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El Niño–Southern Oscillation complexity
Abstract
El Niño events are characterized by surface warming of the tropical Pacific Ocean and weakening of equatorial trade winds that occur every few years. Such conditions are accompanied by changes in atmospheric and oceanic circulation, affecting global climate, marine and terrestrial ecosystems, fisheries and human activities. The alternation of warm El Niño and cold La Niña conditions, referred to as the El Niño–Southern Oscillation (ENSO), represents the strongest year-to-year fluctuation of the global climate system. Here we provide a synopsis of our current understanding of the spatio-temporal complexity of this important climate mode and its influence on the Earth system.
... A major mode of climate variability that can influence climatological conditions globally, including in Canada, is the El-Niño Southern Oscillation (ENSO) [8]. ENSO's variation in sea surface temperatures (SST) over the equatorial Pacific typically cycles every~2-7 years, thus its presence and effects are recognized on interannual, rather than multidecadal, timescales [6,7,9,10]. ENSO influences global crop yields, although that influence can be positive or negative depending on the region and on the type of crop [6]. ...
... To a large extent, variability in Pacific and Atlantic SST strongly dictate climatological conditions on land, including SAT and precipitation [9]. The ENSO climate pattern is defined by an index which reflects deviations in SST over the central equatorial Pacific region, referred to as the Niño 3.4 region (5˚N-5˚S, 170˚W-120˚W) from the base period of 1981-2010 [9]. ...
... To a large extent, variability in Pacific and Atlantic SST strongly dictate climatological conditions on land, including SAT and precipitation [9]. The ENSO climate pattern is defined by an index which reflects deviations in SST over the central equatorial Pacific region, referred to as the Niño 3.4 region (5˚N-5˚S, 170˚W-120˚W) from the base period of 1981-2010 [9]. Similarly, the AMO index is defined by deviations in SST over the North Atlantic basin (0˚-70˚N and 75˚W-7˚W) [15]. ...
Surface air temperature (SAT) and precipitation in Prairie (Western) and Maritime (Eastern) Canada are influenced by the El Niño Southern Oscillation (ENSO) and Atlantic Multidecadal Oscillation (AMO), respectively. However effects of ENSO and AMO on major crop yield in Canada is yet to be understood. Here we investigate the longest record (1908–2017) of wheat, barley, and oat yield as well as its associated risk with summer (May-September) ENSO and AMO interannual and multidecadal variability in Prairie and Maritime, respectively. We used generalized linear models with autocorrelative residuals to assess region- and crop-specific associations between ENSO, AMO, surface air temperatures, and precipitation on crop yield. After adjusting for covariates our models show that a positive phase of the AMO (in comparison to negative phase) significantly reduces the risk of Maritime crop yields by ~3–12%, with both extreme heat and wet precipitation found to be significant risk factors for reducing yields. Summer El Niño or La Niña was found to have a small, insignificant effect on yield in the Prairie region, with no effects found on crops in Maritimes. Therefore, analysis of Atlantic oceanic variability can offer insight into major crop yield variability in Maritime Canada.
... Such linkages give rise to nontrivial feedbacks, generating self-sustained spatiotemporal patterns [3,4]. An example is the El Niño Southern Oscillation (ENSO), a recurrent pattern of natural variability emerging from air-sea interaction in the tropical Pacific Ocean [5,6]. Other examples include the Asian Monsoon, the Indian Ocean Dipole, the Atlantic Niño, just to cite a few [7][8][9]. ...
... Large values of causal strengths D j appear in the equatorial Pacific and are maximized in the eastern part of the basin. This is expected as the interannual variability in the tropical Pacific is dominated by the El Niño Southern Oscillation (ENSO) pattern confined in the equatorial region [6]. We then consider only the statistical significant responses through the proposed null model 5. ...
... 15) in Fig. 5(c). The strongest mode of variability at interannual time scales is in the tropical Pacific, as expected [6]. Physically, this means that, at interannual time scales, the variability in the tropical Pacific is able to influence a larger part of the world compared to other regions with smaller strength. ...
The Earth climate is a complex and high-dimensional dynamical system. At large scale its variability is dominated by recurrent patterns, interacting with each others on a vast range of spatial and temporal scales. Identifying such patterns and their linkages offers a powerful strategy to simplify, study and understand climate dynamics. We propose a data-driven framework to first reduce the dimensionality of a spatiotemporal climate field into a set of regional modes and then infer their time-dependent causal links. Causality is inferred through the fluctuation-response formalism, as shown in Baldovin et al. (2020) [1]. The framework allows us to estimate how a spatiotemporal system would respond to local external perturbation, therefore inferring causal links in the interventional sense. We showcase the methodology on the sea surface temperature field in two cases with different dynamics: weekly variability in the tropical Pacific and monthly variability over the entire globe. In both cases, we demonstrate the usefulness of the methodology by studying few individual links as well as "link maps", visualizing the cumulative degree of causation between a given region and the whole system. Finally, each climate mode is ranked in terms of its "causal strength", quantifying its relative ability to influence the system dynamics. We argue that the methodology allows to explore and characterize causal relationships in high-dimensional spatiotemporal fields in a rigorous and physical way.
... Rossby wave trains triggered by ENSO can be influenced by the background mean flow . This implies that ENSO teleconnections might be significantly influenced by both internal decadal climate variability and the external forcing of global warming, and this is the subject of much debate (e.g., Deser et al., 2017;Dieppois et al., 2021;Domeisen et al., 2019;Timmermann et al., 2018). In addition, there was an abrupt climate shift in the tropical Pacific circulation in 1976-1977(Trenberth & Stepaniak, 2001, which has changed the characteristics of ENSO and its teleconnections (Clark et al., 2003;Wang, 1995;Zhang et al., 1998). ...
... In addition to the dependence of ENSO teleconnections to the spatial pattern of the maximum SST anomalies over the equatorial Pacific, the complexity of ENSO in terms of its intensity and temporal evolution (Timmermann et al., 2018) adds to the uncertainty in ENSO global teleconnections. For example, Zhang et al. (2015) argued that a positive IOD often coincides with EP El Niño events and the correlation becomes stronger as the intensity of EP El Niño increases. ...
The El Niño‐Southern Oscillation (ENSO) is a major component of the Earth's climate that largely influences global climate variability through long‐distance teleconnections. Rossby wave trains emerging from the tropical convection and their propagation into extratropical regions are the key mechanism for tropical and extratropical teleconnections. Despite significant progress in the understanding of ENSO teleconnections over the recent past decades, several important issues have remained to be addressed. The global atmospheric teleconnections of ENSO vary substantially with the seasonal cycle, on the decadal timescale, and under the influence of global warming. It is essential to separate the internal decadal variability of ENSO teleconnections from changes caused by the external forcing of global warming. However, the post‐satellite observations are not long enough to compose a large number of ENSO events to distinguish the decadal variability of ENSO teleconnections from changes related to increasing greenhouse concentrations. The current climate models also suffer from common biases, such that they are unable to properly reproduce both the tropical mean state and some features of ENSO. Nevertheless, observational records can be extended back in time via reconstruction methods. Efforts have also already been made to remove some main common biases of climate models and to improve the representation of ENSO characteristics. The reliable reconstructed data along with a large number of ensemble members of the improved climate model simulations can be applied to advance our understanding of ENSO global teleconnections and their responses to internal decadal variability and externally forced global warming.
This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change
Climate Models and Modeling > Earth System Models
Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change
... Climate teleconnections are commonly defined as low-frequency variability in the atmosphere and oceans, where climatological variables at distant points on the earth are significant correlated [28]. Regional weather system including temperature, precipitation, storm tracks, severe weather, and drought are affected by these teleconnections and the periods range from several weeks to several decades. ...
... North Atlantic Oscillation (NAO) is one of the most prominent teleconnection patterns in all seasons, and the imprint of NAO has been detected by Pearson correlation climate network [30,31]. El Niño-Southern Oscillation (ENSO) is the strongest inter-annual signal of the tropical air-sea coupled system in the Pacific [28,32,33]. The effects of ENSO, also called teleconnections, emphasize its remote impacts on global climate. ...
A climate anomaly is defined as the difference between a climate variable and a baseline, which is often the climate normal. In climate change studies, climate anomalies are more important than the average climate. Positive and negative extremes of climate anomalies are equally important, because they represent quite opposite climate events. Therefore, extreme event based synchronization is a proper choice for measuring the similarity of event-like series. However, the traditional event synchronization method cannot incorporate positive and negative extremes simultaneously. In this study, a newly proposed event synchronization (ES) measure is adopted as similarity measure of climate anomaly time series, where both positive and negative extremes are identified as extreme events. Then, global complex climate networks based on this similarity measure of surface air temperature (SAT) anomaly time series have been constructed and analyzed. Exponential function and power function have been fitted to the empirical degree distribution of positive and negative climate networks, respectively. The prominent atmospheric teleconnection pattern (North Atlantic Oscillation, NAO) as well as the remote impacts of ENSO have been correctively detected by the global climate networks. The advantages of ES-based complex networks have also bee discussed. This study provides an illustrative example of constructing complex climate network model for nonlinearly correlated climate time series with both positive and negative extremes.
... There are generally stronger mean height anomalies under La Niña, despite El Niño events being generally stronger than La Niñas. The variation in ENSO's response across seasons may be connected to the variability in the manifestation of ENSO events (Timmermann et al. 2018), which could affect the source location of poleward propagating atmospheric waves in the subtropics. Equally, the lower signal-to-noise ratio outside the dominant teleconnection season in JJA and SON likely allows greater mixing from other sources of variability. ...
... Contrasting the apparent hemispheric-wide response to the SAM with the regional response to ENSO suggests there is a weaker coupling between the mean height and HF variance fields. The consistent presence of multiple ENSO-like signals in retrieved MCA modes may be related to the differing effects of central Pacific (CP) and eastern Pacific (EP) El Niño events (Timmermann et al. 2018)}ENSO spatial structure in the tropics affects the location of the Rossby wave source in the subtropics and likely impacts how the ENSO teleconnection manifests. A future study should aim to document the spatial structure of extratropical teleconnections for CP and EP events, better contextualizing retrieved patterns. ...
In this study, we explore linkages between the monthly mean 500hPa height field (Z500) and its high-frequency variability over two-eight day periods, a proxy for Southern Hemisphere storm track activity. We apply Maximum Covariance analysis to identify leading modes of co-variability between the Z500 and high-frequency variance anomalies on monthly and sub-monthly timescales, using two reanalyses products. We also calculate covariance with indices of large-scale variability (Southern Annular Mode (SAM), El Niño-Southern Oscillation (ENSO) and Zonal Wave 3 (ZW3)). We find large-scale circulation patterns emerge as prominent modes of co-variability, particularly SAM and ENSO, accounting for 11.1% and 7.8% of co-variability on the monthly scale. The seasonal cycle plays a prominent role in explaining variability in both SAM and ENSO interactions with the storm track. We find that despite a broadly linear response, both SAM and ENSO teleconnections present additional complexities and non-linearities. Despite strong ZW3 signals in the mean height field, its influence on the high-frequency variance field remains unclear. We conclude the mean height field is most likely strongly linked with high-frequency variance, but interference from other influences may lead to an inconsistent response. We note both fields have an apparent hemispheric response to the SAM, but ENSO has a more regional response and ZW3 a relatively incoherent response. This suggests ENSO and ZW3 patterns depend less on feedbacks between the storm track and the mean height field than the SAM.
... We do likewise in this paper but focus on the 2018-20 protracted El Niño episode and its wider impacts on Australia within a historical perspective. Allan et al. (2019) showed that protracted ENSO episodes were just another "flavor" of ENSO (Capotondi et al. 2015;Timmermann et al. 2018), and that their signature pattern of periods of over 2 yr of persistent western equatorial Pacific Niño-4 region (58N-58S, 1608E-1508W) SST anomalies was analogous to the Modoki El Niño and La Niña phases (Ashok et al. 2007;Weng et al. 2007;Ashok and Yamagata 2009) and central Pacific (CP) ENSOs (Ashok and Yamagata 2009;Capotondi et al. 2015;Timmermann et al. 2018). It was also noted that the ocean-atmosphere interactions underlying classical events, which occur on interannual time scales, also underlie protracted episodes, but that the latter involve the interplay of quasi-biennial, interannual, and quasi-decadal ENSO signals (Tourre et al. 2001). ...
... We do likewise in this paper but focus on the 2018-20 protracted El Niño episode and its wider impacts on Australia within a historical perspective. Allan et al. (2019) showed that protracted ENSO episodes were just another "flavor" of ENSO (Capotondi et al. 2015;Timmermann et al. 2018), and that their signature pattern of periods of over 2 yr of persistent western equatorial Pacific Niño-4 region (58N-58S, 1608E-1508W) SST anomalies was analogous to the Modoki El Niño and La Niña phases (Ashok et al. 2007;Weng et al. 2007;Ashok and Yamagata 2009) and central Pacific (CP) ENSOs (Ashok and Yamagata 2009;Capotondi et al. 2015;Timmermann et al. 2018). It was also noted that the ocean-atmosphere interactions underlying classical events, which occur on interannual time scales, also underlie protracted episodes, but that the latter involve the interplay of quasi-biennial, interannual, and quasi-decadal ENSO signals (Tourre et al. 2001). ...
