Editing IPCC:AR6/WGI/Chapter-4 (section)

== 4.8 Low-likelihood, High-warming Storylines ==

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Previous IPCC assessments have primarily assessed the projected ''likely'' range of changes (e.g., [[#Collins--2013|Collins et al., 2013]] ; see also Box 1.1). The focus on the ''likely'' range partly results from the design of model intercomparison projects that are not targeted to systematically assess the upper and lower bounds of projections, which in principle would require a systematic sampling of structural and parametric model uncertainties. The upper and lower bounds of model projections may further be sensitive to the missing representation of processes and to deep uncertainties about aspects of the climate system ( [[IPCC:Wg1:Chapter:Chapter-1#1.2.3.1|Section 1.2.3.1]] ).

However, a comprehensive risk assessment requires taking into account high potential levels of warming whose likelihood is low, but potential impacts on society and ecosystems are high ( [[#Xu--2017|Xu and Ramanathan, 2017]] ; [[#Sutton--2018|Sutton, 2018]] ). Climate-related risks have been argued to increase with increasing levels of global warming even if their likelihood decreases ( [[#O’Neill--2017|O’Neill et al., 2017]] ). Thus, it has recently been argued that an assessment that is too narrowly focused on the ''likely'' range potentially ignores the changes in the physical climate system associated with the highest risks ( [[#Sutton--2018|Sutton, 2018]] ; see [[IPCC:Wg1:Chapter:Chapter-1#1.4.4.1|Section 1.4.4.1]] ).

Given that the CMIP experiments can be considered ensembles of opportunity that are not designed for probabilistic assessments, alternative approaches such as physically plausible high-impact scenarios ( [[#Sutton--2018|Sutton, 2018]] ) or storylines have been suggested to investigate the tail of the distribution ( [[#Lenderink--2014|Lenderink et al., 2014]] ; [[#Zappa--2017|Zappa and Shepherd, 2017]] ; [[#Kjellström--2018|Kjellström et al., 2018]] ; [[#Shepherd--2018|Shepherd et al., 2018]] ; see [[IPCC:Wg1:Chapter:Chapter-1#1.4.4|Section 1.4.4]] ). Such storylines informed by a combination of process understanding, model evidence, and paleo information can be used for risk assessment and adaptation planning to test how well adaptation strategies would cope if the impacts of climate change were more severe than suggested by the ''likely'' model range ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.4|Section 1.4.4]] ). Note that by definition the lower bound of the ''likely'' model range ( [[#box-4.1|Box 4.1]] ) is equally likely as the upper bound. However, low-warming storylines are not specifically assessed in this section to focus on storylines associated with highest risks. This section further focuses on storylines of high and very high global warming levels along with their manifestation in global patterns of temperature and precipitation changes. However, this does not account for the largest potential changes at regional levels, which would require taking into account storylines of regional changes dependent on changes in atmospheric circulation, land–atmosphere interactions, and regional to local feedbacks.

This section adopts an approach suggested in [[#Sutton--2018|Sutton (2018)]] . Since changes in temperature and precipitation tend to increase with the level of warming ( [[#4.6.1|Section 4.6.1]] ), low-likelihood, high-warming storylines are here illustrated for a level of warming consistent with the upper bound of the assessed ''very likely'' range ( [[#4.3.4|Section 4.3.4]] ) and for a level of warming above the ''very likely'' range. ECS and TCR are the dominant sources of uncertainty in projections of future warming under moderate to strong emissions scenarios ( [[#7.5.7|Section 7.5.7]] ). Thus, a very high level of warming may occur if ECS and TCR are close to or above the upper bound of the assessed ''very likely'' range, which, to agree with historical trends, would require a strong historical aerosol cooling and/or strong SST pattern effects, combined with strong positive cloud feedback and substantial biases in paleoclimate temperature reconstructions, each of which are assessed as either ''unlikely'' or ''very unlikely'' , though not ruled out (Section 7.5.5).

