Convection-generated gravity waves in the tropical lower stratosphere from Aeolus wind profiling, GNSS-RO and ERA5 reanalysis

Ratynski, Mathieu; Khaykin, Sergey; Hauchecorne, Alain; Alexander, Joan; Mariaccia, Alexis; Keckhut, Philippe; Mangin, Antoine

doi:https://doi.org/10.5194/egusphere-2025-394

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https://doi.org/10.5194/egusphere-2025-394

Preprints

26 Feb 2025

| 26 Feb 2025

Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Convection-generated gravity waves in the tropical lower stratosphere from Aeolus wind profiling, GNSS-RO and ERA5 reanalysis

Mathieu Ratynski, Sergey Khaykin, Alain Hauchecorne, Joan Alexander, Alexis Mariaccia, Philippe Keckhut, and Antoine Mangin

Abstract. The European Space Agency's Aeolus satellite, equipped with the Atmospheric LAser Doppler INstrument (ALADIN), provides global provides near-global wind profiles from the surface to about 30 km altitude. These wind measurements enable the investigation of atmospheric dynamics, including gravity waves (GWs) in the upper troposphere and lower stratosphere (UTLS). This study analyzes ALADIN wind observations and ERA5 reanalysis, by deriving GWs kinetic energy (Ek) distributions, examining their temporal and spatial variability throughout the tropical UTLS. A prominent hotspot of enhanced GW activity is identified by Aeolus, migrating from the Indian Ocean in Boreal Summer to the Maritime Continent in Boreal Winter, closely matching outgoing longwave radiation minima and thus highlighting convective origins. Results show that ERA5 consistently underestimates Ek in convective regions, especially over the Indian Ocean, where conventional wind measurements are sparse. Additional comparisons with Global Navigation Satellite System Radio Occultation (GNSS-RO) measurements of GW potential energy (Ep) corroborate these findings and suggest significant underrepresentation of convection-driven wave activity in reanalyses. A multi-instrumental exploratory analysis also allows to verify the empirical grounding of the established Ek to Ep ratio, as well as the wavelength of the waves retrieved by Aeolus. By filling critical wind data gaps, Aeolus emerges as a key tool for improving the representation of GWs, particularly in remote tropical regions. When combined with GNSS-RO measurements, Aeolus data provides new insights into how convective processes drive GW generation, revealing opportunities to refine reanalysis products and model parameterizations, as well as improving the energy ratio.

Received: 28 Jan 2025 – Discussion started: 26 Feb 2025

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Mathieu Ratynski, Sergey Khaykin, Alain Hauchecorne, Joan Alexander, Alexis Mariaccia, Philippe Keckhut, and Antoine Mangin

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RC1: 'Comment on egusphere-2025-394', Anonymous Referee #1, 28 Feb 2025 reply

