university of oklahoma graduate college a … · 2014. 3. 11. · university of oklahoma graduate...

UNIVERSITY OF OKLAHOMA

GRADUATE COLLEGE

A PRELIMINARY CLIMATOLOGY OF U.S. ICE STORM FREQUENCY AND A

COMPARISON BETWEEN NORTHEAST U.S. ICE STORM FREQUENCY AND

TELECONNECTIONS

A THESIS

SUBMITTED TO THE GRADUATE FACULTY

In partial fulfillment of the requirements for the

Degree of

MASTER OF SCIENCE IN METEOROLOGY

By

CARLY ELAINE KOVACIK

Norman, Oklahoma

2013

A PRELIMINARY CLIMATOLOGY OF U.S. ICE STORM FREQUENCY AND A COMPARISON BETWEEN NORTHEAST U.S. ICE STORM FREQUENCY AND

TELECONNECTIONS

A THESIS APPROVED FOR THE SCHOOL OF METEOROLOGY

BY

_____________________________________________ Dr. Kevin Kloesel, Chair

_____________________________________________ Dr. Steven Cavallo

_____________________________________________ Dr. Frederick Carr

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Acknowledgements

First and foremost I would like to thank my advisor, Dr. Kevin Kloesel, for

his endless support and patience over the past two years. I cannot express my

appreciation towards the opportunity that he has given me to obtain a Master’s

degree from one of the most respectable schools in meteorology. Dr. Kloesel has

been one of the most inspiring, outgoing, respectful, and kind-hearted people I

have met and I am grateful to have had the opportunity to work under him. He

enjoyed challenging me, yet never gave me more than I could handle and was

always willing to lend a hand or listen to me when I needed it most. I am truly

honored to say that I have had the pleasure of interacting with him over the past

several years. I could not have asked for a better advisor.

I would also like to thank my committee members, Dr. Frederick Carr and

Dr. Steven Cavallo, for taking the time out of their busy schedules and career to be

a part of my research experience. They have provided a great deal of support and

guidance over the past several months that I am very grateful for.

I am also honored to have had the opportunity to work with Sid Sperry. I

cannot thank Sid enough for his patience and enthusiasm towards my thesis topic.

Working with Sid has opened many doors in my life that I would have never

thought possible, and for that I am grateful. I am thankful to know that I will have

an everlasting connection with him and the people of the Oklahoma Association of

Electric Cooperatives (OAEC). Interning with Sid and OAEC has been such a

positive experience and has helped me mature in multiple ways.

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With this, I would also like to thank the Southern Climate Impacts Planning

Program and the Oklahoma Climatological Survey for the opportunity to expand on

my research from REU and for financially supporting me over the past several

years. The people associated with these two organizations are one of a kind and I

would not have traded the opportunity to get to know them for anything in the

world. Everyone has been extremely supportive of my research and future goals,

and I appreciate that.

Mark Shafer of the Oklahoma Climatological Survey deserves a special

thank you. Mark was an extremely supportive mentor during my REU internship

and was enthusiastic about me returning to OU as a graduate student. Throughout

the past two years, he took the time to meet with me and discuss my research. He

also provided helpful advice about career planning and long-term goals. This has

been very important to me and I appreciate his generosity and respect.

A personal thank you is given to Nicole Grams for her help with GIS and

ArcMap throughout the extent of my thesis. A graduate student herself at the time,

she put aside some of her valuable time to introduce to me to the basics of plotting

data on a map, as well as providing prompt advice and feedback when I

encountered problems. Without her help, I would still be staring at a blank map of

the United States and it is safe to say you would not be reading this thesis right

now.

A special thanks goes out to Eric Jacobsen for his patience and support. Eric

has been there for me every step of the way throughout my graduate career at OU,

from establishing a true friendship with me to lending a helping hand during study

vi

sessions to introducing me to programming code relevant to my research. The ride

through graduate school would not have been as smooth (or enjoyable) without

his genuine character or emotional support.

I would like express my gratitude towards my parents for all of their

financial and emotional support and help over the years. It is through them that I

have had the opportunity to obtain several college degrees. They have challenged

me and made me work hard towards my goals, but they supported me every step

of the way and picked me up each time I fell. I will never be able to thank these two

influential and wonderful people as much as would like to. Words cannot describe

how honored I feel to be their daughter. I would not be where I am today without

them.

Finally, a special thanks also goes out to all of my friends at OU who have

reached out to establish an everlasting friendship with me and who have always

been more than willing to provide an extra hand when I needed it the most. I

cannot express how grateful I am to have shared this wonderful journey with you

all. I would like to personally thank: Kristen Cassady, Yunsung Hwang, Eric

Jacobsen, Ariel Cohen, Jill Hardy, Chris Kerr, Rebecca McCarter, and Paul Downes.

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Table of Contents

LIST OF FIGURES .............................................................................................................................. VIII

ABSTRACT .......................................................................................................................................... XIII

CHAPTER 1: INTRODUCTION ............................................................................................................ 1 1.1 MOTIVATION .................................................................................................................................................. 1

1.1.1 Research Experience for Undergraduates (REU) Project ...................................................... 2 1.2 FREEZING RAIN CHARACTERISTICS ........................................................................................................... 4 1.3 GOAL AND IMPORTANCE .............................................................................................................................. 5

CHAPTER 2: PREVIOUS ICE STORM RESEARCH ......................................................................... 7

CHAPTER 3: METHODOLOGY ........................................................................................................ 32

CHAPTER 4: PRELIMINARY CLIMATOLOGY OF ICE STORMS ACROSS THE U.S. ........... 44 4.1 ICE STORM FREQUENCY BETWEEN 1966-1977 .................................................................................. 44 4.2 ICE STORM FREQUENCY BETWEEN 1998-2011 .................................................................................. 45 4.3 FREQUENCY DIFFERENCES BETWEEN 1966-1977 AND 1998-2011 DATA.................................. 47 4.4 DATA INCONSISTENCIES ........................................................................................................................... 51

4.4.1 Definition of an Ice Storm .................................................................................................................. 51 4.4.2 Ice Accretion Measurement Methods ........................................................................................... 53 4.4.3 Storm Data Reporting ......................................................................................................................... 54 4.4.4 Automated Surface Observing Systems (ASOS) ....................................................................... 56

CHAPTER 5: INTRODUCTION TO TELECONNECTIONS .......................................................... 60 5.1 ATLANTIC MULTIDECADAL OSCILLATION (AMO)................................................................................ 60

5.1.1 Introduction to the AMO .................................................................................................................... 60 5.1.2 Existing Literature: AMO ................................................................................................................... 61

5.2 NORTH ATLANTIC OSCILLATION (NAO) ................................................................................................ 63 5.2.1 Positive NAO ............................................................................................................................................. 64 5.2.2 Negative NAO .......................................................................................................................................... 65 5.2.3 Existing Literature: NAO .................................................................................................................... 65

5.3 ARCTIC OSCILLATION (AO) ...................................................................................................................... 67 5.3.1 Positive AO ................................................................................................................................................ 68 5.3.2 Negative AO .............................................................................................................................................. 69 5.3.3 Existing Literature: AO ....................................................................................................................... 69

5.4 EL NINO-SOUTHERN OSCILLATION (ENSO) ......................................................................................... 73 5.4.1 El Nino ........................................................................................................................................................ 74 5.4.2 La Nina ....................................................................................................................................................... 74 5.4.3 Existing Literature: ENSO.................................................................................................................. 75

CHAPTER 6: ICE STORM FREQUENCY IN NEW ENGLAND AND ITS ASSOCIATION WITH TELECONNECTION PATTERNS. ..................................................................................................... 78

6.1 DECADAL ICE STORMS IN NEW ENGLAND .............................................................................................. 78 6.1.1 Ice Storm Frequency Versus the AMO .......................................................................................... 88 6.1.2 Ice Storm Frequency Versus the NAO ........................................................................................... 92 6.1.3 Ice Storm Frequency Versus the AO .............................................................................................. 95 6.1.4 Ice Storm Frequency Versus ENSO ................................................................................................ 99

CHAPTER 7: SUMMARY AND CONCLUSIONS ........................................................................... 111

REFERENCES ...................................................................................................................................... 115

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List of Figures Figure 1.1 The total number of ice storms documented in the Southern Plains of

the United States between 2000-2009. The highest frequency of ice storms is evident across southwest Oklahoma, and extends northeast into central Missouri.

Figure 1.2 Same as in Figure 1.1, except NWS WFO boundaries are overlaid on

the ice storm distribution. Notable changes in the number of documented ice storms can be seen across several WFO boundaries, particularly in parts of Missouri and Texas.

Figure 2.1 A graphic from Changnon (2002) showing the amount of loss, in

millions of dollars, from ice storms between 1949-2000 for all regions of the United States. In parentheses, the average loss per catastrophe, in millions of dollars, is shown for all region of the United States. The Northeast experienced the most loss.

Figure 2.2 A graphic taken from Changnon (2002) showing the number of ice

storm catastrophes in all regions of the United States between 1949-2000. The number of catastrophes with losses is shown in parentheses. The Northeast experienced the most catastrophes.

Figure 2.3 The average number of freezing rain days across the United States

between 1949-2000 (Changnon 2002). The highest average is in the Northeast, extending down into the Appalachians and west into the central United States.

Figure 2.4 The number of hours of freezing rain in the United States between

1976-1990. The Northeast, Mid Atlantic, Appalachians, and parts of the Midwest and Plains experienced the highest number of freezing rain hours.

Figure 2.5 Graphic from Call (2008). Left: The average seriousness rating of an

ice storm on a scale of 2-5. Oklahoma, Arkansas, and Tennessee had the highest rating. Right: The average rank of ice storms relative to other hazardous weather phenomenon. Ice storms ranked highest around the Ohio Valley.

Figure 3.1 An entry contained within a monthly publication of NCDC’s Storm

Data. Each entry provides specific details of a hazardous weather event. This entry is for an ice storm/mixed precipitation event in New Jersey that includes the date of the event, the counties affected, the duration of the event, estimated losses, and an overview of the event and its associated impacts.

ix

Figure 3.2 Annual global temperature anomalies associated with El Nino(red),

La Nina (blue), and Neutral (gray) seasons between 1950-2012. A notable change was observed around 1978, where La Nina became associated with positive global temperatures anomalies and El Nino became associated with higher positive anomalies. This image was provided by NCDC.

Figure 4.1 The total number of ice storms during the winter seasons between

1966-1977 across the contiguous United States. New England experienced the most ice storms, while areas west of the Rockies and the Deep South experienced the least.

Figure 4.2 The total number of ice storms during the winter seasons between

1998-2011. The southern portion of the Northeast experienced the most ice storms, while regions west of the Rockies and the Deep South experienced the least.

Figure 4.3 The total number of winter season ice storms between 1998-2011

subtracted from the total winter season ice storms between 1966-1977. Red colors indicate more ice storms between 1966-1977 and blue colors indicate more ice storms between 1998-2011. A notable shift was seen across the Northeast and an increase in ice storms was found across the Midwest and central United States.

Figure 4.4 The difference in winter season ice storms across the Northeast.

More ice storms occurred in the northern Northeast between 1966-1977, while more ice storms occurred in the southern Northeast between 1998-2011.

Figure 4.5 An example of an ASOS site. Photo provided by www.meteo.psu.edu.

Figure 6.1 The average number of ice storms in the Northeast between 1966-

1969. The highest average was across New Hampshire, southern Maine, and northern and eastern Massachusetts.

Figure 6.2 The average number of ice storms in the Northeast during the 1970s.

The highest average was found across New Hampshire, southern Maine, and northern and eastern Massachusetts.


The highest averages were located farther south than the previous two decades, with maxima across eastern New York and eastern Pennsylvania.

x

Figure 6.4 The average number of ice storms in the Northeast during the 1990s. The 1990s was the most active decade with highest averages across eastern New York, eastern Pennsylvania, and northwest New Jersey.


The highest average was found across eastern Pennsylvania and northwest New Jersey near a mountainous area.

Figure 6.6 The normalized value of the AMO representing each winter season of

study. The AMO was negative between the winters of 1966-1997. A positive phase has been present since.

Figure 6.7 The average number of ice storms in the Northeast during winter

seasons where the AMO index was negative. A large portion of the area averaged between 1-2 ice storms.

Figure 6.8 The average number of ice storms in the Northeast during winter

seasons in which the AMO is positive. The highest average was found across the Poconos Mountains region of eastern Pennsylvania and northwest New Jersey.

Figure 6.9 The normalized NAO index for the winter seasons between 1966-

2011. A positive phase has been present during most seasons between 1987-2008.

Figure 6.10 The average number of ice storms in the Northeast during negative

NAO winter seasons. Most locations experienced an average of 1-2 ice storms, except areas near Lake Ontario and coastal areas.

Figure 6.11 The average number of ice storms in the Northeast during positive

NAO winter seasons. The averages closely resemble the results of the negative NAO seasons, with most locations experiencing 1-2 ice storms.

Figure 6.12 The normalized value of the AO index for the winter seasons

between 1966-2011. The AO has been oscillatory, but has shown trends towards a negative phase since the 1990s.

Figure 6.13 The average number of ice storms in the Northeast during negative

AO winter seasons. Higher averages extend northeast from Maryland to Maine.


AO winter seasons. Higher averages were seen over a broader area than during negative seasons, most notably across eastern New York.

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AO winter seasons. Higher averages were seen over a broader area than during negative seasons, most notably across eastern New York.

Figure 6.16 The average number of ice storms in the Northeast during El Nino

winter seasons. High averages were located across the northern Northeast, while low averages were located across the southern Northeast.

Figure 6.17 The average number of ice storms in the Northeast during La Nina

winter seasons. Higher averages were found across the southern Northeast compared to the northern Northeast.

Figure 6.18 The average number of ice storms in the Northeast during ENSO-

neutral winter seasons. No significant trend was noted, as most locations averaged around one ice storm.

Figure 6.19 A spatial comparison of the average number of winter season ice

storms across the Northeast during negative AMO (left) and El Nino seasons (right). The higher frequency is located across the northern portion of the Northeast.

Figure 6.20 Same as Figure 6.20, but for positive AMO (left) and La Nina seasons

(right). The higher frequency was found across the southern portion of the Northeast.

Figure 6.21 The average number of winter season ice storms in the Northeast

when El Nino and negative AMO conditions are present together. A maximum frequency is located over the northern portion of the Northeast and a minimum is found over the southern portion of the Northeast.

Figure 6.22 The average number of winter season ice storms in the Northeast

when La Nina and positive AMO conditions are present together. A maximum is found across the southern portion of the Northeast and a minimum is found across the northern portion of the Northeast.

Figure 6.23 The number of hours of freezing rain in Portland, Maine between

1966-2011 taken from the ASOS site. The number of freezing rain hours increased between the 1960s and the 1990s, with a peak near 1999. The number of hours began to decrease in recent years.

xii

Figure 6.24 The number of hours of freezing rain in Albany, New York between the winter seasons of 1966-2011. No trend was found, although the 1990s experienced the highest number of freezing rain hours.

xiii

Abstract

Ice storms are a severe winter weather phenomenon involving damaging

accumulations of freezing rain. While the atmospheric conditions conducive to the

formation of freezing rain are well known, few studies have analyzed spatial and

temporal changes of ice storms over an extended period of time. To address this, a

preliminary climatology of ice storm frequency was developed across the

contiguous United States for the winter seasons (December, January, and

February) between 1966-1977 and 1998-2011. These periods were chosen

because they were associated with notable changes in global temperature

anomalies associated with the El Nino-Southern Oscillation (ENSO). The most

notable shift in ice storm frequency between the two time periods was observed

over the northeast U.S. and hypothesized to be associated with changes in global

atmospheric circulations. An increase in ice storm frequency was also seen across

the central United States and Southern Plains, but was hypothesized to be an

artifact of reporting improvements. A climatology of northeast United States ice

storms from 1966-2011 was then compared to phase changes of the North Atlantic

Oscillation (NAO), the Arctic Oscillation (AO), the Atlantic Multidecadal Oscillation

(AMO), and ENSO. Ice storm frequency was highest across the northern portion of

the Northeast when El Nino conditions were present with negative AMO

conditions, while ice storm frequency was highest across the southern portion of

the Northeast when La Nina conditions were present with positive AMO

conditions. Inconsistencies within the existing definition of an ice storm and ice

accretion measurements were encountered and determined to hinder the accuracy

xiv

of existing ice storm climatologies, though the extent is unclear. All preliminary

conclusions drawn from this study will serve as a basis for future quantitative

studies that will improve short term forecasting and preparation strategies.

1

Chapter 1: Introduction

1.1 Motivation

Winter weather has a pronounced impact on both lives and property across

the contiguous United States. Numerous studies have been conducted on these

types of events to better understand the dynamical aspects, synoptic evolution,

corresponding mesoscale features, and associated hazards to improve short-term

forecast ability. A majority of these studies have focused on severe snowstorm or

blizzard events, however, mixed precipitation events and ice storms are also of

great hazard.

