2021
DOI: 10.1002/joc.7164
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Unravelling the influence of subjectivity on ranking of CMIP6 based climate models: A case study

Abstract: The skill of General Circulation Models (GCMs) in mimicking the observed climate is assessed through various procedures and are ranked. The performance of a GCM is site-specific and the ranking pattern varies spatially. In general, a set of best performing GCMs is extracted to study the impact of climate change. As, there is no universally accepted ranking procedure, the ranking of GCMs is prone to subjectivity. In this study, it is aimed to address the effect of this subjectivity on the GCM rankings. The past… Show more

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Cited by 29 publications
(14 citation statements)
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“…The entropy technique is used to determine the weightage of each performance indicator used in the study [28,46]. The entropy for each indicator j is calculated by using Equation (2).…”
Section: Weight Computing Technique (Entropy Technique)mentioning
confidence: 99%
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“…The entropy technique is used to determine the weightage of each performance indicator used in the study [28,46]. The entropy for each indicator j is calculated by using Equation (2).…”
Section: Weight Computing Technique (Entropy Technique)mentioning
confidence: 99%
“…A lower L p indicates the best suitable RCM. Equation ( 6) represents the expression for the L p metric [46].…”
Section: Compromise Programmingmentioning
confidence: 99%
“…In order to rank the performance of earlier studies, GCM outputs were spatially re-gridded to a common grid resolution of 2°Â 2° (Ahmed et al 2019b;Noor et al 2019), 2.5°Â 2.5° (Johnson & Sharma 2009;Eghdamirad et al 2017;Raju et al 2017;Jiang et al 2019), and 3°Â 3° (Woldemeskel et al 2014). Due to the fact that some researchers preferred to re-grid the GCM data into a resolution of the observed gridded dataset (Tiwari et al 2014;Pour et al 2018;Hassan et al 2020;Homsi et al 2020;Anil et al 2021), the collected GCM outputs were spatially re-gridded to 0.25°Â 0.25°. However, the GCM outputs were bias-corrected using the empirical quantile mapping method after re-gridding the raw GCM outputs to the spatial resolution of 0.25°Â 0.25°to remove the inherent biases.…”
Section: Cmip6 Gcm Datasetsmentioning
confidence: 99%
“…(IPCC 2013;Tiwari et al 2014;McSweeney et al 2015). Apart from these uncertainties, reference datasets and performance evaluation metrics also influence the performance of GCMs (Anil et al 2021) used. Due to all these uncertainties associated with GCMs, the selection of GCMs is becoming a challenge in terms of ranking them using the simulation of the current climate (Raju et al 2017;Kamworapan & Surussavadee 2019).…”
Section: Graphical Abstract Introductionmentioning
confidence: 99%
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