Download Climatology of Freeze-Thaw Days in the Conterminous United

Climatology of Freeze-Thaw Days in the Conterminous United States: 1982-2009
A Thesis Submitted in Partial Fulfillment
of the Requirements for the Degree of
Master of Arts in Geography
By
Jason S. Haley
May, 2011
Thesis written by
Jason Stewart Haley
B.A., Kent State University, 2009
M.A., Kent State University 2011
Approved by
__________________________________, Advisor, Dr. Scott Sheridan
__________________________________, Chair, Department of Geography,
Dr. Mandy Munro-Stasiuk
__________________________________, Dean, College of Arts and Sciences,
Dr. John R. D. Stalvey
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TABLE OF CONTENTS
Page
LIST OF FIGURES……………………………………………………………………….v
LIST OF TABLES………………………………………………………………………..vi
ACKNOWLEDGEMENTS……………...………………………………………………vii
CHAPTER 1
INTRODUCTION…...……………………………………………………………1
CHAPTER 2
LITERATURE REVIEW…………………………………………………………3
2.1 – General Information Regarding Freeze Thaw Cycles………………3
2.2 – The Impact of Climate Trends and Variability on Winters
Across the Study Area………………………………….7
2.3 – Implications of Freeze Thaw……………………………….……….9
CHAPTER 3
DATA AND METHODOLOGY………………………………………………...11
3.1 – Data………………………………………………………………...11
3.2 – Methodology……………………………………………………….13
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CHAPTER 4
RESULTS………………………………………………………………………..15
4.1 – FT Interpolation Descriptions……………………………………...15
4.2 – FT Region Descriptions, Location, and Climatology……………...25
4.3 – Annual Trends in FT Days by Region……………………………..31
4.4 – FT Seasonal Temporal Analysis by Region…………………….....34
4.5 – Urban/Rural FT Analysis…………………………………………..39
4.6 – EFT Interpolation Descriptions……………………………….……42
4.7 – EFT Region Descriptions, Location, and Climatology…………….50
4.8 – Annual Trends in EFT Days by Region……………………………57
4.9 – EFT Seasonal Temporal Analysis by Region…………….………..58
4.10 – Urban/Rural EFT Analysis………………………………...……..59
CHAPTER 5
DISCUSSION…………………………………………………………………....66
5.1 – Overview…………………………………………………………...66
5.2 – Issues Encountered During the Study…………………………..….69
CHAPTER 6
CONCLUSION………………………………………………………………….72
CHAPTER 7
REFERNCES…………………………..………………………………………...74
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LIST OF FIGURES
Figure
Page
2.1 – Mean Annual Frequency (days) of Freeze Thaw Cycles……………………………5
4.1 – Interpolation of Mean Monthly FT Days Across the Study Area………………….16
4.2 – Interpolation of Mean Annual FT Days Across the Study Area…………………...24
4.3 – FT Clusters…………………………………………………………………………26
4.4 – Average FT Days by Region……………………………………………………….27
4.5 – Annual Mean FT Days by Region……………………………………………...….33
4.6 – Seasonal Mean FT Days by FT Cluster……………………………………………35
4.7 – Urban and Rural Stations…………………………………………………………..40
4.8 – Interpolation of Mean Monthly EFT Days Across the Study Area………………..43
4.9 – Interpolation of Mean Annual EFT Days Across the Study Area…………………50
4.10 – EFT Clusters……………………………………………………………………...52
4.11 – Average EFT Days by Region……………………………………………………53
4.12 – Annual Mean EFT Days by Region………………………………………………58
4.13 – Seasonal Mean EFT Days by EFT Cluster……………………………………….60
4.14 – Annual Heartland Urban and Rural EFT Days…………………………………...65
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LIST OF TABLES
Table
Page
4.1 – Annual and Seasonal Mean FT Day Trends Per Year and P-values……………….32
4.2 – FT Urban/Rural Station Count in FT Analysis……………………………….……40
4.3 – Urban/Rural FT Day Trends……………………………………………………….41
4.4 – Urban/Rural FT Day Trend P-values……………………………………………....41
4.5 – Annual and Seasonal Mean EFT Day Trends Per Year and P-values…………..…57
4.6 – EFT Urban/Rural Station Count in EFT Analysis………………………………....64
4.7 – Urban/Rural EFT Day Trends……………………………………………………...64
4.8 – Urban/Rural EFT Day Trend P-values…………………………………………….64
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ACKNOWLEDGEMENTS
This project has come a long way since it was conceived in Geneva, Switzerland
in the spring of 2009. I spent countless hours in the lab programming the data, displaying
it in GIS, running the temporal analysis, and writing the results. Completing this thesis is
one of my greatest accomplishments.
There are many people I’d like to thank for their time and support, without it I
don’t think I could have completed this in timely matter, or even at all. First, I would like
to thank my advisor, Dr. Scott Sheridan. Without his guidance, knowledge, and support I
would have never finished. He has been a integral part of this research from its inception
in Geneva. Secondly, I would like to thank my committee members, Dr. Tom Schmidlin
and Dr. Emariana Taylor. Dr. Schmidlin’s help with sources and his own in depth
knowledge of freeze thaw from his own research were invaluable. Without Dr. Taylor’s
help in the GIS lab The projecting and mapping of my variables would have taken much
longer. It is my pleasure to have worked with both of them.
One of the people I am the most thankful for is the support and help of Dr. Kevin
Butler at the University of Akron. Without his help the data I used in this study would
have never made it past its NCDC format and the study would have never been
completed. Finally, I’d like to thank my family and friends, especially Derrin Smith and
Christina Longo, for their support. Without their support and kind words I would have
lost sanity somewhere down the line.
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CHAPTER 1
INTRODUCTION
The combination of precipitation and temperatures crossing the freezing point has
to be dealt with when planning almost any kind of construction or repair in the
conterminous United States. These freeze thaw cycles make water expand and contract
which can damage and destroy natural features and man-made structures over long term
exposure.
Although freeze thaw activity across the conterminous United States has been
researched in the past, there is not a lot of research on their specific occurrence, and no
major work on it has been completed recently. Modern database and data storage
technology was not available when the work that focused exclusively on freeze thaw
activity was published in the past. By using a variety of statistical and mapping software
this study will be able to look at freeze thaw activity from a wider perspective than past
research.
The purpose of this thesis is to analyze temperature data from 1982-2009 to
determine the effect climate trends and variability have had on freeze thaw days, (a 24
hour period when one or more freeze thaw cycle occurs), create regions based on freeze
thaw day patterns, and look at differences between urban and rural areas within these
regions. These data will help to determine the modern spatial variability of freeze thaw,
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and also look at how this variability has changed over the study period. These data will
be used to will also identify where and when these changes were the most significant.
Two types of freeze thaw activity will be examined in this study. The first is just
the occurrence of freeze thaw days. The second is the occurrence of extreme freeze thaw
days. Extreme freeze thaw days are defined as when the daily high is 9° F (5° C) or more
above the freezing point and the daily low is 9° F (5° C) or more below the freezing
point. This criterion was created specifically for this study in order to look at the
occurrence and spatial variability of more damaging freeze thaws during the study period.
Both of these freeze thaw variables’ occurrences from 1982 to 2009 will be examined
across the conterminous United States, within regions (created for this study using freeze
thaw data), and an urban versus rural analysis. Each of these analyses will be examined
by annual, monthly, and seasonal occurrences during the study period. The end result
will provide trends that present general trends in freeze thaw and extreme freeze thaw
activity during the study, as well as where and when significant changes occurred.
CHAPTER 2
LITERATURE REVIEW
Freezing and thawing, or a freeze thaw cycle, is an effect that occurs across much
of the conterminous United States at some point of the year. Although frost is something
that is consistently occurring through many parts of the year, its cycle and definition have
been sparsely researched (e.g. Hershfield, 1974; Russel, 1943; Schmidlin et al. 1987;
Visher, 1945). With little recent research on the topic, these are the only studies that
provide a general background for the research of this subject.
This section looks at past research done in a variety of areas regarding freeze thaw
cycles. Areas that will be explored include: (1) general information regarding freeze
thaw, (2) winters in the United States and the impact of climate trends and variability on
freeze thaw, and (3) implications of freeze thaw.
