Download Chapter 3: Climate variability

Chapter 3
CHAPTER 3
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
Climate Variability
CLIMATE VARIABILITY ............................................................................................. 1
INTRODUCTION ........................................................................................................................... 2
FLOODS, DROUGHTS, AND HEAT WAVES ............................................................................ 2
AIR MASSES ................................................................................................................................ 3
MECHANISMS OF SEASONAL-TO-INTERANNUAL VARIABILITY ....................................... 6
CLIMATIC EXTREMES .............................................................................................................. 11
LAND SURFACE PROCESSES ................................................................................................ 14
TABLES ....................................................................................................................................... 16
FIGURE LEGENDS.................................................................................................................... 17
Ecological Climatology
3.1 Introduction
The previous chapter focused on the mean state of the atmosphere, first in terms of annual
temperature and precipitation and then seasons. However, the realized temperature and precipitation in any
year can deviate markedly from this mean. Mean monthly January and July temperatures measured over a
342-year period in central England illustrate the nature of climate variability (Figure 3.1). Between 1659
and 2000, January temperature averaged 3.2 °C but ranged from a high of 7.5 °C to a low of -3.1 °C. July
temperature averaged 15.9 °C but ranged from a high of 19.5 °C to a low of 13.4 °C. This chapter
examines climate variability at seasonal-to-interannual timescales.
3.2 Floods, droughts, and heat waves
Figure 3.2 (color plate) shows the standard deviation for monthly January and July surface air
temperature over the period 1950 to 1979. Three geographic patterns are evident: high latitudes have
greater interannual variability in temperature than tropical latitudes; interior continental regions have
greater interannual variability than coastal regions; and oceans have lower interannual variability than
continents. These patterns are particularly evident in January. Interannual variability is considerably less in
July, but the same geographic patterns hold.
Precipitation also varies from year to year. The summer of 1993 brought the worst flooding on
record in Midwest United States (Halpert et al. 1994; Changnon 1996). Over 7 million hectares (ha) of
land (1 ha =10 000 m2) were flooded, causing 15 to 20 billion U.S. dollars in damage and 48 deaths. At St.
Louis, Missouri, the Mississippi River was above flood stage for 80 days and above record flood stage for
23 consecutive days. At Davenport, Iowa, the river was above flood stage for 43 days. Many of the large
rivers that flow into the Mississippi also experienced major and record flooding. During this period, the
northern and central Plains states and Midwest states received twice the normal precipitation (Figure 3.3).
Precipitation from June through August was more than 100 mm to 200 mm above normal in a wide region
and was more than 300 mm above normal in a smaller region centered on Iowa.
In addition to floods, central and western regions of the United States are prone to recurring
droughts. Droughts persist longer in interior portions of the country than in areas closer to the coasts (Karl
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and Koscielny 1982; Karl 1983; Diaz 1983). The summers of 1934, during the infamous Dust Bowl, and
1956 were two of the most extreme drought years in the 1900s. In these years, extensive portions of the
central Plains and West experienced extremely dry soils with a Palmer Drought Severity Index of –4 or
less (Figure 3.4). The summer of 1988 was also a severe drought (Figure 3.5) that caused 40 billion U.S.
dollars in damage and 5000 to 10 000 heat-related deaths (Riebsame et al. 1990). These three years
illustrate the geographic variability of droughts. The 1934 drought was widespread throughout the Great
Plains while the 1956 drought was concentrated in the central and southern Great Plains and the 1988
drought was focused on the northern Great Plains. A 1930s magnitude Dust Bowl drought has occurred
once or twice a century over the past 300 to 400 years (Karl and Koscielny 1982; Woodhouse and
Overpeck 1998). Indeed, observations show recurring droughts throughout the 1900s, with prolonged
drought throughout the Great Plains in the 1930s and in the central and southern Plains in the 1950s
(Figure 3.6).
Even areas that seemingly have ample annual rainfall are prone to drought. Precipitation in the
summer of 1993 was more than 100 mm below normal in the southeastern states of Virginia, North
Carolina, South Carolina, Georgia, and Florida (Figure 3.3). This drought, during which much of the
Southeast received only half its normal rain and was 2 °C to 3 °C warmer than normal, was the second
driest and the hottest on record in many states. The summer of 1995 brought a severe drought to Northeast
United States. Precipitation from March through August was 150 mm to 200 mm below normal along the
East Coast from Virginia to Maine (Halpert et al. 1996). A similar devastating drought developed in the
summer of 1999. In the Northeast states, the period April through August was the driest on record, with
rainfall at least 25 mm below normal in each of the five months.
