It has become increasingly clear that different communities, particularly meteorologists, hydrologists and ecologists have different perceptions of the relative importance, the time constants and even the 'laws' governing the processes occurring at the land surface. These different perceptions were being encoded into numerical schemes, all of which captured some of the attributes of the soil and vegetation, and the water, energy and momentum exchanges but none of which were exactly similar. A common element, and one of critical importance, is soil moisture. This recognition prompted the decision to hold the 'Soil Moisture Simulation Workshop' in November 1994. The workshop was co-sponsored by the WCRP/GEWEX/ Project for Intercomparison of Landsurface Parameterization Schemes (PILPS) and the IGBP GAIM project, the Regional Interactions of Climate and Ecosystems, (RICE).
The Regional Interactions of Climate and Ecosystems project (RICE) was an early GAIM activity and was completed in 1997. The purpose of the RICE activity was to improve our understanding regarding the relationships between climate and terrestrial ecosystems by concentrating upon changes (natural and human-imposed) to terrestrial ecosystems and regional climate. The goals of RICE were to: (1) ascertain the regional effects of vegetation and soils on climates simulated by global models, (2) establish the sensitivity of vegetation and ecological schemes to regional climates derived from global models, and (3) facilitate the integration of new vegetation/ecological schemes into global models. Within this framework, RICE focused on three main components: (1) regional vegetation/climate interactions, (2) effects of soil moisture formulations in land surface and ecosystem models, and (3) coupling vegetation and climate models
One of the expected consequences of climate change from increasing greenhouse gases over the next century will be changes in distribution of biomes (and rates of carbon pool cycling). These changes will, in turn, modify the climate changes. Present model simulations of climate change from greenhouse warming assume prescribed distributions of biomes and non-interactive scenarios for changing atmospheric carbon dioxide. Two-way coupling between climate change on the one hand and the effects of biome distribution and carbon fluxes, on the other hand, must eventually be addressed. A prerequisite to carrying out such two-way coupling is first the validation of the individual subcomponents. There is presently developing a considerable body of information regarding the effects of vegetation changes on regional climates. Thus, an important activity of this project has been to synthesize this present body of knowledge, identify gaps and put it into a global framework. Likewise, as a second activity, we explored how to use GCM supplied regional climate excursions to drive simulations of terrestrial ecosystems or changes in biome distributions.
RICE addressed uncertainties in the prediction of global biome distributions driven by simulated climate. Although GCMs and vegetation models have been widely used in the assessment of climatic impacts on ecological systems, there is common agreement that there remain large uncertainties in the GCM predictions. Specifically, climate predictions are often inaccurate at the regional level. Hence, the use of climate models in the estimation of the impact of climatic change on global and regional ecosystems remains highly problematic. In order to evaluate the reliability of climate models with respect to ecosystem modeling, two global equilibrium vegetation models, BIOME1 (Prentice et al. 1992) and a version of the Holdridge scheme (Holdridge, 1967) were used in conjunction with several climate model experiments from the Model Evaluation Consortium for Climate Evaluation (MECCA) project (Ciret and Henderson-Sellers 1997). The objective was to identify which simulated ecosystems were sensitive to the biases of the climate simulations.
The GCMs and global vegetation models were linked in a one way mode (i.e. no feedback from the vegetation model to the GCM is allowed). Vegetation distributions predicted by the two biogeography models using simulated climates were compared against the plant distributions predicted using observed climate.
The results indicate that the overall performance of coarse resolution climate models with respect to vegetation prediction was rather poor. The discrepancies between vegetation distributions computed from observed and simulated climatologies represented more than 50% of land area. The differences in vegetation predictions were generally due to the overestimation of the soil moisture index and precipitation, to the overestimation of growing degree days and to the underestimation of the annual minimum temperatures. Certain biomes appeared to be particularly sensitive to the biases in the simulated climates (e.g. grassland, xerophytic woods). Overall, the discrepancies in vegetation predictions were predominantly due to biases in the simulation of the hydrology (i.e. soil moisture index and total annual precipitation) and these results indicate that the uncertainties in the simulation of the soil moisture availability should be carefully evaluated before a high degree of confidence can be vested in the prediction of vegetation, and moreover in the prediction of vegetation change.
