Investigating the predictability of North Atlantic sea surface height

Fraser, Robert; Palmer, Matthew D.; Roberts, C. D.; Wilson, Chris; Copsey, Dan; Zanna, Laure

doi:10.1007/s00382-019-04814-0

Cited by 6 publications

(4 citation statements)

References 60 publications

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“…Part of the reason for this lack of forecasting information, beyond the tidal prediction determined by astronomical cycles and perhaps some climate variability parameters, are limitations using current-generation global climate models to skillfully predict the sea level conditions in many U.S. coastal regions (Long et al, 2021). Although climate models do show skill predicting seasonal sea level anomalies in some places (Miles et al, 2014;McIntosh et al, 2015;Widlansky et al, 2017;Fraser et al, 2019;Sheridan et al, 2019;Shin and Newman, 2021;Frederikse et al, 2022), the real-time application of these models for coastal sea level information has been thusfar limited to the tropical Pacific Islands (e.g., https://uhslc.soest. hawaii.edu/sea-level-forecasts/).…”

Section: Introductionmentioning

confidence: 99%

A novel statistical approach to predict seasonal high tide flooding

et al. 2022

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Sea level rise is increasing the frequency of high tide flooding in coastal communities across the United States. Although the occurrence and severity of high-tide flooding will continue to increase, skillful prediction of high tide flooding on monthly-to-annual time horizons is lacking in most regions. Here, we present an approach to predict the daily likelihood of high tide flooding at coastal locations throughout the U.S. using a novel probabilistic modeling approach that relies on relative sea-level rise, tide predictions, and climatological non-tidal residuals as measured by NOAA tide gauges. The structure of the model will also enable future incorporation of mean sea level anomaly predictions from numerical, statistical, and machine learning forecast systems. A retrospective skill assessment using the climatological sea level information indicates that this approach is skillful at 61 out of 92 NOAA tide gauges where at least 10 high tide flood days occurred from 1997–2019. In this case, a flood day occurs when the observed water level exceeds the gauge-specific high tide flood threshold. For these 61 gauges, on average 35% of all floods are accurately predicted using this model, with over half of the floods accurately predicted at 18 gauges. The corresponding False-Alarm-Rate is less than 10% for all 61 gauges. Including mean sea level anomaly persistence at leads of 1 and 3 months further improves model skill in many locations, especially the U.S. Pacific Islands and West Coast. Model skill is shown to increase substantially with increasing sea level at nearly all locations as high tides more frequently exceed the high tide flooding threshold. Assuming an intermediate amount of relative sea level rise, the model will likely be skillful at 93 out of the 94 gauges projected to have regular flooding by 2040. These results demonstrate that this approach is viable to be incorporated into NOAA decision-support products to provide guidance on likely high tide flooding days. Further, the structure of the model will enable future incorporation of mean sea level anomaly predictions from numerical, statistical, and machine learning forecast systems.

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Section: Introductionmentioning

confidence: 99%

A novel statistical approach to predict seasonal high tide flooding

et al. 2022

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“…In an investigation of the drivers of global sea-level variability, Roberts et al (2016) showed that in one ocean eddy-permitting modeling system (NEMO) large-scale patterns of sea-level variability are predictable months-to-years in advance for many regions, at least when comparing the forecasts to the model analysis. Another study demonstrated that North Atlantic SSH anomalies are potentially predictable by using a linear inverse modeling statistical approach to assess interannual variability in a coupled climate model (Fraser et al, 2019). Recently, Shin and Newman (2021) assessed SSH retrospective forecast skill using a similar statistical model that they applied globally, and found it to be capable of complementing the ensemble-mean forecast from five climate models that they also assessed.…”

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confidence: 99%

Seasonal Forecasting Skill of Sea‐Level Anomalies in a Multi‐Model Prediction Framework

et al. 2021

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“…The SSHA can exert substantial influence on the frequency and severity of extreme sea level events, and it is very concerned by ship navigation, fishery resource forecasting, marine engineering and industry (Lumban‐Gaol et al., 2017; Tanajura et al., 2015). Therefore, accurate prediction of SSHA holds immense significance (Fraser et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

The Sensitive Area for Targeting Observations of Paired Mesoscale Eddies Associated With Sea Surface Height Anomaly Forecasts

Jiang,

Duan,

Wang

2024

JGR Oceans

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For paired mesoscale eddies, the most sensitive initial errors in relation to sea surface height anomaly (SSHA) forecasts are investigated by utilizing the conditional nonlinear optimal perturbation (CNOP) method in a two‐layer quasigeostrophic model and then the sensitive areas are identified accordingly. For counter‐rotating eddies, the CNOP initial errors primarily occur within the eddies themselves, especially in areas characterized by clear high‐to low‐velocity gradients, accompanied by shear structures; while for co‐rotating eddies, besides sharing the feature of the former, their CNOP initial errors also concentrate between the two eddies and are obliquely tangential to their boundaries with a positive and negative shear structure. The utility of the CNOP initial errors in determining the sensitive areas for target observation is assessed through observing system simulation experiments (OSSEs). The OSSEs indicate that giving priority to the implementation of target observations in the sensitive areas identified by large CNOP initial errors, whether within eddies or between two co‐rotating eddies, particularly with a specific array guided by the shear structure of CNOP initial errors, leads to a notable improvement in SSHA forecasting. Finally, the effectiveness of the target observations, especially within the sensitive area situated between two co‐rotating eddies, is interpreted from the perspective of barotropic instability. The results of this study offer valuable scientific insights into the targeted observation of paired mesoscale eddies. These findings have the potential to provide important guidance for initializating paired mesoscale eddies, ultimately contributing to improvements in SSHA forecast accuracy.

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Investigating the predictability of North Atlantic sea surface height

Cited by 6 publications

References 60 publications

A novel statistical approach to predict seasonal high tide flooding

A novel statistical approach to predict seasonal high tide flooding

Seasonal Forecasting Skill of Sea‐Level Anomalies in a Multi‐Model Prediction Framework

The Sensitive Area for Targeting Observations of Paired Mesoscale Eddies Associated With Sea Surface Height Anomaly Forecasts

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