An ensemble of 11 regional climate model (RCM) projections are analysed for Denmark from a hydrological modelling inputs perspective. Two bias correction approaches are applied: a relatively simple monthly delta change (DC) method and a more complex daily distribution-based scaling (DBS) method. Differences in the strength and direction of climate change signals are compared across models and between bias correction methods, the statistical significance of climate change is tested as it evolves over the 21st century, and the impact of choice of reference and change period lengths is analysed as it relates to assumptions of stationary in current climate and change signals. Both DC and DBS methods are able to capture mean monthly and seasonal climate characteristics in temperature (T), precipitation (P), and potential evapotranspiration (ETpot). For P, which is comparatively more variable by day, the DC approach is insufficient at recreating projected regimes while the DBS correction method can transfer changes in the mean as well as the variance, improving the characterisation of temporal dynamics as well as heavy precipitation events. Climate change signals in the near-future (2011–2040) are hidden by natural variability and are therefore not significant, in the mid-future (2041–2070) the significance of climate change signals depend on the choice of climate model, and in the far future (2071–2100) climate change signals are strong across all models and variables. Some models already display significant differences in climate variables within the past timeframe for Denmark. Current climate characteristics are not necessarily stationary and the temporal positioning of a reference period might impact the magnitude of relative climate change. Reference and change period lengths over 15 years are adequate in size to overcome natural variability and still have stationarity in the climate change signal within the periods.
Seaby, L. P., Refsgaard, J. C., Sonnenborg, T., Stisen, S., Christensen, J. H., & Jensen, K. H. (2013). Assessment of robustness and significance of climate change signals for an ensemble of distribution-based scaled climate projections. Journal of Hydrology, 486, 479-493. https://doi.org/10.1016/j.jhydrol.2013.02.015