The sensitivity of vegetation models to changes in the spatial horizontal resolution of GCM was also investigated. The climate integrations were derived from a set of experiments undertaken by Williamson et al. (1995) in which the NCAR Community Climate Model, CCM2, was run with increasing spatial resolutions. The global scale vegetation prediction was improved with increasing the spatial resolution in the climate simulations. These results confirm those from Claussen and Esch (1994) who found that the GCM ECHAM-T42 generated better climate simulations with respects to ecosystem prediction than ECHAM-T21. However the best results were not necessarily obtained with the highest resolution. For instance a more "realistic'' vegetation distribution was obtained with the BIOME model using the T42 climate integration instead of the T63 climate integration. It must also be noted that certain biases in the climate simulation persisted and in some cases were enhanced at higher resolution.
In summary, predicting accurate biome patterns remained, in some regions, particularly poor. The biomes which clearly benefited from the increased spatial resolution of CCM2 were cool forests, seasonal tropical forest, tundra and hot desert. Therefore, the use of simulated climatologies is not yet fully satisfactory to accurately predict the distribution of global biomes . It is nevertheless possible to increase our level of confidence in predicting vegetation patterns by carefully evaluating the performance of the vegetation models driven by simulated climatologies and by identifying the causes of the biases.
The interactions between vegetation and atmosphere are a key issue in climate system modeling, however the vegetation characteristics represented currently in the soil-vegetation-atmosphere transfer schemes (SVAT) are often prescribed and are not allowed to fully respond to the climate forcing. Hence it was decided that a necessary first step was to improve the representations of the short-term dynamics of the vegetation functions in land-surface schemes (i.e. seasonal and interannual variations of the vegetation functions). This component of the RICE project focused on selected regions and/or vegetation types for which the simulation of the hydrological cycle and of the plant-water relations are particularly critical, such as seasonally dry tropics.
The parameter chosen for this analysis was the Leaf Area Index (LAI) because of its critical role in controlling evapotranspiration and rainfall interception. Simulations were performed for grassland savannas in Western Africa and the Victoria River District in Northern Australia where the marked seasonality of LAI is induced by the pronounced seasonality of soil water content. An approach was developed to simulate the seasonal variations of LAI. The aim was to employ a model simple enough to be implemented without delay in a GCM, and which could be driven by simulated climate variables (including soil moisture variables), generated by the GCM, LMD (Sadourney and Laval 1984). The approach consisted of using a daily plant primary productivity and phenology model paramaterized for the savanna system at the Lamto Scientific Site, Western Africa. The phenology model simulated living and dead LAI and was coupled to the land surface scheme, SECHIBA (Ducoudré et al., 1993) with climatological forcing from the LMD general circulation model.
The coupled model realistically reproduced seasonal variations in LAI for both the Australian and African savannas. These results may be partly attributed to the accurate simulation of the precipitation regime by LMD, particularly in semiarid northern Australia. Two climate variables: (1) incoming shortwave radiation (Rs), and (2) a soil retention coefficient (Us) provided by LMD, were biased as errors produced by one may have partially offset the other. For instance, a decrease in Rs would lead to an increase in soil moisture, particularly at the end of the growing season. It was noted, however, that it would be difficult to evaluate whether improving one variable would improve the other and subsequently improve LAI predictions (Ciret et al., in review). By introducing a seasonal component to a previously fixed land surface vegetation scheme, the results of these linked models strongly suggest that to accurately predict biosphere-atmosphere interactions and the consequences of climate change, an appropriate phenological component is necessary to the development of dynamic vegetation models.
Soil moisture is a key component in the land surface schemes as it is closely related to evaporation and thus to the apportioning of sensible and latent heat fluxes. Accurate prediction for soil moisture is crucial for the simulation of the hydrological cycle and of soil and vegetation biochemistry and thereby plays a significant role in atmospheric models, hydrological models and ecological models. The major objective of the soil moisture workshop was to increase our understanding of the parameterization of soil moisture in different schemes and to provide a quantitative assessment of soil moisture simulation in current land surface schemes.
