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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... During El Niño, trade winds are weakened or reversed, leading to reduced warm water transport to the western Pacific and abnormal warming in the central and eastern Pacific. Conversely, Responsible Editor: Philippe Garrigues during La Niña, intensified trade winds enhance the westward displacement of warm sea surface temperatures, resulting in anomalous cooling (Timmermann et al. 2018;Chen and Fang 2023). ...
... These results reflect a stronger ENSO teleconnection response when considering CP SST anomalies, whereas it appears unclear with the EP. According to Zhang et al. (2018), this phenomenon could be attributed to the instability of the La Niña-NAO teleconnection and the asymmetry between ENSO warm and cold events, which stems from the positioning of the longitudinal SST anomalies (Takahashi et al. 2011;Capotondi et al. 2015;Timmermann et al. 2018;Feng et al. 2019). This phenomenon is potentially illustrated by the E8 and E9 scenarios (CP ENSO and EP ENSO events respectively), which indicate a directional change in the teleconnection, accompanied by opposing correlations in linear indices which underscores the importance of independently considering both types of ENSO events. ...
... Deep convection at the tropical level is highly dependent on the total SST (climatological SST + SST anomalies), with heightened convection occurring when the total SST is elevated (Dommenget et al. 2013;Okumura 2019). In the case of CP events, the total SST tends to be higher than in the EP region during ENSO development (Takahashi et al. 2011;Timmermann et al. 2018;Okumura 2019). This SST disparity leads to varying ocean-to-atmosphere heat fluxes (Kao and Yu 2009;Yan et al. 2022), triggering a faster response in the atmosphere, boosting the convection process when CP event occurs. ...
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The Central-Pacific (CP) and Eastern-Pacific (EP) types of El Niño-Southern Oscillation (ENSO) and their ocean–atmosphere effect cause diverse responses in the hydroclimatological patterns of specific regions. Given the impact of ENSO diversity on the North Atlantic Oscillation (NAO), this study aimed to determine the relationship between the ENSO-NAO teleconnection and the ENSO-influenced precipitation patterns in Colombia during the December–February period. Precipitation data from 1981 to 2023, obtained from the Climate Hazards Group (CHIRPS), were analyzed using nine ENSO and NAO indices spanning from 1951 to 2023. Using Pearson’s correlation and mutual information (MI) techniques, nine scenarios were devised, encompassing the CP and EP ENSO events, neutral years, and volcanic eruptions. The results suggest a shift in the direction of the ENSO-NAO relationship when distinguishing between the CP and EP events. Higher linear correlations were observed in the CP ENSO scenarios (r > 0.65) using the MEI and BEST indices, while lower correlations were observed when considering EP events along with the Niño 3 and Niño 1.2 indices. MI show difference in relationships based on the event type and the ENSO index used. Notably, an increase in the non-linear relationship was observed for the EP scenarios with respect to correlation. Both teleconnections followed a similar pattern, exhibiting a more substantial impact during CP ENSO events. This highlights the significance of investigating the impacts of ENSO on hydrometeorological variables in the context of adapting to climate change, while acknowledging the intricate diversity inherent to the ENSO phenomenon.
... The El Niño-Southern Oscillation (ENSO) is an important basin-scale year-to-year fluctuation in the global climate system, and it strongly interacts with the rest of the weather-climate continuum involving timescales ranging from synoptic through multidecadal to centennial. Due to its significant global impacts and teleconnections (reviewed by Cai et al. 2019;Yang et al. 2018;Yeh et al. 2018;Yuan et al. 2018), the dynamics and predictability of ENSO have long been hot topics in scientific research (reviewed by Cai et al. 2021, McPhaden et al. 2020, Tang et al. 2018, Timmermann et al. 2018and Yeh et al. 2014. Based on the classic ENSO instability theory (Bjerknes 1969;Jin 1997;Jin et al. 2020), the air-sea coupled Bjerknes positive feedback mechanism provides the fundamental growth mechanism of ENSO, and the recharge-discharge processes of the upper-ocean heat content in the equatorial Pacific give rise to a delayed negative feedback that causes the ENSO phase transition. ...
... However, as the understanding of ENSO improves, the ENSO diversity has been found to be an important issue affecting its prediction (Cai et al. 2021;Capotondi et al. 2015;Timmermann et al. 2018). The main periods of ENSO have a wide range of 2-7 years, which were found to be separately concentrated in the 3-7-yr low-frequency (LF) and 2-3-yr quasi-biennial (QB) bands (An and Wang 2000;Barnett 1991;Jiang et al. 1995;Kao and Yu 2009;Rasmusson et al. 1990;Ropelewski et al. 1992;Yeo et al. 2017). ...
... The spatiotemporal characteristics of the space-time diversity of ENSO depend on the relative activities and phase differences of the ENSO modes with changing intensities (Wang and Ren 2020). In addition, the two major positive oceanic feedbacks, the thermocline feedback (TH) and zonal advective feedback (ZA), play different roles in the two ENSO modes that the ZA is relatively more important for the QB mode than the LF mode (Bejarano and Jin 2008;Timmermann et al. 2018;Xie and Jin 2018). ...
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The super long La Niña phenomenon, which has an extremely long duration, like the recent 2020–2023 La Niña event, is less concerned than the super El Niño. In this study, we identify five super long La Niña events after 1950 and investigate roles of the 2–3-year quasi-biennial (QB) and 3–7-year low-frequency (LF) ocean–atmosphere coupled processes of El Niño–Southern Oscillation (ENSO), and the interdecadal background in forming the large-scale prolonged negative sea surface temperature anomalies (SSTAs) in the central to eastern equatorial Pacific during these events. We group the five events into the thermocline-driven type (the 1983–1986 and 1998–2002 events) and the wind-driven type (the 1954–1957, 1973–1976, and 2020–2023 events). The former inherited a sufficiently discharged state of equatorial upper-ocean heat content from the preceding super El Niño and dominated by the thermocline feedback, leading to a LF oceanic dynamical adjustment to support the maintenance of negative ENSO SSTAs. The latter were promoted by the relatively more important zonal advective feedback and Ekman pumping feedback and deeply affected by a strongly negative equatorial zonal wind stress background state that sourced from the strong negative phase of the Interdecadal Pacific Oscillation. Besides, the QB ENSO variability with casual contributions during these events is less important. Results show that both the LF ENSO variability and the interdecadal Pacific background could assist in generating prolonged La Niñas.
... This mechanism is realistically simulated by our model, consistent with the emerging paradigm that ENSO is not a cycle; instead, it is best described as a series of events 47 . Observed and simulated ENSO variability is energized every time an El Niño event is excited by stochastic forcing, triggering a subsequent La Niña, with the cycle breaking down as conditions return to neutral without triggering a subsequent El Niño event 45,48 . This asymmetric evolution is key to quantifying the strength of coupled feedbacks because each phase is triggered by different physical processes. ...
... This analysis is consistent with the emerging model that El Niño events are triggered by stochastic wind fluctuations and amplified by a positive Bjerknes feedback involving the expansion of the WPWP with a lesser role 45 for subsurface thermal anomalies generated by a preceding La Niña, as indicated by many theories of ENSO 27 . By contrast, large negative thermocline depth anomalies and associated negative vertical thermal advection do play a part in the onset of La Niña (Extended Data Fig. 3, right). ...
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El Niño events, the warm phase of the El Niño–Southern Oscillation (ENSO) phenomenon, amplify climate variability throughout the world¹. Uncertain climate model predictions limit our ability to assess whether these climatic events could become more extreme under anthropogenic greenhouse warming². Palaeoclimate records provide estimates of past changes, but it is unclear if they can constrain mechanisms underlying future predictions3–5. Here we uncover a mechanism using numerical simulations that drives consistent changes in response to past and future forcings, allowing model validation against palaeoclimate data. The simulated mechanism is consistent with the dynamics of observed extreme El Niño events, which develop when western Pacific warm pool waters expand rapidly eastwards because of strongly coupled ocean currents and winds6,7. These coupled interactions weaken under glacial conditions because of a deeper mixed layer driven by a stronger Walker circulation. The resulting decrease in ENSO variability and extreme El Niño occurrence is supported by a series of tropical Pacific palaeoceanographic records showing reduced glacial temperature variability within key ENSO-sensitive oceanic regions, including new data from the central equatorial Pacific. The model–data agreement on past variability, together with the consistent mechanism across climatic states, supports the prediction of a shallower mixed layer and weaker Walker circulation driving more frequent extreme El Niño genesis under greenhouse warming.
... Skillful ENSO prediction can help reduce societal and economic impacts caused by this natural phenomenon, and assist in managing natural resources and the environment 8,9 . Therefore, it is critical to predict ENSO early and accurately 1,10 . ...
... The recent development of AI, particularly deep learning (DL) technology, in Earth science has led to significant progress in ENSO prediction 9,10,22-28 . Ham et al. (HKL19 hereafter) demonstrated significantly improved forecasting skill compared to previous dynamical and traditional statistical forecasts by utilizing sea surface temperature (SST), ocean heat content, and sea surface winds as inputs in their DL model 1,9,10,[25][26][27] . Their Convolutional Neural Networks-based model could predict the strength of ENSO events by 17 months in advance; here the effective forecast length is defined as the period when the correlation between the forecast ENSO index and the observed true value drops to 0.50. ...
