the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 09 Jul 2024
The aerosol pathway is crucial for observationally constrained climate sensitivity and anthropogenic forcing
Abstract. Climate sensitivity and aerosol forcing are two of the most central, but uncertain, quantities in climate science that are crucial for assessing historical climate as well as future climate predictions. Here, we use a Bayesian approach to estimate the inferred climate sensitivity and aerosol forcing using observations of temperature and global ocean heat content and prior knowledge of effective radiative forcing (ERF) over the industrial period. Due to limited information on uncertainties related to the time evolution of aerosol forcing, we perform a range of sensitivity analyses with idealized aerosol time evolution. The estimates are sensitive to the aerosol forcing pathway with the mean estimate of inferred climate sensitivity ranging from 2.0 to 2.4 K, present-day (2019 relative to 1750) aerosol ERF ranging from -0.7 to -1.1 W m-2 and anthropogenic ERF ranging from 2.6 to 3.1 W m-2. Using observations and forcing up to and including 2022, the inferred effective climate sensitivity is 2.2 K with a 1.6 to 3.0 K 90 % uncertainty range. Analysis with more freely evolving aerosol forcing between 1950 and 2014 shows a strong negative aerosol forcing trend in the latter part of the 20th century that is not consistent with observations. Although we test our estimation method with strongly idealized aerosol ERF pathways, our posteriori estimates of the climate sensitivities end up in the weaker end of the range assessed in the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). As our method only includes climate feedbacks that have occurred over the historical period, it does not include the pattern effect, i.e. where climate feedbacks are dependent on the pattern of warming which will likely change into the future. Adding the best estimate of the pattern effect from IPCC AR6, our climate sensitivity estimate is almost identical to the IPCC AR6 best estimate and very likely range.
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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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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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Supplement
(7614 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-2024-2030', Anonymous Referee #1, 06 Aug 2024
The aerosol pathway is crucial for observationally constrained climate sensitivity and anthropogenic forcing
Skeie et al.
This paper is interesting and useful for comparing different aerosol modeling frameworks, especially for analyzing the impact of the structure of the time history of aerosol forcing. The authors replace aerosol ERF from AR6 with various idealized alternative pathways and also extend temperature, ocean heat content, and ERF time series using the most up-to-date versions of these data. I have a few major and minor comments.
Major comments
Idealized pathways: I wonder if the authors could more clearly describe the advantages and disadvantages of these various idealized pathways, vs. using a simple aerosol forcing model whose free parameters can vary in the Bayesian inversion and therefore yield different time histories of aerosol forcing. I am thinking of the simple aerosol forcing model in Stevens et al., 2015, and employed in Smith et al., 2021b and Albright et al., 2021, which are cited in this paper. How does the present approach change the results and allow for more flexibility? What would the results look like if using a simple aerosol forcing model (using SO2, and/or also including BC and OC)?
Co-variance among parameters: One of the advantages of the Bayesian framework is that it yields a joint distribution of uncertain parameters. Aerosol forcing and ECS_inf are presented independently. Could these values be shown in a joint pdf?
Other interesting parameters regarding how heat is mixed into the ocean (“e.g. mixed layer depth, air-sea heat exchange coefficient, vertical diffusivity in the ocean and upwelling velocity”, line 517) are presented. I was interested in how these parameters traded off in the various scenarios, and whether strongly differing scenarios of idealized pathways showed different parameter covariance? Could that parameter covariance provide physical insights?
NH vs. SH temperatures: It could be useful, I think, to provide physical insights why modifying the aerosol pathway over certain time periods changes the ECS_inf and other parameters more than changing it over other periods. For example: “The observed temperature and OHC used in the estimation do not allow for a weakening in the anthropogenic ERF in the second half of the century.” Is this result dependent on using hemispheric temperatures, or are similar results obtained when using global temperatures? Is there a particular decade in the second half of the century that emerges as most important for not allowing for more negative aerosol radiative forcing? Is it before / during / after peak emissions of SO2?
Role of ocean heat uptake observations in the model: I was also interested to know how much ocean heat content constrained the model and added additional information, compared to surface temperatures, but, if I am understanding correctly, I did not see this discussed in much detail in the paper. Could the authors comment on it, or refer to a previous discussion of the role of surface temperatures vs. ocean heat content in a previous work by this team? Thank you!
Minor comments
Line 78, for known reasonS. Small typo.
In Figs. 2 and 3, panel c, I found the different lines / cases difficult to distinguish.
