How well do Earth System Models reproduce observed aerosol changes during the Spring 2020 COVID-19 lockdowns?

Digby, Ruth A. R.; Gillett, Nathan P.; Monahan, Adam H.; von Salzen, Knut; Gkikas, Antonis; Song, Qianqian; Zhang, Zhibo

doi:https://doi.org/10.5194/egusphere-2023-432

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https://doi.org/10.5194/egusphere-2023-432

Preprints

11 Apr 2023

| 11 Apr 2023

How well do Earth System Models reproduce observed aerosol changes during the Spring 2020 COVID-19 lockdowns?

Ruth A. R. Digby, Nathan P. Gillett, Adam H. Monahan, Knut von Salzen, Antonis Gkikas, Qianqian Song, and Zhibo Zhang

Abstract. One side effect of the Spring 2020 COVID-19 lockdowns was a rapid reduction in aerosol and aerosol precursor emissions. These emission reductions provide a unique opportunity for model evaluation, and to assess the potential efficacy of future policy decisions. We investigate changes in observed regional aerosol burdens during the COVID-19 lockdowns, and compare these observed anomalies to predictions from Earth System Models forced with COVID-19-like reductions in aerosol and greenhouse gas emissions. Despite the dramatic economic and lifestyle changes associated with the pandemic, most anthropogenic source regions do not exhibit detectable changes in satellite retrievals of total or dust-subtracted aerosol optical depth. Only India exhibits an aerosol optical depth anomaly that exceeds internal variability. These conclusions are broadly reproduced by Earth System Models when confounding factors have been accounted for. We present a systematic assessment of the contributions of internal variability, model input uncertainty, and observational sampling to the aerosol signal, and highlight the impacts of observational uncertainty on model evaluation analyses. These results are encouraging, suggesting that current Earth System Models may be able to realistically capture the effects of future emission reductions.

Received: 08 Mar 2023 – Discussion started: 11 Apr 2023

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19 Feb 2024

How well do Earth system models reproduce the observed aerosol response to rapid emission reductions? A COVID-19 case study

Ruth A. R. Digby, Nathan P. Gillett, Adam H. Monahan, Knut von Salzen, Antonis Gkikas, Qianqian Song, and Zhibo Zhang

Atmos. Chem. Phys., 24, 2077–2097, https://doi.org/10.5194/acp-24-2077-2024,https://doi.org/10.5194/acp-24-2077-2024, 2024

Short summary

Ruth A. R. Digby, Nathan P. Gillett, Adam H. Monahan, Knut von Salzen, Antonis Gkikas, Qianqian Song, and Zhibo Zhang

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-432', Anonymous Referee #1, 21 May 2023
In this study, the authors did lots of work to estimate AOD changes during spring 2020, based on satellite remote sensing products, and to evaluate AOD responses to emission reductions in several CovidMIP models. Unfortunately, the different satellite instruments gave such a large spread in AOD changes, even with dust (as one of the natural species) excluded in the retrievals. Strong regional dependence of the robustness of observational estimates and model performance is found, but drivers behind this are unclear. The analysis of CovidMIP models does not add much to the literature, beyond the original CovidMIP paper (Jones et al., 2021) and other published studies. These are the major concerns leading to my hesitation to recommend the current manuscript for publication. The CanESM5 sensitivity tests are more interesting and potentially revealing. The novelty and science significance of this paper may be increased by focusing more on in-depth analysis of the sensitivity experiments on the roles of meteorological factors and possibly microphysical processes driving the response of aerosols to emission reductions in spring 2020.
Below are a few more specific comments:
The abstract lacks quantitative results either from the observational estimates or model analyses.

Line 4 (and several other places): strictly aerosol optical depth rather than aerosol burden is estimated in this study, which should be made clear.

Line 39-41: This statement is inaccurate. Jones et al. (2021) did specifically compare regional AOD changes among the participating Earth system models.

Line 46: Please be more specific about what kinds of observed changes being used for model evolution purposes. Global or regional climate models use many different observational data for the evaluation purpose.

Line 61-64: depending on the purpose of obtaining aerosol concentrations, it can be a big problem of using a column optical property (AOD) as a proxy for aerosol concentration or aerosol burden mentioned in the first science question.

Line 78 (and section 2.1): It is too vague and generic to name meteorological conditions as one of the determining factors of AOD. In addition to the emissions, one should at least speak to the transport and sink terms of atmospheric aerosols such as dry and wet deposition in aerosol budget equation, although the detailed aerosol chemical and microphysical processes are sometimes even more important, depending on the aerosol types.

