Editing IPCC:AR6/WGI/Chapter-8 (section)

==== 8.5.3.1 Non-linearities in Large-scale Atmospheric Circulation and Precipitation ====

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Since AR5, there is further evidence that the pattern-scaling technique has limitations ( [[#Lopez--2014|Lopez et al., 2014]] ; [[#Wartenburger--2017|Wartenburger et al., 2017]] ; [[#Tachiiri--2019|Tachiiri et al., 2019]] ), and that alternative approaches, such as multiple regressions using the land–sea warming contrast as an additional predictor, offer added value ( [[#Joshi--2013|Joshi et al., 2013]] ). The simplest traditional pattern-scaling approach approximates future changes by the product of a time-evolving global surface temperature change and a pattern that varies spatially but is constant across time, scenarios, and models. This technique was shown to be more robust across scenarios rather than across models, with better results for temperature compared with precipitation ( [[#Tebaldi--2014|Tebaldi and Arblaster, 2014]] ; see also [[IPCC:Wg1:Chapter:Chapter-4#4.2.4|Section 4.2.4]] ). One approach which avoids scaling is to consider a period in a different scenario with the same global surface temperature change ( [[#Herger--2015|Herger et al., 2015]] ). It is attractive as it provides patterns of any temporal resolution that are consistent across variables. Nonetheless, this technique is still only based on global surface temperature and is not necessarily suitable for precipitation changes projected in stabilized versus transient scenarios (at the same global warming level) given the fast-atmospheric adjustment to GHG radiative forcing (Sections 8.2.1 and 8.4.1.1).

Even in a theoretical climate system governed by linear processes, pattern-scaling assumptions can fail because the different forcing time response of different parts of the Earth system cause evolving spatial warming patterns ( [[#Good--2016a|Good et al., 2016a]] ). This occurs primarily because different feedbacks occur at different time scales ( [[#Armour--2013|Armour et al., 2013]] ; [[#Andrews--2015|Andrews et al., 2015]] ), which in turn implies that the atmospheric circulation and water cycle is dependent both on the level of warming and the rate of change ( [[#Ceppi--2018|Ceppi et al., 2018]] ). The usual distinction between the fast adjustment to increased GHG concentrations and the slower response to SST warming ( [[#8.2.2.2|Section 8.2.2.2]] ) may, however, not be sufficient to explain the time evolution of the hydroclimatic response at the regional scale, especially in subtropical land areas where this response critically depends on shifts in atmospheric circulation associated with distinct ‘fast’ (typically five to ten years, that is however much slower than the atmospheric adjustment assessed in [[#8.2.1|Section 8.2.1]] ) and slow SST warming patterns ( [[#Zappa--2020|Zappa et al., 2020]] ). The changing balance between the water cycle response to anthropogenic GHG and aerosol forcings is another source of non-linearity across time and global warming levels (Ishizaki et al. , 2013; Rowell et al. , 2015; Y. Liu et al. , 2019b; Wilcox et al., 2020) .

Non-linearities in the climate response are thought to arise from multiple factors. These include state-dependent ice-albedo feedback and its potential influence on Northern Hemisphere (NH) storm tracks (Peings and Magnusdottir, 2014; [[#Semenov--2015|Semenov and Latif, 2015]] ; see also Cross-Chapter Box 10.1 and [[#8.6.1|Section 8.6.1.2]] ); a state-dependent sensitivity of tropical precipitation to increased SST ( [[#Schewe--2017|Schewe and Levermann, 2017]] ; [[#He--2018|He et al., 2018]] ); a complex response of the Atlantic meridional overturning circulation (AMOC; Sections 9.2.4.1 and 8.6.1.1) and its model- and magnitude dependent teleconnections with regional temperature and precipitation (Kageyama et al., 2013; [[#Jackson--2015|Jackson et al., 2015]] ; [[#Qasmi--2017|Qasmi et al., 2017]] , 2020); and other atmospheric and terrestrial ( [[#8.5.3.2|Section 8.5.3.2]] ) processes such as cloud and land surface feedbacks (Ceppi and Gregory, 2017; [[#King--2019|King, 2019]] ). The response of convective precipitation may exhibit non-linearities because it is itself modulated by both dynamics and atmospheric water content, each responding independently to warming (Chadwick and Good, 2013; [[#Neupane--2013|Neupane and Cook, 2013]] ).

