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

=== 4.2.6 Display of Model Agreement and Spread ===

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Maps of multi-model mean changes provide an average estimate for the forced model climate response to a certain forcing. However, they do not include any information on the robustness of the response across models nor on the significance of the change with respect to unforced internal variability ( [[#Tebaldi--2011|Tebaldi et al., 2011]] ). Models can consistently show absence of significant change, in which case they should not be expected to agree on the sign of a change (e.g., [[#Tebaldi--2011|Tebaldi et al., 2011]] ; [[#Knutti--2013|Knutti and Sedláček, 2013]] ; [[#Fischer--2014|Fischer et al., 2014]] ). If a multi-model mean map of precipitation shows no change, it is unclear whether the models consistently project insignificant changes or whether projections span both significant increases and significant decreases. Several methods have been proposed to distinguish significant conflicting signals from agreement on no significant change ( [[#Tebaldi--2011|Tebaldi et al., 2011]] ; [[#Knutti--2013|Knutti and Sedláček, 2013]] ; [[#McSweeney--2013|McSweeney and Jones, 2013]] ; [[#Zappa--2021|Zappa et al., 2021]] ). A set of different methods have been introduced in the literature to display model robustness and to put a climate change signal into the context of internal variability. Box 12.1 in AR5 provides a detailed assessment of different methods of mapping model robustness and Cross-Chapter Box Atlas.1 provides an update of recent proposals including the methods used in this Report.

Most methods for quantifying robustness assume that only one realization from each model is applied. There are challenges that arise from having heterogeneous multi-model ensembles with many members for some models and single members for others ( [[#Olonscheck--2017|Olonscheck and Notz, 2017]] ; [[#Evin--2019|Evin et al., 2019]] ). Furthermore, the methods that map model robustness usually ignore that sharing parametrizations or entire components across coupled models can lead to substantial model interdependence ( [[#Fischer--2011|Fischer et al., 2011]] ; [[#Kharin--2012|Kharin et al., 2012]] ; [[#Knutti--2013|Knutti et al., 2013]] , 2017; [[#Leduc--2015|Leduc et al., 2015]] ; [[#Sanderson--2015|Sanderson et al., 2015]] , 2017; [[#Annan--2017|Annan and Hargreaves, 2017]] ; [[#Boé--2018|Boé, 2018]] ; [[#Abramowitz--2019|Abramowitz et al., 2019]] ). This may lead to a biased estimate of model agreement if a substantial fraction of models is interdependent. The methodologies and results in this literature since AR5 are higher in quality and clarity. However, quantifying and accounting for model dependence in a robust way remains challenging ( [[#Abramowitz--2019|Abramowitz et al., 2019]] ). Furthermore, absence of significant mean change in a certain climate variable does not imply absence of substantial impact, because there may be substantial change in variability, which is typically not mapped ( [[#McSweeney--2013|McSweeney and Jones, 2013]] ).

Chapter 4 uses the advanced approach, taking into account the sign and significance of the change (Cross-Chapter Box Atlas.1, approach C). Where not applicable, such as due to a lack of the necessary model output, the simple method is used taking into account only agreement on the sign of the change across the multi-model ensemble (Cross-Chapter Box Atlas.1, approach B). The advanced approach is similar to the method used in AR5 but isolates conflicting signals as proposed in [[#Zappa--2021|Zappa et al. (2021)]] . It uses three mutually exclusive categories and distinguishes (i) areas with significant change and high model agreement (no overlay), (ii) areas with no change or no robust change (diagonal lines), and (iii) areas with significant change but '''low agreement''' (crossed lines). Category (i) marks areas where the climate change signals ''likely'' emerge from internal variability, where two-thirds or more of the models project changes greater than internal variability and 80% or more of the models agree on the sign of the change. Category (ii) marks areas where fewer than two-thirds of the models project changes greater than internal variability, and category (iii) marks areas with significant but conflicting signals, where two-thirds or more of the models project changes greater than internal variability but less than 80% agree on the sign of the change.