... The latter consists of standard single-year moderate ENSO events, multiyear events (Yu and Fang 2018), extreme El Niños (Chen et al. 2015;Capotondi et al. 2018;Sun and Yu 2009), and delayed El Niños (Hu andFedorov 2016, 2017). These spatiotemporal irregularities are called ENSO complexity (Timmermann et al. 2018;Hayashi and Watanabe 2017;Boucharel et al. 2021). ...
... This oscillation between EP-dominant and CP-dominant regimes indicates that the decadal variability plays an important role in the underlying dynamics, which is parameterized through a simple linear stochastic differential equation with multiplicative noise (10f), with no explicit dependence on the state variables in the faster time scales (Yang et al. 2021). They, together with additional small Gaussian white noise s uẆ u , s hẆ h , s CẆ C , and s EẆ E , characterize the irregularity and multiscale features of the ENSO complexity (Timmermann et al. 2018;Fang and Xie 2020). ...
Article
Studying the response of a climate system to perturbations has practical significance. Standard methods in computing the trajectory-wise deviation caused by perturbations may suffer from the chaotic nature that makes the model error dominate the true response after a short lead time. Statistical response, which computes the return described by the statistics, provides a systematic way of reaching robust outcomes with an appropriate quantification of the uncertainty and extreme events. In this paper, information theory is applied to compute the statistical response and find the most sensitive perturbation direction of different El Niño–Southern Oscillation (ENSO) events to initial value and model parameter perturbations. Depending on the initial phase and the time horizon, different state variables contribute to the most sensitive perturbation direction. While initial perturbations in sea surface temperature (SST) and thermocline depth usually lead to the most significant response of SST at short and long ranges, respectively, initial adjustment of the zonal advection can be crucial to trigger strong statistical responses at medium range around 5–7 months, especially at the transient phases between El Niño and La Niña. It is also shown that the response in the variance triggered by external random forcing perturbations, such as the wind bursts, often dominates the mean response, making the resulting most sensitive direction very different from the trajectory-wise methods. Finally, despite the strong non-Gaussian climatology distributions, using Gaussian approximations in the information theory is efficient and accurate for computing the statistical response, allowing the method to be applied to sophisticated operational systems. Significance Statement The purpose of this work is to better understand how El Niño–Southern Oscillation (ENSO) responds to changes in its initial state and internal dynamics or external forcings. A statistical quantification of this response allows for the comprehension of the triggering conditions and the effect of climate change on the occurrence frequency and strength of each type of ENSO event. Such a study also allows to detect the most sensitive perturbation directions, which has practical significance in guiding anthropogenic activities. The approach used to study the response in this work is through the framework of information theory, which allows for an unbiased and robust assessment of the statistical response that is not affected by the turbulent dynamics of the system.
... The typical life cycle of ENSO consists of its growth in boreal spring or summer, reaching its peak in winter, and decay in the following spring (Iwakiri & Watanabe, 2021;Jin et al., 1994;Tziperman et al., 1994). The oscillatory behaviour of ENSO has an asymmetry with respect to its temporal evolution and the transition among its phases such as El Niño (warm phase), La Niña (cold phase) and neutral (Dommenget et al., 2013;Neelin et al., 2000;Timmermann et al., 2018). A study by Cole et al. (2002) discussed that the El Niño phase generally ends quickly (almost 1 year) whereas the La Niña phase lasts longer (multi-year La Niña). ...
... A study by Cole et al. (2002) discussed that the El Niño phase generally ends quickly (almost 1 year) whereas the La Niña phase lasts longer (multi-year La Niña). This asymmetry emphasizes the importance of the nonlinear dynamical response of the equatorial Pacific, from the remote teleconnections originating from the Indian Ocean, Atlantic Ocean and extratropical (north and south) Pacific (Fan & Meng, 2023;Izumo et al., 2010;Timmermann et al., 2018). ...
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This study examines the asymmetry in the Indian summer monsoon rainfall (ISMR) response over India and its four homogeneous regions to two distinct types of temporal evolution in La Niña. We have shown this uneven response by analysing the large‐scale dynamics over tropical Indo‐Pacific region for the period 1951–2022. We have identified two types of La Niña events during monsoon season (June–September) based on whether they evolved from El Niño or La Niña from preceding boreal winter season (December–February). India receives significantly more (less) rainfall during La Niña years, when it was preceded by El Niño (La Niña) in the preceding winter. We further observed the spatial diversity of rainfall over India with a northeast–southwest dipole pattern. When La Niña years were preceded by El Niño, positive surface pressure anomaly over west‐north Pacific, low‐level westerlies and moisture transport favoured the rainfall over south peninsula and west‐central India. Whereas moisture divergence associated with anomalous lower‐tropospheric anticyclone over west‐north Pacific suppressed the rainfall over Indo‐Gangetic plains. However, when La Niña years were preceded by La Niña in winter, the absence of westerlies and weak moisture transport subdued the rainfall over south peninsula and west‐central India. At the same time, moisture convergence and a greater number of monsoon depressions favoured rainfall over north‐west India. This study also looked at how well eight Copernicus Climate Change Service (C3S) models predicted ISMR and SST for two types of La Niña with April initial conditions during the period 1993–2016. Models were able to capture the spatial pattern of SST anomalies over Indo‐Pacific Ocean, but all models could not capture the spatial pattern of ISMR. However, in terms of intensity, six out of eight models could predict more (less) ISMR when it was preceded by El Niño (La Niña), coinciding with the observed anomaly.
... El Niño-Southern Oscillation (ENSO) is the major mode of interannual climate variability that exerts significant impacts on global climate, ecosystems, and socio-economic developments 1,2 . Monthly sea surface temperature (SST) data are crucial to better understand the detailed features of ENSO occurrence and development, including the relationship between onset timing and duration of ENSO events 3 , the diversity in the persistence of multi-year El Niño events 4 , and the responses of super El Niño events to SSTA in Indian and Atlantic Oceans 5 . ...
... ENSO comprises different flavors, distinguished by the spatial distribution of sea surface temperature (SST) anomalies Timmermann et al., 2018), which were associated to different impacts (e.g., Ashok et al., 2007) (Figure 1). Thus, the ENSO-MHW connection can be expected to also be affected by the diversity of ENSO events. ...
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Plain Language Summary Marine heatwaves (MHWs) are periods of prolonged, extremely warm ocean temperatures that have caused widespread ecological and socioeconomic impacts worldwide. The predictability of these events can be improved if we can find connections between regional events and larger climatic drivers, such as El Niño‐Southern Oscillation (ENSO). Both the positive phase of ENSO, El Niño, and its negative phase, La Niña, have been linked to MHWs in various parts of the world. However, not all El Niño and La Niña events are the same, leading to uncertainty in the relationship between ENSO and MHWs. Due to the limited duration of the observational record, a major issue arises with the lack of examples of different types of El Niño and La Niña events in observations. To overcome this challenge, we utilized 10,000 years of simulated data from a near‐global linear inverse model to generate many more samples of possible global ocean temperature configurations. We find strong differences between regional MHWs and different types of El Niño and La Niña events. In some regions, the probability of MHWs is 12 times more likely under long‐lasting El Niño events.
... By strengthening the Walker circulation during La-Niña developing phase, the lower-level equatorial easterly winds cause the convergence, deep convection, and upper tropospheric divergence over the ISM region, which is closely associated with the strong precipitation in summer over the Indian subcontinent ( Fig. 1d and e), and vice versa during El-Nino developing phase. However, the spatiotemporal complexity of ENSO can lead to a wide range of diverse and far-reaching responses [8][9][10][11][12][13][14][15][16][17] . Moreover, the response to the same ENSO forcing can vary due to regional highfrequency variability, non-linear interactions with other climate factors and pre-existing atmospheric, oceanic, and terrestrial conditions [18][19][20][21][22][23][24][25][26] . ...
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The Indian summer monsoon (ISM) rainfall interannual variability is known to be strongly linked to the El-Niño–Southern Oscillation (ENSO). This linear relationship is the primary factor in controlling the interannual variation in ISM precipitation. However, there are many outlier cases, and such deviations pose significant challenges in seasonal prediction over this region. Here we show that such challenges can be attributed to anomalous atmospheric pressure patterns in the Western North Pacific (WNP) region. The anticyclonic circulation anomaly over WNP region causes the easterly wind toward the Indian subcontinent, leading to positive precipitation anomalies with stronger low-level moist convergence, while the cyclonic circulation decreases ISM precipitation. The linear baroclinic model simulation results further support that the WNP circulation pattern can serve as an independent factor for forecasting precipitation over India. The WNP circulation anomaly play the crucial role generating ISM precipitation particularly for July and September. Our study suggests that the role of the WNP circulation anomaly should be carefully considered as the secondary prevailing mechanism on the subseasonal timescale during the boreal summer in addition to the ENSO signal.
... The long-term SST variability patterns induced by ENSO+ events are well known: warming in the eastern Pacific and cooling in the western Pacific [32]. The findings showed that the Indian Ocean also warms during ENSO+ events, as do some regions of the South Atlantic (figures S2(e) and (f)). ...
