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Temperature is a major variable in the Earth's climate system, which plays an important role in energy exchange interactions between the Earth's surface and the atmosphere. There are various sources for temperature estimation, including ground stations, satellite products, reanalysis datasets, and multi-source weighted-ensemble datasets. Reanalysis datasets are generated by combining different types of observational data for a certain time in numerical weather prediction models and using ground and satellite observations. The purpose of this research is to investigate the performance of the ERA5- Land and AgERA5 reanalysis datasets as well as the MSWX multi-source dataset to determine reliable datasets for estimating temperature in Iran. First, we evaluated the temporal variations of the three datasets against the station data. We used the air temperature from 98 stations for 30 years from 1991 to 2020. Three metrics including Root Mean Square Error, Bias, and Index of agreement were used to evaluate ERA5-Land, AgERA5, and MSWX datasets.Then, considering the seven main climate zones of Iran, the spatiotemporal quality of the annual mean temperature was evaluated in different climate zones. The results showed that all three datasets have less error and bias in the estimation of the minimum temperature of Iran. However, AgERA5 and MSWX significantly showed less error in the estimation of the maximum temperature with RMSE of 1.74℃ and 1.42℃, respectively. On the other hand, the ERA5-Land dataset shows overestimation (5.05℃) and high error values (5.07℃) over the country-averaged. The results showed that the MSWX dataset has a better performance in estimating Iran's temperature with an average bias of 1℃. The interannual variations and decreasing and increasing trends of temperature in three datasets with correlation above 0.86 in all climate zones of Iran show a high consistency with observational data.The RMSE in all three datasets reaches its maximum in the winter season in mountainous climate zones of the country. This may be caused by snow-albedo feedback in mountainous climate zones. The findings showed that performing bias correction and downscaling methods as they have been done in MSWX and AgERA5, significantly improved the reanalysis dataset compared to the direct model output. Nevertheless, in the southwest of the Caspian Sea, the bias-corrected MSWX and AgERA5 show more errors than ERA5-Land. In general, the values of bias and RMSE in all three datasets are affected by the physical schemes of the model, parameterization, and data assimilation system, or the downscaling and bias correction methods. However, sources of bias can be different in different seasons of the year. The monthly spatial distribution of temperature in Iran shows that the minimum temperatures are located in the middle of Alborz and the northwest mountains of the country, and the coldest month of the year is January with a temperature of -10.8℃. The maximum temperatures in Iran are located in the southwest of the country and the southern coasts, and the hottest month of the year is July with an average temperature of 42.38℃. [ABSTRACT FROM AUTHOR]

Arctic sea-ice extent has strongly decreased since the beginning of satellite observations in the late 1970s. While several drivers are known to be implicated, their respective contribution is not fully understood. Here, we apply the Liang-Kleeman information flow method to five different large ensembles from the Coupled Model Intercomparison Project Phase 6 (CMIP6) over the 1970-2060 period to investigate the extent to which fluctuations in winter sea-ice volume, air temperature and ocean heat transport drive changes in subsequent summer Arctic sea-ice extent. This allows us to go beyond classical correlation analyses. Results show that air temperature is the most important controlling factor of summer sea-ice extent at interannual time scale, and that winter sea-ice volume and Atlantic Ocean heat transport play a secondary role. If we replace air temperature by net shortwave and downward longwave radiations, we find that the sum of influences from both radiations is almost similar to the air temperature influence, with the longwave radiation being dominant in driving changes in summer sea-ice extent. Finally, we find that the influence of air temperature is more prominent during periods of large sea-ice reduction and that this temperature influence has overall increased since 1970. [ABSTRACT FROM AUTHOR]

Lake is an important water resources in Mongolia, which has undergone a large variation in past decades. However, it is still challenging to monitor long-term changes in lake water storage (LWS) due to the lack of lake level monitoring and long-term satellite altimetry data for Mongolian lakes. According to the Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM) and the Joint Research Centre (JRC) dataset, we estimated the LWS changes of 55 Mongolian lakes (> 10 km2) from 1991 to 2020. The results showed that the LWS increased by 40.24 km3 from 1991 to 1997, especially for northwestern Mongolia with 31.47 km3. However, the LWS decreased by 32.44 km3 from 1998 to 2010, and the lakes in the northwestern and southern decreased by 20.24 km3 (62%) and 7.38 km3 (23%), respectively, and then the LWS continued to decrease by 10.22 km3 from 2011 to 2020. The precipitation was the primary cause of lake change, which could explain 45.13% of LWS change based on Generalized Linear Model, followed by temperature (26.33%) and irrigated area (16.72%). Analyzing the changing characteristic and driving mechanisms of LWS can provide a scientific basis for local water resources management and planning. [ABSTRACT FROM AUTHOR]

We study the air temperature with 10 years of hourly measured in 12 automatic weather stations, located in the State of Bahia (Brazil), considering mainly its auto‐ and cross‐correlation. We found that, in general, there is a positive linear trend, indicating warming in this variable for all stations. Also, we observe that the probability distribution function clearly exhibits a bimodal distribution, meaning that there are two main seasonal cycles (the largest relating to winter, the other to summer). In order to have a more robust statistical analysis, detrended fluctuation analysis (DFA) method was applied in these time‐series to analyzing data where traditional methods might struggle due to nonstationary. In this case, DFA was useful in identifying long‐range autocorrelations in these time‐series (with persistent and antipersistent behavior) with specific seasonal time‐scales (1 day and 1 year). However, if we quantify the cross‐correlations between the air temperature in Salvador and the other stations, by ρDCCA, we found that detrended cross‐correlation analysis cross‐correlation coefficient is always positive with two peaks, the first is located at n = 24 (1 day) and the second at n = 8766 (1 year). In this sense, ρDCCA coefficient provides insights how air temperature in Salvador is correlated with air temperature in other weather stations over different time‐scales, helping to understand their long‐term interactions. These results, for example, allow us to identify temporal scales that may be useful to better understand trends in the air temperature and that can help us further answer global warming questions. [ABSTRACT FROM AUTHOR]

The regional features of changes in the normals of the main climatic parameters (air temperature, precipitation, water vapor tension, atmospheric pressure at sea level) on the territory of Russia for three 30-year periods, 1961–1990, 1981–2010, and 1991–2020, are studied. Seasonal changes in the regime of the main climatic parameters in certain regions of Russia over the past decades have been revealed. In the last thirty years, there has been a slowdown in warming in all seasons on the territory of Russia. Summer precipitation normals of 1991–2020 decreased in most of the country, however, a significant increase in precipitation was found in the southern regions of Yakutia, the Amur oblast, and Primorye. For the regime of atmospheric pressure at sea level, the greatest changes are manifested in winter. The differences in climatic normals obtained in this work can be considered as one of the indicators of climate change that manifest themselves differently in different regions of such a vast country as Russia. The information can be useful for developing preventive adaptation measures under changing climate. [ABSTRACT FROM AUTHOR]

The rapid acceleration of urbanization has a serious impact on the urban ecological environment. It is of great importance to carry out numerical simulation research on urban spatial thermal environment for improving urban ecological environment. At present, there are few studies on the influence and accuracy evaluation of different vegetation settings in computational fluid dynamics (CFD) on the simulation results. Aiming to quantify impacts of vegetation changes and vegetation model settings on the numerical simulation of urban three-dimensional thermal environment, this study 1) based on Gaofen-2 remote sensing data and CFD model, the wind and heat environment at the block scale was simulated and analysed; 2) The vegetation model is assumed to be three models including cold source, constant temperature wall, and porous medium. Based on the heat transfer and cooling mechanism of vegetation, these three different vegetation models and their related CFD parameter settings are used to simulate the 3D spatial thermal environment. The research results show that the distribution of temperature has a great correlation with the direction of wind. The temperature distribution at different heights at night did not show significant differences. Due to the obvious convective heat transfer between the building surface and the atmosphere, the temperature in the upwind direction is significantly lower than that in the downwind direction. At a height of 2 m and below, setting the vegetation as the wall model has the best performance, and at a height above 5 m, the results of the wind and heat environment of the three setting methods are basically similar. The wind environment results of the same vegetation setting are different at different heights. [ABSTRACT FROM AUTHOR]

