins significantly

different from the climatologies produced using T42 or higher. This is also consistent with

the results described by Tibaldi et al. (1990) and Boyle (1993) in analyzing the ECMWF

model. The tendency of models to produce unrealistically strong zonal winds as resolution

is increased has also been recognized for some time. This has been ascribed to deficiences

in modeling the drag exerted by orography on the zonal mean flow (e.g., Wallace et al.

1983, Pahner et al. 1986). At low resohltion, unrealistically weak momentum transports

compensate for orographic errors, leading to more realistic (weaker) zonal winds. We note

that, in our c`ase, unrealistically strong zonal surface and upper-level winds are observed

even at low resolution when we increase the accuracy of vorticity advection (L42). While

we would not expect orographic effects to play a major role on our errors in the southern

hemisphere, their effects in the northern hemisphere may be quite substantial. We will

return to this point and explore the source of these errors more closely in section 5.

Annual mean temperatures at 100 mb are shown in Fig. 16. We begin by noting that all

simulations are colder than the corresponding analysis. In the tropics, this temperature

bias is ,-_ 5°C. At both poles, but particularly in the southern henlisphere, the cold pole

bias is worsened as resolution and/or order-of-accuracy are incre`ased. The low-resolution,

low order-of-accuracy simulation, L22, is nearly 15°C warmer than the corresponding highres()luti()n Control run, while the low resolution, fourth-order simulation, L44, is practically

identical to the Control run. It is interesting to note that most of the additional cold

bias l>etween L22 and L44 results from adding more accurate temperature Mvection (L42

---+ L44), rather than just more accurate vortieity advection (L22 --+ L42). While lower

polar temperatures are consistent with the enhanced westerlies we found with increasing

resolution and order-of-accuracy, it should also be noted that these results are contrary to

th()se fimnd by Mahlman and Umscheid (1987), in which temperatures above 50 mb were

12
shown to increase (toward observational wdues) with increasing resolution in the 40-level

GFDL "SKYHI" GCM. Similarly, Chen and Bates (1995) found a reduction in the cold pole

bias when the GEOS model was run with a semi-Lagrangian dynamical cores. However, in

this case it is not clear whether the change in dynamical cores provided a real increase in

accuracy.

Figure 17a shows the annual mean precipitable water differences between the Control run

and the four experiments, while Fig. 17b shows the diffe.rences between the simulations

and the GEOS-1 DAS analysis. It is clear from Fig. 17a that the low-resolution, low

,wder-of-accuracy run (L22) has a distinctly different moisture climatology than the other

simulations, with a significant moisture deficit in the tropics and excessive moisture at

middle to .high latitudes, ,as compared to the Control experiment (H22). Interestingly,

just the use of flmrth-order w)rticity advection (L42) substantially removes this bias, and

little additional inlprovement is seen when fourth-order advection of specific humidity is

included (L44). We. also see little impact when fourth-order accuracy is included at. 2° x

2.5 ° rvsoluti,nl (H44).

Fr,nn Fig. 17ll we see that all simulations exhibit a dry bias along tile equator, and a moist

bias ,m either side in the sub-tropics. In general, the simulations are producing a moist

bias at middle to high latitudes in the northern hemisphere and a dry bias at middle to

high latitudes in the southern hemisphere. Since L22 was wetter than the Control in the

s(mthm'n hemisphere, it is somewhat closer to the analysis in this region.

4.2 Eddy Statistics

Giw:n the changes in the zonal mean win& temperature, and specific humidity shown in the

previous section, we may anticipate significant changes in the fluxes of momentum, }mat,

and moisture due to transient and stationary eddies. Figures 18-20 show the vertically

integrated poleward fluxes of momentum, heat, and moisture fronl all experiments, as well

as Dora the GEOS-1 DAS. Transient and stationary quantities are defined as in section a.a.

Turning our attention first to the momentum fluxes (Fig. 18), we see that in the southern

henfisphere the analysis shows poleward flux equatorward of ,-_ 60 ° and equatorward flux

at higher latitudes. All experiments but L22 reproduce this pattern. In L22 tile transition

between poleward and equatorward flux is displaced equatorward some 10 °, _usis the latitude

of inaxinmm converg(,nce. The magnitudes of the fluxes, however, are best simulated by L22,

whil(: an exaggerated maximum in westerly momentum flux conw:rgence is clearly evident

in the. other runs. In the northern hemisphere there is also a very systematic increase in

the transient momentum flux with increasing order-of-accuracy and resolution. Comparing

these results with the analysis, we see that this systematic increase takes us away from the

observations, in spite of our increased accuracy. The analysis, in fact, most closely resembles

the 4 ° x 5° second-order result. (L22).

Similar tmhavior can t)e seen in the northern hemisphere stationary fluxes. At high latitudes

(> 60°N)_ where the analysis shows an equatorward flux, there is a systematic tendency for

13
increasingpolewardflux with increasedaccuracy.In general,all runsshowa polewardbias

in the stationarymomentumflux outsidethe tropics. Sincethe meridionalcomponentof

the Eliassen-Palmflux is the negativeof themeridionalmomentumflux, this impliestoo

muchequatorwardpropogationof stationarywaves-- a biaswhichis probablyrelatedto

errorsin the zonalmeanflow.

Figure19showsthesensitivityof theverticallyintegratedheatflux to resolutionandorder-

_ff-_ecuracy.All modelrunsproduceexcessivetransientheatfluxesin both hemispheres.