Land surface schemes used in atmospheric models and ecosystem models have as their principal commonalty the need to simulate soil moisture. While atmospheric models require accurate descriptions of the state and fluxes of water at the surface to assure the realistic partitioning of incoming energy into sensible and latent heat fluxes, terrestrial ecosystem models require this same information to predict the cycling of carbon and nutrients through various organic and inorganic phases. Both groups, insofar as model validation is concerned, are interested in accurate descriptions of soil water and hydrology in general as one of the best sources, in some cases the only source, of validation data.
The treatment of these processes in a particular model is largely determined by the spatial and temporal resolution of the model employed. Atmospheric models have short time steps (less than 0.5 hours), but large spatial resolution of tens to hundreds of kilometers because of numerical and dynamical constraints. Hydrological and ecosystem models operate under other constraints, and have time steps in the order of days to months. Because the numerics involved are relatively simple compared to atmospheric models, there is more flexibility in specifying the spatial resolution which is often constrained by the availability of spatially explicit data sets and the aims of the particular application rather than by numerical considerations.
This polarity between spatial and temporal resolution for atmospheric and hydrological models, when considering the application of each over large spatial domains, leads to a polarity in the treatment of the soil moisture processes. With very short time steps, the land surface schemes used in atmospheric models must treat the vertical movement of water and heat in the soil mechanistically. However, with a horizontal spatial resolution on the order of tens to hundreds of kilometers, explicit treatments of the horizontal movements of water at the surface are necessarily very coarse. On the other hand, with longer time steps, in the order of days to months, it is neither necessary nor possible for hydrological models to assess the details of vertical water movements in the soil column, but with a greater flexibility in the definition of the horizontal spatial resolution of the land surface, these models can and occasionally do incorporate much more sophisticated diagnoses of horizontal movements of water at and under the surface. Ecosystem modelers are concerned with some aspects of both the atmosphere and hydrological processes but tend to consider time and space scales closer to hydrology than meteorology.
While the separate research communities seem to have been fairly well served by this arrangement to date, these simple divisions are disintegrating rapidly. Hydrological models and ecosystem models are being 'plugged into' GCMs and the space and time scale incompatibilities must be recognized and overcome. The precise horizontal distribution of fluxes at the surface may be less important to the accurate assessment of long-term patterns of atmospheric dynamics than is an accurate assessment of the magnitude of those fluxes at short time steps. On the other hand, knowing the relative horizontal distribution of the quantities and fluxes of surface (including subsurface) water is critical to the accurate assessment of the major drainage features and structural and dynamic aspects of terrestrial ecosystems, while the exact magnitude of these quantities and fluxes may not be as critical. There remains one common factor: soil moisture.
In short, soil moisture is a key component in land surface schemes and is of great significance to atmospheric models, hydrological models and ecological models. Soil and vegetation play an important role in the hydrological cycle and influence atmospheric systems on time scales from several hours to many years. Soil water content is closely related to evaporation and thus to the partitioning of sensible and latent heat fluxes at the surface. Soil moisture, the atmospheric boundary layer, and convective clouds are a coupled system leading to feedbacks in short term weather forecasting models and to longer-term climate variability and change. Apart from solar radiation and soil nutrients, the availability of soil moisture is the key to plant growth and to the net production of crops. For these reasons, the intercomparison of soil moisture simulation in land surface schemes was the focus of the joint PILPS and RICE workshop. Figure 5.1 shows a schematic of the models for soil hydrological processes and Table 5.1 indicates the experiments that were conducted.
Figure 5.1: Schematic illustration of runoff and drainage for land surface schemes represented in the workshop. According to the number of soil layers, the schemes are classified into group I (single layer models), group II (two layer models), group III (three layer models) and group IV (multi-layer models).
