Invited Review Paper Review of approaches for selection and ensembling of GCMs

Global climate models (GCMs) are developed to simulate past climate and produce projections of climate in future. Their roles in ascertaining regional issues and possible solutions in water resources planning/management are appreciated across the world. However, there is substantial uncertainty in the future projections of GCM(s) for practical and regional implementation which has attracted criticism by the water resources planners. The present paper aims at reviewing the selection of GCMs and focusing on performance indicators, ranking of GCMs and ensembling of GCMs and covering different geographical regions. In addition, this paper also proposes future research directions. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/). doi: 10.2166/wcc.2020.128 ://iwaponline.com/jwcc/article-pdf/11/3/577/716759/jwc0110577.pdf Komaragiri Srinivasa Raju Department of Civil Engineering, BITS Pilani, Hyderabad Campus, Hyderabad 500 078, India Dasika Nagesh Kumar (corresponding author) Department of Civil Engineering, Indian Institute of Science, Bangalore 560 012, India; Divecha Centre for Climate Change, Indian Institute of Science, Bangalore 560 012, India and Interdisciplinary Centre for Water Research, Indian Institute of Science, Bangalore 560 012, India E-mail: nagesh@iisc.ac.in

(), based on their studies in Zhejiang Province, in Southeast China, concluded that most GCMs were not able to predict the observed spatial patterns, due to insufficient resolution. Benedict et al. () analysed spatial resolution of GCMs and global hydrological models for the Rhine and Mississippi basins. Higher resolution GCMs yielded improved precipitation budget for the Rhine whereas no substantial improvement was found for the Mississippi. Above all, lack of precise observed data to assess the simulating ability of GCMs is another concern (https://www.tau.ac.il/∼colin/ courses/CChange/CC5.pdf; https://www.climate.gov/mapsdata/primer/climate-models; https://www.ipcc-data.org/ guidelines/pages/gcm_guide.html). Keeping this in view, Tian et al. () suggested uncertainty assessment along the whole climate modelling chain.

MULTI-MODEL ENSEMBLE OF GCMS WITHOUT EXPLICIT EVALUATION
There are several studies where multi-model ensembles were 2. The indicator can be selected category-wise (example: one from error, one from correlation coefficient, one from skill score). If required, a composite indicator can be formulated.
3. Compute weights of chosen indicators using either a rating method, analytic hierarchy process (AHP) or their fuzzy extension. Accordingly, for evaluation, the highest weighted indicator can be considered.

Lastly, the principal component analysis of indicators
can be explored for dimension reduction, to arrive at (say) two components which will be sufficient and useful for evaluating the GCMs.
We suggest, these perspectives can be considered as a future research area.

Handling uncertainty in indicators
Observation       Some data mining algorithms that facilitate clustering of GCMs are also briefly described below:

Ranking of GCMs
• K-means cluster analysis can group GCMs into relatively homogeneous clusters, that is, GCMs in a cluster expectedly more analogous to each other than those in the other clusters (Raju & Nagesh Kumar , ). In brief, the procedure of K-means is as follows: (1)   • ELECTRE-TRI assigns GCMs to some predefined ordered clusters (Rogers et al. ; Raju et al. ).
The limit between two consecutive clusters is demarcated by a profile. Clusters are mutually exclusive which means that one GCM cannot be entrusted to two different clusters. Construction and exploitation of an outranking relation is the basis for allotting GCMs to clusters. Most of these validation techniques work with inter-cluster and intra-cluster distances.
To augment the earlier studies on the topic: • Researchers can use published literature to select suitable GCMs and ensembling for their study area.
• Research institutions can take the lead, and region-wise ranking of GCMs may be done (if not done already or outcomes of previous studies not confirmed) in a robust manner. These GCMs can be frozen to avoid repetitive efforts of researchers. Time thus saved, can be utilised for analysing the projected outcomes and validating them.
• One of the crucial aspects in assessment of GCMs is comparison with observed data which may have such limitations such as, accessibility to all researchers, authenticity, confidence in the data, acquisition procedures and permissions to use the data. It is suggested to have a central database at least for each country where data can be easily accessible. In our opinion, more accessibility of data to users may provide better outcomes which may provide confidence zones to planners for impact studies.

SUMMARY AND CONCLUSIONS
This state-of-the-art review paper has provided insights into mainly three aspectsperformance indicators, ranking of GCMs and ensembling of GCMs. This study discussed the role of performance indicators, various types of performance indicators, necessity of GCM evaluation and relevant challenges and the basis of MME. Future research directions can be in terms of handling uncertainty in indicators, and ranking of GCMs both in fuzzy-and stochastic-based decision-making perspectives. In addition, various relevant MME-based approaches that provide optimum ensembling are also presented. In addition, a number of techniques which facilitate ranking and ensembling are also part of the paper.
Brief but relevant conclusions are as follows: Perera, Editor-in-Chief, for encouraging them to write this article and also for his constructive criticism throughout the review process. Some views of esteemed researchers were quoted verbatim to convey their views without losing the meaning. Acknowledgements are due to the esteemed reviewers for providing valuable suggestions which helped us to think more critically while revising this paper.