The Pennsylvania State University
The Graduate School
Department of Geography
A SYNOPTIC CLIMATOLOGY OF CONTRAIL OUTBREAKS AND ASSOCIATED
SURFACE TEMPERATURE IMPACTS FOR TWO SUB-REGIONS OF THE
CONTINENTAL UNITED STATES
A Thesis in
Geography
by
Jase E. Bernhardt
2013 Jase E. Bernhardt
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Master of Science
May 2013
The thesis of Jase Bernhardt was reviewed and approved* by the following:
Andrew Carleton
Professor of Geography
Thesis Advisor
Brent Yarnal
Professor of Geography & Associate Department Head
Karl Zimmerer
Professor of Geography
Head of the Department of Geography
*Signatures are on file in the Graduate School
iii
ABSTRACT
The artificial cloudiness, or “contrail cirrus,” that results from multiple jet contrail
outbreaks can last for several hours and alter the radiation budget and, thereby, surface
temperatures. An extensive database of satellite-derived “clear-sky” contrail outbreaks over two
regions of the continental United States – the South and the Midwest – for two mid-season
months of 2008 and 2009 was used to determine the potential climatic impact of contrail
outbreaks on surface temperature. Events spanning at least one half of the diurnal cycle of
temperature and covering at least 10,000 km2 were selected for study. The aggregated impact of
outbreaks on maximum and minimum temperatures and on the diurnal temperature range (DTR)
was determined by comparing the departures at stations overlain by outbreaks with those at
adjacent stations having similar synoptic and land surface conditions, but not experiencing
contrail cloudiness. A synoptic climatology (i.e., composite average) of upper troposphere (UT)
variables (temperatures, specific humidity, horizontal winds, and vertical lapse rate) for these
longer-lasting jet contrail outbreaks was also developed to link the impacts of contrail outbreaks
with conditions favorable for their formation. The results at the South stations during the month
of January and the Midwest stations during the month of April both indicate a statistically
significant suppression of DTR at outbreak stations versus adjacent non-outbreak stations. The
South outbreaks generally covered a larger area, occurred in conjunction with an anomalously
cooler UT, and were more likely to take place in a region of warm air advection. Moreover, large
wind shear values and strong gradients of the aforementioned UT variables across the outbreak
box, symptomatic of a baroclinic environment, characterize the formation of the long-lived
contrail outbreaks. The results of this study demonstrate the impact jet contrails have on short-
term weather and climate through the reduction of DTR. The findings can also be used to assist
iv
with the development of a system for real-time forecasting of jet contrails based on the UT
conditions present.
v
TABLE OF CONTENTS
LIST OF FIGURES ................................................................................................................. vi
LIST OF TABLES ................................................................................................................... viii
ACKNOWLEDGMENTS ....................................................................................................... viiii
Chapter 1 Introduction ............................................................................................................ 1
Formation and Observation of Jet Contrail Outbreaks ..................................................... 1
Potential Climatic Impacts of Jet Contrail Outbreaks ...................................................... 5
Chapter 2 Data and Methodologies ......................................................................................... 10
The Outbreaks .................................................................................................................. 10
Selection of Station Pairs for DTR Comparison .............................................................. 16
NARR Data and Development of the Composite Synoptic Environmental Conditions .. 18
Chapter 3 DTR Impacts of Longer-Lived Jet Contrail Outbreaks .......................................... 23
Chapter 4 Synoptic Climatology of Longer-Lived Jet Contrail Outbreaks ............................ 27
The South January 2008 and 2009 Jet Contrail Outbreaks .............................................. 27
The Midwest April 2008 and 2009 Jet Contrail Outbreaks ............................................. 37
Comparison of the January South and April Midwest Outbreak UT Conditions ............ 46
Chapter 5 Summary, Discussion, and Conclusions ................................................................ 49
Appendix A Sample 500 mb Geopotential Height Imagery ................................................... 57
Appendix B Sample Upper Tropospheric Wind Shear Imagery ............................................. 60
vi
LIST OF FIGURES
Figure 1-1: September 11th – 14
th, 2001 DTR anomalies (a) and mean frequency of
contrails for October 1977-79 and 2000-01 (b) ............................................................... 3
Figure 1-2: Satellite-derived changes in contrail frequency between 1977-79 and 2000-
02, normalized to every 100 images ................................................................................ 8
Figure 2-1: Regions of higher contrail outbreak frequency, January 2000-02 ........................ 11
Figure 2-2: Regions of higher contrail outbreak frequency, April 2000-02 ........................... 12
Figure 2-3: January 2009 outbreak frequency anomaly (departure from January 2000-02
averages) ......................................................................................................................... 12
Figure 2-4: April 2009 outbreak frequency anomaly (departure from April 2000-02
averages) ......................................................................................................................... 13
Figure 2-5: Starting times of jet contrail outbreaks selected .................................................. 15
Figure 2-6: Locations of bounding boxes for January 2008 and 2009 longer-lived
outbreaks ......................................................................................................................... 16
Figure 2-7: Locations of bounding boxes for April 2008 and 2009 longer-lived outbreaks .. 17
Figure 2-8: Locations of central outbreak box for January 2008 and 2009 outbreaks in
relation to all outbreak bounding boxes ........................................................................... 21
Figure 2-9: Locations of central outbreak box for April 2008 and 2009 outbreaks in
relation to all outbreak bounding boxes ........................................................................... 22
Figure 4-1: 250 mb temperature composite for January South region outbreaks. .................. 30
Figure 4-2: 250 mb temperature composite anomaly for January South region outbreaks ..... 30
Figure 4-3: 250 mb zonal wind composite for January South region outbreaks...................... 31
Figure 4-4: 250 mb zonal wind composite anomaly for January South region outbreaks. ...... 31
Figure 4-5: 250 mb specific humidity composite anomaly for January South region
outbreaks. ......................................................................................................................... 32
Figure 4-6: Sample 200 mb temperature gradient for 1/8/09 outbreak .................................... 33
Figure 4-7: Composite vertical temperature profile for January South outbreaks. .................. 34
vii
Figure 4-8: Composite vertical temperature anomalies for January South outbreaks ............. 34
Figure 4-9: Composite vertical zonal wind profile for January South outbreaks. ................... 35
Figure 4-10: Composite vertical zonal wind anomalies for January South outbreaks. ............ 35
Figure 4-11: 250 mb temperature composite for April Midwest region outbreaks. ............... 40
Figure 4-12: 250 mb temperature composite anomaly for April Midwest region outbreaks ... 40
Figure 4-13: 250 mb zonal wind composite for April Midwest region outbreaks. .................. 41
Figure 4-14: 250 mb zonal wind composite anomaly for April Midwest region outbreaks. ... 41
Figure 4-15: 250 mb specific humidity composite anomaly for April Midwest region
outbreaks. ......................................................................................................................... 42
Figure 4-16: Composite vertical temperature profile for April Midwest outbreaks. ............... 43
Figure 4-17: Composite vertical temperature anomalies for April Midwest outbreaks ........... 44
Figure 4-18: Composite vertical zonal wind profile for April Midwest outbreaks. ................. 44
Figure 4-19: Composite vertical zonal wind anomalies for April Midwest outbreaks. ........... 45
viii
LIST OF TABLES
Table 2-1: Information for the selected longer-lived jet contrail outbreaks analyzed in
this study .......................................................................................................................... 14
Table 3-1: DTR impacts of longer-lived jet contrail outbreaks. ............................................. 24
Table 3-2: Composite DTR impacts of jet contrail outbreaks ................................................ 26
Table 3-3: Composite DTR Impacts of days before and after jet contrail outbreaks .............. 26
Table 4-1: Composite horizontal gradients of UT variables for January South region
outbreaks .......................................................................................................................... 32
Table 4-2: Composite station UT data for January South region outbreaks ........................... 33
Table 4-3: Synoptic analysis of January South outbreaks ...................................................... 36
Table 4-4: Composite horizontal gradients of UT variables for April Midwest region
outbreaks .......................................................................................................................... 42
Table 4-5: Composite station UT data for April Midwest region outbreaks .......................... 43
Table 4-6: Synoptic analysis of April Midwest outbreaks ...................................................... 45
ix
ACKNOWLEDGMENTS
I am deeply indebted to my advisor, Andrew Carleton, for his close guidance throughout
the process of completing this thesis. I would also like to thank the other members of my
committee, Brent Yarnal and Paul Knight, for their valuable insights, as well as Sonia Miller for
her technical assistance. Partial funding for this research was provided by National Science
Foundation grants 0819396 and 0819416.
Chapter 1
Introduction
Particularly since the Industrial Revolution, humans have exerted a significant influence
on global climate. The most important anthropogenic influence on surface temperature has been
the emissions of greenhouse gases, as well as of various types of aerosols. However, society has
also had other, more subtle – but still detectable – effects on Earth’s climate system. Among these
additional impacts are land use- land cover changes and jet aviation contrails. Aviation wields a
dual influence on climate, as planes emit greenhouse gases and also initiate artificial cirrus-like
clouds known as contrails (Lee et al. 2009). Although the impacts of contrails are not as well
understood or studied as greenhouse gases, it is generally accepted that they contribute to
regional-scale and global warming. In addition, numerous papers have suggested that contrails
reduce diurnal temperature range (DTR), which could have both beneficial and detrimental
influences on society. This chapter summarizes the impacts of jet contrails on the climate system.
