~~ ~ ~~ ~ ~~ 

NASA Contractor Report 185509 

Implementing the Simple Biosphere 

Model (SiB) in a General Circulation 

Model: Methodologies and Results 

N. Sat0 D. A. Randall 

Japanese Meteorological Agency 

Tokyo, Japan Fort Collins, Colorado 

Colorado State University 

P. J. Sellers 

University of Maryland 

College Park, Maryland 

E. K. Schneider, J. Shukla, 

J. L. Kinter 111, Y.-T. Hou, 

and E. Albertazzi 

University of Maryland 

College Park, Maryland 

Prepared for 

the National Aeronautics and Space Administration 

under Grants NAGW-492 and NAGW-557 

National Aeronautics and 

Space Administration 

Office of Management 

Scientific and Technical 

Information Division 

1989 
TABLE OF CONTENTS 

1. 

2. 

3. 

4. 

5. 

Table of Contents 

Abstract 

INTRODUCTION 

DESCRIPTION OF THE MODEL AND EXPERIMENTAL DESIGN 

OVERVIEW OF THE SIMPLE BIOSPHERE MODEL (SiB) 

3.1 Model Philosophy 

3.2 Model Structure 

3.3 Atmospheric Boundary Conditions for SiB 

3.4 Prognostic Physical State Variables of SiB and Their 

Governing Equations 

3.5 Implementation of SiB into the NMC Spectral Model 

3.6 Anticipated Impact of Implementing SiB 

COMPARISON OF SIMULATIONS AS PRODUCED BY CONTROL 

AND SiB VERSIONS OF THE NMC GCM. 

4.1 Diurnal Variation of Surface and Near-Surface 

Processes 

4.1.1 Tropical Rainforest, Amazon Basin 

4.1.2 Coniferous Forest, Canada 

4.1.3 Cultivated Area, Central Europe 

4.1.4 Sahara Desert 

Time Tendency of Regional Mean Energy 

and Moisture Budgets. 

4.2.1 Energy Budget 

4.2.2 Hydrological Cycle 

4.2.3 

4.2 

Soil Heat Conduction and Temperature 

4.3 Global Surface Field 

4.3.1 Summer Case 

4.3.2 Winter Case 

Impact on the Large Scale Atmosphere Circulation 

4.4.1 Summer Case 

4.4.2 Winter Case 

4.4 

SUMMARY AND DISCUSSION 

APPENDIX A Analyt i c function for C, and ch based on 

Monin-Obukhov similari t y theory. 

APPENDIX B Derivation of effective PBL mass and forcing. 

APPENDIX C Treatment of spatially non-uniform precipitation 

in SiB. 

APPENDIX D Initialization of global soil mo i sture fields 

fields for the Simple Biosphere Model (SiB). 

APPENDIX E Initialization of soil temperature. 

Acknowledgements 

References 

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ABSTRACT 

The Simple Biosphere Model (SiB) of Sellers et al. (1986) was designed to simulate 

the interactions between the Earth’s land surface and the atmosphere by treating the 

vegetation explicitly and realistically, thereby incorporating biophysical controls on the 

exchanges of radiation, momentum, sensible and latent heat between the two systems. 

This paper describes the steps taken to implement SiB in a modified version of the 

National Meteorological Center’s spectral GCM. The coupled model (SiB-GCM) was used 

to produce summer and winter simulations. The same GCM was used with a conventional 

hydrological model (Ctl-GCM) to produce comparable ‘control) summer and winter 

simulations. 

It was found that SiB-GCM produced a more realistic partitioning of energy at the 

land surface than Ctl-GCM. Generally, SiB-GCM produced more sensible heat flux and 

less latent heat flux over vegetated land than did Ctl-GCM and this resulted in the 

development of a much deeper daytime planetary boundary layer and reduced precipitation 

rates over the continents in SiB-GCM. In the summer simulation, the 200 mb jet stream 

and the wind speed at 850 mb were slightly weakened in the SiB-GCM relative to the 

Ctl-GCM results and equivalent analyses made from observations. 

PRECEDING PAGE BLANK NOT FILMED 
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1. INTRODUCTION 

This report and the paper of Sat0 et al. (1989) describe a study in which a simple 

but biophysically based model of the terrestrial vegetation was implemented in a General 

Circulation Model (GCM) of the atmosphere. The motivation for the study was to assess 

the impact of implementing such a scheme on the simulated climate with particular 

reference to (i) observations where available and, (ii) a parallel simulation produced with 

an abiotic land surface model. The paper of Sat0 et al. (1989) reported on the results of 

the study: this report is complementary to the the paper in providing a detailed review of 

the technical aspects of the implementation and an expanded discussion of the reuslts. 

Over the last decade, the scientific community has recognized that the interaction 

between the land surface and the atmosphere may play a significant role in the climate 

system; specifically, the fluxes of radiation, momentum and sensible and latent heat 

between the two systems may influence the atmospheric motion, temperature, humidity 

and precipitation fields which in turn feed back onto the spatial distribution of the sinks 

and sources of these fluxes on the land surface. 

Until recently, the surface properties which regulate the exchange of radiation, 

momentum and heat between the land and the atmosphere have been regarded as separate 

items which may be independently prescribed as boundary conditions within a GCM. 

Although this approach is not very realistic, it lends itself to the investigation of land 

surface-atmosphere interactions by way of sensitivity studies conducted with GCM’s. The 

three land surface properties which ultimately govern these interactions are the albedo 

(radiative transfer), surface roughness (momentum transfer) and the surface hydrolo ical 

parameterization prescription (sensible and latent heat transfer), all of which have %een 

the subject of in d ividual sensitivity studies. 

First, the land surface albedo is known to vary from about 12% (tropical rain 

forest) to around 35% (Sahara desert) over the snowfree land, see Shuttleworth et al. 

(198413) and Matthews (1984). Charney et al. (1977) executed a series of GCM sensitivity 

experiments centered on albedo changes in the Sahel which indicated that an increase in 

the albedo of the region would lead to a decrease in the surface evaporation rate and a 

reduction in the precipitation rate in the same area. This study has been followed b 

similar ones on the Sahel/Sahara region (Sud and Fennessy (1982) and Chervin (1979)l 

which produced the same result qualitatively if not quantitatively. Carson and Sangster 

(1981) performed a more radical experiment by comparing two simulation runs performed 

with a GCM wherein the global land surface albedo, a!, was prescribed as 0.1 or 0.3. The 

higher albedo case was associated with a lowered precipitation rate over the land, (4.6 

mm/day for a! = 0.1 and 3.4 mm/day for a! = 0.3). 

The effect of changing surface roughness length (zo) fields was investigated in a 

study by Sud and Smith (1985): The roughness length of the world’s deserts was reduced 

from 45 cm to 0.2 mm which brought about changes in the fields of horizontal water vapor 

convergence and convective precipitation. 

Not surprisingly, many sensitivity studies have been devoted to investigating the 

role of land surface hydrology in the exchange of energy between the surface and 

atmosphere. Walker and Rowntree (1977), Carson and Sangster (1981), Shukla and Mintz 

(1982), Warrilow (1986) and others reviewed in Mintz (1984) have performed experiments 

ranging from global prescriptions of totally wet versus totally dry land surfaces to 

alterations in regional soil moisture capacities. In almost all cases, reduced land surface 

evaporation rates led to reduced precipitation rates in the continental interiors. 

1 
P 

All of the above experiments served to demonstrate that the specification of albedo, 

roughness and perhaps most importantly, the ‘surface wetness’ could have important 

impacts on atmospheric fields. 1984) that these 

description of the land surface evapotranspiration process as conventionally specified in 

GCM’s. Dickinson (1984) designed a land surface model which explicitly refers to the 

effects of vegetation which has since been used for a simulation study of the climatic 

impact of large scale deforestation of the Amazon Basin; see Dickinson and HendersonSellers (1988). Sellers et al. (1986) constructed the Simple Biosphere model (SiB) which 

incorporates much of the philosophy of the Dickinson (1984) model while differing in detail. 

Both of these models have undergone sensitivity studies which indicate that as well as 

being realistic, the recognition of the role of vegetation in GCM’s should lead to more 

reasonable descriptions of the land surface-atmosphere fluxes. This paper reports on one 

such study, in which the conventional land surface hydrology model used in a research 

version of the National Meteorological Center (NMC) GCM was replaced by SiB. Changes 

in the simulated surface and atmospheric fields as a result of this implementation are 

discussed with reference to relevant observations where available. 

It was pointed out by Dickinson 

experiments, though illustrative, were unrealistic, particularly wit h regard to the 

2. DESCRIPTION OF THE MODEL AND EXPERIMENTAL DESIGN 

The National Meteorological Center (NMC) spectral eneral circulation model 

ela (1980 , but has recently been modified by us for use as a meteorological research tool. 

These mo ‘fications are reviewed below and include the implementation of a more efficient 

radiation scheme by Harshvardhan et al. (1987); the incorporation of a simple curve-fitted 

function in place of the full Monin-Obukhov scheme used to calculate surface transfer 

coefficients and resistances; inclusion of the Mellor and Yamada (1982) vertical diffusion 

scheme; and improvement of the time integration scheme used for the lower atmosphere 

and surface physics. 

The model formulation of dynamics is described in Sela (1980) and Kinter et al. 

(1988) describe the physics, initialization procedures and boundary conditions. The model 

has a rhomboidal truncation at total wave number 40, Laplacian-square type horizontal 

diffusion to avoid spectral blocking and is discretized into 18 vertical levels of which the 

PBL may occupy five or six. It has a shallow convection scheme (Tiedtke et al. (1984) as 

well as a large-scale precipitation and cumulus convection scheme after Kuo (1965) which 

serves as a moist process parameterization. The model has three soil layers with prognostic 

temperatures at 2.5 cm, 10 cm, and 50 cm depth; the temperature at 500 cm is kept 

constant. The surface hydrology is represented by a bucket model (Manabe, 1969) wherein 

the wetness is defined as the soil moisture divided by three quarters of maximum field 

capacity which is taken to be 150 mm on all land surfaces. These model ‘physics’ were 

taken from the 1986 version of NMC medium range forecast model. 

1. 

was originally designed and applied as a medium range B orecast (MRF) model, see 

1 yM) 

Some parameterizations were changed: 

A more efficient radiation scheme was incorporated which allowed simulation of the 

diurnal cycle; see Harshvardhan et al. 1987). The long wave radiation scheme is 

radiation scheme is based on that of Lacis and Hansen (1974) as modified by Davies 

(1982). The solar radiation scheme was further modified to allow for partial 

cloudiness. The new long wave scheme has less spectral resolution than the 

pre+xisting scheme of Fels and Schwarzkopf (1975), but it is not thought that this 

based on that of Harshvardhan and A orsetti (1984) and the short wave (solar) 

2 
simplification gives rise to serious errors. The short wave and long wave radiation 

tendencies are now calculated every hour and every three hours, respectively, which 

allows effective resolution of the diurnal cycle. 

The description of the aerodynamic resistances (or surface drag coefficient) for 

momentum and sensible/latent heat transfer between the lowest model layer and 

the surface was based on the Monin-Obukhov similarity theory as used in the 

E2-physics package of the Geophysical Fluid Dynamics Laboratory (GFDL) model 

(Miyakoda and Sirutis, 1986). When the virtual temperature correction is 

neglected, the surface drag coefficient depends on only two nondimensional 

parameters, the Richardson number and zr/z0, where zr is a reference height located 

within the lowest model layer and zo is the surface roughness length. A simple 

analytic function was developed that gives results close to those calculated with the 

original formulation (Appendix A). This function is used in order to avoid the 

costly iterations necessary for calculating the Monin-Obukhov length. 

The level 2 second-rder closure model of Mellor and Yamada, (1982) was 

implemented as a parameterization of vertical diffusion in the PBL. This has been 

used in the GFDL spectral model (Miyakoda and Sirutis, 1977) and the fine mesh 

grid model of the Japanese Meteorolo ical A ency (Yamagishi, 1980). In this 

version of the scheme, the eddy trans pff er coe ficient is a diagnostic function of 

Richardson number which makes it computationally expensive in operation. 

The surface and PBL physics were coupled so that heat and mass exchanges 

between the surface and atmosphere satisfy ener y and mass conservation laws (see 

time-integrate the coupled system. 

This modified version of the NMC GCM was used as the control model (Ctl-GCM) 

in this study. 

The hydrological model in place in Ctl-GCM is the same as that used in the NMC 

and GFDL GCM’s. The scheme consists of a conceptual bucket for each land point which 

is filled by precipitation and emptied by evaporation and runoff. The rate of evaporation is 

determined by the product of a potential evaporation rate; that is, the rate that would 

result from a saturated surface with the same roughness and surface temperature; and a 

wetness value, which is taken as a simple linear function of the level of water in the bucket. 

The scheme has the attraction of being conceptually simple, but cannot be considered a 

realistic description of the energy partition process as it occurs in nature. The most 

important omission in the bucket model is that of biophysical control of transpiration: 

vegetation can exert a considerable resistance on the transfer of water from soil to 

atmosphere and this can reduce the evapotranspiration rate to a level significantly below 

that calculated with the bucket formulation. Sellers (1987) discusses this and related 

shortcomings of the bucket model and concludes that it will almost always overestimate 

evaporation rates over the land. In this paper, we investigate the effect of replacing the 

conventional bucket hydrology model used in Ctl-GCM with the Simple Biosphere model 

(SiB) of Sellers et al. (1986). 

SiB is described in Sellers et al. (1986) and is reviewed briefly in Sec. 3 of this 

paper; basically, it is a model which directly addresses the effect of vegetation on land 

surface-atmosphere interactions, by modeling those physiological and physical 

(biophysical) processes which influence radiation, momentum and heat transfer. This may 

be compared with the abiotic schemes currently used in most GCM’s, see Sellers (1987). 

Here, we investigate the effect of replacing the conventional bucket hydrology model used 

2. 

3. 

. 

4. 

Sec. 4). An implicit scheme with explicit coef 8 cients was used with iteration to 

3 
in Ctl-GCM with the Simple Biosphere model SiB) of Sellers et al. (1986). SiB directly 

those physiolo ical and biophysical processes which influence radiation, momentum, mass 

Implementation of SiB necessitated a number of modifications to the control version 

of the GCM, as discussed in Section 3 of this paper. The complete Ctl-GCM combination 

was then used to produce 30-day summer and winter simulations which are compared to 

equivalent simulations produced by the Ctl-GCM, see Sec. 4. In both SiB-GCM and 

Ctl-GCM, the same boundary conditions for sea surface temperature (SST), sea ice 

distribution, initial snow amount and topography were used. The other land surface 

boundary conditions or parameters were treated differently in the two models. 

Surface albedo in Ctl-GCM is interpolated from the seasonal climatology of Posey 

and Clapp (1964). In SiB-GCM, the albedo is calculated with a ve etation canopy 

radiation model based on the two stream approximation (Sellers, 1984. The albedo 

depends on vegetation type and state and the solar zenith angle (see Dorman and Sellers, 

1989). Figure 1 shows the difference between the SiB-GCM and Ctl-GCM albedo fields 

for the June-July eriod. Over most of the continental areas except for north of 50" the 

Posey and Clapp 6964) albedo field is unrealistically low for reasons discussed in Dorman 

and Sellers (1989). The high albedo to the north of 65"N in Ctl-GCM is appropriate for 

snow-covered conditions. 

addresses the effect of vegetation on land su d ace-atmosphere interactions by modeling 

and heat trans f er 

90N 

60N 

30N 

EQ 

30s 

60s 

90s 

Figure 1: Difference map of the global albedo fields as generated by SiB-GCM and as prescribed for Ctl-GCM from the data of Posey and Clapp (1964). The 

fields are averages for the June-July Period. The solid contours indicate 

regions where SiB-GCM generates higher albedo values, the broken contours 

indicate where Ctl-GCM has higher albedo values. In all cases, the contour 

interval is 0.05. 

