Editing IPCC:AR6/WGI/Chapter-7 (section)

=== 7.3.4 Other Agents ===

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In addition to the large anthropogenic ERFs associated with WMGHGs and atmospheric aerosols assessed in Sections 7.3.2 and 7.3.3, land-use change, contrails and aviation-induced cirrus, and light-absorbing particles deposited on snow and ice have also contributed to the overall anthropogenic ERF and are assessed in Sections 7.3.4.1, 7.3.4.2 and 7.3.4.3. Changes in solar irradiance, galactic cosmic rays, and volcanic eruptions since pre-industrial times combined represent the natural contribution to the total (anthropogenic + natural) ERF and are discussed in Sections 7.3.4.4, 7.3.4.5 and 7.3.4.6.

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==== 7.3.4.1 Land Use ====

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Land-use forcing is defined as those changes in land-surface properties directly caused by human activity rather than by climate processes (see also [[IPCC:Wg1:Chapter:Chapter-2#2.2.7|Section 2.2.7]] ). Land-use change affects the surface albedo. For example, deforestation typically replaces darker forested areas with brighter cropland, and thus imposes a negative radiative forcing on climate, while afforestation and reforestation can have the opposite effect. Precise changes depend on the nature of the forest, crops and underlying soil. Land-use change also affects the amount of water transpired by vegetation ( [[#Devaraju--2015|Devaraju et al., 2015]] ). Irrigation of land directly affects evaporation ( [[#Sherwood--2018|Sherwood et al., 2018]] ), causing a global increase of 32,500 m <sup>3</sup> s <sup>−1</sup> due to human activity. Changes in evaporation and transpiration affect the latent heat budget, but do not directly affect the top-of-atmosphere (TOA) radiative fluxes. The lifetime of water vapour is so short that the effect of changes in evaporation on the greenhouse contribution of water vapour are negligible ( [[#Sherwood--2018|Sherwood et al., 2018]] ). However, evaporation can affect the ERF through adjustments, particularly through changes in low-cloud amounts. Land management affects the emissions or removal of GHGs from the atmosphere (such as CO <sub>2</sub> , CH <sub>4</sub> , N <sub>2</sub> O). These emissions changes have the greatest effect on climate ( [[#Ward--2014|Ward et al., 2014]] ), however they are already included in GHG inventories. Land-use change also affects the emissions of dust and biogenic volatile organic compounds (BVOCs), which form aerosols and affect the atmospheric concentrations of ozone and methane (Section 6.2.2). The effects of land use on surface temperature and hydrology were recently assessed in SRCCL ( [[#Jia--2019|Jia et al., 2019]] ).

Using the definition of ERF from ( [[#7.1|Section 7.1]] , the adjustment in land-surface temperature is excluded from the definition of ERF, but changes in vegetation and snow cover (resulting from land-use change) are included ( [[#Boisier--2013|Boisier et al., 2013]] ). Land-use change in the mid-latitudes induces a substantial amplifying adjustment in snow cover. Few climate model studies have attempted to quantify the ERF of land-use change. T. [[#Andrews--2017|]] [[#Andrews--2017|Andrews et al. (2017)]] calculated a very large surface albedo ERF (–0.47 W m <sup>–2</sup> ) from 1860 to 2005 in the HadGEM2-ES model, although they did not separate out the surface albedo change from snow cover change. HadGEM2-ES is known to overestimate the amount of boreal trees and shrubs in the unperturbed state ( [[#Collins--2011|Collins et al., 2011]] ) so will tend to overestimate the ERF associated with land-use change. The increases in dust in HadGEM2-ES contributed an extra –0.25 W m <sup>–2</sup> , whereas cloud cover changes added a small positive adjustment (0.15 W m <sup>–2</sup> ) consistent with a reduction in transpiration. A multi-model quantification of land-use forcing in CMIP6 models (excluding one outlier) ( [[#Smith--2020b|Smith et al., 2020b]] ) found an IRF of –0.15 ± 0.12 W m <sup>–2</sup> (1850–2014), and an ERF (correcting for land-surface temperature change) of –0.11 ± 0.09 W m <sup>–2</sup> . This shows a small positive adjustment term (mainly from a reduction in cloud cover). CMIP5 models show an IRF of –0.11 [–0.16 to –0.04] W m <sup>–2</sup> (1850–2000) after excluding unrealistic models ( [[#Lejeune--2020|Lejeune et al., 2020]] ).

