the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Jun 2023
Global Observations of Aerosol Indirect Effects from Marine Liquid Clouds
Abstract. Interactions between aerosols and liquid clouds are one of the largest sources of uncertainty in the historical radiative forcing of climate. One widely shared goal to reduce this uncertainty is to decompose radiative anomalies arising from aerosol-cloud interactions into components associated with changes in cloud-droplet number concentration (Twomey effect), liquid water path adjustments, and cloud-fraction adjustments. However, there has not been a quantitative foundation for simultaneously estimating these components with global satellite observations. Here we present a method for assessing shortwave radiative flux anomalies from the Twomey effect and cloud adjustments over ocean between 55° S and 55° N. We find that larger aerosol concentrations are associated with widespread cloud brightening from the Twomey effect, a positive radiative adjustment from decreasing liquid water path in subtropical stratocumulus regions, and a negative radiative adjustment from increasing cloud fraction in the subtropics and midlatitudes. The Twomey effect and total cloud adjustment contribute -0.77±0.25 W m-2 and -1.02±0.43 W m-2, respectively, to the effective radiative forcing since 1850 over the domain (95 % confidence). Our findings reduce uncertainty in these components of aerosol forcing and suggest that cloud adjustments make a larger contribution to the forcing than is commonly believed.
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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
(13573 KB) - Metadata XML
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Supplement
(3019 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-2023-1436', Ying Chen, 14 Jul 2023
Aerosol-cloud interactions (ACI) continuously consist one of the largest uncertainties in climate radiative forcing and projections. This study combines a large ensemble of satellite observations and a statistical relation-regression method to estimate radiative forcing associated with key ACI elements, including Twomey effect, liquid water path (LWP) adjustment and cloud fraction adjustment. They found cloud fraction adjustment could be much more important than commonly believed and larger than Twomey effect in ACI cooling; while, LWP adjustment leads to slightly warming globally. The scope fit well with ACP. The manuscript is well written, the results are scientific interesting and politically meaningful supported by sound methodology. I am happy to recommend for publication after a few minor revisions.
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Minor concerns:
1) Authors find the LWP adjustment leads to warming almost everywhere globally (Fig. 2); however, recent studies, which also use a large ensemble of satellite observations, reported that LWP adjustment leads to SW cooling on a large-scale (Manshausen et al., 2022;Rosenfeld et al., 2019). Could you please add some more discussion about this discrepancy? Â
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2) Page-3 bottom equation. Here, authors describe radiation anomaly as a function of cloud fraction (C), and the partial dependency: dR/dCrl, where C is partitioned by effective radius (r) and LWP (l). I wonder that are ‘r’ and ‘l’ the most important controlling-factors for C, or is there also other factors would largely impact ‘C’ and the partial dependency relationship (dR/dC)?
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3) Some more clarification about the method would help audience better understand it. A) line-100 (and after), what does ‘anomaly’ here refer to, do you mean anomaly to the climatological value (temporal averaged, or also spatial averaged)? B) line-120: some description about how do you remove the climatological seasonal cycle and linear trend. C) line-125: explain 46-49% variance – how do you measure variance and lead to this conclusion?
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4) Fig. 1. Joint histogram. I do not quite understand this figure. Does the color indicate the values of cloud fraction (a) and SW kernel (b)? If yes, then this is not a joint histogram, it is a heatmap plot. A histogram should show the probability density function (or counts) of data.
Moreover, Fig. 1b. the kernel dR/dC should be depended on latitude/longitude/day-of-the-year/surface-albedo. Does all of these factors are controlled, e.g., fixed to an average value, and only allow re and LWP to vary?
Â
5) Data open-access. SW kernel data is a key factor use in this study and generated in this study. I feel that making the global distribution of this dataset open-access would largely improve the reproducibility of this study, and also enhance its contribution to the community. Â
Â
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References:
Manshausen, P., Watson-Parris, D., Christensen, M. W., Jalkanen, J.-P., and Stier, P.: Invisible ship tracks show large cloud sensitivity to aerosol, Nature, 610, 101-106, 10.1038/s41586-022-05122-0, 2022.