Table 5.1: Soil Moisture Workshop Experiments
Pre-workshop ExperimentsExp 1: Control experiment with pre-workshop version of schemes; Syn-workshop ExperimentsExp 11: Control experiment with surface aerodynamic parameters specified for every time step; |
From the workshop results, three major general conclusions were made. Firstly, there exists large differences in soil moisture modeling and other modeled variables; even for simulations run with high quality atmospheric forcing data and carefully chosen parameters. Therefore, the prediction of soil moisture in climate change, weather forecast or hydrological simulations cannot be considered as reliable when the forcing data are much less accurate and the information required for specifying land surface parameters is crude. Secondly, current land surface schemes are profoundly different in structure and in their treatment of various land surface processes such as evaporation, transpiration and drainage, with the differences in scheme structure apparently being most important. Finally, land surface schemes comprise two closely coupled components responsible for: the partitioning of sensible and latent heat fluxes and the partitioning of evaporation and runoff-drainage; and among these it is the treatment of runoff and drainage which deserves much improvement and more careful consideration.
In this phase of RICE research, a pair of experiments was conducted from which the sensitivity of one global climate model to imposed "dynamic" changes in vegetation structure could be assessed. The timescale chosen was intentionally as short as possible from the point of view of ecological changes (one year). Although this is much too short a time step to represent, with any verity, ecological changes, it is useful in the context of the half century climate simulations undertaken here. The sensitivity of a global climate model to annually imposed changes in functional vegetation type was examined. This sensitivity testing, a necessary first step towards full model coupling, employs a methodology analogous to "instantaneous" deforestation or "instantaneous" doubling of stomatal resistance.
Transitions between different biome or ecosystem types are a function of a wide range of processes which operate on many time and space scales. Rapid shifts (<5 years) generally, of necessity, involve decreases in biomass. examples include severe drought, frost, hurricanes and fire. medium (10-50 years) "recovery" of forests can be observed in benign environments such as the s.e. asian islands and if human influence is removed. much longer (50-500 years) ecological succession can be simulated but at present only by presuming a fixed climate or a prescribed changing climate (i.e. known in advance and independent of vegetation changes). in simulations of future enhanced greenhouse conditions, the climate cannot be assumed to be fixed or known a priori nor can human influence be denied. thus it must be assumed that rapid as well as longer-term vegetation changes may occur as climate changes. there is a variety of ways of considering the changes in the continental surface characteristics associated with these vegetation disturbances:
simulate climate change presuming that vegetation remains fixed and when climate equilibrates generate continental ecology. both (a) and (b) ignore impacts of vegetation on climate.
use results of (b) and continue at equilibrium to achieve a new climate as modified by the new vegetation. this approach, which may become an iterative process, also assumes that there is a future point at which climate change will stop (presumably at doubled or tripled c02) so that vegetation can "catch-up". it ignores shorter-term climatic effects on vegetation due to droughts, frosts and fires and other extreme climate events and their feedbacks.
apply a similar vegetation type diagnostic model at intervals during a transient or equilibrating climatic change assuming that this would capture faster ecological changes (i.e. reductions in biomass) but speed-up slower (increasing biomass) changes.
employ a "dynamic" vegetation model globally over a long enough time frame for both vegetation and climate to equilibrate (0-500 years).
all published gcm simulations to date have used technique (a). ipcc 2 and other impact assessors adopted technique (b). a few people have tried (c). it is assumed that technique (e) is optimum but such "dynamic" vegetation models, presumably based on the principles underlying succession models have not yet been created. this use of succession type models for global simulations of climate and vegetation changes demands recognition of the fact that not only do the climate and the biomes change but also the cause-and-effect relationships between them are subject to change.
technique (a) is accepted by gcms‹because it is easy (?) ‹although it ignores vegetation feedbacks. technique (b) is apparently acceptable to impact modellers although it too ignores vegetation feedbacks. technique (e) is the goal; (c) and (d) may be steps towards this goal. rice explored technique (d) and chose a 1-year meshing (coupling) period.
the relationship between vegetation and climate is symbiotic but not exclusive: soils, fauna and human activities all impact vegetation (and climate). in an idealized globe where only climate and (above ground) vegetation co-exist it is possible to recognize a range of timescales: slow transitions, "speedy" opportunistic proliferations and die-back, instantaneous wind-throw and fire.
