El Nino-Southern Oscillation Phases Research Articles

Advanced Peak Phase of ENSO under Global Warming

Abstract El Niño–Southern Oscillation (ENSO) is the leading mode of interannual ocean–atmosphere coupling in the tropical Pacific, greatly influencing the global climate system. Seasonal phase locking, which means that ENSO events usually peak in boreal winter, is a distinctive feature of ENSO. In model future projections, the ENSO sea surface temperature (SST) amplitude in winter shows no significant change with a large intermodel spread. However, whether and how ENSO phase locking will respond to global warming are not fully understood. In this study, using Community Earth System Model Large Ensemble (CESM-LE) projections, we found that the seasonality of ENSO events, especially its peak phase, has advanced under global warming. This phenomenon corresponds to the seasonal difference in the changes in the ENSO SST amplitude with an enhanced (weakened) amplitude from boreal summer to autumn (winter). Mixed layer ocean heat budget analysis revealed that the advanced ENSO seasonality is due to intensified positive meridional advective and thermocline feedback during the ENSO developing phase and intensified negative thermal damping during the ENSO peak phase. Furthermore, the seasonal variation in the mean El Niño–like SST warming in the tropical Pacific favors a weakened zonal advective feedback in boreal autumn–winter and earlier decay of ENSO. The advance of the ENSO peak phase is also found in most CMIP5/6 models that simulate the seasonal phase locking of ENSO well in the present climate. Thus, even though the amplitude response in the winter shows no model consensus, ENSO also significantly changes during different stages under global warming.

Fingerprints of El Niño Southern Oscillation on global and regional oceanic chlorophyll-a timeseries (1997–2022)

With the advancement of present-day ocean color satellites, the spatiotemporal variations of oceanic Chlorophyll-a (Chl-a) concentration, a proxy for phytoplankton population, can be monitored at regional and global scales. Estimating long-term changes in Chl-a concentration, however, is mainly constrained by the limited availability of ocean color data and the significant influence of climate oscillations like the El Niño Southern Oscillation (ENSO). In this study, we investigate the influence of ENSO on regional and global Chl-a timeseries using two ocean color datasets spanning from September 1997 to December 2022. Our analysis found that global Chl-a concentration exhibits a significant increasing trend over the study period. Most of the increasing trends are detected in the Southern Hemisphere (SH) and high-latitudinal regions, including the Southern Ocean. In light-limited regions like the Southern Ocean, enhanced Chl-a concentration shows a strong positive correlation with warming sea surface temperature (SST). Conversely, most oceans in the Northern Hemisphere (NH) exhibit decreasing trends, which are highly correlated with warming SST and ENSO phases. Additionally, we identify distinctive fingerprints of four extreme ENSO events: two extreme El Niño events (1997–1998 and 2015–2016) and two triple La Niña events (1998–2001 and 2020–2022) in these timeseries. Furthermore, empirical orthogonal function (EOF) analysis reveals that the dominant mode of interannual Chl-a variability is associated with an ENSO-like pattern, accounting for about 13 % of the total variability. Intriguingly, this principal mode is highly correlated with the phases of ENSO (R = -0.87) and Pacific Decadal Oscillation (PDO) (R = −0.90) on an interannual scale. This study provides insights into assessing the impacts of ENSO on long-term Chl-a trend estimation.

Interannual variation of summer southwest monsoon rainfall over the monsoon core regions of the eastern Bay of Bengal and its relationship with oceans

The study looked at how summer monsoon rainfall in the eastern Bay of Bengal area changes from year to year due to Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO). Study used rainfall data and sea surface temperature data to see these variations. It's found that during ENSO positive phase, rainfall decreased in the eastern coastal region of the Bay of Bengal but increased in the northern Indo-Myanmar region. The opposite happened during ENSO negative phase. The study used a special analysis method called EOF and Morlet wavelet power-spectrum analysis to look for important patterns in the rainfall data and did correlation analysis to understand what causes abnormal rainfall in these regions. The study also found that the local convection and water vapor flux during ENSO positive phase are related to the anomalous rainfall in the Monsoon Core region. Rainfall is made stronger by the unusual anticyclone circulation in the upper troposphere. A strong/weak Mainland Indochina southwest monsoon (MSwM) in the positive or negative phase of ENSO can bring excess/less moisture to wet/dry the local southwest summer rainfall. In northern Indo-Myanmar, the anomalous rainfall is not only relied on the intensity of the MSwM but also the frequency of western disturbances also influences the regional rainfall, and further study need to develop.

