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Land-atmosphere coupling is a key factor for understanding the mean hydroclimatological conditions in South America. In this study, we analyzed how land-atmosphere coupling modulates the aridity conditions in the South American continent through experiments performed with a regional climate model. We assessed changes in precipitation (P), potential evaporation (PET), aridity index (ɸ = PET / P) and vapor pressure deficit (VPD) in experiments with and without soil moisture - atmosphere coupling. Additionally, we analyzed how air dry enthalpy (Hd), moist enthalpy (Hw) and total enthalpy (Ht) change between the coupled and uncoupled experiments, which help to identify patterns in aridity conditions. When analyzing ɸ, we found opposite responses in central-eastern Brazil and southeastern South America (SESA), the two main hotspots of land-atmosphere coupling. On the other hand, a widespread amplification of VPD is found. Both VPD and enthalpy are locally driven by changes in temperature and water vapor content and reflect wetter conditions in central-eastern Brazil and drier conditions in SESA. Changes in regional circulation, described in previous studies, increase moisture convergence in Brazil and help to explain the response of each individual metric when coupling is enabled. [ABSTRACT FROM AUTHOR]

Climate models are useful tools for monthly to decadal prediction of the evolution of climate. This study assesses how CMIP6 models represent soil moisture-latent heat regimes and coupling processes between the land and atmosphere. Metrics considered are terrestrial and atmospheric coupling indices to show the nature and strength of the coupling over Africa, focusing on the March to May (MAM) and June to August (JJA) seasons over East, Central, and West Africa. Characterization of the annual cycle indicates that model biases are highest during the peak of the rainfall season and least during the dry season, while soil moisture biases correspond with rainfall. Models show appreciable sensitivity to regional characteristics; there was model consensus in representing East Africa and the Sahel as regions of limited soil moisture, while major differences were noted in the wet regime over Central Africa. Most CMIP6 models tend to overestimate the strength of the terrestrial and atmospheric coupling pathways over East and Southern Africa. Inter-model differences in coupling indices could be traced to their inter-annual variability rather than the mean biases of the variables considered. These results encourage further advancement of land surface schemes in the next generation of climate models for a better representation of climate over Africa. [ABSTRACT FROM AUTHOR]

Southeast Asia's Low-Latitude Highland （LLH） is one of the hotspots of land-atmosphere coupling in the world， with its land-atmosphere interaction has significant impacts on climate， hydrology and environment.This study employs Uniform Design （UD） method to conduct 48 groups of simulation using different parameterization schemes of Weather Research and Forecasting （WRF） model.By optimizing the parameterization schemes， the variables related to land-atmosphere interaction in this area are simulated and evaluated.The findings are as follows： （1） The ensemble of 48 simulation groups demonstrates good performance for near-surface air temperature， near-surface specific humidity， surface downward longwave radiation， surface upward longwave radiation and surface soil temperature， with average Taylor Skill Score （TSS） values exceeding 0.8； for near-surface wind speed， precipitation， surface sensible heat flux， surface latent heat flux， surface downward shortwave radiation and surface upward shortwave radiation， the ensemble simulation can adequately capture the characteristics of these variables， with average TSS values ranging between 0.4 and 0.8； but for surface soil moisture， the ensemble simulation performance is poor， with average TSS values less than 0.4.The variability among different simulation groups is minimal for near-surface wind speed， precipitation， surface latent heat flux， surface downward shortwave radiation， surface upward shortwave radiation， surface soil temperature and surface soil moisture （TSS range < 0.2）； but for the surface sensible heat flux， the variability among different simulation groups is significant （TSS range > 0.3）.（2） The optimal parameterization schemes based on equal-weighted averaged TSS can enhance simulation accuracy for near-surface air temperature， near-surface specific humidity， surface downward longwave radiation， surface upward longwave radiation and surface soil temperature， with correlation coefficients exceeding 0.9 and minor deviations from reference values.However， this optimization could not significantly improve simulation performance for near-surface wind speed， precipitation， surface sensible heat flux， surface latent heat flux and surface soil moisture， where deviations remain substantial.（3） The optimal parameterization schemes can reasonably capture the spatial and temporal features of land-atmosphere coupling， showing strong coupling strength in northeast and southwest LLH， with temporal correlation coefficient greater than 0.9.Nonetheless， the simulated values of coupling strength is generally weaker than the reference values， primarily due to poor simulation performance of surface latent heat flux and surface downward shortwave radiation.

The 2022 Yangtze mega-flash drought is characterized by strong intensity and rapid development both in time and space, accompanied by a persistent anticyclonic circulation anomaly. However, the causes of the extreme event remain elusive given the multiscale nature of drought. Here we presented a brief overview for the oceanic and terrestrial causes of the mega-flash drought during the summer of 2022, and estimated the risk in a changing climate. Using the soil moisture percentile as the drought index, it was found that the drought expanded to the entire Yangtze River basin within two months, with 80% of basin under severe drought conditions at the end of August. Both the intensity and onset speed of the 2022 mega-flash drought were ranked as the first during the past 62 years, with return periods of 86 and 259 years, respectively. The results of composite analysis showed that the spring La Niña can facilitate the abrupt change from a wet/normal condition in May–June to drought in July–August over the Yangtze River basin, which was beneficial for the increase of flash drought intensity and onset speed in 2022. The analysis through the linear regression also indicated that the unprecedented intensity was associated with the negative phase of the Pacific Decadal Oscillation. Quantified by a coupling strength index for soil moisture and vapor pressure deficit, it was found that there was a strong land-atmosphere coupling over the Yangtze River basin during July–August 2022. The attribution by using CMIP6 climate models suggested that land-atmosphere coupling increased the risks of flash drought intensity and onset speed like 2022 by 61%±6% and 64%±7% under natural climate forcings, and the synergy of coupling and anthropogenic climate change would increase the risks by 75%±22% and 85%±12%. Our findings emphasized the role of land-atmosphere coupling combined with anthropogenic climate change in intensifying flash droughts. [ABSTRACT FROM AUTHOR]

Irrigation, a significant human activity, impacts regional water cycles and climate. However, its effect on the diurnal cycle of precipitation (DCP) and the associated water cycle remains uncertain. In this study, we investigate the influence of irrigation in the North China Plain on precipitation and water cycle by utilizing a regional weather model with a water vapor tracing scheme. Our findings indicate that, during the afternoon, irrigation in the plain area generates a cooling effect, intensifying the thermal contrast between the mountains and adjacent plain and strengthening the mountain-plains solenoid. This leads to an increase in the amount and frequency of afternoon precipitation in the northwestern mountains, while the southeastern plains experience a decrease. This cooling and moistening effects of irrigation inhibit the development of the planetary boundary layer, posing a challenge for air to reach the lifting condensation level, which hinders cloud and precipitation formation and delays the peak of afternoon precipitation. Although irrigation's direct contribution to total precipitation does not exceed 6%, it is responsible for approximately 30% of the total precipitation changes induced by irrigation. The precipitation recycling ratio in the plain is elevated under irrigation, especially in the mountains, likely due to the enhanced moisture and altered local circulations. Irrigation's positive impact on precipitation is more pronounced under dry soil conditions, while under wet soil conditions, this positive impact diminishes and may even result in a negative impact. [ABSTRACT FROM AUTHOR]

Soil moisture is an essential climate variable of the Earth system. Understanding its spatiotemporal dynamics is essential for predicting weather patterns and climate variability, monitoring and mitigating the effects and occurrence of droughts and floods, improving irrigation in agricultural areas, and sustainably managing water resources. Here we review in depth how soils can remember information on soil moisture anomalies over time, as embedded in the concept of soil moisture memory (SMM). We explain the mechanisms underlying SMM and explore its external and internal drivers; we also discuss the impacts of SMM on different land surface processes, focusing on soil‐plant‐atmosphere coupling. We explore the spatiotemporal variability, seasonality, locality, and depth‐dependence of SMM and provide insights into both improving its characterization in land surface models and using satellite observations to quantify it. Finally, we offer guidance for further research on SMM. Plain Language Summary: Our review paper takes an in‐depth look at soil moisture memory, which is how soil records its moisture history over time and space. Analogous to human psychology, which seeks to understand how a person's/society's memory influences his/her present and future behavior, understanding soil moisture memory encourages consideration of how such memory determines present state and might determine future behavior of soils exposed to environmental disturbances. Soil moisture memory can be affected by a variety of factors, both external (e.g., weather extremes) and internal (soil's unique properties). It affects everything from the air to the way our landscapes respond to disasters like droughts, wildfires, and floods. We also studied how this phenomenon affects the balance of water and energy in our environment, the health of our plants, and even how it communicates with the atmosphere. We show how it can change depending on where you are on the planet, the time of year, and how deep you dig into the soil. We offer scientists insights into how weather and land surface models can become more accurate by accounting for soil moisture memory. Its understanding not only helps us predict and manage our environment, but also provides opportunities for exciting scientific discoveries. Key Points: Atmospheric forcings, land use and management, and soil processes and mechanisms explain how and why soil moisture memory emerges in ecosystemsNonlocality of moisture memory, its spread across different regions, and its interaction with large‐scale climate phenomena are underexploredFurther advances in land surface models and closer integration of model simulations and observations are needed to better characterize moisture memory [ABSTRACT FROM AUTHOR]

