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Long-term trends in the historical frequency of environments supportive of atmospheric convection are unclear, and only partially follow the expectations of a warming climate. This uncertainty is driven by the lack of unequivocal changes in the ingredients for severe thunderstorms (i.e., conditional instability, sufficient low-level moisture, initiation mechanism, and vertical wind shear). ERA5 hybrid-sigma data allow for superior characterization of thermodynamic parameters including convective inhibition, which is very sensitive to the number of levels in the lower troposphere. Using hourly data we demonstrate that long-term decreases in instability and stronger convective inhibition cause a decline in the frequency of thunderstorm environments over the southern United States, particularly during summer. Conversely, increasingly favorable conditions for tornadoes are observed during winter across the Southeast. Over Europe, a pronounced multidecadal increase in low-level moisture has provided positive trends in thunderstorm environments over the south, central, and north, with decreases over the east due to strengthening convective inhibition. Modest increases in vertical wind shear and storm-relative helicity have been observed over northwestern Europe and the Great Plains. Both continents exhibit negative trends in the fraction of environments with likely convective initiation. This suggests that despite increasing instability, thunderstorms in a warming climate may be less likely to develop due to stronger convective inhibition and lower relative humidity. Decreases in convective initiation and resulting precipitation may have long-term implications for agriculture, water availability, and the frequency of severe weather such as large hail and tornadoes. Our results also indicate that trends observed over the United States cannot be assumed to be representative of other continents.

Air pollution continues to have a significant impact on Europeans living in urban areas, and episodes of elevated PM x are responsible for a large number of premature deaths (mostly due to heart disease and stroke) each year. According to the annual EEA reports, Poland is one of the most polluted countries in Europe, experiencing high PM x concentrations during winter that mostly result from large emissions and unfavourable weather conditions in combination with environmental features. Thus, in addition to implementing municipal mitigation strategies, alerting residents to pollution episodes through accurate PM x forecasting is necessary. This research aimed to assess the feasibility of short-term PM x forecasting via machine learning (ML) and the subsequent identification of the primary meteorological covariates. The data comprised 10 years of hourly winter PM 10 and PM 2.5 concentrations measured at 11 urban air quality monitoring stations, including background, traffic, and industrial sites, in four large Polish agglomerations, viz., Poznań, Kraków, Łódź, and Gdańsk, which cover areas with high population density and diverse environments that extend from the Baltic Sea coast (Tricity) through the lowlands (Poznań and Łódź) to the highlands (Kraków). We tested four ML models: AIC-based stepwise regression, two tree-based algorithms (random forests and XGBoost), and neural networks. Employing analysis and cross-validation, we found that XGBoost performed the best, followed by random forests and neural networks, and stepwise regression performed the worst. This ranking was apparent in the threshold exceedance values of the binary forecasts obtained via regression. Overall, our results confirm the high applicability of ML to short-term air quality prediction with the perfect prog approach.

The paper presents historical (1971-2015) and scenario-based (2006-2100) changes in surface water temperatures in 10 lakes of Poland. The analysis of historical measurement (1971-2015) showed that mean annual lake surface water temperature (LSWT) was characterised by an increasing tendency by 0.37°C∙dec-1 on average, and was higher by 0.01°C∙dec-1 than air temperature in the analogical period. The highest increase in LSWT was recorded in spring months (April, May) and in summer (July). The future changes in LSWT was based on simulations of 33 AOGCMs available in the scope of CMIP5 project for RCPs: 2.6, 4.5, 6.0, and 8.5. The developed empirical-statistical downscaling models (ESD) use the air temperature field as predictors, with consideration of autocorrelation for two preceding months. ESD models are characterised by high quality of reconstruction of water temperatures in the historical period, with correlation from 0.82 (December, February) to 0.93 (July). The future CMIP5 scenarios for the period 2006-2100 assume an increase in air temperature at the end of the 21st century from +1.8°C (RCP 2.6) to +5.1°C (RCP 8.5) in reference to the period 1971-2005. According to the downscaling models, this corresponds to an increase in water temperature in the analysed lakes ranging from +1.4°C (RCP 2.6) to +4.2°C (RCP 8.5) in the years 2081-2100, respectively, with evident variability between the adopted emission paths beginning from the period 2041-2060. At a monthly scale, water temperature will increase the slowest in February (2081-2100: RCP 2.6 = +0.5°C, RCP 8.5 = +1.8°C). The highest increase in temperature will occur from May to August (RCP 8.5 = +6°C in June).Substantial effects of transformations of the thermal regime are already observed today, e,g. in the reduction of the ice season length. According to developed scenarios, a further considerable increase in water temperature will be the primary factor determining the transformation of lake ecosystems. The obtained results provide a theoretical basis for further research conducted in the scope of many disciplines, among others hydrology, hydrobiology, ecology, water management, energy production, etc. In the case of Poland, issues related to low water resources per capita are particularly important. Contemporary studies concerning changes in water resources showed that the natural factor playing the key role in their reduction is temperature increase and therefore it should constitute for the possibly fast development of multidisciplinary concepts of mitigation policy to potential impact of climate change. 