3.3 Air masses
Changes in temperature and precipitation from one year to the next reflect changes in the types of
air occurring in a region. Air moving across Earth’s surface acquires temperature and moisture
characteristics of the underlying surface. As they drift to different regions, these air masses carry with them
the temperature and moisture of the region over which they formed. Air masses originate over several
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source regions and typically cover thousands of square kilometers. The longer the air remains over a source
region, the more likely it is to attain the region’s characteristics. The polar and subtropical highs are ideal
source regions for air masses. In contrast to middle latitudes, where temperature and humidity are
constantly changing, these high pressure areas have relatively consistent (hot, cold, moist, dry) features.
There are four broad categories of air masses differentiated by whether the air originates in polar or
tropical regions and over continents or oceans. Air masses that originate in polar regions (polar air) are
colder than those from tropical regions (tropical air). Because land is drier than oceans, air masses that
originate over continents (continental air) are drier than air masses that originate over oceans (maritime
air). Maritime air is also milder, especially during winter, than continental air.
Several types of air masses frequent North America (Figure 3.7). Air masses that originate over
Canada, called continental polar air, are cold and dry. Arctic air, which originates over the ice- and snowcovered Arctic Ocean, is extremely cold. These air masses can readily move into central United States
because no topographic barriers prevent their movement southwards. In contrast, the mountains of western
United States generally prevent this air from moving into the Pacific Northwest. Continental polar air and
Arctic air are associated with unseasonably cold winter temperatures. In summer, however, southward
excursions of this air bring relief from heat and humidity. Cool, moist, maritime polar air originates over
the North Pacific and North Atlantic. In winter, this air is generally warmer than continental polar air
because the oceans are warmer than land. Along the Pacific coast, this air loses much of its moisture as rain
or snow when it moves inland over the Coastal Mountains. Some of this air penetrates over the Rocky
Mountains, where it brings milder temperatures than continental polar air. Maritime tropical air is found
along the California coast and in eastern United States. Maritime tropical air formed over the subtropical
Pacific is warm and moist, producing heavy rainfall as it moves onto the West Coast. East of the Rocky
Mountains, maritime tropical air originates over the Gulf of Mexico and Caribbean Sea. In summer,
northwards excursion of this air into eastern United States brings oppressive heat and humidity. Maritime
tropical air is most prevalent in the Southeast, where the Bermuda high pumps hot, moist air from the Gulf
of Mexico northwards into the United States. Continental tropical air, which is hot and dry, originates in
the deserts of Mexico and Southwest United States. This air, which is restricted to Southwest United
States, is associated with clear skies and hot temperatures.
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Chapter 3 – Climate Variability
Storms, which bring what we commonly call weather, occur along the fronts where these air
masses collide. The sharp contrasts in temperature and humidity among these air masses can generate
severe weather. The location of these collisions in much of the United States is governed by geography.
The United States lies within a broad geographic region from latitudes 30 °N to 60 °N known as the middle
latitudes that separates cold polar regions from hot tropical regions. In the west, the Coastal Mountains
block the maritime influences of the Pacific Ocean from extending far inland. Along the East Coast, the
prevailing westerly winds limit the maritime influences of the Atlantic Ocean. Instead, the Gulf of Mexico
provides moisture for precipitation over central and eastern United States. Between the Rocky Mountains
and the Appalachian Mountains, in the interior of the continent, there are no significant obstructions to air
movement. Cold arctic air can sweep down from the north. Warm, moist tropical air can sweep up from the
Gulf of Mexico. Storms develop where these air masses collide.
The jet stream, which marks the boundary between warm and cold air masses, guides the location
of these air masses and movement of storms. In the Northern Hemisphere, these bands of fast moving,
westerly wind between 10 km to 15 km aloft develop when the south-to-north temperature gradient creates
high altitude pressure differences that initiate wind flow to the north; the Coriolis effect deflects the winds
to the east. The strength and location of these winds depends on the temperature gradient. A large
temperature contrast creates a large pressure difference between warm and cold air, resulting in strong
winds. In winter, when there is a sharp temperature gradient across North America (Figure 2.21), strong
winds dip far to the south. Much of the northern half of the United States is dominated by continental polar
air. The Pacific Northwest has a mixture of maritime tropical and polar air. Continental tropical air covers
the Southwest. The Southeast is a transition zone between continental polar air in the north and maritime
tropical air to the south. In summer, the temperature gradient is much weaker (Figure 2.21). The jet streams
weaken and move north into southern Canada. Continental polar air is displaced far north. Much of the
eastern half of the United States has maritime tropical air. The Southwest is still covered with continental
tropical air. The West Coast has mild maritime air. These air masses collide in the interior lowlands
between the Rocky and Appalachian Mountains.
