To understand the role of the land surface in seasonal prediction, we envision the hydrologic cycle over land as a loop, where variations in upstream components affect the behavior of the downstream components to varying degrees, depending on location, season, synoptic situation, etc. At its simplest, this can be viewed as an entirely local phenomenon with no external controls on the loop. In fact, the loop is not truly a closed system. It is this property that leads to both positive and negative feedbacks, and to the deterioration of “signal” as one traces the impact of a perturbation in one component of the hydrologic cycle around through the system. The survival and propagation of climate anomaly signals through the hydrologic cycle is a mechanism for realizable climate predictability over land. By interrupting the hydrologic cycle in a coupled land-atmosphere climate model at various locations in the feedback loop, we may quantify the loss of signal in a meaningful way, and increase our understanding of the roles of the processes involved. This approach is applied to a large suite of boreal summer seasonal simulations with the COLA GCM.

Specification of observed precipitation as in input to the land surface component of the climate model conveys a signal through the hydrologic cycle that increases the skill in the simulation of soil wetness, latent heat flux, and even the predicted model precipitation over many areas. However, the average gain in unrealized predictability of rainfall was less than 10% when averaged over all land points, suggesting that the hydrologic feedback loop is not strong in this model. Spliting the loop into components, we find that signal loss on the global scale is of similar magnitude (about 70%) from atmosphere to land, and then from land to atmosphere. However, there is tremendous regional variability. The precipitation signal is conveyed strongly to soil wetness in rainy regions, but predictive skill in evapotranspiration arises primarily in dry regions. Unlike the case of specified precipitation, use of realistic land surface initial conditions impacts soil wetness skill in arid regions most strongly.