Contributions of tropical-extratropical oceans to the prediction skill of ENSO after 2000

2020 ◽  
Author(s):  
Liang Shi ◽  
Ruiqiang Ding ◽  
Yu-heng Tseng
Keyword(s):  
Lead Time ◽  
Prediction Models ◽  
Sea Surface ◽  
Statistical Prediction ◽  
Tropical Atlantic ◽  
Ocean Variability ◽  
Enso Prediction ◽  
Enso Predictability ◽  
Statistical Prediction Model ◽  
Northern Tropical Atlantic

<p>The skills of most ENSO prediction models have declined significantly since 2000. This decline may be due to a weakening of the correlation between tropical predictors and ENSO. Moreover, the effects of extratropical ocean variability on ENSO have increased during this period. To improve ENSO predictability, we investigate the influence of the tropical-extratropical Atlantic and Pacific sea surface temperature(SST) on ENSO during the periods of pre-2000 and post-2000. We find that the influence of the northern tropical Atlantic(NTA) SST on ENSO has significantly increase since 2000. Meanwhile, there is a much earlier and stronger SST responses between NTA SST and ENSO over the central-eastern Pacific during June–July–August in the post-2000 period compared with the pre-2000 period. Furthermore, the extratropical Pacific SST predictors for ENSO still retain a ~10-month lead time after 2000. We use SST signals in the extratropical Atlantic and Pacific to predict ENSO using a statistical prediction model. These results reveal a significant improvement in ENSO prediction skills. These results indicate that the Atlantic and Pacific SSTAs can make substantial contributions to ENSO prediction, and can be further used to enhance ENSO predictability after 2000.</p>

scholarly journals Statistical Prediction of Summer Rainfall and Vegetation in the Ethiopian Highlands

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mark R. Jury
Keyword(s):  
Surface Temperature ◽  
Multivariate Regression ◽  
Lead Time ◽  
Late Summer ◽  
Tropical Indian Ocean ◽  
Summer Rainfall ◽  
Statistical Prediction ◽  
Tropical Atlantic ◽  
Zonal Winds ◽  
Predictive Algorithms

Year-to-year fluctuations of Ethiopia climate are investigated to develop statistical predictions at one-season lead time. Satellite vegetation data from NASA and rainfall from ARC2 are the basis for analysis. The “target” seasons are May–July and August–October, while “predictors” are December–February and March–May, respectively. Global fields of surface temperature, sea level air pressure, and upper and lower level zonal winds are employed in point-to-field correlations. After step-wise multivariate regression, the leading predictors are: surface temperature across Europe (cold-favourable), 850 mb zonal winds over the tropical Atlantic (easterly-favourable), and surface temperature in the tropical Indian Ocean (cold-favourable). Predictive algorithms for early and late rainfall exhibit a consistentr2fit of ~0.50, while those for vegetation reach ~0.65 in late summer, indicating that fluctuations in food resources could be forewarned.

scholarly journals Winter Persistence Barrier of Sea Surface Temperature in the Northern Tropical Atlantic Associated with ENSO

2011 ◽  
Vol 24 (9) ◽  
pp. 2285-2299 ◽  
Author(s):  
Ruiqiang Ding ◽  
Jianping Li
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
El Niño ◽  
El Nino ◽  
Sea Surface ◽  
Delayed Response ◽  
Tropical Atlantic ◽  
Sign Reversal ◽  
Enso Events ◽  
Northern Tropical Atlantic

Abstract This study investigates the persistence characteristics of the sea surface temperature anomaly (SSTA) in the northern tropical Atlantic (NTA). It is found that a persistence barrier exists around December and January. This winter persistence barrier (WPB) is prominent during the mature phase of strong ENSO events but becomes indistinct during weak ENSO and normal (non-ENSO) events. During strong El Niño events, the NTA SSTA shows a reversal in sign and a rapid warming during December and January. It is possible that this SSTA sign reversal reduces the persistence, leading to the occurrence of the NTA WPB. The present analyses indicate a dynamic relationship among the Pacific ENSO, the NTA SSTA, and the NTA WPB on a quasi-biennial time scale: a strong El Niño event is usually preceded by a strong La Niña event, which leads to a sign reversal of the NTA SSTA in winter as a delayed response to ENSO, finally resulting in the NTA WPB. Analyses also suggest that the NTA WPB is affected by the North Atlantic Oscillation (NAO). The NAO enhances the persistence of the NTA SSTA during winter, tending to weaken the NTA WPB.