Ill thesouthernhemispherethepositionof themaximumtransientflux improvessomewhat

with increasingaccuracyand/orresolution.Weseea similarresultfor thestationaryflux

ill the southernhemisphere,with the magnitude,also improving with increased order-of-

:_eeuracy and resolution. In the northern hemisphere the systematic increase in the poleward

heat flux by transient eddies is partially compensated by a systematic decrease in the flux

by the stationary flow with increasing order-of-accuracy and resolution. The analyzed flux

obtained from the GEOS-1 DAS, however, more closely resembles the low-resolution, low

order-of-accuracy result, with weaker flux from the transient flow and greater flux from the

stationary flow. It is also interesting to note that improved aceurary of advection in the

momentum equation (L22 ---* L42) produces changes in the heat fluxes ms great &s those

from improved accuracy of adveetion in the thermodynamic equation (L42 ---* L44). This

iv in contrast to the behavior of the momentum fluxes, which were mostly sensitive to the

formulation of momentum advection itself.

Finally, Fig. 20 shows the sensitivity of the vertically integrated moisture flux to resolution

and order-of-accuracy. Interestingly, we see that contrary to the behavior of the transient

momentum and heat fluxes, which increased with increasing accuracy and resolution, the

transient flux of moisture decreases with increased accuracy and resolution. Also, increasing

the accllracy has a signifiicant impact on the moisture fluxes even at high resolution (H22

H44), whereas the heat and momentum fiuxes were not very affected by order-of-accuracy at

th(_ higher resohltion. This points out the care that must be taken in advecting water vapor,

even in the horizontal, and suggests that this ,aspect should be improved in fllture versions of

the model. It is encouraging that in the southern hemisphere, where model biases can easily

affect the analysis, the transient flux from the fourth-order, 2° x 2.5 ° resolution simulation

(H44) is closer to the analysis than to the simulation produced from the model used in the

GEOS-1 DAS (H22). In the northern hemisphere mid-latitudes (poleward of 40°N) there is

a systematic decrease in the stationary moisture flux with increasing order-of-accuracy and

resolution. This decrease, however, moves the simulation away from the analysis, with L22

again appearing to be the most realistic.

We have seen that several of the model biases presented in section 3 (such `as the cold pole

bimses and the associated errors in verticM shear, the westerly hi`as in surface winds, and

the excessive transient fluxes of heat and momentum) are not simply a result of inadeq,ate accuracy in the discretization of the hydrodynamics. Rather, we have shown that

in the northern hemisphere incre`aging the accuracy of the hydrodynaxnics has consistently

produced zonal climatologies of wind, temperature, and meridional fluxes of heat and momentum which are further from the analysis. We will show in the following section that

these biases are closely linked to the lack of sufficient upper-level drag `associated with sub

14
grid-scaleorographicgravitywaves,andt.he lack of a sufficiently resolved stratosphere.

5 The Effects of Gravity Wave Drag

As shown in the preceding sections, the GEOS GCM suffers from several egregious errors

which manifest themselves during the northern hemisphere winter season as cold bi,'Lses at

both poles and westerly bi,'uses in both .jets, a severe zonality of the sea-level pressure and

weak 300 mb eddy heights, enhanced transient fluxes, and weakened stationary fluxes in the

northern hemisphere. Similar errors have been encountered by other authors (for example,

Pahner et al. 1986, McFarlane 1987, and Boer and Lazare 1988) and, ,as mentioned earlier,

have been ascribed to deficienees in modeling the drag exerted by orography on the zonal

lnean flow. Typical of these studies is that the poleward transport of westerly momentum

by transient, eddies systematically incre,'uses when horizontal resolution is incremsed. These

increases in the momentmn transport must be balanced by changes in the mountain torque

and/or incremses in the surface frictional drag, thus requirinp4 an inereKse in surface zonal

wind. While most of these studies have fimnd these errors at resolutions equivalent to 2°

x 2.5 ° r_solntion or higher, we have shown that in the GEOS model they occur even at

low res()lution when fourth-order accuracy is used. Further insight into the source of these

errors may be gained by examining the simulated and mmlyzed angular momentum budgets.

We will fi)cus our attention first on the GEOS-1 GCM simulation (H22) and the GEOS-1

analysis.

The tend(;ncy of the vertically integrated and zonally averaged relative angular momentum

may be expressed as

/,, /0 /0' 0 ' __ ' 1 0[.,,M,.]cosCd_+ .f [_,l _.cos¢-- 0-_ [(.M,.)] .q _,cos¢ 0¢ .q

__ [71"0(I).._] t.;-b-_j -[,,-,],_.cos¢+jO 1 _.0 [TrM,.]Ada.-7

(1)

where square brackets denote a zonal mean,

Mr -= ua cos

is the relative angular momentum,

_l.q= O,.,,,,.s- r,,o,,,)/_

is the mass of the model atmosphere per unit area, and all other notation is standard.

The last term on the right-hand-side of Eq.(1) is the data-driven torque introduced by the

m_alysis, and can be expanded ms:

f0_ o [_M,.]__do- .q = .£ " \b-i] A " eos, £g +fo " ,,, g "

15
• ( 0.,,

Here. we see that _t('xM,.)A is proportional to both the analysis increments of wind, _.7b-7}A'

and sm'face pressure, (_) although this latter tet'nl is quite small. In the long-term A _

mean, the time change of the relative nmmentmn can be neglected ill (1): a.s emn the term

proportional to .f, since the mean ma.ss flux across a latitude circle must be small. Tlms,

a balance will be obtained between the remaining terms predicted by the lnodel (fi'ictional

torque, mountain torque, and momentmn convergence) and the systematic error correeted

l,y _ (TrM,.) A.

