mloz: A Highly Efficient Machine Learning-Based Ozone Parameterization for Climate Sensitivity Simulations Article Swipe

PDF

Yiling Ma , Nathan Luke Abraham , Stefan Versick , Roland Ruhnke , Andrea Schneidereit , Ulrike Niemeier , Francis Back , Peter Braesicke , Peer Nowack ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2509.20422

Atmospheric ozone is a crucial absorber of solar radiation and an important greenhouse gas. However, most climate models participating in the Coupled Model Intercomparison Project (CMIP) still lack an interactive representation of ozone due to the high computational costs of atmospheric chemistry schemes. Here, we introduce a machine learning parameterization (mloz) to interactively model daily ozone variability and trends across the troposphere and stratosphere in standard climate sensitivity simulations, including two-way interactions of ozone with the Quasi-Biennial Oscillation. We demonstrate its high fidelity on decadal timescales and its flexible use online across two different climate models -- the UK Earth System Model (UKESM) and the German ICOsahedral Nonhydrostatic (ICON) model. With atmospheric temperature profile information as the only input, mloz produces stable ozone predictions around 31 times faster than the chemistry scheme in UKESM, contributing less than 4 percent of the respective total climate model runtimes. In particular, we also demonstrate its transferability to different climate models without chemistry schemes by transferring the parameterization from UKESM to ICON. This highlights the potential for widespread adoption in CMIP-level climate models that lack interactive chemistry for future climate change assessments, particularly when focusing on climate sensitivity simulations, where ozone trends and variability are known to significantly modulate atmospheric feedback processes.

Related Topics

Truth And Reconciliation Commission Of Canada

Alanis Morissette

2025 Nba Draft

28 Years Later

Concepts

No concepts available.

Metadata

Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2509.20422
PDF: https://arxiv.org/pdf/2509.20422
OA Status: green
OpenAlex ID: https://openalex.org/W4414787388

All OpenAlex metadata

Raw OpenAlex JSON

OpenAlex ID: https://openalex.org/W4414787388

Canonical identifier for this work in OpenAlex
DOI: https://doi.org/10.48550/arxiv.2509.20422

Digital Object Identifier
Title: mloz: A Highly Efficient Machine Learning-Based Ozone Parameterization for Climate Sensitivity Simulations

Work title
Type: preprint

OpenAlex work type
Language: en

Primary language
Publication year: 2025

Year of publication
Publication date: 2025-09-24

Full publication date if available
Authors: Yiling Ma, Nathan Luke Abraham, Stefan Versick, Roland Ruhnke, Andrea Schneidereit, Ulrike Niemeier, Francis Back, Peter Braesicke, Peer Nowack

