Air Temperature Variations Research Articles

Efficiency Assessment on Roof Geometry and Trombe Wall Shape for Improving Buildings’ Heating Performance

It is crucial to consider structural design issues in Trombe wall (T-wall) buildings to promote more suitable indoor climates and thermal comfort standards. Therefore, the present study examined the impact of two different T-wall designs and six different roof types on the energy and operational efficiency of a building located in a low-temperature and high-humidity winter climate. Ansys-CFX 15.0 software was employed to simulate the thermal and fluid dynamics behavior of the T-wall system, and flow, thermal comfort, energy, and exergy analyses were conducted. Three-dimensional simulation results and the pertinent literature data showed a good level of agreement, and the accuracy of the model was ensured. Outcomes revealed an average air velocity variation of 0.186 m/s and maximum average indoor air temperature variation of 3.3 °C between the six roof geometries. The highest air speed (0.988 m/s) was recorded for the gambrel roof while the lowest one (0.802 m/s) was recorded for the typical flat roof. The shed roof right with a rounded T-wall was more comfortable for standing and sitting activity than the others for the two T-wall shapes, and, at Y = 0.6 m and Y = 1.1 m, the average predicted percentages of dissatisfied (PPD) values were 31 and 28%, respectively. Furthermore, it was determined in the study that solar radiation intensity and T-wall and roof geometries had a significant effect on energy and exergy efficiency, and high energy and exergy efficiencies were achieved at higher solar intensity values. The best energy and exergy efficiencies were obtained for the butterfly and shed roof configurations. This study can serve as a reference for the thermal environment design of buildings with T-walls.

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.

Numerical analysis of the energy efficiency of drying a flooded masonry wall by applying a variable drying air temperature profile

The masonry wall initially saturated with moisture and without internal or external water sources was considered to simulate the drying of a wall after a flood. Numerical calculations using the in-house non-equilibrium heat and moisture model were performed to investigate the impact of variable drying air temperature profiles on the drying efficiency of the thermo-injection method. A drying process lasting twelve days was simulated. Based on previous studies, four drying air temperature variation strategies, i.e., jump, stepwise, periodic, and constant temperature (reference), were computed and compared. The drying air temperature profile was changed from 20°C to 60°C with heating intervals of 24 h and different characterization strategies. The jump strategy changed rapidly in a single step. Stepwise one changed by 10°C after each heating interval, and the period strategy changed from 20°C to 60°C or from 60°C to 20°C after each heating interval. Furthermore, the relative humidity of the drying air corresponded to the three seasons in Poland (i.e., winter, summer, and spring) and ranged between 70% and 90% at ambient conditions. It was found that the proposed drying strategies with variable temperature profiles can reduce energy consumption compared to the reference strategy with a constant temperature.

Indoor Microclimatic Conditions and Air Pollutant Concentrations in the Archaeological Museum of Abdera, Greece

Indoor microclimate conditions and air pollutant concentrations (O3, TVOC, CO, CO2, and particulate matter mass concentrations in six size bins) were measured in the Greek Archaeological Museum of Abdera, which houses priceless works of art from the birthplace of the ancient philosopher Democritus. The monitoring campaign took place during the spring and summer months, when there were the greatest number of visitors. In the exhibition rooms, daily variations in relative humidity ranged from 4% to 10%, and daily variations in air temperature ranged from 0.9 °C to 2.6 °C. These uncontrolled changes may endanger the housed antiquities. The microclimate in the storage rooms varied substantially less than in the exhibition halls due to dehumidifiers and the lack of visitors. Concerning air pollution, indoor O3 concentrations were higher than the recommended limit values for the conservation of artwork. Even more worrisome are particulate matter mass concentrations above the air quality guidelines. Despite the fact that the building is well insulated and that only artificial lighting is used in the exhibition halls, it is difficult to achieve adequate conditions for the protection of the works of art.

