Abstract
Soil moisture plays a key role in water, carbon and energy exchanges between the land surface and the atmosphere. Therefore, a better representation of this variable in the Land-Surface Models (LSMs) used in climate modelling could significantly reduce the uncertainties associated with future climate predictions. In this study, the ESA-CCI soil moisture (SM) combined product (v4.2) has been confronted to the simulated top-first layers/cms of the ORCHIDEE LSM (the continental part of the IPSL Earth System Model) for the years 2008-2016, to evaluate its potential to improve the model using data assimilation techniques. The ESA-CCI data are first rescaled to match the climatology of the model and the signal representative depth is selected. Results are found to be relatively consistent over the first 20 cm of the model. Strong correlations found between the model and the ESA-CCI product show that ORCHIDEE can adequately reproduce the observed SM dynamics. As well as considering two different atmospheric forcings to drive the model, we consider two different model parameterizations related to the soil resistance to evaporation. The correlation metric is shown to be more sensitive to the choice of meteorological forcing than to the choice of model parameterization. Therefore, the metric is not optimal in highlighting structural deficiencies in the model. In contrast, the temporal autocorrelation metric is shown to be more sensitive to this model parameterization, making the metric a potential candidate for future data assimilation experiments.
Highlights
Land-surface models (LSMs) are crucial components of climate models, representing the matter and energy transfers at the continental interface with the atmosphere
Since the ESA-Climate Change Initiative (CCI) soil moisture (SM) product has undergone a rescaling process with the GLDAS-Noah LSM, a direct comparison with the ORCHIDEE model outputs is likely misleading as those models have different soil representation: spatial distribution, textural and hydraulic properties and vertical discretization
Agreement between the ORCHIDEE and ESA-CCI SM products should only be evaluated in terms of relative dynamics unless the observations are first bias corrected or scaled to be consistent with the model climatology
Summary
Land-surface models (LSMs) are crucial components of climate models, representing the matter and energy transfers at the continental interface with the atmosphere. They play a key role on the climate model simulations, past, present, and future predictions. SM governs the partition between runoff and infiltration and is a key element of the global water cycle. Correctly representing this variable in LSMs could highly improve both short- and long-term forecasts [5,6,7,8,9]
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