Motivation

Land-Atmosphere (L-A) interactions are a key component in earth’s climate system. At the land surface, incoming solar radiation is absorbed, stored and transformed into an exchange of momentum, energy and mass with the atmosphere. Due to this coupling, L-A processes control the state of the planetary boundary layer and constitute the lower boundary condition for all atmospheric circulations on earth. An in-depth understanding of L-A interactions is therefore essential to comprehend the dynamics of the whole weather and climate system and to accurately describe it in models over all spatial and temporal scales. This includes weather prediction, medium-range up to sub-seasonal forecasts, and climate projections.

Fig.1: Feedback processes in the L-A system.

However, still to date, L-A interactions are poorly understood. Recent studies clearly show that fundamental knowledge gaps exist particularly with respect to the impact of heterogeneous land surface structures, the partitioning of evapotranspiration and the effects of entrainment on the L-A system. Since these processes are still insufficiently represented in standard modeling approaches, L-A interactions are inadequately simulated. Errors in state-of-art model systems are the consequence, affecting simulation results of all compartments of the weather and climate system.

This applies in particular to compartments of the L-A system with a strong L-A coupling, like agricultural landscapes in transition zones between moisture- and energy-limited conditions, as they prevail in Central Europe. Changes in the L-A system can therefore have severe socio-economic implications, since agricultural landscapes cover » 50 % of the European landscape and are responsible for the food security of millions of people. Thus, an improved understanding of the L-A system over these agricultural regions and an advanced description of its atmospheric feedback processes in models is of great scientific and socio-economic importance, in order to lay the foundation for maintaining crop yields and food security in the future.