From the journal Environmental Science: Atmospheres Peer review history

26-Apr-2021

Dear Dr Wang:

Manuscript ID: EA-COM-03-2021-000021
TITLE: Aerosol presence reduces diurnal temperature change: An interval when the COVID-19 pandemic reduced aerosols revealed the climate effect

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Reviewer 1

This study proposed that the COVID-19 pandemic led to a reduction in aerosols and an increase in the diurnal temperature range (DTR). It is an interesting topic and well fits the scope of the journal. However, the discussion is somehow descriptive and jumps to the conclusion on the interpretation of DTR increase. Thus, more in-depth analysis is expected to be provided. Here are some issues that need to be addressed for better supporting the conclusions.

The monthly AOD anomaly and its relationship with DTR are missing. Did the inverse relationship between aerosol and DTR also hold true in all the months? As demonstrated by some existing work, AOD and PM2.5 during the COVID was not always lower than the corresponding climatological mean. For instance, AOD and PM2.5 in Beijing and the surrounding area in Jan. and Mar. were higher than the climatological mean (Le et al., 2020; Filonchyk et al.,2020). But in the present work, DTR also showed a notable increase in this time period in Figure 2. Thus, it is not likely that aerosol was the main contributor to DTR increment from this perspective. Adding AOD or PM2.5 anomaly in Figure 2 will help and make it more clear. A scatter plot of AOD(PM2.5) anomaly and DTR can also provide more direct evidence.

Another issue is that this work conducted little analysis of the interannual variability of climatological conditions. Many studies, including Le et al., 2020, have analyzed the abnormal meteorological conditions in 2020. The climatological variation may also cause the anomaly in meteorological fields, including DTR.

This work states that DTRs during the COVID-19 are greater than 3 standard deviations above the climatological mean, that is 2-4°C increases according to Figure 2. I think one simple way to identify the role of aerosol is to estimate the radiative forcing base on AOD changes in Figure 1, and then roughly calculate the temperature perturbations due to such an RF value according to the relationship of radiative forcing and climate sensitivity.

Overall, this work presents an interesting phenomenon of the AOD decrease and DTR increase during the COVID-19 pandemic. But more solid evidence is needed to support the conclusion that AOD played the dominant role in it.

Reviewer 2

This manuscript describes temperature change due to decrease in aerosol during the COVID-19 pandemic in China. The topic addressed is interesting and will contributes to analysis to understand aerosol-climate interactions using observations and climate models for this period.
However, in order to indicate the effect of aerosol reduction on DTRs during the COVID-19 pandemic, it is necessary to make a distinction between long-term changes in DTRs caused by global warming, improvement of air pollution, etc. and changes in DTRs caused by the lockdown.
I recommend changing the period shown in Fig. 2 from February to June in 2020 to July in 2019 to June in 2020 including the period before the COVID-19 pandemic. If aerosol reduction by the lockdown has a strong influence on DTRs changes, the difference of DTRs from climatology will become large after June in 2020.

This text has been copied from the PDF response to reviewers and does not include any figures, images or special characters.

Reviewer #1 (Comments to the Author):

Reviewer #1 General Comments: This study proposed that the COVID-19 pandemic led to a reduction in aerosols and an increase in the diurnal temperature range (DTR). It is an interesting topic and well fits the scope of the journal. However, the discussion is somehow descriptive and jumps to the conclusion on the interpretation of DTR increase. Thus, more in-depth analysis is expected to be provided. Here are some issues that need to be addressed for better supporting the conclusions.
[Response] We sincerely appreciate the reviewer’s constructive comments that have greatly helped us to improve this study. Following these suggestions and comments, we have substantially revised the manuscript. Details of the revision are provided below. We hope that this revised version will answer your concerns and meet the Journal’s criteria as a Communication paper, which entails preliminary research findings that are novel and original. We believe our work is a pilot study to examine the inverse relationship between AOD and DTR during the COVID-19 pandemic, and hopefully it will inspire more observation and model simulation research on the climatic forcing of aerosols.

