Mapping the dependence of BC radiative forcing on emission region and season
- 1Finnish Meteorological Institute, Climate Research, Helsinki, Finland
- 2University of Helsinki, Institute for Atmospheric and Earth System Research, Helsinki, Finland
- 3Finnish Environment Institute, Climate and Air Pollution, Helsinki, Finland
- 4Norwegian Meteorological Institute, Oslo, Norway
- 5CICERO Center for International Climate Research, Oslo, Norway
- 1Finnish Meteorological Institute, Climate Research, Helsinki, Finland
- 2University of Helsinki, Institute for Atmospheric and Earth System Research, Helsinki, Finland
- 3Finnish Environment Institute, Climate and Air Pollution, Helsinki, Finland
- 4Norwegian Meteorological Institute, Oslo, Norway
- 5CICERO Center for International Climate Research, Oslo, Norway
Abstract. For short-lived climate forcers such as black carbon (BC), the atmospheric concentrations, radiative forcing (RF) and, ultimately, the subsequent effects on climate, depend on the location and timing of the emissions. Here, we employ the NorESM1-Happi version of Norwegian Earth System Model to systematically study how the RF associated with BC emissions depends on the latitude, longitude and seasonality of the emissions. The model aerosol scheme is run in an offline mode, to allow for an essentially noise-free evaluation of the RF associated with even minor changes in emissions. Both the BC direct RF (dirRF) and the RF associated with BC in snow/ice (snowRF) are calculated for emissions in 192 latitude-longitude boxes covering the globe, both for seasonally uniform emissions and for emissions in each of the four seasons separately. We also calculate a rough estimate of the global temperature response to regional emissions, and provide a fortran-based tool to facilitate the further use of our results.
Overall, the results demonstrate that the BC RFs strongly depend on the latitude, longitude and season of the emissions. In particular, the global-mean dirRF normalized by emissions (direct specific forcing; dirSF) depends much more strongly on the emission location than suggested by previous studies that have considered emissions from continental/subcontinental-scale regions. Even for seasonally uniform emissions, dirSF varies by more than a factor of ten depending on emission location. These variations correlate strongly with BC lifetime, which varies from less than 2 days to 11 days. BC dirSF is largest for emissions in tropical convective regions and in subtropical and midlatitude continents in summer, both due to the abundant solar radiation and strong convective transport, which increases BC lifetime and the amount of BC above clouds. The dirSF is also relatively large for emissions in high-albedo high-latitude regions such as Antarctica and Greenland. The dependence of snow specific forcing (snowSF) on the emission location is even larger. While BC emissions originating from most low-latitude regions result in negligible snowSF, the maxima of snowSF for emissions in polar regions greatly exceed the largest values of dirSF for low-latitude emissions. The large magnitude of snowSF for high-latitude BC emissions suggests that, for a given mass of BC emitted, also the climate impacts are largest for high-latitude emissions.
The additivity of the RFs resulting from BC emissions in different regions and seasons is also investigated. It is found that dirRF is almost additive for current-day emissions: summing the RFs computed for individual regions/seasons without considering BC emissions from elsewhere overestimates dirRF by less than 10 %. For snowRF, the overestimate is somewhat larger, ~20 %.
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Journal article(s) based on this preprint
Petri Räisänen et al.
Interactive discussion
Status: closed
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RC1: 'Comment on acp-2022-288', Anonymous Referee #1, 12 Jun 2022
Review of “Mapping the dependence of BC radiative forcing on emission region and season”.
Summary
This paper uses a large set of simulations with the NorESM1-Happi aerosol scheme in an offline mode (i.e., aerosols do not effect climate) to quantify the BC radiative forcing (direct and snow/ice) in response to BC emissions perturbations in 192 grid boxes. Seasonally uniform emissions, as well as emissions in each season are separately evaluated. The authors find the BC RFs significantly vary as a function of emission location (spatially) and season. When normalized by emissions, the direct specific BC RF varies by up to a factor of 10 (even larger for the corresponding snow/ice RF). The authors also discuss reasons for the spatial variability (e.g., enhanced lofting in tropical convective regions, which enhances the BC lifetime and the corresponding RF). The authors also show that for present-day BC emissions, the RF is nearly additive across regions/seasons (within ~10%); however, the indirect BC RF is not.
