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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]

This manuscript investigates the effect of diabatic imparity on two-dimensional ( 2 - D ) transient fins having naturally obtained non-linear variation of temperature-dependent conductivity. The diabatic imparity arises due to asymmetrical convection coefficient on the surfaces, implemented using different Biot numbers on each respective surface of the fin, which resembles pragmatic industrial applications. The transient solution of 2-D fin has been obtained using a Lattice Boltzmann ( LB ) solver, firstly, the suitability of 2 - D LB formulation is established with the validity of the existing results, and subsequently extending the LB formulation for the present study. The numerical solution is determined under the two types of step-changing boundary conditions at the fin root having (i) heat flow and (ii) temperature . Results have been plotted graphically, and these include instantaneous isotherms , equilibrium isotherms , and threshold iso-temporal lines. Reported results facilitate the fin designer to examine the effect of diabatic imparity on the attainment of steady-state and occurrence of transverse temperature gradient on 2 - D transient fin. The reported results provided are pertinent to the pragmatic application and are seldom reported in the literature. The outcomes of the present investigation are eminently informative to practising engineers and will serve as design monograms. [ABSTRACT FROM AUTHOR]

This study presents an unmanned aerial vehicle (UAV) platform used to resolve horizontal and vertical patterns of CO2 and CH4 mole fractions within the lower part of the atmospheric boundary layer. The obtained data contribute important information for upscaling fluxes from natural ecosystems over heterogeneous terrain, and for constraining hot spots of greenhouse gas (GHG) emissions. This observational tool, therefore, has the potential to complement existing stationary carbon monitoring networks for GHGs, such as eddy covariance towers and manual flux chambers. The UAV platform is equipped with two gas analyzers for CO2 and CH4 which are connected sequentially. In addition, a 2D anemometer is deployed above the rotor plane to measure environmental parameters including 2D wind speed, air temperature, humidity, and pressure. Laboratory and field tests demonstrate that the platform is capable of providing data with reliable accuracy, with good agreement between the UAV data and tower-based measurements of CO2 and CH4 , and wind speed. Using interpolated maps of GHG mole fractions, with this tool we assessed the signal variability over a target area, and identified potential hot spots. Our study shows that the UAV platform provides information about the spatial variability of the lowest part of the boundary layer, which up to this date remains poorly observed, especially in remote areas such as the Arctic. Furthermore, using the profile method, it is demonstrated that the GHG fluxes from a local source can be calculated. Although subject to large uncertainties over the area of interest, the comparison between the eddy covariance method and UAV-based calculations showed acceptable qualitative agreement. [ABSTRACT FROM AUTHOR]

Taylor's Frozen Turbulence Hypothesis (TH) is a critical assumption in turbulent theory and practice which allows time series of point measurements of turbulent variables to be translated to the spatial domain via the mean wind. Using a 3D array of fibre-optic distributed temperature sensing in the atmospheric surface layer over an idealized desert site we present a systematic investigation of the applicability of Taylor's Hypothesis to atmospheric surface layer flows over a variety of conditions: unstable, near-neutral, and stable atmospheric stabilities; and multiple measurement heights between the surface and 3 m above ground level. Both spatially integrated and spatially scale-dependent eddy velocities are investigated by means of time-lagged streamwise two-point correlations and compared to the mean Eulerian wind. We find that eddies travel slower than predicted by TH at small spatial separations, as predicted by TH at separations typically between 5 and 16 m, and faster than predicted by TH at larger spatial separations. In unstable atmospheric conditions the spatial separation at which eddy velocity is larger than Eulerian velocity decreases with height. [ABSTRACT FROM AUTHOR]

Climate change has myriad impacts on ecosystems, but the mechanisms by which it affects individual species can be difficult to pinpoint. One strategy to discover such mechanisms is to identify a specific ecological factor related to survival or reproduction and determine how that factor is affected by climate. Here we used Landsat imagery to calculate water clarity for 127 lakes in northern Wisconsin from 1995 to 2021 and thus investigate the effect of clarity on the body condition of an aquatic visual predator, the common loon (Gavia immer). In addition, we examined rainfall and temperature as potential predictors of water clarity. Body mass tracked July water clarity strongly in loon chicks, which grow chiefly in that month, but weakly in adult males and females. Long‐term mean water clarity was negatively related to chick mass but positively related to adult male mass, suggesting that loons foraging in generally clear lakes enjoy good foraging conditions in the long run but might be sensitive to perturbations in clarity during chick‐rearing. Finally, chick mass was positively related to the density of docks, perhaps because angling removes large fishes and thus boosts the abundance of the small fishes on which chicks depend. Water clarity itself declined strongly from 1995 to 2021, was negatively related to July rainfall, and was positively related to July air temperature. Our findings identified both long‐term and short‐term water clarity as strong predictors of loon foraging efficiency, and suggest that climate change, through water clarity, impacts freshwater ecosystems profoundly. Moreover, our results identified the recent decrease in water clarity as a likely cause of population decline in common loons. [ABSTRACT FROM AUTHOR]

The role of atmospheric humidity in the evolution of endotherms' thermoregulatory performance remains largely unexplored, despite the fact that elevated humidity is known to impede evaporative cooling capacity. Using a phylogenetically informed comparative framework, we tested the hypothesis that pronounced hyperthermia tolerance among birds occupying humid lowlands evolved to reduce the impact of humidity‐impeded scope for evaporative heat dissipation by comparing heat tolerance limits (HTLs; maximum tolerable air temperature), maximum body temperatures (Tbmax), and associated thermoregulatory variables in humid (19.2 g H2O m−3) versus dry (1.1 g H2O m−3) air among 30 species from three climatically distinct sites (arid, mesic montane, and humid lowland). Humidity‐associated decreases in evaporative water loss and resting metabolic rate were 27%–38% and 21%–27%, respectively, and did not differ significantly between sites. Decreases in HTLs were significantly larger among arid‐zone (mean ± SD = 3.13 ± 1.12°C) and montane species (2.44 ± 1.0°C) compared to lowland species (1.23 ± 1.34°C), with more pronounced hyperthermia among lowland (Tbmax = 46.26 ± 0.48°C) and montane birds (Tbmax = 46.19 ± 0.92°C) compared to arid‐zone species (45.23 ± 0.24°C). Our findings reveal a functional link between facultative hyperthermia and humidity‐related constraints on evaporative cooling, providing novel insights into how hygric and thermal environments interact to constrain avian performance during hot weather. Moreover, the macrophysiological patterns we report provide further support for the concept of a continuum from thermal specialization to thermal generalization among endotherms, with adaptive variation in body temperature correlated with prevailing climatic conditions. [ABSTRACT FROM AUTHOR]

