Peer Nowack

Biography

Academic Background

Dr Peer Nowack uses numerical models and machine learning to address key challenges in atmospheric chemistry, climate science, and atmospheric physics. In particular, he is interested in methods to understand and reduce uncertainty in regional climate change projections, the development of computationally efficient parameterizations for the latest generation of Earth system models, seasonal forecasting, extreme weather events in a changing climate, and air pollution.

Selected Publications

• Nowack et al. Causal networks for climate model evaluation and constrained projections. Nature Communications 11, 1415 (2020).
• Runge, Nowack et al. Detecting and quantifying causal associations in large nonlinear time series datasets. Science Advances 5, eaau4996 (2019).
• Nowack et al. Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations, Environmental Research Letters 13, 104016 (2018).
• Nowack et al. On the role of ozone feedback in the ENSO amplitude response under global warming, Geophysical Research Letters 44, 3858-3866 (2017).
• Nowack et al. Stratospheric ozone changes under solar geoengineering: implications for UV exposure and air quality, Atmospheric Chemistry and Physics 16, 4191-4203 (2016).
• Nowack et al. A large ozone-circulation feedback and its implications for global warming assessments, Nature Climate Change 5, 41-45 (2015).

Link to Google Scholar.

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