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

=== 10.4.3 Future Regional Changes: Robustness and Emergence of the Anthropogenic Signal ===

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Regional climate projections are one key element of the multiple lines of evidence that are used for climate risk assessments as well as for adaptation and policy decisions at regional scales (Sections 10.3.3.9 and 10.5). Regional climate projections can be separated into two components: the regional-scale forced response or regional-scale climate sensitivity when normalized by the global mean temperature change ( [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ) and the climate internal variability characterizing the future period or global warming level under scrutiny. This section assesses a few methodological aspects related to robustness and emergence properties of the regional-scale forced response as well as the possible influence of internal variability on the emergence of the anthropogenic signal.

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==== 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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==== 10.4.3.2 Emergence of the Anthropogenic Signal at Regional Scale ====

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This section provides an assessment of the different approaches used in emergence studies as well as sensitivities to methodological choices. The section then focuses on the possible influence of internal variability on future emergence of the simulated mean precipitation anthropogenic signal at regional scales with some illustrative examples.

In climate science, emergence or distinguishability of a signal refers to the appearance of a persistent change in the probability distribution and/or temporal properties of a climate variable compared with that of a reference period ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.2|Section 1.4.2]] ; [[#Giorgi--2009|Giorgi and Bi, 2009]] ; [[#Mahlstein--2011|Mahlstein et al., 2011]] , [[#Mahlstein--2012|2012]] ; [[#Hawkins--2012|Hawkins and Sutton, 2012]] ). Similar to anthropogenic climate change detection (Cross-Working Group Box: Attribution in Chapter 1), signal emergence can be detected, at least initially, without identifying the physical causes of the emergence ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.2|Section 1.4.2]] ). In the context of human influence on climate, the objective of emergence studies is the search for the appearance of a signal characterizing an anthropogenically-forced change relatively to the climate variability of a reference period, defined as the noise.

Precise definitions of signal and noise as well as a metric to measure the relative importance of the signal are key ingredients of the emergence framework and depend on the framing question. In particular, emergence study results can depend on the specific definitions of signal and noise such as the level of spatial and temporal aggregation ( [[#McSweeney--2013|McSweeney and Jones, 2013]] ). For instance, grid-point scale emergence will likely be delayed compared with region-average emergence ( [[IPCC:Wg1:Chapter:Chapter-11#11.2.4|Section 11.2.4]] and [[IPCC:Wg1:Chapter:Atlas-1-figure-2|Cross-Chapter Box Atlas.1, Figure 2]] ; [[#Fischer--2013|Fischer et al., 2013]] ; [[#Maraun--2013b|Maraun, 2013b]] ; [[#Lehner--2017a|Lehner et al., 2017a]] ). The signal is often estimated by a running mean multi-decadal average or probability distribution function of the physical variable under scrutiny in order to avoid false emergence due to manifestation of multi-decadal internal variability ( [[#King--2015|King et al., 2015]] ). In the case of extremes such as climate records, a notion of multi-year persistence or recurrence can also be used to fully characterize the anthropogenic signal and its emergence ( [[#Christiansen--2013|Christiansen, 2013]] ; [[#Bador--2016|Bador et al., 2016]] ).

Emergence is also sensitive to the noise characteristics: assuming a common signal definition, larger signal-to-noise values and earlier emergence will arise if the noise is based on decadal mean variability rather than interannual variability ( [[#Kusunoki--2020|Kusunoki et al., 2020]] ). Depending on the framing question, the noise can include or omit external natural forcing such as volcanic and solar forcing ( [[#Zhang--2018|Zhang and Delworth, 2018]] ; [[#Silvy--2020|Silvy et al., 2020]] ). Furthermore, emergence results are very sensitive to the choice and length of the reference period ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.1|Section 1.4.1]] ). The reference period can be the pre-industrial, the very recent past or even a time-evolving baseline, depending on both the framing and assumption that adaptation to the current climate has already occurred ( [[#King--2015|King et al., 2015]] ; [[#Zhang--2018|Zhang and Delworth, 2018]] ; [[#Brouillet--2020|Brouillet and Joussaume, 2020]] ). These choices will then determine the type of simulations and periods that will be used to construct the noise distribution. Finally, the permanence of future emergence cannot be taken for granted when emergence occurs in the late-21st century based on simulations ending in 2100 ( [[#Hawkins--2014|Hawkins et al., 2014]] ; [[#King--2015|King et al., 2015]] ; [[#Lehner--2017a|Lehner et al., 2017a]] ).