it might be more realistic to select a meshing period of 5, 10 or even 50 or 100 years, because some vegetation changes are slow e.g. forest development or change in forest composition. elongation of the coupling time step, in some senses, brings technique (d) closer to technique (c) but also removes shorter time-scale feedbacks of disturbed vegetation on climate. there seems to be no obvious "best choice" of timescale for coupling. in the interests of reducing cpu investment in this preliminary assessment of technique (d), rice chose to use the shortest possible time period for meshing the continental surface characteristics and the climate: one year.
the biosphere-atmosphere transfer scheme (bats), incorporates a single vegetation, or canopy, layer, a multiple-layer soil scheme and provision for snow cover on the land-surface. the scheme has been subjected to stability and sensitivity tests both with the ncar community climate model and in off-line mode. the bats scheme has evolved as a result of these experiments so that the current version (bats1e) which is reported here, although substantially the same as that described in dickinson et al., does incorporate some corrections and improvements to earlier versions described in the literature. bats can treat a wide range of different surface types, soil characteristics and vegetation covers. at a given grid point, a seasonally dependent fraction of surface covered by vegetation is specified; the remaining fraction is assumed to be covered by bare soil. the fractional vegetation cover varies seasonally based on the assumption of a maximum value when the temperature of the total soil profile is above 298k and decreasing in a quadratic fashion to a specified minimum value when this temperature is below 273k. (it must be recognized that this temperature-only dependence of fractional vegetation cover neglects the important effects of soil moisture availability).
in the presence of vegetation, the temperatures of air within the canopy and the foliage are calculated diagnostically via an energy balance equation which includes canopy-ground and canopy-atmosphere radiative and sensible heat exchanges, transpiration from stomatal pores and evaporation of intercepted moisture. the transpiration rate is calculated using a resistance formulation which includes the aerodynamic resistance to fluxes of moisture and heat from the foliage and the mechanical resistance encountered by the diffusion of moisture from inside a leaf to outside (or stomatal resistance). stomatal resistance depends on the flux of photosynthetically active radiation, leaf temperature and vapor pressure deficit and is modified to account for the root resistance to soil water uptake by the canopy but varies only between specified limits.
bats normally uses 18 vegetation types when coupled to the ncar gcm, ccm1-oz, to represent both natural and agricultural ecosystems. a set of eleven vegetation functional types have been derived. there are sixteen parameters associated with each of these functional types when coupling to the global climate model via bats. the vegetation model is "coupled" to the climate model by use of annually-averaged biotemperatures and total annual precipitation. six experiments were conducted to examine the role of an interactive biosphere in simulations of the climate system. initially, three doubled-co2 simulations were utilized:
a "standard" instantaneous doubling experiment using a specified vegetation distribution appropriate to present day observations, in which the atmospheric concentration of CO2 was raised to 660 ppmv and the GCM allowed to equilibrate;
a fast, transient experiment, in which the atmospheric concentration of CO2 was increased gradually over 35 years until the amount doubled. The climate model was then allowed to equilibrate with this CO2 level held constant; and
a doubled C02 experiment with prescribed vegetation cover (fixed at present-day distributions) and continued from the end of case (i). In addition, a control (fixed vegetation) and interactive vegetation for 1xCO2 experiments were performed. The fast, transient, doubling used a 2% per annum compound rate of increase justifiable if recent estimates of projected emissions are taken into account. The transient experiment permitted an evaluation of the final impact on climate of different rates of vegetation change (slower in the transient than in the instantaneously doubled CO2 experiment).
Figure 5.2 compares the zonally and multi-year averaged difference between 2xCO2 (fixed or interactive vegetation) and 1xCO2 (fixed vegetation), Stevenson screen air temperature and total precipitation for January and July. Large temperature differences were seen in July in high latitude locations where sea-ice differences occurred whereas tropical precipitation was sensitive to the incorporation of interactive vegetation. In common with most simulations of greenhouse climates, the temperature change signals were statistically significant (the lowest curves on the temperature graphs in Figure 2 are of one standard deviation) while the precipitation changes were generally not significant. On the other hand, the differences between fixed and interactive vegetation simulations were always smaller than the model's natural variability.