Severe compound drought-heatwave events were observed over three regions of the Contiguous United States (CONUS), Northwest (NW), Great Plains (GP), and Northeast (NE) regions, during July and August 2022. In this study, we have found that the developments of these drought-heatwave events were shaped by different land-atmosphere coupling behaviors which are associated with water and energy limitation regimes in these regions. In the NW and GP regions, the surface soil moisture (SM) and evapotranspiration (ET) were coupled through water-limited processes. Heatwaves in these two regions were affected by the decrease of ET and the available SM due to the precipitation deficit. This type of land-atmosphere coupling was especially prominent in the GP. In the NE region, the heatwave governed ET through the increase of potential ET (PET) based on energy-limited coupling, which played a crucial role in the development of drought.The impacts of the different land-atmosphere coupling behaviors on the predictability of the 13-km Geophysical Fluid Dynamics Laboratory (GFDL) System for High-resolution prediction on Earth-to-Local Domains (SHiELD) were also investigated by checking its 10-day forecasts during the same period. The analysis was particularly focused on the GP and NE regions, where different land-atmosphere coupling behaviors were observed. The model's warm bias in the GP region was associated with the overestimated net radiation, and the bias was further amplified through the water-limited coupling. In the NE region, the PET-related variables, including surface air temperature, influenced the predictability of drought onset by limiting ET through the energy-limited coupling. Based on our findings, this study highlights the crucial role of land-atmosphere coupling behaviors and provides a scientific strategy for enhancing the model predictability of compound drought-heatwaves.

Compound flash drought and heatwave (FDHW) events have garnered increasing amounts of attention due to their substantial impacts on agriculture, water resources, and public health. However, studies on their intensity and classification in China are limited. In this study, we classified FDHW events in China from 1980 to 2022 using a classification framework designed to address regional patterns and explore their characteristics further. The results showed that FDHW events in northern China mostly occurred in early to mid-summer, whereas in southern China, excluding the Southwest River Basin, they occurred predominantly in mid to late summer. Furthermore, the spatial coverage of FDHW events across China significantly expanded. From 1980 to 2022, FDHW events in China evolved toward higher intensities and longer durations. This trend was especially notable in the Jiang-Huai River Basin, the main grain-producing region and a densely populated area of China. From the perspective of land‒atmosphere coupling, the amplifying effect of flash droughts and high temperatures increased with their intensity. When high temperatures reached the extreme level, the amplification effect on flash droughts was evident: 35.76% from the water deficit perspective and 38.82% from the soil moisture perspective. During extreme flash droughts, the amplification effect on high temperatures intensified: 41.51% from the water deficit perspective and 45.06% from the soil moisture perspective. The Southwest River Basin became a hotspot for the interaction between flash droughts and high temperatures. This study has implications for developing science-based policies to tackle risks in the water, energy and food sectors in China.

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]

Accurate soil moisture initial conditions in dynamical subseasonal forecast systems are known to improve the temperature forecast skill regionally, through more realistic water and energy fluxes at the land-atmosphere interface. Recently, results from a multi-model coordinated experiment have provided evidence of the primal contribution of the initial surface and subsurface soil temperature over the Tibetan Plateau for capturing a hemispheric scale atmopsheric teleconnection leading to improved subseasonal forecasts. Yet, both the soil temperature and water content are key components of the soil enthalpy and we hypothesize that properly initializing one of them without modifying the other in a consistent manner can alter the soil thermal equilibrium, thereby potentially reducing the benefit of land initial conditions on subsequent atmospheric forecasts. This study builds on the protocol of the above-mentioned multi-model experiment, by testing three different land initialization strategies in an Earth system model. Results of this pilot study suggest that a better mass and energy balance in land initial conditions of the Tibetan Plateau triggers a wave train which propagates through the northern hemisphere mid-latitudes, resulting in an improved large scale circulation and temperature anomalies over multiple regions of the globe. While this study is based on a single case, it strongly advocates for enhanced attention towards preserving the soil energy equilibrium at initialization to make the most of land as a driver of atmospheric extended-range predictability. [ABSTRACT FROM AUTHOR]

The debate on the sign of the soil moisture-precipitation feedback remains open. On the one hand, studies using global coarse-resolution climate models have found strong positive feedback. However, such models cannot represent convection explicitly. On the other hand, studies using km-scale regional climate models and explicit convection have reported negative feedback. Yet, the large-scale circulation is prescribed in such models. This study revisits the soil moisture-precipitation feedback using global, coupled simulations conducted for 1 y with explicit convection and compares the results to coarse-resolution simulations with parameterized convection. We find significant differences in a majority of points with feedback that is weaker and dominantly negative with explicit convection. The model with explicit convection is more often in a wet regime and prefers the triggering of convection over dry soil in the presence of soil moisture heterogeneity, in contrast to the coarse-resolution model. Further analysis indicates that the feedback not only between soil moisture and evapotranspiration but also between evapotranspiration and precipitation is weaker, in better agreement with observations. Our findings suggest that coarse-resolution models may not be well suited to study aspects of climate change over land such as changes in droughts and heatwaves. [ABSTRACT FROM AUTHOR]

Drying soil has been conducive to a high frequency of extreme high-temperature events over many regions worldwide in recent decades. However, changes in the intraseasonal variability of soil moisture can also influence the likelihood of extremely high temperatures. Although previous investigators have examined the association between extremely high temperatures and large-scale atmospheric circulation variability, the role of land–atmosphere coupling dominated by soil moisture variability in extremely high temperatures, particularly over the Eurasian continent, is not well understood. In this study, on the basis of the Land Surface, Snow, and Soil Moisture Model Intercomparison Project, we found that land–atmosphere feedback amplified the variability of soil moisture in most regions of Eurasia during summer from 1980 to 2014. This amplification of soil moisture variability is closely correlated with more intensive intraseasonal variability of surface air temperature and more frequent occurrences of extreme high-temperature events, particularly in Europe, Siberia, Northeast Asia, and the Indochina Peninsula. This correlation implies that increasing the intraseasonal variability of soil moisture results in a high likelihood of heat extremes during summer in most parts of Eurasia except Asian desert areas. On the intraseasonal timescale, the land–atmosphere coupling increases the variability of surface sensible heat flux and net long-wave radiation heating the atmosphere by intensifying the soil moisture variability, thus amplifying the variability of surface air temperature and enhancing the extreme high-temperature days. This finding demonstrates the importance of changes in intraseasonal soil moisture variability for the increasing likelihood of heat extremes in summer.

The generalized complementary relationship (CR) rooted on land–atmosphere coupling has been widely used to study evaporation over various ecosystems. Evaporation estimation models based on CR usually contain two parameters. The first one controls the shape of CR, and the second one is related to potential evaporation (Epo). Previous studies have found substantial variations in these parameters, but how they are affected by land surface and/or the atmosphere under conditions with varying land–atmosphere coupling remains unclear. In this study, the land and atmospheric controls for the generalized CR were comparatively investigated focusing on the two parameters of the sigmoid type function by using the data of 24 grassland and 19 forest flux sites and by considering the contrasting land–atmosphere coupling strength. The strong coupling to the outer atmosphere at the forest sites resulted in a large variation in the Epo‐related parameter and its significant correlation with the mean relative humidity. The shape parameter had a large variation at the grassland sites due to the weak coupling with the outer atmosphere and was significantly correlated with the mean soil water content. The simple parameterizations for the two parameters based on the correlations improved the predictions of monthly actual evaporation compared with the parameterizations for the fixed parameters corresponding to ecosystem types. The effects of land surface wetness and local advections on the generalized CR over grasslands and forests were also discussed respectively. Key Points: Generalized CR is differently controlled by the land surface and atmosphere over grasslands and forests due to land–atmosphere couplingThe shape parameter exhibited a large variation at grassland sites and was significantly correlated with long‐term land surface wetnessThe Epo‐related parameter showed a large variation at the forest sites and was significantly correlated with the mean relative humidity [ABSTRACT FROM AUTHOR]

Abstract 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.

