Abstract:Recent studies demonstrated the existence of a conspicuous atmospheric combination mode (C-mode) originating from nonlinear interactions between El Niño–Southern Oscillation (ENSO) and the Pacific warm pool annual cycle (AC). Here we find that the C-mode exhibits prominent decadal amplitude variations during the ENSO decaying boreal spring season. It is revealed that the Atlantic multidecadal oscillation (AMO) can largely explain this waxing and waning in amplitude. A robust positive correlation between ENSO a… Show more
“…On the other hand, the negative or inverse correlation between ENSO and AMO can be explained straightforwardly by the fact that during the negative AMO phase, both El Niño and La Niña events tend to be stronger than during a positive AMO phase 41 . Moreover, this inverse correlation between ENSO and AMO can be explained by the so-called atmospheric “bridge-thermocline feedback” 41 , 42 . Figure 1 Sea surface temperature (SST) for the main development region (MDR) for Atlantic tropical cyclones 36 (black); El Niño-Southern Oscillation (ENSO) SST (based on El Niño 3 region) 37 (red); North Atlantic Atlantic Multidecadal Oscillation (AMO) SST averaged over the North Atlantic ocean 37 (green); Long-term Atlantic tropical cyclone counts (TC) 36 (blue).…”
The wavelet local multiple correlation (WLMC) is introduced for the first time in the study of climate dynamics inferred from multivariate climate time series. To exemplify the use of WLMC with real climate data, we analyse Last Millennium (LM) relationships among several large-scale reconstructed climate variables characterizing North Atlantic: i.e. sea surface temperatures (SST) from the tropical cyclone main developmental region (MDR), the El Niño-Southern Oscillation (ENSO), the North Atlantic Multidecadal Oscillation (AMO), and tropical cyclone counts (TC). We examine the former three large-scale variables because they are known to influence North Atlantic tropical cyclone activity and because their underlying drivers are still under investigation. WLMC results obtained for these multivariate climate time series suggest that: (1) MDRSST and AMO show the highest correlation with each other and with respect to the TC record over the last millennium, and: (2) MDRSST is the dominant climate variable that explains TC temporal variability. WLMC results confirm that this method is able to capture the most fundamental information contained in multivariate climate time series and is suitable to investigate correlation among climate time series in a multivariate context.
“…On the other hand, the negative or inverse correlation between ENSO and AMO can be explained straightforwardly by the fact that during the negative AMO phase, both El Niño and La Niña events tend to be stronger than during a positive AMO phase 41 . Moreover, this inverse correlation between ENSO and AMO can be explained by the so-called atmospheric “bridge-thermocline feedback” 41 , 42 . Figure 1 Sea surface temperature (SST) for the main development region (MDR) for Atlantic tropical cyclones 36 (black); El Niño-Southern Oscillation (ENSO) SST (based on El Niño 3 region) 37 (red); North Atlantic Atlantic Multidecadal Oscillation (AMO) SST averaged over the North Atlantic ocean 37 (green); Long-term Atlantic tropical cyclone counts (TC) 36 (blue).…”
The wavelet local multiple correlation (WLMC) is introduced for the first time in the study of climate dynamics inferred from multivariate climate time series. To exemplify the use of WLMC with real climate data, we analyse Last Millennium (LM) relationships among several large-scale reconstructed climate variables characterizing North Atlantic: i.e. sea surface temperatures (SST) from the tropical cyclone main developmental region (MDR), the El Niño-Southern Oscillation (ENSO), the North Atlantic Multidecadal Oscillation (AMO), and tropical cyclone counts (TC). We examine the former three large-scale variables because they are known to influence North Atlantic tropical cyclone activity and because their underlying drivers are still under investigation. WLMC results obtained for these multivariate climate time series suggest that: (1) MDRSST and AMO show the highest correlation with each other and with respect to the TC record over the last millennium, and: (2) MDRSST is the dominant climate variable that explains TC temporal variability. WLMC results confirm that this method is able to capture the most fundamental information contained in multivariate climate time series and is suitable to investigate correlation among climate time series in a multivariate context.
