Soil moisture (SM) is crucial in land-atmosphere (LA) interactions, regulating evapotranspiration and influencing moisture and energy budgets, which impact weather, precipitation, and the broader climate system. Despite efforts to quantify LA coupling using observational data, reanalysis, or combined observation-model products, challenges remain due to inconsistencies and discrepancies among datasets. This highlights the need for observational data to assess model performance, diagnose errors in model structures, and potentially improve their physical process. Flux tower sites, while valuable for in-situ observations, have limited global distribution and short durations. Conversely, satellite data offer high-quality, long-term, and globally distributed observations but are prone to random errors, which can degrade estimates of LA coupling.     As a result, there is an incomplete picture of the reality of global LA coupling for model validation and calibration. This study introduces a method for deriving the Pearson correlation coefficient between SM time series from the Soil Moisture Active Passive (SMAP) satellite and observation-based latent heat flux (LE) products from the Global Land Evaporation Amsterdam Model (GLEAM), FluxCom, and Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration (CAMELE). While SMAP data correlate closely with in-situ SM measurements, they exhibit stochastic random errors, which can reduce the accuracy of SM-LE correlation. On the other hand, SM variability typically resembles a first-order Markov process, enabling the estimation of the ratio between variance of random error and temporal variance in SM measurements. Given that the sequence of random error variances is unknown, it is not feasible to derive an error-free time series by eliminating random errors. As a solution, we propose a mathematical methodology to estimate the corrected correlation, considering random errors in satellite-based SM data. The accuracy of corrected correlations will be further evaluated using in-situ measurements (AmeriFlux network). We aim to construct a global depiction of SM-LE correlation from observationally gridded data with a quantification of uncertainties and potentially establish a new benchmark for model validation.

Soil moisture (SM) analyses and assessments hold significance for numerous applications in the fields of hydrometeorology and agriculture. Throughout history, flux tower sites have been a primary source of data for observationally-based SM examinations and evaluations of landatmosphere interaction. However, these monitoring stations are not evenly distributed worldwide. One of the ways in which the comprehensive understanding of how land and atmosphere interact can be improved is by incorporating remotely sensed SM observations. The Soil Moisture Active Passive (SMAP) satellite is one of the satellite resources which closely aligns with in-site observations. However, the remote sensing nature of SMAP data means that it is prone to unpredictable random distortions. Since variations in SM tend to follow a fundamental Markov process, they typically display a specific "red noise" pattern of variability. On the other hand, satellite data that incorporates random fluctuations exhibits a more uniform "white noise" pattern at higher frequencies, which contrasts with the anticipated red noise pattern. Furthermore, gaps in SMAP data are not randomly distributed; due to its orbital characteristics, the satellite experiences regular instances of missing data during its 8-day orbital cycle, differing depending on the orbital pass. This introduces additional anomalies in the power spectrum, performed through examining correlations in the time series data, leading to recurring spikes at intervals of 8, 4 (half of 8), 2 and 2/3 (one-third of 8), and 2 days (one-fourth of 8). These spectral spikes become broader due to small variations in the satellite's orbit. To make the satellite data most effective for assessing land-atmosphere interactions, which tend to rely on estimates of covariability of SM with other environmental variables, it is crucial to minimize the impact of random distortions and systematic missing data. A technique for adjusting the power spectrum, and thus the time series, of SM has been developed to minimize the influence of orbital harmonic spikes in the gridded Level 3 (L3) SMAP dataset. This is achieved by fitting a catenary function to the power spectrum between the harmonic spikes and then removing their influence. The adjusted spectrum is then aligned with soil moisture data from the surface layer, collected from sites within the AmeriFlux network (in-situ flux tower data). These sites demonstrate relatively minimal distortion and exhibit SM power spectra that closely resemble those generated by offline land surface models (LSMs), which are free of random noise by nature. Using validated spectral data from gridded LSM-based datasets, an improved global L3 SMAP dataset is being generated that accounts for noise and harmonic effects. This presentation will showcase the outcomes of this technique in enhancing SMAP data and its temporal correspondence with observational data.

