Strong Relations Between ENSO and the Arctic Oscillation in the North American Multimodel Ensemble

L’Heureux, Michelle; Tippett, Michael K.; Kumar, Arun; Butler, Amy H.; Ciasto, Laura M.; Ding, Qinghua; Harnos, Kirstin; Johnson, Nathaniel C.

doi:10.1002/2017gl074854

Cited by 29 publications

(32 citation statements)

References 38 publications

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“…Nevertheless, for the prediction on timescales of weeks to months, there exist recent promising improvements in prediction skill. For winter, some facets Journal of Geophysical Research: Atmospheres 10.1029/2019JD030923 of the extratropical NH circulation such as the North Atlantic Oscillation (NAO; e.g., Hurrell et al, 2001;Walker, 1928) are predictable to some degree with seasonal prediction systems (Baker et al, 2018;Dobrynin et al, 2018;L'Heureux et al, 2017;Stockdale et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

The Role of the Stratosphere in Subseasonal to Seasonal Prediction: 2. Predictability Arising From Stratosphere‐Troposphere Coupling

et al. 2020

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The stratosphere can have a significant impact on winter surface weather on subseasonal to seasonal (S2S) timescales. This study evaluates the ability of current operational S2S prediction systems to capture two important links between the stratosphere and troposphere: (1) changes in probabilistic prediction skill in the extratropical stratosphere by precursors in the tropics and the extratropical troposphere and (2) changes in surface predictability in the extratropics after stratospheric weak and strong vortex events. Probabilistic skill exists for stratospheric events when including extratropical tropospheric precursors over the North Pacific and Eurasia, though only a limited set of models captures the Eurasian precursors. Tropical teleconnections such as the Madden‐Julian Oscillation, the Quasi‐Biennial Oscillation, and El Niño–Southern Oscillation increase the probabilistic skill of the polar vortex strength, though these are only captured by a limited set of models. At the surface, predictability is increased over the United States, Russia, and the Middle East for weak vortex events, but not for Europe, and the change in predictability is smaller for strong vortex events for all prediction systems. Prediction systems with poorly resolved stratospheric processes represent this skill to a lesser degree. Altogether, the analyses indicate that correctly simulating stratospheric variability and stratosphere‐troposphere dynamical coupling are critical elements for skillful S2S wintertime predictions.

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Section: Introductionmentioning

confidence: 99%

The Role of the Stratosphere in Subseasonal to Seasonal Prediction: 2. Predictability Arising From Stratosphere‐Troposphere Coupling

et al. 2020

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“…Another way of illustrating the signal‐to‐noise paradox involves the idea of constructing a hypothetical perfect model to estimate model predictability and interpreting the skill estimate from the perfect model as “potential skill” (Mehta et al, ; Tang et al, ; Kumar et al, ; Jin et al, ; L'Heureux et al, ). Here the perfect model approach relies on the interchangeability of ensemble members with the observations if the underlying probability distribution of the true state is perfectly sampled by the model.…”

Section: Introductionmentioning

confidence: 99%

How confident are predictability estimates of the winter North Atlantic Oscillation?

