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Abstract:
A large ensemble of 24 bias-corrected and uncorrected regional climate model (RCM) simulations is used to investigate climate change impacts on water supply patterns over Germany using the seasonal winter and summer Standardized Precipitation Index (SPI) based on 6-month precipitation sums. The climate change signal is studied comparing SPI characteristics for the reference period 1971-2000 with those of the 'near' (2036-2065) and the 'far' (2071-2100) future. The spread of the climate change signal within the simulation ensemble of bias-corrected versus non-corrected data is discussed. Ensemble scenarios are evaluated against available observation-based data over the reference period 1971-2000. After correcting the model biases, the model ensemble underestimates the variability of the precipitation climatology in the reference period, but replicates the mean characteristics. Projections of water supply patterns based on the SPI for the time periods 2036-2065 and 2071-2100 show wetter winter months during both future time periods. As a result soil drying may be delayed to late spring extending into the summer period, which could have an important effect on sensible heat fluxes. While projections indicate wetting in summer during 2036-2065, drier summers are estimated towards the south-west of Germany for the end of the 21st century. The use of the bias correction intensifies the signal to wetter conditions for both seasons and time periods. The spread in the projection of future water supply patterns between the ensemble members is explored, resulting in high spatial differences that suggest a higher uncertainty of the climate change signal in the southern part of Germany. It is shown that the spread of the climate change signals between SPIs based on single ensemble members is twice as large as the difference between the mean climate change signal of SPIs based on bias-corrected and uncorrected precipitation. This implies that the sensitivity of the SPI to the modelled precipitation bias is small compared to the range of the climate change signals within our ensemble. Therefore, the SPI is a very useful tool for climate change studies allowing us to avoid the additional uncertainties caused by bias corrections. © Author(s) 2013.
