Estimating Uncertainty in Long Term Total Ozone Records from Multiple Sources

Wednesday, 17 December 2014
Stacey M Frith1, Richard S Stolarski2, Richard D McPeters3 and Natalya A Kramarova1, (1)Science Systems and Applications, Inc., Lanham, MD, United States, (2)Johns Hopkins University, Baltimore, MD, United States, (3)NASA Goddard SFC, Greenbelt, MD, United States

Total ozone measurements derived from the TOMS and SBUV backscattered solar UV instrument series cover the period from late 1978 to the present. As the SBUV series of instruments comes to an end, we look to the 10 years of data from the AURA Ozone Monitoring Instrument (OMI) and two years of data from the Ozone Mapping Profiler Suite (OMPS) on board the Suomi National Polar-orbiting Partnership satellite to continue the record. When combining these records to construct a single long-term data set for analysis we must estimate the uncertainty in the record resulting from potential biases and drifts in the individual measurement records. In this study we present a Monte Carlo analysis used to estimate uncertainties in the Merged Ozone Dataset (MOD), constructed from the Version 8.6 SBUV/SBUV2 series of instruments. We extend this analysis to incorporate OMI and OMPS total ozone data into the record and investigate the impact of multiple overlapping measurements on the estimated error. We also present an updated column ozone trend analysis and compare the size of statistical error (error from variability not explained by our linear regression model) to that from instrument uncertainty.