Reconciling differences in stratospheric ozone composites

Abstract

Abstract. Observations of stratospheric ozone from multiple instruments now span three decades; combining these into composite datasets allows long-term ozone trends to be estimated. Recently, several ozone composites have been published, but trends disagree by latitude and altitude, even between composites built upon the same instrument data. We confirm that the main causes of differences in decadal trend estimates lie in (i) steps in the composite time series when the instrument source data changes and (ii) artificial sub-decadal trends in the underlying instrument data. These artefacts introduce features that can alias with regressors in multiple linear regression (MLR) analysis; both can lead to inaccurate trend estimates. Here, we aim to remove these artefacts using Bayesian methods to infer the underlying ozone time series from a set of composites by building a joint-likelihood function using a Gaussian-mixture density to model outliers introduced by data artefacts, together with a data-driven prior on ozone variability that incorporates knowledge of problems during instrument operation. We apply this Bayesian self-calibration approach to stratospheric ozone in 10 • bands from 60 • S to 60 • N and from 46 to 1 hPa (∼ 21-48 km) for 1985-2012. There are two main outcomes: (i) we independently identify and confirm many of the data problems previously identified, but which remain unaccounted for in existing composites; (ii) we construct an ozone composite, with uncertainties, that is free from most of these problems -we call this the BAyeSian Integrated and Consolidated (BASIC) composite. To analyse the new BASIC composite, we use dynamical linear modelling (DLM), which provides a more robust estimate of long-term changes through Bayesian inference than MLR. BASIC and DLM, together, provide a step forward in improving estimates of decadal trends. Our results indicate a significant recovery of ozone since 1998 in the upper stratosphere, of both northern and southern midlatitudes, in all four composites analysed, and particularly in the BASIC composite. The BASIC results also show no hemispheric difference in the recovery at midlatitudes, in contrast to an apparent feature that is present, but not consistent, in the four composites. Our overall conclusion is that it is possible to effectively combine different ozone composites and account for artefacts and drifts, and that this leads to a clear and significant result that upper stratospheric ozone levels have increased since 1998, following an earlier decline.

Evidence for a continuous decline in lower stratospheric ozone offsetting ozone layer recovery

“…Despite a lack of clear recovery in total column ozone, ozone appears to be significantly recovering in the upper stratosphere above 10 hPa in multiple ozone composites that merge observations from various space missions, especially at mid-latitudes Laine et al, 2014;WMO, 2014;Tummon et al, 2015;Harris et al, 2015;Steinbrecht et al, 2017;Ball et al, 2017;Frith et al, 2017;Sofieva et al, 2017;Bourassa et al, 2017). Trends are almost always presented as percentage change per decade, which does not illuminate the contribution to the column ozone changes.…”

“…However, it has been difficult to confirm (WMO, 2014;Harris et al, 2015;Steinbrecht et al, 2017) because (i) ozone is typically integrated over wide latitude bands and/or total column ozone is considered, both of which may lead to cancellation of opposing trends; (ii) large dynamical variability unaccounted for in regression analysis together with shorter time series lead to higher uncertainties (Tegtmeier et al, 2013); (iii) below 20 km there are large ozone gradients, with low ozone concentrations close to the tropopause; and (iv) composite-data merging techniques have hindered identification of robust changes Ball et al, 2017).…”

“…Finally, issues remain in the attribution and identification of ozone recovery usually performed through multiple linear regression (MLR) analysis that can lead to biased trend estimates (Damadeo et al, 2014;Ball et al, 2017) due to geolocation biases (Sofieva et al, 2014), vertical resolution (Kramarova et al, 2013), and satellite drifts and biases from merging data into composites (WMO, 2014;Tummon et al, 2015;Harris et al, 2015;Ball et al, 2017). Most studies consider either piecewise linear trends (PWLTs) or the equivalent effective stratospheric chlorine (EESC) proxy to represent the influence of hODSs on long-term ozone changes (Newman et al, 2007).…”

An updated version of a gap-free monthly mean zonal mean ozone database

“…Davis et al (2016) developed one of the more recent vertically resolved ozone data sets: SWOOSH. This data set has already been used in a number of studies for a wide variety of analyses Steinbrecht et al, 2017;Ball et al, 2017). Therefore, SWOOSH is an ideal candidate for the evaluation of the newly developed BSVertOzone database.…”

An updated version of a gap-free monthly mean zonal mean ozone database

“…7) and OzOne Satellite Homogenized data set). This data set has already been used in a number of studies for a wide variety of analyses (Harris et al, 2015;Steinbrecht et al, 2017;Ball et al, 2017). Therefore, SWOOSH is an ideal candidate for the The lower panel of Fig.…”