Impact of Sea Surface Temperature Forcing on Weeks 3 and 4 Forecast Skill in the NCEP Global Ensemble Forecasting System

Yuejian Zhu NOAA/NWS/NCEP/EMC, College Park, Maryland

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Xiaqiong Zhou IMSG, NOAA/NWS/NCEP/EMC, College Park, Maryland

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Malaquias Peña Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut

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Wei Li IMSG, NOAA/NWS/NCEP/EMC, College Park, Maryland

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Christopher Melhauser IMSG, NOAA/NWS/NCEP/EMC, College Park, Maryland

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Dingchen Hou NOAA/NWS/NCEP/EMC, College Park, Maryland

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Abstract

The Global Ensemble Forecasting System (GEFS) is being extended from 16 to 35 days to cover the subseasonal period, bridging weather and seasonal forecasts. In this study, the impact of SST forcing on the extended-range land-only global 2-m temperature, continental United States (CONUS) accumulated precipitation, and MJO skill are explored with version 11 of the GEFS (GEFSv11) under various SST forcing configurations. The configurations consist of 1) the operational GEFS 90-day e-folding time of the observed real-time global SST (RTG-SST) anomaly relaxed to climatology, 2) an optimal AMIP configuration using the observed daily RTG-SST analysis, 3) a two-tier approach using the CFSv2-predicted daily SST, and 4) a two-tier approach using bias-corrected CFSv2-predicted SST, updated every 24 h. The experimental period covers the fall of 2013 and the winter of 2013/14. The results indicate that there are small differences in the ranked probability skill scores (RPSSs) between the various SST forcing experiments. The improvements in forecast skill of the Northern Hemisphere 2-m temperature and precipitation for weeks 3 and 4 are marginal, especially for North America. The bias-corrected CFSv2-predicted SST experiment generally delivers superior performance with statistically significant improvement in spatially and temporally aggregated 2-m temperature RPSSs over North America. Improved representation of the SST forcing (AMIP) increased the forecast skill for MJO indices up through week 2, but there is no significant improvement of the MJO forecast skill for weeks 3 and 4. These results are obtained over a short period with weak MJO activity and are also subject to internal model weaknesses in representing the MJO. Additional studies covering longer periods with upgraded model physics are warranted.

© 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: Yuejian Zhu, yuejian.zhu@noaa.gov

Abstract

The Global Ensemble Forecasting System (GEFS) is being extended from 16 to 35 days to cover the subseasonal period, bridging weather and seasonal forecasts. In this study, the impact of SST forcing on the extended-range land-only global 2-m temperature, continental United States (CONUS) accumulated precipitation, and MJO skill are explored with version 11 of the GEFS (GEFSv11) under various SST forcing configurations. The configurations consist of 1) the operational GEFS 90-day e-folding time of the observed real-time global SST (RTG-SST) anomaly relaxed to climatology, 2) an optimal AMIP configuration using the observed daily RTG-SST analysis, 3) a two-tier approach using the CFSv2-predicted daily SST, and 4) a two-tier approach using bias-corrected CFSv2-predicted SST, updated every 24 h. The experimental period covers the fall of 2013 and the winter of 2013/14. The results indicate that there are small differences in the ranked probability skill scores (RPSSs) between the various SST forcing experiments. The improvements in forecast skill of the Northern Hemisphere 2-m temperature and precipitation for weeks 3 and 4 are marginal, especially for North America. The bias-corrected CFSv2-predicted SST experiment generally delivers superior performance with statistically significant improvement in spatially and temporally aggregated 2-m temperature RPSSs over North America. Improved representation of the SST forcing (AMIP) increased the forecast skill for MJO indices up through week 2, but there is no significant improvement of the MJO forecast skill for weeks 3 and 4. These results are obtained over a short period with weak MJO activity and are also subject to internal model weaknesses in representing the MJO. Additional studies covering longer periods with upgraded model physics are warranted.