Madden‐Julian Oscillation Contributes to the Skewed Intraseasonal PNA in El Niño and La Niña Winters

AbstractThe impact of the Madden‐Julian oscillation (MJO) on the intraseasonal PNA (ISPNA) was investigated and was found to be modulated by the El Niño‐Southern Oscillation (ENSO), which reasonably explains the skewness of the ISPNA during El Niño and La Niña winters. It was shown that the intensity and periodicity of the ISPNA was much stronger and slightly longer in La Niña winters than in the El Niño winters. The phase‐locked association between the ISPNA and MJO indicate that this skewness was controlled by the MJO. The northward Rossby wave activities derived from the tropics associated with the MJO to the subtropical Pacific sector of the ISPNA clarified that the stronger intensity of the MJO convection in the western Pacific during the La Niña winters, as well as the slower eastward propagation of the MJO, led to the asymmetric intensity and period of the ISPNA in the two ENSO phases.

Comparative Analysis of Machine Learning Models for Tropical Cyclone Intensity Estimation

Estimating tropical cyclone (TC) intensity is crucial for disaster reduction and risk management. This study aims to estimate TC intensity using machine learning (ML) models. We utilized eight ML models to predict TC intensity, incorporating factors such as TC location, central pressure, distance to land, landfall in the next six hours, storm speed, storm direction, date, and number from the International Best Track Archive for Climate Stewardship Version 4 (IBTrACS V4). The dataset was divided into four sub-datasets based on the El Niño–Southern Oscillation (ENSO) phases (Neutral, El Niño, and La Niña). Our results highlight that central pressure has the greatest effect on TC intensity estimation, with a maximum root mean square error (RMSE) of 1.289 knots (equivalent to 0.663 m/s). Cubist and Random Forest (RF) models consistently outperformed others, with Cubist showing superior performance in both training and testing datasets. The highest bias was observed in SVM models. Temporal analysis revealed the highest mean error in January and November, and the lowest in February. Errors during the Warm phase of ENSO were notably higher, especially in the South China Sea. Central pressure was identified as the most influential factor for TC intensity estimation, with further exploration of environmental features recommended for model robustness.

Predictability of the early summer surface air temperature over Western South Asia

Variability of the Surface Air Temperature (SAT) over the Western South Asia (WSA) region leads to frequent heatwaves during the early summer (May-June) season. The present study uses the European Centre for Medium-Range Weather Forecast’s fifth-generation seasonal prediction system, SEAS5, from 1981 to 2022 based on April initial conditions (1-month lead) to assess the SAT predictability during early summer season. The goal is to evaluate the SEAS5’s ability to predict the El Niño-Southern Oscillation (ENSO) related interannual variability and predictability of the SAT over WSA, which is mediated through upper-level (200-hPa) geopotential height anomalies. This teleconnection leads to anomalously warm surface conditions over the region during the negative ENSO phase, as observed in the reanalysis and SEAS5. We evaluate SEAS5 prediction skill against two observations and three reanalyses datasets. The SEAS5 SAT prediction skill is higher with high spatial resolution observations and reanalysis datasets compared to the ones with low-resolution. Overall, SEAS5 shows reasonable skill in predicting SAT and its variability over the WSA region. Moreover, the predictability of SAT during La Niña is comparable to El Niño years over the WSA region.

