Florida State University Libraries
Honors Theses
The Division of Undergraduate Studies
2012
Multi-Decadal Variations of Durations of
Extreme Temperatures in the Southeastern
United States
Rochelle Worsnop
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THE FLORIDA STATE UNIVERSITY
COLLEGE OF ARTS & SCIENCES
MULTIDECADAL VARIATIONS OF DURATIONS OF EXTREME TEMPERATURES
IN THE SOUTHEASTERN UNITED STATES
By
ROCHELLE WORSNOP
A Thesis submitted to the
Department of Earth, Ocean and Atmospheric Sciences
in partial fulfillment of the requirements for graduation with
Honors in the Major
Degree Awarded:
Spring 2012
1
The members of the Defense Committee approve the thesis of Rochelle Worsnop defended on
April 9, 2012.
______________________________
Associate Professor, Dr. Mark Bourassa
Thesis Director
______________________________
Assistant Research Scientist, Dr. Lydia Stefanova
Outside Committee Member
______________________________
Associate Professor, Dr. Philip Sura
Committee Member
2
ACKNOWLEDGEMENTS
This research was funded by the United States Department of Agriculture (USDA) and the
National Oceanic and Atmospheric Administration (NOAA). I thank Dr. Bourassa for his
support and for advising me through this process, Dr. Sura for providing me support and
statistical advice, Dr. Stefanova for giving me confidence and helping me with scientific
wording, and Melissa Griffin for her unending assistance throughout the project. Her support and
belief in me has helped me accomplish more than I ever thought I could. I would also like to
thank Shawn Smith and Kathy Fearon for their guidance and writing critiques and Dr. O’Brien
and COAPS as a whole for providing me with such a great learning opportunity. I would also
like to thank Evan Kalina for his encouragement and support.
3
TABLE OF CONTENTS
LIST OF FIGURES……………………………………………………………………5
LIST OF TABLES……………………………………………………………………..7
ABSTRACT…………………………………………………………………………….8
INTRODUCTION………………………………………………………………….......9
METHODOLOGY……………………………………………………………………..10
DEFINING CLIMATE REGIMES…………………………………………………...13
SEASONAL COUNTS OF SUMMER-DAY AND WINTER-DAY EXTREMES…15
LIKELIHOOD OF A SUMMER-DAY AND A WINTER-DAY EXTREME
OCCURRING..………………………………………………………………………….18
SPATIAL STRUCTURE OF THE PROBABILITY OF A STATION EXPERIENCING
EXACTLY ONE SUMMER-DAY AND ONE WINTER-DAY EXTREME………..21
SPATIAL STRUCTURE OF THE DURATIONS OF SUMMER-DAY AND WINTERDAY EXTREMES……………………………………………………………………….23
CONCLUSION…………………………………………………………………………...28
REFERENCES…………………………………………………………………………...30
4
LIST OF FIGURES
Fig 2.1. Station coverage map of the 113 COOP stations in the Southeast, which includes
Alabama, Florida, Georgia, South Carolina, and North Carolina
Fig. 2.2. An example of negative skewness in the left-hand side of the minimum temperature
distribution (blue) of anomalies during winter for Hendersonville, NC.
Fig 3.1. Annual average temperature for Florida sampled by the Oklahoma Climatological
Survey. The x-axis shows years in decades and the y-axis shows temperature in °F. The red
shaded region represents the warmer historical period and the blue shaded region represents the
cooler historical period based on a five-year annual temperature tendency.
Fig 3.2. Same as Fig 3.1, except with three 20-year study periods outlined. The period between
the red lines is this study’s Warm Regime subset, the period between the blue lines is this study’s
Cold Regime subset, and the period between the green lines is this study’s Modern Record.
Fig 4.1. Count of winter-day extreme events [days in December, January, and February (DJF)
with minimum temperatures that are ≤ the 1st percentile of the DJF daily minimum temperature
distribution for a particular station] for Camp Hill, AL. The x-axis shows the years in the
station’s period of record and the y-axis shows the number of times that an daily minimum
temperature met or exceeded the 1st percentile (dark blue) threshold and the 99th percentile (light
blue) threshold. The Cold Regime subset (1959–1978) is the period contained by the vertical
lines.
Fig 4.2. Count of summer-day extreme events for Camp Hill, AL.
The x-axis shows the years in the station’s period of record and the y-axis shows the number of
times that the daily maximum temperature met or exceeded the 99th percentile (dark red)
threshold and the 1st percentile (light red) threshold. The Warm Regime subset (1935-1954) is
the period between the vertical lines.