© 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: Yuejian Zhu, yuejian.zhu@noaa.gov
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  • Adams, D., and A. Comrie, 1997: The North American monsoon. Bull. Amer. Meteor. Soc., 78, 21972213, https://doi.org/10.1175/1520-0477(1997)078<2197:TNAM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnston, A., and R. Livezey, 1987: Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Wea. Rev., 115, 10831126, https://doi.org/10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buizza, R., P. Houtekamer, G. Pellerin, Z. Toth, Y. Zhu, and M. Wei, 2005: A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems. Mon. Wea. Rev., 133, 10761097, https://doi.org/10.1175/MWR2905.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, C.-P., Y. Zhang, and T. Li, 2000: Interannual and interdecadal variations of the East Asian summer monsoon and tropical Pacific SSTs. Part I: Roles of the subtropical ridge. J. Climate, 13, 43104325, https://doi.org/10.1175/1520-0442(2000)013<4310:IAIVOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, W., and H. Van den Dool, 2003: Sensitivity of teleconnection patterns to the sign of their primary action center. Mon. Wea. Rev., 131, 28852899, https://doi.org/10.1175/1520-0493(2003)131<2885:SOTPTT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Côté, J., S. Gravel, A. Méthot, A. Patoine, M. Roch, and A. Staniforth, 1998: The operational CMC–MRB Global Environmental Multiscale (GEM) model. Part I: Design considerations and formulation. Mon. Wea. Rev., 126, 13731395, https://doi.org/10.1175/1520-0493(1998)126<1373:TOCMGE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, X., J.-Y. Lee, P.-C. Hsu, H. Taniguchi, B. Wang, W. Wang, and S. Weaver, 2013: Multi-model MJO forecasting during DYNAMO/CINDY period. Climate Dyn., 41, 1067, https://doi.org/10.1007/s00382-013-1859-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gottschalck, J. M., and Coauthors, 2010: A framework for assessing operational Madden–Julian oscillation forecasts: A CLIVAR MJO Working Group project. Bull. Amer. Meteor. Soc., 91, 12471258, https://doi.org/10.1175/2010BAMS2816.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, H., and Y. Zhu, 2016: Development of verification methodology for extreme weather forecasts. Wea. Forecasting, 32, 479491, https://doi.org/10.1175/WAF-D-16-0123.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, J., and H. Pan, 2011: Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System. Wea. Forecasting, 26, 520533, https://doi.org/10.1175/WAF-D-10-05038.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, J., M. L. Witek, J. Teixeira, R. Sun, H.-L. Pan, J. K. Fletcher, and C. S. Bretherton, 2016: Implementation in the NCEP GFS of a hybrid eddy-diffusivity mass-flux (EDMF) boundary layer parameterization with dissipative heating and modified stable boundary layer mixing. Wea. Forecasting, 31, 341351, https://doi.org/10.1175/WAF-D-15-0053.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hou, D., Z. Toth, Y. Zhu, and W. Yang, 2008: Impact of a stochastic perturbation scheme on global ensemble forecast. 19th Conf. on Probability and Statistics, New Orleans, LA, Amer. Meteor. Soc., 1.1, https://ams.confex.com/ams/88Annual/webprogram/Paper134165.html.

  • Hou, D., and Coauthors, 2014: Climatology-calibrated precipitation analysis at fine scales: Statistical adjustment of Stage IV toward CPC gauge-based analysis. J. Hydrometeor., 15, 25422557, https://doi.org/10.1175/JHM-D-11-0140.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hunke, E. C., W. H. Lipscomb, and A. K. Turner, 2010: Sea-ice models for climate study: Retrospective and new directions. J. Glaciol., 56, 11621172, https://doi.org/10.3189/002214311796406095.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H.-M., P. J. Webster, V. E. Toma, and D. Kim, 2014: Predictability and prediction skill of the MJO in two operational forecasting systems. J. Climate, 27, 53645378, https://doi.org/10.1175/JCLI-D-13-00480.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, https://doi.org/10.1175/BAMS-D-12-00050.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kleist, D. T., and K. Ide, 2015: An OSSE-based evaluation of hybrid variational–ensemble data assimilation for the NCEP GFS. Part II: 4DEnVar and hybrid variants. Mon. Wea. Rev., 143, 452470, https://doi.org/10.1175/MWR-D-13-00350.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kleist, D. T., D. F. Parrish, J. C. Derber, R. Treadon, W. S. Wu, and S. Lord, 2009: Introduction of the GSI into the NCEP Global Data Assimilation System. Wea. Forecasting, 24, 16911705, https://doi.org/10.1175/2009WAF2222201.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kurihara, Y., M. A. Bender, R. E. Tuleya, and R. J. Ross, 1995: Improvements in the GFDL hurricane prediction system. Mon. Wea. Rev., 123, 27912801, https://doi.org/10.1175/1520-0493(1995)123<2791:IITGHP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liebmann, B., and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 12751277.