Reviewer #1 Major Comment 1: The monthly AOD anomaly and its relationship with DTR are missing. Did the inverse relationship between aerosol and DTR also hold true in all the months? As demonstrated by some existing work, AOD and PM2.5 during the COVID was not always lower than the corresponding climatological mean. For instance, AOD and PM2.5 in Beijing and the surrounding area in Jan. and Mar. were higher than the climatological mean (Le et al., 2020; Filonchyk et al.,2020). But in the present work, DTR also showed a notable increase in this time period in Figure 2. Thus, it is not likely that aerosol was the main contributor to DTR increment from this perspective. Adding AOD or PM2.5 anomaly in Figure 2 will help and make it more clear. A scatter plot of AOD(PM2.5) anomaly and DTR can also provide more direct evidence.
[Response] Thank you for this constructive comment. In the revision, we have first extended the study period to a 1-year range from July 2019 to June 2020, to conduct direct comparisons of DTR anomalies between the periods before and during the COVID-19 pandemic. Besides, following your suggestion, we added scatterplots of the monthly AOD anomaly and DTR anomaly before and during the lockdown period at both national scale and regional scale, so that it allows for direct investigation of their association (Fig. R1). Results show that the inverse relationship between aerosol and DTR is held true in almost all the months, with just a few exceptions (such as January in Beijing region). The following descriptions in the revised manuscript highlight the potential association between aerosol concentration level and DTR, which is consistent with our previous findings: “The possible relationship between the monthly DTR anomaly and the AOD anomaly is investigated before and during the lockdown period (Fig. 3). The inverse relationship between AOD reduction and the DTR increment is generally held true in almost all months, though their sensitivity varies among regions. Moreover, the anomalies of DTR and AOD are larger during the COVID-19 pandemic than those before the lockdown period with a few exceptions.” (page 3, lines 86-90 in the track version of revised manuscript).

In previous research, Filonchyk et al. (2020) pointed out that AOD in Beijing and surrounding area increased in January compared to the year before 2020, and then decreased in February and March, results that are compatible with our analyses. Le et al. (2020) found out that both AOD and PM2.5 in that area have increased during the period from January 23 to February 13 compared to a 5-year period mean level before the pandemic. We also observed this unexpected increase of AOD during January in the Beijing region (Fig. R1b), which may be related to the specific atmospheric circulation condition during that time period (Le et al., 2020). Overall, our analyses provide further evidence of the inverse relationship between aerosol concentration levels and DTR in most months before and during the COVID-19 pandemic. Moreover, these important references have been cited in the revised manuscript.

It should be noted that, as a Communications paper, this work aims to report the inverse relationship between aerosol presence and DTR, upon which additional, in-depth analyses combing observations and climate model simulations for this lockdown period can be performed. We could not declare that aerosol was the “main contributor” to the DTR increment at this stage, as there is really large uncertainty in aerosol modelling. To avoid potential confusion, we have modified some descriptions in the revised manuscript: “This anomaly has never occurred before in the 21st century and is at least in part associated with the observed reduction in aerosols.” (page 1, lines 26-28). “We argue that the increase is, at least in part, associated with the observed reduction in aerosols.” (page 4, lines 128-129).

Figure R1 Scatterplot for the monthly mean DTR anomaly with the corresponding AOD anomaly during July 2019 to January 2020 (blue) and during February to June in 2020 (red) from the 2000-2019 climatological values in China (a) and the five regions (b-f).

Reviewer #1 Major Comment 2: Another issue is that this work conducted little analysis of the interannual variability of climatological conditions. Many studies, including Le et al., 2020, have analyzed the abnormal meteorological conditions in 2020. The climatological variation may also cause the anomaly in meteorological fields, including DTR.
[Response] Thank you for raising this concern. Indeed, DTR anomaly could arise from a number of factors such as the inter-annual variability of climatological condition and atmospheric circulation, which may also impact other meteorological variables. Actually, in the early stage of this research, we have examined many other meteorological fields and could not find concrete changes like the observed national-wide DTR departures (Fig. R2). Therefore, we mainly focused on the potential association between aerosol concentration level and DTR. In the revised manuscript, we have presented the monthly mean values of three other variables: “Besides, DTR anomaly could arise from a number of factors such as the inter-annual variability of climatological condition and atmospheric circulation, which may also impact other meteorological fields. Three other variables, namely, precipitation, wind speed and dew point temperature are examined by using the GSOD dataset. However, the national mean values of the three variables from February to June of 2020 fall in the historical range of the preceding 19 years (Fig. 4), implying that the changes of climatological variation may not the main drivers of the substantially increased DTR during the COVID-19 pandemic.” (page 3, lines 90-97).

Figure R2 Comparison of precipitation (a), wind speed (b) and dew point temperature (c) from 2001-2019 climatology data and those for every month from February to June during the COVID-19 pandemic in China. The mean, 1 standard deviation range, and minimum-maximum range of the 2000-2019 climatology data are represented as black line, dark shadow, and light shadow, respectively. The mean values of 2020 are represented as blue line.