Earth system models currently struggle to account for the complex effects that land surface heterogeneity can have on land-atmosphere interactions. Subgrid land surface heterogeneity is currently not well accounted for in land-atmosphere interactions in earth system models. There have been attempts to include the impact of this heterogeneity on the atmosphere, but they ignore the development of coherent secondary circulations that can be driven by spatial differential surface heating. A wealth of literature, particularly large-eddy simulation (LES) based studies, shows that these circulations have significant impacts on the development and organization of clouds. In this work, we describe a two-column model with a parameterized circulation driven by atmospheric virtual potential temperature profiles, differences in near surface temperature between the two columns, patterns of surface heterogeneity, and the mean background wind. Key aspects of the proposed model structure are compared with LES output, and the model is then implemented between two otherwise independent single column models. While some avenues for improvement exist, when the circulations are parameterized, we see increased cloud development and realistic changes to the mean profiles of temperature and moisture. The proposed model qualitatively matches expectations from the literature and LES, and points to the potential success of its future implementation in coarse grid models.

Evaporation is controlled by soil moisture (SM) availability when conditions are not extremely wet. In such a moisture-limited regime, land-atmosphere coupling is active, and a chain of linked processes allow land surface anomalies to affect weather and climate. How frequently any location is in a moisture-limited regime largely determines the intensity of land feedbacks on climate. Conventionally this has been quantified by shifting probability distributions of SM, but the boundary between moisture-limited and energy-limited regimes, called the critical soil moisture (CSM) value, can also change. CSM is an emergent property of the land-atmosphere system, determined by the balance of radiative, thermal and kinetic energy factors. We propose a novel framework to separate the contributions of these separate effects on the likelihood that SM lies in the moisture-limited regime. We confirm that global warming leads to a more moisture-limited world. This is attributed to reduced SM in most regions: the moisture effect. CSM changes mainly due to shifts in the surface energy budget, significantly affecting 27% of the globe in analyzed climate change simulations. However, consistency among Earth system models regarding CSM change is low. The poor agreement hints that variability of CSM in models and the factors that determine CSM are not well represented. The fidelity of CSM in Earth system models has been overlooked as a factor in water cycle projections. Careful assessment of CSM in nature and for model development should be a priority, with potential benefits for multiple research fields including meteorology, hydrology, and ecology.

High temperature extremes accompanied by drought have led to serious ramifications for environmental and socio-economic systems. Thus, improving the predictability of heat-wave events is a high priority. One key to achieving this is to better understand land-atmosphere interactions. Recent studies have documented a hypersensitive regime in the soil moisture-temperature relationship when soil dries below a critical low threshold, air temperatures increase at a greater rate as soil moisture declines. In this study, we explore the mechanisms linking low soil moisture to high air temperatures. From in-situ observations, we confirm that the hypersensitive regime acts throughout the chain of energy processes from land to atmosphere. A simple energy-balance model indicates that the cause of the hypersensitive regime is the dramatic drop in evaporative cooling that occurs when soil moisture dries to the permanent wilting point, below which latent heat flux almost ceases. Precisely how a model represents the relationship between evapotranspiration and soil moisture is found to be essential to describe the occurrence of hypersensitive regime. Thus, we advocate that climate models should ensure a realistic representation of land-atmosphere interactions to obtain reliable forecasts of extremes and climate projections, aiding the assessment of climate vulnerability and adaptation.

Coupled land-atmosphere models exchange grid cell mean values of states and fluxes even as they separately simulate subgrid variability. The characteristics of atmospheric heterogeneity driven by land surface heterogeneity are examined in large eddy simulations. The degree of spatial variance in the atmosphere is highly correlated with the spatial variance of land surface fluxes, but the impacts are entirely at scales larger than 10-30 km. Between 1-10 km throughout most of the day there is no difference in power spectra over heterogeneous and homogeneous surfaces, as synoptic situations and internal dynamics associated with atmospheric turbulent transfer is insensitive to surface heterogeneity. To improve forecasts, the ability of land surface heterogeneity to organize mesoscale circulations affecting boundary layer evolution and convective development should be represented in non-cloud-resolving models, as modern convective parameterizations can account for such subgrid processes. This will require the exchange of subgrid information between land and atmosphere models.

