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The coupling of the land with the planetary boundary layer (PBL) on diurnal time scales is critical to regulating the strength of the connection between soil moisture and precipitation. To improve understanding of land-atmosphere (L-A) interactions, recent studies have focused on the development of diagnostics to quantify the strength and accuracy of the land-PBL coupling at the process level. In this paper, the authors apply a suite of local land-atmosphere coupling (LoCo) metrics to modern reanalysis (RA) products and observations during a 17-yr period over the U.S. southern Great Plains. Specifically, a range of diagnostics exploring the links between soil moisture, evaporation, PBL height, temperature, humidity, and precipitation is applied to the summertime monthly mean diurnal cycles of the North American Regional Reanalysis (NARR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Climate Forecast System Reanalysis (CFSR). Results show that CFSR is the driest and MERRA the wettest of the three RAs in terms of overall surface-PBL coupling. When compared against observations, CFSR has a significant dry bias that impacts all components of the land-PBL system. CFSR and NARR are more similar in terms of PBL dynamics and response to dry and wet extremes, while MERRA is more constrained in terms of evaporation and PBL variability. Each RA has a unique land-PBL coupling that has implications for downstream impacts on the diurnal cycle of PBL evolution, clouds, convection, and precipitation as well as representation of extremes and drought. As a result, caution should be used when treating RAs as truth in terms of their water and energy cycle processes. [ABSTRACT FROM AUTHOR]

Normalized mutual information (NMI) is a nonparametric measure of the dependence between two variables without assumptions about the shape of their bivariate data distributions, but the implementation and interpretation of NMI in the coupled climate system is more complicated than for linear correlations. This study presents a joint approach combining correlation and NMI to examine land and ocean surface forcing of U.S. drought at varying lead times. Based on the distribution of correlation versus NMI between a source variable (local or remote forcing) and target variable [e.g., summer precipitation in the southern Great Plains (SGP)], newly proposed one-tail significance levels for NMI combined with two-tailed significance levels of correlation enable us to discern linearity and nonlinearity dominant regimes in a more intuitive way. Our analysis finds that NMI can detect strong linear relationships like correlations, but it is not exclusively tuned to linear relationships as correlations are. Also, NMI can further identify nonlinear relationships, particularly when there are clusters and blank areas (high density and low density) in joint probability distributions between source and target variables (e.g., detected between soil moisture conditions in eastern Montana from mid-February to mid-August and summer precipitation in the SGP). The linear and nonlinear information are found to be sometimes mixed and rather convoluted with time, for instance, in the subtropical Pacific of the Southern Hemisphere, revealing relationships that cannot be fully detected by either NMI or correlation alone. Therefore, this joint approach is a potentially powerful tool to reveal complex and heretofore undetected relationships. [ABSTRACT FROM AUTHOR]

During the summer, the midwestern United States, which covers the main U.S. corn belt, has a net loss of surface water as evapotranspiration exceeds precipitation. The net moisture gain into the atmosphere is transported out of the region to the northern high latitudes through transient eddy moisture fluxes. How this process may change in the future is not entirely clear despite the fact that the corn-belt region is responsible for a large portion of the global supply of corn and soybeans. We find that increased CO2 and the associated warming increase evapotranspiration while precipitation reduces in the region, leading to further reduction in precipitation minus evaporation in the future. At the same time, the poleward transient moisture flux increases, leading to enhanced atmospheric moisture export from the corn-belt region. However, storm-track intensity is generally weakened in the summer because of a reduced north–south temperature gradient associated with amplified warming in the midlatitudes. The intensified transient eddy moisture transport as the storm track weakens can be reconciled by the stronger mean moisture gradient in the future. This is found to be caused by the climatological low-level jet transporting more moisture into the Great Plains region as a result of the thermodynamic mechanism under warmer conditions. Our results, for the first time, show that in the future the U.S. Midwest corn belt will experience more hydrological stress due to intensified transient eddy moisture export, leading to drier soils in the region. Significance Statement: The U.S. corn belt has dominated corn production in the United States since the 1850s, accounting for more than one-third of the global supply of corn, and is the world's largest source of soybeans. Yet how the region's hydroclimate responds to future climate change is still unclear. Here we found that increased CO2 and the associated warming increase evapotranspiration that takes more moisture out of the soil, thus leading to a drying effect. At the same time, the storms are becoming weaker and are shifted northward, leading to less precipitation in the corn-belt region. The excess moisture from the evapotranspiration is transported out of the corn-belt region by eddies. These processes together contribute to the corn belt's increased hydrological stress in the future. [ABSTRACT FROM AUTHOR]