2.1 General Information Regarding Freeze Thaw Cycles
Freeze thaw cycles are sometimes described using different terminology, e.g. frost
change-days (Todhunter 1996), but for the purposes of this study they will be referred to
as freeze thaw days. Although different terminologies are used, the definition of a freeze
thaw day is constant. The seminal research on the topic was published by Hershfield
(1974), who looked at freeze thaw days across the conterminous United States.
Hershfield defined a freeze thaw day: “if the temperature crossed the freezing point
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during a calendar day.” Which means the daily low temperature has to be at least 31º F
and the daily high temperature had to be at least 33º F. Therefore freeze thaw days can
be measured by how many days in a year a freeze thaw cycle occurred. What is not
included in this definition of freeze thaw days is how many times the temperature crosses
the freezing point during one day. Hershfield’s data were determined by using maximum
and minimum air temperature at about 5 feet above the ground. Hershfield used data
from 1300 stations across the conterminous United States. These data points were
derived from 200 first-order National Weather Service (NWS) stations and 1,100
cooperative-observer stations, a significant upgrade from the 200 stations that were used
by Visher (1945). Hershfield used Visher’s method of summing the number of days the
low temperature was below freezing was recorded and subtracting the number of days
where the high temperature was below freezing as well. If the daily high or low was 32º
F it was not counted as a freeze thaw day. Hershfield used these data to make a map of
the number of freeze thaw cycles per year. This map was displayed as an isoline map
showing mean, monthly, and annual frequency (days) of freeze thaw days [Figure 2.1].
The findings on freeze thaw occurrences in general show the highest amount of
freeze thaw days occurring in the mountainous regions of the country, especially the
mountains in the western portion of the country. Not only are the highest levels of freeze
thaw days found there, but the highest level of variability over short distances occur in
mountainous regions as well. The eastern areas of the country have far fewer spatial
changes in the amount of freeze thaw days. These areas also have less variability over
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large areas. Within the eastern portion of the country, the highest numbers of freeze-thaw
days occur in the north and the least occur in the south.
Figure 2.1 Mean Annual Frequency (days) of Freeze Thaw Cycles (Hershfield 1974)
Hershfield also examined freeze thaw day occurrences by month. Mountainous
areas in the west have freeze thaw days two out of three days annually. Hershfield found
that freeze thaw days there occur primarily in the spring, summer, and fall months. In the
winter the temperature generally stays below the freezing point in northern and
mountainous areas; because of these lower temperatures, fewer freeze thaw cycles occur.
However, most of the country has freeze thaw activity in the winter months. Only in
areas where daily high temperatures are consistently below freezing during the winter
months are lower numbers of freeze thaws observed, largely in northern states and
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mountainous areas. Russell (1943) found that consistently lower temperatures in
northern and mountainous areas results in a pattern which areas with the coldest winters
have similar counts of freeze thaw days compared to more areas with more moderate
winter temperatures. Schmidlin et al. (1987) also noted that freeze thaw activity in the
northeast areas of the country has double peaks in December and March because of
below freezing temperatures during the colder months of the winter. Furthermore, in the
winter, the southeast and southwest coasts experience their only freeze thaw cycles, as
the rest of the year the temperature does not drop below freezing. Peterson et al. (2008)
found that the period of the year in which freezes occur is becoming earlier in the spring
and later in the fall; the result is a longer growing season and a decrease in the snow
season. This is especially true since 1980, around the time when climate variability is
believed to have become more significantly driven by anthropogenic forces. These
forces are projected to further increase variability into the future.
Ho et al. (2005) looked at freeze thaw cycles in Toronto, Canada in a changing
climate. This study only used three stations and looked at freeze thaw cycles in relation
to road repair costs. Ho et al. (2005) found the urban heat island effect has an impact on
freeze thaw cycles. The impact was a slightly lower number of freeze thaw cycles, but
the decrease is not substantial in and around Toronto. All of the stations in Ho’s study
show a decreasing number of freeze thaw cycle days from 1960 to 1989 primarily in
April and October.
2.2 The Impact of Climate Trends and Variability on Winters across the United
States
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Changes in freeze thaw days over the study period (1982-2009) are caused by
changes in the climate over time. The sum of terrestrial and extraterrestrial climate
change are multiple processes which cause the variability and trends of the climate
everywhere on the planet. Terrestrial factors include volcanic emissions, atmospheric
content, and surface reflectivity (Pidwirny 2010). Extraterrestrial factors include solar
output and earth-sun geometry. The change in the climate that these processes produce is
both a naturally occurring phenomenon and the result of anthropogenic causes.
Increases in greenhouse gases in the atmosphere during the past few decades,
compared to the past thousand years, are unlikely of natural origin (Pittcock 2005). It is
probable that humans have contributed to this increase in greenhouse gases and are likely
continuing to cause to the global temperature to rise. Through industry and humanity's
reliance on fossil fuels, CO2 levels are high, widespread of cattle and sheep farming
contribute above normal levels of methane, and water vapor levels are slowly growing,
all of which contribute to further warming.
Climate trends help with an understanding of how the planet’s climate was in the
past, and also helps look at how climate may present itself in the future. However,
anthropogenic factors have probably changed the evolution of the climate in the absence
of mankind. Based on the knowledge of climate over the last millennium, the last three
decades of the twentieth century were the warmest (Jones et al. 2001). Overall the
twentieth century has the strongest global warming trend of the millennium with a 0.6° C
per century trend. The twentieth century was also 0.2° C above the millennial mean.
Since the 1850’s this 0.6° C warming has had a large seasonal contrast with a 0.8° C
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increase during winter and only 0.4° C in the summer (Jones et al. 2001). This is
especially important to this study because warmer winter months since 1950 will almost
certainly affect the number and intensity of freeze thaw days. Climate data before the
1850’s includes proxy data from ice cores, tree rings, and corals and these data sources
are not nearly accurate enough for extremely sensitive research. However, from the use
of proxy data, it is accepted that there have been general warming trends from the year
1000 to the present (Jones et al. 2001).
Winter climate trends are especially important to this study. There has been a
general warming trend over winters and springs over the past century in North America
(Schwartz et al. 2006). From 1948-1999 there was a decrease in the number of frost days
(days when the minimum temperature was less than 32º F), resulting in the growing
season being longer by 2.6-3.9 days on the west coast and by 0.9-1.2 days on the east
coast (Easterling 2002).
Freeze thaw cycles in the eastern and western parts of the conterminous United
States have behaved differently from each other (Easterling 2002). Kunkel et al. (2004)
found that the 100° W longitude line best divided the country for both an east and west
analysis, very similar to the findings of Easterling (2002). Kunkel et al. (2004) found that
east of the 100° W line, the growing season is lengthening by around three days a
century. West of the 100° W line the growing season is lengthening by around nineteen
days per century. Both of these trends were statistically significant at the 95% level of
confidence. In regards to the freeze thaw analysis in Kunkel’s study, these findings are
very significant, because they match Easterling’s (2002) and Hershfield’s (1974) findings
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that freeze thaw activity is more dynamic and variable in the western portion of the
conterminous United States.
Field et al. (2007) found that between 1955 and 2005 annual mean temperature
increased in North America, with the largest temperature changes occurring in the spring
and winter. Increased mean temperatures during spring and winter probably indicate an
increased level of freeze thaw cycle activity. During these colder months, Field et al.
(2007) observed increasing daytime and nighttime temperatures in the northern United
States and into Canada, with nighttime temperatures being more affected. Warmer
temperatures led to less extreme winter cold in northern cities, likely resulting in greater
freeze thaw activity in the colder winter months.
2.3 Implications of Freeze Thaw
When Ho et al. (2006) examined climate change’s effects on freeze thaw days in
Toronto, Canada they were looking at freeze thaw days in order to predict the costs
required to maintain Toronto’s roads. Freeze thaw cycles affect water by making it
expand and contract as it cools or warms respectively. As water freezes it expands, when
it melts the water contracts. In soil, freeze thaw of water can cause a creep in walls and
fence posts. Freeze thaw cycles also aid in the creation of pot holes in roads. These two
effects of freeze thaw cycles have a great economic and environmental impact, especially
the developed areas in northern parts of the conterminous United States.