Photometric technology, characterized by its compact structure and relatively high stability, finds wide application in measuring airglow spectra. This instrumentation is anticipated to assume a pivotal role as the primary equipment for extensive network observations of middle and upper atmospheric temperatures in China, thereby providing crucial support for space environmental monitoring and atmospheric dynamic research. Nevertheless, susceptibility to various factors such as instrument inconsistency, variability in observation conditions, and alterations in the background atmospheric environment across different stations poses a challenge, potentially resulting in data inconsistencies in network observations. In response to these challenges, we propose a multiple-parameter iterative inversion (MPII) algorithm for temperature retrieval based on a mesospheric airglow spectrum photometer (MASP) developed by our research group. This algorithm accurately identifies the center of the image circle, corrects image distortion, and thereby obtains an accurate synthetic spectrum reflective of actual observations. It encompasses five adjustable parameters: sky background light, atmospheric temperature, filter temperature, optical system focal length, and degree of synthetic spectrum modulation. Compared to traditional methods, significant enhancements in the accuracy of the inverted temperature are achieved. To validate the effectiveness of the MPII algorithm, we conducted combined active and passive remote sensing synchronous measurements using MASP in conjunction with a sodium fluorescence Doppler lidar developed by the National Space Science Center. By utilizing the lidar temperature as a reference, atmospheric background radiation is mitigated from the MASP data, and the temperature is inverted using the MPII algorithm. Comparative analysis with the traditional method reveals that temperatures calculated by the MPII algorithm exhibit better consistency than those observed by the lidar. [ABSTRACT FROM AUTHOR]

Warm temperature exposure during winter has reportedly resulted in the apparent negation of chilling in several fruit species. This study was conducted to investigate the floral and vegetative response of two pistillate kiwifruit cultivars to intermittent warm temperature interruption during chilling accumulation. Dormant 1-year-old canes of Actinidia chinensis ‘AU Golden Dragon’ and Actinidia deliciosa ‘AU Fitzgerald’ were collected in December 2018 and 2019 (334 and 360 chilling units, respectively), shortly after leaf abscission. Canes were cut to 10 nodes after removing the first six basal nodes, placed in jars filled with distilled water, and transferred to respective chilling treatments. Treatments included continuous chilling (CC) (in addition to base chilling) at 1-week (168 chilling units) increments (0–5 weeks) and chilling exposure at the same increments with intermittent warm temperature (WT). For the WT treatments, each week of chilling was followed by 3 days of exposure to warm conditions. Chilling and warm temperature exposure were simulated by 7/4°C and 25/17.2 °C (day/night) air temperatures, respectively, using separate climate-controlled growth chambers. After treatments, canes were forced in a third chamber at 21.1 to 25.0°C with light-emitting diode lighting. Vegetative budbreak, floral bud number (from here on defined as floral response), and floral development stage were recorded for each cane at 2-day intervals. For ‘AU Golden Dragon’, WT did not result in any reduced floral response at any of the observed chilling levels. However, lower mean floral response was observed with WT, as compared with CC for ‘AU Fitzgerald’ at 5 weeks of chilling over the 2 years (P = 0.05). WT also lessened the effect of apical dominance with respect to vegetative/floral response to node position for both cultivars. Chilling type had no significant effect on vegetative response in either cultivar. Estimated chilling requirements (CC) in this experiment were similar to those reported previously for these cultivars. Results suggest that A. chinensis cultivars may respond more favorably than A. deliciosa to the erratic winter temperature patterns experienced in the southeastern United States. [ABSTRACT FROM AUTHOR]

Yerba mate (Ilex paraguariensis) is renowned for its nutritional and pharmaceutical attributes. A staple in South American (SA) culture, it serves as the foundation for several traditional beverages. Significantly, the pharmaceutical domain has secured numerous patents associated with this plant's distinctive properties. This research delves into the climatic influence on yerba mate by leveraging the CMIP6 model projections to assess potential shifts brought about by climate change. Given its economic and socio-cultural significance, comprehending how climate change might sway yerba mate's production and distribution is pivotal. The CMIP6 model offers insights into future conditions, pinpointing areas that are either conducive or adverse for yerba mate cultivation. Our findings will be instrumental in crafting adaptive and mitigative strategies, thereby directing sustainable production planning for yerba mate. The core objective of this study was to highlight zones optimal for Ilex paraguariensis cultivation across its major producers: Brazil, Argentina, Paraguay, and Uruguay, under CMIP6's climate change forecasts. Our investigation encompassed major producing zones spanning the North, Northeast, Midwest, Southeast, and South of Brazil, along with the aforementioned countries. A conducive environment for this crop's growth features air temperatures between 21 to 25 °C and a minimum precipitation of 1200 mm per cycle. We sourced the current climate data from the WorldClim version 2 platform. Meanwhile, projections for future climatic parameters were derived from WorldClim 2.1, utilizing the IPSL-CM6A-LR model with a refined 30-s spatial resolution. We took into account four distinct socio-economic pathways over varying timelines: 2021–2040, 2041–2060, 2061–2081, and 2081–2100. Geographic information system data aided in the spatial interpolation across Brazil, applying the Kriging technique. The outcomes revealed a majority of the examined areas as non-conducive for yerba mate cultivation, with a scanty 12.25% (1.5 million km2) deemed favorable. Predominantly, these propitious regions lie in southern Brazil and Uruguay, the present-day primary producers of yerba mate. Alarming was the discovery that forthcoming climatic scenarios predominantly forecast detrimental shifts, characterized by escalating average air temperatures and diminishing rainfall. These trends portend a decline in suitable cultivation regions for yerba mate. [ABSTRACT FROM AUTHOR]

Aerosols significantly affect cloud microphysics and energy budget in different ways. The contribution of the direct, semi‐direct, and indirect effects of aerosols on radiation are here investigated over the North Atlantic tropical ocean under different aerosol loadings. The Weather Research and Forecasting Model is used to perform a set of numerical idealized experiments, which are forced with prescribed aerosol profiles. We evaluate the effects of aerosols on modeled shallow clouds and surface radiative budget. The results indicate that large aerosol loadings are associated with enhanced cloudiness and reduced precipitation. While the change in rainfall is mainly due to the larger number of smaller droplets, the change in cloudiness is attributed to the effects of absorbing aerosols, mainly dust particles, which are responsible for a rise of temperature that feeds back onto specific humidity. As in the boundary layer the increase of moisture dominates, the net effect is a higher relative humidity, which favors the formation of thin low non‐precipitating clouds. The feedback accounts for a dynamical change in the lower troposphere: shortwave radiation absorption increases temperature at the top of the marine atmospheric boundary‐layer and reduces entrainment of warm and dry air, increasing low level moisture content. Despite the overall increase in cloudiness, daytime cloud cover is reduced. The semi‐direct effect of aerosols on clouds results in a warming of the surface, opposite to the indirect effect. [ABSTRACT FROM AUTHOR]

Escape behavior is a common antipredator strategy among wild animals. Here, we investigated the effect of four factors on the vigilance behavior of the endemic Cyprus rock agama (Laudakia cypriaca). Flight initiation distance (FID, the minimum distance to which an observer can approach a lizard before it flees) was measured in relation to the type of location (tourist vs. nontourist area), the observer's starting distance, air temperature, and substrate temperature. We collected data for 39 agamas in tourist areas and 34 of these lizards in nontourist areas. As a whole, the mean starting distance was 10.5 m and the FID was 3.6 m. The average substrate temperature was 34.0°C and the average air temperature 29.6°C. Only the type of area affected the agamas' escape decisions with FID being 1.8 m shorter in tourist areas than in nontourist areas (2.7 m vs. 4.5 m). This is probably due to the habituation of lizards to the presence of humans in the former areas. This study shows that tourism strongly affects the behavior of lizards, which may have consequences for the functioning of the population. Tourists can increase the safety of lizards by creating a human shield to deter predators. Once the tourist season is over, lizards may become more vulnerable to predators. [ABSTRACT FROM AUTHOR]

