Editing IPCC:AR6/WGI/Chapter-10 (section)

==== 10.3.4.3 Role of Internal Variability ====

<div id="h3-36-siblings" class="h3-siblings"></div>

A regional climate projection based on a single simulation from a single global model or driving a single RCM alone will inevitably be affected by not considering the internal variability (Figure 10.10). This is mainly due to the dominant influence of the chaotic atmospheric circulation on regional climate variability, in particular at mid- to high latitudes. Internal variability is an irreducible source of uncertainty for mid- to long-term projections with an amplitude that typically decreases with increasing spatial scale and lead time (Sections 1.4.3 and 4.2.1). However, regional-scale studies show that both large- and local-scale internal variability together can still represent a substantial fraction of the total uncertainty related to hydrological cycle variables, even at the end of the 21st century ( [[#Lafaysse--2014|Lafaysse et al., 2014]] ; [[#Vidal--2016|Vidal et al., 2016]] ; [[#Aalbers--2018|Aalbers et al., 2018]] ; [[#Gu--2018|Gu et al., 2018]] ).

<div id="_idContainer036" class="Basic-Text-Frame"></div>

[[File:b5a447f469f04d352b1f3ff6157251f9 IPCC_AR6_WGI_Figure_10_10.png]]

'''Figure 10.10''' '''|''' '''Observed and projected changes in austral summer (December to February) mean precipitation in Global Precipitation Climatoloy Centre (GPCC), Climatic Research Unit Time Series (CRU TS) and 100 members of the Max Planck Institute for Meteorology Earth System Model (MPI-ESM. (a)''' 55-year trends (2015–2070) from the ensemble members with the lowest (left) and highest (right) trend (% per decade, baseline 1995–2014). '''(b)''' Time series (%, baseline 1995–2014) for different spatial scales (from top to bottom: global averages; South-Eastern South America; grid boxes close to São Paulo and Buenos Aires) with a five-point weighted running mean applied (a variant on the binomial filter with weights [1-3-4-3-1]). The brown (green) lines correspond to the ensemble member with weakest (strongest) 55-year trend and the grey lines to all remaining ensemble members. Box-and-whisker plots show the distribution of 55-year linear trends across all ensemble members, and follow the methodology used in Figure 10.6. Trends are estimated using ordinary least squares. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

Analysis of multi-model archives such as CMIP or CORDEX simulation results cannot easily disentangle model uncertainty and uncertainty related to internal variability. Since AR5, the development of single-model (global model and/or RCM) initial-condition large ensembles (SMILEs) has emerged as a promising way to robustly assess the regional-scale forced response to external forcings and the respective contribution of internal variability and model uncertainty to future regional climate changes ( [[IPCC:Wg1:Chapter:Chapter-4#4.2.5|Section 4.2.5]] ; [[#Deser--2014|Deser et al., 2014]] , 2020; [[#Kay--2015|Kay et al., 2015]] ; [[#Sigmond--2016|Sigmond and Fyfe, 2016]] ; [[#Aalbers--2018|Aalbers et al., 2018]] ; [[#Bengtsson--2019|Bengtsson and Hodges, 2019]] ; [[#Dai--2019|Dai and Bloecker, 2019]] ; [[#Leduc--2019|Leduc et al., 2019]] ; [[#Maher--2019|Maher et al., 2019]] ; [[#von%20Trentini--2019|von Trentini et al., 2019]] ; [[#Lehner--2020|Lehner et al., 2020]] ). The recent development of a multi-model archive of SMILE simulations facilitates the quantification and comparison of the influence of internal variability on global model-based regional climate projections between different models ( [[#Deser--2020|Deser et al., 2020]] ; [[#Lehner--2020|Lehner et al., 2020]] ). Another related development is the more frequent use of observation-based statistical models to assess the influence of internal variability on regional-scale global and regional model projections ( [[#Thompson--2015|Thompson et al., 2015]] ; [[#Salazar--2016|Salazar et al., 2016]] ). However, these methods often implicitly assume that regional-scale internal variability does not change under anthropogenic forcing, which is a strong assumption that does not seem to hold at regional and local scales ( [[#LaJoie--2016|LaJoie and DelSole, 2016]] ; [[#Pendergrass--2017|Pendergrass et al., 2017]] ; W. [[#Cai--2018|]] [[#Cai--2018|Cai et al., 2018]] ; [[#Dai--2019|Dai and Bloecker, 2019]] ; [[#Mankin--2020|Mankin et al., 2020]] ; [[#Milinski--2020|Milinski et al., 2020]] ).