Influences on North‐Atlantic summer climate from the El Niño‐Southern Oscillation

AbstractSeasonal‐range predictability of summer climate in northwestern Europe is generally considered to be low. This is an increasing issue given the worsening impact of summer heatwaves, droughts and intense convective rainfall in a rapidly changing climate. In wintertime, predictive skill in the region is derived from a variety of sources, not least teleconnections with the El Niño‐Southern Oscillation (ENSO). Summer ENSO teleconnections, however, are often considered to be negligible. In this paper, we revisit the topic of summer teleconnections between ENSO and the North Atlantic‐European region. We build on previous work identifying upper tropospheric responses to tropical forcing, since dynamical teleconnections are most apparent at this level. Our results confirm that significantly increased geopotential heights are found stretching over the North‐Atlantic region and into western Europe when La Niña conditions are prevalent during summer. This pattern is part of the previously identified ‘circumglobal’ pattern of wider northern‐hemisphere height changes. We then look for these responses in a range of climate models used in operational seasonal prediction. While parts of the circumglobal pattern are weakly present, none of them produce the response seen over the North Atlantic, even when the effect of sampling on the observed teleconnection is accounted for. We additionally estimate the contribution of the previous (wintertime) phase of ENSO on the following summer. We find a significant delayed response, particularly in heights, to the earlier phase. The combination of the delayed and current responses gives height anomalies that are larger, on average, when ENSO changes phase from winter to summer. Finally, we show that a modest level of regional prediction skill from ENSO does exist. There is a contribution to skill in heights from the previous ENSO phase, but the equivalent contribution to the skill of zonal winds is smaller.

The advance of El Niño phase locking from period 1982–2000 to 2001–2022

The mature phases of El Niño-Southern Oscillation (ENSO) events exhibit a distinct tendency to peak towards the end of the calendar year, a phenomenon commonly referred to as ENSO phase locking. This phase locking is a fundamental property of the ENSO. The observed characteristics of phase locking are intricately tied to the seasonality of ENSO-related Sea Surface Temperature (SST) growth rate. In this study, notable observational evidence is presented: the strength of phase locking of El Niño weakened, and the phase locking advanced to peak at an earlier time during the period 2001 to 2022 compared to 1982 to 2000. The advancement of El Niño Phase Locking is explored by analyzing the contributions of different oceanic feedbacks to the El Niño phenomenon. Specifically, our findings highlight the significant role of nonlinear advective dynamic heating (NDH), which is influenced by the decrease in equatorial pacific surface zonal current anomaly and the equatorial pacific surface zonal gradient of sea SST anomaly. This investigation enhances our understanding of the evolving dynamics of El Niño phase locking, shedding light on the intricate interplay of oceanic feedbacks in influencing this fundamental aspect of ENSO behavior.

Comparison of machine learning models in forecasting different ENSO types

Accurate forecasting of the El Niño Southern Oscillation (ENSO) plays a critical role in mitigating the impacts of extreme weather conditions linked to ENSO variability on ecosystems. This study evaluates the performance of six machine learning models in forecasting two ENSO types: the Central Pacific El Niño (Niño 4 index) and the East Central Pacific El Niño (Niño 3.4 index). The models analyzed include the Feed Forward Neural Network (FFNN), Long Short-term Memory (LSTM) neural network, eXtreme Gradient Boosting Regressor, K-Nearest Neighbors Regressor, Gradient Boosting Regressor, and Support Vector Regressor, using the ENSO index lagged by six months as the predictor. The models were trained on the monthly ENSO indices from 1870 to 1992 and tested from 1993 to 2023. We also assess the relative predictability of the two ENSO types. Events were defined as when the ENSO index exceeded ±0.4. Our evaluation during the testing period reveals that for the analyzed models, the deep neural network models (LSTM and FFNN) demonstrated superior performance in forecasting ENSO at a 6-month lead time. Furthermore, all models achieved impressive all-season correlations ranging from 0.93 to 0.97 and threat score for the ENSO phases between 0.71 to 0.88 for Niño 3.4 events, and 0.72 to 0.93 for Niño 4 events. The predictability of the two ENSO types depended on the model and strength of the ENSO event. Considering both ENSO phases, La Niña events were forecasted with a higher accuracy relative to El Niño events, and all models, besides the deep learning models, notably fell short in capturing the extreme 2015/2016 Central Pacific El Niño event. These results highlight the potential of machine learning models, particularly the deep learning approaches, for skillful ENSO forecasting, by leveraging its historical data.

