High-resolution carbon cycle data assimilation with multiple satellite observations

Research Project, 2026 – 2029

Anthropogenic activities (e.g. fossil fuel burning and land use change) have caused large amounts of CO2 emissions into the atmosphere, which have increased atmospheric CO2 concentrations from 280 ppm in the pre-industrial era to more than 400 ppm today. The rise in atmospheric CO2 concentrations has been assessed to be the dominant driver of global warming (Stocker et al., 2013). Terrestrial ecosystems are a major carbon sink for atmospheric CO2, and thus play a vital role in controlling global warming. However, the changing climate and more frequent and severe climate extremes such as droughts could potentially disrupt the terrestrial CO2 sink, and subsequently lead to accelerated climate change. How terrestrial ecosystems have been and will be affected by climate change and extremes remain largely unknown, to a large part because of insufficient observations and large uncertainties in terrestrial ecosystem models.

Data assimilation describes a class of methods for combining theory—usually in the form of a numerical model—and observations in a statistically optimal way. These methods make it possible to fill in gaps in the observations using process-based understanding of the system being observed, while at the same time reducing model uncertainties and biases using observational evidence. This project aims at improving our understanding of the terrestrial carbon cycle by developing data assimilation methods for combining state-of-the-art ecosystem models and newly available in-situ and remote sensing observations.