scholarly journals Benchmarking prediction skill in binary El Niño forecasts

2021 ◽  
Author(s):  
Xinjia Hu ◽  
Jan Eichner ◽  
Eberhard Faust ◽  
Holger Kantz
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Prediction Method ◽  
Surrogate Data ◽  
Prediction Skill ◽  
Lead Times ◽  
Small Data ◽  
Predictive Skill ◽  
Forecast Validation

AbstractReliable El Niño Southern Oscillation (ENSO) prediction at seasonal-to-interannual lead times would be critical for different stakeholders to conduct suitable management. In recent years, new methods combining climate network analysis with El Niño prediction claim that they can predict El Niño up to 1 year in advance by overcoming the spring barrier problem (SPB). Usually this kind of method develops an index representing the relationship between different nodes in El Niño related basins, and the index crossing a certain threshold is taken as the warning of an El Niño event in the next few months. How well the prediction performs should be measured in order to estimate any improvements. However, the amount of El Niño recordings in the available data is limited, therefore it is difficult to validate whether these methods are truly predictive or their success is merely a result of chance. We propose a benchmarking method by surrogate data for a quantitative forecast validation for small data sets. We apply this method to a naïve prediction of El Niño events based on the Oscillation Niño Index (ONI) time series, where we build a data-based prediction scheme using the index series itself as input. In order to assess the network-based El Niño prediction method, we reproduce two different climate network-based forecasts and apply our method to compare the prediction skill of all these. Our benchmark shows that using the ONI itself as input to the forecast does not work for moderate lead times, while at least one of the two climate network-based methods has predictive skill well above chance at lead times of about one year.

scholarly journals Benchmarking Prediction Skill in Binary El Niño Forecasts

2021 ◽  
Author(s):  
Xinjia Hu ◽  
Jan Eichner ◽  
Eberhard Faust ◽  
Holger Kantz
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Prediction Method ◽  
Surrogate Data ◽  
Prediction Skill ◽  
Lead Times ◽  
Small Data ◽  
Predictive Skill ◽  
Forecast Validation

Abstract Reliable El Niño Southern Oscillation (ENSO) prediction at seasonal-to-interannual lead times would be critical for different stakeholders to conduct suitable management. In recent years, new methods combining climate network analysis with El Niño prediction claim that they can predict El Niño up to 1 year in advance by overcoming the spring barrier problem (SPB). Usually this kind of method develops an index representing the relationship between different nodes in El Niño related basins, and the index crossing a certain threshold is taken as the warning of an El Niño event in the next few months. How well the prediction performs should be measured in order to estimate any improvements. However, the amount of El Niño recordings in the available data is limited, therefore it is difficult to validate whether these methods are truly predictive or their success is merely a result of chance. We propose a benchmarking method by new surrogate data for a quantitative forecast validation for small data sets. We apply this method to a naïve prediction of El Niño events based on the Oscillation Niño Index (ONI) time series, where we build a data-based prediction scheme using the index series itself as input. In order to assess the network-based El Niño prediction method, we reproduce two different climate network-based forecasts and apply our method to compare the prediction skill of all these. Our benchmark shows that using the ONI itself as input to the forecast does not work for moderate lead times, while at least one of the two climate network-based methods has predictive skill well above 30 chance at lead times of about one year.

scholarly journals Seasonality in Prediction Skill and Predictable Pattern of Tropical Indian Ocean SST

2015 ◽  
Vol 28 (20) ◽  
pp. 7962-7984 ◽  
Author(s):  
Jieshun Zhu ◽  
Bohua Huang ◽  
Arun Kumar ◽  
James L. Kinter III
Keyword(s):  
Indian Ocean ◽  
El Niño ◽  
El Nino ◽  
Tropical Indian Ocean ◽  
Prediction Skill ◽  
Lead Times ◽  
Predictable Pattern ◽  
Basin Mode ◽  
The Difference ◽  
Atmospheric Variations

Abstract Seasonality of sea surface temperature (SST) predictions in the tropical Indian Ocean (TIO) was investigated using hindcasts (1982–2009) made with the NCEP Climate Forecast System version 2 (CFSv2). CFSv2 produced useful predictions of the TIO SST with lead times up to several months. A substantial component of this skill was attributable to signals other than the Indian Ocean dipole (IOD). The prediction skill of the IOD index, defined as the difference between the SST anomaly (SSTA) averaged over 10°S–0°, 90°–110°E and 10°S–10°N, 50°–70°E, had strong seasonality, with high scores in the boreal autumn. In spite of skill in predicting its two poles with longer leads, CFSv2 did not have skill significantly better than persistence in predicting IOD. This was partly because the seasonal nature of IOD intrinsically limits its predictability. The seasonality of the predictable patterns of the TIO SST was further explored by applying the maximum signal-to-noise (MSN) empirical orthogonal function (EOF) method to the predicted SSTA in March and October, respectively. The most predictable pattern in spring was the TIO basin warming, which is closely associated with El Niño. The basin mode, including its associated atmospheric anomalies, can be predicted at least 9 months ahead, even though some biases were evident. On the other hand, the most predictable pattern in fall was characterized by the IOD mode. This mode and its associated atmospheric variations can be skillfully predicted only 1–2 seasons ahead. Statistically, the predictable IOD mode coexists with El Niño; however, the 1994 event in a non-ENSO year (at least not a canonical ENSO year) can also be predicted at least 3 months ahead by CFSv2.

