The Monsoon over the Barents Sea and Kara Sea

In the Arctic (mainly in its European sector) there is statistically detectable seasonal reversal wind pattern. The combination of seasonally warm (cold) land surfaces in arctic areas together with cool (cool) sea surface of Arctic seas not covered by ice is conducive to the formation of a monsoon like system. On the other hand, the predominance of the cyclonic regime during all seasons makes it difficult to answer the question of whether the Arctic region belongs to the monsoon type pattern. In this study, the monsoon features of atmospheric circulation over the Barents and Kara Seas were analysed. To extract specific monsoon signs, atmospheric circulation systems (separately for areas of each sea) were divided into ten weather types. Their appearance and statistics were compared with indicators of regional circulation. A significant part of intra-annual monsoon variability is associated with the configuration of such modes as the North Atlantic Oscillation and the Scandinavia teleconnection patterns. For example, during the winter season, the monsoon currents (from land to sea) occur only with a positive North Atlantic Oscillation index. With the prevalence of other modes of variability, the direction of the winds can be different, and the regular monsoon circulation pattern is changed by chaotic regime. In summer, northern streams (from sea to land) are realized on the western periphery of cyclones, regenerating and stabilizing over the Kara Sea. As for anomalies, the nature of the monsoons is manifested in the statistics of extreme winds even without selecting data on the regimes of variability. So, in winter, maximum speeds fall on the southern streams, and in the summer—on the northern ones. Large precipitation anomalies during all seasons, as one would expect, are encountered most often with the cyclonic type of circulation.

An Extreme Value Analysis of Wind Speed over the European and Siberian Parts of Arctic Region

Multiyear observed time series of wind speed for selected points of the Arctic region (data of station network from the Kola Peninsula to the Chukotka Peninsula) are used to highlight the important peculiarities of wind speed extreme statistics. How largest extremes could be simulated by climate model (the INM-CM4 model data from the Historical experiment of the CMIP5) is also discussed. Extreme value analysis yielded that a volume of observed samples of wind speeds are strictly divided into two sets of variables. Statistical properties of one population are sharply different from another. Because the common statistical conditions are the sign of identity of extreme events we therefore hypothesize that two groups of extreme wind events adhere to different circulation processes. A very important message is that the procedure of selection can be realized easily based on analysis of the cumulative distribution function. The authors estimate the properties of the modelled extremes and conclude that they consist of only the samples, adhering to one group. This evidence provides a clue that atmospheric model with a coarse spatial resolution does not simulate special mechanism responsible for appearance of largest wind speed extremes. Therefore, the tasks where extreme wind is needed cannot be explicitly solved using the output of climate model. The finding that global models are unable to capture the wind extremes is already well known, but information that they are members of group with the specific statistical conditions provides new knowledge. Generally, the implemented analytical approach allows us to detect that the extreme wind speed events adhere to different statistical models. Events located above the threshold value are much more pronounced than representatives of another group (located below the threshold value) predicted by the extrapolation of law distributions in their tail. The same situation is found in different areas of science where the data referring to the same nomenclature are adhering to different statistical models. This result motivates our interest on our ability to detect, analyze, and understand such different extremes.

Extreme Values of Wind Speed over the Kara Sea Based on the ERA5 Dataset

Extreme values of wind speed were studied based on the highly detailed ERA5 dataset covering the central part of the Kara Sea. Cases in which the ice coverage of the cells exceeded 15% were filtered. Our study shows that the wind speed extrema obtained from station observations, as well as from modelling results in the framework of mesoscale models, can be divided into two groups according to their probability distribution laws. One group is specifically designated as black swans, with the other referred to as dragons (or dragon-kings). In this study we determined that the data of ERA5 accurately described the swans, but did not fully reproduce extrema related to the dragons;these extrema were identified only in half of ERA5 grid points. Weibull probability distribution function (PDF) parameters were identified in only a quarter of the pixels. The parameters were connected almost deterministically. This converted the Weibull function into a one-parameter dependence. It was not clear whether this uniqueness was a consequence of the features of the calculation algorithm used in ERA5, or whether it was a consequence of a relatively small area being considered, which had the same wind regime. Extremes of wind speed arise as mesoscale features and are associated with hydrodynamic features of the wind flow. If the flow was non-geostrophic and if its trajectory had a substantial curvature, then the extreme velocities were distributed according to a rule similar to the Weibull law.