The discussion identifies the key gaps in our understanding of contrail-climate impacts, including
those lacunae that form the basis of this thesis research.
Formation and Observation of Jet Contrail Outbreaks
Contrails are the condensation trails left by jet engines as they release heated, moist
particles and water vapor into the far colder ambient upper troposphere (UT). The plane’s engines
create turbulence, mixing these two types of air together. If the ambient atmosphere is sufficiently
humid, contrails form in narrow lines following the path of the jet. When the atmosphere is
relatively humid, contrails have the potential to last for several hours, slowly spreading due to the
shear associated with the jet stream. In a less humid UT, contrails may last only briefly, or not be
2
produced at all. Hence, jet contrails can be an indicator of the general state of the UT. If contrails
do not form, then there is likely dry air and stable conditions present in the UT, but if they do
form and persist, the UT is likely at or near saturation and less stable, sometime in conjunction
with a trough or cyclone. Contrails are not unique to one geographical area as they can form just
about anywhere, although they are obviously more common in areas of frequent jet traffic, such
as portions of the United States (U.S.) and Europe (National Weather Service, 2011).
A few case studies have shown that contrails have been of historical importance (e.g.,
Ryan et al. 2011; Travis et al. 2004). One of the first examples of widespread jet contrails
occurred during World War II. At that time, commercial air travel was not common, so the high
number of military aircraft involved in allied bombing raids over Europe was highly anomalous.
When synoptic weather conditions were favorable, extensive contrails were observed over
southeastern England, which served as the launching pad for these bombing missions. The
contrails remained for up to several hours, which resulted in a suppressed diurnal temperature
range (DTR) at the surface. This statistically significant reduction in DTR was only found in
locations overflown by a high volume of aircraft, showing that contrails were responsible (Ryan
et al. 2011).
Another instance of historic events playing a role in contrails research, particularly the
potential role in suppressing DTR, was the grounding of commercial aviation in the U.S. after the
attacks of September 11th, 2001. The lack of flights was especially significant over heavily
trafficked regions of the U.S., such as the Midwest. Although tragic, this event presented an ideal
opportunity for climate researchers (Travis et al. 2004). During the three days after the attacks
when no flights occurred, conditions favorable for contrail development existed over the Midwest
and other U.S. sub-regions. Indeed, in the absence of contrails, a statistically significant increase
in DTR was observed not only in this region, but also in portions of the Northeast and
3
Intermountain West (Figure 1-1) – all areas that would have seen contrails had planes been flying
as normal. Interestingly, in the post-9/11 case, it was found that maximum temperatures were
more affected by the lack of contrails than were minimum temperatures. This finding seems
logical as air traffic frequencies are highest during the day, so the lack of any contrails would
result in a larger impact than at night. Daytime temperatures would further be increased due to the
reduction in planetary albedo caused by the reduction in high clouds. Additionally, the research
confirmed that jet contrails most often form in advance of frontal cyclones and convective storms
due to the moistening of the UT at the leading edge of these systems (DeGrand et al. 2000; Travis
et al. 2004).
Jet contrails can be easily visible from the surface during the day, unless obstructed by a
significant amount of low and mid- levels clouds. Similarly, the linear cloud formations
Figure 1-1: September 11th – 14
th, 2001 DTR anomalies (a) and mean frequency of contrails for
October 1977-79 and 200-01 (b) (Travis et al. 2004).
4
characteristic of newly-formed contrails are highly discernible on different types of satellite
imagery, primarily in the visible and infrared (IR) portions of the spectrum, with the IR being the
more useful to researchers because it can be used at any time of day (Carleton and Lamb 1986).
IR imagery also has the advantage of being able to highlight contrails due to their very low
temperatures of below -45 oC (given a location in the upper atmosphere). Persisting contrails –
features that thin vertically and spread laterally – are those contrails most likely to be seen on
satellite images, and also to have the greatest impact on DTR. In these contrails, the “cloud
greenhouse” effect may outweigh the cloud albedo affect, potentially leading to a net surface
warming (as compared to clear-sky conditions). Furthermore, contrails can usually be
distinguished from natural cirrus on satellite IR imagery as they are often shorter and oriented in
different directions from their natural counterparts. Haphazard, intersecting patterns of multiple
linear contrails known as outbreaks also indicate persisting contrails in an area of high jet traffic,
Satellite studies of persisting contrails have also revealed other notable features of outbreaks.
Outbreaks tend to form in groups on meso- or synoptic- scales, and may recur over several days
while conditions remain favorable (Carleton et al. 2008). Satellite observations also confirm that
the majority of contrail outbreaks are associated with frontal cyclones; many form in the warm
advection regions in advance of the cyclone, although some occur in the cold air (DeGrand et al.
2000). Although contrails are able to form in clear air, high pressure conditions, this category is
dwarfed in frequency by synoptic situations where they form in conjunction with natural cirrus
associated with frontal cyclones and baroclinic waves.
As the atmospheric conditions surrounding contrail development became more apparent,
an empirical model was developed to predict widespread contrail outbreaks in key regions of the
U.S. (Travis et al. 1997). Such a model is useful to both climatologists and weather forecasters;
the latter would utilize the model results to better predict when contrails form. The Travis et al.
(1997) model utilized two variables affecting contrail development: the average temperature in
5
the 300 to 100 mb layer, and the column-integrated water vapor between 700 to 100 mb. It was
shown that moderate values of these two variables were necessary for contrail formation in the
presence of jets. Formation temperatures were most conducive in the -50 to -60 oC range, as
lower temperatures signified an air mass containing too little water vapor to foster clouds, while
warmer temperatures limited the development of ice crystals. Meanwhile, water vapor “counts”
derived from GOES water vapor image brightness temperatures were most ideal between 170 and
190 K for contrails; less water vapor represented air lacking sufficient moisture to support clouds,
while values over 190 typically meant there was enough moisture to form only natural (cirrus)
clouds. The statistical model took into account temperature and water vapor content, as well as
the relationship between the two, to calculate the probability of widespread contrail formation for
66 outbreak cases in January and April 1987. The model worked well in contrail and non-contrail
prediction for those 66 events, indicating that UT temperature and water vapor content are among
the conditions important for the formation of outbreaks of persistent contrails, and further that the
study of UT conditions associated with contrail outbreaks can help improve their prediction.
Potential Climatic Impacts of Jet Contrail Outbreaks
The success of the Travis et al. (1997) contrail forecasting method demonstrates that
improved understanding of contrail outbreak formation could also have direct applications to
improving weather and short-term climate projections due to contrails’ effect on surface
temperature and the DTR. For example, the National Weather Service Model Output Statistics
(MOS) take into account a variety of climatological and current weather data when composing a
forecast for a station, but the potential for contrail formation is not one of the variables used.
Notwithstanding, because widespread, persistent contrails affect DTR noticeably, they likely
produce forecast errors in MOS products because this additional cloud cover causes daily
6
maximum temperatures to be consistently overestimated and minimum temperatures to be
underestimated (Travis et al. 2011). Integrating elements of a contrail forecast model into the
MOS forecasts might help to reduce this bias. Such improvements could be helpful in several
ways. For instance, when a winter storm is approaching, forecasters need to know the surface
temperatures leading up to the storm in order to predict precipitation type and resultant snow
accumulation or ice accretion. Contrail formation ahead of the storm system, altering
temperatures even just a degree or two either way, can be crucial, as this seemingly small
difference is important when predicting precipitation types and accumulation. Thus, knowledge
of contrail outbreak locations and associated synoptic conditions is important.
Contrails will continue to form as a result of jet traffic for the foreseeable future, and are
therefore taken into account by the IPCC and other climate change organizations employing
climate models (EPA Aircraft Contrails Factsheet, 2000). Nonetheless, there is still considerable
uncertainty over the precise forcing of contrails on the climate system. Global observation of
contrails is limited, and their exact radiative properties are still not entirely known. However, the
IPCC 4th Assessment Report (2007) estimated that linear contrails had a net radiative forcing of
roughly 0.010 Wm-2
– a modest warming effect (Lee et al. 2009). Two primary physical
processes govern the long-term climate impact from contrails, which are also responsible for their
effect on DTR. Clouds reflect incoming solar radiation away from the Earth, decreasing the
amount of shortwave radiation that reaches the surface during the daytime. However, this effect is
more than offset by the ability of contrails to absorb and emit longwave radiation back down to
the Earth, allowing more heat to be trapped in the atmosphere at night. As a result, thin cirrus and
persisting contrails tend to warm the Earth’s surface (Minnis et al. 2004). By including contrails
into Global Climate Model (GCM) simulations, the Minnis study determined that the maximum
forcing from jet contrails is between 0.006 and 0.025 Wm-2
, while causing a net warming at the
surface of about 0.2 to 0.3 oC per decade. Moreover, Lee et al. (2009) predicted that overall
7
radiative forcing from contrails will increase to about 0.020 Wm-2
in 2020 and, depending on the
model used, to between 0.037 and 0.055 Wm-2
by 2050. It must be noted that any projections of
contrail impacts are highly dependent on their optical depth (i.e., a dimensionless quantity
describing the transparency of the cloud, which is a function of the ice crystal density and size).
