the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A process-evaluation of the impact of precipitation on aerosol particle number size distributions in three Earth System Models
Abstract. Accurately modeling the cloud condensation nuclei (CCN) budget is a key factor in reducing uncertainty in aerosol–cloud interactions in Earth system models. Wet deposition—the removal of particles by precipitation—is a major CCN sink, but rainfall can also trigger a replenishment phase via the formation and growth of new particles, partially offsetting losses. In this study, we evaluate how three general circulation models represent the size- and time-resolved effects of precipitation on the particle number size distribution (PNSD) and the CCN budget. The evaluation is based on correlations between the PNSD and the precipitation rates along back trajectories from three long-term measurement stations. To better isolate the role of precipitation from confounding factors, we also apply a Machine Learning approach (XGBoost), training a separate regression model for each site and data source using a minimal set of physically relevant predictors.
Our results show that at the two high-latitude stations, the models underestimate CCN replenishment following precipitation, with too weak new particle formation and growth. At ATTO, in contrast, two of the models overestimate this effect, simulating an immediate CCN source after rainfall. Observations also suggest that CCN removal is weaker during colder conditions, a pattern that models struggle to capture—either overestimating or underestimating the precipitation effect, depending on the model. The XGBoost analysis confirms the key findings of the correlation analysis while helping to correct for likely confounding influences, showing promise for disentangling spurious correlations and controlling for unrelated factors in model evaluation.
Competing interests: Some authors are members of the editorial board of ACP.
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 paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-2559', Ruth Price, 21 Jul 2025
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RC2: 'Comment on egusphere-2025-2559', Joseph Carton-Kelly & Anthony Jones (co-review team), 01 Aug 2025
This paper by Blichner et al looks to investigate how much of an impact precipitation can have on the CCN budget. Precipitation is typically seen as a CCN sink and so research into how precipitation can lead to new aerosols that can then grow and potentially become part of the CCN budget once again is important. They explore how precipitation can lead to good conditions for new particle formation and an increase in smaller aerosols due to downdrafts brought about by precipitation events. They explore how different processes affect the particle number size distribution (PNSD) after rainfall. This work is building on ideas suggested by Khadir et al (2023) and introduces three Earth system models to see how well they predict changes to PNSD along back trajectories from precipitation events.
They have selected three different models, nudged to ERA interim analyses, which use different in-cloud and below cloud processing schemes. They also look at how the models treat phase dependency, redistribution of aerosols and aerosol transport differently. This gives them a range of approaches to modelling aerosols and clouds which can then be attributed to different sensitivities that the models exhibit, compared to observations taken at three different sites (two high latitude sites and one tropical). They primarily explore the correlation between precipitation and number concentration and how this differs for different aerosol sizes, with different times away from precipitation event. They discuss their results process by process to gain an understanding of how each affects the aerosol number size distribution, back through time. They also use XGBoost, a machine learning algorithm that is good at predicting outcomes from a small number of input features. This is used to support the arguments their main methods suggest by looking at whether the same patterns seen in the correlation figures can be picked out from the predictions of these machine learnt models. They also do some feature analysis to help pick out potential correlations that shouldn't be attributed to precipitation.
The main results from this paper show that models are underpredicting new particle formation and growth after precipitation, compared to observations. They perform better at the ATTO site, where the increase in small particles is linked to downdrafts instead. It is unclear however whether improved model performance is for the right reasons, with some existing hypotheses impossible within the models available. They also see further verification of previous work that showed particle growth at the ATTO site was much too high in several of the models. XGBoost presented an interesting new way to further analyse these correlations and potentially helped to identify questionable correlations seen in other methods.
This paper is written very well with a clear narrative path throughout the sections, introducing all the important concepts as well as previous work that this is building upon. This paper will fit within the current literature around this topic, exploring some new novel results that provide further understanding of how models perform when looking at the CCN budget after precipitation. The conclusions are clear and show scope for future research, outside of this paper. The title, abstract and results reflect the work that has been done. I believe this paper should be published, once minor comments (outlined below) have been addressed.
General comments:
Thought 1 (Introduction): The discussion of the paper is primarily on a weather timescale (4 days) or so however the paper starts by talking about the impact of aerosols on climate and human health. I know the general discussion after the first few lines is in aerosol dynamics which are at a weather timescale but I think it would be good to introduce aerosol impacts on weather timescales in these first few lines.