4 
Surface roughness length, zo, is time invariant in Ctl-GCM while in SiB-GCM it 

depends on the vegetation type and month of the year (see Dorman and Sellers, 1989). The 

major differences between the two models are to be found in the deserts of China and 

Mongolia: the roughness lengths there are around 30 cm in Ctl-GCM and 1 cm in 

SiB-GCM which may be compared to values of 1 cm reported by Matthews (1984). 

up to now, the normal practice in Ctl-GCM has been to update the global soil 

moisture field at daily intervals during a simulation, thus effectively maintaining the 

wetness at or near its ‘climatological’ value at every grid point. This procedure was 

retained for the Ctl-GCM runs discussed in this paper, but a test was executed in which 

Ctl-GCM calculated the time+?volution of the soil moisture. It was found that there were 

only small differences between the wetnesses and heat flux fields produced by those two 

versions of the Ctl-GCM (see later sections). In SiB-GCM, soil moisture was predicted 

throughout all simulations. 

The soil moisture and temperature initializations are reviewed in Appendices D and 

E respectively. As discussed in Sellers et al. (1986), the initialization of the soil moisture 

field is particularly problematical as it has a relatively long ‘memory’; the soil column in 

mid latitude zones is capable of storing several hundred mm of water which translates to 

one or two months worth of evapotranspiration and runoff. In Appendix D, we describe 

the technique for initializing the SiB-GCM soil moisture field so as to make it equivalent 

to the Ctl-GCM initial field; equivalent, that is, in that a given SiB-GCM grid point is 

assumed to have been exposed to moreor-less the same climatic history as its Ctl-GCM 

counterpart up until the start of the simulation run. This technique allows us to transform 

the Ctl-GCM initial soil moisture fields into equivalent SiB-GCM fields. The former 

observations of precipitation and air temperature.) The accuracy of these initial fields is 

questionable, a fact which is all the more disquieting as (a) it is certain that soil moisture 

initialization has a profound effect on the ensuing time-series of surface heat flux fields and 

(b the simulation runs discussed in this paper are comparatively short. The obvious 

soil moisture climatologies, an option that was not available to us during this study due to 

constraints on computer time. 

A final comment on the effects of the soil moisture initialization: within the upper 

range of soil wetness values; that is, greater than about 0.5, both models’ responses are 

almost completely insensitive to the soil wetness, see Sellers and Dorman (1987). It is only 

when the soil moisture content drops below these values that we expect an impact on the 

calculated evapotranspiration rate. It will be shown in later discussion that the greatest 

differences between the land surface evaporation rates calculated by the two models 

occurred in zones with relatively high soil moisture contents. 

The two GCMs, Ctl-GCM and SiB-GCM, were integrated for 50 days from 

00 UTC June 15, 1986, and 30 days from 00 UTC December 15, 1985. The simulated fields 

were compared to investigate the impact of including a biophysically-based surface 

parameterization in a GCM. Particular attention has been paid to the differences between 

the land surface hydrology and energy budgets produced by the two models. 

Two additional 30-day summer case experiments were made: A SiB-GCM run 

with a reduced soil moisture initialization in North America and a Ctl-GCM run in which 

the time-evolution of the soil moisture was calculated rather than prescribed from 

climatology . 

were prepared by Willmott et al. (1985) using an hydrological budget metho 6 forced by 

so 1 ution to the problem is to run both GCM’s for several years to generate self-consistent 

5 
Following the completion of all the experiments, four coding errors were found; one 

in Ctl-GCM, one in SiB-GCM and two common to both models. In Ctl-GCM, the surface 

sensible heat flux was estimated by integrating the time rate of change in virtual 

temperature (rather than actual temperature) over the atmospheric column. This 

diagnostic error leads to errors in the Ctl-GCM sensible heat flux fields on the order of 1%. 

In SiB-GCM, the moisture contribution to the time-tendency of the virtual temperature 

due to vertical diffusion was treated incorrectly which again is thou ht to lead to errors on 

the order of 1%. Third, eddy diffusivities in SiB-GCM were ! our times as large as 

equivalent values obtained with the original Mellor-Yamada (1982) code. Last, in 

implementing the Mellor and Yamada (1982) scheme, the interlayer Richardson numbers 

were multiplied by a factor of two (relative to the original scheme) which leads to more 

rapid transitions from stable to unstable transfer conditions within the atmospheric 

column. This last error is common to both GCMs, but is not thought to have a severe 

impact on the simulations as for most of the time the transfer coefficients are operating on 

the ‘plateau’ regions (i.e., stable or unstable conditions) where the eddy coefficients are not 

sensitive to the value of the Richardson number. 

3. OVERVIEW OF THE SIMPLE BIOSPHERE MODEL (SiB) 

Here we briefly describe the philosophy, structure, atmospheric boundary 

conditions and prognostic variables of SiB. This is followed by a description of the 

implementation of SIB into the GCM and a discussion of the anticipated impact of SiB on 

the calculation of the surface heat, moisture and energy budgets. 

3.1 Model Philosophy 

In most GCMs, including the original NMC GCM and Ctl-GCM, the fluxes of 

radiation, heat (sensible and latent) and momentum across the land surface-atmosphere 

boundary are conceptualized as largely separate processes, so that a given grid point has an 

independently specified albedo, an independently specified roughness length and an 

independently formulated dependence of evapotranspiration on soil moisture. This last 

generally takes the form of the bucket model described in the preceding section. 

The interactions between the land surface and the atmosphere which are of direct 

climatological significance on time scales of seconds to years are discussed in Sellers 

et al. (1986). They may be briefly summarized as follows: 

Radiation Absorption: The spectral properties of leaves and multiple 

reflections between them make vegetative canopies highly absorbent in the 

visible 

(i) 

thetically active radiation - PAR) wavelength interval 

and moderately reflective in the near-infrared region 

In contrast, bare ground generally exhibits a relatively flat 

over the interval of 0.4 - 4.0 p. 

(ii ) Biophysical Control of Evapotranspiration: Higher plants regulate the 

amount of gas exchange (and hence water loss) between their saturated 

interior tissues and the external air by means of valve-like structures on the 

leaf surface stomates). These appear to respond to the environmental 

potential so as to maximize the ratio of COz influx to water vapor flux (see 

Farquhar and Sharkey, 1982) or to conserve moisture in times of water 

stress. Besides the active physiological control of evapotranspiration, plants 

also influence the rate at which free water on the land surface is returned to 

conditions o 1 PAR flux density, temperature, humidity and leaf water 

6 
the atmosphere: vegetative canopies intercept precipitation which can be 

directly re-evaporated to produce relatively high but short-lived releases of 

latent heat into the atmosphere. 

Momentum Transfer: Vegetative canopies usually present a rough, porous 

surface to the nearsurface airflow. The resultant turbulence enhances the 

transport of sensible and latent heat away from the surface while exerting a 

drag force which may be significantly larger than that produced by bare 

ground. 

Soil Moisture Availability: The depth and density of the vegetation root 

systems and the physical properties of the soil determine the amount of soil 

moisture available for evapotranspiration. 

‘Insulation’: The soil surface under a dense vegetation canopy intercepts less 

radiation and may also be aerodynamically sheltered. As a result, the energy 

available to the covered soil is small and the component terms of the soil 

energy budget (evaporation, sensible heat flux and ground heat flux) are 

correspondingly reduced. 

In designing the SiB model, the philosophy was to model the vegetation itself and 

thereby calculate the radiation, momentum, heat and mass transfer properties of the 

surface in a consistent way. The morphological and physiological characteristics of the 

vegetation community at a grid point are used to derive the coefficients which govern the 

fluxes between the surface and the atmosphere. All of these fluxes depend upon the state 

of the vegetated surface and the atmospheric boundary conditions. 

In SiB, the wor ’s vegetation communities are classified into twelve types or 

morphological and physiological characteristics of a given vegetation community into the 

model parameters are given in Sellers et al. (1986) and the data sets and methodologies 

used to define the global distribution of vegetation types and attributes in SiB-GCM are 

reviewed in Dorman and Sellers (1989). 

3.2 Model Structure. 

In SiB, vegetation is divided into two morphological groups: Trees or shrubs which 

constitute the upper story or canopy vegetation, and the ground cover which consists of 

grasses and other herbaceous plants. Either, both or neither of these vegetation covers may 

be present in a given grid area. The upper story vegetation consists of perennial plants 

with persistent roots assigned to a fixed depth taken to be the bottom of the second soil 

layer. The ground cover is made up of annual plants and may have a timevarying root 

depth. There is a upper, thin soil layer (soil layer l), from which there can be a significant 

rate of withdrawal of water by direct evaporation into the air when the pores of the soil are 

at or near saturation. Beneath the root zone (soil layer 2), there is an underlying recharge 

layer (soil layer 3) where the transfer of water is governed only by gravitational drainage 

and hydraulic diffusion. The parameters required for each vegetation type in SiB are listed 

in Table 1 of Sellers et al. (1986). Values for many of the parameters are given in Dorman 

and Sellers (1989). 

(iii ) 

(i.1 

(VI 

biomes, see Fig. 2. B etailed descriptions of the methods used to transform the 

7 
Figure 2: Distribution of SIB vegetation types on the 128 x 102 Gaussian grid used in the 

NMC GCM. 

l-broadleaf evergreen 

2-broadleaf deciduous 

3-mixed broadleaf 

deciduous and 

needleleaf evergreen 

&needleleaf evergreen 

&needleleaf deciduous 

6-gras.s and broadleaf 

deciduous shrubs 

'I-grass 

&broadleaf deciduous, shrub, 

growth singly, in patches or 

groups 

%-broadleaf deciduous, shrub, 

growth singly, or in patches 

(semi-desert) 

T-tundra 

Daesert 

C-cultivated land represented by 

*--glacier 

wheat 

The sites used for the diurnal variation studies in Sec. 4.1 are marked with 

solid dots. Energy and moisture budget analyses were made for the four 

areas encircled by solid lines, see Sec. 4.2. 

8 
3.3 Atmospheric Boundary Conditions for SiB 

The upper boundary conditions by SiB are as follows: 

Ar temperature, vapor pressure and wind speed of the lowest model layer; 

Solar zenith angle, p. 

Five components of the incident radiation; F(o) 

Tr, e,, ur. 

Visible or PAR (< 0.72 p) direct beam radiation, 

Visible or PAR (< 0.72 p) diffuse radiation, 

Near Infrared (0.72 - 4.0 p) direct beam radiation, 

Near Infrared (0.72 - 4.0 pm) diffuse radiation, 

Thermal Infrared (8.0 - 12.0 pm) diffuse radiation, 

s,b(o)’ 

s, d(o)’ 

n,dW 

t,d(o)’ 

F 

F 

Fn,b(o)’ 

F 

F 

Large scale and convective precipitation rates; PL, Pc. 

3.4 Prognostic physical state variables of SiB and their governing equations 

SiB has eight prognostic physical-state variables: three temperatures (one for the 

canopy vegetation, Tc, one for both the ground cover and the soil surface, T,, and a deep 

soil temperature, Td); two interception water stores (one for the canopy Mc, and one for 

the ground cover, Mg); and three soil moisture stores (W1, W2, and Wd. The subscripts refers to the 

soil surface. The combined subscript gs refers to the ground (ground cover vegetation and 

soil) and d refers to the deep soil. The subscripts 1, 2, and 3 refer to soil layers. 

The governing equations for the three temperatures, Tc, T, and Td are 

and refer to canopy and ground cover vegetation, respectively, and 

and 

2(Rn,, - H,, - XE,,) ATd = Y 

F 

where 

= temperature, K 

= absorbed net radiation, W/m2 = sensible heat flux, W/m2 = evapotranspiration rate, kg/m2/s 

= canopy heat capacity, J/m2/K 

= latent heat of vaporization, J/kg 

= day length, s 

= effect.ive heat capacity of soil, J/m2/K. 

(3) 

The dot superscript (9) represents partial differentiation with respect to time. 

9 
In Eqs. (2) and (3), the original ‘slab’ model used to describe soil heat flux in SiB 

has been replaced by the force-restore formulation of Deardorff (1977). E,, the evapotranspiration from the canopy, has two components: i) E,,, eva oration of water from the 

wetted fraction of the canopy (canopy interception loss), and ii P Ed,-, transpiration of soil 

water extracted by the root system and lost from the dry fraction of the canopy. 

E,,, the evapotranspiration from the ground cover and the surface soil layer has 

three components: i) E,, and ii) Ed which correspond to Ewc and Edc for the ground 

cover, and iii) E,, direct evaporation ofwater from the surface soil layer. 

The governing equations for the two interception water stores are 

MC = Pc-Dc-Ew,/pw, and (4) 

Mg = P,-D,-Ewg/Pw (5) where 

M,, M, = water stored on the leaves, m 

P,, P, = rate of precipitation interception, m/s 

D,, D, = water drainage rate, m/s 

E,,, E,, = rate of evaporation of water from the wet portions of the 

leaves, kg/m2/s 

pw = density of water, kg/m3 

The governing equations for the three soil moisture stores are 

Wl 

W2 

W3 

w1, w2, w3 

and 

and 

where 

P1 

KS 

PO 

P 

soil moisture wetness in the three soil layers 

volumetric soil moisture in layer i, m3/m3 

value of 6i at saturation, m3/m3 

thickness of ith soil layer, m 

flow between i and itl soil layer, m/s 

gravitational drainage from recharge soil moisture store, m/s 

canopy, ground cover abstraction of soil moisture by transpiration 

from the ith soil layer, kg/m2/s 

infiltration of precipitation into the upper soil moisture store, m/s 

minimum (Po, K,) 

0, when W1= 1 

hydraulic conductivity of saturated soil, m/s 

effective precipitation rate on soil surface, m/s 

Si/ 6s 

P-(Pc+*P,)+(Dc+D) 

Precipitation rate above t i e canopy, m/s 

10 
The precipitation excess, (Po - PI), joins the gravitational outflow from the lowest 

soil moisture store, Q3, to produce runoff, Qr, 

Qr = Po-P1+Q3 (9) 

Equations (1) through (8) represent the governing equations for SiB and may be 

used in conjunction with time series of meteorological data to simulate the energy balance 

of a given site. 

3.5. Implementation of SiB into the NMC Spectral Model 

Our experience to date has shown that it is necessary to adapt the implementation 

of SiB into the individual GCM in question. As first implemented in the Goddard Laboratory for Atmospheres (GLA) Fourth-order GCM, all of the prognostic variables except for 

T, and T were timeintegrated with a forward differencing scheme. The two ‘fast’ 

variables 9,‘ and T, (which have relatively rapid time rates of change due to the lar e 

are constant through the time step. This scheme produced no numerical ill-effects as in 

the GLA GCM, the lowest model layer is very thick (-100 mb) which makes T, and e, 

relatively insensitive to the addition of sensible and latent heat fluxes over short (5 1800 

secs) time steps. The backward differencing technique used to solve (1) and (2) therefore 

involved taking partial derivatives of the right hand side of (1) and (2) with respect to Tc 

and T,, and solving for AT, and AT,,, the net changes in T, and T,, over a time 

step. 

Implementation of SiB into the University of California at Los Angeles/Goddard 

Laboratory for Atmospheres (UCLA/GLA) model of Randall et al. (1985) could not be 

achieved so simply. The UCLA/GLA model has a planetary boundary layer (PBL) 

formulation in which the PBL height, TB, may vary from a few millibars (N 10 m) to over 

180 mb (- 1.5 km). The time rates of change of the PBL potential temperature, OB, and 

the PBL mixing ratio, are highly variable, being large when the PBL is shallow, and small 

when the PBL is deep. The formulations governing TB, OB, and qB are complex in 

themselves as they involve surface heating terms, internal PBL dynamics and entrainment 

terms. 

A scheme was developed which ensures the solution of (1) and (2) with equations 

for the time rate of change of the PBL height, potential temperature and mixing ratio 

without numerical problems. Basically the variables OB and qB are added to the ‘fast’ 

variable set and appropriate time derivatives of T,, T,, OB and qB are calculated at 

the beginning of each time step. (TB is advanced using a forward differencing scheme). 

These equations are then solved using a simple backward differencing scheme. Ths 

tation in the mo 

In the NMC model, the Mellor and Yamada (1982) vertical diffusion model (level 2) 

was incorporated. In this parameterization, there are no explicit prognostic variables that 

describe the state of the PBL: the whole atmosphere, including the PBL, is discretized 

into layers and the state variables such as temperature, specific humidity and wind velocity 

are defined and predicted on each level; specifically, temperature and wind are defined at 

all of the model’s 18 levels while specific humidity is defined at the 12 lowest levels. These 

atmospheric variables were coupled with the SiB prognostic variables, Tc and T,,, so 

ratio of the flux terms Rn, H, XE) to the heat conduction/storage terms scaled by 8 , 

and A) were integrated b y a backward implicit scheme which assumes that I r, e, and Ur 

implementation the UCLAlGLA model) was used as the basis for the SIB implemenversion of the NMC model. 

