The challenges posed by climate change research and the desire to have adequate assessments of climate and environmental responses to various land forcings have motivated the development of land data and land process models over the last several decades. Emerging in conjunction with the these advancement and environmental and climate concern was a strong interest in land-atmosphere (L-A) interaction studies, which have evolved from focusing only on biophysical process-atmosphere interaction, to including photosynthesis, ecosystem, and biogeochemical processes. Meanwhile, more subsystems in the land processes, such as snow/glaciers, urban, and fire module have been developed. The principal physical and biophysical considerations in these developments are briefly reviewed.
Using these land models, L-A interaction research has largely focused on the local feedback between land and atmosphere. The remote effect of land processes on precipitation prediction has not been addressed. A new idea has recently been developed that utilizes information on spring land surface temperature/subsurface temperature (LST/SUBT) anomalies over the Tibetan Plateau (TP) and Rocky Mountains (RM) to improve prediction of subsequent summer droughts/floods over several regions over the world, East Asia and North America in particular. The work was performed in the framework of a GEWEX Initiative (LS4P). More than 40 institutions worldwide have participated in this effort.
The LS4P first experiment has identified 8 hotspot regions in the world where June precipitation is significantly associated with anomalies of May TP land temperature. Consideration of the TP LST/SUBT effect has produced about 25%-50% of observed precipitation anomalies in most hotspot regions. The LS4P also identifies an out-of-phase oscillation between the TP and RM surface temperatures and a TP-RM Circumglobal (TRC) wave train from the TP through the Bering Strait to the western part of North America. The multiple models have shown more consistency in the hotspot regions along the TRC wave train. For comparison, the global sea surface temperature (SST) effect has also been tested and 6 regions with significant SST effects were identified, explaining about 25-50% of precipitation anomalies over most of these regions. The results from the LS4P experiment suggests that the TP LST/SUBT remote effect is a first-order source of S2S precipitation predictability, comparable to that of the SST effect.