image: To improve the estimation accuracy of the terrestrial carbon cycle, it is often necessary to integrate atmospheric carbon cycle models and observations (top-down) and terrestrial carbon cycle models and observations (bottom-up) into a system that effectively utilizes multisource, multiscale, and multiangle remote sensing observations and ground measurements. view more
Credit: ©Science China Press
Data Assimilation is a type of technology based on the mathematical principles of combining as much data as possible with explicit dynamical models to constrain the evolution of a dynamic system. It has been broadly applied in terrestrial carbon cycle research to provide accurate estimates for carbon metrics such as CO2 emissions and holds the promise of combining the plethora of data available on the terrestrial carbon cycle. It is critical to enable the development of more target carbon mitigation strategies and policies to achieve 'carbon neutrality'. However, there have been few overviews summarizing the current state of terrestrial carbon-cycle data assimilation and synthesizing the progress for the future direction of land carbon data assimilation systems. Recently, scientists from the Tibetan Research Institute, Chinese Academy of Sciences (CAS), Northwest Institute of Eco-Environment and Resources CAS, Institute of Geographic Sciences and Natural Resources Research CAS, and Lanzhou University, frame data assimilation approaches for the land carbon cycle applications.
The authors overviewed the approaches that are essentially needed to achieve the optimal fusion of a model with observational data while considering the respective errors in the model and the observations. Based on the progress in carbon cycle models and measurement techniques in recent years, the researchers summarized the mathematical principles for data assimilations and listed the major challenges in the terrestrial carbon cycle, including the “equifinality” of models, the identifiability of model parameters, the estimation of representativeness errors in surface fluxes, remote sensing observations, and model-data error characterization. The authors highlight the emerging opportunities to fuse multisource observations into a coherent carbon data assimilation system as the new satellites for monitoring solar-induced chlorophyll fluorescence and vegetation biomass and massive ground-based measurements became available. This study provides a framework for the next generation of land surface data assimilation systems and offers the pathway to help improve the estimation of carbon cycles at the global and regional scales to serve future carbon management strategies for achieving the goals of carbon neutrality.
To access the article, click here “Terrestrial carbon cycle model-data fusion: Progress and challenges”.
Journal
Science China Earth Sciences