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The giant impact between proto-Earth and a Mars-sized planet called Theia resulted in the formation of the Earth–Moon system, and the silicate mantles of the initial bodies may have partly been vaporized. Here, we develop a machine learning potential for MgSiO3 based on the data from first-principles calculations to estimate its critical point. The variations in pressure along different isotherms yield the position of the critical point of MgSiO3 at 0.54 g cm−3 and 6750 ± 250 K, which agrees with the previous theoretical estimation. We also simulate the MgSiO3 melt under a spectrum of critical conditions to understand the changes in coordination environment with density and temperature. The fourfold Si–O coordination hardly changes with increasing density at 3000 K. However, with increasing temperature, the dominance of four-coordinated Si–O diminishes rapidly as density decreases. Regarding Mg–O coordination, the overall trend, which varies with temperature and density, remains largely consistent with Si–O but with a greater diversity in the types of coordination due to more bond breaking events. Our work opens a new avenue by employing machine learning methods to estimate the critical point of silicates. [ABSTRACT FROM AUTHOR]

Surface air temperature (t2m) data are essential for understanding climate dynamics and assessing the impacts of climate change. Reanalysis products, which combine observations with retrospective short‐range weather forecasts, can provide consistent and comprehensive datasets. ERA5 represents the state‐of‐the‐art in global reanalyses and supplies initial and boundary conditions for higher‐resolution regional reanalyses designed to capture finer‐scale atmospheric processes. However, these products require validation, especially in complex terrains like Italy. This study analyses the capability of different reanalysis products to reproduce t2m fields over Italy during the 1991–2020 period. The analyses encompass ERA5, ERA5‐Land, the MEteorological Reanalysis Italian DAtaset (MERIDA), the Copernicus European Regional ReAnalysis (CERRA), and the Very High‐Resolution dynamical downscaling of ERA5 REAnalysis over ITaly (VHR‐REA_IT). The validation we conduct pertains to both the spatial distribution of 30‐year seasonal and annual normal values and the daily anomaly records. Each reanalysis is compared with observations projected onto its respective grid positions and elevations, overcoming any model bias resulting from an inaccurate representation of the real topography. Key findings reveal that normal values in reanalyses closely match observational values, with deviations typically below 1°C. However, in the Alps, winter cold biases sometimes exceed 3°C and show a relation with the elevation. Similar deviations occur in the Apennines, Sicily, and Sardinia. Conversely, VHR‐REA_IT shows a warm bias in the Po Valley up to 3°C in summer. Daily anomalies generally exhibit lower errors, with MERIDA showing the highest accuracy and correlation with observational fields. Moreover, when aggregating daily anomalies to annual time scales, the errors in the anomaly records rapidly decrease to <0.5°C. The results of this study empower reanalysis users across multiple sectors to gain a more profound insight into the capabilities and constraints of different reanalysis products. The knowledge and the characterization of the reanalyses t2m bias against observations can indeed be crucial when incorporating these products into their research and practical applications. [ABSTRACT FROM AUTHOR]

The out‐of‐phase mode of winter surface air temperature anomalies (SATAs) over northern Central Asia (NCA; 45°–65°N, 40°–100°E) between January and February is investigated in this study. This mode corresponds to warm (cold) SATAs in January (February) over NCA and is mainly modulated by the enhanced tropical convection anomalies over the Maritime Continent in previous late December, associated with MJO phase 4. These tropical convection anomalies can induce anomalous tropospheric Rossby‐wave sources over the North Pacific in late December. The eastward‐propagating Rossby‐wave train, triggered by these anomalous Rossby‐wave sources, can cause negative and positive tropospheric geopotential height anomalies over the Greenland–Scandinavia region and NCA in the following early–mid‐January, subsequently leading to warm SATAs over NCA in January. The negative geopotential height anomalies over the Greenland–Scandinavia region in early–mid‐January can trigger upward‐propagating wave activity fluxes (WAFs) into the stratosphere, resulting in negative stratospheric geopotential height anomalies in late January–early February. These stratospheric anomalies, by triggering downward‐propagating WAFs, can in turn lead to positive tropospheric geopotential height anomalies over the Greenland–Scandinavia region in early February. These anomalies over the Greenland–Scandinavia region can maintain themselves in the following mid‐ and late February by feedback of anomalous storm tracks, and cause negative geopotential height anomalies and subsequently cold SATAs over NCA in February by triggered southeastward‐propagating Rossby‐wave train. [ABSTRACT FROM AUTHOR]

This research explores the feasibility of using date seeds (DS), an agricultural waste, for the adsorption of malachite green (MG) dye from synthesized wastewater. The characterization of the DS before and after adsorption was accomplished by FTIR, SEM, BET, and EDX measurements. Batch adsorption experiments were investigated for MG dye adsorption from aqueous solution onto the DS. The effect of different parameters such as solution pH, adsorbent dose, contact time, temperature, and the initial dye concentration were studied. The optimum pH, adsorbent dose, temperature, and contact time for the dye removal were found to be 5, 0.1 g, 25 °C, and 30 min, respectively. The equilibrium studies for the data with Langmuir, Freundlich, and Temkin isotherms showed that Freundlich isotherm is the best model to describe the adsorption of MG onto the DS particles which has a heterogeneous surface. It was found that the adsorption process follows a pseudo-second-order kinetic model which revealed that the intra-particle diffusion stage is the rate-controlling stage for the process. The thermodynamic parameters ΔG, ΔS, and ΔH suggest the possibility of chemisorption and physisorption simultaneously and indicate the exothermic and spontaneous characters of the adsorption of MG dye on DS with negative values of ΔH and ΔG. This study used agriculture waste (date seeds) which is proved to be an environmentally friendly and low-cost adsorbent. The date seeds were shown to be a promising adsorbent, demonstrating high surface area and well-developed porosity. The prepared adsorbent will have a great impact on wastewater treatment technology and possible applications at a large scale. Thus, widespread and great progress in this area can be expected in the future. [ABSTRACT FROM AUTHOR]

Diagnosing the role of internal variability over recent decades is critically important for both model validation and projections of future warming. Recent research suggests that for 1980–2022 internal variability manifested as Global Cooling and Arctic Warming (i‐GCAW), leading to enhanced Arctic Amplification (AA), and suppressed global warming over this period. Here we show that such an i‐GCAW is rare in CMIP6 large ensembles, but simulations that do produce similar i‐GCAW exhibit a unique and robust internally driven global surface air temperature (SAT) trend pattern. This unique SAT trend pattern features enhanced warming in the Barents and Kara Sea and cooling in the Tropical Eastern Pacific and Southern Ocean. Given that these features are imprinted in the observed record over recent decades, this work suggests that internal variability makes a crucial contribution to the discrepancy between observations and model‐simulated forced SAT trend patterns. Plain Language Summary: When comparing model simulations of Earth's recent warming to real‐world observations, differences may arise from several factors. Two important factors are the model errors in the simulated response to increased greenhouse gases, and natural fluctuations within the climate system that produced discrepancies between observations and models. Thus, quantifying the role of these natural fluctuations is important for the assessment of model‐observation differences. Previous studies have shown that natural climate variability has depressed global warming and enhanced Arctic warming. By compositing the multi‐decadal trend patterns from CMIP6 simulations in which natural variability warms the Arctic but has a global cooling effect, we find that the majority of these model simulations also produce enhanced warming in the Barents and Kara Seas and cooling in the Tropical Eastern Pacific and Southern Ocean due to natural variability. Since these are the exact features imprinted on observed surface temperature changes over 1980–2022, our work suggests that natural variability is an important component of several noteworthy differences between models and observations. Key Points: Internal variability has enhanced Arctic warming but suppressed global warming over 1980–2022This manifestation of internal variability is rare in model simulations but has a robust global surface air temperature (SAT) trend patternThis internal SAT pattern features warming in the Barents and Kara Sea and cooling of the Tropical Eastern Pacific and Southern Ocean [ABSTRACT FROM AUTHOR]