As the jet streams flow from west to east, they weave a wave-like pattern of ridges and troughs
with a pronounced dip (trough) in eastern North America (Figure 3.8). A trough allows cold air to move
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Ecological Climatology
southward while a ridge allows warm air northward. The amplitude of the waves (i.e., the distance between
successive ridges or troughs) varies over time. Zonal flow, characterized by weak ridges and troughs,
confines cold air to the north. Mild temperatures prevail over much of the United States as less Arctic air
flows south. In winter, a deep trough extending far south allows cold air to penetrate into central and
eastern United States, bringing below normal temperatures. When the trough is displaced to the west, cold,
dry air prevails in the west while mild conditions occur in the east. In summertime, displacement of the jet
stream into Canada can restrict continental polar air to the north and bring oppressive heat and humidity
when maritime tropical air blankets much of eastern United States.
3.4 Mechanisms of seasonal-to-interannual variability
Many factors determine the air masses that dominate a geographic region at a particular time. The
atmosphere is a chaotic system in which small-scale atmospheric events may have large-scale
consequences. This chaotic behavior has been aptly characterized by the infamous butterfly effect by which
a butterfly flapping its wings in Asia can cause events affecting weather over the United States (Lorenz
1993). Chaos theory puts a limit on the predictability of weather beyond several days because forecasters
can never precisely know all the conditions affecting weather. However, over longer timescales of seasons
some general patterns are evident. In particular, tropical atmospheric circulation and precipitation are
strongly linked to the temperature of the underlying sea surface (Shukla 1998). Changes in tropical
circulation and precipitation influence seasonal temperature and precipitation throughout the world.
The trade winds usually blow from east to west near the equator (Figure 2.8). These winds drive
warm surface water westward across the tropical Pacific with the result that the western Pacific near
Australia and Indonesia has an extensive pool of warm surface water with temperatures in excess of 28°C
(Figure 3.9). In contrast, water in the eastern Pacific off the Peruvian and Ecuadorian coast of South
America is several degrees cooler as the warm surface water is replaced by deep cold water. This pattern of
warmest temperatures in the west and coldest temperatures in the east is evident throughout the year,
though it is greatest in late summer and early autumn when sea surface temperatures in the eastern tropical
Pacific reach a minimum. Surface waters subsequently warm and reach maximum temperatures in winter
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Chapter 3 – Climate Variability
and spring. In contrast, sea surface temperatures in the western tropical Pacific are normally warm yearround.
The pattern of warm surface water in the western tropical Pacific and cold surface water in the
eastern tropical Pacific is accompanied by a large-scale atmospheric circulation across the Pacific known as
the Walker Circulation (Figure 3.10). This circulation is characterized by low-level easterly winds and
upper-level westerly winds across the Pacific. Over the western tropical Pacific, where the air is warmed
by the warm seas, there is low surface air pressure and ascending motion. In this region, the warm, moist
air rises to form deep clouds that produce heavy rains over northern Australia, Indonesia, and the
Philippines. In contrast, the eastern tropical Pacific, where the air is colder, has higher surface pressure and
descending air motion. Rainfall is light in this region of sinking rather than rising air.
Every few years this pattern changes, and there is a large-scale warming of sea surface
temperature in the tropical Pacific east of the dateline known as El Niño. This period of warmer than
normal sea surface temperatures is often followed by a cold phase (La Niña) in which waters in the eastern
Pacific are abnormally cold. El Niño, or warm episodes, and La Niña, or cold episodes, are opposite
extremes of the El Niño/Southern Oscillation (ENSO) cycle. El Niño refers to the warming of surface
waters while Southern Oscillation refers to changes in the Walker Circulation.
El Niño episodes are characterized by abnormally warm sea surface temperatures across the
eastern equatorial Pacific. During a strong El Niño episode such as in 1998, the 28 °C isotherm extends
well east of the dateline and ocean temperatures are more than 2 °C to 3 °C warmer than normal between
the dateline and the west coast of South America (Figure 3.11). During El Niño episodes, the normal
contrast between high pressure over the eastern tropical Pacific and low pressure over the west, which
drives the easterly trade winds, diminishes. This is seen in the Southern Oscillation Index, which quantifies
the difference in sea level pressure between Tahiti and Darwin, Australia. Prolonged periods of a negative
phase, with below normal air pressure at Tahiti and above normal air pressure at Darwin, coincide with
abnormally warm water in the eastern tropical Pacific. This reflects a reduced strength of the Walker
Circulation (Figure 3.12). Higher than normal air pressure in the west over Indonesia and northern
Australia and lower than normal air pressure in the eastern tropical Pacific weakens the trade winds; warm
water drifts farther east, from the dateline to South America. Rainfall patterns follow the warm water, with
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Ecological Climatology
dry conditions over northern Australia, Indonesia, and the Philippines and heavy rains along the equator
from the dateline east to the South American coast where sea surface temperatures reach 28 °C or more
(Figure 3.13).
