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The sporadic E‐layer (Es) exhibits unique opportunity for exploring the coupling from lower to upper atmosphere. It was found that the East Asia is with the highest intensity and occurrence probability of Es. By using the long‐term data of 21 ionosonde stations in China and Japan over the past 60 yrs, this paper explores the probable control on the Es layer from the lower layers. It is found that the intensity of the Es layer is strongly correlated with the surface atmospheric temperature, terrain, and land‐sea boundary. The correlation coefficient of the intensity of Es with surface temperature is as high as 0.8204, while that with the terrain and land‐sea boundary is up to 0.6668. Based on the coupling between the lower and upper atmosphere, this paper reveals the probable controls from lower layers on the intensity of the Es in East Asia. Plain Language Summary: As an occasional ionized layer of the ionosphere, the sporadic E‐layer (Es) exhibits unique opportunity for exploring the coupling from lower to upper atmosphere. Despite extensive research on both behavior and mechanism of the Es, none have adequately explained the strong inhomogeneous of its global morphology. It is found in fact that the East Asia is with the highest intensity and occurrence probability of Es. By using the long‐term data of 21 ionosondes in China and Japan over the past 60 yrs, this letter explores the formation mechanism of the strongest Es regions in the world. It is found that the intensity of the Es layer is strongly correlated with the surface atmospheric temperature, terrain, and land‐sea boundary. The correlation coefficient between the intensity of Es and surface temperature is highest, while that with the terrain and land‐sea boundary is also high. By analyzing the data as long as six decades, this letter reveals the probable controls from lower layers on the intensity of the Es in East Asia. Key Points: By analyzing the six‐decade data of 21 ionosondes in East Asia, it reveals probable controls on the strongest Es layer from lower layersFor the first time, it is found that the intensity of Es layer is highly correlated with the surface atmospheric temperatureAn interesting coupling from lower to upper atmosphere may be found by taking advantage of observations longer than half of a century [ABSTRACT FROM AUTHOR]

In this paper, we constructed the Exospheric Temperature Models (ETM) on the basis of CHAMP and GRACE data using different empirical orthogonal functions (EOFs). The EOFs of the exospheric temperature can be derived either from satellite data directly or from the outputs of the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) and MSIS models by applying the Principal Component Analysis method. Then, the thermospheric mass densities calculated from ETM are used to compare with the observed data in order to evaluate the performance of different ETM models. It was found that all these three models can provide good specification of thermospheric density including day‐night, seasonal, and latitudinal variations. However, the ETM based on CHAMP and GRACE data gives a better performance in modeling the Equatorial Thermospheric Anomaly and the Midnight Density Maximum features than the MSIS‐ETM and TIEGCM‐ETM. Specifically, independent SWARM‐C data comparison showed that the Relative Deviations and corresponding Root‐Mean‐Square‐Errors of our Texo models are less than 8.9% and 22.8%, much better than the MSIS‐00 model. Plain Language Summary: Exospheric temperature, the atmospheric temperature at an altitude above 300 km, is a critical parameter used in most thermospheric density models. In this study, we use different mathematical functions to construct the exospheric temperature models (ETM) named ETM, MSIS‐ETM, and Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM)‐ETM, respectively based on the CHAMP and GRACE density observations. The mathematical functions can be derived either using the data‐driven method or using the model‐driven method. Our purpose is to evaluate the performance of these constructed models in reflecting the thermosphere, and we found that all these models can provide good specification of the thermosphere such as the typical day‐night, seasonal, and latitudinal variation characteristics. Overall, the ETM constructed by using the data‐driven method gives a better performance than the MSIS‐ETM and TIEGCM‐ETM, and it can effectively simulate the physical features that are observed in the thermosphere. Our results indicate that compared with the model‐driven method utilizing the sufficient data‐driven PCA method to bend the exospheric temperature model is also an effective way. Key Points: Exospheric Temperature Models (ETM) are constructed on the basis of CHAMP and GRACE data using different empirical orthogonal functions (EOFs)The three ETM models can provide good specification of thermospheric density after they are embedded in the MSISThe ETM with EOFs directly from the observations gives a better performance than those with EOFs obtained from the MSIS and Thermosphere Ionosphere Electrodynamics General Circulation Model models [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the relationships between soil moisture, cloud cover, solar radiation, air temperature and humidity, and precipitation during the warm half of the year in East‐Central Europe over the years 1971–2020. The temporal and spatial variability of these meteorological elements is presented in association with the occurrence of anticyclonic blocking events over the study area. It demonstrates that changes in soil moisture in East‐Central Europe point to the combined influence of many meteorological factors resulting from the atmospheric circulation, and are an indicator of the comprehensive relationships among those factors. The main factors affecting soil water content are precipitation and evapotranspiration, which in turn depend on air humidity, cloudiness, intensity of solar radiation and air temperature. The increase in the frequency and duration of sequences of days with blocking events in East‐Central Europe has contributed to an increased probability of longer periods with soil moisture negative anomalies. [ABSTRACT FROM AUTHOR]

