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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]

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]

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]

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]

GNSS radio occultation (RO) observations play an increasingly important role in monitoring climate changes and numerical weather forecasts in the upper troposphere and stratosphere. The magnitudes of the RO bending angle are small at these altitudes, and therefore residual ionospheric error (RIE) is critical to retrieve vertical profiles of atmospheric temperature and refractivity. The latter represent the state variables of the weather and climate models. RIEs remain poorly characterized in terms of the global geographical distribution and its variations with the local time and altitude influenced by the solar cycle and solar-geomagnetic disturbances. In this study we developed a new method to determine RIE from the RO excess phase measurement on a profile-by-profile basis. The method, called Φ ex -gradient method, is self-sufficient and based on the vertical derivative of the RO excess phase (Φ ex ) profile, which can be applied to individual RO bending angle observations for RIE correction. In addition to the RIE in bending angle measurements, RIEs are found in the RO Φ ex measurements in the upper atmosphere where an exponential dependence is expected and in small-scale temperature variance of the RO retrieval. We found that the RIE values derived from the Φ ex -gradient method can be both positive and negative, which is fundamentally different from the k -method that produces only the positive RIE values. The new algorithm reveals a latitude-dependent diurnal variation with a larger daytime negative RIE (up to ~3 μrad) in the tropics and subtropics. Based on the observed RIE climatology, a local-time dependent RIE representation is used to evaluate its impacts on reanalysis data. We examined these impacts by comparing the data from the Goddard Earth Observing System (GEOS) data assimilation (DA) system with and without the RIE. The RIF impact on GEOS DA temperature is mainly confined to the polar regions of stratosphere. Between 10 hPa and 1 hPa the temperature differences are ~1 K and exceed ~3–4 K in some cases. These results further highlight the need for RO RIE correction in the modern DA systems. [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]

The purpose of this first article is to provide a physical basis for atmospheric Rossby waves at the tropopause to clarify their properties and improve our knowledge of their role in the genesis of extreme precipitation and heat waves. By analogy with the oceanic Rossby waves, the role played by the pycnocline in ocean Rossby waves is replaced here by the interface between the polar jet and the ascending air column at the meeting of the polar and Ferrel cell circulation or between the subtropical jet and the descending air column at the meeting of the Ferrel and Hadley cell circulation. In both cases, the Rossby waves are suitable for being resonantly forced in harmonic modes by tuning their natural period to the forcing period. Here, the forcing period is one year as a result of the variation in insolation due to solar declination. A search for cause-and-effect relationships is performed from the joint representation of the amplitude and phase of (1) the velocity of the cold or warm modulated airflows at 250 mb resulting from Rossby waves, (2) the geopotential height at 500 mb, and (3) the precipitation rate or ground air temperature. This is for the dominant harmonic mode whose period can be 1/16, 1/32, or 1/64 year, which reflects the intra-seasonal variations in the rising and falling air columns at the meeting of the polar, Ferrel, and Hadley cell circulation. Harmonics determine the duration of blocking. Two case studies referring to extreme cold and heat waves are presented. Dual cyclone–anticyclone systems seem to favor extreme events. They are formed by two joint vortices of opposite signs reversing over a period, concomitantly with the involved modulated airflows at the tropopause. A second article will be oriented toward (1) the examination of different case studies in order to ascertain the common characteristics of Rossby wave patterns leading to extreme events and (2) a map of the globe revealing future trends in the occurrence of extreme events. [ABSTRACT FROM AUTHOR]