The Tibetan Plateau (TP) impacts local and remote atmospheric circulations, wherein it mechanically and thermally affects air masses or airflows. Moreover, the TP provides a key channel for substance transport between the troposphere and the stratosphere. This study reviews recent advances in research regarding land–atmosphere coupling processes over the TP. The TP experiences climate warming and wetting. Climate warming has caused glacier retreat, permafrost degradation, and a general increase in vegetation density, while climate wetting has led to a significant increase in the number of major lakes, primarily through increased precipitation. Local and regional climates are affected by interactions between the land and the atmosphere. Namely, the TP drives surface pollutants to the upper troposphere in an Asian summer monsoon (ASM) anticyclone circulation, before spreading to the lower stratosphere. Further, the thermal forcing of the TP plays an essential role in the ASM. TP forcing can modulate hemispheric‐scale atmospheric circulations across all seasons. The TP interacts with remote oceans through a forced atmospheric response and is substantially affected by the evolution of the Earth's climate via promoting Atlantic meridional overturning circulation and eliminating Pacific meridional overturning circulation. The extensive influence of the TP is facilitated by its coupling with the ASM in the summer; whereas its winter influence on climate mainly occurs through Rossby waves. The observed increasing trends of temperature and precipitation over the TP are projected to continue throughout the 21st century. Plain Language Summary: The impact of the Tibetan Plateau on atmospheric circulation and the climate has been of great interest to the scientific community. Here, we review the literature on the mechanisms of its climate effects, to provide an overview of recent progress in the field and directives for future research. Key Points: We summarize recent advances in climate change research and observations of the land–atmosphere coupling processes over the Tibetan Plateau (TP)We highlight the impact of the TP on the global climate, including atmospheric species transport, circulation, and air‐sea interactionsWe conclude projected future climate changes over the TP and discuss future research directives for assessing the TP's global climate impact [ABSTRACT FROM AUTHOR]

The representation of land–atmosphere coupling in forecast models can significantly impact weather prediction. A previous case study in Northern India incorporating both model and observational data identified atmospheric biases in a high‐resolution forecast linked to soil moisture that impacted the representation of the monsoon trough, an important driver of regional rainfall. The aim of the current work is to understand whether this behavior is present in operational forecasts run by the India National Centre for Medium Range Weather Forecasting (NCMRWF). We utilize satellite observations and reanalysis to evaluate model fields in June, July, August, and September forecasts from 2020. Our analysis reveals systematic rapid growth in warm boundary layer biases during the daytime over North West India, which weaken overnight, consistent with excessive daytime surface sensible heat flux. The cumulative effect of these biases produces temperatures more than 4K warmer in 60‐h forecasts. These effects are enhanced by dry surface conditions. The biases impact circulation in the forecasts, which have implications for regional rainfall. The spatial distribution of warm biases in the Indo‐Gangetic Plain is remarkably consistent with the location of areas equipped for irrigation, a process that is not explicitly represented in the model. Our results provide compelling evidence that the development of an irrigation scheme within the model is needed to address the substantial forecast biases that we document. [ABSTRACT FROM AUTHOR]

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Abstract The representation of land–atmosphere coupling in forecast models can significantly impact weather prediction. A previous case study in Northern India incorporating both model and observational data identified atmospheric biases in a high‐resolution forecast linked to soil moisture that impacted the representation of the monsoon trough, an important driver of regional rainfall. The aim of the current work is to understand whether this behavior is present in operational forecasts run by the India National Centre for Medium Range Weather Forecasting (NCMRWF). We utilize satellite observations and reanalysis to evaluate model fields in June, July, August, and September forecasts from 2020. Our analysis reveals systematic rapid growth in warm boundary layer biases during the daytime over North West India, which weaken overnight, consistent with excessive daytime surface sensible heat flux. The cumulative effect of these biases produces temperatures more than 4K warmer in 60‐h forecasts. These effects are enhanced by dry surface conditions. The biases impact circulation in the forecasts, which have implications for regional rainfall. The spatial distribution of warm biases in the Indo‐Gangetic Plain is remarkably consistent with the location of areas equipped for irrigation, a process that is not explicitly represented in the model. Our results provide compelling evidence that the development of an irrigation scheme within the model is needed to address the substantial forecast biases that we document.

The NASA Goddard Earth Observing System (GEOS) employs a hybrid four‐dimensional ensemble‐variational (Hybrid‐4DEnVar) atmospheric data assimilation system to provide global, near‐real time weather analysis and forecast products. This study introduces a land analysis into the GEOS Hybrid‐4DEnVar system to additionally assimilate L‐band (1.4 GHz) brightness temperature observations over land from the NASA Soil Moisture Active Passive (SMAP) satellite mission, which are highly sensitive to surface (∼0–5 cm) soil moisture. This weakly coupled land analysis impacts the simulated and forecast atmosphere through the land–atmosphere exchange processes encoded in the atmospheric model. Retrospective assimilation experiments for boreal summer 2017 were conducted with the system at 50 km horizontal resolution. The SMAP assimilation is shown to mitigate errors in screen‐level (2 m) specific humidity (q2m) and temperature (T2m), with regional reductions in the root‐mean‐square error (RMSE) of q2m and maximum daily T2m by up to 0.4 g·kg−1 and 0.3 K, respectively. These improvements are somewhat smaller than those found in a precursor study that used the atmospheric analysis in the simpler, 3‐dimensional variational (3DVar) atmospheric assimilation configuration of the current GEOS reanalysis; Hybrid‐4DEnVar provides overall better near‐surface background estimates than 3DVar and therefore leaves less room for improvement. Finally, in the Hybrid‐4DEnVar system with SMAP assimilation, forecasts of q2m and T2m have significantly improved anomaly correlation and RMSE (99% confidence) at lead times out to 5 days compared to the Hybrid‐4DEnVar system without SMAP assimilation, with medium‐range lead times extended by ∼3 hr for q2m and ∼2 hr for T2m. Slight but nevertheless significant improvements are also seen for temperature forecasts at the 925 and 850 hPa levels and lead times out to 4 days. Humidity forecasts at 925 and 850 hPa are improved out to 1.5‐day lead time with 90% confidence. [ABSTRACT FROM AUTHOR]

Although evapotranspiration (ET) from the land is a key variable in Earth system models, the accurate estimation of ET based on physical principles remains challenging. Parameters used in current ET models are largely empirically based, which could be problematic under rapidly changing climatic conditions. Here, we propose a physically based ET model that estimates ET based on the surface flux equilibrium (SFE) theory and the maximum entropy production (MEP) principle. We derive an expression for aerodynamic resistance based on the MEP principle, then propose a novel ET model that integrates the SFE model and the MEP principle. The proposed model, which is referred to as the SFE‐MEP model, becomes equivalent to the MEP state in non‐equilibrium conditions when turbulent mixing is weak and the land surface is dry. Under conditions meeting land‐atmosphere equilibrium, the SFE‐MEP model is similar to ET estimation based on the SFE model. This blended nature of the SFE‐MEP ET model allows accurate ET estimation for most inland regions by overcoming the ET overestimation issue of the SFE model in dry conditions. As a result, the SFE‐MEP model significantly improves the performance of SFE ET estimation, particularly for arid regions. The proposed model and its high accuracy of ET estimation enable novel insight into various Earth system models as it does not require any empirical parameters and only uses readily obtainable meteorological variables including reference height air temperature, relative humidity, available energy, and radiometric surface temperature. Plain Language Summary: Terrestrial evaporation, also known as evapotranspiration (ET), is a central variable controlling the water, energy, and carbon cycles. However, it is difficult to estimate ET from physical principles due to complex interactions between the land and the atmosphere. An emerging theory of surface flux equilibrium (SFE) suggests that atmospheric observations reflect land surface conditions owing to a land‐atmosphere coupling, making it possible to estimate ET only using meteorological information. Nevertheless, we demonstrate that if the equilibrium is not quickly achieved, particularly in dry regions, the emerging SFE theory cannot properly estimate ET. We resolve this problem by introducing the maximum entropy production principle, a thermodynamic principle that can be applicable to non‐equilibrium conditions. By combining two theories with complementary relationships, we significantly improve ET estimation performance. In our approach, empirical and adjustable parameters are not required and ET can be easily estimated based on a land‐atmosphere coupling by using meteorological information and land surface temperature. Key Points: Identify non‐equilibrium conditions where the surface flux equilibrium theory does not perform wellPropose a model to improve the performance of evapotranspiration estimation compared to the surface flux equilibrium approachThe proposed model only uses readily obtainable meteorological variables and radiometric surface temperature [ABSTRACT FROM AUTHOR]

