Search

By observing well‐known sea surface temperature (SST) indices and outputs from Climate Forecast System version 2 (CFSv2) hindcasts and predictions, in this study, we developed a hybrid statistical downscaling prediction model (HSDPM) based on a timescale decomposition approach, which effectively improved the forecasting ability of summer (June–August) precipitation over Hainan Island (HNSP) in China; this precipitation presented multiple obvious timescale variabilities. We found that the interannual variability of the HNSP is closely related to the observed North Atlantic tripole SST anomalies in the preceding winter and the summer sea level pressure over the South China Sea by CFSv2 predicted in March. On the other hand, the interdecadal variability in the HNSP is linked to the prewinter Pacific Interdecadal Oscillation (PDO) according to the observations, the CFSv2 predictions of the summer surface air temperature over the tropical central‐east Pacific. On this basis, both the interannual and interdecadal components of the HNSP are effectively predicted using the corresponding predictors via multiple regression; thus, the HSDPM is ultimately established by combining the above two components. Compared with the original CFSv2 model, the HSDPM model achieves a considerable improvement in performance in predicting the HNSP. Specifically, the temporal correlation coefficient, ratio of root‐mean‐square error and anomaly sign consistency rate between the observed and HSDPM‐predicted HNSPs are 0.70, 17.4% and 80.5%, respectively, which are significantly greater than the above three forecasting index values of 0.23, 38.3% and 48.8%, respectively, obtained from the original CFSv2 predictions. The application of the HSDPM may be beneficial for drought and flood prevention and mitigation in Hainan Island. [ABSTRACT FROM AUTHOR]

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

The seasonal cycle (SC) of surface air temperature is a substantial element in climatology. Some basic and important topics in climate change studies are based on accurate and reliable estimation of SCs, such as percentile‐based indices of extremes and probability density function (PDF) changes of daily temperatures. For both of them, most studies characterized SCs using averages of multi‐days windows, which is not smooth and accurate enough representing the extreme thresholds and climatological normal of SCs. It is necessary to construct smooth and reasonable SCs for more accurate estimation of temperature changes on extreme thresholds and PDFs. In this study, we propose a flexible method based on generalized additive models for location scale and shape and penalized b‐spline smoothing technique with respective distributions to construct smooth SCs and SC for extreme temperatures (SCETs). The accuracy of the constructed smooth SCETs is good with the estimation biases of percentiles tending to be zero. The constructed smooth SCET also exhibits good stability over time, such that the magnitude changes of temperatures on each calendar day are close to climatic changes of mean temperature when the concerned period shifts. Based on the constructed smooth SCs, climatic changes by seasons and PDFs between two periods, 1961–1990 and 1991–2020, are examined over China. The increase of thresholds for hot extremes in spring during the recent period is prominent, while the increase of thresholds for daytime cold extremes in summer over a part of central to southern China is also notable. The smooth SCs and SCETs based on our flexible statistical modelling framework can characterize daily extreme temperatures reasonably and accurately, and should be expected to have more applications for a better understanding of climate changes related to distributions and seasonal cycles. [ABSTRACT FROM AUTHOR]

The purpose of this research is to identify changes in the spatiotemporal distribution and severity of meteorological droughts in Ukraine between 1946 and 2020. In order to determine the role of precipitation and atmospheric evaporative demand on drought severity, two drought indices were applied for comparative analysis: the standardized precipitation index (SPI) and the standardized evaporation precipitation index (SPEI). Both indices were calculated on 3‐ and 12‐month timescales. The gridded dataset of monthly air temperature and atmospheric precipitation for Ukraine with high spatial resolution (0.1° × 0.1°), developed in the Ukrainian Hydrometeorological Institute, was used. Results of time series analysis of drought indices showed at least 20 episodes, in which more than 25% of the territory of Ukraine was affected by drought of varying intensity. Trends show an increase in drought severity determined mostly by a strong observed increase in the atmospheric evaporative demand. Tendencies towards an increase in the duration and intensity of droughts were observed mainly in the southwestern, central and northern regions of Ukraine. The main cause of the increase was droughts in spring and summer. The temporal analysis of the spatial distribution of areas affected by drought during the identified main drought episodes showed different patterns in drought propagation across the territory of Ukraine. [ABSTRACT FROM AUTHOR]

Under global warming, the summer surface air temperature (SAT) change signal in the northern mid‐latitudes is generally the most significant, while the SAT change in North China (NC) shows distinct local characteristics. Using simulations from the Coupled Model Intercomparison Project Phase 6 and observation from Berkeley Earth Surface Temperature, this study reveals an unusually weak signal of summer SAT change in NC since the 1960s. This uniquely weak signal can be attributed to the small net contribution of external forcings, mainly from anthropogenic greenhouse gases (GHG) and aerosols (AA). Compared to other land regions in the same latitudinal band, the GHG‐induced warming was weaker and AA‐induced cooling was stronger in NC, resulting in the weakest SAT change signal and thus the lowest signal‐to‐noise ratio. This weaker GHG‐induced warming plays a more important role in the SAT change difference between NC and other land regions in the same latitudinal zone. Under different emission scenarios in the future, the signal‐to‐noise ratio in NC will become as large as those in the other land regions in the same latitudinal band and the northern hemisphere, which is partly due to the smaller relative differences in the SAT change signal between NC and the other two regions. The projections of SAT change indicate that climate change in NC will probably become more violent and vulnerable. [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]

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]

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]

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]

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]

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]

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]

Exposure biases are a pervasive non‐climatic change in land air temperature records which have been introduced as a result of changes in the way thermometers were sheltered from solar radiation and the elements over time. Exposure biases have not been widely accounted for in observational records, due to difficulties detecting/correcting the bias using traditional homogenisation techniques; therefore, exposure biases still contribute significant uncertainty to the early period in global temperature compilations. Here, an empirical approach to address the bias arising from the introduction of Stevenson‐type screens from the late‐19th century is presented. The approach consists of: (1) an empirical analysis of 54 parallel measurement series to identify the characteristics of the exposure bias in four exposure classes; (2) the development of bias‐estimation models based on an analysis of which variables influence the bias; and (3) the application of the models to an extended version of CRUTEM5 (CRUTEM5_ext), based on exposure metadata, to quantify and reduce the bias. Step one identified differences between the temperatures recorded in Stevenson screens and early exposures, which vary seasonally, diurnally, and with location and exposure class. The largest biases (in mean temperatures) were found in freestanding exposures (up to −0.78°C annually) and in summer, while the smallest biases were generally found in wall‐mounted exposures (near‐0°C annually) and in winter. Significant relationships between the bias and temperature, downward top of atmosphere and/or received shortwave downward solar radiation were found in each exposure class and led to the development of three regression‐based bias‐estimation models. Application of these models to 1,960 mid‐latitude stations in CRUTEM5_ext, resulted in small (≤0.016°C) positive adjustments to the Northern Hemisphere mid‐latitude mean before 1880, and larger (≤−0.1°C) negative adjustments to the Northern and Southern Hemisphere mid‐latitude means between 1882–1934 and 1856–1900, respectively. Larger adjustments were estimated regionally: up to −0.57°C annually and −0.79°C seasonally in individual grid cells. [ABSTRACT FROM AUTHOR]

