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Carbon Assimilation and Modelling of the European Land Surfaces |
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| Within the carbon cycle research community two quite distinct modelling techniques are used to estimate net land-atmosphere CO2 fluxes. In inverse studies, atmospheric CO2 measurements are used in conjunction with atmospheric transport modelling. These studies produce useful estimates of the large-scale geographical distribution of sources and sinks (Bousquet at al., 1999), but they are unconstrained by what is already understood about physiological and ecological processes, and cannot currently provide good estimates at the policy-relevant scale of countries. In forward modelling, models of land carbon uptake and release are developed based on an understanding of the relevant processes (e.g. the response of photosynthesis to CO2, the response of microbial respiration to climate), and then these models are integrated forward in time to produce predictions of the temporal and spatial variability of land-carbon sinks (Cramer et al., 2001). Estimates of the land carbon balance produced by forward modelling to the current day from the past are constrained by the understanding of the system embodied in the model (e.g. conservation of carbon and nitrogen), but not constrained by any direct observations of the carbon cycle (e.g. flux measurements, forest inventories, CO2 flask measurements). To date, the inverse and forward modelling approaches have not been intelligently combined into a carbon data assimilation system, in which the data used in inverse modelling and the understanding embodied in forward modelling can be used simultaneously to produce the best estimates of current sources and sinks. The development of such a data assimilation for carbon is one of the main goals of CAMELS. The nowcasting system will adopt data assimilation techniques as used in numerical weather prediction (NWP). In NWP, data assimilation is used to produce a best estimate of the initial state of the atmosphere based on recent observations, and on information contained in the last numerical forecast. The numerical forecast is consistent with our understanding of the atmosphere (as embodied in the underlying model), but may differ from the real weather because of inadequacies in the model or the initial conditions. By contrast, direct observations provide a more reliable estimate of the true state of the system but tend to be local. Data assimilation for NWP acts by nudging the modelled state to get a good fit to local observations. In this way, the model provides a tool to interpolate local and often sparse observations to produce a large-scale picture which is consistent with constraints provided by both the observations and the model itself. A major innovation from CAMELS will to develop an equivalent system, i.e. a carbon cycle data assimilation system (CCDAS). The combination of mechanistic forward modelling and observational constraints will provide unique high-resolution maps of the European land carbon sink, which can be broken down into the relative contributions arising from land management (as covered under the Kyoto protocol) and other environmental factors. Summary of Innovative Elements
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