Overall, this is a well written paper that presents comprehensive model simulations and interesting results relevant for SLCF mitigation. The main novelty here is the use of much smaller emission regions (5x5 grid boxes, as opposed to continental scale). This is more relevant to BC mitigation. This in turn leads to the conclusion that the BC specific RF (spatially/seasonally) varies more than previously recognized.
Limitations of the present study (which are discussed in Section 6.2) include the fact the authors are not able to quantify the rapid adjustments, which have been shown to be very important for the climate impacts (e.g., Stjern et al. 2017). See also:
Smith, C. J., Kramer, R. J., Myhre, G., Forster, P. M., Soden, B. J., Andrews, T., et al. (2018). Understanding rapid adjustments to diverse forcing agents. Geophysical Research Letters, 45, 12,023– 12,031. https://doi.org/10.1029/2018GL079826
Specific Comments
L130 “the semidirect effect of BC cannot be included”. Shouldn’t this be more general, i.e., rapid adjustments cannot be included? Semi-direct effects traditionally refer to clouds alone, but there are several rapid adjustments including those associated with the clouds.
Section 2.3 In the context of BC emissions, the two main sources are fossil fuel and biomass burning. In contrast to fossil fuel BC emissions, biomass burning BC emissions are likely less easily controlled to mitigation policies. Is there any utility in separating the two? Probably beyond the scope of this work, but perhaps the authors could comment.
L200. In the context of convective lofting, see also:
Park, S., and Allen, R. J. (2015), Understanding influences of convective transport and removal processes on aerosol vertical distribution, Geophys. Res. Lett., 42, 10,438– 10,444, doi:10.1002/2015GL066175.
- AC1: 'Reply on RC1', Petri Räisänen, 04 Aug 2022
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RC2: 'Comment on acp-2022-288', William Collins, 17 Jun 2022
This is a useful study of the effect of emission location on BC forcing efficiency. As the authors themselves note, this is a single model study with lots of uncertainty. However they explain the physical processes behind their results which are likely to be more generally applicable even if the absolute numbers are not. This will be useful to publish after addressing the points below.
I understand the reluctance for detailed indirRF calculations since they are not likely to be additive, but it might still be useful to see the regional variation. A version of fig 4 for indirRF could be added to the supplement. Why is the indirRF positive? Does mixing with BC reduce the nucleating ability of SO4, or does it reduce the SO4 lifetime? How is indirRF calculated? Is it a double call as in Ghan 2013?
Even though they can’t be addressed in this study, there could be a bit more mention of meteorological adjustments to BC, for instance the increased stabilisation of the atmospheric profile, and how they might affect the conclusions. Stjern et al. is cited, but not the discussions in that paper, also there are Samset papers. These meteorological adjustments will be included implicitly in the Shindell and Faluvegi coefficients. What they term “efficacy” is really an accounting for adjustments.
Introduction: Could also cite Aamaas et al. 2016 and Bellouin et al. 2016 papers from ECLIPSE.
Line 85: This should mention the magnitude of indirRF here - it seems to be ~25% of dirRF.
Line 130: Suggest to use “meteorological adjustments” or “rapid adjustments” rather than “semi-direct effect” to align with IPCC terminology.
Line 136: Give some explanation of how F_air is calculated. Presumably this is from a double call to the radiation scheme with zero BC in the advancing step.
Section 2.3: This might comment on how the mixing affects other species. Does mixing with SO4 affect the lifetime of BC, i.e. do BC emissions have a lower lifetime if they are emitted in a high SO4 region? Does mixing with BC affect the lifetime of SO4, i.e. is there an indirect effect of BC on SO4 RF and if so is this included in F_air?
Figure 1. I suggest splitting into COARSE-REAL which shows the impact of resolution, and then RECONST-REAL which shows the additivity. I’m not sure RECONST-REAL is that useful since it mixes these two effects.
Line 240: Why aren’t the reconstructed fields for REAL and COARSE actually identical as opposed to “virtually” identical. Similarly, in fig S1 are the emissions for RECONST and COARSE identical, and if not, why not?