Tropospheric Ozone levels in and around southern city Bengaluru, India

The diurnal to seasonal ozone (O3) pre-cursors (NO, NO2, CO, VOCs) trends over local climatic zones (LCZs) with the changes in weather patterns with respect to land cover classes determines the O3 trends and useful for air quality management. In this work, diurnal to seasonal patterns of O3 concentrations are analysed across the different LCZs in Bengaluru region. The Central/State Pollution Control Boards (CPCB/SPCB) operate more than 500 continuous air quality and weather monitoring stations across the India. Hourly O3 concentration data were collected from CPCB for 5 different LCZs within the Bengaluru region over a period of four years (2019-2022). Time series analyses were conducted to examine the temporal variations in O3 levels. The results showed significant fluctuations in diurnal trends of O3 due to the influence of air temperature and relative humidity variations across the five LCZs within Bengaluru region. There were distinct seasonal trends in O3 concentrations with maximum (60-65 ppb) observed during the winter and summer seasons and that of minimum concentration in the range 20-25 ppb during the monsoon months. The observed maximum ozone levels (60 -65 ppb) found to be beyond the threshold national ambient air quality standard (NAAQ) and the accumulation exposure over threshold of 40 ppb (AOT 40) during the winter and summer. The O3 concentration trends are in line with the published research on premature mortality about 1200 annually for Karnataka state for chronic obstructive pulmonary disease owing to population exposure to O3 beyond its threshold level of NAAQ. Also, the annual rice yield loss is about 2% (~0.1 Metric Tonne) with the AOT 40 to paddy crop for Karnataka state. Thus, O3 diurnal to seasonal trends serve the first-hand information to local authorities for the action plan of air quality management taking into consideration O3 precursors, wind speed, and wind direction.

Unveiling Environmental Influences on Sustainable Fertilizer Production through Insect Farming

Entomocomposting is fast and environmentally friendly, boosts soil quality and crop production, and improves resilience to climate change. The black soldier fly larvae (BSFL) catalyze the composting process, but their efficiency is highly influenced by environmental factors and the quality of the substrate. This study employs response surface methodology to discern physical–chemical factors that influence the nutrient quality of BSF frass fertilizer. Internet of Things (IoT) sensors were deployed to monitor in real-time both independent variables (air temperature, moisture content, humidity, and substrate temperature) and dependent variables (nitrogen, phosphorous, and potassium); the data were relayed to the cloud. A non-linear regression model was used to study the relationship between the dependent and independent variables. Results showed that air humidity and air temperature did not have a significant effect on nitrogen and phosphorus accumulation in frass fertilizer, respectively, but phosphorus was significantly influenced by air humidity. On the other hand, neither air temperature nor moisture content has a significant effect on potassium concentration in frass fertilizer. We found that an air temperature of 30 °C and 41.5 °C, substrate temperature of 32.5 °C and 35 °C, moisture content between 70 and 80%, and relative humidity beyond 38% can be conducive for the production of high-quality BSF frass fertilizer. Model validation results showed better robustness of prediction with R2 values of 63–77%, and Radj2 values of 62–76% for nitrogen, phosphorous, and potassium. Our findings highlight the potential for the application of digital tools as a fast and cost-effective decision support system to optimize insect farming for the production of high-quality frass fertilizer for use in sustainable agriculture and crop production.

Interdecadal shift of the North Pacific Oscillation and its nonstationary relationship with East Asian climate

A pronounced shift of the North Pacific Oscillation (NPO), including its teleconnection with the East Asian winter monsoon, has been observed in recent decades, but the underlying mechanism remains unclear. In this study, we found that interannual variation of the NPO and its relationship with the dominant modes of winter mean and extreme temperatures over East Asia (EA) experienced a significant interdecadal shift in the mid-1980s. Before the shift (during 1950–1985), the NPO emerged and was maintained as a strong regional north-south dipole, manifesting a remarkable tropical-extratropical teleconnection. It significantly modulated the second mode of air temperature over EA (depicted by a north-south dipole) and extreme temperature events over southern EA, mainly through horizontal advection and adiabatic heating induced by the anomalous circulation in the southern lobe of the NPO. After the shift (during 1986–2021), the NPO was characterized by an intensified northern lobe and a weakened and northwestward shifted southern lobe, and by a close teleconnection with the Rossby wave trains at the mid-higher latitudes. In the meantime, tropical-extratropical teleconnections became apparently weak. Correspondingly, the NPO was mainly linked to the interannual variation of air temperature and extreme events over northern EA via horizontal advection. Importantly, the decadal shift of the relationships of the NPO with the Arctic Oscillation and El Niño–Southern Oscillation played an apparent role in modulating EA climate.