Karcher et al. (2009) analyzed optical depths of contrails from observations and models,
determining that they could vary between 0.05 to 0.5, according to meteorological factors present
for contrail development and duration time. The results of these studies confirm that contrail
outbreaks impact both short-term weather and longer-term climate, although the effects can vary
between outbreaks.
According to Travis et al. (2007), contrail frequency over the U.S. increased by 101.5%
when comparing the three-year periods 1977-1979 and 2000-2002, although considerable
variance in these frequency changes were seen both at spatial scales (Figure 1-2) and seasonal
scales. Although the increase in jet traffic between the two periods explained some of the overall
increase, this change in contrail frequency was largely explained by differences in atmospheric
conditions near the tropopause level. The tropopause separates the troposphere, where clouds
usually form, and the stratosphere, which is too stable and dry to support cloud- and contrail-
formation. A higher and therefore colder tropopause ensures that more jets fly in the UT as
opposed to the lower stratosphere, making contrail formation more likely. The Travis et al. (2007)
study determined that increases in tropopause height and associated decreases in tropopause
temperature between the 1977-79 and 2000-02 periods are reflected in the corresponding increase
in contrail frequency, especially over the Midwest and Eastern U.S. where contrails increased the
most.
8
In summary, it is clear that persisting contrails, especially when and where they comprise
outbreaks, influence the shortwave and longwave radiation streams, and thereby the net radiation.
Accordingly, contrails’ role in surface temperature conditions (e.g., the DTR) is likely to be
suppressive. Yet, despite this knowledge and the previously mentioned satellite surveys of jet
contrail outbreaks and the synoptic conditions associated with their formation, the role of
contrails in DTR is still not adequately known, including their spatial and temporal dependence.
This study, therefore, has the following two objectives:
1. To determine UT synoptic conditions associated with the formation and persistence of
longer-lived (≥ 6-hour) jet contrail outbreaks.
2. To quantify the impact of these outbreaks on the surface DTR on two sub-regions of
the U.S. characterized by high frequencies of contrails (the South and Midwest) and for
two contrasting mid-season moths (January in the South and April in the Midwest).
Figure 1-2: Satellite-derived changes in contrail frequency between 1977-79 and 2000-02,
normalized to every 100 images (Travis et al. 2007).
9
To achieve these objectives, 42 contrail outbreaks in 2008 and 2009 from a previous
satellite survey (Carleton et al. 2013) were selected for analysis. The UT synoptic climatology of
these outbreak events was determined using North American Regional Reanalysis (NARR) data
on temperature, humidity, and winds. These conditions and their departures from normal were
composited for each month and region. Surface temperature data for stations overlain by these
longer-lived contrail outbreaks was obtained, and compared to nearby stations not being impacted
by the outbreak, but with otherwise similar characteristics (e.g., elevation) and large-scale
conditions (e.g., position in trough/ridge system), to assess the contrails’ effects on DTR. Chapter
2 further examines the NARR data, the method of identifying the contrail outbreaks, and the
approach used to develop the synoptic climatology and DTR comparisons. Chapter 3 presents
how the outbreak affects DTR, while Chapter 4 presents details on the synoptic climatology of
the longer-lived contrail outbreaks. Finally, Chapter 5 discusses these results and their
significance, and provides concluding remarks on the study and its motivation for future work.
Chapter 2
Data and Analysis Methodologies
This chapter comprises three sections. The first section clarifies the analysis and selection
process used – specifically how the outbreaks were identified and why they were chosen. The
second and third sections, respectively, discuss the acquisition and manipulation of the NARR
and other data used to determine the climatology of the synoptic outbreaks and their impacts on
the DTR.
The Outbreaks
Carleton et al. (2008) described a methodology used to manually determine the presence
and spatial extent of jet contrail outbreaks using Advanced Very High Resolution Radiometer
(AVHRR) satellite thermal IR data for mid-season months of 2000-02. When three or more
contrails in the same general location persisted for at least four to six hours (i.e., they were
observed on multiple consecutive satellite images), they were counted as an outbreak. A
bounding box comprising two pairs of latitude and longitude coordinates enclosed all of the
contrails in the outbreak was also determined for each outbreak identified on the AVHRR.
Finally, the approximate time the outbreak commenced was noted. Silva (2009) recently applied
this method to the four mid-season months (January, April, July, and October) of 2008 and 2009;
the data generated by that analysis are used here.
For the purposes of this study and therefore to determine the potential impact the
outbreaks had on DTR, each selected outbreak must have spanned at least one half of the diurnal
cycle of temperature (early morning minimum or mid-afternoon maximum). The outbreaks also
11
had to encompass an area of at least 10,000 km2, as defined by the size of its bounding box, so
that surface weather stations being affected by each outbreak could be discerned. Finally, each
potential longer-lived outbreak was verified on the IR satellite data to confirm its occurrence in
an area of limited natural cloud cover (i.e., clear to partly clear skies).
Silva (2009) classified the outbreaks by region of formation – with the primary regions
being the Midwest, South, and Northeast – and found that the regions of maximum development
vary by mid-season month (Figures 2-1 and 2-2). For the present study, two separate months and
regions were investigated, outbreaks in the South region during January 2008 and 2009 and
outbreaks in the Midwest region during April 2008 and 2009, because contrail outbreak
frequency is at a maximum in those regions during those months (Figures 2-3 and 2-4). This
study also analyzes how longer-lived outbreaks affect DTR, so other factors affecting DTR that
could confound interpretation of the results were controlled. Primary among these additional
factors is snowcover, which alters the solar radiation receipt at the surface due to its high albedo.
Therefore, the focus of the investigation of the South region was January, as this area typically
experiences little snowcover, whereas the focus for the Midwest was April because snowcover is
not common there at that time.
Figure 2-1: Regions of higher contrail outbreak frequency, January 2000-02 (Carleton et al.
2013).
12
Figure 2-2: Regions of higher contrail outbreak frequency, April 2000-02 (Carleton et al. 2013).
Figure 2-3: January 2009 outbreak frequency anomaly (departure from January 2000-02
averages) (Carleton et al. 2013).
13
A total of 42 outbreaks fit the aforementioned criteria and were analyzed in this study
(Table 2-1). On average the outbreaks covered an area of 245,800 km2. Most outbreaks were first
identified as taking place on satellite images representing the early morning or mid-afternoon
hours. However, a quarter of the outbreaks began during the late evening hours, and lasted
overnight (Figure 2-5). Outbreaks occurring during the early morning, mid-afternoon, and late
evening hours are expected, given the high frequency of flights over the Continental U.S. during
these times (Minnis et al. 1997). Generally, although most outbreak bounding boxes were
rectangles oriented west-to-east, some were oriented north-to-south and a few were almost perfect
squares. Of the outbreaks chosen, twenty-four occurred during January in the South (Figure 2-6),
and 18 took place in April in the Midwest (Figure 2-7). In each region, the outbreaks occurred
Figure 2-4: April 2009 outbreak frequency anomaly (departure from April 2000-02 averages)
(Carleton et al. 2013).
14
over a fairly wide geographical area – up to 1,500 km apart – although they all overlapped with at
least one other outbreak and up to eight separate outbreaks could cover the same location.
Table 2-1: Information for the jet contrail outbreaks analyed in this study.