Ice storms are dangerous and destructive winter weather events. Freezing

rain and freezing drizzle produce hazardous environmental conditions with

significant societal impacts that can last from several days to several weeks.

Industries that are affected by these events include power, transportation,

aviation, insurance, public safety, etc. Minor glaze accumulation causes pedestrian

and traffic accidents, while severe ice storms cause power outages, delays and

closings of ground and air transportation, property damage, and physical injury

(Rauber et al. 2001). Despite these impacts, an accurate climatology of ice storms

across the contiguous United States has not been developed. With a sparse data

archive for a winter weather phenomenon with harsh impacts, it remains crucial to

expand the research in this area. Improvements in ice storm knowledge and

research will better ice storm forecasts and warnings from operational

meteorologists.

2

1.1.1 Research Experience for Undergraduates (REU) Project

Kovacik et al. (2010) worked closely with the Oklahoma Climatological

Survey in Norman, Oklahoma to develop a preliminary climatology of ice storm

frequency and distribution across the Southern Plains of the United States using

Storm Data. It was initially concluded that a belt of higher ice storm frequency

extended from southwest Oklahoma, northeastward, into central Missouri (Figure

1.1). However, when overlaying National Weather Service (NWS) Weather

Forecast Office (WFO) boundaries over the ice storm distribution, frequency

inconsistencies were noted between offices (Figure 1.2). This is most apparent

between the St. Louis, Missouri WFO and Springfield, Missouri WFO, between the

Tulsa, Oklahoma WFO and Wichita, Kansas WFO, and between the San Antonio,

Texas WFO and its neighboring WFOs (Kovacik et al. 2010).

3

Figure 1.1: The total number of ice storms documented in the Southern Plains of the United States between 2000-2009. The highest frequency of ice storms is evident

across southwest Oklahoma, and extends northeast into central Missouri.

Figure 1.2: Same as in Figure 1.1, except NWS WFO boundaries are overlaid on the ice storm distribution. Notable changes in the number of documented ice storms can

be seen across several WFO boundaries, particularly across parts of Missouri and Texas.

4

After qualitatively comparing ice storm frequency and distribution with

WFO boundaries, and noting the limitations of Storm Data, it was concluded that

the degree of inconsistency in ice storm reporting across the Southern Plains was

too severe to determine whether a belt of higher frequency exists across parts of

the Southern Plains. It was also concluded that developing a representative

climatology of ice storms within this region of the United States is a challenge

(Kovacik et al. 2010).

1.2 Freezing Rain Characteristics

The atmospheric characteristics that constitute an ice storm are complex.

Freezing rain is commonly embedded within a mid-latitude cyclone, which can

have a spatial extent ranging from hundreds to thousands of kilometers (km),

implying that the winter storm as a whole contains complex synoptic and

mesoscale characteristics. Freezing rain is considered a mesoscale phenomenon,

and is usually located within a narrow swath of the parent storm, making the

precise location and severity difficult to forecast (Rauber et al. 2001).

In order to understand ice storms from a meteorological or climatological

perspective, the formation mechanisms must be introduced. There are two

common environmental setups conducive to the formation of freezing rain

(Rauber et al. 1999). The most common involves a layer of above freezing air

(melting layer) located in the lower troposphere (between 700 and 800 hPa) that

is bounded above and below by sub-freezing air. Initially, a precipitation particle

falling through this atmosphere is frozen until it encounters the melting layer,

5

where it melts into a raindrop. As the raindrop approaches the surface, it

experiences a layer of sub-freezing air (Rauber et al. 1999). This freezing layer is

shallow and does not allow the appropriate amount of time for the raindrop to

freeze back into solid form. Instead, it becomes supercooled and freezes on contact

with any structure or object it encounters at the surface. Multiple particles

experiencing this process will cause an accumulation of ice to build over an

exposed surface. This process is termed the “melting process” (Rauber et al. 1999)

and has been speculated to be the primary formation mechanism for about 62% of

all freezing rain events (Bernstein 2000).

The second process conducive for freezing rain is the collision and

coalescence of droplets. This method is termed the “warm rain process” and is

common in stratiform precipitation events (Rauber et al. 1999). The vertical

profile of the atmosphere is below freezing, but only cold enough to support the

formation of supercooled droplets, which form within shallow cloud layers.

Freezing precipitation associated with this process is usually light and in the form

of drizzle. Although the accumulation of freezing drizzle can be dangerous at the

surface, it is especially hazardous aloft to the aviation industry, as heavy ice

accretion can impact aircraft performance and has lead to several accidents

(Rauber et al. 1999).

1.3 Goal and Importance

With the aforementioned shortcomings in the freezing rain literature and

the difficulty in forecasting winter weather, the primary goal of this study was to

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develop a preliminary climatology of ice storms across the contiguous United

States. The climatology was intended to yield spatial and temporal changes in ice

storm frequency over time. An investigation of potential meteorological sources

was performed after a qualitative analysis of the ice storm frequency and

distribution, with a focus on teleconnection patterns, including the El Nino-

Southern Oscillation (ENSO), the Arctic Oscillation (AO), the North Atlantic

Oscillation (NAO), and the Atlantic Multidecadal Oscillation (AMO). The results of

this study are aimed to address critical issues of the available data, particularly

inconsistencies in ice storm reporting and ice storm measurements over time,

along with the differing criteria applied to ice storm warnings issued by

operational meteorologists. In addition, these results yielded preliminary

comparisons between a high impact winter weather phenomenon and

teleconnection patterns that can be expanded upon, quantitatively, in future

studies.

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Chapter 2: Previous Ice Storm Research

Studies conducted on frozen precipitation events far outnumber those

conducted on freezing precipitation events. Perhaps the most well known ice

storm climatology that is currently available was completed by Stanley Changnon

in 2002. Changnon (2002) documented all freezing rain catastrophes between

1949-2000 using two datasets. The first dataset was developed by the property-

insurance industry, which is based on an insured property loss threshold of a

specific dollar amount and includes a record of all catastrophic natural hazards in

each state with information pertaining to the experienced losses for major

insurance firms. The second dataset was created by the Association of American

Railroads strictly for freezing rain events, which contains information regarding

the date of the occurrence of an ice storm, a radial ice thickness measurement, and

the associated damage caused by an ice storm. According to Changnon (2002),

these two datasets contained the necessary and reliable economical and spatial

characteristics of ice storms needed to develop an accurate and informative

climatology across the United States.

Changnon (2002) concluded that the national loss total between 1949-2000

from all freezing rain events was estimated at $18 billion, with an annual average

of $187 million. The average annual ice storm loss between the years of 1988 and

1995 was estimated at $226 million, which accounted for 60% of the total winter

storm losses in the United States. These totals have been corrected for inflation

over the years and correspond to the losses in dollar amounts representing the

8

economy of the most recent decade (Changnon 2002). Figure 2.1 shows a graphic

from Changnon (2002) that includes the amount of loss, in millions of dollars, from

ice storm catastrophes between 1949-2000 across all regions of the United States.

The highest loss total was found in the northeast United States, with the southeast

and central United States having second and third highest totals, respectively. This

figure also shows the average losses (in millions) per ice storm catastrophe, which

were also highest in the Northeast (Changnon 2002).

Figure 2.1: A graphic from Changnon (2002) showing the amount of loss, in millions of dollars, from ice storms between 1949-2000 for all regions of the United States. In parentheses, the average loss per catastrophe, in millions of dollars, is shown for all

region of the United States. The Northeast experienced the most loss.

The Northeast was also found to have the highest frequency of ice storm

catastrophes than any other region in the United States (Figure 2.2), with 39

catastrophes during the period of study. The value in parentheses represents the

number of catastrophes with losses, which was highest in the Northeast.

9

Figure 2.2: A graphic taken from Changnon (2002) showing the number of ice storm catastrophes in all regions of the United States between 1949-2000. The number of catastrophes with losses is shown in parentheses. The Northeast experienced the

most catastrophes.

Changnon (2002) also plotted the average number of days with freezing

rain across the United States (Figure 2.3). The maximum was found over the

Northeast with an average of 5-7days per year, although it is unclear whether this

was influenced by population density. Higher averages were also found over the

upper Midwest, but Changnon (2002) concluded that, although this region

experiences frequent freezing rain days, it usually does not result in large property

losses. Comparing the average number of freezing rain days to the associated

losses, Changnon (2002) concluded that, despite the lower number of freezing rain

days in the South compared to New England, associated losses were higher in the

South, suggesting that the impacts of ice storms are more detrimental in this

region.

10

Figure 2.3: The average number of freezing rain days across the United States between 1949-2000 (Changnon 2002). The highest average is in the Northeast, and

extends down into the Appalachians and west into the Central United States.

Based on railroad data, Changnon (2002) assessed ice thickness values

representative of the ice storms that occurred in each region of the United States.

The largest ice thickness (5 centimeters) was found in New England, the Deep

South, and the Southern Plains, while the smallest thickness was found in the

upper Midwest and the Pacific Northwest. Average thickness values in the United

States ranged from half an inch to an inch and a half, indicating important regional

ice storm differences. Interestingly, the average size was highest in the Deep South

and Southern Plains and Lower Midwest (Changnon 2002).

Changnon (2002) pointed out several limitations in previous studies of

damaging freezing rain storms that were relevant throughout the extent of this

thesis (discussed in more detail in chapter 4). The first limitation was the available

datasets. Changnon (2002) noted that the main disadvantages of these datasets

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include the length of record, which can vary depending on the database used but is

generally too short to develop a climatology, and the quality and reliability of the

information contained. This has prevented researchers from assessing accurate

economic losses of past storms and analyzing key aspects of these storms, such as

ice thickness and storm size. Changnon (2002) also stated that, in addition to

available datasets, point measures that have been made by first order stations

(FOS) of the National Weather Service (NWS) have been too widely spaced to allow

for meaningful measures of ice storm characteristics. It should also be noted, for

purposes of current and future projects, that data from railroads has been

discontinued (Changnon 2002).

Changnon and Karl (2003) investigated the spatial and temporal variations

of freezing rain in the contiguous United States between 1948-2000. They used a

dataset containing freezing rain reports across the United States from 988 FOS

stations and cooperative substations of the NWS. While many previous studies

relied on FOS data, the addition of the cooperative substations in Changnon and

Karl (2003) resulted in a more detailed spatial resolution of all freezing rain events

across the country. Similar to Changnon (2002), Changnon and Karl (2003) found

that most freezing rain events in the contiguous United States have occurred east

of the Rocky Mountains, with the exception of the Pacific Northwest. There was a

noticeable decrease in freezing rain events westward across the High Plains, as

well as from inland locations to the shore between North Carolina and

Massachusetts. A maximum of seven freezing rain days per year was located in the

Adirondack region of New York. It was determined that freezing rain days in the

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Pacific Northwest and Appalachian Region were the result of cold air damming and

the interaction of cold air with an extratropical cyclone (Changnon and Karl 2003).

Freezing rain days across the Upper Plains were associated with arctic high

pressure systems and the frequent intrusion of low pressure systems from Canada,

while freezing rain in the Midwest was largely associated with frontal activity.

While frontal activity was the cause for many freezing rain events across the

Northeast, the orography also played a role, similar to the Pacific Northwest and

the Appalachian Region. Although freezing rain occurs in the South, the lack of cold

air intrusions and extratropical cyclone tracks limit the number of freezing rain

days (Changnon and Karl 2003).

Changnon and Karl (2003) also examined the month of the earliest and

latest freezing rain occurrences in the United States. For the Plains, October was

the earliest month of occurrence, while the latest occurrence ranged from April to

May. Across the Midwest, the earliest occurrence was found to be in November,

while the latest occurrence was in April. Across the Northeast, the earliest

occurrence was found in October across northern New York and northern New

England, and during November for the remainder of the area. The latest

occurrence in this region was in April. The South experienced its earliest

occurrence in November or December and its latest occurrence in March or

February. There was evidence of a latitudinal dependence for the latest

occurrences of freezing rain across the country. It was also found that the most

susceptible areas experienced a freezing rain “season” lasting about six months.

Changnon and Karl (2003) found January to be the most active month over the

13

eastern half of the country, as well as the Pacific Northwest. December averages

were highest across the Great Plains. In general, the sum of all of the recorded

freezing rain days for December and January was about 60% of the seasonal total

for the Midwest and Northeast, 70% for the South, between 35% and 50% in the

northern Plains, and 50% south and west of the Plains (Changnon and Karl 2003).

For related information regarding the other regions of the country, refer to the

actual paper.

Changnon and Karl (2003) also found that a higher frequency of ice storms

was found across the eastern half of the United States between 1948-1964, while a

higher frequency was found across the western half of the country between 1982-

1998. The remaining years were found to have a lower frequency of freezing rain

days across the entire country.

In addition to Changnon’s work, Robbins and Cortinas (2001) examined the

synoptic environments associated with freezing rain across the contiguous United

States, albeit during a shorter time scale (1976-1990), with the use of hourly

surface observations for 489 weather stations south of latitude 49N. Their results

yielded four regions which corresponded to a high frequency of freezing rain

hours. These are shown in Figure 2.4 and included the Catskill and Allegheny

region of the Northeast, the Piedmont of North Carolina and Virginia, the Columbia

Basin region of the Pacific Northwest, and the Midwest extending from Missouri to

Pennsylvania (Robbins and Cortinas 2001).

14

Figure 2.4: The number of hours of freezing rain in the United States between 1976-1990. The Northeast, Mid Atlantic, Appalachians, and parts of the Midwest and

Plains experienced the highest number of freezing rain hours.

Using these results and analyzing rawinsonde observations for locations

within these areas, freezing rain events were usually located in the proximity of a

warm front. In most cases, other forms of precipitation accompanied the freezing

rain, with variable temporal scales (Robbins and Cortinas 2001).

Robbins and Cortinas (2001) then examined characteristics of vertical

atmospheric profiles to assess similarities and differences in freezing rain across

different regions of the United States. They found that most profiles exhibit a warm

layer with a median depth of 1,324 meters above a sub-freezing layer with a

median depth of 613 meters. In general, the coldest temperature within the low

level freezing layer was not located at the surface. In most cases, the melting

process found within the warm layer dominated the freezing process below. Upon

investigating local parameters within the rawinsonde data, Robbins and Cortinas

15

(2001) concluded that local topography plays a major role in freezing rain

ingredients across each region studied. Robbins and Cortinas (2001) also decided

that variations in the local environments during freezing rain events across the

country suggest that the synoptic processes may also vary. Results of further

analysis indicated that freezing rain events in regions east of the Appalachian

Mountains were generally the result of cold air damming (Robbins and Cortinas

2001). In the central United States, freezing rain was associated with isentropic lift

over a stationary front or a closed cyclone. Freezing rain in the Northeast was

associated with cyclones located in the Southern Plains that moved northeastward.

Bernstein (2000) analyzed regional and local influences on freezing

precipitation. Bernstein (2000) developed a climatology of freezing precipitation

characteristics by studying surface data from 207 weather stations and six upper

air sites within the United States that received at least ten hours of freezing

precipitation per year between the years of 1961-1992. Freezing rain was most

often associated with a surface wind between the northeast and east and freezing

drizzle was associated with a surface wind from the north or east. Relatively few

freezing precipitation events were evident when the surface wind was from the

south or southwest. This agrees reasonably well with past studies that have

indicated that most freezing precipitation occurs on the colder side of warm or

stationary fronts (or to the northeast of surface cyclone). Freezing drizzle was

found to be more common than freezing rain, especially during times in which the

wind was not from a northerly direction. This is because the main formation

16

mechanism for drizzle is through collision coalescence, which can be triggered in

relatively warm and shallow clouds (Bernstein 2000).

Bernstein (2000) selected a specific upper air site in several regions of the

contiguous United States. Just east of the Rocky Mountains, most freezing

precipitation events were categorized as freezing drizzle associated with a surface

wind supporting an upslope flow regime. Arctic cold frontal passages and local

terrain changes also played an important role in freezing precipitation

development. All freezing drizzle events were found to develop under conditions

sufficient for collision coalescence as opposed to the melting process (Bernstein

2000).

Bernstein (2000) next analyzed a site in the Columbia River Basin. Most

events were reported as freezing drizzle, but there were more freezing rain events

in this area than in the eastern Rockies. For a majority of the freezing drizzle

events, surface winds were usually calm, from the northeast, or the southwest with

a strong anticyclone located near the Washington/Idaho border. Most analyzed

soundings indicated cold air pooling or fog within the basin and a shallow layer of

saturation, indicative of collision coalescence and freezing drizzle. For the freezing

rain events, the most common synoptic setup included a southward moving

anticyclone near the four corners region, with strong low pressure moving in from

the northeastern Pacific (Bernstein 2000).

Next, Bernstein (2000) examined a site in the Midwest/Northern Plains

region. This area experienced more freezing precipitation events than the previous

two, and most events were classified as freezing drizzle events, as opposed to

17

freezing rain. For both freezing rain and drizzle events, the area was located north

of a warm or stationary front, with a low located in the central or southern Plains,

and high pressure located over Ontario or Quebec. In most cases of freezing

precipitation, the surface winds were from the northeast. Locations near and

within the Great Lakes experienced frequent occurrences of freezing drizzle when

the wind was offshore of the lake (Bernstein 2000).

