The components of a ‘SVAT’ scheme and their effects on a GCM's hydrological cycle

Advancesin WaterResources17 (1994) 61-78 © 1994 Elsevier Science Limited Printed in Great Britain. All rights reserved 0309-1708/94/$07.00

ELSEVIER

The components of a 'SVAT' scheme and their effects on a GCM's hydrological cycle Randal D. Koster & Max J. Suarez NASA/Goddard Space Flight Center, Greenbelt, Maryland 20771, USA

'Bucket'-type land surface models are being replaced in some general circulation model (GCM) climate studies by 'SVAT' (Surface Vegetation-Atmosphere Transfer) models, which feature an explicit treatment of vegetation control over the surface energy balance. The evaporation calculations of a typical SVAT model differ from those of a bucket model in at least four fundamental ways: (a) the SVAT model typically allows a greater variety of environmental stresses to limit evapotranspiration; (b) it generally includes a canopy interception reservoir; (c) the control of the land surface over evaporation in a SVAT model is influenced by the atmosphere; and (d) the land surface control in a SVAT model varies on short time scales. Global fields and grid square diurnal cycles illustrate the hydrological cycle produced in a 20-year simulation with an atmospheric GCM coupled to a SVAT model. A sensitivity analysis then examines the relative importance of the SVAT/ bucket differences in terms of their effects on the simulated hydrological cycle. The interception reservoir exerts more control over global evaporation than does either the vapor pressure deficit stress or the temperature stress. The effect of the temperature stress is, in fact, insignificant. The time variability of land surface control over the surface energy balance in a SVAT model significantly increases moisture convergence over land. only is the surface energy balance at a point controlled by complicated interactions between vegetation, topography, and available soil moisture (among other things) but all of these quantities vary spatially, with varying degrees of spatial correlation between them. Despite this difficulty, the realistic modeling of land surface processes is critical. Mintz 23 and others have established that changes in land surface conditions can have a profound effect on the GCM's hydrological cycle. The evolution of land surface models (LSMs) has been guided by the desire to increase their realism and thereby the accuracy of the GCM's climate. The earliest interactive LSMs, known as 'bucket' models, represent the land surface as a simple soil reservoir that is filled by precipitation and emptied by evaporation, with the evaporation rate controlled by the reservoir's water level. 19 These models ignore the explicit control of vegetation over the surface energy balance, prompting the recent development of more complex 'SVAT' (Surface-Vegetation-Atmosphere Transfer) models 1'7'28 that include canopy (stomatal) resistances that vary with environmental stress. A parallel branch of LSM development focuses on the manipulation of statistical distributions to account for subgrid variability in soil moisture, precipitation, and other quantities. 8'9 Avissar

1 INTRODUCTION A general circulation model (GCM) of the atmosphere provides a unique setting for the analysis of the atmospheric branch of the global hydrological cycle. The GCM's chief advantage is its ability to simulate moisture fluxes between all GCM reservoirs, allowing it to generate a complete set of global precipitation, evaporation, and other hydrological data fields over most time scales of interest. GCMs, however, do have two important limitations in hydrological research. First, they cannot provide data at a scale smaller than the grid square, which typically covers an area of 400 x 400 km; this drastically limits their use for studying many hydrological processes at the land surface, such as the effect of storm structure on basin-scale runoff production. Second, and perhaps more important, GCMs are notorious for their inability to reproduce certain climatic features (e.g. local precipitation rates), which calls into question the hydrological insights they provide. The land surface boundary condition in a G C M is particularly difficult to model. A crucial role of the land surface is to partition incoming solar energy into fluxes of latent and sensible heat, and the processes by which this partitioning is determined are very complex. Not 61

R. D. Koster, M. J. Suarez

62

and Verstraete 4 provide a thorough review of current land surface modeling strategies. The recent increase in LSM complexity has produced benefits and raised new problems. The added physics in some of the later models - - particularly the explicit incorporation of vegetation effects - - should produce more realistic diurnal and seasonal cycles of land surface fluxes and should improve land surface response to changing climatic conditions. The increased complexity, though, also requires the specifications of numerous extra parameters, many of which have imprecise definitions and essentially unmeasurable values at the grid-square scale. Furthermore, with added complexity comes added difficulty in interpreting model results, i.e. in isolating the land surface processes responsible for specific climatic features. Many of the modelers working with the newer, more complex LSMs have no intention of returning to the bucket model approach, feeling that the advantages of the newer models outweigh the disadvantages. The anticipated widespread use of SVAT models in GCM climate studies demands a thorough understanding and documentation of their influence on the simulated global hydrological cycle. One pioneering study was performed by Sato eta/., 26 who found that the replacement of a bucket model by the SiB model 28 in a GCM resulted in reduced global precipitation and evaporation rates. The present paper provides a more detailed picture of the influence of a SVAT scheme on a GCM's global hydrological cycle. It first identifies the characteristics of a SVAT model that distinguish it from a bucket model. It then outlines the climatology established in a 20-year simulation using a GCM coupled to a revised version of the SVAT model of Koster and Suarez. 14 Finally, in a sensitivity analysis, it isolates and evaluates the effects of the different SVAT components on simulated GCM climate.

and p is the air density, e is the ratio of the molecular weight of water vapor to that of dry air, Ps is the surface pressure, es(Tc) is the saturated vapor pressure at the surface temperature To, eref is the vapor pressure in the overlying air, and r a is the aerodynamic resistance to water vapor transport provided by the atmosphere. The factor/3bucket is usually a simple monotonic function of soil moisture content w, which varies from 0 for fully dry soil to 1 for saturated soil; /3bucket thus implicitly incorporates the effects of 'surface' resistance, allowing the land surface to reduce evaporation as the soil dries. We emphasize that Ep is not the potential evaporation in the usual hydrologic sense; z2 it is the evaporation that would occur from a saturated surface at the current temperature, not at the corresponding equilibrium wet surface temperature. For SVAT models, on the other hand, a simplified form of the evaporation equation (assuming a single temperature at the surface) is E = pe (es(Tc) - eref) Ps ra + rsurf

where rsurf is the effective surface resistance to water transport. For this discussion, we assume that rsurf accounts for the different pathways allowed for evaporation in a SVAT model: plant transpiration, evaporation from bare soil, evaporation from a canopy interception reservoir, and evaporation of snow. When coupled to a surface energy balance, eqn (3) can be proven equivalent to the Penman-Monteith evapotranspiration formulation. Factoring Ep, as defined in eqn (2), from the right hand side of eqn (3) leads to an effective '/3 function' for SVAT models: E = /3SvATEp

