Introduction

It is now generally accepted that land surface processes can have a significant impact on the global climate, [e.g. Budyko, 1956; Budyko, 1974; Budyko andSokolov, 1978; Garratt, 1993; Geiger, 1965; Geiger et al., 1995; Mintz, 1984]. Various different land surface schemes for use in climate and numerical weather prediction models have been developed, ranging from simple bucket schemes [Budyko, 1956; Manabe, 1969] up to fairly complex models including an explicit representation of vegetation [e.g. Dickinson et al., 1986; Sellers et al., 1986]. These models have been tested in intercompa-rison studies, revealing a wide range in model performances [Henderson-Sellers et al., 1996; Schulz et al., 1998]. Numerical experiments have been used to identify the most important factors of land surface-atmosphere interactions, which have turned out to include evapotranspiration [Shukla and Mintz, 1982], water-holding capacity [Milly and Dunne, 1994], albedo [Charney et al., 1977] and surface roughness [Sud et al., 1988]. All of those land surface parameters can change dramatically during deforestation and other forms of land conversion, with important consequences for regional and global climate [Chase et al., 2000; Lean and Warrilow, 1989; Polcher and Laval, 1994; Shukla et al., 1990]. To a large extent, these surface fluxes and parameters are controlled by the vegetation cover, which in turn is largely determined by the climate [Box, 1981; Holdridge, 1947]. Those links then create biogeographic feedbacks between terrestrial vegetation and the atmosphere, which have been found to alter climate sensitivities to imposed changes in surface cover [Gutman, 1984], with indications of creating new climate-vegetation equilibria [Charney et al., 1975; Claussen et al., 1997; Ganopolski et al., 1998]. Adequate representation of the plant-controlled surface hydrology has also been found to be important for the quality of numerical weather prediction [Viterbo and Beljaars, 1995] and for the simulated climate in a number of nested limited-area models [Christensen et al., 1997].

Much of the close link between vegetation activity and climate-relevant land surface processes can be attributed to the large water requirement of land plants. Because vegetation growth is limited by water availability over the largest part of the terrestrial surface, plants tend to maximise water use by controlling transpiration through their stomata, small pores on the leaf surface [Jones, 1983], as well as through efficient rooting strategies [Kleidon and Heimann, 1998]. As a consequence, more than half of land evapotranspiration happens as plant-controlled stomatal transpiration [Budyko, 1974; Budyko and Sokolov, 1978]. This explains why a number of recent land surface schemes have attempted to include physiological processes, such as carbon uptake, in conjunction with energy and water exchanges [e.g. Sellers et al., 1996].

On a global scale, the state and evolution of land surfaces can best be characterised using data from space-borne remote sensing platforms that have now been available for approximately 20 years. The presence of vegetation can be identified rather easily with optical sensors because its reflectance shows a strong contrast between the visible and the near-infrared optical domains [ Verstraete, 1994]. Provided the link between vegetation activity, the terrestrial water balance and climate is captured in an appropriate modelling framework, remote sensing of vegetation can in principle be used to infer information about other climate relevant parameters, such as soil moisture status. One such modelling framework that uses the technique of data assimilation will be introduced in the present study.

When combined with models, remote sensing data can be used for either initialisation, forcing, evaluation, or assimilation. Which of these is used in a particular case will depend on the requirements and the intended use of the model as well as the type of information extracted from the remote sensing data. Initialisation and forcing always require appropriate algorithms to derive parameters from the remote sensing data that can be used in the models concerned. In the case of evaluation, an analogous strategy is to compare parameters derived from remote sensing with state variables of the model that represent the same physical or biophysical quantitity. Since the signal recorded by the satellite sensor is usually the result of complex radiative interaction between the physical system under investigation and other factors, such as the state of the atmosphere or the surface background, this strategy requires the solution of an inverse problem [see e.g. Verstraete and Pinty, 1996; Verstraete and Pinty, 2000]. An alternative strategy that can be used for evaluation is therefore to add a description of the measurement process to the model: in this case, it is possible to predict the signal that should have been observed by the sensor under the given viewing conditions in a forward manner, and compare it to the actual measurement. - Both techniques can also be used for assimilation of satellite data, which in effect is an extension of the method of model evaluation through formalisation of subsequent model adjustments (see below for further discussion of this method).

