Predictability of climate at seasonal and longer time scales stems from the interaction of the atmosphere with slowly varying components of the climate system such as the ocean and the land surface. However, much of the improvement so far has been obtained over ocean. In contrast, the lack of observations to constrain the model complexity over land has determined the development of different prediction systems for different time scales, which is believed to considerably limit the current level of performance and usefulness of predictions. While benefit from daily verification, the models that are developed for short time-scales (Weather to seasonal climate predictions) include only that part of the surface and near-surface variability for which observations are available and that can be suitably modelled/initialized in order to positively contribute to the forecasts (verification-based approach). Therefore, to limit prediction errors, short time-scale models do not include those processes related to vegetation and their seasonal, interannual and sub-grid variability. On the other hand, for the interannual and longer time scales, the Earth system models used for climate variability/change research contain comprehensive soil-vegetation-atmosphere-transfer schemes that are intended to represent as many processes as possible, even those that are still poorly constrained or understood (process-based approach).
Because energy supply, demand and infrastructures are strongly affected by climate, energy sector has been recently included as a priority area in the Global Framework for Climate Services. Since most of the applications of climate predictions would serve energy, social and economic interests that are land-based, there is an urgent need to improve probability forecasts over land.
Objectives:
-Develop novel observational constraints to understand and improve modeling of land-climate interactions and feedbacks.
-Understand the land limitations that are affecting current prediction models across scales.
-Enhance Earth System predictions across scales by obtaining a practicable seamless development of verifiable land surface processes.
-Exploit performance/usefulness of improved Earth System predictions over land to provide valuable information to end-users in the energy sector.
