the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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.
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Status: open (until 02 Aug 2026)
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RC1: 'Comment on egusphere-2026-1666', Anonymous Referee #1, 25 Jun 2026
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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.ReplyCitation: https://doi.org/
10.5194/egusphere-2026-1666-RC1
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