    • Search Google Scholar
    • Export Citation
  • Lindzen, R. S., 1987: On the development of the theory of the QBO. Bull. Amer. Meteor. Soc., 68, 329337, https://doi.org/10.1175/1520-0477(1987)068<0329:OTDOTT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ling, J., P. Bauer, P. Bechtold, A. Beljaars, R. Forbes, F. Vitart, M. Ulate, and C. Zhang, 2014: Global versus local MJO forecast skill of the ECMWF model during DYNAMO. Mon. Wea. Rev., 142, 22282247, https://doi.org/10.1175/MWR-D-13-00292.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Q., S. J. Lord, N. Surgi, Y. Zhu, R. Wobus, Z. Toth, and T. Marchok, 2006: Hurricane relocation in global ensemble forecast system. 27th Conf. on Hurricanes and Tropical Meteorology, Monterey, CA, Amer. Meteor. Soc., P5.13, https://ams.confex.com/ams/pdfpapers/108503.pdf.

  • Lorenz, E., 1969a: The predictability of a flow which possesses many scales of motion. Tellus, 21, 289307, https://doi.org/10.3402/tellusa.v21i3.10086.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, E., 1969b: Three approaches to atmosphere predictability. Bull. Amer. Meteor. Soc., 50, 345349.

  • Lorenz, E., 1982: Low-order models of atmospheric circulations. J. Meteor. Soc. Japan, 60, 255267, https://doi.org/10.2151/jmsj1965.60.1_255.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, Y. R., and Coauthors, 2016: The Southern China Monsoon Rainfall Experiment (SCMREX). Bull. Amer. Meteor. Soc., 98, 9991013, https://doi.org/10.1175/BAMS-D-15-00235.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, J., Y. Zhu, D. Hou, X. Zhou, and M. Peña, 2014: Ensemble transform with 3D rescaling initialization method. Mon. Wea. Rev., 142, 40534073, https://doi.org/10.1175/MWR-D-13-00367.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madden, R., and P. Julian, 1971: Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci., 28, 702708, https://doi.org/10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madden, R., and P. Julian, 1972: Description of global-scale circulation cells in the tropics with a 40–50 day period. J. Atmos. Sci., 29, 11091123, https://doi.org/10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Melhauser, C., W. Li, Y. Zhu, X. Zhou, M. Peña, and D. Hou, 2016: Exploring the impact of SST on the extended range NCEP Global Ensemble Forecast System. STI Climate Bulletin, NWS/Office of Science and Technology Integration, Silver Spring, MD, 30–34, http://www.nws.noaa.gov/ost/climate/STIP/41cdpw_digest.htm.

  • Pegion, K., and P. Sardeshmukh, 2011: Prospects for improving subseasonal predictions. Mon. Wea. Rev., 139, 36483666, https://doi.org/10.1175/MWR-D-11-00004.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ramsay, B. H., 1998: The Interactive Multisensor Snow and Ice Mapping System. Hydrol. Processes, 12, 15371546, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1537::AID-HYP679>3.0.CO;2-A.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rex, D., 1950: Blocking action in the middle troposphere and its effect upon regional climate. Tellus, 2, 275301.

  • Saha, S., and Coauthors, 2006: The NCEP Climate Forecast System. J. Climate, 19, 34833517, https://doi.org/10.1175/JCLI3812.1.

  • 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
  • Saha, S., and Coauthors, 2014: The NCEP Climate Forecast System version 2. J. Climate, 27, 21852208, https://doi.org/10.1175/JCLI-D-12-00823.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sela, J., 1980: Spectral modeling at the National Meteorological Center. Mon. Wea. Rev., 108, 12791292, https://doi.org/10.1175/1520-0493(1980)108<1279:SMATNM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shelly, A., P. Xavier, D. Copsey, T. Johns, J. M. Rodríguez, S. Milton, and N. Klingaman, 2014: Coupled versus uncoupled hindcast simulations of the Madden–Julian oscillation in the Year of Tropical Convection. Geophys. Res. Lett., 41, 56705677, https://doi.org/10.1002/2013GL059062.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toth, Z., and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74, 23172330, https://doi.org/10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toth, Z., and E. Kalnay, 1997: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125, 32973319, https://doi.org/10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toth, Z., Y. Zhu, and T. Marchok, 2001: The use of ensembles to identify forecasts with small and large uncertainty. Wea. Forecasting, 16, 463477, https://doi.org/10.1175/1520-0434(2001)016<0463:TUOETI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van den Dool, H., S. Saha, and Å. Johansson, 2000: Empirical orthogonal teleconnections. J. Climate, 13, 14211435, https://doi.org/10.1175/1520-0442(2000)013<1421:EOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F., G. Balsamo, R. Buizza, L. Ferranti, S. Keeley, L. Magnusson, F. Molteni, and A. Weisheimer, 2014: Sub-seasonal predictions. ECMWF Tech. Memo. 734, 45 pp., https://www.ecmwf.int/sites/default/files/elibrary/2014/12943-sub-seasonal-predictions.pdf.

