Spatiotemporal modes of global sea surface temperature variability

Zhang, XiangMing; Chen, Chen; Lian, Tao; Chen, Dake

doi:10.1007/s11430-016-0160-9

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…For all four interpolated data sets, the top three regionally averaged theoretical thresholds required by the SLR method reside in the North Atlantic, the North Pacific, and the central‐eastern equatorial Pacific (Figures a–d). Note that the SSTs in these three regions exhibit strong decadal to multidecadal variations (Zhang et al, ). In the North Atlantic, the SST variations are associated mainly with the AMO (Enfield et al, ), which exhibits a period at the multidecadal time scale.…”

Section: Revisiting the Global Sst Trends Between 1881 And 2013mentioning

confidence: 99%

Investigating the Uncertainty in Global SST Trends Due to Internal Variations Using an Improved Trend Estimator

et al. 2018

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A new criterion was proposed recently to measure the influence of internal variations on secular trends in a time series. When the magnitude of the trend is greater than a theoretical threshold that scales the influence from internal variations, the sign of the estimated trend can be interpreted as the underlying long‐term change. Otherwise, the sign may depend on the period chosen. An improved least squares method is developed here to further reduce the theoretical threshold and is applied to eight sea surface temperature (SST) data sets covering the period 1881–2013 to investigate whether there are robust trends in global SSTs. It is found that the warming trends in the western boundary regions, the South Atlantic, and the tropical and southern‐most Indian Ocean are robust. However, robust trends are not found in the North Pacific, the North Atlantic, or the South Indian Ocean. The globally averaged SST and Indian Ocean Dipole indices are found to have robustly increased, whereas trends in the zonal SST gradient across the equatorial Pacific, Niño 3.4 SST, and the Atlantic Multidecadal Oscillation indices are within the uncertainty range associated with internal variations. These results indicate that great care is required when interpreting SST trends using the available records in certain regions and indices. It is worth noting that the theoretical threshold can be strongly influenced by low‐frequency oscillations, and the above conclusions are based on the assumption that trends are linear. Caution should be exercised when applying the theoretical threshold criterion to real data.

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Section: Revisiting the Global Sst Trends Between 1881 And 2013mentioning

confidence: 99%

Investigating the Uncertainty in Global SST Trends Due to Internal Variations Using an Improved Trend Estimator

et al. 2018

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“…The warming and cooling events are termed El Niño and La Niña, respectively. Because of its tremendous global impact (Philander, 1990;Lian et al, 2014a;Sun et al, 2015;Lian and Tang, 2017;Wang et al, 2016;Zhang et al, 2017), ENSO has been a focus of oceanic and atmospheric research since 1980s, and significant progress has been made in observing, understanding and predicting ENSO (Chen et al, 1995(Chen et al, , 2004Neelin et al, 1998;McPhaden et al, 1999;Chen and Cane 2008). However, there are still many un- * Corresponding author (email: dchen@sio.org.cn) solved problems.…”

Section: Introductionmentioning

confidence: 99%

Genesis of the 2014–2016 El Niño events

Lian

Chen

Tang

2017

Sci. China Earth Sci.

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The 2015/2016 El Niño was one of the strongest El Niño events in history, and this strong event was preceded by a weak El Niño in 2014. This study systematically analyzed the dynamical processes responsible for the genesis of these events. It was found that the weak 2014 El Niño had two warming phases, the spring-summer warming was produced by zonal advection and downwelling Kelvin waves driven by westerly wind bursts (WWBs), and the autumn-winter warming was produced by meridional advection, surface heating as well as downwelling Kelvin waves. The 2015/2016 extreme El Niño, on the other hand, was primarily a result of sustained zonal advection and downwelling Kelvin waves driven by a series of WWBs, with enhancement from the Bjerknes positive feedback. The vast difference between these two El Niño events mainly came from the different amount of WWBs in 2014 and 2015. As compared to the 1982/1983 and 1997/1998 extreme El Niño events, the 2015/2016 El Niño exhibited some distinctive characteristics in its genesis and spatial pattern. We need to include the effects of WWBs to the theoretical framework of El Niño to explain these characteristics, and to improve our understanding and prediction of El Niño.

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Contributions of internal climate variability in driving global and ocean temperature variations using multi-layer perceptron neural network

Xiao

Liu

et al. 2022

Advances in Climate Change Research

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Spatiotemporal modes of global sea surface temperature variability

Cited by 3 publications

References 29 publications

Investigating the Uncertainty in Global SST Trends Due to Internal Variations Using an Improved Trend Estimator

Investigating the Uncertainty in Global SST Trends Due to Internal Variations Using an Improved Trend Estimator

Genesis of the 2014–2016 El Niño events

Contributions of internal climate variability in driving global and ocean temperature variations using multi-layer perceptron neural network

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