For SSP1-2.6, the warming consistent with the upper bound of the assessed ''very likely'' range corresponds to a warming of 1.5°C in 2081–2100 relative to 1995–2014 and 2.4°C relative to 1850–1900 ( [[#4.3.4|Section 4.3.4]] ), a warming well above the 2°C warming level even in SSP1-2.6. Based on different lines of evidence, Figure 4.41 illustrates by how much such a low-likelihood, high-warming storyline exceeds the warming pattern consistent with the assessed best estimate GSAT warming of 0.9°C relative to 1995–2014. The first estimate (Figure 4.41, second row) is based on the assumption that the multi-model mean temperature pattern scales linearly with global mean warming. While linear scaling provides an appropriate approximation for changes in temperatures patterns at lower levels of warming ( [[#4.2.4|Section 4.2.4]] ), this assumption cannot easily be tested for an extrapolation to higher levels of warming. Thus, a second estimate (Figure 4.41, third row) is based on the average of the five models that simulate a GSAT warming most consistent with the upper bound of the assessed ''very likely'' range ( [[#4.3.4|Section 4.3.4]] and Box 4.1; note some of the models share components). The two estimates for the annual mean temperature pattern for a low-likelihood, high-warming storyline consistently show a warming pattern that substantially exceeds the best estimate warming pattern in most regions except around the North Atlantic and the parts of the Arctic. Pattern scaling suggests more than 50% warming above the best estimate, with 2°C–3°C warming over much of Eurasia and North America and more than 4°C warming relative to 1995–2014 over the Arctic (Figure 4.41c). The other approach based on five models shows less warming than the best estimate and even larger area of cooling in the North Atlantic but more warming than the best estimate over much of the tropical Pacific, Atlantic, around Antarctica and other the land regions (Figure 4.41e).

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[[File:11d4f5c156c1a324e1cbd4ef08e36cbc IPCC_AR6_WGI_Figure_4_41.png]]

'''Figure''' '''4.41 |''' '''High-warming storylines for changes in annual mean temperature. (a, b)''' Changes in 2081–2100 relative to 1995–2014 consistent with the assessed best global surface air temperature (GSAT) estimate (0.9°C and 3.5°C relative to 1995–2014 for SSP1-2.6 and SSP5-8.5, respectively). The CMIP6 multi-model mean is linearly pattern-scaled to the best GSAT estimate. '''(c–h)''' Annual mean warming above the best estimate, relative to panels (a) and (b) respectively; note the different colour bar in a high and very high-warming storyline for 2081–2100. '''(c, d)''' Multi-model mean warming pattern scaled to very high GSAT level corresponding to the upper bound of the assessed ''very likely'' range (4.8°C for SSP5-8.5 and 1.5°C for SSP1-2.6; see [[#4.3.4|Section 4.3.4]] ). '''(e, f)''' Average of five models with high GSAT warming nearest to the upper estimate of the ''very likely'' range (CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-1-HR, EC-Earth3 for SSP1-2.6 and ACCESS-CM2, CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-1-HRfor SSP5-8.5); '''(g, h)''' Average of four and five models, respectively (ACCESS-CM2, HadGEM3-GC31-LL, HadGEM3-GC31-MM, UKESM1-0-LL for SSP1-2.6 and CanESM5, CanESM5-CanOE, HadGEM3-GC31-LL: HadGEM3-GC31-MM, UKESM1-0-LL for SSP5-8.5) projecting very high GSAT warming exceeding the ''very likely'' range. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

For the high-emissions scenarios SSP3-7.0 and SSP5-8.5, a high-warming storyline is associated with wide-spread warming that exceeds the already high best-estimate warming by another 35–50%. For SSP5-8.5, this corresponds to a warming of 1°C–3°C in addition to the best estimate over most land regions, which implies more than 6°C relative to 1995–2014 over most extratropical land regions and Amazonia. Over large parts of the Arctic, annual mean temperatures increase by more than 10°C relative to 1995–2014 in such a high-warming storyline under SSP5-8.5. The two lines of evidence yield more consistent patterns for SSP5-8.5 than for SSP1-2.6, but there are substantial differences concerning whether the strongest warming above the best estimate occurs over the tropics or extratropical land regions.

