Editing IPCC:AR6/WGI/Chapter-4 (section)

=== 4.2.4 Pattern Scaling ===

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Projected climate change is typically represented in this chapter for specific future periods. One important source of uncertainty in projections presented for fixed future epochs (time-slabs/time-slices) is the underlying scenario used; another is the structural uncertainty associated with model climate sensitivity. Presenting projections and associated measures of uncertainty for specific periods (see Sections 4.4 and 4.5) remains the most widely applied methodology towards informing climate change impact studies. It is becoming increasingly important from the perspective of climate change and mitigation policy, however, to present projections also as a function of the change in global mean temperature (i.e., global warming levels, GWLs). They are expressed either in terms of changes of global mean surface temperature (GMST) or GSAT (see [[IPCC:Wg1:Chapter:Chapter-1#1.6.2|Section 1.6.2]] and Cross-Chapter Box 2.3). For example, the IPCC SR1.5 ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) assessed the regional patterns of warming and precipitation change for GMST increase of 1.5°C and 2°C above 1850–1900 levels. Techniques used to represent the spatial variations in climate at a given GWL are referred to as pattern scaling.

In the ‘traditional’ methodology as applied in AR5 ( [[#Collins--2013|Collins et al., 2013]] ), patterns of climate change in space are calculated as the product of the change in GSAT at a given point in time and a spatial pattern of change that is constant over time and the scenario under consideration, and which may or may not depend on a particular climate model ( [[#Allen--2002|Allen and Ingram, 2002]] ; [[#Mitchell--2003|Mitchell, 2003]] ; [[#Lambert--2009|Lambert and Allen, 2009]] ; [[#Andrews--2010|Andrews and Forster, 2010]] ; [[#Bony--2013|Bony et al., 2013]] ; [[#Lopez--2014|Lopez et al., 2014]] ). This approach assumes that external forcing does not affect the internal variability of the climate system, which may be regarded a stringent assumption when taking into account decadal and multi-decadal variability ( [[#Lopez--2014|Lopez et al., 2014]] ) and the potential non-linearity of the climate change signal. Moreover, pattern scaling is expected to have lower skill for variables with large spatial variability ( [[#Tebaldi--2014|Tebaldi and Arblaster, 2014]] ). Pattern scaling also fails to capture changes in boundaries that move poleward such as sea ice extent and snow cover ( [[#Collins--2013|Collins et al., 2013]] ), and temporal frequency quantities such as frost days that decrease under warming but are bounded at zero. Spatial patterns are also expected to be different between transient and equilibrium simulations because of the long adjustment time scale of the deep ocean.

Further developments of the AR5 approach have since explored the role of aerosols in modifying regional climate responses at a specific degree of global warming and also the effect of different GCMs and scenarios on the scaled spatial patterns ( [[#Frieler--2012|Frieler et al., 2012]] ; [[#Levy--2013|Levy et al., 2013]] ). Furthermore, the modified forcing-response framework ( [[#Kamae--2012|Kamae and Watanabe, 2012]] , 2013; [[#Sherwood--2015|Sherwood et al., 2015]] ), which decomposes the total climate change into fast adjustments and slow response, identifies the fast adjustment as forcing-dependent and the slow response as forcing-independent, scaling with the change in GSAT.

For precipitation change, there is near-zero fast adjustment for solar forcing but suppression during the fast-adjustment phase for CO <sub>2</sub> and black-carbon radiative forcing ( [[#Andrews--2009|Andrews et al., 2009]] ; [[#Bala--2010|Bala et al., 2010]] ; [[#Cao--2015|Cao et al., 2015]] ). By contrast, the slow response in precipitation change is independent of the forcing. This indicates that pattern scaling is not expected to work well for climate variables that have a large fast-adjustment component. Even in such cases, pattern scaling still works for the slow response component, but a correction for the forcing-dependent fast adjustment would be necessary to apply pattern scaling to the total climate change signal. In a multi-model setting, it has been shown that temperature change patterns conform better to pattern scaling approximation than precipitation patterns ( [[#Tebaldi--2014|Tebaldi and Arblaster, 2014]] ).

[[#Herger--2015|Herger et al. (2015)]] have explored the use of multiple predictors for the spatial pattern of change at a given degree of global warming, following the approach of [[#Joshi--2013|Joshi et al. (2013)]] that explored the role of the land–sea warming ratio as a second predictor. They found that the land–sea warming contrast changes in a non-linear way with GSAT, and that it approximates the role of the rate of global warming in determining regional patterns of climate change. The inclusion of the land–sea warming contrast as the second predictor provides the largest improvement over the traditional technique. However, as pointed out by [[#Herger--2015|Herger et al. (2015)]] , multiple-predictor approaches still cannot detect non-linearities (or internal variability), such as the apparent dependence of spatial temperature variability in the mid- to high latitudes on GSAT (e.g., Fischer and Knutti, 2014; [[#Screen--2014|Screen, 2014]] ).

An alternative to the traditional pattern scaling approach is the time-shift method described by [[#Herger--2015|Herger et al. (2015)]] which is applied in this chapter (also called the epoch approach; see [[#4.6.1|Section 4.6.1]] ). When applied to a transient scenario such as SSP5-8.5, a future time-slab is referenced to a particular increase in the GSAT (e.g., 1.5°C or 2°C of global warming above pre-industrial levels). The spatial patterns that result represent a direct scaling of the spatial variations of climate change at the particular level of global warming. An important advantage of this approach is that it ensures physical consistency between the different variables for which changes are presented ( [[#Herger--2015|Herger et al., 2015]] ). The internal variability does not have to be scaled and is consistent with the GSAT change. Furthermore, the time-shift method allows for a partial comparison of how the rate of increase in GSAT influences the regional spatial patterns of climate change. For example, spatial patterns of change for global warming of 2°C can be compared across the SSP2-4.5 and SSP5-8.5 scenarios. Direct comparisons can also be obtained between variations in the regional impacts of climate change for the case where global warming stabilizes at, for instance, 1.5°C or 2°C of global warming, as opposed to the case where the GSAT reaches and then exceeds the 1.5°C or 2°C thresholds ( [[#Tebaldi--2018|Tebaldi and Knutti, 2018]] ). An important potential caveat is that forcing mechanisms such as aerosol radiative forcing are represented differently in different models, even for the same SSP. This may imply different regional aerosol direct and indirect effects, implying different regional climate change patterns. Hence, it is important to consider the variations in the forcing mechanisms responsible for a specific increase in GSAT towards understanding the uncertainty range associated with the variations in regional climate change. A minor practical limitation of this approach is that stabilization scenarios at 1.5°C or 2°C of global warming, such as SSP1-2.6, do not allow for spatial patterns of change to be calculated from these scenarios at higher levels of global warming, while it is possible in scenarios such as SSP5-8.5 ( [[#Herger--2015|Herger et al., 2015]] ).

In this chapter, the spatial patterns of change as a function of GWLs (defined in terms of the increase in the GSAT relative to 1850–1900) are thus constructed using the time-shift approach, thereby accounting for various non-linearities and internal variability that influence the projected climate change signal. This implies a reliance on large ensemble sizes to quantify the role of uncertainties in regional responses to different degrees of global warming. The assessment in [[#4.6.1|Section 4.6.1]] also explores how the rate of global warming (as represented by different SSPs), aerosol effects, and transient as opposed to stabilization scenarios influence the spatial variations in climate change at specific levels of global warming.

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