Are Changes in Atmospheric Circulation Important for Black Carbon Aerosol Impacts on Clouds, Precipitation, and Radiation?

Johnson, Ben; Haywood, Jim M.; Hawcroft, Matt

doi:10.1029/2019jd030568

Cited by 36 publications

(49 citation statements)

References 84 publications

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“…The anthropogenic emissions of BC were specified as 5 Tg yr -1 in CMIP5 (Year 2000 as PD) but have increased to 8 Tg yr -1 in CMIP6 (Year 2014 as PD). With CMIP5 emissions, the BC ERF from HadGEM3-GA7.1 was found to be 0.17 W m -2 (Johnson et al, 2019) and is comparable to direct BC forcing from other CMIP5 model estimates (Myhre et al, 2013a).…”

Section: Aerosols and Aerosol Precursorssupporting

confidence: 69%

“…The BC ERF was +0.32 W m -2 , coming mostly from the IRF (0.38 W m -2 ) and small negative offset of -0.06 W m -2 from rapid adjustments. As noted in Johnson et al (2019), BC absorption leads to strong cloud adjustments but the SW and LW components of these almost cancel in HadGEM3-GA7.1 and UKESM1 (see Table 3). This contrasts with many other models where the combination of low cloud enhancements and reductions in upper-level cloud typically result in more substantial negative adjustments, making the BC ERF on average about half the magnitude of the IRF (Stjern et al, 2017).…”

Section: Aerosols and Aerosol Precursorsmentioning

confidence: 68%

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Assessment of pre-industrial to present-day anthropogenic climate forcing in UKESM1

O’Connor¹,

Abraham²,

Dalvi³

et al. 2020

Preprint

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Abstract. Quantifying forcings from anthropogenic perturbations to the Earth System (ES) is important for understanding changes in climate since the pre-industrial period. In this paper, we quantify and analyse a wide range of present-day (PD) anthropogenic climate forcings with the UK's Earth System Model (ESM), UKESM1, following the protocols defined by the Radiative Forcing Model Intercomparison Project (RFMIP) and the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP). In particular, by quantifying effective radiative forcings (ERFs) that include rapid adjustments within a full ESM, it enables the role of various climate-chemistry-aerosol-cloud feedbacks to be quantified. Global mean ERFs are 1.83, 0.13, &#8722;0.33, and 0.93&#8201;W&#8201;m&#8722;2 at the PD (Year 2014) relative to the pre-industrial (PI; Year 1850) for carbon dioxide, nitrous oxide, ozone-depleting substances, and methane, respectively. The PD total greenhouse gas ERF is 2.89&#8201;W&#8201;m&#8722;2, larger than the sum of the individual GHG ERFs. UKESM1 has an aerosol forcing of &#8722;1.13&#8201;W&#8201;m&#8722;2. A relatively strong negative forcing from aerosol-cloud interactions and a small negative instantaneous forcing from aerosol-radiation interactions are partially offset by a substantial forcing from black carbon absorption. Internal mixing and chemical interactions mean that neither the forcing from aerosol-radiation interactions nor aerosol-cloud interactions are linear, making the total aerosol ERF less than the sum of the individual speciated aerosol ERFs. Tropospheric ozone precursors, in addition to exerting a positive forcing due to ozone, lead to oxidant changes which in turn cause an indirect aerosol ERF, altering the sign of the net ERF from nitrogen oxide emissions. Together, aerosol and tropospheric ozone precursors (near-term climate forcers, NTCFs) exert a global mean ERF of &#8722;1.12&#8201;W&#8201;m&#8722;2, mainly due to changes in the cloud radiative effect. There is also a negative PD ERF from land use (&#8722;0.32&#8201;W&#8201;m&#8722;2). It is outside the range of previous estimates, and is most likely due to too strong an albedo response. In combination, the net anthropogenic ERF is potentially biased low (1.61&#8201;W&#8201;m&#8722;2) relative to other estimates, due to the inclusion of non-linear feedbacks and ES interactions. By including feedbacks between greenhouse gases, stratospheric and tropospheric ozone, aerosols, and clouds, some of which act non-linearly, this work demonstrates the importance of ES interactions when quantifying climate forcing. It also suggests that rapid adjustments need to include chemical as well as physical adjustments to fully account for complex ES interactions.

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Section: Aerosols and Aerosol Precursorssupporting

confidence: 69%

Section: Aerosols and Aerosol Precursorsmentioning

confidence: 68%

Assessment of pre-industrial to present-day anthropogenic climate forcing in UKESM1

O’Connor¹,

Abraham²,

Dalvi³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similar conclusions exist over land only, where the multi-model mean (MMM) warming is even larger at 0.36 K over the entire time period (Table 1). Enhanced land warming is consistent with the land-sea warming contrast (Sutton et al, 2007;Joshi et al, 2008), which may also act to increase aerosol burden itself Allen et al, 2019b), implying a climate change penalty to air quality. Interestingly, models that include both aerosol and ozone reductions (Aer+O3) yield similar surface warming relative to the models that include aerosol reductions (Aer) alone (0.07 versus 0.06 K decade −1 , respectively).…”

