Evaluation of CMIP5 Global Climate Models for Simulating Climatological Temperature and Precipitation for Southeast Asia

Abstract

This study evaluates the performances of all forty different global climate models (GCMs) that participate in the Coupled Model Intercomparison Project Phase 5 (CMIP5) for simulating climatological temperature and precipitation for Southeast Asia. Historical simulations of climatological temperature and precipitation of the 40 GCMs for the 40-year period of 1960–1999 for both land and sea and those for the century of 1901–1999 for land are evaluated using observation and reanalysis datasets. Nineteen different performance metrics are employed. The results show that the performances of different GCMs vary greatly. CNRM-CM5-2 performs best among the 40 GCMs, where its total error is 3.25 times less than that of GCM performing worst. The performance of CNRM-CM5-2 is compared with those of the ensemble average of all 40 GCMs (40-GCM-Ensemble) and the ensemble average of the 6 best GCMs (6-GCM-Ensemble) for four categories, i.e., temperature only, precipitation only, land only, and sea only. While 40-GCM-Ensemble performs best for temperature, 6-GCM-Ensemble performs best for precipitation. 6-GCM-Ensemble performs best for temperature and precipitation simulations over sea, whereas CNRM-CM5-2 performs best over land. Overall results show that 6-GCM-Ensemble performs best and is followed by CNRM-CM5-2 and 40-GCM-Ensemble, respectively. The total errors of 6-GCM-Ensemble, CNRM-CM5-2, and 40-GCM-Ensemble are 11.84, 13.69, and 14.09, respectively. 6-GCM-Ensemble and CNRM-CM5-2 agree well with observations and can provide useful climate simulations for Southeast Asia. This suggests the use of 6-GCM-Ensemble and CNRM-CM5-2 for climate studies and projections for Southeast Asia.

Assessing the Representation of Synoptic Variability Associated With California Extreme Precipitation in CMIP6 Models

“…In this study we utilize the latest suite of state of the art GCMs in the CMIP6 ensemble to evaluate their representations of synoptic‐scale flow patterns associated with precipitation over California. Previous evaluations of CMIP models have typically focused on variables of direct societal impact, for example, precipitation and temperature (Jiang et al., 2015; Kamworapan & Surussavadee, 2019; Kim et al., 2019; Koutroulis et al., 2016; Nguyen et al., 2017; Sheffield et al., 2013; Sillmann, Kharin, Zhang, et al., 2013; Wuebbles et al., 2014). Meanwhile, a separate branch of the literature utilizes the GCMs for dynamical downscaling over regions of interest (Chen et al., 2018; Gorguner et al., 2019; Harding et al., 2013; Ishida et al., 2017; Wang & Kotamarthi, 2015; Zhang & Colle, 2018).…”

Evaluation of General Circulation Models over the Upper Ouémé River Basin in the Republic of Benin

“…The performance of a model varies according to the climate parameter taken into account for its evaluation. This is the case, for example, in the studies carried out in Pakistan [8,25], Southeast Asia [2] and West Africa [1]. Similarly, for the same climate parameter, the performance of a model can vary from one statistical criterion to another over the same region.…”

“…Humans can only protect themselves from the inherent damage and manage the consequences of climate change by thinking about the development of projections of future changes. General circulation models (GCMs) are the most reliable means of estimating future climate change in an atmosphere where the concentration of greenhouse gases continues to increase significantly [1][2][3]. A climate model can be used to simulate past or future climate (climate projection).…”

Effects of climate change and land cover on the distributions of a critical tree family in the Philippines

“…A pairwise Pearson’s correlation analysis was performed on the bioclimatic variable to exclude highly correlated variables (|r|> 0.7) 59 , 60 ; variables bio02, bio10, bio11, bio13 and bio14 were retained. To model the effects of climate change, future projections of those bioclimatic variables from five best Coupled Model Inter-Comparison Project (CMIP 5 ) Global Circulation Models (GCMs) 61 for Southeast Asia 62 : Canadian Earth System Model, second generation (CanESM2); Community Earth System Model, version 1 of Biogeochemistry (CESM1-BGC); Community Earth System Model, version 5.0 of the Community Atmosphere Model (CESM1-CAM5); Centre National de Recherches Météorologiques, Climate Model version 5 (CNRM-CM5); and Model for Interdisciplinary Research On Climate, version 5 (MIROC5), were also sourced from CHELSA and averaged for two representative concentration pathways (RCPs): RCP 4.5 and 8.5 for 2070 58 . The future climate scenarios assumed that global annual greenhouse gas (GHG) emissions would peak by 2040 and subsequently decline substantially for RCP 4.5, or that GHG emissions would continue to rise until 2070 for RCP 8.5, respectively a “best-case” and “worst-case” scenario (RCP 2.6 was not considered as it was considered to be too unrealistic, assuming peak emissions by 2020 and subsequent decline) 63 .…”