Thesis Applying Two Binned Methods to the Simple Biosphere Model (sib) for Improving the Representation of Spatially Varying Precipitation and Soil Wetness

Representing subgrid-scale variability is a continuing challenge for modelers, but is crucial for accurately calculating the exchanges of energy, moisture, and momentum between the land surface and atmospheric boundary layer. Soil wetness is highly spatially variable and difficult to resolve at grid length scales (~100 km) used in General Circulation Models (GCMs). Currently, GCMs use an area average precipitation rate that results in a single soil wetness value for the entire grid area, and due to the highly nonlinear relationship between soil wetness and evapotranspiration, significant inaccuracies arise in the calculation of the grid area latent heat flux. Using a finer GCM resolution will not solve this problem completely and other methods of modeling need to be considered. For this study, the binned and alternative binned method of Sellers et al. (2007) are applied to the Simple Biosphere Model (SiB) for improving the representation of spatially varying precipitation, soil wetness and surface-atmosphere fluxes. The methods are tested in a dry, semi-arid, and wet biome for two off-line precipitation distribution experiments, and results are compared to an explicit method, which is ideal for resolving subgrid-scale variability, and the bulk method (area averaged), which is currently in use with GCMs. Results indicate that the alternative binned method better captures the spatial variability in soil wetness and grid area flux calculations produced by the explicit method, and deals realistically with spatially varying precipitation at little additional computational cost to the bulk method. iii ACKNOWLEDGEMENTS I would like to acknowledge and greatly thank the following people who have made the completion of my thesis possible: I thank my advisor, Dr. A. Scott Denning, for the opportunity to work with the best scientists in our field at CMMAP. I also thank my committee members, Dr. David A. Randal and Dr. Jorge A. Ramirez, for their interest in my research. I also express my gratitude to Dr. Ian T. Baker for all of his scientific insight and mentoring, and the Biocycle Group for all of their support and encouragement. Finally, I am grateful for the support and understanding from my family and friends.