Air Temperature Research Articles

Extreme ultra-short-term changes in air temperature in January in southern Poland (the example of Sosnowiec) against the background of atmospheric circulation conditions

Abstract Large short-term changes in air temperature affect the functioning of living organisms in the environment and human activities. For this reason, a study of extreme positive and negative 10 min temperature changes and their causes related to atmospheric circulation was undertaken. Air temperature data for January in the period 2001–2017 in southern Poland (Sosnowiec) were analysed. Extreme ultra-short-term temperature changes were considered to be values less than or equal to 0.1 percentile (extreme temperature drops) and greater than or equal to 99.9 percentile (extreme temperature increases). The extreme ultra-short-term negative air temperature change was − 7.2°C/10 min, while the positive change was + 3.5 °C/10 min. Extreme ultra-short-term negative air temperature changes in southern Poland in January occur most frequently with the advection of air from the west (43% of cases), the inflow of maritime Polar old (transformed) air over Poland (25% of cases), western cyclonic (Wc) and north-western cyclonic (NWc) situations, a total of 34% of cases), the passage of the atmospheric front over southern Poland (59% of cases), especially a cold front (68% of cases with a front). Extreme ultra-short-term positive changes of air temperature in southern Poland in January occur most frequently with the advection of air from the south-west (43% of cases), the inflow of continental Polar air (42% of cases), anticyclonic situations (72% of cases), the occurrence of a high-pressure wedge over southern Poland (26% of cases), situations without atmospheric front (80% of cases). The direction of advection plays a secondary role in determining the values of extreme short-term changes in air temperature. More important is the speed of the influx of this air mass. Extreme ultra-short-term temperature changes are partly explained by circulation conditions. Rapid temperature changes can also occur as a result of small-scale processes in the atmosphere.

Effect of the uncertainty of autocorrelation estimation on trend analysis

Autocorrelation is ubiquitous in real-life time series. For trend analysis, positive autocorrelation mainly increases the statistical uncertainty of the detected trend. The theoretical framework has been established to quantify how the autocorrelation can impact trend analysis if its characteristic is known precisely. However, if the autocorrelation is also uncertain like that estimated in real-life data, then the consequences are yet unclear. The situation is further complicated by the concurrence of both short- and long-range correlation (SLRC), e.g., in air temperature. In this study, we use an appropriate minimal model with one short-range autoregressive parameter and one long-range fractional parameter to account for such SLRC, and then accordingly propose a framework to integrate the effect of the uncertainty of autocorrelation estimation into trend analysis, on the basis of the detrended fluctuation analysis and Akaike weights. This framework can also obtain a joint probability density function of the estimated autoregressive and fractional parameters for measuring their uncertainty, identifying their combined optimal estimates, uncovering their strong interdependence, and determining their two-dimensional confidence region. The effectiveness and applicability of the proposed framework are verified by a numerical experiment and a case study of daily air temperature anomalies at the Potsdam station. This study can provide a solid theoretical basis to underpin our understanding of the autocorrelation effect on trend analysis in theory and applications. Published by the American Physical Society 2024

Assessment of the Impact of Meteorological Variables on Lake Water Temperature Using the SHapley Additive exPlanations Method

The water temperature of lakes is one of their fundamental characteristics, upon which numerous processes in lake ecosystems depend. Therefore, it is crucial to have detailed knowledge about its changes and the factors driving those changes. In this article, a neural network model was developed to examine the impact of meteorological variables on lake water temperature by integrating daily meteorological data with data on interday variations. Neural networks were selected for their ability to model complex, non-linear relationships between variables, often found in environmental data. Among various architectures, the Artificial Neural Network (ANN) was chosen due to its superior performance, achieving an R2 of 0.999, MSE of 0.0352, and MAE of 0.1511 in validation tests. These results significantly outperformed other models such as Multi-Layer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM). Two lakes (Lake Mikołajskie and Sławskie) differing in morphometric parameters and located in different physico-geographical regions of Poland were analyzed. Performance metrics for both lakes show that the model is capable of providing accurate water temperature forecasts, effectively capturing the primary patterns in the data, and generalizing well to new datasets. Key variables in both cases turned out to be air temperature, while the response to wind and cloud cover exhibited diverse characteristics, which is a result of the morphometric features and locations of the measurement sites.

