General Comments

Overall, the paper is interesting and uses an updated version of WRF-Chem with the Noah-MP LSM (i.e., the NASA-Unified Weather Research and Forecasting model with online chemistry).  The paper covers relatively important aspects of the complex interactions between changes in reactive nitrogen (Nr) fluxes and ozone formation in the eastern U.S., and the relationships and implications of such changes with changing land/biosphere-atmosphere interactions.  The paper highlights such interactions that are relatively less appreciated in the community in regards to land surface data/processes, Nr fluxes, and ozone.   Indeed, the paper attempts to cover a lot of complex land-atmosphere-chemistry topics. However, many arguments and discussions, appear only cursory with minimal to no supporting scientific evidence/analyses or quantitative assessments provided for justification. I provide explicit comments below that pertain to some of these issues in the paper, and suggestions to improve them.

Also,  the manuscript also appears flawed in its grammar and sentence structure, where a thorough proof-read of the English writing should have been done prior to submission.  To return the review comments in a timely manner, I can only provide the detailed grammatical and sentence structure errors in the Abstract and Introduction sections, with general issues in other sections that need to be thoroughly revised (see Technical Corrections below).  The grammatical errors are persistent through the remaining manuscript, and it is imperative that the manuscript is thoroughly checked for grammar and English writing in all sections before submission of the revised version.  Otherwise, in my opinion, the paper is not acceptable for publication.

Specific Comments

2 Methods

Line 103:  Model spinup description of “late April” is not specific enough.  What was the exact number of days used for spinup in April?

Lines 144-145.  OK, but seems that omission of detailed fertilizer inputs and bi-directional ammonia fluxes (BIDI-NH3) in the modeling system is an oversight considering all other advances.  The BIDI-NH3 approach has been shown to have significant impacts on overall deposition of Nr species and aerosols.  Can this in any way be rectified, or at least discussed in terms of model uncertainty using only a unidirectional approach in areas of significant ammonia fluxes (e.g., agricultural lands)?  This becomes an issue later in the results section of the paper (i.e., Lines 351-353).

Lines 161-163:  Agreed, but there has been recent work that addresses and quantifies this issue of using satellite retrievals to infer NOx emissions.  See (Silvern et al., 2019; https://acp.copernicus.org/articles/19/8863/2019/) and Zhen et al., 2021; https://pubmed.ncbi.nlm.nih.gov/34149109/). Indeed there is much uncertainty given the increasingly large role of background NO2 emission sources.

Line 164:  Please describe more about what plume rise approach is used and uncertainties.  Plume rise has a major impact on wildfire emission transport.

Lines 171-172:  And due to rising agricultural, plant fertilizer application and emissions. 

Line 191:  I think this section is better titled:  “Chemical observations from satellites, aircraft, and ozonesondes.”

Lines 215 – 231:  While this is a very useful assimilation and comparison using satellite SM products, I think some uncertainty should be explained regarding lack of deeper soil moisture observations and understanding, and implications for drought.  The top-level soil measurements at first 5 cm, woefully neglects the important impacts of rootzone SM on drought. 

Lines 219-223:  This is a very broad statement, and no definitely understanding on how results presented here either qualitatively or quantitatively agree/disagree with the NADM (e.g., spatiotemporal comparisons).  Please revise and be more explicit.

Lines 243-245:  Understood that long-term direct chemical flux measurements are limited, but what about the CASTNET (dry deposition) and NADP (wet deposition) networks?

3 Results

Lines 318-339:  I find parts of this section very speculative, and qualitative, where it is difficult to follow the emphasis (also due to writing issues, noted below).   

Lines 343-346:  So are the differences in dry deposition contributions a result of overestimated wet deposition in other literature/models, or underestimations of wet deposition based on WRF-Chem? This is a confusing and rather contradictory argument.

Lines 341-365:  Again, this paragraph largely compares the results from this modeling system based on WRF-Chem to other literatures, and has some rather speculative arguments.  I think much of this paragraph could be trimmed, improved writing (see below), and improved discussion.  Its plausible this section is more an assessement of the WRF-Chem modeling evaluated generally, and mainly qualitatively against other models, and some measurements.  Not sure what is new here.

Lines 367-379:  I think this could be improved by directly comparing the modeled spatial Nr deposition to critical load thresholds for different vegetation types and how it has changed from 2018-2023.   