Fig 5.1. Albany, GA: The overall binomial (black bar) is computed to determine the probability
of occurrence of a winter-day extreme. The selected time periods are plotted (colored stars)
against the 90% confidence interval (gray lines). Data from Albany, GA show that the selected
20-year periods were outside the confidence intervals at one event per season, and the Cold
Regime subset and Modern Record were above the confidence interval for two and three winterday extreme events per season.
Fig 6.1. Likelihood of exactly one winter-day extreme (daily minimum temperature ≤ 1st
percentile of the daily winter minimum temperature distribution for a particular station)
5
occurring during the Warm Regime and Cold Regime subsets and the Modern Record. The (+)’s
indicate a statistically significant increased likelihood, (-)’s indicated a statistically significant
decreased likelihood, and (o)’s indicate no change in the likelihood of one winter-day extreme
event occurring during the given period.
Fig 6.2. Same as Fig 6.1 except for a summer-day extreme (daily maximum temperature ≥ 99th
percentile of the daily summer maximum temperature distribution for a particular station).
Fig 7.1. One- and two-day durations of summer-day extremes (daily maximum temperature ≥
99th percentile of the daily summer maximum temperature distribution for a particular station)
during the Warm Regime subset. The number of times (0-30) that a summer-day event occurs for
a one- or two-day duration is shown by the color bar. The (+)’s indicate a statistically significant
increased likelihood, (-)’s indicate a statistically significant decreased likelihood, and (o)’s
indicate no change in the likelihood of one summer-day extreme occurring during the Warm
Regime subset.
Fig 7.2. Same as Fig 7.1, except for winter-day extremes (daily minimum temperature ≤ 1st
percentile of the daily winter minimum temperature distribution for a particular station) during
the Cold Regime subset.
Fig 7.3. One-day durations of summer-day extremes (left) and winter-day extremes (right) during
the Modern Record. The number of times (0-30) that a winter-day or summer-day extreme
temperature event occurs with a duration of one day is shown by the color bar. The (+)’s indicate
an increased likelihood, (-)’s indicated a decreased likelihood, and (o)’s indicate no change in the
likelihood of one summer-day extreme or one winter-day extreme occurring during the Modern
Record.
6
LIST OF TABLES
Table 5.2. Table of the number or stations that have statistically significant increases, decreases,
and no changes in the likelihood of exactly one winter-day and summer-day extreme occurring
during the Warm Regime and Cold Regime subsets and the Modern Record.
7
ABSTRACT
The number of extreme temperature occurrences and their durations in the southeast
United States varies during three periods: the Warm Regime subset (WRs), the Cold Regime
subset (CRs), and the Modern Record (MR). Multidecadal variations in the regional patterns of
the counts and durations of summer-day and winter-day extremes reveal that during the MR, the
extreme temperature counts and durations in Florida are more consistent with a WRs setup
whereas these parameters for the inland states (Alabama, Georgia, North Carolina, and South
Carolina) are more consistent with a CRs setup. We also found that during the CRs (WRs), the
majority of stations show a statistically significant increase in the likelihood of exactly one
winter-day (summer-day) extreme occurrence. During the MR, both inland and coastal stations
show a statistically significant increase in the likelihood of exactly one winter-day occurrence.
This increased likelihood is not seen during the MR for a summer-day extreme occurrence.
Patterns in the behavior of summer-day and winter-day extremes during the CRs and WRs may
provide insight about how extreme temperatures will behave in future periods, if the period is
forecasted to have similar setups to that of the CRs or WRs. Insight about the duration and counts
of extreme temperatures is useful to the agricultural community, power industries, and health
officials.
8
CHAPTER 1
INTRODUCTION
Understanding the behavior of extreme temperatures is essential to evaluating a changing
climate. The number of occurrences and the duration of extreme temperatures are good measures
of how the tails of the temperature distribution are changing with time. This study quantifies the
occurrence and duration of summer-day extremes, which are days in June, July, and August
(JJA) with a maximum temperature that is ≥ the 99th percentile of the JJA daily maximum
temperature distribution, and winter-day extremes, which are days in December, January, and
February (DJF) with a minimum temperature that is ≤ the 1st percentile of the DJF daily
minimum temperature distribution. The threshold for a summer-day and a winter-day extreme is
station and season dependent.
The study will focus on five states in the United States collectively called the Southeast:
Alabama, Florida, Georgia, South Carolina, and North Carolina. Several studies have
documented that there are climatological warm and cool periods in the Southeast (Diaz and
Quayle 1980; Dettinger et al. 1995; and Williams et al. 2010). The study conducted by Williams
et al. (2010) found that there is a multi-decadal oscillation in the long-term temperature record;
the oscillation shifts from warmer-than-normal temperatures during the period of 1920–57
(Warm Regime) to cooler-than-normal temperatures during the period of 1958–98 (Cold
Regime). DeGaetano and Allen (2002) showed that there have been increases and decreases in
trends in the number of occurrences of extreme temperatures throughout multiple periods in the
contiguous United States. These findings suggest that occurrences of extreme temperatures, like
the long-term temperature record, in the Southeast may vary on multidecadal scales.
