General Comments

This study investigates the potential impacts of urbanization and rising SSTs on tropical cyclones impacting Shanghai based on convection-permitting WRF simulations of five tropical cyclones. The authors find a significant impact from increasing SSTs, including consistent increases to cyclone radius, maximum rainfall rate, and 10-m wind speed over the cyclone area that are more variable over Shanghai itself, as well as a southward shift in the cyclone track. They also find that urbanization has only a small impact on tropical cyclones, with almost no effect on large-scale cyclone characteristics but a slight increase in rainfall and decrease in wind speed over Shanghai itself. The latter half of the paper also includes an analysis of potential mechanisms, concluding that increased SSTs enhance lower tropospheric temperatures, wind speeds, and pressure anomalies, which increase cyclone size and intensity, and produce a southward shift of the cyclone tracks through enhancement of the Fujiwhara effect.

The results are clear, well-presented, and compelling. The paper is also beautifully written and was a genuine pleasure to read. The authors did a good job of evaluating potential cyclone impacts across multiple metrics, distinguishing between large-scale changes and those affecting Shanghai in particular, and proposing and demonstrating plausible mechanisms for the effects they observed. My biggest substantive comments have to do with the figures, some of which I find to be insufficiently explained and which use color schemes that are confusing and even, in some cases, deceptive. Most of my remaining comments are looking for additional clarification on certain choices that were made in the methods or analysis rather than objections to those choices. Overall, I am recommending minor revisions.

Specific Comments

Event selection – time period. On line 86, why focus on the period from 2018-2022 for selecting TCs? Why not, for example, the five most destructive TCs impacting Shanghai in the last twenty or even thirty years instead?

Event selection – trajectory. You specified that the TCs under consideration had to directly impact Shanghai (line 87), but given the southward track shift you observed under higher SSTs isn’t it possible that cyclones that would otherwise have passed north of the city could shift to hit it instead? This also comes up on line 262, as it seems to me that the southward shift may spare Shanghai from some landfalls but could equally easily expose it to cyclones that would otherwise pass harmlessly north.

WRF resolution. On line 104, you describe the vertical resolution as 45 layers going up to 50 hPa. Are those levels terrain-following, hybrid, or pressure-following? And is the layer spacing constant or does it change with height? At least a few studies (Wu et al 2019 in Acta Oceanologica Sinica; Ma et al 2012 in Asia-Pacific Journal of Amostpheric Sciences) indicate that the vertical resolution in specific parts of the atmosphere can have a noticeable effect on TC simulation with WRF.

Urbanization case definition. Why choose the lowest urban development scenario, SSP1, for your increased urbanization case (line 112-113)? This seems designed to under-estimate the potential impact of urbanization.

SST case definition. Near the end of the paper, you acknowledge a potential pattern effect of SST change which is in contrast to the uniform warming you imposed (line 280-281), so why did you go with a uniform warming case (line 118)? Particularly when you already have the pattern effect, at least in a climatological sense, in Figure S1.   

Significance or Confidence Intervals on TC metrics. Figures 5 and 6 look a lot at changes in key TC metrics from the CTR case, and line 151-152 discusses deviations from the control track in km. Is there any way to establish statistical significance of these changes, using either the bias from observations or some spread in these metrics from an ensemble of TC simulations? For example, some of the magnitudes discussed are quite small (line 186-187 discusses a “consistent” change in maximum rainfall and maximum wind speed in Figure 6, but those changes are on the order of 1-4 mm/h and 1-2 m/s, which seems very small to attribute to a significant impact of urbanization). I realize this could be challenging to quantify, but if there is any way to do so I think it would make it much easier to interpret Figures 5 and 6.

Domain for calculating I_max and W_max. In Figure 6, the maximum rainfall and wind speed are calculated just over the Shanghai domain, but in Figures 5 and 7 it is not clear to me the domain that was used to find these maxima. Is it within the radius R during the timesteps that Shanghai was also in that radius, similar to Figure 2c?