Bernstein (2000) then described the freezing precipitation conditions

common for the Mid Atlantic and Great Lakes Region. This area received an equal

amount of freezing rain and freezing drizzle events. The most common synoptic

setup was a strong anticyclone in the wake of a strong cyclone and its associated

cold front, with surface winds generally from the west or northwest. On occasion,

freezing precipitation events developed when a warm front was located across

Virginia, in association with a surface low to the south and an anticyclone to the

north. In general, local and synoptic features allowed for collision coalescence to

be the dominant freezing precipitation formation process and, hence, freezing

drizzle was more common than freezing rain (Bernstein 2000).

The next site was selected to represent the inland region of the Southeast,

east of the Appalachian Mountains. This area received the most freezing

precipitation out of the aforementioned areas, with around 43 hours reported

annually (Bernstein 2000). Most events were categorized as freezing rain as

opposed to freezing drizzle. Northeast winds in association with an anticyclone in

the Northeast commonly supplied the cold air to this region, which became

dammed against the mountains. Low pressure was typically present to the south.

18

These conditions usually led to the formation of freezing rain events. Most freezing

drizzle events were the result of low pressure located to the east or off the coast of

this region (Bernstein 2000).

In the Northeast, freezing precipitation is largely influenced by the Atlantic

Ocean. The average annual hours of freezing precipitation matches that of the

southern region, but there was a slightly higher percentage of freezing drizzle

events over freezing rain events (Bernstein 2000). Surface winds were typically

from the north and neither onshore or downslope in nature. Local features in the

topography, coupled with maritime air from oceanic sources commonly produced

freezing precipitation. For most freezing drizzle events, a strong low was located

offshore with high pressure over eastern Canada, or a warm front located across

southern New England in association with low pressure over the Great Lakes and

high pressure in Canada. Freezing rain events tended to occur with the same

synoptic setups, but the differences in the cloud temperatures and depth provided

the development of a melting layer, resulting in an event characterized by the

melting process rather than collision coalescence (Bernstein 2000).

Bernstein (2000) concluded that the proximity to both topographical and

oceanic features plays a significant role in the formation and characteristics of

freezing precipitation events. By examining locations within many regions of the

contiguous United States susceptible to these events, a better understanding of the

geographical distribution of both freezing drizzle and freezing rain could be

obtained. However, by studying only one site in each region, the results obtained

cannot be applied to all locations within that region. The detailed mechanisms by

19

which both topography and marine sources aid in precipitation developed still

warrants future study for more concrete and representative results (Bernstein

2000).

Rauber et al. (2001) analyzed synoptic patterns and soundings in order to

develop a climatology of freezing precipitation across the United States east of the

Rocky Mountains. Rauber et al. (2001) analyzed three-hourly surface charts

corresponding to 411 freezing precipitation events listed in Storm Data and

compared these charts to sounding sites that reported freezing drizzle or freezing

rain for a particular event at launch time. Seven synoptic-scale surface features

were found to be associated with freezing precipitation events east of the Rockies,

with four of these patterns not associated with specific topographical features, and

three associated with topographical features. Rauber et al. (2001) found that most

freezing precipitation reports were across the Central and Southern Plains, and

east of the Appalachian Mountains, with fewer reports north and south of these

areas.

The first feature not associated with topography was the arctic front. It was

the most common weather pattern associated with freezing precipitation within

the domain of study (Rauber et al. 2001). Freezing precipitation lasted an average

of 1-2 days and was typically freezing drizzle. It was common for warmer air to

rise over a cold dome of high pressure and form a shallow cloud layer, causing a

narrow band of freezing drizzle to develop on the cold side of the surface C

isotherm. If deeper clouds developed, freezing rain was experienced (Rauber et al.

2001).

20

Another surface feature conducive for freezing precipitation development,

but not influenced by topography, was the warm front/occlusion sector of an

extratropical cyclone. In this region, warm air advection and frontal overrunning

allow for the development of freezing rain and freezing drizzle north of the surface

0 C isotherm, parallel to the front. In general, freezing precipitation events that

developed in this manner lasted less than twelve hours (Rauber et al. 2001).

The next feature analyzed was a cyclone-anticylone setup. This develops

when the previously mentioned features occur simultaneously, introducing the

proper ingredients for a freezing precipitation event, as well as a strong pressure

gradient force (PGF), conducive to high winds. The enhanced PGF is usually located

near the region of freezing precipitation, sometimes allowing heavy ice to

accumulate. These events typically lasted around a day with a surface high located

to the north of a surface low (Rauber et al. 2001).

The last non-topographically influenced surface feature associated with

freezing precipitation was the western quadrant of an arctic high pressure system.

The southerly flow west of the high is often associated with warm air advection

(Rauber et al. 2001). Where the southerly flow is enhanced, perhaps by a

developing low in the Rockies or the East coast, freezing precipitation is likely to

develop. The precipitation is found north of the surface 0 C isotherm in a circular

pattern and lasts around a day (Rauber et al. 2001).

The interaction of synoptic surface features and topographical features is

most common along the east side of the Appalachian Mountains. One of the most

common processes associated with this is cold air damming (Rauber et al. 2001).

21

A low-level cold dome associated with cold air damming can interact with a

surface anticyclone, or an Atlantic cyclone. In the case of an anticyclone, an arctic

air mass is located over the eastern United States, with an associated surface ridge

of cold air extending south (Rauber et al. 2001). Warmer air from the Atlantic

Ocean rises over the cold dome dammed against the mountains, allowing for the

development of freezing precipitation. In the case of the Atlantic cyclone, low

pressure develops in the Gulf of Mexico or along the Atlantic Coast and moves

northeastward while cold air is dammed against the mountains. As the surface low

moves, warm air advection and rising motion in the northwest quadrant can

enhance the development of freezing precipitation. The low may also enhance the

PGF, which enhances the easterly flow onshore, leading to heavy precipitation


Cold air trapping is another freezing rain mechanism involved when surface

features interact with topography. This is common in the northern Appalachians as

a cyclone in the Rockies moves eastward and provides warm air on both sides of

the mountain while cold air has been trapped in the valleys (Rauber et al. 2001).

Rauber et al. (2001) next analyzed soundings that represented all of the

aforementioned freezing precipitation mechanisms. Most soundings that exhibited

a melting layer had a maximum temperature of at least 2 C in that region. This

maximum temperature correlated well with the depth of the melting layer, as the

area is bounded on both sides by the 0 C isotherm. This indicates that a higher

thickness must be present to support higher maximum temperatures (Rauber et al.

2001). Soundings pertaining to the passage of an arctic front showed the highest

22

maximum temperatures within the melting layer, due to the common penetration

of the front into the Deep South. Rauber et al. (2001) also concluded that the

presence of a melting layer does not guarantee that precipitation will form through

the melting process if cloud tops are located within the warm layer.

Rauber et al. (2001) next examined the distribution of minimum

temperatures within the low-level freezing layer. Most events represented a

minimum temperature of around - C, except arctic air mass events, which usually

produced colder minimum temperatures. The minimum temperature and depth of

the cold layer did not correlate well (Rauber et al. 2001). Unlike the warm layer

aloft, the cold layer is not bounded on both sides by C isotherm. In addition, the

coldest temperature was not always located at the surface, as these temperatures

were often found to be warmer than 0 C. This was suggested to be due to above

surface measurements or evaporation mechanisms (Rauber et al. 2001). It was

concluded that the coldest temperatures in the freezing layer were found between

200-600m above the surface.

Rauber et al. (2001) analyzed the mean 1000-500 hectopascal (hPa)

thickness during times in which freezing precipitation was reported at the surface,

soundings indicated freezing rain, or one of the previously mentioned synoptic

patterns were present. The mean thickness value was 5456m with a standard

deviation of 58m. Thickness values tended to be lower for vertical profiles that did

not contain a melting layer. The thickness values did not represent a drastic

difference regarding freezing drizzle versus freezing rain events (Rauber et al.

2001). A latitudinal dependence was found between thickness values and the

23

synoptic patterns conducive to freezing precipitation. Rauber et al. (2001)

concluded by stating that the 1000-500 hPa thickness is a poor predictor of

freezing precipitation occurrence, however, the 1000-850 hPa thickness may be a

better predictor of the location of freezing precipitation events, since lower

tropospheric temperature patterns are important.

Rauber et al. (2001) briefly looked at the dominating wind direction and

speed during freezing precipitation events, for ice accumulation purposes. Surface

winds were typically from the east or northeast, while winds aloft were from the

southwest. This represented a veering wind profile, commonly associated with

warm air advection. The surface winds were highest when the anticyclone-cyclone

pattern was present, owing to the stronger PGF that develops in response to this

sharp change in pressure (Rauber et al. 2001).

Rauber et al. (1999) discussed the significance of the warm rain process

and the melting process during freezing precipitation events by analyzing

rawinsonde soundings east of the Rocky Mountains. The soundings were divided

into categories based on the cloud top temperature, the presence or absence of a

melting layer, and the altitude of the cloud top relative to the warm layer.

Rauber et al. (1999) found that most soundings in the North-Central Plains

had no warm layer and were associated with freezing drizzle and low cloud tops.

In some cases, a melting layer was present, but existed well above cloud top, so all

precipitation remained supercooled. In this environment, cloud tops were shallow

and precipitation was mostly freezing drizzle. These two environments were most

24

often associated with the passage of an arctic front over the Plains (Rauber et al.

1999).

Across the southern and northeastern United States, most soundings

indicated that the cloud top resided within the warm layer aloft, with a deep layer

of dry air above. These environments develop as shallow arctic fronts that

approach the Gulf Coast. These fronts stall or redevelop as warm fronts and allow

for overrunning conditions, which produce freezing precipitation (Rauber et al.

1999).

Environments in which the cloud top existed above the melting layer

represented about 15% of the soundings (Rauber et al. 1999). This environment

suggests that the cloud tops were colder and deeper than the aforementioned

environments, although precipitation still fell as freezing drizzle. This environment

was commonly noted across the East Coast, particularly in cold air damming

situations (Rauber et al. 1999). Freezing rain tended to be most dominant in cases

where a deep layer of moisture was present and a melting layer existed with

clouds tops having temperatures colder than -10C. These conditions were most

often seen east of the Appalachian Mountains, just north of the Ohio Valley, and

sometimes across the Midwest in association with surface cyclones or warm fronts


This study suggests that the warm rain process, as opposed to the melting

process, was more common, as it was observed in approximately 75% of the

soundings analyzed. The frequent occurrence of freezing drizzle, in association

25

with the multiple environments in which it can develop is particularly important,

as many aircraft icing events are the result of freezing drizzle (Rauber et al. 1999).

David Call (2009) assessed the changes in ice storm impacts between 1886-

2000. The most widespread and longest lasting impact of ice storms is the

disruption of electrical service. Other impacts include transportation disruptions,

business closings, agricultural losses, etc. Call (2009) used newspaper articles and

selected nine severe ice storms that affected the United States to asses these

impacts. Call (2009) concluded that widespread power outages associated with

severe ice storms can last for several weeks after an event. While the details

remain unclear, power losses were found to last longer during the more recent ice

storms. Call (2009) also found that rural customers tend to lose electrical service

for a longer period of time compared to urban customers, as utility companies

initially concentrate on restoring the high-priority lines. Other adverse effects that

result from power outages include carbon monoxide poisoning from improper

generator use and fire caused by methods of lighting and heating (Call 2009).

Call (2009) also examined the relationship between ice storms and other

meteorological hazards. Specifically, he studied the relationship between ice

storms and extreme cold. Ice storms that are followed by long periods of cold

weather have longer cleanup periods and pose a greater chance for carbon

monoxide poisoning and hypothermia. Call (2009) then investigated the

relationship between wind and ice. Wind was found to have an effect on ice

accumulation and can create adverse working conditions, ultimately prolonging

power outages.

26

Similar to Changnon, Call (2009) noted some of the limitations of available

freezing rain datasets. One of these limitations is the temporal inconsistencies that

inevitably yield coarser spatial resolution and incomplete datasets. Due to the lack

of quality data, results of many studies of ice storms in the literature vary widely.

The biggest issue Call (2009) faced was the challenging task of collecting reliable

quantitative information regarding ice storms.

From an impacts perspective, Bernstein et al. (1997) analyzed the

relationship between aircraft icing and synoptic scale conditions by analyzing the

surface features, forcing mechanisms, surface mass origins, and precipitation types

within 37 winter weather cases between 1993-1995 and comparing this data to

pilot reports (PIREPS). It was noted that most of the PIREPs occurred within arctic

air masses, and relatively few were found in air masses of Gulf origin. It was

speculated that the size of the arctic air mass, and the associated air temperature

were key aspects of these results (Bernstein et al. 1997). Relative to surface

cyclones, PIREPS tended to be highest on the cold side of active and/or stationary

warm front, due to the relatively high cloud tops, low surface temperatures, high

moisture content, and widespread precipitation often found in this area. In

addition, near a warm frontal zone, wind shear can affect the production of

supercooled liquid water available to freeze on aircraft. Higher wind shear may

influence the size of the supercooled water within a cloud deck through an

enhancement of the collision coalescence process (Bernstein et al. 1997). Higher

amounts of supercooled liquid water create a larger hazard to aircraft. It was also

found that a greater number of PIREPS were located near stationary warm or

27

arctic fronts, as opposed to active fronts. The density of PIREPS increased along

and behind surface troughs. The reason for this remains unclear to the authors.

Regions on or behind drylines were typically found to be ice free due to the dry

nature of this particular phenomenon. Overall, according to PIREPS reports, the

locations with the highest threats for icing included those within arctic air masses,

and air masses advected into the East and East coast regions, ahead of active and

stationary warm fronts (Bernstein et al. 1997).

Bernstein et al. (1997) noted that many PIREPS listed snow or rain as the

dominant precipitation type, perhaps due to the greater areal coverage of rain

and/or snow compared to freezing precipitation. Although rain and snow tended

to be the dominant precipitation type, freezing precipitation was found to pose the

highest threat to aircraft, as any observation of this type of precipitation is

indicative of large supercooled droplets existing throughout a given depth of the

atmosphere. While snow may be reported at a surface station or PIREP at the

ground, supercooled liquid likely exists farther aloft, in which case freezing

precipitation may pose a threat to aircraft (Bernstein et al. (1997). It was also

found that freezing drizzle was particularly hazardous to aircraft, whereas freezing

rain was typically reported within in precipitation transition zones and posed less

of a threat. From a general standpoint, it was found that the highest threat for icing

was found when freezing precipitation was observed at the surface, as opposed to

aloft (Bernstein et al. 1997).

One major issue that was encountered in this study was the information

provided by the PIREPs. Often, their geographical distribution is a function of the

28

location of airline hubs. This issue is similar to those presented by Changnon

(2002) and Call (2009).

With the NWS serving as the primary source for warning issuance across

the United States, David Call (2008) assessed the NWS warning procedures for ice

storms. Call (2008) attempted to address the seriousness taken by warning

coordination meteorologists (WCMs) by examining the warning products issued in

a storm’s advance, the office’s contact with emergency responders, and forms of

outreach to the public. Call (2008) sent a survey of questions to all WCMs in

weather forecast offices where ice storm catastrophes reached ten or more. This

included the Southern Plains, parts of the Midwest, and all states east of the

Mississippi River. The survey ranked “seriousness” associated with particular

hazardous weather phenomena on a scale of 2 to 5, with 5 being most serious. The

seriousness pertaining to ice storms was evident in all regions selected, but

depended on an office’s location (Call 2008). Therefore, some offices ranked other

inclement weather events as “more serious” than ice storms. The states that

ranked ice storms highest were Oklahoma, Aransas, and Tennessee. Figure 2.5

shows the seriousness rating of ice storms across this region of study, along with

the rank of ice storms relative to other hazards across the region of study (Call

2008).

29

Figure 2.5: Graphic from Call (2008). Left: The average seriousness rating of an ice storm on a scale of 2-5. Oklahoma, Arkansas, and Tennessee had the highest rating.

Right: The average rank of ice storms relative to other hazardous weather phenomenon. Ice storms ranked highest around the Ohio Valley.

The survey contained a hypothetical ice storm event asking the WCMs how

they would respond to it 72 hours out. Most WCMs would issue an advisory, wait

for several future model runs, account for nonmeteorological factors, or consult

with adjacent offices (Call 2008). As time got closer to the event, many would issue

a winter storm watch. The NWS Central Region offices within the domain tended to

issue watches earlier than other regions. Most WCMs would issue warnings about

24 hours in advance of an ice storm. However, the expected ice accumulation

played a major role in deciding between issuing a warning or an advisory, which

varied between regions. Of the warnings issued, the text product itself focused on

power outages and travel/transportation disruptions (Call 2008).

In response to the issued text products, there were some key differences

between WFOs. The length of the product varied, the intended audience differed,

30

and the intensity and language varied. Call (2008) did not elaborate on the

consequences of these differences, but did state that all offices should assess a

specified audience(s), include an appropriate amount of information in the

warning, and suggest an appropriate level of language to address the issue (Call

2008).

