Land surface phenology along urban to rural gradients in the U.S. Great Plains

Soil Moisture Influence on the Incidence of Summer Mesoscale Convective Systems in the U.S. Great Plains

Monthly Weather Review ◽

10.1175/mwr-d-21-0140.1 ◽

2021 ◽

Author(s):

Rachel Gaal ◽

James L. Kinter

Keyword(s):

Soil Moisture ◽

Great Plains ◽

Land Surface ◽

Large Scale ◽

Mesoscale Convective Systems ◽

Antecedent Soil Moisture ◽

Initiation Point ◽

Convective Systems ◽

Mesoscale Convective ◽

The U.S

AbstractMesoscale convective systems (MCS) are known to develop under ideal conditions of temperature and humidity profiles and large-scale dynamic forcing. Recent work, however, has shown that summer MCS events can occur under weak synoptic forcing or even unfavorable large-scale environments. When baroclinic forcing is weak, convection may be triggered by anomalous conditions at the land surface. This work evaluates land surface conditions for summer MCS events forming in the U.S. Great Plains using an MCS database covering the contiguous United States east of the Rocky Mountains, in boreal summers 2004-2016. After isolating MCS cases where synoptic-scale influences are not the main driver of development (i.e. only non-squall line storms), antecedent soil moisture conditions are evaluated over two domain sizes (1.25° and 5° squares) centered on the mean position of the storm initiation. A negative correlation between soil moisture and MCS initiation is identified for the smaller domain, indicating that MCS events tend to be initiated over patches of anomalously dry soils of ~100-km scale, but not significantly so. For the larger domain, soil moisture heterogeneity, with anomalously dry soils (anomalously wet soils) located northeast (southwest) of the initiation point, is associated with MCS initiation. This finding is similar to previous results in the Sahel and Europe that suggest that induced meso-β circulations from surface heterogeneity can drive convection initiation.

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Dryness over the U.S. Southwest, a Springboard for Cold Season Pacific SST to Influence Warm Season Drought over the U.S. Great Plains

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0029.1 ◽

2021 ◽

Vol 22 (1) ◽

pp. 63-76

Author(s):

Yizhou Zhuang ◽

Amir Erfanian ◽

Rong Fu

Keyword(s):

Great Plains ◽

Land Surface ◽

Rainfall Variability ◽

Warm Season ◽

Summer Precipitation ◽

Cold Season ◽

Early Summer ◽

Moisture Condition ◽

Moisture Advection ◽

The U.S

AbstractAlthough the influence of sea surface temperature (SST) forcing and large-scale teleconnection on summer droughts over the U.S. Great Plains has been suggested for decades, the underlying mechanisms are still not fully understood. Here we show a significant correlation between low-level moisture condition over the U.S. Southwest in spring and rainfall variability over the Great Plains in summer. Such a connection is due to the strong influence of the Southwest dryness on the zonal moisture advection to the Great Plains from spring to summer. This advection is an important contributor for the moisture deficit during spring to early summer, and so can initiate warm season drought over the Great Plains. In other words, the well-documented influence of cold season Pacific SST on the Southwest rainfall in spring, and the influence of the latter on the zonal moisture advection to the Great Plains from spring to summer, allows the Pacific climate variability in winter and spring to explain over 35% of the variance of the summer precipitation over the Great Plains, more than that can be explained by the previous documented west Pacific–North America (WPNA) teleconnection forced by tropical Pacific SST in early summer. Thus, this remote land surface feedback due to the Southwest dryness can potentially improve the predictability of summer precipitation and drought onsets over the Great Plains.

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The Physics of Drought in the U.S. Central Great Plains

Journal of Climate ◽

10.1175/jcli-d-15-0697.1 ◽

2016 ◽

Vol 29 (18) ◽

pp. 6783-6804 ◽

Cited By ~ 42

Author(s):

Ben Livneh ◽

Martin P. Hoerling

Keyword(s):

Soil Moisture ◽

Great Plains ◽

Land Surface ◽

Growing Season ◽

Surface Model ◽

Moisture Conditions ◽

Soil Moisture Variability ◽

The U.S ◽

Rainfall Deficits ◽

Moisture Variability

Abstract 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.

