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Extreme heat events are becoming increasingly common, and the short‐term acclimation of photosynthesis will have a large impact on plant performance. Trees in lowland tropical forests, which are hypothesized to have limited abilities to tolerate rising temperatures, may need to rely on short‐term acclimation of their more plastic traits, like photosynthetic thermal tolerance, to persist in the face of increasingly variable climates. Here, we investigated seasonal acclimation of thermal tolerances in plant species of the moist Amazon. Specifically, we measured the photosynthetic thermal tolerances of six common woody Amazonian species at the beginning and at the end of the dry season to determine the species' abilities to acclimate to intra‐annual changes to climate. In addition, we used the natural thermal gradient present at our research site to test the acclimation of individual plants to maximum air temperatures not currently observed elsewhere in the moist lowland Amazon (up to ~43°C). Between seasons, there were significant overall increases in the thermal tolerances of three species (i.e. higher thermal tolerances in the hotter dry season), suggesting that leaf megathermy is prominent in these species. Also, three species acclimated their thermal tolerances to microsite‐level differences in seasonal temperature maxima, suggesting closer fidelity between leaf and air temperatures (i.e. limited homeothermy) for these species. Our results show that some woody species from the moist Amazon can acclimate their thermal tolerances over short time‐scales, although acclimation is likely insufficient to overcome thermal stress during extreme temperature events. Some species may therefore be more sensitive to heatwaves than others, which could impact survival and composition of tropical lowland forests into the future. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

The applicability and accuracy of high‐resolution Real‐Time Meso‐scale Analysis (RTMA) system is assessed over India for the first time. The RTMA is a high‐spatial (2.5 km) and temporal resolution analysis system for near‐surface weather conditions. It is used to simulate near‐surface air temperature over India during the Heatwave (HW) period 12th to 20th April 2023. The verification analysis of temperature using the GLDAS gridded temperature shows reasonable improvement in the analysis from RTMA by capturing regional features compared to first‐guess. The spatial and temporal verification using the IMD station observations also confirms the value addition of RTMA in capturing the locations of recorded highest Tmax and their daily variations. The location‐specific heat stress analysis of RTMA shows high skill over many locations during HW days. Heat stress regions have been accurately brought out in the RTMA. Hence, the RTMA can be used for now‐casting and severe weather monitoring. Plain Language Summary: Heat wave (HW) alert systems require accurate high‐resolution surface weather data for better adaption strategies. Real‐Time Meso‐scale Analysis (RTMA) system configured to generate surface weather parameters at a high‐spatial (2.5 km) and temporal (3 hrs) resolution at National Center for Medium Range Weather Forecasting (NCMRWF) for the very first time in India. We verified its performance in representing the HW conditions and extreme temperatures over the Indian region relative to India Meteorological Department (IMD) observations/Global Land Data Assimilation System (GLDAS) during the HW period of 12th to 20th April 2023. Our results indicate significant improvement in the spatial and temporal variations of daily maximum temperature simulated by RTMA system. We have noted lower absolute mean errors of ∼ 0.02 for the RTMA in predicting the Tmax over the HW affected regions of Bihar, Odisha, West Bengal, and East Uttar Pradesh when compared to the GFS model. These improvements have led to the better identification of the locations of higher heat risk through the computation of the Heat Index and highlight the applicability of RTMA. Overall this study clearly brings out the usability of RTMA final analysis fields for now‐casting and severe weather monitoring. Key Points: It is a first‐of‐its‐kind study to understand the applicability and accuracy of high‐resolution RTMA over India by analyzing heatwaveRTMA temperature analysis shows higher skill over the heatwave regions and locations as compared to the background fields and observationsThe study suggested that the RTMA final analysis fields are highly useable for severe weather monitoring and impact‐based weather forecasts [ABSTRACT FROM AUTHOR]

Wet and cold biases on the Tibetan Plateau (TP) commonly exist in global and regional climate simulations. Previous studies have explored the possible causes of wet and cold biases and contributed to reducing these biases. However, the connection between wet and cold biases remains insufficiently addressed. Our research indicates that the TP wet bias converts into positive snow amount bias not continually but efficiently and concentratedly, under the control of snow phenology in different regions. Furthermore, the complex relationship between snow amount, snow coverage and surface albedo restricts the transformation of snow amount to surface albedo bias, and thus to cold bias. Our research highlights the spatio‐temporally divergent transformation of wet to cold bias on the TP during snow season, providing a novel perspective to understand the intrinsic connection between wet and cold biases and improve climate simulations on the TP. Plain Language Summary: Due to the harsh environment, observational data are limited on the Tibetan Plateau (TP), so climate model is one of necessary tools for climate researches. However, both global and regional climate models generally overestimate precipitation (wet bias) and underestimate near surface air temperature (cold bias) on the TP. Previous studies have pointed out the possible causes of the wet and cold biases, and partly reduced these biases, leaving the connection between these biases unaddressed. Our study finds that the conversion of wet to cold bias during snow season is divergent in different areas of the TP. Under the control of snow duration in different areas, the wet bias efficiently and concentratedly transforms into larger snow amount. Furthermore, the complex relationship between snow amount, snow coverage and surface albedo restricts the transformation of snow amount to surface albedo bias, and thus to cold bias. We reveal the spatio‐temporally divergent transformation of wet to cold bias on the TP during snow season, and these conclusions will contribute to a better improvement of snow and climate modeling on the TP. Key Points: WRF simulations significantly overestimate snow amount and delay snow phenology on the Tibetan Plateau (TP)Snowfall bias is not always transformable into snow amount bias, depending on snow phenologyComplex relationship between snow amount and surface albedo restricts the transformation of wet to cold bias on the TP [ABSTRACT FROM AUTHOR]

Summary: Current estimates of temperature effects on plants mostly rely on air temperature, although it can significantly deviate from leaf temperature (Tleaf). To address this, some studies have used canopy temperature (Tcan). However, Tcan fails to capture the fine‐scale variation in Tleaf among leaves and species in diverse canopies.We used infrared radiometers to study Tleaf and Tcan and how they deviate from air temperature (ΔTleaf and ΔTcan) in multispecies tropical tree plantations at three sites along an elevation and temperature gradient in Rwanda.Our results showed high Tleaf (up to c. 50°C) and ΔTleaf (on average 8–10°C and up to c. 20°C) of sun‐exposed leaves during 10:00 h–15:00 h, being close to or exceeding photosynthetic heat tolerance thresholds. These values greatly exceeded simultaneously measured values of Tcan and ΔTcan, respectively, leading to strongly overestimated leaf thermal safety margins if basing those on Tcan data. Stomatal conductance and leaf size affected Tleaf and Tcan in line with their expected influences on leaf energy balance.Our findings highlight the importance of leaf traits for leaf thermoregulation and show that monitoring Tcan is not enough to capture the peak temperatures and heat stress experienced by individual leaves of different species in tropical forest canopies. [ABSTRACT FROM AUTHOR]

Summary Climate change not only leads to higher air temperatures but also increases the vapour pressure deficit (VPD) of the air. Understanding the direct effect of VPD on leaf gas exchange is crucial for precise modelling of stomatal functioning. We conducted combined leaf gas exchange and online isotope discrimination measurements on four common European tree species across a VPD range of 0.8–3.6 kPa, while maintaining constant temperatures without soil water limitation. In addition to applying the standard assumption of saturated vapour pressure inside leaves (ei), we inferred ei from oxygen isotope discrimination of CO2 and water vapour. ei desaturated progressively with increasing VPD, consistently across species, resulting in an intercellular relative humidity as low as 0.73 ± 0.11 at the highest tested VPD. Assuming saturation of ei overestimated the extent of reductions in stomatal conductance and CO2 mole fraction inside leaves in response to increasing VPD compared with calculations that accounted for unsaturation. In addition, a significant decrease in mesophyll conductance with increasing VPD only occurred when the unsaturation of ei was considered. We suggest that the possibility of unsaturated ei should not be overlooked in measurements related to leaf gas exchange and in stomatal models, especially at high VPD. [ABSTRACT FROM AUTHOR]

