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Over a period of 30 years (1993–2022), headwater catchments in the Slavkov Forest (Czech Republic) exhibited a robust increase in stream water DOC (dissolved organic carbon) concentrations following a significant reduction in acidic atmospheric deposition. Sulfur deposition decreased from 34 kg ha−1 yr−1 in 1993 to 2.6 kg ha−1 yr−1 in 2022. Three Norway-spruce-dominated research sites—Černý Potok (CEP), a 15.2 ha peatbog catchment, Lysina (LYS), a 27.3 ha granitic catchment, and Pluhův Bor (PLB), a 21.6 ha serpentinite catchment, were investigated. The three–year average DOC concentration increased from 48.2 mg L−1 (1993–1995) to 68.3 mg L−1 (2020–2022) at CEP (0.69 mg L−1 yr−1). LYS showed an increase from 16.9 mg L−1 to 25.4 mg L−1 (0.30 mg L−1 yr−1 annually). The largest increase was recorded at PLB, with an increase from 15.7 mg L−1 to 36.7 mg L−1 (0.89 mg L−1 yr−1). A decline in ionic strength was identified as the main driver of the DOC increase. The annual runoff declined significantly at CEP and LYS from 465 mm to 331 mm as a result of rising air temperatures and reduced precipitation between 2014 and 2022. PLB (average of 266 mm) did not show a statistically significant decline. Recently, PLB experienced significant deforestation that likely lowered transpiration and thus increased catchment runoff. As a result, DOC fluxes did not change significantly at CEP (average 210 kg ha−1 yr−1) and LYS (90 kg ha −1 yr−1). However, PLB's DOC flux more than doubled, increasing from 44 to 106 kg ha−1 yr−1. Drivers connected with global change, such as increasing temperatures, or potential chemical drivers, such as reductions in Al concentrations and pH changes, were not able to explain the observed changes in DOC concentra tions and fluxes. [ABSTRACT FROM AUTHOR]

The subject of the analysis were the climatic and bioclimatic conditions of the mountainous areas in Central Europe from 2005 to 2022. The study was conducted based on meteorological data from 4 stations located in 2 mountain ranges in Central Europe, which were obtained from the Ogimet database. The analysis examined the course of mean air temperature, winter days, precipitation, snow cover, wind speed, horizontal visibility, as well as the number of days with thunderstorms and fog. Subsequently, bioclimatic indices were analyzed based on the wind chill index (WCI) and the climate severity index by Osokin (So). The results confirm an increase in mean air temperature and a decrease in the number of winter days. The most significant change in mean annual air temperature was recorded at the Carphatian stations: Varful Omu (0.59 °C/10 years) and tomnica (0.49 °C/10 years). The largest change in the WCI value was recorded at the Carpathian station Varful Omu (51 W/m²/10 years), while the So index was 1.4/10 years. [ABSTRACT FROM AUTHOR]

Here we present a new eastern Baltic Chironomidae training set (TS) containing 35 sites that was collected and merged with neighbouring published Finnish (82 lakes) and northern part of the Polish (nine lakes) TSs. Chironomidae, non‐biting midges, are known to be strongly responsive to the July air temperature and are widely used to infer palaeotemperature. Several modern analogue‐based TSs necessary for calibrating the relationships between mean July air temperature (MJAT) and chironomids are available for Europe. However, none of these is representative of the transitional climate typical for eastern Baltic (Estonia, Latvia, Lithuania). The Finno–Baltic–Polish TS contains 121 sites and covers a geographically continuous 70–50°N latitudinal and 7 °C (12.1–19.2 °C) MJAT gradient. Canonical correspondence analysis revealed that, among the tested environmental variables (pH, water depth, dissolved oxygen, MJAT), the MJAT explains the highest amount of variation, both for the eastern Baltic separately and the Finno–Baltic–Polish TSs. The weighted averaging–partial least squares‐based cross‐validation test reveals that the Finno–Baltic–Polish TS has a low root mean square error of prediction (0.7 °C) confirming the high reliability of the TS. The temperature optima of the taxa included in the new Finno–Baltic–Polish TS and widely used Swiss–Norwegian TS were examined. The observed dissimilarities can be attributed to the differences in the temperature ranges represented by the TS, the taxonomic identification level, the general cosmopolitan taxa distribution patterns and the influence of TS‐specific geographic position, climatic or environmental conditions. The new Finno–Baltic–Polish TS adds to the knowledge on the modern distribution of Chironomidae taxa and widens the geographical area of reliable Chironomid‐based MJAT reconstructions into the eastern European lowland. [ABSTRACT FROM AUTHOR]

Seasonal climate predictions can assist with timely preparations for extreme episodes, such as dry or wet periods that have associated additional risks of droughts, fires and challenges for water management. Timely warnings for extreme warm summers or cold winters can aid in preparing for increased energy demand. We analyse seasonal forecasts produced by three different methods: (1) a multi-linear statistical forecasting system based on observations only; (2) a non-linear random forest model based on observations only; and (3) process-based dynamical forecast models. The statistical model is an empirical system based on multiple linear regression that is extended to include the trend over the previous 3 months in the predictors, and overfitting is further reduced by using an intermediate multiple linear regression model. This results in a significantly improved El Niño forecast skill, specifically in spring. Also, the Indian Ocean dipole (IOD) index forecast skill shows improvements, specifically in the summer and autumn months. A hybrid multi-model ensemble is constructed by combining the three forecasting methods. The different methods are used to produce seasonal forecasts (three-month means) for near-surface air temperature and monthly accumulated precipitation seasonal forecast with a lead time of one month. We find numerous regions with added value compared with multi-model ensembles based on dynamical models only. For instance, for June, July and August temperatures, added value is observed in extensive parts of both Northern and Southern America, as well as Europe. [ABSTRACT FROM AUTHOR]

The Younger Dryas-Holocene transition represents a period of significant thermal change, comparable in magnitude to modern warming, yet in a colder context and without the effect of anthropogenic disturbance. This is useful as a reference to tackle how biodiversity is affected by temperature in natural conditions. Here, we addressed the thermal change during this period in a southern Baltic coastal lake (Konarzewo Lake, Poland), as inferred by chironomid remains. We evaluated changes in chironomid communities and used Hill numbers to explore how commonness and rarity underlie biodiversity changes attributable to warming. We found evidence of warming at Konarzewo Lake during the Younger Dryas-Holocene transition, with inferred temperatures in the Younger Dryas period supporting the NW–SE gradient in Younger Dryas summer temperatures across Europe. Chironomid communities drastically changed during the thermal transition. However, Hill numbers showed no response to temperature when rare morphotypes were emphasized (order q = 0) or a weak response when they were balanced with common morphotypes (order q = 1). Hill number of order q = 2, emphasizing the most common morphotypes, consistently increased with temperature across different sample sizes or coverages. This illustrates how common morphotypes, rather than the rare ones, may boost biodiversity facing warming. [ABSTRACT FROM AUTHOR]

Relative to drought and heat waves, the effect of winter warming on forest CO2 fluxes during the dormant season has less been investigated, despite its relevance for net CO2 uptake in colder regions with higher carbon content in soils. Our objective was to test the effect of the exceptionally warm winter in 2020 on the winter CO2 budget of cold-adapted evergreen needle-leaf forests across Europe, and identify the contribution of soil and air temperature to changes in winter CO2 fluxes in response to warming. Our hypothesis was that warming in winter leads to higher emissions across colder sites due to increased ecosystem respiration. To test this hypothesis, we used 98 site-year eddy covariance measurements across 14 evergreen needle-leaf forests (ENFs) distributed from north to south of Europe (from Sweden to Italy). We used a data-driven approach to quantify the effect of air and soil temperature on changes in net ecosystem productivity (NEP) during the warm winter of 2020. Our results showed that the impact of warming was different across sites, as in the lower altitude and lower latitude sites positive soil temperature anomalies were larger, while positive air temperature anomalies were larger in the northern latitude and high-altitude sites. Warming in winter led to a divergent response across the sites. Out of 14 sites only in 3 sites net ecosystem productivity declined in winter significantly in response to warming. In addition, we observed that in the colder sites daytime NEP (that is dominated by photosynthesis) declined with warming of the air in winter, whereas in the warmer sites daytime NEP increased with warming of the soil. This shows that warming of the air – if not translated into a direct warming of the soil– might not trigger productivity in winter if the soil within the rooting zone remains frozen. Forests within the same plant functional type category can exhibit differing reactions to winter warming and to predict their responses accurately it is crucial to account for variations in local climate, physiology, and structure simultaneously. [ABSTRACT FROM AUTHOR]