Color scale for Figures 5 and 6. I don’t understand the color mapping used in these figures, and on fairly close inspection actually find them quite deceptive. As far as I can tell, each panel (single heat map) uses a shared colorbar, which has a diverging colormap where the lowest values are red and the highest are blue. But the intention of the figure seems to be to compare changes from the control simulation, so in my view there should really be a separate color scale for each row (otherwise the highest-intensity values will always be on the most and least intense TCs, so you don’t really see changes even when they do exist across the middle of the pack), such that the CTR simulation is grey for each TC, any increase from that is red, and any decrease is blue, with the two sides symmetric so that the intensity of the color indicates either the absolute or relative (which may be better, since the text mostly discusses changes as a percent of the control case) change from the control case. As is, there are some very confusing cases where the text describes, for example, a three-fold increase in the radius of Jongdari in the SST3 scenario (line 160), but the corresponding box in Figure 5a is the palest shade in the Jongdari row, which would imply the opposite. Either way, the color scaling should be explained in the caption, particularly since it is not provided as a colorbar on the figure.

Diverging colormap for uniformly increasing values. A minor point, but S1 uses a diverging red-blue colormap even though the underlying values are uniformly positive and represent a change in temperature over time. I found this unnecessarily confusing, and it took me a while to realize that the blue areas are still an increase in SST, just a smaller one.

Definition of Delta in Figure 7. It was not clear to me what the Delta in rainfall and wind represents. In “after landfall”, for example, is the change calculated as a change over the Shanghai domain from the moment of landfall to one time step (1 hour) after landfall? 6 hours after landfall? In that case, line 196 is confusing to me, as it states that an enhancement of rainfall both before after landfall indicates that Jongdari decays slowly after reaching land; if rainfall increases after landfall, it’s not decaying at all yet, right? And if the change is actually between the CTR case and a SST/U3km case, I’m still not sure what time(s) “before” and “after” refers to. This is another case where I think a more detailed explanation in the figure caption of what the Delta I_max and Delta W_max^10m actually represent is needed.

Fujiwhara effect seen in all cases. The finding that the Fujiwhara effect played a role in the response of all 5 TCs to increased SSTs was particularly interesting to me (line 221-222). Was there no secondary pressure low in the CTR case, and one developed in all 5 cases under increased SSTs? Or did the secondary low already exist but just got stronger/closer to the primary TC? Is there any explanation for why this change in the effect strength happens under increasing SST? Explaining why the Fujiwhara effect gets stronger would help to support the claim made in line 296-297, particularly since about 61% of current TCs occur as doubles (line 228), which is a large number but wouldn’t necessarily produce any change in the other 39% of TCs unless the increase in SSTs actually makes double-TC events more frequent rather than just changing the range/strength of interaction.

Influence of El Nino. In line 266, you state “anomalously warmer SST, so-called El Nino, significantly influences typhoons on a large scale.” However, the support for this point that follows seems to be based on a single El Nino year (2023) and subsequent TC season. I think you either need more references to support the claim that El Ninos in general can influence typhoons on a large scale or to soften that claim to a possibility rather than a surety.

Technical Corrections

Line 44: “there are not many evidences” -> “there is not much evidence”

Line 92: “followed the TC central location” -> “following the TC central location”? I’m not sure I understand this sentence structure, though

Line 105 and in Table 2: “Yonsei University scheme (YSU) scheme” -> “Yonsei University (YSU) scheme”

Figure 7 caption: “landfall from Shanghai” -> “landfall in Shanghai”

Line 203: “T” -> “T^850”

Line 225: “The typhoon” -> “Typhoons” and “has been found normally moving” -> “have been found to move” or similar

Line 238: “amount of water vapor context” get rid of “context”

Line 242: “enhances the upward” -> “enhances upward”

Line 243: the citation should be in-text, no parentheses

Line 249-250: “marking a record-breaking four-time landfall” not sure what this refers to; did the TC make landfall four times? Or was the record broken by a factor of four?

