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Increasing vapor pressure deficit (VPD) increases atmospheric demand for water. While increased evapotranspiration (ET) in response to increased atmospheric demand seems intuitive, plants are capable of reducing ET in response to increased VPD by closing their stomata. We examine which effect dominates the response to increasing VPD: atmospheric demand and increases in ET or plant response (stomata closure) and decreases in ET. We use Penman‐Monteith, combined with semiempirical optimal stomatal regulation theory and underlying water use efficiency, to develop a theoretical framework for assessing ET response to VPD. The theory suggests that depending on the environment and plant characteristics, ET response to increasing VPD can vary from strongly decreasing to increasing, highlighting the diversity of plant water regulation strategies. The ET response varies due to (1) climate, with tropical and temperate climates more likely to exhibit a positive ET response to increasing VPD than boreal and arctic climates; (2) photosynthesis strategy, with C3 plants more likely to exhibit a positive ET response than C4 plants; and (3) plant type, with crops more likely to exhibit a positive ET response, and shrubs and gymniosperm trees more likely to exhibit a negative ET response. These results, derived from previous literature connecting plant parameters to plant and climate characteristics, highlight the utility of our simplified framework for understanding complex land‐atmosphere systems in terms of idealized scenarios in which ET responds to VPD only. This response is otherwise challenging to assess in an environment where many processes coevolve together. Plain Language Summary Plants can sense increasing dryness in the air and close up the pores on their leaves, preventing water loss. However, drier air also naturally demands more water from the land surface. Here we develop a simplified theory for when land surface water loss increases (atmospheric demand dominates) or decreases (plant response dominates) in response to increased dryness in the air. This theory provides intuition for how ecosystems regulate water in response to changes in atmospheric dryness. According to the theory, ecosystems are capable of broad range of behavior in response to increased atmospheric dryness, from strongly reducing water loss to allowing large increases in water loss. Ecosystem behavior depends both on environmental conditions and plant type. Key Points We derive a simplified analytical model for ecosystem‐scale evapotranspiration response to changes in vapor pressure deficit Ecosystems exhibit a range of behavior, from reductions to increases in evapotransipration, in response to increasing vapor pressure deficit The choice of stomatal conductance model fundamentally alters the relationship between evapotranspiration and vapor pressure deficit

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 State The eclipse resulted in significant cooling and moistening near the surface and in the boundary layer, leading to a surface inversion layer It also weakened surface winds, turbulent mixing, heat fluxes, but caused a robust rise in near‐surface CO2 concentrations

The feedback of topsoil moisture (SM) content on convective clouds and precipitation is not well understood and represented in the current generation of weather and climate models. Here, we use functional decomposition of satellite‐derived SM and cloud vertical profiles (CVP) to quantify the relationship between SM and the vertical distribution of cloud water in the central US. High‐dimensional model representation is used to disentangle the contributions of SM and other land‐surface and atmospheric variables to the CVP. Results show that the sign and strength of the SM‐cloud‐precipitation feedback varies with cloud height and time lag and displays a large spatial variability. Positive anomalies in antecedent 7‐hr SM and land‐surface temperature enhance cloud reflectivity up to 4 dBZ in the lower atmosphere about 1–3 km above the surface. Our approach presents new insights into the SM‐cloud‐precipitation feedback and aids in the diagnosis of land‐atmosphere interactions simulated by weather and climate models. Plain Language Summary This paper focuses on the observational analysis of how soil moisture (SM) influences the vertical cloud‐water distribution throughout the day. By analyzing data from Soil Moisture Active Passive (SMAP) and Dual‐frequency Precipitation Radar (DPR), we gain insights into how antecedent SM affects cloud‐water reflectivity at different heights in the lower atmosphere. Our data‐driven approach produces spatial maps of SM's contribution to cloud reflectivity and rainfall in the central US as a function of cloud height and SM time lag. Our method will help diagnose weather and climate model biases. Key Points Functional decomposition of satellite‐measured soil moisture (SM) and cloud vertical profiles (CVP) provides insights into SM‐CVP feedbacks The sign and strength of this feedback varies with height, time lag, and geographic location, in agreement with qualitative studies Our approach can be used to diagnose weather and climate model biases as they relate to land‐atmosphere coupling