The performance of solvent-based direct air capture across geospatial and temporal climate regimes

IntroductionLiquid-solvent direct air capture (DAC) is a prominent approach for carbon dioxide removal but knowing where to site these systems is challenging because it requires considering a multitude of interrelated geospatial factors. Two of the most pressing factors are: (1) how should DAC be powered to provide the greatest net removal of CO2 and (2) how does weather impact its performance?.MethodsTo investigate these questions, this study develops a process-level model of a liquid-solvent DAC system and couples it to a 20-year dataset of temperature and humidity conditions at a ~9km resolution across the contiguous US.Results and discussionWe find that the amount of CO2 sequestered could be 30% to 50% greater than the amount of CO2 removed from the atmosphere if natural gas is burned on site to power DAC, but that the optimal way to power DAC is independent of capture rate (i.e., weather), depending solely on the upstream GHG intensity of electricity and natural gas. Regardless of how it is powered, air temperature and humidity conditions can change the performance of DAC by up to ~3x and can also vary substantially across weather years. Across the continuous US, we find that southern states (e.g., Gulf Coast) are preferrable locations for a variety of reasons, including higher and less variable air temperature and relative humidity. Lastly, we also find the performance of liquid-solvent DAC calculated with monthly means is within 2% of the estimated performance calculated with hourly data for more than a third of the country, including in the states with weather most favorable for liquid-solvent DAC.

Climate Variability of Air Temperature and Its Warming Trends in the Arabian Gulf

Over a 41-year period (1979–2019), the climate of the Arabian Gulf (Gulf) exhibited notable variability. This variation, influenced by mesoscale and globalscale meteorological and climatic determinants, showed temperature fluctuations, predominantly in the Northern Gulf (N Gulf). The N Gulf recorded an average temperature of 25.9 °C, a noticeable 3 °C higher than the broader Gulf average. This period marked extreme temperature records, with a peak at 53.8 °C in July 2016, paralleling the highest known temperatures in Asia. Data reveals a warming trend (0.1–0.6 °C per decade) in the region, with the N Gulf’s rise surpassing global trends. The years 2014–2019 were notably warmer, impacting overall trends, and 1997 was the transitional year shifting from cooler to warmer climates. The N Gulf’s seasonal dynamics are also transforming, with extended summers and shortened winters. In this study, we employed empirical orthogonal function (EOF) decomposition analysis to examine the interannual to multidecadal fluctuations in air temperature within the Gulf from 1979 to 2019, using hourly air temperature data from the Watch Forcing Dataset (WFDE5) generated from the fifth generation (ERA5) European Centre for Medium-Range Weather Fore-casts (ECMWF). Through this method, we discerned three principal EOF modes, which together accounted for 82% of the air temperature variability in the Gulf, highlighting external influences such as North Atlantic Oscillation (NAO), El Nino-Southern Oscillation (ENSO), and Atlantic Multidecadal Oscillation (AMO). The data for these external influences were sourced from the National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory. These findings stress the pressing need for detailed climate studies and mitigation strategies for a rapidly warming Gulf region, especially the N Gulf.

Joint influence of the North Atlantic sea surface temperature and the Barents sea-ice concentration on the dipole pattern of Eurasian surface air temperature in March

In recent decades, an increase in the frequency of extreme events in early spring across Eurasia has resulted in significant impacts on crop yields and socio-economic development. However, the variations of March surface air temperature (SAT) over Eurasia and mechanisms are still unclear. This study aims to investigate the dominant mode of the March surface air temperature (SAT) over Eurasia during 1979–2022, and its relationship with sea surface temperature (SST) and sea ice concentration (SIC). The results reveal the prevalence of a dipole mode of the Eurasian SAT anomalies (SATAs) in March characterized by positive SATAs in northern (50°N–80°N) Eurasia and negative ones in southern (10°N–45°N) region. Further analyses demonstrate that this dipole mode of Eurasian SATAs shows significant relationships with a positive phase of the North Atlantic tripole SST anomalies (NAT SSTAs), as well as with the decline of Barents Sea SIC anomalies (SICAs). Associated with the NAT SSTAs and the Barents Sea SICAs during March, two Rossby wave trains propagate towards Eurasia through different ways, one is propagating eastwards from the mid-latitude of the North Atlantic to Eurasia, and another one spreads from the polar region to Eurasia. Modulated by these two wave trains, anomalous near-surface southwesterly (northeasterly) wind induces more (less) warm air towards northern Eurasia, while more (less) cloud emerges in southern Eurasia and causes less (more) solar radiation reaching southern Eurasia, leading to the dipole mode of the Eurasian SATAs in March. These results are further confirmed by numerical experiments from the version 5 of the Community Atmosphere Model forced by the above mentioned SSTAs and SICAs.