Some SVAT models extend beyond typical bucket models by including more sophisticated formulations for surface albedo, momentum transfer, and other land surface characteristics. We limit our discussion to the most distinctive difference between the bucket and SVAT models - - the formulation of evaporation. In bucket models, the evaporation rate E is typically computed as the product of a 'potential' evaporation Ep and a factor/3: E = /3bucketEp

(1)

where e o =

p ' (es(Tc) - eref) Ps

ra

(2)

(4)

where /3SVAT --

2 DISTINGUISHING FEATURES OF BUCKET AND SVAT MODELS

(3)

ra r a + rsurf

(5)

We can compare the bucket and SVAT model frameworks by comparing the forms of/3bucket and /3SVAT, viewing /3 as the control of the land surface over the evaporation rate. The comparison, along with an examination of rsurf, reveals some fundamental differences between the evaporation formulations of bucket and SVAT models. The first involves the ability of SVAT models to limit evaporation when environmental conditions are suboptimal. Of course, bucket and SVAT models both allow drier soils to produce lower evaporation rates, though via very different mechanisms. Typical SVAT schemes, however, also account for the response of plant stomata to photosynthetically active radiation (PAR) and to other environmental stresses. Plants, for example, act to limit water loss when temperatures are too high or when humidities are too low.

The components of a 'SVAT' scheme and their effects on a GCM's hydrological cycle The land surface model of Koster and Suarez 14 computes transpiration by equating rsurf to the product of an unstressed canopy resistance rc - - a function of vegetation type and P A R - - and separable environmental stress factors that depend on soil moisture w, ambient temperature T, and vapor pressure deficit VPD: rsurf =

rc(veg.type, PAR)gl(w)g2(T)g3(VPD )

(6)

(This formulation, also used in SiB,28 is derived from Jarvisl3). The stress terms act to increase rsurf and thus reduce /3SVAT. Their inclusion adds significantly to the number of parameters requiring specification for each surface type. Note that the bucket model's/3 function contains no analogues for g2(T) and g3(VPD). A second important difference between bucket and SVAT models involves the canopy interception reservoir. Bucket models typically assume that rainwater either runs off the surface or infiltrates the soil; evaporation is necessarily below the potential rate if the soil moisture lies below some critical level. In contrast, SVAT models usually allow some rainwater to remain on the surface of leaves and ground litter (i.e. within an interception reservoir), and this interception water later evaporates without surface resistance. Thus, when computing the evaporation from the interception reservoir, rsurf is set to zero, leading to a/3SVAa" of 1 - the interception water evaporates at the potential rate. Evaporation rates produced by a SVAT model increase sharply following precipitation events. The capacity of the interception reservoir is typically made a function of leaf area index. The third important difference is the most subtle, involving the very structure of the/3 functions. The form of eqn (5) implies that, unlike a bucket model, a SVAT model's /3 function depends on both land surface and atmospheric conditions (through ra) and is always less than one, at least for the calculation of transpiration. With a SVAT model, atmospheric conditions can either heighten or reduce the ability of the soil to control evaporation - - for example, the land surface exerts only a small influence on evaporation rate when r a is large. The/3 computed for transpiration is always below one because rsurf has a minimum value of r c, which typically is of the same order as r a during the day. Transpiration in a SVAT model, therefore, never occurs at the potential rate, regardless of soil moisture availability. A bucket model, in contrast, always evaporates at the potential rate when the soil is saturated. An additional important difference between bucket and SVAT models is related to the first three. The dependence of r a on boundary layer stability and the dependence of rsurf on the interception reservoir, the PAR, and the environmental stresses result in strong diurnal variations in/3SVAa'- These variations are absent in /3bucket, due to the fact that soil moisture changes relatively little in the course of a day. The choice of a SVAT model, with its added

63

computational expense, over a bucket model is justifiable if the SVAT model increases the realism of the GCM's climatological means, variability, and response to external forcing. In practice, though, some of the characteristic SVAT features outlined above may be superfluous, having a negligible (and perhaps even a deleterious) effect on simulated climate. This is explored in Section 5.

3 MODELING ENVIRONMENT Our analysis of the SVAT formulation is performed in the GCM environment (as opposed to an offline environment) because only the GCM can account for feedbacks between the land surface, the overlying atmosphere, and the general circulation.

3.1 The atmospheric GCM We use the ARIES GCM, which was developed at the NASA/Goddard Laboratory for Atmospheres. It is a multi-level primitive equation model that uses a latitude-longitude coordinate in the horizontal and a standard sigma-coordinate in the vertical. Its prognostic variables are the two horizontal wind components, the potential temperature, and the water vapor mixing ratio. The radiation parametrization is, with minor modifications, that presented in Harshvardhan et al.Jl The IR spectrum is divided into four broadbands: the 15 #m CO2 band, the 9.6#m 0 3 band, and two water vapor bands. Water vapor continuum absorption is included in the 500-1200cm -l region. In the shortwave parametrization, the solar spectrum is divided into two regions: one with wavelengths shorter than 0.69 #m (ultra violet + visible) and the other with wavelengths longer than 0.69#m (near infrared). Scattering by clouds is computed using the delta-Eddington approximation. The surface layer parametrization closely follows that proposed by Louis et al. 18 The parametrization provides drag coefficients and diffusivities for heat and momentum that depend on the Richardson number. It thus includes the effect of stability of the atmospheric surface layer in the computation of the surface fluxes. The model also includes a dry convective adjustment scheme. Penetration convection originating in the boundary layer is parametrized using the Relaxed ArakawaSchubert (RAS) scheme, 25 which is a simple and efficient implementation of the Arakawa-Schubert 2 scheme. Unlike the standard Arakawa-Schubert 2 scheme, RAS considers only one cloud at a time, and rather than adjusting fully every hour or two, it does a series of partial adjustments that tend to relax the state toward equilibrium. The model also includes a parametrization of large-scale condensation and a scheme to fill spurious 'negative' water vapor produced by the dynamics.

