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LOTUS : Long-term Ozone Trends and Uncertainties in the Stratosphere
In this paper, we compare model calculations of ozone profiles and their variability for the period 1998 to 2016 with satellite and lidar profiles at five ground-based stations. Under the investigation is the temporal impact of the stratospheric halogen reduction (chemical processes) and increase in greenhouse gases (i.e., global warming) on stratospheric ozone changes. Attention is given to the effect of greenhouse gases on ultraviolet-B radiation at ground level. Our chemistry transport and chemistry climate models (Oslo CTM3 and EMAC CCM) indicate that (a) the effect of halogen reduction is maximized in ozone recovery at 1–7 hPa and observed at all lidar stations; and (b) significant impact of greenhouse gases on stratospheric ozone recovery is predicted after the year 2050. Our study indicates that solar ultraviolet-B irradiance that produces DNA damage would increase after the year 2050 by +1.3% per decade. Such change in the model is driven by a significant decrease in cloud cover due to the evolution of greenhouse gases in the future and an insignificant trend in total ozone. If our estimates prove to be true, then it is likely that the process of climate change will overwhelm the effect of ozone recovery on UV-B irradiance in midlatitudes.
Abstract. Detecting a tropospheric 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 temporal variability. To enhance trend detection we have developed a sophisticated statistical approach that utilizes a geoadditive model to assess ozone 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 integrated fit method filters out the unstructured noise through a statistical regularization (i.e. a roughness penalty), by taking advantage of the additional correlated data points available on the pressure surfaces above and below the surface of interest. We have applied this technique to the trend analysis of the vertically 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 to detect a consistent trend estimate, and its effectiveness for reducing the associated uncertainty in the noisy profile data due to low sampling frequency. We also conducted a sensitivity analysis of frequent IAGOS profiles above Europe (approximately 120 profiles per month) 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 typical sampling strategy of 4 profiles-per-month 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 14 profiles per month when using the integrated fit method for calculating trends; when the integrated fit method is not applied, the sampling frequency had to be increased to 18 profiles per month to achieve the same result. While our method improves trend detection from sparse data sets, the key to substantially reducing the uncertainty is to increase the sampling frequency.
One of the primary motivations of the LOTUS effort is to attempt to reconcile the discrepancies in ozone trend results from the wealth of literature on the subject. Doing so requires investigating the various methodolo-gies employed to derive long-term trends in ozone as well as to examine the large array of possible variables that feed into those methodologies and analyse their impacts on potential trend results. Given the limited amount of time, the LOTUS group focused on the most common methodology of multiple linear regression and performed a number of sensitivity tests with the goal of trying to establish best practices and come to a consensus on a single regression model to use for this study. This chapter discusses the details and results of the sensitivity tests before describing the components of the final single model that was chosen and the reasons for that choice. 4.1 Regression methodology MLR methods have been used for trend detection in ozone time series for decades. They evolved into the most commonly used approach in the community, with many smaller or more substantial variations of a baseline method having been developed. For the LOTUS project, we decided to base our sensitivity tests on a MLR with an iterative lag-1 autocorrelation correction (see Appendix B of Damadeo et al., 2014). In general, the regression problem can be written as, (4.1), where y is the length n vector of observations, β is the length m vector of proxy coefficients, X is the n × m matrix of proxies, and ε are the fit residuals. The goal of the regression procedure is to find the values of β which mi-nimise the quantity (4.2), where Ω is the covariance matrix of the observations. The problem admits a direct solution, (4.3), which can also be used to obtain an error estimate for the proxy coefficients assuming the covariance matrix is correctly specified. The regression is performed in an iterative procedure (Cochrane and Orcutt, 1949) with Ω set to unity for the first iteration. The first iteration is equivalent to an unweighted least squares fit. After the first iteration , the autocorrelation coefficient, ρ, is calculated through, (4.4), where ε is the mean value of the residuals. Typically, the autocorrelation coefficient is on the order of 0.2-0.3. For the next iteration, the covariance matrix is updated taking into account the observed autocorrela-tion (Prais and Winsten, 1954) with modifications by Savin and White (1978) if gaps are present in the data. The procedure is repeated until the autocorrelation coefficient has converged within a tolerance level of 0.01. The final error estimate is calculated by scaling the covariance matrix to match the observed variance of the residuals. This baseline MLR was used for sensitivity tests to decide which proxies to use in the final "LOTUS regression" model, for evaluations of possible lags for proxies, and for the evaluation of weighted or unweighted regressor data. The final setup of the "LOTUS regression" model is described in more detail in Section 4.5. 4.2 Proxies Proxies are used in multiple regression analyses to represent the observed variability in the parameter being modeled, in this case ozone. There is a wealth of literature concerning the viability of various proxies to represent dynamical and chemical processes that affect ozone (e.g., WMO, 2011; WMO, 2014; and references therein). We brief ly describe the most common proxies for ozone trend analyses below and provide information on where these proxies may be found. Our focus is therefore not to provide detailed studies about the effects of these proxies on ozone distribution but rather a short estimate about their inf luence mechanism (dynamical or chemical) and a description on how the proxy has been implemented in regression models before. The listed links for the proxies are not exhaustive and should only be seen as a subset of all available possible sources.
