Southern Oscillation Research Articles

A probabilistic forecast for multi-year ENSO using Bayesian convolutional neural network

Abstract A robust El Niño Southern Oscillation (ENSO) prediction is essential for monitoring the global climate, regional monsoons, and weather extremes. Despite dedicated efforts spanning decades, the precise prediction of ENSO events through numerical modeling beyond a couple of seasonal lead times remains a daunting challenge. The advent of deep learning-based approaches marks a transformative era in climate and weather prediction. However, many machine learning-based studies attempting ENSO prediction are confined to singular estimates, lacking adequate quantification of uncertainty in learned parameters and overlooking the crucial need for a nuanced understanding of ENSO prediction confidence. Here, we introduce a deep learning-based Bayesian convolutional neural network model that provides robust probabilistic predictions for ENSO with a lead time of up to 9–10 months across all seasons. The Bayesian layers within the convolutional neural network maintain the capability to predict a distribution of learned parameters. Augmented with bias correction, our model reproduces the amplitude of the Niño 3.4 index with fidelity for lead up to 9–10 months. The inherent capacity for uncertainty modeling enhances the reliability of bayesian neural networks (BNNs), making them particularly valuable in operational services. This research holds substantial socio-economic implications as it enhances our forecasting capabilities and rigorously quantifies forecast uncertainties, providing valuable insights for planning and policymaking.

Asymmetric ENSO teleconnections in a symmetric CO2 concentration pathway

Abstract El Niño-Southern Oscillation (ENSO) is the strongest interannual phenomenon occurring in the tropical Pacific, significantly affecting the entire world. The ENSO response to increasing CO2 concentrations have been extensively studied, but the reverse scenario is still not well understood. Here, we investigate the hysteresis of ENSO teleconnections in a CO2 removal simulation of an earth system model. During the ramp-up period of CO2 concentrations, Pacific-North American and Pacific-South American patterns are intensified, with the eastward shift of their poles, which are even further intensified during the ramp-down period. This ENSO teleconnection hysteresis is closely linked to the tropical-origin hysteresis, in which, during the ramp-down periods, the prevalence of the eastern-Pacific type El Niño leads to the hysteresis in the eastern Hemispheric ENSO teleconnections and the enhanced ENSO skewness and the eastward shift of ENSO-induced tropical atmospheric convection do to that in the western Hemispheric ENSO teleconnection. The alterations by the tropical origin are predominantly associated with intensified southward migration of the Intertropical Convergence Zone, along with a stronger El Niño-like warming trend. We also demonstrate that the hysteretic change in the mid-latitude mean state over the North Pacific region could lead to hysteresis of the ENSO teleconnection without invoking a tropical origin.

Widening of wind-stress anomalies amplifies ENSO in a warming climate

Abstract Climate change simulations generally indicate the strengthening of El Niño Southern Oscillation (ENSO) sea surface temperature (SST) variability through the 21st century, yet a robust physical mechanism explaining this change across different models is still lacking, and the projections for ENSO amplitude exhibit a large spread. Most commonly, changes in the background state of the tropical Pacific are invoked to explain these changes of ENSO. Here we show that changes in the structure of wind-stress anomalies associated with ENSO are potentially as important as these background state changes. Specifically, changes in the magnitude, meridional width, and zonal structure of wind-stress anomalies can explain approximately 53% of the inter-model variance in the projected change of ENSO magnitude through the 21st century as well as 43% in ENSO periodicity changes. Among these changes in the wind structure, the meridional widening of wind anomalies plays the most important role. To demonstrate that these changes are indeed critical, we develop a hybrid model of ENSO based on the Community Earth System Model version 2, which incorporates a dynamical ocean coupled to a simplified statistical atmosphere within the tropical Pacific. In the absence of external forcing and corresponding mean-state changes, the imposed changes in wind stress anomalies in this hybrid model result in an increase of ENSO amplitudes of nearly 10% along the equator. Our results are also theoretically supported by a recharge-oscillator model that incorporates the meridional wind structure. Thus, changes in the structure of wind-stress anomalies, together with changes in the mean state, likely play a critical role in the projected strengthening of ENSO.

