Synoptic-Scale Meteorological Patterns Associated with Heavy Rainfall in the Minnesota Region

Rory Laiho aDepartment of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado

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Katja Friedrich aDepartment of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado

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Andrew C. Winters aDepartment of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado

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Abstract

Situated in the Upper Midwest, Minnesota’s midcontinental location places it in a climate transition zone between eastern U.S. humid conditions and western semiarid conditions as well as between warm, moist air from the Gulf of Mexico to the south and drier, polar air to the north. Potential adverse impacts on ecosystems due to changing climate and precipitation patterns, together with ongoing flash flooding risks, indicate that heavy rainfall occurrence and distribution are important considerations for Minnesota. This research used ERA5 reanalysis data with 0.25° grid spacing during May–September 1959–2021 to investigate the synoptic-scale drivers of Minnesota heavy rainfall. The study utilized a neural network, self-organizing map (SOM) technique to identify sea level pressure patterns and precipitation patterns associated with heavy rainfall and used composite analysis to explore the relationships between synoptic-scale conditions and environmental parameters during heavy rain hours. Six sea level pressure patterns were identified, three of which represented advancing surface cyclones and accounted for >70% of the heavy rain hours. The spatial distribution of heavy rainfall was represented by six precipitation patterns. The greatest frequency of heavy rain hours was associated with the northwest precipitation pattern, followed by the southwest and southeast patterns. Analysis of the frequency of pressure and heavy rain precipitation pattern pairs revealed that the top five most frequent pairs were associated with advancing surface cyclones and >26% of the total heavy rain hours. Composite analysis of environmental parameters showed that favorable conditions related to moisture and lift were associated with heavy rainfall.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Rory Laiho, rory.laiho@colorado.edu

Abstract

Situated in the Upper Midwest, Minnesota’s midcontinental location places it in a climate transition zone between eastern U.S. humid conditions and western semiarid conditions as well as between warm, moist air from the Gulf of Mexico to the south and drier, polar air to the north. Potential adverse impacts on ecosystems due to changing climate and precipitation patterns, together with ongoing flash flooding risks, indicate that heavy rainfall occurrence and distribution are important considerations for Minnesota. This research used ERA5 reanalysis data with 0.25° grid spacing during May–September 1959–2021 to investigate the synoptic-scale drivers of Minnesota heavy rainfall. The study utilized a neural network, self-organizing map (SOM) technique to identify sea level pressure patterns and precipitation patterns associated with heavy rainfall and used composite analysis to explore the relationships between synoptic-scale conditions and environmental parameters during heavy rain hours. Six sea level pressure patterns were identified, three of which represented advancing surface cyclones and accounted for >70% of the heavy rain hours. The spatial distribution of heavy rainfall was represented by six precipitation patterns. The greatest frequency of heavy rain hours was associated with the northwest precipitation pattern, followed by the southwest and southeast patterns. Analysis of the frequency of pressure and heavy rain precipitation pattern pairs revealed that the top five most frequent pairs were associated with advancing surface cyclones and >26% of the total heavy rain hours. Composite analysis of environmental parameters showed that favorable conditions related to moisture and lift were associated with heavy rainfall.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Rory Laiho, rory.laiho@colorado.edu
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  • Agel, L., M. Barlow, S. B. Feldstein, and W. J. Gutkowski Jr., 2018: Identification of large-scale meteorological patterns associated with extreme precipitation in the US northeast. Climate Dyn., 50, 18191839, https://doi.org/10.1007/s00382-017-3724-8.

    • Search Google Scholar
    • Export Citation
  • Algarra, I., J. Eiras-Barca, G. Miguez-Macho, R. Nieto, and L. Gimeno, 2019: On the assessment of the moisture transport by the Great Plains low-level jet. Earth Syst. Dyn., 10, 107119, https://doi.org/10.5194/esd-10-107-2019.

    • Search Google Scholar
    • Export Citation
  • American Meteorological Society, 2021: Heavy rain. Glossary of Meteorology, https://glossary.ametsoc.org/wiki/Heavy_rain.

  • Andresen, J., S. Hilberg, and K. Kunkel, 2012: Historical climate and climate trends in the Midwestern USA. U.S. National Climate Assessment Midwest Tech. Input Rep., 18 pp., https://glisa.umich.edu/wp-content/uploads/2021/02/MTIT_Historical.pdf.

