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Marlene Kretschmer, Dim Coumou, Jonathan F. Donges, and Jakob Runge

Abstract

In recent years, the Northern Hemisphere midlatitudes have suffered from severe winters like the extreme 2012/13 winter in the eastern United States. These cold spells were linked to a meandering upper-tropospheric jet stream pattern and a negative Arctic Oscillation index (AO). However, the nature of the drivers behind these circulation patterns remains controversial. Various studies have proposed different mechanisms related to changes in the Arctic, most of them related to a reduction in sea ice concentrations or increasing Eurasian snow cover.

Here, a novel type of time series analysis, called causal effect networks (CEN), based on graphical models is introduced to assess causal relationships and their time delays between different processes. The effect of different Arctic actors on winter circulation on weekly to monthly time scales is studied, and robust network patterns are found. Barents and Kara sea ice concentrations are detected to be important external drivers of the midlatitude circulation, influencing winter AO via tropospheric mechanisms and through processes involving the stratosphere. Eurasia snow cover is also detected to have a causal effect on sea level pressure in Asia, but its exact role on AO remains unclear. The CEN approach presented in this study overcomes some difficulties in interpreting correlation analyses, complements model experiments for testing hypotheses involving teleconnections, and can be used to assess their validity. The findings confirm that sea ice concentrations in autumn in the Barents and Kara Seas are an important driver of winter circulation in the midlatitudes.

Full access
Sem Vijverberg, Maurice Schmeits, Karin van der Wiel, and Dim Coumou

Abstract

Extreme summer temperatures can cause severe societal impacts. Early warnings can aid societal preparedness, but reliable forecasts for extreme temperatures at subseasonal-to-seasonal (S2S) time scales are still missing. Earlier work showed that specific sea surface temperature (SST) patterns over the northern Pacific Ocean are precursors of high temperature events in the eastern United States, which might provide skillful forecasts at long leads (~50 days). However, the verification was based on a single skill metric, and a probabilistic forecast was missing. Here, we introduce a novel algorithm that objectively extracts robust precursors from SST linked to a binary target variable. When applied to reanalysis (ERA-5) and climate model data (EC-Earth), we identify robust precursors with the clearest links over the North Pacific. Different precursors are tested as input for a statistical model to forecast high temperature events. Using multiple skill metrics for verification, we show that daily high temperature events have no predictive skill at long leads. By systematically testing the influence of temporal and spatial aggregation, we find that noise in the target time series is an important bottleneck for predicting extreme events on S2S time scales. We show that skill can be increased by a combination of 1) aggregating spatially and/or temporally, 2) lowering the threshold of the target events to increase the base rate, or 3) adding additional variables containing predictive information (soil moisture). Exploiting these skill-enhancing factors, we obtain forecast skill for moderate heat waves (i.e., 2 or more hot days closely clustered together in time) with up to 50 days of lead time.

Open access
Marlene Kretschmer, Dim Coumou, Laurie Agel, Mathew Barlow, Eli Tziperman, and Judah Cohen

Abstract

The extratropical stratosphere in boreal winter is characterized by a strong circumpolar westerly jet, confining the coldest temperatures at high latitudes. The jet, referred to as the stratospheric polar vortex, is predominantly zonal and centered around the pole; however, it does exhibit large variability in wind speed and location. Previous studies showed that a weak stratospheric polar vortex can lead to cold-air outbreaks in the midlatitudes, but the exact relationships and mechanisms are unclear. Particularly, it is unclear whether stratospheric variability has contributed to the observed anomalous cooling trends in midlatitude Eurasia. Using hierarchical clustering, we show that over the last 37 years, the frequency of weak vortex states in mid- to late winter (January and February) has increased, which was accompanied by subsequent cold extremes in midlatitude Eurasia. For this region, 60% of the observed cooling in the era of Arctic amplification, that is, since 1990, can be explained by the increased frequency of weak stratospheric polar vortex states, a number that increases to almost 80% when El Niño–Southern Oscillation (ENSO) variability is included as well.

Open access
Chiem van Straaten, Kirien Whan, Dim Coumou, Bart van den Hurk, and Maurice Schmeits

Abstract

Reliable subseasonal forecasts of high summer temperatures would be very valuable for society. Although state-of-the-art numerical weather prediction (NWP) models have become much better in representing the relevant sources of predictability like land and sea surface states, the subseasonal potential is not fully realized. Complexities arise because drivers depend on the state of other drivers and on interactions over multiple time scales. This study applies statistical modeling to ERA5 data, and explores how nine potential drivers, interacting on eight time scales, contribute to the subseasonal predictability of high summer temperatures in western and central Europe. Features and target temperatures are extracted with two variations of hierarchical clustering, and are fitted with a machine learning (ML) model based on random forests. Explainable AI methods show that the ML model agrees with physical understanding. Verification of the forecasts reveals that a large part of predictability comes from climate change, but that reliable and valuable subseasonal forecasts are possible in certain windows, like forecasting monthly warm anomalies with a lead time of 15 days. Contributions of each driver confirm that there is a transfer of predictability from the land and sea surface state to the atmosphere. The involved time scales depend on lead time and the forecast target. The explainable AI methods also reveal surprising driving features in sea surface temperature and 850 hPa temperature, and rank the contribution of snow cover above that of sea ice. Overall, this study demonstrates that complex statistical models, when made explainable, can complement research with NWP models, by diagnosing drivers that need further understanding and a correct numerical representation, for better future forecasts.

Open access
Ruud Sperna Weiland, Karin van der Wiel, Frank Selten, and Dim Coumou

Abstract

Persistent hot–dry or cold–wet summer weather can have significant impacts on agriculture, health, and the environment. For northwestern Europe, these weather regimes are typically linked to, respectively, blocked or zonal jet stream states. The fundamental dynamics underlying these circulation states are still poorly understood. Edward Lorenz postulated that summer circulation may be either fully or almost intransitive, implying that part of the phase space (capturing circulation variability) cannot be reached within one specific summer. If true, this would have major implications for the predictability of summer weather and our understanding of the drivers of interannual variability of summer weather. Here, we test the two Lorenz hypotheses (i.e., fully or almost intransitive) for European summer circulation, capitalizing on a newly available very large ensemble (2000 years) of present-day climate data in the fully coupled global climate model EC-Earth. Using self-organizing maps, we quantify the phase space of summer circulation and the trajectories through phase space in unprecedented detail. We show that, based on Markov assumptions, the summer circulation is strongly dependent on its initial state in early summer with the atmospheric memory ranging from 28 days up to ~45 days. The memory is particularly long if the initial state is either a blocked or a zonal flow state. Furthermore, we identify two groups of summers that are characterized by distinctly different trajectories through phase space, and that prefer either a blocked or zonal circulation state, respectively. These results suggest that intransitivity is indeed a fundamental property of the atmosphere and an important driver of interannual variability.

Open access
Gabriele Messori, Emanuele Bevacqua, Rodrigo Caballero, Dim Coumou, Paolo De Luca, Davide Faranda, Kai Kornhuber, Olivia Martius, Flavio Pons, Colin Raymond, Kunhui Ye, Pascal Yiou, and Jakob Zscheischler
Open access