Selection of multi-model ensemble of GCMs for the simulation of precipitation based on spatial assessment metrics

Abstract

Abstract. The climate modelling community has trialled a large number metrics to evaluate the temporal performance of the Global Circulation Models (GCMs) for the selection of GCMs, while very little attention has been given to spatial performance of GCMs which is equally important. This study evaluated the performance of 20 Coupled Model Intercomparison Project 5 (CMIP5) GCMs pertaining to their skills in simulating mean annual, monsoon and winter precipitation over Pakistan using state-of-the-art spatial metrics; SPAtial EFficiency, Goodman–Kruskal's lambda, Fractions Skill Score, Cramer’s V, Mapcurves, and Kling-Gupta efficiency for the period 1961–2005. The multi-model ensemble (MME) precipitation was generated through intelligent merging of simulated precipitation of selected GCMs employing Random Forest (RF) regression and Simple Mean (SM). The results indicated some differences in the ranks of GCMs for different metrics. The overall ranks indicated NorESM1-M, CESM1-CAM5, GFDL-CM3 and GFDL-ESM2G as the best GCMs in simulating the spatial patterns of mean annual, monsoon and winter precipitation over Pakistan. MME precipitation generated based on the best performing GCMs showed more similarities with observed precipitation compared to precipitation simulated by individual GCMs. The MME developed using RF displayed better performance than the MME-based on SM. Multiple spatial metrics have been used for the first time for selecting GCMs based on their capability to mimic the spatial patterns of annual and seasonal precipitation. The approach suggested in the present study can be extended to any number of GCMs and climate variables and applicable to any region for the suitable selection of an ensemble of GCMs to reduce uncertainties in climate projections.

Temperature-based phenology model for predicting the present and future establishment and distribution of recently invasiveSpodoptera frugiperda(J. E. Smith) in India

“…Future temperature data (years 2050 and 2070) in the present study were used from the ensemble of eight general circulation models (GCMs) (BCC-CSM1-1; GISS-ER-R; HADGEM2-AO; HadGEM2-ES; IPSL-CM5A-MR; MIROC-ESM-CHEM; MRI-CGCM3; Nor-ESM1-M) and four greenhouse gas concentration trajectories scenarios (RCP 2.6, RCP 4.5, RCP 6.0, RCP 8.5) as proposed in the Fifth Assessment of the Intergovernmental Panel for Climate Change to reduce GCM and scenario-based uncertainties (CMIP5) (Moss et al, 2010). A simple arithmetic average of the ensemble members with equal weight is generally more reliable over a single model approach (Her et al, 2016;Kim et al, 2016;Ahmed et al, 2019). Therefore, in the present study, we used an arithmetic average ensemble of eight GCMs.…”

Impact of Climate Change on Water Balance Components and Droughts in the Guajoyo River Basin (El Salvador)

“…Using this validation-based approach, we are assuming that a model's ability to simulate an observed baseline climate will be representative of the same model's ability to project future climate. The selection of GCMs has been analyzed in a number of studies (e.g., [41,53,54]). However, there is not yet sufficient scientific consensus with respect to both the identification of unsatisfactory models, and the relation between apparently poor performance to the plausibility of future projections [55].…”

Evaluation of CMIP6 GCMs for simulations of temperature over Thailand and nearby areas in the early 21st century

“…The multi-model ensemble is a method created by multiple model simulations. Considering the summary by several previous studies, this method can reduce the biases and uncertainties of the simulations associated with GCMs ( Ahmed et al., 2018 ; He et al., 2019 ; Raju and Kumar, 2020 ; Yan et al., 2015 ). In addition, several researchers have demonstrated that the multi-model ensemble method can improve climate simulation performance compared to single models ( Chhin and Yoden, 2018 ; Hughes et al., 2014 ; Kamworapan and Surussavadee, 2019 ; Raju and Kumar, 2014 , 2015 ).…”