Southern Oscillation Research Articles

Understanding the Relationship Between South Pacific Oscillation and ENSO

AbstractThe boreal summer South Pacific Oscillation (SPO), the dominant mode of atmospheric variability over the South Pacific, has been proposed as a potential precursor to the development of El Niño‐Southern Oscillation (ENSO). However, this study demonstrates that the SPO signal during the developing summer of ENSO events is primarily driven by the concurrent ENSO‐related tropical Pacific sea surface temperature anomalies, challenging its role as an independent predictor of ENSO. We further show that those winter ENSO events preceded by a summer SPO signal begin to develop before the SPO signal in the extratropics appear, and incorporating the summer SPO signal into ENSO persitence model does not add additional information for ENSO hindcast. The observational findings are further corroborated by evidence that the lead relationship of SPO over ENSO in climate models is mainly determined by their simulated ENSO teleconnection to SPO during summer.

Western Pacific Warm Pool δ18O Response to the El Niño‐Southern Oscillation

AbstractTropical Pacific seawater and precipitation stable oxygen isotope data aid in understanding modern oceanic and atmospheric interactions, and these data are particularly valuable as they are archived in isotope‐based paleoclimate records. However, the absence of modern seawater isotope time series limits the ability to identify the atmospheric influences on these data, precluding robust paleoclimate interpretations. We present a new 10 year sub‐monthly record of seawater and precipitation stable oxygen isotope values (δ18Osw and δ18Op) from Koror, Palau. Our dataset indicates that temporally, δ18Osw is strongly influenced by local δ18Op. Both monthly δ18Osw and δ18Op are highly correlated with outgoing longwave radiation across the tropical Pacific, reflecting a Walker Circulation imprint on the surface ocean. Changes in the Palau δ18Osw—salinity relationship correspond to NINO3.4 variability, indicating a difference in how these variables record El Niño Southern Oscillation (ENSO) information, but demonstrating the utility of δ18Osw to reconstruct ENSO variability in the western tropical Pacific.

The El Niño Southern Oscillation and the salinity of land and water in the United States.

The El Niño-Southern Oscillation (ENSO) is a periodic climate phenomenon with important consequences for weather and socioeconomic variables worldwide. This paper investigates the effect of El Niño on the concentration of salts in soils and fresh water. Our analysis shows effects that are statistically significant and large in magnitude. In particular, in response to El Niño events, soil salinity increases by 21% in the northern region of the United States, where average temperatures are positively affected, while it decreases by 29% on average in the southern tiers of the country, where temperatures decline. Similarly, fresh water salinity increases by 9% in warmer counties, while it is 53% lower in cooler counties.

Dynamics of Surface Chlorophyll and the Asymmetric Response of the High Productive Zone in the Peruvian Sea: Effects of El Niño and La Niña

ABSTRACTThis study analyzes over 26 years of satellite data to explore the seasonal and interannual variability of surface chlorophyll‐a from September 1997 to March 2024, focusing on the impacts of the El Niño‐Southern Oscillation (ENSO). We investigate how these phases affect the High Productivity Zone (HPZ) during two seasonal periods: The Warm Season (December to April) and the Cold Season (June to September). From an ENSO‐neutral perspective, the HPZ is more extensive and exhibits higher chlorophyll concentrations during the Warm Season. Conversely, in the Cold Season, low chlorophyll concentrations are found and the area of the HPZ is reduced by 88,000 km2. Under El Niño conditions, chlorophyll‐a concentrations decrease consistently along the near‐offshore region, but a counter‐intuitive increase is observed within the 25 km from shore. La Niña phase enhances chlorophyll in the near‐offshore region while reducing it in the very nearshore waters. This study further highlights the asymmetric response of various physical drivers (such as upwelling strength and light) to different ENSO phases. This finding underscores the intricate interplay between ENSO and marine productivity, where the response can vary seasonally. These findings underscore a spatially complex response of the Peruvian coastal productivity to ENSO phases. Understanding these dynamics is vital for predicting future shifts in coastal productivity and for developing adaptive management strategies to mitigate the impacts of climate variability on marine resources.