Relative to drought and heat waves, the effect of winter warming on forest CO2 fluxes during the dormant season has less been investigated, despite its relevance for net CO2 uptake in colder regions with higher carbon content in soils. Our objective was to test the effect of the exceptionally warm winter in 2020 on the winter CO2 budget of cold-adapted evergreen needleleaf forests across Europe, and identify the contribution of soil and air temperature to changes in winter CO2 fluxes in response to warming. Our hypothesis was that warming in winter leads to higher emissions across colder sites due to increased ecosystem respiration. To test this hypothesis, we used 98 site-year eddy covariance measurements across 14 evergreen needleleaf forests (ENFs) distributed from north to south of Europe (from Sweden to Italy). We used a data-driven approach to quantify the effect of air and soil temperature on changes in net ecosystem productivity (NEP) during the warm winter of 2020. Our results showed that the impact of warming was different across sites, as in the lower altitude and lower latitude sites positive soil temperature anomalies were larger, while positive air temperature anomalies were larger in the northern latitude and high-altitude sites. Warming in winter led to a divergent response across the sites. Out of 14 sites only in 3 sites net ecosystem productivity declined in winter significantly in response to warming. In addition, we observed that in the colder sites daytime NEP (that is dominated by photosynthesis) declined with warming of the air in winter, whereas in the warmer sites daytime NEP increased with warming of the soil. While warming increases ecosystem respiration, it might not trigger productivity in winter if the soil within the rooting zone remains frozen. Forests within the same plant functional type category can exhibit differing reactions to winter warming and to predict their responses accurately it is crucial to account for variations in local climate, physiology, and structure simultaneously. [ABSTRACT FROM AUTHOR]

Irrigation is a crucial land use practice to adapt agriculture to unsuitable climate and soil conditions. Aiming to improve the growth of plants, irrigation modifies the soil condition, which causes atmospheric effects and feedbacks through land–atmosphere interaction. These effects can be quantified with numerical climate models, as has been done in various studies. It could be shown that irrigation effects, such as air temperature reduction and humidity increase, are well understood and should not be neglected on local and regional scales. However, there is a lack of studies including the role of vegetation in the altered land–atmosphere interaction. With the increasing resolution of numerical climate models, these detailed processes have a chance to be better resolved and studied. This study aims to analyze the effects of irrigation on land–atmosphere interaction, including the effects and feedbacks of vegetation. We developed a new parameterization for irrigation, implemented it into the REgional climate MOdel (REMO2020), and coupled it with the interactive MOsaic-based VEgetation module (iMOVE). Following this new approach of a separate irrigated fraction, the parameterization is suitable as a subgrid parameterization for high-resolution studies and resolves irrigation effects on land, atmosphere, and vegetation. Further, the parameterization is designed with three different water application schemes in order to analyze different parameterization approaches and their influence on the representation of irrigation effects. We apply the irrigation parameterization for southwestern Europe including the Mediterranean region at a 0.11 ∘ horizontal resolution for hot extremes. The simulation results are evaluated in terms of the consistency of physical processes. We found direct effects of irrigation, like a changed surface energy balance with increased latent and decreased sensible heat fluxes, and a surface temperature reduction of more than -4 K as a mean during the growing season. Further, vegetation reacts to irrigation with direct effects, such as reduced water stress, but also with feedbacks, such as a delayed growing season caused by the reduction of the near-surface temperature. Furthermore, the results were compared to observational data, showing a significant bias reduction in the 2 m mean temperature when using the irrigation parameterization. [ABSTRACT FROM AUTHOR]

Extreme thermal conditions have been known for a long time as is evidenced by the oldest preserved historical accounts referring to weather. However, direct data, that is, air temperature measurements, which began in Europe in the 17th century, are the only reliable source of information. They provide insight into how climate evolved over the characteristic period of the climate timeline known as the Little Ice Age. This study focuses on extreme climatic phenomena, referred to here as exceptionally cold (EC) and exceptionally warm (EW) months and seasons that occurred in the early instrumental period (until 1830). The research is based on average monthly air temperature values (of different lengths) from five meteorological stations in Europe. The Central England Temperature series (CET) offers the longest series that spans 172 years whereas Padua has the shortest, 57 years. Consequently, the year 1659, which is the start date of the Central England Temperature series, marks the beginning of the study period for the research presented in this article. Thermally anomalous periods are identified based on the standard deviation of the average temperature from the corresponding long‐term average (1831–2020). The highlights of the study include the identification of the pronounced prevalence at all five stations of EC months and seasons when compared to EW months and seasons, especially in Western Europe (CET) during the Maunder Minimum. The study also finds a lack of synchronicity in the timing of the anomalous periods under investigation in the different parts of Europe. [ABSTRACT FROM AUTHOR]

Vertical temperature profiles influence the wind power generation of large offshore wind farms through stability-dependent effects such as blockage and gravity waves. However, numerical tools that are used to model these effects are often computationally too expensive to cover the large variety of atmospheric states occurring over time. Generally, an informed decision about which representative nonidealized situations to simulate is missing because of the lack of easily available information on representative vertical profiles, taking into account their spatiotemporal variability. Therefore, we present a novel framework that allows a smart selection of vertical temperature profiles. The framework consists of an improved analytical temperature model for the atmospheric boundary layer and lower troposphere, a subsequent clustering of these profiles to identify representatives, and last, a determination of areas with similar spatiotemporal characteristics of vertical profiles. When applying this framework on European ERA5 data, physically realistic representatives were identified for Europe, excluding the Mediterranean. Two or three profiles were found to be dominant for the open ocean, whereas more profiles prevail for land. Over the open ocean, weak temperature gradients in the boundary layer and a clear capping inversions are widespread, and stable profiles are absent except in the region of the East Icelandic Current. Interestingly, according to the ERA5 data, at its resolution, coastal areas and seas surrounded by land behave more similar to the land areas than to the open ocean, implying that a larger set of model integrations are needed for these areas to obtain representative results for offshore wind power assessments in comparison with the open ocean. SIGNIFICANCE STATEMENT: Numerical tools used to simulate the effect of large, offshore wind farms on neighboring farms and the atmosphere are very expensive. Therefore, they can only be computed for a limited number of cases. As temperature is one of the most important parameters in these kinds of simulations, this work provides a new vertical temperature model and an analysis framework that allows for a smart selection of these cases such that they ideally represent the full variation of the atmosphere's temperature profiles. [ABSTRACT FROM AUTHOR]

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Based on the ERA5 reanalysis since the 1950s, this study investigates the coherent inter‐decadal variations of the winter continental surface air temperature (SAT) between the Eurasian (EUA) and the North American continents in the past 70 years. The SAT variation over the two continents can be characterized with five typical periods successively from a 'both‐warmer' (1950s–1960s), to a 'both‐colder' (1960s–1980s), and then a 'EUA‐warmer' (1980s–1990s) followed by another 'both‐warmer' (1990s–2000s) and a 'EUA‐colder' (2000s–2010s) period. Such periodic variation of the SAT is closely coupled with changes of the low isentropic‐level meridional mass transport (IMMT), as manifested by the coupling relationship between the midlatitude SAT and the net IMMT out of the polar circle, in terms of their zonal patterns and geographical patterns. The SAT variation can be well reconstructed with the first two leading maximum covariance analysis (MCA) modes between the SAT and IMMT. The first leading MCA mode (MCA1) mainly dominates a 'EUA‐warmer/colder' pattern, while the second (MCA2) dominates the 'both‐warmer/colder' pattern. There exists a significant lead–lag correlation between the MCA1 and MCA2, which determines the changing SAT pattern in the five successive periods. Furthermore, the MCA1 and MCA2, especially their lead–lag coupling after the 1975 is significantly associated with the Pacific decadal oscillation (PDO) which dominates the much‐intensified MCA1 and the related 'EUA‐warmer/colder' SAT pattern since then. The Atlantic multi‐decadal oscillation mainly dominates the third leading MCA mode with it's effects on the SAT over Europe coincidently to amplify the PDO‐related SAT changes. [ABSTRACT FROM AUTHOR]

