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.