La Niña episodes feature abnormally cold surface water. During a strong La Niña such as in 1989,
the 28 °C isotherm is restricted to west of the dateline along the equator and sea surface temperatures are a
few degrees colder than normal between the dateline and western South America (Figure 3.14). During La
Niña episodes, air pressure is lower than normal over Indonesia and higher than normal over the eastern
tropical Pacific (a positive Southern Oscillation Index). This reflects an enhanced Walker Circulation
(Figure 3.12). The strong east-to-west pressure difference drives strong easterly surface winds across the
Pacific from the Galapagos Islands to Indonesia. The warm pool of surface water does not drift eastwards
across the dateline. As a result, the equatorial Pacific from the dateline to South America is extremely dry
while northern Australia, Indonesia, and the Philippines receive heavy rainfall (Figure 3.13).
El Niños typically last 6 to 12 months and recur every few years. Extension of sea surface
temperature records back to 1950 shows that the El Niño cycle has an average period of about 4 years,
although it varies from between 2 to 7 years. Historical records show El Niño has occurred periodically
over the past few centuries (Quinn et al. 1987). Figure 3.15 (color plate) illustrates the El Niño cycle from
1979 through 1998. Warm episodes, with warmer than normal water east of the dateline, occurred during
1982-83, 1986-87, 1991-93, 1994-95, and 1997-98. This period featured two of the strongest El Niños on
record (1982-83, 1997-98) and two consecutive warm episodes without an intervening cold episode (199193, 1994-95). Cold episodes, with colder than normal water east of the dateline, occurred in 1984-85,
1988-89, and 1995-96. During warm events, precipitation was above normal east of the dateline and below
normal west of the dateline.
El Niño is a cyclically recurring event in which small changes in the strength of the easterly
surface winds along the equator lead to changes in oceanic circulation. This affects sea surface
temperatures and rainfall, which feed back to affect the strength of the easterlies until a mature El Niño
event is established. The El Niño cycle represents a coherent, large-scale fluctuation in ocean temperature,
rainfall, air pressure, and atmospheric circulation across the tropical Pacific. The shifts in tropical
atmospheric circulation alter global atmospheric circulation and influence temperature and precipitation
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Chapter 3 – Climate Variability
worldwide. These changes are most apparent between December and February, though there are important
patterns throughout the year (Figure 3.16). During this time, wetter than normal conditions occur along the
northwest coast of South America, southern Brazil and central Argentina along the southeast coast of South
America, and central east Africa. California and the Gulf Coast of the United States also tend to be wetter
than normal. Drier than normal conditions occur over northeast South America and southern Africa.
Temperatures are warmer than normal across southern Africa, southeast Asia, southeastern Australia,
Japan, southern Alaska, much of Canada, and southeastern Brazil. Cooler than normal temperatures occur
in southeastern United States. The opposite patterns generally occur during La Niñas (Figure 3.17).
The changes in climate and weather created by El Niño and La Niña can have devastating
socioeconomic consequences (Glantz 1996). The estimated worldwide economic impact of the 1982-83
event, generally agreed to be one of the strongest on record with extensive floods, snowstorms, droughts,
and fires, is 8 billion U.S. dollars. The 1997-98 warm event is also one of the strongest on record. Some
consequences of this El Niño for the United States were below normal hurricane activity in the North
Atlantic and wetter than normal conditions in California and the Southeast (Bell and Halpert 1998).
Locally, El Niño can bring severe hardship as torrential rains cause extensive flooding and landslides (e.g.,
California 1997-98) or disruption of commercial fisheries. This is particularly true along the Pacific Coast
of South America, where the warm waters of El Niño replace the cold, nutrient-rich waters of the coastal
upwelling zone in which fish thrive.
The climatic impacts of El Niño are particularly strong in North America in winter. The dramatic
contrast in the winters of 1994-95 and 1995-96 provides an indication of the role of El Niño in shaping
climate (Halpert et al. 1996; Halpert and Bell 1997). The winter of 1994-95 was 3 °C to 5 °C warmer than
normal in western and central Canada and Northeast United States (Figure 3.18, color plate). More than
250 record high temperatures were set across northern and eastern United States from November through
January. Precipitation was below normal for much of this region (Figure 3.19, color plate). The Rocky
Mountain states also had record or near-record warmth, but above normal precipitation. California and the
Southwest had above normal precipitation, bringing long-term relief to an abnormally dry decade in
California but also new problems – extensive flooding and landslides.
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In the spring of 1995, Pacific sea surface temperatures began to decrease, and by the following
winter a cold La Niña had developed. The Pacific Northwest experienced above normal precipitation and
flooding (Figure 3.19, color plate). The Southwest and southern Plains had abnormally hot, dry conditions.