The CMIP6 projections constitute the basis of our latest understanding of the climate response to anthropogenic forcing. However, there is still considerable uncertainty in the projections, especially at the regional scale. One way to constrain the uncertainty is by comparing the models historical climate change signals against observations and investigate the physical reasons for divergences. Here, we assess the signal‐to‐noise ratio (S/N) of surface air temperature (SAT), precipitation (PREC) and soil moisture (SM) over Europe for a set of CMIP6 historical simulations and compare them against the E‐OBS observational product and the ERA5 reanalysis. We found considerable divergences between the CMIP6 ensemble mean S/N and that of E‐OBS and ERA5, as well as between ERA5 and E‐OBS. The latter indicates that the S/N is affected by data coverage. We show that the differences among model signals are associated with different atmospheric circulation responses. We also investigate the potential relationships between the models' signals and climatological biases, and we found evidence that the models with a warm climatological bias in southern Europe tend to have smaller SAT signals (warm less). Finally, we found no apparent relationship between SM biases and the warming signal, suggesting that the mechanism by which SM–atmosphere interactions affect climate variability does not explain the mean changes. However, there is a tendency for models with higher SM to dry faster than models with lower SM. [ABSTRACT FROM AUTHOR]

North Atlantic Oscillation (NAO) has a significant impact on surrounding winter weather and climate. However, the causes of its occasional reversal between early and late winter remain unclear. This study proposes a mechanism for the mid‐winter reversal of NAO from the perspective of local midlatitude air‐sea interaction. Strong sea surface temperature (SST) tripole events, which are defined by empirical orthogonal function of winter‐mean interannual North Atlantic SST anomalies, are primarily induced by NAO in early winter and peak in January. In late winter, the persistent SST tripole exerts active feedback on atmosphere through diabatic heat and transient eddy forcing. The resulting atmospheric circulation anomalies exhibit an almost reversed NAO pattern in February, which forms a wavetrain originating above the Gulf Stream and propagating to the Middle East and weakens the former SST tripole. Consequently, significant reversals of air temperature anomalies occur in Europe and the Caspian Sea area between February and December. Plain Language Summary: The North Atlantic Oscillation (NAO) is a crucial atmospheric system that significantly impacts the weather and climate of the surrounding regions during winter. However, it often reverses between early and late winter, and the reasons for its mid‐winter reversal remain unclear. The NAO behavior can be influenced by multiple factors, such as atmospheric internal processes, underlying surface, and remote climate system, which makes the issue more complex. This study highlights the role of underlying sea surface temperature anomalies (SSTAs) in the mid‐winter reversal of NAO. The North Atlantic SSTAs are closely associated with the NAO and exhibit a tripole pattern. For strong sea surface temperature (SST) tripole events, the NAO primarily induces the early winter North Atlantic SST tripole. The SSTAs develop in early winter, peak in January, and feedback on the atmospheric circulation in late winter. The atmospheric circulation anomalies exhibit an almost reversed NAO pattern in February, forming a wavetrain that propagates above the Gulf Stream to the Middle East. As a result, during the warm phase of the SST tripole, Europe experiences colder temperatures, and the Caspian Sea experiences warmer temperatures in February than usual, as opposed to the positive NAO's control over warm Europe and cold Caspian Sea in December. Key Points: In early winter, North Atlantic Oscillation (NAO) forces a North Atlantic sea surface temperature (SST) tripole, which persists and tends to induce a nearly reversed NAO pattern in FebruarySST tripole induces a wavetrain over the Gulf Stream to the Middle East via diabatic heating and transient eddy forcing in FebruaryAs a result, there are remarkable reversals of air temperature anomalies in Europe and Caspian Sea area between February and December [ABSTRACT FROM AUTHOR]

The relationship between body size, colour and hairiness of Procladius of the Diptera family Chironomidae and climate measured as annual mean air temperature was analysed. The study was based on 453 males representing 31 species from 253 localities in 27 countries in Europe and six elsewhere. Wing length as well as three other characters reflecting body size showed significant decrease with warmer climate conditions. The results are in line with several other studies of insects concluding that adult insects are smaller in warmer climate, also considering the implications of global warming. Likewise, all five characters representing colour lightness showed significantly lighter coloured Procladius specimens with warmer climate. The relationship between hairiness and annual mean air temperature was weaker than that of size or colour and temperature, but all five characters studied showed a statistically significant decrease in hairiness with warmer climate. The biological relevance of smaller size, lighter colour and less hairiness in warmer climate can be related to several factors regulating flight and swarming, including less need of protection against cold weather, overheating avoidance, mating success agility and predator escape. [ABSTRACT FROM AUTHOR]

The increasing intensity and frequency of high-temperature events in response to climate change can potentially impact the aviation industry, since aircraft takeoff and landing performances depend on near-surface air temperature. Previous studies have combined climate data with aircraft technical data to estimate the future impact of rising high temperatures on aircraft takeoff. They found a decrease of maximum takeoff weights and the lengthening of takeoff distances. The Mediterranean region is a climate change "hot spot" area, specially concerned by extreme high-temperatures increase. In this study, the magnitude and trends of the daily maximum near-surface temperature extremes in summer were analysed over major airports in Southwestern Europe. Trends in the period 1961–2014 were analysed from observations and reanalysis. Future changes by 2021–2050 and 2071–2100, with respect to 1961–2005, were analysed from simulations performed with Regional and Global Climate Models (RCMs and GCMs). Before assessing future climate projections, climate models were evaluated in present climate, and the RCM and GCM ensembles were compared to each other. No clear added value was found for RCMs over GCMs in present climate at the airport scale in these terms. GCMs project larger temperature changes than RCMs over the same locations. Multi-model ensemble mean projected changes under the RCP8.5 scenario range between + 1.7 and + 3.2 ∘ C by the near term, and between + 4.9 and + 8.5 ∘ C by the long term, across the airports and the RCM and GCM ensembles. This increase of high-temperature extremes would impact airport operations. Adaptation or mitigation policies would become necessary. [ABSTRACT FROM AUTHOR]

Intra-Annual Density Fluctuations (IADFs) are an important wood functional trait that determine trees' ability to adapt to climatic changes. Here, we use a large tree-ring database of 11 species from 89 sites across eight European countries, covering a climatic gradient from the Mediterranean to northern Europe, to analyze how climate variations drive IADF formation. We found that IADF occurrence increases nonlinearly with ring width in both gymnosperms and angiosperms and decreases with altitude and age. Recently recorded higher mean annual temperatures facilitate the formation of IADFs in almost all the studied species. Precipitation plays a significant role in inducing IADFs in species that exhibit drought tolerance capability, and a growth pattern known as bimodal growth. Our findings suggest that species with bimodal growth patterns growing in western and southern Europe will form IADFs more frequently, as an adaptation to increasing temperatures and droughts. [ABSTRACT FROM AUTHOR]

Irrigation is a crucial land use practice to adapt agriculture to unsuitable climate and soil conditions. Aiming for improving the growth of plants, irrigation modifies the soil condition, which causes atmospheric effects and feedbacks through land-atmosphere interaction. These effects can be quantified with numerical climate models, as has been done in various studies. It could be shown that irrigation effects, such as air temperature reduction and humidity increase are well understood and should not be neglected on local and regional scales. However, there is a lack of studies including the role of vegetation in the altered land-atmosphere interaction. With the increasing resolution of numerical climate models, these detailed processes have a chance to be better resolved and studied. This study aims for analyzing the effects of irrigation on land-atmosphere interaction, including the effects and feedbacks of vegetation. We developed a new parameterization for irrigation and implemented it into the REgional climate MOdel REMO2020, coupled with the interactive MOsaicbased VEgetation module iMOVE. Following this new approach of a separate irrigated fraction, the parameterization is suitable as a subgrid parameterization for high-resolution studies and resolves irrigation effects on land, atmosphere, and vegetation. Further, the parameterization is designed with three different water application schemes in order to analyze different parameterization approaches and their influence on the representation of irrigation effects. We apply the irrigation parameterization for South-West Europe including the Mediterranean region on 0.11° horizontal resolution for hot extremes. The simulation results are evaluated in terms of the consistency of physical processes. We found direct effects of irrigation, like a changed surface energy balance with increased latent and decreased sensible heat fluxes, and a surface temperature reduction of more than -4 K as mean during the growing season. Further, vegetation reacts to irrigation with direct effects, such as reduced water stress, but also with feedbacks, such as a delayed growing season caused by the reduction of the near-surface temperature. Furthermore, the results were compared to observational data showing a significant bias reduction in the 2 m mean temperature when using the irrigation parameterization. [ABSTRACT FROM AUTHOR]