Based on a simple model, it was also suggested that the Indian summer monsoon may exhibit a moisture-advection feedback which allows multiple stable states as boundary conditions change (Zickfeld et al., 2005). However, limitations of this theory and comprehensive GCMs suggest a near-linear monsoon response to a broad range of radiative forcings (Boos and Storelvmo, 2016). Non-linear precipitation responses to global warming have been reported in the Indo-Pacific, where a linear increase in SSTs can trigger non-linear changes in precipitation and a shift in the ITCZ depending on the relative amplitudes of uniform and structured SST anomalies ( C.T.Y. [[#Chung--2014|]] [[#Chung--2014|Chung et al., 2014]] ; [[#Toda--2018|Toda and Watanabe, 2018]] ).

Compared to atmospheric circulation and seasonal mean precipitation, extreme precipitation has been found to scale more accurately with local and global mean temperature (Chou et al., 2012; [[#Pendergrass--2015|Pendergrass et al., 2015]] ). The projected increase in the magnitude of extreme precipitation is generally proportional to the global warming level, with an increase of around 7% per 1°C warming ( [[IPCC:Wg1:Chapter:Chapter-11#11.4.5|Section 11.4.5]] ) although this rate shows seasonal and geographical variations and is slightly less for five-day than for one-day precipitation maxima. Projected changes in extreme precipitation are the result of both thermodynamical and more model-dependent and potentially less linear dynamical contributions ( [[#Pfahl--2017|Pfahl et al., 2017]] ). Projected changes in precipitation extremes are also potentially sensitive to a non-linear response of spatial convective organization (Pendergrass et al., 2016), and can exhibit a quadratic rather than linear response to global warming (Pendergrass et al., 2019).

Within CMIP6, the linearity to CO <sub>2</sub> forcing can be assessed through the comparison of the model response to abrupt doubling compared with abrupt quadrupling of atmospheric CO <sub>2</sub> ( [[#Webb--2017|Webb et al., 2017]] ). Preliminary analyses based on CMIP5 models showed that annual precipitation changes following a doubling step change in CO <sub>2</sub> from pre-industrial levels are not necessarily consistent with the response to the step from doubling to quadrupling despite a similar change in radiative forcings ( [[#Good--2016a|Good et al., 2016a]] ; [[#Ceppi--2017|Ceppi and Shepherd, 2017]] ). Beyond the visual comparison of the climate response at various global warming levels (e.g., Figure 4.35), the linearity across global warming levels can be assessed by using the highest emissions scenario and comparing seasonal mean relative precipitation changes at +2°C versus +4°C above pre-industrial (1850–1900) temperatures (Figure 8.25). The results support the previous finding ( [[#Good--2016b|Good et al., 2016b]] ) that a second 2°C warming does not necessarily lead to the same precipitation anomaly pattern as the first 2°C, especially in the tropics where regional differences can be large but not necessarily consistent among different models. They are also consistent with a recent analysis of CMIP5 models showing that the projected drying in the Mediterranean and in Chile is substantially faster than the increase in GSAT, and therefore does not scale linearly with global warming ( [[#Zappa--2020|Zappa et al., 2020]] ).

In summary, there is ''high confidence'' that continued global warming will further amplify GHG-induced changes in large-scale atmospheric circulation and precipitation. Nonetheless, there are cases where regional water cycle changes are not linearly related to global warming due to the interaction of multiple forcings, feedbacks and time scales ( ''medium confidence'' , see also Sections 4.2.4, 7.4.3 and 8.2.1). Aridity in subtropical regions is highly sensitive to fast shifts in large-scale atmospheric circulation so are particularly susceptible to such non-linearities.

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