A “protracted” El Niño episode occurred from March–April 2018 to April–May 2020. It was manifested by the interlinked Indo-Pacific influences of two components of El Niño phases. Positive Indian Ocean dipoles (IODs) in 2018 and 2019 suppressed the formation of northwest cloud bands and southern Australia rainfall, and a persistent teleconnection, with enhanced convection generated by positive Niño-4 region sea surface temperature (SST) anomalies and strong subsidence over eastern Australia, exacerbated this Australian drought. As with “classical” El Niño–Southern Oscillation (ENSO) events, which usually last 12–18 months, protracted ENSO episodes, which last for more than 2 yr, show a similar pattern of impacts on society and the environment across the Indo-Pacific domain, and often extend globally. The second half of this study puts the impact of the 2018–20 protracted El Niño episode on both the Australian terrestrial agricultural and marine ecophysiological environments in a broader context. These impacts are often modulated not only by the direct effects of ENSO events and episodes, but by interrelated local to region ocean–atmosphere interactions and synoptic weather patterns. Even though the indices of protracted ENSO episodes are often weaker in magnitude than those of major classical ENSO events, it is the longer duration of the former that poses its own set of problems. Thus, there is an urgent need to investigate the potential to forecast protracted ENSO episodes, particularly when the mid-2020 to current 2022 period has been experiencing a major protracted La Niña episode with near-global impacts.
Significance Statement
The major 2018–20 Australian drought and its terrestrial and marine impacts were caused by a “protracted” El Niño episode, exacerbated by global warming. Indo-Pacific ocean–atmosphere interactions resulted in a persistent positive western Pacific Niño-4 sea surface temperature anomaly during the period 2018–20 and positive Indian Ocean dipoles (IODs) in 2018 and 2019. These suppressed rainfall across eastern Australia and limited northwest Australian cloud band rainfall across southern Australia. Australian agricultural and ecophysiological impacts caused by protracted El Niño–Southern Oscillation (ENSO) episodes permeate, overstress, and expose society, infrastructure, and livelihoods to longer temporal-scale pressures than those experienced during shorter “classical” ENSO events. Thus, there is an urgent need to investigate the potential to forecast protracted ENSO episodes.
... ENSO originates in the tropical Pacific through interactions between the ocean and the atmosphere, but its environmental and socioeconomic impacts are felt worldwide (McPhaden et al., 2006). Thanks to the continuous observation in the tropical Pacific by the Tropical Ocean Global Atmosphere (TOGA) program, our understanding of ENSO has made significant process (Wang and Picaut, 2004;McPhaden et al., 2010), and has continued to evolve as new layers of complexity that refers to the diversity in spatial patterns, amplitude and temporal evolution (Timmermann et al., 2018). ...
... Previous studies had focused on ENSO STPD (e.g., Timmermann et al., 2018;Jin, 2022). ...
A spatiotemporal oscillator model for El Ni\~no/Southern Oscillation (ENSO) is constructed based on the sea surface temperature (SST) and thermocline depth dynamics. The model is enclosed by introducing a proportional relationship between the gradient in SST and the oceanic zonal current and can be transformed into a standard wave equation that can be decomposed into a series of eigenmodes by cosine series expansion. Each eigenmode shows a spatial mode that oscillates with a natural frequency. The first spatial mode, that highlights SST anomaly (SSTA) contrast in the eastern and western Pacific, the basic characteristics of the eastern Pacific (EP) El Ni\~no, oscillates with a natural period of around 4.3 years, consistent with the quasi-quadrennial (QQ) mode. The second spatial mode, that emphasizes SSTA contrast between the central and the eastern, western Pacific, the basic spatial structure of the central Pacific (CP) El Ni\~no, oscillates with a natural period of 2.3 years that is half of the first natural period, also consistent with the quasi-biennial (QB) modes. The combinations of the first two eigenmodes with different weights can feature complex SSTA patterns with complex temporal variations. In open ocean that is far away from the coastlines, the model can predict waves propagating both eastward and westward. Besides, the net surface heating further complicates the temporal variations by exerting forced frequencies. The model unifies the temporal and spatial variations and may provide a comprehensive viewpoint for understanding the complex spatiotemporal variations of ENSO.
... El Niño is subjected to complexity (Timmermann et al., 2018), with event-by-event diversity in terms of magnitude (Peng et al., 2019(Peng et al., , 2020J.-Z. Wang & Wang, 2021), spatial structure (e.g., Kao & Yu, 2009;X. ...
... emphasizes the link between the special PS SLA and the temporal evolution diversity of ENSO. However, ENSO complexity includes the diversity in patterns, amplitude and temporal evolution (Timmermann et al., 2018). Therefore, we further examined the impacts of ENSO flavors (EP and CP types; see Text 1 in Supporting Information S1 for detail) and amplitude. ...
The Philippine Sea (PS) tends to show sea-level falling and cyclonic upper-layer circulation anomalies during the developing stage (spring and summer) of El Niño, with notable influences on the circulation, climate, and biosystems of downstream regions. These changes show diversity across individual events, partly associated with ENSO complexity and bringing uncertainties to scientific attribution and prediction. Here, we focus on the positive sea-level anomalies (SLAs) and anticyclonic circulation anomalies in the boreal spring and summer of 2006 and 2009, which are opposite in sign to the changes observed during other El Niño events. Correspondingly, exceptional changes were seen in downstream regions, such as the enhanced Indonesian throughflow in the Makassar Strait. Our analysis highlights the persistent equatorial easterly wind anomalies during the preceding winter, which were likely favorable for these special changes in 2006 and 2009. This is confirmed by sensitivity experiments of a simplified ocean model. The results show that the negative off-equatorial wind stress curls in the western and central North Pacific play the key role in causing positive SLAs in the PS through evoking downwelling Rossby waves, and equatorial winds played a secondary role. Further analysis indicates that such special changes of the PS tend to take place in late-onset weak El Niño events following the La Niña conditions.
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... We use CI provided by NOAA. CI MEI is provided bimonthly by an empirical orthogonal function that combines different variables including sea surface pressure and temperature (Di Lorenzo et al., 2023;Timmermann et al., 2018;Wolter & Timlin, 1993). Since the data are bimonthly, they should be interpolated to generate daily values to be used as an additional feature for the prediction of dUT1. ...
Rapid provision of Earth Orientation Parameters (EOPs, here polar motion and dUT1) is indispensable in many geodetic applications and also for spacecraft navigation. There are, however, discrepancies between the rapid EOPs and the final EOPs that have a higher latency, but the highest accuracy. To reduce these discrepancies, we focus on a data‐driven approach, present a novel method named ResLearner, and use it in the context of deep ensemble learning. Furthermore, we introduce a geophysically‐constrained approach for ResLearner. We show that the most important geophysical information to improve the rapid EOPs is the effective angular momentum functions of atmosphere, ocean, land hydrology, and sea level. In addition, semi‐diurnal, diurnal, and long‐period tides coupled with prograde and retrograde tidal excitations are important features. The influence of some climatic indices on the prediction accuracy of dUT1 is discussed and El Niño Southern Oscillation is found to be influential. We developed an operational framework, providing the improved EOPs on a daily basis with a prediction window of 63 days to fully cover the latency of final EOPs. We show that under the operational conditions and using the rapid EOPs of the International Earth Rotation and Reference Systems Service (IERS) we achieve improvements as high as 60%, thus significantly reducing the differences between rapid and final EOPs. Furthermore, we discuss how the new final series IERS 20 C04 is preferred over 14 C04. Finally, we compare against EOP hindcast experiments of the European Space Agency, on which ResLearner presents comparable improvements.
... 4 other tropical ocean basins as well as to the mid-latitudes may benefit by alleviating the cold bias (Alexander et al., 2002;Yeh et al. 2018;Timmermann et al. 2018). For example, in CMIP5 models the cold bias leads to a westward shift in the atmospheric response to ENSO over the North Pacific and significant underestimation of the ENSOrelated precipitation anomalies over California (Bayr et al., 2019b). ...
We investigate the origin of the equatorial Pacific cold sea surface temperature (SST) bias and its link to wind biases, local and remote, in the Kiel Climate Model (KCM). The cold bias is common in climate models participating in the 5 th and 6 th phases of the Coupled Model Intercomparison Project. In the coupled experiments with the KCM, the interannually varying NCEP/CFSR wind stress is prescribed over four spatial domains: globally, over the equatorial Pacific (EP), the northern Pacific (NP) and southern Pacific (SP). The corresponding EP SST bias is reduced by 100%, 52%, 12% and 23%, respectively. Thus, the EP SST bias is mainly attributed to the local wind bias, with small but not negligible contributions from the extratropical regions. Erroneous ocean circulation driven by overly strong winds cause the cold SST bias, while the surface-heat flux counteracts it. Extratropical Pacific SST biases contribute to the EP cold bias via the oceanic subtropical gyres, which is further enhanced by dynamical coupling in the equatorial region.
The origin of the wind biases is examined by forcing the atmospheric component of the KCM in a stand-alone mode with observed SSTs and simulated SSTs from the coupled experiments. Wind biases over the EP, NP and SP regions originate in the atmosphere model. The cold EP SST bias substantially enhances the wind biases over all three regions, while the NP and SP SST biases support local amplification of the wind bias. This study suggests that improving surface-wind stress, at and off the equator, is a key to improve mean-state equatorial Pacific SST in climate models.
... The research area selected for this study is the Paci c Ocean as it has signi cant in uence on climate variability, particularly over equatorial zone. In the interannual timescale, the ocean and atmosphere variability in the Paci c Ocean are predominantly in uenced by anomalous climate mode of ENSO, which involves non-periodic uctuation in winds, sea surface temperatures (SST), and air pressure of the underlying atmosphere (Southern Oscillation) across the equator over the tropical eastern Paci c Ocean (Philander, 1985;Holton and Dmowska, 1989;Timmermann et al., 2018). In addition, this area is selected because there are numerous previous studies on that area which can thoroughly validate our results, as the primary aim this study is merely to investigate the applicability of the on-board microwave radiometer to capture the atmospheric variability during ocean-atmosphere phenomena (in this case ENSO). ...
Since its first launching, the ability of satellite Altimetry in providing reliable and accurate ocean geophysical information of the sea surface height (SSH), significant wave height (SWH), and wind speed has been proven by numerous research, as it was designed for observing the ocean dynamics through nadir range measurement between satellite and the sea surface. However, to achieve high level accuracy, environmental and geophysical effects on the range measurement must be accurately determined and corrected, in particularly the effects from the atmospheric water vapor which can divert altimeter range up to 3–45 cm. Thus, satellite Altimetry is originally equipped by the on-board microwave radiometer to measure the water vapour content for correcting the range measurement. To our knowledge, no one has attempted to apply the on-board radiometer for atmospheric studies. In this present work, we attempt to optimize the on-board radiometer data for studying the atmosphere variability due to the El Niño–Southern Oscillation (ENSO) phenomena. We convert the on-board water vapor data into the precipitable water vapour (PWV), and we then investigate whether the derived PWV can capture the variability of ocean-atmosphere phenomena due to ENSO as accurate as the traditional Altimetry-derived sea level anomaly (SLA). Based on our analysis using the empirical orthogonal function (EOF), the results show convincing argument that Altimetry-derived PWV are reliable in examining the atmospheric fluctuation as the correlation of its primary principal component time series (PC1) with Oceanic Nino Index is higher (0.87) than SLA (0.80). The correlations between two dominant principal components (PC1 and PC2) of PWV and SLA are high, which are approximately 0.93 and − 0.67 for PC1 and PC2, respectively. These results may reinforce the confidence in the ability of satellite Altimetry for ocean-atmospheric studies.
... A 180-year segment of the tropical Pacific synthetic data averaged over 5°S-5°N is shown in Figure 2b to analyze ENSO diversity (Capotondi et al., 2015;Timmermann et al., 2018). Despite the symmetries between El Niño and La Niña due to the linear, Gaussian assumption, ENSO varies in its phase evolution He et al., 2020). ...
This study addresses how to model and predict large‐scale climate variability, such as the El Niño–Southern Oscillation (ENSO). We introduce a framework for inferring the macroscale causal structure of the climate system using a spatial‐dimension reduction and high‐dimensional variable selection. The framework encodes the causal structure into a structural causal model, which captures the mechanisms and diversity of ENSO. It thus has a potential to reveal other physical processes within the climate system. The model predicts ENSO at a 1‐month lead time with high accuracy, and the recursive predictions at multi‐month leads are still reliable, even in a different climate state. The stand‐alone oceanic experiments capture the observed oceanic response, proving the model's capability to predict large‐scale climate variability using fragmentary information. This study demonstrates the potential for inferring causal structures to explain, model, and predict large‐scale climate variability such as ENSO.
... Figure 7 presents the time series of the PRCPTOT, R30mm, R95p, and RX5day anomalies in ABOV and STCZ. First, there is the marked interannual variability of precipitation caused by lower frequency phenomena, such as El Niño-Southern Oscillation (Timmermann et al., 2018) and South Atlantic Dipole (Bombardi et al., 2014), which modulate the frequency and intensity of systems such as fronts and cyclones in the Southeastern region of Brazil. In ABOV, despite the statistically non-significant trends, PRCTOT increased by +69 mm/decade (Figure 7(a)) and R30mm increased by +0.6 day/decade (Figure 7(b)). ...