Midlatitude Europe (ME) emerges as a prominent heatwave hotspot with rapid increases in summer surface air temperature and heatwave days since 1979, surpassing the global land averages by approximately 2.6 and 2.3 times, respectively. The circulation analogs‐based dynamic adjustment reveals that approximately 38% and 35% of these trends result from shifts in zonal dipolar circulation patterns over the North Atlantic (NA) and Europe, crucial for the enhanced warming compared to the global land average. The circulation changes are associated with warming sea surface temperatures in the NA. This warming pattern resembles the Atlantic Multidecadal Variability and is predominantly induced by greenhouse gases. Moreover, the stronger air temperature response in ME to decreased aerosols amplifies warming, contributing to the rapid increase in heatwave frequency. These findings highlight a prominent influence of anthropogenic forcings on the swift surge of European heatwaves compared to global land, with a potential implication for adaptation strategies and risk management. Plain Language Summary: Midlatitude Europe is experiencing a significant increase in heatwaves, with summer temperatures and heatwave occurrences rising much faster than the global land averages since 1979. One of the key contributors to this rapid warming is the changes in zonal dipolar circulation patterns over the NA and Europe, which are linked to the warm sea surface temperatures in the NA. This warming pattern, resembling the Atlantic Multidecadal Variability, is largely caused by greenhouse gases and additionally influenced by reduced aerosols and natural forcing. Furthermore, the stronger response of air temperatures in midlatitude Europe to decreased aerosol emissions intensifies summer warming, contributing to a rapid increase in heatwave days. These findings highlight the significant impact of anthropogenic forcings on the observed surge of heatwaves in Europe compared to global land, with an important implication for developing strategies to adapt to these changing climate conditions and managing the associated risks. Key Points: Midlatitude Europe exhibits more rapid increases in summer heatwave frequency than the global land averages in recent decadesAtmospheric circulation changes contributing about one third of the observed warming trend, further intensify the rapid increases in summer heatwave frequencyLocal air temperature response to reduced aerosol emissions contributes about half of the enhanced warming compared to the global land average [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]

The impact of land variables on temperature forecasts in atmospheric cycling is often underestimated or overlooked. This oversight primarily occurs due to the abundance of meteorological measurements available for assimilation and partly because soil states are assumed to be quickly reset by atmospheric forcing, such as precipitation, justifying no spin‐ups or no updates of soil states during cycling. In this study, by updating soil moisture every 6 hr using different analysis data sets for May 2019, considerable discrepancies were found, highlighting large uncertainties in soil moisture analysis. Different soil moisture analyses produced systematically different temperature forecasts, with errors growing over cycles to be comparable to a typical error magnitude of 2‐m temperature observations (∼2°K). This study demonstrates that temperature forecasts are significantly influenced by whether and how soil moisture is updated, not only near the surface but also up to the low‐mid troposphere and throughout the cycles. Plain Language Summary: This study examines the impact of soil moisture on temperature forecasts in the context of atmospheric cycling. During the 6‐hr cycling for May 2019 over the CONUS domain, updating soil moisture systematically affects temperature forecast bias (>±1°K) in the low‐mid troposphere throughout the cycles. We also observe significant uncertainties across state‐of‐the‐art soil moisture analysis data sets, highlighting the challenges of land data assimilation. Key Points: Temperature forecast errors are significantly affected by whether or how soil moisture is updated in atmospheric cyclingLarge uncertainties are found across different soil moisture analysis data setsUpdating soil moisture alone can mitigate temperature forecast bias in the low‐mid troposphere, systematically improving forecast accuracy [ABSTRACT FROM AUTHOR]

The mechanisms by which clouds impact the variability of the mid‐latitude atmosphere are poorly understood. We use an idealized, dry atmospheric model to investigate the relationship between Atmospheric Cloud Radiative Effects (ACRE) and annular mode persistence. We force the model with time‐varying diabatic heating that mimics the observed ACRE response to the Southern Annular Mode (SAM). Realistic ACRE forcing reduces annular mode persistence by 5 days (−16%), which we attribute to a weakening of low‐frequency eddy forcing via modified low‐level temperature gradients, though this effect is partly compensated by reduced frictional damping due to zonal wind anomalies becoming more top‐heavy. The persistence changes are nonlinear with respect to the amplitude of ACRE forcing, reflecting nonlinearities in the response of the eddy forcing. These results highlight the ACRE's impact on low‐frequency eddy forcing as the dominant cause of changes in annular mode persistence. Plain Language Summary: In this study, we conduct novel modeling experiments aimed at understanding how clouds affect the unforced variability of jet streams—concentrated bands of eastward wind in the mid‐latitude atmosphere. We focus on direct heating and cooling of the atmosphere by clouds, called Atmospheric Cloud Radiative Effects (ACRE), and how these impact the annular mode—the most significant type of jet stream variability, which consists of north‐south shifts of the jet's position. Using a simplified atmospheric model, we investigate how the persistence and structure of the annular mode are impacted by heatings and coolings that mimic the observed ACRE response to the Southern Hemisphere Annular Mode (SAM). We find that the ACRE reduces the persistence of the annular mode (i.e., deviations of the jet stream from its mean position tend to persist for less time), which we attribute to a weakening of the feedback mechanism by which atmospheric waves strengthen the jet where it is already strongest. We identify some compensating effects, but, overall, our results highlight that ACRE impacts jet streams via low‐altitude temperature gradients and atmospheric wave generation. Key Points: The response of Atmospheric Cloud Radiative Effects (ACRE) to the Southern Annular Mode (SAM) reduces SAM's persistenceThe decrease in SAM's persistence is attributed to weakened low‐frequency eddy forcing via disruption of low‐level temperature gradientsThe change in persistence is nonlinear with respect to the amplitude of ACRE forcing [ABSTRACT FROM AUTHOR]

Accurate air temperatures underpin environmental research. Most professional meteorological air temperature measurements still expose thermometers within traditional, naturally ventilated screens. Their representation of true air temperature depends on screen airflow, and therefore local winds. Accuracies of daily maximum (Tmax) and minimum (Tmin) air temperatures are assessed by comparison between a naturally ventilated large conventional screen and a co‐located aspirated reference screen. In over 1200 days' data, the naturally ventilated Tmin and Tmax both showed small (median < 0.06°C) cold bias, but, in 1% of cases, warm Tmax bias and cold Tmin bias >|1°C|. The Tmin cold bias is associated with calm clear nights, and the Tmax warm bias events with calm winter days at low sun angles, allowing solar heating of the screen. The prevalence of poor natural ventilation, potentially affecting Tmin and Tmax, is estimated across European sites. Poor ventilation occurred at Tmin for 12% of values, and at Tmax for 4%. Climatological averaging will reduce these effects, but, without corroborating wind data, statistical changes in Tmin or Tmax, including identifying “Tropical Nights” (Tmin > 20°C) or occurrences of winter extremes, may have limited value. Wider adoption of aspirated thermometer screens, with an initial overlap period, will largely eliminate these effects. [ABSTRACT FROM AUTHOR]

