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

=== 4.2.5 Quantifying Various Sources of Uncertainty ===

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The AR5 assessed with ''very high confidence'' that climate models reproduce the general features of the global-scale annual mean surface temperature increase over the historical period, including the more rapid warming in the second half of the 20th century, and the cooling immediately following large volcanic eruptions. Furthermore, because climate and Earth system models are based on physical principles, they were assessed in AR5 to reproduce many important aspects of observed climate. Both aspects were argued to contribute to our confidence in the models’ suitability for their application in quantitative future predictions and projections ( [[#Flato--2013|Flato et al., 2013]] ). This Report assesses (in [[IPCC:Wg1:Chapter:Chapter-3#3.8.2|Section 3.8.2]] ) with ''high confidence'' that for most large-scale indicators of climate change, the recent mean climate simulated by the latest generation climate models underpinning this assessment has improved compared to the models assessed in AR5, and with ''high confidence'' that the multi-model mean captures most aspects of observed climate change well. These assessments form the foundation of applying climate and Earth system models to the projections assessed in this chapter. Where appropriate, the assessment of projected changes is accompanied by an assessment of process understanding and model evaluation.

That said, fitness-for-purpose of the climate models used for long-term projections is fundamentally difficult to ascertain and remains an epistemological challenge ( [[#Parker--2009|Parker, 2009]] ; [[#Frisch--2015|Frisch, 2015]] ; [[#Baumberger--2017|Baumberger et al., 2017]] ). Some literature exists comparing previous IPCC projections to what has unfolded over the subsequent decades ( [[#Cubasch--2013|Cubasch et al., 2013]] ), and recent work has confirmed that climate models since around 1970 have projected global surface warming in reasonable agreement with observations once the difference between assumed and actual forcing has been taken into account ( [[#Hausfather--2020|Hausfather et al., 2020]] ). However, the long-term perspective to the end of the 21st century or even out to 2300 takes us beyond what can be observed in time for a standard evaluation of model projections, and in this sense the assessment of long-term projections will remain fundamentally limited.

The spread across individual runs within a multi-model ensemble represents the response to a combination of different sources of uncertainties ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.3|Section 1.4.3]] ), specifically: scenario uncertainties, climate response uncertainties (also referred to as model uncertainties) related to parametric and other structural uncertainties in the model representation of the climate system, and internal variability (e.g., [[#Hawkins--2009|Hawkins and Sutton, 2009]] ; [[#Kirtman--2013|Kirtman et al., 2013]] ). While the nature of these uncertainties was introduced in [[IPCC:Wg1:Chapter:Chapter-1#1.4.3|Section 1.4.3]] , this subsection assesses methods to disentangle different sources of uncertainties and quantify their contributions to the overall ensemble spread.

As discussed extensively in AR5 ( [[#Collins--2013|Collins et al., 2013]] ), ensemble spread in projections performed with different climate models accounts for only part of the entire model uncertainty, even when considering the uncertainty in the radiative forcing in projections ( [[#Vial--2013|Vial et al., 2013]] ) and forced response. The AR5 uncertainty characterisation ( [[#Kirtman--2013|Kirtman et al., 2013]] ) followed [[#Hawkins--2009|Hawkins and Sutton (2009)]] and diagnosed internal variability through a high-pass temporal filter. This approach has deficiencies particularly if internal variability manifests on the multi-decadal time scales ( [[#Deser--2012a|Deser et al., 2012a]] ; [[#Marotzke--2015|Marotzke and Forster, 2015]] ) and is classified as (model) response uncertainty instead of internal variability. Single-model initial-condition large ensembles revealed that the AR5 approach underestimates the role of internal variability uncertainty and overestimates the role of model uncertainty ( [[#Maher--2018|Maher et al., 2018]] ; [[#Stolpe--2018|Stolpe et al., 2018]] ; [[#Lehner--2020|Lehner et al., 2020]] ) particularly at the local scale while yielding a reasonable approximation for uncertainty separation for GSAT ( [[#Lehner--2020|Lehner et al., 2020]] ).

Single-model initial-condition large ensembles thus represent a crucial step towards a cleaner separation of model uncertainty and internal variability than available for AR5 ( [[#Deser--2014|Deser et al., 2014]] , 2016; [[#Saffioti--2017|Saffioti et al., 2017]] ; [[#Sippel--2019|Sippel et al., 2019]] ; [[#Milinski--2020|Milinski et al., 2020]] ; [[#von%20Trentini--2020|von Trentini et al., 2020]] ; [[#Maher--2021|Maher et al., 2021]] ). Novel approaches have been proposed to further quantify internal variability in multi-model ensembles ( [[#Hingray--2014|Hingray and Saïd, 2014]] ; [[#Evin--2019|Evin et al., 2019]] ; [[#Hingray--2019|Hingray et al., 2019]] ). For time horizons beyond the limit of decadal predictability ( [[#Branstator--2010|Branstator and Teng, 2010]] ; [[#Meehl--2014|Meehl et al., 2014]] ; [[#Marotzke--2016|Marotzke et al., 2016]] ), such as in the CMIP6 projections, the simulations are starting from random rather than assimilated initial conditions. Internal variability constitutes an uncertainty in the projection of the climate in a future period of 10 or 20 years that is irreducible, but can be precisely quantified for individual models using sufficiently large initial-condition ensembles ( [[#Fischer--2013|Fischer et al., 2013]] ; [[#Deser--2016|Deser et al., 2016]] , 2020; [[#Hawkins--2016|Hawkins et al., 2016]] ; [[#Pendergrass--2017|Pendergrass et al., 2017]] ; [[#Luo--2018|Luo et al., 2018]] ; [[#Dai--2019|Dai and Bloecker, 2019]] ; [[#Maher--2019|Maher et al., 2019]] ).