Nearly all WCMs contacted local and state emergency managers or

conducted conference calls in advance of an ice storm (Call 2008). The results

varied geographically in response to contacting utility companies, for various

reasons that can be found in the actual paper. It was noted that many state

governments sponsor an annual winter weather awareness week as a way for the

public to assess the risks of winter weather. Some WCMs also hosted workshops

on winter weather awareness or sent out monthly newsletters (Call 2008).

It was found that most offices within the regions of the domain take ice

storms seriously and, in turn, are taking the appropriate actions in both preparing

and warning the general public (Call 2008). Although this is vital in protecting life

and property, this study did not address the accuracy or appropriate lead time of

such forecasts, which is an important part of this research. In addition, the flaws

within the text products/automated warning systems were addressed and remain

a concern for future ice storms in these regions (Call 2008).

While many of the aforementioned studies have addressed specific regions

of the United States that commonly experience freezing rain, the synoptic patterns

conducive to freezing rain, and the impacts associated with significant

accumulations of freezing rain, they have yet to focus on the spatial and temporal

31

changes in freezing rain frequency over time. In addition to this, previous studies

have mentioned shortcomings in the available data, but failed to address this issue

in detail or suggest a more accurate method of documentation. This study filled in

these gaps by analyzing the spatial and temporal changes in ice storms frequency

across the United States over time, while also discussing in detail the main

shortcomings associated with the documentation of ice storms. The addition of this

project to the existing literature will provide a better foundation for future studies

that will eventually lead to a more concrete understanding of this phenomenon.

32

Chapter 3: Methodology

The goal of this study was to develop a preliminary climatology of ice storm

frequency across the contiguous United States and compare the results to changes

in global circulation patterns, namely those associated with teleconnection

patterns that influence the United State’s climate. This study is separated into

three sections: The first section presents the development of a national ice storm

climatology. The second section, influenced by the analysis of the results of the

first section, introduces an ice storm climatology across the northeast United

States. The third section provides the comparison of the ice storm climatology for

the Northeast to phases in selected teleconnection patterns.

Before the first section could be conducted, a representative definition of an

ice storm had to be implemented and the appropriate timescales and dataset had

to be selected. As presented by Call (2008) in Chapter 2, there exists an

appreciable amount of variance in the definition of an ice storm across the

different regions of the United States. To avoid these issues, this study adopted its

own definition of an ice storm that could be applied to all regions of the United

States. An “ice storm” in this study refers to a winter weather event containing

freezing rain or freezing drizzle that caused negative impacts, including hazardous

travel conditions, power outages, personal injury, and/or fatalities, regardless of

the amount of ice accumulation or presence of other precipitation types

throughout the duration of the event. To be classified as an ice storm, freezing rain

had to be considered the primary cause of negative impacts if it was present with

other winter precipitation. Ice accumulation was eliminated from the definition

33

due to the inconsistency in accretion measurements, the differences in public

preparation strategies across different regions of the country, and the differing ice

storm warning criteria among NWS WFOs across the country.

After determining an appropriate definition for an ice storm, a seasonal

time scale was established. Although many previous studies have considered a

combined-seasonal time scale (i.e., Sept-April), this study focused only on the

winter months of December, January, and February (DJF). These months are

typically the coldest months experienced for most regions east of the Rocky

Mountains, where previous studies have indicated most ice storms occur.

December of each winter season was associated with the year prior to that of

January and February.

A detailed and long-term dataset containing archived information of all

freezing rain events that have occurred across the contiguous United States was

determined next. Storm Data, a publically available database provided by the

National Climatic Data Center (NCDC) since 1959 containing monthly archived

information of all hazardous weather phenomena, proved to be the most

informative, long-term, and detailed dataset available for this study. The

information contained within the monthly entries of Storm Data is written

individually by each NWS WFO across the United States and is later published by

NCDC and available for public use. Although the information is provided by each

WFO, the reports can come from untrained observers within the general public.

Despite this disadvantage, it was decided that the information within Storm Data

34

was accurate for the intended purpose of this study, because the information can

be the result of verified warnings from meteorologists and trained spotters.

Within each monthly issue of Storm Data, significant weather events are

listed alphabetically by state, along with the date, the counties and/or general area

affected, a start and end time of the event, a proposed financial estimate of

losses/damage, and a synopsis of the event. Figure 3.1 provides an example of an

entry listed in a monthly publication of Storm Data that was determined to be

fairly representative of all entries encountered during this study.

Figure 3.1: An entry contained within a monthly publication of NCDC’s Storm Data. Each entry provides specific details of a hazardous weather event. This entry is for an

ice storm/mixed precipitation event in New Jersey that includes the date of the event, the counties affected, the duration of the event, estimated losses, and an

overview of the event and its associated impacts.

This particular entry contains important information regarding an ice

storm that affected Sussex, Warren, Morris, and Hunterdon counties of New Jersey

on day 8 of a particular month and year. The far left side of the entry includes the

state abbreviation and affected zones within the state. The area to the right of this

35

lists the affected counties in bold, along with the date of the event underneath and

to the left of the counties, and the start and end time of the event underneath and

to the right. The numbers to the right of this information represent the financial

loss estimates, if applicable. The far right of the entry lists the particular hazardous

weather event. A synopsis of the entire event is then located at the bottom. The

amount of information contained within the synopsis varied for each case, but

generally discussed the synoptic conditions leading up to the event, a specific

weather type(s) present, and associated impacts of the event.

In this particular case, the synopsis was extremely useful in determining

whether to include Hunterdon County within the spatial extent of the ice storm.

Despite Hunterdon being listed under “Winter Weather/Mix,” the synopsis stated

that freezing rain did fall and that sleet/snow were not present, so conditions were

assumed to be similar to those counties listed under “Ice Storm,” and Hunterdon

was included within the spatial extent of the ice storm on this particular day. The

synopsis helped conclude that “Winter Weather/Mix” did not include any other

precipitation type aside from freezing rain, so all impacts, although minor in

Hunterdon, were assumed to be from ice. Despite cases of erroneous data and the

subjective nature of determining events that classify as an ice storm, the

supplemental information provided within the synopsis was considered to be

more reliable than freezing rain reports from a weather instrument (ASOS) or

other method of ice storm reporting (refer to Ryerson and Ramsay 2006).

In many cases, a county map of each state was consulted in determining the

spatial extent of an ice storm, particularly when multiple winter weather types

36

within a state were present on the same date. This was also necessary for most

entries prior to 1990 when the WFOs listed the general areas affected within their

boundaries rather than the specific counties (i.e., eastern Wyoming, southeast

Colorado, etc.). Although the interpretation of generalized regions within a state

can be considered highly subjective, any errors introduced through this method

were assumed to not affect results in a destructive manner. For the case presented

in Figure 3.1, a county map of New Jersey was consulted after reading the synopsis,

but before confidently determining if Hunterdon could have been affected by

freezing rain. The close proximity of Hunterdon County to Sussex, Warren, and

Morris Counties solidified the decision to include it in the spatial extent of the

event. All cases were spatially evaluated in the same manner.

Next, a temporal scale was defined for the climatology. The scale was

chosen to include a number of years that could be statistically compared to

available teleconnection index data. For convenience, the temporal scale was

chosen based upon an ENSO graphic provided in Figure 3.2. This graphic

represents the global temperature anomalies (in reference to global temperature

data recorded over the past century) associated with La Nina, El Nino, and ENSO-

Neutral years between 1950-2012. Prior to around 1978, La Nina was associated

with negative global temperature anomalies, and after 1978 La Nina was

associated with positive global temperature anomalies. In addition to this

observation, global temperature anomalies associated with El Nino began to

increase after 1978. Based on these global observations and the length of the

chosen database, this study compared ice storm frequency between the years of

37

1966-1977 to those between 1998-2011. 1966-1977 was chosen to represent the

years associated with negative La Nina temperature anomalies and lower positive

temperature anomalies of El Nino, and 1998-2011 was chosen to represent years

associated with positive La Nina temperature anomalies and higher positive

temperature anomalies of El Nino. Ice storms were only documented for the

winter seasons of these two periods. The two periods were initially analyzed

separately to determine regions with the highest ice storm frequency and were

later compared to one another (despite the difference in the length of the periods)

to develop preliminary conclusions regarding potential changes in ice storm

frequency across the country over the past several decades.

38

Figure 3.2: Annual global temperature anomalies associated with El Nino (red), La Nina (blue), and Neutral (gray) seasons between 1950-2012. A notable change was

observed around 1978, where La Nina became associated with positive global temperatures anomalies and El Nino became associated with higher positive

anomalies. This image was provided by NCDC.

With a definition of an ice storm, an established database, appropriate

temporal scales, and an understanding of both the relevant information and errors

within the chosen database, a distribution of ice storm frequency for the

contiguous United States was developed. The winter weather types listed within

Storm Data that were investigated included: Ice Storm, Winter Weather, Wintry

Mix, Freezing Rain, Freezing Drizzle, Freezing Fog, Winter Storm, and on rare

occasions, Snowstorm. Investigating the winter weather types that were not listed

strictly as an “Ice Storm” was very important because most freezing rain events

39

were accompanied by snow and/or sleet, and were listed under “Winter Weather”

or “Wintry Mix”. Each county listed under the title “Ice Storm” was automatically

counted, while those listed under any of the other aforementioned categories had

to be further investigated, and oftentimes subjectively categorized. Through the

information provided within the synopsis under these titles and the simultaneous

consultation of county maps, all associated ice storms for each county within the

contiguous United States were documented, and each county was given a specific

number of reported ice storms for each winter month during the period of study.

The comparison of ice storm frequency between the two periods of study

yielded distinct results across several regions of the United States. As will be

discussed thoroughly in Chapter 4, it was concluded that the results across the

Northeast may be of meteorological significance, while results across other regions

of the United States may have been due to reporting improvements. For the

remainder of the study, the analysis focused solely on the Northeast. The new

domain contained the states of Maryland, Delaware, Pennsylvania, New Jersey,

New York, Connecticut, Rhode Island, Massachusetts, Vermont, New Hampshire,

and Maine. In order to obtain respectable results regarding the potential

meteorologically-induced changes in ice storm frequency within this region, the

data gap between the winter seasons 1977-1998 was filled using the same

procedure as stated earlier. Therefore, all ice storms that occurred in the Northeast

during the winter seasons between 1966-2011 were documented, establishing 45

years of data to represent the climatology. The results were then normalized by

40

decade to provide a more detailed interpretation of changes in both spatial

distribution and frequency.

Based on the normalized results, it was hypothesized that changes in global

atmospheric circulations, particularly those via teleconnections, may play a role in

the frequency and spatial distribution of ice storms across the northeast United

States. The teleconnections chosen for this investigation included: The Atlantic

Multidecadal Oscillation (AMO), the El Nino-Southern Oscillation (ENSO), the

North Atlantic Oscillation (NAO), and the Arctic Oscillation (AO). The phases of

these teleconnections are represented by a positive or negative index value that

can be obtained via several statistical methods. ENSO and NAO index values are

obtained by normalizing surface pressure variations (Climate Prediction Center).

The AO index is calculated by projecting daily 1000 hPa height anomalies onto the

leading empirical orthogonal function (EOF) (Climate Prediction Center). The AMO

index is determined by detrending the area weighted average of gridded sea

surface temperature anomalies (Earth System Research Laboratory). Additional

mathematical and statistical details behind the calculations of the index values for

each teleconnection were beyond the scope of this study and the values provided

publically by the Climate Prediction Center (CPC) were assumed to be both

accurate and sufficient for this analysis. Next, a normalized index value

representing each winter season between 1966-2011 for each teleconnection

(NAO, AO, AMO, and ENSO) was calculated for comparison with the ice storm data.

For the NAO, AO, and AMO this was done by taking an average value of the indices

provided by CPC for the months of December, January, and February. It was

41

determined that this value was representative of the phase of the selected

teleconnection for a given winter season throughout the period of study. A positive

phase of these teleconnections had an average index value 1. Similarly, a negative

phase had an average index values ≤-1. All values in between -1 and 1 were

ignored because a neutral phase is not commonly addressed in the scientific

literature.

The same approach was taken for ENSO, but because CPC provides a 3-

monthly determined index value, as opposed to an index value for each individual

month, five 3-month index values had to be averaged in order to fully represent

the months of December, January, and February. These included the index values

for the following 3-month groups: OND, NDJ, DJF, JFM, and FMA where each letter

represents a specific calendar month. While this approach is certain to contain

errors by including averaged index information for months outside of the

designated winter season, it was determined to be sufficient for the purpose of

generating preliminary results. Similar to the other teleconnections, averaged

index values 1 represented an El Nino season, while averaged index values ≤-1

were La Nina seasons. However, contrary to the other teleconnection categories,

values between –1 and 1 were classified as an ENSO-Neutral season, and were not

ignored. This follows similar methods performed during previous and current

studies within the scientific literature.

Once all index values were averaged for each winter season and each

teleconnection, and placed into one of the aforementioned categories, they were

compared to the distribution of ice storm frequency across the Northeast. As will

42

be shown in Chapter 4, teleconnection indices were only compared to the

Northeast because of a pronounced change in ice storm frequency found in that

region. This was conducted by plotting all documented freezing rain events across

the Northeast that corresponded to the winter seasons with a positive phase and

negative phase (and neutral for ENSO) of each teleconnection individually. For

example, all ice storms that occurred in the Northeast during positive AMO winter

seasons were plotted together and all ice storms associated with negative AMO

winter seasons were plotted. Next, qualitative conclusions regarding the spatial

extent and frequency of freezing rain across the Northeast for each phase of every

teleconnection were made, and the associated ice storm distribution for each

teleconnection was compared to the other teleconnections also.

For data quality control purposes, and a comparison with the results

provided by Storm Data, hourly freezing rain data from the Portland, Maine and

Albany, New York ASOS sites for the years 1960-2010 were plotted. These sites

were chosen because they each represented a northern and southern section of the

Northeast, respectively. The raw data from each ASOS site was filtered to include

only the years, winter months, days, and hours with freezing rain reports. The total

number of hours of freezing rain reported at each site for each winter month over

all years provided was then calculated. Qualitative conclusions were then drawn

after comparing the ice storm distribution to the hours of freezing rain provided

by ASOS. A comparison of the results between these two methods will determine

similarities in the distribution of ice storm frequency across the Northeast over

time.

43

The remainder of this thesis contains the following sections: Chapter 4

includes the spatial extent and frequency of ice storms across the United States for

the two time periods of study, the importance of the data inconsistencies

encountered while developing these distributions, and the transition of the domain

to the northeast United States. Chapter 5 provides an introduction of each

telelconnection involved in this study and brief background of previous studies.

Chapter 6 presents a comparison of ice storm frequency with each phase of the

selected teleconnection, and the similarities between the teleconnections

themselves in association with ice storm frequency. Chapter 7 provides the

conclusions of this study, along with several recommendations for future studies.

44

Chapter 4: Preliminary Climatology of Ice Storms across the U.S.

After evaluating all of the ice storms documented in Storm Data, a national

climatology was developed for the winter seasons between 1966-1977 and 1998-

2011. These time periods were chosen in reference to the global climate

temperature anomaly changes associated with ENSO. Ice storm frequency and

distribution for each period was analyzed separately and then compared to one

another to assess potential spatial and temporal changes across the United States.

4.1 Ice Storm Frequency between 1966-1977

Figure 4.1 shows the total number of ice storms across the contiguous

United States for the winter seasons between 1966-1977. The highest frequency of

ice storms is found in the northern portion of the northeast United States,

particularly across Maine, New Hampshire, and Massachusetts, where over 30 ice

storms were documented. This may be due to coastal and marine features, or

extratropical cyclone tracks and characteristics. Several other maxima in

frequency, although less intense, are found across Maryland, Delaware, and the

western spine of the Appalachian Mountains in North Carolina and Virginia where

15-20 ice storms were reported. Cold air damming and extratropical cyclones are

responsible for the higher frequency in the Appalachian Mountain region. Across

the central United States, the highest frequency is found in eastern Oklahoma,

perhaps influenced by a greater number of ice storm reports. The number of ice

storms decreases west of the Rocky Mountains, where the highest frequency of ice

storms is found near the Columbia River Basin in Washington and Oregon.

45

Topography may influence ice storm frequency across this region as particular

flow regimes allow shallow cold air to become trapped by higher terrain. Coastal

influences may also play an indirect role in extratropical cyclone characteristics

and tracks within this area as well.

Figure 4.1: The total number of ice storms during the winter seasons between 1966-1977 across the contiguous United States. New England experienced the most ice

storms, while areas west of the Rockies and the Deep South experienced the least.

4.2 Ice Storm Frequency between 1998-2011

Figure 4.2 shows the distribution of ice storms during the winter seasons

between 1998-2011. Similar to Figure 4.1, the highest number of ice storms is

found in the Northeast. However, the maximum is much further south and west,

with reports of over 30 documented ice storms across parts of east Pennsylvania

46

and northwest New Jersey. A secondary maximum of 25-30 ice storms across the

Northeast is found across the Adirondack and Catskill regions of eastern New York.