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et al. 2019

Quart J Royal Meteoro Soc

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Atmospheric seasonal predictability in winter over the Euro-Atlantic region is studied with an emphasis on the signal-to-noise paradox of the North Atlantic Oscillation. Seasonal hindcasts of the ECMWF model for the recent period show, in agreement with other studies, that correlation skill over Greenland and parts of the Arctic is higher than the signal-to-noise ratio implies. This leads to the paradoxical situation where the real world appears more predictable than the models suggest, with the forecast ensembles being overly dispersive (or underconfident). However, it is demonstrated that these conclusions are not supported by the diagnosed relationship between ensemble mean root-mean-square error (RMSE) and ensemble spread which indicates a slight under-dispersion (overconfidence). Furthermore, long atmospheric seasonal hindcasts suggest that over the 110-year period from 1900 to 2009 the ensemble system is well calibrated (neither overnor under-dispersive). The observed skill changed drastically in the middle of the twentieth century and paradoxical regions during more recent hindcast periods were strongly under-dispersive during mid-century decades. Due to non-stationarities of the climate system in the form of decadal variability, relatively short hindcasts are not sufficiently representative of longer-term behaviour. In addition, small hindcast sample size can lead to skill estimates, in particular of correlation measures, that are not robust. It is shown that the relative uncertainty due to small hindcast sample size is often larger for correlation-based than for RMSE-based diagnostics. Correlation-based measures like the RPC are shown to be highly sensitive to the strength of the predictable signal, implying that disentangling of physical deficiencies in the models on the one hand, and the effects of sampling uncertainty on the other hand, is difficult. Given the current lack of a causal physical mechanism to unravel the puzzle, our hypotheses of non-stationarity and sampling uncertainty provide simple yet plausible explanations for the paradox.

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“…NMME model data is provided at a relatively coarse 1 • ×1 • resolution, but is useful for capturing large-scale oceanic variability and drivers of that variability that offer predictability. The NMME's ability to capture relevant modes of variability has been well-documented (Becker et al, 2014;L'Heureux et al, 2017) and the consistent set of hindcasts offers the opportunity to evaluate the applicability of the system for targeted predictions. In particular, the NMME model output can supply lateral boundary conditions and surface forcing, after proper downscaling, for high-resolution regional forecasting models like those described in Sections "JISAO's Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE)" and "Bering Sea Modeling and Forecasting."…”

Section: Global Dynamical Modelsmentioning

confidence: 99%

Observational Needs Supporting Marine Ecosystems Modeling and Forecasting: From the Global Ocean to Regional and Coastal Systems

et al. 2019

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Many coastal areas host rich marine ecosystems and are also centers of economic activities, including fishing, shipping and recreation. Due to the socioeconomic and ecological importance of these areas, predicting relevant indicators of the ecosystem state on sub-seasonal to interannual timescales is gaining increasing attention. Depending on the application, forecasts may be sought for variables and indicators spanning physics (e.g., sea level, temperature, currents), chemistry (e.g., nutrients, oxygen, pH), and biology (from viruses to top predators). Many components of the marine ecosystem are known to be influenced by leading modes of climate variability, which provide a physical basis for predictability. However, prediction capabilities remain limited by the lack of a clear understanding of the physical and biological processes Frontiers in Marine Science | www.frontiersin.org 1 October 2019 | Volume 6 | Article 623 Capotondi et al. Marine Ecosystems Modeling and Forecastinginvolved, as well as by insufficient observations for forecast initialization and verification. The situation is further complicated by the influence of climate change on ocean conditions along coastal areas, including sea level rise, increased stratification, and shoaling of oxygen minimum zones. Observations are thus vital to all aspects of marine forecasting: statistical and/or dynamical model development, forecast initialization, and forecast validation, each of which has different observational requirements, which may be also specific to the study region. Here, we use examples from United States (U.S.) coastal applications to identify and describe the key requirements for an observational network that is needed to facilitate improved process understanding, as well as for sustaining operational ecosystem forecasting. We also describe new holistic observational approaches, e.g., approaches based on acoustics, inspired by Tara Oceans or by landscape ecology, which have the potential to support and expand ecosystem modeling and forecasting activities by bridging global and local observations.

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Strong Relations Between ENSO and the Arctic Oscillation in the North American Multimodel Ensemble

Cited by 29 publications

References 38 publications

The Role of the Stratosphere in Subseasonal to Seasonal Prediction: 2. Predictability Arising From Stratosphere‐Troposphere Coupling

The Role of the Stratosphere in Subseasonal to Seasonal Prediction: 2. Predictability Arising From Stratosphere‐Troposphere Coupling

How confident are predictability estimates of the winter North Atlantic Oscillation?

Observational Needs Supporting Marine Ecosystems Modeling and Forecasting: From the Global Ocean to Regional and Coastal Systems

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