The contribution of land-use change to albedo changes has recently been investigated using MODIS and AVHRR to attribute surface albedo to geographically specific land-cover types ( [[#Ghimire--2014|Ghimire et al., 2014]] ). When combined with a historical land-use map ( [[#Hurtt--2011|Hurtt et al., 2011]] ) this gives a SARF of –0.15 ± 0.01 W m <sup>–2</sup> for the period 1700–2005, of which approximately –0.12 W m <sup>–2</sup> is from 1850. This study accounted for correlations between vegetation type and snow cover, but not the adjustment in snow cover identified in T. [[#Andrews--2017|]] [[#Andrews--2017|Andrews et al. (2017)]] .

The indirect contributions of land-use change through biogenic emissions is very uncertain. Decreases in BVOCs reduce ozone and methane ( [[#Unger--2014|Unger, 2014]] ), but also reduce the formation of organic aerosols and their effects on clouds ( [[#Scott--2017|Scott et al., 2017]] ). Adjustments through changes in aerosols and chemistry are model dependent ( [[#Zhu--2019b|Zhu et al., 2019b]] ; [[#Zhu--2020|Zhu and Penner, 2020]] ), and it is not yet possible to make an assessment based on a limited number of studies.

The contribution of irrigation (mainly to low-cloud amount) is assessed as –0.05 <sup></sup> [–0.1 to 0.05] W m <sup>–2</sup> for the historical period ( [[#Sherwood--2018|Sherwood et al., 2018]] ).

Because the CMIP5 and CMIP6 modelling studies are in agreement with [[#Ghimire--2014|Ghimire et al. (2014)]] , that study is used as the assessed albedo ERF. Adding the irrigation effect to this gives an overall assessment of the ERF from land-use change of –0.20 ± 0.10 W m <sup>–2</sup> ( ''medium confidence'' ). Changes in ERF since 2014 are assumed to be small compared to the uncertainty, so this ERF applies to the period 1750–2019. The uncertainty range includes uncertainties in the adjustments.

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==== 7.3.4.2 Contrails and Aviation-induced Cirrus ====

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ERF from contrails and aviation-induced cirrus is taken from the assessment of [[#Lee--2020|Lee et al. (2020)]] , at 0.057 [0.019 to 0.098] W m <sup>–2</sup> in 2018 (see Section 6.6.2 for an assessment of the total effects of aviation). This is rounded up to address its ''low confidence'' and the extra year of air traffic to give an assessed ERF over 1750–2019 of 0.06 [0.02 to 0.10] W m <sup>–2</sup> . This assessment is given ''low confidence'' due to the potential that processes missing from the assessment would affect the magnitude of contrails and aviation-induced cirrus ERF.

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==== 7.3.4.3 Light-absorbing Particles on Snow and Ice ====

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In AR5, it was assessed that the effects of light-absorbing particles (LAPs) did probably not significantly contribute to recent reductions in Arctic ice and snow ( [[#Vaughan--2013|Vaughan et al., 2013]] ). The SARF from LAPs on snow and ice was assessed to 0.04 [0.02 to 0.09] W m <sup>–2</sup> ( [[#Boucher--2013|Boucher et al., 2013]] ), a range appreciably lower than the estimates given in AR4 ( [[#Forster--2007|Forster et al., 2007]] ). This effect was assessed to be ''low confidence'' ( ''medium evidence'' , ''low agreement'' ) (Table 8.5 in [[#Myhre--2013b|Myhre et al., 2013b]] ).

