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

==== 10.4.1.1 Optimal Fingerprinting Methods ====

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Optimal fingerprint regression-based methods have been applied to detection and attribution of mean temperature anthropogenic signal in several regions of the world such as Canada, India, central Asia, northern and western China, Australia, and North Africa ( [[#Xu--2015|Xu et al., 2015]] ; [[#Li--2017|]] [[#Li--2017|C. Li et al., 2017]] ; [[#Dileepkumar--2018|Dileepkumar et al., 2018]] ; Y. [[#Wang--2018|]] [[#Wang--2018|Wang et al., 2018]] ; [[#Peng--2019|Peng et al., 2019]] ; [[#Wan--2019|Wan et al., 2019]] ). The influence of anthropogenic forcing, and in particular that of greenhouse gases (GHGs), is robustly detected in annual and seasonal mean temperatures for all considered regions. Most of the observed regional temperature changes since the mid-twentieth century can only be explained by external forcings, with anthropogenic influence being the dominant factor. GHG increase is found to be the primary factor of the anthropogenic-induced warming while the aerosol forcing leads to a cooling offsetting a fraction of the GHG change ( [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|C. Li et al., 2016]] , 2017). While the influence of external natural forcing can often be detected as well, its contribution to observed changes is usually much smaller ( [[#Li--2017|]] [[#Li--2017|C. Li et al., 2017]] ; [[#Wan--2019|Wan et al., 2019]] ). Temperature detection results are found to be robust to the use of different observational datasets and detection methodologies ( [[#Dileepkumar--2018|Dileepkumar et al., 2018]] ).

Detection of mean precipitation changes caused by human influence is much more difficult, due to a larger role of internal variability at regional to local scales, as well as substantial modelling and observational uncertainty ( [[#Wan--2015|Wan et al., 2015]] ; [[#Sarojini--2016|Sarojini et al., 2016]] ; [[#Li--2017|]] [[#Li--2017|C. Li et al., 2017]] ). However, multi-decadal precipitation changes due to anthropogenic forcing have been detected for several regions. [[#Ma--2017b|Ma et al. (2017b)]] show that anthropogenic forcing has strongly contributed to the observed shift of China daily precipitation towards heavy precipitation. The observed weakening of the East Asia summer monsoon, also known as the southern flooding and northern drought pattern has been partially linked to anthropogenic forcing ( [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.4.2|Section 8.3.2.4.2]] ; [[#Song--2014|Song et al., 2014]] ; [[#Zhou--2017|Zhou et al., 2017]] ; [[#Tian--2018|Tian et al., 2018]] ). Changes in GHGs lead to increasing precipitation over southern China, while changes in anthropogenic aerosols over East Asia are the dominant factors determining drought conditions over northern China ( [[#Song--2014|Song et al., 2014]] ; [[#Tian--2018|Tian et al., 2018]] ). Based on all-forcing and single-forcing simulation ensembles with a high-resolution model, [[#Delworth--2014|Delworth and Zeng (2014)]] found that the observed long-term regional austral autumn and winter rainfall decline over southern and particularly south-west Australia is partially reproduced in response to anthropogenic changes in GHGs and ozone in the atmosphere, whereas anthropogenic aerosols do not contribute to the simulated precipitation decline. In contrast, the observed increase of north-west Australian summer rainfall since 1950 has been partially attributed to anthropogenic aerosol based on CMIP5 detection and attribution single-forcing simulations ( [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.4.6|Section 8.3.2.4.6]] ; [[#Dey--2019a|Dey et al., 2019a]] , [[#Dey--2019b|b]] ).

It is noteworthy that these methods require a very significant reduction of spatial and temporal dimensions in order to reliably estimate the co-variance matrix of internal variability (an entire region is thus often considered as being only one or a few spatial points that represent the spatial average of the whole region or a few sub-regions; time samples are often 5- or 10-year averages). Finally, model bias is rarely considered in statistical models used in detection and attribution regional studies, while it has been shown to have a strong impact on the stability of detection results and their associated confidence intervals when increasing the spatial dimension ( [[#Ribes--2013|Ribes and Terray, 2013]] ). New statistical methods are emerging to provide some alternative to standard optimal fingerprinting but they have not yet been evaluated and applied at regional scales ( [[IPCC:Wg1:Chapter:Chapter-3#3.2.2|Section 3.2.2]] ).

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