Abstract
AbstractWe used empirical–statistical downscaling in a pseudoreality context, in which both large-scale predictors and small-scale predictands were based on climate model results. The large-scale conditions were taken from a global climate model, and the small-scale conditions were taken from dynamical downscaling of the same global model with a convection-permitting regional climate model covering southern Norway. This hybrid downscaling approach, a “perfect model”–type experiment, provided 120 years of data under the CMIP5 high-emission scenario. Ample calibration samples made rigorous testing possible, enabling us to evaluate the effect of empirical–statistical model configurations and predictor choices and to assess the stationarity of the statistical models by investigating their sensitivity to different calibration intervals. The skill of the statistical models was evaluated in terms of their ability to reproduce the interannual correlation and long-term trends in seasonal 2-m temperature T2m, wet-day frequency fw, and wet-day mean precipitation μ. We found that different 30-yr calibration intervals often resulted in differing statistical models, depending on the specific choice of years. The hybrid downscaling approach allowed us to emulate seasonal mean regional climate model output with a high spatial resolution (0.05° latitude and 0.1° longitude grid) for up to 100 GCM runs while circumventing the issue of short calibration time, and it provides a robust set of empirically downscaled GCM runs.
Highlights
Global climate models (GCMs) are our main tool for providing plausible outlooks for what future climate conditions we can expect with changes in atmospheric greenhouse gas concentrations and other forcings (IPCC AR5; CMIP5)
Impact of using bias-corrected GCM fields in empirical–statistical downscaling (ESD) The impact of utilizing bias-corrected fields in ESD was investigated by using bias-corrected GCM data (T2m,VP2m, and psl from the NorESM1-M model) as predictor data
Using predictor fields where the climatological monthly mean had been corrected based on reanalysis data did not result in different ESD results than those based on the original uncorrected GCM fields
Summary
Global climate models (GCMs) are our main tool for providing plausible outlooks for what future climate conditions we can expect with changes in atmospheric greenhouse gas concentrations and other forcings (IPCC AR5; CMIP5). They are designed to capture large-scale phenomena and processes in the climate system; the amount of details they can provide is limited by design and computational resources. To provide information on local scales needed for impact studies and climate change adaptation, it is necessary to downscale the global models (Takayabu et al 2015).
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