the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Mar 2025
Nonlinear effects of the stratospheric Quasi‐Biennial Oscillation on ENSO modulating PM2.5 over the North China Plain in early winter
Abstract. The North China Plain (NCP) experiences severe air pollution, with PM2.5 (fine particulate matter with an aerodynamic diameter ≤ 2.5 μm) as the primary pollutant, especially in early winter (November to December). The PM2.5 concentrations in this period is significantly modulated by the El Niño-Southern Oscillation (ENSO). In this study, we have found that the stratospheric Quasi-Biennial Oscillation (QBO) exerts a nonlinear impact on the relationship between ENSO and PM2.5 concentrations over the NCP in early winter. During the easterly QBO (EQBO) phase, ENSO’s influence on PM2.5 concentration is stronger compared to the westerly QBO (WQBO) phase. In El Niño and EQBO years, PM2.5 concentrations rise due to meteorological factors like a shallower boundary layer, higher relative humidity, and intensified southerly wind anomalies. Conversely, during La Niña and EQBO years, PM2.5 levels decrease due to opposite meteorological conditions. The study attributes these changes to planetary wave dynamics. During El Niño and EQBO years, upward-propagating planetary waves in mid-latitudes enhance upper-level divergence over Eurasia, strengthening westerlies. These westerlies guide Rossby wave trains into Northeast Asia, forming a strong anomalous anticyclone that worsens air pollution over the NCP. In La Niña and EQBO years, downward-propagating planetary waves induce divergence in sub-polar regions, strengthening westerlies that facilitate La Niña-related wave trains. These wave trains trigger cyclonic circulation over Northeast Asia, improving air quality in the NCP. These findings underscore the complex interplay between ENSO, QBO, and atmospheric dynamics in shaping regional air pollution.
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RC1: 'Comment on egusphere-2025-285', Anonymous Referee #1, 06 Jun 2025
The paper "Nonlinear effects of the stratospheric Quasi‐Biennial Oscillation on ENSO modulating PM2.5 over the North China Plain in early winter" by An et al. investigates the combined effect of the QBO and ENSO on the PM2.5 concentration over the North China Plain. Main findings are that PM2.5 is enhanced for El Nino and westward QBO conditions, while PM2.5 is reduced for La Nina and eastward QBO. This effect is explained by variations in wind speed and direction, boundary layer height, and humidity.
Overall, the paper is a thorough study, it is well written, and of interest and relevance for the readership of ACP.
The paper is therefore recommended for publication in ACP after minor revisions.My main comments are:
(1) the authors should include the model equations they assume for their multivariate linear regressions
(2) you should explain a bit why enhanced humidity would lead to enhanced PM2.5, but not to precipitation that would wash out air pollution
Please find more Specific and Technical comments below.
Specific comments:
(1) Fig.1d: what is the source of the SST data?
(2) l.116 onward:
You should state more clearly in the text that multivariate regression analyses are performed. The underlying linear models should be given as additional equations.
(3) l.120: In the text you should add the information that the regression coefficients in Figs.2b and 2d have opposite sign in most regions of China, while in the North China Plain (NCP), which is the main focus of your work, PM2.5 anomalies do not change much.(4) l.153: The point with the humidity was not completely clear to me!
If humidity is high enough, precipitation would form and wash out air pollution.
Please clarify whether or not this is a relevant mechanism during conditions of enhanced humidity in November/December over the NCP.
This should also be clarified in the discussion around l.246.(5) l.442: In addition to the white dots, there are also stippled areas in Fig.2. Do these areas refer to another different level of significance?
I would suppose that the white dots refer to 80%, and the black grid areas to 90% of significance, like in Fig.5.
Please explain and correct!
(6) Fig.3: to avoid confusion, please use same notation as in Fig.2
(ENSO- instead of ENSO^-1 and QBO- instead of QBO^-1)
(7) Fig.6, Fig.7: Green lines represent the Yellow River and the Yangtze River? Please add this information.Technical corrections:
(1) l.40:
Given its the crucial in shaping global weather and climate
->
Given its crucial role in shaping global weather and climate
(2) reference Austin and Tu (2004) is missing in the References list
(3) l.105: Palmer -> Palm
(4) l.107: reference Chen et al. (2013) is not in the references list, did you mean Chen et al. (2003) ?
(5) l.117: It is clearly -> It is evident
(6) l.119:
whether is La Nina or El Nino, these is no
->
whether ENSO is in the La Nina or El Nino phase, there is no
(7) l.130: The now question is -> The question now is
(8) l.138: Fig.3 -> Fig.4
(9) l.142: (Fig. 4a and 4c) -> (Fig. 5a and 5c)
(10) l.148: 4e and 4g -> 5e and 5g
(11) l.155: remining -> remaining
(12) l.199: 9c 9e and 9g) -> 9c, 9e, and 9g)
(13) l.203: OBO -> QBO
(14) l.442: areas of significant -> areas of significance
(15) Caption of Fig.7: Is interval = 1, or 0.5? Please check!Citation: https://doi.org/10.5194/egusphere-2025-285-RC1 -
AC1: 'Reply on RC1', Xiadong An, 02 Jul 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-285/egusphere-2025-285-AC1-supplement.pdf
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AC1: 'Reply on RC1', Xiadong An, 02 Jul 2025
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RC2: 'Comment on egusphere-2025-285', Anonymous Referee #2, 06 Jun 2025
The authors analyze the effects of the QBO on the ENSO influence on
North China Plains pollution. The present work would benefit from substantial
improvements, namely in the methodology, quality of presentation and
level of discussion.My impression is that the ENSO is the leading factor, and the QBO importance
is secondary. This needs to be quantified numerically, beyond the simple Fig. 3
analysis.The description and selection of data needs improvement (see detailed comments).
