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

=== 3.7.1 North Atlantic Oscillation and Northern Annular Mode ===

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The Northern Annular Mode (NAM; also known as the Arctic Oscillation) is an oscillation of atmospheric mass between the Arctic and northern mid-latitudes, analogous to the Southern Annular Mode (SAM; [[#3.7.2|Section 3.7.2]] ). It is the leading mode of variability of sea-level pressure in the northern extratropics but also has a clear fingerprint through the troposphere up to the lower stratosphere, with maximum expression in boreal winter ( [[#Kidston--2015|Kidston et al., 2015]] ). The North Atlantic Oscillation (NAO) can be interpreted as the regional expression of the NAM and captures most of the related variance in the troposphere over a broad North Atlantic/Europe domain. Indices measuring the state of the NAO correlate highly with those of the NAM, and teleconnection patterns for both modes are rather similar ( [[#Feldstein--2006|Feldstein and Franzke, 2006]] ). A detailed description of the NAM and the NAO as well as their associated teleconnection over land is given in Annex IV.2.1.

AR5 found that while models simulated correctly most of the spatial properties of the NAM, substantial inter-model differences remained in the details of the associated teleconnection patterns over land ( [[#Flato--2013|Flato et al., 2013]] ). The AR5 reported that most models did not reproduce the observed positive trend of the NAO/NAM indices during the second half of the 20th century. It was unclear to what extent this failure reflected model shortcomings and/or if the observed trend could be simply related to pronounced internal climate variability. The AR5 accordingly did not make an attribution assessment for the NAO/NAM.

New studies since AR5 continue to find that CMIP5 models reproduce the spatial structure and magnitude of the NAM reasonably well ( [[#Lee--2013|Lee and Black, 2013]] ; [[#Zuo--2013|Zuo et al., 2013]] ; [[#Davini--2014|Davini and Cagnazzo, 2014]] ; [[#Ying--2014|Ying et al., 2014]] ; [[#Ning--2016|Ning and Bradley, 2016]] ; [[#Deser--2017b|Deser et al., 2017b]] ; [[#Gong--2017|Gong et al., 2017]] ) although the North Pacific SLP anomalies remain generally too strong ( [[#Zuo--2013|Zuo et al., 2013]] ; [[#Gong--2017|Gong et al., 2017]] ) and the subtropical North Atlantic lobe of SLP anomalies conversely too weak ( [[#Ning--2016|Ning and Bradley, 2016]] ) in many models. Such overall biases noted in both CMIP3 and CMIP5 ( [[#Davini--2014|Davini and Cagnazzo, 2014]] ) persist in CMIP6 historical simulations, even though the multi-model multi-member ensemble mean spatial correlation between modelled and observed NAM is slightly higher (Figure 3.33a,d,g). Regarding the NAO, the majority of CMIP5 models very successfully simulate its spatial structure ( [[#Lee--2019|Lee et al., 2019]] ) and its associations with extratropical jet, storm track and blocking variations over a broad North-Atlantic/Europe domain ( [[#Davini--2014|Davini and Cagnazzo, 2014]] ) and over land through teleconnections ( [[#Volpi--2020|Volpi et al., 2020]] ). The good performance of the models is confirmed in CMIP6 with a marginal improvement of the averaged observation-model spatial correlation (Figure 3.33b,e,h) and better skill based on other evaluation metrics ( [[#Fasullo--2020|Fasullo et al., 2020]] ). The slight underestimation of the SLP anomalies related to the NAO centres of actions over the Azores and Greenland–Iceland–Norwegian Seas remain unchanged compared to CMIP5.