The solid lines in Fig. 21 show the nmmentmn torques d,u'ing the northern hemisphere

winter season fin" the GEOS-1 analysis and the GEOS-1 simulation (H22). We see that

the frictional torque due to surface, stress is considerably larger in the simulation than in

the analysis. This difference is ,xssoeiated with the stronger surface winds in the model

simulation (see Fig. 1), which give rise to unrealistically large sources (in the tropics) and

sinks (at mid-latitudes) of westerly momentum. Consistent with the, se increased sources

and sinks is the inere,xse in the meridional flux of westerly momentum described in section

3.3. Also evident in Fig. 21 is a complete lack of inountain torque poleward of 60N in

the model simulation due to the severe zonality of the pressure field discussed in section

3.2. In this region the analysis shows the mountain torque balanced by an influx of easterly

montentunl, while in the simulation the atmosphere is bringing in westerly momentum as

in middle latitudes which is balanced by an unrealistic fl'ietional torque. Finally, we also

show the residual between the fl'ietional torque, the mountain torque, and the convergence

of angular lll()nlelltllnl. We see that ill the simulation the residual is small, ilnplying that

there are no large spurious sources of monlent,mt. 2 During the, GEOS-1 DAS assimilation,

h()wever, the analysis had to introduce a significant easterly torque t() remain close t() the

()l)se.rvati(mal data. This analysis torque can be thought of as a c()rrecti(m t() systematic

err,)rs in model numerics and/or paranleterizations.

Tlmse results, t,_ge.ther with those pres,mted ill sections 3 and 4_ are suggestive of tile

need fiw a gravity wave drag paranleterizati,m in the GEOS GCM. Recently, Zhou eL al.

(1996) introduced a gravity wave drag parameterization into the Goddard Laboratory t})l'

Aim,spheres (GLA) 4th-order GCM. This A-grid GCM was the predecessor of tile Cgrid GEOS-1 GCM and uses many similar alg, withms and physical parameterizations. In

t.h(qr rop_n't, they flmnd that many of the same biases flmnd in the GEOS simulations

(fin" example, the anomalous low pressure in the north polar region) were corrected in

their model by gravity wave drag. Therefl-wc, we decided to test their gravity wave drag

tmrametcrization in the GEOS system.

The gravity wave drag paranleterization of Zhou et al. (1996) is a modified version of

Vernekar ,',t al. (1992), which was based on Alpert et al. (1988). In this version, the gravity

w;we st.ress at the surface is based on that deriw,d by Pierrehmnbert (1986) and is given

by:

'-'This is gtatit_'ing lmcanse the nunu.rical s('lt_.ll,,, wc nsvd ,h,os not g,uuantec angular momentmn

('OllNl'l V_t[ toll.

16
I< s,,I= _7 1 4- f_2] ' (2)

wher(' F,. = Nh/U is the Froude number, N is the Brunt - Vdisdlii fl'equency, U is tile

sm'facc wind speed: h is the standard deviation of the sub-grid scale orography, and g* is

the wavelength of the nlonochromatic gravity wave in the direction of the low-level wind.

A m()dificaticm introduced by Zhou et al. (1996) allows for the momentum flux to escape

thr,,lgh the top of the model. For our study, h is set, to the subgrid scale, standard deviation

,,f tim Navy 10 minute by 10 minute topography, but is not allowed to exceed 400 m.

To test the gravity wave drag parameterization, we ran a 26-nlonth simulation similar to

those describe, d ill section 4 using tile 4° x 5° flmrth-order model, L44. Ill section 4 we have

shown that all of the model biases exhibited by tile C,mtrol GEOS-1 GCM also exist and

arc well represented by tile 4° x 5° flmrth-order model. Refln'ing again to Fig. 21, the dashed

lines depict the momentum torques obtained using tile gravity wave drag parameterizatiolL

It is clear that L44 with gravity wave drag simulates very well the surface stress, mountain

t,n(lUe, and m,mmntum convergence depicted by the OEOS DAS analysis. Moreover, tile

residual t(,rque from this run (which is tile torque introduced by tile gravity waw _. drag

parameterization) is very similar to the torque introduced by the analysis increments. This

is a good indication that the systematic errors corrected by the assimilation resulted from

the at_sence .f gravity wave drag ill the GEOS-1 model.

The effects of gravity wave drag on the momentum budget are seen lllore clearly in Fig. 22,

which shows the difference.s in torques obtained with and without gravity wave drag, for

nm't.hern hemisphere D,JF. We see that gravity wave drag exerts an easterly torque between

20°N and 50°N and weak westerly torques at low and high latitudes. We might expect this

to be balanced simply by a decrease in the frictional drag and mountain torque.s associated

with decreased surface winds. But this is not at, all the cage. Changes in mountain torque are

w_ry small and changes in frictional drag are distributed very differently fl-om the gravity

wave drag. In fact, the frictional torque changes sign around 40°N ahnost exactly

at, the latitude of maximum gravity wave drag, and the two are in the same direction

equatorward of this latitude to 20°N. The outstanding result ill Fig. 22 is the large change in

the momentum transports. Equatorward of 45°N anomalous westerly convergence balances

tile gravity wave drag and Dictional drag. At higher latitudes the inean balance is between

tile change in momentum transport and surface fl'iction, with the gravity wave drag again

being in the same direction as the frictional torque.

These changes in the fluxes can be seen nlore dramatically ill Fig. 23, where we show the

vertically integrated annual mean eddy fluxes of momentum aim heat by the transient and

stationary flow from the analysis, as well ,xs fl'om L44 with and without gravity wave drag.

As shown in section a.a, without gravity wave drag the flux of momentum and heat by transient eddies is much too strong. In the northern hemisphere the heat flux by the stationary

flow is much too weak, while the momentum flux by the stationary flow lacks the equatorward component at high latitudes. With bile inchlsion of gravity wave drag, however,

all of these fluxes are substantially improved. The transient wave statistics for momentum

17
and heatarepracticallyidenticalto thoseof the analysisin the northernhemisphere.In

additiom the heatflux dueto the stationaryflowhasdramaticallyincreasedto a magnitudecomparableto that from the analysis,whilea significantequatorwardcomponenthas

enlerge, d at. high latitudes for the momentum flux by the stationary eddies.