The manuscript presents an analysis of gravity wave (GW) kinetic energy distributions, derived from new Aeolus satellite wind profiles, that shows great promise in pushing the needle forward in the construction of observational constraints of gravity waves and their impacts on upper troposphere/lower stratosphere circulation. A methodology is presented for deriving the kinetic energy associated with small-scale GWs in regions of deep convection in the tropics over a period spanning June 2019 to August 2022. Comparisons with ERA5 suggest that the reanalysis product underestimates GW-associated kinetic energy; conversely, GW-associated potential energy comparisons between ERA5 and temperature-profiles from an independent instrument (GNSS-RO) show much more consistency, suggesting that the use of kinetic energy highlights a distinct feature of the GW energy spectrum that is not typically assessed (and, incidentally, is not well represented in ERA5). The authors further speculate that this underestimate may reflect lack of assimilated direct wind observations, in contrast to temperatures, which are assimilated. All in all, the manuscript does a good job of presenting a new dataset with all necessary caveats, while also making a generally convincing case that this new data will be valuable. To this end, I recommend acceptance, pending that minor revisions be made to address the following concerns:
#1. Page 4: There is no description of the GW drag parameterization employed in ERA5. In particular, does the model have an explicit parameterization for non-orographic GW drag due to parameterized convection? If so, what is it and how has it been evaluated/performed in past assessments? This will be important in terms of interpreting the dearth of kinetic energy in the model, relative to the Aeolus-derived energy.
#2. Page 7: Presumably the definition of "background" based on "the arguments presented in Alexander et al. (2008b)" apply to past analysis of temperature, not wind, profiles, no? More generally, it would be good for the reader to have a better sense of the sensitivity of the profiles depicted in Figure 1a to choice of grid box averaging domain, the temporal period over which profiles are averaged (currently set to 7 days, etc.), etc. I imagine the authors have already done this sensitivity analysis, so they could consider showing in an appendix figure.
#3. Equation (1): This notation becomes slightly confusing/counterintuitive as the text moves on, since the meridional component often goes to zero due to the pointing vector retaining its approximate angle at ~100 degrees. In other words, V_HLOS would be more intuitively referred to as U_HLOS (or something similar) since, indeed, it primarily reflects the zonal component of the flow. Is there any particular reason why "v" is used instead of something more generic? I suggest changing.
#4. Figure 14, lines 354-355: The first sentence of this paragraph does not make sense to me. In particular, the bit referring to "ERA5 shows a considerable reduction" is vague. Reduction relative to what? Please clarify.
#5. Figure 5: The temporal resolution labeled on the y-axes of these hovmoller plots is too high/unnecessary as it crowds the figures. Please show only every other two or three months. Same comment applies to Figure 7.
#6. Figure 16, Discussion concluding Section 3.2: The discussion here seems weak and understates the disagreement between the Aeolus and ERA5 Ek temporal patterns. The second-to-last paragraph highlights the common features between Aeolus and ERA5, but I think the plots look very different. In particular, the hotspots coincident with low OLR are totally missing in ERA5 (Fig. 5b). The phrasing in the text, however, seems to suggest that the differences are only minor. Please rephrase.
#7. Section 4: Doesn't the ratio of Ek/Ep (shown for ERA-5 in Fig. 8a) suggest that these two quantities are extremely different and not meaningful to compare with each other? I appreciate that the authors want to move beyond traditional (conservative) analysis and attempt to do a bit more, but Figure 8a suggests that the two quantities are in much more disagreement than the discrepancy predicted by llinear wave theory (i.e., factor of 4, not factor of 2). My suggestion here is to introduce Figure 8a earlier as a way to more directly address the concerns with comparing potential and kinetic energy (within a self-consistent product like ERA-5).
#8. Last paragraph on page 19 (lines 467-470): How do you know it's the failure to assimilate the winds directly that's causing the poor representation of GW-associated EK? In principle, one might be able to capture these features using a convective non-orgraphic gravity wave drag parameterization within the ERA-5 model, no? In other words, the assimilation is one way to correct the problem, but an alternative approach is to tackle the model bias directly. However, without having more knowledge about the underlying GW drag parameterization in the model it's hard for the reader to know how many degrees of freedom are afforded to the modeler. Can the authors please comment on the role played here by model bias? And how this is/is not handled by the GW drag parameterization?
#9. Discussion: No mention is made of how these observations might be used to develop constraints on the momentum fluxes (which is what modelers seek most). Is that something that the author has considered? This is a challenging question, so I am not seeking any complete answers here; I am just wondering if the author can speculate in a sentence or two how to potentially bridge V_HLOS with the momentum fluxes.

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Citation: https://doi.org/10.5194/egusphere-2025-394-RC1
CC1: 'Comment on egusphere-2025-394', Brian Mapes, 20 Mar 2025 reply

The work described here flows from a unique and novel data source, a wonder of the age, and seems fairly sound. However, the figures are not all they could be, and not fully explained, lacking any statistical analysis or mentions of evident artifacts. Many (incomplete) data handling details and other science issues are sprinkled though a rather smooth vague narrative, with too little scientific circumspection. With a modest amount more care, perhaps aided by senior author input, this paper could become as excellent as the data deserve.

The manuscript suffers from three problems common in the dissertation-to-literature translation, detailed further below but listed thematically here:

1. Key technical details for understanding the results are too buried. In place of crisp exposition and taut circumspection are meandering vague mentions of issues around methods, defensive at times and elsewhere a sales pitch for the arbitrary trade-offs settled on. This may be how a committee explained a complex recipe to a student, but is not ideal for a paper addressing peer researchers.

2. Meandering threads are also present as a means for a student to telegraph to a committee their awareness of a reading list, cited often in vague mentions rather than claim-supporting paraphrases of the content. This may be good for dissertations written to a captive readership, but is not ideal for peer researchers. As just one instance, not all winds on larger scales than the filter belong in a named list of Mode Types: the wind is the wind.

3. Findings and interpretations are a bit vague, unobservant of the actual figure set, and repetitive (for instance the “especially Indian Ocean” trope appearing several times). False color scales are unhelpfully distorting, and questions raised by the figures and results are not pursued with a scientifically committed vigor. This may embody the rationally bounded commitment of a student to the physical problem at hand, but ideally a senior coauthor might exert the leadership to bring more depth of inquiry and perspective.

Issue 1. Clarifying key technical details.
Fig. 1 was very helpful but the text not so clear.

