the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 12 Aug 2026
Development and Evaluation of Online Gas-Phase Chemistry in the Global Variable-Resolution Atmospheric Model iAMAS (v2.5.1): Resolution-Dependent Surface Ozone over North China
Abstract. Atmospheric composition is governed by multiscale processes spanning global transport to local chemistry. Simultaneously capturing these scales is essential, yet conventional modeling trades global coverage for regional detail restricted by artificial boundaries. Global variable-resolution (VR) modeling provides a unified alternative, yet online gas-phase chemistry within nonhydrostatic VR frameworks remains limited, and how mesh refinement alters coupled physical–chemical responses is not fully understood. Here, we develop and evaluate an online gas-phase chemistry framework in the VR model iAMAS (v2.5.1). Operating on a unified unstructured mesh without lateral boundaries, the modular framework couples KPP-generated SAPRC-99 tropospheric chemistry, Linoz stratospheric ozone, Fast-J photolysis, emissions, transport, and deposition. Global baseline simulations at ~60 km resolution (U60km) for July 2019 validate large-scale tracer distributions (O3, NO2, HCHO, CO) and surface ozone diurnal cycles across China. Regionally refined simulations at ~4 km over North China (V4km) demonstrate how online VR chemistry resolves cross-scale physical–chemical coupling over complex terrain. V4km recovers the observed ridge-high/valley-low nighttime ozone pattern, reversing spatial correlation from R=−0.40(U60km) to R=+0.46 by resolving slope circulations and heterogeneous NOx emissions within a shallow nocturnal boundary layer. Refinement also reorganizes urban–rural and day–night ozone responses: higher urban NOx suppresses daytime ozone peak under VOC-limited conditions, whereas reduced coarse-grid NOx dilution weakens rural nighttime titration and sustains higher ozone. These results demonstrate that mesh refinement in a global VR model alters the coupled dynamical–chemical state rather than merely sharpening concentration gradients, establishing iAMAS as a modular platform for seamless global-to-regional atmospheric-composition studies.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-4483', Anonymous Referee #1, 23 Sep 2026
- RC2: 'Comment on egusphere-2026-4483', Anonymous Referee #2, 26 Sep 2026
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RC3: 'Comment on egusphere-2026-4483', Anonymous Referee #3, 01 Oct 2026
General comments
This manuscript presents an online gas-phase chemistry framework in the global variable-resolution iAMAS model and evaluates simulations with a quasi-uniform 60 km mesh and a mesh refined to approximately 4 km over North China. The comparison of simulation results at two different grid resolutions showed interesting results regarding the improvement in the simulated nighttime ridge–valley contrast in surface ozone. Simulating atmospheric chemistry at a variable grid resolution in a global framework is important to understand many important processes such as multi-scale processes. The combination of model development, global evaluation, and a regional application in this manuscript is well suitable for GMD. The manuscript is generally well organized and the main results are clearly presented. Therefore, I would recommend a minor revision if the following comments could be properly addressed.
Major comments
(1) One of the key points proposed by this study is that VR can better describe diurnal cycles of ozone over the refined area, and the manuscript suggest this was mainly attributed to better representations of heterogeneous distribution of NOx emission, topographic relief, and titration process. There are two main issues with it. First, the raw anthropogenic emission was from HTAP with 0.1 degree grid resolution, which is even coarser than the V4km grid. Both U60km and V4km shall suffer from uncertainties associated with emission file remapping. Thus it is necessary to describe how the 0.1 degree HTAP was interpolated into 4km grid. And if the authors want to distinguish the specific benefit of emission heterogeneous emission, an addition sensitivity simulation is recommend to run V4km with a similar U60km emission (i.e., all 4km grids in the same U60km grid use the same emission) and compare it with the baseline V4km simulation. Second, this is more critical, the manuscript mentioned that biogenic VOC was not included in the simulation, but FNR was applied to examine O3 sensitivity. Although the refined area with 4km grids is mainly over North China which has relatively much lower BVOC emission as compared to other areas with dense vegetation, this method may still suffer from cropland emission considering the simulation period was July. I would recommend a more careful examination of the potential influence from BVOC emission because it is very important for O3 sensitivity which is one of the key points in this manuscript. Since the supplementary material applied WRF-Chem simulation, it would be helpful to use that model to check if including BVOC would affect the conclusion, or at least show the spatial distribution and monthly variations of BVOC emission over North China.
(2) It is necessary to strengthen the evaluation of the meteorology simulation results underlying the ozone interpretation. The explanation of nighttime ozone differences relies on slope flows, boundary-layer depth, and turbulent mixing. Although some of the figures showed V4km surely provides more details (especially spatial heterogeneity) of the mat variables, the bias against observations is still unknown. Please provide a brief evaluation of relevant meteorological variables over the refined region, using available surface observations or other independent data where possible. Wind speed and direction, near-surface temperature, and boundary-layer structure would be particularly relevant. This evaluation would help to indicate whether the modeled changes in circulation and mixing are credible.
