Using SMOS soil moisture data combining CO2 flask samples to constrain carbon fluxes during 2010-2015 within a Carbon Cycle Data Assimilation System (CCDAS)

摘要

The terrestrial carbon cycle is an important component of the global carbon budget due to its large gross exchange fluxes with the atmosphere and their sensitivity to climate change. Terrestrial biosphere models show large uncertainties in simulating carbon fluxes, which impact global carbon budget assessments. The land surface carbon cycle is tightly controlled by soil moisture through plant physiological processes. Accurate soil moisture observations thereby have the potential to improve the modeling of carbon fluxes in a model-data fusion framework. We employ the Carbon Cycle Data Assimilation System (CCDAS) to assimilate six years of surface soil moisture provided by the SMOS satellite in combination with global-scale observations of atmospheric CO2 concentrations. We find that assimilation of SMOS soil moisture exhibits better performance on soil hydrology modeling at both global and site-level than only assimilating atmospheric CO2 concentrations, and it improves the soil moisture simulation particularly in mid- to high-latitude regions where the plants suffer from water stress frequently. The optimized model also shows good agreements with inter-annual variability in simulated Net Primary Productivity (NEP) and Gross Primary Productivity (GPP) from an atmospheric inversion (Jena CarboScope) and the up-scaled eddy covariance flux product (FLUXNET-MTE), respectively. Correlation between SIF (Solar Induced Fluorescence) and optimized GPP also shows to be the highest when soil moisture and atmospheric CO2 are simultaneously assimilated. In general, CCDAS obtains smaller annual mean NEP values (1.8 PgC/yr) than the atmospheric inversion and an ensemble of Dynamic Global Vegetation Models (DGVMs), but larger GPP values (167.8 PgC/yr) than the up-scaled eddy covariance dataset (FLUXNET-MTE) and the MODIS based GPP product for the years 2010 to 2015. This study demonstrates the high potential of constraining simulations of the terrestrial biosphere carbon cycle on inter-annual time scales using long-term microwave observations of soil moisture.