“…When compositing the longitudinal positions of maximum tropical Pacific SST anomalies for each ENSO winters during the different AMO phases, we can also obtain this significant difference at the 95% confidence level (Table 3). These results are well in line with the previous studies (Yu et al ., 2015; Park et al ., 2019; Geng et al ., 2020), which pointed out a physical linkage between North Atlantic multidecadal warming and the zonal structure change of ENSO SST anomalies. In their arguments, a positive AMO leads to an intensification of the Pacific subtropical high and/or stronger‐than‐average background trade winds in both zonal and meridional components, which in turn favours the development of more CP El Niño events in the tropical Pacific (Yu et al ., 2015).…”
Section: Possible Modulation Mechanisms By the Amomentioning
The amount of winter precipitation over southeast China (SCWP) is significantly affected by the El Niño-Southern Oscillation (ENSO), with generally an enhanced SCWP response to El Niño events. In this paper, we find that this relationship shows remarkable nonstationarities on the multidecadal timescale. Running ENSO-SCWP correlation maintains statistically significant before the mid-1960s and after the mid-1990s but not during the interim period, which exhibits an approximate synchronized temporal evolution with the Atlantic Multidecadal Oscillation (AMO). A statistical method for interpreting nonstationary relationships is then applied to quantitatively verify and measure this possible AMO modulation. It is demonstrated that the nonstationary ENSO-SCWP correlation can be expressed to a large extent by the influence of the AMO-ENSO nonlinear effect (AMO*Niño3.4) on the SCWP. This influence intensifies the ENSO-SCWP relationship during the positive AMO phase but weakens it during the negative AMO phase. Therefore, the nonstationarity in the ENSO-SCWP relationship comes predominantly from an AMO modulation via this key nonlinear effect. We further suggest that a positive AMO favours a westward shift of ENSO sea surface temperature (SST) anomaly pattern in the tropical Pacific. Correspondingly, the anomalous western North Pacific (WNP) anticyclone also migrates westward, which gives rise to pronounced moisture transports and vertical motions over the adjacent southeast China, thereby resulting in a significant ENSO-SCWP relationship. In contrast, during the negative AMO phase, the eastward-shifted ENSO SST and WNP anticyclonic anomalies produce significant atmospheric anomalies primarily to the east of the Chinese mainland, thus having less effect on the SCWP.
“…The negative (positive) phase of the AMO corresponds to an increase (decrease) in the ENSO amplitude (Dong & Sutton, 2007; Timmermann et al., 2007). Because the AMO causes significant changes in the ENSO amplitude, which in turn drives the extratropical circulation and climate effects (Geng et al., 2017, 2020; Gong et al., 2020; Zhao et al., 2022), a question naturally arises: is the relationship between ENSO and WP modulated by the AMO? If so, what are the underlying mechanisms of this modulation?…”
The western Pacific pattern (WP) is one of the most essential wintertime teleconnection patterns in the Northern Hemisphere, with alternating geopotential height anomalies in the subtropical western North Pacific (WNP) and mid-high latitudes of the North Pacific. The WP has far-reaching implications for weather and climate anomalies over mid-high latitudes of Eurasia and North America (Barnston & Livezey, 1987;Linkin & Nigam, 2008;Wallace & Gutzler, 1981), as it is considered a preeminent contributor to weather and climate anomalies in East Asia and the Far East (Lim & Kim, 2013;Pak et al., 2014;Takaya & Nakamura, 2013). Because it aligns with various positions of the jet stream and storm track anomalies, the WP also exerts significant impacts on precipitation anomalies in North America (Linkin & Nigam, 2008;Yuan et al., 2015). In addition, emerging studies have highlighted the important role of WP-induced atmospheric circulations in provoking extreme climate events. One extremely strong and persistent WP event was suggested to be the key circulation pattern fostering the record-breaking cold winter of 2013/14 in North America (Baxter & Nigam, 2015;B. Yu & Zhang, 2015). Therefore, analyzing and understanding the WP and its relevant climatic effects are of critical importance.
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