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