A Nonlinear Full-Field Conceptual Model for ENSO Diversity

Abstract As the strongest year-to-year fluctuation of the global climate system, El Niño–Southern Oscillation (ENSO) exhibits spatial–temporal diversity, which challenges the classical ENSO theories that mainly focus on the canonical eastern Pacific (EP) type. Besides, the complicated interplay between the interannual anomaly fields and the decadally varying mean state is another difficulty in current ENSO theory. To better account for these issues, the nonlinear two-region recharge paradigm model is extended to a three-region full-field conceptual model to capture the physics in the western Pacific (WP), central Pacific (CP), and EP regions. The results show that the extended conceptual model displays a rich dynamical behavior as parameters setting the efficiencies of upwelling and zonal advection are varied. The model can not only generate El Niño bursting behavior but also simulate the statistical asymmetries between the two types of El Niños and the warm and cold phases of ENSO. Finally, since both the anomaly fields and mean states are simulated by the model, it provides a simple tool to investigate their interactions. The strengthening of the upwelling efficiency, which can be seen as an analogy to a cooling thermocline associated with the oceanic tunnel to the midlatitudes, will increase the zonal gradient of the mean state temperature between the WP and EP, i.e., resembling a negative Pacific decadal oscillation (PDO) pattern along the equatorial Pacific. The influence of the zonal advection efficiency is quite the opposite, i.e., its strengthening will reduce the zonal gradient of the mean state temperature along the equatorial Pacific.

Impact of ENSO on the UTLS chemical composition in the Asian Summer Monsoon Anticyclone

The changes in the convection and background dynamics over the Asian Summer Monsoon (ASM) are mainly responsible for the changes in the concentrations of the tropospheric pollutants within the Asian Summer Monsoon Anticyclone (ASMA). In this study, we have investigated the structural and chemical composition changes of the ASMA during different El Niño–Southern Oscillation (ENSO) conditions by making use of reanalysis products and Microwave Limb Sounder (MLS) satellite observations. Our analysis mainly concentrates on the July and August months of 2015 (El Niño) and 2022 (La Niña), which are the cold (Nino Index 3.4 exceeding −0.5 in magnitude throughout the year) and warm (Nino Index exceeding +0.5 in magnitude throughout the year) phase of the ENSO. The ASMA structure during different phases of the ENSO is found to be different when compared with the long-term (2005–2023) mean. The ASMA is found to have a larger zonal extent during La Niña (2022), extending from 20° to 140° E compared to El Niño (2015), which is from 20° to 100–105°E. A significant decrease (increase) in the concentration of the tropospheric species (CO and WV) is noticed within the ASMA in El Niño (La Niña). The decrease in the tropospheric species is clearly evident throughout the upper troposphere. The northeastern edge of the ASMA at 100 hPa and 150 hPa showed quite distinct behaviour with an enhancement (reduction) in the tropospheric species during La Niña (El Niño). Interestingly, there is an increase in O3 concentrations over the northeastern edge of ASMA during 2015, which is attributed to the stratospheric intrusion. Our results also revealed the enhancement of the tropospheric species over the western Pacific region during 2015 but not in 2022. Current findings highlight dynamic shifts linked to varying ENSO phases, leading to notable alterations in both the ASMA structure and Upper Troposphere and Lower Stratosphere (UTLS) chemical composition.

