Probabilistic forecasts of near-term climate change: sensitivity to adjustment of simulated variability and choice of baseline period

The authors of this study recently proposed a resampling method for deriving probabilistic forecasts of near-term climate change and presented some results focusing on temperature and precipitation changes in southern Finland from 1971–2000 to 2011–2020. Here, the sensitivity of the resulting forecasts to two details of the methodology is studied. First, to account for differences between simulated and observed climate variability, a variance correction technique is devised. Second, the sensitivity of the forecasts to the choice of the baseline period is studied. In southern Finland, the variance correction technique generally widens the derived probability distributions of precipitation change, mirroring an underestimate of the observed precipitation variability in climate models. However, the impact on the derived probability distributions of temperature change is small. The choice of the baseline period is generally more important, but again the forecasts of temperature change are less sensitive to different options than those of precipitation change. Crossverification suggests that the variance correction leads to a slight improvement in the potential quality of the probabilistic forecasts, especially for precipitation change. The optimal baseline length appears to be at least 30 yr, and the baseline should be as late as possible (e.g. 1971–2000 is preferable over 1961–1990).