Summer Regional United States Diurnal Temperature Range Variability
With Soil Moisture Conditions
THESIS
Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in
the Graduate School of The Ohio State University
By
Robert Wayne Brewer, B.S.
Graduate Program in Atmospheric Science
The Ohio State University
2015
Master's Examination Committee:
Jeffery Rogers Advisor
Jay Stanley Hobgood
Jialin Lin
Copyrighted by
Robert Wayne Brewer
2015
Abstract
Long-term (1895-2012) soil moisture proxy data are collected and analyzed for its
spatial and temporal variability across the United States in conjunction with air
temperature and diurnal temperature range (DTR) variations over the same period.
Palmer Drought Severity Index (PDSI) summer data were subjected to a Rotated
Principle Component Analysis (RPCA) that identified 10 regions (components) having
unique patterns of PDSI spatial and temporal variability. Four of those regions (RPC1:
Ohio River Valley; RPC2: upper Midwest and eastern Northern Plains; RPC3:
southeastern United States; RPC5: Southern Plains) are analyzed further with regard to
DTR variations. In conjunction to the summer PDSI time series scores produced by the
RPCA, mean DTR, T-max, and T-min (maximum and minimum temperatures) were
obtained using GHCNM station data within each of the regions of interest and analyzed
for trends. The twelve wettest and driest summers were also identified for each of the 4
regions based on the rank of their PDSI time series scores. The average temperature/DTR
for each of these cases (wet or dry) were then compared.
Soil moisture in the Ohio River Valley (RPC1) has an increasing trend throughout
the 20th-21st centuries. T-max shows a downtrend of 0.5°C while T-min has increased ~
0.7°C producing a downward trend in DTR throughout the period of record. The upper
Midwest and eastern Northern Plains (RPC2) produced similar behavior as the Ohio
ii
River Valley with more moist soil conditions at the end of the 20th and early 21st century.
DTR trends downward in this region due to a very clear upward trend in T-min coupled
with a negligible downtrend in T-max. PDSI in the southeastern United States (RPC3)
does not have a strong trend but does show a slight increase. T-max produces a trivial,
but slight increasing trend while T-min shows a stronger increase in temperatures. This
outcome produces a decreasing trend in DTR. Soil moisture in the Southern Plains
(RPC5) shows an overall decline in PDSI. T-max produced a long-term increase of ~
0.6°C. T-min produces an increasing trend slightly larger than that of T-max causing a
very small decreasing DTR trend. The long-term DTR trends in each region seemed to be
mostly influenced by the larger long-term increasing trends of T-min as compared to the
smaller trends in T-max. However, DTR during the most extreme soil moisture summers
(wet or dry) seemed to be influenced more by the variability in T-max, as T-min did not
fluctuate as much.
The 2012 summer drought was used as a case study to evaluate month-to-month
DTR variations in the context of variations in precipitation and drought conditions. On a
statewide and month-to-month basis, 2012 DTR variations almost always declined
(increased) in response to increases (decreases) in rainfall. This variability agrees with
that shown in the DTR soil-moisture portion of the analyses.
iii
Acknowledgements
I would like to recognize my family, friends, Dr. Jialin Lin, Dr. Jay Stanley
Hobgood, and especially Dr. Jeffery Rogers. Without your continued support and
guidance, I would not be where I am today in my academic career. It has truly been an
honor and privilege to surround myself with such knowledge, leadership, and humility
during my time at The Ohio State University. Thank you.
iv
Vita
May 2007…………………………………....New Lebanon Dixie High School
2011…………………………………………B.S. Atmospheric Science, The Ohio State
University
2013 to present……………………………...Graduate Teaching Assistant, Department of
Geography, The Ohio State University
Fields of Study
Major Field: Atmospheric Sciences
v
Table of Contents
Abstract……………………………………………………………………………………ii
Acknowledgements……………………………………………………………………….iv
Vita………………………………………………………………………………………...v
List of Tables…………………………………………………………………………….vii
List of Figures…………………………………………………………………………...viii
Chapter 1: Introduction……………………………………………………………………1
Chapter 2: Literature Review……………………………………………………………...4
Chapter 3: Data and Methodology……………………………………………………….15
Chapter 4: Results………………………………………………………………………..19
Chapter 5: Case Study: The Drought of 2012 ……………………………………………63
Chapter 6: Conclusions…………………………………………………………………..94
References………………………………………………………………………………..99
vi
List of Tables
Table 5.1. U.S. Drought Conditions: End of August 2012
70
Table 5.2. States Analyzed for the U.S. Drought of 2012
78
Table 5.3. Statewide Averages for May 2012
80
Table 5.4. Statewide Averages for June 2012
82
Table 5.5. Statewide Temperature Changes from May-June 2012
85
Table 5.6. Statewide Averages for July 2012
87
Table 5.7. Statewide Temperature Changes from June-July 2012
89
Table 5.8. Statewide Averages for August 2012
91
Table 5.9. Statewide Temperature Changes from July-August 2012
93
vii
List of Figures
Figure 4.1. Spatial Centers of PDSI Components 1-5
20
Figure 4.2. PDSI Scores for the Ohio River Valley (RPC1)
22
Figure 4.3. PDSI Scores for the Upper Midwest/eastern Northern Plains (RPC2)
23
Figure 4.4. PDSI Scores for the southeastern United States (RPC3)
24
Figure 4.5. PDSI Scores for the Southern Plains (RPC5)
25
Figure 4.6. RPC1 Regional Averages of T-max
28
Figure 4.7. RPC2 Regional Averages of T-min
30
Figure 4.8. Ohio River Valley Regional Averages of DTR
31
Figure 4.9. RPC2 Regional Averages of T-max
33
Figure 4.10. RPC2 Regional Averages of T-min
35
Figure 4.11. Upper Midwest/eastern Northern Plains Regional Averages of DTR
36
Figure 4.12. RPC3 Regional Averages of T-max
38
Figure 4.13. RPC3 Regional Averages of T-min
40
Figure 4.14. Southeastern United States Regional Averages of DTR
41
Figure 4.15. RPC5 Regional Averages of T-max
43
Figure 4.16. RPC5 Regional Averages of T-min
44
Figure 4.17. Southern Plains Regional Averages of DTR
46
viii
List of Figures
Figure 4.18. PDSI Wettest vs Driest Summers: RPC1
48
Figure 4.19. T-max, T-min, and DTR for Wettest vs Driest Summers: RPC1
51
Figure 4.20. PDSI Wettest vs Driest Summers: RPC2
52
Figure 4.21. T-max, T-min, and DTR for Wettest vs Driest Summers: RPC2
54
Figure 4.22. PDSI Wettest vs Driest Summers: RPC3
55
Figure 4.23. T-max, T-min, and DTR for Wettest vs Driest Summers: RPC3
57
Figure 4.24. PDSI Wettest vs Driest Summers: RPC5
59
Figure 4.25. T-max, T-min, and DTR for Wettest vs Driest Summers: RPC5
61
Figure 5.1. Climate Division PDSI Values: May-August 2012
67
Figure 5.2. U.S. Drought Monitor: May-August 2012 Drought Expansion
68
Figure 5.3. End of May-End of August U.S. Drought Monitor Drought Severity Ranks 69
Figure 5.4. Climate Division PDSI Values: May-August 1956
72
Figure 5.5. Climate Division PDSI Values: 1956 Drought Peak vs 2012 Drought Peak 73
Figure 5.6. Climate Division PDSI Values: May-August 1988
75
Figure 5.7. Climate Division PDSI Values: May-August 1934
76
Figure 5.8. United States Climate Regions
78
ix
Chapter 1: Introduction
With increasing global temperatures and the melting of both glacial and polar sea
ice, climate change has become reality. The ramifications of climate change have been
highly scrutinized both politically and scientifically as extensive research from the past
and present try to forecast the potential atmospheric and ecological changes that may
come as consequence. One of the oldest climate theories is that of the late Milutin
Milankovitch who suggested that ice ages are related to planetary gravitational influences
on the Earth’s orbit around the sun. Based on these principles, Milankovitch Cycles are
10,000-100,000 year variations occurring amidst ice ages as high latitude solar insolation
waxes and wanes (Smith, 1990; House, 1995; Ruddiman, 2006; Huybers and Curry,
2006; Berger, 2012). Bringing this into today’s perspective, according to these cycles, the
Earth should be in the midst of an extensive period of high insolation and high
temperatures. The distant future however holds a return of declining insolation and
cooling. Since the late 20th and early 21st century however, global anthropogenic carbon
emissions have increased leading to a more noticeable increase in temperatures than the
Milankovitch Cycles suggests, leading to the underlying principles of anthropogenic
global warming and climate change (Shackleton, 2009; United States Environmental
1
Protection Agency, 2012; Hansen and Sato, 2012; National Assessment Synthesis Team,
2014).
In the current era of technology and data collection, the effects of climate change
can be readily monitored and evaluated. With simple datasets containing long-term time
series comprised of daily temperatures and precipitation, short and long-term trends
become apparent as deviations from normal recur in one direction. Soil moisture is one
parameter that plays a role in temperature variability, although the role it plays is not
entirely understood.
Droughts and extreme precipitation events are expected to occur more frequently
with a changing climate (Seager et al., 2009; Gutzler and Robbins, 2010; Mishra et al.,
2010; Mallya et al., 2013). These instances of precipitation variation also influence soil
moisture and are a common occurrence of nature that can affect millions of people
worldwide every year. It can alter water supplies, crop yields, and livestock which are
very important cogs to the well-being of life. Research in problems associated with
drought and flooding has expanded, especially in the United States. More recently,
ongoing drought has impacted the United States from coast to coast with a protracted
drought in the western United States this century and a widespread Midwestern drought
in 2012 (Mallya et al., 2013; Grigg, 2014). Crop loss, wildfires, and dwindling water
reservoirs have become very common in these areas. In the history of the United States,
this is not a rare development. The United States has experienced its fair share of
droughts dating all the way back in its documented history to present day (Woodhouse
and Overpeck, 1998; Cook et al., 1999). Understanding drought and its causes and
2
consequences is an ongoing topic in climate research. The causes of drought involve
numerous meteorological variables including precipitation, air temperature, evaporation
and soil moisture (Diaz, 1983; Dai et al., 1999; Hu and Wilson, 2000) and the
interactions among these that work together to produce the unfavorable conditions.
In a warmer world, atmospheric water vapor is positively correlated with
temperature and will also increase. Because of this, extreme precipitation events have
become more common in the United States. This can be linked to long-term positive
trends in precipitation as a whole and also an increase in localized extreme precipitation
events leading to severe flooding (Kunkel et al., 1999; Groisman et al., 1999; Groisman
et al. 2011). Soil moisture indicates that the late 20th century and early 21st century have
shown characteristics of more moisture and severe precipitation events (Karl and Knight,
1998; Rahmstorf and Coumou, 2011; Peterson et al., 2013).
Associated with the effects of climate change driven by rising temperatures (e.g.
droughts and precipitation events), changes in diurnal temperatures are occurring (Karl et
al., 1993; Dai et al., 1999; Lauritsen and Rogers, 2012) along with component daily
maximum (T-max) and minimum (T-min) values. Diurnal temperature range (DTR) is
one important change potentially related to soil moisture and precipitation variability
associated with climate change in the United States. This thesis will investigate historical
DTR to see how it varies through the 20th-21st centuries in conjunction with climate
change and also how it varies in conjunction with the recent drought of 2012.
3
Chapter 2: Literature Review
A changing climate alters the typical patterns of regional and seasonal
temperatures and precipitation as well as their frequency and severity of events on a daily
scale (Shackleton, 2009). This has become evident throughout the end of the 20th century
and early 21st century as global temperatures have warmed due to large increases of heattrapping gases released from human activity (United States Environmental Protection
Agency, 2012; Hansen and Sato, 2012; National Assessment Synthesis Team, 2014).
Because of this warming, moisture levels in the atmosphere have risen because higher
temperatures permit more mixing of water vapor into the air creating higher humidities.
Karl and Knight (1998) have shown that 20th century and early 21st century trends in
precipitation have increased by about 10% in the United States. The major cause of this
rise has been the increased frequency in heavy and extreme daily precipitation events
(Karl and Knight, 1998). Kunkel et al. (1999) made note that recent years with heavy
precipitation have resulted in several damaging floods across the United States. One of
the worst floods in recent history occurred in 1993 in the upper Mississippi River Basin
resulting in billions of dollars in damage (U.S. Dept. of Commerce, 1994; Eash, 1997).
Instances of dry soil conditions come with extreme precipitation events. Because
of increasing temperatures and variations in weather patterns due to climate change,
another outcome is severe drought. Drought has become a regular occurring phenomenon
4
in the United States since the 1950s as global temperatures started to rise (Easterling et
al., 2007). Drought events have not necessarily trended toward higher or lower frequency,
but Easterling et al. (2007) suggest that temperature increases lead to increases in spatial
drought coverage while the precipitation increases over the last century have limited the
more severe and extreme drought conditions.
By the end of 2012, the United States had found itself in a recovery from one of
the costliest and most widespread dry spells in its history (Mallaya et al., 2013). The
weak winter systems of 2011 coupled with natural climate fluctuations and low sea
surface temperatures stemming from the La Niña of that year, brought very dry
conditions to the United States especially in the Midwest and Northern Plains. During
this time, monthly mean temperatures in the Midwest were warmer than the long-term
averages for the summer months (Mallaya et al., 2013). This drought caused heavy
economic, social, and environmental impacts and ranked as one of the worst in the
country since the 1930s Dust Bowl (Grigg, 2014).
As temperature has changed over the last century, soil moisture has varied as
well. The trends in daily site-based T-max and T-min temperatures can be collectively
analyzed in a combination with diurnal temperature range (DTR) (T-max minus T-min).
With the available data, DTR has decreased worldwide during the last 4-5 decades (Dai
et al., 1999). As technology has advanced, coupled with new atmospheric measurement
technologies and new datasets, it has become easier to investigate the decreasing DTR
trend. More importantly, scientists have been able to identify possible causes of this
trend. Linking the long-term decrease in DTR to drought might give some insight to
5
possible effects of global climate change. This chapter reviews some of the key research
on DTR and soil moisture variability. With the use of this scientific knowledge, this
thesis then delves into the possible relationship between DTR and soil moisture in the
recent global climate change. The research review will mainly assess findings for the
United States although DTR and soil moisture variabilities are global occurrences.