Soil moisture (SM) influences near‐surface air temperature by partitioning downwelling radiation into latent and sensible heat fluxes, through which dry soils generally lead to higher temperatures. The strength of this coupled soil moisture‐temperature (SM‐T) relationship is not spatially uniform, and numerous methods have been developed to assess SM‐T coupling strength across the globe. These methods tend to involve either idealized climate‐model experiments or linear statistical methods which cannot fully capture nonlinear SM‐T coupling. In this study, we propose a nonlinear machine‐learning (ML)‐based approach for analyzing SM‐T coupling and apply this method to various mid‐latitude regions using historical reanalysis datasets. We first train convolutional neural networks (CNNs) to predict daily maximum near‐surface air temperature (TMAX) given daily SM and geopotential height fields. We then use partial dependence analysis to isolate the average sensitivity of each CNN's TMAX prediction to the SM input under daily atmospheric conditions. The resulting SM‐T relationships broadly agree with previous assessments of SM‐T coupling strength. Over many regions, we find nonlinear relationships between the CNN's TMAX prediction and the SM input map. These nonlinearities suggest that the coupled interactions governing SM‐T relationships vary under different SM conditions, but these variations are regionally dependent. We also apply this method to test the influence of SM memory on SM‐T coupling and find that our results are consistent with previous studies. Although our study focuses specifically on local SM‐T coupling, our ML‐based method can be extended to investigate other coupled interactions within the climate system using observed or model‐derived datasets. Plain Language Summary: Soil moisture content influences air temperature by controlling evaporation at the soil surface. Dry soils reduce evaporation which warms the surface and leads to higher air temperatures. Conversely, wet soils generally lead to cooler temperatures. This process results in a coupled relationship between SM and temperature. Soil moisture‐temperature (SM‐T) coupling occurs everywhere but is especially strong in certain areas of the world. Over recent decades, numerous methods have been developed to measure regional differences in SM‐T coupling strength. These studies agree on certain "hot spots" where this coupling relationship is particularly strong. However, these previous studies rely on idealized climate model experiments or linear statistics which cannot fully capture nonlinear SM‐T coupling. To address this, we apply nonlinear ML techniques to investigate SM‐T coupling. Our results show that this method captures the nonlinear characteristics of SM‐T coupling and agrees well with previously documented coupling hot spots. Our method also provides a framework for using ML to investigate other coupled processes in the Earth system. Key Points: We investigate land‐atmosphere interactions by applying machine‐learning (ML) techniques to reanalysis datasetsPartial dependence analysis reveals new insights into nonlinear summertime soil moisture (SM)‐temperature coupling and SM memoryThese relationships broadly agree with previous studies, supporting ML as a method for quantifying surface‐atmosphere coupling [ABSTRACT FROM AUTHOR]

Land–atmosphere coupling is a crucial factor influencing extreme weather and climate. Based on the ERA5‐Land reanalysis data set and sensitivity experiments with the CESM1.2.2 model, this study investigated the possible impacts of land–atmosphere coupling on low temperature extremes (LTEs) during fall and winter over southern Eurasia (SEA). The results suggested that LTEs show evident interdecadal variations in recent 60 years (1958–2017) and exhibit significant positive correlations with soil moisture anomalies based on the ERA5‐Land data set. Numerical simulations (the control run and sensitivity run with prescribed climatological soil moisture) isolated the effects of soil moisture‐related land surface processes and demonstrated that land–atmosphere coupling explains around 70% of temperature variability, increases the near‐surface air temperature by 0.81 K, and favors the reduction of LTEs by 8.20 d·yr−1. In the perturbation scenarios, the impacts of coupling on the frequency and spatial coverage of LTEs are more prominent. Further analysis revealed that the thermal cooling anomalies induced by the coupling could alter the atmospheric circulation, thereby affecting the frequency and intensity of LTEs. When the land–atmosphere interactions are weakened, the surface latent heat flux increases accompanied by intense evapotranspiration, while the sensible heat flux and net infrared radiation decrease. Land surface cooling is apparent, and it weakens with increasing altitude, along with geopotential height anomalies that are positive in the lower troposphere and weaker at higher altitudes. The northeasterly wind anomalies over the East European Plain facilitate the development of cold advection, providing a favorable circulation background for the generation of LTEs over SEA. Plain Language Summary: As a key land surface variable, soil moisture can affect the surface thermal conditions and regulate the surface energy exchange, further trigger anomalous atmospheric circulation, and lead to extreme weather and climate events. In recent 60 years (1958–2017), low temperature extremes (LTEs) have occurred at times over southern Eurasia (SEA) and exhibit close relationships with soil moisture anomalies. Several simulations were conducted to isolate the effects of soil moisture‐related land surface processes on LTEs. When the land–atmosphere interactions are restricted, the frequency and intensity of LTEs in fall and winter consistently increase over SEA. The effects of land–atmosphere coupling on LTEs are spatially consistent under different backgrounds, albeit with certain differences in the magnitude of increase. Further analysis revealed that land surface cooling is evident when land–atmosphere interactions are weakened. The surface latent heat flux increases accompanied by strong evapotranspiration, while the sensible heat flux and net infrared radiation decrease. The positive geopotential height anomalies and northeasterly wind anomalies over SEA facilitate the development of cold advection, which can contribute to more frequent and intense LTEs over SEA. Key Points: Low temperature extremes (LTEs) over southern Eurasia are closely related to soil moisture anomalies and land–atmosphere couplingLand–atmosphere coupling is conducive to LTEs reduction, and the stronger variations the coupling has, the greater impact on LTEs it exertsThermal effects of land–atmosphere coupling and their influence on atmospheric circulation are the key factors affecting LTEs [ABSTRACT FROM AUTHOR]

Understanding the contributions of large-scale atmospheric circulation and local land surface processes to precipitation is essentially important for the climate prediction. This study adopts a dynamic adjustment (DA) approach based on constructed circulation analogs to quantitatively isolate the contribution of atmospheric circulation to summer land precipitation (Pr) over Asian mid-low latitudes during 1980–2019. The atmospheric circulation factor is represented by the 500 hPa geopotential height (Z500), and the residual component after DA is regarded as the contribution from land surface processes via evaporation mainly resulting from soil moisture (SM). The results indicate that the key SM-Pr feedback areas are mainly located in northeast China and the northern Indian Peninsula. The key influencing area of Z500 on the land Pr anomaly shows a "- + -" tripole pattern in the mid-latitude region. Atmospheric circulation determines the magnitude of summer land Pr, while the residual components reflect the land–atmosphere coupling effect and dominate Pr trend. This conclusion is helpful for better understanding the evolution mechanism of summer climate over Asia mid-low latitudes and may also have application value for climate prediction. [ABSTRACT FROM AUTHOR]

Abstract Although evapotranspiration (ET) from the land is a key variable in Earth system models, the accurate estimation of ET based on physical principles remains challenging. Parameters used in current ET models are largely empirically based, which could be problematic under rapidly changing climatic conditions. Here, we propose a physically based ET model that estimates ET based on the surface flux equilibrium (SFE) theory and the maximum entropy production (MEP) principle. We derive an expression for aerodynamic resistance based on the MEP principle, then propose a novel ET model that integrates the SFE model and the MEP principle. The proposed model, which is referred to as the SFE‐MEP model, becomes equivalent to the MEP state in non‐equilibrium conditions when turbulent mixing is weak and the land surface is dry. Under conditions meeting land‐atmosphere equilibrium, the SFE‐MEP model is similar to ET estimation based on the SFE model. This blended nature of the SFE‐MEP ET model allows accurate ET estimation for most inland regions by overcoming the ET overestimation issue of the SFE model in dry conditions. As a result, the SFE‐MEP model significantly improves the performance of SFE ET estimation, particularly for arid regions. The proposed model and its high accuracy of ET estimation enable novel insight into various Earth system models as it does not require any empirical parameters and only uses readily obtainable meteorological variables including reference height air temperature, relative humidity, available energy, and radiometric surface temperature.

Hot and dry conditions pose a substantial risk to global crops. The frequency of co-occurring heat and drought depends on land–atmosphere coupling, which can be quantified by the correlation between temperature and evapotranspiration ( r ( T, ET )). We find that the majority of global croplands have experienced declines in r ( T, ET ) over the past ∼40 years, indicating a shift to a more moisture-limited state. In some regions, especially Europe, the sign of r ( T, ET ) has flipped from positive to negative, indicating a transition from energy-limitation to moisture-limitation and suggesting a qualitative shift in the local climate regime. We associate stronger declines in r ( T, ET ) with faster increases in annual maximum temperatures and larger declines in soil moisture and ET during hot days. Our results suggest that shifts towards stronger land–atmosphere coupling have already increased the sensitivity of crop yields to temperature in much of the world by 12%–37%, as hot days are not only hotter, but also more likely to be concurrently dry.

