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GCM Simulations of tropical rainfall

The aspect of GCM simulations of most relevance to studies of the impact of tropical vegetation changes is rainfall. An assessment has therefore been made of the GCM simulations of rainfall. The observed precipitation according to Jaeger (1976) (from Randall 1982) is shown in figure 3a, c.

In addition to the models listed in table 1, the long established OSU model and the ECMWF medium-range forecast model are included here. The OSU model (Schlesinger and Gates 1980) is a two-layer model with 4° latitude by 5° longitude grid. The boundary-layer parametrization uses constant flux layer assumptions like the GISS model. The model albedos are from Posey and Clapp (1964) and there is a one-layer ground temperature simulation. The hydrology has recently been changed from that described by Carson to one like the GISS model's (Ghan et al. 1982). The version of the ECMWF model used by Tiedtke (1983) for two 50-day experiments is a 1 7/8° by 1 7/8° model with 15 layers. The physics include a one-layer soil treatment but with diffusive linking of both moisture and temperatures to prescribed deep soil values (Tiedtke et al. 1979). The surface and boundary layer fluxes are based on Monin-Oboukhov similarity theory and are dependent on thermal stability and mixing lengths.

In order to relate them to tropical deforestation, the following summary of each model's success in predicting the observed rainfall pattern is restricted to northern South America, Africa, and southern Asia.

AES: (January only) wet areas too far north over all three continents with excessive rain north of the equator over west and east Africa and parts of Asia
ECMWF: good agreement in shape both in February and July. Too wet over north east South America and southern India in February and over all India in July, when the African rainbelt is also too intense and a few degrees too far north
EERM: generally too dry with most of the tropical land below 3 mm/day rainfall
GISS: too wet over North Africa and southern Asia in January. In July (8° by 10° model) rain too far north over Africa and Arabia with eastern South America and Africa also too wet
GLAS 1980: (see fig. 3) good generally; rain too far north over Africa in February
GLAS 1982: African rains too extensive to north and south in January. July African and Indian rainfall about 10° too extensive to the north
LMD: rain belts too narrow and intense but generally well-centred, except dry area over north India in July
MO: good agreement in January except Somalia and south India too wet; generally right July pattern over South America and Africa but too dry, northern India also too dry
NCAR: northern South America and southern Asia too wet both in January and July; ahara too wet in July
OSU: patterns generally good (except western Sahara wet in July); rain too intense in January over South America and especially Africa
GFDL: wet over South-East Asia in January and Sahara in July; most maxima Rhomboidal 30) too intense
GFDL: July pattern good except northern India too dry; India rather wet in (250 km grid point) January; South American maxima too intense January and July.

Overall, the spectral models, especially those of lower resolution, perform less well than the grid point models. There is less evidence of a dependence of quality on resolution for grid point models, with the 4° by 5° GLAS 1980 model probably the best. However, some aspects of that model's simulation in the tropics are less realistic: for example, the upper troposphere is too warm and the flow tends to be too westerly, especially in the upper troposphere near, and south, of the equator in July (Randall 1983).

FIG. 3a. February observed precipitation, taken from Jaeger (1976). The contour interval is 1 mm d-1

FIG. 3b. February simulated precipitation (GLAS). The contour interval is 1 mm d-1 except in congested regions.

FIG. 3c. July observed precipitation, taken from Jaeger (1976). The contour interval is 1 mm d-1.

FIG. 3d. July simulated precipitation (GLAS). The contour interval is 1 mm d1 except in congested regions.

Recommendations for future research

For detailed surveys of the availability of relevant data sets and requirements for future observational, theoretical, and modelling work, reference should be made to the appropriate sections of Eagleson (1981) and ICSU/WMO (1983, sect. 5 and annexes). Space precludes more than a brief statement of the more important needs. In the following, these needs are classified into three types: (a) observational studies; (b) improvements in GCM parametrizations; and (c) GCM experiments.