This study presents the direct relationships between changes in the annual surface temperature of the North Atlantic (SST) and the number of days per year experiencing low flows in the Warta River catchment (WRC) in Central Europe, Poland, in the multi-annual period of 1951–2020. The number of days experiencing low flows (TLF) was used to describe the conditions of hydrological drought in the WRC. Moderately strong (r~0.5) but statistically highly significant (p < 0.001) relationships were found between TLF and the SST in the subtropical (30–40° N, 60–40° W) and subpolar North Atlantic (70° N, 10° W–10° E). With the increase in the annual SST in these parts of the North Atlantic, the number of days in a year experiencing low flows in the WRC also increased. It was determined that besides synchronous (i.e., in the same year) relationships between TLF and SST, asynchronous relations also occurred: the SST changes were one year ahead of the TLF changes. With the increase in the SST in the subtropical and subpolar North Atlantic, the sunshine duration and air temperature in the WRC increased, while the relative humidity decreased. The relationships between precipitation in the WRC and SST were negative (from −0.04 to −0.14), but statistically insignificant (p > 0.2). This indicates that the impact of SST changes on TLF in the WRC is mainly caused by the shaping of the amount of surface evaporation, which strongly increases in years of high SST, and the climatic water balance becomes negative, resulting in an increase in extremely low flows. The analysis of the causes of these relationships shows that the SST changes in the North Atlantic control, through changes in the height of the geopotential (h500), changes in the atmospheric circulation over Europe. In the periods of SST h500 growth over Central Europe, the atmospheric pressure (SLP) increases. That area is more frequently than average under the influence of the Azores High; this leads to an increase in the frequency of anticyclonic weather. A significant increase in the number of TLFs and prolonged periods of hydrological drought in the WRC after 2000 are associated with a strong increase in the SST in the area of the tropical and subtropical North Atlantic. [ABSTRACT FROM AUTHOR]

In Europe, there has been a significant change in the way tillage is approached in recent years. This change is due to a growing awareness among farmers, politicians and society as a whole that soil is not a renewable resource in itself. From an agricultural point of view, the greatest impact on soil condition can be achieved through the use of the applied tillage systems. My research takes this approach as a basis when examining the different tillage systems and their impact on the environment. In this context, conventional and a variety of no-tillage systems are examined in this paper. As a next step, it is examined how the environmental conditions created by the different tillage systems influence the emergence of maize hybrids. The analyses are carried out in a multi-factorial, long-term tillage field experiment. The same batch of the same hybrid seed was sown in several crop years, and the effects of environmental conditions on the emergence process were examined. Environmental effects and emergence-related uptake were measured in the examined plots. Measurements of environmental effects included air temperature, precipitation, soil temperature measured at seeding depth, as well as % cover of stem residue on the surface in the treated plots. The first emergence time measurements of the sown crop in the plots of each treatment were compared and relationships between these factors were investigated. [ABSTRACT FROM AUTHOR]

Time-varying relationships between two atmospheric oscillation modes and precipitation (rainfall and snow) in the western portion of the Carpathian Mountains and the northern part of the Pannonian Plain are investigated in this paper. The Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO) modes were used to explain the intra-annual variability of monthly rainfall totals and new snow over a 36-year period (1981-2017) observed at a dense network of 177 rain gauges and 33 snow rods located throughout the investigated area, respectively. The Continuous Wavelet Transform (CWT) was deployed as a band-pass fllter to quantify the spectral coherence and attribution (based on signal-to-noise ratio) of the NAO and AO modes to the precipitation variability. The temporal and spatial patterns of NAO and AO-related signal variance in monthly rainfall totals and fresh snowpack were evaluated on seasonal and monthly bases. The NAO mode explained the major historically observed snow calamities, such as the snowy cold spell of January 1985 that hit central and southern Europe, bringing heavy snowfall to the investigated region and many other parts of Europe. This event was the beginning of a prolonged cold wave in Europe, with extremely low air temperatures across the continent. Our analyses showed that the AO mode appears to have only a limited effect on the overall variability of new snowpack in late winter. The attribution of NAO and AO modes to the variability of monthly rainfall totals is less pronounced compared to new snowpack. [ABSTRACT FROM AUTHOR]

This study examines the European heatwaves' predictability at subseasonal timescales. Land surface feedbacks and tropical convection, due to their variability at intraseasonal timescales, are taken into consideration and their potential role in extending the predictability beyond the medium range (10 days) is explored. A classification of European heatwaves into five heatwave types is used to discriminate the effects of surface feedbacks and of tropical variability among the different heatwave types. The classification is computed in terms of circulation patterns. By inferring the near‐surface temperature through atmospheric circulation, we aim to identify the predictable component of the heatwave events. All five heatwave circulation patterns are characterized by persistent anticyclonic anomalies located over the region with maximum temperatures. We show that soil moisture deficit is not a required precondition for the occurrence of heatwaves over most of Europe. However, heatwave events over southern Europe exhibit some sensitivity to dry conditions. We use a simplified index to describe the dominant mode of tropical convection at intraseasonal timescales. The index, based on precipitation anomalies, represents the evolution of the Boreal Summer Intraseasonal Oscillation (BSISO). We find that episodes with strong BSISO amplitudes characterized by enhanced convection over India, Bay of Bengal and China sea favour the occurrence of heatwave events over Russia. The results highlight the role of tropical intraseasonal variability in enhancing the predictability of some extreme temperature events over Europe. [ABSTRACT FROM AUTHOR]

Spring forest ephemerals often create homogeneous patches in the understory; however, our knowledge about their stand level characteristics is deficient. Our aims were to examine, parallel to their phenology, the stand level Net Ecosystem CO2 Exchange (NEE) and evapotranspiration (ET) fluxes as well as the dependence of NEE on leaf area (LA), air temperature (Tair) and light (PPFD) in three spring forest geophytes that are widespread in Europe. Furthermore, we compared the leaf and stand level net photosynthesis. The methods used included open chamber measurements with an infrared gas analyser in permanent plots on a weekly basis. The results showed that the stand levels of all three species proved to be carbon sinks from the beginning of the vegetation period until the end of it or until the last phase of fruit formation. The largest amount of carbon sink was observed at the peak of blooming. A positive linear correlation was measured between NEE and PPFD as well as between NEE and LA, while a negative linear regression was measured between NEE and Tair. The remarkable carbon uptake capacity indicates the non-negligible role of geophyte vegetation in the carbon flux of temperate forests. In addition, the research provided new proof about the role of stand level operation, stability and regulation. [ABSTRACT FROM AUTHOR]

The drought event of 2018–2019 significantly affected most of Central Europe. In this study, the development and intensity of drought in the Lusatian Neisse river catchment were evaluated, based on the indices of SPI, SPEI, and low flow periods related to hydrological drought. Furthermore, multiannual variability in the drought indices, as well as the trends for air temperature and precipitation totals, were examined for 1981–2020. In the catchment, deficit of precipitation was noticed from autumn 2017 to spring 2020, additionally accompanied by a high thermal anomaly. In the summer seasons, heat waves occurred, which intensified evaporation and increased water deficit. The meteorological drought already appeared in spring 2018, developed in the following months, and became more intensive. The frequency of days with discharges >Q70% exceeded 55%. According to SPI12 and SPEI12, the episode of 2018–2019 can be assessed as the longest period of severe drought in the whole of 1981–2020. The drought caused various consequences in the region. The deterioration of water quality and selected biological indices was one of the effects. In the sector of agriculture, yield reduction in corn and wheat amounted to 33% and 18% in 2018 and 22% and 9% in 2019. In addition, decrease in hydropower generation by more than 30% was noticed. [ABSTRACT FROM AUTHOR]