Compound climate events are major threats to crop production under climate change. However, the heterogeneity in the impact of compound events on crop yield and its drivers remain poorly understood. Herein, we used empirical approach to evaluate the impact of compound hot–dry (HD) and cold–wet (CW) events on maize yield in China at the county level from 1990 to 2016, with a special focus on the spatial heterogeneity. Our findings indicate comparable impact of extremely compound CW events (−12.8 ± 3.6%) on maize yield loss to extremely compound HD events (−11.3 ± 2.1%). The spatial pattern of compound HD and CW events impacts on maize yield was dominantly associated with moisture regime, followed by management practices and soil properties. Specifically, drier counties and counties with less fraction of clay soil and organic carbon tend to experience greater yield loss due to compound HD events, and wet condition, excessive fertilizer, clay soil and rich organic carbon aggravate the maize yield loss due to compound CW events. Moreover, the land–atmosphere coupling exacerbated the heterogeneous yield impact through divergent heat transfer. In drier regions, the greater proportion of sensible heat creates a positive feedback between drier land and hotter atmosphere. In contrast, the greater proportion of latent heat in wetter regions results in a positive feedback between wetter land and colder atmosphere. Our results highlighted a critical element to explore in further studies focused on the land–atmosphere coupling in agricultural risk under climate change.

Representing subgrid variabilities of land surface processes and their upscaled effects is crucial for global climate modeling. Here, we implement a multiple atmosphere multiple land (MAML) framework in the superparamaterized version of E3SM (SP‐E3SM) to explicitly simulate the subgrid variabilities of land states and fluxes at cloud‐resolving scale and their interactions with atmosphere. Comparing to the standard SP‐E3SM in which all the atmospheric columns of the cloud resolving model embedded within the global atmospheric model grid interact with the same land surface (i.e., multiple atmosphere single land (MASL)), the impact of MAML on the strength of land‐atmosphere coupling is limited, partly because the current implementation mainly facilitates one‐way coupling between the cloud‐resolving model and the land surface model. Despite such limitation, MAML increases the surface latent heat flux at the expense of sensible heat flux, and increases precipitation in India, Amazon, and Central Africa, reducing the model dry bias compared to the standard SP‐E3SM. By employing a normalized gross moist stability (NGMS) diagnostic framework, we find that the increase in precipitation minus evaporation (P‐E) is primarily driven by the change in large‐scale moisture convergence, particularly by the increase of water vapor in the lower atmosphere, while the local effect of total surface energy flux plays a minor role in the P‐E change. More specifically, MAML changes the surface energy partitioning (evaporative fraction), increases the atmosphere water vapor, and further increases P‐E by decreasing the NGMS. Finally, future development in the MAML framework is discussed. Plain Language Summary: Land‐atmosphere interactions such as soil moisture‐precipitation feedback occur at a wide range of spatial scales. For example, spatial variability of surface fluxes such as radiation and precipitation can influence surface latent and sensible heat fluxes, which further influence turbulent mixing processes in the boundary layer and cloud and precipitation. Current global climate models (GCMs) have difficulties in representing land‐atmosphere interactions at scales smaller than the GCM grid (subgrid scales). To meet this challenge, we employ a novel framework, called multiple atmosphere multiple land (MAML), in a superparamaterized version of E3SM (SP‐E3SM) in which a cloud resolving model is embedded within each GCM grid to better resolve clouds and convection. MAML explicitly simulates the subgrid variabilities of land states and fluxes at scale of ∼1 km and provides feedback to the atmosphere. The use of MAML is shown to have a limited effect on the strength of land‐atmosphere coupling due to the current one‐way subgrid land‐atmosphere interaction setup. Despite the limited effect, MAML increases the rainfall in India, Amazon, and Central Africa, which reduces the dry bias in the model. Key Points: A multiple atmosphere multiple land (MAML) framework of land‐atmosphere coupling is implemented in the super‐parameterized E3SM (SP‐E3SM)MAML increases precipitation in India, Central Africa, and Amazon by increasing water vapor and hence large‐scale moisture convergenceMAML's impact on land‐atmosphere interactions is limited by the current setup of one‐way coupling on cloud‐resolving scales [ABSTRACT FROM AUTHOR]

The surface water and energy balances can be coupled or uncoupled depending on whether the evaporation regime is water‐limited or energy‐limited. As the landscape loses soil moisture during drydowns, a transition between the regimes may occur, which signifies a nonlinear change in water‐energy‐carbon coupling. Regions that switch often between these two regimes, that is, are dominated by neither regime, are particularly vulnerable to climate variability and change. To robustly identify these tipping points, we identify drydown events based on global soil moisture data sets from remote sensing. The event identification does not rely on precipitation information and is robust with respect to measurement noise. Then, the soil moisture thresholds delineating the evaporation regime transitions are determined by Sequential Monte Carlo Sampling and a two‐stage parametrization strategy. Based on the estimated soil moisture thresholds across the globe, we estimate observation‐based water availability indices which quantify the nonlinear controls of soil moisture on evaporation. This framework is tested and applied globally using Soil Moisture Active Passive soil moisture retrievals. Combined with a new tippling‐point metric that describes the frequency of evaporation regime transitions, we identify regions that switch often between different evaporation regimes at the global scale. Given unit shifts in soil moisture, these regions will experience the most change in how their surface water and energy are coupled. Plain Language Summary: Evaporation links the land surface water, energy, and carbon balances. Soil moisture can reduce the amount of available energy that is used for evaporation at the land surface. Evaporation can thus change from an energy‐limited regime to a water‐limited regime at a critical soil moisture value. Additionally, at some low soil moisture value, evaporation altogether diminishes to zero (wilting‐point soil moisture threshold). Besides evaporation, drainage can be the dominant hydrology regime of the landscape. This is the case for very wet soils (above so‐called field capacity or soil moisture threshold at which gravity is no longer an adequate force to dewater the porous media). There are thus several hydrologic regimes during interstorm period, separated by three threshold soil moisture values. We develop robust methods to estimate these thresholds using estimates of surface soil moisture from NASA's Soil Moisture Active Passive satellite mission. The regime transitions and thresholds make the hydrologic response of the landscape to climate forcing via a nonlinear process. We develop a new metric to globally map where the landscape water balance is at the tipping‐point between regimes. Landscapes that exist near the tipping point are particularly vulnerable to climate variability and change. Key Points: Remotely sensed soil moisture is used to estimate the thresholds between drainage‐dominated, energy‐ and water‐limited evaporation regimesThe soil moisture thresholds defining the regime changes are mapped globally and shown to be related to climate and soil textureRegions at the tipping‐point between water‐ and energy‐limited evaporation are mapped across the globe [ABSTRACT FROM AUTHOR]

Lifting condensation level (LCL) has long been used to estimate cloud base heights. However, spatial and temporal patterns of cloud bases embedded within atmospheric currents flowing over mountainous terrain still need to be more adequately described. To advance understanding, hourly observations of barometric pressure and ambient and dew point temperatures from 1948 to 2017 were acquired for seven airports located at 40.21° N (average) and crossing the Allegheny Mountains of the northeastern United States. Daily LCL trends were quantified, and large positive (2.3 m yr−1) and negative (−1.3 m yr−1) LCL trends were found to be greatest near seasonal transition dates (17 April and 9 November 2022). Cool season LCLs (795 m) increased significantly (p < 0.007) at five sites resulting in an average LCL increase of 81 m and implying a deeper and drier sub-cloud layer. Average warm season LCLs (773 m) decreased by 23 m, suggesting a deeper convective cloud layer and less sub-cloud evaporation that may facilitate higher hydrometeor growth and precipitation rates. Collective results indicate divergent seasonally averaged LCLs characterized by more rapid seasonal transitions, warmer and less cloudy cool seasons, and cloudier and more humid warm seasons that may be partly attributable to aggressive reforestation and contribute to more significant rainfall events and higher flood risks. [ABSTRACT FROM AUTHOR]