Due to freeze thaw cycles, roads with pot holes damage vehicles, walls bow due
to the creeping, driveways are damage, roofing, gutters, and siding are damaged. All of
this damaged caused by freeze thaw needs to be continuously repaired or replaced
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because of the expansion and contraction of in and around their location. This damage
occurs during the fall, spring, and winter. Natural features such as snowpacks can also be
affected by freeze thaw cycles. Schmidlin et al. (1992) found that fluctuations across the
freezing point affect the growth, metamorphism, and decay of snowpacks. Freeze thaw
cycle activity has a large impact on most parts of the conterminous United States, both
economic and environmental. Freeze thaw cycles cause damage to homes, roads, and
water systems in the winter. They also slowly change the landscape and local features.
CHAPTER 3
DATA AND METHODOLOGY
3.1 Data
A large dataset was required to adequately cover the entire study area of the
conterminous United States. These data consist of daily high and low temperature data
from National Climatic Data Center (NCDC) cooperative weather stations. For each
station, daily high and daily low temperatures were used to evaluate freeze thaw cycles.
To properly assess trends, only stations with at least twenty years of data were used. Only
stations that had a daily observation time of 6:00 AM, 7:00 AM, or 8:00 AM were used
in this study, in order to avoid bias from stations that have different observation times.
From the temperature data, two variables were calculated: freeze thaw days and
extreme freeze thaw days. For the purpose of this study a freeze thaw day event is
defined as any time the temperature crosses the freezing point (A daily low with a
maximum temperature of 31º F and a daily high with a minimum temperature of 33º F) in
a twenty four hour period (Hershfield 1974). Extreme freeze thaw days are defined as a
temperature change that crosses the freezing point with a daily low temperature of 23º F
or lower and the daily high temperature of 41º F or higher during a calendar day.
The daily high and low records from 1982-November 2009 from all available
weather stations in the conterminous United States were downloaded from the NCDC
website. All state data files were combined into one file and all state reference files were
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combined into one file. Both of these processes were completed using Microsoft Disk
Operating System (MS DOS) due to the extremely large size of the data. Statistical
Analysis Software (SAS) and Statistical Packages for Social Sciences (SPSS) were then
used to combine the geographic reference and data attribute files.
Once the entire unedited dataset was completed, all records that did not have a
daily observation time of 6:00 AM, 7:00 AM, or 8:00 AM were removed. This reduced
the number of records from 7.8 million to 1,100,418, where each record represents one
full month of daily high and low temperature data. Further, all stations with less than
twenty years of data were removed, which reduced the number of records to 936,624.
There were a total of 3,508 stations with at least ten years of data, 2,584 stations with
fifteen years, 1,957 stations with twenty years, and 616 stations with thirty years of data.
A random sample of 250 records was generated and then compared against the original
records from the raw NCDC data to confirm that none of the original data had been
corrupted during the data manipulation.
Using the daily high and low temperature records, the number of freeze thaw and
extreme freeze thaw days were calculated (on a per month basis). A new decade variable
was also added to the dataset at this point. These manipulations were completed using
SPSS, SAS, and MS Access. The edited dataset was imported into ArcMap and
displayed as a map of the conterminous United States using a North American
Equidistant Conic Projection. At this stage, the analysis on freeze thaw days and extreme
freeze thaw days began.
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3.2 Methodology
Using SPSS the mean number of freeze thaw days and extreme freeze thaw days
by station over the entire study period was calculated. A mean number of freeze thaw
days and extreme freeze thaw days by month were also calculated. A dataset was then
created from the aforementioned data creation, which included the mean FT and EFT
days for each month and annually during the entire study period. Then, using SPSS, a
series of cluster analyses were run on this dataset in order to determine FT and EFT
regions. An eight cluster analysis was chosen, because it best displayed the data in the
study area, and the FT and EFT cluster numbers were added into the dataset. The
completed analysis was run as a two-step cluster. For the FT and EFT regions, the input
variables were all twelve months’ mean FT or EFT values. Finally the urban and rural
attribute was added into the dataset. Urban/Rural designation was calculated by
assigning all stations within, or ten miles from the boundaries of cities with a population
of 25,000 or greater, which was chosen because it best characterized the division of the
urban/rural split in the study area. All stations that did not meet this classification were
assigned as rural.
Using this station dataset, the study period FT and EFT means were projected
using an equidistant conic projection of the conterminous United States by average FT or
EFT by month and annually. The number of mean FT and EFT days per month were
classified using a defined interval classification scheme based on manual classifications
displayed using seven classes: 0-1, 1-5, 5-10, 10-15, 15-20, 20-25, and 25-30 FT or EFT
days. These classifications were chosen because they capture outliers, particularly in
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areas that have mean FT and EFT day activity at less than one day, integers facilitate easy
interpretation and understanding of FT and EFT day activity. The FT and EFT annual
map symbologies also had seven divisions: 0-25, 25-50, 50-75, 75-100, 100-125, 125150, and 150-250 FT or EFT days. The annual map symbology was also manually
classified in order to identify outliers and facilitate an easy understanding of annual FT
and EFT trends.
Following the station analysis the next step was to interpolate the points as a
continuous flat surface using Linear Kriging in ArcGIS. Interpolated FT and EFT maps
were generated from the average month and annual FT and EFT station. The station data
was then overlayed atop the interpolated surface to generate maps in ArcMap.
Following the general analysis of the entire study area FT and EFT regions were
created based on the FT and EFT cluster analysis. These regions were created based on
the grouping of stations by their assigned cluster number which ranged from 1 to 8 for
both mean FT and EFT days, which were generated in the aforementioned SPSS two-step
cluster analysis. Clusters 7 and 8 were grouped together, in terms of FT and EFT
regional boundaries, because of similarities in elevation and geographic location which
occurred in both the FT and EFT regional analyses. The boundaries for both the FT and
EFT regions were created by dividing a conterminous US shapefile along the boundaries
of the different FT and EFT clusters.
Linear trends were calculated for FT and EFT clusters using Microsoft Excel by
calculating the slope for each variable during the study period. Significance testing was
run in SPSS, which any slopes with p-values below 0.05 were considered significant.
CHAPTER 4
RESULTS
4.1 FT Interpolation Descriptions
The FT year will be considered as July-June as it is typical of other cool-season
climate variables (Figure 4.1) This is because almost all of the study area has no FT
activity in July and the areas that do (3 small mountainous areas in the northwest)
average 1 FT day. From July to August almost all of the study area still has no FT day
activity. Whereas the small areas in the western mountains observed during July grow in
size by August, if any FT day activity still exists, it still only averages 1 FT day.
September is the first of the transitional months into the fall, and when mean FT
day activity begins to affect a much larger portion of the study area. There is a mean of
at least 1 FT day in the western mountains and most of the areas across the northern parts
of the study area. Areas in the western mountains with higher elevations have means
between 5 and 10 FT days. The transition from early fall to winter leads to much more
extensive FT activity in the study area in October. The mountainous areas in the west
have means of 10 to 25 FT days. East of the mountain areas mean FT day activity
increases from south to the north. In most of the southern states there are generally no FT
days, while the lower mid-west has a mean of 1-5 FT days.
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Figure 4.1 Interpolation of Mean Monthly FT days Across the Study Area
(a)July-top (b)August-bottom
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Figure 4.1 (continued) (c) September-top (d) October-bottom
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Figure 4.1 (continued) (e) November-top (f) December-bottom
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Figure 4.1 (continued) (g) January-top (h) February-bottom
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Figure 4.1 (continued) (i) March-top (j) April-bottom
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Figure 4.1 (continued) (k)May-top (l)June-bottom
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The spatial pattern of average FT day activity in November is very similar to that
of October. Values in the western mountains are higher with means of 15 to 25 FT days
and there are higher FT values in all areas east of the Rockies. Areas in southern Texas,
Louisiana, and Florida have less than 1 mean FT day of activity, but the rest of the
southern states have means of 1-5 FT days. The lower mid-west has a mean of 10-15 FT
days, while the northern sections of the study area (as well as areas in Appalachia) have
means of 15-20 FT days. In northern Minnesota there is a mean of 10-15 FT days. The
decrease in northern areas of the study area during this time of year, and into the spring,
is due to mean high temperatures not exceeding the freezing point. This reduction in FT
day activity serves as the division which creates the double peak of FT day activity in the
northern parts of the study area, as well as many stations in the western mountainous
areas.
The months of December and January are very similar in mean FT day activity.
This is the time of the year when FT day activity east of the western mountains is highest
in the middle of the study area with a mean of 15 to 20 FT days. The very southern and
northern parts in this area have less than 5 mean FT days. The northern areas of the
southern states, northern parts of the mid-west and New England have between 10 and 15
mean FT days. The western mountains have between 10 and 25 FT days. On the west
coast there are between 1 and 15 FT days, dependent on the magnitude of influence of the
Pacific Ocean.
