ArticleLiterature Review

# El Niño–Southern Oscillation complexity

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## 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.

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... It interacts strongly with the rest of the weather-climate continuum involving timescales ranging from synoptic through multidecadal to centennial, affecting not only the global climate system but also marine and terrestrial ecosystems, fisheries, human health, and other societal and economic aspects of the Earth system (cf. McPhaden et al., 2006;Cashin et al., 2017;Timmermann et al., 2018;Boucharel et al., 2021). The primary dynamical mechanisms for ENSO's basic features as a canonical oscillator have been extensively investigated in terms of a leading coupled ocean-atmosphere model of the tropical Pacific essentially described by the simple conceptual delayed oscillator/recharge oscillator paradigm (Cane and Zebiak, 1985;Suarez and Schopf, 1988;Battisti and Hirst, 1989;Philander, 1990;Jin and Neelin, 1993;Neelin and Jin, 1993;Jin, 1997aJin, , 1997bNeelin et al., 1998;Wang and Picaut, 2004;Jin et al., 2020). ...
... In the past 2 decades, new layers of complexity of the ENSO phenomenon were identified with important implications for ENSO's impacts and predictability (Timmermann et al., 2018). It has been widely recognized that the observed ENSO pattern diversity and temporal complexity tend to manifest roughly in two dominant ENSO types known as central Pacific (CP) and eastern Pacific (EP) El Niño events Harrison, 2005a, 2005b;Ashok et al., 2007;Weng et al., 2007;Kug et al., 2009;Kao and Yu, 2009), as exemplified by two typical EP and CP El Niño events shown in Figures 1A, B. Associated with the sea surface temperature (SST) anomaly patterns, there is a westward shift of the slanted boundary marking the transition region between positive and negative equatorial ocean subsurface temperature anomalies ( Figures 1C, D), suggesting that different upper thermocline thermal stratification responses may contribute to different dynamical feedback strengths during CP and EP El Niño events (Zhao et al., 2021a). ...
... The observed key features of diversity in patterns and temporal evolution of ENSO, referred to ENSO STPD hereafter, are still inadequately captured by state-of-the-art climate models, despite the increasing successes achieved in simulating the ENSO phenomenon due to better process representations and increased in model resolutions (Planton et al., 2021). This is in part due to the fundamentally sensitive nonlinear dynamics of ENSO STPD and the fact that ENSO STPD involves multiscale interactions between ENSO and other major modes of variability, including the Madden Julian Oscillation (MJO) and Westerly Wind Bursts (WWBs), Tropical Instability Waves (TIWs), Pacific Meridional Modes (PMMs), the Interdecadal Pacific Oscillation (IPO) and/or Pacific Decadal Oscillation (PDO), and modes in other tropical ocean basins (see reviews by Timmermann et al., 2018;Wang, 2018;Yang et al., 2018;Cai et al., 2019). As a result, various biases in the climate mean state, seasonal cycle, the composition of key coupled feedback processes of ENSO, and ENSO's interactions with other modes of variability can all play important roles in hindering the state-of-the-art climate model's capabilities of simulating ENSO and its STPD, which shall affect the performances of these models in projecting changes of ENSO and their associated global impacts under global warming. ...
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The El Niño Southern Oscillation (ENSO) phenomenon, manifested by the great swings of large-scale sea surface temperature (SST) anomalies over the equatorial central to eastern Pacific oceans, is a major source of interannual global shifts in climate patterns and weather activities. ENSO’s SST anomalies exhibit remarkable spatiotemporal pattern diversity (STPD), with their spatial pattern diversity dominated by Central Pacific (CP) and Eastern Pacific (EP) El Niño events and their temporal diversity marked by different timescales and intermittency in these types of events. By affecting various Earth system components, ENSO and its STPD yield significant environmental, ecological, economic, and societal impacts over the globe. The basic dynamics of ENSO as a canonical oscillator generated by coupled ocean–atmosphere interactions in the tropical Pacific have been largely understood. A minimal simple conceptual model such as the recharge oscillator paradigm provides means for quantifying the linear and nonlinear seasonally modulated growth rate and frequency together with ENSO’s state-dependent noise forcing for understanding ENSO’s amplitude and periodicity, boreal winter-time phase locking, and warm/cold phase asymmetry. However, the dynamical mechanisms explaining the key features of ENSO STPD associated with CP and EP events remain to be better understood. This article provides a summary of the recent active research on the dynamics of ENSO STPD together with discussions on challenges and outlooks for theoretical, diagnostic, and numerical modeling approaches to advance our understanding and modeling of ENSO, its STPD, and their broad impacts.
... Regarding our modelling of events' occurrences, we turned to the extant climatic literature. The latter indicates that El Niño phenomenon is linked to unusual increases in sea surface temperatures (SST) in the Pacific Ocean [8]. On this basis, following recent studies [9,10], we chose to model their dynamic of occurrence in Peru from the stochastic process that governs these SST anomalies. ...
... From a climatic perspective, an El Niño phenomenon corresponds to a warm phase of the El Niño Southern Oscillation (ENSO), that is, an abnormal increase in both sea surface temperatures (SST) in the central and/or eastern equatorial regions of the Pacific Ocean and in sea-level atmospheric pressures in its western regions [8]. Different indicators define the conditions of occurrence and the intensity of this phenomenon. ...
... In the climatic literature, physically based models are the primary tool for evaluating the probabilities of occurrence of El Niño events over a given period. Nevertheless, although their ability to simulate El Niño-related dynamics at the global scale has improved markedly in recent years, they still exhibit errors in predictions regarding the amplitude, period, irregularity or spatial patterns of the phenomenon [8]. In this context, alternative parsimonious approaches consist in using statistical estimates derived from past weather pattern analyses [9,10,39,40]. ...
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This study evaluates the socioeconomic risk that extreme El Niño event-related road damages present to Peru by combining an environmental modelling of events’ occurrences in the country with a quantitative modelling of their effects on its economy. The dynamic of occurrence of events is modelled as a stochastic process with a vector autoregressive representation based on historical climatic data, and simulated over a 10-year period with a non-parametric bootstrap procedure. The indirect consequences of events’ related road damages are addressed with a multiregional dynamic computable general equilibrium model through an increase in interregional transportation costs and, more originally, a negative externality effect on activities’ output, which is estimated beforehand using a firm database. We find that extreme El Niño events constitute a significant one-off disaster risk for the country, threatening shifts of − 2.8% in GDP and + 1.9% in poverty rates with an annual probability p = 1.4%. We further show that they also present a longer-term risk, leading to average annual deviations from normal trend by − 0.8% in GDP and + 0.4% in poverty rate with a probability p = 12.6% over a 10-year period. However, we finally show that Peru might reduce these socioeconomic risks associated with these non-frequent but recurrent climatic shocks in constructing more disaster-resilient road infrastructure.
... In the classical viewpoint, ENSO was often regarded as a phenomenon with cyclical attributes 3 , in which the positive and negative phases are El Niño and La Niña, respectively. ENSO is known to show a significant diversity and irregularity 4,5 . The theoretical explanations of these are always grouped into two categories [5][6][7] . ...
... ENSO is known to show a significant diversity and irregularity 4,5 . The theoretical explanations of these are always grouped into two categories [5][6][7] . In the first category, ENSO is viewed as a self-sustained, unstable and naturally oscillatory mode of the coupled ocean-atmosphere system, in which the nonlinearity acts mainly to bound the growing eigenmode and create a finite amplitude of the ENSO cycle. ...
... This is found to be crucial for obtaining the realistic negative-skewed PDF for the simulated T C and therefore for simulating the realistic ENSO complexity. It should also be noted that the theoretical explanations of ENSO are always grouped into two categories [5][6][7] . In the first category, ENSO is viewed as a self-sustained, unstable and naturally oscillatory mode of the coupled ocean-atmosphere system, in which the nonlinearity acts mainly to bound the growing eigenmode and create a finite amplitude of the ENSO cycle. ...
Article
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El Niño-Southern Oscillation (ENSO) exhibits diverse characteristics in spatial pattern, peak intensity, and temporal evolution. Here we develop a three-region multiscale stochastic model to show that the observed ENSO complexity can be explained by combining intraseasonal, interannual, and decadal processes. The model starts with a deterministic three-region system for the interannual variabilities. Then two stochastic processes of the intraseasonal and decadal variation are incorporated. The model can reproduce not only the general properties of the observed ENSO events, but also the complexity in patterns (e.g., Central Pacific vs. Eastern Pacific events), intensity (e.g., 10–20 year reoccurrence of extreme El Niños), and temporal evolution (e.g., more multi-year La Niñas than multi-year El Niños). While conventional conceptual models were typically used to understand the dynamics behind the common properties of ENSO, this model offers a powerful tool to understand and predict ENSO complexity that challenges our understanding of the twenty-first century ENSO.
... With the continuously improved understanding of nature, the spatiotemporal diversity and complexity of ENSO have been progressively highlighted 9,10 . In particular, the observational data shows that the center of anomalous SST is mainly located in the EP from 1980 to 2000, whereas it lies more towards the central Pacific (CP) after 2000 [11][12][13] . ...
... It is important to notice that the shift of the warming center can make significant differences in the air-sea coupling over the equatorial Pacific, which changes the way ENSO affects the global climate and brings serious challenges to its prediction [17][18][19][20][21][22][23][24] . In addition to these two major categories, individual ENSO events further exhibit diverse characteristics in spatial pattern, peak intensity, and temporal evolution, which are known as the ENSO complexity 10 . Thus, developing effective dynamical models that capture the ENSO complexity is of practical importance, not only for improving the understanding of the formation mechanisms of ENSO but advancing the prediction of different ENSO events and the associated varying climatic impacts as well. ...