• Two-parameter complementary models have robustness to account for varying land-atmosphere coupling. • Fixing the E p o -related parameter is advisable under conditions where evaporation is closely coupled with the land surface, such as over grasslands. • Fixing the shape parameter but adjusting the E p o -related parameter is advisable when evaporation is highly coupled to the outer atmosphere, as over forests. Models based on the complementary relationship for estimating evaporation typically incorporate two parameters, one for adjusting the relationship's shape and the other for formulating potential evaporation (E p o). In practical applications, single-parameter versions are often derived by fixing one of these parameters. But there is ongoing debate about which parameter to fix and under what conditions. To address these crucial questions in the application of generalized complementary models, we conducted a comprehensive comparison of the consequences arising from the simplification of three prominent two-parameter generalized complementary models (H2012 by Han et al. (2012), B2015 by Brutsaert (2015), and S2022 by Szilagyi et al. (2022)) to their respective single-parameter versions. This analysis utilized data from 24 grassland and 19 forest flux sites, showcasing varying land-atmosphere coupling dynamics. The results underscore the robustness of the two-parameter scheme in accommodating diverse land-atmosphere coupling. The choice of which parameter to fix depends on the land-atmosphere coupling strength. Under conditions where evaporation is closely coupled with the land surface, as observed over grasslands, fixing the E p o -related parameter while leaving the shape parameter for calibration (as in the simplification of H2012) preserves the dependence of the shape parameter on land surface wetness, albeit with an acceptable reduction in performance. In contrast, when evaporation is closely coupled to the outer atmosphere, as observed over forests, fixing the shape parameter but leaving the E p o -related parameter for calibration (as in the simplifications of B2015 and S2022) maintains the physical correlations between the E p o -related parameters and the major atmospheric factors, also with an acceptable reduction in performance. These findings provide valuable insights for the parameterization of complementary models, aiding in the selection of appropriate parameter-fixing strategies based on the prevailing land-atmosphere coupling conditions. [ABSTRACT FROM AUTHOR]

Abstract Representing subgrid variabilities of land surface processes and their upscaled effects is crucial for global climate modeling. Here, we implement a multiple atmosphere multiple land (MAML) framework in the superparamaterized version of E3SM (SP‐E3SM) to explicitly simulate the subgrid variabilities of land states and fluxes at cloud‐resolving scale and their interactions with atmosphere. Comparing to the standard SP‐E3SM in which all the atmospheric columns of the cloud resolving model embedded within the global atmospheric model grid interact with the same land surface (i.e., multiple atmosphere single land (MASL)), the impact of MAML on the strength of land‐atmosphere coupling is limited, partly because the current implementation mainly facilitates one‐way coupling between the cloud‐resolving model and the land surface model. Despite such limitation, MAML increases the surface latent heat flux at the expense of sensible heat flux, and increases precipitation in India, Amazon, and Central Africa, reducing the model dry bias compared to the standard SP‐E3SM. By employing a normalized gross moist stability (NGMS) diagnostic framework, we find that the increase in precipitation minus evaporation (P‐E) is primarily driven by the change in large‐scale moisture convergence, particularly by the increase of water vapor in the lower atmosphere, while the local effect of total surface energy flux plays a minor role in the P‐E change. More specifically, MAML changes the surface energy partitioning (evaporative fraction), increases the atmosphere water vapor, and further increases P‐E by decreasing the NGMS. Finally, future development in the MAML framework is discussed.

With global warming and more frequent extreme precipitation events in recent years, the phenomenon of warming and humidification in the arid regions of Northwest China (ANWC) has attracted increasing attention. We assessed the coupling effects of soil temperature and moisture on extreme precipitation in the ANWC by using daily precipitation data from CN05.1 and monthly data on soil temperature, soil moisture, and energy from ERA5-land, 1961–2018. After logical partitioning by K-means clustering, the primary influencing routes in each partition were investigated using two indices of extreme precipitation, indicated by precipitation on very wet days (R95P) and the number of extreme precipitation days (R10day). We found that 1) Local extreme precipitation has had a steadily growing impact on overall precipitation. In summer, this impact is primarily driven by an increase in the quantity of extreme precipitation, but in winter, it is primarily driven by an increase in the intensity of single precipitation. 2) The Tianshan Mountains (TM) and Qilian Mountains (QM) are the key locations for the coupling of soil temperature and moisture with the extreme precipitation index. Both locations exhibit a positive coupling state for soil temperature with extreme precipitation with positive coupling in the TM but negative coupling in the QM for soil moisture with extreme precipitation. 3) In the coupling of soil temperature and moisture with energy, the relevant significant regions are almost all over the ANWC throughout the year and all seasons, and the coupling high-value areas are concentrated around the basin. 4) In the TM–Hami Basin (HB)–QM, the coupling between energy and the extreme precipitation index is also stronger. The specific coupling paths have been changing with seasonal and regional changes.

The linear form of the nondimensional complementary relationship (CR) follows from an isenthalpic process of evaporation under a constant surface available energy and unchanging wind. Mixing of external moisture into the boundary layer (BL) alters the dry‐end second‐type boundary condition yielding a polynomial that can be further generalized into a three‐parameter (Priestley‐Taylor α, a, b) power function (PF3), capable of responding to the level of such admixing. With the help of FLUXNET data and setting a = 2 for a possible recapture of the linear and/or polynomial versions of the CR, it is demonstrated that the resulting two‐parameter PF (i.e., PF2) excels among the CR‐based two‐parameter models considered in this study. PF2 is then employed with a globally set constant value of α = 1.1 and 0.5° monthly data across Australia, while calibrating b against the multiyear water‐balance evaporation rate on a cell‐by‐cell basis. The resulting bi‐modal histogram peaks first near b = 2 (recapturing the polynomial CR) when moisture admixing is significant, and then at b → 1 (yielding the linear CR) when mixing effects are negligible. Unlike the linear or polynomial CR versions, PF2 can respond to the general efficiency of external moisture admixing through its parameter b, making it applicable even near sudden discontinuities in surface moisture. A new duality emerges with the PF2: while α accounts for the effect of entrainment of free tropospheric drier air into the BL on the resulting wet‐environment evaporation rate, b does so for moisture on the drying‐environment evaporation rates. Plain Language Summary: The power‐function expansion of the polynomial complementary relationship of evaporation can account for the effect of large‐scale moisture transport into the drying region thus making it more versatile in practical applications. Key Points: The power‐function formulation of the nondimensional complementary relationship (CR) accounts for moisture advectionWhen such admixing is negligible an existing linear version of the nondimensional CR is evokedOtherwise, calibration of the power‐function often recaptures an existing polynomial version of the CR [ABSTRACT FROM AUTHOR]

Recent soil moisture (SM) reconstructions revealed plunging trends and enhanced SM‐temperature couplings over the last two decades in dry regions. However, how SM changed and whether the land‐atmosphere interaction was intensified over time in humid regions remained unknown. Here we reported the first six‐century‐long regional summer SM reconstruction (1360–2000 CE) in western Europe (WE) using three individual tree‐ring δ18O chronologies in England and France. A sharp wet‐to‐dry change occurred around 1820, earlier than 1850–1900 CE, the commonly used historical baseline of anthropogenic climate changes. Enhanced coupling of SM‐temperature followed, with stronger summer sea level pressure anomalies in dry years after the 1820s. Our results reveal that the hotter‐drier regime has also become more frequent in humid WE under global warming. Plain Language Summary: Soil moisture (SM) is a primary indicator for drought conditions, and its relationship with temperature can greatly impact regional climate conditions. So far, the existing studies on SM‐temperature comparisons have been focused on arid or semi‐arid regions. Historical SM variations and their relationship with temperature over time in humid regions remain largely unknown. Here we demonstrated a six‐century regional summer SM timeseries in humid western Europe, using a paleoclimate proxy tree‐ring δ18O. We found an abrupt wet‐to‐dry change occurring around 1820. Since the 1820s, the relationship between SM and temperature was enhanced, indicating that western Europe experienced more hotter‐drier summer more frequently. Our study implies that western Europe are likely to be affected more by hot droughts. Key Points: Regional six‐century‐long summer soil moisture was reconstructed in western EuropeAn abrupt wet‐to‐dry change of soil moisture occurred around 1820Soil moisture‐temperature coupling was strengthened since the 1820s, indicating hotter‐drier climate in western Europe [ABSTRACT FROM AUTHOR]