As a result of these improvements in the flnxes of momentum and heat, the patterns of mean

sea-lew_l pressure and 300 mb eddy heights have also dramatically improved, ms shown for

DJF in Fig. 24. For ease of comparison, we have repeated the results from the GEOS-1

DAS. Most of the problems evident in Figs. 5 and 6 have been eliminated by the inclusion

of gravity wave drag. The extreme low pressures and zonality in the northern hemisphere

have. been removed, while the positions of the Aleutian and Icelandic lows are substantially

imt)roved. It can also be seen that the strength of the 300 mb standing eddies have increased,

while their positions are more accurately simulated.

Associated with these improvements in tile mean sea-level pressure and 300 mb eddy heights,

we also see improvements in the zonal mean wind structure and a reduction of the cold pole

t)ias, shown in Fig. 25 for DJF. The panels on the left show results from the simulation using

L44 with gravity wave drag. As compared to Fig. 1, we see that the jets are closed in both

h,_mist)heres; with substantial _usynlmetry in the .jet inaxinla. The -22°C bi_s found in the

H22 simulation in the northern hemisphere has |men substantially reduced (-4 ° to -6°C).

Tile -32°C bias fimnd in H22 in tile southern hemist)here hms been moderated (-24°C),

but its pattern ha.sremained substantially intact.

While gravity wave drag has dramatically reduced the cold-pole bia.s, it is clear that there

still remain significant errors at high latitudes, particularly in the southern hemisphere.

B,,vilh, and Cheng (1988) and Tsuyuki (1994) have reported reductions in the cold-pole

bias as a result of increased vertical resolution and better representation of the stratospheric

circulation. Boville and Cheng compare two perpetual January simulations, one using 15

levels with a rigid lid at 10 mb and the other using 26 levels extending to 0.1 mb. Results

showed a 20°C warming at 100 mb in tile high resolution experiment. To test whether some

of our remaining biases are due to our low vertical resolution and extent, we ran the L44

model with 46 levels (about 35 below 10 rob) and the top at 0.1 mb. This is the w.rtical

resolution which was used for the DAO's participation in the ASHOE field mission. The

panels on the right of Fig. 25 show DJF results from a two-year simulation. We see that

increasing tile vertical resolution and extent ha.s fllrther reduced the cold-pole bias, with the

northern hemisphere actually becoming warmer than the analysis. While results from both

the gravity wave drag parameterization and the stratospheric GCM are very preliminary,

it is clear that they will produce a marked improvement in the model provided first guess

to the GEOS DAS, and a substantial reduction in the systematic bia.ses currently found in

the analysis increments.

18
6 Summary

In this report have have analyzed the GEOS-1 GCM climatology fl'om a five-year simulation,

and c(mq)ared it to the climatology of the five-year GEOS-1 DAS reanalysis for the period

fr_ml March, 1985 through February, 1990. Due to the wide dissemination of tlle GEOS-1

DAS products to the scientific eomnmnity_ it is important that the behavior of the GCM

used t.o t)r,)duce them be thoroughly analyzed and docmnented. The behavior of physical

t)r, messes in the model such as moist convection, radiative heating, surface fluxes, and

the hydr,_h_gical cycle is the focus of Molod et al. (1996). In this paper we concentrated

on the I)ehavior of the model's circulation statistics, with an emphasis on those biases in

the simulated climate that may affect the analysis through systematic errors in the nmdel

pr(_vidcd first guess. In addition, the lint)act of several developments currently underway

fin" fl,t,,r(, versions ()f tile GEOS system have been explored.

In g(:ncral the GE()S-1 GCM captures the main features of the general circulation. A

,tctaih_d analysis of the. GEOS-1 GCM's climatology of primary fields (wind, temperature,

and sp,:cific h, nnidity), however, has revealed significant systematic biases when compared

with tlm GEOS-1 DAS analysis. The GEOS-1 GCM fails to accurately sinmlate the position

and strength of the northern henfisphere winter Icelandic and Aleutian lows, and shows the

incrcasc.d zonality characteristic of models without a gravity-wave drag parameterization

(Fig. 6). The model also exhibits a strong westerly bias in vertical shears and a significant

(',)1,t bias at both poles (Figs. 1 and 2). Also typical of many ,_ther GCMs (of. Boer et al.

1992). th,' m,Mel pr, Muces a coht bias throughout the tropical troposphere (.-_ 2°C). The

mo(lcl's dist.ril)ution of moisture shows a significant dry bias in the tropics and subtropics at

l(_w,_r lcv,_ls and a wet, bias at middle and high latitudes (Fig. 4). Above 300 mb the model

t)r,Mue,'s a gr,)ss overestimate of tile relative }mlnidity when compared to SAGE data. The

h_w lev,:l dryness in the tropics was shown t,) l)e correlated primarily with the model's cold

ldas thr, mgh,mt the lower troposphere, where`as the middle and high latitude wet bias w,_s

ass(,ciat.ed with errors in relative lmmdity.