Temperature extremes, particularly cold extreme temperatures, affect the agricultural
community in the Southeast. Strawberry farmers in Plant City, Florida, prepare for the
strawberry season as early as December for crops that are picked in the winter months. Winterday extremes below 28°F can eliminate an entire strawberry crop (Pritts 2006). To prevent the
berries from reaching this threshold, farmers coat the fruit with a layer of ice induced by
spraying them with water just before a hard freeze. Multiple days of severe freezes, particularly
consecutive days, increase the risk of the crops being destroyed. Consecutive days of warm
extreme temperatures may result in an increased risk of heat exhaustion and heat stroke for the
human population. These temperature-related impacts are the motivation for analyzing the
number of times that a summer-day and a winter-day extreme is observed for a duration of one,
two, three, four, and five days. In this study, the counts of the durations are analyzed during three
20-year periods: the Warm Regime subset (WRs, 1935–54), the Cold Regime subset (CRs, 1959–
78), and the Modern Record (MR, 1981–2000). The WRs and CRs were selected from the
regimes previously defined by Williams et al. (2010). Comparing patterns in the MR to those
found during the WRs and CRs will reveal whether the MR is more similar to the WRs or to the
CRs. In the future, if a year is forecasted to have temperature patterns similar to that of the WRs
or CRs, the behavior of the extreme temperatures during that year might be predicated.
9
CHAPTER 2
METHODOLOGY
Temperature data include the daily maximum and minimum temperatures from 113 National
Weather Service Cooperative Observation Program (COOP) stations. We use the DS 3200 and
DS 3206 digital datasets collected by the National Climatic Data Center (NCDC). Each dataset
contains data from 8,000 active stations (NCDC 2011); from these, we select 113 COOP stations
(Fig. 2.1) from Alabama, Florida, Georgia, South Carolina, and North Carolina, collectively
referred to as the Southeast.
Fig 2.1. Station coverage map of the 113 COOP stations in the Southeast, which includes
Alabama, Florida, Georgia, South Carolina, and North Carolina.
We defined an extreme temperature by using a percentile approach as opposed to a
standard deviation approach. The latter assumes a normal distribution of maximum and
minimum temperatures, which may not be true for all stations. In a non-Gaussian temperature
distribution, the skewness of the distribution may overemphasize the patterns of extremes
(Henderson and Muller 1997). This is especially true for stations with a positive skewness for the
10
maximum temperatures (defined by the stretching of the right-hand side of the distribution) and a
negative skewness for the minimum temperatures (defined by the stretching of the left-hand side
of the distribution). An illustration of negative skewness in the anomalies of the minimum
temperature distribution is shown in Fig. 2.2.
Since the right-hand side of the maximum temperature distribution and the left-hand side
of the minimum temperature distribution are the focus of this study and are most affected by the
standard deviation method, we will use a percentile approach. The percentile method does not
assume a Gaussian temperature distribution; therefore, it allows for a more accurate depiction of
patterns that are found without overemphasizing the number of extreme temperature events.
Fig. 2.2. An example of negative skewness in the left-hand side of the minimum
temperature distribution (blue) of anomalies during winter for Hendersonville, NC.
We will focus on the 99th (1st) percentile of the maximum (minimum) temperature
distribution. These thresholds represent the hottest maximum temperatures and the coolest
minimum temperatures. We determine the threshold value for an extreme temperature using
these percentages on a station- and season-dependent basis. It is important for the extreme
temperature thresholds to be station dependent because extremes may be more easily met at
some of our stations than at others because of geographic location or urban development.
Creating a seasonally dependent threshold allows us to analyze patterns in different seasons
without biases from temperature values from the remaining seasons. Seasonal analysis also
provides a more useful dataset for the stakeholders, such as strawberry farmers trying to protect
their crops during the winter months.
The threshold values are created from the maximum and minimum temperature
distributions during a static period of record from 1930 to 2009. This static-period method allows
us to use the maximum number of stations from the COOP network while still using a standard
number of years to develop the threshold for each station. Inherently, this enables us to include
stations dating back as far as 1892 as well as those starting in 1930 so that we can obtain a
thorough regional dataset of extreme temperature patterns. An additional criterion is set for the
stations such that the stations cannot contain more than five consecutive years of missing data
from the static period of record (Smith 2006).