With increased atmospheric temperature, temperatures in the shade of trees in parks also increase, and people are faced with high temperature challenges. In this study, thermal comfort in the shade of the trees of an urban park during summer in China was assessed. The subjective responses of the respondents were recorded via questionnaires, and environment parameters were measured. The results show that the air temperature in the shade was 31.1 ± 3.0 °C during the day, and that it peaked at 36.9 °C; the globe temperature was 31.3 ± 3.1 °C, and it peaked at 40.1 ℃. Respondents' clothing insulation was 0.31 ± 0.08 clo, and the effect of clothing adjustment on thermal adaptation was limited. Thermal sensation is linearly related to standard effective temperature (SET), and the upper limit of 80% acceptable SET was 32.1 °C. At different temperature values, the proportion of expected airflow enhancement exceeded 50%. The respondents preferred a neutral-warm sensation. Moreover, there was an obvious thermal adaptation, with thermal history and psychological adaptation being the main factors affecting thermal comfort. This study confirmed the value of shade and provided us with guidance for park planning and design. [ABSTRACT FROM AUTHOR]

An accurate simulation and projection of future warming are needed for a proper policy response to expected climate change. We examine the simulations of the mean global and Arctic surface air temperatures by the CMIP6 (Climate Models Intercomparison Project phase 6) climate models. Most models overestimate the observed mean global warming. Only seven out of 19 models considered simulate global warming that is within ±15% of the observed warming between the average of the 2014–2023 and 1961–1990 reference period. Ten models overestimate global warming by more than 15% and only one of the models underestimates it by more than 15%. Arctic warming is simulated by the CMIP6 climate models much better than the mean global warming. The reason is an equal spread of over and underestimates of Arctic warming by the models, while most of the models overestimate the mean global warming. Eight models are within ±15% of the observed Arctic warming. Only three models are accurate within ±15% for both mean global and Arctic temperature simulations. [ABSTRACT FROM AUTHOR]

This study aimed to examine the spatio-temporal variations in the atmospheric boundary layer height (ABLH) over the Tarim Basin (TB). Monthly ABLH data from the ERA-Interim dataset from January 1979 to December 2018 were used. Periodicity analysis and the Mann–Kendall Abrupt Changes test were employed to identify the change cycle and abrupt change year of the boundary layer height. The Empirical Orthogonal Function (EOF) method was utilized to determine the spatial distribution of the boundary layer height, and the RF method was used to establish the relationship between the ABLH and influencing factors. The results demonstrated that the highest values of ABLH (over 1900 m) were observed in the middle parts of the study area in June, and the ABLH exhibited a significant increase over the TB throughout the study period. Abrupt changes in the ABLH were also identified in 2004, as well as in 2-, 5-, 9-, and 15-year changing cycles. The first EOF ABLH mode indicated that the middle and northeast regions are relatively high ABLH areas within the study area. Additionally, the monthly variations in ABLH show a moderately positive correlation with air temperature, while exhibiting a negative correlation with air pressure and relative humidity. [ABSTRACT FROM AUTHOR]

Baseflow is a critical hydrological variable linked to a variety of environmental factors. The construction of a dam may modify baseflow generation and adjust its environmental dependence via the regulation of discharge. However, its effects on the relationships among air temperature, water temperature, and baseflow remain unclear. Here, we use copula functions to explore the relationships among these variables in the Yangtze River and show how they have changed in response to the Three Gorges Dam (TGD). Because of the buffering effects of the baseflow, baseflow is negatively correlated to water temperature, and the relationship between baseflow and water temperature lessens as air temperature rises. Construction of the TGD attenuates the relationship between baseflow and water temperature when air temperature is low, and this attenuation is especially prominent in winter and spring. Our findings will enhance environmental protection in the Yangtze River and improve the ecological operations of the TGD. [ABSTRACT FROM AUTHOR]

The combined heating method of seat heating and air conditioning (A/C) was applied in the passenger compartment under different experiment conditions, using thermocouples to continuously measure the wall surfaces and air temperatures in the passenger compartment and the passengers' skin temperatures of 17 segments. Meanwhile, a subjective evaluation questionnaire survey was conducted using a nine-point evaluation scale on the local and overall thermal sensation and thermal comfort of the passengers, and the data from the questionnaire were analyzed with the ANOVA method. The results showed that the use of the heating pad directly affected the changes in human skin temperature, which in turn affected the local and overall thermal sensation and thermal comfort. For the two thermally stimulated segments of the back and under the thighs, the skin temperature of the back was higher than that of the thighs. Using the heating pad resulted in a rapid increase in the mean skin temperature in the early period of the experiment. Thermal sensation of the back and under-thighs shifted rapidly towards the hot zone in the first 10 min, and then settled around +3, with even more significant differences between the groups. Thermal sensations in non-thermally stimulated segments changed in relation to their position on the heating pad, with slower changes in those at the "distal" end of the body, the head and the feet. Continued use of the heating pads at lower ambient temperatures maintained overall thermal comfort at a neutral level in the range of 0–1, whereas at higher ambient temperatures there was a gradual deterioration of local and overall thermal comfort. [ABSTRACT FROM AUTHOR]