Figure 3: Why doesn’t dirRF scale with column burden? I would have expected 3(a) and 3(c) to be much more similar since section 6.3 suggests little non-linearity in dirRF. A plot of dirRF/columnBC would be useful in the supplement.
Line 377: The longitudinal variation seems interesting, and very policy relevant.
Section 6.1: Suggest to also compare with ECLIPSE project, Bellouin et al. 2016
Section 6.2: Suggest to also compare with ECLIPSE project, Aamaas et al. 2016 and 2017.
Lines 389: This paragraph would be clearer if it included discussion of meteorological adjustments and ERFs. The reason dirRF for BC has less effect on climate is not because it has lower “efficacy”, it is because there are adjustments that oppose the dirRF so that the ERF is lower than dirRF (e.g. Stjern et al. 2017). Studies of BC efficacy defined in terms of ERF (E.g. Richardson et al. 2019) show an efficacy of around 1.0 when compared to CO2.
Figure 7: This figure is presumably very sensitive to the assumed factor of 3 efficacy for snowRF. What is the uncertainty in this factor of 3? Would the conclusions be qualitatively the same with a lower factor?
Line 518: Suggest to use “meteorological adjustments” or “rapid adjustments” rather than “semi-direct effect” to align with IPCC terminology.
- AC2: 'Reply on RC2', Petri Räisänen, 04 Aug 2022
Peer review completion
Interactive discussion
Status: closed
-
RC1: 'Comment on acp-2022-288', Anonymous Referee #1, 12 Jun 2022
Review of “Mapping the dependence of BC radiative forcing on emission region and season”.
Summary
This paper uses a large set of simulations with the NorESM1-Happi aerosol scheme in an offline mode (i.e., aerosols do not effect climate) to quantify the BC radiative forcing (direct and snow/ice) in response to BC emissions perturbations in 192 grid boxes. Seasonally uniform emissions, as well as emissions in each season are separately evaluated. The authors find the BC RFs significantly vary as a function of emission location (spatially) and season. When normalized by emissions, the direct specific BC RF varies by up to a factor of 10 (even larger for the corresponding snow/ice RF). The authors also discuss reasons for the spatial variability (e.g., enhanced lofting in tropical convective regions, which enhances the BC lifetime and the corresponding RF). The authors also show that for present-day BC emissions, the RF is nearly additive across regions/seasons (within ~10%); however, the indirect BC RF is not.
Overall, this is a well written paper that presents comprehensive model simulations and interesting results relevant for SLCF mitigation. The main novelty here is the use of much smaller emission regions (5x5 grid boxes, as opposed to continental scale). This is more relevant to BC mitigation. This in turn leads to the conclusion that the BC specific RF (spatially/seasonally) varies more than previously recognized.
Limitations of the present study (which are discussed in Section 6.2) include the fact the authors are not able to quantify the rapid adjustments, which have been shown to be very important for the climate impacts (e.g., Stjern et al. 2017). See also:
Smith, C. J., Kramer, R. J., Myhre, G., Forster, P. M., Soden, B. J., Andrews, T., et al. (2018). Understanding rapid adjustments to diverse forcing agents. Geophysical Research Letters, 45, 12,023– 12,031. https://doi.org/10.1029/2018GL079826
Specific Comments
L130 “the semidirect effect of BC cannot be included”. Shouldn’t this be more general, i.e., rapid adjustments cannot be included? Semi-direct effects traditionally refer to clouds alone, but there are several rapid adjustments including those associated with the clouds.
Section 2.3 In the context of BC emissions, the two main sources are fossil fuel and biomass burning. In contrast to fossil fuel BC emissions, biomass burning BC emissions are likely less easily controlled to mitigation policies. Is there any utility in separating the two? Probably beyond the scope of this work, but perhaps the authors could comment.
L200. In the context of convective lofting, see also:
Park, S., and Allen, R. J. (2015), Understanding influences of convective transport and removal processes on aerosol vertical distribution, Geophys. Res. Lett., 42, 10,438– 10,444, doi:10.1002/2015GL066175.
