The Role of the Circulation Patterns in Projected Changes in Spring and Summer Precipitation Extremes in the U.S. Midwest

Liang Chen aClimate and Atmospheric Sciences Section, Illinois State Water Survey, Prairie Research Institute, University of Illinois Urbana–Champaign, Champaign, Illinois
bDepartment of Earth and Atmospheric Sciences, University of Nebraska–Lincoln, Lincoln, Nebraska

Search for other papers by Liang Chen in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0003-1553-2846
,
Trent W. Ford aClimate and Atmospheric Sciences Section, Illinois State Water Survey, Prairie Research Institute, University of Illinois Urbana–Champaign, Champaign, Illinois

Search for other papers by Trent W. Ford in
Current site
Google Scholar
PubMed
Close
, and
Erik Swenson cCenter for Ocean–Land–Atmosphere Studies, George Mason University, Fairfax, Virginia

Search for other papers by Erik Swenson in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Recent studies suggest springtime wet extremes and summertime dry extremes will occur more frequently in the U.S. Midwest, potentially leading to devastating agricultural consequences. To understand the role of circulation patterns in the projected changes in seasonal precipitation extremes, the k-means clustering approach is applied to the large-ensemble experiments of Community Earth System Model, version 2 (CESM2-LE), and ensemble projections of CMIP6. We identify two key atmospheric circulation patterns that are associated with the extremely wet spring and extremely dry summer in the U.S. Midwest. The springtime wet extremes are typically linked to baroclinic waves with a northward shift of the North American westerly jet and positive anomalies in sea level pressure over the western Atlantic, which favor the development of the Great Plains low-level jet. The summertime dry extremes are associated with the development of an anomalous ridge with suppressed storm tracks over the central United States. The projected increase in springtime wet extremes and summertime dry extremes can be attributed to significantly more frequent occurrences of the associated atmospheric regimes. Particularly, the intensity of wet extremes is expected to increase mainly due to the enhanced moisture flux from the Gulf of Mexico. The moisture budget analysis suggests that the precipitation extremes are mainly associated with the dynamic component of atmospheric circulation. CESM2-LE and CMIP6 exhibit good agreement in the projected changes in circulation patterns and precipitation extremes. Our results explain the mechanism of the projected changes in the Midwest seasonal precipitation and highlight the contribution of circulation patterns to hydroclimatic extremes.

© 2023 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: Liang Chen, lchen45@unl.edu

Abstract

Recent studies suggest springtime wet extremes and summertime dry extremes will occur more frequently in the U.S. Midwest, potentially leading to devastating agricultural consequences. To understand the role of circulation patterns in the projected changes in seasonal precipitation extremes, the k-means clustering approach is applied to the large-ensemble experiments of Community Earth System Model, version 2 (CESM2-LE), and ensemble projections of CMIP6. We identify two key atmospheric circulation patterns that are associated with the extremely wet spring and extremely dry summer in the U.S. Midwest. The springtime wet extremes are typically linked to baroclinic waves with a northward shift of the North American westerly jet and positive anomalies in sea level pressure over the western Atlantic, which favor the development of the Great Plains low-level jet. The summertime dry extremes are associated with the development of an anomalous ridge with suppressed storm tracks over the central United States. The projected increase in springtime wet extremes and summertime dry extremes can be attributed to significantly more frequent occurrences of the associated atmospheric regimes. Particularly, the intensity of wet extremes is expected to increase mainly due to the enhanced moisture flux from the Gulf of Mexico. The moisture budget analysis suggests that the precipitation extremes are mainly associated with the dynamic component of atmospheric circulation. CESM2-LE and CMIP6 exhibit good agreement in the projected changes in circulation patterns and precipitation extremes. Our results explain the mechanism of the projected changes in the Midwest seasonal precipitation and highlight the contribution of circulation patterns to hydroclimatic extremes.

© 2023 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: Liang Chen, lchen45@unl.edu

Supplementary Materials

    • Supplemental Materials (PDF 2.94 MB)
Save
  • Agel, L., and M. Barlow, 2020: How well do CMIP6 historical runs match observed northeast U.S. precipitation and extreme precipitation–related circulation? J. Climate, 33, 98359848, https://doi.org/10.1175/JCLI-D-19-1025.1.