Organic Farming in India: A Dual Strategy for Climate Change Adaptation and Mitigation

Recent decades have transformed Indian agriculture from traditional methods to a mechanized system reliant on fossil fuels, chemical fertilizers, and pesticides, increasing greenhouse gas emissions and impacting global climate. Higher greenhouse gas levels, including carbon dioxide, methane, and ozone are likely, causing the observed rise in air temperatures and leading to significant climate shifts. In most subtropical regions, a 4°C rise in global temperatures is anticipated to reduce groundwater and surface water, heightening food demand and threatening global food security. Climate change denotes long-term, significant alterations in climate measurements. A production system that maintains the health of ecosystems, soils, and human populations is known as organic farming (IFOAM 2006). Organic farming is energy-efficient and reduces greenhouse gas emissions by minimizing chemical and fossil fuel use, while enhancing soil carbon, biodiversity and fertility. It reuses plant and animal waste to restore soil nutrients, relies on renewable resources and supports sustainable, low-pollution management. Organic farming not only helps to mitigate climate change but also provides lasting benefits as an adaptation strategy. Effective nutrient management and carbon sequestration in soils are crucial for adapting and mitigating climate change across diverse temperature zones and local conditions.

Wind turbine and PV power prediction using a deterministic data-driven model with variational mode decomposition preprocessing

This study develops a method for accurately forecasting solar radiation (SR), wind speed (WS), and air temperature (AT) for the coming 24 hours in order to predict energy production from photovoltaic (PV) panels and wind turbines (WT) in positive energy buildings. Input data are pre-processed through variational mode decomposition (VMD) for broadband feature extraction, which is then decomposed into smooth modes. The application of the Salp Swarm Algorithm (SSA) aims to optimize VMD parameters to enhance the precision of feature extraction. A thorough data analysis is performed to identify the essential input features. Residual pre-processing between input variables and their decomposed modes further enhances model performance. The stacking algorithm (SA) is used to predict both modes and residuals of the input data. Performance evaluation using metrics such as root mean square error (RMSE), normalized root mean square error (NRMSE), mean absolute error (MAE), and normalized mean absolute error (NMAE) indicates a reduction in error rates across measurement scales. For example, under adverse weather conditions, the NRMSE and NMAE for PV power are 2.50% and 1.95%, respectively.

Climate Response and Radial Growth Dynamics of Pedunculate Oak (Quercus robur L.) Plus Trees and Their Half-Sib Progeny in Periods of Severe Droughts in the Forest-Steppe Zone of Eastern Europe

The dendrochronological parameters of 97 pedunculate oak (Quercus robur L.) trees including 20 plus trees (142-year-old on average) and four half-sib families for four of them were analyzed considering also specifically years of the most severe droughts that were identified using average monthly air temperature and precipitation data. The tree-ring width (TRW) was mostly affected by air temperature that had the largest cross-dating indices (CDI), up to 78% maximum. However, the 32-year Brückner–Egeson–Lockyer cycle (a climatic cycle of approximately 30–40 years that correlates with sunspot activity) was more reflected in the TRW dynamics in plus trees than precipitation and air temperature. A high-frequency of abnormal TRW was clearly observed during drought periods and in the following 2–3 years. Tree radial-growth reduction due to drought stress varied significantly between families. The resistance to drought based on TRW was higher in the maternal plus oak trees than in progeny. Drought resulted in reduced growth during the subsequent year(s); hence, the minimum growth occurred after the actual climate event. Autumn–winter precipitation and weather conditions were of the greatest importance at the onset of active vegetation in April and May. The influence of air temperature on oak growth was the largest in March (r = 0.39, p < 0.05). The strongest positive correlation between precipitation and growth (with r up to 0.38) was observed in May 2023. Plus trees had a high adaptive potential due to the stability of radial growth during drought with high resistance (Rt = 1.29) and resilience (Rs = 1.09) indexes. The offspring of families 1 (Rt = 0.89, Rs = 0.89) and 2 (Rt = 1.04, Rs = 0.87) had similar resistance and resilience, but the recovery indices (Rc) for offspring in families 1, 2 and 3 exceeded the recovery values for plus trees. For offspring in families 3 and 4, the index values were lower. The revealed responses of wood growth of plus trees to climatic parameters estimated as resistance (Rt), resilience (Rs) and recovery (Rc) indexes and similar responses in their progeny can be used in breeding pedunculate oak for wood growth productivity and drought resistance.