Robust assessments and comparisons of past emergence between observations and models are strengthened by the use of consistent definitions of signal and noise ( [[#Lehner--2017a|Lehner et al., 2017a]] ; [[#Hawkins--2020|Hawkins et al., 2020]] ). In the case of future emergence under increasing greenhouse gas emissions, two main approaches have been followed to assess emergence. The first is based on estimating the signal and noise (and sometimes the signal-to-noise ratio as well) in individual models before using the resulting distribution median or mean to construct the final emergence metric ( [[#Hawkins--2012|Hawkins and Sutton, 2012]] ; [[#Maraun--2013b|Maraun, 2013b]] ; [[#Sui--2014|Sui et al., 2014]] ; [[#Barrow--2019|Barrow and Sauchyn, 2019]] ). The second method first estimates the signal as a multi-model mean change and the noise variance as a combination of internal variability and model structural differences ( [[#Giorgi--2009|Giorgi and Bi, 2009]] ; [[#Mariotti--2015|Mariotti et al., 2015]] ; [[#Nguyen--2018|Nguyen et al., 2018]] ). The first approach allows the definition of emergence of the signal relative to internal variability only and treats model error as source of uncertainty ( [[#Maraun--2013b|Maraun, 2013b]] ; [[#Lehner--2017a|Lehner et al., 2017a]] ). The second assumes that the multi-model mean is the optimal estimate of the signal and confounds internal variability and model structural differences in the noise estimate. It is noteworthy that most emergence studies implicitly assume model independence ( [[#Annan--2017|Annan and Hargreaves, 2017]] ; [[#Boé--2018|Boé, 2018]] ; Box 4.1) and therefore sensitivity of emergence results to model selection or weighting is rarely performed ( [[#Akhter--2018|Akhter et al., 2018]] ).

Metrics can vary from a simple signal-to-noise ratio to statistical distributional tests ( [[#King--2015|King et al., 2015]] ; [[#Gaetani--2020|Gaetani et al., 2020]] ) and give median estimates and uncertainty bounds for the date (or time of emergence) corresponding to the exceedance of specific thresholds by the emergence metric. Reconciling future emergence results among different studies is challenging due to their many methodological differences including the choice of the reference period, the selected climate models and scenario, the precise definition of signal and noise and the choice of different signal-to-noise thresholds to characterize robust emergence. Contrasting with binary yes/no statements, emergence can also be viewed as a continuous process characterized by an amplitude or level, for example the value of the signal-to-noise ratio, that is a function of time or global warming level.

Since AR5, the development and production of SMILEs (Sections 4.2.5 and 10.3.4.3) has allowed the assessment of the influence of internal variability on anthropogenic signal emergence. The influence of internal variability, and specifically of the unforced atmospheric circulation, on temperature signal emergence can delay or advance the time of emergence by a decade or two in mid- to high-latitude regions ( [[#Lehner--2017a|Lehner et al., 2017a]] ; [[#Koenigk--2020|Koenigk et al., 2020]] ). Internal variability can also result in small or decreasing decadal to multi-decadal heatwave frequency trends under the historical anthropogenic forcing over most regions, thereby delaying emergence of unprecedented heatwave frequency trends relative to the pre-industrial trend distribution (Sections 11.2–11.3; [[#Perkins-Kirkpatrick--2017|Perkins-Kirkpatrick et al., 2017]] ).

Regional precipitation future changes are much more impacted by internal variability than their temperature counterpart ( [[#Monerie--2017b|Monerie et al., 2017b]] ; [[#Dai--2019|Dai and Bloecker, 2019]] ; [[#Singh--2019|Singh and AchutaRao, 2019]] ; [[#von%20Trentini--2019|von Trentini et al., 2019]] ; [[#Koenigk--2020|Koenigk et al., 2020]] ). Relative to mean temperature changes, this larger influence of internal variability on mean precipitation changes contributes, among other factors ( [[#Sarojini--2016|Sarojini et al., 2016]] ), to a much delayed emergence of the forced precipitation response in observations ( [[#Hawkins--2020|Hawkins et al., 2020]] ). Based on the CMIP6 multi-model ensemble forced with the scenario SSP5-8.5, we assess the future emergence of mean precipitation forced change as a function of GWLs for all AR6 land regions (Figure 10.15a). The methodology is a straightforward adaptation of the standard approach ( [[#Hawkins--2012|Hawkins and Sutton, 2012]] ). While the standard method is only based on the signal-to-noise ratio exceedance of a specified threshold (taken as one), the approach used here assumes that grid-point emergence occurs when the forced change is considered robust following the AR6 WGI definition of robustness for projected changes (Cross-Chapter Box Atlas.1). At a GWL of 1°C, emergence only occurs in high-latitude regions ( [[#Wan--2015|Wan et al., 2015]] ; R. [[#Guo--2019|]] [[#Guo--2019|Guo et al., 2019]] ), albeit with only small (less than 30%) area fraction with robust change. Robust changes in tropical and subtropical regions only appear from GWLs of 1.5°C, for example in south-western South America ( [[#Boisier--2016|Boisier et al., 2016]] ), western Africa ( [[#Hawkins--2020|Hawkins et al., 2020]] ; [[#10.4.2.1|Section 10.4.2.1]] ) and southern Australia ( [[#Delworth--2014|Delworth and Zeng, 2014]] ). Substantial (taken here simply as area fraction greater than 50%) emergence only occurs in some tropical, subtropical and mid-latitude regions when high GWLs (3°C–4°C) are reached. Importantly, even at these high GWL values, there are still a large number of these regions with robust changes covering less than 50% of their area. In contrast, most high-latitude regions have an area fraction with robust changes greater than 80% at GWLs of 3°C and above.

