Impact of Urbanization on the Predictions of Urban Meteorology and Air Pollutants over Four Major North American Cities

Round 1

Reviewer 1 Report

Review comments on “Impact of Urbanization on the Predictions of Urban Meteorology and Air Pollutants over Four Major North American Cities” by Ren et al.

 

The authors investigated the sensitivities of meteorological and chemical predictions to urban effects in four major North American cities in different season with a 2.5km horizontal resolution Environment and Climate Change Canada’s air quality model using the Town Energy Balance (TEB) urban scheme. It is demonstrated that the TEB scheme affects urban temperature by changing surface heat fluxes. The evaluation results show that the predictions with the TEB scheme lead to a great improvement in biases and root-mean-square deviations temperature and humidity predictions in downtown, uptown and suburban areas in the early morning and nighttime.  The TEB scheme also leads to big improvement of predictions of 
nitrogen dioxides, PM2.5 and ground-level ozone in the downtown, uptown and industrial areas in the early morning and nighttime. The paper can be published after modification according the following comments:

Major comments:

• In Figure 5, there are big differences in the afternoon, what causes that big differences?
• Difference in RH is around 2% in Figure 11, is this statistical significant?
• In Figure 22, all T bias are negative, what’s the dynamical/physical mechanisms cause that negative bias? Why the T bias is around -2.5 degree over island?
• In Figure 22, RMSDs for Down Town and Island for Non-TEB are similar but their biases are very different, what reasons cause that?
• Correlation coefficients for NO2 or for NOx without TEB are larger than that with TEB for Down Town, Up Town and Industrial area in Figures 28 and 29. Does this mean that TEB does not positive impact on correlation in those areas?
• All negative bias for O3 in Figure 30 and positive bias for PM2.5 in Figure 31, why the model has systematic bias in O3 and PM2.5?

Minor comments:

• In Figure 13, there are some cutoff in lines, the reader of the paper may want to see the whole line in the plots.
• In Figure 14, there are some cutoff in line, and it will be nice to keep all the lines in the plots.

Author Response

Please see the attachment

Author Response File: Author Response.docx

Reviewer 2 Report

It is the study to see the impact of urbanization on the prediction of urban meteorology and air pollutants.
It is a good study and well organized but i have some comments and suggestion. First and important none about the length of article as unnecessary details are provided. Over all length of article is more and need to reduce it, although it looks a good initial draft but still to reduce it. There are too many figures are provided, is it possible to reduce the number.
Also need to focus on discussion as it is added with conclusion so make it confusion, if possible then separate it. While there are specific comments can be seen in attached reviewed version. 

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 3 Report

Dear authors,

First of all, I would like to congratulate you for the work done behind this paper, and for the paper itself. In my opinion it is very interesting and it adds an important value on urban scale meteorological/climate/air quality numerical modelling fields. However, I have some general comments I would like you address or, at least, think about them.

1. 4 cities are studies here however there is a lack of city inter-comparison. Nevertheless, one of the keywords is “Urbanization impacts”. This topic is important because the physiography of each city plays important role urban fluxes. I would expect that most of the differences found among the 4 cities are linked to their own physiography (namely building heights, urban fraction and green spaces, and street wide). Moreover, the paper cited (28) as the one where physiography information is available only concerns Montreal which is not included here. I suggest the authors to add physiography features on the urban fluxes analysis to make it stronger and more useful.  
2. The amount of data analysed here is huge, thus one cannot expect a small paper. However, in my opinion, the results can be better digest in order to present the analysis in a more succinct way. I would recommend the authors to revise the text again in order to reduce the paper size in about 20-30% (up to 45 pages max).
3. Why is the statistical analysis only presented for Toronto? I suggest including all cities in this analysis in a more succinct way.
4. Section 5 gives important background information about the data which potentially helps the reader to analyse the sensitivity tests in section 3 and 4. Thus, I would recommend to move the section 5 for the beginning (as section 3).

Specific comments:

1. The abstract has more than 300 words, please reduce it to 200 world max (as recommended in the author’s guidelines)
2. Line 42: heating buildings [15] (b is missing)
3. Line 81: Martilli 2003 should be [24]; same in line 156, line 164
4. Section 2.1: Is the 2.5km domain only one including the 4 cities, or there is one specific domain for each city? Please include a table as below with domain geography information of the simulation domains

	Domain 1	Domain 2
Resolution (km)	10	2.5
Ncell (x)
Ncell (y)
Model top (hPa)
Number of vertical levels
Vertical resolution
Source of boundary and initial conditions

 

5. Please give more details about the initialization of the 10km domain. What does EnVAR data assimilation analysis mean in this case? Where does it come from? What is the horizontal resolution?
6. Line 166: double “the” in “because the the hourly…”
7. Line 192: it should be “In addition to traffic heat flux, the heat flux released…”
8. Line 192-195: This paragraph is not about heat fluxes from vehicles, so move it to another section or change the section title. Moreover, to my knowledge TEB does not include A/C on the simulation of heat fluxes. Please explain how it is taking into account in this work.
9. Section 3.1:
   1. What does TOWN mean. Is it a sum of all urban features? If so, how is it so similar to ROOF? In line 231 it is said that it is an aggregation. How did this aggregation made up?
   2. What areas are considered in each city? Is it a specific location, the average of downtown area? In the last case, what are the area-sizes involved in the analysis?
   3. In man
10. In many figures (Fig.3, 12, 13, 14, 16) the data is out of the range.
11. Section 4: The plots are extensively described but little explored. Please point out a reason for the facts in the following lines:
    1. 312-314
    2. 320-325
    3. 333-338
    4. 343-346
    5. 355-356
12. Line 349: “UBL” hasn't been used yet.
13. Line 350: What do you mean by UBL UHI effects?
14. Line 353: The differences are about 1-1.5°C
15. Line 361: “heat fluxes are reduced by TEB”. Do you mean “TEB simulated more heat absortion by urban surfaces”?
16. Line 361-363: This can be because Detroit has higher green fraction?
17. Line 377: If T is dimensionless variable, what is it? Is DT= T₂ -T₁?
18. Line 394: remove “by”
19. Line 415-418: please reformulate the sentence. I think there are 2 sentences here.
20. Line 453: replace “slow” by “Smooth”
21. Figure 5: It is too small to read. Please increase the font size.
22. Section 4.4: a reference to AQHI is missing
23. Section 4.4.3: AQHI is function of O3, NO2 and PM2.5, however PM2.5 is not analyzed here while in the abstract it appears as one of the pollutants under analysis. Please clarify this situation
24. Figure 21 is presented as being the same than Figure 20 but for winter, however it is shows diurnal profile of weekdays and weekend instead of Diff AQHI components. Please clarify this.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The authors' replies solve my concerns, and in my point of view, the paper can be accepted for publishing.

Author Response

Thanks for your decision.