• Baldwin, M. P., and T. J. Dunkerton, 2001: Stratospheric harbingers of anomalous weather regimes. Science, 294, 581584, https://doi.org/10.1126/science.1063315.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baldwin, M. P., D. B. Stephenson, D. W. J. Thompson, T. J. Dunkerton, A. J. Charlton, and A. O’Neill, 2003: Stratospheric memory and skill of extended-range weather forecasts. Science, 301, 636640, https://doi.org/10.1126/science.1087143.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baldwin, M. P., and et al. , 2018: 100 years of progress in understanding the stratosphere and mesosphere. A Century of Progress in Atmospheric and Related Sciences: Celebrating the American Meteorological Society Centennial, Meteor. Monogr., No. 59, 27.1–27.62, https://doi.org/10.1175/AMSMONOGRAPHS-D-19-0003.1.

    • Crossref
    • Export Citation
  • Butler, A., and et al. , 2019: Sub-seasonal predictability and the stratosphere. Sub-seasonal to Seasonal Prediction, A. W. Robertson and F. Vitart, Eds., Elsevier, 585 pp.

  • Charlton, A. J., and L. M. Polvani, 2007: A new look at stratospheric sudden warmings. Part I: Climatology and modeling benchmarks. J. Climate, 20, 449469, https://doi.org/10.1175/JCLI3996.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, M., W. Shi, P. Xie, V. B. S. Silva, V. E. Kousky, R. W. Higgins, and J. E. Janowiak, 2008: Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res., 113, D04110, https://doi.org/10.1029/2007JD009132.

    • Search Google Scholar
    • Export Citation
  • Danabasoglu, G., S. C. Bates, B. P. Briegleb, S. R. Jayne, M. Jochum, W. G. Large, S. Peacock, and S. G. Yeager, 2012: The CCSM4 ocean component. J. Climate, 25, 13611389, https://doi.org/10.1175/JCLI-D-11-00091.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and et al. , 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
  • DelSole, T., and M. K. Tippett, 2014: Comparing forecast skill. Mon. Wea. Rev., 142, 46584678, https://doi.org/10.1175/MWR-D-14-00045.1.

  • Dennis, J. M., and et al. , 2012: CAM-SE: A scalable spectral element dynamical core for the community atmosphere model. Int. J. High Perform. Comput. Appl., 26, 7489, https://doi.org/10.1177/1094342011428142.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Domeisen, D. I. V., and et al. , 2019a: The role of the stratosphere in subseasonal to seasonal prediction. Part I: Predictability of the stratosphere. J. Geophys. Res. Atmos., 124, e2019JD030923, https://doi.org/10.1029/2019JD030920.

    • Search Google Scholar
    • Export Citation
  • Domeisen, D. I. V., and et al. , 2019b: The role of the stratosphere in subseasonal to seasonal prediction. Part II: Predictability arising from stratosphere–troposphere coupling. J. Geophys. Res. Atmos., 124, e2019JD030923, https://doi.org/10.1029/2019JD030923.

