We use a decadal prediction system with the Coupled Model Intercomparison Project Phase 6 version of the coupled Max Planck Institute Earth System Model to predict the probability of occurrence for ...extremely warm summers in the Northern Hemisphere. An assimilation run with Max Planck Institute Earth System Model shows a robust response of summer temperature extremes in northern Europe and northeast Asia to North Atlantic sea surface temperature via a circumglobal Rossby wavetrain. When the North Atlantic is warm, warm summer temperature extremes occur with a probability of 20% and 24% in northern Europe and northeast Asia, respectively. In a cold North Atlantic phase, these probabilities are 0% and 8%. A similar difference in probability of occurrence is found in the initialized climate predictions. Consequently, the likelihood of a warm summer temperature extreme occurring in the examined regions in the next 10 years can be inferred from predictions of North Atlantic temperature.
Plain Language Summary
Extremely warm summers can have a substantial impact on society. Trustworthy predictions of such events several years ahead could help in anticipating the extremes and managing their impacts. In this study, we show that the probability with which a warm summer temperature extreme occurs in northern Europe and northeast Asia can be linked to the surface temperature of the North Atlantic ocean. We further show that North Atlantic ocean surface temperature and the connection between ocean temperature and extreme summer temperature can be predicted. As a result, the probability for extremely warm summers to occur in northern Europe and northeast Asia in the next 10 years can be predicted.
Key Points
Extremely warm summers in northern Europe and northeast Asia occur more frequently when the North Atlantic is warm than when it is cold
A set of initialized CMIP6 decadal hindcasts predicts this dependence of summer temperature extremes on North Atlantic temperature
The likelihood of extremely warm summers to occur in the next 10 years can be inferred from predictions of North Atlantic ocean temperature
We improve seasonal hindcast skill of European summer climate in an ensemble based coupled seasonal prediction system by selecting individual ensemble members based on their respective consistent ...chain of processes that describe a physical mechanism. This mechanism is associated with the second mode of seasonal climate variability in the North-Atlantic-European sector and is contrary to the summer North Atlantic Oscillation. We initially analyse the mechanism in the ERA-Interim reanalysis and then test the influence of the mechanism on European hindcast skill in an initialised coupled seasonal climate model. We show that the mechanism originates in the tropical North Atlantic in spring, where either warm or cold sea surface temperature anomalies (SSTs) are connected with the European climate by an upper-level wave-train. This wave-train is accompanied by a zonal pressure gradient, that in turn influences the climate over central Europe in the following summer. We analyse the seasonal summer hindcast skill in a mixed resolution hindcast ensemble simulation generated by MPI-ESM, with 30 members starting every year in May. While the mean over the full ensemble shows no seasonal hindcast skill in summer, we achieve significant hindcast skill through forming a new mean over subselected ensemble members. For this selection, we test every ensemble member for the proposed consistent chain of connections between the wave-train, the zonal pressure gradient and their impact on European summer temperatures, and find that the processes that describe the mechanism are not represented in every ensemble member. Due to its influence on European summer climate, we use the condition of the persistent spring SSTs to anticipate the phase of the mechanism in each considered year. We thus use statistical relations to select ensemble members generated by a dynamical prediction system. With this approach, we significantly enhance the seasonal hindcast skill and the reliability of the hindcasts in the North-Atlantic-European sector, especially in the areas where the mechanism is showing a prominent signal. Since we only use knowledge that would be available in a real forecast set-up, this approach can potentially be applied in operational ensemble prediction systems.
Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool ...for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.
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