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[[File:b133ef1b4fb9fc8d2307ebd86e869aa9 IPCC_AR6_WGI_Figure_3_33.png]]

Figure 3.33 | '''Model evaluation of NAM, NAO and SAM in boreal winter.''' Regression of Mean Sea Level Pressure (MSLP) anomalies (in hPa) onto the normalized principal component (PC) of the leading mode of variability obtained from empirical orthogonal decomposition of the boreal winter (December–February) MSLP poleward of 20°N for the observed Northern Annular Mode '''(NAM, a)''' , over 20°N–80°N, 90°W–40°E for the North Atlantic Oscillation as shown by the black sector '''(NAO, b)''' , and poleward of 20°S for the Southern Annular Mode '''(SAM, c)''' for the JRA-55 reanalysis. Cross marks indicate regions where the anomalies are not significant at the 10% level based on a t-test. The period used to calculate the NAO/NAM is 1958–2014 but 1979–2014 for the SAM. '''(d–f)''' Same but for the multi-model ensemble (MME) mean from CMIP6 historical simulations. Models are weighted in compositing to account for differences in their respective ensemble size. Diagonal lines show regions where less than 80% of the runs agree in sign. '''(g–i)''' Taylor diagrams summarizing the representation of the modes in models and observations following [[#Lee--2019|Lee et al. (2019)]] for CMIP5 (light blue) and CMIP6 (red) historical simulations. The reference pattern is taken from JRA-55 (a–c). The ratio of standard deviation to that of the reference observations (radial distance), spatial correlation (radial angle) and resulting root-mean-squared errors (solid isolines) are given for individual ensemble members (crosses) and for other observational products (ERA5 and NOAA 20CR version 3, black dots). Coloured dots stand for weighted multi-model mean statistics for CMIP5 (blue) and CMIP6 (light red) as well as for AMIP simulations from CMIP6 (orange). '''(j–l)''' Histograms of the trends built from all individual ensemble members and all the models (brown bars). Vertical lines in black show all the observational estimates. The orange, light red, and light blue lines indicate the weighted multi-model mean of CMIP6 AMIP, CMIP6 and CMIP5 historical simulations, respectively. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

CMIP5 models with a model top within the stratosphere seriously underestimate the amplitude of the variability of the wintertime NAM expression in the stratosphere, in contrast to CMIP5 models which extend well above the stratopause ( [[#Lee--2015|Lee and Black, 2015]] ). However, even in the latter models, the stratospheric NAM events, and their downward influence on the troposphere, are insufficiently persistent ( [[#Charlton-Perez--2013|Charlton-Perez et al., 2013]] ; [[#Lee--2015|Lee and Black, 2015]] ). Increased vertical resolution does not show any significant added value in reproducing the structure and magnitude of the tropospheric NAM ( [[#Lee--2013|Lee and Black, 2013]] ) nor in the NAO predictability as assessed in a seasonal prediction context with a multi-model approach ( [[#Butler--2016|Butler et al., 2016]] ). On the other hand, there is mounting evidence that a correct representation of the Quasi Biennal Oscillation, extratropical stratospheric dynamics (the polar vortex and sudden stratospheric warmings), and related troposphere-stratosphere coupling, as well as their interplay with ENSO, are important for NAO/NAM timing ( [[#Scaife--2016|Scaife et al., 2016]] ; [[#Karpechko--2017|Karpechko et al., 2017]] ; [[#Domeisen--2019|Domeisen, 2019]] ; [[#Domeisen--2019|Domeisen et al., 2019]] ), in spite of underestimated troposphere–stratosphere coupling found in models compared to observations ( [[#O’Reilly--2019b|O’Reilly et al., 2019b]] ).

The observed trend of the NAM and NAO indices is positive in winter when calculated from the 1960s ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.1.1|Section 2.4.1.1]] ) but it includes large multi-decadal variability, which means that the nature of the trend should be interpreted with caution ( [[#Gillett--2013|Gillett et al., 2013]] ). The multi-model multi-member ensemble mean of the trend estimated from historical simulations over that period is very close to zero for both CMIP5 and CMIP6 (Figures 3.33j,k and 3.34a). Even if one cannot rule out that 1958–2014 was an exceptional period of variability, the observational estimates of the wintertime NAO trend lie outside the 5th–95th percentile range of the distribution of trends in the CMIP6 historical simulations, and the observed NAM trends over the same period lie above the 90th percentile. There is a tendency for the CMIP5 models to systematically underestimate the level of multi-decadal versus interannual variability of the winter NAO and jet stream compared to observations (X. [[#Wang--2017|]] [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] ; [[#Bracegirdle--2018|Bracegirdle et al., 2018]] ; [[#Simpson--2018|Simpson et al., 2018]] ). Results from CMIP6 (Figure 3.33j,k) and over the 1958–2019 period (Figure 3.34a) confirm this conclusion and seriously question the ability of the models to simulate long-term fluctuations of the NAO/NAM, independently of its forced or internal origins.