The second moments (horizontal ,nomentunl, heat, and moisture fluxes) also show several

nmd('.l biases and inaccuracies (Figs. 8 and 11). The GEOS-1 GCM consistently overestinlates the meridional flux of westerly momentum and heat due to the transient eddies. In

addition, in the northern hemisphere during DJF the flux of heat due to the stationary

ed(ties is c,msiderat)ly weaker than observed and the stationary momentum flux is of the

wrong sign at high latitudes.

hi the see,rod pm't of this report, we focused on the sensitivity of the GEOS GCM to

res_dution and order-of-accuracy. While the advantages of higher-order aecur_y for passiw_ tracer advection are readily apparent, the effect of higher-order accuracy on a GCM's

climatoh_gy is not ,as obvious. We have shown that increasing the resolution and/or the

order-,ff-accuraey in the GEOS GCM has a very significant impact on the zonal mean flow

and on the second-moment statistics. In general, the climatology produced in the secondorder, low resolution (4 ° x 5° ) experiment is significantly different from all other model

generated climatologies (fourth-order, low resolution ,'_s well ms second- and fourth-order,

19
highresolution(2° x 2.5° )). In thesouthernhemisphere,thelow-order,low-resolutionsimulationshowsa nlarkedequatorwardshift of the zonal.jetand largebi_Lsesin momentum,

heat,andmoisturefluxeswhencompared to the higher resolution run (Figs. 14, 18, 19, and

20). Most of these bi,'uses appear to be related to the simulation of the transient eddies, with

increased accuracy enhancing transient fluxes of heat and momentum and decrea.sing fluxes

of moisture. In this region, where the total fluxes are dominated by the transient contribution, the higher-order and higher-resohltion experiments are usually in closer agreement

with the GEOS-1 DAS analysis.

In the northern hemisphere, where the role of fluxes due to the stationary eddies is significant, the zonal mean flow and heat and momentum fluxes tend systematically away

fi'om the GEOS-1 DAS analysis ,as accuracy and resolution are inere`ased (Figs. 15, 18,

19, and 20). When comparing only model generated results, however, it w_s shown that

the low-resolution, fourth-order simulations were much closer to the higher resolution Control sinmlation. Increasing the order-of-accuracy clearly improves the dynamical aspects

of the sinmlations (in the sense that they are closer to the higher resolution results), even

though these more accurate runs are filrther fi'om the GEOS-1 DAS analysis. These results are consistent with the suggestion of Palmer et al. (1986) that increasing resolution

and/or order-of-accuracy removes a cancellation of errors between the hydrodynamics and

sub grid-scale parameterizations that occurs at low resolution and low order-of-accuracy.

We conclude this study by examing the impact of gravity wave drag on the climatology

produced by the GEOS GCM. An examination of the angular momentum budgets fl'om

the GEOS-1 GCM and the five-year GEOS-1 DAS reanalysis for DJF shows the need

fin' an e_usterly torque at mid-latitudes in the northern hemisphere to reduce the westerly

hi`as (Fig. 21). This e`asterly torque is similar to that produced by gravity wave drag

parameterizations currently used in nlany models. Experiments using the gravity wave

drag paranlc.terization of Zhou et al. (1996) yielded significant and beneficial impacts on

both the time-mean flow and the transient statistics of the GEOS-1 GCM climatology, and

have elilninated most of the worst bi`ases in the GEOS-1 GCM sinmlation. An examination

of the. angular momentum budget from this run indicates that the resulting gravity wave

torque is similar to the data-driven torque introduced by the GEOS-1 DAS analysis which

was l)erfl_l'med without gravity wave drag. It was shown that, the inclusion of gravity wave

drag results in large changes in both the mean flow and in eddy fluxes (Figs. 23 and 24).

The result is a more accurate simulation of surface stress (through a reduction in the surface

wind strength), of momltain torque (through a redistribution of mean sea-level pressure),

and of lnonlentuln convergence (through a reduction in the flux of westerly momentmn by

transient flow eddies).

Acknowled.qements This work w_s supported by the EOS Interdisciplinary Science Program

aim by the NASA Research and Applications Program. Computer resources and flmding

were provided by the EOS Project through the Scientific ComputationM Facility of the

Data Assimilation OIfice.

2O
Appendix

Fourth-Order Scheme for the Shallow Water Equations

In this sect,ion we present a new fourt,h-order enstrophy-conserving differencing of t,he mom(_ntum ,,quation. To simplify tile t)rcsent.ati, m, we apply t,he scheme to t,he shMlow water

equat,i(ms in Cartesian coordinates, but its extension to the three-dimensionM primitive

C(luati(ms (m the sphere is straightforward.

A detailed'derivat, ion of the scheme is presented in Su,'trez and Takacs (1995). For reasons

outlined there, we consider this scheme a fourth-order version of the Sadourny scheme used

by Burridge and Haseler (1977). The scheme is based on the Arakawa C-grid and conserves

(!nerg/y and t)ot, ent, i,_l enstrophy for non-divergent flow. Also, it is fourth-order only in the

sense that it, reduces to tile flmrth-order Arakawa (1966) Jaeobian for non-divergent flow.

It, thus provides fourt.h-order accuracy only in t,he advect, ion of a second-order w_rt,icity by

the non-divergent, part of the flow.

Following Sadom'ny (1975) aim Arakawa and Lamb (1981), we begin by writing the shallow

water ,,q,mtions fin" tile t,wo wind components ('u.,v), and the. fluid det)th, h,, in 'wect,orillV_tl"i_ult'" fol'lll its:

O'H. 0

-- = --111_,'1, -- [(I'+ K]

0v _ 7flzu 0 [_+K]

cOt - _ '

o,, r0,,,,0,,,,1 cO_ L o.,: + -571 '

whel'e

(.f + ()

h

is t.h(_ I)()t, elltial vorticity, .f is the Coriolis parameter, ( is t,he relative vorticity,

K is the kinetic energy per unit mass,

K=_I [u2+v2]

,I, = g (h+ I,,.,),

g is the acceleration of gI'avity_ and h._ is the height of the surface topograt)hy.

21
F()lh-)wingArakawaand Lamb(1981),wediscretizetheshallowwatersystemas:

\Ot)_+_,j Ax ,+b/',+lo+._ #'+_,/',o+1⁄2 7i+ 5,3 :_,3-_

Az1 [ , ] t T_,_-_ It_,3tl _O-- 7 zt.7,3--1

I

1 F

,_ , u , + : _+},/'i+,_,j+ m+},j+," , Ay [ '-7o '-7,J

1[, /'i--I .--i?a..