scholarly journals Leveraging multi-model season-ahead streamflow forecasts to trigger advanced flood preparedness

2021 ◽  
Author(s):  
Colin Keating ◽  
Donghoon Lee ◽  
Juan Bazo ◽  
Paul Block
Keyword(s):  
Prediction Models ◽  
Superior Performance ◽  
Statistical Prediction ◽  
Exceedance Probability ◽  
Early Action ◽  
Physically Based ◽  
Streamflow Forecasts ◽  
Operational Practices ◽  
Statistical Prediction Model ◽  
Seasonal Streamflow

Abstract. Disaster planning has historically allocated minimal effort and finances toward advanced preparedness, however evidence supports reduced vulnerability to flood events, saving lives and money, through appropriate early actions. Among other requirements, effective early action systems necessitate the availability of high-quality forecasts to inform decision making. In this study, we evaluate the ability of statistical and physically based season-ahead prediction models to appropriately trigger flood early preparedness actions based on a 75 % or greater probability of surpassing the 80th percentile of historical seasonal streamflow for the flood-prone Marañón River and Piura River in Peru. The statistical prediction model, developed in this work, leverages the asymmetric relationship between seasonal streamflow and the ENSO phenomenon. Additionally, a multi-model (least squares combination) is also evaluated against current operational practices. The statistical and multi-model predictions demonstrate superior performance compared to the physically based model for the Marañón River by correctly triggering preparedness actions in all four historical occasions. For the Piura River, the statistical model proves superior to all other approaches, and even achieves an 86 % hit rate when the required threshold exceedance probability is reduced to 50 %, with only one false alarm. Continued efforts should focus on applying this season-ahead prediction framework to additional flood-prone locations where early actions may be warranted and current forecast capacity is limited.

scholarly journals Leveraging multi-model season-ahead streamflow forecasts to trigger advanced flood preparedness in Peru

2021 ◽  
Vol 21 (7) ◽  
pp. 2215-2231
Author(s):  
Colin Keating ◽  
Donghoon Lee ◽  
Juan Bazo ◽  
Paul Block
Keyword(s):  
Prediction Models ◽  
Superior Performance ◽  
Statistical Prediction ◽  
Exceedance Probability ◽  
Early Action ◽  
Physically Based ◽  
Streamflow Forecasts ◽  
Operational Practices ◽  
Statistical Prediction Model ◽  
Seasonal Streamflow

Abstract. Disaster planning has historically allocated minimal effort and finances toward advanced preparedness; however, evidence supports reduced vulnerability to flood events, saving lives and money, through appropriate early actions. Among other requirements, effective early action systems necessitate the availability of high-quality forecasts to inform decision making. In this study, we evaluate the ability of statistical and physically based season-ahead prediction models to appropriately trigger flood early preparedness actions based on a 75 % or greater probability of surpassing the 80th percentile of historical seasonal streamflow for the flood-prone Marañón River and Piura River in Peru. The statistical prediction model, developed in this work, leverages the asymmetric relationship between seasonal streamflow and the ENSO phenomenon. Additionally, a multi-model (least-squares combination) is also evaluated against current operational practices. The statistical prediction demonstrates superior performance compared to the physically based model for the Marañón River by correctly triggering preparedness actions in three out of four historical occasions, while both the statistical and multi-model predictions capture all four historical events when the required threshold exceedance probability is reduced to 50 %, with only one false alarm. For the Piura River, the statistical model proves superior to all other approaches, correctly triggering 28 % more often in the hindcast period. Continued efforts should focus on applying this season-ahead prediction framework to additional flood-prone locations where early actions may be warranted and current forecast capacity is limited.

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