  • Vitart, F., and Coauthors, 2017: The Subseasonal to Seasonal (S2S) Prediction Project Database. Bull. Amer. Meteor. Soc., 98, 163173, https://doi.org/10.1175/BAMS-D-16-0017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wallace, J., and D. Gutzler, 1981: Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev., 109, 784812, https://doi.org/10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., J. Liu, H.-J. Kim, P. J. Webster, S.-Y. Yim, and B. Xiang, 2013: Northern Hemisphere summer monsoon intensified by mega-El Niño/Southern Oscillation and Atlantic multidecadal oscillation. Proc. Natl. Acad. Sci. USA, 110, 53475352, https://doi.org/10.1073/pnas.1219405110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, W., A. Kumar, J. X. Fu, and M. P. Hung, 2015: What is the role of the sea surface temperature uncertainty in the prediction of tropical convection associated with the MJO? Mon. Wea. Rev., 143, 31563175, https://doi.org/10.1175/MWR-D-14-00385.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X., D. Parrish, D. Kleist, and J. Whitaker, 2013: GSI 3DVar-based ensemble–variational hybrid data assimilation for NCEP Global Forecast System: Single-resolution experiments. Mon. Wea. Rev., 141, 40984117, https://doi.org/10.1175/MWR-D-12-00141.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wei, M., Z. Toth, R. Wobus, and Y. Zhu, 2008: Initial perturbations based on the ensemble transform (ET) technique in the NCEP Global Operational Forecast System. Tellus, 59A, 6279, https://doi.org/10.1111/j.1600-0870.2007.00273.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wheeler, M. C., and 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
  • Whitaker, J., T. Hamill, X. Wei, Y. Song, and Z. Toth, 2008: Ensemble data assimilation with the NCEP Global Forecast System. Mon. Wea. Rev., 136, 463482, https://doi.org/10.1175/2007MWR2018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. Elsevier, 676 pp.

    • Crossref
    • Export Citation
  • Wu, W., R. J. Purser, and D. F. Parrish, 2002: Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev., 130, 29052916, https://doi.org/10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiang, B., M. Zhao, X. Jiang, S. Lin, T. Li, X. Fu, and G. Vecchi, 2015: The 3–4-week MJO prediction skill in a GFDL coupled model. J. Climate, 28, 53515364, https://doi.org/10.1175/JCLI-D-15-0102.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., W. Li, and M. E. Mann, 2016: Scale-dependent regional climate predictability over North America inferred from CMIP3 and CMIP5 ensemble simulations. Adv. Atmos. Sci., 33, 905918, https://doi.org/10.1007/s00376-016-6013-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, X., Y. Zhu, D. Hou, and D. Kleist, 2016: A comparison of perturbations from an ensemble transform and an ensemble Kalman filter for the NCEP Global Ensemble Forecast System. Wea. Forecasting, 31, 20572074, https://doi.org/10.1175/WAF-D-16-0109.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, X., Y. Zhu, D. Hou, Y. Luo, J. Peng, and D. Wobus, 2017: The NCEP Global Ensemble Forecast System with the EnKF initialization. Wea. Forecasting, 32, 19892004, https://doi.org/10.1175/WAF-D-17-0023.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, Y., 2005: Ensemble forecast: A new approach to uncertainty and predictability. Adv. Atmos. Sci., 22, 781788, https://doi.org/10.1007/BF02918678.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, Y., Z. Toth, R. Wobus, D. Richardson, and K. Mylne, 2002: The economic value of ensemble-based weather forecasts. Bull. Amer. Meteor. Soc., 83, 7383, https://doi.org/10.1175/1520-0477(2002)083<0073:TEVOEB>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
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