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Regional changes in early, middle, and late winter total snowfall distributions are identified over the continental United States in association with warm and cold phases of the El Niño-Southern Oscillation (ENSO). The analysis is primarily motivated by a desire to improve winter season climate forecasts. Original interest in snowfall associated with ENSO was provided by requests for skiing forecasts during the 1997 ENSO warm phase. Geographic regions with internally similar ENSO warm, cold, and neutral phase snowfall distributions are identified using a composite technique. The composites reveal three early winter, five midwinter, and three late winter regions with shifts in the upper-, middle, and lower-quartile seasonal snowfall. The quartile shifts revealed by the composite technique are important for forecasting applications; however, snowfall impact studies rely more on the absolute magnitude of the change in snowfall at individual stations. Potential impacts of the shifts in snowfall distributions associated with ENSO are discussed using the quartile snowfall magnitudes for the stations in the composites. Shifts in regional snowfall distributions are compared to published ENSO winter climate studies, and hypotheses are presented to relate physical processes to the warm, cold, and neutral phase snowfall distributions.
Principal findings include increased snowfall during an ENSO cold phase relative to warm and neutral phases in the northwestern states from early through midwinter, less (more) snowfall during a cold (warm) phase relative to neutral years in the Northeast, and less snowfall (relative to neutral winters) in both warm and cold phases in the Ohio Valley (early winter) and Midwest (midwinter). Combining these snowfall regions with an ever-improving ability to forecast ENSO warm and cold phases will improve seasonal snowfall forecasts. The results should improve mitigation strategies for agencies adversely impacted by ENSO-induced snowfall anomalies.
Regional changes in early, middle, and late winter total snowfall distributions are identified over the continental United States in association with warm and cold phases of the El Niño-Southern Oscillation (ENSO). The analysis is primarily motivated by a desire to improve winter season climate forecasts. Original interest in snowfall associated with ENSO was provided by requests for skiing forecasts during the 1997 ENSO warm phase. Geographic regions with internally similar ENSO warm, cold, and neutral phase snowfall distributions are identified using a composite technique. The composites reveal three early winter, five midwinter, and three late winter regions with shifts in the upper-, middle, and lower-quartile seasonal snowfall. The quartile shifts revealed by the composite technique are important for forecasting applications; however, snowfall impact studies rely more on the absolute magnitude of the change in snowfall at individual stations. Potential impacts of the shifts in snowfall distributions associated with ENSO are discussed using the quartile snowfall magnitudes for the stations in the composites. Shifts in regional snowfall distributions are compared to published ENSO winter climate studies, and hypotheses are presented to relate physical processes to the warm, cold, and neutral phase snowfall distributions.
Principal findings include increased snowfall during an ENSO cold phase relative to warm and neutral phases in the northwestern states from early through midwinter, less (more) snowfall during a cold (warm) phase relative to neutral years in the Northeast, and less snowfall (relative to neutral winters) in both warm and cold phases in the Ohio Valley (early winter) and Midwest (midwinter). Combining these snowfall regions with an ever-improving ability to forecast ENSO warm and cold phases will improve seasonal snowfall forecasts. The results should improve mitigation strategies for agencies adversely impacted by ENSO-induced snowfall anomalies.
Abstract
The warm phase of the El Niño–Southern Oscillation is known to suppress hurricane activity in the Atlantic basin, and several studies have evaluated the influence of ENSO on hurricane landfalls in the United States. The present analysis focuses on hurricane landfall probabilities in relation to ENSO for landmasses surrounding the Caribbean Sea. La Niña events are found to be associated with an increased probability of hurricane landfalls in the Caribbean as a whole. Regional variations in the impact of ENSO on hurricane landfall probabilities in the Caribbean are identified, including a lack of an El Niño decrease in probability (relative to neutral years) in the east and west Caribbean.
Abstract
The warm phase of the El Niño–Southern Oscillation is known to suppress hurricane activity in the Atlantic basin, and several studies have evaluated the influence of ENSO on hurricane landfalls in the United States. The present analysis focuses on hurricane landfall probabilities in relation to ENSO for landmasses surrounding the Caribbean Sea. La Niña events are found to be associated with an increased probability of hurricane landfalls in the Caribbean as a whole. Regional variations in the impact of ENSO on hurricane landfall probabilities in the Caribbean are identified, including a lack of an El Niño decrease in probability (relative to neutral years) in the east and west Caribbean.
