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Impact of Land Initial States Uncertainty on Subseasonal Surface Air Temperature Prediction in CFSv2 Reforecasts

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  • 1 Department of Atmospheric, Oceanic and Earth Sciences, Center for Ocean–Land–Atmosphere Studies, George Mason University, Fairfax, Virginia
  • | 2 K. Banerjee Centre of Atmospheric and Ocean Studies, University of Allahabad, Allahabad, Uttar Pradesh, India
  • | 3 NOAA/National Centers for Environmental Prediction/Climate Prediction Center, College Park, Maryland
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Abstract

The NCEP CFSv2 ensemble reforecasts initialized with different land surface analyses for the period of 1979–2010 have been conducted to assess the effect of uncertainty in land initial states on surface air temperature prediction. The two observation-based land initial states are adapted from the NCEP CFS Reanalysis (CFSR) and the NASA GLDAS-2 analysis; atmosphere, ocean, and ice initial states are identical for both reforecasts. This identical-twin experiment confirms that the prediction skill of surface air temperature is sensitive to the uncertainty of land initial states, especially in soil moisture and snow cover. There is no distinct characteristic that determines which set of the reforecasts performs better. Rather, the better performer varies with the lead week and location for each season. Estimates of soil moisture between the two land initial states are significantly different with an apparent north–south contrast for almost all seasons, causing predicted surface air temperature discrepancies between the two sets of reforecasts, particularly in regions where the magnitude of initial soil moisture difference lies in the top quintile. In boreal spring, inconsistency of snow cover between the two land initial states also plays a critical role in enhancing the discrepancy of predicted surface air temperature from week 5 to week 8. Our results suggest that a reduction of the uncertainty in land surface properties among the current land surface analyses will be beneficial to improving the prediction skill of surface air temperature on subseasonal time scales. Implications of a multiple land surface analysis ensemble are also discussed.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0024.s1.

This article has a companion article which can be found at http://journals.ametsoc.org/doi/abs/10.1175/JHM-D-20-0025.1.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Publisher’s Note: This article was revised on 18 November 2020 to link it to a companion article and to update the cited reference accordingly.

Corresponding author: Chul-Su Shin, cshin3@gmu.edu

Abstract

The NCEP CFSv2 ensemble reforecasts initialized with different land surface analyses for the period of 1979–2010 have been conducted to assess the effect of uncertainty in land initial states on surface air temperature prediction. The two observation-based land initial states are adapted from the NCEP CFS Reanalysis (CFSR) and the NASA GLDAS-2 analysis; atmosphere, ocean, and ice initial states are identical for both reforecasts. This identical-twin experiment confirms that the prediction skill of surface air temperature is sensitive to the uncertainty of land initial states, especially in soil moisture and snow cover. There is no distinct characteristic that determines which set of the reforecasts performs better. Rather, the better performer varies with the lead week and location for each season. Estimates of soil moisture between the two land initial states are significantly different with an apparent north–south contrast for almost all seasons, causing predicted surface air temperature discrepancies between the two sets of reforecasts, particularly in regions where the magnitude of initial soil moisture difference lies in the top quintile. In boreal spring, inconsistency of snow cover between the two land initial states also plays a critical role in enhancing the discrepancy of predicted surface air temperature from week 5 to week 8. Our results suggest that a reduction of the uncertainty in land surface properties among the current land surface analyses will be beneficial to improving the prediction skill of surface air temperature on subseasonal time scales. Implications of a multiple land surface analysis ensemble are also discussed.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0024.s1.

This article has a companion article which can be found at http://journals.ametsoc.org/doi/abs/10.1175/JHM-D-20-0025.1.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Publisher’s Note: This article was revised on 18 November 2020 to link it to a companion article and to update the cited reference accordingly.

Corresponding author: Chul-Su Shin, cshin3@gmu.edu

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