Reviewer #1 Major Comment 3: This work states that DTRs during the COVID-19 are greater than 3 standard deviations above the climatological mean, that is 2-4°C increases according to Figure 2. I think one simple way to identify the role of aerosol is to estimate the radiative forcing base on AOD changes in Figure 1, and then roughly calculate the temperature perturbations due to such an RF value according to the relationship of radiative forcing and climate sensitivity.
[Response] Thank you for your suggestion. In the revision, we further examine the role of aerosol reduction on the radiative forcing and DTR by employing the WRF-GC model (Lin et al., 2020; Feng et al., 2021). The model is a recent version of an online two-way coupling of the Weather Research and Forecasting model and the GEOS-Chem chemical model (http://wrf.geos-chem.org). The model domain covers the most regions of China (76.5°-133.5° E, 6.3°-44.5° N) at a grid resolution of 27 km × 27 km. All experiments are performed during January 5th to February 28th in 2020, and the results in February are used for analyses.

We design two comparable experiments forced by different anthropogenic emissions. In the control experiment, the monthly anthropogenic emission scenario is derived from the Multi-resolution Emission Inventory for China (MEIC; Zheng et al., 2018) with a resolution of 0.25° × 0.25° at the year 2017, then scale to the emission level of 2019 based on Zheng et al. (2020). In the lockdown experiment, the anthropogenic emissions during the COVID-19 pandemic are estimated based on the reported reduction ratios from 2019 to 2020 (Zheng et al., 2020). The difference between the two experiments can represent the response of radiative forcing to the AOD change during the COVID-19 pandemic.

We focus on the changes of shortwave radiation and long-wave radiation fluxes because they reflect the changes of daytime/nighttime radiation budget. The mean shortwave radiation and long-wave radiation over the locations of GSOD stations in China and the five regions are calculated for each experiment. And their changes between the two experiments are listed in Table R1.

The change in air temperature (T_a) can be roughly estimated using the following decompose equation (Zeng et al., 2017):
∆T_a= 1/f (-Sτ∆α-λ∆E+S(1-α)∆τ+ε_s σT_a^4 ∆ε_a+(ρC_d (T_s-T_a ))/(r_a^2 ) ∆r_a )+∆T_a^cir
The third term on the right attribute to the response of T_a to the changes in τ, which is the atmospheric shortwave transmissivity (the solar radiation reaching the land surface). f is an energy redistribution factor, f^(-1) represents the land surface air temperature sensitivity to 1 W m-2 radiative forcing at the land surface. Thus, the temperature perturbations due to the change of radiation can be estimated as follows:
∆DTR= ((∆SW-∆LW))⁄f
f is given by:
f= (ρC_d)⁄r_a +4ε_s σε_a T_a^3
where ρ (=1.21 kg m-3) is the air density; C_d (=1013 J kg-1 K-1) is the specific heat of air at constant pressure; r_a is the aerodynamic resistance (in s m-1), which can be estimated by wind speed (r_a= 208⁄ws); ε_s is land-surface emissivity, here we treat it as a constant of 0.95; σ (=5.67×10-8 W m-2 K-4) is the Stephan-Boltzmann constant; ε_a is the atmospheric air emissivity, here we treat it as a constant of 0.8.

Table R1. Modelled shortwave radiation change and the estimated temperature perturbations in China and the five regions.
China Beijing Chengdu Wuhan Shanghai Guangzhou
∆SW (W m-2) 3.81 7.04 9.77 12.17 7.57 8.78
∆LW (W m-2) -0.10 -0.22 -0.42 -0.40 -0.63 -0.47
Ws (m s-1) 2.54 2.43 1.94 2.37 3.11 2.42
Ta (K) 283.17 281.57 287.28 287.92 286.79 295.01
∆DTR (K/°C) 0.21 0.40 0.66 0.70 0.37 0.50

Results show that the national average shortwave radiation increase significantly by 3.81 W m-2 (t-student tests, p < 0.01) during February 2020, compared with a slight decrease of long-wave radiation by -0.10 W m-2 (Table R1). The changes of shortwave radiation and long-wave radiation in the five regions are up to 7.04~12.17 W m-2 and -0.63~-0.22 W m-2, respectively, indicating that the pandemic-related reduction of AOD has a substantial influence on the solar absorption by surface. We further calculated the mean wind speed and the mean maximum air temperature during the February in 2020 using the GSOD dataset, as shown in Table R1. Finally, the temperature perturbations due to the changes of radiation from the model simulations are estimated based on the above equations. At the national mean level, the estimated increase in DTR due to the changes in radiative forcing under the effect of the reduction in aerosols is about 0.21 °C. In the five regions, DTRs could increase by 0.40 °C, 0.66 °C, 0.70 °C, 0.37 °C, and 0.50 °C, respectively.

