|The current paper investigates the representation of strong “circumglobal wave events” in three different climate models. A particular focus lies on the role of the upper-level flow versus soil moisture for model biases. First it is shown that the three models in a free-running mode do a pretty reasonable job in representing the anomalies associated with these “extreme circumpolar wave” events as compared with reanalyses. Second, the authors use a valuable set of numerical experiments that was performed in connection with the ExtremeX project in order to find out whether this success is primarily related to the correct representation of the upper-level flow or to the correct representation of the soil moisture conditions. As it turns out, the upper-level flow is considerably more important in this analysis. |
The topic is highly relevant, the paper is overall well written, and the logic seems straightforward in large parts of the text.
However, the longer I think about this paper, the less I understand what I am supposed to learn. Essentially you investigate composites of events, and the events are selected on the basis of the magnitude of one particular zonal wavenumber. Arguably this is a somewhat artificial selection, because it does not necessarily represent any physical/meteorological situation. For instance, if you select a specific wavenumber and the amplitude of this wavenumber is much larger than the amplitude of all other wavenumbers, the situation corresponds to a circumglobal wavetrain. However, if you select a specific large-amplitude wavenumber and at the same time neighboring wavenumbers are of similar (large) amplitude (something which I assume to be rather common), the meteorological situation is more likely to be a large-amplitude Rossby wave in part of the hemisphere with much smaller amplitudes in the rest of the hemisphere. It transpires that the events that you select (based on one specific wavenumber exceeding a threshold) may be a collection of rather different-looking physical situations, and they are not necessarily always associated with a circumglobal wave train. Taken at face value, this would draw into question even the title of your paper, which promises a study of circumglobal Rossby waves. In any case, if you average over many such separate events (through compositing), it is unclear (to me) what this composite is meant to represent.
Possibly I have not fully understood your analysis. But maybe other readers have a similar problem. Therefore, the authors would have to be more explicit and more lucid in their interpretation and work out much more clearly what one really learns from the analysis of this set of simulations.
My second issue is somehow related the previous one. A superficial interpretation of your results suggests that soil moisture is really not important; rather, all you need is to get the upper-level flow right. Maybe this is not how the results should be interpreted, but there is the danger that this impression remains (unless your interpretation becomes much more detailed and explicit). In particular, one of the authors (Sonia Seneviratne) has shown multiple times in previous publications that soil moisture is important to determine summer surface temperatures at least over certain areas over the continents. Again, on a superficial level, the current results seem to contradict these earlier results. I would be interested in Sonia Seneviratne commenting on this issue, and the reader would certainly appreciate if you could clarify.
In my interpretation, your analysis points to a possible model bias in the sense that the models systematically under- or overestimate the spectral power in specific wavenumbers as far as the upper tropospheric circulation is concerned. In your analysis this is somewhat obscured by the fact that you focus on just a few wavenumbers. In reality, the lack of spectral power at a certain wavenumber may reappear as a surplus of spectral power at some other wavenumber. In other words, your selection of events is based on highly incomplete information in spectral space and may, therefore, obscure what is really going on. A more straighforward approach to analyse spectral model biases would be to consider the composite spectral power of all wavenumbers and determine related biases. In addition, to the extent that the meteorology in the lower troposphere is “slaved” to the dynamics in the upper troposphere, nudging the upper troposphere makes obviously a difference, but nudging the lower troposphere does not; this would imply that your results regarding the biases are somewhat trivial.
What’s non-trivial and what I find highly interesting is the fact that individual wavenumbers apparently have a preferred phase. However, this is not the topic of the current paper, and the current paper does not (aim to) further contribute towards an explanation.
Last, but not least, I have an issue with the title, and this mirrors the points that I raised above. I think the title is misleading for two reasons. (1) It suggests that you deal with “circumglobal Rossby waves”, which I would argue is not true. You select events on the basis of highly incomplete information in spectral space, and this does not guarantee that your events are characterized by a circumglobal Rossby wave (see my earlier remarks). (2) You say that “small biases in upper-level circulation create substantial biases in surface imprint”. To me as a reader this suggests that if you select a specific event and introduce just a small upper-tropospheric bias in that event, this results in strong changes in the surface meteorology. But again, this is not what you have shown. The concept “bias” in your analysis represents a comparison between two composites, e.g., one from a free-running model and the other one from reanalysis data. However, the underlying individual events (from which the composites are computed) may be very different and the analysis, hence, does not support the claim raised in the title (the way I understand it). I realize that you tried to summarize the whole content of your paper in the title, but with an analysis as complex as yours this is invariably going to be impossible.
