The contribution of dynamic and thermodynamic causes in changes of precipitation character in Northern Eurasia that is expressed in increase of convective and decrease of large-scale precipitation, is still undetermined. Here, we estimate influence of atmospheric circulation on various characteristics of convective and large-scale precipitation in Northern Eurasia using correlation and regression analyses. We estimate different measures for atmospheric circulation including frequency of cyclones and anticyclones, blocking duration, intensity of the main atmospheric centers of action. Correlation and regression analyses were carried out using nonparametric Mann-Kendall correlation and Theil-Sen estimator. We revealed local response of precipitation on circulation characteristics, which strength varies in space and time. For some regions and seasons, opposite responses for convective and large-scale precipitation were found. Therefore, changing character of precipitation over Northern Eurasia can partly be explained by dynamical factors. Nevertheless, the main reason for increase of convective and decrease of large-scale precipitation is presumably associated with thermodynamics factors, namely increase of surface air temperature and humidity that resulting in convective instability growth.
A comprehensive intercomparison of midlatitude storm characteristics is presented. Extratropical storm characteristics were derived from 16 reanalysis-based objective automated algorithms for cyclone identification and tracking from the IMILAST project and from manual method based on an expert inspection of weather charts. The analysis was carried out for the Siberian region (50–80N, 60–110E) for two seasons (winter of 2007/08 and summer of 2008). Most of the automated algorithms show 1.5–3 times more cyclones and 3–5 times more cyclone tracks in the Siberian region compare to the manual method. The algorithms show a good agreement with the manual method for spatial distribution of cyclones and tracks number with spatial correlation coefficient varies around 0.8–0.9 in summer and around 0.7–0.9 in winter for most of the algorithms. Two ranking measures were used to evaluate similarity of objective algorithms with the manual method.
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