the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global zonal wind variations and responses to solar activity, and QBO, ENSO during 2002–2019
- 1Institute of Electromagnetic Wave, School of Physics, Henan Normal University, Xinxiang, 453000, China
- 2State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China
- 3University of the Chinese Academy of Science, Beijing, 100049, China
- 4Physics Department, Catholic University of America, Washington, DC 20064, USA
- 5NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA
- 6Heliophysics, Planetary Science and Aeronomy Division, National Institute for Space Research (INPE), Sao Jose dos Campos, Sao Paulo, Brazil
- 1Institute of Electromagnetic Wave, School of Physics, Henan Normal University, Xinxiang, 453000, China
- 2State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China
- 3University of the Chinese Academy of Science, Beijing, 100049, China
- 4Physics Department, Catholic University of America, Washington, DC 20064, USA
- 5NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA
- 6Heliophysics, Planetary Science and Aeronomy Division, National Institute for Space Research (INPE), Sao Jose dos Campos, Sao Paulo, Brazil
Abstract. Variations of global wind are important in changing the atmospheric structure and circulation, in the coupling of atmospheric layers, in influencing the wave propagations. Due to the difficulty of directly measuring zonal wind from the stratosphere to the lower thermosphere, we derived the global balance wind (BU), which captured the main feature of the monthly zonal mean wind, to study its variations (i.e., annual, semiannual, terannual, and linear) and responses to QBO (quasi-biennial oscillation), ENSO (El Niño/Southern Oscillation), and solar activity. Same procedure is performed on the MERRA2 zonal wind (MerU) to validate BU and its responses below 70 km. The annual, semiannual, terannual oscillations of BU and MerU have similar amplitudes and phases. The semi-annual oscillation of BU has peaks around 80 km, which are stronger in the southern tropical region and coincide with previous satellite observations. The responses to QBO shift from positive to negative and extend from the equator to higher latitudes with the increasing height. The responses to ENSO and F10.7 are strongest (positive and negatively, respectively) in the southern stratospheric polar jet region below 70 km and exhibit hemispheric asymmetry. While above 70 km, the responses of BU to F10.7 and ENSO are mainly positive. Both BU and MerU exhibit similar linear changes, but the negative linear changes of BU at 50° N are absent in MerU during October–January. The discussions on the possible influences of the temporal intervals and sudden stratospheric warmings (SSWs) on the variations and responses of BU illustrate that: (1) the seasonal variations and the responses to QBO are almost independent on the temporal intervals selected; (2) the responses to ENSO and F10.7 are robust but slightly dependent on the temporal intervals; (3) the linear changes of both BU and MerU depend strongly on the temporal intervals; (4) SSWs affect the magnitudes but do not affect the hemispheric asymmetry of the variations and responses of BU at least in the monthly mean sense. The variations and responses of global zonal wind to various factors are based on BU, which is derived from observations, and thus provide a good complementary to model studies and ground-based observations.
Xiao Liu et al.
Status: open (until 13 Feb 2023)
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CC1: 'Should consider the tidal forcing instead of solar activity', Paul PUKITE, 05 Jan 2023
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Attached is an alternate explanation for the periodicity of stratospheric winds. My observation, independent of the acceptability of the paper under review, is that more progress can be made only after refuting the most plausible explanations.
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RC1: 'Comment on acp-2022-792', Anonymous Referee #1, 18 Jan 2023
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General comments:
This manuscript applies multiple linear regression (MLR) to monthly mean zonal wind data in the stratosphere, mesosphere, and lower thermosphere obtained from SABER observations, MF and meteor radar observations, and MERRA2 meteorological reanalysis to examine the effects of QBO, ENSO, and solar activity as well as seasonal changes and long-term trends. Although many similar studies based on the MLR analyses have been conducted using long-term meteorological reanalysis data, there have been few research above the stratopause due to the difficulty of observing winds. In this sense, the efforts in this manuscript are commendable. On the other hand, the method of MLR analysis and statistical significance are not well documented, and the consideration of the short data period is not sufficient. In addition, English grammar check by a native speaker is also recommended. Therefore, I think that this manuscript needs substantial revision before publication. Detailed comments are given below.