Climate-Informed Management of Irrigated Cotton in Western Kansas to Reduce Groundwater Withdrawals

The Ogallala aquifer, underlying eight states from South Dakota to Texas, is practically non-recharging south of Nebraska, and groundwater withdrawals for irrigation have lowered the aquifer in western Kansas. Subsequent well-yield declines encourage deficit irrigation, greater reliance on precipitation, and producing profitable drought-tolerant crops like upland cotton (Gossypium hirsutum (L.)). Our objective was to evaluate deficit irrigated cotton growth, yield, and water productivity (CWP) in northwest, west-central, and southwest Kansas in relation to El Niño southern oscillation (ENSO) phase effects on precipitation and growing season cumulative thermal energy (CGDD). Using the GOSSYM crop growth simulator with actual 1961–2000 location weather records partitioned by the ENSO phase, we modeled crop growth, yield, and evapotranspiration (ET) for irrigation capacities of 2.5, 3.75, and 5.0 mmd−1 and periods of 4, 6, and 8 weeks. Regardless of location, the ENSO phase did not influence CGDD, but precipitation and lint yield decreased significantly in southwest Kansas during La Niña compared with the Neutral and El Niño phases. Simulated lint yields, ET, CWP, and leaf area index (LAI) increased with increasing irrigation capacity despite application duration. Southwestern Kansas producers may use ENSO phase information with deficit irrigation to reduce groundwater withdrawals while preserving desirable cotton yields.

Influence of Natural Tropical Oscillations on Ozone Content and Meridional Circulation in the Boreal Winter Stratosphere

The dependence of ozone content in the polar stratosphere upon different phases of the quasi-biennial oscillation (QBO) of the zonal wind and the El Niño–Southern Oscillation (ENSO) during winter was studied. The monthly (from November to January) mean residual meridional circulation (RMC) was calculated for four different combinations of the main phases of ENSO and QBO using MERRA2 reanalysis data. It has been demonstrated that the QBO phase manifests itself in different vertical distributions of ozone in the equatorial stratosphere, as well as in strengthening/weakening of the secondary meridional circulation in the tropics. The enhancement of the RMC from the tropical to the polar stratosphere is stronger at altitudes where ozone is higher in the tropics under El Niño conditions. The RMC modification and intensification are observed from ozone-depleted areas under La Niña conditions. A “cumulative” effect is observed by February under La Niña conditions and the easterly QBO, which is expressed in the lowest ozone content in the polar stratosphere. The numerical experiments carried out using the Middle and Upper Atmosphere Model (MUAM) confirmed tendencies in changes in the meridional transport detected from the reanalysis data for different combinations of QBO and ENSO.

Synoptic control of the spatiotemporal variability of fog and low clouds under ENSO phenomena along the Chilean coast (17°-36° S)

The northern and central coasts of Chile have an extensive semi-permanent layer of stratocumulus clouds that produce fog on land, a crucial resource for water-stressed areas. This study examines the spatio-temporal variability of fog and low clouds (FLC) across four climatic zones (17°S-36°S) characterized by arid conditions. Our analysis aims to elucidate the relationship between FLC patterns and the El Niño-Southern Oscillation (ENSO) phenomenon based on 25 years (1998–2022) of GOES satellite images. The variability of FLC shows a marked, although spatially asymmetric, seasonal cycle, with a subtle positive trend in the long-term. Our results suggest that the presence of FLC is controlled by the strength of the thermal inversion (correlation coefficient, r = 0.7), which, in turn, depends on the sea surface temperature (SST) and the subsidence. Specifically, FLC patterns are controlled by SST in the north (r = −0.9) and by subsidence intensity in the south (r = 0.9). Furthermore, our analysis indicates a potential link between ENSO and FLC, which alters the SST-subsidence equilibrium. At 20°S, warm phases of ENSO lead to increased FLC during the summer and decreased FLC during the winter. Conversely, at 30°S, warm phases result in decreased FLC during the summer and increased FLC during the winter. However, during cold phases, this trend is reversed. At 20°S, FLC decreases in summer and increases in winter, while at 30°S, FLC increases in summer and decreases in winter. In summary, our study offers a novel perspective on understanding the large-scale dynamics associated with FLC frequency along the central and northern coasts of Chile, including FLC underlying mechanisms and the long-term influence exerted by ENSO on the phenomenon.