In the fruit processing industry, a large volume of residue is generated, which is usually discarded. However, fruit residues may have appeal as functional ingredients due to the presence of bioactive compound. In this study, the drying of acerola residue in a medium‐sized conventional spouted bed was investigated. The residue was characterized; vitamin C and 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) were determined to evaluate the effect of drying on bioactive compounds. To overcome the difficulty of material fluidization, a mixture composed of in natura acerola residue and acerola residue previously dried in an oven as an inert material was used. The effect of air temperature (40 and 60°C) and velocity (28 and 36 m/s) and amount of in natura residue in the mixture (20% and 30%) was evaluated during drying. As expected, higher temperatures and air velocities and lower percentages of in natura residue favored the reduction of moisture. For a mixture containing 20% in natura residue, air velocity of 36 m/s, and temperature of 60°C, it was possible to achieve a final moisture content suitable for storage (14.23% w.b.) and the highest percentage of DPPH free radical capture activity (85.31%). Moreover, a mixture containing 30% in natura residue, velocity of 28 m/s, and temperature of 40°C conserved more vitamin C (48.30 mg of ascorbic acid/100 g). Finally, spouted bed can be considered a good technique for drying acerola residue as it was able to reduce a high amount of moisture content preserving bioactive compounds during short drying periods at low temperatures. Practical Applications: The acerola processing generates a residue that consists basically of the seeds, bagasse, and peels, which is rich in ascorbic acid, phenolic compounds, flavonoids, anthocyanins, and organic acids. This residue can be used to generate new food products, which reduces the environmental problem of its disposal and the loss of raw material in fruit processing. To extend the residue shelf life, it must be subjected to a drying process. In this paper, the drying of acerola residue was investigated in a medium‐sized spouted bed. The spouted bed proved effectiveness in drying the acerola residue and in conserving bioactive compounds when operating for short periods and with low temperature. Thus, it can be considered as a promising technique for drying food products. Also, the data provided in this study can contribute to the solution of the scale‐up problem in spouted beds. [ABSTRACT FROM AUTHOR]

Separating existing historical precipitation data into solid and liquid precipitation remains a challenge in the study of climate change, extreme precipitation, and hydrological modelling. Based on historical daily air temperature and precipitation data, as well as visual observations of precipitation phase (weather phenomena records) in China mainland, this study proposed a snow‐day direct definition method (SDDM) to determine the threshold air temperature (TAT) of rainfall and snowfall, and analysed the spatial pattern and its influential factors. The main findings include: (1) the TAT based on the SDDM varied from −1.2 to 6.3°C, with a mean value of 2.8°C for the entire study region; (2) TAT was generally higher and more variable in the low‐latitude areas, and the Qinghai‐Tibet Plateau was characterized by an abnormally high average TAT of 5.2°C, almost twice as large as that of the eastern monsoon region; (3) TAT exhibited a significant positive correlation with altitude and negative correlation with precipitation and relative humidity. The results presented in this paper have potential application for studies of large‐scale snowfall climatology and climate change, weather forecasting techniques, and hydrological model parameterization in areas with complex and diverse geographical and climatic conditions. [ABSTRACT FROM AUTHOR]

This work introduces a comprehensive model of sound attenuation and speed on Mars, in light of the recent operation of several microphones on the surface of Mars. The proposed acoustic model calculates the sound speed and attenuation throughout the near‐surface Martian atmosphere based on first‐principles. We evaluate the effects of the seasonal and diurnal cycle of air temperature, pressure and CO2, as well as the concentration of airborne dust on the sound attenuation. The attenuation and speed of sound are most sensitive to the air temperature and, therefore, they vary with the diurnal temperature cycle and to a lesser degree with the seasonal changes in temperature. The speed of sound also varies with the seasonal variations of the concentration of CO2. The main outcome of this work is an acoustic model capable of computing the sound speed and attenuation for any location at the Martian surface at any time of year and any time of day. Plain Language Summary: Sound offers new means of investigating the Martian environment. To study the properties of sound sources on Mars, or to derive atmospheric properties from the way sound travels in the Martian atmosphere, a model of the sound attenuation and speed on Mars is needed. In this paper, we propose such a model that allows the sound attenuation and speed at the Martian surface to be computed at any point and time. We investigate how these two properties change with air temperature, pressure, the chemical composition of the Martian atmosphere, the concentration of airborne dust, and with time of day and season. This paints the picture of Mars as a very contrasting planet acoustically, with a very wide range of attenuation and speed of sound compared to Earth because of the daily variations of temperature and the yearly variations of pressure and chemical composition. Key Points: Attenuation and speed of sound are modeled at every point of the Martian surfaceSound attenuation and speed are most affected by the variation of pressure and temperature associated with the diurnal and seasonal cyclesThe variations of sound attenuation and speed during a given day are very strong compared to Earth [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]

