Editing IPCC:AR6/SR15/Chapter-2 (section)

=== 2.2.1 Geophysical Characteristics of Mitigation Pathways ===

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This section employs the pathway classification introduced in Section 2.1, with geophysical characteristics derived from simulations with the MAGICC reduced-complexity carbon cycle and climate model and supported by simulations with the FAIR reduced-complexity model (Section 2.1). Within a specific category and between models, there remains a large degree of variance. Most pathways exhibit a temperature overshoot which has been highlighted in several studies focusing on stringent mitigation pathways (Huntingford and Lowe, 2007; Wigley et al., 2007; Nohara et al., 2015; Rogelj et al., 2015d; Zickfeld and Herrington, 2015; Schleussner et al., 2016; Xu and Ramanathan, 2017) <sup>[[#fn:r40|40]]</sup> . Only very few of the scenarios collected in the database for this report hold the average future warming projected by MAGICC below 1.5°C during the entire 21st century (Table 2.1, Figure 2.1). Most 1.5°C-consistent pathways available in the database overshoot 1.5°C around mid-century before peaking and then reducing temperatures so as to return below that level in 2100. However, because of numerous geophysical uncertainties and model dependencies (Section 2.2.1.1, Supplementary Material 2.SM.1.1), absolute temperature characteristics of the various pathway categories are more difficult to distinguish than relative features (Figure 2.1, Supplementary Material 2.SM.1.1), and actual probabilities of overshoot are imprecise. However, all lines of evidence available for temperature projections indicate a probability greater than 50% of overshooting 1.5°C by mid-century in all but the most stringent pathways currently available (Supplementary Material 2.SM.1.1, 2.SM.1.4).

Most 1.5°C-consistent pathways exhibit a peak in temperature by mid-century whereas 2°C-consistent pathways generally peak after 2050 (Supplementary Material 2.SM.1.4). The peak in median temperature in the various pathway categories occurs about ten years before reaching net zero CO <sub>2</sub> emissions due to strongly reduced annual CO <sub>2</sub> emissions and deep reductions in CH <sub>4</sub> emissions (Section 2.3.3). The two reduced-complexity climate models used in this assessment suggest that virtually all available 1.5°C-consistent pathways peak and then decline global mean temperature, but with varying rates of temperature decline after the peak (Figure 2.1). The estimated decadal rates of temperature change by the end of the century are smaller than the amplitude of the climate variability as assessed in AR5 (1 standard deviation of about ±0.1°C), which hence complicates the detection of a global peak and decline of warming in observations on time scales of one to two decades (Bindoff et al., 2013) <sup>[[#fn:r41|41]]</sup> . In comparison, many pathways limiting warming to 2°C or higher by 2100 still have noticeable increasing trends at the end of the century, and thus imply continued warming.

By 2100, the difference between 1.5°C- and 2°C-consistent pathways becomes clearer compared to mid-century, not only for the temperature response (Figure 2.1) but also for atmospheric CO <sub>2</sub> concentrations. In 2100, the median CO <sub>2</sub> concentration in 1.5°C-consistent pathways is below 2016 levels (Le Quéré et al., 2018) <sup>[[#fn:r42|42]]</sup> , whereas it remains higher by about 5–10% compared to 2016 in the 2°C-consistent pathways.

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'''Figure 2.1'''

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'''Pathways classification overview.'''
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[[File:fe1759678dc9162b928227cb1f09fd14 figure-2.1-1024x995.png]]

(a) Average global mean temperature increase relative to 2010 as projected by FAIR and MAGICC in 2030, 2050 and 2100; (b) response of peak warming to cumulative CO <sub>2</sub> emissions until net zero by MAGICC (red) and FAIR (blue); (c) decadal rate of average global mean temperature change from 2081 to 2100 as a function of the annual CO <sub>2</sub> emissions averaged over the same period as given by FAIR (transparent squares) and MAGICC (filled circles). In panel (a), horizontal lines at 0.63°C and 1.13°C are indicative of the 1.5°C and 2°C warming thresholds with the respect to 1850–1900, taking into account the assessed historical warming of 0.87°C ±0.12°C between the 1850–1900 and 2006–2015 periods (Chapter 1, Section 1.2.1). In panel (a), vertical lines illustrate both the physical and the scenario uncertainty as captured by MAGICC and FAIR and show the minimal warming of the 5th percentile of projected warming and the maximal warming of the 95th percentile of projected warming per scenario class. Boxes show the interquartile range of mean warming across scenarios, and thus represent scenario uncertainty only.

