Physicist, Meteorologist, Mathematician, or similar (f/m/x)

Shallow cumuli are convective clouds that generally do not precipitate substantially and whose tops rarely exceed 3 km in altitude. While these clouds can be frequently observed at midlatitudes during warm summer days, they are found on a daily basis over the tropical oceans where they play a central role in the water cycle and energy balance. Despite their importance, Earth system models (ESMs) still have difficulties representing their impact on climate, in particular in a warming atmosphere. The Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report in 2023 indeed assessed that climate feedbacks involving low-level tropical clouds remain very uncertain. These uncertainties are to a large extent related to the fact that ESMs generally operate at resolutions too coarse to resolve shallow cumuli: grid spacings smaller than 500 m are required to explicitly represent shallow clouds whereas climate projections are generally performed at resolutions on the order of 100 km. As a consequence, the influence of shallow cumuli on the resolved flow must be approximated using mathematical models known as parameterisations. Since available computational resources do not permit climate simulations at resolutions fine enough to explicitly represent these clouds, improving our understanding of tropical shallow cloud-climate feedbacks thus necessitates the development of enhanced parameterisations capable of modeling their impact with high accuracy. In an attempt to overcome the limitations of traditional parameterisations, this project aims at designing novel data driven, Machine Learning (ML) based models to parameterise the effects of shallow clouds in ESMs.

The candidate will be part of an international team of the European Research Council (ERC) Synergy Grant on „Understanding and Modelling the Earth System with Machine Learning (USMILE, https://www.usmile-erc.eu/)“. During your PhD you will utilize high-resolution ICON simulations (large eddy simulations – LES) to train ML algorithms to represent the effect of shallow convection on the simulated climate in a coarser version of the ICON-ML Model that is developed by the group.

The successful candidate will be expected to:

At the DLR Institute of Atmospheric Physics we provide excellent facilities with opportunities to work with world-renowned experts in the field of Earth system modelling and observations. You will be part of the Earth System Model Evaluation and Analysis Department which develops and applies innovative methods, including ML techniques, for the analysis of Earth system models in comparison to observations. The ultimate goal is to improve climate models and projections with machine learning and spaceborne Earth observations for actionable climate science and technology assessments in aeronautics, space, transport, and energy research. The department is strongly linked to international research activities within the World Climate Research Programme (WCRP), with substantial contributions to CMIP. We are striving to increase the proportion of female employees and therefore particularly welcome applications from women.

Please submit your application including a letter of motivation explaining your interest in this position, curriculum vitae, publication list, documentation of academic degrees and certificates, and two letters of reference.

Dr. Julien Savre-Piou

Institute of Atmospheric Physics

Email: julien.savre-piou@dlr.de