... El Ni o/Southern Oscillation (ENSO) is one of the main climatic modes of variability, the teleconnections of which have worldwide impacts (Timmermann et al., 2018). During El Ni o periods an anomalous sea surface temperature (SST) warming pattern can be identified in the eastern/central Pacific, replaced by an anomalous SST cooling pattern during La Ni a. ...
The specifics of the simulated injection choices in the case of stratospheric aerosol injections (SAI) are part of the crucial context necessary for meaningfully discussing the impacts that a deployment of SAI would have on the planet. One of the main choices is the desired amount of cooling that the injections are aiming to achieve. Previous SAI simulations have usually either simulated a fixed amount of injection, resulting in a fixed amount of warming being offset, or have specified one target temperature, so that the amount of cooling is only dependent on the underlying trajectory of greenhouse gases. Here, we use three sets of SAI simulations achieving different amounts of global mean surface cooling while following a middle‐of‐the‐road greenhouse gas emission trajectory: one SAI scenario maintains temperatures at 1.5°C above preindustrial levels (PI), and two other scenarios which achieve additional cooling to 1.0°C and 0.5°C above PI. We demonstrate that various surface impacts scale proportionally with respect to the amount of cooling, such as global mean precipitation changes, changes to the Atlantic Meridional Overturning Circulation and to the Walker Cell. We also highlight the importance of the choice of the baseline period when comparing the SAI responses to one another and to the greenhouse gas emission pathway. This analysis leads to policy‐relevant discussions around the concept of a reference period altogether, and to what constitutes a relevant, or significant, change produced by SAI.
... It is well established that El Niño is largely preconditioned by a slow buildup of upper ocean heat in the western Pacific warm pool region (e.g., Jin, 1997;Meinen & McPhaden, 2000;Suarez & Schopf, 1988;Wyrtki, 1985). While external forcing is generally not required to initiate El Niño, stochastic forcing has been shown to be central to El Niño onset, growth, and irregularity, as well as ENSO phase asymmetry (e.g., Chang et al., 1996;Kirtman & Schopf, 1998;Lopez & Kirtman, 2015;Penland & Sardeshmukh, 1995;Timmermann et al., 2018). This stochastic forcing consists mainly of episodic westerly wind bursts (WWBs) in the western equatorial Pacific, which are often linked to the convectively-active phase of the Madden-Julian Oscillation (MJO) (e.g., Chiodi et al., 2014;Kessler, 2002;McPhaden, 1999McPhaden, , 2004Puy et al., 2016). ...
Plain Language Summary
Atlantic Niño is the Atlantic counterpart of El Niño in the Pacific, often referred to as El Niño's little brother. It was previously thought to have only regional influence on rainfall variability in West Africa, but a growing number of studies have shown that Atlantic Niño also plays an important role in the development of El Niño–Southern Oscillation, as well as in the formation of powerful hurricanes near the coast of West Africa. This study investigates the development of an extreme Atlantic Niño in the summer of 2021. Here, we show that the 2021 event was preconditioned by warm waters piled up near the South American coast, and then directly triggered by a westerly wind burst event that drove the warm waters eastward. The westerly wind burst event was driven by a patch of tropical thunderstorms that formed across the Indian Ocean and moved slowly eastward across the Pacific, South America, and the Atlantic, also known as the Madden‐Julian Oscillation. Westerly wind bursts driven by the Madden‐Julian Oscillation are fundamental for the development of El Niño in the Pacific, but a previously unidentified driver for Atlantic Niño, and thus may improve our ability to predict future Atlantic Niño events.
... El Niño-Southern Oscillation (ENSO) is the dominant mode of interannual climate variability in the tropical Pacific. Its influence has been identified to be worldwide 1,2 . Therefore, it is pivotal to accurately predict ENSO as early as possible due to its tremendous global impact. ...
El Niño-Southern Oscillation (ENSO) asymmetry in predictability on springtime initial condition remains unclear. From the perspective of the spring predictability barrier (SPB), this paper investigates the ENSO asymmetry in SPB and explores the potential factors that may lead to this asymmetry. Both the observation and 29 Coupled Model Intercomparison Project Phase 6 (CMIP6) models show that the spring sea surface temperature (SST) persistence is significantly higher in El Niño years than that in La Niña years, and the SPB intensity is stronger in La Niña years than that in El Niño years. Through the recharge oscillator model, observation and CMIP6 models, we demonstrate that the nonlinear wind stress response to SST anomalies in spring is the main cause of the asymmetric SPB intensity. By the mixed-layer heat budget of the tropical Pacific in the spring, we further identify that a stronger response of zonal wind stress in El Niño events can cause a stronger zonal advection feedback, which finally leads to a weaker SPB and enhances the predictability of El Niño. In contrast, the cooling SST in the spring only leads to weak easterly anomalies, the zonal advection feedback is relatively weaker, thus SPB is stronger and the predictability of La Niña is lower. From the perspective of SPB, we suggest that El Niño is more predictable than La Niña.
... El Niño-Southern Oscillation (ENSO) is an air-sea coupled phenomenon occurring in the equatorial Pacific with pronounced climatic impacts around the globe [1][2][3] . It has two opposing phases (i.e., El Niño and La Niña), which are not simple mirror images and exhibit striking asymmetries in their dynamics, climate impacts, and predictability [4][5][6][7] . One of the most remarkable nonlinear characteristics of ENSO is its amplitude asymmetry (hereafter referred to as ENSO asymmetry for convenience), which describes the fact that sea surface temperature (SST) anomalies during strong El Niño episodes are more intense and farther east than during La Niña episodes. ...
El Niño-Southern Oscillation (ENSO) sea surface temperature (SST) anomaly skewness encapsulates the nonlinear processes of strong ENSO events and affects future climate projections. Yet, its response to CO 2 forcing remains not well understood. Here, we find ENSO skewness hysteresis in a large ensemble CO 2 removal simulation. The positive SST skewness in the central-to-eastern tropical Pacific gradually weakens (most pronounced near the dateline) in response to increasing CO 2 , but weakens even further once CO 2 is ramped down. Further analyses reveal that hysteresis of the Intertropical Convergence Zone migration leads to more active and farther eastward-located strong eastern Pacific El Niño events, thus decreasing central Pacific ENSO skewness by reducing the amplitude of the central Pacific positive SST anomalies and increasing the scaling effect of the eastern Pacific skewness denominator, i.e., ENSO intensity, respectively. The reduction of eastern Pacific El Niño maximum intensity, which is constrained by the SST zonal gradient of the projected background El Niño-like warming pattern, also contributes to a reduction of eastern Pacific SST skewness around the CO 2 peak phase. This study highlights the divergent responses of different strong El Niño regimes in response to climate change.
... Different SST contrasts were due to temperature changes and geographic extend of the Western Pacific Warm Pool during the El Niño-Southern Oscillation event. SPMNP is located in the western Pacific Ocean, hence El Niño may have caused relative cooling across the study region, while La Niña tends to cause oceanic warming (Timmermann et al., 2018). The years 2019 and 2020 were also El Niño and moderately strong La Niña years, respectively. ...
... In recent work 14 , the authors developed a framework based on autonomous techniques that successfully extracted slowly decaying cycles from a single nonstationary time series. Specifically, the El-Niño Southern Oscillation (ENSO) 15 was extracted as a complex eigenvector of the transfer or Koopman operators built from monthly SST images over the past 50 years. Over this time, there is noticeable warming of the ocean, and one could argue that time-dependent techniques should be applied. ...
An important problem in modern applied science is characterizing the behavior of systems with complex internal dynamics subjected to external forcings from their environment. While a great variety of techniques has been developed to analyze such non-autonomous systems, many approaches rely on the availability of ensembles of experiments or simulations in order to generate sufficient information to encapsulate the external forcings. This makes them unsuitable to study important classes of natural systems such as climate dynamics where only a single realization is observed. Here, we show that operator-theoretic techniques previously developed to identify slowly decaying observables of autonomous dynamical systems provide a powerful means for identifying trends and persistent cycles of non-autonomous systems using data from a \emph{single} trajectory of the system. Using systematic mathematical analysis and prototype examples, we demonstrate that eigenfunctions of Koopman and transfer operators provide coordinates that simultaneously capture nonlinear trends and coherent modes of internal variability. In addition, we apply our framework to two real-world examples from present and past climate dynamics: Variability of sea surface temperature (SST) over the industrial era and the mid-Pleistocene transition (MPT) of Quaternary glaciation cycles. Our results provide a nonparametric representation of SST and surface air temperature (SAT) trends over the industrial era, while also capturing the response of the seasonal precipitation cycle to these trends. In addition, our paleo-climate analysis reveals the dominant glaciation cycles over the past 3 million years and the MPT with a high level of granularity.
... The anomalous sinking motion occurs over the western tropical Pacific and it leads to inflows of ozone-rich air in this sinking area (Thengumthara et al., 2007). In addition to this, ENSO can influence global atmospheric circulations (Timmermann et al., 2018;Yeh et al., 2018), particularly in East Asia through affecting anticyclones in the western North Pacific (Wang et al., 2000;Wu et al., 2003). Generally, El Niño intensifies the western North Pacific subtropical high during the following summer after the wintertime peak (Chen et al., 2019;Xie et al., 2009), which can affect the fate of ozone in this area. ...
Abundance of tropospheric ozone is determined by the interplay among emissions, chemistry, and climate dependent transport. It is important to identify the essential factors that modulate ozone in China and Korea because of increasing ozone trends in this area. In this study, we investigated the correlations of tropospheric ozone in Asia with El Niño Southern Oscillations (ENSO). The model simulated ozone from 2000 to 2020 with Community Atmosphere Model version 6 with chemistry (CAM6‐chem), ECMWF Atmospheric Composition Reanalysis data, and ozone profiles measured in South Korea, Japan, and Hong Kong. These data were utilized to understand the ozone–ENSO relationship in this region. We found that tropospheric ozone in Asia is significantly correlated with the wintertime Niño 3.4 index in El Niño/La Niña developing summer and decaying spring. Particularly tropospheric ozone originated from stratosphere is strongly correlated with the Niño 3.4 index. Ozone concentrations in China and the Korean Peninsula in El Niño developing summer is generally higher than that in La Niña because of anomalous cyclonic/anticyclonic flows in the extratropical region. In El Niño/La Niña decaying spring, the opposite occurs in the same region. In the decaying spring, stronger variability was observed in the subtropical regions of the lower to mid troposphere, indicating higher ozone concentrations during El Niño compared to La Niña. This variability is associated with the fluctuations in the Walker circulation, large‐scale sinking/rising motions, and the accompanying biomass burning events. The model reproduced the ozone variability associated with ENSO in the ozonesonde observations.
... Tales condiciones van acompañadas de cambios en la circulación atmosférica y oceánica, que afectan el clima global, los ecosistemas marinos y terrestres, la pesca y las actividades humanas. La alternancia de condiciones cálidas de El Niño y frías de La Niña, conocida como El Niño-Oscilación del Sur (ENOS), representa la fluctuación más fuerte de un año a otro del sistema climático global (Timmermann et al., 2018). Dada la importancia de los efectos del fenómeno ENOS sobre la producción primaria, el presente trabajo tiene por objetivo realizar una evaluación de los riesgos de pérdidas económicas directas en la producción agrícola extensiva, generadas por eventos de sequias y de excesos hídricos. ...
Los eventos extremos de inundaciones y sequías, asociados con el ciclo El Niño Oscilación Sur (ENOS), son fenómenos recurrentes en la Provincia del Chaco, y causan pérdidas de cosechas y de producción. El presente trabajo tiene por objetivo evaluar los riesgos de pérdidas económicas directas en la producción de algodón, soja, maíz y girasol, generadas por eventos de sequias y de excesos hídricos. Para ello, se estima un indicador de riesgo de rendimientos de cultivos, y, mediante un análisis de correspondencia, se analiza la relación de dependencia entre los rendimientos extremos y las fases El Niño y La Niña. Finalmente se calcula las pérdidas en el Valor Bruto de la Producción (VBP) y se analiza la relación entre estas y las fases del ciclo ENOS. Los resultados muestran que el cultivo de algodón presenta mayor frecuencia de rendimientos extremos (13 de 21 campañas, 62%), pero estos son de baja “intensidad” (máximo 38%); soja y maíz tienen rendimientos extremos con menor frecuencia, 38% y 33%, respectivamente, pero de mayor alcance (69%); y en girasol, la frecuencia es aún más baja, 29%, pero el alcance es mucho más amplio; pérdidas que llegan a alcanzar al 100% de los departamentos. Las fases La Niña estarían asociados a rendimientos extremos altos y muy altos en algodón; a extremos altos, muy altos, y a extremos bajos en soja; y a rendimientos muy altos en maíz y girasol. Las fases El Niño estarían asociados a rendimientos normales en soja y maíz, y extremadamente bajos en girasol. Los años neutros estarían asociados a rendimientos extremadamente bajos y muy bajos en algodón y extremadamente bajos en maíz.
... El Niño-Southern Oscillation (ENSO) is one of the most intense ocean-atmosphere coupling phenomena worldwide [1], characterized by a clear periodicity with a cycle of 2-7 years [2]. Although originating in the tropical Pacific region, ENSO affects various parts of the world through atmospheric teleconnections. ...
