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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]

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]

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]

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]

[Display omitted] • The model performs good in capturing the seasonal pattern and peak values of river water temperature. • Air temperature and day of year are the major factorson modeling river water temperature. • The model is a promising tool to investigate the impact of climate change on river thermal dynamics. Water temperature is an important physical indicator of rivers because it impacts many other physical and biogeochemical processes and controls the metabolism of aquatic species in rivers. Having a good knowledge of river thermal dynamics is of great importance. In this study, an advanced machine learning based model that is fast, accurate and easy to use, namely the nonlinear autoregressive network with exogenous inputs (NARX) neural network, was coupled with Bayesian Optimization (BO) algorithm for optimizing the number of NARX hidden nodes and lagged input/target values and the Bayesian Regularization (BR) backpropagation algorithm for the NARX training, to forecast daily river water temperatures (RWT). Long-term observed data from 18 rivers of the Vistula River Basin, one of the largest rivers in Europe, were used for model testing, and model performance was compared with the air2stream model. The results showed that the NARX-based model performs significantly better than the air2stream model in the calibration and validation stages, and can better capture the seasonal pattern and peak values of RWT. Input combinations impact the performance of the NARX-based model in RWT modeling, and air temperature and the day of the year (DOY) are the major inputs, while streamflow and rainfall play a minor role on modeling RWT at the Vistula River Basin. Considering that future times series of air temperatures are easily accessible from climate models and DOY is easy to be considered in the model, the NARX-based model can serve as a promising tool to investigate the impact of climate change on river thermal dynamics. [ABSTRACT FROM AUTHOR]