From February to March the distribution of mean FT day activity east of the
mountains in the west begins shifting northward. The areas with lower mean FT days
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around Minnesota rise from an average of 1-5 FT days in January to 5-10 in February and
15-20 in March. The spring transitional period causes the second peak of annual FT
activity during this time. The western mountains have average FT counts between 20 and
25 days. This is because of the prevalent clear skies in this region. With clear skies the
sun shines during the day bring temperatures above freezing; at night clear skies facilitate
radiative cooling, which causes temperatures to drop below freezing. By March the
southern states east of the mountains have between 0 (southern Florida and Texas) and 5
mean FT days, the northern mid-west and New England (except for a mean of 20 FT days
in the mountainous areas of the region) have means of 15-20 FT days. The southern
areas of the west coast of the conterminous US have means of 1 to 5 FT days, except for
areas in the Arizona desert and the Bay Area in California (which have no FT activity).
April is the last month to have large widespread FT activity. The mountainous
areas in the west have a mean of 10 to 25 FT days (with only the highest areas of the
Rockies in Colorado having 25). In the south there is no mean FT day activity, in the
lower Midwest there is between 1 and 5 mean FT days. Appalachia has between 1-15
mean FT days. The northern regions have between 10 and 20 mean FT days, with a
small area in northern Maine having between 20 and 25 mean FT days. The west coast
and southwest areas of the study area have between 0 and 5 mean FT days. This pattern
is primarily caused by the transition to summer, latitudinal location, and proximity to
large bodies of water (Such as the Atlantic and Pacific Oceans and to some extent the
lower Great Lakes).
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In the month of May there is no mean FT activity except for means of 1-10 FT
days in the northern regions of the conterminous US, the Appalachians, and in the
mountainous areas in the west which range from 5 to 19 FT days (with the highest values
only being in the most uppermost elevations of the Rocky Mountains). By June there is
no activity anywhere in the study area other than 1-5 FT days in most of the western
mountains and two small areas that have 5-10 FT days in the highest areas of the
Rockies.
Annual mean FT days (Figure 4.2) increase from south to north in the eastern
half of the study area. In the western half FT days are affected by both latitude and
elevation. The areas with the highest elevations in the west have the highest annual FT
activity.
Figure 4.2 Interpolation of Mean Annual FT days Across the Study Area
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4.2 FT Region Descriptions, Location, and Climatology
This section is an overview of the FT regions (Figure 4.3a) that were created
using the results of the cluster analysis (Figure 4.3b). The clusters were created using
the mean monthly FT day values from all twelve months. Clusters 1-6 were kept as
single regions, while clusters 7 and 8 were combined to form the Mountain A & B region.
The mountain regions were combined for this image because they occur in the same areas
and represent changes in elevation within the same mountainous areas. In order to look at
the mean FT occurrence, charts were created by month for each region (Figure 4.4)
26
Figure 4.3 FT Clusters (a) by Region-top (b) by Station-bottom
27
Figure 4.4 Average FT Days by Region
28
The Coastal FT region, as shown in green in Figure 4.3, exists from mid-Oregon
down the west coast of the US and continues through Texas and into South Carolina. It
also occurs along the Mississippi River. FT days in this region, on average, do not occur
from April through October, the transitional months of March and November have 1 to 2
mean FT days, while December through February have 3 to 7 mean FT days. The coastal
FT region has very little to no FT day activity throughout the year due to the warm
climate caused by both latitudinal positioning and its proximity to the Pacific and
Atlantic Oceans and the Gulf of Mexico.
The Inland South FT region, as shown in light green in Figure 4.3, exists on the
coastline of Washington south into the mid-Oregon coast, the inland area of California
and from southern Nevada to northern Arizona, and in mid-Texas across the southern
states to the coast of North Carolina. The Inland South FT region is characterized by
winter months with 10 to 15 mean FT days and 1 to 6 mean FT days in the transitional
months. This region on average has no FT activity from May through September. This
region is affected both by its proximity to large bodies of water, especially in Washington
and Oregon, and in most cases its latitudinal location.
The Transitional FT region, as shown in yellow in Figure 4.3, is present in
multiple areas in the conterminous US, including southern Washington, northern Oregon
and into mid-Idaho. It usually occurs between the more temperate regions (Coastal and
Inland South) and the regions with higher elevation and/or colder winters (Heartland,
Plateau/Appalachian, Mountain A & B). It is present in southern New Mexico into
Kansas and stretches east across the Ohio River Valley, and occurs from Tennessee into
29
the northeast across Virginia into New Jersey, Long Island and north through Cape Cod.
From May through September there is no FT activity, April and October have 3 to 4
mean FT days, March and November have around 11 to 12 mean FT days, while the
winter months have 16 to 18 mean FT days. The Transitional region exemplifies higher
latitudes, the rise in elevation, and the reduction of the influence of large bodies of water
on FT day activity. It is a division from more temperate FT regions and those with much
higher average FT day activity.
The main area of the Heartland region extends from southeastern South Dakota
and northeastern Nebraska eastward across the plains through Iowa into southern
Michigan, Ohio, Pennsylvania and into New York, as shown in orange in Figure 4.3.
The Heartland FT region, like the Inland South has little or no mean FT activity from
May (1 FT day) through September, the transitional months of April and October have 7
to 10 mean FT days, and the months of November through March have 11 to 17 mean FT
days. The climate in these regions is continental, this along with the Heartland in higher
latitudes. It is very variable with hot summers and long winters with freezing
temperatures, although most winter days have a thaw.
The Northern Tier FT region, as shown in red-orange in Figure 4.3, extends
from northern Washington eastward into the Upper Peninsula of Michigan. It continues
in the northern area of Michigan’s Lower Peninsula and east across the highlands of New
York and into New England. In the Northern Tier FT region the peak activity on average
occurs during the transitional months during October-November and March-April.
During these months mean FT day activity ranges from 14 to 18 FT days per month.
30
There is no mean FT activity from June to August, May and September have minimal FT
activity (ranging from 2 to 5 mean FT days); while December through February have 9 to
11 mean FT days per month. The transitional months in the Northern Tier region are the
most active months because during the winter months the daily high temperature tends to
not be above freezing, therefore there is less FT day activity.
The Plateau/Appalachian FT region is present in the Appalachian Mountains
and a small strip north of the Transitional region into New England, as shown in dark
pink in Figure 4.3. It also occurs in northern Washington. There is also a small westeast swath in the Snake River Valley and from southern South Dakota into New Mexico
and up into the four corners area. From May through September there is almost no FT
activity, with no FT activity June to August; April and October have 8 to 10 mean FT
days, and from November to March there are 19 to 21 mean FT days per month. The
climate in the Plateau/Appalachian region has so many FT days due to its occurrence in
areas of high elevation. Throughout most of the year, excluding late spring into early
fall, these areas have many days with daytime temperatures above freezing and nighttime
lows below freezing. This is often due to clear skies which allow plentiful sunlight in
during the day and allow the same daytime energy escape throughout the night. Also, it
should be noted that the cold air sinks during the night into the valleys where many of the
stations used in this study are located.
The Mountain A & B FT region, as shown in light pink in Figure 4.3, is the
combination of the Mountain A cluster and the Mountain B cluster. These are
geographically intertwined; the primary difference between the two regions is elevation
31
(Mountain B includes stations at higher elevations than Mountain A).
The Mountain A
& B region is located from southern Oregon to southern Montana, and goes south into
northern New Mexico, and western Arizona. Mountain A has little or no activity from
June (1 mean FT day) to August (No mean FT activity). Mountain B has mean FT days
12 months of the year, even in the summer months: June (7 mean FT days), July (1 mean
FT Day), and August (3 mean FT Days). From October to April Mountain A has 20 to
25 mean monthly FT days (with the most activity being in the transitional months of
November and March). In the same time period Mountain B has 14 to 25 mean monthly
FT days, with the fewest being in December and January due to average daily highs
below freezing. The largest difference between Mountain A and Mountain B is the mean
FT day activity during May and September. In May Mountain A has 9 mean FT days and
September has 5 mean FT days. In Mountain B May has 18 mean FT days and
September has 14 mean FT days. This is a result of elevation difference between the 2
regions. Mountain B’s higher elevations have many daily lows below freezing even in
the late fall early summer due to clear nighttime skies. The lower elevations of Mountain
A still have cold nights into the summer, but the lower elevation raises the average daily
lows above freezing earlier in the year and maintains them later into the summer and
early fall.