While individual models project even stronger warming over extratropical land regions (Figure 4.41 bottom row), their projected GSAT warming exceeds the assessed ''very likely'' 5–95% range and thus correspond to an ''extremely unlikely'' (below 5% likelihood) storyline. While all the models consistent with such a storyline tend to overestimate the observed warming trend over the historical period ( [[#Brunner--2020|Brunner et al., 2020]] ; [[#Liang--2020|Liang et al., 2020]] ; [[#Nijsse--2020|Nijsse et al., 2020]] ; [[#Tokarska--2020|Tokarska et al., 2020]] ; [[#Ribes--2021|Ribes et al., 2021]] ), some of them show a good representation of several aspects of the present-day climate ( [[#Andrews--2019|Andrews et al., 2019]] ; [[#Sellar--2019|Sellar et al., 2019]] ; [[#Swart--2019|Swart et al., 2019]] ). Such a very high-warming storyline implies widespread warming of more than 1.5°C and 3°C above the best-estimate warming pattern under SSP1-2.6 and SSP5-8.5, respectively. Under SSP1-2.6, this corresponds to more than 3°C warming relative to 1995–2014 over land regions in the northern mid- to high latitudes and more than 6°C in the Arctic (Figure 4.41g). Under SSP5-8.5, such a very high-warming storyline implies more than 8°C warming over parts of Amazonia and more than 6°C over most other tropical land regions (Figure 4.41h).

High-warming storylines are ''very likely'' also associated with substantial changes in the hydrological cycle due to strong thermodynamic changes, which can be amplified or offset by dynamical changes ( [[#Emori--2005|Emori and Brown, 2005]] ; [[#Seager--2014b|Seager et al., 2014b]] ; [[#Chavaillaz--2016|Chavaillaz et al., 2016]] ; [[#Kröner--2017|Kröner et al., 2017]] ; [[#Chen--2019|Chen et al., 2019]] ). Here the assessment of the hydrological cycle in high-warming storylines is limited to changes in annual mean precipitation, but changes in seasonal mean precipitation can be even stronger due to enhanced seasonality in many regions ( [[IPCC:Wg1:Chapter:Chapter-8#box-8.2|Box 8.2]] ).

Quantifying precipitation changes associated with high-warming storylines is challenging since models show the largest changes in precipitation over different regions (Sections [[#4.5.1|Section 4.5.1]] and [[#4.6.1|4.6.1]] ). In some areas, models project opposing signals in different seasons or a combination of decreasing mean and increasing extreme precipitation ( [[#Kendon--2014|Kendon et al., 2014]] ; [[#Ban--2015|Ban et al., 2015]] ; [[#Giorgi--2016|Giorgi et al., 2016]] ; [[#Pendergrass--2017|Pendergrass et al., 2017]] ). Models with the most pronounced GSAT warming are not necessarily associated with the strongest precipitation response in all regions, in part due to projected changes in atmospheric dynamics ( [[#Madsen--2017|Madsen et al., 2017]] ; [[#Zappa--2017|Zappa and Shepherd, 2017]] ; [[#Li--2018|Li et al., 2018]] ).

Different alternative estimates of changes in annual mean precipitation patterns consistent with high-warming levels are compared here. The first estimate (Figure 4.42b) is based on a linear pattern scaling of the multi-model mean precipitation pattern for SSP5-8.5 (Figure 4.42a) to be consistent with the upper bound of the assessed ''very likely'' GSAT range (see above). This estimate is reasonably consistent with the average response of the five models with GSAT warming most consistent with the upper bound of the ''very likely'' warming range (Figure 4.42c) except for Australia. Both estimates show about 30–40% larger changes in annual mean precipitation than the response pattern consistent with the best GSAT estimate. In a high-warming storyline, widespread increases of more than 30% occur in many regions north of 50°N and over parts of the tropics. Around the Mediterranean and other parts of the subtropics, a high-warming storyline is associated with a reduction in annual mean precipitation of more than 30% depending on the season.

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[[File:baa59fd0db3b85880905c442bfb10ca3 IPCC_AR6_WGI_Figure_4_42.png]]

'''Figure 4.42''' '''|''' '''High-warming storylines for changes in annual mean precipitation.'''

