the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 May 2024
Simulations of Snow Physicochemical Properties in Northern China using WRF-Chem
Abstract. Snow is a key component of the cryosphere and has significant impacts on surface energy balance, hydrology, atmospheric circulation, and etc. In addition, numerous studies have indicated that snow impurities, especially nitrate, are sensitive to sunlight and can be photolyzed to emit reactive species including NO2 and HONO, which serve as precursors of O3 and radicals and disturb the overlying atmospheric chemistry. This makes snow a reservoir of reactive species, and this reservoir is particularly important in remote and pristine regions with limited anthropogenic emissions. The magnitude of snow chemical emissions is also influenced by snow physical properties, including snow depth, density and concentrations of light-absorbing impurities (e.g., BC and dust). Exploring and elucidating the emissions and atmospheric consequences of the snow-sourced reactive species require a global or regional model with a snow module. Here, we parameterized atmospheric nitrate deposition and its distributions in snow using a regional chemical transport model, i.e., the WRF-Chem (the Weather Research and Forecasting Model coupled with Chemistry) model, and evaluated the performance of the WRF-Chem model in simulating snow cover, snow depth, and BC, dust and nitrate concentrations with field observations in northern China which is one of the regions with dense and prolong snow cover. The model reasonably reproduces the observed snow cover and depth in northeastern and northwestern China, and the observed snow dust and nitrate concentrations are also reasonably reproduced. These results illustrate the ability of WRF-Chem in simulating snow properties including concentrations of reservoir species in northern China, and in the future, we will incorporate snow nitrate photolysis in the model, exploring the emissions of snow NOx from nitrate photolysis and the impacts on local to regional atmospheric chemistry and air pollutant transformations.
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RC1: 'Comment on gmd-2024-37', Anonymous Referee #1, 18 Jun 2024
Review of “Simulations of snow physiochemical properties in northern China using WRF-Chem”
This paper describes the development of a treatment for nitrate deposition on snow in a mesoscale model and compares the simulations with available meteorological and aerosol deposition data. The new model developments are incremental, extending previous treatments of black carbon and other species to now include nitrate. The observations provide a unique dataset covering a wide geographic region needed to better evaluate model performance. The ability to simulate nitrate deposition is the first step to permit two-way interactions between the atmosphere and the snow. The feedback of snow nitrate on atmosphere is not covered in this paper. This paper is clearly written and is suitable for GMD, but my preference is that more material is needed on the model development (my first major comment).
My first major comment is that the details of how the deposition of nitrate on snow is handled in the model in Section 2.2.1 is far too brief. The authors seem to point back to previous studies to indicate how other aerosol species were handled. However, when I looked at those papers, the description of the model development there is also inadequate. There is some description in this paper at how deposition is treated at the surface, but what is lacking is how nitrate becomes bound to snow in the atmosphere. WRF-Chem does handle cloud-borne aerosol species, but it does not include ice-borne aerosols as indicated by the paper (line 127). So, treating in-cloud scavenging must be parameterized in some way. Then what happens when the snow melts to rain? Some nitrate may simply partition back to the atmosphere before it falls as snow. How is below cloud scavenging by falling snow handled in the model? These are questions that need to be addressed. Given the lack of details it would be difficult to reproduce the results and findings from this study using the information from this paper in its current form.
My second major comment is that it would be useful to also include an evaluation of how well the model did with regard to nitrate as a function of snow depth. The paper focuses only at the topmost snow layer. If there is a way to include a comparison of vertical distribution within the snow that would be very valuable. This will ultimately impact subsequent studies that simulate the feedback of nitrate back to the atmosphere.
My third major comment is regarding the Figures, which I mention in my specific comments. The authors choose to use only spatial comparisons with the observations. Given the uncertainties in color scales, it would be more meaningful to include scatter plots or some other type of plot to provide a better quantitative assessment of the model performance. While this might add length to the paper, it is worth it. And this material could be included in supplemental information materia.
Specific Comments:
Line 78: There is an extra parenthesis.
Line 110: change “of China” to “(USTC) of China”. Suggest changing the first part of the “Distinguished …” sentence. The phrase implies the USTC version is not available to the public. I assume the authors mean the USTC developments are simply not on in the version distributed by NCAR.