In July and August 2022, the Yangtze River basin (YRB) experienced its hottest summer since 1961. The SINTEX‐F2 seasonal prediction system initialized in early May predicted the hotter‐than‐normal summer due to its successful prediction of central Pacific La Niña, negative Indian Ocean Dipole and the resultant warming in the tropical West Pacific‐East Indian Ocean (TWP_EIO). The common SST forcing explains only about 26% to the heatwave strength, while the internal variations in the anomalous warming in the TWP_EIO and Europe, surplus precipitation in Pakistan, and local land‐air interaction account for approximately 65%, based on the analysis of 108 ensemble members. These factors have collectively increased the maximum temperature over the YRB through the enhancement and westward expansion of western North Pacific subtropical high. Our findings quantify the relative contributions of external forcing and internal variations to the unprecedented hot event, offering insights into its forming mechanism and potential predictability. Plain Language Summary: A record‐breaking heatwave event occurred in the YRB in July and August 2022, posing significant risks on human health, power supply, and social economic activities. Recognizing the importance of such an event, our study aims to identify key factors influencing its prediction using the SINTEX‐F2 system. The central Pacific La Niña, negative Indian Ocean Dipole and the resultant warming in the TWP_EIO provide the dominant predictability, but accounts only about 26% of the heatwave strength. However, internal variations in the anomalous warming in the TWP_EIO and Europe, surplus precipitation in Pakistan, and local land‐air interaction collectively explain about 65%. Our results suggest the necessity of large‐ensemble prediction in capturing this kind of unprecedented extreme event. Key Points: The SINTEX‐F2 initialized on 2022 early May captures the warmer‐than‐normal air temperatures in Yangtze River basinThe model successfully predicts co‐occurrences of CP La Niña and negative IOD in 2022However, the direct SST forcing contributes only 26% to the 2022 extreme event in Yangtze River basin [ABSTRACT FROM AUTHOR]

North Atlantic midlatitude cyclones are among the most severe weather systems, causing enormous economic damages and threatening human lives. The cyclone is typically characterized by cyclonic convergent surface winds, strong updrafts, and precipitation. However, extreme surface winds are often observed within the cyclone where downdrafts develop. The present study investigates the dynamical and thermodynamical characteristics of the horizontal winds impinging on the cold frontal surface and the associated downdrafts. It is shown that the cyclonic winds into the cold frontal surface are mainly responsible for the downdrafts that transport the high‐altitude horizontal momentum to the surface and cause intense surface winds. About half of the North Atlantic midlatitude cyclones are accompanied by the downdrafts especially in the southern and western parts of the cyclone center. Plain Language Summary: In the midlatitudes, air temperature decreases toward the poles, and cyclone systems typically travel eastward along regions characterized by strong meridional temperature gradients. Over the cyclone‐influencing area, the cyclonic, counterclockwise rotating, wind induces southward cold advection on the western side of the cyclone center. Therefore, during the early phase of the North Atlantic midlatitude cyclone, the generation of the cold front, which has a steep gradient of temperature to the west of the cyclone center, and that is a common feature. Even after the formation of the cold front, the horizontal winds keep impinging on the frontal surface. Then the horizontal winds blocked by the frontal surface and downdrafts are induced. The downdrafts transport the upper‐level intense wind speed to the lower level strengthening the surface winds. Key Points: Surface winds within North Atlantic midlatitude cyclones are strengthened by strong downdrafts and downward momentum transportsDowndrafts can be generated by the horizontal winds impinging on the cold frontal surfaceCyclones tend to accompany the downdrafts more to the south and west of the cyclone center [ABSTRACT FROM AUTHOR]