The emergence of the JWST and the development of other advanced observatories (e.g. ELTs, LIFE, and HWO) marks a pivotal moment in the quest to characterize the atmospheres of Earth-like exoplanets. Motivated by these advancements, we conduct theoretical explorations of exoplanetary atmospheres, focusing on refining our understanding of planetary climate and habitability. Our study investigates the impact of ozone on the atmosphere of Proxima Centauri b in a synchronous orbit, utilizing coupled climate chemistry model simulations and dynamical systems theory. The latter quantifies compound dynamical metrics in phase space through the inverse of co-persistence (θ) and co-dimension (d), of which low values correspond to stable atmospheric states. Initially, we scrutinized the influence of ozone on temperature and wind speed. Including interactive ozone [i.e. coupled atmospheric (photo)chemistry] reduces the hemispheric difference in temperature from 68 °K to 64 °K, increases (∼+7 °K) atmospheric temperature at an altitude range of ∼20–50 km, and increases variability in the compound dynamics of temperature and wind speed. Moreover, with interactive ozone, wind speed during highly temporally stable states is weaker than for unstable ones, and ozone transport to the nightside gyres during unstable states is enhanced compared to stable ones (∼+800 DU). We conclude that including interactive ozone significantly influences Earth-like exoplanets' chemistry and climate dynamics. This study establishes a novel pathway for comprehending the influence of photochemical species on the climate dynamics of potentially habitable Earth-like exoplanets. We envisage an extension of this framework to other exoplanets. [ABSTRACT FROM AUTHOR]

Here, we introduce the concept of "outdoor days" to describe how climate change can affect quality of life for different communities and individuals. An outdoor day is characterized by moderate temperature, neither too cold nor too hot, allowing most people to enjoy outdoor activities. The number of "outdoor days" is a nonlinear function of the daily surface air temperature. If the latter falls within a specific range describing assumed thermal comfort conditions, then we assign that day as an "outdoor day." Using this function, we describe climate change impacts on temperature differently, compared to other studies which often describe these impacts in terms of the linear averaging of daily surface air temperature. The introduction of this new concept offers another way for communicating how climate change may impact the quality of life for individuals who usually plan their outdoor activities based on how local weather conditions compare to their preferred levels of thermal comfort. Based on our analysis of regional variations in "outdoor days," we present observational and modeling evidence of a north–south disparity in climate change impacts. Under high-emission scenarios, CMIP5 and CMIP6 models project fewer "outdoor days" for people living in developing countries, primarily located in low-latitude regions. Meanwhile, developed countries in mid- and high-latitude regions could gain more "outdoor days," redistributed across seasons. Significance Statement: We introduce a novel concept: outdoor days, characterizing surface air temperature conditions that allow for outdoor activities, such as walking, jogging, and cycling, by most people. We project that under high-emission scenarios of anthropogenic greenhouse gases, a north–south disparity of climate change risk will be enhanced considerably toward the end of this century due to more frequent outdoor days in the wealthy Global North and less frequent outdoor days in the deprived Global South. [ABSTRACT FROM AUTHOR]

Background: Many studies reported associations between long-term exposure to environmental factors and mortality; however, little is known on the combined effects of these factors and health. We aimed to evaluate the association between external exposome and all-cause mortality in large administrative and traditional adult cohorts in Europe. Methods: Data from six administrative cohorts (Catalonia, Greece, Rome, Sweden, Switzerland and the Netherlands, totaling 27,913,545 subjects) and three traditional adult cohorts (CEANS-Sweden, EPIC-NL-the Netherlands, KORA-Germany, totaling 57,653 participants) were included. Multiple exposures were assigned at the residential addresses, and were divided into three a priori defined domains: (1) air pollution [fine particulate matter (PM2.5), nitrogen dioxide (NO2), black carbon (BC) and warm-season Ozone (warm-O3)]; (2) land/built environment (Normalized Difference Vegetation Index--NDVI, impervious surfaces, and distance to water); (3) air temperature (cold- and warm-season mean and standard deviation). Each domain was synthesized through Principal Component Analysis (PCA), with the aim of explaining at least 80% of its variability. Cox proportional-hazards regression models were applied and the total risk of the external exposome was estimated through the Cumulative Risk Index (CRI). The estimates were adjusted for individual- and area-level covariates. Results: More than 205 million person-years at risk and more than 3.2 million deaths were analyzed. In single-component models, IQR increases of the first principal component of the air pollution domain were associated with higher mortality [HRs ranging from 1.011 (95% CI: 1.005-1.018) for the Rome cohort to 1.076 (1.071-1.081) for the Swedish cohort]. In contrast, lower levels of the first principal component of the land/built environment domain, pointing to reduced vegetation and higher percentage of impervious surfaces, were associated with higher risks. Finally, the CRI of external exposome increased mortality for almost all cohorts. The associations found in the traditional adult cohorts were generally consistent with the results from the administrative ones, albeit without reaching statistical significance. Discussion: Various components of the external exposome, analyzed individually or in combination, were associated with increased mortality across European cohorts. This sets the stage for future research on the connections between various exposure patterns and human health, aiding in the planning of healthier cities. [ABSTRACT FROM AUTHOR]

We report on the piezoresistive method for detecting stability loss events in microelectromechanical sensors based on bifurcation. The method involves measuring the resistivity changes of an entire beam to detect snap-through transitions in an electrostatically actuated, bistable double-clamped crystalline silicon (Si) microbeam. The applicability of the suggested approach in two types of sensors—an ambient air temperature sensor or a mean air velocity sensor, is demonstrated. In both cases, the bistable beam, serving as the sensing element, is affected by the electrothermal Joule's heating and air cooling. The measured signal is obtained by monitoring the critical voltages of the snap-through transitions. Piezoresistive sensing is especially suitable for the response monitoring of the exposed to the environment, free-standing heated microbeam sensors, where optical, piezoelectric, or electrostatic interrogation methods are not applicable. The approach can be implemented in various bifurcation microsensors and for response monitoring of bistable actuators. [ABSTRACT FROM AUTHOR]

This document is a detailed description of a study on the oxidative chlorination of alkanes using visible light and HCl as the chlorine reagent. The researchers found that simple alkanes cannot be chlorinated effectively under the same reaction conditions, but functionalized alkanes with electron-withdrawing groups can undergo chlorination. The study includes experimental procedures, results, and characterization of the compounds using NMR analysis. The document also acknowledges the funding source and provides additional supporting information. [Extracted from the article]

