Land surface processes relevant to sub-seasonal to seasonal (S2S) prediction

In this chapter, the role of land-surface interactions with the atmosphere on sub-seasonal timescales is discussed in terms of the physical processes as they are currently understood, as well as the implications for improved predictions. This potential stems from the predictability provided by relatively slowly varying land-surface states like soil moisture, snow cover, and vegetation states. A history of the evolution of land-surface models at operational forecast centers is also provided. We conclude that significant improvements in forecast skill can be made in the short term by treating land and atmosphere as a coupled system throughout the model development process, applying available observations to better calibrate, validate, and initialize land-surface states. Sub-seasonal to seasonal (S2S) prediction time scales are a potentially strong target for land-atmosphere feedbacks to affect the atmosphere.