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

=== 3.7.6 Pacific Decadal Variability ===

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Pacific Decadal Variability (PDV) is the generic term for the modes of variability in the Pacific Ocean that vary on decadal to inter-decadal time scales. PDV and its related teleconnections encompass the Pacific Decadal Oscillation (PDO; [[#Mantua--1997|Mantua et al., 1997]] ; [[#Zhang--1997|Zhang et al., 1997]] ; [[#Mantua--2002|Mantua and Hare, 2002]] ), and an anomalous SST pattern in the North Pacific, as well as a broader structure associated with Pacific-wide SSTs termed the Inter-decadal Pacific Oscillation (IPO; [[#Power--1999|Power et al., 1999]] ; [[#Folland--2002|Folland et al., 2002]] ; [[#Henley--2015|Henley et al., 2015]] ). Since the PDO and IPO indices are highly correlated, this section assesses them together as the PDV (Annex IV.2.6).

AR5 mentioned an overall ''limited'' level of evidence for both CMIP3 and CMIP5 evaluation of the Pacific modes at inter-decadal time scales, leading to ''low confidence'' statements about the models’ performance in reproducing PDV ( [[#Flato--2013|Flato et al., 2013]] ) and similarly ''low confidence'' in the attribution of observed PDV changes to human influence ( [[#Bindoff--2013|Bindoff et al., 2013]] ).

The implication of PDV in the observed slowdown of the GMST warming rate in the early 2000s (Cross-Chapter Box 3.1) has triggered considerable research on decadal climate variability and predictability since AR5 ( [[#Meehl--2013|Meehl et al., 2013]] , 2016b; [[#England--2014|England et al., 2014]] ; [[#Dai--2015|Dai et al., 2015]] ; [[#Steinman--2015|Steinman et al., 2015]] ; [[#Kosaka--2016|Kosaka and Xie, 2016]] ; [[#Cassou--2018|Cassou et al., 2018]] ). Many studies find that the broad spatial characteristics of PDV are reasonably well represented in unforced climate models ( [[#Newman--2016|Newman et al., 2016]] ; [[#Henley--2017|Henley, 2017]] ) and in historical simulations in CMIP5 and CMIP6 (Figure 3.39), although there is sensitivity to the methodology used to remove the externally-forced component of the SST ( [[#Bonfils--2011|Bonfils and Santer, 2011]] ; [[#Xu--2018|Xu and Hu, 2018]] ). Compared with CMIP3 models, CMIP5 models exhibit overall slightly better performance in reproducing PDV and associated teleconnections ( [[#Polade--2013|Polade et al., 2013]] ; [[#Joshi--2017|Joshi and Kucharski, 2017]] ), and also smaller inter-model spread ( [[#Lyu--2016|Lyu et al., 2016]] ). CMIP6 models on average show slightly improved reproduction of the PDV spatial structure than CMIP5 models (Figure 3.39a–c; [[#Fasullo--2020|Fasullo et al., 2020]] ). SST anomalies in the subtropical South Pacific lobe are, however, too weak relative to the equatorial and North Pacific lobes in CMIP5 pre-industrial control and historical simulations ( [[#Henley--2017|Henley et al., 2017]] ), a bias that remains in CMIP6 (Figure 3.39b).

Biases in the PDV temporal properties and amplitude are present in CMIP5 ( [[#Cheung--2017|Cheung et al., 2017]] ; [[#Henley--2017|Henley, 2017]] ). While model evaluation is severely hampered by short observational records and incomplete observational coverage before satellite measurements started, the duration of PDV phases appears to be shorter in coupled models than in observations, and correspondingly the ratio of decadal to interannual variance is underestimated (Figure 3.39e,f; [[#Henley--2017|Henley et al., 2017]] ). This apparent bias may be associated with overly biennial behaviour of Pacific trade wind variability and related ENSO activity, leaving too weak variability on decadal time scales ( [[#Kociuba--2015|Kociuba and Power, 2015]] ). ENSO influence on the extratropical North Pacific Ocean at decadal time scales is also very diverse among both CMIP3 and CMIP5 models, being controlled by multiple factors ( [[#Nidheesh--2017|Nidheesh et al., 2017]] ). In terms of amplitude, the variance of the PDV index after decadal filtering is significantly weaker in the concatenated CMIP5 ensemble than the three observational estimates used in Figure 3.39e ( ''p'' &lt;0.1 with an F-test). Consequently, the observed PDV fluctuations over the historical period often lie in the tails of the model distributions (Figure 3.39e,f). Even if one cannot rule out that the observed PDV over the instrumental era represents an exceptional period of variability, it is plausible that the tendency of the CMIP5 models to systematically underestimate the low frequency variance is due to an incomplete representation of decadal-scale mechanisms in these models. This situation is slightly improved in CMIP6 historical simulations but remains a concern ( [[#Fasullo--2020|Fasullo et al., 2020]] ). The results of [[#McGregor--2018|McGregor et al. (2018)]] suggest that the under-representation of the variability stems from Atlantic mean SST biases ( [[#3.5.1.2.2|Section 3.5.1.2.2]] ) through inter-basin coupling.