Land–atmosphere energy and moisture exchange can strongly influence local and regional climates. However, high uncertainty exists in the representation of land–atmosphere interactions in numerical models. Parameterization of surface exchange processes is greatly affected by parameter Czil, which, however, is typically defined as a domain-wide constant value. In this study, we examine the sensitivity of regional climate simulations over China to different surface exchange strengths via three Czil schemes (default (without Czil), constant (Czil = 0.1), and dynamic canopy-height-dependent Czil-h schemes) within a 13-km-resolution Weather Research and Forecasting model coupled with the Noah land surface model with multiparameterization options (WRF/Noah-MP). Our results demonstrate that compared to the other two schemes, the Czil-h scheme substantially reduces land–atmosphere coupling strength overestimation, and comparison to Chinese terrestrial ecosystem flux research network (ChinaFLUX) observations reveals the capability of the Czil-h scheme to better match observed surface energy and water variations. The results of the application of the various Czil schemes in four typical climate zones in China demonstrate that the Czil-h simulations achieve the closest agreement with field observations. The Czil-h scheme can narrow the positive discrepancies in the simulated precipitation and surface fluxes and the negative biases of the land surface temperature in Northeast China, North China, eastern Northwest China, and Southwest China. In particular, the above remarkable improvements produced by the Czil-h scheme primarily occur in areas covered with short vegetation. Additionally, the precipitation simulated with the Czil-h scheme exhibits more intricate and uncertain changes compared with surface flux simulations due to the nonlocal impacts of the surface exchange strength resulting from atmospheric fluidity. Overall, our findings highlight the applicability of the dynamic Czil scheme as a better physical alternative to the current treatment of surface exchange processes in atmosphere coupling models. [ABSTRACT FROM AUTHOR]

Land surface processes can significantly influence weather and climate. The Common Land Model version 2005 (CoLM2005) has been coupled to the Global Forecast System of the Global/Regional Assimilation and Prediction System (GRAPES_GFS), which is independently developed by the China Meteorological Administration. Since a new version of CoLM has been developed (CoLM2014) with updated soil basic data and parts of hydrological processes, we coupled CoLM2014 with GRAPES_GFS to investigate whether the land surface model can help to improve the prediction skill of the weather forecast model. The forecast results were evaluated against global validation datasets at different forecasting lengths and over various regions. The results demonstrate that GRAPES_GFS coupled with CoLM2005 and CoLM2014 can both well reproduce the spatial patterns and magnitude of atmospheric variables, and the effective predictable lengths of time are up to 3 days on the global scale and even up to 6 days on regional scales. Moreover, the GRAPES_GFS coupled with CoLM2014 outperforms the original one in predicting atmospheric variables. In addition, GRAPES_GFS coupled with both versions of CoLM reproduce acceptably accurate spatial distribution and magnitude of land variables. GRAPES_GFS coupled with CoLM2014 significantly improves the forecast of land surface state variables compared to the one coupled with CoLM2005, and the improvement signal is more notable than that in atmospheric variables. Overall, this study shows that CoLM is suitable for coupling with GRAPES_GFS, and the improvement of the land surface model in a weather forecast model can significantly improve the prediction skill of both atmospheric and land variables. [ABSTRACT FROM AUTHOR]

Several independent measurements of warm-season soil moisture and surface atmospheric variables recorded at the ARM Southern Great Plains (SGP) research facility are used to estimate the terrestrial component of land-atmosphere coupling (LAC) strength and its regional uncertainty. The observations reveal substantial variation in coupling strength, as estimated from three soil moisture measurements at a single site, as well as across six other sites having varied soil and land cover types. The observational estimates then serve as references for evaluating SGP terrestrial coupling strength in the Community Atmospheric Model coupled to the Community Land Model. These coupled model components are operated in both a free-running mode and in a controlled configuration, where the atmospheric and land states are reinitialized daily, so that they do not drift very far from observations. Although the controlled simulation deviates less from the observed surface climate than its free-running counterpart, the terrestrial LAC in both configurations is much stronger and displays less spatial variability than the SGP observational estimates. Preliminary investigation of vegetation leaf area index (LAI) substituted for soil moisture suggests that the overly strong coupling between model soil moisture and surface atmospheric variables is associated with too much evaporation from bare ground and too little from the vegetation cover. These results imply that model surface characteristics such as LAI, as well as the physical parameterizations involved in the coupling of the land and atmospheric components, are likely to be important sources of the problematical LAC behaviors.

The population of the Sahel region of West Africa has approximately doubled in the past 50 years, and could potentially double again by the middle of this century. This has led to the northward expansion of agricultural areas at the expense of natural savanna, leading to widespread land use -land cover change (LULCC). Because there is strong evidence of significant surface-atmosphere coupling in this region, one of the main goals of the West African Monsoon Modeling and Evaluation project phase II is to provide basic understanding of LULCC on the regional climate, and to evaluate the sensitivity of the seasonal variability of the West African Monsoon to LULCC. The prescribed LULCC is based on the changes from 1950 through 1990, representing a maximum feasible degradation scenario in the past half century. It is applied to 5 state of the art global climate models (GCMs) over a 6-year simulation period. Multiple GCMs are used because the magnitude of the impact of LULCC depends on model-dependent coupling strength between the surface and the overlying atmosphere, the magnitude of the surface biophysical changes, and how the key processes linking the surface with the atmosphere are parameterized within a particular model framework. Land cover maps and surface parameters may vary widely among models; therefore a special effort was made to impose consistent biogeophysical responses of surface parameters to LULCC using a simple experimental setup. The prescribed LULCC corresponds to degraded vegetation conditions, which mainly cause increases in the Bowen ratio and decreases in the surface net radiation, and result in a significant reduction in surface evaporation (upwards of 1 mm day−1 over a large part of the Sahel). This, in turn, mainly leads to less moisture convergence and precipitation over the LULCC zone. The overall impact is a rainfall reduction with every model, which ranges across models from 4 to 25 % averaged over the Sahel, and a southward shift of the rainfall peak in three of the five models which evokes a precipitation dipole pattern which is consistent with the observed pattern for dry climate anomalies over this region. The African Easterly Jet shifts equator-ward, although the strength of this change varies considerably among the models. In most of the models, the main factor causing diabatic cooling of the upper troposphere and enhanced subsidence over the region of LULCC is the reduction of convective heating rates linked to reduced latent heat flux and moisture flux convergence. In broad agreement with previous studies, the impact of degradation on the regional climate is found to vary among the different models, however, the signal is stronger and more consistent between the models here than in previous inter-comparison projects. This is likely related to our emphasis on prioritizing a consistent impact of LULCC on the surface biophysical properties.

The intimate link between soil moisture and precipitation makes it a "chicken-and-egg situation" that challenges climate studies of the continental water cycle. This association is particularly acute over the Mediterranean, increasingly exposed to droughts with climate change. This study aims at deciphering the impact of spring soil moisture state in the Mediterranean on subsequent warm season precipitation. In an idealized setup, two distinct climate models are used to generate extreme dry or wet soil conditions, and run climate simulations initialized and/or forced by these conditions. Changes in precipitation distribution and persistence are analyzed and where applicable compared to composites from a reanalysis. Spring soil moisture anomalies are found to be very persistent, but the precipitation response is largely model dependent. Overall, dry soils lead to a reduction of precipitation for early summer months and conversely for wet soils although with a fainter and less robust signal. On the other hand, wet soils tend to favor the persistence of precipitation throughout summer over several sub-regions. Our results highlight the stringent need to reduce the wide array of uncertainties associated to soil moisture, land-atmosphere coupling and convection in climate models, before ascertaining that soil moisture initialization could provide more skillful sub-seasonal to seasonal precipitation prediction. [ABSTRACT FROM AUTHOR]

Strengthened land‐atmosphere coupling in the northeastern United States (NEUS), accompanied by a positive soil moisture‐rainfall feedback, may lead to more drought. Coupling between the land and atmosphere emerges when low soil moisture values limit surface latent heat flux, or evapotranspiration, so that a majority of absorbed solar radiation is emitted from the surface as sensible heat. In this study, the Weather Research and Forecasting model was run with four prescribed soil moisture levels across 7 years to elucidate the strength of land‐atmosphere coupling under potential, future soil moisture states in the NEUS. Under drier conditions, land‐atmosphere coupling strengthens, and a positive soil moisture‐precipitation feedback develops in all years despite differences in the amount of moisture advected into the study domain. As snowpack decreases and evaporative demand increases, soil conditions may become drier in future summers over the NEUS, resulting in the more frequent development of drought. Key Points: The strength of land‐atmosphere coupling in the northeastern United States (NEUS) may increase under drier soil moisture conditionsA positive soil moisture‐rainfall feedback may develop under land‐atmosphere coupling, increasing the risk for droughtWith decreased snowpack and increased evapotranspiration in the NEUS expected, drought may become more common under climate change [ABSTRACT FROM AUTHOR]

Abstract The Community Earth System Model (CESM) is widely used for the prediction and understanding of climate variability and change. Accurate simulation of the behavior of near surface air temperature (T2m) is critical in such a model for addressing societally relevant problems. However, previous versions of CESM suffered from an overestimation of wintertime T2m variability in Northern Hemisphere (NH) land regions. Here, it is shown that the latest version of CESM (CESM2) exhibits a much improved representation of wintertime T2m variability compared to its predecessor and it now compares well with observations. A series of targeted experiments reveal that an important contributor to this improvement is the local effects of changes to the representation of snow density within the land surface component. Increased snow densities in CESM2 lead to enhanced conductance of the snow layer. As a result, larger heat fluxes across the snow layer are induced in the presence of T2m anomalies, leading to a greater dampening of surface and near surface atmospheric temperature anomalies. The implications for future projections with CESM2 are also considered through comparison of the CESM1 and CESM2 large ensembles. Aligned with the reduction in surface temperature variability, compared to CESM1, CESM2 exhibits reduced ensemble spread in future projections of NH winter mean temperature and a smaller decline in daily wintertime T2m variability under climate change. Overall, this improvement has increased the accuracy of CESM2 as a tool for the study of wintertime T2m variability and change.