Satellite microwave radiance observations are strongly sensitive to sea ice, but physical descriptions of the radiative transfer of sea ice and snow are incomplete. Further, the radiative transfer is controlled by poorly‐known microstructural properties that vary strongly in time and space. A consequence is that surface‐sensitive microwave observations are not assimilated over sea ice areas, and sea ice retrievals use heuristic rather than physical methods. An empirical model for sea ice radiative transfer would be helpful but it cannot be trained using standard machine learning techniques because the inputs are mostly unknown. The solution is to simultaneously train the empirical model and a set of empirical inputs: an "empirical state" method, which draws on both generative machine learning and physical data assimilation methodology. A hybrid physical‐empirical network describes the known and unknown physics of sea ice and atmospheric radiative transfer. The network is then trained to fit a year of radiance observations from Advanced Microwave Scanning Radiometer 2, using the atmospheric profiles, skin temperature and ocean water emissivity taken from a weather forecasting system. This process estimates maps of the daily sea ice concentration while also learning an empirical model for the sea ice emissivity. The model learns to define its own empirical input space along with daily maps of these empirical inputs. These maps represent the otherwise unknown microstructural properties of the sea ice and snow that affect the radiative transfer. This "empirical state" approach could be used to solve many other problems of earth system data assimilation. Plain Language Summary: One way to learn about the earth system would be through machine learning, but typical "supervised learning" already requires good knowledge of the geophysical variables of interest. Quite often this "geophysical state" is not well known and the main observations are from satellites, which measure earth‐emitted radiation. In many cases the links between this observed radiation and the geophysical state are poorly known. This work illustrates a new method which allows both the geophysical state and its links to the satellite observations to be learned at the same time. The specific application is finding the sea ice concentration, which is a difficult problem because there is poor knowledge of the properties of sea ice, such as the air bubbles within it, which can strongly affect the radiation measured by satellites, and poor knowledge of exactly how this affects the satellite observations. The new approach solves this double problem by making daily maps of the sea ice and its properties, along with learning a new model to simulate the satellite measured radiation from the sea ice properties. Similar approaches could improve knowledge of both models and geophysical state in other areas of earth system science. Key Points: A new hybrid of machine learning and data assimilation can infer an unknown state and an unknown model simultaneouslyThis empirical state method could help other chicken and egg earth system problems that are not suitable for supervised machine learningThis work finds a new sea ice surface emissivity model and makes global all‐season maps of sea ice concentration using inverse methods [ABSTRACT FROM AUTHOR]

Here we present a new eastern Baltic Chironomidae training set (TS) containing 35 sites that was collected and merged with neighbouring published Finnish (82 lakes) and northern part of the Polish (nine lakes) TSs. Chironomidae, non‐biting midges, are known to be strongly responsive to the July air temperature and are widely used to infer palaeotemperature. Several modern analogue‐based TSs necessary for calibrating the relationships between mean July air temperature (MJAT) and chironomids are available for Europe. However, none of these is representative of the transitional climate typical for eastern Baltic (Estonia, Latvia, Lithuania). The Finno–Baltic–Polish TS contains 121 sites and covers a geographically continuous 70–50°N latitudinal and 7 °C (12.1–19.2 °C) MJAT gradient. Canonical correspondence analysis revealed that, among the tested environmental variables (pH, water depth, dissolved oxygen, MJAT), the MJAT explains the highest amount of variation, both for the eastern Baltic separately and the Finno–Baltic–Polish TSs. The weighted averaging–partial least squares‐based cross‐validation test reveals that the Finno–Baltic–Polish TS has a low root mean square error of prediction (0.7 °C) confirming the high reliability of the TS. The temperature optima of the taxa included in the new Finno–Baltic–Polish TS and widely used Swiss–Norwegian TS were examined. The observed dissimilarities can be attributed to the differences in the temperature ranges represented by the TS, the taxonomic identification level, the general cosmopolitan taxa distribution patterns and the influence of TS‐specific geographic position, climatic or environmental conditions. The new Finno–Baltic–Polish TS adds to the knowledge on the modern distribution of Chironomidae taxa and widens the geographical area of reliable Chironomid‐based MJAT reconstructions into the eastern European lowland. [ABSTRACT FROM AUTHOR]