Current exterior wall assembly designs for new low-rise residential buildings targeting low-energy demand in heating dominated countries include split-insulation wall and thick-wall assembly designs. Both have been shown to result in thermal efficiency gains compared to building-code minimum assemblies, however long-term hygrothermal performance can vary depending on boundary conditions and the presence of construction deficiencies. Future climate scenarios estimate many heating-dominated climates will experience a reduction in heating-degree day hours and an increase in annual rainfall. Using validated assembly performance data from a Passive House certified facility, a sensitivity analysis is performed to determine the impact of rainwater wetting, air exfiltration and insulation material properties on the hygrothermal response of a thick-wall assembly. Results show that rainwater leakage values of 0.50% and greater of the adhering rainfall on the exterior surface of the assembly results in the greatest risk for failure. The hygrothermal response of the assembly is then examined under a global temperature rise scenario of 3.5°C for five geographic locations across Canada. Results show that an increase in average annual total rainfall does not directly result in an increase in the failure rate of the assembly when a rainwater leak is present. Additional climatic factors, including outdoor air temperature, driving rain and solar radiation received will influence the hygrothermal response of the assembly and need to be considered when modelling the performance under future climate change scenarios. [ABSTRACT FROM AUTHOR]

This study estimates the trends in precipitable water vapor (PWV), atmospheric moisture budget (AMB), and the factors influencing them: air temperature, evapotranspiration (ET), convective available potential energy (CAPE), and vertical velocity (Omega) over the Indian subcontinent using ERA5 reanalysis data sets between 1980 and 2020. PWV is examined across three atmospheric layers (1000–850 hPa: lower layer; 850–500 hPa: middle layer; 500–300 hPa: upper layer), and the entire atmospheric column (EAC; 1000–300 hPa). The observed PWV trends exhibit variability within the EAC, ranging from −0.53 to 1.25 mm/decade across the study area, with the middle layer showing the most pronounced variation (–0.44 to 0.83 mm/decade), followed by the lower layer (0.10 to 0.45 mm/decade), and the upper layer (–0.02 to 0.23 mm/decade). These fluctuations in PWV are attributed to changes in air temperature, ET, CAPE, and Omega. This investigation, however, underscores the necessity of delving into the impacts of these influencing factors on PWV using finer resolution data, to enhance our comprehension of its spatial and temporal dynamics in the region. Furthermore, the annual AMB analysis reveals a declining trend in the study region. These findings collectively contribute to the understanding of regional water-energy cycles and the recent shifts in atmospheric dynamics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Nitrogen losses of nitrous oxide (N2O) in grazed grassland are strongly driven by urine deposition by grazing ruminants. There is robust concern about applying nitrification inhibitor to mitigate nitrogen losses in global grasslands. However, the effect of dicyandiamide (DCD) on N2O emission rates and its driven factors remain unclear due to spatial heterogeneity. In the present study, we synthesized 133 group data from 69 published articles. It was indicated that effect size was -0.784±0.048 (P<0.0001) indicating a reduction of 54.34% in N2O emission rates. There was significant difference on effect size between different dose of nitrogen fertilization. Moderate dosage of DCD application was the best mitigation effect of 56.09%, which was significantly higher than light dosage of DCD application. Mixed effect model results revealed that precipitation, bulk density and air temperature could explain 16.93%, 12.31% and 3.02% variations in effect size. In addition, DCD application was an effective strategy to mitigate N2O emission rates in global grazed grasslands. [ABSTRACT FROM AUTHOR]

Subsurface temperature measurements play a crucial role, for instance, in optimizing geothermal power plants and monitoring heat-storage systems. Previous studies have demonstrated that time-lapse variations in temperature can be correlated with variations in seismic wave speeds, offering the potential for temperature monitoring via seismic surveys. However, an apparent discrepancy has emerged between field and laboratory experiments. Field studies predominantly report positive correlations between temperature and seismic wave speeds, while laboratory experiments often show anticorrelations. This inconsistency underscores the need for a more comprehensive, physics-based understanding of temperature-induced wave speed changes. In this study, we strive to bridge the gap between field and laboratory findings by examining several mechanisms governing temperature-induced seismic wave speed changes, namely the intrinsic temperature dependency of elastic parameters and thermally induced elasticity. We present a physics-based modelling approach to identify the primary mechanisms responsible for temperature-induced seismic wave speed changes. By considering several end-member models, we find that intrinsic temperature dependency of elastic parameters (negative correlation) compete with thermal pressure effects (positive correlation). The precise initial and boundary conditions and physical parameters of the system under consideration will determine the weight of both effects. Temperature-related dilatation does not seem to play an important role. We apply our approach to loosely consolidated sediments in the shallow subsurface of the Groningen region, where subsurface temperature fluctuations are driven by seasonal atmospheric temperature fluctuations roughly between −5 and |$30\, ^\circ$| C. For these models, we predict seasonal temperature-induced changes in body-wave speeds of up to 8 per cent in the first few metres of the subsurface, high-frequency (above 2 Hz) surface wave phase velocity variations in the range of 1–2 per cent, and relative changes in site amplification on the order of 4 per cent. These findings contribute to a more comprehensive understanding of the intricate relationship between temperature and near-surface seismic properties, offering insights for applications as subsurface temperature monitoring systems. [ABSTRACT FROM AUTHOR]

Globally occurring changes in environmental conditions necessitate extending our knowledge of the system‐level mechanisms underlying plant adaptation to multifactorial stress conditions or stress combinations. This is crucial for designing new strategies to maintain plant performance under simultaneous abiotic pressure. Here, we conducted our study at Rohtang Pass and sampled Picrorhiza kurroa leaves along high‐altitude gradient (3400, 3800 and 4100 meters above sea level) in the western Himalayas. The results showed the functional traits associated with morpho‐anatomical structures and eco‐physiological performances are highly variable. The air temperature and relative humidity represent dominant environmental factors among others that significantly regulate plant's physiological performance by adjusting the functional traits in altitude‐specific manner. A trait coordination network is developed among significantly altered plant functional traits, which reveals high‐altitude associated trait‐based adaptation. Moreover, it reveals leaf area shows the highest degree, while photochemical quenching reflects the weighted degree of centrality in the network. Proteomic analysis reveals various stress‐responsive proteins, including antioxidants were accumulated to deal with combined stress factors. Furthermore, a high‐altitudinal protein interaction network unravels key players of alpine plant adaptation processes. Altogether, these systems demonstrate a complex molecular interaction web extending the current knowledge of high‐altitudinal alpine plant adaptation, particularly in an endangered medicinal herb, P. kurroa. Summary statement: The high‐altitude alpine regions are characterised by 'harsh' and 'variable' environmental conditions like sharp temperature shifts, high levels of ultraviolet radiation and low gases availability. In this study, we investigated the mechanisms by which Picrorhiza kurroa withstand the multifactorial alpine environment at morpho‐anatomical, eco‐physiological and molecular levels by examining the plants colonised at three different high altitudes. We observed plant functional traits were stratified by environmental conditions, particularly air temperature and relative humidity, which were underlined by the unique proteome response of P. kurroa at each high‐altitude. [ABSTRACT FROM AUTHOR]