Since DTR is solely related to fluctuations of T-min and T-max, soil moisture and
global climate change can be linked to changes in daily temperature maxima and
nighttime minima. Karl et al. (1993) introduced the notion of decreasing DTR linked to
increased cloudiness and low cloud cover across the United States resulting in increased
daily T-min. Karl blamed a possible combination of observed global warming, increasing
aerosols, and fluctuations in natural climate variability as indirect causes of the increase
in cloudiness (Karl et al., 1993).
Dai et al. (1999) evaluated the effect of wind direction, clouds, water vapor, soil
moisture, and surface wind speed on DTR during summer and autumn seasons. Each
variable was treated separately in the analysis. They used site-averaged data created by
Betts and Ball (1998) based on higher-resolution data collected during the First
International Land Surface Climatology Project (FIFE) in the late 1980s in Kansas. The
FIFE dataset contained 30-min averaged surface air temperature, humidity, winds,
precipitation, total cloud cover, latent and sensible heat fluxes, solar and longwave
radiative fluxes, and daily soil moisture content (Dai et al., 1999). The FIFE dataset
lacked a comprehensive look at cloud cover fluctuations so cloud data was obtained from
6
a nearby weather station from the National Center for Atmospheric Research data
archives.
Dai et al. looked at the possible effects of wind direction on DTR. They narrowed
the FIFE dataset to days of low cloud cover (cc<25%). They monitored the impact warm
and cold air masses had on surface air temperatures and DTR by using clear days that had
predominately southerly and northerly component winds. It was determined that wind
direction alone was not a significant factor in DTR (Dai et al., 1999) although they
expressed concern that other geographic and topographic locations in the United States
might not necessarily exhibit the same result. Although wind direction can significantly
affect daily mean surface air temperature, DTR is more dependent on everyday
unbalanced local forcings such as soil moisture and sensible and latent heat fluxes (Dai et
al., 1999). This will be discussed below.
The effect of varying cloud cover on DTR was investigated using data from nonprecipitating and relatively dry days. These data were separated into clear and cloudy
days (cc<12.5% and cc>62.5%). Cloud base height was also obtained from station reports
to see if cloud type plays a role in affecting DTR. Cloud coverage was found to dampen
DTR by reducing the daytime T-max at the surface (Dai et al., 1999), presumably
through a reduction in surface solar radiation. The width of the cloud base, thickness, and
cloud height are all attributes that can alter DTR. Clouds with low bases are found to be
the most efficient at reducing daily T-max and in turn, DTR (Dai et al., 1999). Cloud
cover can reflect incoming radiation which dampens T-max and can also transmit
downward longwave radiation which can increase daily T-min (Dai et al., 1999).
7
Atmospheric water vapor was evaluated on clear days that were separated into
dry/low humidity and dry/high humidity days. They used the large differences in
downward longwave radiation between the low and high humidity days as the measure of
water vapor on DTR. Their correlation analysis showed that nighttime T-min was
strongly related with surface humidity (Dai et al. 1999). According to Fung et al. (1984)
and Zhang et al. (1995), surface downward longwave radiation is most sensitive to water
vapor and temperature in the lower atmosphere. This is because there is ample moisture
below clouds to help decouple temperature at the surface and the downward longwave
radiation from clouds. Composite analysis determined that enhanced downward
longwave radiation was linked to high humidity days (Dai et al., 1999). The enhancement
of the downward longwave led to increases in T-max and T-min. Their study showed that
high humidity days were associated with a small decrease in DTR.
Soil moisture can play a role in temperature near earth’s surface due to
evaporation and sensible heat loss, as well as through fluxes of longwave radiation. Dai’s
data were split into two categories; clear days with low soil moisture versus clear days
with high soil moisture. Correlation analysis showed that soil moisture was negatively
associated with DTR once other variables were removed (Dai et al., 1999). The higher
the soil moisture the smaller the DTR since daily T-max is limited due to evaporative
cooling occurring at the surface. Water vapor plays a role in T-min as a greenhouse gas
absorbing infrared radiation emitted by the earth’s surface. Increased soil moisture is
associated with precipitation and cloud cover, which also have DTR impacts.
8
To evaluate how surface wind speeds may play a role in modulating DTR, Dai et
al. examined clear days with calm winds versus windy days (> 4.6 m/s). They tried to
determine if wind speed plays a role in sensible and latent heat flux. After analyzing their
correlations, wind speed showed no significant relationship to DTR at the FIFE site.
Sensible and latent heat fluxes showed no significant relationship to DTR between calm
and windy days. Turbulent mixing at the surface on calm days is not a limiting factor for
sensible and latent heat flux (Dai et al., 1999).
In summary, Dai et al. (1999) provided insight into the decline of DTR over the
last four to five decades in the United States. The diurnal cycle of temperature at the
surface is mostly driven by radiative fluxes and moisture (Dai et al., 1999). They
investigated several variables that could modulate radiation at the surface and found that
cloud cover, precipitation, and soil moisture were the biggest contributors to decreasing
DTR. Surface solar heating affects daily T-max while nighttime T-min is controlled by
the greenhouse effect of atmospheric water vapor near the surface. Cloud cover,
precipitation, and soil moisture were accountable for approximately 50% in the reduction
of diurnal temperatures when compared to clear sky days (Dai et al., 1999). Clouds and
soil moisture are able to decrease DTR by reducing T-max due to reflection of solar
radiation and evaporative cooling. Precipitation from clouds affect temperature maximum
by slowing the rate of diurnal warming. Greenhouse gases may be liable for increasing
both the minimum and maximum temperatures, but decreasing DTR is mostly linked to
asymmetric daily forcings (Dai et al., 1999). The authors suggest that analyzing land use
9
changes to see if DTR has changed due to surface evapotranspiration may be useful and
that urbanization may play a role in changing DTR.
Lauritsen and Rogers (2012) sought to confirm the findings of Dai et al. (1999)
using a dataset covering the entire United States and showed regional details of the longterm downward trend of DTR. They investigated the DTR variability over several regions
in relation to moisture variables and atmospheric teleconnections. They found that the
annual increase in T-min has been exceeding the annual increase of T-max across most of
the United States, especially since 1950.
Lauritsen and Rogers (2012) used high resolution gridded data from the Climate
Research Unit (CRU) 2.1 dataset which shows land surface climate parameters dating
from 1901 to 2002 covering the United States and parts of Canada and Mexico. Monthly
T-max and T-min, DTR, and precipitation were used and converted to annual values.
Version 3 of the Global Historical Climatology Network was used in conjunction with
the CRU 2.1 dataset for comparison purposes. Cloud cover data were assembled from
various weather stations across the United States dating from 1891 to 1987. Cloud cover
data from 1987 were obtained from the NCDC which produced hourly data from 1988 to
1996. To ensure a complete set of could cover data to 2002, sunshine reports from the
CRU 2.1 dataset were used to serve as another measure of cloud cover.
To obtain estimates of soil moisture, Lauritsen and Rogers used the Palmer
Drought Severity Index (PDSI). The PDSI ranks from extremely dry (negative values) to
extremely moist (positive) on its scale of measurement. Nine different teleconnection
indices represented the atmospheric circulation including the North Atlantic Oscillation,
10
Arctic Oscillation, North Pacific Index, Southern Oscillation Index, Atlantic Multidecadal Oscillation, Pacific Decadal Oscillation, Niño-3.4 SST Index, the Cold Tongue
SST Index, and finally the Tropical Pacific SST Index.
Lauritsen and Rogers (2012) regionalized DTR variability and identified pinpoint
predictor variables within each region that explain DTR fluctuations. Regionalization was
accomplished using principal component analysis to maximize the variance explained
and a varimax rotation strategy to regionalize all of the patterns of DTR variance across
the United States. Five rotated principal component patterns were identified across the
United States with centers on a narrow winding area of the Midwest and the northeastern
states (RPC1), the southwestern United States (RPC2), the south-central United States
(RPC3), extending from eastern Texas through Louisiana and north to Tennessee, the
Great Plains (RPC4) and the western United States. (RPC5).
Within each of the five RPC regions, mean annual values of T-max, T-min, and
DTR were found along with average values of cloud cover, soil moisture, and
precipitation. A stepwise multiple linear regression was then applied on the variables to
identify the best combinations of predictors that explain the most variance of the
temperature variables (T-min, T-max, and DTR). All five regions found by the RPCA,
excluding the southwestern United States, showed highly significant downward DTR
trends between -1.2°C and -1.9°C over the last century (Lauritsen and Rogers, 2012). The
northeastern United States exhibited a statistically significant upward trend in Tmin and
the DTR fell below 12°C after 1965. Increased cloud cover, precipitation, and soil
moisture all influence this DTR decline. Cloud cover alone explained most of the DTR
11
variance in this region due to the upward 20th century trend (Lauritsen and Rogers, 2012).
The stepwise multiple linear regression indicated that cloud cover explained 63.2% of the
DTR variance while T-min variability was 21% due to cloud cover. Less than 5% of the
variability of T-max was explained by cloud cover. Soil moisture and precipitation
explained very little of the DTR variance in this region.
The southwestern United States did not show a DTR decline over the 20th century
because upward trends occur in both Tmin and T-max leading to an unchanged DTR.
Two specific periods had large DTR variance. From 1946 to 1956, low soil moisture and
precipitation led to very dry conditions in the southwestern United States. T-max was
elevated which actually led to an increase in DTR (Lauritsen and Rogers, 2012). In
contrast, moist conditions occurred from 1982 through 1986 producing low T-max values
and decreased DTR. Throughout the entire century, 39.1% of the DTR variance is due to
soil moisture and precipitation in the Southwest. The Atlantic Multi-decadal Oscillation
produced 55% of the T-max variance during the century. Lauritsen and Rogers
summarized that precipitation is accountable for reducing the daily T-max while
simultaneously increasing T-min, resulting in a large impact on DTR. Soil moisture
inhibited both T-max and Tmin and resulted in a reduced DTR impact.
The south-central United States had higher values of T-max until 1957 that
produced higher DTR (Lauritsen and Rogers, 2012). Increased T-max was especially
associated with a 1950s drought along with low cloud cover, precipitation, and soil
moisture during this dry period. Following 1957, T-max and DTR took a dramatic
12
downturn, occurring along with an increase in cloud cover along with precipitation and
soil moisture. These variables accounted for 66.5% of the DTR variance.
The Northern Plains had a significant decrease in annual DTR through the 20th
century (Lauritsen and Rogers, 2012). This is in large part due to the sizeable upward
trend in T-min coupled with only a small T-max increase. The 1930s drought reduced
soil moisture increasing the T-max and also DTR. 60.2% of the variance of DTR in this
region was explained by cloud cover, precipitation, and soil moisture.
An area in the western United States had a DTR peak in the 1930s followed by a
steady decline. Low soil moisture and precipitation led to the peak in DTR producing
high values of T-max. However, daily T-min has steadily increased, explaining the DTR
decrease following the 1930s. Soil moisture and precipitation explained most of the Tmax in this region while cloud cover explained 22% of the variance in T-min (Lauritsen
and Rogers 2012). A combined effect of all of the moisture variables (cloud cover, soil
moisture, and precipitation) explained 57% of the DTR variance in the western United
States.
Lauritsen and Rogers (2012) produced important United States regional
information on DTR occurring throughout the 20th century. All 5 of the DTR regions
exhibited an increase in T-min throughout the century while T-max varied regionally.
Prolonged sub-periods of wet and dry conditions played a large role in T-max and DTR
variance. Variations in the moisture parameters cloud cover, soil moisture, and
precipitation were all additional causes of DTR changes. The DTR decline was most
noticeable after 1950 when cloud cover started an upward trend. By regionalizing the
13
United States, it was shown that different areas were succumbing to different
combinations of moisture parameters and teleconnections to produce the falling DTR
trend over the last half of the 20th century. Their research helped to further pin point
localized causes of changing DTR.
14
Chapter 3: Data and Methodology
3.1. Data
Air temperature data are obtained from the Global Historical Climatology
Network Monthly (GHCNM) dataset. These data contain monthly average T-max and Tmin values for many United States weather stations within each state. This expansive data
set covers the years from 2012 to years in the 1890s and early 1900s, depending on the
station. It was intended to start the analysis using station data in 1895. Most stations have
some missing values because either the station started reporting after 1895 or the station
missed reports for months or years for other reasons. If only one year of data are missing,
the adjacent summer values are used to form an average for the missing year. In all other
cases, a station can be defaulted to a missing value and will not be further incorporated in
the study. This use of station data records differs from the gridded CRU2 data, used by
Lauritsen and Rogers (2012), which only extended to 2002.
A unique dataset of summer air temperatures and precipitation quantities has been
gathered from the NCDC and organized for a case study of the summer of 2012 along
with climatological averages. Temperatures for this dataset are comprised of either first
order weather stations reporting on individual National Weather Service office websites,
Automated Surface Observing System (ASOS) stations which are part of the United
15
States Historical Climatology Network (USHCN) version 2.5, or GHCN data
(www.ncdc.gov/cag/). These data are available for the Contiguous United States,
statewide, Climate Divisions, Climate Regions, National Weather Service Regions, and
Agricultural Belts that come from the United States Climate Divisional Database, which
extend from 1895 to the present. They are used in an analysis of historical droughtsummer comparisons to the drought of 2012. Data for May, June, July, and August are
averaged to make summer mean T-max and T-min values. These data will also be used
in determining DTR by taking the T-max value at a location minus the T-min value at
that same location for each respective month.
Palmer Drought Severity Index, or PDSI, data are used and are available for
1895-2012. These data are collected from the National Climatic Data Center of the
National Oceanic and Atmospheric Administration (NOAA)
(http://www.ncdc.noaa.gov/sotc/drought). The PDSI is a numerical index value that
measures water content in a soil layer based on precipitation and air temperature data for
the current and past months. A mean summer PDSI value will be obtained for each
summer for each of the 344 United States climate divisions, following the procedures
described for temperature. The climate division PDSI data are used in the rotated
principal component analysis to obtain unique regional PDSI variability patterns.
Precipitation data are also from the climate division dataset and these data are averaged
over the PDSI-regions to help further identify moist and dry summers.