Since AR5 there has been progress in the understanding of the physical state and processes in snow that govern the albedo reduction by black carbon (BC). The SROCC ( [[#IPCC--2019a|IPCC, 2019a]] ) assessed that there is ''high confidence'' that darkening of snow by deposition of BC and other light-absorbing aerosol species increases the rate of snow melt ( [[IPCC:Wg1:Chapter:Chapter-2#2.2|Section 2.2]] in [[#Hock--2019|Hock et al., 2019]] ; [[IPCC:Wg1:Chapter:Chapter-3#3.4|Section 3.4]] in [[#Meredith--2019|Meredith et al., 2019]] ). C. [[#He--2018|]] [[#He--2018|He et al. (2018)]] found that taking into account both the non-spherical shape of snow grains and internal mixing of BC in snow significantly altered the effects of BC on snow albedo. The reductions of snow albedo by dust and BC have been measured and characterized in the Arctic, the Tibetan Plateau, and mid-latitude regions subject to seasonal snowfall, including North America and northern and eastern Asia ( [[#Qian--2015|Qian et al., 2015]] ).

Since AR5, two further studies of global IRF from black carbon on snow deposition are available, with best estimates of 0.01 W m <sup>–2</sup> ( [[#Lin--2014|Lin et al., 2014]] ) and 0.045 W m <sup>–2</sup> ( [[#Namazi--2015|Namazi et al., 2015]] ). Organic carbon deposition on snow and icehas been estimated to contribute a small positive IRF of 0.001 to 0.003 W m <sup>–2</sup> ( [[#Lin--2014|Lin et al., 2014]] ). No comprehensive global assessments of mineral dust deposition on snow are available, although the effects are potentially large in relation to the total effect of LAPs on snow and ice forcing ( [[#Yasunari--2015|Yasunari et al., 2015]] ).

Most radiative forcing estimates have a regional emphasis. The regional focus makes estimating a global mean radiative forcing from aggregating different studies challenging, and the relative importance of each region is expected to change if the global pattern of emissions sources changes ( [[#Bauer--2013|Bauer et al., 2013]] ). The lower bound of the assessed range of BC on snow and ice is extended to zero to encompass [[#Lin--2014|Lin et al. (2014)]] , with the best estimate unchanged, resulting in 0.04 [0.00 to 0.09] W m <sup>–2</sup> . The efficacy of BC on snow forcing was estimated to be 2 to 4 times as large as for an equivalent CO <sub>2</sub> forcing as the effects are concentrated at high latitudes in the cryosphere ( [[#Bond--2013|Bond et al., 2013]] ). However, it is unclear how much of this effect is due to radiative adjustments leading to a higher ERF, and how much comes from a less negative feedback α due to the high-latitude nature of the forcing. To estimate the overall ERF, the IRF is doubled assuming that part of the increased efficacy is due to adjustments. This gives an overall assessed ERF of +0.08 [0.00 to 0.18] W m <sup>–2</sup> , with ''low confidence'' .

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==== 7.3.4.4 Solar ====

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Variations in the total solar irradiance (TSI) represent a natural external forcing agent. The dominant cycle is the solar 11-year activity cycle, which is superimposed on longer cycles ( [[IPCC:Wg1:Chapter:Chapter-2#2.2|Section 2.2]] ). Over the last three 11-year cycles, the peak-to-trough amplitude in TSI has differed by about 1 W m <sup>–2</sup> between solar maxima and minima (Figure 2.2).