  • Barandiaran, D., S.-Y. Wang, and K. Hilburn, 2013: Observed trends in the Great Plains low-level jet and associated precipitation changes in relation to recent droughts. Geophys. Res. Lett., 40, 62476251, https://doi.org/10.1002/2013GL058296.

    • Search Google Scholar
    • Export Citation
  • Barlow, M., and Coauthors, 2019: North American extreme precipitation events and related large-scale meteorological patterns: A review of statistical methods, dynamics, modeling, and trends. Climate Dyn., 53, 68356875, https://doi.org/10.1007/s00382-019-04958-z.

    • Search Google Scholar
    • Export Citation
  • Binau, S., 2009: The historic flash flood event of 18–19 August 2007 in the upper Mississippi river valley: Impacts of terrain and societal response. Extended Abstracts, 23rd Conf. on Weather Analysis and Forecasting/19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 1B.4, https://ams.confex.com/ams/pdfpapers/154238.pdf.

  • Brandt, J. P., 2009: The extent of the North American boreal zone. Environ. Rev., 17, 101161, https://doi.org/10.1139/A09-004.

  • Cassano, E. N., J. M. Glisan, J. J. Cassano, W. J. Gutowski Jr., and M. W. Seefeldt, 2015: Self- organizing map analysis of widespread temperature extremes in Alaska and Canada. Climate Res., 62, 199218, https://doi.org/10.3354/cr01274.

    • Search Google Scholar
    • Export Citation
  • Cavazos, T., 2000: Using self-organizing maps to investigate extreme climate events: An application to wintertime precipitation in the Balkans. J. Climate, 13, 17181732, https://doi.org/10.1175/1520-0442(2000)013<1718:USOMTI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Cook, K. H., E. K. Vizy, Z. S. Launer, and C. M. Patricola, 2008: Springtime intensification of the Great Plains low-level jet and Midwest precipitation in GCM simulations of the twenty-first century. J. Climate, 21, 63216340, https://doi.org/10.1175/2008JCLI2355.1.

    • Search Google Scholar
    • Export Citation
  • Czuba, C. R., J. D. Fallon, and E. W. Kessler, 2012: Floods of June 2012 in northeastern Minnesota. U.S. Geological Survey Scientific Investigations Rep. 2012-5283, 52 pp., https://pubs.usgs.gov/sir/2012/5283/sir2012-5283.pdf.

  • Davenport, F. V., and N. S. Diffenbaugh, 2021: Using machine learning to analyze physical causes of climate change: A case study of U.S. Midwest extreme precipitation. Geophys. Res. Lett., 48, e2021GL093787, https://doi.org/10.1029/2021GL093787.

    • Search Google Scholar
    • Export Citation
  • Debbage, N., P. Miller, S. Poore, K. Morano, T. Mote, and J. M. Shepherd, 2017: A climatology of atmospheric river interactions with the southeastern United States coastline. Int. J. Climatol., 37, 40774091, https://doi.org/10.1002/joc.5000.

    • Search Google Scholar
    • Export Citation
  • Doswell, C. A., III, H. E. Brooks, and R. A. Maddox, 1996: Flash flood forecasting: An ingredients-based methodology. Wea. Forecasting, 11, 560581, https://doi.org/10.1175/1520-0434(1996)011<0560:FFFAIB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Dunkerley, D. L., 2021: Light and low-intensity rainfalls: A review of their classification, occurrence, and importance in landsurface, ecological and environmental processes. Earth-Sci. Rev., 214, 103529, https://doi.org/10.1016/j.earscirev.2021.103529.

    • Search Google Scholar
    • Export Citation
  • Durkee, J. D., L. Campbell, K. Berry, D. Jordan, G. Goodrich, R. Mahmood, and S. Foster, 2012: A synoptic perspective of the record 1–2 May 2010 mid-south heavy precipitation event. Bull. Amer. Meteor. Soc., 93, 611620, https://doi.org/10.1175/BAMS-D-11-00076.1.

    • Search Google Scholar
    • Export Citation
  • ECMWF, 2019: ERA5 reanalysis (0.25 degree latitude-longitude grid). National Center for Atmospheric Research Computational and Information Systems Laboratory Research Data Archive, accessed 18 November 2023, https://doi.org/10.5065/BH6N-5N20.