64

R. D. Koster, M. J. Suarez

3.2 The Land Surface Model Our LSM 14 is a streamlined version of the Simple Biosphere (SiB) model of Sellers et al. 2s with a different strategy (the 'mosaic' strategy) for modeling the firstorder subgrid variability within a GCM grid square. The mosaic strategy consists of dividing the heterogeneous grid square into relatively homogeneous subgrid 'tiles', with each tile representing a different land surface type (deciduous trees, grassland, bare soil, etc.). The tiles interact with the atmosphere independently and maintain their own state variables (temperatures, water reservoir contents). Fluxes of evaporation and sensible heat are weighted by tile area for insertion into the overlying grid box. A similar procedure was devised by Avissar and Pielke3 for use in mesoscale models. Within each tile we apply an energy balance calculation similar to that in SiB, with transpiration resistances that vary with photosynthetically active radiation and environmental stress. In our model, however, each tile holds only one surface type. By ignoring the interaction between neighboring vegetation types through near-surface air, we significantly increase the model's speed. The rationale for this simplification was presented in a separate analysis 15 that shows that even the interaction between the trees and grass of a savanna can usually be ignored without significantly affecting the net surface energy balance. Our LSM also differs from SiB in a number of other ways, e.g. by assuming a single temperature for the ground surface and canopy. The method used to couple the land surface to the atmosphere includes an implicit updating of the surface state and aerodynamic resistance without iteration. The reader is referred to Koster and Suarez 14 for full details and for a discussion of the LSM's behavior in offline tests. A few changes in the structure of the model do require comment. Our control simulation did not include the effect of vapor pressure deficit stress on the effective canopy resistance to transpiration; the effects of this simplification are described in one of the sensitivity tests below. Also, the model features revised formulations for runoff and groundwater diffusion (Koster & Suarez, tech. memo. in preparation).

4 THE CONTROL SIMULATION

4.1 Simulation description Values are assigned to a given tile's surface parameters (some of which vary seasonally, e.g. leaf area index) based solely on the tile's assigned surface type. We defined 11 fundamental surface types for this study: six for vegetated land areas (broadleaf evergreen trees, broadleaf deciduous trees, needleleaf trees, groundcover, broadleaf shrubs, and dwarf trees); three for unvegetated

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....

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O

> I,LI ,_l 2 ,<

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'

g

h latiude

land

\ midlatitude land 00 . . . . .

51 . . . .

1tO . . . . 151 . . . . SIMULATION YEAR

210

Fig. 1. Annual evaporation rate (mm/day) as a function of simulation year for several different regions: tropical land (10S-10N), subtropical land (30S-10S, 10N-30N), midlatitude land (50S-30S, 30N-50N), and high latitude land (70S-50S, 50N-70N). The first 22years of the 27-year simulation are shown. land areas (bare soil, desert soil, and permanent land ice); and two for ocean areas (open ocean and sea ice). The land distributions were derived from a 1° x 1o data set of SiB vegetation biomes provided by Sellers (personal communication), which itself was derived from the data sets of KuchJer16 and Matthews.2°'21 About 3000 separate tiles are distributed among the 1200 land grid squares. The number of tiles in a square depends on the regional surface heterogeneity; some squares contain only one tile, whereas others contain as many as eight. Sea surface temperatures for the simulation are prescribed from climatological means. The solar radiation forcing varies both diurnally and seasonally. The model was initialized with atmospheric conditions and surface temperatures produced in an earlier extended simulation. Soil moisture reservoirs, however, were artificially saturated in order to examine the transient behavior of the climate as it 'dried down' to an equilibrium state. The transient response is not an emphasis of this paper; we merely note that several years were required to reach equilibrium in most regions and that in very high latitudes, the frozen state of the ground maintained some high soil moisture. Figure 1 shows the surface evaporation rate, averaged over all the tiles in several different regions, as a function of time for the first 22year of the simulation. High latitude land required the longest time to adjust. High latitude (50S-70S; 50N-70N) evaporation slightly exceeds midlatitude (30S-50S; 30N-50N) evaporation due to the northward extent of the Asian desert in the model. The simulation continued for a total of 27 years. We classify the first 7 years of simulation as the 'transient regime', leaving a 20-year period during which the climate is essentially at equilibrium. Unless otherwise specified, the fields shown below for the 'control simulation' are averages over this 20-year period.

The components of a 'SVAT" scheme and their effects on a GCM's hydrological cycle EVAPORATION

500

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SUB-SAHARAN

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20 SQUARE

DESERT SOIL

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DESERT SOIL

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BROADLEAF SHRUBS

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BROADLEAF DECIDUOUSTREES 2901 0

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Fig. 2. Mean July diurnal cycles for the grassland (areal coverage = 81%), deciduous trees (8%), broadleaf shrubs (7%), and desert soil (4%) tiles in a sub-Saharan grid square centered at 20E, 14N. (a) Evaporation, (b) temperature. 4.2 Model climatology 4.2.1 Diurnal cycles Half-hourly data from 7 Julys of the control simulation were averaged together to produce the mean diurnal cycles shown in Figs 2 and 3. Figure 2 shows the variation of temperature and evaporation within a subSaharan grid square that contains four tiles: (a) grass (81%); (b) broadleaf deciduous trees (8%); (c) broadleaf shrubs (7%); and (d) desert soil (4%). Note that although the tiles are forced by precisely the same radiation, precipitation, and near-surface atmospheric quantities, their different surface characteristics result in markedly different behaviors. The trees, for example,

evaporate twice as much water as the shrubs. The monitoring of this first order subgrid variability may someday find use in the parametrization of certain subgrid (e.g. mesoscale) processes. Evaporation occurs from bare soil, from the canopy interception reservoir, from snow, and through the vegetation stomata (transpiration). The breakdown of the sub-Saharan grid square's total evaporation into these components, averaged over the four tiles, is shown in Fig. 3. Transpiration is the most important, but evaporation from the interception reservoir is also significant. Of course, the monthly-averaged diurnal cycles hide some of the complex structure seen on individual days. Due to interception loss, total

R. D. Koster, M. J. Suarez

66

EVAPORATION COMPONENTS, SUB-SAHARAN AFRICA SQUARE 5O0

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"E TION

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100

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10

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: ~ =~t_-~-;

15

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Fig. 3. Mean July diurnal cycles of evaporation components, averaged over all of the tiles in the sub-Saharan grid square. a. E V A P O R A T I O N 1000

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Fig. 4. (a) Evaporation (accumulated over 90 min) from the grassland tile of the sub-Saharan grid square as a function of time in a specific 4-day period; (b) precipitation in the square (accumulated over 90 min) during the same 4-day period.

