Preprints
https://doi.org/10.5194/egusphere-2026-1666
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2026-1666
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
27 May 2026
| 27 May 2026
Beyond aerosol size parameter comparison: A practical method for evaluating aerosol microphysical processes of 3D aerosol physics and chemistry models using size-resolved aerosol measurement data - Application to NHM-Chem v2.0
Abstract. Aerosol size is a key parameter for evaluating the climate and environmental impacts of aerosols. However, when discrepancies arise between simulated and observed aerosol size-distribution parameters (e.g., number concentration, mean diameter, and standard deviation), it is often difficult to judge how critical those differences are. Here, we propose a practical recipe for evaluating 3-D aerosol models with size-resolved measurement data: the Pseudo-physical Variable Comparison (PPVC) method. PPVC compares physically meaningful variables (e.g., cloud condensation nuclei concentrations and light extinction coefficients) and processes (e.g., condensation rate and Brownian coagulation rate) that are derived from observed and simulated size distributions of aerosols, together with prescribed representative values of relevant parameters (e.g., hygroscopicity or refractive index), instead of comparing only size parameters. We applied PPVC to evaluate a regional-scale meteorology–chemistry model, NHM-Chem (versions v1.0 and v2.0), using scanning mobility particle sizer (SMPS) and aerodynamic particle sizer (APS) data measured in Tsukuba. The PPVC analysis showed that v2.0 improved the predictability of light extinction coefficients relative to v1.0. Using independent datasets, we also found that NHM-Chem v2.0 generally outperformed v1.0 not only for aerosol optical depth (AOD) but also for the predicted fine-mode fractions of inorganic species, owing to improvements in aerosol size distributions. We propose a new metric to evaluate the overall consistency of simulated aerosol physical parameters: the Distance of Deviations between simulated and observed Aerosol Physical Parameters (DDAP). DDAP for NHM-Chem v1.0 and v2.0 were 4.2 × 10−2 and 3.5 × 10−2, respectively, indicating that the microphysical properties and processes simulated by v2.0 were approximately 17% more consistent with observations than those of v1.0.
How to cite. Kajino, M., Orikasa, N., Kudo, R., Shimizu, A., Nishizawa, T., Jin, Y., Osada, K., Kayaba, S., Kajikawa, T., Ishikawa, R., and Yumimoto, K.: Beyond aerosol size parameter comparison: A practical method for evaluating aerosol microphysical processes of 3D aerosol physics and chemistry models using size-resolved aerosol measurement data - Application to NHM-Chem v2.0, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-1666, 2026.
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.
Download & links
Download & links
- Preprint
(10868 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
-
RC1: 'Comment on egusphere-2026-1666', Anonymous Referee #1, 25 Jun 2026
This paper introduces the Pseudo-Physical Variable Comparison (PPVC) method, uses it to guide the development of NHM-Chem from v1.0 to v2.0, and proposes a summary consistency metric, DD_AP. Rather than comparing size parameters that are hard to interpret (N, D_g, σ_g, M_k), the authors fix a common set of prescribed parameters (κ, density, refractive index, and so on) and derive process-relevant variables from both the observed and simulated n(D): the condensation sink φ, the coagulation sink β, CCN number concentration, and the extinction coefficient σ_ext.The idea is genuinely useful and a good fit for GMD. The model-development record is thorough, the sensitivity tests for D_new, D_hump, the merging scheme, and dt_NPF are well documented, and the two independent datasets are used well. I also appreciate how candid the manuscript is about its own limits. That said, several points need substantive work before the paper is ready. None of these undermine the validity of the science, and all are fixable.General comments:1. NHM-Chem carries five categories with different refractive indices, densities, and hygroscopicities (Fig. 4i/4j even uses per-component optics), so the σ_ext and N_CCN obtained after fixing κ and m are not the values the model's radiation and cloud modules actually produce. An improvement in a PPV is therefore an improvement in the shape of the size distribution, not proof that the model's real optical or CCN behavior