The climatology and variability of the January to February (JF) season in eastern Africa's (EA) precipitation are examined during the 1960–2020 period, as off‐season climate could have dire consequences, considering agricultural practices tie to the seasonal cycle of precipitation. The analysis in this study is divided into four parts. The first is the climatological background of variability during the JF season. Second, the spatiotemporal variability of the leading mode of the JF precipitation is described using an empirical orthogonal function (EOF) method. Third, anomalous atmospheric circulations linked to the variability of the JF precipitation were examined through composite analysis. Fourth, the link between JF precipitation and sea surface temperature (SST) is explored using composite and correlation analyses. The leading mode (EOF1) shows a monopole variation, with a positive anomaly in the entire region accounting for 55.1% of the total variance. EOF1 is linked to the SST anomaly (SSTA) over the tropical Indian Ocean (TIO). A warm (cool) SSTA in the TIO induces diabatic warming/adiabatic cooling (diabatic cooling/adiabatic warming). This leads to the rising (sinking) of warm and moist air (cold and dry air) from the lower to higher (higher to lower) troposphere via the ascending (descending) branch of the Walker circulation and contributes to the upper warm (cold) temperature anomaly centred at ~300 hPa. The warm (cold) anomaly is closely associated with the upper‐level westerly (easterly) and divergence (convergence) anomalies at the upper side of the warm (cold) core, coupled with ascending (descending) and deep wet (dry) anomalies below the warm (cold) core. This induces moisture convergence (divergence) and unstable (stable) conditions that favour (suppresses) precipitation over EA. Consequently, this study may facilitate the prediction of the JF precipitation and decrease in socio‐economic losses in EA. [ABSTRACT FROM AUTHOR]

BACKGROUND: This research aimed to identify the agroclimatic zones in Brazil, excluding Rio Grande do Sul, that are suitable for olive (Olea europaea L.) cultivation, considering both climatic and topographical factors. Olives require specific conditions: moderate winter temperatures (7–15 °C), warmer summers (25–35 °C) and sufficient water during growth and fruit maturation. They can endure some drought, making them a viable option for agricultural diversification. Using daily meteorological data from 1989 to 2023 from NASA‐POWER, this study analyzed variables like air temperature (minimum and maximum) and rainfall. Key climate variables were the mean air temperature in winter (T_w), spring (T_s), summer (T_su) and autumn (T_a) and total annual precipitation (Prec). Criteria for suitability included: T_w between 5 and 20 °C, T_s between 15 and 23 °C, T_su between 15 and 30 °C, T_a between 15 and 22 °C, annual precipitation over 900 mm and altitude below 900 m. Geographic information system software and Python 3.8 were employed for data analysis and zoning. RESULTS: Results indicated that only 1.92% of the analyzed area, mainly in Minas Gerais, was suitable for olive cultivation. High temperatures and low rainfall in Brazil, particularly in the North and Midwest, make 59.56% of the country unsuitable for olive farming. Additionally, 18.58% of the land, mainly in the Northeast, faces challenges due to extreme heat (T_w) and insufficient water supply. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Motivation: Impacts of climate change on marine biodiversity are often projected with species distribution modelling using standardized data layers representing physical, chemical and biological conditions of the global ocean. Yet, the available data layers (1) have not been updated to incorporate data of the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), which comprise the Shared Socioeconomic Pathway (SSP) scenarios; (2) consider a limited number of Earth System Models (ESMs), and (3) miss important variables expected to influence future biodiversity distributions. These limitations might undermine biodiversity impact assessments, by failing to integrate them within the context of the most up‐to‐date climate change projections, raising the uncertainty in estimates and misinterpreting the exposure of biodiversity to extreme conditions. Here, we provide a significant update of Bio‐ORACLE, extending biologically relevant data layers from present‐day conditions to the end of the 21st century Shared Socioeconomic Pathway scenarios based on a multi‐model ensemble with data from CMIP6. Alongside, we provide R and Python packages for seamless integration in modelling workflows. The data layers aim to enhance the understanding of the potential impacts of climate change on biodiversity and to support well‐informed research, conservation and management. Main Types of Variable Contained: Surface and benthic layers for, chlorophyll‐a, diffuse attenuation coefficient, dissolved iron, dissolved oxygen, nitrate, ocean temperature, pH, phosphate, photosynthetic active radiation, total phytoplankton, total cloud fraction, salinity, silicate, sea‐water direction, sea‐water velocity, topographic slope, topographic aspect, terrain ruggedness index, topographic position index and bathymetry, and surface layers for air temperature, mixed layer depth, sea‐ice cover and sea‐ice thickness. Spatial Location and Grain: Global at 0.05° resolution. Time Period and Grain: Decadal from present‐day to the end of the 21st century (2000–2100). Major Taxa and Level of Measurement: Marine biodiversity associated with surface and epibenthic habitats. Software Format: A package of functions developed for Python and R software. [ABSTRACT FROM AUTHOR]

Aim: Squamate fitness is affected by body temperature, which in turn is influenced by environmental temperatures and, in many species, by exposure to solar radiation. The biophysical drivers of body temperature have been widely studied, but we lack an integrative synthesis of actual body temperatures experienced in the field, and their relationships to environmental temperatures, across phylogeny, behaviour and climate. Location: Global (25 countries on six continents). Taxa: Squamates (210 species, representing 25 families). Methods: We measured the body temperatures of 20,231 individuals of squamates in the field while they were active. We examined how body temperatures vary with substrate and air temperatures across taxa, climates and behaviours (basking and diel activity). Results: Heliothermic lizards had the highest body temperatures. Their body temperatures were the most weakly correlated with substrate and air temperatures. Body temperatures of non‐heliothermic diurnal lizards were similar to heliotherms in relation to air temperature, but similar to nocturnal species in relation to substrate temperatures. The correlation of body temperature with air and substrate temperatures was stronger in diurnal snakes and non‐heliothermic lizards than in heliotherms. Body‐substrate and body‐air temperature correlations varied with mean annual temperatures in all diurnal squamates, especially in heliotherms. Thermal relations vary with behaviour (heliothermy, nocturnality) in cold climates but converge towards the same relation in warm climates. Non‐heliotherms and nocturnal species body temperatures are better explained by substrate temperature than by air temperature. Body temperature distributions become left‐skewed in warmer‐bodied species, especially in colder climates. Main Conclusions: Squamate body temperatures, their frequency distributions and their relation to environmental temperature, are globally influenced by behavioural and climatic factors. For all temperatures and climates, heliothermic species' body temperatures are consistently higher and more stable than in other species, but in regions with warmer climate these differences become less pronounced. A comparable variation was found in non‐heliotherms, but in not nocturnal species whose body temperatures were similar to air and substrate irrespective of the macroclimatic context. [ABSTRACT FROM AUTHOR]

Deforestation in the Brazilian Amazon (BA) for cattle and soybean production has significant consequences for the various aspects of the climate system. Land surface modifications due to deforestation directly influence surface energy and moisture availability, hence impacting rainfall patterns, air temperature and the onset of the agricultural rainy season. Here, we assess the forest loss‐related climate risks for the first and second crop seasons of the soy‐maize double cropping system in the BA. We utilized long‐term, daily, remote sensed climate data and annual land‐use maps as input for a machine learning algorithm to isolate the signal of forest loss on the climate. Our findings indicate that forest loss in the BA intensifies the risks of climate change from the local to the regional geographical scale, with the impact being more pronounced at the regional scale. Between 1999 and 2019, largely deforested regions exhibited a delay of approximately 76 days in the onset of the agricultural rainy season. These regions also experienced a 360 mm decrease in rainfall and an increase in maximum air temperature of 2.5°C. In view of these results, there are collective advantages of halting deforestation. Conservation of the Amazon Forest is vital for maintaining the early onset of the agricultural rainy season, favourable temperatures and adequate rainfall volume needed for attaining high yields in the soy‐maize double cropping system. [ABSTRACT FROM AUTHOR]