ENSO is an important climate phenomenon that often causes widespread climate anomalies and triggers various meteorological disasters. Accurately predicting the ENSO variation trend is of great significance for global ecosystems and socio-economic aspects. In scientific practice, researchers predominantly employ associated indices, such as Niño 3.4, to quantitatively characterize the onset, intensity, duration, and type of ENSO events. In this study, we propose the STL-TCN model, which combines seasonal-trend decomposition using locally weighted scatterplot smoothing (LOESS) (STL) and temporal convolutional networks (TCN). This method uses STL to decompose the original time series into trend, seasonal, and residual components. Each subsequence is then individually predicted by different TCN models for multi-step forecasting, and the predictions from all models are combined to obtain the final result. During the verification period from 1992 to 2022, the STL-TCN model effectively captures index features and improves the accuracy of multi-step forecasting. In historical event simulation experiments, the model demonstrates advantages in capturing the trend and peak intensity of ENSO events.
... El Niño-Southern Oscillation (ENSO) is widely acknowledged as the strongest interannual variability in the tropical oceans, with significant effects on the global climate (Yeh et al., 2009). Typically, the ENSO lifecycle, which includes the warm (El Niño) and the cold (La Niña) phases, begins to develop in boreal summer, reaches maturity in winter, and decays in the following spring (Bjerknes, 1969;Li et al., 2017;Timmermann et al., 2018). However, El Niño and La Niña events differ in terms of their spatial patterns, seasonal evolutions, and climatic effects (An and Jin, 2001;DiNezio and Deser, 2014). ...
... Hereinafter, the term pSON EM will be used to refer to those EM events occurring in the pSON season. 1985, 1988, 1989, 1999, 2000pJJA 1991, 1994, 2002, 2018, 20191983, 1997, 1998, 1999, 2008, 2010, 1983, 1987, 19971984, 1985, 1988, 1999, 2010cSON 1990, 1991, 1994, 20191983, 1988, 1997, 1998, 2008, 2010, 1987, 1997, 20021988, 1999, 2010, 2011, 2017 List of Figures ...
The lagged teleconnections of the two types of El Niño–Southern Oscillation (ENSO) on Southeast Asian autumn rainfall (SEAAR) anomalies for lagged time varying from 12 to zero months are investigated for the period 1979 to 2019 using Singular Value Decomposition (SVD) and composite analyses.
It shows that the first two SVD coupled modes always exhibit the patterns of the two types of ENSO for all lagged times. First, the canonical El Niño (EN) from preceding autumn induces significantly wetter SEAAR, due to the transition from EN to La Niña (LN) in the following year. Thereafter, disparities between ENSO and ENSO Modoki teleconnections on SEAAR are most pronounced for the 6-month lag, in which an El Niño Modoki (EM)/La Niña Modoki (LM) occurring in the preceding spring is a strong indication of a much drier/wetter SEAAR. Subsequently, for 3-month and zero lag, both EN and EM (LN and LM) bring drier (wetter) SEAAR. However, EM leads to drier (wetter) conditions in the north (south). Regarding LN and LM, LM causes significantly less rainfall in the Philippines, northern Indochina, and Sumatra.
The differences in SEAAR anomalies under ENSO and ENSO Modoki conditions are linked to a more northward (southward) Walker circulation in EM compared to EN (LM compared to LN). Different evolution patterns, i.e., transition from EM to LM occurs less frequently than EN to LN, particularly with preceding autumn and spring ENSO events, contribute to the distinct lagged teleconnections between the two ENSO types. It also results in longer teleconnection persistence of LM and LN compared to EM and EN.
... This article seeks to provide a comprehensive understanding about the PMM phase asymmetry via a detailed investigation by separating the PMM into successive and stochastic events. In addition, considering that successive and stochastic PMM events occur respectively during the decaying and developing of ENSO events, how the phase asymmetry of these two types of PMM events might respectively contribute to ENSO transition complexity (Timmermann et al., 2018;Yu & Fang, 2018) is also the main goal of this study. ...
Plain Language Summary
In this study, we investigated the influences of the Pacific Meridional Mode (PMM), an important modulator of El Niño‐Southern Oscillation (ENSO), on the diverse transition preference of ENSO events (ENSO transition complexity). We first divided PMM events into two types that are respectively triggered by tropical and extratropical forcing, and named them as successive and stochastic PMM events. Successive PMM events tend to enhance persistency of ENSO events, whereas stochastic PMM events incline to initiate ENSO events from a neutral state. We found that the successive PMM events are negatively asymmetric because the triggering effect of tropical forcing is stronger in the negative phase. This negative asymmetry leads to longer persistency of La Niña events than El Niño events. On the contrary, the stochastic PMM events are positively asymmetric because the growth rate of PMM is stronger in the positive phase. This positive phase asymmetry of stochastic PMM events results in more frequent episodic El Niño events than La Niña events. This study is the first to show the contribution of positively asymmetric stochastic PMM events to higher frequency of episodic El Niño events, which may supplement our understanding of ENSO transition complexity.
... Since Atlantic storm movement is steered by 500-or 700-hPa low-frequency mean winds over the North Atlantic (NA) [11][12][13] , the low-frequency atmospheric circulation over the NA could influence the occurrence of A-RTDW events by steering the storms into the Arctic. Moreover, previous modeling and reanalysis studies both confirmed that the El Niño-Southern Oscillation (ENSO), which is a major driver of global interannual variability [14][15][16] , could have a significant impact on the low-frequency mean winds over the NA and Arctic regions by the ENSO/North Atlantic oscillation (NAO) teleconnection [17][18][19] . Thus, we speculate that ENSO may exert an influence on the occurrence frequency of A-RTDW events. ...
Arctic daily warming is gradually garnering the attention of academics. Here we discuss an interdecadal change around the mid-1980s in the role of the Central Pacific El Niño-Southern Oscillation (CP ENSO) in the occurrence frequency of Arctic daily warming events triggered by Atlantic storms in winter (called the Atlantic pattern-Arctic rapid tropospheric daily warming (A-RTDW) event) and the possible mechanism. Before the mid-1980s, the Central Pacific El Niño/La Niña events weakened/strengthened the Iceland Low (IL); the resulting anomalous northerly/southerly at the east of the IL prevented/favored the A-RTDW event occurrence by leading Atlantic storms away from/into the Arctic. Thus, the CP ENSO could affect the occurrence frequency of A-RTDW events by the CP ENSO/IL teleconnection. In contrast, this role hardly exists after the mid-1980s. Before the mid-1980s, the CP ENSO could affect the polar vortex by planetary wave propagation upwards into the stratosphere to create the CP ENSO/IL teleconnection; thereby, the CP ENSO and A-RTDW could establish a connection. However, after the mid-1980s, the planetary wave associated with CP ENSO could not propagate upwards into the stratosphere; thus, the ENSO/IL teleconnection disappears, resulting in CP ENSO having no effect on the occurrence frequency of A-RTDW events.
... In the real world, no two El Niño events are exactly alike in terms of their onset timing, duration, and other fundamental characteristics (Quinn et al., 1971;Timmermann et al., 2018). Some ENSO events might have not yet developed by boreal summer (Neelin et al., 2000), and other events, especially cold La Niña events, could persist beyond a year, often re-intensifying in the following winter (Okumura & Deser, 2010). ...
Understanding the interaction between the tropical Pacific and Atlantic Oceans has challenged the climate community for decades. Typically, boreal summer Atlantic Niño events are followed by vigorous Pacific events of opposite sign around two seasons later. However, incorporating the equatorial Atlantic information to variabilities internal to the Pacific lends no significant additional predictive skill for the subsequent El Niño‐Southern Oscillation (ENSO). Here we resolve this conundrum in a physically consistent frame, in which the nascent onset of a Pacific event rapidly induces an opposite‐signed summer equatorial Atlantic event and the lead correlation of Atlantic over Pacific is a statistical artifact of ENSO's autocorrelation. This Pacific‐to‐Atlantic impact is limited to a short window around late spring due to seasonally‐amplified Atlantic atmosphere‐ocean coupling. This new frame reconciles the discrepancies between the observed and multi‐model simulated inter‐basin relationship, providing a major advance in understanding seasonally‐modulated inter‐basin climate connections as well as their predictability.
... According to the location of the warm center, these two types of El Niño are referred to as EP and CP El Niño. The CP El Niño shows distinct dynamics and predictability compared with the EP El Niño (Timmermann et al., 2018). For example, the CP El Niño is more difficult to predict than the EP El Niño using the operational models (Ren, Scaife, et al., 2018;Zheng & Yu, 2017). ...
Perturbations in the thermocline and surface zonal current (ZC) play crucial roles in the evolutions of the eastern Pacific (EP) and central Pacific (CP) El Niño events, respectively. Whereas numerous studies have examined the influence of initial uncertainties in ocean temperature on the predictability of El Niño, only a few studies investigated the impact of the initial ZC. Using an air‐sea coupling model, the conditional nonlinear optimal perturbation (CNOP) approach was employed to investigate the maximum impact of initial ZC errors on the El Niño prediction. The optimal initial ZC errors (denoted as CNOP‐Us) that have the severest impact on the El Niño prediction are found to mainly concentrate in the western and central tropical Pacific. The CNOP‐Us cause larger errors in the CP El Niño prediction than in the EP El Niño prediction. Additionally, CNOP‐Us cause rapid sea surface temperature error growth in spring in the EP El Niño prediction but in summer in the CP El Niño prediction. Dynamically, the former is related to the large uncertainties in the meridional current in spring caused by CNOP‐Us, while the latter is related to the strong ZC errors in summer. According to the distributions of CNOP‐Us, reducing the initial ZC errors in the western and central tropical Pacific may be vital in weakening the predictability barrier phenomena and improving the predictions of El Niño diversity.
... At present, domain experts typically address such problems by employing the group mean of a variable group as a stand-in for the group as a whole, or by means of more elaborate standard dimension reduction techniques such as principal component analysis (PCA). For instance, in climate science, the El Niño Southern Oscillation (ENSO) is often represented as either a regional average of sea surface temperatures, or as a principal component in a PCA [14]. Unfortunately, if some of the causal processes at hand happen at smaller scale than averages or principal components can capture, relevant causal information may be lost. ...
Discovering causal relationships from observational data is a challenging task that relies on assumptions connecting statistical quantities to graphical or algebraic causal models. In this work, we focus on widely employed assumptions for causal discovery when objects of interest are (multivariate) groups of random variables rather than individual (univariate) random variables, as is the case in a variety of problems in scientific domains such as climate science or neuroscience. If the group-level causal models are derived from partitioning a micro-level model into groups, we explore the relationship between micro and group-level causal discovery assumptions. We investigate the conditions under which assumptions like Causal Faithfulness hold or fail to hold. Our analysis encompasses graphical causal models that contain cycles and bidirected edges. We also discuss grouped time series causal graphs and variants thereof as special cases of our general theoretical framework. Thereby, we aim to provide researchers with a solid theoretical foundation for the development and application of causal discovery methods for variable groups.
... While ENSO is known to be unambiguously dominant in the Pacific with strong impacts on the shoreline and coastal ecosystems 41,42,51,[57][58][59][60][61][62][63][64] , the possible linkages between ENSO and the key drivers of shoreline change at global scale have not yet been fully explored. In particular, the recent rejuvenation of ENSO research has led to many theoretical breakthroughs in understanding its complex and diverse regimes 65 . On a global basis, other climate modes can also significantly modulate coastal drivers in other ocean basins. ...
Coastal zones are fragile and complex dynamical systems that are increasingly under threat from the combined effects of anthropogenic pressure and climate change. Using global satellite derived shoreline positions from 1993 to 2019 and a variety of reanalysis products, here we show that shorelines are under the influence of three main drivers: sea-level, ocean waves and river discharge. While sea level directly affects coastal mobility, waves affect both erosion/accretion and total water levels, and rivers affect coastal sediment budgets and salinity-induced water levels. By deriving a conceptual global model that accounts for the influence of dominant modes of climate variability on these drivers, we show that interannual shoreline changes are largely driven by different ENSO regimes and their complex inter-basin teleconnections. Our results provide a new framework for understanding and predicting climate-induced coastal hazards.
Climate-related phenomena in Peru have been slowly but continuously changing in recent years beyond historical variability. These include sea surface temperature increases, irregular precipitation patterns and reduction of glacier-covered areas. In addition, climate scenarios show amplification in rainfall variability related to the warmer conditions associated with El Niño events. Extreme weather can affect human health, increase shocks and stresses to the health systems, and cause large economic losses. In this article, we study the characteristics of El Niño events in Peru, its health and economic impacts and we discuss government preparedness for this kind of event, identify gaps in response, and provide evidence to inform adequate planning for future events and mitigating impacts on highly vulnerable regions and populations. This is the first case study to review the impact of a Coastal El Niño event on Peru’s economy, public health, and governance. The 2017 event was the third strongest El Niño event according to literature, in terms of precipitation and river flooding and caused important economic losses and health impacts. At a national level, these findings expose a need for careful consideration of the potential limitations of policies linked to disaster prevention and preparedness when dealing with El Niño events. El Niño-related policies should be based on local-level risk analysis and efficient preparedness measures in the face of emergencies.
