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

==== 4.4.3.5 Pacific Decadal Variability ====

<div id="h3-20-siblings" class="h3-siblings"></div>

Climate variability of the Pacific Ocean on decadal and inter-decadal time scales is described in terms of a number of quasi-oscillatory SST patterns such as the Pacific Decadal Oscillation (PDO; [[#Mantua--1997|Mantua et al., 1997]] ) and the Inter-decadal Pacific Oscillation (IPO; [[#Folland--2002|Folland, 2002]] ), which are referred to as the Pacific Decadal Variability (PDV; [[#Newman--2016|Newman et al., 2016]] ). PDV comprises an inter-hemispheric pattern that varies at decadal to inter-decadal time scales (Figure 3.35). However, although the spatial domains to derive the IPO and PDO indices differ, and despite uncertainty related to trend removal and time-filtering ( [[#Newman--2016|Newman et al., 2016]] ; [[#Tung--2019|Tung et al., 2019]] ), the IPO and PDO are highly correlated in time and they will be assessed together as the PDV (Annex IV, Section AIV.2.6).

The AR5 assessed that near-term predictions of PDV (then referred to as PDO or IPO) were largely model dependent ( [[#Mochizuki--2012|Mochizuki et al., 2012]] ; [[#van%20Oldenborgh--2012|van Oldenborgh et al., 2012]] ), not robust to sampling of initialization start-dates, overall not statistically significant, and worse than persistence ( [[#Doblas-Reyes--2013|Doblas-Reyes et al., 2013]] ), although some studies showed positive skill for PDV ( [[#Mochizuki--2010|Mochizuki et al., 2010]] ; [[#Chikamoto--2013|Chikamoto et al., 2013]] ). The CMIP5 decadal-prediction ensemble yielded no prediction skill of SST over the key PDV centres of action in the Pacific Ocean, both at two-to-five-year and six-to-nine-year forecast averages ( [[#Doblas-Reyes--2013|Doblas-Reyes et al., 2013]] ; [[#Guemas--2013|Guemas et al., 2013]] ; [[#Boer--2019|Boer and Sospedra-Alfonso, 2019]] ).

Since AR5, the processes causing the multi-decadal variability in the Pacific Ocean have become better understood ( [[#Newman--2016|Newman et al., 2016]] ; [[#Henley--2017|Henley, 2017]] ). However, the relative importance oftropical and extratropical processes underlying PDV remains unclear; although it seems to be stochastically driven rather than self-excited ( [[#Liu--2012|Liu, 2012]] ; [[#Liu--2018|Liu and Di Lorenzo, 2018]] ), the South Pacific being a key region for the tropical branch of PDV ( [[#Chung--2019|Chung et al., 2019]] ; [[#Liguori--2019|Liguori and Di Lorenzo, 2019]] ).

Because PDV represents not one, but many dynamical processes, it represents a challenge as a target for near-term climate predictions and projections. The new generation of decadal forecast systems keeps showing poor ( [[#Shaffrey--2017|Shaffrey et al., 2017]] ) to moderate (D.M. [[#Smith--2019|]] [[#Smith--2019|Smith et al., 2019]] ) multi-year prediction skill for PDV, although the potential for forecasting capabilities is demonstrated in case studies ( [[#Meehl--2012|Meehl and Teng, 2012]] ; [[#Meehl--2014|Meehl et al., 2014]] ). For the near-term, a transition of PDV from the negative phase (1999–2012) towards a positive phase is predicted in the coming years (2013–2022; [[#Meehl--2016|Meehl et al., 2016]] ).

The PDV has been shown to influence the pace of global warming ( [[IPCC:Wg1:Chapter:Chapter-3#cross-chapter-box-3.1|Cross-Chapter Box 3.1]] ), but the extent to which PDV is externally forced or internally generated ( [[#Mann--2020|Mann et al., 2020]] ) remains an open question, and there is still no robust evidence. Thus, there is ''low confidence'' on how the PDV will evolve in the near-term ( [[#Bordbar--2019|Bordbar et al., 2019]] ).

<div id="4.4.3.6" class="h3-container"></div>

<span id="atlantic-multi-decadal-variability"></span>