11
Since we are evaluating extreme temperatures on a seasonal scale, we use the following
definitions. A summer-day extreme is defined as a day in June, July, and August (JJA) with a
maximum temperature that is ≥ the 99th percentile of the JJA daily maximum temperature
distribution for a particular station. Similarly, a winter-day extreme is defined as a day in
December, January, and February (DJF) with a minimum temperature that is ≤ the 1st percentile
of the DJF daily minimum temperature distribution for a particular station. We evaluate the
winter and summer seasons because the greatest number of extreme warm temperatures are
expected to occur during the summer and the greatest number of extreme cool temperatures are
expected to occur during the winter in the Southeast.
12
CHAPTER 3
DE
EFINING CLIMATE REGIMES
Multiple parts of this stud
udy evaluate the behaviors of extreme temperature
ures during years
selected from three periods of rec
record in the Southeast: the Warm Regime (WR,, 1920–57), the
Cold Regime (CR, 1958–98), and
nd the Modern Record (MR, 1981–2000). Previou
ious studies have
documented the historical warm aand cool periods in the Southeast (Williams ett al.
a 2010; Diaz
and Quayle 1980; Dittinger et al. 1995). The WR and CR were indicated by mult
ultidecadal shifts
in the long-term temperature reco
cords illustrated in Fig. 3.1 (Williams et al. 2010)
0).
Fig 3.1. Annual average temper
erature for Florida sampled by the Oklahoma
a Climatological
Survey. The x-axis shows years
rs in decades and the y-axis shows temperature
re in °F. The red
shaded region represents the w
warmer historical period and the blue shaded
d region
represents the cooler historical
al period based on a five-year annual temperatture tendency.
xtend to the year 2000 to include the most recent record available
The MR is selected to ext
at the time of the study. We want
nt to have at least 20 years of data to include in the
th MR, and for
that reason, the MR overlaps with
ith the CR. Since the WR and CR are defined from
rom the general
long-term temperature records,, th
the CR temperatures vary from state-to-state. The
he tail end of the
CR in Alabama is cooler than the
he tail end of the CR in Florida. Having a MR tha
hat overlaps the
tail end of the CR helps different
ntiate each state’s cooling and warming responsee during the CR.
A relatively warm CR can be see
een in Fig. 3.1.
13
We select 20 consecutive years from the defined WR and CR to be consistent with the
length of the MR. The 20-year subset selected from the WR is from 1935 to 1954 (WRs) and the
20-year subset we selected from the CR is from 1959 to 1978 (CRs). Hereafter, references to the
WRs and the CRs indicate the 20-year periods used for this study (Fig. 3.2) unless otherwise
stated.
Fig 3.2. Same as 3.1, except with three 20-year study periods outlined. The period between
the red lines is this study’s Warm Regime subset, the period between the blue lines is this
study’s Cold Regime subset, and the period between the green lines is this study’s Modern
Record.
14
CHAPTER 4
SEASONAL COUNTS OF SUMMER-DAY AND WINTER-DAY EXTREMES
Histograms are used to illustrate the number of occurrences of summer-day and winterday extremes for each station’s period of record. These histograms are not based on the
individual periods mentioned in Chapter 3, but instead are created for the station’s entire period
of record so that multidecadal patterns among the regimes can be seen.
Figure 4.1 shows a histogram of the number of times that a winter-day extreme occurred
during a given year in Camp Hill, Alabama. Counts of extreme events with temperatures greater
than or equal to the 99th percentile of the daily minimum temperature distribution are also
plotted (i.e., warm minimums, light blue bars).
Multidecadal oscillations in the number of extreme minimum temperatures can be seen in
Fig. 4.1 as increases (decreases) in the number of winter-day extremes occur during the CRs
(WRs). For this station, ten years from the CRs had winter-day extremes while only three (five)
years from the WRs (MR) had them. Three of the ten years during the CRs had four or more
occurrences of winter-day extremes whereas the years during the WRs (MR) had no more than
two (one) occurrences of winter-day extremes for a given year. The analysis shows that a
majority of stations within the study domain have greater numbers of winter-day extremes during
the CRs than during any other period.
15
Fig 4.1. Count of winter-day extreme events [days in December, January, and February
(DJF) with minimum temperatures that are ≤ the 1st percentile of the DJF daily minimum
temperature distribution for a particular station] for Camp Hill, AL. The x-axis shows the
years in the station’s period of record and the y-axis shows the number of times that an
daily minimum temperature met or exceeded the 1st percentile (dark blue) threshold and
the 99th percentile (light blue) threshold. The Cold Regime subset (1959–78) is the period
contained by the vertical lines.
