An Objective Classification and Analysis of Upper-Level Coupling to the Great Plains Low-Level Jet over the Twentieth Century

D. Alex Burrows Department of Atmospheric and Environmental Sciences, and Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, New York

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Craig R. Ferguson Department of Atmospheric and Environmental Sciences, and Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, New York

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Matthew A. Campbell Department of Atmospheric and Environmental Sciences, and Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, New York

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Geng Xia Department of Atmospheric and Environmental Sciences, and Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, New York

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Lance F. Bosart Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, New York

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Abstract

Low-level jets (LLJ) around the world critically support the food, water, and energy security in regions that they traverse. For the purposes of development planning and weather and climate prediction, it is important to improve understanding of how LLJs interact with the land surface and upper-atmospheric flow, and collectively, how LLJs have and may change over time. This study details the development and application of a new automated, dynamical objective classification of upper-atmospheric jet stream coupling based on a merging of the Bonner–Whiteman vertical wind shear classification and the finite-amplitude local wave activity diagnostic. The classification approach is transferable globally, but applied here only for the Great Plains (GP) LLJ (GPLLJ). The analysis spans the period from 1901 to 2010, enabled by the ECMWF climate-quality, coupled Earth reanalysis of the twentieth century. Overall, statistically significant declines in total GPLLJ event frequency over the twentieth century are detected across the entire GP corridor and attributed to declines in uncoupled GPLLJ frequency. Composites of lower- and upper-atmospheric flow are shown to capture major differences in the climatological, coupled GPLLJ, and uncoupled GPLLJ synoptic environments. Detailed analyses for southern, central, and northern GP subregions further highlight synoptic differences between weak and strong GPLLJs and provide quantification of correlations between total, coupled, and uncoupled GPLLJ frequencies and relevant atmospheric anomalies. Because uncoupled GPLLJs tend to be associated with decreased precipitation and low-level wind speed and enhanced U.S. ridge strength, this finding may suggest that support for drought over the twentieth century has waned.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-18-0891.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: D. Alex Burrows, daburrows@albany.edu

Abstract

Low-level jets (LLJ) around the world critically support the food, water, and energy security in regions that they traverse. For the purposes of development planning and weather and climate prediction, it is important to improve understanding of how LLJs interact with the land surface and upper-atmospheric flow, and collectively, how LLJs have and may change over time. This study details the development and application of a new automated, dynamical objective classification of upper-atmospheric jet stream coupling based on a merging of the Bonner–Whiteman vertical wind shear classification and the finite-amplitude local wave activity diagnostic. The classification approach is transferable globally, but applied here only for the Great Plains (GP) LLJ (GPLLJ). The analysis spans the period from 1901 to 2010, enabled by the ECMWF climate-quality, coupled Earth reanalysis of the twentieth century. Overall, statistically significant declines in total GPLLJ event frequency over the twentieth century are detected across the entire GP corridor and attributed to declines in uncoupled GPLLJ frequency. Composites of lower- and upper-atmospheric flow are shown to capture major differences in the climatological, coupled GPLLJ, and uncoupled GPLLJ synoptic environments. Detailed analyses for southern, central, and northern GP subregions further highlight synoptic differences between weak and strong GPLLJs and provide quantification of correlations between total, coupled, and uncoupled GPLLJ frequencies and relevant atmospheric anomalies. Because uncoupled GPLLJs tend to be associated with decreased precipitation and low-level wind speed and enhanced U.S. ridge strength, this finding may suggest that support for drought over the twentieth century has waned.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-18-0891.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: D. Alex Burrows, daburrows@albany.edu

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  • Arritt, R. W., T. D. Rink, M. Segal, D. P. Todey, C. A. Clark, M. J. Mitchell, and K. M. Labas, 1997: The Great Plains low-level jet during the warm season of 1993. Mon. Wea. Rev., 125, 21762192, https://doi.org/10.1175/1520-0493(1997)125<2176:TGPLLJ>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • AWEA, 2018: Wind facts at a glance. American Wind Energy Association, https://www.awea.org/wind-101/basics-of-wind-energy/wind-facts-at-a-glance.

  • Basara, J. B., J. N. Maybourn, C. M. Peirano, J. E. Tate, P. J. Brown, J. D. Hoey, and B. R. Smith, 2013: Drought and associated impacts in the Great Plains of the United States—A review. Int. J. Geosci., 4, 7281, https://doi.org/10.4236/ijg.2013.46A2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamini, Y., and Y. Hochberg, 1995: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Stat. Soc., 57A, 289300, https://doi.org/10.1111/J.2517-6161.1995.TB02031.X.