The components of a 'SPAT' scheme and their effects on a GCM's hydrological cycle

67

a. Total Evaporation: 20-year Control Run

90N

8.0

60N

4.0 30N 2.0 EQ 1.0 30S 0.5

60S

90S 180

I

J

I

I

1

120W

60W

0

60E

120E

0.0 180

b. Total Evaporation: Walkerand Mintz Data

90N

8.0

60N

4.0

30N

2.0 EQ 1.0 30S

,,

,p,. 0.5

60S

"----z> J

90S 180

0.0 120W

60W

0

60E

120E

180

Fig. 5. Total annual evaporation rates, in ram/day. (a) Simulated, averaged over 20 year; (b) 'observed', as derived by Mintz and Walker (Greg Walker, personal communication). evaporation tends to increase dramatically immediately following a precipitation event, as seen in Fig. 4.

4.2.2 Global evaporation and precipitation fields Figure 5(a) shows the global distribution of simulated annual evaporation rate, in mm/day. For comparison, Fig. 5(b) shows a distribution derived by Mintz and Walker (Walker, personal communication; aspects of the approach are discussed by Liston et al.17). We will refer to the Mintz/Walker distribution as the 'observed' distribution. It was constructed similarly to that of Mintz and Walker, 24 i.e. using a Thornthwaite 3° approach combined with energy and water balance considerations, but it also incorporates global measurements of NDVI (normalized difference vegetation index). The Mintz/Walker data do not consist of direct

evaporation measurements and should not be interpreted as unequivocally correct. These data have, however, been validated against historical runoff records in some large basins. In Figs 6 and 7 we compare the simulated and 'observed' distributions of interception loss and transpiration. These observations are part of the same data set. The large-scale structures of the simulated and observed total evaporation fields are very similar. The GCM, for example, successfully reproduces the subtropical deserts despite the initially saturated soil. Two major discrepancies between the simulated and observed distributions involve the magnitudes of tropical and midlatitude evaporation fluxes. The GCM's apparently excessive tropical evaporation rates probably reflect an excessive contribution from the interception reservoir

68

R. D. Koster, M. J. Suarez

(Fig. 6); independent, detailed measurements in the Amazon 29 suggest that the Mintz/Walker value there, is of the correct order. Averaging the simulated annual evaporation rates over all land surfaces between 70 ° S and 70 ° N, we find that 35% is evaporation of intercepted water, 45% is transpiration, 13 % is evaporation directly from the soil, and 6% is evaporation from snow. The Mintz/Walker data, in contrast, suggest a breakdown of 20% evaporated interception water, 47% transpiration, and 33% bare soil evaporation. Simulated annual precipitation rates are shown in Fig. 8(a), and observed annual rates, consisting of station measurements from the NCAR World Monthly Surface Station Climatology interpolated onto a 2 ° × 2"5 ° grid 27 are shown in Fig. 8(b). The structures

of the two distributions are very similar, particularly in the positions of the tropical rainbelts and subtropical deserts. (At the beginning of the simulation, precipitation rates over the deserts were much higher, in response to the high evaporation rates from the initially saturated soil. The precipitation and evaporation fields co-evolved into the more reasonable fields shown.) As with any GCM, though, some observed features are not reproduced. For example, the GCM underestimates precipitation in the southern part of South America and in Australia, and it overestimates precipitation in northern Asia and Alaska. This qualitative description of how the modeled and observed fields compare is supplemented by a quantitative comparison in Table 1, which lists simulated precipitation, evaporation, and moisture convergence

a. Interception Loss: From20 years of Control Run

90N

8.0

60N

4.0

2.0

3°Nl EQ.

1.0 30S 0.5

60S

90SI 180

I

I

I

I

I

120W

60W

0

60E

120E

0.0 180

b. Interception Loss: Walker and Mintz Data

90N

8.0

60N

4.0

30N

2.0 EQ

1.0 30S

0.5

60S ...------.--....W'-J

90S 180

0.0 120W

60W

0

60E

120E

180

Fig. 6. Total annual evaporation from the interception reservoir, in mm/day. (a) Simulated, averaged over 20 year of the control simulation; (b) 'observed', as derived by Mintz and Walker (Walker, personal communication).

The components of a 'SVAT' scheme and their effects on a GCM's hydrological cycle

69

a. Transpiration: From 20 Years of Control Run

90N

8.0

60N

4.0 30N 2.0 EQ 1.0 30S 0.5 60S

1-

90S 180

I

I

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120W

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0

60E

120E

0.0 180

b. Transpiration: Walker and Mintz Data

9ON

8.0

60N

4.0

30N 2.0 EQ 1.0 30S 0.5

60S

0.0

90S 180

120W

60W

0

60E

120E

180

Fig. 7. Total annual transpiration, in mm/day. (a) Simulated, averaged over 20 year of the control simulation; (b) 'observed', as derived by Mintz and Walker (Walker, personal communication). annual rates, averaged over latitudinal bands, alongside corresponding estimates from the literature. Examined in this form, the model again appears to overestimate evaporation and precipitation in the tropics and high latitudes and underestimate them in midlatitudes. Note, however, the range in the different observational estimates. The Baumgartner and Reichel 5 evaporation estimates for the tropics and subtropics, for example, are much closer to the simulated values than are the Mintz and Walker (personal communication) estimates. Simulated and observed moisture convergence (i.e. P - E) are relatively close, suggesting that errors in the simulated precipitation and evaporation rates are tied together. To summarize, the model reproduces the main features of the global precipitation and evaporation

fields, but it has some trouble generating the proper magnitudes in certain regions, and it probably has biases in the partitioning of evaporation into transpiration and interception loss. We consider the model's accuracy to be sufficient for the sensitivity experiments described below.

