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

=== 3.7.7 Atlantic Multi-decadal Variability ===

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Atlantic Multi-decadal Variability (AMV) refers to a climate mode representing basin-wide multi-decadal fluctuations in surface temperatures in the North Atlantic (Figure 3.40a,f), with teleconnections particularly pronounced over the adjacent continents and the Arctic. The AMV phenomenon is usually assessed through SST anomalies averaged over the entire North Atlantic basin, hereafter the AMV index, but it is associated with many physical processes including three-dimensional ocean circulation, such as AMOC fluctuations ( [[#3.5.4.1|Section 3.5.4.1]] ), gyre adjustments, and salt and heat transport in the entire North Atlantic and subarctic Atlantic basins. The AMV, together with the PDV, has been shown to have modulated GSAT on multi-decadal time scales since pre-industrial times (Cross-Chapter Box 3.1; T. [[#Wu--2019|Wu et al., 2019]] a; [[#Li--2020|Li et al., 2020]] ). A detailed description of the AMV as well as its associated teleconnection over land is given in Annex IV.2.7.

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[[File:638c52fbeddf9c608bea6a7a66b83f19 IPCC_AR6_WGI_Figure_3_40.png]]

Figure 3.40 | '''Model evaluation of Atlantic Multi-decadal Variability (AMV). (a, b)''' Sea surface temperature (SST) anomalies (°C) regressed onto the AMV index defined as the 10-year low-pass filtered North Atlantic (0°–60°N, 80°W–0°E) area-weighted SST* anomalies over 1900–2014 in '''(a)''' ERSST version 5 and '''(b)''' the CMIP6 multi-model ensemble (MME) mean composite obtained by weighting ensemble members by the inverse of each model’s ensemble size. The asterisk denotes that the global mean SST anomaly has been removed at each time step of the computation. 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) show regions where less than 80% of the runs agree in sign. '''(c)''' A Taylor diagram summarizing the representation of the AMV pattern in CMIP5 (each ensemble member is shown as a cross in light blue, and the weighted multi-model mean is shown as a dot in dark blue), CMIP6 (each ensemble member is shown as a cross in red, and the weighted multi-model mean is shown as a dot in orange) and observations over [0°–60°N, 80°W–0°E]. The reference pattern is taken from ERSST version 5 and black dots indicate other observational products (HadISST version 1 and COBE-SST2). '''(d)''' Autocorrelation of unfiltered annual AMV index at lag one year and 10-year low-pass filtered AMV index 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 AMV indices (°C). Boxes and whiskers show the weighted multi-model means, interquartile ranges and 5th and 95th percentiles. '''(f)''' Time series of the AMV index (°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 obtained from all ensemble members. The 5–95% confidence interval for the CMIP6 multi-model mean is shown by the thin dashed line. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

AR5 assessed, based on climate models, that the AMV was primarily internally-driven alongside some contribution from external forcings (mainly anthropogenic aerosols) over the late 20th century ( [[#Bindoff--2013|Bindoff et al., 2013]] ; [[#Flato--2013|Flato et al., 2013]] ). But AR5 also concluded that models show medium performance in reproducing the observed AMV, with difficulties in simulating the time scale, the spatial structure and the coherency between all the physical processes involved ( [[#Flato--2013|Flato et al., 2013]] ).

Climate models analysed since AR5 continue to simulate AMV-like variability as part of their internal variability. This statement is mostly based on CMIP5 pre-industrial control and historical simulations ( [[#Wouters--2012|Wouters et al., 2012]] ; [[#Schmith--2014|Schmith et al., 2014]] ; [[#Menary--2015|Menary et al., 2015]] ; [[#Ruprich-Robert--2015|Ruprich-Robert and Cassou, 2015]] ; [[#Brown--2016b|Brown et al., 2016b]] ; [[#Chen--2016|Chen et al., 2016]] ; [[#Kim--2018a|Kim et al., 2018a]] ) and is also true for the CMIP6 models ( [[#Menary--2018|Menary et al., 2018]] ; [[#Voldoire--2019b|Voldoire et al., 2019b]] ). Models also continue to support links to a wide array of remote climate influences through atmospheric teleconnections ( [[#Martin--2014|Martin et al., 2014]] ; [[#Ruprich-Robert--2017|Ruprich-Robert et al., 2017]] , 2018; [[#Monerie--2019|Monerie et al., 2019]] ; [[#Qasmi--2020|Qasmi et al., 2020]] ; [[#Ruggieri--2021|Ruggieri et al., 2021]] ). Even if debate remains ( [[#Clement--2015|Clement et al., 2015]] ; [[#Cane--2017|Cane et al., 2017]] ; [[#Mann--2020|Mann et al., 2020]] ), there is now stronger evidence for a crucial role of oceanic dynamics in internal AMV that is primarily linked to the AMOC and its interplay with the NAO ( [[#Zhang--2013a|Zhang et al., 2013a]] ; [[#Müller--2015|Müller et al., 2015]] ; [[#O’Reilly--2016b|O’Reilly et al., 2016b]] , 2019a; [[#Delworth--2017|Delworth et al., 2017]] ; [[#Zhang--2017|Zhang, 2017]] ; [[#Sun--2019|Sun et al., 2019]] ; [[#Kim--2020|Kim et al., 2020]] ). However, considerable diversity in the spatio-temporal properties of the simulated AMV is found in both pre-industrial control and historical CMIP5 experiments ( [[#Zhang--2013|Zhang and Wang, 2013]] ; [[#Wills--2019|Wills et al., 2019]] ). Such model diversity is presumably associated with the wide range of coupled processes associated with AMV ( [[#Baker--2017|Baker et al., 2017]] ; [[#Woollings--2018a|Woollings et al., 2018a]] ) including large-scale atmospheric teleconnections and regional feedbacks relating to tropical clouds, Arctic sea ice in the subarctic basins and Saharan dust, whose relative importance and interactions across time scales are specific to each model ( [[#Martin--2014|Martin et al., 2014]] ; [[#Brown--2016b|Brown et al., 2016b]] ).