List of authors in order
Landing page: https://arxiv.org/abs/2509.20422

Publisher landing page
PDF URL: https://arxiv.org/pdf/2509.20422

Direct link to full text PDF
Open access: Yes

Whether a free full text is available
OA status: green

Open access status per OpenAlex
OA URL: https://arxiv.org/pdf/2509.20422

Direct OA link when available
Cited by: 0

Total citation count in OpenAlex

Full payload

id	https://openalex.org/W4414787388
doi	https://doi.org/10.48550/arxiv.2509.20422
ids.doi	https://doi.org/10.48550/arxiv.2509.20422
ids.openalex	https://openalex.org/W4414787388
fwci
type	preprint
title	mloz: A Highly Efficient Machine Learning-Based Ozone Parameterization for Climate Sensitivity Simulations
biblio.issue
biblio.volume
biblio.last_page
biblio.first_page
topics[0].id	https://openalex.org/T11588
topics[0].field.id	https://openalex.org/fields/23
topics[0].field.display_name	Environmental Science
topics[0].score	0.9842000007629395
topics[0].domain.id	https://openalex.org/domains/3
topics[0].domain.display_name	Physical Sciences
topics[0].subfield.id	https://openalex.org/subfields/2306
topics[0].subfield.display_name	Global and Planetary Change
topics[0].display_name	Atmospheric and Environmental Gas Dynamics
topics[1].id	https://openalex.org/T11320
topics[1].field.id	https://openalex.org/fields/19
topics[1].field.display_name	Earth and Planetary Sciences
topics[1].score	0.9824000000953674
topics[1].domain.id	https://openalex.org/domains/3
topics[1].domain.display_name	Physical Sciences
topics[1].subfield.id	https://openalex.org/subfields/1902
topics[1].subfield.display_name	Atmospheric Science
topics[1].display_name	Atmospheric Ozone and Climate
topics[2].id	https://openalex.org/T12120
topics[2].field.id	https://openalex.org/fields/23
topics[2].field.display_name	Environmental Science
topics[2].score	0.9765999913215637
topics[2].domain.id	https://openalex.org/domains/3
topics[2].domain.display_name	Physical Sciences
topics[2].subfield.id	https://openalex.org/subfields/2305
topics[2].subfield.display_name	Environmental Engineering
topics[2].display_name	Air Quality Monitoring and Forecasting
is_xpac	False
apc_list
apc_paid
language	en
locations[0].id	pmh:oai:arXiv.org:2509.20422
locations[0].is_oa	True
locations[0].source.id	https://openalex.org/S4306400194
locations[0].source.issn
locations[0].source.type	repository
locations[0].source.is_oa	True
locations[0].source.issn_l
locations[0].source.is_core	False
locations[0].source.is_in_doaj	False
locations[0].source.display_name	arXiv (Cornell University)
locations[0].source.host_organization	https://openalex.org/I205783295
locations[0].source.host_organization_name	Cornell University
locations[0].source.host_organization_lineage	https://openalex.org/I205783295
locations[0].license
locations[0].pdf_url	https://arxiv.org/pdf/2509.20422
locations[0].version	submittedVersion
locations[0].raw_type	text
locations[0].license_id
locations[0].is_accepted	False
locations[0].is_published	False
locations[0].raw_source_name
locations[0].landing_page_url	http://arxiv.org/abs/2509.20422
locations[1].id	doi:10.48550/arxiv.2509.20422
locations[1].is_oa	True
locations[1].source.id	https://openalex.org/S4306400194
locations[1].source.issn
locations[1].source.type	repository
locations[1].source.is_oa	True
locations[1].source.issn_l
locations[1].source.is_core	False
locations[1].source.is_in_doaj	False
locations[1].source.display_name	arXiv (Cornell University)
locations[1].source.host_organization	https://openalex.org/I205783295
locations[1].source.host_organization_name	Cornell University
locations[1].source.host_organization_lineage	https://openalex.org/I205783295
locations[1].license	cc-by
locations[1].pdf_url
locations[1].version
locations[1].raw_type	article
locations[1].license_id	https://openalex.org/licenses/cc-by
locations[1].is_accepted	False
locations[1].is_published
locations[1].raw_source_name
locations[1].landing_page_url	https://doi.org/10.48550/arxiv.2509.20422
indexed_in	arxiv, datacite
authorships[0].author.id	https://openalex.org/A5020961821
authorships[0].author.orcid	https://orcid.org/0000-0002-6504-4165
authorships[0].author.display_name	Yiling Ma
authorships[0].author_position	first
authorships[0].raw_author_name	Ma, Yiling
authorships[0].is_corresponding	False
authorships[1].author.id	https://openalex.org/A5078321071
authorships[1].author.orcid	https://orcid.org/0000-0003-3750-3544
authorships[1].author.display_name	Nathan Luke Abraham
authorships[1].author_position	middle
authorships[1].raw_author_name	Abraham, Nathan Luke
authorships[1].is_corresponding	False
authorships[2].author.id	https://openalex.org/A5077419543
authorships[2].author.orcid	https://orcid.org/0000-0003-2265-6695
authorships[2].author.display_name	Stefan Versick
authorships[2].author_position	middle
authorships[2].raw_author_name	Versick, Stefan
authorships[2].is_corresponding	False
authorships[3].author.id	https://openalex.org/A5006929022
authorships[3].author.orcid	https://orcid.org/0000-0002-0353-1603