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[[File:1c824a91cc0b7392bb857583d40348dc IPCC_AR6_WGI_Figure_10_15.png]]

'''Figure 10.15''' '''|''' '''Future emergence of anthropogenic signal at regional scale. (a)''' Percentage area of land regions with robust annual mean precipitation change as a function of increasing global warming levels (GWLs). Robustness of the precipitation change is first estimated at each grid-point followed by the estimation of the AR6 region area with robust changes. For each Coupled Model Intercomparison Project Phase 6 (CMIP6) model considered (45 models, one member per model, historical simulations and scenario SSP5-8.5), the annual mean precipitation change is based on the difference between a 20-year average centred on the GWL crossing year and the mean precipitation during the pre-industrial period (1850–1900) taken as a reference. The change is considered to be robust when at least 66% of the models (30 out of 45) have a signal-to-noise ratio greater than one and at least 80% of them (36 out of 45) agree on the sign of change. The signal-to-noise ratio is estimated for each model from the ratio between the change and the standard deviation of non-overlapping 20-year means of the corresponding pre-industrial simulation (scaled by square root of 2 times 1.645). '''(b)''' Time evolution of the percentage area of land region with robust annual mean precipitation change for five AR6 land regions. Thick solid lines represent precipitation changes based on the same CMIP6 ensemble as in (a). Thin solid, dotted and dashed lines represent changes based on the three coupled single-model initial-condition large ensembles (SMILEs) used in Chapter 10, illustrating the influence of internal variability on the emergence of robust change. The change is estimated from the difference between all consecutive 20-year periods from 1900–1919 up to 2081–2100 and the pre-industrial period. The line colour indicates the sign of the robust change given by the multi-model mean (CMIP6) or ensemble mean (SMILE) change: brown (decreasing precipitation) and dark green (increasing precipitation). Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

We now illustrate the potential influence of internal variability on late or lack of emergence for a few AR6 land regions (Figure 10.15b). For each of these AR6 regions, the time evolution of the percentage area with robust annual mean precipitation change is estimated for both the CMIP6 multi-model ensemble and the three coupled SMILEs used throughout Chapter 10. Similarity in percentage area time evolution between CMIP6 and the three coupled SMILEs suggests that internal variability can substantially influence the timing of emergence. For example, internal variability could explain the mid-21st century emergence (percentage area greater than 50%) of the drying and wetting signal over the Mediterranean and South Asia (see also [[#10.6.3|Section 10.6.3]] ) regions, respectively. Internal variability can also contribute to the late and moderate emergence over South-Eastern South America (see also [[#10.4.2|Section 10.4.2]] ) and West South Africa (see also [[#10.6.2|Section 10.6.2]] ). In contrast, it cannot explain the lack of robust changes (percentage area less than 30%) over Western Africa at the end of the 21st century, suggesting that model differences are also contributing to the lack of emergence ( [[#Monerie--2017a|Monerie et al., 2017a]] , b). In addition to different forced signals, the differences of time evolution between the three SMILEs, in particular for African regions, point to the issue of global model performance in accurately representing internal variability and its future changes. While overestimation and underestimation of internal variability in current models have been reported ( [[#Eade--2014|Eade et al., 2014]] ; [[#Laepple--2014|Laepple and Huybers, 2014]] ), methodological challenges to assess the magnitude and spatial pattern of model biases in simulating internal variability, still remain [[#10.3.4.3|Section 10.3.4.3]] ). Therefore, the existence of model biases and the limited knowledge of their characteristics lead to limitations about a precise quantification of internal variability influence on delayed regional-scale emergence.

There is ''high confidence'' that consistency in definitions of signal and noise, choice of the reference period and signal-to-noise threshold, is important to robustly assess the future emergence of anthropogenic signals across different types or generations of models, as well as comparing past emergence results between observations and models. There is ''high confidence'' that internal variability can delay the emergence of the regional-scale mean precipitation anthropogenic signal in many regions, mainly located in the tropics, subtropics and mid-latitudes. An accurate estimation of the delay in regional-scale emergence caused by internal variability remains challenging due to global model biases in their representation of internal variability as well as methodological difficulties to precisely estimate these biases ( ''high confidence'' ).

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