Lines 393-394:  I also think the much higher (lower) correlation coefficients against NO2 (HCHO) in year 2020 deserve to be highlighted and discussed briefly.

Lines 425-428:  Here it would be good to briefly describe the controlling parameters on decreasing CUO in the past 2018-2023, and that projected to continue to decrease in the future climate.  Seems much uncertainty here, and justification is needed for discussion.  If the eastern U.S. is projected to become wetter climate in the future, it would suggest increasing CUO, unless ozone concentrations decrease enough in proportion.  Even with decreasing anthropogenic NOy it plausible that future increases in some GHGs, e.g., CH4, could lead also to widespread increases in ozone concentrations in the future, thus exacerbating CUO increases under a projected wetter climate in Eastern U.S.

Lines 447-449: This seems a relatively small impact of the SMAP SM DA on surface ozone bias and RMSE, relative to other much larger controlling factors on ozone formation and loss processes. 

 Lines 465-466:  This does seems more important locally (e.g., Northern Virginia), and would be much easier to see if paired ozone spatial bias plots (against AQS) were provided for both the Model no-DA vs. Model DA, instead of having to qualitatively compare the surface AQS obs against contour plots in Figure 14.  Ultimately, I am concerned of the statistical significance of these ozone changes, and think some quantification of the significance is needed here. 

Lines 484-508:  In this section 3.2, a provided map of irrigated vs. non-irrigated lands is necessary to interpret the changes in Figure 15.  Also, very difficult to interpret the noisy signal of Nr deposition, and as above comment, the significance of these changes are strongly in question for relevance and understanding.  Suggest a statistical significance test is included on these changes, otherwise, the results presented here are very questionable.

Lines 520-522:  I do not follow this argument, ¼-1/3 as large?  This could stem from writing issues here, but very difficult to take anything from this argument scientifically.

Lines 529-540:  These are very weak scientific arguments, and is only very cursory here with the ozone profiles in Fig. 17b and WRF-Chem/AQS spatial maps of ozone concentrations in Figure 17c-j.  There is really no evidence provided here really isolating the elevated ozone with fire plume transport vs. other sources of extra regional ozone and precursor transport, when simply using clean (unrealistic) vs. base simulations in Figure 18.  Indeed, these interactions are known to be very complex regarding ozone concentrations.  More evidence and potentially source apportionment or sensitivity studies (e.g., simply fires on vs. fires off) would be needed to associate these areas with fire plumes vs. other sources of important precursors, and the related enhancements in daytime surface ozone formation.

Lines 538-539:  Would suggest adding more recent literature on the importance of fires, N deposition, and implications for downwind ecosystems.  This is a growing field of importance. 

https://doi.org/10.1016/j.scitotenv.2022.156130

https://library.wmo.int/records/item/62090-no-3-september-2023; see Pages 7-8

Summary and Suggested Future Directions

Lines 580-595:  I find these arguments significantly broad and not well supported by the presented results in this paper.  From what is presented, it is very difficult to determine, where this WRF-Chem configuration performed “remarkably” better than other platforms.  Better identification, quantitative comparisons, and examples of improved results are needed.  I assume much of this comment is pertaining to the inclusion of Land DA for SM and different simple case studies such as irrigation switches and turning off chemical LBCs, i.e., Clean scenario (Section 3.1-3.3).  However, as presented, it is rather cursory arguments, which are not fully apparent how much better this system is able to represent the interactions of Nr and ozone formation. 

Technical Corrections:

***Detailed grammatical errors and suggestions only shown here for Abstract and Introduction sections***

Abstract

Lines 15-20:  Grammatical error.  Run-on sentence, and needs revision.

Line 20:  Grammatical error.  This statement “compared with and related to” is redundant. 

Lines 23-24:  Grammatical error.  Remove comma.

1 Background, motivation, and goals

Lines 42-44:  Grammatical error.  The sentence structure is very awkward, and needs revision. 

Line 47:  Grammatical error.   Change “O3 via the aerosol radiative” to “O3 via aerosol radiative”.

Lines 47-48:  Grammatical error.  Remove “the” in “via the aerosol radiative effects”.

Lines 48-54:  Grammatical error.   Run-on sentence, and needs revision.

Line 55:  Suggest changing  “would be” to “is”.