Similarly, a multidecadal oscillation is seen in the number of occurrences of extreme
maximum temperatures. A histogram is created of the counts of summer-day extremes and
temperatures that are less than or equal to the 1st percentile of the daily maximum temperature
distribution (i.e., coolest maximums, light red bars). During the WRs, seven years have summerday extremes; three of those years have four or more summer-day extremes for a given year.
The CRs and MR do not have as many years that experience a summer-day extreme. The CRs
(MR) has a summer-day extreme during only one (four) of its years. However, the MR does
show that four or more occurrences of a summer-day extreme happened during three of those
years.
Similarities among the MR, WRs, and CRs allude to the possibility of forecasting how
winter-day and summer-day extremes will behave during a given period depending on the
period’s forecasted similarities to a WRs or CRs.
16
Fig 4.2. Count of summer-day extreme events for Camp Hill, AL.
The x-axis shows the years in the station’s period of record and the y-axis shows the
number of times that the daily maximum temperature met or exceeded the 99th percentile
(dark red) threshold and the 1st percentile (light red) threshold. The Warm Regime (193554) subset is the period between the vertical lines.
17
CHAPTER 5
LIKELIHOOD OF A SUMMER-DAY AND A WINTER-DAY EXTREME OCCURRING
Multidecadal oscillations in the number of occurrences of summer-day and winter-day
extremes lead us to calculate the probability of one to ten extreme events occurring during the
WRs, CRs, and MR. We calculate an overall binomial distribution (BD) of extreme occurrences
using the station’s entire period of record. We also calculate the BD for each of the three periods
to compare to the overall BD. The results of this comparison determine whether the probability
of a station experiencing an extreme temperature event during the three periods is similar to the
probability of having an extreme temperature event during the station’s entire period of record.
Since the three periods are significantly shorter (only 20 years long) than the station’s
period of record, we develop a confidence interval (CI) to determine the uncertainty in the
probability of an extreme temperature event occurring during the three periods. A Monte Carlo
approach is used to re-sample 20 random years from the station’s period of record to develop the
90% CI.
If the BD from a particular period falls within the CI, the station shows no substantial
change in the probability of an extreme temperature event occurring during that particular period
compared to the probability produced from the overall BD of the station. This means that the
extreme temperature occurrences are indistinguishable from a binomial distribution. Similarly, if
the BD from a particular period falls above (below) the CI, then the station shows a significantly
substantial increase (decrease) in the probability of an extreme temperature event occurring
during that period compared to the probability produced from the overall BD. We designate
those stations with a probability that falls above the CI as having an “increased likelihood,” those
that fall below the CI as having a “decreased likelihood,” and those that fall within the CI as
having “no change in the likelihood” of an extreme event occurring during a period.
Figure 5.1 illustrates the aforementioned process for winter-day extremes in Albany,
Georgia. The red dot, blue dot, and green dot represent the WRs, CRs, and MR, respectively.
Notice that there is a 44% chance that exactly one winter-day extreme event will occur during
any winter season in the station’s period of record. The BDs for the CRs and MR fall above the
90% CI and show a probability between 65-70% of exactly one winter-day extreme event
occurring during the CRs and MR. This means that it is statistically more likely for one winterday extreme to occur during the CRs and MR. The BD for the WRs falls below the CI for exactly
one winter-day extreme to occur. There is only a 10% chance for this station to experience a
winter-day extreme during the WRs, making it statistically less likely for exactly one winter-day
extreme event to occur during this period. None of the three periods falls within the CI for the
occurrence of exactly one event for this station. This means that the periods do not follow a
binomial distribution similar to that of the station’s overall BD. Probability analysis is conducted
for each station; the number of stations with each probability scenario is shown in Table 5.2.
18
Fig 5.1. Albany, GA: The overall binomial (black bar) is computed to determine the
probability of occurrence of a winter-day extreme. The selected time periods are plotted
(colored stars) against the 90% confidence interval (gray lines). Data from Albany, GA
show that the selected 20-year periods were outside the confidence intervals at one event
per season, and the Cold Regime subset and Modern Record were above the confidence
interval for two and three winter-day extreme events per season.
19
Table 5.2. Table of the number or stations that have an increased, decreased, and no
change in the likelihood of exactly one winter-day and summer-day extreme occurring
during the Warm Regime and Cold Regime subsets and the Modern Record.