improved. The title phrase "physically meaningful variables … that directly affect climate" overstates this.2. The 17% improvement is driven mainly by the coarse mode (APS), whereas the scientific core of the paper is submicron processes (NPF, condensation, coagulation etc.). The text itself notes that the SMPS_SUB PPVs are "similar except EVR" and that v2.0 is worse for N_CCN,1.0% (Sect. 5.2), so maybe the abstract should not let "17% more consistent" stand as an overall statement about submicron processes.3. The correlation coefficients in Tables 2, 5, and A1 are generally low and occasionally negative (for example EVR APS_MD R = 0.0020; many APS_MD/SS entries in Table 5 with R < 0.05; some negative AOD values in Table 4). This means there is essentially no day-to-day skill, and the improvement rests almost entirely on the Sim./Obs. median ratios and RMSE. That is a useful result in itself, but the paper should state plainly that it demonstrates an improvement in central tendency rather than in temporal skill, and discuss why the correlations are so low (point-measurement representativeness against a 30 km grid box, transport-timing errors, the dry-to-wet RH conversion etc.).4. Code availability: The NHM-Chem v2.0 is not open source, is available only on request, but GMD normally requires the specific model version to be deposited in a permanent, citable archive (for example Zenodo with a DOI) at submission.Specific comments:1. Page 5, lines 15–18 (Sect. 2.1.1): a 5-day spin-up for a one-year aerosol simulation seems short for the buildup of coarse-mode and accumulation-mode aerosol. Please justify that it is sufficient.2. Page 13, lines 4–5 (Sect. 2.3.4): the exclusion of APS data below 1 µm leaves a 0.4–1.0 µm gap. Since extinction efficiency peaks in roughly this range, please quantify the effect of the gap on EVR and the moments here, rather than only mentioning it in the conclusions.3. Page 12, lines 26–30 (end of Sect. 2.3.3): the dry-to-wet growth uses observed or simulated RH. Please clarify how much of the variability in the "observed" PPVs comes from RH and how much from the size distribution.Citation: https://doi.org/
10.5194/egusphere-2026-1666-RC1 -
RC2: 'Comment on egusphere-2026-1666', Anonymous Referee #2, 16 Oct 2026
General Comments:
As someone who primarily focuses on field measurements, our goal is often to provide observations for model validation while leveraging simulations to explore beyond the observed data. This paper proposes a methodology for modelers to break down their simulations and diagnose what really causes the mismatch between simulated and observed aerosol size distributions. Given that aerosols remain one of the most challenging and often highly uncertain parameters in climate models, this methodology offers a potentially very useful framework for future work in atmospheric aerosol studies.
However, I find that some places in the methodological details and assumptions are vague and could benefit from clarification.
- A missing fraction (0.4 um – 1.0 um) in the aerosol size distribution. The observed aerosol size distribution comes from two instruments: SMPS for 10nm–0.4 um, and APS for particles with diameters of 1.0 um and larger, leaving an unmeasured size range between 0.4 um–1.0 um. The authors didn’t explain how they treat this size gap. In Figure 4f, there are some circled data points representing the assumed missing fraction of the size distribution, but the assumptions are not clear except for the aerosol density they applied. While Figure 4 compares modeled and observed distributions separately by instrument, the integrated aerosol parameters are evaluated across the full size range. The authors need to consider detail the interpolation/reconstruction methodology they used to fill this size gap.
- Parameters are not prescribed using the same conditions as the observations. In the comparison of the model simulations and field data, the prescribed parameters or aerosol properties are calculated under different conditions, some at standard temperature (e.g., page 12, line 22) and some at standard pressure (e.g., page 12, line 27), which is not consistent with the field data. This is confusing.
- The accommodation coefficient used in this study is 0.1 (page 10, line 16), but the gas phase selected here is sulfuric acid and/or extremely low-volatility organics, which usually have an accommodation coefficient closer to 1.0, as they will readily condense. I wonder why the authors chose 0.1 here.