This article analyses the winter warm spells (WWS) that occurred in central Mediterranean over the period 1993–2022, examining the daily maximum temperatures collected at eight airport sites located in the Italian Peninsula, belonging to different climate zones. According to the definition proposed in 1999 by the Expert Team on Climate Change Detection and Indices (ETCCDI), a WWS is a sequence of at least six consecutive days when daily maximum air temperature exceeds the calendar day 90th percentile centred on a 5‐day window for a base period. WWS occurring over the entire Italian territory or only over northern/central/southern Italy have been identified and related to the peculiar synoptic conditions. It was found that December is the month most prone to WWS and, on average, WWS last 9.4 days in northern Italy, 6.6 days in central Italy, and 8.5 days in southern Italy. Over the period under investigation, the Italian Peninsula experienced only one common event characterized by persistent high‐pressure systems associated with air subsidence over western Mediterranean and, therefore, with exceptional warming. Finally, it has been proven that the definition of WWS proposed by ETCCDI allows to capture synoptic scale events but, in orographically complex areas such as Italy, underestimates moderate spells, which generally might have a duration of at least 3 days. Consequently, it is important to consider the possibility of reducing the period length threshold used for the detection of WWS when orographically heterogeneous regions are studied. [ABSTRACT FROM AUTHOR]

The South Asian summer monsoon strongly modulates regional temperature and humidity. While extreme dry heat peaks in the pre‐monsoon season, recent literature suggests that extreme humid heat can continue to build throughout the monsoon season. Here we explore the influence of monsoon onset and subseasonal precipitation variability on the occurrence of extreme wet bulb temperatures (Tw) across South Asia. We find that extreme Tw events often occur on rainy days during the monsoon season. However, the influence of precipitation on Tw varies with the background climatology of surface specific humidity. In climatologically drier areas, positive Tw anomalies tend to occur when precipitation increases due to either early onset or wet spells during the monsoon. In contrast, in climatologically humid areas, positive Tw anomalies occur during periods of suppressed precipitation, including both delayed onset and dry spells during the monsoon. Plain Language Summary: The combination of high heat and humidity poses greater risks to human health, productivity, and well‐being relative to elevated temperatures alone. South Asia has already experienced some of the most extreme humid heat observed on Earth. Typically, the highest temperatures in South Asia occur during the pre‐monsoon season in March‐May. While the precipitation and cloudiness associated with the summer monsoon reduce extreme air temperatures, the increased humidity can contribute to the occurrence of extreme humid heat events. Our research finds that a majority of extreme Tw events across South Asia happen on rainy days during the monsoon season. This is especially true for regions where surface humidity is typically low during the monsoon season, and Tw tends to be higher during heavier than usual precipitation spells and during years when the monsoon season starts earlier in the calendar year. In regions with high background surface humidity, however, Tw is more intense when there is less precipitation than usual or when the monsoon onset is delayed. These results help to identify additional times with high risk of heat stress for outdoor workers, and provide a new way of understanding how monsoon variations influence local humid heat. Key Points: In contrast to dry heat, most extreme wet bulb temperature events in South Asia take place on rainy days during the monsoon seasonLocal background specific humidity determines whether wet bulb temperatures are intensified by enhanced or suppressed precipitationBaseline moisture availability also influences whether early or delayed monsoon onset intensifies wet bulb temperature anomalies [ABSTRACT FROM AUTHOR]

Global warming is destabilizing the cryosphere, with consequences for glaciers, permafrost, sea ice and lake ice. Polar lakes have short ice‐free seasons, and small changes in ice cover duration have the potential to provoke alterations to ecosystem structure. However, these lakes are understudied, and the consequences for mixing regimes, thermal structures and biogeochemical processes remain unclear. We measured three annual cycles of dissolved oxygen, temperature and specific conductivity in a lake at ∼83°N to investigate limnological processes and their interannual variability. There were sharp interannual contrasts in lake dynamics, with state shifts in mixing, stratification and oxygen regimes due to air temperature variability and meteorological events. We also observed unusual thermal profiles that were associated with solute gradients. These striking differences underscore the sensitivity of high Arctic lakes to interannual variations in meteorological forcing, and their susceptibility to regime shifts in response to ongoing global change. Plain Language Summary: Lakes are biodiversity refuges, climate regulators and providers of ecosystem services. High Arctic lakes experience brief summer ice‐free periods and their annual dynamics remain largely unexplored despite their location in one of the most rapidly warming regions on Earth. In particular, patterns of thermal structure and oxygen dynamics have rarely been measured. We studied three complete annual cycles in a coastal lake less than 100 km south of the northernmost land on Earth. The observed limnological dynamics highlight the potential for rapid regime shifts in polar lakes and underline their sensitivity to interannual differences in environmental conditions. Key Points: Pronounced shifts in lake oxygen and temperature dynamics occur due to interannual air temperature variation and meteorological episodesDivergent annual trajectories in lake structure and functioning highlight the vulnerability of high Arctic lakes to climate change [ABSTRACT FROM AUTHOR]

Formaldehyde (HCHO) is one of major pollutants in indoor air, and prolonged exposure to HCHO may result in great harm to human health. Therefore, it is crucial to remove HCHO from indoor air at room temperature. In recent years, various room temperature catalysts, which have the advantages of environmental friendliness and energy saving, were developed for high effective catalytic oxidation of HCHO. In this work, research progress and challenges on the room temperature catalytic oxidation of HCHO by using noble metal catalysts and non‐noble metal catalysts are systemically reviewed. Meanwhile, the room temperature catalytic oxidation performance, degradation conditions of HCHO, and room temperature catalytic oxidation mechanism of catalysts was also discussed. [ABSTRACT FROM AUTHOR]

An efficient and practical method for the N‐alkynylation of 7‐azaindoles has been established by using CuI/DMAP catalytic system at room temperature and in open air. This simple protocol has been successfully employed in the synthesis of a wide range of N‐alkynylated 7‐azaindoles with good yields. Also, this approach is well‐suited for large‐scale N‐alkynylation reactions. The designed N‐alkynylated 7‐azaindoles were further subjected to Cu‐/Ir‐catalyzed alkyne–azide cycloaddition (CuAAC/IrAAC) or "click" reaction for the rapid synthesis of 1,4‐/1,5 disubstituted 1,2,3‐triazole decorated 7‐azaindoles. A mechanistic study based on density functional theory (DFT) calculations and ultraviolet–visible (UV) spectroscopic studies revealed that the CuI and DMAP combination formed a [CuII(DMAP)2I2] species, which acts as an active catalyst. The DFT method was used to assess the energetic viability of an organometallic in the C−N bond formation pathway originating from the [CuII(DMAP)2I2] complex. We expect that the newly designed Cu/DMAP/alkyne system will offer valuable insights into the field of Cu‐catalyzed transformations. [ABSTRACT FROM AUTHOR]

This article explores the unique phenomenon of Carlina corymbosa thistle flowers in the Mediterranean having a cooler interior than the surrounding air during the hot summer months. The author conducted temperature measurements and found that the interior of the flowers was consistently cooler than the air outside. The study suggests that this cooling effect is achieved through enhanced transpiration in the flowers, which is more effective in the high ambient temperatures and low humidity of the Mediterranean climate. The article also discusses the potential benefits of this cooling mechanism for the plant's reproduction by attracting pollinators. Further research can be done to test this hypothesis. [Extracted from the article]

