scholarly journals Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier

2019 ◽  
Vol 117 (1) ◽  
pp. 177-183 ◽  
Author(s):  
Jun Meng ◽  
Jingfang Fan ◽  
Josef Ludescher ◽  
Ankit Agarwal ◽  
Xiaosong Chen ◽  
...  
Keyword(s):  
El Niño ◽  
Prediction Models ◽  
El Nino ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Statistical Prediction ◽  
Analysis Tool ◽  
Strong Positive Correlation ◽  
Near Surface ◽  
Spring Predictability Barrier

The El Niño Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. Early and reliable ENSO forecasting remains a crucial goal, due to its serious implications for economy, society, and ecosystem. Despite the development of various dynamical and statistical prediction models in the recent decades, the “spring predictability barrier” remains a great challenge for long-lead-time (over 6 mo) forecasting. To overcome this barrier, here we develop an analysis tool, System Sample Entropy (SysSampEn), to measure the complexity (disorder) of the system composed of temperature anomaly time series in the Niño 3.4 region. When applying this tool to several near-surface air temperature and sea surface temperature datasets, we find that in all datasets a strong positive correlation exists between the magnitude of El Niño and the previous calendar year’s SysSampEn (complexity). We show that this correlation allows us to forecast the magnitude of an El Niño with a prediction horizon of 1 y and high accuracy (i.e., root-mean-square error = 0.23° C for the average of the individual datasets forecasts). For the 2018 El Niño event, our method forecasted a weak El Niño with a magnitude of 1.11±0.23° C. Our framework presented here not only facilitates long-term forecasting of the El Niño magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems.

Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier

2020 ◽  
Author(s):  
Jun Meng ◽  
Jingfang Fan ◽  
Josef Ludescher ◽  
Ankit Agarwala ◽  
Xiaosong Chen ◽  
...  
Keyword(s):  
Prediction Models ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Statistical Prediction ◽  
Analysis Tool ◽  
Strong Positive Correlation ◽  
Near Surface ◽  
Spring Predictability Barrier ◽  
Prediction Horizon ◽  
The Individual

<p>The El Niño Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. An early and reliable ENSO forecasting remains a crucial goal, due to its serious implications for economy, society, and ecosystem. Despite the development of various dynamical and statistical prediction models in the recent decades, the “spring predictability barrier” (SPB) remains a great challenge for long (over 6-month) lead-time forecasting. To overcome this barrier, here we develop an analysis tool, the System Sample Entropy (SysSampEn), to measure the complexity (disorder) of the system composed of temperature anomaly time series in the Niño 3.4 region. When applying this tool to several near surface air temperature and sea surface temperature datasets, we find that in all datasets a strong positive correlation exists between the magnitude of El Niño and the previous calendar year’s SysSampEn (complexity). We show that this correlation allows to forecast the magnitude of an El Niño with a prediction horizon of 1 year and high accuracy (i.e., Root Mean Square Error = 0.23°C for the average of the individual datasets forecasts). For the 2018 El Niño event, our method forecasts a weak El Niño with a magnitude of 1.11±0.23°C.  Our framework presented here not only facilitates a long–term forecasting of the El Niño magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems.</p>

scholarly journals Complexity-based approach for El Nino magnitude forecasting before the spring predictability barrier

2021 ◽  
Author(s):  
Jun Meng
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Southern Oscillation ◽  
Statistical Prediction ◽  
Analysis Tool ◽  
Strong Positive Correlation ◽  
Mean Square ◽  
Near Surface ◽  
Spring Predictability Barrier