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[[File:0b97dbd737fa427f5e11d81685ab718a IPCC_AR6_WGI_Figure_3_34.png]]

Figure 3.34 | '''Attribution of observed seasonal trends in the annular modes to forcings.''' Simulated and observed trends in NAM indices over 1958–2019 '''(a)''' and in SAM indices over 1979–2019 '''(b)''' and over 2000–2019 '''(c)''' for boreal winter (December–February average; DJF) and summer (June–August average; JJA). The indices are based on the difference of the normalized zonally averaged monthly mean sea level pressure between 35°N and 65°N for the NAM and between 40°S and 65°S for the SAM as defined in [[#Jianping--2003|Jianping and Wang (2003)]] and [[#Gong--1999|Gong and Wang (1999)]] , respectively; the unit is decade <sup>–</sup> <sup>1</sup> . Ensemble mean, interquartile ranges and 5th and 95th percentiles are represented by empty boxes and whiskers for pre-industrial control simulations and historical simulations. The number of ensemble members and models used for computing the distribution is given in the upper-left legend. Grey lines show observed trends from the ERA5 and JRA-55 reanalyses. Multi-model multi-member ensemble means of the forced component of the trends as well as their 5–95% confidence intervals assessed from t-statistics, are represented by filled boxes, based on CMIP6 individual forcing simulations from DAMIP ensembles; greenhouse gases in brown, aerosols in light blue, stratospheric ozone in purple and natural forcing in green. Models with at least three ensemble members are used for the filled boxes, with black dots representing the ensemble means of individual models. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Dedicated SST-forced stand-alone atmospheric model experiments (AMIP) suggest that ocean forcing appears to play a role in decadal variability of the NAO and associated fluctuations in the strength of the jet ( [[#Woollings--2015|Woollings et al., 2015]] ). In particular, Atlantic and Indian Ocean SST anomalies ( [[#Fletcher--2015|Fletcher and Cassou, 2015]] ; [[#Baker--2019|Baker et al., 2019]] ; [[#Douville--2019|Douville et al., 2019]] ; [[#Dhame--2020|Dhame et al., 2020]] ) may have contributed to the long-term positive trend of the winter NAO/NAM over the 20th century, but there is only ''low confidence'' in such a causal relationship because of the limitation of the imposed SST approach in AMIP and the uncertainties in observed SST trends among datasets used as forcing of the atmospheric model. The representation of the NAM and NAO spatial structure is slightly improved in AMIP ensembles (Figure 3.33g,h), which also produce slightly larger trends than the historical simulations for the NAO, but not for the NAM.

When calculated over the most recent two decades, the wintertime NAM/NAO trend is weakly negative since the mid-1990s ( [[#Hanna--2015|Hanna et al., 2015]] ). Recent studies based on observations ( [[#Gastineau--2015|Gastineau and Frankignoul, 2015]] ) and dedicated modelling experiments ( [[#Davini--2015|Davini et al., 2015]] ; [[#Peings--2016|Peings and Magnusdottir, 2016]] ) suggest that the recent dominance of negative NAM/NAO could be partly related to the latest shift of the Atlantic Multi-decadal Variability (AMV) to a warm phase (Sections 2.4.4 and 3.7.7). Some recent modelling studies also find that the Arctic sea ice decline might be partly responsible for more recurrent negative NAM/NAO ( [[#Peings--2013|Peings and Magnusdottir, 2013]] ; B.M. [[#Kim--2014|]] [[#Kim--2014|Kim et al., 2014]] ; [[#Nakamura--2015|Nakamura et al., 2015]] ), while other studies do not robustly identify such responses in models (see also Cross-Chapter Box 10.1).

