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Article
Peer-Review Record

Can We Measure a COVID-19-Related Slowdown in Atmospheric CO2 Growth? Sensitivity of Total Carbon Column Observations

Remote Sens. 2020, 12(15), 2387; https://doi.org/10.3390/rs12152387
by Ralf Sussmann * and Markus Rettinger
Reviewer 1:
Reviewer 2: Anonymous
Reviewer 3:
Remote Sens. 2020, 12(15), 2387; https://doi.org/10.3390/rs12152387
Submission received: 17 June 2020 / Revised: 17 July 2020 / Accepted: 21 July 2020 / Published: 24 July 2020
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases and Air Pollution)

Round 1

Reviewer 1 Report

This paper discusses an interesting topic, "feasibility of measuing a COVID-19 related slowdown in atmospheric CO2 growth". A mathematical framework has been developed to derive annual gwoth rates from observed column-averaged carbon dioxide. In general, the paper is well written and can be published with minor revisions.

(1) line 55, the the accuracy of TCCON is 8 ppm, while it is claimed to be 0.8 ppm in Section 2.1.

(2) the equations and the corresponding equation numbers are not centered aligned for many equations, e.g., Eq. (7)

(3) Figure 1, there are four curves in the subset figure, while there are only two legends for them. It is confusing for readers.

(4)It would be better to add a flowchart for the mathematical models in the manuscript.

(5) Figure 2, it is very difficult to identify the curves, which are some times with the same colors.

(6) It seems that there is not any confident conclusion about the COVID-19 related slowdown. What should we do in order to better evaluate this effect?

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

I have reviewed the manuscript entitled “Can we measure a COVID-19 related slowdown in Atmospheric CO2 growth? Sensitivity of Total Carbon Column Observations”.  Authors tried to measure responses of XCO2 measured by TCCON to the social closure due to COVID-19 pandemic. They utilized XCO2 observations from 4 sites and extracted trends of XCO2 using a model fit. Their evidences showed that possible reductions of carbon emissions have not reflected by TCCON XCO2 observations.

       I think this work has a few drawbacks. But it is worth being published because there is really lack of works dedicated to study possible reductions of carbon emissions using observation means. I have some comments as follow.

 

1, a basic argument of this work is that “The 2020 quasi-global lockdown due to the COVID-19 pandemic lead to enormous emission reductions estimated by various recent studies to range up to 8 % for the whole year 2020”. Referring to those papers, I find that a critical parameter, namely change in different activities, is subjectively preset. Therefore, I would regard that argument as a tentative projection but not a conclusion. The final conclusion needs to be proved by measurements or retrospective studies. Currently, there are several studies on responses of air pollutants to the COVID-19 closures. Early evidences from China revealed that NO2 concentrations indeed exhibit evident decline but other major atmospheric pollutants have kept steady, implying anthropogenic emissions may not decline so much as we thought. Given that there is a strong correlation between carbon emissions and air pollutions in China, it is reasonable to inference that anthropogenic carbon emissions could not decline so much either.

 

2, In subsection 2.1, authors introduced how they process the data from TCCON. As mentioned in Liang, et,al Remote Sensing 9 (10), 1033 and Wunch, et,al Atmospheric Measurement Techniques, 2017, 10(6):1-45., it is necessary to screen up original data before further calculating statistics or evident errors could be introduced into results. 

 

3, regarding calculations of annual growth, I am not familiar with authors’ model fit. But, as far as I know the SLT (Seasonal-Trend decomposition procedure based on Loess) is widely used to exact seasonality and annual growth of XCO2 observations of long periods. The SLT technique is a commonly used tool to process CO2 concentrations, especially for those obtained by GLOBALVIEW-CO2. As mentioned in Line 234-235, derived annual growth of this work is similar but still different from the global mean trend reported by the global carbon project. It that possible different techniques yield slight differences of resultant annual growth?

 

4, P7 line 257- 258 “Adopting a scenario of an 8 % COVID-19 related global emission reduction”. Referring to “Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement” which is one of your references, I show a figure from that work. I think the plunge is far more than 8 %. Because you used TCCON measurements before June those data won’t contain signals from the forthcoming months in this year. If there was such an annual reduction of 8%, the change rate in the first 5 months this year would be much larger than what Fig.1 shows.

5, I am not sure whether sites authors selected can reflect signals of CO2 reductions due to the pandemic. Site Park Falls locates in U.S. Many states did not take strong actions coping with COVID-19 until April. It thus would be very reasonable that measurements of this site can not reveal reductions of CO2 emissions. Other 3 sites are in Germany. Chancellor Merkel closed border in March. Assuming quarantine measures started then, I doubt whether those sites can “receive” reduced XCO2 in such a short time, especially considering that France and Britain reacted even later. 

 

6, LINE 498, “fond” spelling mistake.

 

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

The manuscript "Can we measure a COVID-19 related slowdown in atmospheric CO2 growth? Sensitivity of Total Carbon Column Observations" by Sussmann & Rettinger address the question of the detection limits of TCCON to significantly detect the CO2 reduction due to COVID-19. The authors present different mathematical procedures required to overcomes the limitations of the datasets (non-homogeneous sampling during the year, interannual variability, oversampling) and investigate several cases taking into account the growth reduction predicted for Mauna Loa. Despite the negative answer to the question, the work presents relevant results that deserve publication. On the other hand, the methodology is not clearly explained, with relevant parts mentioned in the results section (weekly-aggregates, detection delay definition) but not described in the methodology. My recommendation is a major revision of the manuscript to reorganize all the information properly and also correct several typos. More details and identification of the typos are provided in the attached document 

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

The authors have addressed all my concerns about the manuscript. It can be published after a last minor detail is corrected. In figure 2, the 2-week panel is repeated, instead of the 1-week. Please correct it.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

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