A Multivariate AMV Index and Associated Discrepancies Between Observed and CMIP5 Externally Forced AMV

Yan, Xiaolei; Zhang, Rong; Knutson, Thomas R.

doi:10.1029/2019gl082787

Cited by 34 publications

(36 citation statements)

References 94 publications

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“…In Si and Hu (2017), when the AMOC index from each individual ensemble member of the CESM1_LE is regressed onto the SST of the same ensemble member, the resulting ensemble-mean regression pattern resembles the AMV pattern very well. This may suggest that the internally generated AMV is well related to the AMOC, agreeing with many previous studies (Zhang et al 2016;Yan et al 2019). Therefore, our results emphasize that the proper ocean initialization might be the key for a successful decadal climate prediction due to a slowly evolving processes, such as AMOC.…”

Section: Contribution Of Amoc To a Better Amv Predictionsupporting

confidence: 92%

“…5d,f), which may have contributed to the failure to simulate the observed AMV time evolution, especially for CCSM4his. As indicated by Yan et al (2019), the relationships between externally forced AMV and AMOC are opposite to the relationship of the internally generated AMV and AMOC. In fact, the ensemble-mean AMV and AMOC shown in Figs.…”

Section: Contribution Of Amoc To a Better Amv Predictionmentioning

confidence: 97%

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Predicting the Atlantic Multidecadal Variability from Initialized Simulations

Wang

et al. 2019

Journal of Climate

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In contrast to dominant interannual time-scale variability in other ocean basins, the leading observed mode variability in the Atlantic is characterized as a basinwide seesaw-like sea surface temperature variability between the North and South Atlantic on a multidecadal time scale (approximately 60–80 years), known as the Atlantic multidecadal variability (AMV). AMV has been identified as a key driver for climate shifts that occurred in the mid-1960s and late 1990s. Here we attempt to predict the summer AMV by analyzing decadal prediction experiments from two climate models. Results show that these climate models with proper initialization do a better job than uninitialized historical runs, and are capable of predicting the observed AMV time evolution. Our models predict that the AMV will be in a neutral to slightly negative phase, leading to a warm–dry trend over western Europe and North Africa and a cold–wet trend (cold relative to the warming trend) over southeastern China and Indochina in the next few years.

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Section: Contribution Of Amoc To a Better Amv Predictionsupporting

confidence: 92%

Section: Contribution Of Amoc To a Better Amv Predictionmentioning

confidence: 97%

Predicting the Atlantic Multidecadal Variability from Initialized Simulations

Wang

et al. 2019

Journal of Climate

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“…d Within the confines of the EBM models developed here, the above properties follow from a stronger communication between the deep ocean and mixed layer implicit in the observed data, coupled with the stronger excitation of the deep-ocean oscillators by the stochastic forcing. These processes are but parameterizations of a multitude of feedbacks operating in realistic CMIP5 models, such as the (underestimated in many CMIP5 models) internal variability of AMOC or its response to the North Atlantic Oscillation (Delworth et al 2017;Yan et al 2018) or the generation of basinscale AMO signature via coupled air-sea feedbacks in response to AMOC variability (Bellomo et al 2016;Brown et al 2016;Yuan et al 2016); see Zhang et al (2019) for a review. d From EBM model results, the hemispheric teleconnections and hemispheric-scale multidecadal variability in free runs of CMIP5 models are found to be dominated by PMO, with AMO influence largely confined to the North Atlantic region.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…Finally, initialized near-term prediction systems [see Cassou et al (2018) for a recent summary] show a larger skill of predicting AMO compared to PDO/IPO, although the skill for the former seems to be confined to the Atlantic Ocean region due, tentatively, to underestimated magnitude of multidecadal variability in AMOC (Yan et al 2018) combined, as discussed above, with misrepresentation, in climate models, of air-sea feedbacks that shape and energize the observed basin-scale AMO signature. The resulting weaker-than-observed basin-scale multidecadal AMO signals in coupled climate GCMs (Kravtsov and Callicutt 2017;Kravtsov 2017;Kim et al 2018;Yan et al 2019) are consistent with weak interbasin coupling in free runs of these models (despite an apparent teleconnectivity demonstrated via pacemaker experiments), the loss of potential AMOrelated skill in predicting the PDO/IPO, as well as with the lack, in these models, of the global-scale multidecadal variability matching the magnitude and spatiotemporal structure of such variability diagnosed in the reanalysis data (Kravtsov et al 2018).…”

Section: Introductionmentioning

confidence: 96%

Dynamics and Predictability of Hemispheric-Scale Multidecadal Climate Variability in an Observationally Constrained Mechanistic Model