- In the introduction, it is stated that particles smaller than several 10nm have negligible influence on the climate unless they grow to a bigger size. I find this might need some nuance. Small particles, although do not directly interact with light effectively, they usually dominate total number concentrations and serve as a crucial reservoir for condensation and coagulation, potentially governing the microphysical evolution of the aerosol size distribution. This has also been discussed later in the manuscript when simulations show new particle formation shifted the size distribution. The authors might consider clarifying this earlier in the manuscript.
- New particle formation. Simulation with two different D(new), 1nm vs 9nm, didn’t make much difference in this study. This probably reflects that the extent of new particle formation (rate, number concentration) governs coagulation and thus impacts the size evolution. The modal models are not able to resolve the non-linear relationship between NPF rates, total number concentration, and coagulation sinks as the sectional models. This might be a bigger factor for the consistency of the simulated size distribution (compared with the observation). However, as explained in the paper, sectional models will be much more expensive. But extending the discussion on this a bit more here would add valuable context.
- The paper claimed a 17% improvement in V2.0 (4.2×10-2) compared to V1.0 (3.5×10-2). I wonder how significant the 17% is here; does this value come from a range of values? Could the authors elaborate on which specific size fractions contribute most to the improvement? Importantly, is the 17% reproducible?
Citation: https://doi.org/10.5194/egusphere-2026-1666-RC2
Viewed
Total article views: 341 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 27 May 2026)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 188 | 109 | 44 | 341 | 43 | 40 |
- HTML: 188
- PDF: 109
- XML: 44
- Total: 341
- BibTeX: 43
- EndNote: 40
Viewed (geographical distribution)
Total article views: 305 (including HTML, PDF, and XML)
Thereof 305 with geography defined
and 0 with unknown origin.
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Latest update: 19 Oct 2026
Meteorological Research Institute (MRI), Japan Meteorological Agency (JMA), Tsukuba, Ibaraki 305-0052, Japan
Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8572, Japan
Narihiro Orikasa
Meteorological Research Institute (MRI), Japan Meteorological Agency (JMA), Tsukuba, Ibaraki 305-0052, Japan
Rei Kudo
Meteorological Research Institute (MRI), Japan Meteorological Agency (JMA), Tsukuba, Ibaraki 305-0052, Japan
Atsushi Shimizu
National Institute for Environmental Studies (NIES), Tsukuba, Ibaraki 305-8506, Japan
Tomoaki Nishizawa
National Institute for Environmental Studies (NIES), Tsukuba, Ibaraki 305-8506, Japan
Yoshitaka Jin
National Institute for Environmental Studies (NIES), Tsukuba, Ibaraki 305-8506, Japan
Kazuo Osada
Graduate School of Environmental Studies, Nagoya University, Nagoya, Aichi 464-8601, Japan
Satoko Kayaba
National Institute for Environmental Studies (NIES), Tsukuba, Ibaraki 305-8506, Japan
Tomoki Kajikawa
Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Ibaraki 305-8572, Japan
Meteorological Research Institute (MRI), Japan Meteorological Agency (JMA), Tsukuba, Ibaraki 305-0052, Japan
Rio Ishikawa
Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Ibaraki 305-8572, Japan
Meteorological Research Institute (MRI), Japan Meteorological Agency (JMA), Tsukuba, Ibaraki 305-0052, Japan
Keiya Yumimoto
Research Institute for Applied Mechanics, Kyushu University, Kasuga, Fukuoka 816-8580, Japan
Short summary
Aerosol size distribution is important for understanding the climate impacts of aerosols; however, it is difficult to validate modeled size distributions. This study introduces a new method that compares physical variables, such as cloud condensation nuclei and light extinction coefficients, derived from size distribution measurements. Using data from Tsukuba, Japan, the method shows that the new model (v2.0) improves predictions of aerosol size distribution by about 17% compared to v1.0.
Aerosol size distribution is important for understanding the climate impacts of aerosols;...