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Both seasonal and extreme climate conditions are influenced by long-term natural internal variability. However, in general, long-term hazard variation has not been incorporated into coastal risk assessments. There are coastal regions of high interest, such as urban areas, where a large number of people are exposed to hydrometeorological hazards, and ecosystems, which provide protection, where long-term natural variability should be considered a design factor. In this study, we systematized climate analysis to identify high-interest regions where hazard long-term variability should be considered in risk assessment, disaster reduction, and future climate change adaptation and protection designs. To achieve this goal, we examined the effect of the leading modes of climate variability (Arctic Oscillation, Southern Annular Mode, and El Niño–Southern Oscillation) on the variation in the recurrence of extreme coastal hazard events, including as a first step sea surface temperature, winds, and waves. Neglecting long-term variability could potentially lead to the underperformance of solutions, or even irreversible damage that compromises the conditions of ecosystems for which nature-based solutions are designed.
... Although the development of each ENSO phase involves common ocean-atmosphere feedback processes related to the recharge and discharge of heat 2 , each ENSO cycle varies. Conceptual ENSO models elucidate the average evolution of events and their oscillatory dynamics but fail to explain temporal and spatial deviations 2 Page 2 of 23 AUTHOR SUBMITTED MANUSCRIPT -ERL-117746.R3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 A c c e p t e d M a n u s c r i p t from the mean 15 . Differences in the location of sea surface temperature anomalies (SSTA) are thought to be one unaccounted factor within the oscillation framework that may be important for the event life cycle. ...
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Diverse characteristics of El Niño Southern Oscillation (ENSO) events challenge the traditional view of tropical coupled ocean-atmosphere systems. The probability of a transition from one type of event to another is influenced by multiple factors of which many are projected to change. Here we assess the likelihood of ENSO transitions in observations and climate models, including a distinction between events that peak in the Eastern Pacific (EP) and Central Pacific (CP). We find that the initial ENSO state influences the likelihood of certain transitions and that some transitions are not physically possible or stochastically likely. For example, transitions to CP events are more likely than EP events except from a neutral state. We also find that El Niños tend to occur as singular events compared to La Niñas. While consecutive El Niño and La Niña events of EP type are possible, opposing EP events do not occur in succession. We identify several transitions likely driven by internal dynamical processes including neutral conditions to El Niño, CP El Niño to another El Niño, EP El Niño to CP La Niña, CP La Niña to CP El Niño and La Niña, and EP La Niña to neutral and CP El Niño. Projections of future transitions show an increased probability of transitions to CP El Niño events while transitions to EP La Niña events become less frequent under a high-emissions scenario. Accordingly, transitions to these events become more and less likely, respectively. We also find changes in the likelihood of specific transitions in a warming world: consecutive CP El Niño events become more likely while EP El Niño events become less likely to transition into CP La Niña events. These changes are expected to occur as early as 2050 with some changes to be accelerated by the end of the 21st century.
... State-of-the-art ESMs still contain biases in the eastern equatorial Pacific (Timmermann, 2018) which leads to problems in ...
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Climate change and Artificial Intelligence (AI) are both attracting great interest across society. There is also substantial interest in merging the two sciences, with evidence already that AI can identify earlier precursors to extreme weather events. There are a range of AI algorithms, and selection of the most appropriate one maximizes the amount of additional understanding extractable for any dataset. However, most AI algorithms are statistically based and even with careful splitting between data for training and testing, they arguably remain as emulators. Emulators may make unreliable predictions when driven by out-of-sample forcing and climate change is an example of this, requiring understanding responses to atmospheric Greenhouse Gas (GHG) concentrations that may be substantially higher than present or the recent past. Notable, though, is the emerging AI technique of “equation discovery”. AI-derived equations from data also does not automatically guarantee good performance for new forcing regimes. However, access to equations rather than a statistical emulator guides system understanding, as their variables and parameters often have a better interpretation. Better process knowledge enables judgements as to whether equations are trusted under extrapolation. For many climate system attributes, descriptive equations are not yet fully available or may be unreliable. This uncertainty is hindering the development of Earth System Models (ESMs) which remain the main tool for projections of large-scale environmental change as GHGs rise. Here, we make the case for using AI-driven equation discovery in climate research, given that its outputs are more interpretable in terms of processes. As ESMs are based around the numerical discretisation of equations that describe climate components, equation discovery from new datasets provides a format amenable to direct inclusion into such models where representation of environmental systems is missing. We present three illustrative examples of how AI-led equation discovery may advance future climate science research. These are generating new equations related to atmospheric convection, parameter derivation for existing equations of the terrestrial carbon cycle, and (additional to ESM improvement) the creation of simplified models of large-scale oceanic features to assess Tipping Point (TP) risks.
... Additionally, it may interact with one of the most influential interannual phenomena on Earth, the El Niño Southern Oscillation (ENSO) (Fedorov & Philander, 2000;McPhaden et al., 2011;Okumura, Sun, & Wu, 2017;Sun & Okumura, 2020;Zhao et al., 2016;Zhong et al., 2017). Unlike ENSO, which exhibits large interannual variance from the central to the eastern equatorial Pacific (Timmermann et al., 2018), the strongest variance of TPDV extends from the northeastern subtropic to the equatorial central Pacific, particularly in the Niño4 region ( Figure S1a in Supporting Information S1) (Capotondi et al., 2022;Chunhan et al., 2021;Di Lorenzo et al., 2023;Liu et al., 2022). This spatial structure bears resemblance to the Central Pacific ENSO (Ashok et al., 2007;Capotondi et al., 2022;Sullivan et al., 2016) and the Pacific Meridional Mode (PMM) (Amaya, 2019;Chiang & Vimont, 2004;Stuecker, 2018), suggesting a significant contribution of PMM to TPDV dynamics (Di Lorenzo et al., 2015;Joh & Di Lorenzo, 2019;Zhao et al., 2023). ...
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Plain Language Summary Observations have consistently highlighted prominent decadal climate variability in the Niño4 region, yet the underlying cause of this distinct pattern remains largely elusive. In this study, we use composite analysis during the phase transition of Tropical Pacific Decadal Variability (TPDV) and modeling experiments to elucidate the mechanisms governing the observed decadal climate variability in the Niño4 region compared to other equatorial areas. Our findings reveal that the eastward and upward propagation of negative subsurface temperature anomalies primarily drives the phase reversal of TPDV. Following this transition from positive to negative phase, the Pacific Meridional Mode (PMM) plays a crucial role. Specifically, PMM‐associated wind forcing induces anomalous upwelling and downwelling in the Niño4 and Niño3 regions, respectively. This results in anomalous vertical advection of mean temperature, contributing to the strengthening and weakening of decadal variances in these regions.
... Australia is a land of considerable climatic variability and highly dependent upon regional weather systems, including the El Niño-Southern Oscillation (Timmermann et al. 2018), that make the country susceptible to drought and bushfires (Abram et al. 2021). As a result, agricultural conditions can also vary greatly between seasons. ...
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The continuing effects of climate change require farmers and growers to have greater understanding of how these changes affect crop production. However, while climatic data is generally available to help provide much of that understanding, it can often be in a form not easy to digest. The proposed Combined Location Online Weather Data (CLOWD) framework is an easy-to-use online platform for analysing recent and historical weather data of any location within Australia at the click of a map. CLOWD requires no programming skills and operates in any HTML5 web browser on PC and mobile devices. It enables comparison between current and previous growing seasons over a range of environmental parameters, and can create a plain-English PDF report for offline use, using natural language generation (NLG). This paper details the platform, the design decisions taken and outlines how farmers and growers can use CLOWD to better understand current growing conditions. Prototypes of CLOWD are now online for PCs and smartphones.
... El Niño events, however, are difficult to predict due to their complexity which manifests through a spectrum of intensities, spatial patterns and temporal evolution 135 . Both statistical and dynamical models are routinely employed for ENSO prediction 136 with dynamical models generally outperforming the statistical ones. ...
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An exponential growth in computing power, which has brought more sophisticated and higher resolution simulations of the climate system, and an exponential increase in observations since the first weather satellite was put in orbit, are revolutionizing climate science. Big data and associated algorithms, coalesced under the field of Machine Learning (ML), offer the opportunity to study the physics of the climate system in ways, and with an amount of detail, infeasible few years ago. The inference provided by ML has allowed to ask causal questions and improve prediction skills beyond classical barriers. Furthermore, when paired with modeling experiments or robust research in model parameterizations, ML is accelerating computations, increasing accuracy and allowing for generating very large ensembles at a fraction of the cost. In light of the urgency imposed by climate change and the rapidly growing role of ML, we review its broader accomplishments in climate physics. Decades long standing problems in observational data reconstruction, representation of sub-grid scale phenomena and climate (and weather) prediction are being tackled with new and justified optimism. Ultimately, this review aims at providing a perspective on the benefits and major challenges of exploiting ML in studying complex systems.
... Longer simulations would be required for a deeper understanding of the impact of ENSO on wind/wave climate in the Pacific Ocean. They would also enable to account for the diversity and complexity of the El Niño /La Niña (Capotondi et al. 2015;Timmermann et al. 2018) episodes in the comprehension of the interannual wave climate variability. ...