Abstract
The uncertainties in the NCEP–NCAR reanalysis (NCEPR) products are not well known. Using a newly developed, high-resolution, quality controlled, surface meteorology dataset from research vessels participating in the World Ocean Circulation Experiment (WOCE), regional and global uncertainties are quantified for the NCEPR air–sea fluxes and the component fields used to create those fluxes.
For the period 1990–95, WOCE vessel and gridded NCEPR fields are matched in time and space. All in situ data are subject to data quality review to remove suspect data. Adjustment of ship observations to the reference height of the NCEPR variables, and calculation of air–sea fluxes from the in situ data are accomplished using bulk formulas that take atmospheric stability, height of the measurements, and other adjustments into consideration. The advantages of using this new set of WOCE ship observations include the ability to compare 6-h integrated fluxes (much of the ship data originate from automated observing systems recording continual measurements), and the ability to perform more exhaustive quality control on these measurements. Over 4500 6-h component (sea level pressure, air and sea temperature, winds, and specific humidity) and flux (latent, sensible, and momentum) matches are statistically evaluated to quantify uncertainties between the ship observations and the NCEPR.
Primary results include a significant underestimation in NCEPR near-surface wind speed at all latitudes. The magnitude of the low bias increases at higher ship wind speeds and may be related to large (rms = 2.7 hPa) errors in sea level atmospheric pressure over the entire globe. The pressure biases show the NCEPR to underestimate the amplitude and/or position of both high and low pressures. The NCEPR slightly underestimates the momentum flux, in part, due to the weaker winds. The NCEPR sensible and latent heat fluxes are largely overestimated when compared to the WOCE ship data. Potential sources of this overestimation (e.g., the NCEPR model flux parameterization) are discussed. Using the NCEPR meteorological variables and an independent flux parameterization, the revised NCEPR sensible heat fluxes are closer to the observations, and the biases of the revised NCEPR latent heat flux change sign. Furthermore, while the revised latent heat flux values reduce the magnitude of the bias at higher wind speeds, they increase the bias at (more frequently occurring) moderate wind speeds and thus may not be suitable for many applications.
Abstract
The uncertainties in the NCEP–NCAR reanalysis (NCEPR) products are not well known. Using a newly developed, high-resolution, quality controlled, surface meteorology dataset from research vessels participating in the World Ocean Circulation Experiment (WOCE), regional and global uncertainties are quantified for the NCEPR air–sea fluxes and the component fields used to create those fluxes.
For the period 1990–95, WOCE vessel and gridded NCEPR fields are matched in time and space. All in situ data are subject to data quality review to remove suspect data. Adjustment of ship observations to the reference height of the NCEPR variables, and calculation of air–sea fluxes from the in situ data are accomplished using bulk formulas that take atmospheric stability, height of the measurements, and other adjustments into consideration. The advantages of using this new set of WOCE ship observations include the ability to compare 6-h integrated fluxes (much of the ship data originate from automated observing systems recording continual measurements), and the ability to perform more exhaustive quality control on these measurements. Over 4500 6-h component (sea level pressure, air and sea temperature, winds, and specific humidity) and flux (latent, sensible, and momentum) matches are statistically evaluated to quantify uncertainties between the ship observations and the NCEPR.
Primary results include a significant underestimation in NCEPR near-surface wind speed at all latitudes. The magnitude of the low bias increases at higher ship wind speeds and may be related to large (rms = 2.7 hPa) errors in sea level atmospheric pressure over the entire globe. The pressure biases show the NCEPR to underestimate the amplitude and/or position of both high and low pressures. The NCEPR slightly underestimates the momentum flux, in part, due to the weaker winds. The NCEPR sensible and latent heat fluxes are largely overestimated when compared to the WOCE ship data. Potential sources of this overestimation (e.g., the NCEPR model flux parameterization) are discussed. Using the NCEPR meteorological variables and an independent flux parameterization, the revised NCEPR sensible heat fluxes are closer to the observations, and the biases of the revised NCEPR latent heat flux change sign. Furthermore, while the revised latent heat flux values reduce the magnitude of the bias at higher wind speeds, they increase the bias at (more frequently occurring) moderate wind speeds and thus may not be suitable for many applications.
