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The spatial distribution of compound extremes of air temperature and precipitation was studied over the territory of Eastern Europe for the period 1950–2018. Using daily data on air temperature and precipitation, we calculated the frequency and trends of the four indices—cold/dry (CD), cold/wet (CW), warm/dry (WD) and warm/wet (WW). The connection between these indices and large-scale patterns in the ocean–atmosphere system, such as the North Atlantic Oscillation (NAO), the East Atlantic (EA) and Scandinavia (SCAND) patterns, was also studied. The positive and statistically significant trends in the region were observed for the warm extremes (especially the WW index) in all seasons, with maximum values in the winter season, while negative trends were obtained for the cold extremes. The NAO index has a strong positive and statistically significant correlation with the warm compound indices (WD and WW) in the northern part of Eastern Europe in winter like the EA pattern, but with smaller values. The spatial distribution of the correlation coefficients between compound extremes and the SCAND index in the winter season is opposite to the correlation coefficients with the NAO index. [ABSTRACT FROM AUTHOR]

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This study uses 45 years of observational records from 517 historical surface weather stations over northern Eurasia to examine changing precipitation characteristics associated with increasing air temperatures. Results suggest that warming air temperatures over northern Eurasia have been accompanied by higher precipitation intensity but lower frequency and little change in annual precipitation total. An increase in daily precipitation intensity of around 1%-3% per each degree of air temperature increase is found for all seasons as long as a station's seasonal mean air temperature is below about 15°-16°C. This threshold temperature may be location dependent. At temperatures above this threshold, precipitation intensity switches to decreasing with increasing air temperature, possibly related to decreasing water vapor associated with extreme high temperatures. Furthermore, the major atmospheric circulation of the Arctic Oscillation, Scandinavian pattern, east Atlantic-western Eurasian pattern, and polar-Eurasian pattern also have significant influences on precipitation intensity in winter, spring, and summer over certain areas of northern Eurasia. [ABSTRACT FROM AUTHOR]

High-resolution data are needed in order to assess potential impacts of extreme events on infrastructure in the mid-latitudes. Dynamical downscaling offers one way to obtain this information. However, prior to implementation in any impacts assessment scheme, model output must be validated and determined fitfor- purpose. This study presents the results from two 8-km resolution perfect boundary experiments over Scandinavia. Two different regional climate models were initialised and driven with ERA interim reanalysis from 1990 to 2010. Reference data come from both gridded products and point-based station observations. In addition to the canonical variables of daily precipitation and temperature, winds were also investigated. The models exhibit systematic cold and wet biases on seasonal time scales ( 1K and -50-100%, respectively). However, frequency-based skill scores for daily precipitation and temperature are high, indicating that the distributions of these variables are generally well captured. Wind speeds over the North and Norwegian Seas were simulated more realistically in the models than in the ERA interim reanalysis. However, most importantly, for impacts assessments, the models should be capable of capturing the timing, intensity and location of short-duration extreme events, in particular precipitation. In this respect, both models outperform the reanalysis over the city of Copenhagen, where recent pluvial floods led to costly damages to infrastructure. [ABSTRACT FROM AUTHOR]

The atmosphere-ocean model ECHO-G, run with solar, volcanic and greenhouse gas forcing for the past millennium, is used to analyse winter and summer temperature variability in Scandinavia. Relationships with atmospheric circulation, North Atlantic SSTs and Northern Hemisphere (NH) temperatures are investigated at timescales longer and shorter than 10 yr. The simulated response to volcanic forcing is also analysed. Realistic relationships with the atmospheric circulation, with some deficiencies in summer, are found. High-frequency co-variations with SSTs and NH temperatures are too weak, but low-frequency co-variations with NH temperatures in winter are apparently too strong. The summer cooling response to volcanic forcing is realistic, but the expected winter warming is absent. The simulated long-term temperature evolution agrees broadly with proxy data. Combinations of several forcing factors can lead to decadal and multidecadal anomalies from the centennial trends. Decreased solar forcing can account for cold intervals in both summer and winter. A systematic negative North Atlantic Oscillation (NAO) phase can explain the coldest winter temperatures during 1590–1650. Several strong volcanic forcing events can have contributed to a simultaneous summer cooling. Proxy data also indicate cold summers and winters, and a negative NAO in winter, in the same period. [ABSTRACT FROM AUTHOR]

Predictability of seasonal precipitation in the Nordic countries (Denmark, Finland, Iceland, Norway and Sweden) is investigated using a nine-member ensemble of atmospheric general circulation model simulations with prescribed sea-surface temperature from October 1950 until March 1999. The simulations and corresponding observations from 65 stations in the Nordic countries are used to identify large-scale patterns of seasonal precipitation, the predictability of which is investigated. Subsequently, the identified large-scale patterns are used in a statistical downscaling of the model simulated precipitation. The downscaling, which is of the model output statistics type, yields seasonal predictions for the individual stations. The model simulations of precipitation are compared to predictions of precipitation directly from observed sea-surface temperature using a statistical prediction method and no dynamic model. The two different methods give consistent results. It is demonstrated that seasonal precipitation in the Nordic region contains a weak predictable signal in several seasons. The most skilful predictions can be made in spring, especially in the April-June season when precipitation appears to be influenced both by tropical and North Atlantic sea surface temperature. In particular, the North Atlantic Oscillation in winter appears to influence the North Atlantic sea-surface temperature in spring, which in turn has an effect on precipitation in Scandinavia. [ABSTRACT FROM AUTHOR]

The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR's Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM). [ABSTRACT FROM AUTHOR]