Search

Summary: A long‐standing debate looks at whether air or soil dryness is more limiting to vegetation water use and productivity. The answer has large implications for future ecosystem functioning, as atmospheric dryness is predicted to increase globally while changes in soil moisture are predicted to be far more variable. Here, I review the complexities that contribute to this debate, including the strong coupling between atmospheric and soil dryness, and the widespread heterogeneity in vegetation hydraulic traits, acclimations, and adaptations to water stress. I discuss solutions to improve understanding and modeling of vegetation sensitivity to dryness, including how different types of observational data can be used together to gain insight into vegetation response to water stress across spatial and temporal scales. [ABSTRACT FROM AUTHOR]

Land-atmosphere (L-A) feedbacks are important for understanding regional climate functioning. However, the accurate quantification of feedback strength is challenging due to complex, nonlinear interactions and varying background atmospheric conditions. In particular, the role of cloud water in the terrestrial water cycle is often ignored or simplified in previous L-A feedback studies, which overlook the relationship between evapotranspiration (ET) and cloud water (TQC). This study diagnoses the interactions between , and its dynamics () under different atmospheric conditions by conducting correlation and a novel scaling analysis, based on a coupled regional climate model simulation. Contrasting correlation relationships between , and were identified, indicating the positive feedback between and the dynamics in cloud water. Two types of positive scaling relationships between and were identified by K-means clustering. The analysis shows a contrasting north-south distribution of the scaling relationship that is similar to the spatial distribution of energy-limited and water-limited regimes, highlighting the role of ET regimes in modulating the - scaling relationships. Moreover, the feedback strength and scaling relationship are affected by atmospheric moisture flux dynamics, providing remote moisture sources and altering dry/wet conditions. Our results highlight the role of cloud water in the atmospheric part of the L-A process chain and reveal the effect of different atmospheric conditions on L-A interactions based on the new analysis framework. [ABSTRACT FROM AUTHOR]

On 8 April 2024, a rare total solar eclipse (TSE) passed over western New York State (NYS), the first since 1925 and the last one until 2079. The NYS Mesonet (NYSM) consisting of 126 weather stations with 55 on the totality path provides unprecedented surface, profile, and flux data and camera images during the TSE. Here we use NYSM observations to characterize the TSE's impacts at the surface, in the planetary boundary layer (PBL), and on surface fluxes and CO2 concentrations. The TSE‐induced peak surface cooling occurs 17 min after the totality and is 2.8°C on average with a maximum of 6.8°C. It results in night‐like surface inversion, calm winds, and reduced vertical motion and mixing, leading to the shallowing of the PBL and its moistening. Surface sensible, latent and ground heat fluxes all decrease whereas near‐surface CO2 concentration rises as photosynthesis slows down. Plain Language Summary: On 8 April 2024, a rare total solar eclipse (TSE) passed over western New York State (NYS), the first one since 1925 and the last one until 2079. The entire NYS witnessed at least 88% obscuration at the peak of the eclipse. It provides an excellent opportunity to study the impacts of the TSE. The NYS Mesonet (NYSM), an advanced statewide weather network, has 55 stations on the totality path and provides unprecedented measurements of surface meteorological variables, atmospheric vertical profiles, the heat exchange between the atmosphere and the surface and carbon dioxide (CO2) concentration. It enables one to study the TSE in greater details on a regional scale for the first time. This study found that the moon shadow cools the surface by as much as 6.8°C and creates a surface inversion layer. The cooling calms down winds and vertical mixing, leading to less escape of the water vapor and moistening of the air. It also reduces the heat exchange between the surface and the air. Without sunlight, the photosynthesis shuts down, causing a robust rise in near‐surface CO2 concentration. One‐minute camera images provide a fantastic view of the darkening of the sky during the TSE. Key Points: The New York State Mesonet provided unprecedented surface, profile, flux and image data during the 8 April 2024 total solar eclipse across New York StateThe eclipse resulted in significant cooling and moistening near the surface and in the boundary layer, leading to a surface inversion layerIt also weakened surface winds, turbulent mixing, heat fluxes, but caused a robust rise in near‐surface CO2 concentrations [ABSTRACT FROM AUTHOR]

Massive irrigation across eastern China (EC) could reduce extreme heat by altering energy and water budgets. However, the irrigation effect on increasing humidity has often been missed, especially in humid regions, leaving its effect and mechanism on extreme humid heat (EHH) poorly characterized. We analyzed assemblies of observations and performed regional simulations with more detailed irrigation scheme to explore the irrigation impacts and potential mechanisms on EHH. We find that irrigation in EC, despite having a cooling effect of about 0.2–0.6° ${}^{\circ}$C, leads to an increase of about 0.4–0.8° ${}^{\circ}$C in EHH, with a more intense impact on the Middle‐Lower Yangtze Plain (MLYP) by 0.9° ${}^{\circ}$C. Cooling effect induced by increased latent heat fluxes through irrigation contributes to air deposition, which lowers boundary layer height, raises near‐surface moist enthalpy, and ultimately exacerbates the EHH. Results mechanistically emphasized irrigation impacts on EHH and highlighted the necessity of improving irrigation modeling reality. Plain Language Summary: Eastern China (EC) distributes the North China Plain (NCP, semi‐arid climate) and MLYP (humid climate), the first and third largest irrigated areas of China. Large‐scale irrigation mitigates extreme dry heat (EDH) while moistening surface air, which is more dangerous to humans with EDH and high humidity combined. Recently, the advantages of irrigation on summer EDH have been widely explored, whereas the influence of irrigation on compound extreme humid heat (EHH) remains unclear. Hence, we studied the impacts of irrigation on EDH and EHH in EC and its potential driving mechanisms based on in situ observations and simulations of WRF‐Noah model coupled with a subgrid demand‐driven irrigation scheme. The comprehensive analysis supports that although irrigation reduces temperature, it raises the integrated measure of temperature and humidity, exacerbating deadly heat stress. The decline in the height of planetary boundary layer induced by the irrigation cooling effect could have played an important role. We argue that heat wave mitigation measures in MLYP and other regions with dominantly moist climates that ignore the moistening power of irrigation, overestimate irrigation's advantages for EDH but underestimate the risks of tens of millions of outdoor workers in the region experiencing compound EHH. Key Points: Effects of irrigation on summer extreme dry and humid heat stress in eastern China are investigated through observations and simulationsIrrigation decreases summer dry‐bulb temperatures but increases heat index and wet‐bulb temperatures, especially in the Middle‐Lower Yangtze Plain.Reduction in planetary boundary layer height induced by cooling effects of irrigation could contribute to deteriorating humid heat extremes [ABSTRACT FROM AUTHOR]

Terrestrial processes influence the atmosphere by controlling land‐to‐atmosphere fluxes of energy, water, and carbon. Prior research has demonstrated that parameter uncertainty drives uncertainty in land surface fluxes. However, the influence of land process uncertainty on the climate system remains underexplored. Here, we quantify how assumptions about land processes impact climate using a perturbed parameter ensemble for 18 land parameters in the Community Earth System Model version 2 under preindustrial conditions. We find that an observationally‐informed range of land parameters generate biogeophysical feedbacks that significantly influence the mean climate state, largely by modifying evapotranspiration. Global mean land surface temperature ranges by 2.2°C across our ensemble (σ = 0.5°C) and precipitation changes were significant and spatially variable. Our analysis demonstrates that the impacts of land parameter uncertainty on surface fluxes propagate to the entire Earth system, and provides insights into where and how land process uncertainty influences climate. Plain Language Summary: Land processes can affect climate by controlling the transfer of energy and water from the land to the atmosphere. Previous research has shown that uncertainty surrounding land processes (e.g., photosynthesis and the movement of water through soils) can drive uncertainty in land‐to‐atmosphere fluxes. However, it remains unclear how much that land uncertainty can impact climate. Here, we quantify how climate is sensitive to assumptions about land processes by varying 18 land model parameters to create an ensemble of 36 possible worlds in a global climate model. Land temperature ranges by 2.2°C across this ensemble, mostly due to changes in how much water is evaporated from the land surface. Changing land parameters also drives regionally variable changes in mean precipitation. This study highlights a large and underappreciated impact of land processes in determining the mean climate state, and provides insights into how climate is influenced by land process uncertainty. Key Points: Land processes substantially impact the climatological mean state terrestrial temperature and precipitationLand parameters influence climate predominantly through changing evapotranspiration rather than through other mechanismsWarming driven by land processes activates different atmospheric feedbacks than radiatively‐driven warming [ABSTRACT FROM AUTHOR]

ABSTRACT The Cerrado biome, renowned for its biodiversity and threatened by rapid land transformation, encompasses a vast savanna ecosystem in Brazil. The region is characterised by a seasonal climate, influenced by a myriad of meteorological systems creating diverse and non‐homogeneous rainfall regimes across the region. To account for this heterogeneity, we propose a novel classification of the Cerrado using rainfall data to delineate three distinct climatic regions: Eastern, Southern and Central‐West Cerrado. The Eastern region exhibited the driest and most seasonal climate, marked by high vapour pressure deficit (VPD) and predominantly open‐canopy vegetation. Conversely, the Southern region, characterised by lower seasonality, boasts a higher proportion of forest cover and lower mean VPD. The Central‐West region, encompassing diverse landscapes, featured areas with higher precipitation levels, particularly along the Amazônia border. Furthermore, we conducted trend analyses on observed station data and used CMIP6 models to evaluate future scenarios under differing emissions trajectories. While observed trends in mean rainfall were marginal, VPD demonstrated a notable upward trend of approximately 1% annually throughout the biome. Climate models indicated a substantial drying close to the Amazônia border, and wetter conditions in the southeast. All Cerrado regions are anticipated to experience amplified seasonality and VPD, with VPD projected to surge by approximately 30% (60%) under low (high) emissions scenarios by the end of the century. Notably, the transition from the dry to wet season was the most affected. Our study provides critical insights on how the climatic heterogeneity of the Cerrado shapes vegetation structure distribution and how future changes will exacerbate water stress throughout the biome. These findings underscore the importance of understanding climate variability for effective conservation and management strategies in the region. [ABSTRACT FROM AUTHOR]