Few studies have investigated the spatial patterns of the air temperature urban heat island (AUHI) and its controlling factors. In this study, the data generated by an urban climate model were used to investigate the spatial variations of the AUHI across China and the underlying climate and ecological drivers. A total of 355 urban clusters were used. We performed an attribution analysis of the AUHI to elucidate the mechanisms underlying its formation. The results show that the midday AUHI is negatively correlated with climate wetness (humid: 0.34 K; semi-humid: 0.50 K; semi-arid: 0.73 K). The annual mean midnight AUHI does not show discernible spatial patterns, but is generally stronger than the midday AUHI. The urban–rural difference in convection efficiency is the largest contributor to the midday AUHI in the humid (0.32 ± 0.09 K) and the semi-arid (0.36 ± 0.11 K) climate zones. The release of anthropogenic heat from urban land is the dominant contributor to the midnight AUHI in all three climate zones. The rural vegetation density is the most important driver of the daytime and nighttime AUHI spatial variations. A spatial covariance analysis revealed that this vegetation influence is manifested mainly through its regulation of heat storage in rural land. [ABSTRACT FROM AUTHOR]

Saccharomyces cerevisiae D65 isolated from the Malus asiatica naturally fermented liquid was used as the parental strain for mutation breeding of atmospheric and room temperature plasma (ARTP). The mutant strains with excellent characteristics were obtained through acid resistance test, esterase activity test, and fermentation test multi-step screening. The results showed that the specific activity of total esterase of D65-4D10 was 2428.30 U·g−1, and the ester content in the fermented Malus asiatica wine was 5710.75 ug·L−1, which was 20.69% and 41.64% higher than that of the parental strain, respectively. Moreover, the Malus asiatica wine fermented by D65-4D10 had stronger antioxidant capacity than the control, the highest score in sensory evaluation, and completed fermentation 24 h earlier than commercial yeast. After seven generations of continuous culture, the fermentation performance of D65-4D10 remained stable and had the potential to be applied to Malus asiatica wine fermentation. [ABSTRACT FROM AUTHOR]

Taking gas and the heat transfer process between gas and plate as the research object, the mathematical model of heat transfer in one working cycle by moving variable temperature air mass under the action of sound field is established, which provides a new idea for understanding thermoacoustic effect. The influence factors in the model are analyzed and it is found that the amplitude of the air mass has a significant influence on the heat transfer, and the heat transfer of the air mass in one working cycle is proportional to the square of the amplitude. In a certain working environment, the thermoacoustic refrigerator has a critical operating frequency, and only when the operating frequency is greater than the critical frequency can refrigeration be realized. The critical operating frequency is independent of the amplitude and increases with the increase of the stack temperature gradient. With the pressure belly point as the reference position, the greater the distance from the reference position, the greater the critical operating frequency. On this basis, the idea of short plate overlapping is put forward and the formation mechanism of temperature difference between two ends of plate overlapping is explained. [ABSTRACT FROM AUTHOR]

We have developed a photocatalyst/base/metal-free, visible-light-driven three-component annulation protocol to access fully substituted 2-iminothiazolidin-4-one derivatives from isothiocyanates, amines, and aryl diazoacetates using a low-energy white light source at room temperature in open air. The [2 + 2 + 1] reaction undergoes successive C–N/C–S bond formation in one pot. This transformation proceeds under extremely mild conditions with a good yield and a good functional group tolerance. Mechanistic studies showed that the reaction involves the formation of free carbene intermediates from aryl diazoacetates under visible light irradiation. [ABSTRACT FROM AUTHOR]

Near-surface air temperature (Ta) is a key variable in global climate studies. Global climate models such as ERA5 and CMIP6 predict various parameters at coarse spatial resolution (>9 km). As a result, local phenomena such as the urban heat islands are not reflected in the model's outputs. In this study, we address this limitation by downscaling the resolution of ERA5 (9 km) and CMIP6 (27 km) Ta to 1 km, employing two different machine learning algorithms (XGBoost and Deep Learning). Our models leverage a diverse set of features, including data from satellites (land surface temperature and normalized difference vegetation index), from ERA5 and CMIP6 climate models (e.g., solar and thermal radiation, wind), and from digital elevation models to develop accurate machine learning prediction models. These models were rigorously validated against observations from 98 meteorological stations in the East Mediterranean (Israel) using a standard cross-validation technique as well as a leave-one-group-out on the station ID evaluation methodology to avoid overfitting and dependence on geographic location. We demonstrate the sensitivity of the downscaled Ta to local land cover and topography, which is missing in the climate models. Our results demonstrate impressive accuracy with the Deep Learning-based models, obtaining Root Mean Squared Error (RMSE) values of 0.98 °C (ERA5) and 1.86 °C (CMIP6) for daily Ta and 2.20 °C (ERA5) for hourly Ta. Additionally, we explore the impact of the various input features and offer an extended application for future climate predictions. Finally, we propose an enhanced evaluation framework, which addresses the problem of model overfitting. This work provides practical tools and insights for building and evaluating Ta downscaling models. The code and data are publicly shared online. [ABSTRACT FROM AUTHOR]

Background: Western Montana, USA, experiences complex air pollution patterns with predominant exposure sources from summer wildfire smoke and winter wood smoke. In addition, climate change related temperatures events are becoming more extreme and expected to contribute to increases in hospital admissions for a range of health outcomes. Evaluating while accounting for these exposures (air pollution and temperature) that often occur simultaneously and may act synergistically on health is becoming more important. Methods: We explored short-term exposure to air pollution on children's respiratory health outcomes and how extreme temperature or seasonal period modify the risk of air pollution-associated healthcare events. The main outcome measure included individual-based address located respiratory-related healthcare visits for three categories: asthma, lower respiratory tract infections (LRTI), and upper respiratory tract infections (URTI) across western Montana for ages 0–17 from 2017–2020. We used a time-stratified, case-crossover analysis with distributed lag models to identify sensitive exposure windows of fine particulate matter (PM2.5) lagged from 0 (same-day) to 14 prior-days modified by temperature or season. Results: For asthma, increases of 1 µg/m3 in PM2.5 exposure 7–13 days prior a healthcare visit date was associated with increased odds that were magnified during median to colder temperatures and winter periods. For LRTIs, 1 µg/m3 increases during 12 days of cumulative PM2.5 with peak exposure periods between 6–12 days before healthcare visit date was associated with elevated LRTI events, also heightened in median to colder temperatures but no seasonal effect was observed. For URTIs, 1 unit increases during 13 days of cumulative PM2.5 with peak exposure periods between 4–10 days prior event date was associated with greater risk for URTIs visits that were intensified during median to hotter temperatures and spring to summer periods. Conclusions: Delayed, short-term exposure increases of PM2.5 were associated with elevated odds of all three pediatric respiratory healthcare visit categories in a sparsely population area of the inter-Rocky Mountains, USA. PM2.5 in colder temperatures tended to increase instances of asthma and LRTIs, while PM2.5 during hotter periods increased URTIs. [ABSTRACT FROM AUTHOR]

