All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 118 14 1
PDF Downloads 17 11 0

The Impact of Pacific Ocean Subsurface Data on Operational Prediction of Tropical Pacific SST at the NCEP

View More View Less
  • 1 Climate Prediction Center, NWS/National Centers for Environmental Prediction, Washington. D.C.
  • | 2 Coupled Model Project, NWS/National Centers for Environmental Prediction, Washington, D.C.
  • | 3 Climate Prediction Center, NWS/National Centers for Environmental Prediction, Washington, D.C.
Full access

Abstract

The value of assimilated subsurface oceanic data to statistical predictions of interannual variability of sea surface temperature (SST) at the National Centers for Environmental Prediction (NCEP) is shown. Subsurface temperature data for the tropical Pacific Ocean come from assimilated ocean analysis from July 1982 to June 1993 and from a numerical model forced by observed surface wind stress from 1961 to June 1982. The value of subsurface oceanic data on the operational NCEP canonical correlation analysis (CCA) forecasts of interannual SST variability is assessed. The CCA is first run using only sea level pressure and SST as predictors, and then the subsurface data are added. It is found that use of the subsurface data improves the forecast for lead times of six months or longer, with some seasonal dependence in the improvements. Forecasts of less than six months are not helped by the subsurface data. Greatest improvements occur for forecasts of boreal winter to spring conditions, with less improvements for the rest of the year.

Abstract

The value of assimilated subsurface oceanic data to statistical predictions of interannual variability of sea surface temperature (SST) at the National Centers for Environmental Prediction (NCEP) is shown. Subsurface temperature data for the tropical Pacific Ocean come from assimilated ocean analysis from July 1982 to June 1993 and from a numerical model forced by observed surface wind stress from 1961 to June 1982. The value of subsurface oceanic data on the operational NCEP canonical correlation analysis (CCA) forecasts of interannual SST variability is assessed. The CCA is first run using only sea level pressure and SST as predictors, and then the subsurface data are added. It is found that use of the subsurface data improves the forecast for lead times of six months or longer, with some seasonal dependence in the improvements. Forecasts of less than six months are not helped by the subsurface data. Greatest improvements occur for forecasts of boreal winter to spring conditions, with less improvements for the rest of the year.

Save