On sampling uncertainty of satellite ozone profile measurements

Sofieva, Viktoria; Kalakoski, Niilo; Päivärinta, S.-M.; Tamminen, J.; Laine, Marko; Froidevaux, L.

doi:10.5194/amt-7-1891-2014

Cited by 46 publications

(50 citation statements)

References 17 publications

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“…It is important that not only the individual instrument uncertainties be taken into account, but uncertainties arising from the merging process itself must also be accounted for before the merged data can be properly interpreted. Such uncertainties result from individual instrument uncertainties (absolute calibration, drift, other systematic errors) but also from differences in measurement technique, spatial and temporal resolution, and native vertical coordinate systems of the merged records (e.g., Damadeo et al, 2014;Hassler et al, 2014;Sofieva et al, 2015). How to propagate such uncertainties and assess their impact on derived trends and other long-term signals is an outstanding question within the community (e.g., WMO, 2014;Harris et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Estimating uncertainties in the SBUV Version 8.6 merged profile ozone data set

et al. 2017

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Abstract. The combined record of total and profile ozone measurements from the solar backscatter ultraviolet (SBUV) and SBUV/2 series of instruments, known as the SBUV Merged Ozone Data (MOD) product, constitutes the longest satellite-based ozone time series from a single instrument type and as such plays a key role in ozone trend analyses.Following the approach documented in to analyze the merging uncertainties in the MOD total ozone record, we use Monte Carlo simulations to estimate the potential for uncertainties in the calibration and drift of individual instruments in the profile ozone merged data set. We focus our discussion on the trends and associated merging uncertainty since 2001 in an effort to verify the start of ozone recovery as predicted by chemistry climate models. We find that merging uncertainty dominates the overall estimated uncertainty when considering only the 15 years of data since 2001. We derive trends versus pressure level for the MOD data set that are positive in the upper stratosphere as expected for ozone recovery. These trends appear to be significant when only statistical uncertainties are included but become not significant at the 2σ level when instrument uncertainties are accounted for. However, when we use the entire data set from 1979 through 2015 and fit to the EESC (equivalent effective stratospheric chlorine) we find statistically significant fits throughout the upper stratosphere at all latitudes. This implies that the ozone profile data remain consistent with our expectation that chlorine is the dominant ozone forcing term.

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Section: Introductionmentioning

confidence: 99%

Estimating uncertainties in the SBUV Version 8.6 merged profile ozone data set

et al. 2017

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“…Sampling bias is an error in a computed quantity that arises due to unrepresentative (i.e., insufficient or inhomogeneous) sampling, which induces spurious features in the average estimates (e.g., Aghedo et al, 2011;Foelsche et al, 2011;Toohey et al, 2013;Sofieva et al, 2014) and long-term trends (Lin et al, 2015). Sampling bias may occur when the atmospheric state within the time-space domain over which the average is calculated is not uniformly sampled.…”

Section: Ozonesonde Sampling Bias Estimationmentioning

confidence: 99%

“…The primary technique for sampling bias estimation is to subsample model or reanalysis fields based on the sampling patterns of the measurements and then to quantify differences between the mean fields based on the measurement sampling and those derived from the complete fields. Sampling bias cannot be negligible, even for satellite measurements (Aghedo et al, 2011;Toohey et al, 2013;Sofieva et al, 2014).…”

Section: Ozonesonde Sampling Bias Estimationmentioning

confidence: 99%

Evaluation of ACCMIP ozone simulations and ozonesonde sampling biases using a satellite-based multi-constituent chemical reanalysis

Miyazaki

Bowman

2017

Atmos. Chem. Phys.

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Abstract. The Atmospheric Chemistry Climate Model Intercomparison Project (ACCMIP) ensemble ozone simulations for the present day from the 2000 decade simulation results are evaluated by a state-of-the-art multi-constituent atmospheric chemical reanalysis that ingests multiple satellite data including the Tropospheric Emission Spectrometer (TES), the Microwave Limb Sounder (MLS), the Ozone Monitoring Instrument (OMI), and the Measurement of Pollution in the Troposphere (MOPITT) for [2005][2006][2007][2008][2009]. Validation of the chemical reanalysis against global ozonesondes shows good agreement throughout the free troposphere and lower stratosphere for both seasonal and year-to-year variations, with an annual mean bias of less than 0.9 ppb in the middle and upper troposphere at the tropics and midlatitudes. The reanalysis provides comprehensive spatiotemporal evaluation of chemistry-model performance that compliments direct ozonesonde comparisons, which are shown to suffer from significant sampling bias. The reanalysis reveals that the ACCMIP ensemble mean overestimates ozone in the northern extratropics by 6-11 ppb while underestimating by up to 18 ppb in the southern tropics over the Atlantic in the lower troposphere. Most models underestimate the spatial variability of the annual mean lower tropospheric concentrations in the extratropics of both hemispheres by up to 70 %. The ensemble mean also overestimates the seasonal amplitude by 25-70 % in the northern extratropics and overestimates the inter-hemispheric gradient by about 30 % in the lower and middle troposphere. A part of the discrepancies can be attributed to the 5-year reanalysis data for the decadal model simulations. However, these differences are less evident with the current sonde network. To estimate ozonesonde sampling biases, we computed model bias separately for global coverage and the ozonesonde network. The ozonesonde sampling bias in the evaluated model bias for the seasonal mean concentration relative to global coverage is 40-50 % over the western Pacific and east Indian Ocean and reaches 110 % over the equatorial Americas and up to 80 % for the global tropics. In contrast, the ozonesonde sampling bias is typically smaller than 30 % for the Arctic regions in the lower and middle troposphere. These systematic biases have implications for ozone radiative forcing and the response of chemistry to climate that can be further quantified as the satellite observational record extends to multiple decades.