The urban heat island effect exacerbates independent climate change‐induced shifts toward longer, stronger, and more frequent heat extremes. Environmental inequity, driven by a history of racially motivated urban planning policies, has led particular demographics to bear the worst impacts of urban heat exposure and thus also climate change. These impacts cause adverse health outcomes in the form of heat emergencies. Through a novel demographic and spatial analysis of heat‐related illness Emergency Medical Services data from Richmond, Virginia, this study investigates the relationships between heat health emergencies and intra‐urban heat islands quantified through three heat exposure metrics. We also evaluate the accessibility of built refuge from urban heat in the form of public transit infrastructure, libraries, and government cooling centers in relation to these emergencies. We found that heat emergencies are inequitably distributed among racial, age, and socioeconomic groups in Richmond, particularly among residents identified as Male, Black or African American, 50+ years old, and experiencing mental health, intoxication, and/or homelessness. We found significant associations between the location of these heat emergencies and urban heat islands as estimated from remotely‐sensed surface and community science‐derived air temperature metrics, but not a co‐estimated heat index. We also found that available refuge facilities are insufficiently located to protect individuals with reduced mobility across areas with the highest number of heat‐related health emergencies. Community involvement in the mitigation and management of extreme heat threats, especially for those disproportionately impacted, is necessary to decrease the number of summertime heat illnesses. Plain Language Summary: In a process called the urban heat island effect, urban areas experience higher average temperatures compared to nearby rural areas, largely due to the lack of cooling provided by trees and the heat‐trapping ability of buildings and paved surfaces. While human‐caused climate change is driving average global temperatures to rise, some communities and neighborhoods in Richmond, Virginia experience hotter temperatures than others. This is largely due to a historical lack of government investment in Black and low‐income neighborhoods. Hotter, less resourced neighborhoods experience more heat‐related health emergencies like heat stroke and heat exhaustion. This study found significant relationships between the location of heat‐related illness Emergency Medical Services data from Richmond, Virginia and areas which are measurably hotter. We also found that Richmond has insufficient publicly accessible places for people with limited mobility to escape the heat, including bus stop shelters, libraries, and government cooling centers. Richmonders bear the brunt of heat emergencies unequally, with a higher proportion of heat emergencies among residents identified as Male, Black or African American, 50+ years old, and experiencing mental health, intoxication, and/or homelessness. Impacted community members should be prioritized as cities like Richmond addresses the everyday impacts of climate change and urban heat. Key Points: Heat‐related health emergencies are inequitably distributed in Richmond, Virginia across demographicsRemotely‐sensed surface temperature, community science‐derived air temperature metrics are strong predictors of heat‐related health impactsBuilt refuge is essential for extreme heat management, inequitable access persists for low mobility and vulnerable residents [ABSTRACT FROM AUTHOR]

Identifying how aerosol particles interact with atmospheric water is critical to understand climate and precipitation. Ice‐nucleating particles (INP) trigger the formation of atmospheric ice crystals at higher temperatures than pure water. They are difficult to characterize because of their scarce occurrence, and variability, in the atmosphere, especially at temperatures above −20°C. It has been demonstrated that at these temperatures, biological aerosol particles can contribute significantly to INP number concentration. This study incorporates a series of offline, size‐segregated measurements of INPs collected at the Puy de Dôme station (PUY, 1,465 m a.s.l.) over a 6 month period from October to May, covering the transitions from autumn, winter, to spring. These measurements show a general trend of decreasing particle number concentrations during the winter months and higher concentration during autumn and spring. INP concentrations measured in the range of −5 and −18°C, had concentrations of 0.001 INP/Lair at the warmest temperatures, and between 0.01 and 0.1 INP/Lair at the coldest temperatures. The majority of INP measured at temperatures warmer than −15°C were heat labile, suggesting a biological or organic origin. The INP variability was compared with collocated aerosol physical and chemical properties, allowing us to associate highest INP concentrations with local and marine origins. Following these comparisons, we use aerosol total number concentration to develop a new parameterization. In addition, this parameterization is specifically optimized for warmer temperature INP measurements, and demonstrated a good performance when tested on independent data sets. Plain Language Summary: Understanding how tiny particles in the air (aerosol particles) interact with water in the atmosphere is crucial for studying climate and precipitation. Ice Nuclei Particles (INP) play a role in forming ice crystals in the atmosphere, especially at higher temperatures. This is important because it's been found that biological aerosol particles can significantly contribute to INP at temperatures above −20°C. The study conducted offline measurements of INPs over 6 months at the Puy de Dôme station, covering the transition from autumn to spring. Results showed a general trend of fewer particles during winter and more during autumn and spring. The INP concentrations were highest at temperatures between −5 and −18°C, with warmer temperatures having 0.001 INP per liter of air and colder temperatures having 0.01 to 0.1 INP per liter of air. Most INPs at temperatures above −15°C were heat‐sensitive, indicating a biological or organic origin. This study linked high INP concentrations to local and marine sources by comparing them with aerosol properties. A new parameterization based on total aerosol number concentration, specifically optimized for warmer temperatures, performed well when tested on independent data sets. Key Points: Size‐segregated airborne ice‐nucleating particles (INP) concentrations were measured at an altitude station in Central France over 6 monthsSeasonal variations are observed with a minimum in winter. The majority of INPs is biological and activated above −18°CWe developed a parameterization for predicting INP concentrations at warm temperatures based on total aerosol concentrations [ABSTRACT FROM AUTHOR]