In this chapter variability is defined as

<code> </code> 1.645 * √ <code> 2 </code> σ <sub>yr</sub>

, where

σ <sub>yr</sub>

is the standard deviation of 20-year means in the pre-industrial control simulations (see Cross-Chapter Box, Atlas.1). Category (a) uses a definition very similar to the AR5 method for stippling, except that the model signal is compared to its corresponding internal rather than the multi-model mean variability, to account for the substantial model differences in pre-industrial internal variability ( [[#Parsons--2020|Parsons et al., 2020]] ). Changes smaller than internal variability can have potential impacts particularly if they persist over sustained periods such as several decades. Finally, even when changes do not exceed variability at the grid point level they may exceed variability if aggregated over catchment basins, regions, or continents (Cross-Chapter Box Atlas.1). Maps of mean changes also ignore potential changes in variability addressed by a more comprehensive assessment of changes in temperature variability ( [[#4.5.1|Section 4.5.1]] ) and modes of internal variability ( [[#4.4.3|Section 4.4.3]] ).

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'''Box 4.1 | Ensemble Evaluation and Weighting'''

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The AR5 used a pragmatic approach to quantify the uncertainty in CMIP5 GSAT projections ( [[#Collins--2013|Collins et al., 2013]] ). The multi-model ensemble was constructed by picking one realization per model per scenario. For most quantities, the 5–95% ensemble range was used to characterize the uncertainty, but the 5–95% ensemble range was interpreted as the 17–83% ( ''likely'' ) uncertainty range. The uncertainty was thus explicitly assumed to contain sources not represented by the model range. While straightforward and clearly communicated, this approach had several drawbacks.

# The uncertainty breakdown into scenario uncertainty, model uncertainty, and internal variability ( [[#Cox--2007|Cox and Stephenson, 2007]] ; [[#Hawkins--2009|Hawkins and Sutton, 2009]] ) in AR5 followed [[#Hawkins--2009|Hawkins and Sutton (2009)]] and diagnosed internal variability through a high-pass temporal filter ( [[#Kirtman--2013|Kirtman et al., 2013]] ), but it has since become clear that even multi-decadal trends contain substantial internal variability relative to the forced response in many variables (e.g., [[#Deser--2012a|Deser et al., 2012a]] , 2020; [[#Marotzke--2015|Marotzke and Forster, 2015]] ; [[#Lehner--2020|Lehner et al., 2020]] ); hence a more comprehensive approach is needed.
# The uncertainty characterization ignores observation-based information about internal climate variability during the most recent past, such as is used in initialized predictions. While this may matter little for the long-term projections ( [[#Collins--2013|Collins et al., 2013]] ), it is very important for the near-term future ( [[#Kirtman--2013|Kirtman et al., 2013]] ). The AR5 included additional uncertainty quantification for the near-term projections ( [[#Kirtman--2013|Kirtman et al., 2013]] ), leading to a downward adjustment of assessed near-term GSAT change, which created an inconsistency in the transition from near-term to long-term GSAT assessment in AR5.
# The AR5 used the range of CMIP5 equilibrium climate sensitivity (ECS) side-by-side with the ECS ''likely'' range assessed from multiple lines of evidence (the CMIP5 ensemble, instrumental observations, and paleo-information; [[#Collins--2013|Collins et al., 2013]] ). While the CMIP5 range in ECS and AR5 ECS ''likely'' range did not differ much, the difference did create an inconsistency. Furthermore, AR5 WGIII used the assessed ''likely'' range for ECS in their calculations of carbon budgets ( [[#IPCC--2014|IPCC, 2014]] ), and these uncertainties matter a great deal when assessing remaining carbon budgets consistent with limiting global warming to 1.5°C above pre-industrial levels ( [[#Millar--2017|Millar et al., 2017]] , 2018a, b; Rogelj et al., 2018b; [[#Schurer--2018|Schurer et al., 2018]] ).