The intermittency of gravity wave potential energy (GWPE) in the upper troposphere and stratosphere was investigated using the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) temperature data over three typical mountains (Tibetan Plateau, Rocky Mountains, and Andes). These typical mountains have high sea level elevations but different land–sea contrast. The probability density function (PDF) of GWPE has the independent variable of GWPE and dependent variable of occurrence probability of GWPE over a region. Our analysis showed that the PDFs of GWPE over these three mountains roughly followed lognormal distributions in all heights and months. But, the key parameters (mean value and standard deviation) of lognormal distribution varied with heights and months. Above each mountain, the two key parameters exhibited similar temporal and spatial distributions. They had the largest values around the tropopause region, smaller values in the lower stratosphere (~20–30 km), and larger values in the upper stratosphere (~35–45 km). The intermittency of GWs is represented as the ratio of the GWPE at 50th percentile to the GWPE at 90th percentile. The weakest intermittency was at ~20–30 km (above the zonal mean winds of zero) over the Tibetan Plateau and Rocky Mountains in all months and over the Andes from November to March, respectively. Generally, the weakest intermittency (~0.4) occurred in the region where the key parameters were the smallest around summer. The key parameters of lognormal distribution were dominated by annual variation over the Andes throughout the height range, 8–50 km. However, the semiannual variations are also significant in the lower stratosphere over the Tibetan Plateau and Rocky Mountains. The seasonal variations in the intermittency were not as obvious as those of the key parameters. The lognormal distributions and the intermittencies derived here provide an observational constraint on the tunable parameters in GW parameterization schemes. [ABSTRACT FROM AUTHOR]

This study addresses a crucial gap in understanding the impact of urban morphologies on the canopy urban heat islands (CUHI) effect. The selection of reference stations lacks a unified standard, and their surface air temperature (SAT) sequences are also inevitably influenced by urbanization. However, synchronous observational data from relocated meteorological stations could provide high-quality sample data for studying CUHI. Utilizing remote sensing techniques, the findings of this paper revealed that the observation environment of stations after relocation exhibited remarkable representativeness, with their observation sequences accurately reflecting the local climatic background. The differences in synchronized observation sequences could characterize the CUHI intensity (CUHII). Among the various factors, land use parameters and landscape parameters played particularly significant roles. Furthermore, the fitting performance of the random forest (RF) model for both training and testing data was significantly superior to that of the linear model and support vector regression (SVR) model. Additionally, the influence of local circulation on CUHI could not be overlooked. The mechanisms by which urban morphologies affect CUHII under different circulation backgrounds deserve further investigation. [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]

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]

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]

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]

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]

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]

