Quantifying climate model representation of the wintertime Euro-Atlantic circulation using geopotential-jet regimes

• Published in: Weather and Climate Dynamics
• Main Authors: J. Dorrington, K. Strommen, F. Fabiano
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2022
• Subjects: Meteorology. Climatology; QC851-999; North Atlantic
• Online Access: https://doi.org/10.5194/wcd-3-505-2022; https://doaj.org/article/f16327e30f3840daa30d93a78fede1aa

Even the most advanced climate models struggle to reproduce the observed wintertime circulation of the atmosphere over the North Atlantic and western Europe. During winter, the large-scale motions of this particularly challenging region are dominated by eddy-driven and highly non-linear flows, whose low-frequency variability is often studied from the perspective of regimes – a small number of qualitatively distinct atmospheric states. Poor representation of regimes associated with persistent atmospheric blocking events, or variations in jet latitude, degrades the ability of models to correctly simulate extreme events. In this paper we leverage a recently developed hybrid approach – which combines both jet and geopotential height data – to assess the representation of regimes in 8400 years of historical climate simulations drawn from the Coupled Model Intercomparison Project (CMIP) experiments, CMIP5, CMIP6, and HighResMIP. We show that these geopotential-jet regimes are particularly suited to the analysis of climate data, with considerable reductions in sampling variability compared to classical regime approaches. We find that CMIP6 has a considerably improved spatial regime structure, and a more trimodal eddy-driven jet, relative to CMIP5, but it still struggles with under-persistent regimes and too little European blocking when compared to reanalysis. Reduced regime persistence can be understood, at least in part, as a result of jets that are too fast and eddy feedbacks on the jet stream that are too weak – structural errors that do not noticeably improve in higher-resolution models.