Kravtsov

2020

Journal of Climate

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This paper addresses the dynamics of internal hemispheric-scale multidecadal climate variability by postulating an energy-balance (EBM) model comprising two deep-ocean oscillators in the Atlantic and Pacific basins, coupled through their surface mixed layers via atmospheric teleconnections. This system is linear and driven by the atmospheric noise. Two sets of the EBM model parameters are developed by fitting the EBM-based mixed-layer temperature covariance structure to best mimic basin-average North Atlantic/Pacific sea surface temperature (SST) covariability in either observations or control simulations of comprehensive climate models within the CMIP5 project. The differences between the dynamics underlying the observed and CMIP5-simulated multidecadal climate variability and predictability are encapsulated in the algebraic structure of the two EBM model versions so obtained: EBM CMIP5 and EBM OBS . The multidecadal variability in EBM CMIP5 is overall weaker and amounts to a smaller fraction of the total SST variability than in EBM OBS , pointing to a lower potential decadal predictability of virtual CMIP5 climates relative to that of the actual climate. The EBM CMIP5 decadal hemispheric teleconnections (and, by inference, those in CMIP5 models) are largely controlled by the variability of the Pacific, in which the ocean, due to its large thermal and dynamical memory, acts as a passive integrator of atmospheric noise. By contrast, EBM OBS features a stronger two-way coupling between the Atlantic and Pacific multidecadal oscillators, thereby suggesting the existence of a hemispheric-scale and, perhaps, global multidecadal mode associated with internal ocean dynamics. The inferred differences between the observed and CMIP5 simulated climate variability stem from a stronger communication between the deep ocean and surface processes implicit in the observational data.

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“…The role of aerosols in Atlantic climate variability is a contentious subject of ongoing research (Bellomo et al, 2017; Booth et al, 2012; Evan et al, 2009; Haustein et al, 2019; Yan et al, 2019; Zhang et al, 2013). Previous studies discussed interhemispheric SST gradient over tropical Atlantic (with warmer south and colder north) as robust response to aerosol forcing in climate models during the twentieth century (Biasutti & Giannini, 2006; Booth et al, 2012; Chang et al, 2011; Held et al, 2005).…”

Section: The Relative Roles Of Intrinsic Variability and External Formentioning

confidence: 99%

A Satellite Era Warming Hole in the Equatorial Atlantic Ocean

et al. 2020

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Observations during the satellite era 1979-2018 only depict small sea surface temperature (SST) trends over the Equatorial Atlantic cold tongue region in boreal summer. This lack of surface warming of the cold tongue, termed warming hole here, denotes an 11% amplification of the mean SST annual cycle. The warming hole is driven by a shoaling of the equatorial thermocline, linked to increased wind stress forcing, and damped by the surface turbulent heat fluxes. The satellite era warming deficit is not unusual during the twentieth century-similar weak trends were also observed during the 1890s-1910s and 1940s-1960s. The tendency for surface cooling appears to reflect an interaction of external forcing, which controls the timing and magnitude of the cooling, with the intrinsic variability of the climate system. The hypothesis for externally forced modulation of internal variability is supported by climate model simulations forced by the observed time-varying concentrations of atmospheric greenhouse gases and natural aerosols. These show that increased greenhouse forcing warmed the cold tongue and aerosols cooled it during the satellite era. However, internal variability, as derived from control integrations with fixed, preindustrial values of greenhouse gases and aerosols, can potentially cause larger cooling than observed during the satellite era. Large uncertainties remain on the relative roles of external forcing and intrinsic variability in both observations and coupled climate models.Plain Language Summary The Atlantic cold tongue is a region of locally cooler ocean surface waters that develops just south of the equator in boreal summer, partly reflecting the upwelling of deep cold waters by the action of the southeasterly trade winds. Although there has been considerable global warming since the beginning of global satellite measurements in 1979, there is hardly any surface warming in the Atlantic cold tongue region during 1979-2018. This warming hole is most pronounced in boreal summer. Observations suggest that despite strong heat transfer from the atmosphere to the ocean, the upper ocean may have cooled. Climate model simulations show that variations in external forcing associated with greenhouse gases can cause warming of the cold tongue and aerosols cooling of it. However, model simulations that exclude variations in these external forcing show that the mechanisms internal to the climate system can potentially cause larger cooling than observed during the satellite era. Thus, we attribute the warming hole to a combination of internal variability and external forcing. It must be stressed, however, that understanding the relative roles of internal variability versus external forcing is greatly hampered by large errors in both the observational data sets and climate models.

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A Multivariate AMV Index and Associated Discrepancies Between Observed and CMIP5 Externally Forced AMV

Cited by 34 publications

References 94 publications

Predicting the Atlantic Multidecadal Variability from Initialized Simulations

Predicting the Atlantic Multidecadal Variability from Initialized Simulations

Dynamics and Predictability of Hemispheric-Scale Multidecadal Climate Variability in an Observationally Constrained Mechanistic Model

A Satellite Era Warming Hole in the Equatorial Atlantic Ocean

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