    • Search Google Scholar
    • Export Citation
  • Griffies, S. M., and et al. , 2014: An assessment of global and regional sea level for years 1993-2007 in a suite of interannual CORE-II simulations. Ocean Modell., 78, 3589, https://doi.org/10.1016/j.ocemod.2014.03.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Griffies, S. M., and et al. , 2016: OMIP contribution to CMIP6: Experimental and diagnostic protocol for the physical component of the ocean model intercomparison project. Geosci. Model Dev., 9, 32313296, https://doi.org/10.5194/gmd-9-3231-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hitchcock, P., and P. Haynes, 2016: Stratospheric control of planetary waves. Geophys. Res. Lett., 43, 11 88411 892, https://doi.org/10.1002/2016GL071372.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., 1995: Decadal trends in the North Atlantic Oscillation: Regional temperatures and precipitation. Science, 269, 676679, https://doi.org/10.1126/science.269.5224.676.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., J. J. Hack, D. Shea, J. M. Caron, and J. Rosinski, 2008: A new sea surface temperature and sea ice boundary dataset for the Community Atmosphere Model. J. Climate, 21, 51455153, https://doi.org/10.1175/2008JCLI2292.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., and et al. , 2013: The Community Earth System Model: A framework for collaborative research. Bull. Amer. Meteor. Soc., 94, 13391360, https://doi.org/10.1175/BAMS-D-12-00121.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johansson, Å., 2007: Prediction skill of the NAO and PNA from daily to seasonal time scales. J. Climate, 20, 19571975, https://doi.org/10.1175/JCLI4072.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, C., D. E. Waliser, J. K. E. Schemm, and W. K. M. Lau, 2000: Prediction skill of the Madden and Julian Oscillation in dynamical extended range forecasts. Climate Dyn., 16, 273289, https://doi.org/10.1007/s003820050327.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and et al. , 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H., J. H. Richter, and Z. Martin, 2019: Insignificant QBO–MJO prediction skill relationship in the SubX and S2S subseasonal reforecasts. J. Geophys. Res. Atmos., 124, 12 65512 666, https://doi.org/10.1029/2019JD031416.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirtman, B. P., and et al. , 2014: The North American multimodel ensemble: Phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull. Amer. Meteor. Soc., 95, 585601, https://doi.org/10.1175/BAMS-D-12-00050.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawrence, D. M., and et al. , 2011: Parameterization improvements and functional and structural advances in version 4 of the Community Land Model. J. Adv. Model. Earth Syst., 3, M03001, https://doi.org/10.1029/2011MS00045.

    • Search Google Scholar
    • Export Citation
  • Leutbecher, M., and T. N. Palmer, 2008: Ensemble forecasting. J. Comput. Phys., 227, 35153539, https://doi.org/10.1016/j.jcp.2007.02.014.

  • Lim, Y., and et al. , 2019: Influence of the QBO on MJO prediction skill in the subseasonal-to-seasonal prediction models. Climate Dyn., 53, 16811695, https://doi.org/10.1007/s00382-019-04719-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, H., and et al. , 2014: On the correspondence between mean forecast errors and climate errors in CMIP5 models. J. Climate, 27, 17811798, https://doi.org/10.1175/JCLI-D-13-00474.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Magnusson, L., J. Nycander, and E. Kallen, 2009: Flow-dependent versus flow-independent initial perturbations for ensemble prediction. Tellus, 61A, 194209, https://doi.org/10.1111/j.1600-0870.2008.00385.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mariotti, A., and et al. , 2020: Windows of opportunity for skillful forecasts subseasonal to seasonal and beyond. Bull. Amer. Meteor. Soc., 101, E608E625, https://doi.org/10.1175/BAMS-D-18-0326.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marshall, A. G., and A. A. Scaife, 2010: Improved predictability of stratospheric sudden warming events in an atmospheric general circulation model with enhanced stratospheric resolution. J. Geophys. Res., 115, D16114, https://doi.org/10.1029/2009JD012643.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McIntyre, M. E., 1982: How well do we understand sudden stratospheric warmings? J. Meteor. Soc. Japan, 60, 3765.

  • Murphy, A. H., and E. Epstein, 1989: Skill scores and correlation coefficients in model verification. Mon. Wea. Rev., 117, 572582, https://doi.org/10.1175/1520-0493(1989)117<C0572:SSACCI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NAS, 2016: Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. The National Academies Press, 350 pp., https://doi.org/10.17226/21873.

    • Crossref
    • Export Citation
  • Neale, R. B., and et al. , 2012: Description of the NCAR Community Atmosphere Model (CAM 5.0). NCAR Tech. Note TN-486, 274 pp., www.cesm.ucar.edu/models/cesm1.0/cam/docs/description/cam5_desc.pdf.

