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

==== 10.4.3.1 Robustness of the Anthropogenic Signal at Regional Scale ====

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Standard methodologies to derive the regional forced response include pattern-scaling and the time-shift or epoch approach ( [[IPCC:Wg1:Chapter:Chapter-4#4.2.4|Section 4.2.4]] ; [[#Tebaldi--2014|Tebaldi and Arblaster, 2014]] ; [[#Vautard--2014|Vautard et al., 2014]] ; [[#Herger--2015|Herger et al., 2015]] ; [[#Tebaldi--2018|Tebaldi and Knutti, 2018]] ; [[#Christensen--2019|Christensen et al., 2019]] ). Pattern-scaling assumes that the spatial patterns of regional change, often based on a time-averaged 20- or 30-year period at the end of the 21st century, are roughly constant in time, and simply scale linearly with global mean warming. The time-shift approach defines a target in terms of global warming level (GWL) and locates the time segment, usually 20 or 30 years, in historical or scenario simulations in which global mean warming matches the required GWL ( [[#10.1.2|Section 10.1.2]] and Cross-Chapter Box 11.1). Physical consistency between multiple variables and space-time co-variance are fully preserved in the time-shift approach, which is not the case for pattern-scaling ( [[#Herger--2015|Herger et al., 2015]] ). Importantly, pattern scaling cannot account for the non-linearity arising from either interacting quasi-linear processes ( [[#Chadwick--2013|Chadwick and Good, 2013]] ) and purely non-linear mechanisms, which have been shown to be present in CMIP5 models for high GWL (4°C) and affect precipitation more than temperature at the regional-scale ( [[IPCC:Wg1:Chapter:Chapter-8#8.5.3.1|Section 8.5.3.1]] ; [[#Good--2015|Good et al., 2015]] , 2016). The time-shift approach can also be used to test whether regional climate change patterns depend on the rate of global mean warming and external forcing pathways, in addition to global warming magnitude. A global evaluation of both approaches in projecting the forced temperature and precipitation response for a highly mitigated scenario based on a moderately mitigated one has been performed using a perfect-model framework ( [[#Tebaldi--2018|Tebaldi and Knutti, 2018]] ). The amplitude of errors for both approaches appears to be substantially smaller than model uncertainty approximated by the CMIP5 multi-model spread.

Based on large and coordinated modelling exercises such as CMIP5 and CORDEX, the time-shift approach has been largely used to assess differences in regional climate impacts for different GWLs, with a strong focus on 1.5°C versus 2°C ( [[#Karmalkar--2017|Karmalkar and Bradley, 2017]] ; [[#Dosio--2018|Dosio and Fischer, 2018]] ; [[#Karnauskas--2018|Karnauskas et al., 2018]] ; W. [[#Liu--2018|]] [[#Liu--2018|Liu et al., 2018]] ; [[#Taylor--2018|Taylor et al., 2018]] ; [[#Weber--2018|Weber et al., 2018]] ; Chapter 3, SR1.5, [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ). Comparisons between pattern-scaling and time-shift approaches allow assessment of the scalability of the regional climate change signal and the extent to which pattern-scaling assumptions still hold at regional scale for a wide range of GWL. This was the approach followed by [[#Matte--2019|Matte et al. (2019)]] in their assessment of the scalability of European regional climate projections. Based on EURO-CORDEX projections, they performed a detailed comparison between the pattern scaling and the GWL spatial patterns (GWL range: 1°C, 2°C and 3°C) for different seasons, regional model resolutions, and both temperature and precipitation. High pattern correlation values (greater than 0.9) are found between the scaled pattern and all GWL patterns for temperature. In the case of precipitation, the correspondence is slightly lower, especially in summer, for high GWLs (2°C and 3°C) and much lower for 1°C.

Figure 10.14 illustrates a similar comparison based on the CMIP6 multi-model ensemble forced with the scenario SSP5-8.5 and applied to two large-scale continental areas. The forced response to anthropogenic forcing is simply taken as the CMIP6 multi-model mean of future regional climate change relative to the 1850–1900 reference period. Robustness of the forced response is based on both significance of the change and model agreement about the sign (direction) of change (Cross-Chapter Box Atlas.1; Figure 10.14). Caution has to be exercised against a too literal interpretation of lack of robust change given that significance and sign agreement can be sensitive to spatial and temporal aggregation (Cross-Chapter Box Atlas.1, Figure 2) and lack of a robust change does not necessarily translate to lack of regional-scale climate change impacts ( [[#McSweeney--2013|McSweeney and Jones, 2013]] ; [[#Hibino--2016|Hibino and Takayabu, 2016]] ).

