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.

Spatial and temporal variation in precipitation at the start of the rainy season in tropical Australia

2010 ◽  
Vol 32 (2) ◽  
pp. 215 ◽  
Author(s):  
S. T. Garnett ◽  
G. Williamson
Keyword(s):  
Seed Germination ◽  
El Niño ◽  
Rainy Season ◽  
El Nino ◽  
Southern Oscillation ◽  
Temporal Trends ◽  
Spatial And Temporal Variation ◽  
Economic Implications ◽  
North West ◽  
The North

The patterns of rainfall early in the rainy season vary substantially across northern Australia, even in sites with the same annual average. This has biophysical and economic implications in terms of land and infrastructure management, resource availability and capacity, and access. Daily patterns in long-term rainfall records in Australia north of 23°S subject to regular monsoonal rainfall were compared with threshold levels for dryland and wetland seed germination, initiation of the growing season, patterns of gaps between early storms and the heaviness of the first falls, correlations between thresholds, spatial variation in correlation with the Southern Oscillation Index (SOI) and temporal trends in mean threshold dates. The earliest rains sufficient to cause seed germination or generate fresh fodder occur in the north-west of the Northern Territory with the average date being later to the south, east and west. Initial falls of the rainy season are heaviest, however, on Cape York Peninsula so that the time between first falls and saturation is shortest in the east. The probability of extended gaps between rainfall events increased from north to south. When the SOI is taken into account, no change in timing could be detected at the few sites with records of sufficient duration. However, because of changes in SOI frequency, rains are tending to start earlier in the drier parts of the north and north-west and later in the east. This may be because anthropogenic climate change is resulting in fewer classical El Niño Southern Oscillation events and more frequent El Niño Modoki climate anomalies.

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 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 Typhoon storm surge in the southeast Chinese mainland modulated by ENSO

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xingru Feng ◽  
Mingjie Li ◽  
Yuanlong Li ◽  
Fujiang Yu ◽  
Dezhou Yang ◽  
...  
Keyword(s):  
Storm Surge ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Tide Gauge ◽  
Economic Loss ◽  
Chinese Mainland ◽  
Northwest Pacific ◽  
Potential Predictability ◽  
Marine Disasters

AbstractIn the past decade (2010–2019), the annual maximum typhoon storm surge (AMTSS) accounted for 46.6% of the total direct economic loss caused by marine disasters in Chinese mainland, but its prediction in advance is challenging. By analyzing records of 23 tide-gauge stations, we found that the AMTSSs in Shanghai, Zhejiang and Fujian show significant positive correlations with the El Niño-Southern Oscillation (ENSO). For the 1987–2016 period, the maximum correlation is achieved at Pingtan station, where correlation coefficient between the AMTSS and Niño-3.4 is 0.55. The AMTSS occurring in El Niño years are stronger than those in non-El Niño years by 9–35 cm in these areas. Further analysis suggests that a developing El Niño can greatly modulate the behaviors of Northwest Pacific typhoons. Strong typhoons tend to make landfall in southeast China with stronger intensities and northward shifted landfall positions. This study indicates that the modulation effect by ENSO may provide potential predictability for the AMTSS, which is useful for the early alert and reduction of storm surge damages.

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.

Large-scale environments of successive atmospheric river events leading to compound precipitation extremes in California

2021 ◽  
pp. 1-66
Keyword(s):  
El Niño ◽  
Large Scale ◽  
El Nino ◽  
Southern Oscillation ◽  
Strong Relationship ◽  
Potential Predictability ◽  
Atmospheric River ◽  
The Pacific ◽  
Average Precipitation ◽  
Compound Events

Abstract Successive atmospheric river (AR) events—known as AR families—can result in prolonged and elevated hydrological impacts relative to single ARs due to the lack of recovery time between periods of precipitation. Despite the outsized societal impacts that often stem from AR families, the large-scale environments and mechanisms associated with these compound events remain poorly understood. In this work, a new reanalysis-based 39-year catalog of 248 AR family events affecting California between 1981 and 2019 is introduced. Nearly all (94%) of the inter-annual variability in AR frequency is driven by AR family versus single events. Using K-means clustering on the 500-hPa geopotential height field, six distinct clusters of large-scale patterns associated with AR families are identified. Two clusters are of particular interest due to their strong relationship with phases of the El Niño/Southern Oscillation (ENSO). One of these clusters is characterized by a strong ridge in the Bering Sea and Rossby wave propagation, most frequently occurs during La Niña and neutral ENSO years and is associated with the highest cluster-average precipitation across California. The other cluster, characterized by a zonal elongation of lower geopotential heights across the Pacific basin and an extended North Pacific Jet, most frequently occurs during El Niño years and is associated with lower cluster-average precipitation across California but a longer duration. In contrast, single AR events do not show obvious clustering of spatial patterns. This difference suggests that the potential predictability of AR families may be enhanced relative to single AR events, especially on sub-seasonal to seasonal timescales.

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.

Monthly Modulations of ENSO Teleconnections: Implications for Potential Predictability in North America

2021 ◽  
pp. 1-71
Author(s):  
William E. Chapman ◽  
Aneesh C. Subramanian ◽  
Shang-Ping Xie ◽  
Michael D. Sierks ◽  
F. Martin Ralph ◽  
...  
Keyword(s):  
North America ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Internal Variability ◽  
Forced Response ◽  
Signal To Noise ◽  
Potential Predictability ◽  
Enso Teleconnections ◽  
Upper Level

AbstractUsing a high-resolution atmospheric general circulation model simulation of unprecedented ensemble size, we examine potential predictability of monthly anomalies under El Niño Southern Oscillation (ENSO) forcing and back-ground internal variability. This study reveals the pronounced month-to-month evolution of both the ENSO forcing signal and internal variability. Internal variance in upper-level geopotential height decreases (∼ 10%) over the North Pacific during El Niño as the westerly jet extends eastward, allowing forced signals to account for a greater fraction of the total variability, and leading to increased potential predictability. We identify February and March of El Niño years as the most predictable months using a signal-to-noise analysis. In contrast, December, a month typically included in teleconnection studies, shows little-to-no potential predictability. We show that the seasonal evolution of SST forcing and variability leads to significant signal-to-noise relationships that can be directly linked to both upper-level and surface variable predictability for a given month. The stark changes in forced response, internal variability, and thus signal-to-noise across an ENSO season indicate that subseasonal fields should be used to diagnose potential predictability over North America associated with ENSO teleconnections. Using surface air temperature and precipitation as examples, this study provides motivation to pursue ‘windows of forecast opportunity’, in which statistical skill can be developed, tested, and leveraged to determine times and regions in which this skill may be elevated.

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