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

Abstract

Abstract. 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.

CMIP6 Models Trend Toward Less Persistent European Blocking Regimes in a Warming Climate

“…While Dorrington et al. (2022) showed mid‐latitude variability is much improved in CMIP6, it remains unclear whether similar improvements have occurred in these key teleconnections. The decrease in geopotential‐jet regime persistence is consistent with this projected zonalisation, but subject to considerable inter‐model variability.…”

“…While the near-unanimous model agreement on this trend gives confidence, we must also bear in mind evidence that the representation of Arctic-midlatitude links is too weak in models (Cohen et al, 2019;Smith et al, 2022;Strommen et al, 2022), and that this may bias the "tug of war" toward zonal conditions. While Dorrington et al (2022) showed mid-latitude variability is much improved in CMIP6, it remains unclear whether similar improvements have occurred in these key teleconnections. The decrease in geopotential-jet regime persistence is consistent with this projected zonalisation, but subject to considerable inter-model variability.…”

“…The fraction of principal component variability explained by linear variations in the jet speed was identified for each model via linear regression, and the space of residuals to this linear best fit was used to identify regimes via K-means clustering. For a more in-depth explanation of the method, and expanded motivation, see Dorrington and Strommen (2020) and Dorrington et al (2022). Jet speed was not detrended, as trends were found to be insignificant, but the linear relationship between principal components and jet speed was calculated separately for the historical and future time periods.…”

“…A cubic fit was required to account for the accelerating increase in geopotential height seen in the SSP5-8.5 simulations. The four leading principal components of detrended Z500 were then computed, as in Dorrington et al (2022) and Fabiano et al (2020). A corresponding jet speed time series was also computed, defined as the maximum (oriented Eastward) of 5-day smoothed latitudinally averaged 850 hPa zonal wind speed over the Atlantic domain [60W-0W, 15-75N].…”

Meridional-energy-transport extremes and the general circulation of Northern Hemisphere mid-latitudes: dominant weather regimes and preferred zonal wavenumbers

“…The minimum number of EOFs that explain at least 90 % of the total variance is retained: this corresponds to 16, 19 and 41 EOFs during DJF for the EAT, PAC and NH, respectively, and 22, 25 and 57, respectively, during JJA. An EOF-based dimensionality reduction is a common first step in weather regime detection algorithms (Cassou, 2008;Dawson et al, 2012;Cattiaux et al, 2013;Straus et al, 2017;Dorrington et al, 2022). Sensitivity tests on the number of EOFs retained (not shown) confirm that the regime patterns are robust to this choice (e.g., Fabiano et al, 2020), with the full field recovered in the limit of all EOFs.…”