A new global carbon flux estimation methodology by assimilation of both in situ and satellite CO2 observations

Abstract

Abstract Accurate estimation of carbon removal by terrestrial ecosystems and oceans is crucial to the success of global carbon mitigation initiatives. The emergence of multi-source CO2 observations offers prospects for an improved assessment of carbon fluxes. However, the utility of these diverse observations has been limited by their heterogeneity, leading to much variation in estimated carbon fluxes. To harvest the diverse data types, this paper develops a multi-observation carbon assimilation system (MCAS), which simultaneously integrates both satellite and ground-based observations. MCAS modifies the ensemble Kalman filter to apply different inflation factors to different types of observation errors, addressing the heterogeneity between satellite and in situ data. In commonly used independent validation datasets, the carbon flux derived from MCAS outperformed those obtained from a single source, demonstrating a 20% reduction in error compared to existing carbon flux products. We use MCAS to conduct ecosystem and ocean carbon flux inversion for the period of 2016–2020, which reveals that the 5-year average global net terrestrial and ocean sink was 1.84 ± 0.60 and 2.74 ± 0.49 petagrams, absorbing approximately 47% of human-caused CO2 emissions together, which were consistent with the global carbon project estimates of 1.82 and 2.66 petagrams. All these facts suggest MCAS is a better methodology than those for assimilating single-source observation only.