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

=== 4.4.3 Modes of Variability ===

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This subsection assesses the near-term evolution of the large-scale modes of climate variability. Assessment of the physical mechanisms and the individual feedbacks involved in the future change of each mode and their role on future regional climate variability are provided in Sections [[#8.4.2|8.4.2]] , [[#9.2.3|9.2.3]] and [[#10.1.3|10.1.3]] , and [[IPCC:Wg1:Chapter:Chapter-10#cross-chapter-box-10.1|Cross-Chapter Box 10.1]] .

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==== 4.4.3.1 Northern and Southern Annular Modes ====

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===== 4.4.3.1.1 The NorthernAnnular Mode =====

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The AR5 assessed from CMIP5 simulations that there is only ''medium confidence'' in near-term projections of a northward shift of NH storm track and westerlies, and an associated increase in the NAM index, because of the large response uncertainty and the potentially large influence of internal variability. A tendency in the near term towards a slightly more positive NAM in the three highest emissions scenarios during boreal fall, winter, and spring is apparent in Figure 4.17a. However, in general the projected near-term multi-model mean change in the NAM is small in magnitude compared to the inter-model and/or multi-realization variability within the ensemble (Figure 4.17a; [[#Deser--2012b|Deser et al., 2012b]] , 2017; [[#Barnes--2015|Barnes and Polvani, 2015]] ).

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[[File:8869dd2f6128b93d068ceef75f2e735c IPCC_AR6_WGI_Figure_4_17.png]]

'''Figure 4.17''' '''|''' '''CMIP6 Annular Mode index change (hPa) from 1995–2014 to 2021–2040. (a)''' Northern Annular Mode (NAM); '''(b)''' Southern Annular Mode (SAM). The NAM is defined as the difference in zonal mean sea level pressure (SLP) at 35°N and 65°N ( [[#Li--2003|Li and Wang, 2003]] ) and the SAM as the difference in zonal mean SLP at 40°S and 65°S ( [[#Gong--1999|Gong and Wang, 1999]] ). The shadings are the 5–95% ranges across the simulations. The numbers near the top of each panel are the numbers of model simulations in each SSP ensemble. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

On seasonal to interannual time scales, there is new evidence since AR5 that initialized predictions show lower potential predictability for the boreal winter NAO than the correlation skill with respect to observations ( [[#Eade--2014|Eade et al., 2014]] ; [[#Baker--2018|Baker et al., 2018]] ; [[#Scaife--2018|Scaife and Smith, 2018]] ; [[#Athanasiadis--2020|Athanasiadis et al., 2020]] ). This has been referred to in the literature as a ‘signal-to-noise paradox’ and means that very large ensembles of predictions are needed to isolate the predictable component of the NAO. While the processes that contribute to the predictability of the winter NAO on seasonal time scales may be distinct from the processes that drive multi-decadal trends, there is emerging evidence that initialized predictions also underrepresent the predictability of the winter NAO on decadal time scales (D.M. [[#Smith--2019|]] [[#Smith--2019|Smith et al., 2019]] ). Post-processing and aggregation of initialized predictions may therefore reveal significant skill for predicting the winter NAO on decadal time scales ( [[#Smith--2020|Smith et al., 2020]] ). Considering these new results since AR5, in the near-term it is ''likely'' that any anthropogenic forced signal in the NAM will be of comparable magnitude or smaller than natural internal variability in the NAM ( ''medium confidence'' ).

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===== 4.4.3.1.2 The Southern Annular Mode =====

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The AR5 assessed that it is ''likely'' that increases in GHGs and the projected recovery of the Antarctic ozone hole will be the principal drivers of future SAM trends. Additionally, the positive trend in austral summer/autumn SAM observed over the past several decades ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.1.2|Section 2.4.1.2]] ; [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] in AR5, [[#Hartmann--2013|Hartmann et al., 2013]] ), is ''likely'' to weaken considerably as stratospheric ozone recovers through to the mid-21st century. The effects of ozone depletion and recovery on the SH circulation primarily occur in austral summer, while GHGs influence the SH circulation year round ( [[#Gillett--2013|Gillett and Fyfe, 2013]] ; [[#Grise--2014b|Grise and Polvani, 2014b]] ). Therefore, they are ''likely'' to be the dominant driver of projected circulation changes outside of austral summer ( [[#Gillett--2013|Gillett and Fyfe, 2013]] ; [[#Barnes--2014|Barnes et al., 2014]] ; [[#Solomon--2016|Solomon and Polvani, 2016]] ). Based on current scenarios specifying future atmospheric decline of ozone depleting substances ( [[#WMO--2011|WMO, 2011]] ), chemistry-climate models project the Antarctic ozone hole in October to recover by around 2060 ( [[#WMO--2014|WMO, 2014]] , 2018; [[#Dhomse--2018|Dhomse et al., 2018]] ). Observational evidence since AR5 shows the onset of Antarctic ozone hole recovery ( [[#Solomon--2016|Solomon et al., 2016]] ; [[#WMO--2018|WMO, 2018]] ) that has been attributed to a pause in the summer SAM trend over the past couple of decades ( [[#Saggioro--2019|Saggioro and]] [[#Shepherd--2019|Shepherd, 2019]] ; [[#Banerjee--2020|Banerjee et al., 2020]] ). In austral summer, ozone recovery and increasing GHGs will have opposing effects on the SAM over the next several decades ( [[#Barnes--2014|Barnes et al., 2014]] ).