Humid heatwaves, characterized by high temperature and humidity combinations, challenge tropical societies. Extreme wet‐bulb temperatures (TW) over tropical land are coupled to the warmest sea surface temperatures by atmospheric convection and wave dynamics. Here, we harness this coupling for seasonal forecasts of the annual maximum of daily maximum TW (TWmax). We develop a multiple linear regression model that explains 80% of variance in tropical mean TWmax and significant regional TWmax variances. The model considers warming trends and El Niño and Southern Oscillation indices. Looking ahead, the strong‐to‐very‐strong El Niño at the end of 2023, with an Oceanic Niño Index of ∼2.0, suggests a 2024 tropical land mean TWmax of 26.2°C (25.9–26.4°C), and a 68% chance (24%–94%) of breaking existing records. This method also predicts regional TWmax in specific areas. Plain Language Summary: The heat and humidity in the tropics can be particularly challenging for people to stay comfortable and healthy. This combination of heat and moisture is described using a measure called the wet‐bulb temperature (TW). We found that these extremely humid and hot conditions on land can be predicted about 5 months in advance using a physics‐based statistical model. The forecast is possible because the peak of El Niño comes before the peak in the warmest sea surface temperatures, which affects the maximum TW on land. This prediction can help tropical societies to better prepare for extreme heat. Key Points: Tropical wet‐bulb temperatures (TW) peak around 5 months after El Niño wintersA multiple linear regression model considering the El Niño‐Southern Oscillation index and the long‐term warming trend effectively explains TWmax variabilityOur model quantifies the likelihood of strong El Niño and human‐induced warming pushing TWmax to record‐breaking levels [ABSTRACT FROM AUTHOR]

While previous studies have largely focused on anthropogenic warming characterized by surface air temperature, little is known about the behaviors of human‐perceived temperature (HPT), which describe the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity and/or wind speed. Here we adopted an optimal fingerprinting method to compare seasonal mean HPTs in China with those from simulations conducted with multiple climate models participating in the Coupled Model Intercomparison Project Phase 6. We found clear anthropogenic signals in the observational records of changes in both summer and winter HPTs over the period 1971–2020. Moreover, the anthropogenic greenhouse gas influence was robustly detected, with clear separation from natural and anthropogenic aerosol forcings. The anthropogenic greenhouse gas forcing plays the dominant role (>90%) of human‐perceived warming. Urbanization effects contribute slightly and moderately to the estimated trends in summer and winter HPTs, respectively, in addition to the effects of external forcing. Plain Language Summary: Human influences have been identified in the observed warming quantified by surface air temperature (SAT), but SAT alone is inadequate as a metric for human thermal comfort. Here we focus on human‐perceived temperature (HPT), which describes the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity, and/or wind speed. We isolate anthropogenic impacts on the observed increase in summer and winter HPTs in China during 1971–2020 by comparing observations with state‐of‐the‐art climate models. Results show that the influence of anthropogenic greenhouse gas is detected, with clear separation from other external forcings such as solar and volcanic activities and anthropogenic aerosols. The human‐induced greenhouse gas increases are also found to explain most (>90%) of the observed human‐perceived warming. Along with the effects of large‐scale anthropogenic forcing, urbanization effects also have a slight to moderate influence on the estimated trends in summer and winter HPTs. Our work is an early attempt to provide quantitative evidence for the physiological impacts of anthropogenic global warming and local urbanization on human beings. Key Points: The warming is quantified by human‐perceived temperature that considers the joint effects of temperature, humidity and/or wind speedHuman influence could be robustly detected in both summer and winter human‐perceived warmingThe observed increase in human‐perceived temperature is mostly attributed to anthropogenic greenhouse gas increases [ABSTRACT FROM AUTHOR]

The Arctic is notable as a region where the greatest rate of increase in precipitation associated with global warming is anticipated. The Arctic precipitation simulated by the Coupled Model Intercomparison Project Phase 6 models showed a strong increasing trend since the 1980s. We found that the forcing factor of the trend is a combination of the continued strengthening of greenhouse gas forcing and the leveling off of aerosol forcing dominated in earlier periods. From an energetic perspective, we found that the increased atmospheric radiative cooling and reduced sensible heat transport from lower latitudes contributed equally to the recent increase in Arctic precipitation. The combination of these two energetic factors suggests a doubling of the Arctic amplification factor for precipitation relative to that for temperature. Future Arctic precipitation will change in proportion to the temperature change, and the fractional contributions of the energetic factors will remain stable across various scenarios. Plain Language Summary: The Arctic region is inherently a low‐precipitation area. However, because of global warming, precipitation is expected to increase substantially in the Arctic region compared with the global average when viewed as a percentage change from the original precipitation. This severely affects climate change in the Arctic environment. The latest climate model simulations show that there has been a rapid increase in precipitation in the Arctic region in recent decades. The driving factors behind the rapid increase are the effects of the accelerating growth of greenhouse gas concentrations, which were previously suppressed by the increasing anthropogenic aerosol emissions before the 1980s. Based on the heat budget of the atmosphere, we identified important factors contributing to these precipitation changes. These include enhanced radiative cooling (responding locally to increased air temperature) and reduced heat transport from lower latitudes due to greater temperature increases at higher latitudes. Future precipitation will change in proportion to the temperature change while maintaining consistent fractional contributions across different scenarios. Key Points: Trends in Arctic precipitation in the recent and future decades are examined from multimodel simulationsThe recent rapid increase is driven by accelerating greenhouse gas concentrations and plateauing growth in anthropogenic aerosol emissionsIncreased radiative cooling and reduced poleward sensible heat transport equally contributed to the Arctic precipitation changes [ABSTRACT FROM AUTHOR]

Lake-effect snowfall (LES) occurs when cold air moves across open lakes. LES is expected to occur more frequently over the TP, due to the intensified lake expansion caused by intensified global warming. Thus, there is an urgent need to comprehensively assess the LES over the TP. Here, we revealed that the LES is triggered by westerly southward shift leading to the drop in air temperature and is positively correlated with lake area, wind speed and longitude across 12 large lakes (>300 km2) based on satellite observations and reanalysis data. Using a sensitivity model simulation, we determined that large lakes in the southern TP contributed to more than 50% of the snowfall in the downwind area in 2013. Projections indicate that the westerly-triggered LES will increase under the future RCP4.5 climate warming scenario, highlighting the importance of developing adaptive policies to address the growing risks associated with future LES. [ABSTRACT FROM AUTHOR]

Strengthened by polar amplification, Arctic warming provides direct evidence for global climate change. This analysis shows how Arctic surface air temperature (SAT) extremes have changed throughout time. Using ERA5, we demonstrate a pan-Arctic (>60°N) significant upward SAT trend of +0.62°C decade−1 since 1979. Due to this warming, the warmest days of each month in the 1980s to 1990s would be considered average today, while the present coldest days would be regarded as normal in the 1980s to 1990s. Over 1979–2021, there was a 2°C (or 7%) reduction of pan-Arctic SAT seasonal cycle, which resulted in warming of the cold SAT extremes by a factor of 2 relative to the SAT trend and dampened trends of the warm SAT extremes by roughly 25%. Since 1979, autumn has seen the strongest increasing trends in daily maximum and minimum temperatures, as well as counts of days with SAT above the 90th percentile and decreasing trends in counts of days with SAT below the 10th percentile, consistent with rapid Arctic sea ice decline and enhanced air–ocean heat fluxes. The modulated SAT seasonal signal has a significant impact on the timing of extremely strong monthly cold and warm spells. The dampening of the SAT seasonal fluctuations is likely to continue to increase as more sea ice melts and upper-ocean warming persists. As a result, the Arctic winter cold SAT extremes may continue to exhibit a faster rate of change than that of the summer warm SAT extremes as the Arctic continues to warm. Significance Statement: As a result of global warming, the Arctic Ocean's sea ice is receding, exposing more and more areas to air–sea interactions. This reduces the range of seasonal changes in Arctic surface air temperatures (SAT). Since 1979, the reduced seasonal SAT signal has decreased the trend of warm SAT extremes by 25% over the background warming trend and doubled the trend of cold SAT extremes relative to SAT trends. A substantial number of warm and cold spells would not have been identified as exceptional if the reduction of the Arctic SAT seasonal amplitudes had not been taken into account. As the Arctic continues to warm and sea ice continues to diminish, seasonal SAT fluctuations will become more dampened, with the rate of decreasing winter SAT extremes exceeding the rate of increasing summer SAT extremes. [ABSTRACT FROM AUTHOR]