Identifying time patterns of highland and lowland air temperature trends in Italy and the UK across monthly and annual scales

Abstract. This paper presents a statistical analysis of air temperature data from 32 stations in Italy and the UK up to 2000 m above sea level from 2002 to 2021. The data came from both highland and lowland areas in order to evaluate the differences due to both location and elevation. The analysis focused on detecting trends at annual and monthly timescales, employing ordinary least-squares (OLS), robust S-estimator regression, and Mann–Kendall (MK) and Sen's slope methods. Hierarchical clustering (HCA) using dynamic time warping (DTW) was then applied to the monthly data to analyze the intra-annual pattern similarity of trends within and across the groups. Two different regions of Europe were chosen because of the different climate and temperature trends – namely, the northern UK (smaller trends) and the northwest Italian Alps (larger trends). The main novelty of the work is to show that stations with similar locations and altitudes have similar monthly slopes by quantifying them using DTW and clustering. These results reveal the nonrandomness of different trends throughout the year and between different parts of Europe, with a modest influence of altitude in wintertime. The findings revealed that group average trends were close to the National Oceanic and Atmospheric Administration (NOAA) values for the areas in Italy and the UK, confirming the validity of analyzing a small number of stations. More interestingly, intra-annual patterns were detected commonly at the stations of each of the groups and are clearly different between them. Confirming the different climates, most highland and lowland stations in Italy exhibit statistically significant positive trends, while in the UK, both highland and lowland stations show statistically nonsignificant negative trends. Hierarchical clustering in combination with DTW showed consistent similarity between monthly patterns of means and trends within the group of stations and inconsistent similarity between patterns across groups. The use of the 12 distance correlation matrices (dcor) (one for each month) also contributes to what is the main result of the paper, which is to clearly show the different temporal patterns in relation to location and (in some months) altitude. The anomalous behaviors detected at 3 of the 32 stations, namely Valpelline, Fossano, and Aonoch Mòr, can be attributed, respectively, to the facts that Valpelline is the lowest-elevation station in its group; Fossano is the southernmost of the Italian stations, with some sublittoral influence; and Aonoch Mòr has a large number of missing values. In conclusion, these results improve our understanding of temperature spatio-temporal dynamics in two very different regions of Europe and emphasize the importance of consistent analysis of data to assess the ongoing effects of climate change. The intra-annual time patterns of temperature trends could also be compared with climate model results.

Green light enhances the phytochemical preservation of lettuce during postharvest cold storage.

The postharvest lighting environment is a main factor that influences quality preservation for harvested biomass. The objective of this study was to evaluate postharvest changes in bioactive compounds of lettuce with different storage light spectra. The effects of green LEDs with peaks at 500 nm and 530 nm, white LEDs (400-700 nm), and dark storage were evaluated, where light intensity (10 μmol m-2 s-1) and photoperiod (12 h per day) were constant with air temperature at 5°C over the 14 d treatment period. Lettuce stored with 500 nm and 530 nm green LEDs exhibited 1474.5% and 1451.8% (approximately 15.7 and 15.5 times) higher antioxidant activity, respectively, compared to dark storage. Significant improvements in total phenolic content, and 67.5% and 64.8% increases in total soluble solids with 530 nm and 500 nm green LEDs over dark storage were discerned. Exposure to 530 nm green LEDs led to 128.2% (approximately 2.28 times) higher anthocyanin content, a 26.2% increase in carotenoids, and a 95% rise in flavonoid content compared to dark storage. Increases of 26.4% and 16.0% in chlorophyll a content in lettuce stored under 500 nm and 530 nm green LEDs, respectively, and 65.6% and 46.6% rises in the Chlorophyll a/b ratio were observed. Compared to dark storage, green LEDs (500 nm) resulted in a 13.5% higher total chlorophyll content. Findings underscore the positive impact of green LEDs on the nutritional quality of lettuce, providing insight for postharvest practices.