Since AR5, there have been advances in understanding the role of internal climate variability for projected near-term SH circulation trends ( [[#Solomon--2016|Solomon and Polvani, 2016]] ). A large initial-condition ensemble following the RCP4.5 emissions scenario shows a monotonic positive SAM trend in austral winter. In austral summer, the SAM trend over the first half of the 21st century is weaker compared to the strongly positive trend observed and simulated over the late 20th century. In that model, the number of realizations required to identify a detectable change in decadal mean austral winter SAM index from a year 2000 reference state decreased to below five by around 2025–2030 ( [[#Solomon--2016|Solomon and Polvani, 2016]] ). However, in December–January–February (DJF) the same criterion is not met until the second half of the 21st century, owing to the near-term opposing effects of ozone recovery and GHGs on the austral-summer SAM. In austral summer, forced changes in the SAM index in the near-term are therefore ''likely'' to be smaller than changes due to internal variability (Figure 4.17b; [[#Barnes--2014|Barnes et al., 2014]] ; [[#Solomon--2016|Solomon and Polvani, 2016]] ).

CMIP6 models show a tendency in the near-term towards a more positive SAM index especially in the austral winter (June–July–August, JJA; Figure 4.17b). In all seasons, the differences between the central estimates of the change in the SAM index for each SSP are much smaller than the inter-model ensemble spread. The number of CMIP6 realizations in Figure 4.17b is larger than the suggested threshold of five realizations needed to detect a significant near-term change in decadal-mean austral winter SAM index for a single CMIP5 model ( [[#Solomon--2016|Solomon and Polvani, 2016]] ), and yet the 5–95% intervals on the CMIP6 ensemble spread encompass zero for all core SSPs. This suggests both internal variability and model uncertainty contribute to the CMIP6 ensemble spread in near-term SAM index changes. Based on these results, it is ''more likely than not'' that in the near-term under all assessed SSP scenarios the SAM index would become more positive than in present-day in austral autumn, winter and spring.

An influence of forcing agents other than stratospheric ozone and GHGs, such as anthropogenic aerosols, on SAM changes over the historical period has been reported in some climate models ( [[#Rotstayn--2013|Rotstayn, 2013]] ), but the response across a larger set of CMIP5 models is not robust ( [[#Steptoe--2016|Steptoe et al., 2016]] ) and depends on how tropospheric temperature responds to aerosols ( [[#Choi--2019|Choi et al., 2019]] ). This gives ''low confidence'' in the potential influence of anthropogenic aerosols on the SAM in the future.

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==== 4.4.3.2 El Niño–Southern Oscillation ====

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The AR5 assessed that it is ''very likely'' that the ENSO will remain the dominant mode of interannual variability in the future but did not specify its change in near term. A subset of CMIP5 models that simulate the ENSO Bjerknes index most realistically show an increase of ENSO SST amplitude in the near-term future and decline thereafter ( [[#Kim--2014|Kim et al., 2014]] ). However, detection of robust near-term changes of ENSO SST variability in response to anthropogenic forcing is difficult to achieve due to pronounced unforced low-frequency modulations of ENSO ( [[#Wittenberg--2009|Wittenberg, 2009]] ; [[#Maher--2018|Maher et al., 2018]] ; [[#Wengel--2018|Wengel et al., 2018]] ). Figure 4.10 in [[#4.3.3.2|Section 4.3.3.2]] , using CMIP6 models, also shows no robust change in ENSO SST variability in the near term.

While there is no strong model consensus on the change in amplitude of ENSO SST variability, the amplitude of ENSO-associated rainfall variability ''likely'' increases ( [[#Power--2013|Power et al., 2013]] ; [[#Cai--2015|Cai et al., 2015]] ). Analysis of CMIP6 models shows a slight increasing trend in amplitude of rainfall variability over Niño 3.4 region in the near term attributable to mean moisture increase, regardless of changes in ENSO SST variability (Figure 4.10). However, there are no distinguishable changes in the rainfall variability among five SSPs with significant model spread in the near term. Hence, no robust change in amplitude of ENSO SST and rainfall variability is expected in the near term although the rainfall variability slightly increases ( ''medium confidence'' ).

