Abstract
Selection of a suitable general circulation model (GCM) ensemble is crucial for effective water resource management and reliable climate studies in developing countries with constraint in human and computational resources. A careful selection of a GCM subset by excluding those with limited similarity to the observed climate from the existing pool of GCMs developed by different modeling centers at various resolutions can ease the task and minimize uncertainties. In this study, a feature selection method known as symmetrical uncertainty (SU) was employed to assess the performance of 26 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCM outputs under Representative Concentration Pathway (RCP) 4.5 and 8.5. The selection was made according to their capability to simulate observed daily precipitation (prcp), maximum and minimum temperature (Tmax and Tmin) over the historical period 1980–2005 in the Niger Delta region, which is highly vulnerable to extreme climate events. The ensemble of the four top-ranked GCMs, namely ACCESS1.3, MIROC-ESM, MIROC-ESM-CHM, and NorESM1-M, were selected for the spatio-temporal projection of prcp, Tmax, and Tmin over the study area. Results from the chosen ensemble predicted an increase in the mean annual prcp between the range of 0.26% to 3.57% under RCP4.5, and 0.7% to 4.94% under RCP 8.5 by the end of the century when compared to the base period. The study also revealed an increase in Tmax in the range of 0 to 0.4 °C under RCP4.5 and 1.25–1.79 °C under RCP8.5 during the periods 2070–2099. Tmin also revealed a significant increase of 0 to 0.52 °C under RCP4.5 and between 1.38–2.02 °C under RCP8.5, which shows that extreme events might threaten the Niger Delta due to climate change. Water resource managers in the region can use these findings for effective water resource planning, management, and adaptation measures.
Highlights
IntroductionGeneral circulation models (GCMs) are numerical representations of the atmosphere, ocean, and land surface processes developed based on physical laws and physical-based empirical relationships
General circulation models (GCMs) are numerical representations of the atmosphere, ocean, and land surface processes developed based on physical laws and physical-based empirical relationships.general circulation model (GCM) simulations are essential tools for assessing the impact of climate change for a range of human and natural systems [1]
A suitable set of GCM ensembles for simulating the spatio-temporal changes in both prcp, Tmin, and Tmax were selected based on their performances in simulating the observed climate research unit (CRU) datasets using the symmetrical uncertainty (SU) filter using 26 GCM outputs under RCP4.5 and RCP8.5 emission scenarios
Summary
General circulation models (GCMs) are numerical representations of the atmosphere, ocean, and land surface processes developed based on physical laws and physical-based empirical relationships. Three algorithms known as ‘filters’, ‘wrappers’, and the ‘hybrid’ of filters and wrappers have been used by the past performance approach in the selection of the GCM subset by ranking the GCMs concerning a climate variable(s) based on their past performance [11,20]. Many studies have been conducted to determine the performance of GCM outputs by employing various wrappers and filters with respect to gridded data, which include clustering hierarchy [14], weighted skill score [28], spectral analysis [29], Bayesian weighting [30], and information entropy [31] Various statistical indicators such as correlation coefficients [22] have been used for GCM evaluation, ranking, and selection.
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