16
3.2. Methods
Spatial and temporal patterns of United States soil moisture data are created using
a Principal Component Analysis (PCA) on the PDSI dataset correlation matrix. PCA
reduces the dimensions in data, leaving only its basic spatial component modes of
variability. The principal components are the key modes in explaining the majority of the
dataset variance. The eigenvectors initially obtained by the PCA maximize the variance
explained in each component of the analysis such that all eigenvectors following the first
are orthogonal. Due to this orthogonality constraint, second and higher eigenvectors do
not show any unique patterns of spatial variability that may be present in the dataset. To
resolve this issue, the eigenvectors are subjected to a rotation procedure that transforms
them into a nonorthogonal linear structure. This leads to compact patterns that regionalize
the variance in the dataset (Lauritsen and Rogers, 2012) although the patterns may
overlap spatially and are not orthogonal.
The Rotated Principal Component Analysis (RPCA) identifies 10 United States
regions (components) having unique patterns of PDSI spatial and temporal variability.
The regional centers defined by the RPCA are identified by using the component
numerical loadings, which are similar to correlation components with values of L
between -1 and +1. Each component will have a unique clustered set of grid points over
the United States where the loadings reach critical large values. Loadings are similar to
correlation coefficients and in this case L > 0.71 is chosen since L² ≈ 0.50 (50%)
indicating that no other (among 9 remaining) RPCs would explain as much of the climate
division’s variance as did the one having L > 0.71. Of the 10 significant regions
17
identified by the analysis, 4 of them were considered key regions of interest for further
analysis in this thesis.
The wettest and driest summers were then identified from the time series scores of
the rotated principal components (RPC) for the 4 selected regions. The time series scores
for each RPC generally have values between -3 and +3, much like standard deviations,
and remain orthogonal to each other as part of the rotation procedure. The scores for each
region are highly correlated to the original PDSI values occurring at the grid points in
those regions. The mean DTR, T-max and T-min values (along with standard deviations)
are obtained for these wettest and driest summers using the GHCNM station data lying
within each of those regions. The temperature/DTR means for the two sets of cases, wet
versus dry, are compared and tested for statistical significance using a two-tailed t-test.
Concluding is work is a case study of 2012 temperature and DTR conditions relative to
the composite means of historic past.
18
Chapter 4: Results
4.1. Rotated Principal Components of summer PDSI
Summer soil moisture indices (PDSI) from all United States climate divisions for
1895-2012 were subjected to a rotated principal component analysis (RPCA) that
produced 10 regions (components) exhibiting relatively unique soil moisture variability.
Four of these regions will be discussed in detail below, focusing mostly on the regions
east of the Rocky Mountains that explained the greatest amounts of dataset variance. The
spatial component of the RPCA is the loadings, which numerically express how much of
the PDSI variability in each climate division is represented in a particular rotated
principal component (RPC). The variability of each climate division was considered to be
part of a particular individual RPC if it had a loading L > 0.71. The time series of the
RPC scores are shown below. The process of rotation of principal components
orthogonalizes all of the RPC time series but they are spatially uncorrelated among
themselves.
The RPCA produces 10 regions exhibiting unique moisture variability from 18952012, each uncorrelated to the variability in the other components. These are shown in
Fig. 4.1. The cumulative explained variance for the 10 patterns is 67.2%. The component
regions evaluated further here are RPCs 1, 2, 3, and 5 with component 4 for the Pacific
Northwest United States being left out of further analysis along with lesser components
19
6-10 that explain much less dataset variance. RPC1 centers on Illinois, Indiana,
Kentucky, and Ohio (Fig. 4.1). RPC2, which isolated the upper Midwest, contains Iowa,
Minnesota, Nebraska, North Dakota, South Dakota, and Wisconsin. Alabama, Georgia,
North Carolina, and South Carolina establish the southeastern region known as RPC3.
Finally, RPC5 includes Kansas, Oklahoma, and Texas and is generally called the
Southern Plains. The 4 RPCs explained 20.4%, 11.6%, 9.3%, and 4.7% of the total
dataset variance, respectively. The number of loadings (L) >0.71 was relatively limited in
each RPC and as such, Fig. 4.1 shows the climate divisions where L is as low as L= 0.6.
Fig. 4.1: United States Climate Divisions with the highest RPC loadings on each of the
first 5 summer PDSI components. The map symbols represent the core of climate
divisions with the highest loadings (> 0.6) for a particular RPC. Each RPC is represented
by a different symbol or shading to tell them apart. Any climate division where loadings
failed to reach 0.6 on any RPC are represented as small dots. Climate divisions
represented with a cross (+) had loadings in excess of 0.6 but occurring on a lower ranked
RPCs (6 through 10).
20
Fig. 4.1 illustrates that regional centers of unique spatial and temporal PDSI
variability occur across many parts of the United States. This area contains valuable farm
and graze land that are very important to the well-being of the economy and health of the
United States. While the areas would be prone to wet summers most are also subject to
drought conditions. Fig. 4.1 shows the spatial locations of the central core of PDSI
centers while other key features in each region’s long-term time series represent the moist
and dry events occurring in history. The remainder of this section focuses on time
variability associated with RPCs 1, 2, 3, and 5 representing respectively the Ohio River
Valley, the upper Midwest, the southeastern United States, and the Southern Plains.
Figs. 4.2 through 4.5 represent time series of the RPC scores based on the PDSI
for the summer months of June, July, and August over the last century (1895-2012) at the
4 regional centers. The RPC scores for each region are highly correlated to the regional
mean PDSI but their scores are orthogonal with every other region. PDSI ranges on a
numeric scale from 4.0 (extremely moist) to -4.0 (extremely dry) but the RPC time series
scores range from -3 (dry) to +3 (moist), effectively representing standard deviations.
Values greater than 0 are considered moist conditions while values less than 0 are
considered dry.
Fig. 4.2 represents the time series of PDSI through RPC1 for the Ohio River
Valley. According to the time series, RPC1 experienced drier conditions prior to 1955.
Some of the drier summers included 1901, 1934, 1936, 1941, 1953, and 1954 while wet
summers occurred in 1927 and 1950. Following 1955, higher soil moisture conditions
were prevalent. The driest summers included 1991 and 2012 but they are apparently not
21
as dry as the just mentioned pre-1955 summers. This variability is represented around the
least-squares linear trend line associated with the time series that indicates that PDSI on
average is on the rise in this region.
Fig. 4.2: Time series of RPC1 scores representing the orthogonalized PDSI values for the
Ohio River Valley.
Fig. 4.3, for RPC2 contains the upper Midwest and eastern Northern Plains states,
and has a similar looking PDSI trend in its scores as RPC1. Dry conditions dominated the
first half of the 20th century followed by moist conditions during the second half into the
beginning of the 21st century. From 1910-1941, only 6 summers had moist scores above
22
zero with only one such summer during the dry summers of 1929-1941. The summers of
1931 and 1934 are among the driest, along with 1988 and 1989. The summer with
greatest soil moisture is 1993, when persistent heavy rains in the upper Mississippi and
Missouri river basins contributed to widespread severe flooding farther down the river
basins around eastern Missouri (Wahl et al., 1993; Changnon, 1996; Eash, 1997).
Fig. 4.3: Time series of RPC2 scores representing the orthogonalized PDSI values for the
Upper Midwest.
The time series of RPC3 represents PDSI variations in the southeastern United
States (Fig. 4.4). RPC3 also shows a trend towards increasingly moist soil conditions
23
through the 20th century although the trend is not as strong as in RPCs 1 and 2. A notable
feature is the extended series of moist summers from 1957 through 1976 as well as the
recent series of dry summers after 2005. It was also persistently dry from 1950-1956. The
region was not particularly affected by excessively dry conditions in the 1930s and most
of the driest summers do not correspond to those of RPC1 or 2, including 1988 and 2012.
Overall, it would appear that dry summers have occurred more frequently in recent
decades than occurred in RPC1 and 2. This reduces the size of the upward trend despite
many moist summers with large positive scores.
Fig. 4.4: Time series of RPC3 scores representing the orthogonalized PDSI values for the
southeastern United States.
24
Fig. 4.5 represents RPC5 and the southern Plains of the United States. The PDSI
scores in this region were the only one of the four to show a trend towards drier soil
conditions although the trend is negligible. The 1930s drought was severe from 19331940 but the southern Plains were particularly dry in the 1950s drought from 1952-1956
(Fig. 4.5). These droughts are followed by some of the wettest years in the record. No
major drought occurs again until 1997 and this region is particularly dry in 2011 and
2012. The wettest summers are 1987 and 2007.
Fig. 4.5: Time series of RPC5 scores representing the orthogonalized PDSI values for the
Southern Plains.
25
4.2. Regional T-max, T-min, and DTR, 1895-2012
A major goal of this work is to analyze how DTR has changed throughout the last
century in the United States by focusing exclusively on regions that have the greatest
variability in soil moisture. Previous studies such as Dai et al. (1999) and Lauritsen and
Rogers (2012) have shown decreasing DTR with time across the United States since
1950. By only using summer temperature data, this phenomenon is investigated and
compared for each of the RPC regions. This section will discuss how regional average Tmax and T-min have fluctuated throughout the last century based on available GHCNM
station data. Time series show regional temperature changes are linked to the decreasing
DTR trend. Summer averaged T-max and T-min time series are presented while standard
deviations about the regional mean among the stations are also plotted. This indicates the
degree of variation in the temperatures and DTR among the stations in each region in
each summer.
Figs. 4.6-4.8 depict the times series of mean T-max, T-min, and DTR for stations
of the Ohio River Valley (RPC1) from 1895 through 2012. Fig. 4.6 shows the regional
average T-max over the last century. Regional T-max has large fluctuations but overall
exhibits a downward trend over the span of the century. According to the T-max trend
line, mean T-max decreases about 0.5°C over the period of record. There are a few years
that stand out on this T-max time series. First, the year 1915 has a regional T-max of
approximately 26°C, the lowest for a summer over the entire span of the regional dataset.
1936 also stands out on the time series as the warmest summer regional T-max for this
region. This year was very dry summer mostly associated for a heat wave and drought
26
that took place in the Midwest. Following these two dates, regional average T-max
became progressively lower based on the trend line in Fig. 4.6. A span of consistently
unusually low T-max summers occurs from about 1960-1976 followed by more
interannual variability after 1976 and higher T-max.
The standard deviations of the summer T-max values (Fig. 4.6) among the
stations of the Ohio River Valley is relatively large in the early 20th century but becomes
much lower, especially following the mid-1980s. Most stations chosen in this analysis
had temperature data that started as close as possible to 1895, while a few stations started
data collection following that date. Overall missing data tended to occur early in the 20th
century as observation stations were developing. These factors may partly be affecting
the temporal variation in the standard deviations throughout Fig. 4.6.
Figure 4.7 illustrates the progression of regional T-min averages in the Ohio River
Valley. The time series clearly shows that regional T-min for this area has increased over
the last century. This positive trend line increases about 0.7 °C through the period of
record. The summer of 1926 exhibited the lowest T-min than any other summer in the
record, just as its corresponding T-max value (Fig. 4.7) had been very low. Temperatures
decreased to an average of under 14°C. Comparing Fig. 4.2, 1926 was an average year
based on precipitation in this region according to its PDSI score. The highest average Tmin in this region occurred more recently in 2010 when average T-min were over 18°C.
T-min were consistently relatively high into the 1930s but starting around 1985, the Tmin values begin to rival and even exceed those of the 1930s. T-min were consistently
low in the 1960s. The summer of 2010 was dry based on PDSI (Fig. 4.2), but not very
27
28
28
88
Fig. 4.6: Regional Averaged T-max for RPC1.
28
extreme. The standard deviations around the regional mean summer T-min is much more
evenly and randomly distributed in Fig. 4.7 than occurs for T-max in Fig. 4.6. The
standard deviations about the station-based regional T-min values are not as consistently
high in the early decades of the 20th century, as it had been for T-max. The T-min
standard deviations show little trend over the period of record.
The DTR time series for region RPC1 (Fig. 4.8) shows that average summer DTR
is on the decline in the Ohio River Valley over the last century. Despite the century-long
downward trend (Fig. 4.8), DTR increased from 1895 onward into the peak DTR
summers of the 1930s. The peak DTR in the 1930s corresponds to the very dry and warm
(high T-max) summers of this decade. Four summers, 1930, 1936, 1988, and 2012 appear
as years where DTR was much larger than others (Fig. 4.8), with the latter two summers
having somewhat lower DTR than 1930 and 1936, apparently in keeping with the
decreasing long-term trend line. The very dry summer of 1936 (Fig. 4.2) exhibited the
largest DTR overall. The regional average DTR was slightly over 15°C, with the average
DTR over the entire period of record being just under 12.5°C. Overall, Ohio River Valley
DTR is much lower than the rest of the period of record after 1965, with the exception of
the drought summers of 1988 and 2012. The standard deviation of the DTR among the
regional stations also steadily declines through time, seemingly mirroring the downtrend
noted in the T-max time series (Fig. 4.6). It appears to become abruptly lower after 1952.
Figs. 4.9-4.11 show the time series of T-max, T-min, and DTR for the upper
Midwest and portions of the eastern Northern Plains (RPC2). Based on similarities
between Figures 4.2 and 4.3, one would expect the variability in RPC2 to broadly
29
30
Fig. 4.7: Regional Averaged T-min for RPC1.
30
31
Fig. 4.8: Ohio River Valley DTR.
31
resemble that of RPC1.
The upper Midwest has T-max variability around a steady non-trending regional
average of approximately 26.9°C for the period of record (Fig. 4.9) as opposed to the
downtrend in Fig. 4.6. The period from 1930-1943 exhibits unusually high T-max values.
The summers of 1915 and 1992 exhibited lower temperatures than the average. The
summer of 1915 was one of the wettest in Figure 4.3 while 1992 was the summer after
the eruption of Mt. Pinatubo when the United States overall had its third coldest summer
in 77 years (geography.about.com/od/globalproblemsandissues/a/pinatubo.htm) . RPC2 also had
warmer than average temperatures in 1936 and 1988. These years produced almost
identical regional average high temperatures. Both summers were among some of the
driest summers on record in this region (Fig. 4.3). The trend line established in Figure 4.9
depicts a very flat but slight down turn representing a decreasing regional T-max average.
The standard deviation about the regional mean T-max values among stations is higher
early in the 20th century than in the second half of that century.