<p>The El Niño Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. Early and reliable ENSO forecasting remains a crucial goal, due to its serious implications for economy, society, and ecosystem. Despite the development of various dynamical and statistical prediction models in the recent decades, the “spring predictability barrier” remains a great challenge for long-lead-time (over 6 mo) forecasting. To overcome this barrier, here we develop an analysis tool, System Sample Entropy(SysSampEn), to measure the complexity (disorder) of the system composed of temperature anomaly time series in the Niño 3.4 region. When applying this tool to several near-surface air temperature and sea surface temperature datasets, we find that in all datasets a strong positive correlation exists between the magnitude of El Niño and the previous calendar year’s SysSampEn(complexity). We show that this correlation allows us to forecast the magnitude of an El Niño with a prediction horizon of 1 y and high accuracy (i.e., root-mean-square error=0.25<sup>◦</sup>C for the average of the individual datasets forecasts). For the recent two 2018 and 2019 El Niño events, our method forecasted weak El Niños with magnitudes of 1.11±0.23<sup>◦</sup>C and 0.69±0.25<sup>◦</sup>C, both within one root-mean-square error comparing to the observed magnitudes, i.e. 0.9<sup>◦</sup>C and 0.6<sup>◦</sup>C. Our framework presented here not only facilitates long-term forecasting of the El Niño magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems.</p>

scholarly journals El Niño–Global Atmospheric Oscillation as the Main Mode of Interannual Climate Variability

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1443
Author(s):  
Ilya V. Serykh ◽  
Dmitry M. Sonechkin
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
The Arctic ◽  
Northern Eurasia ◽  
Main Mode ◽  
Trade Winds ◽  
Near Surface ◽  
Atmospheric Oscillation

The interannual variability of the global mean monthly anomalies of near-surface air temperature, sea-level pressure, wind speed near the surface, amount of precipitation and total cloudiness was investigated. The amplitudes of the anomalies of these hydrometeorological characteristics between opposite phases of the Global Atmospheric Oscillation (GAO) were calculated. The regional element of the GAO in the tropics of the Indian and Pacific Oceans is the Southern Oscillation. The results show that the oscillations of these characteristics are associated with the GAO not only in the tropical belt of the Earth but also in the middle and high latitudes, especially in the Arctic and northern Eurasia. The physical mechanism by which the transition of the GAO from the negative to the positive phase influences the weakening of the Pacific trade winds, and, as a consequence, the onset of El Niño is described.

scholarly journals Indian Ocean Dipole and El Niño/Southern Oscillation impacts on regional chlorophyll anomalies in the Indian Ocean

2013 ◽  
Vol 10 (10) ◽  
pp. 6677-6698 ◽  
Author(s):  
J. C. Currie ◽  
M. Lengaigne ◽  
J. Vialard ◽  
D. M. Kaplan ◽  
O. Aumont ◽  
...  
Keyword(s):  
Indian Ocean ◽  
El Niño ◽  
Indian Ocean Dipole ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Near Surface ◽  
Climate Modes ◽  
The Indian Ocean

Abstract. The Indian Ocean Dipole (IOD) and the El Niño/Southern Oscillation (ENSO) are independent climate modes, which frequently co-occur, driving significant interannual changes within the Indian Ocean. We use a four-decade hindcast from a coupled biophysical ocean general circulation model, to disentangle patterns of chlorophyll anomalies driven by these two climate modes. Comparisons with remotely sensed records show that the simulation competently reproduces the chlorophyll seasonal cycle, as well as open-ocean anomalies during the 1997/1998 ENSO and IOD event. Results suggest that anomalous surface and euphotic-layer chlorophyll blooms in the eastern equatorial Indian Ocean in fall, and southern Bay of Bengal in winter, are primarily related to IOD forcing. A negative influence of IOD on chlorophyll concentrations is shown in a region around the southern tip of India in fall. IOD also depresses depth-integrated chlorophyll in the 5–10° S thermocline ridge region, yet the signal is negligible in surface chlorophyll. The only investigated region where ENSO has a greater influence on chlorophyll than does IOD, is in the Somalia upwelling region, where it causes a decrease in fall and winter chlorophyll by reducing local upwelling winds. Yet unlike most other regions examined, the combined explanatory power of IOD and ENSO in predicting depth-integrated chlorophyll anomalies is relatively low in this region, suggestive that other drivers are important there. We show that the chlorophyll impact of climate indices is frequently asymmetric, with a general tendency for larger positive than negative chlorophyll anomalies. Our results suggest that ENSO and IOD cause significant and predictable regional re-organisation of chlorophyll via their influence on near-surface oceanography. Resolving the details of these effects should improve our understanding, and eventually gain predictability, of interannual changes in Indian Ocean productivity, fisheries, ecosystems and carbon budgets.

