• Assel, R. A., 1998: The 1997 ENSO event and implication for North American Laurentian Great Lakes winter severity and ice cover. Geophys. Res. Lett., 25, 10311033, doi:10.1029/98GL00720.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnston, A. G., M. K. Tippett, H. M. van den Dool, and D. A. Unger, 2015: Toward an improved multimodel ENSO prediction. J. Appl. Meteor. Climatol., 54, 15791595, doi:10.1175/JAMC-D-14-0188.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Becker, E. J., H. van den Dool, and M. Peña, 2013: Short-term climate extremes: Prediction skill and predictability. J. Climate, 26, 512531, doi:10.1175/JCLI-D-12-00177.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Becker, E. J., H. van den Dool, and Q. Zhang, 2014: Predictability and forecast skill in NMME. J. Climate, 27, 58915906, doi:10.1175/JCLI-D-13-00597.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clites, A. H., J. Wang, K. B. Campbell, A. D. Gronewold, R. Assel, X. Bai, and G. A. Leshkevich, 2014: Cold water and high ice cover on Great Lakes in spring 2014. Eos, Trans. Amer. Geophys. Union, 95, 305312, doi:10.1002/2014EO340001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Croley, T. E., II, 2000: Using Meteorology Probability Forecasts in Operational Hydrology. American Society of Civil Engineers, 206 pp.

    • Crossref
    • Export Citation
  • Gent, P. R., and Coauthors, 2011: The Community Climate System Model version 4. J. Climate, 24, 49734991, doi:10.1175/2011JCLI4083.1.

  • Gronewold, A. D., and V. Fortin, 2012: Advancing Great Lakes hydrological science through targeted binational collaborative research. Bull. Amer. Meteor. Soc., 93, 19211925, doi:10.1175/BAMS-D-12-00006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gronewold, A. D., A. H. Clites, T. S. Hunter, and C. A. Stow, 2011: An appraisal of the Great Lakes advanced hydrologic prediction system. J. Great Lakes Res., 37, 577583, doi:10.1016/j.jglr.2011.06.010.

    • Search Google Scholar
    • Export Citation
  • Herring, S. C., M. P. Hoerling, T. C. Peterson, and P. A. Stott, 2014: Explaining extreme events of 2013 from a climate perspective. Bull. Amer. Meteor. Soc., 95, S1S96, doi:10.1175/1520-0477-95.9.S1.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hunter, T. S., A. H. Clites, K. B. Campbell, and A. D. Gronewold, 2015: Development and application of a monthly hydrometeorological database for the North American Great Lakes—Part I: Precipitation, evaporation, runoff, and air temperature. J. Great Lakes Res., 41, 6577, doi:10.1016/j.jglr.2014.12.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jia, L., and Coauthors, 2015: Improved seasonal prediction of temperature and precipitation over land in a high-resolution GFDL climate model. J. Climate, 28, 20442062, doi:10.1175/JCLI-D-14-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kerr, R. A., 2011: Vital details of global warming are eluding forecasters. Science, 334, 173174, doi:10.1126/science.334.6053.173.

  • Kirtman, B. P., and Coauthors, 2014: The North American Multimodel Ensemble: Phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull. Amer. Meteor. Soc., 95, 585601, doi:10.1175/BAMS-D-12-00050.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, D. H., A. H. Clites, and J. P. Keillor, 1997: Assessing risk in operational decisions using Great Lakes probabilistic water level forecasts. Environ. Manage., 21, 4358, doi:10.1007/s002679900004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lofgren, B. M., A. D. Gronewold, A. Acciaioli, J. Cherry, A. Steiner, and D. Watkins, 2013: Methodological approaches to projecting the hydrologic impacts of climate change. Earth Interact., 17, doi:10.1175/2013EI000532.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, F., X. Yuan, and A. Ye, 2015: Seasonal drought predictability and forecast skill over China. J. Geophys. Res. Atmos., 120, 82648275, doi:10.1002/2015JD023185.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merryfield, W. J., and Coauthors, 2013: The Canadian seasonal to interannual prediction system. Part I: Models and initialization. Mon. Wea. Rev., 141, 29102945, doi:10.1175/MWR-D-12-00216.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Millerd, F., 2011: The potential impact of climate change on Great Lakes international shipping. Climatic Change, 104, 629652, doi:10.1007/s10584-010-9872-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mo, K. C., and D. P. Lettenmaier, 2014: Hydrologic prediction over the conterminous United States using the National Multi-Model Ensemble. J. Hydrometeor., 15, 14571472, doi:10.1175/JHM-D-13-0197.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mortsch, L. D., and F. H. Quinn, 1996: Climate change scenarios for Great Lakes Basin ecosystem studies. Limnol. Oceanogr., 41, 903911, doi:10.4319/lo.1996.41.5.0903.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Noorbakhsh, N., and R. Wilshaw, 1990: Forecasting water levels in the Great Lakes using multiple linear regression and time series analyses of net basin supplies. Proc. Great Lakes Water Level Forecasting and Statistics Symp., Windsor, ON, Canada, Great Lakes Environmental Research Laboratory, 53–61.

  • Notaro, M., K. Holman, A. Zarrin, E. Fluck, S. Vavrus, and V. Bennington, 2013: Influence of the Laurentian Great Lakes on regional climate. J. Climate, 26, 789804, doi:10.1175/JCLI-D-12-00140.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Lenic, E. A., D. A. Unger, M. S. Halpert, and K. S. Pelman, 2008: Developments in operational long-range climate prediction at CPC. Wea. Forecasting, 23, 496515, doi:10.1175/2007WAF2007042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodionov, S., and R. Assel, 2003: Winter severity in the Great Lakes region: A tale of two oscillations. Climate Res., 24, 1931, doi:10.3354/cr024019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2014: The NCEP Climate Forecast System version 2. J. Climate, 27, 21852208, doi:10.1175/JCLI-D-12-00823.1.

