| 27 Jun 2025
Global shifts in mountain wave turbulence within high resolution climate models
Abstract. Using a multi-model approach, this paper quantified global changes in moderate or greater mountain wave turbulence (MWT) within a high-end warming scenario. Initial results found model resolution dependency apparent, therefore three high resolution global climate modelled datasets were used within the analysis; HadGEM3-GC3.1-HM (25 km), EC-Earth-3P-HR (36 km) and MPI-ESM1.2-XR (34 km). Regional dependencies developed around each model and index, with seasonal components an important contributor to results. A sub-continental approach was developed, focusing on all regions in which MWT arose. On average, the North American continent projected an increase in MWT, but a decrease over the Rocky Mountain range. This decrease was apparent in all seasons but northern hemisphere (NH) winter, with an increase of +60.6 % over the 101 year investigation period. NH summer, spring and autumn dropped by -58.3 %, -41.2 % and -30.9 %. Over several mountain ranges an increase was evident, particularly over Greenland and regions in Asia. However, a drop in MWT also arose over the Alps, Atlas and northern and central Andes. Southern Andes and the Himalayas had seasonal differences resulting in a mix of projected outcomes. A final aim arose around the connection to low-level, surface wind flow and MWT production. This paper found links between MWT trends and the shift in projected median surface wind flow. The aviation sector should be aware of the future projections in MWT, particularly for those were large increase over the 101 year period were evident, such as Asia, Greenland and the Antarctic.
This manuscript explores regional trends in mountain wave turbulence (MWT), linked to climate change, in high-resolution global climate models. They use a suite of indices and models to explore these trends and also link them to changes in low-level winds. This is an important topic, and the manuscript adds useful information. The results demonstrate significant dependence on the model, region, and index used, highlighting significant uncertainty in any conclusions about future trends. Only a few locations consistently demonstrate statistically significant trends (some negative, some positive), which itself is an important result. My main concerns with the manuscript are that it is missing important detail on the methods use, and the discussion could be expanded on and improved in some areas, with limitations further explored.
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Major comments:
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1 – What thresholds are used to diagnose MWT for each index to create Figure 1 (and are then used for the remainder of the manuscript)? This is not included anywhere and expanded detail on the methods are necessary for this work to be reproducible and complete. What do the frequency distributions of these indices look like? I suggest that the methods section needs to be expanded to include this detail.
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2 – There should be some additional discussion about the appropriateness of using these indices in climate models as the majority of cited work (except Kim et al. 2023) apply the indices to higher resolution NWP models. Does the very smooth topography of climate models pose a significant impediment to doing this properly (even for the highest resolution grids)? I assume that for the indices you are using the native model topography to define the terrain gradient (though this is not stated anywhere and should be). What would the results look like if you used the model fields to define the CAT index, but a higher resolution topography dataset to define the terrain slope (i.e., the same terrain slope for every model)? Would that change the results? My concern is that smooth topography doesn’t identify the complexity of the terrain, reducing the overall area of identified MWT locations (Greenland is a good example, where MWT is only identified in near-coastal regions, whereas previous studies, e.g., Doyle et al. 2005 J. Atmos. Sci., have observed MWT closer to the centre of the continent, i.e., not only along the steepest slopes).
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3 – What statistical technique is being used to determine statistical significance of the trend lines? As far as I can tell, significance is determined if the range of slopes (determined by error bars) are entirely positive or negative. But what is used to determine the error bars? It is also worth commenting more clearly about the fact that most of the linear fits appear as significant (i.e., strong correlation, which would probably pass statistical tests), and the ‘significance’ testing referred to is with regards to the gradient of these fitted lines being significantly different from zero.
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4 – I had difficulty ascertaining exactly where there were actual significant trends (either positive or negative) and suggest that Figure 11 could be modified to make this clearer. Perhaps a 4th column for the overall results (combining from the three models) which identify the mean trend (whether it be positive, negative, or close to zero) and the reliability or significance of this trend. Once this is done, I suspect there will be few locations with a significant trend – if this is true, this ‘null’ result still has a lot of value.
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Other minor comments.
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Line 35. Suggest you cite Clark et al. (2000, J. Atmos. Sci., 1105-1131) here.
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Line 37. Suggest you cite Foudad et a. (2024, J. Geophys. Res., https://doi.org/10.1029/2023JD040261 here, noting the uncertainty in future trends due to model variations etc.
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Line 65-68. Unclear of purpose of these sentences. Could be removed or re-written.
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Line 110-115. Are these differences purely related to topography resolution or are they impacted by impact of model resolution on flow features
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Line 142 and numerous occurrences elsewhere. The word ‘propagate’ seems like the wrong word to use here to talk about trends.
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Line 177-182. Could be rewritten to be clearer. The two conditions could be described earlier in the methods section.
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Line 274. Suggest cite Doyle et al. 2005 here
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Line 290. ‘previously touched’ – unclear, suggest rewrite
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Line 335. ‘if applicable’ – unclear, suggest rewrite
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Line 347. ‘tug of war…’ - unclear, suggest rewrite
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Line 510. It would be useful to describe a more detailed comparison between your results and those published by Kim et al. How are trends different etc.
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Figure quality. Many of the figures were difficult to interpret as the axes ranges were often too large. For example, if the y-range of Fig. 8 was changed to -100 to -50, the data and trends would be easier to interpret.
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End of review
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