Uncertainties in emissions of greenhouse gases and aerosols that affect future radiative forcings are represented by selected SSP scenarios (Sections 1.6.1 and 4.2.2). In addition to emission uncertainties, SSPs represent uncertainties in land use changes ( [[#van%20Vuuren--2011|van Vuuren et al., 2011]] ; [[#Ciais--2013|Ciais et al., 2013]] ; [[#O’Neill--2016|O’Neill et al., 2016]] ; [[#Christensen--2018|Christensen et al., 2018]] ). Additional uncertainty comes from climate carbon-cycle feedbacks and the residence time of atmospheric constituents, and are at least partly accounted for in emissions-driven simulations as opposed to concentration-driven simulations ( [[#Friedlingstein--2014|Friedlingstein et al., 2014]] ; [[#Hewitt--2016|Hewitt et al., 2016]] ). The climate carbon-cycle feedbacks affect the transient climate response to cumulative CO <sub>2</sub> emissions (TCRE). Constraining this uncertainty is crucial for the assessment of remaining carbon budgets consistent with global mean temperature levels ( [[#Millar--2017|Millar et al., 2017]] ; [[#IPCC--2018a|IPCC, 2018a]] ) and is covered in [[IPCC:Wg1:Chapter:Chapter-5|Chapter 5]] of this Report. Finally, there are uncertainties in future solar and volcanic forcing ( [[#cross-chapter-box-4.1|Cross-Chapter Box 4.1]] ).

The relative magnitude of model uncertainty and internal variability depends on the time horizon of the projection, location, spatial and temporal aggregation, variable, and signal strength ( [[#Rowell--2012|Rowell, 2012]] ; [[#Fischer--2013|Fischer et al., 2013]] ; [[#Deser--2014|Deser et al., 2014]] ; [[#Saffioti--2017|Saffioti et al., 2017]] ; [[#Kirchmeier-Young--2019|Kirchmeier-Young et al., 2019]] ). New literature published after AR5 systematically discusses the role of different sources of uncertainty and shows that the relative contribution of internal variability is larger for short than for long projection horizons ( [[#Marotzke--2015|Marotzke and Forster, 2015]] ; [[#Lehner--2020|Lehner et al., 2020]] ; [[#Maher--2021|Maher et al., 2021]] ), larger for high latitudes than for low latitudes, larger for land than for ocean variables, larger at station level than for continental or global means, larger for annual maxima/minima than for multi-decadal means, larger for dynamic quantities (and, by implication, precipitation) than for temperature ( [[#Fischer--2014|Fischer et al., 2014]] ).

The method introduced by [[#Hawkins--2009|Hawkins and Sutton (2009)]] and applied to GSAT projections reveals that by the end of the 21st century, the fraction contribution of the climate model response uncertainty to the total uncertainty is larger in CMIP6 than in CMIP5 whereas the relative contribution of scenario uncertainty is smaller ( [[#Lehner--2020|Lehner et al., 2020]] ). This is the case even when sub-selecting pathways and scenarios that are most similar in CMIP5 and CMIP6, that is, the range from RCP2.6 to RCP8.5 vs SSP1-2.6 to SSP5-8-5, respectively ( [[#Lehner--2020|Lehner et al., 2020]] ). The larger range of response uncertainty is further consistent with the larger range of TCR and GSAT warming for a comparable pathway in CMIP6 than CMIP5 ( [[#Forster--2020|Forster et al., 2020]] ; [[#Tokarska--2020|Tokarska et al., 2020]] ).

Some uncertainties are not, or only partially accounted for in the CMIP6 experiments, such as uncertainties in natural forcings from solar and volcanic forcings, long-term Earth system feedbacks including land–ice feedbacks, groundwater feedbacks ( [[#Smerdon--2017|Smerdon, 2017]] ) or some long-term carbon-cycle feedbacks ( [[#Fischer--2018|Fischer et al., 2018]] ). Where appropriate, this chapter uses results from non-CMIP ESMs or EMICs to assess the role of these feedbacks. Still other uncertainties – such as further pandemics, nuclear holocaust, global natural disaster such as tsunami or asteroid impact, or fundamental technological change such as fusion – are not accounted for at all.

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