Wallace J.M., Hobbs P.V. Atmospheric Science. An Introductory Survey

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424 Climate Dynamics

warmth of the tropical and subtropical continents

relative to the surrounding oceans in the summer

hemisphere lifts the pressure surfaces and induces

horizontal divergence at upper tropospheric levels,

maintaining relatively low sea-level pressure over

the continents relative to the surrounding oceans.

The pressure contrast between land and sea drives an

onshore flow of moist, boundary-layer air, triggering

deep convection over land, as depicted schematically

in Fig. 10.9.

In the real atmosphere the seasonally varying

distribution of precipitation (Fig. 1.25) is also influ-

enced by the land–sea geometry, the distribution of

mountain ranges, and the underlying sea-surface

temperature distribution. These combined influences

account for distinctive regional features such as the

summer rainfall maximum over the Bay of Bengal

and the extensive subtropical desert regions that

experience very little summer rainfall.

The tropospheric jet stream (Fig. 1.11) and the

baroclinic wave activity associated with the midlatitude

storm tracks tend to be strongest during wintertime,

when the equator-to-pole temperature gradient is

strongest.

At the longitude of Japan, where stationary

planetary waves generated by the land–sea con-

trasts and the flow over the Himalayas reinforce the

Jan

Mar May

Jul

Sep

Nov

Jan Mar

Depth (m)

°C

100

110

120

130

140

Fig. 10.7 Temperature (in °C) averaged over a region in the

central North Pacific (28°42 °N, 180°160 °W) as a

function of calendar month and depth showing the climato-

logical-mean annual cycle. [Based on data from World Ocean

Atlas, NOAA National Oceanographic Data Center (1994).

Courtesy of Michael Alexander.]

A notable exception is the North Pacific, where baroclinic wave activity tends to be suppressed during midwinter relative to late

autumn and early spring.

–180 –150 –120 –90 –60 –30 0 30 60 90 120 150

W m

–2

December–February

June–August

Fig. 10.6 Net radiation at the top of the atmosphere

in December–February and June–August in units of W m

2

[Based on data from the NASA Earth Radiation Budget

Experiment. Courtesy of Dennis L. Hartmann.]

moderating the contrast between winter and sum-

mer temperatures. Heat of fusion is taken up by the

cryosphere when ice melts during the warm season.

A comparable quantity of heat is released during the

cold season when sea ice freezes and thickens and

snow and ice particles that freeze within the atmos-

phere precipitate onto ice sheets and glaciers.

The large difference in the annual range of surface

air temperature over the continents and oceans shown

in Fig. 10.8 reflects widely differing heat capacities of

the underlying surfaces. The largest ranges (50 K)

are observed over the interior of Eurasia, North

America, and Antarctica, in areas far away from (or

shielded by mountain ranges) from the moderating

influence of the oceans.

Land–sea temperature contrasts at low latitudes

of the summer hemisphere force continental-scale

monsoon circulations. In analogy with Fig. 7.21, the

P732951-Ch10.qxd 12/09/2005 09:12 PM Page 424

10.2 Climate Variability 425

tropospheric jet stream, the climatological mean

westerly wind component at the 250-hPa level exceeds

70 m s

1

(Fig. 10.10). The Aleutian and Icelandic lows in

the sea-level pressure field (Fig. 1.19) also tend to

be most pronounced during wintertime, when the high

latitude oceans are much warmer than the continents.

In contrast, subtropical anticyclones tend to be

strongest during summer, when they are reinforced by

the monsoon-related land–sea pressure contrasts.

10.2 Climate Variability

Much of what we know about the causes of year-to-

year climate variability is based on numerical experi-

ments with atmospheric models like those described

in Section 7.3. Imagine a pair of experiments: one

run in which the bottom boundary conditions (e.g.,

sea-surface temperature, sea ice extent, soil moisture)

are prescribed to vary from one year to the next in

Fig. 10.8 The “Find the Continents” game! Climatological-mean July minus January surface air temperature in °C. [Courtesy of

Todd P. Mitchell.]

80°

–10

–

–40

WARM

COLD

Fig. 10.9 Idealized representation of the monsoon circula-

tions. The islands represent the subtropical continents in the

summer hemisphere. Solid lines represent isobars or height

contours near sea level (lower plane) and near 14 km or

150 hPa (upper plane). Short solid arrows indicate the sense

of the cross-isobar flow. Vertical arrows indicate the sense of

the vertical motions in the middle troposphere. Regions that

experience of summer monsoon rainfall are also indicated.

