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# 1
Hempel, Sabrina • Frieler, Katja • Warszawski, Lila • Schewe, Jacob • Piontek, Franziska
Abstract: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) is a community-driven modelling effort bringing together impact models across sectors and scales to create consistent and comprehensive projections of the impacts of different levels of global warming. This entry holds the input data of the ISIMIP Fast Track Initiative consisting of bias corrected daily data for from the following five CMIP5 Global Climate Models (GCMs): GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM and NorESM1-M. Bias corrections has been processed by Sabrina Hempel at PIK and is described in "A trend-preserving bias correction -- the ISIMIP approach" by Hempel et al. (2013)The input data section of the ESGF project referenced in this entry holds the initial version of the bias-corrected GCM input data and was used to force impact models in the ISIMIP Fast Track phase. It should only be used for the ISIMIP2 catch-up experiments for sectors that were already part of the Fast Track phase. For all other purposes, i.e. future runs for new ISIMIP 2 sectors and modeling exercises with no relation to ISIMIP, the corrected and extended version published under the ISIMIP 2 ESGF project should be used. It overcomes several limitations in adjusting the daily variability (denoted as ISIe in Hempel et al., 2013). Data access links are provided to the PIK node of the Earth System Grid Federation (ESGF, https://esg.pik-potsdam.de/). There is currently no directly linked data available, please take a look at the input data of the ISIMIP Fast Track Initiative via https://esg.pik-potsdam.de/search/isimip-ft/. For technical support please have a look at the ESGF FAQ (http://esgf.github.io/esgf-swt/index.html) and the tutorials (https://www.earthsystemcog.org/projects/cog/tutorials_web).
Statistical bias correction is commonly applied within climate impact modeling to correct climate model data for systematic deviations of the simulated historical data from observations. Methods are based on transfer functions generated to map the distribution of the simulated historical data to that of the observations. Those are subsequently applied to correct the future projections. Thereby the climate signal is modified in a way not necessarily preserving the trend of the original climate model data. Here, we present the bias correction method that was developed within ISIMIP, the first Inter-Sectoral Impact Model Intercomparison Project. ISIMIP is designed to synthesize impact projections in the agriculture, water, biome, health, and infrastructure sectors at different levels of global warming. However, bias-corrected climate data that are used as input for the impact simulations could be only provided over land areas. To ensure consistency with the global (land + ocean) temperature information the bias correction method has to preserve the warming signal. Here we present the applied bias correction method that preserves the absolute changes in monthly temperature, and relative changes in monthly values of precipitation and the other variables needed for ISIMIP. The proposed methodology represents a modification of the transfer function approach applied in the Water Model Intercomparison Project (WaterMIP). Correction of the monthly mean is followed by correction of the daily variability about the monthly mean.
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