20
CHAPTER 6
SPATIAL STRUCTURE OF THE PROBABILITY OF A STATION EXPERIENCING
EXACTLY ONE SUMMER-DAY AND ONE WINTER-DAY EXTREME
Analysis of the probability plots similar to Fig. 5.1 provides insight about the magnitude
of the probability increase or decrease of an extreme event occurring during a particular period
compared to the station’s entire period of record. Regional patterns in the probabilities can be
seen by plotting the probabilities on a spatial map of the Southeast. Figure 6.1 shows the
likelihood of exactly one winter-day extreme occurring during the WRs, CRs, and MR. The plot
shows that during the WRs, the likelihood of a winter-day extreme happening in the Southeast is
decreased, meaning that the binomial distribution of the WRs is below the 90% confidence
interval of the overall mean binomial distribution. During the CRs, the likelihood of the station
experiencing a winter-day extreme increases at 72 of the 113 stations (63.7%) in the domain. The
MR appears to have a similar setup to that of the CRs except that only 41 stations exhibit an
increased likelihood of a winter-day extreme occurring.
This analysis is also conducted for summer-day extremes (Fig. 6.2). During the WRs, it is
statistically more likely for the majority of stations in the domain to experience exactly one
summer-day extreme. All but one station during the CRs shows either a decreased likelihood or
no change in the likelihood of one summer-day extreme occurrence. The MR shows variability
in the likelihood of a summer-day extreme occurrence. Seventeen stations exhibit an increased
likelihood and fifteen stations exhibit a decreased likelihood. All except three stations that show
a decreased likelihood are situated in inland areas and all except three stations that show an
increased likelihood are located along the coast. This reveals that during the MR, coastal regions
are more likely to experience exactly one summer-day extreme than inland stations are. This
inland versus coastal region separation is not seen in the probabilities of a winter-day extreme
occurrence during the MR.
21
Fig 6.1. Likelihood of exactly one winter-day extreme (daily minimum temperature ≤ 1st
percentile of the daily winter minimum temperature distribution for a particular station)
occurring during the Warm Regime and Cold Regime subsets, and the Modern Record.
The (+)’s indicate a statistically significant increased likelihood, (-)’s indicated a
statistically significant decreased likelihood, and (o)’s indicate no change in the likelihood
of one winter-day extreme event occurring during the given period.
Fig 6.2. Same as Fig 6.1 except for a summer-day extreme (daily maximum temperature ≥
99th percentile of the daily summer maximum temperature distribution for a particular
station).
22
CHAPTER 7
SPATIAL STRUCTURE OF THE DURATIONS OF SUMMER-DAY AND WINTERDAY EXTREMES
To further investigate the behavior of extreme temperatures in the Southeast, we examine
the duration of summer-day and winter-day extremes during the WRs, CRs, and MR. We
calculate the number of times that the summer-day and winter-day extremes were observed for
durations of one, two, three, four, and five days. The likelihood of exactly one summer-day and
one winter-day extreme occurring during a period (relative to the whole record) is also plotted
along with the counts. Figure 7.1 shows the relationship between the probability of an extreme
event occurring and how many times it occurs within a given period.
In Fig. 7.1, the probability results show that for 39 stations it is statistically more likely
for a summer-day extreme to occur during the WRs. This likelihood is consistent with the
likelihoods displayed in Fig. 6.2 and does not change as the duration changes. For instance, an
increased likelihood for exactly one occurrence of a summer-day extreme maintains the same
likelihood for durations of one, two, three, four, and five days for a given subset. However, the
number of occurrences of an extreme event can vary among the different durations. All of the 39
stations had 5 or more occurrences of a summer-day extreme that lasted for only 1 day. Four of
these stations had thirty or more occurrences of a summer-day extreme.
Stations with increased likelihood of summer-day extremes that have five or more oneday events are likely to have five or more two-day events during the WRs. During the WRs,
56.4% of the stations that have an increased likelihood of five or more occurrences of a one-day
summer-day extreme also had five or more occurrences of a two-day summer-day extreme. Only
7.6% of stations have three-day summer-day extremes (not shown). None of the stations has
more than five occurrences of summer-day extremes that last four or five days during the WRs.
A cluster of stations with increased likelihood are located inland. The peninsula of
Florida does not show a significant increase in the likelihood of a summer-day extreme
occurrence during the WRs. This is possibly due to the geography of Florida that causes frequent
sea breezes during the summer. These sea breezes help moderate the temperature in this region
of the state and therefore may prohibit reaching extreme warm temperatures.
During the CRs, the majority (68%) of stations show a decreased likelihood of a summerday extreme occurrence. Only twenty-nine stations show five or more occurrences of a one-day
summer-day extreme. None of the stations have more than five occurrences of summer-day
extremes that last two or more days during the CRs (not shown).
23
Fig 7.1. One- and two-day durations of summer-day extremes (daily maximum
temperature ≥ 99th percentile of the daily summer maximum temperature distribution for a
particular station) during the Warm Regime subset. The number of times (0-30) that a
summer-day event occurs with a one- or two-day duration is shown by the color bar. The
(+)’s indicate a statistically significant increased likelihood, (-)’s indicate a statistically
significant decreased likelihood, and (o)’s indicate no change in the likelihood of one
summer-day extreme occurring during the Warm Regime subset.