Another important consideration concerns the potential weighting of model contributions to an ensemble, based on model independence, model performance during the historical period, or both. Such model weighting (in fact, model selection) was performed in AR5 for projections of Arctic sea ice ( [[#Collins--2013|Collins et al., 2013]] ), but that particular application has subsequently been shown by [[#Notz--2015|Notz (2015)]] to be contaminated by internal variability, making the resulting weighting questionable (Stroeve and [[#Notz--2015|Notz, 2015]] ). For a general cautionary note, see [[#Weigel--2010|Weigel et al. (2010)]] . Approaches that take into account internal variability and model independence have been proposed since AR5 ( [[#Knutti--2017|Knutti et al., 2017]] ; [[#Boé--2018|Boé, 2018]] ; [[#Abramowitz--2019|Abramowitz et al., 2019]] ; [[#Brunner--2020|Brunner et al., 2020]] ).

There are hence good reasons for basing an assessment of future global climate on lines of evidence in addition to the projection simulations. However, despite some progress, no universal, robust method for weighting a multi-model projection ensemble is available, and expert judgement must be included, as it did for AR5, in the assessment of the projections. The default in this chapter follows the AR5 approach for GSAT ( [[#Collins--2013|Collins et al., 2013]] ) and interprets the CMIP6 5–95% ensemble range as the ''likely'' uncertainty range.

Additional lines of evidence enter the assessment particularly for the most important indicator of global climate change, GSAT. The CMIP6 ensemble generally shows larger projected warming by the end of the 21st century, relative to the average over the period 1995–2014, than the CMIP5 ensemble ( [[#4.3.1|Section 4.3.1]] ). The warming has increased in part because of models with higher ECS in CMIP6, compared to CMIP5 ( ''high confidence'' ) (e.g., [[#Meehl--2020|Meehl et al., 2020]] ; [[#Tokarska--2020|Tokarska et al., 2020]] ; [[#Zelinka--2020|Zelinka et al., 2020]] ; J. [[#Zhu--2020|]] [[#Zhu--2020|Zhu et al., 2020]] ), and in part because of higher ERF in CMIP6 than in CMIP5 (e.g., [[#Tebaldi--2021|Tebaldi et al., 2021]] , [[#4.6.2|Section 4.6.2]] ). Because change in several other important climate quantities scales with change in GSAT ( [[#4.2.4|Section 4.2.4]] ), bringing in additional lines of evidence is particularly important for the GSAT assessment.

The Chapter 4 assessment uses information from the following sources:

