Overview | Land-Atmosphere Feed

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.

Objectives

The overarching goal of the LAFI research unit is to deepen the general understanding of Land-Atmosphere (L-A) processes over agricultural regions in Europe and to quantify their atmospheric feedback processes.

In order to achieve this ambitious goal, six research objectives are formulated in LAFI on 1) alternative similarity theories, 2) the impact of land-surface heterogeneity, 3) partitioning evapotranspiration, 4) understanding entrainment, 5) synergistic characterization of L-A feedback, and 6) droughts or heatwaves potentially investigated by ad-hoc field observations.

Fig. 2: The organization of LAFI in six Objectives (Os) and three Cross-Cutting Working Groups (CCWGs)

Our research highlights include: A) The observation of L-A system processes and feedbacks at two sites dominated by agricultural land use: I) The Land-Atmosphere Feedback Observatory (LAFO) at the University of Hohenheim enhanced by a worldwide-unparalleled synergy of instruments, e.g., combining for the first time measurements of water stable isotopes, fiber-optic distributed temperature sensors, and scanning lidar systems, II) the Meteorological Observatory Lindenberg-Richard Aßmann Observatory (MOL-RAO) of the German Meteorological Service providing a long-term data set for studying the statistics of droughts and heatwaves. B) Understudied and poorly understood processes in the L-A system, C) Improvement and application of L-A system models down to the large eddy scales with advanced representation of vegetation and water stable isotopes. D) Application of deep learning (DL) methods for identifying key and potentially novel factors in process descriptions. Based on this combination of research components, we will characterize the multi-dimensional phase space of L-A system variables with various process-based metrics over an entire vegetation period in order to reach the overarching LAFI goal.

Structure

LAFI consists of a network of ten closely intertwined projects addressing the six LAFI research objectives.

In order to achieve these research objectives, information on L-A interactions is required in unprecedented detail to be able to capture the whole complexity of the L-A system with all its processes and feedbacks. Thus, observation and modeling data are needed which go beyond the purposes of standard approaches. Such data need to comprise simultaneous measurements of key variable profiles and transport processes across all components of the L-A system as well as simulations of the meso-scale atmospheric conditions and its interactions with the local surface conditions from diurnal to seasonal time scales. However, this kind of data can only be produced by means of an inclusive combination of three-dimensional observations and high-resolution modeling. In the ten LAFI projects, this is realized by an unparalleled synergy of lidar systems, fiber-optic distributed temperature sensors (FODS), water stable isotope measurements and satellite remote sensing data in combination with a state-of-the-art modeling chain from the meso-gamma scale (~2 km) down to the micro-gamma scale (~ 2 m).

Collaboration across the LAFI projects will be fostered by three Cross Cutting Working Groups on Deep Learning, Sensor Synergy and Upscaling, as well as the LAFI Multi-model Experiment.

On the basis of this worldwide unique combination of L-A measurements (in-situ and remote sensing) and simulations at different resolutions, for the first time, a detailed and at the same time all-encompassing analysis of the L-A system with its individual key processes and spatial and temporal feedbacks is possible over the entire vegetation period.

Fig. 3: Composition of the dedicated research teams adressing the six Objectives (Os) and their contributions to the three Cross-Cutting Working Groups (CCWGs)