    • Search Google Scholar
    • Export Citation
  • Berg, L. K., and P. J. Lamb, 2016: Surface properties and interactions: Coupling the land and atmosphere within the ARM Program. The Atmospheric Radiation Measurement (ARM) Program: The First 20 Years, Meteor. Monogr., No. 57, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0044.1.

    • Crossref
    • Export Citation
  • Blackadar, A. K., 1957: Boundary layer wind maxima and their significance for the growth of nocturnal inversions. Bull. Amer. Meteor. Soc., 38, 283290, https://doi.org/10.1175/1520-0477-38.5.283.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonner, W. D., 1968: Climatology of the low level jet. Mon. Wea. Rev., 96, 833850, https://doi.org/10.1175/1520-0493(1968)096<0833:COTLLJ>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bouillon, S., M. A. Morales Maqueda, V. Legat, and T. Fichefet, 2009: An elastic-viscous-plastic sea ice model formulated on Arakawa B and C grids. Ocean Modell., 27, 174184, https://doi.org/10.1016/j.ocemod.2009.01.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Budikova, D., T. W. Ford, and T. J. Ballinger, 2017: Connections between north-central United States summer hydroclimatology and Arctic sea ice variability. Int. J. Climatol., 37, 44344450, https://doi.org/10.1002/joc.5097.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Burrows, D. A., C. R. Ferguson, and L. F. Bosart, 2019: Great Plains low-level jet occurrence and upper-level coupling in CERA-20C. NCAR Research Data Archive, Computational and Information Systems Laboratory, accessed 29 July 2019, https://doi.org/10.5065/KDB5-9X72.

    • Crossref
    • Export Citation
  • Butchart, N., and E. E. Remsberg, 1986: The area of the stratospheric polar vortex as a diagnostic for tracer transport on an isentropic surface. J. Atmos. Sci., 43, 13191339, https://doi.org/10.1175/1520-0469(1986)043<1319:TAOTSP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Case, J. L., B. T. Zavodsky, F. J. Lafontaine, and J. R. Bell, 2014: Real-time green vegetation fraction for land surface and numerical weather prediction models. IEEE Trans. Geosci. Remote Sens., 52, 17721786, https://doi.org/10.1109/TGRS.2013.2255059.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, G., J. Lu, D. A. Burrows, and R. L. Leung, 2015: Local finite-amplitude wave activity as an objective of midlatitude extreme weather. Geophys. Res. Lett., 42, 10 95210 960, https://doi.org/10.1002/2015GL066959.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Danco, J. F., and E. R. Martin, 2018: Understanding the influence of ENSO on the Great Plains low-level jet in CMIP5 models. Climate Dyn., 51, 15371558, https://doi.org/10.1007/s00382-017-3970-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., R. D. Koster, and Z. Guo, 2006: Do global models properly represent the feedback between land and atmosphere? J. Hydrometeor., 7, 11771198, https://doi.org/10.1175/JHM532.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dole, R. M., and N. D. Gordon, 1983: Persistent anomalies of the extratropical Northern Hemisphere wintertime circulation: Geographical distribution and regional persistence characteristics. Mon. Wea. Rev., 111, 15671586, https://doi.org/10.1175/1520-0493(1983)111<1567:PAOTEN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fast, J. D., and M. D. McCorcle, 1990: A two-dimensional numerical sensitivity study of the Great Plains low-level jet. Mon. Wea. Rev., 118, 151164, https://doi.org/10.1175/1520-0493(1990)118<0151:ATDNSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, X., K. Haines, C. Liu, E. De Boisséson, and I. Polo, 2018: Improved SST–precipitation intraseasonal relationships in the ECMWF coupled climate reanalysis. Geophys. Res. Lett., 45, 36643672, https://doi.org/10.1029/2018GL077138.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferguson, C. R., and G. Villarini, 2014: An evaluation of the statistical homogeneity of the Twentieth Century Reanalysis. Climate Dyn., 42, 28412866, https://doi.org/10.1007/s00382-013-1996-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferguson, C. R., and D. M. Mocko, 2017: Diagnosing an artificial trend in NLDAS-2 afternoon precipitation. J. Hydrometeor., 18, 10511070, https://doi.org/10.1175/JHM-D-16-0251.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fichefet, T., and M. A. Maqueda, 1997: Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics. J. Geophys. Res., 102, 12 60912 646, https://doi.org/10.1029/97JC00480.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ford, T. W., A. D. Rapp, and S. M. Quiring, 2015: Does afternoon precipitation occur preferentially over dry or wet soils in Oklahoma? J. Hydrometeor., 16, 874888, https://doi.org/10.1175/JHM-D-14-0005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frye, J. D., and T. L. Mote, 2010: The synergistic relationship between soil moisture and the low-level jet and its role on the prestorm environment in the southern Great Plains. J. Appl. Meteor. Climatol., 49, 775791, https://doi.org/10.1175/2009JAMC2146.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Higgins, R. W., Y. Yao, E. S. Yarosh, J. E. Janowiak, and K. C. Mo, 1997: Influence of the Great Plains low-level jet on summertime precipitation and moisture transport over the central United States. J. Climate, 10, 481507, https://doi.org/10.1175/1520-0442(1997)010<0481:IOTGPL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hodges, D., and Z. Pu, 2019: Characteristics and variations of low-level jets in the contrasting warm season precipitation extremes of 2006 and 2007 over the southern Great Plains. Theor. Appl. Climatol., 136, 753771, https://doi.org/10.1007/S00704-018-2492-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoerling, M. P., J. Eischeid, A. Kumar, R. Leung, A. Mariotti, K. Mo, S. D. Schubert, and R. Seager, 2014: Causes and predictability of the 2012 Great Plains drought. Bull. Amer. Meteor. Soc., 95, 269282, https://doi.org/10.1175/BAMS-D-13-00055.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holton, J. R., 1967: The diurnal boundary layer wind oscillation above sloping terrain. Tellus, 19, 200205, https://doi.org/10.3402/tellusa.v19i2.9766.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, C. S.-Y., and N. Nakamura, 2016: Local finite-amplitude wave activity as a diagnostic of anomalous weather events. J. Atmos. Sci., 73, 211229, https://doi.org/10.1175/JAS-D-15-0194.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, C., and L. M. V. Carvalho, 2018: The influence of the Atlantic multidecadal oscillation on the eastern Andes low-level jet and precipitation in South America. npj Climate Atmos. Sci., 1, 40, https://doi.org/10.1038/S41612-018-0050-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kam, J., J. Sheffield, and E. F. Wood, 2014: Changes in drought risk over the contiguous United States (1901–2012): The influence of the Pacific and Atlantic Oceans. Geophys. Res. Lett., 41, 58975903, https://doi.org/10.1002/2014GL060973.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kendall, M. G., 1975: Rank Correlation Methods. 4th ed. Charles Griffin, 199 pp.