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Pacific islands are widely exposed to several strong wave events all year long. However, comprehensive analyses of coastal vulnerabilities to wave climates and their extremes are often lacking in those islands. In the present paper, the wave climate around the reef of New Caledonia is analyzed using a 28-year simulation performed with the Wave Watch III model, and accounting for realistic wind intensity forcing from tropical cyclones. Four mean wave regimes are defined with clustering methods, and are shown to vary along the reef depending on its main orientation. The western reef is mostly exposed to energetic south-western swells (significant height over 1.5 m, peak period of ~ 12 s) generated in the Tasman Sea that are reinforced during austral winter. The northern sector and the Loyalty Islands, are hit by shorter waves (~ 8 to 9 s period) coming from the south-east to the north-east, with height ranging on average from 0.8 m in the Loyalty Channel to 1.5 m at the northern tip of the Grande Terre reef. These waves mainly result from the south-eastern trade winds that blow over the central south-western Pacific all year long. In austral summer, additional swell remotely generated by both the extra-tropical westerlies and the north-eastern trade winds of the northern hemisphere reach the north-eastern reef of the archipelago. These wave regimes also strongly vary in response to the interannual El Niño-Southern Oscillation. El Niño events tend to increase the frequency of the south-western swell regime in austral spring and fall, and of the south-eastern trade wind waves in austral summer. In contrast, during La Niña, waves generated in the northern hemisphere are more likely to reach New Caledonia all year long. Finally, extreme wave events and their return periods were assessed. Wave amplitude reaching 7 m is estimated to occur every 100 years. On the west and southern tip of the Grande Terre reef, extreme waves are 80% of the time westerly waves generated by storms in the Tasman Sea or in the Coral Sea, while on the eastern reefs (Loyalty Islands and Channel), 70% of the extreme wave episodes are associated to tropical cyclone-induced waves. During La Niña episodes, more tropical cyclones pass by New Caledonia, increasing their contribution to extreme wave events along the western and southern coasts of the island. Conversely, in El Niño conditions, the exposure to tropical cyclone-induced waves is predominantly concentrated on the northeastern side.
... [29]), recent research is focused on understanding the ENSO 'diversity' and its different impacts ( [5,34]). The most popular procedure is perhaps to classify based on where the SST anomalies are found: (i) central or (ii) eastern Pacific (referred as type 'C' and type 'E' , respectively; [6,33,36]). However, the recent extreme warming in the far-eastern equatorial Pacific has drawn back again attention from the scientific community. ...
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We examine the relationship between convectively coupled waves, the Madden–Julian Oscillation (MJO), and extreme precipitation over the western coast of South America during Coastal El Niño (COEN) events for the period spanning 1980–2023. Two types of COEN can be distinguished: (i) that occur in association with large-scale El Niño Southern Oscillation (ENSO) (e.g. 1982/83, 1997/98), and (ii) more ‘local’ COEN–when anomalous sea surface temperature take place over the far-eastern equatorial Pacific only (e.g. 2017, 2023). During both types of COEN events, increased rainfall along the western coast of South America is associated with intense Kelvin wave activity. In addition, westward inertio-gravity (WIG) waves, Rossby waves, and the MJO exhibit increased activity during local COEN events. During the recent extreme COEN 2017 and 2023, heavy rainfall occurred alongside significant WIGs, Kelvin, Rossby, and MJO events with unprecedented amplitudes propagating along western South America. Our results suggest that the probability of extreme precipitation under Coastal ENSO in western South America is strongly modulated by wave activity.
... Given its substantial impact on the US, ENSO serves as the primary source of seasonal forecasting skill. Spatial and temporal features of ENSO differ considerably between events, potentially affecting teleconnections and its impact on the US climate (Infanti & Kirtman, 2016;Kao & Yu, 2009;Timmermann et al., 2018). ...
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To better understand and improve the prediction of the seasonal surface temperature (TS) across the United States, we employed convolutional neural network (CNN) models trained on the Community Earth System Model Version 2 Large Ensemble (CESM2 LENS). We used lagged sea surface temperatures (SST) over the tropical Pacific region, containing the information of the El Niño Southern Oscillation (ENSO), as input for the CNN models. ENSO is the principal driver of variability in seasonal US surface temperatures (TSUS) and employing CNN models allows for spatiotemporal aspects of ENSO to be analyzed to make seasonal TSUS predictions. For predicting TSUS, the CNN models exhibited significantly improved skill over standard statistical multilinear regression (MLR) models and dynamical forecasts across most regions in the US, for lead times ranging from 1 to 6 months. Furthermore, we employed the CNN models to predict seasonal TSUS during extreme ENSO events. For these events, the CNN models outperformed the MLR models in predicting the effects on seasonal TSUS, suggesting that the CNN models are able to capture the ENSO‐TSUS teleconnection more effectively. Results from a heatmap analysis demonstrate that the CNN models utilize spatial features of ENSO rather than solely the magnitude of the ENSO events, indicating that the improved skill of seasonal TSUS is due to analyzing spatial variation in ENSO events. The proposed CNN model demonstrates a promising improvement in prediction skill compared to existing methods, suggesting a potential path forward for enhancing TSUS forecast skill from subseasonal to seasonal timescales.
... Li et al., 2020;Sallée et al., 2021). The impact usually reverses during La Niña (Torrence & Webster, 1999) although usually weaker relative to El Niño (An & Jin, 2004;Timmermann et al., 2018) and imply that the effect of La Niña teleconnections is not a simple reversal of El Niño teleconnections. ...
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... The El Niño-Southern Oscillation (ENSO) is a source of climate variability at global level. This phenomenon is partially characterized by oscillations of temperature in the surface waters of the Pacific Ocean and is divided into three phases: warm (El Niño), cold (La Niña) and neutral (Timmermann et al., 2018). In South America, the ENSO is closely related to anomalies in weather and crops (Anderson et al., 2017). ...
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... ENSO has the strongest interannual climate variability in tropical oceans and exhibits significant seasonal phase locking characteristics (Timmermann et al., 2018). In our analysis of the tropical oceans, we employed a temporal window spanning from March of the preceding year (March ( 1)) to February of the subsequent year (February (+1)) as a single sample for SVD analysis. ...
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Understanding how climate variability affects oilseed yields is crucial for ensuring a stable oil supply in regions such as China, where self‐sufficiency in edible vegetable oils is low. Here, we found coherent patterns in the interannual variability of Sea Surface Temperature (SST) anomalies and percent crop yield anomalies in the three ocean basins, and then quantified the contribution of these SST modes to oilseed crop yield anomalies. Our analysis revealed that, at the national level, the six tropical SST modes collectively accounted for 51% of soybean, 52% of rapeseed, and 33% of peanut yield anomalies in China. Tropical Indian Ocean variability exerts the greatest impact on soybean and peanut yield variability, whereas the most significant impact on rapeseed yield anomalies is attributed to El Niño‐Southern Oscillation. Finally, this study examined the specific ways in which changes in SST modes can affect oilseed crop yields using changes in local meteorological variables. Our findings revealed the relationship between tropical SST variability and oilseed crop yields, providing a detailed understanding of the diverse connections between SST modes and oilseed crop yield. This study deepens our knowledge of the influence of climate variability on agriculture, offering valuable insights for devising strategies to mitigate the adverse effects of climate variability on oilseed crop production in China.
... The development and improvement of coupled climate models have indeed played an important role in ENSO research. Considerable and encouraging progress has been made in the past decades in coupled climate model simulations of basic aspects of ENSO, such as the amplitude, the distribution and evolution of SST anomalies in the equatorial Pacific associated with ENSO, the frequency, and the diversity of ENSO [4,24,25,[41][42][43][44][45]. However, the results of the current study-a study focusing on evaluating the simulation of the seasonal phase-locking of ENSO by coupled climate models-indicate that the simulation of the ENSO winter peak phenomenon by climate models is still biased in a significant way in many models, and that correctly simulating the seasonal phase-locking phenomenon of ENSO is still a great challenge for CGCMs. ...
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El Niño–Southern Oscillation (ENSO) usually peaks in the boreal winter—November to January of the following year. This particular feature of ENSO is known as the seasonal phase locking of ENSO. In this study, based on 34 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), the seasonal phase-locking characteristics of the model-simulated El Niño and La Niña events are evaluated in terms of the evolution of the SST anomalies associated with ENSO and the probability distribution of the peak month—the time at which ENSO peaks. It is found that CMIP6 models underestimate the phase-locking strength of ENSO for both El Niño and La Niña events. The ensemble mean peak month matches the observations, but the inter-model spread is large. The models simulate the phase locking of El Nino events better than that of La Niña events, and the large simulation bias of CMIP6 for La Niña phase-locking in the models may have an impact on the simulation of seasonal phase-locking in the ENSO.
... The value of q is about 0.01(℃ × month) 1 between February to March and reaches the mean maximum value of 0.12 (℃ × month) 1 between June to October. The decrease of q in spring is consistent with previous studies, which also found that the air-sea coupling strength is weakest in spring (Jin et al., 2020;Timmermann et al., 2018). However, the zero value of q in spring does not imply an uncoupled atmosphere and ocean, but rather a weakly coupled state, as the box model is oversimplified. ...
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Plain Language Summary The El Niño‐Southern Oscillation (ENSO) is a climate phenomenon characterized by the periodic fluctuation of Sea Surface Temperature (SST) anomaly in the tropical Pacific, which has a significant impact on global weather and climate patterns. However, predicting ENSO evolution several seasons in advance is challenging due to the Spring Predictability Barrier (SPB) phenomenon, which refers to the reduced accuracy of ENSO forecasts during the spring. Our investigation into the nonlinear dynamic characteristics of ENSO systems revealed that the strong surface heating process in spring could be a contributing factor to the SPB. The strong springtime surface heating increases the SST in the eastern tropical Pacific and further affects the different coupled processes between ocean and atmosphere, finally raising the chaotic degree of ENSO systems. As the chaotic degree of ENSO systems increases, initial errors in the forecast model grow more rapidly, which may lead to the SPB phenomenon.