Original Creation for this Report using SR15 scenario database, public, 2018

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==== 2.2.1.1 Geophysical uncertainties: non-CO <sub>2</sub> forcing agents ====

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Impacts of non-CO <sub>2</sub> climate forcers on temperature outcomes are particularly important when evaluating stringent mitigation pathways (Weyant et al., 2006; Shindell et al., 2012; Rogelj et al., 2014b, 2015a; Samset et al., 2018) <sup>[[#fn:r43|43]]</sup> . However, many uncertainties affect the role of non-CO <sub>2</sub> climate forcers in stringent mitigation pathways.

A first uncertainty arises from the magnitude of the radiative forcing attributed to non-CO <sub>2</sub> climate forcers. Figure 2.2 illustrates how, for one representative 1.5°C-consistent pathway (SSP2-1.9) (Fricko et al., 2017; Rogelj et al., 2018) <sup>[[#fn:r44|44]]</sup> , the effective radiative forcings as estimated by MAGICC and FAIR can differ (see Supplementary Material 2.SM1.1 for further details). This large spread in non-CO <sub>2</sub> effective radiative forcings leads to considerable uncertainty in the predicted temperature response. This uncertainty ultimately affects the assessed temperature outcomes for pathway classes used in this chapter (Section 2.1) and also affects the carbon budget (Section 2.2.2). Figure 2.2 highlights the important role of methane emissions reduction in this scenario, in agreement with the recent literature focussing on stringent mitigation pathways (Shindell et al., 2012; Rogelj et al., 2014b, 2015a; Stohl et al., 2015; Collins et al., 2018) <sup>[[#fn:r45|45]]</sup> .

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'''Changes and uncertainties in effective radiative forcings (ERF) for one 1.5°C-consistent pathway (SSP2-19) as estimated by MAGICC and FAIR.'''
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[[File:973385e9fbc985c7954e74a64d1ed321 figure-2.2-1024x975.jpg]]

The lines are indicative of the total effective radiative forcing from all anthropogenic sources (solid lines) and for non-CO <sub>2</sub> agents only (dashed lines), as represented by MAGICC (red) and FAIR (blue) relative to 2010, respectively. Vertical bars show the mean radiative forcing as predicted by MAGICC and FAIR of relevant non-CO <sub>2</sub> agents for year 2030, 2050 and 2100. The vertical lines give the uncertainty (1 standard deviation) of the ERFs for the represented species.

Original Creation for this Report using the SR15 scenario database

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For mitigation pathways that aim at halting and reversing radiative forcing increase during this century, the aerosol radiative forcing is a considerable source of uncertainty (Figure 2.2) (Samset et al., 2018; Smith et al., 2018) <sup>[[#fn:r46|46]]</sup> . Indeed, reductions in SO <sub>2</sub> (and NO <sub>x</sub> ) emissions largely associated with fossil-fuel burning are expected to reduce the cooling effects of both aerosol radiative interactions and aerosol cloud interactions, leading to warming (Myhre et al., 2013; Samset et al., 2018) <sup>[[#fn:r47|47]]</sup> . A multimodel analysis (Myhre et al., 2017) <sup>[[#fn:r48|48]]</sup> and a study based on observational constraints (Malavelle et al., 2017) <sup>[[#fn:r49|49]]</sup> largely support the AR5 best estimate and uncertainty range of aerosol forcing. The partitioning of total aerosol radiative forcing between aerosol precursor emissions is important (Ghan et al., 2013; Jones et al., 2018; Smith et al., 2018) <sup>[[#fn:r50|50]]</sup> as this affects the estimate of the mitigation potential from different sectors that have aerosol precursor emission sources. The total aerosol effective radiative forcing change in stringent mitigation pathways is expected to be dominated by the effects from the phase-out of SO <sub>2</sub> , although the magnitude of this aerosol-warming effect depends on how much of the present-day aerosol cooling is attributable to SO <sub>2</sub> , particularly the cooling associated with aerosol–cloud interaction (Figure 2.2). Regional differences in the linearity of aerosol–cloud interactions (Carslaw et al., 2013; Kretzschmar et al., 2017) <sup>[[#fn:r51|51]]</sup> make it difficult to separate the role of individual precursors. Precursors that are not fully mitigated will continue to affect the Earth system. If, for example, the role of nitrate aerosol cooling is at the strongest end of the assessed IPCC AR5 uncertainty range, future temperature increases may be more modest if ammonia emissions continue to rise (Hauglustaine et al., 2014) <sup>[[#fn:r52|52]]</sup> .