Outbreak
Date/Time
Outbreak Station Non- Outbreak
Station
Direction of Non-
Outbreak Station
from Outbreak
January South
15z 1/3/2008 Little Rock, AR Memphis, TN East
12z 1/4/2008 Abilene, TX Wichita Falls, TX North
12z 1/7/2008 Lake Charles, LA Hattiesburg, MS East
18z 1/7/2008 Alexandria, LA Baton Rouge, LA East
3z 1/8/2008 Natchez, MS Texarkana, AR West
3z 1/8/2008 Anniston, AL Montgomery, AL South
15z 1/11/2008 Memphis, TN Jackson, MS South
18z 1/14/2008 Inverness, FL Arcadia, FL South
12z 1/15/2008 Jacksonville, FL Lakeland, FL South
21z 1/18/2008 Greensboro, NC Richmond, VA North
03z 1/22/2008 Atlanta, GA Valdosta, GA South
18z 1/23/2008 Gainesville, FL Waycross, GA North
3z 1/26/2008 London-Corbin, KY Louisville, KY North
18z 1/29/2008 Valdosta, GA Orlando, FL South
15z 1/30/2008 Fort Myers, FL Okeechobee, FL East
03z 1/1/2009 Tallahassee, FL Albany, GA North
15Z 1/1/2009 Columbus, GA Athens, GA North
03Z 1/5/2009 Little Rock, AR Shreveport, LA South
15Z 1/8/2009 Monroe, LA Tuscaloosa, AL North
03Z 1/11/2009 Jacksonville, FL Ocala, FL South
18Z 1/11/2009 Jacksonville, FL Ocala, FL South
03Z 1/18/2009 Daytona Beach, FL Jacksonville, FL North
21Z 1/26/2009 Denison, TX Houston (Bush), TX South
21Z 1/28/2009 Victoria, TX San Angelo, TX North
April Midwest
06z 04/02/2008 Peru, IL Decatour, IL South
12z 04/06/2008 Carbondale sewage plant
(IL)
Evansville, IN
East
18z 04/07/2008 Columbus, IN Bowling Green, KY East
12z 04/13/2008 Algona, IA Huron, SD West
15
18z 04/14/2008 Findlay, OH Kalamazoo, MI West
12z 04/22/2008 Wynne, AR Fort Smith, AR West
12z 04/25/2008 Jackson, TN Huntsville, AL East
12z 04/26/2008 Chicago- Midway Saint Louis, MO South
18z 04/27/2008 Brookings, SD Hastings, NE West
09z 04/29/2008 Springfield, IL Carbondale sewage
plant (IL) South
03z 04/02/2009 Erie, PA Buffalo, NY North
18z 04/04/2009 Peoria, IL Indianapolis, IN South
03z 04/15/2009 Kansas City, MO Oklahoma City, OK South
21z 04/20/2009 Nebraska City, NE Aberdeen, SD North
09z 04/21/2009 Wichita, KS Sioux City, IA North
12z 04/22/2009 Erie, PA Toledo, OH West
15z 04/26/2009 Wichita, KS Dodge City, KS West
21z 04/27/2009 Otter Tail, MN Aberdeen, SD West
Figure 2-5: Starting times of jet contrail outbreaks selected.
0
2
4
6
8
10
12
00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z
Fre
qu
en
cy
Outbreak Time
16
Figure 2-6: Locations of bounding boxes for January 2008 and 2009 longer-lived outbreaks.
Selection of Stations Pairs for DTR Analysis
To determine the impact of the longer-lived jet contrail outbreaks on DTR in the South
(January) and Midwest (April), surface observed temperature data were obtained from the Northeast
Regional Climate Center’s CLIMOD system. The daily DTR was determined as the difference
17
between the daily maximum and daily minimum temperature at a particular station. The DTR
anomaly from normal was also determined by comparing the daily DTR to the normal DTR
(defined as the difference between the normal minimum and normal maximum during the
station’s period of record, usually at least 30 years). For each longer-lived outbreak, the DTR and
DTR anomalies at two adjacent stations were chosen for comparison: one station overlain by
contrails, and the other nearby but not experiencing the outbreak. To isolate the impact of the
contrail outbreak on DTR, the two chosen stations possessed similar physical, climatic, and
meteorological conditions. Elevation, proximity to water, general soil moisture conditions, and
land cover were also held as similar as possible for the station pairs. Additionally, the synoptic-
Figure 2-7: Locations of bounding boxes for April 2008 and 2009 longer-lived outbreaks.
18
scale weather pattern was also taken into account for station selection: the outbreak and non-
outbreak station pairs for each event needed to be under the influence of the same weather regime
(e.g., under high pressure, in a trough axis, etc.).
Airport weather stations were used almost exclusively for the DTR analysis. Two
National Weather Service Cooperative Observer sites served as proxies when airport data were
missing. The non-outbreak stations could be any direction from the outbreak stations (north,
south, etc.). These stations and their direction from the outbreak station are noted in Table 2-1.
NARR Data and Development of the Composite Synoptic Environmental Conditions
The composite synoptic environments of the longer-lived contrail outbreaks utilized
NARR data, which were manipulated and visualized using the Grid Analysis and Display System
(GrADS). GrADS allowed for easy management of the data through pre-written codes and tools
tailored to environmental data. Separate composites were created for the two months/regions. The
NARR dataset is the National Center for Environmental Prediction’s (NCEP) high resolution
combined model and assimilated dataset. It is available over North America at high spatial and
temporal resolutions; approximately 0.3 degrees by 0.3 degrees, for every three hours (i.e., eight
times daily), and for 29 levels of the atmosphere (Mesinger et al. 2006). The NARR dataset is
currently available for the 32-year period 1979-2010, thus comprising the study period mid-
season months.
Several different atmospheric variables (temperature, specific humidity, zonal wind, and
wind shear) in the UT were selected in the NARR data to create the composite synoptic
environmental conditions of longer-lived contrail outbreaks in the South (January) and Midwest
19
(April). These variables were analyzed at the 300 mb, 250 mb, and 200 mb levels, as contrails
typically form at the jet cruising altitude of 10 to 12 km (DeGrand et al. 2000).
Composite average and anomaly numerical data for UT temperature, specific humidity,
and zonal wind were computed, along with their horizontal gradients, over the bounding box area
of each outbreak. The anomalies were based on the departures from normal over the 32 years of
NARR data. The three variables (along with their horizontal gradients and vertical wind shear)
were selected for analysis as their anomalies have been shown to be associated with increased
outbreak frequency (DeGrand et al. 2000), and comprise the three critical variables for contrail
formation and persistence (Appelman 1953; Schrader 1997; Hanson and Hanson 1995; Jensen et
al. 1998). Moreover, the maximum value, minimum value, and grid box average of each of the
three atmospheric variables was computed for all 42 outbreaks. The horizontal gradient of each
variable was also calculated by first taking the difference between the maximum and minimum,
and then dividing by the diagonal length across the rectangular grid box. Last, the maximum,
minimum, average, and gradient data for each outbreak were compared to the normals and
composited for the two months and regions.
Composite average and anomaly numerical data (from the nearest grid point in the
gridded NARR data) was also computed for the individual stations inside the bounding box being
affected by the contrail outbreaks. The actual value, anomaly, and standardized anomaly were
calculated for the UT temperature, specific humidity, and zonal wind for each outbreak and
composited for the two months and regions. Vertical gradients of temperature and zonal wind
over each station were also calculated numerically. Specifically, the directional wind shear
between 300 mb and 100 mb and the temperature lapse rate between 300 mb and 200 mb were
determined for each outbreak station, composited by variable, and compared to normal. Wind
shear between 100 mb and 300 mb was necessary because often the directional change between
300 mb and 200 mb was too small to be useful and indicative of the overall pattern. The sign of
20
the advection present in the UT for each outbreak was also established using the directional wind
shear data. Winds turning clockwise between 300 mb and 100 mb represented warm air
advection, while winds turning counter-clockwise between the two levels represented cold air
advection. Advection was determined for use in this analysis because it helps give a sense of the
larger-scale synoptic setup controlling the outbreak formation area (DeGrand et al. 2000).
To determine the synoptic pattern present throughout the entire atmosphere, vertical
composite profiles of the temperature, specific humidity, and zonal wind between 1000 mb and
100 mb were created for the outbreak stations and compared to normal. To clarify the synoptic
patterns in the UT associated with the outbreaks, composite maps across a representative grid box
for the two months and regions were created. The actual (conditions during the outbreak) and
anomaly (departure from normal) patterns of 250 mb temperature, specific humidity, and zonal
wind were utilized for these maps. The maps comprised a central grid box of the outbreaks in
each region, which allowed the UT conditions in the typical outbreak region to be composited, as
the majority of the outbreaks covered part or all of the central grid box. Figures 2-8 and 2-9 show
the location of the two central grid boxes used for the 250 mb composites, in relation to all of the
outbreaks (Figures 2-6 and 2-7). The representative grid box for each month and region was in a
fixed location, at the average location of the all of the region’s outbreaks, as determined by the
ArcGIS centroid tool. The size of the box was established as the average size of all the outbreak
bounding boxes. Lastly, to determine the synoptic circulation category associated with each
outbreak, the 500 mb height pattern present over each outbreak was determined from the NARR
data, and each outbreak was categorized as being inside a trough axis, between a trough and
ridge, or in a flat wave. These three categories are a simplified version of the categorization done
by DeGrand et al. (2000, Figure 11a.) for outbreaks, which included six additional categories-
closed low, closed high, at or near ridge axis, zonal flow with a weak height gradient, jet stream
21
maximum, and unclassified. All but one of the outbreaks in the present study fell broadly into the
three categories used, which allowed for the simplification.
Figure 2-8: Locations of central outbreak box for January 2008 and 2009 outbreaks in relation to all
outbreak bounding boxes.
22
Figure 2-9: Locations of central outbreak box for April 2008 and 2009 outbreaks in relation to all
outbreak bounding boxes.
Chapter 3
DTR Impacts of Longer-Lived Jet Contrail Outbreaks
In both regions and mid-season months, the longer-lived contrail outbreaks suppressed
DTR at the outbreak station versus at the adjacent non-outbreak station (Tables 3-1, 3-2). This
reduction in DTR underneath outbreaks is statistically significant at greater than the 95% level for
each studied month and region, although the outbreaks in the South during January had an even
stronger impact on the DTR. For those 24 outbreaks, DTR averaged 6.417 oF smaller than at the
nearby non-outbreak stations. For the 18 Midwest April outbreaks, the DTR was suppressed an
average of 5.278 oF. Although most outbreaks reduced DTR at the outbreak station, this was not
always the case (Table 3-1). In four of the January outbreaks (South) and three of the April
outbreaks (Midwest), DTR was either the same or slightly greater at the station affected by the
contrail outbreak. It is possible that these cases were a result of microclimatological or
observational biases in the stations used that could not be accounted for in the station selection
process. It is even more likely that the DTR suppression caused by each outbreak varies
considerably, with maximum suppressions of 27 oF (during April) and 25
oF (during January).
