EGU2020-2436
https://doi.org/10.5194/egusphere-egu2020-2436
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Towards fast machine learning parameterizations of stratospheric ozone feedbacks in climate change simulations

Peer Nowack1,2, Nathan Luke Abraham3,4, and Peter Braesicke5
Peer Nowack et al.
  • 1School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom (P.Nowack@uea.ac.uk)
  • 2Grantham Institute, Department of Physics, and the Data Science Institute, Imperial College London, London, United Kingdom
  • 3National Centre for Atmospheric Science, United Kingdom
  • 4Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
  • 5IMK-ASF, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany

There is a plethora of ways in which the representation of upper tropospheric and stratospheric ozone (‘ozone feedbacks’) can affect the outcome of climate change simulations. Prominent examples include modulations of the tropospheric and stratospheric circulation, climate sensitivity, cloud formation, and stratospheric water vapour (e.g. [1-8]). Here I first revisit some recent work providing evidence for such effects. I then provide an update on a recently developed machine learning parameterization for ozone using the UK Earth System Model (UKESM1, [9]). Such a parameterization could adequately represent ozone feedbacks without adding the high computational expense of a fully interactive atmospheric chemistry scheme. The parameterization could also provide several notable scientific advantages, for example concerning the treatment of important chemistry-climate model biases. Finally, I put my results into the context of several other methods suggested as potential means for addressing ozone-related effects in idealized climate sensitivity simulations, also considering the still substantial uncertainties related to modelling ozone [10,11] and associated climate feedbacks [5,12].

References:

[1] Son et al. (2008), The impact of stratospheric ozone recovery on the Southern Hemisphere westerly jet. Science 320, 1486, doi:10.1126/science.1155939.

[2] Dietmüller et al. (2014), Interactive ozone induces a negative feedback in CO2-driven climate change simulations, Journal of Geophysical Research: Atmospheres 119, 1796-1805, doi:10.1002/2013JD020575.

[3] Chiodo & Polvani (2016), Reduction of climate sensitivity to solar forcing due to stratospheric ozone feedback, Journal of Climate 29, 4651-4663, doi:10.1175/JCLI-D-15-0721.1.

[4] Chiodo & Polvani (2017), Reduced Southern Hemispheric circulation response to quadrupled CO2 due to stratospheric ozone feedback, Geophysical Research Letters 43, 465-474, doi:10.1002/2016GL071011.

[5] Nowack et al. (2015), A large ozone-circulation feedback and its implications for global warming assessments. Nature Climate Change 5, 41-45, doi:10.1038/nclimate2451.

[6] Nowack et al. (2017), On the role of ozone feedback in the ENSO amplitude response under global warming, Geophysical Research Letters 44, doi:10.1002/2016GL072418.

[7] Nowack et al. (2018), The impact of stratospheric ozone feedbacks on climate sensitivity estimates. Journal of Geophysical Research: Atmospheres 123, 4630-4641, doi:10.1002/2017JD027943.

[8] Rieder et al. (2019), Is interactive ozone chemistry important to represent polar cap stratospheric temperature variability in Earth-System Models?, Environmental Research Letters 14, 044026, doi: 10.1088/1748-9326/ab07ff.

[9] Nowack et al. (2018), Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations, Environmental Research Letters 13, 104016, doi:10.1088/1748-9326/aae2be.

[10] Chiodo & Polvani (2019), The response of the ozone layer to quadrupled CO2 concentrations: implications for climate, Journal of Climate 31, 3893-3907, doi:10.1175/JCLI-D-17-0492.1.

[11] Keeble et al. (2020), Evaluating stratospheric ozone and water vapour changes in CMIP6 models from 1850-2100, Atmospheric Chemistry and Physics Discussions.

[12] Dacie et al. (2019), A 1D RCE study of factors affecting the tropical tropopause layer and surface climate. Journal of Climate 32, 6769-6782, doi:10.1175/JCLI-D-18-0778.1.

How to cite: Nowack, P., Abraham, N. L., and Braesicke, P.: Towards fast machine learning parameterizations of stratospheric ozone feedbacks in climate change simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2436, https://doi.org/10.5194/egusphere-egu2020-2436, 2020

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