Hibernation is one of the most important behaviours of bats of the temperate zone. During winter, when little food or liquid water is available, hibernation in torpor lowers metabolic costs. However, the timing of emergence from hibernation is crucial for the resumption of the reproductive process in spring. Here, we investigate the spring emergence of six bat species or pairs of bat species of the genera Myotis and Plecotus at five hibernation sites in Central Europe over 5 years. Using generalized additive Poisson models (GAPMs), we analyze the influence of weather conditions (air and soil temperature, atmospheric pressure, atmospheric pressure trend, rain, wind, and cloud cover) as predictors of bat activity and separate these extrinsic triggers from residual motivation to emerge from hibernation (extrinsic factors not studied; intrinsic motivation). Although bats in a subterranean hibernaculum are more or less cut off from the outside world, all species showed weather dependence, albeit to varying degrees, with air temperature outside the hibernaculum having a significant positive effect in all species. The residual, potentially intrinsic motivation of species to emerge from their hibernacula corresponds to their general ecological adaptation, such as trophic specialization and roosting preferences. It allows the definition of three functional groups (high, medium and low residual activity groups) according to the degree of weather dependence of spring activity. A better knowledge of the interplay of extrinsic triggers and residual motivation (e.g., internal zeitgebers) for spring emergence will help to understand the flexibility of a species to adapt to a changing world. [ABSTRACT FROM AUTHOR]

The onset of periods with very high or low air temperatures has aroused general interest for a long time, which is understandable since it has many dangerous effects directly affecting humans. Such perilous climatic phenomena include exceptionally cold and exceptionally warm months, which this study investigates for all Europe and for its five physico‐geographic regions over the 190‐year period of 1831–2020. Therefore, the research in this paper includes two periods characteristic of the history of climate – the late Little Ice Age (LIA) and present‐day warming. The studies are based on average monthly air temperature values from 40 weather stations in Europe. In this paper, exceptionally cold months (ECMs) or exceptionally warm months (EWMs) are considered to have occurred when the average air temperature at a station differed from the respective long‐term average by at least two standard deviations. The highlights of the study include the identification of a drop in the number of ECMs by 20 over the entire 190‐year period, and a highly statistically significant increase in EWMs by 44 between 1980 and 2020. These changes proceeded with different intensities from one physico‐geographic region of Europe to another. [ABSTRACT FROM AUTHOR]

Future global temperature change will have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore future climate change. However, ESMs have great uncertainty and often run at a coarse spatial resolution (usually about 2°). Accurate high‐spatial‐resolution temperature dataset are needed to improve our understanding of temperature variations and for many other applications. We apply Super resolution (SR) in computer vision using deep learning (DL) to merge 31 ESMs data. The proposed method performs data merging, bias‐correction, and spatial downscaling simultaneously. The CRU TS (Climate Research Unit gridded Time Series) data is used as reference data in the model training process. To find a suitable DL method, we select five SR methodologies with different structures. We compare the performances of the methods based on mean square error (MSE), mean absolute error (MAE) and Pearson correlation coefficient (R). The best method is used to merge the projected monthly data (1850–1900), and monthly future scenarios data (2015–2100), at the high spatial resolution of 0.5°. Results show that the merged data have considerably improved performance compared with individual ESM data and their ensemble mean (EM), both spatially and temporally. The MAE shows significant improvement; the spatial distribution of the MAE widens along the latitudes in the Northern Hemisphere. The MAE of merged data is ranging from 0.60 to 1.50, the South American (SA) has the lowest error and the Europe has the highest error. The merged product has excellent performance when the observation data is smooth with few fluctuations in the time series. This work demonstrates the applicability and effectiveness of the DL methods in data merging, bias‐correction and spatial downscaling when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632. [ABSTRACT FROM AUTHOR]

Future global temperature change will have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore future climate change. However, ESMs have great uncertainty and often run at a coarse spatial resolution (usually about 2°). Accurate high‐spatial‐resolution temperature dataset are needed to improve our understanding of temperature variations and for many other applications. We apply Super resolution (SR) in computer vision using deep learning (DL) to merge 31 ESMs data. The proposed method performs data merging, bias‐correction, and spatial downscaling simultaneously. The CRU TS (Climate Research Unit gridded Time Series) data is used as reference data in the model training process. To find a suitable DL method, we select five SR methodologies with different structures. We compare the performances of the methods based on mean square error (MSE), mean absolute error (MAE) and Pearson correlation coefficient (R). The best method is used to merge the projected monthly data (1850–1900), and monthly future scenarios data (2015–2100), at the high spatial resolution of 0.5°. Results show that the merged data have considerably improved performance compared with individual ESM data and their ensemble mean (EM), both spatially and temporally. The MAE shows significant improvement; the spatial distribution of the MAE widens along the latitudes in the Northern Hemisphere. The MAE of merged data is ranging from 0.60 to 1.50, the South American (SA) has the lowest error and the Europe has the highest error. The merged product has excellent performance when the observation data is smooth with few fluctuations in the time series. This work demonstrates the applicability and effectiveness of the DL methods in data merging, bias‐correction and spatial downscaling when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632. [ABSTRACT FROM AUTHOR]

Droughts and heatwaves are among the most impactful climate extremes. Their co-occurrence can have devastating consequences on natural and human systems. Early information on seasonal timescales on their possible occurrence is beneficial for many stakeholders. Seasonal climate forecast has gradually become more used; but limited skill in certain regions and seasons still hinders a broader use. Here we show that a simple forecast metric from a multi-system ensemble, the signal to noise ratio, can help overcome some limitations in the boreal summer. Forecasts of maximum daily near surface air temperature and precipitation in boreal summers with high signal to noise ratio tend to coincide with observed larger deviations from the mean than years with small signal to noise ratio. The same metric also helps identify processes relevant to seasonal climate predictability. Here we show that a positive phase of a North Atlantic Sea surface dipole during boreal spring may favor the occurrence of dry and hot summers in Europe. [ABSTRACT FROM AUTHOR]

Condensation trails (contrails) from aircraft exhausts and contrail cirrus clouds play a significant role in the atmosphere's radiation balance and the climate. The aim of this study is to analyse the physical properties of contrails with the use of satellite data and determine the atmospheric conditions favoring their formation and persistence. The contrails are detected on satellite images provided by the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) radiometer onboard the Meteosat Second Generation (MSG) satellites, using a modified version of the contrail detection algorithm by Mannstein et al. Int J Remote Sens 20(8):1641–1660. (1999) for the year 2016. The study area covers central and western Europe. Five contrail detection hotspots are located, with contrail density depending on time of day and season. The length of the detected contrails ranges between 225 and 292.5 km and the mean width between 5.1 and 8.1 km. The spatial contrail cover reaches up to 0.085% of the scene, with larger values being detected between 8 and 10 UTC and during winter. Results of the comparison with the ERA-5 reanalysis database show that contrail formation and persistence is favored in ice saturated areas (RHI≈100%), when the air temperature is between 204 and 232 Κ (− 69.2 to − 41.2 °C) and the specific humidity between 0.025 and 0.0625 g/kg. The favorable wind direction and speed lie between W and SW (240–300°) and 10 and 30 m/s, respectively. [ABSTRACT FROM AUTHOR]

A suitable indoor climate positively affects the lifespan of historical building structures. The path to an agreeable climate begins with monitoring current conditions. Considerable attention is given to monitoring the indoor climate of historical buildings. The motivation for monitoring air temperature and surface temperatures, relative air humidity or airflow can be, for example, the installation of heating, the occurrence of biotic damage, and others. Through the analysis of the most frequently used keywords, a strong connection was found, for example, between thermal comfort and the church. This review also summarises the various reasons for conducting microclimate monitoring studies in historical religious buildings on the European continent. It is supplemented with an evaluation of the monitoring methodology from the chosen period of the year point of view, the measured parameters, and the length of the interval between the recordings of quantities. It was found that in more than one-third of the cases, the recording time was less than or equal to 15 min, but mostly less than or equal to 1 h. Quite often, monitoring results are used to calibrate a simulation model describing the hydrothermal behaviour of a historical object under various operation alternatives (e.g., influence of ventilation, climate change, occupancy, etc.). This way, it is possible to test various intelligent systems in the virtual world without much risk before they are used in an actual building application. [ABSTRACT FROM AUTHOR]

