Applications of terrestrial remote sensing to climate modeling

Terrestrial processes are an important component of climate. Climate can be viewed as a nonlinear dynamical system which generates statistics to be compared with observational statistics. The surface is forced by net radiation balanced by sensible and latent fluxes, and by precipitation balanced by evapotranspiration, soil moisture storage, and runoff. These balances depend on detailed geographic descriptions of parameters required by the modeling. These details are constrained by satellite remote sensing (as demonstrated by various recent studies) with consequent substantial improvement in climate models. Some of these parameters, especially those involving vegetation, may be evolved with the climate system. When climate models characterize their radiative processes consistent with the remote sensing algorithms useful for their detection, they become physically more realistic and provide a suitable modeling framework for forward modeling data assimilation. In particular, the 3D nature of canopy radiation needs to be represented in climate models and how this connects trees and bushes to various underlying surfaces.