The Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI) is a member of the Helmholtz Association (HGF) and funded by federal and state government. AWI focuses on polar and marine research in a variety of disciplines such as biology, oceanography, geology, geochemistry and geophysics thus allowing multidisciplinary approaches to scientific goals.
Master thesis: Machine Learning for High-Resolution Paleoclimate Modeling
Background
Coarse-resolution (~1°) global climate models are widely used in paleoclimate research but are unable to explicitly represent many small-scale oceanic and atmospheric features that shape regional variability and extremes. High-resolution climate simulations can resolve these processes but remain computationally expensive, strongly limiting their applicability for long simulations and ensemble studies. Recent advances in machine learning offer a promising alternative by enabling data-driven reconstruction of fine-scale climate information from coarse model output (Oyama, 2023; Mardani, 2025).
Model performance will be evaluated using climate-aware diagnostics, going beyond traditional image similarity metrics. This includes assessing spatial variance, spectral characteristics, and the representation of extremes and coherent structures relevant for paleoclimate interpretation, such as marine heatwaves (Hayashida, 2020) or monsoonal precipitation (Liu et al., 2023). By combining modern machine learning techniques with state-of-the-art climate simulations, your thesis will contribute to an emerging research direction at the interface of climate dynamics, paleoclimate modeling, and data science.
The main supervisor is Prof. Dr. Gerrit Lohmann. During the project you will be supported by two PhD students, one with a background in physics and the other with a background in data science, and be a part of the Paleoclimate Dynamics section at the Alfred Wegener Institute (AWI). We are an international and dynamic team: https://www.awi.de/en/science/climate-sciences/paleoclimate-dynamics.html
Coarse-resolution (~1°) global climate models are widely used in paleoclimate research but are unable to explicitly represent many small-scale oceanic and atmospheric features that shape regional variability and extremes. High-resolution climate simulations can resolve these processes but remain computationally expensive, strongly limiting their applicability for long simulations and ensemble studies. Recent advances in machine learning offer a promising alternative by enabling data-driven reconstruction of fine-scale climate information from coarse model output (Oyama, 2023; Mardani, 2025).
Model performance will be evaluated using climate-aware diagnostics, going beyond traditional image similarity metrics. This includes assessing spatial variance, spectral characteristics, and the representation of extremes and coherent structures relevant for paleoclimate interpretation, such as marine heatwaves (Hayashida, 2020) or monsoonal precipitation (Liu et al., 2023). By combining modern machine learning techniques with state-of-the-art climate simulations, your thesis will contribute to an emerging research direction at the interface of climate dynamics, paleoclimate modeling, and data science.
The main supervisor is Prof. Dr. Gerrit Lohmann. During the project you will be supported by two PhD students, one with a background in physics and the other with a background in data science, and be a part of the Paleoclimate Dynamics section at the Alfred Wegener Institute (AWI). We are an international and dynamic team: https://www.awi.de/en/science/climate-sciences/paleoclimate-dynamics.html
Your Tasks
- You will explore machine learning–based methods for super-resolving – translating low-resolution into high-resolution fields – paleoclimate simulations.
- Using paired climate model simulations of a past warm climate state (mid-Holocene), you will train deep learning models – such as convolutional neural networks or diffusion models – to learn the statistical relationship between coarse and fine spatial scales.
- The focus will be on physically relevant variables such as sea surface temperature or near-surface winds.
Your Profile
- Enrolled at an University
- Backgorund in Physics, Oceanography/Meteorology, Applied Mathematics, or Data Science
- Experience with Python
- Interests in Machine Learning and Climate Science
Further Information
The place of employment will be Bremerhaven, 27570.
Start: as soon as possible
References
Oyama et al. 2023. Deep Generative Model Super-Resolves Spatially Correlated Multiregional Climate Data. Scientific Reports 13 (1): 5992. https://doi.org/10.1038/s41598-023-32947-0.
Mardani et al. 2025. Residual Corrective Diffusion Modeling for Km-Scale Atmospheric Downscaling. Communications Earth & Environment 6 (1): 124. https://doi.org/10.1038/s43247-025-02042-5.
Hayashida et al. 2020. Insights into Projected Changes in Marine Heatwaves from a High-Resolution Ocean Circulation Model. Nature Communications 11 (1): 4352. https://doi.org/10.1038/s41467-020-18241-x.
Liu et al. 2023. The East Asian Summer Monsoon Response to Global Warming in a High Resolution Coupled Model: Mean and Extremes. Asia-Pac J Atmos Sci 59, 29–45. https://doi.org/10.1007/s13143-022-00285-2
The place of employment will be Bremerhaven, 27570.
Start: as soon as possible
References
Oyama et al. 2023. Deep Generative Model Super-Resolves Spatially Correlated Multiregional Climate Data. Scientific Reports 13 (1): 5992. https://doi.org/10.1038/s41598-023-32947-0.
Mardani et al. 2025. Residual Corrective Diffusion Modeling for Km-Scale Atmospheric Downscaling. Communications Earth & Environment 6 (1): 124. https://doi.org/10.1038/s43247-025-02042-5.
Hayashida et al. 2020. Insights into Projected Changes in Marine Heatwaves from a High-Resolution Ocean Circulation Model. Nature Communications 11 (1): 4352. https://doi.org/10.1038/s41467-020-18241-x.
Liu et al. 2023. The East Asian Summer Monsoon Response to Global Warming in a High Resolution Coupled Model: Mean and Extremes. Asia-Pac J Atmos Sci 59, 29–45. https://doi.org/10.1007/s13143-022-00285-2
You are interested?
Then please send us your application with Cover letter an CV (with all documents merged into one PDF file) by e-mail to: Nina Öhlckers (nina.oehlckers@awi.de) or Alexander Thorneloe (alexander.thorn@awi.de).
Then please send us your application with Cover letter an CV (with all documents merged into one PDF file) by e-mail to: Nina Öhlckers (nina.oehlckers@awi.de) or Alexander Thorneloe (alexander.thorn@awi.de).