11 
that energy and mass are conserved and an implicit scheme was used to integrate the 

system stably in time. The other (slowly-varying) SiB variables were integrated with a 

forward scheme. 

The tendencies for the time rate of change of the temperature, 81, and specific 

humidity, q1, of the lowest atmospheric layer resulting from the transfer of heat fluxes 

from the surface are 

MTel = FT+# 

P P1 

and 

C, = specific heat of air, J/kg/K 

g = acceleration due to gravity, m/s2 

H = total surface sensible heat flux, W m2 

E = total surface evaporation rate, kg / m2/s 

Ap1 = lowest atmospheric layer thickness, mb 

In order to integrate the coupled system stably in time, the equations were made implicit 

with explicit coefficients. 

12 
In Eqs. (14) and (15) no time derivatives of the surface net radiation terms, Rn, 

and Rn,,, were calculated as these are small compared to the other terms. Flux 

derivatives like were evaluated at time level t-At and once the system was 

integrated, Tc, T,,, 81, q1 at time level t+At were used to compute the new values of 

the non-neutral aerodynamic transfer coefficients. The four equations were then used to 

integrate Tc, T,,, 81, q1 again. The time tendencies for the first layer were obtained by 

dividing - and q t+At - q by 2At. Time tendencies of potential 

temperature and specific humidity at other levels were computed by solving the matrices of 

the diffusion equations successively from the bottom to the top of the atmosphere 

(Appendix E). These tendencies were added to the tendencies due to advection and other 

physical processes to give the total tendency for each prognostic variable. 

In treating the vertical diffusion of momentum, an implicit noniterative time 

integration scheme with explicit coefficients was used. The surface drag coefficient was 

evaluated using Tc, T,,, 81, q1 at time level t+At after the first integration of Eqs. (12) 

to (15). 

In Ctl-GCM, the 

atmospheric variables (temperature at 18 levels and specific humikty at 12 levels) were 

coupled with the ground surface temperature and soil temperatures at 10 cm and 50 cm 

depth. The system was integrated using the same implicit time integration scheme with 

explicit transfer coefficients. Iterations were made using a modified Newton-Raphson 

method to insure against numerical oscillations. 

As currently implemented, SiB has a significant impact on the computer time 

required for a simulation; SiB-GCM is typically 20 percent more expensive 

(computationally) than Ctl-GCM. However, it should be noted that as yet no great effort 

has been dedicated to making the code more efficient. 

aH 

K 

Ctl-GCM uses the same basic time integration scheme 

3.6 Anticipated impact of implementing SiB 

The impact of implementing SiB into the GCM can be anticipated by comparing 

the surface parameterizations of SiB-GCM and Ctl-GCM. 

First, there are some differences between the albedo field calculated by SiB-GCM 

and that prescribed in Ctl-GCM, see Fig. 1. Generally, the SiB-GCM albedos are 

significantly higher over vegetated regions, leading to reductions in the surface net 

radiation of around 5% in SiB-GCM relative to Ctl-GCM. 

Second, there are some differences between the two roughness length fields, but 

these are not thought to contribute greatly to the differences between the performances of 

the two models except for the extreme cases referred to in a previous section (China, 

Mongolia). This is because the aerodynamic resistance is inversely related to the logarithm 

of the roughness length and so the turbulent heat flux calculations are relatively insensitive 

to fairly large changes in the absolute value of this parameter, see Sellers and Dorman 

(1987). 

Third and most importantly, Ctl-GCM and SiB-GCM calculate the partition of 

energy at the surface in very different ways. The effect of this can be simply illustrated by 

comparing the energy budgets calculated by the two surface arameterizations for a 

densely vegetated area. If time-invariant forcing (Tr, e,, Ur, Fg)) is assumed and the 

radiation budgets for the two models are taken as approximately equal, heat storage terms 

13 
may be neglected and equilibrium surface fluxes and surface temperatures may be 

calculated with the two parameterizations. Under these conditions, the total energy 

budgets may be written as 

Rn = HB+ XEB 

Rn = H,+XE, 

R, = net radiation, W/m2 

H = sensible heat flux, W m2 

E = evaporation rate, kg / m2/s 

X = latent heat of vaporization, J/kg 

The subscripts B and s refer to the ‘bucket’ and SiB parameterizations, respectively. 

different equilibrium surface temperatures. 

The sensible heat fluxes calculated by the two models are different due to the 

= surface temperature for SiB, bucket parameterizations, respectively 

K. S,B 

T 

p,cp = density, specific heat of air, respectively; kg/m3, J/kg/K. 

ra = aerodynamic resistance between surface and lowest model layer, 

s/m 

= 1/(CH Ur) 

CH = heat transfer coefficient. 

For Ctl-GCM, the evaporation rate is given by the so-called ‘Beta-function’. 

= saturated vapor pressure at temperature T, kPa 

= psychrometric constant, kPa/K. 

= 0.066 kPa/K at 15°C. 

W = soil moisture content, mm 

Wma, = Maximum value of W, 

= 150mm 

14 
In SiB, the calculation of evapotranspiration is fairly complex, involving treatments 

for two layers of vegetation and the evaporative contribution from the bare soil. However, 

for a dense vegetation canopy, like a tropical forest, the evapotranspiration rate can be 

approximated by 

rc = bulk stomatal (canopy) resistance, s/m 

The essential term in Eq. (18b) is rc, the bulk stomatal or canopy resistance of the 

Equations (16), (17), and (18) can be combined and the surface temperatures, T, 

eliminated using the linear approximation for saturated vapor pressure as a 

dry vegetation. 

and TB, 

function of temperature as shown in Monteith (1973) to yield 

ARn + C & ra = 

ARn + C Sera 

ra + rc 

& = va or pressure deficit, kPa 

temperature Tr, kPa/K 

= e* P T,) -e, 

= 

= 0.11 kP,/K at 15°C 

A slope of saturated vapor pressure versus temperature curve at 

Equation (19b) is simply the Penman-Monteith evapotranspiration equation, see 

Monteith (1973). For the same meteorological (Tr, e,, ur) and radiative (Rn) forcings, the 

ratio of the calculated evapotranspiration rates is then given by 

(20) 

A + 7 T: 1 

- 

% A + r/P 

For a tropical forest site, ra is typically IO s/m and, if soil moisture is not 

limiting, p = 1 and rc N 100 s/m, see Shuttleworth et al (1984, a,b), which yields a 

value of EB/Es = 4.75! Furthermore, to obtain EB/Es = 1, ,6 must be reduced to 0.091 

which implies a soil wetness of W = 0.07, clearly below the range of reasonable ‘tuning’ for 

a humid region. 

The above analysis is extreme as it assumes that these is no feedback from the 

surface evaporation rate on the atmospheric forcing. In Ctl-GCM, the huge latent heat 

fluxes calculated in the first few timesteps should bring about an unrealistically cold, stable 

and humid PBL which then reduces subsequent evaporation rates to less extreme values. 

Nonetheless, we should expect to see marked differences between the surface heat fluxes 

calculated by the two models with associated differences in their near surface climates. 

15 
4. COMPARISON OF SIMULATIONS AS PRODUCED BY CONTROL AND SiB 

VERSIONS OF THE NMC GCM. 

In this section, the simulations generated by the SiB-GCM and Ctl-GCM runs are 

compared with each other and with observations over a range of spatial scales. 

First, analyses of surface and near-surface processes for representative grid points 

within each model see Fig. 2) were compared with equivalent field measurements. In 

latent heat fluxes for a tro ical forest, a coniferous forest and an agricultural site are 

compared with observations P see Sec. 4.1). 

Second, the simulated time-series of the energy and moisture budgets of four large 

regions: the Amazon Basin, central and eastern United States, southern Asia and the 

Sahara (see Fig. 2) - are discussed with reference to observations where available 

(Sec. 4.2). 

Third, the global fields of latent heat flux evapotranspiration), sensible heat flux 

Fourth and last, the impacts of the two land surface parameterization schemes on 

particular, the 30 4 ay means of the simulated hourly net radiation, sensible heat and 

and precipitation generated by SiB-GCM and Ctl 4 CM are compared (Sec. 5.3). 

the large scale atmospheric circulation are discussed (Sec. 4.4). 

4.1 Diurnal variation of surface and near+urface processes. 

Time series of fluxes and other variables for several grid points over the globe were 

examined to see how the implementation of SiB affects the diurnal variation of surface and 

PBL processes. The first 30 days of the SiB-GCM and Ctl-GCM runs were analyzed for 

this study; 30 rather than 50 days were used as the surface energy budget changed rapidly 

and in a spatially heterogeneous way in the last 20 days of the Ctl-GCM run, see later 

sections. 

4.1.1 Tropical rain forest, Amazon Basin 

Figures 3 through 9 show 30-day means of the simulated diurnal variation of 

various physical parameters at a tropical forest grid point near Manaus in the Amazon 

basin (59"W, 3"s) for the period June 15-July 14. Some equivalent observations of 

Shuttleworth et al. (1984a,b, and private comm.) are also presented for comparison. 

The difference between the albedos used by the two models may be seen in Fig. 3. 

In SiB-GCM the albedo is computed using a two stream approximation model (Sellers, 

1985) which calculates the dependency of the albedo on the solar zenith angle. The 

minimum albedo at noon, (11.5%), and the daily mean albedo (12.6%) as calculated by 

SiB-GCM a ree well with the observations made over the Amazon forest (Shuttleworth, 

1984b); 11.0 o and 12.5% respectively. The albedo used in Ctl-GCM Posey and Clapp, 

1964) is a climatological one and has a seasonal variation. It is unre istically small for 

reasons discussed in Dorman and Sellers (1989). 

Because the albedos are different, there is a slight difference between the net 

radiation values calculated by SiB-GCM and Ctl-GCM (Figs. 4a,b); but the peak 

observed net radiation is 50-100 W/m2 less than both of the simulated values. However, 

the observed net radiation is always significantly smaller than the sum of the observed 

latent and sensible heat fluxes which suggests that there may be some errors in the 

observed heat fluxes and or net radiation values. Additionally, it is likely that the fixed 

1 b 

zonal mean climatologica 1/ cloudiness used in both GCM's leads to errors in the simulations. 

16 
Control I ----- 

_,---------------- 

I'i 0 6 1 I I Local I Time I 12 ,~ (Hours) 18 

SiB Rn Observed Rn 

SiB H -k Observed H 

0 Observed E 

--_ -_--_ 

- SiB XE 

> 

P 

C 

w 

Figure 3: 3O-day mean (June 15 - July 14) 

surface albedo at a tropical forest 

grid area near Manaus (59'W, 

3's) as simulated by SiB-GCM 

(solid line) and as given by Posey 

and Clapp (1964) for Ctl-GCM 

(dashed line). The period June 15 - July 14 is used for all the 30-day 

summer means referred to below. 

Control Rn 0 Observed Rn 

+ Observed H 

o Observed LE 

- _- - - - 

-_-- Control H 

- Control XE 

6 12 18 

Local Time (Hours) Local Time (Hours) 

Figure 4: 30-day surface energy balance for the tropical forest grid point near Manaus as 

simulated by SiB and aa observed by Shuttleworth et al. (1984a). The lines on 

the figure represent simulation, the points (circles and crosses) represent the 

means of observations taken over the period June 15, 1984 -July 14, 1984 at a 

site 30 km from Manaus, Brazil. a) SiB-GCM b) Ctl-GCM 

17 
The heat flux into the soil is not shown in Figs. 4a and 4b. Typically, this term is 

calculated to be around 20 W/m2 durin the day and -20 W/m2 at night. The daily mean 

models. 

Figures 4a and 4b dramatically illustrate the improvement of the simulated surface 

energy budget due to the implementation of SiB-GCM. The simulations may be directly 

compared with the observations: 30 days (June 15 - July 14, 1984) of the Shuttleworth 

et al. (1984a) data set were averaged to produce the observed hourly fluxes shown in 

Figs. 4a and 4b. In SiB-GCM the maximum values of latent heat flux, 380 W/m2, and 

sensible heat flux, 170 W/m2, agree fairly well with these observations. In Ctl-GCM 

(Fig. 4b), however, almost all of the net radiation is used for evaporation while the sensible 

heat flux is almost zero. 

This unrealistic partitioning of net radiation in Ctl-GCM is a direct consequence of 

using the bucket model parameterization of surface evaporation. In the bucket model, the 

atmosphere is directly connected to the surface by the aerodynamic resistance which is 

relatively small when the surface layer is neutral or unstable, see Eq. 18a). In SiB-GCM, 

order of magnitude larger than aerodynamic resistance over the Amazon forest, see 

Eq. (18b). (In both SiB-GCM and Ctl-GCM, sensible heat fluxes are only regulated by 

the aerodynamic resistances). The morning and midday values of the aerodynamic 

resistance, ra, are around 10 s/m in both SiB-GCM and Ctl-GCM, but the stomatal 

resistance, rc, plus leaf boundary layer resistance, rb, in SiB-GCM never drops below 100 

s/m. According to Shuttleworth et al. (1984a), daytime values of the aerodynamic 

value is very small and is an insigni B cant part of the surface energy budget in both 

the transpired water also has to traverse the stomatal resistance w b ch is typically one 

I I 

I I 

I I 

I 

I 

- - 

I 

I 

Figure 5: 304ay mean (June 15 - July 14) 

resistances at Manaus simulated by 

SiB4CM and Ctl-CCM. ra is 

surface (stomatal) and aerodynamic 

18 
0 Observed T, 

-. -____ __ Si6 Tgs 

SiB T, 

- SiB T, 

---_ 

303 

302 

301 

300 

299 

~III~III~IJI~

e - @e 

e 

e - e 

e 

e - e 

- e4 e 

302 

30 1 

300 

: 299 

2 

8 298 

3 

c 

E 

I297 

296 

Local Time (Hours) 

Figure 6a: 30-day (June 15 - July 14) mean 

surface and soil temperatures at 

Manaus simulated by SiB-GCM. 

The solid line is for the kmpcrature at the lowest model atmospheric level, Tm (a = 0.995) the 

coarse dashed line for the canopy 

temperature, Tc, and the dotted 

line is for the ground surfacc/cover 

temperature, Tgs. The series of 

solid dots represent the 3O-day 

mean hourly air temperatures 45 m 

above ground (10 m above canopy 

top) as observed by Shuttleworth 

et al. (1984a) at Manaus for the 

period June 15, 1984 - July 14, 

1984. 

6 12 18 

Local Time (Hours) 

Figure 6b: 30-day (June 15 - July 14) mean 

surface and soil temperature at 

Manaus simulated by Ctl4CM. 

The solid line is for the tempcrature at the lowest model level 

(0 = 0.995), Tm, and the dashed 

line line is for the ground surface 

temperature, Ts. The series of 

solid dots represent 30-day mean 

hourly air temperatures at 45 m 

above ground (10 m above canopy 

top) aa observed by Shuttleworth 

et al. (1984a) at Manaus for the 

period June 15, 1984 - July 14, 

1984. 

resistance, ra, estimated from eddy correlation data, frequently dropped to around 10 s/m 

and the mean minimum value of the canopy resistance for the tropical forest was estimated 

to be around 130 s/m from the flux observations. The SiB-GCM simulations of ra and rb 

+ rc concur with these observations. Additionally, a 'bump' in the diurnal course of the 

stomatal resistance, centered on 1100-1200 local time, may also be seen in Fig. 5. This 

feature corresponds to the midday depression in stomatal conductance and photosynthesis as observed in Oak shrub by Tenhunen et al. 1984) and is caused by the vegetations' 

space at this time of day. The reproduction of this feature indicates that the SiB-GCM is 

realistically simulating the coupled system of biological and near-surface meteorological 

processes. 

response to the elevated temperatures and raise 6 vapor pressure deficit in the canopy air 

19 
Because of stomatal resistance, SiB-GCM generates more sensible heat than 

Ctl-GCM in most vegetated areas. This is associated with higher surface temperatures 

and a larger diurnal range in SiB-GCM. Figure 6a shows the SiB canopy, ground surface 

and the air temperature at the lowest GCM level. Figure 6b shows corresponding results 

for Ctl-GCM: ground surface temperature and the air temperature of the lowest 

atmospheric level are shown. The monthly mean air temperature 44.6 m above ground 

(average canopy top height is 35 m) as observed by Shuttleworth et al. (1984a,b) is shown 

in both figures. The observed temperature reaches 302 K with a diurnal range of about 

6 K: the equivalent simulated values from SiB-GCM, 302 K and 6.5 K respectively, are 

comparable to the observed values and are more realistic than the values calculated by 

Ctl-GCM (298 K and 3 K). 

