Editing IPCC:Wg1:Chapter:Chapter-1-comments (section)

==== 1.5.3.4 Models of Lower Complexity ====

<div id="h3-32-siblings" class="h3-siblings"></div>

'''Earth system models of intermediate complexity''' (EMICs) complement the model hierarchy and fill the gap between conceptual, simple climate models and complex GCMs or ESMs ( [[#Claussen--2002|Claussen et al., 2002]] ). EMICs are simplified; they include processes in a more parameterized, rather than explicitly calculated, form and generally have lower spatial resolution compared to the complex ESMs. As a result, EMICs require much less computational resource and can be integrated for many thousands of years without supercomputers ( [[#Hajima--2014|Hajima et al., 2014]] ). The range of EMICs used in climate change research is highly heterogeneous, ranging from zonally averaged or mixed-layer ocean models coupled to statistical-dynamical models of the atmosphere, to low-resolution three-dimensional ocean models coupled to simplified dynamical models of the atmosphere. An increasing number of EMICs include interactive representations of the global carbon cycle, with varying levels of complexity and numbers of processes considered ( [[#Plattner--2008|Plattner et al., 2008]] ; [[#Zickfeld--2013|Zickfeld et al., 2013]] ; [[#MacDougall--2020|MacDougall et al., 2020]] ). Given the heterogeneity of the EMIC community, modellers tend to focus on specific research questions and develop individual models accordingly. As for any type of models assessed in this Report, the set of EMICs undergoes thorough evaluation and fit-for-purpose testing before being applied to address specific climate aspects.

EMICs have been used extensively in past IPCC reports, providing long-term integrations on paleoclimate and future time scales, including stabilization pathways and a range of commitment scenarios, with perturbed physics ensembles and sensitivity studies, or with simulations targeting the uncertainty in global climate–carbon cycle systems (e.g., [[#Meehl--2007b|Meehl et al., 2007b]] ; [[#Collins--2013|Collins et al., 2013]] ). More recently, a number of studies have pointed to the possibility of systematically different climate responses to external forcings in EMICs and complex ESMs ( [[#Frölicher--2015|Frölicher and Paynter, 2015]] ; [[#Pfister--2017|Pfister and Stocker, 2017]] , 2018) that need to be considered in the context of this report. For example, [[#Frölicher--2015|Frölicher and Paynter (2015)]] showed that EMICs have a higher simulated realized warming fraction (i.e., the TCR/ECS ratio) than CMIP5 ESMs and speculated that this may bias the temperature response to zero carbon emissions. But, in a recent comprehensive multi-model analysis of the zero CO <sub>2</sub> emissions commitment, [[#MacDougall--2020|MacDougall et al. (2020)]] did not find any significant differences between EMICs and ESMs in committed temperatures 90 years after halting emissions. While some EMICs contribute to parts of the CMIP6-endorsed MIPs, a coordinated EMICs modelling effort similar to those carried out for AR4 ( [[#Plattner--2008|Plattner et al., 2008]] ) and AR5 ( [[#Eby--2013|Eby et al., 2013]] ; [[#Zickfeld--2013|Zickfeld et al., 2013]] ) is not in place for IPCC AR6; however, EMICs are assessed in a number of chapters. For example, Chapters 4 and 5 use EMICs in the assessment of long-term climate change beyond 2100 (Section 5.5); zero-emissions commitments, overshoot and recovery ( [[IPCC:Wg1:Chapter:Chapter-4#4.7|Section 4.7]] ); consequences of CO <sub>2</sub> removal (CDR) on the climate system and the carbon cycle (Sections 4.6 and 5.6); and long-term carbon cycle–climate feedbacks (Section 5.4).

'''Physical emulators and simple climate models''' make up a broad class of heavily parametrized models designed to reproduce the responses of the more complex, process-based models, and provide rapid translations of emissions, via concentrations and radiative forcing, into probabilistic estimates of changes to the physical climate system. The main application of emulators is to extrapolate insights from ESMs and observational constraints to a larger set of emissions scenarios (Cross-Chapter Box 7.1). The computational efficiency of various emulating approaches opens new analytical possibilities, given that ESMs take a lot of computational resources for each simulation. The applicability and usefulness of emulating approaches are however constrained by their skill in capturing the global mean climate responses simulated by the ESMs (mainly limited to global mean or hemispheric land/ocean temperatures) and by their ability to extrapolate skilfully outside the calibrated range.