Regional average T-min for RPC2 follows a clear warming trend throughout the
last century continuing into present day (Fig. 4.10). The trend starts around 13°C in 1895
and has increased to almost 14.25°C in 2012. The warming of regionally averaged T-min
was also present in RPC1 (Fig. 4.7) but it was not as large a trend as in RPC2. Within the
last 30 years, especially after 1982, summer T-min has on several occasions been among
the highest on record. At the same time however, the summers of 1985, 1992, 2004, and
2009 are among the lowest T-min. This mimics the behavior of PDSI in Fig. 4.3 showing
that summer soil moisture levels are on the increase and could help explain why T-min is
32
33
Fig. 4.9: Regional Averaged T-max for RPC2.
33
increasing with time. Cloud cover and moisture have been found to inhibit cooling.
Standard deviations about the mean T-min among the stations of the region are
consistently higher early in the period of record until the early 1950s, after which many
summers have lower standard deviations, especially after the early 1980s.
Fig. 4.11 depicts the extent of DTR in the upper Midwest and eastern Northern
Plains for the last century. When you combine the long-term steady regional T-max and
notable warming of T-min in this region, the DTR exhibits a decreasing trend (Fig. 4.11)
that, at about 1.1°C, is almost as large as the T-min upward trend. There is a clear decline
in DTR from the beginning of the last century to present day albeit in a somewhat steplike fashion. For example, DTR appears highest from about 1910-1935, then it declines in
value somewhat until about 1990, followed by some of the lowest DTR values following
1990. The downward step after 1935 is associated with a large decrease in T-max (Fig.
4.7) while coinciding T-min (Fig. 4.8) does not decrease. The stepdown in DTR
following 1989 occurs as T-max remains relatively low (Fig. 4.7) but T-min has some of
its highest values (Fig. 4.8).
Summer 2012 had a relatively large DTR amidst a preceding 20+ year tendency
for low summer DTRs. Most of the extreme DTR summers depicted in Fig. 4.11 are also
summers that are particularly wet or dry. The summers of 1910, 1933, 1936, and 1988 all
rank within the driest summers on record also shown in Fig. 4.3. The dry summer of 1976
does not fall within that category. Summers with wet conditions such as 1951, 1993, and
2010 also were years where DTR also exhibited the smallest range within the last
century. The standard deviations appear to mirror the same low frequency behavior with
34
35
Fig. 4.10: Regional Averaged T-min for RPC2.
35
36
Fig. 4.11: Upper Midwest and eastern Northern Plains DTR.
36
high deviations early in the record and some of the lowest after 1990 (Fig. 4.11).
The southeastern United States, RPC3, shows some similarities to temperature
and DTR patterns in RPC1 and 2, but it has its uniquely different characteristics (Figs.
4.12-4.14). Regional averaged T-max exhibits an overall non-existent trend amidst
oscillating summer temperatures (Fig. 4.12). The highest summer T-max values occur in
1952, 1954, 2010, and 2011. The summer of 2011 essentially represents the highest Tmax, breaking records from the 1950s. Summer T-max in the Southeast is particularly
below normal from 1960-1976. From that period onward, T-max is on a warming trend
throughout the second half of the century. Standard deviations about the mean T-max
values are also relatively low through the middle 20th century indicating the consistency
of the low temperatures among the regional GHCNM stations. Fig. 4.4, shows a period in
the 1960s that carried over into the 1970s where soil conditions were very moist. These
summers ranked within the wettest on record according to averaged PDSI values. This
can explain the cooled regional averaged T-max for RPC3 as cloud cover or frequent
precipitation can suppress T-max values (Lauristen and Rogers, 2012). Rogers (2012)
discusses how low summer temperatures in the Southeast from 1961-1976 are due to a
combination of some cloudy summers and some sunny summers with dominant polar air
masses. Following these years, drier conditions developed producing warmer T-max. The
trend line slightly picks up this warming trend with very small positive slope (Fig. 4.12).
T-min time series for the southeastern United States (Fig. 4.13) summer T-min
during the last century had some similar characteristics to the T-max variability in Fig.
4.12. RPC3 T-min leading into the 21st century also shows a warming trend. The T-min
37
38
Fig. 4.12: Regional Averaged T-max for RPC3.
38
time series mimics the wet summer characteristics in the 1960s and 1970s with very low
minimum temperatures. Starting in 1967, there is a clear warming trend in T-min. The
trend line after 1967 would likely be more pronounced than that shown for the entire
period of record. The summer of 2010 in RPC3 really stands out on the time series as
having the warmest regionally averaged T-min on record, breaking the record set earlier
in 2005. Looking at the summer of 2010 of RPC3 in Fig. 4.4, 2010 was dry, but doesn’t
register as one of the driest on record. The summer T-min standard deviation about the
mean based on the stations representing the southeast shows a relatively unique gradual
increase through the period of record.
Because the warming trend in T-min of RPC3 is larger than the near-zero trend in
T-max, DTR will still show a decreasing trend. This is shown in Fig. 4.14 based on the
overall regional DTR trend, the mean southeastern DTR goes from 11.5°C in 1895 to
about 11.24°C by the beginning of the 21st century. The DTR from 1960-1976 is not
particularly notable, these summers had low values in both T-max and T-min.
Particularly low DTR summer values only occur between 1989 and 2005. Large DTR
occurs in 1925-1932 and into the mid-1950s. Comparing Figure 4.14 with 4.4 reveals
some answers to the behavior of DTR over the time span of data in RPC3. Drier summers
in the beginning of the 20th century (Fig. 4.4) (e.g. 1911, 1914, and 1925) correspond to
summers of larger DTR (Fig. 4.14) peaking in the summer of 1955 which was also a dry
summer. The wet period in the 1960s and 1970s start the decline in DTR (Fig. 4.14). The
smallest DTRs occurred in the more recent summers of 2003 and 2005 where both
summers rank as the first and second wettest summers in RPC3 (Fig. 4.4). The latter half
39
40
Fig. 4.13: Regional Averaged T-min for RPC3.
40
41
Fig. 4.14: Southeastern United States DTR.
41
of the 20th century leading into the 21st century seem to be influencing the decline in DTR
more than the dry summers that occurred in the beginning of the 20th century.
Finally, Figs. 4.15-4.17 depict the T-max, T-min, and DTR time series for the
Southern Plains (RPC5). Fig. 4.15 shows that summer T-max exhibits a warming trend
over the 20th century into the 21st century. This is the only region of the 4 analyzed here
that has a clear upward T-max trend. The trend magnitude is roughly 0.6°C over the
period of record. The summer of 2011 had the highest T-max over the period of record,
exceeding those of 1934 and 1936. The summers exhibiting the highest averaged T-max
occurred in the years of 1934 and 2011 and rank as dry summers in Fig. 4.5. This would
explain the very warm temperatures during these summers. During dry conditions, cloud
cover is usually at a minimum. The driest summer in RPC5 occurred in 1956 but didn’t
produce the warmest summer of the record. T-max during this summer was ~ 2°C lower
than that of 1934 and 2011. The cooler summers of 1915 and 1992 can be found on Fig.
4.5 as some of the wettest summers on record. The seasonal standard deviations of T-max
among the regional stations is highly variable through the period of record, especially in
the first 3 decades.
The time series of regionally averaged T-min in the southern plains (Fig. 4.16)
almost identically mimics the behavior of T-max in Fig. 4.15. T-min shows the same
warming trend from the beginning to end of the dataset having a magnitude of about
0.7°C, slightly larger than that for T-max. The two warmest summer T-min occur in the
same years as the T-max in 1934 and 2011. The summers of 1915, 1920, and 1992 show
the lowest regionally average T-min. Figure 4.15 also shows that T-max during these
42
43
Fig. 4.15: Regional Averaged T-max of RPC5.
43
44
Fig. 4.16: Regional Averaged T-min for RPC5.
44
summers were abnormally cool as well. Similar to T-min exhibited in the other RPCs
(Figs. 4.7, 4.10, and 4.13), a long-term trend in T-min warming is shown (Fig. 4.16).
Large annual T-min station standard deviations occur early in the record.
The time series of DTR for RPC5 is relatively unique among the 4 regions in that
is has a near zero trend (Fig. 4.17). DTR in the Southern Plains does not seem to be
changing like the other regions. Even though the trend line does not show much visually,
the slope is just slightly negative. This means that DTR is in fact decreasing over the
entire 20th century into the beginning of the 21st century for all the 4 regions studied in
this analysis supporting other works based on this topic. The largest DTR of RPC5 took
place in the summer of 1936 and recent high DTR values occur in 2011 and 2012.
Although 1936 was not one of the driest summers on record for this region, PDSI values
indicate that this summer experienced dry soil conditions. The smallest DTR occurred in
1992 which has been established as one of the wettest and coldest summers on record
(Fig. 4.5). The wettest and driest PDSI values for each region will be discussed in the
next section.
45
46
Fig. 4.17: Southern Plains DTR.
46
4.3. Wettest vs Driest summers: Regional T-max, T-min, DTR, and PDSI
Previous studies have evaluated how diurnal temperature range (DTR) fluctuates
for given dry or moist soil conditions (Karl et al., 1986; Dai et al., 1997; Dai et al., 1999).
Lower DTR is linked to years of higher precipitation compared to years with lower PDSI
values when DTR is larger. In this section, T-max, T-min, and DTR are calculated and
compared among the 12 wet and dry summer cases for each region. The 12 wettest and
driest summers are determined based on the actual summer mean soil moisture (PDSI)
index value for the group of climate divisions forming the region. The highest or lowest
PDSI summers generally correspond to the highest and lowest PDSI scores shown in
Section 4.1 (Figs. 4.2-4.5) although there are some differences. The selection of the 12
most moist and dry summers did not include any prior to 1910 as fewer stations were
available in that era that reported temperature and were limit the reliability of the
analysis.
Even numbered figures between Figs. 4.18-4.25 show the 12 wettest summers
versus the 12 driest summers in each respective RPCA region. Wet versus dry was
determined by summer soil moisture PDSI values. Odd numbered figures portraying
PDSI show wet summers with a blue bar while dry summers are indicated by an orange
bar. In these figures, T-max (darker) and T-min (lighter) are shades of green for the 12
wettest summers while DTR is represented by a blue dot. T-max and T-min for the 12
driest summers are represented with red and orange bars while their corresponding DTR
is shown by yellow dots.
The Ohio River Valley (RPC1) 12 highest and lowest PDSI summers are
47
48
Fig. 4.18: The 12 summers with the highest (blue) and lowest (red) PDSI values from 1910-2012 for the Ohio River Valley (RPC1).
48
represented by Fig. 4.18. Three quarters of the driest summers occurred prior to the peak
dry summer of 1954 (PDSI of ≈ -2.3 (Fig. 4.18)) with a period in the 1930s containing
the majority of the lowest soil moisture. The more recent year of 2012 was relatively dry
ranking within the dozen driest PDSI scores with a value similar to the dry period that
occurred in the 1930s. The majority of the wettest summers occurred in the latter half of
the 20th century leading into the more recent year of 2009. The two wettest summers
occurred in the first half of the 20th century in the years 1927 and 1950. The summer of
1950 was the wettest summer of record with an average PDSI score for the region
topping at ≈ 2.4.
Fig. 4.19 shows the twelve wettest and driest summers within the Ohio River
Valley (RPC1) throughout the last century in terms of T-max, T-min, and DTR.
Averaged summer T-max is clearly higher during summers that were dry, with
magnitudes all exceeding T-max occurring in any of the wet summers. The same is true
for DTR with magnitudes in excess of 13°C (right-side Y-axis in Fig. 4.19) in every dry
summer and values below that level restricted to wet summers. Based on these averages,
T-max was almost 3°C warmer during drier summers than wet, which is over 5°F.
The Ohio River Valley became moister over the period of record (Fig. 4.2) and Tmax declined during that period (Fig. 4.6). Fig. 4.19 confirms that dry conditions are
linked to hot summer T-max while moist soils are linked to cooler summers. T-min did
not show as much variation as between wet and dry years as T-max, but there was still a
slight difference. Average T-min was, on average, just over 1°C higher in dry summers
compared to wet conditions. DTR displays an increase slightly above 2°C for drier
49
summers compared to wetter years. T-max and DTR values in 1988 and 2012 rival those
in magnitude of the early 20th century droughts only exceeded by 1930 and 1936. The
results in Fig. 4.19 indicate that both T-max and T-min are higher in dry conditions in the
Ohio River Valley, more so for the former. The higher DTR in dry summers agrees with
findings in Dai et al. (1999) and Lauritsen and Rogers (2012), but the higher T-min in dry
summers is not expected. Higher T-min may occur because of stronger daytime solar
heating in dry summers that helps keep T-min values higher overnight.
The PDSI values of the wettest and driest summers of the upper Midwest are
represented by the time-magnitude plot in Fig. 4.20. One similarity RPC2 has with RPC1
is the extremely dry summers that occurred in the 1930s. This means there was a vast
area of the United States experiencing drought-like conditions for a long period of time.
As with the Ohio River Valley, most of the driest summers in terms of PDSI occurred in
the first half of the 20th century. The second half is characterized by summers with
extremely moist soil conditions. The summer of 1993 exhibited soil moisture conditions
with an average PDSI value of ≈ 2.6 (Fig. 4.20). This ranks wetter than any summer that
occurred in the Ohio River Valley. The driest summer occurred in 1934 with a PDSI
value of ≈ 2.7. This is also drier than any PDSI value occurring in any of the driest
summers of RPC1. The summers of 1931, 1934, and 1936 registered as extremely dry
summers in both the upper Midwest and the Ohio River Valley. The dust bowl is very
noticeable in both regions. The summers of the 1930s rank among the driest for each
region respectively. The summer of 2012 was particularly dry for both regions. This
summer will individually be discussed in Chapter 5.
50
RPC1 Wettest vs Driest Summers: T-max, T-min, DTR
15.5
15
14.5
14
25
13.5
13
20
12.5
12
15
11.5
2012
2009
2008
2004
1996
1993
1990
1988
1981
1974
1973
1964
1958
1954
1953
1950
1941
1936
1934
1931
1930
1927
11
1925
10
1914
51
Temperature (Celcius)
30
Diurnal Temperature Range (Celcius)
35
Year
Tmin-Dry
Tmax-Dry
Tmin-Wet
Tmax-Wet
DTR-Dry
DTR-Wet
Fig. 4.19: T-max, T-min, and DTR (all °C) during the 12 wettest and 12 driest summers since 1895 in the Ohio River
Valley (RPC1). T-max is represented by green (wet) and red (dry) vertical bars; T-min by light green (wet) and orange
(dry) and DTR is represented by blue dots (wet) and yellow dots (dry) respectively.