This paper is devoted to the modeling of altitude-dependent patterns of ozone variations over time. Umkehr ozone profiles (quarter of Umkehr layer) from 1978 to 2011 are investigated at two locations: Boulder (USA) and Arosa (Switzerland). The study consists of two statistical stages. First we approximate ozone profiles employing an appropriate basis. To capture primary modes of ozone variations without losing essential information, a functional principal component analysis is performed. It penalizes roughness of the function and smooths excessive variations in the shape of the ozone profiles. As a result, data-driven basis functions (empirical basis functions) are obtained. The coefficients (principal component scores) corresponding to the empirical basis functions represent dominant temporal evolution in the shape of ozone profiles. We use those time series coefficients in the second statistical step to reveal the important sources of the patterns and variations in the profiles. We estimate the effects of covariates – month, year (trend), quasi-biennial oscillation, the solar cycle, the Arctic oscillation, the El Niño/Southern Oscillation cycle and the Eliassen–Palm flux – on the principal component scores of ozone profiles using additive mixed effects models. The effects are represented as smooth functions and the smooth functions are estimated by penalized regression splines. We also impose a heteroscedastic error structure that reflects the observed seasonality in the errors. The more complex error structure enables us to provide more accurate estimates of influences and trends, together with enhanced uncertainty quantification. Also, we are able to capture fine variations in the time evolution of the profiles, such as the semi-annual oscillation. We conclude by showing the trends by altitude over Boulder and Arosa, as well as for total column ozone. There are great variations in the trends across altitudes, which highlights the benefits of modeling ozone profiles.
The ultimate goal of LOTUS is to improve confidence in calculated ozone trend values via an improved under-standing of the uncertainties. Chapter 3 highlighted many of the challenges facing analyses of long-term ozone time series, and despite the fact that many of those challenges still need to be addressed, it is worthwhile to assess the trend results from this work in such a way as to be able to place those in the context of previous work. This chapter highlights the results of taking the “LOTUS regression” model from Chapter 4 and applying it to the different data sets (i.e., satellite, ground, and model) at different resolu-tions comparable to those in previous ozone assessments and comprehensive studies (e.g., WMO, 2014; Harris et al., 2015; Steinbrecht et al., 2017). The individual satellite-based trend results are then combined to obtain a single mean ozone trend profile with respective uncertainty es-timates. This important yet challenging final step in the assessment has been the cause of debate in the community in recent years. Different methods for combining the indi-vidual trend results are discussed and explained, and the final trend profile estimates are analysed for significance.
This peer-reviewed Report summarises the main results obtained during the first year of the LOTUS activity, which was targeted at providing timely inputs to the 2018 WMO/UNEP Ozone Assessment. This deadline defined the scope of this Report and focused the LOTUS activities primarily on changes in ozone levels in the middle and the upper stratosphere outside the polar regions as observed by merged satellite records. These studies were complemented by an analysis of ground-based data and recent model data provided by the CCMI (Morgenstern et al., 2017). The short timeline of this work necessitated leaving several pressing topics for the second phase of LOTUS, most notably: the significance of recent trends in the lower stratosphere, the attribution of trends to ODSs and GHGs using model data, and estimates of trends in the polar regions.
