Editing IPCC:AR6/WGI/Chapter-3 (section)

==== 3.3.2.1 Paleoclimate Context ====

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A fact hindering detection and attribution studies in precipitation and other hydrological variables is the large internal variability of these fields relative to the anthropogenic signal. This low signal-to-noise ratio hinders the emergence of the anthropogenic signal from natural variability. Moreover, the sign of the change depends on location and time of the year. Paleoclimate records provide valuable context for observed trends in the 20th and 21st century and assist with the attribution of these trends to human influence (see also ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.3.1|Section 2.3.1.3.1]] ). By nature, hydrological proxy data represent regional conditions, but taken together can represent large-scale patterns. As an example of how paleorecords have helped assessing the origin of changes, we consider some, mainly subtropical, regions which have experienced systematic drying in recent decades (see also Section 8.3.1.3). Paleoclimate simulations of monsoons are assessed in [[#3.3.3.2|Section 3.3.3.2]] .

Records of tree ring width have provided evidence that recent prolonged dry spells in the Levant and Chile are unprecedented in the last millennium ( ''high confidence'' ) ( [[#Cook--2016a|Cook et al., 2016a]] ; [[#Garreaud--2017|Garreaud et al., 2017]] ). East Africa has also been drying in recent decades ( [[#Rowell--2015|Rowell et al., 2015]] ; [[#Hoell--2017|Hoell et al., 2017]] ), a trend that is unusual in the context of the sedimentary paleorecord spanning the last millennium ( [[#Tierney--2015|Tierney et al., 2015]] ). This may be a signature of anthropogenic forcing but cannot yet be distinguished from natural variability ( [[#Hoell--2017|Hoell et al., 2017]] ; [[#Philip--2018|Philip et al., 2018]] ). Likewise, tree rings indicate that the 2012–2014 drought in the south-western United States was exceptionally severe in the context of natural variability over the last millennium, and may have been exacerbated by the contribution of anthropogenic temperature rise ( ''medium confidence'' ) ( [[#Griffin--2014|Griffin and Anchukaitis, 2014]] ; [[#Williams--2015|Williams et al., 2015]] ). Furthermore, [[#Williams--2020|Williams et al. (2020)]] used a combination of hydrological modelling and tree-ring reconstructions to show that the period from 2000 to 2018 was the driest 19-year span in south-western North America since the late 1500s. Nonetheless, tree rings also indicate the presence of prolonged megadroughts in western North America throughout the last millennium that were more severe than 20th and 21st century events ( ''high confidence'' ) ( [[#Cook--2004|Cook et al., 2004]] , 2010, 2015). These were associated with internal variability ( [[#Coats--2016|Coats et al., 2016]] ; [[#Cook--2016b|Cook et al., 2016b]] ) and indicate that large-magnitude changes in the water cycle may occur irrespective of anthropogenic influence (see also [[#McKitrick--2019|McKitrick and Christy, 2019]] ).

Paleoclimate records also allow for model evaluation under conditions different from present-day. The AR5 concluded that models can successfully reproduce to first-order patterns of past precipitation changes during the Last Glacial Maximum (LGM) and mid-Holocene, though simulated precipitation changes during the mid-Holocene tended to be underestimated ( [[#Flato--2013|Flato et al., 2013]] ). Further analysis of CMIP5 models confirmed these results but has also revealed systematic offsets from the paleoclimate record ( [[#DiNezio--2013|DiNezio and Tierney, 2013]] ; [[#Hargreaves--2014|Hargreaves and Annan, 2014]] ; [[#Harrison--2014|Harrison et al., 2014]] , 2015; [[#Bartlein--2017|Bartlein et al., 2017]] ; [[#Scheff--2017|Scheff et al., 2017]] ; [[#Tierney--2017|Tierney et al., 2017]] ). [[#Harrison--2014|Harrison et al. (2014)]] concluded that CMIP5 models do not perform better in simulating rainfall during the LGM and mid-Holocene than earlier model versions despite higher resolution and complexity. However, prescribing changes in vegetation and dust was found to improve the match to the paleoclimate record ( [[#Pausata--2016|Pausata et al., 2016]] ; [[#Tierney--2017|Tierney et al., 2017]] ) suggesting that vegetation feedbacks in the CMIP5 models may be too weak ( ''low confidence'' ) ( [[#Hopcroft--2017|Hopcroft et al., 2017]] ). [[#Brierley--2020|Brierley et al. (2020)]] compared the latitudinal gradient of annual precipitation changes in the European–African sector simulated by CMIP6 models for the mid-Holocene with pollen-based reconstructions and showed that models generally reproduce the direction of changes seen in the reconstructions (Figure 3.11). They do not show a robust signal in area averaged rainfall over most European regions where quantitative reconstructions exist, which is not incompatible with reconstructions. Over the Sahara/Sahel and West Africa regions, where reconstructions suggest positive anomalies during the mid-Holocene, both CMIP5 and CMIP6 models also simulate a rainfall increase, but it is much weaker (see also ( [[#3.3.3.2|Section 3.3.3.2]] ). Overall, however, large discrepancies remain between simulations and reconstructions.

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[[File:33efc5a39a5a4f520a1db9ebb7ebbd01 IPCC_AR6_WGI_Figure_3_11.png]]

Figure 3.11 | '''Comparison between simulated annual precipitation changes and pollen-based reconstructions in the mid-Holocene (6000 years ago).''' The area-averaged changes relative to the pre-industrial control simulations over five regions ( [[#Iturbide--2020|Iturbide et al., 2020]] ) as simulated by CMIP6 models (individually identifiable, one ensemble member per model) and CMIP5 models (blue) are shown, stretching from the tropics to high-latitudes. All regions contain multiple quantitative reconstructions of changes relative to present day; their interquartile range are shown by boxes and with whiskers for their full range excluding outliers. Figure is adapted from [[#Brierley--2020|Brierley et al. (2020)]] . Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

[[#Liu--2018|Liu et al. (2018)]] evaluated the soil moisture changes that occurred during the LGM and concluded that the multi-model median from CMIP5 is consistent with available paleo-records in some regions, but not in others. CMIP5 models accurately reproduce an increase in moisture in the western United States, related to an intensified winter storm track and decreased evaporative demand ( [[#Oster--2015|Oster et al., 2015]] ; [[#Ibarra--2018|Ibarra et al., 2018]] ; [[#Lora--2018|Lora, 2018]] ). On the other hand, CMIP5 models show a wide variety of responses in the tropical Indo-Pacific region, with only a few matching the pattern of change inferred from the paleoclimate record ( [[#DiNezio--2013|DiNezio and Tierney, 2013]] ; [[#DiNezio--2018|DiNezio et al., 2018]] ). The variable response across models is related to the effect of the exposure of the tropical shelves during glacial times, which variously intensifies or weakens convection in the rising branch of the Walker cell, depending on model parameterization ( [[#DiNezio--2011|DiNezio et al., 2011]] ). For the Last Interglacial, CMIP6 models reproduce the proxy-based increased precipitation relative to pre-industrial in the North African, South Asian and North American regions, but not in Australia ( [[#Scussolini--2019|Scussolini et al., 2019]] ).

In summary, there is ''medium confidence'' that CMIP5 and CMIP6 models can reproduce broad aspects of precipitation changes during paleo reference periods, but large discrepancies remain. Further assessment of model performance and comparison between CMIP5 and CMIP6 during past climates can be found in [[#3.8.2.1|Section 3.8.2.1]] .

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