Table 1: There might be too few models. Are they outliers among the 12 CovidMIP models?

Line 228-231: How was the first assumption tested?

Line 284: This statement about contribution of dust to total AOD is inaccurate and can be misleading. It highly depends on season and region. Globally, dust contributes to less than 25% of annual total AOD.
Citation: https://doi.org/10.5194/egusphere-2023-432-RC1
RC2: 'Comment on egusphere-2023-432', Anonymous Referee #2, 23 May 2023

The manuscript “How well do Earth System Models reproduce observed aerosol changes during the Spring 2020 COVID-19 lockdowns?” use the COVID lockdown and the following emission reduction for model evaluation. Modelled changes in aerosol optical depth (AOD) due to COVID restrictions and satellite retrieved AOD in March, April, May (MAM) 2020 are compared. The Earth System Models and observations show consistent results in Europe and India, where India is the only region considered with a significant reduction in AOD in MAM 2020 in the observations. In China and Northern Hemisphere as a whole, the modelled reduction in AOD is overestimated. Using one model, a systematic assessment of the influence of meteorology, baseline emissions, size of COVID emission reductions are done. The spread in the observations of AOD is a limiting factor of further constraining the models.
The manuscript is well structured and presented, and I have only a few comments.
The uncertainties in the satellite AOD products precludes a further constraint on the models responses to emission reduction. As this method outlined here, also can be used to evaluate response to future emission reductions, a bit more on future direction of satellite AOD evaluation would have been good.
In the abstract:
L4: “observed regional aerosol burdens during” It is not aerosol burden that is assessed, but AOD (as a proxy for aerosol burden).
Consider swapping section 2.3 and 2.2?
Table 1: The mineral dust column can be misread as if models include mineral dust or not in the simulations. Maybe replace “Mineral dust?” by “od550dust” or “Mineral dust output”.
Section 3.2: Could be useful with a table of the satellite AOD products.
ACROS-C is used in Figure 1, but not mentioned in section 3.2.1.
L253: “the Northern Hemisphere as a whole” From figure 1 and text elsewhere, the “as a whole” is not entirely correct as it is 0N-70N. Replace “as a whole” with (0N-70N).
Figure 1 (and 2 and 3) contain a lot of information. It could be useful to add more information to the legend, maybe first present what is included in the timeseries (the six models and the observations with symbol and black line). Then, as a separate box or just below, the 2020 values (Square: control, diamond: covid pert, MMEc). For MMEc maybe only show the square and not the line, as I was looking at the time series when I first looked at the plot. See also if filled, black outline, opaque/semi-transparent can be indicated inside the figure as well. I am not able to see if the results are plotted opaque or semi-transparent. Possible to use filled or not filled symbols instead?
Figure caption: Delete “horizontal offset for visual clarity” Already mentioned that the right side of the panel was for 2020 and “2020 values” are the titles of the subpanels.
L313: It is hard by eye to see the difference in the trend between observations and models for the reference period (2015-2019).
L359-362: This was a bit unclear. Just note that the MMEc is as in Figure 2.

Citation: https://doi.org/10.5194/egusphere-2023-432-RC2
AC1: 'Response to RC1 and RC2', Ruth Digby, 04 Jul 2023

We thank the reviewers for their helpful comments on our manuscript. Our responses to both reviewers are included in the attached document.

Citation: https://doi.org/10.5194/egusphere-2023-432-AC1

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-432', Anonymous Referee #1, 21 May 2023
In this study, the authors did lots of work to estimate AOD changes during spring 2020, based on satellite remote sensing products, and to evaluate AOD responses to emission reductions in several CovidMIP models. Unfortunately, the different satellite instruments gave such a large spread in AOD changes, even with dust (as one of the natural species) excluded in the retrievals. Strong regional dependence of the robustness of observational estimates and model performance is found, but drivers behind this are unclear. The analysis of CovidMIP models does not add much to the literature, beyond the original CovidMIP paper (Jones et al., 2021) and other published studies. These are the major concerns leading to my hesitation to recommend the current manuscript for publication. The CanESM5 sensitivity tests are more interesting and potentially revealing. The novelty and science significance of this paper may be increased by focusing more on in-depth analysis of the sensitivity experiments on the roles of meteorological factors and possibly microphysical processes driving the response of aerosols to emission reductions in spring 2020.
Below are a few more specific comments:
The abstract lacks quantitative results either from the observational estimates or model analyses.

Line 4 (and several other places): strictly aerosol optical depth rather than aerosol burden is estimated in this study, which should be made clear.