The article discusses the 2nd A3 Foresight Program Workshop held in Zhuhai, China, where scholars from China, South Korea, and Japan shared research on climate change, monsoons, extreme events, and climate impacts in Northeast Asia. The workshop aimed to establish a joint Asian hub for collaborative research and promote Future Earth activities in the region. Topics covered included climate changes over Northeast Asia, Asian monsoons, climate-aerosol interactions, and climate impacts on ecology and society. The workshop facilitated exchanges and collaborations among scientists from the three countries, with plans for future workshops and enhanced cooperation. [Extracted from the article]

Understanding the dynamics of the planetary boundary layer (PBL) is crucial for comprehending land-atmosphere interactions. This study utilizes three years of Doppler wind lidar measurements from June 2019 to June 2022 to investigate PBL dynamics over Hefei, a city in the Western Yangtze River Delta, China. We focus on the seasonal and diurnal variations in key characteristics, such as wind profiles, shear intensity, turbulent mixing, low-level jets (LLJs), and mixing layer heights (MLH). Results show that horizontal wind speeds accelerated more rapidly above 3 km, with the predominant westerly winds (270°±15°) in all seasons. The vertical depth of high wind zone (> 8 m s-1) during the day is found generally deeper than at night, particularly in winter. In Hefei, LLJs primarily form at sunset and dissipate by noon, typically at altitudes between 0.5 and 0.6 km throughout the year, except in July. LLJ occurrences are most frequent in spring (31.7 %), followed by summer (24.7 %), autumn (22.3 %), and winter (21.3 %). Summer LLJs are most intensified, extending up to 1.5 km. The larger wind gradient below the jets significantly enhances turbulence and shear intensity near the ground at night. The seasonal average MLH peaks between 2:00 p.m. and 3:00 p.m., reaching approximately 1.2 km in spring and summer. Cloud cover raises MLH by about 100 m at night but decreases it by 200 m at the afternoon peak. This study provides insights into lidar-based PBL dynamics and highlights implications for local standards concerning low-altitude economic activities. [ABSTRACT FROM AUTHOR]

Our understanding of drought evolution and land-atmosphere interactions under climate mitigation scenarios remains limited. Here, we analyzed future drought under net-zero and net-negative emission scenarios using the Community Earth System Model version 2, particularly focusing on three atmospheric CO2 states: linearly increases, decreases, and a return to the initial state. Interestingly, results revealed that net-zero emissions are more effective for drought mitigation than net-negative targets. Drying trends and drought characteristics — such as the duration, frequency, intensity, and area expansion are prominently increased under net-negative emissions due to higher potential evapotranspiration (PET). This is because the soil moisture and temperature couplings are stronger over drought regions and years, especially under net-negative forcing, with notable impacts in Central Africa and South Asia. Nevertheless, both target scenarios offer regional benefits, such as weakened dryness. This suggests that mitigating CO2 alone may not be sufficient to manage future droughts, highlighting the need for advanced water management strategies. [ABSTRACT FROM AUTHOR]

Tropical precipitation P has been found to be related to column relative humidity r by a simple relationship known as the moisture–precipitation relationship P(r). Based on one decade of daily ERA5 reanalysis data, we test whether P(r) is able to reproduce the tropical land–ocean precipitation contrast measured by χ, the ratio between mean precipitation over land and ocean. We find that P(r) captures the mean seasonal cycle of χ as long as we account for the fact that P(r) is distinct over land and ocean, and that it varies seasonally. Typical values of χ above 0.86 imply that precipitation is enhanced over land, relative to the ocean. We therefore investigate next whether this enhancement is due to the differences in P(r) and/or in the humidity distribution between land and ocean. We show that, rather than enhancing precipitation, the presence of land modifies P(r) in such a way that precipitation over land is disfavored compared to over ocean. Precipitation enhancement over land is instead explained by the modified terrestrial humidity distribution that features a more pronounced tail towards high r values compared to the one over ocean. All results rest on an accurate construction of P(r) from the underlying data. Simple fit models such as an exponential function that were proposed by previous studies are unable to capture the seasonal cycle of χ and fail to explain land–ocean differences in precipitation. [ABSTRACT FROM AUTHOR]

This research paper discusses a case study of Kelvin-Helmholtz (KH) waves in the atmosphere, specifically focusing on a KH wave event observed above Dubrovnik, Croatia on October 9, 2015. The authors conducted a numerical simulation using the Weather Research and Forecasting (WRF) model to analyze the atmospheric conditions that led to this particular KH event. The simulation suggested the presence of KH waves at altitudes corresponding to the observed KH clouds, and revealed a sheared layer in the middle troposphere and a saturated layer at a specific altitude, providing further evidence of the association between the KH clouds and the favorable conditions for KH waves. The article concludes that the WRF model has the potential to simulate even shorter wavelengths of KH waves with a finer grid resolution. [Extracted from the article]

Precipitation in eastern China exhibits large interannual variability during July with the northward movement of the monsoon rain belt. Thus, eastern China usually experiences severe droughts and floods in July. However, the influences of underlying surface thermal drivers, particularly the land factors, remain poorly understood. This study investigates the leading modes of July precipitation in eastern China and their potential influencing factors. The first and second empirical orthogonal function (EOF) modes show meridional dipole and tripolar precipitation anomalies in eastern China, respectively. The EOF1 mode is found to be closely associated with sea surface temperature (SST) anomalies in the tropical Pacific and North Atlantic Oceans in June, while the EOF2 mode is mainly linked to anomalous Indian Ocean SST and Indochina Peninsula soil moisture in June. During years with a strong El Niño–South Oscillation (ENSO) signal, the EOF1 mode is mainly related to the enhanced Walker and Hadley circulations associated with the cold tropical Pacific SST anomalies. In contrast, during years with a weak ENSO signal, the Eurasian midlatitude wave train and the westward zonal overturning circulation associated with tripole-like North Atlantic SST anomalies play a leading role. The EOF2 mode is mainly influenced by Indian Ocean SST anomalies that alter the Walker circulation and by soil moisture anomalies in the Indochina Peninsula that induce an anomalous regional cyclonic circulation. Numerical experiments further demonstrated that the combined effects of soil moisture and SST exert a more substantial impact than their individual effects. These results emphasize the importance of surface thermal factors in understanding regional climate dynamics. [ABSTRACT FROM AUTHOR]

The establishment of SPAC (soil–plant‐atmosphere continuum) stations is essential for comprehensive monitoring of land‐atmosphere interactions and ecological and hydrological processes. This paper addresses the critical limitations of existing observation networks, which often rely on single‐aspect observations, resulting in insufficient data for a holistic understanding of SPAC dynamics. Specifically, SPAC stations provide critical multi‐variable observations that enhance process‐based model calibration and physical constraints and improve the empirical basis of data‐driven models. Advanced technologies such as machine learning and remote sensing are proposed to transform current weather and soil moisture stations into quasi‐SPAC sites capable of estimating carbon and water flux data. Additionally, the strategic placement of new SPAC sites in regions projected to be sensitive to future climate change and climate risks, as indicated by models such as CMIP6, is recommended. Furthermore, promoting comprehensive observational systems like Europe's Integrated Carbon Observation System (ICOS) in other regions, establishing a unified management framework and coordinating the upgrading of existing global observation networks are essential steps. Ultimately, the proposed enhancements will advance global ecological and hydrological studies, providing a more integrated and accurate understanding of the SPAC system and its responses to climate variability. [ABSTRACT FROM AUTHOR]

The purpose of this study is to determine whether urban greenhouse gas (GHG) fluxes can be quantified from tower-based mole fraction measurements using Monin–Obukhov similarity theory (MOST). Tower-based GHG mole fraction networks are used in many cities to quantify whole-city GHG emissions. Local-scale, micrometeorological flux estimates would complement whole-city estimates from atmospheric inversions. CO2 mole fraction and eddy-covariance flux data at an urban site in Indianapolis, Indiana, from October 2020 through January 2022 are analyzed. Using MOST flux–variance and flux–gradient relationships, CO2 fluxes were calculated using these mole fraction data and compared to the eddy-covariance fluxes. MOST-based fluxes were calculated using varying measurement heights and methods of estimating stability. The MOST flux–variance relationship method showed good temporal correlation with eddy-covariance fluxes at this site but overestimated flux magnitudes. Fluxes calculated using flux–gradient relationships showed lower temporal correlation with eddy-covariance fluxes but closer magnitudes to eddy-covariance fluxes. Measurement heights closer to ground level produce more precise flux estimates for both MOST-based methods. For flux–gradient methods, flux estimates are more accurate and precise when low-altitude measurements are combined with a large vertical separation between measurement heights. When stability estimates based on eddy-covariance flux measurements are replaced with stability estimates based on the weather station or net radiation data, the MOST-based fluxes still capture the temporal patterns measured via eddy covariance. Based on these results, MOST can be used to estimate the temporal patterns in local GHG fluxes at mole fraction tower sites, complementing the small number of eddy-covariance flux measurements available in urban settings. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recent drought events have significantly affected navigation through the Rhine River and the transportation of goods due to low water levels. It has become imperative to analyze the conditions in which these events occur to establish actions to develop adaptive measures and avoid monetary losses. The main focus of this study is to calibrate the hydrological model for extremely low water levels and to test its performance during the 2018 drought event in the Rhine River basin. WRF-Hydro was developed to complement the land-atmosphere interactions with the meteorological model Weather Research and Forecasting (WRF), and it has been mainly used to study flood events. In this study, we simulated the Rhine River basin's streamflow using the meteorological ERA5 reanalysis dataset as input data. The calibration period is 2016–2017, during which the influence of several parameters on the streamflow was evaluated and contrasted with the daily observed discharge values at gauging stations along the river. Land use cover and terrain slope were used to create spatially distributed parameter maps, thus avoiding the calibration process of testing a range of values, substantially reducing computational demands. During calibration, the importance of modeling realistic outflow values of Lake Constance became apparent due to its significant contribution to the upper Rhine basin. However, WRF-Hydro's lake module resulted in an overly strong dampening of streamflow. Lake Constance was, therefore, represented without the lake module, resulting in more realistic hydrographs and statistical scores. Overall, the calibration and validation process demonstrated that WRF-Hydro is capable of reproducing the variability of the discharge along the Rhine and capturing low water levels observed during the 2018 drought event. These results suggest that WRF-Hydro is a suitable model for analyzing recent and future drought events in the Rhine River basin under different climate conditions. [ABSTRACT FROM AUTHOR]

