• Brankovic, C., , T. N. Palmer, , and L. Ferranti, 1994: Predictability of seasonal atmospheric variations. J. Climate, 7, 217237.

  • Dirmeyer, P. A., 2001: An evaluation of the strength of land-atmosphere coupling. J. Hydrometeor., 2, 329344.

  • Dirmeyer, P. A., , A. J. Dolman, , and N. Sato, 1999: The Global Soil Wetness Project: A pilot project for global land surface modeling and validation. Bull. Amer. Meteor. Soc., 80, 851878.

    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., , R. D. Koster, , and Z. Guo, 2006: Do global models properly represent the feedback between land and atmosphere? J. Hydrometeor., 7, 11771198.

    • Search Google Scholar
    • Export Citation
  • Ek, M. B., , K. E. Mitchell, , Y. Lin, , E. Rogers, , P. Grunmann, , V. Koren, , G. Gayno, , and J. D. Tarpley, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation
  • Fan, Y., , and H. van den Dool, 2008: A global monthly land surface air temperature analysis for 1948-present. J. Geophys. Res., 113, D01103, doi:10.1029/2007JD008470.

    • Search Google Scholar
    • Export Citation
  • Higgins, R. W., , W. Shi, , E. Yarosh, , and R. Joyce, 2000: Improved United States precipitation quality control system and analysis. NCEP–Climate Prediction Center, Atlas 7, Climate Prediction Center. [Available online at http://www.cpc.noaa.gov/research_papers/ncep_cpc_atlas/7/index.html.]

    • Search Google Scholar
    • Export Citation
  • Hou, Y.-T., , S. Moorthi, , and K. A. Campana, 2002: Parameterization of solar radiation transfer in the NCEP models. NCEP Office Note 441, 46 pp. [Available online at http://www.emc.ncep.noaa.gov/officenotes/newernotes/on441.pdf.]

    • Search Google Scholar
    • Export Citation
  • Ji, M., , A. Leetmaa, , and J. Derber, 1995: An ocean analysis system for seasonal to interannual climate studies. Mon. Wea. Rev., 123, 460481.

    • Search Google Scholar
    • Export Citation
  • Kanamitsu, M., , W. Ebisuzaki, , J. Woollen, , S.-K. Yang, , J. J. Hnilo, , M. Fiorino, , and G. L. Potter, 2002: NCEP-DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 16311648.

    • Search Google Scholar
    • Export Citation
  • Koren, V., , J. Schaake, , K. Mitchell, , Q. Duan, , F. Chen, , and J. Baker, 1999: A parameterization of snowpack and frozen ground intended for NCEP weather and climate models. J. Geophys. Res., 104, 19 56919 585.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., , M. J. Suarez, , and M. Heiser, 2000: Variance and predictability of precipitation at seasonal-to-interannual timescales. J. Hydrometeor., 1, 2646.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Coauthors, 2004a: Regions of strong coupling between soil moisture and precipitation. Science, 305, 11381140.

  • Koster, R. D., and Coauthors, 2004b: Realistic initialization of land surface states: Impacts on subseasonal forecast skill. J. Hydrometeor., 5, 10491063.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Coauthors, 2006: GLACE: The Global Land–Atmosphere Coupling Experiment: Part 1: Overview. J. Hydrometeor., 7, 590610.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., , Z. Guo, , R. Yang, , P. A. Dirmeyer, , K. Mitchell, , and M. J. Puma, 2009: On the nature of soil moisture in land surface models. J. Climate, 22, 43224335.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Coauthors, 2010: Contribution of land initialization to subseasonal forecast skill: First results from a multi-model experiments. Geophys. Res. Lett., 37, L02402, doi:10.1029/2009GL041677.

    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20, 131141.

  • Lu, C.-H., , Z. Guo, , and K. Mitchell, 2005: Response of precipitation to soil moisture constraints in the NCEP global model simulations for GLDAS. Preprints, 85th AMS Annual Meeting, San Diego, CA, Amer. Meteor. Soc., 4.4. [Available online at http://ams.confex.com/ams/pdfpapers/87070.pdf.]

    • Search Google Scholar
    • Export Citation
  • Mitchell, K. E., and Coauthors, 2004: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J. Geophys. Res., 109, D07S90, doi:10.1029/2003JD003823.

    • Search Google Scholar
    • Export Citation
  • Pan, H.-L., , and L. Mahrt, 1987: Interaction between soil hydrology and boundary layer developments. Bound.-Layer Meteor., 38, 185202.

    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., , N. A. Rayner, , T. M. Smith, , D. C. Stokes, , and W. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15, 16091625.

    • Search Google Scholar
    • Export Citation
  • Robock, A., , K. Y. Vinnikov, , G. Srinivasan, , J. K. Entin, , S. E. Hollinger, , N. A. Speranskaya, , S. Liu, , and A. Namkhai, 2000: The global soil moisture data bank. Bull. Amer. Meteor. Soc., 81, 12811299.

    • Search Google Scholar
    • Export Citation
  • Rodell, M., and Coauthors, 2004: The global land data assimilation system. Bull. Amer. Meteor. Soc., 85, 381394.

  • Saha, S., and Coauthors, 2006: The NCEP climate forecast system. J. Climate, 19, 34833517.

  • Shukla, J., 1998: Predictability in the midst of chaos: A scientific basis for climate forecasting. Science, 215, 14981501.