Climate change is reshaping coastal wetlands worldwide, driving ecosystem shifts like mangrove poleward expansion into saltmarshes in tropical‐temperate transitional zones. Though warming is recognized as the primary driver, a lack of detailed field studies limits our ability to predict mangrove responses to rapid climate warming.Here, we characterized how mangroves vary across a temperature gradient at 18 sites along Florida's Gulf of Mexico coast (USA). We used minimum air temperature (Tmin) derived from daily data from 1989 to 2021 as the independent variable and applied plot‐based and synoptic approaches to quantify species‐specific mangrove variation at community, population, and individual levels. We then used these results to spatially project future mangrove ecosystem properties under multiple warming scenarios.Across the Tmin gradient from −10.8 to −1.4°C, mangrove canopy height and coverage ranged from 0.4 to 11.5 m and 15% to 98%, respectively, with both exhibiting sigmoidal increases with Tmin. Estimated mangrove aboveground biomass ranged from 0 to 496.7 Mg/ha and showed a positive linear relationship with Tmin due both to the tall tree stratum's increased biomass per tree and higher abundance.While the population abundance and coverage of Rhizophora mangle and Laguncularia racemosa had positive linear relationships with Tmin, Avicennia germinans exhibited a significant quadratic relationship, reflecting the higher freeze tolerance of this species. Such tolerance may stem from A. germinans' higher morphological plasticity observed at the individual level, adapting to cold stress by exhibiting a more shrub‐like architecture at colder sites.Based on these field‐derived quantitative relationships, we projected substantial increases in mangrove coverage and canopy height near current range limits, with tall A. germinans dominating in the north and R. mangle dominating the centre and south of the study region.Synthesis. To better predict the ecological consequences in coastal wetlands under future climate change, it is essential to understand how mangroves respond to winter temperature regimes across a temperature gradient. Collectively, these cross‐level and species‐specific results advance our understanding of mangrove temperature sensitivity and provide information about the future of coastal wetland structure and function in response to a changing climate. [ABSTRACT FROM AUTHOR]

Temperature influences nearly all aspects of fitness. However, reproduction is often more thermally sensitive than survival. Thermoregulation must maintain performance in both components of fitness to buffer populations from environmental change.We assessed the fitness benefits of thermoregulation in Enchenopa binotata treehoppers. Under realistic mesocosm conditions, we quantified fine‐scale microclimates using 3D‐printed operative temperature models. We then compared operative temperatures to treehopper body temperatures and translated patterns of thermoregulation into variation in survival and reproduction. We also assessed two thermoregulatory mechanisms: precise microclimate choice and heat‐escape behaviours. Finally, we applied our results to evaluate if arthropod thermoregulation is accurately characterized by two theoretical models commonly used to simulate responses to environmental change.We found substantial thermal variation at fine spatial scales relevant to insects: at a single point in time, temperatures within 30‐cm‐tall plants spanned ranges up to 19°C (23–42°C).Lethal operative temperatures were common when air temperatures were high. However, heat escapes allowed treehoppers to almost entirely avoid lethal temperatures.By contrast, individuals thermoconformed in the absence of lethal operative temperatures. This finding suggests that precise microclimate choice imposes high costs due to thermal uncertainty at fine spatial scales. Furthermore, given the narrow range of temperatures in which reproduction occurs, thermoregulation is unlikely to maintain reproduction.Thermoregulation was most effective in the lowest‐quality and most spatially variable thermal habitats. Treehopper thermoregulation therefore more closely follows cost–benefit models of thermoregulation compared to models that account for inhibited movement at extreme temperatures.Overall, even if thermoregulation can prevent lethal heat stress, it may have limited capacity to buffer arthropods and other small ectotherms from environmental change if it cannot maintain reproductive performance. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Cooling systems provide a safe thermal environment for the reliable operation of IT equipment in data centers (DCs) while generating significant energy consumption. Therefore, to achieve energy savings in cooling system control under dynamic thermal distribution in DCs, this paper proposes a multi-setpoint cooling control approach based on deep reinforcement learning (DRL). Firstly, a thermal model based on the XGBoost algorithm is constructed to precisely evaluate the thermal distribution in the rack room to guide real-time cooling control. Secondly, a multi-set point cooling control approach based on the deep Q-network algorithm (DQN-MSP) is designed to finely regulate the supply air temperature of each air conditioner by capturing the thermal fluctuations to ensure the dynamic balance of cooling supply and demand. Finally, we adopt the extended CloudSimPy simulation tool and the real workload trace of the PlanetLab system to evaluate the effectiveness and performance of the proposed approach. The simulation results show that the proposed control solution effectively reduces the cooling energy consumption by over 2.4% by raising the average air supply temperature of the air conditioner while satisfying the thermal constraints. [ABSTRACT FROM AUTHOR]

Reanalysis datasets provide a continuous picture of the past climate for every point on Earth. They are especially useful in areas with few direct observations, such as Siberia. However, to ensure these datasets are sufficiently accurate they need to be validated against readings from meteorological stations. Here, we analyse how values of six climate variables—the minimum, mean and maximum 2‐metre air temperature, snow depth (SD), total precipitation and wind speed (WSP)—from three reanalysis datasets—ERA‐Interim, ERA5 and ERA5‐Land—compare against observations from 29 meteorological stations across Siberia and the Russian Far East on a daily timescale from 1979 to 2019. All three reanalyses produce values of the mean and maximum daily 2‐metre air temperature that are close to those observed, with the average absolute bias not exceeding 1.54°C. However, care should be taken for the minimum 2‐metre air temperature during the summer months—there are nine stations where correlation values are <0.60 due to inadequate night‐time cooling. The reanalysis values of SD are generally close to those observed after 1992, especially ERA5, when data from some of the meteorological stations began to be assimilated, but the reanalysis SD should be used with caution (if at all) before 1992 as the lack of assimilation leads to large overestimations. For low daily precipitation values the reanalyses provide good approximations, however they struggle to attain the extreme high values. Similarly, for the 10‐metre WSP; the reanalyses perform well with speeds up to 2.5 ms−1 but struggle with those above 5.0 ms−1. For these variables, we recommend using ERA5 over ERA‐Interim and ERA5‐Land in future research. ERA5 shows minor improvements over ERA‐Interim, and, despite an increased spatial resolution, there is no advantage to using ERA5‐Land. [ABSTRACT FROM AUTHOR]

Increased Arctic air temperatures and evaporative fluxes have coincided with more frequent and destructive high‐latitude wildfires. Arctic fires impact ecosystems and people, especially at the community‐level by degrading air quality, destroying agriculture, and threatening life and property. Central Eastern Interior (CEI) Alaska is one such region that has recently experienced the effects of wildfire activity related to warming air temperatures. To improve our ability to identify fire weather events and assess their potential for extreme outbreaks at actionable lead times relevant to fire weather forecasters and managers, new metrics and approaches need to be established and applied toward understanding the physical mechanisms underlying such wildland fire characteristics. Our study uses a new, regional atmospheric circulation metric, the Alaska Blocking Index (ABI), to describe midtropospheric air pressure around Alaska, which is subsequently related to CEI fire weather conditions at the Predictive Service Area (PSA) scale in climatological and extreme events frameworks. Of note, during years of high fire activity, Build‐Up Index (BUI) values tend to be anomalously high during the duff and drought phases across the CEI PSAs, though comparatively lower BUI values are still associated with high fire activity in the Tanana Zone‐South (AK03S) PSA. Likewise, extreme BUI values are strongly tied to high ABI values and well‐defined upper‐air ridging circulation patterns in the duff and drought periods. The statistical skill of mean daily ABI values in the 6–10 day period preceding extreme duff period BUI values is modest (τ2 > 14%) in the Upper Yukon Valley (AK02) PSA, a hotbed of wildland fire activity. Extremes in ABI and CEI BUI often occur in tandem, yielding regional predictability of upper‐air weather patterns and extremes and underlying surface weather conditions, by statistical and/or dynamical forecast models, imperative for local community and governmental organizations to effectively manage and allocate Alaska's fire weather resources. [ABSTRACT FROM AUTHOR]