Satellite observations show that cumulus is one of the dominant cloud types in the summer over the Tibetan Plateau (TP), indicating prevalent shallow convection (ShCn). However, the impacts of ShCn parameterization on simulations of near-surface atmospheric variables and land-atmosphere interaction over the TP remain largely unknown. This study conducts simulations at 5-km grid-spacing with the Weather Research and Forecasting (WRF) model for the TP in July and August of three years. The result with/without the University of Washington (UW) ShCn scheme is evaluated against in situ observations. The evaluation shows that the ShCn scheme improves the simulations of 2-m air temperature, relative humidity, precipitation amount, and the diurnal cycle of precipitation. It also reduces light rainfall, implying its potential to lessen the general bias of "too much light rain" in climate models. Furthermore, the simulation with the scheme shows that ShCn brings moist air from the planetary boundary layer (PBL) into the free atmosphere, generally lowering and delaying the accumulation of convective available potential energy, reducing precipitation amount and soil moisture. This air exchange also reduces cloud water content and enhances surface net radiation, strengthening sensible heat flux and evaporation. This, in turn, enables the PBL to grow through both air entrainment and surface sensible heating, leading to warmer and drier low-level profiles. These impacts are most pronounced in the interior of the TP, where strong surface sensible heat and low-level convergence trigger convection, but the lack of water vapor inhibits deep convection. [ABSTRACT FROM AUTHOR]

The adequate representation of interactions between the land surface and the atmosphere is of crucial importance in modern numerical weather prediction (NWP) systems. In this context, this study examines how errors in the planetary boundary layer (PBL) depend on the quality of near-surface prediction over land for medium-range NWP. Two series of 10-day forecasts from Environment and Climate Change Canada (ECCC)'s global deterministic prediction system were evaluated: one similar to what is currently used in ECCC's operational systems and the other with improved land surface modeling and land data assimilation. An objective evaluation was performed for the 2019 summer season in North America, with a special emphasis on three specific areas: northern Canada, the central US, and the southeastern US. The results indicate that the impact of the new land surface package is more difficult to interpret in the PBL than it is at the screen level. The error differences between the two experiments are quite distinct for the three regions examined. As expected, random errors (standard deviations) for air temperature and specific humidity in the PBL are directly linked with their own random errors at the screen level, with correlation coefficients decreasing from a value of one at the surface to values of about 0.2–0.3 a few kilometers above the surface. Less expected, however, is the fact that random errors in the lower atmosphere also strongly depend on changes in air temperature biases at the surface. Warmer near-surface conditions lead to increased random errors for air temperature in the lower atmosphere, in association with the development of the deeper PBL, with greater spatial variability. This finding is of particular interest when evaluating new configurations of NWP systems for implementation in national meteorological and environmental prediction centers. [ABSTRACT FROM AUTHOR]

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Temperature fluctuations and related factors are among the main causes of climate change. Understanding the temporal and spatial variations in temperature can shed light on how species respond to climate change. Plants generally persist in suitable microclimates in response to environmental change; however, examining long-term temperature variations within a species' range can be challenging using field observations. Thermal remote sensing, on the other hand, provides multi-scale time-series data with good coverage and regularity to overcome the challenges associated with field observations in environmental monitoring. Although changes in land surface temperature (LST) affect climate, hydrological processes, land-atmosphere interactions, and ecological activities, this metric has not received much research attention. This study aimed to analyze changes in habitat suitability and microclimatic conditions for Moringa peregrina. Seasonal changes in LST within the distribution range of the species were also investigated. To this aim, mean seasonal LST was computed in Google Earth Engine using the daily MODIS/006/MYD13A2 product from 2003 to 2023. Subsequently, a binary habitat suitability map was created based on the true skill statistic (TSS). The Mann-Kendall test was used to analyze seasonal LST trends. Major trends in LST were quantified based on the z-score, and compatibility with habitat suitability was evaluated via GAP analysis and the Kappa index. Seasonal temperature trends were evaluated by comparing each season with the following season using binary comparison. Landforms at presence points were regarded as microclimates and the sensitivity of microclimates to LST was evaluated using two methods: Principal component analysis (PCA) was used to quantify seasonal LST heterogeneity and the random forest (RF) approach was used to evaluate the effect of environmental parameters on habitat suitability within microclimates. The Kappa index revealed a weak overlapping between suitable / unsuitable habitat and the surfaces affected by the trend of changes. Moreover, the suitable habitat of Moringa peregrina in spring, autumn and winter is spatially overlapped by areas that have shown an increasing LST trend, and the presence points have not experienced an increasing temperature trend only in the summer. The findings show that the analysis of seasonal trends in LST provides insights into the effect of LST on habitat suitability and the condition of vegetation. The current study clearly shows that seasonal changes have had a significant impact on the distribution and habitat suitability of M. peregrina, particularly during summer and winter. Improved habitat suitability and range expansion were observed throughout the year. The study also highlights the role of landforms in regulating temperature. Landforms such as local ridges with minimal temperature fluctuations and regions near the Oman Sea were identified as potential future habitats due to favorable humidity conditions. [ABSTRACT FROM AUTHOR]

Current global climate models (GCMs), limited to grid‐scale land‐atmosphere coupling, cannot represent subgrid urban‐rural precipitation contrasts. This study develops an innovative two‐way subgrid land‐atmosphere coupling framework in the National Center for Atmospheric Research (NCAR) Community Earth System Model version 2 (CESM2) to explicitly resolve land‐atmosphere interaction over subgrid individual land units. Results show that urban heat island (UHI) leads to the urban rainfall effect (URE), which in turn alleviates overestimated UHI over China in CESM2. The URE manifests as a shift toward more heavy precipitation and less light precipitation in world urban areas than in surrounding rural counterparts. This feature is consistent with available observations. In heavy precipitation situations, the UHI promotes atmospheric instability and enhances atmospheric water vapor holding capacity, resulting in more heavy precipitation in urban areas. Conversely, in light precipitation situations, the UHI and decreased evaporation from urban impermeable surfaces diminish atmospheric relative humidity, suppressing light precipitation. Plain Language Summary: Given the grid‐scale land‐atmosphere coupling (operating at a scale of ∼100–200 km), all the current global climate models cannot represent precipitation over urban areas. This obstructs the explicit simulation and future projections of precipitation in world urban regions, where over half of the world's population resides and a substantial portion of economic activities occurs. To address this issue, this study develops a novel land‐atmosphere coupling framework that explicitly allows land‐atmosphere interaction over individual land units within the grid cell in global climate models. The modeling results show a greater daily average of precipitation intensities on precipitation days in urban areas than in rural areas due to urban warming, with a shift toward more heavy precipitation and less light precipitation. These findings indicate an escalation in the risk of extreme precipitation and flooding in urban regions around the globe, as urban land continues to expand. Key Points: A novel framework of two‐way subgrid land‐atmosphere coupling for GCMs is developed and implemented in CESM2Overestimated urban heat island over China in CESM2 is significantly alleviated by explicitly representing urban rainfall effectsWorld urban regions feature more heavy precipitation and less light precipitation than the surrounding rural areas [ABSTRACT FROM AUTHOR]

Urbanization alters land surface properties in absorbing, reflecting and emitting radiation as well as infiltrating, evaporating and storing water. This consequently modifies surface energy and water fluxes and, thus, urban climates. Weak synoptic flow, clear sky conditions and higher surface temperatures in cities compared to their rural surroundings create a buoyancy‐driven atmospheric circulation, in which surface energy fluxes become the main determinants of urban daytime overheating. Here, we demonstrate the role of surface energy fluxes for warming and cooling processes in the urban canopy layer under buoyancy‐driven atmospheric conditions. We improve and apply an integrated CFD‐GIS modeling approach to provide a detailed analysis of fine‐scale land‐atmosphere interactions and assess the surfaces' profound implications on energy and water exchange. We show that variations in the ratios of the surface energy fluxes to the net radiation can be separated from meteorological conditions (wind speed, air temperature and incoming solar radiation) and emissivity values, varying explicitly with changes in land surface type and water availability for vegetated areas. Based on the energy flux ratios, we introduce an approach to assess the surface‐induced warming and cooling effect and its contribution to urban overheating in the urban canopy layer, under buoyancy‐driven atmospheric conditions, directly applicable to strategic urban planning for climate change adaptation. Independent of meteorological conditions, this approach can be used to evaluate different surface materials (both natural and artificial) and climate adaptation measures, such as urban nature‐based solutions and blue‐green infrastructures, and to monitor changes in the energy and water balance. Plain Language Summary: The aim of this study is to better understand the impact of different surface characteristics (both natural and artificial) on soil‐surface‐atmosphere interactions and, consequently, on the urban meteorology. This is important to improve the assessment of climate change impacts and adaptation strategies in cities. We present an approach, directly applicable in strategic urban planning for climate change adaptation, that can describe the surface‐induced warming and cooling effect under low wind and clear sky conditions, when surface thermal forcing is the dominant factor in urban overheating. Separable from meteorological conditions (wind speed, air temperature and incoming solar radiation) and emissivity values, our approach can be used to rapidly evaluate changes to the surface characteristics, for example, through the implementation of urban nature‐based solutions and blue‐green infrastructure in cities. We highlight the importance of water availability in heat mitigation strategies to ensure evaporative cooling from green areas, and the hazardous potential of impermeable artificial surfaces to increase diurnal and nocturnal heat transfer. Key Points: Variations in the ratios of surface energy fluxes to net radiation separable from meteorological conditions and emissivity valuesA Bowen ratio lower than 0.52 for vegetated surfaces necessary to maintain a prevailing evaporative cooling effectLack of water from precipitation and irrigation exacerbates the heat transfer between the soil, surface and atmosphere [ABSTRACT FROM AUTHOR]