Lines 58-59:  Grammatical error.  Awkward sentence structure, and cannot understand the connection the author is making with  “…and carbon dioxide (CO2) concentration as well as plants’ physiological conditions.”

Lines 62-64:  Grammatical errors and inappropriate verbiage.  “…continue to decrease there due..”, “…for studies on Nr and O3, attention should…”, and “imported”.

Line 68:  Grammatical error.  Need comma, “…and the estimated background O3, as well as…”

Lines 74-75:  Grammatical error. Run-on sentence, and needs revision.

Line 76:  Grammatical error.  Awkward sentence structure.  “…limits the capability of understanding air quality there and evaluating…”

Line 78:  Awkward verbiage.  Suggest changing “is anticipated to”  to “will”.

Lines 80-85:  Grammatical error.  Run-on sentence, and needs revision.

Lines 88-97:  This is too long for a bulleted list, particularly difficult to read in bullet 3).  Suggest separating it out of the paragraph and shortening into more bullets to make easier to read and understand.   

Results

Lines 318-339:  I find this section needs significant writing improvements, as discussed above.  Also, it would be best to break this paragraph up into multiple paragraphs.

Lines 341-365:  Writing needs significant improvement and needs multiple paragraphs.       

Lines 381-431:  Writing needs significant improvement and sentence structure needs substanitial improvement.  Currently it is difficult to follow the arguments.

Note:  Similar writing improvements are needed through the remaining results section.

Summary and Suggested Future Directions

Technically, this section also needs similar significant writing improvements and is very cumbersome to read.  Highly recommend thorough proof-reading in revised manuscript. 

The MS (egusphere-2024-484) by Huang conduced model simulation of Reactive nitrogen in and around the northeastern and Mid-Atlantic US, and analyzed its influence on O3 and plant, it shows a lot of model simulation work by considering different model setups and also analyzing so many components, which follows Huang’s previous studies as list in references. However, the performance of model simulation, especially for dry/wet deposition and the influence of O3 on plants should be furthered carefully evaluated, which can be potential large uncertainty for this study.

other comments as:

Line 111-113, “Noah-MP’s CO2 forcing for 2018, 2019, 2020, 2022, 2023’s warm seasons were set to 410, 412, 415, 420, and 423 ppmv, respectively, based on measurements at the Mauna Loa Observatory and its nearby Maunakea Observatories for part of 2023”, why this study choose GHG background values of Mauna Loa as the CO2 forcing for the urban area, which can have much higher CO2 concentration as >450 ppm, and affect photosynthesis of plants.

Line 155, “missions Database for Global Atmospheric Research version 5 based on the Community Emissions Data”, please illustrate what the spanning years for EDGAR v5.0 for these pollution species.  

Line 165-174, the authors mentioned the annual variations of different pollution species, it’s much better to illustrated them with time series figure than worlds.

Section 2.2.3 ground-based observations, why the site based PM2.5 PM10 the components SO42- NO3- NH4+ were not compared with model simulations, which can support your model’s performance regarding atmospheric chemical reaction, dry/wet deposition and pollution emissions.

Section 2.3.2 I am still wondering whether the plant models in your study can well represent the harmful O3 effect on stomate, where the parameters and plant model structure can largely affect your evaluation.

Section 3.1 usually the model simulated results should first compare with observations (not all species, depends on what observations the authors have as illustrated in method section) to verify the performance of model. It’s easy to run the model and analyzed model simulations, but it’s hard to tell us whether the simulations from your model parameter and emission setup were reliable. Here on line 407, I just notice your comparison with surface O3, and I am not that confident your model can well simulate the spatial-temporal variations of O3 changes, as you only displayed the averages of a period, instead of hourly observations, with considerable bias. Have you considered the impact of stratospheric intrusion on ozone enhancement in the lower troposphere with upper O3 boundary condition scheme?

Section 3.2 irrigation approaches, on line 486, the “Ozone perturbs gross primary productivity more strongly (up to 20–30%) than transpiration”, as I mentioned above, whether there are observation-based study that displayed similar results? because the plants model can not well simulate the feedback between O3 and plant. To me The GPP decreased by 20-30% only caused by O3 is not reliable, see the situations in China and India, large O3 concentrations occurred in summer, but the influence on crop production did not change too much. From my experience, even the influence of O3 on plants have not been well investigated by field observations, how can it be well represented by model equation and structure?