Line 39-41: This statement is inaccurate. Jones et al. (2021) did specifically compare regional AOD changes among the participating Earth system models.

Line 46: Please be more specific about what kinds of observed changes being used for model evolution purposes. Global or regional climate models use many different observational data for the evaluation purpose.

Line 61-64: depending on the purpose of obtaining aerosol concentrations, it can be a big problem of using a column optical property (AOD) as a proxy for aerosol concentration or aerosol burden mentioned in the first science question.

Line 78 (and section 2.1): It is too vague and generic to name meteorological conditions as one of the determining factors of AOD. In addition to the emissions, one should at least speak to the transport and sink terms of atmospheric aerosols such as dry and wet deposition in aerosol budget equation, although the detailed aerosol chemical and microphysical processes are sometimes even more important, depending on the aerosol types.

Table 1: There might be too few models. Are they outliers among the 12 CovidMIP models?

Line 228-231: How was the first assumption tested?

Line 284: This statement about contribution of dust to total AOD is inaccurate and can be misleading. It highly depends on season and region. Globally, dust contributes to less than 25% of annual total AOD.
Citation: https://doi.org/10.5194/egusphere-2023-432-RC1
RC2: 'Comment on egusphere-2023-432', Anonymous Referee #2, 23 May 2023

The manuscript “How well do Earth System Models reproduce observed aerosol changes during the Spring 2020 COVID-19 lockdowns?” use the COVID lockdown and the following emission reduction for model evaluation. Modelled changes in aerosol optical depth (AOD) due to COVID restrictions and satellite retrieved AOD in March, April, May (MAM) 2020 are compared. The Earth System Models and observations show consistent results in Europe and India, where India is the only region considered with a significant reduction in AOD in MAM 2020 in the observations. In China and Northern Hemisphere as a whole, the modelled reduction in AOD is overestimated. Using one model, a systematic assessment of the influence of meteorology, baseline emissions, size of COVID emission reductions are done. The spread in the observations of AOD is a limiting factor of further constraining the models.
The manuscript is well structured and presented, and I have only a few comments.
The uncertainties in the satellite AOD products precludes a further constraint on the models responses to emission reduction. As this method outlined here, also can be used to evaluate response to future emission reductions, a bit more on future direction of satellite AOD evaluation would have been good.
In the abstract:
L4: “observed regional aerosol burdens during” It is not aerosol burden that is assessed, but AOD (as a proxy for aerosol burden).
Consider swapping section 2.3 and 2.2?
Table 1: The mineral dust column can be misread as if models include mineral dust or not in the simulations. Maybe replace “Mineral dust?” by “od550dust” or “Mineral dust output”.
Section 3.2: Could be useful with a table of the satellite AOD products.
ACROS-C is used in Figure 1, but not mentioned in section 3.2.1.
L253: “the Northern Hemisphere as a whole” From figure 1 and text elsewhere, the “as a whole” is not entirely correct as it is 0N-70N. Replace “as a whole” with (0N-70N).
Figure 1 (and 2 and 3) contain a lot of information. It could be useful to add more information to the legend, maybe first present what is included in the timeseries (the six models and the observations with symbol and black line). Then, as a separate box or just below, the 2020 values (Square: control, diamond: covid pert, MMEc). For MMEc maybe only show the square and not the line, as I was looking at the time series when I first looked at the plot. See also if filled, black outline, opaque/semi-transparent can be indicated inside the figure as well. I am not able to see if the results are plotted opaque or semi-transparent. Possible to use filled or not filled symbols instead?
Figure caption: Delete “horizontal offset for visual clarity” Already mentioned that the right side of the panel was for 2020 and “2020 values” are the titles of the subpanels.
L313: It is hard by eye to see the difference in the trend between observations and models for the reference period (2015-2019).
L359-362: This was a bit unclear. Just note that the MMEc is as in Figure 2.

Citation: https://doi.org/10.5194/egusphere-2023-432-RC2
AC1: 'Response to RC1 and RC2', Ruth Digby, 04 Jul 2023

We thank the reviewers for their helpful comments on our manuscript. Our responses to both reviewers are included in the attached document.

Citation: https://doi.org/10.5194/egusphere-2023-432-AC1

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Ruth Digby on behalf of the Authors (04 Jul 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (07 Oct 2023) by Xiaohong Liu

RR by Anonymous Referee #1 (22 Oct 2023)

RR by Anonymous Referee #2 (23 Oct 2023)

RR by Anonymous Referee #3 (24 Oct 2023)

Suggestions for revision or reasons for rejection

Review of "How well do Earth System Models reproduce the observed aerosol response to rapid emission reductions? A COVID-19 case study" by Digby et al.