In the 1980s, ground-based monitoring of the ozone layer played a key role in the discovery of the Antarctic Ozone Hole as well as in the first documentation of significant winter and spring long-term downward trends in the populated mid-latitude regions. The article summarizes the close-to-hundred-year-long history of ground-based measurements of stratospheric ozone, and more recent observations of constituents that influence its equilibrium. Ozone observations began long before the recognition of the impact of increasing emissions of manmade ozone-depleting substances on ozone and therefore on UV levels, human health, ecosystems and the Earth climate. The historical ozone observations prior to 1980s are used as a reference for the assessments of the state of the ozone layer linked to the enforcement of the Montreal Protocol. In this paper, we describe the worldwide monitoring networks and their ozone observations used to determine long-term trends with an accuracy of a few percent per decade. Since 1989, the ground-based monitoring activities have provided support for the amendments of the Montreal Protocol (MP). They include monitoring of (a) the ozone total column and the vertical distribution at global scale, (b) the ozone-depleting substances (ODS) related to the MP such as chlorofluorocarbons (CFCs), and their decomposition products in the stratosphere, and (c) the atmospheric species playing a role in ozone depletion, e.g., nitrogen oxides, water vapor, aerosols, polar stratospheric clouds. We highlight important accomplishments in the atmospheric monitoring performed by the Global Atmosphere Watch program (GAW) run under the auspices of the World Meteorological Organization (WMO) and by the Network for the Detection of Atmospheric Composition Change (NDACC). We also address the complementary roles of ground-based networks and satellite instruments. High-quality ground-based measurements have been used to evaluate ozone variabilities and long-term trends, assess chemistry climate models, and check the long-term stability of satellite data, including more recently the merged satellite time-series developed for the detection of ozone recovery at global scale, which might be further modified by climate change.
This paper is focusing on the representativeness of single lidar stations for zonally averaged ozone profile variations over the middle and upper stratosphere. From the lower to the upper stratosphere, ozone profiles from single or grouped lidar stations correlate well with zonal means calculated from the Solar Backscatter Ultraviolet Radiometer (SBUV) satellite overpasses. The best representativeness with significant correlation coefficients is found within ±15° of latitude circles north or south of any lidar station. This paper also includes a multivariate linear regression (MLR) analysis on the relative importance of proxy time series for explaining variations in the vertical ozone profiles. Studied proxies represent variability due to influences outside of the earth system (solar cycle) and within the earth system, i.e. dynamic processes (the Quasi Biennial Oscillation, QBO; the Arctic Oscillation, AO; the Antarctic Oscillation, AAO; the El Niño Southern Oscillation, ENSO), those due to volcanic aerosol (aerosol optical depth, AOD), tropopause height changes (including global warming) and those influences due to anthropogenic contributions to atmospheric chemistry (equivalent effective stratospheric chlorine, EESC). Ozone trends are estimated, with and without removal of proxies, from the total available 1980 to 2015 SBUV record. Except for the chemistry related proxy (EESC) and its orthogonal function, the removal of the other proxies does not alter the significance of the estimated long-term trends. At heights above 15 hPa an inflection point between 1997 and 1999 marks the end of significant negative ozone trends, followed by a recent period between 1998 and 2015 with positive ozone trends. At heights between 15 and 40 hPa the pre-1998 negative ozone trends tend to become less significant as we move towards 2015, below which the lower stratosphere ozone decline continues in agreement with findings of recent literature.
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With 33 years of continuous measurements, Thessaloniki has one of the world’s longest time series of the total ozone column (TOC) derived from a single monochromator Brewer spectrophotometer deployed since March 1982. We use the multi-decadal time series as a unique dataset for analysis of long-term changes in TOC and its parametric dependencies. We apply multiple linear regression (MLR) to monthly mean values of TOC and five proxies in the framework of a piece-wise linear trend (PWLT) model. We also perform a nonlinear time series decomposition using singular spectrum analysis (SSA) to obtain the monthly mean trend, periodicity and residual noise at mid-latitude. Both models confirm that the negative trend in TOC from 1982 exhibits a turning point during 1997 and strongly anti-correlates (ρ = −0.69 for MLR and ρ = −0.95 for SSA) with the amount of the ozone-depleting substances as represented by the EESC. Post-1997 ozone recovery is found to be evident but not statistically-significant, for the majority of the months, in the linear model. The reconstruction of TOC from the noise-free nonlinear trend model extracted from SSA is an improvement over the standard MLR linear PWLT model (ρ = 0.89 as compared to ρ = 0.76).