In contrast to winter, the observed trend of the NAO index over 1958–2014 is overall negative in summer and is associated with more recurrent blocking conditions over Greenland, in particular since the mid-1990s, thus contributing to the acceleration of melting of the Arctic sea ice ( [[#3.4.1.1|Section 3.4.1.1]] ) and Greenland Ice Sheet ( [[#3.4.3.2|Section 3.4.3.2]] ; [[#Fettweis--2013|Fettweis et al., 2013]] ; [[#Hanna--2015|Hanna et al., 2015]] ; [[#Ding--2017|Ding et al., 2017]] ). The origin of the negative trend of the summer NAO has not been clearly identified, and is hypothesized to be the result of combined influences ( [[#Lim--2019|Lim et al., 2019]] ), though trends in summertime NAO should also be interpreted with caution because of the presence of strong multi-decadal variability. The recent observed negative NAO prevalence and related blocking over Greenland is not present in any of the CMIP5 models ( [[#Hanna--2018|Hanna et al., 2018]] ).

Regarding the influence of external forcings since pre-industrial times, AR5 noted that CMIP5 models tend to show an increase in the NAM in response to greenhouse gas increases ( [[#Bindoff--2013|Bindoff et al., 2013]] ). Based on the CMIP5 historical ensemble, [[#Gillett--2013|Gillett and Fyfe (2013)]] however showed that such a trend is not significant in all seasons. A multi-model assessment of eight CMIP5 models found a NAM increase in response to greenhouse gases, but no robust influence of aerosol changes ( [[#Gillett--2013|Gillett et al., 2013]] ). As for ozone depletion, there is no robust detectable influence on long-term trends of the NAO/NAM ( [[#Karpechko--2018|Karpechko et al., 2018]] ) in contrast to the SAM ( [[#3.7.2|Section 3.7.2]] ), but there are indications that extreme Arctic ozone depletion events and their surface expression are linked to an anomalously strong NAM episodes ( [[#Calvo--2015|Calvo et al., 2015]] ; [[#Ivy--2017|Ivy et al., 2017]] ). However, the direction of causality here is not clear.

Conclusions on external forcing influences on the NAM are supported by CMIP6 results based on single forcing ensembles (Figure 3.34a). Positive trends are found in historical simulations over 1958–2019 in boreal winter and are mainly driven by greenhouse gas increases. No significant trends are simulated in response to anthropogenic aerosols, stratospheric ozone or natural forcing. Albeit weak and not statistically significant, the sign of the multi-model mean forced response due to natural forcing is consistent with the observed reduction of solar activity since the 1980s ( [[IPCC:Wg1:Chapter:Chapter-2#2.2.1|Section 2.2.1]] ) whose influence would have favoured the negative phase of wintertime NAM/NAO based on the fingerprint of the nearly periodical 11-year solar cycle extracted from models ( [[#Scaife--2013|Scaife et al., 2013]] ; [[#Andrews--2015|Andrews et al., 2015]] ; [[#Thiéblemont--2015|Thiéblemont et al., 2015]] ) or observations ( [[#Gray--2016|Gray et al., 2016]] ; [[#Lüdecke--2020|Lüdecke et al., 2020]] ). But such an NAO response to solar forcing remains highly uncertain and controversial, being contradicted by longer proxy records over the last millennium ( [[#Sjolte--2018|Sjolte et al., 2018]] ) and modelling evidence ( [[#Gillett--2013|Gillett and Fyfe, 2013]] ; [[#Chiodo--2019|Chiodo et al., 2019]] ). For all seasons and for all individual forcings, uncertainties remain in the estimation of the forced response in the NAM trend as evidenced by considerable model spread (Figure 3.34a) and because the simulated forced component has small amplitude compared to internal variability.