  • Fay, P. A., J. D. Carlisle, A. K. Knapp, J. M. Blair, and S. L. Collins, 2000: Altering rainfall timing and quantity in a mesic grassland ecosystem: Design and performance of rainfall manipulation shelters. Ecosystems, 3, 308319, https://doi.org/10.1007/s100210000028.

    • Search Google Scholar
    • Export Citation
  • Frelich, L. E., and P. B. Reich, 2009: Wilderness conservation in an era of global warming and invasive species: A case study from Minnesota’s boundary waters Canoe area wilderness. Nat. Areas J., 29, 385393, https://doi.org/10.3375/043.029.0405.

    • Search Google Scholar
    • Export Citation
  • Glisan, J. M., W. J. Gutowski Jr., J. J. Cassano, E. N. Cassano, and M. W. Seefeldt, 2016: Analysis of WRF extreme daily precipitation over Alaska using self-organizing maps. J. Geophys. Res. Atmos., 121, 77467761, https://doi.org/10.1002/2016JD024822.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2023a: ERA5 hourly data on pressure levels from 1940 to the present. Copernicus Climate Change Service Climate Data Store, accessed 20 March 2023–9 October 2023, https://doi.org/10.24381/cds.bd915c6.

  • Hersbach, H., and Coauthors, 2023b: ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service Climate Data Store, accessed 29 March 2023–12 September 2023, https://doi.org/10.24381/cds.adbb2d47.

  • Hewitson, B. C., and R. G. Crane, 2002: Self-organizing maps: Applications to synoptic climatology. Climate Res., 22, 1326, https://doi.org/10.3354/cr022013.

    • Search Google Scholar
    • Export Citation
  • Hogg, E. H., J. P. Brandt, and B. Kochtubajda, 2005: Factors affecting interannual variation in growth of western Canadian aspen forests during 1951–2000. Can. J. For. Res., 35, 610622, https://doi.org/10.1139/x04-211.

    • Search Google Scholar
    • Export Citation
  • Jentsch, A., and C. Beierkuhnlein, 2008: Research frontiers in climate change: Effects of extreme meteorological events on ecosystems. C. R. Geosci., 340, 621628, https://doi.org/10.1016/j.crte.2008.07.002.

    • Search Google Scholar
    • Export Citation
  • Jiang, Q., and Coauthors, 2021: Evaluation of the ERA5 reanalysis precipitation dataset over Chinese Mainland. J. Hydrol., 595, 125660, https://doi.org/10.1016/j.jhydrol.2020.125660.

    • Search Google Scholar
    • Export Citation
  • Knapp, A. K., and Coauthors, 2002: Rainfall variability, carbon cycling, and plant species diversity in a Mesic Grassland. Science, 298, 22022205, https://doi.org/10.1126/science.1076347.

    • Search Google Scholar
    • Export Citation
  • Knapp, A. K., and Coauthors, 2008: Consequences of more extreme precipitation regimes for terrestrial ecosystems. BioScience, 58, 811821, https://doi.org/10.1641/B580908.

    • Search Google Scholar
    • Export Citation
  • Kunkel, K. E., D. R. Easterling, D. A. R. Kristovich, B. Gleason, L. Stoecker, and R. Smith, 2012: Meteorological causes of the secular variations in observed extreme precipitation events for the conterminous United States. J. Hydrometeor., 13, 11311141, https://doi.org/10.1175/JHM-D-11-0108.1.

    • Search Google Scholar
    • Export Citation
  • Laiho, R., K. Friedrich, and A. C. Winters, 2023: Characteristics of warm season heavy rainfall in Minnesota. Wea. Forecasting, 38, 163177, https://doi.org/10.1175/WAF-D-21-0186.1.

    • Search Google Scholar
    • Export Citation
  • Lavers, D. A., and G. Villarini, 2013: Atmospheric rivers and flooding over the central United States. J. Climate, 26, 78297836, https://doi.org/10.1175/JCLI-D-13-00212.1.

    • Search Google Scholar
    • Export Citation
  • Lin, X., and A. Y. Hou, 2012: Estimation of rain intensity spectra over the continental United States using ground radar-gauge measurements. J. Climate, 25, 19011915, https://doi.org/10.1175/JCLI-D-11-00151.1.