authorships[3].author.display_name	Roland Ruhnke
authorships[3].author_position	middle
authorships[3].raw_author_name	Ruhnke, Roland
authorships[3].is_corresponding	False
authorships[4].author.id	https://openalex.org/A5045095782
authorships[4].author.orcid
authorships[4].author.display_name	Andrea Schneidereit
authorships[4].author_position	middle
authorships[4].raw_author_name	Schneidereit, Andrea
authorships[4].is_corresponding	False
authorships[5].author.id	https://openalex.org/A5083485437
authorships[5].author.orcid	https://orcid.org/0000-0003-0088-8364
authorships[5].author.display_name	Ulrike Niemeier
authorships[5].author_position	middle
authorships[5].raw_author_name	Niemeier, Ulrike
authorships[5].is_corresponding	False
authorships[6].author.id	https://openalex.org/A5016970678
authorships[6].author.orcid
authorships[6].author.display_name	Francis Back
authorships[6].author_position	middle
authorships[6].raw_author_name	Back, Felix
authorships[6].is_corresponding	False
authorships[7].author.id	https://openalex.org/A5010787142
authorships[7].author.orcid	https://orcid.org/0000-0003-1423-0619
authorships[7].author.display_name	Peter Braesicke
authorships[7].author_position	middle
authorships[7].raw_author_name	Braesicke, Peter
authorships[7].is_corresponding	False
authorships[8].author.id	https://openalex.org/A5029231689
authorships[8].author.orcid	https://orcid.org/0000-0003-4588-7832
authorships[8].author.display_name	Peer Nowack
authorships[8].author_position	last
authorships[8].raw_author_name	Nowack, Peer
authorships[8].is_corresponding	False
has_content.pdf	False
has_content.grobid_xml	False
is_paratext	False
open_access.is_oa	True
open_access.oa_url	https://arxiv.org/pdf/2509.20422
open_access.oa_status	green
open_access.any_repository_has_fulltext	False
created_date	2025-10-10T00:00:00
display_name	mloz: A Highly Efficient Machine Learning-Based Ozone Parameterization for Climate Sensitivity Simulations
has_fulltext	False
is_retracted	False
updated_date	2025-11-06T06:51:31.235846
primary_topic.id	https://openalex.org/T11588
primary_topic.field.id	https://openalex.org/fields/23
primary_topic.field.display_name	Environmental Science
primary_topic.score	0.9842000007629395
primary_topic.domain.id	https://openalex.org/domains/3
primary_topic.domain.display_name	Physical Sciences
primary_topic.subfield.id	https://openalex.org/subfields/2306
primary_topic.subfield.display_name	Global and Planetary Change
primary_topic.display_name	Atmospheric and Environmental Gas Dynamics
cited_by_count	0
locations_count	2
best_oa_location.id	pmh:oai:arXiv.org:2509.20422
best_oa_location.is_oa	True
best_oa_location.source.id	https://openalex.org/S4306400194
best_oa_location.source.issn
best_oa_location.source.type	repository
best_oa_location.source.is_oa	True
best_oa_location.source.issn_l
best_oa_location.source.is_core	False
best_oa_location.source.is_in_doaj	False
best_oa_location.source.display_name	arXiv (Cornell University)
best_oa_location.source.host_organization	https://openalex.org/I205783295
best_oa_location.source.host_organization_name	Cornell University
best_oa_location.source.host_organization_lineage	https://openalex.org/I205783295
best_oa_location.license
best_oa_location.pdf_url	https://arxiv.org/pdf/2509.20422
best_oa_location.version	submittedVersion
best_oa_location.raw_type	text
best_oa_location.license_id
best_oa_location.is_accepted	False
best_oa_location.is_published	False
best_oa_location.raw_source_name
best_oa_location.landing_page_url	http://arxiv.org/abs/2509.20422
primary_location.id	pmh:oai:arXiv.org:2509.20422
primary_location.is_oa	True
primary_location.source.id	https://openalex.org/S4306400194
primary_location.source.issn
primary_location.source.type	repository
primary_location.source.is_oa	True
primary_location.source.issn_l
primary_location.source.is_core	False
primary_location.source.is_in_doaj	False
primary_location.source.display_name	arXiv (Cornell University)
primary_location.source.host_organization	https://openalex.org/I205783295
primary_location.source.host_organization_name	Cornell University
primary_location.source.host_organization_lineage	https://openalex.org/I205783295
primary_location.license
primary_location.pdf_url	https://arxiv.org/pdf/2509.20422
primary_location.version	submittedVersion
primary_location.raw_type	text
primary_location.license_id
primary_location.is_accepted	False
primary_location.is_published	False
primary_location.raw_source_name
primary_location.landing_page_url	http://arxiv.org/abs/2509.20422
publication_date	2025-09-24
publication_year	2025
referenced_works_count	0
abstract_inverted_index.4	137
abstract_inverted_index.a	3, 46
abstract_inverted_index.--	96
abstract_inverted_index.31	125
abstract_inverted_index.In	146
abstract_inverted_index.UK	98
abstract_inverted_index.We	78
abstract_inverted_index.an	10, 28
abstract_inverted_index.as	115
abstract_inverted_index.by	160
abstract_inverted_index.in	19, 64, 132, 175
abstract_inverted_index.is	2
