Evaluation of Multi-Model Hindcasts of Overland Precipitation for Georgia

Author:

Davitashvili T.1,Kutaladze N.2,Kvatadze R.3,Megrelidze L.2,Mikuchadze G.2,Samkharadze I.4

Affiliation:

1. Ilia Vekua Institute of Applied Mathematics of Ivane Javakhishvili Tbilisi State University, 2 University Street, 0186, Tbilisi, Georgia

2. National Environmental Agency of Ministry of Environment Protection and Agriculture of Georgia, 150, David Agmashenebeli Ave., 0112, Tbilisi, Georgia

3. Georgian Research and Educational Networking Association GRENA, 4a, Chovelidze Street, 0108, Tbilisi, Georgia

4. Ivane Javakhishvili Tbilisi State University, 1, Chavchavadze Ave., 0179, Tbilisi, Georgia

Abstract

This study evaluates the ability of several Regional Climate Models (RCMs) to simulate rainfall patterns in the South Caucasus region. In total, 8 RCM simulations were assessed against the CRU observational database over different domains, among them two from the Coordinated Regional Climate Downscaling Experiment (CORDEX). Seasonal climatology, annual rainfall cycles and interannual variability in RCM outputs were estimated for 8 homogeneous sub-regions against several observational datasets. Different metrics covering from monthly and seasonal to annual time scales are analyzed over the region of interest. The results confirm the distinct capabilities of climate models in capturing the local features of the climatic conditions of the South Caucasus region. At the same time, the analysis shows significant deviations in individual models depending on the sub-region and season; however, the ensemble mean is in better agreement with observations than individual models. Overall, the analysis presented here demonstrates that, the multi-model ensemble mean adequately simulates rainfall in the South Caucasus and, therefore, it can be used to assess future climate predictions for the region. This work promotes the selection of RCM runs with reasonable performance in the South Caucasus region, from which, for the first time, a high-resolution bias-adjusted climate database can be generated for future risk assessment and impact studies.

Publisher

North Atlantic University Union (NAUN)

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