Question: Ecological indicator values (EIVs) reflect species' optimal conditions on an environmental gradient, such as temperature. Averaged over a community, they are used to quantify thermophilization stemming from climate change, i.e. the reshuffling of communities toward more warm‐adapted species. In forests, understorey plant communities do not keep up with global warming and accumulate a climatic debt. Although the causes are still debated, this thermal lag may be partly explained by forest microclimate buffering. For the first time, we test whether community means of EIVs are able to capture microclimate (here, under forest canopies) temperature across, or also within forests. Location: 157 forest plots across three French deciduous forests covering a large macroclimatic gradient. Methods: To assess whether EIVs can be used to infer the mean and range of microclimate temperature in forests, we measured understorey air temperature for ca. 1 year (10 months) with sensors located 1 m above the ground. We surveyed bryophytes and vascular plants within 400‐m2 plots, and computed floristic temperature from ordinal‐scale EIVs (Ellenberg, Julve) and degree‐scale EIVs (ClimPlant, Bryophytes of Europe Traits) for both temperature and continentality, i.e. temperature annual range. Finally, we fitted linear models to assess whether EIVs could explain the mean and range of microclimate temperature in forests. Results: Vascular plant and bryophyte communities successfully reflected differences in mean annual temperatures across forests but largely failed to do so for microclimate variation within forests. Bryophytes did not perform better than vascular plants to infer microclimate conditions. The annual range of microclimate temperatures was poorly associated with ordinal‐scale EIVs for continentality but was positively correlated with degree‐scale EIVs for annual range within lowland forests, especially for vascular plant communities. Conclusion: Overall, the capabilities of EIVs to infer microclimate was inconsistent. Refined EIVs for temperature are needed to capture forest microclimates experienced by understorey species. [ABSTRACT FROM AUTHOR]

Advection is a prevailing meteorological phenomenon in the arid and semi‐arid environments that directly affects the crop and soil hydrology. It could have a great impact on the rate of standard crop evapotranspiration (ETc) in the irrigated areas. Therefore, it is required to take it into consideration while computing crop water requirement through the physically based meteorological procedures. The objectives of this study are (1) simple modification of the Penman–Monteith (PM) equation in calculation of the grass reference evapotranspiration (ETo) to implement the local advection, (2) comparing the single (Kc) and dual crop coefficient (Kcb) of two quinoa cultivars (Titicaca, and Q5) with and without advection correction and (3) presenting a simple model to calculate the advection factor using the maximum air temperature and mean relative humidity for the future crop growth modelling studies. Both Q5 and Titicaca showed unexpectedly very high ETc and potential transpiration (Tp) as 1568 mm and 1003 mm, and 1156 mm and 829 mm, respectively, due to occurrence of regional advection, whereas very high unrealistic Kc and Kcb values for Q5 revealed the impact of strong local advection and external energy. Therefore, we modified ETo to implement the advection effect through introducing the advection factor, ETc/Rn (Rn is the net radiation), as a function of maximum air temperature and mean relative air humidity during the growing season [ETc/Rn = exp (0.025Tmax – 0.015RHavg)] which resulted in higher ETo values, and consequently lower and more realistic Kc and Kcb. As a result, modified maximum Kc values of 1.14 and 1.55 and the modified maximum Kcb of 0.94 and 1.0 were obtained for Titicaca and Q5 cultivars, respectively. This procedure leads to a more accurate site‐specific Kc estimation and indirectly reliable ETc estimation as a function of advection factor and its multiplication by the non‐modified ETo. Furthermore, for direct estimation of ETc through the PM equation, the coefficients of aerodynamic and canopy resistance components of PM equation were modified for estimation of ETc by the non‐modified Rn, which resulted in accurate estimation of ETc. [ABSTRACT FROM AUTHOR]

River estuaries are habitats for a variety of organisms, including many temperature‐sensitive species; water temperatures in estuaries are affected by several factors as they are influenced by both terrestrial and marine environments. Therefore, understanding the factors that influence river estuaries are essential for environmental management. However, no information exists on temperature change at high temporal resolution and over a wide area; moreover, the relationship with environmental factors has not yet been clarified. Here, we have described the actual status of water temperature change in riverine estuaries and its relationship with environmental factors for a wide area of the Japanese archipelago. Our results indicated that 217 of 294 rivers showed a significant monthly increase in water temperature. The average annual change in rates of water temperature increase was the highest in October at 0.090°C yr−1 and lowest in February at 0.068°C yr−1. Furthermore, snowfall, air temperature increase, sea water temperature, and land use were identified as factors increasing the rate of water temperature increase. Factors influencing the increase in river estuarine water temperature varied from month to month—with meteorological factors being the strongest influencers from spring to fall, and anthropogenic factors in winter. Our findings emphasize the importance of considering not only meteorological and oceanographic phenomena, but also anthropogenic influences and topographic features to understand the pattern of water temperature changes in river estuaries. [ABSTRACT FROM AUTHOR]

The accurate modeling of urban microclimate is a challenging task given the high surface heterogeneity of urban land cover and the vertical structure of street morphology. Recent years have witnessed significant efforts in numerical modeling and data collection of the urban environment. Nonetheless, it is difficult for the physical‐based models to fully utilize the high‐resolution data under the constraints of computing resources. The advancement in machine learning (ML) techniques offers the computational strength to handle the massive volume of data. In this study, we proposed a modeling framework that uses ML approach to estimate point‐scale street‐level air temperature from the urban‐resolving meso‐scale climate model and a suite of hyper‐resolution urban geospatial data sets, including three‐dimensional urban morphology, parcel‐level land use inventory, and weather observations from a sensor network. We implemented this approach in the City of Chicago as a case study to demonstrate the capability of the framework. The proposed approach vastly improves the resolution of temperature predictions in cities, which will help the city with walkability, drivability, and heat‐related behavioral studies. Moreover, we tested the model's reliability on out‐of‐sample locations to investigate the modeling uncertainties and the application potentials to the other areas. This study aims to gain insights into next‐gen urban climate modeling and guide the observation efforts in cities to build the strength for the holistic understanding of urban microclimate dynamics. Plain Language Summary: Estimating air temperature at street‐level is a difficult task because of the complex environment in cities and the limitations of the current urban numerical models. In recent years, with the rapid development of data collection and analysis techniques, it is possible to fully utilize the hyper‐local data harvested from urban areas by advanced machine learning algorithms. In this study, we present a modeling pipeline to estimate point‐scale street‐level air temperature from conventional urban weather model and a suite of hyper‐resolution urban data sets. These data sets were collected using state‐of‐art techniques, such as sub‐meter level Light Detection and Ranging technology and wireless weather observation network. We demonstrated the use of this modeling method over the City of Chicago and estimated the street‐level temperature. The modeling results have multiple real‐world applications, such as to provide navigation suggestions to reduce thermal discomfort of the pedestrians as an example. Moreover, it is possible to expand the use of our model to other areas given the current data availability. The results of this study can also help the development of the next‐generation urban climate and weather models and guide observation efforts in cities. These together can build the strength for the holistic understanding of urban microclimate dynamics. Key Points: The study presents a modeling framework to estimate street‐level air temperature using a suite of detailed urban climate informaticsModel results showed hyper‐local urban features have significant impacts on street‐level temperature but with a limited influence radiusThe investigations on model sensitivity implies the existence of the optimum scale in urban modeling and critical locations in observation [ABSTRACT FROM AUTHOR]

Metrics to quantify regulation of plant water status at the daily as opposed to the seasonal scale do not presently exist. This gap is significant since plants are hypothesised to regulate their water potential not only with respect to slowly changing soil drought but also with respect to faster changes in air vapour pressure deficit (VPD), a variable whose importance for plant physiology is expected to grow because of higher temperatures in the coming decades. We present a metric, the stringency of water potential regulation, that can be employed at the daily scale and quantifies the effects exerted on plants by the separate and combined effect of soil and atmospheric drought. We test our theory using datasets from two experiments where air temperature and VPD were experimentally manipulated. In contrast to existing metrics based on soil drought that can only be applied at the seasonal scale, our metric successfully detects the impact of atmospheric warming on the regulation of plant water status. We show that the thermodynamic effect of VPD on plant water status can be isolated and compared against that exerted by soil drought and the covariation between VPD and soil drought. Furthermore, in three of three cases, VPD accounted for more than 5 MPa of potential effect on leaf water potential. We explore the significance of our findings in the context of potential future applications of this metric from plant to ecosystem scale. [ABSTRACT FROM AUTHOR]