Comparative Evaluation of Niño1+2 and Niño3.4 Indices in Terms of ENSO Effects Over the Euro‐Mediterranean Region

ABSTRACTGlobal or regional impacts of El Niño Southern Oscillation (ENSO) have predominantly been investigated through the Niño3.4 index, representing the sea surface temperature (SST) variations in the central Tropical Pacific. In this study, we comparatively evaluated the usefulness of Niño1+2, a relatively less utilised index that represents SST variability in the Eastern Tropical Pacific. In our analyses, we focused on ENSO impacts on Euro‐Mediterranean (EM) climate variability during boreal winter, using data from the NCEP/NCAR Reanalysis. The correlation analysis involving Niño1+2 depicts more distinct temperature and precipitation patterns over the EM region. Amongst the SST‐based Niño indices, it has the highest correlation with the East Atlantic index (0.47, statistically significant at > 99% confidence level), a prominent regional teleconnection associated primarily with the strength of the East Atlantic ridge, which produces dipole‐type climate patterns between East Atlantic/Western Europe and Central/Eastern Mediterranean. Moreover, its lagged correlations with the following spring (0.39), summer (0.31), and autumn (0.36) are all statistically significant at ≥ 99% confidence levels. The composite analysis shows that different Niño regions have distinct effects on atmospheric circulation and climate in the EM region. The Niño1+2 index is particularly helpful in identifying the years when warm SST anomalies of El Niño extend to the Eastern Equatorial Pacific, which results in a reversal of temperatures across the EM region. Thus, this study suggests that Niño1+2 is a useful index for studying climate variability and predictability in the EM region, especially when used in conjunction with other Niño indices, as it captures some ENSO features that they may not encompass.

A reconstructed PDO history from an ice core isotope record on the central Tibetan Plateau

Ice core oxygen isotope (δ18O) records from low-latitude regions preserve high-resolution climate records in the past, yet the interpretation of these ice core δ18O records is still facing difficulty due to the uncertainty of ice core dating. Here we present a new established δ18O time series from Qiangtang (QT) No. 1 ice core retrieved from the central Tibetan Plateau. Due to the vague seasonal signals in the QT ice core, we investigated the spectral properties of δ18O record with depth and discussed the implications of significant spectral power peaks in the QT ice core. We employed a variational mode decomposition (VMD) analysis for the upper part of the QT ice core to decompose the δ18O depth series in order to separate the El Niño Southern Oscillation (ENSO) mode, a signal strongly preserved in the QT ice core δ18O record. With this approach, we established a time series of 335 years (1677–2011 CE) for the upper 50 m of the QT ice core. Subsequently, we examined the frequency of the new established δ18O time series and detected strong signals of the bidecadal and multidecadal modes of Pacific Decadal Oscillation (PDO). The PDO consists of two modes with periods of approximately 25–35 years and 50–70 years, and we found that the 50–70 years periodicity has persisted since 1700 CE, succeeded by dominance of the 25–75 years periodicity after 1900 CE. Additionally, we analyzed the δ18O series of the QT ice core during the past century and determined that the increasing frequency of El Niño events is an important factor contributing to the increase in recent ice core δ18O.

Interdecadal Variation in the Impact of Arctic Sea Ice on El Niño–Southern Oscillation: The Role of Atmospheric Mean Flow

