Potential supervisors - Call for 2022 (and an additional selection of potential PhD topics)

For you application, please choose between 1 - 3 potential supervisors from the table below whose research areas are of particular interest to you. There is no need to get in touch with individual supervisors at this stage.

Additionally, you will find the description of a number of PhD topics which have already been specified in more detail below the table.

Specific projects, including a mutually beneficial advising arrangement within one of the department’s working groups, will be developed after the interviews with the prospective candidate only. To guide this process, candidates are asked to indicate a general area of interest, whether that be topical or methodological, and explain this interest in the context of their Letter of Motivation and the structure of the department.

MPI-M Brovkin, V.Ilyina, T. 
  Jungclaus, J. 
  Korn, P. 
please see below Landschützer, P. 
  Marotzke, J. 
  Müller, W. 
  Mikolajewicz, U. 
  von Storch, J.-S. 
Uni HHAment, F.Ament, F.Baehr, J.Drupp, M.
Bühler, S.Hort, M.Behrens, J.Gerber, A.
Leitl, B.Beer, C.Notz, D.Held, H.
Žagar, N.  Lange, A.
   Perino, G.
   Schneider, U.



Additional selection of potential PhD topics

The ocean is estimated to absorb around a quarter of the annual man-made CO2 emissions, yet, this important carbon sink and its variability in time are still poorly constrained from observations, particularly in the southern hemisphere, where limited ship traffic only supplies few observations. One fleet with the potential to fill these gaps has thus far received little attention: sailboats. This PhD position allows for the unique opportunity to use an observational network of high-quality measurements of the sea surface partial pressure of CO2 aboard sailboats in combination with the existing ship network to better constrain the ocean carbon sink. Depending on the research interests of the applicant, the project may focus on one or more of the following research areas:

  1. Development of a new machine learning-based tool (e.g. building on the SOM-FFN method developed within the OAS group) to provide a novel data constraint of the air-sea CO2 exchange and focus on its evaluation.
  2. small-scale (meso- and sub mesoscale) ocean features and their role in the global ocean carbon cycle, providing a new dataset to improve and evaluate high-resolution Earth System Models.
  3. reduce the uncertainty linked to the air-sea CO2 flux with its overall impact amounting to a significant step change in our ability to monitor the success of climate mitigation efforts
  4. the role of the Southern Ocean in the global climate system and the inter-annual through decadal variations of this important carbon sink in a changing climate.

Simulations conducted with coarse-resolution climate models have revealed the existence of tipping points in the vegetation-climate system and put forward the idea that a too large deforestation of the Amazon forest could lead to a regime shift with a permanent and irreversible loss of the Amazon forest. The goal of this PhD project is to investigate the resilience of the Amazon forest and its likelihood to tip using a new class of climate models. Such models employ a grid spacing of a few kilometers that, in contrast to their predecessors, allows them to explicitly represent some of the key processes involved in the interactions between the atmosphere and the biosphere.

Phd projects looking deeper into the coupling between radiation and climate, either with storm resolving model simulations or with simple 1D radiative convective equilibrium models are also welcome.