Model and Observation for Surface-Atmosphere Interactions

The MOSAI (Model and Observation for Surface-Atmosphere Interactions) project (ANR 2020) aims to assess and improve the interactions between the surface and the atmosphere in weather and climate numerical prediction models through long-term observations. This project is motivated by the fact that surface fluxes are the second cause of biases in large-scale models. Three lines of work are planned between 2021 and 2025: 1/ to provide the community with quality measurements accompanied by a parameter of representativeness of this local measurement in the heterogeneous landscape, 2/ to set up accurate comparison methods that allow to trace the weaknesses of the parameterizations, 3/ to improve the representation of surface-atmosphere interactions for heterogeneous surfaces in the models.

MOSAI project objectives

Sketches of land surface-atmosphere interactions over real heterogeneous landscape (left) and in ESM grid-mesh (right)

The meteorological phenomena draw their energy from the Earth’s surface and dissipate most of their energy close to the surface. The land surface, through its topography, soil moisture, temperature or vegetation activity, impacts the atmosphere from daily to seasonal time scale (Dirmeyer and Halder, 2016, Betts et al., 2017): afternoon convection, cloud formation and evolution (Milovac et al., 2016), mesoscale circulations (Taylor et al., 2012) or planetary waves (Koster et al., 2014). Land surface processes have been shown influential for extremes events, such as atmospheric drought and heat waves (Wang et al., 2015). Through these phenomena, the land surface can consequently impact climate trajectories (Seneviratne et al., 2010). An accurate assessment of the Land-Atmosphere (L-A) exchanges, and their correct representation, are therefore essential for weather and climate forecasts.

The Global Energy and Water cycle Exchanges (GEWEX) and World Climate Research Program (WCRP) have pointed out for the last ten years the importance of the L-A coupling for weather and climate models. The Working Group on Numerical Experimentation (WGNE) survey on systematic errors (Feb. 2019) established that the outstanding errors in the modelling of surface fluxes of momentum and sensible and latent heat is the second most important issue. Earth System Models (ESM) and Numerical Weather Prediction (NWP) often have large biases in their representation of surface-atmosphere flux when compared to observations. The detailed quantification and reduction of these biases are still on-going efforts in many modelling centres. The MOSAI project aims at contributing to this effort.

The first step to achieve this objective is to conduct a fair and correct evaluation of the L-A interactions simulated by ESM and NWP. This is based on (1) reliable references against which the simulated L-A exchanges can be evaluated, and, (2) relevant comparison methods able to point out the ESM and NWP weaknesses. These points define the two first scientific objectives of MOSAI project.

The first scientific objective (O1) is to investigate and determine the uncertainty and representativeness of L-A exchanges measured over heterogeneous landscapes. MOSAI project will take advantage of long-term measurements acquired at ACTRIS and ICOS instrumented sites and will systematically estimate the measurement errors, the surface energy balance (SEB) non-closure issue, and, the representativeness of the local measurements in the heterogeneous landscape, at the grid mesh scale. This is a necessary step towards a fair evaluation of the models in order to avoid blaming the ESM for wrong reasons or doubting the measurements.

The second scientific objective (O2) is to propose and test two methods to evaluate the L-A exchanges in ESM using long-term measurements. These methods will go further than point-to-point, time-to-time or case-study comparisons, and will identify the ESM and NWP weaknesses according to the environmental conditions. The first method leans on a set of sensitivity 1D and 3D simulations, whereas the second method is based on a statistical model learning approach.

The second step of the project concerns the improvement of the L-A exchanges simulated by the ESM and NWP. The coupling between land surface models (LSM) and atmospheric models is based on several simplifications which are different when considering Large Eddy Simulation (LES), weather or climate models. The third scientific objective (O3) addresses some of these underlying simplifications in the coupling between LSM and atmospheric models, and their impacts on the simulated L-A exchanges.