Changes are projected for the boreal biome with complex and variable effects on forest vegetation including drought‐induced tree mortality and forest loss. With soil and atmospheric conditions governing drought intensity, specific drivers of trees water stress can be difficult to disentangle across temporal scales. We used wavelet analysis and causality detection to identify potential environmental controls (evapotranspiration, soil moisture, rainfall, vapor pressure deficit, air temperature and photosynthetically active radiation) on daily tree water deficit and on longer periods of tree dehydration in black spruce and tamarack. Daily tree water deficit was controlled by photosynthetically active radiation, vapor pressure deficit, and air temperature, causing greater stand evapotranspiration. Prolonged periods of tree water deficit (multi‐day) were regulated by photosynthetically active radiation and soil moisture. We provide empirical evidence that continued warming and drying will cause short‐term increases in black spruce and tamarack transpiration, but greater drought stress with reduced soil water availability. Plain Language Summary: This research explores how climate change could impact the water stress experienced by black spruce and tamarack trees in the western boreal forest of Canada. We focused on a key measure called "tree water deficit" to understand if the trees were under stress due to insufficient water. We examined how tree water deficit relates to environmental factors such as temperature, sunlight, and soil moisture. The findings revealed that, on a daily basis, factors like sunlight and temperature cause trees to release more water into the air. However, over longer periods (days to weeks), the amount of water in the soil becomes crucial, suggesting that trees might face water stress during dry spells. So, while trees could grow more on hotter, sunnier days, they could also experience water stress and reduced growth if the soil becomes too dry for an extended period. This study helps us grasp how various factors interact to influence tree water stress in the boreal forest, providing insights important for managing these ecosystems in a changing climate. Key Points: A novel approach to determine environmental controls of tree water deficit across time scales with wavelet analysis and Granger causalitySoil moisture emerges as a significant control of tree water deficit in boreal trees at longer scales (multi‐days)Daily productivity gains with warming will be mitigated by decreased soil water availability in longer periods of tree water deficit [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]

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]

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]

Strawberries are an economically valuable fruit in Japan; hence, their production must be maintained throughout the year. In this study, we investigated the effect of temperature on the number of days required for each fruit growth and ripening stage in order to obtain a basic knowledge for the highly accurate prediction and control of fruit harvest time. We planted the 'Koiminori' variety in artificial climate chambers, and then the effect of the average air temperature on the number of days required for each stage was analysed in four stages. The results showed that the correlation between temperature and the number of days required for fruit maturity was high at the white stage, moderate at the flowering and turning stages, and weak at the green stage. In comparing our proposed method which estimates the entire maturation period of the strawberries by totalling the predicted number of days required for each stage with the conventional method of estimating the entire maturation period at once, our proposed method significantly reduced the mean absolute error. However, the difference was slight at 0.18 days. This result suggests that the number of days required for each stage should be optimized for prediction by adding parameters other than air temperature as independent variables. [ABSTRACT FROM AUTHOR]

The efficacious forecasting of single‐station atmospheric temperature profiles can provide essential support for the structural design and flight missions of spacecrafts in near space. However, empirical models and reference atmospheric models most are calculations of the average state of the atmosphere profiles. Numerical assimilation models require expensive computational costs to improve the accuracy for medium and long‐term forecasting. It has been still a challenge to refined predict short‐term temperature profiles of near space at a low‐cost. We present a temperature profile operator method for refined modeling in the stratosphere by fusing the ability of Long Short‐Term Memory (LSTM) networks or its variants‐ bidirectional LSTM (BiLSTM) to exploit time series correlated information and deep operator networks (DeepONets) to approximate the solution operator of temperature profiles. It consists of three subnetworks. The first subnetwork is used to approximate the discrete temperature profile function, the second net is applied to represent the spatial information of pressure heights, and the third branch is utilized to encode the time domain of the temperature profile operator. We first use the hourly low latitude temperature data (20–50 km) from ERA5 for training, verification and iterative testing in the next 48 hr. The results denote that the temperature profile operator network has a stable and low error of cumulative generalization, and the BiLSTM operator significantly outperform the other models. We also apply two scenarios to testing the refined applicability of the high latitude temperature profile operator and the mid latitude wind profile operator in the stratosphere. This work provides a novel perspective for us to study the refined single‐station modeling of the upper and middle atmosphere. Plain Language Summary: The temporal and spatial distribution mechanism of physical parameters in near space is very complex. It is of great significance to carry out fine modeling and forecasting of atmospheric temperature profiles for spacecrafts in near space. In this study, taking a single‐station temperature profile in the stratosphere as the object, the mathematical mapping problem of temperature operator is realized by a new deep learning method (deep operator networks). This provides a new research perspective for fine spatiotemporal modeling of temperature profiles. The proposed temperature operator shows low cumulative error performance in forecasting low latitude temperature profiles, and is also suitable for high latitude temperature profiles and mid latitude wind profiles in the stratosphere. Key Points: A deep neural operator method is utilized to forecast hourly atmospheric temperature profiles in the stratosphereThe temperature profile operator network based on BiLSTM denotes lower cumulative error in multi‐step iterative forecastingThe proposed method exhibits potential performance in different locations and atmospheric variables [ABSTRACT FROM AUTHOR]