180°

0°

°W

°E

Fig. 10.10 January climatological mean zonal wind speed at

the jet stream (250-hPa) level. Contour interval 15 m s

1

. The

zero contour is bold; positive contours, indicative of westerlies,

are solid and negative contours, indicative of easterlies, are

dashed. [Based on data from the NCEP-NCAR Reanalysis.

Courtesy of Todd P. Mitchell.]

P732951-Ch10.qxd 12/09/2005 09:12 PM Page 425

426 Climate Dynamics

accordance with historical data over, say, the 20th cen-

tury and the other a run of equal length in which these

boundary conditions are prescribed to vary in accor-

dance with seasonally varying, climatological-mean

values, year after year. Because weather is inherently

unpredictable beyond a time frame of a few weeks, any

resemblance between the observed and simulated 100-

year sequence daily synoptic charts in the two simula-

tions must be viewed as entirely fortuitous. However,

the first run may exhibit climate variability attributable

to the year-to-year variations in the boundary con-

ditions, whereas the variability in the second run is

generated exclusively by dynamical processes operat-

ing within the atmosphere. The ratio of the standard

deviation of the year-to-year variability as simulated in

the two experiments provides a measure of the relative

importance of the boundary forced variability relative

to the internally generated variability.

Numerical experiments of this kind have been

conducted using many different models. The results

can be summarized as follows.

(1) Most of the year-to-year variability of the

tropical atmosphere is boundary forced

(i.e., attributable to year-to-year variations in the

prescribed boundary conditions, particularly sea-

surface temperature over the tropical oceans). In

the more realistic models, the simulated year-to-

year variations in tropical climate bear a strong

resemblance to the observed variations.

(2) At extratropical latitudes boundary forcing

and internal atmospheric dynamics both make

important contributions to the observed year-to

year climate variability. Of the various

contributors to the boundary forcing, tropical

sea-surface temperature appears to be of

primary importance for the northern

hemisphere winter climate. Variations in soil

moisture and vegetation contribute to the

month-to-month persistence of summertime

climate anomalies. If the observed values

of these fields are prescribed, the simulated

year-to-year variations in extratropical climate

are correlated with the observed variations,

but not as strongly as in (1).

(3) The influences of year-to-year variations in

sea-ice extent and extratropical sea-surface

temperature are more subtle. By running

ensembles of simulations, in which each

member is started from different initial

conditions but is forced with the same

prescribed sequence of boundary conditions,

it is possible to identify weak bondary-forced

“signals” that stand out above the internally

generated “sampling noise.”

(4) Most of the intraseasonal variability of the

extratropical wintertime circulation appears to

be generated internally within the atmosphere.

Variability generated by the interactions between

the atmosphere and more slowly varying compo-

nents of the Earth system is referred to as coupled

climate variability. Climate variability may also be

externally forced,e.g., by volcanic eruptions, varia-

tions in solar emission, or changes in atmospheric

composition induced by human activities.

Consider the climatic variable x, which could

represent monthly-, seasonal-, annual-, or even

decadal-mean temperature at a prescribed lati-

tude, longitude, and height above the Earth’s

surface. Let X be the climatological-mean value

of x.The departure of x from its (seasonally

varying) climatological mean value, namely

(10.1)xx  X

is referred to as the anomaly in x.For example, a

temperature 3 °C below normal is equivalent to a

temperature anomaly of 3°C.

The variance of x about the climatological mean

(10.2)

where the denotes a time mean over the refer-

ence period upon which the climatology is based.

( )

x

 (x  X)

10.1 Some Basic Climate Statistics

The formalism developed in Box 10.1 is also applicable to boundary-layer quantities. The overbars represent time-averaged quantities

and the primed quantities represent fluctuations about the mean that occur in association with boundary-layer turbulence.

is the temporal variance.The spatial variance, defined in an analogous manner, is a measure of the variability of x about its spatial mean.x

Continued on next page

P732951-Ch10.qxd 12/09/2005 09:12 PM Page 426

10.2 Climate Variability 427

Variance, a positive definite quantity with units

of the square of the variable under examination

(e.g., °C

for temperature), is a measure of the

amplitude of the variability (or dispersion) of x

about its climatological-mean value. The standard

deviation or root mean squared (r.m.s.) amplitude

of the variations in x about the time mean

(10.3)

is widely used as a measure of the dispersion. Note

that variance and standard deviation do not carry

algebraic signs.

