scholarly journals A Statistical/Dynamical Model for North Atlantic Seasonal Hurricane Prediction

2020 ◽  
Vol 47 (20) ◽  
Author(s):  
P. J. Klotzbach ◽  
L.‐P. Caron ◽  
M. M. Bell
Keyword(s):  
North Atlantic ◽  
Dynamical Model ◽  
Hurricane Prediction

scholarly journals Improved simulation of 19th- and 20th-century North Atlantic hurricane frequency after correcting historical sea surface temperatures

2021 ◽  
Vol 7 (26) ◽  
pp. eabg6931
Author(s):  
Duo Chan ◽  
Gabriel A. Vecchi ◽  
Wenchang Yang ◽  
Peter Huybers
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
20Th Century ◽  
North Atlantic ◽  
Sea Surface ◽  
Model Data ◽  
Atmospheric Models ◽  
Decadal Variations ◽  
Hurricane Prediction ◽  
19Th And 20Th Century

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.

North Atlantic Seasonal Hurricane Prediction

2017 ◽  
pp. 315-328 ◽  
Author(s):  
Philip J. Klotzbach ◽  
Mark A. Saunders ◽  
Gerald D. Bell ◽  
Eric S. Blake
Keyword(s):  
North Atlantic ◽  
Hurricane Prediction

scholarly journals A Consensus Model for Seasonal Hurricane Prediction

2010 ◽  
Vol 23 (22) ◽  
pp. 6090-6099 ◽  
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.

scholarly journals Observational evidence of European summer weather patterns predictable from spring

2017 ◽  
Vol 115 (1) ◽  
pp. 59-63 ◽  
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.

The Atmospheric Response to North Atlantic SST Trends, 1870–2019

2020 ◽  
Author(s):  
Kristopher B. Karnauskas ◽  
Lei Zhang ◽  
Dillon J. Amaya
Keyword(s):  
North Atlantic ◽  
Atmospheric Response

The Currents of the North Atlantic

1892 ◽  
Vol 34 (872supp) ◽  
pp. 13940-13941
Author(s):  
Richard Beynon
Keyword(s):  
North Atlantic ◽  
The North ◽  
The North Atlantic
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