... It is crucial in simulating the correct occurrence frequencies of both the CP and the EP El Niños. One additional small constant c 2 is further added to (10), which guarantees all the variables have climatology with zero mean since otherwise the nonlinearity can cause a slight shift of the mean state. Another nonlinearity incorporated here is the damping coefficient in the SST equation. ...
Preprint
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El Ni\~no-Southern Oscillation (ENSO) is the most prominent interannual climate variability in the tropics and exhibits diverse features in spatiotemporal patterns. In this paper, a simple multiscale intermediate coupled stochastic model is developed to capture the ENSO diversity and complexity. The model starts with a deterministic and linear coupled interannual atmosphere, ocean and sea surface temperature (SST) system. It can generate two distinct dominant linear solutions that represent the eastern Pacific (EP) and the central Pacific (CP) El Ni\~nos, respectively. In addition to adopting a stochastic model for characterizing the intraseasonal wind bursts, another simple stochastic process is developed to describe the decadal variation of the background Walker circulation. The latter links the two dominant modes in a simple nonlinear fashion and advances the modulation of the strength and occurrence frequency of the EP and the CP events. Finally, a cubic nonlinear damping is adopted to parameterize the relationship between subsurface temperature and thermocline depth. The model succeeds in reproducing the spatiotemporal dynamical evolution of different types of the ENSO events. It also accurately recovers the strongly non-Gaussian probability density function, the seasonal phase locking, the power spectrum and the temporal autocorrelation function of the SST anomalies in all the three Ni\~no regions (3, 3.4 and 4) across the equatorial Pacific. Furthermore, both the composites of the SST anomalies for various ENSO events and the strength-location bivariate distribution of equatorial Pacific SST maxima for the El Ni\~no events from the model simulation highly resemble those from the observations.
... El Niño-Southern Oscillation (ENSO) is an irregular climate signal with a period of 2-7 years in the tropical Pacific Ocean and often grows to be exceptionally strong under unstable air-sea interactions (Bjerknes, 1969;Philander, 1983), causing large global climatic anomalies and hence affecting many regions even far from the tropical area (Yu et al., 2012). Studies have suggested that each ENSO event may differ in spatial structure, temporal evolution, amplitude and trigger Timmermann et al., 2018). One view is that there may be two different types of ENSO, referred to as the eastern Pacific (EP)-type event and the central Pacific (CP)-type event (Yu and Kao, 2007;Kao and Yu, 2009), and the differences in the details of sea surface temperature (SST) anomaly patterns between EP and CP events will lead to different remote teleconnection patterns and effects on the global climate Ashok et al., 2007;Timmermann et al., 2018). ...
... Studies have suggested that each ENSO event may differ in spatial structure, temporal evolution, amplitude and trigger Timmermann et al., 2018). One view is that there may be two different types of ENSO, referred to as the eastern Pacific (EP)-type event and the central Pacific (CP)-type event (Yu and Kao, 2007;Kao and Yu, 2009), and the differences in the details of sea surface temperature (SST) anomaly patterns between EP and CP events will lead to different remote teleconnection patterns and effects on the global climate Ashok et al., 2007;Timmermann et al., 2018). In recent decades, with the increased occurrence of CP El Niño relative to EP El Niño, the predictability of two ENSO types has attracted widespread attention (Lee and McPhaden, 2010). ...
Article
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The global impact of an El Niño–Southern Oscillation (ENSO) event can differ greatly depending on whether it is an eastern Pacific (EP)-type event or a central Pacific (CP)-type event. Reliable predictions of the two types of ENSO are therefore of critical importance. Here we construct a deep neural network with multichannel structure for ENSO (named ENSO-MC) to simulate the spatial evolution of sea surface temperature (SST) anomalies for the two types of events. We select SST, heat content and wind stress (i.e., three key ingredients of Bjerknes feedback) to represent coupled ocean–atmosphere dynamics that underpin ENSO, achieving skilful forecasts for the spatial patterns of SST anomalies out to 1 year ahead. Furthermore, it is of great significance to analyse the precursors of EP-type or CP-type events and identify targeted observation sensitive areas for the understanding and prediction of ENSO. Precursors analysis is to determine what type of initial perturbations will develop into EP-type or CP-type events. Sensitive area identification is to determine the regions where initial states tend to have the greatest impacts on the evolution of ENSO. We use the saliency map method to investigate the subsurface precursors and identify the sensitive areas of ENSO. The results show that there are pronounced signals in the equatorial subsurface before EP events, while the precursory signals of CP events are located in the northern Pacific. It indicates that the subtropical precursors seem to favour the generation of the CP-type El Niño and that the EP-type El Niño is more related to the tropical thermocline dynamics. Furthermore, the saliency maps show that the sensitive areas of the surface and the subsurface are located in the equatorial central Pacific and the equatorial western Pacific respectively. The sensitivity experiments imply that additional observations in the identified sensitive areas can improve forecasting skills. Our results of precursors and sensitive areas are consistent with the previous theories of ENSO, demonstrating the potential usage and advantages of the ENSO-MC model in improving the simulation, understanding and observations of the two ENSO types.
... This oscillating cycle presents three phases, warm, cool and neutral, which strongly affect the precipitation pattern along the Pacific Ocean and the South-American continent. The warm phase, known as El Niño, corresponds to the above-average warming of the eastern tropical Pacific Ocean and the weakness of easterly winds that, depending on the phenomenon's intensity, can even blow from west to east (Timmermann et al., 2018). The cool phase, known as La Niña, is characterized by the cooling of the ocean surface and by the strengthening of easterly winds. ...
... However, the frequency of this phenomenon is not constant, neither in intensity nor in time. The ONI varied between 0.5 and 2.6°C during warm phases, and between -2 and -0.5°C during cool phases, with a frequency between 2 and 10 years (Timmermann et al., 2018). Monthly precipitation anomalies compared to ONI and PDO indices. ...
... On seasonal time scales, the El Niño-Southern Oscillation (ENSO) is a major source of predictive skill for atmospheric circulation anomalies (McPhaden et al. 2006;Timmermann et al. 2018). Therefore, assessing and improving ENSO prediction is one of the most important challenges in the modeling and seasonal forecasting community (e.g., Barnston et al. 2012). ...
... The lowest skill values occur during JJA at longer leads and MAM for February initial conditions, which may be due to the spring predictability barrier, as this remains a large challenge that limits the models' skill in forecasting ENSO (e.g., Chen et al. 2020;Zhang et al. 2021). Nonetheless, the correlation analysis indicates that the Saudi-KAU CGCM skill remains statistically significant at approximately the 6-month lead time, and therefore, it can be potentially useful for regional climate services providing early warning of seasonal climate forecasts for precipitation and temperature that depend on the predictions of the ENSO (e.g., Timmermann et al. 2018). ...
Article
This paper assesses the skill of the Saudi-King Abdulaziz University coupled ocean–atmosphere Global Climate Model, namely Saudi-KAU CGCM, in forecasting the El Niño-Southern Oscillation (ENSO)-related sea surface temperature. The model performance is evaluated based on a reforecast of 38 years from 1982 to 2019, with 20 ensemble members of 12-month integrations. The analysis is executed on ensemble mean data separately for boreal winter (December to February: DJF), spring (March to May: MAM), summer (June to August: JJA), and autumn (September to November: SON) seasons. It is found that the Saudi-KAU model mimics the observed climatological pattern and variability of the SST in the tropical Pacific region. A cold bias of about 0.5–1.0 °C is noted in the ENSO region during all seasons at 1-month lead times. A statistically significant positive correlation coefficient is observed for the predicted SST anomalies in the tropical Pacific Ocean that lasts out to 6 months. Across varying times of the year and lead times, the model shows higher skill for autumn and winter target seasons than for spring or summer ones. The skill of the Saudi-KAU model in predicting Niño 3.4 index is comparable to that of state-of-the-art models available in the Copernicus Climate Change Service (C3S) and North American Multi-Model Ensemble (NMME) projects. The ENSO skill demonstrated in this study is potentially useful for regional climate services providing early warning for precipitation and temperature variations on sub-seasonal to seasonal time scales.
... It uses the Held and Suarez hydrostatic spectral dynamical core (Held and Suarez 1994) expressed in the vorticity-divergence form derived by Bourke (1974). A set of parameterizations takes care of processes such as Another source of uncertainty related to ENSO forcing and its teleconnections is the impact of the SST bias in stateof the-art models (Timmermann et al. 2018). Many coupled general circulation models exhibit a cold SST bias in the equatorial Pacific Ocean (reminiscent of a La Niña-like state), which leads to an overly westward displaced rising branch of the Walker Circulation. ...
... required to induce the convective anomalies (Timmermann et al. 2018). ...