Thought 2 (Introduction into Methods): HYSPLIT and GDAS1 are introduced in both sections without any references to the specific model/method used with regards to HYSPLIT and no reference for GDAS1. I think a more substantial introduction and explanation of these two things would be appropriate as well as a reference to the GDAS1 dataset and HYSPLIT.
Thought 3 (Methods): I think that the models are well outlined however I think the table for comparison is too complicated and a lot of it is repeated in the individual models sections. I struggle, using the table and the individual sections to fully grasp the differences between the models. I believe a simpler table that just outlines each part and an additional section at the end, comparing the models in terms of schemes and complexity (linked back to the introduction when wet deposition scheme complexity was discussed) would be useful to support later discussions of model performance.
Thought 4 (results): I think that the figures within figures could benefit from labelling with a), b), c) etc so that when they are referred to, they can be referred to as 2b) and so on to make it clear which figure is being referenced. Also some of the figures have faint lines for some reason (see figure 8). Figure 7 could do with being split into two plots (one for nighttime and one for all) as it is hard to read the results. Overall the spacing between the figures and size of the figures could be tweaked to remove some of the white space too.
Specific comments:
[L3] “Wet” rather than “et”
[L5] can do general circulation models (GCM) in brackets here rather than later
[L10] new particle formation (NPF) in brackets here rather than later
[F1L1] “Illustration” not “Illustratration”
[F1L6] “points” rather than “point”
[F1L11] Reference Khadir et al (2023) at this point as this plot type is taken from that paper
[L50] “Borne” not “born”
[L62] A reference to a paper explaining why precipitation formed via ice formation is expected to scavenge less than liquid precipitation formation
[L120] A suitable reference for each of the different sites related to NPF or precipitation
[L126] This second hypothesis contradicts the one by Zhu et al. Which one is more plausible or are both plausible in different scenarios? A further comment on this contradiction would be beneficial.
[L128] Linked to F1L11 but this should be in figure caption aswell/alternatively
[L131] “,for example,”
[L171, L204, L244] Why these relaxation times? Further detail would be useful.
[L191] “area” not “are”
[L209] “scavenging” to “scavenging”
[L229] “includes” not “include”
[T2L4] “Jun-Aug” instead of “Jun-Jul”
[T2L11-13] Might read better if you swap Dry and Dry-to-Wet around as then goes through the year.
[L351] swap S1 and S2 around
[L418] “at least in” not “just at least”
[L427] “S7))” not just “S7)”
[L429] “10-30nm)” not “10-30nm”
[L468] remove anyway
[F7] Night needs to clearly be a star in the legend
[L496] “one can see here” not “one can here see”
[L497] need something between “stations, the models” like a “whereas” or change the sentence structure
[F8] Could you differentiate between the seasons? Also address the white out effect of some of the lines
[L526] “Fig S22” not “Figs S22”
[L530] “to be more” not “to more”
[L581] struggles to reproduce two of the models, not NorESM and observations instead of current wording
[L586] “XGBoost” not “XGboost”
[L618] “72-80h” not “72-8h”
[L658] “to be a lower” not “to be lower”
[L673] this needs fixing to make sense (remove the NPF?)
[L682] “much too large” to “too many”
[L692] “Is” not “are”
[L699] Need to make it more clear that XGBoost agrees with overall correlations but attributes different reasons – some more explanation would be useful
Citation: https://doi.org/10.5194/egusphere-2025-2559-RC2
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General comments
This study uses Earth System Model output, in-situ observations, reanalysis data, back-trajectories, and machine learning to investigate the effects of recent precipitation on the aerosol size distribution at three locations. Specifically, the authors evaluate the models with respect to a hypothesis from the literature that the removal of large particles by precipitation can trigger new particle formation events at these sites. The study represents a novel and valuable contribution to a subject in the scope of ACP: aerosol-cloud interactions and new particle formation processes, the climate effects of which are notoriously uncertain due to the need for complex, unconstrained parameterisations in ESMs.
The authors make very interesting conclusions about new particle formation-precipitation interactions, which lead to important recommendations for future research. However, the results in this article are complex due to the fact that the authors have undertaken a very thorough analysis of the data available, (including several different variables, models, locations, seasons, data products and sensitivity tests). Hence, the article would benefit from some simplification, including more synthesis of how these multiple strands of evidence lead to specific conclusions about individual physical processes in ESMs. I also recommend more discussion of the sensitivity of the results to the use of different meteorological datasets. As such, I recommend that this article should be published following some revisions to address these points. Specific recommendations are given below.
Ruth Price
Specific comments
Technical corrections