As global temperatures rise, droughts are becoming more frequent and severe. To predict how drought might affect plant communities, ecologists have traditionally designed drought experiments with controlled watering regimes and rainout shelters. Both treatments have proven effective for simulating soil drought. However, neither are designed to directly modify atmospheric drought. Here, we detail the efficacy of a silica gel atmospheric drought treatment in outdoor mesocosms with and without a co‐occurring soil drought treatment. At California State University, Los Angeles, we monitored relative humidity, temperature, and vapor pressure deficit every 10 min for 5 months in bare‐ground, open‐top mesocosms treated with soil drought (reduced watering) and/or atmospheric drought (silica dehumidification packets suspended 12 cm above soil). We found that silica packets dehumidified these mesocosm microclimates most effectively (−5% RH) when combined with reduced soil water, regardless of the ambient humidity levels of the surrounding air. Further, packets increased microclimate vapor pressure deficit most effectively (+0.4 kPa) when combined with reduced soil water and ambient air temperatures above 20°C. Finally, packets simulated atmospheric drought most consistently when replaced within 3 days of deployment. Our results demonstrate the use of silica packets as effective dehumidification agents in outdoor drought experiments. We emphasize that incorporating atmospheric drought in existing soil drought experiments can improve our understandings of the ecological impacts of drought. [ABSTRACT FROM AUTHOR]

The UK contribution to the Detection and Attribution Model Intercomparison Project (DAMIP), part of the sixth phase of the Climate Model Intercomparison Project (CMIP6), is described. The lower atmosphere and ocean resolution configuration of the latest Hadley Centre global environmental model, HadGEM3‐GC3.1, is used to create simulations driven either with historical changes in anthropogenic well‐mixed greenhouse gases, anthropogenic aerosols, or natural climate factors. Global mean near‐surface air temperatures from the HadGEM3‐GC31‐LL simulations are consistent with CMIP6 model ensembles for the equivalent experiments. While the HadGEM3‐GC31‐LL simulations with anthropogenic and natural forcing factors capture the overall observed warming, the lack of marked simulated warming until the 1990s is diagnosed as due to aerosol cooling mostly offsetting the well‐mixed greenhouse gas warming until then. The model has unusual temperature variability over the Southern Ocean related to occasional deep convection bringing heat to the surface. This is most prominent in the model's aerosol only simulations, which have the curious feature of warming in the high southern latitudes, while the rest of the globe cools, a behavior not seen in other CMIP6 models. This has implications for studies that assume model responses, from different climate drivers, can be linearly combined. While DAMIP was predominantly designed for detection and attribution studies, the experiments are also very valuable for understanding how different climate drivers influence a model, and thus for interpretating the responses of combined anthropogenic and natural driven simulations. We recommend institutions provide model simulations for the high priority DAMIP experiments. Plain Language Summary: We describe the UK submission to the Detection and Attribution Model Intercomparison Project (DAMIP), using the HadGEM3‐GC3.1 climate model. The model's near‐surface temperature responses to different human and natural climate drivers are compared with other climate models and observed temperature changes. The experiments help to understand the evolution of the model's simulated historical global temperatures. One of the more interesting model features is the variability in the Southern Ocean which manifests itself as occasional surface warming due to deep ocean heat coming to the surface. This behavior, which occurs more often in simulations that cool than in simulations that warm, appears to be unusual compared to other models. The investigation of this model behavior demonstrates that DAMIP model experiments are not just useful for climate change detection and attribution, but also for understanding how a model responds to different climate drivers. Climate model participation in DAMIP is encouraged. Key Points: The UK's contribution to the Detection and Attribution Model Intercomparison project (DAMIP) is describedThe climate model's global temperature response to different anthropogenic and natural drivers is examined and compared to other modelsSouthern Ocean temperature variability is unusual and sensitive to climate driver [ABSTRACT FROM AUTHOR]

The atmosphere‐ocean is a highly coupled system with significant diurnal and hourly variations. However, current coupled models usually lack sub‐diurnal scale processes at the air‐sea interface due to the finite vertical resolution for ocean discretization. Previous modeling studies showed that sub‐diurnal scale air‐sea interaction processes are important for ocean mixing. Here, by designing an integrated sub‐diurnal parameterization (ISDP) scheme which combines different temperature profiling functions, we stress sub‐diurnal air‐sea interactions to better represent the local ocean mixing. This scheme has been implemented into two coupled models which contributed to the Climate Model Intercomparison Project (CMIP), referenced by the Intergovernmental Panel on Climate Change—Community Earth System Model and Coupled Model version 2. The results show that the ISDP scheme improves model simulations with better climatology and more realistic spectra, especially in the tropics and North Pacific Ocean. With the scheme, the tropical cold tongue bias is significantly relaxed by reducing the overestimation of ocean upper mixing, and the cold bias of North Pacific Ocean is reduced due to the improvement on currents and net heat fluxes. Our scheme may help better the simulation and prediction skills of coupled models when their horizontal resolution becomes fine but vertical resolution remains relatively coarse as it describes high‐frequency air‐sea interactions more realistically. Plain Language Summary: The atmosphere and ocean interact with each other. In these interactions, changes may occur over different time periods. Some changes take years or months, while others happen in days or hours. The atmosphere changes quickly throughout the day, such as air temperature, wind, and rain. These quick changes in the atmosphere can affect the ocean rapidly. Similarly, there are quick changes in the ocean. These quick changes also affect the atmosphere in return. Understanding these quick changes is important. However, it is difficult to perfectly capture the quick changes in the climate models because they do not always represent the ocean behavior accurately. In this study, we developed a new scheme (named the sub‐diurnal scale parameterization, ISDP) to better represent these quick changes. We added this new scheme into two widely‐used climate models. These models are important tools for studying climate change. Our new scheme represents better the ocean mixing and interaction based on the local weather conditions. When we use ISDP, the climate models get better at simulating climate. They're more accurate in showing ocean temperatures in the tropics. These models make the tropical oceans seem colder than they really are, but our scheme, to some extent, fixes this problem. The results also show the more accurate ocean temperatures in the North Pacific Ocean. Our new scheme has a great potential to make climate models more accurate. Key Points: An integrated sub‐diurnal scale parameterization (ISDP) scheme has a more appropriate representation for local ocean mixingThe ISDP scheme has been implemented into two models which contributed to the Climate Model Intercomparison Project, referenced by IPCC: Community Earth System Model and Coupled Model version 2The model tropical cold tongue bias with ISDP is relaxed by reducing the overestimation of ocean upper mixing [ABSTRACT FROM AUTHOR]