... A higher variance in mode-induced hazards was observed for the ENSO than for the polar climate modes (AO and SAM). This finding is consistent with the existing knowledge of the complexity of the ENSO; its magnitude, amplitude, and skewness render its predictability and modelling challenging, in contrast to annular modes, which exhibit more stable behaviour 10 . Moreover, for short-term projections (less than 15 years), the natural variability is the major source of uncertainty in climate predictions 55,56 . ...
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The El Niño-Southern Oscillation (ENSO) is the dominant mode of tropical climate variability. Understanding its sensitivity to climate states is of societal and ecosystem importance given the unabated global warming. Paleoclimate archives and climate models suggest that ENSO activity depends on mean state conditions. However, due to climate model biases, short observational record and proxy-data uncertainties, evaluating ENSO sensitivity remains challenging. Here we combine state-of-the-art model simulations of past climates and future warming to evaluate ENSO activity throughout a wide range of climate states. We find that the sensitivity of ENSO to the background climate is nonlinear and tied to the climatological position of the tropical Pacific convection centers, namely the Intertropical and South Pacific Convergence Zones. Simulations with atmospheric CO2 lower than today display a poleward shift of the convection centers and weakened ENSO. Moderate equatorward shifts of the convection centers occur under CO2-induced warming increasing ENSO activity, while strong equatorward shifts reduce ENSO variability in extreme CO2 warming scenarios, resulting in a permanent El Niño-like mean state. Furthermore, we find that Eastern Pacific El Niños are more sensitive to the background state than Central El Niño events. Our results provide a comprehensive mechanism of how tropical Pacific mean state modulates ENSO activity.
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El Niño typically induces cooling in the Southwest Pacific Ocean during austral summers, usually leading to decreased marine heatwave frequency and severity. However, the 2016 extreme El Niño unexpectedly coincided with the longest and most extensive marine heatwave ever recorded in the region. This heatwave, spanning over 1.7 million square kilometers, persisting for 24 days with a peak intensity of 1.5°C, resulted in massive coral bleaching and fish mortality. This exceptional warming resulted from anomalously strong shortwave radiation and reduced heat loss via latent heat fluxes, owing to low wind speed and increased air humidity. These anomalies are attributed to a rare combined event "Madden-Julian Oscillation and extreme El Niño." Following 10 February, the rapid dissipation of this marine heatwave results from the most intense cyclone ever recorded in the South Pacific. The hazardous ecological impacts of this extreme event highlight the needs for improving our understanding of marine heatwave-driving mechanisms that may result in better seasonal predictions.
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This study mainly investigated the dynamic synoptic eddy (SE) feedback to interdecadal change in the impact of the winter Siberian high (SH) on subsequent ENSO development. It was found that a significant negative SH-ENSO correlation dominated since the mid-1980s (denoted as P2), which was extremely weak before the 1980s (denoted as P1). By applying a transformed vorticity budget analysis, focus was paid on the North Pacific where the air-sea responses linking the SH and ENSO development were closely associated with SE feedback. When the SH was anomalously enhanced, the Aleutian Low (AL) responded over the North Pacific. The dynamic SE feedback during P2 positively contributed to maintain the AL until early summer, induced the generation and intensification of an anomalous subtropical anticyclone, and thereby formed the negative North Pacific Oscillation phase. These air-sea responses favored the occurrence of Bjerknes feedback established around summer and finally grew into a La Niña event. During P1, the AL could hardly persist due to the rapid decay of dynamic SE feedback in spring, making the subsequent air-sea responses penniless. The distinct dynamic SE feedback was primarily because the intensity of spring SE activity was significantly weaker during P2 than during P1. By utilizing SE structure decomposition, it was found that the SE structure is more prone to being changed during P2, resulting in the generation of SE vorticity fluxes with a larger magnitude through multiplying basic SE velocity by anomalous SE vorticity and obtaining the SE forcing.
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The importance of spring Tibetan Plateau (TP) precipitation is increasingly recognized. This study investigated the primary spatiotemporal features of spring Tibetan Plateau (TP) precipitation and revealed its pronounced impacts from El Niño–Southern Oscillation (ENSO). The spring TP precipitation anomalies are majorly featured by a west–east spatial pattern with interannual variations correlated with the spring central-Pacific (CP) ENSO. This west–east precipitation pattern leads to more precipitation in the western TP and less in the eastern TP under the spring CP El Niño, which is reversed under the spring CP La Niña. The ENSO-related Walker circulation variations and induced Indian Ocean warming that further excite anomalous zonal–vertical and meridional–vertical circulations south of TP are crucial to the spring TP precipitation through configuring the opposite anomalies of vertical motions and moisture convergence over western to eastern TP. The CP ENSO could be a potential precursor to the spring TP precipitation owing to its relatively long predictability and a summer persistence barrier.
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We use coupled and atmosphere-only simulations from the Precipitation Driver and Response Model Intercomparison Project to investigate the impacts of Asian anthropogenic sulfate aerosols on the link between the El Niño-Southern Oscillation (ENSO) and the East Asian Winter monsoon (EAWM). In fully-coupled simulations, aerosol-induced cooling extends southeastward to the Maritime Continent and the north-western Pacific. Remotely, this broad cooling weakens the easterly trade winds over the central Pacific, which reduces the east-west equatorial Pacific sea surface temperature gradient. These changes contribute to increasing ENSO's amplitude by 17 %, mainly through strengthening the zonal wind forcing. Concurrently, the El Niño-related warm SST anomalies and the ensuing Pacific-East Asia teleconnection pattern (i.e. the ENSO-EAWM link) intensify, leading to an increased EAWM amplitude by 18 % in the coupled simulations. Therefore, in response to the increasing frequency of El Niño and La Niña years under Asian aerosol forcing, the interannual variability of the EAWM increases, with more extreme EAWM years. The opposite variations in the interannual variability of the EAWM to Asian aerosols in atmosphere-only simulations (−19 %) further reflect the importance of ENSO-related atmosphere-ocean coupled processes. A better understanding of the changes of the year-to-year variability of the EAWM in response to aerosol forcing is critical to reducing uncertainties in future projections of variability of regional extremes, such as cold surges and flooding, which can cause large social and economic impacts on densely populated East Asia.
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Atmospheric carbon dioxide (CO2) accounts for the largest radiative forcing among anthropogenic greenhouse gases. There is, therefore, a pressing need to understand the rate at which CO2 accumulates in the atmosphere, including the interannual variations (IAVs) in this rate. IAV in the CO2 growth rate is a small signal relative to the long-term trend and the mean annual cycle of atmospheric CO2, and IAV is tied to climatic variations that may provide insights into long-term carbon–climate feedbacks. Observations from the Orbiting Carbon Observatory-2 (OCO-2) mission offer a new opportunity to refine our understanding of atmospheric CO2 IAV since the satellite can measure over remote terrestrial regions and the open ocean, where traditional in situ CO2 monitoring is difficult, providing better spatial coverage compared to ground-based monitoring techniques. In this study, we analyze the IAV of column-averaged dry-air CO2 mole fraction (XCO2) from OCO-2 between September 2014 and June 2021. The amplitude of the IAV, which is calculated as the standard deviation of the time series, is up to 1.2 ppm over the continents and around 0.4 ppm over the open ocean. Across all latitudes, the OCO-2-detected XCO2 IAV shows a clear relationship with El Niño–Southern Oscillation (ENSO)-driven variations that originate in the tropics and are transported poleward. Similar, but smoother, zonal patterns of OCO-2 XCO2 IAV time series compared to ground-based in situ observations and with column observations from the Total Carbon Column Observing Network (TCCON) and the Greenhouse Gases Observing Satellite (GOSAT) show that OCO-2 observations can be used reliably to estimate IAV. Furthermore, the extensive spatial coverage of the OCO-2 satellite data leads to smoother IAV time series than those from other datasets, suggesting that OCO-2 provides new capabilities for revealing small IAV signals despite sources of noise and error that are inherent to remote-sensing datasets.
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The southern Great Plains experience fluctuating precipitation extremes that significantly impact agriculture and water management. Despite ongoing efforts to enhance forecast accuracy, the underlying causes of these climatic phenomena remain inadequately understood. This study elucidates the relative influence of the tropical Pacific and Atlantic basins on April–May–June precipitation variability in this region. Our partial ocean assimilation experiments using the Community Earth System Model unveil the prominent role of interbasin interaction, with the Pacific and Atlantic contributing approximately 70% and 30%, respectively, to these interbasin contrasts. Our statistical analyses suggest that these tropical interbasin contrasts could serve as a more reliable indicator for late-spring precipitation anomalies than El Niño–Southern Oscillation. The conclusions are reinforced by analyses of seven climate forecasting systems within the North American Multi-Model Ensemble, offering an optimistic outlook for enhancing real-time forecasting of late-spring precipitation in the southern plains. However, the current predictive skills of the interbasin contrasts across the prediction systems are hindered by the lower predictability of the tropical Atlantic Ocean, pointing to the need for future research to refine climate prediction models further. Significance Statement Agriculture and infrastructure in the southern plains face challenges from severe late-spring precipitation extremes. Traditional predictors like El Niño–Southern Oscillation (ENSO) lose effectiveness during the critical spring-to-summer transition, creating a forecasting gap. This study introduces the concept of tropical interbasin interactions, known to enhance seasonal predictability for late-spring precipitation in the southern plains. Novel climate model experiments highlight contributions from the tropical Pacific and Atlantic, offering a promising predictability that potentially surpasses the limitations of ENSO-based predictions. These outcomes hold the potential for developing operational forecasts of late-spring precipitation anomalies in the southern plains, enabling proactive risk management.
