Progress in Monthly Climate Forecasting with a Physical Model

1981 ◽  
Vol 62 (12) ◽  
pp. 1666-1675 ◽  
Author(s):  
Julian Adem ◽  
William L. Donn
Keyword(s):  
United States ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Physical Model ◽  
Long Range ◽  
Surface Albedo ◽  
Sea Surface ◽  
Climate Forecasting ◽  
Temperature And Precipitation ◽  
Ice Conditions

A long-range forecasting technique, based on a physical model that emphasizes thermodynamics, is applied to the prediction of anomalies of temperature and precipitation for the Northern Hemisphere. Monthly forecasts are initialized with the sea surface temperature, 700 mb temperature and surface albedo, including variable snow-ice conditions. Application to the hot spell and drought in the summer of 1980 for the contiguous United States shows very encouraging skill when verified for the standard 100-station NOAA grid.

Sea Surface Temperature–Precipitation Relationship in Different Reanalyses

2013 ◽  
Vol 141 (3) ◽  
pp. 1118-1123 ◽  
Author(s):  
Arun Kumar ◽  
Li Zhang ◽  
Wanqiu Wang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Scales ◽  
Department Of Energy ◽  
Sea Surface ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis ◽  
Temperature And Precipitation ◽  
The Relationship ◽  
Modern Era

Abstract The focus of this investigation is how the relationship at intraseasonal time scales between sea surface temperature and precipitation (SST–P) varies among different reanalyses. The motivation for this work was spurred by a recent report that documented that the SST–P relationship in Climate Forecast System Reanalysis (CFSR) was much closer to that in the observation than it was for the older generation of reanalyses [i.e., NCEP–NCAR reanalysis (R1) and NCEP–Department of Energy (DOE) reanalysis (R2)]. Further, the reason was attributed either to the fact that the CFSR is a partially coupled reanalysis, while R1 and R2 are atmospheric-alone reanalyses, or that R1 and R2 use the observed weekly-averaged SST. The authors repeated the comparison of the SST–P relationship among R1, R2, and CFSR, as well as two recent generations of atmosphere-alone reanalyses, the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and the ECMWF Re-Analysis Interim (ERA-Interim). The results clearly demonstrate that the differences in the SST–P relationship at intraseasonal time scales across different reanalyses are not due to whether the reanalysis system is coupled or atmosphere alone, but are due to the specification of different SSTs. The SST–P relationship in different reanalyses, when computed against a single SST for the benchmark, demonstrates a relationship that is common across all of the reanalyses and observations.

Present and Past Sea Surface Temperatures: A Recipe for Better Seasonal Climate Forecasts

2020 ◽  
Vol 35 (4) ◽  
pp. 1221-1234
Author(s):  
Matthew B. Switanek ◽  
Joseph J. Barsugli ◽  
Michael Scheuerer ◽  
Thomas M. Hamill
Keyword(s):  
United States ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
The United States ◽  
Cold Season ◽  
Sea Surface ◽  
Precipitation Forecast ◽  
The North ◽  
Model Ensembles ◽  
Tropical Sea

AbstractMonthly tropical sea surface temperature (SST) data are used as predictors to make statistical forecasts of cold season (November–March) precipitation and temperature for the contiguous United States. Through the use of the combined-lead sea surface temperature (CLSST) model, predictive information is discovered not just in recent SSTs but also from SSTs up to 18 months prior. We find that CLSST cold season forecast anomaly correlation skill is higher than that of the North American Multimodel Ensemble (NMME) and the SEAS5 model from the European Centre for Medium-Range Weather Forecasts (ECMWF) when averaged over the United States for both precipitation and 2-m air temperature. The precipitation forecast skill obtained by CLSST in parts of the Intermountain West is of particular interest because of its implications for water resources. In those regions, CLSST dramatically improves the skill over that of the dynamical model ensembles, which can be attributed to a robust statistical response of precipitation in this region to SST anomalies from the previous year in the tropical Pacific.

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