The southern Plains states received 50% less precipitation than normal accompanied by occasional
outbreaks of summer-like heat with temperatures above 32 °C. The drought continued into summer, with
rainfall totals among the lowest 10% on record for much of the region. The hot, dry, and frequently windy
conditions fueled wildfires in the Southwest and withered crops throughout Colorado, Nebraska, Kansas,
Oklahoma, and Texas. Temperatures were 1 °C to 3 °C colder than normal over much of Canada and
Northeast United States (Figure 3.18, color plate). Overall, the winter of 1995-96 was colder and wetter
than normal in western and central Canada and northern United States.
The climatic contrast between the winters of 1994-95 and 1995-96 illustrate the prominent
changes in atmospheric circulation and climate over North America associated with the ENSO cycle
(Figure 3.20). During El Niño, the jet stream pattern flattens and produces more zonal flow across the
North Pacific and North America. The excessive rain in the West during the 1994-95 El Niño was caused
by a persistent low pressure off the West Coast, which resulted in an 18° latitude southward shift in the
location of the subtropical Pacific jet stream. This directed storms into California and the Southwest rather
than the Pacific Northwest. In contrast, a persistent high pressure developed in the east, shifting the polar
jet stream 15° latitude north of its normal location. This prevented the buildup of cold air over central and
western Canada and directed weak weather systems across Canada rather than into the United States.
In the cold phase, the wavelike pattern to the jet stream over North America is amplified, bringing
increased meridional flow across the continent (Figure 3.20). A weaker low pressure system develops in
the Gulf of Alaska. This allows the buildup of colder than normal air in the Northwest, which often moves
south into the northern Great Plains. The subtropical Pacific jet stream directs major storms into the Pacific
Northwest. Reduced storm activity in the south brings abnormally hot, dry conditions to the Southwest and
southern Plains. In the east, the polar jet stream dips south of its normal position at times, producing cold
and stormy conditions in the mid-Atlantic and Northeast states.
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The seesaw pattern of El Niño and La Niña is a naturally recurring mode of interannual climate
variability. A similar type of oscillation occurs in the North Atlantic, where north-to-south variation in
atmospheric pressure between the subtropical high, centered on the Azores in winter, and the Icelandic low
(Figure 2.8) affects atmospheric circulation. The climatic effects of the North Atlantic Oscillation are most
pronounced in winter. In the positive phase, pressure is lower than normal over the region of the Icelandic
low and higher than normal across the subtropical Atlantic. The deepened Icelandic low drives cold polar
air onto Greenland and Labrador, where temperatures are colder than normal. Stronger than normal middle
latitude westerlies force maritime air onto northern Europe, where temperatures are milder than normal.
The strengthened subtropical high forces warm subtropical air into southeast United States and cool
northerly air into the Mediterranean. The reverse patterns occur in the negative phase, when winters are
generally colder than normal over northern Europe and eastern United States. As with El Niño, the North
Atlantic Oscillation is well observed in historical records (Jones et al. 1997; Hurrell and van Loon 1997).
Changes in the North Atlantic Oscillation are linked to sea surface temperatures in the tropics (Hoerling et
al. 2001).
The El Niño/Southern Oscillation and the North Atlantic Oscillation are just two of many sources
of interannual climate variability. However, it is becoming clear that they are second only to the change in
seasons in terms of their impact on global climate (Hurrell 1996; Dai et al. 1997; Thompson and Wallace
2001). For example, the seasonal migration of the intertropical convergence zone causes large seasonal
changes in tropical precipitation (Figure 2.23, color plate). El Niño and La Niña produce large interannual
changes in tropical precipitation (Figure 3.13). Together, El Niño and the North Atlantic Oscillation
account for almost one-half of the variance in Northern Hemisphere extratropical (20 °N to 90 °N) winter
temperatures over the past 60 years and influence extratropical patterns of winter precipitation (Hurrell
1996; Dai et al. 1997).
3.5 Climatic extremes
Although it is convenient to describe climate in terms of climatic means, other statistics that
account for climate variability are as important if not more important. Climatic means – the statistical
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composite of many daily weather events – often do not provide the appropriate information. It is the
extreme events such as heatwaves, floods, and droughts that have the largest impact on human and natural
landscapes. These events are best characterized by probabilities of occurrence.
Consider, for example, the length of growing season (i.e., the period during which daily minimum
temperatures are above freezing) for Boulder, Colorado. Based on a 52-year period between 1949 and
2000, the average dates of last frost in spring (the beginning of the growing season) and first frost in
autumn (the end of the growing season) are May 3 and October 5, respectively. However, the earliest last
spring frost is on April 10 and the latest is on June 3; the earliest autumn frost is September 12 and the
latest is on October 31. By considering the frequency distribution of these frost dates, one can construct the
probability that the growing season will begin or end on a certain date (Figure 3.21). For example, in all 52
years the growing season began on or after April 10. Based on this, there is a high chance that in any given
year the earliest start of the growing season will be on or after April 10. The observations also show that in
22 of the 52 years, the growing season began by April 30. Thus, there is a ( 22 / 52)100 = 42% chance that
in any given year the growing season will begin by April 30 and a 58% chance that it will begin at a later
date. The median date of last spring frost is May 1, meaning that in 50% of years the growing season
begins on a earlier date and in 50% of years the growing season begins on a later date. Two weeks later, by
May 15, the chance of a later frost is only 15%. These data show there is always a risk of frost between
April 10 and June 3, but this risk decreases over time. In autumn, the opposite pattern occurs; the risk of
frost increases with time.