# The CMIP6 multi-model ensemble ( [[#Eyring--2016|Eyring et al., 2016]] ), augmented if appropriate by the CMIP5 ensemble ( [[#Taylor--2012|Taylor et al., 2012]] ).
# Single-model large initial-condition ensembles (e.g., [[#Kay--2015|Kay et al., 2015]] ; [[#Sigmond--2016|Sigmond and Fyfe, 2016]] ; [[#Maher--2019|Maher et al., 2019]] ) and combinations of control runs with CMIP transient simulations (e.g., [[#Thompson--2015|Thompson et al., 2015]] ; [[#Olonscheck--2017|Olonscheck and Notz, 2017]] ) to characterize internal variability. Several analyses using multiple large ensembles have recently become available and add robustness to the results ( [[#Maher--2018|Maher et al., 2018]] , 2019, 2020, 2021; [[#Deser--2020|Deser et al., 2020]] ; [[#Lehner--2020|Lehner et al., 2020]] ).
# Assessed best estimates, ''likely'' , and ''very likely'' ranges of ECS and TCR, from process understanding, warming in the instrumental record, paleoclimates, and emergent constraints (Tables 7.13 and 7.14, and Section 7.5). The ECS and TCR ranges are converted into GSAT ranges using as an emulator a two-layer energy balance model (EBM, e.g., [[#Held--2010|Held et al., 2010]] ) that is driven by the effective radiative forcing (ERF) assessed in [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] ( [[#cross-chapter-box-7.1|Cross-Chapter Box 7.1]] ). Assuming for the ERF resulting from a doubling of the CO <sub>2</sub> concentration, ∆ F <sub>2 × CO2</sub> = 4.0 W m <sup>–2</sup> (close to the best estimate of 3.93 W m <sup>–2</sup> , Section 7.3), and using the so-called zero-layer approximation to the EBM (e.g., [[#Marotzke--2015|Marotzke and Forster, 2015]] ; [[#Jiménez-de-la-Cuesta--2019|Jiménez-de-la-Cuesta and Mauritsen, 2019]] ) permits a one-to-one translation of any pair of ECS and TCR into a pair of climate feedback parameter α and ocean heat uptake coefficient κε , using the simple equations α = – ∆ F <sub>2 × CO2</sub> ECS <sup>–1</sup> and κε = ∆ F <sub>2 × CO2</sub> TCR <sup>–1</sup> – ∆ F <sub>2 × CO2</sub> ECS <sup>–1</sup> (e.g., [[#Jiménez-de-la-Cuesta--2019|Jiménez-de-la-Cuesta and Mauritsen, 2019]] ; see [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] for a detailed discussion). The results are displayed in Box 4.1, Figure 1 and are used in the synthesis GSAT assessment in [[#4.3.4|Section 4.3.4]] .
# Model independence diagnosed a priori, based on shared model components for atmosphere, ocean, land surface, and sea ice of CMIP5 models ( [[#Boé--2018|Boé, 2018]] ). CMIP5 models have been re-sampled assuming that two models sharing either the atmosphere or the ocean component are effectively the same model ( [[#Maher--2021|Maher et al., 2021]] ). Downweighting CMIP5 models that share a component with another has substantial influence on diagnosed model agreement on change in ENSO ( [[#Maher--2021|Maher et al., 2021]] ), but has negligible influence (much less than 0.1°C) on the ensemble mean and range of GSAT change over the 21st century. No corresponding diagnosis exists yet for CMIP6 models, and no weighting based on a-priori independence is applied here.
# Performance in simulating the past and a-posteriori independence based on comparison against observations ( [[#Knutti--2017|Knutti et al., 2017]] ; [[#Abramowitz--2019|Abramowitz et al., 2019]] ). This approach has been applied to CMIP6-simulated GSAT and has led to a substantial reduction in model range ( [[#Brunner--2020|Brunner et al., 2020]] ; [[#Liang--2020|Liang et al., 2020]] ; [[#4.3.4|Section 4.3.4]] ). The CMIP6-simulated Arctic sea ice area has been compared to the observed record, and models have been selected whose ensemble range across their individual realizations ( [[#Olonscheck--2017|Olonscheck and Notz, 2017]] ) includes the observational range of uncertainty. A larger fraction of these selected simulations show an ice-free Arctic in September before 2050, compared to the entire CMIP6 ensemble (Notz and SIMIP Community, 2020; [[#4.3.2|Section 4.3.2]] ).
# A linear inverse method (kriging) has combined the entire GSAT record since 1850 with the CMIP6 historical simulations to produce constrained projections for the 21st century; again the reduction in range has been substantial ( [[#Ribes--2021|Ribes et al., 2021]] ; [[#4.3.4|Section 4.3.4]] ; [[#4.3.4|Section 4.3.4]] ).
# Emergent constraints (e.g., [[#Hall--2006|Hall and Qu, 2006]] ; [[#Cox--2018|Cox et al., 2018]] ; [[#Brient--2020|Brient, 2020]] ), which for the post-1970 warming have been applied to the CMIP5 ( [[#Jiménez-de-la-Cuesta--2019|Jiménez-de-la-Cuesta and Mauritsen, 2019]] ) and CMIP6 ensembles ( [[#Nijsse--2020|Nijsse et al., 2020]] ; [[#Tokarska--2020|Tokarska et al., 2020]] ) and have likewise led to a substantial reduction in GSAT ensemble range ( [[#4.3.4|Section 4.3.4]] ).
# Climate predictions initialized from recent observations (e.g., [[#Kirtman--2013|Kirtman et al., 2013]] ) and the Decadal Climate Prediction Project (DCPP) contribution to CMIP6 ( [[#Boer--2016|Boer et al., 2016]] ; [[#Smith--2020|Smith et al., 2020]] ; [[#Sospedra-Alfonso--2020|Sospedra-Alfonso and Boer, 2020]] ). Initialized predictions for the period 2019–2028 exist for eight DCPP models and are used here ( [[#box-4.1|Box 4.1]] , Figure 1 and [[#4.4.1|Section 4.4.1]] ). The DCPP results have been drift-removed and referenced to the time-averaged hindcasts for 1995–2014 lead-year by lead-year, following ( [[#Kharin--2012|Kharin et al., 2012]] ; [[#Kruschke--2016|Kruschke et al., 2016]] ).

