Dr Peer Nowack uses numerical models and machine learning to address key challenges in climate science, atmospheric physics, and atmospheric chemistry. 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.

Peer Nowack is now Tenure-Track Professor at the Karlsruhe Institute of Technology (KIT) in Germany. The website of his new Chair for AI in Climate and Environmental Sciences can be found here.

Selected Publications

• Ceppi and Nowack. Observational evidence that cloud feedback amplifies global warming. PNAS 118, e2026290118 (2021).
• 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.

Chemistry, data science, machine learning, climate science.

• Machine learning
• Atmospheric chemistry and physics
• Air pollution
• Extreme weather
• Climate sensitivity
• Causality algorithms
• Ozone
• Climate change