Article
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Understanding the natural and forced variability of the general circulation of the atmosphere and its drivers is one of the grand challenges in climate science. In particular, it is of paramount importance to understand to what extent the systematic error of global climate models affects the processes driving such variability. This is done by performing a set of simulations (ROCK experiments) with an intermediate complexity atmospheric model (SPEEDY), in which the Rocky Mountains orography is modified (increased or decreased) to influence the structure of the North Pacific jet stream. For each of these modified-orography experiments, the climatic response to idealized sea surface temperature (SST) anomalies of varying intensity in the El Niño Southern Oscillation (ENSO) region is studied. ROCK experiments are characterized by variations in the Pacific jet stream intensity whose extension encompasses the spread of the systematic error found in state-of-the-art climate models. When forced with ENSO-like idealised anomalies, they exhibit a non-negligible sensitivity in the response pattern over the Pacific North American region, indicating that a change/bias in the model mean state can affect the model response to ENSO. It is found that the classical Rossby wave train response generated by ENSO is more meridionally oriented when the Pacific jet stream is weaker, while it exhibits a more zonal structure when the jet is stronger. Rossby wave linear theory, used here to interpret the results, suggests that a stronger jet implies a stronger waveguide, which traps Rossby waves at a lower latitude, favouring a more zonally oriented propagation of the tropically induced Rossby waves. The shape of the dynamical response to ENSO, determined by changes in the intensity of the Pacific Jet, affects in turn the ENSO impacts on surface temperature and precipitation over Central and North America. Furthermore, a comparison of the SPEEDY results with CMIP6 models behaviour suggests a wider applicability of the results to more resources-demanding, complete climate GCMs, opening up to future works focusing on the relationship between Pacific jet misrepresentation and response to external forcing in fully-fledged GCMs.
... The focus of this work is the Pacific Ocean between 20°N and 20°S, where the El Niño Southern Oscillation (ENSO) is the main mode of climate variability. ENSO is an oscillatory mode driving, with its warm and cold phases, El Niño and La Niña, the most dramatic year-to-year variation of Earth's climate system [27]. ENSO affects rainfall patterns, tropical cyclogenesis and the likelihood of droughts and floods, and freshwater availability. ...
... ENSO affects rainfall patterns, tropical cyclogenesis and the likelihood of droughts and floods, and freshwater availability. ENSO also impacts food security, with cascading effects on health, water, sanitation, education, and overall increased mortality [27][28][29][30]. In light of its great societal relevance, the tropical Pacific is a much studied region, and therefore a convenient, well-known, test case. ...
Article
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The threat of global warming and the demand for reliable climate predictions pose a formidable challenge because the climate system is multiscale, high-dimensional and nonlinear. Spatiotemporal recurrences of the system hint to the presence of a low-dimensional manifold containing the high-dimensional climate trajectory that could make the problem more tractable. Here we argue that reproducing the geometrical and topological properties of the low-dimensional attractor should be a key target for models used in climate projections. In doing so, we propose a general data-driven framework to characterize the climate attractor and showcase it in the tropical Pacific Ocean using a reanalysis as observational proxy and two state-of-the-art models. The analysis spans four variables simultaneously over the periods 1979–2019 and 2060–2100. At each time t, the system can be uniquely described by a state space vector parametrized by N variables and their spatial variability. The dynamics is confined on a manifold with dimension lower than the full state space that we characterize through manifold learning algorithms, both linear and nonlinear. Nonlinear algorithms describe the attractor through fewer components than linear ones by considering its curved geometry, allowing for visualizing the high-dimensional dynamics through low-dimensional projections. The local geometry and local stability of the high-dimensional, multivariable climate attractor are quantified through the local dimension and persistence metrics. Model biases that hamper climate predictability are identified and found to be similar in the multivariate attractor of the two models during the historical period while diverging under the warming scenario considered. Finally, the relationships between different subspaces (univariate fields), and therefore among climate variables, are evaluated. The proposed framework provides a comprehensive, physically based, test for assessing climate feedbacks and opens new avenues for improving their model representation.
... The El Niño-Southern Oscillation (ENSO) is a major interannually reoccurring mode of Earth's climate system that originates naturally from oceanatmosphere interactions in the tropical Pacific and can affect the weather and climate worldwide. ENSO has been observed to undergo decadal variations and modulations [1][2][3], including the intensity, asymmetry of its two phases (El Niño and La Niña), different types of El Niño and multiyear La Niña and El Niño events. One well-known example occurred during the so-called 1976-1977 climate shift [4], with El Niño events emerging frequently in the tropical Pacific during the 1980s and 1990s. ...
... Processes causing SST changes in the tropical Pacific include positive and negative feedbacks that operate naturally within the climate system [3]. During a La Niña event, its typical conditions are shown schematically in Fig. 1b [6]. ...
Article
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The frequent occurrences of the second-year surface cooling condition in the eastern equatorial Pacific, as observed in late 2021, are attributed to decadal changes in the thermocline depth, which determine the relative dominances of local cooling effect in the east and subsurface warming effect remotely from the west. Coupled models need to adequately represent these processes in a balanced way, thus being able to successfully predict the observed sea surface temperature evolution in late 2021.
... The quasi-biweekly oscillation is another intra-seasonal mode, but with a more localized effect, that also affects WNP TC activity (Li and Zhou 2013). The El Niño-Southern Oscillation (ENSO) is known as the main phenomenon occurring at interannual time scales (Trenberth and Caron 2000;Timmermann et al. 2018;Kim et al. 2020). ENSO has significant impacts on WNP TCs (Kim et al., 2011). ...
Article
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Based on satellite data after 1979, we find that the tropical cyclone (TC) variations in the Western North Pacific (WNP) can be divided into three-periods: a high-frequency period from 1979-1997 (P1), a low-frequency period from 1998-2010 (P2), and a high-frequency period from 2011-2020 (P3). Previous studies have focused on WNP TC activity during P1 and P2. Here we use observational data to study the WNP TC variation and its possible mechanisms during P3. Compared with P2, more TCs during P3 are due to the large-scale atmospheric favorable conditions of vertical velocity, relative vorticity and relative humidity. Warm sea surface temperature (SST) anomalies are found during P3 and migrate from east to west, which is also favorable for TC genesis. The correlation between the WNP TC frequency and SST shows a significant positive correlation around the equator and a significant negative correlation around 36°N, which is similar to the warm phase of the Pacific Decadal Oscillation (PDO). The correlation coefficient between the PDO and TC frequency is 0.71, above the 95% confidence level. The results indicate that the increase of the WNP TC frequency during 2011-2020 is associated with the PDO and warm SST anomalies.
... Questions as to changes in the spatial ENSO characteristics and which forced or unforced mechanisms exactly govern periods with enhanced long-range skill (Timmermann et al., 2018;Wengel et al., 2018) will require more detailed research in the future. We conclude by noting that these extensive reforecast experiments can potentially also become useful data sets to train deep learning algorithms and other machine learning techniques (Ham et al., 2019;LeCun et al., 2015;Rasp et al., 2018) in order to facilitate the development of powerful and reliable dynamical long-range forecasting systems for the future. ...
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Plain Language Summary El Niño Southern Oscillation (ENSO) is an irregular periodic variation in ocean temperatures and winds over the tropical Pacific Ocean that occurs every few years with varying intensity of its several‐month‐long cold and warm phases. Using a modern forecast model as well as observations of the climate system at the start of the forecast, seasonal forecasts draw on the predictability arising from these slows ENSO fluctuations to provide regular predictions of the atmosphere and ocean for the whole globe 1–2 seasons ahead. Such forecasts are important to warn us about potentially dangerous weather and climate conditions in the months to come. How well can we predict ENSO and what confidence do we have in our future abilities to forecast it? Here we present results from an unprecedented extensive model data set of retrospective ENSO forecasts 2 years into the future which were repeated every year from 1901 to 2010. By comparison with observations, we find that our ability to forecast ENSO did not simply degrade the further back in time we went. Rather, the skill had a distinct minimum during the 1930s to 1950s with better forecasts before and after. We explore factors that play a role in explaining this unexpected behavior.
... As mentioned above, SEJ and associated upper circulation in summer vary in response to ENSO events. Considering the ENSO complexity (Timmermann et al., 2018;Wang, 2018), case analysis of SEJ during the typical El Niño events are necessary. There have been many studies about the 1997-1998 case analysis of the SCS, including the SCS warm event , the upwelling anomaly off the coast of Vietnam (Kuo et al., 2004), the chlorophyll concentration anomaly in the western SCS (Zhao & Tang, 2007), and the upwelling anomaly on the northern continental shelf of the SCS (Jing et al., 2011). ...
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The interannual variation of the South China Sea (SCS) summertime eastward jet (SEJ) contributes to the regional climate and ecology. Based on the Simple Ocean Data Assimilation data set, this study conducts a case analysis of SEJ in the SCS during two El Niño developing years, 1982 and 1997. In both years, the summer‐mean circulation presented a stronger SEJ with an obvious dipole pattern; however, the enhancement of SEJ was significantly weaker in 1997 than that in 1982. The reasons for enhanced SEJ and the difference of the enhancement magnitude (e.g., 256% and 52%) between 1982 and 1997 are explored. The climatological summer‐mean southwesterly wind stress is strongest in the middle SCS and decreases northward and southward (i.e., an arch‐shaped structure), forming a wind stress curl field conducive to the generation of SEJ. There is a low‐level anticyclonic anomaly during El Niño developing summer, with its northern flank enhancing the SCS monsoon. However, the structures of wind anomaly in the SCS are different in 1982 and 1997, which may relate to the earlier or later development of El Niño events modulated by the eastward migration of Madden‐Julian oscillation. In 1982, the northern flank of the anomalous anticyclone strengthened the climatological arch‐shaped structure, effectively enhancing the wind stress curls and SEJ. Whereas, in 1997, the northward deviation of the northern flank weakened the arch‐shaped structure north of 11°N, reduced the positive wind stress curl around 11°N–14.2°N, and caused the weaker enhancement of SEJ in 1997 than in 1982.
