A comparative study of TOPSIS‐based GCMs selection and multi‐model ensemble

Abstract

General circulation models (GCMs) are developed to simulate the past climate and generate future climate predictions. In the context of global warming, their important roles in identifying possible solutions to water resources planning/management are recognized by the world. However, in actual and regional implementation, due to many factors like initial and boundary conditions, parameters and model structures and so forth, there are great variabilities and uncertainties across the future climate projections of GCMs outputs, which has attracted criticism from water resources planners. Thus, the GCMs usually must be evaluated for assessing their performances in simulating the historical observations. Currently, there are many different conclusions and opinions as to whether the optimal individual model is more advantageous or whether the combined consideration of MME works better. The purpose of this paper is to compare the advantages and disadvantages between the selection of the optimal single GCM and multi‐model ensemble (MME) based on one of the more objective selection methods called TOPSIS. The results show that the performances of GCMs in simulating precipitation and temperature in different climate subregions over China are not identical. For CMIP6 GCMs simulations, the optimal precipitation GCM is CMCC‐CM2‐SR5 for EC, MIROC6 for SC, SWC and NWC, CESM2‐WACCM for NC and NEC, and FGOALS‐g3 for QTP. As for temperature, only NESM3 and BCC‐ESM1 are the optimal GCM for QTP and NWC, respectively; in other subregions, MME is better than single GCM. In general, a simple arithmetic averaging approach employed to generate the MME model is not superior to the optimal GCM, although the error metric with the observed data is reduced, at the cost of a severe compression of the interannual variability.

Hydrological Drought and Flood Projection in the Upper Heihe River Basin Based on a Multi-GCM Ensemble and the Optimal GCM

“…The method is based on the principle that the chosen alternative should have the shortest distance from the ideal solution and the furthest distance from the anti-ideal solution. In this study, we use the entropy-weighted TOPSIS method to prioritize the 10 bias-corrected GCM outputs [34,35] according to their performances in simulating the observed precipitation and the maximum and minimum temperatures from 1987 to 2014. The procedures are as follows [34]:…”

“…In this study, we use the entropy-weighted TOPSIS method to prioritize the 10 bias-corrected GCM outputs [34,35] according to their performances in simulating the observed precipitation and the maximum and minimum temperatures from 1987 to 2014. The procedures are as follows [34]:…”

“…It should be noted that, for the contrary trends in the future runoff projections from the MME and the optimal model, it is hard to say which one is more accurate, since both of them have uncertainty. The MME may involve inferior GCMs and thus reduce the reliability of climate projections, and the optimal model depends on many factors such as evaluation methods, evaluation indicators, and so forth, which result in the optimal model not being uniquely determined [2,[29][30][31][32][33][34]. In addition to the uncertainty involved in the GCM selection process, we assumed that the optimal model selected based on historical data would be applicable to future periods, which also carries some uncertainties since we cannot judge whether the meteorological elements in the future will still follow the laws of historical periods.…”

“…Some research prefers to use a multi-model ensemble (MME) as a potential solution for reducing the reliance of climate projections on a single model and the possible errors caused by an inferior choice of GCM (e.g., [2,[29][30][31]). Some believe that to abandon some GCMs with poor performance in projections and to select a few optimal GCMs from a pool is very necessary [32], and they regard the optimal GCM as generally corresponding to the best performance and less uncertainty [33,34]. However, undeniably, the selection of the optimal GCM usually depends on many factors such as evaluation methods and evaluation indicators [34,35].…”

“…Some believe that to abandon some GCMs with poor performance in projections and to select a few optimal GCMs from a pool is very necessary [32], and they regard the optimal GCM as generally corresponding to the best performance and less uncertainty [33,34]. However, undeniably, the selection of the optimal GCM usually depends on many factors such as evaluation methods and evaluation indicators [34,35]. Since not much evidence suggests that the MME and the optimal GCM will agree on the sign and magnitude of hydrological extreme responses, we will use both of them to assess the future drought and flood responses to climate change in this study, and then visit the similarities and differences on the conclusions arising therefrom.…”