  • Kumar, S. V., and Coauthors, 2006: Land information system: An interoperable framework for high resolution land surface modeling. Environ. Modell. Software, 21, 14021415, https://doi.org/10.1016/j.envsoft.2005.07.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Laloyaux, P., E. De Boisséson, and P. Dahlgren, 2017: CERA-20C: An Earth system approach to climate reanalysis. ECMWF Newsletter, No. 150, ECMWF, Reading, United Kingdom, 25–30, https://doi.org/10.21957/ffs36birj2.

    • Crossref
    • Export Citation
  • Laloyaux, P., and Coauthors, 2018: CERA-20C: A coupled reanalysis of the twentieth century. J. Adv. Model. Earth Syst., 10, 11721195, https://doi.org/10.1029/2018MS001273.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, G., Y. Liu, and S. Endo, 2013: Evaluation of surface flux parameterizations with long-term ARM observations. Mon. Wea. Rev., 141, 773797, https://doi.org/10.1175/MWR-D-12-00095.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Livezey, R. E., and W. Y. Chen, 1983: Statistical field significance and its determination by Monte Carlo techniques. Mon. Wea. Rev., 111, 4659, https://doi.org/10.1175/1520-0493(1983)111<0046:SFSAID>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madec, G., 2008: NEMO ocean engine. Institut Pierre-Simon Laplace Note du Pole de Modelisation 27, 386 pp.

  • Mann, H. B., 1945: Nonparametric tests against trend. Econometrica, 13, 245259, https://doi.org/10.2307/1907187.

