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

=== 4.2.1 Models, Model Intercomparison Projects, and Ensemble Methodologies ===

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Similar to the approach used in AR5 ( [[#Flato--2013|Flato et al., 2013]] ), the primary lines of evidence of this chapter are comprehensive climate models (atmosphere–ocean general circulation models, AOGCMs) and Earth system models (ESMs); ESMs differ from AOGCMs by including representations of various biogeochemical cycles. We also build on results from ESMs of intermediate complexity (EMICs; [[#Claussen--2002|Claussen et al., 2002]] ; [[#Eby--2013|Eby et al., 2013]] ) and other types of models where appropriate. This chapter focuses on a particular set of coordinated multi-model experiments known as model intercomparison projects (MIPs). These frameworks recommend and document standards for experimental design for running AOGCMs and ESMs to minimize the chance of differences in results being misinterpreted. CMIP is an activity of the World Climate Research Programme (WCRP), and the latest phase is CMIP6 ( [[#Eyring--2016|Eyring et al., 2016]] ). To establish robustness of results, it is vital to assess the performance of these models in terms of mean state, variability, and the response to external forcings. That evaluation has been undertaken using the CMIP6 ‘Diagnostic, Evaluation and Characterization of Klima’ (DECK) and historical simulations in [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] of this Report, which concludes that there is ''high confidence'' that the CMIP6 multi-model mean captures most aspects of observed climate change well ( [[IPCC:Wg1:Chapter:Chapter-3#3.8.3.1|Section 3.8.3.1]] ).

This chapter draws mainly on future projections referenced both against the period 1850–1900 and the recent past, 1995–2014, performed primarily under ScenarioMIP ( [[#O’Neill--2016|O’Neill et al., 2016]] ). This allows us to assess both dimensions of integration across scenarios ( [[#4.3|Section 4.3]] ) and global warming levels ( [[#4.6|Section 4.6]] ) as discussed in [[IPCC:Wg1:Chapter:Chapter-1|Chapter 1]] ( [[IPCC:Wg1:Chapter:Chapter-1#1.6|Section 1.6]] ). Other MIPs also target future scenarios with a focus on specific processes or feedbacks and are summarized in Table 4.1.

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'''Table 4.1 |''' '''Model Intercomparison Projects (MIPs) utilized in Chapter 4.'''

{| class="wikitable"
|-
| MIP/Experiment

| Usage

| Chapter/Section

| Reference

|-
| DECK, 1%, 4×CO <sub>2</sub>

| Diagnosing climate sensitivity

| Assessed in Chapter 7

ECS and TCR used in GSAT assessment

| [[#Eyring--2016|Eyring et al. (2016)]]

|-
| CMIP6 Historical

| Evaluation, baseline

| Assessed in Chapter 3,

used in Chapter 4 to cover reference period

| [[#Eyring--2016|Eyring et al. (2016)]]

|-
| ScenarioMIP

| Future projections

| Used throughout Chapter 4

| [[#O’Neill--2016|O’Neill et al. (2016)]]

|-
| AerChemMIP

| Aerosols and trace gases

| 4.4.4

| [[#Collins--2017|Collins et al. (2017)]]

|-
| C4MIP

| CO <sub>2</sub> emissions-driven simulations

| 4.3.1

| C.D. [[#Jones--2016a|Jones et al. (2016a)]]

|-
| CDRMIP

| Carbon dioxide removal

| 4.6.3

| [[#Keller--2018|Keller et al. (2018)]]

|-
| DCPP

| Near-term climate change

| 4.2.3, Box 4.1, 4.4

| [[#Boer--2016|Boer et al. (2016)]]

|-
| GeoMIP

| Solar radiation modification

| 4.6.3

| [[#Kravitz--2011|Kravitz et al. (2011)]]

|-
| PDRMIP

| Forcing dependence of precipitation

| 4.5.1

| [[#Myhre--2017|Myhre et al. (2017)]]

|-
| SIMIP

| Sea ice assessment

| 4.3

| [[#Notz--2016|Notz et al. (2016)]]

|-
| ZECMIP

| Zero emissions commitment

| 4.7.1

| [[#Jones--2019|Jones et al. (2019)]]

|-
| CMIP5

| RCP scenario assessment

| 4.6.2, 4.7.1

| [[#Taylor--2012|Taylor et al. (2012)]]

|}

Multi-model ensembles provide the central focus of projection assessment. While single-model experiments have great value for exploring new results and theories, multi-model ensembles additionally underpin the assessment of the robustness, reproducibility, and uncertainty attributable to model internal structure and processes variability ( [[#4.2.5|Section 4.2.5]] ; [[#Hawkins--2009|Hawkins and Sutton, 2009]] ). Techniques underlying the combination of evaluation and weighting that are applied in this chapter are synthesized in Box 4.1.