The 2021 heatwave over Western North America (WNA) led to record‐breaking air temperatures and human‐perceived heat stress (humidex) values. The event was accompanied by drier conditions driven by prolonged atmospheric blocking. During the heatwave, the maximum 6‐day means of humidex and temperature (HX‐6 and TX‐6) exhibited larger anomalies (6.70 and 5.57°C) compared to the 95th percentiles (HX95 and TX95) (4.12 and 3.73°C), relative to 1981–2021 extended summer (June‐September) averages. Extreme indices of humidex show faster and larger increases than those of temperature, reflecting the nonlinear positive relationship between humidex and temperature. Future projections from a multi‐model ensemble of 19 Coupled Model Intercomparison Project Phase six (CMIP6) Global Climate Models (GCMs) clearly show an increase in humidex and temperature extremes, especially under intermediate and high emissions scenarios. Humidex indices (HX‐6 and HX95) show faster and larger increases than temperature indices (TX‐6 and TX95) for the same future years and global warming levels. Controlling for differences in GCM climate sensitivity to greenhouse gas forcing yields robust projections at various global warming levels, reducing the ranges of projected changes from the multi‐model ensemble. At 3.0°C global warming from pre‐industrial, the multi‐model ensemble projects occurrences of HX‐6, TX‐6, HX95, and TX95 over WNA that exceed 2021 levels to occur every 3.9, 1.7, 1.4, and 2.2 years, respectively, increasing to almost annually at 4.0°C. Plain Language Summary: In 2021 summer, Western North America (WNA) experienced an unprecedented heatwave, breaking air temperature and human‐perceived heat stress (humidex) records despite lower humidity due to prolonged atmospheric blocking. During the heatwave, consecutive 6‐day means of heat stress (HX‐6) and temperature (TX‐6) showed larger anomalies (6.70 and 5.57°C) compared to the 95th percentiles (HX95 and TX95) (4.12 and 3.73°C) over the region, relative to their averages over the 1981–2010 summer period (June‐September). Heat stress increased faster and more significantly than air temperature due to their nonlinear relationship. Future projections from 19 climate models reveal clear increases in heat stress and temperature, especially under intermediate and high emission scenarios. Heat stress indices show larger and faster increases than temperature indices at the same future years and global warming levels. Accounting for uncertainty related to climate model sensitivity enhances projection reliability. At 3.0°C global warming from pre‐industrial levels, 2021‐level events over WNA are projected to occur every few years, becoming an almost annual occurrence at 4.0°C. The study underscores the critical need for robust projections using multi‐model ensembles. Key Points: Unprecedented 2021 summer heatwave over Western North America resulted in record‐breaking humidex and air temperature extremesLarger increases in humidex than temperature, exceeding their 2021 levels, under intermediate and high emissions scenariosRobust projections of multi‐model ensemble, mitigating climate sensitivity uncertainty of GCMs response to greenhouse gas emissions [ABSTRACT FROM AUTHOR]

Projecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM‐LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM‐LIN method improved R2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM‐LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation. Plain Language Summary: Natural hazards, such as flooding and extreme heat, can damage our infrastructure and local communities. These hazards often stem from extreme weather conditions such as heavy precipitation and hot temperatures. Global climate change is expected to affect precipitation and air temperature, leading to more extreme weather events and hazards in the future. Predicting frequency and severity of these future events is key to protect our infrastructure and communities. Climate models have been widely used to project statistics of future extremes. However, they come with uncertainties due to numerical approximations, low spatial resolutions etc. Consequently, predictions generated by these models are inherently uncertain. This paper used a new approach to improve future projections of extreme climatic events, focusing on South Florida. Our findings revealed substantial improvements in projecting future events, particularly for extreme precipitation. We also showed that nationally and globally derived simulations may not be suitable for accurately projecting heavy precipitation, but can suffice for air temperature and low/medium precipitation rates. This paper offers promising avenues for refining projections of future weather events. By enhancing our ability to anticipate upcoming weather events more accurately, we can better protect our infrastructure and communities against the challenges posed by climate change. Key Points: A hybrid statistical approach to improve bias correction of GCM precipitation and air temperature simulationsMajor improvements in both climatic variables, especially extreme precipitation, using our hybrid statistical approach in South FloridaCoarse resolution downscaled data may be insufficient for deriving high quantile precipitation at the regional scale [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]

Surface anticyclones connected to the ridge of an upper‐tropospheric Rossby wave are the main dynamical drivers of mid‐latitude summer heatwaves. It is, however, unclear to what extent an anomalously low zonal phase speed of the wave in the upper troposphere is necessary for persistent temperature extremes at the surface. Here, we use spectral decomposition to separate fast and slow synoptic‐scale waves. A composite analysis of ERA5 reanalysis data reveals that, while in some regions heatwaves become more frequent during episodes of weak or no phase propagation, temperature extremes in other regions are commonly associated with more rapidly eastward propagating Rossby waves. Reflected in the mean heatwave duration as well, this relationship is possibly linked to a longitudinal phase preference of slow and fast waves or a meridional storm track shift. These findings open up new questions about the influence of mid‐latitude dynamics on temperature extremes. Plain Language Summary: High pressure systems tend to be associated with mid‐latitude summer heatwaves, defined as multiple consecutive hot days. The persistence of heatwaves raises the question whether the associated high pressure system has to be anomalously persistent as well to cause a heatwave. Using a combination of observational data and atmospheric model output that is commonly regarded as ground truth for prediction purposes we find that this is not the case. By re‐distributing the location of on‐average high air pressure, some regions experience an increased heatwave frequency even if the atmospheric circulation is less persistent than usual. Understanding the link between persistent surface temperature extremes and the atmospheric circulation is important for the prediction and projection of extreme events in a warming climate. Key Points: Phase speed of synoptic‐scale waves is crucial for where, but less important for whether heatwaves occurA longitudinal phase preference of slow and fast waves is reflected in the mean heatwave durationChanges in phase speed are linked to a meridional storm track shift [ABSTRACT FROM AUTHOR]

Over recent decades, the southeastern United States (Southeast) has become increasingly well represented by the terrestrial climate proxy record. However, while the paleo proxy records capture the region's hydroclimatic history over the last several centuries, the understanding of near surface air temperature variability is confined to the comparatively shorter observational period (1895‐present). Here, we detail the application of blue intensity (BI) methods on a network of tree‐ring collections and examine their utility for producing robust paleotemperature estimates. Results indicate that maximum latewood BI (LWBI) chronologies exhibit positive and temporally stable correlations (r = 0.28–0.54, p < 0.01) with summer maximum temperatures. As such, we use a network of LWBI chronologies to reconstruct August‐September average maximum temperatures for the Southeast spanning the period 1760–2010 CE. Our work demonstrates the utility of applying novel dendrochronological techniques to improve the understanding of the multi‐centennial temperature history of the Southeast. Plain Language Summary: Tree‐ring data are important sources of paleoclimate information, which allow for the longer‐term evaluation of modern climate values and trends. Compared to much of North America, the Southeastern United States (Southeast) contains substantially fewer paleoclimate records from tree rings, and no estimates of past temperature variability which extend before the observational period. Employing a recently developed technique, which uses light reflectance properties of wood to obtain a representative metric of tree‐ring density, we develop a network of temperature‐sensitive tree‐ring records across the Southeast. These records enable us to reconstruct late summer maximum temperatures across the region spanning the period 1760–2023 CE. As few ground‐based, pre‐instrumental temperature records previously existed for this region, our reconstruction allows for an improved understanding of the region's multi‐centennial climatic history. Key Points: Maximum latewood blue intensity from tree rings can effectively be used to develop paleotemperature estimates for the southeastern USThe fidelity of tree‐ring density parameters for paleoclimate reconstruction are influenced by disturbance regimes and microsite conditionsCompared to the last 260 years, regional 20th‐century maximum late summer temperatures are not characterized by unprecedented positive trend [ABSTRACT FROM AUTHOR]

High‐resolution climate projections are valuable resources for understanding the regional impacts of climate change and developing appropriate adaptation/mitigation strategies. In this study, we developed a 10‐km gridded hydrometeorological dataset over India by dynamic downscaling of the bias‐corrected Community Earth System Model (CESMv1) climate projections under RCP8.5 scenario using the state‐of‐the‐art Weather Research and Forecasting (WRF) model. The downscaled CESM dataset (DSCESM) is archived in the World Data Center for Climate (WDCC) portal at three temporal resolutions (daily, monthly and monthly climatology) for current (2006–2015), mid‐century (2041–2050) and end‐century (2091–2100) periods. The dataset includes 2‐m air temperature, total accumulated precipitation, wind speed, relative humidity, sensible and latent heat fluxes, along with surface shortwave and outgoing longwave radiation. All the DSCESM variables were evaluated against reanalysis data and station observations for the period 2006–2015. This dataset can help us quantitatively understand regional climate change in India. It can also be used in conjunction with agricultural, hydrological, fire and other application models for climate change impact assessment on various sectors to help develop effective adaptation/mitigation strategies. [ABSTRACT FROM AUTHOR]

