The spring 2011 final stratospheric warming above Eureka: anomalous dynamics and chemistry

Adams, C.; Strong, Kimberly; Zhao, Xiaoyi; Bourassa, Adam; Daffer, W. H.; Degenstein, D. A.; Drummond, J. R.; Farahani, E.; Fraser, A.; Lloyd, N. D.; Manney, G. L.; McLinden, Chris A.; Rex, Markus; Roth, Chris; Strahan, S. E.; Walker, Kaley A.; Wohltmann, Ingo

doi:10.5194/acp-13-611-2013

Cited by 16 publications

(13 citation statements)

References 63 publications

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“…The four longer wavelengths are used for the ozone retrieval. The TCO is calculated by analyzing the relative intensities at these different wavelengths using the Bass and Paur (1985) ozone cross section.…”

Section: Brewermentioning

confidence: 99%

“…These results (at Eureka) are better than the high-latitude agreement reported by Hendrick et al (2011), who found that SAOZ TCO (1990TCO ( -2008 was systematically lower than Brewer TCO at Sodankylä (67 • N, 27 • E) by 3 %-4 %, with the largest discrepancies in the spring and fall. Hendrick et al (2011) suggested that this bias was due to the temperature dependence (Kerr, 2002;Kerr et al, 1988;Scarnato et al, 2009;Van Roozendael et al, 1998;Zhao et al, 2016b) and uncertainty in the ozone cross section (Bass and Paur, 1985) used in Brewer measurements. The agreement between the UT-GBS, SAOZ, and Brewer in Adams et al (2012) (and this study) is notable given the challenges of taking ZS-DOAS measurements at 80 • N, particularly in the summer when measurements within the NDACC-recommended SZA range are not available.…”

Section: Weather Impacts On Tco Accuracymentioning

confidence: 99%

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Assessing the impact of clouds on ground-based UV–visible total column ozone measurements in the high Arctic

et al. 2019

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Abstract. Zenith-Sky scattered light Differential Optical Absorption Spectroscopy (ZS-DOAS) has been used widely to retrieve total column ozone (TCO). ZS-DOAS measurements have the advantage of being less sensitive to clouds than direct-sun measurements. However, the presence of clouds still affects the quality of ZS-DOAS TCO. Clouds are thought to be the largest contributor to random uncertainty in ZS-DOAS TCO, but their impact on data quality still needs to be quantified. This study has two goals: (1) to investigate whether clouds have a significant impact on ZS-DOAS TCO, and (2) to develop a cloud-screening algorithm to improve ZS-DOAS measurements in the Arctic under cloudy conditions. To quantify the impact of weather, 8 years of measured and modelled TCO have been used, along with information about weather conditions at Eureka, Canada (80.05∘ N, 86.41∘ W). Relative to direct-sun TCO measurements by Brewer spectrophotometers and modelled TCO, a positive bias is found in ZS-DOAS TCO measured in cloudy weather, and a negative bias is found for clear conditions, with differences of up to 5 % between clear and cloudy conditions. A cloud-screening algorithm is developed for high latitudes using the colour index calculated from ZS-DOAS spectra. The quality of ZS-DOAS TCO datasets is assessed using a statistical uncertainty estimation model, which suggests a 3 %–4 % random uncertainty. The new cloud-screening algorithm reduces the random uncertainty by 0.6 %. If all measurements collected during cloudy conditions, as identified using the weather station observations, are removed, the random uncertainty is reduced by 1.3 %. This work demonstrates that clouds are a significant contributor to uncertainty in ZS-DOAS TCO and proposes a method that can be used to screen clouds in high-latitude spectra.

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Section: Brewermentioning

confidence: 99%

Section: Weather Impacts On Tco Accuracymentioning

confidence: 99%

Assessing the impact of clouds on ground-based UV–visible total column ozone measurements in the high Arctic

et al. 2019

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“…Satellite-based estimates of NO 2 columns have been widely used in studies of atmospheric chemistry, air quality, and climate (greenhouse gas trends and lifetimes), and in the evaluation of chemistry transport models (CTMs). In the stratosphere, satellite-based NO 2 observations have been used to study factors influencing ozone loss and recovery [e.g., Adams et al, 2013] as well as long-term trends in stratospheric composition [e.g., Dirksen et al, 2011, Hendrick et al, 2012. In the troposphere, satellite NO 2 records show the sources [van der A et al, 2008;Lin et al, 2010;Ghude et al, 2013;Mebust et al, 2011;Mebust and Cohen, 2013], spatial patterns [Martin et al, 2003;Toenges-Schuller et al, 2006;Russell et al, 2010;Hilboll et al, 2013a], and trends of NO x emissions [Richter et al, 2005;Kim et al, 2006;Zhang et al, 2007;Boersma et al, 2008;McLinden et al, 2012;de Wildt et al, 2012;Russell et al, 2012;Duncan et al, 2013;Streets et al, 2013;Vinken et al, 2014], NO x lifetimes [Schaub et al, 2007;Lamsal et al, 2010;Beirle et al, 2011], and the impact of population and economic activity on NO x emissions .…”