Land Surface Models (LSMs) are essential to reproduce biophysical processes modulated by vegetation and to predict the future evolution of the land‐climate system. To assess the performance of an ensemble of LSMs (JSBACH, JULES, ORCHIDEE, CLM, and LPJ‐GUESS) a consistent set of land surface energy fluxes and leaf area index (LAI) has been generated. Relationships of interannual variations of modeled surface fluxes and LAI changes have been analyzed at global scale across climatological gradients and compared with those obtained from satellite‐based products. Model‐specific strengths and deficiencies were diagnosed for tree and grass biomes. Results show that the responses of grasses are generally well represented in models with respect to the observed interplay between turbulent fluxes and LAI, increasing the confidence on how the LAI‐dependent partition of net radiation into latent and sensible heat are simulated. On the contrary, modeled forest responses are characterized by systematic bias in the relation between the year‐to‐year variability in LAI and net radiation in cold and temperate climates, ultimately affecting the amount of absorbed radiation due to LAI‐related effects on surface albedo. In addition, for tree biomes, the relationships between LAI and turbulent fluxes appear to contradict the experimental evidences. The dominance of the transpiration‐driven over the observed albedo‐driven effects might suggest that LSMs have the incorrect balance of these two processes. Such mismatches shed light on the limitations of our current understanding and process representation of the vegetation control on the surface energy balance and help to identify critical areas for model improvement. Key Points The covariability of land biophysics and changes in vegetation density predicted by Land Surface Models is compared with satellite data Biophysical properties of vegetation are explored across climatological gradients of temperature and precipitation Model‐specific and systematic strengths and deficiencies are diagnosed separately for tree and grass biomes

Cold pools influence convective initiation and organization, dust lofting, and boundary layer properties, but little is known about their interactions with the land surface, particularly in dry continental environments. In this study, two‐way cold pool‐land surface interactions are investigated using high‐resolution idealized simulations of an isolated, transient cold pool evolving in a dry convective boundary layer. Results using a fully interactive land surface demonstrate that sensible heat fluxes are suppressed at the center of the cold pool but enhanced at the edge due to the spatial patterns of land surface cooling and the air temperature and wind speed perturbations. This leads to cold pool dissipation from the edge inward. Latent heat fluxes are primarily suppressed within the cold pool, and the magnitude of this suppression is controlled by competition between atmospheric and land surface effects. By comparing the fully interactive land surface simulation to a simulation with imposed surface fluxes, the land surface‐cold pool feedbacks are shown to reduce the cold pool lifetime, extent, and intensity by up to 50% and influence the pattern of boundary layer turbulent kinetic energy recovery, which have significant implications for cold pool‐induced convective initiation. Key Points An isolated cold pool evolving in a dry convective boundary layer is simulated at high resolution Sensible heat fluxes are enhanced at the cold pool's edge and suppressed in the center, leading to dissipation from the edge inward Land surface interactions reduce the cold pool lifetime, area, and intensity by up to 50% and affect the boundary layer evolution

There has been a long debate on the extent to which precipitation relies on terrestrial evaporation (moisture recycling). In the past, most research focused on moisture recycling within a certain region only. This study makes use of new definitions of moisture recycling to study the complete process of continental moisture feedback. Global maps are presented identifying regions that rely heavily on recycled moisture as well as those that are supplying the moisture. An accounting procedure based on ERA‐Interim reanalysis data is used to calculate moisture recycling ratios. It is computed that, on average, 40% of the terrestrial precipitation originates from land evaporation and that 57% of all terrestrial evaporation returns as precipitation over land. Moisture evaporating from the Eurasian continent is responsible for 80% of China's water resources. In South America, the Río de la Plata basin depends on evaporation from the Amazon forest for 70% of its water resources. The main source of rainfall in the Congo basin is moisture evaporated over East Africa, particularly the Great Lakes region. The Congo basin in its turn is a major source of moisture for rainfall in the Sahel. Furthermore, it is demonstrated that due to the local orography, local moisture recycling is a key process near the Andes and the Tibetan Plateau. Overall, this paper demonstrates the important role of global wind patterns, topography and land cover in continental moisture recycling patterns and the distribution of global water resources.

Diverse urban‐induced rainfall anomalies across different cities highlight the need for additional insights into land‐atmosphere interactions over complex urban environments. Based on empirical analyses of 144 warm‐season storms and high‐resolution numerical simulations over Nanjing, China, we show divergent patterns of urban‐induced rainfall anomalies for storms with contrasting synoptic conditions, despite of rainfall enhancement over downtown from a climatological perspective. We propose two simple gage‐based metrics to characterize both the thermal and turbulent conditions in pre‐storm environment, and classify storms into different groups. Our results show that elevated rainfall magnitudes and heavy rainfall frequency are equally expected in either downtown or suburb regions (upwind or downwind). This is mainly dictated by the relative dominance of urban‐induced thermal perturbations and mechanical turbulence (i.e., related to surface roughness) under different synoptic conditions. We develop four paradigms of urban rainfall modification, and thus provide a predictive understanding of rainfall anomalies in urban environments. Plain Language Summary The diverse characteristics of urban rainfall anomalies from previous studies highlight intricate processes about the land‐atmosphere interactions and urban rainfall modification. We conduct empirical analyses of in situ rainfall observations and numerical analyses of high‐resolution Weather Research and Forecasting model simulations during the warm season months for 2014–2017 over and around Nanjing, China. Although the urban‐induced temperature anomalies dictate enhanced rainfall over downtown from the climatological perspective, positive rainfall anomalies for storms with different pre‐storm environments are observed over suburbs, including different positions over upwind and downwind regions. We categorize 144 storms into different groups based on two independent indices characterizing urban‐induced thermal perturbations and mechanical turbulence prior to each storm. Our results emphasize that the relative dominance of thermal and turbulent conditions in pre‐storm environment plays a critical role in determining the divergent urban rainfall anomalies. An in‐depth understanding of urban rainfall modification is provided to contribute to hydrometeorological engineering design and fine‐scale flood forecasting. Key Points We show divergent urban‐induced rainfall anomalies with contrasting thermal and turbulent conditions in the pre‐storm environments The divergence is dictated by the relative dominance of urban‐induced thermal perturbations and mechanical turbulence We provide four paradigms of urban rainfall modification that can be transferable to other worldwide cities