The relative importance of oceanic modes to global surface air temperature (SAT) and terrestrial precipitation during 1934–2020 was investigated using a variety of statistical and dynamical system methods. Through singular spectrum analysis, we present the distribution of decadal (10–20‐year), multidecadal (20–50‐year), and secular (>50‐year) variabilities of global SAT and terrestrial precipitation anomalies. Three sea surface temperature modes were identified by singular value decomposition that affect the low‐frequency variabilities of global SAT and terrestrial precipitation anomalies—namely, global warming (GW), the Interdecadal Pacific Oscillation (IPO), and the Atlantic Multidecadal Oscillation (AMO). The relative importance GW, the IPO, and the AMO in SAT and terrestrial precipitation was obtained through a normalized multivariate information flow analysis. Besides a high information flow percentage (60%) from GW to global SAT, especially over the northern Indian Ocean and tropical West Pacific, information flow from the AMO to the northwestern Pacific was also found. In terms of terrestrial precipitation, a large area with a wet trend was found over Eurasia at mid‐to‐high latitudes, and this trend was especially remarkable in the boreal winter half‐year (November–April), as compared with that in the boreal summer half‐year (May–October). By employing artificial neutral networks with a self‐organized map to cluster the key patterns of vertically integrated water vapour flux, we found that the synoptic circulation related to the wet trend is characterized by westerly flow that transports water vapour from the northeastern Atlantic to Eurasia, which is favourable for precipitation there both in the boreal winter and summer half‐year. [ABSTRACT FROM AUTHOR]

Meteorological processes are investigated during the development of deep convective clouds over the Yellow Sea inside a quasi‐stationary rainy front in the East Asian region. WRF‐Chem simulations were conducted to assess the effects of aqueous‐phase chemistry on the concentrations of PM2.5 and hydrometers in deep convective clouds and the associated precipitation. A reduction in the south‐north air temperature gradient induced an inactive quasi‐stationary rainy front with nonlinear zonal clouds during the Changma period in the East Asian region in 2017, causing intensified surface heating due to solar radiation. A mesoscale cyclone was formed near the Shantung Peninsula inside the inactive quasi‐stationary rainy front, extending the eastward tongue of the troughs across the Yellow Sea. Deep convective clouds (CC16 on July 16, CC17 on July 17, and CC18 on July 18) formed in a severe mode on the east side of the mesoscale cyclone over the Yellow Sea at midnight as the warm type, as the warm and humid south‐southwesterly winds present at that time produced convection energy. PM2.5 from eastern China flowed into warm‐type CC16, CC17, and CC18 areas, producing more cloud droplets below 500 hPa via activation. In contrast, the release of additional condensation heat amplifies updrafts within convective clouds during efficient nighttime environmental cooling, thereby enhancing the production of cold‐type ice, reaching altitudes of up to 15 km. The WRF‐Chem PM2.5 outcomes indicate a significant increase in heavy rainfall, exceeding 10 mm hr−1, particularly in the context of the development of cold‐type convective clouds. Plain Language Summary: The regional phenomenon of summer rain, known as Changma in Korea, usually occurs with a quasi‐stationary rainy front that forms between the continental polar air mass over the East Asian region and the maritime tropical air mass over the northwest Pacific. An inactive quasi‐stationary rainy front with nonlinear zonal clouds induced by a reduction in the south‐north air temperature gradient during the Changma period led to the efficient absorption of solar radiation on the surface of the East Asian region. In addition, convective clouds have frequently developed over the Yellow Sea inside the inactive quasi‐stationary rainy front, resulting in extreme events with large amounts of short‐term precipitation in inland Korea. We found that aqueous‐phase chemistry in convective clouds led to the production of PM2.5 and nucleated cloud droplets, enhancing precipitation on 16–18 July 2017. While warm‐type convective clouds developed due to warm and humid south‐southwesterly flows over the Yellow Sea, WRF‐Chem‐simulated PM2.5 promoted cold‐type ice production to a top convection height of 15 km. The entrainment of near‐surface PM2.5 aerosols in the south‐southwesterly flows from eastern China and the secondary activations over the boundary layer clouds enhanced heavy rainfall to more than 10 mm hr−1 in cold‐type convective clouds. Key Points: A reduction in the air temperature gradient induced an inactive rainy front, forming a mesoscale cyclone over the Yellow SeaPM2.5 aerosols in eastern China flowed out to the Yellow Sea, wherein the PM2.5 inflows contributed to the development of cold‐type cloudsCold‐type clouds promoted heavy rainfall exceeding 10 mm hr−1 over the Yellow Sea and Korea [ABSTRACT FROM AUTHOR]

Radiative Coolers (RCs) represent an emerging technology that has the potential to significantly reduce urban heat and improve indoor/outdoor thermal comfort in the built environment. Broadband and Selective Radiative Coolers (BRCs and SRCs, respectively) are the two main types of RCs that have been proposed for application in the built environment. Being both typically characterized by high solar reflectance, the former emits thermal radiation within the overall infrared spectrum, whilst the latter emits thermal radiation mainly within the Atmospheric Window (AW) waverange. A variety of both types of RCs have been developed and tested in both in‐lab and in‐field experimental campaigns. Yet, all experiments comprise small scale specimens that do not represent real‐life scale components of the built environment. In addition, no meso scale assessments have been performed with respect to the RCs' performance at a city scale. Here, for the first time, the thermo‐optical performance of both BRCs and SRCs is introduced and investigated in a multilayer Urban Canopy Model (UCM) coupled with extended Weather Research and Forecasting model (WRF) and we simulated 20 different scenarios representing the vast majority of urban layouts. The outcomes show that both types of RCs maintain lower surface temperature and air temperature at 2 m height inside the canyon, compared to conventional roofs. In addition, a city scale application of RCs has been found capable of decreasing ambient temperature up to 1.6°C as potentially experienced by pedestrians. [ABSTRACT FROM AUTHOR]

It is well known that a massive demand for thermal comfort has resulted in a sharp rise in energy use for residential space heating and cooling. This article presents numerical simulations of a room coupled with an earth‐to‐air heat exchanger system operating at the climatic condition of 21.2497° N, 81.6050° E for both summer and winter seasons. The present study involves an estimate of monthly variations in soil temperature at different depths and a suitable geometrical arrangement of pipe is proposed. The thermal performance of the earth‐to‐air heat exchanger system in terms of the outlet air temperature and effectiveness has been calculated. It is shown that with an effectiveness of 0.77 in summer and 0.78 in winter, the present EAHE system reduces the ambient air temperature from 38 to 28.2°C in the summer and it rises from 13 to 22.9°C in winter, respectively. Finally, an improvement in the thermal comfort condition of the room exposed to the solar radiation has been examined at different time intervals and different heights of the room. During the summer the room temperature dropped by 4.2°C while in winter it rose by 5.2°C. Furthermore, the environmental effects of system have been evaluated, showing energy savings of 3.90 kWh/day during summer and 4.83 kWh/day during winter leading to a decrease in CO2 emissions of 6.13 kg/day in summer and 7.59 kg/day in winter. [ABSTRACT FROM AUTHOR]

The relationship between air temperature and sovereign bond returns is founded on competing paradigms: macroeconomic, behavioral and energy demand‐based. Which of these theoretical mechanisms receives support from data? To answer this, we examined four decades of bond data from 31 countries. Overall, daily temperature positively affects government bond returns. A 10°F rise leads to an increase in sovereign bond returns between 0.22 and 0.85 basis points. We also document evidence of asymmetric and nonlinear price responses to both temperature levels and shocks. Our results survive a battery of robustness checks and lend support to the macroeconomic and behavioral paradigms, albeit not the energy demand‐based view. [ABSTRACT FROM AUTHOR]