Raw sago hampas (RSH) was grafted with oxygen-containing functional group through a functionalization process to form esterified sago hampas (ESH) for U(VI) adsorption and evaluated using adsorption kinetics, isotherm, and thermodynamics studies. Adsorption kinetics studies show that both adsorbents follow pseudo-second-order model while isotherm studies conclude that both adsorption processes fitted Sips isotherm model. Thermodynamic studies indicate both adsorption processes exhibited by the RSH and ESH were endothermic but spontaneous and non-spontaneous respectively. Strongs acids are tested to be the best desorption agents to release U(VI) from surfaces' of RSH and ESH. [ABSTRACT FROM AUTHOR]

The present study deals with the observation of Rn-222 emanation process in terms of its exhalation rate for 48 h from sub-surface soil at 3 h intervals, using a continuous Rn-222 monitoring system RAD7 at Sidho-Kanho-Birsha University campus in Purulia district, located in the easternmost part of the Chotanagpur Plateau, India based on local weather conditions, over five different seasons. For every season, the diurnal variations of the Rn-222 exhalation rate increase after sunset, reaching to its maximum at mid-night and thereafter starts decreasing until it falls to the minimum around noon time. Local meteorological parameters such as ambient temperature, relative humidity, atmospheric pressure were also recorded during the observation period to study the possible influences of these parameters on the soil gas Rn-222 emission rates using Pearson's correlation matrix. There exists a strong positive correlation between relative humidity and Rn-222 exhalation rate and a positive correlation between atmospheric pressure and Rn-222 exhalation rate for all the observation periods. On the other hand, negative correlations between the atmospheric temperature and the Rn-222 exhalation rate exist for all the seasons. [ABSTRACT FROM AUTHOR]

In the present study, the tropical tropopause inversion layer (TIL) Kelvin waves are extracted from the Global Navigation Satellite System (GNSS) radio occultation (RO) temperature data of multiple missions from January 2007 to December 2020. We focus on the variations of TIL Kelvin waves in two longitude regions, the Maritime Continent (MC; 90°–150°E) and the Pacific Ocean (PO; 170°–230°E). The results show that over both regions, ENSO leads to the opposite variations of TIL Kelvin wave temperature amplitude during different ENSO phases. Specifically, during La Niña, the strong (weak) deep convection over MC (PO) leads to strengthened (weakened) static stability. With enhanced easterly (westerly) winds and strengthened (weakened) static stability, the TIL Kelvin wave temperature amplitudes are stronger (weaker) over MC (PO). The opposite phenomenon occurs during El Niño. The zonal-mean zonal winds affect TIL Kelvin wave temperature amplitudes by two mechanisms. First, the prevalence of easterlies (westerlies) in the upper troposphere affects the upward propagation of Kelvin waves, resulting in stronger (weaker) TIL Kelvin wave temperature amplitudes over MC (PO). Second, the TIL Kelvin wave temperature amplitude peaks about 2 months before the zero-wind line of the descending westerly QBO phase occurs, due to dissipation on the critical line. Additionally, the rapid increase of zonal-mean static stability significantly affects the annual variation of TIL Kelvin wave temperature amplitudes. They both reach maxima during DJF and minima during JJA, which should be related to the annual cycles of temperature and ozone mixing ratio in the TIL. Significance Statement: Recent studies indicate that the Kelvin wave temperature amplitudes in the tropical tropopause inversion layer (TIL) exhibit distinct characteristics compared with those in other height levels, while the modulation mechanisms of the TIL Kelvin waves need further investigation. The present study aims to study the differences in the variabilities and the modulation factors of TIL Kelvin waves over two longitude regions. Our findings suggest that the different responses of background conditions during ENSO phases influence the spatiotemporal distribution of the TIL Kelvin waves. Besides, the zonal winds and the static stability significantly affect the temporal variations of TIL Kelvin waves. Our work fills the research gap of TIL Kelvin waves and contributes to understanding the dynamics of tropical tropopause variations. [ABSTRACT FROM AUTHOR]

This study investigates monthly streamflow modeling at Kale and Durucasu stations in the Black Sea Region of Turkey using remote sensing data. The analysis incorporates key meteorological variables, including air temperature, relative humidity, soil wetness, wind speed, and precipitation. The study also investigates the accuracy of multivariate adaptive regression (MARS) with Kmeans clustering (MARS-Kmeans) by comparing it with single MARS, M5 model tree (M5Tree), random forest regression (RF), multilayer perceptron neural network (MLP). In the first modeling stage, principal component regression is applied to diverse input combinations, both with and without lagged streamflow (Q), resulting in twenty-three and twenty input combinations, respectively. Results demonstrate the critical role of including lagged Q for improved model accuracy, as models without lagged Q exhibit significant performance degradation. The second stage involves a comparative analysis of the MARS-Kmeans model with other machine-learning models, utilizing the best-input combination. MARS-Kmeans, incorporating three clusters, consistently outperforms other models, showcasing superior accuracy in predicting monthly streamflow. [ABSTRACT FROM AUTHOR]

Aims: Quantitative characterization of the time-lag effect between canopy temperature and atmospheric temperature and its controlling factors in the agricultural ecosystem may contribute to a higher inversion accuracy of soil water content using canopy-air temperature information. Methods: Tc of winter wheat were continuously monitored, and the data of such environmental factors as solar radiation (Rs), atmospheric temperature (Ta), relative humidity (RH) and soil water content (SWC) were simultaneously collected. Results: Hysteresis existed between Tc and Ta over the diel cycles, and different weather and irrigation levels did not change the direction of the time lag loop. the key driver regulating the diel hysteresis pattern between Tc and Ta varied under different weather: on rainy days, key driver was Rs while on cloudy and sunny days, the key driver was RH. the multiple regression model indicated that together Rs, Ta, RH, and SWC explained 58 ± 10% of the variation of time-lag effect. Path analysis showed on rainy days the key driver (Rs and RH) could enhance the time-lag effect through other indirect factors (Ta and SWC); on cloud days the key driver (RH and SWC) could inhibit the time-lag effect through other indirect factors (Ta); On sunny days this mutual inhibition was further significant. Conclusions: These findings indicated a dynamic process of time-lag effect between Tc and Ta with different weather and different irrigation levels. This study contributes to the understanding of the time-lag effect and its driving factors and this analysis provides the basis for further improvement in monitoring crop water deficit. [ABSTRACT FROM AUTHOR]