Linking Projected Changes in Seasonal Climate Predictability and ENSO Amplitude

Abstract Recent studies have shown that potential predictability and actual forecast skill have varied throughout the historical record, primarily due to natural decadal variability. In this study, we explore whether and how potential predictability is projected to change in the future as a distinct response to anthropogenic climate change. We estimate the potential predictability of El Niño–Southern Oscillation (ENSO) as well as global surface temperature, precipitation, and upper-atmospheric circulation anomalies from 1921 to 2100, within a perfect model framework, using five coupled model large ensembles. We find that historical and projected ENSO amplitude changes generate global-scale shifts in climate predictability via ENSO-driven changes in the signal-to-noise ratio of seasonal forecasts, with a 10% change in Niño-3.4 standard deviation leading to a 14% change in globally averaged forecast skill at 12-month lead. This relationship suggests that potential predictability changes across much of the globe in the coming decades could be linked to anthropogenic climate change of ENSO. However, since current models substantially disagree on the sign and intensity of projected ENSO change, the trajectory of future global predictability changes cannot yet be determined. This problem is demonstrated by widely varying predictability changes seen across the five large ensembles, with models exhibiting a robust increase, robust decrease, or no significant change in predictability, depending upon their respective projected ENSO amplitude trends. Our results highlight the need for climate model development aimed at better capturing past forced and unforced changes to ENSO variability, which is necessary (if not sufficient) to constrain projected changes to climate predictability worldwide.

High-resolution groundwater storage anomalies in the Middle and Lower Yangtze River Basin of China using machine learning fusion of in-situ wells, satellite gravity and hydrological model.

Groundwater plays a key role in the water cycle and is used to meet industrial, agricultural, and domestic water demands. High-resolution modeling of groundwater storage is often challenging due to the limitations of observation techniques and mathematical methods. In this study, two machine learning (ML) algorithms, namely random forest (RF) and artificial neural networks (ANNs), were employed to estimate groundwater level anomaly (GWLA) and groundwater storage anomaly (GWSA) with a 0.25° resolution using multi-source datasets, including in-situ wells, the Gravity Recovery and Climate Experiment (GRACE), land surface models and hydrogeological parameters. The results indicated the ANN algorithm outperformed the RF algorithm in predicting the spatiotemporal variations of the shallow and deep GWLA in the Middle and Lower Yangtze River Basin (MLYRB). Hence, the ANN algorithm was used to construct a model for predicting the GWSA over the 2005-2017 period. The GWSA exhibited an increasing linear rate of about 0.77±0.30mm/yr in almost the entire area, except in the Han River Basin (HRB), where GWSA decreased by -1.18±0.38mm/yr due to decreased precipitation amounts. The occurrence of seasonal variations in the deep GWSA showed lead time effects compared with those in the shallow GWSA, ranging from 0 to 1 month and 1 to 2 months in the humid and relatively dry areas, respectively. It was found that the ANN-based model results showed pronounced responses of the GWSA to the drought events. The standard groundwater drought index (SGDI) was further calculated to assess the spatiotemporal characteristics of the groundwater drought events. The results revealed the occurrence of a severe drought event in 2011, as well as pronounced impacts of the El Niño-Southern Oscillation (ENSO) events on the GWSA. This study demonstrated the effectiveness of in-situ groundwater measurements in enhancing the reliability of ML-based GWSA prediction. Furthermore, the constructed high-resolution groundwater variation features in this study can provide water resource managers with enhanced information and valuable insights into climate-induced groundwater changes.

Triggering of a 2500-year coral shutdown in northern South China Sea by coupled East Asian Monsoon and El Niño–Southern Oscillation

Triggering of a 2500-year coral shutdown in northern South China Sea by coupled East Asian Monsoon and El Niño–Southern Oscillation

A pioneering approach to deterministic rainfall forecasting for wet period in the Northern Territory of Australia using machine learning

A pioneering approach to deterministic rainfall forecasting for wet period in the Northern Territory of Australia using machine learning

Climate-induced expansion of Lyme disease in east central Ohio

ABSTRACT The geographical distribution of Lyme disease has been attributed to changes in Earth’s climate and associated distribution of its vector, ticks of the genus Ixodes. This study focuses on the impact of climatic and meteorological conditions on Lyme disease transmission in East Central Ohio, an emerging hotspot of cases. Using county-level data from 2001 to 2023, we analyzed the relationship between Lyme disease cases and temperature, precipitation, and the Southern Oscillation Index (SOI) using a distributed lag nonlinear model (DLNM). Results show that warmer winter temperatures, higher precipitation, and negative SOI values (El Niño conditions) were significantly associated with increased Lyme disease incidence and displayed delayed effects of 6 to18 months. These findings suggest that climate change, with its potential to bring milder winters and increased spring and summer rainfall, may further exacerbate Lyme disease cases in Ohio.