Albedo

  1. Data sets on actual surface albedo are needed both for GCM experiments and to provide a basis for future assessments of changes (a).
  2. The dependence of vegetation albedo on moistness of foliage and of soil should be studied to allow specification of these dependences in GCMs (a).
  3. The dependence of albedos on soil moisture as well as on vegetation should be included in GCMs on the basis of the above studies and used in vegetation change experiments (b, c).

Hydrology

  1. Research is needed to overcome the fundamental parametrization problems of transfer of theory and models based on microscale observations to the GCM grid scale, allowing for inhomogeneities of soils, slopes, vegetation, precipitation, etc. (a,b).
  2. Particular efforts are needed on the parametrization of runoff, which is generally very crudely represented in GCMs (b).
  3. The evaporation of intercepted rainfall and the effects of root depth variations need to be included in GCMs to allow proper assessments of the sensitivities to vegetation changes (b).
  4. GCM experiments are needed to assess the sensitivities to variations in runoff and root depths associated with vegetation changes (c).

Roughness

  1. Assessments of GCM formulations in relation to the surface stress are desirable in view of the large sensitivity to its formulation found in the GISS model by Hansen et al. (1983) (b).
  2. Sensitivity experiments are needed to estimate the GCM response to perturbations of surface roughness (z0) (c).
  3. the actual variations of z0 are poorly known. Observational studies of the relation of z0 to vegetation are required if GCMs are found to be sensitive to it (a).

General

The simulation of cloudiness in GCMs is generally poor. Special attention should continue to be given to this aspect of GCMs to represent realistically the feedbacks between clouds and surface parameters (b).

Symbols and abbreviations

C soil thermal capacity
C0 soil thermal capacity = pgCD
Cs turbulent coefficient depending on surface roughness and atmosphericstability
cp specific heat of air
D soil depth
E vertical moisture flux or evaporation
Ep potential evaporation
G downward heat flux into soil
G(z) subsurface heat flux
H vertical heat flux
K stability dependent diffusion coefficient
unit vertical vector
L latent heat of condensation
M water flux in soil
Ms snowmelt
N soil source/sink water term (melt/freeze)
P precipitation or net sink of moisture
PR rainfall
Qg soil source/sink heat term (moisture phase changes)
R gas constant of air
downward radiative flux at wavelength A
downward longwave radiative flux
Rs net vertical radiative flux
RN(0) net vertical radiative flux at land surface
Rs0) downward solar radiation flux at land surface
T temperature
To surface temperature
horizontal wind vector
near-surface windspeed
W soil moisture content
Y(0) surface runoff
Z depth from surface in air/soil
V vector gradient operator
( ) mean
( )' deviation from mean
e *­ emissivity
e *¯ absorptivity
f Coriolis parameter
g force due to gravity
hu non-linear function of soil moisture content
m soil moisture content for top soil layer
p pressure
p* surface pressure
q specific humidity
raE atmospheric resistance to water vapour transfer
raH atmospheric resistance to heat transfer
rs surface resistance
t time
w vertical velocity (dz/dt)
ze height of lowest model layer
z0 roughness length
a * reflectivity of surface
b function of normalized soil moisture
s vertical co-ordinate value p/p*
d q atmosphere near surface saturation deficit
d T temperature gradient
X excess value of surface value X0, over mean-surface value Xs
q potential temperature
x ratio of gas constant to specific heat at constant pressure
l wavelength of radiation
l g soil thermal conductivity
p density
pg soil density
s s Stefan's constant
t upward momentum flux
f geopotential
v 0 diurnal frequency
*AES Atmospheric Environment Service
ANMRC Australian Numerical Meteorology Research Centre
CCSAS Siberian Academy of Science Computing Centre
ECMWF European Centre for Medium Range Weather Forecasting
*EERM l'Etablissement d'Etudes et de Recherches Météorologiques
*GFDL Geophysical Fluid Dynamics Laboratory
*GISS Goddard Institute for Space Studies
*GLAS Goddard Laboratory for Atmospheric Sciences
*LMD Laboratoire de Météorologie Dynamique
*MO Meteorological Office
*NCAR National Centre for Atmospheric Research
OSU Oregon State University
UCLA University of California, Los Angeles

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