... The periodicity of this phenomenon is highly irregular, but the prediction of the warm phase and of its intensity has considerable importance due to their major impact on global climate and economy (Solow et al., 1998;McPhaden, Zebiak, & Glantz, 2006;Berry & Okulicz-Kozaryn, 2008). Predictions of the El Niño phenomenon with DL tools are currently getting an increasing amount of attention in both geosciences and the AI/ML communities (Timmermann et al., 2018;Ham, Kim, & Luo, 2019). The considered data set contains the observed historical monthly El-Niño Southern Oscillation data anomalies in the period 1950 to 2007 (Nino3.4 ...
Article
Classification problems in the small data regime (with small data statistic T and relatively large feature space dimension D) impose challenges for the common machine learning (ML) and deep learning (DL) tools. The standard learning methods from these areas tend to show a lack of robustness when applied to data sets with significantly fewer data points than dimensions and quickly reach the overfitting bound, thus leading to poor performance beyond the training set. To tackle this issue, we propose eSPA+, a significant extension of the recently formulated entropy-optimal scalable probabilistic approximation algorithm (eSPA). Specifically, we propose to change the order of the optimization steps and replace the most computationally expensive subproblem of eSPA with its closed-form solution. We prove that with these two enhancements, eSPA+ moves from the polynomial to the linear class of complexity scaling algorithms. On several small data learning benchmarks, we show that the eSPA+ algorithm achieves a many-fold speed-up with respect to eSPA and even better performance results when compared to a wide array of ML and DL tools. In particular, we benchmark eSPA+ against the standard eSPA and the main classes of common learning algorithms in the small data regime: various forms of support vector machines, random forests, and long short-term memory algorithms. In all the considered applications, the common learning methods and eSPA are markedly outperformed by eSPA+, which achieves significantly higher prediction accuracy with an orders-of-magnitude lower computational cost.
... This process is facilitated and amplified by thermocline and zonal advective feedbacks involved in the Bjerknes feedback. Due to the El Niño-La Niña asymmetry, wind anomalies of El Niño and La Niña do not completely offset but rectify onto the mean state 47,54,55 . We calculate changes in ENSO amplitude as the difference in Niño3.4 standard deviation between the twentieth and twenty-first centuries. ...
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The Southern Ocean is a primary heat sink that buffers atmospheric warming and has warmed substantially, accounting for an outsized portion of global warming-induced excess heat in the climate system. However, its projected warming is highly uncertain and varies substantially across climate models. Here, using outputs from Coupled Model Intercomparison Project phase six models, we show that Southern Ocean warming during the twenty-first century is linked to the change in amplitude of the El Niño–Southern Oscillation (ENSO). Models simulating a larger increase in ENSO amplitude systematically produce a slower Southern Ocean warming; conversely, a smaller increase in ENSO amplitude sees a stronger warming. The asymmetry in amplitude and teleconnection between El Niño and La Niña produce cumulative surface wind anomalies over the southern high latitudes, impacting Southern Ocean heat uptake. The magnitude of inter-model ENSO variations accounts for about 50% of the uncertainty in the projected Southern Ocean warming.
... The reason why SST and HC are used as inputs is that it is well known that ocean HC resides a memory for the future ENSO evolution. In addition, the SST pattern is important because it modulates equatorial wind variability, which is a key to the ENSO evolution 7,35,36 . ...
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To learn spatiotemporal representations and anomaly predictions from geophysical data, we propose STANet, a spatiotemporal neural network with a trainable attention mechanism, and apply it to El Niño predictions for long-lead forecasts. The STANet makes two critical architectural improvements: it learns spatial features globally by expanding the network’s receptive field and encodes long-term sequential features with visual attention using a stateful long-short term memory network. The STANet conducts multitask learning of Nino3.4 index prediction and calendar month classification for predicted indices. In a comparison of the proposed STANet performance with the state-of-the-art model, the accuracy of the 12-month forecast lead correlation coefficient was improved by 5.8% and 13% for Nino3.4 index prediction and corresponding temporal classification, respectively. Furthermore, the spatially attentive regions for the strong El Niño events displayed spatial relationships consistent with the revealed precursor for El Niño occurrence, indicating that the proposed STANet provides good understanding of the spatiotemporal behavior of global sea surface temperature and oceanic heat content for El Niño evolution.
... El Niño-Southern Oscillation (ENSO), as the most significant interannual variability in the tropics (Bjerknes, 1969;Cheng, 2020;Neelin et al., 1998;Timmermann et al., 2018;Wei et al., 2020;Zhu et al., 2017), exerts considerable influence on the HC, through which ENSO affects global climate (Freitas et al., 2016;Guo et al., 2020;Yang and Huang, 2021;Zhang and Wang, 2013). The zonal-mean HC is strengthened and reduced in spatial extent during El Niño events, whereas it is weakened and broadened during La Niña events Lu et al., 2008;Stachnik and Schumacher, 2011). ...
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The Hadley circulation (HC) variability induced by ENSO (ENSO-driven HC) plays an important role in the changes of the tropical and even global climate. In the present study, we investigated the changes in ENSO-driven HC using the historical and RCP8.5 (SSP585 in CMIP6) runs from 34 models in CMIP5 and 24 models in CMIP6, and separated the roles of the changes in the ENSO SST and background SST using three atmospheric model experiments (amip, amip-p4K, and amip-future4K) in 8 CMIP5 and 7 CMIP6 models. The results showed that although the zonal-mean ENSO-driven HC strengthens slightly, the ENSO-driven HC is significantly intensified in a warmer climate from a regional perspective, especially over the eastern Pacific. The intensified ENSO-driven HC over the eastern Pacific dominates the zonal-mean changes after the counteraction of the changes in different longitudes. When the state-of-the-art CMIP5 and CMIP6 models project a robust El Niño-like warming pattern for the background SST changes in the eastern Pacific, the background SST changes enhance the changes in ENSO-driven HC over the eastern Pacific. In contrast, the changes of ENSO SST show large intermodel spread, resultantly playing a weak role in ENSO-driven HC in the multi-model ensemble, although an intensified ENSO SST can strengthen ENSO-driven HC over the eastern Pacific and vice versa. The contributions of the ENSO SST are stronger than that of the background SST, and dominate the zonal-mean changes in ENSO-driven HC.
... The equatorial Pacific is the home to El Niño -Southern Oscillation (ENSO) which has far-reaching influences on the weather patterns over the globe (e.g., McPhaden et al., 2020;Timmermann et al., 2018;Yeh et al., 2018). ...
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Obduction is the large-scale, irreversible upward water transport from the permanent pycnocline to the surface mixed layer. Using Hybrid Coordinate Ocean Model (HYCOM), this study explores the impact of atmospheric intraseasonal oscillations (ISOs) on the Pacific equatorial obduction that plays a fundamental role in regulating the tropical Pacific climate. Parallel HYCOM experiments forced by atmospheric fields with and without ISO variability are compared to assess the effect of ISOs on the ocean. The results suggest that the ISOs can reduce the annual obduction rate of the western and central equatorial Pacific Ocean (WCEPO; 150°E-160°W, 5°S-5°N) by ∼12%, and their impact on the eastern Pacific is rather limited. The ISOs cause prominent intraseasonal variability of mixed layer depth (MLD) in the WCEPO primarily through surface wind forcing, which in turn affect the two key processes controlling the annual obduction rate. First, the intraseasonal MLD deepening events narrow down the total time windows for obduction by ∼30% and thereby give rise to the decrease in obduction rate; second, it leads to high-frequency entrainment events and enhances the entrainment rate by ∼18%. The increased entrainment rate is overwhelmed by the reduced time windows for effective entrainment, and the net effect of ISOs is to reduce the obduction rate and modify its seasonal cycle. This work highlights the importance of atmospheric ISOs in the equatorial ocean dynamics, with implications for tropical surface temperature and climate variabilities and biogeochemical cycle.
... Any statistical characterization of ENSO complexity (Timmermann et al. 2018) is ridden with limitations stemming from the choices of the extraction methodology (Monahan et al. 2009), datasets (Alory et al. 2007;Deser et al. 2010;Kennedy 2014), and climatological baseline (Verdon-Kidd 2018). The limitation in the measures of ENSO and the uncertainty in estimates of global warming would further compound the uncertainties of ENSO-forced climate signals. ...
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Low-frequency changes in the tropical Indian Ocean surface temperature have previously been investigated in the context of the Indian Ocean basin-wide (IOBM) and dipole (IOD) modes. The IOBM and IOD are the leading eigenmodes estimated from a traditional anomaly of SST. This approach ignores the possibility of multiple seasonal cycles (SCs) having different geographic patterns and interannually modulating amplitudes. The analyses presented here are anchored on the four sets of multivariate seasonal cycles independently extracted from the monthly observations of sea surface temperature (SST), surface wind, and surface pressure variations. We show that the secular warming, encapsulated by the monotonic variations of the first SC of SST (SST-SC1), differs from the previous linear trend patterns and has the most significant variance in the Indian Ocean Warm Pool (IOWP). Hence, these warming tendencies quantify the monotonic expansion rates of IOWP. The most significant interannual responses of Indian Ocean SST to remote forces (such as El Niño and La Niña) are also captured by SST-SC1. Unlike the traditional IOBM but similar to SST-SC1's secular warming, these remotely forced interannual signals also have considerable variances in IOWP. The interannual variations in SST's third seasonal cycle (i.e., SST- SC3) inherit SST-SC3's dipole pattern but diverge from classical IOD in many aspects and are predominantly controlled by local processes. However, they are insufficient to account for the total interannual signals on their own. The collective interannual variations of four seasonal cycles - with significant variances off Africa's eastern shores - demonstrate basin-wide unipolar patterns. Hence, SST interannual signals in the north-western Indian Ocean and the constantly growing warming in IOWP influence climate and weather over countries surrounding the Indian Ocean. Thus, this study offers a simple way to separate three types of climate signals: secular, internal, and remotely induced climate fluctuations.
