Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: description and applications

Abstract

Abstract. Land surface models are important for improving our understanding of the Earth system. They are continuously improving and becoming better in representing the different land surface processes, e.g., the Community Land Model version 5 (CLM5). Similarly, observational networks and remote sensing operations are increasingly providing more data, e.g., from new satellite products and new in situ measurement sites, with increasingly higher quality for a range of important variables of the Earth system. For the optimal combination of land surface models and observation data, data assimilation techniques have been developed in recent decades that incorporate observations to update modeled states and parameters. The Parallel Data Assimilation Framework (PDAF) is a software environment that enables ensemble data assimilation and simplifies the implementation of data assimilation systems in numerical models. In this study, we present the development of the new interface between PDAF and CLM5. This newly implemented coupling integrates the PDAF functionality into CLM5 by modifying the CLM5 ensemble mode to keep changes to the pre-existing parallel communication infrastructure to a minimum. Soil water content observations from an extensive in situ measurement network in the Wüstebach catchment in Germany are used to illustrate the application of the coupled CLM5-PDAF system. The results show overall reductions in root mean square error of soil water content from 7 % up to 35 % compared to simulations without data assimilation. We expect the coupled CLM5-PDAF system to provide a basis for improved regional to global land surface modeling by enabling the assimilation of globally available observational data.

Can a Sparse Network of Cosmic Ray Neutron Sensors Improve Soil Moisture and Evapotranspiration Estimation at the Larger Catchment Scale?

“…The scatter plot for Wüstebach in Figure A3 indicates that modeled soil moisture updates reach an upper plafond, which is defined by the maximum possible porosity in the model, which is determined by the soil texture and pedotransfer function. However, the remarkably high porosity of the forest soil is due to the very high content of organic material in the topsoil, so the real porosity of Wüstebach cannot be represented in the model by the soil texture alone (Strebel et al., 2022). Therefore, the upper porosity limit inhibits further improvement of soil moisture characterization.…”

Seasonal crop yield prediction with SEAS5 long-range meteorological forecasts in a land surface modelling approach

“…The reference simulations showed higher correlations between the simulated and observed surface soil moisture 540 than the forecast experiments, which could be expected given the wrong timing of precipitation events in the seasonal weather predictions, while still showing similar systematic differences compared to all products. Earlier studies with CLM3.5 (e.g., Zhao et al, 2021;Hung et al, 2022) and CLM5 (e.g., Strebel et al, 2022) found pronounced discrepancies in CLM-simulated soil moisture contents and field measurements. In this context, data assimilation has proven to be a valuable technique to reproduce better soil moisture dynamics (Strebel et al, 2022).…”

“…Earlier studies with CLM3.5 (e.g., Zhao et al, 2021;Hung et al, 2022) and CLM5 (e.g., Strebel et al, 2022) found pronounced discrepancies in CLM-simulated soil moisture contents and field measurements. In this context, data assimilation has proven to be a valuable technique to reproduce better soil moisture dynamics (Strebel et al, 2022). 545…”

“…Bishes (station 18) and Bennets (station 19) that are part of the CosmOz network (Hawdon et al, 2014). For DE-NRW, CRNS measurements were obtained from the four COSMOS-Europe stations Selhausen, Merzenhausen, Aachen and Heinsberg (Bogena et al, 2022). For this comparison, simulation outputs from the closest grid point to the respective station were averaged with the weighting approach after Schrön et al (2017).…”

“…However, also the reference simulations were not able to represent these fluctuations (Figure A2). Heinsberg) (Bogena et al, 2022) to the simulated SMC at the closest grid point respectively. The comparisons showed that the reference simulations forced with reanalysis generally produced higher SMCs than the forecast simulations and corresponded better with CRNS measurement in terms of fluctuation intensity and magnitudes than SMCs from forecast simulations for single sites (Figure A3).…”