Optimizing the characteristic cooling curves of PCM-integrated thermally active buildings: Experimental and numerical investigations

Phase change materials (PCMs) exploit their cyclic phase change (solidification and melting) to enable latent heat storage. While incorporating them in thermally activated building systems can potentially enhance energy savings and thermal comfort, they can introduce significant challenges to the building energy management system. This work first reports the result of small-scale side-by-side experiments on two thermally active cuboids with and without BioPCMs at different supply water and PCM melting temperatures, as well as irradiance levels. Next, a dynamic model is developed in TRNSYS v18.05 for a typical design of an office space under hot desert climate conditions. The model is then integrated into a multi-objective optimization routine using NSGA-II algorithms to identify the optimal characteristic cooling curves, melting temperatures, and PCM thicknesses that simultaneously minimize energy consumption and maximize the percentage of comfortable hours. The experimental observations of the PCM-integrated cuboid highlighted energy savings between 80.45 and 84.56 %, compared to the reference cuboid, at nearly the same levels of indoor temperatures. The PCM panels reduced the cooling capacity of the radiant floor but also dampened air temperature variations by up to 83.17 %. Besides, higher solar irradiance levels, encountered in desert areas, were found to enhance the cooling capacity by up to 46.8 %. The Pareto solutions highlighted the possibility of achieving acceptable unmet hours of 3.16 % using PCMs and without the need for an assisting air system, compared to 14.1 % without PCMs. Besides, at the same unmet hours of the reference system, the PCMs reduced the total energy consumption and the peak cooling power by 9.75 and 41.2 %, which translates into substantial operation and capital investments, respectively. Moreover, the study demonstrated by example the importance of calibrating the commonly used cooling curves of thermally active buildings when planning for PCM integration.

Assessing the Impacts of Climate Change-induced Variations in Air Temperature and Precipitation on Plant Physiological and Soil Microbial Processes with DNDC Model

Abstract The DNDC (DeNitrification-DeComposition) model (version 9.5) was applied to predict the differences in transpiration and photosynthesis rates of perennial grasses (red clover and timothy), and autotrophic respiration of a sandy Spodosol. The input parameters for two growing seasons (from 1st of May to 31st of August in 2010 and 2015) contrasting in meteorological conditions were used in the modeling experiment. In 2010, the mean air temperature of the period was 14.1 ±3.3 °C and the total precipitation – 0.1796 m, while in 2015 the mean air temperature was 16.8 ±5.5 °C and the total precipitation – 0.538 m. These meteorological parameters were unfavorable for plants in 2010 and favorable in 2015. The results have shown that the DNDC model adequately predicted the weather-induced differences in total and mean transpiration rates of perennial grasses: 0.12204 m. and 0.00099 ±0.00040 m.day−1, respectively, under favorable meteorological conditions of 2015 and 0.05969 m. and 0.00049 ±0.00035 m.day−1, respectively, under unfavorable meteorological conditions of 2010. Dynamics of daily transpiration rates of plants was significantly (r = 0.34 p <0.001) correlated with soil water content only under unfavorable meteorological conditions. Mean values of simulated photosynthesis rates were equal to 84.4 ±27.9 kg.C.ha−1.day−1 in 2015 and 52.3 ±23.4 kg.C.ha-1.day−1 in 2010. There were significant differences (p <0.001) in the mean values of photosynthesis rates between the two weather scenarios. The results of one-way analysis of variance (ANOVA) have shown that the rates of autotrophic respiration were significantly (p <0.001) higher under favorable (8.14 ±2.25 kg.C.ha−1.day−1) than under unfavorable (5.17 ±2.19 kg.C.ha−1.day−1) meteorological conditions.

ANALYSIS OF CLIMATE CHANGE EFFECT ON EXTREME PRECIPITATION EVENT IN CONSTANTINE ALGERIA

Extreme precipitation events are critical climatic indicators that offer substantial insights into climate change and its variability. This study aims to investigate whether climate change has impacted precipitation frequencies in the Constantine region of Algeria, known for its semi-arid climate with hot-dry summers and cold-wet winters. Temporal variations of monthly air temperature and precipitation were analyzed from 1981 to 2014. Statistical analysis identified a threshold for estimating monthly extreme precipitation during the period from 1981 to 2005 using the Gumbel distribution method. Additionally, a climatic projection of precipitation for return periods of 50 (t = 50) and 100 (t = 100) years was conducted to forecast extreme precipitation values in the future. The results indicate a gradual increase in precipitation levels during the study period, with values generally remaining below extreme thresholds. This suggests no significant escalation in the occurrence of extreme precipitation events. Furthermore, the findings highlight that climate change has had a more pronounced impact on altering air temperature than on influencing extreme precipitation trends.

