'LOAC gas exchange' by Dr. Peter Landschützer


Dr. Peter Landschützer's short biography:

Dr. Peter Landschützer is a marine researcher at the Max-Planck Institute for Meteorology in Hamburg with a strong background in ocean biogeochemistry and statistical data analysis. His scientific research has a focus on quantification of the exchange of carbon dioxide (CO2) between the ocean and the atmosphere on seasonal, inter-annual and decadal timescales
He carried out his PhD research at the University of East Anglia (UEA) as an Early Stage Researcher within the Marie Curie GREENCYCLES II project. At UEA he contributed to surface ocean CO2 data collection and developed a novel 2-step neural network technique. With this method, he provided the first observation-based, monthly maps of the Atlantic Ocean carbon sink over a full decade and further on global ocean air-sea flux maps over 3 decades. 
His key findings include the recent reinvigoration of the Southern Ocean carbon sink. In his research he found large decadal variations in the global ocean carbon sink that suggest a dynamic ocean carbon cycle, an intriguing outcome and contrary to model results, requiring research into the processes driving this variation. His data-based air-sea flux maps are further used within the Surface Ocean pCO2 Mapping intercomparison project (SOCOM) and the annual release of the Global Carbon Budget.

Lecture's abstract - 'LOAC gas exchange':

‘No substitute exists for adequate observations. [...] Models will evolve and improve, but, without data, will be untestable, and observations not taken today are lost forever. [...] Today’s climate models will likely prove of little interest in 100 years. But adequately sampled, carefully calibrated, quality controlled, and archived data for key elements of the climate system will be useful indefinitely.’ (Wunsch et al. 2013)
This quote illustrates the importance of observational data and their importance in understanding the global carbon cycle as well as testing current climate models. However, the spatial and temporal heterogeneity of the available measurements today makes a direct evaluation of climate models nearly impossible.
The lecture will focus on how we can use carbon cycle measurements to test climate models. It will focus in particular on data extrapolation and numerical modeling techniques commonly used in marine carbon cycle research. The PhD students will learn about several different methods, their applications as well as their strengths and weaknesses. They will learn how to identify the best tools for data extrapolation and how to interpret and evaluate the output from these methods, with the aim to be able and produce state-of-the-art datasets to evaluate climate system models.

Recommended background publication on this presentation:

Landschützer, P., Gruber, N., Bakker, D. C. E., Schuster, U., Nakaoka, S., Payne, M. R., Sasse, T. P. and Zeng, J.: A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink. Biogeosciences, 10, 7793–7815, (2013). Doi:10.5194/bg-10-7793-2013.