This paper compares the performance of a vertical-finned microchannel heat exchanger (VMHX) and a 2-row fin-tube heat exchanger (FTHX) under wet and frosting conditions. The VMHX features vertically oriented fins with an extension at the windward side and parallel microchannel tubes, allowing for efficient water drainage and better performance than conventional microchannel heat exchangers with parallel serpentine fins. However, the performance of the VMHX has not been sufficiently compared to that of FTHX. After conducting experiments under specific operating conditions, the VMHX exhibits better water drainage performance than the FTHX at higher inlet air velocities, higher maximum heat flow, and time-integrating heat flow under certain operating conditions. Although the heating time is relatively shorter than the 2-row FTHX, these results suggest that the VMHX may be a more suitable option for applications requiring efficient water drainage and heat transfer. Additionally, the operating conditions significantly affect the heat exchanger's performance. The performance of VMHX is analyzed under different inlet air velocities, inlet air temperatures, and evaporation temperatures. The results indicate that frost itself had no effect on the heat exchanger performance and that the decrease of the inlet air flow rate was the main factor that decreased the heat exchanger performance. Overall, the results of this study provide valuable insights into the performance of VMHX and FTHX under wet and frosting conditions and the impact of various operating parameters on VMHX performance. These findings can inform the development of more efficient and reliable heat exchangers for various applications. [ABSTRACT FROM AUTHOR]

We conducted a comprehensive study on the diversity of Silene aucheriana Boiss. populations by analyzing both morphological data and nrDNA ITS sequences. Maximum parsimony and Bayesian methods on representative material from 15 populations throughout Iran demonstrated that they did not form a monophyletic group. Strong positive correlation occurred between epipetalous filaments length to claw (EFLC), basal leaf length (BLL), basal leaf width (BLW), and plant height (PLH) with elevation. While negative correlation achieved between anthophore length (AnL) with the average air temperature and rainfall. Canonical Correspondence Analysis (CCA) showed that most morphological traits were more correlated to elevation than to average rainfall and temperature. Multivariate analysis of morphological traits with ITS analysis displayed a slight divergence between two types of regions based on their elevation. [ABSTRACT FROM AUTHOR]

Based on our primary database of the flower opening time (FOT) and flower exposure duration (FED) of 1114 rice (Oryza sativa ssp. indica) landraces, we examined the influences of growing season, sunrise time as well as day maximum and minimum temperatures on the anthesis behaviour of indica rice landraces of South and Southeast Asia, flowering in summer and winter in 3 consecutive years (2020–2022). We also compared the FOT and FED on sunny and cloudy days of a small set of landraces, and also during summer and winter. Our data show that rice florets tend to open later in the morning and lengthen the sunrise‐to‐anthesis duration (SAD) on hotter sunny days during tropical summer than during winter and on cloudy days. These findings contradict the widely held conjecture, based on studies conducted at colder latitudes, that rice flowers open earlier in the morning to avoid heat stress. We propose that indica rice landraces are sufficiently adapted to tropical summer because they were selected and bred over millennia to withstand heat stress during tropical summer, so their FOT and SAD are weakly influenced by high day temperatures. However, the significant reduction in FED of these landraces, whose flowers open later in mid‐day, seems to be an adaptive mechanism to avoid longer exposure to rising air temperature approaching day maximum temperature. [ABSTRACT FROM AUTHOR]

Water temperature responses to climate change may vary across Earth's estuaries. To understand how climate change influences estuarine surface water temperature, we need global, long‐term records of estuarine temperature. Here, we generated surface water temperature data over 1060 estuaries globally using Landsat 5, 7, and 8 from 1985 to 2022 and compared water warming rates with local air temperature warming rates. Forty‐seven percent of Earth's estuaries are warming, with a global average warming rate of 0.070 ± 0.004°C yr−1 (median = 0.060°C yr−1). Estuaries at higher latitudes showed rapid warming. A 1°C increase in air temperature could lead to a 0.81°C increase in estuarine surface water warming and 1.3°C increase in estuaries above 60.5°N. We inferred the potential influences over estuarine warming based on distinct global spatial patterns in water and air warming and discussed the effects of warming water temperature on estuarine metabolism and water quality. [ABSTRACT FROM AUTHOR]

Although trends toward earlier ice‐out have been documented globally, the links between ice‐out timing and lake thermal and biogeochemical structure vary spatially. In high‐latitude lakes where ice‐out occurs close to peak intensity of solar radiation, these links remain unclear. Using a long‐term dataset from 13 lakes in West Greenland, we investigated how changing ice‐out and weather conditions affect lake thermal structure and oxygen concentrations. In early ice‐out years, lakes reach higher temperatures across the water column and have deeper epilimnia. Summer hypolimnia are the warmest (~ 11°C) in years when cooler air temperatures follow early ice‐out, allowing full lake turnover. Due to the higher potential for substantive spring mixing in early ice‐out years, a warmer hypolimnion is associated with higher dissolved oxygen concentrations. By affecting variability in spring mixing, the consequences of shifts in ice phenology for lakes at high latitudes differ from expectations based on temperate regions. [ABSTRACT FROM AUTHOR]

The urban heat island effect has become a common meteorological phenomenon in urban areas in recent years. Research on urban thermal environments has used various technologies, such as remote sensing, on-site/mobile measurement, and model simulation. This study employed satellite images to comprehensively identify the correlations between thermal environments and the urban morphology in Taipei, Taiwan. This study applied the local climate zone (LCZ) scheme to classify urban land types by surface characteristics. The LCZ classification result was used as the basic unit for assessing and calculating urban forms by the landscape ecological metrics (LEM). For the thermal environment information, this study collected air temperature data by mobile measurements and combined it with records from weather stations. The results showed that different urban form compositions were associated with thermal environments. Built-up areas with higher levels of edge density tended to have higher heat intensity. For two regions with the same proportion of green spaces and built environments, the temperature varies with different configurations. This study can evaluate the urban thermal environments and assist urban planners who do not have a meteorological background in finding urban climate issues and developing heat mitigation strategies. [ABSTRACT FROM AUTHOR]

The results of a phenological study of 78 species of perennial plants from biomorphological groups of chamaephytes, hemicryptophytes, and geophytes over a 20-year period (1996–2015) are discussed. Against the background of air temperature and precipitation changes of the warm season in Novosibirsk, the timing shift in phenological events have been analyzed using calculated linear trends. It is found that the trends for species groups are multidirectional and vary significantly in magnitude. At the same time, most of the shifts in phenology are due, not to trends, but to the interannual variability of climatic indicators. [ABSTRACT FROM AUTHOR]

Large-eddy simulations are used to evaluate mean profile similarity in the convective boundary layer (CBL). Particular care is taken regarding the grid sensitivity of the profiles and the mitigation of inertial oscillations in the simulation spin-up. The nondimensional gradients ϕ for wind speed and air temperature generally align with Monin–Obukhov similarity across cases but have a steeper slope than predicted within each profile. The same trend has been noted in several other recent studies. The Businger-Dyer relations are modified here with an exponential cutoff term to account for the decay in ϕ to first-order approximation, yielding improved similarity from approximately 0.05 z i to above 0.3 z i , where z i is the CBL depth. The necessity for the exponential correction is attributed to an extended transition from surface scaling to zero gradient in the mixed layer, where the departure from Monin–Obukhov similarity may be negligible at the surface but becomes substantial well below the conventional surface layer height of 0.1 z i . [ABSTRACT FROM AUTHOR]