The paper is focusing on the representativeness of single lidar stations for zonally averaged ozone profile variations over the middle and upper stratosphere. From the lower to the upper stratosphere, ozone profiles from single or grouped lidar stations correlate well with zonal means calculated from (Solar Backscatter Ultraviolet Radiometer (SBUV) overpasses. The best representativeness is found within a few degrees of latitude north or south of any lidar station. The latitude range with significant correlation coefficients (> 0.4) spans about ±10° in the mid-stratosphere (around 30 hPa) and becomes much larger in the upper stratosphere (around 2 hPa), where it spans a large part of the entire globe. The paper includes also a multiple linear regression analysis on the relative importance of proxy time series for explaining variations in the vertical ozone profiles. Studied proxies represent variability due to influences outside of the earth system (solar cycle), variability due to dynamic processes (the Quasi Biennial Oscillation (QBO), the Arctic Oscillation (AO), Antarctic Oscillation (AAO), El Niño Southern Oscillation (ENSO)), due to volcanic aerosol (Aerosol Optical Depth (AOD)), due to tropopause height changes (including global warming) and due to manmade contributions to chemistry (Equivalent Effective Stratospheric Chlorine (EESC)). Ozone trends are estimated, with and without removal of proxies, from the total available 1980 to 2015 SBUV record. Except for the chemistry related proxy (EESC), the use of the other proxies does not alter the significance of the estimated long-term trends. At heights above 10 hPa an “inflection point” between 1997 and 1999 marks the end of significant negative ozone trends, followed by a recent period of positive ozone change over the period 1998-2015. At heights below 15 hPa the pre-1998 negative ozone trends tend to become insignificant as we move towards 2015.
In this paper, we present a merged dataset of ozone profiles from several satellite instruments: SAGE II on ERBS, GOMOS, SCIAMACHY and MIPAS on Envisat, OSIRIS on Odin, ACE-FTS on SCISAT, and OMPS on Suomi-NPP. The merged dataset is created in the framework of the European Space Agency Climate Change Initiative (Ozone_cci) with the aim of analyzing stratospheric ozone trends. For the merged dataset, we used the latest versions of the original ozone datasets. The datasets from the individual instruments have been extensively validated and intercompared; only those datasets which are in good agreement, and do not exhibit significant drifts with respect to collocated ground-based observations and with respect to each other, are used for merging. The long-term SAGE–CCI–OMPS dataset is created by computation and merging of deseasonalized anomalies from individual instruments. The merged SAGE–CCI–OMPS dataset consists of deseasonalized anomalies of ozone in 10° latitude bands from 90° S to 90° N and from 10 to 50 km in steps of 1 km covering the period from October 1984 to July 2016. This newly created dataset is used for evaluating ozone trends in the stratosphere through multiple linear regression. Negative ozone trends in the upper stratosphere are observed before 1997 and positive trends are found after 1997. The upper stratospheric trends are statistically significant at midlatitudes and indicate ozone recovery, as expected from the decrease of stratospheric halogens that started in the middle of the 1990s and stratospheric cooling.