The efficiency of evaporative media typically ranges from 80% to 90%. The most effective systems can reduce the temperature of dry air to 95% of the wet-bulb temperature, while the least effective systems can only accomplish 50%. The rate of evaporation efficiency experiences little decline over time. The climate in Iraq has a more extended period of summer in comparison to other nations. The ambient temperature during this season exceeds 50oC, making the evaporative cooling system appropriate for this region. The evaporative cooler in this study is created by incorporating multiple heat exchanger stages (water-air) to cool the input air indirectly and indirectly before cooling directly inside the traditional evaporative cooler. When compared to compression refrigeration systems, the multistage evaporative cooler helps to reduce energy use, environmental pollution, the effects of global warming, and manufacturing costs. The multistage evaporative cooler is appropriate for use in homes and sizable structures with high relative humidity and temperatures. The developed evaporative cooler's experimental findings demonstrated that the addition of a cooling stage to a conventional cooler reduces the out-dry bulb temperature by about 50% and the specific humidity by about 80%. [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]

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]

Recent climatic changes, most evident in air temperature, also significantly impact sensible conditions, particularly affecting the human body during the summer season in urban areas. This study utilized hourly values of air temperature (t, °C) and relative humidity (RH, %) for 2008–2022 obtained from nine measurement points located in the city of Poznań. The measurement network was set up and supported by the Department of Meteorology and Climatology of Adam Mickiewicz University in Poznań. Based on these data, thermal conditions were characterized using the Humidex (HD), as well as "hot days" (Tmax > 30 °C) and heat waves determined based on them. The conducted research revealed variability in thermal conditions across the city. The highest average Tmax (27.4 °C) and HD values (31.3 °C), as well as the greatest average number of hot days (15 days), were recorded in the compact, dense city center. In contrast, the lowest values (respectively: 25.9 °C, 27.5 °C, 8 days) were observed in areas with a significantly greater proportion of green spaces located further from the center. Furthermore, significant trends of change were observed: an annual average Tmax increase of 1.2–1.6 °C/decade and HD values showing an increase of 1.5–3.0 °C/decade. This study also highlighted the noteworthy impact of urbanization and the presence of urban greenery on the frequency and total duration of heat waves. Between 2008 and 2022, the area of compact development experienced 26 heat waves lasting a total of 115 days, whereas the area with a high proportion of greenery recorded 14 cases and 55 days, respectively. The longest recorded heat wave, spanning 9 days, occurred in July 2010. Individual heat waves identified based on Tmax (>30 °C) may exhibit significant variations in terms of perceived conditions, as indicated by HD. [ABSTRACT FROM AUTHOR]

