scholarly journals Reply to “Comment on changes in the rates of North Atlantic major hurricane activity during the 20th century”

2001 ◽  
Vol 28 (14) ◽  
pp. 2873-2874
Author(s):  
James B. Elsner
Keyword(s):  
20Th Century ◽  
North Atlantic ◽  
Major Hurricane ◽  
Hurricane Activity

scholarly journals Changes in the rates of North Atlantic major hurricane activity during the 20th century

2000 ◽  
Vol 27 (12) ◽  
pp. 1743-1746 ◽  
Author(s):  
James B. Elsner ◽  
Thomas Jagger ◽  
Xu-Feng Niu
Keyword(s):  
20Th Century ◽  
North Atlantic ◽  
Major Hurricane ◽  
Hurricane Activity

Skillful Prediction of Monthly Major Hurricane Activity in the North Atlantic with Two‐way Nesting

2019 ◽  
Vol 46 (15) ◽  
pp. 9222-9230 ◽  
Author(s):  
Kun Gao ◽  
Jan‐Huey Chen ◽  
Lucas Harris ◽  
Yongqiang Sun ◽  
Shian‐Jiann Lin
Keyword(s):  
North Atlantic ◽  
The North ◽  
Major Hurricane ◽  
Hurricane Activity ◽  
The North Atlantic

scholarly journals Dominant effect of relative tropical Atlantic warming on major hurricane occurrence

Science ◽  
2018 ◽  
Vol 362 (6416) ◽  
pp. 794-799 ◽  
Author(s):  
H. Murakami ◽  
E. Levin ◽  
T. L. Delworth ◽  
R. Gudgel ◽  
P.-C. Hsu
Keyword(s):  
North Atlantic ◽  
Warming Trend ◽  
Sea Surface ◽  
Global Ocean ◽  
Tropical Atlantic ◽  
Surface Warming ◽  
Key Factor ◽  
Resolution Model ◽  
Major Hurricane ◽  
Hurricane Activity

Here we explore factors potentially linked to the enhanced major hurricane activity in the Atlantic Ocean during 2017. Using a suite of high-resolution model experiments, we show that the increase in 2017 major hurricanes was not primarily caused by La Niña conditions in the Pacific Ocean but rather triggered mainly by pronounced warm sea surface conditions in the tropical North Atlantic. Further, we superimpose a similar pattern of North Atlantic surface warming on data for long-term increasing sea surface temperature (a product of increases in greenhouse gas concentrations and decreases in aerosols) to show that this warming trend will likely lead to even higher numbers of major hurricanes in the future. The key factor controlling Atlantic major hurricane activity appears to be the degree to which the tropical Atlantic warms relative to the rest of the global ocean.

scholarly journals North Atlantic 20th century multidecadal variability in coupled climate models: sea surface temperature and ocean overturning circulation

Ocean Science ◽  
2011 ◽  
Vol 7 (3) ◽  
pp. 389-404 ◽  
Author(s):  
I. Medhaug ◽  
T. Furevik
Keyword(s):  
Surface Temperature ◽  
20Th Century ◽  
North Atlantic ◽  
General Circulation ◽  
General Circulation Models ◽  
Meridional Overturning Circulation ◽  
Overturning Circulation ◽  
Atlantic Sector ◽  
The North ◽  
The North Atlantic

Abstract. Output from a total of 24 state-of-the-art Atmosphere-Ocean General Circulation Models is analyzed. The models were integrated with observed forcing for the period 1850–2000 as part of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report. All models show enhanced variability at multi-decadal time scales in the North Atlantic sector similar to the observations, but with a large intermodel spread in amplitudes and frequencies for both the Atlantic Multidecadal Oscillation (AMO) and the Atlantic Meridional Overturning Circulation (AMOC). The models, in general, are able to reproduce the observed geographical patterns of warm and cold episodes, but not the phasing such as the early warming (1930s–1950s) and the following colder period (1960s–1980s). This indicates that the observed 20th century extreme in temperatures are due to primarily a fortuitous phasing of intrinsic climate variability and not dominated by external forcing. Most models show a realistic structure in the overturning circulation, where more than half of the available models have a mean overturning transport within the observed estimated range of 13–24 Sverdrup. Associated with a stronger than normal AMOC, the surface temperature is increased and the sea ice extent slightly reduced in the North Atlantic. Individual models show potential for decadal prediction based on the relationship between the AMO and AMOC, but the models strongly disagree both in phasing and strength of the covariability. This makes it difficult to identify common mechanisms and to assess the applicability for predictions.

scholarly journals Predicting Atlantic Tropical Cyclone Seasonal Activity in April

2009 ◽  
Vol 24 (2) ◽  
pp. 436-455 ◽  
Author(s):  
Elinor Keith ◽  
Lian Xie
Keyword(s):  
Tropical Cyclone ◽  
Gulf Of Mexico ◽  
Confidence Level ◽  
Complex Problem ◽  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Random Chance ◽  
Major Hurricane ◽  
Hurricane Activity ◽  
The U.S