    • Search Google Scholar
    • Export Citation
  • Loikith, P. C., B. R. Lintner, and A. Sweeney, 2017: Characterizing large-scale meteorological patterns and associated temperature and precipitation extremes over the northwestern United States using self-organizing maps. J. Climate, 30, 28292847, https://doi.org/10.1175/JCLI-D-16-0670.1.

    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., C. F. Chappell, and L. R. Hoxit, 1979: Synoptic and meso-α scale aspects of flash flood events. Bull. Amer. Meteor. Soc., 60, 115123, https://doi.org/10.1175/1520-0477-60.2.115.

    • Search Google Scholar
    • Export Citation
  • Minnesota Department of Natural Resources, 2024a: Coteau moraines subsection. Minnesota Department of Natural Resources, accessed 27 June 2024, https://www.dnr.state.mn.us/ecs/251Bb/index.html.

  • Minnesota Department of Natural Resources, 2024b: North shore highlands subsection. Minnesota Department of Natural Resources, accessed 27 June 2024, https://www.dnr.state.mn.us/ecs/212Lb/index.html.

  • Minnesota Forest Resources Council, 2020: Climate change and Minnesota’s forests. Accessed 26 June 2023, https://mn.gov/frc/assets/Climate_Change_and_Minnesota%27s_Forests_2020_tcm1162-471265.pdf.

  • Minnesota Pollution Control Agency, 2005: Minnesota land surface elevation. Minnesota Pollution Control Agency, accessed 17 November 2023, https://stormwater.pca.state.mn.us/index.php?title=File:Minnesota_land_surface_elevation.jpg.

  • Moore, B. J., P. J. Neiman, F. M. Ralph, and F. E. Barthold, 2012: Physical processes associated with heavy flooding rainfall in Nashville, Tennessee, and vicinity during 1–2 May 2010: The role of an atmospheric river and mesoscale convective systems. Mon. Wea. Rev., 140, 358378, https://doi.org/10.1175/MWR-D-11-00126.1.

    • Search Google Scholar
    • Export Citation
  • Moore, B. J., K. M. Mahoney, E. M. Sukovich, R. Cifelli, and T. M. Hamill, 2015: Climatology and environmental characteristics of extreme precipitation events in the southeastern United States. Mon. Wea. Rev., 143, 718741, https://doi.org/10.1175/MWR-D-14-00065.1.

    • Search Google Scholar
    • Export Citation
  • Moss, P., and Coauthors, 2017: Adapting to climate change in Minnesota 2017 Report of the Interagency Climate Adaptation Team. Minnesota Pollution Control Agency, 67 pp., https://www.lrl.mn.gov/docs/2019/other/190662.pdf.

  • Mundahl, N. D., and A. M. Hunt, 2011: Recovery of stream invertebrates after catastrophic flooding in southeastern Minnesota, USA. J. Freshwater Ecol., 26, 445457, https://doi.org/10.1080/02705060.2011.596657.

    • Search Google Scholar
    • Export Citation
  • NOAA/National Weather Service, 2023a: Environmental parameters and indices. Accessed 18 November 2023, https://www.weather.gov/lmk/indices.

  • NOAA/National Weather Service, 2023b: Glossary. Accessed 18 November 2023, https://www.weather.gov/glossary/.

  • Ramseyer, C. A., and Coauthors, 2022: Identifying eastern US atmospheric river types and evaluating historical trends. J. Geophys. Res. Atmos., 127, e2021JD036198, https://doi.org/10.1029/2021JD036198.

    • Search Google Scholar
    • Export Citation
  • Reich, P. B., R. Bermudez, R. A. Montgomery, R. L. Rich, K. E. Rice, S. E. Hobble, and A. Stefanski, 2022: Even modest climate change may lead to major transitions in boreal forests. Nature, 608, 540545, https://doi.org/10.1038/s41586-022-05076-3.

    • Search Google Scholar
    • Export Citation
  • Rowe, S. T., and G. Villarini, 2013: Flooding associated with predecessor rain events over the Midwest United States. Environ. Res. Lett., 8, 024007, https://doi.org/10.1088/1748-9326/8/2/024007.

    • Search Google Scholar
    • Export Citation
  • Rudolph, J. V., K. Friedrich, and U. Germann, 2012: Model-based estimation of dynamic effect on twenty- first-century precipitation for Swiss river basins. J. Climate, 25, 28972913, https://doi.org/10.1175/JCLI-D-11-00191.1.