Heatwaves and droughts are becoming more common and severe in Europe, causing changes in tree phenology, disrupting the sequestration of carbon and causing tree mortality on a continental scale. The responses of leaf shedding to heatwaves and droughts remain uncertain, although temperate deciduous forests may shed their leaves if exposed to extreme heat and water stress. Little information, however, is available about the extent and recurrence of early leaf shedding induced by drought, likely because it occurs in small forest patches and can be discriminated only during a few weeks. We used highly spatiotemporal Sentinel‐2 data as evidence of widespread drought‐induced early leaf shedding in Europe during 2017–2021. We estimated the timing of leaf shedding from NDVI time series and a threshold‐based method that extracts the end of the growing season. Then, we evaluated the heatwave and drought impacts at the end of season by analysing the z‐score of Landsat‐7 and ‐8 land surface temperature and the ERA5‐Land air temperature and aridity index. The 10‐m resolution Sentinel‐2 data identified early leaf shedding not detected by the low‐resolution (250 m) MODIS sensor. Early leaf shedding was observed across Europe during the entire study period and its occurrence was linked to preceding anomalously high temperatures and arid conditions. Our results also indicated that mean summer NDVI decreased significantly in the years following early leaf shedding, suggesting a legacy decline in vegetation productivity. Our study demonstrates that decametric satellite data can be used to monitor the responses of forests to extreme climate events at the canopy level and indicates that early leaf shedding associated with heatwaves and droughts is more widespread and frequent across the continent than previously thought. [ABSTRACT FROM AUTHOR]

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Observing system experiments were undertaken within the 4D-Var data assimilation of the Météo-France global numerical weather prediction (NWP) model. A 6-month period was chosen (October 2019–March 2020) where 40 million observations per day were assimilated. The importance of in situ observations provided by aircraft, radiosondes, and surface weather stations, despite their small fractional amount (7%), has been confirmed particularly in the Northern Hemisphere. Moreover, the largest impact over Europe in terms of root-mean-square error (RMSE) scores comes from surface observations. Satellite data play a dominant role over tropical regions and the Southern Hemisphere. Microwave radiances have a more pronounced impact on the long range and on the humidity field than infrared radiances, despite being less numerous (10% versus 80%). Bending angles impact significantly the quality of the upper-troposphere–lower-stratosphere temperature of the tropics and Southern Hemisphere. Atmospheric motion vectors (AMVs) are beneficial in wind forecasts at low and high levels in the tropics and the Southern Hemisphere, but also in the humidity field. Such impacts are only significant during the first 48 h of the forecasts. Scatterometer winds have an impact restricted to low levels that is kept at longer ranges. A comparison with forecast sensitivity–observation impact studies over a 3-month period using the same measure of short-range (24-h) forecast errors reveals that the ranking between the major observing systems is kept between these two ways of measuring observation impact in NWP. From our conclusions, recommendations are provided on possible evolutions of the global observing system for NWP. [ABSTRACT FROM AUTHOR]

Scots pine has been considered a resistant tree species not only to adverse habitats but also to drought for a long time for its pioneering character, enabling it to thrive at significantly nutrient-poor and dry sites where the competitive pressure of other tree species is reduced. It has also some physiological properties adapted for the increased efficiency of water management. However, the increased and cumulated drought stress in Central Europe after 2013, which has accelerated significantly since 2015, showed the limits of the Scots pine´s sustainable prosperity in many sites of its present occurrence. The research analyses 101 studies published in the period from 1975 to 2022, representing basic properties, ecophysiological aspects, health status development, growth and fertility of Scots pine related to drought stress. It also deals with the influence of the tree species mixing effect and silvicultural management on the health status of Scots pine stands. The review aims to provide comprehensive material for understanding the development of Scots pine´s health and growth, as well as optimising its use under ongoing global climate change conditions characterised by an increase in annual air temperatures and by irregular distribution of precipitation, leading to the increasing frequency of drought periods. Methodological recommendations for silviculture in pine forests are also mentioned here. [ABSTRACT FROM AUTHOR]

The study specifies the circulation conditions, the frequency of occurrence, long‐term changes, and thermal characteristics of air masses over east‐central Europe in the years 1951–2020. The division of air masses utilized by The Polish Institute of Meteorology and Water Management was used, where, based on the geographical location of the source areas, Arctic (A), Polar maritime fresh (mPf), Polar maritime old (transformed) (mPo), Polar maritime warm (mPw), Polar continental (cP), and Tropical (T) air masses are distinguished. It has been shown that western circulation dominates in this area (60% of days on average), during which the advection of maritime air masses occurs. The share of Arctic air mass flowing from the north (about 12% of days a year) and Tropical air mass from the southern sector (4% of days) is clearly smaller. The results of the research have shown that the frequency of the majority of the analysed air masses was subject to short‐term changes in the studied period. On the other hand, long‐term changes in this frequency concerned two air masses flowing from lower latitudes (mPw and T). In the case of mPw, in the period 1951–2020 there was a significant increase in frequency, not only on a yearly basis, but also in all seasons. In the last two decades, the frequency of Tropical air mass has also increased significantly, while the increase took place only in the summer period. Therefore, the increase in air temperature recorded in east‐central Europe since the 1990s, mainly in the warm half of the year, should to a large extent be attributed to both the greater frequency of the inflow of air masses from lower latitudes (mPw and T) and the increase in temperature in virtually all separated air masses, which in turn is a result of progressing global warming. [ABSTRACT FROM AUTHOR]

The response of the Euro‐Atlantic wintertime circulation to climate change is deeply uncertain. The Atlantic jet is caught in a "tug‐of‐war" between rapid warming trends in both the tropics and the Arctic leading to debate over the changing "waviness" of the jet, which is subject to strong non‐linearity and internal variability. From the complementary perspective of weather regimes, there is considerable uncertainty in how atmospheric blocking will alter under climate change. By applying the hybrid approach of geopotential‐jet regimes to 6th phase of the coupled model inter‐comparison project projections, we show that the centers of action of anticyclonic regimes hardly alter even under severe warming. Instead, regimes are expected to become less persistent, with zonal flow conditions becoming more prevalent, although models disagree on the details of regime changes. Finally, we show the regime response can be captured qualitatively in a simple Lorenz‐like model, emphasizing the conceptual link between observed regimes and those in basic mathematical systems. Plain Language Summary: The impact of climate change on European weather can be broken into two components: a thermodynamic part relating to increasing air temperature and humidity, and a dynamic part relating to changes in the atmospheric circulation such as the direction and strength of prevailing winds. While the thermodynamic part is relatively well understood, the dynamic part is very uncertain and this is a major problem in constraining European climate projections. Looking at the winter season, we study the dynamic response of CMIP6 models under climate change using so‐called "regimes," and show that the types of prevailing circulation are not predicted to change strongly. However the regimes are projected to be less long lived. We also show that these features can be well captured in a simple 5 equation model of regime dynamics, providing a potentially useful tool for understanding regime systems in more detail. Key Points: The spatial structure of anticyclonic circulations over Europe are projected to stay the same under climate changeThe persistence of anticyclonic circulations are in general expected to decrease, although there is considerable inter‐model variabilityWe show that these qualitative features of the atmospheric response can be reproduced in a simple forced regime model [ABSTRACT FROM AUTHOR]

