Comparing Statistical Downscaling and Arithmetic Mean in Simulating CMIP6 Multi-Model Ensemble over Brunei

Abstract

The climate is changing and its impacts on agriculture are a major concern worldwide. The impact of precipitation will influence crop yield and water management. Estimation of such impacts using inputs from the General Circulation Models (GCMs) for future years will therefore assist managers and policymakers. It is therefore important to evaluate GCMs on a local scale for an impact study. As a result, under the Shared Socioeconomic Pathways (SSPs) future climate scenarios, namely SSP245, SSP370, and SSP585, simulations of mean monthly and daily precipitation across Brunei Darussalam in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) were evaluated. The performance of two multi-model ensemble (MME) methods is compared in this study: the basic Arithmetic Mean (AM) of MME and the statistical downscaling (SD) of MME utilizing multiple linear regression (MLR). All precipitation simulations are bias-corrected using linear scaling (LS), and their performance is validated using statistical metrics such as Root Mean Square Error (RMSE) and coefficient of determination (R2). The adjusted mean monthly precipitation during the validation period (2010–2019) shows an improvement, especially for the SD model with R2 = 0.85, 0.86 and 0.84 for SSP245, SSP370 and SSP585, respectively. Although the two models produced unsatisfying results in producing annual precipitation. Future analysis under the SD model shows that there will be a much lower average monthly trend in comparison with the observed trend. On the other hand, the forecasted monthly precipitation under AM predicted the same rainfall trend as the baseline period in the far future. It is projected that the annual precipitation in the near future will be reduced by at least 27% and 11% under the SD and AM models, respectively. In the long term, less annual precipitation changes for the SD model (17%). While the AM model estimated a decrease in precipitation by at least 14%.