4.3 Annual Trends in FT Days by Region
This section will cover annual mean occurrences of FT days by region from 1982
to 2009 (Figure 4.5) and the linear trends for trends for each region (Table 4.1).
32
Table 4.1 Annual and Seasonal Mean FT Day Trends Per Year and P-values
(1982-2009) with Bolded numbers representing statistically significant change
(p<.05)
Coastal
Inland South
Transitional
Heartland
Northern Tier
Plateau/Appalachian
Mountain A
Mountain B
Annual
Trend
-0.105
-0.119
-0.138
-0.159
-0.272
-0.040
-0.216
-0.739
Sig
0.360
0.481
0.360
0.552
0.291
0.834
0.275
0.031
Winter
Trend
-0.037
-0.156
-0.002
-0.102
-0.110
0.115
-0.014
-0.244
Sig
0.679
0.163
0.983
0.546
0.534
0.324
0.929
0.141
Spring
Trend
0.004
0.030
-0.090
-0.008
-0.066
-0.053
-0.115
-0.303
Sig
0.851
0.697
0.105
0.914
0.458
0.546
0.228
0.011
Summer
Trend
0.000
0.000
0.000
-0.001
-0.010
-0.001
-0.034
-0.149
Sig
0.273
0.715
0.605
0.009
0.021
0.321
0.084
0.026
Autumn
Trend
-0.012
-0.002
-0.057
-0.126
-0.185
-0.125
-0.107
-0.078
Sig
0.671
0.902
0.498
0.162
0.075
0.106
0.267
0.585
Although the more temperate FT cluster regions show less variability over time,
they clearly accent the shifts in the FT cluster regions. The Mountain B FT cluster had an
annual trend of -0.739 FT days per year, which was statistically significant (p = 0.031),
which made it the only cluster to have a significant trend in annual FT numbers during
the study period. However, this trend was driven by low values in the late 1990s and the
early 2000s, mean annual FT day activity values at the end of the study period are lower
to the mean annual FT day values at the beginning of the study. The Northern Tier and
Heartland FT cluster’s mean annual FT day activity became more variable after 1996
with much more annual variability between years. Overall, there were less mean annual
FT days in 1985, and in 2000-01. There was also a large increase in mean annual FT day
activity in 2001-2002 in all of the FT cluster regions.
33
Figure 4.5
Annual Mean FT Days by Region
34
4.4 FT Seasonal Temporal Analyses by Region
This section will look at seasonal mean FT day trends during the study period by
FT cluster region (Figure 4.6) and the overall trends, and significances, for each region
(Table 4.1). Spring and summer values are from 1982 to 2009, winter values are from
1983-2009, and autumn values are from 1982 to 2008.
As with the annual FT trends there was not a lot of significant FT trends in the
seasonal analysis of the FT clusters, although most seasonal clusters had decreases during
the study period that were not significant. In the Mountain B, several significant results
were noted. Summers had small amounts of FT activity, but had a significant decrease of
-0.149 FT days/year. Spring had a significant decrease of -0.303 FT days/year.
35
Figure 4.6 Seasonal Mean FT Days by FT Cluster
(a) Coastal-top (b) Inland South-bottom
36
Figure 4.6 (continued) (c) Transitional-top (d) Heartland-bottom
37
Figure 4.6 (continued) (e) Northern Tier –top (f) Plateau/Appalachian-bottom
38
Figure 4.6 (continued) (g) Mountain A-top (h) Mountain B-bottom
39
4.5 Urban/Rural FT Analysis
FT day activity was also analyzed based on whether a station was Urban or Rural
(Figure 4.7). As mentioned in the methodology chapter, urban stations were defined as
stations located within, or no more than ten miles from, an urban area with 25,000
inhabitants or more; rural was defined as any other station that did not meet the
aforementioned criterion. Some clusters, like the Northern Tier, Mountain A and
Mountain B clusters, did not have enough urban areas to complete a relevant analysis
(Table 4.2). Most FT stations did not show significant urban/rural trends during the
study period (Table 4.3 and Table 4.4). Out of the urban and rural trend analysis, almost
no areas had a significant decrease in FT day activity during the study period. Urban and
rural areas in the Heartland experienced significant negative change during summer
months (though the mean is almost no FT activity). Another area with statistically
significant negative change was the Transitional cluster. It had a negative change in
urban areas in the spring, and a negative rural change in the summer during the study
period.
40
Figure 4.7 Urban and Rural Stations
Table 4.2 FT Urban/Rural Station Count in FT Analysis
Cluster
Coastal
Inland South
Transitional
Heartland
Northern Tier
Plateau/Appalachian
Mountain A
Mountain B
Urban
88
44
60
46
6
32
2
9
Rural
180
224
273
220
163
236
32
53
41
Table 4.3 Urban/Rural FT Day Trends with statistically significant change (p<.05)
in Bold
Coastal
Inland South
Transitional
Heartland
Northern Tier
Plateau/ Appalachian
Mountain A
Mountain B
Annual
Rural
-0.080
-0.261
-0.103
-0.147
-0.285
-0.036
-0.716
-0.303
Urban
-0.165
0.044
-0.296
-0.225
0.216
-0.126
-0.456
0.312
Winter
Rural
-0.008
-0.088
0.010
-0.104
-0.118
0.117
-0.267
-0.051
Urban
-0.102
-0.036
-0.054
-0.088
0.154
0.090
0.409
0.195
Spring
Rural
0.002
-0.042
-0.075
-0.001
-0.069
-0.056
-0.296
-0.143
Urban
0.008
0.000
-0.157
-0.049
0.006
-0.056
-0.244
0.053
Summer
Rural
0.000
0.000
0.000
-0.001
-0.010
-0.001
-0.135
-0.036
Urban
0.000
-0.088
0.000
-0.002
0.005
-0.001
-0.416
-0.015
Autumn
Rural
-0.011
-0.091
-0.047
-0.120
-0.189
-0.118
-0.055
-0.119
Urban
-0.012
-0.091
-0.103
-0.166
-0.054
-0.203
-0.184
-0.023
Table 4.4 Urban/Rural FT Day Trend P-values with statistically significant change
(p<.05) in Bold
Coastal
Inland South
Transitional
Heartland
Northern Tier
Plateau/ Appalachian
Mountain A
Mountain B
Annual
Rural
0.504
0.126
0.499
0.589
0.273
0.850
0.038
0.134
Urban
0.143
0.730
0.053
0.368
0.341
0.577
0.471
0.166
Winter
Rural
0.929
0.426
0.911
0.546
0.509
0.329
0.121
0.746
Urban
0.234
0.441
0.557
0.573
0.429
0.430
0.129
0.219
Spring
Rural
0.941
0.340
0.174
0.989
0.441
0.542
0.013
0.138
Urban
0.657
0.182
0.011
0.468
0.962
0.473
0.315
0.656
Summer
Rural
0.277
0.905
0.441
0.016
0.018
0.289
0.051
0.072
Urban
0.358
0.200
0.443
0.013
0.505
0.552
0.000
0.478
Autumn
Rural
0.704
0.166
0.581
0.189
0.071
0.131
0.704
0.232
Urban
0.611
0.166
0.214
0.069
0.650
0.023
0.520
0.823
42
4.6 EFT Interpolation Descriptions
The descriptions of the monthly patterns in mean EFT day activity (Figure 4.8)
begin in July. In July the only mean EFT activity occurs in the high elevations of the
Northern Rockies with a mean of 1 EFT day. Through the month of August areas with
EFT activity move south into the areas with the highest elevations in the western
mountain ranges (around Colorado and New Mexico). Areas in the highest elevations
with any EFT activity do not have more than 1 EFT day. September marks the beginning
of the transitional part of the year in the northern and mountainous areas of the study
area. The western mountains have means of 1 to 10 EFT days, with the highest mean
numbers of EFT days occurring in areas of higher elevation. Northern areas, including
northern New England have means of 1 to 5 EFT days.
October is the first month that most of the study area becomes active. The
western mountain have means of 5 to 20 EFT days, the northern areas of the
conterminous US have means of 5 to 10 EFT days (excluding the lower Great Lakes), the
lower mid-west and southern areas of the mountainous west have a mean of 1 EFT day
(excluding most of the Appalachians, which have means of around 5 EFT days). The
mean EFT day activity in October marks the first of the double peaks of activity in most
of the northern areas and much of the western mountainous areas.