Climate model simulations can be performed in either ‘concentration-driven’ or ‘emissions-driven’ configurations reflecting whether the CO <sub>2</sub> concentration is prescribed to follow a pre-defined pathway or is simulated by the Earth system models in response to prescribed emissions of CO <sub>2</sub> (Box 6.4, [[#Ciais--2013|Ciais et al., 2013]] ). The majority of CMIP6 experiments are conducted in concentration-driven configurations in order to enable models without a fully interactive carbon cycle to perform them, and throughout most of this chapter we present results from those simulations unless otherwise stated. Concentrations of other greenhouse gases are always prescribed. However, the SSP5-8.5 scenario has also been performed in emissions-driven configuration (‘esm-ssp585’) by 10 ESMs, and in [[#4.3.1.1|Section 4.3.1.1]] we assess the impact on simulated climate over the 21st century.

Internal variability complicates the identification of forced climate signals, especially when considering regional climate signals over short time scales (up to a few decades), such as local trends over the satellite era ( [[#Hawkins--2009|Hawkins and Sutton, 2009]] ; [[#Deser--2012a|Deser et al., 2012a]] ; [[#Xie--2015|Xie et al., 2015]] ; [[#Lovenduski--2016|Lovenduski et al., 2016]] ; [[#Suárez-Gutiérrez--2017|Suárez-Gutiérrez et al., 2017]] ). Large initial-condition ensembles, where the same model is run repeatedly under identical forcing but with initial conditions varied through small perturbations or by sampling different times of a pre-industrial control run, have substantially grown in their use since AR5 ( [[#Deser--2012a|Deser et al., 2012a]] ; [[#Kay--2015|Kay et al., 2015]] ; [[#Rodgers--2015|Rodgers et al., 2015]] ; [[#Hedemann--2017|Hedemann et al., 2017]] ; [[#Stolpe--2018|Stolpe et al., 2018]] ; [[#Maher--2019|Maher et al., 2019]] ). Such large ensembles have shown potential to quantify uncertainty due to internal variability ( [[#Hawkins--2016|Hawkins et al., 2016]] ; [[#McCusker--2016|McCusker et al., 2016]] ; [[#Sigmond--2016|Sigmond and Fyfe, 2016]] ; [[#Lehner--2017|Lehner et al., 2017]] ; [[#McKinnon--2017|McKinnon et al., 2017]] ; [[#Marotzke--2019|Marotzke, 2019]] ) and thereby extract the forced signal from the internal variability, which can be calibrated against observational data to improve the reliability of probabilistic climate projections over the near and mid-term ( [[#O’Reilly--2020|O’Reilly et al., 2020]] ). Moreover, they allow the investigation of forced changes in internal variability (e.g., [[#Maher--2018|Maher et al., 2018]] ). A key assumption is that a given model skilfully represents internal variability; structural uncertainty is not accounted for.

A complementary approach that represents structural uncertainty in a given model is stochastic physics ( [[#Berner--2017|Berner et al., 2017]] ). The approach has proven useful in representing structural uncertainty on seasonal climate time scales ( [[#Weisheimer--2014|Weisheimer et al., 2014]] ; [[#Batté--2015|Batté and Doblas-Reyes, 2015]] ; [[#MacLachlan--2015|MacLachlan et al., 2015]] ). Stochastic physics can markedly improve the internal variability in a given model ( [[#Dawson--2015|Dawson and Palmer, 2015]] ; [[#Wang--2016|Wang et al., 2016]] ; [[#Christensen--2017|Christensen et al., 2017]] ; [[#Davini--2017|Davini et al., 2017]] ; [[#Watson--2017|Watson et al., 2017]] ; [[#Strømmen--2018|Strømmen et al., 2018]] ; [[#Yang--2019|Yang et al., 2019]] ). Stochastic physics can also correct long-standing mean-state biases ( [[#Sanchez-Gomez--2016|Sanchez-Gomez et al., 2016]] ) and can influence the predicted climate sensitivity ( [[#Christensen--2019|Christensen and Berner, 2019]] ; [[#Strommen--2019|Strommen et al., 2019]] ; [[#Meccia--2020|Meccia et al., 2020]] ).

Perturbed-physics ensembles ( [[#Murphy--2004|Murphy et al., 2004]] ) are also used to systematically account for parameter uncertainty in a given model. Uncertain model parameters are identified and ranges in their values selected that conform to emergent observational constraints (see [[IPCC:Wg1:Chapter:Chapter-1#1.5.4.2|Section 1.5.4.2]] ). These parameters are then changed between ensemble members to sample the effect of parameter uncertainty on climate ( [[#Piani--2005|Piani et al., 2005]] ; [[#Sexton--2012|Sexton et al., 2012]] ; [[#Johnson--2018|Johnson et al., 2018]] ; [[#Regayre--2018|Regayre et al., 2018]] ). It is possible to weight ensemble members according to some performance metric or emergent constraint (e.g., [[#Fasullo--2015|Fasullo et al., 2015]] ; [[IPCC:Wg1:Chapter:Chapter-1#1.5.4.7|Section 1.5.4.7]] ) to improve the ensemble distribution (Box 4.1).

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