This study investigates the observed El Niño Southern Oscillation (ENSO) dynamics for the eastern Pacific ( EP ) and central Pacific ( CP ) events in reference to the canonical ENSO ( T ). We use the recharge oscillator (ReOsc) model concept to describe the ENSO phase space, based on the interaction of sea surface temperature ( T ) and thermocline depth ( h ), for the different types of ENSO events. We further look at some important statistical characteristics, such as power spectrum and cross-correlation, as essential parameters for understanding the dynamics of ENSO. The results show that the dynamics of the CP and EP events are very different from each other and from the canonical ENSO events. The canonical ENSO ( T ) events fit closest to the idealised ReOsc model and has the most clearly oscillating ENSO phase space, suggesting it is the most predictable ENSO index. The EP index is similar to the canonical ENSO, but the phase space transitions are less clear, suggesting less of an oscillatory nature and the index is more focussed on extreme El Niño and discharge states. The CP index, in turn, does not have a clear propagation through all phases and are strongly skewed towards the La Niña state. The interaction between CP and h are much weaker, making the mode less predictable. Wind forced shallow water model simulations show that the CP winds do not force significant h tendencies, strongly reducing the delayed negative feedback, which is essential for the ENSO cycle.
Plain Language Summary
Sea surface temperature (SST) is one of the important indicators as well as drivers of climate variability over the globe. SST varies not only due to changes in surface heat fluxes but also due to changes in effective heat capacity as mainly determined by mixed layer depth (MLD). However, the observed characteristics of the latter process associated with the MLD anomalies are limited. In this study, we propose a new metric called “Flux Divergence Angle (FDA),” which can quantify the relative importance of MLD and surface heat flux to the SST variability. Using this metric, we find that the MLD anomalies tend to amplify the local SST variability in the extra‐tropics during spring and summer. On the other hand, MLD anomalies tend to suppress the SST variability in the eastern tropical Pacific during December‐January‐February. This paper provides, for the first time, the global picture of relative importance of MLD anomalies to the SST variability based on observations.
Understanding the multi-scale variabilities of global sea surface temperature (GSST) is extremely critical for deepening the comprehension of surface climate change. Great efforts have been made to study the multi-scale features of GSST, however, aiming to fully reveal the local features, here we propose a combined approach, incorporating an adaptive method named Ensemble Empirical Mode Decomposition (EEMD), and Pairwise-Rotated EOF (REOF), to separate signals on various frequency bands and eliminate the confounded EOF signatures. The results show that the explained variance of high-frequency components (HFC) in the equatorial central-eastern and south mid-latitude Pacific could reach more than 60%. The grid points where the variance contributions of low-frequency components (LFC) are greater than 40% are mainly concentrated in the subpolar North Atlantic and the Southern Ocean in both Pacific and Atlantic sectors, while that for secular trend (ST) hitting beyond 60% are displayed in the North Indian Ocean, the Southern Ocean from the tip of southwest Africa expanded to the southern side of Australia, Indo-western Pacific, east of the continents in both hemispheres and tropical Atlantic. By applying the EOF/REOF analysis, the leading modes of the HFC, LFC, and ST are then yielded. It is found that the patterns of the HFC are associated with El Niño-South Oscillation (ENSO) diversity, inferring the dominance and independence of the Eastern Pacific (EP) and Central Pacific (CP) El Niño. Meanwhile, Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO) emerge in the rotated modes of the LFC, with the former exhibiting an Atlantic-Pacific coupling.
This study compares the evolution of atmospheric and oceanic anomalies as well as predictions for the two most recent triple‐dip La Niña events in 1998‐2001 and 2020‐2023. Subsurface cooling in the equatorial Pacific was stronger and more persistent during 1998‐2001. In contrast, surface easterly winds were stronger during 2020‐2023 as was the east‐west sea surface temperature (SST) contrast along the equator. We argue that in the absence of appreciable equatorial Pacific heat discharge, persistent and strong surface trade winds and a strengthened mean zonal SST contrast across the tropical Pacific contributed to the development of the 2020‐2023 triple‐dip La Niña. In terms of the surface layer heat balance, the growth and maintenance of unusually cold SSTs during the triple‐dip La Niña in 1998‐2001 were mainly the result of ocean vertical entrainment and diffusion, as well as meridional advection, associated with enhanced equatorial upwelling; while for the triple‐dip La Niña in 2020‐2023, zonal advection was the largest contributor. The two events were mostly well predicted by multi‐model averages at 1‐8 month lead times. We hypothesize that mean state change with enhanced zonal SST contrast and trade winds over the last several decades altered the physical processes associated with the growth and maintenance of the most recent La Niña, affecting its predictability. Successful prediction in real‐time of the 2020‐2023 event more than half a year in advance was surprising because there was little memory in oceanic heat content which is often considered a key predictor.
El Niño–Southern Oscillation (ENSO) events are a major predictor of Asian spring precipitation. However, it is unclear whether ENSO events with different intensities have similar or different impacts on Asian spring precipitation. This question is explored in this study. According to their intensity, ENSO events are divided into strong and moderate (S‐ENSO and M‐ENSO) events. The analysis indicates that S‐ENSO events have stronger sea surface temperature (SST) anomalies over the tropical Pacific and Indian oceans, and warm/cold events can lead to significantly more/less precipitation over Central Asia and East China as well as less/more precipitation over the Indo‐China Peninsula, Tibetan Plateau, and Maritime Continent. The S‐ENSO events significantly affect the Walker circulation and West Pacific anticyclone and excite an eastward‐propagating wave train over the midlatitude Northern Hemisphere. The changes in these atmospheric circulations further alter the dynamic and moisture conditions over the aforementioned Asian regions, consequently leading to precipitation anomalies. In contrast, the M‐ENSO events have relatively weak SST anomalies over the tropics, and the warm/cold events can result in more/less precipitation over Central Asia, Mongolia and northern China and less/more precipitation over northeastern Siberia, South China and the Maritime Continent. The impact of M‐ENSO events on spring precipitation over the Asian regions mainly occurs due to modulating effects on the western Pacific pattern, Aleutian Low, and anomalous anticyclones over northwestern Asia. The results in this study indicate that ENSO events of different intensities may have different impacts on Asian spring precipitation, which should be considered when predicting it.
El Niño‐Southern Oscillation (ENSO) flavors have been defined to characterize ENSO events and their teleconnections. Studying El Niño flavor evolution during the Holocene period can provide valuable insights into changes over long time scales. We investigated ENSO flavor evolution using simulations spanning the last 6,000 years and present‐day observations. Two approaches to computing ENSO flavors, in agreement in the present, lead to opposite trends in the last 6,000 years. The methods also differ significantly in their representation of ENSO flavor patterns. However, incorporating the sensitivity of the methods to calibration periods and mean state changes yields similar interpretations of ENSO variability changes. Both methods suggest an increase in El Niño variability spreading to the west and east tropical Pacific over the past 6,000 years. Standardizing El Niño flavor definitions is necessary for meaningful comparisons between studies and robust climate variability analysis.
In the summer of 2022, a long-lasting La Niña entered its third year. In Asia, southern China was in the grip of a historic drought while heavy rainfall ravaged Pakistan. Using a climate model forced by observed sea surface temperatures (SST) over the equatorial Pacific, we show that the back-to-back La Niña events from 2020 to 2022 is a key contributor to the global SST pattern in 2022 including the negative-phase Pacific decadal oscillation and exceptionally strong negative Indian Ocean dipole. The model reproduces the observed precipitation pattern over South and East Asia, including enhanced rainfall over Pakistan–northwest India and reduced rainfall over southern China. Additional model simulations indicate that the negative Indian Ocean dipole combined with La Niña reduces southern China rainfall by causing anomalous subsidence and anticyclonic flows. These results highlight the dominant role of long-lasting La Niña in modulating rainfall over heavily populated monsoon Asia.
Most El Niño events occur sporadically and peak in a single winter1–3, whereas La Niña tends to develop after an El Niño and last for two years or longer4–7. Relative to single-year La Niña, consecutive La Niña features meridionally broader easterly winds and hence a slower heat recharge of the equatorial Pacific6,7, enabling the cold anomalies to persist, exerting prolonged impacts on global climate, ecosystems and agriculture8–13. Future changes to multi-year-long La Niña events remain unknown. Here, using climate models under future greenhouse-gas forcings¹⁴, we find an increased frequency of consecutive La Niña ranging from 19 ± 11% in a low-emission scenario to 33 ± 13% in a high-emission scenario, supported by an inter-model consensus stronger in higher-emission scenarios. Under greenhouse warming, a mean-state warming maximum in the subtropical northeastern Pacific enhances the regional thermodynamic response to perturbations, generating anomalous easterlies that are further northward than in the twentieth century in response to El Niño warm anomalies. The sensitivity of the northward-broadened anomaly pattern is further increased by a warming maximum in the equatorial eastern Pacific. The slower heat recharge associated with the northward-broadened easterly anomalies facilitates the cold anomalies of the first-year La Niña to persist into a second-year La Niña. Thus, climate extremes as seen during historical consecutive La Niña episodes probably occur more frequently in the twenty-first century.
The present study analyzes the temporal variability of carbon monoxide (CO) over the Manaus Metropolitan Region (MMR) and its relations with nearby fires based on data obtained by the environmental satellite AQUA, for the 2003–2020 period. For this purpose, wavelet transform analyses and wavelet coherence analyses were used. The results show a well-defined seasonal behavior, with an increase and decrease in mean CO concentrations during dry and wet seasons, respectively. Semiannual and annual scales represent around 95 % of CO temporal variability in lower troposphere (500 to 1,000 hPa) and are associated with rains and fires dynamics in the region. In terms of interannual variability, multiple variability scales (1.2–2, 2.5–3 and 4.5–6 years) were observed, which explain around 10–15 % of concentration variability near surface. The results suggest that climatic variations, associated with the tropical Pacific and Atlantic sea surface temperature variations, on these different time scales, affect rain dynamics and, consequently, fires and CO concentration. Specifically, in 2015/16, the combined effect from different variability scales acted to prolong the dry period over the region, which contributed to increase fires and the CO to reach higher values compared to previous years. These results show a new aspect of the importance of evaluating the combined effect of different climate variability scales on CO concentrations in the atmosphere.
Using hindcasts produced by a coupled climate model, this study evaluates whether the model can forecast the observed spatiotemporal complexity in the El Niño−Southern Oscillation (ENSO) during the period 1982−2011: the eastern Pacific (EP), central Pacific-I (CP-I) and -II (CP-II) types of El Niño, and the multi-year evolution events of El Niño occurred in 1986–1988 (i.e., 1986/87/88 El Niño) and La Niña occurred in 1998–2000 (i.e., 1998/99/00 La Niña). With regard to the spatial complexity, it is found that the CP-I type of El Niño is the easiest to hindcast, the CP-II is second, and the EP is most difficult to hindcast as its amplitude is significantly underestimated in the model used here. The model deficiency in hindcasting the EP El Niño is related to a warm bias in climatological sea surface temperatures (SSTs) in the tropical eastern Pacific. This warm bias is related to model biases in the strengths of the Pacific Walker circulation and South Pacific high, both of which are notably weaker than observed. As for the temporal complexity, the model successfully hindcasts the multi-year evolution of the 1998/99/00 La Niña but fails to accurately hindcast the 1986/87/88 El Niño. This contrasting model performance in hindcasting multi-year events is found to be related to a cold bias in climatological SSTs in the tropical central Pacific. This cold bias result enables the model La Niña, but not El Niño, to activate intrabasin tropical‒subtropical interactions associated with the Pacific Meridional Mode that produce the multi-year evolution pattern.
The recharge oscillator model of the El Niño Southern Oscillation (ENSO) describes the ENSO dynamics as an interaction between the eastern tropical Pacific sea surface temperatures (T) and subsurface heat content (thermocline depth; h), defining a dynamical cycle with different phases. h is often approximated on the basis of the depth of the 20 °C isotherm (Z20). In this study we will address how the estimation of h affects the representation of ENSO dynamics. We will compare the ENSO phase space with h estimated based on Z20 and based on the maximum gradient in the temperature profile (Zmxg). The results illustrate that the ENSO phase space is much closer to the idealised recharge oscillator model if based on Zmxg than if based on Z20. Using linear and non-linear recharge oscillator models fitted to the observed data illustrates that the Z20 estimate leads to artificial phase dependent structures in the ENSO phase space, which result from an in-phase correlation between h and T. Based on the Zmxg estimate the ENSO phase space diagram show very clear non-linear aspects in growth rates and phase speeds. Based on this estimate we can describe the ENSO cycle dynamics as a non-linear cycle that grows during the recharge and El Nino state, and decays during the La Nina states. The most extreme ENSO states are during the El Nino and discharge states, while the La Nina and recharge states do not have extreme states. It further has faster phase speeds after the El Nino state and slower phase speeds during and after the La Nina states. The analysis suggests that the ENSO phase speed is significantly positive in all phases, suggesting that ENSO is indeed a cycle. However, the phase speeds are closest to zero during and after the La Nina state, indicating that the ENSO cycle is most likely to stall in these states.
Introduction
As global temperatures continue to rise, extreme weather phenomena such as El Niño and the Southern Oscillation (ENSO) near the equatorial Pacific Ocean are occurring more frequently and leading to tropical cyclones, droughts, and a series of extreme weather disasters. Accurately predicting ENSO in advance can greatly reduce the serious damage to human society, economy, and ecological environment. However, existing methods often neglect the data relation between geographical regions and meteorological factors, hindering the accuracy of ENSO prediction.