The Middle Pleistocene Reinsdorf sequence at the Lower Palaeolithic sites of Schöningen offers the opportunity to reconstruct a rarely well‐preserved post‐Holsteinian environmental transition from an interglacial into a glacial phase along with its highly dynamic interjacent climatic oscillations. Combining biological proxies, element composition and stable isotope ratios of two lakeshore sequences at excavation site 13 II, we demonstrate repeated variations in climate, hydrology and catchment vegetation cover. New ostracod‐based quantitative mean summer and winter air temperature reconstructions with the Mutual Ostracod Temperature Range (MOTR) method provide the first detailed information about the temperature evolution. The interglacial temperature maximum, probably corresponding to Marine Isotope Stage 9e, is followed by a first dry phase and, during the younger part of the Reinsdorf sequence, by a second dry period. Both were marked by lower precipitation/evaporation ratios, reduced vegetation cover in the catchment and increased surface inflows from springs. Temperature reconstructions of these two steppe (open woodland) phases yield very narrow ranges for mean January (−4–0 °C) and July (+17–19 or +17–21 °C) air temperatures, demonstrating that, while summers were similar to those of today, winters were at least 1 °C colder, hinting at a more pronounced continental climate. Precise temperature estimates for the interjacent woodland and steppe (woodland) phase are hindered by generally wider ranges produced by the MOTR method (January mean −4–3 °C, July mean +15–21 °C). The development of a more extensive vegetation cover, reducing surface runoff and erosion in favour of increased river and groundwater discharge, as indicated by a shift in microfossil and stable isotope records, suggests generally more humid climates with higher precipitation/evaporation ratios as well as reduced seasonal temperature variations. [ABSTRACT FROM AUTHOR]

The breeding phenology of birds is often timed to coincide with a peak in food availability. However, the shortening of the vegetation period with increasing elevation may force bird species at high elevations to breed earlier in relation to optimal environmental conditions due to time constraints. We investigated differences in fledging dates in five Alpine woodland songbird species along an elevational gradient from 1500 to 2200 m in Switzerland. We estimated fledging dates from a nationwide citizen science bird monitoring dataset and used the date when the proportion of observations of 'fledged young' reached 50% among all observations indicating breeding behaviour. This measure had the advantage that we could estimate average timing of the broods across a wide geographic range and over many years without the need to search for individual nests. We then compared differences in timing of the broods with climatic conditions and larch budburst across different elevational bands. The daily mean air temperature of 10–15°C was reached 34–38 days later at 2200 m compared to 1500 m, which is a similar delay as found in previous reports on snow melt‐out date. The average delay in larch budburst was 19.2 days at 2200 m compared to 1500 m. In comparison, the average timing of the birds' broods was only 5.4 days later in coal tits and 0.5 days later in Alpine tits at 2200 compared to 1500 m (the two species for which we had the narrowest interval estimates). Also, the estimated delay at higher elevations in the broods of song thrushes, mistle thrushes and Eurasian chaffinches was relatively small. Rather than postponing breeding dates to better environmental conditions later in the season that would match the earlier conditions at low elevation, songbirds breeding at higher elevations may thus have evolved adaptations to cope with the harsher conditions. [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]

We installed a meteorological recording system at Badwater (elev. −75 m), the lowest point in Death Valley, California and recorded data over the period 1998–2019. A second station (the Outhouse Station) was established nearby from 2014 to 2019. Here, we report on and publicly archive the data from these two stations. Of interest was the comparison between two air temperature measurements at the Badwater Station, the first with an aspirated platinum resistance temperature device and the second with a thermistor probe in a passive sun shield. During the hottest periods of the summer when temperatures were typically between 30°C at night and 50°C daily peak, the passively shielded sensor indicated up to 0.5°C warmer than the aspirated temperature sensor due to radiative effects. The data suggest a correction for radiative heating of (T–35)/30, for T > 35°C, where, T, is the uncorrected temperature reading of a passively shielded sensor subtracted after any calibration at lower temperatures. Our station was the first precision temperature measurements at Badwater. A longer record exists for the reporting station near the visitor's centre at the Furnace Creek. The summer temperature maxima at the Badwater site correlate well with the values the same day from the Furnace Creek site. The daily maximum temperatures in winter at the Badwater site appear to be about 1°C lower than at the Furnace Creek site. The largest differences are in the minimum temperatures for which the Badwater site averages about 2–3°C warmer than the Furnace Creek site. [ABSTRACT FROM AUTHOR]

Vehicle‐based mobile observations are taken across the world every day by operational and research meteorological organizations, public transportation agencies, and private car manufacturers. Whether directly weather‐related (e.g., air temperature) or not (e.g., wiper speed), the coverage and frequency of these observations holds the promise of filling in gaps between fixed observing stations and greatly improving situational awareness and weather forecasting, from road surface condition‐specific applications and winter road maintenance to urban and street‐level numerical weather prediction and beyond. However, in order to take advantage of these observations, the weather, water, and climate enterprise must work together with the transportation enterprise across academic, public, and private sectors to provide a mechanism for obtaining these data, so that the benefits of using these unconventional observations may be realized. [ABSTRACT FROM AUTHOR]

The Arctic is impacted by some of the fastest temperature changes observed on Earth, but the impact on the terrestrial arthropod fauna is unclear. Acute physiological thermal limits of terrestrial ectotherms from high latitudes often exceed the local air temperatures, suggesting that they may be able to cope with increasing temperatures. However, knowledge on how Arctic terrestrial arthropods cope with elevated temperatures for longer periods is lacking.Here we investigate how acclimation temperature and exposure time affect the acute physiological heat tolerance of five terrestrial arthropod species (Neomolgus littoralis, Megaphorura arctica, Nysius groenlandicus, Psammotettix lividellus and Nabis flavomarginatus) immediately after collection in Arctic and sub‐Arctic habitats.We show that although acute heat tolerances are relatively high, even exposure to moderate (temperature span assessed ca. 3–29°C) acclimation temperatures for a 24 h period have strong negative effects on heat tolerance for four of the five species. Similarly, exposure time negatively affected heat tolerance, but depending on species and temperature.Together our results suggest that exposure to even moderately elevated temperatures for periods of 24 h or even shorter can lead to lower acute heat tolerance for cold adapted terrestrial arthropod species from sub‐Arctic and Arctic regions. Consequently, climate change leading to extended periods of mildly elevated temperatures may have strong negative effects on these species. We argue that this aspect is currently overlooked when assessing the ability of arthropods from Arctic and sub‐Artic regions to cope with climate changes as such predictions are typically based on acute heat tolerance estimates and with the assumption of beneficial acclimation responses. Read the free Plain Language Summary for this article on the Journal blog. [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]

The article discusses a study on the thistle Carlina corymbosa, which has the unique ability to cool its flowerheads in the hot and dry Mediterranean summer. The flowerheads of C. corymbosa were found to be cooler than the surrounding air, particularly during the hottest part of the day. This cooling effect is achieved through enhanced transpiration, similar to the way sweating pitchers made of porous clay, known as "botijos" in Spanish, cool down the water inside them through evaporation. The study suggests that the cooler flowerheads may attract more pollinators, such as bees, and increase pollination success. [Extracted from the article]