Urban surface and near-surface air temperatures are known to be often higher than their rural counterparts, a phenomenon now labeled as the urban heat island effect. However, whether the elevated urban temperatures are more persistent than rural temperatures at timescales commensurate to heat waves has not been addressed despite its importance for human health. Combining numerical simulations by a global climate model with a surface energy balance theory, it is demonstrated here that urban surface and near-surface air temperatures are significantly more persistent than their rural counterparts in cities dominated by impervious materials with large thermal inertia. Further use of these materials will result in even stronger urban temperature persistence, especially for tropical cities. The present findings help pinpoint mitigation strategies that can simultaneously ameliorate the larger magnitude and stronger persistence of urban temperatures. [ABSTRACT FROM AUTHOR]

Slow and long-term variations of sea surface temperature anomalies have been interpreted as a red-noise response of the ocean surface mixed layer to fast and random atmospheric perturbations. How fast the atmospheric noise is damped depends on the mixed layer depth. In this work we apply this theory to determine the relevant spatial and temporal scales of surface layer thermal inertia in lakes. We fit a first order auto-regressive model to the satellite-derived Lake Surface Water Temperature (LSWT) anomalies in Lake Garda, Italy. The fit provides a time scale, from which we determine the mixed layer depth. The obtained result shows a clear spatial pattern resembling the morphological features of the lake, with larger values (7.18± 0.3 m) in the deeper northwestern basin, and smaller values (3.18 ± 0.24 m) in the southern shallower basin. Such variations are confirmed by in-situ measurements in three monitoring points in the lake and connect to the first Empirical Orthogonal Function of satellite-derived LSWT and chlorophyll-a concentration. Evidence from our case study open a new perspective for interpreting lake-atmosphere interactions and confirm that remotely sensed variables, typically associated with properties of the surface layers, also carry information on the relevant spatial and temporal scales of mixed-layer processes. [ABSTRACT FROM AUTHOR]

Accurate air temperature measurements are essential in eddy covariance systems, not only for determining sensible heat flux but also for applying the density effect corrections (DEC) to water vapor and CO2 fluxes. However, the influence of wind-induced vibrations of mounting structures on temperature fluctuations remains a subject of investigation. This study examines 30-min average temperature variances and fluxes using eddy covariance systems, combining Campbell Scientific Anemometer Thermometry (CSAT3B) with closely co-located fine-wire thermocouples alongside LI-COR CO2/H2O gas analyzers at multiple heights above a sagebrush ecosystem. The variances of sonic temperature after humidity corrections (Ts) and sensible heat fluxes derived from are underestimated (e.g., by approximately 5 % for temperature variances and 4 % for sensible heat fluxes at 40.2 m, respectively) as compared with those measured by a fine-wire thermocouple (Tc). Spectral analysis illustrates that these underestimated variances and fluxes are caused by the lower energy levels in the Ts spectra than the Tc spectra in the low frequency range (natural frequency < 0.02 Hz). This underestimated Ts spectra in the low frequency range become more pronounced with increasing as wind speeds, especially when wind speed exceeds 10 m s-1. Moreover, the underestimated temperature variances and fluxes cause overestimated water vapor and CO2 fluxes through DEC. Our analysis suggests that these underestimations when using Ts are likely due to wind-induced vibrations affecting the tower and mounting arms, altering the time of flight of ultrasonic signals along three sonic measurement paths. This study underscores the importance of further investigations to develop corrections for these errors. [ABSTRACT FROM AUTHOR]

A detailed understanding of snow cover and its possible feedback on climate change on the Tibetan Plateau (TP) is of great importance. However, spatiotemporal variability in snow phenology (SP) and its influencing factors on the TP remain unclear. Based on the daily gap-free snow cover product (HMRFS-TP) with 500 m resolution, this study investigated the spatiotemporal variability in snow cover days (SCDs), snow onset date (SOD), and snow end date (SED) on the TP from 2002 to 2022. A structural equation model was used to quantify the direct and indirect effects of meteorological factors, geographical location, topography, and vegetation greenness on SP. The results indicate that the spatial distribution of SP on the TP was extremely uneven and exhibited temporal heterogeneity. SP showed vertical zonality influenced by elevation (longer SCD, earlier SOD, and later SED at higher elevations). A total of 4.62 % of the TP area had a significant decrease in SCDs, at a rate of -1.74 d yr -1. The SOD of 2.34 % of the TP area showed a significant delayed trend, at a rate of 2.90 d yr -1 , while the SED of 1.52 % of the TP area had a significant advanced trend, at a rate of at -2.49 d yr -1. We also found a strong elevation dependence for the trend in SCDs (R=-0.73). Air temperature, precipitation, wind speed, and shortwave radiation can directly affect SP as well as indirectly affect it by influencing the growth of vegetation, whereas the direct effect was much greater than the indirect effect. Geographical location (latitude and longitude) and topographic conditions (elevation and slope) indirectly affected SP by modulating meteorological conditions and the growth of vegetation. Vegetation primarily influences SP by intercepting the snow and regulating the balance of the solar radiation budget. Regarding the total effect, air temperature was found to be the dominant factor. This study contributes to the understanding of snow variation in response to global warming over the past 2 decades by providing a basis for predicting future environmental and climate changes and their impacts on the TP. [ABSTRACT FROM AUTHOR]

Land snow is considered one of the important Earth system elements altering sub-seasonal to seasonal (S2S) atmospheric variability and predictability. However, the causal relationship in the snow–atmosphere interaction and its impact on S2S predictability are still not clearly understood. In this study, we investigated the sub-monthly causal relationship between observed snow cover (SC) and surface air temperature (SAT) in the Northern Hemisphere. We used Liang–Kleeman information flow analysis to scrutinise the direction of causation and identify "cold spots" where SC conditions actively influence SAT on a sub-monthly timescale. The cold spots were identified by geographical location and season: North Eurasia in September and October; East Siberia in October and May; Canada in November; East Asia in November and March; Central Asia in October and November; and East Europe in March. Results based on snow water equivalent instead of SC also confirmed the cold spots identified in SC. Furthermore, we evaluated the SC–SAT causal relation in operational S2S prediction models. The results indicated that the S2S models underestimate the SC influence on SAT to greater or lesser degrees, implying the deficiencies in the models. This study emphasises the importance of faithfully reproducing the SC effect on SAT in S2S models for further possible improvements in sub-seasonal prediction skill. The findings renew a fundamental understanding of the snow–atmosphere interaction and sub-seasonal predictability arising from land snow conditions. [ABSTRACT FROM AUTHOR]