Sensitivity of the Northern Hemisphere Warming Trend to Snowpack Variability

Abstract A marked decrease in land surface snow cover within the Northern Hemisphere under global warming will warm the atmosphere near the land surface. However, minimal information exists regarding the contribution of snowpack variation to interannual surface air temperature variability. The current study investigates the effects caused by snow water equivalent (SWE) change on surface air temperature (SAT). To this end, an SWE pacemaker experiment of land–atmosphere coupling (AMIP-type) is undertaken for the period 1901–2010, during which the Model for Interdisciplinary Research on Climate, version 6 (MIROC6), atmospheric general circulation model’s SWE is continuously nudged toward SWE derived from an land-only (LMIP-type) experiment. Compared to a reference MIROC6 simulation without SWE nudging, the spatial correlation of 1980–2010 interannual SWE trends in our experiment with those in GlobSnow observation data increased by 0.31 over the Northern Hemisphere. Similarly, the linear correlation of interannual SAT with reanalysis data is greater by 0.04, 0.04, 0.08, and 0.07 for autumn, winter, spring, and summer over the region from the reference experiment, respectively. It is shown that due to this SWE nudging, the modeled interannual SAT change in the Northern Hemisphere becomes more accurate. Through a surface energy budget analysis, changes in SAT are attributed to changes in surface albedo, soil evaporation, and soil temperature. Areas of greater contribution of SWE variability to SAT variability appear to shift from south to north areas as snow melts. These results highlight surface albedo, snow hydrological, and land heat storage effects through which the SWE interannual trend on the land surface significantly controls atmospheric air temperature variability near the land surface. Significance Statement This study investigates the contribution of land snowpack to surface air temperature in the Northern Hemisphere using a climate model updated with observation-based estimates of snow water equivalent. A marked decrease in snowpack under global warming is expected to warm the atmosphere near the land surface. Variations in surface albedo, soil evaporation, and soil temperature through the snow–soil–atmosphere processes during the early snow-melting season are crucial for accurately simulating surface air temperature in the Northern Hemisphere. Moreover, terrestrial snowpack is a significant factor in global warming rates.

Characterizing Long-term Astroclimate Parameters at the Muztagh-Ata Site in the Pamir Plateau with ERA5 and MERRA-2 Data

Abstract The Muztagh-Ata site, an excellent high-altitude ground-based astronomical observing site was discovered and monitored in the eastern part of the Pamir Plateau in the southwestern Xinjiang Uygur Autonomous Region of China. This site has been systematically monitored using various observational parameters since the spring of 2017. Yet, the site lacks long-term monitoring and statistical characterization of key variables such as: precipitable water vapour (PWV) and air temperature. These factors directly influence whether a site is suitable for hosting large ground-based telescopes across optical, infrared, submillimeter, and millimeter wavelengths in later stages. In this study, we utilized atmospheric reanalysis datasets from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and ERA5, the fifth edition of the European Centre for Medium-Range Weather Forecasts (ECMWF). These datasets were combined with local observations from the weather station at the Muztagh-Ata site. Following the validation of ground-based and satellite data, we conducted a comparative analysis of PWV and temperature trends at the Muztagh-Ata site over a period of 24 years. The weighted annual mean nighttime temperature and PWV increase at rates of 0.18 ∼ 0.38 ○C decade−1 and 0.02 ∼ 0.15 mm decade−1, respectively. The nighttime PWV slightly decrease during the winter with rates of −0.01 ∼ −0.03 mm decade−1. This findings reveal that the PWV and temperature variation patterns at the Muztagh-Ata site are consistently stable, particularly in the results derived from the ERA5 dataset. The comprehensive conditions at this site are highly suitable and advantageous for hosting large optical, infrared, submillimeter, and millimeter wavelengths telescope installations.

