Assessing several downscaling methods for daily minimum and maximum temperature in a mountainous area. Are we able to statistically simulate a warmer climate in the Pyrenees?

Mountain areas are one of the most vulnerable areas to climate change, due to the large amount of natural resources they contribute to society. Moreover, the announced increase in temperature for the next few decades may have uncertain consequences for the ecosystems and landscapes of such territories. To face this challenge, it is necessary to test the capacity to simulate the climate of warm periods using observed data. In the present contribution, different perfect prog (PP) downscaling methods were evaluated to simulate the minimum and maximum daily temperature in a 1x1 km grid in the Pyrenees (Spain, France & Andorra) for the period 1985-2015. To obtain the results, several combinations of predictors, different geographical domains of such predictors, as well as different reanalysis databases were used, to check how much they can influence the prediction skill. In addition, different metrics were calculated to evaluate the bias, the similarity in the observed and predicted distributions, the temporal correlation, etc.

The results obtained reflect that the regression models better represent the warm periods using the observed data, as well as a lower bias. The present study will facilitate the decision making on which method of downscaling PP is more useful to reproduce the future temperature in the Pyrenees.

 

Keywords: Statistical downscaling, perfect prog, Pyrenees, daily temperature.