The observed warming of the surface air temperature (SAT) over the last 50 years has not been homogenous. There are strong differences in the temperature changes both geographically and on different time frames. Here, we review the observed diurnal asymmetry in the global warming trend: the night-time temperatures have increased more rapidly than day-time temperatures. Several explanations for this asymmetric warming have been offered in the literature. These generally relate differences in the temperature trends to regionalized feedback effects, such as changes to cloud cover, precipitation or soil moisture. Here, we discuss a complementary mechanism through which the planetary boundary layer (PBL) modulates the SAT response to changes in the surface energy balance. This reciprocal relationship between boundary-layer depth and temperature response can explain a part of why the night-time has warmed more rapidly than the daytime. We used a multi-linear regression model to compare the effect of the PBL, cloud cover, precipitation and soil moisture on the SAT. From this, we demonstrate that it is the boundary-layer depth which is the strongest predictor of the strength of temperature trends in the boreal annual cycle, and in all seasons except the summer.

Total cloud fraction over the Arctic (north of 60°N) has been evaluated and intercompared based on 16 Arctic cloud climatologies from different satellite and surface observations and reanalyses. The Arctic annual-mean total cloud fraction is about 0.70±0.03 according to different observational data. It is greater over the ocean (0.74±0.04) and less over land (0.67±0.03). Different observations for total cloud fraction are in a better agreement in summer than in winter and over the ocean than over land. An interannual variability is higher in winter than in summer according to all observations. The Arctic total cloud fraction has a prominent annual cycle according to most of the observations. The time of its maximum concurs with the time of the sea ice extent minimum (early summerlate autumn) and vice versa (late spring). The main reason for the discrepancies among observations is the difference in the cloud-detection algorithms, especially when clouds are detected over the ice/snow surface (during the whole year) or over the regions with the presence of strong low-tropospheric temperature inversions (mostly in winter). Generally, reanalyses are not in a close agreement with satellite and surface observations of cloudiness in the Arctic.

Precipitation extremes are widely thought to intensify with global warming due to an exponential growth following the Clausius–Clapeyron (C–C) equation which links the atmosphere water vapor saturation pressure with air temperature. However, a number of recent studies based on station and reanalyzes data for the contemporary period showed that scaling rates between extreme precipitation and temperature strongly depend on temperature range, moisture availability, and a region of interest. Being performed for some regions, such estimates, however, lack for Northern Eurasia, where prominent temperature changes and rapid shift from large-scale to convective precipitation are observed. Here, we examine the scaling between daily precipitation extremes and surface air temperature (SAT) over Russia for 1966–2017 using meteorological station data and for 1979–2020 using ERA5 reanalysis. The precipitation-temperature relation is examined for total precipitation and, separately, for convective and large-scale precipitation types. In winter, we reveal a general increase in extreme precipitation of all precipitation types according to the C–C relationship. For the Russian Far East region, the stratiform precipitation extremes scale with SAT following even super C–C rates, about two times as fast as C–C. However, in summer we find a peak-like structure of the precipitation-temperature scaling, especially for the convective precipitation in the southern regions. Extreme precipitation reaches their peak values at the temperature range between 15 °C and 20 °C. At higher temperatures, the negative scaling prevails. Analyzed data show a pronounced decrease in relative humidity with increasing surface temperatures beyond the 15 °C–20 °C threshold. This indicates that moisture availability is the major factor for the peak-shaped relationship between extreme precipitation and temperature revealed by our analysis.

The last comprehensive statistics of tornadoes in northern Eurasia (NE) were published more than 30 years ago. This paper introduces a new database of tornadoes in NE that spans from the tenth century to 2016. The database, compiled using various sources, contains 2879 tornado cases over land and water and includes tornado characteristics. Tornadoes are common for most regions of NE, with a density reaching four cases per 10⁴ km² in 1900–2016 in some regions. Tornadoes over land have distinct annual and diurnal cycles: they form mostly in May–August, with a maximum in June, and during daytime, with a maximum at 1700–1800 local time. Waterspouts form in all months with a maximum in late summer and mostly at 0900–1300 local time. Most tornadoes are weak and short lived. The Fujita-scale intensity is ≤F1 for 80% and ≥F3 for 3% out of all rated tornadoes. Half last less than 10 min. The average annual number of all tornadoes over land is around 150, including 10 and 2 tornadoes with ≥F2 and ≥F3 intensity, respectively. Annually, 1–2 tornadoes lead to casualties and result in 2.9 fatalities and 36.3 injuries. Despite the incompleteness of the dataset, our results show that tornadoes in NE, although being rare, are not as extremely rare as has been thought before. The results illustrate the substantial underestimation of tornado threat by the general public, researchers, and meteorologists, and unambiguously indicate the need for systematic assessments and forecasting of tornadoes by national weather services.