Predictability study of the observed and simulated European climate using linear regression
Blender R, Luksch U, Fraedrich K, Raible CC
Quart. J. Roy. Meteorol. Soc.
submitted April 2002


Abstract:
Monthly mean temperature anomalies in the regions England, Germany and Scandinavia are predicted by linear regression. Predictors are monthly mean teleconnection indices, North Atlantic sea surface temperatures (SST), projected on the first three Empirical Orthogonal Functions (EOF), and European climate variables (temperature, sea level pressure, and precipitation) averaged in the three predictand regions. The predictors are chosen separately for each month according to their correlation with the predictand. Observations from 1870-1999 and data from a 600 years integration with the coupled atmosphere-ocean general circulation model are used to assess and compare the forecast skill. The skill of the empirical model is measured by the anomaly correlation coefficient (ACC) and the explained variance (EV).

For one month lead time, the ACC for observations is up to 0.6 (EV < 35%) for February/March and August/September in the three regions. The skill for the simulated data is lower (up to ACC = 0.5) and its seasonal dependence differs from that of the observations. Main predictors are the preceding temperatures in the predictand region. The simulated data is split in segments to estimate the distribution of skill. For lead times up to one year there is skill (ACC >0.3) in the observations for England (spring and late summer), and Scandinavia (August-September), but none in Germany. The observed two-month mean England temperature in spring and late summer can be predicted with 6 months lead time using the first two North Atlantic SST EOF coefficients for 1970 to 1996 with 1870-1969 as training set. A leave-two-out cross-validation in 1870-1999 shows an obvious reduction of skill. Beyond one month, the skill in simulated data is much lower than in the observations. The linear regression prediction scheme shows up as a method to evaluate general circulation models.

KeyWords Plus:
European Climate, Predictability, Monthly forecasts, GCM simulation

Addresses:
Blender R, Luksch U, Fraedrich K, Univ Hamburg, Inst Meteorol, Bundesstr 55, D-20146 Hamburg, Germany
Raible C.C., Climate and Environmental Physics, Physics Institute, University of Bern, Sidlerstr. 5, CH-3012 Bern, Switzerland.

Reprints:
Raible CC, Climate and Environmental Physics, Physics Institute, University of Bern, Sidlerstrasse 5, CH-3012 Bern, Switzerland, raible@climate.unibe.ch