  • Nie, Y., A. A. Scaife, H.-L. Ren, R. E. Comer, M. B. Andrews, P. Davis, and N. Martin, 2019: Stratospheric initial conditions provide seasonal predictability of the North Atlantic and Arctic Oscillations. Environ. Res. Lett., 14, 034006, https://doi.org/10.1088/1748-9326/ab0385.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nishimoto, E., and S. Yoden, 2017: Influence of the stratospheric quasi-biennial oscillation on the Madden–Julian oscillation during austral summer. J. Atmos. Sci., 74, 11051125, https://doi.org/10.1175/JAS-D-16-0205.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pegion, K., and et al. , 2019: The Subseasonal Experiment (SubX): A multimodel subseasonal prediction experiment. Bull. Amer. Meteor. Soc., 100, 20432060, https://doi.org/10.1175/BAMS-D-18-0270.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Richter, J. H., F. Sassi, and R. R. Garcia, 2010: Toward a physically based gravity wave source parameterization in a general circulation model. J. Atmos. Sci., 67, 136156, https://doi.org/10.1175/2009JAS3112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Richter, J. H., A. Solomon, and J. T. Bacmeister, 2014: Effects of vertical resolution and nonorographic gravity wave drag on the simulated climate in the Community Atmosphere Model, version 5. J. Adv. Model. Earth Syst., 6, 357383, https://doi.org/10.1002/2013MS000303.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Richter, J. H., C. Deser, and L. Sun, 2015: Effects of stratospheric variability on El Niño teleconnections. Environ. Res. Lett., 10, 124021, https://doi.org/10.1088/1748-9326/10/12/124021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riddle, E. E., A. H. Butler, J. C. Furtado, J. L. Cohen, and K. Kumar, 2013: CFSv2 ensemble prediction of the wintertime Arctic Oscillation. Climate Dyn., 41, 10991116, https://doi.org/10.1007/s00382-013-1850-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Robertson, A. W., A. Kumar, M. Peña, and F. Vitart, 2015: Improving and promoting subseasonal to seasonal prediction. Bull. Amer. Meteor. Soc., 96, ES49ES53, https://doi.org/10.1175/BAMS-D-14-00139.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scaife, A. A., C. K. Folland, L. V. Alexander, A. Moberg, and J. D. Knight, 2008: European climate extremes and the North Atlantic Oscillation. J. Climate, 21, 7283, https://doi.org/10.1175/2007JCLI1631.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scaife, A. A., and et al. , 2014a: Predictability of the quasi-biennial oscillation and its Northern winter teleconnection on seasonal to decadal timescales. Geophys. Res. Lett., 41, 17521758, https://doi.org/10.1002/2013GL059160.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scaife, A. A., and et al. , 2014b: Skillful long-range prediction of European and North American winters. Geophys. Res. Lett., 41, 25142519, https://doi.org/10.1002/2014GL059637.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Son, S. W., Y. Lim, C. Yoo, H. Hendon, and J. Kim, 2017: Stratospheric control of the Madden–Julian oscillation. J. Climate, 30, 19091922, https://doi.org/10.1175/JCLI-D-16-0620.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stan, C., D. M. Straus, J. S. Frederiksen, H. Lin, E. D. Maloney, and C. Schumacher, 2017: Review of tropical-extratropical teleconnections on intraseasonal time scales. Rev. Geophys., 55, 902937, https://doi.org/10.1002/2016RG000538.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stockdale, T. N., F. Molenti, and L. Ferranti, 2015: Atmospheric initial conditions and the predictability of the Arctic Oscillation. Geophys. Res. Lett., 42, 11731179, https://doi.org/10.1002/2014GL062681.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tripathi, O. P., and et al. , 2016: Examining the predictability of the stratospheric sudden warming of January 2013 using multiple NWP systems. Mon. Wea. Rev., 144, 19351960, https://doi.org/10.1175/MWR-D-15-0010.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tsujino, H., and et al. , 2018: JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do). Ocean Modell., 130, 79139, https://doi.org/10.1016/j.ocemod.2018.07.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tsujino, H., and et al. , 2020: Evaluation of global ocean–sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2). Geosci. Model Dev., 13, 36433708, https://doi.org/10.5194/gmd-13-3643-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F., and A. W. Robertson, 2018: The sub-seasonal to seasonal prediction project (S2S) and the prediction of extreme events. npj Climate Atmos. Sci., 1, 3, https://doi.org/10.1038/S41612-018-0013-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wheeler, M. C., and H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 19171932, https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • White, C. J., and et al. , 2017: Potential applications of Subseasonal-to-Seasonal (S2S) predictions. Meteor. Appl., 24, 315325, https://doi.org/10.1002/met.1654.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. International Geophysics Series, Vol. 100, Academic Press, 704 pp.