The standardized anomaly

is a dimensionless measure of the amplitude of

the departure from the mean. For variables such

as monthly-mean temperature, sea-level pressure

and geopotential height, which tend to be

normally distributed,

64% of randomly selected

x* #

x



(x)



(x) #

√

x



√

(x  X)

standardized anomalies exhibit absolute values

less than 1.0, 95% of them less than 2.0, and

99.9% of them less than 3.0. Hence, for such

distributions, a standardized anomaly x* with a

value of 1.0 or 1.0 can be considered typical in

terms of r.m.s. amplitude.

Figure 10.11 shows an example of a seasonal

mean 500-hPa height field together with the corres-

ponding anomaly and standardized anomaly fields.

In contrast to the baroclinic waves discussed in

Section 8.1.1, which exhibit typical zonal wave-

lengths of 40° of longitude, the anomalies in

monthly and seasonal mean fields like the ones

shown in this example are larger in scale and they

are not particularly wave like.

Now let us consider the relationship between

two time series x(t) and y(t), which might repre-

sent a series of values of the same climatic

variable at two different geographical locations or

might represent two different variables at the

same location. It is assumed that x(t) and y(t) span

a common period of record. The dimensionless

statistic

Continued on next page

Possessing a bell-shaped histogram centered on the climatological-mean value, with a width (defined as the distance between the center

and the inflection points) of 1 standard deviation.

(a)

(b) (c)

(d)

(e)

Fig. 10.11 (a) The mean 500-hPa height field averaged over the late winter season January–March 1998. (b) The

climatological-mean wintertime (January–March) 500-hPa height field based on the period of record 1958–1999; contour

interval 60 m, the 5100-, 5400- and 5700-m contours are bold. (c) The climatological-mean standard deviation of

the wintertime-mean (January–March) 500-hPa height field based on the same period of record; contour interval 9 m, the

54-m contour is bold. (d) The anomaly field for January–March 1998, calculated by subtracting (b) from (a); contour

interval 30 m, the zero contour is bold, and dashed contours denote negative values. (e) The standardized anomaly field,

calculated by dividing (d) by (c); contour interval 0.6 standard deviations, the zero contour is bold, and dashed contours

denote negative values. [Based on data from the NCEP-NCAR Reanalyses. Courtesy of Roberta Quadrelli.]

10.1 Continued

P732951-Ch10.qxd 12/09/2005 09:12 PM Page 427

428 Climate Dynamics

(10.4)

called the correlation coefficient between x and y,

is a measure of the degree to which x and y are lin-

early related (i.e., that one is simply a linear multi-

ple of the other). Values of r range from 1 to 1.

To illustrate how the correlation coefficient can

be used to describe the structure of a spatial field,

we show, in Fig. 10.12, scatter plots of standardized

wintertime mean sea-level pressure anomalies over

Iceland versus sea-level pressure at three different

locations. In plot A, y Iceland sea-level pressure

is plotted against sea-level pressure at a nearby

location; in this case x* and y* tend to be of like

sign so that and the data points in the

scatter plot lie preferentially in the first and third

quadrants of the plot. In such situations x and y

are said to be positively correlated. Plot B shows

pressure over Iceland versus sea-level pressure

r  x

 0



xy



(x)



(y)

over southern England; in this case the correlation

is weak (i.e., r  0). Plot C shows that sea-level

pressure anomalies over Iceland and Portugal tend

to be of opposing sign (i.e., negatively correlated:

and the points in the plot lie prefer-

entially in the second and fourth quadrants.

Statistically significant linear correlations between

climatic variables such widely spaced locations are

evidence of what are referred to as teleconnections.

In general, the higher the value of srs,the

tighter the clustering of the dots along the 45° (or

135°) axis in scatter plots of x* versus y*. It can be

shown that in the limit, as , the points line

up perfectly, in which case, y*  rx* or, alterna-

tively, x*  ry*. If r  0, there is no linear relation

between x and y and the least-squares best fit in

the scatter plot is the horizontal line that passes

through the centroid of the data points. Exercise

10.9 shows that the fraction of the variance of x

and y that is common to the two variables (in a

linear sense) is given by r

 r  : 1

r  x

 0

10.1 Continued

The correlation coefficient r can also be interpreted as the slope of the least-squares best fit regression line for y regressed upon x;

i.e., the slope of the staight line, passing through the origin, for which the mean squared error (y

 rx

)

, summed over all data points

in the time series, is minimized (see Exercise 10.9).

