the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Aug 2023
Fingerprints of the COVID-19 economic downturn and recovery on ozone anomalies at high-elevation sites in North America and Western Europe
Abstract. With a few exceptions, most studies on tropospheric ozone (O3) variability during and following the COVID-19 economic downturn focused on high-emission regions or urban environments. In this work, we investigated the impact of the societal restriction measures during the COVID-19 pandemic on surface O3 at several high-elevation sites across North America and Western Europe. Monthly O3 anomalies were calculated for 2020 and 2021, with respect to the baseline period 2000–2019, to explore the impact of the economic downturn initiated in 2020 and its recovery in 2021. In total, 41 high-elevation sites were analyzed: 5 rural or mountaintop stations in Western Europe, 19 rural sites in the Western US, 4 sites in the Western US downwind of highly polluted source regions, 4 rural sites in the eastern US, plus 9 mountaintop or high-elevation sites outside Europe and the United States to provide a “global” reference. In 2020, the European high-elevation sites showed persistent negative surface O3 anomalies during spring (March–May, i.e., MAM) and summer (June–August, i.e., JJA), except for April. The pattern was similar in 2021, except for June. The rural sites in the Western US showed similar behavior, with negative anomalies in MAM and JJA 2020 (except for August), and MAM 2021. The JJA 2021 seasonal average was influenced by strong positive anomalies in July, due to large and widespread wildfires across the Western US. The polluted sites in the Western US showed negative O3 anomalies during MAM 2020, and a slight recovery in 2021, resulting in a positive average anomaly for MAM 2021 and a pronounced month-to-month variability in JJA 2021 anomalies. The Eastern US sites were also characterized by below average O3 for both MAM and JJA 2020, while in 2021 the negative values exhibited an opposite structure compared to the Western US sites, which were influenced by wildfires. Concerning the rest of the World, a global picture could not be drawn, as the sites, spanning a range of different environments, did not show consistent anomalies, with a few sites not experiencing any notable variation. Moreover, we also compared our surface anomalies to the variability of mid-tropospheric O3 detected by the IASI satellite instrument. Negative anomalies were observed by IASI, consistent with published satellite and modeling studies, suggesting that the anomalies can be largely attributed to the reduction of O3 precursor emissions in 2020.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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Supplement
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3075 KB) - Metadata XML
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Supplement
(10980 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-1737', Anonymous Referee #1, 14 Sep 2023
General comments
This paper investigates the fingerprints of Covid-19 on 41 elevated mountain sites over the world, mainly in the USA and Europe. The scientific interest of the paper is very important, regarding the ozone chemistry related to sources and sinks. The paper is excellently written and well organised with the different chapters.
The measurements sites and the methods for data selection are well described and the data selection is accurate, with night-time values or daily 8h maximum averages for some stations. The use of IASI data is a good choice for comparing with satellite data.
The quantification of the 2020-2021 anomalies is well explained and discussed, related to the emissions reductions shown in Table3.
The conclusion is robust, due to the high number of sites and the O3 reduction is comparable to the IASI data.
All figures are excellent quality, easily understandable and well commented in the text. The supplementary material is also excellent quality.
This paper is suitable for publication.
Minor comments
The author should very briefly discuss about a possible reduction of stratosphere/troposphere exchanges in the period, as a non-negligible part of free troposphere ozone is coming from the stratosphere and as the stations are located in altitude.
Citation: https://doi.org/10.5194/egusphere-2023-1737-RC1 - CC1: 'Comment on egusphere-2023-1737', Rodrigo Seguel, 26 Sep 2023
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RC2: 'Comment on egusphere-2023-1737', Anonymous Referee #2, 09 Oct 2023
This manuscript calculates trends and anomalies in ozone concentrations at high elevations stations and explores the response in these metrics due to changes in behavior during the COVID-19 pandemic. This is additionally explored by examining temporal profiles in satellite retrievals of O3 column data.
Overall, the paper is a thorough and well written account of the changes experienced at the chosen sites. Subject to some clarification of the methods used and the specific comments below, this paper should be accepted for publication.
General Comments
In sections 2.1.2 the authors describe the calculation of the O3 anomalies after detrending and de-seasoning the timeseries. I have two primary concerns with this section:
- While I believe the process described here is sound, my concern is that as written, reproducing the methods from this description is not facile.
- There is substantial mixing of mean averaging with median seeking methods (i.e. quantile regression at 50%).
Restructuring of this section would go a long way to allay these concerns, and I would consider the inclusion of a simple flowchart in the SI making it overtly clear which steps are applied in what order. A good example of the language that is hard to follow surrounds L114: “Last, we used the differences calculated in the previous step…”. Are these differences referring to the resulting de-seasonalised timeseries, which makes sense for a trend, or is this referring to the climatological year, from which one could feasibly calculate anomalies – I believe the authors are referring the former, but I hope this illustrates the uncertainty that is introduced throughout this section. Being explicit with the use of “mean” or “median” over “average” would also help the reader keep the steps clear.
On the second point, my major concern is that the climatological year has been calculated via mean averaging, but then timeseries derived using this to remove seasonality have their trends defined by the median (via QR). After Chang et al, 2023 trend analysis using QR is the preferred method here, but I would question why de-seaonalisation was not conducted using a climatological year calculated using the median also, as one would surely want this to be less sensitive to outliers in any given year.
Specific comments
Line 101 – “The deseasonalization allows to produce a more…” Should perhaps read: “The deseasonalization allows the production of a more…”
Figure 2. – The use of two colours per p-value is not well described. Is hue used to denote trend sign, and saturation for significance? In the previous figure a very similar colour pallet was used to show region, which has now been moved the shape in this figure. If this is the case, the use of a different colour pallet here for significance (one colour only) and allowing the x-axis to denote trend direction would be much clearer.