Climate-driven range expansion via long-distance larval dispersal

Climate-driven warming and changes in major ocean currents enable poleward larval transport and range expansions of many marine species. Here, we report the population genetic structure of the gastropod Kelletia kelletii, a commercial fisheries species and subtidal predator with top-down food web effects, whose populations have recently undergone climate-driven northward range expansion. We used reduced representation genomic sequencing (RAD-seq) to genotype 598 adults from 13 locations spanning approximately 800 km across the historical and expanded range of this species. Analyses of 40747 single nucleotide polymorphisms (SNPs) showed evidence for long-distance dispersal of K. kelletii larvae from a central historical range site (Point Loma, CA, USA) hundreds of km into the expanded northern range (Big Creek, CA), which seems most likely to result from transport during an El Niño-Southern Oscillation (ENSO) event rather than consistent on-going gene flow. Furthermore, the high genetic differentiation among some sampled expanded-range populations and their close genetic proximity with distinct populations from the historical range suggested multiple origins of the expanded-range populations. Given that the frequency and magnitude of ENSO events are predicted to increase with climate change, understanding the factors driving changes in population connectivity is crucial for establishing effective management strategies to ensure the persistence of this and other economically and ecologically important species.

Quantifying impacts of two ENSO patterns on South China sea summer monsoon onset

The onset of the South China Sea (SCS) Summer Monsoon (SCSSM) is usually in May, strongly influencing the beginning of the wet season over the surrounding regions, such as South China and the Philippine Islands. The onset date is significantly affected by the El Niño-Southern Oscillation (ENSO), which manifests as two major types: the Eastern-Pacific (EP) and Central-Pacific (CP) patterns. However, the impacts of these patterns on the SCSSM onset await quantification. Here, using a novel statistical metric, binary combined linear regression, we find a dominant role of the CP pattern rather than the EP pattern, as a moderate CP El Niño/La Niña tends to cause SCSSM onset delay/advance by around 4.7/5.1 days in its decaying year. The anomalous SST structure of CP El Niño leads to persistent anomalous anti-cyclonic winds over the western North Pacific from April to June, suppressing the local convection and impeding the SCSSM winds. In comparison, the EP El Niño impact decays quickly in May and hardly affects the SCSSM onset. This, hence, weakens the relationship between the SCSSM onset and ENSO. These results help improve the SCSSM onset prediction by involving diverse ENSO impacts.

Simulating the ENSO impact on the distribution and fate of floating litter particles in the Northern South China Sea

The Pearl River delivers a large amount of plastic waste to the Pearl River Estuary (PRE) and adjacent Northern South China Sea (NSCS) region each year. However, the transport of floating litter after release is difficult to predict due to the complex hydrodynamic conditions caused by the climate variability. A regional ocean circulation model coupled with a Lagrangian particle tracking model is utilized in this study to simulate the distribution and fate of floating litter particles in the Pearl River Estuary (PRE) and Northern South China Sea (NSCS) under the influence of El Niño - Southern Oscillation (ENSO) event. Simulations are conducted during all four seasons (spring, summer, fall, and winter) in typical El Niño, La Niña, and ENSO-neutral year. The model reveals that most floating litter remains within Lingding Bay before being transported westward by the counterclockwise circulation over the NSCS and arriving at the Qiongzhou Strait. After crossing the Strait, the debris is carried by the counterclockwise circulation of the Beibu Gulf, and eventually arriving at the coasts of Vietnam and Laos. The ENSO warm (El Niño) and cold (La Niña) phases disrupt circulation patterns and modulate the amount of Pearl River runoff, thereby altering the transport pathways and grounding probabilities of floating litter. During La Niña years, floating litter particles spread over a wider area, travel longer distances, and have lower beaching probabilities. Conversely, during El Niño year, floating litter particles tend to remain within Lingding Bay for longer durations, with some debris entrained towards the Hong Kong region. This study underscores the impact of climate mode of variability in influencing the litter sources, fate and transport and accumulation at estuarine-coastal oceans, which will provide critical scientific insights for plastic pollution management in the PRE - NSCS region, which is a newly identified hotspot for floating litter and microplastic pollution in global oceans.

