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Land–atmosphere (L-A) interactions are a main driver of Earth’s surface water and energy budgets; as such, they modulate near-surface climate, including clouds and precipitation, and can influence the persistence of extremes such as drought. Despite their importance, the representation of L-A interactions in weather and climate models remains poorly constrained, as they involve a complex set of processes that are difficult to observe in nature. In addition, a complete understanding of L-A processes requires interdisciplinary expertise and approaches that transcend traditional research paradigms and communities. To address these issues, the international Global Energy and Water Exchanges project (GEWEX) Global Land–Atmosphere System Study (GLASS) panel has supported “L-A coupling” as one of its core themes for well over a decade. Under this initiative, several successful land surface and global climate modeling projects have identified hot spots of L-A coupling and helped quantify the role of land surface states in weather and climate predictability. GLASS formed the Local Land–Atmosphere Coupling (LoCo) project and working group to examine L-A interactions at the process level, focusing on understanding and quantifying these processes in nature and evaluating them in models. LoCo has produced an array of L-A coupling metrics for different applications and scales and has motivated a growing number of young scientists from around the world. This article provides an overview of the LoCo effort, including metric and model applications, along with scientific and programmatic developments and challenges.

The Twentieth Century Reanalysis (20CR), which spans the 138 year period from 1871 to 2008, was intended for a variety of climate applications, including long‐term trend assessment. Because over land 20CR only assimilates surface pressure observations and their count increases by an order of magnitude over the course of the record, a key question is whether the 20CR is homogenous and hence suitable for detecting climate‐related changes. We use three statistical methods (Pettitt and Bai‐Perron tests and segmented regression) to detect abrupt shifts in multiple hydrometeorological variable mean and uncertainty fields over the central United States. For surface air temperature and precipitation, we use the Climate Research Unit (CRU) time series data set for comparison. We find that for warm‐season months, there is a consensus change point among all variables between 1940 and 1950, which is not substantiated by the CRU record. While we cannot say with certainty that these shifts in the 20CR analysis fields are artificial, our statistical analyses, coupled with a visual inspection of the underlying assimilated observational count time series, strongly point to this conclusion. Our recommendation is therefore for users to restrict climate trend applications over the central United States to the second half century of the 20CR record, after observational density has stabilized. Key Points The 20CR is affected by inhomogeneities 20th century CRU precipitation and temperature show no trend or change point Only the most recent half century of 20CR is suitable for climate trend analysis

Soil moisture plays a key role in controlling land-atmosphere interactions, with implications for water resources, agriculture, climate, and ecosystem dynamics. Although soil moisture varies strongly across the landscape, current monitoring capabilities are limited to coarse-scale satellite retrievals and a few regional in-situ networks. Here, we introduce SMAP-HydroBlocks (SMAP-HB), a high-resolution satellite-based surface soil moisture dataset at an unprecedented 30-m resolution (2015–2019) across the conterminous United States. SMAP-HB was produced by using a scalable cluster-based merging scheme that combines high-resolution land surface modeling, radiative transfer modeling, machine learning, SMAP satellite microwave data, and in-situ observations. We evaluated the resulting dataset over 1,192 observational sites. SMAP-HB performed substantially better than the current state-of-the-art SMAP products, showing a median temporal correlation of 0.73 ± 0.13 and a median Kling-Gupta Efficiency of 0.52 ± 0.20. The largest benefit of SMAP-HB is, however, the high spatial detail and improved representation of the soil moisture spatial variability and spatial accuracy with respect to SMAP products. The SMAP-HB dataset is available via zenodo and at https://waterai.earth/smaphb . Measurement(s) wetness of soil Technology Type(s) computational modeling technique Factor Type(s) geographic location • temporal interval Sample Characteristic - Environment land • surface soil Sample Characteristic - Location contiguous United States of America Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14582265

The Twentieth Century Reanalysis (20CR) holds the distinction of having the longest record length (140-year; 1871–2010) of any existing global atmospheric reanalysis. If the record can be shown to be homogenous, then it would be the first reanalysis suitable for long-term trend assessments, including those of the regional hydrologic cycle. On the other hand, if discontinuities exist, then their detection and attribution—either to artificial observational shocks or climate change—is critical to their proper treatment. Previous research suggested that the quintupling of 20CR’s assimilated observation counts over the central United States was the primary cause of inhomogeneities for that region. The same work also revealed that, depending on the season, the complete record could be considered homogenous. In this study, we apply the Bai-Perron structural change point test to extend these analyses globally. A rigorous evaluation of 20CR’s (in)homogeneity is performed, composed of detailed quantitative analyses on regional, seasonal, inter-variable, and intra-ensemble bases. The 20CR record is shown to be homogenous (natural) for 69 (89) years at 50 % of land grids, based on analysis of the July 2 m air temperature. On average 54 % (41 %) of the grids between 60°S and 60°N are free from artificial inhomogenetites in their February (July) time series. Of the more than 853,376 abrupt shifts detected in 26 variable fields over two monthly time series, approximately 72 % are non-climate in origin; 25 % exceed 1.8 standard deviations of the preceding time series. The knock-on effect of inhomogeneities in 20CR’s boundary forcing and surface pressure data inputs to its surface analysis fields is implicated. In the future, reassessing these inhomogeneities will be imperative to achieving a more definitive attribution of 20CR’s abrupt shifts.