Precipitation, because of the devastating effects of floods, is a climatic variable that is best
considered in terms of the probability of extreme events. The risk of extreme large rainfall events is
quantified by a ‘depth-duration-frequency’ analysis (Dunne and Leopold 1978; Dingman 1994). This
analysis quantifies the likelihood that in any particular year (e.g., on average once every 10 years) a certain
amount of rainfall (e.g. 50 mm) will fall over a certain duration (e.g., 3 hours). The probability a storm of a
particular intensity (or depth) and duration will occur at least once in any given year is obtained from
historical rainfall data. Table 3.1 shows maximum 1-hour rainfall amounts measured at Chicago, Illinois,
over a 24-year period. First, the n rainfall amounts are ranked from largest to smallest, assigning the largest
rainfall a score of one and the smallest rainfall a score of n, which in this example is 24. Then, the
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probability of greater rainfall is defined for each rainfall amount. This probability shows, for example,
there is a 4% chance (i.e., the probability is 0.04) that the largest 1-hour rainfall will exceed 58.9 mm in
any given year. There is a 52% chance that this rainfall amount will exceed 35.8 (the mean). These data are
illustrated in Figure 3.22, along with additional data obtained at the same site for 6-hour and 24-hour
rainfall amounts. For all three durations, storms generating large amounts of rainfall are rare. Small storms
are much more common.
The recurrence interval, also called the return period, is the average time between events. It is the
inverse of probability. If the probability that the 1-hour rainfall will exceed about 60 mm in any given year
is 0.04, one would expect, on average, that 4 years out of 100 will have a 1-hour rainfall greater than 60
mm. In this case, the average time between these events is 25 years. The data in Figure 3.22 can be used to
determine the expected 1-, 6-, and 24-hour rainfalls for particular recurrence intervals. For example, there
is a 50% chance that the 1-, 6-, and 24-hour rainfalls will exceed approximately 35 mm, 55 mm, and 70
mm, respectively, in a given year. These are the rainfall amounts for a 2-year recurrence interval. There is
4% chance that these rainfall amounts will exceed about 60 mm, 135 mm, and 160 mm, respectively, in a
given year. These are the 25-year storms. For any given duration, the largest rainfall totals are produced by
rare storms with a long return period. Frequently recurring storms with a short return period produce less
rainfall.
It is important to note, however, that there is no guarantee a 25-year event will occur once every
25 years. If an event has a probability 1 / n of occurring in any particular year, the average time between
events is n years. However, this is only an average. In any given year, the probability of the event
happening is 1 / n . If the event happens in one year, the probability of it happening the next year is still
1 / n . In this way, events can occur in consecutive years. If the probability of an event happening in a
particular year is 1 / n , the probability the event does not happen is 1 − (1 / n ) and the probability that the
event does not happen for two consecutive years is ⎣⎡1−(1/ n ) ⎦⎤ ⎣⎡1−(1/ n ) ⎦⎤ . The probability the event does not
m
occur at all during m years is ⎣⎡1−(1/ n ) ⎤⎦ and the probability it happens at least once in m years is
m
1 − ⎣⎡1−(1/ n ) ⎤⎦ . For example, a 50-year event has a 2% chance of occurring in any given year, but there is
only a 64% chance it happens at least once in 50 years. There is an 87% chance it happens at least once in
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100 years and a 95% chance it happens at least once in 150 years. For recurrence intervals of 5 years or
more, there is only about a 66% chance the event will happen at least once during a period of time equal to
the recurrence interval (Figure 3.23).
Just as there is considerable variation within the United States in annual rainfall totals, there is
also much spatial variability in the frequency and intensity of storms (Figure 3.24). This variability arises
because the characteristics of storms vary depending on the type of storm. In middle latitude regions, the
heaviest rainfall over long periods of time comes from storms generated by fronts or hurricanes. Frontal
activity occurs when air masses of different temperatures and humidity collide. These storms generally
have moderate precipitation rates, extend over large areas (hundreds to thousands of square kilometers),
and last for periods of several hours to days. Hurricanes, which are common during summer and autumn
along the Gulf and Atlantic coasts, affect large areas and deliver high rates of rainfall for several hours to a
few days. The heaviest short duration rainfall comes from thunderstorms. Thunderstorms are most common
in summer, when intense solar radiation heats the ground, warming the surface air, and causing it to rise.