Role of the Spring Sea Surface Temperature over Southeastern Indian Ocean in Bridging the Indian Ocean Dipole and Subsequent ENSO

Abstract A significant relationship exists between the Indian Ocean Dipole (IOD) and the following year’s El Niño-Southern Oscillation (ENSO), in spite of the uncertainty in the associated mechanisms. Here we show that the spring sea surface temperature (SST) anomalies over the southeastern Indian Ocean (SEIO) play a bridging role in the teleconnection of the IOD and subsequent year’s ENSO. A positive IOD could induce a positive tendency of SST in the SEIO region from autumn to winter, primarily through the cloud-radiation-SST feedback, forming an anomalous SEIO warming which persists into the subsequent spring. As the oceanic forcing dominates in spring, the SEIO warming can induce/maintain the anomalous anticyclonic circulation over the western North Pacific according to the Gill-model response, accompanied by easterly wind anomalies over the western equatorial Pacific that generate eastward propagating upwelling Kelvin waves. As a result, anomalous cooling appears in the central-eastern tropical Pacific in the following seasons, manifesting as a La Niña mode. Additionally, the role of the ocean channel (i.e. the Indonesian Throughflow) in connecting the spring SEIO SST and the subsequent eastern Pacific SSTs is also discussed.

Spatial variability of ENSO-based monsoon rainfall in the coastal region of southeastern India

ABSTRACT Climate variability during El Niño and La Niña events is believed to synch with the monsoon fluctuation globally. Studying these phenomena is crucial for predicting meteorological factors like temperature and precipitation. This study investigates the spatial variability of southwest monsoon rainfall (SMR) due to El Niño and La Niña on the southeastern coast of India. Monthly average rainfall data for the coastal districts of Tamil Nadu, Andhra Pradesh, and Odisha from 1970 to 2020 were analyzed. The analysis revealed that the El Niño Southern Oscillation (ENSO) had a distinct impression on these districts. Results showed that during El Niño, Tamil Nadu and Andhra Pradesh received below-normal rainfall with an 82% probability, while above-normal rainfall occurred during La Niña, with probabilities of 65 and 70%, respectively. In Odisha, the probability of below-normal rainfall during El Niño is 41%, and above-normal rainfall during La Niña was 40%. However, the impact on Odisha was insignificant and did not align with conventional ENSO patterns. This study improves understanding of rainfall variability along the southeastern coast of India, highlighting the need for regional climate forecasting. These insights can aid policymakers in developing drought management and flood preparedness strategies, providing a valuable resource for enhancing climate prediction models.

Climate-weather interactions modulating tropical cyclones over the Mascarene Islands 2000–2024

Tropical cyclones (TC) over the Mascarene Islands of the south-west Indian Ocean have been studied using meteorological reanalysis, satellite observations and local measurements in December to March season in the period 2000–2024. An innovative methodology was employed to create an index of TC impacts: multiplying the daily maximum wind and standardized maximum rainfall within the area 19–22 S, 54.5-58.5E, then summing to monthly interval above a threshold of 50. This time series was regressed onto a variety of fields in the Dec-Mar season to determine how climate-weather interactions modulate TCs near the islands of Mauritius and Reunion. The results highlight the importance of outflow from east Africa that passes northern Madagascar and generates cyclonic vorticity. Statistical links with Pacific El Nino Southern Oscillation and the Indian Ocean Dipole were weak but offer an advance warning of risk. Global regression maps show an inter-hemispheric dipole in SST and upper-level geopotential height (warm and high to south), associated with a zonal overturning circulation during austral summers with greater TC impacts in the Mascarene Islands. Trends over the longer period 1980–2024 were mapped to identify warming and moistening in the Mascarene area, and low level northwesterlies in the equatorial belt. The case of TC Dina 22 Jan 2002 was analyzed for meteorological structure and impacts, to place the statistical results in local context.