Land surface temperature anomalies can be linked to changes in local surface energy balance, although the relationship between surface temperature variability and individual radiative processes remains unclear. In this paper, we quantify the contributions of surface radiative effects and non‐radiative heat fluxes to the variance of monthly land surface temperature using European Centre for Medium‐Range Weather Forecasts Reanalysis v5 data and Coupled Model Intercomparison Project Phase 6 simulations. The surface energy budget equation is used to link changes in surface radiation, surface heat fluxes and land surface temperature. Subsequently, surface radiation is decomposed into the radiative effects of clouds, air temperature, surface albedo and relative humidity using radiative kernels. The contributions of these radiative processes, including their coupling effects, are quantified using covariance matrices. The results reveal the air temperature radiative effect to be the most significant contributor to the variability of land surface temperature. In addition, the covariance terms reveal important coupling effects. For example, the contribution from the cloud radiative effect is found to be substantially dampened by its coupling with surface heat fluxes. The air temperature radiative effect is further decomposed into a forcing component and a feedback component using different regression methods, in an attempt to separate the air temperature radiative effect as the driver of the surface temperature variability. The cloud radiative effect becomes the primary contributor to the variance of surface temperature after separating the air temperature feedback, while the contribution of the air temperature radiative forcing remains important. Plain Language Summary: Land surface temperature is critical environmental variable. Through a statistical analysis of the European Centre for Medium‐Range Weather Forecasts Reanalysis v5 data and Coupled Model Intercomparison Project Phase 6 multi‐model simulations, we have explained the land surface temperature variability in relation to the surface energy fluxes. Specifically, we have attributed the variability of monthly surface radiation to the effects of different meteorological variables such as clouds, air temperature, surface albedo and relative humidity. Our findings suggest that the radiative effect of air temperature is the primary contributor to the variance of land surface temperature in most regions, although this effect includes a strong feedback effect of air temperature to surface temperature changes. After separating this feedback effect, the cloud radiative effect becomes the primary contributor. On the other hand, the contribution from cloud radiative effect is significantly counteracted by its coupling with surface heat fluxes. Key Points: The variance of land surface temperature is decomposed to contributions from surface radiative effects and heat fluxesThe radiative effect of air temperature is found to contribute the most to surface temperature variabilityThe cloud radiative effect is found to be the primary contributor after decoupling the air and surface temperature variations [ABSTRACT FROM AUTHOR]

The water resources of the Third Pole (TP), highly sensitive to climate change and glacier melting, significantly impact the water and food security of millions in Asia. However, projecting future spatial‐temporal runoff changes for TP's mountainous basins remains a formidable challenge. Here, we've leveraged the long short‐term memory model (LSTM) to craft a grid‐scale artificial intelligence (AI) model named LSTM‐grid. This model has enabled the production of hydrological projections for the seven major river basins of TP. The LSTM‐grid model integrates monthly precipitation, air temperature, and total glacier mass changes (total_GMC) data at a 0.25‐degree model grid. Training the LSTM‐grid model employed gridded historical monthly runoff and evapotranspiration data sets generated by an observation‐constrained cryosphere‐hydrology model at the headwaters of seven TP river basins during 2000–2017. Our results demonstrate the LSTM grid's effectiveness and usefulness, exhibiting a Nash‐Sutcliffe Efficiency coefficient exceeding 0.92 during the verification periods (2013–2017). Moreover, river basins in the monsoon region exhibited a higher rate of runoff increase compared to those in the westerlies region. Intra‐annual projections indicated notable increases in spring runoff, especially in basins where glacier meltwater significantly contributes to runoff. Additionally, the LSTM‐grid model aptly captures the runoff changes before and after the turning points of glacier melting, highlighting the growing influence of precipitation on runoff after reaching the maximum total_GMC. Therefore, the LSTM‐grid model offers a fresh perspective for understanding the spatiotemporal distribution of water resources in high‐mountain glacial regions by tapping into AI's potential to drive scientific discovery and provide reliable data. Plain Language Summary: Water resources of the Third Pole (TP) significantly impact the water and food security in Asia. However, projecting future spatial‐temporal runoff changes for the TP's mountain basins remains a challenge. Here, we've leveraged the long short‐term memory (LSTM) model to craft a gridded artificial intelligence model (named LSTM‐grid). Trained by the outputs of an observation‐constrained distributed cryosphere‐hydrology model, the LSTM‐grid has enabled reliable spatiotemporal runoff and evapotranspiration projections for the headwaters of seven TP rivers (Yellow, Yangtze, Mekong, Salween, Brahmaputra, Ganges, Indus) till 2100. Our projections show that the river basins in the monsoon region exhibit a higher rate of runoff increase compared to those in the westerlies region. In particular, the proposed approach in this study can reasonably capture the runoff changes before and after the turning points of glacier melting without prior knowledge, highlighting the growing influence of precipitation on runoff after reaching the maximum total glacier mass changes (of a river basin). Hence, the LSTM‐grid model provides a fresh perspective for understanding the spatiotemporal distribution of water resources in high‐mountain glacial regions. Key Points: We use artificial intelligence and an observation‐constrained cryosphere‐hydrology model to project future runoff for seven high‐mountain Third Pole basinsResults show that river basins in the monsoon region exhibited a higher rate of runoff increase compared to those in the westerlies regionThe proposed approach can aptly simulate runoff changes before and after the turning points of glacier melting without prior knowledge [ABSTRACT FROM AUTHOR]

Water quality and freshwater ecosystems are affected by river discharge and temperature. Models are frequently used to estimate river temperature on large spatial and temporal scales due to limited observations of discharge and temperature. In this study, we use physically based river routing and temperature models to simulate daily discharge and river temperature for rivers in 138 basins in Alaska, including the entire Yukon River basin, from 1990–2021. The river temperature model was optimized for ice free months using a surrogate‐based model optimization method, improving model performance at uncalibrated river gages. A common statistical model relating local air and water temperature was used as a benchmark. The physically based river temperature model exhibited superior performance compared to the benchmark statistical model after optimization, suggesting river temperature model optimization could become more routine. The river temperature model demonstrated high sensitivity to air temperature and model parameterization, and lower sensitivity to discharge. Validation of the models showed a Kling‐Gupta Efficiency of 0.46 for daily river discharge and a root mean square error of 2.04°C for daily river temperature, improving on the non‐optimized physical model and the benchmark statistical model, which had root mean square errors of 3.24 and 2.97°C, respectively. The simulation shows that rivers in northern Alaska have higher maximum summer temperatures and more variability than rivers in the Central and Southern regions. Furthermore, this framework can be readily adapted for use across models and regions. Plain Language Summary: Accurate data on the volume and temperature of river water are essential for understanding how changing river conditions affect water quality and freshwater ecosystems. However, direct measurements of river parameters are often lacking, leading researchers to rely on models for estimation. In this study, we utilized advanced models and techniques to compute daily water volume and temperature in 138 basins across Alaska, including the entirety of the Yukon River basin, spanning from 1990 to 2021. Our findings indicated that rivers in northern Alaska exhibited higher maximum summer water temperatures and more significant temperature fluctuations compared to those in the central and southern regions. Our analysis highlighted that adjusting air temperature and the model's internal variables were crucial in minimizing errors in river temperature prediction. We improved the accuracy of the river temperature model by applying a technique to refine the model output based on the limited available river measurements. Comparing our enhanced model to a simpler statistical approach, we observed superior performance once the necessary adjustments were implemented. Key Points: Our model system produced an accurate, high‐resolution, daily hindcast of Alaskan river temperature and discharge from 1990 to 2021We increased model performance by employing a new optimization of the River Basin Model and forcing it with a climate‐land surface modelA sensitivity analysis highlights important drivers of river temperatures in each region and the need for optimization [ABSTRACT FROM AUTHOR]

