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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 ii 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 iii 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 iv 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 v 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 vi 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. vii 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, 1 2 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 3 4 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 5 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 6 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 7 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 8 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 9 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 10 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 11 12 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. 13 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 14 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. 15 16 Figure 4.1 Interpolation of Mean Monthly FT days Across the Study Area (a)July-top (b)August-bottom 17 Figure 4.1 (continued) (c) September-top (d) October-bottom 18 Figure 4.1 (continued) (e) November-top (f) December-bottom 19 Figure 4.1 (continued) (g) January-top (h) February-bottom 20 Figure 4.1 (continued) (i) March-top (j) April-bottom 21 Figure 4.1 (continued) (k)May-top (l)June-bottom 22 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 23 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). 24 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 25 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 REFERENCES Aguado, E and J. E. Burt. 2007. Understanding Weather and Climate: 196198. Prentice Hall, Upper Saddle River, New Jersey. Easterling, D. R. 2002. Recent Changes In Frost Days And The Frost-Free Season In The United States. Bulletin of the American Meteorological Society, 83 1327-1332. Field, C.B., L.D. Mortsch,, M. Brklacich, D.L. Forbes, P. Kovacs, J.A. Patz, S.W. Running and M.J. Scott, 2007: North America. 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