Figure 7.2, shows the likelihood of experiencing one winter-day extreme during the CRs
and the number of times that a winter-day extreme occurs with one- and two-day durations.
Fifty-nine stations show that it is statistically more likely for a winter-day extreme to occur
during the CRs. This likelihood is also consistent with the likelihoods displayed in Fig. 6.1.
Forty-nine of those stations have five or more occurrences of a one-day winter-day extreme.
During the CRs, 69% of the stations that have five or more occurrences of a one-day winter-day
extreme also have five or more occurrences of a two-day winter-day extreme. This is consistent
with the behavior of summer-day extremes during the WRs. Stations with increased likelihood of
winter-day extremes that have five or more one-day events are likely to have five or more twoday events during the CRs . Only 2% of stations have three-day winter-day extremes (not shown).
None of the stations have more than five occurrences of winter-day extremes that last four or five
days during the CRs.
The clustering of “increased likelihood” of one extreme event occurrence at inland
locales seen in Fig. 7.1 for the summer-day extremes is also seen in Fig. 7.2 for the winter-day
extremes. The clustering occurs at the plateau region, referred to as the Piedmont, between the
24
Atlantic coastal region and the Appalachian Mountains. High pressure systems situated north of
the Appalachians Mountains during the winter can result in cold-air damming, which causes cold
air to funnel down from the north and settle in the Piedmont. Cold-air damming might be the
cause of the increased likelihood of a winter-day extreme occurrence during the CRs for stations
in this region.
Stations in the Florida peninsula show increases in the likelihood of a winter-day
occurrence during the CRs; recall that this is not the case for summer-day extremes during the
WR subset. This may be the result of arctic cold fronts dipping down as far south as central
Florida, causing multiple extreme cold temperatures.
During the WRs, all of the stations show either a decreased likelihood or no change in the
likelihood of a winter-day extreme occurrence. However, 62.8% of the stations show five or
more occurrences of a one-day winter-day extreme. None of the stations have more than five
occurrences of winter-day extremes that last two or more days during the WRs (not shown).
Fig 7.2. Same as Fig 7.1, except for winter-day extremes (daily minimum temperature ≤ 1st
percentile of the daily winter minimum temperature distribution for a particular station)
during the Cold Regime subset.
25
Figure 7.3 shows the likelihood of experiencing one summer-day or one winter-day
extreme during the MR and the number of times that a summer-day or winter-day extreme
occurs with a one-day duration. A comparison of the durations will allow us to determine how
extreme temperatures behave during different seasons in the MR. Thirty-one stations show an
increased likelihood of one winter-day extreme occurrence, whereas only seventeen stations
show an increased likelihood of one summer-day extreme occurrence during the MR.
The number of stations that experience five or more extremes with a one-day duration is
nearly the same (~70) for the summer and winter during the MR. The maximum number of times
that a summer-day extreme with one-day duration occurs is greater than the maximum number of
times that a winter-day extreme with one-day duration occurs during the MR. Summer-day
extremes of one-day duration occurred thirty or more times at five stations and winter-day
extremes of one-day duration occurred up to twenty times at only one station.
Geographic patterns are also seen in the counts and likelihoods of exactly one extreme
temperature event occurrence during the MR. Extremes in Florida are similar to a WRs with
multiple stations exhibiting an increased likelihood of an occurrence of a summer-day extreme.
In addition, 56% of Florida stations have ten or more occurrences of a one-day summer-day
extreme. This pattern is similar to the general WRs, but it varies from Florida’s pattern of
summer-day extremes during the WRs. During the WRs, only stations in the panhandle of Florida
showed an increased likelihood of a summer-day extreme occurring, except for one station,
whereas during the MR, only stations in central and south Florida show this increased likelihood.
This suggests that central and south Florida have a more WRs setup than northern Florida does
during the MR. Stations in the other four states, especially in the Piedmont region show
decreases in the likelihood of a summer-day extreme occurrence during the MR.
Although extreme temperature patterns in parts of Florida have similarities to the WRs
patterns, the other four states have patterns similar to those of the CRs. The area with the most
stations exhibiting an increased likelihood of a winter-day extreme occurrence is still in the
Piedmont region.
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Fig 7.3. One-day durations of summer-day extremes (left) and winter-day extremes (right)
during the Modern Record. The number of times (0-30) that an summer-day or winter-day
extreme temperature event occurs with a duration of one day is shown by the color bar.