The large sensible heat flux generated by SiB-GCM also helps the PBL to grow 

deeper and make the transport of heat, moisture and momentum flux in the PBL more 

efficient. Figures 7a and 7b show the calculated diurnal variation of potential temperature 

for the five lowest GCM levels (a = 0.9950, 0.9815, 0.9605, 0.9205, 0.8565) in SiB-GCM 

and Ctl-GCM, respectively. With SiB-GCM the simulated PBL grows up to level 4 (i.e., 

the PBL depth is about 80 mb) and sometimes up to level 5 (150 mb). On the other hand 

in the Ctl-GCM simulations, PBL growth is capped at the third level which means that 

the PBL depth does not exceed 20 mb in this area. Martin et al. (1988) observed the PBL 

6 12 18 

Local Time (Hours) 

30 1 

295, 

6 12 18 

Local Time (Hours) 

Figure 7: Simulated 304ay (June 15 -July 14) mean hourly potential ternpcraturcs fur 

the lowest five model levels (0 - 0.9950, 0.9815, 0.9CO5, 0.9205, 0.SSGS) at 

Manaus. a) SiI3-GCM b) Ctl-GCM 

20 
structure over the Amazonian rain forest near Manaus for the three week period, July 15 - 

August 7, 1985, during which the PBL depth grew to about 170 mb (1600 m on fine days 

about 130 mb (1200 m) which concurs with the PBL growth simulation by SiB-GCM. 

The more unstable daytime conditions associated with the SiB-GCM simulation 

give rise to shear stresses which are roughly double those generated by Ctl-GCM, see 

Fig. 8; the 30-day mean maximum daily shear stresses are 0.28 and 0.17 Pa, respectively. 

These figures may be compared with the friction velocity measurements reported in. 

Shuttleworth et 

with the SiB-GCM calculation. 

and 80 to 90 mb (800 m) on cloudy days. On the average, the maximum P L L depth was 

which are equivalent to shear stress values ranging from 0.01 

Pa (nighttime, to over 0.5 Pa (midday, unstable conditions . These higher shear stresses are probably associated with moderately large sensible heat d uxes, consistent 

6 12 18 

Local Time (Hours) 

Figure 8: 3O-day (June I5 - July 14) mean hourly rurface 

wind atreso at Manaur dmulated by SIB-GCM 

(solid line) and Ctl-GCM (dashed line) for the 

Junc-July period. 

21 
Kousky (1980) reported a strong diurnal pattern of precipitation in north-east 

Brazil. The time of maximum precipitation differed greatly depending on the region. It is 

interesting to see if the GCM's generate any kind of diurnal variation in precipitation. 

Figure 9 shows the simulated diurnal variation of hourly precipitation rate and the 

observations of Shuttleworth et al. (1984a). In both simulations, precipitation is mostly 

convective with a strong diurnal variation. The simulated peak is in the afternoon in both 

cases which is not inconsistent with the observations. 

1.0'1~'~'1'1'I'I'I'I'1'I'I'~ 

- a - 

0.8 - - 

- - 

,*--\... 

, '- I , I , - 0.6 - 

Figure 9: Diurnal variation of the 30-day (June 15 - July 14) mean precipitation 

simulated for Manaus by the two GCMB for the JuncJuly period. The solid 

line is for convective precipitation in SiB-GCM, the coarse dashed line is for 

largc-scale precipitation in SiB-GCM, the dotted line is for convective 

precipitation in Ctl-GCM and the fine dashed line is for largc-scale 

precipitation in Ctl-GCM. The solid dots are for equivalent observations by 

Shuttleworth et al. (1984a). 

22 
.....--- SiB Rn 0 Observed Rn 

---- SIB H + Observed H 

- SiB LE 0 Observed XE 

a 

x 

N 

E 

s 

X 

3 

U 

- 

Local Time (Hours) Local Time (Hours) 

Figure 10: 3O-day (June 15 - July 14) mean surface energy balance for an evergreen 

coniferous forest grid area (98' W, 55' N) in central Canada M simulated by 

SiB-GCM. The points marked on the figure are for equivalent quantities as 

measured by McNaughton and Black (1973) over a Douglas fir forest in British 

Columbia on July 20, 1970. a) SiB4CM b) Ctl-GCM 

- - - - - - - Control Rn 

--- Control H 

o Observed Rn 

+ Observed H 

Control LE 0 Observed LE 

- 

N 

E 

s 

X 

3 

U 

- 

4.1.2 Coniferous Forest 

Figure 10a (lob) shows the SiB-GCM (Ctl-GCM) simulations of latent and 

sensible heat flux for a grid point in Canada (98"W, 55.N); this point is represented as 

evergreen coniferous forest in SiB-GCM. As before, the means for the first 30 days (June 

15 -July 14) of the Northern Hemisphere summer simulation are shown. Estimated fluxes 

for one day at a coniferous forest site in Western Canada calculated from observations by 

McNaughton and Black (1973) are shown in both figures. On five other days in July 

(1970) the observed net radiation, latent and sensible heat fluxes were larger but the 

Bowen ratio was always around one during the day. The simulated Bowen ratio in 

SiB-GCM is also very close to unity during the day whereas in Ctl-GCM it is almost zero. 

4.1.3 Cultivated Area, Central Europe 

Figure lla (llb) shows the latent and sensible heat fluxes for a grid point at (17" E, 

49"N), a cultivated area in Central Europe, as simulated by SiB-GCM (Ctl-GCM). 

Observed fluxes recorded over a barley field at Ruthe in West Germany on June 20 - 22, 

23 
_---_ _. Si0 Rn 0 Observed Rn 

- SiB o Observed XE 

---- SiB H + Observed H 

I 

N 

E 

X 3 

LL 

- 

++ 

6 12 18 

Local Time (Hours) 

1""'1'1'1'1'1 

Control Rn 0 Observed Rn 

Control H + Observed H 

o Observed XE 

.______ 

_--_ 

- Control XE 

++ 

18 'p""'""" 12 

Local Time (Hours) 

Figure 11: 3May (June 14 -July 15) mean surface energy balance for a cultivated grid 

area (17' E, 49' N) in central Europe. The points marked on the figure are for 

equivalent quantities M measured by van der Ploeg et al. (1980) over a barley 

field on 22 June 1977. a) SiB-GCM b) Ctl-GCM 

1979 are also shown (van der Ploeg et al., 1980). As in the case of the Amazon rain forest 

and the evergreen coniferous forest in Canada, Ctl-GCM generates far too much latent 

heat flux whereas the time series of latent heat flux in SiB-GCM compares well with 

observations. Figure 12 shows the surface albedo simulated by SiB-GCM and the prescribed Posey and Clapp (1964) (Ctl-GCM) albedo for the region; observations from a 

wheat field 50 km from Ruthe, West Germany are also shown. It appears that SiB-GCM 

generates a realistic albedo while the Ctl-GCM value is too low and does not vary with 

time. 

4.1.4 Sahara Desert 

The diurnal variation of ground surface temperature at a Saharan grid point (O"E, 

23"N) is shown in Fig. 13. SiB-GCM has a higher surface albedo which contributes to the 

lower maximum ground surface temperature compared to Ctl-GCM. 

Susskind (pers. comm.) retrieved daytime (14:30 LT) and nighttime (2:30 LT) 

ground surface temperatures from satellite data. The estimated daytime and nighttime 

temperatures around (0" E, 23" N) for July, 1979 were 324 K and 296 K, respectively, which 

24 
3OA---- Observations z;troI ;;I 

25 

-- 304 

9 15 

/- 

SIB Td 

CONTROL T50 

CONTROL TI 

. . . . . . 

SIB Tgs . . . . . .. . .. . . . . . ____ 

324 1 e 

Y 320- 

a 

2 316- 

L 

a 

U 

a 

Figure 12: 304ay (June 15 - July 14) mean 

surface albedo at the West German 

grid point an simulated by 

SiB-GCM (eolid line) and 

Ctl-GCM (dashed line). The 

crosses represent observed fluxes for 

a wheat field at Volkenrude, West 

Germany as reported in van der 

Ploeg et al. (1980) for 20-21 June, 

1979. 

Figure 13: 304ay (June 15 -July 14) means 

of the diurnal variation of the 

ground surface temperature for a 

grid point in the Sahara Desert 

(O'E, 23") as calculated by 

SiB-GCM and Ctl-GCM. Td 

and Tgs are the SIB ground 

surface and deep soil temperatures, respectively, T50 and Ts 

are the deep soil (50 cm) and 

surface temperatures for the control 

model (Ctl-GCM), respectively. 

25 
gives a diurnal range of 28 K. These may be compared with the equivalent simulated 

daytime ground surface temperatures of 326.5 K in Ctl-GCM and 323 K in SiB-GCM. 

The nighttime temperatures, 310 K in Ctl-GCM and 305 K in SiB-GCM, seem to be too 

high compared with observations even when uncertainties in the retrieval procedure are 

taken into account. One possible explanation is that the fixed zonal mean clouds in the 

GCMs reduces the amount of nocturnal cooling over the deserts. Excessive downward 

sensible heat flux from the atmosphere to the surface or heat conducted from the deep soil 

layer may also contribute to the higher nighttime ground surface temperatures. 

4.2 Time tendency of regional mean energy and moisture budgets. 

Area-averaged budget analyses were made for 50 days (June 15 - August 4) of 

summer simulations by both GCMs to find out how the regional energy and moisture 

budgets changed during the course of the numerical simulations. Four continental regions 

were selected: the Amazon Basin, Central and Eastern U.S.A., Asia, and the Sahara desert 

(Fig. 2). 

In SiB-GCM, the Amazon basin is uniformly covered by tropical rain forest; the 

Central and Eastern U.S. consists mainly of cultivated land, grassland and some mixed 

forest; the Asian region consists of heterogeneous land cover including tropical rain forest, 

cultivated land and desert; and the Sahara desert consists of sand with a very sparse shrub 

cover. 

4.2.1 Energy Budget 

The daily mean surface energy budget of SiB-GCM (Ctl-GCM) for the Amazon 

Basin is shown in Fig. 14a (14b) and is summarized in Table 1. The assumption of a fixed 

zonal mean climatological cloudiness and ozone distribution results in very similar surface 

insolations for the two models. This mean value, 210 W/m2, is close to, but a little larger 

than the July climatological value, 195 W/m2, as given by Budyko (1974 , but these comGCMs. There is some difference between the net solar radiation fluxes calculated by the 

two GCMs because of the different albedos used; 12% in SiB-GCM and 7.7% Posey and 

Clapp, 1964) in Ctl-GCM. This albedo difference results in a 10 W/m2 or a 5 d o difference 

in the net radiation. 

The striking improvement of SiB-GCM over Ctl-GCM is shown in the partition of 

net radiation into latent and sensible heat fluxes. In the Ctl-GCM simulation, almost all 

of the net radiation is used for evaporation and the sensible heat flux is negligible. In 

SiB-GCM, 84% of the net radiation goes to latent heat flux which gives a Bowen ratio of 

0.21. This difference results from the different formulations used to calculate 

evapotranspiration in the two GCMs, see Sec. 3.6. 

The latent heat fluxes, 130 W/m2 (SiB-GCM) and 162 W/m2 (Ctl-GCM), correspond to 4.5 mm/day SiB-GCM) and 5.5 mm/day (Ctl-GCM) of evapotranspiration. 

for the entire Amazon Basin to give a figure in the range of 3.1 to 4.0 mm/day. If we take 

the uncertainty of this estimate and seasonal variation into consideration, the evaporation 

rate given by the SiB-GCM, 4.5 mm/day, is in better agreement with the estimates 

obtained from observations than Ctl-GCM. 

parisons should not be extended too far in view of the prescribed clou d! iness used in the 

Salati (1987) summarize d published estimates of the mean annual evapotranspiration rate 

26 
N 

E 

s 

X 

3 

i 

SB-GCM: Amazon 611 5-814 

280 11 

240 1 i 

c 1 

i & 120 zl 

c 

W 

Ctl-GCM: Amazon 611 5-814 

2801rn' 240 

__. .. .. _________._._.. -..-----. (1) ___._____.. -.---- N 

- ..... -.- ..._______.__.______..... -..-- 

E 

160 / 

X 

2 

> 

ii 

P 

C 

w 

Figure 14a: The daily .mean rurface enem 

budget calculakd by SiB-GCM for 

the AmaEon Basin area shown in 

Fig. 2 for a 5O-day summer shulation run. Each energy band 

represents: 

l-solar radiation reflected by the 

2-net 106s by longwave radiation 

Ja-latent heat flux by transpiration and bare soil evaporation 

Jb-latent heat flux by interception 

loss 

3a+Jb--total latent heat flux 

kcnsible heat flux 

The top solid line represents the 

total downward solar radiation 

flux, the second dotted line 

represents the net downward solar 

radiation, the third solid line 

represents the net downward 

radiation and the bottom solid line 

represents heat transferred to soil 

by conduction. If this lowest line 

is negative, the heat transfer is 

from the soil to the air. 

surface 

Figure 14b: The daily mean surface energy 

budget calculated by Ctl-GCM for 

the Amazon Basin for a 50-day 

summer (June 15 - July 14) 

simulation run. Each energy band 

represents: 

1-olar radiation reflected by the 

2-net loss by longwave radiation 

J-latent heat flux 

kensible heat flux 

the meaning of each line is the 

same as in Fig. 14a. 

surface 

21 
SIB-GCM: N. America 6115-814 

240 

............ 

- 

N 

E 

g 160 

w 

........... . ._.., .. .. 

-----__d__J\/ LLJdJ 1 L I ClLLL, I I I, LL‘ I, 1 ,, LL,, L,, I, L, , il ,> duI 

0 10 20 30 40 

Dav 

Figure 14c: The same aa Fig. 14a except for central 

and eastern U.S. 

280 

240 

200 

E 

3 160 

N 

X 

U 

- 

> 120 P 

f 

W 

80 

40 

0 

3 

Sib-GCM: Sahara 611 5-814 

11, , , , - 11, I , I I , I IT ,,, ‘-TI r! 11 I I1 17 1-1-1 

Figure 14e: The same aa Fig. 14a except for the 

Sahara Desert. 

Ctl-GCM: N. America 6115-814 

280 I-----? 

240 ..................................... 

w 

80 

t 

Figure 14d: The same as Fig. 14b except for centrd 

and eastern U.S. 

Ctl-GCM: Sahara 611 5-814 

280 

2 50 

fi 

I 1 ............................................................................................. 

- 

N 

E 

160 

1 1 

40 1 

0 10 20 30 40 50 

Dav 

Figure 14f The same as Fig. 14b except for the 

Sahara Descrt. 

28 
280 

240 

200 

160 

120 

8o 

40 

0 

0 

A 

N 

E 

i5 

E 

X 

3 

> 

ii 

C 

w 

N 

E 

3 

X 

3 

ii 

>. 

P 

a 

C 

w 

T-TmTT 1111 11 t 111 m mlrl---r 

Figure 14g: The same as Fig. 14a except for Asia. 

- 

0 

- 0 

- 

............................................................. ................................ 

- - - 

- 8 

,,----- ..____ ........... .............................................. 

...... ' ..................... @- . - - ~ , , 

@ 

./&'-. 

-1 - . ". - ._--- 

- 

-@ 

l,,,rl,,,,l,r,rl,,,l~,,,,~,,,,,,,,,~,,,,~,,~ 

10 20 30 40 50 

280 

240 

200 

Ctl-GCM: 6/15-814 

I''"I''''I''''I''''1''''1''''I''''I'~'~I~'~~ 

- - -~ig~~~ 14h: The same as Fig. 14b except for Asia. - - 

......................................................... 0 - 

0 

............................... 

- - 

- - 

. 160 

120 

80 

- 

- 

- 

- 

- 

29 

L 

o----- IIII,I,,,,,,,,,l,,,,l,,,,I,,,,,,,,,,,,,,I,,,, 
Table 1 The mean surface energy budget as calculated by (a) Ctl-GCM, and (b) 

SiB-GCM for the four regions outlined in Fig. 1: Amazon Basin, Central 

and Eastern United States, Sahara Desert and Asia. The means are for the 

50-day (June 15 - August 4) summer simulations in all cases. The symbols 

used in the columns represent the following fluxes (W/m2). 