... In the tropical Pacific (~ 30°S-30°N), El Niño-Southern Oscillation (ENSO) is the dominant mode of ocean-atmosphere coupled variability on interannual timescales, with strong amplitude in the equatorial Pacific (5°S-5°N) sea surface temperature anomalies (SSTAs; Timmermann et al. 2018). Additionally, the tropical Pacific also exhibits pronounced ENSO-like variability on decadal timescales, but with meridionally broader SSTAs in the eastern Pacific (Zhang et al. 1997), referred to as tropical Pacific decadal variability (TPDV; Power et al. 2021). ...
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The tropical Pacific exhibits decadal El Niño-Southern Oscillation (ENSO)-like variability, characterized by meridionally broad sea surface temperature anomalies in the eastern Pacific. In this study, we focus on the variability in the equatorial Pacific band (5°S–5°N), termed equatorial Pacific decadal variability (EPDV). While it is known that ocean dynamics plays an essential role in EPDV, the simulations by air-sea thermodynamically coupled slab ocean models (SOM) obscure the nature of the role of ocean dynamics. To confront this issue, we use a mechanically decoupled simulation, which isolates the effects of thermodynamic coupling processes and mean ocean circulation on EPDV. Thus, by comparing the simulation to a SOM, we investigate the role of mean ocean circulation and show that it plays a role in damping EPDV, primarily through mean equatorial Pacific upwelling. By comparing the simulation to a fully coupled dynamic ocean model (DOM), we examine the role of anomalous wind-driven ocean circulation and demonstrate that it plays a role in amplifying EPDV. Further, this amplification strength overwhelms the upwelling damping effect, resulting in the anomalous wind-driven ocean circulation forcing EPDV. Finally, we examine the origin of EPDV in the DOM and show that it originates from a zonal dipole mode in the tropical Pacific, which is strongly associated with decadal modulation of ENSO amplitude. Taking EPDV as an example, our study advances the understanding of the two distinct dynamical systems (SOM and DOM), benefiting the physical interpretation of other climate variabilities.
... Even though the area burned in the Amazon presented peaks related to years with climate anomalies, the temporal distribution of the area burned in the Amazon followed overall deforestation trends ( Figure S10), with higher rates during the 1990s and the first five years of the 2000s [9,21]. In the Cerrado, the same trend was broken by the peaks caused by extremely dry years (e.g., 1987,1998,2007,2010,2015,2016,2017), most of them related to the El Niño Southern Oscillation [73]. Pantanal was the biome where the burned area best responded to drier climate conditions, where the extent of fire was associated with years of severe droughts, 1999 and 2020 being record years in burned area [57]. ...
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Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km2) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil.
... Due to the complicated space-time SST variability (Fan and Schneider, 2012;Strobach et al., 2020;Girishkumar et al., 2021) and ENSO's complexity (Capotondi et al., 2015;Paek et al., 2017;Hu and Fedrov, 2018;Timmermann et al., 2018;Hu et al., 2020). Only using the regional averaged SSTA to define the traditional Niño indices is not enough to characterize the typical SST events, more aspects from SST field (such as mean state of SST, local extreme SSTA patterns, see Williams and Patricola, 2018) or more information from other fields (Wolter et al., 2011;Wiedermann et al., 2016) are required. ...
Article
... In recent years, more attention has been paid to the asymmetry between El Niño and La Niña (Ohba and Ueda, 2009;Karori et al., 2013;Zhang R. et al., 2014;Zhang et al., 2015;Guo et al., 2017;Timmermann et al., 2018;Geng et al., 2019;Chen et al., 2022;Song et al., 2022). The El Niño has more super strong events and decays much faster than the La Niña (Song et al., 2022), while the La Niña tends to sustain longer and is more likely to re-intensify after the first peak, i.e., develop to the multiyear La Niña (denoted as MYLN) (Zheng et al., 2015;Okumura et al., 2017). ...
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The multiyear La Niña (MYLN) is characterized by longer duration, bimodal feature, more continuous circulation anomaly, and different climate impacts compared to the canonical single-peak La Niña. In this study, we focus on the evolving impacts of the MYLN on precipitation in southern China, which mainly occur in boreal winter and summer and correspond to significantly less precipitation and frequency of extreme rainfall. Results show that such impacts have remarkable differences between the first and second half of the MYLN lifecycle. In the first boreal winter when the MYLN reaches its first peak, the precipitation in southern China decreases significantly, while it tends to be insignificantly anomalous in the next winter. In the summer after its first peak, the MYLN has no apparent impact on precipitation in southern China, but when it basically disappears in the next summer, precipitation decreases significantly in southern China. Such seasonally evolving features in the impacts of the MYLN on precipitation in southern China can be mainly interpreted by the patterns of the anomalous cyclonic circulation in northwestern subtropical Pacific during the first peak winter and the decaying summer of the MYLN, which favors an anomalous reduction of moisture supply over southern China.
... To predict ENSO evolution, one of the challenges is mainly from the non-cyclical feature and diversity of ENSO evolution. The ENSO diversity includes the variations in the intensity, spatial pattern, temporal evolution, and predictability of individual events (Capotondi et al., 2015;Choi et al., 2013;Timmermann et al., 2018). One of the asymmetries is that the discharge associated with El Niño is stronger than the recharge linked to La Niña (Kessler, 2002;Li et al., 2020, Figure 1b). ...
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Plain Language Summary For the climate anomaly at seasonal‐interannual time scales, the El Niño‐Southern Oscillation (ENSO) is one of the key players and the major source of the predictability. As a quasi‐cyclical phenomenon, the oceanic thermocline fluctuation along the equator determines the ENSO phase turnaround. One of the challenges of forecasting the ENSO is from the non‐cyclical feature and diversity of ENSO evolution. We note that, on average, the amplitude of the asymmetric part of thermocline fluctuation is about 1/3 of that of the symmetric part. Also, preceding thermocline anomalies in the western and central deep tropical Pacific contribute to the recharge phase of ENSO, while preceding thermocline anomalies in the western (eastern) tropical Pacific have a positive (negative) contribution to the discharge phase of ENSO. Such asymmetric connections with the preceding thermocline fluctuation are mainly due to the asymmetric wind stress anomalies associated with El Niño and La Niña.
... In the South Pacific sector of the Antarctic margin, an AMOC collapse would further contribute to projected Amundsen Sea Low changes 41 , with this low pressure system deepening by ~2 hPa in AMOC-off (Methods). The AMOC teleconnection to the Pacific could also have further climatic impacts, such as setting the pace of decadal global temperature rise 42 or altering the nature and frequency of El Niño/Southern Oscillation (ENSO) events 43 . Our study has also demonstrated AMOC teleconnections to other remote locations, including the atmospheric circulation over the west Antarctic, with links to ice melt 44,45 and the strength of the subtropical highs in the Southern Hemisphere. ...
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Climate projections suggest a weakening or collapse of the Atlantic Meridional Overturning Circulation (AMOC) under global warming, with evidence that a slowdown is already underway. This could have significant ramifications for Atlantic Ocean heat transport, Arctic sea ice extent and regional North Atlantic climate. However, the potential for far-reaching effects, such as teleconnections to adjacent basins and into the Southern Hemisphere, remains unclear. Here, using a global climate model we show that AMOC collapse can accelerate the Pacific trade winds and Walker circulation by leaving an excess of heat in the tropical South Atlantic. This tropical warming drives anomalous atmospheric convection, resulting in enhanced subsidence over the east Pacific and a strengthened Walker circulation and trade winds. Further teleconnections include weakening of the Indian and South Atlantic subtropical highs and deepening of the Amundsen Sea Low. These findings have important implications for understanding the global climate response to ongoing greenhouse gas increases.
... A significant reduction in winter precipitation in South China is noted along with a CP El Niño. However, for La Niña events, sea surface temperature anomaly patterns are not distinct, in contrast to El Niño diversity (Ren and Jin, 2011;Ashok et al., 2017;Timmermann et al., 2018). Zhang et al. (2014) showed that the impact of La Niña on autumn precipitation over South China has been relatively stable over the past few decades, leading to significant precipitation deficits over South China. ...
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An extreme drought appeared in South China from October 2020 to March 2021. During that time, sea surfacetemperatures exhibited an unprecedented warm center over the northwest Pacific (NWP) and a cold center over the tropicaleastern Pacific (La Niña). This study demonstrates the combined effects of an exceptionally warm NWP and a moderate LaNiña are closely linked to the anomalous drought in South China. The sea surface temperature anomaly in these two regionsinduced a steeper horizontal geopotential height gradient over South China. As a result, anomalous northeasterly windsprevailed over South China, altering water vapor transport and moisture convergence. A simplified atmospheric generalcirculation model also verifies the influence of the NWP warm anomaly on South China precipitation. This study points outthat the sea surface temperature variation in the NWP was important to the occurrence of extreme drought in South Chinafrom October 2020 to March 2021.
... The El Niño-Southern Oscillation (ENSO) is recognized as one of the most prominent interannual variabilities in the climate system (Philander 1983) and has been extensively explored due to its profound global impacts [see the review by Wang (2019)]. After the 1990s, a new flavor of El Niño with the warming center in the central tropical Pacific (hereafter referred to as CP-El Niño), which is different from the conventional El Niño with a warming center in the eastern tropical Pacific (hereafter referred to as EP-El Niño), hoves into view of researchers [see the review by Timmermann et al. (2018)]. Though the CP-El Niño possesses a much smaller amplitude of sea surface temperature (SST) anomalies than the EP-El Niño, their climate effects are comparable and completely different in some regions (Ashok et al. 2007). ...
