|Date||Wed, 9 Jan 2019 07:14:06 +0000|
We are seeking an enthusiastic and motivated PhD student to work on cloud-climate feedbacks at the Institute of Atmospheric Physics of the German Aerospace Center in Oberpfaffenhofen, Germany.
The aim of this PhD thesis is to reduce the large spread in cloud-climate feedbacks by developing and applying new emergent constraints using multi-model projections of climate change. The analysis requires investigation of relevant dynamical and cloud physical processes in order to establish plausible physical explanations between predictors and predictands. The results are then used to quantify the uncertainties in the simulated cloud-climate feedbacks and their contribution to climate sensitivity with the ultimate goal of contributing to reducing the uncertainties in projected future climate change.
Using simulations that are contributed to the Coupled Model Intercomparison Project (CMIP phases 3, 5, 6), the work will include the following key tasks:
• Assessing the robustness of previously published emergent constraints for cloud feedbacks by applying the emergent constraints to different multi-model ensembles and by using different observational datasets and taking into account observational uncertainties.
• Investigation of dynamical and cloud physical processes in order to develop new emergent constraints for cloud feedbacks based on plausible physical mechanisms. This includes application of machine learning methods to identify such mechanisms.
• Constraining equilibrium climate sensitivity with the newly developed emergent constraints and others from the published literature.
• Contributions to the development of Earth System Models (ESMs) by evaluating the next generation ESMs, identifying potential model deficiencies in the representation of clouds and the hydrological cycle, and quantifying the uncertainties in simulated cloud-climate feedbacks and their contributions to climate sensitivity.
• Master/diploma or equivalent degree in physics, meteorology or atmospheric sciences
• Fluency in English (written and spoken)
• Excellent analytical skills, and the ability to work both, independently and as part of a team
• Good programming skills, preferably with experience in high performance computing and with data analysis tools (such as Python, etc)
• Interest in climate research and machine learning methods
• Enthusiasm, motivation and creativity
• Experience in Earth system modeling and climate science is an advantage
• Basic knowledge in data analysis and visualization is desirable
This is a half-time funded position (i.e. 50%) for up to three years. Further information and online application are available at: https://www.dlr.de/dlr/jobs/en/desktopdefault.aspx/tabid-10596/1003_read-31467/ . Applications will be accepted until the position is filled. For more information contact Axel Lauer, email@example.com
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