Ranking of CMIP5 GCM Skills in Simulating Observed Precipitation over the Lower Mekong Basin, Using an Improved Score-Based Method

Abstract

This study assessed the performances of 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) in reproducing observed precipitation over the Lower Mekong Basin (LMB). Observations from gauge-based data of the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) precipitation data were obtained from 1975 to 2004. An improved score-based method was used to rank the performance of the GCMs in reproducing the observed precipitation over the LMB. The results revealed that most GCMs effectively reproduced precipitation patterns for the mean annual cycle, but they generally overestimated the observed precipitation. The GCMs showed good ability in reproducing the time series characteristics of precipitation for the annual period compared to those for the wet and dry seasons. Meanwhile, the GCMs obviously reproduced the spatial characteristics of precipitation for the dry season better than those for annual time and the wet season. More than 50% of the GCMs failed to reproduce the positive trend of the observed precipitation for the wet season and the dry season (approximately 52.9% and 64.7%, respectively), and approximately 44.1% of the GCMs failed to reproduce positive trend for annual time over the LMB. Furthermore, it was also revealed that there existed different robust criteria for assessing the GCMs’ performances at a seasonal scale, and using multiple criteria was superior to a single criterion in assessing the GCMs’ performances. Overall, the better-performed GCMs were obtained, which can provide useful information for future precipitation projection and policy-making over the LMB.

Assessing the Performance of CMIP5 Global Climate Models for Simulating Future Precipitation Change in the Tibetan Plateau

“…The Sscore describes the degree of overlap between the observed and simulated probability distributions. The improved rank score method distinguishes relative error indices from the nonrelative error indices, which could provide consistent results in multiple criteria assessment [29,30]. In this study, M, SD, Z, Slope, and BS are relative error indices, while r_t, r_s, and Sscore are nonrelative error indices.…”

“…However, the fact that these three models perform relatively better for TP does not means that they will perform as well in other regions. Previous studies also showed that the performance of GCMs depends on the region they are applied to [29,32]. Therefore, the performance of GCMs should be assessed at the regional scale to obtain more reliable information.…”

“…TP is located in East Asia. The study area in this work is limited to [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] • N and 74-106 • E (Figure 1). TP features a complex topography, snow mountains, glaciers, permafrost, and lakes.…”

“…Table 1 provides summary information on the GCMs, whereas more details are available on the CMIP5 website. In view of the different spatial resolutions of the GCMs, precipitation data of the GCMs were all resampled to a 2 • × 2 • grid using a bilinear interpolation approach [29].…”

“…Many studies have been conducted to assess the performance of precipitation simulations of the CMIP5 GCMs using various methods in different regions [24][25][26][27][28][29]. Fu et al [30] evaluated the performance of 25 GCMs using a score-based method in southeastern Australia.…”