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

Abstract. Large uncertainties and variability prevent the detection of statistically significant ozone trends from individual ground-based instruments 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 time series towards reducing trends 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 time series are merged by the BAyeSian Integrated and Consolidated (BASIC) algorithm, adapted to consider propagated measurement uncertainties and the agreement between individual time series 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 a more representative time series by robustly handling outliers. Accordingly, BASIC reduces average uncertainties of the trend estimates by 15.3 % relative to the weighted-mean approach. Our results confirm robust positive trends in the upper stratosphere and show predominantly negative significant regional trends in the middle and lower stratosphere. This study establishes a consolidated, global ground-based reference to be used for comparison with global satellite-based ozone trends.

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Published
2026-02-09
DOI
https://doi.org/10.5194/egusphere-2026-113
Primary Topic
Atmospheric Ozone and Climate
Type
article
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article

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

Gunter Stober, Glen McConville, Roeland Van Malderen, P. W. Duff et al.
Atmospheric Ozone and Climate
article

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

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

Abstract

Abstract. Large uncertainties and variability prevent the detection of statistically significant ozone trends from individual ground-based instruments 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 time series towards reducing trends 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 time series are merged by the BAyeSian Integrated and Consolidated (BASIC) algorithm, adapted to consider propagated measurement uncertainties and the agreement between individual time series 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 a more representative time series by robustly handling outliers. Accordingly, BASIC reduces average uncertainties of the trend estimates by 15.3 % relative to the weighted-mean approach. Our results confirm robust positive trends in the upper stratosphere and show predominantly negative significant regional trends in the middle and lower stratosphere. This study establishes a consolidated, global ground-based reference to be used for comparison with global satellite-based ozone trends.

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), 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)
National Science Foundation, National Aeronautics and Space Administration, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Fonds De La Recherche Scientifique - FNRS, University of Toronto, Centre National de la Recherche Scientifique, Université de Liège, Environment and Climate Change Canada, National Oceanic and Atmospheric Administration, Natural Sciences and Engineering Research Council of Canada, Office of Polar Programs, Eurostars
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