Near-surface air temperature (Ta) estimation by geostationary meteorological satellites is mainly carried out under clear-sky conditions. In this study, we propose an all-weather Ta estimation method utilizing FY-4A Advanced Geostationary Radiation Imager (AGRI) and the Global Forecast System (GFS), along with additional auxiliary data. The method includes two neural-network-based Ta estimation models for clear and cloudy skies, respectively. For clear skies, AGRI LST was utilized to estimate the Ta (Ta,clear), whereas cloud top temperature and cloud top height were employed to estimate the Ta for cloudy skies (Ta,cloudy). The estimated Ta was validated using the 2020 data from 1211 stations in China, and the RMSE values of the Ta,clear and Ta,cloudy were 1.80 °C and 1.72 °C, while the correlation coefficients were 0.99 and 0.986, respectively. The performance of the all-weather Ta estimation model showed clear temporal and spatial variation characteristics, with higher accuracy in summer (RMSE = 1.53 °C) and lower accuracy in winter (RMSE = 1.88 °C). The accuracy in southeastern China was substantially better than in western and northern China. In addition, the dependence of the accuracy of the Ta estimation model for LST, CTT, CTH, elevation, and air temperature were analyzed. The global sensitivity analysis shows that AGRI and GFS data are the most important factors for accurate Ta estimation. The AGRI-estimated Ta showed higher accuracy compared to the ERA5-Land data. The proposed models demonstrated potential for Ta estimation under all-weather conditions and are adaptable to other geostationary satellites. [ABSTRACT FROM AUTHOR]

Accurate air temperatures underpin environmental research. Most professional meteorological air temperature measurements still expose thermometers within traditional, naturally ventilated screens. Their representation of true air temperature depends on screen airflow, and therefore local winds. Accuracies of daily maximum (Tmax) and minimum (Tmin) air temperatures are assessed by comparison between a naturally ventilated large conventional screen and a co‐located aspirated reference screen. In over 1200 days' data, the naturally ventilated Tmin and Tmax both showed small (median < 0.06°C) cold bias, but, in 1% of cases, warm Tmax bias and cold Tmin bias >|1°C|. The Tmin cold bias is associated with calm clear nights, and the Tmax warm bias events with calm winter days at low sun angles, allowing solar heating of the screen. The prevalence of poor natural ventilation, potentially affecting Tmin and Tmax, is estimated across European sites. Poor ventilation occurred at Tmin for 12% of values, and at Tmax for 4%. Climatological averaging will reduce these effects, but, without corroborating wind data, statistical changes in Tmin or Tmax, including identifying "Tropical Nights" (Tmin > 20°C) or occurrences of winter extremes, may have limited value. Wider adoption of aspirated thermometer screens, with an initial overlap period, will largely eliminate these effects. [ABSTRACT FROM AUTHOR]

Snow avalanche run-out is controlled by flow regime, which is heavily dependent on snow temperature and water content. Existing avalanche models calculate temperature change and snow melting arising from frictional work, snowcover entrainment and particle collisions, but ignore the influence of the ambient air temperature. Here, we propose an experimentally-based relationship to describe the sensible heat flux between the flowing avalanche and the ambient air. A snow avalanche case study and a sensitivity analysis are performed using the proposed model. Modeling results suggest a marginal contribution of heat to the avalanche core, but one, that nonetheless can have significant effect on avalanche runout. For wet (warm) avalanches, higher air temperature will amplify water lubrication effects, leading to longer avalanche run-out. Warmer air temperatures also influence the behaviour of dry, cold avalanches, as the presence of meltwater increases snow cohesion, restricting the formation of strong powder clouds and reducing the avalanche run-out. Colder air temperatures facilitate the formation of more fluidized powder snow avalanches with long run-out and strong air-blasts. The proposed model quantifies the temperature change in the avalanche and can be included in avalanche hazard assessments in which temperature and flow regime are important elements, including the understanding of air temperature and avalanche flow in a changing climate. [ABSTRACT FROM AUTHOR]

Besides the rapid retreating trend of Arctic sea-ice extent (SIE), this study found the most outstanding low-frequency variation of SIE to be a 4–6-year periodic variation. Using a clustering analysis algorithm, the SIE in most ice-covered regions was clustered into two special regions: Region-1 around the Barents Sea and Region-2 around the Canadian Basin, which were located on either side of the Arctic Transpolar Drift. Clear 4–6-year periodic variation in these two regions was identified using a novel method called "running linear fitting algorithm". The rate of temporal variation of the Arctic SIE was related to three driving factors: the regional air temperature, the sea-ice areal flux across the Arctic Transpolar Drift, and the divergence of sea-ice drift. The 4–6-year periodic variation was found to have always been present since 1979, but the SIE responded to different factors under heavy and light ice conditions divided by the year 2005. The joint contribution of the three factors to SIE variation exceeded 83% and 59% in the two regions, respectively, remarkably reflecting their dynamic mechanism. It is proven that the process of El Niño–Southern Oscillation (ENSO) is closely associated with the three factors, being the fundamental source of the 4–6-year periodic variations of Arctic SIE. [ABSTRACT FROM AUTHOR]

Elevated temperatures in urban centers have become a common problem in cities around the world. However, the climate problems in suburban areas are equally severe; there is an urgent need to find zero-carbon ways to mitigate this problem. Recent studies have revealed the thermal performance of vegetation, buildings, and water surfaces. They functioned differently regarding the climate at different periods of the day. Accordingly, this study synthesizes remote sensing technology and meteorology station observation data to deeply explore the differences in the role of each climate-influencing factor in the suburban areas of Chengdu. The land surface temperature (LST) and air temperature (Ta) were used as thermal environmental indicators, while the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), and altitude were used as environmental factors. The results showed that the relevant influences of the environmental factors on the climate in the sample areas were significantly affected by the time of the day. The NDVI (R2 = 0.5884), NDBI (R2 = 0.3012), and altitude (R2 = 0.5638) all showed strong correlations with Ta during the night (20:00–7:00), which gradually weakened after sunrise, yet the NDWI showed a poorer cooling effect during the night, which gradually strengthened after sunrise, reaching a maximum at 15:00 (R2 = 0.5012). One reason for this phenomenon was the daily weather changes. These findings facilitate the advancement of the understanding of the climate in suburban areas and provide clear directions for further thermal services targeted towards people in different urban areas. [ABSTRACT FROM AUTHOR]

The study characterized the temporal and spatial variability in greenhouse gas (GHG) fluxes (CO2, CH4, and N2O) between December 2020 and November 2021 and their regulating drivers in the subtropical wetland of the Indian Himalayan foothill. Five distinct habitats (M1—sloppy surface at swamp forest, M2—plain surface at swamp forest, M3—swamp surface with small grasses, M4—marshy land with dense macrophytes, and M5—marshy land with sparse macrophytes) were studied. We conducted in situ measurements of GHG fluxes, microclimate (AT, ST, and SMC(v/v)), and soil properties (pH, EC, N, P, K, and SOC) in triplicates in all the habitat types. Across the habitats, CO2, CH4, and N2O fluxes ranged from 125 to 536 mg m−2 h−1, 0.32 to 28.4 mg m−2 h−1, and 0.16 to 3.14 mg m−2 h−1, respectively. The habitats (M3 and M5) exhibited higher GHG fluxes than the others. The CH4 flux followed the summer > autumn > spring > winter hierarchy. However, CO2 and N2O fluxes followed the summer > spring > autumn > winter. CO2 fluxes were primarily governed by ST and SOC. However, CH4 and N2O fluxes were mainly regulated by ST and SMC(v/v) across the habitats. In the case of N2O fluxes, soil P and EC also played a crucial role across the habitats. AT was a universal driver controlling all GHG fluxes across the habitats. The results emphasize that long-term GHG flux monitoring in sub-tropical Himalayan Wetlands has become imperative to accurately predict the near-future GHG fluxes and their changing nature with the ongoing climate change. [ABSTRACT FROM AUTHOR]

