El Nino-Southern Oscillation Phases Research Articles

Impact of Sudden Stratospheric Warmings on the Stratosphere‐To‐Troposphere Transport of Ozone

AbstractSudden stratospheric warmings (SSWs) can significantly impact tropospheric weather systems. Previous studies suggest that SSWs may also influence stratosphere‐to‐troposphere transport (STT), but their spatial and temporal distribution and mechanisms are not fully understood. The complex relationships between SSWs and the El Niño‐Southern Oscillation (ENSO) have also made it difficult to isolate the effects of SSWs on STT. From an idealized ENSO simulation with the WACCM4 model using a stratospheric origin ozone tracer, we investigate the effect of SSWs on the STT of ozone under different ENSO phases. We find a significant increase in lower tropospheric ozone from the SSW onset up to 3 months later over the Arctic, North America, and Europe, regardless of the ENSO phase. This study highlights the significant influence of SSWs on STT on a subseasonal scale. Our results also emphasize the need to consider SSWs when addressing the ENSO impact on STT.

Methodology for forecasting precipitation related to El Niño when historical meteorological data are incomplete

The interactions between the ocean and the atmosphere produce various climatic phenomena, such as the El Niño-Southern Oscillation (ENSO), that influence hydrological systems. This study serves as a basis for water resource management and planning in reservoirs and hydroelectric generation, in both the short and long term. A methodology is proposed to study the potential influence of extreme phases of ENSO on the amount of monthly and seasonal precipitation in areas where the weather stations do not have a complete historical data record. A case study is presented of the seasonal precipitation forecast for the largest water reservoir in Mexico. Data from 18 weather stations in the hydrological sub-region Grijalva-La Concordia, in the state of Chiapas, were examined, the highest on the river course of the rainwater catchment dam of the Grijalva Hydroelectric Complex, the most important complex of its kind in Mexico. From the results it is expected that climate change will bring more frequent El Niño phases. The proposed methodology has been validated, making it an effective tool to evaluate the effects of ENSO on precipitation. The methodology is applicable to other regions, particularly in developing countries where historical information is often incomplete.

Real-time ENSO forecast skill evaluated over the last two decades, with focus on the onset of ENSO events

This paper provides an updated assessment of the “International Research Institute for Climate and Society’s (IRI) El Niño Southern Oscillation (ENSO) Predictions Plume”. We evaluate 253 real-time forecasts of the Niño 3.4 index issued from February 2002 to February 2023 and examine multimodal means of dynamical (DYN) and statistical (STAT) models separately. Forecast skill diminishes as lead time increases in both DYN and STAT forecasts, with peak accuracy occurring post-northern hemisphere spring predictability barrier and preceding seasons. The DYN forecasts outperform STAT forecasts with a pronounced advantage in forecasts initiated from late boreal winter through spring. The analysis uncovers an asymmetry in predicting the onset of cold and warm ENSO episodes, with warm episode onsets being better forecasted than cold onsets in both DYN and STAT models. The DYN forecasts are found to be valuable for predicting warm and cold ENSO episode onsets at least several months in advance, while STAT forecasts are less informative about ENSO phase transitions. The results indicate that predicting ENSO onset is challenging and that the ability to do so is both model- and event-dependent.

Predicting Australian energy demand variability using weather data and machine learning

Abstract Managing energy systems requires understanding the variability of energy demand, which on daily timescales is driven primarily by the weather. Historical records of demand typically cover 1–2 decades which may be too short to capture the range of possible demand, particularly for a climate with high interannual variability such as that of Australia. Predicting demand using long records of weather data opens the possibility of more robustly estimating true demand variability. We estimate daily energy demand between 2010 and 2019 for Australian states in the National Electricity Market using machine learning with reanalysis weather variables as predictors. We assess the performance of these models and examine their behaviour to identify which weather variables are most important for predicting demand. We then use the models to estimate demand for the period 1959–2022. We use this 64-year record to quantify how the probability of high demand days can change compared to individual 10-year periods and when conditioned by the phase of the El Ni n ~ o Southern Oscillation (ENSO). Energy demand can be accurately predicted with weather, with median errors of 2%–4% on years omitted from the training. We show that the probability of extreme demand over different 10-year periods can vary from half to twice as likely, depending on the decade. When further conditioned on ENSO phase, the probabilities can be up to 7 times higher than when using the 64-year period, implying a risk of overestimating weather-related energy demand if shorter records are used. We conclude that machine learning methods can accurately predict energy demand using only weather data, enabling us to estimate demand variability over longer time horizons than is possible with demand observations. These longer records are important when attempting to quantify tail risks of demand, and so can help to inform the design of energy systems.