Abstract This study reveals the remarkable interdecadal changes in the influence of boreal winter Arctic sea ice concentration (SIC) anomalies (ASICAs) in the Greenland–Barents Seas on the subsequent El Niño–Southern Oscillation (ENSO) development. Winter ASICA is strongly associated with the subsequent winter ENSO before the late 1980s and after the late 2000s, but their connection is weak during the 1990s and the 2000s. The interdecadal variations in the influence of ASICA on ENSO are associated with changes in the spatial structure of the ASICA-induced North Pacific atmospheric anomalies. During high-correlation periods, winter SIC increases in the Greenland–Barents Seas lead to tropospheric cooling via the suppression of upward surface heat fluxes, which further trigger an atmospheric teleconnection from the Arctic to the North Pacific. The accompanying North Pacific Oscillation–like atmospheric anomalies result in sea surface temperature (SST) warming in the subtropical North Pacific, which extends southward into the tropical Pacific via wind–evaporation–SST feedback and leads to surface westerly anomalies over the tropical western Pacific in the following summer. The tropical western Pacific westerly wind anomalies impact the subsequent ENSO development via triggering positive Bjerknes air–sea interaction. During low-correlation periods, atmospheric anomalies over the North Pacific generated by the winter ASICA are located more northward and cannot induce marked subtropical North Pacific SST anomalies and thus have a weak impact on the following ENSO development. Numerical experiments suggest that the interdecadal variation in the spatial structure of the North Pacific atmospheric anomalies induced by the winter ASICA is partly attributed to change in the atmospheric mean flow. Significance Statement Arctic sea ice is an important component of the global climate system. Studies have shown that Arctic sea ice has decreased significantly in recent decades, which is considered to be one of the most important responses of Earth’s climate system to global climate change. A number of studies have shown that Arctic sea ice anomalies have significant impacts on weather and climate over mid–high latitudes through tropospheric and stratospheric processes. Recent studies have suggested that the effects of Arctic sea ice anomalies could extend to the tropics via air–sea interactions. El Niño–Southern Oscillation (ENSO) is the strongest air–sea coupled system in the tropics and can have a significant impact on climate over the globe. ENSO is also considered to be one of the most important sources of subseasonal–seasonal climate predictability over many parts of the globe. It is therefore important to study the factors for the ENSO occurrence. A recent study showed that Arctic sea ice anomalies during boreal winter in the Greenland–Barents Seas have a significant impact on the following winter ENSO. In this study, we further reveal that the influence of winter Arctic sea ice anomalies in the Greenland–Barents Seas on the subsequent ENSO is unstable and has undergone significant interdecadal variation. We then investigate the mechanisms underlying the interdecadal variation via observational analysis and numerical simulations. The results of this study not only have implications for improving the prediction of ENSO but also could improve our understanding of the physical link between high-latitude climate systems and tropical air–sea coupling systems.

Climate drivers of phytoplankton production along the Chilean coast

The west coast of South America is known for its high primary productivity. The level of phytoplankton can be measured through satellite images that detect chlorophyll (Chl), which is dependent on several oceanographic and meteorological parameters. Climate drivers such as El Niño Southern Oscillation (ENSO) and the Southeast Pacific Subtropical Anticyclone (SPSA) affect these parameters and, consequently, the phytoplankton. The objective of this study was to identify the impact of ENSO on SPSA, climate variables, and phytoplankton patterns. Composites were created using the years selected with either strongly positive or negative ENSO to understand their influence on different parameters. To create the Chl composite, it was necessary to extend it using Canonical Correlation Analysis (CCA) based on the sea surface temperature (SST) pattern. The study concludes that ENSO has a noticeable impact on Chl, mainly in the Southern Zone during the warm season. This is driven by the expansion of SPSA to the South, which increases the sea level pressure (SLP) in that region. However, predicting the Chl concentration has a high degree of uncertainty due to its complexity.

The history of the El Niño–Southern Oscillation and sea surface salinity during 1376–1500 CE reconstructed by Porites coral δ18O from Huangyan Island, South China Sea

The El Niño–Southern Oscillation (ENSO) is the most influential climatic phenomenon affecting global ecosystems, water use, and agriculture on an interannual scale. However, limited instrumental records make it difficult to fully understand the characteristics of ENSO events. In this study, we used both monthly-resolved Porites coral δ18O records, i.e., living Porites corals of δ18O–HYDL4 (1992–2015) and subfossil Porites corals of δ18O–HYD3 (1376–1500 CE), from Huangyan Island in the South China Sea (SCS), to reconstruct the moderate intensity ENSO and sea surface salinity (SSS) during 1376–1500 CE. The results show that the SSS was higher but the frequency of moderate-to-high intensity ENSO events was lower than the present during 1376–1500 CE. ENSO activity was generally similar to or slightly lower than that of today, but it included several very strong ENSO events in the first substage of 1376–1450 CE, while it was relatively quiet in the second substage of 1451–1500 CE. More moderate-intensity ENSO events may have occurred in the relatively warm climate. The variation in coral δ18O was dominated by multiple factors in this region. SST, ENSO and the Pacific Decadal Oscillation (PDO) may be the dominant factors influencing the changes in coral δ18O at different timescales.