The fractional variability in the solar irradiance, over the solar cycle and between solar cycles, is much greater at short wavelengths in the 200–400 nanometre (nm) band than for the broad visible/infrared band that dominates TSI ( [[#Krivova--2006|Krivova et al., 2006]] ). The IRF can be derived simply by Δ ''TSI'' × (1 – albedo)/4 irrespective of wavelength, where the best estimate of the planetary albedo is usually taken to be 0.29 and Δ ''TSI'' represents the change in total solar irradiance ( [[#Stephens--2015|Stephens et al., 2015]] ). (The factor 4 arises because TSI is per unit area of Earth cross section presented to the Sun and IRF is per unit area of Earth’s surface). The adjustments are expected to be wavelength dependent. [[#Gray--2009|Gray et al. (2009)]] determined a stratospheric temperature adjustment of –22% to spectrally resolved changes in the solar radiance over one solar cycle. This negative adjustment is due to stratospheric heating from increased absorption by ozone at the short wavelengths, increasing the outgoing longwave radiation to space. A multi-model comparison ( [[#Smith--2018b|Smith et al., 2018b]] ) calculated adjustments of –4% due to stratospheric temperatures and –6% due to tropospheric processes (mostly clouds), for a change in TSI across the spectrum (Figure 7.4). The smaller magnitude of the stratospheric temperature adjustment is consistent with the broad spectral change rather than the shorter wavelengths characteristic of solar variation. A single-model study also found an adjustment that acts to reduce the forcing ( [[#Modak--2016|Modak et al., 2016]] ). While there has not yet been a calculation based on the appropriate spectral change, the –6% tropospheric adjustment from [[#Smith--2018b|Smith et al. (2018b)]] is adopted along with the [[#Gray--2009|Gray et al. (2009)]] stratospheric temperature adjustment. The ERF due to solar variability over the historical period is therefore represented by 0.72 × Δ ''TSI'' × (1 – albedo)/4 using the TSI timeseries from ( [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] [[IPCC:Wg1:Chapter:Chapter-2#2.2.1|Section 2.2.1]] ).

The AR5 ( [[#Myhre--2013b|Myhre et al., 2013b]] ) assessed solar SARF from around 1750 to 2011 to be 0.05 [0.00 to 0.10] W m <sup>–2</sup> which was computed from the seven-year mean around the solar minima in 1745 (being closest to 1750) and 2008 (being the most recent solar minimum). The inclusion of tropospheric adjustments that reduce ERF (compared to SARF in AR5) has a negligible effect on the overall forcing. Prior to the satellite era, proxy records are used to reconstruct historical solar activity. In AR5, historical records were constructed using observations of solar magnetic features. In this assessment historical time series are constructed from radiogenic compounds in the biosphere and in ice cores that are formed from cosmic rays ( [[#Steinhilber--2012|Steinhilber et al., 2012]] ).

In this assessment the TSI from the Paleoclimate Model Intercomparison Project Phase 4 (PMIP4) reconstruction is used ( [[IPCC:Wg1:Chapter:Chapter-2#2.2.1|Section 2.2.1]] ; [[#Jungclaus--2017|Jungclaus et al., 2017]] ). Proxies constructed from the <sup>14</sup> C and <sup>10</sup> Be radiogenic records for the SATIRE-M model ( [[#Vieira--2011|Vieira et al., 2011]] ) and <sup>14</sup> C record for the PMOD model ( [[#Shapiro--2011|Shapiro et al., 2011]] ) for the 1745 solar minimum provide ERFs for 1745–2008 of –0.01, –0.02 and 0.00 W m <sup>–2</sup> respectively. An independent dataset from the National Oceanic and Atmospheric Administration’s Climate Data Record ( [[#Coddington--2016|Coddington et al., 2016]] ; [[#Lean--2018|Lean, 2018]] ) provides an ERF for 1745–2008 of +0.03 W m <sup>–2</sup> . One substantially higher ERF estimate of +0.35 W m <sup>–2</sup> derived from TSI reconstructions is provided by [[#Egorova--2018|Egorova et al. (2018)]] . However, the estimate from [[#Egorova--2018|Egorova et al. (2018)]] hinges on assumptions about long-term changes in the quiet Sun for which there is no observed evidence. [[#Lockwood--2020|Lockwood and Ball (2020)]] analysed the relationship between observed changes in cosmic ray fluxes and recent, more accurate, TSI data and derived ERF between –0.01 and +0.02 W m <sup>–2</sup> , and [[#Yeo--2020|Yeo et al. (2020)]] modelling showed the maximum possible ERF to be 0.26 ± 0.09 W m <sup>–2</sup> . Hence the [[#Egorova--2018|Egorova et al. (2018)]] estimate is not explicitly taken into account in the assessment presented in this section.

