The Role of the Land Surface Background State in Climate Predictability
Open Access
- 1 June 2003
- journal article
- Published by American Meteorological Society in Journal of Hydrometeorology
- Vol. 4 (3) , 599-610
- https://doi.org/10.1175/1525-7541(2003)004<0599:trotls>2.0.co;2
Abstract
Skill in ensemble-mean dynamical seasonal climate hindcasts with a coupled land–atmosphere model and specified observed sea surface temperature is compared to that for long multidecade integrations of the same model where the initial conditions are far removed from the seasons of validation. The evaluations are performed for surface temperature and compared among all seasons. Skill is found to be higher in the seasonal simulations than in the multidecadal integrations except during boreal winter. The higher skill is prominent even beyond the first month when the direct influence of the atmospheric initial state elevates model skill. Skill is generally found to be lowest during the winter season for the dynamical seasonal forecasts. This is in contrast to the multiyear integrations, which show some of the highest skill during winter—as high as the dynamical seasonal forecasts. The reason for the differences in skill during the nonwinter months is attributed to the severe climate drift in the long ... Abstract Skill in ensemble-mean dynamical seasonal climate hindcasts with a coupled land–atmosphere model and specified observed sea surface temperature is compared to that for long multidecade integrations of the same model where the initial conditions are far removed from the seasons of validation. The evaluations are performed for surface temperature and compared among all seasons. Skill is found to be higher in the seasonal simulations than in the multidecadal integrations except during boreal winter. The higher skill is prominent even beyond the first month when the direct influence of the atmospheric initial state elevates model skill. Skill is generally found to be lowest during the winter season for the dynamical seasonal forecasts. This is in contrast to the multiyear integrations, which show some of the highest skill during winter—as high as the dynamical seasonal forecasts. The reason for the differences in skill during the nonwinter months is attributed to the severe climate drift in the long ...Keywords
This publication has 37 references indexed in Scilit:
- Influence of Soil Moisture on the Asian and African Monsoons. Part II: Interannual VariabilityJournal of Climate, 2002
- Climate Drift in a Coupled Land–Atmosphere ModelJournal of Hydrometeorology, 2001
- Using a Global Soil Wetness Dataset to Improve Seasonal Climate SimulationJournal of Climate, 2000
- A Forecast Product that Maximizes Utility for State–of–the–Art Seasonal Climate PredictionBulletin of the American Meteorological Society, 2000
- Present-Day Capabilities of Numerical and Statistical Models for Atmospheric Extratropical Seasonal Simulation and PredictionBulletin of the American Meteorological Society, 1999
- The Pilot Phase of the Global Soil Wetness ProjectBulletin of the American Meteorological Society, 1999
- Quantification of dust-forced heating of the lower troposphereNature, 1998
- The Anomalous Rainfall over the United States during July 1993: Sensitivity to Land Surface Parameterization and Soil Moisture AnomaliesMonthly Weather Review, 1996
- The Effect of SST and Soil Moisture Anomalies on GLA Model Simulations of the 1988 U.S. Summer DroughtJournal of Climate, 1993
- Estimate of Dynamical Predictability from NMC DERF ExperimentsMonthly Weather Review, 1989