The lack of observational data for use in evaluating the realism of model-based land–atmosphere feedback signal and strength has been deemed a major obstacle to future improvements to seasonal weather prediction by the Global Land–Atmosphere Coupling Experiment (GLACE). To address this need, a 7-yr (2002–09) satellite remote sensing data record is exploited to produce for the first time global maps of predominant coupling signals. Specifically, a previously implemented convective triggering potential (CTP)–humidity index (HI) framework for describing atmospheric controls on soil moisture–rainfall feedbacks is revisited and generalized for global application using CTP and HI from the Atmospheric Infrared Sounder (AIRS), soil moisture from the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E), and the U.S. Climate Prediction Center (CPC) merged satellite rainfall product (CMORPH). Based on observations taken during an AMSR-E-derived convective rainfall season, the global land area is categorized into four convective regimes: 1) those with atmospheric conditions favoring deep convection over wet soils, 2) those with atmospheric conditions favoring deep convection over dry soils, 3) those with atmospheric conditions that suppress convection over any land surface, and 4) those with atmospheric conditions that support convection over any land surface. Classification maps are produced using both the original and modified frameworks, and later contrasted with similarly derived maps using inputs from the National Aeronautics and Space Administration (NASA) Modern Era Retrospective Analysis for Research and Applications (MERRA). Both AIRS and MERRA datasets of CTP and HI are validated using radiosonde observations. The combinations of methods and data sources employed in this study enable evaluation of not only the sensitivity of the classification schemes themselves to their inputs, but also the uncertainty in the resultant classification maps. The findings are summarized for 20 climatic regions and three GLACE coupling hot spots, as well as zonally and globally. Globally, of the four-class scheme, regions for which convection is favored over wet and dry soils accounted for the greatest and least extent, respectively. Despite vast differences among the maps, many geographically large regions of concurrence exist. Through its ability to compensate for the latitudinally varying CTP–HI–rainfall tendency characteristics observed in this study, the revised classification framework overcomes limitations of the original framework. By identifying regions where coupling persists using satellite remote sensing this study provides the first observationally based guidance for future spatially and temporally focused studies of land–atmosphere interactions. Joint distributions of CTP and HI and soil moisture, rainfall occurrence, and depth demonstrate the relevance of CTP and HI in coupling studies and their potential value in future model evaluation, rainfall forecast, and/or hydrologic consistency applications.

The exchange of carbon, water, and energy fluxes between the land and the atmosphere plays a vital role in shaping global change and extreme events. Yet our understanding of the theory of this surface-atmosphere exchange, represented via land surface models (LSMs), continues to be limited, highlighted by marked biases in model-data benchmarking exercises. Here, we leveraged the PLUMBER2 dataset of observations and model simulations of terrestrial sensible heat, latent heat, and net ecosystem exchange fluxes from 153 international eddy-covariance sites to identify the meteorological conditions under which land surface models are performing worse than independent benchmark expectations. By defining performance relative to three sophisticated out-of-sample empirical models, we generated a lower bound of performance in turbulent flux prediction that can be achieved with the input information available to the land surface models during testing at flux tower sites. We found that land surface model performance relative to empirical models is worse at edge conditions – that is, LSMs underperform in timesteps where the meteorological conditions consist of coinciding relative extreme values. Conversely, LSMs perform much better under “typical” conditions within the centre of the meteorological variable distributions. Constraining analysis to exclude the edge conditions results in the LSMs outperforming strong empirical benchmarks. Encouragingly, we show that refinement of the performance of land surface models in these edge conditions, consisting of only 12 %–31 % of all site-timesteps, would see large improvements (22 %–114 %) in an aggregated performance metric. Better performance in the edge conditions could see mean relative improvements in the aggregated metric of 77 % for the latent heat flux, 48 % for the sensible heat flux, and 36 % for the net ecosystem exchange on average across all LSMs and sites. Precise targeting of model development towards these meteorological edge conditions offers a fruitful avenue to focus model development, ensuring future improvements have the greatest impact.