Meteorological observations of surface air temperature have provided fundamental data for climate change detection and attribution. However, the weather stations are unevenly distributed, and are still very sparse in remote regions. The possible sampling error is well known, but not well quantified because we are lack of the adequate and regularly distributed measurements. The high resolution of satellite land surface temperature retrieval during night time provide a nice proxy for near surface temperature as both temperatures controlled by surface longwave radiative cooling and the nocturnal temperature inversion depress land‐atmosphere turbulent exchange. The sampling error of mean value and trend were assessed by comparing station point measurements (pixel of ∼0.01°) with grid (1°) mean and national mean from 2001 to 2021. This method permits us to make the first assessment of under‐sampling error and spatial representative error on both national mean and trend of air temperature during nighttime collected at ∼2,400 weather stations over China. The sampling error in national mean temperature is more than 3°C. The under‐sampling error due to lack of observation explains two thirds and the spatial representative error due to the difference between station and grid/regional mean elevation contribute the other one third. The sampling error in trend account for one third of the national mean trend. The urban heat island effect associated with urbanization around the weather stations (spatial representative error) can explain four fifths of the sampling error in trend, which is consistent with existing studies based on air temperature collected at paired weather station. Plain Language Summary: Meteorological observations provide fundamental measurements of surface air temperature. These station‐based temperature records are usually processed to grid data sets considering the uneven distribution of meteorological stations. The gridding procedure represents a non‐uniformly sampling. In this paper, we quantified the sampling error over China using remote sensed temperature record as proxy of near surface temperature during nighttime. The causes of sampling error both in mean value and trend were both quantified. Key Points: Sampling error of mean and trend of temperature collected at weather stations at nighttime over China were investigatedThe sampling error in national mean temperature were found to be more than 3°C due to uneven distribution of weather stationsThe sampling error in temperature trend account for one thirds of the national mean temperature trend due to local urbanization [ABSTRACT FROM AUTHOR]

An accurate prediction model can be required for the greenhouse air temperature, such as Model Predictive Control. Long short-term memory neural networks (LSTM) have been widely used to predict time series data, such as air temperature. However, the prediction accuracy of LSTM can be reduced for the long time series data, due to data forgetting. In this study, the LSTM model was combined with the attention mechanism to construct the LSTM-AT model. The query vector, key vector, and value vector were calculated, according to attention mechanism and output states of LSTM’s hidden layer. The similarity between the query and key vector was calculated to obtain the similarity score. Softmax function was used to obtain attention distribution for the normalization processing. The larger the attention value was, the higher the relevance of input information to the task objective was. The dot product operation was carried out with the normalized weight and value vector to obtain the output of the attention mechanism. The local information integration and data dimension transformation were carried out through the full connection layer. Finally, the output data was obtained in the output layer of the LSTM-AT model. The weights were assigned to the output states of LSTM’s hidden layer, according to the degree of importance. The forgetting of long time series data was effectively solved to improve the prediction accuracy of indoor air temperature. The prediction performances were verified and compared on the LSTM-AT, LSTM, recurrent neural network (RNN), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM) model in the different prediction horizons (12, 24 and 48 h). The results showed that the prediction accuracy of the five models shared a decreasing trend with the increase in prediction time. The maximum and minimum RMSE for the LSTM-AT model were 1.34 ℃ and 0.59 ℃, respectively. The maximum and minimum RMSE for the rest four models were 3.37 ℃ and 0.66 ℃, respectively. The maximum and minimum MAPE for the LSTM-AT model were 8.14% and 2.48%, respectively. The maximum and minimum MAPE for the rest four models were 38.7% and 2.90%, respectively. Therefore, the prediction accuracy of the LSTM-AT model was higher than that of rest four models. The average RMSE for LSTM-AT, LSTM, GRU, RNN, and BiLSTM were 0.89, 1.42, 1.89, 2.10, and 1.51 ℃, respectively. The average MAPE for LSTM-AT, LSTM, GRU, RNN, and BiLSTM were 4.26%, 8.96%, 13.57%, 17.70%, and 10.67%, respectively. The sort data of the prediction model was ranked in descending order of the LSTM-AT, LSTM, BiLSTM, GRU, and RNN. The prediction performances of the LSTM-AT and LSTM model were compared under different weather conditions (sunny, cloudy, and rainy), in order to further explore the universality of the LSTM-AT model. The minimum and maximum RMSE for LSTMAT were 0.26℃ and 0.70 ℃, respectively. The minimum and maximum RMSE for LSTM were 0.68 ℃ and 1.57 ℃, respectively. The minimum and maximum MAPE for LSTM-AT were 1.61% and 10.51%, respectively. The minimum and maximum MAPE for LSTM were 4.27% and 25.07%, respectively. The prediction accuracy of the LSTM-AT model was higher than LSTM in all weather conditions. The LSTM-AT model has a higher prediction accuracy to accurately predict the indoor air temperature. [ABSTRACT FROM AUTHOR]

The frequency of snowfall and rainfall is expected to change due to the warming climate. However, trends in liquid and solid phases are not linearly related to air temperature trends. This paper discusses the impact of thermal properties of circulation types (CTs) on the trends in snowy and rainy days in Poland in the period 1966–2020. The visual observations from 42 synoptic stations, which constitute the most-reliable information on precipitation type, were used to identify the precipitation phase. In most CTs, the air temperature increased between 1966–1985 and 2001–2020, but at various rates depending on the type of circulation. Positive tendencies in the thermal properties of CTs contributed to decreasing trends in winter snowfall and increasing trends in winter rainfall. The rate of tendencies in the probability of the precipitation phases depended on the average temperature and the intensity of warming, in particular CTs. In winter, both the snowfall and rainfall tendencies were the strongest for those CTs with average air temperatures (ATs) close to the freezing point, particularly when the average had crossed that threshold between the years 1966–1985 and 2001–2020. The most rapid tendencies in winter snowfall and rainfall, and in the spring mixed phase were induced by N and NW air advection under cyclonic conditions, bringing air from the rapidly warming Arctic. No trends in the winter mixed precipitation probability resulted from its various tendencies in particular CTs. The probability of snowfall increased during air advection from the southeastern sector, particularly in winter. [ABSTRACT FROM AUTHOR]

Riparian corridors often act as low-land climate refugia for temperate tree species in their southern distribution range. A plausible mechanism is the buffering of regional climate extremes by local physiographic and biotic factors. We tested this idea using a 3-year-long microclimate dataset collected along the Ciron river, a refugia for European beech (Fagus sylvatica) in southwestern France. Across the whole network, canopy gap fraction was the main predictor for spatial microclimatic variations, together with two other landscape features (elevation above the river and woodland fraction within a 300m radius). However, within the riparian forest only (canopy gap fraction < 25%, distance to the river < 150m), variations of up to -4°C and + 15% in summertime daily maximum air temperature and minimum relative humidity, respectively, were still found from the plateau to the cooler, moister river banks, only ~ 5-10m below. Elevation above the river was then identified as the main predictor, and explained the marked variations from the plateau to the banks much better than canopy gap fraction. The microclimate measured near the river is as cool but moister than the macroclimate encountered at 700-1000m asl further east in F. sylvatica's main distribution range. Indeed, at all locations, we found that air relative humidity was higher than expected from a temperature-only effect, suggesting that extra moisture is brought by the river. Our results explain well why beech trees in this climate refugium are restricted to the river gorges where microtopographic variations are the strongest and canopy gaps are rare. [ABSTRACT FROM AUTHOR]

Europe and the Mediterranean are considered climate change hot spots. This is the reason why this paper focuses on the analysis of the trends of essential climate variables in a Mediterranean country, Greece. The analyzed period is 1991–2020, and the dataset used is ERA5-Land (produced by the European Center for Medium-Range Weather Forecasts), which has global coverage and an improved resolution of ~9 × 9 km compared to other datasets. Significant climatic changes across Greece have been put in evidence during the analyzed period. More specifically, the country averaged a 30-year trend of temperature of +1.5 °C, locally exceeding +2 °C, and this increasing trend is positively correlated with the distance of the areas from the coasts. Accordingly, the number of frost days has decreased throughout the country. In terms of rainfall, a major part of Greece has experienced increasing annual rainfall amounts, while 86% of the Greek area has experienced a positive trend of days with heavy rainfall (>20 mm). Finally, a multiple signal of the trend of consecutive dry days was found (statistically non-significant in the major part of Greece). [ABSTRACT FROM AUTHOR]

