the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
CMIP6 Multi-model Assessment of Northeast Atlantic and German Bight Storm Activity
Abstract. We assess the evolution of Northeast Atlantic and German Bight storm activity in the CMIP6 multi-model ensemble, as well as the Max Plack Institute Grand Ensemble with CMIP6 forcing (MPI-GE), using historical forcing and three emission scenarios. We define storm activity as upper percentiles of geostrophic wind speeds, obtained from horizontal gradients of mean sea-level pressure. We detect robust downward trends for Northeast Atlantic storm activity in all scenarios, and weaker but still downward trends for German Bight storm activity. In both the multi-model ensemble and the MPI-GE,we find a projected increase in the frequency of westerly winds over the Northeast Atlantic and northwesterly winds over the German Bight, and a decrease in the frequency of easterly and southerly winds over the respective regions. We also show that despite the projected increase in the frequency of wind directions associated with increased cyclonic activity, the upper percentiles of wind speeds from these directions decrease, leading to lower overall storm activity. Lastly, we detect that the change in wind speeds strongly depends on the region and percentile considered, and that the most extreme storms may become stronger or more likely in the German Bight in a future climate despite reduced overall storm activity.
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Status: open (until 29 May 2025)
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RC1: 'Comment on egusphere-2025-111', Anonymous Referee #1, 03 Apr 2025
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Review for: CMIP6 Multi-model Assessment of Northeast Atlantic and German Bight Storm Activity, by Krieger and Weisse.
My recommendation is that this paper is rejected based on the scope of the journal as describe here: https://www.earth-system-dynamics.net/about/aims_and_scope.html
Specifically, it is stated that “Regional studies drawing conclusions of chiefly regional or local relevance are considered outside of the journal's scope.” Unfortunately, I fail to see how this manuscript does anything other than “draw conclusions of chiefly regional or local relevance”.
Having said that, I enjoyed reading the manuscript and strongly recommend the authors to consider submitting to a more relevant journal. I have provided my full review comments below with the intention that they may be useful to consider for this purpose.
Summary
The manuscript examines future projections of storminess in the Northeast Atlantic region based on simulations from CMIP6 and the MPI Grand Ensemble, each using a range of SSP scenarios. The storminess diagnostic employed has been developed by the lead author in previous studies using long-term direct station observations of surface pressure and as such provides a unique method for comparing the models to the real world. The results are broadly consistent with previous studies, though the diagnostic employed provides some new insights into the relationship between future changes in storminess and changes in wind direction. Overall I’d be very happy to see this work published, subject to the following comments being addressed.
Main Comments
My main concern is that as presented, the study appears rather incremental. There are many studies examining model projections of North Atlantic storminess (as you summarise in your introduction), and your key conclusion of an overall reduction in storminess in future model projections but with an increase in the intensity of the most extreme storms, has been noted numerous times before. Please tweak the framing of your work to address this concern (particularly in the introduction) to better inform the reader exactly how this study aims to advance current understanding. Formulating one or two explicit research questions might help with this.
To my mind, one key advance is the comparison of storminess between the climate models and the long-term dataset of direct observations, because the vast majority of climate model studies just compare against reanalysis products. However, this comparison is not mentioned in the abstract, and even a basic description of the observational dataset is omitted from the manuscript. I’d urge you to make more of this aspect in the text, and to extend the observational comparison to all relevant figures (e.g. 3, 4, 6, 7, 8) if possible.
Your storminess diagnostics are annual in the sense that you don’t subset the data to a particular season. However, I imagine most of the >95%ile geostrophic wind events happen in autumn/winter and so your projected future changes represent most closely the changes in these seasons. Given projected future changes in storminess contain important seasonal variations, please add a discussion on this point to aid interpretation.
Other Comments
Abstract: The last two sentences appear contradictory because you state “the upper percentiles of winds speeds from these directions decrease” and then “the most extreme storms may become stronger or more likely”. I think the former is referring to the 95th percentile of the wind speeds whereas the latter is referring to more extreme percentiles. Please clarify.
L25 and the following paragraphs: Please be explicit about the seasonality of the projected changes in storminess presented in these papers. Some I know explicitly refer to winter only, and others I am not sure about.
L56: “upper wind speed percentiles” is unclear (I thought it meant upper-tropospheric wind speeds initially). Please clarify, e.g. “upper percentiles of near-surface wind speeds”. Similar comment applies to L58.
L91: Is CMIP6 psl data daily means or instantaneous?
L97: Just to be clear, do you standardise the annual 95th percentiles for each triangle separately, or average them together and then standardise?
L115: I presume that the gradients are computed using the distances between the model grid points (which differ for each model), rather than the original station locations? Please specify.
L133: The observed timeseries has not been introduced. Please add a description of it in section 2.
L146: You claim that “the full pool of ensemble members can represent the variability present in the observations”, but this is misleading and clearly must depend on the timescale examined. If I understand correctly, all the timeseries are independently standardised, so the interannual variability is by construction captured by the ensemble, at least during the period 1960-1990. What you show are ten year running means, so I assume your claim is something like “the full pool of ensemble members can represent the variability on decadal timescales”. Please clarify.
Section 4: Several recent papers have highlighted deficiencies in the ability of climate models to simulate multi-decadal variability in the North Atlantic, and have questioned the reliability of model projections in this region as a result (e.g. see here, and references therein: Smith et al., 2025, https://doi.org/10.1038/s41558-025-02277-2). Given their importance, I’d urge you to extend your discussion to include reference to them and relate to the findings of your study.
L290: This paper presents a statistical methodology for assessing future changes from multi-model ensembles of differing sizes, which is very relevant to your suggestion: Zappa et al. (2013) A multimodel assessment of future projections of North Atlantic and European extratropical cyclones in the CMIP5 climate models. Journal of Climate, 26(16), pp.5846-5862.
Fig 3 caption: Please state the periods over which trends are computed (I assume the full experiment periods, but best to be precise).
Fig 4 caption: Are daily geostrophic wind directions? Please clarify.
Fig 8: To what extent are the differences here statistically robust? Can you construct confidence intervals? (here and/or Fig 9)
Typos
Fig 7 caption: Repeated “the”
L225: “increase” -> “increase in frequency”
Citation: https://doi.org/10.5194/egusphere-2025-111-RC1
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