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Understanding the causes for discrepancies between modelled and observed regional climate trends is important for improving present-day climate simulation and reducing uncertainties in future climate projections. Here, we analyse the performance of coupled climate models in reproducing regional precipitation trends during the satellite era. We find statistically significant observed drying in southwestern North America and wetting in the Amazon during the period 1979–2014. Historical climate model simulations do not capture these observed precipitation trends. We trace this discrepancy to the inability of coupled simulations to capture the observed Pacific trade wind intensification over this period. A linear adjustment of free running historical simulations, based on the observed strengthening of the Pacific trade winds and modeled ENSO teleconnections, explains the discrepancy in precipitation trends. Furthermore, both the Pacific trade wind trends and regional precipitation trends are reproduced in climate simulations with prescribed observed sea surface temperatures (SST), underscoring the role of the tropical Pacific SST patterns.
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Besides the rapid retreating trend of Arctic sea-ice extent (SIE), this study found the most outstanding low-frequency variation of SIE to be a 4–6-year periodic variation. Using a clustering analysis algorithm, the SIE in most ice-covered regions was clustered into two special regions: Region-1 around the Barents Sea and Region-2 around the Canadian Basin, which were located on either side of the Arctic Transpolar Drift. Clear 4–6-year periodic variation in these two regions was identified using a novel method called “running linear fitting algorithm”. The rate of temporal variation of the Arctic SIE was related to three driving factors: the regional air temperature, the sea-ice areal flux across the Arctic Transpolar Drift, and the divergence of sea-ice drift. The 4–6-year periodic variation was found to have always been present since 1979, but the SIE responded to different factors under heavy and light ice conditions divided by the year 2005. The joint contribution of the three factors to SIE variation exceeded 83% and 59% in the two regions, respectively, remarkably reflecting their dynamic mechanism. It is proven that the process of El Niño–Southern Oscillation (ENSO) is closely associated with the three factors, being the fundamental source of the 4–6-year periodic variations of Arctic SIE.
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The increasing ocean warming due to climate change significantly threatens regional marine ecosystems by raising the frequency and severity of extreme temperature events. This study examines patterns and trends of maximum annual sea surface temperature (Tmax) in the Eastern China Seas from 1985 to 2022. The results show a significant warming trend in Tmax, exceeding the global average, with notable differences between southern and northern regions. The northern Tmax warming rate is faster, with occurrence times significantly advancing, while the southern Tmax warming rate is slower, with occurrence times significantly delayed. The southern Tmax and its timing are closely correlated with the annual maximum air temperature and its timing. In the north, Tmax timing is influenced by latent heat flux (QLH); a significant increase in August QLH inhibits the continued rise of SST, causing Tmax to advance. The study also highlights a significant increase in marine heatwaves at Tmax timing, with higher Tmax indicating a higher occurrence probability. By elucidating these Tmax trends and dynamics, our study enhances understanding of regional climate impacts, supporting targeted conservation efforts and adaptive ecosystem management strategies in the Eastern China Seas.
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The Indonesian throughflow (ITF) regulates heat and freshwater distributions of the Indo-Pacific Oceans and fundamentally affects the climate. The past decade has witnessed acute interannual variations in the Makassar Strait – the main ITF inflow passage, reaching monthly extremes of 1.9 Sv (1 Sv ≡ 10 ⁶ m ³ s ⁻¹ ) in 2015 and 16.6 Sv in 2017, compared with a mean transport of ~12 Sv. The Pacific Ocean dynamics dictated by El Niño/Southern Oscillation (ENSO) cannot fully explain these variations and the role of the Indian Ocean (IO) dynamics remains uncertain. Here, we use a 0.1°, quasi-global ocean model to cleanly isolate the impact of the IO dynamics on the ITF. The wind-driven IO dynamics are found to play a significant role in either buffering or driving ITF variability. The buffering effect is commonly seen during strong ENSO events, while the driving effect arises from Indian Ocean dipole (IOD) events independent of ENSO. Notably, the IO dynamics buffered the weak ITF extreme of 2015 by ~35% and contributed to the strong ITF extreme of 2017 by ~23%. Our study aids in the prediction of regional climate extremes under the intensifying ENSO and IOD scenarios expected in the future.
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The Southern Great Plains experience fluctuating precipitation extremes that significantly impact agriculture and water management. Despite ongoing efforts to enhance forecast accuracy, the underlying causes of these climatic phenomena remain inadequately understood. This study elucidates the relative influence of the tropical Pacific and Atlantic basins on AprilMay-June precipitation variability in this region. Our partial ocean assimilation experiments using the Community Earth System Model unveil the prominent role of inter-basin interaction, with the Pacific and Atlantic contributing approximately 70% and 30%, respectively, to these inter-basin contrasts. Our statistical analyses suggest that these tropical inter-basin contrasts could serve as a more reliable indicator for late-spring precipitation anomalies than the El Nino Southern Oscilla- ˜ tion. The conclusions are reinforced by analyses of seven climate forecasting systems within the North American Multi-Model Ensemble, offering an optimistic outlook for enhancing real-time forecasting of late-spring precipitation in the Southern Plains. However, the current predictive skills of the inter-basin contrasts across the prediction systems are hindered by the lower predictability of the tropical Atlantic Ocean, pointing to the need for future research to refine climate prediction models further.
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The southwestern tropical Pacific is a key center for the Interdecadal Pacific Oscillation (IPO), which regulates global climate. This study introduces a groundbreaking 627-year coral Sr/Ca sea surface temperature reconstruction from Fiji, representing the IPO’s southwestern pole. Merging this record with other Fiji and central tropical Pacific records, we reconstruct the SST gradient between the southwestern and central Pacific (SWCP), providing a reliable proxy for IPO variability from 1370 to 1997. This reconstruction reveals distinct centennial-scale temperature trends and insights into Pacific-wide climate impacts and teleconnections. Notably, the 20th century conditions, marked by simultaneous basin-scale warming and weak tropical Pacific zonal-meridional gradients, deviate from trends observed during the past six centuries. Combined with model simulations, our findings reveal that a weak SWCP gradient most markedly affects IPO-related rainfall patterns in the equatorial Pacific. Persistent synchronous western and central Pacific warming rates could lead to further drying climate across the Coral Sea region, adversely affecting Pacific Island nations.
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El Niño–Southern Oscillation (ENSO) provides an important source of global seasonal-interannual predictability, while its prediction encounters bottlenecks. Besides the slow-varying air-sea feedbacks, high-frequency atmospheric signals (HFAS) act on ENSO evolution. This study revisits the role of atmospheric initial signals on ENSO prediction using an atmosphere-ocean coupled model. Two sets of sensitivity hindcasts are conducted. One utilizes a pure SST-nudging for initialization so that no observed atmospheric internal signal is assimilated. The other applies a combination of the SST-nudging and spectral nudging of JRA-55 reanalysis, which not only assimilates the observed atmospheric states and hence realizes skillful predictions of HFAS at the initial stage, but also improves the oceanic initial conditions (ICs), especially around the thermocline. Unexpectedly, the better atmosphere-ocean ICs neither improve ENSO prediction nor overcome the spring prediction barrier. Further analysis of Bjerknes stability index suggests that underestimated negative feedbacks and insufficient responses of ocean currents and thermocline slope to atmospheric internal winds may account for the failure. Nevertheless, assimilating the atmospheric information partly improves the prediction of El Niño onset, especially for two recent extreme cases (i.e., 1997/98 and 2015/16). This improvement is associated with better representations of initial westerly wind bursts (WWBs) that excite subsequent downwelling equatorial Kelvin waves. However, due to unpredictable WWBs and underestimated wind response to the SST warming owing to the cold tongue biases in boreal summer, the zonal advective feedbacks are underestimated and thus further development of El Niño cannot be well predicted. This study suggests the importance of initial atmospheric signals despite the limitation, prompting further efforts to improve model physics.
Chapter
This chapter provides an introduction into Chaps. 9 and 10, focussed on abiotic processes. Earth abiotic processes are influenced by interactions between terrestrial and extra-terrestrial forces inherited form the origins of the universe. Chaotic dynamics of abiotic processes are driving forces of biological evolution, providing energetic sources together with constraints for organic life with different factor combinations in marine and terrestrial habitats. Physical properties of water together with particular dynamic characteristics and giant dimensions of the three-dimensional oceanic habitats were preconditions for the evolution of animal life on the energetic basis of microorganisms. Physical properties of the atmosphere and lithosphere were primary preconditions for the evolution of terrestrial vegetation with trenchant consequences for the evolution of terrestrial animal life forms.