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This study highlights significant recent increases in water and air temperatures in the rapidly developing city of Belgrade, Serbia to raise awareness of the anticipated negative impacts and the urgent need to develop appropriate mitigation strategies. We investigate the mean annual water temperature trends at the confluence of the Sava and Danube Rivers, along with air temperature trends at the Belgrade meteorological station, spanning from 1956 to 2020. Results reveal a consistent increase in temperature across all three measuring stations, with the Danube experiencing a rise of 0.34 °C/decade, the Sava at 0.44 °C/decade, and Belgrade's air temperature increasing by 0.39 °C/decade. Employing the Rescaled Adjusted Partial Sums method, sharp rises in water temperature were pinpointed in 1989 for the Sava and 1990 for the Danube, while Belgrade's air temperature surge began in 1998. The highest intensity of air temperature increase within the recent period (1998–2020) was observed at the Belgrade observatory, reaching 0.76 °C/decade. Notably, the Sava exhibited a faster increase in water temperature over the last thirty years compared to the Danube. August marked the peak average water temperature for both rivers, while July recorded the highest average air temperature in Belgrade. Despite differing flow rates, both rivers exhibit similar hydrological regimes, with maximum flows occurring in April and minimum flows in August for the Sava, and October for the Danube. Seasonal temperature increases were most pronounced in summer, notably in August, with the smallest rises occurring during cold periods. Additionally, an inverse proportional relationship between mean annual water temperatures and discharges was observed at both river stations. The overall findings suggest that the increase in both air and water temperatures is more pronounced during the warmer part of the year. [ABSTRACT FROM AUTHOR]

The Qinghai-Xizang Plateau （QXP） is an important herbage producing area， ecological barrier and water conservation area， the vegetation ecological process on which can directly affect the changes of China and even East Asia.With global warming， the phenological period of vegetation on the QXP is constantly changing， affecting climate and ecosystem through carbon cycle and hydrothermal cycle， etc.The study of phenological change and its influencing factors has become a key issue， and the construction of models that can realize future phenological prediction is of great scientific significance.In this paper， based on the Normalized Difference Vegetation Index acquired by satellites during 2000 -2020 （MODIS NDVI）， the dynamic threshold method was used to extract the start of growing season （SOS） on the QXP， and its spatiotemporal variation was analyzed in combination with vegetation types， so as to construct multiple phenological models of SOS， air temperature and soil moisture， exploring the hydrothermal conditions required for different regions and types of vegetation to start growing.The results showed that： （1） From 2000 to 2020， the overall SOS advance trend of the QXP was most significant in the eastern part of the region， where the SOS advance rate exceeded 10 d·（10a）-1.Coniferous forests， scrub， meadows， and alpine vegetation cover areas had a high percentage of SOS advance， and grasslands had about 50 % of slightly delayed areas.（2） The eastern and northern regions of the QXP showed an obvious warming and humidification trend.The average annual temperature rise rate was about 0.36 ℃·（10a）-1， and the average annual soil moisture increase rate was about 3.8×10-4 m3·m-3 （p

The article presents the scientific results of a study assessing agroclimatic resources in agricultural landscapes located in various natural zones of the Turkestan region of the RK (Republic of Kazakhstan). The research methods included classical and modern methods of mathematical statistics using digital technology and time series graphs to develop a mathematical model for climatic and hydrological indicators. Assessment of changes in indicators of agroclimatic resources in agricultural landscapes for 1941–2020 showed that the sum of air temperature, evaporation from the water surface and radiation balance of the daytime surface, characterising the energy resources of landscapes, increased by 10–15%, which contributes to increase in the total water consumption of agricultural land by 10–12%. Meanwhile, the decreasing tendency of the amount of precipitation by 5–10% in all natural climatic zones of the region has become one of the factors leading to a decrease in the natural moisture supply of the soil and vegetation cover of landscapes by 10–15%, acting as important environment-forming and ecological functions. The combined impact of these environment-forming factors has become the key reason for the increase in the deficit of agricultural land water consumption by 15–20%, the reduction of solar energy costs for the soil-forming process by 10–15% and the increase of the climate aridisation, and has become a signal for the need in the safety of agricultural activities, requiring the development of a set of adaptive measures to mitigate this process in the region.

Abstract Arctic sea-ice extent has strongly decreased since the beginning of satellite observations in the late 1970s. While several drivers are known to be implicated, their respective contribution is not fully understood. Here, we apply the Liang-Kleeman information flow method to five different large ensembles from the Coupled Model Intercomparison Project Phase 6 (CMIP6) over the 1970-2060 period to investigate the extent to which fluctuations in winter sea-ice volume, air temperature and ocean heat transport drive changes in subsequent summer Arctic sea-ice extent. This allows us to go beyond classical correlation analyses. Results show that air temperature is the most important controlling factor of summer sea-ice extent at interannual time scale, and that winter sea-ice volume and Atlantic Ocean heat transport play a secondary role. If we replace air temperature by net shortwave and downward longwave radiations, we find that the sum of influences from both radiations is almost similar to the air temperature influence, with the longwave radiation being dominant in driving changes in summer sea-ice extent. Finally, we find that the influence of air temperature is more prominent during periods of large sea-ice reduction and that this temperature influence has overall increased since 1970.

Abstract Lake is an important water resources in Mongolia, which has undergone a large variation in past decades. However, it is still challenging to monitor long-term changes in lake water storage (LWS) due to the lack of lake level monitoring and long-term satellite altimetry data for Mongolian lakes. According to the Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM) and the Joint Research Centre (JRC) dataset, we estimated the LWS changes of 55 Mongolian lakes (> 10 km2) from 1991 to 2020. The results showed that the LWS increased by 40.24 km3 from 1991 to 1997, especially for northwestern Mongolia with 31.47 km3. However, the LWS decreased by 32.44 km3 from 1998 to 2010, and the lakes in the northwestern and southern decreased by 20.24 km3 (62%) and 7.38 km3 (23%), respectively, and then the LWS continued to decrease by 10.22 km3 from 2011 to 2020. The precipitation was the primary cause of lake change, which could explain 45.13% of LWS change based on Generalized Linear Model, followed by temperature (26.33%) and irrigated area (16.72%). Analyzing the changing characteristic and driving mechanisms of LWS can provide a scientific basis for local water resources management and planning.

The issue of global climate change is one of the most important challenges for modern environmental science. In this regard, assessment of greenhouse gas emissions from soils under agroecosystems is an extremely important task. The main purpose of the study was to assess the potential emissions of greenhouse gases (CO2 and N2O) from the soils under the winter wheat agroecosystem during its spring-summer growing season under the climate change in Ukraine. A comprehensive model of greenhouse gas emissions from agroecosystem soils was used as a theoretical basis. The study considered the main agroclimatic regions of Ukraine, such as Polissia, Forest-Steppe, Northern Steppe, and Southern Steppe. The modelling was performed for average long-term conditions of 1981–2020 and under scenario conditions (using the RCP4.5 climate scenario) for the decades of 2021–2030, 2031–2040, and 2041–2050. Estimates of CO2 emissions were obtained for the agroclimatic regions of Ukraine. According to the results, the largest CO2 emissions (0.886-0.940 t C-CO2/ha) will be expected when the average productive moisture supplies in the arable soil layer for the spring-summer period of winter wheat vegetation are 20–40 mm and the air temperature is 12–15 °C. Rise in the level of moisture reserves, averaged for the period, for more than 40 mm and the air temperature of 11–12 °C is accompanied by decrease in the level of CO2 emission (0.645–0.658 t C-CO2/ha). In arid conditions, with a decrease in the period-average moisture supplies in the arable soil layer to less than 20 mm and heightened average air temperatures (14.5–15.2 °C), a decrease in CO2 emissions is expected (0.755–0.770 t C-CO2/ha). The study showed that the highest N2O emission (1.087–1.703 kg N-N2O/ha) is expected at a high level of moistening (Md being equal to 0.474–0.713 relative units and air temperature – 11.3–13.0 °C) with a high intensity of nitrification and denitrification processes. Conditions characterizing the average level of N2O emission (0.657–0.890 kg N-N2O/ha) are formed under the moistening at a level of Md equaling 0.296–0.447 relative units and air temperature of 13.4–14.7 °C. There is a slowdown in denitrification processes and an increase in the share of emissions owing to nitrification. It has been found that the lowest N2O emission (0.508–0.537 kg N–N2O/ha) is expected under conditions of severe and very severe drought (according to the Md criterion), with a heightened temperature averaged at the level of 14.5–15.2 °C during the spring-summer period of winter wheat vegetation.