From the mid 1990s to the late 2000s the consistency of electrochemical cell ozonesonde long term records has been compromised by differences in manufacturers, Science Pump and ENSCI, and differences in recommended sensor solution concentrations, 1.0 % potassium iodide (KI) and the one half dilution 0.5 %. To investigate these differences a number of organizations independently undertook comparisons of the various ozonesonde types and solution concentrations, resulting in 197 ozonesonde comparison profiles. The goal is to derive transfer functions to allow measurements outside of standard recommendations, for sensor composition and ozonesonde type, to be converted to a standard measurement and thus homogenize the data to the expected accuracy of 5 % (10 %) in the stratosphere (troposphere). Subsets of these data have been analyzed previously and intermediate transfer functions derived. Here all the comparison data are analyzed to compare: 1) differences in sensor solution composition for a single ozonesonde type, 2) differences in ozonesonde type for a single sensor solution composition and 3) the manufacturer’s recommendations of 1.0 % KI solution for Science Pump and 0.5 % KI for ENSCI. From the recommendations it is clear that ENSCI ozonesondes and 1.0 % KI solution result in higher amounts of ozone sensed. The results indicate that differences in solution composition and in ozonesonde type display little pressure dependence at pressures ≥ 30 hPa and thus the transfer function can be characterized as a simple ratio of the less sensitive to the more sensitive method. This ratio is 0.96 for both solution concentration and ozonesonde type. The ratios differ at pressures
The Network for the Detection of Atmospheric Composition Change (NDACC) is an international global network of more than 80 stations making high quality measurements of atmospheric composition that began official operations in 1991 after five years of planning. Originally named the Network for the Detection of Stratospheric Change (NDSC), the goal of NDACC is to observe changes in the chemical and physical state of the stratosphere and upper troposphere and to assess the impact of such changes on the lower troposphere and climate. NDACC’s origins, station locations, organizational structure and data archiving are described. NDACC is structured around categories of ground-based observational techniques, timely cross-cutting themes (ozone, water vapour, measurement strategies and emphases), satellite measurement systems, and theory and analyses. To widen its scope, NDACC has established formal collaborative agreements with eight other Cooperating Networks. A brief history is provided, major accomplishments of NDACC during its first 25 years of operation are reviewed, and a forward-looking perspective is presented.
Ozone forms in the Earth's atmosphere from the photodissociation of molecular oxygen, primarily in the tropical stratosphere. It is then transported to the extratropics by the Brewer-Dobson circulation (BDC), forming a protective ozone layer around the globe. Human emissions of halogen-containing ozone-depleting substances (hODSs) led to a decline in stratospheric ozone until they were banned by the Montreal Protocol (MP), and since 1998 ozone in the upper stratosphere shows a likely recovery. Total column ozone (TCO) measurements of ozone between the Earth's surface and the top of the atmosphere, indicate that the ozone layer has stopped declining across the globe, but no clear increase has been observed at latitudes outside the polar regions (60–90). Here we report evidence from multiple satellite measurements that ozone in the lower stratosphere between 60° S and 60° N has declined continuously since 1985. We find that, even though upper stratospheric ozone is recovering in response to the MP, the lower stratospheric changes more than compensate for this, resulting in the conclusion that, globally (60°&tinsp;S–60° N), stratospheric column ozone (StCO) continues to deplete. We find that globally, TCO appears to not have decreased because tropospheric column ozone (TrCO) increases, likely the result of human activity and harmful to respiratory health, are compensating for the stratospheric decreases. The reason for the continued reduction of lower stratospheric ozone is not clear, models do not reproduce these trends, and so the causes now urgently need to be established. Reductions in lower stratospheric ozone trends may partly lead to a small reduction in the warming of the climate, but a reduced ozone layer may also permit an increase in harmful ultra-violet (UV) radiation at the surface and would impact human and ecosystem health.
Ozone profile trends over the period 2000 to 2016 from several merged satellite ozone data sets and from ground-based data measured by four techniques at stations of the Network for the Detection of Atmospheric Composition Change indicate significant ozone increases in the upper stratosphere, between 35 and 48 km altitude (5 and 1 hPa). Near 2 hPa (42 km), ozone has been increasing by about 1.5 % per decade in the tropics (20° S to 20° N), and by 2 to 2.5 % per decade in the 35 to 60° latitude bands of both hemispheres. At levels below 35 km (5 hPa), 2000 to 2016 ozone trends are smaller and not statistically significant. The observed trend profiles are consistent with expectations from chemistry climate model simulations. This study confirms positive trends of upper stratospheric ozone already reported, e.g., in the WMO/UNEP Ozone Assessment 2014 or by Harris et al. (2015). Compared to those studies, three to four additional years of observations, updated and improved data sets with reduced drift, and the fact that nearly all individual data sets indicate ozone increase in the upper stratosphere, all give enhanced confidence. Uncertainties have been reduced, for example for the trend near 2 hPa in the 35 to 60° latitude bands from about ±5 % (2σ) in Harris et al. (2015) to less than ±2 % (2σ). Nevertheless, a thorough analysis of possible drifts and differences between various data sources is still required, as is a detailed attribution of the observed increases to declining ozone-depleting substances and to stratospheric cooling. Ongoing quality observations from multiple independent platforms are key for verifying that recovery of the ozone layer continues as expected.
