| 12 Feb 2026
Brief communication: Anthropogenic aerosol forcing of European windstorms in CMIP6 climate models
Abstract. A recently developed set of historical storm reconstructions, which were extensively validated by insurance loss data, revealed how European windstorm damages were three times higher in the 1980s and '90s compared to a few decades before and since. A better understanding of these slower fluctuations could improve how this costly risk is managed. Here, we explore the impacts of anthropogenic aerosols (AA) on European property damage using results from DAMIP (Detection and Attribution Model Intercomparison Project) climate model experiments. Multimodel mean DAMIP results indicate AA boosted European wind losses by 45 % in the late 20th century relative to preindustrial times, with the signal varying from zero to 100 % between the six models. A review of results from previous climate studies suggested the signal is more likely to be at the higher end of this range, though significant uncertainties remain. The results indicate AA forcing could have been a major driver of recent multidecadal changes in European windstorm losses. Further research into observational and modelling uncertainties would benefit those exposed to this risk.
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Summary: The manuscript analyses ensembles of simulations from five general circulation models covering the period 1950-present, driven solely by changes in anthropogenic aerosol (AA) forcings. The goal is to identify the AA's signature in the economic losses from high winds across Europe. These simulations are compared with control simulations, with constant external forcing set at preindustrial levels.
The main conclusion is that the AA signal in wind-related losses is clear, although the exact mechanisms are hampered by high model spread.
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Recommendation: In my opinion, the tesseract question is interesting, and the results could be useful, despite the large model spreads. However, I also see several aspects in the manuscript that need clear improvement. My main concerns include the statistical analysis and several paragraphs concerning the description of internal and externally forced variability and the role of the AMOC, which, in my opinion, are quite unclear.
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Main points:
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1) Basically, the only statistical analysis conducted to identify the impact of AA on wind-related losses is a t-test to assess changes in the mean between two periods. I have several issues with this approach:
- The AA is not constant in time, and so the impact of AA on wind-related losses should display a comparable time evolution. I am aware that the complexity of the mechanisms involved, including the purported impact of AA on the AMOC and its feedback on storminess may involve some temporal lag, but from the model time series shown in Figure 2b only one model shows a time evolution can be roughly compared to the time evolution of the short-wave downwelling radiative forcing shown, for ins125)tance, in Hassan et al. The SWR displays a very clear maximum in the last 2 decades of the 20th century, but the time series of losses shown in this manuscript (Figure 2b) does not show, by far, this type of behaviour, with the possible exception of the model CanESM5.
Therefore, I think a more sophisticated statistical methodology should be applied to identify the AA signal in wind-related losses. A stationary t-test is not enough
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2) The way to conduct the t-test is unclear. The data description states that the time series of wind-related losses has been smoothed by a -20year Butterworth filter ‘to highlight the decadal variations in the results ‘(line
125). Does this mean that the t-test has been applied to the smoothed data? Also, the ensuing paragraph seems to indicate that the standard deviations of the series, needed to estimate the ‘separation’ of the mean values, have been calculated using the smoothed values. If this is the case, then the calculation is not correct, because the number of degrees of freedom (the sqrt(N) by which the standard deviation is divided ) is not the number of time steps, but much less. Otherwise, one could artificially inflate the precision of the mean estimate, but smoothing the original data will. This needs to be clearly explained.
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Particular points
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3) The explanation of what constitutes internal variability is not correct and should be thoroughly revised. For instance, the text states that ‘ Climate models consistently produce slower climate variations in the North Atlantic sector in the absence of all external forcings, which is referred to as internal variability. This sentence is misleading. First, internal climate variations are produced by models forced by constant forcing (not the absence of forcing); secondly, internal variability occurs at all time scales (from second-scale turbulence to millennial-scale ocean currents.
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‘This multidecadal driver can be broken down into two different components.’.
Internal variability is not a ‘driver’, and this sentence may lead to confounding internal variability with external climate drivers.
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‘The first concerns the Atlantic Meridional Overturning Circulation (AMOC’.
The AMOC is a term that describes a three-dimensional climate system. It is not ‘internal‘ or ‘external’ variability per se. Actually, the manuscript later describes the impact of an external forcing (AA) on the AMOC. This paragraph is really not well-structured and sounds superficial.
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‘ The second component is atmosphere-based, and consists of shorter-timescale extremes of sufficient magnitude to alter multidecadal averages of storminess’.
Again, the atmospheric variability may be internal or externally forced, at all time scales, not only the slow variations. Although high-frequency internal variability is also reflected in low-frequency variability (through sampling variability), some atmospheric processes generate intrinsic low-frequency internal variability.
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Next, the manuscript cites EL Niño as an example of atmospheric internal variability, whereas it is very well known that ENSO arises through the coupling of the Tropical Ocean with the Tropical atmosphere.
There are many other aspects in this discretion that need revision. I really hate to be harsh, but this part of the introduction, in my opinion, needs a complete revision.
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In addition, this part of the introduction is really not necessary for the manuscript's goal. I interpret the author as suggesting that AA may directly affect the atmospheric circulation, and that the AMOC, in turn, modifies the meridional heat transport and thus the atmospheric circulation. Whereas the direct path is very rapid, the AMOC path may display a delay of several years, due to the sluggish response of the deeper ocean. But this is only my interpretation of what the author wish to say after reading the whole manuscript. The issue of internal or externally forced variability is, in my opinion, just steering the flow of the text from the relevant messages, even if it were properly phrased.
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4) ‘Observed multidecadal changes in European storm losses align with the AA-forced signals’
Is there a reference for this? I really doubt that this sentence, as written, can be correct, since wind-related losses would be primarily affected by GDP and/or population growth. Does the author mean ‘normalised’ losses? Beyond socio-economic factors, is there really evidence that AA is the main physical factor affecting wind-related losses, given the large internal variability of the atmosphere? If the sentence were correct, it would imply that atmospheric variations are almost entirely driven by aerosol forcing, which is not true. This sentence really requires a very solid backup and very careful phrasing (what does ‘align’ mean exactly?).
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5) In brief, sulphates have produced the largest radiative forcing in the industrial period, growing from a relatively small amount at the start of the 20th century to peaks in the 1980s and 1990s'
I guess the author means the largest *changes* in short-wave anthropogenic forcings. The largest radiative forcing is by far the sun. This is an example of inaccurate writing that can be found elsewhere in the manuscript.
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6) The contribution of socioeconomic factors to the wind-related losses is taken into account by multiplying the physical (wind) factors by the population in a particular grid-cell (equation on page 4, equations are not numbered!) . It seems the population is considered constant over time (?). This would be quite unrealistic, as population and GDP growth over time would, I think, increase the exposure to wind extremes and thus dominate any trends in wind-driven losses. It would be rather easy to include population or GDP growth. Is there a reason not to do so? This point should at least be discussed.