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[[File:1c60cae6202f2ab8868a790176e35a5e IPCC_AR6_WGI_Figure_10_14.png]]

'''Figure 10.14''' '''|''' '''Robustness and scalability of anthropogenic signals at regional scale. (a)''' Spatial patterns of European and African summer (June to August) surface air temperature change (in °C °C <sup>–1</sup> ) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean (45 models, one member per model, historical simulations and scenario SSP5-8.5) at different global warming levels (GWLs) and the end-21st century scaling pattern estimated from the multi-model mean difference between 2081–2100 and the pre-industrial period (1850–1900) divided by the corresponding global mean warming. The scale of all GWL patterns has been adjusted to a global mean warming of 1°C (for example, the resulting 3°C spatial pattern has been divided by three). The scales of the GWL patterns have to be multiplied by their threshold values to obtain the actual simulated warming. The metrics shown in the bottom left corner of the GWL pattern plots indicate the spatial pattern correlation and the root-mean-square difference between the GWL patterns and the scaling pattern. The number in bold just above the metrics gives the number of used CMIP6 models (out of 45) that have reached the GWL threshold. Areas with robust change (at least 66% of the models have a signal-to-noise ratio greater than one and 80% or more of the models agree on the sign of the change) are coloured with no pattern overlaid (Cross-Chapter Box Atlas.1). Areas with a significant change (at least 66% of the models have a signal-to-noise ratio greater than one) and lack of model agreement (meaning that less than 80% of the models agree on the sign of the change) are marked by cross-hatching. Areas with no change or no robust change (less than 66% of the models have a signal-to-noise ratio greater than one) are marked by negatively sloped hatching. '''(b)''' Same as (a) but for North, Central and South America annual mean precipitation relative change (percent °C <sup>–1</sup> ). The baseline for precipitation climatology is 1850–1900. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

If projected regional mean temperature (Figure 10.14a) and precipitation (Figure 10.14b) changes were to scale linearly with global mean warming, the adjusted spatial patterns would be congruent with each other at different GWLs. While pattern scaling seems to be a reasonable first-order approximation for both temperature and precipitation changes in tropical and high latitude regions (high pattern correlation values), there are a number of regions exhibiting substantial amplitude differences at different GWLs (northern Africa and Middle East, southern and eastern Europe for temperature; south-western North America, Chile and north-eastern Brazil for precipitation). These differences hint at the possible influence of non-linear mechanisms ( [[#Good--2015|Good et al., 2015]] ), including soil-moisture feedbacks ( [[#Seneviratne--2010|Seneviratne et al., 2010]] ; [[#Vogel--2017|Vogel et al., 2017]] ), a time-dependent balance between the different contributions of fast and slow response to greenhouse gas forcing as well as changing SST response patterns ( [[#Long--2014|Long et al., 2014]] ; [[#Good--2016|Good et al., 2016]] ; [[#Ceppi--2018|Ceppi et al., 2018]] ; [[#Zappa--2020|Zappa et al., 2020]] ). Decreasing spatial pattern amplitude with increasing GWL suggests that the initial transient regional response overshoots the long-term change in regions such as northern Africa for summer temperature and south-western South America for precipitation ( [[#Zappa--2020|Zappa et al., 2020]] ). In the latter region, long simulations with stabilized GHG concentrations even suggest a change of sign when near-equilibrium is reached ( [[#Sniderman--2019|Sniderman et al., 2019]] ). The reverse behaviour, increasing pattern amplitude with increasing GWL, is seen for summer temperature in southern and eastern Europe and for precipitation in south-western North America ( [[#Sniderman--2019|Sniderman et al., 2019]] ; [[#Zappa--2020|Zappa et al., 2020]] ), suggesting that, in these regions, the initial transient response is lagging global mean warming and final regional climate change will be reached once GHG concentrations are stabilized.

There is ''high confidence'' that the time-evolving contribution of different mechanisms operating at different time scales can modify the amplitude of the regional-scale response of temperature, and both the amplitude and sign of the regional-scale response of precipitation, to anthropogenic forcing. These mechanisms include non-linear temperature, precipitation and soil-moisture feedbacks, and slow and fast response of SST patterns and atmospheric circulation changes to increasing GHGs.

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