The use of a fairly dated reanalysis product is not justified; while it may be useful
to retain it as a reference for other studies, the study should mainly rely
on up-to-date datasets, such as ERA5 (already used, partially).
Please move the legacy reanalysis contents to the supplement, or remove;
any difference should anyway be explained.
The period of analysis needs to be given, currently many dates are provided
for the various products, which is confusing.
The authors mention using two PM2.5 products, but the results seems to
agree only qualitatively. How do the the products differ?The methodology should be improved, as it is unclear why the authors
selected the Nov-Dec season. In the introduction, Nov-Jan is also mentioned.
Given that nonlinear diagnostics are computed, the authors need to
explain if high-resolution data (e.g., at least daily) were used,
as signals would otherwise be smoothed.The authors ascribe PM2.5 changes to changes in atmospheric circulation. However, my understanding
is that haze conditions can be extremely local, e.g. due to the presence of urban settlements
in valleys. The discussion needs to be enhanced to explain relevant local features, including sources.
Relevant analysis should be included, to understand the relative contributions of
meteorology but also of primary and secondary sources.The motivation of section 4 is lacking. Why do these diagnostics matter?
The authors need to clarify how their work is distinct from previous
papers (several papers from the first author are cited) and what is the new understanding,
see e.g. the non-exhaustive list of works below (none cited, as far as I can see).
Based on a simple online search, a number of references seem missing:An et al. 2018 https://acp.copernicus.org/articles/18/1863/2018/ on ENSO and winter pollution in China
Jiang et al. 2017 https://link.springer.com/article/10.1007/s13351-017-6412-z on drivers on haze in China
Li et al. 2023 https://doi.org/10.5194/acp-23-1533-2023 on summer ozone pollution in China and QBO
Lu et al. 2022 https://doi.org/10.1029/2022JD036938 on ENSO and EASM and summer ozone pollution in China
Ray et al. 2020 https://www.nature.com/articles/s41561-019-0507-3 on QBO and trace gases
Sun et al. 2018 https://doi.org/10.1029/2018JD028534 on ENSO and winter pollution in East China
Zhang et al. 2025 https://doi.org/10.1029/2024JD041825 for a model study of QBO/ENSO influence on ozone transport
Zhao et al. 2016 https://www.nature.com/articles/srep27424 on decadal variability of haze in China and tropical Pacific
The authors are advised to revised the text with care to correct various typos and unsupported statements,
a few reported in the list below. Also the text structure could be improved to avoid long paragraphs
and focus the discussion.
Comments by lineL32 This reference is not suitable. Are there peer-reviewed papers on this 'comprehensiveness'?
E.g. comparing in-situ or satellite-based emissions/concentrations?
L40 unclear 'its the crucial'
L44 I don't get this, which would be a review?
L47 You cannot expect the reader to know in detail the regional geography. Please include a maps
with all the relevant names and features mentioned, perhaps in the supplement.
L64 typo 'underling'
L70 there is virtually no description of the data. Is it in-situ? Satellite? Model? The zenodo
repository does not show the information neither. The link should be given elsewhere, while
here readable information.
L80 The expression 'Fifth major global reanalysis' makes no sense. Is there a minor version as well?
The information on the spatial resolution and temporal coverage are both incorrect,
You probably refer to what you retrieved. Also the reference is to the website, not the paper.
L86 10 hPa is quite high, and here you are looking at instantaneous signals.
Why not using 50 or 70 hPa to characterize a tropospheric process?
Moreover, periods 2015/16 and 2019/20 showcase distinct QBO evolutions,
which should be acknowledged
L87 Based on the plots, there is one point per year. Is this an average of Nov-Dec? This
needs to be explained, and points added in Fig. 1 b/c
L89 The methodology of "Jan Null" is not explained. Please use some method justified in the
literature
Table 1 caption: period should be lowercase
Fig.1 are the units of regression coefficient degC as reported?
L104 The period used should be explained
L117 typo 'clearly'
Fig. 2 one among g or h should be for the QBO?
Fig. 3 I do not understand why you have "-1" subscripts. Are these referring to lagged values?
L142 You are presumably referring to Fig. 5
Fig. 5 Units are not indicated anywhere
L165 by whom?
Fig. 6 This figure definitely differs from Fig. 4. Verify
Fig. 7 'disturbed' in what sense?
L187 A similar statement was already done before
Fig. 8 is hard to read and needs to be improved graphically
L203 what's OBO now?
L211 Unclear sentence
L226 The 'Eurasia hinterland' sounds odd
L237 All this discussion does not seem relevant.
L252 What do you mean? What numerical model? A transport model?
L260 the link for the QBO has moved, verify. Links have been visited one year ago or more!
L263 I can't locate experimental results in this work
Fig. S2 Since all terms have an 'I', it could be safely omitted. What is the 'forecast data' mentioned?
Is it again ERA5?Citation: https://doi.org/10.5194/egusphere-2025-285-RC2 -
AC2: 'Reply on RC2', Xiadong An, 02 Jul 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-285/egusphere-2025-285-AC2-supplement.pdf
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AC2: 'Reply on RC2', Xiadong An, 02 Jul 2025
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