Methods
To overcome this problem, we propose a residual network with geographical and meteorological attention to capture important geographical information and explore the spatio-temporal correlation of different meteorological factors. Specifically, we propose two main attention modules: (1) the Geographical Semantic Information Enhancement Module (GSIEM), which selectively attends to important geographical regions and filters out irrelevant noise through a spatial-axis attention map, and (2) the Meteorological Factors Discriminating Enhancement Module (MFDEM), which aims to learn the spatio-temporal dependency of different meteorological factors using a learnable channel-axis weight map. We then integrate our proposed two attention modules into the backbone using residual connection, enhancing the model's prediction ability.
Results
We conducted extensive experimental comparisons and ablation studies to evaluate the performance of our proposed method. The results show that our method outperforms existing state-of-the-art methods in ENSO prediction, with a significant improvement in prediction accuracy.
Discussion
Our proposed method effectively captures geographical and meteorological information, facilitating accurate ENSO prediction. The attention modules we proposed can effectively filter out irrelevant noise and learn the spatio-temporal dependency of different meteorological factors, contributing to the superior performance of our model. Overall, our study provides a novel approach for ENSO prediction and has great potential for practical applications.
The triple-dip La Niña in 2020-23 is characterized by persisting southeasterly wind anomalies over the tropical central and eastern Pacific. Our results show that the wind anomalies are associated with the anomalously negative phase of the first two leading modes of the annual cycle (antisymmetric and symmetric modes about the equator) of sea surface temperature (SST) in the tropical Pacific. The two modes account for 82.2% and 13.5% of the total variance, linking to the seasonal swing of SST between the northern and southern hemispheres and the temporal evolution of El Niño-Southern Oscillation (ENSO), respectively. During 2020-23, the persistently and anomalously negative phase of the symmetric mode enhances easterly wind over the tropical central Pacific, while the antisymmetric mode strengthens the southeasterly wind over the tropical eastern Pacific. The anomalously negative phase of the antisymmetric mode is associated with the contrast of SST anomalies between the northern and southern hemispheres, which provided a favorable background for the triple-dip La Niña in 2020-23.
Ocean warming is associated with the tropicalization of fish towards higher latitudes. However, the influence of global climatic phenomena like the El Niño Southern Oscillation (ENSO) and its warm (El Niño) and cold (La Niña) phases on tropicalization has been overlooked. Understanding the combined effects of global climatic forces together with local variability on the distribution and abundance of tropical fish is essential for building more accurate predictive models of species on the move. This is particularly important in regions where ENSO-related impacts are known to be major drivers of ecosystem change, and is compounded by predictions that El Niño is becoming more frequent and intense under current ocean warming. In this study, we used long-term time series of monthly standardized sampling (August 1996 to February 2020) to investigate how ocean warming, ENSO and local environmental variability influence the abundance of an estuarine dependent tropical fish species (white mullet Mugil curema) at subtropical latitudes in the southwestern Atlantic Ocean. Our work revealed a significant increasing trend in surface water temperature in shallow waters (<1.5 m) at estuarine and marine sites. However, against our initial expectation, we did not observe an increasing trend in the abundance of this tropical mullet species. Generalized Additive Models revealed complex, non-linear relationships between species abundance and environmental factors operating at large (ENSO's warm and cold phases), regional (freshwater discharge in the coastal lagoon's drainage basin) and local (temperature and salinity) scales across the estuarine marine gradient. These results demonstrate that fish responses to global climate change can be complex and multifaceted. More specifically, our findings suggested that the interaction among global and local driving forces dampen the expected effect of tropicalization for this mullet species in a subtropical seascape.
Plain Language Summary
The tropical Pacific experienced the prolonged cooling conditions during 2020–2022 (often called a triple La Niña), which exerted great impacts on the weather and climate globally. However, physics‐derived coupled models still have difficulty in accurately making long‐lead real‐time predictions for sea surface temperature (SST) evolution in the tropical Pacific. With the rapid development of deep learning‐based modeling, purely data‐driven models provide an innovative way for SST predictions. Here, a transformer‐based deep learning model is used to evaluate its performance in predicting the evolution of SST in the tropical Pacific during 2020–2022 and explore process representations that are important for SST evolution during 2021, including subsurface thermal effect and surface wind forcing on SST, the crucial factors determining the second‐year prolonged La Niña conditions and turning point of SST evolution. A comparison is made between the completely differently constructed physics‐derived dynamical coupled model and the pure‐data driven deep learning model, showing they both can be used for predictions of SST evolution in the 2021 second‐year cooling conditions. This indicates that it is necessary to adequately represent the thermocline feedback in predictive models, either in dynamical coupled models or purely data‐driven models, so that El Niño and Southern Oscillation predictions can be improved.
Purpose
Societies go through complex challenges in the face of the vertiginous increase in disasters, mostly produced by the effects of extreme events. The lack of capacity to deal with disasters is evident, especially in developing countries, as in the case of Peru. Under such a premise, this paper contributes to strengthening the country’s capacities, through an evaluation of national disaster resilience to the El Niño-Southern Oscillation-driven hazards caused by the El Niño disaster event between 2016 and 2017 on the Peruvian coast.
Design/methodology/approach
By reviewing the literature, various hazards were identified, such as heavy rainfalls and cascading hazards, such as floods and landslides. Even though risk assessments were carried out, 169 people died and essential infrastructure was severely impacted and lost. Through a 12-criteria resilience assessment framework sub-divided into sustainable development and disaster risk reduction, a diagnosis of national disaster resilience was carried out, along with a disaster risk management evaluation. Under such assessments, strategic recommendations were proposed to enhance the resilience of the country.
Findings
The lack of resilience of the country is reflected in the evaluated criteria, the most negative being the built environment due to infrastructure system’s vulnerability to hazards, and the lack of social development, despite national economic growth in Peru.
Originality/value
The research is extremely valuable because it bridges the knowledge gap on disaster resilience in Peru. In addition, the methodology, as well as the multi-topic assessment framework, can be used for other analyses, which are key to building greater capacity in nations around the globe.
El Niño–Southern Oscillation (ENSO), the leading mode of global interannual variability, usually intensifies the Hadley Circulation (HC), and meanwhile constrains its meridional extension, leading to an equatorward movement of the jet system. Previous studies have investigated the response of HC to ENSO events using different reanalysis datasets and evaluated their capability in capturing the main features of ENSO-associated HC anomalies. However, these studies mainly focused on the global HC, represented by a zonal-mean mass stream function (MSF). Comparatively fewer studies have evaluated HC responses from a regional perspective, partly due to the prerequisite of the Stokes MSF, which prevents us from integrating a regional HC. In this study, we adopt a recently developed technique to construct the three-dimensional structure of HC and evaluate the capability of eight state-of-the-art reanalyses in reproducing the regional HC response to ENSO events. Results show that all eight reanalyses reproduce the spatial structure of HC responses well, with an intensified HC around the central-eastern Pacific but weakened circulations around the Indo-Pacific warm pool and tropical Atlantic. The spatial correlation coefficient of the three-dimensional HC anomalies among the different datasets is always larger than 0.93. However, these datasets may not capture the amplitudes of the HC responses well. This uncertainty is especially large for ENSO-associated equatorially asymmetric HC anomalies, with the maximum amplitude in Climate Forecast System Reanalysis (CFSR) being about 2.7 times the minimum value in the Twentieth Century Reanalysis (20CR). One should be careful when using reanalysis data to evaluate the intensity of ENSO-associated HC anomalies.
Plain Language Summary
El Niño is a powerful source of year‐to‐year climate variability, with significant impacts on the Hadley Circulation (HC), one of the most important large‐scale atmospheric circulations affecting precipitation and drought in the tropics and subtropics. It has been reported that the spatial distribution and magnitude of sea surface temperature (SST) anomalies under El Niño events can influence the long‐term variability of HC. However, the impact is still inconclusive. Detailed research on how El Niño events modulate the HC is necessary. We investigate the impacts of the meridional gradient of anomalous SST during El Niño development and decay stages on the spatial structure of the HC. The results indicate that the distinct effects of different stages on the HC are mainly due to their associated SST meridional structures. The results explain why the deduced influences of El Niño on the HC in previous work were different. When considering the impacts of El Niño, the stage of the event must be considered. Furthermore, the physical mechanisms that significantly affect the HC in different stages are explained through theory and data analysis. These results and mechanisms help further our understanding of the climate impacts of El Niño and the variability of the HC.
El Nino events, characterized by anomalous warming in the eastern equatorial Pacific Ocean, have global climatic teleconnections and are the most dominant feature of cyclic climate variability on subdecadal timescales. Understanding changes in the frequency or characteristics of El Nino events in a changing climate is therefore of broad scientific and socioeconomic interest. Recent studies(1-5) show that the canonical El Nino has become less frequent and that a different kind of El Nino has become more common during the late twentieth century, in which warm sea surface temperatures (SSTs) in the central Pacific are flanked on the east and west by cooler SSTs. This type of El Nino, termed the central Pacific El Nino (CP-El Nino; also termed the dateline El Nino(2), El Nino Modoki(3) or warm pool El Nino(5)), differs from the canonical eastern Pacific El Nino (EP-El Nino) in both the location of maximum SST anomalies and tropical-midlatitude teleconnections. Here we show changes in the ratio of CP-El Nino to EP-El Nino under projected global warming scenarios from the Coupled Model Intercomparison Project phase 3 multi-model data set(6). Using calculations based on historical El Nino indices, we find that projections of anthropogenic climate change are associated with an increased frequency of the CP-El Nino compared to the EP-El Nino. When restricted to the six climate models with the best representation of the twentieth-century ratio of CP-El Nino to EP-El Nino, the occurrence ratio of CP-El Nino/EP-El Nino is projected to increase as much as five times under global warming. The change is related to a flattening of the thermocline in the equatorial Pacific.
The contribution of the subsurface precursor, defined as the buildup of heat content in the equatorial subsurface prior to El Niño-Southern Oscillation (ENSO) events, to ENSO amplitude and predictability has been unclear for some time. To address the issue, this study implements a careful experimental design to construct three March-initialized precursor ensembles using CCSM4, one ensemble with ENSO-neutral initial conditions, one with a warm precursor in the subsurface, and one with a cold precursor. The initial precursors within each respective ensemble, although generated via identical wind forcing, differ slightly due to intrinsic sources of “noise” in the ocean and atmosphere. The ensembles are then integrated fully-coupled to produce a distribution of outcomes per each type of initial condition. Results show that a precursor is not essential to produce moderate El Niño and the full range of La Niña events, whereas a warm precursor is a necessary condition to generate extreme El Niño. The findings imply that extreme El Niño and the coldest La Niña events are fundamentally different. Presence of a warm (cold) precursor in the initial condition results in a warm (cold) shift and narrowing of the distribution of outcomes, suggesting increased predictability of El Niño (La Niña). Although the cold precursor is not necessary to produce La Niña, its presence in the initial condition reduces La Niña spread more than the warm precursor reduces El Niño spread. Despite the smaller ensemble spread for La Niña, signal-to-noise ratios indicate that El Niño may be more predictable than La Niña.
We investigate the dependence of ENSO atmospheric feedbacks on the mean-state in a perturbed atmospheric physics ensemble with the Kiel Climate Model (KCM) and in CMIP5 models. Additionally, uncoupled simulations are conducted with the atmospheric component of the KCM to obtain further insight into the mean-state dependence. It is found that the positive zonal wind feedback and the negative heat flux feedback, with the short-wave flux as dominant component, are strongly linearly related through sea surface temperature (SST) and differences in model physics are less important. In observations, strong zonal wind and heat flux feedbacks are caused by a convective response in the western central equatorial Pacific (Niño4 region), resulting from an eastward (westward) shift of the rising branch of the Walker Circulation (WC) during El Niño (La Niña). Many state-of-the-art climate models exhibit an equatorial cold SST bias in the Niño4 region, i.e. are in a La Niña-like mean-state. Therefore they simulate a too westward located rising branch of the WC (by up to 30°) and only a weak convective response. Thus, the position of the WC determines the strength of both the amplifying wind and usually damping heat flux feedback, which also explains why biases in these two feedbacks partly compensate in many climate models. Furthermore, too weak atmospheric feedbacks can cause quite different ENSO dynamics than observed, while enhanced atmospheric feedbacks lead to a substantial improvement of important ENSO properties such as seasonal ENSO phase locking and asymmetry between El Niño and La Niña. Differences in the mean-state SST are suggested to be a major source of ENSO diversity in current climate models.
The El Niño/Southern Oscillation (ENSO) is characterized by a seasonal phase locking, with strongest eastern and central equatorial Pacific sea surface temperature (SST) anomalies during boreal winter and weakest SST anomalies during boreal spring. In this study, key feedbacks controlling seasonal ENSO phase locking in the Kiel Climate Model (KCM) are identified by employing Bjerknes index stability analysis. A large ensemble of simulations with the KCM is analyzed, where the individual runs differ in either the number of vertical atmospheric levels or coefficients used in selected atmospheric parameterizations. All integrations use the identical ocean model. The ensemble-mean features realistic seasonal ENSO phase locking. ENSO phase locking is very sensitive to changes in the mean-state realized by the modifications described above. An excessive equatorial cold tongue leads to weak phase locking by reducing the Ekman feedback and thermocline feedback in late boreal fall and early boreal winter. Seasonal ENSO phase locking also is sensitive to the shortwave feedback as part of the thermal damping in early boreal spring, which strongly depends on eastern and central equatorial Pacific SST. The results obtained from the KCM are consistent with those from models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5).
