The QBO and global-scale tropical waves in Aeolus wind observations, radiosonde data, and reanalyses
Abstract. The quasi-biennial oscillation (QBO) of the stratospheric tropical winds influences the global circulation over a wide range of latitudes and altitudes. Although it has strong effects on surface weather and climate, climate models have large difficulties in simulating a realistic QBO, especially in the lower stratosphere. Therefore, global wind observations in the tropical upper troposphere and lower stratosphere (UTLS) are of particular interest for investigating the QBO and the tropical waves that contribute significantly to its driving. In our work, we focus on the years 2018–2022 and investigate the QBO and different tropical wave modes in the UTLS region using global wind observations by the Aeolus satellite instrument, and three meteorological reanalyses (ERA-5, JRA-55, and MERRA-2). Further, we compare these data with observations of selected radiosonde stations. By comparison with Aeolus observations, we find that on zonal average the QBO in the lower stratosphere is well represented in all three reanalyses, with ERA-5 performing best. Averaged over the years 2018–2022, agreement between Aeolus and the reanalyses is better than 1 to 2 m s−1, with somewhat larger differences during some periods. Different from zonal averages, radiosonde stations provide only local observations and are therefore biased by global-scale tropical waves, which limits their use as a QBO standard. While reanalyses perform well on zonal average, there can be considerable local biases between reanalyses and radiosondes. We also find that, in the tropical UTLS, zonal wind variances of stationary waves and the most prominent global-scale traveling equatorial wave modes, such as Kelvin waves, Rossby-gravity waves, and equatorial Rossby waves, are in good agreement between Aeolus and all three reanalyses (in most cases better than 20 % of the peak values in the UTLS). On zonal average, this supports the use of reanalyses as a reference for comparison with free-running climate models, while locally certain biases exist, particularly in the QBO wind shear zones, and around the 2019–2020 QBO disruption.
Manfred Ern et al.
Status: final response (author comments only)
- RC1: 'Comment on egusphere-2023-408', Anonymous Referee #1, 26 Apr 2023
- RC2: 'Comment on egusphere-2023-408', Anonymous Referee #2, 02 May 2023
Manfred Ern et al.
Manfred Ern et al.
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This is an interesting and well-written manuscript, which investigates the QBO and large-scale tropical waves in zonal wind data from Aeolus, three reanalyses datasets, and radiosondes. The authors have found a good agreement between Aeolus wind and the reanalyses on zonal averages. The differences are more pronounced near the strong shear zones, which might be related to the differences in vertical resolutions of the datasets, and also before the QBO disruption. Furthermore, the differences are much more noticeable when the reanalysis are locally compared to radiosonde observations. The results also show good agreement between reanalyses and Aeolus zonal wind in capturing the variances related to global-scale stationary and travelling tropical waves. The focus of the manuscript aligns well with the journal's special issue on Aeolus data and their application. I have only a few minor comments that should help to clarify a few points.
Line 52: It might be useful to mention that the gravity waves are usually not resolved in climate models and their effects are approximated using different parameterization schemes.
Lines 130 to 150: My understating is that the Aeolus and radiosondes winds are interpolated to 0.25 km vertical resolution, but the reanalysis are interpolated to a 0.5 vertical resolution. If this is the case, then how you subtract them in the next sessions?
Figure 1: I am not sure why the one standard deviation is shown with respect to zero. I think it should be shown with respect to the mean differences (i.e., the red line).
Figure 6: Have you tried using a log-scale for plotting the power? I think it would be useful in extracting the interesting features in higher frequencies.
Section 4.2.4. You might want to mention that the peak variances at ~30 km are due to extratropical Rossby waves propagating equatorward from the winter hemisphere and are not related to the equatorial Rossby waves.
Figure A2: I am a little bit surprised by the panel for MRGW. I expected to see a stronger signal in meridional wind than in the zonal wind for MRG waves. In isolating the MRG signal based on the wavenumber-frequency spectrum, have you considered the fact the MRG waves are symmetric in meridional wind (while they are antisymmetric in zonal wind)?
Line 207: The comma before “Also”, should change to a dot.