In summary, I am aware that the current paper uses a quite special (and maybe somewhat artificial and unphysical) setup for the analysis: namely compositing only events that have a large amplitude of a specific wave number. This very special sort of analysis may have a strong imprint on its interpretation, but it is not straightforward (for me) to see and understand this imprint. As a consequence, I am not fully able to follow your interpretations and to appreciate the merits of your analysis.
Line 50: arguably, RRWPs are not the same thing as quasi-stationary Rossby waves, so the former should not be given as “an example” for the latter.
Line 88: “with the duration of sfc weather conditions….”: that’s a somewhat strange formulation. Do you mean “long duration” or “persistence” here?
Line 137: “This added tendency term….”: this sentence is not clear to me, as well as the ensuing sentence.
Line 152: Since your analysis is based on weekly averages, you could (and should) say that you are interested in rather persistent anomalies. For instance, in the abstract you could talk about “summertime persistent (or: quasi-stationary) wave events” instead of just “summertime wave events”.
Line 157: What is an “imprint”? Please define! Here you talk only about surface “imprints”, but in the following plots you also show composite upper tropospheric meridional wind. How are the latter defined?
Line 168: The concept of a “bias” is central to your paper (the word appears even in the title), but the “definitions” that you provide on page 6 are not clear to me. In my understanding, AISI represents a free running model run, and ERA5 represents a dataset. So what is the difference between a model run and a data set? This is way too sloppy, this must be specified much more clearly. (You probably mean the difference between the composites, not the difference between model runs or data sets, but this must be said explicitly).
Line 190: better “variance in wave amplitude” instead of “variance in wave activity”, because “wave activity” has a special meaning in dynamical meteorology (which you do NOT want to refer to here).
Caption Fig 2: not clear to me what the term “bandwidth” refers to here.
Line 204: Well, ERA5 shows a strong preference for one part of the hemisphere as opposed to the other part of the hemisphere. Therefore, since you have not clearly defined “phase locking”, it is not obvious to me that ERA5 is supposed to not show phase-locking for wave-6 and 8.
Lines 229/230: english? In addition, I appreciate your effort to determine statistical significance, but I was not able to extract from Fig. B12 and B13 what areas are significant. Please clarify. Where on the plots should I see “highlighted fuchsia color”?
Line 293: “almost fully….” seems somewhat overstated, I would say “to a large extent….”
Line 304: Ref to Fig 7a seems wrong.
Line 316: Ref to Fig 5b seems wrong.
Line 329: what do the correlation values given in parentheses refer to?
Line 330: better “climatology and variability”.
Line 343: I think that the vertical wind in your nudging experiments is only weakly constrained by the divergence/convergence of the horizontal wind. Rather, having the horizontal wind almost right in these experiments implies the correct forcing in the omega equation and, hence, a good representation of the vertical wind. In other words, I think your statement is correct, but the reason/explanation you give may not be correct.
Line 347: “can be disturbed in the models….”: that’s unclear. I agree that an error in the vertical wind may lead to an error in cloudiness, but there may be other sources for errors in cloudiness (e.g., moisture advection) which may be just as important.
Line 363: froced forced
Line 371/372: do you mean that they yield cirumglobal waves “by design”!?
Line 373: What is the “stationarity of a data set”? also: I hope very much that the results do NOT depend on the method used to compute the FFT! Can you explain what you mean here?
Line 374: I do not understand this. If you provide only the zonal mean to an FFT algorithm, then the wave amplitude would be zero for all zonal wavenumbers except s=0.
Line 376: not “the data is bigger….”, rather “the amount of data is larger by a factor….”
Line 392: “slow to….”??? Also: a simple statistical connection between the upper-level flow and surface temperatures per so is not an “emergent constraint” (the way I understand that concept). Please clarify.