The predictability of the duration of La Niña is assessed using the Community Earth System Model Version 1 (CESM1), a coupled climate model capable of simulating key features of the El Niño/Southern Oscillation (ENSO) phenomenon, including the multi-year duration of La Niña. Statistical analysis of a 1800 year long control simulation indicates that a strong thermocline discharge or a strong El Niño can lead to La Niña conditions that last 2 years (henceforth termed 2-year LN). This relationship suggest that 2-year LN maybe predictable 18 to 24 months in advance. Perfect model forecasts performed with CESM1 are used to further explore the link between 2-year LN and the “Discharge” and “Peak El Niño” predictors. Ensemble forecasts are initialized on January and July coinciding with ocean states characterized by peak El Niño amplitudes and peak thermocline discharge respectively. Three cases with different magnitudes of these predictors are considered resulting in a total of six ensembles. Each “Peak El Niño” and “Discharge” ensemble forecast consists of 30 or 20 members respectively, generated by adding a infinitesimally small perturbation to the atmospheric initial conditions unique to each member. The forecasts show that the predictability of 2-year LN, measured by the potential prediction utility (PPU) of the \({\mathrm{Ni}{\tilde{\mathrm{n}}}\mathrm{o}}\)-3.4 SST index during the second year, is related to the magnitude of the initial conditions. Forecasts initialized with strong thermocline discharge or strong peak El Niño amplitude show higher PPU than those with initial conditions of weaker magnitude. Forecasts initialized from states characterized by weaker predictors are less predictable, mainly because the ensemble-mean signal is smaller, and therefore PPU is reduced due to the influence of forecast spread. The error growth of the forecasts, measured by the spread of the \({\mathrm{Ni}{\tilde{\mathrm{n}}}\mathrm{o}}\)-3.4 SST index, is independent of the initial conditions and appears to be driven by wind variability over the southeastern tropical Pacific and the western equatorial Pacific. Analysis of observational data supports the modeling results, suggesting that the “thermocline discharge” and “Peak El Niño” predictors could also be used to diagnose the likelihood of multi-year La Niña events in nature. These results suggest that CESM1 could provide skillful long-range operational forecasts under specific initial conditions.
Hindcasts and real-time predictions of the east-central tropical Pacific sea surface temperature (SST) from the North American Multimodel Ensemble (NMME) system are verified for 1982–2015. Skill is examined using two deterministic verification measures: mean squared error skill score (MSESS) and anomaly correlation. Verification of eight individual models shows somewhat differing skills among them, with some models consistently producing more successful predictions than others. The skill levels of MME predictions are approximately the same as the two best performing individual models, and sometimes exceed both of them. A decomposition of the MSESS indicates the presence of calibration errors in some of the models. In particular, the amplitudes of some model predictions are too high when predictability is limited by the northern spring ENSO predictability barrier and/or when the interannual variability of the SST is near its seasonal minimum. The skill of the NMME system is compared to that of the MME from the IRI/CPC ENSO prediction plume, both for a comparable hindcast period and also for a set of real-time predictions spanning 2002–2011. Comparisons are made both between the MME predictions of each model group, and between the average of the skills of the respective individual models in each group. Acknowledging a hindcast versus real-time inconcsistency in the 2002–2012 skill comparison, the skill of the NMME is slightly higher than that of the prediction plume models in all cases. This result reflects well on the NMME system, with its large total ensemble size and opportunity for possible complementary contributions to skill.
An ensemble of nine operational ocean reanalyses (ORAs) is now routinely collected, and is used to monitor the consistency across the tropical Pacific temperature analyses in real-time in support of ENSO monitoring, diagnostics, and prediction. The ensemble approach allows a more reliable estimate of the signal as well as an estimation of the noise among analyses. The real-time estimation of signal-to-noise ratio assists the prediction of ENSO. The ensemble approach also enables us to estimate the impact of the Tropical Pacific Observing System (TPOS) on the estimation of ENSO-related oceanic indicators. The ensemble mean is shown to have a better accuracy than individual ORAs, suggesting the ensemble approach is an effective tool to reduce uncertainties in temperature analysis for ENSO. The ensemble spread, as a measure of uncertainties in ORAs, is shown to be partially linked to the data counts of in situ observations. Despite the constraints by TPOS data, uncertainties in ORAs are still large in the northwestern tropical Pacific, in the SPCZ region, as well as in the central and northeastern tropical Pacific. The uncertainties in total temperature reduced significantly in 2015 due to the recovery of the TAO/TRITON array to approach the value before the TAO crisis in 2012. However, the uncertainties in anomalous temperature remained much higher than the pre-2012 value, probably due to uncertainties in the reference climatology. This highlights the importance of the long-term stability of the observing system for anomaly monitoring. The current data assimilation systems tend to constrain the solution very locally near the buoy sites, potentially damaging the larger-scale dynamical consistency. So there is an urgent need to improve data assimilation systems so that they can optimize the observation information from TPOS and contribute to improved ENSO prediction.
The El Niño of 2015-16 was among the strongest El Niño events observed since 1950, and took place almost two decades after the previous major event in 1997-98. Here, perspectives of the event are shared by scientists from three national meteorological or climate services that issue regular operational updates on the status and prediction of the El Niño-Southern Oscillation (ENSO). Public advisories on the unfolding El Niño were issued in the first half of 2015. This was followed by significant growth in sea surface temperature (SST) anomalies, a peak during November 2015 - January 2016, subsequent decay, and its demise during May 2016. The lifecycle and magnitude of the 2015-16 El Niño was well predicted by most models used by national meteorological services, in contrast to the generally over-exuberant model predictions made the previous year. The evolution of multiple atmospheric and oceanic measures demonstrates the rich complexity of ENSO, as a coupled ocean-atmosphere phenomenon with pronounced global impacts. While some aspects of the 2015-16 El Niño rivaled the events of 1982-83 and 1997-98, we show that it also differed in unique and important ways, with implications for the study and evaluation of past and future ENSO events. Unlike previous major El Niños, remarkably above-average SST anomalies occurred in the western and central equatorial Pacific, but were milder near the coast of South America. While operational ENSO systems have progressed markedly over the past several decades, the 2015-16 El Niño highlights several challenges that will continue to test both the research and operational forecast communities.
Strong El Niño events are followed by massive summer monsoon flooding over the Yangtze River basin (YRB), home to about a third of the population in China. Although the El Niño–Southern Oscillation (ENSO) provides the main source of seasonal climate predictability for many parts of the Earth, the mechanisms of its connection to the East Asian monsoon remain largely elusive. For instance, the traditional Niño3.4 ENSO index only captures precipitation anomalies over East Asia in boreal winter but not during the summer. Here we show that there exists a robust year-round and predictable relationship between ENSO and the Asian monsoon. This connection is revealed by combining equatorial (Niño3.4) and off-equatorial Pacific sea surface temperature anomalies (Niño-A index) into a new metric that captures ENSO’s various aspects, such as its interaction with the annual cycle and its different flavors. This extended view of ENSO complexity improves predictability of YRB summer flooding events.
El Niño (EN) is a dominant feature of climate variability on inter-annual time scales driving changes in the climate throughout the globe, and having wide-spread natural and socio-economic consequences. In this sense, its forecast is an important task, and predictions are issued on a regular basis by a wide array of prediction schemes and climate centres around the world. This study explores a novel method for EN forecasting. In the state-of-the-art the advantageous statistical technique of unobserved components time series modeling, also known as structural time series modeling, has not been applied. Therefore, we have developed such a model where the statistical analysis, including parameter estimation and forecasting, is based on state space methods, and includes the celebrated Kalman filter. The distinguishing feature of this dynamic model is the decomposition of a time series into a range of stochastically time-varying components such as level (or trend), seasonal, cycles of different frequencies, irregular, and regression effects incorporated as explanatory covariates. These components are modeled separately and ultimately combined in a single forecasting scheme. Customary statistical models for EN prediction essentially use SST and wind stress in the equatorial Pacific. In addition to these, we introduce a new domain of regression variables accounting for the state of the subsurface ocean temperature in the western and central equatorial Pacific, motivated by our analysis, as well as by recent and classical research, showing that subsurface processes and heat accumulation there are fundamental for the genesis of EN. An important feature of the scheme is that different regression predictors are used at different lead months, thus capturing the dynamical evolution of the system and rendering more efficient forecasts. The new model has been tested with the prediction of all warm events that occurred in the period 1996–2015. Retrospective forecasts of these events were made for long lead times of at least two and a half years. Hence, the present study demonstrates that the theoretical limit of ENSO prediction should be sought much longer than the commonly accepted “Spring Barrier”. The high correspondence between the forecasts and observations indicates that the proposed model outperforms all current operational statistical models, and behaves comparably to the best dynamical models used for EN prediction. Thus, the novel way in which the modeling scheme has been structured could also be used for improving other statistical and dynamical modeling systems.
ENSO variability has a seasonal phase-locking, with SST anomalies on average decreasing during the beginning of the year and SST anomalies increasing during the second half of the year. As a result of this, the ENSO SST variability is smallest in April and the so call ‘spring barrier’ exists in the predictability of ENSO. In this study we analysis how the seasonal phase-locking of surface short wave radiation associated with cloud cover feedbacks contribute to this phenomenon. We base our analysis on observations and simplified climate model simulations. At the beginning of the year, the warmer mean SST in the eastern equatorial Pacific leads to deeper clouds whose anomalous variability are positively correlated with the underlying SST anomalies. These observations highlight a strong negative surface short wave radiation feedback at the beginning of the year in the eastern Pacific (NINO3 region). This supports the observed seasonal phase-locking of ENSO SST variability. This relation also exists in model simulations of the linear recharge oscillator and in the slab ocean model coupled to a fully complex atmospheric GCM. The Slab ocean simulation has seasonal phase-locking similar to observed mostly caused by similar seasonal changing cloud feedbacks as observed. In the linear recharge oscillator simulations seasonal phase-locking is also similar to observed, but is not just related to seasonal changing cloud feedbacks, but is also related to changes in the sensitivity of the zonal wind stress and to a lesser extent to seasonally change sensitivities to the thermocline depth. In summary this study has shown that the seasonal phase-locking, as observed and simulated, is linked to seasonally changing cloud feedbacks.
El Niño-Southern Oscillation (ENSO) consists of irregular episodes of warm El Niño and cold La Niña conditions in the tropical Pacific Ocean1, with significant global socio-economic and environmental impacts1. Nevertheless, forecasting ENSO at lead times longer than a few months remains a challenge2, 3. Like the Pacific Ocean, the Indian Ocean also shows interannual climate fluctuations, which are known as the Indian Ocean Dipole4, 5. Positive phases of the Indian Ocean Dipole tend to co-occur with El Niño, and negative phases with La Niña6, 7, 8, 9. Here we show using a simple forecast model that in addition to this link, a negative phase of the Indian Ocean Dipole anomaly is an efficient predictor of El Niño 14 months before its peak, and similarly, a positive phase in the Indian Ocean Dipole often precedes La Niña. Observations and model analyses suggest that the Indian Ocean Dipole modulates the strength of the Walker circulation in autumn. The quick demise of the Indian Ocean Dipole anomaly in November–December then induces a sudden collapse of anomalous zonal winds over the Pacific Ocean, which leads to the development of El Niño/La Niña. Our study suggests that improvements in the observing system in the Indian Ocean region and better simulations of its interannual climate variability will benefit ENSO forecasts.
Synoptic wind events in the equatorial Pacific strongly influence the El Niño/Southern Oscillation (ENSO) evolution. This paper characterizes the spatio-temporal distribution of Easterly (EWEs) and Westerly Wind Events (WWEs) and quantifies their relationship with intraseasonal and interannual large-scale climate variability. We unambiguously demonstrate that the Madden–Julian Oscillation (MJO) and Convectively-coupled Rossby Waves (CRW) modulate both WWEs and EWEs occurrence probability. 86 % of WWEs occur within convective MJO and/or CRW phases and 83 % of EWEs occur within the suppressed phase of MJO and/or CRW. 41 % of WWEs and 26 % of EWEs are in particular associated with the combined occurrence of a CRW/MJO, far more than what would be expected from a random distribution (3 %). Wind events embedded within MJO phases also have a stronger impact on the ocean, due to a tendency to have a larger amplitude, zonal extent and longer duration. These findings are robust irrespective of the wind events and MJO/CRW detection methods. While WWEs and EWEs behave rather symmetrically with respect to MJO/CRW activity, the impact of ENSO on wind events is asymmetrical. The WWEs occurrence probability indeed increases when the warm pool is displaced eastward during El Niño events, an increase that can partly be related to interannual modulation of the MJO/CRW activity in the western Pacific. On the other hand, the EWEs modulation by ENSO is less robust, and strongly depends on the wind event detection method. The consequences of these results for ENSO predictability are discussed.