scholarly journals Network analysis reveals strongly localized impacts of El Niño

2017 ◽  
Vol 114 (29) ◽  
pp. 7543-7548 ◽  
Author(s):  
Jingfang Fan ◽  
Jun Meng ◽  
Yosef Ashkenazy ◽  
Shlomo Havlin ◽  
Hans Joachim Schellnhuber
Keyword(s):  
El Niño ◽  
El Nino ◽  
Large Fraction ◽  
Surface Air Temperature ◽  
Climatic Conditions ◽  
La Niña ◽  
Near Surface ◽  
La Nina ◽  
Agriculture Industry ◽  
Global Impacts

Climatic conditions influence the culture and economy of societies and the performance of economies. Specifically, El Niño as an extreme climate event is known to have notable effects on health, agriculture, industry, and conflict. Here, we construct directed and weighted climate networks based on near-surface air temperature to investigate the global impacts of El Niño and La Niña. We find that regions that are characterized by higher positive/negative network “in”-weighted links are exhibiting stronger correlations with the El Niño basin and are warmer/cooler during El Niño/La Niña periods. In contrast to non-El Niño periods, these stronger in-weighted activities are found to be concentrated in very localized areas, whereas a large fraction of the globe is not influenced by the events. The regions of localized activity vary from one El Niño (La Niña) event to another; still, some El Niño (La Niña) events are more similar to each other. We quantify this similarity using network community structure. The results and methodology reported here may be used to improve the understanding and prediction of El Niño/La Niña events and also may be applied in the investigation of other climate variables.

scholarly journals Tropical Temperature and Precipitation Responses to Large Volcanic Eruptions: Observations and AMIP5 Simulations

2016 ◽  
Vol 29 (4) ◽  
pp. 1325-1338 ◽  
Author(s):  
A. Meyer ◽  
D. Folini ◽  
U. Lohmann ◽  
T. Peter
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Volcanic Eruptions ◽  
Phase Correlation ◽  
Temperature And Precipitation ◽  
Precipitation Decrease ◽  
El Chichón ◽  
El Chichon

Abstract Tropical land mean surface air temperature and precipitation responses to the eruptions of El Chichón in 1982 and Pinatubo in 1991, as simulated by the atmosphere-only GCMs (AMIP) in phase 5 of the Coupled Model Intercomparison Project (CMIP5), are examined and compared to three observational datasets. The El Niño–Southern Oscillation (ENSO) signal was statistically separated from the volcanic signal in all time series. Focusing on the ENSO signal, it was found that the 17 investigated AMIP models successfully simulate the observed 4-month delay in the temperature responses to the ENSO phase but simulate somewhat too-fast precipitation responses during the El Niño onset stage. The observed correlation between temperature and ENSO phase (correlation coefficient of 0.75) is generally captured well by the models (simulated correlation of 0.71 and ensemble means of 0.61–0.83). For precipitation, mean correlations with the ENSO phase are −0.59 for observations and −0.53 for the models, with individual ensemble members having correlations as low as −0.26. Observed, ENSO-removed tropical land temperature and precipitation decrease by about 0.35 K and 0.25 mm day−1 after the Pinatubo eruption, while no significant decrease in either variable was observed after El Chichón. The AMIP models generally capture this behavior despite a tendency to overestimate the precipitation response to El Chichón. Scatter is substantial, both across models and across ensemble members of individual models. Natural variability thus may still play a prominent role despite the strong volcanic forcing.