−3 −2 −1 0 1 2 3

−3

−2

−1

r = 0.69

−3 −2 −1 0 1 2 3

−3

−2

−1

r = −0.69

−3 −2 −1 0 1 2 3

−3

−2

−1

r = −0.1

Fig. 10.12 Scatter plots of standardized sea-level pressure anomalies. In all three plots, the x axis refers to anomalies at a

grid point over Iceland, indicated by the red dot in the upper left panel. Iceland anomalies are plotted versus anomalies at

(A) Spitzbergen, (B) southern England, and (C) Portugal. Numerical values of the correlation coefficients are indicated in

the scatter plots, and sloping lines represent the equation y*  rx* that corresponds to the least-squares best fit linear

regression line. The map in the upper left panel shows the correlation coefficient between sea-level pressure at Iceland and

sea-level pressure at every grid point: contour interval 0.15; the zero contour is bold and negative values are indicated by

dashed contours. [Based on data from the NCEP-NCAR Reanalyses. Courtesy of Roberta Quadrelli.]

P732951-Ch10.qxd 12/09/2005 09:12 PM Page 428

10.2 Climate Variability 429

10.2.1 Internally Generated Climate

Variability

The day-to-day variability of the geopotential height

field is characterized by a bewildering array of hemi-

spheric patterns. In contrast, the spatial patterns of

month-to-month variability, as manifested in hemi-

spheric or global anomaly fields like those shown in

Fig. 10.11 tend to be simpler. Much of the structure in

such maps can be interpreted in terms of the superpo-

sition of a limited number of preferred spatial patterns

that may appear with either polarity when they are

present. In the northern hemisphere these patterns

are much more prominent during winter than during

summer.

A prominent pattern in the northern hemisphere

wintertime geopotential height field is variously

known as the North Atlantic Oscillation (NAO),the

Arctic Oscillation (AO), or the northern hemisphere

annular mode (NAM).The sea-level pressure signa-

ture of this pattern, shown in Fig. 10.13, is marked by

anomalies of opposing sign over the Arctic polar cap

region and the AtlanticMediterranean sector of

middle latitudes. The Southern hemisphere annular

mode (not shown) is even more symmetric about the

pole and is evident not just during the winter, but

throughout the year.

When sea-level pressure is below normal over

the polar cap region and above normal over the

Mediterranean, the annular mode is said to be in its

high index polarity. At these times the jet streams

and storm tracks tend to be displaced poleward of

their normal positions, temperatures tend to be

unseasonably mild over Eurasia and most the United

States (Fig. 10.14), and northern Europe experiences

–1.0

– 0.5

0.5 1.0

Fig. 10.13 Pattern of sea-level pressure anomalies associated

with the northern hemisphere annular mode (a.k.a., North Atlantic

Oscillation). Colored shading indicates the correlation coefficient

between the time series of the index of the pattern shown in

Fig. 10.17 and the time series of the monthly mean sea-level

pressure anomalies at each grid point. The colors thus indicate

the sign of the anomalies associated with the high index polarity

of the annular mode: the low index polarity is characterized

by anomalies of opposite sign. [Based on November–April data

from the NCEP-NCAR Reanalysis. Courtesy of Todd

P. Mitchell.]

–2.0

–1.0

1.0

2.0

–1.0 –

0.5

0 0.5 1.0

°C

Fig. 10.14 Patterns of surface air temperature anomalies

(top) and standardized precipitation anomalies (bottom)

observed in months when the northern annular mode is in

its high index polarity characterized by below normal sea-level

pressure over the Arctic. Warm colors indicate above normal

temperature and below normal precipitation, and shades of

blue indicate anomalies of opposing sign. Typical amplitudes

of the temperature and standardized precipitation anomalies

are indicated by the color scale. [Courtesy of Todd P. Mitchell.]

P732951-Ch10.qxd 12/09/2005 09:12 PM Page 429

430 Climate Dynamics

heavier than normal rainfall while the Mediterranean

basks in sunshine. In contrast, episodes of abnormally

high pressure over the Arctic (i.e., the low index

polarity of the annular mode) tend to be marked by

relatively frequent occurrence of cold-air outbreaks

over Eurasia and the United States, raising the

demand for heating oil on the world market, and

stormy weather over the Mediterranean.

The sea-level pressure signature of another promi-

nent wintertime pattern, the so-called PacificNorth

American (PNA) pattern, is shown in Fig. 10.15. The

PNA pattern, with its strongest sea-level pressure

anomalies over the North Pacific, has a strong impact

on wintertime climate anomalies downstream over

North America, as documented in Fig. 10.15. When

pressures over the North Pacific are below normal,

temperatures over much of western North America

tend to be relatively mild and precipitation tends to be

abnormally heavy along the coasts of the Gulf of

Alaska and the Gulf of Mexico; Hawaii tends to be dry.