The question whether forcing and initialisation, or evaluation and assimilation of remote sensing data is the preferable method will usually depend on the use of the derived parameter within the model. This parameter may be considered either a cause for the process modelled, or an effect of it, and consequently appear as either input for initialisation or forcing, or as output used for evaluation or assimilation. For example, since atmospheric circulation models have atmospheric parameters as an output, assimilation is the preferred use of atmospheric remote sensing data, such as in numerical weather prediction [e.g. Anderson et al., 1994]. Remotely sensed thermal and microwave radiation, both related to the energy and moisture balance of land surfaces, have also been used for assimilation in the context of regional hydrological studies [Blyth, 1993; McNider et al., 1994; Ottle and Vijal-Madjar, 1994], or numerical weather prediction [van den Hurk et al., 1997]. The opposite situation is found for a number of vegetation models designed to study the global carbon cycle: The design of those models has been focus-sed on the process of carbon uptake and cycling at a given vegetation distribution, and has consequently used a remotely sensed measure of vegetation productivity, fAPAR, as a model input [Potter et al., 1993; Prince, 1991; Ruimy et al., 1996]; fAPAR (fraction of vegetation-absorbed photosyntheti-cally active radiation) is a quantitity that can be derived rather well from optical remote sensing. If, however, vegetation cover and productivity are predicted as a result of climate and soils input data, as in this study, fAPAR may appear as a model output and can thus be used for either evaluation or assimilation.

The main advantage of using fAPAR, or any other parameter, for forcing is that there is no need to model the relevant processes by which this quantity is modified. If, on the other hand, this parameter is used for assimilation, it must at least be possible to represent those processes in some simplified form, such that the adjusted model, following assimilation, is able to reproduce them correctly. For example, it is not necessary to include the extent to which human activity modifies vegetation activity by irrigation and agriculture, if fAPAR is used to force a vegetation model. If fAPAR is assimilated, however, the impact of agriculture and other factors not modelled explicitly may be accounted for by modifying the length of the growing season, the water balance, and the vegetated fraction of a model grid cell (see below).

The advantage of data assimilation is generally that it allows to account for varying degrees of uncertainty in the satellite data; a special case of which are data gaps. This is important for optical satellite remote sensing, because in some very cloudy regions (e.g. tropical rainforests) and at high latitude during the winter (when the sun is far away from zenith), no reliable observations of surface conditions may exist for one to several months per year. This situation has led to the development of data where additional information has been used to fill in gaps occurring in the satellite data [Los et al., 1994]. Here, assimilation offers the important advantage that remotely sensed information may be combined with other, independent information in systematic and accountable way. In the case of fAPAR, this independent information consists of climate and soils data that, which, in an appropriate model, can be used to derive vegetation cover on a global scale [e.g. Box, 1981; Haxeltine and Prentice, 1996], This is particularly important when process descriptions carry large uncertainties, as it is the case for vegetation modelling [Knorr, 2000]: assimilation allows to assign uncertainties to both remote sensing derived and model internal parameters, and, through combining both sources of information, to reduce the overall modelling uncertainty. In contrast to this method, forcing with satellite data does not allow for adjusting the model itself, with the consequence that other state variables of the model may be inconsistent with the one that is prescribed from the satellite data. An example where forcing the model with high fAPAR at very low soil moisture content results in strongly negative carbon gain of the vegetation is given in Section 5.

Despite the advantages described above, no global-scale assimilation of fAPAR or other vegetation related remotely sensed parameters has yet been attempted, neither for vegetation nor for climate models containing a suitable vegetation description within their surface schemes. The reason may be that vegetation models are still a relatively recent development, and that only few surface schemes are able to predict vegetation cover and activity [e.g. IBIS, Foley et al., 1996], However, some evaluation of vegetation models against satellite data has been performed, either against derived quantities like fAPAR [Knorr, 1997], or by combining the vegetation model with a radiative transfer model of atmosphere, vegetation and soil [Knorr et al., 1995].

In this study, global monthly fields of fAPAR derived from satellite data are compared to monthly fields of fAPAR as predicted by the Biosphere Energy-Transfer Hydrology (BETHY) scheme [Knorr, 1997; Knorr, 2000], which is a model of surface processes, photosynthesis and land-biosphere carbon balance forced "off-line" by monthly climate and fixed soil input data. Both fields are intended to represent one average year under mean climatic conditions. Differences between them are then used to adjust the model in a suitable assimilation procedure. Although the model uses fields of actual vegetation types as input, the amount of leaf area for each month is derived entirely from the water and carbon balance.

Assimilation and forcing of the BETHY scheme with satellite derived fAPAR is first tested against observations of soil moisture from a rainforest site in Amazonia. This site represents conditions of natural vegetation under severe water limitation for a significant part of the year. One of the model parameters that has a particularly large impact on the water balance and is adjusted during assimilation is the maximum plant-available soil moisture content. Global fields of this model parameter, adjusted following the assimilation procedure, are then used in a sensitivity analysis with the ECHAM-4 climate model [Roeckner et al., 1996], with the purpose of assessing the impact of including information contained in the satellite data on the simulated climate. The results should then indicate what benefit could be gained from including an active biosphere in a climate model, i.e. a combined surface and vegetation model that predicts vegetation amount based on the simulated climate itself.

A further aim of this study is to outline and demonstrate a methodology that could lead to some valuable improvements in the fields of climate simulation and vegetation modelling. Its main advantage is that it ensures consistency between derived parameters, model dynamics and observations within their specific degrees of accuracy, and an adequate representation of the coupling between atmospheric circulation and the terrestrial carbon and water cycles.

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