While PDV is primarily understood as an internal mode of variability ( [[#Si--2017|Si and Hu, 2017]] ), there are some indications that anthropogenically induced SST changes project onto PDV and have contributed to its past evolution ( [[#Bonfils--2011|Bonfils and Santer, 2011]] ; [[#Dong--2014a|Dong et al., 2014a]] ; [[#Boo--2015|Boo et al., 2015]] ; [[#Xu--2018|Xu and Hu, 2018]] ). However, the level of evidence is ''limited'' because of the difficulty in correctly separating internal versus externally forced components of the observed SST variations, and because it is unclear whether the dynamics of the PDV are operative in this forced SST change pattern. Over the last two to three decades which encompass the period of slower GMST increase (Cross-Chapter Box 3.1), [[#Smith--2016|Smith et al. (2016)]] found that anthropogenic aerosols have driven part of the PDV change toward its negative phase. A similar result is shown in [[#Takahashi--2016|Takahashi and Watanabe (2016)]] who found intensification of the Pacific Walker circulation in response to aerosol forcing ( [[#3.3.3.1.2|Section 3.3.3.1.2]] ). Indeed, CMIP6 models simulate a negative PDV trend since the 1980s (Figure 3.39f), which is much weaker than internal variability. However, a response to anthropogenic aerosols is not robustly identified in a large ensemble of a model ( [[#Oudar--2018|Oudar et al., 2018]] ), across CMIP5 models ( [[#Hua--2018|Hua et al., 2018]] ), or in idealized model simulations ( [[#Kuntz--2016|Kuntz and Schrag, 2016]] ). Alternatively, inter-basin teleconnections associated with the warming of the North Atlantic Ocean related to the mid-1990s phase shift of the AMV ( [[#McGregor--2014|McGregor et al., 2014]] ; [[#Chikamoto--2016|Chikamoto et al., 2016]] ; [[#Kucharski--2016|Kucharski et al., 2016]] ; X. [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] a; [[#Ruprich-Robert--2017|Ruprich-Robert et al., 2017]] ), and also warming in the Indian Ocean ( [[#Luo--2012|Luo et al., 2012]] ; [[#Mochizuki--2016|Mochizuki et al., 2016]] ), could have favoured a PDV transition to its negative phase in the 2000s. Considering the possible influence of external forcing on Indian Ocean decadal variability ( [[#3.7.4|Section 3.7.4]] ) and AMV ( [[#3.7.7|Section 3.7.7]] ), any such human influence on PDV would be indirect through changes in these ocean basins, and then imported to the Pacific via inter-basin coupling. However, this human influence on AMV, and how consistently such inter-basin processes affect PDV phase shifts, are uncertain. Other modelling studies find that anthropogenic aerosols can influence the PDV ( [[#Verma--2019|Verma et al., 2019]] ; [[#Amiri-Farahani--2020|Amiri-Farahani et al., 2020]] ; [[#Dow--2020|Dow et al., 2020]] ). It is however unclear whether and how much those forcings contributed to the observed variations of PDV. In CMIP6 models, the temporal correlation of the multi-model ensemble mean PDV index with its observational counterpart is insignificant and negligible (Figure 3.39f), suggesting that any externally-driven component in historical PDV variations was weak. Lastly, the multi-model ensemble mean computed from CMIP6 historical simulations shows slightly stronger variation than the CMIP5 counterpart, suggesting a greater simulated influence from external forcings in CMIP6. Still, the fraction of the forced signal to the total PDV is very low (Figure 3.39f), in contrast to AMV ( [[#3.7.7|Section 3.7.7]] ). Consistently, [[#Liguori--2020|Liguori et al. (2020)]] estimate that the variance fraction of the externally-driven to total PDV is up to only 15% in a multi-model large ensemble of historical simulations. These findings support an assessment that PDV is mostly driven by internal variability since the pre-industrial era. The sensitivity of ensemble-mean PDV trends to the ensemble size ( [[#Oudar--2018|Oudar et al., 2018]] ), and the dominance of the ensemble spread over the ensemble mean in the 60-year trend of the equatorial Pacific zonal SST gradient in large ensemble simulations ( [[#Watanabe--2021|Watanabe et al., 2021]] ), also support this statement.

