41378: Physicist, Data Scientist or similar - Developing Machine Learning Based Cloud Parametrizations for Climate Models

German Aerospace Center (DLR)

Oberpfaffenhofen, Germany

Work group:

Institute of Atmospheric Physics

Area of research:

PHD Thesis

Job description:

Despite significant progress in climate modelling over the last few decades, fundamental biases and substantial uncertainty in the model responses remain. Specifically, the range of climate model estimates for equilibrium climate sensitivity to doubling of CO2 concentration has not decreased since the 1970s. One of the largest contributors to uncertainties in climate sensitivity stems from parametrization of cloud processes occurring on scales smaller than the grid resolution in the atmosphere. Typical parameterizations are still failing at correctly representing clouds, rainfall, and their response to a changing climate, as well as regional variations in hydrological and climate response. This limits the models’ ability to accurately simulate and project global but also regional climate, the hydrological cycle and extreme events. While efforts are underway to develop convection resolving high resolution global climate models where some of the physical processes can be explicitly modelled, for model experiments that need to cover long time periods or for those that require additional complexity beyond the traditional physical model setup, coarser model simulations will continue to be required. A potentially promising way forward that is addressed in this thesis is to develop machine learning and especially deep learning technique-based cloud parametrizations for climate models.


The PhD student will use high-resolution simulation (3D Cloud resolving models), which can resolve the big clouds to define a machine learning parameterization and will implement the new parameterization into a global general circulation model for climate projections.

Development of deep learning techniques that can emulate subgrid convective processes in coarse-resolution state information (e.g. mean temperature, humidity, tracer transport) Implementation into a general circulation model Matching observations using modern minimization techniques compared to remote sensing observations

At DLR-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 which develops and applies innovative methods for the analysis of Earth system models in comparison to observations with the aim to better understand and project the Earth system. The department is strongly linked to international research activities within the World Climate Research Programme (WCRP), with substantial contributions to the Coupled Model Intercomparison Project (CMIP). The PhD thesis is carried out in close collaboration with Prof. Pierre Gentine (Columbia University, New York, USA) and includes regular visits of his research group. It also benefits from close collaboration 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. 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.

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Quote Reference: 41378