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Land-Cover Change and the “Dust Bowl” Drought in the U.S. Great Plains

Journal of Climate ◽

10.1175/jcli-d-17-0515.1 ◽

2018 ◽

Vol 31 (12) ◽

pp. 4657-4667 ◽

Cited By ~ 7

Author(s):

Qi Hu ◽

Jose Abraham Torres-Alavez ◽

Matthew S. Van Den Broeke

Keyword(s):

Land Cover ◽

Great Plains ◽

Land Surface ◽

Large Scale ◽

Surface Condition ◽

Moisture Flux ◽

Dust Bowl ◽

Large Scale Circulation ◽

The U.S ◽

Sst Anomalies

The North American Dust Bowl drought during the 1930s had devastating environmental and societal impacts. Comprehending the causes of the drought has been an ongoing effort in order to better predict similar droughts and mitigate their impacts. Among the potential causes of the drought are sea surface temperature (SST) anomalies in the tropical Pacific Ocean and strengthened local sinking motion as a feedback to degradation of the land surface condition leading up to and during the drought. Limitations on these causes are the lack of a strong tropical SST anomaly during the drought and lack of local anomaly in moisture supply to undercut the precipitation in the U.S. Great Plains. This study uses high-resolution modeling experiments and quantifies an effect of the particular Great Plains land cover in the 1930s that weakens the southerly moisture flux to the region. This effect lowers the average precipitation, making the Great Plains more susceptible to drought. When drought occurs, the land-cover effect enhances its intensity and prolongs its duration. Results also show that this land-cover effect is comparable in magnitude to the effect of the 1930s large-scale circulation anomaly. Finally, analysis of the relationship of these two effects suggests that while lowering the precipitation must have contributed to the Dust Bowl drought via the 1930s land-cover effect, the initiation of and recovery from that drought would likely result from large-scale circulation changes, either of chaotic origin or resulting from combinations of weak SST anomalies and other forcing.

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On the Link between Summer Dry Bias over the U.S. Great Plains and Seasonal Temperature Prediction Skill in a Dynamical Forecast System

Weather and Forecasting ◽

10.1175/waf-d-19-0023.1 ◽

2019 ◽

Vol 34 (4) ◽

pp. 1161-1172 ◽

Cited By ~ 2

Author(s):

Constantin Ardilouze ◽

Lauriane Batté ◽

Bertrand Decharme ◽

Michel Déqué

Keyword(s):

Great Plains ◽

Land Surface ◽

Climate Models ◽

Warm Season ◽

Seasonal Temperature ◽

Forecast System ◽

Temperature And Precipitation ◽

The Impact ◽

The U.S ◽

Dry Bias

Abstract Soil moisture anomalies are expected to be a driver of summer predictability for the U.S. Great Plains since this region is prone to intense and year-to-year varying water and energy exchange between the land and the atmosphere. However, dynamical seasonal forecast systems struggle to deliver skillful summer temperature forecasts over that region, otherwise subject to a consistent warm-season dry bias in many climate models. This study proposes two techniques to mitigate the impact of this precipitation deficit on the modeled soil water content in a forecast system based on the CNRM-CM6-1 model. Both techniques lead to increased evapotranspiration during summer and reduced temperature and precipitation bias. However, only the technique based on a correction of the precipitation feeding the land surface throughout the forecast integration enables skillful summer prediction. Although this result cannot be generalized for other parts of the globe, it confirms the link between bias and skill over the U.S. Great Plains and pleads for continued efforts of the modeling community to tackle the summer bias affecting that region.