Greece and the surrounding areas experienced an early warm spell with characteristics of a typical summer Mediterranean heatwave in mid-May 2020. The maximum 2 m temperature at Kalamata (southern Greece) reached 40 °C on 16 May and at Aydin (Turkey), it was 42.6 °C on 17 May. There was a 10-standard deviation positive temperature anomaly (relative to the 1975–2005 climatology) at 850 hPa, with a southwesterly flow and warm advection over Greece and western Turkey from 11 to 20 May. At 500 hPa, a ridge was located over the Eastern Mediterranean, resulting in subsidence. The aims of this study were (a) to investigate the prevailing synoptic conditions during this event in order to document its occurrence and (b) to assess whether this out-of-season heatwave was predictable on subseasonal timescales. The subseasonal predictability is not a well-researched scientific topic in the Eastern Mediterranean Sea. The ensemble global forecasts from six international meteorological centres (European Centre for Medium-Range Weather Forecasts—ECMWF, United Kingdom Met Office—UKMO, China Meteorological Administration—CMA, Korea Meteorological Administration—KMA, National Centers for Environmental Prediction—NCEP and Hydrometeorological Centre of Russia—HMCR) and limited area forecasts using the Weather Research and Forecasting model with the Advanced Research dynamic solver (WRF) forced by Climate Forecast System version 2 (CFSv.2; NCEP) forecasts were evaluated for lead times ranging from two to six weeks using statistical scores. WRF was integrated using two telescoping nests covering Europe, the Mediterranean basin and large part of the Atlantic Ocean, with a grid spacing of 25 km, and Greece–western Turkey at 5 km. The results showed that there were some accurate forecasts initiated two weeks before the event's onset. There was no systematic benefit from the increase of the WRF model's resolution from 25 km to 5 km for forecasting the 850 hPa temperature, but regarding the prediction of maximum air temperature near the surface, the high resolution (5 km) nest of WRF produced a marginally better performance than the coarser resolution domain (25 km). [ABSTRACT FROM AUTHOR]

The Qinghai-Tibetan Plateau (QTP), the source of many major Asian rivers, is sensitive to climate change, affecting billions of people's livelihoods across Asia. Here, we developed high-resolution projections of precipitation and daily maximum/minimum temperatures at 0.1° spatial resolution over the QTP. The projections are based on the output from seven global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for historical (1979–2013) and projected (2015–2100) climates across four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). An updated nonstationary cumulative distribution function matching method (called CNCDFm) was used to remove model systemic bias. We verify the necessity of taking into account altitude in downscaling processes and the validity of nonstationary bias correction. Compared to the historical period, the climate in the QTP in the 21st century is warmer (1.2–5.1 °C, for maximum surface temperature) and wetter (3.9–26.8%) according to the corrected GCM projection. For precipitation, the Indus River (IDR), Tarim River (TMR), Inner of Qiangtang Basin (IQTB), Yarlung Zangbo (YLZBR), and Qaidam Basin (QDB) showed growth well above the global average across high radiative forcing scenarios, which could have a profound impact on the regional hydrological cycle. However, there is great uncertainty in precipitation prediction, which is demonstrated by a very low signal-to-noise ratio (SNR) and a large difference between Bayesian model averaging (BMA) and multi-model averages (MMAs). This bias-corrected dataset is available for climate change impact research in the QTP at the subregion scale. [ABSTRACT FROM AUTHOR]

To adapt to Earth's rapidly changing climate, detailed modelling of thermal stress is needed. Dangerous stress levels are becoming more frequent, longer, and more severe. While traditional measurements of thermal stress have focused on air temperature and humidity, modern measures including radiation and wind speed are becoming widespread. However, projecting such indices has presented a challenging problem, due to the need for appropriate bias correction of multiple variables that vary on hourly timescales. In this paper, we aim to provide a detailed understanding of changing thermal stress patterns incorporating modern measurements, bias correction techniques, and hourly projections to assess the impact of climate change on thermal stress at human scales. To achieve these aims, we conduct a case study of projected thermal stress in central Hobart, Australia for 2040–2059, compared to the historical period 1990–2005. We present the first hourly metre-scale projections of thermal stress driven by multivariate bias-corrected data. We bias correct four variables from six dynamically downscaled General Circulation Models. These outputs drive the Solar and LongWave Environmental Irradiance Geometry model at metre scale, calculating mean radiant temperature and the Universal Thermal Climate Index. We demonstrate that multivariate bias correction can correct means on multiple time scales while accurately preserving mean seasonal trends. Changes in mean air temperature and UTCI by hour of the day and month of the year reveal diurnal and annual patterns in both temporal trends and model agreement. We present plots of future median stress values in the context of historical percentiles, revealing trends and patterns not evident in mean data. Our modelling illustrates a future Hobart that experiences higher and more consistent numbers of hours of heat stress arriving earlier in the year and extending further throughout the day. [ABSTRACT FROM AUTHOR]