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

==== 4.4.3.6 Atlantic Multi-decadal Variability ====

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The Atlantic Multi-decadal Variability (AMV) is a large-scale climate mode accounting for the main fluctuations in North Atlantic SST on multi-decadal time scales (Section AIV.2.7). The AMV influences air temperatures and precipitation over adjacent and remote continents, and its undulations can partially explain the observed variations in the NH mean temperatures ( [[#Steinman--2015|Steinman et al., 2015]] ). The origin of this variability is still uncertain. Ocean dynamics (e.g., changes in the AMOC), external forcing, and local atmospheric forcing all seem to play a role ( [[#Menary--2015|Menary et al., 2015]] ; [[#Ruprich-Robert--2015|Ruprich-Robert and Cassou, 2015]] ; [[#Brown--2016|Brown et al., 2016]] ; [[#Cassou--2018|Cassou et al., 2018]] ; [[#Wills--2019|Wills et al., 2019]] ). Recent studies have discussed that the ocean dynamics play an active role in generating AMV ( [[#Oelsmann--2020|Oelsmann et al., 2020]] ) and its interplay with the NAO ( [[#Vecchi--2017|Vecchi et al., 2017]] ; R. [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] ; [[#Kim--2020|Kim et al., 2020]] ), although natural and anthropogenic external forcing might be crucial in modulating its amplitude and timing ( [[#Bellucci--2017|Bellucci et al., 2017]] ; [[#Bellomo--2018|Bellomo et al., 2018]] ; [[#Andrews--2020|Andrews et al., 2020]] ; Borchertet al., 2021; [[#Mann--2021|Mann et al., 2021]] ; see Sections 3.7.7 and AIV.2.7).

The AR5 assessed with high confidence that initialized predictions can improve the skill for temperature over the North Atlantic, in particular in the sub-polar branch of AMV, compared to the projections, for the first five years (see AR5 WGI Figures 11.3 and 11.4). However, non-initialized predictions showed positive correlation over the same time-range as well, consistent with the notion that part of this variability is caused by external forcing ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.7|Section 3.7.7]] ).

Since AR5, near-term initialized predictions, both multi-model ( [[#Bellucci--2015a|Bellucci et al., 2015a]] ; [[#García-Serrano--2015|García-Serrano et al., 2015]] ; D.M. [[#Smith--2019|]] [[#Smith--2019|Smith et al., 2019]] ) and single-model ensembles ( [[#Marotzke--2016|Marotzke et al., 2016]] ; [[#Simpson--2018|Simpson et al., 2018]] ; [[#Yeager--2018|Yeager et al., 2018]] ; Hermanson et al., 2020; [[#Bilbao--2021|Bilbao et al., 2021]] ), confirm substantial skill in hindcasting North Atlantic SST anomalies on a time range of eight to ten years. On the same time range, [[#Borchert--2021|Borchert et al. (2021)]] show a substantial improvement in the prediction of the subpolar gyre SST (the northern component of the AMV) in CMIP6 models compared to CMIP5, in both initialized and non-initialized simulations. The higher skill of CMIP6 models can be attributed to a more accurate response of SST variations in the subpolar gyre to natural forcing, possibly originating from the AMOC-related delayed response to volcanic eruptions ( [[#Hermanson--2020|Hermanson et al., 2020]] ).

Initialization contributes to the reduction of uncertainty and to predicting subpolar SST amplitude ( [[#Borchert--2021|Borchert et al., 2021]] ). Yet, skill in predicting the AMV is not always translated into equally successful predictions of temperature and precipitation over the nearby land and ocean regions ( [[#Langehaug--2017|Langehaug et al., 2017]] ). This might be related to systematic model errors in the simulation of the spatial and temporal structure of the AMV and too weak associated teleconnections ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.7|Section 3.7.7]] ), and also to the larger noise in regional land variables compared to the AMV index. However, AMV predictions can be used as proxies to predict other variables such as precipitation over Western Europe and Eurasia and SAT over Mediterranean, Northern Europe and north-east Asia ( [[#Årthun--2018|Årthun et al., 2018]] ; [[#Borchert--2019|Borchert et al., 2019]] ; [[#Ruggieri--2021|Ruggieri et al., 2021]] ) whose relationship with North Atlantic SST is robust in observations, but not well captured in climate models.

Encouraging results about the prediction of land precipitation linked to the warm AMV phase ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.7|Section 3.7.7]] and Annex IV, Figure AIV.2.7) on a two-to-nine-year time scale are reported in the multi-model study by D.M. [[#Smith--2019|]] [[#Smith--2019|Smith et al. (2019)]] . Positive correlations with observations are found in the Sahel, South America, the Maritime Continent. Analyses from large-ensemble decadal prediction systems such as the community Earth system model decadal prediction large ensemble (CESM-DPLE; [[#Yeager--2018|Yeager et al., 2018]] ) show an improvement with respect to the CMIP5 decadal hindcasts ( [[#Martin--2014b|Martin and Thorncroft, 2014b]] ) in forecasting Sahel precipitation over three to seven years, which is consistent with the current understanding of AMV impact over Africa ( [[#Mohino--2016|Mohino et al., 2016]] ; D.M. [[#Smith--2019|]] [[#Smith--2019|Smith et al., 2019]] ). CESM-DPLE predicts drought conditions over the Sahel through 2020, which is compatible with a shift towards a negative phase of AMV as a result of a weakening of the AMOC, advocated by a number of studies ( [[#Hermanson--2014|Hermanson et al., 2014]] ; [[#Robson--2014|Robson et al., 2014]] ; [[#Yeager--2015|Yeager et al., 2015]] ).

In summary, the ''confidence'' in the predictions of AMV and its effects is ''medium'' . However, there is ''high'' ''confidence'' that the AMV skill over five-to-eight-year lead times is improved by using initialized predictions, compared to non-initialized simulations.

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