Overcooled talus slopes are generally described as islands of sporadic permafrost below the lower alpine limit of permafrost. The negative thermal anomaly of the ground is mainly consecutive to the internal ventilation of the deposit, but it is also conditioned by multiple factors as topography, slope aspect and incline, openwork structure and coarseness of the deposit, air temperature, solar radiation and wind regime. Therefore, the study of the spatiotemporal dynamics of ventilation processes allows a better understanding of the phenomenon. At Cannon Cliff, New Hampshire (USA), several field visits and environmental monitoring allowed us to describe the varying nature and significance of the ventilation mechanisms that can be observed at the ground surface and associated with both the intensity and direction of the airflows in a talus debris accumulation/protalus rampart system. The thermal negative anomalies are strong enough to lower the ground temperature to the point of preserving ice during the late spring and summer seasons. The monitoring of the gradient between external (air) and internal (talus) temperatures coupled with several dendroecological and geomorphological analyses provided a complete environmental picture of the impacts, feedback and extent of the phenomenon. [ABSTRACT FROM AUTHOR]

An attractive candidate to reduce dependence on fossil fuels and replace them by renewable sources is represented by concentrated solar power (CSP) technology, whose wide deployment is still hindered by its high investment costs. In CSP linear collector plants, a key component with high technological content may be identified in receiver tubes, that are traversed by heat transfer fluid and located on focal line on which solar radiation is concentrated by mirrors. The proposed work aims at developing spectrally selective coatings for receiver tubes able to overcome state‐of‐the‐art technological limitation in terms of both vacuum encapsulation and maximum operative temperature, currently limited at 550°C for molten salts CSP plants. To this aim, a multilayer structure was designed and developed for coating a receiver tube to be employed in open‐air environment at temperature of 600°C. The multilayer coating is based on tungsten chromium titanium infrared reflector and includes a solar absorber layer developed with two alternative methods of reactive sputtering and thermal oxidation respectively, finally covered by antireflective and barrier bi‐layer of aluminum oxide and silica. Thermal stability was demonstrated of the new developed coatings, since only small variations were measured of the optical parameters after more than 2000 h of aging in open air conditions at temperature of 600°C (on average less than 5%), with final measured solar absorptance αs of 73% and 87% and thermal emissivity εth at 600°C of 18% and 27% for aged multilayer structures including the absorber layer obtained through thermal oxidation and sputtering process, respectively. These results pave the way for further improvement in terms of both optical performance and operative temperature increase, allowing to enhance efficiency and lower costs, thus contributing to ease to deployment of CSP technology. [ABSTRACT FROM AUTHOR]

High temporal resolution geostationary hyperspectral infrared sounders can simultaneously profile atmospheric temperature and moisture, and track moisture features to provide 3D horizontal winds in clear and partially cloudy scenarios. The thermodynamic information obtained has been integral to enhancing tropical cyclone (TC) forecasts. However, the potential benefits derived from the dynamic information provided by geostationary hyperspectral infrared sounders (GeoHIS) are yet to be fully comprehended for numerical weather prediction (NWP). With the re‐estimated observation error and multivariate hydrometeor background error covariance, the 3D horizontal winds from the Geostationary Interferometric Infrared Sounder (GIIRS) are synergistically assimilated with the hydrometeor information. The impact of the GIIRS‐derived 3D horizontal wind assimilation on Typhoon Maria (2018) and Lekima (2019) analysis and forecast are evaluated. The results show that the assimilating GIIRS‐derived 3D horizontal wind notably reduces the RMSEs of analysis and forecast, particularly in the U and V components. The improvement in accuracy of large‐scale fields substantially enhances the forecasts of typhoons' track and maximum wind speed, as well as the central sea‐level pressure. Moreover, the prediction of the spatial distribution and intensity for landfall precipitation is also improved. The detailed diagnoses of the Maria show that a more southerly subtropical high generated by the additional horizontal wind assimilation improves the rainfall spatial distribution, and the reasonable water vapor transportation caused by the improved dynamic conditions corrects the precipitation intensity. This work highlights the importance of dynamic information in TC forecasting and emphasizes the need for efficient and high‐precision 3D wind measurements to improve NWP. Plain Language Summary: Accurately describing atmospheric winds is vital in weather forecasting, particularly in predicting severe weather such as typhoons. High temporal resolution measurements from geostationary hyperspectral infrared sounder (GeoHIS) offer the weather cube of atmospheric temperature and moisture and provide three‐dimensional (3D) horizontal wind profiles by tracking the moisture features. With upcoming more hyperspectral sounders being deployed in the geostationary ring on operational geostationary weather satellites, it is essential to understand the benefit of dynamic information for numerical weather prediction (NWP). Using 3D wind experimental data from a high temporal resolution Geostationary Interferometric Infrared Sounder (GIIRS) onboard FengYun‐4A, the observation errors are characterized, and cycling assimilation and forecasting experiments are conducted to assess the impact of assimilating 3D wind for Typhoon Maria (2018) and Typhoon Lekima (2019). Improving the model's initial conditions with GIIRS‐derived 3D winds enhances the typhoons' track and intensity forecast accuracy. It helps correct the biased forecast of rainfall spatial distribution and intensity after the typhoons made landfall. The improvements are primarily due to the typhoon's better environmental conditions, particularly the more accurate dynamical conditions, which result in the correction of subtropical high position and water vapor transport. This study emphasizes the need for 3D wind observations and the benefit of weather forecasting, especially in predicting extreme weather events such as tropical cyclones. Accurate wind measurements can help us prepare and respond better to potentially dangerous weather phenomena. Key Points: GIIRS‐derived 3D horizontal wind provides key dynamic information for NWP model and is synergistically assimilated with other observationsAssimilating GIIRS‐derived 3D horizontal wind improves typhoon analysis and forecasting, especially track, intensity, and precipitationSynergistic assimilation of geostationary satellite dynamic and hydrometeor data improves rainfall forecast by optimizing water vapor transportation [ABSTRACT FROM AUTHOR]