Figure 2.2 shows that there are substantial differences in the evolution of estimated effective radiative forcing of non-CO <sub>2</sub> forcers between MAGICC and FAIR. These forcing differences result in MAGICC simulating a larger warming trend in the near term compared to both the FAIR model and the recent observed trends of 0.2°C per decade reported in Chapter 1 (Figure 2.1, Supplementary Material 2.SM.1.1, Chapter 1, Section 1.2.1.3). The aerosol effective forcing is stronger in MAGICC compared to either FAIR or the AR5 best estimate, though it is still well within the AR5 uncertainty range (Supplementary Material 2.SM.1.1.1). A recent revision (Etminan et al., 2016) <sup>[[#fn:r53|53]]</sup> increases the methane forcing by 25%. This revision is used in the FAIR but not in the AR5 setup of MAGICC that is applied here. Other structural differences exist in how the two models relate emissions to concentrations that contribute to differences in forcing (see Supplementary Material 2.SM.1.1.1).

Non-CO <sub>2</sub> climate forcers exhibit a greater geographical variation in radiative forcings than CO <sub>2</sub> , which leads to important uncertainties in the temperature response <sub> </sub> (Myhre et al., 2013) <sup>[[#fn:r54|54]]</sup> . This uncertainty increases the relative uncertainty of the temperature pathways associated with low emission scenarios compared to high emission scenarios (Clarke et al., 2014) <sup>[[#fn:r55|55]]</sup> . It is also important to note that geographical patterns of temperature change and other climate responses, especially those related to precipitation, depend significantly on the forcing mechanism (Myhre et al., 2013; Shindell et al., 2015; Marvel et al., 2016; Samset et al., 2016) <sup>[[#fn:r56|56]]</sup> (see also Chapter 3, Section 3.6.2.2).

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==== 2.2.1.2 Geophysical uncertainties: climate and Earth system feedbacks ====

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Climate sensitivity uncertainty impacts future projections as well as carbon-budget estimates (Schneider et al., 2017) <sup>[[#fn:r57|57]]</sup> . AR5 assessed the equilibrium climate sensitivity (ECS) to be ''likely'' in the 1.5°–4.5°C range, ''extremely unlikely'' less than 1°C and ''very unlikely'' greater than 6°C. The lower bound of this estimate is lower than the range of CMIP5 models (Collins et al., 2013) <sup>[[#fn:r58|58]]</sup> . The evidence for the 1.5°C lower bound on ECS in AR5 was based on analysis of energy-budget changes over the historical period. Work since AR5 has suggested that the climate sensitivity inferred from such changes has been lower than the 2 × CO <sub>2</sub> climate sensitivity for known reasons (Forster, 2016; Gregory and Andrews, 2016; Rugenstein et al., 2016; Armour, 2017; Ceppi and Gregory, 2017; Knutti et al., 2017; Proistosescu and Huybers, 2017) <sup>[[#fn:r59|59]]</sup> . Both a revised interpretation of historical estimates and other lines of evidence based on analysis of climate models with the best representation of today’s climate (Sherwood et al., 2014; Zhai et al., 2015; Tan et al., 2016; Brown and Caldeira, 2017; Knutti et al., 2017) <sup>[[#fn:r60|60]]</sup> suggest that the lower bound of ECS could be revised upwards, which would decrease the chances of limiting warming below 1.5°C in assessed pathways. However, such a reassessment has been challenged (Lewis and Curry, 2018) <sup>[[#fn:r61|61]]</sup> , albeit from a single line of evidence. Nevertheless, it is premature to make a major revision to the lower bound. The evidence for a possible revision of the upper bound on ECS is less clear, with cases argued from different lines of evidence for both decreasing (Lewis and Curry, 2015, 2018; Cox et al., 2018) <sup>[[#fn:r62|62]]</sup> and increasing (Brown and Caldeira, 2017) <sup>[[#fn:r63|63]]</sup> the bound presented in the literature. The tools used in this chapter employ ECS ranges consistent with the AR5 assessment. The MAGICC ECS distribution has not been selected to explicitly reflect this but is nevertheless consistent (Rogelj et al., 2014a) <sup>[[#fn:r64|64]]</sup> . The FAIR model used here to estimate carbon budgets explicitly constructs log-normal distributions of ECS and transient climate response based on a multi-parameter fit to the AR5 assessed ranges of climate sensitivity and individual historic effective radiative forcings (Smith et al., 2018) <sup>[[#fn:r65|65]]</sup> (Supplementary Material 2.SM.1.1.1).

