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Matthew A. Campbell, Craig R. Ferguson, D. Alex Burrows, Mark Beauharnois, Geng Xia, and Lance F. Bosart


The Great Plains (GP) low-level jet (GPLLJ) contributes to GP warm season water resources (precipitation), wind resources, and severe weather outbreaks. Past research has shown that synoptic and local mesoscale physical mechanisms (Holton and Blackadar mechanisms) are required to explain GPLLJ variability. Although soil moisture–PBL interactions are central to local mechanistic theories, the diurnal effect of regional soil moisture anomalies on GPLLJ speed, northward penetration, and propensity for severe weather is not well known. In this study, two 31-member WRF-ARW stochastic kinetic energy backscatter scheme ensembles simulate a typical warm season GPLLJ case under CONUS-wide wet and dry soil moisture scenarios. In the GP (24°–48°N, 103°–90°W), ensemble mean differences in sensible heating and PBL height of 25–150 W m−2 and 100–700 m, respectively, at 2100 UTC (afternoon) culminate in GPLLJ 850-hPa wind speed differences of 1–4 m s−1 12 hours later (0900 UTC; early morning). Greater heat accumulation in the daytime PBL over dry soil impacts the east–west geopotential height gradient in the GP (synoptic conditions and Holton mechanism) resulting in a deeper thermal low in the northern GP, causing increases in the geostrophic wind. Enhanced daytime turbulent mixing over dry soil impacts the PBL structure (Blackadar mechanism), leading to increased ageostrophic wind. Overnight geostrophic and ageostrophic winds constructively interact, leading to a faster nocturnal GPLLJ over dry soil. Ensemble differences in CIN (~50–150 J kg−1) and CAPE (~500–1000 J kg−1) have implications for severe weather predictability.

Free access
Ryann A. Wakefield, Jeffrey B. Basara, Jason C. Furtado, Bradley G. Illston, Craig. R. Ferguson, and Petra M. Klein


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.

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Xiaogang He, Hyungjun Kim, Pierre-Emmanuel Kirstetter, Kei Yoshimura, Eun-Chul Chang, Craig R. Ferguson, Jessica M. Erlingis, Yang Hong, and Taikan Oki


As a basic form of climate patterns, the diurnal cycle of precipitation (DCP) can provide a key test bed for model reliability and development. In this study, the DCP over West Africa was simulated by the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) during the monsoon season (April–September) of 2005. Three convective parameterization schemes (CPSs), single-layer simplified Arakawa–Schubert (SAS), multilayer relaxed Arakawa–Schubert (RAS), and new Kain–Fritsch (KF2), were evaluated at two horizontal resolutions (20 and 10 km). The Benin mesoscale site was singled out for additional investigation of resolution effects. Harmonic analysis was used to characterize the phase and amplitude of the DCP. Compared to satellite observations, the overall spatial distributions of amplitude were well captured at regional scales. The RSM properly reproduced the observed late afternoon peak over land and the early morning peak over ocean. Nevertheless, the peak time was early. Sensitivity experiments of CPSs showed similar spatial patterns of rainfall totals among the schemes; CPSs mainly affected the amplitude of the diurnal cycle, while the phase was not significantly shifted. There is no clear optimal pairing of resolution and CPS. However, it is found that the sensitivity of DCP to CPSs and resolution varies with the partitioning between convective and stratiform, which implies that appropriate partitioning needs to be considered for future development of CPSs in global or regional climate models.

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Joseph A. Santanello Jr., Paul A. Dirmeyer, Craig R. Ferguson, Kirsten L. Findell, Ahmed B. Tawfik, Alexis Berg, Michael Ek, Pierre Gentine, Benoit P. Guillod, Chiel van Heerwaarden, Joshua Roundy, and Volker Wulfmeyer


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.

Open access