Linear contrails and contrail cirrus induced by global aviation have long been known to contribute to climate change by warming the atmosphere. Besides increasing global cirrus cloudiness, aviation may also alter the properties of natural cirrus clouds by soot emissions which lead to more heterogeneous freezing. During the first COVID-19 lockdown in Europe, changes in the properties and occurrence of cirrus clouds were determined with the lidar measurements of CALIPSO, which are presumed to be caused by the corresponding reduction in civil aviation. In the 10 years before the COVID-19 outbreak, however, aviation grew strongly in terms of CO 2 emissions and flight densities in Europe. In this study, 10-year lidar measurements with CALIPSO are analysed to determine the seasonality and long-term trends in cirrus clouds as well as their correlations with the ambient temperatures and air traffic. The results show that there is a distinct seasonal cycle in the occurrence rates (ORs) and particle linear depolarization ratio (PLDR) of cirrus clouds. In addition, cirrus clouds appear within a broader altitude range in winter than in summer and they are characterized by larger OR and PLDR values in winter than in summer. The monthly medians of PLDR as well as their deseasonalized time series in the 10-year period before COVID-19 both show positive trends, which are statistically significant according to the Mann–Kendall (MK) significance test. However, the ORs of cirrus clouds show a negative trend, which might be connected with the background meteorological conditions. Since the cirrus PLDR strongly depends on the ambient temperatures, the contributions induced by temperature are further removed from the cirrus PLDR with a simple linear regression model. The derived residuals show significant positive trends according to the MK test. To compare the cirrus PLDR and air traffic (with the CO 2 emissions from aviation as a proxy), the deseasonalization of both datasets were previously conducted since the seasonal cycles in both are not consistent. The deseasonalized time series determined for the cirrus PLDR and CO 2 emissions from aviation both show increasing trends and their correlation coefficient is r=0.54 at the confidence level above 99.5 %. Finally, comparisons between the cirrus PLDR and aviation in every season were made and revealed a strong correlation in other seasons than in summer. [ABSTRACT FROM AUTHOR]

Afforestation is an important mitigation strategy to climate change due to its carbon sequestration potential. Besides this positive biogeochemical effect on global CO2 concentrations, afforestation also affects the regional climate by changing the biogeophysical land surface characteristics. In this study, we investigate the effects of an idealized global CO2 reduction to pre-industrial conditions by a Europe wide afforestation experiment on the regional longwave radiation balance, starting in the year 1986 from a continent entirely covered with grassland. Results show that the impact of biogeophysical processes on the surface temperatures is much stronger than of biogechemical processes. Furthermore, biogeophysically induced changes of the surface temperatures, atmospheric temperatures and moisture concentrations are as important for the regional greenhouse effect as the global CO2 reduction. While the greenhouse effect is strengthened in winter, it is weakened in summer. On annual total, a Europe-wide afforestation has a regional warming effect, despite reduced CO2 concentrations. Thus, even for an idealized reduction of the global CO2 concentrations to pre-industrial levels, the European climate response to afforestation would still be dominated by its biogeophysical effects. [ABSTRACT FROM AUTHOR]

Recent years have seen a significant increase in weather anomalies in Central Europe (51°22′ N, 22°64′ E), which makes it more difficult to predict yields of winter wheat and to estimate prices on world exchanges. Eight-year (2007–2015) field observations of winter wheat cv. Turnia were carried out in order to determine the relationship between selected meteorological indicators (e.g., the values of average daily air temperature, daily sums of precipitation, and the number of days with precipitation) and the biometric traits and seed yield of winter wheat. In addition, an attempt was made to identify the meteorological parameters that determine an increase in stem elongation during the growing season. The relationship between yield parameters and biometric parameters was evaluated using Spearman's non-parametric correlation coefficients. The regression models for analysis of the dependence of yield parameters and biometric parameters on meteorological elements (temperature, precipitation totals, and days with precipitation) in each development period was used. The study showed that from the beginning of vegetative growth to the flowering stage, growth and development were most advanced in plants for which the average temperature was about 8–9 °C, and the number of days with precipitation was about 41. During generative growth, spike length is 80% determined by the precipitation total. Grain weight is 68% negatively determined by the temperature during dormancy (the higher the temperature during dormancy, the lower the weight of the kernel). A positive relationship was shown between yield and the precipitation total during the period from sowing to the onset of dormancy (r = 0.37). Winter wheat yield was 69% dependent on the density at harvest (the greater the density, the higher the final yield). On the basis of the 8-year results of the study, we conclude that the weather is highly variable from year to year, and therefore breeders should direct their research towards creating variants with much greater plasticity and high tolerance for unfavourable weather patterns. [ABSTRACT FROM AUTHOR]

Variability of the North Atlantic Oscillation (NAO) drives wintertime temperature anomalies in the Northern Hemisphere. Dynamical seasonal prediction systems can skilfully predict the winter NAO. However, prediction of the NAO‐dependent air temperature anomalies remains elusive, partially due to the low variability of predicted NAO. Here, we demonstrate a hidden potential of a multi‐model ensemble of operational seasonal prediction systems for predicting wintertime temperature by increasing the variability of predicted NAO. We identify and subsample those ensemble members which are close to NAO index statistically estimated from initial autumn conditions. In our novel multi‐model approach, the correlation prediction skill for wintertime Central Europe temperature is improved from 0.25 to 0.66, accompanied by an increased winter NAO prediction skill of 0.9. Thereby, temperature anomalies can be skilfully predicted for the upcoming winter over a large part of the Northern Hemisphere through increased variability and skill of predicted NAO. Plain Language Summary: Wintertime temperature in the Northern Hemisphere is regulated by the variations of atmospheric pressure, represented by the so‐called North Atlantic Oscillation (NAO). The NAO's phase—negative or positive—is associated with the pathways of cold and warm air masses leading to cold or warm winters in Europe. While the NAO phase can be predicted well, predictions of the NAO‐dependent air temperature remain elusive. Specifically, it is challenging to predict the strength of the NAO, the most important requirement for the accurate prediction of wintertime temperature. Here, we improve wintertime temperature prediction by increasing the strength of the predicted NAO. We use observation based autumn Northern Hemisphere ocean and air temperature, as well as ice and snow cover for statistical estimation of the first guess NAO for the upcoming winter. Then, we sub‐select only those simulations from the multi‐model ensemble, which are consistent with our first guess NAO. As a result, based on these selected members, the wintertime temperature prediction is substantially improved over a large part of the Northern Hemisphere. Key Points: Amplitude and skill of predicted North Atlantic Oscillation (NAO) improve significantly by subsampling of ensemble of existing seasonal prediction systemsAmplified NAO variability leads to significant improvement in predicting the upcoming winter temperature anomalies in the Northern Hemisphere [ABSTRACT FROM AUTHOR]

Since our climate is in a rapid changing phase, it is crucial to get information about the regional patterns of basic climatic parameters and indices. The EURO-CORDEX project provides high-quality regional climate model outputs, but these raw datasets are not convenient for the application in wider geoscience studies. According to the authors' knowledge, there is a lack of published spatial information about basic climate parameters and indices in Central Europe and especially in the broader Carpathian region therefore the basic aim of this study to fill this gap. The study presents the future trends in daily air temperature and precipitation and various climatic indices in the broader Carpathian Basin region during the 21st century. The indices are calculated using multi-model average temperature and precipitation data from EURO-CORDEX model simulations for the future time periods (2021-2050, 2071-2100) and emission scenarios (RCP4.5, RCP8.5). The indices present the future trends of the heat load, energy demand as well as extreme precipitation and drought characteristics. Based on the results the temperature increase is obvious and the heat load and energy demand quantifying indices follow the temperature trend. However, the trend is difficult to evaluate in the case of precipitation. The changes in the precipitation and the related indices can be considered small and appear within the regions. The future changes are the most considerable in the Carpathian Basin, but the entire examined region faces crucial changes in the following decades. [ABSTRACT FROM AUTHOR]