  • Marengo, J. A., W. R. Soares, C. Saulo, and M. Nicolini, 2004: Climatology of the low-level jet east of the Andes as derived from the NCEP–NCAR reanalyses: Characteristics and temporal variability. J. Climate, 17, 22612280, https://doi.org/10.1175/1520-0442(2004)017<2261:COTLJE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martineau, P., G. Chen, and D. A. Burrows, 2017: Wave events: Climatology, trends, and relationship to Northern Hemisphere winter blocking and weather extremes. J. Climate, 30, 56755697, https://doi.org/10.1175/JCLI-D-16-0692.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCabe, G. J., M. A. Palecki, and J. L. Betancourt, 2004: Pacific and Atlantic Ocean influences on multidecadal drought frequency in the United States. Proc. Natl. Acad. Sci. USA, 101, 41364141, https://doi.org/10.1073/pnas.0306738101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Melillo, J. M., T. Richmond, and G. W. Yohe, 2014: Climate Change Impacts in the United States: The Third National Climate Assessment. U.S. Global Change Research Program, 841 pp., https://www.globalchange.gov/browse/reports/climate-change-impacts-united-states-third-national-climate-assessment-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mitchell, M. J., R. W. Arritt, and K. Labas, 1995: A climatology of the warm season Great Plains low-level jet using wind profiler observations. Wea. Forecasting, 10, 576591, https://doi.org/10.1175/1520-0434(1995)010<0576:ACOTWS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muñoz, E., and D. B. Enfield, 2011: The boreal spring variability of the Intra-Americas low-level jet and its relation with precipitation and tornadoes in the eastern United States. Climate Dyn., 36, 247259, https://doi.org/10.1007/s00382-009-0688-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakamura, N., and D. Zhu, 2010: Finite-amplitude wave activity and diffusive flux of potential vorticity in eddy–mean flow interaction. J. Atmos. Sci., 67, 27012716, https://doi.org/10.1175/2010JAS3432.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakamura, N., and A. Solomon, 2011: Finite-amplitude wave activity and mean flow adjustments in the atmospheric general circulation. Part II: Analysis in the isentropic coordinate. J. Atmos. Sci., 68, 27832799, https://doi.org/10.1175/2011JAS3685.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Newton, C. W., 1967: Severe convective storms. Advances in Geophysics, Vol. 12, Academic Press, 257–308, https://doi.org/10.1016/S0065-2687(08)60377-5.

    • Crossref
    • Export Citation
  • Ortegren, J. T., P. A. Knapp, J. T. Maxwell, W. P. Tyminski, and P. T. Soulé, 2011: Ocean–atmosphere influences on low-frequency warm-season drought variability in the Gulf Coast and southeastern United States. J. Appl. Meteor. Climatol., 50, 11771186, https://doi.org/10.1175/2010JAMC2566.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rife, D. L., J. O. Pinto, A. J. Monaghan, C. A. Davis, and J. R. Hannan, 2010: Global distribution and characteristics of diurnally varying low-level jets. J. Climate, 23, 50415064, https://doi.org/10.1175/2010JCLI3514.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ruiz-Barradas, A., and S. Nigam, 2013: Atmosphere-land surface interactions over the southern Great Plains: Characterization from pentad analysis of DOE ARM field observations and NARR. J. Climate, 26, 875886, https://doi.org/10.1175/JCLI-D-11-00380.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schubert, S. D., M. J. Suarez, P. J. Pegion, R. D. Koster, and J. T. Bacmeister, 2004: On the cause of the 1930s Dust Bowl. Science, 303, 18551859, https://doi.org/10.1126/science.1095048.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sen, P. K., 1968: Estimates of the regression coefficient based on Kendall’s tau. J. Amer. Stat. Assoc., 63, 13791389, https://doi.org/10.1080/01621459.1968.10480934.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shapiro, A., E. Fedorovich, and S. Rahimi, 2016: A unified theory for the Great Plains nocturnal low-level jet. J. Atmos. Sci., 73, 30373057, https://doi.org/10.1175/JAS-D-15-0307.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sisterson, D. L., R. A. Peppler, T. S. Cress, P. J. Lamb, and D. D. Turner, 2016: The ARM Southern Great Plains (SGP) Site. The Atmospheric Radiation Measurement (ARM) Program: The First 20 Years, Meteor. Monogr., No. 57, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0004.1.

