Euro-Atlantic weather Regimes in the PRIMAVERA coupled climate simulations: impact of resolution and mean state biases on model performance

Abstract

Recently, much attention has been devoted to better understand the internal modes of variability of the climate system. This is particularly important in mid-latitude regions like the North-Atlantic, which is characterized by a large natural variability and is intrinsically difficult to predict. A suitable framework for studying the modes of variability of the atmospheric circulation is to look for recurrent patterns, commonly referred to as Weather Regimes. Each regime is characterized by a specific large-scale atmospheric circulation pattern, thus influencing regional weather and extremes over Europe. The focus of the present paper is the study of the Euro-Atlantic wintertime Weather Regimes in the climate models participating to the PRIMAVERA project. We analyse here the set of coupled historical simulations (hist-1950), which have been performed both at standard and increased resolution, following the HighresMIP protocol. The models' performance in reproducing the observed Weather Regimes is assessed in terms of different metrics, focussing on systematic biases and on the impact of resolution. We also analyse the connection of the Weather Regimes with the Jet Stream latitude and blocking frequency over the North-Atlantic sector. We find that-for most models-the regime patterns are better represented in the higher resolution version, for all regimes but the NAO-. On the other side, no clear impact of resolution is seen on the regime frequency of occurrence and persistence. Also, for most models, the regimes tend to be more tightly clustered in the increased resolution simulations, more closely resembling the observed ones. However, the horizontal resolution is not the only factor determining the model performance, and we find some evidence that biases in the SSTs and mean geopotential field might also play a role.

HighResMIP versions of EC-Earth: EC-Earth3P and EC-Earth3P-HR – description, model computational performance and basic validation

“…15f); the same applied to EC-Earth2.2 when compared to ERA-40 (Hazeleger et al, 2012). EC-Earth rightly captures the circumglobal pattern associated with the NAO at upper levels (e.g., Branstator, 2002;García-Serrano and Haarsma, 2017), particularly the elongated lobe over the North Atlantic and the two centers of action over the North Pacific (Fig. 15a-c).…”

A regime view of future atmospheric circulation changes in Northern mid-latitudes

“…The reanalysis data from a combination of the European Centre for Medium-Range Weather Forecast (ECMWF) ERA-40 (1964-1978Uppala et al, 2005 and ERA-Interim (1979Dee et al, 2011 is used as a reference. Selecting a different reanalysis product as NCEP does not affect the results, as discussed in Fabiano et al (2020). For the computation of the Weather Regimes we consider the wintertime (NDJFM) daily mean geopotential height at 500hPa ("data" in the following).…”

“…For practical reasons, data are first interpolated to a 2.5°x 2.5°grid using a bilinear interpolation. As discussed in Fabiano et al (2020), since we are interested in the large scale patterns, this does not impact on results. Since in Section 4.2 we evaluate the role of some drivers in determining the inter-model spread of the response in SSP5-8.5 and RCP8.5 scenarios, monthly averages of the atmospheric temperature (ta) at different vertical levels and wind (ua) in the stratosphere are also used.…”

“…The Weather Regimes are computed using the WRtool Python package (Fabiano et al, 2020). We focus here on latitudes between 30°and 90°N and consider separately two longitudinal sectors: the Euro-Atlantic (EAT, from 80°W to 40°E) and the Pacific-North American (PNA, from 140°E to 80°W).…”

“…For the EAT sector, we set the number of regimes to 4, as is widely documented in literature (Michelangeli et al, 1995;Cassou, 2008;Dawson et al, 2012;Madonna et al, 2017;Strommen et al, 2019;Fabiano et al, 2020). For the PNA sector, we choose 4 clusters as in Straus et al (2007) and Weisheimer et al (2014), although a different number of clusters could be a viable alternative as argued in Straus et al (2007) and favoured by other studies (e.g.…”