This manuscript compares AOD in a series of model experiments with COVID emission reductions to observations. It concludes with mostly a null result that the changes in Aerosol Optical Depth due to COVID19 induced emission changes/reductions during March-May 2020 were mostly not significant, except over India. The manuscript I think is mostly okay, and with some substantial revisions (mostly minor, I don't think much new work is needed) it should be acceptable for ACP. I don't think AOD is a great metric, but I think as far as the scope of the paper the conclusions are well justified. It is hard to publish a mostly null result, and it should be encouraged.

I make a variety of suggestions and comments below that will need clarification:

Page 3, L60: how was COVID different than long term changes? You mention co-emission of species. Did COVID reductions change that? My guess is probably: energy emissions were the same, but transport emissions were reduced. Maybe discuss this?

Page 4, L115: You might want to note explicitly that satellite retrieval error and model error are two other significant error sources.

Page 6, L170: How accurate was the 2 year blip assumption? Can you compare it to mobility data until now? Since it sounds like you are going to conclude emissions matters, maybe you can show the assumptions versus actual observations/inventories?

Page 7, L186: Is Sigmond et al the reference for the CanESM5 AOD spread or do you need to show a figure?

Page 10, L260: I don’t think using the models to define observational anomalies is really appropriate. Why don’t you use the pre-covid period and it’s variability to define the mean and variability for the t-test?

Page 10, L280: are you doing this at each point or some global mean? I hope it is at each point: there is information in the pattern.

Page 11, L300: note that MAM = March - May?

Page 11, L304: define “detectable”

Page 14, L370: Please clarify this is the same model as the CanAM5 in the previous plots (you corrected it for this paper).

Page 15, L389: I don’t agree, it is in line with one of the data sets, and that is unchanged from the regular emission version given the spread.

Page 15, L397: you just said Europe is improved with the new emissions. Be consistent or more precise.

Page 16, L417: for the 2020 covid decrease. It does NOT improve earlier agreement.

Page 17, L420: Again, I don’t agree with this interpretation. What about anomalies in 2017 and 2018? Please be specific here.

Page 17, L431: but the inter-annual variability is not reproduced. the observations tend to have the same sign of inter-annual anomalies and the nudged model does not. Please explain how this is consistent?

Page 18, L465: in the previous section you implied that nudging did improve agreement (please explain or make consistent).

Hide

ED: Publish subject to minor revisions (review by editor) (16 Nov 2023) by Xiaohong Liu

AR by Ruth Digby on behalf of the Authors (05 Dec 2023) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (30 Dec 2023) by Xiaohong Liu

AR by Ruth Digby on behalf of the Authors (03 Jan 2024) Author's response Manuscript

Journal article(s) based on this preprint

19 Feb 2024

How well do Earth system models reproduce the observed aerosol response to rapid emission reductions? A COVID-19 case study

Ruth A. R. Digby, Nathan P. Gillett, Adam H. Monahan, Knut von Salzen, Antonis Gkikas, Qianqian Song, and Zhibo Zhang

Atmos. Chem. Phys., 24, 2077–2097, https://doi.org/10.5194/acp-24-2077-2024,https://doi.org/10.5194/acp-24-2077-2024, 2024

Short summary

Ruth A. R. Digby, Nathan P. Gillett, Adam H. Monahan, Knut von Salzen, Antonis Gkikas, Qianqian Song, and Zhibo Zhang

Supplement

https://doi.org/10.5194/egusphere-2023-432-supplement

Ruth A. R. Digby, Nathan P. Gillett, Adam H. Monahan, Knut von Salzen, Antonis Gkikas, Qianqian Song, and Zhibo Zhang

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Short summary

The COVID-19 lockdowns reduced aerosol emissions. We ask whether these reductions affected regional aerosol optical depth (AOD), and compare the observed changes to predictions from Earth System Models. Only India shows an observed AOD reduction outside of typical variability. Models overestimate the response, but when necessary factors have been accounted for, the agreement is improved. Our results suggest that current models can realistically predict the effects of future emission changes.


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How well do Earth System Models reproduce observed aerosol changes during the Spring 2020 COVID-19 lockdowns?

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Interactive discussion

Interactive discussion

Peer review completion

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Suggestions for revision or reasons for rejection

Suggestions for revision or reasons for rejection

Journal article(s) based on this preprint

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