Today, air temperature (AT) is the most critical climatic indicator. This indicator accurately defines global warming and climate change, despite the fact that it has effects on different things, including the environment, hydrology, agriculture, and irrigation. Accurate and timely AT forecasting is crucial since it supplies more significant details that can create credibility for future planning. This study proposes innovative hybrid models that integrate a convolutional neural network (CNN) with a long short-term memory (LSTM) neural network and a gated recurrent unit (GRU) to perform one-day ahead AT predictions. For this purpose, the daily AT data obtained from 2012 to 2019 at the Adana and Ankara meteorological stations over Türkiye under Continental and Mediterranean climate conditions are used. The hybrid GRU-CNN and LSTM-CNN models are compared with various traditional statistical and machine-learning models such as feed-forward neural network, adaptive neuro-fuzzy inference system, autoregressive moving average, GRU, CNN, and LSTM. The success of the prediction models is evaluated utilizing various statistical criteria (MAE, RMSE, NSE, and R2) and visual comparisons. The results show that the proposed hybrid GRU-CNN and LSTM-CNN models in one day-ahead AT predictions yield the best results among all models with high accuracy. [ABSTRACT FROM AUTHOR]

Understanding the trend of seasonal rainfall in the context of climate change is crucial for the maintenance of regional water resources management. The present study examines the seasonal rainfall trend in the Mahi River basin, India by using the wavelet transform and clustering method. Daily gridded rainfall data (0.25° × 0.25° spatial resolution) for 51 grids from 1970 to 2020 have been taken from India Meteorological Department (IMD). The monthly, seasonal, and annual statistic has been analyzed for 51 years. We have also plotted the relationship between precipitation trend–elevation. After that, homogeneous precipitation regions are delineated with hierarchical clustering analysis. Results reveal that seasonal precipitation over the basin clusters into 4 subregions for monsoon and 3 subregions for winter, pre-monsoon, and post-monsoon seasons. After the regionalization of the subregions, the periodicity and the inter-seasonal relationship were analyzed using continuous wavelet transform (CWT). In addition, it was clear that the cross-correlation between pre-monsoon and monsoon seasons had a significant periodic change in the last 30 years over the basin. The basin is located adjacent to the Arabian sea, which makes the basin more sensitive to a natural event like ENSO, it can cause changes in ocean temperatures and atmospheric circulation patterns that affect precipitation patterns during the study period. It is expected that some natural phenomena like monsoon variability, low-level jet streams, and many more also have an impact on the rainfall patterns of the basin. Thus, the understanding of seasonal precipitation variation provides a practical reference for water resources management, agricultural planning, and a forecast of precipitation in different regions and river basins of India which may give a better climate change indication. [ABSTRACT FROM AUTHOR]

There is growing concern about the fate of tropical forests in the face of rising global temperatures. Doughty et al. (2023) suggest that an increase in air temperature beyond ∼4 °C will result in massive death of tropical forest leaves and potentially tree death. However, this prediction relies on assumptions that likely underestimate the heat tolerance of tropical leaves. [ABSTRACT FROM AUTHOR]

A powerful way to predict how ecological communities will respond to future climate change is to test how they have responded to the climate of the past. We used climate oscillations including the Pacific Decadal Oscillation (PDO), North Pacific Gyre Oscillation, and El Niño Southern Oscillation (ENSO) and variation in upwelling, air temperature, and sea temperatures to test the sensitivity of nearshore rocky intertidal communities to climate variability. Prior research shows that multiple ecological processes of key taxa (growth, recruitment, and physiology) were sensitive to environmental variation during this time frame. We also investigated the effect of the concurrent sea star wasting disease outbreak in 2013–2014. We surveyed nearly 150 taxa from 11 rocky intertidal sites in Oregon and northern California annually for up to 14-years (2006–2020) to test if community structure (i.e., the abundance of functional groups) and diversity were sensitive to past environmental variation. We found little to no evidence that these communities were sensitive to annual variation in any of the environmental measures, and that each metric was associated with < 8.6% of yearly variation in community structure. Only the years elapsed since the outbreak of sea star wasting disease had a substantial effect on community structure, but in the mid-zone only where spatially dominant mussels are a main prey of the keystone predator sea star, Pisaster ochraceus. We conclude that the established sensitivity of multiple ecological processes to annual fluctuations in climate has not yet scaled up to influence community structure. Hence, the rocky intertidal system along this coastline appears resistant to the range of oceanic climate fluctuations that occurred during the study. However, given ongoing intensification of climate change and increasing frequencies of extreme events, future responses to climate change seem likely. [ABSTRACT FROM AUTHOR]

The Arctic sea ice cover has decreased rapidly over the last few decades both in extent and thickness. Here we present multi‐year (2013–2022) observations of sea ice thickness in the northwestern Barents Sea based on Upward Looking Sonar measurements and show that the winter sea ice has become thicker over the last decade. Sea ice thickness from the Pan‐Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) reproduces both the observed variability and recent 10‐year trend and shows that this thickening (0.24 m decade−1) has not been seen since the 1990s. Using PIOMAS we find that the recent increase in sea ice thickness can be explained by increased sea ice freezing as a result of lower temperatures in the ocean and in the atmosphere. The recent thickening is set in the context of a long‐term thinning trend, with PIOMAS showing much thinner ice now than in the 1980s. Plain Language Summary: The Arctic sea ice cover is becoming smaller and thinner due to global warming. We have measured sea ice thickness in the Barents Sea since 2013, and find that the ice thickness has increased since the measurements were initiated, contrary to what we would expect in a warming world. The Arctic sea ice cover can, however, increase for periods typically up to a decade due to natural climate variability. We find that increased sea ice formation due to lower ocean and air temperatures has caused the recent thickening of the ice cover. We also find that the Barents Sea ice thickness has decreased since the 1980s, and despite the recent thickening, the ice cover is much thinner now than it used to be. Key Points: Upward Looking Sonar measurements in the Barents Sea show increased sea ice thickness during the last decadeIn an ice‐ocean reanalysis (PIOMAS) the recent thickening is due to increased ice freezing, associated with lower ocean and air temperaturesThe long‐term thickness trend is negative, meaning that despite recent thickening, ice is now much thinner than in the 1980s [ABSTRACT FROM AUTHOR]