    • Search Google Scholar
    • Export Citation
  • Runkle, J., K. E. Kunkel, R. Frankson, D. R. Easterling, and S. M. Champion, 2022: Minnesota State Climate Summary 2022. NOAA Tech. Rep. NESDIS 150-MN, 4 pp., https://statesummaries.ncics.org/downloads/Minnesota-StateClimateSummary2022.pdf.

  • Schuenemann, K. C., J. J. Cassano, and J. Finnis, 2009: Synoptic forcing of precipitation over Greenland: Climatology for 1961–99. J. Hydrometeor., 10, 6078, https://doi.org/10.1175/2008JHM1014.1.

    • Search Google Scholar
    • Export Citation
  • Schultz, D. M., and Coauthors, 2019: Extratropical cyclones: A century of research on meteorology’s centerpiece. A Century of Progress in Atmospheric and Related Sciences: Celebrating the American Meteorological Society Centennial, Meteor. Monogr., No. 59, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0015.1.

  • Schumacher, R. S., and R. H. Johnson, 2006: Characteristics of U.S. extreme rain events during 1999–2003. Wea. Forecasting, 21, 6985, https://doi.org/10.1175/WAF900.1.

    • Search Google Scholar
    • Export Citation
  • Schumacher, R. S., T. J. Galarneau Jr., and L. F. Bosart, 2011: Distant effects of a recurving tropical cyclone on rainfall in a midlatitude convective system: A high-impact predecessor rain event. Mon. Wea. Rev., 139, 650667, https://doi.org/10.1175/2010MWR3453.1.

    • Search Google Scholar
    • Export Citation
  • Swales, D., M. Alexander, and M. Hughes, 2016: Examining moisture pathways and extreme precipitation in the U.S. intermountain west using self-organizing maps. Geophys. Res. Lett., 43, 17271735, https://doi.org/10.1002/2015GL067478.

    • Search Google Scholar
    • Export Citation
  • Swemmer, A. M., A. K. Knapp, and H. A. Snyman, 2007: Intra-seasonal precipitation patterns and above-ground productivity in three perennial grasslands. J. Ecol., 95, 780788, https://doi.org/10.1111/j.1365-2745.2007.01237.x.

    • Search Google Scholar
    • Export Citation
  • Ummenhofer, C. C., and G. A. Meehl, 2017: Extreme weather and climate events with ecological relevance: A review. Philos. Trans. Roy. Soc., B372, 20160135, https://doi.org/10.1098/rstb.2016.0135.

    • Search Google Scholar
    • Export Citation
  • U.S. EPA, 2023: Ecoregions of North America Level 1 Ecoregions. Accessed 21 June 2023, http://www.epa.gov/eco-research/ecoregions.

  • Vettigli, G., 2018: MiniSom: Minimalistic and NumPy-based implementation of the self organizing map. GitHub, https://github.com/JustGlowing/minisom/.

  • Virtanen, P., and Coauthors, 2020: SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2.

  • Whittaker, L. M., and L. H. Horn, 1981: Geographical and seasonal distribution of North American cyclogenesis, 1958–1977. Mon. Wea. Rev., 109, 23122322, https://doi.org/10.1175/1520-0493(1981)109<2312:GASDON>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Winkler, J. A., 1988: Climatological characteristics of summertime extreme rainstorms in Minnesota. Ann. Assoc. Amer. Geogr., 78, 5773, https://doi.org/10.1111/j.1467-8306.1988.tb00191.x.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., J. A. Biederman, N. A. Pierce, D. L. Potts, C. J. Devine, Y. Hao, and W. K. Smith, 2021: Precipitation temporal repackaging into fewer, larger storms delayed seasonal timing of peak photosynthesis in a semi-arid grassland. Funct. Ecol., 36, 646658, https://doi.org/10.1111/1365-2435.13980.

    • Search Google Scholar
    • Export Citation
  • Zhang, W., and G. Villarini, 2019: On the weather types that shape the precipitation patterns across the U.S. Midwest. Climate Dyn., 53, 42174232, https://doi.org/10.1007/s00382-019-04783-4.

    • Search Google Scholar
    • Export Citation
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