Figure 3. – Referring the reader to fig. 1 for the definitions of the colours is not good, as the figure + caption should stand alone much more readily. The regions could be added to the y-axis to make the groupings clear.
Line 194, relating to figure 3. The wording here could be changed as I don’t think “clearly shows widespread… …in 2020” is strictly true. A similar statement could be made about 2009 or 2015.
Line 217, 2021 falling in the top 5 of 18 years – essentially means 2021 falls in ~ top 1/3rd of years over that period? This could be more clearly phrased.
Table 3. This table is attempting to show too much information at once, could be separated out into two to avoid the use of the parenthesis notation, which interrupts being able to read down the columns clearly.
Citation: https://doi.org/10.5194/egusphere-2023-1737-RC2 - AC1: 'Response to reviewers: egusphere-2023-1737', Davide Putero, 25 Oct 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1737', Anonymous Referee #1, 14 Sep 2023
General comments
This paper investigates the fingerprints of Covid-19 on 41 elevated mountain sites over the world, mainly in the USA and Europe. The scientific interest of the paper is very important, regarding the ozone chemistry related to sources and sinks. The paper is excellently written and well organised with the different chapters.
The measurements sites and the methods for data selection are well described and the data selection is accurate, with night-time values or daily 8h maximum averages for some stations. The use of IASI data is a good choice for comparing with satellite data.
The quantification of the 2020-2021 anomalies is well explained and discussed, related to the emissions reductions shown in Table3.
The conclusion is robust, due to the high number of sites and the O3 reduction is comparable to the IASI data.
All figures are excellent quality, easily understandable and well commented in the text. The supplementary material is also excellent quality.
This paper is suitable for publication.
Minor comments
The author should very briefly discuss about a possible reduction of stratosphere/troposphere exchanges in the period, as a non-negligible part of free troposphere ozone is coming from the stratosphere and as the stations are located in altitude.
Citation: https://doi.org/10.5194/egusphere-2023-1737-RC1 - CC1: 'Comment on egusphere-2023-1737', Rodrigo Seguel, 26 Sep 2023
-
RC2: 'Comment on egusphere-2023-1737', Anonymous Referee #2, 09 Oct 2023
This manuscript calculates trends and anomalies in ozone concentrations at high elevations stations and explores the response in these metrics due to changes in behavior during the COVID-19 pandemic. This is additionally explored by examining temporal profiles in satellite retrievals of O3 column data.
Overall, the paper is a thorough and well written account of the changes experienced at the chosen sites. Subject to some clarification of the methods used and the specific comments below, this paper should be accepted for publication.
General Comments
In sections 2.1.2 the authors describe the calculation of the O3 anomalies after detrending and de-seasoning the timeseries. I have two primary concerns with this section:
- While I believe the process described here is sound, my concern is that as written, reproducing the methods from this description is not facile.
- There is substantial mixing of mean averaging with median seeking methods (i.e. quantile regression at 50%).
Restructuring of this section would go a long way to allay these concerns, and I would consider the inclusion of a simple flowchart in the SI making it overtly clear which steps are applied in what order. A good example of the language that is hard to follow surrounds L114: “Last, we used the differences calculated in the previous step…”. Are these differences referring to the resulting de-seasonalised timeseries, which makes sense for a trend, or is this referring to the climatological year, from which one could feasibly calculate anomalies – I believe the authors are referring the former, but I hope this illustrates the uncertainty that is introduced throughout this section. Being explicit with the use of “mean” or “median” over “average” would also help the reader keep the steps clear.
On the second point, my major concern is that the climatological year has been calculated via mean averaging, but then timeseries derived using this to remove seasonality have their trends defined by the median (via QR). After Chang et al, 2023 trend analysis using QR is the preferred method here, but I would question why de-seaonalisation was not conducted using a climatological year calculated using the median also, as one would surely want this to be less sensitive to outliers in any given year.
Specific comments
Line 101 – “The deseasonalization allows to produce a more…” Should perhaps read: “The deseasonalization allows the production of a more…”
Figure 2. – The use of two colours per p-value is not well described. Is hue used to denote trend sign, and saturation for significance? In the previous figure a very similar colour pallet was used to show region, which has now been moved the shape in this figure. If this is the case, the use of a different colour pallet here for significance (one colour only) and allowing the x-axis to denote trend direction would be much clearer.
Figure 3. – Referring the reader to fig. 1 for the definitions of the colours is not good, as the figure + caption should stand alone much more readily. The regions could be added to the y-axis to make the groupings clear.
Line 194, relating to figure 3. The wording here could be changed as I don’t think “clearly shows widespread… …in 2020” is strictly true. A similar statement could be made about 2009 or 2015.
Line 217, 2021 falling in the top 5 of 18 years – essentially means 2021 falls in ~ top 1/3rd of years over that period? This could be more clearly phrased.
Table 3. This table is attempting to show too much information at once, could be separated out into two to avoid the use of the parenthesis notation, which interrupts being able to read down the columns clearly.
Citation: https://doi.org/10.5194/egusphere-2023-1737-RC2 - AC1: 'Response to reviewers: egusphere-2023-1737', Davide Putero, 25 Oct 2023
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Paolo Cristofanelli
Kai-Lan Chang
Gaëlle Dufour
Gregory Beachley
Cédric Couret
Peter Effertz
Daniel A. Jaffe
Dagmar Kubistin
Jason Lynch
Irina Petropavlovskikh
Melissa Puchalski
Timothy Sharac
Barkley C. Sive
Martin Steinbacher
Carlos Torres
Owen R. Cooper
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3075 KB) - Metadata XML
-
Supplement
(10980 KB) - BibTeX
- EndNote
- Final revised paper