Line 111: “boasts” is a strong word. Suggest changing to “the USTC version includes supplemental functionality such as online …”
Line 115: This version of MOSAIC is rather old and does not include newer versions that have treatments of SOA, which is often a large fraction of total particulate mass. While this assumption will underestimate total PM deposition on snow, perhaps it does not matter for this time of year and when focusing on nitrate, BC, and dust observations. Some discussion on this topic seems warranted. Another area to expand upon is how calcium is treated in the model, since the authors compare predicted calcium with observations. My understanding is that by default, the model assumes a certain percentage of other inorganics are calcium, so one could tweak that ratio to better agree with observations. I am not saying that has happened here, but the reader needs to understand some details of the model to understand why the model is predicting certain species.
Figure 6: the color scale needs revision to have more gradients to better understand differences between the observations and simulated values. Also a scatter plot of obs vs model would be useful. As in Figure 5, it is best to zoom in a focus on the areas with observations.
Lines 127-128: Is this process included by Chapman et al. (2008), or has it been added later?
Line 128: by “convection” I assume you mean parameterized shallow and deep convective clouds that are subgrid scale? Please be more specific.
Lines 155-159: The way the text is phrased is sounds like snow photolysis has been included, but elsewhere the authors indicate that the feedback to the atmosphere is not included. Suggest revising this text to eliminate the possible confusion introduced.
Figure 1: Samples is misspelled in the figure. Put a space between the year and month. What is the difference between red dots and red stars?
Line 288: This sentence is provided as a motivation for the model evaluation of meteorological variables. But the accuracy is a bit more complicated than the authors note here. The accuracy will also depend on how well the model simulates synoptic circulations and the treatment of cloud microphysics, in addition to other factors.
Lines 290-291: I thought the Morrison microphysics scheme contains a snow specie, but there the authors suggest otherwise. Perhaps a bit more discussion is needed here.
Figures 2-3: Perhaps it would be useful to include a scatter plot of obs vs model as an extra panel. The dots could be colored by region to show performance differences between northern and southern China.
Figure 4: Fraction is misspelled in the figure. It would be better if the MODIS and WRF panels were together (maybe left vs right), so that reader can compare them easier. Has the MODIS data been averaged to the 36 km WRF grid? This would provide a fairer comparison. Then a scatter plot (perhaps by region) would provide a more meaningful quantification of the difference.
Figure 5: This figure could be improved by only including one China-wide plot that only shows the boxes. The small panels are more important and could then be larger.
Line 406: The statement “the model agreed well with the observations” is too vague and subjective. Please give some #’s or better description.
Lines 401-420: Since there are no BC obs, what is the value of this section to the main goal of the paper to describe nitrate? This section could be deleted. If not, provide a better justification for including it.
Figure 8: There is something in the lower right corner that is unreadable. If unimportant it should be removed.
Figure 9: Same comment as Figure 5. Again a scatter plot comparing obs and model is needed. Another figure is needed to show trends from month to month. It is hard to infer this, but the authors are claiming good predictions in the trends in the conclusions.
Lines 534-535: This statement is subjective and would be useful to include some #’s on bias, correlation, etc. regarding what constitutes “effectively replicates”
Line 538: this statement talks about validation with BC, but there were no observed BC in this study. So, are the authors implying the BC deposition is similar to other studies in a climatological sense? This needs more clarification.
Line 540: “validity of the results” is an another vague, subjective statement. What do the authors mean by “assessing the simulation outcomes.” It seems to me the assessing part was the evaluation of the simulated nitrate with observations, which is part of the “validity of the results” phrase. So the authors need to better communicate a message here.
Line 541: What is “slight”? Can you give a percentage? That would be more meaningful.
Line 544: Could part of this be due to not including the feedback of nitrate back to the atmosphere? If so, that would mean that the present results should be biased high compared to observations. But that doesn’t seem to be the case at all locations.
Line 545: Is there a stray “w”?
Citation: https://doi.org/10.5194/gmd-2024-37-RC1 -
AC1: 'Reply on RC1', Xia Wang, 11 Oct 2024
We are grateful to the reviewer for his/her time and efforts reviewing this manuscript. We have carefully read through the comments and made responses as well as relevant revisions in the manuscript. Please find our point-by-point response (black) and the corresponding revisions (blue) as follows. Please find the attached file for the detailed responses.