A high frequency of ice storms is still evident over Maryland, Delaware, and the

western Appalachian Mountain range in North Carolina. A noticeable increase in

ice storm frequency is seen over the central United States, particularly across parts

of the Midwest and the Southern Plains. Reports of 15-25 ice storms were

documented across eastern Iowa, western and southern Illinois, southeast

Missouri, northeast Kansas, and central and western Oklahoma. West of the Rocky

Mountains, a higher frequency is still seen over parts of the Columbia River Basin

in Washington and Oregon.

47

Figure 4.2: The total number of ice storms during the winter seasons between 1998-2011. The southern portion of the Northeast experienced the most ice storms, while

regions west of the Rockies and the Deep South experienced the least.

4.3 Frequency Differences between 1966-1977 and 1998-2011 Data

Despite a difference in the length of the time periods, Figures 4.1 and 4.2

indicate that the most notable changes in ice storm frequency between the two

time periods occurred across the Northeast, the Midwest, and the Southern Plains.

Figure 4.3 depicts a difference in total ice storm frequency between the two time

periods. The total number of ice storms between the winter seasons of 1998-2011

was subtracted from the total number of ice storms between the winter seasons of

1966-1977 (i.e., past-present). The warmer colors (reds and oranges) depict a

higher ice storm frequency during the winter seasons between 1966-1977 and the

cooler colors (blues and purples) depict a higher frequency of ice storms during

48

the winter seasons between 1998-2011. The largest difference is seen across the

Northeast, where a higher frequency of ice storms occurred from 1966-1977

across the northern portion of the Northeast, and a higher frequency occurred

across the southern portion of the Northeast between 1998-2011. Across most of

the central United States, more ice storms were documented between the winter

seasons of 1998-2011. West of the Rocky Mountains, there has been a slight

increase in ice storm frequency in recent years, but due to the low frequency found

during both periods individually, it does not appear to be significant compared to

other regions of the United States.

49

Figure 4.3: The total number of winter season ice storms between 1998-2011 subtracted from the total winter season ice storms between 1966-1977. Red colors

indicate more ice storms between 1966-1977 and blue colors indicate more ice storms between 1998-2011. A notable shift was seen across the Northeast and an

increase in ice storms was found across the Midwest and central United States.

The distribution of ice storms across the Northeast reveals an inflection in

frequency that extends along the western state borders of Vermont,

Massachusetts, and Connecticut (Figure 4.4). Areas east of this inflection

experienced more ice storms between the winter seasons of 1966-1977, whereas

areas west of the inflection experienced more ice storms between the winter

seasons of 1998-2011. It was hypothesized that this recent westward and

50

southward shift in ice storm frequency across the Northeast was due to

meteorological sources, while the increase in ice storms across the Midwest and

Southern Plains was thought to be related to improvements in ice storm reporting.

This does not guarantee that an increase in ice storm frequency across the central

United States is not meteorologically significant, but for this study this area was

not investigated further. The domain of this study was then shifted to the

Northeast to examine the notable frequency shift in detail. Changes in global

circulation patterns associated with teleconnection patterns, and their potential

relationship with ice storm distribution across the Northeast were investigated

and compared to the ice storm distributions. The teleconnection patterns chosen

in this study included the Atlantic Multidecadal Oscillation (AMO), the North

Atlantic Oscillation (NAO), the Arctic Oscillation (AO), and the El Nino-Southern

Oscillation (ENSO).

51

Figure 4.4: The difference in winter season ice storms across the Northeast. More ice storms occurred in the northern Northeast between 1966-1977, while more ice

storms occurred in the southern Northeast between 1998-2011.

4.4 Data Inconsistencies

Before comparing global circulation patterns and the ice storm frequency

distributions across the Northeast, critical data issues that were encountered

during this analysis need to be addressed.

4.4.1 Definition of an Ice Storm

What constitutes an ice storm may seem straightforward, however, the

results presented thus far show the inconsistency in ice storm reporting across the

52

United States. For example, according to the NWS, an ice storm is defined as an

event in which damaging accumulations of ice are expected during a period of

freezing rain. Ice accretion amounts are considered damaging when ice

accumulates to a quarter of an inch or more. The American Meteorological Society

(AMS) definition of an ice storm is: “a storm characterized by a fall of freezing

liquid precipitation that forms a glaze on terrestrial objects and creates hazards.”

Although these two definitions share similarities, there are noticeable differences.

The NWS uses ice accumulations of at least a quarter of an inch as a classification

measurement, whereas AMS uses the broader term of glaze. These differences in

categorizing a freezing rain event determine how it is documented on a national

level. Despite the NWS’s definition of an ice storm, the WFOs within the NWS

report ice storms differently. Call (2008) addressed these issues and stated that

some of the Regional Offices in the NWS have issued supplemental directives and

nonmeteorologcial criteria regarding ice storms for their WFOs into their

definition. These can be readily identified in figures 4.1 and 4.2. In Figure 4.1, a

noticeable difference in reporting can be seen across the Oklahoma and Arkansas

border, as well as the Louisiana and Arkansas border. Such an abrupt change in ice

storm distribution does not seem meteorologically possible. What constitutes an

ice storm for the WFO in eastern Oklahoma is different than what constitutes an

ice storm for the WFO in western Arkansas. Other WFO boundaries can be

indentified across the country in addition to this example. These inconsistencies

that exist across the country skew the actual number of ice storms that have

occurred since reporting methods began. Inconsistent reporting mechanisms make

53

it impossible to develop a highly accurate climatology of these winter weather

events, which hinders advancements in research and improvements in forecasting.

4.4.2 Ice Accretion Measurement Methods

One potential cause for the inconsistencies within the definition of an ice

storm is the difficulty in measuring ice accretion. Ice accretion measurements are

essential, as the NWS requires glaze accumulations for public safety purposes and

the issuance of winter storm warnings. Changnon (2002) stated that there has

never been a long-term sustained measure of ice thickness by the NWS. NWS

regions commonly use a threshold of approximately a quarter of an inch or more,

despite the fact that smaller amounts can introduce hazards and cause damage. In

addition, quantitative measures of ice accumulation have not yet been available on

a standard surface to humans or automated weather observers (Changnon 2002).

Glaze accumulations can also vary significantly over short geographic distances,

which makes representative reports more difficult, especially over sparsely

populated areas (Ryerson and Ramsay 2006). Also, the shape, orientation, and the

thermal properties of terrestrial objects on which ice accretes cause variations in

observed ice accretion. Although freezing rain can create semi-uniform ice

cylinders around some objects, other atmospheric conditions such as wind and

precipitation rate can prevent the development of such uniform shapes, making a

representative measurement near impossible (Ryerson and Ramsay 2006).

The United States federal government has made an effort to define ice

thickness as “the vertical depth of ice on a horizontal surface.” Many media and

54

NWS observers measure ice accretion as the maximum thickness of ice observed

on any available surface to represent the event (Ryerson and Ramsay 2006). Such

surfaces are exposed differently to the wind and falling precipitation. Some argue

that a standard method involving computation of the radial ice thickness using an

assumed ice density after the weight of the ice laden object is removed should be

implemented (Ryerson and Ramsay 2006). Such a technique could be

implemented by the general public and by operational meteorologists, but would

be difficult to incorporate into a weather forecast model. With the obvious need for

ice measurement improvement, the implementation of more accurate freezing rain

sensors in the Automated Surface Observing Systems (ASOS) across the country is

being evaluated (Ryerson and Ramsay 2006).

Inconsistent methods of ice accretion measurements prevent the

development of a solid and nationally accepted definition of an ice storm. Without

adequate solutions to both of these problems, reliable research efforts to

understand these storms will not be applicable.

4.4.3 Storm Data Reporting

As previously mentioned in chapter 3, NCDC’s Storm Data database

contains the most extensive archive of significant past weather events over the

United States. While it is useful for the evaluation of the development, movement,

and associated societal impacts caused by particular hazardous weather

phenomena, it has disadvantages that are of crucial importance for data mining

purposes. Despite the role of the NWS in issuing the final monthly Storm Data

55

reports for NCDC, the documented storm conditions and financial-loss information

contained within each entry are questionable because reports can come from

untrained observers (Changnon 2002). Therefore, the data within Storm Data

contain errors and are often inconsistent between states or WFOs (Changnon

2002). Inconsistencies in the data between states and WFOs make it extremely

difficult to develop an accurate climatology or improve the understanding of any

type of hazardous weather phenomenon.

In addition to the untrained observers’ role in Storm Data documentation,

the context presented within Storm Data’s entries has varied over the past several

decades. Until 1990, vague information, including the description of the affected

areas, financial loss estimates, and a brief (if any) synopsis of the actual event was

available to the public. After 1990, a more detailed analysis of each event was

provided, including specific counties affected, concise damage estimates, and an

informative synopsis of the atmospheric characteristics associated with the event.

With a noticeably more extensive method of documenting significant weather

events, the reliability of all entries prior to about 1990 is subject to question. In

addition to the quality of reports before 1990, many WFOs often failed to submit a

report on time or never submitted a report to NCDC. This has also contributed to

the data inconsistencies observed among states, which negatively affects attempts

at climatological data analysis.

In some cases, the title of an entry within Storm Data can be misleading. For

ice storms in particular, Storm Data does not list all freezing rain events as an “Ice

Storm” or “Glaze.” Some significant freezing rain events are labeled with snow and

56

sleet under “Winter Storm,” “Wintry Mix,” or “Winter Weather.” The information

relating to the ice storm within the previously mentioned titles is found within the

synopsis of the event. Therefore, when strictly looking for a significant freezing

rain event, all events documented with a winter weather title must be carefully

investigated. Failure to investigate every winter weather event individually

significantly undermines the actual number of ice storms that have occurred since

the onset of nationwide documentation. This will falsify the results of any

climatological analysis related to ice storms and freezing rain events.

4.4.4 Automated Surface Observing Systems (ASOS)

With all of the aforementioned disadvantages of using Storm Data, many

studies use ASOS reports to develop climatologies or to perform a quantitative

data analysis. The ASOS network across the United States has become the primary

surface weather observing system (Ryerson and Ramsay 2006) (Figure 4.5).

Almost 75% of the ASOS sites across the country have received a freezing rain

sensor known as the Goodrich Sensor Systems 872C3. This particular sensor is

programmed to provide freezing rain and freezing drizzle reports, but is unable to

provide a quantitative estimate of ice accretion. The sensor detects freezing rain by

sensing a mass of ice on a 25 millimeter-long by 6 millimeter-diameter vertical

cylindrical probe, which vibrates when ice free (Ryerson and Ramsay 2006).

Recently, a new algorithm was developed based on raw data from the sensor to

provide ice accretion measurements. This algorithm has been approved for

implementation, and the associated software changes have been made, but have

57

yet to be released by the NWS (Ryerson and Ramsay 2006). While reliable ice

accretion data seem probable in the coming years, ASOS currently has several

disadvantages.

Figure 4.5: An example of an ASOS site. Photo provided by www.meteo.psu.edu.

Despite the promising results of the new algorithm, the original sensor will

continue to be used. A common problem with the sensor is the mechanical

response to a specific mass of ice, which often varies from sensor to sensor

(Ryerson and Ramsay 2006). The sensor was delivered to each ASOS site after

passing a manufacturer’s rate test in which a sensor’s response to a mass of ice

was required to be within 20% of a nominal value. A sample of rate tests indicated

that sensor responses were distributed around ±20% of the nominal value

(Ryerson and Ramsay 2006). In addition, ice bridging at the base of the probe can

58

cause frequency increases, which was found to affect the estimates of ice accretion

and even temporarily halt freezing rain reports.

The freezing rain sensor can also experience “overrides” in which mixed

precipitation readings can alter actual accumulation reports. If snow is identified

by the sensor during a freezing rain event, any decrease in frequency reported by

the sensor will be attributed as wet snow, and freezing rain will no longer be

reported.

During freezing rain events where temperatures remain near freezing, slow

probe cooling can occur, which can cause the freezing rain sensor to undergo a

deicing cycle. During this time, the sensor may fail to report ice accretion for up to

45 minutes. Although this type of situation is uncommon, it can disguise a

hazardous freezing rain event as a brief period of icing or no icing event at all.

To avoid “false alarm” reporting, there exists a frequency change threshold

within the freezing rain sensor upon which a specific frequency difference must be

met before a freezing rain event will be reported. With this threshold, an average

of about 6% of all minutes during icing events have accretion amounts that do not

meet the threshold and, therefore, do not get reported.

The collection efficiency, which is the likelihood that the particles will strike

the probe, is related to the size and the orientation of the probe, the wind speed,

and the drop size distribution. Hence, smaller particles are likely to be deflected

around the probe during high wind events, or freeze on contact on the sensor

before reaching the probe, while larger particles are more likely to penetrate the

probe and be detected. This will prevent the correct frequency of the sensor from

59

being attained and, hence, ASOS output will not be representative of the actual

event.

Micrometeorological conditions can have an effect on the freezing rain

sensor as well. Such conditions may allow ice accretion on an ASOS network, but

not on any nearby surfaces, or vice versa. Local parameters such as wind speed

and direction are examples of common micrometeorological factors.

Nonmeteorological factors are also important, including, topography, nearby

radiation sources, internal thermal properties of objects, and the orientation of an

object. In similar ways as the micrometeorological sources, these sources will also

affect the freezing the rain sensor and ASOS output.

The implementation of the new ice accretion algorithm in ASOS will

improve ice thickness measurements, which may allow for a more nationally

accepted definition of ice storms in the near future. It may also serve to provide

more accurate reports for future Storm Data reports, which will then be a more

accurate source for climatological purposes. While it will also enhance the appeal

of using ASOS reports for data analysis, the aforementioned disadvantages are

associated with the freezing rain sensor, which will remain in use for the

foreseeable future. Therefore, although improvements in ice thickness

measurements will be beneficial in a variety of ways during ice storms in the

future, until a better freezing rain sensor is developed, ASOS will contain some

erroneous data.

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Chapter 5: Introduction to Teleconnections

The westward shift in ice storm frequency across the Northeast was

hypothesized to be influenced by changes in teleconnection phases. Before

comparing the spatial and temporal characteristics of ice storms across the

Northeast with teleconnection indices, a brief overview of each teleconnection is

provided. An introduction of each teleconnection is provided first, followed with a

description of each phase, and the existing literature discussing the global

influence.

5.1 Atlantic Multidecadal Oscillation (AMO)

5.1.1 Introduction to the AMO

The AMO was identified by Michael Schlesinger and Navin Ramankutty in

1994 (Schlesinger 1994). It is defined as a linearized, long-term series of sea

surface temperature (SST) changes in the Atlantic Ocean, with a cool and warm

phase that remains dominant for about 20-40 years. Conditions have been

representative of a warm phase since the mid-1990s (NOAA Physical

Oceanography Division). A calculated index value is associated with each phase,

which is based on average annual SST anomalies in the North Atlantic Region

(Knudsen et al. 2011). Some scientists are skeptical of the AMO’s

representativeness of natural variability due to the influence of greenhouse gases

on SSTs that may influence the signal.

Many scientists have speculated a relationship between ocean circulation

and climate variability for nearly half a century, but most have not compared the

61

AMO to climate changes. This may be due to the recent discovery of the

teleconnection, the short record span of SST measurements, or the difficulty in the

ability to isolate purely natural variations with the AMO to the anthropogenic

component of the signal into the SST averaging. Even with a lack of long-term data,

evidence of a long-term oscillation in the North Atlantic region has been noted in

tree rings and ice cores (Knudsen et al. 2011). Research has also proposed that the

AMO notably affects the climate of Europe, North America, and the tropical Atlantic

(Knudsen et al. 2011).

5.1.2 Existing Literature: AMO

Dima and Lohmann conducted a study in 2006 to determine a mechanism

that may influence the AMO. While there are many factors that may influence the

AMO, Dima and Lohmann (2006) suggested that changes in sea level pressure and

associated changes in the wind field have an effect on sea ice export and

freshwater balance, likely changing the large-scale ocean circulation. Further

investigation revealed that the thermohaline circulation plays an active role in the

AMO signal, as well as the hemispheric wave number, sea ice export, and ocean-

atmospheric interactions. The amount of sea ice transported into the Atlantic

Ocean was found to influence the thermohaline circulation (Dima and Lohmann

2006). When the thermohaline circulation strengthens or weakens, the North

Atlantic SSTs will warm and cool, respectively. It was unclear how much the AMO

signal lagged from these “forcings” or how long it takes the thermohaline

circulation to adjust to these changes (Dima and Lohmann 2006). These

62

mechanisms can be thought of as occurring in a cycle: If SST anomalies are

positive, the thermohaline circulation strengthens. The atmospheric response is

represented by a surface low over the Atlantic Ocean. This signal may affect the

Pacific region, where the Aleutian low may be weakened. The maximum amplitude

of this signal is found after about 15 years (Dima and Lohmann 2006). Eventually,

gradients over the Arctic become large and sea ice export is enhanced. The higher

freshwater influx into the Atlantic Ocean leads to a phase change in the AMO and

the cycle repeats itself, with opposite conditions (Dima and Lohmann 2006).