Exceptionally Strong Equatorial Intermediate Current Events in the Indian Ocean Associated with Climate Modes

Abstract Knowledge of the effects of climate modes on the equatorial intermediate current (EIC) remains limited. This paper investigates exceptional events of the EIC in the Indian Ocean and their relationships with climate modes at various time scales by using observations, reanalysis outputs, and a continuously stratified linear ocean model (LOM). A mooring at 80°E from 2015 to 2019 revealed four exceptionally strong EIC events, occurring in 2015 July–August (JA), 2016 January–February (JF), 2016 JA, and 2019 JF. Component analysis revealed that these exceptional events are attributed to the co-occurrence of the seasonal components peaking during JF and JA, as well as the larger current anomalies associated with intraseasonal and interannual components. In the intraseasonal band, the Madden–Julian oscillation (MJO) generates a significant EIC anomaly through a 40–50-day process involving equatorial waves. The MJO exerts a substantial effect when the amplitude of the MJO index exceeds 1 and the oscillation is in phase 4. In the interannual band, El Niño–Southern Oscillation and the Indian Ocean dipole (IOD) can each independently contribute to the EIC anomaly. This contribution initiates during the mature phase and persists throughout the subsequent year. Concurrent occurrences of these effects can lead to larger interannual anomalies. Notably, El Niño–positive IOD (El Niño–pIOD) and La Niña–negative IOD (La Niña–nIOD) synergies are most pronounced in August and in January of the following year. Significance Statement Four exceptional equatorial intermediate current (EIC) events characterized by strong eastward velocities are observed in the Indian Ocean during 2015–19. This study explored their underlying dynamics and their relationships with climate modes. Climatologically, the EIC has seasonal cycles with peaks in January–February and July–August, which is helpful in producing exceptional EIC events. However, the occurrence of these events is attributed to the intensity of the intraseasonal and interannual variability that occurs around the seasonal cycle peaks. This study revealed that climate modes, including the Madden–Julian oscillation (MJO), El Niño–Southern Oscillation (ENSO), and Indian Ocean dipole (IOD), contribute to exceptional EIC events in the intraseasonal and interannual bands. Notably, their influence is comparable, albeit contingent upon specific conditions.

Evaluating constraints on offshore wind farm installation across the Taiwan Strait by exploring the influence of El Niño-Southern Oscillation on weather window assessment

The transition to renewable energy sources, such as offshore wind farms, is essential in mitigating climate change. Taiwan has set ambitious targets to harness wind energy from the Taiwan Strait, but offshore wind farm installations are highly dependent on weather conditions, particularly wind speeds. This study examines the relationship between the El Niño-Southern Oscillation (ENSO) and offshore wind farm installation by assessing weather windows—periods with wind speeds below 12 meters per second at a height of 100 meters for at least 12 hours. Our analysis shows that during La Niña years, the number of feasible weather windows decreases by up to 40%, particularly between October and June, compared to neutral and El Niño years. This decrease can be as high as fourfold in December, significantly impacting installation schedules. Seasonal variations are also notable, with wind speeds exceeding 12 m/s in winter 66.4% of the time, compared to 29.4% in spring, making spring and summer the most favorable periods for installation. However, even during these favorable seasons, La Niña years can bring higher wind speeds, necessitating careful planning. These results underscore the importance of integrating ENSO forecasts into project planning to avoid installation delays and optimize installation timelines. By leveraging seasonal and interannual climate variability predictions, decision-makers can improve the resilience of offshore wind farm projects and ensure efficient energy transition strategies.

Potential climate predictability of renewable energy supply and demand for Texas given the ENSO hidden state.

Climate variability influences renewable electricity supply and demand and hence system reliability. Using the hidden states of the sea surface temperature of tropical Pacific Ocean that reflect El Niño-Southern Oscillation (ENSO) dynamics that is objectively identified by a nonhomogeneous hidden Markov model, we provide a first example of the potential predictability of monthly wind and solar energy and heating and cooling energy demand for 1 to 6 months ahead for Texas, United States, a region that has a high penetration of renewable electricity and is susceptible to disruption by climate-driven supply-demand imbalances. We find a statistically significant potential for oversupply or undersupply of energy and anomalous heating/cooling demand depending on the ENSO state and the calendar month. Implications for financial securitization and the potential application of forecasts are discussed.

