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.