Impact of green vegetation fraction on atmosphere/land-surface models

Vegetation plays a significant role in determining the partition of surface sensible and latent heat fluxes, so green vegetation fraction must be represented adequately in numerical weather prediction models, seasonal climate prediction models, and in climate variability models. Vegetation fraction changes from year to year and can have a different seasonal evolution from climatology, e.g. due to an advance or delay in spring 'green-up', departures from normal in low level temperature and precipitation, changes in irrigation or harvesting practices, forest fires, deforestation, desertification, hailstorm, and flooding/drought effects on the growth of vegetation. Therefore, it is necessary to assess the impact of the change in vegetation fraction on the physical processes of the land-surface simulated in prediction models (weather or climate) by conducting sensitivity tests with the prediction model. Our particular interest in the present study is the impact of drought-induced vegetation-cover anomalies over the central continental US (CONUS) in NCEP's mesoscale weather prediction model (WRF).

Recently, NESDIS has begun routine operational production of a 14-km global field of realtime, weekly, green vegetation fraction (GVF) from the AVHRR sensor aboard NOAA polar orbiting satellites. In this study, this new data set and its corresponding multi-year climatology are tested separately in NCEP's Noah land surface model coupled to the 5-km mesoscale WRF model in 2-3 day simulations during the growing season. The resulting predictions of surface fluxes, 2-meter dew-point and air temperature, and precipitation over the Midwest will be compared between the control run and experimental run that, respectively, use the climatology and realtime weekly update of the green vegetation fraction.