Several feedbacks of the Earth system, involving the carbon cycle, non-CO <sub>2</sub> GHGs and/or aerosols, may also impact the future dynamics of the coupled carbon–climate system’s response to anthropogenic emissions. These feedbacks are caused by the effects of nutrient limitation (Duce et al., 2008; Mahowald et al., 2017) <sup>[[#fn:r66|66]]</sup> , ozone exposure (de Vries et al., 2017) <sup>[[#fn:r67|67]]</sup> , fire emissions (Narayan et al., 2007) <sup>[[#fn:r68|68]]</sup> and changes associated with natural aerosols (Cadule et al., 2009 <sup>[[#fn:r69|69]]</sup> ; Scott et al., 2018) <sup>[[#fn:r70|70]]</sup> . Among these Earth system feedbacks, the importance of the permafrost feedback’s influence has been highlighted in recent studies. Combined evidence from both models (MacDougall et al., 2015; Burke et al., 2017; Lowe and Bernie, 2018) <sup>[[#fn:r71|71]]</sup> and field studies (like Schädel et al., 2014; Schuur et al., 2015) <sup>[[#fn:r72|72]]</sup> shows ''high agreement'' that permafrost thawing will release both CO <sub>2</sub> and CH <sub>4</sub> as the Earth warms, amplifying global warming. This thawing could also release N <sub>2</sub> O (Voigt et al., 2017a, b) <sup>[[#fn:r73|73]]</sup> . Field, laboratory and modelling studies estimate that the vulnerable fraction in permafrost is about 5–15% of the permafrost soil carbon (~5300–5600 GtCO <sub>2</sub> in Schuur et al., 2015) <sup>[[#fn:r74|74]]</sup> and that carbon emissions are expected to occur beyond 2100 because of system inertia and the large proportion of slowly decomposing carbon in permafrost (Schädel et al., 2014) <sup>[[#fn:r75|75]]</sup> . Published model studies suggest that a large part of the carbon release to the atmosphere is in the form of CO <sub>2</sub> (Schädel et al., 2016) <sup>[[#fn:r76|76]]</sup> , while the amount of CH <sub>4</sub> released by permafrost thawing is estimated to be much smaller than that CO <sub>2</sub> . Cumulative CH <sub>4</sub> release by 2100 under RCP2.6 ranges from 0.13 to 0.45 Gt of methane (Burke et al., 2012; Schneider von Deimling et al., 2012, 2015) <sup>[[#fn:r77|77]]</sup> , with fluxes being the highest in the middle of the century because of maximum thermokarst lake extent by mid-century (Schneider von Deimling et al., 2015) <sup>[[#fn:r78|78]]</sup> .

The reduced complexity climate models employed in this assessment do not take into account permafrost or non-CO <sub>2</sub> Earth system feedbacks, although the MAGICC model has a permafrost module that can be enabled. Taking the current climate and Earth system feedbacks understanding together, there is a possibility that these models would underestimate the longer-term future temperature response to stringent emission pathways (Section 2.2.2).

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