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Effects of Observational Uncertainty and Models Similarity on Climate Change Projections
KAUST DepartmentPhysical Science and Engineering (PSE) Division
Earth Science and Engineering Program
Extreme Computing Research Center
KAUST Grant NumberREP/1/3268-01-01
Permanent link to this recordhttp://hdl.handle.net/10754/690130.1
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AbstractClimate change projections (CCPs) are based on the multimodel means of individual climate model simulations that are assumed to be independent. However, model similarity leads to projections biased toward the largest set of similar models and the underestimation of uncertainties. We assessed the influence of similarities in CMIP6 through CMIP3 CCPs. We ascertained model similarity due to shared physics/dynamics and initial conditions by comparing simulated spatial temperature and precipitation with the corresponding observed patterns and accounting for inter-model spread relative to the spread across observational datasets. After accounting for similarity, the information from 57 CMIP6, 47 CMIP5, and 24 CMIP3 models could be explained by just 11 effective models, without significant differences in globally averaged climate change statistics. The effective models showed a smaller globally averaged temperature rise of 0.25ºC (~0.5ºC–1ºC in some regions) by the end of 21 century relative to the multimodel mean of all models for socioeconomic pathways 5–8.5.
CitationPathak, R., Prasad, D. H., Karumuri, A., & Hoteit, I. (2023). Effects of Observational Uncertainty and Models Similarity on Climate Change Projections. https://doi.org/10.21203/rs.3.rs-2448114/v1
SponsorsThe Program for Climate Model Diagnosis and Intercomparison is acknowledged for making CMIP model data publicly available. The supercomputing facility at King Abdullah University of Science and Technology (KAUST) is acknowledged for providing fast computation and analysis support. NCAR-NCL and MATLAB software were used for data processing and visualization. This research was supported by the Office of Sponsored Research at KAUST under the Virtual Red Sea Initiative (REP/1/3268-01-01), the Saudi ARAMCO Marine Environmental Research Center, and the Climate Change Center at KAUST.
PublisherResearch Square Platform LLC
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