Predictions of climate change over Europe using statistical and dynamical downscaling techniques

Abstract

Statistical and dynamical downscaling predictions of changes in surface temperature and precipitation for 2080–2100, relative to pre-industrial conditions, are compared at 976 European observing sites, for January and July. Two dynamical downscaling methods are considered, involving the use of surface temperature or precipitation simulated at the nearest grid point in a coupled ocean–atmosphere general circulation model (GCM) of resolution ∼300 km and a 50 km regional climate model (RCM) nested inside the GCM. The statistical method (STAT) is based on observed linear regression relationships between surface temperature or precipitation and a range of atmospheric predictor variables. The three methods are equally plausible a priori, in the sense that they estimate present-day natural variations with equal skill. For temperature, differences between the RCM and GCM predictions are quite small. Larger differences occur between STAT and the dynamical predictions. For precipitation, there is a wide spread between all three methods. Differences between the RCM and GCM are increased by the meso-scale detail present in the RCM. Uncertainties in the downscaling predictions are investigated by using the STAT method to estimate the grid point changes simulated by the GCM, based on regression relationships trained using simulated rather than observed values of the predictor and the predictand variables (i.e. STAT_SIM). In most areas the temperature changes predicted by STAT_SIM and the GCM itself are similar, indicating that the statistical relationships trained from present climate anomalies remain valid in the perturbed climate. However, STAT_SIM underestimates the surface warming in areas where advective predictors are important predictors of natural variability but not of climate change. For precipitation, STAT_SIM estimates the simulated changes with lower skill, especially in January when increases in simulated precipitation related to a moister atmosphere are not captured. This occurs because moisture is rarely a strong enough predictor of natural variability to be included in the specification equation. The predictor/predictand relationships found in the GCM do not always match those found in observations. In January, the link between surface and lower tropospheric temperature is too strong. This is also true in July, when the links between precipitation and various atmospheric predictors are also too strong. These biases represent a likely source of error in both dynamical and statistical downscaling predictions. For example, simulated reductions in precipitation over southern Europe in summer may be too large. Copyright © 2000 British Crown Copyright