Rosenfeld, D., Zhu, Y., Wang, M., Zheng, Y., Goren, T., and Yu, S.: Aerosol-driven droplet concentrations dominate coverage and water of oceanic low-level clouds, Science, 363, eaav0566, 10.1126/science.aav0566, 2019.
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Citation: https://doi.org/10.5194/egusphere-2023-1436-RC1 - RC2: 'Comment on egusphere-2023-1436', Jianhao Zhang, 31 Jul 2023
- AC1: 'Comment on egusphere-2023-1436', Casey Wall, 12 Aug 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1436', Ying Chen, 14 Jul 2023
Aerosol-cloud interactions (ACI) continuously consist one of the largest uncertainties in climate radiative forcing and projections. This study combines a large ensemble of satellite observations and a statistical relation-regression method to estimate radiative forcing associated with key ACI elements, including Twomey effect, liquid water path (LWP) adjustment and cloud fraction adjustment. They found cloud fraction adjustment could be much more important than commonly believed and larger than Twomey effect in ACI cooling; while, LWP adjustment leads to slightly warming globally. The scope fit well with ACP. The manuscript is well written, the results are scientific interesting and politically meaningful supported by sound methodology. I am happy to recommend for publication after a few minor revisions.
Â
Â
Minor concerns:
1) Authors find the LWP adjustment leads to warming almost everywhere globally (Fig. 2); however, recent studies, which also use a large ensemble of satellite observations, reported that LWP adjustment leads to SW cooling on a large-scale (Manshausen et al., 2022;Rosenfeld et al., 2019). Could you please add some more discussion about this discrepancy? Â
Â
2) Page-3 bottom equation. Here, authors describe radiation anomaly as a function of cloud fraction (C), and the partial dependency: dR/dCrl, where C is partitioned by effective radius (r) and LWP (l). I wonder that are ‘r’ and ‘l’ the most important controlling-factors for C, or is there also other factors would largely impact ‘C’ and the partial dependency relationship (dR/dC)?
Â
3) Some more clarification about the method would help audience better understand it. A) line-100 (and after), what does ‘anomaly’ here refer to, do you mean anomaly to the climatological value (temporal averaged, or also spatial averaged)? B) line-120: some description about how do you remove the climatological seasonal cycle and linear trend. C) line-125: explain 46-49% variance – how do you measure variance and lead to this conclusion?
Â
4) Fig. 1. Joint histogram. I do not quite understand this figure. Does the color indicate the values of cloud fraction (a) and SW kernel (b)? If yes, then this is not a joint histogram, it is a heatmap plot. A histogram should show the probability density function (or counts) of data.
Moreover, Fig. 1b. the kernel dR/dC should be depended on latitude/longitude/day-of-the-year/surface-albedo. Does all of these factors are controlled, e.g., fixed to an average value, and only allow re and LWP to vary?
Â
5) Data open-access. SW kernel data is a key factor use in this study and generated in this study. I feel that making the global distribution of this dataset open-access would largely improve the reproducibility of this study, and also enhance its contribution to the community. Â
Â
Â
References:
Manshausen, P., Watson-Parris, D., Christensen, M. W., Jalkanen, J.-P., and Stier, P.: Invisible ship tracks show large cloud sensitivity to aerosol, Nature, 610, 101-106, 10.1038/s41586-022-05122-0, 2022.
Rosenfeld, D., Zhu, Y., Wang, M., Zheng, Y., Goren, T., and Yu, S.: Aerosol-driven droplet concentrations dominate coverage and water of oceanic low-level clouds, Science, 363, eaav0566, 10.1126/science.aav0566, 2019.
Â
Citation: https://doi.org/10.5194/egusphere-2023-1436-RC1 - RC2: 'Comment on egusphere-2023-1436', Jianhao Zhang, 31 Jul 2023
- AC1: 'Comment on egusphere-2023-1436', Casey Wall, 12 Aug 2023
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Trude Storelvmo
Anna Possner
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(13573 KB) - Metadata XML
-
Supplement
(3019 KB) - BibTeX
- EndNote
- Final revised paper