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This study analyses the El Niño-Southern Oscillation (ENSO) phase space as simulated by the Coupled Model Intercomparison Projects 5 and 6 (CMIP5 and CMIP6) models. The ENSO phase space describes the ENSO cycle between the sea surface temperature (SST) anomaly in the eastern equatorial Pacific ( T ) and the equatorial mean thermocline depth anomaly ( h ). We find that the characteristics out-of-phase cross-correlation between T and h is shifted to negative values in CMIP models, suggesting that the coupling between T and h is regionally sifted to the east compared to the observed central Pacific. If we consider the CMIP models with an eastward shifted h then the models have better agreements with the observed characteristics. While the models can capture some of the non-linear aspects with high correlations, they do largely underestimate the strength of non-linear ENSO aspects. They underestimate the likelihood of extreme El Niño and discharge states, they cannot capture the enhanced growth rates during the recharge state, the enhanced decay after the discharge state nor the reduced phase transitions after the La Niña phases. Weaker than observed wind-SST feedback and weaker h variability are likely some of the reasons why models cannot fully capture the non-linear ENSO phase space dynamics. Further, we found no indication of significant improvements from the CMIP 5 to 6 ensemble, suggesting that the two ensembles are essentially the same in terms of their ENSO dynamics. There is, however, a large spread within the model ensembles, leading to models with quite different ENSO dynamics.
Article
Earth's climate underwent secular structural evolution and cooling since the Pliocene Warmth, punctuated by increasing thermal gradients between the Western Pacific Warm Pool (WPWP) and the surrounding cooler regions of Eastern Pacific Cold Tongue and extra-tropics. One key mechanism for deciphering the climate evolution since Pliocene is the shoaling of the ocean thermocline, but much remains unknown about the upper-water structure of the western Pacific Warm Pool, the heat engine of the climate system, and its linkage to global ocean circulations. Here, by a newly made series of millennial-resolved δ18O and δ13C records of paired mixed-layer and thermocline dwelling planktonic foraminifera from IODP Site U1489 drilled in the center of WPWP, the trends of cooling and respired carbon content decreasing in the WPWP thermocline since ~4 Ma are revisited and diagnosed. Our data allows us to propose that, the tropical Pacific upper-water circulation cell had shoaled and shrunk and the vertical mixing from deeper waters may have increased, since the Pliocene. Development towards a modern-style Warm Pool was initially onset at ~3.1 and finalized at ~0.7 Ma, respectively.
Article
A surface layer (upper 20 m depth) heat budget analysis, derived from a hindcast regional-scale ocean modeling experiment, was employed to examine the underlying mechanisms behind the emergence of sea surface temperature anomalies (SSTA) in the Indonesian seas during El Niño events over the 1995–2019 course. Prior to the emergence of warm SSTA, which typically appeared following the mature phase of El Niño and lasted for almost a year, apparent anomalous heat accumulation occurred for at least 2–4 months and peaked in conjunction with the climatic event. Further examination revealed possible east–west distinct dynamics in the heat budget variations within the region during El Niño years. The anomalous heat accumulation in the western part of Indonesian seas (Java Sea) was predominantly caused by modulation in the surface net heat flux. Whereas in the eastern part (Banda Sea), the ocean circulation also exerted important influence in addition to the surface net heat flux. The ocean circulation in the eastern Indonesian seas notably contributed to moderate the effect of surface net heat flux during El Niño growth. Moreover, the same ocean circulation was responsible for prolonging the anomalous heat accumulation in the eastern Indonesian seas from mature to decay phase of the El Niño, ultimately resulted in warmer SSTA than that in the western part. The study conducted here provides additional insights on how the Indonesian seas responded to the El Niño and further reaffirms the idea that the climatic event results in anomalous warming across the Indonesian seas.
Chapter
Natural climate oscillations can cause extreme events such as floods and droughts. The mid-1970s is known for the occurrence of a global Climate Shift, defined as a short period when both interannual and interdecadal climate oscillations changed phases, which led the climate to a new state. Climate Shift has affected many regions and is considered an unprecedented event. In this chapter, we describe the main research finding related to Climate Shift that affected the precipitation regime in several areas worldwide. This review highlights the importance of future studies of climate variability and climate change to improve water resource management.
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全球变暖背景下,中国东北夏季洪涝干旱灾害发生频繁,对人类和自然系统造成严重影响。但是,目前东北夏季降水预测水平较低,远不能满足防灾、减灾的需求。中国东北汛期降水主要集中在盛夏(7~8月),其年际变率与年代际变率相当。本文聚焦在分析春季土壤温度在中国东北盛夏降水年际分量预测中的作用。研究发现中国东北盛夏降水年际分量与欧洲中东部春季土壤温度年际分量存在显著负相关关系、与青藏高原东部和西亚东北部春季土壤温度年际分量存在显著正相关关系。春季关键区土壤温度异常对应下游地区盛夏土壤温度异常,从而引起东亚盛夏大气环流异常,高空西风急流偏强偏北、西太平洋副热带高压偏强偏北,进而造成中国东北水汽辐合与上升增强,引起中国东北盛夏降水增强。进一步采用欧洲中东部、青藏高原东部和西亚东北部春季土壤温度年际分量建立了中国东北盛夏降水年际分量的季节预测模型,1979~2021年留一法交叉检验时间相关系数在GLDAS-Noah、ERA5 和CRA/Land三套数据中最高可达0.64,2012~2021年的后报试验时间相关系数在三套数据中最高可达0.78,表明春季土壤温度在中国东北盛夏降水年际分量预测中起到关键作用。研究成果能够为提高中国东北夏季降水预测提供科学基础,并易于应用到实际预测。 Under the influence of global warming, floods and droughts often occur in summer over northeastern China, resulting in serious consequences to human and natural systems. However, the current seasonal prediction of summer precipitation over northeastern China is still low and far from satisfying the needs of disaster prevention and reduction. The precipitation during the rainy season in northeastern China is primarily concentrated in midsummer (July and August), and its interannual variability is comparable to that of interdecadal variability. The focus of this study is on examining the predictive role of the interannual variability of soil temperature in the interannual variability of midsummer precipitation over northeastern China. The results revealed a substantial negative correlation between the interannual variability of midsummer precipitation over northeastern China, the interannual variability of spring soil temperature over central and Eastern Europe, and a significant positive correlation with the interannual variability of spring soil temperature over eastern Qinghai Tibet Plateau and northeastern West Asia. The abnormal soil temperature in the key areas in spring relates to the abnormal soil temperature in the downstream region during the midsummer, thereby causing abnormal atmospheric circulation in East Asia during the midsummer. Furthermore, the upper-level westerly jet is strong and northward, and the western Pacific subtropical high is northward, leading to elevated water vapor convergence and upward movement over northeastern China, giving rise to increased precipitation in the midsummer. A seasonal prediction model for the interannual variability of spring soil temperature over central and Eastern Europe, eastern Qinghai Tibet Plateau, and northeastern West Asia was constructed to predict the interannual variability of summer precipitation over northeastern China. Moreover, the TCC (Time Correlation Coefficient) of the leave-one-out cross-validation could reach a maximum of 0.64, and hindcast during 2012–2021 could reach a maximum of 0.78 in the GLDAS-Noah, ERA5, and CRA/Land datasets, demonstrating that spring soil temperature plays a crucial role in predicting the interannual component of midsummer precipitation over northeastern China. This study offers a scientific basis for enhancing the prediction of summer precipitation over northeastern China to be easily applied to actual predictions.
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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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El Niño (EN) is a dominant feature of climate variability on inter-annual time scales driving changes in the climate throughout the globe, and having wide-spread natural and socio-economic consequences. In this sense, its forecast is an important task, and predictions are issued on a regular basis by a wide array of prediction schemes and climate centres around the world. This study explores a novel method for EN forecasting. In the state-of-the-art the advantageous statistical technique of unobserved components time series modeling, also known as structural time series modeling, has not been applied. Therefore, we have developed such a model where the statistical analysis, including parameter estimation and forecasting, is based on state space methods, and includes the celebrated Kalman filter. The distinguishing feature of this dynamic model is the decomposition of a time series into a range of stochastically time-varying components such as level (or trend), seasonal, cycles of different frequencies, irregular, and regression effects incorporated as explanatory covariates. These components are modeled separately and ultimately combined in a single forecasting scheme. Customary statistical models for EN prediction essentially use SST and wind stress in the equatorial Pacific. In addition to these, we introduce a new domain of regression variables accounting for the state of the subsurface ocean temperature in the western and central equatorial Pacific, motivated by our analysis, as well as by recent and classical research, showing that subsurface processes and heat accumulation there are fundamental for the genesis of EN. An important feature of the scheme is that different regression predictors are used at different lead months, thus capturing the dynamical evolution of the system and rendering more efficient forecasts. The new model has been tested with the prediction of all warm events that occurred in the period 1996–2015. Retrospective forecasts of these events were made for long lead times of at least two and a half years. Hence, the present study demonstrates that the theoretical limit of ENSO prediction should be sought much longer than the commonly accepted “Spring Barrier”. The high correspondence between the forecasts and observations indicates that the proposed model outperforms all current operational statistical models, and behaves comparably to the best dynamical models used for EN prediction. Thus, the novel way in which the modeling scheme has been structured could also be used for improving other statistical and dynamical modeling systems.
