Observations and Global Modeling

The analysis and interpretation of large data sets are among the core competences of our research group. While we do not collect these observations ourselves, we are involved in or lead their gathering and synthesis, such was the case for the CARINA and MAREMIP projects. We also use advanced statistical methods, such as artificial neural networks, to analyze the observations and extract their main essence. A key objective of our data-based work is to analyze the observations in close interaction with the modeling activities, in order to create synergies. To this end, we are also employing data-assimilation methodologies including Kalman filter-based methods or genetic algorithms.

Our global model activities focus on the use of NCAR's Community Earth System Model (CESM) and have the aim to better understand the ocean's role in the global carbon cycle and the interactions between the different biogeochemical cycles within the ocean and how they have evolved through time, particularly in response to the anthropogenic perturbation.

Currently, our research in this area focuses on (i) global assessment of the variability of the oceanic carbon sink based on pCO2 observations, (ii) the determination of the global oceanic sink for anthropogenic CO2 in the recent decades based on observations of the changes in the ocean's inorganic carbon content, (iii) the anthropogenic perturbation of the marine nitrogen cycle, and (iv) the development of a Kalman filter-based assimilation system to analyze atmospheric CO2 observations over Switzerland (CarboCount CH project).