Our model experiments confirm the important role of the pandemic-related AOD reductions in increasing (decreasing) the daytime (nighttime) surface radiation absorption and the maximum (minimum) air temperature. The increased maximum air temperature in the daytime, compared with the decreased minimum air temperature during the nighttime, could result in an increase of DTR. These results suggest that the AOD changes are partially responsible for the observed DTR anomaly, though the magnitude of model estimated temperature change is lower than the observed DTR departures. This discrepancy could be contributed from many issues, such as the complex mechanisms in aerosol-cloud interactions, the uncertainties in model parameterizations, and the effect of weather variability (Ming et al., 2021; Wang et al., 2021). Nonetheless, both the observed large departures of DTR and the model-simulated radiation perturbations indicate a significant change in the climatic forcing effect of aerosols, whose concentration levels were reduced during the COVID-19 pandemic.

The above information has been included in the revised manuscript (pages 3-4, lines 99-115) and the Supplementary Information.

Reviewer #1 Major Comment 4: Overall, this work presents an interesting phenomenon of the AOD decrease and DTR increase during the COVID-19 pandemic. But more solid evidence is needed to support the conclusion that AOD played the dominant role in it.
[Response] We are grateful for your compliments on our paper and the constructive suggestions.
To conduct more in-depth analyses and present more solid evidence, we have first extended the study period to a 1-year range from July 2019 to June 2020 in the revised manuscript. Results show that AOD (DTR) has substantially negative (positive) departures during the pandemic (Fig. R3b). While the national mean AOD change before the lockdown period is only half of the change during the lockdown (Fig. R3), and the monthly mean DTRs in the first 7 months are generally fall in the climatological range of the preceding 19 years (Fig. R4). The related descriptions are provided in the revised manuscript (Figs. 1 and 2; pages 2-3, lines 39-84).

Figure R3 Spatial pattern of mean AOD changes during the period from July in 2019 to January in 2020 (a), and the period from February to June in 2020 (b) compared with the climatology pattern of the preceding 19 years in China (shades); standard deviation departures of average DTRs during the two periods from the climatological values at weather stations (dots). Regional analyses were conducted in the 4° × 4° regions around Beijing, Chengdu, Wuhan, Shanghai, and Guangzhou. AOD data was derived from the MODIS-Terra MOD08 M3 v6.1 dataset.


Figure R4 Comparison of diurnal temperature ranges (DTRs) from 2000-2019 climatology data and those for every month from July 2019 to June 2020 before and during the COVID-19 pandemic in China (a) and the five regions (b-f). The mean, 1 standard deviation range, and minimum-maximum range of the climatology data extending from July 2000 to June 2019 are represented as red line, dark shadow, and light shadow, respectively. The mean DTRs from July 2019 to June 2020 are represented as black line.

Besides, we have also added a scatterplot for the monthly mean DTR anomaly with the corresponding AOD anomaly before and during the lockdown period (Fig. R1), analyzed the inter-annual variability of other meteorological fields (Fig. R2), and confirmed the role of aerosol reduction by using the climate model (Supplementary Information), as described in the responses of Comment 1 to Comment 3. In addition, to avoid potential confusion, we have modified the previous descriptions that the DTR anomaly is at least in part caused by the observed reduction in aerosols to “associated with” in the revised manuscript.

References
Feng, X. et al. WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry-meteorology interactions. Geosci. Model Dev. Discuss. 2021, https://doi.org/10.5194/gmd-2020-441, in review.
Filonchyk, M., Hurynovich, V., Yan, H., Gusev, A. and Shpilevskaya, N. Impact assessment of COVID-19 on variations of SO2, NO2, CO and AOD over East China. Aerosol Air Qual. Res. 2020, 20, 1530–1540.
Le, T., Wang, Y., Liu, L., Yang, J., Yung, Y. L., Li, G. and Seinfeld, J. H. Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China. Science, 2020, 369, 702–706.
Lin, H., et al. WRF-GC (v1.0): online coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.2.1) for regional atmospheric chemistry modeling – Part 1: Description of the one-way model. Geosci. Model Dev. 2020, 13, 3241–3265.
Ming, Y., et al. Assessing the influence of COVID-19 on the shortwave radiative fluxes over the East Asian Marginal Seas. Geophys. Res. Lett. 2021, 48, GL091699.
Wang, N., et al. Air quality during COVID-19 lockdown in the Yangtze River Delta and the Pearl River Delta: Two different responsive mechanisms to emission reductions in China. Environ. Sci. Technol. 2021, 55, 5721−5730.
Zeng, Z., et al. Climate mitigation from vegetation biophysical feedbacks during the past three decades. Nature Clim. Change, 2017, 7, 432–436.
Zheng, B., et al. Trends in China’s anthropogenic emissions since 2010 as the consequence of clean air actions. Atmos. Chem. Phys. 2018, 18, 14095–14111.
Zheng, B., Zhang, Q., Geng, G., Shi, Q., Lei, Y., and He, K. Changes in China's anthropogenic emissions during the COVID-19 pandemic. Earth Syst. Sci. Data Discuss. 2020, https://doi.org/10.5194/essd-2020-355, in review.  
Reviewer #2 (Comments to the Author):

