Verification and analysis of a climate simulation of temperature and pressure fields over Norway and Svalbard

The monthly mean 2 m temperature and sea-level pressure (SLP) fields from the most recent integration (GSDIO) with the Max Planck Institute's global coupled climate model ECHAM4/OPYC3 are compared to historical data over Norway including Svalbard. For temperature, observations from selected stations are directly compared to values at grid points nearby. For SLP, modelled and observed gridded fields over the area 20 degrees W-40 degreesE, 50-85 degreesN are compared by means of empirical orthogonal function (EOF) analysis. Finally, the connections between SLP and temperature over Norway are deduced for both historical data and results from the GSDIO integration and then compared to each other. The GSDIO 'control climate' grid point temperatures over Norway are in most cases found to be realistic, whenever it is possible to find stations with similar altitude and distance from the coast. The GSDIO 'future climate' indicates an annual mean warming of 0.2 to 0.5 degreesC decade(-1) on the Norwegian mainland, and 0.8 degreesC decade(-1) at the Svalbard grid point up to 2050. The strongest warming is simulated in winter, in the inland, and at high latitudes. The GSDIO 'control climate' SLP gradients imply on average westerly winds over Norway that are too weak. The GSDIO 'future climate', however, indicates an increase in the westerly wind component. Observations after 1960 show an increase in the westerly field of the same magnitude as in the GSDIO results during the same period. The observed connections between atmospheric circulation and temperatures in Norway are satisfactorily reproduced in the GSDIO integration, especially in winter. The winter warming in the GSDIO integration may partly be explained by the increase in the westerly wind component. On the Norwegian mainland, a linear regression model based on atmospheric circulation indices accounts for 1/3 to 2/3 of the 'scenario' warming in January. In July, the linear regression model does not account for any warming at all, The warming which is not accounted for by the linear regression model may be caused by non-linear processes (e.g. air-sea-ice interactions) or directly connected to changes in the climate forcing.