Quantifying the acceleration of multidecadal global sea surface warming driven by Earth’s energy imbalance

Abstract Global mean sea surface temperature (GMSST) is a fundamental diagnostic of ongoing climate change, yet there is incomplete understanding of multi-decadal changes in warming rate and year-to-year variability. Exploiting satellite observations since 1985 and a statistical model incorporating drivers of variability and change, we identify an increasing rate of rise in GMSST. This accelerating ocean surface warming is physically linked to an upward trend in Earth’s energy imbalance (EEI). We quantify that GMSST has increased by 0.54 ± 0.07 K for each GJ m–2 of accumulated energy, equivalent to 0.17 ± 0.02 K decade‒1 (W m‒2)‒1. Using the statistical model to isolate the trend from interannual variability, the underlying rate of change of GMSST rises in proportion with Earth’s energy accumulation from 0.06 K decade–1 during 1985–89 to 0.27 K decade–1 for 2019–23. While variability associated with the El Niño Southern Oscillation triggered the exceptionally high GMSSTs of 2023 and early 2024, 44% (90% confidence interval: 35%–52%) of the +0.22 K difference in GMSST between the peak of the 2023/24 event and that of the 2015/16 event is unexplained unless the acceleration of the GMSST trend is accounted for. Applying indicative future scenarios of EEI based on recent trends, GMSST increases are likely to be faster than would be expected from linear extrapolation of the past four decades. Our results provide observational evidence that the GMSST increase inferred over the past 40 years will likely be exceeded within the next 20 years. Policy makers and wider society should be aware that the rate of global warming over recent decades is a poor guide to the faster change that is likely over the decades to come, underscoring the urgency of deep reductions in fossil-fuel burning.

Possible Mechanisms behind Dominant Modes of Seasonal Cycle of Rainfall over India

Abstract The South Asian summer monsoon that occurs over four months from near summer solstice (June) to near Autumnal equinox (September) evolves within the season due to interaction with other climate components. Since most climate patterns are seasonally phase-locked, their interaction and feedback with monsoon should also be season-dependent. Here, using rain-gauge-based precipitation over India for seventy-four years, we show that the onset phase of the monsoon (June) is uncorrelated with rest of the season (July to September). Propagation of Madden Julian Oscillation (MJO) over the Indian Ocean triggers northward-propagation of precipitable water that plays a key role during the onset phase. MJO appears over the Western equatorial Indian Ocean both during the early and late Central Indian onset years. Normal and slow MJO phase propagation during early and late onset years respectively give rise to interannual variations in June rainfall. After onset, till the end of the monsoon season, interannual variability of the dominant seasonal mode of precipitation is regulated by the phase of the El Nino Southern Oscillation (ENSO) through anomalous moisture convergence. We show that, in June, ENSO doesn’t play key role because of the opposing impact on wind and moisture. However, from July to September, ENSO perturbs wind and moisture in a way that gives rise to positive feedback between monsoon, ENSO, and large-scale circulation. Such feedback of monsoon with ENSO results in a coherent impact of ENSO on monsoon precipitation in July-September. Our results suggest that dominant mechanisms for interannual variations of Indian monsoon rainfall in June and the rest of the season are different.

El Niño–Southern Oscillation Prediction Based on the Global Atmospheric Oscillation in CMIP6 Models

In this work, the preindustrial control (piControl) and Historical experiments results from climatic Earth system models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) are analyzed for their ability to predict the El Niño–Southern Oscillation (ENSO). Using the principal component method, it is shown that the Global Atmospheric Oscillation (GAO), of which the ENSO is an element, is the main mode of interannual variability of planetary anomalies of surface air temperature (SAT) and atmospheric sea level pressure (SLP) in the ensemble of 50 CMIP6 models. It turns out that the CMIP6 ensemble of models reproduces the planetary structure of the GAO and its west–east dynamics with a period of approximately 3.7 years. The models showed that the GAO combines ENSO teleconnections with the tropics of the Indian and Atlantic Oceans, and with temperate and high latitudes. To predict strong El Niño and La Niña events, we used a predictor index (PGAO) obtained earlier from observation data and reanalyses. The predictive ability of the PGAO is based on the west–east propagation of planetary structures of SAT and SLP anomalies characteristic of the GAO. Those CMIP6 models have been found that reproduce well the west–east spread of the GAO, with El Niño and La Niña being phases of this process. Thanks to this, these events can be predicted with approximately a year’s lead time, thereby overcoming the so-called spring predictability barrier (SPB) of the ENSO. Thus, the influence of global anomalies of SAT and SLP on the ENSO is shown, taking into account that it may increase the reliability of the early forecast of El Niño and La Niña events.

