On the need for bias correction of regional climate change projections of temperature and precipitation

Abstract

[1] Within the framework of the European project ENSEMBLES (ensembles-based predictions of climate changes and their impacts) we explore the systematic bias in simulated monthly mean temperature and precipitation for an ensemble of thirteen regional climate models (RCMs). The models have been forced with the European Centre for Medium Range Weather Forecasting Reanalysis (ERA40) and are compared to a new high resolution gridded observational data set. We find that each model has a distinct systematic bias relating both temperature and precipitation bias to the observed mean. By excluding the twenty-five percent warmest and wettest months, respectively, we find that a derived second-order fit from the remaining months can be used to estimate the values of the excluded months. We demonstrate that the common assumption of bias cancellation (invariance) in climate change projections can have significant limitations when temperatures in the warmest months exceed 4-6°C above present day conditions. Citation: Christensen, J. H., F. Boberg, O. B. Christensen, and P. Lucas-Picher (2008), On the need for bias correction of regional climate change projections of temperature and precipitation, Geophys. Res. Lett., 35, L20709,

Impacts of climate change and land-use scenarios on Margaritifera margaritifera, an environmental indicator and endangered species

“…When the aim is to forecast hydrological processes (present case), numerical simulations of climatic variables cannot be used without some form of data processing to remove the existing biases (Christensen et al, 2008;Sharma et al, 2007). So, the errors coming with the HadRM3 simulations had to be corrected in this study.…”

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