To further evaluate the DTR suppression of the contrail outbreaks, DTR at each
“adjacent” station (outbreak, non-outbreak) pair was normalized by comparing the observed DTR
for the outbreak to the long-term station average DTR for that date. These results also indicate
that the contrail outbreaks reduced DTR and that their impact on DTR outweighed the impact of
the larger-scale synoptic pattern on DTR. This effect was again most apparent during the January
South outbreaks, with the non-outbreak stations having a DTR of 2.833 oF above normal, and the
outbreak stations having a DTR of 2.708 oF below normal. For the April Midwest outbreaks, the
non-outbreak stations had a DTR of 2.722 oF above normal, while the outbreak stations had a
24
DTR of 1.611 oF below normal. Table 3-2 shows the composite DTR differences for each set of
outbreaks. To further distinguish the DTR impact of the contrail outbreak from other possible
influences, the DTR at each station for the day before and after the outbreak was determined
(Table 3-3). For each set of outbreak events, the average DTR difference between the outbreak
and non-outbreak stations was considerably smaller on both the day before and the day after the
outbreak. For the January South outbreaks, the DTR was only suppressed by 1.25 oF at the
outbreak stations on the day before or after the actual outbreak, compared to the non-outbreak
station. Moreover, for the April Midwest outbreaks, the DTR was only reduced by 2.639 oF at the
outbreak versus non-outbreak stations on the day before and after the actual outbreak. Unlike the
contrail outbreak days, neither the DTR suppressions during the day before and the day after the
outbreak were statistically significant, nor was there a statistically significant difference between
the DTR reduction of the day before or the day after the outbreak.
Table 3-1: DTR impacts of longer-lived jet contrail outbreaks.
Outbreak
Date/Time
Outbreak
DTR
Non-
Outbreak
DTR
DTR
Difference
Outbreak
DTR
Anomaly
Non-
Outbreak
DTR
Anomaly
January (South)
15z 1/3/2008 18 17 1 1 -1
12z 1/4/2008 27 29 -2 3 6
12z 1/7/2008 12 20 -8 -7 -3
18z 1/7/2008 11 21 -10 -10 1
3z 1/8/2008 11 27 -16 -8 10
3z 1/8/2008 19 26 -7 -1 4
15z 1/11/2008 16 23 -7 -1 4
18z 1/14/2008 20 27 -7 -4 2
12z 1/15/2008 22 23 -1 -1 4
21z 1/18/2008 11 16 -5 -8 -2
03z 1/22/2008 8 18 -10 -11 -7
18z 1/23/2008 10 35 -25 -13 8
25
3z 1/26/2008 9 6 3 -10 -10
18z 1/29/2008 28 29 -1 8 7
15z 1/30/2008 22 29 -7 0 6
03z 1/1/2009 18 32 -14 -6 8
15Z 1/1/2009 23 25 -2 3 7
03Z 1/5/2009 10 8 2 -8 -12
15Z 1/8/2009 20 20 0 0 0
03Z 1/11/2009 19 26 -7 -3 2
18Z 1/11/2009 19 26 -7 -3 2
03Z 1/18/2009 37 40 -3 15 18
21Z 1/26/2009 17 22 -5 -5 1
21Z 1/28/2009 23 39 -16 4 13
April (Midwest)
06z 04/02/2008 18 24 -6 -4 4
12z 04/06/2008 25 26 -1 2 3
18z 04/07/2008 31 32 -1 8 9
12z 04/13/2008 6 33 -27 -18 9
18z 04/14/2008 22 25 -3 2 1
12z 04/22/2008 20 23 -3 -2 1
12z 04/25/2008 20 26 -6 -3 2
12z 04/26/2008 18 21 -3 -1 1
18z 04/27/2008 16 26 -10 -8 2
09z 04/29/2008 17 26 -9 -4 1
03z 04/02/2009 27 30 -3 9 13
18z 04/04/2009 26 26 0 5 5
03z 04/15/2009 27 27 0 5 4
21z 04/20/2009 21 23 -2 -3 -2
09z 04/21/2009 24 29 -5 0 4
12z 04/22/2009 10 21 -11 -8 -1
15z 04/26/2009 19 25 -6 -4 -1
21z 04/27/2009 20 19 1 -5 -6
26
Table 3-2: Composite DTR impacts of jet contrail outbreaks.
Outbreak
Month, Region
Outbreak
DTR
Non-
Outbreak
DTR
DTR
Difference
Outbreak DTR
Anomaly
Non-
Outbreak DTR
Anomaly
Janaury, South 17.917 24.333 -6.417 -2.708 2.833
April, Midwest 20.389 25.667 -5.278 -1.611 2.722
Table 3-3: Composite DTR Impacts of days before and after jet contrail outbreaks.
Outbreak
Month, Region
Day Outbreak Non-Outbreak Outbreak DTR –
Non- Outbreak DTR
Janaury, South Day of
Outbreak
17.917 24.333 -6.417
Day before
and day after
22.458 23.708 -1.25
April, Midwest Day of
Outbreak
20.389 25.667 -5.278
Day before
and day after
20.694 23.333 -2.639
27
Chapter 4
Synoptic Climatology of Longer-Lived Jet Contrail Outbreaks
Two sub-regional synoptic climatologies (composites of synoptic environmental
conditions) of longer-lived contrail outbreaks were developed, one for the 24 outbreaks in the
South during January 2008 and 2009, and the other for the 18 outbreaks in the Midwest during
April 2008 and 2009. The two climatologies reveal key similarities and differences amongst the
several atmospheric variables analyzed.
The South January 2008 and 2009 Jet Contrail Outbreaks
The composite maps for the 24 January South outbreaks show internally consistent
patterns among all of the outbreaks. Throughout the representative grid box, there were
anomalously low temperatures at 250 mb – about -49 and -52 oC, or 0.4 to 1.6
oC below the 32-
year normal (Figures 4-1, 4-2). Moreover, the composite 250 mb zonal winds showed the
southern edge of a jet streak, with wind speeds of 43 to 46 ms-1
in the northwestern area of the
representative grid box. These winds were 4 to 6 ms-1
faster than normal. The rest of the grid box
outside the jet streak had zonal winds near or below normal (Figures 4-3, 4-4), thereby giving an
enhanced horizontal anomaly gradient. Finally, the 250 mb specific humidity was near normal
over the entire grid box (Figure 4-5).
Numerically, the UT variables also showed only slight deviations from normal across the
outbreak grid boxes. At 300 mb, the grid box-average specific humidity for the 24 outbreaks was
about 25% lower than normal; 1.647 x 10-4
g kg-1
, as opposed to the normal value of 2.194 x 10-4
g kg-1
. Moreover, the grid-box average 300 mb temperatures and zonal winds were both near
normal. Average 300 mb temperature was -42.937 oC (0.265
oC above normal) while 300 mb
zonal wind was 36.873ms-1
(1.4 ms-1
above normal). At 200 mb, the temperature showed a
28
slightly positive anomaly from normal, with a composite gridbox average of 59.240 oC (1.534
oC
below normal). These results suggest that there need not be anomalous UT conditions for the
formation of long-lived contrail outbreaks; however, temperatures below -40 oC at the contrail
formation level were a necessary condition.
Although the average (as well as maximum and minimum) values of the atmospheric
variables in the grid boxes were not significantly different from normal when all outbreaks were
composited, some of the associated horizontal gradients across the grid boxes were significantly
enhanced (Table 4-1). In particular, anomalously large horizontal gradients of 300 mb specific
humidity, 300 mb zonal wind, and 200 mb temperature were several times larger than normal and
statistically significant at the 95% level. The composite average grid-box minimum value of those
three UT variables was always below normal during long-lived outbreaks, while the composite
average maximum was always above normal, leading to the larger than normal gradients. Figure
4-6 shows an example of such a large gradient for a particular outbreak (1/08/2009), in this case
in the 200 mb temperature field. The bolded square shows the outbreak bounding box, which is
over half-covered by a zone of very tight temperature gradient, from relatively warm in the north-
eastern area of the box to cold in the western area.
Enhanced vertical gradients of the UT variables were also present in the outbreak station
composites. At the 300 mb level over the outbreak station, the average temperature was slightly
above normal, while the average temperature at 200 mb was below normal. This difference
resulted in a composite temperature change between 300 and 200 mb of 16.22 oC, a 2.52
oC larger
difference than normal (Table 4-2). A visual comparison of the composite outbreak station
vertical temperature profile with the normal profile confirms this large gradient in the UT, as
temperature transitioned from above average below the UT to below normal in and above it
(Figure 4-7). Figure 4-8 shows the departure from normal of the temperature profile, highlighting
29
the warmer than average lower and middle tropospheric levels, the colder than normal UT, and
the shift between these two zones at around 300 mb for the outbreak stations.
