An Integrated Approach for Detecting Long-Term Trends from Sparse Tropospheric Ozone Profiles

Detecting an ozone trend from sparsely sampled ozonesonde profiles (typically once per week) is challenging due to the noise in the time series resulting from ozone’s high variability in the troposphere. To enhance trend detection we have developed a sophisticated statistical approach that utilizes a geoadditive model to assess the variability across a time series of vertical profiles. Treating the profile time series as a set of individual time series on discrete pressure surfaces, a class of smoothing spline ANOVA (analysis of variance) models is used for the purpose of jointly modeling multiple correlated time series (on separate pressure surfaces) by their associated seasonal and interannual variabilities. This method filters out the unstructured noise through a statistical regularization (i.e. a roughness penalty). We have applied this technique to the trend analysis of the vertical correlated time series of tropospheric ozone observations from 1) IAGOS (In-service Aircraft for a Global Observing System) commercial aircraft profiles above Europe and China, and 2) NOAA GMD’s (Global Monitoring Division) ozonesonde records at Hilo, Hawaii and Trinidad Head, California. We illustrate the ability of this technique for detecting a consistent trend estimate, and its effectiveness for reducing the associated uncertainty in the noisy profile data due to low sampling frequency. Furthermore, a sensitivity analysis of frequent IAGOS profiles above Europe (approximately 120 profiles per month) is performed to determine how many profiles in a month are required for reliable long-term trend detection. When ignoring the vertical correlation we found that a 4 profile-per-month sampling frequency results in 7% of sampled trends falling outside the 2-sigma uncertainty interval derived from the full data set, with associated 10% of mean absolute percentage error. We determined that an optimal sampling frequency is 15 profiles per month. While our method improves trend detection from sparse data sets, the key to substantially reducing the uncertainty is to increase the sampling frequency.