Statistical Downscaling and Projection of Future Temperature Change for Tabriz City, Iran

EasyChair Preprint 1813, version 1

Abstract

In the 21st century, Climate change has become one of the prominent global challenges which threats the world, and the changes in climate extremes are estimated to have catastrophic consequences on human society and the natural environment. To overcome the spatial-temporal inadequacy of the GCMs, Linking large-scale General Circulation Model (GCM) data with small-scale local climatic data highly comes to the fore. In this paper, two statistical downscaling techniques (i.e., LARS-WG, SDSM) was employed for assessing the fluctuations of temperature predictand for Tabriz city, Iran. To study the impact of climate change over the region, the periods of 1961-1990 and 1991-2005 were used as the baseline and validation period, respectively. The result of climate projection for the temperature predictand by both approaches revealed the point that the city will experience an increasing trend in minimum and maximum temperatures for the horizon of 2041-2060. The average temperature will increase by 2.9 and 3.7 (°C) under RCP4.5 and 8.5, respectively. Also, the results disclosed that both models represented the same performance for minimum and maximum predictands, although the monthly correlation of observed and simulated during the baseline period in LARS-WG model was slightly higher than the SDSM.

Keyphrases: General Circulation Models, Statistical downscaling, Tabriz city temperature, climate change

@booklet{EasyChair:1813,
  author    = {Aida Hosseini Baghanam and Vahid Nourani and Ali Sheikhbabaei and Arshia Jedary Seifi},
  title     = {Statistical Downscaling and Projection of Future Temperature Change for Tabriz City, Iran},
  howpublished = {EasyChair Preprint 1813},
  year      = {EasyChair, 2019}}