Institute of Atmospheric Physics
Area of research:
Modes of natural climate variability from interannual to multi-decadal time scales are important as they have large impacts on regional and even global climate with attendant socio-economic impacts. These modes are emergent and spontaneously occurring phenomena and need not exhibit the same chronological sequence in models as in nature. Their statistical properties (e.g., time scale, autocorrelation, spectral characteristics, and spatial patterns) however should be captured by climate models. Despite their importance, systematic evaluation of these modes remains a daunting task given the wide range to consider, the length of the data record needed to adequately characterize them, the importance of sub-surface oceanic processes and uncertainties in the observational records. Evaluation of the major modes of variability is to date largely based on means, climatologies, or spectral properties which cannot always reveal whether a climate model correctly simulates the dynamical mechanisms between different climatological processes, for example the interplay between ENSO and the Pacific Decadal Oscillation or lagged long-distance teleconnections. It is therefore a priority to find new approaches of evaluating climate models in an efficient way that reveals linkages between geographical regions or time intervals and uncovers the relevant processes. The paradigm of causal discovery provides methods to estimate such dynamical dependencies from data time series and can help to understand whether a model simulates specific phenomena for the right reasons.
In this thesis, Earth system model simulations will be used to identify and project dependency structures in the major modes of variability. This will be done by:
Identifying the major modes of variability in the Coupled Model Intercomparison Project Phase 6 (CMIP6, Eyring et al. (2016a)) simulations using the Earth system model evaluation tool (ESMValTool, Eyring et al. (2016b)). Identifying dependency structures in the major modes of variability in CMIP6 simulations using an existing algorithm for advanced methods on causal discovery (Runge et al., 2015). Comparing resulting causal interdependency networks to observations Assess how these dependency structures are changing in the future under different forcing scenarios.
At the DLR Institute of Atmospheric Physics (IPA) we provide excellent facilities with opportunities to work with world-renowned experts in the field of Earth system modelling and observations. The PhD student will be part of the Earth System Model Evaluation and Analysis Department at IPA (https://www.dlr.de/pa/en/desktopdefault.aspx/tabid-10557/18322_read-42768/). The department develops innovative methods for the evaluation and analysis of Earth system models in comparison to observations with the aim to better understand and project the Earth system. The PhD thesis is part of a close collaboration project with the Climate Informatics Group of the DLR Institute of Data Sciences and its external collaboration partners from the Friedrich-Schiller-University and the Max-Planck-Institute for Biogeochemistry in Jena and includes the possibility of shorter research stays in Jena. We are striving to increase the proportion of female employees and therefore particularly welcome applications from women. Disabled applicants with equivalent qualifications will be given preferential treatment.