The terms ‘emulator’ and ‘simple climate model’ (SCM) are different, although they are sometimes used interchangeably. SCM refers to a broad class of lower-dimensional models of the energy balance, radiative transfer, carbon cycle, or a combination of such physical components. SCMs can also be tuned to reproduce the calculations of climate-mean variables of a given ESM, assuming that their structural flexibility can capture both the parametric and structural uncertainties across process-oriented ESM responses. When run in this setup, they are termed emulators. Simple climate models do not have to be run in ‘emulation’ mode, though, as they can also be used to test consistency across multiple lines of evidence with regard to ranges in ECS, TCR, TCRE and carbon cycle feedbacks (Chapters 5 and 7). Physical emulation can also be performed with very simple parameterizations (‘one-or-few-line climate models’), statistical methods like neural networks, genetic algorithms, or other artificial intelligence approaches, where the emulator behaviour is explicitly tuned to reproduce the response of a given ESM or model ensemble (Chapters 4, 5 and 7).

Current emulators and SCMs include the generic impulse response model outlined in [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] of AR5 (AR5-IR; Supplementary Material 8.SM.11 of [[#Myhre--2013|Myhre et al., 2013]] ), two-layer models ( [[#Held--2010|Held et al., 2010]] ; [[#Rohrschneider--2019|Rohrschneider et al., 2019]] ; [[#Nicholls--2020|Nicholls et al., 2020]] ), and higher-complexity approaches that include upwelling, diffusion and entrainment in the ocean component (e.g., MAGICC Version 5.3 ( [[#Raper--2001|Raper et al., 2001]] ; [[#Wigley--2009|Wigley et al., 2009]] ); Version 6/7 ( [[#Meinshausen--2011a|Meinshausen et al., 2011a]] ); OSCAR ( [[#Gasser--2017|Gasser et al., 2017]] ); CICERO SCM ( [[#Skeie--2017|Skeie et al., 2017]] ); FaIR ( [[#Millar--2017a|Millar et al., 2017a]] ; [[#Smith--2018|Smith et al., 2018]] ); and a range of statistical approaches ( [[#Schwarber--2019|Schwarber et al., 2019]] ; [[#Beusch--2020b|Beusch et al., 2020b]] ). An example of recent use of an emulator approach is an early estimate of the climate implications of the COVID-19 lockdowns (Cross-Chapter Box 6.1; [[#Forster--2020|Forster et al., 2020]] ).

Since AR5, simplified climate models have been developed further, and their use is increasing. Different purposes motivating development include: being as simple as possible for teaching purposes (e.g., a two-layer energy balance model); being as comprehensive as possible to allow for propagation of uncertainties across multiple Earth system domains (MAGICC and others); or focusing on higher-complexity representation of specific domains (e.g., OSCAR). The common theme motivating many models is to improve parameterizations that reflect the latest findings in complex ESM interactions – such as the nitrogen cycle addition to the carbon cycle, or tropospheric and stratospheric ozone exchange – with the aim of emulating their global mean temperature response. Also, within the simple models that have a rudimentary representation of spatial heterogeneity (e.g., four-box simple climate models), the ambition is to represent heterogeneous forcers such as black carbon more adequately ( [[#Stjern--2017|Stjern et al., 2017]] ), provide an appropriate representation of the forcing–feedback framework (e.g., [[#Sherwood--2015|Sherwood et al., 2015]] ), investigate new parameterizations of ocean heat uptake, and implement better representations of volcanic aerosol-induced cooling ( [[#Gregory--2016a|Gregory et al., 2016a]] ).

MAGICC ( [[#Wigley--2009|Wigley et al., 2009]] ; [[#Meinshausen--2011a|Meinshausen et al., 2011a]] ) and FaIR ( [[#Smith--2018|Smith et al., 2018]] ) were used in IPCC SR1.5 ( [[#IPCC--2018|IPCC, 2018]] ) to categorize mitigation pathways into classes of scenarios that peak near 1.5°C, overshoot 1.5°C, or stay below 2°C. The SR1.5 ( [[#Rogelj--2018b|Rogelj et al., 2018b]] ) concluded that there was ''high agreement'' on the relative temperature response of pathways, but ''medium agreement'' on the precise absolute magnitude of warming, introducing a level of imprecision in the attribution of a single pathway to a given category.