The Combined and Isolated Impacts of El Niño and Positive Indian Ocean Dipole Events on South American Precipitation During Austral Winter and Spring Depending on the Atlantic Multidecadal Oscillation Phases

ABSTRACTThe isolated/combined impacts of the positive Indian Ocean Dipole (pIOD) and El Niño (EN) events on precipitation in South America (SA) were investigated during austral winter and spring for the 1901–2012 period, considering both Atlantic Multidecadal Oscillation (AMO) phases. Under the warm phase (WAMO), EN events are well characterised in winter and, in spring, are accompanied by anomalous warming of the Tropical North Atlantic (TNA); thus, variations in the Walker circulation and the northward shifted Intertropical Convergence Zone (ITCZ) reduce precipitation over northern SA. In the cold AMO phase (CAMO), EN events are weak in winter. At the same time, an intense cooling in the equatorial North Atlantic, favoured by the CAMO, enhances moisture transport from the Amazon to central and southeastern Brazil, increasing precipitation in the South Atlantic Convergence Zone region. As EN develops in spring, the anticyclone off the east coast of SA associated with the Pacific‐South American (PSA) pattern decreases (increases) precipitation in central (southeastern) SA. The pIOD events predominantly occur during WAMO phase, when warming in the TNA is favoured by AMO. In winter, the moisture transport to northern SA is weakened, and the ITCZ remains northward shifted, inhibiting the precipitation over northeastern Brazil and southeastern Amazon. In spring, pIOD intensified ascending motions in equatorial Atlantic, increasing precipitation over northeastern Brazil. A wave train from the Indian Ocean strengthens the South Atlantic subtropical high, suppressing precipitation in central and eastern SA. EN‐pIOD events are well established in both AMO phases, though the sea surface temperature anomalies in the TNA depend on the AMO. During WAMO, reduced precipitation in western Amazon and northeastern SA is influenced by the Walker circulation, while in CAMO, TNA cooling enhances moisture transport from the Amazon into southeastern SA, where the PSA pattern and wave train from the Indian Ocean increase precipitation.

Non-Negligible Factors Influence Tree-Ring-Based Temperature Reconstruction and Comparison over Mid-Latitude China

Warm-season mean maximum temperature changes over mid-latitude regions have been attracting increasing attention amid the background of global warming. In this study, we present three tree-ring width chronologies: Tongbai Mountain (TBM; 1916–2014), Shimen Mountain (SMM; 1663–2014), and Xinlong (XL; 1541–2014), derived respectively from the eastern Qinling Mountains, north–central China, and the eastern Tibetan Plateau. Therein, TBM and SMM are newly developed, while XL is a reanalysis. Correlation analysis with climatic factors reveals that these three chronologies exhibit the highest correlation with the May–July mean maximum temperature. Based on these chronologies, we conducted reconstructions of the May–July mean maximum temperature. Spatial correlation analysis of each reconstruction with concurrent observed data, as well as comparisons with nearby temperature reconstructions, indicates their large-scale representativeness. However, during the common period of 1916–2014, the three chronologies show weak correlations with each other at the interannual timescale. Furthermore, the 11-year running correlation coefficients among the three reconstructions fluctuated during this common period. Additionally, fluctuations were observed between the reconstructions from SMM and XL during the overlapping period of 1668–2009, suggesting that tree-ring-based temperature reconstructions may be inconsistent when compared over mid-latitude China. These inconsistent changes can be attributed to the regional differences in the May–July mean maximum temperature change, the influence of different precipitation signals on the maximum temperature, and the El Niño–Southern Oscillations.