Editing IPCC:AR6/WGI/Chapter-6 (section)

=== 6.4.1 Historical Estimates of Regional Short-lived Climate Forcing ===

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The highly heterogeneous distribution of SLCF abundances (Section 6.3) translates to strong heterogeneity in the spatial pattern and temporal evolution of forcing and climate responses due to SLCFs. This section assesses the spatial patterns of the current forcing due to aerosols and their historical evolution by region.

In AR5, the ''confidence'' in the spatial patterns of aerosol and ozone forcing was lower than that for the global mean because of the large spread in the regional distribution simulated by global models, and was assessed as ''medium'' . The AR5 assessment was based on aerosol and ozone RFs, and aerosol ERFs (with fixed SSTs) from ACCMIP and a small sample of CMIP5 models ( [[#Myhre--2013|Myhre et al., 2013]] ; [[#Shindell--2013|Shindell et al., 2013]] ). For this assessement, the spatial distribution of aerosol ERF due to human-induced changes in aerosol concentrations over 1850–2014 is quantified based on results from a seven-member ensemble of CMIP6 ESMs including interactive gas and aerosol chemistry analysed in AerChemMIP. There is insufficient information to estimate the spatial patterns of ozone ERF from CMIP6, however, the spatial patterns in SLCF ERF are dominated by that from aerosol ERF over most regions (e.g., [[#Shindell--2015|Shindell et al., 2015]] ). The aerosol ERF includes contributions from both direct aerosol–radiation (ERFari) and indirect aerosol–cloud interactions (ERFaci; [[IPCC:Wg1:Chapter:Chapter-7#7.3.3|Section 7.3.3]] ), and is computed as the difference between radiative fluxes from simulations with time-evolving aerosol and their precuror emissions, and identical simulations but with these emissions held at their 1850 levels ( [[#Collins--2017|Collins et al., 2017]] ). Both simulations are driven by time-evolving sea surface temperatures (SSTs) and sea ice from the respective coupled model historical simulation, and therefore, differ from ERFs computed using fixed pre-industrial SST and sea ice fields ( [[IPCC:Wg1:Chapter:Chapter-7#7.3.1|Section 7.3.1]] ), but the effect of this difference is generally small ( [[#Forster--2016|Forster et al., 2016]] ). A correction for land surface temperature change ( [[IPCC:Wg1:Chapter:Chapter-7#7.3.1|Section 7.3.1]] ) is not available from these data to explicitly quantify the contribution from adjustments. The ESMs included here used the CMIP6 anthropogenic and biomass-burning emissions for ozone and aerosol precursors but varied in their representation of the natural emissions, chemistry and climate characteristics contributing to spread in the simulated concentrations (Section 6.3) and resulting forcings, partly reflecting uncertainties in the successive processes ( [[#Thornhill--2021b|Thornhill et al., 2021b]] ).

The geographical distribution of the ensemble-mean aerosol ERF over the 1850–2014 period is highly heterogeneous (Figure 6.10a) in agreement with AR5. Negative ERF is greatest over and downwind of most industrialized regions in the Northern Hemisphere and to some extent over tropical biomass-burning regions, with robust signals. The largest negative forcing occurs over Eastern Asia and Southern Asia, followed by Europe and North America, reflecting the changes in anthropogenic aerosol emissions in recent decades (Section 6.2). Positive ERF <sub></sub> over high albedo areas, including cryosphere, deserts and clouds, also found in AR5 and attributed to absorbing aerosols, are not robust across the small CMIP6 ensemble applied here. Regionally aggregated shortwave (SW) and longwave (LW) components of the aerosol ERF <sub></sub> exhibit similar large variability across regions (Figure 6.10b). The SW flux changes come from aerosol–radiation and aerosol–cloud interactions while the small positive LW flux changes come from aerosol–cloud interactions (related to liquid-water path changes ( [[IPCC:Wg1:Chapter:Chapter-7#7.3.2.2|Section 7.3.2.2]] ). These spatial patterns in aerosol ERF are similar to the patterns reported in AR5.