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In recent decades, the tropical Pacific frequently experiences a new type of El Niño with warming center in the central tropical Pacific (i.e., the CP-El Niño) with distinct global climate effect to the traditional El Niño (i.e., EP-El Niño). Predicting the El Niño diversity is still a huge challenge for climatologists partly due to the precursory signals of El Niño events with different type is unclear. In the present study, a novel precursory signal of the CP-El Niño event that presents a negative sea surface temperature anomaly in the eastern tropical Pacific (i.e., EP-cooling mode) is revealed. The transition from the EP-cooling mode to CP-El Niño is explained by the basin-scale air–sea coupling in the tropical Pacific and teleconnections between the tropical and North Pacific. With the EP-cooling mode as a predictor, the forecast skill for the CP-El Niño in hindcast experiments is obviously improved by using regression models. The results in the present study are therefore instructive for promoting a better understanding of El Niño diversity and predictability.
... El Niño is a typical extreme event causing a strong influence on the worldwide weather and climate [1][2][3]. Each El Niño event can last for several months, in which sea surface temperature (SST) over the tropical Pacific gets anomalously warm compared to that in the normal periods. ...
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El Niño is the long-lasting anomalous warming of sea surface temperature (SST) and surface air temperature (SAT) over the tropical Pacific. Each El Niño event has its unique impact on the overlaying atmosphere, where the warming exhibits diversity in spatiotemporal patterns. It still remains an open question for discriminating the El Niño diversity, since the single area-averaging SST index often fails to distinguish the impact of the event diversity, which is partially due to the nonlinear and non-uniform variations of the warming patterns. Here, we introduced the Dynamical Systems metrics (DSMs) to measure instantaneous dimensions and persistence of the SAT warming patterns over the tropical Pacific. Our results show that different SAT warming patterns can be discriminated by their corresponding values of dimension and persistence, then the central Pacific and eastern Pacific El Niño events can be discriminated by DSM. Particularly, through the analyses of El Niño events, we can interpret the physical meaning of DSM parameters applied to the space-time SAT field: an instantaneous dimension reflects whether the sub-regions of the SAT field are consistently varying and to what degree the spatial pattern of anomalies is homogeneous, while the instantaneous persistence indicates how long an anomalous SAT pattern can be maintained. This work analyzes the spatiotemporal variability of El Niño from a dynamical system perspective, and DSM may also serve as a useful tool to study extreme events related to SST anomalies.
... For the earlier periods the weaker coupling between WWV and SST and the stronger thermal damping weaken the ENSO variability. ENSO dynamics have clearly changed since the climate shift in the 1970s favoring a stronger thermocline feedback and a weaker zonal advective feedback , An and Wang 2000, Timmermann et al 2018, which is consistent with the observed increase in periodicity and variability. The simplified ReOsc model used here represents a coupled mode, based on the thermocline feedback, and does not include the zonal advective feedback (Fang and Zheng 2018). ...
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We use a conceptual recharge oscillator model to identify changes in El Niño and the Southern Oscillation (ENSO) statistics and dynamics during the observational record. The variability of ENSO has increased during the 20th century. The cross-correlation between sea surface temperature (SST) and warm water volume (WWV) has also changed during the observational record. From the 1970s onwards, the SST drives WWV anomalies with a lead-time of ten months and the WWV feedbacks onto the SST with a lead-time of eight months. This is reminiscent of a recharge-discharge mechanism of the upper ocean heat content. The full recharge-discharge mechanism is only observed from the 1970s onwards. This could be the result of the degradation of the quality of observations in the early part of the 20th century. However, it may also be a consequence of decadal changes in the coupling between WWV and SST. Additional analysis fitting the recharge oscillator model to the coupled state-of-the-art climate models indicates that ENSO properties show little decadal changes in the climate models. The disagreement in changes in ENSO properties between the reanalysis and the climate models can be due to errors in the available observational data or due to the models missing the low frequency variability and decadal wind trends. Longer and more reliable observational records would be required to validate our results.
... The El Niño-Southern Oscillation (ENSO), is a dominant source of interannual climate variability, and plays an important role in regulating the frequency and intensity of global climate changes (Gupta and Jain, 2020;Trenberth, 2020;Vecchi and Wittenberg, 2010). ENSO has a typical periodicity of 2-7-years, which impacts temperature and precipitation around the globe via oceanic and atmospheric teleconnections (Cobb et al., 2013;Lin and Qian, 2019;Timmermann et al., 2018;Wang and Fiedler, 2006). Understanding the climate characteristics and dynamics of ENSO are essential for predictions of interannual climate changes under a range of expected global warming scenarios. ...
... Oscillatory phenomena can be abundantly observed in many scientific fields. Examples are the El-Nino-Southern Oscillation in climatology (Timmermann et al., 2018), the Belousov-Zhabotinsky reaction in chemistry (Hudson and Mankin, 1981), predator-prey relationships in biology (Danca et al., 1997) or electrophysiological brain activity in neuroscience (Buzsáki and Draguhn, 2004). Oscillatory phenomena are often analyzed by means of spectral analysis methods like Fourier-or Wavelet analysis (Muthuswamy and Thakor, 1998;Oehrn et al., 2018). ...
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In many scientific fields including neuroscience, climatology or physics, complex relationships can be described most parsimoniously by non-linear mechanics. Despite their relevance, many neuroscientists still apply linear estimates in order to evaluate complex interactions. This is partially due to the lack of a comprehensive compilation of non-linear methods. Available packages mostly specialize in only one aspect of non-linear time-series analysis and most often require some coding proficiency to use. Here, we introduce NoLiTiA, a free open-source MATLAB toolbox for non-linear time series analysis. In comparison to other currently available non-linear packages, NoLiTiA offers (1) an implementation of a broad range of classic and recently developed methods, (2) an implementation of newly proposed spatially and time-resolved recurrence amplitude analysis and (3) an intuitive environment accessible even to users with little coding experience due to a graphical user interface and batch-editor. The core methodology derives from three distinct fields of complex systems theory, including dynamical systems theory, recurrence quantification analysis and information theory. Besides established methodology including estimation of dynamic invariants like Lyapunov exponents and entropy-based measures, such as active information storage, we include recent developments of quantifying time-resolved aperiodic oscillations. In general, the toolbox will make non-linear methods accessible to the broad neuroscientific community engaged in time series processing.
... The higher frequency of IOD occurrences in the current decade (Cai et al., 2018) can be linked to the increase in wind speeds along the Kerala coast. Decadal oscillation may change the current trends; however, the increasing trend is more likely to be continued for next decade (Zhang et al., 2018;Timmermann et al., 2018). This brings out the possibility of a gradual change in wind climate along the Kerala coast in the near future. ...
Article
... [37][38][39][40][41][42][43]). The latter may differ significantly, leading to the identification of two El Niño types: the Eastern Pacific (EP) El Niño, characterized by maximum SST warming in the Eastern Pacific, and Central Pacific (CP) El Niño, with the highest anomaly located in the center of the tropical Pacific [44][45][46]. The amplitude of observed SST anomalies may also serve as a characteristic of ENSO diversity: moderate versus extreme/strong events, where 'strong' El Niño events are usually of the EP type (e.g., 1982/83, 1997/98) and 'moderate' are of the CP type [47]. ...
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El Niño Southern Oscillation (ENSO) invokes the release of a large amount of heat and moisture into the tropical atmosphere, inducing circulation anomalies. The circulation response to ENSO propagates both horizontally poleward and vertically into the stratosphere. Here, we investigate the remote response of the polar stratosphere to ENSO using reanalysis data, along with composite and regression analysis. In particular, we focus on inter-event variability resulting from two ENSO types (the Eastern Pacific (EP) and the Central Pacific (CP) El Niño) and the inter-hemispheric difference in the ENSO responses. Consistent with previous results, we show that ENSO is associated with a weakening in the stratospheric polar vortex but emphasize that the polar stratosphere response strongly depends on the ENSO types, differs between the hemispheres, and changes from the lower to middle stratosphere. The main inter-hemispheric asymmetry manifests in response to the EP El Niño, which is not significant in the Southern Hemisphere, while CP events are associated with pronounced weakening in the polar vortex in both hemispheres. The weakening in the stratospheric polar vortex arguably results from the intensification in the wave flux from the troposphere into the stratosphere and is accompanied by increased heat transport. The latter causes stratospheric warming in the Artic and Antarctic and slows zonal currents. The response of the lower stratosphere circulation to ENSO is approximately the opposite to that of the middle stratosphere.
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Studies show anthropogenic aerosols (AAs) can perturb regional precipitation, including the tropical rain belt and monsoons of the Northern Hemisphere (NH). In the NH mid-latitudes, however, the impact of AAs on regional climate and precipitation remains uncertain. This work investigates the influence of AAs on wintertime precipitation along the North American Pacific Coast using models from the Coupled Model Intercomparison Project phase 6 (CMIP6). Over the early to mid-20th century, when U.S. and European AA and precursor gas emissions rapidly increased, a robust wintertime precipitation dipole pattern exists in CMIP6 all-forcing and AA-only forcing simulations, with wetting of the southern Pacific Coast (southward of 40N) and drying to the north. A corresponding dynamical dipole pattern also occurs--including strengthening of the east Pacific jet southward of 40N and weakening to the north--which is related to a Rossby wave teleconnection that emanates out of the tropical Pacific. Over the 21st century, when AAs are projected to decrease, an opposite hydro-dynamic dipole pattern occurs, including drying southward of 40N (including California) and wetting to the north. Although Pacific Coast precipitation is dominated by natural variability, good multi-model agreement in the forced component of Pacific Coast precipitation change exists, with the AA pattern (north south dipole) dominating the greenhouse gas (uniform) pattern in the historical all-forcing simulations. A high level of agreement in individual model-realization trends also exists, particularly for the early part of the 20th century, suggesting a robustness to the human signature on Pacific Coast precipitation changes. Thus, historical precipitation responses along the Pacific Coast are likely to have been driven by a mixture of natural variability and forced changes. Natural variations appear to drive a large fraction of this change, but human influences (i.e.,~aerosols) are likely to have preconditioned the variability of the climate in this region.
