A Statistical/Dynamical Model for North Atlantic Seasonal Hurricane Prediction

Confidence in dynamical and statistical hurricane prediction is rooted in the skillful reproduction of hurricane frequency using sea surface temperature (SST) patterns, but an ensemble of high-resolution atmospheric simulation extending to the 1880s indicates model-data disagreements that exceed those expected from documented uncertainties. We apply recently developed corrections for biases in historical SSTs that lead to revisions in tropical to subtropical SST gradients by ±0.1°C. Revised atmospheric simulations have 20% adjustments in the decadal variations of hurricane frequency and become more consistent with observations. The improved simulation skill from revised SST estimates not only supports the utility of high-resolution atmospheric models for hurricane projections but also highlights the need for accurate estimates of past and future patterns of SST changes.

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A Mechanism and Simple Dynamical Model of the North Atlantic Oscillation and Annular Modes

Journal of the Atmospheric Sciences ◽

10.1175/1520-0469(2004)061<0264:amasdm>2.0.co;2 ◽

2004 ◽

Vol 61 (3) ◽

pp. 264-280 ◽

Cited By ~ 116

Author(s):

Geoffrey K. Vallis ◽

Edwin P. Gerber ◽

Paul J. Kushner ◽

Benjamin A. Cash

Keyword(s):

North Atlantic Oscillation ◽

North Atlantic ◽

Dynamical Model ◽

Annular Modes ◽

The North ◽

The North Atlantic ◽

The North Atlantic Oscillation ◽

Simple Dynamical Model

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North Atlantic Seasonal Hurricane Prediction

Climate Extremes - Geophysical Monograph Series ◽

10.1002/9781119068020.ch19 ◽

2017 ◽

pp. 315-328 ◽

Cited By ~ 11

Author(s):

Philip J. Klotzbach ◽

Mark A. Saunders ◽

Gerald D. Bell ◽

Eric S. Blake

Keyword(s):

North Atlantic ◽

Hurricane Prediction

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A Consensus Model for Seasonal Hurricane Prediction

Journal of Climate ◽

10.1175/2010jcli3686.1 ◽

2010 ◽

Vol 23 (22) ◽

pp. 6090-6099 ◽

Cited By ~ 9

Author(s):

Thomas H. Jagger ◽

James B. Elsner

Keyword(s):

North Atlantic ◽

Bayesian Model Averaging ◽

Southern Oscillation ◽

Model Averaging ◽

Seasonal Prediction ◽

Consensus Model ◽

Probability Models ◽

Nao Index ◽

Hurricane Activity ◽

Hurricane Prediction

Abstract The authors apply a procedure called Bayesian model averaging (BMA) for examining the utility of a set of covariates for predicting the distribution of U.S. hurricane counts and demonstrating a consensus model for seasonal prediction. Hurricane counts are derived from near-coastal tropical cyclones over the period 1866–2008. The covariate set consists of the May–October monthly averages of the Atlantic SST, North Atlantic Oscillation (NAO) index, Southern Oscillation index (SOI), and sunspot number (SSN). BMA produces posterior probabilities indicating the likelihood of the model given the set of annual hurricane counts and covariates. The September SSN covariate appears most often in the higher-probability models. The sign of the September SSN parameter is negative indicating that the probability of a U.S. hurricane decreases with more sunspots. A consensus hindcast for the 2007 and 2008 season is made by averaging forecasts from a large subset of models weighted by their corresponding posterior probability. A cross-validation exercise confirms that BMA can provide more accurate forecasts compared to methods that select a single “best” model. More importantly, the BMA procedure incorporates more of the uncertainty associated with making a prediction of this year’s hurricane activity from data.

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Observational evidence of European summer weather patterns predictable from spring

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1713146114 ◽

2017 ◽

Vol 115 (1) ◽

pp. 59-63 ◽

Cited By ~ 26

Author(s):

Albert Ossó ◽

Rowan Sutton ◽

Len Shaffrey ◽

Buwen Dong

Keyword(s):

Atmospheric Circulation ◽

North Atlantic ◽

Summer Rainfall ◽

Specific Pattern ◽

Sea Surface ◽

Dynamical Model ◽

Sea Surface Temperatures ◽

Weather Patterns ◽

Surface Temperatures ◽

Wide Range

Forecasts of summer weather patterns months in advance would be of great value for a wide range of applications. However, seasonal dynamical model forecasts for European summers have very little skill, particularly for rainfall. It has not been clear whether this low skill reflects inherent unpredictability of summer weather or, alternatively, is a consequence of weaknesses in current forecast systems. Here we analyze atmosphere and ocean observations and identify evidence that a specific pattern of summertime atmospheric circulation––the summer East Atlantic (SEA) pattern––is predictable from the previous spring. An index of North Atlantic sea-surface temperatures in March–April can predict the SEA pattern in July–August with a cross-validated correlation skill above 0.6. Our analyses show that the sea-surface temperatures influence atmospheric circulation and the position of the jet stream over the North Atlantic. The SEA pattern has a particularly strong influence on rainfall in the British Isles, which we find can also be predicted months ahead with a significant skill of 0.56. Our results have immediate application to empirical forecasts of summer rainfall for the United Kingdom, Ireland, and northern France and also suggest that current dynamical model forecast systems have large potential for improvement.

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