We investigated positive temperature (warm) and negative precipitation (dry) biases in convection-permitting model (CPM) simulations for Europe (2.2 km grid spacing) that were considerably larger than in equivalent regional climate model (RCM) simulations (12 km grid spacing). We found that improvements in dry biases could be made by (1) using a more complex runoff scheme which takes into account topography and groundwater, (2) delaying the onset of water stress in vegetation to enhance transpiration, (3) changing the microphysics scheme to CASIM (Cloud AeroSol Interacting Microphysics) which also decreases heavy rainfall and increases light rainfall. Increasing soil moisture to the critical point can remove dry precipitation biases in southern Europe but not in northern areas, indicating that soil moisture limitation is a key contributor to precipitation biases in the south only. Instead, in the north, changing the cloud scheme of the model has more impact on precipitation biases. We found that the more intense and intermittent nature of rainfall in the CPM, which is more realistic leads to different canopy interception compared to the RCM. This can impact canopy evaporation, evapotranspiration and feed back on precipitation. Increasing rainfall storage in the canopy only leads to small improvements in warm biases, since it still fills rapidly with intense CPM rainfall, suggesting the need for an additional moisture store via improved groundwater modelling or surface pooling. Overall, this work highlights the challenge of correctly capturing land surface feedbacks in CPMs, which play an important role in future climate projections in some regions. [ABSTRACT FROM AUTHOR]

Selin Co basin, a representative lake basin situated in the central region of the Tibetan Plateau (TP), is characterized by extensive areas of frozen ground and has undergone a remarkable expansion in its lake area. Evapotranspiration, as an important variable within the lake water cycle, as well as its influencing factors are still under debate at this basin. Actual evapotranspiration (ETa) plays a vital role in influencing land-atmosphere interaction processes and the hydrological cycle. By quantifying the amount of water that is actually transpired by vegetation and evaporated from soil surfaces, ETa provides important information about the mechanisms controlling evapotranspiration. In this study, the advection-aridity (AA) model was employed to estimate ETa in Selin Co basin. The estimated ETa and its influencing factors were investigated between 2001-2018, with an emphasis on examining both spatial and temporal trends. The results demonstrated that the multiyear mean ETa in the basin was 430.3 mm (excluding lakes) with an overall significantly decreasing trend (-5.8 mm/yr). In terms of its seasonal variation, ETa exhibited highest value during summer, while experiencing comparatively lower rates in autumn and spring. Furthermore, the spatial distribution of both the annual and seasonal ETa exhibited a similar decreasing trend from the southeast to the northwest of the basin. The sensitivity of several meteorological variables to modifications in ETa was also examined. Results indicated that specific humidity was the predominant variable, followed by net radiation, air temperature, precipitation, atmospheric pressure, and wind speed. In addition, the area covered by permafrost and seasonally frozen ground was approximately about 1.58 × 104 km2 and 4.22 × 104 km2 in the basin, and permafrost degradation in the northern region of the Selin Co basin resulted in increased average soil moisture content and underground ice melting, leading to a subsequent increase in ETa. [ABSTRACT FROM AUTHOR]

The maximum information entropy production model (MaxEnt), the relative humidity at equilibrium approach (ETRHEQ), and the Surface Flux Equilibrium model (SFE) are three recently developed models to estimate evapotranspiration. Although the connection between ETRHEQ and SFE is evident, no attempts have been made to investigate the congruence, distinctions, or potential complementarity between the two models and MaxEnt. Our mathematical analysis demonstrates that minimizing the vertical variance of RH in ETRHEQ is equivalent to minimizing the dissipation function of energy fluxes in MaxEnt, under the assumption of the same eddy diffusivity of heat and water vapor and with a specific expression for the ratio between the thermal inertia terms for H and LE. The connection between ETRHEQ, SFE, and MaxEnt is independent of Monin‐Obukhov similarity theory (MOST)'s extremum solution, and MOST's extreme solution can be viewed as equivalent to introducing a constant correction factor to account for atmospheric stability. While ETRHEQ and MaxEnt can be united within a single hydrometeorological framework, they diverge in their approaches to modeling evapotranspiration, particularly in how they address the roles of vegetation and land surface heterogeneity. More importantly, the unified framework suggests that turbulence fluxes within the atmospheric boundary layer adhere to the principles of maximum information entropy production. The way in which dissipation, along with its associated entropy production, is established using information entropy theory deviates from traditional thermodynamic entropy formulations. Exploring the connection between thermodynamic and information entropy and developing proper formulations of dissipation for energy fluxes presents an appealing avenue for prospective research. Plain Language Summary: This paper seeks to establish a common theory for explaining the effectiveness of the maximum information entropy production model (MaxEnt), the relative humidity at equilibrium approach (ETRHEQ), and the Surface Flux Equilibrium model (SFE) in estimating evapotranspiration over a wide range of conditions. It uncovers that under reasonable assumptions, ETRHEQ's method in terms of minimizing the vertical variance in relative humidity is equivalent to MaxEnt's approach to minimizing dissipation. The united theory suggests that the movement of air and energy in the lower atmosphere follows the principles of maximum information entropy production, a concept that is a bit different from traditional ideas of entropy in thermodynamics. Looking into how thermodynamic entropy and information entropy are connected and figuring out the right way to calculate dissipation and entropy production for energy flux could lead to new insights in atmospheric science and land‐atmosphere interactions. Key Points: The study presented a pure theorical analysis to unifying MaxEnt, ETRHEQ, and SFE within the same hydrometeorological frameworkMinimizing the vertical variance of RH in ETRHEQ is equivalent to minimizing the dissipation function of energy fluxes in MaxEntThe connection between MaxEnt, ETRHEQ, and SFE is independent of Monin‐Obukhov similarity theory (MOST)'s extremum solution [ABSTRACT FROM AUTHOR]

High temperature extremes accompanied by drought have led to serious ramifications for environmental and socio‐economic systems. Thus, improving the predictability of heat‐wave events is a high priority. One key to achieving this is to better understand land‐atmosphere interactions. Recent studies have documented a hypersensitive regime in the soil moisture‐temperature relationship: when soil dries below a critical low threshold, called the soil moisture breakpoint, air temperatures increase at a greater rate as soil moisture declines. Whether such a hypersensitive regime is rooted in land surface processes and whether this soil moisture breakpoint corresponds to a known plant critical value, the permanent wilting point (WP), below which latent heat flux almost ceases, remains unclear. In this study, we explore the mechanisms linking low soil moisture to high air temperatures. From in situ observations, we confirm that the hypersensitive regime acts throughout the chain of energy processes from land to atmosphere. A simple energy‐balance model indicates that the hypersensitive regime occurs when there is a dramatic drop in evaporative cooling, which happens when soil moisture dries toward the permanent WP, suggesting that the soil moisture breakpoint is slightly above the permanent WP. Precisely how a model represents the relationship between evapotranspiration and soil moisture is found to be essential to describe the occurrence of the hypersensitive regime. Thus, we advocate that weather and climate models should ensure a realistic representation of land‐atmosphere interactions to obtain reliable forecasts of extremes and climate projections, aiding the assessment of heatwave vulnerability and adaptation. Plain Language Summary: Hot temperature extremes combined with droughts have caused significant problems for the environment and economies. Improving prediction of heat‐wave events is of utmost importance. This can be achieved by a better understanding of how land conditions affect near surface atmosphere and vice versa. Recent evidences have shown that when the soil becomes very dry and below a certain threshold, even a slight decrease in soil moisture yields a substantial increase in air temperature. However, the behind mechanism remains unclear. In this study, we validate that hypersensitive regimes indeed result from energy transmission from land to atmosphere by using observations. Subsequently, we built a simple model to explore how air temperature correlates to land wetness conditions. Our model indicates that hypersensitive regime occurs when there is a dramatic drop in evaporation when soil moisture dries to the permanent wilting point, below which water is no longer drawn from the soil by plant roots. The diminished evaporation significantly curtails the cooling effect on the atmosphere. Notably, the model's representation of evaporation behavior fundamentally governs the occurrence of hypersensitive regimes. To achieve reliable forecasts of climate extremes and projections, a realistic depiction of land‐atmosphere interactions is indispensable. Key Points: Hypersensitive regime acts throughout the chain of energy processes from land to atmosphereHypersensitive regime occurs when soil moisture dries to the permanent wilting pointModel's representation of evapotranspiration fundamentally governs the occurrence of hypersensitive regimes [ABSTRACT FROM AUTHOR]

Abstract Terrestrial processes influence the atmosphere by controlling land‐to‐atmosphere fluxes of energy, water, and carbon. Prior research has demonstrated that parameter uncertainty drives uncertainty in land surface fluxes. However, the influence of land process uncertainty on the climate system remains underexplored. Here, we quantify how assumptions about land processes impact climate using a perturbed parameter ensemble for 18 land parameters in the Community Earth System Model version 2 under preindustrial conditions. We find that an observationally‐informed range of land parameters generate biogeophysical feedbacks that significantly influence the mean climate state, largely by modifying evapotranspiration. Global mean land surface temperature ranges by 2.2°C across our ensemble (σ = 0.5°C) and precipitation changes were significant and spatially variable. Our analysis demonstrates that the impacts of land parameter uncertainty on surface fluxes propagate to the entire Earth system, and provides insights into where and how land process uncertainty influences climate.

