Description: The relationships between the dominant modes of interannual variability of Diurnal Temperature Range (DTR) over Europe and large-scale atmospheric circulation and sea surface temperature anomaly fields are investigated through statistical analysis of observed and reanalysis data. It is shown that the dominant DTR modes as well as their relationship with large-scale atmospheric circulation and sea surface temperature anomaly fields are specific for each season. During winter the first and second modes of interannual DTR variability are strongly related with the North Atlantic Oscillation and the Scandinavian pattern, while the third mode is related with the Atlantic Multidecadal Oscillation. Strong influence of the Atlantic Multidecadal Oscillation and the Arctic Oscillation on spring DTR modes of variability was also detected. During summer the DTR variability is influenced mostly by a blocking-like pattern over Europe, while the autumn DTR variability is associated with a wave-train like pattern, which develops over the Atlantic Ocean and extends up to Siberia. It is also found that the response of DTR to global sea surface temperature is much weaker in spring and summer comparing to winter and autumn. A correlation analysis reveals a strong relationship between DTR modes of variability and the Cloud Cover anomalies during all seasons. The influence of the potential evapotranspiration and precipitation anomalies on DTR modes of variability is strongest during summer, but it is significant also in spring and autumn. It is suggested that a large part of interannual to decadal DTR variability over Europe is induced by the large-scale climate anomaly patterns via modulation of cloud cover, precipitation and potential evapotranspiration anomaly fields.

Description: Heat stress is projected to intensify with global warming, causing significant socioeconomic impacts and threatening human health. Wet-bulb temperature (WBT), which combines temperature and humidity effects, is a useful indicator for assessing regional and global heat stress variability and trends. However, the variations of European WBT and their underlying mechanisms remain unclear. Using observations and reanalysis datasets, we demonstrate a remarkable warming of summer WBT during the period 1958–2021 over Europe. Specifically, the European summer WBT has increased by over 1.08C in the past 64 years. We find that the increase in European summer WBT is driven by both near-surface warming temperatures and increasing atmospheric moisture content. We identify four dominant modes of European summer WBT variability and investigate their linkage with the large-scale atmospheric circulation and sea surface temperature anomalies. The first two leading modes of the European WBT variability exhibit prominent interdecadal to long-term variations, mainly driven by a circumglobal wave train and concurrent sea surface temperature variations. The last two leading modes of European WBT variability mainly show interannual variations, indicating a direct and rapid response to large-scale atmospheric dynamics and nearby sea surface temperature variations. Further analysis shows the role of global warming and changes in midlatitude circulations in the variations of summer WBT. Our findings can enhance the understanding of plausible drivers of heat stress in Europe and provide valuable insights for regional decision-makers and climate adaptation planning.

Description: 〈jats:title〉Abstract〈/jats:title〉 〈jats:p〉This study quantifies the state-of-the-art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multi-model dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–2020 for predictions of Pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on June 1, July 1, August 1, and September 1. This diverse set of statistical and dynamical models can individually predict linearly detrended Pan-Arctic SIE anomalies with skill, and a multi-model median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to Pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and Central Arctic sectors. The skill of dynamical and statistical models is generally comparable for Pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least three months in advance.〈/jats:p〉