REGION 

Amazon 

Central & 

Eastern 

U.S. 

Sahara 

Desert 

Asia 

s= 

Ln = 

Rn = 

(1-0r)S = 

XET = 

XE, = 

XE = 

H= 

G= 

XE H G 

4.6 -3.1 

S (1-a)S Ln Rn "T XE, 

211.1 194.8 -31.5 163.3 - - 161.8 

260.2 231.8 -34.9 196.9 - - 176.4 18.4 2.1 

289.6 217.4 -92.4 125.0 - - 3.1 117.1 4.8 

281.9 236.6 -50.1 186.5 - - 134.5 50.9 1.1 

Insolation 

net short wave absorbed by the surface). 

net radiation, available ener y. 

Transpired latent heat flux tSiB only). 

interception loss plus soil evaporation latent heat flux 

(SiB only). 

Total latent heat flux (sum of XE, and XE, in SiB). 

sensible heat flux. 

ground heat flux 

net longwave (a 6 sorbed by the surface). 

Amazon 

Central & 

Eastern 

us. 

Sahara 

Desert 

Asia 

(a) Mean (lio-day) surface energy budget calculated by Ctl-GCM. 

210.9 184.7 -30.6 154.1 

261.8 220.2 -39.9 180.3 

292.2 202.0 -83.1 118.9 

283.5 223.4 -54.0 169.4 

(b) Mean (SO-day) surface energy budget calculated by SiB-GCM. 

114.9 E 18.0 

*: 

135.0 

G 

-3.0 

4.2 

1.7 

0.8 

30 
The time series of interception loss for the Amazon as calculated by SiB-GCM is 

shown in Fig. 14a and in Table 1. The timeaveraged value, 36 W/m2, represents 28% of 

the total evapotranspiration. Shuttleworth et al. (1984a) reported an equivalent value of 

30% for their measurements taken at a site outside Manaus. An annual mean value of 35% 

was reported in Salati (1987) for a small basin (25 km2) 60 km north of Manaus. Salati 

(1987) also mentions the report of Franken et al. (1982) which gives a value of 33% for a 

one-year period at another site 40 km from Manaus. In general then, the interception loss 

rate calculated by SiB-GCM seems to be realistic when compared to the observations. 

This must be partly due to the formulation used to account for the hydrological effects 

associated with the non-uniform spatial distribution of convective rainfall (Appendix C). 

Figures 14c and 14d show the daily mean surface energy budget for the central and 

eastern U.S. region. The calculated downward solar radiation at the surface is around 260 

W/m2 which is a bit less than the climatological value of 275 W/m2 given by Budyko 

1974) and Sellers (1965). The small difference in net radiation, 20 W/m2, comes partly I rom the higher albedo in SiB-GCM versus Ctl-GCM, 16% vs 11%, and partly from the 

increased net longwave radiation flux associated with the higher surface temperature and 

drier PBL in SiB-GCM. As in the case of the Amazon Basin, the marked difference 

between the SiB-GCM and Ctl-GCM results is to be found in the partition of the net 

radiation into sensible and latent heat fluxes: The Bowen ratio is 0.3 in SiB-GCM and 0.1 

in Ctl-GCM. 

In Ctl-GCM, there is an increase in the sensible heat flux and a decrease in the 

latent heat flux toward the end of the 504ay summer simulation which is a result of a 

progressive reduction in the prescribed climatological soil moisture content. Figure 16b 

shows how the Ctl-GCM soil moisture was decreased from 0.88 to 0.45 over the 50 days. 

In the parallel SiB-GCM run, the equivalent (prognostic soil wetness (Fig. 16a) decreased 

slow change in soil moisture is that SiB has a greater soil moisture capacity than the 

bucket model: the total depth of the moisture carryin zones is typically 1490 mm, 

an initial soil moisture content for SiB-GCM of which some 300 mm is available for 

evapotranspiration. This is twice the maximum soil water holding capacity 150 mm) of 

Miyakoda and Sirutis (1986). 

Figures 14e and 14f show the daily mean energy budget for the Sahara Desert. The 

total insolation in both GCM’s decreases from 295 W/m2 to 280 W/m2 over the course of 

the 504ay run due to the decrease in the solar inclination. The net solar radiation is 217 

W m2 in Ctl-GCM and 202 W/m2 in SiB-GCM. The 15 W/m2 difference is due to the 

this difference in the net solar radiation is approximately compensated by differences in the 

net terrestrial radiation loss so the resulting net radiation is almost the same; 125 W/m2 in 

Ctl-GCM and 119 W/m2 in SiB-GCM. It is interesting to note that the net radiation in 

this region comprises only 42% of the incoming solar radiation as opposed to 7040% for 

the Amazon basin and U.S.; this is due to the high surface albedo and low atmospheric 

humidity in the desert regions. Both models give very low evapotranspiration rates in this 

area so almost all of the net radiation is returned to the atmosphere as sensible heat flux. 

Figures 14g and 14h show the daily mean energy budget for the Asian region 

indicated in Fig. 2. Because this region consists mainly of desert with relatively little 

vegetated land, the conspicuous change of surface energy partition encountered in the 

very slowly, changing from 0.70 to 0.65 in 50 days. T b. e main reason for this relatively 

porosity is 0.46 and the initial soil wetness is 0.7. These v 9 ues combine to give 480 mm as 

the UCLA/GLA GCM of Randall et al. (1985) and the GFDL GCM as 6 escribed by 

di / ference in surface albedo which is 31% in SiB-GCM and 25% in Ctl-GCM. However, 

31 
Amazon Basin and in Central and Eastern U.S.A. is not seen so clearly here, but there is 

some increase in the Bowen ratio in SiB-GCM. 

4.2.2 Hydrological Cycle 

The simulated hydrological balance of the Amazon basin is shown in Figs. 15a and 

15b. The precipitation rate is 6 mm/day in SiB-GCM and 7 mm/day in Ctl-GCM. From 

the climatological precipitation chart (Fig. F2d of Jaeger, 1976) we see that the northern 

half of the Amazon basin is wet and the southern half is dry in summer. If an area average 

is taken, we get 44 mm/day for the climatological rainfall rate in June-July for the entire 

Ainazon Basin. Compared with this observed value, both GCM's, especially Ctl-GCM, 

give more precipitation. The 1 mm/day decrease in the SiB-GCM versus the Ctl-GCM 

precipitation rate corresponds to the reduction in the SiB-GCM evaporation rate. The 

same situation holds in North America and Asia. In these regions, the rainfall over land 

seems to be directly affected by the regional evapotranspiration rate. 

a l2 

10 

8 

- r6 

2 

E4 - 

X 

E2 

ZI 

4- 

30 

-2 

-4 

_----- Runoff 

_. -. -__ -. Horizontal Moisture Convergence 

_--- Evapotranspiration 

- Precipitation 

SiB-GCM: Amazon 611 5-814 

""'I 

_____- Runoff 

. . . . -. - __ Horizontal Moisture Convergence 

____ Evapotranspiration 

- Precipitation 

Cd-GCM: Amazon 6116-814 

.. 

.. 

:: 

i ,,: -4 

i 

0 10 20 30 40 50 

Day 

Figure 15: The daily mean moisture budget calculated ty SiB-GCM in the Amazon 

Basin for the 50-da.y summer (June 15 - July 14) simulation run. The solid 

line is for precipitation, the coarse dashed line is for evaporation, the fine 

dashed line is for runoff and the dotted line is for the atmospheric horizontal 

moisture convergence. All values are in mm/day. 

a) SiB-GCM b) Ctl-GCM 

32 
From Figs. 15a and 15b, we see that the precipitation exceeds evaporation by 1.5 

mm/day on the average, with the difference made up by large scale horizontal moisture 

convergence. This advected moisture contributes about 25% (SiB-GCM) and 20% 

(Ctl-GCM) to the total rainfall in the region. 

The runoff rate in SiB-GCM is 2.5 mm/day on the average. This amount exceeds 

the difference between the rainfall rate and evaporation rate by 1 mm/day and implies 

that the total soil moisture content in SiB is steadily decreasing (see Fig. 16a). Oltman et 

al. (1964) estimated the annual mean outflow from the Amazon basin to be about 2.5 

mm/day from observations of the Amazon river velocity profile and stage. There is a fairly 

considerable annual variation in this quantity, but the June to August period appears to 

bracket this mean value while showing a steadily decreasing outflow rate. This decrease in 

the outflow is consistent with a fall in the average soil moisture content for the basin as 

calculated by SiB. 

In Ctl-GCM, runoff is generated by the difference between rainfall and evaporation 

when the soil is saturated. When soil moisture was predicted (see below) it decreased 

significantly, but because soil moisture in this main Ctl-GCM run was interpolated from 

monthly climatology, the soil wetness in the Amazon basin was almost always at the 

saturated value, see Fig. 15b. 

Runoff ------ 

.. . . . . . . ..Horizontal Moisture Convergence 

---- Evapotranspiration 

- Precipitation 

SIB-GCM: N. America 6/15-8/4 

lot 8 

X 

ii 

0 10 20 30 40 50 

Day 

Runoff 

Horizontal Moisture Convergence 

_--___ 

._. - -. -. 

---- Evapotranspiration 

- Precipitation 

Ctl-GCM: N. America 6/15-8/4 

lot 

Figure 15c: The same as Fig. 15a except for central 

and eastern U.S. 

Figure 15d: The same as Fig. 15b except for central 

and eastern U.S. 

33 
The calculated moistures budget for the U.S. region are presented in Figs. 15c and 

15d. In SiB-GCM (Ctl-GCM) the calculated precipitation rate is 4.7 mm/day (6.2 

mm/day) and the evaporation rate is 4.5 mm/day (6.0 mm/day). In both cases, the 

time-mean of the horizontal convergence of moisture by the large scale atmos heric 

circulation is very small. This is supported by the analysis of Rasmusson (l968f who 

computed the horizontal moisture convergence over the same Central and Eastern U.S. 

region for each month of the year from 5 ears of radiosonde data. He estimated the 

surface evaporation rates to be 2.7 mm J day and 3.1 mm/day in June and July, 

respectively, whereas the horizontal moisture convergences were very small for the same 

period. The evapotranspiration rate obtained from the simulations exceeds the estimates of 

Rasmusson (1968) by 50% in SiB-GCM and by 100% in Ctl-GCM. (The corresponding 

evapotranspiration rate of 4.5 mm/day in SiB-GCM seems close to the values observed for 

a forest without soil moisture stress: 5 mm/day is given by Swift (1975) as the June-July 

mean for a deciduous forest in the southern Appalachians and 4 mm/day is given by 

Luxmoore (1983) as the July-September mean for a deciduous forest in the Eastern U.S.). 

From the hydrological analyses of the Amazon and North American regions, we 

note that the higher precipitation rates over land in Ctl-GCM were associated with higher 

evapotranspiration rates over the same area and not from an increase in horizontal 

moisture convergence. This leads us to suggest that the precipitation over these regions is 

in large part controlled by the transpiration there. In order to check this hypothesis, another SiB-GCM experiment was performed with a reduced initial soil moisture content. 

A 30-day SiB-GCM run was performed using the same initial conditions as the 

50-day summer SiB-GCM run except that in North America the initial soil wetness was 

set to 0.45 wherever it exceeded this value. Figure 15g shows the moisture budget for the 

central and eastern U.S. region as calculated for this run. Once again, the time-averaged 

horizontal moisture convergence is almost zero and the reduced evapotranspiration rate, 

down from the 4.5 mm/day of the original 50-day SiB-GCM run to less than 3.5 mm/day 

in this reduced soil moisture case, is reflected in a corresponding 1 mm/day decrease in the 

time-averaged precipitation rate. This result strongly supports the hypothesis that the 

evapotranspiration and rainfall rates are tightly coupled in this region during the summer 

months. 

Returning to the original summer run, the runoff generated for the U.S. region by 

the SiB-GCM is 1 mm/day and is not in equilibrium with the precipitation rate minus the 

evaporation rate. As in the case of the Amazon Basin the runoff is caused by the drainage 

from the recharge zone of the SiB soil moisture model. 

The moisture budget for Asia for the summer run is shown in Figs. 15e and 15f. 

The precipitation rate in SiB-GCM (Ctl-GCM) is 7.2 mm/day (8.5 mm/day), evapotranspiration 3.8 mm/day (4.7 mm day), horizontal moisture convergence 3.4 mm/day (3.8 

(1.3 mmlday) in SiB-GCM compared with that in Ctl-GCM comes partly from a decrease 

in the evapotranspiration rate (0.9 mm/day) and partly from a decrease in the horizontal 

moisture convergence (0.4 mm/day). The main characteristic to note for this region is that 

horizontal convergence is calculated to be a significant contributor to the precipitation in 

both GCM’s for this region. 

mm/day) and runoff 3.0 mm/ d ay (1.1 mm/day). The decrease in precipitation 

34 
Runoff 

Horizontal Moisture Convergence 

Evapotranspiration 

PreciDitation 

----_ 

_..___. 

--- 

- 

-2. 

-4. 

Runoff 

Horizontal Moisture Convergence 

Evapotranspiration 

----- 

.___ ___ ---- 

- Precipitation 

Cd-GCM: Asia 6115-014 

lot i 

X 

ii 

Ot- -21 

0 10 20 30 40 50 0 1 10 20 30 40 50 

Figure 15e: The same as Fig. 15a except for Asia. 

Runoff ------ 

.... ~ __.__ Horizontal Moisture Convergence 

---- Evapotranspiration 

- Precipitation 

SiB-GCM: N. America 6/16-7114 

lot i 1 I 

8/ 

Figure 15f: The same as Fig. 15b except for Asia. 

Figure 15g: The daily mean moisture budget M 

calculated by SiB-GCM for 30 

days (June 15 - July 14) in the 

eastern and central U.S. where the 

initial soil wetness was limited to a 

maximum of 0.45. Other conditions and symbols are the same as 

for the 504ay summer SiB-GCM 

simulation (Fig. 15a). 

35 
Asia 

N. America 

. . __ ._._ . ____ 

-Am azo n 

1.0 

.a 

.6 

.4 

0 

- 

- 

- --- \ 

--- -_- ---__-_ --_ __ --_ 

- 

........................................ .... ............ ..... ...... 

I I1 I I IS I, 11 11 I I I I a I I1 I I I I I I I I I I I I I I I I I I I I I,, < 

10 20 30 40 

A ..:- 

Asia 

N. America 

.- __ _--_ 

- Amazon 

Ctl-GCM: 6/15-8/4 

1.2 ~ 

Figure 16a: The evolution of the soil wetness 

calculated by SiB-GCM during a 

50-day summer (June 15 - August 4) simulation run. The 

depth-weighted average of the 

wetnesses of the surface cone, root 

zone, and recharge cone was taken. 

The solid line is for the Amazon 

basin, the dashed line is for the 

central and eastern U.S. region and 

the dotted line is for the Asia 

region; see Fig. 2. 

1 \ ', , '. . I \ 

0.6 1 

I . I 

.................... 

......... 0.4 

Figure 16b: The .ame M Fig. 16a except for 

Ctl-GCM. Here the tim-volution of soil wetncss follows the 

prescribed climatology of Willmott 

et al. (1985). The soil wetness in 

Ctl-GCM is derived by dividing 

soil moisturea by 150.0.75 mm, 

three quarters of field capacity. 

Soil wetnesses exceeding 1.0 are set 

to 1.0 in the GCM. 

... 

0 10 20 30 40 50 

Day 

36 
Because runoff is less than precipitation minus evaporation in SiB-GCM, the soil 

moisture in the region increases slowly from 0.45 to 0.50 durin the run (Fig. 16a). At the 

0.54 (Fig. 16b). 

Lastly, it will be remembered that a decision was made early in the study to 

continuously update the soil moisture content in Ctl-GCM in line with the climatological estimates of Willmott et al. (1985). An additional 30-day Ctl-GCM run was 

performed in which the soil moisture was predicted and updated by the model. Figure 16c 

shows the predicted time evolution of soil moisture which may be directly compared with 

Fig. 16b. It may be seen that the predicted soil moisture decreases more rapidly in the 

Amazon region, but more slowly in the central and eastern U.S. as compared with the 

imposed climatology. 

