Procedure for selecting GCM datasets for climate risk assessment

Abstract

General Circulation Models (GCMs) are indispensable tools to project future climate. It is not realistic or necessary to use all GCM datasets when assessing climate risks and building adaptive capacity. Thus, a rational procedure for selecting GCM datasets is needed. It is also required to classify weather stations into climate zones and then suggest a suitable list of GCM datasets to avoid weather stations with similar climate patterns but using different GCM datasets. The purpose of this study is to establish a process for selecting GCM datasets for a region. The process consists of climate zonation, applicability ranking, and a model similarity check. Principal component analysis (PCA) and cluster analysis are used to classify regional weather stations into climate zones. The weighted average ranking (WAR) method and demerit point system (DPS) are then used to rank the GCM performance using CMIP5 (Coupled Model Intercomparison Project Phase 5) datasets. The GCM family tree is then applied to screen out highly similar GCMs before generating a GCM suggestion list. Taiwan is chosen as the study area for this investigation. Taiwan receives monthly mean precipitation data from 25 weather stations. The weather stations were clustered into ten climate zones with different GCM datasets suggested for each zone. The top five GCM datasets suggested for Taiwan by the WAR method are HadGEM2-AO, CESM1-CAM5, CCSM4, MIROC5, and GISS-E2-R while those suggested by the DPS method are CSIRO-Mk3-6-0, HadGEM2-AO, CESM1-CAM5, MIROC5, and CCSM4. The GCM selection process presented in this study is applicable to other regions to assist users in finding GCM datasets suitable for their research.

Selection of CMIP5 GCM Ensemble for the Projection of Spatio-Temporal Changes in Precipitation and Temperature over the Niger Delta, Nigeria

“…This becomes more difficult when the underlying preference model, like weights assignable to different attributes for some parameters, are considered. To overcome these challenges, a technique that aggregates and combines information from different sources such as the weighting method [55], frequency of occurrence majority rule [56], and numerical averaging [13] can be employed. In this study, the ranks of the GCMs relating to each grid point were computed for each climate variable based on the computed SU weights for the 22 grid points, considering all 26 GCMs.…”

Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study

“…GCM simulations are associated with large uncertainties arising from different sources including model resolution, mathematical formulation, initial assumptions, and calibration processes that restrict the use of all GCMs for reliable projections of climate at the regional or local scale (Hijmans et al 2005;Foley 2010; Chen et al 2011;Northrop 2013;Khan et al 2018a;Salman et al 2018;Sun et al 2018;Ahmed et al 2019c). Therefore, a subset of GCMs is suggested by removing the less skilled models in simulating observed climate to minimize uncertainties in projection (Lutz et al 2016;Lin and Tung 2017;Khan et al 2018b;Salman et al 2018;Ahmed et al 2019b).…”

Low Impact Development Planning and Adaptation Decision-Making under Climate Change for a Community against Pluvial Flooding

“…The GCMs are selected based on the criteria of the trend The weather data used for the designed community are obtained from the station closest to the reference community (Hsinchu station). The GCMs are selected based on the criteria of the trend similarity (R 2 ) and differences (root mean square error; RMSE) of the baseline mean monthly precipitation (1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005) between the data produced by different GCMs at the nearest data point and the observed data of Hsinchu station [50]. Three GCMs are selected taking into consideration the required computation efforts.…”