In contrast to AR5, the solar ERF in this assessment uses full solar cycles rather than solar minima. The pre-industrial TSI is defined as the mean from all complete solar cycles from the start of the <sup>14</sup> C SATIRE-M proxy record in 6755 BCE to 1744 CE. The mean TSI from solar cycle 24 (2009–2019) is adopted as the assessment period for 2019. The best estimate solar ERF is assessed to be 0.01 W m <sup>–2</sup> , using the <sup>14</sup> C reconstruction from SATIRE-M, with a ''likely'' range of –0.06 to +0.08 W m <sup>–2</sup> ( ''medium confidence'' ). The uncertainty range is adopted from the evaluation of [[#Lockwood--2020|Lockwood and Ball (2020)]] using a Monte Carlo analysis of solar activity from the Maunder Minimum to 2019 from several datasets, leading to an ERF of –0.12 to +0.15 W m <sup>–2</sup> . The [[#Lockwood--2020|Lockwood and Ball (2020)]] full uncertainty range is halved as the period of reduced solar activity in the Maunder Minimum had ended by 1750 ( ''medium confidence'' ).

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==== 7.3.4.5 Galactic Cosmic Rays ====

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Variations in the flux of galactic cosmic rays (GCR) reaching the atmosphere are modulated by solar activity and affect new particle formation in the atmosphere through their link to ionization of the troposphere ( [[#Lee--2019|Lee et al., 2019]] ). It has been suggested that periods of high GCR flux correlate with increased aerosol and CCN concentrations and therefore also with cloud properties (e.g., [[#Dickinson--1975|Dickinson, 1975]] ; [[#Kirkby--2007|Kirkby, 2007]] ).

Since AR5, the link between GCR and new particle formation has been more thoroughly studied, particularly by experiments in the CERN CLOUD chamber (Cosmics Leaving OUtdoor Droplets; [[#Dunne--2016|Dunne et al., 2016]] ; [[#Kirkby--2016|Kirkby et al., 2016]] ; [[#Pierce--2017|Pierce, 2017]] ). By linking the GCR-induced new particle formation from CLOUD experiments to CCN, [[#Gordon--2017|Gordon et al. (2017)]] found that the CCN concentration for low-clouds differed by 0.2–0.3% between solar maximum and solar minimum. Combined with relatively small variations in the atmospheric ion concentration over centennial time scales ( [[#Usoskin--2015|Usoskin et al., 2015]] ), it is therefore unlikely that cosmic ray intensity affects present-day climate via nucleation ( [[#Yu--2014|Yu and Luo, 2014]] ; [[#Dunne--2016|Dunne et al., 2016]] ; [[#Pierce--2017|Pierce, 2017]] ; [[#Lee--2019|Lee et al., 2019]] ).

Studies continue to seek a relationship between GCR and properties of the climate system based on correlations and theory. [[#Svensmark--2017|Svensmark et al. (2017)]] proposed a new mechanism for ion-induced increase in aerosol growth rate and subsequent influence on the CCN concentration. The study does not include an estimate of the resulting effect on atmospheric CCN concentration and cloud radiative properties. Furthermore, Svensmark et al. (2009, 2016) find correlations between GCRs and aerosol and cloud properties in satellite and ground-based data. Multiple studies investigating this link have challenged such correlations ( [[#Kristjánsson--2008|Kristjánsson et al., 2008]] ; [[#Calogovic--2010|Calogovic et al., 2010]] ; [[#Laken--2016|Laken, 2016]] ).

AR5 concluded that the GCR effect on CCN is too weak to have any detectable effect on climate and no robust association was found between GCR and cloudiness ( [[#Boucher--2013|Boucher et al., 2013]] ). Published literature since AR5 robustly supports these conclusions with key laboratory, theoretical and observational evidence. There is ''high confidence'' that GCRs contribute a negligible ERF over the period 1750–2019.