In Figs. 16b and 16c, the differences between the simulated and climatological 

timeseries of soil moisture are due to regional differences in the precipitation-evaporation 

ratios as simulated by Ctl-GCM and calculated by Willmott et al. (1986). However, the 

difference in the wetness values associated with the prognostic and climatological soil 

moisture fields was comparatively small and so the difference between the global fields of 

evapotranspiration and sensible heat flux produced by the two Ctl-GCM runs was slight; 

at day 30, the version with the predicted soil moisture gave rise to a sensible heat flux of 

only 10 W/m2 less over the U.S. than that obtained with the climatological soil moisture 

field. There was virtually no difference in the heat flux fields calculated for the Amazon by 

the two Ctl-GCM runs at this time. 

same time the climatological soil moisture prescribed in Ctl -4 CM increases from 0.43 to 

0) 

C 

4- 

2 .8 

- .- 

0 

m 

N. America --- 

- Amazon 

. 

--- -\ 

- \ 

\ '. /" '.-/./\ - 

.6 - 

31 
4.2.3 Soil Heat Conduction and Temperature 

Figure 17a (17b) shows the area-averaged ground surface temperature and deep soil 

temperature (soil temperature at 50 cm depth) in SiB-GCM (Ctl4CM) for the four 

regions. In Ctl-GCM, the displayed surface temperature was derived from the daily mean 

upward longwave radiation flux at the surface; it was found that this did not differ much 

from the daily mean of the surface temperature. The deep soil temperature in SiB-GCM 

shows some initial adjustment due to uncertainties in its initialization; however, analysis of 

the surface ener y budget showed that the daily mean heat conduction into or out of the 

soil was usually P ess than 10 W/m2 and so was not critical to the surface energy budget. 

SB-GCM: 6/15-814 

1 

306 

- c 

- _---- - -. Sahara 4 

et' 

I 

Figure 17a: The deep soil temperature, Td, at 

00 GMT and daily mean surface 

temperature, Tsi for a 50-day 

(June 14 - August 4) period 

summer simulation run by 

SiB4CM. The daily mean 

surface temperature was derived 

from the daily mean upward 

longwave flux at the surface and 110 

ie a weighted mean of the canopy 

and ground temperature. The top 

pair of lines are for the Sahara 

Desert, the second pair for central 

and eastern U.S., the third pair are 

for the Amazon and the bottom 

pair are for Asia. 6 K waa added 

to the temperatures for central and 

eastern US. for easier display. 

38 
I 

t 

i 

Ctl-GCM: 611 5-8-4 

---_ ---___ -- 

Amazon 

294 

Figure 17b: The same as Fig. 17a, but for Ctl-GCM. The deep soil temperature, T50, is 

at 50 cm depth. 

4.3 Global Surface Field 

4.3.1 Summer Case 

The surface latent heat flux for the first 30 days in the summer simulation by SiBGCM (Ctl-GCM) is shown in Fig. 18a 18b) and the difference in Fig. 18c. Except for the 

deserts, the latent heat flux in Ctl-G (5 M over the continents is usually more than 150 

W/m2 and sometimes exceeds 200 W/m2 in parts of North America and South Asia. In 

SiB-GCM, latent heat fluxes of more than 150 W/m2 are observed only in South Asia. 

The difference map shows that SiB-GCM gives 25-50 W/m2 (1-2 mm/day) less evapotranspiration over most vegetated land. 

we compared the latent heat flux calculated with Sib-GCM with the global map 

of climatological latent heat flux for June as estimated by Budyko 1974). Budyko’s 

example, his estimated latent heat fluxes in the Amazon basin and centralleastern U.S. are 

80 W/m2 and 60 W/m2, respectively, which are much less than the observed values of 

90-120 W/m2 and 90 W/m2 for the respective regions cited in the previous subsection. 

The global map of surface net radiation (Budyko, 1974) shows values which were typically one half of the downward solar radiation over the land surface. The simulations from both 

GCM’s suggest that this only happens over deserts where the surface albedo is high and the 

estimates were found to be less than the simulated value over all I and surfaces; for 

39 
0 60E 120E 1 80 120w 60 W 0 

Figure 18a: Global field of mean surface latent heat flux in the SiB-GCM summer 

simulation for the first 30-day period (June 15 - July 14). The contour 

interval is 50 W/m2. Land areas with mean latent heat flus in excess of 150 

W/mZ have been shaded. 

90N 

60N 

30N 

EQ 

30s 

60s 

90s 

0 60E 120E 180 120w 60W 0 

Figure 18b: The same as Fig. 18a except for Ctl-GCM. 

40 
90N 

60N 

30N 

EQ 

30s 

60s 

90s 

0 60E 120E 1 80 120w 60W 0 

Figure 18c: The difference between the 30-day (June 15 - July 14) mean latent heat 

fluxes of the summer simulations: SiB-GCM minus Ctl4CM The contour 

interval is 25 W/m2. The land areas where Ctl4CM produces > 25 W/m2 of 

latent heat flux compared to SiB-GCM have been shaded. 

atmospheric humidity is low. Table 1 shows that over the Sahara desert, about 58% of the 

incoming solar radiation is lost by reflection and longwave emission and the remaining 42% 

is available for sensible and latent heat fluxes. Over densely vegetated regions like the 

Amazon basin, only about 30% is lost (see Figs. 14a and 14b). Although the net loss of 

surface energy by terrestrial radiation is dependent on the amount and height of the 

prescribed cloud in both the climatological estimation (Budyko, 1974) and the GCMs, 

Budyko’s value does seem to severely underestimate the net radiation and consequently the 

latent and sensible heat fluxes. 

Figure 19a (19b) shows the mean daily precipitation rate simulated by SiB-GCM 

Ctl-GCM) and Fig. 19c the difference between them. For comparison, a climatological I, eld for the June-July period (Jaeger, 1976) is presented in Fig. 19d. Generally, the 

simulated precipitation pattern agrees well with the climatology, but the Ctl-GCM field is 

marked by excessive precipitation in North America and northern Eurasia. The difference 

map clearly illustrates the reduction of precipitation over vegetated land in SiB-GCM. 

There is a northward shift of the monsoon rainbelt in Asia in SiB-GCM relative to 

Ctl-GCM. 

The surface sensible heat flux for the first 30 days in the summer simulation by 

SiB-GCM (Ctl-GCM) is shown in Figs. 20a and 20b with the difference shown in Fig. 20c. 

Except for the arid zones, the time-averaged sensible heat flux in Ctl-GCM is very small 

over land and even negative over the tropical forests while in SiB-GCM, the time 

averaged sensible heat flux is more than 25 W/m2 almost everywhere over the vegetated 

land. The negative value observed in the defference field in northern China is a result of 

the higher albedo and wetter soil in SiB-GCM, the wetter soil being due to the northward 

shift of the rainbelt in China in SiB-GCM. 

41 
0 120E 180 120w 6OW 0 

Figure 19a: Mean daily total (convective & large de) precipitation in the SiB4CM 

mmmer simulations for the first 3O-day (lune 15 - July 14) period. The 

contour .._ intervals are 1.0, 2.0, 4.0 and 8.0 mm/day. Areas with rainfall rates 

greater than 4.0 mm/day have been shaded. 

90N 

6ON 

30N 

M 

30s 

60s 

90s 

42 
90N 

60N 

30N 

EQ 

30s 

60s 

90s 

0 60E 120E 180 120w 60W 0 

Figure 19c: The difference between the 3May (June 15 - July 14) mean daily 

precipitation for the summer simulations: SiB-SCM minus Ctl-GCM. The 

contour interval is 1 mm/day. The land arean where Ctl-GCM produces > 1 

mm/day of precipitation compared to SiB-GCM have been shaded. 

60N 

30N 

EQ 

30s 

60s 

90s 

0 60E 120E 180 120w 60W 0 

Figure 19d: Mean observed daily precipitation for June-July from Jaeger (1975). 

43 
90N 

60N 

30N 

EQ 

30s 

60s 

90s 

Figure 20a: Global field of the mean surface sensible heat flux in the SiB-GCM summer 

simulation for the first 304ay period (June 15 - July 14). The contour 

interval is 25 W/m2. Land areas with mean sensible heat fluxes in excess of 25 

W/m2 have been shaded. 

0 

90N 

60N 

30N 

EQ 

30s 

60s 

90s 0 60E 120E 180 120w 60W 0 

Figure 20b: The same as Fig. 20a except for Ctl-GCM. 

44 
90N 

60N 

30N 

EQ 

30s 

60s 

90s 

0 60E 120E 180- 120w 60W 0 

Figure 20c: The difference of the 304ay (June 15 -July 14) mean sensible heat fluxes for 

the summer (June 15 - July 14) simulations: SiB-GCM minus Ctl-GCM. 

The contour interval is 25 W/m*. Land areas where SiB-GCM produces > 25 

W/m2 of sensible heat flux compared to Ctl-GCM have been shaded. 

Figures 21a and 21b show the daytime PBL depth as simulated by SiB-GCM 

(Ctl-GCM). The PBL depth was derived by counting the number of layers where the 

304ay mean heating rate generated by vertical diffusion exceeded 1 K/day. This 

definition was found to agree well with the conventional method of using potential 

temperature gradients. 

Except for the deserts, the PBL depth in Ctl-GCM is calculated to be about 20 mb 

over the land which is unrealistically shallow; this is a direct result of the small sensible 

heat flux generated by Ctl-GCM. In SiB-GCM the PBL depth is more than 60 mb over 

most of the vegetated land and in the Amazon basin, the simulated depth of 100 mb agrees 

well with the observations of Martin et al. (1988). In both GCM’s the PBL grew to 

300 mb at some desert grid points. This very high value is consistent with the report of 

Blake et al. (1983) that the PBL depth in the Saudi Arabian desert could reach 550 mb. 

In SiB-GCM, the daytime PBL in the SiB-GCM is more unstable than that of 

Ctl-GCM, and so momentum transport is more efficient in the former model. This results 

in a larger surface wind stress in SiB-GCM over land. Figures 22a and 22b show the 

zonally-averaged zonal surface wind stress in SiB-GCM (Ctl-GCM). The value over 

ocean is almost the same for both GCMs and agrees fairly well with that estimated from 

surface data by Hellerman and Rosenstein (1983). Over the continents, the SiB-GCM 

value is almost twice as large as the Ctl-GCM value. 

45 
0 

Figure 21a: Mean maximum PBL height in the SiB4CM summer (June 15 - July 14) 

simulation The contour interval is 30 mb. Land areas where the mean 

maximum PBL height excce;is 60 mb have been shaded. 

90N 

60N 

30N 

EO 

30s 

60s 

90s 

Figure 21b: The same as Fig. 21a except for Ctl-GCM. 

46 
t 

01 

E 

\ 

2 

a 

a 

Y 

9) 

v) 

1 

C 

L 

L 

- 

s 

9) 

0 

a 

L 

L 

a 

v) 

.2 

.1 

0 

-. 1 

-.2 

I I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

90 60 30 0 -30 -60 -! 

Latitude 

Figure 22a: Zonally averaged zonal wind stress in N/m2 in the SiB-GCM summer 

(June 15 -July 14) simulation. 

4- 

E 

z 

\ 

Y 

a 

a 

0 

L 

.. 

v) 

U 

c - 

3 

0 

0 

a 

c 

L 

a 

v) 

.1 

0 

-. 1 

\ I 

\ I 

\I 

\I 

'I 

I/ 

\. 

90 60 30 0 -30 -60 -90 

Latltude 

Figure 22b: The same as Fig. 2% except for Ctl4CM. 

47 

t 
4.3.2 Winter Case 

In the winter simulation, the two 30-day runs were made with the surface drag 

parameterization of Deardorff (1972) which gave a slightly larger surface drag coefficient 

under stable and neutral conditions than the formulation based on the Monin-Obukhov 

model used in the summer simulations. This different formulation tended to reduce the 

Bowen ratio slightly, but the overall difference between the two GCMs was the same as in 

the summer simulation. 

Figures 23, 24, and 25 show the differences between the latent heat nux, sensible 

heat 5ux and precipitation 30-day (December 15 - January 13) mean fields as calculated 

by SiB-GCM and Ctl-GCM for the 30-day winter simulation. The SiB-GCM enhances 

sensible heat 5ux by up to 50 W/m2 with comparable decreases in the latent heat flux. 

The reduced sensible heat flux over Australia calculated by SiB-GCM is associated with 

its higher desert albedo values. 

The simulated precipitation patterns from both GCM’s are in good agreement with 

the climatology except in the Amazon basin. The climatological precipitation field exhibits 

one local maxima in the region; one in the northeast and another in the southeast of Brazil. 

The SiB-GCM is associated with decreased precipitation over land (through the reduction 

of evapotranspiration) but SiB did not change the basic precipitation pattern; it seems that 

other physics parameterizations in the GCM may be responsible for this problem. 

0 60E 120E 180 120w 60 W 0 

Figure 23: Difference field of latent heat flux an calculated by SiB-GCM and Ctl-GCM 

for the 304ay winter (December 15 - January 13) simulations: SiB-GCM 

minus Ctl-GCM. The contour interval is 25 W/m2. Land arean where 

Ctl-GCM produces > 25 W/m2 of latent heat flux compared to SiB-GCM 

have been shaded. 

48 
0 

Figure 24: 

60E 120E 180 120w 60W 0 

Difference field of sensible heat flux as calculated by SiB-GCM and Ctl-GCM 

for the 304ay winter (December 15 - January 13) simulations: SiB-GCM 

minus Ctl-GCM. The contour interval ia 25 W/m2. Land areas where 

SiB-GCM produces > 25 W/m2 of sensible heat flux compared to Ctl-GCM 

have been shaded. 

0 

Figure 25: Difference field of mean daily precipitation rate (largtscale plus convective) as 

calculated by SiB-GCM and Ctl-CCM for the 304ay winter (December 15 - January 13) simulations: SiB-GCM minus Ctl-GCM. The contour interval is 

1 mm/day. Land areas where Ctl-GCM produces more than 1 mm/day of 

precipitation than SiB-GCM have been shaded. 

49 
4.4 Impact on the Large Scale Atmosphere Circulation 

The impact of SiB on the large scale atmospheric circulation is briefly discussed 

here. Clearly, the shortness of the simulation runs performed with SiB-GCM and 

Ctl-GCM and the uncertainties in the soil moisture initialization (see earlier sections and 

Appendix D) prevent us from drawing definitive conclusions. Longer runs, where the 

effects of the soil moisture initialization scheme can be disregarded, must be carried out to 

fully investigate the interactions between the surface energy balance and the large scale 

circulation. 

4.4.1 Summer Case 

Figures 26a and 26b show the 200 mb zonal winds for the first 30 days of the 

summer simulation by SiB-GCM Ctl-GCM). The weakening of the tropospheric jet in 

SiB-GCM compared with Ctl-G & M is noticeable in the northern hemisphere. Wind 

speeds of more than 30 m/s are observed in the NMC analysis (Fig. 26c) and in the 

Ctl-GCM simulated field, but not in the SiB-GCM simulated field. Although not clearly 

visible in the maps, the jet stream has also shifted slightly northward in the SiB-GCM 

simulation. 

90N 

60N 

30N 

EQ 

30s 

60s 

0 90s 

0 60E 120E 180 120w 60W 

Figure 26a: Mean sonal wind at 200 mb in the SiB-GCM summer (June 15 - July 14) 

si.nulation for the firat 30 days. The contour interval ie 10 m/r. 

50 
90N 

60N 

30N 

EQ 

30s 

60s 

90s 

0 60E 120E 180 120w 60W 0 

Figure 26b: The same aa Fig. 26a except for Ctl-GCM. 

90N 

60N 

30N 

EQ 

30s 

60s 

90s 

Figure 26c: Mean zonal wind at 200 mb from'the NMC analysis for the period June 15 - July 14, 1986. 

I 

51 
90N 

60N 

30N 

EQ 

30s 

60s 

90s 

0 60E 120E 180 120w 60W 0 

Figure 27a: Mean eddy geopotential height at 500 mb in the SiB-GCM for the summer 

(June 15 -July 14) simulation. The contour interval is 40 rn. 

52 
t 

I 

I 

SON 

60N 

30N 

EQ 

30s 

60s 

90s 

0 60E 120E 180 120w 60W 0 

Figure 27c: Eddy gcopotential height at 500 mb from NMC analyses averaged for the 

period June 15 -July 14, 1986. The contour interval is 40 m. 