SUMMARY:
The variance of u and temperature T fluctuations, sub-weekly in period and Fourier bandpassed to 1-9km wavelengths in the vertical, are averaged over a UTLS layer (about 11-25 km after vertical smoothing). The 500m common grid is mentioned far from the other data details (line 124) requiring a second read and search. Half the variance (with rescaling factor for T') is energy. Was the averaging density-weighted (like a physical energy interpretation in J/kg should be), or is it just 1/2 a height-averaged variance? The text is silent.

Lidar Wind profiles:
A side-looking spaceborne lidar measures u(z) along its line of sight, which is almost the zonal direction (that wasn’t clear to this reader without web searches). Profiles were processed to isolate deviations from weekly 20x5-degree Lon-lat averages. Then Fourier filtering passed shorter than 9km vertical wavelengths (on a 500m grid so 1km is the shortest). The square of that filtered deviation profile was vertically smoothed with a 7km boxcar, then averaged over a layer (line 203 is ambiguous, why "49 points?” of 500m depth?) The layer is summed from 1km below the tropopause (about 14km in tropics) to 22km (is this mass weighted?), to make seasonal maps (smoothed how? not mentioned) and about 3-weekly (looks like, from figures? not mentioned) longitude-time sections. Artifical slow trends due to (squared) instrument noise variance increasing were estimated and subtracted.

Identical processing was applied to RO T(z) and ERA5 winds and T (interpolated on the same grid, again please mention in the data section not introduction). This makes an excellent baseline of comparisons and opportunity for interpretation!

How many pages did the above take to describe in the manuscript? Too many, a tedious read to fish out key details in order to bring a skepticism the somewhat slick text seemed to lack. Lines 172-174 are a good example of sales tone taking over: “making it possible”…”without introducing significant biases”…”configuration mitigates errors”…”ensuring reliable… and robust…” Declarations of success are not very scientific ways to express understandings of trade-offs. The real strength is *identical* processing of comparison datasets. Tossaway adverbs (e.g. “strongly”, “specifically") also set a slick tone in places, undercutting reader trust in the self-skepticism of those best positioned to see problems.

Issue 2: Clarifying results

2.1 Maps of KE and PE in the tropics and subtropics (Fig. 4) are clearly smoothed, but no mention is made of how and how much and why. Was raw data really too rough for scientific readers’ eyes, or is the paper trying to be too smooth? It is surprising to this reader how strongly confined to the equatorial belt the energy is even in solstice seasons. Is there some kind of conditioning or weighting behind this feature, or is it truly an aspect of convection as a source of signal (’the ITCZ’?)? Or is there an effect of the Coriolis force somehow suppressing sub-weekly sub-7km layer fluctuations? Silence about the smoothing undercuts reader confidence.

2.2 Why no mention of the obvious artifacts in DJF2019-JJA2020, with zero in weak areas, or JJA2019 with 5+ in those areas? Are we all looking at the same figure here and describing its characteristics from the most obvious to the most subtle? Reader confidence is again at stake.

2.3. The color scale for positive-definite variance, especially when discussed in a linear meaning like energy, should not have perceptual jumps like this one. Gray shading would be the honest choice, or a single color. This perceptual nonlinearity may be the source of the several-times-repeated “especially Indian Ocean” trope which otherwise seemed inscrutable to this viewer. Seychelles, Diego Garcia, Indonesia; are the other equatorial oceans really so much better covered with “conventional” wind soundings? And anyway, does ERA really get its UTLS wind variability from assimilating rawinsonde data into its imbalanced flow manifold? Did a desire to say something and move on override a thoughtful scientific assessment of the differences between reanalysis and observations, differences fluffed by the redness of a (not accessibility recommended) color map, and perhaps a misinterpretation of u fluctuations in the upper troposphere in the Maritime Continent wet season (detailed below)?

2.4. Fig. 2 is about the anisotropy of waves in ERA5. Is this the second thing a reader wants in a paper about new observations? A value of 2 means isotropic waves, <2 suggests E-W elongation of the variance ellipse, >2 a N-S elongation. Here is a case where a color scale with a perceptual steep part could make sense, but 2 belongs there. Here a less meaningful choice was made. The focus is estimating something like the KE “missing” from the zonal (or LOS) component only, rather than the issue of isotropy which implies something interesting about sources. But all only in the reanalysis, before the first comparisons to even make that dataset as a relevant one. Might a better choice for a second figure be continuing raw obervations (Figs. 3-4) with discussions of the obvious artifacts? Let the ERA5 comparisons wait.

