Assessment and ranking of CMIP5 GCMs performance based on observed statistics over Cauvery river basin – Peninsular India

Assessing information on climate change over a regional scale is made possible through general circulation models (GCMs). However, developers generally have a dilemma in selecting suitable GCM for regional scale downscaling to reduce the computational burden. Ranking of GCMs based on various conditi...

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Bibliographic Details
Published in:Arabian journal of geosciences 2020-11, Vol.13 (22), Article 1200
Main Authors: Loganathan, Parthiban, Mahindrakar, Amit Baburao
Format: Article
Language:English
Subjects:
Air temperature
Climate change
Climate models
Climate studies
Computer applications
Earth and Environmental Science
Earth science
Earth Sciences
Environmental assessment
General circulation models
Hydrologic data
Intercomparison
Nonparametric statistics
Original Paper
Parameters
Performance evaluation
Precipitation
Ranking
Regional analysis
Regional development
Reliability analysis
River basins
Statistical analysis
Statistical methods
Statistics
Trend analysis
Trends
Weather
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Summary:Assessing information on climate change over a regional scale is made possible through general circulation models (GCMs). However, developers generally have a dilemma in selecting suitable GCM for regional scale downscaling to reduce the computational burden. Ranking of GCMs based on various conditions will help these purposes, and the present study evaluates the performance of GCMs using various performance evaluation parameters for ranking. Performance of twenty-six Coupled Model Intercomparison Project Phase 5 (CMIP5) GCMs was assessed in the present study to evaluate and rank the predictability of near-surface air temperature (tas) and precipitation (pr). The non-parametric trend existing in observed data from 35 stations is compared with GCM projected trends using Mann-Kendall trend analysis to assess the model reliability. Performance evaluation parameters such as percentage BIAS (PBIAS %), normalized root mean squared error (NRMSE %) and coefficient of determination ( R 2 ). Neither of the CMIP5 GCM performed consistently well throughout all four seasons. Also, models performing better in projecting temperature statistics are poor in capturing the precipitation trends and vice versa. The seasonal ranking of GCMs based on their ability to reproduce the regional weather condition would help in selecting suitable GCM for regional climate studies.
ISSN:1866-7511
1866-7538
DOI:10.1007/s12517-020-06217-6