Despite new efforts since AR5 to reconstruct the NAO beyond the instrumental record, it is still very challenging to assess the role of external forcings in the apparent multi-decadal to centennial variability present throughout the last millennium. Large uncertainties remain in the reconstructed NAO index that are sensitive to the types of proxies and statistical methods ( [[#Trouet--2012|Trouet et al., 2012]] ; [[#Ortega--2015|Ortega et al., 2015]] ; [[#Anchukaitis--2019|Anchukaitis et al., 2019]] ; [[#Cook--2019|Cook et al., 2019]] ; [[#Hernández--2020|Hernández et al., 2020]] ; [[#Michel--2020|Michel et al., 2020]] ) and reconstructed NAO variations are often not reproduced using pseudo-proxy approaches in models ( [[#Lehner--2012|Lehner et al., 2012]] ; [[#Landrum--2013|Landrum et al., 2013]] ). At low frequency, it remains challenging to evaluate if the observed or reconstructed signal corresponds to an actual change in the NAO intraseasonal to interannual intrinsic properties or rather to a change in the mean background atmospheric circulation changes projecting on a specific phase of the mode. Consequently, conflicting results emerge in the attribution of reconstructed long-term variations in the NAO to solar forcing, whose influence thus remains controversial ( [[#Gómez-Navarro--2013|Gómez-Navarro and Zorita, 2013]] ; [[#Moffa-Sánchez--2014|Moffa-Sánchez et al., 2014]] ; [[#Ortega--2015|Ortega et al., 2015]] ; [[#Ait%20Brahim--2018|Ait Brahim et al., 2018]] ; [[#Sjolte--2018|Sjolte et al., 2018]] ; [[#Xu--2018|Xu et al., 2018]] ). Influences from major volcanic eruptions appear to be more robust ( [[#Ortega--2015|Ortega et al., 2015]] ; [[#Swingedouw--2017|Swingedouw et al., 2017]] ) even if some modelling experiments question the amplitude of the response, which mostly projects on the positive phase of the NAM/NAO ( [[#Bittner--2016|Bittner et al., 2016]] ). The forced response is dependent on the strength, seasonal timing and location of the eruption but may also depend on the mean climate background state ( [[#Zanchettin--2013|Zanchettin et al., 2013]] ) and/or the phases of the main modes of decadal variability such as the AMV ( [[#3.7.7|Section 3.7.7]] ; [[#Ménégoz--2018|Ménégoz et al., 2018]] ).

Finally, there is some evidence of an apparent signal-to-noise problem referred to as ‘paradox’ in seasonal and decadal hindcasts of the NAO over the period 1979–2018 ( [[#Scaife--2018|Scaife and Smith, 2018]] ), which suggests that the NAO response to external forcing, SST or sea ice anomalies could be too weak in models. The weakness of the signal has been related to troposphere-stratosphere coupling which is too intermittent ( [[#O’Reilly--2019b|O’Reilly et al., 2019b]] ) and to chronic model biases in the persistence of NAO/NAM daily regimes, which is critically underestimated in coupled models ( [[#Strommen--2019|Strommen and Palmer, 2019]] ; [[#Zhang--2019|Zhang and Kirtman, 2019]] ), and which does not exhibit significant improvement when model resolution is increased ( [[#Fabiano--2020|Fabiano et al., 2020]] ). Note, however, that the apparent signal-to-noise problem may be dependent on the period analysed over the 20th century, which questions its interpretation as a general characteristic of coupled models ( [[#Weisheimer--2020|Weisheimer et al., 2020]] ).

In summary, CMIP5 and CMIP6 models are skilful in simulating the spatial features and the variance of the NAM/NAO and associated teleconnections ( ''high confidence'' ). There is ''limited evidence'' for a significant role for anthropogenic forcings in driving the observed multi-decadal variations of the NAM/NAO from the mid 20th century. Confidence in attribution is ''low'' : (i) because there is a large spread in the modelled forced responses which is overwhelmed anyway by internal variability; (ii) because of the apparent signal-to-noise problem; and (iii) because of the chronic inability of models to produce a range of trends which encompasses the observed estimates over the last 60 years.

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