Quantifying Downstream Climate Impacts of Sea Surface Temperature Patterns in the Eastern Tropical Pacific Using Clustering

El Niño–Southern Oscillation (ENSO) phases and flavors, as well as off-equatorial climate modes, strongly influence sea surface temperature (SST) patterns in the eastern tropical Pacific and downstream climate. Prior studies rely on EOFs (which characterize fractional SST variance) to diagnose climate-scale SST structures, limiting the ability to link individual ENSO flavors with downstream phenomena. Hierarchical and k-means clustering methods are used to construct Eastern Pacific patterns from the ERSST dataset spanning 1950 to 2021. Cluster analysis allows for the direct linkage of individual SST years/seasons to ENSO phase, providing insight into ENSO flavors and associated downstream impacts. In this study, four clusters are revealed, each depicting unique SST patterns influenced by ENSO and Pacific Meridional Mode (PMM) phases. A case study demonstrating the utility of the clusters was also carried out using accumulated cyclone energy (ACE) in the Atlantic and Eastern Pacific basins. Results showed that Eastern Pacific (EP) El Niño suppresses Atlantic tropical cyclone (TC) activity, while Central Pacific (CP) La Niña enhances it. Further, EP El Niño, coupled with positive PMM, amplifies ACE. Ultimately, the methods used herein offer a cleaner analysis tool for identifying dominant SSTA patterns and employing those patterns to diagnose downstream climatic effects.

Influence of Spring Precipitation over Maritime Continent and Western North Pacific on the Evolution and Prediction of El Niño–Southern Oscillation

Previous studies suggested that spring precipitation over the tropical western Pacific Ocean can influence the development of El Niño–Southern Oscillation (ENSO). To identify crucial precipitation patterns for post-spring ENSO evolution, a singular value decomposition (SVD) method was applied to spring precipitation and sea surface temperature (SST) anomalies, and three precipitation and ENSO types were obtained with each highlighting precipitation over the Maritime Continent (MC) or western north Pacific (WNP). High MC spring precipitation corresponds to the slow decay of a multi-year La Niña event. Low MC spring precipitation is associated with a rapid El Niño-to-La Niña transition. High WNP spring precipitation is related to positive north Pacific meridional mode and induces the El Niño initiation. Among the three ENSO types, ocean current and heat content behave differently. Based on these spring precipitation and oceanic factors, a statistical model was established aimed at predicting winter ENSO state. Compared to a full dynamical model, this model exhibits higher prediction skills in the winter ENSO phase and amplitude for the period of 1980–2022. The explained total variance of the winter Niño-3.4 index increases from 43% to 75%, while the root-mean-squared error decreases from 0.82 °C to 0.53 °C. The practical utility and limitations of this model are also discussed.

Event-based evaluation of operational ENSO forecasting models in 2002–2020: Implications for seasonal water resources management

Given the practical implications of El Niño-Southern Oscillation (ENSO) forecasting for developing decision support systems and its importance in global climate prediction on seasonal to interannual scales, significant efforts have been made to enhance ENSO forecasts. This study presents an event-based evaluation of ENSO forecasts and discusses the implications of such forecasts to water resources management. Using ENSO forecasts for the period 2002–2020, this study evaluates and compares the results of two types of forecasting models, i.e., statistical and dynamical models, in predicting Sea Surface Temperatures (SSTs). Forecasting skills are evaluated for distinct target seasons via two metrics, spearman correlation and mean squared error. In addition, the performance of the two types of models at different lead times are also evaluated. Results reveal that the forecasting skills of these models are comparable, both of which exhibit higher forecasting skills for the boreal fall-winter season and lower skills for the boreal spring season. Event-based analyses show that dynamical and statistical models under-forecast SST anomaly at the onset month for El Niño events, although the forecasting error diminishes with a reduced forecasting lead for most occasions. For La Niña events, SST anomaly forecasting errors could be positive or negative. It is also difficult for the models to accurately predict the quick shift from one ENSO phase to another. Factors that contribute to such challenges are discussed. The implication of ENSO forecasts for water resources management, mainly streamflow forecasts, for a regional water supply utility in the southeastern United States is also discussed. Results from this study provide insights into ENSO forecasting skills and their practical implications.

