Dr Peer Nowack is an Imperial College Research Fellow at the Grantham Institute, the Department of Physics and the Data Science Institute. In addition, he is a University Lecturer in Atmospheric Chemistry and Data Science at the University of East Anglia (UEA website).
For his research, Peer addresses interdisciplinary challenges in climate science, atmospheric physics and atmospheric chemistry. In particular, he uses and develops numerical models and machine learning techniques to understand and reduce uncertainty in regional climate change projections. Further research interests include the development of computationally efficient parameterizations for the latest generation of Earth system models, seasonal weather forecasting, extreme weather events in a changing climate, and air pollution.
- Machine learning
- Climate sensitivity
- Atmospheric composition
- Air pollution
- Atmospheric physics and chemistry
- Earth observations
- Earth system modelling
- Causality algorithms
- Electric Vehicle Fleet Optimisation for Lowering Vehicle Emissions (EVOLVE)
- Science and Solutions for a Changing Planet DTP
- Independent Research Fellow, Imperial College London. Since 08/2017.
- Postdoctoral Research Associate, Department of Chemistry, University of Cambridge. 2016 - 2017
- ASI Data Science Fellowship, CMC Markets, London. Development of machine learning models to measure the dependence of business performance on market volatility and to identify new business opportunities. 01/2017 - 03/2017
- PhD, Department of Chemistry, University of Cambridge. Numerical simulations of ozone changes in the atmosphere: impacts on projections of climate sensitivity, the El Niño Southern Oscillation and solar geoengineering. 2012 - 2016
- BSc exchange programme, University of Cambridge.
Bachelor thesis and 3rd-4th year lecture courses. 2010 - 2011
- BSc in Interdisciplinary Sciences (Physics, Computer Science, Chemistry), ETH Zurich, Switzerland. 2008 - 2012
et al., 2020, Causal networks for climate model evaluation and constrained projections, Nature Communications, Vol:11, ISSN:2041-1723
et al., 2019, Detecting and quantifying causal associations in large nonlinear time series datasets, Science Advances, Vol:5, ISSN:2375-2548, Pages:1-15
et al., 2018, Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations, Environmental Research Letters, Vol:13, ISSN:1748-9326
Nowack PJ, 2018, The impact of stratospheric ozone feedbacks on climate sensitivity estimates, Journal of Geophysical Research: Atmospheres, Vol:123, ISSN:2169-897X, Pages:4630-4641
et al., 2017, On the role of ozone feedback in the ENSO amplitude response under global warming, Geophysical Research Letters, Vol:44, ISSN:0094-8276, Pages:3858-3866
et al., 2016, Stratospheric ozone changes under solar geoengineering: implications for UV exposure and air quality, Atmospheric Chemistry and Physics, Vol:16, ISSN:1680-7316, Pages:4191-4203
et al., 2015, A large ozone-circulation feedback and its implications for global warming assessments, Nature Climate Change, Vol:5, ISSN:1758-678X, Pages:41-45
et al., 2019, Machine learning parameterizations for ozone: climate model transferability, 9th International Workshop on Climate Informatics, UCAR, https://sites.google.com/view/climateinformatics2019/proceedings, Pages:263-268