Ensemble reforecasts of recent warm-season weather: Impacts of a dynamic vegetation parameterization
| Autor: | Adriana Beltrán-Przekurat, Roger A. Pielke, Curtis H. Marshall |
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| Rok vydání: | 2008 |
| Předmět: |
Global Forecast System
Atmospheric Science Ecology Paleontology Soil Science Forestry Forcing (mathematics) Vegetation Aquatic Science Oceanography Warm season Atmospheric sciences Geophysics Space and Planetary Science Geochemistry and Petrology Climatology Regional Atmospheric Modeling System Earth and Planetary Sciences (miscellaneous) Environmental science Boundary value problem Precipitation Baseline (configuration management) Physics::Atmospheric and Oceanic Physics Earth-Surface Processes Water Science and Technology |
| Zdroj: | Journal of Geophysical Research. 113 |
| ISSN: | 0148-0227 |
| Popis: | [1] The impact of dynamic vegetation on ensemble re-forecasts of recent warm-season weather over the continental U.S. was assessed using the Regional Atmospheric Modeling System (RAMS) and a fully coupled dynamic vegetation version of RAMS, the General Energy and Mass Transfer–RAMS (GEMRAMS). Two 10-member ensembles were produced for the June-August periods of 2000 and 2001. For each period, one of the members used the standard RAMS, and the other the GEMRAMS version. Initial and lateral boundary conditions were provided by a re-forecast produced with the NCEP Seasonal Forecast Model (SFM). In addition, a pair of “baseline” simulations was produced using the NCEP Reanalysis, the “perfect” global forecast, as initial and lateral boundary conditions. Precipitation in the regional ensembles was largely controlled by the driving large-scale forcing. A large precipitation bias exists over the regional domain in the SFM itself that is amplified in the simulations. For the time periods and model set-up considered in this work, under an explicitly predictive model configuration, the use of a more complex parameterization of land-surface processes with dynamic vegetation added little value to the skill of the seasonal forecast over the regional domain. This is a consequence of the strong dependence of the regional model results on the lateral boundary conditions provided by the parent global model. Even the use of an ensemble of predictions does not remove all of the biases that are inherent in the parent global model. |
| Databáze: | OpenAIRE |
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