Section: Introductionmentioning

confidence: 99%

Revising the slant column density retrieval of nitrogen dioxide observed by the Ozone Monitoring Instrument

et al. 2015

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Nitrogen dioxide retrievals from the Aura/Ozone Monitoring Instrument (OMI) have been used extensively over the past decade, particularly in the study of tropospheric air quality. Recent comparisons of OMI NO2 with independent data sets and models suggested that the OMI values of slant column density (SCD) and stratospheric vertical column density (VCD) in both the NASA OMNO2 and Royal Netherlands Meteorological Institute DOMINO products are too large, by around 10–40%. We describe a substantially revised spectral fitting algorithm, optimized for the OMI visible light spectrometer channel. The most important changes comprise a flexible adjustment of the instrumental wavelength shifts combined with iterative removal of the ring spectral features; the multistep removal of instrumental noise; iterative, sequential estimates of SCDs of the trace gases in the 402–465 nm range. These changes reduce OMI SCD(NO2) by 10–35%, bringing them much closer to SCDs retrieved from independent measurements and models. The revised SCDs, submitted to the stratosphere‐troposphere separation algorithm, give tropospheric VCDs ∼10–15% smaller in polluted regions, and up to ∼30% smaller in unpolluted areas. Although the revised algorithm has been optimized specifically for the OMI NO2 retrieval, our approach could be more broadly applicable.

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“…In this work, vertical profiles of MERRA-2 ozone (Wargan et al, 2017), temperature, pressure, and scaled potential vorticity (sPV) over Eureka were computed using the Jet and Tropopause Products for Analysis and Characterization (JETPAC) package described by Manney et al (2011. The sPV is potential vorticity scaled in "vorticity units" to give a similar range of values at each level, which can be used to identify the location of the polar vortex (Dunkerton and Delisi, 1986;Manney et al, 1994;Adams et al, 2013;Zhao et al, 2017). The profile data are on 72 model levels with 3-hour temporal resolution and approximately 1-km vertical spacing near the tropopause.…”

Section: Merra-2mentioning

confidence: 99%

Assessing the Impact of Clouds on UV-visible Total Column Ozone Measurements in the High Arctic

Zhao¹,

Bognar²,

Fioletov³

et al. 2018

Preprint

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Abstract. Zenith-Sky scattered light Differential Optical Absorption Spectroscopy (ZS-DOAS) has been used widely to retrieve total column ozone (TCO). ZS-DOAS measurements have the advantage of being less sensitive to clouds than direct-sun measurements. However, the presence of clouds still affects the quality of ZS-DOAS TCO. Clouds are thought to be the largest contributor to random uncertainty in ZS-DOAS TCO, but their impact on data quality still needs to be quantified. This study has two goals: (1) to study whether clouds have a significant impact on ZS-DOAS TCO, and (2) to develop a cloud-screening algorithm to improve ZS-DOAS measurements in the Arctic under cloudy conditions. To quantify the impact of weather, eight years of measured and modelled TCO have been used, along with information about weather conditions at Eureka, Canada (80.05° N, 86.41° W). Relative to direct-sun TCO measurements by Brewer spectrophotometers and modelled TCO, a positive bias is found in ZS-DOAS TCO measured in cloudy weather, and a negative bias is found for clear conditions, with differences of up to 5 % between clear and cloudy conditions. A cloud-screening algorithm is developed for high-latitudes using the colour index calculated from ZS-DOAS spectra. The quality of ZS-DOAS TCO datasets is assessed using a statistical uncertainty estimation model, which suggests a 3–4 % random uncertainty. The new cloud-screening algorithm reduces the random uncertainty by 0.6 %. If all measurements collected during cloudy conditions, as identified using the weather station observations, are removed, the random uncertainty is reduced by 1.3 %. This work demonstrates that clouds are a significant contributor to uncertainty in ZS-DOAS TCO and proposes a method that can be used to screen clouds in high-latitude spectra.

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The spring 2011 final stratospheric warming above Eureka: anomalous dynamics and chemistry

Cited by 16 publications

References 63 publications

Assessing the impact of clouds on ground-based UV–visible total column ozone measurements in the high Arctic

Assessing the impact of clouds on ground-based UV–visible total column ozone measurements in the high Arctic

Revising the slant column density retrieval of nitrogen dioxide observed by the Ozone Monitoring Instrument

Assessing the Impact of Clouds on UV-visible Total Column Ozone Measurements in the High Arctic

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