Atmospheric chemistry and transport models (ACTMs) are crucial to understanding sources and impacts of reactive nitrogen (Nr) chemistry and its potential mitigation. In the manuscript, the authors undertook the first evaluation of the global version of the EMEP MSC-W ACTM driven by WRF meteorology (1º×1º resolution), with a focus on surface concentrations and wet deposition of N and S species relevant to investigation of atmospheric Nr and secondary inorganic aerosol (SIA). The results of model-measurement comparisons, conducted both spatially and temporally, covering 9 monitoring networks worldwide, showed an overall nice agreement between simulated and observed data. The authors found that simulations of primary pollutants (e.g. NH3) are sensitive to the choice of different inventories of primary emissions (e.g. China), but much less so for secondary components (e.g. NH4+). Furthermore, comparisons of 2010 and 2015 surface concentrations between model and measurement demonstrated that the model captured well the overall spatial and seasonal variations of the major inorganic pollutants, and their wet deposition in different regions worldwide. The model showed better correlations with annual average measurements for networks in Southeast Asia, Europe and North America than in East Asia (data for 2015), suggesting potential issues with the measurements in the latter network. Temporally, both model and measurement agree on higher NH3 concentrations in spring and summer, and lower concentrations in winter. The authors also reported high correlations between measured and modelled NH4+ precipitation concentrations in all regions except East Asia (receiving greater anthropogenic activities). They evaluated model-measurement bias for various atmospheric Nr species in different networks as well. The greater uniformity in spatial correlations than in biases suggested that the major driver of model-measurement discrepancies were shortcomings in absolute emissions rather than in modelling the atmospheric processes. In summary, this study supported the application of this model framework for global analysis of current and potential future budgets and deposition of Nr and SIA. The authors’ work provides strong implications of modeling the atmospheric processes regionally and globally but the key point is the (relatively) accurate emission inventory of Nr species. The manuscript fits well the scope of this journal and merits to be published after minor revisions as follows.

L80: wet deposition of Nr

L109-110: use the abbreviation of reference expression by“(Vieno et al., 2010; 2014; 2016)”

L127-128: As you mentioned here, all inventories were aggregated to 1° × 1° resolution internally in the model. Could you make some comments on the uncertainties due to this aggregation (e.g. from resolutions of 0.1×0.1^o and 0.5×0.5^o)? I have the same concern on uncertainties for the re-assigned 11 selected nomenclature for sources of air pollution sectors in all inventory emission sector-layers.

L966: Delete “14, 16(2014-08-21)”before “14, 8435-8447” and add the “doi” number before “2014”.

Review of Ge et al. gmd-2021-166

Overarching comment:

The manuscript provides a comprehensive evaluation of reactive nitrogen concentrations and wet deposition simulated by the WRF/EMEP-MSC-W modeling system against a suite of surface measurement networks over Europe, Asia, and North America. The goal of the manuscript is to establish the credibility of this modeling system in simulating the atmospheric transport and fate of reactive nitrogen on global scales for studying processing affecting concentrations and quantifying the impacts of policy scenarios. The results suggest that the performance of the modeling system is on par with the performance of other contemporary global-scale modeling systems. While I do not expect this general conclusion to change, there are several aspects of the manuscript that should be strengthened prior to its final publication as detailed in my main and specific comments below. The manuscript is well written and structured clearly, the presentation quality of all figures and tables is good, and references to other published studies on similar topics are provided where appropriate. I enjoyed reading Section 4 that provides a nice discussion of the inherent limitations of using surface measurements as criterion for establishing model credibility.

Main comments:

1. It is not clear to me whether the authors used the available observations at their native temporal resolution (e.g. hourly, daily, weekly) and matched these native-resolution observations to the corresponding model values (for non-missing hours / days / weeks) to compute observed and modeled annual averages for the subsequent analyses, or whether they relied on pre-computed observed annual averages provided by some or all of the networks and then matched them to modeled annual averages (computed from all 8760 hours in a given year). Section 2.2 and the discussion in Section 4 suggest the latter approach was taken, but given that this approach can lead to temporal mismatching as discussed in Section 4, I don’t see why such a choice would have been made. At least for the North American and European networks I am familiar with (EMEP, NAPS, NTN, AMoN, and EPA Air Data), native resolution data is readily available (e.g. https://aqs.epa.gov/aqsweb/airdata/download_files.html, http://nadp.slh.wisc.edu/data/NTN/ntnAllsites.aspx, https://data-donnees.ec.gc.ca/data/air/monitor/national-air-pollution-surveillance-naps-program/Data-Donnees/2010/ContinuousData-DonneesContinu/HourlyData-DonneesHoraires/?lang=en ) and should be used in the analysis to eliminate this factor of uncertainty.
2. Given that one of the key uses of ACTMs such as WRF/EMEP-MSC-W is to quantify the effects of emission scenarios as stated by the authors in both the abstract and summary, an analysis of how well the model captured historic changes in concentrations and deposition (due to changes and variations in both emissions and meteorology) can help establish its credibility for that purpose. The two simulations performed for 2010 and 2015 with the ECLIPSEe emission inventories seem to offer such an opportunity. I strongly encourage the authors to consider adding a section that quantifies emission changes between 2010 and 2015 and then also compares observed and modeled concentration and deposition changes between the two years. While for some pollutants and regions the changes may be dominated by interannual meteorological variability, for others the concentration changes may show a clear link to emission changes.