5 SENSITIVITY EXPERIMENTS

5.1 Measurementof sensitivity The sensitivity simulations, designed to evaluate the importance of the SVAT/bucket differences outlined in Section 2, were initialized with conditions generated late in the control simulation. Because soil moisture requires time to adjust to any imposed formulation change, we

70

R. D. Koster, M. J. Suarez a. Total Precipitation: 20-year Control Run

90N

16.0

60N

8.0

30N

4.0

EQ

2.0

30S

1.0

60S

0.5

90SI 180

I

I

I

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120W

60W

0

60E

120E

0.0 180

b. Total Precipitation: Observations

90N

16.0

60N

8.0

30N

4.0

EQ

2.0

30S

1.0

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0.5

90S -~au.u 180

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120W

v.v

I

60W

r v.v

0

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60E

u,~

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120E

v.v

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0.0

180

Fig. 8. Total annual precipitation, in mm/day. (a) Simulated, averaged over 20 year; (b) NCAR observations over 11 year, as gridded by Schubert et al. 27 monitored each simulation as it approached climatic equilibrium and then extended the simulation at least one year into the essentially equilibrated climate. Measures of model sensitivity are provided in Table 2. The first line lists the mean annual precipitation and evaporation rates from the 20-year control simulation, averaged over each of four large-scale regions: tropical land (10S-10N), subtropical land (30S-10S and 10N30N), midlatitude land (50S-30S and 30N-50N) and high latitude land (70S-50S and 50N-70N). We will compare these means to those generated in the sensitivity simulations. The standard deviations of yearly precipitation and evaporation, as determined from the 20 yearly values in the control run, are provided next. Note that each value represents the standard deviation of an areally-averaged

flux, not the average standard deviation of a grid square flux. Analysis of the NCAR precipitation data suggests that the listed standard deviations are too small in the tropics and subtropics (probably due to the model's prescription of climatological SSTs and to the problems associated with aggregating point observations) and are about right in midlatitudes and high latitudes. The remaining lines of Table 2 show the changes generated over each region in each sensitivity simulation. To produce each number, we determined the precipitation or evaporation generated in a single year of the sensitivity simulation and subtracted from it the corresponding 20-year control run mean. (We examine the final year of each sensitivity simulation, after first confirming that it is typical for the simulation's equilibrated climate.) A change is considered significant

The components of a 'SVAT' scheme and their effects on a GCM's hydrological cycle

71

Table 1. Precipitation, evaporation, and moisture convergence over land from published climatologies and from the 20-year control simulation

Tropics 10S-10N

Subtropics 10-30

Midlatitudes 30-50

High latitudes 50-70

Precipitation (mm/year) BR 1836 Jaegar 1697 NCAR 1652 Model 1996

773 756 754 842

562 629 553 486

505 457 472 679

Evaporation (mm/year) BR 1123 WM 840 Model 1377

557 404 559

405 368 331

251 229 383

Moisture convergence (mm/year) BR 713 WM 857 Model 618

216 352 283

157 261 154

254 228 296

BR stands for Baumgartner and Reichel, 5 WM stands for the Mintz and Walker data, and NCAR stands for Schubert e t al. 27 Precipitation data is also taken from Jaeger. 12

if it lies outside the range of interannual variability, as defined by the listed standard deviations. Changes exceeding two standard deviations are identified in the table with an asterisk.

achieved, the canopy temperature may remain in its neighborhood enough to minimize the stress. We completely disable the temperature stress in Simulation la; i.e. g2(T) in eqn (6) is set to 1. A global difference map (not shown) indicates that the resulting precipitation and evaporation changes are spotty and are generally statistically insignificant. The small role of temperature stress in this model is further demonstrated in Table 2. Disabling the temperature stress produces no significant changes in precipitation or evaporation over any of the latitudinal bands. Simulation l b addresses the vapor pressure deficit stress, which is active whenever the ambient canopy air is subsaturated. For high relative humidities, the stress is inconsequential; the stress increases, though, with vapor pressure deficit, until transpiration is effectively prevented (at a VPD of about 30-40mb, depending on vegetation type).

5.2 Environmental stresses

The first sensitivity simulation examines the role of temperature stress. In the control simulation, this stress increases transpiration resistance whenever the canopy temperature deviates from the optimal temperature, and it shuts down transpiration at temperatures below or above prescribed critical values. (The critical values for broadleaf deciduous trees are, for example, 273 K and 318 K, and those for grassland are 283 K and 328 K; for all types they are at least 4 0 K apart.) In principle, although the optimal temperature is never precisely

Table 2. Typical 1-year P and E for sensitivity experiments minus the corresponding control run 20-year means, in ram/year. Standard deviations of annual P and E from the control run are provided for comparison. Differences exceeding two standard deviations are marked with an asterisk

Tropical land 10S-10N P

Subtropical land 10-30S; 10-30N E

Midlatitude land 30-50S; 30-50N

P

E

P

E

High latitude land 50-70S; 50-70N P

E

Control, 20 year mean (standard deviation)

1996 (29)

1377 (18)

842 (16)

559 (9)

486 (20)

331 (14)

679 (10)

383 (5)

Sensitivity test la. Temperature stress disabled 1b. Vapor pressure deficit stress enabled 2. Interception reservoir removed 3a. Fixed beta run 3b. Linear 'bucket' model

Ap -54

/xE -12

/xp -3

~E 2

~e 21

/xE 9

~e 10

~E 7

- 110*

-130"

-87*

-75*

-85*

-50*

10

-4

-288*

-403*

-180"

-173"

-122"

-105"

-45*

-64*

-268* -203*

-1 -129"

