Abstract

In order to find a suitable climate model to forecast future temperature change in Fars province of Iran, three different Global Climate Models (GCMs); that is HADCM3 with scenarios A2 and B2, CCCMA-A2 and ECHOG with scenario A2a, were compared on coordinate point and whole area basis. GCM temperature variable was taken from Internet (http//www.cera-dkrz.de) and local measured minimum and maximum temperature were taken from 27 Synoptic Weather Stations (1989-2007) in Fars province and neighbouring areas. For downscaling GCMs, a variation of different regression models, namely; linear, second order, third order and multiple linear regression of stepwise type were tried in the form of 6 Methods using a detailed error analysis. In our study, the variables were minimum and maximum temperature and GCM model selection criteria were MSE and SS (Skill Score). The results showed that GCM model selection for the area depended on selection criteria and the kind of variable (being either minimum or maximum temperature). In most parts of the area, CCCMA-A2 was the best with the least error for minimum temperature and ECHOG-A2a for maximum temperature. Also, multiple linear regression of stepwise type, among other regression models, proved to be the best method of downscaling having the least error in all comparisons.

Six methods were then used to obtain temperature from 1950 to 2100. Results of the multiple linear regression of step wise type as the best method showed that the average monthly temperature in the control run (1995-2009) was 292.83 and for future period (2085-2099) was 297.95 degrees Kelvin showing temperature increase of 5.12 degrees for the next 90 years.

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