Abstract Seasonal hurricane forecasts are continuing to develop skill, although they are still subject to large uncertainties. This study uses a new methodology of cross-correlating variables against empirical orthogonal functions (EOFs) of the hurricane track density function (HTDF) to select predictors. These predictors are used in a regression model for forecasting seasonal named storm, hurricane, and major hurricane activity in the entire Atlantic, the Caribbean Sea, and the Gulf of Mexico. In addition, a scheme for predicting landfalling tropical systems along the U.S. Gulf of Mexico, southeastern, and northeastern coastlines is developed, but predicting landfalling storms adds an extra layer of uncertainty to an already complex problem, and on the whole these predictions do not perform as well. The model performs well in the basin-wide predictions over the entire Atlantic and Caribbean, with the predictions showing an improvement over climatology and random chance at a 95% confidence level. Over the Gulf of Mexico, only named storms showed that level of predictability. Predicting landfalls proves more difficult, and only the prediction of named storms along the U.S. southeastern and Gulf coasts shows an improvement over random chance at the 95% confidence level. Tropical cyclone activity along the U.S. northeastern coast is found to be unpredictable in this model; with the rarity of events, the model is unstable.

scholarly journals 20th-century sea-ice variations from observational data

2001 ◽  
Vol 33 ◽  
pp. 444-448 ◽  
Author(s):  
John E. Walsh ◽  
William L. Chapman
Keyword(s):  
Sea Ice ◽  
20Th Century ◽  
North Atlantic ◽  
Climate Models ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Empirical Orthogonal Functions ◽  
Global Climate Models ◽  
The Past ◽  
Ice Coverage

AbstractIn order to extend diagnoses of recent sea-ice variations beyond the past few decades, a century-scale digital dataset of Arctic sea-ice coverage has been compiled. For recent decades, the compilation utilizes satellite-derived hemispheric datasets. Regional datasets based primarily on ship reports and aerial reconnaissance are the primary inputs for the earlier part of the 20th century. While the various datasets contain some discrepancies, they capture the same general variations during their period of overlap. The outstanding feature of the time series of total hemispheric ice extent is a decrease that has accelerated during the past several decades. The decrease is greatest in summer and weakest in winter, contrary to the seasonality of the greenhouse changes projected by most global climate models. The primary spatial modes of sea-ice variability diagnosed in terms of empirical orthogonal functions, also show a strong seasonality. The first winter mode is dominated by an opposition of anomalies in the western and eastern North Atlantic, corresponding to the well-documented North Atlantic Oscillation. The primary summer mode depicts an anomaly of the same sign over nearly the entire Arctic and captures the recent trend of sea-ice coverage.

Improving Multiseason Forecasts of North Atlantic Hurricane Activity

2008 ◽  
Vol 21 (6) ◽  
pp. 1209-1219 ◽  
Author(s):  
James B. Elsner ◽  
Thomas H. Jagger ◽  
Michael Dickinson ◽  
Dail Rowe
Keyword(s):  
North Atlantic ◽  
Monte Carlo Sampling ◽  
Sea Surface ◽  
Economic Losses ◽  
Atlantic Hurricane ◽  
Time Period ◽  
Hurricane Season ◽  
Hurricane Activity ◽  
Term Management

Abstract Hurricanes cause drastic social problems as well as generate huge economic losses. A reliable forecast of the level of hurricane activity covering the next several seasons has the potential to mitigate against such losses through improvements in preparedness and insurance mechanisms. Here a statistical algorithm is developed to predict North Atlantic hurricane activity out to 5 yr. The algorithm has two components: a time series model to forecast average hurricane-season Atlantic sea surface temperature (SST), and a regression model to forecast the hurricane rate given the predicted SST value. The algorithm uses Monte Carlo sampling to generate distributions for the predicted SST and model coefficients. For a given forecast year, a predicted hurricane count is conditional on a sampled predicted value of Atlantic SST. Thus forecasts are samples of hurricane counts for each future year. Model skill is evaluated over the period 1997–2005 and compared against climatology, persistence, and other multiseasonal forecasts issued during this time period. Results indicate that the algorithm will likely improve on earlier efforts and perhaps carry enough skill to be useful in the long-term management of hurricane risk.

Statistical–Dynamical Predictions of Seasonal North Atlantic Hurricane Activity

2011 ◽  
Vol 139 (4) ◽  
pp. 1070-1082 ◽  
Author(s):  
Gabriel A. Vecchi ◽  
Ming Zhao ◽  
Hui Wang ◽  
Gabriele Villarini ◽  
Anthony Rosati ◽  
...  
Keyword(s):  
High Resolution ◽  
North Atlantic ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Atlantic Hurricane ◽  
Uncertainty Estimates ◽  
Hurricane Season ◽  
Hurricane Activity ◽  
Hurricane Forecasting

Abstract Skillfully predicting North Atlantic hurricane activity months in advance is of potential societal significance and a useful test of our understanding of the factors controlling hurricane activity. In this paper, a statistical–dynamical hurricane forecasting system, based on a statistical hurricane model, with explicit uncertainty estimates, and built from a suite of high-resolution global atmospheric dynamical model integrations spanning a broad range of climate states is described. The statistical model uses two climate predictors: the sea surface temperature (SST) in the tropical North Atlantic and SST averaged over the global tropics. The choice of predictors is motivated by physical considerations, as well as the results of high-resolution hurricane modeling and statistical modeling of the observed record. The statistical hurricane model is applied to a suite of initialized dynamical global climate model forecasts of SST to predict North Atlantic hurricane frequency, which peaks during the August–October season, from different starting dates. Retrospective forecasts of the 1982–2009 period indicate that skillful predictions can be made from as early as November of the previous year; that is, skillful forecasts for the coming North Atlantic hurricane season could be made as the current one is closing. Based on forecasts initialized between November 2009 and March 2010, the model system predicts that the upcoming 2010 North Atlantic hurricane season will likely be more active than the 1982–2009 climatology, with the forecasts initialized in March 2010 predicting an expected hurricane count of eight and a 50% probability of counts between six (the 1966–2009 median) and nine.

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