Major comments:
- Time interval of the data
It seems that 18 years are too short to fit the 11-year solar cycle. Although the authors evaluated its impacts by changing the time interval, half or more of the data periods overlap, which does not seem very meaningful. Rather, a comparison using 40 years of MERRA2 data would be more meaningful. As the authors say, the MLS has been assimilated since 2004, but its effect appears to be strong only for the vertical structure of temperature, not so much for the meridional gradient of temperature and the distribution of zonal wind that is related to the meridional gradient of temperature.
- Method of MLR analysis
From the explanation in section 2.2, it appears that eq. (2) is applied to data for 216 months over 18 years, in which case only one regression coefficient is obtained for the entire period. On the other hand, section 3 shows that regression coefficients were obtained for each month, suggesting that eq. (2) without including the seasonal variation term was actually applied to 18 years of data for each month. In that case, I do not know how the seasonal variation was estimated. The authors need to properly explain the MLR method.
- Multicollinearity
In the MLR analysis, multicollinearity often leads to meaningless results. The authors need to evaluate and indicate whether the correlations between regressors are sufficiently small before performing the MLR analysis.
- Statistical significance
In this manuscript, the regression coefficient is considered statistically significant if it is greater than 1σ. However, there is no description of how σ is calculated. In addition, when determining whether a regression coefficient is statistically significant in the MLR analysis, it is common practice to use the p-value of each regression coefficient. Unless there is a special reason to use σ, the p-value should be used (e.g., Mitchell et al. (2015)).
- Impact of SSW
In general, if the effect of SSW is large, it should occur that the regression coefficient is not statistically significant despite its large value. The authors should first check to see if this is the case, especially in the high latitudes of the winter northern hemisphere.
Furthermore, it is questionable whether it makes sense to apply the MLR analysis to spline interpolated data. Also, it should be explicitly stated which latitude bands were replaced by spline interpolation. Looking at Fig. 10c, it appears that all winters were replaced by spline interpolation, but major SSW does not occur every year. It should be explicitly stated by what criteria SSW is defined.
Minor comments:
- L. 143-145
Is it safe to consider data from a single point observation as the same as the zonal average, even though it is a monthly average? For example, how does this compare to the data of Smith et al. (2017)?
- Fig. 2
It is hard to see the phases from the arrows. I recommend to show the amplitudes by contours and the phases by colors.
- L. 237
Please clarify how the annual mean response was calculated. Is it an annual mean of the regression coefficient for each month? Or did you apply the MLR to the data including whole months (216 months)?
- L. 275
higher southern (northern) latitudes in summer (winter) → higher latitudes in the winter hemisphere
- L. 275-277
I cannot see the signal at 50S/N at z=50-80 km.
- L. 381-420
Trend fitting is sensitive to the values at both edge points. The authors need to mention this point.
- L. 455-456
I think that the seasonal asymmetry is explained by semiannual and terannual components to some extent.
References:
Mitchell, D.M., Gray, L.J., Fujiwara, M., Hibino, T., Anstey, J.A., Ebisuzaki, W., Harada, Y., Long, C., Misios, S., Stott, P.A. and Tan, D. (2015), Signatures of naturally induced variability in the atmosphere using multiple reanalysis datasets. Q.J.R. Meteorol. Soc., 141: 2011-2031. https://doi.org/10.1002/qj.2492
Smith, A. K., Garcia, R. R., Moss, A. C., & Mitchell, N. J. (2017). The Semiannual Oscillation of the Tropical Zonal Wind in the Middle Atmosphere Derived from Satellite Geopotential Height Retrievals, Journal of the Atmospheric Sciences, 74(8), 2413-2425. Retrieved Jan 18, 2023, from https://journals.ametsoc.org/view/journals/atsc/74/8/jas-d-17-0067.1.xml
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RC2: 'Comment on acp-2022-792', Anonymous Referee #2, 25 Jan 2023
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The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2022-792/acp-2022-792-RC2-supplement.pdf
Xiao Liu et al.
Xiao Liu et al.
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