Macroclimate drives vegetation distributions, but fine‐scale topographic variation can generate microclimate refugia for plant persistence in unsuitable areas. However, we lack quantitative descriptions of topography‐driven microclimatic variation and how it shapes forest structure, diversity, and composition. We hypothesized that topographic variation and the presence of the forest overstory cause spatiotemporal microclimate variation affecting tree performance, causing forest structure, diversity, and composition to vary with topography and microclimate, and topography and the overstory to buffer microclimate. In a 20.2‐ha inventory plot in the North American Great Plains, we censused woody stems ≥1 cm in diameter and collected detailed topographic and microclimatic data. Across 59‐m of elevation, microclimate covaried with topography to create a sharp desiccation gradient, and topography and the overstory buffered understory microclimate. The magnitude of microclimatic variation mirrored that of regional‐scale variation: with increasing elevation, there was a decrease in soil moisture corresponding to the difference across ~2.1° of longitude along the east‐to‐west aridity gradient and an increase in air temperature corresponding to the difference across ~2.7° of latitude along the north‐to‐south gradient. More complex forest structure and higher diversity occurred in moister, less‐exposed habitats, and species occupied distinct topographic niches. Our study demonstrates how topographic and microclimatic gradients structure forests in putative climate‐change refugia, by revealing ecological processes enabling populations to be maintained during periods of unfavorable macroclimate. [ABSTRACT FROM AUTHOR]

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

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

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]

The research on the trends of mountainous aridity and the issue of elevation‐dependent aridity (EDA) has long been hindered by limitations in data validity. In this study, we present global monthly merged data sets of Vapor Pressure Deficit and Aridity Index (AI) from 1960 to 2020, aiming to detect and attribute elevation‐dependent patterns of atmospheric and soil aridity in six representative high mountainous areas worldwide. Our findings reveal that most of the mountainous regions experienced significant aridification after the 1970s (p < 0.05). The Rocky Mountains and Ethiopian Highlands are identified as the most vulnerable areas, with both atmospheric and soil aridity increasing by 13% across all elevation gradients. Significant humidification has been observed only in High Asian Regions at 4,000 m above sea level with an approximate decrease of 11% in soil AI. In both temporal and spatial patterns, soil aridity exhibits stronger heterogeneity compared to atmospheric aridity, with certain regions and seasons showing humidification, despite the overall aridification trend. Elevation‐dependent aridity is observed in two‐thirds of the mountains, but whether high altitude alleviates or amplifies aridity depends on the pattern of precipitation changes at different elevations. The rise in air temperature is the primary driving factor for soil and atmospheric aridification, contributing to over 50% of each. In two‐thirds of the regions, changes in precipitation exacerbate soil aridity. Simulations show that human activities are closely related to the ongoing prolonged atmospheric aridification. This study contributes to a comprehensive understanding of global mountainous aridity evolution projected under climate change. Plain Language Summary: Due to limitations in data validity, the conclusions regarding mountainous aridity changes and related elevation‐dependent aridity issues have often been contradictory. To address this issue, we generated a comprehensive data set of drought indices by integrating observed and simulated data, covering the period from 1960 to 2020. Our analysis revealed that the 1970s marked a significant turning point for aridity patterns in mountainous regions globally. Since then, except for the High Asian Regions, both soil and atmospheric aridity have exhibited a rapid increase in mountainous areas worldwide, with the most severe aridification observed in the Rocky Mountains and Ethiopian Highlands. We also identified a certain relationship between the severity of aridification and elevation gradients, although it is not a straightforward relationship where higher elevations necessarily experience more severe aridification. Importantly, our attribution analysis suggests that human activities may impact the observed atmospheric aridification in mountainous regions on a global scale. Key Points: The period from 1970 to 1980 witnessed a significant shift from humidification to aridification in most mountainous regions worldwideThe relationship between changes in precipitation and elevation gradients shapes the distinct patterns of elevation‐dependent aridityProlonged atmospheric aridification in mountainous regions may not be solely attributed to natural variability [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]

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]

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]

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]