Results from eight regional climate models (RCMs) participating in the Impact of Initialized Land Temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P) initiative of the Global Energy and Water Exchanges (GEWEX) are examined and compared with observations over the Tibetan Plateau (TP). The RCM common domain covers most areas of East Asia with a horizontal resolution of 20–30 km. The model simulation covers a period from April to September in each year between 1991 and 2015. This study explores the RCMs' ability for seasonal climate simulation over the TP, focusing on the summer monsoon climate as part of the LS4P initiative. An intercomparison is made among eight RCMs for precipitation, surface air temperature, mid-troposphere atmospheric circulation, moisture conditions, and surface energy fluxes. It shows that the downscaling characteristics differ significantly between two major RCM types. The RegCM4 models show positive precipitation biases over the entire TP, especially over the south and southeast TP, while the WRF models mostly show both positive and negative precipitation biases over the TP with relatively high spatial correlation between simulated and observed precipitation. The multi-model ensemble mean produces overall smaller precipitation biases than most individual RCMs, with the largest biases over the southeastern TP, and smaller surface air temperature biases over most areas of the TP, especially over the central and southwestern TP. Moreover, the ensemble mean can better reproduce the inter-annual variation of precipitation and surface air temperature than most RCMs with proper magnitude. Sensitivity analyses using RegCM4 with different physics parameterizations show that varying land and cumulus schemes may induce large precipitation differences over the TP by affecting moisture and atmospheric circulation conditions in the lower and upper troposphere, respectively. Moreover, turbulent heat and radiation fluxes differences are associated with the temperature differences between different RegCM4 models. [ABSTRACT FROM AUTHOR]

The slowly varying soil temperature can exert local and nonlocal influences on regional climate system, and may thus provide a critical source of subseasonal-to-seasonal climate prediction. In this study, we identify that soil temperature in May over the key region of Central Asia (42 °N–50 °N, 62 °E–80°E, KRCA) from Noah, Mosaic, CLM and ERA-interim datasets is closely linked to variations of the surface air temperature, daily maximum temperature and hot days over Northeastern China in summer (June–July–August), with correlation coefficients of regional average detrended time series ranging from 0.42 to 0.54, and all significant at the 99% confidence level for the period of 1979–2018. The possible physical mechanism behind the substantial downstream impacts of soil temperature over Central Asia are explored via diagnostical analysis combined with regional climate model experiments. Warmer soil temperature in May over the KRCA can keep to the summer and cause positive anomalies of geopotential height in summer over Northeastern China through the Rossby wave propagation, and associated stronger subsidence warming, less cloud cover, more solar radiation reaching the surface, higher planetary boundary layer, and stronger thermal advection at 850 hPa, which provide favorable conditions for warmer surface air temperature particularly in the daytime as well as more hot days. This study further reveals that soil temperature over Central Asia in May makes important contribution to prediction of summer surface air temperature, daily maximum temperature and hot days over Northeastern China in terms of regional average time series and spatial patterns. Our findings highlight the previously-unknown substantial role of antecedent soil temperature condition over Central Asia for summer surface air temperature variation and prediction over Northeastern China. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau, as a major elevated heat source, plays a critical role in the Asian monsoon and global climate. Observational data revealed significant correlations between spring surface air temperature in the Tibetan Plateau and downstream summer precipitation interannual variations. Sensitivity experiments using the regional Climate-Weather Research and Forecasting model (CWRF) were conducted to understand the physical processes and mechanisms underlying such delayed teleconnections. A positive temperature forcing was imposed over the plateau on the surface and subsurface soil layers only at the initial conditions around May 1st, 2003. This regional forcing quickly induces positive perturbations in local air temperature and, more importantly, maintains its signal in local soil, especially deep layers, for several months. Consequently, the soil temperature serves as a charged capacitor to modulate the planetary atmospheric circulation and through Rossby wave chains to cause significant summer precipitation anomalies over broad regions. This relayed teleconnection pattern is consistent with that identified from observational data records and CWRF climate simulations during 1980–2015. Diagnostic analyses of observations and simulations suggest that the Tibetan Plateau heating significantly impacts summer East Asian monsoon climate through influencing the South Asian High and shifting the East Asian jet. [ABSTRACT FROM AUTHOR]

This study applies three widely used land models (SSiB, CLM, and Noah-MP) coupled in a regional climate model to quantitatively assess their skill in preserving the imposed ± 5 °C anomalies on the initial land surface and subsurface temperature (LST/SUBT) and generating the 2-m air temperature (T2m) anomalies over Tibetan Plateau (TP) during May–August. The memory of the LST/SUBT initial anomalies (surface/soil memory) is defined as the first time when time series of the differences in daily LST/SUBT cross the zero line during the simulation, with the unit of days. The memory of the T2m anomalies (T2m memory) is defined in the same way. The ensemble results indicate that the simulated soil memory generally increases with soil depth, which is consistent with the results based on the observations with statistic methods. And the soil memory is found to change rapidly with depth above ~ 0.6–0.7 m and vary slowly below it. The land models have fairly long soil memories, with the regional mean 1.0-m soil memory generally longer than 60 days. However, they have short T2m memory, with the regional means generally below 20 days. This may bring a big challenge to use the LST/SUBT approach on the sub-seasonal to seasonal (S2S) prediction. Comparison between the three land models shows that CLM and Noah-MP have longer soil memory at the deeper layers (> ~ 0.05 m) while SSiB has longer T2m/surface memories and near-surface (≤ ~ 0.05 m) soil memory. As a result, it is difficult to say which land model is optimal for the application of the LST/SUBT approach on the S2S prediction. The T2m/surface/soil memories are various over TP, distinct among the land models, and different between the + 5 °C and − 5 °C experiment, which can be explained by both changes in the surface heat fluxes and variances in the hydrological processes over the plateau. [ABSTRACT FROM AUTHOR]

A field campaign in a valley near Seefeld, Austria, well known for the frequent occurrence of cold‐air pools, was conducted to identify the processes leading to the formation and erosion of the cold‐air pool. Here, we focus on a case study in January 2020 that featured cold‐air pool formation interrupted by a wind disturbance. Simulations with the Weather Research and Forecasting model were performed at a horizontal grid spacing of 40 m and compared with measurement results. The model was able to reproduce the intense cooling in the beginning of the night and the cold‐air pool erosion in the middle of the night caused by the wind disturbance, but stronger winds than observed prevented the cold‐air pool from fully re‐establishing in the model after the disturbance. The dominant cooling processes were long‐wave radiative heat loss and turbulent exchange, both of which are parametrized and cool the air locally. Advection was the most important warming contribution during the cold‐air pool disturbances, especially its cross‐valley and vertical components. Owing to numerical constraints and the shallow nature of the cold‐air pool, its extent was limited to the lowest model level. Further improvements to the cold‐air pool's representation in the model would require a finer grid resolution. [ABSTRACT FROM AUTHOR]