This study investigates atmospheric conditions’ influence on the mean and extreme characteristics of PM 10 concentrations in Poznań during the period 2006–2013. A correlation analysis was carried out to identify the most important meteorological variables influencing the seasonal dynamics of PM 10 concentrations. The highest absolute correlation values were obtained for planetary boundary layer height ( r  = −0.57), thermal (daily minimum air temperature: r  = −0.51), anemological (average daily wind speed: r  = −0.37), and pluvial (precipitation occurrence: r  = −0.36) conditions, however the highest correlations were observed for temporal autocorrelations (1 day lag: r  = 0.70). As regulated by law, extreme events were identified on the basis of daily threshold value i.e. 50 μg m −3 . On average, annually there are approximately 71.3 days anywhere in the city when the threshold value is exceeded, 46.6 % of those occur in winter. Additionally, 83.7 % of these cases have been found to be continuous episodes of a few days, with the longest one persisting for 22 days. The analysis of the macro-scale circulation patterns led to the identification of an easy-to-perceive seasonal relations between atmospheric fields that favour the occurrence of high PM 10 concentration, as well as synoptic situations contributing to the rapid air quality improvement. The highest PM 10 concentrations are a clear reaction to a decrease in air temperature by over 3 °C, with simultaneous lowering of PBL height, mean wind speed (by around 1 m s −1 ) and changing dominant wind directions from western to eastern sectors. In most cases, such a situation is related to the expansion of a high pressure system over eastern Europe and weakening of the Icelandic Low. Usually, air quality conditions improve along with an intensification of westerlies associated with the occurrence of low pressure systems over western and central Europe. Opposite relations are distinguishable in summer, when air quality deterioration is related to the inflow of tropical air masses originating over the Sahara desert.

To study urban heat island (UHI), Landsat 5 TM data and in situ measurements of air temperature from nine points in Poznań (Poland) for the period June 2008–May 2013 were used. Based on data from measurement points located in different types of land use, the surface urban heat island (SUHI) maps were created. All available and quality-controlled Landsat 5 TM images from 15 unique days were used to obtain the characteristics of land surface temperature (LST) and UHI intensity. In addition, spatial analysis of UHI was conducted on the basis of Corine Land Cover 2006 dataset. In situ measurements at a height of 2 m above ground level show that the UHI is a common occurrence in Poznań with a mean annual intensity of 1.0 °C. The UHI intensity is greater during the warm half of the year. Moreover, results based on the remote sensing data and the Corine Land Cover 2006 indicate that the highest value of the mean LST anomalies (3.4 °C) is attained by the continuous urban fabric, while the lowest value occurs within the broad-leaved forests (−3.1 °C). To re-count from LST to the air temperature at a height of 2 m above ground level ( T agl ), linear and non-linear regression models were created. For both models, coefficients of determination equal about 0.80, with slightly higher value for the non-linear approach, which was applied to estimate the T agl spatial variability over the city of Poznań.

The main objective of this study was to investigate the variability in annual counts and the northern extent of cyclones in the North Atlantic in the years 1970–2019. Cyclones were divided into tropical cyclones (TCs), called hurricanes in the Atlantic, and extratropical cyclones (ETCs), transformed from TCs. Linear regression methods and Pearson’s correlation coefficient were applied. The trend in numbers is upward for both types of cyclones. The maximum annual northern extent of TCs shows a decreasing trend, while that of ETCs is clearly increasing. Hurricane numbers show a moderate positive correlation (correlation coefficient 0.31) with the annual Southern Oscillation Index (SOI) and a negative correlation (−0.34) with the annual North Atlantic Oscillation (NAO) index. For the SOI in the months of the second half of the year, there is a strong correlation (up to 0.51) with the number of TCs in September–October. The highest correlation (0.65) is observed between the number of TCs and the annual Atlantic Multi-decadal Index (AMO). The number of TCs have been shown to correlate positively with the water temperature of the North Atlantic and western Pacific, and negatively with the eastern Pacific Ocean. A significant relationship has also been recorded between SST and the maximum annual extent of extratropical cyclones to the north and east (correlation coefficient of 0.4 to 0.6).