, • n _ #i-' -- t?Y_" Ay I #7,Jt7 _t ,3+g 7,3t 7 _-l,j+

1

- A--y [(0 + K)i,j+R - (0 + K)i,j] ,

+_ *

i-1⁄2,J+lUi-1⁄2,J+l]

= i [. ot/_,j - A,TA?)_":+_,,_ -

]

-:j,3 7,J+ 7 1⁄2

where

ui+._,j = hi+± iui a_ l _Z2X?I

--y

"0. . l =- h. . , l'o. ._ n A.T,

2'7+7 _'-/+ 7 _'3-t- 7

--2: ]-

h_+1⁄2,j = _(h_+lj+h;j),

hi,j+ 1⁄2 = (h,i,j+l + h,i,j).

Re.fer to the stencil shown in Fig. 26 for the position of the wtrious variables on the Cgrid. We see that second-order differencing is used for the pressure gradient term and the

continuity equation. The advection of potential vorticity is governed by the form of the

t)aramct(,.rs c_, fl, 3', /S, u, and p..We write these as:

where

S ,_r

(c_,/3, 7, [i)i+1⁄2,j = e (a, fl, 7, 3)i+1⁄2,j + (1 - e)(n,,/4, 3', 6)i+._,j ,

£,

el':

_+:5'J

#,+_,j

_ 1 (rli+_,a+: s+7 ,. , +Tli+,- ,_ 12 " ' /'+7-a+_ 7,a-=.,] '

- 121(*l'+va! +:v +,_'-74-7_ +,_,++._s,) ,

22
-- 1 (I]_+/.j_[_/ -[- '/ . I ' 1 "/L I ) 12 /,+_,./-_ "qi "4-7

--

121(_1'+74-7 ) - I 4-1.]'+74+7 ) • ) 4-7 ]'+5,J:) .7

J)

_tlld

N

el'i+ i

.7.J

I #' + 5 ,J

iX"

%+1⁄24

_ 1 (q_+_4-' +_/;-' ' +Tk,' ,:_

12 7 .74+_ -rT,jt_/ ,

1 (_Ji+.-',4+:'+_ :' '+" ' ')

-- 12 . 7 /'+74+7 "'-7,J-7/ '

-- 121(_li+{.s-7" :_ + _li-..7,;-.7:_ ' 4- _li-. _,j+._ .) ,

1 (qi :' " ' +_1' ' " +zl;_ " ' _

12 ', -:_,s-.7 '-_,J-,7 +7,J+7/ '

l }' I

,+.7,:,+"

#_' t L:7,J+L77

__ -(1-_)@;+_ '-'l,-' j+1⁄2) _ _j+_

- _ m+_.,-, - _'J_+,j+,)

Th(_ s(_c()nd-(,rder scheme used by Burridge and Haseler (1977) t,hen corresponds to e = 1,

and t.h(_ new fi)m'th-order scheme corresponds t() e = _.

In the ab()ve equations, n_ _ _ _ is defined as

"_t_,Jt_

W]I(_I'(_

_tT,Jt 7

£i+_,j+._ + .fi+' .+'

. '7,7 .7

--:try

_tT,Tt 7

( ,) 11i+1,j+_- _'#¢7 '" = _-t-7,J

£i+_ .+' = - _

--xy

h._ , ._, = (hi+l j+! -F hi j+l + hi,j -t- hi+l,j)/4 _+Td± 7 , ,

Tlw fl)rm ()f K for which the scheme conserves energy is:

K_j = -_

whel'e

--. 1 2

,,,-_j = _(",,+{,3 +"? , .)

1

,,-_,_ = ±(,?., +,,?. ,).

2 'o*7 ',J-7

23
Using this discrete form of the kinetic energy in the three-dimensional GCM will result in

the computational instability discussed by Hollingsworth et al. (1983). A modification of

this form which removes the instability is presented in Suarez and Takacs (1995).

24
Z

Z

t/_

ec:J

¢-

O

N

¢9

I

t_

¢D

Figure 1: Five year average of Northern Hemisphere winter zonal mean u-winds (m sec -l)

for the GEOS-1 GCM, the GEOS-1 DAS, and the u-wind and temperature difference (GCMDAS).

25
Figure2: Fiveyearaverageof NorthernHemispheresummerzonalmeanu-winds(m sec-] )

fortheGEOS-1GCM,theGEOS-1DAS,andtheu-windandtemperaturedifference(GCMDAS).

26
Figure 3: Five year average of Northern Hemisphere winter and summer seasonal means

of the zonal mean v-wind (m sec -l) for the GEOS-1 GCM, the GEOS-1 DAS, _nd the

uniti_lized ECMWF analysis.

27
Figure4: Five yearaverageof JJA seasonalmeanof the zonalmeana) specifichumidity

(g kg-1) for the GEOS-1GCM,b) specifichumidity differencefor the GCM-DAS,c) specifichumiditydifferencedueto temperature,d) specifichumiditydifferencedueto relative

humidity,e)blendedrelativehumidity usingGEOS-1DASandSAGE,f) relativehumidity

from theGEOS-I GCM.

28
(D

I

O0

0

i,I

g

0

0

l

0

i,i

O

g

Q

W

W

-g

_0

Figure 5: Five year average of Northern Hemisphere winter seasonal means of the 200-

mb u-wind (5 (msec -]) contours, with negatives shaded) and 300-mb eddy-heights (60 m

contours, with light shading < 60 m and dark shading > 60 m) for the GEOS-1 GCM and

the GEOS-1 DAS.

29
Of)

a

I

hA

Figure 6: Five year average of Northern Hemisphere winter and summer seasonal means of

Sea-Level Pressure (4 mb contours, with light shading < 988 mb and dark shading > 1020

rob) for the GEOS-1 GCM and the GEOS-1 DAS.

