Role of the Atmospheric Moisture Budget in Defining the Precipitation Seasonality of the Great Lakes Region

Samar Minallah Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, Michigan

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Allison L. Steiner Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, Michigan

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Abstract

Precipitation in the Great Lakes region has a distinct seasonal cycle that peaks in early summer, followed by a decline in August and a secondary peak in September. This seasonality is often not captured by models, which necessitates understanding of the driving mechanisms to ascertain the model biases. This study analyzes the atmospheric moisture budget using reanalysis datasets to assess the role of regional evapotranspiration and moisture influx from remote origins in defining the precipitation seasonality, and to understand how the Great Lakes modulate spatial patterns and magnitudes of these components. Specifically, the land–water thermal contrast yields large seasonal variations in the evaporative fluxes and creates distinctive localized spatial patterns of moisture flux divergence. We find considerable month-to-month variations in both evapotranspiration and the net moisture transport through the boundaries, where they play a cooperative (contrasting) role in amplifying (dampening) the moisture content available for precipitation and total precipitable water. Our seasonal analysis suggests that the misrepresentation of the budget quantities in models, for example, in simulation of moisture transport processes and parameterization schemes, can result in an anomalous precipitation behavior and, in some cases, violation of the atmospheric moisture mass balance, resulting in large residual magnitudes. We also identify conspicuous differences in the representation of moisture budget components in the various reanalyses, which can alter their representation of the regional hydroclimates.

Supplemental information related to this paper is available at the Journals Online website:https://doi.org/10.1175/JCLI-D-19-0952.s1.

© 2020 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: Samar Minallah, minallah@umich.edu

Abstract

Precipitation in the Great Lakes region has a distinct seasonal cycle that peaks in early summer, followed by a decline in August and a secondary peak in September. This seasonality is often not captured by models, which necessitates understanding of the driving mechanisms to ascertain the model biases. This study analyzes the atmospheric moisture budget using reanalysis datasets to assess the role of regional evapotranspiration and moisture influx from remote origins in defining the precipitation seasonality, and to understand how the Great Lakes modulate spatial patterns and magnitudes of these components. Specifically, the land–water thermal contrast yields large seasonal variations in the evaporative fluxes and creates distinctive localized spatial patterns of moisture flux divergence. We find considerable month-to-month variations in both evapotranspiration and the net moisture transport through the boundaries, where they play a cooperative (contrasting) role in amplifying (dampening) the moisture content available for precipitation and total precipitable water. Our seasonal analysis suggests that the misrepresentation of the budget quantities in models, for example, in simulation of moisture transport processes and parameterization schemes, can result in an anomalous precipitation behavior and, in some cases, violation of the atmospheric moisture mass balance, resulting in large residual magnitudes. We also identify conspicuous differences in the representation of moisture budget components in the various reanalyses, which can alter their representation of the regional hydroclimates.

Supplemental information related to this paper is available at the Journals Online website:https://doi.org/10.1175/JCLI-D-19-0952.s1.

© 2020 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: Samar Minallah, minallah@umich.edu

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  • Assel, R., K. Cronk, and D. Norton, 2003: Recent trends in Laurentian Great Lakes ice cover. Climatic Change, 57, 185204, https://doi.org/10.1023/A:1022140604052.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Banacos, P. C., and D. M. Schultz, 2005: The use of moisture flux convergence in forecasting convective initiation: Historical and operational perspectives. Wea. Forecasting, 20, 351366, https://doi.org/10.1175/WAF858.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Basile, S. J., S. A. Rauscher, and A. L. Steiner, 2017: Projected precipitation changes within the Great Lakes and western Lake Erie basin: A multi-model analysis of intensity and seasonality. Int. J. Climatol., 37, 48644879, https://doi.org/10.1002/joc.5128.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berrisford, P., and Coauthors, 2011. The ERA-Interim archive: Version 2.0. ERA Rep. Series 1, 27 pp., https://www.ecmwf.int/sites/default/files/elibrary/2011/8174-era-interim-archive-version-20.pdf.