Abstract Massive irrigation across eastern China (EC) could reduce extreme heat by altering energy and water budgets. However, the irrigation effect on increasing humidity has often been missed, especially in humid regions, leaving its effect and mechanism on extreme humid heat (EHH) poorly characterized. We analyzed assemblies of observations and performed regional simulations with more detailed irrigation scheme to explore the irrigation impacts and potential mechanisms on EHH. We find that irrigation in EC, despite having a cooling effect of about 0.2–0.6°C, leads to an increase of about 0.4–0.8°C in EHH, with a more intense impact on the Middle‐Lower Yangtze Plain (MLYP) by 0.9°C. Cooling effect induced by increased latent heat fluxes through irrigation contributes to air deposition, which lowers boundary layer height, raises near‐surface moist enthalpy, and ultimately exacerbates the EHH. Results mechanistically emphasized irrigation impacts on EHH and highlighted the necessity of improving irrigation modeling reality.

Soil moisture can significantly influence weather and climate via land‒atmosphere interactions over the Tibetan Plateau. However, the temporal and spatial preferences of precipitation for soil moisture anomalies and the underlying mechanisms over the plateau have not been determined. Using multiple satellite data sets (including Global Precipitation Measurement precipitation data and Soil Moisture Active Passive and Advanced SCATterometer soil moisture data) and ERA5 reanalysis data, the temporal and spatial soil moisture–precipitation coupling (SMPC) relationships in seven summers during 2015–2021 over the plateau are quantified based on a percentile‐based method. The satellite observations show prevalent positive temporal SMPC across the plateau, indicating that wetter‐than‐normal soil conditions tend to lead to more afternoon precipitation. While ERA5 generally aligns with satellite findings, it underestimates areas with positive temporal SMPC. Both the satellite and ERA5 data show that spatial SMPC relationships are usually statistically insignificant, but a few regions show significant positive relationships, that is, precipitation is more likely to occur over soils wetter than the surrounding soils. Moreover, the satellite observations suggest an inter‐event positive correlation between the temporal and spatial SMPC relationships. ERA5 agrees with the satellite‐based results over the western plateau but shows discrepancies over the eastern plateau. The temporal and spatial variations in soil moisture modulate the partitioning of surface heat fluxes, planetary boundary layer height, and lifting condensation level, promoting moist convection and afternoon precipitation. The findings from this study shed new light on SMPC and have important implications for precipitation forecasting over the plateau. Plain Language Summary: Soil moisture can influence precipitation via land–atmosphere water and energy exchanges over the Tibetan Plateau. Such impacts manifest itself in different ways. Temporally, precipitation could preferentially occur on antecedent soils that are either wetter or drier than normal. Spatially, precipitation could preferentially occur on antecedent soils that are either wetter or drier than the surrounding soils. In this study, we investigate the effects of soil moisture on precipitation in summer over the plateau from separate temporal and spatial perspectives using satellite and reanalysis data sets. Satellite data suggest that wetter‐than‐normal soil conditions often lead to more afternoon precipitation. While the ERA5 generally supports satellite findings, they underestimate areas with a positive temporal relationship. Both the satellite and ERA5 data show that in many areas, the spatial relationship between soil moisture and precipitation is not statistically significant. However, in a few regions, precipitation is more likely to occur over soils wetter than the surrounding soils. Soil moisture‐induced changes in surface energy and water fluxes influence the atmospheric boundary layer, leading to the spatiotemporal preference of afternoon precipitation over soil moisture anomalies. This work helps improve our understanding of the effects of soil moisture on precipitation over the plateau. Key Points: The temporal and spatial preferences of afternoon precipitation over soil moisture anomalies over the Tibetan Plateau are investigatedPrecipitation preferentially occurs over antecedent soil that is wetter than normal and wetter than the surrounding soilsSoil moisture changes the surface energy partitioning and affects subsequent precipitation by adjusting the atmospheric boundary layer [ABSTRACT FROM AUTHOR]

Surface solar radiation, as a primary energy source, plays a pivotal role in governing land–atmosphere interactions, thereby influencing radiative, hydrological, and land surface dynamics. Ground-based instrumentation and satellite-based observations represent two fundamental methodologies for acquiring solar radiation information. While ground-based measurements are often limited in availability, high-temporal- and spatial-resolution, gridded satellite-retrieved solar radiation products have been extensively utilized in solar radiation-related studies, despite their inherent uncertainties in accuracy. In this study, we conducted an evaluation of the accuracy of two high-resolution satellite products, namely Himawari-8 (H8) and Moderate Resolution Imaging Spectroradiometer (MODIS), utilizing data from a newly established solar radiation observation system at the Beijing Normal University (BNU) station in Beijing since 2017. The newly acquired measurements facilitated the generation of a firsthand solar radiation dataset comprising three components: Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI). Rigorous quality control procedures were applied to the raw minute-level observation data, including tests for missing data, the determination of possible physical limits, the identification of solar tracker malfunctions, and comparison tests (GHI should be equivalent to the sum of DHI and the vertical component of the DNI). Subsequently, accurate minute-level solar radiation observations were obtained spanning from 1 January 2020 to 22 March 2022. The evaluation of H8 and MODIS satellite products against ground-based GHI observations revealed strong correlations with R-squared (R2) values of 0.89 and 0.81, respectively. However, both satellite products exhibited a tendency to overestimate solar radiation, with H8 overestimating by approximately 21.05% and MODIS products by 7.11%. Additionally, solar zenith angles emerged as a factor influencing the accuracy of satellite products. This dataset serves as crucial support for investigations of surface solar radiation variation mechanisms, future energy utilization prospects, environmental conservation efforts, and related studies in urban areas such as Beijing. [ABSTRACT FROM AUTHOR]

The climate of the Tibetan Plateau (TP) has experienced substantial changes in recent decades as a result of the location's susceptibility to global climate change. The changes observed across the TP are closely associated with regional land–atmosphere interactions. Current models and satellites struggle to accurately depict the interactions; therefore, critical field observations on land–atmosphere interactions outlined here provide necessary independent validation data and fine-scale process insights for constraining reanalysis products, remote sensing retrievals, and land surface model parameterizations. Scientific data sharing is crucial for the TP since in situ observations are rarely available under these harsh conditions. However, field observations are currently dispersed among individuals or groups and have not yet been integrated for comprehensive analysis. This has prevented a better understanding of the interactions, the unprecedented changes they generate, and the substantial ecological and environmental consequences they bring about. In this study, we collaborated with different agencies and organizations to present a comprehensive dataset for hourly measurements of surface energy balance components, soil hydrothermal properties, and near-surface micrometeorological conditions spanning up to 17 years (2005–2021). This dataset, derived from 12 field stations covering a variety of typical TP landscapes, provides the most extensive in situ observation data available for studying land–atmosphere interactions on the TP to date in terms of both spatial coverage and duration. Three categories of observations are provided in this dataset: meteorological gradient data (met), soil hydrothermal data (soil), and turbulent flux data (flux). To assure data quality, a set of rigorous data-processing and quality control procedures are implemented for all observation elements (e.g., wind speed and direction at different height) in this dataset. The operational workflow and procedures are individually tailored to the varied types of elements at each station, including automated error screening, manual inspection, diagnostic checking, adjustments, and quality flagging. The hourly raw data series; the quality-assured data; and supplementary information, including data integrity and the percentage of correct data on a monthly scale, are provided via the National Tibetan Plateau Data Center (10.11888/Atmos.tpdc.300977, Ma et al., 2023a). With the greatest number of stations covered, the fullest collection of meteorological elements, and the longest duration of observations and recordings to date, this dataset is the most extensive hourly land–atmosphere interaction observation dataset for the TP. It will serve as the benchmark for evaluating and refining land surface models, reanalysis products, and remote sensing retrievals, as well as for characterizing fine-scale land–atmosphere interaction processes of the TP and underlying influence mechanisms. [ABSTRACT FROM AUTHOR]

Previously, the types of impacts of land–atmosphere interactions have scarcely been clarified systematically. In this article, we present a classification of these impacts based on modeling boundary layer variables/parameters, which is grouped into local, regional, and remote impacts. In the narrow sense, land surface processes (LSPs) influence the atmospheric state via vertical land–atmosphere coupling at local scales, which is referred to as local LSP impacts. However, local LSP impacts can lead to the advection effect due to the horizontal heterogeneity in the parameters over a region, which can be defined as regional LSP impacts. Furthermore, remote LSP impacts on the regional atmospheric state are induced by some land/sea surface variables/parameters over remote key areas of the Earth's surface, which are conventionally taken as strong signals of climate variation. Of the three impacts, local impacts are the most important essential, as the other two types of impacts are derived from these impacts. We describe the quantification of local impacts based on our previous studies from the perspective of modeling approaches, and we discuss some issues related to these impacts. Previous investigations showed that local LSP impacts are mostly stronger than regional LSP impacts, e.g., the diabatic process is dominant in the physical processes responsible for daily maximum temperatures, and two first-order physical processes including vertical diffusion largely induce changes in surface wind speed in China. Finally, some aspects for future research are noted. This study provides insights into the research on land–atmosphre interactions at different scales. [ABSTRACT FROM AUTHOR]