Figure 5.2 January and July differences (2x CO2 -1xCO2) in zonally averaged (land, ocean and sea-ice) screen temperature (K), total precipitation (% of 1x CO2) with interactive and fixed continental vegetation. All differences were with a 1x CO2 simulation with fixed vegetation. For screen temperature, one standard deviation of the 1x CO2 (control) values is shown (lowest curve).
Doubling of CO2 (without any change at the land surface) resulted in a weaker winter branch (decreased between the equator and 35¡) but a slightly stronger summer branch (increased from the equator to 35¡) indicating enhanced circulation. The presence of an interactive biosphere produced similar changes in the meridional circulation. The winter branch of the Hadley circulation was diminished and the summer branch was enhanced. Differences at higher latitudes were much smaller and harder to identify clearly. Changes in meridional circulation induced by allowing the biosphere to respond to the climate were similar in character and of the same magnitude as changes induced by doubled CO2. In both cases, there was stronger low-level flow over summertime tropical oceans which has the potential to operate as a positive feedback by increasing evaporation.
In summary, it is doubtful if vegetation and climate are ever in equilibrium. Certainly, these experiments, which couple a very simple (eleven class) representation of vegetation functional form to a fairly standard global climate model, exhibited feedbacks operating in both directions: the climate alters the vegetational form and changing vegetation modified the climate. A conclusion from this project suggests that an interactive vegetation will modify climate due to the doubling of atmospheric CO2. The most direct changes were enhanced continental evaporation which prompted intensification of the atmospheric circulation in the tropics and, in turn, enhanced oceanic evaporation. This second affirmative is a trying result for those content to generate vegetation post facto. At a minimum, it means their diagnosis is incomplete since their analysis neglects feedbacks between climate and vegetation. There is no one, simple solution to the "best means" of coupling models of terrestrial vegetation into global climate models. Sensitivity studies of the types described here are essential first steps towards "dynamic" modelling of global change.
In elevated CO2 conditions, plants tend to exhibit increased stomatal resistance so that water lost through transpiration is decreased for the same uptake of carbon in photosynthesis. Although this response varies widely as a function of plant type, availability of sunlight, soil water and nutrients and inter-species competition, there are many examples of observations of CO2 "fertilization" enhancing plant water use efficiency.
It has been shown in numerous numerical modelling experiments that decreased evaporation from the continents can cause changes to the regional climate and to the global climate. Generally, the imposition of stomatal resistance on the parameterization of continental evapotranspiration causes decreases in the latent heat flux over the land and reductions in both the convective and large-scale precipitation. Other experiments in which the effect of stomatal resistance has been introduced also show decreased evaporative fluxes, warmer near-surface temperatures, reduced near-surface humidities and, in some cases, an increase in the diurnal cycle of the near-surface air temperature.
It is reasonable to hypothesize that as CO2 increases in the atmosphere, plant stomatal resistance to transpiration will also increase. This increase is likely to cause a decrease in transpiration. These changes may both compound and confound the direct changes to the climate caused by the CO2 increase itself. No studies have yet assessed the combined impacts of doubling atmospheric CO2 and plant stomatal resistance in a global climate model. Here, the sensitivity of a global climate model to these two effects was investigated. The term "stomatal resistance" is employed to describe the parameterization of the effect of vegetation on transpiration of moisture from the soil to the atmosphere. Although a more correct term might be canopy conductance as it applies to a "big leaf" land surface scheme, stomatal resistance is retained here in line with the current terminology in land surface modelling.
Although there have been many attempts to use global climate models to predict future climates as CO2 and other greenhouse gas concentrations increase the results reported here are the first results from a global climate model in which both CO2 concentrations and plant stomatal resistance were increased. When the imposition of doubled stomatal resistance was combined with an instantaneous doubling of atmospheric carbon dioxide, the global response was rather small: total annual precipitation was slightly reduced c£ the "standard" greenhouse experiment but annual mean temperature increase was little affected. However, comparison of the continental responses show that the combined effect of CO2 and rs increases may be different from either imposed separately. In all cases, evaporation decreases and temperatures, sensible heat and runoff increase but the magnitude of the responses differ and this combines to produce different impacts on, for example, total soil moisture.