These storms produce brief but intense bursts of rainfall over small areas.
3.6 Land surface processes
Atmospheric and oceanic processes and their coupling dominate much of the study of seasonal-tointerannual climate variability. However, land surface processes contribute significantly to climate
variability. The presence of snow is now recognized as an important initial condition required for accurate
weather forecasts (Barnett et al. 1988, 1989; Walsh and Ross 1988; Cohen and Rind 1991; Walland and
Simmonds 1997). Low absorption of solar radiation by snow-covered surfaces prevents the surface from
warming during the day. On warm days, a large portion of net radiation at the surface is used to melt snow
and sensible heat is transferred from the warm air to the colder melting snow pack. Similarly, soil water
content is an important determinant of seasonal precipitation forecasts (Fennessy and Shukla 1999; Pielke
et al. 1999a; Douville and Chauvin 2000; Koster et al. 2000). Recycling of precipitation in
evapotranspiration can lead to a positive feedback in which wet soils pump more moisture into the
atmosphere, which enhances rainfall and further wets the soil. Conversely, dry soils, with their low rates of
14
Chapter 3 – Climate Variability
evapotranspiration, can reduce rainfall. Retention of precipitation by soil and the influence of soil water on
subsequent evapotranspiration contribute to and amplify interannual precipitation variability over tropical
and middle latitudes (Koster and Suarez 1995, 1996; Koster et al. 2000).
15
Ecological Climatology
3.7 Tables
Table 3.1. Largest 1-hour rainfalls from 1949 to 1972 at Chicago, Illinois
Raw data
Year
Ranked data
Rainfall
Rank,
Rainfall, x
Exceedence
(mm)
r(x)
(mm)
probability, P(X>x)
1949
20.1
1
58.9
0.04
1950
42.9
2
52.8
0.08
1951
38.1
3
52.3
0.12
1952
30.2
4
46.2
0.16
1953
25.9
5
45.2
0.20
1954
35.8
6
44.4
0.24
1955
28.7
7
42.9
0.28
1956
27.2
8
40.9
0.32
1957
52.8
9
39.4
0.36
1958
26.4
10
39.4
0.40
1959
52.3
11
38.9
0.44
1960
46.2
12
38.1
0.48
1961
35.1
13
35.8
0.52
1962
38.9
14
35.1
0.56
1963
23.1
15
30.2
0.60
1964
16.5
16
29.5
0.64
1965
21.8
17
28.7
0.68
1966
45.2
18
27.2
0.72
1967
39.4
19
26.4
0.76
1968
40.9
20
25.9
0.80
1969
39.4
21
23.1
0.84
1970
44.4
22
21.8
0.88
1971
29.5
23
20.1
0.92
1972
58.9
24
16.5
0.96
Note: Exceedence probability is the probability that rainfall (X) exceeds a certain value (x) and is equal to
the
ranked
score
( r ( x) )
divided
by
the
number
P ( X > x ) = r ( x ) /( n + 1) .
Source: Data from Dingman (1994, p. 146).
16
of
observations
plus
one
( n +1)
or
Chapter 3 – Climate Variability
3.8 Figure Legends
Figure 3.1. Mean monthly January and July temperature measured in central England from 1659 to 2000.
The thick solid line shows the 30-year mean temperature. Data provided by the Climatic Research Unit
(University of East Anglia, Norwich). Manley (1974) and Parker et al. (1992) describe this temperature
record.
Figure 3.2. Standard deviation of monthly air temperature for the period 1950 to 1979. Top: January.
Bottom: July. Data from Shea (1986) and provided by the National Center for Atmospheric Research
(Boulder, Colorado).
Figure 3.3. Precipitation for June through August 1993. Top: Total precipitation. Regions with greater than
400 mm precipitation are shaded. Bottom: Departure from normal. Regions more than 100 mm above
normal are darkly shaded. Regions more than 100 mm below normal are lightly shaded. Data from Xie and
Arkin (1997) extended through 1998 and provided by the National Center for Atmospheric Research
(Boulder, Colorado).
Figure 3.4. Summertime Palmer Drought Severity Index. Values between –2 and +2 indicate near normal
moisture conditions. Values less than –2 indicate increasing severity of drought and are darkly shaded.
Values greater than +2 indicate increasing moisture surplus and are lightly shaded. Top: 1934. Bottom:
1956. Data from Cook et al. (1999) and provided by the National Geophysical Data Center (National
Oceanic and Atmospheric Administration, Boulder, Colorado).
Figure 3.5. Summertime Palmer Drought Severity Index. Top: 1988. Bottom: 1993. Data from Cook et al.
(1999).