Reviewer #2 General Comments: This manuscript describes temperature change due to decrease in aerosol during the COVID-19 pandemic in China. The topic addressed is interesting and will contributes to analysis to understand aerosol-climate interactions using observations and climate models for this period.
[Response] We sincerely appreciate the reviewer’s compliments on the broad interest and the interpretation of our paper. We are also grateful for the reviewer’s constructive suggestions, which have helped us improve this study.

Reviewer #2 Major Comment 1: However, in order to indicate the effect of aerosol reduction on DTRs during the COVID-19 pandemic, it is necessary to make a distinction between long-term changes in DTRs caused by global warming, improvement of air pollution, etc. and changes in DTRs caused by the lockdown. I recommend changing the period shown in Fig. 2 from February to June in 2020 to July in 2019 to June in 2020 including the period before the COVID-19 pandemic. If aerosol reduction by the lockdown has a strong influence on DTRs changes, the difference of DTRs from climatology will become large after June in 2020.
[Response] Thank you for this helpful comment. Following your suggestion, we have extended the study period to a 1-year range from July 2019 to June 2020, to conduct a direct comparison between the first 7 months before the COVID-19 pandemic and the last 5 months during the pandemic (Figs. R3 and R4). All the meteorological data from July 2000 to June 2020 are screened by the strict quality control approach, and 361 gauges with valid temperature observation are finally used in the revision. We have updated the metrics, added more descriptions and modified some sentences accordingly. The additional analyses prove the general consistency of our findings, which indicate an inverse relationship between aerosol concentration levels and diurnal temperature ranges.

Results show that AOD has reduced remarkably across the country during the lockdown period (Fig. R3). The national mean AOD change during the lockdown is -0.0682 (95% confidence interval [-0.0749 -0.0616]; p < 0.01) compared with the 19-year period climatology pattern during February to June of 2001-2019, nearly double of the change from July 2019 to January 2020 before the lockdown period (-0.0345; 95% confidence interval [-0.0389 -0.0300]; p < 0.01). Almost all stations show higher DTRs during the lockdown period compared with the climatological mean levels during the preceding 19 years (Fig. R3b). While before the lockdown period, 74.0% stations show the DTR departures between -1 to 2 standard deviations (Fig. R3a). In addition, DTRs in all these five months during the COVID-19 pandemic are greater than 3 standard deviations above the climatological mean DTR — an anomaly that has never occurred before in the 21st century. In contrast, monthly mean DTRs in the seven months before the COVID-19 pandemic are generally fall in the climatological range except for December in 2019 (Fig. R4). The above information has been included in the revised manuscript (Figs. 1, 2; pages 2-3, lines 39-84 in the track version of revised manuscript).

Besides, we have also added comparisons for the monthly mean DTR anomaly with the corresponding AOD anomaly before and during the lockdown period (Fig. R1), and analyzed the inter-annual variability of other meteorological fields (Fig. R2), to conduct more in-depth analyses. In addition, we confirmed the role of aerosol reduction on the radiative forcing and DTR by employing the WRF-GC model (Supplementary Information). The related descriptions and discussions are involved in the revised manuscript (pages 3-4, lines 86-115). Both the observed large departures of DTR and the model simulated radiation perturbations indicate a significant change in the climatic forcing effect of aerosols, whose concentration levels were reduced during the COVID-19 pandemic.

10-Jun-2021

Dear Dr Wang:

Manuscript ID: EA-COM-03-2021-000021.R1
TITLE: Aerosol presence reduces diurnal temperature change: An interval when the COVID-19 pandemic reduced aerosols revealed the climate effect

Thank you for submitting your revised manuscript to Environmental Science: Atmospheres. After considering the changes you have made, I am pleased to accept your manuscript for publication in its current form. I have copied any final comments from the reviewer(s) below.

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Reviewer 2

The manuscript has been revised well. I think this manuscript is acceptable.