Recent studies suggest that the AMO may be related to drought across the

Midwest and Southwest United States, with more droughts experienced during the

warm phase (NOAA). The AMO may also affect rainfall over Florida, with higher

annual rainfall amounts occurring during warm phase. In addition to these

changes, the AMO may have an effect on hurricane frequency and intensity by

altering the circulation and overturning of Atlantic waters (NOAA). Knight et al.

(2006) found that the AMO may affect hurricane frequency and development

through its associated changes in vertical wind shear. Analysis of data through one

AMO cycle indicates that vertical wind shear had decreased during the transition

from a positive to negative AMO (Knight et al. 2006). A negative correlation was

found between SSTs and vertical wind shear, indicating a possible influence from

the AMO. In middle latitudes, Knight et al. (2006) found that positive AMO

conditions were associated with broad cyclonic pressure anomalies over the

Atlantic Ocean and Europe. Pressure changes were strongest in the winter months,

63

but most widespread during the summer months. The United States was affected

most during the summer months (Knight et al. 2006).

Burakowski et al. (2010) noted a recent warming trend across the

northeast United States and has stated that the AMO has been linked to North

American summer climate and river flows, but it remains unclear what impact the

AMO has on winter climate in the northeast United States. Comparing the phase of

the AMO to the mean temperature and snow cover days, no trend was indentified

(Burakowski et al. 2010).

It can be concluded that changes in the thermohaline circulation of the

Atlantic Ocean affect SSTs, which causes fluctuations in climate patterns across the

world. The AMO index was developed to describe this SST change, and as a

measure of comparison to climate fluctuations across the world. However, the lack

of research due to its recent discovery and controversial association with

greenhouse gas interference makes it impossible to conclude how changes in the

phase of the AMO affects specific regions of the United States, particularly the

Northeast.

5.2 North Atlantic Oscillation (NAO)

The NAO is a climatic phenomenon related to fluctuations in sea level

pressure across two regions of the North Atlantic Ocean. The NAO was originally

defined by Sir Gilbert Walker (Walker and Bliss 1932). Walker defined an index

consisting of a linear combination of these parameters at selected stations. Positive

values of the index indicated a strong low near Iceland, a strong high around 40N

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latitude, and strong westerly flow. Negative values indicated a weak low near

Iceland and a weak high around 40N latitude. Walker and Bliss (1923) emphasized

these relationships through dynamical methods beyond the scope of this study.

Van Loon (1978) and Wallace and Gutzler (1981) suggested Walker’s

method contained flaws and performed their own analysis of the NAO. Presently,

the NAO consists of a semi-permanent area of low pressure near Iceland and a

semi-permanent area of high pressure near the Azores. Semi-permanent indicates

that these pressure centers can change location on a seasonal time scale, allowing

for the accurate use of other temporary pressure anomalies to define the phase of

the NAO (State Climate Office of North Carolina). The structure and strength of

these high and low pressure anomalies can fluctuate frequently, which alters the

alignment of the polar and subtropical jet streams. Research has shown that the

changes within the jet stream influence temperature and precipitation

distributions over the eastern United States (State Climate Office of North

Carolina).

5.2.1 Positive NAO

During the positive phase of the NAO, both the Icelandic low and the Azores

high strengthen. This strengthening increases the pressure gradient force (PGF)

over the North Atlantic region, causing an increase in the westerly wind field (State

Climate Office of North Carolina). A stronger belt of westerlies across this region

confines polar air to high latitudes, and allows it to flow away from the North

American continent, rather than move southward into the United States. These

65

conditions result in above average geopotential heights, temperatures, and

precipitation patterns across the eastern portion of the United States.

5.2.2 Negative NAO

During the negative phase of the NAO, both the Icelandic low and the

Azores high weaken, which suppresses the PGF across the North Atlantic region

(State Climate Office of North Carolina). A weak PGF corresponds to meager

westerly flow across middle latitudes, thus allowing polar air to advect farther

southward into the United States. Middle and upper level troughing occurs across

the eastern United States, which results in below normal geopotential heights,

temperatures, and precipitation patterns within this region (State Climate Office of

North Carolina).

5.2.3 Existing Literature: NAO

Hurrell (1995) compared decadal trends in the NAO to regional

temperature and precipitation patterns across the United States using ice-core

data. The ice-core data has revealed abrupt changes in North Atlantic climate on a

decadal time scale that were hypothesized to be related to fluctuations within the

NAO. To investigate this, Hurrell (1995) used calculated index values of >+1 and <-

1 for positive and negative NAO phases, respectively, and found that differences

between the two pressure anomalies can be up to 15 hPa. In addition, it is possible

for a phase to persist for several decades. When averaging over a timescale that

encompasses the duration of NAO records, a negative NAO was present from 1900-

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1930, a positive phase was present from 1940-1970, and a negative phase became

dominant around 1980. With these results, Hurrell (1995) then coupled modes of

variability between SLP and surface temperature and SST fields using statistical

analysis. Results suggested that temperature anomalies near and within the North

Atlantic region are strongly related to the phase of the NAO, as opposed to other

teleconnections. Such changes in the mean circulation patterns over the North

Atlantic are tied to changes in storm track and synoptic eddy activity. This affects

the transport of moisture and regional precipitation patterns, which has an effect

on polar ice quantity. Hurrell (1995) also proposed that variations within

planetary waves may produce spatially coherent large scale patterns of anomalies,

but this was not investigated with respect to phase changes of the NAO.

Groenert et al. (2002) briefly investigated two cool season precipitation

events in the northeastern United States to the phase of the NAO. Groenert et al.

(2002) hypothesized that a comparison between the NAO phase and the regional

scale atmospheric circulation is more reliable if the NAO index is calculated from

500 hPa height data, as opposed to sea level data. After calculating a daily NAO

index from domain averaged 500 hPa heights, it was compared to two winter

storm case studies: a snow event and an ice storm. It was found that the snow

event occurred during a negative to positive NAO regime change, while the ice

storm occurred during a positive to negative NAO regime change (Groenert et al.

2002).

Prior to the snow event, a large scale 500 hPa trough was located over the

North Atlantic, bounded on the north by a 500 hPa ridge near Iceland (Groenert et

67

al. 2002). This anomaly pattern is characteristic of a negative NAO. Explosive

cyclogenesis, corresponding to the development of a downstream jet and ridge and

was suggested to be representative of a transition to positive NAO conditions

(Groenert et al. 2002).

During the ice storm, NAO index values transitioned from positive to

weakly negative values, which persisted for several weeks before becoming

strongly negative (Groenert et al. 2002). There was no deep cyclogenesis present,

indicating that this process is not an essential factor for an NAO regime change. A

shortwave trough over the eastern United States was responsible for downstream

development of a trough and ridge setup over the northern and southern portion

of the NAO domain, respectively. This caused an NAO regime change from positive

to negative, at which time the ice storm began to develop (Groenert et al. 2002).

It was concluded that both of these significant winter storms in the

northeast United States was associated with a phase change in the NAO. The phase

change of the NAO was caused by upstream perturbations in the mean 500 hPa

flow field (Groenert et al. 2002). Despite these results, this study should have

incorporated more events and repeated the same EOF procedure done by others

who use 1000 hPa heights.

5.3 Arctic Oscillation (AO)

The arctic oscillation is a calculated index that describes the state of the

atmospheric circulation over polar latitudes (State Climate Office of North

Carolina). The AO is characterized by the strength of cyclonic motion

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encompassing latitudes at and north of 55N in the Northern Hemisphere. The

cyclonic motion is commonly referred to as the polar vortex. The sign of the index

over polar latitudes is usually opposite to the index over middle latitudes. Thus,

the strength of the AO is described as the amount of arctic air that penetrates into

middle latitudes. The AO does not have an average periodicity, but the largest

variability is found during the cold season (Climate Prediction Center). The AO can

fluctuate on a daily, monthly, seasonal, or annual time scale. Over the past century,

the AO has been oscillatory, however, since the 1970s it has trended towards a

positive phase (Climate Prediction Center). Similar to the NAO, the AO has two

phases: positive and negative and often shares the same phase with the NAO.

5.3.1 Positive AO

During a positive phase of the AO, the polar vortex strengthens, causing

below average geopotential heights across polar latitudes and above average

geopotential heights across middle latitudes, especially the eastern United States.

This setup leads to a strengthening of the westerly flow, which forces colder air to

remain in polar latitudes. This keeps extratropical cyclone tracks north of the

United States and results in above average temperatures east of the Rockies

Mountains, particularly across the Northeast United States (State Climate Office of

North Carolina).

69

5.3.2 Negative AO

The opposite conditions occur when the AO is negative. The polar vortex

weakens, higher geopotential heights are found across the Arctic region, and lower

geopotential heights are located across middle latitudes (State Climate Office of

North Carolina). The westerly flow weakens, allowing arctic air to penetrate south

into the United States and extratropical cyclones to propagate into the United

States. This results in below normal temperatures across most of the eastern

United States, particularly the Northeast (State Climate Office of North Carolina).

5.3.3 Existing Literature: AO

In 2000, Deser published a paper claiming that the AO and the NAO tend to

resemble one another, however the AO contains more important features. Due to

the zonal symmetry of the AO, Deser (2000) emphasized its similarity to the

circulation of the lower stratosphere during the winter and the spatial pattern of

the circulation variability of the Southern Hemisphere. The notable positive trend

found over recent decades is indicative of a strengthening polar vortex from sea

level to the lower stratosphere. Deser (2000) investigated the significance of the

AO’s zonal symmetry, claiming that the AO exhibits anomalies of one sign over

polar latitudes and anomalies of the opposite sign across mid-latitudes over the

Atlantic-European and Pacific sectors. After investigating the degree of correlation

of these areas, Deser (2000) found that the Atlantic and Arctic time series are most

related, while the Atlantic and Pacific time series are least related. The temporal

coherence between Atlantic and Pacific middle latitudes is weak, suggesting that

70

the annual character of the AO is a reflection of the dominance of its Arctic center,

rather than coordinated behavior between Atlantic and Pacific centers related to

the AO. Deser (2000) also noted that the AO and the annular mode of the Southern

Hemisphere are similar, as the strongest correlations in the Southern Hemisphere

are between polar latitudes and primary middle latitudes.

Bamzai (2002) compared snow cover variability to the AO index on a

hierarchy of time scales. Bamzai (2002) found that the number of snow-free days

increased over the time period of study. On a weekly time scale, snow anomalies

were shown to express the largest magnitude when the AO leads the snow by one

week. During a negative AO, snow anomalies were higher than during the positive

phase of the AO in North America and Eurasia (Bamzai 2002). There was no

connection found between Northern Hemisphere winter snow cover extent and

winter season AO index values on a monthly time scale. On a seasonal time scale,

there was an inverse relationship between the AO index and the winter season

snowfall. The main conclusions of Bamzai (2002) were: a) snowfall usually lags the

AO by a period of several weeks b) there is no significant relationship found when

the AO lags snow cover c) an inverse relationship between the AO index and snow

cover over most of the Northern Hemisphere was found when investigating most

time scales.

Cohen and Barlow (2005) compared the similarities between the AO, NAO,

and warming surface temperatures. Most research has suggested that

temperatures patterns of both indices are similar, so it has been suggested that the

AO and NAO are closely linked to global warming. Cohen and Barlow (2005)

71

suggested that SSTs, stratospheric cooling and warming, and warming of the

tropical Pacific may affect the trends in the NAO and AO. Although the relationship

between the AO and NAO is not fully understood, it was found that the AO and NAO

were in a positive trend beginning in the 1970s through the 1990s, however, it

appears that the AO and NAO indices have become negative in recent years. When

comparing the 2000s to the past 30 years, the trends for both indices are weak or

nonexistent (Cohen and Barlow 2005). There has been a significant positive trend

in surface temperatures over the past several decades, but a neutral trend in the

AO, suggesting that there is no evidence of dynamical amplification of hemispheric

warming associated with decadal trends in the AO. There was not enough evidence

to conclude a relationship exists between North Atlantic SSTs and the phase of the

AO (Cohen and Barlow 2005).

In the stratosphere, the increase in greenhouse gases may cause cooling

(assuming that cooling is preferential over higher latitudes), which strengthens the

polar vortex and yields a positive AO (Cohen and Barlow 2005). The analysis in

this study reveals that the stratospheric AO signal has been in an opposite sign to

that of the tropospheric AO signal during the past few decades. When investigating

SSTs in the Pacific in relation to the NAO and AO, it was concluded that the Pacific

SSTs may force variability over the Atlantic sector but they are unrelated to trends

in either the NAO or AO. The time series between Pacific SSTs and the AO diverged

over the past several decades, most notably during the most recent decade (Cohen

and Barlow 2005). It has also been proposed that snow cover influences the phase

and strength of the winter AO, as the snow cover trend over the previous few

72

decades matches the AO index almost identically. Comparing everything together

in relation to global warming, it appears that the AO is not a dominant factor in the

surface temperature warming trend while warmer Pacific SSTs may (Cohen and

Barlow 2005). It was concluded that the NAO and AO may affect regional surface

temperatures across portions of Europe and North America , the trends in phase

have been oscillatory, whereas the trend in temperatures have remained strongly

positive. Therefore, anthropogenic forcing and other regional anomalies in the

Pacific may be playing a larger and more consistent role in climate change across

the world (Cohen and Barlow 2005).

Wettstein and Mearns (2002) focused on the influence of the NAO and AO

on temperatures across the northeast United States and Canada. They determined

that, although spatial differences are noted between the definitions of the NAO and

AO, they were found to have a correlation coefficient of .75 during the winter

seasons of the period studied. Mean minimum temperatures expressed a negative

relationship with the AO index in far northern portions of the northeast, while

southern portions of the northeast experienced increasing temperatures with a

positive AO (Wettstein and Mearns 2002). The mean maximum temperatures

decreased when the AO index was more positive in far northern parts of the

Northeast and increased when the AO index increased in southern parts of the

Northeast. During the winter, a spatial gradient in the diurnal temperature range

existed over the northeast, ranging from small positive values in the southern

portion to near 1C in AO positive phase versus AO negative phase for the

northeast portion (Wettstein and Mearns 2002). It was noted that the maximum

73

NAO-AO effect on mean and extreme temperatures across the Northeast was found

in late winter to early spring. The authors noted that these results were obtained

from statistical analyses and it would be wise to analyze temperature and NAO-AO

relationships dynamically and synoptically before any solid conclusions can be

drawn.

5.4 El Nino-Southern Oscillation (ENSO)

ENSO was first identified by Sir Gilbert Walker as a naturally occurring

phenomenon involving air pressure differences and fluctuations in ocean

temperatures across the equatorial Pacific (Earth System Research Laboratory).

The ocean temperatures fluctuate between warmer than normal conditions and

cooler than normal conditions across the eastern equatorial Pacific and are

referred to as El Nino and La Nina, respectively. Near-normal ocean temperatures

are referred to as ENSO-Neutral. The Southern Oscillation refers to the large-scale

changes in surface air pressure between the eastern tropical and western tropical

Pacific Ocean. The index value representing this change is calculated from

fluctuations in the surface pressure between Tahiti and Darwin, Australia (Climate

Prediction Center). Negative Southern Oscillation Index (SOI) values correspond to

below normal pressure over Tahiti and above normal pressure over Darwin, which

reduces the strength of the trade winds and creates low pressure over the eastern

equatorial Pacific (Climate Prediction Center). Low atmospheric pressure is

associated with warm ocean water and high atmospheric pressure is associated

with cold water, so El Nino conditions occur when the SOI is negative. For a

74

positive SOI value, opposite conditions occur and La Nina develops (Climate

Prediction Center).

5.4.1 El Nino

El Nino is characterized by a negative SOI index and warmer than normal

SSTs across the eastern equatorial Pacific (State Climate Office of North Carolina).

The effects of El Nino are strongest during the northern hemisphere winter

because ocean temperatures are warmest, supplying moisture and instability that

enhance convection within the eastern equatorial Pacific region. The convection

alters the position of the jet stream such that it becomes active over parts of the

United States. Across the United States, winters are warmer over the Northeast

and warmer and drier over the Northwest and Midwest with below average

snowfall. Winters are wetter across the Southwest and wetter and cooler across

the Southeast. Regions not mentioned do not produce noticeable climate changes

during El Nino winters. El Nino conditions also alter the jet stream such that the

vertical wind shear increases across the Caribbean and Atlantic, suppressing

tropical cyclone activity (State Climate Office of North Carolina).

.

5.4.2 La Nina

La Nina is characterized by a positive SOI index and cooler than normal

SSTs across the eastern equatorial Pacific (State Climate Office of North Carolina).

Cooler SSTs correspond to higher stability and less convection across the Pacific.

