Our paper on the value added in seasonal weather prediction as a result of adding a dynamic vegetation parameterization has appeared. The paper is
(2008), Ensemble re-forecasts of recent warm-season weather: impacts of a dynamic vegetation parameterization, J. Geophys. Res., doi:10.1029/2007JD009480, in press.
The abstract reads
“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 reforecast 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 setup 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.”
This paper reinforces two issues that have been repeatedly emphasized on climate science and elsewhere:
1. Adding additional real-world complexity to weather and climate prediction models makes skillful forecasts more difficult. If this difficulty is so serious with respect to seasonal weather predictions, it will be even more so for multi-decadal global climate predictions.
2. The regional models cannot correct for biases (i.e. errors) in the parent global model. Regional dynamic model downscaling based on output from parent models (such as the IPCC multi-decadal projections) cannot add predictive skill over and beyond what is already present in the parent model. If the parent model (e.g. an IPCC simulation) does not have all of the climate forcings and feedbacks that are important on multi-decadal climate time scales, the regional model cannot correct for these shortcomings.