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 precipitation Land parameters influence climate predominantly through changing evapotranspiration rather than through other mechanisms Warming driven by land processes activates different atmospheric feedbacks than radiatively‐driven warming

This study assesses the ecohydrological effects of recent meteorological droughts in tropical South America based on multiple sources of data, and investigates the possible mechanisms underlying the drought response and recovery of different ecohydrological systems. Soil drought response and recovery lag behind the meteorological drought, with delays longer in the dry region (Nordeste) than in the wet region (Amazonia), and longer in deep soil than in shallow soil. Evapotranspiration (ET) and vegetation in Nordeste are limited by water under normal conditions and decrease promptly in response to the onset of shallow soil drought. In most of the Amazon where water is normally abundant, ET and vegetation indices follow an increase-then-decrease pattern, increase at the drought onset due to increased sunshine and decrease when the drought is severe enough to cause a shift from an energy-limited regime to a water-limited regime. After the demise of meteorological droughts, ET and vegetation rapidly recover in Nordeste with the replenishment of shallow soil moisture (SM), but take longer to recover in southern Amazon due to their dependence on deep SM storage. Following severe droughts, the negative anomalies of ET and vegetation indices in southern Amazon tend to persist well beyond the end of soil drought, indicating drought-induced forest mortality that is slow to recover from. Findings from this study may have implications on the possibility of a future forest dieback as drought is projected to become more frequent and more severe in a warmer climate.

A growing constellation of satellites is providing near‐global coverage of column‐averaged CO2 observations. Launched in 2019, NASA’s OCO‐3 instrument is set to provide XCO2 observations at a high spatial and temporal resolution for regional domains (100 × 100 km). The atmospheric column version of the Stochastic Time‐Inverted Lagrangian Transport (X‐STILT) model is an established method of determining the influence of upwind sources on column measurements of the atmosphere, providing a means of analysis for current OCO‐3 observations and future space‐based column‐observing missions. However, OCO‐3 is expected to provide hundreds of soundings per targeted observation, straining this already computationally intensive technique. This work proposes a novel scheme to be used with the X‐STILT model to generate upwind influence footprints with less computational expense. The method uses X‐STILT generated influence footprints from a key subset of OCO‐3 soundings. A nonlinear weighted averaging is applied to these footprints to construct additional footprints for the remaining soundings. The effects of subset selection, meteorological data, and topography are investigated for two test sites: Los Angeles, California, and Salt Lake City, Utah. The computational time required to model the source sensitivities for OCO‐3 interpretation was reduced by 62% and 78% with errors smaller than other previously acknowledged uncertainties in the modeling system (OCO‐3 retrieval error, atmospheric transport error, prior emissions error, etc.). Limitations and future applications for future CO2 missions are also discussed. Plain Language Summary Several satellites are providing near‐global observations of Earth’s atmospheric carbon dioxide (CO2). One example is NASA’s new OCO‐3 instrument which is set to provide spatially dense CO2 measurements over targeted areas. Measurements may contain signals of emissions from cities and power plants. One method of finding the source(s) of observed CO2 is using a Lagrangian particle dispersion model such as X‐STILT. This model takes OCO‐3 measurements and runs atmospheric transport backwards in time to trace out the sources affecting these measurements. However, OCO‐3 and future satellite missions will yield many measurements, significantly increasing the computational cost for X‐STILT and other similar models. This paper presents an algorithm that will reduce the computational effort for X‐STILT by tracing the sources of only a subset of OCO‐3 measurements and then infers (interpolates) the rest. The following two questions are addressed: (1) How many OCO‐3 measurements does X‐STILT need for the interpolations to be accurate? (2) How do meteorology and topography affect the accuracy of the interpolations? Applying the algorithm on simulated OCO‐3 data at two test cities—Los Angeles and Salt Lake City—the time required to elucidate the CO2 sources was reduced by 62% and 78%, respectively. Key Points Determining sources of spatially dense XCO2 observations with LPDM techniques can become time intensive and strain computational resources Presented in this work is an interpolation scheme that eases the computational burden of spatially dense XCO2 source determination studies Evaluating the efficiency of this interpolation scheme revealed reductions of >50% in computational time at two testing locations