The (+)’s indicate a statistically significant increased likelihood, (-)’s indicate a statistically
significant decreased likelihood, and (o)’s indicate no change in the likelihood of one
summer-day extreme or one winter-day extreme occurring during the Modern Record.
Spatial plots of the two-day durations (not shown) of summer-day and winter-day
extremes during the MR reveal the same geographic patterns as the one-day duration plots do.
Winter-day extremes that last for two days occur five or more times at fifty-five stations while
the summer-day extremes of the same length occur only five or more times at fifteen stations.
This suggests that winter-day extremes persist longer than summer-day extremes during the MR.
Four stations have five or more occurrences of winter-day extremes that persist for three days
whereas no stations have five or more occurrences of summer-day extremes that last for three
days during the MR.
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CHAPTER 8
CONCLUSION
Renewed interest in extreme temperatures in the southeastern United States is a result of
increased interest in climate change and agricultural risk in the last decade. To assess how
extreme temperatures will behave seasonally in the future, we must first evaluate their behavior
in the past, in particular, how many extreme temperatures occur and how long they persist. Here,
we analyze extreme temperatures for the standard winter and summer seasons at 113 NWS
COOP stations in Alabama, Florida, Georgia, North Carolina, and South Carolina. We define a
winter-day extreme as a day in December, January, and February (DJF) with a minimum
temperature that is ≤ the 1st percentile of the DJF daily minimum temperature distribution for a
particular station. Similarly, a summer-day extreme is a day in June, July, and August (JJA) with
a maximum temperature that is ≥ the 99th percentile of the JJA daily maximum temperature
distribution for a particular station.
For each station, we count the number of times that a winter-day and a summer-day
extreme occur during the station’s period of record. We then calculate the probability of a
winter-day and a summer-day extreme occurring during the Cold Regime subset (CRs, 1959–78),
Warm Regime subset (WRs, 1935–54), and the Modern Record (MR, 1981–2000) to determine
whether the likelihood of exactly one occurrence of a winter-day or a summer-day extreme is
statistically increased, decreased, or does not change. In addition, we count the number of times
that a winter-day and summer-day extreme is observed for a duration of one, two, three, four,
and five days during the three periods.
Counts of winter-day and summer-day extremes show a multidecadal oscillation
consistent with the WRs and CRs. Increased counts of winter-day extremes generally occur
during the CRs and increased counts of summer-day extremes generally occur during the WRs.
Counts of winter-day and summer-day extremes vary from station to station during the MR.
Spatial plots reveal that a winter-day (summer-day) extreme is statistically more likely to
occur during the CRs (WRs) and statistically less likely to occur during the WRs (CRs). During
the MR subset, coastal regions are statistically more likely to experience exactly one summerday extreme than inland stations are. This inland versus coastal region separation is not seen in
the probabilities of a winter-day extreme occurrence during the MR.
Analysis of the durations of winter-day and summer-day extremes during the three
periods shows that stations with an increased likelihood of a summer-day extreme that have five
or more one-day events are likely to have five or more two-day events during the WRs. The
majority (56.4%) of the stations that have an increased likelihood and five or more occurrences
of a one-day summer-day extreme also had five or more occurrences of a two-day summer-day
extreme during the WRs. Only 7.6% of stations have three-day summer-day extremes. No
stations have more than five occurrences of summer-day extremes that last for four or five days
during the WRs. During the CRs, summer-day extremes generally do not last more than one day.
Similarly, 69% of the stations with an increased likelihood of a winter-day extreme and five or
more one-day events also had five or more two-day events during the CRs.
28
The number of stations that experience five or more extremes with a one-day duration is
nearly the same (~70) for the summer and winter during the MR. The maximum number of times
that a summer-day extreme with one-day duration occurs is greater than the maximum number of
times that a winter-day extreme with one-day duration occurs during the MR. Summer-day
extremes of one-day duration occurred thirty or more times at five stations and winter-day
extremes occurred up to twenty-five times at only one station. Patterns in the counts and
durations of extreme temperatures in peninsular Florida are similar to the WRs while the other
four states show patterns similar to the CRs during the MR.
The behavior of extreme temperatures, in particular their duration, has significant impacts
on the agricultural community, power suppliers, and human health. Identifying patterns in the
durations of extremes during climatologically warm and cool periods may help to forecast the
behavior of winter-day and summer-day extremes in coming years. As shown in this study,
during the MR, patterns in parts of Florida resembled the patterns seen in the WRs. If a given
year is forecasted to be more or less like the WRs or CRs, then analysis like this can be used to
predict how long the hottest maximum and coldest minimum temperatures will persist. Future
work will include the transition seasons and will extend the MR to include the years 2010 and
2011 so that we can assess how extreme temperatures are behaving in the most recent years as
well as test the sensitivity of our record length.
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