Applying Automated Underway Ship Observations to Numerical Model EvaluationAbstract
Numerical models are used widely in the oceanic and atmospheric sciences to estimate and forecast conditions in the marine environment. Herein the application of in situ observations collected by automated instrumentation on ships at sampling rates ≤5 min is demonstrated as a means to evaluate numerical model analyses. Specific case studies use near-surface ocean observations collected by a merchant vessel, an ocean racing yacht, and select research vessels to evaluate various ocean analyses from the Hybrid Coordinate Ocean Model (HYCOM). Although some specific differences are identified between the observations and numerical model analyses, the purpose of these comparisons is to demonstrate the value of high-sampling-rate in situ observations collected on ships for numerical model evaluation.
Abstract
Numerical models are used widely in the oceanic and atmospheric sciences to estimate and forecast conditions in the marine environment. Herein the application of in situ observations collected by automated instrumentation on ships at sampling rates ≤5 min is demonstrated as a means to evaluate numerical model analyses. Specific case studies use near-surface ocean observations collected by a merchant vessel, an ocean racing yacht, and select research vessels to evaluate various ocean analyses from the Hybrid Coordinate Ocean Model (HYCOM). Although some specific differences are identified between the observations and numerical model analyses, the purpose of these comparisons is to demonstrate the value of high-sampling-rate in situ observations collected on ships for numerical model evaluation.
Abstract
A case study investigation into the meridional and horizontal circulation over the South Pacific Ocean is presented for the 1986–89 El Niño–Southern Oscillation (ENSO) cycle. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses, annual average fields are created for the years before and after the 1987 minimum (warm phase) and 1989 maximum (cold phase) in the Southern Oscillation index. The analyses reveal a shift in the split jet stream over the south Pacific sector(180°–120°W)from a strong subtropical jet (STJ) and weak polar front jet (PFJ) during the warm phase to a weak STJ and strong PFJ during the cold phase.
Analysis of the momentum budget reveals how the split jet in the upper troposphere over South Pacific Ocean evolved during the 1986–89 ENSO cycle. During the warm phase, the strong STJ is associated with advection of the mean flow momentum flux from the Australian sector, which is approximately balanced by a large negative ageostrophic term; the PFJ is primarily associated with eddy momentum convergence, which is partially counterbalanced by the ageostrophic term. During the cold phase, the weakened STJ is related to an increasingly negative ageostrophic term and a less positive mean flow momentum convergence. The strengthened PFJ is associated with an increase in the convergence of eddy momentum flux that is mainly composed of 2.5–6-day poleward momentum transport from midlatitudes and 7–30-day equatorward momentum transport from high latitudes. In general, the impacts of eddy stress on the STJ and the mean momentum divergence on the PFJ in this sector are small.
The variations in the split jet may reflect the poleward propagation of the ENSO signal via the South Pacific convergence zone. The implications for the high southern latitudes are discussed as interannual variations are found in the low-level easterlies near Antarctica and the Amundsen Sea low.
Abstract
A case study investigation into the meridional and horizontal circulation over the South Pacific Ocean is presented for the 1986–89 El Niño–Southern Oscillation (ENSO) cycle. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses, annual average fields are created for the years before and after the 1987 minimum (warm phase) and 1989 maximum (cold phase) in the Southern Oscillation index. The analyses reveal a shift in the split jet stream over the south Pacific sector(180°–120°W)from a strong subtropical jet (STJ) and weak polar front jet (PFJ) during the warm phase to a weak STJ and strong PFJ during the cold phase.
Analysis of the momentum budget reveals how the split jet in the upper troposphere over South Pacific Ocean evolved during the 1986–89 ENSO cycle. During the warm phase, the strong STJ is associated with advection of the mean flow momentum flux from the Australian sector, which is approximately balanced by a large negative ageostrophic term; the PFJ is primarily associated with eddy momentum convergence, which is partially counterbalanced by the ageostrophic term. During the cold phase, the weakened STJ is related to an increasingly negative ageostrophic term and a less positive mean flow momentum convergence. The strengthened PFJ is associated with an increase in the convergence of eddy momentum flux that is mainly composed of 2.5–6-day poleward momentum transport from midlatitudes and 7–30-day equatorward momentum transport from high latitudes. In general, the impacts of eddy stress on the STJ and the mean momentum divergence on the PFJ in this sector are small.
The variations in the split jet may reflect the poleward propagation of the ENSO signal via the South Pacific convergence zone. The implications for the high southern latitudes are discussed as interannual variations are found in the low-level easterlies near Antarctica and the Amundsen Sea low.