 Specific comments:

Abstract, line 27: change “measurement” to “measurements”

Abstract, line 33: suggest changing “most” and “least” to “largest” and “smallest”

Section 1, line 49: Do we know which components (SIA, OM) drive this increase in PM2.5 burden reported by Shaddick et al. (2020)?

Section 1, lines 70 – 75: The work by Tan et al. (https://acp.copernicus.org/articles/18/6847/2018/) could also be referenced here

Section 2.1, line 87: consider changing “with implementation” to “used for applications”

Section 2.1, lines 104 – 112: Please provide additional information on the WRF configuration options and input datasets used in these simulations (e.g. land use / land cover database, microphysics scheme, cumulus parameterization scheme, PBL scheme, radiation scheme, land-surface model, etc.). Please also discuss whether the land use / land cover information used in the WRF simulations is consistent with the information used in the EMEP MSC-W biogenic emission and deposition calculations.

Section 2.1, lines 133 - 149: It appears that no day-of-week or hour-of-day temporal profiles were applied to either the ECLIPSEe or HTAP emissions. If this is correct, what was the rationale for not applying such profiles?

Section 2.2, lines 173 – 195: please see my first main comment regarding the use observations with their native temporal resolution.

Section 2.2, lines 181 – 185: What was the rationale for not including wet deposition measurements from CAPMoN (https://www.canada.ca/en/environment-climate-change/services/air-pollution/monitoring-networks-data/canadian-air-precipitation.html) to strengthen the analysis in Section 3.3?

Section 2.2, lines 186 – 187: Please make sure to clearly distinguish between “R” and “RT” in the supplement and their first uses in the text, tables, and figures.

Section 3.1.1., line 216: consider changing “total gridded differences” to “the total number of grid cells”

Section 3.1.2, lines 292 – 295: I suggest extracting and comparing the HTAP and ECLIPSEe emissions for the grid cells corresponding to these locations (maybe in addition to some surrounding grid cells) to gain further insights into the relative importance of potential localized emission differences (i.e. localized model uncertainty) vs. localized phenomena affecting the measurements.

Section 3.1.2, line 328: I suggest avoiding the term “bias” since the analysis presented here does not constitute a rigorous verification of emission inventories.

Section 3.2.1, lines 374 – 375: “there is no significant difference between model and measurements in most Southeast Asia countries” – This seems like an overstatement given that FAC2 is only about 0.5 for EANET NH3.

Section 3.2.1, lines 394 – 397: This again seems like an overstatement and selective reading of evaluation metrics. The FAC2 values for many of the EANET pollutant measurements are quite low, and many of these FAC2 values as well as NME and NMB values are worse for EANET than NNDMN.

Section 3.2.3, lines 471 – 472: What was the rationale for not including AIRBASE over Europe? If it was included, the number and type of sites might be more comparable to the NO2 and SO2 measurement sites over North America.

Section 3.2.3, lines 474 – 476: It is likely the location of the sites which are often source-oriented. SO2 emissions from the largest sector (power generation) are directly measured and included in the EPA emission inventory that was used in HTAP. Does the ECLIPSEe inventory not use this available information about measured SO2 emissions from the U.S. power sector?

Section 3.2.3, lines 480 – 481: As for SO2, this is likely due to the local-scale nature of many NO2 measurement sites.

Section 3.2.3, line 482: I suggest replacing “secondary” with “aerosol” since there are other secondary pollutants (e.g. HNO3) in Table 4 besides sulfate, nitrate, and ammonium.

Section 3.2.4, lines 505 – 506: As noted in my first main comment, higher temporal resolution data is also available over the U.S. and Canada and should be used here as well.

Section 3.2.4, Figure 11: Please add results for available North American networks (e.g. AMoN) here and in Figure S6.

Section 4, lines 666 – 676: Please see my first main comment about using observational data at their native temporal resolution for the analysis presented in this manuscript.

Section 4, lines 704 – 705: “unmatched temporal coverage” should not be an issue if model-observation matching is performed at the native temporal resolution of the observations as discussed in my main comment.

Section 5, lines 763 – 764: Please see my second main comment about adding an analysis of observed and modeled 2010 vs. 2015 changes in concentrations to evaluate the modeling system’s ability to capture such changes which are at least partly driven by changes in emissions.