From November through January mean EFT day activity in the northern and
mountainous west areas drops to a mean of 1 to 5 EFT days and 5 to 15 days,
respectively. Southern areas of the western mountains have means of 10 to 25 EFT days.
43
Figure 4.8 Interpolation of Mean Monthly FT days Across the Study Area
(a) July-top (b) August-bottom
44
Figure 4.8 (continued) (c) September-top (d) October-bottom
45
Figure 4.8 (continued) (e) November-top (f) December-bottom
46
Figure 4.8 (continued) (g) January-top (h) February-bottom
47
Figure 4.8 (continued) (i) March-top (j) April-bottom
48
Figure 4.8 (continued) (k) May-top (l) June-bottom
49
In the southern non-mountainous areas there exist between 5 and 15 EFT days. The very
southern portions of Florida and Texas have no EFT activity.
February through March have increased EFT activity areas in the northern tier
around Minnesota (a mean of 5 to 15 EFT days) and most of the western mountains (a
mean of 10 to 20 EFT days). Other than the southernmost parts of the study area (which
have no EFT day activity) the south had a mean of 1 to 5 EFT days. Most of the
heartland averages 5 to 10 EFT days. Areas around the upper Great Lakes, Appalachia
into New England have means of 10 to 15 EFT days during the beginning of the spring
transitional period as well.
April is the last month to have widespread EFT day activity throughout the study
area. The western mountains have means of 10 to 20 EFT days. Most of the northern
areas of the conterminous US has a mean 5 to15 EFT days, while areas to the south have
means of 5 to 10 EFT days. Areas from Kansas to Ohio have between 1 and 5 mean EFT
days, while the southern areas of the study have no EFT day activity.
During the month of May the only portions of the study area with a mean EFT
day activity are the western mountains (which have 5 to 15 mean EFT days), the northern
area of the study region (Which has 1 to 5 mean EFT days), and parts of Appalachia
(which have a mean of 1 EFT day). The only areas in the conterminous US that have any
mean EFT day activity in June are the areas with high elevations in the western
mountains, which have 1 to 5 mean EFT days.
Annual mean EFT days (Figure 4.9) increase from south to north in the eastern
half of the study area. In the western half EFT days are affected by both latitude and
50
elevation. The areas with the highest elevations in the west have the highest annual EFT
activity. Compared to annual mean FT days, the annual distribution of EFT days in the
study area had a very similar distribution, although there are a few differences such as
lower overall EFT day values and lower values along the Mississippi River and its
tributaries.
Figure 4.9 Interpolation of Mean Annual EFT days Across the Study Area
4.7 EFT Region Descriptions, Location, and Climatology
This section is an overview of the EFT regions (Figure 4.10a) that were created
using the results from the cluster analysis (Figure 4.10b). The clusters were created
51
using the mean monthly EFT day values from all twelve months. Clusters 1-6 were made
into single regions, while clusters 7 and 8 were again combined to form the Mountain A
& B region. To examine EFT occurrences charts were created by month for each of the
cluster regions (Figure 4.11)
The Coastal EFT region, as shown in green in Figure 4.10, extends along almost the
entire west coast and continues into the parts of the southwest into mid-Texas and
stretches up the Mississippi River and along the coast to the South Carolina coastline.
There are also a few small areas of the Coastal EFT region that occur in southern New
Jersey/northern Delaware, and most of Upper Peninsula of Michigan. The Coastal EFT
region has very little EFT activity. There is no EFT activity from April to October,
November and March average 1 to 2 mean EFT days, while December to February
average between 3 to 4 mean EFT days. Like the Coastal FT region most of the Coastal
EFT region is temperate due to latitude or proximity to large water bodies or both.
However the addition of those areas in northern Michigan to the EFT cluster occur due to
the lack of EFT days caused by daily sub-freezing high temperatures, the cloudy nature
of the Great Lakes climate, or the small wintertime daily temperature range during the
winter months.
52
Figure 4.10 EFT Clusters (a) by Region-top (b) by Station-bottom
53
Figure 4.11 Average EFT days by Region
54
The Inland South EFT region, as shown in light green in Figure 4.10, is very
similar in location to the Inland South FT region. It occurs in southern California and
continues across Arizona, into southern New Mexico and into southwest Texas. Once in
Texas, it extends east to the North Carolina coastline. The Inland South EFT region has
no EFT activity from May to September, 1 mean EFT day in April and October, 5 to 6
mean EFT days in March and November, and from December to February 9 to 12 mean
EFT days. Like the Coastal FT region the Inland South EFT region is characterized by
its location in the lower latitudes.
The Transitional EFT region, as shown in yellow in Figure 4.10, like the
Transitional FT region, effectively serves as a division between the more temperate EFT
regions (Coastal and Inland South) and the more active EFT regions (Heartland, Plateau,
Northern Tier, and Mountain A & B). The Transitional EFT region is present in the north
of the Inland South region and into southern South Dakota. It also occurs from Kentucky
to the Virginia coastline and northward through most of the megalopolis. The
Transitional region has almost no EFT activity in May (1 mean EFT day) and June
through September (0 mean EFT days), has 5 mean EFT days in April and October, and
the months of November through March have 12 to 14 mean EFT days. The Transitional
EFT cluster’s characteristics are influenced by the rise in elevation, the reduction of the
influence of large bodies of water on EFT day activity, and the continental nature of the
region’s climate.
The Heartland EFT region, as shown in orange in Figure 4.10, exists in multiple
areas across the conterminous US. The EFT Heartland occurs on the Washington/Oregon
55
border, from Minnesota to Missouri and east to New York. There are a few scattered
areas in West Virginia. The Heartland EFT region is characterized with little mean EFT
activity in May (1 mean EFT day) and no mean EFT activity June through September, the
months of October through April have 5 to 6 mean EFT days with the most activity
during the transitional and winter months from November to March (5 to 9 mean EFT
days) . Like the FT Heartland region the EFT Heartland is continental with variable
transitional seasons with no activity in the summer and less EFT activity in the late fall,
winter, and early spring due to the average daily temperature not being high enough
and/or the average daily low not being low enough to qualify for an EFT day.
The Northern Tier EFT region, as shown in red-orange in Figure 4.10, occurs in
multiple areas across the conterminous US. It occurs in northern Oregon to Idaho,
Washington to Michigan, parts of Appalachia into northern New England. The Northern
Tier EFT region has no EFT activity June through August, 2 to 4 mean EFT days in May
and September, 10 to 13 mean EFT days in the transitional seasons of March, April,
October, and November, and 5 to 8 mean EFT days during the winter months. The
Northern Tier EFT region has the most EFT activity during the transitional seasons and
much less activity in the winter because the small daily temperature ranges and average
daily high temperatures are not high enough to qualify as EFT days. During the
transitional seasons the average daily high and low temperature variability encourages
EFT activity.
The Plateau EFT region, as shown in dark pink in Figure 4.10, occurs from
Arizona/New Mexico to Montana. The Plateau EFT region is characterized as exhibiting
56
zero mean EFT activity June to August, 1 mean EFT day in May and September, 8 to 9
mean EFT days in April and October, and 17 to 20 mean EFT days November to March.
The Plateau EFT region mostly occurs in the high elevation areas directly east of the
Rocky Mountains and between mountain ranges in the southwestern conterminous US.
This region has large amounts of EFT days from the late fall and early into the spring due
to elevation and the effect of daytime warming and nighttime cooling caused by prevalent
clear skies.
Like the Mountain A & B FT region the Mountain A & B EFT region, as shown
in light pink in Figure 4.10, is the combination of the Mountain A cluster and the
Mountain B cluster. As with the Mountain A & B region the primary difference between
the two regions is elevation. The Mountain A & B region is located in almost the same
location of the Mountain A & B FT region from California to Colorado, and south into
New Mexico. Mountain A has little or no activity from June (1 mean EFT day) to
August (No mean EFT activity). In May and September Mountain A has 9 and 5 mean
EFT days respectively. During the winter months Mountain A has more mean EFT days
(15 to 19 mean EFT days). In the transitional months of October, November, March and
April Mountain A has 19 to 23 mean EFT days. Mountain B has mean EFT days 12
months of the year, even in the summer months: June (6 mean EFT days), July (a mean
of 1 EFT day), and August (3 mean EFT days). In May and September Mountain B has
15 and 13 mean EFT days respectively. In the winter months Mountain B has 10 to 14
mean EFT days. The increased number of EFT days in Mountain B is due to lower
57
average daily high temperatures in the elevations of Mountain B. Mountain B has 16 to
22 mean EFT days in the transitional months of October, November, March, and April.
