How far the CORDEX high-resolution data represents observed precipitation: an analysis across Indian mainland
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ORIGINAL PAPER
How far the CORDEX high-resolution data represents observed precipitation: an analysis across Indian mainland Subbarao Pichuka 1 & Rajib Maity 2 Received: 10 May 2018 / Accepted: 12 August 2020 # Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract Coordinated Regional Climate Downscaling Experiment (CORDEX) has developed high-resolution, dynamically downscaled data across the world. This paper investigates the spatio-temporal correspondence between CORDEX data for South Asia (CORDEX-SA) and observed precipitation data to assess its reliability. The entire Indian mainland is considered as study region. Both monthly and seasonal analyses are undertaken along with the extreme magnitudes during historical (1971–2005) and future (2006–2100) periods. The outputs of two regional climate models (RCMs), viz. regional climate model, version-4.4 (RegCM4) and Rossby Centre regional Atmospheric model, version-4 (RCA4), participating in CORDEX-SA program, are considered for medium (RCP4.5) and high (RCP8.5) emission scenarios. The CORDEX-SA is found to well represents in central India, but significant bias in the mean precipitation (± 200 mm) is noticed at northern, north-eastern, southern and coastal regions. The inferior performance of both the RCMs is noticed over high rainfall regions. Specifically, more than 70% area is found to have mean bias more than ± 100 mm. Performance of RCA4 is better in North and Himalayan regions, where 67% of area is found to be within the aforementioned threshold, which is 23.64% in case of RegCM4. Performance of RegCM4 is marginally better in the peninsular India as compared with RCA4. In case of extreme magnitudes, the insignificant correspondance between CORDEX-SA data and observed precipitation is noticed over the many parts of India. Results of this study are important to check the applicability of CORDEX data in various impact assessment studies.
1 Introduction The socio-economic status of any agricultural dependent country is greatly influenced by the occurrence of precipitation. The water resource development and management of a region directly depends on the amount of precipitation in that region. Precipitation, being a vital process in the hydrological cycle, varies over short spatial scale. Therefore, the spatial variability of precipitation is essential for making suitable policies in a particular region. Hence, prior assessment of the future precipitation can be helpful in various sectors, for instance, water resource management and agricultural productivity (Ramos et al. 2012; Nepal and Shrestha 2015; Pradhan et al. 2015). * Rajib Maity [email protected] 1
Department of Civil Engineering, National Institute of Technology Andhra Pradesh, Tadepalligudem, India
2
Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
In this context, general circulation models (GCMs) have projected the global precipitation for the twenty-first century. However, due to their coarse resolution, the GCM outputs are incap
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