Reconstructed centennial precipitation-driven water storage anomalies in the Nile River Basin using RecNet and their suitability for studying ENSO and IOD impacts

While the Gravity Recovery And Climate Experiment (GRACE) and its Follow-On (GFO) missions have offered valuable observations for monitoring total water storage anomalies (TWSA), their short record constrains our ability to study the complete range and long-term variability of TWSA in the Nile River Basin (NRB). Previous studies reconstructing TWSA in this region either relied on specific hydrological models or did not consider spatial correlations among the TWSA grids. Here, we employ RecNet, a deep learning model capable of providing independent TWSA observations without relying on hydrological models while considering spatial correlations, to reconstruct precipitation-driven TWSA in the NRB from 1923 to 2022. The reconstructed data are validated by comparisons with the Global Land Data Assimilation System (GLDAS), the WaterGAP Global Hydrology Model (WGHM), the water balance budget, long-term runoff data, and GRACE-REC (i.e., a global reconstruction dataset freely available online). Subsequently, the suitability of the reconstructed data for studying El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) impacts within the NRB is assessed. Dividing the NRB into four sub-regions, i.e., the Lake Victoria Basin (LVB), the Bahr el Jebel and Bahr el Ghazal basins (BJBG), Ethiopian Highlands region (EH), and the Lower Nile River Basin (LNRB), it is shown that RecNet successfully reconstructs precipitation-driven TWSA over BJBG and EH, achieving correlation coefficient (CC), normalized root mean square error (NRMSE), and Nash–Sutcliffeefficiency (NSE) of 0.94/0.11/0.88 and 0.96/0.09/0.91 during the testing period, respectively. Additionally, RecNet’s reconstruction shows better agreement with GLDAS and WGHM than GRACE-REC, correlating well with runoff and the water balance budget in these regions. The relatively poor performance in the LVB and LNRB regions could be attributed to the substantial influence of Lake Victoria and the arid climate, respectively. Correlation analysis and wavelet coherence analysis identify significant coherence between ENSO/IOD and the reconstructed TWSA in BJBG and EH, with CC values of −0.68/0.34 and −0.82/0.56, respectively. This study provides centennial reconstructed TWSA data that could be useful in climate change/variability studies and water resource management within the NRB.

Individual tree mortality: Risks of climate change in the eastern Brazilian Amazon region

The mortality of trees in humid tropical forests plays a fundamental role in understanding forest development, particularly after disturbances such as those caused by logging and extreme weather events. The aim of this study was to evaluate estimates of individual tree mortality following Reduced Impact Logging (RIL) in the Eastern Brazilian Amazon at biennial intervals from 2005 to 2012. RIL is based on operations planning, personnel training, and investments in forest management, and harvesting through RIL must: (a) minimize environmental damage, (b) diminish operation cost by increasing work efficiency, and (c) reduce operational waste. A mortality model was constructed based on the estimation of three distance-independent competition-indices (DII) and five models for predicting the probability of individual tree mortality. The Kolmogorov-Smirnov statistical test was used to determine the most representative model, from which a Neural Network Autoregressive (NNAR) model was constructed to forecast mortality after RIL. Mortality data was correlated with the El Niño–Southern Oscillation (ENSO) and climate (Rainfall, Maximum, Minimum, and Average air temperature). The tested models showed similar and accurate estimates with R2 exceeding 0.90, although underestimation and overestimation trends were observed. The NNAR satisfactorily represented species mortality over the simulated years. The period from 2012 to 2014 was characterized by a Neutral and Weak El Niño event, and exhibited the highest mortality value for a 25 cm DBH (diameter at breast height), the smallest DBH class measured in this study. In the correlation matrix analysis, maximum air temperature showed the highest positive correlation with trees mortality. Despite the challenges in estimating individual tree mortality in tropical forests after selective logging, accurate estimates were achieved using traditional regression techniques and NNAR. These results can support technical and silvicultural decisions regarding forest management in the Eastern Amazon region of Brazil.

Statistical Response of ENSO Complexity to Initial Condition and Model Parameter Perturbations

Abstract 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.