51
52
Fig. 4.20: Same as Fig. 4.18 but for the upper Midwest (RPC2).
52
The upper Midwest (RPC2; Fig. 4.21) has similar characteristics to those
discussed above about the Ohio River Valley. As with RPC1, T-max is higher on average
during summers that were abnormally dry. In RPC2, T-max is over 2°C warmer in dry
summers than in abnormally wet summers. The largest T-max (dark green bars) in some
wet summers exceed T-max of some of the dry summers (red bars) in the upper Midwest
(e.g., 1983). T-min does not seem to show much variation between dry and wet summers.
T-min is less than a half degree Celsius warmer during the driest summers relative to the
wet cases. This would suggest that the DTR difference between the driest and wettest
summers in RPC2 can mostly be linked to the increase in T-max during the driest
summers. The T-max and DTR of summer 1988 rivals in magnitude the values for the
1930s and earlier drought summers, but the same does not hold for summer 2012 and
some of the other dry summers. Still, DTR in the driest summers exceeds that of any of
the 12 wettest summers (Fig. 4.21).
The most extreme summers in the southeastern United States (RPC3) are largely
different than those of any other region. Fig. 4.22 shows the driest summer PDSI values
are confined to both the beginning and end of the 20th century into the beginning of the
21st century. The driest soil moisture conditions in RPC3 occurred in the summer of 1914
with a regionally averaged PDSI value of ≈-2.1 (Fig. 4.22). This PDSI value is not quite
as negative as in the driest summers of RPC1 or RPC2. The majority of the wettest soil
conditions occurred in the second half of the 20th century leading into the 21st century
much like RPC1 and RPC2. The summer of 2003 ranked as the wettest summer of the
dataset registering a regional average PDSI value of ≈2.6 (Fig. 4.22). This is very
53
RPCA2 Wettest vs Driest Summers: T-max, T-min, DTR
16
15.5
15
14.5
25
14
20
13.5
13
15
12.5
10
12
11.5
5
11
Year
Tmin-Dry
Tmax-Dry
Tmin-Wet
Tmax-Wet
Fig. 4.21: Same as Fig. 4.19 but for the upper Midwest (RPC2).
54
DTR-Dry
DTR-Wet
2012
2010
2008
1994
1993
1989
1988
1986
1984
1983
1979
1977
1956
1951
1945
1944
1940
1937
1936
1934
1933
1931
10.5
1915
0
1911
54
Temperature (Celcius)
30
Diurnal Temperature Range (Celcius)
35
55
Fig. 4.22: Same as Fig. 4.18 but for the southeastern United States (RPC3).
55
similar to soil moisture indices that occurred ten years prior in the summer of 1993 in
RPC2 (Fig. 4.20). Hurricane Isabel brought torrential rainfall in September to this region
that had already been experiencing high soil moistures from severe weather that occurred
during the summer. The driest summers in RPCs 1-3 are all very similar in terms of
magnitude when an average “abnormal” dry summer PDSI is found. All three regions
produce an abnormally dry summer PDSI value of approximately -1.6. However, RPC3
exhibited the largest magnitudes of the wettest summer PDSI scores.
Fig. 4.23 shows the wettest and driest southeastern United States summers
throughout the last century in terms of T-max, T-min, and DTR. The driest summers in
this this region generally bookend a time period of relatively wet summers during the
1960s and 70s. T-max in this region does not show as much of a difference from dry to
wet summers. The average T-max for the driest summers came in at about 1.5°C higher
than the wettest. The T-max for all dry summers climbs well over 30°C, while all wet
summers have mean T-max closer to 30°C. T-min displays an even smaller amount of
variation between wet and dry summers most of which have values near 20°C. During the
driest summers, T-min is barely a quarter of a degree Celsius on average warmer than Tmin during a wet summer. DTR values in RPC3 do not resemble the same pattern as in
RPCs 1-2, where the DTR in a drier summer was always larger than that during a wet
summer. DTR is a little erratic in that some summers with wet conditions had a higher
DTR than in some summers having dry conditions (e.g. 1929, 1975, and 1976). Overall,
DTR exhibits a larger range during dry summers compared to wet with the difference
56
13.5
30
13
12.5
25
12
20
11.5
15
11
10
10.5
Year
Tmin-Dry
Tmax-Dry
Tmin-Wet
Tmax-Wet
DTR-Dry
Fig. 4.23: Same as Fig. 4.19 but for the southeastern United States (RPC3).
57
DTR-Wet
2011
2007
2005
2003
2000
1989
1986
1981
1976
1975
1973
1971
1964
1961
1955
1954
1949
1931
1929
1928
9.5
1927
0
1925
10
1914
5
Diurnal Temperature Range (Celcius)
35
1911
57
Temperature (Celcius)
RPC3 Wettest vs Driest Summers: T-max, T-min, DTR
being a little over 1°C, a smaller difference than in the previous 2 regions. A lot of this is
to blame on T-min. It appears that the southeastern United States does not cool off as
much as the upper Midwest or Ohio River Valley. This might be attributable to a greater
humidity in the southeastern United States relative to the other regions.
Fig. 4.24 depicts the rankings of the wettest and driest summer PDSI values in the
Southern Plains states defined as RPC5. Almost all of the driest summers occurred within
the first half of the 20th century from 1911 to 1956. There are also dry summers in the
early 21st century including more recent summers since 2006 (Fig. 4.24). As in the upper
Midwest and Ohio River Valley, the summer of 2012 registers as one of the driest
summers on record in this region. The driest summer of the record in the Southern Plains
occurred in 1956 with an average score of approximately -2.5 (Fig. 4.24). This ranks as
the second driest regionally averaged PDSI value from all 4 regions behind the summer
of 1934 in the upper Midwest (Fig. 4.20). The end of the second half of the 20th century
experienced the wettest summers in terms of soil moisture (Fig. 4.24). The 1990s alone
accounted for a third of the wettest dozen summers recorded in the Southern Plains. The
summer with the highest soil moisture occurred in 2007 with a PDSI score of ≈ 2.8. This
average is wetter than any other summer PDSI value from any other region.
The Southern Plains states of Kansas, Oklahoma, and Texas (RPC5) experience
very hot summers in spite of soil moisture conditions. Fig. 4.25 illustrates that T-max
during the driest and wettest years maintain an average over 33°C or 91°F. The majority
of the driest summers took place within the first half of the century, while most of the
latter half has consisted with more years featuring wetter conditions. T-max in the driest
58
59
Fig. 4.24: Same as Fig. 4.18 but for the Southern Plains (RPC5).
59
summers have higher temperatures than that of T-max during wet summers all cases but
one (1955 is not warmer than 1957). What sets the Southern Plains apart from the other
regions is that the average difference of over 3°C in T-max between dry and wet
summers. Some of the dry summers had very large average T-max values. The summers
of 1934 and 2011 produced average T-max in the mid-90s averaged over all weather
stations of the three states (Fig. 4.25). Average T-min for these wettest and driest years is
about 20°C. Like the other RPC regions, T-min exhibits warmer temperatures during the
driest conditions compared to the wettest. T-min in drier summers is a little over 1°C
warmer than T-min during the wettest summers. DTR is affected in the same way. DTR
is about 1.8°C larger in the driest summers compared to the wettest. There is one wet
summer (1973) in which DTR was slightly higher than the DTR during the dry summers.
In this section, averaged T-max and T-min were obtained over the 4 regions for
each of the 12 wettest and driest summers. DTR was then calculated by taking the simple
difference in T-max and T-min for every summer per region. Based on Figs. 4.19, 4.21,
4.23, and 4.25, T-max seemed to vary the most between wet and dry summers for each
region. In some regions such as the Southeast, T-max varied widely amongst individual
dry, and even wet summers (Fig. 4.23). It was however generally larger in dry summers
compared to wet summers.
T-max exhibits small trends (Figs. 4.6 and 4.15) or no trend at all (Figs. 4.9 and
4.12) over the period of 1895-2012. The results in this section indicate that T-max is
much higher in dry summers, most of which occur in the early 20th century. Increases in
soil moisture since 1950 in every region but RPC5 have likely been important in
60
15.5
35
15
30
14.5
25
14
20
13.5
15
13
10
Year
Tmin-Dry
Tmax-Dry
Tmin-Wet
Tmax-Wet
Fig. 4.25: Same as Figure 4.19 but for the Southern Plains states (RPC5)
61
DTR-Dry
DTR-Wet
2012
2011
2007
2006
1997
1995
1993
1992
1987
1973
1957
1956
1955
1954
1953
1945
1942
1941
1934
1925
12
1918
0
1917
12.5
1915
5
Diurnal Temperature Range (Celcius)
40
1911
61
Temperature (Celcius)
RPC5 Wettest vs Driest Summers: T-max, T-min, DTR
suppressing any tendency for increased T-max. T-min for all regions did not seem to be
affected as much by soil conditions. By looking at Figs. 4.19, 4.21, 4.23, and 4.25, T-min
does not fluctuate very much during a summer when extreme soil moisture conditions
(wet or dry) was classified. T-min however was generally higher in dry summers than in
wet soil condition summers. Using annually averaged data, Lauritsen and Rogers (2012;
their Fig. 3) showed that while soil moisture has an important regional impact on T-max,
its relation to T-min is much weaker. The T-min - PDSI relation is negative in their study,
indicating dry conditions are linked to higher T-min.
In terms of DTR for the wet and dry summers of each region, fluctuations in Tmax play an overall larger role in determining the extent of DTR. This agrees with results
from Lauritsen and Rogers (2012) which, while PDSI explains some of the regional
variance in DTR, cloud cover is more important in all regions while in some areas the
importance of precipitation variability also exceeds that of soil moisture in influencing
DTR. The overall increase in DTR seems to be more heavily related to the change in Tmax when it comes to comparing the wettest versus the driest years for each region.
62
Chapter 5: Case Study: The Drought of 2012
5.1. Overview of the summer drought of 2012
The recent summer of 2012 brought the most widespread severe drought
conditions across the central United States in over a half century. Figures 4.19, 4.21 and
4.25 pointed out that the summer of 2012 ranked as one of the top twelve driest years in
RPCs 1 (Ohio River Valley), 2 (Upper Midwest), and 5 (Southern Plains) respectively.
This chapter will specifically focus on how this summer unfolded in these regions as well
as other nearby states and how it compares to dry soil conditions that occurred in the past.
This chapter will also take a look at PDSI values obtained over the summer of 2012 and
analyze how they changed as the months progressed. The 4 maps in Fig. 5.1 show the
month to month progression of the drought of 2012 in terms of soil moisture conditions
as measured by the PDSI. As Fig. 5.1 shows, most of the extreme drought conditions
were confined to the southwestern and the southeastern United States leading into the
summer in May. By the end of June however, the drought intensified in the western states
as well as along a swath extending from eastern Texas to southern Michigan. The
environment in the Midwest United States progressed to drought in a month between
May and June. By the end of June, most of the continental United States was
experiencing some extent of drought which continued for the remainder of the summer.
The drought intensified in July (Fig. 5.1) although the expansion of its areal coverage
63
may have been minimal. The drought expanded in the Central Plains and Midwest but
was reduced in the southeastern states. In August, the areal extent of drought remained
relatively fixed, with some areas improving in the Southeast, and the overall severity of
the drought changed little.
Fig. 5.2 illustrates the progression of the summer 2012 drought as presented by
the U.S. Drought Monitor (http://www.drought.gov/drought). The U.S. Drought Monitor
is jointly produced by the National Drought Mitigation Center at the University of
Nebraska-Lincoln, the United States Department of Agriculture, and NOAA. Produced
every Thursday, the map is based on climatic, hydrologic, soil conditions, and also
reports generated from observations from individual and agency contributors all over the
United States. Fig. 5.3 better shows the ranking system used by the U.S. Drought Monitor
where areas within states can be ranked from having no drought to abnormal dryness
(D0) through D4 which is exceptional drought.
The August map (Fig. 5.2), places the greatest drought severity from the Rockies
across the Central Plains into the Midwest and Ohio River Valley. The map sequence
also gives more indication of drought intensification from July to August than appears in
Fig. 5.1. Fig. 5.2 also downplays the severity of the drought along the Rocky Mountain
States, relative to the PDSI.
Fig. 5.3 shows the percentage of land mass in the United States associated with
each of the U.S. Drought Monitor’s drought categories both at the end of May and the
final week of August 2012. By the end of May, 64% (100% minus 35.98%) of the United
States was experiencing some extent of drought whether it be abnormally dry conditions
64
(D0) all the way to exceptional drought (D4; Fig. 5.3). Just over 70% of the D0-D4 area
experiencing drought ranked in the two lowest categories D0 or D1 (45.08% divided by
64%). This means roughly 30% of the areas with dry conditions were suffering from
severe through exceptional drought (D2-D4). A substantial change in drought coverage
takes place from May 29th to August 28th in 2012.
By the end of August, almost 78% of the United States was considered to be
experiencing drought (D0-D4). This was up from 64% at the end of May. Of all areas
experiencing dryness, only 45% were characterized by abnormally dry or moderate
drought conditions (D0-D1). This meant the majority of areas experiencing drought
(55%) had severe conditions or worse (D2-D4) at the end of August. The biggest change
from May 29th to August 28th was the expansion of areas covered by extreme (D3) and
exceptional (D4) drought. Of the total area of the United States experiencing drought at
the end of May, ~8% were categorized as D3-D4 drought severity. By the end of August,
areas of drought with severity D3-D4 conditions had risen to ~30% coverage, an increase
of 22% (Fig. 5.3).
Table 5.1 further shows the statistics from the U.S. Drought Monitor on August
28, 2012 (http://droughtmonitor.unl.edu/MapsAndData/MapArchive.aspx). Table 5.1
makes drought comparisons between the last full week of August and earlier periods. At
the beginning of the year of 2012, 49.59% of the United States was experiencing dry
conditions. The majority of these areas were classified as moderately or abnormally dry.