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The ocean heat content (OHC) and associated surface stratification in tropical oceans are ideal variables to monitor global climate change. However, the variations in surface stratification of the tropical Indian Ocean at the millennial time scale have been less understood. In this context, the present study aimed at reconstructing the sea surface temperature (SST) and thermocline water temperature (TWT) using Mg/Ca of planktonic foraminifera in the sediments of the Andaman Sea, which is one of the hot centers in the Indo-Pacific warm pool (IPWP) over the past 42 ka. The study results confirm the SST changes are consistent in response to the high-latitude temperature records with strong sub-orbital and millennial variations, while the TWT patterns trend remarkably different from the millennial-scale SST and show the variations correspond to the natural summer insolation that triggers the latitudinal movements of the Intertropical Convergence Zone (ITCZ). Furthermore, salinity influences the thermal differences (ΔT=SST-TWT) by varying the vertical temperature and stratification. Analysis of available data shows that the mean northward position of ITCZ would input more freshwater into the Andaman Sea. Freshwater lids can reduce mixing by acting as effective barriers and preventing heat from transferring from the surface to the subsurface, thereby causing subsurface cooling and increased stratification. In contrast, more southward ITCZ (boreal winter monsoon) supplies less freshwater into the Andaman Sea. A highly saline surface water and air cooling promotes mixing and subsurface warming, resulting in decreased stratification. The study results reveal a millennial-scale vertical thermal seesaw synchronizing in the tropical Indian Ocean with abrupt Atlantic meridional overturning circulation (AMOC) changes. During the Bolling-Allerod (B/A) warming period, the dynamic AMOC maintains the surface water stratification by controlling the subsurface cooling processes with increased precipitation and freshwater brought by the northward ITCZ. In contrast, Younger Dryas (YD) and Heinrich events (H) are characterized by reduced summer monsoon precipitation associated with the sluggish AMOC and southward ITCZ, resulting in less stratification and subsurface warming in the Andaman Sea. The thermal proxy records adopted in the study afford robust evidence on the millennial-scale variability in surface stratification driven synchronously by AMOC and regional evaporation vs. precipitation changes in the tropical Indian Ocean, offering new insights into the impacts of the IPWP OHC on abrupt climate changes.
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The meso‐scale enhancement of surface turbulent fluxes at the air‐sea interface is driven by the meso‐scale surface wind speed variability, especially the gustiness velocity and the meso‐scale wind speed standard variation. This study proposes a parameterization of these two variables. A large dataset based on the operational 2.5~km‐AROME convection permitting model is used in a coarse‐graining framework, to quantify various quantities that are subgrid at the scale of a 100‐km resolution global circulation model grid cell. This provides a learning dataset to help build the parameterization. The analysis of two case studies of intense wind speed meso‐scale variability, combined with the literature review, provides a physically‐based set of twelve potential predictors, accounting for the convection activity and the large‐scale dynamics. The least absolute shrinkage and selection operator then frames a penalized multivariate linear regression approach to objectively identify the most relevant predictors. Five predictors are selected for predicting the gustiness velocity: the updraft mass flux at the lifting condensation level, the density current spreading velocity, the large‐scale horizontal shear and divergence and the large‐scale wind speed. The parameterization of the meso‐scale wind speed standard deviation requires an additional predictor, namely the cold‐pool object aggregation index. The proposed parameterization performs significantly better than the previously published parameterizations and is able to capture 80%, 99% and 93% of the meso‐scale enhancement of the momentum, sensible heat and latent heat fluxes, respectively. In the perspective of a global circulation model implementation, in which some predictors may be unavailable, simpler versions of the parameterization, i.e. involving fewer predictors, are also discussed.
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Surface currents in the equatorial Pacific play a key role in determining thermohaline structures in the upper ocean, thus acting to modulate the tropical Pacific mean state and interannual variability. In turn, the thermohaline structure and its variability can also affect surface currents by changing ocean density and pressure gradients. Currently, the thermohaline effects on the surface currents have not been well understood, especially for the salinity role. In this study, the thermohaline effects on surface currents in the equatorial Pacific are systematically evaluated based on an analytical scheme, which is used to calculate surface currents in producing the Ocean Surface Current Analysis Real‐time (OSCAR) product. Several calculations are performed with the original OSCAR‐based scheme and an improved scheme including the salinity effect. It is demonstrated that the thermohaline effects are generally dominated by the temperature role, but the salinity effect plays a non‐negligible role near the dateline and in the far eastern Pacific. The thermohaline effects are quantified and highlighted on seasonal and interannual time scales. For annual mean surface currents, temperature and salinity effects work together to weaken zonal currents north of the equator, with their reduced amplitude by about half. Seasonally, the thermohaline effects are dominant in spring due to the weakened wind effect. Interannually, the salinity effect explains up to 25% of the total variance in the far eastern equatorial Pacific. This study highlights the substantial contributions of thermohaline anomalies to surface current variabilities, in which salinity can play an important contributing role.
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This study shows the influence of ENSO over the seasonal and diurnal variability of the Orinoco Low‐Level Jet (OLLJ) during the December–January–February (DJF) season. Results show changes in the occurrence and intensity of the OLLJ winds during the phases of ENSO (El Niño and La Niña) in relation to Neutral conditions. During El Niño (La Niña), the OLLJ days decrease (increase) over the mid and exit of the OLLJ corridor. Furthermore, the mean wind vertical profiles (and the intensity of the diurnal cycle) decrease (increase) with values ranging from 5 to 9 m/s. We highlight that the OLLJ days decrease (increase) in January (February) during El Niño (La Niña) up to 24% (15%). These changes are modulated by the pressure gradient between the Atlantic Subtropical High and the Near Equatorial Trough regions that decreases (increases) during El Niño (La Niña) and thus, induces a decrease (increase) of the OLLJ winds. Moreover, the water vapor flux and precipitation reduce during both phases of ENSO along the OLLJ corridor. This work shows new insights on the OLLJ features during ENSO, which contributes to improving our understanding of the processes that influence the hydro‐climatology of northern South America.
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Siberian High (SH) is the dominant pressure system located in the mid-high latitudes of Eurasia during boreal wintertime. This study reveals a triggering impact of SH variation in preceding winter on the following ENSO events, and gives a possible explanation via diagnosing the SH-associated air-sea response over the tropical Pacific and North Pacific. When SH is anomalously enhanced (suppressed) during boreal winter, an Aleutian Low enhanced (suppressed) response will occur over the downstream North Pacific. The Aleutian Low response gradually evolves into a meridional dipole structure similar to the negative (positive) phase of the North Pacific Oscillation (NPO) during the following spring and early summer. Correspondingly, the oceanic response in the North Pacific features a pattern similar to the negative (positive) phase of the Victoria mode. These SH-associated air-sea responses over the subtropical North Pacific will be maintained and further delivered into the tropical Pacific through the so-called seasonal footprinting mechanism, which favors the Bjerknes feedback established around boreal summer and finally grows into a La Niña (El Niño).
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The dust cycle is an important element of the Earth system, and further understanding of the main drivers of dust emission, transport, and deposition is necessary. The El Niño–Southern Oscillation (ENSO) is the main source of interannual climate variability and is likely to influence the dust cycle on a global scale. However, the causal influences of ENSO on dust activities across the globe remain unclear. Here we investigate the response of dust activities to ENSO using output from Coupled Modeling Intercomparison Project Phase 6 (CMIP6) historical simulations during the 1850–2014 period. The analyses consider the confounding impacts of the Southern Annular Mode, the Indian Ocean Dipole, and the North Atlantic Oscillation. Our results show that ENSO is an important driver of dry and wet dust deposition over the Pacific, Indian, and Southern oceans and parts of the Atlantic Ocean during 1850–2014. Over continents, ENSO signature is found in America, Australia, parts of Asia, and Africa. Further, ENSO displays significant impacts on dust aerosol optical depth over oceans, implying the controls of ENSO on the transport of atmospheric dust. Nevertheless, the results indicate that ENSO is unlikely to exhibit causal impacts on regional dust emissions of major dust sources. While we find high consensus across CMIP6 models in simulating the impacts of ENSO on dust deposition and transport, there is little agreement between models for the ENSO causal impacts on dust emission. Overall, the results emphasize the important role of ENSO in global dust activities.