  • Xie, P., M. Chen, S. Yang, A. Yatagai, T. Hayasaka, Y. Fukushima, and C. Liu, 2007: A gauge-based analysis of daily precipitation over East Asia. J. Hydrometeor., 8, 607626, https://doi.org/10.1175/JHM583.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S., H. Ma, J. S. Boyle, S. A. Klein, and Y. Zhang, 2012: On the correspondence between short- and long-time-scale systematic errors in CAM4/CAM5 for the Year of Tropical Convection. J. Climate, 25, 79377955, https://doi.org/10.1175/JCLI-D-12-00134.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yeager, S. G., and et al. , 2018: Predicting near-term changes in the Earth system: A large ensemble of initialized decadal prediction simulations using the Community Earth System Model. Bull. Amer. Meteor. Soc., 99, 18671886, https://doi.org/10.1175/BAMS-D-17-0098.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yoo, C., and S.-W. Son, 2016: Modulation of the boreal wintertime Madden-Julian Oscillation by the stratospheric quasi-biennial oscillation. Geophys. Res. Lett., 43, 13921398, https://doi.org/10.1002/2016GL067762.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Subseasonal Prediction with and without a Well-Represented Stratosphere in CESM1

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  • 1 Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado
  • | 2 Department of Atmospheric, Oceanic, and Earth Sciences, George Mason University, Fairfax, Virginia
  • | 3 Colorado State University, Fort Collins, Colorado
  • | 4 School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, New York
  • | 5 NOAA/NCEP/Climate Prediction Center, College Park, Maryland
  • | 6 Innovim, Inc., College Park, Maryland
  • | 7 Full Stack Science LLC, Cary, North Carolina
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Abstract

There is a growing demand for understanding sources of predictability on subseasonal to seasonal (S2S) time scales. Predictability at subseasonal time scales is believed to come from processes varying slower than the atmosphere such as soil moisture, snowpack, sea ice, and ocean heat content. The stratosphere as well as tropospheric modes of variability can also provide predictability at subseasonal time scales. However, the contributions of the above sources to S2S predictability are not well quantified. Here we evaluate the subseasonal prediction skill of the Community Earth System Model, version 1 (CESM1), in the default version of the model as well as a version with the improved representation of stratospheric variability to assess the role of an improved stratosphere on prediction skill. We demonstrate that the subseasonal skill of CESM1 for surface temperature and precipitation is comparable to that of operational models. We find that a better-resolved stratosphere improves stratospheric but not surface prediction skill for weeks 3–4.

Significance Statement

There is a growing demand in society for understanding sources of predictability on subseasonal to seasonal time scales. In this work we demonstrate that the CESM1 research Earth system model can be utilized as a subseasonal prediction model and show that its subseasonal prediction skill is comparable to that of operational models. We also show that the inclusion of a well-resolved stratosphere does not improve the subseasonal (week 3–4 averaged) forecast of temperature and precipitation at the surface.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WAF-D-20-0029.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: Jadwiga H. Richter, jrichter@ucar.edu

Abstract

There is a growing demand for understanding sources of predictability on subseasonal to seasonal (S2S) time scales. Predictability at subseasonal time scales is believed to come from processes varying slower than the atmosphere such as soil moisture, snowpack, sea ice, and ocean heat content. The stratosphere as well as tropospheric modes of variability can also provide predictability at subseasonal time scales. However, the contributions of the above sources to S2S predictability are not well quantified. Here we evaluate the subseasonal prediction skill of the Community Earth System Model, version 1 (CESM1), in the default version of the model as well as a version with the improved representation of stratospheric variability to assess the role of an improved stratosphere on prediction skill. We demonstrate that the subseasonal skill of CESM1 for surface temperature and precipitation is comparable to that of operational models. We find that a better-resolved stratosphere improves stratospheric but not surface prediction skill for weeks 3–4.

Significance Statement

There is a growing demand in society for understanding sources of predictability on subseasonal to seasonal time scales. In this work we demonstrate that the CESM1 research Earth system model can be utilized as a subseasonal prediction model and show that its subseasonal prediction skill is comparable to that of operational models. We also show that the inclusion of a well-resolved stratosphere does not improve the subseasonal (week 3–4 averaged) forecast of temperature and precipitation at the surface.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WAF-D-20-0029.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: Jadwiga H. Richter, jrichter@ucar.edu

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