Reliable early forecasting of extreme summer air temperatures is essential for effectively managing and mitigating the socioeconomic damage caused by thermal disasters. Numerical weather prediction models have become valuable tools for forecasting air temperature; however, they incur high computational costs, resulting in coarse spatial resolution and systematic bias owing to imperfect parametrization. To address these problems, we developed a novel statistical downscaling and bias correction method (named DeU‐Net) for the maximum and minimum air temperature (Tmax and Tmin respectively) forecasts obtained from the Global Data Assimilation and Prediction System with a spatial resolution of 10 to 1.5 km over South Korea through the fusion of deep learning (i.e., U‐Net) and spatial interpolation. In this study, we used a methodology to decompose statistically downscaled Tmax and Tmin forecasts into temporal dynamics over South Korea and spatial fluctuations by pixels. When comparing the proposed DeU‐Net with the dynamical downscaling model (i.e., Local Data Assimilation and Prediction System) and support vector regression‐based statistical downscaling model at the seen and unseen stations for forecasting the next‐day Tmax and Tmin, DeU‐Net showed the highest spatial correlation and the lowest root‐mean‐square error in all cases. In a qualitative evaluation, DeU‐Net successfully produced a detailed spatial distribution most similar to the observations. A further comparison extending the forecast lead time to 7 days indicated that the proposed DeU‐Net is a better downscaling approach than support vector regression, regardless of the forecast lead time. These results demonstrate that bias‐corrected high spatial resolution air temperature forecasts with relatively long forecast lead times in summer can be effectively produced using the proposed model for operational forecasting. [ABSTRACT FROM AUTHOR]

Cold‐air pooling is an important topoclimatic process that creates temperature inversions with the coldest air at the lowest elevations. Incomplete understanding of sub‐canopy spatiotemporal cold‐air pooling dynamics and associated ecological impacts hinders predictions and conservation actions related to climate change and cold‐dependent species and functions. To determine if and how cold‐air pooling influences forest composition, we characterized the frequency, strength, and temporal dynamics of cold‐air pooling in the sub‐canopy at local to regional scales in New England, USA. We established a network of 48 plots along elevational transects and continuously measured sub‐canopy air temperatures for 6–10 months (depending on site). We then estimated overstory and understory community temperature preferences by surveying tree composition in each plot and combining these data with known species temperature preferences. We found that cold‐air pooling was frequent (19–43% seasonal occurrences) and that sites with the most frequent inversions displayed inverted forest composition patterns across slopes with more cold‐adapted species, namely conifers, at low instead of high elevations. We also observed both local and regional variability in cold‐air pooling dynamics, revealing that while cold‐air pooling is common, it is also spatially complex. Our study, which uniquely focused on broad spatial and temporal scales, has revealed some rarely reported cold‐air pooling dynamics. For instance, we discovered frequent and strong temperature inversions that occurred across seasons and in some locations were most frequent during the daytime, likely affecting forest composition. Together, our results show that cold‐air pooling is a fundamental ecological process that requires integration into modeling efforts predicting future forest vegetation patterns under climate change, as well as greater consideration for conservation strategies identifying potential climate refugia for cold‐adapted species. [ABSTRACT FROM AUTHOR]

Studying atmospheric temperature characteristics is crucial under climate change, as it helps us to understand the changing patterns in temperature that have significant implications for the environment, ecosystems, and human well-being. This study presents the comprehensive analysis of the spatiotemporal variability of scaling behavior of daily temperature series across the whole Indian mainland, using a Multifractal Detrended Fluctuation Analysis (MFDFA). The analysis considered 1° × 1° datasets of maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), and diurnal temperature range (DTR) (TDTR = Tmax − Tmin) from 1951 to 2016 to compare their scaling behavior for the first time. Our results indicate that the Tmin series exhibits the highest persistence (with the Hurst exponent ranging from 0.849 to unity, and a mean of 0.971), and all four-temperature series display long-term persistence and multifractal characteristics. The variability of the multifractal characteristics is less significant in North–Central India, while it is highest along the western coast of India. Moreover, the assessment of multifractal characteristics of different temperature series during the pre- and post-1976–1977 period of the Pacific climate shift reveals a notable decrease in multifractal strength and persistence in the post-1976–1977 series across all regions. Moreover, for the detection of climate change and its dominant driver, we propose a new rolling window multifractal (RWM) framework by evaluating the temporal evolution of the spectral exponents and the Hurst exponent. This study successfully captured the regime shifts during the periods of 1976–1977 and 1997–1998. Interestingly, the earlier climatic shift primarily mitigated the persistence of the Tmax series, whereas the latter shift significantly influenced the persistence of the Tmean series in the majority of temperature-homogeneous regions in India. [ABSTRACT FROM AUTHOR]

The changing global climate, characterized by rising surface air temperatures, shifting precipitation patterns, and heightened occurrences of extreme weather events, is anticipated to profoundly impact the environment, economy, and society worldwide. This impact is particularly acute in African nations like Angola, where crucial sectors, such as agriculture, rely heavily on climate variability and exhibit limited adaptive capacity. Given that the majority of Angola's agriculture is rain-fed and serves as a vital source of livelihood for the populace, the country is especially vulnerable to climate change, particularly in its southern region. Climate change has caused severe damage in Angola, especially in the southern part of the country, where the worst droughts in decades have affected over 3.81 million people, resulting in food and water shortages. Between 2005 and 2017, climate-related disasters cost the country about 1.2 billion US dollars, further exacerbating the economic and social challenges faced by the population. This study presents a systematic review of the effects of climate change on agriculture in Angola, with a focus on the southern region. Employing the PRISMA2020 methodology, the review examined 431 documents from databases such as Scopus and Web Science, spanning from 1996 to 2023, with 63 meeting inclusion criteria. The review reveals a paucity of research on the short and long-term impacts of climate change on Angolan agriculture. Projections indicate a rise in temperatures and a general decrease in precipitation, with the southern region experiencing a more pronounced decline. Agricultural productivity may suffer significantly, with models suggesting a potential 7% reduction by 2050. [ABSTRACT FROM AUTHOR]

In this study, the radiation scheme BCC-RAD (Beijing Climate Center RADiative transfer model) developed for global climate models is implemented into the Global–Regional Integrated SysTem (GRIST) model as an alternative to the default RRTMG (general circulation model (GCM) version of the Rapid Radiative Transfer Model) scheme. Its impact on the simulated climate is comprehensively evaluated under different physics parametrization packages, in comparison with both the CERES (partly from ERA5 reanalysis) observations and multi-model results from CMIP6. The results indicate that under the default physics parameterization package of GRIST (PhysC), BCC-RAD improved the simulated global mean cloud cover by ~3% and the clear-sky outgoing longwave radiation by ~5.6 W/m2. Upon the inclusion of the PhysCN parameterization package, BCC-RAD exhibited further improvement in simulated cloud cover and radiative forcing (particularly longwave radiative forcing, the bias of which decreases from −9.2 W/m2 to −1.8 W/m2), leading it to be closer to observations than RRTMG. Additionally, BCC-RAD improved the simulation of atmospheric temperature and hence notably diminished the apparent overestimation of atmospheric humidity seen in RRTMG. This study demonstrates the advantages of BCC-RAD over RRTMG in certain aspects of the GRIST-simulated climate, verifying its capability for the climate-oriented configuration of GRIST. [ABSTRACT FROM AUTHOR]