Ozone stratospheric trends from regional Bayesian composite of ground-based partial columns

Abstract. Large uncertainties and variability in individual ground-based instrument records limit the detection of statistically significant ozone trends, particularly in the lower stratosphere. Available merging studies are typically performed by latitude bands on satellite-based data records. This study derives correlation-based regional composites of ground-based timeseries towards reducing trend uncertainties. We address fundamental heterogeneities resulting from grouping individually homogenized ground-based datasets to enable robust merging. Uneven temporal and vertical resolutions of five ozone measurement techniques (Ozonesondes, FTIR, Dobson Umkehr, Lidar and Microwave radiometers) are handled by integrating monthly mean ozone profiles in two sets of four independent partial columns. Spatial heterogeneity is resolved by defining coherent regions using the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis. Regional timeseries are merged by the BAyeSian Integrated and Consolidated (BASIC) algorithm, adapted to consider propagated measurement uncertainties and the agreement between individual timeseries by Principal Component Analysis (PCA). Trends for the 2000–2024 period are then estimated by Multiple Linear Regression using the LOTUS model. We compare BASIC with a conventional weighted mean. While the weighted mean fails to capture variability during periods of low instrument consensus, BASIC produces more representative timeseries by robustly handling outliers. Accordingly, for the selected regions, BASIC reduces average uncertainties of the trend estimates by 9.4 % relative to the weighted-mean approach. Our results support positive trends in the upper stratosphere, predominantly negative trends in the middle stratosphere and non-significant trends in the lower stratosphere. This study establishes a consolidated ground-based reference to be used for comparison with global satellite-based ozone trends.

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Publication Details

Journal
Atmospheric chemistry and physics
Published
2026-07-23
DOI
https://doi.org/10.5194/acp-26-10303-2026
Primary Topic
Atmospheric Ozone and Climate
Type
article
Field-Weighted Citation Impact
0.00

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article

Ozone stratospheric trends from regional Bayesian composite of ground-based partial columns

Alexander Haefele, Gunter Stober, M. Cazorla, Wolfgang Stremme et al.
Atmospheric chemistry and physics
Atmospheric Ozone and Climate
article

Ozone stratospheric trends from regional Bayesian composite of ground-based partial columns

Alexander Haefele, Gunter Stober, M. Cazorla, Wolfgang Stremme, Wolfgang Steinbrecht, Irina V. Petropavlovskikh, Kimberly Strong, Michael Gill, Glen McConville, Roeland Van Malderen, Raphael Köhler, Emmanuel Mahieu, P. W. Duff, Anne M. Thompson, Sophie Godin‐Beekmann, Ya. A. Virolainen, Gary A. Morris, James W. Hannigan, Rigel Kivi, Shin‐Ya Ogino, J. Mellqvist, Richard Querel, Peter Effertz, Eliane Maillard Barras, Jülian Gröbner, B. Kois, Isao Murata, Tomoo Nagahama, Rolf Rüfenacht, Ralf Sussmann, Ryan M. Stauffer, Caroline Jonas, Antoine Vadès, Louis Mirallie, Gérard Ancellet, Matthias M. Frey, Debra E. Kollonige, Gerald E. Nedoluha, Corinne Vigouroux, Thierry Leblanc, Nicholas Jones
article en

Abstract

Abstract. Large uncertainties and variability in individual ground-based instrument records limit the detection of statistically significant ozone trends, particularly in the lower stratosphere. Available merging studies are typically performed by latitude bands on satellite-based data records. This study derives correlation-based regional composites of ground-based timeseries towards reducing trend uncertainties. We address fundamental heterogeneities resulting from grouping individually homogenized ground-based datasets to enable robust merging. Uneven temporal and vertical resolutions of five ozone measurement techniques (Ozonesondes, FTIR, Dobson Umkehr, Lidar and Microwave radiometers) are handled by integrating monthly mean ozone profiles in two sets of four independent partial columns. Spatial heterogeneity is resolved by defining coherent regions using the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis. Regional timeseries are merged by the BAyeSian Integrated and Consolidated (BASIC) algorithm, adapted to consider propagated measurement uncertainties and the agreement between individual timeseries by Principal Component Analysis (PCA). Trends for the 2000–2024 period are then estimated by Multiple Linear Regression using the LOTUS model. We compare BASIC with a conventional weighted mean. While the weighted mean fails to capture variability during periods of low instrument consensus, BASIC produces more representative timeseries by robustly handling outliers. Accordingly, for the selected regions, BASIC reduces average uncertainties of the trend estimates by 9.4 % relative to the weighted-mean approach. Our results support positive trends in the upper stratosphere, predominantly negative trends in the middle stratosphere and non-significant trends in the lower stratosphere. This study establishes a consolidated ground-based reference to be used for comparison with global satellite-based ozone trends.

Atmospheric chemistry and physicsVol. 26(14)
Karlsruhe Institute of Technology (DE), NSF National Center for Atmospheric Research (US), Royal Meteorological Institute of Belgium (BE), University of Bern (CH), Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (DE), Finnish Meteorological Institute (FI), United States Naval Research Laboratory (US), Centre National de la Recherche Scientifique (FR), Oeschger Centre for Climate Change Research (CH), Goddard Space Flight Center (US), Japan Agency for Marine-Earth Science and Technology (JP), Federal Office of Meteorology and Climatology MeteoSwiss (CH), Jet Propulsion Laboratory (US), Laidlaw College (NZ), University of Liège (BE), Cooperative Institute for Research in Environmental Sciences (US), Deutscher Wetterdienst (DE), St Petersburg University (RU), University of Toronto (CA), University of Colorado Boulder (US), Université de Versailles Saint-Quentin-en-Yvelines (FR), Royal Belgian Institute for Space Aeronomy (BE), Tohoku University (JP), University of Wollongong (AU), Sorbonne Université (FR), Met Éireann (IE), Institut Pierre-Simon Laplace (FR), Physikalisch-Meteorologisches Observatorium Davos (CH), Institute of Meteorology and Water Management (PL), Laboratoire atmosphères, milieux, observations spatiales (FR), Garmisch-Partenkirchen Medical Center (DE), Adnet Systems (United States) (US), Nagoya University (JP), Chalmers University of Technology (SE), Universidad San Francisco de Quito (EC), University of Maryland, Baltimore County (US), Universidad Nacional Autónoma de México (MX)
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Openalex Percentile: Top 12%
Atmospheric Ozone and Climate
DOIOpen Access PDFOpenAlex
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