- AC1: 'Reply on RC1', Petri Räisänen, 04 Aug 2022
-
RC2: 'Comment on acp-2022-288', William Collins, 17 Jun 2022
This is a useful study of the effect of emission location on BC forcing efficiency. As the authors themselves note, this is a single model study with lots of uncertainty. However they explain the physical processes behind their results which are likely to be more generally applicable even if the absolute numbers are not. This will be useful to publish after addressing the points below.
I understand the reluctance for detailed indirRF calculations since they are not likely to be additive, but it might still be useful to see the regional variation. A version of fig 4 for indirRF could be added to the supplement. Why is the indirRF positive? Does mixing with BC reduce the nucleating ability of SO4, or does it reduce the SO4 lifetime? How is indirRF calculated? Is it a double call as in Ghan 2013?
Even though they can’t be addressed in this study, there could be a bit more mention of meteorological adjustments to BC, for instance the increased stabilisation of the atmospheric profile, and how they might affect the conclusions. Stjern et al. is cited, but not the discussions in that paper, also there are Samset papers. These meteorological adjustments will be included implicitly in the Shindell and Faluvegi coefficients. What they term “efficacy” is really an accounting for adjustments.
Introduction: Could also cite Aamaas et al. 2016 and Bellouin et al. 2016 papers from ECLIPSE.
Line 85: This should mention the magnitude of indirRF here - it seems to be ~25% of dirRF.
Line 130: Suggest to use “meteorological adjustments” or “rapid adjustments” rather than “semi-direct effect” to align with IPCC terminology.
Line 136: Give some explanation of how F_air is calculated. Presumably this is from a double call to the radiation scheme with zero BC in the advancing step.
Section 2.3: This might comment on how the mixing affects other species. Does mixing with SO4 affect the lifetime of BC, i.e. do BC emissions have a lower lifetime if they are emitted in a high SO4 region? Does mixing with BC affect the lifetime of SO4, i.e. is there an indirect effect of BC on SO4 RF and if so is this included in F_air?
Figure 1. I suggest splitting into COARSE-REAL which shows the impact of resolution, and then RECONST-REAL which shows the additivity. I’m not sure RECONST-REAL is that useful since it mixes these two effects.
Line 240: Why aren’t the reconstructed fields for REAL and COARSE actually identical as opposed to “virtually” identical. Similarly, in fig S1 are the emissions for RECONST and COARSE identical, and if not, why not?
Figure 3: Why doesn’t dirRF scale with column burden? I would have expected 3(a) and 3(c) to be much more similar since section 6.3 suggests little non-linearity in dirRF. A plot of dirRF/columnBC would be useful in the supplement.
Line 377: The longitudinal variation seems interesting, and very policy relevant.
Section 6.1: Suggest to also compare with ECLIPSE project, Bellouin et al. 2016
Section 6.2: Suggest to also compare with ECLIPSE project, Aamaas et al. 2016 and 2017.
Lines 389: This paragraph would be clearer if it included discussion of meteorological adjustments and ERFs. The reason dirRF for BC has less effect on climate is not because it has lower “efficacy”, it is because there are adjustments that oppose the dirRF so that the ERF is lower than dirRF (e.g. Stjern et al. 2017). Studies of BC efficacy defined in terms of ERF (E.g. Richardson et al. 2019) show an efficacy of around 1.0 when compared to CO2.
Figure 7: This figure is presumably very sensitive to the assumed factor of 3 efficacy for snowRF. What is the uncertainty in this factor of 3? Would the conclusions be qualitatively the same with a lower factor?
Line 518: Suggest to use “meteorological adjustments” or “rapid adjustments” rather than “semi-direct effect” to align with IPCC terminology.
- AC2: 'Reply on RC2', Petri Räisänen, 04 Aug 2022
Peer review completion
Journal article(s) based on this preprint
Petri Räisänen et al.
Data sets
Data and code for the manuscript "Mapping the dependence of BC radiative forcing on emission region and season" by Petri Räisänen et al. (submitted to ACP) Petri Räisänen https://doi.org/10.23728/FMI-B2SHARE.6808480A473E437FA56F6BF05E8D6A8B
Model code and software
BC radiative forcing and climate response tool, version 1.0.0 Petri Räisänen https://doi.org/10.5281/zenodo.6461647
Petri Räisänen et al.
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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.
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