Conventional global climate models are prone to producing unrealistic land-atmosphere coupling signals. Cumulus and convection parameterizations are natural culprits but the effect of bypassing them with explicitly resolved convection on global land-atmosphere coupling dynamics has not been explored systematically. We apply a suite of modern land-atmosphere coupling diagnostics to isolate the effect of cloud Superparameterization in the Community Atmosphere Model (SPCAM) v3.5, focusing on both the terrestrial segment (i.e., soil moisture and surface turbulent fluxes interaction) and atmospheric segment (i.e., surface turbulent fluxes and precipitation interaction) in the water pathway of the land-atmosphere feedback loop. At daily timescales, SPCAM produces stronger uncoupled terrestrial signals (negative sign) over tropical rainforests in wet seasons, reduces the terrestrial coupling strength in the Central Great Plain in American, and reverses the coupling sign (from negative to positive) over India in the boreal summer season—all favorable improvements relative to reanalysis-forced land modeling. Analysis of the triggering feedback strength (TFS) and amplification feedback strength (AFS) shows that SPCAM favorably reproduces the observed geographic patterns of these indices over North America, with the probability of afternoon precipitation enhanced by high evaporative fraction along the eastern United States and Mexico, while conventional CAM does not capture this signal. We introduce a new diagnostic called the Planetary Boundary Layer (PBL) Feedback Strength (PFS), which reveals that SPCAM exhibits a tight connection between the responses of the lifting condensation level, the PBL height, and the rainfall triggering to surface turbulent fluxes; a triggering disconnect is found in CAM.

Global atmospheric "storm‐resolving" models with horizontal grid spacing of less than 5 km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that could be used to improve computationally affordable coarse‐grid global climate models across a range of climates, reducing uncertainties in regional precipitation and temperature trends. Here, machine learning of nudging tendencies as functions of column state is used to correct the physical parameterization tendencies of temperature, humidity, and optionally winds, in a real‐geography coarse‐grid model (FV3GFS with a 200 km grid) to be closer to those of a 40‐day reference simulation using X‐SHiELD, a modified version of FV3GFS with a 3 km grid. Both simulations specify the same historical sea‐surface temperature fields. This methodology builds on a prior study using a global observational analysis as the reference. The coarse‐grid model without machine learning corrections has too few clouds, causing too much daytime heating of land surfaces that creates excessive surface latent heat flux and rainfall. This bias is avoided by learning downwelling radiative flux from the fine‐grid model. The best configuration uses learned nudging tendencies for temperature and humidity but not winds. Neural nets slightly outperform random forests. Forecasts of 850 hPa temperature gain 18 hr of skill at 3–7 days leads and time‐mean precipitation patterns are improved 30% by applying the ML correction. Adding machine‐learned wind tendencies improves 500 hPa height skill for the first five days of forecasts but degrades time‐mean upper tropospheric temperature and zonal wind patterns thereafter. Plain Language Summary: Global weather and climate models can be made more realistic by using a finer computational grid, but this is too expensive for routine use. We design a machine‐learned correction to make a more economical coarse‐grid model better track a fine‐grid reference version of this model. The correction is trained using a limited, computationally affordable, period of fine‐grid model output. It is applied interactively during the coarse‐grid simulation. As desired, adding the correction substantially improves how well weather forecasts and time‐mean rainfall patterns with the coarse‐grid model match the fine‐grid reference. Key Points: With a state‐dependent machine learning correction, a coarse‐grid global atmospheric model evolves more like a fine‐grid model versionThe skill of coarse‐grid weather forecasts and time‐mean rainfall are improved compared to the fine‐grid storm‐resolving reference modelAccounting for cloud biases by correcting surface downwelling radiation improves surface turbulent fluxes and precipitation over land [ABSTRACT FROM AUTHOR]

Evolution of the autumn snowpack has been considered as a potential source for the subseasonal predictability of winter surface air temperature, but its linkage to precipitation variability has been less well discussed. This study shows that the snow water equivalent (SWE) over the Urals region in early (1–14) November is positively associated with precipitation in southern China during 15–21 November and 6–15 January, based on the study period 1979/80–2016/17. In early November, a decreased Urals SWE warms the air locally via diabatic heating, indicative of significant land–atmosphere coupling over the Urals region. Meanwhile, a stationary Rossby wave train originates from the Urals and propagates along the polar-front jet stream. In mid (15–21) November, this Rossby wave train propagates downstream toward East Asia and, combined with the deepened East Asian trough, reduces the precipitation over southern China by lessening the water vapor transport. Thereafter, during 22 November to 5 January, there are barely any obvious circulation anomalies owing to the weak land–atmosphere coupling over the Urals. In early (6–15) January, the snowpack expands southward to the north of the Mediterranean Sea and cools the overlying atmosphere, suggestive of land–atmosphere coupling occurring over western Europe. A stationary Rossby wave train trapped in the subtropical westerly jet stream appears along with anomalous cyclonic circulation over Europe, and again with a deepened East Asian trough and less precipitation over southern China. The current findings have implications for winter precipitation prediction in southern China on the subseasonal timescale.

Abstract Global atmospheric “storm‐resolving” models with horizontal grid spacing of less than 5 km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that could be used to improve computationally affordable coarse‐grid global climate models across a range of climates, reducing uncertainties in regional precipitation and temperature trends. Here, machine learning of nudging tendencies as functions of column state is used to correct the physical parameterization tendencies of temperature, humidity, and optionally winds, in a real‐geography coarse‐grid model (FV3GFS with a 200 km grid) to be closer to those of a 40‐day reference simulation using X‐SHiELD, a modified version of FV3GFS with a 3 km grid. Both simulations specify the same historical sea‐surface temperature fields. This methodology builds on a prior study using a global observational analysis as the reference. The coarse‐grid model without machine learning corrections has too few clouds, causing too much daytime heating of land surfaces that creates excessive surface latent heat flux and rainfall. This bias is avoided by learning downwelling radiative flux from the fine‐grid model. The best configuration uses learned nudging tendencies for temperature and humidity but not winds. Neural nets slightly outperform random forests. Forecasts of 850 hPa temperature gain 18 hr of skill at 3–7 days leads and time‐mean precipitation patterns are improved 30% by applying the ML correction. Adding machine‐learned wind tendencies improves 500 hPa height skill for the first five days of forecasts but degrades time‐mean upper tropospheric temperature and zonal wind patterns thereafter.

In recent decades, irrigated agriculture has expanded dramatically over the Southeastern United States (SEUS). The trend is more likely to continue in future given the need to further improve crop productivity and its resilience against droughts, however, the impact of these SEUS land cover changes remains unknown. This study investigates how and to what extent rain-fed to irrigation-fed (RFtoIF) transition in the SEUS region modulates precipitation spatially and temporally under a severe drought meteorological condition. In this study, we perform three Weather Research Forecasting model simulations with varying degrees of irrigated crop areas with meteorological boundary conditions of a record-breaking 2007 drought in the SEUS region. Results show that the SEUS irrigation expansion reduces both the convective triggering potential and low-level humidity index through land-atmospheric interaction. This is accompanied by reduction in the height of atmospheric boundary layer (ABL)-lifting condensation level crossing and increase in the convective available potential energy. These modulations within the ABL provide a favorable condition for strong deep convection during the drought period. However, the impact on precipitation is heterogeneous, with crop areas undergoing RFtoIF transition experiencing an overall reduction in precipitation while other landcovers experiencing an increase. The reduction in precipitation over RFtoIF transitioned croplands is in part due to moisture redistribution aided by generation of an anomalous high-pressure system. The results highlight the complexity of response of precipitation to irrigation expansion in the SEUS, and underscore the need to perform spatially-explicit analysis for mitigating risks to water resources and food security.