The D/H ratio in water on Mars, Rwater, is 4–6× the Earth ratio, signifying past water loss to space. Recently, measurements have revealed high values of the D/H ratio in hydrogen, Ratomic, in the thermosphere during southern summer. Here, we use a photochemical model to explore the potential drivers of Ratomic, testing three: thermospheric temperatures, excess mesospheric water, and changing insolation. We find that Ratomic can achieve values between 15× the Earth ratio (due to water) and 23× the Earth ratio (due to temperature). The effects arise because H escape is diffusion‐limited, while D escape is energy‐limited. Our results underscore how Ratomic reflects mesospheric dynamics, and the need for concurrent measurements of mesospheric water, thermospheric temperatures, and Ratomic to understand seasonal changes in the martian water cycle and atmospheric loss. Plain Language Summary: The high ratio of deuterium (D) to hydrogen (H) measured in water molecules on Mars indicates that much of Mars' past water has escaped to space. Recent measurements of the D/H ratio in the atoms themselves using data from the MAVEN spacecraft have revealed a ratio as high as 100 times the Earth value. In this work, we use a computational model of the Mars atmosphere to explore whether the large values could be caused by seasonal changes in three atmospheric parameters: the upper atmospheric temperature, the presence of extra water vapor in the middle atmosphere, and the incoming solar radiation. We find that temperature and water vapor have comparable effects, with each leading to an atomic D/H ratio similar to those found by MAVEN observations. We also explain how temperature and water affect the dynamics of H and D in the atmosphere to cause the change in the ratio. Key Points: Seasonal increases in exobase temperature or mesospheric water can enhance the upper atmospheric atomic D/H ratio up to 15–23 times VSMOWThe enhancement occurs due to dynamical differences, leading to similarities in D/H ratio but differences in abundance and escapeConcurrent measurements of temperatures, water vapor, and the D/H ratio will enhance our understanding of atmospheric escape from Mars [ABSTRACT FROM AUTHOR]

Mountains near the equator have very unique temperature regimes due to the tropical latitude and alpine altitude. How climate change will impact these temperature regimes is not clear as there are so few temperature records in these environments. This study attempts to characterize the near‐surface air and ground temperature regime for the Teleki Valley (3,200–4,200 m. a.s.l) on the western slope of Mount Kenya using a set of six temperature loggers placed at 200 m elevation intervals. Temperature was recorded at 30 min intervals from September 2021 to October 2022. The mean diurnal temperature range varied from 6°C to 14°C, with maximum daily swings in excess of 25°C. There was a distinct seasonality in temperature, with a hot season during the months of January‐March, where it was roughly 3°C warmer than the coolest months. Diurnal temperature swings were also highest in March. This seasonality is driven by radiation and moisture: higher radiation during the vernal equinox combined with clear skies during the dry season made for higher temperatures. Elevation was the dominant spatial gradient determining temperature regimes, but rock and vegetation cover played a large role in modifying near‐surface air and ground temperature. The highest elevation logger (4,200 m. a.s.l), measuring ground temperature, showed muted seasonal and diurnal swings, creating for warmer temperatures than lower elevation loggers. This demonstrates the importance of thermal refugia at these high elevations. This also makes predictions of plant and animal responses to climate change difficult, as uniform upward shifts would not necessarily maintain thermal niches. Plain Language Summary: Tropical alpine areas have few detailed records of climate particularly in the zone near the surface where most organisms live. This study investigated the near ground temperature regime of the Teleki Valley of Mount Kenya using loggers placed at 200 m elevation intervals. The results showed that temperature can vary considerably over short distances and over time, with marked daily and seasonal temperature patterns. Elevation, topography and cover drive these differences as do macro‐climactic factors of wind patterns and solar radiation. The implication is that biotic responses to climate change will not be straight‐forward but may be site‐ and species‐specific. Key Points: Near‐surface temperature regime of the alpine zone of Mount Kenya has a seasonal element that is driven in part by solar radiation changesElevation and topography drive spatial differences in temperature in the alpine zoneThere is a diversity of microclimates which may play a crucial role in survival of biotic communities under climate change [ABSTRACT FROM AUTHOR]