3O
90N

GEOS-1 GCM Eddy Sea-Level Pressure (DJF)

60N

30N

30S60S90S

0 60E 12'OE 180 120W 60W 0

90N

GEOS-1 DAS Eddy Seo-Level Pressure (DJF)

60N

30N

EQ

30S

60S90S , _ _

0 60E 120E 180 120W 60W 0

Figure 7: Five year average of DJF seasonal mean of the eddy Sea-Level Pressure (4 mb

contours, with light shading < -4 mb and dark shading > 4 rob) for the GEOS-1 GCM and

the GEOS-1 DAS.

31
#

/

//

/!

II

z

\

i

,o=

i

Figure 8: Five year average of DJF and JJA seasonal means of the vertically integrated

momentum flux (m 2 sec -2) , and kinetic energy (m 2 sec -2) , due to the transient and

stationary flow for the GEOS-1 GCM and the GEOS-1 DAS.

32
(D

<(

C)

T'--

O

ta.l

¢D

iiOi .!

iI_. ..do

¢,o

Z

o

eo

,?,

o

(JD

Z

0

(q.,) _JnssaJa

,9, 0 ....._._._i.i._.

o _ o 6 6 66&&o _ o o o o o ooooo

(q.,) *Jns*.,._d (q_) ..m_.Jd

Figure 9: Five year a_nnua_l mean average of horizontal momentun flux (m _ sec -2) and

kinetic energy (m 2 sec -_) due to the transient flow for the GEOS-1 GCM and the GEOS-1

DAS.

33
Figure 10: Fiveyear annualmeanaverageof horizontalmomentunflux (m2 sec-2) and

kineticenergy(m2sec-2) dueto thestationaryflowfortheGEOS-1GCMandtheGEOS-1

DAS.

34
.--3

,>.

Z

u'l 0 _ 0

' "7

(Oep+el/m)

oo_o

II Z

/

/

/

IIIIIII

Figure 11: Five year average of DJF and JJA seasonal means of the vertically integrated heat

(m see -t deg) , and moisture (m sec -1 g kg-1) fluxes due to the transient and stationary

flow for the GEOS-1 GCM and the GEOS-1 DAS.

35
Z Z

or)

a

i

0

LLI

0

Z

0

0

I

0

0

i

Figure 12: Five year annual mean average of meridional he_t flux (msec -1 deg) due to the

transient and stationary flow for the G_3OS-1 GCM and the GEOS-1 DAS.

36
Of)

n

%..---

C)

hA

I :

(/)

O

hA

Z

Z

t_ _,

O

,o

¢D

_?, oO

L

.,#::

Figure 13: Five year annual mean average of meridional moisture flux (m see -1 _ kg- 1)

due to the transient and stationary flow for the GEOS-1 GCM and the GEO$-I DAS.

37
0

cO

0

I I I

Figure 14: Two year annual and zonal mean u-wind (In sec -1) in the southern hemisphere

for the Control (H22) run, experiments L22, L42, L44, H44, and the GEOS-1 DAS.

38
n

E

0

0

"ID

e"

me

I

e'-

Q.

g3

Om

-r

cO

.C

L

0

Z

1:

0

in

I

I

!

I

I

I

t

0

I

Z

o

z

o

z

o

z

o

z

,o

z

z

Z

.o

Z

-o

7,

o

z

0

aO

z

g

z

z

.g

Z

Z

.o

OI*1

I i

I i

I

/

i

I I

,o

Figure 15: Two year annual and zonal mean u-wind (m sec -1) in the aorthern hemisphere

for the Control (H22) run, experiments L22, L42, L44, H44, and the GEOS-I DAS.

39
il

I

i

\

*°

o

Z

0

O)

Z

'0

Figure 16: Two year annual and zonal mean temperature (deg K) at 100 mb for the Control

(H22) run, experiments L22, L42, IA4, H44, and the GEOS-1 DAS.

4O
0.25

Precipitable Water Anomalies

0.20-

0.15

0.10

c_ 0.05

-.xE o.oo

C)

-0.05

-0.I0

-0.15

-0.20

-0.25

90S 66s

L22 - Control (H22)

L42 - Control (H22)

L44 - Control (H22)

H44 - Control (1422)

._7--:.i'...-\,' _,/AI/'"---"::_"

30S E'Q 30N 60N ION

0.30

C'_

0.25

0.20

0.15

0.10

E° o.05

o.oo ,[ ,_,k-. : !\--]

10 I '....

",. ...... V

-0151

-0.20

90S 60S 30S E'Q

f"x,

.... H22 - GEOS DAS

-- L22 - GEOS DAS

L42 - GEOS DAS

........... L44 - GEOS DAS

.... H44 - GEOSDAS

36N 6(3N 90N

Figure 17: Two year annual and zonal mean differences in precipitable water (g tin -2)

between a) experiments L22 through H44 and the Control (H22), and b) between L22

through H44, the Control (H22), and the GEOS-1 DAS.

41
!

Figure 18: Vertically integrated poleward momenluni flux for the Control (H22) run, experiments L22, L42, L44, H44, and the GEOS-1 I)AS, dlle to transient and stationary

_low.

42
.... :::4.,i Z

, , , , , | , i i , ,

I I

(51poi_/uJ)

/

Figure 19: Vertically integrated poleward heat flux for the Control (ti22) run, experiments

L22, L42, L44, H44, and the GEOS-1 DAS, due to transient and stationary flow.

43
"o-

->

W

x

la.

8 "

Mem

O

10

L

Q

3

Q

O

I1.