    • Search Google Scholar
    • Export Citation
  • Agel, L., M. Barlow, C. Skinner, F. Colby, and J. Cohen, 2021: Four distinct northeast US heat wave circulation patterns and associated mechanisms, trends, and electric usage. npj Climate Atmos. Sci., 4, 31, https://doi.org/10.1038/s41612-021-00186-7.

    • Search Google Scholar
    • Export Citation
  • Akinsanola, A. A., G. J. Kooperman, K. A. Reed, A. G. Pendergrass, and W. M. Hannah, 2020: Projected changes in seasonal precipitation extremes over the United States in CMIP6 simulations. Environ. Res. Lett., 15, 104078, https://doi.org/10.1088/1748-9326/abb397.

    • Search Google Scholar
    • Export Citation
  • Cao, F., T. Gao, L. Dan, Z. Ma, X. Chen, L. Zou, and L. Zhang, 2019: Synoptic-scale atmospheric circulation anomalies associated with summertime daily precipitation extremes in the middle–lower reaches of the Yangtze River basin. Climate Dyn., 53, 31093129, https://doi.org/10.1007/s00382-019-04687-3.

    • Search Google Scholar
    • Export Citation
  • Chen, L., and T. W. Ford, 2021: Effects of 0.5°C less global warming on climate extremes in the contiguous United States. Climate Dyn., 57, 303319, https://doi.org/10.1007/s00382-021-05717-9.

    • Search Google Scholar
    • Export Citation
  • Chen, L., and T. W. Ford, 2023: Future changes in the transitions of monthly-to-seasonal precipitation extremes over the Midwest in Coupled Model Intercomparison Project Phase 6 models. Int. J. Climatol., 43, 255–274, https://doi.org/10.1002/joc.7756.

    • Search Google Scholar
    • Export Citation
  • Chen, W., D.-B. Jiang, X.-M. Lang, and Z.-P. Tian, 2021: Understanding the cold biases of CMIP5 models over China with weather regimes. Adv. Climate Change Res., 12, 373383, https://doi.org/10.1016/j.accre.2021.05.002.

    • Search Google Scholar
    • Export Citation
  • Chou, C., and C.-W. Lan, 2012: Changes in the annual range of precipitation under global warming. J. Climate, 25, 222235, https://doi.org/10.1175/JCLI-D-11-00097.1.

    • Search Google Scholar
    • Export Citation
  • Cook, B. I., J. S. Mankin, K. Marvel, A. P. Williams, J. E. Smerdon, and K. J. Anchukaitis, 2020: Twenty-first century drought projections in the CMIP6 forcing scenarios. Earth’s Future, 8, e2019EF001461, https://doi.org/10.1029/2019EF001461.

    • Search Google Scholar
    • Export Citation
  • Danabasoglu, G., and Coauthors, 2020: The Community Earth System Model version 2 (CESM2). J. Adv. Model. Earth Syst., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916.

    • Search Google Scholar
    • Export Citation
  • 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
  • Dong, J., P. A. Dirmeyer, F. Lei, M. C. Anderson, T. R. H. Holmes, C. Hain, and W. T. Crow, 2020: Soil evaporation stress determines soil moisture-evapotranspiration coupling strength in land surface modeling. Geophys. Res. Lett., 47, e2020GL090391, https://doi.org/10.1029/2020GL090391.

    • Search Google Scholar
    • Export Citation
  • Feng, X., B. Huang, G. Tintera, and B. Chen, 2019: An examination of the Northern Hemisphere mid-latitude storm track interannual variability simulated by climate models—Sensitivity to model resolution and coupling. Climate Dyn., 52, 42474268, https://doi.org/10.1007/s00382-018-4378-x.

    • Search Google Scholar
    • Export Citation
  • Feng, Z., L. R. Leung, S. Hagos, R. A. Houze, C. D. Burleyson, and K. Balaguru, 2016: More frequent intense and long-lived storms dominate the springtime trend in central US rainfall. Nat. Commun., 7, 13429, https://doi.org/10.1038/ncomms13429.

    • Search Google Scholar
    • Export Citation
  • Ford, T. W., and C. F. Labosier, 2017: Meteorological conditions associated with the onset of flash drought in the eastern United States. Agric. For. Meteor., 247, 414423, https://doi.org/10.1016/j.agrformet.2017.08.031.

