the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Jun 2026
Impacts of mesh refinement on the simulation of a long-range transported extreme dust storm over East Asia in the global variable-resolution model (iAMAS v2.6.3)
Abstract. Long-range transported dust storms challenge regional high-resolution models because lateral boundary conditions constrain the consistent representation of dust emission, transport, and deposition. Here we use the integrated Atmospheric Model Across Scales (iAMAS v2.6.3), a global variable-resolution physics-chemistry coupled model, to simulate the extreme East Asian dust storm from 13 to 18 March 2021. Three experiments adopt a globally quasi-uniform 50 km mesh (U50 km) and two source-refined meshes at 16–50 km (V16 km) and 4–50 km (V4 km). Evaluated against reanalysis and ground-based observations, the simulations reproduce the large-scale synoptic evolution, while mesh refinement captures more detailed near-surface dynamical features and terrain-channeled winds through improved topographic representation. The model reasonably represents 10-m wind speed, surface PM10 concentrations, and aerosol optical depth. Relative to U50 km, total dust emissions over East Asia increase by 35.24 % in V16 km and 54.98 % in V4 km, as refined meshes resolve localized friction-velocity enhancement and nonlinear saltation-threshold exceedances. By altering emission evolution and its interaction with atmospheric circulation, mesh refinement also affects the spatial distribution of transported dust. The contrast between V16 km and V4 km reveals regional heterogeneity in downwind dust mass loading, with V4 km capturing localized wet-scavenging enhancement not evident in V16 km and reducing dust loading over the Yangtze River Delta by 30.49 % relative to U50 km. This research emphasizes the importance of mesh refinement for representing long-range transported dust storms and provides a basis for future evaluations of multiple dust emission schemes in global variable-resolution models.
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Status: open (until 25 Aug 2026)
- RC1: 'Comment on egusphere-2026-3149', Anonymous Referee #1, 23 Jul 2026 reply
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- 1
In this study, titled “Impacts of mesh refinement on the simulation of a long-range transported extreme dust storm over East Asia in the global variable-resolution model (iAMAS v2.6.3)” by Xue et al., the authors conducted three experiments, with the highest model resolution increased from 50 km to 16 km to 4 km, to simulate an extreme dust storm event in East Asia. The authors analyzed the impact of model resolution on simulated dust aerosol properties and processes.
1.In section 2.2, Dust emission scheme, the authors could highlight the factors affected by model resolution.
2.Line 179-180: Add more information about the dust source function (e.g., definition, range, physical meaning) and model resolution in Figure 1. Same for Figure 1 title. Do variable-resolution models use the same dust source function, with 50 km resolution?
3.Line 304-306: Since the resolution of ERA5 data is 0.25-degree and lower than that of variable-resolution models, how does the interpolation, from low to high resolution, affect the simulation results? Also, “Meteorological initial conditions and surface boundary fields are obtained from the ERA5 reanalysis at 6-hourly intervals”, does the model read initial conditions every 6 hour?
4.Figure 2: The grid lines and differences in grid sizes in (a)-(c) are not clear.
5.When evaluating the simulated large-scale circulation and wind speed, the authors could quantify the model performance using spatial correlation coefficients, scatterplot, root mean square error, etc. This can provide evidence for the descriptions such as “generally well reproduced”, “reasonably represented”, “comparable spatial pattern”, “broadly consistent”, (Line 365-370). For 10-m wind speed, besides temporal correlation coefficients in Table 2, the scatterplot (like Figure 6) could be used to show how well the models capture the extreme wind speed in dust source regions.
6.Line 367: For mean sea level pressure in TKD region, ERA5 shows a low pressure and wind convergence, but models do not capture this, especially during March 15-16. Since TKD is a key source of dust, this bias may affect dust transport. Are the model results regridded to ERA5 grids? If so, the authors could show the differences between simulations and ERA5 in supplementary information.
7.Line 381-382: Figure 3 does not show the information about “terrain transitions”, and the audiences might not be familiar with the definitions of Hexi Corridor/Mongolian Plateau. The authors could overlay the winds on topography and highlight the regions with significant differences in winds if possible.
8.Line 463: What do the authors mean by “more spatially diffuse”? The dust in U50 goes farther from source to downwind regions? On March 16 (Figure 5), over the downwind Shandong Peninsula, V16 and V4 show larger concentrations (>500 ug/m3) than U50 does (200-500 ug/m3). This suggests that dust in V16 and V4 goes farther. Where are “elevated background concentrations” (Line 465-466) located at? For the “positive mean bias presented in Fig. 6” (Line 466-467), most of the observed concentrations are below 400 ug/m3 while simulated concentrations are 200-1300 ug/m3, so, I guess these points are associated with stations in northwestern regions in Figure 6 where all three models show higher bias in concentrations.
9.Line 489-492: How about the effects of dry deposition near dust source regions on dust concentrations/transport?
10.Section 3.1.4: The authors could compare the simulated spatial distribution of AOD with satellite observations/reanalysis data. Does the model output include dust optical depth? If so, the authors could evaluate it using observation/reanalysis data such as MERRA2. What other aerosol species are considered in the model? The biases in anthropogenic aerosols could affect the model performance in simulating AOD in the downwind regions of dust.
11.Line 583-585: What does “the strongest” mean? It refers to magnitude or spatial range? What do the authors mean by “consistent enhancement”?
12.Line 643-645: Both the model resolution and topography can affect the low-level circulation. Therefore, to separate these two factors, the authors may need to use higher-resolution model with coarse-resolution topography. In TKD (40N, 90E), the topography differences are small (<10m, Figure 9 d, e), but the differences in wind friction field are relatively large (-0.2~-0.3, Figure 10 d, e).
13.Figure 12: With mesh refinement, dust burden decreases in the region with large topography gradient (30-40N, 105E). This may be related to higher topography blocking dust transport. In northeastern China (40-45N, 110-130E), V16km shows decreases in dust burden, but V4km shows increase. Which model has model performance regarding this? The authors may compare the simulation results with observations/reanalysis data of dust burden or optical depth.
14.Figure 13: The authors may change the range of colorbar to show values below 10 mg/m2/day, because the current figure shows precipitation decrease (Figure 13c, blue color) but wet scavenging does not (Figure 13f, white color) as the changes are less than 10 mg/m2/day. Changing colorbar could be more consistent with Table 5 which shows wet scavenging in NCP decreases (from 6.733 to 1.109 mg/m2/day) and support the conclusion “precipitation differences … modulate dust removal” (Line 755-757).
15.Line 764-767: What do the authors mean by “more consistent”? How to quantify it? Do you mean the contribution from emission changes to dust burden changes is larger than that from wet removal changes? Also, in these source regions, what are the changes in dust dry deposition as the model resolution increases?
16.Line 776: Besides “precipitation structure”, how about the effects of model resolution on clouds? How does the in-cloud and below-cloud wet scavenging change as model resolution increases?