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              <p>The rate at which land surface soils dry following rain events is an important feature of the climate system. Surface soil moisture (SSM) drydowns, i.e., the soil moisture temporal dynamics following a significant rainfall event, play a crucial role in determining the surface water budget. In particular, they influence the partitioning between runoff, drainage, and evaporation. They are also important when predicting the water availability for vegetation, and the occurrences of droughts and heatwaves. As such, improved understanding and characterization of the drivers of SSM drydowns will give fundamental and combined insight into the coupling between the carbon, water, and energy cycles. Furthermore, the associated variations of land surface temperature (LST) during drydowns can be used to understand evapotranspiration rates and calibrate the surface-soil thermal properties.Within the Climate Change initiative (cci), efforts are ongoing by the soil moisture and LST communities to collate the growing amounts of Earth observation data into user-friendly global products of high temporal and spatial resolutions (SM_cci and LST_cci, respectively). These products provide a unique opportunity to evaluate and calibrate land surface models used to represent the terrestrial part of wider Earth system models. In this presentation, we demonstrate how LST_cci and SM_cci can be used in synergy to improve key model parameters through data assimilation techniques and hence improve the representation of SSM drydowns in the model.Using the ORCHIDEE land surface model, we first show how the SM_cci and LST_cci products can be used to identify sensitive model parameters. We then test the complementarity of both data streams by assimilating them both individually and simultaneously, helping us identify the information content brought by each data stream. The optimizations are performed using ORCHIDAS, the Bayesian data assimilation framework set up around ORCHIDEE. We conclude by evaluating drydowns simulated by the optimized models.</p>
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