    • Search Google Scholar
    • Export Citation
  • Frankcombe, L. M., M. H. England, J. B. Kajtar, M. E. Mann, and B. A. Steinman, 2018: On the choice of ensemble mean for estimating the forced signal in the presence of internal variability. J. Climate, 31, 56815693, https://doi.org/10.1175/JCLI-D-17-0662.1.

    • Search Google Scholar
    • Export Citation
  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis For Research And Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Search Google Scholar
    • Export Citation
  • Gibson, P. B., A. J. Pitman, R. Lorenz, and S. E. Perkins-Kirkpatrick, 2017: The role of circulation and land surface conditions in current and future Australian heat waves. J. Climate, 30, 99339948, https://doi.org/10.1175/JCLI-D-17-0265.1.

    • Search Google Scholar
    • Export Citation
  • Grady, K. A., L. Chen, and T. W. Ford, 2021: Projected changes to spring and summer precipitation in the midwestern United States. Front. Water, 3, 780333, https://doi.org/10.3389/frwa.2021.780333.

    • Search Google Scholar
    • Export Citation
  • Hartigan, J. A., and M. A. Wong, 1979: Algorithm AS136: A K-means clustering algorithm. Appl. Stat., 28, 100108, https://doi.org/10.2307/2346830.

    • Search Google Scholar
    • Export Citation
  • Hernandez, M., and L. Chen, 2022: Future land precipitation changes over the North American monsoon region using CMIP5 and CMIP6 simulations. J. Geophys. Res. Atmos., 127, e2021JD035911, https://doi.org/10.1029/2021JD035911.

    • 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
  • Hoerling, M., J. Eischeid, A. Kumar, R. Leung, A. Mariotti, K. Mo, S. 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.

    • Search Google Scholar
    • Export Citation
  • Jézéquel, A., P. Yiou, and S. Radanovics, 2018: Role of circulation in European heatwaves using flow analogues. Climate Dyn., 50, 11451159, https://doi.org/10.1007/s00382-017-3667-0.

    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and Coauthors, 2015: The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bull. Amer. Meteor. Soc., 96, 13331349, https://doi.org/10.1175/BAMS-D-13-00255.1.

    • Search Google Scholar
    • Export Citation
  • Kleinman, P. J. A., M. S. Srinivasan, C. J. Dell, J. P. Schmidt, A. N. Sharpley, and R. B. Bryant, 2006: Role of rainfall intensity and hydrology in nutrient transport via surface runoff. J. Environ. Qual., 35, 12481259, https://doi.org/10.2134/jeq2006.0015.

    • Search Google Scholar
    • Export Citation
  • Kunkel, K. E., T. R. Karl, M. F. Squires, X. Yin, S. T. Stegall, and D. R. Easterling, 2020: Precipitation extremes: Trends and relationships with average precipitation and precipitable water in the contiguous United States. J. Appl. Meteor. Climatol., 59, 125142, https://doi.org/10.1175/JAMC-D-19-0185.1.

    • Search Google Scholar
    • Export Citation
  • Li, M., Q. Sun, M. A. Lovino, S. Ali, M. Islam, T. Li, C. Li, and Z. Jiang, 2022: Non-uniform changes in different daily precipitation events in the contiguous United States. Wea. Climate Extremes, 35, 100417, https://doi.org/10.1016/j.wace.2022.100417.

    • Search Google Scholar
    • Export Citation
  • Li, Y., K. Guan, G. D. Schnitkey, E. DeLucia, and B. Peng, 2019: Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States. Global Change Biol., 25, 23252337, https://doi.org/10.1111/gcb.14628.

    • Search Google Scholar
    • Export Citation
  • Loecke, T. D., A. J. Burgin, D. A. Riveros-Iregui, A. S. Ward, S. A. Thomas, C. A. Davis, and M. A. St. Clair, 2017: Weather whiplash in agricultural regions drives deterioration of water quality. Biogeochemistry, 133, 715, https://doi.org/10.1007/s10533-017-0315-z.

    • Search Google Scholar
    • Export Citation
  • Lutsko, N. J., J. W. Baldwin, and T. W. Cronin, 2019: The impact of large-scale orography on Northern Hemisphere winter synoptic temperature variability. J. Climate, 32, 57995814, https://doi.org/10.1175/JCLI-D-19-0129.1.