scholarly journals Representation of Boreal Winter MJO and Its Teleconnection in a Dynamical Ensemble Seasonal Prediction System

2018 ◽  
Vol 31 (21) ◽  
pp. 8803-8818 ◽  
Author(s):  
Hyerim Kim ◽  
Myong-In Lee ◽  
Daehyun Kim ◽  
Hyun-Suk Kang ◽  
Yu-Kyung Hyun
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Seasonal Prediction ◽  
La Niña ◽  
Prediction Skill ◽  
Boreal Winter ◽  
Teleconnection Patterns ◽  
La Nina ◽  
Forecast Lead Time

This study examines the representation of the Madden–Julian oscillation (MJO) and its teleconnection in boreal winter in the Global Seasonal Forecast System, version 5 (GloSea5), using 20 years (1991–2010) of hindcast data. The sensitivity of the performance to the polarity of El Niño–Southern Oscillation (ENSO) is also investigated. The real-time multivariate MJO index of Wheeler and Hendon is used to assess MJO prediction skill while intraseasonal 200-hPa streamfunction anomalies are used to evaluate the MJO teleconnection. GloSea5 exhibits significant MJO prediction skill up to 25 days of forecast lead time. MJO prediction skill in GloSea5 also depends on initial MJO phases, with relatively enhanced (degraded) performance when the initial MJO phase is 2 or 3 (8 or 1) during the first 2 weeks of the hindcast period. GloSea5 depicts the observed MJO teleconnection patterns in the extratropics realistically up to 2 weeks albeit weaker than the observed. The ENSO-associated basic-state changes in the tropics and in the midlatitudes are reasonably represented in GloSea5. MJO prediction skill during the first 2 weeks of the hindcast is slightly higher in neutral and La Niña years than in El Niño years, especially in the upper-level zonal wind anomalies. Presumably because of the better representation of MJO-related tropical heating anomalies, the Northern Hemispheric MJO teleconnection patterns in neutral and La Niña years are considerably better than those in El Niño years.

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 Dynamical, Statistical–Dynamical, and Multimodel Ensemble Forecasts of Australian Spring Season Rainfall

2011 ◽  
Vol 139 (3) ◽  
pp. 958-975 ◽  
Author(s):  
Eun-Pa Lim ◽  
Harry H. Hendon ◽  
David L. T. Anderson ◽  
Andrew Charles ◽  
Oscar Alves
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Prediction Method ◽  
Rainfall Anomaly ◽  
Ensemble Prediction ◽  
Spring Season ◽  
Multimodel Ensemble ◽  
Austral Spring ◽  
Probabilistic Forecasts

Abstract The prediction skill of the Australian Bureau of Meteorology dynamical seasonal forecast model Predictive Ocean Atmosphere Model for Australia (POAMA) is assessed for probabilistic forecasts of spring season rainfall in Australia and the feasibility of increasing forecast skill through statistical postprocessing is examined. Two statistical postprocessing techniques are explored: calibrating POAMA prediction of rainfall anomaly against observations and using dynamically predicted mean sea level pressure to infer regional rainfall anomaly over Australia (referred to as “bridging”). A “homogeneous” multimodel ensemble prediction method (HMME) is also introduced that consists of the combination of POAMA’s direct prediction of rainfall anomaly together with the two statistically postprocessed predictions. Using hindcasts for the period 1981–2006, the direct forecasts from POAMA exhibit skill relative to a climatological forecast over broad areas of eastern and southern Australia, where El Niño and the Indian Ocean dipole (whose behavior POAMA can skillfully predict at short lead times) are known to exert a strong influence in austral spring. The calibrated and bridged forecasts, while potentially offering improvement over the direct forecasts because of POAMA’s ability to predict the main drivers of springtime rainfall (e.g., El Niño and the Southern Oscillation), show only limited areas of improvement, mainly because strict cross-validation limits the ability to capitalize on relatively modest predictive signals with short record lengths. However, when POAMA and the two statistical–dynamical rainfall forecasts are combined in the HMME, higher deterministic and probabilistic skill is achieved over any of the single models, which suggests the HMME is another useful method to calibrate dynamical model forecasts.

scholarly journals The Modulating Influence of Indian Ocean Sea Surface Temperatures on Australian Region Seasonal Tropical Cyclone Counts

2017 ◽  
Vol 30 (13) ◽  
pp. 4843-4856 ◽  
Author(s):  
H. A. Ramsay ◽  
M. B. Richman ◽  
L. M. Leslie
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Relative Vorticity ◽  
Sea Surface ◽  
Late Winter ◽  
Australian Region ◽  
Predictive Skill