How Do Forecast Model Biases Affect Large-Scale Teleconnections That Control Southwest U.S. Precipitation? Part I: S2S Models

Abstract We analyze biases in subseasonal forecast models and their effect on Southwest United States (SWUS) precipitation prediction (2–6-week time scale). Cluster analyses identify three primary wave trains associated with SWUS precipitation: a meridional El Niño–Southern Oscillation (ENSO)–type wave train, an arching Pacific–North American (PNA)–type wave train, and a circumglobal zonal wave train. Compared to reanalysis, the models overrepresent the arching pattern, underrepresent the zonal pattern, and produce mixed results for the meridional pattern. The arching pattern overrepresentation is linked to model mean flow biases in the midlatitude–subpolar North Pacific, which cause a westward retraction of the region of forbidden linear Rossby wave propagation. The zonal pattern underrepresentation is linked to westerly biases in the subtropical jet, which cause a westward retraction of the waveguide in the midlatitude eastern North Pacific and divert wave trains southward. These results are confirmed using linear, barotropic ray-tracing analysis. In addition to mean state biases, the models also contain errors in their representation of the Madden–Julian oscillation (MJO). Tropical convection anomalies associated with the MJO are too weak and incoherent at lead times greater than 2 weeks when compared to reanalysis. Additionally, there is a strong SWUS precipitation signal as far out as 5 weeks after a strong MJO in reanalysis, associated with its persistent eastward propagation, but this signal is absent in the models. Our results indicate that there is still significant room for improvement in subseasonal predictions if we can reduce model biases in the background flow and improve the representation of the MJO.

ENSO‐Modulated Variability in Winter Shelf Circulation of the Northern South China Sea

AbstractWe combined long‐term observational data from 1999 to 2021 with numerical simulations to study the seasonal changes in the currents along the continental shelf in the Northern South China Sea (NSCS) during winter and how these changes relate to the El Niño‐Southern Oscillation (ENSO). Our results indicate that during El Niño events, the eastward movement of the warm pool in the tropical Pacific Ocean not only leads to colder sea surface temperatures and a stronger Kuroshio current intrusion but also significantly affects the formation of a high‐pressure system in the subtropics over the NSCS. In El Niño years, an anomalous anticyclonic wind stress curl in the South China Sea weakens the northeast winter monsoon, which in turn weakens the cyclonic shelf circulation. The first principal component from the multivariate empirical orthogonal function decomposition, which accounts for 49.02% of the total variance, shows a significant correlation of 0.64 with the Niño 3.4 index, indicating the circulation's sensitivity to tropical climate changes. Our analysis of the winter shelf circulation, based on the along‐isobath depth‐integrated vorticity equation, reveals that the exchange of water across the isobaths over the shelf is mainly controlled by the nonlinear advection of relative vorticity, with wind stress curl and bottom stress curl playing a less significant role in regulating the structure of these exchanges. The combined effect of baroclinic forces and topography likely governs the dynamics over the slope.

Does climate risk as barometers for specific clean energy indices? Insights from quartiles and time-frequency perspective

This study presents the first analysis of the nexus between the Southern Oscillation Index (SOI), a measure of climate risk, and segmented clean energy indices (such as solar, renewable, and bioenergy). Our research findings indicate that (i) the Granger quantile causality significance of SOI on segmented clean energy indices is asymmetric across different conditional quantiles. Significant predictability of SOI is observed only at the 0.25 and 0.75 quantile levels for all segmented clean energy indices, except for the WilderHill Clean Energy Index and NASDAQ OMX Fuel Cell Index. (ii) The clean energy market is significantly influenced by SOI under bullish market conditions. Impacts of SOI on all clean energy markets are nearly negligible when clean energy indices are at the median and lower quantile levels. (iii) The influence of strong La Niña episodes on segmented clean energy indices is more pronounced than during periods of intense El Niño phenomena. (iv) SOI exhibited a positive correlation at mid-term and long-term frequencies with segmented Clean Energy sectors, excluding bioenergy, for the majority of the sample period. Our conclusions provide deeper insights for investors managing clean energy investments in extreme climate conditions. Additionally, they offer useful information for policymakers to formulate viable economic policies addressing climate change, ensuring energy security, and facilitating a safer transition to clean energy.

Revisiting the inconsistent influence of El Niño-Southern Oscillation on the equatorial Atlantic