Rapid-onset droughts, known as flash droughts, can have devastating impacts on agriculture, water resources, and ecosystems. The ability to predict flash droughts in advance would greatly enhance our preparation for them and potentially mitigate their impacts. Here, we investigate the prediction skill of the extreme 2012 flash drought over the U.S. Great Plains at subseasonal lead times (3 weeks or more in advance) in global forecast systems participating in the Subseasonal Experiment (SubX). An additional comprehensive set of subseasonal hindcasts with NASA’s GEOS model, a SubX model with relatively high prediction skill, was performed to investigate the separate contributions of atmospheric and land initial conditions to flash drought prediction skill. The results show that the prediction skill of the SubX models is quite variable.While skillful predictions are restricted to within the first two forecast weeks in most models, skill is considerably better (3–4 weeks or more) for certain models and initialization dates. The enhanced prediction skill is found to originate from two robust sources: 1) accurate soil moisture initialization once dry soil conditions are established, and 2) the satisfactory representation of quasi-stationary cross-Pacific Rossby wave trains that lead to the rapid intensification of flash droughts. Evidence is provided that the importance of soil moisture initialization applies more generally to central U.S. summer flash droughts. Our results corroborate earlier findings that accurate soil moisture initialization is important for skillful subseasonal forecasts and highlight the need for additional research on the sources and predictability of drought-inducing quasi-stationary atmospheric circulation anomalies. [ABSTRACT FROM AUTHOR]

The southern Great Plains (SGP) experienced a record-breaking drought in 2011, in which the excessively dry conditions established quickly in spring (i.e., April) and extended into summer. A regional climate model is used (after its evaluation) to simulate this April drought and investigate how a soil moisture anomaly could affect the development of its precipitation deficit. The authors examine how the local thermodynamic structure of the overlying atmosphere contributes to soil moisture feedbacks and how these feedbacks are connected to nonlocal mechanisms. The simulations establish a zonal gradient in the (generally positive) feedback strength [i.e., a significant (negligible) precipitation increase over the eastern (western) SGP] under an SGP-wide wet soil moisture anomaly and spatially similar evapotranspiration (ET) increments. This pattern is dominated by convective precipitation and consistent with spatial gradients in parameters relevant to moist convection, including the precipitable water, the low-level instability and humidity, and the local cloud water content. All these variables are sensitive to a wet soil moisture anomaly, but precipitation responds differently to their changes in different locations. Furthermore, the impacts of the soil moisture anomaly on various large-scale atmospheric fields are related to the spatial structure of feedback strength. Additionally, the weaker feedback over the western SGP occurs in a region of relatively strong subsidence and changes little with a westward expansion of the anomaly area, whereas nonlocal soil moisture impacts-in particular, moisture advection from the west-are important for the stronger feedback over the eastern SGP. [ABSTRACT FROM AUTHOR]

The semiarid U.S. Great Plains is prone to severe droughts having major consequences for agricultural production, livestock health, and river navigation. The recent 2012 event was accompanied by record deficits in precipitation and high temperatures during the May-August growing season. Here the physics of Great Plains drought are explored by addressing how meteorological drivers induce soil moisture deficits during the growing season. Land surface model (LSM) simulations driven by daily observed meteorological forcing from 1950 to 2013 compare favorably with satellite-derived terrestrial water anomalies and reproduce key features found in the U.S. Drought Monitor. Results from simulations by two LSMs reveal that precipitation was directly responsible for between 72% and 80% of the soil moisture depletion during 2012, and likewise has accounted for the majority of Great Plains soil moisture variability since 1950. Energy balance considerations indicate that a large fraction of the growing season temperature variability is itself driven by precipitation, pointing toward an even larger net contribution of precipitation to soil moisture variability. To assess robustness across a larger sample of drought events, daily meteorological output from 1050 years of climate simulations, representative of conditions in 1979-2013, are used to drive two LSMs. Growing season droughts, and low soil moisture conditions especially, are confirmed to result principally from rainfall deficits. Antecedent meteorological and soil moisture conditions are shown to affect growing season soil moisture, but their effects are secondary to forcing by contemporaneous rainfall deficits. This understanding of the physics of growing season droughts is used to comment on plausible Great Plains soil moisture changes in a warmer world. [ABSTRACT FROM AUTHOR]