The Australian region seasonal tropical cyclone count (TCC) maintained a robust statistical relationship with El Niño–Southern Oscillation (ENSO), with skillful forecasts of above (below) average TCC during La Niña (El Niño) years from 1969 until about 1998, weakening thereafter. The current study identifies an additional climate driver that mitigates the loss of predictive skill for Australian TCC after about 1998. It is found that the seasonal Australian TCC is strongly modulated by a southwest-to-northeast-oriented dipole in Indian Ocean sea surface temperature anomalies (SSTAs), referred to here as the transverse Indian Ocean dipole (TIOD). The TIOD emerges as the leading mode of detrended Indian Ocean SSTAs in the Southern Hemisphere during late winter and spring. Active (inactive) TC seasons are linked to positive (negative) TIOD phases, most notably during August–October immediately preceding the TC season, when SSTAs northwest of Australia, in the northeast pole of the TIOD, are positive (negative). To provide a physical interpretation of the TIOD–TCC relationship, 850-hPa zonal winds, 850-hPa relative vorticity, and 600-hPa relative humidity are composited for positive and negative TIOD phases, providing results consistent with observed TCC modulation. Correlations between ENSO and TCC weaken from 1998 onward, becoming statistically insignificant, whereas the TIOD–TCC correlation remains statistically significant until 2003. Overall, TIOD outperforms Niño-4 SSTA as a TCC predictor (46% skill increase since about 1998), when used individually or with Niño-4. The combination of TIOD and Niño 4 provide a skill increase (up to 33%) over climatology, demonstrating reliably accurate seasonal predictions of Australian region TCC.

Sources of Subseasonal Skill and Predictability in Wintertime California Precipitation Forecasts

2021 ◽  
Author(s):  
Michelle L. L'Heureux ◽  
Michael K. Tippett ◽  
Emily J. Becker
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Longwave Radiation ◽  
Lead Times ◽  
Strongly Correlated ◽  
Zonal Winds ◽  
Wide Range ◽  
Upper Level ◽  
Tropical Sea

AbstractThe relation between the El Niño-Southern Oscillation (ENSO) and California precipitation has been studied extensively and plays a prominent role in seasonal forecasting. However, a wide range of precipitation outcomes on seasonal timescales are possible, even during extreme ENSO states. Here, we investigate prediction skill and its origins on subseasonal timescales. Model predictions of California precipitation are examined using Subseasonal Experiment (SubX) reforecasts for the period 1999–2016, focusing on those from the Flow-Following Icosahedral Model (FIM). Two potential sources of subseasonal predictability are examined: the tropical Pacific Ocean and upper-level zonal winds near California. In both observations and forecasts, the Niño-3.4 index exhibits a weak and insignificant relationship with daily to monthly averages of California precipitation. Likewise, model tropical sea surface temperature and outgoing longwave radiation show only minimal relations with California precipitation forecasts, providing no evidence that flavors of El Niño or tropical modes substantially contribute to the success or failure of subseasonal forecasts. On the other hand, an index for upper-level zonal winds is strongly correlated with precipitation in observations and forecasts, across averaging windows and lead times. The wind index is related to ENSO, but the correlation between the wind index and precipitation remains even after accounting for ENSO phase. Intriguingly, the Niño 3.4 index and California precipitation show a slight but robust negative statistical relation after accounting for the wind index.

El Niño dynamics and long lead climate forecasts

2016 ◽  
Author(s):  
Joan Ballester ◽  
S. Bordoni ◽  
D. Petrova ◽  
X. Rodó
Keyword(s):  
El Niño ◽  
Global Economy ◽  
El Nino ◽  
Southern Oscillation ◽  
Oscillatory Behavior ◽  
Lead Times ◽  
Weather Extremes ◽  
Potential Predictability ◽  
Economic Implications ◽  
Climate Forecasting

El Niño-Southern Oscillation (ENSO) is a climatic phenomenon in the tropical Pacific arising from interactions between the ocean and the atmosphere on timescales ranging from months to years. ENSO generates the most prominent climate alterations known worldwide, even very far from where it forms. It affects weather extremes, landslides, wildfires or entire ecosystems, and it has major impacts on human health, agriculture and the global economy. Reliable forecasts of ENSO with long lead times would represent a major achievement in the climate sciences, and would have huge positive societal and economic implications. Here we provide a review of our current understanding of ENSO as a major source of climate predictability worldwide, emphasizing four main aspects: 1) differences between weather and climate forecasting, and existing limitations in both types of prediction; 2) main mechanisms and interactions between the atmosphere and the ocean explaining the dynamics behind ENSO; 3) different theories that have been formulated regarding the oscillatory behavior and the memory sources of the phenomenon; and 4) the upper limit in its potential predictability and current research endeavors aimed at increasing the lead time of climate predictions.

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