Line 273: “tropical regions” New York and Tokyo (2 of the 3 listed cities) are mid-latitude, not tropical

This study examines the effects of sea surface temperature (SST) warming and urbanisation on typhoon hazards and risks in Shanghai, a city often prone to their effects. This is done by comparing WRF experiments with uniform SST warming and land use change for five recent damaging typhoons. The results show that typhoons increase in size with warming SSTs, making their associated wind and rainfall more hazardous. However, increases in urbanisation has little effect on the synoptic evolution of the typhoon, instead increasing the local rainfall due to increased surface roughness and moisture convergence.

Overall, this manuscript is very well written, succinct, and easy to follow. The methodology, results, and discussions are all sound, and there is clear impact and relevance to disaster management. There is also a good discussion of the limitations and avenues for future research. I recommend this paper for publication after minor revisions addressing the following comments.

General comments:

1. The authors suggest that a southward shift of tracks due to the Fujiwhara effect may reduce cyclone impacts in Shanghai. However, the case studies selected were only ones that impacted Shanghai to begin with. Is it possible that typhoons that would normally make landfall north of Shanghai will now directly hit Shanghai, resulting in greater effects? Some further discussion of this, particularly around lines 262-263, may be beneficial.
2. Figure 9 presents differences between CTR and U3km for In-fa, however this seems to be largely a null result (i.e. no significant changes). Meanwhile, Figure S5 presents results that show substantial differences between the simulations, and these are discussed in the text. Because of this, I would suggest moving Fig S5 into the main body of the text, and Fig 9 into supplementary material.
3. Some of the colour maps used in figures could be improved. Notably, a blue-red diverging colour map is frequently used where a sequential colour map would be more suitable. This applies to Figs 3, 5, 6, 8, 9, S1, S2 and S4.
4. A study by Yin et al. (2021) has previously the effects of SST warming on typhoon hazards in Shanghai and could be included in the discussions.

Yin, K., Xu, S., Zhao, Q., Zhang, N. and Li, M., 2021. Effects of sea surface warming and sea-level rise on tropical cyclone and inundation modeling at Shanghai coast. Natural hazards, 109(1), pp.755-784.

Specific comments:

L11-12: “…and their dynamic systems” is unclear. What specifically does this refer to?

L44: change to “there is not much evidence that supports…”

L92-93: given that all TCs analysed here are from 2018 onward, you could just say the temporal resolution is 3 hrs.

L98: MODIS should be defined.

L104-105: WSM and MM5 should be defined.

Fig S1 caption: “temporary” should be “temporal”.

L128: “radius” or “distance” is perhaps more suitable than “buffer”.

L142: I’m not sure that a percentage bias is appropriate for MSLP here, given it tends to only vary between approx. 950-1050 hPa.

L143: I don’t think “climate dynamics” is suitable here since this isn’t a climate study. Consider replacing this part with something like “the tracks and intensities are well simulated, including an increase in SLP_min as the TCs move over land”.

Fig 3: the red contour showing Shanghai is a little difficult to see. Consider changing this to a different colour or otherwise making it clearer. Also relevant for Figs 8 and 9, where the red line overlies red shading.

L159: it may be convenient to redefine the variables for the reader.

L163: “exposure” could be changed to “exposure time” etc. to specify this paragraph is referring to the time of the cyclone.

L168-169: The first sentence here feels like it should belong in the previous paragraph, otherwise it could be removed.

L178: Are the quantities shown in Fig 6 averaged over the Shanghai domain, or are these values at a single grid point?

L187-188: I recommend changing the words “positive/negative effect” to remove any connotations. Something simple like “can increase/decrease” may work. Also see L198.

L210-211: “equivalent in intensity” is only really true for SST2 and SST3 experiments.

L213-214: does In-fa actually move southward/southwestward, or is it just that its northward propagation is slowed?

L225: change to “Typhoons in the Northern Hemisphere have been found to normally move northward…”

L225-231: It also appears that raising SSTs allows the model to spin up TCs more frequently and rapidly, potentially leading to more double (or more) TC activity events and hence more common Fujiwhara effects. Has this been observed in recent decades? Some further discussion could be beneficial.

L238: should this be “water vapour content”?

L243: the authors for the references here should be outside the brackets.

L266: should this “warmer SST” related to El Nino be specific to the central/east Pacific?