Fig. 2a. Why is the posterior Skeie2018 so close / strongly constrained by the AR5 prior, whereas Base is less constrained by the AR6 prior? I see a description around line 175 but no reason given (is it elsewhere?)
Line 496-7, could “strong” be more clearly defined with a number value/range? Around -2 W/m2?
Citation: https://doi.org/10.5194/egusphere-2024-2030-RC1 - AC1: 'Reply on RC1', Ragnhild Bieltvedt Skeie, 18 Sep 2024
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RC2: 'Comment on egusphere-2024-2030', Anonymous Referee #2, 20 Aug 2024
Review of “The aerosol pathway is crucial for observationally constrained climate sensitivity and anthropogenic forcing” by Skie et al.
The paper investigates the important ways aerosols impact the constraining of climate sensitivity. The sensitivity tests and calculations are a welcome addition to the literature. I only have a few minor comments listed below that need to be addressed prior to publication.
Minor Comments
- Grammar: I think the title should be "observationally constraining”?
- General: Need to leave space between paragraphs, it was hard to read blocks of text
- Line 41: Earth Energy Balance -> Earth energy balance
- Line 106: Why not use the Oceanic Niño Index/Niño3.4, the most widely used index of ENSO?
- Figures 2-3: Please provide more frequent x-axis labels, perhaps every 10 years. It is difficult to judge the years discussed in the text with 50 years spacing on the graph.
- Line 250: Please elaborate on what exactly saturation means here as it's not fully clear from the sentences that follow. Saturation to a reader may sound like a huge perturbation like a 10xBC type experiment.
- Line 282: Perhaps worth noting that even multi-millennial simulations don't equilibrate sometimes, i.e., T_s still keeps increasing.
- Figure 4 and surrounding discussion on pattern effect: Perhaps Dessler (2020) (https://doi.org/10.1175/JCLI-D-19-0476.1) should be mentioned in this discussion as it relates quite closely to the discussion here. Furthermore, how should one reconcile these results with his results where \Delta \lambda = 0.2 Wm^-2K^-1 on average?
- Line 343: At line 68 it's mentioned the lifetime of aerosols is days yet here it's mentioned that these aerosols can have an impact for decades. Could you please reconcile this for the reader?
- Line 352: Sherwood et al. (2020) (https://doi.org/10.1029/2019RG000678) showed that paleoclimate has a strong constraint. Could that be relevant here? If so, please discuss it.
- Line 439: Perhaps worth mentioning updated literature here: Raghuraman et al. (2023) (https://doi.org/10.1175/JCLI-D-22-0555.1) has separated this. They find that the observed SW trend is due to 40% ERF, 30% SW cloud feedback, and 30% surface albedo + SW water vapor feedbacks.
- Line 514: What does m_t stand for/mean exactly?
- Line 516: Why is climate sensitivity an input? Shouldn't it be an output? Please clarify/explain further.
Citation: https://doi.org/10.5194/egusphere-2024-2030-RC2 - AC2: 'Reply on RC2', Ragnhild Bieltvedt Skeie, 18 Sep 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-2030', Anonymous Referee #1, 06 Aug 2024
The aerosol pathway is crucial for observationally constrained climate sensitivity and anthropogenic forcing
Skeie et al.
This paper is interesting and useful for comparing different aerosol modeling frameworks, especially for analyzing the impact of the structure of the time history of aerosol forcing. The authors replace aerosol ERF from AR6 with various idealized alternative pathways and also extend temperature, ocean heat content, and ERF time series using the most up-to-date versions of these data. I have a few major and minor comments.
Major comments
Idealized pathways: I wonder if the authors could more clearly describe the advantages and disadvantages of these various idealized pathways, vs. using a simple aerosol forcing model whose free parameters can vary in the Bayesian inversion and therefore yield different time histories of aerosol forcing. I am thinking of the simple aerosol forcing model in Stevens et al., 2015, and employed in Smith et al., 2021b and Albright et al., 2021, which are cited in this paper. How does the present approach change the results and allow for more flexibility? What would the results look like if using a simple aerosol forcing model (using SO2, and/or also including BC and OC)?
Co-variance among parameters: One of the advantages of the Bayesian framework is that it yields a joint distribution of uncertain parameters. Aerosol forcing and ECS_inf are presented independently. Could these values be shown in a joint pdf?
Other interesting parameters regarding how heat is mixed into the ocean (“e.g. mixed layer depth, air-sea heat exchange coefficient, vertical diffusivity in the ocean and upwelling velocity”, line 517) are presented. I was interested in how these parameters traded off in the various scenarios, and whether strongly differing scenarios of idealized pathways showed different parameter covariance? Could that parameter covariance provide physical insights?