Introduction. The purpose of the study is to assess the dynamics of changes in air temperature indicators and its impact on agronomic systems, based on long-term observations of the V. A. Mikhelson Meteorological Observatory of RSAU – MTAA. Materials and Methods. Using the Mathcad program, graphs of changes in average annual temperatures were constructed using a third-order approximation polynomial, graphs of the theoretical normal density distribution and normal temperature distribution function, as well as graphs of intra-annual temperature distribution for 30 years. Research results and Discussion. The values of the relative error of average annual temperatures were calculated, the normal distribution of average annual temperatures was checked using the Pearson criterion, after which the probability of deviation of the empirical hours falling into intervals from the theoretical hours was found. Conclusion. As a result of the research, a significant increase in average annual air temperature was established against the background of a general reduction in the duration of the cold period. Increase in temperature over the period 1961–1990 amounted to 0.9 °C, which is significantly higher than the average values for the globe. The results of the study reflect the general trend of warming in the context of global climate change and can be used to improve the forecasting of weather conditions, as well as to optimize technological processes in crop production.

We analyze the changes in snow depth at different time scales (from within a day to over many years) and its dependence on the precipitation phases, wind regime, and air temperature. The study employs observational data for snow cover and regular meteorological records of the air temperature (2 m), precipitation, and wind (direction and speed at 10 m) in 2002–2022. The data were processed by classical climatological methods. To compare the data on snow depth with precipitation phases, air temperature, wind speed and direction, we used temporal interpolation. It is shown that solid precipitation occurs most often, when the annual distribution of precipitation is considered. A significant percentage of precipitation in the liquid phase is observed during the Antarctic summer and Antarctic autumn. The portion of the mixed precipitation is the smallest throughout the year. The influence of the precipitation phases on the accumulation/melting of snow has seasonal character. The period from April to November is favorable for snow accumulation. In December, the solid precipitation leads to an increase in snow depth, but the mixed and liquid phases are accompanied the melting of the snow cover. The most significant snow cover grows smaller in January-February due to melting. The emphasis is on the local effect of the snow depth decrease due to strong winds in a setting with the accompanying effect of the thermal factor. Further analysis showed that the parameter most closely associated with snow cover depth reduction was a combination of wind speed and direction. Snow cover depth was reduced the most in January–March due to melting, yet on a daily scale, the reduction’s intensity was not the highest. The highest frequency of cases of intense reduction in snow cover depth by more than 1 cm/3h is seen if the wind is either northerly, northeasterly, or southerly. The most frequent reduction in snow cover depth is seen under the northerly and northeasterly winds and positive temperatures. The north and northeastern air masses’ advection is mostly associated with heat advection, and thus, the snow cover depth is reduced by melting. The eastern, northeastern, and southeasterly winds can be connected to the effect of the foehn winds due to the closeness of the continent. The most frequent occurrence of a significant reduction in the snow cover under the southern wind is noted under a high wind speed and negative air temperature.

The aim of this study was to present the characteristics of a hot airflow in a conveyor type dampers under the hot air temperature changes. The amount of space occupied by the damper was reduced by 50 %, and by efficiently managing the damper's airflow rate even in the face of extreme external conditions and constant air volume proportional control, a conveyor-type damper with a significant energy-saving effect has been explored. It was configured to control the degree to which it was opened by operating the folding conveyor drive mechanism, enabling the damper to modify airflow. In this study, the mass flow rate of heated air through the conveyor-type damper increased in proportion to the damper port's expansion. The mass flow rate of air decreased as the temperature of air intensified. In addition, the thermal energy of the air flowing in the conveyor-type damper improved in ratio to the increase in the damper opening. The findings indicate a 50 % reduction in the damper's occupied space, indicating a significant energy-saving impact of the conveyor-type damper. This is because the damper's air volume is effectively controlled even in the face of severe external conditions, as it is proportionately controlled throughout. The understanding of the properties of airflow for the hot temperature change is strengthened by this research. Furthermore, this study may provide useful direction for future research and industry optimization. [ABSTRACT FROM AUTHOR]

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Abstract Global climate change notably influences meteorological variables such as temperature, affecting regions and countries worldwide. In this study, monthly average temperature data spanning 73 years (1950–2022) were analyzed for 28 stations in the city centers across seven regions of Turkey. The station warming rates (SWR) were calculated for selected stations and the overall country using Singular Spectrum Analysis (SSA) and Least Square Polynomial Fit (LSPF) methods. The temperature trend in Turkey exhibited a decline until the late 1970s, followed by a continuous rise due to global warming. Between 1980 and 2022, the average SWR in Turkey was found to be 0.52 °C/decade. The SWR was determined to be the lowest in Antakya (0.28 °C/decade) and the highest in Erzincan (0.69 °C/decade). The relationship between SWR and latitude, longitude, altitude, and distance to Null Island (D2NI) was explored through linear regression analysis. Altitude and D2NI were found to be the most significant variables, influencing the SWR. For altitude, the correlation coefficient (R) was 0.39 with a statistically significant value (p) of 0.039. For D2NI, R, and p values were 0.39 and 0.038, respectively. Furthermore, in the multiple regression analysis involving altitude and D2NI, R and p values were determined to be 0.50 and 0.029, respectively. Furthermore, the collinearity analysis indicates no collinearity between altitude and D2NI, suggesting that their effects are separated in the multiple regression.

The ice regime of the Caspian Sea has pronounced influence on the heat and moisture exchange of the reservoir with the atmosphere, the state of the ecosystem, as well as human marine activities, including shipping, fishing, construction of hydraulic structures, etc. Consequently, the development of existing ideas about the causes of changes in the characteristics of the ice regime of such water bodies is actual and socially significant problem of limnology, hydrometeorology, ecology, and navigation. This study was aimed at determination the frequency of occurrence of atmospheric blockings over the Caspian region with standings longer 5 days in winter period of 1959–2022, and investigation of influence of them on the ice regime in the Northern area of the Sea. The following information and data were used: changes in hourly mean values of atmospheric pressure at the sea level, geopotential of isobaric surfaces 850, 500 and 300 hPa, presented in the ERA5 reanalysis; and observational data on air temperature and ice cover from hydrometeorological stations located in the Caspian region of Kazakhstan and Russia. It has been established that in the winter during a long standing of atmospheric blockings the mean daily air temperatures noticeably drop. The values of all the studied characteristics of every atmospheric blocking which occurred in 1959–2022 were estimated as well the influence of them on the ice regime in the North of the Caspian Sea was analyzed. The relationship between a decrease in the frequency of atmospheric blockings (AB), sums of negative air temperatures on its coasts, and the ice cover thickness in the corresponding areas of coastal waters has been revealed in the region. The longer the total AB duration, the lower is the air temperature, and the ice thickness in February is larger.

Arctic amplification (A A) of the climate warming is understood as the excess of the surface air temperature rise in the Arctic over the same process in the non-Arctic latitudes, and it is a fundamental characteristic of the climate during periods of warming. The positive feedback between albedo and shrinking of the sea ice coverage has been identified as the first possible cause of A A. The article presents quantitative estimates of the relationship between the summer decrease and autumn-winter restoration of the ice coverage with the rise of the surface air temperature in the marine Arctic based on data of observations. The mean monthly values of the temperature in the marine Arctic and the mean monthly values of areas covered by the sea ice in the Arctic Ocean and the Arctic seas for the period 1989–2020 were used. It has been found that the observed warming and the decrease of the ice coverage are accompanied by a growth of inter-monthly changes in the coverage and the same in the air temperature. Negative trends in increments of the ice coverage in May–July is indicative of a long-term growth in inter-monthly shrinkage of the ice coverage due to increasing melting, while negative trends in increments of the temperature in the same months is suggestive of a slowdown in the temperature rise from month to month, presumably due to the increasing heat consumption for the snow and ice melting and the water heating. The positive correlation confirms the relation between growing negative inter-monthly differences in the ice coverage and decreasing positive differences in the air temperature during these months. Based on that we determined a sensitivity of the inter-monthly increments of the temperature to the increments of the ice coverage, which was used to estimate the weakening of the positive air temperature trend in May–July over the Arctic Ocean and over the seas of the Northern Sea Route (NSR). Estimating of the relationship between month-to-month changes in temperature and ice coverage in the autumn-winter months meet difficulties due to the strong influence of external heat influx. Only in November and January a slowdown in the air temperature drop from October to November and from December to January had been revealed, with a positive trend in the ice coverage increments.