Land use and land cover changes (LULCCs) can influence surface temperature through local and nonlocal biophysical processes, which remain inadequately addressed. In this study, we separate the local and nonlocal effects of historical (1850–2014) LULCCs based on model outputs from the Coupled Model Intercomparison Project Phase 6. We also attempt to explore the sources of intermodel differences in the effects of LULCCs. The multimodel mean shows a cooling effect of −0.05°C (with an intermodel range of −0.24–0.06°C) at the global scale due to cropland and pastureland expansion, consisting of dominant nonlocal cooling of −0.06°C (with an intermodel range of −0.26–0.06°C) and slight local warming of 0.01°C (with an intermodel range of −0.01–0.05°C). The modeling results show some clear consistency in the effects of LULCCs despite considerable intermodel uncertainties. The local effects cause warming at low latitudes and cooling in boreal regions via changes in upward shortwave radiation and sensible and latent heat fluxes. The nonlocal effects mainly cause cooling via decreases in downward longwave radiation and increases in upward shortwave radiation. Intermodel differences in the total effects are dominated by those in the nonlocal effects, which are further attributed to divergent changes in downward longwave radiation and sensible heat flux across the models. This study highlights the importance of the nonlocal effects of LULCCs in terms of strength and intermodel uncertainty, with implications for designing land‐based solutions aimed at climate change mitigation. Plain Language Summary: Land use and land cover changes (LULCCs) can alter surface properties and thereby influence surface temperature through land–atmosphere interactions. LULCCs influence the climate not only at the location of LULCCs (local effects) but also elsewhere (nonlocal effects) through atmospheric and oceanic feedbacks. However, the local and nonlocal effects of realistic LULCCs still lack explicit investigation. In this study, we quantify the local and nonlocal effects of historical (1850–2014) LULCCs and their intermodel differences based on a set of simulations from four earth system models. The multimodel mean suggests that historical cropland and pastureland expansion has caused a nonlocal cooling effect of −0.06°C and a local warming effect of 0.01°C, creating a net cooling effect of −0.05°C at the global scale. The local and nonlocal effects of LULCCs can be attributed to changes in surface fluxes and atmospheric feedbacks, whereas the strength and sign vary substantially across the models. Large intermodel differences in the total effects of LULCCs mostly result from those in the nonlocal effects. This study highlights the importance of the nonlocal effects of LULCCs and has implications for developing land‐use strategies aimed at climate change mitigation. Key Points: The local and nonlocal biophysical effects of historical land use and land cover changes (LULCCs) are investigated using CMIP6 modelsThe multimodel mean shows that, overall, historical LULCCs cool the land surface through nonlocal processesThe intermodel divergence in the total effects of historical LULCCs mainly results from low intermodel agreement on the nonlocal effects [ABSTRACT FROM AUTHOR]

Diagnosing and predicting evaporation through satellite‐based surface energy balance (SEB) and land surface models (LSMs) faces challenges due to the non‐linear responses of aerodynamic (ga) and stomatal conductance (gcs) to concurrent soil and atmospheric drought. Despite a soaring popularity to refine gcs formulation in LSMs by integrating soil‐plant hydraulics, SEB models often overlook the utility of gcs. This oversight is attributed to the overriding emphasis on reducing ga uncertainties and the lack of coordination between these two modeling communities. This disengagement between modeling communities poses a persistent challenge in understanding divergent evaporation estimates during intense soil‐atmospheric drought. Here we conducted a theoretical experiment over two contrasting European forest sites to examine the sensitivity of conductances and evaporative fluxes to a water‐stress factor (β‐factor), coupled with land surface temperature (LST) and vapor pressure deficit (representing soil and atmospheric drought proxy). Utilizing a non‐parametric diagnostic model (Surface Temperature Initiated Closure, STIC) and a prognostic model (Community Land Model, CLM5.0), the analysis revealed that the β‐factor, alongside different functional forms of conductances and the loose coupling of CLM5.0 conductances to LST, significantly influenced the response of the two models to soil and atmospheric drought. These discrepancies propagated in the estimates of evaporative fluxes between STIC and CLM5.0. The analysis reaffirms the need for a consensus on theory and models capturing the sensitivity of biophysical conductances to soil‐atmospheric drought interplay. It emphasizes the need for fostering collaboration between modeling communities to enhance the prediction of evaporation in complex environmental conditions. Plain Language Summary: The regulation of plant water loss through evaporation is governed by two key physical and biological attributes: aerodynamic and stomatal conductance. The magnitude and variability of these conductances and their degree of regulation on evaporation is heavily dependent on how the conductances respond to the conjugate dryness from the soil and the atmosphere. Because these conductances cannot be typically measured at a large scale, the majority of the global evaporation models use different mechanistic functions to estimate them, which involves many empirical parameters. Such methods do not fully capture the evaporation variability of ecosystems during water stress, leading to large errors in water cycle monitoring. Our model‐based synthetic experiment shows how two structurally different models with different functional forms of the conductances respond very differently to emerging soil‐atmospheric water stress and produce divergent estimates of evaporation in a variety of dry and wet conditions. This study not only provides insights into the role of conjugate effects of soil and atmospheric drought in explaining the conductances and evaporation variability but also suggests a novel perspective for reconciling predictive and remote sensing evaporation models, benefiting water management, testing plant water use theories, and understanding land‐atmosphere interactions. Key Points: Diagnostic (STIC) and prognostic (CLM5.0) evaporation models show distinct levels of sensitivity to water stressSTIC and CLM5.0 evaporation models better agree in simulating the energy fluxes as compared to underlying biophysical conductanceMajor differences in the simulated stomatal conductance are due to divergences in the physiological assumptions of the two evaporation modeling approaches [ABSTRACT FROM AUTHOR]

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Anthropogenic activity drives extensive tropical deforestation, particularly in Southeast Asia where 16% of total forest cover was lost between 2000 and 2020. While land surface changes significantly affect the atmosphere, their net impact on convective clouds is not well‐constrained. Here, we use satellite data to demonstrate long‐term deforestation in Southeast Asia robustly alters cloud properties and provide the first observational evidence that the magnitude of this response depends on the atmospheric environment. Deforestation drives a shift toward more widespread, shallower clouds during the daytime, with amplified effects in dry inland areas compared with moist coastal regions. Aerosols only weakly modulate the cloud fraction response, but offset the cloud top height response to deforestation, suggesting the influence of aerosol indirect effects. We conclude that the local signature of forest loss is not uniform, and regional differences in climatology must be considered when assessing deforestation impacts on clouds and the climate system. Plain Language Summary: Humans are driving widespread deforestation in the tropics. Changes to the land surface following forest loss are generally known to affect the atmosphere, but it is hard to tell how deforestation will impact clouds in a given area. Here, we focus on Southeast Asia, a region of the world facing dramatic large‐scale deforestation. We use two decades of satellite data to estimate how the loss of tropical forests impacts cloud properties. On average, we find that deforestation leads to more widespread and shallower clouds. We then look further into how this cloud response to deforestation depends on other environmental factors like moisture and aerosols. This gives us a better idea of which regions are most sensitive to changes in forest cover. Overall, our results show there is an observable cloud response to deforestation, but this response may be stronger in some regions than in others depending on underlying moisture and aerosol conditions. As forest loss continues in Southeast Asia and across the world, it is important to further study these region‐dependent interactions between the atmosphere and the land surface so we can better understand the impacts of human‐driven deforestation on weather and climate. Key Points: Deforestation in Southeast Asia drives a robust shift toward more widespread and shallower clouds on an annual timescaleThis effect has been debated in modeling studies, but we demonstrate this observationally using two decades of satellite dataSome regions are especially vulnerable to deforestation‐driven changes in clouds, depending on atmospheric moisture and aerosol loading [ABSTRACT FROM AUTHOR]

Large differences in projected future annual precipitation increases in North America exists across 27 CMIP6 models under four emission scenarios. These differences partly arise from weak representations of land-atmosphere interactions. Here we demonstrate an emergent constraint relationship between annual growth rates of future precipitation and growth rates of historical temperature. The original CMIP6 projections show 0.49% (SSP126), 0.98% (SSP245), 1.45% (SSP370) and 1.92% (SSP585) increases in precipitation per decade. Combining observed warming trends, the constrained results show that the best estimates of future precipitation increases are more likely to reach 0.40–0.48%, 0.83–0.93%, 1.29–1.45% and 1.70–1.87% respectively, implying an overestimated future precipitation increases across North America. The constrained results also are narrow the corresponding uncertainties (standard deviations) by 13.8–31.1%. The overestimated precipitation growth rates also reveal an overvalued annual growth rates in temperature (6.0–13.2% or 0.12–0.37°C) and in total evaporation (4.8–14.5%) by the original models' predictions. These findings highlight the important role of temperature for accurate climate predictions, which is important as temperature from current climate models' simulations often still have systematic errors. [ABSTRACT FROM AUTHOR]

Soil–Vegetation–Atmosphere Transfer (SVAT) models are a promising avenue towards gaining a better insight into land surface interactions and Earth's system dynamics. One such model developed for the academic and research community is the SimSphere SVAT model, a popular software toolkit employed for simulating interactions among the layers of vegetation, soil, and atmosphere on the land surface. The aim of the present review is two-fold: (1) to deliver a critical assessment of the model's usage by the scientific and wider community over the last 15 years, and (2) to provide information on current software developments implemented in the model. From the review conducted herein, it is clearly evident that from the models' inception to current day, SimSphere has received notable interest worldwide, and the dissemination of the model has continuously grown over the years. SimSphere has been used so far in several applications to study land surface interactions. The validation of the model performed worldwide has shown that it is able to produce realistic estimates of land surface parameters that have been validated, whereas detailed sensitivity analysis experiments conducted with the model have further confirmed its structure and architectural coherence. Furthermore, the recent inclusion of novel functionalities in the model, as outlined in the present review, has clearly resulted in improving its capabilities and in opening up new opportunities for its use by the wider community. SimSphere developments are also ongoing in different aspects, and its use as a toolkit towards advancing our understanding of land surface interactions from both educational and research points of view is anticipated to grow in the coming years. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) and fractional vegetation cover (FVC) are very important parameters in land–atmosphere interactions. In this study, a very simple but robust and mechanism-based method was developed to derive FVC data based on the relationships between the canopy gap fraction, LAI, and direct solar extinction coefficient. For validation, the LAI data and NDVI-based and mechanism-based FVC data were assimilated into the integrated urban land model (IUM). Using the mechanism-based FVC data as the input, the simulation of the annual average land surface temperatures (LSTs) in the Beijing area were improved compared with those using the NDVI-based FVC data as the input. [ABSTRACT FROM AUTHOR]