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==== 7.3.4.6 Volcanic Aerosols ====

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There is large episodic negative radiative forcing associated with sulphur dioxide (SO <sub>2</sub> ) being ejected into the stratosphere from explosive volcanic eruptions, accompanied by more frequent smaller eruptions (Figure 2.2 and Cross-Chapter Box 4.1). From SO <sub>2</sub> gas, reflective sulphate aerosol is formed in the stratosphere where it may persist for months to years, reducing the incoming solar radiation. The volcanic SARF in AR5 ( [[#Myhre--2013b|Myhre et al., 2013b]] ) was derived by scaling the stratospheric aerosol optical depth (SAOD) by a factor of –25 W m <sup>–2</sup> per unit SAOD from [[#Hansen--2005b|Hansen et al. (2005b)]] . Quantification of the adjustments to SAOD perturbations from climate model simulations have determined a significant positive adjustment driven by a reduction in cloud amount (Figure 7.4; [[#Marshall--2020|Marshall et al., 2020]] ). Analysis of CMIP5 models provides a mean ERF of –20 W m <sup>–2</sup> per unit SAOD ( [[#Larson--2016|Larson and Portmann, 2016]] ). Single-model studies with successive generations of Hadley Centre climate models produce estimates between –17 and –19 W m <sup>–2</sup> per unit SAOD ( [[#Gregory--2016|Gregory et al., 2016]] ; [[#Marshall--2020|Marshall et al., 2020]] ), with some evidence that ERF may be non-linear with SAOD for large eruptions ( [[#Marshall--2020|Marshall et al., 2020]] ). Analysis of the volcanically active periods of 1982–1985 and 1990–1994 using the CESM1(WACCM) aerosol–climate model provided an SAOD-to-ERF relationship of –21.5 (± 1.1) W m <sup>–2</sup> per unit SAOD ( [[#Schmidt--2018|Schmidt et al., 2018]] ). Volcanic SO <sub>2</sub> emissions may contribute a positive forcing through effects on upper tropospheric ice clouds, due to additional ice nucleation on volcanic sulphate particles ( [[#Friberg--2015|Friberg et al., 2015]] ; [[#Schmidt--2018|Schmidt et al., 2018]] ), although one observational study found no significant effect ( [[#Meyer--2015|Meyer et al., 2015]] ). Due to ''low agreement'' , the contribution of sulphate aerosol effects on ice clouds to volcanic ERF is not included in the overall assessment.

Non-explosive volcanic eruptions generally yield negligible global ERFs due to the short atmospheric lifetimes (a few weeks) of volcanic aerosols in the troposphere. However, as discussed in ( [[#7.3.3.2|Section 7.3.3.2]] , the massive fissure eruption in Holuhraun, Iceland persisted for months in 2014 and 2015 and did in fact result in a marked and persistent reduction in cloud droplet radii and a corresponding increase in cloud albedo regionally ( [[#Malavelle--2017|Malavelle et al., 2017]] ). This shows that non-explosive fissure eruptions can lead to strong regional and even global ERFs, but because the Holuhraun eruption occurred in Northern Hemisphere winter, solar insolation was weak and the observed albedo changes therefore did not result in an appreciable global ERF ( [[#Gettelman--2015|Gettelman et al., 2015]] ).

The ERF for volcanic stratospheric aerosols is assessed to be –20 ± 5 W m <sup>–2</sup> per unit SAOD ( ''medium confidence'' ) based on the CMIP5 multi-model mean from the [[#Larson--2016|Larson and Portmann (2016)]] SAOD forcing efficiency calculations combined with the single-model results of [[#Gregory--2016|Gregory et al. (2016)]] , [[#Schmidt--2018|Schmidt et al. (2018)]] and [[#Marshall--2020|Marshall et al. (2020)]] . This is applied to the SAOD time series from ( [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] [[IPCC:Wg1:Chapter:Chapter-2#2.2.2|Section 2.2.2]] ) to generate a time series of ERF and temperature response shown in ( [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] (Figure 2.2 and Figure 7.8, respectively). The period from 500 BCE to 1749 CE, spanning back to the start of the record of [[#Toohey--2017|Toohey and Sigl (2017)]] , is defined as the pre-industrial baseline and the volcanic ERF is calculated using an SAOD anomaly from this long-term mean. As in AR5, a pre-industrial to present-day ERF assessment is not provided due to the episodic nature of volcanic eruptions.

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