Utilizing atmospheric temperature observed from Mars Years 33–36 by the imaging ultraviolet spectrograph (IUVS) onboard the Mars atmosphere and volatile evolution (MAVEN) and Mars climate sounder (MCS) onboard Mars Reconnaissance Orbiter (MRO), we derive the diurnal and semidiurnal thermal tides from 30 to 160 km. Vertical phase velocities of the migrating tides indicate their upward propagation above 100 km during the dust season (solar longitude, Ls 240°–300°). During the non‐dust season (Ls 30°–150°), the diurnal eastward wavenumber 2 (DE2) and wavenumber 3 (DE3) tides can propagate upward from the lower atmosphere to ∼140 km. The seasonal variation of DE2 and DE3 amplitudes in the thermosphere corresponds well to their counterparts in the lower atmosphere, primarily controlled by their Hough (1, 1) modes. The upward propagation of these tides could potentially impact the vertical coupling between the Martian lower and upper atmosphere. Plain Language Summary: Atmospheric thermal tides are perturbations caused by the absorption of solar radiation in the atmosphere, a phenomenon in Earth's and other planetary atmospheres. The vertical propagation of tides plays a crucial role in transporting energy and momentum vertically within the atmosphere. Observing atmospheric temperatures at different local times makes it possible to determine various tidal components' amplitude and propagation characteristics. Based on data from different satellites, this study investigates the characteristics of tidal amplitude and propagation on Mars. The migrating tides, which move westward in sync with the sun's motion, can propagate upward in the region above 100 km during the dust season. During the non‐dust season on Mars, the diurnal eastward propagating wavenumber 2 (DE2) and wavenumber 3 (DE3) tide exhibits apparent upward propagation from the atmosphere below 40 km into the thermosphere. As Mars has weaker gravity and a lower exobase altitude than Earth, the vertical propagation of tides may directly transmit energy from lower atmospheric activities to the upper layers of the Martian atmosphere, influencing the atmospheric escape rate. Key Points: The thermal tides are derived from temperature observations by two satellites with different altitude rangesMigrating tides are strong in the mesosphere and thermosphere and can propagate upward from 100 km during the Martian dust seasonDuring the non‐dusty season, the diurnal eastward wavenumber 2 (DE2) and 3 (DE3) tides propagate from the troposphere to the thermosphere [ABSTRACT FROM AUTHOR]

A minequake of magnitude ML 4.1 occurred on 18 May 2020 early in the morning at the LKAB underground iron ore mine in Kiruna, Sweden. This is the largest mining‐induced earthquake in Scandinavia. It generated acoustic signals observed at three infrasound arrays at 9.3 (KRIS, Sweden), 155 (IS37, Norway), and 286 km (ARCI, Norway) distance. We perform full‐waveform focal mechanism inversion based on regional seismic data and local infrasound data. These independently highlight that this event was dominated by a shallow‐depth collapse in agreement with in‐mine seismic station data. However, regional infrasound data cannot inform the inversion process without an accurate model of atmospheric winds and temperatures. Yet, our numerical simulations demonstrate a potential of using local and regional infrasound data to constrain an event's focal mechanism and depth. Plain Language Summary: The largest mining‐induced earthquake in Scandinavia (ML 4.1) occurred on 18 May 2020 early in the morning at the LKAB underground iron ore mine in Kiruna, Sweden. The seismic waves coupled to the atmosphere and propagated large distances as sound waves which were observed at three infrasound arrays at 9.3 (KRIS, Sweden), 155 (IS37, Norway), and 286 km (ARCI, Norway) distance. Our seismic and acoustic modeling results highlight a strong collapse event within the northern section of the mine. The modeling of acoustic and seismic waves across the Earth‐atmosphere suggests that sound wave data can help when determining the location and properties of a seismic source. Key Points: Seismic and acoustic records indicate a strong collapse at shallow depth of 1 ± 0.5 km for the 2020 Kiruna minequakeFocal mechanisms and depths of shallow seismic sources can be retrieved from local infrasound recordsInversions using regional infrasound data is possible when accurate weather models are available [ABSTRACT FROM AUTHOR]

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

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

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]

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

Multiple lines of evidence confirm a widespread increase in vegetation growth across China over the past few decades. The relationship between vegetation growth and water availability is thought to be becoming stronger under climate change, that is, water constraints on vegetation growth have been increasing. However, our understanding of how water constraints have influenced these vegetation greening trends, especially those climate change‐driven ones, remains limited. Here, we conduct a comprehensive evaluation of recent water constraints and their implications for vegetation growth in China between 1982 and 2015. By analyzing the spatiotemporal patterns of the relationship between vegetation growth and water availability, we reveal recent water constraints and their changes hidden within an overall greening trend in China. Further analysis demonstrates that two climate change‐related categories, defined broadly as "climate" (e.g., air temperature, precipitation, and so on) and "CO2" (i.e., atmospheric carbon dioxide), have exerted varying levels of importance in regulating vegetation growth across different water constraints. With increasing water constraints, the proportion of the climate‐dominated area has significantly risen, while that of the CO2‐dominated area has sharply declined. Our findings highlight that water constraints can mediate the dominance of climate and atmospheric CO2 on vegetation growth. This has the great potential to exacerbate the uncertainty surrounding current and future sustainable vegetation greening trends. Plain Language Summary: China's vegetation growth has shown a broad increase under climate change and human activities over the past few decades. At the same time, changes in vegetation water demand, terrestrial water availability, and climatic factors are expected to lead to increasing water constraints on vegetation growth. However, whether water constraints have influenced climate change‐driven vegetation greening trends remains unclear. In this study, we provide a comprehensive assessment of recent water constraints and their implications for vegetation growth in China between 1982 and 2015. To do this, we analyze the spatiotemporal patterns of the relationship between vegetation growth and water availability to reveal recent water constraints hidden within China's vegetation greening. Further analysis shows that climate and atmospheric CO2 have exerted varying levels of importance in regulating vegetation growth across different water constraints. With increasing water constraints, more regions become climate‐dominated and fewer become CO2‐dominated. Overall, our findings highlight that water constraints can mediate the dominance of climate and atmospheric CO2 on vegetation growth. This will help decision‐makers recognize the uncertainty of sustainable vegetation greening trends. Key Points: We reveal recent water constraints and their changes hidden within China's widespread vegetation greeningClimate and atmospheric CO2 have exerted varying levels of importance in regulating vegetation growth across different water constraintsWith increasing water constraints, more of China's vegetated regions become climate‐dominated and fewer become CO2‐dominated [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]

Understanding how a warmer and more variable climate will affect the productivity and stability of perennial forages in the establishment year is essential for determining forage management strategies resilient to the changing climate. We conducted an experiment from 2020–2021 and repeated it in 2021–2022 to determine how warmer and more variable temperatures affect yield of an alfalfa (Medicago sativa L.)–orchardgrass (Dactylis glomerata L.) mixture, as well as on the abundance of a winter annual weed, chickweed (Stellaria media), during the establishment year. We utilized open‐top chambers (OTCs) to manipulate air temperature in situ to impose three treatments: (1) constant warming (OTC present year‐round), (2) fluctuating warming (OTC present year‐round but temporarily removed prior to abrupt cold snaps), and (3) control (no OTC, ambient air temperature). OTCs increased air temperature within the alfalfa–orchardgrass canopy by an average of 0.45 and 0.72°C in Year 1 and Year 2, respectively; however, the effects of OTCs on air temperature varied temporally within and between years. Constant warming decreased alfalfa frost injury compared with the control, while the control and fluctuating warming resulted in similar levels of frost injury. Warmer temperature within the OTCs neither affected chickweed biomass nor the biomass of all other weed species. Both constant and fluctuating warming increased forage dry matter yield at the first harvest timepoint in the spring of both years, the period when preceding ambient air temperature tended to be cooler than other harvest timepoints. We found that the constant warming increased total forage yield (summed over the entire year) in Year 1, when precipitation was well above the 30‐year normal for our region, but decreased total forage yield in Year 2, when summer precipitation was substantially below the 30‐year normal. As global temperature continues to rise, yield loss of cool‐season perennial forages, such as alfalfa and orchardgrass, may be exacerbated in years without adequate precipitation. Therefore, exploring heat‐ and drought‐tolerant alternative forages, as well as management strategies that improve resilience to weather variability, will be increasingly important. [ABSTRACT FROM AUTHOR]