Additional studies since AR5 corroborate that CMIP5-era models tend to underestimate many aspects of observed AMV and its SST fingerprint. On average, the duration of modelled AMV episodes is too short, the magnitude of AMV is too weak and its basin-wide SST spatial structure is limited by the poor representation of the link between the tropical North Atlantic and the subpolar North Atlantic/Nordic seas ( [[#Martin--2014|Martin et al., 2014]] ; [[#Qasmi--2017|Qasmi et al., 2017]] ). Such mismatches between observed and simulated AMV (Figure 3.40c–e) have been associated with intrinsic model biases in both mean state ( [[#Menary--2015|Menary et al., 2015]] ; [[#Drews--2016|Drews and Greatbatch, 2016]] ) and variability in the ocean and overlying atmosphere. For instance, compared to available observational data CMIP5 models underestimate the ratio of decadal to interannual variability of the main drivers of AMV, namely the AMOC, NAO and related North Atlantic jet variations ( [[#3.7.1|Section 3.7.1]] ; [[#Bracegirdle--2018|Bracegirdle et al., 2018]] ; [[#Kim--2018b|Kim et al., 2018b]] ; [[#Simpson--2018|Simpson et al., 2018]] ; [[#Yan--2018|Yan et al., 2018]] ), which has strong implications for the simulated temporal statistics of AMV, AMV-induced teleconnections ( [[#Ault--2012|Ault et al., 2012]] ; [[#Menary--2015|Menary et al., 2015]] ) and AMV predictability.

The increase of AMV variance in CMIP6 models (stronger magnitude and longer duration) seems to be explained by the enhanced variability in the subpolar North Atlantic SST (Figure 3.40b,c), which is particularly pronounced in some models, associated with greater variability in the AMOC ( [[#3.5.4.1|Section 3.5.4.1]] ; [[#Voldoire--2019a|Voldoire et al., 2019a]] ; [[#Boucher--2020|Boucher et al., 2020]] ) and greater GMST multi-decadal variability ( [[#3.3.1|Section 3.3.1]] and Figure 3.40c–f; [[#Voldoire--2019b|Voldoire et al., 2019b]] ; [[#Parsons--2020|Parsons et al., 2020]] ). The decadal variance in SST in the subpolar North Atlantic seems now to be slightly overestimated in CMIP6 compared to observational estimates, while the AMV-related tropical SST anomalies remain weaker in line with CMIP5 (Figure 3.40b). The mechanisms producing the tropical-extratropical relationship at decadal time scales remain poorly understood despite stronger evidence since AR5 for the importance of the subpolar gyre SST anomalies in generating tropical changes through atmospheric teleconnection ( [[#Caron--2015|Caron et al., 2015]] ; [[#Ruprich-Robert--2017|Ruprich-Robert et al., 2017]] ; [[#Kim--2020|Kim et al., 2020]] ). Significant discrepancies remain in the simulated AMV spatial pattern when historical simulations are compared to multivariate observations ( [[#Yan--2018|Yan et al., 2018]] ; [[#Robson--2020|Robson et al., 2020]] ).