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Diagnosing the Nature of Land–Atmosphere Coupling: A Case Study of Dry/Wet Extremes in the U.S. Southern Great Plains

Journal of Hydrometeorology ◽

10.1175/jhm-d-12-023.1 ◽

2013 ◽

Vol 14 (1) ◽

pp. 3-24 ◽

Cited By ~ 58

Author(s):

Joseph A. Santanello ◽

Christa D. Peters-Lidard ◽

Aaron Kennedy ◽

Sujay V. Kumar

Keyword(s):

Great Plains ◽

Land Surface ◽

Critical Role ◽

Land Surface Model ◽

Coupled System ◽

Surface Model ◽

Test Bed ◽

Southern Great Plains ◽

Pbl Scheme ◽

The U.S

Abstract Land–atmosphere (L–A) interactions play a critical role in determining the diurnal evolution of land surface and planetary boundary layer (PBL) temperature and moisture states and fluxes. In turn, these interactions regulate the strength of the connection between surface moisture and precipitation in a coupled system. To address model deficiencies, recent studies have focused on development of diagnostics to quantify the strength and accuracy of the land–PBL coupling at the process level. In this paper, a diagnosis of the nature and impacts of local land–atmosphere coupling (LoCo) during dry and wet extreme conditions is presented using a combination of models and observations during the summers of 2006 and 2007 in the U.S. southern Great Plains. A range of diagnostics exploring the links and feedbacks between soil moisture and precipitation is applied to the dry/wet regimes exhibited in this region, and in the process, a thorough evaluation of nine different land–PBL scheme couplings is conducted under the umbrella of a high-resolution regional modeling test bed. Results show that the sign and magnitude of errors in land surface energy balance components are sensitive to the choice of land surface model, regime type, and running mode. In addition, LoCo diagnostics show that the sensitivity of L–A coupling is stronger toward the land during dry conditions, while the PBL scheme coupling becomes more important during the wet regime. Results also demonstrate how LoCo diagnostics can be applied to any modeling system (e.g., reanalysis products) in the context of their integrated impacts on the process chain connecting the land surface to the PBL and in support of hydrological anomalies.

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Assessment of Remotely Sensed and Modelled Soil Moisture Data Products in the U.S. Southern Great Plains

Remote Sensing ◽

10.3390/rs12122030 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2030

Author(s):

Bo Jiang ◽

Hongbo Su ◽

Kai Liu ◽

Shaohui Chen

Keyword(s):

Soil Moisture ◽

Great Plains ◽

Land Surface ◽

European Space Agency ◽

Remotely Sensed ◽

The Other ◽

Short Term ◽

Southern Great Plains ◽

The U.S

Soil moisture (SM) plays a crucial role in the water and energy flux exchange between the atmosphere and the land surface. Remote sensing and modeling are two main approaches to obtain SM over a large-scale area. However, there is a big difference between them due to algorithm, spatial-temporal resolution, observation depth and measurement uncertainties. In this study, an assessment of the comparison of two state-of-the-art remotely sensed SM products, Soil Moisture Active Passive (SMAP) and European Space Agency Climate Change Initiative (ESACCI), and one land surface modeled dataset from the North American Land Data Assimilation System project phase 2 (NLDAS-2), were conducted using 17 permanent SM observation sites located in the Southern Great Plains (SGP) in the U.S. We first compared the daily mean SM of three products with in-situ measurements; then, we decompose the raw time series into a short-term seasonal part and anomaly by using a moving smooth window (35 days). In addition, we calculate the daily spatial difference between three products based on in-situ data and assess their temporal evolution. The results demonstrate that (1) in terms of temporal correlation R, the SMAP (R = 0.78) outperforms ESACCI (R = 0.62) and NLDAS-2 (R = 0.72) overall; (2) for the seasonal component, the correlation R of SMAP still outperforms the other two products, and the correlation R of ESACCI and NLDAS-2 have not improved like the SMAP; as for anomaly, there is no difference between the remotely sensed and modeling data, which implies the potential for the satellite products to capture the variations of short-term rainfall events; (3) the distribution pattern of spatial bias is different between the three products. For NLDAS-2, it is strongly dependent on precipitation; meanwhile, the spatial distribution of bias represents less correlation with the precipitation for two remotely sensed products, especially for the SMAP. Overall, the SMAP was superior to the other two products, especially when the SM was of low value. The difference between the remotely sensed and modeling products with respect to the vegetation type might be an important reason for the errors.

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