    • Crossref
    • Export Citation
  • Song, J., K. Liao, R. L. Coulter, and B. M. Lesht, 2005: Climatology of the low-level jet at the Southern Great Plains Atmospheric Boundary Layer Experiments site. J. Appl. Meteor., 44, 15931606, https://doi.org/10.1175/JAM2294.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Squitieri, B. J., and W. A. Gallus, 2016a: WRF forecasts of Great Plains nocturnal low-level jet-driven MCSs. Part I: Correlation between low-level jet forecast accuracy and MCS precipitation forecast skill. Wea. Forecasting, 31, 13011323, https://doi.org/10.1175/WAF-D-15-0151.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Squitieri, B. J., and W. A. Gallus, 2016b: WRF forecasts of Great Plains nocturnal low-level jet-driven MCSs. Part II: Differences between strongly and weakly forced low-level jet environments. Wea. Forecasting, 31, 14911510, https://doi.org/10.1175/WAF-D-15-0150.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Theil, H., 1950a: A rank-invariant method of linear and polynomial regression analysis, i. Proc. K. Ned. Akad. Wet., 53A, 386392.

  • Theil, H., 1950b: A rank-invariant method of linear and polynomial regression analysis, ii. Proc. K. Ned. Akad. Wet., 53A, 521525.

  • Theil, H., 1950c: A rank-invariant method of linear and polynomial regression analysis, iii. Proc. K. Ned. Akad. Wet., 53A, 13971412.

    • Search Google Scholar
    • Export Citation
  • Thorne, P. W., and R. S. Vose, 2010: Reanalyses suitable for characterizing long-term trends. Bull. Amer. Meteor. Soc., 91, 353361, https://doi.org/10.1175/2009BAMS2858.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ting, M., and H. Wang, 2006: The role of the North American topography on the maintenance of the Great Plains summer low-level jet. J. Atmos. Sci., 63, 10561068, https://doi.org/10.1175/JAS3664.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., G. W. Branstator, and P. A. Arkin, 1988: Origins of the 1988 North American drought. Science, 242, 16401645, https://doi.org/10.1126/science.242.4886.1640.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uccellini, L. W., 1980: On the role of upper tropospheric jet streaks and leeside cyclogenesis in the development of low-level jets in the Great Plains. Mon. Wea. Rev., 108, 16891696, https://doi.org/10.1175/1520-0493(1980)108<1689:OTROUT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uccellini, L. W., and D. R. Johnson, 1979: The coupling of upper and lower tropospheric jet streaks and implications for the development of severe convective storms. Mon. Wea. Rev., 107, 682703, https://doi.org/10.1175/1520-0493(1979)107<0682:TCOUAL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walters, C. K., and J. A. Winkler, 2001: Airflow configurations of warm season southerly low-level wind maxima in the Great Plains. Part I: Spatial and temporal characteristics and relationship to convection. Wea. Forecasting, 16, 531551, https://doi.org/10.1175/1520-0434(2001)016<0531:ACOWSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weaver, S. J., and S. Nigam, 2008: Variability of the Great Plains low-level jet: Large-scale circulation context and hydroclimate impacts. J. Climate, 21, 15321551, https://doi.org/10.1175/2007JCLI1586.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weaver, S. J., A. Ruiz-Barradas, and S. Nigam, 2009: Pentad evolution of the 1988 drought and 1993 flood over the Great Plains: An NARR perspective on the atmospheric and terrestrial water balance. J. Climate, 22, 53665384, https://doi.org/10.1175/2009JCLI2684.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wexler, H., 1961: A boundary layer interpretation of the low-level jet. Tellus, 13, 368378, https://doi.org/10.3402/tellusa.v13i3.9513.

  • Whiteman, C. D., X. Bian, and S. Zhong, 1997: Low-level jet climatology from enhanced rawinsonde observations at a site in the southern Great Plains. J. Appl. Meteor., 36, 13631376, https://doi.org/10.1175/1520-0450(1997)036<1363:LLJCFE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, S., X. Ding, D. Zheng, and Q. Li, 2007: Depiction of the variations of Great Plains precipitation and its relationship with tropical central-eastern Pacific SST. J. Appl. Meteor. Climatol., 46, 136153, https://doi.org/10.1175/JAM2455.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, L., S. Zhong, J. A. Winkler, D. L. Doubler, X. Bian, and C. K. Walters, 2017: The inter-annual variability of southerly low-level jets in North America. Int. J. Climatol., 37, 343357, https://doi.org/10.1002/joc.4708.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yue, S., P. Pilon, B. Phinney, and G. Cavadias, 2002: The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol. Processes, 16, 18071829, https://doi.org/10.1002/hyp.1095.

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  • Zhu, J., and X.-Z. Liang, 2013: Impacts of the Bermuda high on regional climate and ozone over the United States. J. Climate, 26, 10181032, https://doi.org/10.1175/JCLI-D-12-00168.1.

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