4.8 Annual Trends in EFT Days by Region
This section looks at annual mean occurrences of EFT days by region from 1982
to 2008 (Figure 4.12) and the annual trends for each region (Table 4.5).
Table 4.5 Annual and Seasonal Mean EFT Day Trends and P-values (1982-2009)
with Bold Font Marking Significant P-values
Coastal
Inland
South
Transitional
Heartland
Northern
Tier
Plateau
Mountain A
Mountain B
Annual
Trend
-0.056
-0.077
Sig
0.458
0.523
Winter
Trend
0.002
-0.112
Sig
0.965
0.207
Spring
Trend
-0.006
0.010
Sig
0.743
0.878
Summer
Trend
0.000
0.000
Sig
0.949
0.713
Autumn
Trend
-0.012
-0.005
Sig
0.588
0.790
-0.047
-0.152
-0.162
0.790
0.347
0.377
0.042
-0.040
-0.064
0.690
0.641
0.559
-0.030
-0.012
0.019
0.644
0.834
0.777
0.000
-0.001
-0.012
0.481
0.247
0.004
-0.052
-0.095
-0.164
0.508
0.121
0.060
0.002
-0.168
-0.596
0.993
0.361
0.039
0.1600
-0.042
-0.258
0.284
0.755
0.038
-0.025
-0.082
-0.221
0.813
0.337
0.037
-0.001
-0.033
-0.137
0.373
0.115
0.020
-0.104
-0.039
-0.017
0.291
0.735
0.903
58
Figure 4.12 Annual Mean EFT Days by Region
All of the annual EFT clusters had either a negative trend in mean annual EFT
day activity during the study period or else experienced virtually no change. There were
no significant increases in mean annual EFT day activity in any of the eight clusters.
The Mountain B FT cluster had a significant negative trend during the study
period in the autumn, winter, and spring. There was a high number of mean EFT days in
2002, but this was followed by negative trends until the end of the study period in 2009.
4.9 EFT Seasonal Temporal Analyses by Region
This section looks at seasonal mean EFT day trends during the study period by
EFT cluster region (Figure 4.13). Spring and summer values represent the period from
59
1982 to 2009, winter values represent 1983-2009, and autumn values represent 1982 to
2008. The Northern Tier EFT cluster had a significant summer decrease of -0.012 EFT
days/year during the study period, however there is no EFT activity in this cluster during
the summer months. The Mountain B EFT cluster had significant downward trends in
EFT days during summer , -0.137 days/year (p= 0.020), winter; -0.258 days/year
( p=0.038), and spring; -0.221 days/year (p=0.037).
4.10 Urban/Rural EFT Analysis
EFT day activity was also analyzed based on whether a station was Urban or
Rural (Figure 4.7). Some clusters, like the Northern Tier, Mountain A, and Mountain B
clusters, did not have enough urban areas to complete a relevant analysis (Table 4.6).
Other than the annual Heartland EFT stations, none of the stations or clusters
showed significant urban/rural change over the study period (Table 4.7 and Table 4.8).
In the Heartland EFT cluster the rural areas had more annual EFT days than the urban
areas (Figure 4.14). However, the urban areas had a large and significant decrease in
EFT day activity (A trend of -0.336 days/year). Rural areas experienced a decrease as
well (A trend of -0.106 days/year), but this decrease was not significant.
60
Figure 4.13 Seasonal Mean EFT Days by EFT Cluster
(a) Coastal-top (b) Inland South-bottom
61
Figure 4.13 (continued) (c) Transitional-top (d) Heartland-bottom
62
Figure 4.13 (continued) (e) Northern Tier –top (f) Plateau-bottom
63
Figure 4.13 (continued) (g) Mountain A-top (h) Mountain B-bottom
64
Table 4.6 EFT Urban/Rural Station Counts in EFT Analysis
Cluster
Coastal
Inland South
Transitional
Heartland
Northern Tier
Plateau
Mountain A
Mountain B
Urban
103
57
24
76
11
9
2
7
Rural
221
295
212
296
162
123
35
41
Table 4.7 Urban/Rural EFT Day Trends with statistically significant change (p<.05)
in Bold
Annual
Rural
Coastal
Inland South
Transitional
Heartland
Northern
Tier
Plateau/
Appalachian
Mountain A
Mountain B
Urban
Winter
Rural
Urban
Spring
Rural
Urban
Summer
Rural
Urban
Autumn
Rural
Urban
-0.061
-0.044
-0.042
-0.106
-0.056
-0.242
-0.106
-0.336
0.001
0.060
0.038
-0.030
-0.003
-0.087
0.068
-0.075
-0.008
-0.031
-0.024
0.005
-0.004
-0.051
-0.079
-0.086
0.000
0.000
0.000
0.000
0.000
0.000
-0.001
-0.001
-0.013
-0.035
-0.045
-0.081
-0.009
-0.070
-0.112
-0.156
-0.188
0.296
-0.065
0.012
0.013
0.114
-0.017
0.004
-0.177
0.084
0.026
0.146
-0.254
-0.296
0.452
0.368
0.180
-0.278
-0.088
-0.027
-0.368
0.210
-0.023
-0.590
-0.103
-0.081
-0.030
0.062
-0.001
-1.143
-0.034
0.000
-1.218
-0.021
-0.102
0.566
-0.049
-0.150
0.343
0.049
Table 4.8 Urban/Rural EFT Day Trend P-values with statistically significant change
(p<.05) in Bold
Annual
Rural
Coastal
Inland South
Transitional
Heartland
Northern
Tier
Plateau/
Appalachian
Mountain A
Mountain B
Urban
Winter
Rural
Urban
Spring
Rural
Urban
Summer
Rural
Urban
Autumn
Rural
Urban
0.438
0.713
0.818
0.531
0.447
0.059
0.523
0.014
0.981
0.554
0.722
0.737
0.951
0.339
0.490
0.312
0.682
0.438
0.714
0.934
0.817
0.205
0.195
0.105
0.673
0.312
0.302
0.448
0.263
0.238
0.232
0.033
0.565
0.587
0.573
0.206
0.650
0.254
0.130
0.005
0.307
0.199
0.549
0.944
0.847
0.253
0.001
0.547
0.040
0.555
0.907
0.850
0.165
0.383
0.635
0.162
0.242
0.023
0.525
0.865
0.010
0.146
0.828
0.174
0.234
0.592
0.955
0.613
0.339
0.072
0.115
0.998
0.086
0.379
0.300
0.062
0.683
0.246
0.351
0.689
65
Figure 4.14 Annual Heartland Urban and Rural EFT Days
CHAPTER 5
DISCUSSION
5.1 Overview
The results from this study mirror those of Hershfield (1974). The spatial
distribution of annual FT days across the conterminous United States is very similar to
Hershfield’s map (with most annual FT day activity in the mountainous areas in the
west). This study also confirmed the double-peak of FT day activity in the spring and
autumn in the northern and mountainous areas of the study area.
The new EFT variable, never included in a freeze thaw study before, showed
widespread activity in most of the study area during the duration of the study. EFT days
were designed to examine the occurrence and spatial variability of freeze thaw cycles that
have a higher potential to damage infrastructure. The areas with the most EFT activity
were in the northern and mountainous areas of the conterminous United States. Like the
areas with the most FT activity, the areas with the most EFT activity had double-peaks of
EFT occurrence in the spring and autumn. However, temperate and coastal areas in the
study area had the most EFT day activity in the winter. This is because most of the days
when the daily low temperature is 23° F or less occur during the winter in these areas.
Cluster analysis allowed for a more detailed analysis than Hershfield (1974).
Some cluster regions were identified with the double-peak in FT/EFT activity. Regions
with more temperate climates had a single peak in FT/EFT activity in the winter, while
66
67
regions with colder winters had the double-peak in FT/EFT day activity. FT regions with
a single-peak include the Coastal, Inland South, Transitional, and the
Plateau/Appalachian regions. FT regions with a double-peak include the Heartland,
Northern Tier, and the Mountain A and B regions. EFT regions with a single-peak in
EFT include the Coastal, Inland South, Transitional, and Plateau regions. EFT regions
with the double-peak include the Heartland, Northern Tier, and Mountain A and B
regions. For both FT and EFT regions, areas that have higher FT and EFT day
occurrences have a double-peak in activity, except the Plateau/Appalachian FT cluster
and the Plateau EFT cluster (which had high FT and EFT values from November to
March with only slightly higher values in the winter months).