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==== 4.4.3.3 Indian Ocean Basin and Dipole Modes ====

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Important modes of interannual climate variability with pronounced climate impacts in the Africa–Indo-Pacific areas of the globe are the Indian Ocean Dipole (IOD), which is closely related to, and often coincides with, ENSO phases ( [[#Stuecker--2017|Stuecker et al., 2017]] ), and the Indian Ocean basin (IOB) mode. This is often described as a capacitor effect in response to ENSO ( [[#Xie--2009|Xie et al., 2009]] ; [[#Du--2013|Du et al., 2013]] ) and can feed back onto ENSO evolution ( [[#Cai--2019|Cai et al., 2019]] ). IOD and IOB are extensively described in [[IPCC:Wg1:Chapter:Annex-iv|Annex IV]] (Section AIV2.4).

The projected climate mean state changes in the tropical Indian Ocean resemble a positive IOD state, with faster warming in the west compared to the east. This mean state change will potentially lead to a reduction in the amplitude of IOD events, albeit with no robust change in IOD frequency ( [[#Cai--2014b|Cai et al., 2014b]] ). There is no robust evidence yet suggesting a cessation of IOD variability or a significant change in the IOB mode in the near-term.

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==== 4.4.3.4 Tropical Atlantic Modes ====

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Interannual variability of the tropical Atlantic can be described in terms of two main climate modes: the Atlantic equatorial mode and the Atlantic meridional mode (AMM; Annex IV, Section AIV2.5). The Atlantic equatorial mode, also commonly referred to as the Atlantic Niño or Atlantic Zonal Mode, is associated with SST anomalies near the equator, peaking in the eastern basin, while the AMM is characterized by an inter-hemispheric gradient of SST and wind anomalies. Both modes are associated with changes in the ITCZ and related winds and exert a strong influence on the climate in adjacent and remote regions.

Despite considerable improvements in CMIP5 with respect to CMIP3, most CMIP5 models have difficulties in simulating the mean climate of the tropical Atlantic ( [[#Mohino--2019|Mohino et al., 2019]] ) and are not able to correctly simulate the main aspects of Tropical Atlantic Variability (TAV) and associated impacts. This is presumably the main reason why there is a lack of specific studies dealing with near-term changes in tropical Atlantic modes. Nevertheless, AR5 reported that the ocean is more predictable than continental areas at the decadal time scale ( [[#Kirtman--2013|Kirtman et al., 2013]] ). In particular, the predictability in the tropical oceans is mainly associated with decadal variations of the external forcing component. Since the AMV affects the tropical Atlantic, near-term variations of the AMV can modulate the equatorial mode and the AMM as well as associated impacts.

There are no specific studies focusing on near-term changes in tropical Atlantic modes; nevertheless, decadal predictions show that although the North Atlantic stands out in most CMIP5 models as the primary region where skill might be improved because of initialization, encouraging results have also been found in the tropical Atlantic ( [[#Meehl--2014|Meehl et al., 2014]] ). The effect of initialization in the tropical Atlantic is not only visible in surface temperature but also in the subsurface ocean ( [[#Corti--2015|Corti et al., 2015]] ). In particular, initialization improves the skill via remote ocean conditions in the North Atlantic subpolar gyre and tropical Pacific, which influence the tropical Atlantic through atmospheric teleconnections ( [[#Dunstone--2011|Dunstone et al., 2011]] ; [[#Vecchi--2014|Vecchi et al., 2014]] ; [[#García-Serrano--2015|García-Serrano et al., 2015]] ). Improvements of some aspects of climate prediction systems (initialization techniques, large ensembles, increasing model resolution) have also led to skill improvements over the tropical Atlantic ( [[#Pohlmann--2013|Pohlmann et al., 2013]] ; [[#Monerie--2017|Monerie et al., 2017]] ; [[#Yeager--2017|Yeager and Robson, 2017]] ).

Recent studies have shown that the AMV can modulate not only the characteristics of the Atlantic Niños, but also their inter-basin teleconnections (Indian and Pacific). In particular, the Atlantic Niño–ENSO relationship is strongest during negative AMV phases ( [[#Martín-Rey--2014|Martín-Rey et al., 2014]] ; [[#Losada--2016|Losada and Rodríguez-Fonseca, 2016]] ) when equatorial Atlantic SST variability is enhanced ( [[#Martín-Rey--2017|Martín-Rey et al., 2017]] ; [[#Lübbecke--2018|Lübbecke et al., 2018]] ).

Based on CMIP5 and available CMIP6 results, we conclude that there is a lack of studies on the near-term evolution of TAV and associated teleconnections for a comprehensive assessment. However, some studies show that despite severe model biases there are skilful predictions in the mean state of tropical Atlantic surface temperature several years ahead ( ''medium confidence'' ), though skill in simulated variability has not been assessed yet. Decadal changes in the Atlantic Niño spatial configuration and associated teleconnections might be modulated by the AMV, but there is ''limited evidence'' and therefore ''low confidence'' in these results.

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

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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]] ).

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==== 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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