  • Bosilovich, M. G., and Coauthors, 2008: Evaluation of global precipitation in reanalyses. J. Appl. Meteor. Climatol., 47, 22792299, https://doi.org/10.1175/2008JAMC1921.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosilovich, M. G., F. R. Robertson, and J. Chen, 2011: Global energy and water budgets in MERRA. J. Climate, 24, 57215739, https://doi.org/10.1175/2011JCLI4175.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosilovich, M. G., and Coauthors, 2015: MERRA-2: Initial evaluation of the climate. NASA Tech. Memo. NASA/TM-2015-104606/Vol. 43, 145 pp., https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich803.pdf.

  • Dagan, G., P. Stier, and D. Watson-Parris, 2019: Analysis of the atmospheric water budget for elucidating the spatial scale of precipitation changes under climate change. Geophys. Res. Lett., 46, 10 50410 511, https://doi.org/10.1029/2019GL084173.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Decremer, D., and Coauthors, 2014: Which significance test performs the best in climate simulations? Tellus, 66A, 23139, https://doi.org/10.3402/tellusa.v66.23139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dominguez, F., and Coauthors, 2006: Impact of atmospheric moisture storage on precipitation recycling. J. Climate, 19, 15131530, https://doi.org/10.1175/JCLI3691.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • d’Orgeville, M., and Coauthors, 2014: Climate change impacts on Great Lakes Basin precipitation extremes. J. Geophys. Res., 119, 10 79910 812, https://doi.org/10.1002/2014JD021855.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fujisaki-Manome, A., and Coauthors, 2017: Turbulent heat fluxes during an extreme lake-effect snow event. J. Hydrometeor., 18, 31453163, https://doi.org/10.1175/JHM-D-17-0062.1.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gottschalck, J., and Coauthors, 2005: Analysis of multiple precipitation products and preliminary assessment of their impact on global land data assimilation system land surface states. J. Hydrometeor., 6, 573598, https://doi.org/10.1175/JHM437.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grover, E. K., and P. J. Sousounis, 2002: The influence of large-scale flow on fall precipitation systems in the Great Lakes Basin. J. Climate, 15, 19431956, https://doi.org/10.1175/1520-0442(2002)015<1943:TIOLSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harris, I., and Coauthors, 2014: Updated high-resolution grids of monthly climatic observations—The CRU TS3.10 dataset. Int. J. Climatol., 34, 623642, https://doi.org/10.1002/joc.3711.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hayhoe, K., J. Van Dorn, T. Carley, N. Schlegal, and D. Wuebbles, 2010: Regional climate change projections for Chicago and the U.S. Great Lakes. J. Great Lakes Res., 36, 721, https://doi.org/10.1016/j.jglr.2010.03.012.

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

    • Search Google Scholar
    • Export Citation
  • Kim, J., V. Y. Ivanov, and S. Fatichi, 2015: Climate change and uncertainty assessment over a hydroclimatic transect of Michigan. Stochastic Environ. Res. Risk Assess., 30, 923944, https://doi.org/10.1007/s00477-015-1097-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lamb, P. J., D. H. Portis, and A. Zangvil, 2012: Investigation of large-scale atmospheric moisture budget and land surface interactions over U.S. Southern Great Plains including for CLASIC (June 2007). J. Hydrometeor., 13, 17191738, https://doi.org/10.1175/JHM-D-12-01.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lélé, M. I., and L. M. Leslie, 2016: Intraseasonal variability of low-level moisture transport over West Africa. Climate Dyn., 47, 35753591, https://doi.org/10.1007/s00382-016-3334-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, L. F., W. H. Li, and A. P. Barros, 2013: Atmospheric moisture budget and its regulation of the summer precipitation variability over the Southeastern United States. Climate Dyn., 41, 613631, https://doi.org/10.1007/s00382-013-1697-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X. P., and Coauthors, 2010: Hydroclimate and variability in the Great Lakes region as derived from the North American Regional Reanalysis. J. Geophys. Res., 115, D12104, https://doi.org/10.1029/2009JD012756.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lopez, P., 2007: Cloud and precipitation parameterizations in modeling and variational data assimilation: A review. J. Atmos. Sci., 64, 37663784, https://doi.org/10.1175/2006JAS2030.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343360, https://doi.org/10.1175/BAMS-87-3-343.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Notaro, M., and Coauthors, 2013: Influence of the Laurentian Great Lakes on regional climate. J. Climate, 26, 789804, https://doi.org/10.1175/JCLI-D-12-00140.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Notaro, M., V. Bennington, and S. Vavrus, 2015: Dynamically downscaled projections of lake-effect snow in the Great Lakes basin. J. Climate, 28, 16611684, https://doi.org/10.1175/JCLI-D-14-00467.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peltier, W. R., and Coauthors, 2018: Uncertainty in future summer precipitation in the Laurentian Great Lakes basin: Dynamical downscaling and the influence of continental-scale processes on regional climate change. J. Climate, 31, 26512673, https://doi.org/10.1175/JCLI-D-17-0416.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151057, https://doi.org/10.1175/2010BAMS3001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Şahin, S., and Coauthors, 2015: Large scale moisture flux characteristics of the Mediterranean basin and their relationships with drier and wetter climate conditions. Climate Dyn., 45, 33813401, https://doi.org/10.1007/s00382-015-2545-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schneider, U., and Coauthors, 2014: GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol., 115, 1540, https://doi.org/10.1007/s00704-013-0860-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scott, R. W., and F. A. Huff, 1996: Impacts of the Great Lakes on regional climate conditions. J. Great Lakes Res., 22, 845863, https://doi.org/10.1016/S0380-1330(96)71006-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seager, R., and N. Henderson, 2013: Diagnostic computation of moisture budgets in the ERA-Interim reanalysis with reference to analysis of CMIP-archived atmospheric model data. J. Climate, 26, 78767901, https://doi.org/10.1175/JCLI-D-13-00018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shafran, P., J. Woollen, W. Ebisuzaki, W. Shi, Y. Fan, R. W. Grumbine, and M. Fennessy, 2004: Observational data used for assimilation in the NCEP North American Regional Reanalysis. 14th Conf. on Applied Climatology, Seattle, WA, Amer. Meteor. Soc., 1.4, https://ams.confex.com/ams/84Annual/techprogram/paper_71689.htm.