    • Search Google Scholar
    • Export Citation
  • Magee, M. R., and Coauthors, 2019: Scientific advances and adaptation strategies for Wisconsin lakes facing climate change. Lake Reservoir Manage., 35, 364381, https://doi.org/10.1080/10402381.2019.1622612.

    • Search Google Scholar
    • Export Citation
  • Mastrantonas, N., P. Herrera-Lormendez, L. Magnusson, F. Pappenberger, and J. Matschullat, 2021: Extreme precipitation events in the Mediterranean: Spatiotemporal characteristics and connection to large-scale atmospheric flow patterns. Int. J. Climatol., 41, 27102728, https://doi.org/10.1002/joc.6985.

    • Search Google Scholar
    • Export Citation
  • Milinski, S., N. Maher, and D. Olonscheck, 2020: How large does a large ensemble need to be? Earth Syst. Dyn., 11, 885901, https://doi.org/10.5194/esd-11-885-2020.

    • Search Google Scholar
    • Export Citation
  • Moore, T. L., J. S. Gulliver, L. Stack, and M. H. Simpson, 2016: Stormwater management and climate change: Vulnerability and capacity for adaptation in urban and suburban contexts. Climatic Change, 138, 491504, https://doi.org/10.1007/s10584-016-1766-2.

    • Search Google Scholar
    • Export Citation
  • NOAA, 2022: U.S. billion-dollar weather and climate disasters. National Centers for Environmental Information, https://doi.org/10.25921/stkw-7w73.

  • O’Gorman, P. A., and T. Schneider, 2009: The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proc. Natl. Acad. Sci. USA, 106, 14 77314 777, https://doi.org/10.1073/pnas.0907610106.

    • Search Google Scholar
    • Export Citation
  • Pal, J. S., and E. A. B. Eltahir, 2001: Pathways relating soil moisture conditions to future summer rainfall within a model of the land–atmosphere system. J. Climate, 14, 12271242, https://doi.org/10.1175/1520-0442(2001)014<1227:PRSMCT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Paxton, A., J. T. Schoof, T. W. Ford, and J. W. F. Remo, 2021: Extreme precipitation in the Great Lakes region: Trend estimation and relation with large-scale circulation and humidity. Front. Water, 3, 782847, https://doi.org/10.3389/frwa.2021.782847.

    • Search Google Scholar
    • Export Citation
  • Rao, R., and Y. Li, 2003: Management of flooding effects on growth of vegetable and selected field crops. HortTechnology, 13, 610616, https://doi.org/10.21273/HORTTECH.13.4.0610.

    • Search Google Scholar
    • Export Citation
  • Rodgers, K. B., and Coauthors, 2021: Ubiquity of human-induced changes in climate variability. Earth Syst. Dyn., 12, 13931411, https://doi.org/10.5194/esd-12-1393-2021.

    • Search Google Scholar
    • Export Citation
  • Schubert, S. D., M. J. Suarez, P. J. Pegion, R. D. Koster, and J. T. Bacmeister, 2004: Causes of long-term drought in the U.S. Great Plains. J. Climate, 17, 485503, https://doi.org/10.1175/1520-0442(2004)017<0485:COLDIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Seager, R., and G. A. Vecchi, 2010: Greenhouse warming and the 21st century hydroclimate of southwestern North America. Proc. Natl. Acad. Sci. USA, 107, 21 27721 282, https://doi.org/10.1073/pnas.0910856107.

    • Search Google Scholar
    • Export Citation
  • Shirzaei, M., and Coauthors, 2021: Persistent impact of spring floods on crop loss in U.S. Midwest. Wea. Climate Extremes, 34, 100392, https://doi.org/10.1016/j.wace.2021.100392.

    • Search Google Scholar
    • Export Citation
  • Sippel, S., J. Zscheischler, M. D. Mahecha, R. Orth, M. Reichstein, M. Vogel, and S. I. Seneviratne, 2017: Refining multi-model projections of temperature extremes by evaluation against land–atmosphere coupling diagnostics. Earth Syst. Dyn., 8, 387403, https://doi.org/10.5194/esd-8-387-2017.

    • Search Google Scholar
    • Export Citation
  • Straus, D. M., 2019: Clustering techniques in climate analysis. Oxford Research Encyclopedia of Climate Science, Oxford University Press, https://doi.org/10.1093/acrefore/9780190228620.013.711.