The Scientific Case Against Net Zero: Falsifying the Greenhouse Gas Hypothesis

The UK Net Zero by 2050 Policy was undemocratically adopted by the UK government in 2019. Yet the science of so-called ‘greenhouse gases’ is well known and there is no reason to reduce emissions of carbon dioxide (CO2), methane (CH4), or nitrous oxide (N2O) because absorption of radiation is logarithmic. Adding to or removing these naturally occurring gases from the atmosphere will make little difference to the temperature or the climate. Water vapor (H2O) is claimed to be a much stronger ‘greenhouse gas’ than CO2, CH4 or N2O but cannot be regulated because it occurs naturally in vast quantities. This work explores the established science and recent developments in scientific knowledge around Net Zero with a view to making a rational recommendation for policy makers. There is little scientific evidence to support the case for Net Zero and that greenhouse gases are unlikely to contribute to a ‘climate emergency’ at current or any likely future higher concentrations. There is a case against the adoption of Net Zero given the enormous costs associated with implementing the policy, and the fact it is unlikely to achieve reductions in average near surface global air temperature, regardless of whether Net Zero is fully implemented and adopted worldwide. Therefore, Net Zero does not pass the cost-benefit test. The recommended policy is to abandon Net Zero and do nothing about so-called ‘greenhouse gases’.

Counteracting Impacts of ENSO and the Aleutian Low on Winter Surface Air Temperature over Japan Sea Rim Region

Abstract Exploring the mechanisms modulating Japan Sea Rim (JSR) winter surface air temperature (JSRWT) holds significant scientific and societal importance. The present study unravels the counteracting impacts of El Niño–Southern Oscillation (ENSO) and the Aleutian low (AL) on JSRWT. Although ENSO and the AL have a significant positive correlation on interannual timescale, they exert totally opposite impacts on JSRWT. The positive Niño-3.4 sea surface temperature anomalies (SSTAs) usually lead to positive JSRWT anomalies, while an enhanced AL often results in negative JSRWT anomalies. The tropical convection anomalies associated with the positive Niño-3.4 SSTA induce two tropical to extratropical Rossby wave trains, leading to a barotropic anomalous anticyclone over northeast Asia, and thereby positive JSRWT anomalies. Conversely, the strengthened AL perturbs the westerly jet and induces an eastward-propagating Rossby wave train, leading to a barotropic anomalous cyclone over northeast Asia and negative JSRWT anomalies. The northeastward-propagating Rossby wave induced by the ENSO-related tropical convection anomalies over the Indian Ocean offsets the downstream Rossby wave induced by the strengthened AL, leading to counteracting impacts of ENSO and the AL on JSRWT. Finally, a novel index for monitoring JSRWT is proposed based on the indices of ENSO and AL. Significance Statement Despite the advantage of ENSO as a well-known predictability source, the seasonal predictability of Japan Sea Rim winter surface air temperature (JSRWT) is far from satisfactory. Here, we discovered that the Aleutian low (AL) has an independent influence on JSRWT contrasting with that of ENSO. When the AL and ENSO are in-phase, JSRWT shows no significant anomalies, but the JSRWT anomalies are largely amplified when AL and ENSO are out-of-phase. Therefore, a novel index for monitoring JSRWT is proposed based on the indices of ENSO and AL. These findings shed light on the low capacity of the seasonal prediction of JSRWT when only considering ENSO as the precursor.