-93* -28

4 -1

-24 -47*

3 -7

-16 40*

-28* 20*

72

R. D. Koster, M. J. Suarez a. RELATIVE HUMIDITY DIFFERENCES: SIMULATION IB - CONTROL

90N

~

0.4

~

0.,3

60N

: .~

0.2

~

,.'3ON

0.1 0.0: 0 .050

EO

~

o

0.0 --0. 0.050

30S

-0.1 -0.2

60S

-0.3 90S L 180

120W

L 6OW

J 0

L 6DE

L 120E

I 180

b. EVAPORATION DIFFERENCES (mm/doy):SIMU~TlON 18 - CONTROL

90N

3.0

2.0

<

60N

-0.4

1.0 30N

:d

0.250 0.0

EQ

-0.250 30S -1.0 60S

-2.0 90S 180

I 120W

n 6OW

I 0

I 60E

I 120E

-3.0 180

Fig. 9. Difference maps (experiment minus control) showing the changes produced in a single year of Simulation lb relative to the 20-year control run. (a) Relative humidity, (b) evaporation, in mm/day. Recall that we disabled this stress in the control simulation; we had set the term g3 (VPD) in eqn (6) to 1. The difficulty in including this stress is that it can instigate a strong positive feedback process in a coupled land/ atmosphere model - - when the stress acts to reduce evaporation, the near-surface humidity is then reduced, leading to a higher stress. This positive feedback is not bad in itself, but if the GCM systematically underestimates low level humidities, as ours does (even over the ocean, where the land surface has little effect) the feedback can reduce transpiration rates catastrophically. We re-activated the vapor pressure deficit stress in Simulation lb. The regionally-averaged precipitation and evaporation rates are reduced by several standard deviations everywhere but in high latitudes (Table 2). The importance of the vapor pressure deficit stress

clearly overshadows that of the temperature stress in our modeling environment. This is certainly due in part to the associated positive feedback, found to be operative in Simulation lb; strong reductions in near-surface relative humidity over continents in the simulation are spatially correlated with reductions in evaporation rate (Fig. 9). The strength of this feedback emphasizes the fact that the successful use of a vapor pressure deficit stress term is contingent on the production of realistic surface humidities. Soil moisture availability limits evaporation in both bucket and SVAT models, and thus a simple disabling of soil moisture stress in a sensitivity simulation would not be informative. Simulation 3b, described below, will demonstrate the effect of replacing a SVAT model's evaporation formulation with that of a bucket model.

The components of a 'SVAT' scheme and their effects on a G C M ~ hydrological cycle 90N

o. PRECIPI~TION: EXPERIMENT - CONTROL

--

73

INTERCEPTIONRESERVOIR REMOVED

5.0 4.0

60N

3.0

30N

EQ

4~ 305

"

2.0 1.0 0.5 0.0

-o.5 -1.0

-2.0 -3.0

60S

-4.0 90S

180

-5.0

120W

60W

0

b. EVAPORATION: EXPERIMENT - CONTROL

90N ,

60E --

120E

180

INTERCEPTIONRESERVOIR REMOVED

5.0 4.0

IE 60N

3.0 2.0 1.0

30N

0.5

EQ

0.0 -0.5 -1.0

30S

-2.0 -3.0

60S

-4.0 90S 180

, 120W

L 60W

i 0

, 60E

i 120E

-5.0 180

Fig. 10. Differencemaps (experiment minus control) showing the average changes produced in 10 (equilibrated) years of Simulation 2 relative to the 20-year control run. (a) Precipitation, in mm/day, (b) evaporation, in mm/day.

5.3 The interception reservoir The interception reservoir in the control run has a capacity equal to the leaf area index multiplied by 0.1 mm. For example, broadleaf evergreen trees and foliated broadleaf deciduous trees can hold about 0"5mm of water, and needleleaf trees can hold about 1 mm. Given the magnitude of interception loss rates both in the simulation results and in the observational estimates, the removal of the interception reservoir might be expected to affect significantly the model's total evaporation. To test this, we disabled the interception reservoir in Simulation 2. Any precipitation falling onto the canopy was either added to the soil or removed from the system as surface runoff. Table 2 shows the resulting strong,

statistically significant reductions in evaporation and precipitation in each latitudinal band. The interception reservoir has a much larger effect on surface hydrological fluxes than either of the tested environmental stresses. Furthermore, the precipitation and evaporation reductions are spatially correlated (Fig. 10). Local precipitation in the GCM responded strongly to the imposed change in the surface water vapor source, which in turn probably resulted in a further reduction of local evaporation. Recall that in the tropics, the interception reservoir's contribution to the total evaporation appears too large when compared to the estimates of Mintz and Walker (Fig. 7); the sensitivity may therefore be exaggerated there. The sensitivity is also strong, though, in midlatitudes, where the model's agreement with the

74

R. D. Koster, M. J. Suarez

observational estimates is much better. (The simulated and 'observed' estimates of midlatitude interception loss are 84mm/year and 73mm/year, respectively.) The presence of an interception reservoir clearly increases the evaporation rates produced by a SVAT model. 5.4 Model framework and time scale of variability

The last set of sensitivity simulations examines the SVAT model's framework for calculating evaporation and the SVAT model's diurnally-varying control over the surface energy balance. Simulation 3a is a highly controlled experiment in which a bucket-type land surface formulation is forced to mimic exactly the time-averaged behavior of the SVAT model. Simulation 3b employs an interactive bucket model typical of those used in GCMs. Both simulations were extended to produce a full 10 years of equilibrated climate. 5.4.1 'Fixing' the land surface to reproduce timeaveraged S V A T behavior

In Simulation 3a, we compute evaporation as E :/3nxedEp,

(7)

in analogy with eqn (4). In contrast to/TSVAt, however, /3fixedconsists of preassigned values that allow eqn (7) to mimic the model framework implied by eqns (4) and (5) to the fullest extent possible. Simulation 3a is designed to demonstrate how the two other important characteristics of a SVAT model's 13 function, namely its functional dependence on atmospheric properties and its strong diurnal and synoptic-scale variability (see Section 2) help define the hydrology generated by a SVAT model. The definition of /3nx~d below ensures that the land surface's timeaveraged control over the evaporation rate will be precisely the same in Simulation 3a as it is in the control run. Since the/3fixed values are prescribed, however, and since they vary only seasonally, they have no functional dependence on r a and exhibit no short-term variability. The definition of /3fixed also precludes interannual variability. A full seasonal cycle of pre-assigned /3fixed values is determined for each tile based on diagnostics stored during several years of the control simulation. At every time step of the control simulation, and at every tile, we accumulated into special diagnostics both the evaporation rate E and the potential evaporation rate Ep: E = pees(Te) - eref