Climatic oscillation based 3-dimensional drought risk assessment over India

Drought risk assessment is essential for the efficient design of water resource infrastructures and agricultural planning. Conventional drought studies define droughts either in a spatial or temporal framework without considering the simultaneous temporal and spatial progression of droughts. Spatiotemporal drought (SPD), a dynamic and complex phenomenon, has been relatively understudied due to the challenges in developing algorithms for their identification. In this study, we use a 3-dimensional drought identification framework to identify the SPD events across India. Further, as large-scale ocean-atmospheric phenomena namely El Niño Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO) play an important role in modulating the occurrence of droughts in India, we investigated the influence of these climatic oscillations on SPD characteristics across different climatic regions of India. Further, ENSO-based drought risk assessment using drought characteristics and the synergy between univariate and multivariate drought risks are also evaluated. Results show that 1) there are significant seasonal and spatial variations in occurrences and magnitude of SPD characteristics for the different climate phases. SPDs during the ENSO positive (El Niño) and the EQUINOO negative phases have higher severity, especially over the monsoon core region. In northwest India, the median drought severity during El Niño events is over three times higher than La Niña events. Across most of India, a major portion of SPD event onset occurs during the monsoon seasons, however in the north-western and south-eastern regions an equal portion of drought onset occurs during the pre-monsoon and post-monsoon season, respectively. 2) Further, the ENSO-based drought risk assessment reveals that severe and longer-duration SPD events are associated with El Niño phases, making ENSO a crucial factor in drought frequency analysis. 3) The synergy between the univariate and multivariate drought risks conditioned on ENSO reveal that overall drought risks are linked to the magnitude of individual drought characteristics which have a strong association with ENSO phases. Thus, design criteria for drought resilient infrastructure should be based on the dominant drought characteristics influenced by ENSO phases for effective drought risk assessment. Overall, the study underscores the significant impact of ENSO on SPDs in India and the importance of incorporating ENSO information in drought frequency analysis for reliable risk assessments.

Climate Extremes in the New Zealand Region: Mechanisms, Impacts and Attribution

ABSTRACTAs global surface temperatures have increased with human‐induced climate change, notable compound climate extremes in the New Zealand (NZ) region associated with atmospheric heatwaves (AHWs) and marine heatwaves (MHWs) have occurred in the past 6 years. Natural modes of variability that also played a key role regionally include the Interdecadal Pacific Oscillation (IPO), El Niño/Southern Oscillation (ENSO) and changes in the location and strength of the westerlies as seen in the Southern Annular Mode (SAM). Along with mean warming of 0.8°C since 1900, a negative phase of the IPO, La Niña phase of ENSO and a strongly positive SAM contributed to five compound warm extremes in the extended austral summer seasons (NDJFM) of 1934/35, 2017/18, 2018/19, 2021/22 and 2022/23. These are the most intense coupled ocean/atmosphere (MHWs/AHWs) heatwaves on record with average temperature anomalies over land and sea +0.8°C to 1.1°C above 1991–2020 averages. The number of days above 25°C and above the 90th percentile of maximum temperature has increased, while the number of nights below 0°C and below the 10th percentile has decreased. Coastal waters around NZ recently experienced their longest MHW in the satellite era (1982‐present) of 289 days through 2023. The estimated recurrence interval reduces from 1 in 300‐years for the AHW event during the 1930s climate to a 1 in 25‐year event for the most recent decade. Consequences include major loss of ice of almost one‐third volume from Southern Alps glaciers from 2017 to 2021 with rapid melt of seasonal snow in all four cases. Above‐average temperatures in the December/January grape flowering period resulted in advances in veraison (the onset of ripening); and higher‐than‐average grape yields in 2022 and 2023 vintages. Marine impacts include widespread sea‐sponge bleaching around northern and southern NZ.

Climate pattern effects on global economic conditions

This study investigates how El Niño–Southern Oscillation (ENSO) climate patterns affect global economic conditions. Prior research suggests that ENSO phases, particularly El Niño, influence economic outcomes, but with limited consensus on their broader macroeconomic impacts. Using a novel monthly dataset from 20 economies, covering 80% of global output from 1999 to 2022, we employ a global augmented vector autoregression with local projections (GAVARLP) model. The empirical findings suggest that El Niño boosts output with minimal inflationary effects, reducing global economic policy uncertainty, while La Niña raises food inflation, which can amplify aggregate inflation as a “second-round” effect, amplifying uncertainty. These findings shed light on the transmission channels of climate shocks and highlight the significant role of ENSO in shaping global economic conditions, emphasizing why climate shocks should be a concern for policy markers.

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 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 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 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 (R = −0.90) on an interannual scale. This study underscores the significance of assessing the impacts of ENSO on long-term Chl-a trend estimation.

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.

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.