Land-atmosphere feedbacks act through process chains that link variables in the land-atmosphere system. For the global energy and water cycles, the first link in the chain is soil moisture. Flux tower sites provide in-situ observations, including land surface states, surface fluxes, and nearsurface atmospheric states, to validate these links; however, they are unevenly distributed over the globe. Therefore, to obtain a global view of observationally based land-atmosphere coupling metrics, satellite data are useful. Among satellite products, the Soil Moisture Active Passive (SMAP) satellite provides the closest match to in-situ observations. However, SMAP exhibits stochastic random noise that can deflate coupling estimates. Since soil moisture variability closely follows a first-order Markov process, it typically has a distinct red noise spectrum. Satellite data with random noise has a whiter spectrum at high frequencies that can be compared to the expected red spectrum. Also, missing data in SMAP are not entirely random; its 8-day repeating polar orbit creates a cadence of missing data for both ascending and descending overpasses, depending on the location. This creates additional artifacts in the power spectrum, calculated through lagged autocovariance in the time series, with harmonic spikes at 8, 4 (8/2), 2 2/3 (8/3), and 2 (8/4) days that broaden due to the satellite's orbital variations. To be optimally useful for quantifying land-atmosphere feedbacks, the effects of random noise and periodic missing data must be minimized. A power spectrum adjustment technique has been designed to remove the orbital harmonic spikes from Level 3 (L3) SMAP data. This is achieved by fitting and removing a catenary function to the power spectrum between harmonic spikes. This adjusted spectrum is then scaled to match surface layer soil moisture observations at sites of the AmeriFlux network (in-situ data), which exhibit relatively low noise and have spectra that are very similar to those produced by offline land surface models (LSMs). Utilizing validated spectral data from gridded LSM-based datasets, a global L3 SMAP product with removed noise and harmonic effects is being produced. We will present results quantifying the extent to which this technique improves SMAP data and its temporal correlation with observations.

Land-atmosphere interactions are central to the evolution of the atmospheric boundary layer and the subsequent formation of clouds and precipitation. Existing global climate models represent these connections with bulk approximations on coarse spatial scales, but observations suggest that small-scale variations in surface characteristics and co-located turbulent and momentum fluxes can significantly impact the atmosphere. Recent model development efforts have attempted to capture this phenomenon by coupling existing representations of subgrid-scale (SGS) heterogeneity between land and atmosphere models. Such approaches are in their infancy and it is not yet clear if they can produce a realistic atmospheric response to surface heterogeneity. Here, we implement a parameterization to capture the effects of SGS heterogeneity in the Community Earth System Model (CESM2), and compare single-column simulations against high-resolution Weather Research and Forecasting (WRF) large-eddy simulations (LESs), which we use as a proxy for observations. The CESM2 experiments increase the temperature and humidity variances in the lowest atmospheric levels, but the response is weaker than in WRF-LES. In part, this is attributed to an underestimate of surface heterogeneity in the land model due to a lack of SGS meteorology, a separation between deep and shallow convection schemes in the atmosphere, and a lack of explicitly represented mesoscale secondary circulations. These results highlight the complex processes involved in capturing the effects of SGS heterogeneity and suggest the need for parameterizations that communicate their influence not only at the surface but also vertically.

Contemporary Earth system models mostly ignore the sub-grid scale (SGS) heterogeneous coupling between the land surface and atmosphere. To aid in the development of coupled land and atmosphere SGS parameterizations for global models, we present a study of different aspects of highly-realistic sub-100 km scale land-surface heterogeneity. The primary experiment is a set of simulations of September 24, 2017 over the Southern Great Plains (SGP) site using the Weather Research and Forecasting (WRF) model with 100-m horizontal resolution. The overall impact of land-surface heterogeneity is evaluated by comparing cloud and turbulent kinetic energy (TKE) production in large-eddy simulations (LESs) using heterogeneous and homogeneous surface fields (namely sensible and latent heat fluxes) specified by an offline field-scale resolving land-surface model (LSM). The heterogeneous land surface leads to significantly more cloud and TKE production. We then isolate specific sources of heterogeneity by using selectively domain-wide averaged fields in the LSM. It is found that heterogeneity in the land surface created by precipitation is effectively responsible for the increases in cloud and TKE production, while rivers and soil type have a negligible impact and land cover has only a small impact. Additional experiments modify the Bowen ratio in the surface fields and the initial wind profile of the heterogeneous case to clarify the results seen. Finally two additional days at the SGP site are simulated showing a similar increase in cloud production in heterogeneous cases.

