Feedbacks between the land surface and the atmosphere, manifested as mass and energy fluxes, are strongly correlated with soil moisture, making soil moisture an important factor in land-atmosphere interactions. It is shown that a reduction of the uncertainty in subsurface properties such as hydraulic conductivity (K) propagates into the atmosphere, resulting in a reduction in uncertainty in land-atmosphere feedbacks that yields more accurate atmospheric predictions. Using the fully coupled groundwater-to-atmosphere model ParFlow-WRF, which couples the hydrologic model ParFlow with the Weather Research and Forecasting (WRF) atmospheric model, responses in land-atmosphere feedbacks and wind patterns due to subsurface heterogeneity are simulated. Ensembles are generated by varying the spatial location of subsurface properties while maintaining the global statistics and correlation structure. This approach is common to the hydrologic sciences but uncommon in atmospheric simulations where ensemble forecasts are commonly generated with perturbed initial conditions or multiple model parameterizations. It is clearly shown that different realizations of K produce variation in soil moisture, latent heat flux, and wind for both point and domain-averaged quantities. Using a single random field to represent a control case, varying amounts of K data are sampled and subsurface data are incorporated into conditional Monte Carlo ensembles to show that the difference between the ensemble mean prediction and the control saturation, latent heat flux, and wind speed are reduced significantly via conditioning of K. By reducing uncertainty associated with land-atmosphere feedback mechanisms, uncertainty is also reduced in both spatially distributed and domain-averaged wind speed magnitudes, thus improving the ability to make more accurate forecasts, which is important for many applications such as wind energy.
All Science Journal Classification (ASJC) codes
- Atmospheric Science
- Coupled models
- Stochastic models