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ENSO variability has a seasonal phase-locking, with SST anomalies on average decreasing during the beginning of the year and SST anomalies increasing during the second half of the year. As a result of this, the ENSO SST variability is smallest in April and the so call ‘spring barrier’ exists in the predictability of ENSO. In this study we analysis how the seasonal phase-locking of surface short wave radiation associated with cloud cover feedbacks contribute to this phenomenon. We base our analysis on observations and simplified climate model simulations. At the beginning of the year, the warmer mean SST in the eastern equatorial Pacific leads to deeper clouds whose anomalous variability are positively correlated with the underlying SST anomalies. These observations highlight a strong negative surface short wave radiation feedback at the beginning of the year in the eastern Pacific (NINO3 region). This supports the observed seasonal phase-locking of ENSO SST variability. This relation also exists in model simulations of the linear recharge oscillator and in the slab ocean model coupled to a fully complex atmospheric GCM. The Slab ocean simulation has seasonal phase-locking similar to observed mostly caused by similar seasonal changing cloud feedbacks as observed. In the linear recharge oscillator simulations seasonal phase-locking is also similar to observed, but is not just related to seasonal changing cloud feedbacks, but is also related to changes in the sensitivity of the zonal wind stress and to a lesser extent to seasonally change sensitivities to the thermocline depth. In summary this study has shown that the seasonal phase-locking, as observed and simulated, is linked to seasonally changing cloud feedbacks.
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El Niño-Southern Oscillation (ENSO) consists of irregular episodes of warm El Niño and cold La Niña conditions in the tropical Pacific Ocean1, with significant global socio-economic and environmental impacts1. Nevertheless, forecasting ENSO at lead times longer than a few months remains a challenge2, 3. Like the Pacific Ocean, the Indian Ocean also shows interannual climate fluctuations, which are known as the Indian Ocean Dipole4, 5. Positive phases of the Indian Ocean Dipole tend to co-occur with El Niño, and negative phases with La Niña6, 7, 8, 9. Here we show using a simple forecast model that in addition to this link, a negative phase of the Indian Ocean Dipole anomaly is an efficient predictor of El Niño 14 months before its peak, and similarly, a positive phase in the Indian Ocean Dipole often precedes La Niña. Observations and model analyses suggest that the Indian Ocean Dipole modulates the strength of the Walker circulation in autumn. The quick demise of the Indian Ocean Dipole anomaly in November–December then induces a sudden collapse of anomalous zonal winds over the Pacific Ocean, which leads to the development of El Niño/La Niña. Our study suggests that improvements in the observing system in the Indian Ocean region and better simulations of its interannual climate variability will benefit ENSO forecasts.
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Synoptic wind events in the equatorial Pacific strongly influence the El Niño/Southern Oscillation (ENSO) evolution. This paper characterizes the spatio-temporal distribution of Easterly (EWEs) and Westerly Wind Events (WWEs) and quantifies their relationship with intraseasonal and interannual large-scale climate variability. We unambiguously demonstrate that the Madden–Julian Oscillation (MJO) and Convectively-coupled Rossby Waves (CRW) modulate both WWEs and EWEs occurrence probability. 86 % of WWEs occur within convective MJO and/or CRW phases and 83 % of EWEs occur within the suppressed phase of MJO and/or CRW. 41 % of WWEs and 26 % of EWEs are in particular associated with the combined occurrence of a CRW/MJO, far more than what would be expected from a random distribution (3 %). Wind events embedded within MJO phases also have a stronger impact on the ocean, due to a tendency to have a larger amplitude, zonal extent and longer duration. These findings are robust irrespective of the wind events and MJO/CRW detection methods. While WWEs and EWEs behave rather symmetrically with respect to MJO/CRW activity, the impact of ENSO on wind events is asymmetrical. The WWEs occurrence probability indeed increases when the warm pool is displaced eastward during El Niño events, an increase that can partly be related to interannual modulation of the MJO/CRW activity in the western Pacific. On the other hand, the EWEs modulation by ENSO is less robust, and strongly depends on the wind event detection method. The consequences of these results for ENSO predictability are discussed.
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It has been previously proposed that two El Niño (EN) regimes, strong and moderate, exist but the historical observational record is too short to establish this conclusively. Here, 1200 years of simulations with the GFDL CM2.1 model allowed us to demonstrate their existence in this model and, by showing that the relevant dynamics are also evident in observations, we present a stronger case for their existence in nature. In CM2.1, the robust bimodal probability distribution of equatorial Pacific sea surface temperature (SST) indices during EN peaks provides evidence for the existence of the regimes, which is also supported by a cluster analysis of these same indices. The observations agree with this distribution, with the EN of 1982-1983 and 1997-1998 corresponding to the strong EN regime and all the other observed EN to the moderate regime. The temporal evolution of various indices during the observed strong EN agrees very well with the events in CM2.1, providing further validation of this model as a proxy for nature. The two regimes differ strongly in the magnitude of the eastern Pacific warming but not much in the central Pacific. Observations and model agree in the existence of a finite positive threshold in the SST anomaly above which the zonal wind response to warming is strongly enhanced. Such nonlinearity in the Bjerknes feedback, which increases the growth rate of EN events if they reach sufficiently large amplitude, is very likely the essential mechanism that gives rise to the existence of the two EN regimes. Oceanic nonlinear advection does not appear essential for the onset of strong EN. The threshold nonlinearity could make the EN regimes very sensitive to stochastic forcing. Observations and model agree that the westerly wind stress anomaly in the central equatorial Pacific in late boreal summer has a substantial role determining the EN regime in the following winter and it is suggested that a stochastic component at this time was key for the development of the strong EN towards the end of 1982.
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This study examines preindustrial simulations from Coupled Model Intercomparison Project, phase 3 (CMIP3), models to show that a tendency exists for El Nino sea surface temperature anomalies to be located farther eastward than La Nina anomalies during strong El Nino Southern Oscillation (ENSO) events but farther westward than La Nina anomalies during weak ENSO events. Such reversed spatial asymmetries are shown to force a slow change in the tropical Pacific Ocean mean state that in return modulates ENSO amplitude. CMIP3 models that produce strong reversed asymmetries experience cyclic modulations of ENSO intensity, in which strong and weak events occur during opposite phases of a decadal variability mode associated with the residual effects of the reversed asymmetries. It is concluded that the reversed spatial asymmetries enable an ENSO-tropical Pacific mean state interaction mechanism that gives rise to a decadal modulation of ENSO intensity and that at least three CMIP3 models realistically simulate this interaction mechanism.
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We analyse the ability of CMIP3 and CMIP5 coupled ocean–atmosphere general circulation models (CGCMs) to simulate the tropical Pacific mean state and El Niño-Southern Oscillation (ENSO). The CMIP5 multi-model ensemble displays an encouraging 30 % reduction of the pervasive cold bias in the western Pacific, but no quantum leap in ENSO performance compared to CMIP3. CMIP3 and CMIP5 can thus be considered as one large ensemble (CMIP3 + CMIP5) for multi-model ENSO analysis. The too large diversity in CMIP3 ENSO amplitude is however reduced by a factor of two in CMIP5 and the ENSO life cycle (location of surface temperature anomalies, seasonal phase locking) is modestly improved. Other fundamental ENSO characteristics such as central Pacific precipitation anomalies however remain poorly represented. The sea surface temperature (SST)-latent heat flux feedback is slightly improved in the CMIP5 ensemble but the wind-SST feedback is still underestimated by 20–50 % and the shortwave-SST feedbacks remain underestimated by a factor of two. The improvement in ENSO amplitudes might therefore result from error compensations. The ability of CMIP models to simulate the SST-shortwave feedback, a major source of erroneous ENSO in CGCMs, is further detailed. In observations, this feedback is strongly nonlinear because the real atmosphere switches from subsident (positive feedback) to convective (negative feedback) regimes under the effect of seasonal and interannual variations. Only one-third of CMIP3 + CMIP5 models reproduce this regime shift, with the other models remaining locked in one of the two regimes. The modelled shortwave feedback nonlinearity increases with ENSO amplitude and the amplitude of this feedback in the spring strongly relates with the models ability to simulate ENSO phase locking. In a final stage, a subset of metrics is proposed in order to synthesize the ability of each CMIP3 and CMIP5 models to simulate ENSO main characteristics and key atmospheric feedbacks.
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Forecast skill of the APEC Climate Center (APCC) Multi-Model Ensemble (MME) seasonal forecast system in predicting two main types of El Niño-Southern Oscillation (ENSO), namely canonical (or cold tongue) and Modoki ENSO, and their regional climate impacts is assessed for boreal winter. The APCC MME is constructed by simple composite of ensemble forecasts from five independent coupled ocean-atmosphere climate models. Based on a hindcast set targeting boreal winter prediction for the period 1982–2004, we show that the MME can predict and discern the important differences in the patterns of tropical Pacific sea surface temperature anomaly between the canonical and Modoki ENSO one and four month ahead. Importantly, the four month lead MME beats the persistent forecast. The MME reasonably predicts the distinct impacts of the canonical ENSO, including the strong winter monsoon rainfall over East Asia, the below normal rainfall and above normal temperature over Australia, the anomalously wet conditions across the south and cold conditions over the whole area of USA, and the anomalously dry conditions over South America. However, there are some limitations in capturing its regional impacts, especially, over Australasia and tropical South America at a lead time of one and four months. Nonetheless, forecast skills for rainfall and temperature over East Asia and North America during ENSO Modoki are comparable to or slightly higher than those during canonical ENSO events.