(8)

/3rtxed for each month and each tile from:

/3fixed,month Up

where the overbars denote climatological means for each month. Interpolating /3fixed between the monthly averages produces instantaneous values. (This procedure is analogous to that of Delworth & Manabe, 6 who fixed the GFDL GCM's bucket model soil moisture amounts to climatological mean values in order to examine the land surface's control over atmospheric variability.) Table 2 indicates that except in high latitudes, Simulation 3a produces negligible changes in evaporation. (The high latitude change necessarily reflects a reduction in potential evaporation.) The model framework implied by 13svat thus has little effect on the timeaveraged energy balance of the land surface. (See also Table 3.) The interesting result is that despite the negligible changes in evaporation, tropical and subtropical precipitation rates are significantly reduced. The difference maps for precipitation and evaporation in Fig. 11 were generated by subtracting the 10-year mean fields for Simulation 3a from the 20-year mean fields for the control simulation. Changes in evaporation are small everywhere. Tropical precipitation, however, is clearly shifted off of the continents and onto the neighboring oceans, even to the extent that an isolated reduction over the tip of India is seen. These patterns are statistically significant at the 95% confidence level. This result has important implications. The GCM's large scale convergence patterns are somehow influenced by the changes imposed at the surface. Since differences between the long-term energy balances of the control simulation and Simulation 3a are small, the more logical candidate to explain the precipitation differences is short-term (diurnal and synoptic) variability in the surface energy balance. In other words, the large-scale circulation and hydrological cycle appear to be sensitive to the time distribution of surface fluxes, not just to the time-mean energy balance. Further analysis shows that changes in moist convective precipitation account for the bulk of the changes in total precipitation, as might be expected in the tropics. The diurnal cycles in the control simulation - - for some reason that we do not fully understand - - are more Table 3. Spatially-averaged annual energy balance components for tropical and subtropical land (30S-30N)

Ps ra+rc-eff and Ep : pe es (Tc) - eref Ps ra

(9)

where rc_efr is the effective canopy resistance, accounting for all evaporation components. We then computed

(10)

Control, 20-year mean Simulation 3a, 10-year mean

Abs SW

E

H

Net LW

210.54

65.60

56.41

88.52

206.88

65.59

54.85

86.44

Abs SW = absorbed shortwave radiation (W), E = evaporation (W), H = sensible heat flux (W), and Net LW = net longwave radiation (W).

The components of a 'SVAT' scheme and their effects on a GCM~ hydrological cyc& 90N

o. PRECIPITATION:EXPERIMENT - CONTROL

--

75 5.0

FIXEDB E ~ RUN

~ 60N

4.0

~

3.0 2.0

30N

1.0 0.5

EQ

0.0 -0.5

30S

-1.0 -2.0

-3.0

60S

-4.0 90S 180

-5.0 120W

90N

60W

0

60E

b. EVAPORATION: EXPERIMENT - CONTROL

--

120E

180

FIXED BETA RUN

5.0 4.0

60N ~

~

'

3.0 2.0 1.0

"

0.5

t.

EQ

0.0 -0.5

30S

-1.0

~

-2.0 -3.0

60S

-4.0 90S 180

I

I

I

t

I

120W

60W

0

60E

120E

-5.0 180

Fig. 11. Difference maps (experiment minus control) showing the average changes produced in the 10 (equilibrated) years of Simulation 3a relative to the 20-year control run. (a) Precipitation, in mm/day, (b) evaporation, in mm/day. amenable to convective activity. Perhaps a temporal correlation between land surface control over the surface energy balance and the atmospheric conditions conducive to convection is critical. Results from a supplemental simulation (not shown here), in which we employed eqn (7) with #fixed values derived from Simulation 2, are similar, demonstrating that the SVAT model's interception reservoir is not the source of the short-term variabifity affecting tropical precipitation. 5.4.2 Use of a typical bucket model In a bucket model, flbucket varies only with soil moisture, which itself varies slowly in time. Thus, the diurnal (and to some extent, the synoptic-scale) variations in #SVAT that were removed in Simulation 3a would also be absent in a simulation using an interactive bucket

model. We might therefore expect a bucket model simulation to produce shifts in large-scale convergence off the continents similar to those in Fig. 11. This is tested in Simulation 3b. In Simulation 3b, the SVAT evaporation formulation is replaced by that of a typical bucket model, this time with soil moisture contents that vary interactively with GCM climate. Evaporation is computed using eqns (1) and (2), with /~bucket set equal to the volumetricallyaveraged degree of saturation in the land surface's top two soil layers. (The two layers have a combined water holding capacity of about 0.6 m below trees, 0.2 m below grass and shrubs, and 0"008 m for bare soil types.) This particular #bucket formulation is similar to that used in the NASA/GISS Model II GCM, l° though here we are using soil depths consistent with the control simulation.

76

R. D. Koster, M. J. Suarez

90N

o. PRECIPITATION: EXPERIMENT - CONTROL

--

LINEAR BUCKET MODEL

5.0

60N

3.0 2.0

30N l

°L E

1.0 0.5

Q

0.0 -0.5

6os !

~

-3o -4.0

90S 180

-5.0 120W

90N

60W

0

b. EVAPORATION: EXPERIMENT - CONTROL

60E --

120E

180

LINEAR BUCKET MODEL

5.0

,,o 60N

3.0 2.0

~ .

EQ

; ,

0.0

,

30S

-1.0 -2.0

60S

-3.0 -4.0

90S 180

I 120W

i 60W

i 0

i 60E

a 120E

-5.0 180

Fig. 12. Difference maps (experiment minus control) showing the average changes produced in the 10 (equilibrated) years of Simulation 3b relative to the 20-year control run. (a) Precipitation, in mm/day, (b) evaporation, in mm/day.

