DOWNSCALING STANDARDIZED PRECIPITATION INDEX VIA MODEL OUTPUT STATISTICS

Hasan Tatli

Abstract

This study investigates the possible impacts of future climate change on meteorological drought events in Turkey by using a new statistical downscaling technique based on polytomous logistic regression, denoted as the model output statistics (MOS) technique. It is designed to downscale the drought classes of the 12-month Standardized Precipitation Index (SPI). The main goal of a downscaling procedure is to determine the influences of large-scale climatic variability and the projected changes on the local scale-regional variables. The large-scale predictors used in this study were obtained from the output of the Second Generation Canadian Coupled General Circulation Model (CGCM2) simulations, run from 1940 to 2100 for three socioeconomic scenarios, namely control, with the constraint of the 20th century atmospheric concentration of greenhouse gases, and the SRES A2 and B2 scenarios. Observations from 96 meteorological stations were used to estimate 12-month SPI values for the period 1940-2010, leaving the last 10 years for validation against the results simulated by the CGCM2. The MOS results derived from the control climate simulation agree with the observed patterns for present climate. The MOS results derived from future climate scenarios lead to conclude that there is a decreased probability of very wet and extremely wet conditions. In addition, the probabilities of near-normal conditions will decrease in the Black Sea coast and will increase towards the Marmara Transition and continental eastern Anatolia regions.

 

Keywords

Climate of Turkey, statistical downscaling, logistic regression, standard precipitation index (SPI).

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