the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Jan 2025
Aerosol impacts on regional climate: chaotic or physical effect?
Abstract. Aerosols have significant impacts on regional climate, which has been widely investigated with numerical experiments. However, uncertainties of simulated aerosol impact due to long-standing chaotic effect remain unclear. Here we propose a diagnostic method based on large ensemble simulations and random sampling algorithm to unveil the chaos-induced uncertainties in simulated aerosol climatic impacts that is overlooked in previous studies. Taking dust impacts on Indian summer monsoon system as a demonstration, our findings reveal that, while dust generally enhances the large-scale summer monsoon circulation consistently among ensemble members, its impacts on regional systems, such as monsoon depressions, exhibit significant chaotic effect: the simulated aerosol impacts on precipitation from individual ensemble member differ substantially, even inversely. Through quantitative analysis, we demonstrate that the magnitude of these chaotic effects diminishes following a N-½ relationship with ensemble size N. Furthermore, our results indicate that statistical significance testing alone may be insufficient for robust attribution of dust impacts, as even small ensembles can yield statistically significant yet contradictory results. This study emphasizes the necessity of employing adequate ensemble sizes to capture reliable physical impacts of aerosol on regional climate.
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RC1: 'Comment on egusphere-2024-4037', Anonymous Referee #1, 25 Feb 2025
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Review of Jeng et al., Aerosol impacts on regional climate: chaotic or physical effect?
This study proposes an analysis of the role of weather and climate stochasticity impacting the response of the Indian monsoon precipitation to Arabian dust regional radiative forcing. It relies on the statistical analysis of a large ensemble of regional short-term simulations based on a global, dust interactive, atmospheric model to discuss the regional significance of ‘true’ physical dust induced response vs purely chaotic internal variability response. It also examines the number of ensemble members needed to achieve converging and robust results. It outlines that some sub-region like central India where precipitation response depends on meso-scale weather system organization are more prone to internal variability compared to other region where the impact of large scale flow dominates. The study concludes that most of existing studies looking at dust impact on Indian monsoon precipitation did not properly account for these effects, explaining divergence in results especially for central India.
The topic is definitely relevant to ACP, the paper and methodology are in general appropriate, clearly written. Although the topic of stochastic effects / internal variability affecting sensitivity and climate change studies has been explored, seeing it applied on the specific issue of dust /Indian monsoon interaction is definitely interesting in my opinion. Overall I find the paper suitable for publication, after taking into account the following points :
Major comment :
My main criticism concerns the Authors generalizing their conclusion a bit quickly regarding other existing climatic studies. Indeed the proposed simulation protocol uses a lot of members (50), but it also explore the dust induced response on a relatively short time scale of 20 days, characteristic of June of a given year. So each members includes a limited number of meso-scale events for example. Climatic studies are often based on multi-year simulation and examine the impacts of dust on seasonal and yearly averaged precipitation, they thus includes many events and part of the internal variability effect might be smoothed out when averaging. I am not saying that the internal variability does not affect these studies, I am sure it does, but perhaps the convergence toward a consistent physical signal is achieved faster (i.e. with less members) when dealing with climate length simulations. I advise the Authors to be cautious when making conclusion “at climate scale” and regarding “the Indian Monsoon”, or to clearly demonstrate how their results can be be generalized.
Regarding the radiative forcing and significant precipitation response obtained through a robust ensemble average, it would be also good to recall that the corresponding patterns and magnitude reflect a specific june 10-30 2016 situation, which does not cover the entire variability of dust - Indian monsoon interactions.
Finally I did not really understand the method for discussing the validity of statistical significance tests in few ensemble member studies, please see specific comments.
Minor/specific comments :
Title : Perhaps a title more focused on dust and the region of study would be more appropriate.
L45 -50 ; An other useful reference focusing on regional climate models Internal variability
O’Brien et al.. Clim Dyn 37, 1111–1118 (2011). https://doi.org/10.1007/s00382-010-0900-5
L 141 : the simulations are 20 days long, representing a specific month of a specific year. Can we say this protocol « captures the Indian monsoon » ? To me the experiment is closer to a meteorological experiment than a climatological experiment. The intra-seasonal and interannual variability of the ISM are here not captured.
L150 : Could you please mention at this stage if only dust radiative effects or both dust radiative and microphysical effects are taken into account in the experiments ? I understand it was stated in the conclusion.
Question: Despite the simulation time scale being relatively short and since IV develops from small perturbations, can the fact that SST are forced in your experiments affect the noise to signal ratio (and so the relative impact of internal variability) ? Fixed SST creates basically a constant supply of energy and moisture for the perturbations to develop without consistent dampening, perhaps this is likely to enhance stochasticity especially in convective regions. This would also be a contextual difference with climatic studies which consider an interactive ocean /SST.
2.2.2 Generating Perturbed Initial Conditions for Ensembles: It seems that two distinct perturbation protocols are presented but I did not really understand why at this stage. Are they compared later on ?
L 295 : This robust effect of dust on precipitation (100%enhancement) is here quite large and significant. This is a strong result that would need to be more discussed in light of other studies. As I mentioned earlier, caution should be taken regarding how this result can be representative of “dust impact on monsoon” regarding the time-scale addressed.
L360 : check also the previous O’Brien et al. ref which identifies a similar behavior in the convergence as a function of ensemble members.
L 365 : As stated earlier, these studies are based on longer model integrations where the temporal average might already dampen the IV effect seen at shorter time scale . In other words perhaps a 10 member ensemble considering multiyear, seasonal means (which includes many events) could be more robust than the author suggest based on 50 members ensemble of 20 day simulations (which each includes a limited numbers of events). When considering multiyear seasonal means, the convergence towards a physical effect in function of ensemble members might be perhaps faster. So less ensemble member required.
L385. Figure 9. I was wondering how different are the radiative forcings (especially TOA) for E1 and E2.
L395 and Figure 9: About statistical significance: I did not really grasp the method and conclusion here. If you select the E1 and E2 samples to be representative of a type of precipitation response, you automatically increase the statistical significance of the results just due to this preferential sampling, compared to a sample which would contain members with variable type of responses. From this I don’t see how to conclude that statistical tests applied to climatic simulations with small ensemble are not meaningful. Maybe I missing something (or a statistical background), that could be explained furthermore.
L420: Particularly I think when convection is an important component of the meso-scale systems. Orography induced meso-scale system for instance might be less chaotics in term of response to dust.
L435: and other studies.
Citation: https://doi.org/10.5194/egusphere-2024-4037-RC1
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