  • Trenberth, K. E., and C. J. Guillemot, 1995: Evaluation of the global atmospheric moisture budget as seen from analyses. J. Climate, 8, 22552272, https://doi.org/10.1175/1520-0442(1995)008<2255:EOTGAM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., J. T. Fasullo, and J. Mackaro, 2011: Atmospheric moisture transports from ocean to land and global energy flows in reanalyses. J. Climate, 24, 49074924, https://doi.org/10.1175/2011JCLI4171.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Z. Q., and Coauthors, 2017: Atmospheric moisture budget and its regulation on the variability of summer precipitation over the Tibetan Plateau. J. Geophys. Res., 122, 614630, https://doi.org/10.1002/2016JD025515.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Willmott, C. J., and K. Matsuura, 2001: Terrestrial air temperature and precipitation: Monthly and annual time series (1950–1999). Center for Climatic Research, Department of Geography, University of Delaware, accessed 1 May 2019, http://climate.geog.udel.edu/~climate/html_pages/download.html.

  • Yang, Q., and Coauthors, 2014: Atmospheric moisture budget and spatial resolution dependence of precipitation extremes in aquaplanet simulations. J. Climate, 27, 35653581, https://doi.org/10.1175/JCLI-D-13-00468.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, S., and Coauthors, 2009: Variations of U.S. regional precipitation and simulations by the NCEP CFS: Focus on the Southwest. J. Climate, 22, 32113231, https://doi.org/10.1175/2009JCLI2532.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zangvil, A., D. H. Portis, and P. J. Lamb, 2001: Investigation of the large-scale atmospheric moisture field over the midwestern United States in relation to summer precipitation. Part I: Relationships between moisture budget components on different timescales. J. Climate, 14, 582597, https://doi.org/10.1175/1520-0442(2001)014<0582:IOTLSA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zangvil, A., D. H. Portis, and P. J. Lamb, 2004: Investigation of the large-scale atmospheric moisture field over the midwestern United States in relation to summer precipitation. Part II: Recycling of local evapotranspiration and association with soil moisture and crop yields. J. Climate, 17, 32833301, https://doi.org/10.1175/1520-0442(2004)017<3283:IOTLAM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, L., A. Kumar, and W. Q. Wang, 2012: Influence of changes in observations on precipitation: A case study for the Climate Forecast System Reanalysis (CFSR). J. Geophys. Res., 117, D08105, https://doi.org/10.1029/2011JD017347.

    • Search Google Scholar
    • Export Citation
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