2.5. Fig. 5 is a nice comparison, although again distorted by the use of false color for a linear positive quantity. Variance is a jumpy quantity from squaring the data (Fig. 1 shows this nicely) so statistical significance is tricky and surprising. It is customary to use a RATIO rather than a DIFFERENCE of variance (which has no meaning), subject to the F-test for significance. Many students are surprised by how hard it is to pass the F-test with quite a few degrees of freedom. The student should consult a table and appreciate the issue. No statistical testing is evident in the work, surprisingly. That can be overdone or a distraction, but none at all seems weak, again undermining reader confidence.

2.6. Here is an actual scientific error I suspect: In the wet season over the Maritime Continent, convection is strong and localized and organized on island-strait and diurnal mesoscales that models struggle to represent. The intense divergent outflow of convection in the upper few km of the troposphere (squared) is not UTLS gravity wave energy! The 14-point smoother smears squared wind from a 4.5km layer of the upper troposphere into the averaging layer. Might the pat, recipe-like data analysis choices (Issue 1 above) and an insufficiently critical success-declaring mindset be combining here in a genuine misinterpretation? Sensitivity to this vertical smearing of non-wave wind variance should be assessed.

2.6b Figure 7, again a ratio (F tested) would be more meaningful than a difference. The lack of RO signal in the Maritime Continent wet season further strengthens my belief that the Ek is a misinterpretation of convective outflows.

2.7. The discussion of wave sources seems shallow. There are various mechanisms including temporally varying convective heat sources (which might set vertical wavenumber and frequency), mountains and transient mountains of lofted air in shear (which might set horizontal wavenumber and phase speed), and more. Would they be anisotropic? The phrase “trade winds” appears in the context of Fig. 2 (anisotropy), as if the surface wind direction has something to do with anisotropy at the tropopause level. Does it? Might one of the senior authors add a little depth?

2.8 Likewise the meaning and source of the strong, anisotropic (more meridional) “waves” (isotropy ratio >2) on the midlatitude edges could be thought about more deeply. Are all subweekly meridional wind fluctuations (squared), from 4km below the tropopause, really UTLS gravity waves, or was that just a tidy story for students?

2.9 Figure 8: at last a variance ratio, but only to be taken at face value (with no credible-interval estimation from the F test) in light of some vague gestures at theory whose linearity is considered an easy target. Line 478 says “Fig. 8 presents a detailed analysis” but literally it is just a data plot, with no analysis at all. Too much sales and not enough product for this reader.

2.10 Figure 9: Panel a: Here might be the misinterpretation of convective outflow again, further exaggertated by the false color scheme. Panel b: what is a “dominant” wavelength? Anyone who looks at spectra knows that peak detection is far from trivial and every spectrum is always broad and usually red (more variance falls in eachoi of the wider bins at the low frequency end). What does geometric wavelength really signify over a layer whose stratification goes from upper tropospheric (almost neutral) to 22km (highly stratified)? How does the range here (6500-12000m) relate to the filter which supposedly excludes >9km? Is the spectrum basically red like all geophysical spectra, such that the widest bin near the longest permitted wavelength at the edge of the filter’s passband has the most variance? Does that deserve the word “dominant”? Is this figure worth including, or just a thesis figure looking for a place? Is the red exaggeration here the source of the “especially Indian Ocean” trope repeated several times? It's not exactly over the Indian ocean. The authorial prose should reflect a close look, as a reader brings.

3. Discussion should be rewritten with care and thought, in light of all the above. A celebration of this amazing dataset, a technological marvel from such long hard efferts by so many, deserves more science value than a too-easy critique of reanalysis and/or underlying voids in data sources (common over all the equatorial oceans), and some vague words about how nature is not linear. Some senior author voice could help, if a bit of leadership can be mustered from a committee. Congratulations to so so many people contributing to make this possible! Wonderful data.

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Citation: https://doi.org/10.5194/egusphere-2025-394-CC1

Mathieu Ratynski, Sergey Khaykin, Alain Hauchecorne, Joan Alexander, Alexis Mariaccia, Philippe Keckhut, and Antoine Mangin

Data sets

Pertubation Profiles Dataset Mathieu Ratynski https://doi.org/10.5281/zenodo.8113261

Mathieu Ratynski, Sergey Khaykin, Alain Hauchecorne, Joan Alexander, Alexis Mariaccia, Philippe Keckhut, and Antoine Mangin

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Short summary

This study investigates how tropical convection generates gravity waves, which play a key role in transporting energy across the atmosphere. By combining Aeolus satellite data with ERA5 reanalysis data and radio-occultation measurements, we identified significant wave activity overlooked by ERA5, particularly over the Indian Ocean. Aeolus fills major gaps in wind data, offering a clearer picture of wave dynamics and challenging assumptions about their behavior, improving climate models.


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