Vertical patterns in zonal winds commensurate with the temperature profiles were also
present over the outbreak stations. At and near the surface, zonal wind speeds were relatively
weak and below normal, suggesting high pressure. Between about 900 and 300 mb, though, zonal
winds were greater than normal and consistent with the temperature profile, but they switched
back to below normal just above the 300 mb level (Figure 4-9, 4-10). Directional shear in the UT
over the outbreak stations also exhibited an anomalous pattern comparable to the long-term
normals. The composite absolute directional shear between 300 and 100 mb for the outbreaks was
6.27 degrees, well above the climatological normal of 0.90 degrees. Fourteen of the 24 outbreaks
had associated clockwise turning of the wind through the UT (i.e., warm air advection), while 10
of the outbreaks had counter-clockwise turning of the wind (cold air advection).
There were three distinct types of larger-scale 500 mb patterns – which follow the
classification scheme found in De Grand et al. (2000) – present during the 24 outbreaks. Eighteen
of the outbreaks occurred between a trough and ridge axis, of which 12 were east of the trough
and 6 were east of the ridge. The remaining 6 outbreaks were in a trough axis. Table 4-3 classifies
each outbreak into its 500 mb and shear category, and also contains their numerical horizontal
gradients anomalies of specific humidity (at 300 mb) and temperature (at 200 and 300 mb).
Notably, the horizontal gradients of 300 mb specific humidity and 200 mb temperature were each
greater than normal for all but one of the outbreaks.
30
Figure 4-1: 250 mb temperature composite for January South region outbreaks.
Figure 4-2: 250 mb temperature composite anomaly for January South region outbreaks.
31
Figure 4-3: 250 mb zonal wind composite for January South region outbreaks.
Figure 4-4: 250 mb zonal wind composite anomaly for January South region outbreaks.
32
Figure 4-5: 250 mb specific humidity composite anomaly for January South region outbreaks.
Table 4-1: Composite horizontal gradients of UT variables for January South region
outbreaks. The ‘X normal’ denotes how many times greater than normal the gradient for
the composite of the selected outbreaks was.
300 mb SH 300 mb Temp 300 mb U Wind 200 mb Temp
Gradient
(/km)
Gradient
(C/km)
Gradient (m/s/km) Gradient
(C/km)
Outbreak 1.304E-07 0.0034 0.0206 0.0053
Normal 4.680E-08 0.0031 0.0056 0.0007
Anomaly 8.364E-08 0.0002 0.0150 0.0045
X normal 2.787 1.071 3.660 7.137
33
Figure 4-6: Sample 200 mb temperature gradient for 1/8/09 outbreak.
Table 4-2: Composite station UT data for January South region outbreaks.
Gradients Outbreak Normal Anomaly Standardized
Anomaly
300 mb T -57.204 -55.307 -1.897 -0.483
200 mb T -41.256 -41.605 0.346 0.103
300 mb SH 1.52E-6 1.42E-6 9.69E-06 0.129
300 mb U 34.607 34.188 0.419 0.014
Lapse rate Outbreak Normal Anomaly
300 - 200 mb T 16.219 13.695 2.524
34
Figure 4-7: Composite vertical temperature profile for January South outbreaks.
Figure 4-8: Composite vertical temperature anomalies for January South outbreaks.
35
Figure 4-9: Composite vertical zonal wind profile for January South outbreaks.
Figure 4-10: Composite vertical zonal wind anomalies for January South outbreaks.
36
Table 4-3: Synoptic Analysis of January South outbreaks.
Date CIRCUL Δ
|SHEAR|
S (C,
W)
ΔT-grad,
300 mb
ΔT-grad,
200 mb
ΔSH-grad Area
1/3/2008 East of
Ridge
3.16 W 4.40E-04 2.40E-03 4.13E-05 7.58E+05
1/4/2008 East of
Trough
-1.74 W 8.03E-04 4.65E-03 3.78E-05 2.55E+05
1/7/2008 East of
Trough
0.61 C 2.76E-05 4.67E-03 1.64E-05 5.18E+04
1/7/2008 East of
Trough
3.36 W 1.90E-03 2.85E-03 3.22E-05 1.48E+06
1/8/2008 East of
Trough
0.38 C 1.95E-03 -1.67E-
03
6.05E-05 2.96E+05
1/8/2008 East of
Trough
8.70 W 5.58E-06 6.29E-03 3.42E-07 2.58E+05
1/11/2008 Trough
Axis
9.44 W 8.82E-04 2.40E-03 2.70E-05 7.93E+05
1/14/2008 Trough
Axis
3.50 C 2.57E-04 8.39E-03 8.65E-05 6.11E+05
1/15/2008 East of
Trough
2.95 W 1.20E-03 4.37E-03 1.69E-04 2.15E+05
1/18/2008 East of
Trough
10.23 W 2.98E-04 9.80E-03 1.20E-04 1.59E+05
1/22/2008 East of
Trough
-0.81 C 6.97E-04 4.38E-03 1.33E-05 5.06E+04
1/23/2008 East of
Trough
7.68 C -8.64E-
04
7.03E-04 6.83E-05 1.60E+05
1/26/2008 East of
Ridge
6.22 W 1.56E-03 8.32E-03 1.17E-05 1.77E+04
1/29/2008 East of
Ridge
2.82 W -1.87E-
03
3.40E-03 5.70E-05 1.82E+05
1/30/2008 Trough
Axis
14.58 W -2.97E-
03
4.07E-03 1.19E-04 1.52E+05
1/1/2009 East of
Ridge
2.78 W 1.31E-03 1.71E-03 5.52E-05 5.34E+05
1/1/2009 East of
Ridge
-0.74 W 6.31E-04 3.19E-03 4.53E-05 5.00E+05
1/5/2009 East of
Trough
4.8 C 4.10E-03 2.46E-04 2.02E-04 2.60E+05
1/8/2009 Trough
Axis
21.43 C -3.04E-
03
1.86E-02 9.00E-05 3.99E+05
1/11/2009 East of
Trough
0.62 W -1.00E-
03
2.59E-03 8.96E-05 6.41E+04
1/11/2009 Trough
Axis
1.13 C 6.49E-06 4.19E-03 2.51E-04 1.50E+05
1/18/2009 East of
Trough
16.84 W -1.23E-
03
4.98E-03 1.63E-04 4.03E+04
37
The Midwest April 2008 and 2009 Jet Contrail Outbreaks
The composite maps for the representative grid box maps covering the 18 Midwest
region April outbreaks demonstrated internally consistent patterns, with near normal conditions.
The 250 mb temperature increased from west to east across the grid box, ranging from
approximately -51.7 to -52.7 oC (Figure 4-11). This resulted in negative temperature anomalies of
up to 0.6 oC in the southern and western portions of the grid box, and positive temperature
anomalies of up to 0.4 oC in the northern and eastern portions (Figure 4-12). The 250 mb zonal
wind composite map indicates above-normal westerly wind speed (up to 3 ms-1
) over nearly the
entire grid box (Figure 4-13). Zonal winds averaged 24 to 28 ms-1
, with a jet streak apparent in
the southern and western portions of the grid box (Figure 4-14). Additionally, both 250 mb
specific humidity values and their anomalies were rather variable throughout the grid box (Figure
1/26/2009 East of
Ridge
-0.19 C -1.68E-
04
2.64E-03 1.16E-04 5.61E+03
1/28/2009 Trough
Axis
11.09 C -5.33E-
04
6.60E-04 -8.72E-06 4.60E+04
Average: 6 East of
Ridge
5.37* 14 W 1.83E-04 4.33E-
03*
8.10E-05* 3.10E+05
12 East
of
Trough
10 C
6 Trough
Axis
Table 4-3 Note: Synoptic analysis of 24 contrail outbreaks analyzed in January 2008 and January
2009. Circulation categories (CIRCUL) at 500 hPa follow DeGrand et al. (2000). Both the
departure of the absolute value of vertical shear from the long-term normal (Δ |SHEAR| = S, m/s),
and the sign of the temperature advection (S, C = Cold, W= Warm) pertain to the layer 300-100
hPa. Departures of the horizontal gradients of temperature (ΔT-grad, in oC/km) and specific
humidity (ΔSH-grad, in g/kg/km) from the long-term normals at 300 hPa (and 200 hPa for
temperature) are calculated across the extent of each outbreak. Asterisks (*) indicate statistical
significance at p < 0.05 level.
38
4-15), suggesting that specific humidity was not a key UT variable controlling contrail outbreak
formation.
Small UT anomalies were also present in the composite numerical outbreak grid box
averages for the April outbreaks. The average 300 mb temperature was -45.614 oC (0.835
oC
below normal). The average 200 mb temperature, however, was -55.479 oC (1.292
oC above
normal), meaning a smaller than normal temperature lapse rate was occurring in the UT. The
average 300 mb zonal wind was 21.845 ms-1
; 0.584 ms-1
below normal, and 300 mb specific
humidity was 12% lower than normal- indicating that large anomalies in these UT variables were
not necessary for longer-lived outbreaks.
