Excessive Momentum and False Alarms in Late‐Spring ENSO Forecasts

Tippett, Michael K.; L’Heureux, Michelle; Becker, Emily; Kumar, Arun

doi:10.1029/2020gl087008

Cited by 12 publications

(9 citation statements)

References 70 publications

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“…There is recent evidence that, in initialized seasonal climate prediction models, El Niño false alarms have increased in the last half of the 1982-2020 record 179 , and this may be tied to errors in simulated linear SST trends across the equatorial Pacific Ocean 36,180 . As described earlier, many state-of-the-art seasonal climate prediction models have stronger positive trends in the central and eastern tropical Pacific than found in the observations.…”

Section: Consequences Of Equatorial Pacific Sst Trendsmentioning

confidence: 99%

On the future zonal contrasts of equatorial Pacific climate: Perspectives from Observations, Simulations, and Theories

et al. 2022

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Changes in the zonal gradients of sea surface temperature (SST) across the equatorial Pacific have major consequences for global climate. Therefore, accurate future projections of these tropical Pacific gradients are of paramount importance for climate mitigation and adaptation. Yet there is evidence of a dichotomy between observed historical gradient trends and those simulated by climate models. Observational records appear to show a “La Niña-like” strengthening of the zonal SST gradient over the past century, whereas most climate model simulations project “El Niño-like” changes toward a weaker gradient. Here, studies of these equatorial Pacific climate trends are reviewed, focusing first on data analyses and climate model simulations, then on theories that favor either enhanced or weakened zonal SST gradients, and then on notable consequences of the SST gradient trends. We conclude that the present divergence between the historical model simulations and the observed trends likely either reflects an error in the model’s forced response, or an underestimate of the multi-decadal internal variability by the models. A better understanding of the fundamental mechanisms of both forced response and natural variability is needed to reduce the uncertainty. Finally, we offer recommendations for future research directions and decision-making for climate risk mitigation.

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Section: Consequences Of Equatorial Pacific Sst Trendsmentioning

confidence: 99%

On the future zonal contrasts of equatorial Pacific climate: Perspectives from Observations, Simulations, and Theories

et al. 2022

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“…Instead, following the approach in Tippett et al (2020), we evaluated the skill of the tendency of both SST and SSH anomalies over the 6 months after initialization [i.e., the skill of    …”

Section: Assessing the Impact Of The Secular Trend On Skillmentioning

confidence: 99%

“…Instead, following the approach in Tippett et al. (2020), we evaluated the skill of the tendency of both SST and SSH anomalies over the 6 months after initialization [i.e., the skill of

x (6) - x (0)

rather than

boldx (6)

]. This skill metric removes the impact of the trend component, to the extent that it is relatively constant over any 6‐month increment, as is largely the case for the LIM.…”

Section: Hindcast Skill Evaluationmentioning

confidence: 99%

Seasonal Predictability of Global and North American Coastal Sea Surface Temperature and Height Anomalies

Shin

Newman

2021

Geophysical Research Letters

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Coastal marine ecosystems are strongly impacted by changes in ocean circulation and stratification, which lead to the geographic redistribution of available nutrients and oxygen and, in turn, marine species (Capotondi, Jacox, et al., 2019;Doney et al., 2012). Therefore, skillful prediction of coastal ocean conditions is increasingly needed for marine resource management (Jacox et al., 2020). In fact, within the Large Marine Ecosystems (LMEs), relatively large coastal zones where primary productivity is higher than in the open ocean, sea surface temperature (SST) hindcast skill from the numerical models of the North American Multi-Model Ensemble (NMME; Kirtman et al., 2014) is large enough to be potentially useful (Hervieux et al., 2019;Stock et al., 2015). Recently, in a comprehensive review of seasonal-to-interannual prediction methods and their skill within North American coastal marine ecosystems, Jacox et al. (2020) suggested not only that prediction skill may be maximized via a hybrid statistical-numerical prediction approach, but also that statistical prediction alone could be competitive with numerical prediction.This study explores North American coastal forecast skill using a global Linear Inverse Model (LIM; Penland & Sardeshmukh, 1995; hereafter PS95). The LIM is an empirical dynamical model that may diagnose potential spatiotemporal variations of skill, since its forecasts are often competitive with numerical model forecasts. For example, Newman and Sardeshmukh (2017; hereafter NS17) showed that a tropical LIM had similar tropical Indo-Pacific SST skill to the grand ensemble mean of the NMME operational models and that the LIM itself largely predicted the spatial and temporal skill variations of both models. LIMs have been constructed for other regions, including the Pacific and the Atlantic Ocean basins (

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“…These are El Niño events which are predicted by the models, but end up failing to occur in reality. Tippett et al (2020) identified several El Niño false alarms while analyzing model "excessive momentum, " in which the observed Niño-3.4 SST index tendency during the spring is too strongly correlated with the forecast tendency of ENSO into the fall. In other words, positive springtime changes in the observed Niño-3.4 index often beget a positive forecast trajectory in the Niño-3.4 index.…”

Section: Relating Recent El Niño False Alarms With Errors In Trends O...mentioning

confidence: 99%

“…In other words, positive springtime changes in the observed Niño-3.4 index often beget a positive forecast trajectory in the Niño-3.4 index. Tippett et al (2020) showed that out of all springtime busts during 1982-2018, four of nine occurred in the last decade (2011, 2013, 2014, and 2017) and they were all El Niño false alarms-a concerning development for seasonal climate forecasting if it continues.…”

Section: Relating Recent El Niño False Alarms With Errors In Trends O...mentioning

confidence: 99%

Prediction Challenges From Errors in Tropical Pacific Sea Surface Temperature Trends

2022

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Models in the North American Multi-Model Ensemble (NMME) predict sea surface temperature (SST) trends in the central and eastern equatorial Pacific Ocean which are more positive than those observed over the period 1982–2020. These trend errors are accompanied by linear trends in the squared error of SST forecasts whose sign is determined by the mean model bias (cold equatorial bias is linked to negative trends in squared error and vice versa). The reason for this behavior is that the overly positive trend reduces the bias of models that are too cold and increases the bias of models that are too warm. The excessive positive SST trends in the models are also linked with overly positive trends in tropical precipitation anomalies. Larger (smaller) SST trend errors are associated with lower (higher) skill in predicting precipitation anomalies over the central Pacific Ocean. Errors in the linear SST trend do not explain a large percentage of variability in precipitation anomaly errors, but do account for large errors in amplitude. The predictions toward a too warm and wet tropical Pacific, especially since 2000, are strongly correlated with an increase in El Niño false alarms. These results may be relevant for interpreting the behavior of uninitialized CMIP5/6 models, which project SST trends that resemble the NMME trend errors.

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Excessive Momentum and False Alarms in Late‐Spring ENSO Forecasts

Cited by 12 publications

References 70 publications

On the future zonal contrasts of equatorial Pacific climate: Perspectives from Observations, Simulations, and Theories

On the future zonal contrasts of equatorial Pacific climate: Perspectives from Observations, Simulations, and Theories

Seasonal Predictability of Global and North American Coastal Sea Surface Temperature and Height Anomalies

Prediction Challenges From Errors in Tropical Pacific Sea Surface Temperature Trends

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