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Abstract. Remote sensing is increasingly being used as a cost-effective and practical solution for the rapid evaluation of impacts from wildland fires. The present study investigates the use of the support vector machine (SVM) classification method with multispectral data from the Advanced Spectral Emission and Reflection Radiometer (ASTER) for obtaining a rapid and cost effective post-fire assessment in a Mediterranean setting. A further objective is to perform a detailed intercomparison of available burnt area datasets for one of the most catastrophic forest fire events that occurred near the Greek capital during the summer of 2007. For this purpose, two ASTER scenes were acquired, one before and one closely after the fire episode. Cartography of the burnt area was obtained by classifying each multi-band ASTER image into a number of discrete classes using the SVM classifier supported by land use/cover information from the CORINE 2000 land nomenclature. Overall verification of the derived thematic maps based on the classification statistics yielded results with a mean overall accuracy of 94.6% and a mean Kappa coefficient of 0.93. In addition, the burnt area estimate derived from the post-fire ASTER image was found to have an average difference of 9.63% from those reported by other operationally-offered burnt area datasets available for the test region.

This is the author accepted manuscript. The final version is available from EGU via the DOI in this record. ; The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over 2 decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. In this paper, we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models. We have also created supplementary tables that describe, in thorough mathematical detail, the structure of each model. ; S. Rabin was supported by a National Science Foundation Graduate Research Fellowship and by the Carbon Mitigation Initiative, and along with S. Hantson and A. Arneth would like to acknowledge support by the EU FP7 projects BACCHUS (grant agreement no. 603445) and LUC4C (grant agreement no. 603542). This work was supported, in part, by the German Federal Ministry of Education and Research (BMBF), through the Helmholtz Association and its research programme ATMO, and the HGF Impulse and Networking 5 fund. F. Li was funded by the National Natural Science Foundation of China under Grant No. 41475099 and the CAS Youth Innovation Promotion Association Fellowship. The UK Met Office contribution was funded by BEIS under the Hadley Centre Climate Programme contract (GA01101). G. A. Folberth also wishes to acknowledge funding received from the European Union's Horizon 2020 research and innovation programme under grant agreement No 641816 (CRESCENDO). J. O. Kaplan was supported by the European Research Council (COEVOLVE, 313797).

REanalysis of the TROpospheric chemical composition over thepast 40 years (RETRO)Objectives:• exploit (often under-utilised) existing data sets from ground based stations,aircraft, and satellite instruments, integrating these into common datasets,• develop tools for the analysis, interpretation and exploitation of the data,• formulate recommendations for future measurement strategies,• assess changes in trace compound emissions and their effect on troposphericchemical composition and aerosols, and the associated radiative forcing, overthe past 40 years,• provide an assessment of uncertainties caused by climate variability,• evaluate emission control strategies in Europe,• predict changes over the next 20 years in tropospheric composition, andradiative forcing through model studies using the emission scenarios definedfor the IPCC 2001 climate assessment,• analyze the magnitude of intercontinental pollutant transport.Scientific achievements:• first detailed, comprehensive and consistent data sets on global emissionsfrom fossil and biofuel combustion and from open vegetation burningcovering the time period 1960-2000; available as gridded data sets with0.5°×0.5° and monthly mean resolution,• first global long-term atmospheric chemistry integrations with several stateof-the-art models using the ERA-40 meteorological data, the RETROemissions and other constrains in a consistent and well-documented manner,• analysis of key parameters controlling the interannual and seasonalvariability and the longer-term trends in the tropospheric composition relatedto ozone and its precursors,• development of new software tools for the analysis of observational data andmodel results; standardisation of model output and data formats anddefinition of model evaluation metrics and skill scores,• development of a comprehensive data base for tropospheric compositionobservations with complete metadata definition and a user-friendly interfacefor data access,• multi-model analysis of specific scenarios related to power generation andthe ...