This study analyzes the atmospheric forcing of upwelling occurrence along differently oriented coastlines of the southern Baltic Sea basin. The mean daily sea surface temperature (SST) data from the summer seasons (June–August) of the years 1982–2017 made the basis for the detection of upwelling cases. For the atmospheric part of the analysis, monthly indices of four macroscale circulation patterns were used: North Atlantic Oscillation (NAO), Scandinavian (SCAND), East Atlantic (EA) and East Atlantic/Western Russia (EATL/WRUS). In order to identify the local airflows and wind conditions, zonal and meridional regional circulation indices were constructed and introduced to the analysis. Within the southern Baltic Sea basin, upwelling occurs most frequently along the zonally oriented southern coasts of Sweden, and least frequently along the southern (Polish) and eastern (Lithuanian-Latvian) coasts. Among the macroscale circulation patterns, the SCAND has the strongest impact on the horizontal flow of surface sea waters in the southern Baltic, which triggers upwelling. The summer NAO and EA appeared to have a weak effect on upwelling occurrence, and EATL/WRUS have the smallest impact. Local circulation indices allowed us to recognize the atmospheric control of upwelling frequency better than the indices of the macroscale patterns. Anomalies in upwelling frequency are their highest at the positive/negative phase of the zonal circulation, particularly along the southern and eastern coast of the southern Baltic Sea basin.

Observed proximity soundings from Europe are used to highlight how well environmental parameters discriminate different kind of severe thunderstorm hazards. In addition, the skill of parameters in predicting lightning and waterspouts is also tested. The research area concentrates on central and western European countries and the years 2009–15. In total, 45 677 soundings are analyzed including 169 associated with extremely severe thunderstorms, 1754 with severe thunderstorms, 8361 with nonsevere thunderstorms, and 35 393 cases with nonzero convective available potential energy (CAPE) that had no thunderstorms. Results indicate that the occurrence of lightning is mainly a function of CAPE and is more likely when the temperature of the equilibrium level drops below −10°C. The probability for large hail is maximized with high values of boundary layer moisture, steep mid- and low-level lapse rates, and high lifting condensation level. The size of hail is mainly dependent on the deep layer shear (DLS) in a moderate to high CAPE environment. The likelihood of tornadoes increases along with increasing CAPE, DLS, and 0–1-km storm-relative helicity. Severe wind events are the most common in high vertical wind shear and steep low-level lapse rates. The probability for waterspouts is maximized in weak vertical wind shear and steep low-level lapse rates. Wind shear in the 0–3-km layer is the best at distinguishing between severe and extremely severe thunderstorms producing tornadoes and convective wind gusts. A parameter WMAXSHEAR multiplying square root of 2 times CAPE (WMAX) and DLS turned out to be the best in distinguishing between nonsevere and severe thunderstorms, and for assessing the severity of convective phenomena.

Phenology is primarily seen as an indicator of the impacts of climate change. The strongest biological signal of climatic change is revealed by phenological data from the period after 1990. Unfortunately, the Polish nationwide network of phenological monitoring was terminated in 1992, and was only reactivated in 2005. Here, we attempt to reconstruct late spring phenophases of flowering of Syringa vulgaris L. and Aesculus hippocastanum L. across several sites in Poland from 1951 to 2014 using the GIS-based approach (if observations from neighboring stations were available) and multiple regression modeling with stepwise screening and bootstrap resampling. It was found that the air temperature and its indices explain over 60% of the variance, giving an accuracy of 3.0–3.4 days (mean absolute error) and correlation coefficients of 0.83 and 0.78 for lilac and horse chestnut, respectively. Altogether, both plant species showed a statistically significant advancement in the onset of flowering with an average rate of 1.7 days per decade. We also found that the final trend is the result of rapid acceleration of the increase in air temperature after the 1990s, while most of the trends for late spring were ambiguous before that period.

This paper constitutes the sensitivity study of application the Polar WRF model to the Svalbard area with testing selected parameterizations, including planetary boundary layer, radiation and microphysics schemes. The model was configured, using three one-way nested domains with 27 km, 9 km and 3 km grid cell resolutions. Results from the innermost domain were presented and compared against measured wind speed and air temperature at 10 meteorological stations. The study period covers two months: June 2008 and January 2009. Significant differences between simulations results occurred for planetary boundary layer (PBL) schemes in January 2009. The Mellor-Yamada-Janjic (MYJ) planetary boundary layer (PBL) scheme resulted in the lowest errors for air temperature, according to mean error (ME), mean absolute error (MAE) and correlation coefficient values, where for wind speed this scheme was the worst from all the PBL schemes tested. In the case of June 2008, shortwave and longwave radiation schemes influenced the results the most. Generally, higher correlations were obtained for January, both for air temperature and wind speed. However, the model performs better for June in terms of ME and MAE error statistics. The results were also analyzed spatially, to summarize the uncertainty of the model results related to the analyzed parameterization schemes groups. Significant variability among simulations was calculated for January 2009 over the northern part of Spitsbergen and fjords for the PBL schemes. Standard deviations for monthly average simulated values were up to 3.5°C for air temperature and around 1 m s-1 for wind speed.