'''(a)''' Estimates for annual mean precipitation changes in 2081–2100, relative to 1995–2014, consistent with the best global surface air temperature (GSAT) estimate derived by linearly scaling the CMIP6 multi-model mean changes to a GSAT change of 3.5°C. '''(b, c)''' Estimates for annual mean precipitation changes in 2081–2100 relative 1995–2014 in a storyline representing a physically plausible high-global-warming level. '''(b)''' Multi-model-mean precipitation scaled to high-global-warming level (corresponding to 4.8°C, the upper bound of the ''very likely'' range; see [[#4.3.4|Section 4.3.4]] ). '''(c)''' Average of five models with GSAT warming nearest to the high level of warming (ACCESS-CM2, CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-1-HR) '''(d)''' Annual mean precipitation changes in four of the five individual model simulations averaged in (c). '''(e, f)''' Local upper estimate (95% quantile across models) and lower estimate (5% quantile across models) at each grid point. Information at individual grid points comes from different model simulations and illustrates local uncertainty range but should not be interpreted as a pattern. '''(g)''' Area fraction of changes in annual mean precipitation 2081–2100 relative to 1995–2014 for (i) all CMIP6 model simulations (thin black lines), (ii) models shown in (c) (red lines), and (iii) models showing very high warming above the models shown in (c) (dark red lines). The grey range illustrates the 5–95% range across CMIP6 models and the solid black line the area fraction of the multi-model mean pattern shown in (a). Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

Both the multi-model mean and the pattern-scaled responses show a smoother pattern than in individual simulations ( [[#Tebaldi--2007|Tebaldi and Knutti, 2007]] ; [[#Knutti--2010|Knutti et al., 2010]] ), because the multi-model mean filters out internal variability and because model differences in the location of the largest change tend to cancel. Individual model simulations show opposing signs in precipitation change such as over parts of Australia, the west coast of North America, parts of West Africa and India (Figure 4.42d), which tend to offset in the multi-model mean response. The spatial probability distribution of precipitation changes shows that areas of strong precipitation increase or decrease occur in all models (Figure 4.42g, see also [[#4.6.1|Section 4.6.1]] ). However, due to the spatial smoothing, the multi-model mean response shows a lower area fraction of drying than most of the individual models ( [[#Tebaldi--2007|Tebaldi and Knutti, 2007]] ; [[#Knutti--2010|Knutti et al., 2010]] ). The five models with GSAT warming consistent with a high-warming storyline and the two models projecting GSAT warming exceeding the ''very likely'' GSAT warming range show a much larger area fraction of drying and somewhat larger fraction of strong precipitation increases than the multi-model mean (Figure 4.42b–d).

The high-warming storyline shown in Figure 4.42b,c does not correspond to an upper or lower estimate of annual precipitation increase and decrease over individual locations, which in many regions may differ in the sign of the response (Figure 4.42e,f) due to differences in the model response and internal variability ( [[#Madsen--2017|Madsen et al., 2017]] ). Figure 4.42e,f illustrates upper and lower local estimates corresponding to the 5–95% model range of local uncertainties as opposed to the global-warming storylines. Note, however, that Figure 4.42e,f does not show a physically plausible global precipitation response pattern, because information at the different grid points is taken from different model simulations.

Again, the manifestation of changes in the hydrological cycle for a high-warming storyline is not limited to precipitation, but would substantially affect other variables such as soil moisture, runoff, atmospheric humidity, and evapotranspiration. The changes are also not limited to annual mean precipitation but may be stronger or weaker for individual seasons and for precipitation extremes and dry spells.

While this assessment is limited to temperature and precipitation, such a high-warming storyline would manifest itself also in other climate variables ( [[#Sanderson--2011|Sanderson et al., 2011]] ) assessed in this chapter such as Arctic sea ice, atmospheric circulation changes, and sea level rise ( [[#Ramanathan--2008|Ramanathan and Feng, 2008]] ; [[#Xu--2017|Xu and Ramanathan, 2017]] ; [[#Steffen--2018|Steffen et al., 2018]] ).

In summary, while high-warming storylines – those associated with global warming levels above the upper bound of the assessed ''very likely'' range – are by definition ''extremely unlikely'' , they cannot be ruled out. For SSP1-2.6, such a high-warming storyline implies warming well above rather than well below 2°C ( ''high confidence'' ). Irrespective of scenario, high-warming storylines imply changes in many aspects of the climate system that exceed the patterns associated with the best estimate of GSAT changes by up to more than 50% ( ''high confidence'' ).

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