Recent offline land data assimilation results and future steps towards coupled DA at Meteo-France

Land data assimilation aims to monitor the evolution of soil and vegetation variables. These variables are driven by climatic conditions and by anthropogenic factors such as agricultural practices. Monitoring terrestrial surfaces involves a number of variables of the soil-plant system such as land cover, snow, surface albedo, soil water content and leaf area index. These variables can be monitored by integrating satellite observations into models. This process is called data assimilation. Integrating observations into land surface models is particularly important in changing climate conditions because environmental conditions and trends never experienced before are emerging. Because data assimilation is able to weight the information coming from contrasting sources of information and to account for uncertainties, it can produce an analysis of terrestrial variables that is the best possible estimation. In this work, data assimilation is implemented at a global scale by regularly updating the model state variables of the ISBA land surface model within the SURFEX modelling platform: the LDAS-Monde sequential assimilation approach. Model-state variable analysis is done for initializing weather forecast atmospheric models. Weather forecast relies on observations to a large extent because of the chaotic nature of the atmosphere. Land variables are not chaotic per se but rapid and complex processes impacting the land carbon budget such as forest management (thinning, deforestation, ...), forest fires and agricultural practices are not easily predictable with a good temporal precision. They cannot be monitored without integrating observations as soon as they are available. We focus on the assimilation of leaf area index (LAI), using land surface temperature (LST) for verification. We show that (1) analyzing LAI together with root-zone soil moisture is needed to monitor the impact of irrigation and heat waves on the vegetation, (2) LAI can be forecasted after properly initializing ISBA. This paves the way to more interactive assimilation of land variables into numerical weather forecast and seasonal forecast models, as well as in atmospheric chemistry models.