Attribution of seasonal soil moisture prediction uncertainties
Zaitao Pan, St. Louis University, St. Louis, MO; and R. Horton, B. Tentinger, and M. Segal
Since spring 2002, we have used Penn State/NCAR MM5 to predict growing season soil moisture (SM) variation. It was found that MM5 along with its land surface scheme (OSU) persistently under-predicts soil moisture content, especially in the deeper soil. We examined other models and/or land surface schemes including NCAR LSM and CLM and found that they also tend to have a similar dry bias in SM prediction. This study attempts to identify the causes for the underestimation in SM by carrying out multiple seasonal predictions. The results show that the underprediction of SM in the topsoil was mainly caused by a low-bias in model's precipitation, the input into the soil profile. The dry bias in the deep layers, on the other hand, was found to be associated with the soil model's unrealistic parameterization of retention property and bottom drainage. Experiments with different retention curves can alleviate, to some extent, the over-drying problem. However, the unrealistic specification of bottom drainage appears to be the main cause for the dry bias. After more realistic specification of bottom SM flux including mimicking water table dynamics can largely remove the dry bias problem.
Joint Poster Session 1, Land-Atmosphere Interactions (Joint with 18th Conference on Climate Variability and Change and 20th Conference on Hydrology)
Tuesday, 31 January 2006, 9:45 AM-11:00 AM, Exhibit Hall A2
Previous paper Next paper
Browse or search entire meeting
AMS Home Page