The association of mean air temperature with the incidence of Type 2 diabetes

The association of mean air temperature with the incidence of Type 2 diabetes

Assessment of lake Markakol’s physical and chemical condition with consideration of eutrofication

The study was conducted in 2023 and comprised a comparative assessment of the state of Lake Markakol’s aquatic ecosystems in surface and bottom water strata. The analysis of bottom water temperatures showed correlation between this parameter and dissolved oxygen concentrations, indicating that pollution caused by organic impurities leads to lake eutrophication, in turn, pro-pelling the extinction of aquatic life. Although the main water physicochemical parameters of Markakol Lake correspond to the oligotrophic type, the shifts in dissolved oxygen and phos-phate content, as well as growth of aquatic vegetation indicate its transition to the mesotrophic type. Higher phosphate content in water is a consequence of pollution disturbing the biological balance, as well as stimulating the reservoir’s eutrophication and increased biological productivity, i.e. algal bloom. In addition, phosphate ions serve an informative indicator of Hazard Class 3 (organoleptically hazardous) anthropogenic pollution. Based on the correlation factor (r), two statistical models were considered for the target lake: 1) Water Pollution: nutri-ent concentrations depending on surface and bottom water temperatures (Model 1); and 2) changes in Water Pollution Indices (biogenic, heavy metals, mean) because of air temperature growth due to global warming of 0.25°C/decade (Model 2).

Finding Critical Factors for Developing Dynamic Models to Optimize Greenhouse Solar Dryer’s Environmental Conditions

This paper is focused on the formulation of a mathematical model for greenhouse solar dryer systems in the context of determining the principal drivers of efficiency of the solar dryer. The model was built by evaluating the correlation coefficients of each term and only those terms which had high levels of significance were retained. To test this model, experiments were replicated with measurements being taken in the different sections of the greenhouse to provide a large spectrum of the environmental conditions involved. The values of the technical coefficients are predicated on internal air temperature, humidity ratio and weight of the product with fixed scales, avoiding sophisticated complex models prone to overfitting. By using terms with high correlation, the model is less likely to be affected by outliers and total correlation or cross correlation is low, thereby making the model practical. The outcomes of this research, therefore, offer a scientifically viable and a practical approach to enhancing drying processes in greenhouse solar dryer systems.

Changes in morphological and physiological traits of urban trees in response to elevated temperatures within an Urban Heat Islands.

Urban Heat Islands (UHI) are a common phenomenon in metropolitan areas worldwide where the air temperature is significantly higher in urban areas than in surrounding suburban, rural or natural areas. Mitigation strategies to counteract UHI effects include increasing tree cover and green spaces to reduce heat. The successful application of these approaches necessitates a deep understanding of the thermal tolerances in urban trees and their susceptibility to elevated urban temperatures. We evaluated how the photosynthetic thermal optimum (Topt), photosynthetic heat tolerance (T50), and key leaf thermoregulatory morphological traits (leaf area, specific leaf area, leaf width, thickness and LDMC) differ between conspecific trees growing in 'hot [UHI]' vs. 'cool' parts of Montreal, Canada (with a difference of 3.4°C in air temperature), to assess the ability of seven common tree species to acclimation to higher temperatures. We hypothesized that individuals with hotter growing temperatures would exhibit higher Topt and T50, as well as leaf thermoregulatory morphological traits aligned with conservative strategies (e.g., reduced leaf area and increased leaf mass) compared to their counterparts in the cooler parts of the city. Contrary to our a priori hypotheses, leaf area increased with growing temperatures and only four of the seven species had higher T50 and only three had higher Topt values in the hotter area. These results suggest that many tree species cannot acclimate to elevated temperatures and that the important services they provide, such as carbon capture, can be negatively affected by high temperatures caused by climate change and/or the UHI effect. The ability vs inability of tree species to acclimate to high temperatures should be considered when implementing long term tree planting programs in urban areas.