We simulated the seasonal temperature evolution in the atmosphere of Antarctica and the Arctic focusing on infrared processes. Contributions by other processes were parametrized and kept fixed throughout the simulations. The model was run for current CO2 and CH4 and for doubled concentrations. For doubling CH4 the warming in Antarctica is restricted to the lowest few hundred meters above the surface while in the Arctic we find a warming in the whole troposphere. We find that the amount of water is the main driver for the differences between both polar regions. When increasing both, CO2 and CH4 from pre‐industrial values to current concentrations, and averaged over the whole troposphere, we find a warming of 0.42 K for the Arctic and a slight cooling of 0.01 K for Antarctica. Our results contribute to the understanding of the lack of warming seen in Antarctica throughout the last decades. Plain Language Summary: In 2015 we have initiated a discussion on a fundamental property of the radiation in the atmosphere over Antarctica: The negative greenhouse effect (Schmithüsen et al., 2015, https://doi.org/10.1002/2015GL066749). A negative greenhouse effect means, the atmosphere emits more radiation to space than it receives from the surface. This results in a cooling somewhere in the Antarctic atmosphere during some months of the year, when increasing CO2. We now simulate how the Antarctic atmospheric temperature responds in all altitude levels to CO2 and CH4 increases, and show this is different from the temperature response in the Arctic. We show for example, that an increase in CH4 cools nearly the whole troposphere, although the response for CH4 is much lower in amplitude than for CO2. We find that the amount of water is the main driver for the differences between both polar regions. Since the amount of water vapor strongly depends on temperature, the colder Antarctic atmosphere responds differently to the Arctic when greenhouse gases increase. Our studies could be one important factor when understanding the lack of warming in Antarctica throughout the last decades. Key Points: We simulate the temperature development in both polar regions in the infrared and find that doubling CO2 and CH4 lead to opposing forcingsThe different amount of water vapor shows to be responsible for the differences in warming/cooling in both polar regionsIn Antarctica doubling CH4 leads to a cooling of almost the whole troposphere, a future increase in H2O could invert this [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is the largest permafrost distribution zone at high‐altitude in the mid‐latitude region. Climate change has caused significant permafrost degradation on the TP, which has important impacts for the eco‐hydrological processes. In this study, the frost number is utilized to calculate the frost number (F) based on the air freezing/thawing index obtained from the downscaled Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets. A novel method is proposed to determine the frost number threshold (Ft) for diagnosing permafrost distribution. Then the simulated permafrost distribution maps are compared with the existing permafrost distribution map, employing the Kappa coefficient as the measure of classification accuracy to identify the optimal Ft. Finally, the permafrost distribution on the TP under different Shared Socio‐economic Pathways (SSP) scenarios are diagnosed with the optimal Ft. Simulation results demonstrate that across all scenarios, the rates of permafrost degradation during the mid‐future period (2040–2060) remain comparable to those observed in the baseline period (2000), ranging from 33% ± 3% to 53% ± 4%. Conversely, during the far‐future (2080–2099), the permafrost degradation rates display significant variation across different scenarios, ranging from 37% ± 4% to 96% ± 3%. The profound impacts of permafrost degradation on the TP are reflected in decreasing trends in soil moisture and runoff, as well as a slower increasing trend in Normalized Difference Vegetation Index (NDVI) compared to other regions, indicating negative impacts on vegetation growth. Plain Language Summary: The Tibetan Plateau, the highest plateau in the world and the largest high‐altitude permafrost region, is experiencing permafrost degradation due to climate change, significantly impacting eco‐hydrological processes in this region. In this study, we used the frost number model with air temperature to simulate the distribution of permafrost on the Tibetan Plateau under different scenarios. The results show that permafrost on the Tibetan Plateau is projected to degrade in the 21st century, especially under high‐emission scenarios. The degradation of permafrost will likely reduce soil moisture and runoff. Additionally, vegetation growth in areas with permafrost degradation is expected to be slow. These findings are of great significance for understanding permafrost changes on the Tibetan Plateau and their impacts on eco‐hydrological processes. Key Points: A new method using the frost number model with Kappa coefficient is proposed to diagnose permafrost distributionPermafrost on the Tibetan Plateau will experience the least degradation (33% ± 3%) under SSP126, and the most (96% ± 3%) under SSP585 in 2080–2099Permafrost degradation on the Tibetan Plateau is anticipated to reduce soil moisture and runoff, adversely affecting vegetation growth [ABSTRACT FROM AUTHOR]

Gas–steam combined cycle power plants are the most efficient electricity‐generation units based on fossil fuels. However, these power plants are prone to efficiency decrease in hot climates as high ambient temperatures adversely influence the gas turbine's output. The present study investigated the effect of incorporating a solar absorption refrigeration (SAR) system into an actual combined cycle power plant for the first time. First, the energy and exergy analyses were performed using THERMOFLOW software. Then, the influence of the ambient temperature (10°C–52.5°C) on the power plant's performance and its components was investigated. The SAR system was then used to cool the compressor's input air and improve the power generation capacity by employing TRNSYS software. The results showed that the power plant reached its maximum efficiency at an ambient temperature of 26.6°C. However, its overall efficiency and net power generation were dropped with a further increase in the ambient temperature. Employing the SAR system for each gas turbine in the power plant on a sunny day until 2 p.m. would decrease the compressor's input air temperature. For example, for the refrigeration capacities of 450, 700, and 1000 tons for each gas turbine, the temperature was reduced by nearly 3°C, 5°C, and 7°C, respectively. Under the same condition, power generation capacity improved by 12.5, 24, and 32.5 MW, and the overall efficiency rose by 0.6%, 1.4%, and 2%. Such an increase in power and efficiency occurred during peak demand, which was significant. [ABSTRACT FROM AUTHOR]