The jet stream tends to be suppressed across the United States. Cooler and wetter

75

conditions are found across the Northwest and wetter conditions, but near normal

temperatures, are found across the Midwest. It is drier and warmer across the

Southwest and Southeast and wetter and warmer across the Mid Atlantic and

Northeast. Other regions of the United States do not produce any noticeable

climate changes during La Nina winters (State Climate Office of North Carolina).

5.4.3 Existing Literature: ENSO

Ropelewski and Halpert (1986) analyzed North American precipitation and

temperature patterns associated with ENSO. Using monthly precipitation totals

and mean temperatures of surface meteorological station data, Ropelewski and

Halpert (1986) found that the Mid Atlantic, High Plains, Great Basin, and Gulf of

Mexico regions revealed a coherent ENSO response in precipitation patterns. Areas

near the Gulf of Mexico and the Great Basin experienced above normal

precipitation during ENSO seasons. It was speculated that the High Plains also

experienced above normal precipitation during ENSO season, however the results

were inconclusive (Ropelewski and Halpert 1986). Northwest North America, the

southeast United States, and eastern Canada showed a coherent ENSO response in

temperature patterns. Northwest North America experienced above normal

temperatures during most ENSO events, while the southeast United States

experienced below average temperatures in most cases (Ropelewski and Halpert

1986). Further investigation of eastern Canada led to a conclusion that the Pacific

Decadal Oscillation (PDO) may have a larger influence on temperatures than ENSO.

No other areas in North America showed a clear relationship with ENSO and it

76

remains unclear how large an influence other teleconnection patterns have in

North America compared to ENSO (Ropelewski and Halpert 1986).

Kiladis and Diaz (1989) investigated global climate anomalies with

extremes in the Southern Oscillation using station precipitation and temperature

data. Temperature anomalies in the United States were more pronounced than

those over Eurasia during both El Nino and La Nina, due to the proximity of

America to the equatorial Pacific. However, precipitation changes in response to

the SO were more pronounced over Eurasia than in North America. During the

winter months, North America showed the largest middle latitude response

compared to any other region in the world (Kiladis and Diaz 1989). During El Nino,

above normal temperatures and below normal precipitation were found across

Alaska and southern Canada, while the opposite pattern was seen over the

southeastern United States (Kiladis and Diaz 1989). It was speculated that the PDO

may influence this response. The strongest signal with the SO was a positive

correlation between SSTs in the eastern equatorial Pacific and surface

temperatures in the tropics (Kiladis and Diaz 1989).

Shabbar and Khandekar (1995) analyzed the impacts of ENSO on the

temperature field over Canada using gridded surface temperature data and 1000-

500 hPa thickness data. They found that positive surface temperature anomalies

spread eastward from the west coast of Canada to Labrador during the winter

season when El Nino is present. During La Nina winters, negative surface

temperature anomalies spread southeastward from Yukon to the Great Lakes.

Western Canada surface temperatures were influenced by both phases of ENSO,

77

whereas eastern Canada temperatures were influenced only during El Nino. The

impact of ENSO was strongest during the winter months. It is possible that regions

in the United States bordering these sections of Canada could experience similar

influences by ENSO, but these were not investigated.

78

Chapter 6: Ice Storm Frequency in the Northeast and its

Association with Teleconnection Patterns.

A pronounced westward shift in ice storm frequency between the periods

of 1966-1977 and 1998-2011 across the northeast United States was noted in

Chapter 4, and it was hypothesized that this may have been induced by

meteorological sources. Specifically, phases changes of teleconnections were

thought to influence the spatial and temporal characteristics of ice storms within

the Northeast. Other factors related to global warming may also be responsible for

the westward shift, however, this topic was not investigated. Before this

comparison was conducted, ice storm frequency in the Northeast was normalized

to determine when the shift may have occurred. The comparison between

teleconnection phases and ice storm frequency and distribution was then

performed for the winter seasons between 1966-2011.

6.1 Decadal Ice Storms in the Northeast

As seen in Chapter 4, there appears to be a westward shift in winter ice

storm frequency across the Northeast between the time periods of 1966-1977 and

1998-2011. While data inconsistencies were present in this region throughout the

extent of this climatology, it was hypothesized that there still exists some

meteorological influence behind the shift. The meteorological sources

hypothesized to influence ice storm frequency in the Northeast are the climate

anomalies associated with variability of global circulation patterns, better known

as teleconnections. In order to compare ice storm frequency over the Northeast to

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teleconnections, the winter seasons between 1977-1998 had to be accounted for.

The average number of winter ice storms per decade was calculated to pinpoint a

timeframe in which a regional shift in ice storm frequency may have occurred.

Figures 6.1-6.5 show the normalized number of documented ice storms across

New England for each decade, beginning in the late 1960s and ending in the 2000s.

From this point forward, a only a specific domain was analyzed, which included the

states of Pennsylvania, Maryland, Delaware, New Jersey, New York, Connecticut,

Rhode Island, Massachusetts, Vermont, New Hampshire, and Maine. Ice storm

frequencies for other states outside of this domain that can be seen in upcoming

figures are subject to error, since the data gap was not filled for those states (refer

back to Chapter 3).

Since the temporal scale of this study began in 1966, the 1960s consisted of

only three winter seasons, where the highest average number of ice storms is

found over the northern portion of New England, including the states of Maine,

New Hampshire, Vermont, Massachusetts, Connecticut, and Rhode Island. All of

these states experienced an average of at least one ice storm during this period.

Far eastern Vermont, central and southern New Hampshire, southern Maine, and

eastern Massachusetts experienced the highest number of ice storms, with an

average of 2-3 storms. A relatively high frequency of ice storms is also found over

Maryland and Delaware, with an average of 1-2 ice storms. Isolated sections of

northern Maryland experienced an average of 2-3 ice storms. A minimum is found

over western Pennsylvania, with no reported ice storms. Most areas in the

80

Northeast experienced an average of at least one ice storm during these three

winter seasons.

Figure 6.1: The average number of ice storms for each winter season in the Northeast between 1966-1969. The highest average is across New Hampshire, southern Maine,

and northern and eastern Massachusetts.

The 1970s consisted of ten winter seasons where the highest average

number of ice storms is found over northern portions of the Northeast, including

the states of Maine, New Hampshire, Vermont, Massachusetts, Connecticut, and

Rhode Island (Figure 6.2). These states experienced an average of 1-2 ice storms

during this decade. Similar to the results from the late 1960s, southern Maine,

central and southern New Hampshire, and northern and eastern Massachusetts

averaged 2-3 ice storms. There is a slight increase in the average number of ice

81

storms from the late 1960s into the 1970s across southern New York and eastern

Pennsylvania. The higher frequency that is found over Maryland and Delaware is

still evident, with a slight decrease in ice storm frequency over northern Maryland.

Western Pennsylvania also experienced a slight increase in ice storm frequency

from the late 1960s into the 1970s. There is an abrupt change in ice storm

frequency across eastern Pennsylvania into New Jersey (and southern New York

and New Jersey). This is an example of a data inconsistency within the information

presented in Storm Data. There was likely a higher average of ice storms across the

northern portion of New Jersey, however, due to incomplete or erroneous

reporting, the data was skewed in that region of the Northeast. Nonetheless, the

average number of documented ice storms in the Northeast during the 1970s

closely matches the average number of ice storms during the late 1960s, with the

highest average found over the far northern Northeast across portions of Maine,

New Hampshire, and Massachusetts.

82

Figure 6.2: The average number of ice storms in the Northeast during the 1970s. The highest average is found across New Hampshire, southern Maine, and northern and

eastern Massachusetts.

During the winter seasons of the 1980s, there is a noticeable change in both

ice storm frequency and the location of the highest averages across the Northeast

(Figure 6.3). The maximum frequency of ice storms is less than that of previous

two decades. During the 1980s, only several counties in southern New York

reported an average of 2-3 storms. The highest average aside from this is located

over eastern and southern New York, eastern Pennsylvania, and parts of southern

New Hampshire, where an average of 1-2 ice storms were documented. The

remaining areas of the Northeast experienced an average of about one ice storm.

Not only is the average number of ice storms lower in the 1980s, but the maximum

in frequency has shifted westward into New York and Pennsylvania. Portions of

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the northern Northeast that experienced an average of 2-3 ice storms during the

late 1960s and 1970s averaged about one ice storm during the 1980s, with a few

exceptions in southern New Hampshire. It seems that the westward shift that was

discussed earlier may have occurred during the 1980s. However, this observation

is subject to error as data inconsistencies are still obvious over portions of the

Northeast during this period, with the most notable being across northern New

Jersey. Despite errors in the available data during the 1980s, fewer ice storms

were documented and the highest number of storms was found to have shifted

southward and westward as compared to the previous two decades towards parts

of the western southern Northeast.

84

Figure 6.3: The average number of ice storms in the Northeast during the 1980s. The highest averages are located farther south than the previous two decades, with

maxima across eastern New York and eastern Pennsylvania.

The winter seasons during the 1990s were active across much of the

Northeast as compared to the previous decades (Figure 6.4). The greatest

frequency of ice storms is found during this decade, with nearly all portions of the

Northeast experiencing a higher average number of ice storms. The highest

frequency is found over Vermont, eastern and southern New York, eastern

Pennsylvania, and far northwest New Jersey. These areas experienced an average

of 2-3 ice storms, with some locations across New York and Pennsylvania

averaging 4-5 storms. The location of the maximum in frequency matches that

seen during the 1980s, however, the total number of reported storms increased.

85

Most areas across the northern Northeast experienced a higher number of ice

storms in the 1990s as compared to the 1980s, however, totals are still lower

compared to the late 1960s and 1970s. Most areas in the Northeast experienced an

average of 1-2 ice storms during this decade. The data inconsistency between

Pennsylvania, New York, and New Jersey has been resolved during this decade, as

reports from northern New Jersey better resemble the results of surrounding

areas. This shows that the information presented within Storm Data significantly

improved during the 1990s. It is unclear how this improvement in data quality

affected ice storm frequency across the Northeast during this particular decade. It

is possible that the higher frequencies are the result of better data quality. It is also

possible that winters during the 1990s were particularly active. The main

conclusions from Figure 6.4 are that nearly all areas in the Northeast experienced

an average of 1-2 ice storms, the westward shift in ice storm frequency is still

evident when compared to the first two decades that were analyzed, and the

frequency of ice storms increased across portions of New York and Pennsylvania

since the beginning of this study, where the westward shift is most noticeable.

86

Figure 6.4: The average number of ice storms in the Northeast during the 1990s. The 1990s was the most active decade with highest averages across eastern New York,

eastern Pennsylvania, and northwest New Jersey.

During the most recent decade, there seems to be a noticeable decrease in

the spatial coverage of higher frequencies in ice storms across the Northeast,

compared to the 1990s (Figure 6.5). However, several areas experienced the same

average number of storms as previous decades. The maximum number of ice

storms is found over a small portion of eastern Pennsylvania and northwest New

Jersey, with an average of 4-5 ice storms. A few counties in eastern Pennsylvania

averaged over five ice storms. The remainder of the region reported 1-2 ice

storms. The westward shift still appears evident, however, the broad coverage of

higher frequency that was seen over previous decades, especially the 1990s, has

87

noticeably declined to only include a small section of the southern Northeast. In

this region, topography may have played an important role in precipitation type, as

the highest frequency was found in the vicinity of the Poconos Mountains.

Figure 6.5: The average number of ice storms in the Northeast during the 2000s. The highest average was found across eastern Pennsylvania and northwest New Jersey

near a mountainous area.

It seems evident that a westward shift in higher ice storm frequency over

the Northeast occurred during the 1980s, barring any inconsistencies in data

coverage. During the late 1960s and 1970s, the highest number of ice storms was

found in the northern Northeast. During the 1980s, 1990s, and 2000s, the highest

number of ice storms was found over the southern Northeast. The 1990s seem to

have the highest ice storm frequency. Although the spatial coverage of higher

frequencies of ice storms seems to decrease during the 2000s, the highest

88

frequency appears to be slightly further south than the previous two decades and

equal to that of the 1990s. Therefore, not only has the distribution of ice storms

changed during the past four decades, the average number of ice storms has

increased during the past two decades. Now that the distribution of ice storms has

been documented for the entire period of study, changes in ice storm frequency

can now be compared with the climate anomalies associated with teleconnections.

6.1.1 Ice Storm Frequency Versus the AMO

As stated in Chapter 5, the AMO is a teleconnection that refers to the sea

surface temperature anomalies in the North Atlantic Ocean. The AMO was

hypothesized to be of importance when studying ice storm frequency across New

England due to the proximity of the coast to the Northeast. Figure 6.6 shows the

average value of the AMO index for each winter season between 1966 and 2011.

Figure 6.6: The normalized value of the AMO representing each winter season of study. The AMO was negative between the winters of 1966-1997. A positive phase

has been present since.

89

The AMO index was largely negative between 1966-1997. From about 1997

to present day, the index became positive, with a few seasons being classified as

negative in between. Figure 6.7 shows the average number of ice storms

documented during the winter seasons between 1966-2011 that are associated

with a negative AMO index. In general, most of the Northeast experienced an

average of 1-2 ice storms, but several locations, particularly in southern New York,

experienced between 2-3 ice storms. The areas with the lowest average include

western New York, western and central Pennsylvania, and many coastal locations.

Despite the maximum number of ice storms in southern New York, there is a

broader area of higher ice storm frequency across the northern Northeast

compared to the southern Northeast.

Figure 6.7: The average number of ice storms in the Northeast during winter seasons where the AMO index was negative. A large portion of the area averaged between 1-

2 ice storms.

90

The distribution of ice storms across the Northeast is noticeably different

during winter seasons in which the AMO is positive (Figure 6.8). In addition to this,

there seems to be a higher average number of ice storms in some locations during

a positive AMO. These results also show some similarity to ice storm frequency

during the 2000s. A maximum in frequency is found over the Poconos Mountains

of eastern Pennsylvania and northwest New Jersey, with an average of 4-5 ice

storms in some locations. Comparing ice storm frequency between negative and

positive AMO seasons, there is a distinct decrease in frequency from an average of

1-2 storms during negative AMO seasons to around an average of one ice storm

during positive AMO seasons across the northern Northeast, particularly across

New Hampshire, Maine, Vermont, and Massachusetts. There is a slight westward

shift in frequency from negative AMO seasons to positive AMO seasons. Negative

AMO seasons seem to be associated with a higher frequency of ice storms across

the northern Northeast and positive AMO seasons seem to be associated with a

higher frequency of ice storms across the southern Northeast. These results were

not analyzed further, so it is not known how statistically significant this

relationship is, nor how the synoptic evolution of cyclones is affected by changes in

the AMO, or how other oceanographic variables affect the AMO and associated

Northeast precipitation type (This statement holds true for the remaining

analysis).

91

Figure 6.8: The average number of ice storms in the Northeast during winter seasons in which the AMO is positive. The highest average is found across the Poconos

Mountains region of eastern Pennsylvania and northwest New Jersey.

6.1.2 Ice Storm Frequency Versus the NAO

Ice storm frequency was next compared to the NAO, as many previous

studies have indicated that changes in the NAO are known to affect temperature

and precipitation patterns across the eastern United States. Figure 6.9 shows the

average value of the NAO index for each winter season between 1966-2011.

92

Figure 6.9: The normalized NAO index for the winter seasons between 1966-2011. A positive phase has been present during most seasons between 1987-2008.

The index represents a fairly oscillatory pattern for this particular period of

study. It may be interpreted that the index was most oscillatory between the

winters of 1966-1990 and then remained largely positive from about 1987-2008,

with a few seasons in between characterized by a negative phase. Figure 6.10

shows the distribution of ice storm frequency in response to winter seasons

characterized by a negative NAO index. Most areas of the Northeast experienced an

average of 1-2 ice storms during this phase. The maximum in frequency extends

across the Northeast in a southwest to northeast manner, beginning in Maryland

and extending into Maine. The highest number of ice storms is reported across the

Poconos Mountains and parts of southern New York, where an average of 1-3 ice

storms were documented. The minimum is found across western and central

Pennsylvania, western New York, and coastal locations. The distribution of ice

93

storms during negative NAO seasons does not seem correlated with the results

from either phase of the AMO.

Figure 6.10: The average number of ice storms in the Northeast during negative NAO winter seasons. Most locations experienced an average of 1-2 ice storms, except

areas near Lake Ontario and coastal areas.

Shown in figure 6.11 is the average number of ice storms during winter

seasons in which the NAO is positive. Similar to the negative phase of the NAO, the

maximum frequency extends from southwest to north-northeast across the region,

with the highest averages found in southern New York and a few locations near the

Poconos Mountains. Most areas in the Northeast averaged 1-2 ice storms, with

southern New York and the Poconos Mountains averaging 2-3 ice storms. A

minimum is found across western New York, western and central Pennsylvania,

94

northern Maine, and coastal locations, with an average around one ice storm.

Comparing ice storm frequency to negative and positive NAO seasons, there does

not seem to be a unique difference. There may be a slight northward shift of the

higher frequencies during positive seasons as compared to negative seasons. The

highest averages are generally located in the same area during both phases, with

the highest average during the negative phase located in the Poconos Mountains

and the highest average during the positive phase located over southern New York.

