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Satellite image showing tracks (long bands of clouds)

Satellite image showing shiptracks in the Pacific

Credit: NASA/MODIS

Peter Manshausen (he/him)

Graduate Student (Marie Curie ESR)

Research theme

  • Climate physics

Sub department

  • Atmospheric, Oceanic and Planetary Physics

Research groups

  • Climate processes
peter.manshausen@physics.ox.ac.uk
Atmospheric Physics Clarendon Laboratory
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I am a DPhil student on the iMIRACLI programme working on aerosol-cloud interactions. I study the relationship between aerosols and cloud properties both with traditional methods like shiptracks and with novel methods. These methods come from the fields of machine learning and causal inference. An understanding of causality is very important in this topic, because we rely on 'passive' observations rather than active experiments or interventions. Because of this, observed correlations of aerosols and cloud properties are not necessarily causal. One confounding factor, which acts on both, is humidity. In my project, I try to untangle the complex network of aerosols, cloud properties, and environmental factors. 

More specifically, my project focuses on so-called opportunistic experiments, where a pollution point source allows us to compare polluted and unpolluted clouds. A striking example of these opportunistic experiments are ship tracks, which look like contrails from airplanes, but are caused by ship pollution particles growing clouds (rather than the additional water like in contrails). In my work, I show how cloud properties are changed by ships even when no track is visible to the eye, and how this is different from the visible case. This has important implications for our understanding of clouds, as well as climate change. 

Research interests

aerosol-cloud interactions
machine learning for climate
shiptracks
causal inference

Selected publications

Invisible ship tracks show large cloud sensitivity to aerosol

Nature Springer Nature 610:7930 (2022) 101-106
Peter Manshausen, Duncan Watson-Parris, Matthew Christensen, Jukka-Pekka Jalkanen, Philip Stier

ClimateBench v1.0: a benchmark for data-driven climate projections

Journal of Advances in Modeling Earth Systems American Geophysical Union 14:10 (2022) e2021MS002954
Duncan Watson-Parris, Y Rao, D Olivie, Ø Seland, Peer Nowack, C Camps-Valls, philip Stier, Shahine Bouabid, M Dewey, E Fons, J Gonzalez, Paula Harder, Kai Jeggle, J Lenhardt, Peter Manshausen, M Novitasari, L Ricard, C Roesch
See all publications

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