the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global Ground-based Tropospheric Ozone Measurements: Reference Data and Individual Site Trends (2000–2022) from the TOAR-II/HEGIFTOM Project
Abstract. Tropospheric ozone trends from models and satellites are found to diverge. Ground-based (GB) observations are used to reference models and satellites but GB data themselves might display station biases and discontinuities. Re-processing with uniform procedures, the TOAR-II Working Group Harmonization and Evaluation of Ground-based Instruments for Free-Tropospheric Ozone Measurements (HEGIFTOM) homogenized public data from 5 networks: ozonesondes, In-service Aircraft for a Global Observing System (IAGOS) profiles, solar absorption Fourier-Transform Infrared (FTIR) spectrometer measurements, Lidar observations, and Dobson Umkehr data. Amounts and uncertainties for total tropospheric ozone (“TrOC”, surface to 300 hPa), free and lower tropospheric ozone, are calculated for each network. We report trends (2000 to 2022) for these segments using Quantile Regression (QR) and Multiple Linear Regression (MLR) for 55 datasets, including 6 multi-instrument stations. The findings: (1) Median TrOC trends computed with QR and MLR trends are essentially the same; (2) Pole-to-pole, across all longitudes, TrOC trends fall within +3 ppbv/decade to -3 ppbv/decade, equivalent to (-4 % to + 8 %)/decade depending on site. (3) The greatest fractional increases occur over most tropical/subtropical sites with decreases at northern high latitudes but these patterns are not uniform. (4) Post-COVID trends are smaller than pre-COVID trends for Northern Hemisphere mid-latitude sites. In summary, this analysis conducted in the frame of TOAR-II/HEGIFTOM shows that high-quality, multi-instrument, harmonized data over a wide range of ground sites provide clear standard references for TOAR-II models and evolving tropospheric ozone satellite products for 2000–2022.
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RC1: 'Comment on egusphere-2024-3736', Anonymous Referee #1, 03 Feb 2025
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This manuscript uses homogenized ozone data from ozonesondes, IAGOS, FTIR, Lidar, and Umkehr instruments to analyze global trends in total, free, and lower tropospheric ozone. The authors compared different regressions and explored reasons for trend differences among stations with co-located instruments. Overall, this manuscript is excellent, and these ozone datasets and analyses are critically needed in the ozone community. I appreciate the authors’ careful consideration and handling of sparse and complex ozone data. I have only minor comments.
Page 2, Line 72-73: There is a reference to “Ref to Elementa collection”. Please fix this so it is a real, traceable citation.
Page 5, Line 154: Are there any results from the ASOPOS WMO GAW report that influenced how the ozonesonde data was handled?
Page 7, Line 175: I have some concerns about calculating monthly averages from locations where only one or two ozonesonde measurements are available within a given month. Previous literature suggests that somewhere between 3-18 observations per month are needed for accurate and representative time series (e.g., Christiansen et al., 2022; Lu et al., 2019; Chang et al., 2020; Wang et al., 2022). Could the authors perform some kind of short analysis that compares trends from once- or twice-monthly samples to trends derived from more frequently sampled sites? One thought could be to use a site that has many observations each month, then randomly select two observations to use from each month. Would the trends be similar to those derived from the full dataset? At the least, a discussion of the uncertainty involved in using these very sparse ozonesonde datasets is warranted.
Page 8: The IAGOS profiles are integrated so that the concentration is also reported in DU. Why is this not also done for ozonesondes?
Section 2.5: While the inclusion of Lidar data is desirable, as it summarizes nighttime trends, I am not sure it is appropriate to compare those nighttime trends to other instruments that measure during daylight hours. Nighttime ozone trends are known to be different from daytime (e.g., Yan et al., 2018). Perhaps the authors could discuss those nighttime trends as a separate section without comparison to daytime observations. Does the fact that these are nighttime measurements or some other aspect of Lidar sensing (e.g., sensitivity to the lower atmosphere, the filling in of missing data using models) explain the biases identified in Section 4.1.1 when Lidars are compared to the other instruments?
Figures:
Figure 6. The legend in the upper left corner is difficult to use. A few suggestions: 1) box off the legend so it does not appear to be another data point, and 2) provide more than one length/concentration for an easier visual reference.
References:
Chang, K.-L., Cooper, O. R., Gaudel, A., Petropavlovskikh, I., and Thouret, V.: Statistical regularization for trend detection: an integrated approach for detecting long-term trends from sparse tropospheric ozone profiles, Atmos. Chem. Phys., 20, 9915–9938, https://doi.org/10.5194/acp-20-9915-2020, 2020.
Christiansen, A., Mickley, L. J., Liu, J., Oman, L. D., and Hu, L.: Multidecadal increases in global tropospheric ozone derived from ozonesonde and surface site observations: can models reproduce ozone trends?, Atmos. Chem. Phys., 22, 14751–14782, https://doi.org/10.5194/acp-22-14751-2022, 2022.
Lu, X., Zhang, L., Zhao, Y., Jacob, D. J., Hu, Y., Hu, L., Gao, M., Liu, X., Petropavlovskikh, I., McClure-Begley, A., and Querel, R.: Surface and tropospheric ozone trends in the Southern Hemisphere since 1990: possible linkages to poleward expansion of the Hadley circulation, Sci. Bull., 64, 400–409, https://doi.org/10.1016/j.scib.2018.12.021, 2019.
Wang, H., Lu, X., Jacob, D. J., Cooper, O. R., Chang, K.-L., Li, K., Gao, M., Liu, Y., Sheng, B., Wu, K., Wu, T., Zhang, J., Sauvage, B., Nédélec, P., Blot, R., and Fan, S.: Global tropospheric ozone trends, attributions, and radiative impacts in 1995–2017: an integrated analysis using aircraft (IAGOS) observations, ozonesonde, and multi-decadal chemical model simulations, Atmos. Chem. Phys., 22, 13753–13782, https://doi.org/10.5194/acp-22-13753-2022, 2022.
Yan, Y., Lin, J., and He, C.: Ozone trends over the United States at different times of day, Atmos. Chem. Phys., 18, 1185–1202, https://doi.org/10.5194/acp-18-1185-2018, 2018.
Citation: https://doi.org/10.5194/egusphere-2024-3736-RC1
Data sets
HEGIFTOM homogenized ozone profile and TrOC datasets R. Van Malderen et al. http://hegiftom.meteo.be
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