Droughts represent a significant source of social and economic damage in the southeast United States. Having sufficient warning of these extreme events enables managers to prepare for and potentially mitigate the severity of their impacts. A seasonal hydrologic forecast system can provide such warning, but current forecast skill is low during the convective season when precipitation is affected by regionally varying land surface heat flux contributions. Previous studies have classified regions into coupling regimes based on the tendency of surface soil moisture anomalies to trigger convective rainfall. Until now, these classifications have been aimed at assessing the long-term dominant feedback signal. Sufficient focus has not been placed on the temporal variability that underlies this signal. To better understand this aspect of coupling, a new classification methodology suitable at daily time scales is developed. The methodology is based on the joint probability space of surface soil moisture, convective triggering potential, and the low-level humidity index. The methodology is demonstrated over the U.S. Southeast using satellite remote sensing, reanalysis, and hydrological model data. The results show strong persistence in coupling events that is linked to the land surface state. A coupling-based drought index shows good agreement with the temporal and spatial variability of drought and highlights the role of coupling in drought recovery. The implications of the findings for drought and forecasting are discussed.

The increasing availability of remote sensing products for all components of the terrestrial water cycle makes it now possible to evaluate the potential of water balance closure purely from remote sensing sources. We take precipitation (P) from the TMPA and CMORPH products, a Penman‐Monteith based evapotranspiration (E) estimate derived from NASA Aqua satellite data and terrestrial water storage change (ΔS) from the GRACE satellite. Their combined ability to close the water budget is evaluated over the Mississippi River basin for 2003–5 by estimating streamflow (Q) as a residual of the water budget and comparing to streamflow measurements. We find that Q is greatly overestimated due mainly to the high bias in P, especially in the summer. Removal of systematic biases in P reduces the error significantly. However, uncertainties in the individual budget components due to simplifications in process algorithms and input data error are generally larger than the measured streamflow.

Recent summers in the United States have been plagued by intense droughts that have caused significant damage to crops and have had a large impact on society. The ability to forecasts such events would allow for preparations that could help reduce the impact on society. Coupled land–atmosphere–ocean models were created to provide such forecasts but there are large uncertainties associated with their predictions. The predictive skill of these models is particularly low during the convective season due to the weaker connections with the oceans and an increase in the land–atmosphere interactions. To better understand the degradation of forecasts skill during the summer months and its connection to the land–atmosphere interactions we analyze National Centers for Environmental Prediction’s Climate Forecast System Version 2 (CFSv2) in terms of its climatological land–atmosphere interactions. To do this we use a recently developed classification of land–atmosphere interactions and other diagnostic variables to compare the reanalysis from the Climate Forecast System (CFSR) with CFSv2 re-forecasts (CFSRR) over the period 1982–2009. Coupling in the CFSRR tends toward the wet coupling regime for most areas east of the Rocky Mountains. Although the specific mechanism driving CFSRR to wet coupling state varies by region, the overall cause is enhanced vegetation rooting depth, originally implemented to address a near-surface warm bias in CFSR. The long-term tendency to wet coupling precludes the forecast model from consistently predicting and maintaining drought over the continental US.

Global “hot spots” for land–atmosphere coupling have been identified through various modeling studies—both local and global in scope. One hot spot that is common to many of these analyses is the U.S. southern Great Plains (SGP). In this study, we perform a mesoscale analysis, enabled by the Oklahoma Mesonet, that bridges the spatial and temporal gaps between preceding local and global analyses of coupling. We focus primarily on east–west variations in seasonal coupling in the context of interannual variability over the period spanning 2000–15. Using North American Regional Reanalysis (NARR)-derived standardized anomalies of convective triggering potential (CTP) and the low-level humidity index (HI), we investigate changes in the covariance of soil moisture and the atmospheric low-level thermodynamic profile during seasonal hydrometeorological extremes. Daily CTP and HI z scores, dependent upon climatology at individual NARR grid points, were computed and compared to in situ soil moisture observations at the nearest mesonet station to provide nearly collocated annual composites over dry and wet soils. Extreme dry and wet year CTP and HI z-score distributions are shown to deviate significantly from climatology and therefore may constitute atmospheric precursors to extreme events. The most extreme rainfall years differ from climatology but also from one another, indicating variability in the strength of land–atmosphere coupling during these years. Overall, the covariance between soil moisture and CTP/HI is much greater during drought years, and coupling appears more consistent. For example, propagation of drought during 2011 occurred under antecedent CTP and HI conditions that were identified by this study as being conducive to positive dry feedbacks demonstrating potential utility of this framework in forecasting regional drought propagation.