2011 was also a dry year over almost half of the United States. On August 30, 2011,
11.21% of the United States had exceptional drought compared to 6.04% on August 28,
65
2012 indicating exceptionally dry conditions also existed in summer 2011. However the
2011 drought was not as expansive as that of 2012, as much more surface area was
experiencing D0-D3 level drought in 2012 relative to 2011.
66
67
Fig. 5.1: Climate Division average PDSI values from May through August 2012. Data are from the NCDC.
(http://www.ncdc.noaa.gov/temp-and-precip/drought/historical-palmers/maps)
67
68
Fig. 5.2: 2012 drought expansion as shown by the U.S. Drought Monitor
68
69
Fig. 5.3: Drought ranking system used by the U.S. Drought Monitor; percentage
of each drought category at the end of May to the end of August 2012
69
Table 5.1: Data directly from the U.S. Drought Monitor website for the end of August
2012. Previous year (2011) and beginning of year drought statistics are shown and can be
compared to Aug. 21, 2012 United States drought conditions.
70
5.2. Comparing the drought of 2012 to droughts of historic past
How bad was the drought of 2012? Looking back in history to find dry conditions
as extensive as those of 2012, one would have to go back to the summer of 1956, (Fig.
5.4). It was discussed in Chapter 4 how the summer of 2012 ranked as one of the driest
summers in the timeframe of the data collected in RPC1 (Ohio River Valley), RPC2
(upper Midwest and eastern Northern Plains), and RPC5 (Southern Plains) (see Fig. 5.1).
Similarly to the 2012 summer, the summer of 1956 ranks as one of the driest in RPC2
and RPC5. Unlike the summer of 2012 where drought expansion and severity peaked in
August, conditions following the summer of 1956 worsen as drought conditions peaked
in December (Fig. 5.5) leading to one of the longest droughts in United States history. By
the middle of the summer of 2012, the drought had become so expansive across the
United States that it ranked right behind 1956 as one of the largest droughts in history
(http://www.ncdc.noaa.gov/sotc/drought/2012/8). Fig. 5.4 shows the expansiveness of the
drought of 1956 in terms of PDSI through the summer of that year. Comparing this figure
with Fig. 5.1, one can see that the drought in 2012 covered a larger land mass than the
drought in 1956 by the end of August. The drought of 1956 was located mainly in the
central and Southern Plains with large areas of area experiencing PDSI values of -4 or
worse ranking them in the extreme drought category. Fig. 5.5 shows the peak of the
drought of 1956, occurring in December of that year, compared to the peak of dryness in
2012, which occurred in August. The two droughts covered a lot of the same land area in
terms of extreme drought with 2012 having a slightly larger overall land coverage.
71
72
Fig. 5.4: Climate Division average PDSI values from May through August 1956. Data are from the NCDC.
(http://www.ncdc.noaa.gov/temp-and-precip/drought/historical-palmers/maps).
72
73
Fig. 5.5: Peak of the 1956 drought (December) compared to the peak of the 2012 drought (August).
73
The extremely warm temperatures and dry soil conditions experienced in 2012
across the United States were also very similar to droughts that occurred as recently as
1988 and as far back as 1934. The drought of 1988 (Fig. 5.6) was a wide spread event
covering much of the northern United States and Great Lakes states with moderate to
extreme drought. The summer of 1988 registered in RPCs 1 and 2 was one of the driest
summers on record. The drought of 1988 was well known for its costly forest fires
including devastation to Yellowstone National Park. The drought of 2012 brought very
similar temperatures to that of 1988 as well as precipitation values.
One of the worst and most expansive droughts in history occurred in 1934 which
spanned much of the central and western United States (Fig. 5.7). The summer of 1934
appeared as one of the driest in RPCs 1, 2, and 5 (Southern Plains). This summer ranked
as the overall driest in the upper Midwest region. The drought of 1934 represents the
largest expansion of extreme drought in United States history. The mid-1930s era was
known as the “dust bowl” both for the extreme dryness on the Southern Plains as well as
expansive dust storms that were prevalent during this time. Poor soil management
strategies during this time allowed soil to dry out, turn to dust, and become susceptible to
wind erosion (Worster, 1979).
74
75
Fig. 5.6: Climate Division average PDSI values from May through August 1988. Data are from the NCDC.
(http://www.ncdc.noaa.gov/temp-and-precip/drought/historical-palmers/maps).
75
76
Fig. 5.7: Climate Division average PDSI values from May through August 1934. Data are from the NCDC.
(http://www.ncdc.noaa.gov/temp-and-precip/drought/historical-palmers/map).
76
5.3. Temperature change and precipitation in summer 2012
This section will analyze month to month changes from May to August 2012 in Tmax, T-min, DTR, and precipitation. Temperature and precipitation data from the
summer of 2012 were collected and analyzed from 21 states comprising parts, or all of
five of the nine climate regions identified (Fig. 5.8) by the National Climatic Data Center
(NCDC) and used by the National Oceanic and Atmospheric Administration (NOAA)
(http://www.ncdc.noaa.gov/monitoring-references/maps/us-climate-regions.php). These
nine regions were created due to their consistent climate characteristics which make
climate anomalies occurring in current times easier to identify against historical averages
(Karl and Koss, 1984). Table 5.2 lists the specific states from which NWS weather data
were collected for the 2012 summer.
Four tables (Tables 5.3, 5.4, 5.6, and 5.8) depict the monthly mean T-max, T-min,
DTR, and precipitation totals of the 21 states (Table 5.2) from May to August 2012.
Three others (Tables 5.5, 5.7, and 5.9) were created to depict the monthly fluctuations in
T-max, T-min, DTR, and precipitation compared to seasonal changes. DTR is calculated
from the air temperature data as was average monthly temperature along with departures
from normal. Precipitation departures from normal are also available for the
corresponding states. Highlighted values in Tables 5.3, 5.4, 5.6, and 5.8 note the state
with the highest T-max, highest T-min, largest DTR, highest average temperature, largest
departure from normal temperature, lowest and highest precipitation, and finally the
greatest departure from normal. Highlighted values in Tables 5.5, 5.7, and 5.9 represent
T-max, T-min, and DTR changes larger than seasonally expected values. Average
77
Fig. 5.8: United States Climate Regions (http://www.ncdc.noaa.gov/monitoringreferences/maps/us-climate-regions.php#references) used by NOAA. The 5 regions
indicated by a star are those from which NWS station data are gathered during MayAugust 2012. The states of these regions are listed in Table 5.2.
2012 Drought in the United States
Arkansas
Colorado
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Michigan
Minnesota
Mississippi
Missouri
Nebraska
Ohio
Oklahoma
South
Dakota
Tennessee
Texas
Wisconsin
North
Dakota
Wyoming
Table 5.2: States for which NWS first-order station data were collected for the months of
May-August 2012
78
temperature change between months is also indicated by a red color if it is warmer than
normal or blue if cooler.
Table 5.3 shows data for the month of May leading into the summer of 2012.
Every state shown experienced warmer than normal temperatures for that time of year.
Ohio led the way with mean temperatures 3.5°C above normal. The state that was closest
to having normal temperatures for May was North Dakota (+0.81°C). Precipitation was
below normal in all of the states in the study excluding Wisconsin and Minnesota, which
received 7.14 cm above normal. Arkansas had just 3.73 cm of precipitation, 9.40 cm
below normal, which was the most below normal for any state in the study. Colorado
received the lowest amount of precipitation of any other state in May and was
coincidentally also the state with the largest DTR. Colorado, Nebraska, and Wyoming for
the most part displayed the largest DTR of any of the 21 states in the study for all of the
investigated months (May-August 2012). Based on previous studies (e.g., Dai et al.,
1999), the high DTR values are likely due to climatologically low precipitation compared
to the other states, very low surface humidities (little Gulf of Mexico moisture), and
relatively high topographic elevations. A combination of higher than normal temperatures
and lack of precipitation were the initial precursors for the drought that occurred in these
21 states in the summer of 2012.
As May turned to June, drought conditions worsened (Figs. 5.1 and 5.2). As states
warmed up in June, the Plains states continued to have average temperature departures
well above normal especially in Wyoming, Colorado, South Dakota, Nebraska, and
Kansas (Table 5.4). The Midwestern states continued to experience warmer than normal
79
MAY 2012
AVG
AVG
AVG DTR Monthly DPTR FM Total
DPTR FM
STATE
Temperature Temperature
(°C)
AVG
Normal PRECIP
Normal
MAX (°C)
MIN (°C)
TEMP
TEMP
(cm) PRECIP(cm)
(°C)
(°C)
Wyoming
18.06
2.00
16.06
10.03
+0.89
3.89
-1.75
Colorado
21.67
4.33
17.33
13.00
+1.92
2.31
-2.69
N. Dakota
20.00
5.61
14.39
12.81
+0.81
5.18
-0.89
S. Dakota
22.11
7.17
14.94
14.64
+1.31
7.09
-0.33
Nebraska
25.00
8.61
16.39
16.81
+2.11
5.18
-3.68
Kansas
28.56
12.61
15.94
20.58
+3.08
3.73
-6.02
Oklahoma
29.50
15.61
13.89
22.56
+2.44
5.46
-6.68
Texas
30.94
17.39
13.56
24.17
+1.58
7.32
-2.13
Arkansas
29.56
16.22
13.33
22.89
+2.44
3.73
-9.40
Louisiana
30.56
18.67
11.89
24.61
+1.50
5.51
-6.99
Tennessee
27.94
14.78
13.17
21.36
+2.36
8.26
-3.10
Mississippi
30.17
16.83
13.33
23.50
+1.58
7.95
-3.84
Minnesota
21.28
8.00
13.28
14.64
+2.28
15.14
+7.14
Iowa
25.33
11.39
13.94
18.36
+2.89
8.92
-1.57
Missouri
27.67
14.06
13.61
20.86
+2.89
5.49
-6.65
Wisconsin
22.33
8.28
14.06
15.31
+2.69
12.22
+3.18
Illinois
26.94
13.17
13.78
20.06
+3.25
6.27
-4.32
Indiana
26.89
12.78
14.11
19.83
+3.47
6.99
-3.73
Michigan
22.17
7.78
14.39
14.97
+2.92
7.62
-0.36
Ohio
26.06
12.22
13.83
19.14
+3.50
9.35
-0.48
Kentucky
27.39
14.11
13.28
20.75
+2.69
10.80
-0.48
Table 5.3: May 2012 state averaged T-max (°C) and T-min (°C) temperatures with
departures from normal. Monthly precipitation (cm) is also included along with
departures from normal. The average DTR (°C) is the difference T-max – T-min.
80
temperatures although departures from normal were lower than in May (Table 5.3).
Conditions in Colorado quickly deteriorated with temperatures over 3.5°C warmer than
normal and precipitation 2.82 cm below normal. Average temperature during the day in
Colorado reached 29.33°C with the low temperatures falling to 10.56°C. This led to an
average DTR of 18.78°C, the largest of any of the 21 states for the month of June.
Outside of neighboring Wyoming, DTR for this area was greater than or equal to 2.5°C
larger than any other state. Like Colorado, Wyoming also received below average
precipitation albeit a larger deficit of 3.43 cm. These temperatures and precipitation
values suggest net clear sky conditions, especially at night, allowing the air temperatures
to drop considerably as shown by the large DTR values (Table 5.4). Precipitation values
were below normal for every state in the study for June except for Minnesota, which
received a surplus of 0.56 cm (Table 5.4). In some states, absolute values of the
departures from normal were larger than the actual rainfall totals thus indicating
precipitation was less than half of normal. Conditions in the Midwest were the worst in
terms of dryness as many states were over 4.5 cm short of normal precipitation. Indiana
saw the largest deficit of precipitation suffering a loss of 7.16 cm of rainfall, receiving
roughly 30% of normal.
Table 5.5 shows the temperature changes from May to June that occurred within
the 21 states analyzed. Air temperatures should seasonally increase in all states during the
progression of May to June. The normal month to month changes were calculated based
on monthly average temperature changes dating from 1895-2011 (Column 8). This allows
for comparison to the month to month temperature changes that occurred during the
81
JUNE 2012
AVG
AVG
AVG
Monthly DPTR FM
Total
DPTR FM
STATE
Temperature Temperature
DTR
AVG
Normal
PRECIP
Normal
MAX (°C)
MIN (°C)
(°C)
TEMP
TEMP (°C)
(cm)
PRECIP(cm)
(°C)
Wyoming
26.39
7.78
18.61
17.08
+2.81
1.14
-3.43
Colorado
29.33
10.56
18.78
19.94
+3.58
1.07
-2.82
N. Dakota
25.72
11.56
14.17
18.64
+1.47
6.65
-2.11
S. Dakota
29.11
13.61
15.50
21.36
+2.61
5.54
-3.18
Nebraska
31.06
14.94
16.11
23.00
+2.81
4.19
-5.23
Kansas
33.33
17.22
16.11
25.28
+2.25
5.49
-4.83
Oklahoma
33.17
19.11
14.06
26.14
+1.14
6.22
-4.01
Texas
35.06
20.89
14.17
27.97
+1.44
4.72
-2.64
Arkansas
32.72
18.39
14.33
25.56
+0.75
5.03
-5.16
Louisiana
32.94
21.17
11.78
27.06
+0.47
10.80
-1.22
Tennessee
30.44
15.83
14.61
23.14
-0.17
5.46
-5.28
Mississippi
32.22
19.06
13.17
25.64
-0.14
8.79
-1.83
Minnesota
26.06
12.61
13.44
19.33
+1.67
11.07
+0.56
Iowa
28.39
15.44
12.94
21.92
+1.25
7.14
-4.70
Missouri
31.00
16.61
14.39
23.81
+0.97
4.83
-6.96
Wisconsin
26.50
12.56
13.94
19.53
+1.64
8.48
-2.13
Illinois
29.67
15.44
14.22
22.56
+0.61
4.39
-6.02
Indiana
29.44
14.56
14.89
22.00
+0.53
3.28
-7.16
Michigan
26.11
12.17
13.94
19.14
+1.69
7.62
-0.69
Ohio
28.39
14.11
14.28
21.25
+0.67
5.38
-4.65
Kentucky
30.11
14.89
15.22
22.50
-0.11
3.81
-6.96
Table 5.4: June 2012 state averaged T-max (°C) and T-min (°C) temperatures with
departures from normal. Monthly precipitation (cm) is also included along with
departures from normal. The average DTR (°C) is the difference T-max – T-min.