The accurate partitioning of precipitation is important for snowpack and streamflow simulations in mountainous regions. However, most hydrologic models employ a partitioning method based on near‐surface air temperature (Ta) and the sensitivity of hydrologic simulations to the selection of the precipitation partitioning method (PPM) under climate change has been underexplored. To address this, we compared simulations using two PPMs, a conventional Ta‐based scheme and a wet‐bulb temperature (Tw)‐based approach (TA and TW) with the Variable Infiltration Capacity model. Our study focused on the Colorado River Basin (CRB) which is vulnerable to future snowfall and streamflow reductions due to climate warming. Historical simulations demonstrated the improved performance of the TW scheme in simulating snowfall fraction (SF) and snow water equivalent (SWE) in relation to in situ observations and a gridded SWE product. Subsequently, we evaluated the influence of the PPM selection on snowpack and streamflow projections from eight climate models under two emissions scenarios. The ensemble median results revealed more substantial decline in annual SF with the TW scheme as compared to the TA method (−12% vs. −9%) from the historical (1976–2005) to the far future (2066–2095) period, especially at lower elevations. Hydrologic simulations showed that the TA scheme underestimated reductions in SWE and streamflow (Q) under warming as compared to the TW scheme. This finding has important implications on future projections for the CRB and in other mountainous basins where climate warming can shift conditions from snowfall to rainfall. Key Points: The precipitation partitioning method (PPM) impacts the simulation of snowfall and snowpack dynamics in the Colorado River Basin (CRB)The selection of PPM also determines the sensitivity of streamflow projections in the CRB under climate change scenariosSpatial variations in the impact of PPM reveal distinct climate change sensitivities in high and low elevation regions in the CRB [ABSTRACT FROM AUTHOR]

Understanding temporal trends in low streamflows is important for water management and ecosystems. This work focuses on trends in the occurrence rate of extreme low‐flow events (5‐ to 100‐year return periods) for pooled groups of stations. We use data from 1,184 minimally altered catchments in Europe, North and South America, and Australia to discern historical climate‐driven trends in extreme low flows (1976–2015 and 1946–2015). The understanding of low streamflows is complicated by different hydrological regimes in cold, transitional, and warm regions. We use a novel classification to define low‐flow regimes using air temperature and monthly low‐flow frequency. Trends in the annual occurrence rate of extreme low‐flow events (proportion of pooled stations each year) were assessed for each regime. Most regimes on multiple continents did not have significant (p < 0.05) trends in the occurrence rate of extreme low streamflows from 1976 to 2015; however, occurrence rates for the cold‐season low‐flow regime in North America were found to be significantly decreasing for low return‐period events. In contrast, there were statistically significant increases for this period in warm regions of NA which were associated with the variation in the Pacific Decadal Oscillation. Significant decreases in extreme low‐flow occurrence rates were dominant from 1946 to 2015 in Europe and NA for both cold‐ and warm‐season low‐flow regimes; there were also some non‐significant trends. The difference in the results between the shorter (40‐year) and longer (70‐year) records and between low‐flow regimes highlights the complexities of low‐flow response to changing climatic conditions. Key Points: Temporal trends in occurrence rates of extreme low‐flow events computed: analysis based on pooling stations by low‐flow regimesFor 1976–2015, low flow events become more frequent in some warm regimes (related to Pacific Decadal Oscillation), and less frequent in some cold regimesFor 1946–2015, low flow events become less frequent across multiple warm and cold regimes [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]

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]

Ponds are common freshwater habitats in the European landscape that substantially contribute to local and regional biodiversity. Chironomids often dominate invertebrate communities in ponds but are usually disregarded in ecological studies due to relatively complicated taxonomy and identification issues. We present a comprehensive overview of the chironomid diversity in 246 ponds spanning a wide range of conditions extending from the Pannonian Plain to the Carpathians. Altogether, we recorded 225 taxa including 192 species from six subfamilies (Podonominae, Tanypodinae, Diamesinae, Prodiamesinae, Orthocladiinae and Chironominae). However, the chironomid taxa inventory is far from complete and about 16% of the total diversity of pond‐dwelling chironomids remains undetected. Chironomid alpha diversity showed a significant unimodal pattern along the elevation gradient with the highest number of taxa per pond expected around 790 m a.s.l. Gamma diversity also peaked in mid‐elevations (600–800 m), and the common chironomid taxa partitioned the 2100‐m long altitudinal gradient relatively evenly. The heterogeneity of chironomid communities among ponds measured as beta diversity was significantly higher in elevations below 800 m. Temperature and the proportion of surrounding forests significantly influenced alpha diversity of chironomid communities, while urban land cover and pond size had no significant effect. Ponds with a mean annual air temperature of approximately 4.8°C and a low proportion of surrounding forests are expected to harbour the most diverse chironomid communities. Our study showed that chironomids represent a very diverse and often exceptionally rich group of pond‐dwelling macroinvertebrates. Given the high diversity and broad range of occupied niches, chironomids should not be overlooked in pond ecology studies. On the contrary, they should be considered a potential model group. [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]

Arctic warming has significant environmental and social impacts. Arctic long‐term warming trend is modulated by decadal‐to‐multidecadal variations. Improved understanding of how different external forcings and internal variability affect Arctic surface air temperature (SAT) is crucial for explaining and predicting Arctic climate changes. We analyze multiple observational data sets and large ensembles of climate model simulations to quantify the contributions of specific external forcings and various modes of internal variability to Arctic SAT changes during 1900–2021. We find that the long‐term trend and total variance in Arctic‐mean SAT since 1900 are largely forced responses, including warming due to greenhouse gases and natural forcings and cooling due to anthropogenic aerosols. In contrast, internal variability dominates the early 20th century Arctic warming and mid‐20th century Arctic cooling. Internal variability also explains ∼40% of the recent Arctic warming from 1979 to 2021. Unforced changes in Arctic SAT are largely attributed to two leading modes. The first is pan‐Arctic warming with stronger loading over the Eurasian sector, accounting for 70% of the unforced variance and closely related to the positive phase of the unforced Atlantic Multidecadal Oscillation (AMO). The second mode exhibits relatively weak warming averaged over the entire Arctic with warming over the North American‐Pacific sector and cooling over the Atlantic sector, explaining 10% of the unforced variance and likely caused by the positive phase of the unforced Interdecadal Pacific Oscillation (IPO). The AMO‐related changes dominate the unforced Arctic warming since 1979, while the IPO‐related changes contribute to the decadal SAT changes over the North American‐Pacific Arctic. Plain Language Summary: The Arctic warms much faster than the rest of the world, leading to significant local and remote influences. Warming in the Arctic is not uniform over time, with decadal‐to‐multidecadal variations upon the long‐term trend. The changes in Arctic surface air temperature (SAT) can be attributed to either instrinct variability within the climate system or external forcings including anthropogenic factors such as greenhouse gases emission and natural factors such as volcanic eruptions. Understanding of the relative contributions of internal variability and external forcing to observed changes in Arctic SAT is crucial for improving Arctic climate projections in coming decades. By synthesizing multiple observational data sets and large‐ensemble climate simulations, we find that the Arctic experienced long‐term warming with some periods of slowdown in response to external forcing, which largely explains the overall change since 1900. Internal variability, particularly the multidecadal oscillation in the North Atlantic, dominates the early 20th century warming and mid‐20th century cooling and significantly contributes to the recent rapid warming since 1979. A regression‐based rescaling method removes systematic biases in model‐simulated response (in comparison with observations), ensuring that our results are not influenced by the choice of climate models, as long as they are under the same historical forcing. Key Points: Externally forced Arctic warming dominates the trend and variance in Arctic surface air temperature during 1900–2021Most of the internally generated Arctic temperature changes are related to the unforced Atlantic Multidecadal OscillationInternal variability explains 40% of the Arctic warming from 1979 to 2021, while greenhouse gases and natural forcings account for the rest [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]