It has been previously proposed that two El Niño (EN) regimes, strong and moderate, exist but the historical observational record is too short to establish this conclusively. Here, 1200 years of simulations with the GFDL CM2.1 model allowed us to demonstrate their existence in this model and, by showing that the relevant dynamics are also evident in observations, we present a stronger case for their existence in nature. In CM2.1, the robust bimodal probability distribution of equatorial Pacific sea surface temperature (SST) indices during EN peaks provides evidence for the existence of the regimes, which is also supported by a cluster analysis of these same indices. The observations agree with this distribution, with the EN of 1982-1983 and 1997-1998 corresponding to the strong EN regime and all the other observed EN to the moderate regime. The temporal evolution of various indices during the observed strong EN agrees very well with the events in CM2.1, providing further validation of this model as a proxy for nature. The two regimes differ strongly in the magnitude of the eastern Pacific warming but not much in the central Pacific. Observations and model agree in the existence of a finite positive threshold in the SST anomaly above which the zonal wind response to warming is strongly enhanced. Such nonlinearity in the Bjerknes feedback, which increases the growth rate of EN events if they reach sufficiently large amplitude, is very likely the essential mechanism that gives rise to the existence of the two EN regimes. Oceanic nonlinear advection does not appear essential for the onset of strong EN. The threshold nonlinearity could make the EN regimes very sensitive to stochastic forcing. Observations and model agree that the westerly wind stress anomaly in the central equatorial Pacific in late boreal summer has a substantial role determining the EN regime in the following winter and it is suggested that a stochastic component at this time was key for the development of the strong EN towards the end of 1982.
Nonlinear interactions between ENSO and the Western Pacific warm pool annual cycle generate an atmospheric combination mode (C-mode) of wind variability. We demonstrate that C-mode dynamics are responsible for the development of an anomalous low-level North-West Pacific anticyclone (NWP-AC) during El Niño events. The NWP-AC is embedded in a large-scale meridionally anti-symmetric Indo-Pacific atmospheric circulation response and has been shown to exhibit large impacts on precipitation in Asia. In contrast to previous studies, we find the role of air-sea coupling in the Indian Ocean and North-West Pacific only of secondary importance for the NWP-AC genesis. Moreover, the NWP-AC is clearly marked in the frequency domain with near-annual combination tones, which have been overlooked in previous Indo-Pacific climate studies. Furthermore, we hypothesize a positive feedback loop involving the anomalous low-level NWP-AC through El Niño and C-mode interactions: the development of the NWP-AC as a result of the C-mode acts to rapidly terminate El Niño events. The subsequent phase shift from retreating El Niño conditions towards a developing La Niña phase terminates the low-level cyclonic circulation response in the Central Pacific and thus indirectly enhances the NWP-AC and allows it to persist until boreal summer. Anomalous local circulation features in the Indo-Pacific (such as the NWP-AC) can be considered a superposition of the quasi-symmetric linear ENSO response and the meridionally anti-symmetric annual cycle modulated ENSO response (C-mode). We emphasize that it is not adequate to assess ENSO impacts by considering only interannual timescales. C-mode dynamics are an essential (extended) part of ENSO and result in a wide range of deterministic high-frequency variability.
El Niño Southern Oscillation (ENSO) is a naturally occurring mode of tropical Pacific variability, with global impacts on society and natural ecosystems. While it has long been known that El Niño events display a diverse range of amplitudes, triggers, spatial patterns, and life cycles, the realization that ENSO's impacts can be highly sensitive to this event-to-event diversity is driving a renewed interest in the subject. This paper surveys our current state of knowledge of ENSO diversity, identifies key gaps in understanding, and outlines some promising future research directions.
In this study, the authors investigate the connection between the South Pacific atmospheric variability and the tropical Pacific climate in models of different degrees of coupling between the atmosphere and ocean. A robust mode of variability, defined as the South Pacific meridional mode (SPMM), is identified in a multi-model ensemble of climate model experiments where the atmosphere is only thermodynamically coupled to a slab ocean mixed layer. The physical interpretation of the SPMM is nearly identical to the North Pacific meridional mode (NPMM) with the off-equatorial southeast trade wind variability altering the latent heat flux and sea surface temperature (SST) and initiating a wind–evaporation–SST feedback that propagates signals into the tropics. The authors also show that a positive cloud feedback plays a role in the development of this mode, but this effect is model dependent. While physically analogous to the NPMM, the SPMM has a stronger expression in the equatorial Pacific and directly perturbs the zonal gradients of SST and sea level pressure (SLP) on the equator, thus leading to ENSO-like variability despite the lack of ocean–atmosphere dynamical coupling. Further analysis suggests that the SPMM is also active in fully coupled climate models and observations. This study highlights the important role of the Southern Hemisphere in tropical climate vari-ability and suggests that including observations from the data-poor South Pacific could improve the ENSO predictability.
Tropical cyclones (TCs) are a hazard to life and property and a prominent element of the global climate system, therefore understanding and predicting TC location, intensity and frequency is of both societal and scientific significance. Methodologies exist to predict basin-wide, seasonally-aggregated TC activity months, seasons and even years in advance. We show that a newly developed high-resolution global climate model can produce skillful forecasts of seasonal TC activity on spatial scales finer than basin-wide, from months and seasons in advance of the TC season. The climate model used here is targeted at predicting regional climate and the statistics of weather extremes on seasonal to decadal timescales, and is comprised of high-resolution (50km×50km) atmosphere and land components, and more moderate resolution (~100km) sea ice and ocean components. The simulation of TC climatology and interannual variations in this climate model is substantially improved by correcting systematic ocean biases through “flux-adjustment.” We perform a suite of 12-month duration retrospective forecasts over the 1981-2012 period, after initializing the climate model to observationally-constrained conditions at the start of each forecast period – using both the standard and flux-adjusted versions of the model. The standard and flux-adjusted forecasts exhibit equivalent skill at predicting Northern Hemisphere TC season sea surface temperature, but the flux-adjusted model exhibits substantially improved basin-wide and regional TC activity forecasts, highlighting the role of systematic biases in limiting the quality of TC forecasts. These results suggest that dynamical forecasts of seasonally-aggregated regional TC activity months in advance are feasible.
Westerly wind bursts (WWBs) that occur in the western tropical Pacific are believed to play an important role in the development of El Niño events. Here, following the study of Lengaigne et al. (Clim Dyn 23(6):601-620, 2004), we conduct numerical simulations in which we reexamine the response of the climate system to an observed wind burst added to a coupled general circulation model. Two sets of twin ensemble experiments are conducted (each set has control and perturbed experiments). In the first set, the initial ocean heat content of the system is higher than the model climatology (recharged), while in the second set it is nearly normal (neutral). For the recharged state, in the absence of WWBs, a moderate El Niño with a maximum warming in the central Pacific (CP) develops in about a year. In contrast, for the neutral state, there develops a weak La Niña. However, when the WWB is imposed, the situation dramatically changes: the recharged state slides into an El Niño with a maximum warming in the eastern Pacific, while the neutral set produces a weak CP El Niño instead of previous La Niña conditions. The different response of the system to the exact same perturbations is controlled by the initial state of the ocean and the subsequent ocean-atmosphere interactions involving the interplay between the eastward shift of the warm pool and the warming of the eastern equatorial Pacific. Consequently, the observed diversity of El Niño, including the occurrence of extreme events, may depend on stochastic atmospheric processes, modulating El Niño properties within a broad continuum.
It is now widely recognized that El Niño-Southern Oscillation (ENSO) occurs in more than one form, with the canonical eastern Pacific (EP) and more recently recognized central Pacific (CP) ENSO types receiving the most focus. Given that these various ENSO "flavors" may contribute to climate variability and long-term trends in unique ways, and that ENSO variability is not limited to these two types, this study presents a framework that treats ENSO as a continuum but determines a finite maximum number of statistically distinguishable representative ENSOpatterns. Aneural network-based cluster analysis called self-organizing map (SOM) analysis paired with a statistical distinguishability test determines nine unique patterns that characterize the September-February tropical Pacific SST anomaly fields for the period from 1950 through 2011. These nine patterns represent the flavors of ENSO, which include EP, CP, and mixed ENSO patterns. Over the 1950-2011 period, the most significant trends reflect changes in La Niña patterns, with a shift in dominance of La Niña-like patterns with weak or negative western Pacific warm pool SST anomalies until the mid-1970s, followed by a dominance of La Niña-like patterns with positive western Pacific warm pool SST anomalies, particularly after the mid-1990s. Both an EP and especially a CP El Niño pattern experienced positive frequency trends, but these trends are indistinguishable fromnatural variability. Overall, changes in frequencywithin the ENSO continuum contributed to the pattern of tropical Pacific warming, particularly in the equatorial eastern Pacific and especially in relation to changes of La Niña-like rather than El Niño-like patterns.
Observations and climate simulations exhibit epochs of extreme El Niño/Southern Oscillation (ENSO) behavior that can persist for decades. Previous studies have revealed a wide range of ENSO responses to forcings from greenhouse gases, aerosols, and orbital variations – but they have also shown that interdecadal modulation of ENSO can arise even without such forcings. The present study examines the predictability of this intrinsically-generated component of ENSO modulation, using a 4000-year unforced control run from a global coupled GCM (GFDL-CM2.1) with a fairly realistic representation of ENSO. Extreme ENSO epochs from the unforced simulation are reforecast using the same ("perfect") model, but slightly-perturbed initial conditions. These 40-member reforecast ensembles display potential predictability of the ENSO trajectory, extending up to several years ahead. However, no decadal-scale predictability of ENSO behavior is found. This indicates that multidecadal epochs of extreme ENSO behavior can arise not only intrinsically, but delicately, and entirely at random. Previous work had shown that CM2.1 generates strong, reasonably-realistic, decadally-predictable high-latitude climate signals, as well as tropical and extratropical decadal signals that interact with ENSO. However, those slow variations appear not to lend significant decadal predictability to this model's ENSO behavior, at least in the absence of external forcings. While the potential implications of these results are sobering for decadal predictability, they also suggest an expedited approach to model evaluation and development – in which large ensembles of short runs are executed in parallel, to quickly and robustly evaluate simulations of ENSO. Further implications are discussed for decadal prediction, attribution of past and future ENSO variations, and societal vulnerability.
El Niño events, the warm phase of the El Niño/Southern
Oscillation (ENSO), are known to affect other tropical ocean basins
through teleconnections. Conversely, mounting evidence suggests that
temperature variability in the Atlantic Ocean may also influence ENSO
variability. Here we use reanalysis data and general circulation models
to show that sea surface temperature anomalies in the north tropical
Atlantic during the boreal spring can serve as a trigger for ENSO
events. We identify a subtropical teleconnection in which spring warming
in the north tropical Atlantic can induce a low-level cyclonic
atmospheric flow over the eastern Pacific Ocean that in turn produces a
low-level anticyclonic flow over the western Pacific during the
following months. This flow generates easterly winds over the western
equatorial Pacific that cool the equatorial Pacific and may trigger a La
Niña event the following winter. In addition, El Niño
events led by cold anomalies in the north tropical Atlantic tend to be
warm-pool El Niño events, with a centre of action located in the
central Pacific, rather than canonical El Niño events. We suggest
that the identification of temperature anomalies in the north tropical
Atlantic could help to forecast the development of different types of El
Niño event.
It is vital to understand how the El Niño-Southern Oscillation
(ENSO) has responded to past changes in natural and anthropogenic
forcings, in order to better understand and predict its response to
future greenhouse warming. To date, however, the instrumental record is
too brief to fully characterize natural ENSO variability, while large
discrepancies exist amongst paleo-proxy reconstructions of ENSO. These
paleo-proxy reconstructions have typically attempted to reconstruct the
full temporal variability of ENSO, rather than focusing simply on its
variance. Here a new approach is developed that synthesizes the
information on common low frequency variance changes from various proxy
datasets to obtain estimates of ENSO variance. The method is tested
using surrogate data from two coupled general circulation model (CGCM)
simulations. It is shown that in the presence of dating uncertainties,
synthesizing variance information provides a more robust estimate of
ENSO variance than synthesizing the raw data than identifying its
running variance. We also examine whether good temporal correspondence
between proxy data and instrumental ENSO records implies a good
representation of ENSO variance. A significant improvement in
reconstructing ENSO variance changes is found when combining several
proxies from diverse ENSO-teleconnected source regions, rather than by
relying on a single well-correlated location, suggesting that ENSO
variance estimates provided derived from a single site should be viewed
with caution. Finally, identifying the common variance signal in a
series of existing proxy based reconstructions of ENSO variability over
the last 600 yr we find that the common ENSO variance over the period
1600-1900 was considerably lower than during 1979-2009.
This study examines preindustrial simulations from Coupled Model Intercomparison Project, phase 3 (CMIP3), models to show that a tendency exists for El Nino sea surface temperature anomalies to be located farther eastward than La Nina anomalies during strong El Nino Southern Oscillation (ENSO) events but farther westward than La Nina anomalies during weak ENSO events. Such reversed spatial asymmetries are shown to force a slow change in the tropical Pacific Ocean mean state that in return modulates ENSO amplitude. CMIP3 models that produce strong reversed asymmetries experience cyclic modulations of ENSO intensity, in which strong and weak events occur during opposite phases of a decadal variability mode associated with the residual effects of the reversed asymmetries. It is concluded that the reversed spatial asymmetries enable an ENSO-tropical Pacific mean state interaction mechanism that gives rise to a decadal modulation of ENSO intensity and that at least three CMIP3 models realistically simulate this interaction mechanism.