High temperatures that prevail at sun-exposed sites such as heaths, depressions, or on slopes, increase the risk of desiccation for rove beetles (Staphylinidae). Therefore, atmospheric and sub-atmospheric humidity determine the microclimates and thus habitat preference and activity of xerothermophilous species. In the nature reserve Marienfließ, a heathland in the south of Mecklenburg-Western Pomerania, Northeast German Lowlands, air temperature and humidity were recorded year-round along a transect at 10 sites, i.e. where the vegetation was more dense and more humid than where the vegetation was sparse and it was drier, using data loggers placed in the litter layer that recorded the conditions every 30 min in 2021. Monthly visits were made to determine the biomass of vegetation, soil and litter properties, edaphic rove beetle fauna and total abundance and and that of individual species at 1 m2 sites. Statistical analysis of 365 diurnal cycles per site, with pronounced amplitude in air temperature and humidity, clearly delineated the cooler and more humid sites from warmer and drier ones. Between 10 and 22 of the 30 xerothermophilous species (73.3%), such as, e.g. Quedius persimilis Mulsant & Rey, 1876, Xantholinus gallicus Coiffait, 1956, Cousya longitarsis (Thomson, 1867) and Euaesthetus superlatus Peyerimhoff, 1937, were recorded only at the cooler and moist sites on the heath. Comparison of the abundances and distribution of species revealed that the xerothermophilous heath-dwelling and desiccation-prone species prefer rather constant environmental conditions, i.e. niches in cool and moist habitats on warm and dry heathland.

Evaluating air temperature perturbation is important to know the anthropic activity's effect on the environmental system. The study case was La Malinche volcano, concerning the urban, agricultural, and forest environments. The air temperature data (average, maximum, minimum, standard deviation and range), was analyzed by prin- cipal components analysis (PCA), and the Kruskal-Wallis (K-W) test. Data analyses were made on a diurnal (warming and cooling rates), daily and monthly basis. The K-W test showed that warming and cooling rates are significantly different between the agricultural, urban, and forest zones, despite the north and south sides of La Malinche volcano had significant differences. The PCA indicated more perturbation concerning the cooling rates of air temperature among the environments than the warming rates. The average, maximum, and minimum air temperature of the urban environment and the standard deviation and range of the agricultural environment were the highest. The minimum air temperature changes more than the maximum in the volcano's urban, agricultural, and forest south side. The K-W test showed that the environmental conditions differed sig- nificantly based on average and maximum. The daily air temperature on the north side of La Malinche Volcano was very different from the south side. The PCA with average, maximum, minimum, standard deviation, and range showed that the environments are modified. The average monthly air temperature in the agricultural and forest areas was lower than average. Minimum air temperature increase was more accentuated in urban areas than in agriculture and forest areas and increased more than maximum.

Woody vegetation and the atmosphere are closely related, which determines the relevance of studying this interaction within the framework of the problem of global climate change and the resilience of terrestrial ecosystems to external influences. For the research region, the Komi Republic, climate warming has been revealed, expressed in increased air temperature in the summer. The warmest year over the years of observation was 2013, when the average air temperature in the summer was 17.2 °C, while the longterm annual average norm was 14.6 °C. The growth and productivity of woody plants in a forest community depend on the amount of moisture in the soil. In the middle subzone of the taiga, moisture reserves are 60–200 mm, so woody plants do not lack it. In this regard, transpiration rates of Scots pine and Siberian spruce needles in different types of forest under fluctuating weather conditions has been studied. The results of the research have shown that at elevated air temperatures, low amounts of precipitation and favourable soil water supply, the transpiration rate in pine increases by 1.5 times, and in spruce by 3 times. This is probably due to the fact that the spruce stomata react especially quickly to changes in external conditions, which is determined by the increased sensitivity of the needles to moisture conditions. It has been established that during the same period there is the closest relationship between the transpiration rate and the air temperature (r = 0.7–0.9). In different types of forest, the transpiration rate in pine varies from 4 to 16 %, in spruce within 5 %. An assessment of changes in the pine and spruce needles transpiration rates in response to changing weather conditions has shown that this process is largely determined by the species membership. Pine is a light-demanding species with a taproot system. Spruce is a shade-tolerant species with a shallow root system.

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The study analyzed available data series of the Sava River's water temperature measured at the Zagreb gauging station. Official data from the Croatian Meteorological and Hydrological Service (DHMZ) in Zagreb were utilized. Over the 73 years from 1948 to 2020, there are only 53 years with complete measurement records. Despite this limiting fact, it was considered important to analyze the behavior of the Sava River's water temperatures in Zagreb over the past 70 years, during which a significant increase in air temperatures has been observed in the region, particularly in the city of Zagreb. Analyses were conducted on the characteristic (minimum, mean, and maximum) water temperatures over timescales of years, months, and days. The relationship between water temperatures (TW) and air temperatures (TA) measured at the Grič Observatory and the flows (Q) of the Sava River in Zagreb were investigated. A trend of rising water temperatures was observed throughout the entire period from 1948 to 2020, with the intensity significantly increasing in the recent period starting from 1988. The trend of rising air temperatures is the primary driver of the increase in the water temperatures of the Sava River in Zagreb. However, the impact of reduced water flow, especially during the warm season, should not be overlooked. This effect is amplified by the observed trend of decreasing minimum flows of the Sava during the warm season, from June to September. As a result, the most significant rise in water temperatures of the Sava River in Zagreb occurs during prolonged low-water summer periods, particularly in July. A strong trend has been observed in the increasing number of days per year with mean daily water temperatures of the Sava River exceeding 20 °C. This higher water temperature occurs increasingly earlier in the year, lasts longer, and ends later, often extending into September. [ABSTRACT FROM AUTHOR]

In this study, the Atmospheric Infrared Sounder (AIRS) Observations for Model Intercomparison Projects (Obs4MIPs) V2.1 tropospheric air temperature, specific humidity, and relative humidity data are utilized to evaluate the global tropospheric temperature and humidity simulations in the fully coupled global climate models from the Coupled Model Intercomparison Project phases 3, 5, and 6 (CMIP3, CMIP5, and CMIP6), and possible simulation improvement in CMIP6 models in comparison to CMIP3 and CMIP5 models. Our analyses indicate that all three phases of CMIP models share similar tropospheric air temperature, specific humidity, and relative humidity biases in their multi‐model ensemble means relative to AIRS. Cold biases up to 4 K and positive relative humidity biases up to 20% are found in the free troposphere almost globally with maxima over the mid‐latitude storm tracks. Warm biases up to 2 K are seen over the Southern Ocean in the lower troposphere. Positive specific and relative humidity biases exist over the off‐equatorial oceans while negative specific and relative humidity biases are seen near the equator in the tropical free troposphere, which are related to the double‐intertropical convergence zone bias in the models. Both the air temperature and specific humidity biases are important to the relative humidity biases except in the tropical free troposphere where the specific humidity biases dominate. The tropospheric air temperature, specific humidity, and relative humidity biases are reduced from CMIP3 to CMIP5 and to CMIP6 at almost all pressure levels except at 300 hPa for specific humidity and in the boundary layer for relative humidity. Plain Language Summary: In this study, we use the Atmospheric Infrared Sounder (AIRS) data to assess how well the fully coupled global climate models simulate temperature and humidity in the troposphere. We focus on the climate models from the recent three phases of Coupled Model Intercomparison Project (CMIP3, CMIP5, and CMIP6) and investigate if there is any bias reduction in the recent CMIP6 models compared to the earlier CMIP3 and CMIP5 models. To conduct our evaluation, we compare the CMIP model ensemble averages with the AIRS data and find that all three phases of CMIP models exhibit similar biases in tropospheric temperature and humidity. For example, cold biases up to 4 K and positive relative humidity biases up to 20% are seen in the free troposphere over most regions, with the maximum biases over the midlatitudes. Positive specific and relative humidity biases are found over the off‐equatorial oceans while negative specific and relative humidity biases are seen near the equator in the tropical free troposphere. We also note a possible temperature and humidity bias reduction from the CMIP3 models to the CMIP5 models and then to the CMIP6 models at most pressure levels. Key Points: CMIP Phase 3, 5 and 6 climate models share similar tropospheric air temperature, specific and relative humidity biases relative to Atmospheric Infrared SounderCold biases and positive relative humidity biases are prevalent in free troposphere almost globally with maxima over the mid‐latitude oceansDouble‐intertropical convergence zone bias‐related specific and relative humidity biases are evident in tropical free troposphere: positive (negative) off (near) the equator [ABSTRACT FROM AUTHOR]