Interplay of Environmental Factors, Genetic Susceptibility, and Sleep Disturbances predict Bipolar Disorder’s Relapses: preliminary results from a pilot study

IntroductionPredicting acute affective episodes in individuals with Bipolar Disorder (BD) remains a clinical challenge. Specific environmental stressors, including air pollution, noise, and temperature variations might worsen affective symptoms or sleep in the general population, but their role in BD relapses is often overlooked. Indeed, they might exacerbate BD by perturbing circadian rhythms – fundamental aspects of BD.ObjectivesWe thereby present the protocol of this pilot study and future preliminary data. We aim to longitudinally assess sleep alterations, mood fluctuations, and environmental exposure to several factors (air pollutants, climate, noise, artificial light-at-night, green space access) in patients with BD and to check the association of these variables with BD relapses.MethodsIn this pilot study, we will recruit 40 patients with BD in a 6-month prospective study. Patients were assessed during baseline, at 3 and 6 months. Data recollected will consist of a subjective (questionnaires) and objective (through meteorological stations) evaluation of physical environmental factors around the home residence; clinical assessment of mood and circadian rhythms, and continuous tracking of sleep-wake patterns, energy, and movement using actigraphy.ResultsExpected results will show that exposure to a worse environment (higher pollution, noise, light exposure, climate) will be associated with worse BD outcomes (i.e., relapse, mood symptoms, sleep alterations).ConclusionsWe will be sharing preliminary data from our ongoing study, offering insights into early patterns and findings that shed light on our objectives.Disclosure of InterestNone Declared

Covariability of decadal surface air temperature variability over Myanmar with sea surface temperature based on singular value decomposition analysis

The Myanmar’ Southeast Asian country is currently experiencing environmental changes, with temperature change being one the major contributing factors. Although many studies have shown the contribution of anthropogenic activities, the factors sustaining the observed increase in air temperature (TEMP) are not fully understood. We examined the interdependence of the surface TEMP with the sea surface temperature (SST) from 1971 to 2020 on a decadal timescale to predict changes in TEMP over a longer time period. Our analysis showed a pronounced interdecadal change in TEMP, with the highest intensity observed in the 2010s. The results show that the dominant modes of the global SST significantly influence the TEMP variation in the region at the decadal time scale. Indian Ocean (IO) SST-singular value decomposition (SVD)1 presents significant positive (negative) correlations in the southeast, and central (southwest) related to warming (cooling) TEMP in the east, northern tip, and northwest (south-Yangon and Taninthayi, and some parts of the north). The second and third modes of SST-SVD are cooling (warming) SST over the east of the Bay of Bengal (BoB), and along the Myanmar coast, which are associated with cooling(warming) TEMP patterns in the region. Significant negative correlations of decadal TEMP at the annual scale with Pacific Decadal Oscillation (PDO) and North Pacific (NP), the MAMJ with Atlantic Multidecadal Oscillation (AMO) and November–December–January–February (NDJF) with PDO were evidenced. Meanwhile, significant positive correlations were obtained between TEMP and AMO (NP) at the annual scale (NDJF), respectively. These findings provide valuable insights into decadal-scale TEMP patterns and their relationships with SSTs, contributing to a better understanding of TEMP variability in Myanmar, which may be helpful in climate prediction. Predicting surface TEMP on a decadal timescale is helpful in environmental management.

Projected changes in daily precipitation, temperature and wet‐bulb temperature across Arizona using statistically downscaled CMIP6 climate models

AbstractTo evaluate future changes in the climate system, the outputs from coarse‐resolution global climate models (GCMs) need to be downscaled to a finer scale, making them more directly applicable for impact assessment. Here we focus on examining the projected changes of three key climate variables (precipitation, air temperature, and wet bulb temperature) across Arizona (south‐western United States). We use daily outputs from GCMs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and bias correct and downscale them to a 4‐km resolution. Through leave‐one‐out cross‐validation, we compare various bias correction methods and identify that the empirical quantile mapping approach performs the best regardless of the climate variable. Then, we analyse the projected bias‐corrected outputs for two future periods (Mid‐of‐Century: 2015–2048; End‐of‐Century: 2067–2100) with respect to the 1981–2014 period, under four shared socioeconomic pathway scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). Our results show that Arizona's climate is projected to become overall warmer and wetter, more so towards the end of this century and for higher emission scenarios. Additionally, our findings project an increase in wet bulb temperature and cooling degree days, implying the ongoing warming climate's potential impacts on public health and the economy. These results provide a baseline understanding of climate change impacts in the state and highlight the projected changes in the future in response to the climate scenarios.