The appropriate ensemble size for a robust use of SMILEs depends on the model and physical variable being investigated, the spatial and time aggregation being performed, the magnitude of the acceptable error and the type of questions one seeks to answer ( [[#Deser--2012|Deser et al., 2012]] , 2017b; [[#Kang--2013|Kang et al., 2013]] ; [[#Wettstein--2014|Wettstein and Deser, 2014]] ; [[#Dai--2019|Dai and Bloecker, 2019]] ; [[#Maher--2019|Maher et al., 2019]] ). It is noteworthy that the recent development of ensembles with a very large ensemble size (greater than 100) have led to new insights and methodologies to robustly assess the required ensemble size for questions such as the estimation of the forced response to external forcing or a forced change in modes of internal variability, such as ENSO, and its associated teleconnections ( [[#Herein--2017|Herein et al., 2017]] ; [[#Maher--2018|Maher et al., 2018]] ; [[#Haszpra--2020|Haszpra et al., 2020]] ; [[#Milinski--2020|Milinski et al., 2020]] ).

The use of SMILEs assumes that they have a realistic representation of internal variability and its evolution under anthropogenic climate change ( [[#Eade--2014|Eade et al., 2014]] ; [[#McKinnon--2017|McKinnon et al., 2017]] ; [[#McKinnon--2018|McKinnon and Deser, 2018]] ; [[#Chen--2019|Chen and Brissette, 2019]] ). Assessing the realism of simulated internal variability for past and current climates remains an active research field with a number of issues such as the shortness and uncertainties of the observed record, in particular in data-scarce regions ( [[#10.2.2.3|Section 10.2.2.3]] ), the signal-to-noise paradox ( [[IPCC:Wg1:Chapter:Chapter-4#4.4.3.1|Section 4.4.3.1]] ; [[#Scaife--2018|Scaife and Smith, 2018]] ), uncertainty in past observed external forcing estimates (Chapters 2, 6 and 7) and the limitations of assumptions underlying the statistical methods used to derive observational large ensembles ( [[#McKinnon--2017|McKinnon et al., 2017]] ; [[#McKinnon--2018|McKinnon and Deser, 2018]] ; [[#Castruccio--2019|Castruccio et al., 2019]] ). Calibration methods inspired by weather and seasonal forecasts can be used to improve the reliability of regional-scale climate projections from large ensembles ( [[#Brunner--2019|Brunner et al., 2019]] ; [[#O’Reilly--2020|O’Reilly et al., 2020]] ). Interestingly, reliability is improved when the calibration is performed separately for the dynamical and residual components of the ensemble resulting from dynamical adjustment ( [[#10.4.1|Section 10.4.1]] ; [[#O’Reilly--2020|O’Reilly et al., 2020]] ).

Importantly, accurately partitioning uncertainty in regional climate projections can provide an incentive for immediate action, accepting a large range of possible outcomes due to internal variability, while confounding model uncertainty with internal variability may be understood as a lack of knowledge and lead to delayed action in adaptation decision-making ( [[#10.5.3|Section 10.5.3]] ; [[#Maraun--2013b|Maraun, 2013b]] ; [[#Mankin--2020|Mankin et al., 2020]] ).

There is ''high confidence'' that the availability of SMILEs allows a robust assessment of the relative contributions of model uncertainty and internal variability in regional-scale projection uncertainty. There is ''high confidence'' that the use of SMILEs with appropriate ensemble size leads to an improved estimate of regional-scale forced response to an external forcing as well as of the full spectrum of possible changes associated with internal variability. There is ''high confidence'' that these improved estimates are beneficial for characterizing the full distribution of outcomes that is a key ingredient of climate information for robust decision-making and risk-analysis frameworks.

<div id="10.3.4.4" class="h3-container"></div>

<span id="designing-and-using-ensembles-for-regional-climate-change-assessments-to-take-uncertainty-into-account"></span>