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[[File:f624a371b10fb678d8209889f1f1964c IPCC_AR6_WGI_Figure_3_39.png]]

Figure 3.39 | '''Model evaluation of the Pacific Decadal Variability (PDV). (a, b)''' Sea surface temperature (SST) anomalies (°C) regressed onto the Tripole Index (TPI; [[#Henley--2015|Henley et al., 2015]] ) for 1900–2014 in '''(a)''' ERSST version 5 and '''(b)''' CMIP6 multi-model ensemble (MME) mean composite obtained by weighting ensemble members by the inverse of the model ensemble size. A 10-year low-pass filter was applied beforehand. Cross marks in (a) represent regions where the anomalies are not significant at the 10% level based on a t-test. Diagonal lines in (b) indicate regions where less than 80% of the runs agree in sign. '''(c)''' A Taylor diagram summarizing the representation of the PDV pattern in CMIP5 (each ensemble member is shown as a cross in light blue, and the weighted multi-model mean as a dot in dark blue), CMIP6 (each ensemble member is shown as a cross in red, and the weighted multi-model mean as a dot in orange) and observations over 40°S–60°N and 110°E–70°W. The reference pattern is taken from ERSST version 5 and black dots indicate other observational products: Hadley Centre Sea Ice and Sea Surface Temperature data set version 1 (HadISST version 1) and Centennial in situ Observation-Based Estimates of Sea Surface Temperature version 2 (COBE-SST2). '''(d)''' Autocorrelation of unfiltered annual TPI at lag one year and 10-year low-pass filtered TPI at lag 10 years for observations over 1900–2014 (horizontal lines), 115-year chunks of pre-industrial control simulations (open boxes) and individual historical simulations over 1900–2014 (filled boxes) from CMIP5 (blue) and CMIP6 (red). '''(e)''' As in (d), but showing standard deviation of the unfiltered and filtered TPI (°C). Boxes and whiskers show weighted multi-model means, interquartile ranges and 5th and 95th percentiles. '''(f)''' Time series of the 10-year low-pass filtered TPI (°C) in ERSST version 5, HadISST version 1 and COBE-SST2 observational estimates (black) and CMIP5 and CMIP6 historical simulations. The thick red and light blue lines are the weighted multi-model mean for the historical simulations in CMIP5 and CMIP6, respectively, and the envelopes represent the 5th–95th percentile ranges across ensemble members. The 5–95% confidence interval for the CMIP6 multi-model mean is given in thin dashed lines. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

In CMIP5 last millennium simulations, there is no consistency in temporal variations of PDV across the ensemble ( [[#Fleming--2016|Fleming and Anchukaitis, 2016]] ). This supports the notion that PDV is internal in nature. However, this issue remains difficult to assess because paleoclimate reconstructions of PDV have too poor a level of agreement for a rigorous model evaluation in past millennia ( [[#Henley--2017|Henley, 2017]] ).

To conclude, there is ''high confidence'' that internal variability has been the main driver of the PDV since pre-industrial times, despite some modelling evidence for potential external influence. This assessment is supported by studies based on large ensemble simulations that found the dominance of internally-driven PDV, and the CMIP6-based assessment (Figure 3.39). As such, PDV is an important driver of decadal internal climate variability which limits detection of human influence on various aspects of decadal climate change on global to regional scales ( ''high confidence'' ). Model evaluation of PDV is hampered by short observational records, spatial incompleteness of observations before the satellite observation era, and poor agreement among paleoclimate reconstructions. Despite the limitations of these model-observation comparisons, CMIP5 models, on average, simulate broadly realistic spatial structures of the PDV, but with a clear bias in the South Pacific ( ''medium confidence'' ). CMIP5 models also ''very'' ''likely'' underestimate PDV magnitude. CMIP6 models tend to show better overall performance in spatial structure and magnitude of PDV, but there is ''low confidence'' in this assessment due to the lack of literature.

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