In this paper, we present a merged dataset of ozone profiles from several satellite instruments: SAGE II on ERBS, GOMOS, SCIAMACHY and MIPAS on Envisat, OSIRIS on Odin, ACE-FTS on SCISAT, and OMPS on Suomi-NPP. The merged dataset is created in the framework of European Space Agency Climate Change Initiative (Ozone_cci) with the aim of analyzing stratospheric ozone trends. For the merged dataset, we used the latest versions of the original ozone datasets. The datasets from the individual instruments have been extensively validated and inter-compared; only those datasets, which are in good agreement and do not exhibit significant drifts with respect to collocated ground-based observations and with respect to each other, are used for merging. The long-term SAGE-CCI-OMPS dataset is created by computation and merging of deseasonalized anomalies from individual instruments. The merged SAGE-CCI-OMPS dataset consists of deseasonalized anomalies of ozone in 10° latitude bands from 90° S to 90° N and from 10 to 50 km in steps of 1 km covering the period from October 1984 to July 2016. This newly created dataset is used for evaluating ozone trends in the stratosphere through multiple linear regression. Negative ozone trends in the upper stratosphere are observed before 1997 and positive trends are found after 1997. The upper stratospheric trends are statistically significant at mid-latitudes in the upper stratosphere and indicate ozone recovery, as expected from the decrease of stratospheric halogens that started in the middle of the 1990s.
Ozone profile trends over the period 2000 to 2016 from several merged satellite ozone data sets and from ground-based data by four techniques at stations of the Network for the Detection of Atmospheric Composition Change indicate significant ozone increases in the upper stratosphere, between 35 and 48 km altitude (5 and 1 hPa). Near 2 hPa (42 km), ozone has been increasing by about 1.5 % per decade in the tropics (20° S to 20° N), and by 2 to 2.5 % per decade in the 35° to 60° latitude bands of both hemispheres. At levels below 35 km (5 hPa), 2000 to 2016 ozone trends are smaller and not statistically significant. The observed trend profiles are consistent with expectations from chemistry climate model simulations. Using three to four more years of observations and updated data sets, this study confirms positive trends of upper stratospheric ozone already reported, e.g., in the WMO/UNEP Ozone Assessment 2014, or by Harris et al. (2015). The additional years, and the fact that nearly all individual data sets indicate these increases, give enhanced confidence. Nevertheless, a thorough analysis of possible drifts and differences between various data sources is still required, as is a detailed attribution of the observed increases to declining ozone depleting substances and to stratospheric cooling. Ongoing quality observations from multiple independent platforms are key for verifying that recovery of the ozone layer continues as expected.
While it has become evident that the ozone trend has changed sign around the mid-1990s, the debate is still open as to how uncertain the profile trend estimates are. There is currently no community-wide consensus whether there is statistically significant observational evidence for the recovery of stratospheric ozone. As a step forward, we review the studies that have been carried out in recent years, e.g., in the context of the WCRP/SPARC SI2N initiative and the last WMO/UNEP ozone assessment, with particular focus on how the uncertainties in trends have been estimated. In these frameworks many satellite and ground-based ozone monitoring teams have provided high-quality, long-term measurement records of the vertical distribution of ozone at global and local scales from the ground up to the mesosphere. Comprehensive inter-comparisons of these data records have provided substantial insight into, e.g., the long-term stability of individual data records, one of the main sources of uncertainty in single-instrument ozone trend estimates. The combined use of the observations acquired by different instruments has definitely helped to reduce the statistical uncertainty in regression analyses for the post-2000 period. However, doing so also introduces the intricate problem of quantifying the systematic uncertainty in multi-instrument trend estimates, since other factors than long-term stability come into play as well. We discuss the merits and the limitations of past and recent evaluations. Since most important prerequisites are met, we make a case for a coordinated effort to take one of the remaining crucial hurdles and obtain more realistic uncertainty estimates for stratospheric ozone trend assessments. We are hopeful that this will soon pave the way for a concordant picture on the second stage of ozone recovery.