To understand future climate change, it is critical to fully understand the past and present climate system. Information about this is encrypted in paleoclimate archives such as ice- cores or bore-hole temperatures reflecting the temperature of glaciers such as Antarctica. However, it is difficult to de-convolve and combine this knowledge as proxy records are often time-uncertain, are noisy, sparse and record the climate in very different ways.
Recently, there have been advances in understanding the ice-core recording process. Based on this, proxy system models enabling the production of “digital” cores from climate model simulations, i.e. numerical forward models, were developed. Further, bore-hole temperatures and isotope data deliver complementary information. Still, the challenge to optimally invert the process from the climate to the ice-core record and to reconstruct the climate state from such sparse, noisy and diverse data is largely unresolved.
This PhD project aims to improve on the borehole inversion methods as well as on the climate field reconstruction technique by means of modern techniques in numerical modeling and Bayesian inference to optimally combine the various information sources. In collaboration with data science and statistics experts and experts from paleo-climate research, this project aims to develop and test a new reconstruction technique that could provide a better quantitative access of paleo-climate data and insight into the past climate evolution.
- Set up, i.e. discretize and efficiently implement forward models, given by advection diffusion equations, for glacier bore-hole temperature profiles.
- Develop and test inversion methods for the relationship of water isotopes and temperature using Bayesian inference
- Use advanced Bayesian hierarchical modeling techniques to combine the information from water-isotopes and borehole temperatures to reconstruct the local temperature evolution. Ideally, this will be extended to spatio-temporal field reconstructions making use of the spatial physical covariance structure from reanalysis data.
You will test this model using surrogate data from simulated (‘digital’) ice-cores – Based on simulated cores and the developed framework, you will optimize the sampling strategy and show the feasibility and limitations of combined isotope and borehole thermometry to reconstruct the temperature evolution of Antarctica.
- Strong analytical, mathematical and statistical skills
- Proficiency in a programming language (preferably Python or C/C++)
- A degree (Master, Diploma) in mathematics, computer science, physics, climate sciences, or a related field
- Excellent English language skills, both written and spoken
- Experience in numerical analysis and Bayesian methods is a benefit
Previous experience with ice-cores or (paleo)climate research is an advantage.
For further information please contact Thomas Laepple (tlaepple at awi dot de) or Peter Zaspel (p dot zaspel at jacobs-university.de). The place of employment will be the Jacobs University Bremen
This position is limited to 3 years. The salary will be paid in accordance with the German “Collective Agreement for the Public Service of the Federation” (Tarifvertrag des öffentlichen Dienstes, TVöD Bund), up to salary level 13 (100%).
All doctoral candidates will be members of AWI’s postgraduate program POLMAR and thus benefit from a comprehensive training program and extensive support measures.