  • Seager, R., M. Hoerling, S. Schubert, H. Wang, B. Lyon, A. Kumar, J. Nakamura, and N. Henderson, 2015: Causes of the 2011 to 2014 California drought. J. Climate, 28, 69977024, doi:10.1175/JCLI-D-14-00860.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thiaw, W. M., and V. B. Kumar, 2015: NOAA’s African desk: Twenty years of developing capacity in weather and climate forecasting in Africa. Bull. Amer. Meteor. Soc., 96, 737753, doi:10.1175/BAMS-D-13-00274.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, D., C. J. Martinez, W. D. Graham, and S. Hwang, 2014: Statistical downscaling multimodel forecasts for seasonal precipitation and surface temperature over the southeastern United States. J. Climate, 27, 83848412, doi:10.1175/JCLI-D-13-00481.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vernieres, G., M. Rienecker, R. Kovach, and C. Keppenne, 2012: The GEOS-iODAS: Description and evaluation. NASA Tech. Memo. NASA/TM-2012–104606, 73 pp. [Available online at http://ntrs.nasa.gov/search.jsp?R=20140011278.]

  • Wood, E. F., S. D. Schubert, A. W. Wood, C. D. Peters-Lidard, K. C. Mo, A. Mariotti, and R. S. Pulwarty, 2015: Prospects for advancing drought understanding, monitoring, and prediction. J. Hydrometeor., 16, 16361657, doi:10.1175/JHM-D-14-0164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, X., J. K. Roundy, E. F. Wood, and J. Sheffield, 2015: Seasonal forecasting of global hydrologic extremes: System development and evaluation over GEWEX basins. Bull. Amer. Meteor. Soc., 96, 18951912, doi:10.1175/BAMS-D-14-00003.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, S., M. J. Harrison, A. Rosati, and A. Wittenberg, 2007: System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon. Wea. Rev., 135, 35413564, doi:10.1175/MWR3466.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Application of the NMME in the Development of a New Regional Seasonal Climate Forecast Tool

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  • 1 Colorado State University, Department of Atmospheric Science, Fort Collins, Colorado, and National Oceanic and Atmospheric Administration/Great Lakes Environmental Research Laboratory, Ann Arbor, Michigan
  • 2 National Oceanic and Atmospheric Administration, Great Lakes Environmental Research Laboratory, and Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan
  • 3 U.S. Army Corps of Engineers, Detroit District, Detroit, Michigan
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ABSTRACT

The National Oceanic and Atmospheric Administration’s Climate Prediction Center (CPC) provides access to a suite of real-time monthly climate forecasts that compose the North American Multi-Model Ensemble (NMME) in an attempt to meet the increasing demands for monthly to seasonal climate prediction. While the North American and global map-based forecasts provided by CPC are informative on a broad or continental scale, operational and decision-making institutions need products with a much more specific regional focus. To address this need, we developed a Region-Specific Seasonal Climate Forecast (RSCF–NMME) tool by combining NMME forecasts with regional climatological data. The RSCF–NMME automatically downloads and archives data and is displayed via a dynamic web-based graphical user interface. The tool has been applied to the Great Lakes region and utilized as part of operational water-level forecasting procedures by the U.S. Army Corps of Engineers, Detroit District (USACE-Detroit). Evaluation of the tool, compared with seasonal climate forecasts released by CPC, shows that the tool can provide additional useful information to users and overcomes some of the limitations of the CPC forecasts. The RSCF–NMME delivers details about a specific region’s climate, verification observations, and the ability to view different model forecasts. With its successful implementation within an operational environment, the tool has proven beneficial and thus set a precedent for expansion to other regions where there is a demand for region-specific seasonal climate forecasts.

National Oceanic and Atmospheric Administration–Great Lakes Environmental Research Laboratory Publication Number 1831.

© 2017 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 E-MAIL: Rebecca A. Bolinger, becky.bolinger@colostate.edu

ABSTRACT

The National Oceanic and Atmospheric Administration’s Climate Prediction Center (CPC) provides access to a suite of real-time monthly climate forecasts that compose the North American Multi-Model Ensemble (NMME) in an attempt to meet the increasing demands for monthly to seasonal climate prediction. While the North American and global map-based forecasts provided by CPC are informative on a broad or continental scale, operational and decision-making institutions need products with a much more specific regional focus. To address this need, we developed a Region-Specific Seasonal Climate Forecast (RSCF–NMME) tool by combining NMME forecasts with regional climatological data. The RSCF–NMME automatically downloads and archives data and is displayed via a dynamic web-based graphical user interface. The tool has been applied to the Great Lakes region and utilized as part of operational water-level forecasting procedures by the U.S. Army Corps of Engineers, Detroit District (USACE-Detroit). Evaluation of the tool, compared with seasonal climate forecasts released by CPC, shows that the tool can provide additional useful information to users and overcomes some of the limitations of the CPC forecasts. The RSCF–NMME delivers details about a specific region’s climate, verification observations, and the ability to view different model forecasts. With its successful implementation within an operational environment, the tool has proven beneficial and thus set a precedent for expansion to other regions where there is a demand for region-specific seasonal climate forecasts.

National Oceanic and Atmospheric Administration–Great Lakes Environmental Research Laboratory Publication Number 1831.

© 2017 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 E-MAIL: Rebecca A. Bolinger, becky.bolinger@colostate.edu
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