Journal of Water and Climate Change (Feb 2024)

Selection of representative general circulation models under climatic uncertainty for Western North America

  • Seyed Kourosh Mahjour,
  • Giovanni Liguori,
  • Salah A. Faroughi

DOI
https://doi.org/10.2166/wcc.2024.541
Journal volume & issue
Vol. 15, no. 2
pp. 686 – 702

Abstract

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Climate change research uses an ensemble of general circulation model runs (GCMs-runs) to predict future climate under uncertainties. To reduce computational costs, this study selects representative GCM-runs (RGCM-runs) for Western North America (WNA) based on their performance in replicating historical climate conditions from 1981 to 2005 and projecting future changes from 1981–2010 to 2071–2100. This evaluation is conducted under two representative concentration pathways (RCPs) scenarios, RCP4.5 and RCP8.5, from the Coupled Model Intercomparison Project 5. By using an envelope-based selection technique and a multi-objective distance-based approach, we identify four RGCM-runs per RCP representing diverse climatic conditions, including wet-warm, wet-cold, dry-warm, and dry-cold. Compared to the full-set, these selected runs show a decreased mean absolute error (MAE) between the reference and RGCM-runs concerning the monthly average mean air temperature (T̄) and precipitation (P̄). For RCP4.5, T̄ MAE is 0.45 (vs. 0.58 in the full-set) and P̄ MAE is 0.31 (vs. 0.42). For RCP8.5, T̄ MAE is 0.51 (vs. 0.75) and P̄ MAE is 0.25 (vs. 0.36). The lower MAE values in the RGCM-run set indicate closer alignment between predicted and reference values, making the RGCM-run suitable for climate impact assessments in the region. HIGHLIGHTS Pioneered a multistep framework for representative general circulation model run (RGCM-run) selection in Western North America, considering computing limitations and ensuring diverse climatic scenarios.; Compared temperature and precipitation variability under RCP4.5 and RCP8.5, aiding decision-making in the context of climate change.; Demonstrated substantial reduction in mean absolute error for vital climatic variables using selected RGCM-runs, reducing uncertainty.;

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