Assessment of Impact of ESA CCI Land Cover Information for Global Climate Model Simulations
Khlystova, Iryna G.1; Loew, A.1; Kloster, S.1; Hagemann, S.1; Defourny, P.2; Brockmann, Carsten3; Bontemps, Sophie2
1Max Plank Institute for Meteorology, GERMANY; 2Université Catholique de Louvain, BELGIUM; 3Brockmann Consult GmbH, GERMANY

Addressing the issues of climate change, the European Space Agency has recently initiated the Global Monitoring of Essential Climate Variables program (ESA Climate Change Initiative). The main objective is to realize the full potential of the long-term global Earth Observation archives that ESA has established over the last thirty years. Due to a well organized data access and transparency for the data quality, as well as long-term scientific and technical support, the provided datasets become very attractive for the use in the Earth System Modeling. The Max Plank Institute for Meteorology is contributing to the ESA CCI over the Climate Modeler User Group (CMUG) activities and is responsible for providing a modeler perspective on the Land Cover and Fire Essential Climate Variables.
The new ESA Land Cover ECV has recently released a new global 300-m land cover dataset. This dataset is supported by an interactive tool which allows flexible horizontal re-scaling and conversion from currently accepted satellite specific land classes to the model-specific Pants Functional Type (PFT) categorization, universal for the most land vegetation models. Such dataset is an ideal starting point for the generation of the land cover information for the initialization of models land cover states. In this presentation, we show how the usage of this new dataset affects the model performance, comparing to the standard JSBACH set-up, in terms of the energy and water fluxes. For this, we performed a number of offline simulations with original standard JSBACH land cover and with the new ESA CCI land cover product, as well as with the CCI Land Cover pre-curser, the MODIS Land Cover dataset.
The usage of different land cover information is also expected to have an impact on the model-simulated above ground biomass, and thus have an impact on the calculated carbon emissions from fires. In the scope of ESA CCI Fire ECV, we have implemented a mechanism for integration of satellite fire products, using the GFEDv3 burned area dataset as a pre-cursor dataset for the fire ECV. Here, we also analyze the impact of different land cover information used in JSBACH on the simulated biomass load and fire carbon emissions.