It was concluded that the changes in the phase of the NAO generally do not reflect

a change in the frequency of ice storms or the location of the maximum occurrence

over the Northeast. These results may seem contrary to recent studies regarding

the NAO and precipitation type over the Northeast, however, it must be noted that

most of these studies put most emphasis on frozen precipitation. Again, any

statistical, meteorological, or oceanographic details relating to the two phases of

NAO were not investigated.

95

Figure 6.11: The average number of ice storms in the Northeast during positive NAO winter seasons. The averages closely resemble the results of the negative NAO

seasons, with most locations experiencing 1-2 ice storms.

6.1.3 Ice Storm Frequency Versus the AO

Similar to the NAO, previous studies have linked changes in the AO to

changes in the temperature and precipitation patterns across the eastern United

States. The Figure 6.12 shows the average AO index value for each winter season

between 1966-2011.

96

Figure 6.12: The normalized value of the AO index for the winter seasons between 1966-2011. The AO has been oscillatory, but has shown trends towards a negative

phase since the 1990s.

Similar to the NAO, the AO pattern for this period of study was fairly

oscillatory, but there does seem to be evidence of a trend towards more negative

indices since the mid-1990s (with the exception of a few seasons). A negative

phase was present during most winter seasons between 1966-1990, with several

seasons in the 1970s and 1980s in a positive phase. The early 1990s were

characterized by a strong positive phase. Comparing ice storm frequency to the

negative phase of the AO, it was found that the distribution of higher frequencies

displayed a southwest to northeast trend across the Northeast (Figure 6.13). This

distribution extends from Maryland into Pennsylvania, New York, Massachusetts,

Vermont, New Hampshire, and Maine, with an average of 1-2 storms. A maximum

is found over a small portion of the Poconos Mountains where an average of 2-3 ice

97

storms was reported. All other areas in the Northeast experienced an average of

about one ice storm during all negative AO phases. Some similarities can be seen in

the distribution of ice storms during both negative AO and negative NAO seasons,

with a slightly broader area of higher frequency found during negative NAO

seasons as compared to negative AO seasons. Slightly higher averages are found

over the Poconos Mountains region during negative NAO seasons as compared to

the negative AO.

Figure 6.13: The average number of ice storms in the Northeast during negative AO winter seasons. Higher averages extend northeast from Maryland to Maine.

Figure 6.14 shows the distribution of ice storm frequency during winter

seasons in which the AO was positive. Compared to the negative phase, a

noticeable increase in ice storm frequency is found over eastern and southern New

York, and a broader area of higher frequency is seen over the Poconos area of

98

eastern Pennsylvania. These areas experienced an average of 2-3 ice storms during

positive AO seasons. An increase in frequency across Vermont was also noted. All

other areas experienced an average of 1-2 ice storms when the AO was positive. Ice

storm frequency seems to increase during positive AO seasons in parts of New

York and Pennsylvania, with a broader distribution of higher frequency across the

entire region as well, compared to negative AO seasons. Comparing the results of

positive AO seasons to that of positive NAO seasons, the area of highest frequency

is similar between the two, however, a higher number of storms were reported

during positive AO seasons.

Figure 6.14: The average number of ice storms in the Northeast during positive AO winter seasons. Higher averages were seen over a broader area than during negative

seasons, most notably across eastern New York.

99

6.1.4 Ice Storm Frequency Versus ENSO

The El Nino-Southern Oscillation index is a well-known climatological tool

for forecasting temperature and precipitation pattern changes across the United

States. Although the effects of ENSO are thought to be relatively weak across the

Northeast compared to other regions of the country, a signal does exist and it is

important to study the associated impacts across this region. Figure 6.15 shows

the average value of the ENSO index for each winter season between 1966-2011.

Unlike the previous teleconnection indices that were presented, ENSO is

categorized by three phases: El Nino, La Nina, and Neutral. El Nino was categorized

as having an index value 1. Similarly, La Nina was categorized as having an index

value ≤-1. Neutral was categorized as having an index value between ±1.

Figure 6.15: The normalized value for ENSO during the winter seasons between 1966-2011. The pattern has been oscillatory and no solid trends were indentified.

100

Similar to the NAO and AO, the ENSO index represents an oscillatory

pattern. Several strong El Nino seasons were found between 1984-1998 and

several strong La Nina seasons were found between 1999-2010. Several other

seasons experienced El Nino and La Nina conditions, albeit somewhat weak. Most

seasons were categorized by a neutral phase. Figure 6.16 shows the average

number of ice storms in the Northeast during seasons in which the ENSO index

represented El Nino conditions. The highest frequency of ice storms is found over

the northern Northeast, with the highest averages located in southern New

Hampshire and far southern Maine (3-4 ice storms). Most states within the

northern Northeast experienced an average of 1-3 ice storms during El Nino

seasons, with the exception of Rhode Island, where there was likely a data

inconsistency. A relatively lower frequency is found across the southern Northeast,

where an average of one ice storm is found in most areas, with a few locations in

eastern Pennsylvania and New York averaging the same number of storms as the

northern Northeast.

101

Figure 6.16: The average number of ice storms in the Northeast during El Nino winter seasons. High averages are located across the northern Northeast, while low

averages are located across the southern Northeast.

During La Nina seasons, the highest frequency is found across eastern and

southern New York, and the Poconos Mountains (Figure 6.17). Almost all areas had

an average of 1-2 ice storms, with the exception being western New York and some

coastal locations. Comparing La Nina seasons to El Nino seasons over the

Northeast, there is a broader area that averages over one storm during La Nina

seasons, but there seems to be a higher frequency of ice storms (particularly for

some locations) during El Nino seasons. During El Nino seasons the maximum is

found over the northern Northeast and during La Nina seasons, the maximum is

found over the southern Northeast. This represents a westward shift in ice storm

102

frequency, from El Nino conditions to La Nina conditions, which is similar to the

results obtained during the AMO comparison.

Figure 6.17: The average number of ice storms in the Northeast during La Nina winter seasons. Higher averages are found across the southern Northeast compared

to the northern Northeast.

During ENSO-neutral seasons, ice storm frequency is not as high as that

during El Nino or La Nina seasons, with the maximum in frequency located in

eastern Pennsylvania with between 2-3 storms (Figure 6.18). Surrounding

locations in eastern Pennsylvania, eastern New York, southern Vermont, central

and southern New Hampshire, and southern Maine experienced an average of 1-2

ice storms during neutral seasons. All other areas experienced an average of one

ice storm during neutral seasons. Neutral seasons were similar to La Nina seasons

103

in that the highest frequency is located over southern New England, in this case the

Poconos Mountains.

Figure 6.18: The average number of ice storms in the Northeast during ENSO-neutral winter seasons. No significant trend was noted, as most locations averaged around

one ice storm.

Based on the results presented thus far, a potential association between ice

storm frequency and the phases of ENSO, the AO, and the AMO was found. Looking

more closely at the results of these three teleconnections, it seems as though El

Nino seasons and negative AMO seasons yield a similar distribution of ice storms

(Figure 6.19). There also seems to be a similar distribution of ice storms resulting

from La Nina seasons and positive AMO seasons (Figure 6.20). During seasons

which are characterized by El Nino and negative AMO conditions, there seems to

104

be a higher frequency of ice storms located across the northern half of the

Northeast. During seasons in which La Nina and positive AMO conditions are

present, there seems to be a higher number of ice storms reported in southern

parts of the Northeast. The AO results did not yield strong similarities with ENSO

and/or the AMO as the latter two showed with one another.

Figure 6.19: A spatial comparison of the average number of winter season ice storms across the Northeast during negative AMO (left) and El Nino seasons (right). The higher frequency is located across the northern portion of the

Northeast.

105

Figure 6.20: Same as Figure 6.20, but for positive AMO (left) and La Nina seasons (right). The higher frequency was found across the southern portion of the

Northeast.

To investigate the potential association between ENSO, the AMO, and ice

storm frequency, all ice storms that have been documented during seasons in

which El Nino conditions were present with negative AMO conditions were

plotted, as well as all ice storms that have been documented when La Nina

conditions were present with positive AMO conditions (Figures 6.21 and 6.22,

respectively). During El Nino seasons that were coupled with negative AMO

seasons, there tends to be a higher frequency of ice storms located in the northern

Northeast, with relatively few, if any, reported in the southern Northeast. Across

the northern Northeast, a higher frequency is found across southern New

Hampshire, southern Maine, and far northern Massachusetts where an average of

106

3-4 storms were reported. A maximum in ice storm frequency is found over

southern New Hampshire, where an average of 4-5 storms was noted. Most of the

surrounding states averaged around 2-3 storms, with coastal areas averaging

slightly less. The southern Northeast yielded a much lower average as compared

to the northern portion, with a majority of the states experiencing an average of

around one ice storm under these conditions.

Figure 6.21: The average number of winter season ice storms in the Northeast when El Nino and negative AMO conditions are present together. A maximum frequency is located over the northern portion of the Northeast and a minimum is found over the

southern portion of the Northeast.

During seasons in which La Nina was coupled with positive AMO

conditions, more ice storms were reported in the southern half of the Northeast,

particularly over portions of Pennsylvania and New Jersey. The highest average is

107

found over the Poconos Mountains in eastern Pennsylvania and northwest New

Jersey, where an average of 3-5 ice storms were reported. A few counties in

eastern Pennsylvania averaged over five ice storms during these conditions. A

secondary maximum is found over eastern New York in the lower Adirondack

region, where an average of 3-5 ice storms was reported. Most of the surrounding

areas of the southern Northeast averaged between 1-3 ice storms, with the lowest

average of around one ice storm found across western New York. A noticeably low

average is found over the northern half of the Northeast, where an average of

around one ice storm was reported.

Figure 6.22: The average number of winter season ice storms in the Northeast when La Nina and positive AMO conditions are present together. A maximum is found across the southern portion of the Northeast and a minimum is found across the

northern portion of the Northeast.

108

These two comparisons yield preliminary associations between two

teleconnection patterns and ice storm frequency. A statistical analysis was not

conducted to verify these comparisons, so a relationship between ENSO, the AMO,

and ice storm frequency across New England during the winter season may or may

not exist. However, it appears possible that there may be an association between

the three variables based on the results presented above.

Recall from chapter 4 the advantages and disadvantages that were

discussed between using Storm Data and ASOS data. For data quality control

purposes, a comparison between freezing rain reports in Storm Data and freezing

rain reports from two ASOS sites was performed. This was done to verify the

westward shift in ice storm frequency across the Northeast. As stated in chapter 3,

only freezing rain data from Portland, Maine and Albany, New York was used to

represent a northern and southern section of the Northeast, respectively. Figure

6.23 shows the number of freezing rain hours between the winter seasons of 1966-

2011 from the ASOS site in Portland, Maine.

109

Figure 6.23: The number of hours of freezing rain in Portland, Maine between 1966-2011 taken from the ASOS site. The number of freezing rain hours increased between

the 1960s and the 1990s, with a peak near 1999. The number of hours began to decrease in recent years.

To match the westward shift suggested by the Storm Data reports, Portland

should have experienced more hours of freezing rain during the winter seasons of

before the 1980s and fewer hours of freezing rain during the winter seasons after

the 1980s. The results from the Portland ASOS do not reflect this and show a more

hours of freezing rain in recent decades.

The same process was conducted for the ASOS site in Albany, New York

(Figure 6.24). To match the ice storm reports from Storm Data, the Albany ASOS

should have reported a higher number of freezing rain hours during the 1990s and

2000s compared to the 1960s and 1970s. The number of hours of freezing rain in

Albany between the winter seasons of 1966-2011 was oscillatory, with peaks in

110

the mid 1960s, late 1980s, early 1990s, and late 2000s. The results favor higher

freezing hours during several periods between the 1990s and 2000s compared to

the 1960s and 1970s, but do not show a distinct trend towards more numerous

freezing rain hours as the reports from Storm Data do.

Figure 6.24: The number of hours of freezing rain in Albany, New York between the winter seasons of 1966-2011. No trend was found, although the 1990s experienced

the highest number of freezing rain hours.

It was concluded that a change in ice storm frequency across the Northeast

does exist, but when comparing Storm Data reports to ASOS reports, it is unclear

when this may have occurred or how significant the change may be. It also

remains unclear which data source is more reliable, further emphasizing the need

for a national method of defining and reporting ice storms.

111

Chapter 7: Summary and Conclusions

A preliminary climatology of ice storm frequency across the contiguous

United States was developed using Storm Data reports between the winter seasons

of 1966-1977 and 1998-2011. Important data inconsistencies were encountered

along the way, including opposing definitions of ice storms used in operational

settings, multiple methods of measuring ice accretion, weather instrument and

human reporting errors, and lack of a significant amount of attention to this winter

threat in the research community. Upon analysis of the national data, it was

hypothesized that there may have been a westward shift in ice storm frequency in

the Northeast from northern sections to southern sections. More ice storms were

documented in the northern portion of the Northeast during the winter seasons

between 1966-1977 and more ice storms were reported in the southern portion of

the Northeast during the winter seasons between 1998-2011. Normalizing the

data revealed that this shift may have occurred during the 1980s. An increase in

ice storm frequency was also observed across the Midwest, but was believed to be

due to improvements in ice storm reporting and population change.

The domain of this study shifted focus strictly to the Northeast, where a

climatology of ice storm frequency and distribution was compared to phase

changes of selected teleconnections between the winter seasons of 1966-2011.

Qualitatively, it was concluded that the ice storm frequency and distributions were

most affected by the AMO and the ENSO, particularly when El Nino seasons were

coupled with negative AMO seasons, and La Nina seasons were coupled with

positive AMO seasons. During El Nino and negative AMO seasons, the highest

112

frequency of ice storms was found over the far northern portion of the Northeast,

particularly across New Hampshire, southern Maine, and northern Massachusetts.

During La Nina and positive AMO seasons, the highest frequency of ice storms was

located over the southern portion of the Northeast, particularly across eastern

Pennsylvania, northwest New Jersey, and eastern New York. No notable change in

ice storm frequency was noted between negative and positive phases of NAO, or

between either phase of the NAO and another teleconnection phase. This same

conclusion was made for the AO, although there was an increase in frequency

across eastern New York during positive AO seasons compared to negative AO

seasons. Spatially, neither phase of AO was seen to relate to any phase of the other

teleconnections. While the results of this study hold value, no solid conclusions

could be made between ice storm frequency across the Northeast in relation to

telelconnections, or to whether ice storm frequency has changed across the

contiguous United States due to meteorological factors.

A comparison between Storm Data reports and ASOS reports did not verify

the existence of a shift in ice storm frequency across the Northeast. The ASOS

reports showed no conclusive evidence of a shift from northern sections to

southern sections of the Northeast, while the reports documented in Storm Data

do. It was determined that the existence and the temporal and spatial

characteristics of the shift remains unclear.

While future studies on ice storms will be beneficial towards our

understanding, the lack of a widely accepted and followed definition of an ice

storm will prevent a consistent method of reporting. This is especially true within

113

the NWS WFOs, who use supplemental criteria in determining whether to issue ice

storm advisories, watches, or warnings. If no advisory or warning is issued, it will

not be reported, and will not be documented in databases. Along with this,

preparation strategies vary across different regions of the United States, leading to

inconsistencies of ice storm characteristics. Light ice accumulation in the southern

United States is likely to have a higher impact than light icing in the Northeast,

where freezing rain is more frequent. The South may document the event as an ice

storm, while the Northeast may ignore it.

Ice accretion measurement methods need to be redefined before an

accurate ice storm climatology can be developed. This is especially true since the

NWS uses an ice threshold before issuing warnings. Ice accretions could be under

this threshold yet cause damage. In addition, ice accretes on surfaces differently,

depending on thermal properties, wind speeds, and orientation. A point

measurement on one object may not be representative of the overall ice

accumulation, causing the event to be dismissed or erroneously exaggerated. In

addition, weather instruments have yet to acquire an ice accretion sensor,

although an algorithm for this has been developed, making reports instruments

erroneous at times.

With no proper way of defining an ice storm, or measuring ice

accumulation, it is nearly impossible to develop an accurate climatology of ice

storm frequency or distribution, as such inconsistencies skew data within the

existing datasets. This also makes any quantitative method designed to yield

statistically significant results of ice storm frequency and atmospheric circulation

114

changes challenging. The overall conclusions of this study indicate that furthering

ice storm research is currently hampered by inconsistencies in the realm of data

documentation. This study hopes to emphasize the importance of the need to

evaluate how the meteorological community addresses elements pertaining to

freezing rain events, in hopes that there will be a step forward.

Once data inconsistencies are resolved, an accurate climatology of the

contiguous United States can be developed. Then, a better idea of ice storm

frequency changes can be evaluated and compared to changes in global circulation,

synoptic patterns, mesoscale features, etc. Upon doing this, a quantitative

approach using a statistical analysis will reveal representative results, in addition

to the qualitative conclusions. This will yield a better understanding ice storms,

both spatially and temporally, which will improve ice storm forecasting and save

lives and property.

115

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