Implications of Russia-Ukraine war on land surface temperature and air quality: long-term and short-term analysis.

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.

Regime Shifts in Lake Oxygen and Temperature in the Rapidly Warming High Arctic

AbstractGlobal warming is destabilizing the cryosphere, with consequences for glaciers, permafrost, sea ice and lake ice. Polar lakes have short ice‐free seasons, and small changes in ice cover duration have the potential to provoke alterations to ecosystem structure. However, these lakes are understudied, and the consequences for mixing regimes, thermal structures and biogeochemical processes remain unclear. We measured three annual cycles of dissolved oxygen, temperature and specific conductivity in a lake at ∼83°N to investigate limnological processes and their interannual variability. There were sharp interannual contrasts in lake dynamics, with state shifts in mixing, stratification and oxygen regimes due to air temperature variability and meteorological events. We also observed unusual thermal profiles that were associated with solute gradients. These striking differences underscore the sensitivity of high Arctic lakes to interannual variations in meteorological forcing, and their susceptibility to regime shifts in response to ongoing global change.

Impact of the Eurasian Zonal Circulation on the Interannual Variability of Winter Surface Air Temperature and Subseasonal Temperature Reversal in North China

AbstractThis study focuses on the interannual variability of winter mean surface air temperature (SAT) and subseasonal SAT reversal in North China, which have profound impacts on national life and economic activities. The analysis explores the temporal and spatial distribution characteristics of these variations and their relationship with the Eurasian Zonal Circulation (EZ). The findings reveal distinct interdecadal changes in North China’s winter mean SAT, along with a general subseasonal reversal of cold and warm conditions. In comparison to the North Atlantic oscillation/Arctic Oscillation, the EZ has a more significant influence on the interannual variability of winter and subseasonal reversals. Strong EZ years are associated with the weakening of the Siberian High and the Ural Blocking High, causing the East Asian Trough to move eastward, which leads to the retention of cold air from the north and increased SAT in North China. Conversely, weak EZ years exhibit opposite circulation patterns. The winter mean EZ is influenced by preceding autumn Arctic sea ice concentrations (SIC). Reduced autumn SIC results in decreased heat flux and reduced eddy energy, leading to substantial attenuation of the winter storm track across Scandinavia and Western Asia. This attenuation, mediated through the interaction between eddies and mean flow, triggers a slowdown of Eurasian zonal circulation and intensification of the Siberian High. These conditions favor a positive phase of the winter Scandinavia pattern, facilitating the transport of cold Arctic air into North China. Likewise, subseasonal reversals in SIC influence corresponding alterations in the EZ.

VARIATIONS OF δ18О AND δ2H VALUES OF PRECIPITATION IN MOSCOW FROM 2017 TO 2019

To reveal variations in the iso topic composition of O and H in the atmospheric precipitation in Moscow and the processes influencing the isotope composition, all events of precipitation in 2017-2019 were sampled at the Meteorological Observatory of the Moscow State University: 158 samples in 2017, 119 samples in 2018 and 143 samples in 2019. The study is a prolongation of continuous measurements of the isotope composition of precipitation, started by authors in 2014. The study of the isotope composition of precipitation at the MSU Meteorological Observatory was supported by the IAEA and became a part of the Global Network of Isotopes in Precipitation (GNIP) database. It has been found that the intra-annual variability of the isotop e composition of precipitation has a pronounced seasonality. The most isotopically heavy precipitation falls from May to August, and the most isotop ically light precipitation at December-February, mainly due to seasonal air temperature variations. The ratio of the average monthly δ18O values in precipitation and air temperature for the study period varied from 0.34 to 0.39‰/°C, which is consistent with the previously obtained data for precipitation in Moscow. The δ2H-δ18O ratio in precipitation was clos e to that of the Global Meteoric Water Line, pointing to the equilibrium conditions during precipitation formation. It was established that in the summer months isotopic composition is significantly influenced by undercloud evaporation. The deuterium excess va lues in precipitation are not markedly seasonal; however, lower dexc values (below the 3-year average of 11‰) are typical for the summer months (July-August). It is most likely due to undercloud evaporation in conditions of low relative humidity and high air temperatures. Higher dexc values (above 11‰) prevailed from October to April.