Recent progress in simulating North Atlantic weather regimes

By: Alex Baker

Weather is chaotic. Low-pressure weather systems bring rainfall; areas of high pressure block the passage of these weather systems. Take this year so far, for instance. February and March were much wetter than average, and April and May much drier. May, in particular, was England’s driest—and the U.K.’s sunniest—on record, with similar conditions enjoyed across much of Western and Central Europe. Such swings in weather are down to where low- or high-pressure conditions prevail, and for how long these synoptic situations persist.

One way to make sense of this variability is by identifying so-called weather regimes, reoccurring patterns of high and low pressure across the central and eastern North Atlantic and Europe. Conventionally, meteorologists recognise four Euro-Atlantic regimes: the positive and negative phases of the North Atlantic Oscillation (NAO+ and NAO–, respectively), Scandinavian blocking (SB), and a North Atlantic ridge pattern (AR)—more on each presently. How often these regimes occur not only dictates regional weather, but also plays a role in whether a season becomes wetter or drier over time, and is important on longer, climatological timescales too.

Weather regimes exhibit characteristic spatial patterns of high- and low-pressure centres, visualised here using geopotential height data from which the climatological mean seasonal cycle was removed (see Figure 1). (Geopotential height is a common variable used to infer atmospheric circulation patterns; it tells us altitude above mean sea level, accounting for gravitational variations over Earth’s surface.) The North Atlantic Oscillation’s positive and negative phases describe variability between the Icelandic Low and the Azores High. During NAO+, high-pressure conditions prevail over much of Central Europe and the Mediterranean. During NAO–, the high-pressure anomaly sits over Greenland and low pressure spans much of continental Europe. Scandinavian blocking is the occurrence of a high-pressure anomaly over western Scandinavia and the North Sea. The Atlantic Ridge pattern is characterised by high pressure over the central North Atlantic at a latitude of about 55°N. Each regime roughly corresponds to a preferred position of the North Atlantic jet stream.

Figure 1: The Euro-Atlantic weather regimes, based on daily geopotential height data at the 500-mb isobaric level from the ERA40 and ERA-Interim reanalyses. The four regimes are the positive and negative phases of the North Atlantic Oscillation (NAO+ and NAO–, respectively), Scandinavian blocking (SB), and a North Atlantic ridge pattern (AR). Regimes patterns visualised following removal of the climatological mean seasonal cycle. Figure adapted from Fabiano et al., 2020.

In a recent paper published in Climate Dynamics, led by Federico Fabiano of the Institute of Atmospheric Sciences and Climate, Consiglio Nazionale delle Ricerche, we examined how well six current-generation, fully coupled global climate models are able to represent Euro-Atlantic weather regimes—their spatial patterns, their persistence, and how realistically distinct the regimes are. Here, I focus on regime patterns, and how well those simulated by low- and high-resolution climate models compare with reference datasets: the ERA-40 and ERA-Interim reanalyses.

Establishing whether or not global climate models can reproduce each regime’s characteristic spatial pattern is important because these patterns are related to where westerly storm systems track and make landfall downstream over Europe—and where these storms’ impacts will be felt. Do high-resolution models reproduce real-world regime patterns better than standard, low-resolution models? To assess this, we calculated pattern correlations between the models and reanalyses. Overall, we found that the NAO+, SB and AR regime patterns are better represented at high resolution, but the NAO– regime is not (see Figure 2). Why NAO– is something of an outlier here will be the subject of future research. Additionally, the AR regime shows greater variance than the other regimes. We also found that simulated regimes are more realistically distinct from one another (to use the jargon, more tightly ‘clustered’) at high resolution and better match the reanalyses.

Figure 2: Pattern correlations between low- (teal) or high-resolution (red) models and reanalyses (black) for each weather regime. Perfect model representation of a regime’s observed spatial pattern is indicated by a pattern correlation coefficient of 1. From distributions of 30-yr bootstrapping for each model, the ensemble mean (dot), median (horizontal line), interquartile range (boxes), 10th and 90th percentiles (bars), and minimum and maximum values across all available ensemble members (triangles) are shown. Figure adapted from Fabiano et al., 2020.

However, increasing models’ resolution had little impact on the frequency and duration of weather regimes. The evidence suggests that these errors are due to biases in simulated sea-surface temperatures and the mean geopotential height field. Simulating realistic regime persistence in models is important because prolonged wet and dry periods, like those seen across Europe earlier this year, are very often related to the persistence of a single regime. This research suggests that increasing model resolution alone is not enough; developments in model physics and dynamics are needed to better simulate North Atlantic weather regimes.

Author’s note

The climate models in this study participate in the sixth phase of the World Climate Research Programme’s Coupled Model Intercomparison Project (CMIP6), the modelling framework underpinning the Intergovernmental Panel on Climate Change’s Assessment Reports that are indispensable for global climate policy-making. These model simulations (hist-1950) were supported by the European Commission-funded PRIMAVERA project, the European contribution to HighResMIP, a CMIP6-endorsed and coordinated assessment of the impact of increasing model resolution, which is documented by Haarsma et al., 2016.


Fabiano, F. et al., 2020. Euro-Atlantic Weather Regimes in the PRIMAVERA coupled climate simulations: impact of resolution and mean state biases on model performance. Climate Dynamics 54, 5031–5048. .

Haarsma, R. J. et al., 2016. High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6. Geoscientific Model Development 9, 4185–4208. 

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