The interception reservoir is disabled, as are the environmental stress terms. A comparison of this /3bucket with flSVAT shows that the bucket model would have to maintain significantly lower soil moisture contents to reproduce SVAT model evaporation rates. Soil moisture contents in Simulation 3b were indeed reduced considerably throughout the globe. These reductions are very likely unrealistic; the tendency for a bucket model to underestimate soil moisture is well documented.22 The associated evaporation changes indicated in Table 2 for the subtropics, midlatitudes and high latitudes are small. These small changes are probably coincidental, considering that the bucket model has no interception reservoir, and that this reservoir is known from Simulation 2 to have

a significant effect on evaporation rates. A different formulation would have resulted in larger evaporation changes. 26 Significant changes were, however, produced in the tropics. Figure 12 presents difference maps calculated from the 10 year of Simulation 3b and the 20 year of the control, in analogy to those in Fig. 11. Changes in precipitation greatly exceed those of evaporation in the tropics, indicating a modification of the large-scale circulation. As in Simulation 3a, the formulation change imposed in Simulation 3b forced some moisture convergence off of the continents and onto the oceans. The large-scale circulation in the GCM again appears to be sensitive to the time distribution of the surface energy balance. flbucket

The components of a ' S V A T ' scheme and their effects on a GCM's hydrological cycle

6 SUMMARY This paper explores the fundamental differences between two types of land surface models currently used with GCMs: bucket models, which limit evaporation through a simple soil moisture function, and SVAT models, which explicitly model the effects of vegetation on the surface energy budget. Typical SVAT models differ from bucket models by explicitly accounting for additional environmental stresses, by accounting for an interception reservoir, by allowing the atmosphere to modulate land surface control, and by incorporating diurnal and synoptic scale variability into this control. We examine the climatological impacts of these differences through a G C M sensitivity study. The G C M environment is necessary; an offline environment would be unable, for example, to reproduce the significant changes in the large-scale circulation seen in Simulations 3a and 3b. The 20-year control simulation captures the main features of observed precipitation and evaporation fields. As with any GCM, however, the magnitudes of these fluxes appear incorrect in certain regions. The sensitivity simulations reveal that our SVAT model's temperature stress term has a negligible effect on model climatology. The vapor pressure deficit stress, in contrast, is found to be important, though this is at least partly due to a magnification of model errors caused by a strong positive feedback. This problem will affect any G C M having a bias in low-level humidity. Removal of the interception reservoir significantly reduces evaporation and precipitation rates over the Earth's continents. Perhaps the most interesting result from the sensitivity analysis is that changing the diurnal and synoptic-scale variability of the energy balance (while retaining the time-mean) has a significant effect on the large-scale convergence of moisture over the tropical continents. The low variability in land surface control associated with a bucket model tends to force some tropical moisture convergence off of the continents and onto the neighboring oceans. Our G C M is possibly oversensitive to land surface processes; only additional studies with different GCMs can determine the validity of this result. For our GCM, in any case, the convergence changes in Simulations 3a and 3b represent a behavior of a SVAT model that a bucket model cannot reproduce.

ACKNOWLEDGMENTS We thank Catherine Jones, Andrew Loughe, and Gary Krueger for their help in processing the simulated and observed data, and Greg Walker and Siegfried Schubert for providing the observational data. The comments of two anonymous reviewers proved very helpful. This work was supported by the Climate and Hydrological

77

Systems Modeling and Data Analysis Program, Mission to Planet Earth, NASA Headquarters.

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19. 20.

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R. D. Koster, M. J. Suarez on Planetary Boundary Layer Parametrization, Reading, 1982, pp. 59-80. Manabe, S. Climate and the ocean circulation, I. The atmospheric circulation and the hydrology of the Earth's surface. Mon. Weather Rev., 97 (1969) 739-74. Matthews, E. Prescription of land-surface boundary conditions in GISS GCM II: a simple method based on high resolution vegetation data bases, NASA Tech. Memo. 86096, Goddard Flight Space Centre, Institute for Space Studies, New York, NY, USA, 1984. Matthews, E. Atlas of archived vegetation, land-use and seasonal albedo data sets. NASA Tech. Memo. 86199, Goddard Flight Space Centre, Institute for Space Studies, New York, NY, USA, 1985. Milly, P. C. D. Potential evaporation and soil moisture in General Circulation Models. J. Climate, 5 (1992) 209-26. Mintz, Y. The sensitivity of numerically simulated climates to land-surface boundary conditions. In The Global Climate, ed. J. Houghton, Cambridge University Press, Cambridge, UK, 1984, pp. 79-105. Mintz, Y. & Walker, G. K. Global fields of soil moisture and land-surface evapotranspiration derived from observed precipitation and surface air temperature. J. Appl. Meteorol., 32 (1993) 1305-34.

25. Moorthi, S. & Suarez, M. Relaxed Arakawa-Schubert: a parametrization of moist convection for general circulation models. Mon. Weather Rev., 120 (1992) 9781002. 26. Sato, N., Sellers, P., Randall, D., Schneider, E., Shukla, J., Kinter, J., Hou, Y.-T. & Albertazzi, E. Effects of implementing the simple biosphere model in a general circulation model. J. Atmos. Sci., 46 (1989) 2757-82. 27. Schubert, S., Wu, C.-Y., Zero, J., Schemm, J.-K., Park, C.-K. & Suarez, M. Monthly means of selected climate variables for 1985-1989, NASA Tech. Memo. 104565, Goddard Space Flight Centre, Greenbelt, MD, USA, 1992. 28. Sellers, P. J., Mintz, Y., Sud, Y. C. & Dalcher, A. A simple biosphere model (SiB) for use within General Circulation Models. J. Atmos. Sci., 43 (1986) 505-31. 29. Sellers, P. J., Shuttleworth, W. J., Dorman, J., Dalcher, A. & Roberts, J. Calibrating the simple biosphere model for Amazonian tropical forest using field and remote sensing data, I. Average calibration with field data. J. Appl. Meteorol., 28 (1989) 727-59. 30. Thornthwaite, C. W. An approach toward a rational classification of climate. Geogr. Rev., 38 (1948) 55-94.