Zonal responses to doubling stomatal resistances showed similarities at 1 x and 2xCO2 with the most marked responses occurring between about 44¡-58¡N. In this latitudinal zone, which is predominantly boreal forest in the BATS vegetation distribution, surface air temperature was highest when both CO2 and rs was doubled but the combined effect of 2xCO2 and 2 x rs was to greatly reduce summer evaporation as compared with the standard stomatal resistance in either the current or greenhouse climates.
Figures 5.3(a) and (b) show the statistically significant areas of response in July for 2xCO2 and 2xrs minus 1xCO2 and 1xrs. The most noticeable difference between 1xCO2 and 2xCO2 was that the boreal response to increased stomatal resistance was increased but the tropical response decreased. Although increasing CO2 intensified the global hydrological cycle producing more precipitation and evaporation, the land areas of the northern mid-latitudes were so strongly affected by the increase in stomatal resistance that there were large regions of decreased evaporation. The same latitude band (44¡-58¡N) also showed statistically significant increases in total soil moisture despite the global decrease. Apparently, incorporating a plausible increase in stomatal resistance in a complex land surface scheme in a greenhouse simulation can reverse a major conclusion of the IPCC Science Report that soil moisture "decreases over northern mid-latitude continents in summer".
Figure 5.3: (a) Evapotranspiration t values at 95% significance levels for July differences between 2 x CO2 and 2xrs minus 1 x CO2 and 1 x rs. (b) As (a) but for total soil moisture. (c) Range of partitioning of total annual precipitation between evaporation and runoff plus drainage for a series of off-line simulations. The total evaporation comprised between about 60% and 90% of the total precipitation (865 mm), depending upon the land surface scheme employed. Interestingly, the simplest (bucket) scheme (I) and BATS (A) were almost coincident on this graph. The other schemes were intentionally not identified.
Figure 5.4: Geographic distribution of the simulated difference (double - standard stomatal resistance) for evapotranspiration (W m-2) in July for 2x CO2.
To date, most predictions of the impact of increasing greenhouse gases have been conducted with rather simple "bucket" land surface schemes which have shown significant decreases in soil moisture in the summertime in northern mid latitudes. It is possible that the inclusion of a more complex representation of the land surface and/or the inclusion of increased stomatal resistance may modify the conclusions drawn to date about changes in soil moisture. Thompson and Pollard (1995) compared a bucket and a more complex model (LSX) at 2xCO2 and found that although the sign of the responses in soil moisture were similar, the magnitudes of the changes were much smaller with LSX. These results suggest that with both 2xCO2 and 2 x rs, BATS predicted increased soil moisture in northern mid latitudes and note that in a series of off-line simulations of a single catchment, BATS partitioned total annual precipitation between runoff and evaporation in almost exactly the same proportions (Figure 3(c)).
Overall, although the impact of doubling stomatal resistance can be as large as the effect of doubling atmospheric CO2, the effects were not global (being confined to the continents and, preferentially, to forested regions). Conclusions drawn from "greenhouse" simulations which fail to take account of the likely changes in stomatal resistance or to the land surface scheme employed may give rise to results of different sign (soil moisture) and different geographic distribution (surface air temperature) than those with simple prescriptions.
The goals of the "Regional Interactions of Climate and Ecosystems" (RICE) project were to:
These are important and challenging goals and ones which cannot be quickly achieved. Nonetheless, the results presented demonstrate that two-way interactions between vegetation and soils and climate are important. The participants of the RICE project found significant feedbacks and unexpected sensitivities in the parameterization schemes studied.
The simulation of soil moisture controls the short-term response of soils and vegetation to precipitation and evaporation demand. It also controls the very long-term capacity for a regional climate to support an ecosystem. The formulation of soil moisture in climate and ecosystem models demands a great deal more study.
Coupling of biome models to climate models begins the development of Biospheric Global Climate Models (BGCM's). the results presented here and elsewhere show that the biome, or biogeography models can be tuned to produce realistic results for the present climate, but that true coupling is as difficult as between the physical components of a GCM: atmosphere, ocean, and sea-ice.