NH vs. SH temperatures: It could be useful, I think, to provide physical insights why modifying the aerosol pathway over certain time periods changes the ECS_inf and other parameters more than changing it over other periods. For example: “The observed temperature and OHC used in the estimation do not allow for a weakening in the anthropogenic ERF in the second half of the century.” Is this result dependent on using hemispheric temperatures, or are similar results obtained when using global temperatures? Is there a particular decade in the second half of the century that emerges as most important for not allowing for more negative aerosol radiative forcing? Is it before / during / after peak emissions of SO2?
Role of ocean heat uptake observations in the model: I was also interested to know how much ocean heat content constrained the model and added additional information, compared to surface temperatures, but, if I am understanding correctly, I did not see this discussed in much detail in the paper. Could the authors comment on it, or refer to a previous discussion of the role of surface temperatures vs. ocean heat content in a previous work by this team? Thank you!
Minor comments
Line 78, for known reasonS. Small typo.
In Figs. 2 and 3, panel c, I found the different lines / cases difficult to distinguish.
Fig. 2a. Why is the posterior Skeie2018 so close / strongly constrained by the AR5 prior, whereas Base is less constrained by the AR6 prior? I see a description around line 175 but no reason given (is it elsewhere?)
Line 496-7, could “strong” be more clearly defined with a number value/range? Around -2 W/m2?
Citation: https://doi.org/10.5194/egusphere-2024-2030-RC1 - AC1: 'Reply on RC1', Ragnhild Bieltvedt Skeie, 18 Sep 2024
-
RC2: 'Comment on egusphere-2024-2030', Anonymous Referee #2, 20 Aug 2024
Review of “The aerosol pathway is crucial for observationally constrained climate sensitivity and anthropogenic forcing” by Skie et al.
The paper investigates the important ways aerosols impact the constraining of climate sensitivity. The sensitivity tests and calculations are a welcome addition to the literature. I only have a few minor comments listed below that need to be addressed prior to publication.
Minor Comments
- Grammar: I think the title should be "observationally constraining”?
- General: Need to leave space between paragraphs, it was hard to read blocks of text
- Line 41: Earth Energy Balance -> Earth energy balance
- Line 106: Why not use the Oceanic Niño Index/Niño3.4, the most widely used index of ENSO?
- Figures 2-3: Please provide more frequent x-axis labels, perhaps every 10 years. It is difficult to judge the years discussed in the text with 50 years spacing on the graph.
- Line 250: Please elaborate on what exactly saturation means here as it's not fully clear from the sentences that follow. Saturation to a reader may sound like a huge perturbation like a 10xBC type experiment.
- Line 282: Perhaps worth noting that even multi-millennial simulations don't equilibrate sometimes, i.e., T_s still keeps increasing.
- Figure 4 and surrounding discussion on pattern effect: Perhaps Dessler (2020) (https://doi.org/10.1175/JCLI-D-19-0476.1) should be mentioned in this discussion as it relates quite closely to the discussion here. Furthermore, how should one reconcile these results with his results where \Delta \lambda = 0.2 Wm^-2K^-1 on average?
- Line 343: At line 68 it's mentioned the lifetime of aerosols is days yet here it's mentioned that these aerosols can have an impact for decades. Could you please reconcile this for the reader?
- Line 352: Sherwood et al. (2020) (https://doi.org/10.1029/2019RG000678) showed that paleoclimate has a strong constraint. Could that be relevant here? If so, please discuss it.
- Line 439: Perhaps worth mentioning updated literature here: Raghuraman et al. (2023) (https://doi.org/10.1175/JCLI-D-22-0555.1) has separated this. They find that the observed SW trend is due to 40% ERF, 30% SW cloud feedback, and 30% surface albedo + SW water vapor feedbacks.
- Line 514: What does m_t stand for/mean exactly?
- Line 516: Why is climate sensitivity an input? Shouldn't it be an output? Please clarify/explain further.
Citation: https://doi.org/10.5194/egusphere-2024-2030-RC2 - AC2: 'Reply on RC2', Ragnhild Bieltvedt Skeie, 18 Sep 2024
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Ragnhild Bieltvedt Skeie
Magne Aldrin
Terje K. Berntsen
Marit Holden
Ragnar Bang Huseby
Gunnar Myhre
Trude Storelvmo
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1826 KB) - Metadata XML
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Supplement
(7614 KB) - BibTeX
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- Final revised paper