Abstract
Changes in the peak wind gust magnitude in association with the warm and cold phases of the El Niño–Southern Oscillation (ENSO) are identified over the contiguous United States. All calculations of the peak wind gust are differences in the extreme phases of ENSO (warm and cold) relative to neutral for all stations in the study that pass the completeness criteria. Monthly composites were created for all years in the study (1 January 1948 through 31 August 1998). The differences in the mean peak wind gust are calculated for each month. A nonparametric statistical test was invoked to determine significant shifts in the extreme phase distributions. Differences in the frequency of gale-force wind gusts were also calculated.
The results show a dominant, ENSO cold phase wintertime signal. Regions most greatly affected are the Pacific Northwest, Southwest, the Great Plains, and the region extending from the Great Lakes through the Ohio River valley, and southwest into Texas. During the cold phase months from November to March, these regions experience an overall increase in the gustiness of the winds. The warm phase is associated with overall decreased gustiness in the Pacific Northwest during these months; however, the signal is of a lesser magnitude. There is also an observed decrease in the central Great Plains during the warm phase months of April and June. These results, along with improved ENSO forecasting, can work toward mitigating adverse effects of strong wind gusts and increase the utilization of wind power.
Abstract
Changes in the peak wind gust magnitude in association with the warm and cold phases of the El Niño–Southern Oscillation (ENSO) are identified over the contiguous United States. All calculations of the peak wind gust are differences in the extreme phases of ENSO (warm and cold) relative to neutral for all stations in the study that pass the completeness criteria. Monthly composites were created for all years in the study (1 January 1948 through 31 August 1998). The differences in the mean peak wind gust are calculated for each month. A nonparametric statistical test was invoked to determine significant shifts in the extreme phase distributions. Differences in the frequency of gale-force wind gusts were also calculated.
The results show a dominant, ENSO cold phase wintertime signal. Regions most greatly affected are the Pacific Northwest, Southwest, the Great Plains, and the region extending from the Great Lakes through the Ohio River valley, and southwest into Texas. During the cold phase months from November to March, these regions experience an overall increase in the gustiness of the winds. The warm phase is associated with overall decreased gustiness in the Pacific Northwest during these months; however, the signal is of a lesser magnitude. There is also an observed decrease in the central Great Plains during the warm phase months of April and June. These results, along with improved ENSO forecasting, can work toward mitigating adverse effects of strong wind gusts and increase the utilization of wind power.
Abstract
Techniques are presented for the computation and quality control of true winds from vessels at sea. Correct computation of true winds and quality-control methods are demonstrated for complete data. Additional methods are presented for estimating true winds from incomplete data. Recommendations are made for both existing data and future applications.
Quality control of automated weather station (AWS) data at the World Ocean Circulation Experiment Surface Meteorological Data Center reveals that only 20% of studied vessels report all parameters necessary to compute a true wind. Required parameters include the ship’s heading, course over the ground (COG), speed over the ground, wind vane zero reference, and wind speed and direction relative to the vessel. If any parameter is omitted or incorrect averaging is applied, AWS true wind data display systematic errors. Quantitative examples of several problems are shown in comparisons between collocated winds from research vessels and the NASA scatterometer (NSCAT). Procedures are developed to identify observational shortcomings and to quantify the impact of these shortcomings in the determination of true wind observations.
Methods for estimating true winds are presented for situations where heading or COG is missing. Empirical analysis of two vessels with high-quality AWS data showed these estimates to be more accurate when the vessel heading is available. Large differences between the heading and COG angles at low ship speeds make winds estimated using the course unreliable (direction errors exceeding 60°) for ship speeds less than 2.0 m s−1. The threshold where the direction difference between a course estimated and true wind reaches an acceptable level (±10°) depends upon the ship, winds, and currents in the vessel’s region of operation.
Abstract
Techniques are presented for the computation and quality control of true winds from vessels at sea. Correct computation of true winds and quality-control methods are demonstrated for complete data. Additional methods are presented for estimating true winds from incomplete data. Recommendations are made for both existing data and future applications.
Quality control of automated weather station (AWS) data at the World Ocean Circulation Experiment Surface Meteorological Data Center reveals that only 20% of studied vessels report all parameters necessary to compute a true wind. Required parameters include the ship’s heading, course over the ground (COG), speed over the ground, wind vane zero reference, and wind speed and direction relative to the vessel. If any parameter is omitted or incorrect averaging is applied, AWS true wind data display systematic errors. Quantitative examples of several problems are shown in comparisons between collocated winds from research vessels and the NASA scatterometer (NSCAT). Procedures are developed to identify observational shortcomings and to quantify the impact of these shortcomings in the determination of true wind observations.
