Impacts of the Atlantic Equatorial Mode in a warmer climate

Mohíno, Elsa; Losada, Teresa

doi:10.1007/s00382-015-2471-y

Cited by 41 publications

(26 citation statements)

References 63 publications

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“…For instance, ref. [112] found that projected warmer SSTs cause a modification of the influence of the AZM on the Tropical Atlantic rainfall, the Asian monsoon, and the land surface temperature in the extratropics, even when the AZM characteristics remains unchanged. In [113] it was found that the doubling of atmospheric CO2 leads to a northward shift of the ITCZ in summer and fall and to a damping of evaporation anomalies that weakens the WES feedback in the northern TA.…”

Section: Variability At Longer Time Scalesmentioning

confidence: 98%

Tropical Atlantic Variability: Observations and Modeling

2019

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We review the state-of-the-art knowledge of Tropical Atlantic Variability (TAV). A well-developed observing system and sustained effort of the climate modeling community have improved our understanding of TAV. It is dominated by the seasonal cycle, for which some mechanisms have been identified. The interannual TAV presents a marked seasonality with three dominant modes: (i) the Atlantic Zonal Mode (AZM), (ii) the Atlantic Meridional Mode (AMM) and (iii) the variability in the Angola–Benguela Front (ABF). At longer time scales, the AMM is active and low-frequency variations in the strength, periodicity, and spatial structure of the AZM are observed. Also, changes in the mean position of the ABF occur. Climate models still show systematic biases in the simulated TAV. Their causes are model-dependent and relate to drawbacks in the physics of the models and to insufficient resolution of their atmospheric and oceanic components. The identified causes for the biases can have local or remote origin, involving the global ocean and atmospheric circulation. Although there is not a clear consensus regarding the role of model resolution in the representation of the TAV, eddy-resolving ocean models combined with atmospheric models with enhanced horizontal and vertical resolutions simulate smaller biases.

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Section: Variability At Longer Time Scalesmentioning

confidence: 98%

Tropical Atlantic Variability: Observations and Modeling

2019

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“…This pattern could be related to the Atlantic Equatorial Mode (AEM), which describes a quasiperiodic interannual warming and cooling of the equatorial Atlantic SST. 62,63 It has been shown that AEM can a ect rainfall over both adjacent and remote regions, including West Africa, 64 Brazil, 65 and even central Europe. 66 Finally, at 64-128 month time-scales [ Fig.…”

Section: B Scale-specific Cross-degree Patternsmentioning

confidence: 99%

Disentangling the multi-scale effects of sea-surface temperatures on global precipitation: A coupled networks approach

Ekhtiari

Agarwal

Marwan

et al. 2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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The oceans and atmosphere interact via a multiplicity of feedback mechanisms, shaping to a large extent the global climate and its variability. To deepen our knowledge of the global climate system, characterizing and investigating this interdependence is an important task of contemporary research. However, our present understanding of the underlying large-scale processes is greatly limited due to the manifold interactions between essential climatic variables at di erent temporal scales. To address this problem, we here propose to extend the application of complex network techniques to capture the interdependence between global elds of sea-surface temperature (SST) and precipitation (P) at multiple temporal scales. For this purpose, we combine timescale decomposition by means of a discrete wavelet transform with the concept of coupled climate network analysis. Our results demonstrate the potential of the proposed approach to unravel the scale-speci c interdependences between atmosphere and ocean and, thus, shed light on the emerging multiscale processes inherent to the climate system, which traditionally remain undiscovered when investigating the system only at the native resolution of existing climate data sets. Moreover, we show how the relevant spatial interdependence structures between SST and P evolve across timescales. Most notably, the strongest mutual correlations between SST and P at annual scale (8-16 months) concentrate mainly over the Paci c Ocean, while the corresponding spatial patterns progressively disappear when moving toward longer timescales .

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“…Similar to other modes of equatorial SST variability, it is the source of a number of teleconnections (e.g. Janicot et al 1998;Mohino and Losada 2015), both regionally and globally. Through its close relationship with the Inter-Tropical Convergence Zone, it especially affects rainfall variability over the surrounding continents, exerting an important socio-economic impact (Hirst and Hastenrath 1983).…”

Section: Introductionmentioning

confidence: 99%