Most studies of land-atmosphere coupling have focused on bivariate linear statistics like correlation. However, more complex dependencies exist, including nonlinear relationships between components of land-atmosphere coupling and the transmutability of relationships between soil moisture and surface heat fluxes under different environmental conditions. In this study, a technique called multivariate mutual information, based on information theory, is used to quantify how surface heat fluxes depend on both surface energy and wetness conditions, i.e. net radiation and soil moisture, across the globe by season using reanalysis data. Such interdependency is then decomposed into linear and nonlinear contributions, which are further decomposed as different components explainable as the unique contribution from individual land surface conditions, redundant contributions shared by both land surface conditions, and the synergistic contribution from the coaction of net radiation and soil moisture. The dependency linearly contributed from soil moisture bears a similar global pattern to previously identified hot spots of coupling. The linear unique contributions of net radiation and soil moisture are mainly nonoverlapping, which suggests two separate regimes are governed by either energy or water limitations. These patterns persist when the nonlinearity is superimposed, thus reinforcing the validity of the land-atmospheric coupling hot spot paradigm and the spatial division of energy-limited as well as water-limited regions. Nevertheless, strong nonlinear relationships are detected, particularly over subtropical regions. Synergistic components are found across the globe, implying widespread multidimensional physical relationships among net radiation, soil moisture, and surface heat fluxes that previously had only been inferred locally.

The 2018 drought and heatwave over Europe was exceptional over northern Europe, with unprecedented forest fires in Sweden, searing heat in Germany and water restrictions in England. Monthly, daily and hourly data from ERA5, verified with soil moisture and surface flux measurements over Britain, are examined to investigate the subseasonal-to-seasonal progression of the event and the diurnal evolution of tropospheric profiles to quantify the anomalous land surface contribution to heat and drought. Data suggest the region entered a rare condition of becoming a “hot spot” for land-atmosphere coupling, which exacerbated the heatwave across much of northern Europe. Land-atmosphere feedbacks were prompted by unusually low soil moisture over wide areas, which generated moisture limitations on surface latent heat fluxes, suppressing cloud formation, increasing surface net radiation and driving temperatures higher during several multi-week episodes of extreme heat. We find consistent evidence in field data and reanalysis of a breakpoint threshold of soil moisture at most locations, below which surface fluxes and daily maximum temperatures become hypersensitive to declining soil moisture. Similar recent heatwaves over various parts of Europe in 2003, 2010 and 2019, combined with dire climate change projections, suggest such events could be on the increase. Land-atmosphere feedbacks may play an increasingly important role in exacerbating extremes, but could also contribute to their predictability on subseasonal time scales.

Past and projected changes in global hydroclimate in Earth system models have been examined. The Budyko framework that relates the partitioning of precipitation into evaporation to a location’s aridity has been modified to account for the effect of interannual terrestrial water storage and compared to traditional methods. The new formulation better fits climate model data over most of the globe. Old and new formulations are used to quantify changes in the spatial patterns of hydroclimate based locally on year-to-year variations water and energy cycle variables. Focus is on multi-model median responses to changing climate. The changes in hydroclimate from preindustrial to recent historical (1965-2014) conditions often have different patterns and characteristics than changes due only to increasing CO2. For simulations with gradually increasing CO2, differing model treatments of vegetation are found specifically to have categorically different impacts on hydroclimate, particularly altering the relationship between aridity and the fraction of precipitation contributing to evaporation in models that predict vegetation changes. Models that predict vegetation phenology have consistently different responses to increasing CO2 than models that do not. Dynamic vegetation models show more widespread but less consistent differences than other models, perhaps reflecting their less mature state. Nevertheless, there is clearly sensitivity to vegetation that illustrates the importance of including the representation of biospheric shifts in Earth system models.