Box 4.1, Figure 1 shows annual mean GSAT simulated by CMIP6 models for both the historical period and forced by scenario SSP2-4.5 until 2100, combined with various characterizations of uncertainty. First, internal variability is estimated with the 50-member ensemble simulated with CanESM5. The 5–95% ensemble range for annual mean GSAT in CanESM5 is slightly below 0.4°C; in other CMIP6 large ensembles this range is about 0.5°C (MIROC6, IPSL-CM6A) and slightly above 0.6°C (S-LENS/EC-Earth3). The CMIP5 large ensemble MPI-GE shows a range of slightly below 0.5°C ( [[#Bengtsson--2019|Bengtsson and Hodges, 2019]] ), in reasonable agreement with observed variability ( [[#Maher--2019|Maher et al., 2019]] ). There is thus ''high confidence'' in the CMIP6-simulated level of internal variability in annual mean GSAT, as displayed in Box 4.1, Figure 1.

Second, Section 7.5 ''very likely'' ECS and TCR ranges are converted into GSAT ranges with the EBM as an emulator using, in this example, SSP2-4.5 radiative forcing information. Because the ECS and TCR assessments in Section 7.5 are based on multiple lines of evidence and the EBM physics are well understood, there is likewise ''high confidence'' in the EBM-emulated warming. Third, the initialized-forecast ensembles from eight CMIP6 DCPP models are shown in the inset, for the period 2019–2028. During this period, the initialized forecasts are consistent, within internal variability, with the EBM-emulated range, further adding to the ''high confidence'' in the assessed-GSAT range.

The constrained range of GSAT change is useful for quantifying uncertainties in changes of other climate quanties that scale well with GSAT change, such as September Arctic sea ice area, global mean precipitation, and many climate extremes (Cross-Chapter Box 11.1). However, there are also quantities that do not scale linearly with GSAT change, such as global mean land precipitation, atmospheric circulation, AMOC, and modes of variability, especially ENSO SST variability. Because we do not have robust scientific evidence to constrain changes in other quantities, uncertainty quantification for their changes is based on CMIP6 projections and expert judgement. For the assessment for changes in GMSL, the contribution from land-ice melt has been added offline to the CMIP6 simulated contributions from thermal expansion, consistent with [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] (Section 9.6).

[[File:b5a07ed31e0fc1b9384e6fbb11840f02 IPCC_AR6_WGI_Box_4_1_Figure_1.png]]
'''Box 4.1 Figure 1'''

'''|'''

'''CMIP6 annual mean global surface air temperature (GSAT) simulations and various contributions to uncertainty in the projections ensemble.''' The figure shows anomalies relative to the period 1995–2014 (left y-axis), converted to anomalies relative to 1850–1900 (right y-axis); the difference between the y-axes is 0.85°C (Cross-Chapter Box 2.3). Shown are historical simulations with 39 CMIP6 models (grey) and projections following scenario SSP2-4.5 (dark yellow; thin lines: individual simulations; heavy line; ensemble mean; dashed lines: 5% and 95% ranges). The black curve shows the observations-based estimate (HadCRUT5; [[#Morice--2021|Morice et al., 2021]] ). Light blue shading shows the 50-member ensemble CanESM5, such that the deviations from the CanESM5 ensemble mean have been added to the CMIP6 multi-model mean. The green curves are from the emulator and show the central estimate (solid) and ''very likely'' range (dashed) for GSAT. The inset shows a cut-out from the main plot and additionally in light purple for the period 2019–2028 the initialized forecasts from eight models contributing to DCPP ( [[#Boer--2016|Boer et al., 2016]] ); the deep-purple curve shows the average of the forecasts. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

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