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Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations

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Title: Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations
Authors: Nowack, PJ
Braesicke, P
Haigh, J
Abraham, NL
Pyle, J
Voulgarakis, A
Item Type: Journal Article
Abstract: A number of studies have demonstrated the importance of ozone in climate change simulations, for example concerning global warming projections and atmospheric dynamics. However, fully interactive atmospheric chemistry schemes needed for calculating changes in ozone are computationally expensive. Climate modelers therefore often use climatological ozone fields, which are typically neither consistent with the actual climate state simulated by each model nor with the specific climate change scenario. This limitation applies in particular to standard modeling experiments such as preindustrial control or abrupt 4xCO2 climate sensitivity simulations. Here we suggest a novel method using a simple linear machine learning regression algorithm to predict ozone distributions for preindustrial and abrupt 4xCO2 simulations. Using the atmospheric temperature field as the only input, the regression reliably predicts three-dimensional ozone distributions at monthly to daily time intervals. In particular, the representation of stratospheric ozone variability is much improved compared with a fixed climatology, which is important for interactions with dynamical phenomena such as the polar vortices and the Quasi-Biennial Oscillation. Our method requires training data covering only a fraction of the usual length of simulations and thus promises to be an important stepping stone towards a range of new computationally efficient methods to consider ozone changes in long climate simulations. We highlight key development steps to further improve and extend the scope of machine learning-based ozone parameterizations.
Issue Date: 9-Oct-2018
Date of Acceptance: 14-Sep-2018
URI: http://hdl.handle.net/10044/1/64829
DOI: https://dx.doi.org/10.1088/1748-9326/aae2be
ISSN: 1748-9326
Publisher: Institute of Physics (IoP)
Journal / Book Title: Environmental Research Letters
Volume: 13
Issue: 10
Copyright Statement: © 2018 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence . Any further distribution of this work must maintain attribution to the author ( s ) and the title of the work, journal citation and DOI.
Sponsor/Funder: Natural Environment Research Council (NERC)
Funder's Grant Number: DCR00470
Keywords: Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Environmental Sciences
Meteorology & Atmospheric Sciences
Environmental Sciences & Ecology
climate change
climate sensitivity
ozone
parameterization
machine learning
big data
climate modeling
STRATOSPHERIC OZONE
ATMOSPHERIC CHEMISTRY
POLAR VORTEX
CIRCULATION MODEL
QUADRUPLED CO2
FEEDBACK
RADIATION
DESIGN
IMPACT
CMIP5
Ozone
Machine Learning
Climate Change
Numerical Analysis, Computer-Assisted
Computer Simulation
Artificial Intelligence
Regression Analysis
Atmosphere
Earth Sciences
Climate
Air Pollutants
Statistics
MD Multidisciplinary
Publication Status: Published
Article Number: 104016
Online Publication Date: 2018-09-20
Appears in Collections:Space and Atmospheric Physics
Physics
Centre for Environmental Policy



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