Section: Global Climate and Air Quality Trendsmentioning

confidence: 66%

Climate and air quality impacts due to mitigation of non-methane near-term climate forcers

et al. 2020

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Abstract. It is important to understand how future environmental policies will impact both climate change and air pollution. Although targeting near-term climate forcers (NTCFs), defined here as aerosols, tropospheric ozone, and precursor gases, should improve air quality, NTCF reductions will also impact climate. Prior assessments of the impact of NTCF mitigation on air quality and climate have been limited. This is related to the idealized nature of some prior studies, simplified treatment of aerosols and chemically reactive gases, as well as a lack of a sufficiently large number of models to quantify model diversity and robust responses. Here, we quantify the 2015–2055 climate and air quality effects of non-methane NTCFs using nine state-of-the-art chemistry–climate model simulations conducted for the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP). Simulations are driven by two future scenarios featuring similar increases in greenhouse gases (GHGs) but with “weak” (SSP3-7.0) versus “strong” (SSP3-7.0-lowNTCF) levels of air quality control measures. As SSP3-7.0 lacks climate policy and has the highest levels of NTCFs, our results (e.g., surface warming) represent an upper bound. Unsurprisingly, we find significant improvements in air quality under NTCF mitigation (strong versus weak air quality controls). Surface fine particulate matter (PM2.5) and ozone (O3) decrease by -2.2±0.32 µg m−3 and -4.6±0.88 ppb, respectively (changes quoted here are for the entire 2015–2055 time period; uncertainty represents the 95 % confidence interval), over global land surfaces, with larger reductions in some regions including south and southeast Asia. Non-methane NTCF mitigation, however, leads to additional climate change due to the removal of aerosol which causes a net warming effect, including global mean surface temperature and precipitation increases of 0.25±0.12 K and 0.03±0.012 mm d−1, respectively. Similarly, increases in extreme weather indices, including the hottest and wettest days, also occur. Regionally, the largest warming and wetting occurs over Asia, including central and north Asia (0.66±0.20 K and 0.03±0.02 mm d−1), south Asia (0.47±0.16 K and 0.17±0.09 mm d−1), and east Asia (0.46±0.20 K and 0.15±0.06 mm d−1). Relatively large warming and wetting of the Arctic also occur at 0.59±0.36 K and 0.04±0.02 mm d−1, respectively. Similar surface warming occurs in model simulations with aerosol-only mitigation, implying weak cooling due to ozone reductions. Our findings suggest that future policies that aggressively target non-methane NTCF reductions will improve air quality but will lead to additional surface warming, particularly in Asia and the Arctic. Policies that address other NTCFs including methane, as well as carbon dioxide emissions, must also be adopted to meet climate mitigation goals.

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“…Overall, the differences in the N 50 and N tot model biases are small although the model to observed ratio is higher for N 50 over the remote oceans than for N tot highlighting potential biases in N 50 and subsequently CCN. However, definitive conclusions on the model performance here are difficult to draw, given the potentially large inter-annual variability in these simulated variables combined with the uncertainty in the observations (Johnson et al, 2019a;Watson-Parris et al, 2019 Figure 10. Comparison of simulated N50 (total particle concentration with diameter > 50 nm) (cm −3 ) from (a) UKESM1 and (b) GC3.1 against (c) gridded observations from a combinations of ground-based, ship and aircraft campaigns.…”

Section: Aerosol Numbermentioning

confidence: 99%

Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations

Mulcahy¹,

Johnson²,

Jones³

et al. 2020

Preprint

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Abstract. We document and evaluate the aerosol schemes as implemented in the physical and Earth system models, HadGEM3-GC3.1 (GC3.1) and UKESM1, which are contributing to the 6th Coupled Model Intercomparison Project (CMIP6). The simulation of aerosols in the present-day period of the historical ensemble of these models is evaluated against a range of observations. Updates to the aerosol microphysics scheme are documented as well as differences in the aerosol representation between the physical and Earth system configurations. The additional Earth-system interactions included in UKESM1 leads to differences in the emissions of natural aerosol sources such as dimethyl sulfide, mineral dust and organic aerosol and subsequent evolution of these species in the model. UKESM1 also includes a stratospheric-tropospheric chemistry scheme which is fully coupled to the aerosol scheme, while GC3.1 employs a simplified aerosol chemistry mechanism driven by prescribed monthly climatologies of the relevant oxidants. Overall, the simulated speciated aerosol mass concentrations compare reasonably well with observations. Both models capture the negative trend in sulfate aerosol concentrations over Europe and the eastern United States of America (US) although the models tend to underestimate the sulfate concentrations in both regions. Interactive emissions of biogenic volatile organic compounds in UKESM1 lead to an improved agreement of organic aerosol over the US. Simulated dust burdens are similar in both models despite a 2-fold difference in dust emissions. Aerosol optical depth is biased low in dust source and outflow regions but performs well in other regions compared to a number of satellite and ground-based retrievals of aerosol optical depth. Simulated aerosol number concentrations are generally within a factor of 2 of the observations with both models tending to overestimate number concentrations over remote ocean regions, apart from at high latitudes, and underestimate over Northern Hemisphere continents. Finally, UKESM1 includes for the first time a representation of a primary marine organic aerosol source. The impact of this new aerosol source is evaluated. Over the pristine Southern Ocean, it is found to improve the seasonal cycle of organic aerosol mass and cloud droplet number concentrations relative to GC3.1 although underestimations in cloud droplet number concentrations remain. This paper provides a useful characterization of the aerosol climatology in both models facilitating the understanding of the numerous aerosol-climate interaction studies that will be conducted as part of CMIP6 and beyond.

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Are Changes in Atmospheric Circulation Important for Black Carbon Aerosol Impacts on Clouds, Precipitation, and Radiation?

Cited by 36 publications

References 84 publications

Assessment of pre-industrial to present-day anthropogenic climate forcing in UKESM1

Assessment of pre-industrial to present-day anthropogenic climate forcing in UKESM1

Climate and air quality impacts due to mitigation of non-methane near-term climate forcers

Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations

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