Urban artificial surfaces and structures induce modifications in land–atmosphere interactions, affecting the exchange of energy, momentum, and substances. These modifications stimulate urban climate formation by altering the values and dynamics of atmospheric parameters, including cloud-related features. This study evaluates the presence and quantifies the extent of such changes over Sofia, Bulgaria. The findings reveal that estimations of low-level cloud base height (CBH) derived from lifting condensation level (LCL) calculations may produce unexpected outcomes due to microclimate influence. Ceilometer data indicate that the CBH of low-level clouds over urban areas exceeds that of surrounding regions by approximately 200 m during warm months and afternoon hours. Moreover, urban clouds exhibit reduced persistence relative to rural counterparts, particularly pronounced in May, June, and July afternoons. Reanalysis-derived low-level cloud cover (LCC) shows no significant disparities between urban and rural areas, although increased LCC is observed above the western and northern city boundaries. Satellite-derived cloud products reveal that the optically thinnest low-level clouds over urban areas exhibit slightly higher cloud tops, but the optically thickest clouds are more prevalent during warm months. These findings suggest an influence of urbanization on cloudiness, albeit nuanced and potentially influenced by the city size and surrounding physical and geographical features. [ABSTRACT FROM AUTHOR]

The scientific literature has many studies evaluating numerical weather prediction (NWP) models. However, many of those studies averaged across a myriad of different atmospheric conditions and surface forcings that can obfuscate the atmospheric conditions when NWP models perform well versus when they perform inadequately. To help isolate these different weather conditions, we used observations from the U.S. Climate Reference Network (USCRN) obtained between 1 January and 31 December 2021 to distinguish among different near-surface atmospheric conditions [i.e., different near-surface heating rates ( d T / d t ), incoming shortwave radiation (SWd) regimes, and 5-cm soil moisture (SM05)] to evaluate the High-Resolution Rapid Refresh (HRRR) Model, which is a 3-km model used for operational weather forecasting in the United States. On days with small (large) d T / d t , we found afternoon T biases of about 2°C (−1°C) and afternoon SWd biases of up to 170 W m−2 (100 W m−2), but negligible impacts on SM05 biases. On days with small (large) SWd, we found daytime temperature biases of about 3°C (−2.5°C) and daytime SWd biases of up to 190 W m−2 (80 W m−2). Whereas different SM05 had little impact on T and SWd biases, dry (wet) conditions had positive (negative) SM05 biases. We argue that the proper evaluation of weather forecasting models requires careful consideration of different near-surface atmospheric conditions and is critical to better identify model deficiencies in order to support improvements to the parameterization schemes used therein. A similar, regime-specific verification approach may also be used to help evaluate other geophysical models. Significance Statement: Improving weather forecasting models requires careful evaluations against high-quality observations. We used observations from the U.S. Climate Reference Network (USCRN) and found that the performance of the High-Resolution Rapid Refresh (HRRR) Model varies as a function of differences in near-surface heating and solar radiation. This finding indicates that model evaluations need to be conducted under varying near-surface weather conditions rather than averaging across multiple weather types. This new approach will allow for model developers to better identify model deficiencies and is a useful step to helping improve weather forecasts. [ABSTRACT FROM AUTHOR]

Earth system models currently struggle to account for the complex effects that land surface heterogeneity can have on land‐atmosphere interactions. There have been attempts to include the impact of this heterogeneity on the atmosphere, but they ignore the development of coherent circulations that can be driven by spatial differential surface heating. A wealth of literature, particularly large‐eddy simulation (LES) based studies, shows that these circulations have significant impacts on the development and organization of clouds. In this work, we describe a two‐column model with a parameterized circulation driven by atmospheric virtual potential temperature profiles, differences in near surface temperature between the two columns, patterns of surface heterogeneity, and the mean background wind. Key aspects of the proposed model structure are compared with LES output, and the model is then implemented between two otherwise independent single column models. While some avenues for improvement exist, when the circulations are parameterized, we see increased cloud development and realistic changes to the mean profiles of temperature and moisture. The proposed model qualitatively matches expectations from the literature and LES, and points to the potential success of its future implementation in coarse grid models. Plain Language Summary: This work addresses the challenge of incorporating land surface heterogeneity into earth system models to better understand land‐atmosphere interactions. Current models struggle to account for the complex effects of subgrid land surface heterogeneity on these interactions, especially when a warmer region near a cooler region can cause a circulation to occur. The study proposes a two‐column model that includes a parameterized circulation driven by vertical temperature profiles, surface temperature differences, surface heterogeneity patterns, and the background wind. The model is compared to high resolution large‐eddy simulation (LES) output for 3 days in the Southern Great Plains. The results show that the model qualitatively reproduces patterns observed in LES and the existing literature, primarily that cloud production increases and concentrates over warmer surface areas. The model's success suggests its potential implementation in coarse grid models to explore regional and global atmospheric impacts of subgrid land surface heterogeneity. Additionally, the similarities between land surface heterogeneity circulations and other thermally driven circulations indicate potential applicability in subgrid‐scale parameterization of sea and lake breezes. While limitations and opportunities for improvement exist, overall this work represents a promising step toward understanding the impacts of subgrid heterogeneity on cloud production and atmospheric processes in earth system models. Key Points: A parameterized circulation between two otherwise independent columns is described and evaluated for three simulation daysLarge‐eddy simulations (LES) show substantial agreement with the overall circulation model structure proposedWhen implemented, the parameterized circulation yields similar changes in the atmospheric profiles and cloud production to LES [ABSTRACT FROM AUTHOR]

Understanding interactions between low clouds and land surface fluxes is critical to comprehending Earth's energy balance, yet their relationships remain elusive, with discrepancies between observations and modeling. Leveraging long‐term field observations over the Southern Great Plains, this investigation revealed that cloud‐land interactions are closely connected to cloud‐land coupling regimes. Observational evidence supports a dual‐mode interaction: coupled stratiform clouds predominate in low sensible heat scenarios, while coupled cumulus clouds dominate in high sensible heat scenarios. Reanalysis data sets, MERRA‐2 and ERA‐5, obscure this dichotomy owing to a shortfall in representing boundary layer clouds, especially in capturing the initiation of coupled cumulus in high sensible heat scenarios. ERA‐5 demonstrates a relatively closer alignment with observational data, particularly in capturing relationships between cloud frequency and latent heat, markedly outperforming MERRA‐2. Our study underscores the necessity of distinguishing different cloud coupling regimes, essential to the understanding of their interactions for advancing land‐atmosphere interactions. Plain Language Summary: Low cloud interactions with the Earth's surface in the Southern Great Plains are examined to understand the coupling between low clouds and the land surface. Clouds play a major role in Earth's radiation energy balance and therefore the climate system, yet the cloud‐land interaction relationship is complicated and not well‐understood. Based on analyses of long‐term field observations, we find that cloud‐land interactions in this region are closely related to cloud‐land coupling regimes. Observational evidence supports a dual‐mode interaction: coupled stratiform clouds that dominate in low sensible heat states and coupled cumulus that dominate in high sensible heat states. The reanalysis data sets exhibit a common shortcoming of a deficit in boundary layer cloud parameterization, with a poorer representation of the initiation of coupled cumulus especially. This study highlights the importance of understanding the different cloud coupling regimes that interact with the land, which is essential for advancing weather and climate models. Key Points: This study develops a diagnostic approach for untangling cloud‐land relationships across distinct cloud coupling regimesField observations are utilized to assess performances of reanalysis data in representing cloud‐land interaction across different regimesFindings emphasize the importance of differentiating cloud coupling regimes in observational and modeling studies of boundary layer clouds [ABSTRACT FROM AUTHOR]

Southeastern South America (SESA) is a highly productive agricultural region and a hot spot for land‐atmosphere interactions. To evaluate the impact of dry soil moisture anomalies (SMAs) on SESA climate and the sensitivity of the regional climate response to the location of SMAs, we perform three experimental simulations using the Community Earth System Model (CESM) with prescribed dry SMAs over (a) SESA, (b) western SESA, and (c) eastern SESA. The dry SESA and eastern SESA simulations show widespread negative precipitation anomalies. In contrast, the dry western SESA simulation shows positive precipitation anomalies over northeastern Argentina, which are associated with the enhanced southward moisture flux co‐located with the South American low‐level jet exit region. A composite analysis of extremely dry cases over western SESA using reanalysis data agrees with the findings from our CESM experiment. These findings have potential implications for subseasonal forecasting in this region. Plain Language Summary: Large‐scale soil moisture anomalies evolve slowly and can provide an opportunity for better weather forecasting at timescales longer than 2 weeks. Therefore, it is critical to understand the causal physical mechanism and evaluate whether the regional climate response is sensitive to the location of soil moisture anomalies, especially in a productive agricultural region like southeastern South America (SESA). Using a numerical climate model, we simulate the impacts of dry soil over (a) SESA, (b) western SESA, and (c) eastern SESA. The simulations show that dry soil over western SESA can alter regional atmospheric circulation in the proximity of the existing corridor of poleward moisture transport, hence enhancing rainfall over northeastern Argentina. Conversely, dry soil over eastern SESA or the entire SESA region results in less precipitation because enhanced northerly transport is not co‐located with the low‐level wind corridor. Analysis of a dataset that incorporates observations supports our findings from numerical simulations. Key Points: The impact of dry soil moisture anomalies (SMAs) on southeastern South America (SESA) regional climate is sensitive to the location of SMAsThis study provides a causal mechanism linking soil moisture to precipitation via atmospheric circulationWhen western SESA has dry soil, it generates anomalous geostrophic wind, which is co‐located with the low‐level jet exit region [ABSTRACT FROM AUTHOR]