abstract_inverted_index.of	6, 31, 39, 72, 139
abstract_inverted_index.on	83, 191
abstract_inverted_index.to	34, 51, 153, 166, 202
abstract_inverted_index.we	44, 148
abstract_inverted_index.and	9, 57, 62, 86, 103, 198
abstract_inverted_index.are	200
abstract_inverted_index.due	33
abstract_inverted_index.for	172, 183
abstract_inverted_index.its	80, 87, 151
abstract_inverted_index.the	20, 35, 60, 75, 97, 104, 116, 129, 140, 162, 170
abstract_inverted_index.two	92
abstract_inverted_index.use	89
abstract_inverted_index.This	168
abstract_inverted_index.With	110
abstract_inverted_index.also	149
abstract_inverted_index.from	164
abstract_inverted_index.gas.	13
abstract_inverted_index.high	36, 81
abstract_inverted_index.lack	27, 180
abstract_inverted_index.less	135
abstract_inverted_index.mloz	119
abstract_inverted_index.most	15
abstract_inverted_index.only	117
abstract_inverted_index.than	128, 136
abstract_inverted_index.that	179
abstract_inverted_index.when	189
abstract_inverted_index.with	74
abstract_inverted_index.Earth	99
abstract_inverted_index.Here,	43
abstract_inverted_index.ICON.	167
abstract_inverted_index.Model	22, 101
abstract_inverted_index.UKESM	165
abstract_inverted_index.costs	38
abstract_inverted_index.daily	54
abstract_inverted_index.known	201
abstract_inverted_index.model	53, 144
abstract_inverted_index.ozone	1, 32, 55, 73, 122, 196
abstract_inverted_index.solar	7
abstract_inverted_index.still	26
abstract_inverted_index.times	126
abstract_inverted_index.total	142
abstract_inverted_index.where	195
abstract_inverted_index.(CMIP)	25
abstract_inverted_index.(ICON)	108
abstract_inverted_index.(mloz)	50
abstract_inverted_index.German	105
abstract_inverted_index.System	100
abstract_inverted_index.UKESM,	133
abstract_inverted_index.across	59, 91
abstract_inverted_index.around	124
abstract_inverted_index.change	186
abstract_inverted_index.faster	127
abstract_inverted_index.future	184
abstract_inverted_index.input,	118
abstract_inverted_index.model.	109
abstract_inverted_index.models	17, 95, 156, 178
abstract_inverted_index.online	90
abstract_inverted_index.scheme	131
abstract_inverted_index.stable	121
abstract_inverted_index.trends	58, 197
abstract_inverted_index.(UKESM)	102
abstract_inverted_index.Coupled	21
abstract_inverted_index.Project	24
abstract_inverted_index.climate	16, 66, 94, 143, 155, 177, 185, 192
abstract_inverted_index.crucial	4
abstract_inverted_index.decadal	84
abstract_inverted_index.machine	47
abstract_inverted_index.percent	138
abstract_inverted_index.profile	113
abstract_inverted_index.schemes	159
abstract_inverted_index.two-way	70
abstract_inverted_index.without	157
abstract_inverted_index.However,	14
abstract_inverted_index.absorber	5
abstract_inverted_index.adoption	174
abstract_inverted_index.feedback	206
abstract_inverted_index.fidelity	82
abstract_inverted_index.flexible	88
abstract_inverted_index.focusing	190
abstract_inverted_index.learning	48
abstract_inverted_index.modulate	204
abstract_inverted_index.produces	120
abstract_inverted_index.schemes.	42
abstract_inverted_index.standard	65
abstract_inverted_index.chemistry	41, 130, 158, 182
abstract_inverted_index.different	93, 154
abstract_inverted_index.important	11
abstract_inverted_index.including	69
abstract_inverted_index.introduce	45
abstract_inverted_index.potential	171
abstract_inverted_index.radiation	8
abstract_inverted_index.runtimes.	145
abstract_inverted_index.CMIP-level	176
abstract_inverted_index.greenhouse	12
abstract_inverted_index.highlights	169
abstract_inverted_index.processes.	207
abstract_inverted_index.respective	141
abstract_inverted_index.timescales	85
abstract_inverted_index.widespread	173
abstract_inverted_index.Atmospheric	0
abstract_inverted_index.ICOsahedral	106
abstract_inverted_index.atmospheric	40, 111, 205
abstract_inverted_index.demonstrate	79, 150
abstract_inverted_index.information	114
abstract_inverted_index.interactive	29, 181
abstract_inverted_index.particular,	147
abstract_inverted_index.predictions	123
abstract_inverted_index.sensitivity	67, 193
abstract_inverted_index.temperature	112
abstract_inverted_index.troposphere	61
abstract_inverted_index.variability	56, 199
abstract_inverted_index.Oscillation.	77
abstract_inverted_index.assessments,	187
abstract_inverted_index.contributing	134
abstract_inverted_index.interactions	71
abstract_inverted_index.particularly	188
abstract_inverted_index.simulations,	68, 194
abstract_inverted_index.stratosphere	63
abstract_inverted_index.transferring	161
abstract_inverted_index.computational	37
abstract_inverted_index.interactively	52
abstract_inverted_index.participating	18
abstract_inverted_index.significantly	203
abstract_inverted_index.Nonhydrostatic	107
abstract_inverted_index.Quasi-Biennial	76
abstract_inverted_index.representation	30
abstract_inverted_index.Intercomparison	23
abstract_inverted_index.transferability	152
abstract_inverted_index.parameterization	49, 163
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	9
citation_normalized_percentile