Explaining the spatially variable impacts of flood‐generating mechanisms is a longstanding challenge in hydrology, with increasing and decreasing temporal flood trends often found in close regional proximity. Here, we develop a machine learning‐informed approach to unravel the drivers of seasonal flood magnitude and explain the spatial variability of their effects in a temperate climate. We employ 11 observed meteorological and land cover (LC) time series variables alongside 8 static catchment attributes to model flood magnitude in 1,268 catchments across Great Britain over four decades. We then perform a sensitivity analysis to assess how a 10% increase in precipitation, a 1°C rise in air temperature, or a 10 percentage point increase in urban or forest LC may affect flood magnitude in catchments with varying characteristics. Our simulations show that increasing precipitation and urbanization both tend to amplify flood magnitude significantly more in catchments with high baseflow contribution and low runoff ratio, which tend to have lower values of specific discharge on average. In contrast, rising air temperature (in the absence of changing precipitation) decreases flood magnitudes, with the largest effects in dry catchments with low baseflow index. Afforestation also tends to decrease floods more in catchments with low groundwater contribution, and in dry catchments in the summer. Our approach may be used to further disentangle the joint effects of multiple flood drivers in individual catchments. Plain Language Summary: We developed a machine learning‐based approach to investigate why the effects of changes in climate and land cover (LC) on floods vary spatially. To inform the model, we used climate and LC data for 1,268 catchments in Great Britain over four decades. We found that increasing rainfall and urban development tend to lead to larger floods, especially in rivers fed largely by groundwater. In contrast, rising air temperature and afforestation, in the absence of any changes in rainfall, lead to smaller floods, particularly in areas where rivers are less fed by groundwater. We believe that our findings can be used to develop more targeted flood management strategies. Key Points: We employ partial dependence analysis and sensitivity testing to assess where changes in climate or land cover might affect flooding the mostRising precipitation and urbanization tend to lead to larger floods in catchments with high baseflow contribution and low runoff ratioRising air temperature with unchanged precipitation lowers flood magnitudes, especially in dry catchments with low baseflow index [ABSTRACT FROM AUTHOR]

Spring snowmelt occurs for a short duration on an annual time scale, but their timings considerably affect the carbon and hydrological cycle in high‐latitude ecosystems. Here, we developed a simple snowmelt model, treating the ecosystem surface as a bulk‐surface layer. Energy fluxes across this bulk surface and the snow‐soil boundary determine snow temperature and the energy utilized for snowmelt. Parameterizing the bulk surface using decade‐long eddy covariance site data from two Alaskan open black spruce forests offered an opportunity to quantitatively evaluate meteorological drivers affecting snowmelt timings without the needs for detailed canopy information. The sensitivity analysis suggested that the total snowfall on the forest floor, ranging from 0.35 m in 2016 to about 1 m in 2018 and 2020, was the most crucial driver for snowmelt timing. This factor accounted for a 10‐day difference in the interannual variations in snow disappearance dates. The importance of the snowfall varied from year to year, and in 2013, the late snowmelt was characterized by low air temperatures, which increased sensible heat loss from the snowpack. The importance of atmospheric radiation was revealed in relatively warm years, such as 2016 and 2019. Our modeling approach necessitates adjusting one empirical parameter that reflects the heat conductivity from the bulk surface to the snow, based on observations. Nevertheless, despite this need for adjustment, the bulk‐surface approach helps identify important meteorological drivers underlying observed snowmelt within a simple theoretical framework. Plain Language Summary: It is essential to understand why and how spring snowmelt occurs in boreal forests because of its impact on both the biological and hydrological processes in the ecosystem. However, meteorological drivers related to snowmelt often change simultaneously, which makes it challenging to determine which single driver is vital for snowmelt. To address this challenge, we constructed a simple snowmelt model that treats the ecosystem surface as a single bulk layer. The simple model bypasses the needs for detailed canopy information and is easily constrained by observed data. Applying the model to decadal observation data from Alaskan boreal forests, we found that snowfall, air temperature, and atmospheric radiation were important meteorological drivers for explaining interannual variations in snowmelt timing. Each of them explained about 1–2 weeks of snowmelt disappearance dates. We also demonstrated that the model helped explain why the snowmelt was late in 2013 and early in 2016 and 2019 and why snow disappearance dates differed between study sites. While a simple model has its limitations, it can be beneficial for understanding snowmelt characteristics within a simple theoretical framework. Key Points: A model‐based approach was introduced to delineate the roles of meteorological drivers on interannual variations in snowmelt timingSnowfall was the most important driver of the interannual variation in snowmelt timing followed by air temperature and atmospheric radiationLate snowmelt in 2013 was attributed to low air temperature, increasing sensible heat loss from the snowpack [ABSTRACT FROM AUTHOR]

An increase in winter air temperature can amplify snowmelt and sublimation in mountain regions with implications to water resources and ecological systems. Winter Warm Spells (WWS) are defined as a winter period (December to February, DJF) of at least 3 consecutive days with daily maximum temperature anomaly above the 90th percentile (using a moving‐average of 15 days between 2001 and 2013). We calculate WWS for every 4‐km grid cell within an atmospheric model over western North America to characterize WWS and analyze snow ablation and their changes in a warmer climate. We find that days with ablation during WWS represent a small fraction of winter days (0.6 days), however, 49% of total winter ablation (33.4 mm/DJF) occurs during WWS. Greater extreme ablation rates (99th percentile) occur 18% more frequently during WWS than during non‐WWS days. Ablation rates during WWS in humid regions are larger (9 mm d−1) than in dry regions (7 mm d−1) in a warmer climate, which can be explained by differences in the energy balance and the snowpack's cold content. We find that warmer (0.8°C), longer (1.8 days) and more frequent (3.7 more events) WWS increase total winter ablation (on average 109% or 18 mm/DJF) in a warmer climate. Winter melt during WWS in warm and humid places is expected to increase about 3 times more than in the cold and dry region. This study provides a comprehensive description of WWS and their impact on snowpack dynamics, which is relevant to reservoir operations and water security. Plain Language Summary: Extreme maximum winter temperatures (Winter Warm Spells, WWS) can affect the snow in winter by increasing snowmelt, with consequences for mountain water supplies. In this study we quantify snowpack response to WWS in the mountains of western North America and its sensitivity to a warmer future climate using numerical models, observations, and statistical tools. We find that 49% of snow lost in winter happens during WWS, despite having only 0.6 days with WWS per winter. Across the mountains, the humid climatic regions are more affected by WWS than arid regions. We show that snowpack ablation during WWS will increase in a warmer climate, which will put more stress on reservoir and water security planning. Key Points: Winter Warm Spells account for 49% of snowpack winter ablation (16.5 mm), despite only representing 0.6 days on averageHistorical Winter Warm Spells increase in frequency (3.7 more events), duration (1.8 days) and magnitude (0.8°C) in a warmer climateWinter snow ablation driven by melt (93.6%) is expected to increase in a warmer climate (147%), especially in humid regions [ABSTRACT FROM AUTHOR]