Subseasonal Atmospheric Variability and El Niño Waveguide Warming: Observed Effects of the Madden–Julian Oscillation and Westerly Wind Events*

2014 ◽  
Vol 27 (10) ◽  
pp. 3619-3642 ◽  
Author(s):  
Andrew M. Chiodi ◽  
D. E. Harrison ◽  
Gabriel A. Vecchi
Keyword(s):  
El Niño ◽  
General Circulation ◽  
El Nino ◽  
Southern Oscillation ◽  
Equatorial Pacific ◽  
Madden Julian Oscillation ◽  
Westerly Wind ◽  
Near Surface ◽  
Wind Events ◽  
Westerly Wind Events

Abstract Westerly wind events (WWEs) have previously been shown to initiate equatorial Pacific waveguide warming. The relationship between WWEs and Madden–Julian oscillation (MJO) activity, as well as the role of MJO events in initiating waveguide warming, is reconsidered here over the 1986–2010 period. WWEs are identified in observations of near-surface zonal winds using an objective scheme. MJO events are defined using a widely used index, and 64 are identified that occur when the El Niño–Southern Oscillation (ENSO) is in its neutral state. Of these MJO events, 43 have one or more embedded WWEs and 21 do not. The evolution of sea surface temperature anomaly over the equatorial Pacific waveguide following the westerly surface wind phase of the MJO over the western equatorial Pacific is examined. Waveguide warming is found for the MJO with WWE events in similar magnitudes as following the WWEs not embedded in an MJO. There is very little statistically significant waveguide warming following MJO events that do not contain an embedded WWE. The observed SST anomaly changes are well reproduced in an ocean general circulation model forced with the respective composite wind stress anomalies. Further, it is found that the occurrence of an MJO event does not significantly affect the likelihood that a WWE will occur. These results extend and confirm the earlier results of Vecchi with a near doubling of the period of study. It is suggested that understanding the sources and predictability of tropical Pacific westerly wind events remains essential to improving predictions of the onset of El Niño events.

Quantifying the Non-Gaussianity of Wintertime Daily Maximum and Minimum Temperatures in the Southeast

2013 ◽  
Vol 26 (3) ◽  
pp. 838-850 ◽  
Author(s):  
Lydia Stefanova ◽  
Philip Sura ◽  
Melissa Griffin
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
La Niña ◽  
Daily Maximum ◽  
Seasonal Temperature ◽  
Southeast United States ◽  
La Nina ◽  
Maximum Temperatures

Abstract In this paper the statistics of daily maximum and minimum surface air temperature at weather stations in the southeast United States are examined as a function of the El Niño–Southern Oscillation (ENSO) and Arctic Oscillation (AO) phase. A limited number of studies address how the ENSO and/or AO affect U.S. daily—as opposed to monthly or seasonal—temperature averages. The details of the effect of the ENSO or AO on the higher-order statistics for wintertime daily minimum and maximum temperatures have not been clearly documented. Quality-controlled daily observations collected from 1960 to 2009 from 272 National Weather Service Cooperative Observing Network stations throughout Florida, Georgia, Alabama, and South and North Carolina are used to calculate the first four statistical moments of minimum and maximum daily temperature distributions. It is found that, over the U.S. Southeast, winter minimum temperatures have higher variability than maximum temperatures and La Niña winters have greater variability of both minimum and maximum temperatures. With the exception of the Florida peninsula, minimum temperatures are positively skewed, while maximum temperatures are negatively skewed. Stations in peninsular Florida exhibit negative skewness for both maximum and minimum temperatures. During the relatively warmer winters associated with either a La Niña or AO+, negative skewnesses are exacerbated and positive skewnesses are reduced. To a lesser extent, the converse is true of the El Niño and AO−. The ENSO and AO are also shown to have a statistically significant effect on the change in kurtosis of daily maximum and minimum temperatures throughout the domain.