The creation of FT and EFT regions allowed for a more detailed analysis of areas
with similar FT and EFT activity. The clustering displayed areas that would not normally
be compared. These areas have very similar FT and EFT activity. This is because the
clustering was based on the number of mean FT/EFT days by month, not on the actual
daily high and low temperature data. The clustering worked very well overall, and the
regions it created reflect regions where the characteristics of FT/EFT change. The few
areas that appear to be out of place are areas with similar FT/EFT activity that have never
been considered as similar before. Examples of this include: areas in the EFT Coastal
cluster in the Michigan’s Upper Peninsula and southern New Jersey (which have very
little EFT activity in the winter, like the temperate coastal areas in this cluster); and the
Washington/Oregon border, which is in the Heartland EFT and the Transitional FT
68
cluster. However, these areas only appear to be out of place, the cluster analysis properly
identified these areas.
Like Hershfield’s study, the highest amounts of FT and EFT activity in took place
in the mountainous areas of the west (in the Mountain A and B FT and EFT regions,
which were both composed of their respective Mountain A and Mountain B clusters). In
the higher elevations of the mountainous areas there was FT/EFT activity in all twelve
months of the year; while the lower elevations had FT/EFT activity ten months of the
year (July and August had no activity for both variables). The double-peak in FT/EFT
activity is the most prevalent in this part of the study area, with the areas of higher
elevation having the largest contrast in activity.
Overall, there was more FT activity (around 13-25 days per month from October
to April) than EFT activity (10-23 days from October to April), which was expected due
to the definition of an EFT day. However, there are comparable amounts of EFT days to
FT days in the Mountain A and B region due to a larger daily temperature range within
the region, suggesting that just about every freeze-thaw is an extreme freeze-thaw. For
the FT/EFT analysis the lowest values from October to April were during the winter in
the areas of higher elevation, which tend to have daily high temperatures below freezing
during this period. The highest FT and EFT values in the Mountain A and B region occur
in the transitional seasons of October (24 FT days and 23 EFT days), November (23 FT
days and 20 EFT days), March (25 FT days and 23 EFT days), and April (24 FT days and
19 EFT days).
69
Linear trend analysis on the FT and EFT clusters was completed by season and
annually to look at the change in FT/EFT activity during the study period (1982-2009).
All significant changes showed a negative trend in FT and EFT activity. For FT: the
Mountain B cluster had an annual trend of -0.739 days/year, a spring trend of -0.303
days/year, and a summer trend of -0.149 days/year. For EFT activity: the Mountain B
cluster had an annual trend of -0.596 days/year, a winter trend of -0.258 days/year, a
spring trend of -0.221 days/year, and a summer trend of -0.137 days/year.
The Mountain B FT and EFT clusters’ negative trends show that the areas with
the most FT and EFT activity experienced a decrease in FT and EFT activity during the
study period. Although most of the study area did not experience significant trends, the
mountainous areas are the most interesting part of any freeze thaw research in the
conterminous United States because of the high number of FT/EFT days that occur there.
5.2 Issues Encountered During the Study
A variety of issues were encountered during this study. These issues included the
format and volume of the data used in the study, limitations of software, and the length of
the study period.
The data that were used for this study came from the National Climatic Data
Center (NCDC), which was the only available source for the long-term daily high and
low temperatures for weather stations needed to complete this study. Also, to have
enough data for the conterminous United States during the entire study period a very
large volume of data was needed. In this study 1,957 stations met the 20 year time
requirement to be included as a FT/EFT station. The problem with this many stations
70
was that they come from a much larger selection of stations in the study area. This made
it difficult to individually examine station histories and their locations, or cases within
each station’s data as in the methodology of Ho et al (2005) in their examination of
Toronto stations. Also, a high concentration of stations did not exist in the mountainous
western areas of the study area, which is where the most significant FT/EFT activity
takes place. Another issue with the NCDC data is that the daily high and low
temperatures are recorded at the standardized elevation of five feet above the ground.
Because the coldest daily low temperatures tend to occur at ground level, which is where
freeze thaw activity has the largest impact, the daily low temperature values used in this
study are probably slightly warmer than the actual minimum ground temperatures for
each station. Furthermore freeze thaw impacts the ground, and there exist no available
ground temperature data that would provide results for a freeze thaw study.
To map and display the FT/EFT data ESRI’s ArcGIS was used. The main
problem with this software is that the temporal data from the NCDC proved hard to map.
Instead of having one point for each station with its associated attributes by date the
software displayed many points on top of each other. To counter this, averages of the
entire study period for annual, monthly, and seasonal FT/EFT day activity were created.
Averaging the data allowed the data display and mapping, but limited analysis had to be
completed to SPSS and Microsoft Excel.
Another issue with the GIS software was the interpolation of the data. Although
the values that the interpolation process created display an expected pattern based on the
FT map created by Hershfield (1975), it is not a perfect science. Interpolation can only
71
create values within the station’s data range. Because the only available points were in
the conterminous United States (from the NCDC) and were not available for Canada or
Mexico there are portions on the periphery of the study area that were not interpolated.
Also, the large scale of the interpolation makes it hard to examine small areas within the
study area.
As previously mentioned, the mountainous areas where the most FT/EFT activity
occurs did not have as many stations as other parts of the study area. Therefore, the
interpolated results in that area are much more generalized than other areas with more
station data. Fewer stations in this area and the process of interpolation mitigated the
influence of local topography in the areas where it is the most important. The best way to
address this problem is to have more stations in the mountainous areas and to use PRISM
(http://www.prism.oregonstate.edu/) or a similar interpolator. Without accurate, detailed
data on the topography in these areas studies on this subject will lack accuracy.
Lastly, this study examines FT/EFT activity from 1982-2009. This period is long
enough to look at trends across the conterminous United States, but there is an
uncertainty of time trends with different stations coming and going in each cluster.
However, a longer study period, with stations that have data during the entire study
period, would allow for better idea of long-term FT/EFT trends within the study area.
CHAPTER 6
CONCLUSION
Modern freeze thaw activity in the conterminous United States reflects the freeze
thaw research from the past. The interpolation of FT and EFT activity in the study area
allowed large amounts of FT data to be easily displayed. Also, the creation of FT/EFT
regions in the study area aided freeze thaw research by grouping areas of like activity
together, which displayed where FT/EFT activity varied in the study area. Also, it
allowed for a sub-analysis that identified where, and in what type of climate regions,
changes in FT/EFT activity occurred (which was in two clusters confined to the Rocky
Mountains). During the study period of 1982-2009 any significant change that occurred
in freeze thaw (FT) / extreme freeze thaw (EFT) activity was negative, especially in the
higher elevation of the mountainous west.
Although this study updated old freeze thaw research and added new variables
and FT/EFT regions, there is a large amount of research that still can be done with freeze
thaw activity in the conterminous United States. New technology and better data in the
future will only help uncover how climate trends and variability affect freeze thaw
activity within the conterminous United States.
72
73
Freeze thaw is an area that has been sparsely researched. Some of the factors that
can be researched in the future are longer study periods, which could yield potentially
more significant look at FT/EFT trends. More regionalized study areas would allow for
more intimate examinations of FT/EFT activity within the conterminous United States,
whereas smaller study areas would provide a more valid urban/rural analysis of FT/EFT
activity.
It would be impossible to talk about the future of freeze thaw research without
addressing data and software. Hopefully a better dataset will be available that would be
easier to use and integrate into a GIS and statistical environment. A dataset with more
points in the western mountains would help with FT/EFT research. Potential studies may
be able to utilize the PRISM (http://www.prism.oregonstate.edu/) dataset in order to
examine FT/EFT activity based on a much more localized topography than the
interpolated data in this study.
If GIS software can evolve in a manner that temporal data can be displayed and
processed effectively a much more detailed set of temporal freeze thaw maps could be
generated. This would make a long-term trend analysis that includes maps of the data
over time a possibility and hopefully expand of the relatively small amount of freeze
thaw research.
CHAPTER 7
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