Integrated terrestrial system models predict the coupled water, energy and biogeochemical cycles. Simulations with these models are affected by uncertainties of model parameters, initial and boundary conditions, atmospheric forcings and the biophysical processes. Data assimilation (DA) can quantify and reduce the uncertainty. This has been tested intensively for single compartment models, but far less for integrated models with multiple compartments. We constructed a virtual reality (VR) with a coupled land surface‐subsurface model under the Terrestrial Systems Modeling Platform, which mimics the Neckar catchment in southern Germany. Soil moisture and groundwater level (GWL) data extracted from the simulated VR are used as measurements to be assimilated with state‐only/state‐hydraulic parameter estimation. Soil moisture DA improves soil moisture characterization in the vertical profile and the neighboring grid cells, with a 40 ∼ 60% reduction of root mean square error (RMSE) over the observation points. In spite of a small ensemble size of 64 members, assimilating soil moisture data improved saturated hydraulic conductivity estimation around the measurement locations. The characterization of evapotranspiration and river discharge only show limited improvements (1% at observation points and less than 0.1% in RMSE at 3 selected gauge locations respectively). GWL DA not only improves the GWL characterization (76 ∼ 88% RMSE reduction at observation locations) but also soil moisture for some cases. In addition, a clear improvement in GWL characterization is observed up to 8 km from the observations, and updating the model states of the saturated zone only instead of the complete domain gives better performance.

We present in this work the development of a solar data assimilation method based on an axisymmetric mean field dynamo model and magnetic surface data, our mid-term goal is to predict the solar quasi cyclic activity. Here we focus on the ability of our algorithm to constrain the deep meridional circulation of the Sun based on solar magnetic observations. To that end, we develop a variational data assimilation technique. Within a given assimilation window, the assimilation procedure minimizes the differences between data and the forecast from the model, by finding an optimal meridional circulation in the convection zone, and an optimal initial magnetic field, via a quasi-Newton algorithm. We demonstrate the capability of the technique to estimate the meridional flow by a closed-loop experiment involving 40 years of synthetic, solar-like data. By assimilating the synthetic magnetic proxies annually, we are able to reconstruct a (stochastic) time-varying meridional circulation which is also slightly equatorially asymmetric. We show that the method is robust in estimating a flow whose level of fluctuation can reach 30% about the average, and that the horizon of predictive capability of the method is of the order of 1 cycle length.

We show how magnetic observations of the Sun can be used in conjunction with an axisymmetric fluxtransport solar dynamo model in order to estimate the large-scale meridional circulation throughout the convection zone. Our innovative approach rests on variational data assimilation, whereby the distance between predictions and observations (measured by an objective function) is iteratively minimized by means of an optimization algorithm seeking the meridional flow which best accounts for the data. The minimization is performed using a quasi-Newton technique, which requires the knowledge of the sensitivity of the objective function to the meridional flow. That sensitivity is efficiently computed via the integration of the adjoint flux-transport dynamo model. Closed-loop (also known as twin) experiments using synthetic data demonstrate the validity and accuracy of this technique, for a variety of meridional flow configurations, ranging from unicellular and equatorially symmetric to multicellular and equatorially asymmetric. In this well-controlled synthetic context, we perform a systematic study of the behavior of our variational approach under different observational configurations, by varying their spatial density, temporal density, noise level, as well as the width of the assimilation window. We find that the method is remarkably robust, leading in most cases to a recovery of the true meridional flow to within better than 1%. These encouraging results are a first step towards using this technique to i) better constrain the physical processes occurring inside the Sun and ii) better predict solar activity on decadal time scales.