In this Report, there are two notable uses of simple climate models. One is the connection between the assessed range of ECS in Chapter 7, and the projections of future global surface air temperature (GSAT) change in Chapter 4, which is done via a two-layer model based on [[#Held--2010|Held et al. (2010)]] . It is also used as input to sea level projections in Chapter 9. The other usage is the transfer of Earth system assessment knowledge to WGIII, via a set of models (MAGICC, FaIR, CICERO-SCM) specifically tuned to represent the WGI assessment. For an overview of the uses, and an assessment of the related Reduced Complexity Model Intercomparison Project (RCMIP), see [[#Nicholls--2020|Nicholls et al. (2020)]] and Cross-Chapter Box 7.1.

<div id="box-1.3" class="h2-container box-container"></div>

'''Box 1.3 | Emissions Met''' '''rics in AR6 WGI'''

<div id="h2-30-siblings" class="h2-siblings"></div>

Emissions metrics compare the radiative forcing, temperature change, or other climate effects arising from emissions of CO <sub>2</sub> against those from emissions of non-CO <sub>2</sub> radiative forcing agents (such as CH <sub>4</sub> or N <sub>2</sub> O). They have been discussed in the IPCC since the First Assessment Report and are used as a means of aggregating emissions and removals of different gases and placing them on a common (‘CO <sub>2</sub> equivalent’, or ‘CO <sub>2</sub> -eq’) scale.

AR5 included a thorough assessment of common pulse emissions metrics, and how these address various indicators of future climate change ( [[#Myhre--2013|Myhre et al., 2013]] ). Most prominently used are the global warming potentials (GWPs), which integrate the calculated radiative forcing contribution following an idealized pulse (or one-time) emission, over a chosen time horizon ( [[#IPCC--1990a|IPCC, 1990a]] ), or the global temperature change potential (GTP), which considers the contribution of emissions to the global-mean temperature at a specific time after emission. Yet another metric is the global precipitation change potential (GPP), used to quantify the precipitation change per unit mass of emission of a given forcing agent ( [[#Shine--2015|Shine et al., 2015]] ).

As an example of usage, the Paris Rulebook [Decision 18/CMA.1, annex, paragraph 37] states that

Each Party shall use the 100-year time-horizon global warming potential (GWP) values from the IPCC Fifth Assessment Report, or 100-year time-horizon GWP values from a subsequent IPCC assessment report as agreed upon by the ‘Conference of the Parties serving as the meeting of the Parties to the Paris Agreement’ (CMA), to report aggregate emissions and removals of GHGs, expressed in CO <sub>2</sub> -eq. Each Party may in addition also use other metrics (e.g., global temperature potential) to report supplemental information on aggregate emissions and removals of GHGs, expressed in CO <sub>2</sub> -eq.

Since AR5, improved knowledge of the radiative properties, lifetimes and other characteristics of emitted species, and the response of the climate system, have led to updates to the numerical values of a range of metrics (Table 7.15). Another key development is a set of metrics that compare a pulse emission of CO <sub>2</sub> (as considered by GWP and GTP) to step-changes of emission rates for short-lived components (i.e., also considering emissions trends). Termed GWP* (which also includes a pulse component) and combined global temperature change potential (CGTP), these metrics allow the construction of a near-linear relationship between global surface temperature change and cumulative CO <sub>2</sub> and CO <sub>2</sub> -eq emissions of both short- and long-lived forcing agents ( [[#Allen--2016|Allen et al., 2016]] ; [[#Cain--2019|Cain et al., 2019]] ; [[#Collins--2020|Collins et al., 2020]] ). For example, the temperature response to a sustained methane reduction has a similar behaviour to the temperature response to a pulse CO <sub>2</sub> removal (or avoided emission).

In this Report, recent scientific developments underlying emissions metrics, as relevant for WGI, are assessed in full in Section 7.6. In particular, see Box 7.3, which discusses the choice of metric for different usages, and Section 7.6.1, which treats the challenge of comparing the climate implication of emissions of short-lived and long-lived compounds. Also, the choice of metric is of key importance when defining and quantifying net zero GHG emissions (Box 1.4 and Section 7.6.2). [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] applies metrics to attribute GSAT change to short-lived climate forcer (SLCF) and long-lived GHG emissions from different sectors and regions (Section 6.6.2).

The metrics assessed in this Report are also used, and separately assessed, by WGIII. See Cross-Chapter Box 2 and Annex B in [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] of the WGIII contribution to AR6.

<div id="1.5.4" class="h2-container"></div>

<span id="modelling-techniques-comparisons-and-performance-assessments"></span>