Long‐term satellite land surface temperature (LST) data are desirable to augment 2m air temperatures (T2m) measured in situ and as an independent measure of surface temperature change. However, previous studies show variable agreement between LST and T2m time series. The objective of this study is to assess the stability and trends in six new LST data sets from the European Space Agency's Climate Change Initiative for LST (LST_cci). LST anomalies are compared with homogenized station T2m anomalies over Europe, which verifies all six data sets are well coupled (LST vs T2m anomaly correlations and slopes: 0.6–0.9). The temporal stability of the LST_cci data is assessed through a comparison with the T2m anomaly time series. Only the LST_cci data sets for the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Aqua and the Advanced Along‐Track Scanning Radiometer (AATSR) appear stable; the MODIS/Terra, ATSR‐2, and multisensor InfraRed and MicroWave data sets show non‐climatic discontinuities associated with changes in sensor and/or drift over time. For MODIS/Aqua (2002–2018), significant trends in LST of 0.64–0.66 K/decade compare well with the equivalent T2m trends of 0.52–0.59 K/decade. The LST and T2m trends for AATSR (2002–2012) are found to be statistically insignificant, likely due to the comparatively short study period and specific years available for analysis. No evidence is found to suggest that trends calculated using cloud‐free InfraRed observations are affected by clear‐sky bias. This study suggests that satellite LST data can be used to assess warming trends over land and for other climate applications if the required homogeneity is assured. Plain Language Summary: Long‐term changes in surface temperature due to climate change are usually studied using near‐surface air temperatures (T2m) from weather stations. However, these data are not available everywhere and changes in T2m are poorly understood in some regions (e.g., parts of Africa). This study assesses the potential for satellite‐observed land surface temperatures (LST) to help fill these gaps. The study finds that LST and T2m provide very similar information on surface temperatures and therefore LST can provide useful information where T2m data are unavailable. However, some of the LST data sets are found to be "unstable" and suffer from jumps and changes over time that result from for example, changes in satellite instrument or a change in the orbit of the satellite platform. These unstable LST data sets cannot be used to measure changes in surface temperature. However, the study finds that for "stable" LST data sets, which do not suffer from these non‐climatic jumps and changes, the changes in LST over time are very similar to, or even the same as, the changes in T2m. Therefore, this work suggests that satellite LST data could be used to measure changes in surface temperature where there are not enough stations to make these measurements using T2m. Key Points: The stability of six satellite land surface temperature data sets is assessed using homogenized station air temperatures over EuropeOnly two data sets are stable; the other four show non‐climatic discontinuities associated with a change in sensor and/or drift over timeSignificant trends in land surface temperature of ∼0.65 K/decade over Europe are found for one of the stable data sets over 2002–2018 [ABSTRACT FROM AUTHOR]

Presently, the forests of one of the most economically important tree species in Europe—Norway spruce [Picea abies (L.) Karst.]—have been disrupted and are in rapid decline due to a combination of several natural factors: extreme drought, heatwaves, and secondary damage caused by bark beetle outbreaks. The vulnerability of these forests has increased considerably over the past decade, and remote sensing methods can theoretically improve the identification of endangered forest stands. The main objective was to determine the relationship between remotely sensed characteristics of vegetation (using the normalized difference vegetation index—NDVI) and annual tree-ring growth in 180 trees through precipitation and air temperature. The research was conducted at six research plots in lowland spruce forests (319–425 m a.s.l.) in the central Czech Republic. No significant correlation between NDVI and annual ring width was observed. The primary factor limiting radial growth was lack of precipitation in the growing season; subsequently, spruce trees reacted negatively to air temperatures. A higher correlation with NDVI was observed on sites susceptible to drought, but overall, NDVI and RWI did not show similarities. This result describes that NDVI is a poor indicator for identifying low radial growth in Norway spruce stands on non-native localities in the studied area. [ABSTRACT FROM AUTHOR]

The North Atlantic Oscillation (NAO) index is often characterized by independent positive and negative NAO events with a characteristic spatial pattern and a typical lifetime of around 1 week. These events are separated by periods of near‐neutral NAO conditions. Here, we challenge this view by showing in reanalysis and observed data that the strength and spatial shape of NAO events depends on the NAO index prior to the window of 1 week and this dependency is most pronounced for negative NAO events. The influence is seen in the mean sea level pressure, and in other important features, including blocking frequency and jet stream characteristics, and also in air surface temperature and precipitation in parts of Europe. This new appreciation is important for efforts to improve methods for subseasonal‐to‐seasonal predictions of NAO. Plain Language Summary: Northern European winters differ. Some are mild and moist with prevailing westerly winds and some are cold and dry, dominated by the Siberian air masses from East. When the Northern European winters are mild, the winter in Greenland is cold, and vice versa. This is the North Atlantic Oscillation (NAO) and its companion, the Europe–Greenland temperature seesaw. They were described in qualitative terms already in the late eighteenth century. Nowadays, the NAO is often described as a series of independent positive or negative events with a duration of around 1 week with intermittent periods of near‐neutral conditions and variable duration. The net result of these events determines the NAO strength of the winter. We show that this picture is too simplified because the NAO outside the time horizon of 1 week influences the strength of the individual NAO events. This is most pronounced for the negative events where this influence can both distort and amplify or diminish the pattern of the event. Also the typical temperatures and precipitation patterns associated with NAO are influenced significantly by this effect. One important consequence for our discovery is the improved prospects of doing subseasonal‐to‐seasonal predictions. Key Points: The daily North Atlantic Oscillation (NAO) index consists of positive and negative events of duration around 1 week separated by periods of near‐neutral conditionsThe strength and spatial shape of negative NAO events depends on NAO‐index values prior to the eventBlocking frequencies, jet strength and position, and surface temperature and precipitation have similar dependencies [ABSTRACT FROM AUTHOR]

Climate change increases the frequency and intensity of heat waves, bringing along multiple adverse impacts on ecosystems, human health, societies, and the economy. Groundwater influences the near surface air temperature evolution through land–atmosphere interactions. Using simplified and shallow groundwater representations, reproducing heat waves in a regional climate model (RCM) is challenging. Currently, RCMs applied over Europe exhibit a warm bias. This study analyzes heat waves over a 13‐year evaluation period, comparing the terrestrial systems modeling platform (TSMP) with an explicit groundwater representation to a EURO‐CORDEX RCM ensemble, the ERA5 reanalysis, and observations. The TSMP multiannual heat wave statistics are consistent with observations and reanalysis data. We attribute the lower absolute deviations of heat wave metrics simulated by TSMP to the improved hydrology including 3D groundwater flow. The findings emphasize the importance of hydrological process representation in RCMs. Plain Language Summary: Heat waves severely impact the ecosystem, society, and the economy. Climate change will amplify these events in the near future, urging a correct representation of heat waves in climate models. Especially during warm and dry periods, there is a strong connection from the groundwater stored in the subsurface through the land surface to the atmosphere. Commonly applied models have a simplified representation of groundwater and cannot establish a correct connection to the atmosphere, simulating too high temperatures over Europe. To show the advantage of a model with a complex groundwater representation, we compare such a model with 12 representative climate models. The results indicate lower deviations from heat wave statistics of observations with the inclusion of complex groundwater. Our findings emphasize the importance of including groundwater dynamics representation in climate models. Key Points: Multiannual heat wave statistics from a regional climate model including groundwater dynamics are consistent with observationsRegional climate models with simplified groundwater representations overestimate observed multiannual heat wave statisticsExplicit groundwater treatment increases the reliability of heat wave simulations with implications for regional climate projections [ABSTRACT FROM AUTHOR]

The genetic pool of valuable old ornamental cultivars and their in situ maintenance is threated by climate change. Meanwhile, ornamental plants like roses make up an important share of both gardens and urban green spaces, where they are particularly vulnerable to multi-stress growth conditions. The aim of this research was to evaluate the effect of changing climatic conditions on growth and flowering of 11 historic climber roses through long-term studies (2000–2017) conducted in Central Europe. The evaluation of plants consisted of assessment of frost damage and the timing of early phenological stages (starting of bud break, leaf unfolding), as well as gathering data on the beginning, fullness, and end of flowering and its abundance. Frost damage was not observed in any year only in 'Mme Plantier' and did not occur for any cultivar after the winter in the years 2007, 2008, and 2014. Only a little damage to one-year shoots was recorded after the winter in the years 2015–2017. Frost damage to 'Alberic Barbier', 'Albertine', 'Chaplin's Pink Climber', 'Orange Triumph clg', and 'Venusta Pendula' led to pruning to ground level in every year excluding those listed above. Frost damage of once-blooming roses limited their flowering; however, the many-year datasets showed a trend for decreased frost damage and improved abundance of flowering, and these results can be interpreted as a response to the increase of average air temperature. The timing of bud breaking and leaf development in all climber roses was strictly correlated with average air temperature in the dormancy period. The reactions of climber roses to weather conditions confirmed the influence of climatic changes on ornamental crop plants in Central Europe, introducing the potential possibility for the wider application of climber roses, but without certainty of flowering every year. [ABSTRACT FROM AUTHOR]