There is additional evidence since AR5 that external forcing has been playing an important role in shaping the timing and intensity of the observed AMV since pre-industrial times ( [[#Bellomo--2018|Bellomo et al., 2018]] ; [[#Andrews--2020|Andrews et al., 2020]] ). The time synchronisation between observed and multi-model mean AMV SST indices is significant in both CMIP5 and CMIP6 historical simulations, while the explained variance of the forced response in CMIP6 appears stronger (Figure 3.40d–f). The competition between greenhouse gas warming and anthropogenic sulphate aerosol cooling has been proposed to be particularly important over the latter half of the 20th century ( [[#Booth--2012|Booth et al., 2012]] ; [[#Steinman--2015|Steinman et al., 2015]] ; [[#Murphy--2017|Murphy et al., 2017]] ; [[#Undorf--2018a|Undorf et al., 2018a]] ; [[#Haustein--2019|Haustein et al., 2019]] ). The latest observed AMV shift from the cold to the warm phase in the mid-1990s at the surface ocean is well captured in the CMIP6 forced component and may be associated with the lagged response to increased AMOC due to strong anthropogenic aerosol forcing over 1955–1985 ( [[#Menary--2020|Menary et al., 2020]] ) in combination with the rapid response through surface flux processes to declining aerosol forcing and increasing greenhouse gas influence since then. However, natural forcings may have also played a significant role. For instance, volcanic forcing has been shown to contribute in part to the cold phases of the AMV-related SST anomalies observed in the 20th century ( [[#Terray--2012|Terray, 2012]] ; [[#Bellucci--2017|Bellucci et al., 2017]] ; [[#Swingedouw--2017|Swingedouw et al., 2017]] ; [[#Birkel--2018|Birkel et al., 2018]] ). Over the last millennium, natural forcings including major volcanic eruptions and fluctuations in solar activity are thought to have driven a larger fraction of the multi-decadal variations in the AMV than in the industrial era, with some interplay with internal processes ( [[#Otterå--2010|Otterå et al., 2010]] ; [[#Knudsen--2014|Knudsen et al., 2014]] ; [[#Moffa-Sánchez--2014|Moffa-Sánchez et al., 2014]] ; J. [[#Wang--2017|]] [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] ; [[#Malik--2018|Malik et al., 2018]] ; [[#Mann--2021|Mann et al., 2021]] ), but other studies question the role of natural forcings over this period ( [[#Zanchettin--2014|Zanchettin et al., 2014]] ; [[#Lapointe--2020|Lapointe et al., 2020]] ).

Model evaluation of the AMV phenomenon remains difficult because of short observational records (especially of detailed process-based observations), the lack of stationarity in the variance, spatial patterns and frequency of the AMV assessed from modelled SST ( [[#Qasmi--2017|Qasmi et al., 2017]] ), difficulties in estimating the forced signals in both historical simulations and observations ( [[#Tandon--2015|Tandon and Kushner, 2015]] ), and because of probable interplay between internally and externally-driven processes ( [[#Watanabe--2019|Watanabe and Tatebe, 2019]] ). Furthermore, models simulate a large range of historical anthropogenic aerosol forcing ( [[#Smith--2020|Smith et al., 2020]] ) and questions often referred to as signal-to-noise paradox have been raised concerning the models’ ability to correctly simulate the magnitude of the response of AMV-related atmospheric circulation phenomena, such as the NAO ( [[#3.7.1|Section 3.7.1]] ), to both internally and externally generated changes ( [[#Scaife--2018|Scaife and Smith, 2018]] ). Related methodological and epistemological uncertainties also call into question the relevance of the traditional basin-average SST index to assessing the AMV phenomenon ( [[#Zanchettin--2014|Zanchettin et al., 2014]] ; [[#Frajka-Williams--2017|Frajka-Williams et al., 2017]] ; [[#Haustein--2019|Haustein et al., 2019]] ; [[#Wills--2019|Wills et al., 2019]] ).

To summarize, results from CMIP5 and CMIP6 models together with new statistical techniques to evaluate the forced component of modelled and observed AMV, provide ''robust evidence'' that external forcings have modulated AMV over the historical period. In particular, anthropogenic and volcanic aerosols are thought to have played a role in the timing and intensity of the negative (cold) phase of AMV recorded from the mid-1960s to mid-1990s and subsequent warming ( ''medium confidence'' ). However, there is ''low confidence'' in the estimated magnitude of the human influence. The limited level of confidence is primarily explained by difficulties in accurately evaluating model performance in simulating AMV. The evaluation is severely hampered by short instrumental records but also, equally importantly, by the lack of detailed and coherent long-term process-based observations (for example of the AMOC, aerosol optical depth, surface fluxes and cloud changes), which limit our process understanding. In addition, studies often rely solely on simplistic SST indices that may be hard to interpret ( [[#Zhang--2016|Zhang et al., 2016]] ) and may mask critical physical inconsistencies in simulations of the AMV compared to observations ( [[#Zhang--2017|Zhang, 2017]] ).

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