Methods for estimating true winds are presented for situations where heading or COG is missing. Empirical analysis of two vessels with high-quality AWS data showed these estimates to be more accurate when the vessel heading is available. Large differences between the heading and COG angles at low ship speeds make winds estimated using the course unreliable (direction errors exceeding 60°) for ship speeds less than 2.0 m s−1. The threshold where the direction difference between a course estimated and true wind reaches an acceptable level (±10°) depends upon the ship, winds, and currents in the vessel’s region of operation.
Abstract
Changes in the frequency of occurrence of snowfall during El Niño–Southern Oscillation (ENSO) events are presented for the continental United States. This study is motivated by the need to improve winter climate forecasts for government agencies (i.e., U.S. Department of Transportation and Department of Energy) and winter entertainment facilities and the need for climatological studies. Daily snowfall data from 442 stations in the U.S. Historical Climatology Network are utilized. Selected stations each have more than 20 yr with 15 or more snowfall events per year during a 97-yr (1900–97) period of study. Three categories are created for each ENSO phase, based on the magnitude of daily snowfall amounts (in millimeters)—light: (0–50.8], moderate: (50.8–152.4], and heavy: (152.4–304.8]. Differences between neutral and cold or warm ENSO winters are created to show regions with increased or decreased occurrences in each snowfall category. Statistical tests are applied at each station to provide confidence levels for the identified changes in snowfall frequency. Simple field significance tests are completed for regions that show coherent ENSO signals. Results reveal several regions with significant changes in the frequency of occurrence of snowfall between neutral and cold or warm ENSO phases. For example, the Pacific Northwest has increased (decreased) occurrences of light, moderate, and heavy snowfalls during the cold- (warm-) phase ENSO winter, with the exception of light snows during the warm phase. Other regions with significant changes include the northern and eastern Great Lakes, the Northeast Corridor, and New England. The results may allow government agencies and private companies to mitigate adverse impacts of winter storms based on predictions of upcoming ENSO phases. Winter entertainment facilities, such as ski resorts, may actually benefit from these results. Combined with other winter-precipitation studies and the ever-improving ability to forecast each ENSO phase, this analysis of snow-event frequencies should aid in preparation for winter storms.
Abstract
Changes in the frequency of occurrence of snowfall during El Niño–Southern Oscillation (ENSO) events are presented for the continental United States. This study is motivated by the need to improve winter climate forecasts for government agencies (i.e., U.S. Department of Transportation and Department of Energy) and winter entertainment facilities and the need for climatological studies. Daily snowfall data from 442 stations in the U.S. Historical Climatology Network are utilized. Selected stations each have more than 20 yr with 15 or more snowfall events per year during a 97-yr (1900–97) period of study. Three categories are created for each ENSO phase, based on the magnitude of daily snowfall amounts (in millimeters)—light: (0–50.8], moderate: (50.8–152.4], and heavy: (152.4–304.8]. Differences between neutral and cold or warm ENSO winters are created to show regions with increased or decreased occurrences in each snowfall category. Statistical tests are applied at each station to provide confidence levels for the identified changes in snowfall frequency. Simple field significance tests are completed for regions that show coherent ENSO signals. Results reveal several regions with significant changes in the frequency of occurrence of snowfall between neutral and cold or warm ENSO phases. For example, the Pacific Northwest has increased (decreased) occurrences of light, moderate, and heavy snowfalls during the cold- (warm-) phase ENSO winter, with the exception of light snows during the warm phase. Other regions with significant changes include the northern and eastern Great Lakes, the Northeast Corridor, and New England. The results may allow government agencies and private companies to mitigate adverse impacts of winter storms based on predictions of upcoming ENSO phases. Winter entertainment facilities, such as ski resorts, may actually benefit from these results. Combined with other winter-precipitation studies and the ever-improving ability to forecast each ENSO phase, this analysis of snow-event frequencies should aid in preparation for winter storms.
Abstract
El Niño–Southern Oscillation (ENSO) is a natural, coupled atmospheric–oceanic cycle that occurs in the tropical Pacific Ocean on an approximate timescale of 2–7 yr. ENSO events have been shown in previous studies to be related to regional extremes in weather (e.g., hurricane occurrences, frequency and severity of tornadoes, droughts, and floods). The teleconnection of ENSO events to extreme weather events means that the ability to classify an event as El Niño or La Niña is of interest in scientific and other applications.