Contributions of Initial Conditions and Meteorological Forecast to Subseasonal-to-Seasonal Hydrological Forecast Skill in Western Tropical South America.

Hydrological predictions at subseasonal-to-seasonal (S2S) time scales can support improved decision-making in climate-dependent sectors like agriculture and hydropower. Here, we present an S2S hydrological forecasting system (S2S-HFS) for western tropical South America (WTSA). The system uses the global NASA Goddard Earth Observing System S2S meteorological forecast system (GEOS-S2S) in combination with the generalized analog regression downscaling algorithm and the NASA Land Information System (LIS). In this implementation study, we evaluate system performance for 3-month hydrological forecasts for the austral autumn season (March-May) using ensemble hindcasts for 2002-17. Results indicate that the S2S-HFS generally offers skill in predictions of monthly precipitation up to 1-month lead, evapotranspiration up to 2 months lead, and soil moisture content up to 3 months lead. Ecoregions with better hindcast performance are located either in the coastal lowlands or in the Amazon lowland forest. We perform dedicated analysis to understand how two important teleconnections affecting the region are represented in the S2S-HFS: El Niño-Southern Oscillation (ENSO) and the Antarctic Oscillation (AAO). We find that forecast skill for all variables at 1-month lead is enhanced during the positive phase of ENSO and the negative phase of AAO. Overall, this study indicates that there is meaningful skill in the S2S-HFS for many ecoregions in WTSA, particularly for long memory variables such as soil moisture. The skill of the precipitation forecast, however, decays rapidly after forecast initialization, a phenomenon that is consistent with S2S meteorological forecasts over much of the world.

Risk transference between climate variability and financial derivatives: Implications for global food security

We investigate the impacts of the El Niño-Southern Oscillation (ENSO) on global food security by using information embedded in financial derivatives. Using a state-of-the-art general circulation model (GCM) climate forecasting system that generates observations on the phase and magnitude of ENSO, we create novel indices that track its uncertainty throughout time. Together with the option implied volatilities of core food inputs (wheat, maize, rice, and soybean), we model the time-varying volatility spillover from ENSO to each commodity, capturing the direction and intensity of risk transference. Simulating Gaussian and non-Gaussian impulse responses that mimic an increase in climate variability show a persistent impact on the price uncertainty of the commodities lasting up to three months. We also show that shocks are contingent on the phase of ENSO, with warmer conditions in the Eastern Pacific (i.e., the El Niño phase) increasing risk transference for soybean and rice. In contrast, the La Niña phase has a material influence on the uncertainty of wheat and maize. In all, we reveal a volatility transmission channel of climate variability that affects financial markets, suggesting valuable inferences about the impact of climate shocks can be derived from the rich information embedded in commodity-linked derivatives.

Regional Responses of Vegetation Productivity to the Two Phases of ENSO

AbstractThe two phases of El‐Niño‐Southern Oscillation (ENSO) influence both regional and global terrestrial vegetation productivity on inter‐annual scales. However, the major drivers for the regional vegetation productivity and their controlling strengths during different phases of ENSO remain unclear. We herein disentangled the impacts of two phases of ENSO on regional carbon cycle using multiple data sets. We found that soil moisture predominantly accounts for ∼40% of the variability in regional vegetation productivity during ENSO events. Our results showed that the satellite‐derived vegetation productivity proxies, gross primary productivity from data‐driven models (FLUXCOM) and observation‐constrained ecosystem model (Carbon Cycle Data Assimilation System) generally agree in depicting the contribution of soil moisture and air temperature in modulating regional vegetation productivity. However, the ensemble of weakly constrained ecosystem models exhibits non‐negligible discrepancies in the roles of vapor pressure deficit and radiation over extra‐tropics. This study highlights the significance of water in regulating regional vegetation productivity during ENSO.