Dust storms are one of the important natural hazards that affect the lives of inhabitants all around the world, especially in North Africa and the Middle East. In this study, wind speed, wind direction, and air temperature patterns are investigated in one of the dustiest cities in Sistan Basin, Zahedan City, located in southeast Iran, over a 17-year period (2004–2020) using a WRF model and ground observation data. The city is located near a dust source and is mostly affected by local dust storms. The World Meteorology Organization (WMO) dust-related codes show that the city was affected by local dust, with 52 percent of the total dust events occurring during the period (2004–2021). The city's weather station reported that 17.5% and 43% were the minimum and maximum dusty days, respectively, during 2004–2021. The summer and July were considered the dustiest season and month in the city. Since air temperature, wind speed, and wind direction are important factors in dust rising and propagation, these meteorological factors were simulated using the Weather Research and Forecasting (WRF) model for the Zahedan weather station. The WRF model's output was found to be highly correlated with the station data; however, the WRF simulation mostly overestimated when compared with station data during the study period (2004–2020). The model had a reasonable performance in wind class frequency distribution at the station, demonstrating that 42.6% of the wind was between 0.5 and 2, which is in good agreement with the station data (42% in the range of 0.5–2). So, the WRF model effectively simulated the wind class frequency distribution and the wind direction at Zahedan station, despite overestimating the wind speed as well as minimum, maximum, and average air temperatures during the 17-year period. [ABSTRACT FROM AUTHOR]

Context: There is a gap of knowledge on the effects of fine resolution landcover patterns on the distribution of air temperatures within neighborhoods, as well as on how these effects may differ depending on temporal (i.e., summer and winter, diurnal and nocturnal), and spatial (i.e. extent) scales. Objectives: (1) Evaluate the effects of compositional and configurational fine-scale landcover patterns on the spatial distribution of air temperatures within neighborhoods. (2) Determine differences between winter and summer seasons and diurnal and nocturnal periods. (3) Evaluate if these effects relate to the spatial extent used for the analysis. Methods: Relationships between four landscape metrics and air temperature within four contrasting neighborhoods located in Santiago. Landcover was classified in six classes (built up, barren, grass, evergreen, deciduous, woody) from 1.5m resolution satellite images and temperature acquired from dataloggers located within neighborhoods. Linear mixed models were used for testing the relationships at six spatial extents. Results: Landcover composition and configuration influence temperatures within neighborhoods, but these effects can greatly differ depending on the season, time of the day and extent of analysis. Grass and evergreen trees show the highest effects on neighborhood´s temperatures among the six landcover classes. Grass reduces summer temperatures at smaller extents but may increase temperatures at larger extents. Evergreen trees play a major role during the winter season increasing coldest nocturnal temperatures at all the analyzed extents. These vegetation effects appear to be mostly associated with the average and largest size of their respective patches. Conclusions: Fine-scale landcover patterns play a role in regulating temperatures within neighborhoods, but these effects depend on the season, time of the day and spatial extent. Researchers and decision makers must be aware that results obtained at a given scale cannot be directly translated to another scales. [ABSTRACT FROM AUTHOR]

The aim of this article is to show the most significant factors influencing the indoor environment in winter considering the operating conditions of an older stable modified for housing 12 horses and an indoor riding arena for teaching and sports purposes. This research focused on assessing the influences affecting the internal environment from the point of view of the construction of the building and ventilation control in the operating conditions of working and non-working days. The analysis of the results showed that the massive masonry structure has sufficient thermal insulation and accumulation, which was manifested by good temperature stability inside the stable of 7.2 ± 1.7 °C when the outside air temperature was −4.80 ± 1.5 °C. At low outdoor air temperatures of −6.44 ± 0.4 °C, the following conditions were found: a higher relative air humidity (76.0 ± 5.3%), a high CO2 concentration 2317.1 ± 931.7 ppm, and a high airborne dust concentration PM10 = 231.94 ± 19.13 μg·m−3 and PM2.5 = 160.13 ± 6.28 μg·m−3. Therefore, it is necessary to improve the solution and function of the stable ventilation. The small size of the windows and their uneven distribution (average daylight factor ei from 0.313 ± 0.154 to 0.835 ± 0.309) caused insufficient daylight in some individual boxes. [ABSTRACT FROM AUTHOR]

Thermal Discomfort Index has traditionally relied on parameters such as air temperature and relative humidity, obtained either from meteorological ground stations or through land-physical approaches estimated independently by satellites. These methods often fall short in adequately capturing both seasonal and detailed local spatial variations. This study addresses these limitations by establishing the Surface Thermal Discomfort Index (STDI), a composite of the Meteorological Discomfort Index (MDI) and the Discomfort Index over the land surface (DI-Land). Focused on Ibu Kota Negara Nusantara (IKN) in East Kalimantan and neighboring cities, MDI is derived from reanalysis data (ERA5-Land), validated with ground station data, while DI-Land is produced primarily from Landsat-8. An equal weighting factor was applied to MDI and DI-Land for estimating STDI. Results indicate that STDI captures both seasonal and spatial variations, reaching peak level in May and October, and hitting a low point in July. The spatial distribution of STDI is influenced by landuse types. In 2023, IKN experienced an STDI of 26.2 °C, while Balikpapan and Samarinda recorded at 26.5 and 26.4 °C, respectively. Compared to previous study in Jakarta, IKN and neighboring cities's STDI are higher up to 0.2 °C, remaining within the partially comfortable range in the tropics. Projecting IKN's development until 2045, an annual MDI increase of 0.01 °C is anticipated. Moreover, a 4% rise in built-up areas is expected to elevate STDI by 0.1–0.2 °C. This study provides insights into the thermal discomfort status in cities across East Kalimantan, anticipating a gradual increase in discomfort levels during the development of IKN. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP), the Third Pole of the world, has experienced significant warming over the past several decades. Previous studies have mostly relied on station-observed air temperature (Ta), reanalysis data, and remotely sensed land surface temperature (LST) to analyze the warming trend over the TP. However, the uneven distribution of stations, the poor spatial resolution of reanalysis data, and the differences between LST and Ta may lead to biased warming rates. This paper first maps Ta over the TP from 2001 to 2020 based on multi-source remote sensing data, and then quantifies the spatio-temporal variations of remotely sensed Ta and elevation dependent warming (EDW) of this region. The monthly mean Ta is estimated using machine learning (ML) method year by year, and its accuracy is validated based on station-observed Ta. The coefficient of determination (R2 ranges from 0.97 to 0.98 and the mean absolute error (MAE) ranges from 1.01 to1.04 °C. The remotely sensed Ta is used to analysis warming trend and EDW over the TP. The overall warming trend of the TP during 2001–2020 is 0.17 ℃/10a, and warming mainly distributed in the eastern TP, central TP and western Kunlun Mountains. Among the four seasons, autumn shows the most significant warming, tripling the annual warming rate. Winter shows a significant cooling trend, with the warming rate of -0.18 ℃/10a. The study also reveales the existence of EDW at both the annual and seasonal scales. This paper suggests the potential of remotely sensed Ta in global warming study, and also provides an improved understanding of climate warming over the TP. [ABSTRACT FROM AUTHOR]

High temperatures that prevail at sun-exposed sites such as heaths, depressions, or on slopes, increase the risk of desiccation for rove beetles (Staphylinidae). Therefore, atmospheric and sub-atmospheric humidity determine the microclimates and thus habitat preference and activity of xerothermophilous species. In the nature reserve Marienfl ieß, a heathland in the south of Mecklenburg-Western Pomerania, Northeast German Lowlands, air temperature and humidity were recorded year-round along a transect at 10 sites, i.e. where the vegetation was more dense and more humid than where the vegetation was sparse and it was drier, using data loggers placed in the litter layer that recorded the conditions every 30 min in 2021. Monthly visits were made to determine the biomass of vegetation, soil and litter properties, edaphic rove beetle fauna and total abundance and and that of individual species at 1 m2 sites. Statistical analysis of 365 diurnal cycles per site, with pronounced amplitude in air temperature and humidity, clearly delineated the cooler and more humid sites from warmer and drier ones. Between 10 and 22 of the 30 xerothermophilous species (73.3%), such as, e.g. Quedius persimilis Mulsant & Rey, 1876, Xantholinus gallicus Coiff ait, 1956, Cousya longitarsis (Thomson, 1867) and Euaesthetus superlatus Peyerimhoff, 1937, were recorded only at the cooler and moist sites on the heath. Comparison of the abundances and distribution of species revealed that the xerothermophilous heath-dwelling and desiccation-prone species prefer rather constant environmental conditions, i.e. niches in cool and moist habitats on warm and dry heathland. [ABSTRACT FROM AUTHOR]