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“…The first approach is problematic if a measurement from the chosen standard is not available for every measurement from the data set to be adjusted. For the second approach, reanalysis data, as shown, for example, by Foelsche et al (2011), or temporally and spatially highly resolved CTM output, as shown, for example, by Toohey et al (2013) and Sofieva et al (2014), can be used as a transfer standard, capturing small-scale ozone variability. Here we chose the second approach for generating a homogeneous data set, since measurements from some data sources are limited to a very small geographical region and time period, such that coincidences with the SAGE II measurements, the chosen standard, are sparse.…”

Section: Homogenizationmentioning

confidence: 99%

“…Using CTM output as an evaluation and adjustment tool for coarsely distributed global ozone measurements is not a novel idea. In Sofieva et al (2014) Flow chart describing the different modification and adjustment steps that are applied to the ozone measurements before they are used in the monthly mean zonal mean calculation. Note that ISBC refers to the inter-satellite bias correction, which is described in Sect.…”

Section: Chemistry-transport Model (Ctm) Datamentioning

confidence: 99%

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

Haßler

Kremser

Bodeker

et al. 2018

Earth Syst. Sci. Data

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Abstract. An updated and improved version of a global, vertically resolved, monthly mean zonal mean ozone database has been calculated -hereafter referred to as the BSVertOzone (Bodeker Scientific Vertical Ozone) database. Like its predecessor, it combines measurements from several satellite-based instruments and ozone profile measurements from the global ozonesonde network. Monthly mean zonal mean ozone concentrations in mixing ratio and number density are provided in 5 • latitude bins, spanning 70 altitude levels (1 to 70 km), or 70 pressure levels that are approximately 1 km apart (878.4 to 0.046 hPa). Different data sets or "tiers" are provided: Tier 0 is based only on the available measurements and therefore does not completely cover the whole globe or the full vertical range uniformly; the Tier 0.5 monthly mean zonal means are calculated as a filled version of the Tier 0 database where missing monthly mean zonal mean values are estimated from correlations against a total column ozone (TCO) database. The Tier 0.5 data set includes the full range of measurement variability and is created as an intermediate step for the calculation of the Tier 1 data where a least squares regression model is used to attribute variability to various known forcing factors for ozone. Regression model fit coefficients are expanded in Fourier series and Legendre polynomials (to account for seasonality and latitudinal structure, respectively). Four different combinations of contributions from selected regression model basis functions result in four different Tier 1 data sets that can be used for comparisons with chemistry-climate model (CCM) simulations that do not exhibit the same unforced variability as reality (unless they are nudged towards reanalyses). Compared to previous versions of the database, this update includes additional satellite data sources and ozonesonde measurements to extend the database period to 2016. Additional improvements over the previous version of the database include the following: (i) adjustments of measurements to account for biases and drifts between different data sources (using a chemistry-transport model, CTM, simulation as a transfer standard), (ii) a more objective way to determine the optimum number of Fourier and Legendre expansions for the basis function fit coefficients, and (iii) the derivation of methodological and measurement uncertainties on each database value are traced through all data modification steps. Comparisons with the ozone database from SWOOSH (Stratospheric Water and OzOne Satellite Homogenized data set) show good agreement in many regions of the globe. Minor differences are caused by different bias adjustment procedures for the two databases. However, compared to SWOOSH, BSVertOzone additionally covers the troposphere. Version 1.0 of BSVertOzone is publicly available at https://doi.org/http://doi.org/10

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On sampling uncertainty of satellite ozone profile measurements

Cited by 46 publications

References 17 publications

Estimating uncertainties in the SBUV Version 8.6 merged profile ozone data set

Estimating uncertainties in the SBUV Version 8.6 merged profile ozone data set

Evaluation of ACCMIP ozone simulations and ozonesonde sampling biases using a satellite-based multi-constituent chemical reanalysis

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

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