ENSO is most often classified using indices that indicate the warmth and coolness of equatorial tropical Pacific Ocean sea surface temperatures (SSTs). Another commonly used index is based on sea level pressure differences measured across the tropical Pacific Ocean. More recently, other indices have been proposed and have been shown to be effective in describing ENSO events. There is currently no consensus within the scientific community as to which of many indices best captures ENSO phases. The goal of this study is to compare several commonly used ENSO indices and to determine whether or not one index is superior in defining ENSO events; or alternatively, to determine which indices are best for various applications.
The response and sensitivity of the SST-based indices and pressure-based indices are compared. The Niño-4 index has a relatively weak response to El Niño; the Niño-1+2 index has a relatively strong response to La Niña. Analysis of the sensitivity of the indices relative to one another suggests that the choice of index to use in ENSO studies is dependent upon the phase of ENSO that is to be studied. The Japan Meteorological Agency (JMA) index is found to be more sensitive to La Niña events than all other indices. The Southern Oscillation, Niño-3.4, and Niño-4 indices are almost equally sensitive to El Niño events and are more sensitive than the JMA, Niño-1+2, and Niño-3 indices.
Abstract
El Niño–Southern Oscillation (ENSO) is a natural, coupled atmospheric–oceanic cycle that occurs in the tropical Pacific Ocean on an approximate timescale of 2–7 yr. ENSO events have been shown in previous studies to be related to regional extremes in weather (e.g., hurricane occurrences, frequency and severity of tornadoes, droughts, and floods). The teleconnection of ENSO events to extreme weather events means that the ability to classify an event as El Niño or La Niña is of interest in scientific and other applications.
ENSO is most often classified using indices that indicate the warmth and coolness of equatorial tropical Pacific Ocean sea surface temperatures (SSTs). Another commonly used index is based on sea level pressure differences measured across the tropical Pacific Ocean. More recently, other indices have been proposed and have been shown to be effective in describing ENSO events. There is currently no consensus within the scientific community as to which of many indices best captures ENSO phases. The goal of this study is to compare several commonly used ENSO indices and to determine whether or not one index is superior in defining ENSO events; or alternatively, to determine which indices are best for various applications.
The response and sensitivity of the SST-based indices and pressure-based indices are compared. The Niño-4 index has a relatively weak response to El Niño; the Niño-1+2 index has a relatively strong response to La Niña. Analysis of the sensitivity of the indices relative to one another suggests that the choice of index to use in ENSO studies is dependent upon the phase of ENSO that is to be studied. The Japan Meteorological Agency (JMA) index is found to be more sensitive to La Niña events than all other indices. The Southern Oscillation, Niño-3.4, and Niño-4 indices are almost equally sensitive to El Niño events and are more sensitive than the JMA, Niño-1+2, and Niño-3 indices.
Abstract
Regional variations in North Atlantic hurricane landfall frequency along the U.S. coastline are examined in relation to the phase of El Niño–Southern Oscillation (ENSO). ENSO warm (cold) phases are known to reduce (increase) hurricane activity in the North Atlantic basin as a whole. Using best-track data from the U.S. National Hurricane Center, regional analysis reveals that ENSO cold-phase landfall frequencies are only slightly larger than neutral-phase landfall frequencies along the Florida and Gulf coasts. However, for the East Coast, from Georgia to Maine, a significant decrease in landfall frequency occurs during the neutral ENSO phase as compared to the cold phase. Along the East Coast, two or more major (category 3 or above) hurricanes never made landfall in the observational record (1900–2004) during a single hurricane season classified as an ENSO neutral or warm phase.
Abstract
Regional variations in North Atlantic hurricane landfall frequency along the U.S. coastline are examined in relation to the phase of El Niño–Southern Oscillation (ENSO). ENSO warm (cold) phases are known to reduce (increase) hurricane activity in the North Atlantic basin as a whole. Using best-track data from the U.S. National Hurricane Center, regional analysis reveals that ENSO cold-phase landfall frequencies are only slightly larger than neutral-phase landfall frequencies along the Florida and Gulf coasts. However, for the East Coast, from Georgia to Maine, a significant decrease in landfall frequency occurs during the neutral ENSO phase as compared to the cold phase. Along the East Coast, two or more major (category 3 or above) hurricanes never made landfall in the observational record (1900–2004) during a single hurricane season classified as an ENSO neutral or warm phase.