The 500 mb eddy geopotential height for the first 30 days of the summer simulation 

as simulated by SiB-GCM (Ctl-GCM) is shown in Fig. 27a (27b). When compared with 

the NMC analysis the simulations by both GCM’s appear to be better in the northern 

rather than the southern hemisphere (Fig. 27c). The wavy pattern over the Eurasian 

continent in the SiB-GCM simulation is flatter than in the Ctl-GCM simulation while the 

trough to the north of the Caspian Sea is completely missed in the SiB-GCM simulation. 

4.4.2 Winter Case 

The simulation of the circulation in the northern hemisphere is very good in both 

GCM’s when compared with the analyzed field. Ctl-GCM simulates the ridge around 

Lake Baikal better than SiB-GCM, but the ridge to the south of New Zealand seems to be 

better represented in SiB-GCM. 

5. SUMMARY AND DISCUSSION 

The Simple Biosphere Model (SIB) developed by Sellers et al. (1986) was 

implemented in a modified version of the NMC spectral model. The SiB-GCM was 

integrated for 50 days in a (boreal) summer simulation and for 30 days in a (boreal) winter 

simulation. The simulated surface and atmospheric fields were compared with those 

produced by the control GCM (Ctl-GCM) which has the same physics package as 

SIB-GCM except for the ‘bucket’ hydrological model and the three soil layers with 

predicted temperatures. 

Ctl-GCM was found to give too much evaporation over the vegetated land; 

sometimes the latent heat flux was almost equivalent to the net radiation. On the other 

hand, SiB-GCM generated sensible and latent fluxes over the land which compared well 

53 
with observations. The improvement in the calculated Bowen ratio in SiB-GCM comes 

from the explicit treatment of transpiration control by stomatal resistance. 

There is a secondary effect due to the generally higher land-surface albedos 

calculated by SiB-GCM as compared to the prescribed fields used in Ctl-GCM. This is 

thought to alter the total available energy by about 6% in vegetated areas with a similar 

impact on the evapotranspiration rate. The stomatal resistance effect, however, accounts 

for the rest of the reduction in evapotranspiration, typically 25% or greater over the moist 

vegetated regions. This result corresponds with those in the sensitivity study of Sellers and 

Dorman (1987). 

The increase in the sensible heat flux 25-50 W/m2) over the vegetated land 

more realistic diurnal ranges in the surface and PBL temperatures. The daytime PBL 

depth was 60-100 mb over vegetated land in SiB-GCM compared with the unrealistic 

value of 20 mb calculated by Ctl-GCM. A deeper PBL and more active momentum 

exchange over the land led to an increase in the surface stress calculated by SiB-GCM. 

The SiB-GCM June -July precipitation rate was 1 to 2 mm/day lower than in the 

Ctl-GCM run in the Amazon basin, the African tropics, North America, the Soviet Union 

and Europe. The monsoon rainbelt in China moved northward, but overall the precipitation there also decreased. This decrease in the precipitation rate corresponded roughly 

to the decrease in the time-averaged latent heat flux, i.e., about 25-50 W/m2. 

SiB-GCM still generated more rain than the climatological precipitation in some 

places over land. This may be due to inadequacies in the physical parameterizations in the 

GCM or it could be that SiB-GCM still calculates evapotranspiration rates that are too 

high because of problems in the soil moisture initialization. Over the central and eastern 

United States, the simulated horizontal moisture convergence in June-July was almost 

zero in both GCMs and was in good agreement with observations, but the evaporation rate 

and hence the precipitation rate in the region was far lower in SiB-GCM. This suggests that the excessive precipitation calculated over land by Ctl-GCM may be the result of an 

overestimation of the evaporation rate. An experiment was performed to explore this 

hypothesis by integrating SiB-GCM with decreased initial soil moisture in central and 

eastern U.S. (The soil moisture initialization was suspect as it is an uncertain part of any 

GCM model). The results showed that the simulated horizontal moisture convergence 

remained almost zero and the smaller evapotranspiration rate over land directly 

contributed to a reduction in the precipitation rate. 

In the course of the 50-day simulation, soil wetness in SiB-GCM changed very 

slowly. This was partly due to the large soil moisture capacity in the SiB formulation and 

partly due to the small differences between evapotranspiration and precipitation. 

In Ctl-GCM soil wetness was updated every day from values interpolated from 

monthly climatology. Over vegetated land in the Northern Hemisphere the soil moisture 

decreased rapidly over the June - August period. This brought about a gradual increase in 

the sensible heat flux and a decrease in the latent heat flux which led in turn to a reduced 

precipitation rate. 

The realism of SiB-GCM and Ctl-GCM on smaller spatial scales was also 

scrutinized. The diurnal variation and the mean value of the albedo as calculated with 

SiB-GCM were found to be satisfactory when compared with observations while the 

albedos used in Ctl-GCM were generally too low. The interception loss fraction is difficult 

to verify, but at least in the Amazon basin the SiB-GCM value of 28% of the total latent 

calculated by SiB-GCM was associated with hig 6 er surface temperatures and larger and 

54 
heat flux seems to be reasonable. Overall, SiB-GCM appears to give a more consistent 

and realistic simulation of hydrological processes and near-surface atmospheric conditions 

than Ctl-GCM. 

It should also be noted that the differences between the values of sensible heat flux, 

evapotranspiration and precipitation calculated by the two models are maximized in the 

humid vegetated regions of the summer (growing season) hemisphere. Under the prevailing 

conditions of low relative humidity and high radiation loads the eva oration rate is very 

sensitive to the value of the stomatal resistance, which is around 100 sfm in SiB-GCM and 

effectively zero in Ctl-GCM. 

A preliminary assessment of the impact of SiB-GCM on the large scale atmospheric circulation was also made. The large-scale circulation changed only slightly in the 

northern hemisphere winter simulation. This is understandable because of the small land 

area in the southern hemisphere and because the continents in the northern hemisphere 

receive little solar radiation in winter. 

In the summer simulation there was some noticeable impact: the 200 mb jet stream 

and the wind speed at 850 mb were found to be unfavorably weakened in SiB-GCM which 

also failed to simulate the stationary trough in the 500 mb geopotential height. We do not 

yet understand the causality of this effect. Clearly, the above comments must be regarded 

as very preliminary in view of the shortness of the simulation runs (50 days or less) and the 

uncertainties in the soil moisture initialization. 

In summary, the implementation of SiB in the GCM has improved the simulated 

partition of absorbed energy at the land surface. Generally, the explicit recognition of 

stomatal resistance terms in the SIB latent heat €lux formulation has resulted in a 

reduction in the land surface evapotranspiration rate compared to the bucket model 

calculation. In the summer simulations, this led to approximately matching reductions in 

the continental precipitation rates in SiB-GCM. The reduction in evapotranspiration and 

rainfall rates over the land due to SiB seems to be supported by the available observations. 

t 

55 
APPENDIX A 

ANALYTIC FUNCTION FOR Cm AND ch BASED ON 

MONIN-OBUKHOV SIMILAFUTY THEORY. 

where Cm, ch are multiplied by the magnitude of the wind velocity to yield the drag 

coefficients for momentum transfer and heat/moisture transfer. 

Fi = F: = [ln(z,/z,)/0.40] * 0.87581 - 1.5630 (A4 

stable region : Ri>O 

F,,,/F: = Fh/Fg = exp[--1.8900 + (1.8900)2 + 5.0519~1 (A.3) 

x = ln(l.O + 10.815 fi) 

unstable region : Ri>O 

Fm/Fi = exp[ 1.2270 - (1.2270)2 + 1.2642~1 

x = ln(l.O - 1.2743 Ri) 

= exp[1.3462 - (1.3462)2 + 1.8380~1 

I x = ln(l.O -3.4805 Ri) 

I where Fi and FE are the neutral values of Fm and Fh, zr is the reference height, zo is 

the roughness length and Ri is the Richardson number. 

the analytic function above are presented in Fig. A.1. In all cases the reference height zr 

was set to 20 m or to half the height of the lowest model level. 

I The curves derived using the original Monin-Obukhov formulation and those using 

56 1 
t 

-1.0 1 L 4 

-1.2 -.8 -.4 a .4 .8 1.2 

Rlchardron Number 

Figure A.la: Surface drag coefficient for momentum transfer. The solid lines were derived 

from Monin-Obukhov SirnilariLy ttcory and the dashed lines from the analytic 

function described in Appcndix A. The range of the Richardson number is 

from -1.25 to 1.25. The roughness length, zo, rangcs from 100 crn (for the top 

line), 10 cm, 1 cm, 0.1 cm to 0.01 cm (for the bottom line). 

-1.0 1 

c. 

-1.2 -.8 -.4 0 0.4 .8 1.2 

Richardson Number 

Figure A-lb: The -me as Fig. A.la except that thc heat/moisturc transfer cocfii&nt is 

shown. 

57 
APPENDIX B 

DEWATION OF EFFECTIVE PBL MASS AND FORCING 

r' 

The vertical diffusion tendencies for potential temperature, 0 , and specific 

humidity, q , are given by 

rrr 

where Kh is the vertical diffusion coefficient for heat and moisture transfer. The two 

equations are discretized in the vertical direction using -coordinates. 

where Ak and Bk are coefficients which arise from the vertical discretization scheme. 

If an implicit scheme with explicit coefficients is used 

t-At @:+At - @k 

2At 6k 2 

A* z 2AtA7B*~2AtB, 

Then the system is solved by elimination. 

58 
APPENDIX C 

TREATMENT OF SPATIALLY NON-UNIFORM PRECIPITATION IN SiB. 

I 

I 

A simple scheme is used in SiB to account for the hydrological effects resulting 

from the spatial non-uniformity of convective precipitation. In nature, the net effect of 

the ‘clumped’ precipitation patterns typical of convective storms is to increase the 

area-averaged canopy throughfall and surface (infiltration excess runoff rates relative to 

the equivalent rates roduced by a spatially uniform rainfall fi el ds. In most GCM’s, a 

single (area-averagedf figure for convective precipitation is produced for each grid-area for 

each time step. In SiB, we assume that this rainfall amount is spatially distributed as 

shown in Fig. C.l and given by Eq. C.1. 

= relative amount of convective precipitation as a function of 

fractional area of grid area x, (O<x<l), falling within a time step IC(4 

ac,bc,cc = constants 

Figure C.l: Precipitation area-amount 

relationships used in SIB. The 

variable x refen to fraction of the 

grid area, the variable rden 

to the relative amount of precipitation. Note that the large scale 

precipitation, Ip( x), is almost 

invariant over the grid area while 

convective precipitation, IC( is 

non-uniformly distributed. 

59 
The constants a, b, c were obtained by comparison with the data of Ruprecht and Gray (1976) and are normalized such that 

1 J I,(x) dx = 1 

0 

The values chosen for convective precipitation were a, = 20, b, = 20, cc = 0.2063-8. For 

precipitation generated by large scale systems, the 

b, = 20 and cp = 0.9999 which means that in the 

types of precipitation (most models can produce both types 

- (Pc a, + Pp a,) e-bx + (P, cc + Pp cp) 

combined to give a single area-amount function by: 

PI (C.3) (4 

P total precipitation during a time step, mm = Pc + P, 

P,,Pp convective, large scale (uniform) precipitation rates during a time-step, respectively, mm 

The term PI can now be interpreted as an amount distribution which may be 

used to calculate throughfall rates and infiltration excess. 

Firstly, the direct throughfall term in SiB is defined as that fraction of the rainfall 

that falls through holes and gaps in the vegetation canopy. (Such holes and gaps are 

assumed to be uniformly spread over the grid area.) The definition of this direct fraction is 

given in Sellers et al. (1986), Sec. 7(b); here it is referred to as G; 0 < G < 1. 

= 

= 

(4 

Then, 

Dd = GP 

Dd = direct throughfall, mm 

The amount of rainfall actually intercepted (though not necessarily retained) by the 

canopy is then given by 

The proportion of the grid area for which the canopy has intercepted enough 

rainfall to equal or exceed its saturation limit, xs, is then given by the solution to 

S, = canopy storage limit, mm 

M, = intercepted water stored on canopy at the beginning of the time step, 

mm 
Combining (C.6) with (C.3) we have 

where a N (Pcac + P ap)/P and so forth. Reference to Fig. (C.2) will show the physical 

significance of xs an8how it is used to calculate the canopy drainage term, D, 

= P( 14) ( l-e-bxS) + cxS - (S, - M,) xs 

P'M 

SC 

MC 

8 

7 

6 

5 Intercepted Into 

4 

Canopy Drainage 

3 Pre-Existing Store 

2 

1 

0 0.5 

XSC 

MC 

Figure C.2: Dynamics of precipitation interception by a vegetation canopy in SIB. The 

amount of water already stored in the canopy prior to rainfall interception, 

Mc, is considered to be uniformly distributed over the grid area (see hatched 

area). The integral of the water amount above Mc represents to total 

amount of water intercepted by the canopy. xs is the proportion of the grid 

area where the intercepted rainfall plus the preexisting canopy water storage, 

Mc, exceeds the canopy storage limit Sc. All water above the Sc linrit drains 

off the canopy, all below it is added to the canopy interception store. 

Total throughfall to the ground, D, is then given by 

D=D d -k Dc I 

61 
The amount of water remaining on the canopy, i.e., available for direct evaporation 

is given by: 

= Mc+P-D MC ttl t (C.10) 

For the next time step, this water is assumed to be uniformly spread over the 

vegetation canopy in the grid area which is clearly inconsistent with the representation 

shown above and the illustration shown in Fig. (C.2). However, one can assume that the 

resulting errors are small as the non-uniformity of intercepted precipitation will only 

determine the timing of evaporation loss unless convective storms are immensely 

concentrated in time and space. 

A similar calculation to that shown in (C.1) through (C.10) is performed for the 

ground cover so that an effective precipitation rate reaches the soil surface. With very 

heavy rainfall, the local infiltration capacity of the soil can be 'beaten' by the rainfall rate 

and (infiltration excess) overland flow can be generated. For the sake of simplicity, we 

assume the same area-amount relationship for the rainfall (in fact this would have been 

distorted by interception in the canopy, etc.). A soil hydraulic conductivity for the surface 

layer is calculated from Eq. 50 of Sellers et al. 1986) and this is used to calculate an 

equivalent depth of water that the soil can absorb uring the time step. 

Pi = KAt (C.11) 

Pi = K = soil hydraulic conductivity, mm s-1 

At = time step duration, s 

amount of water that soil can absorb during the time step, mm 

The fraction of the grid area giving rise to infiltration excess is calculated by 

(C.12) 

Ds = amount of throughfall and vegetation drainage reaching the soil surface, 

mm 

The amount of infiltration excess is then simply given by 

(C.13) 

where the term inside the integral is similar to that in (C.8). 

when snow falls, Eqs. C.l to C.10 are used, but not C.ll to C. 13; that is, the 

snow can build up and fall off the vegetation, but accumulates on (and does not run off) 

the ground. 

This scheme is in place in all current SiB implementations in GCMs. 

62 
APPENDIX D 

INITIALIZATION OF GLOBAL SOIL MOISTURE FIELDS FOR THE 

SIMPLE BIOSPHERE MODEL (SiB) 

It was recognized in Sellers et al. (1986) that SiB would be sensitive to the soil 

moisture initialization as this can have a marked effect on the calculated 

evapotranspiration rate and hence on all the components of the surface energy balance. 

Unfortunately, the soil moisture climatologies currently used by GCMs are not directly 

compatible with SiB as the formulations used to derive them are drastically different from 

the biophysically-based calculations used in SiB. This appendix reviews a methodology 

that can be used to transform the existing soil moisture fields into SiB-compatible soil 

moisture fields; the method requires no other data than the existing soil moisture 

climatology and the distributed SiB parameter set. For a number of reasons the 

methodology has severe limitations. 

1. 

from two principal sources. 

EXISTING SOIL MOISTURE CLIMATOLOGIES FOR GCM'S 

The fields of soil moisture content used for the initialization of GCMs originate 

Mintz and Serafini (in prep.). Mintz and Serafini used a simple water 

budget equation set to calculate time varying global soil moisture fields on 

a4" by 5" grid. 