Figure 3.6. Summertime Palmer Drought Severity Index from 1900 to 1995 as a function of latitude
(vertical axis) and year (horizontal axis). Data are averaged for the Great Plains between longitudes 103°
W and 91° W. Contours greater than +2 are lightly shaded. Contours less than –2 are darkly shaded. Data
from Cook et al. (1999).
17
Ecological Climatology
Figure 3.7. Air mass source regions and typical paths for North America. cA, continental Arctic air. cP,
continental polar air. mP, maritime polar air. mT, maritime tropical air. cT, continental tropical air.
Figure 3.8. Effect of a trough and ridge over North America on temperature. Top: A deep trough in the
east. Bottom: A deep trough in the west.
Figure 3.9. Climatological sea surface temperature (1950 through 1998) for the equatorial Pacific between
latitudes 30° S and 30° N. Temperatures warmer than 28 °C are shaded. Top: January through March.
Bottom: July through September. Data from Reynolds and Smith (1995) extended through 1998 and
provided by the National Center for Atmospheric Research (Boulder, Colorado).
Figure 3.10. Main features of the Walker Circulation, December to February, normal conditions. Shading
indicates progressively warmer sea surface temperature.
Figure 3.11. Sea surface temperatures in the Pacific during an El Niño episode. Top: Sea surface
temperature during January through March 1998. Temperatures warmer than 28 °C are shaded. Bottom:
Difference from the climatological mean. Temperature anomalies greater than 1 °C are shaded. Data from
Reynolds and Smith (1995) extended through 1998.
Figure 3.12. Changes in the Walker Circulation. Top: El Niño episodes. Bottom: La Niña episodes.
Shading indicates progressively warmer sea surface temperature.
Figure 3.13. Contrast in precipitation across the tropical Pacific between El Niño and La Niña episodes.
Top: The 1998 El Niño. Bottom: The 1989 La Niña. Data from Xie and Arkin (1997) extended through
1998.
Figure 3.14. Sea surface temperatures during a La Niña episode. Top: Sea surface temperature during
January through March 1989. Temperatures warmer than 28 °C are shaded. Bottom: Difference from the
climatological mean. Temperature anomalies less than -0.5 °C are shaded. Data from Reynolds and Smith
(1995) extended through 1998.
18
Chapter 3 – Climate Variability
Figure 3.15. The El Niño/Southern Oscillation cycle from 1979 through 1998. Graphs show monthly sea
surface temperature (Reynolds and Smith 1995) and precipitation (Xie and Arkin 1997) anomalies (vertical
axis) averaged over the equatorial Pacific between latitudes 5° S to 5° N as a function of longitude
(horizonal axis). For this figure, longitudes start at 120° E (western Pacific), increase to dateline (180°),
and increase to 280° (corresponding to 80° W) near the South American coast.
Figure 3.16. El Niño related changes in temperature and precipitation. Top: December through February.
Bottom: June through August. Source: Climate Prediction Center (National Centers for Environmental
Prediction, National Oceanic and Atmospheric Administration, Washington, D.C.).
Figure 3.17. La Niña related changes in temperature and precipitation. Top: December through February.
Bottom: June through August. Source: Climate Prediction Center (National Centers for Environmental
Prediction, National Oceanic and Atmospheric Administration, Washington, D.C.).
Figure 3.18. North American temperatures during El Niño and La Niña. Temperature is the departure from
the climatological normal. Top: The winter 1994-95 El Niño. Bottom: The winter 1995-96 La Niña. Data
from Jones (1994) extended through 1998 and provided by the Climatic Research Unit (University of East
Anglia, Norwich).
Figure 3.19. North American precipitation during El Niño and La Niña. Precipitation is the percent of the
climatological normal. Top: The winter 1994-95 El Niño. Bottom: The winter 1995-96 La Niña. Data from
Xie and Arkin (1997) extended through 1998.
Figure 3.20. Dominant climate conditions during El Niño and La Niña. Top: The winter 1994-95 El Niño.
Bottom: The winter 1995-96 La Niña.
Figure 3.21. Probability of frost in Boulder, Colorado. Top: Last frost in spring. Bottom: First frost in
autumn.
19
Ecological Climatology
Figure 3.22. Rainfall depth-duration-frequency analysis. Top: Exceedence probability for rainfalls of 1-, 6, and 24-hours duration. Bottom: Rainfall amount in relation to duration for 2-, 5-, 10-, and 25-year
recurrence intervals.
Figure 3.23. Probability of an event occurring at least once over time intervals from 1 year to 1000 years
for recurrence intervals of 2, 5, 10, 50, and 100 years.
Figure 3.24. Rainfall amounts in inches for 10-year 30-minute storms. One inch is 25.4 mm. Adapted from
Hershfield (1961). See also Higgins et al. (1996).
20