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[[File:631a947caeba367e8cec53a4eed8186a IPCC_AR6_WGI_Figure_6_10.png]]

'''Figure 6.10 |''' '''Multi-model mean effective radiative forcings (ERFs) over the recent-past (1995-2014) induced by aerosol changes since 1850.''' Panel '''(a)''' shows the spatial distribution of the net ERF with area-weighted global mean ERF shown at the lower right corner. Uncertainty is represented using the advanced approach: no overlay indicates regions with robust signal, where ≥66% of models show change greater than variability threshold and ≥80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal, where &lt;66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where ≥66% of models show change greater than variability threshold and &lt;80% of all models agree on sign of change. For more information on the advanced approach, please refer to the Cross-Chapter Box Atlas.1. Panel '''(b)''' shows the mean shortwave and longwave ERF for each of the 14 regions defined in the Atlas. Violins in panel (b) show the distribution of values over regions where ERFs are significant. ERFs are derived from the difference between top of the atmosphere (TOA) radiative fluxes for Aerosol Chemistry Model Intercomparison Project (AerChemMIP) experiments ''histSST'' and ''histSST-piAer'' ( [[#Collins--2017|Collins et al., 2017]] ) averaged over 1995–2014 (Box 1.4, Chapter 1). The results come from seven Earth system models: MIROC6, MPI-I-ESM-1-2-HAM, MRI-ESM2-0, GFDL-ESM4, GISS-E2-1-G, NorESM2-LM and UKESM-0-LL. These data can be seen in the Interactive Atlas. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

Time evolution of 20-year means of regional net aerosol ERF shows that the regions are divided into two groups depending on whether the mean ERF attains its negative peak value in the 1970s–1980s (e.g., Europe, North America) or in the late 1990s–2000s (e.g., Asia, South America; Figure 6.11). Qualitatively, this shift in the distribution of ERF trends is consistent with the regional long-term trends in aerosol precursor emissions (Section 6.2; Figures 6.18 and 6.19) and their abundances (Section 6.3). However, at finer regional scales, there are regions where sulphate aerosols are still following an upward trend (e.g., Southern Asia; Section 6.3.5) implying that the trends in ERF may not have shifted for these regions. The continental-scale ERF <sub></sub> trends are also in line with the satellite-observed AOD trends assessed in [[IPCC:Wg1:Chapter:Chapter-2#2.2.6|Section 2.2.6]] . Global mean ERF reaches maximum negative values in the mid-1970s and its magnitude gradually decreases thereafter. This weakening of the negative forcing since 1990 agrees with findings that attribute this to a reduction in global mean SO <sub>2</sub> emissions combined with an increase in global BC ( [[#Myhre--2017|Myhre et al., 2017]] ). Uncertainties in model-simulated aerosol ERF distribution and trends can result from inter-model variations in the representation of aerosol–cloud interactions and aerosol microphysical processes as also demonstrated by [[#Bauer--2020|Bauer et al. (2020)]] .

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[[File:02d6b81f8579b92f37bfd2b1b4716097 IPCC_AR6_WGI_Figure_6_11.png]]

'''Figure 6.11 |''' '''Time evolution of 20-year multi-model mean averages of the annual area-weighted mean regional net effective radiative forcings (ERFs) due to aerosols''' '''for each of the 14 major regions in the Atlas, and global mean, using the models and model experiments as in Figure 6.''' '''10.''' Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

In summary, the spatial and temporal distribution of the net aerosol ERF from 1850–2014 is highly heterogeneous ( ''high confidence'' ). Globally, there has been a shift from increase to decrease of the negative net aerosol ERF driven by trends in aerosol and their precursor emissions ( ''high confidence'' ). However, the timing of this shift varies by continental-scale region and has not occurred for some finer regional scales.

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