EGU2020-927, updated on 12 Jun 2020
https://doi.org/10.5194/egusphere-egu2020-927
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Fidelity of CORDEX Evaluation runs under Non-stationary climate

Swati Singh1, Kaustubh Salvi2, Subimal Ghosh1,2, and Subhankar Karmakar1,3
Swati Singh et al.
  • 1Indian Institute of Technology, Bombay, Climate Studies, Mumbai, India (swatisingh0705@gmail.com)
  • 2Indian Institute of Technology, Bombay, Department of Civil Engineering, Mumbai, India (kaustubh.a.salvi@gmail.com, subimal.ghosh@gmail.com)
  • 3Indian Institute of Technology, Bombay, Centre for Environmental Science and Engineering, Mumbai, India (subhankar.karmakar@gmail.com)

The downscaling approaches: Statistical and Dynamic, developed for regional climate predictions, have both advantages and limitations. The statistical downscaling is computationally inexpensive but suffers from the violation of the assumption of stationarity in statistical (predictor-predictand) relationship. The dynamical downscaling is assumed to take care of stationarity but suffers from the biases associated with various sources.  Here we propose a joint approach of both the methods by applying statistical methods: bias correction & statistical downscaling to Coordinated Regional Climate Downscaling Experiment (CORDEX) evaluation runs. The evaluation runs are considered as perfect simulations of CORDEX Regional Climate Models (RCMs) with the boundary conditions by ERA-Interim reanalysis data. The statistical methods are also applied to ERA-Interim reanalysis data and compared with observation data for Indian Summer Monsoon characteristics. We evaluate the ability of statistical methods under the non-stationary environment by taking the difference of years close to extreme future runs (RCP8.5) as warmer years and preindustrial runs as cooler years. We find statistical downscaling of CORDEX evaluation runs shows skill in reproducing the signal of non-stationarity. The study can be extended methods by applying statistical downscaling to CORDEX RCMs with the CMIP5 boundary conditions. 

How to cite: Singh, S., Salvi, K., Ghosh, S., and Karmakar, S.: Fidelity of CORDEX Evaluation runs under Non-stationary climate, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-927, https://doi.org/10.5194/egusphere-egu2020-927, 2019