Mg/Ca ratio in stalagmites primarily serves as an indicator of past hydrological conditions rather than temperature variations. Here, we present an Mg/Ca ratio record in a stalagmite from northern China, spanning ice age termination II. A unique finding is that Mg/Ca ratio shows a moderately strong positive correlation with cave air temperature obtained from clumped isotopes data in the same stalagmite. The stable hydrogeochemistry of source water and high temperature sensitivity of Mg partition coefficient plausibly explain this temperature dependence. Furthermore, we establish an Mg/Ca‐temperature calibration for stalagmites from mid‐latitudes, encompassing a temperature range between ∼2 and ∼15°C, where Mg/Ca = 14.221 × e0.073×T (R2 = 0.40). Applying this function to Mg/Ca ratio from similar stalagmites could tentatively provide a quantitative regional temperature record over millennial and centennial timescales. This research highlights Mg/Ca ratio in stalagmites as a potential palaeo‐temperature proxy and introduces, for the first time, a tentative Mg/Ca‐temperature calibration function for speleothems. Plain Language Summary: Stalagmite is one type of speleothems that grow from the floor of caves. The geochemical indexes within stalagmites, such as Mg/Ca ratio, can help us to understand the past climate changes. Here, we conducted a study on a stalagmite from northern China during ice age termination II. Traditionally, Mg/Ca ratios in stalagmites have been used to investigate past hydrological conditions. However, we found that Mg/Ca ratio matches the cave air temperature obtained from clumped isotopes data within the same stalagmite. This correlation was explained by the stable chemistry of the source water and the sensitivity of Mg partition coefficient to temperature. We also developed a tentative formula to calculate past temperature from Mg/Ca ratio in similar speleothems. Key Points: A Chinese stalagmite at termination II shows moderately strong positive correlation between Mg/Ca ratio and clumped isotopes‐temperatureStable drip water hydrogeochemistry and high temperature sensitivity of Mg partition coefficient may explain this temperature dependenceWe tentatively establish, for the first time, an Mg/Ca‐temperature calibration function for speleothems [ABSTRACT FROM AUTHOR]

Herein, poly(aniline‐co‐pyrrole) has been synthetized and investigated to adsorb phenylbutazone (PBZ), ceftiofur (CEF) and prednisolone (PRD) in aqueous medium as proof of concept. After proper adsorbent characterization, some studies were carried out in batch process, i. e., contact time, pH, and concentration. The adsorption mechanism has been conducted using kinetic and isothermal models. It was found that the reaction equilibrium was reached in 20 min for all analytes. The best fit for the experimental data were obtained employing the models of dual‐site Langmuir–Freundlich, Fritz–Schlunder and Langmuir, with R2=0.999, 0.952 and 0.974, for PBZ, CEF and PRD, respectively. For PBZ, CEF and PRD, poly(aniline‐co‐pyrrole) provided a Qmax=238.8 mg g−1 and Kmax=0.086 L g−1, Q=0.028 mg g−1 and Kmax=3.258 L g−1, Q=13.66 mg g−1 and KL=0.005 L g−1, respectively. Finally, the copolymer was evaluated in solid phase extraction, which showed recovery around 70 % for PBZ, offering also exciting opportunities for further application in the separation process. [ABSTRACT FROM AUTHOR]

Black ice, a phenomenon that occurs abruptly owing to freezing rain, is difficult for drivers to identify because it mirrors the color of the road. Effectively managing the occurrence of unforeseen accidents caused by black ice requires predicting their probability using spatial, weather, and traffic factors and formulating appropriate countermeasures. Among these factors, weather and traffic exhibit the highest levels of uncertainty. To address these uncertainties, a study was conducted using a Monte Carlo simulation based on random values to predict the probability of black ice accidents at individual road points and analyze their trigger factors. We numerically modeled black ice accidents and visualized the simulation results in a geographical information system (GIS) by employing a sensitivity analysis, another feature of Monte Carlo simulations, to analyze the factors that trigger black ice accidents. The Monte Carlo simulation allowed us to map black ice accident occurrences at each road point on the GIS. The average black ice accident probability was found to be 0.0058, with a standard deviation of 0.001. Sensitivity analysis using Monte Carlo simulations identified wind speed, air temperature, and angle as significant triggers of black ice accidents, with sensitivities of 0.354, 0.270, and 0.203, respectively. We predicted the probability of black ice accidents per road section and analyzed the primary triggers of black ice accidents. The scientific contribution of this study lies in the development of a method beyond simple road temperature predictions for evaluating the risk of black ice occurrences and subsequent accidents. By employing Monte Carlo simulations, the probability of black ice accidents can be predicted more accurately through decoupling meteorological and traffic factors over time. The results can serve as a reference for government agencies, including road traffic authorities, to identify accident-prone spots and devise strategies focused on the primary triggers of black ice accidents. [ABSTRACT FROM AUTHOR]

The morphological complexity of urban environments results in a high spatial and temporal variability of the urban microclimate. The consequent demand for high-resolution atmospheric data remains a challenge for atmospheric research and operational application. The recent widespread availability and increasing adoption of low-cost mobile sensing offer the opportunity to integrate observations from conventional monitoring networks with microclimatic and air pollution data at a finer spatial and temporal scale. So far, the relatively low quality of the measurements and outdoor performance compared to conventional instrumentation has discouraged the full deployment of mobile sensors for routine monitoring. The present study addresses the performance of a commercial mobile sensor, the MeteoTracker (IoTopon Srl), recently launched on the market to quantify the microclimatic characteristics of the outdoor environment. The sensor follows the philosophy of the Internet of Things technology, being low cost, having an automatic data flow via personal smartphones and online data sharing, supporting user-friendly software, and having the potential to be deployed in large quantities. In this paper, the outdoor performance is evaluated through tests aimed at quantifying (i) the intra-sensor variability under similar atmospheric conditions and (ii) the outdoor accuracy compared to a reference weather station under sub-optimal (in a fixed location) and optimal (mobile) sensor usage. Data-driven corrections are developed and successfully applied to improve the MeteoTracker data quality. In particular, a recursive method for the simultaneous improvement of relative humidity, dew point, and humidex index proves to be crucial for increasing the data quality. The results mark an intra-sensor variability of approximately ± 0.5 °C for air temperature and ± 1.2 % for the corrected relative humidity, both of which are within the declared sensor accuracy. The sensor captures the same atmospheric variability as the reference sensor during both fixed and mobile tests, showing positive biases (overestimation) for both variables. Through the mobile test, the outdoor accuracy is observed to be between ± 0.3 to ± 0.5 °C for air temperature and between ± 3 % and ± 5 % for the relative humidity, ranking the MeteoTracker in the real accuracy range of similar commercial sensors from the literature and making it a valid solution for atmospheric monitoring. [ABSTRACT FROM AUTHOR]

Despite efforts to mitigate climate change, annual greenhouse gas emissions continue to rise, which may lead to the global warming of our planet. Buildings' thermal energy needs are inherently linked to climate conditions. Consequently, it is crucial to evaluate how climate change affects these energy demands. Despite extensive analysis, a comprehensive assessment involving a diverse range of building types has not been consistently conducted. The primary objective of this research is to perform a coherent evaluation of the influence of climate changes, construction element properties, and the Heating, Ventilation, and Air Conditioning (HVAC) system type of control on the energy requirements of six buildings (residential, services, and commercial). The buildings are considered to be located in a temperate Mediterranean climate. Our focus is on the year 2070, considering three distinct climatic scenarios: (i) maintaining the current climate without further changes, (ii) moderate climate changes, and (iii) extreme climate changes. The buildings are distributed across three different locations, each characterized by unique climatic conditions. Buildings' envelope features a traditional External Thermal Insulation Composite System (ETICS) and expanded polystyrene (EPS) serves as thermal insulation material. Two critical design factors are explored: EPS thickness ranging from 0 (no insulation) to 12 cm; and horizontal external fixed shading elements varying lengths from 0 (absence) to 150 cm. Six alternative setpoint ranges are assessed for the HVAC system control: three based on the Predicted Mean Vote (PMV) and three based on indoor air temperature (Tair). Results were obtained with a validated in-home software tool. They show that, even under extreme climate conditions, the application of thermal insulation remains energetically favorable; however, its relative importance diminishes as climate severity increases. Then, proper insulation design remains important for energy efficiency. The use of external shading elements for glazing (e.g., overhangs, louvers) proves beneficial in specific cases. As climate changes intensify, the significance of shading elements grows. Thus, strategic placement and design are necessary for good results. The HVAC system's energy consumption depends on the level of thermal comfort requirements, on the climate characteristics, and on the building's type of use. As climate change severity intensifies, energy demands for cooling increase, whereas energy needs for heating decrease. However, it is essential to recognize that the impact of climate changes on HVAC system energy consumption significantly depends on the type of building. [ABSTRACT FROM AUTHOR]