Analysis of the Properties and Thermal Behavior of Low-Temperature Phase Change Materials (PCMs) That Can Be Applied in Heating and Ventilation Systems

This article analyses the use of low-temperature PCMs in devices supplementing a room ventilation system to prevent the overcooling effect. In this study, the phase change is numerically simulated in an axisymmetric system consisting of two tubes. One is filled with RT11HC with an initial temperature of 0 °C, while air with an inlet temperature of 20 °C flows through the other, heating the PCM and causing it to melt. Calculations are performed using commercial software with the apparent heat method for a system of given dimensions. Spatial distributions of the system temperature and liquid volume fraction at different time moments (from 0 to 120 min) are determined. It is found that the results depended mainly on the method of determining the latent heat. For the beginning of the charging process (t < 40 min), the values of the liquid phase fraction determined by the H and S methods are similar, while the one determined by the G method is definitely higher (even three times at t = 10 min). In turn, the outlet air temperature determined by the S method is lower than that determined by the other methods. The size and shape of the mesh have no significant effect on the results.

A 4 km daily gridded meteorological dataset for China from 2000 to 2020.

Multi-variate gridded meteorological data with high spatial resolution play a key role in studies related to climate change. This study constructed a 4 km daily gridded meteorological dataset for mainland of China (China Daily Meteorological Dataset; CDMet) from 2000 to 2020. The dataset includes nine meteorological variables: 2-meter air temperature (maximum, minimum, and mean temperatures), total precipitation, skin temperature, 10-meter wind speed, relative humidity, surface pressure, and sunshine duration. CDMet was generated using an adaptive interpolation scheme, which employed thin-plate spline and random forest methods to construct the interpolation model. Six combinations of location and terrain information were designed and used as covariates in the model together with reanalysis data. Validation with independent observation stations and existing datasets showed that CDMet has acceptable accuracy, reasonable seasonal variability, and precise spatial distribution, and its accuracy is comparable to that of other datasets. Due to its comprehensive variables and high resolution, CDMet can be used as input data for hydrological, agricultural, and ecological models.

Tracing human influence on rising surface air temperature in Venezuela.

The rise in surface air temperature (SAT) in Venezuela, leading to the loss of all its glaciers, underscores the urgency of understanding human contributions to this phenomenon. This study investigates the impact of anthropogenic climate forcings on SAT across Venezuela, employing observational data, multi-model simulations, and optimal fingerprinting method. Anthropogenic forcings have driven a 0.40-0.85 C SAT rise during the industrial era, with land use (LU) emerging as a significant driver (0.36-0.68 C), surpassing greenhouse gases (GHGs) (0.10-0.62 C). Conversely, anthropogenic aerosols (Aaer) exhibit a cooling effect (- 0.93 to - 0.25 C) on SAT. Projections under Representative Concentration Pathways 4.5 indicate substantial SAT increases by the 21st century's end, underscoring human-induced SAT rise. Effective management of regional Aaer and LU changes in Venezuela holds the potential for mitigating current and future warming and its subsequent impacts on the fragile ecosystem of this region.

Reduced soil moisture drives leaf anatomical shifts more than chronically elevated temperatures in European temperate trees.

Chronic reductions in soil moisture combined with high air temperatures can modify tree carbon and water relations. However, little is known about how trees acclimate their foliar structure to the individual and combined effects of these two climate drivers. We used open-top chambers to determine the multi-year effects of chronic air warming (+5 °C) and soil moisture reduction (-50%) alone and in combination on the foliar anatomy of two European tree species. We further investigated how these climate drivers affected the relationship between foliar anatomy and physiology/chemistry in young downy oak and European beech trees. After 4 years, reduced soil moisture led to development of thinner leaves with a narrower epidermis and lower gas exchange for oak and beech, but to a lesser extent in the latter. In contrast, prolonged warming did not affect the anatomical and physiological/chemical traits in either species. Warming also did not exacerbate the impacts of dry soils, highlighting soil moisture as the key driver in leaf anatomical shifts. While soil moisture altered oak foliar anatomy, and the physiology and chemistry of both species, our work revealed a limited acclimation potential towards more drought- and heat-tolerant leaves as conditions become drier and warmer, suggesting potentially high vulnerability of both species to future climate predictions.