¢,===j

"o-

->

(6_I/6oIs/,,,)

0

_,.o

4/)

,o

O"

,_ I,----I >

GO

Z

Z

I

[6_I__sl,,,) (_/5 _o_I,,,)

Figure 20: Vertically integrated polew_rd moisture flux for the Control (H22) run, experi-

_nents L22, L42, L44, H44, and the GEOS-1 DAS, due to transient _nd stationary flow.

44
90N

6ON'

3ON'

EQ"

30S"

60S"

90S

9ON"

Momentum Torque (DJF)

Frictionol Torque Mountoin Torque

CEOS-" DAS

CEOS-" CCM

L44 w/CWD

-1.5-i.2-(}.9-().6-0.3 0 0:3 0:6

Residual

o:9 1:2 _.5

6ON"

3ON"

EO"

30S"

60S"

9O5

.5-1.2 -0.9 -6.6-0.5 -1.5-i.2-6.9-().6-6.3 6 0:5 0:6 0[9 1:2 1.5

Figure 21: Five year Northern Hemisphere winter seasonal averages of momentum torques

for (a) the GEOS-1 DAS, and (b) the GEOS-1 GCM. Also shown is the two year DJF

seasonal average for (c) the GEOS GCM with Gravity Wave Drag (L44 w/GWD). (Units

are in 106 Nm -1 )

45
90N

80N

70N

60N

50N

40N

30N

20N

ION

GWO-Control Momentum Torque (DJF)

!

!

/

/

<,

\

Frictional

Mountain

Convergence

C;WD

EQ

-0.9 -0.6 -0.3 0 0.3 0.6 0.9

Figure 22: Two year DJF seasonal mean difference of momentum torques from the GEOS

GCM with Gravity Wave Drag (L44 w/GWD) and the control (L44). (Units are in 106 N

m -1 )

46
Z Z

Z

I

i

Z

Figure 23: Vertically integrated annua_l mean momentum and heat flux due to transient

and stationary flow for the GEOS-1 DAS and experiments L44 and L44 w/GWD.

47
Figure 24: DJF Seasonal mean Sea-Level Pressure and 300 mb Eddy Heights from the

GEOS GCM w/GWD and the GEOS-1 DAS.

48
a

o

o

o

or)

o

i,i

o

O

k.

t/)

r-_

3

O

N

Z

Z

Z

O

Z

Z

Figure 25: Two year _verage of DJF zonal mean u-winds (m sec -1) for the GEO,q GCM

w/GWD and the Stratospheric GEOS GCM w/GWD, and their temperature differences

(GCM-DAS).

49
Ay

.......... (i-

/]

?1., i .

z--7,7

7

.......... _" ! - I

Ax

- I 2J. "--I ......... _. I , _ .........

,J-F7 _,3±7 .... ............. _+7,j+7

# v 11,

hi,j

............. ?_i_jLi (i+.

?L.-- i •

7 6

11.

;_'J-7_.........

Figm'e 26: Stencil showing the position and indexing of the prognostic fields u, v, and h,

together with the vorticity, (, and t,he scheme parameters c_, #, 7, 6, v, and 11..

50
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54
Previous Volumes in This Series

Volume 1

September 1994

Documentation of the Goddard Earth Observing System

(GEOS) general eirculati,m model - Version 1

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

Volume 2

October 1994

Direct solution of the implicit fornmlation of fourth order

horizontal diffusion for gridpoint models on the sphere

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

Volume 3

December 1994

An efficient thermal infrared radiation parameterization for

use in general circulation models

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

Volume 4

January 1995

Documentation of the Goddard Earth Observing System

(GEOS) Data Assimilation System - Version 1

James Pfaendtner, Stephen Bloom, David Lamieh,

Michael Seablom, Meta Sienkiewicz, James Stobie,

and Arlindo da Silva

Volume 5

April 1995

Docmncntation of the Aries-GEOS dynamical core: Version 2

Max J. Suarez and Lawrence L. Takacs

Volume 6

April 1995

A Multiyear Assimilation with the GEOS-1 System:

Overview and Results

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

Wayne ttiggins, Yelena Kondratyeva, Andrea Molod,

Lawrence Takacs, Michael Seablom, and Richard

Rood

Volume 7

September 1995

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

Assimilation

Siegfried D. Schubert and Richard B. Rood

55
Volume 8

March 1996

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

Volume 9

March 1996

Energy and Water Balance Calculations in the Mosaic LSM

Randal D. Koster and Max J. Suarez

56



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Technical Report Series on Global Modeling and Data Assimilation

Volume 10 - Dynamical Aspects of Climate Simulations Using the

GEOS General Circulation Model

6. AUTHOR(S)

Lawrence L. Takacs and Max J. Suarez

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

Climate and Radiation Branch

Data Assimilation Office

Goddard Space Flight Center

Greenbelt, Maryland

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Takacs: General Sciences Corporation, Laurel, Maryland;

Suarez: Goddard Space Flight Center, Greenbelt, Maryland

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13. ABSTRACT (Maximum 200 words)

The general circulation simulated by Version 1 of the Goddard Earth Observing System (GEOS- 1)

general circulation model is compared with the 5-year reanalysis recently completed using the

GEOS- 1 data assimilation system. Emphasis is on the comparison of dynamical quantities, such as

heat and momentum fluxes. The results indicate that, while many features are well simulated, the

model exhibits a number of severe biases. These include a cold bias at both poles and an associated

westerly bias at upper levels, a strong low-level westerly bias during northern hemisphere winter, a

dry tropical boundary layer and excessive moisture at all levels outside the tropics, too much

poleward heat and momentum flux by transient eddies, and too little heat flux by stationary eddies.

The effects on these biases of increasing resolution and order of accuracy, of including a parameterization of gravity wave drag, and of increasing the vertical extent of the model are also examined.

14. SUBJECT TERMS

Goddard Earth Observing System, GEOS-1, general circulation model, data

assimilation system, dynamical quantities, heat and momentum fluxes

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