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The El Niño-Southern Oscillation (ENSO) phenomenon features rich sea surface temperature (SST) spatial pattern variations dominated by the Central Pacific (CP) and Eastern Pacific (EP) patterns during its warm phase. Understanding such ENSO pattern diversity has been a subject under extensive research activity. To provide a framework for unveiling the fundamental dynamics of ENSO diversity, an intermediate coupled model based on the Cane-Zebiak-type framework, named RCZ, is established in this study. Compared with the original Cane-Zebiak model, RCZ consists of revised model formulation and well-tuned parameterization schemes. All model components are carefully validated against the observations via the standalone mode, in which the observed anomalous SST (wind stress) forcing is prescribed to drive the atmospheric (oceanic) component. The superiority of RCZ’s model components over those in the original Cane-Zebiak model is evidenced by their better performance in simulating the observations. Coupled simulation with RCZ satisfactorily reproduces aspects of the observed ENSO characteristics, including the spatial pattern, phase-locking, amplitude asymmetry, and, particularly, ENSO diversity/bi-modality. RCZ serves as a promising tool for studying dynamics of ENSO diversity as it resolves most of the relevant processes proposed in the literature, including atmospheric nonlinear convective heating, oceanic nonlinear dynamical heating, and the ENSO/westerly wind burst interaction.
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Understanding the direct and indirect impact of the Pacific and Atlantic Oceans on precipitation in the region of Northeast Brazil (NEB) is crucial for monitoring unprecedented drought events. We propose nonlinear methods of phase coherence and generalized event synchronization analysis to understand the underlying mechanism. In particular, the relationships between sea surface temperature (SST) variability and the standard precipitation index are interpreted as direct interactions, while the relationships between surrounding oceans are interpreted as indirect effects on the precipitation. Our results reveal a dominant role of tropical North Atlantic for precipitation deficit and droughts, particularly in recent decades. Meanwhile, the indirect Pacific‐North Atlantic phase synchronizations have significant influence on and reinforcement of the droughts in NEB. Furthermore, we find that the instantaneous angular frequencies of precipitation and SST are drastically changed after very strong El Niño and La Niña events, therefore resulting in a higher probability of phase coherence.
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This study focuses on the influence of the North Pacific Victoria mode (VM) on the persistence of the sea surface temperature anomalies (SSTAs) of El Niño–Southern Oscillation (ENSO). Both observational data and outputs from phases 5 and 6 of the Coupled Model Intercomparison Project show that VM events can enhance the persistence of ENSO SSTAs and reduce the intensity of the spring persistence barrier (SPB) of ENSO SSTAs. The possible reasons for these phenomena are that the SSTAs develop (decay) slowly and do not experience a rapid sign reversal during their decaying phase, indicating a relatively weak ENSO SPB occurs in the spring for strong VM cases. However, during weak VM cases, they transit quickly from positive (negative) SSTAs into negative (positive) SSTAs. The amplitudes of the SSTAs during weak VM cases are relatively greater in the mature phase than those during strong VM cases, resulting in a relatively strong ENSO SPB. Furthermore, VM events can affect the strength of the westerly wind anomalies over the western Pacific. The magnitude of the westerly wind anomalies is weaker when a strong ENSO co‐occurs with a strong VM event than when it is associated with a weak VM event. In addition, the subsurface water during the strong VM case transits slowly from positive to negative while that transits relatively fast during weak VM cases, which are consistent with the evolutions of SSTAs. Thus, they together affect the recharge/discharge oscillation process of ENSO, which eventually lead to a strong or a weak ENSO SPB phenomenon occurring in the spring during strong or weak VM cases, respectively. The findings offer a new sight for studying the persistence of ENSO SSTAs.
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In the Niño3.4 region (tropical Pacific, 5°S–5°N, 170°–120°W), sea surface temperature (SST) changes are highly correlated with temperature variations in the upper 40-m layer. This study explores the upper-ocean heat budget in the Niño3.4 region using Ocean Reanalysis System 5 (ORAS5) monthly data from 1979 to 2018, with a focus on ocean heat transports at lateral boundaries in the top 40-m layer and their correlation with temperature variations. In the region, there is a well-defined structure of opposite meridional circulation in the upper and lower parts of the thermocline, characterized by divergence in the upper layer above 40 m and convergence in the lower layer. The change of mean temperature in the upper layer is determined by the sum of zonal, meridional, and vertical heat transports, which, however, tend to largely compensate for each other. In general, part of the surface heat flux from the atmosphere to the ocean and the heat transport from the subsurface ocean are transported out of the domain by meridional and zonal currents, leaving only a tiny part to warm or cool the upper ocean. The amplitude of the net surface heat flux effective for the entire 40-m layer of the ocean is weaker than the lateral heat transport. On an interannual timescale, variations of heat transports in both zonal and meridional are positively correlated with temperature anomalies, while the vertical heat transport from the subsurface ocean is negatively correlated. Composite analyses for five El Niño events and five La Niña events also revealed that there is a positive contribution of horizontal transport convergence to temperature anomalies during the evolution of El Niño (warming) and La Niña (cooling), while vertical transport acts against temperature variations.
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Climate change includes the change of the long-term average values and the change of the tails of probability density functions, where the extreme events are located. However, obtaining average values are more straightforward than the high temporal resolution information necessary to catch the extreme events on those tails. Such information is difficult to get in areas lacking sufficient rain stations. Thanks to the development of Satellite Precipitation Estimates with a daily resolution, this problem has been overcome, so Extreme Precipitation Indices (EPI) can be calculated for the entire Colombian territory. However, Colombia is strongly affected by the ENSO (El Niño—Southern Oscillation) phenomenon. Therefore, it is pertinent to ask if the EPI’s long-term change due to climate change is more critical than the anomalies due to climate variability induced by the warm and cold phases of ENSO (El Niño and La Niña, respectively). In this work, we built EPI annual time series at each grid-point of the selected Satellite Precipitation Estimate (CHIRPSv2) over Colombia to answer the previous question. Then, the Mann-Whitney-Wilcoxon test was used to compare the samples drawn in each case (i.e., change tests due to both long-term and climatic variability). After performing the analyses, we realized that the importance of the change depends on the region analyzed and the considered EPI. However, some general conclusions became evident: during El Niño years (La Niña), EPI’s anomaly follows the general trend of reduction -drier conditions- (increase; -wetter conditions-) observed in Colombian annual precipitation amount, but only on the Pacific, the Caribbean, and the Andean region. In the Eastern plains of Colombia (Orinoquía and Amazonian region), EPI show a certain insensitivity to change due to climatic variability. On the other hand, EPI’s long-term changes in the Pacific, the Caribbean, and the Andean region are spatially scattered. Still, long-term changes in the eastern plains have a moderate spatial consistency with statistical significance.
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Plain Language Summary El Niño‐Southern Oscillation (ENSO) is the most significant interannual signal on Earth and its influence is worldwide, thus its predictability and prediction draw much attention. One interesting feature is that the model makes better prediction for the observation than predicting itself, referred as signal‐to‐noise paradox, even in a simple ENSO model: recharge oscillator model (ROM). Previous studies have shown that the higher persistence of the observation than model may play a role in this paradox. Here, we find that a paradox can occur in ROM even when persistence of the observation is lower. By using the analytical and numerical solutions of the ROM, it is suggested that a larger ENSO growth rate and a shortened period can cause the paradox even when the persistence of the observation is lower. By using the second‐order regression model to do ENSO forecasts during 1910–2010, we suggest that a paradox occurs after 1960 is caused by a larger ENSO growth rate and a shortened ENSO period.
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Proxy records from across the Southern Hemisphere show significant local to regional scale variability in climatic and environmental conditions during late Marine Isotope Stage 3 and early Marine Isotope Stage 2, prior to the global last glacial maximum (LGM; 26.5–19.0 kyr). Although not necessarily synchronous across the hemisphere, the regional signature of these pre-26.5 kyr ‘events’ suggests greater complexity of events preceding the global LGM in the Southern Hemisphere than in the North. Here we explore climatic and environmental variability across the Southern Hemisphere during two time-slices: 32 ± 1 kyr (representing the period of Southern Hemisphere summer insolation minimum) and 21 ± 1 kyr (representing the period of maximum global ice volume), based on previously published palaeoclimate proxy data. Temperatures were already approaching glacial levels across the Southern Hemisphere at 32 ± 1 kyr and minimum temperatures were attained in many records at ~21 ± 1 kyr. Furthermore, the descent into minimum temperatures occurred later in Antarctica than elsewhere in the Southern Hemisphere. Effective precipitation was more variable, with evidence for both increased and decreased moisture availability across the hemisphere during each time slice. The pattern of effective precipitation indicates that local factors likely played a more significant role in driving moisture availability compared to temperature. Our findings indicate that the onset of full-glacial conditions across the Southern Hemisphere occurred prior to the attainment of global maximum ice volume.
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Coastal zones are fragile and complex dynamical systems that are increasingly under threat from the combined effects of anthropogenic pressure and climate change. Yet, the key environmental factors that drive regional coastline changes remain poorly quantified. Here, using global satellite derived shoreline positions from 2000 to 2017 and a variety of reanalysis products, we demonstrate that coastlines are under the influence of 3 main drivers: the sea-level, and also ocean waves and fluvial inputs. The relative contribution of each of the drivers vary across the global coastline, with about a third exhibiting a clear dominance of one of the drivers (60% for sea level, 30% for rivers and 10% for waves). Furthermore, by establishing that these environmental forcing are all substantially constrained by El Niño Southern Oscillation (ENSO) at interannual time scales, we derive a conceptual global model of yearly shoreline changes that integrates the complex and diverse planetary climate influence of ENSO on each of these 3 drivers. This model reproduces well the observed shoreline changes with a global correlation of 0.4 and up to 0.6 in the tropical belt. We believe it represents a new solid physical and mathematical framework for understanding ENSO-driven littoral hazards as well as an efficient yet simple approach for their prediction.
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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.
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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.
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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.
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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).
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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.
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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.
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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.
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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.
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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.
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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.
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