  • Shukla, J., , and Y. Mintz, 1982: Influence of land-surface evapotranspiration on the Earth’s climate. Science, 282, 14981501.

  • Tribbia, J., , and D. P. Baumhefner, 1988: Estimates of the predictability of low-frequency variability with a spectral general circulation model. J. Atmos. Sci., 45, 23062317.

    • Search Google Scholar
    • Export Citation
  • Vinnikov, K. Y., , A. Robock, , N. A. Speranskaya, , and C. A. Schlosser, 1996: Scales of temporal and spatial variability of midlatitude soil moisture. J. Geophys. Res., 101, 71637174.

    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., , E. M. Rasmusson, , T. P. Mitchell, , V. E. Kousky, , E. S. Sarachik, , and H. von Storch, 1998: On the structure and evolution of ENSO-related climate variability in the tropical Pacific: Lessons from TOGA. J. Geophys. Res., 103, 14 24114 259.

    • Search Google Scholar
    • Export Citation
  • Wu, W., , and R. E. Dickinson, 2004: Time scales of layered soil moisture memory in the context of land–atmosphere interactions. J. Climate, 17, 27522764.

    • Search Google Scholar
    • Export Citation
  • Xie, P., , and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 25392558.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., , W. Wang, , and J. Wei, 2008: Assessing land-atmosphere coupling using soil moisture from the Global Land Data Assimilation System and observational precipitation. J. Geophys. Res., 113, D17119, doi:10.1029/2008JD009807.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 13 13 1
PDF Downloads 2 2 0

Summer-Season Forecast Experiments with the NCEP Climate Forecast System Using Different Land Models and Different Initial Land States

View More View Less
  • 1 Environmental Modeling Center, National Centers for Environmental Prediction, Camp Springs, and I. M. Systems Group, Rockville, Maryland
  • | 2 Environmental Modeling Center, National Centers for Environmental Prediction, Camp Springs, Maryland
  • | 3 Environmental Modeling Center, National Centers for Environmental Prediction, Camp Springs, and I. M. Systems Group, Rockville, Maryland
  • | 4 Environmental Modeling Center, National Centers for Environmental Prediction, Camp Springs, Maryland
© Get Permissions
Restricted access

Abstract

To examine the impact from land model upgrades and different land initializations on the National Centers for Environmental Prediction (NCEP)’s Climate Forecast System (CFS), extensive T126 CFS experiments are carried out for 25 summers with 10 ensemble members using the old Oregon State University (OSU) land surface model (LSM) and the new Noah LSM. The CFS using the Noah LSM, initialized in turn with land states from the NCEP–Department of Energy Global Reanalysis 2 (GR-2), Global Land Data System (GLDAS), and GLDAS climatology, is compared to the CFS control run using the OSU LSM initialized with the GR-2 land states. Using anomaly correlation as a primary measure, the summer-season prediction skill of the CFS using different land models and different initial land states is assessed for SST, precipitation, and 2-m air temperature over the contiguous United States (CONUS) on an ensemble basis.

Results from these CFS experiments indicate that upgrading from the OSU LSM to the Noah LSM improves the overall CONUS June–August (JJA) precipitation prediction, especially during ENSO neutral years. Such an enhancement in CFS performance requires the execution of a GLDAS with the very same Noah LSM as utilized in the land component of the CFS, while improper initializations of the Noah LSM using the GR-2 land states lead to degraded CFS performance. In comparison with precipitation, the land upgrades have a relatively small impact on both of the SST and 2-m air temperature predictions.

Corresponding author address: Rongqian Yang, Environmental Model Center (W/NP2, Room 207), National Centers for Environmental Prediction, 5200 Auth Rd., Camp Springs, MD 20746. E-mail: rongqian.yang@noaa.gov

Abstract

To examine the impact from land model upgrades and different land initializations on the National Centers for Environmental Prediction (NCEP)’s Climate Forecast System (CFS), extensive T126 CFS experiments are carried out for 25 summers with 10 ensemble members using the old Oregon State University (OSU) land surface model (LSM) and the new Noah LSM. The CFS using the Noah LSM, initialized in turn with land states from the NCEP–Department of Energy Global Reanalysis 2 (GR-2), Global Land Data System (GLDAS), and GLDAS climatology, is compared to the CFS control run using the OSU LSM initialized with the GR-2 land states. Using anomaly correlation as a primary measure, the summer-season prediction skill of the CFS using different land models and different initial land states is assessed for SST, precipitation, and 2-m air temperature over the contiguous United States (CONUS) on an ensemble basis.

Results from these CFS experiments indicate that upgrading from the OSU LSM to the Noah LSM improves the overall CONUS June–August (JJA) precipitation prediction, especially during ENSO neutral years. Such an enhancement in CFS performance requires the execution of a GLDAS with the very same Noah LSM as utilized in the land component of the CFS, while improper initializations of the Noah LSM using the GR-2 land states lead to degraded CFS performance. In comparison with precipitation, the land upgrades have a relatively small impact on both of the SST and 2-m air temperature predictions.

Corresponding author address: Rongqian Yang, Environmental Model Center (W/NP2, Room 207), National Centers for Environmental Prediction, 5200 Auth Rd., Camp Springs, MD 20746. E-mail: rongqian.yang@noaa.gov
Save