Towards an improved representation of the land-surface in seasonal forecasts

Recent studies have shown that the treatment of the land surface and its coupling to the atmosphere is a limiting factor in the skill of seasonal forecasts. Over land, predictive skill is limited by errors originating both from the physical representation of the land-surface processes in the model and from inaccuracies in the initial conditions used to initialize these models. These limitations hinder our ability to produce reliable seasonal predictions, which are increasingly important for climate-sensitive sectors such as agriculture and water resource management.

As part of the Copernicus Climate Change Service Evolution (CERISE) project, significant efforts are underway to address these challenges. A key strategy involves the implementation of advanced land-surface data assimilation techniques, which aim to better represent the state of the land surface at the start of a forecast. Currently land-surface initial conditions for the systems contributing to the Copernicus Climate Change Service (C3S) are generated with free-running land-surface models.

Another promising area of development is the inclusion of time-varying vegetation in the forecast systems. Currently, vegetation characteristics such as leaf area index and land-cover type are held fixed throughout the forecast period. However, research suggests that seasonal changes in vegetation may provide an important source of predictability. By allowing vegetation to evolve realistically over time, models may better capture feedback mechanisms between the land surface and atmosphere leading to improved forecast skill.

In this presentation, we will discuss the impacts of these recent developments on seasonal forecast performance and discuss future prospects for further enhancing land-surface representation to improve forecasts.