82
summer of 2012. In 2012, average May-June temperature changes range from a warming
of about 2°C to ~7°C (Column 7). The normal May-June temperature change in these
states is a much smaller range of warming from ~3.5-5.5°C.
Wyoming, Colorado, and the Northern Plains states of North and South Dakota
warmed more than any other state in the transition from May to June. These states also
warmed more than what occurs seasonally (Table 5.5 Columns 2, 4, and 8). Wyoming
saw the highest T-max change at a little over an 8 °C increase in statewide averaged Tmax from May to June. Most states examined had below the normal seasonal warming,
primarily because May (Table 5.3) departures were very large while those of June (Table
5.4) were smaller. Monthly average temperatures were above normal for both May and
June for most of these states.
All of the states excluding North Dakota, Nebraska, Louisiana, Mississippi, Iowa,
and Michigan had a larger increase in T-max than in T-min leading to an increase in DTR
between the 2 months. Drought conditions became noticeably worse in Wyoming and
Colorado (Figs. 5.1) as PDSI values took a turn to extreme drought conditions. These two
states would continue to experience extreme drought for the rest of the 2012 summer. Tmax and T-min both increased well above seasonal normal changes in these two states as
well. In the Dakotas and Nebraska, T-max and T-min also increased above the average,
but there was little change in DTR unlike Wyoming and Colorado. Nebraska had a
substantial change in drought in June (Fig. 5.1), but the Dakotas changed only slightly.
Drought generally intensified from May to June in the region between Texas and
Ohio (Figs. 5.1 and 5.2). As this area became drier, a net increase in DTR occurred in
83
most of the states with Iowa and Wisconsin excluded (Table 5.5); despite the smaller than
usual increases in both T-max and T-min. Iowa became drier from May to June (Fig.
5.1), but had a small net decrease in DTR (Table 5.5). Mississippi became somewhat
moister (Fig. 5.1) between May and June and its DTR went down slightly. Overall (Table
5.5), DTR increased in many states as the drought worsened from May to June.
July (Table 5.6) had the warmest temperatures over the summer of 2012 with state
wide average T-max between 29°C-38°C. Oklahoma had the highest average T-max of
37.50, which was over 3°C warmer than normal. The average daily temperature for
Oklahoma was also the warmest for any state in July at 29.94°C. Along with the
scorching heat, Oklahoma had one of the smallest amounts of precipitation for any state
(Table 5.6) with only 2.95 cm, which was almost 5 cm below normal precipitation.
Nebraska received the smallest amount of precipitation at 2.62 cm, 5.05 cm below
normal. As in June, the Midwest was still experiencing extreme heat with temperatures
ranging from 2 to 3.5°C warmer than normal. Illinois had the largest anomaly of the
region with temperatures 3.47°C warmer than normal for July. Nightly average Wyoming
temperatures were much lower than any other state in Table 5.6 resulting in the largest
DTR for the month. Even though temperatures were much higher in the month of July,
average precipitation values were not as far below normal in June. States such as
Colorado, Texas, Louisiana, Tennessee, Mississippi, Michigan, and Kentucky received
above normal rainfall. DTR values on average were lower in these wet states compared to
the drier states. Louisiana received the most precipitation out of any state in July at 19.66
cm, which was almost 5.5 cm above normal. Louisiana had the highest T-min (Table 5.6)
84
STATE
TEMPERATURE CHANGES FROM MAY TO JUNE 2012
1.
2.
3.
4.
5.
6.
7.
Δ
Normal
Δ
Normal
Δ
Normal Δ AVG
T-MAX
Δ
T-MIN
Δ
DTR
Δ
TEMP
2012 T-MAX
2012
T-MIN 2012
DTR
2012
(°C)
(°C)
(°C)
(°C)
(°C)
(°C)
(°C)
8.
Normal
Δ AVG
TEMP
(°C)
Wyoming
+8.33
+5.89
+5.78 +4.39 +2.56 +1.50
+7.06
+5.14
Colorado
+7.67
+5.83
+6.22 +4.72 +1.44 +1.11
+6.94
+5.28
N. Dakota
+5.72
+4.72
+5.94 +5.61
-0.22
-0.89
+5.83
+5.17
S. Dakota
+7.00
+5.22
+6.44 +5.61 +0.56
-0.39
+6.72
+5.42
Nebraska
+6.06
+5.44
+6.33 +5.56
-0.28
-0.11
+6.19
+5.50
Kansas
+4.78
+5.56
+4.61 +5.50 +0.17 +0.06
+4.69
+5.53
Oklahoma
+3.67
+4.83
+3.50 +4.94 +0.17
-0.11
+3.58
+4.89
Texas
+4.11
+3.83
+3.50 +4.06 +0.61
-0.22
+3.81
+3.94
Arkansas
+3.17
+4.28
+2.17 +4.44 +1.00
-0.17
+2.67
+4.36
Louisiana
+2.39
+3.28
+2.50 +3.67
-0.11
-0.39
+2.44
+3.47
Tennessee
+2.50
+4.06
+1.06 +4.56 +1.44
-0.50
+1.78
+4.31
Mississippi +2.06
+3.67
+2.22 +4.06
-0.17
-0.39
+2.14
+3.86
Minnesota +4.78
+4.94
+4.61 +5.67 +0.17
-0.72
+4.69
+5.31
Iowa
+3.06
+4.94
+4.06 +5.44
-1.00
-0.50
+3.56
+5.19
Missouri
+3.33
+4.67
+2.56 +5.06 +0.78
-0.39
+2.94
+4.86
Wisconsin
+4.17
+5.06
+4.28 +5.50
-0.11
-0.44
+4.22
+5.28
Illinois
+2.72
+5.11
+2.28 +5.17 +0.44
-0.06
+2.50
+5.14
Indiana
+2.56
+5.00
+1.78 +5.22 +0.78
-0.22
+2.17
+5.11
Michigan
+3.94
+5.39
+4.39 +5.39
-0.44
0.00
+4.17
+5.39
Ohio
+2.33
+4.78
+1.89 +5.11 +0.44
-0.33
+2.11
+4.94
Kentucky
+2.72
+4.28
+0.78 +4.83 +1.94
-0.56
+1.75
+4.56
Table 5.5: Temperature changes that occurred from May to June 2012. The May to June
2012 progressions of T-max and T-min are represented in Columns 1 and 3. Diurnal
temperature range is shown in Col. 5. Comparative long-term normal values are in
Columns 2, 4, and 6. Columns 7 and 8 represent average temperature change in 2012 and
seasonal normal average temperature changes respectively.
85
resulting in the smallest DTR of any state in July. Amidst all of the precipitation that
Louisiana received, monthly average temperatures were still warmer than normal (Table
5.6). Amongst the wetter states, Arkansas experienced a similar DTR but remained dry,
receiving 2.41 cm of precipitation below normal.
During an average summer, the transition from June to July should bring warmer
temperatures across the United States (Column 8; Table 5.7). Compared to seasonal
warming, the change of monthly temperatures in 2012 was very extreme. Only four states
(Wyoming, Colorado, Texas, and Louisiana) had June-July temperature increases that
were below seasonal average changes (Column 7; Table 5.7). Some states in the Midwest
(Illinois, Indiana, and Kentucky) experienced average temperature change twice as large
as occurs normally. Texas had no change in T-max from June to July but did experience a
warming in T-min that was below seasonal fluctuations. This resulted in an average DTR
change 1°C smaller than normal (Column 5; Table 5.7). Not only were average
temperatures in July warmer than normal (Table 5.6), the change in both T-max and Tmin between June and July for most of the states were larger than normal.
The Midwest and Plains drought worsened in July according to PDSI values (Fig.
5.1) and the U.S. Drought Monitor (Fig. 5.2). Combining the decrease in soil moisture
and extreme seasonal warming, the drought of 2012 was only getting worse. Parts of
Illinois, Indiana, Kentucky, and Missouri had joined the ranks of extreme drought with
PDSI values of -4.00 and below by the end of July. Extreme drought conditions in the
Rocky Mountain States along with areas in the Plains saw no improvement in soil
moisture conditions throughout July. Portions of southeastern Texas received drought
86
JULY 2012
AVG
AVG
AVG
Monthly DPTR FM
Total
STATE
Temperature Temperature
DTR
AVG
Normal
PRECIP
MAX (°C)
MIN (°C)
(°C)
TEMP
TEMP (°C)
(cm)
(°C)
Wyoming
29.83
12.44
17.39
21.14
+2.58
2.84
Colorado
29.78
13.11
16.67
21.44
+1.89
5.77
N. Dakota
30.72
16.06
14.67
23.39
+2.81
5.61
S. Dakota
33.94
18.00
15.94
25.97
+3.39
3.91
Nebraska
35.11
18.22
16.89
26.67
+3.03
2.62
Kansas
37.28
20.94
16.33
29.11
+3.08
3.25
Oklahoma
37.50
22.39
15.11
29.94
+2.33
2.95
Texas
35.06
22.00
13.06
28.53
+0.58
6.86
Arkansas
35.44
22.50
12.94
28.97
+2.14
7.16
Louisiana
33.00
22.89
10.11
27.94
+0.22
19.66
Tennessee
33.06
20.83
12.22
26.94
+1.86
14.61
Mississippi
33.39
22.11
11.28
27.75
+0.61
17.83
Minnesota
30.00
17.00
13.00
23.50
+3.03
8.33
Iowa
33.33
19.50
13.83
26.42
+3.19
2.97
Missouri
36.11
21.28
14.83
28.69
+3.36
4.32
Wisconsin
30.67
16.94
13.72
23.81
+3.22
8.10
Illinois
34.67
20.67
14.00
27.67
+3.47
3.56
Indiana
33.78
19.78
14.00
26.78
+3.19
6.43
Michigan
30.06
16.11
13.94
23.08
+3.00
9.12
Ohio
31.78
18.78
13.00
25.28
+2.47
8.76
Kentucky
33.39
20.50
12.89
26.94
+2.36
14.07
Table 5.6: July 2012 state averaged T-max (°C) and T-min (°C) temperatures with
departures from normal. Monthly precipitation (cm) is also included along with
departures from normal. The average DTR (°C) is the difference T-max – T-min.
87
DPTR FM
Normal
PRECIP(cm)
-0.36
+0.43
-0.97
-2.51
-5.05
-5.33
-4.42
+0.74
-2.41
+5.49
+3.28
+5.18
-0.81
-6.99
-5.36
-1.52
-5.89
-3.45
+1.35
-1.50
+2.84
relief, albeit very small-scale, as most of the state continued to show moderate drought or
worse (Fig. 5.1). Louisiana also improved in soil moisture as only two northern climate
regions within the state were experiencing drought conditions at the end of July.
DTR variability in the Midwest between June and July responded to changes in
precipitation amount. For example, DTR decreased in Colorado, Texas, Louisiana,
Tennessee, Mississippi, and Kentucky between June and July (Table 5.7) while the
rainfall went up between the 2 months (Table 5.6). This was especially true in Tennessee
and Kentucky where rainfall was well over 2.5 cm above normal (Table 5.6) and DTR
fell over 2°C between June and July. Louisiana saw 5.49 cm of rainfall above normal in
July (Table 5.6), the most of any state in the record, but only had a DTR decrease of
1.67°C (Table 5.7). However, in Kansas, Missouri, and Illinois, little change in DTR
occurs (Table 5.7) and soil conditions remained relatively dry and rainfall over 5 cm
below normal. Rainfall was 6.99 cm below normal in Iowa between June and July and
DTR increased by 0.89°C. Minnesota was not as adversely affected by drought
conditions in June or July, with slightly below normal rain in both months, while DTR
was relatively low compared to all the other Midwestern states. The conditions between
June and July reveal the changes that can occur on a state level as opposed to a regional
level. They also reveal that the basic physical links between precipitation PDSI and DTR
are confirmed.
In August (Table 5.8), temperature departures were within a degree of normal in
most states. At this point in the year, the drought had already taken its toll on a lot of
areas. Precipitation was much more variable across this month with high rainfall in
88
STATE
TEMPERATURE CHANGES FROM JUNE TO JULY 2012
1.
2.
3.
4.
5.
6.
7.
Δ
Normal
Δ
Normal Δ DTR Normal Δ AVG
T-MAX
Δ
T-MIN
Δ
2012
Δ DTR
TEMP
2012 T-MAX
2012
T-MIN
(°C)
(°C)
2012
(°C)
(°C)
(°C)
(°C)
(°C)
8.
Normal
Δ AVG
TEMP
(°C)
Wyoming
+3.44
+4.94
+4.67
+3.61
-1.22
+1.33
+4.06
+4.28
Colorado
+0.44
+3.11
+2.56
+3.28
-2.11
-0.17
+1.50
+3.19
N. Dakota
+5.00
+4.00
+4.50
+2.83
+0.50
+1.17
+4.75
+3.42
S. Dakota
+4.83
+4.44
+4.39
+3.22
+0.44
+1.22
+4.61
+3.83
Nebraska
+4.06
+3.78
+3.28
+3.11
+0.78
+0.67
+3.67
+3.44
Kansas
+3.94
+3.22
+3.72
+2.78
+0.22
+0.44
+3.83
+3.00
Oklahoma
+4.33
+2.94
+3.28
+2.28
+1.06
+0.67
+3.81
+2.61
Texas
0.00
+1.39
+1.11
+1.44
-1.11
-0.06
+0.56
+1.42
Arkansas
+2.72
+2.17
+4.11
+1.89
-1.39
+0.28
+3.42
+2.03
Louisiana
+0.06
+1.00
+1.72
+1.28
-1.67
-0.28
+0.89
+1.14
Tennessee
+2.61
+1.56
+5.00
+2.00
-2.39
-0.44
+3.81
+1.78
Mississippi +1.17
+1.11
+3.06
+1.61
-1.89
-0.50
+2.11
+1.36
Minnesota +3.94
+2.89
+4.39
+2.72
-0.44
+0.17
+4.17
+2.81
Iowa
+4.94
+2.67
+4.06
+2.44
+0.89
+0.22
+4.50
+2.56
Missouri
+5.11
+2.72
+4.67
+2.28
+0.44
+0.44
+4.89
+2.50
Wisconsin
+4.17
+2.67
+4.39
+2.72
-0.22
-0.06
+4.28
+2.69
Illinois
+5.00
+2.22
+5.22
+2.28
-0.22
-0.06
+5.11
+2.25
Indiana
+4.33
+2.11
+5.22
+2.11
-0.89
0.00
+4.78
+2.11
Michigan
+3.94
+2.61
+3.94
+2.67
0.00
-0.06
+3.94
+2.64
Ohio
+3.39
+2.17
+4.67
+2.28
-1.28
-0.11
+4.03
+2.22
Kentucky
+3.28
+1.78
+5.61
+2.17
-2.33
-0.39
+4.44
+1.97
Table 5.7: Temperature changes that occurred from June to July 2012. The June to July
2012 progressions of T-max and T-min are represented in Columns 1 and 3. Diurnal
temperature range is shown in Col. 5. Comparative long-term normal values are in
Columns 2, 4, and 6. Columns 7 and 8 represent average temperature change in 2012 and
seasonal normal average temperature changes respectively.