Abstract The impact of El Niño-Southern Oscillation (ENSO) on the equatorial Atlantic Zonal Mode (AZM) is investigated using two atmospheric reanalysis products and 44 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). While the impact of ENSO on equatorial Atlantic SST is inconsistent, as previously reported, there is a very robust influence of decaying ENSO events in boreal spring on the contemporaneous surface zonal winds over the equatorial Atlantic, with El Niño events associated with anomalous easterlies that cool the equatorial Atlantic. This dynamic impact is counteracted by a thermodynamic impact, in which El Niño events cause changes in surface heat fluxes that lead to warming over the tropical Atlantic. The thermodynamic influence is active throughout the lifecycle of the ENSO event, but the dynamic influence is highly seasonal with a pronounced peak in boreal spring. Thus, a quickly decaying El Niño event will lead to warming, while a slowly decaying event will lead to cooling in the equatorial Atlantic. The ENSO-AZM relation is further complicated by partially independent Atlantic variability, in particular that associated with the South Atlantic subtropical dipole (SASD). Prediction experiments with a simple linear regression model support the role of the SASD as an AZM precursor. Many CMIP6 models show skillful predictions of the June-July-August AZM based on SST indices in the preceding December-January-February, with correlations above 0.5. When predictions are restricted to ENSO years, even more models exceed this threshold, but skill in the reanalyses remains relatively low, possibly due to insufficient training data.

Strengthening of the Equatorial Pacific Upper‐Ocean Circulation Over the Past Three Decades

AbstractThirty years (1993–2022) of concurrent satellite and in‐situ observations show a long‐term strengthening of the equatorial Pacific upper‐ocean circulation. Enhanced southeasterly and cross‐equatorial winds have caused an annual mean, basin‐wide acceleration of the equatorial westward near‐surface currents by ∼20% and an acceleration of poleward flow north (south) of the equator by ∼60% (∼20%). Additional moored velocity data reveal a deepening of the Equatorial Undercurrent (EUC) core at 170°W and significant shoaling at 140°W and 110°W, but no significant changes in EUC core velocity. The strongest subsurface zonal velocity trends are observed above the EUC core in the central to eastern Pacific and occur before and after the seasonal maximum of EUC core velocity, causing enhanced upper‐ocean vertical current shear. Consistent with trends of the 20°C isotherm depth along the equatorial Pacific, a significant basin‐wide steepening of the equatorial thermocline is observed. Both the accelerating equatorial current system and the enhanced thermocline slope are consistent with an observed steepening of the zonal sea surface height gradient due to increased wind‐driven westward mass transport at the surface. During February‐March, both surface and subsurface currents along the equator show eastward velocity trends, in contrast to westward near‐surface current trends during the remainder of the year. The trend reversal is attributed to both a long‐term shift in equatorial Kelvin wave activity and to the impact of strong interannual variability due to El Niño Southern Oscillation and other modes of natural variability on decadal to multidecadal time scales.

Long-Term Teleconnections Between Global Circulation Patterns and Interannual Variability of Surface Air Temperature over Kingdom of Saudi Arabia

Surface air temperature (SAT) variability is investigated for advancing our understanding of the climate patterns over the Kingdom of Saudi Arabia (KSA). SAT variability reveals significant warming trends, particularly from 1994 onward, as demonstrated by nonlinear and linear trend analysis. This warming is linked to global climate patterns, which serve as significant indicators for studying the effects of climate change on surface air temperature patterns across the KSA. The empirical orthogonal function (EOF) method is employed for analyzing SAT due to its effectiveness in extracting dominant patterns of variability during the winter (DJF) and summer (JJA) seasons. The first mode (EOF1) for both seasons shows positive variability across the KSA, explaining more than 45% of the variance. The second mode (EOF2) indicates negative variability in central and northern regions. The third mode (EOF3) describes positive variability but with lower variance over time. PC1 is used to describe the physical mechanism of SAT variability and correlations with global sea surface temperature (SST). The physical mechanism shows that the variability in Mediterranean troughs during the winter season and high pressure over the Indian Ocean and central Asia controls SAT variability over the KSA. The correlation coefficients (CCs) were calculated during the winter and summer season between the SAT of the KSA and six teleconnection indices, El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Atlantic Meridional Mode (AMM), Pacific Warm Pool (PWP), North Atlantic Oscillation (NAO), and Tropical North Atlantic (TNA) SST for the period from 1994 to 2022. ENSO shifts from positive to negative correlations with SAT from winter to summer. IOD shows a diminished correlation with SAT due to the absence of upper air dynamics. PWP consistently enhances surface warming in both seasons through upper air convergence during both seasons. AMM and NAO have a non-significant impact on SAT; however, TNA contributes warming over central and northern parts during winter and summer seasons. The seasonal SAT variations emphasize the significant role of ENSO, PWP, and TNA across the seasons. The findings of this study can be helpful for seasonal predictability in the KSA.