Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces

Abstract

Abstract. LDAS-Monde is a global offline land data assimilation system (LDAS) that jointly assimilates satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the ISBA (Interaction between Soil Biosphere and Atmosphere) land surface model (LSM). This study demonstrates that LDAS-Monde is able to detect, monitor and forecast the impact of extreme weather on land surface states. Firstly, LDAS-Monde is run globally at 0.25∘ spatial resolution over 2010–2018. It is forced by the state-of-the-art ERA5 reanalysis (LDAS_ERA5) from the European Centre for Medium Range Weather Forecasts (ECMWF). The behaviour of the assimilation system is evaluated by comparing the analysis with the assimilated observations. Then the land surface variables (LSVs) are validated with independent satellite datasets of evapotranspiration, gross primary production, sun-induced fluorescence and snow cover. Furthermore, in situ measurements of SSM, evapotranspiration and river discharge are employed for the validation. Secondly, the global analysis is used to (i) detect regions exposed to extreme weather such as droughts and heatwave events and (ii) address specific monitoring and forecasting requirements of LSVs for those regions. This is performed by computing anomalies of the land surface states. They display strong negative values for LAI and SSM in 2018 for two regions: north-western Europe and the Murray–Darling basin in south-eastern Australia. For those regions, LDAS-Monde is forced with the ECMWF Integrated Forecasting System (IFS) high-resolution operational analysis (LDAS_HRES, 0.10∘ spatial resolution) over 2017–2018. Monitoring capacities are studied by comparing open-loop and analysis experiments, again against the assimilated observations. Forecasting abilities are assessed by initializing 4 and 8 d LDAS_HRES forecasts of the LSVs with the LDAS_HRES assimilation run compared to the open-loop experiment. The positive impact of initialization from an analysis in forecast mode is particularly visible for LAI that evolves at a slower pace than SSM and is more sensitive to initial conditions than to atmospheric forcing, even at an 8 d lead time. This highlights the impact of initial conditions on LSV forecasts and the value of jointly analysing soil moisture and vegetation states.