Unveiling Deviations from IPCC Temperature Projections through Bayesian Downscaling and Assessment of CMIP6 General Circulation Models in a Climate-Vulnerable Region

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 11; p. 1831
• Main Authors: Ferreiro-Lera, Giovanni-Breogan, Penas, Angel, del Río, Sara
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-06-2024
• Subjects: Artificial intelligence; Artificial satellites in remote sensing; Bayesian analysis; Climate change; Climate models; CMIP6; Comparative analysis; Drought; Empirical Bayesian Kriging; ERA5 Land; General circulation models; Global Circulation Models; Global warming; Goodness of fit; Intergovernmental Panel on Climate Change; Machine learning; Mathematical models; Pixels; Precipitation; Regions; Simulation; Temperature; Trends; Spain; Mediterranean Area
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16111831

The European Mediterranean Basin (Euro-Med), a region particularly vulnerable to global warming, notably lacks research aimed at assessing and enhancing the widely used remote climate detection products known as General Circulation Models (GCMs). In this study, the proficiency of GCMs in replicating reanalyzed 1981–2010 temperature data sourced from the ERA5 Land was assessed. Initially, the least data-modifying interpolation method for achieving a resolution match of 0.1° was ascertained. Subsequently, a pixel-by-pixel evaluation was conducted, employing five goodness-of-fit metrics. From these metrics, we compiled a Comprehensive Rating Index (CRI). A Multi-Model Ensemble using Random Forest was constructed and projected across three emission scenarios (SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5) and timeframes (2026–2050, 2051–2075, and 2076–2100). Empirical Bayesian Kriging, selected for its minimal data alteration, supersedes the commonly employed Bilinear Interpolation. The evaluation results underscore MPI-ESM1-2-HR, GFDL-ESM4, CNRM-CM6-1, MRI-ESM2-0, CNRM-ESM2-1, and IPSL-CM6A-LR as top-performing models. Noteworthy geospatial disparities in model performance were observed. The projection outcomes, notably divergent from IPCC forecasts, revealed a warming trend of 1 to over 2 °C less than anticipated for spring and winter over the medium–long term, juxtaposed with heightened warming in mountainous/elevated regions. These findings could substantially refine temperature projections for the Euro-Med, facilitating the implementation of policy strategies to mitigate the effects of global warming in vulnerable regions worldwide.