The Maritime Continent (MC) has experienced significant anthropogenic land use changes, mainly deforestation, which has led to local surface warming and marked convergence in the lower troposphere and divergence in the upper. The remote consequences of this deforestation remain unclear and present considerable uncertainties. In this study, we employ a fully coupled climate model and a linear baroclinic model to explore the effects of altered land‐atmosphere interactions due to MC deforestation on high‐latitude climates. Our series of idealized experiments demonstrates that MC deforestation can induce upper‐level diabatic heating. This generates a barotropic Rossby wave that moves poleward, drawing energy from the subtropical jet across the Central to Eastern Pacific regions via eddy‐mean flow interactions. Such interactions amplify the Aleutian Low, promoting the northward transport of warm air, leading to notable warming anomalies. This influx of warmth contributes to sea ice melt, initiating a positive ice‐albedo feedback. A lapse‐rate feedback is also observed in adjacent high‐latitude land areas, amplifying terrestrial warming. These reinforcing feedbacks, combined with the direct temperature transport enabled by the strengthened Aleutian Low, cumulatively result in pronounced high‐latitude warming originally due to the tropical land use changes. Plain Language Summary: Deforestation in the Maritime Continent (MC) has far‐reaching implications, extending to remote climatic areas. This study explores the mechanisms by which deforestation in the MC impacts climates in middle‐to‐high latitudes, especially during the boreal winter. The deforestation effect originates from the MC, traveling along the subtropical jet. The interactions between localized and larger‐scale atmospheric flows play a pivotal role in this transmission. These interactions bolster the Aleutian Low during winter, resulting in the warming of the Bering Sea. This warming results from the direct transport of warm air, facilitated by the intensified Aleutian Low and feedback loops enhanced by the ice albedo feedback and changes in radiations. Our idealized experiments show that MC deforestation can strengthen the Aleutian Low and lead to a warmer Bering Sea in the winter. Key Points: Deforestation in the Maritime Continent triggers a chain reaction in winter Rossby wave dynamics and strengthens the Aleutian LowThe intensified Aleutian Low transports warm air from lower latitudes to the Bering Sea region, resulting in significant low‐level warmingLocal lapse rate changes and ice‐albedo feedback jointly enhance low‐level warming [ABSTRACT FROM AUTHOR]

In recent years, extreme droughts in the United States have increased in frequency and severity, underlining a need to improve our understanding of vegetation resilience and adaptation. Flash droughts are extreme events marked by the rapid dry down of soils due to lack of precipitation, high temperatures, and dry air. These events are also associated with reduced preparation, response, and management time windows before and during drought, exacerbating their detrimental impacts on people and food systems. Improvements in actionable information for flash drought management are informed by atmospheric and land surface processes, including responses and feedbacks from vegetation. Phenologic state, or growth stage, is an important metric for modeling how vegetation modulates land–atmosphere interactions. Reduced stomatal conductance during drought leads to cascading effects on carbon and water fluxes. We investigate how uncertainty in vegetation phenology and stomatal regulation propagates through vegetation responses during drought and non-drought periods by coupling a land surface hydrology model to a predictive phenology model. We assess the role of vegetation in the partitioning of carbon, water, and energy fluxes during flash drought and carry out a comparison against drought and non-drought periods. We selected study sites in Kansas, USA, that were impacted by the flash drought of 2012 and that have AmeriFlux eddy covariance towers which provide ground observations to compare against model estimates. Results show that the compounding effects of reduced precipitation and high vapor pressure deficit (VPD) on vegetation distinguish flash drought from other drought and non-drought periods. High VPD during flash drought shuts down modeled stomatal conductance, resulting in rates of evapotranspiration (ET), gross primary productivity (GPP), and water use efficiency (WUE) that fall below those of average drought conditions. Model estimates of GPP and ET during flash drought decrease to rates similar to what is observed during the winter, indicating that plant function during drought periods is similar to that of dormant months. These results have implications for improving predictions of drought impacts on vegetation. [ABSTRACT FROM AUTHOR]

Land evapotranspiration (ET) plays a crucial role in Earth's water–carbon cycle, and accurately estimating global land ET is vital for advancing our understanding of land–atmosphere interactions. Despite the development of numerous ET products in recent decades, widely used products still possess inherent uncertainties arising from using different forcing inputs and imperfect model parameterizations. Furthermore, the lack of sufficient global in situ observations makes direct evaluation of ET products impractical, impeding their utilization and assimilation. Therefore, establishing a reliable global benchmark dataset and exploring evaluation methodologies for ET products is paramount. This study aims to address these challenges by (1) proposing a collocation-based method that considers non-zero error cross-correlation for merging multi-source data and (2) employing this merging method to generate a long-term daily global ET product at resolutions of 0.1° (2000–2020) and 0.25° (1980–2022), incorporating inputs from ERA5L, FluxCom, PMLv2, GLDAS, and GLEAM. The resulting product is the Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE). CAMELE exhibits promising performance across various vegetation coverage types, as validated against in situ observations. The evaluation process yielded Pearson correlation coefficients (R) of 0.63 and 0.65, root-mean-square errors (RMSEs) of 0.81 and 0.73 mm d -1 , unbiased root-mean-square errors (ubRMSEs) of 1.20 and 1.04 mm d -1 , mean absolute errors (MAEs) of 0.81 and 0.73 mm d -1 , and Kling–Gupta efficiencies (KGEs) of 0.60 and 0.65 on average at resolutions of 0.1 and 0.25°, respectively. In addition, comparisons indicate that CAMELE can effectively characterize the multiyear linear trend, mean average, and extreme values of ET. However, it exhibits a tendency to overestimate seasonality. In summary, we propose a reliable set of ET data that can aid in understanding the variations in the water cycle and has the potential to serve as a benchmark for various applications. The dataset is publicly available at 10.5281/zenodo.8047038 (Li et al., 2023b). [ABSTRACT FROM AUTHOR]

This research examines the suitability of four land surface schemes (LSSs) in Weather Research and Forecasting (WRF) model over different West Africa's (WA) climatological zones, namely Guinea, Savanna, and Sahel. The four LSSs include Noah, Noah (Multiparameterization) MP, Noah MP with the groundwater option (Noah GW), and Community Land Model Version 4 (CLM4). A 3-month simulation was carried out with each LSS during July–September 2012. Temperature and dew point temperature (Dpt) were evaluated using the ERA5 dataset, while precipitation was evaluated using the TRMM product. Based on the three variables, CLM4 is most suitable for the Guinea zone; Noah, Noah MP, and CLM4 perform equally well for Savanna; and Noah MP is most suitable for the Sahel zone. In general, and over all zones, Noah MP is most suitable for precipitation simulation, while CLM4 is the best for dew point temperature as Noah MP and Noah GW are equally suitable for 2 m temperature. Noah GW overestimates surface moisture, altering surface fluxes, increasing evaporative fraction, and increasing convective activities (especially in semi-arid areas), which led to a significant bias in temperature and precipitation. Also, over the Savanna and Sahel zone, a strong African Easterly Jet (AEJ) with a weak Tropical Easterly Jet (TEJ) in Noah led to lower rainfall. In contrast, weak AEJ with strong TEJ in Noah MP and Noah GW caused higher rainfall. Further studies would be to evaluate the sensitivity of each LSS under different initial conditions, and their land-atmosphere interactions strength over the zones. [ABSTRACT FROM AUTHOR]

We present a variable‐resolution global chemistry‐climate model (AM4VR) developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) for research at the nexus of US climate and air quality extremes. AM4VR has a horizontal resolution of 13 km over the US, allowing it to resolve urban‐to‐rural chemical regimes, mesoscale convective systems, and land‐surface heterogeneity. With the resolution gradually reducing to 100 km over the Indian Ocean, we achieve multi‐decadal simulations driven by observed sea surface temperatures at 50% of the computational cost for a 25‐km uniform‐resolution grid. In contrast with GFDL's AM4.1 contributing to the sixth Coupled Model Intercomparison Project at 100 km resolution, AM4VR features much improved US climate mean patterns and variability. In particular, AM4VR shows improved representation of: precipitation seasonal‐to‐diurnal cycles and extremes, notably reducing the central US dry‐and‐warm bias; western US snowpack and summer drought, with implications for wildfires; and the North American monsoon, affecting dust storms. AM4VR exhibits excellent representation of winter precipitation, summer drought, and air pollution meteorology in California with complex terrain, enabling skillful prediction of both extreme summer ozone pollution and winter haze events in the Central Valley. AM4VR also provides vast improvements in the process‐level representations of biogenic volatile organic compound emissions, interactive dust emissions from land, and removal of air pollutants by terrestrial ecosystems. We highlight the value of increased model resolution in representing climate–air quality interactions through land‐biosphere feedbacks. AM4VR offers a novel opportunity to study global dimensions to US air quality, especially the role of Earth system feedbacks in a changing climate. Plain Language Summary: NOAA's Geophysical Fluid Dynamics Laboratory has developed a new variable‐resolution global chemistry‐climate model for research at the nexus of US climate and air quality extremes. In contrast with the global models contributing to the latest Intergovernmental Panel on Climate Change Report, this model features more than 10 times finer spatial resolution over the contiguous US, allowing it to better resolve cities, mountain valleys, thunderstorms, and urban‐to‐rural air quality variations. This model features much improved representation of regional rainfall extremes, drought, and severe air pollution events in diverse US air basins, including California. Notably, this model reduces the central US dry‐and‐warm bias that has persisted in many generations of climate models. As global climate change leads to more hot and dry weather, the resulting droughts are creating dust‐prone bare lands or stressing plants, making them less able to remove ozone pollution from the air. These effects are included in this model, with particular focus on integrating physical, chemical, and biological components at high spatial resolution to understand Earth system feedbacks to US air quality extremes in a changing climate. Key Points: A new variable‐resolution global chemistry‐climate model has been developed for research at the nexus of US climate and air quality extremesThis model unifies component advances in physics, chemistry and land‐atmosphere interactions within a seamless variable‐resolution frameworkThis model features much improved US regional precipitation, drought, and air quality extremes compared to previous models [ABSTRACT FROM AUTHOR]

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