Central Chile (CC, latitudes 32–40°S) experienced very active fires in 2017 and 2023. These fires burned large areas and killed many people. These unprecedented fires for CC presented a need for more defined fire weather conditions on the synoptic scale. In this paper, fire weather conditions were analyzed using various satellite-derived fire data (hotspots, HSs), wind streamlines, distribution maps of wind flow and temperature, and various synoptic-scale weather maps. Results showed that local winds, known as Puelche, blew on the peak fire days (26 January 2017 and 3 February 2023). The number of HSs on these days was 2676 and 2746, respectively, about 90 times the average (30). The occurrence of Puelche winds was confirmed by streamlines from high-pressure systems offshore of Argentina to the study area in CC. The formation of strong winds and high-temperature areas associated with Puelche winds were identified on the Earth survey satellite maps. Strong winds of about 38 km h−1 and high temperatures above 32 °C with low relative humidity below 33% were actually observed at the weather station near the fire-prone areas. Lastly, some indications for Puelche winds outbreaks are summarized. This paper's results will be used to prevent future active fire occurrences in the CC. [ABSTRACT FROM AUTHOR]

The world is currently witnessing the military operations of Russia invading Ukraine, leading to missile bombing and shelling on various parts. Although the economic ill effects are more conspicuous and much talked about, the environmental impacts are grimmer and more devastating but ironically are less in the news. Hence, in this work, we focused on the environmental impact of the Russia-Ukraine war by quantifying the long-term (2001 to 2023) and short-term temperature changes using land surface temperature (LST) and air temperature (AT) as proxies and monitoring changes in air quality, mainly methane (CH4), carbon monoxide (CO) and carbon dioxide (CO2), between 2021 and 2022. We used NASA MODIS FIRMS fire points from 24th February 2022 to 08th September 2023 to prepare the heat map for identifying the regions heavily devastated by bombing. Thus, parts of Kiev, Lviv, Luhansk, Odesa, Donetsk, Kherson, etc., in Ukraine were chosen for assessing the LST, AT variations during the peak season of war along with analysis of long-term and short-term changes. We used MODIS Terra LST and Emissivity, ERA 5 AT, CH4, CO2 from AIRS and CO from Sentinel 5P. The results of the LST showed an average increase of around 2.32 °C (2022–2023), 3.44 °C (2021 and 2022) in parts of Ukraine and an increase of about 2 °C from COVID time, whilst a decrease of about 1 °C during COVID. This increase in LST will cause enhanced warming, thus changing the regional climate in a shorter time frame. A consistent upward trend in CH4, CO and CO2 is seen from 2019 to 2023. These heat waves and pollution will grip Ukraine and cause menace due to the cumulative effect of heat waves, changing climate and the aftermaths of war. This would be catastrophic as it might lead to a widespread impact on human health, agricultural yield and infrastructure, to name a few. [ABSTRACT FROM AUTHOR]

To predict current climate changes and their manifestations in the watershed of the Assa-Talas river basin, a research base has been created based on many years of information and analytical materials from RSE «Kazhydromet» and «Kyrgyzhydromet», the World Meteorological Organization (WMO), the reference and information portal «Weather and Climate», allowing to study trends on a spatio-temporal scale. Based on the established research base for the weather stations Susamyr, Talas, Kyzyl-Adyr, Nurlykent, Taraz, Saudakent and Oyyk, located in the catchment areas of the Assa-Talas river basin, graphs of time series of average annual air temperatures and annual precipitation amounts were constructed using classical and modern methods mathematical statistics based on standard application packages of digital technology and equations of their linear trend were obtained, which made it possible to develop a mathematical model of the growth rate of climatic and hydrological indicators, which has a fairly high physical and mathematical meaning, based on the law of nature. An analysis of the growth rate of climatic indicators in the catchment area of the Assa-Talas River basin shows that their quantitative values for all meteorological stations do not coincide, that is, in modern conditions, the growth rate of average annual air temperatures in comparison with the growth rate of annual precipitation is twice as high, which contributes to increasing the shortage of water consumption of natural and cultivated agricultural land up to 25% and reducing the surface hydrological runoff up to 15% in comparison with the middle of the twentieth century, ensuring the water security of the region. [ABSTRACT FROM AUTHOR]

Avian preen gland helps birds cope with their environment, although its overall functioning remains unclear. We shed light on the complexity of the preen gland's functioning by studying how multiple factors associate with gland morphology (size and shape) and physiology (wax secreted) in barn owls Tyto alba. Individual factors (sex, breeding stage, body condition) were more important predictors of preen gland than environmental factors (temperature, humidity, brood size). Sex, depending on breeding stage in adults, influenced preen gland traits, pointing to the preen gland's regulation by sex hormones and a greater pressure on females to protect their eggs, offspring and themselves throughout reproduction. Adults and fledglings in better condition had larger glands, pointing to the existence of physiological costs. Temperature and humidity, in interaction with plumage coloration, also influenced but to a lesser extent preen gland traits, suggesting that melanin pigmentation and preen gland act as superseding mechanisms when protecting plumage against microorganisms. Finally, fledglings living in larger broods had larger glands, suggesting a role for the social environment in preen gland's functioning. Overall, our study supports the idea that preen gland functions in diverse biological contexts within the same species and is thus subject to multiple selective pressures. [ABSTRACT FROM AUTHOR]

The article presents the scientific results of a study assessing agroclimatic resources in agricultural landscapes located in various natural zones of the Turkestan region of the RK (Republic of Kazakhstan). The research methods included classical and modern methods of mathematical statistics using digital technology and time series graphs to develop a mathematical model for climatic and hydrological indicators. Assessment of changes in indicators of agroclimatic resources in agricultural landscapes for 1941-2020 showed that the sum of air temperature, evaporation from the water surface and radiation balance of the daytime surface, characterising the energy resources of landscapes, increased by 10-15%, which contributes to increase in the total water consumption of agricultural land by 10-12%. Meanwhile, the decreasing tendency of the amount of precipitation by 5-10% in all natural climatic zones of the region has become one of the factors leading to a decrease in the natural moisture supply of the soil and vegetation cover of landscapes by 10-15%, acting as important environment-forming and ecological functions. The combined impact of these environment-forming factors has become the key reason for the increase in the deficit of agricultural land water consumption by 15-20%, the reduction of solar energy costs for the soil-forming process by 10-15% and the increase of the climate aridisation, and has become a signal for the need in the safety of agricultural activities, requiring the development of a set of adaptive measures to mitigate this process in the region. [ABSTRACT FROM AUTHOR]

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The guntea loach, Lepidocephalichthys guntea, is categorically common freshwater fish in Southeast Asia. Current study is the first elucidation on the reproductive feature of L. guntea including population structure, sex ratio (SR), size at first maturity (Lm), breeding period, and condition factor, emphasizing on the effect of environmental factors on reproduction of this fish in the Payra River (Southern Bangladesh) during July 2021 to June 2022. Using various conventional gears, 1128 individuals (534 males and 594 females) have been collected. Total length (TL), standard length (SL), and body weight (BW) of each fish were measured. Ovaries were cautiously dissected, removed, and precisely weighed. TL ranges from 4.6 to 9.7 cm (BW = 0.7–9.27 g) for male and 4.6–10.3 cm (BW = 0.8–10.75 g) for female. Both male (47.34%) and female (52.66%) populations were the leading group in 7.00–7.99 cm TL. Overall SR was not notably altered from anticipated value of 1:1 (male:female = 1:1.11). Nonetheless, monthly variations of SR specified females were considerably outnumbered males in each month excluding March–May. Lm range was 6.4–7.0 cm, so larger than Lm is recommended to exploit. Monthly changes in GSI indicated that the main spawning season was from March to June. The spawning season was substantially correlated with rainfall, nonetheless with temperature. Additionally, relative weight indicated that habitat was imbalanced with higher predators. A fishing ban is recommended during peak spawning to protect L. guntea in the Payra River and its surroundings based on current research. [ABSTRACT FROM AUTHOR]