  • Sudharsan, N., S. Karmakar, H. J. Fowler, and V. Hari, 2020: Large-scale dynamics have greater role than thermodynamics in driving precipitation extremes over India. Climate Dyn., 55, 26032614, https://doi.org/10.1007/s00382-020-05410-3.

    • Search Google Scholar
    • Export Citation
  • Sun, C., and X.-Z. Liang, 2020: Improving US extreme precipitation simulation: Dependence on cumulus parameterization and underlying mechanism. Climate Dyn., 55, 13251352, https://doi.org/10.1007/s00382-020-05328-w.

    • Search Google Scholar
    • Export Citation
  • Swanston, C., and Coauthors, 2018: Vulnerability of forests of the Midwest and Northeast United States to climate change. Climatic Change, 146, 103116, https://doi.org/10.1007/s10584-017-2065-2.

    • Search Google Scholar
    • Export Citation
  • Tan, Y., S. Yang, F. Zwiers, Z. Wang, and Q. Sun, 2022: Moisture budget analysis of extreme precipitation associated with different types of atmospheric rivers over western North America. Climate Dyn., 58, 793809, https://doi.org/10.1007/s00382-021-05933-3.

    • Search Google Scholar
    • Export Citation
  • Thomas, C., A. Voulgarakis, G. Lim, J. Haigh, and P. Nowack, 2021: An unsupervised learning approach to identifying blocking events: The case of European summer. Wea. Climate Dyn., 2, 581608, https://doi.org/10.5194/wcd-2-581-2021.

    • Search Google Scholar
    • Export Citation
  • Urban, D. W., M. J. Roberts, W. Schlenker, and D. B. Lobell, 2015: The effects of extremely wet planting conditions on maize and soybean yields. Climatic Change, 130, 247260, https://doi.org/10.1007/s10584-015-1362-x.

    • Search Google Scholar
    • Export Citation
  • Wang, S.-Y. S., and Coauthors, 2015: An intensified seasonal transition in the central U.S. that enhances summer drought. J. Geophys. Res. Atmos., 120, 88048816, https://doi.org/10.1002/2014JD023013.

    • Search Google Scholar
    • Export Citation
  • Westcott, N. E., S. E. Hollinger, and K. E. Kunkel, 2005: Use of real-time multisensor data to assess the relationship of normalized corn yield with monthly rainfall and heat stress across the central United States. J. Appl. Meteor., 44, 16671676, https://doi.org/10.1175/JAM2303.1.

    • Search Google Scholar
    • Export Citation
  • Yazdanfar, Z., and A. Sharma, 2015: Urban drainage system planning and design—Challenges with climate change and urbanization: A review. Water Sci. Technol., 72, 165179, https://doi.org/10.2166/wst.2015.207.

    • 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
  • Zhang, Y., T. F. Keenan, and S. Zhou, 2021: Exacerbated drought impacts on global ecosystems due to structural overshoot. Nat. Ecol. Evol., 5, 14901498, https://doi.org/10.1038/s41559-021-01551-8.

    • Search Google Scholar
    • Export Citation
  • Zhao, T., and A. Dai, 2022: CMIP6 model-projected hydroclimatic and drought changes and their causes in the twenty-first century. J. Climate, 35, 897921, https://doi.org/10.1175/JCLI-D-21-0442.1.

    • Search Google Scholar
    • Export Citation
  • Zhou, S., and Coauthors, 2019: Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity. Proc. Natl. Acad. Sci. USA, 116, 18 84818 853, https://doi.org/10.1073/pnas.1904955116.

    • Search Google Scholar
    • Export Citation
  • Zhou, W., L. R. Leung, F. Song, and J. Lu, 2021: Future changes in the Great Plains low-level jet governed by seasonally dependent pattern changes in the North Atlantic subtropical high. Geophys. Res. Lett., 48, e2020GL090356, https://doi.org/10.1029/2020GL090356.

    • Search Google Scholar
    • Export Citation
  • Zhou, W., L. R. Leung, and J. Lu, 2022: Seasonally dependent future changes in the U.S. Midwest hydroclimate and extremes. J. Climate, 35, 1727, https://doi.org/10.1175/JCLI-D-21-0012.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 1028 651 32
Full Text Views 278 151 9
PDF Downloads 353 155 16