89
Louisiana and Mississippi while several drier states were less than 2.5 cm below normal.
Arkansas DTR continued to increase between July and August despite the 2.62 cm of
rainfall above normal. DTR values of Louisiana and Mississippi were the smallest of any
state but remained steady during this very wet month (Table 5.8), compared to June when
conditions were dry. Colorado and Wyoming remain warmer than normal in August
(Table 5.8) accompanied by very small amounts of rainfall relative to their averages for
August. Wyoming received the least amount of precipitation out of any state at 0.66 cm.
Similar to previous months in 2012, DTR values remained the largest within Wyoming
with Colorado and the Northern Plains states (ND, SD, NE, KS) experienced relatively
high DTR. This was due partly to a substantial drop in T-min in some states but also due
to a continuing lack of rainfall in August.
As July turns to August, mean temperatures, not unexpectedly, begin to decline in
every state except Texas (Table 5.9). Although most states in August were still
unseasonably warm, they were not far from normal (Table 5.8). Outside of Texas the
average temperature change declined more than is seasonally expected in every state
(Columns 7 and 8; Table 5.9). DTR on the other hand increased in most states as T-min
declined more than T-max. Only Mississippi and Michigan had a decrease in DTR
between July and August.
As rainfall increased in Louisiana and Mississippi, conditions in the upper
Midwest and Plains continued to deteriorate (Fig. 5.1), as shown by the rain departures in
Table 5.8. Many climate divisions along the Gulf Coast were very moist by the end of
August as the Plains and Midwest trended towards extremely dry. The cooling shown in
90
AUGUST 2012
AVG
AVG
AVG
Monthly DPTR FM
Total
DPTR FM
STATE
Temperature Temperature
DTR
AVG
Normal
PRECIP
Normal
MAX (°C)
MIN (°C)
(°C)
TEMP
TEMP (°C)
(cm)
PRECIP(cm)
(°C)
Wyoming
28.39
10.00
18.39
19.19
+1.72
0.66
-2.01
Colorado
28.44
11.28
17.17
19.86
+1.33
2.72
-2.39
N. Dakota
27.94
11.44
16.50
19.69
+0.31
3.86
-1.50
S. Dakota
30.33
12.94
17.39
21.64
+0.25
2.84
-2.59
Nebraska
31.39
14.11
17.28
22.75
+0.33
2.69
-4.14
Kansas
32.94
16.50
16.44
24.72
-0.47
6.63
-1.30
Oklahoma
35.28
19.72
15.56
27.50
+0.28
6.15
-1.14
Texas
35.78
21.67
14.11
28.72
+1.00
4.67
-1.22
Arkansas
33.56
20.00
13.56
26.78
+0.39
11.10
+2.62
Louisiana
32.83
22.67
10.17
27.75
+0.14
22.50
+10.72
Tennessee
30.56
17.89
12.67
24.22
-0.33
9.09
-0.51
Mississippi
32.00
20.78
11.22
26.39
-0.47
22.12
+11.94
Minnesota
26.39
12.17
14.22
19.28
+0.22
5.18
-3.58
Iowa
29.06
14.22
14.83
21.64
-0.28
7.32
-2.54
Missouri
32.22
16.89
15.33
24.56
+0.06
6.20
-3.20
Wisconsin
26.50
12.56
13.94
19.53
+0.33
5.94
-3.61
Illinois
30.50
15.72
14.78
23.11
+0.03
8.89
+0.05
Indiana
29.33
15.06
14.28
22.19
-0.31
10.29
+1.47
Michigan
26.22
13.17
13.06
19.69
+0.75
6.93
-0.97
Ohio
28.72
15.06
13.67
21.89
+0.17
7.54
-1.27
Kentucky
30.39
16.94
13.44
23.67
-0.25
8.05
-1.30
Table 5.8: August 2012 state averaged T-max (°C) and T-min (°C) temperatures with
departures from normal. Monthly precipitation (cm) is also included along with
departures from normal. The average DTR (°C) is the difference T-max – T-min.
91
Table 5.9 did not bring relief for the drought of 2012 as low soil moisture continued to be
extensive. The drought of 2012 had peaked by the end of August in terms of land
coverage, but would continue to be prevalent throughout the end of the year in northern
parts of the south and the Northern Plains.
92
STATE
TEMPERATURE CHANGES FROM JULY TO AUGUST 2012
1.
2.
3.
4.
5.
6.
7.
Δ
Normal
Δ
Normal Δ DTR Normal
Δ AVG
T-MAX
Δ
T-MIN
Δ
2012
Δ DTR
TEMP
2012 T-MAX
2012
T-MIN
(°C)
(°C)
2012
(°C)
(°C)
(°C)
(°C)
(°C)
8.
Normal
Δ AVG
TEMP
(°C)
Wyoming
-1.44
-1.11
-2.44
-1.06
+1.00
-0.06
-1.94
-1.08
Colorado
-1.33
-1.22
-1.83
-0.83
+0.50
-0.39
-1.58
-1.03
N. Dakota
-2.78
-0.83
-4.61
-1.56
+1.83
+0.72
-3.69
-1.19
S. Dakota
-3.61
-1.06
-5.06
-1.33
+1.44
+0.28
-4.33
-1.19
Nebraska
-3.72
-1.28
-4.11
-1.17
+0.39
-0.11
-3.92
-1.22
Kansas
-4.33
-0.83
-4.44
-0.83
+0.11
0.00
-4.39
-0.83
Oklahoma
-2.22
-0.22
-2.67
-0.56
+0.44
+0.33
-2.44
-0.39
Texas
+0.72
-0.06
-0.33
-0.39
+1.06
+0.33
+0.19
-0.22
Arkansas
-1.89
-0.28
-2.50
-0.61
+0.61
+0.33
-2.19
-0.44
Louisiana
-0.17
+0.06
-0.22
-0.28
+0.06
+0.33
-0.19
-0.11
Tennessee
-2.50
-0.39
-2.94
-0.67
+0.44
+0.28
-2.72
-0.53
Mississippi
-1.39
-0.11
-1.33
-0.44
-0.06
+0.33
-1.36
-0.28
Minnesota
-3.61
-1.39
-4.83
-1.44
+1.22
+0.06
-4.22
-1.42
Iowa
-4.28
-1.33
-5.28
-1.28
+1.00
-0.06
-4.78
-1.31
Missouri
-3.89
-0.72
-4.39
-0.94
+0.50
+0.22
-4.14
-0.83
Wisconsin
-4.17
-1.50
-4.39
-1.28
+0.22
-0.22
-4.28
-1.39
Illinois
-4.17
-1.06
-4.94
-1.17
+0.78
+0.11
-4.56
-1.11
Indiana
-4.44
-1.00
-4.72
-1.17
+0.28
+0.17
-4.58
-1.08
Michigan
-3.83
-1.39
-2.94
-0.89
-0.89
-0.50
-3.39
-1.14
Ohio
-3.06
-1.11
-3.72
-1.06
+0.67
-0.06
-3.39
-1.08
Kentucky
-3.00
-0.61
-3.56
-0.72
+0.56
+0.11
-3.28
-0.67
Table 5.9: Temperature changes that occurred from July to August 2012. The July to
August 2012 progressions of T-max and T-min are represented in Columns 1 and 3.
Diurnal temperature range is shown in Col. 5. Comparative long-term normal values are
in Columns 2, 4, and 6. Columns 7 and 8 represent average temperature change in 2012
and seasonal normal average temperature changes respectively.
93
Chapter 6: Conclusions
The purpose of this work was to investigate the behavior of spatial and temporal
DTR through the 20th and 21st centuries in relation to variability in soil moisture that
could range from extreme dryness to moist conditions. Principal component analysis was
applied to a historical Palmer Drought Severity Index dataset producing 10 significant
regions of the United States exhibiting the most unique soil moisture variability. The
analysis was subjected to a rotation procedure leaving 4 regions east of the Rocky
Mountains that account for the most soil moisture variability of the dataset. The 4
analyzed regions were the Ohio River Valley (RPC1), the upper Midwest and eastern
Northern Plains (RPC2), the southeastern United States (RPC3), and the Southern Plains
(RPC5). T-max, T-min, and DTR were acquired for all available locations within the 4
regions and then analyzed for long-term trends in comparison to variability in long-term
soil moisture.
Soil moisture indices in the Ohio River Valley exhibited an overall increase in
PDSI throughout the 20th century into the early 21st century. Prior to 1955, drier
conditions were prevalent. Long-term T-max in this region shows a downward trend of
~0.5°C to date. T-min has shown an overall increase of 0.7°C. Due to the overall trends
in daily temperatures, DTR in the Ohio River Valley has decreased over the period of
94
record. The beginning of the 20th century actually saw an increase in DTR as drier
conditions were persistent but the latter half of the 20th century and early 21st centuries
have seen more moisture causing an overall decrease in DTR.
The upper Midwest and eastern Northern Plains (RPC2) showed similar results to
RPC1 with drier conditions early in the record with moist conditions in the last half of the
20th century into the 21st. PDSI has trended towards more moisture in this region. Longterm T-max in RPC2 has shown a steady non-trending regional average throughout the
period of record with an overall very small decline in T-max. Two summers of very high
T-max were experienced in 1936 and 1988 that can be attributed to very dry conditions
that influence the slight negative trend in overall T-max. T-min however, exhibits a very
clear warming trend. Several summers following 1983 have been the highest T-min of the
record producing a larger warming profile than is shown in RPC1. Because of the clear
warming in T-min, overall DTR has decreased in RPC2 with the summers of 1951, 1993,
and 2010 exhibiting the lowest DTR of the record. These summers correspond to very
high PDSI summers indicting very moist conditions.
The southeastern United States hasn’t shown a strong trend in PDSI over the
course of record but has shown a slight overall increase in PDSI much lower than the
trends in moisture in RPCs 1 and 2. More recent dry conditions have made the PDSI
trend not as positive as the other regions. T-max in RPC3 does not produce an overall
trend due to oscillating summer temperatures throughout the period of record. Cool and
wet conditions in the 1960s-70s gave way to drier and warmer temperatures closing the
20th century producing a very negligible but slight increase in overall T-max. T-min in
95
RPC3 shows a similar but more positive long-term trend than T-max, as lower
temperatures were experienced in the 1960s and 1970s followed by warming with the
highest T-min in the more recent summer of 2010. This summer was dry according to
PDSI but not considered as extreme as other dry summers that occurred in this region.
DTR in the southeastern United States shows an overall decrease due in large part to the
overall larger increase in T-min than T-max. The wet period through the 1960s and 1970s
starts the decline in DTR with the lowest DTR summers occurring in the late 20th and
early 21st century, influencing the DTR trend more than the early dry summers in the
record.
Soil moisture in the Southern Plains (RPC5) produced an overall decline in in
PDSI although it is very negligible. RPC5 shows wetter conditions at the beginning of the
record giving way to dry conditions early in the 20th century with constant soil moisture
until the late 20th into the 21st century. T-max in this region produced on overall longterm increase of ~0.6°C with the highest T-max in 2011. RPC5 was the only region to
show an overall increase in T-max. The summers of 1934 and 2011 exhibited the
warmest T-max of the record which were also linked to dry soil moisture conditions. The
driest summer in RPC5 took place in 1956 but produced T-max ~2°C cooler than the
summers of 1934 and 2011. T-min shows a warming trend slightly larger than that of Tmax. The summers of 1934 and 2011 also produced the warmest T-min. Long-term DTR
shows a near zero trend but does in fact decrease slightly over the course of record.
However, when analyzing the most extreme soil moistures either wet or dry, Tmax seems to vary the most in all regions. T-min on the other hand did not seem to
96
fluctuate based on whether or not the soil moistures were wet or dry. In general, both Tmax and T-min tend to be warmer during drier summers compared to cooler during wet
summers. Variability in T-max seemed to play the largest role in DTR during extreme
soil moisture cases as T-min did not fluctuate as much. As the case study of the drought
of 2012 proved, DTR is larger for summers exhibiting dry soil moistures. As the climate
continues to change, DTR should continue to shrink as heavy precipitation events
become more common. DTR has been decreasing in the areas of the United States that
have increases in long-term soil moisture.
As this thesis focuses on one specific variable that impacts the variability of
DTR (soil moisture), future work could focus on the influence of other parameters. This
work briefly discussed the effect that precipitation has on temperatures. This could be
expanded to analyze variability in precipitation in available data. Investigation of
potential case studies of precipitation events across the United States can be conducted
while probable temperature variations can be analyzed. Comparing the effects of cloud
cover during precipitation events on temperatures compared to the effects of nonprecipitating cloud cover would also be interesting. It seems cloud cover plays a role in
limiting T-max while providing insulation for the Earth keeping T-min elevated at night
(Frich, 1992; Karl et al., 1993; Dai et al., 1999). Humidity is an interesting variable as
well due to the fact that atmospheric water vapor is on the rise from increasing air and
ocean water temperatures. The effects of varying humidites could also affect temperature.
With changing climates, it would be helpful to determine what types of these occurrences
are becoming most common, as these events will be critical in influencing the long-term
97
trends in temperature into the future. Educating the public about the societal and
economic impacts of a changing climate brings upon us important underlying issues.
What do these changes in temperature mean for life moving forward? What are the
consequences of a changing climate? Can it be stopped or mitigated? These types of
questions will continue to surface and be debated until there is a universal understanding
backed by scientific evidence. Continued production of high resolution computer models
with increased effectiveness and accuracies will be critical in forecasting the weather and
climates on Earth in the future.
98
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