2016
DOI: 10.1175/jcli-d-15-0469.1
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Dynamical Consistency of Reanalysis Datasets in the Extratropical Stratosphere

Abstract: Reanalysis data provide a good estimate of global atmospheric temperature and wind fields. However, the available reanalysis datasets reveal nonnegligible discrepancies in their mean state and temporal variability. In this study, the quality of eight reanalysis datasets is evaluated by examining their dynamical consistency in the extratropical stratosphere. The dynamical consistency is quantified by computing the residual of the zonal-mean momentum equation. The residual is generally small in the lower stratos… Show more

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Cited by 28 publications
(32 citation statements)
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“…These changes near the tropopause may increase the persistence of the negative NAM phase in the troposphere (Fig. 3b), potentially providing a source of predictive skill for up to 60 days after the occurrence of the SSW (Maycock and Hitchcock, 2015). Following the SSW, the stratospheric ozone over the polar cap is greatly enhanced (Fig.…”
Section: Composite Analysismentioning
confidence: 99%
“…These changes near the tropopause may increase the persistence of the negative NAM phase in the troposphere (Fig. 3b), potentially providing a source of predictive skill for up to 60 days after the occurrence of the SSW (Maycock and Hitchcock, 2015). Following the SSW, the stratospheric ozone over the polar cap is greatly enhanced (Fig.…”
Section: Composite Analysismentioning
confidence: 99%
“…Mitchell et al (2015) performed a multiple linear regression analysis on the same nine reanalyses to test the robustness of their variability. Martineau et al (2016) intercompare eight reanalyses (ERA-40, ERA-I, R1, R2, CFSR, JRA-25, JRA-55, and MERRA) for dynamical consistency of wintertime stratospheric polar vortex variability. Kawatani et al (2016) compare the representation of the monthly mean zonal wind in the equatorial stratosphere with a focus on the quasi-biennial oscillation (QBO; Baldwin et al, 2001) among nine reanalyses (R1, R2, CFSR, ERA-40, ERA-I, JRA-25, JRA-55, MERRA, and MERRA-2).…”
Section: Introductionmentioning
confidence: 99%
“…However, an accurate, process-based quantification remains elusive as a result of a large spread in the solar UV and ozone measurements (Ermolli et al 2013;Hood et al 2015;Ball et al 2016), uncertainties among reanalysis datasets (Dee et al 2011;Lu et al 2015;Mitchell et al 2015a;Martineau et al 2016), and model biases (Mitchell et al 2015b;Dhomse et al 2016).…”
Section: Introductionmentioning
confidence: 99%