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AC1: 'Reply on RC1', Xia Wang, 11 Oct 2024
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RC2: 'Comment on gmd-2024-37', Anonymous Referee #2, 28 Aug 2024
Snow is a key component of the cryosphere and has significant impacts on surface energy balance, hydrology, atmospheric circulation, and more. Moreover, snow is significant in atmospheric chemistry, where snow impurities such as nitrate are sensitive to sunlight and can be photolyzed to emit reactive species including NO2 and HONO, which can significantly disturb atmospheric chemistry, especially in pristine regions. An accurate description of the emission and atmospheric consequences of snow-emitted reactive species is hence important for assessing the atmosphere environment. To address this issue, the authors parameterized atmospheric nitrate deposition and its distributions in snow using WRF-Chem model, the performance of the simulations in snow depth, and BC, dust and nitrate concentrations are well validated by field observations in northern China. Overall, this paper is well written and will be very helpful to the related research communities to improve the understanding of snow-atmosphere interactions and its influence on environments. I think this work is suitable for publication in GMD if the following concerns can be addressed:
Major comments
- You point out that to simulate snow nitrate photolysis and its impacts on overlying atmospheric chemistry, one need to obtain snow cover, snow depth, and snow physical and chemical properties, including snow density; impurities, including BC, dust; and nitrate. Other studies have parameterized most factors except snow nitrate concentration, which was the primary contribution of your work. However, your title was “Simulations of Snow Physicochemical Properties in Northern China using WRF-C”, and the abstract includes much descriptions about the simulation and validations of snow cover, snow depth, and BC and dust concentrations, which ware not belonging to your work. In contrast, the description about snow nitrate simulation, the primary contribution of your work, was not enough. So, I suggest some necessary revisions to the title and abstract to emphasis your highlight on snow nitrate simulation. For example, in your abstract, the quantitative performance in nitrate concentration simulation, the bias analysis, and possible bias sources should be included to show the readers how good is your simulation. In addition, the results should more focus on snow nitrate simulation.
- The descriptions on method were not clear:
(a) Line 180, from Equations 2 and 3, horizontal diffusion was not concluded in wet deposition calculation, is its influence was insignificant?
(b) Line 192, form Equation 4, the unit of MNITS should be same to ΔF×dtime/ΔWsno. However, in Equation 5, the unit of MNITS was same to ΔF×dtime/ΔWsno×Δt.
(c) Line 193, ∆F is the cumulative wet and dry deposition of atmospheric nitrate during the entire period between the newly fallen snow and the previous time step. This means the unit of ∆F was kg m-2. If so, the unit of the second term in Equation 5 was not kg kg-1. Please check.
(d) Line 219-220, you mentioned “the nitrate concentrations in each snow layer are determined by factors such as atmospheric deposition rates, the amount of new snowfall, layer combinations and divisions, and meltwater flushing (Oleson et al., 2010b; Flanner et al., 2012; Flanner et al., 200”, how did you consider the layer combinations and divisions in your simulation.
(e) Line 185, you assumed a mix with the top 2cm layer? Do you have any references? For dry deposition, I can agree with your assumption, but for wet deposition, such an assumption may induce significant bias.
(f) Line 212, the scavenging ratio for nitrate was assigned to 0.2. From my knowledge, the nitrate was much soluble. Assigning a low scavenging ratio should add more discussions.
(g) In Equation 6, what did qi+1ci+1 represent, please clarify.
Minor comments:
- Suggest to add scatter plots of simulated versus observed data for simulation validations, especially for snow nitrate.
- Line 415, BCS increase during the melting period should be mainly due to melt enrichment (Doherty et al., 2013)
- Line 506-509 add necessary references to support your discussions.
- Suggest to add more quantitative bias analysis, especially for Section 3.4.2 Nitrate concentrations and spatial distribution.
- Line 543-549 add necessary references to support your discussions.
- Line 516-519 you mentioned comparing simulated monthly values with observed daily values should be cautioned due to the significant temporal fluctuations in NITS, why did you do daily-to-daily comparisons as you can output daily results.
- Line 543-549 is repeated by Line 555-563 more or less, please simplify.
References:
Doherty, S. J., T. C. Grenfell, S. Forsstro¨m, D. L. Hegg, R. E. Brandt, and S. G. Warren (2013), Observed vertical redistribution of black carbon and other insoluble light-absorbing particles in melting snow, J. Geophys. Res. Atmos., 118, doi:10.1002/jgrd.50235
Citation: https://doi.org/10.5194/gmd-2024-37-RC2 -
AC2: 'Reply on RC2', Xia Wang, 11 Oct 2024
We are grateful to the reviewer for his/her time and efforts reviewing this manuscript. We have carefully read through the comments and made responses as well as relevant revisions in the manuscript. Please find our point-by-point response (black) and the corresponding revisions (blue) in the attached file for the detailed responses.
Model code and software
xwang/wrfchem_ustc3.5: wrfchem_ustc 3.5 including nitrate concentrations in snow Xia Wang et al. https://zenodo.org/records/10586762
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