(3) Figure 10 showed mean cycles of V4km and U60km against observation, which is an important proof for the advantages of finer mesh grid. But the data was from spatially averages, which may conceal compensating errors across stations. Please consider reporting station-level statistics or the distribution of hourly bias and RMSE for the two configurations. Moreover, the comparison would benefit more if it can separate complex-terrain, urban, and rural sites as this can provide more solid demonstrations for the key statements regarding emission heterogeneity and nitration.
Specific comments
(1) Abstract line28: Please consider rephrase “heterogeneous NOx emissions” as it is somewhat misleading as “NOx emission from heterogeneous processes”.
(2) Lines 40–45: The description of typical global and regional model resolutions is somewhat broad. Please avoid implying that all global chemistry models are limited to 100–200 km or that regional models are necessarily unable to resolve the relevant processes. For example, this manuscript also applied WRF-Chem for nearly global simulation although it is usually considered as a regional model.
(3) Lines 82–92: Please cite the relevant MPAS-A descriptions where the dynamical core and numerical methods are introduced, so that inherited functionality is readily distinguishable from new iAMAS development.
(4) Lines 124–151: SAPRC-99 is described as containing 82 species, whereas the initialization procedure refers to 49 surrogate species. Please explain the relationship between these two numbers and how the remaining species are initialized.
(5) Lines 145–151: Please clarify which species are initialized from climatology, which are initialized from idealized profiles, and how the short-lived species reach a chemically consistent state during spin-up.
(6) Line149: The description of NOx as a “long-lived” species is too general, particularly for the polluted boundary layer. Please revise this wording and distinguish NOx from longer-lived reservoir species.
(7) Lines 153–155: Please clarify why cloud attenuation in Fast-J is estimated using a relative-humidity-based liquid water path parameterization rather than the cloud fields available from the model microphysics. Is aerosol attenuation included?
(8) Lines 179–180: Please list the species subject to wet scavenging and briefly discuss whether the omission of in-cloud removal could affect soluble nitrogen reservoirs during the summer simulation.
(9) Lines 190–191: The dynamical time steps differ by a factor of 20 between U60km and V4km. Please state whether chemistry, photolysis, emissions, and deposition use the same update intervals in both runs. Otherwise, differences in temporal integration may contribute to the apparent resolution response.
(10) Lines 195–203: Please state whether discontinuities occur at the boundaries between five-day blocks, particularly for wind, boundary-layer variables, and surface ozone.
(11) Figure 9: Please clarify whether the reported correlations use individual station values or values averaged within model grid cells. This matters particularly for U60km, where multiple stations may share one modeled value.
(12) Lines 387–399 and Figure 11: Please specify whether the displayed HCHO/NO2 ratio uses simulated columns sampled consistently with the cited threshold study. Consider restricting regime interpretation to sunlit hours.
(13) Figure 12: Larger nighttime Kzh near urban sites is consistent with enhanced vertical exchange, but it does not establish the direction or magnitude of the ozone flux. Please qualify the proposed downward transport unless it can be diagnosed directly.
(14) Lines 427–435: Please separate the robust finding that the refined simulation better reproduces the nighttime terrain-related ozone pattern from the more tentative explanation of the processes responsible for that improvement. The later part may require more sensitivity simulations to demonstrate as mentioned in the main comments.
(15) The manuscript emphasizes computational tractability. A short comparison of grid size, runtime, and the additional cost of online chemistry for the two configurations would make this aspect more concrete.
Citation: https://doi.org/10.5194/egusphere-2026-4483-RC3
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This paper developed an online gas-phase chemistry framework in the global variable-resolution (VR) model iAMAS (v2.5.1), and demonstrated the improvements in the ozone simulation with 4-km regionally refined simulations compared with a 60-km global baseline simulations. The 4-km refined simulations showed substantially improved capability in resolving surface ozone patterns over complex terrain, particularly the observed ridge-high/valley-low nighttime ozone distribution over North China. The refined simulation also captured distinct resolution-dependent urban-rural and day-night ozone responses, which the authors attribute to improved representation of terrain-driven circulations, spatial heterogeneity in NOx emissions, and boundary-layer processes. Overall, this study presents a potentially useful framework for global-to-regional atmosphere chemistry modeling and provides interesting result on how regional mesh refinement can affect simulated ozone distributions and associated physical-chemical processes. Therefore, I recommend minor revision.
Major comment:
Minor comment: