the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
How well are aerosol–cloud interactions represented in climate models? – Part 2: Isolating the aerosol impact on clouds following the 2014–15 Holuhraun eruption
Abstract. Aerosols significantly influence Earth’s radiative balance, yet considerable uncertainty exists in the underpinning mechanisms, particularly those involving clouds. These aerosol-cloud interactions (ACIs) are the most uncertain element in anthropogenic radiative forcing, hampering our ability to constrain Earth’s climate sensitivity and understand future climate change. The 2014–2015 Holuhraun volcanic eruption in Iceland released sulphur dioxide (SO2) into the lower troposphere on a level comparable to continental-scale emissions. The resultant volcanic plume across a near-pristine North Atlantic Ocean presents an ideal opportunistic experiment to explore the representation of ACIs within general circulation models (GCMs). We present Part 2 of a two-part inter-model comparison study that utilises satellite remote sensing observations to assess modelled cloud responses to the volcanic aerosol within 8 state-of-the-art GCMs during September and October 2014. We isolate the aerosol effect from meteorological variability and find that the GCMs adeptly capture the observed cloud microphysical changes associated with the ACI first indirect effect (i.e., Twomey effect). Meanwhile, a clear divergence exists in the GCM responses of large-scale cloud properties, namely cloud liquid water content, that are expected from the precipitation suppression mechanism of the ACI second indirect effect (i.e., rapid adjustments). We propose that this is due to limitations and differences in the autoconversion schemes under high aerosol loading. Despite the individual GCM differences, the collective large-scale responses of the multi-model ensemble agree well with observations. Finally, our multi-model ensemble estimates that Holuhraun had a global radiative forcing of -0.018 ± 0.007 Wm−2 across September and October 2014.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
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Status: open (until 15 Apr 2025)
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RC1: 'Comment on egusphere-2025-835', Anonymous Referee #1, 07 Apr 2025
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Review of "How well are aerosol–cloud interactions represented in climate models? – Part 2: Isolating the aerosol impact on clouds following the 2014–15 Holuhraun eruption" by Jordan et al
This manuscript compares general circulation model simulations of the effects on clouds and radiation from the 2014-15 Holuhraun volcanic eruption in Iceland to satellite observations of clouds and aerosols.In general the paper is well written, but needs some important clarifications as noted in specific comments below. I have several major concerns:
1. I’m confused why the authors use monthly means and do not try to actually use daily data, especially from models to get a more process focus. This lessens the utility of the paper, but I think they have the data to look at.
2. The author focus on the 'predominantly volcanically polluted (PVP) region using SO2 values. But many of the effects are outside of the PVP regions. Why is that? This might be a case where the PVP region could be better defined if it were done daily rather than monthly given the evolution of emissions and meteorological variability.
I would strongly advise looking at daily data with at least one model to see if it matters for the correlation and attrition, and the volcanically affected or not.
3. More model description is warranted (see specific comments below).
Specific comments:
Page 4, L88: Absent all volcanic emissions? Or just Holuhraun?
Page 5, L92: Explain how the GCMs do this (or not). Are aerosols and cloud drops prognostic in all the models? Or are cloud drops diagnostic (set as a function of aerosols and activation). I assume CNRM is diagnostic number, but autoconversion (Kessler) does NOT depend on drop number, while it does for Menon and KK? A bit more explanation is warranted.
Page 5, L93: Aerosols affect entrainment in the models? How? It thought most GCMs did not include this.
Page 8, L178: so that implies that the area is not really pristine, but can be affected by anthropogenic emissions as well as Holuhraun.
Page 8, L182: but you are still averaging over months. Since you have the meteorology, why not look at daily data? More points, larger gradients. This smooths out the analysis and reduces the chances your differentials are affected by averaging.
Page 8, L186: what is the ‘null hypothesis’? Are you testing significance? Stippled points are significance? Please clarify.
Page 9, L190: but the PVP region also does NOT have a significant change.
Page 9, L191: again, this argues that region is polluted.
Page 9, L192: I would argue they do NOT capture observations well since the largest observed changes are NOT in PVP regions, while the models have largest changes in the PVP regions.
Page 10, Figure 3: are these box plots individual location averages? What gives the spread. It has not been well defined.
Page 11, L208: what about the ‘polluted’ region S. Of the PVP region? Shouldn’t you comment on that: seems like MODIS might have a larger effect than the models.
Page 11, L214: exasperated is not the right word. I think you mean ‘increased’ or exacerbated. That’s still a bit awkward.
Page 11, L228: Isn’t the frequency of precipitation (and susceptible cloud) also important? It’s not really the mean, it’s the number of days that it is effective at precip suppression.
Page 12, L:230: Figure 4: is this a lot or a little precip in the PVP regions. It’s hard to tell from the figure or from what you have said here.
Page 12, L237: Are they really ‘excellently’ capturing the spatial distribution of LWP. What are the potential issues in measuring LWP?
Page 13, L239: I don’t think the ensemble matching the magnitude of the observed really means anything: if you only included one model from each family you might get a different answer. Some models have very different patterns. I think you could do more to look at the spatial distribution of effects: why are there large effects OUTSIDE of the PVP regions in models and obs? Do they correlate with aerosols? Nd? Precip even? Does this hold at a daily scale? Not just s smeared out monthly average.
Page 14, L246: But figure 6 shows some large spread in met effects (large blue box and whiskers) and of different sign. What is going on?
Page 15, L254: that seems pretty self evident and consistent with lots of other work.
Page 15, L257: you should figure out how KK varies across this subset of models: is it tuned differently?
Page 15, L258: is the lack of CF response shown anywhere?
Page 17, L266: Is a reader to read in figure 8 that almost all of the points have significant differences? even ones where the difference is nearly zero (e.g. regions where it switches from positive to negative). That does not seem correct for significance testing…
Page 17, L268: it’s not noise if it is significant? It’s significant meteorological variability right?
Page 17, L285: if clouds get thicker, then low clouds will trap more LW and reduce OLR. I think it’s a consequence of higher TAU (more LWP). The LW offsets the SW somewhat.
Page 17, L291: also due to more daylight….
Page 19, L304: this global efficiency seems very dependent on location and timing of emissions no? Is it really relevant? You only put in emissions for Sept and Oct? Did you calculate effects through February? If not, why not? Seems simple to do.
Citation: https://doi.org/10.5194/egusphere-2025-835-RC1
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