The equations used were: 

(i) 

'MS 

S = available soil moisture, mm 

Smax = maximum value of S; 150 mm 

P = rainfall rate, mm/day 

E = evapotranspiration rate, mm/day 

= Mintz and Serafini Beta-function which 

'MS regulates the evapotranspiration rate 

E, = potential evapotranspiration rate, mm/day. 

63 
The expression for pXtS was determined by Mintz and Serafini by curve-fit to the 

data of Nappo (1975) which were based on observations of soil moisture depletion by 

individual maize plants growing in pots. The potential evapotranspiration rate, E,, was 

determined from the method of Thornthwaite (1948) and is principally a function of air 

temperature, Ta. 

To calculate the time-varying fields of soil moisture, monthly mean values of P 

and T, were obtained from Jae er (1976) and the NCAR data archive respectively and 

were used to integrate Eq. (D.l) f or each 4” by 5” grid area using a time step of one day. 

This procedure was repeated for 7 years of integration after which the difference between 

the values of S taken on the same calendar day of years 6 and 7 were less than 0.01 mm 

for every grid area. (The fields were initialized to zero on the first time step). These fields 

of soil moisture are currently used by the UCLA/GLA model and the GLA 4th-order 

model. In most GLA and UCLA/GLA GCM applications, the soil moisture fields are not 

updated prognostically but from daily interpolations of the time-varying fields calculated 

by Mintz and Serafini (in preparation). 

(ii) Geophysical Fluid Dynamics Laboratory (GFDL) 

A similar scheme to that of Mintz and Serafini (in prep.) has been in 

use at GFDL and at the National Meteorological Center (NMC) for some 

years; see Gordon, (1986). 

A long term integration of the GFDL Zodiac GCM was used to 

generate fields of S via Eq. (D.1). In this case, the fields of P and E used 

in (D.l) are produced from the model’s own calculations where E is 

calculated by (D.2) with DG given by 

where 

= GFDL ‘Beta-function’ ’G 

Sm,, = 150mm 

The potential evaporation rate, E,, is calculated as follows 

r a 

P, c, 

potential evaporation rate, mm/day 

surface temperature, K 

saturated vapor pressure of the lowest model layer, mb 

aerodynamic resistance between the surface and the 

lowest model layer, s/m 

the density, specific heat of air, kg/m and J/kg/K, 

respectively 

psychrometric constant, mb/K 

latent heat of evaporization, J/kg 

number of seconds in a day. 

64 
I 

I 

The fields generated from the long-term GCM integration have been used as a 

standard soil moisture initialization for the GFDL model and the NMC medium range 

forecast (MRF) model. 

Recently, Willmott et al. (1985) performed another off-line analysis of the global 

soil moisture fields in a similar manner to that of Mintz and Serafini (in prep.). These 

fields (which are based on the ,OMS function) are currently used in the University of 

Maryland’s Center for Ocean-Land-Atmosphere version of the NMC MRF 86 model 

(Ctl-GCM) for initialization. For our purposes, however, we treat these fields as if they 

had been derived by the NMC bucket hydrological model (i.e., the PG function) as 

regardless of how the initial fields are derived, they impact the circulation through the 

hydrological model in place in the GCM. 

2. 

following reasons: 

INCOMPATIBILITY OF EXISTING AND SiB MOISTURE CLIMATOLOGIES. 

The existing soil moisture initialization fields cannot be used directly by SiB for the 

(i) 

(ii) 

(iii) 

T 

The value of Smax, the total amount of available soil moisture, is fixed at 150 

mm in the Mintz and Serafini and GFDL cases, but is variable in SiB depending 

on the biome. In SIB, for practical purposes the available soil moisture storage 

ranges from 60 mm in deserts to 415 mm in forested regions. 

The fractional wetness, W = S/Smax, as given by the existing fields, cannot be 

used for initializing SiB because the extremely low values of W predicted by 

(D.l) and (D.2) are physically unrealistic. Both of the Beta-functions, pBs 

and PG, produce excessively high values of E/E, when W 5 0.6, see Sellers 

(1987). The main reason for this is that vegetation cannot extract water from 

the soil when W drops below about 0.3. Most observations and simulations; 

for example, see Sellers and Dorman (1987), indicate that the evapotranspiration rate of a vegetated surface is largely insensitive to W when W is 

greater than about 0.6, declines curvilinearly between 0.6 and about 0.3 and is 

near-zero thereafter. 

The values of E, as given by overestimate the 

stress-free evapotranspiration et al. (1986). 

This effect is not likely to be so 

In conclusion it would be unwise to use the Mintz-Serafini or the GFDL-generated 

fields of soil moisture for SiB initialization directly. This applies to both the absolute 

values, S, and the wetness fraction, W. 

3. TRANSFORMING THE EXISTING SOIL MOISTURE FIELDS INTO 

SiB-COMPATIBLE SOIL MOISTURE FIELDS 

The transformation scheme used to generate SiB soil moisture fields is simple, 

robust and does not require any more data than the existing fields themselves and the SiB 

parameter set. The scheme is based on two representations of the soil moisture depletion 

process, one of which is assumed to be effective for near saturated conditions (Sec. 4.1) and 

the other for very dry conditions (Sec. 4.2). A simple interpolation formula is used to 

combine the two methods to determine intermediate soil moisture contents (Sec. 4.3). 

65 
3.1 Soil moisture depletion under wet conditions 

A given grid area is assumed to be saturated at time t = 0 and is then exposed to 

an evaporative demand, E,. After a specified time T, the grid area would have soil 

moisture contents of S,, or S,, if the pat, or fiG functions were applied, and a soil 

moisture content S, if SiB were used. The values of SMs, S, and S, could be then 

considered transforms of each other. 

The process of integrating (D.l) over time is relatively strai htforward. With the 

transpiration formulation had to be considerably simplified so that it could be applied to 

the existing fields. To this end, the following assumptions were made. 

The initial state of the Beta-function soil column is saturation; that is, 

S = SmaX. The equivalent initial state assumed for the SiB soil column is the 

moisture content retained against gravitational drainage. This implies a soil 

moisture potential, $ , of order -1, from which the appropriate soil moisture 

content can be estimated from 

full version of SiB, however, the problem is more complex an B so the SIB evapo- 

(i) 

$ = $sw-B 

where 

?c) = soil moisture potential m 

lo, = soil moisture potential I! m when W = 1 

B = empirical coefficient 

Both $s and B are dependent on soil type in SiB. For a given SiB biome, 

(D.6) may be solved for $ = -1 to give an estimate of Wo, the starting value 

of the SiB soil wetness when t = 0. 

The value of E used in the Beta-function calculations is taken to be roughly 

grid area. This is a less defensible assumption as firstly, the evapotranspiration 

rate from a SiB grid area is dependent on the vegetation type, and secondly, the 

value of E, used in the Beta-function is liable to be an over+stimate, see 

Sellers (1987). 

(ii) equivalent to t K e unstressed evapotranspiration rate from the equivalent SiB 

to yield a 

are made: 

(iii) 

(iv) 

(4 

(vi) 

(vii) 

(viii) 

The SiB calculation of the surface energy balance can now be drastically simplified 

formulation similar to a Beta-function. To do this, some further assumptions 

The ground and canopy surface temperatures are equal; T, = T,, 

One vegetation layer is dominant. 

The leaf water potential is approximately equal to the soil moisture potential in 

the root zone, $. 

Soil moisture stress, exercised through $J, is the only significant stress operating 

on the vegetation’s transpiration rate (see, Sellers et al. 1986). 

The surface resistances are large compared to the aerodynamic resistances. 

The air is relatively dry. 

66 

I 
Incorporation of these assumptions into the SiB formulation yields 

0s = Vf($) + (1-V) h, rsurfo rsurf 

ES = 

SiB-derived evapotranspiration rate, mm/day 

PS = 

SiB-based Beta function 

V = dominant vegetation layer cover fraction 

h, = soil air humidity, kg/kg. 

rsurfo = Soil surface resistance at saturation, s/m. 

rsurf = Soil surface resistance, s/m 

f($) = leaf water potential stress factor. 

The leaf water potential stress factor, f($), is given by 

$1,$2 = leaf water potentials at which the stomata start to close 

and are completely closed, respectively, m. 

0 < f($) < 1 

Where 

Eq. (D.67. 

is calculated from the empirical formulation of Clapp and Hornberger (1978)) see 

The process of transforming the Beta-function fields is now fairly strai htforward. 

time, T, that each grid area is exposed to the same evaporative demand, E,. To do this, 

the precipitation rate in (D.l) is ignored which yields 

The link between the Beta-function and SiB soil moisture contents is speci k ed by the 

dS 

= -& 

rn 

S MS,G,S 

dS = -Ep [ Tdt 

0 

SO 

,, 

'dt dS = -Ep 

and SO J G 0 

where 

So = starting soil moisture content, mm 

= S,, for Beta-function calculations, mm 

(D. 10) 

(D.ll) 

= [$] l'B for SiB calculations, mm 

S = Soil moisture after time T, mm, where subscript refers to MS,G or SiB 

I 

I 

67 
The SiB transform of the Mintz-Serafini or GFDL soil moisture fields is simply 

achieved by assuming E, is the same in both cases so that 

- Iss dS 

% - 

SO Is”’” SO 

(D.12) 

In (D.12), the fields of SYS,, and So are known and the functions PMS, /3, and 

Ps have been specified in (D.3), (D.4), and (D.7) respectively. (D.12) can therefore be 

solved quite easily for S,, the SiB soil moisture value. The left hand side of (D.12) is 

solved analytically to yield: 

(D.13) 

and, 

S, < 0.75SmaX 

= Smax-S, S, > 0.75SDax (D.14) 

The integration of the right hand side of (D.12 is performed numerically to create 

existing data sets are used to determine the left hand side of (D.12) for each grid area, 

using (D.13) and (D.14), and these are then used to obtain the corresponding values of S, 

from the look up table. 

a look-up table for each SiB biome. In the WETNES 8 program, soil moisture values from 

3.2 Soil moisture depletion under dry conditions. 

In dry areas, it is assumed that the soil moisture content has reached a quasiequilibrium state which is characterized by the value of the Beta-function, PMs or PG. 

Under these conditions the soil moisture content for SiB, S,, is determined from the 

matching SiB soil moisture availability value as given by (D.6). 

where q9 is determined from the solution of 

- 4 - pkls,G 

68 

(D.15) 

(D.16) 
3.3 Interpolation of SiB soil moisture values for all wetness conditions. 

It is assumed that the methods outlined in Secs. 4.1 and 4.2 are appropriate to wet 

and dry conditions respectively. A single value of SsiB is calculated simply by taking the 

minimum of S, and S,. 

S S iB = Min (Ss, Sn) (D.17) 

4. SUMMARY 

The method discussed above has a number of obvious shortcomings. It does serve, 

however, to give SiB-compatible soil moisture fields that will approximately reflect the 

evapotranspiration climatology of the region. The major features of the results from the 

procedure are as follows: 

In mid-latitude zones where SiB has deep soils, the method will yield relatively 

high values of wetness fraction for SiB. This is because the soil moisture deficit 

given by (D.12) is calculated in absolute terms. 

Where the existing climatologies give very low values, i.e., W 5 0.1, SiB will 

yield values of the order of W = 0.25, which is equivalent to unavailable soil 

moisture. 

(iii) The maximum value of the initial SiB wetness fraction will be about 

W = 0.80. 

All of the simplifications used to calculate the SiB fields ensure that a fairly high 

degree of uncertainty exists in these values. When a long integration with SiB is executed, 

the resultant fields of soil moisture will be used for future initializations. 

(i) 

(ii) 

t 

69 
APPENDIX E 

INITIALIZATION OF SOIL TEMPERATURE 

we have adopted a simple soil temperature initialization sclleme for Ctl-GCM: 

the mean annual air surface temperature is used as the soil temperature at 500 cm depth, 

Tg3, the ener y balance equation .was then solved to obtain the ground surface 

These two surface 

temperatures were averaged to give the initial ground surface temperature, Ts. The deep 

soil temperatures T,1 and Tg2 (at 10 cm and 50 cm respectively) were initialized as 

follows 

temperatures B or midday and night-time radiation conditions. 

Tg1 - 0.99 Ts + 0.01 Tg3 

Tg2 - 0.9 Ts + 0.1 Tg3 

In SiB-GCM, Tg3 was used as the initial estimate of deep soil temperature, Td. SiB was 

then integrated for five time steps with noon time and zero solar radiation conditions while 

the atmospheric temperature and specific humidity were held constant. These two ground 

surface temperatures were averaged to give the initial surface temperature T,. The deep 

soil temperature Td was initialized as follows 

Td = 0.9 T, + 0.1 Tg3 (E4 

As discussed in Sec. 4, this soil temperature initialization was found to be 

satisfactory and heat conduction in the soil was generally an insignificant part of the 

time-averaged surface energy balance. 

, 

70 
ACKNOWLEDGEMENTS 

The first author is grateful to the Ja an Meteorological Agency and to the Center 

for Ocean-Land-Atmosphere Interactions P University of Maryland) for facilitating his 

visit to the University of Maryland. Thanks are also extended to Dr. Harshvardhan and 

Mr. Tom Corsetti for help with some of the radiation code, and to Jeff Dorman and Larry 

Marx for organizing the SIB vegetation parameter sets. Dr. Jim Shuttleworth provided the 

Amazon data sets which were used for comparisons with the ‘point’ flux estimates for 

Manaus, Amazonas in Secion 4. This research was supported by NSF grant 

ATM-8414660, NASA grants NAG5492 and NAGW-557. Special thanks go to Marlene 

Schlichtig for typing and proofreading several drafts of the manuscript. 

71 
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76 

1 

1 
Report Documentation Page 

7. Key Words (Suggested by Author(s1) 

1. Report No. 2. Government Accession No. 

18. Distribution Statement 

NASA CR-185509 

4. Title and Subtitle 

9. Securirv C!assif. (of this report) 

Implementing the Simple Biosphere Model (SiB) in a 

General Circulation Model: Methodologies and Results 

20. Security Classif. (of this page) 21. No. of pages 22. Price 

7. Authods) 

N. Sato, P. J. Sellers, D. A. Randall, 

E. K. Schneider, J. Shukla, J. L. Kinter 111, 

Y.-T. Hou, and E. Albertazzi 

9. Performing organization Name and Address 

Center for Ocean-Land-Atmosphere Interactions 

Department of Metephology 

University of Maryland 

College Park, MD 20742 

12. Sponsoring Agency Name and Address 

National Aeronautics and Space Administration 

Washington, DC 20546 

Unclassified I Unclassified 

15. Supplementary Notes 

Technical Monitor: G. Asrar 

Code EEL, NASA Headquarters 

Washington, DC 20546 

76 A05 

3. Recipient's Catalog No. 

5. Report Date 

August 1989 

6. Performing Organization Code 

8. Performing Organization Report No. 

10. Work Unit No. 

11. Contract or Grant No. 

NAGW-492 and NAGW-557 

13. Type of Report and Period Covered 

Contractor Report 

14. Sponsoring Agency Code 

EEL 

16.Abstract The Simple Biosphere Model (SiB)of Sellers et a1 (1986) was designed to 

simulate the interactions between the Earth's land surface and the atmosphere by 

treating the vegetation explicitly and realistically, thereby incorporating biophysical controls on the exchanges of radiation, momentum, sensible and latent heat 

between the two systems. 

a modified version of the National Meteorological Center's spectral GCM. The 

coupled model' (SiB-GCM) was used with a conventional hydrological model (Ctl-GCM) tc 

produce summer and winter simulations. The same GCM was used with a conventional 

hydrological model (Ctl-GCM) to produce comparable 'control' summer and winter 

variations. 

This paper describes the steps taken to implement SiB in 

It was found that SiB-GCM produced a more realistic partitioning of energy 

at the land surface than Ctl-GCM. Generally, SiB-GCM produced more sensible heat 

flux and less latent heat flux over vegetated land than did Ctl-GCM and this 

resulted in the development of a much deeper daytime planetary boundary and reduced 

precipitation rates over the continents in SiB-GCM. In the summer simulation, the 

200 mb jet stream and the wind speed at 850 mb were slightly weakened in the SiB-GCI 

relative to the Ctl-GCM results and equivalent analyses made from observations. 

General CirculationModel (GCM) 

Biosphere-Atmosphere Interaction 

.Planetary Boundary Layer (PBL) 

Unclassified - Unlimited 

Subject Category 46 

I 

I 

NASA-Langley, 1989