One prominent mode of the variability in the summer wet‐bulb temperature (WBT) over eastern China exhibits a distinct north‒south dipole pattern. This pattern demonstrates a positive center in northern China (NC), while a negative center is observed in southern China (SC). Our results indicate that the dipole mode of WBT could be partially ascribed to the impact of spring snow anomalies in eastern Europe–western Siberia (EEWS). The reduction in the snow depth, coupled with dry soil conditions, enhances the surface heat flux and consequently leads to an increase in the near‐surface air temperature. The signal of soil moisture could persist from spring to summer, stimulating the generation of zonal Rossby waves. Consequently, the significant wave flux anomalies propagate from EEWS downstream and influence the atmospheric circulation over eastern China. These patterns play a role in the increase in the surface air temperature and moisture accumulation over NC, ultimately leading to the establishment of the dipole mode of WBT over eastern China in summer. Further analysis indicates that the atypical low sea surface temperature in the tropical Ocean, induced by the El Niño‐Southern Oscillation, establishes a climatic context favorable for the persistence of an anomalous cyclone in the Northwest Pacific (NWP) throughout the summer season. The strengthened convection over the NWP and SC induces a dipole pattern of atmospheric circulation by stimulating a meridional wave train. This pattern creates favorable temperature and moisture conditions, contributing to the development of the dipole mode of the summer WBT across eastern China. Plain Language Summary: In recent decades, the social loss associated with high‐temperature and high‐humidity conditions has sharply increased. The wet‐bulb temperature (WBT) is widely regarded as a highly effective metric for characterizing the combined stress of humidity and heat. In this study, we emphasized one of the important modes (a north‒south dipole pattern) of the variability in the summer WBT over eastern China and revealed the corresponding underlying mechanisms. We found that mid‐high and low latitudinal dynamic processes may exert a synergistic effect on WBT over eastern China. On one side, spring snow depth anomalies could be coupled with spring soil moisture anomalies over eastern Europe–western Siberia and subsequently influence the dipole mode of the summer WBT over eastern China by triggering the stimulation of zonal Rossby waves. On the flip side, the anomalous low sea surface temperature in the tropical Ocean, driven by the ENSO, could yield a climatic state that supports the prolonged existence of an anomalous cyclone over the Northwest Pacific throughout the summer season. This cyclone exerts a significant impact on the dipole mode of the summer WBT over eastern China. Our study compensates for the lack of humid heat effect‐related research across eastern China and contributes to establishing a solid theoretical foundation for comprehending and forecasting the summer WBT in the region. Key Points: One significant pattern of the variability in the summer wet‐bulb temperature (WBT) over eastern China is manifested as a north‒south dipole modeAnomalous spring snowfall over Eurasia can impact the dipole mode of summer WBT over eastern China via the stimulation of zonal Rossby wavesThe anomalous sea surface temperature over tropical ocean, triggered by El Niño‐Southern Oscillation, establishes a climatic context conducive to the dipole mode over eastern China [ABSTRACT FROM AUTHOR]

This study investigates the response of surface air temperature (SAT) to strong tropical volcanic eruptions (STVEs), focusing on the seasonal anchoring of SAT recovery, using the Coupled Model Intercomparison Project 5 (CMIP5) and CMIP6 climate model outputs. The decrease in SAT was more significant in the Northern Hemisphere than in the Southern Hemisphere. After the STVEs, shortwave radiation at the surface (SW) decreased rapidly and reached a minimum in approximately 6 months, whereas the decrease in SAT lasted for approximately 1 year; thus, the SAT minima lagged behind those of SW. The recovery of SAT from the major minimum SAT after the STVEs occurred in the same seasons, the second boreal autumn and winter. The SAT recovery occurred during the climatologically low solar radiation season after the STVEs. During this season, the decreasing effects of the downward SW (DSRS) by volcanic aerosols could become negligible. The weakening of the decrease in SAT and DSRS had a seasonal cycle in the high‐latitude regions of both hemispheres, which could be a significant factor in the seasonal alignment of returning SATs. Furthermore, the SAT response to the El Niño–Southern Oscillation (ENSO) was examined using composite analysis based on the initial state of ENSO. The influence of ENSO on surface cooling after the STVE was at least 1/5 that of the STVEs and might exceed that of the STVEs, particularly in moderate STVEs. These results were commonly found in the CMIP5 and CMIP6 climate models. Plain Language Summary: Strong tropical volcanic eruptions (STVEs) transport volcanic sulfur aerosols into the stratosphere, affecting downward solar radiation and decreasing surface air temperature (SAT) for several years. After the 1991 Pinatubo eruption, the global average surface air temperature dropped by 0.5 K. We investigated the SAT response to the STVEs using the simulation results from multiple climate models. Analyzing many simulations increases the reliability of our results. As is well known from previous studies, a SAT drop has been clearly observed in the Northern Hemisphere. In addition, the SAT response to volcanic aerosols was weakened in the second northern winter after the STVEs, even when the STVE occurred in northern spring and fall. Because solar radiation at the Earth's surface is climatologically weak in northern winter, the effects of volcanic aerosols on SAT decrease can be negligibly small in northern winter. Therefore, the recovery of SAT after STVEs occurred in the northern winter. Key Points: Surface air temperature (SAT) responses to strong tropical volcanic eruptions (STVEs) were investigated using CMIP5/6 climate modelsDecrease in SAT after the STVEs tended to stop in the second winter, even though the timing of the eruption was differentSeasonally anchoring SAT recovery after STVEs is related to weak solar radiation in winter [ABSTRACT FROM AUTHOR]

This study focuses on the interannual variability of winter mean surface air temperature (SAT) and subseasonal SAT reversal in North China, which have profound impacts on national life and economic activities. The analysis explores the temporal and spatial distribution characteristics of these variations and their relationship with the Eurasian Zonal Circulation (EZ). The findings reveal distinct interdecadal changes in North China's winter mean SAT, along with a general subseasonal reversal of cold and warm conditions. In comparison to the North Atlantic oscillation/Arctic Oscillation, the EZ has a more significant influence on the interannual variability of winter and subseasonal reversals. Strong EZ years are associated with the weakening of the Siberian High and the Ural Blocking High, causing the East Asian Trough to move eastward, which leads to the retention of cold air from the north and increased SAT in North China. Conversely, weak EZ years exhibit opposite circulation patterns. The winter mean EZ is influenced by preceding autumn Arctic sea ice concentrations (SIC). Reduced autumn SIC results in decreased heat flux and reduced eddy energy, leading to substantial attenuation of the winter storm track across Scandinavia and Western Asia. This attenuation, mediated through the interaction between eddies and mean flow, triggers a slowdown of Eurasian zonal circulation and intensification of the Siberian High. These conditions favor a positive phase of the winter Scandinavia pattern, facilitating the transport of cold Arctic air into North China. Likewise, subseasonal reversals in SIC influence corresponding alterations in the EZ. Plain Language Summary: The winter surface air temperatures (SAT) in North China exhibit notable interdecadal variations and distinct intra‐seasonal changes. These intra‐seasonal variations lead to subseasonal reversals, where early winter may be unusually warm, followed by later winter turning unusually cold, or vice versa. These subseasonal SAT fluctuations are influenced by atmospheric circulation anomalies. In this study, we investigate the spatial and temporal patterns of the mean winter SAT and subseasonal SAT reversals in North China, focusing on their interannual variability. We emphasize the significant impact of anomalous zonal circulation over the middle and high latitudes in Eurasia and explore the relative influence of the Arctic as a boundary forcing factor in circulation anomalies. Key Points: The winter surface air temperature (SAT) and subseasonal SAT reversal in North China have noteworthy interannual variationsThe Eurasian Zonal Circulation (EZ) strongly influences winter SAT and subseasonal reversal variabilityThe physical mechanisms linking autumn Arctic sea ice concentrations to EZ is examined [ABSTRACT FROM AUTHOR]

Surprising significant underperformance of the polynomial complementary relationship (PCR) of evaporation (Szilagyi et al., 2017, https://doi.org/10.1002/2016jd025611) by Wang et al. (2023, https://doi.org/10.1029/2023jd038683) is caused by the (a) station‐by‐station application of a grid‐based estimation procedure of the Priestley‐Taylor parameter (α) value, and (b) choice of the wind function. Application of the Rome wind function in the Penman equation together with either a well‐chosen single (constant) α or α as a function of the wet‐environment air temperature, should result in much improved evaporation estimates by the PCR in line with previous studies. Plain Language Summary: The serious underperformance of the polynomial complementary relationship of evaporation as employed by Wang et al. (2023, https://doi.org/10.1029/2023jd038683) can be significantly improved by the application of a properly chosen constant value of its α parameter or by relating it to the wet‐environment air temperature demonstrated by Szilagyi and Crago (2023, https://doi.org/10.1016/j.jhydrol.2023.129917). Key Points: Such underperformance of the polynomial complementary relationship (PCR) has never been reportedIt is caused mainly by the improper application of its parameter estimation procedureWith the proper Priestley‐Taylor parameter the PCR performs significantly better [ABSTRACT FROM AUTHOR]