Large horizontal gradients of the UT variables occurred across the 18 outbreak grid boxes
(Table 4-4). Notably, the gradients of 200 mb temperature, 300 mb zonal wind, and 300 mb
specific humidity, were all several times larger than normal and statistically significant at the
95% level. They resulted from grid box-maximum values of each variable being higher than
normal, and grid box-minimum values being lower than normal.
An anomalous pattern in the vertical temperature profile emerged for the composite of
the outbreak station conditions (Figure 4-16). Between the surface and 300 mb, temperature was
consistently 1 to 2 oC below normal over the outbreak stations (Figure 4-17). However, above
300 mb, the temperature decreased at a much smaller rate than normal, and formed an inversion
located at around 175 mb, where the temperature peaked at 2 oC above normal. This vertical
temperature pattern for outbreaks also resulted in a smaller than normal lapse rate between 300
mb and 200 mb over the 18 Midwest stations (Table 4-5).
The vertical zonal wind profiles over the outbreak stations showed a consistent signature
with the temperature. Zonal winds were stronger than average between the surface and 300 mb,
peaking at nearly 3.5 ms-1
above normal at 500 mb (Figure 4-18, 4-19). Above 300 mb, though,
winds speeds were below normal. Normally, zonal winds peak at about 26 ms-1
at the 200 mb
39
level; however, for the outbreak station composite, zonal winds peaked at just 24 ms-1
.
Noteworthy is that the directional shear occurred over the outbreak stations: the average absolute
wind shear between 300 and 100 mb was 16.53 degrees, 15.10 degrees greater than the
climatological normal of 1.43 degrees. Three outbreaks in particular had exceptionally large wind
shear values of at least 40 degrees. Eleven outbreaks had counter-clockwise turning (cold air
advection), while 7 had clockwise turning (warm air advection), and one outbreak had no
directional wind shear. This result indicates that there is a range of advection conditions
associated with Midwest April outbreaks.
Similar to the January South region outbreaks, there were three different types of larger-
scale 500 mb patterns present during the 18 Midwest April outbreaks. Eleven of the outbreaks
were between a trough and ridge axis, of which 6 were east of the trough and 5 were east of the
ridge. The remaining 6 outbreaks took place in a trough axis, while one was situated in a zonal
weak gradient flow (as defined by DeGrand et al. 2000). Table 4-6 classifies each outbreak into
its 500 mb and shear category and also contains their numerical horizontal gradients of specific
humidity and temperature. Notably, the horizontal gradients of 300 mb specific humidity and 200
mb temperature, as well as shear between 300 and 100 mb, were each larger than normal for all
but one of the outbreaks.
40
Figure 4-11: 250 mb temperature composite for April Midwest region outbreaks.
Figure 4-12: 250 mb temperature composite anomaly for April Midwest region outbreaks.
41
Figure 4-13: 250 mb zonal wind composite for April Midwest region outbreaks.
Figure 4-14: 250 mb zonal wind composite anomaly for April Midwest region outbreaks.
42
Figure 4-15: 250 mb specific humidity composite anomaly for April Midwest region outbreaks.
Table 4-4: Composite horizontal gradients of UT variables for April Midwest region outbreaks.
The ‘X normal’ denotes how many times greater than normal the gradient for the composite of
the selected outbreaks was.
300 mb SH 300 mb Temp 300 mb U Wind 200 mb Temp
Gradient (/km) Gradient
(C/km)
Gradient (m/s/km) Gradient
(C/km)
Outbreak 1.342E-07 0.0056 0.0224 0.0075
Normal 2.775E-08 0.0033 0.0051 0.0012
Anomaly 1.068E-07 0.0023 0.0173 0.0063
X normal 4.837 1.691 4.413 6.350
43
Table 4-5: Composite station UT data for April Midwest region outbreaks.
Gradients Outbreak Normal Anomaly Standardized
Anomaly
300 mb T -57.204 -55.307 -1.897 -0.483
200 mb T -41.256 -41.605 0.346 0.103
300 mb SH 1.52E-6 1.42E-6 9.69E-06 0.129
300 mb U 34.606 34.188 0.419 0.014
Lapse rate Outbreak Normal Anomaly
300 - 200 mb T 16.219 13.695 2.524
Figure 4-16: Composite vertical temperature profile for April Midwest outbreaks.
44
Figure 4-17: Composite vertical temperature anomalies for April Midwest outbreaks.
Figure 4-18: Composite vertical zonal wind profile for April Midwest outbreaks.
45
Figure 4-19: Composite vertical zonal wind anomalies for April Midwest outbreaks.
Table 4-6: Synoptic Analysis of April Midwest outbreaks.
Date CIRCUL Δ
|SHEAR|
S (C,
W)
ΔT-grad,
300 mb
ΔT-grad,
200 mb
ΔSH-
grad
Area
4/2/2008 Flat
Wave
3.60 Cold 3.77E-03 2.26E-03 6.35E-08 8.46E+04
4/6/2008 East of
Trough
4.93 Warm -3.41E-
04
-1.69E-
04
2.53E-08 1.19E+05
4/7/2008 East of
Trough
5.36 Warm 6.36E-03 5.63E-03 5.13E-08 1.32E+05
4/13/2008 Trough
Axis
9.83 Cold 1.02E-03 5.88E-03 3.88E-10 5.36E+05
4/14/2008 Trough
Axis
9.90 Cold 8.49E-03 1.04E-03 7.71E-08 2.20E+04
4/22/2008 East of
Ridge
4.39 Cold -3.08E-
04
4.62E-03 1.01E-07 1.87E+05
4/25/2008 East of
Trough
21.17 Warm -6.33E-
04
5.83E-03 7.19E-08 1.92E+04
4/26/2008 Trough
Axis
2.00 Warm 4.32E-03 1.70E-02 8.61E-08 1.83E+04
46
Comparison of the January South and April Midwest Outbreak UT Conditions
The two months and regions analyzed in this study were not entirely similar in terms of
the synoptic patterns associated with outbreaks, although some important similarities exist.
Specifically, large horizontal gradients in the UT variables and strong wind shear were present for
both mid season months and regions, while specific humidity was typically below normal in the
4/27/2008 Trough
Axis
73.23 Cold 4.46E-03 1.12E-02 5.25E-09 1.54E+05
4/29/2008 East of
Ridge
35.67 Cold 1.32E-03 5.50E-03 2.25E-08 2.44E+05
4/2/2009 East of
Trough
16.32 Cold 1.00E-05 2.55E-03 -5.00E-
10
4.66E+03
4/4/2009 East of
Ridge
3.40 Warm 8.78E-04 2.55E-03 4.54E-08 2.82E+04
4/15/2009 East of
Ridge
16.69 Cold 1.74E-03 6.28E-03 1.55E-07 3.69E+05
4/20/2009 Trough
Axis
46.01 Cold 2.01E-03 2.20E-03 1.00E-07 7.20E+04
4/21/2009 East of
Ridge
40.40 Cold -1.01E-
04
1.93E-02 6.23E-08 1.94E+05
4/22/2009 Trough
Axis
0.00 NONE 1.03E-03 1.12E-02 4.60E-08 5.73E+04
4/26/2009 East of
Trough
0.82 Warm -2.33E-
03
4.99E-04 7.64E-07 3.67E+04
4/27/2009 East of
Trough
3.76 Cold 9.28E-03 6.97E-03 2.40E-07 6.10E+05
Average: 1 Flat
Wave
16.53* 11 C 2.28E-03 5.77E-
03*
1.06E-
07*
1.60E+05
6 Trough
Axis
6 W
6 East of
Trough
5 East of
Ridge
Note: Synoptic analysis of 18 contrail outbreaks analyzed in April 2008 and April 2009.
Circulation categories (CIRCUL) at 500 hPa follow DeGrand et al. (2000). Both the departure of
the absolute value of vertical shear from the long-term normal (Δ |SHEAR| = S, m/s) and the sign
of the temperature advection (S, C = Cold, W= Warm) pertain to the layer 300-100 hPa.
Departures of the horizontal gradients of temperature (ΔT-grad, in oC/km) and specific humidity
(ΔSH-grad, in g/kg/km) from the long-term normals at 300 hPa (and 200 hPa for temperature) are
calculated across the extent of each outbreak. Asterisks (*) indicate statistical significance at p <
0.05 level.
47
UT. Furthermore, neither set of outbreaks occurred during times of statistically significant
departures from normal of the UT variables in the representative and outbreak grid boxes,
although their horizontal gradients of the UT variables were significantly larger than normal. The
outbreaks in each region generally took place during the same time of day, primarily during the
daylight or late evening hours, when air traffic typically peaks.
