Mar 13 – 15, 2017
Université Pierre et Marie Curie, Paris
Europe/Paris timezone

Uncertainty analysis of atmospheric variations from ground-based observations

Mar 13, 2017, 5:30 PM
1h 30m


poster ROAST Drinks + posters


Dr Kai-Lan Chang (NOAA)


In year 2016, total column ozone levels continue to be influenced by the remaining levels of the man-made ozone depleting substances in the atmosphere. Ozone recovery is a much more subtle process as compared to the ozone depletion of the 1980s. The quality of ozone observations is important for understanding and interpreting of the trends. The interannual ozone variability is driven by various natural and climate related forcing. The sampling limitation of the ground-based networks complicates analysis of the state of ozone recovery globally and locally. Therefore, the detection of the recovery rates needs to be addressed with understanding of the measurement uncertainties. In the other hand, surface ozone is a greenhouse gas and pollutant detrimental to human health and crop and vegetation productivity. The TOAR (Tropospheric Ozone Assessment Report) is designed to address the role of surface ozone in Earth's atmospheric chemistry processes. Both total column and surface ozone variations estimated using ground-based stations are challenged by data inhomogeneity in time and by the irregularity of the spatial distribution of stations, as well as by interruptions in observation records. Understanding and measuring the inherent uncertainty in long-terms ozone changes is crucial for the understanding of recovery of ozone layer and effects of climate change on ozone recovery. We conduct a spatial and temporal trend analysis using the generalized additive mixed model (GAMM) analysis. We illustrate the methodology to the SBUV and the TOAR dataset.

Primary author

Dr Kai-Lan Chang (NOAA)


Dr irina Petropavlovskikh (NOAA/CIRES)

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