scholarly journals Evaluation of ENSO Prediction Skill Changes since 2000 Based on Multimodel Hindcasts

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 365
Author(s):  
Shouwen Zhang ◽  
Hui Wang ◽  
Hua Jiang ◽  
Wentao Ma
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Global Climate ◽  
Water Volume ◽  
Lead Times ◽  
The North ◽  
Spring Predictability Barrier ◽  
Sea Surface Temperature Anomalies ◽  
Warm Water Volume

In this study, forecast skill over four different periods of global climate change (1982–1999, 1984–1996, 2000–2018, and 2000–2014) is examined using the hindcasts of five models in the North American Multimodel Ensemble. The deterministic evaluation shows that the forecasting skills of the Niño3.4 and Niño3 indexes are much lower during 2000–2018 than during 1982–1999, indicating that the previously reported decline in forecasting skill continues through 2018. The decreases in skill are most significant for the target months from May to August, especially for medium to long lead times, showing that the forecasts suffer more from the effect of the spring predictability barrier (SPB) post-2000. Relationships between the extratropical Pacific signal and the El Niño-Southern Oscillation (ENSO) weakened after 2000, contributing to a reduction in inherent predictability and skills of ENSO, which may be connected with the forecasting skills decline for medium to long lead times. It is a great challenge to predict ENSO using the memory of the local ocean itself because of the weakening intensity of the warm water volume (WWV) and its relationship with ENSO. These changes lead to a significant decrease in the autocorrelation coefficient of the persistence forecast for short to medium lead months. Moreover, for both the Niño3.4 and Niño3 indexes, after 2000, the models tend to further underestimate the sea surface temperature anomalies (SSTAs) in the El Niño developing year but overestimate them in the decaying year. For the probabilistic forecast, the skills post-2000 are also generally lower than pre-2000 in the tropical Pacific, and in particular, they decayed east of 120° W after 2000. Thus, the advantages of different methods, such as dynamic modeling, statistical methods, and machine learning methods, should be integrated to obtain the best applicability to ENSO forecasts and to deal with the current low forecasting skill phenomenon.

scholarly journals Multiyear Statistical Prediction of ENSO Enhanced by the Tropical Pacific Observing System

2020 ◽  
Vol 33 (1) ◽  
pp. 163-174 ◽  
Author(s):  
Desislava Petrova ◽  
Joan Ballester ◽  
Siem Jan Koopman ◽  
Xavier Rodó
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Computational Cost ◽  
Tropical Pacific ◽  
Statistical Prediction ◽  
Tropical Ocean ◽  
Statistical Dynamic ◽  
Dynamic Components ◽  
The Tropical Pacific

AbstractThe theoretical predictability limit of El Niño–Southern Oscillation has been shown to be on the order of years, but long-lead predictions of El Niño (EN) and La Niña (LN) are still lacking. State-of-the-art forecasting schemes traditionally do not predict beyond the spring barrier. Recent efforts have been dedicated to the improvement of dynamical models, while statistical schemes still need to take full advantage of the availability of ocean subsurface variables, provided regularly for the last few decades as a result of the Tropical Ocean–Global Atmosphere Program (TOGA). Here we use a number of predictor variables, including temperature at different depths and regions of the equatorial ocean, in a flexible statistical dynamic components model to make skillful long-lead retrospective predictions (hindcasts) of the Niño-3.4 index in the period 1970–2016. The model hindcasts the major EN episodes up to 2.5 years in advance, including the recent extreme 2015/16 EN. The analysis demonstrates that events are predicted more accurately after the completion of the observational array in the tropical Pacific in 1994, as a result of the improved data quality and coverage achieved by TOGA. Therefore, there is potential to issue long-lead predictions of this climatic phenomenon at a low computational cost.

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