With half-cut photovoltaic (PV) modules being the dominant technology on the market, there is an increasing necessity for accurate modeling of this module type. Circuit simulators such as Simulink are widely used to study different topics regarding photovoltaics, often employing a solar cell block available from the Simcape library. The purpose of this work is to validate this model against measurements for a partially shaded half-cut PV module. Diverse shading scenarios are created by varying the number of shaded substrings, the number of shaded solar cells in the substring, and the shading level. For every shading scenario, the PV module's I-V curve is measured, along with in-plane irradiance, air temperature, and module temperature. A comprehensive evaluation of simulation accuracy is presented. The results confirm a high accuracy of the model with mean nRMSE values of 2.2% for I-V curves and 2.8% when P-V curves are considered. It is found that the simulation errors tend to increase when increasing the number of shaded substrings. At the same time, no obvious dependency of simulation accuracy on the shading level or the number of shaded solar cells in the substring is found. [ABSTRACT FROM AUTHOR]

In this study, we develop an Atmospheric Motion Vector (AMV)-based method for retrieving wind vectors using 183.31 GHz water-vapor absorption channels. The method involves tracking water-vapor features from image triplets and subsequently deriving wind fields from motion vectors. The height of the derived wind for each channel is determined by calculating the weighing function peak using monthly averaged ERA5 reanalysis data. By utilizing Microwave Humidity Sounder-II (MWHS-II) brightness temperatures from the five channels centered around 183.31 GHz, wind vectors are retrieved within the Arctic region for the entire year of 2022. The retrieval quality is evaluated through comparative analysis with ERA5 reanalysis data and the Visible Infrared Imaging Radiometer Suite (VIIRS) wind product. The resultant vector root mean square errors (RMSEs) are approximately 4.5 m/s for the three lower-height channels and 5.5 m/s for the two upper-height channels. These findings demonstrate a wind retrieval performance comparable to the existing methods, highlighting its potential for augmenting wind availability at lower height levels. [ABSTRACT FROM AUTHOR]

Lakes on the Qinghai Tibet Plateau (QTP) are widely distributed spatially, and they are mostly seasonally frozen. Due to global warming, the thickness and phenology of the lake ice has been changing, which profoundly affects the regional climate evolution. There are a few studies about lake ice in alpine regions, but the understanding of climatological characteristics of lake ice on the QTP is still limited. Based on a field experiment in the winter of 2022, the thermal conductivity of Qinghai Lake ice was determined as 1.64 W·m−1·°C−1. Airborne radar ice thickness data, meteorological observations, and remote sensing images were used to evaluate a quasi-steady ice model (Leppäranta model) performance of the lake. This is an analytic model of lake ice thickness and phenology. The long-term (1979–2017) ice history of the lake was simulated. The results showed that the modeled mean ice thickness was 0.35 m with a trend of −0.002 m·a−1, and the average freeze-up start (FUS) and break-up end (BUE) were 30 December and 5 April, respectively, which are close to the field and satellite observations. The simulated trend of the maximum ice thickness from 1979 to 2017 (0.004 m·a−1) was slightly higher than the observed result (0.003 m·a−1). The simulated trend was 0.20 d·a−1 for the FUS, −0.34 d·a−1 for the BUE, and −0.54 d·a−1 for the ice duration (ID). Correlation and detrending analysis were adopted for the contribution of meteorological factors. In the winters of 1979–2017, downward longwave radiation and air temperature were the two main factors that had the best correlation with lake ice thickness. In a detrending analysis, air temperature, downward longwave radiation, and solar radiation contributed the most to the average thickness variability, with contributions of 42%, 49%, and −48%, respectively, and to the maximum thickness variability, with contributions of 41%, 45%, and −48%, respectively. If the six meteorological factors (air temperature, downward longwave radiation, solar radiation, wind speed, pressure, and specific humidity) are detrending, ice thickness variability will increase 83% on average and 87% at maximum. Specific humidity, wind, and air pressure had a poor correlation with ice thickness. The findings in this study give insights into the long-term evolutionary trajectory of Qinghai Lake ice cover and serve as a point of reference for investigating other lakes in the QTP during cold seasons. [ABSTRACT FROM AUTHOR]

Accurate estimation for vegetation net primary production (NPP) at high resolution is essential for ecosystem management and at local to global scales. However, no reliable methods are available to achieve fine-resolution estimation. In this study, a new NPP estimation system was developed by integrating high spatial resolution remote sensing images, tree ring measurements, and process-based model CASA. The system was illustrated by estimating the historical NPP of caragana shrublands from 2011 to 2021 in Dingbian County, China. The results showed that: (1) by employing the biomass model and tree ring measurements, an NPP-crown width model was established to enable a large number of samples for calibrating the CASA model on high spatial resolution images without continuous field measurements. The calibrated parameters, including maximum NDVI, minimum NDVI, and maximum light use efficiency, were determined to be 0.415, 0.022 and 0.025, respectively; (2) according to the estimation by the calibrated CASA model, the NPP in Dingbian County revealed an increasing trend from 2011 to 2021, and the NPP in southern hilly and gully regions was significantly higher than the other regions; (3) among the different indices, max temperature of warmest month had a significant negative effect on the NPP of cagarana shrublands, whereas annual precipitation had a positive effect. In the northern and central regions, air temperature predominantly influenced NPP, while in the southern regions, both temperature and precipitation played a joint role. The implementation in this study illustrated that the NPP estimation system could provide reliable NPP with high resolution and efficient to monitor and detect dominant factors affecting NPP for ecosystem management. [ABSTRACT FROM AUTHOR]

The aim of the work was to determine important parameters of the course of π-A isotherms, which can determine the HLB (hydrophilic-lipophilic balance) value of surfactant mixtures with selected structural features, such as a straight or branched hydrocarbon chain and a double bond, using RSM (response surface methodology) computational methods. Mixtures of surfactants derived from fatty acids and sorbitan with specific HLB values were evaluated by Langmuir trough. The resulting elasticity modules (ELM) and molecules surfaces (SAM) were evaluated via response surface methodology and respective equations were calculated. The π-A isotherm determined in a Langmuir trough and the ELM and SAM parameters determined on the basis of this isotherm may be useful for determining the HLB of a fixed surfactant mixture. The RSM method used, in which ELM and SAM were assumed as two independent variables, can be a useful technique for tracking the influence of individual molecular characteristics on the hydrophilic-lipophilic properties of mixtures of surfactant compounds. Changes in HLB as a dependent variable can be described as a function of ELM and SAM. [ABSTRACT FROM AUTHOR]