The two sets of outbreaks varied substantially when considering the larger-scale synoptic
patterns present at 500 mb. Twelve of the 24 January South outbreaks occurred east of a trough,
while 14 of the 24 had associated warm advection occuring in the UT (Table 4-3). However, the
April Midwest outbreaks were almost evenly split bewteen the three circulation categories (east
of trough, east of ridge, in trough axis), and also had a majority of outbreaks occuring with cold
advection present in the UT. The April outbreaks generally had a warmer than normal UT,
especially at and above 250 mb, as the occurrence of cold advection did not necessarily preclude
a warmer than normal UT. Conversly, the January South outbreaks had an anomalously cold UT
above 300 mb, whereas the lower and mid levels of the atmosphere were warmer than normal for
the January outbreaks, but colder than normal for the April Midwest outbreaks. These differences
also were manifest in the vertical temperature profiles over the outbreak stations and in the
composite outbreak grid box data. In particular, the vertical temperature profiles (throughout the
entire troposphere) show stark differences, especially in the UT, as a slight inversion around 200
mb was present over the composite of the April Midwest outbreaks, while a larger than normal
decrease in temperature occurred in the composite of the January outbreaks. These differences
indicate that there was not one particular synoptic setup (e.g., in a trough or ridge axis) which
favored the formation of longer-lived contrails.
The zonal wind patterns also showed some differences between the two sets of regional
and mid-season month outbreaks. Climatologically, the South region outbreaks occurred with a
stronger westerly jet stream, as the long-term normal zonal winds peaked at around 42 ms-1
at 200
48
mb, while those in the Midwest outbreaks peaked at only 27 ms-1
. Each of the outbreaks had
weaker than normal jets, with negative anomalies in the UT above 300 mb, although the
anomalies were twice as large in the Midwest outbreaks versus those in the South. Moreover,
zonal winds were stronger than normal below 300 mb for both sets of outbreaks, although for the
South region between 900 mb and the surface, zonal winds were weaker than normal.
Last, the January South region outbreaks, on average, covered a greater area than the
April Midwest outbreaks. The average Midwest outbreak grid box had an area of approximately
160,000 km2, while the South region grid boxes were nearly twice as large on average; about
310,000 km2 (Tables 4-3, 4-6). The outbreak grid boxes for each month and region also had
different orientations. Nine of the 18 April Midwest outbreak grid boxes were oriented north-to-
south (i.e., rectangular), with 6 being west-to-east, and 3 were square. Of the January South grid
boxes, though, 16 were oriented north-to-south, 4 were west-to-east, and 4 were square.These
results indicate that the area of contrail clouds did not need to be oriented in a particular direction
(e.g., north-to-south or east-to-west) to persist and create outbreaks.
49
Chapter 5
Summary, Discussion, and Conclusions
This chapter first discusses the results presented in Chapter 4 and suggests physical
explanations for them. Then, to motivate future work on the topic of contrail outbreaks and
climate feedbacks, it places the results of this study in the context of the broader-scale dialogue
on human-climate interactions involving jet aviation.
The findings indicate that the presence of longer-lived jet contrail outbreaks suppresses
the DTR at the underlying surface stations. For each set of outbreaks (January South, April
Midwest), there was a statistically significant suppression of DTR at stations compared to nearby
stations not overlain by contrails. The fact that the DTR station differences were not statistically
significant on either the days before or after the outbreak further validates this conclusion. This
result confirms prior work and physical reasoning (e.g., Travis et al. 2004), as the additional
cloudiness from jet contrail outbreaks helps reduce surface shortwave radiation receipt during the
day, and enhance longwave energy back-radiation receipts at night, thereby lowering the daily
maximum temperature and raising the daily minimum temperature. Although the January South
outbreaks had a slightly greater absolute impact on the DTR (reducing DTR by 1.139 oF more),
this difference was not statistically significant. However, the type of synoptic pattern present in
conjunction with the outbreaks may have influenced DTR. Those outbreaks occurring with UT
cold advection suppressed surface DTR by 7.57 oF (when comparing outbreak to non-outbreak
stations), while outbreaks occurring with warm advection only reduced DTR by 3.90 oF.
Similarly, outbreaks occurring in a 500 mb trough axis suppressed surface DTR by 9.08 oF,
contrasted with just 4.62 oF for those outbreaks located between trough and ridge axes. A possible
explanation for these variations is that the cold advection and associated trough axis in the two
regions were generally more favorable for cloud formation and development, because cold air
50
over a warm surface leads to instability and vertical motion, so the jet contrails that formed
during these conditions were more extensive and longer-lasting, thus having a greater opportunity
to affect DTR.
This research also confirms and extends the suggestion that specific UT conditions
accompany most longer-lived jet contrail outbreaks – at least those outbreaks affecting DTR in
the two regions and mid-season months studied. In particular, anomalously large horizontal
gradients of UT temperature, specific humidity, and zonal wind, along with strong vertical shear
in the UT, appeared necessary for outbreaks to form and persist. Collectively, these conditions are
representative of baroclinic zones, which suggests that their presence may have been necessary
for contrail outbreaks to be sufficiently well developed to influence DTR. Baroclinic zones occur
near troughs and jet streaks, and the composite grid box maps of zonal wind for each set of
outbreaks (January South, April Midwest) confirmed that the typical outbreak occurred in the
vicinity of a 250 mb jet streak and in conjunction with a large horizontal gradient of zonal wind.
The large zonal wind gradient helps explain why the station composites showed below-normal
zonal winds in the UT, because the compositing process “averages out” the steep wind gradients
and therefore suggests below-normal winds. Moreover, some outbreaks occurred within the 500
mb trough axis, and all except one took place in an amplified wave pattern, either between the
trough and ridge or within the trough axis. The fact that the outbreaks within a trough axis and/or
with strong cold advection and wind shear had the greatest impact on DTR underscores the
importance of baroclinic zones for the formation of DTR-suppressing jet contrail outbreaks. As
baroclinicity can occur at most times of the year in the extratropics, and with a wide range of
conditions (e.g., the type of air mass present), it is understandable that the results did not show
outbreak-to-outbreak consistency in the general UT conditions (e.g., temperature, zonal wind,
specific humidity at a particular pressure level, as well as vertical temperature profile). Therefore,
it is reasonable to conclude, at least for the 42 jet contrail outbreaks studied for the South and
51
Midwest sub-regions of the U.S. in January 2008-09 and April 2008-09, that baroclinic zones
provide a favorable location for jet contrail outbreaks to form, persist, and be sufficient to
suppress surface DTR. This finding reinforces the importance of contrails to surface temperatures
in those regions underlain by high frequencies of jet aircraft.
There are potential societal impacts associated with a suppressed DTR. One likely
consequence is an extended growing season in regions of frequent jet flights. Higher minimum
temperatures suggest that, the first frost of the season would be delayed in contrail-prone areas,
while the last frost of the spring or late winter would happen earlier. Growing season is also likely
increasing due to other anthropogenic factors (Hayhoe et al. 2007), but separating the effect of
contrails from these other influences would be useful to decision-makers when detailing their
impacts. DTR changes would also result in changes to annual heating and cooling degree days, a
measure of how much energy is necessary given the departure of a day’s average temperature
from 65 degrees. Depending on both the timing of contrail events and interplays with local
microclimates, contrails could either increase or reduce energy demands for heat or air
conditioning. During the summer, contrails could reduce the maximum temperatures by a few
degrees, thereby reducing energy requirements. In contrast, at night, a contrail-induced increase
in minimum temperatures could result in a net increase in energy consumption. At other times,
the moderating effect of jet contrails could potentially have the opposite impact of reducing
energy use, e.g., if temperatures do not fall as low during cold winter nights, not as much
residential heat is necessary. Intentionally manipulating surface temperature – specifically
increasing minimum temperatures in northeastern U.S. cities on cold nights through cloud
seeding and the formation of jet contrails – has been proposed as an early form of geo-
engineering (Detwiler and Cho 1982; Detwiler and Pratt 1984).
A wide range of extensions and applications of this research are possible and especially
pertinent as air travel continues to increase. Additional regions and outbreak months should be
52
studied to determine if the conclusions drawn from this work are more generally applicable in
space and time. Moreover, shorter-lived outbreaks over the study regions should be analyzed to
compare with the results of the longer-lived outbreaks. In terms of applications, the findings of
this research and follow-up studies could contribute to real-time forecasting of contrail outbreaks
in the near future, as the specific conditions necessary for their formation and persistence are
better understood. Given that longer-lasting outbreaks significantly suppress DTR, their
prediction and observation should be factored into weather forecast models (e.g., MOS) to better
predict surface temperatures. Finally, a better understanding of the formation and climatic
impacts of jet contrail outbreaks could bolster policy recommendations that help to curb this
human influence on weather and climate by making air traffic and airplanes more efficient. For
example, strategies to be employed could include re-routing planes around areas of favorable UT
conditions for longer-lived outbreaks, or flying at lower altitudes when and where these
conditions occur (Williams and Noland 2005).
53
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Appendix A
Sample 500 mb Geopotential Height Maps
Figure A1: Contrail outbreak in trough axis. 12Z 4/13/2008 outbreak.
60
Appendix B
Sample Upper Tropospheric Wind Shear Profiles
Figure B1: Sample of cold air advection over an outbreak. 18Z 4/27/2008 outbreak.