The processes that give rise to the annular modes

and PNA pattern are not fully understood. The

annular modes involve north–south shifts in the

storm tracks (i.e., the belts of strongest baroclinic

wave activity) and the surface westerlies. When the

annular modes are in their high index polarity, the

storm tracks and associated westerly wind belts

are shifted poleward of their climatological-mean

positions, and vice versa. These shifts are believed to

occur as a result of the interactions between the

waves and the mean westerly winds upon which

they are superimposed. The PNA pattern, which has

no clearly defined southern hemisphere counterpart,

involves the alternating extension and retraction of

the downstream end of the jet stream that passes just

to the south of Japan in Fig. 10.10. When the PNA

pattern is in its high index polarity, the jet extends

farther east into the central Pacific than in Fig. 10.10,

and vice versa. These extensions and retractions of

the jet are believed to be the manifestation of a form

of instability of the northern hemisphere wintertime

climatological-mean stationary-wave pattern.

The annular modes and the PNA pattern play

important roles in the variability of the extratropical

circulation on timescales of decades and longer. Of

particular interest is the tendency toward the positive

polarity of both northern and southern annular

modes from the 1970s to the 1990s (Fig. 10.17). The

positive trends in the indices of the annular modes

–1.0

–0.5

0.5 1.0

Fig. 10.15 As in Fig. 10.13, but for the PacificNorth American

pattern. [Courtesy of Todd P. Mitchell.]

–2.0

–1.0

1.0

2.0

°C

–1.0 –0.5

0 0.5 1.0

Fig. 10.16 As in Fig. 10.14, but for the PacificNorth American

pattern. [Courtesy of Todd P. Mitchell.]

P732951-Ch10.qxd 12/09/2005 09:12 PM Page 430

10.2 Climate Variability 431

are indicative of a poleward shift of the wintertime

storm tracks and the westerly wind belt in both

hemispheres. During the winters since 1977 the PNA

pattern has exhibited a preference for its high index

polarity, with below normal sea-level pressure over

the Gulf of Alaska and a relative absence of cold air

masses over Alaska and western Canada.

10.2.2 Coupled Climate Variability

The northern hemisphere annular mode and the

PacificNorth American pattern are believed to be

generated by processes within the atmosphere because

they emerge as the leading modes of variability in

extended runs of atmospheric general circulation

models in which the bottom boundaries are held

fixed. This section considers coupled climate variability

involving interactions between the atmosphere and

other components of the Earth system. Under certain

conditions, these interactions can give rise to modes

of variability that are qualitatively different from

the kinds of variability that the atmosphere is capable

of generating through its own internal dynamical

processes.

a. Coupling with the tropical ocean

The most prominent coupled mode involving the

atmosphere and ocean is known as ENSO (a hybrid

acronym in which the first two letters refer to El Niño,

a recurrent pattern of positive sea-surface temperature

anomalies in the equatorial Pacific

(Fig. 10.18) and

the second two letters refer to the associated global

pattern sea-level pressure (Fig. 10.19), which is known

as the Southern Oscillation

11,12

). Time series of equato-

rial Pacific sea-surface temperature (an indicator of the

status of El Niño) and sea-level pressure at Darwin,

Australia (an indicator of the state of the Southern

−2

−1

−2

−1

Northern Annular Mode

Pacific North American Pattern

Southern Annular Mode

−1

−2

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Year

Fig. 10.17 Time series of standardized indices of the annular modes and the PNA pattern. The northern hemisphere indices are

shown for the winter months (November–March) only, whereas the index of the southern annular mode is shown for all calendar

months. [Courtesy of Todd P. Mitchell.]

The name El Niño, Spanish for male child, was used by Ecuadorian and Peruvian fishermen to refer to a warm coastal current that

often appears Christmas. The name is now more widely used to denote episodic warmings of the entire region of the equatorial Pacific cov-

ered by the warm colored shading in Fig. 10.18.

The name Southern Oscillation was coined by Walker in the 1920’s to denote a global pattern of climate anomalies that included the

sea-level pressure anomalies shown in Fig. 10.19.

Gilbert Thomas Walker (1869–1958) English mathematician and meteorologist. Attended St. Paul’s School, London, where, according

to his own account, he spent 2 years on the classical side but was then “sent in disgrace” to the mathematical side when he made some

appalling linguistic blunders. While at St. Paul’s he was awarded a prize for a gyroscope that he made. In 1897 he published an original paper

on the flight of boomerangs, which he was expert at throwing. He also wrote articles on the mathematics of billiards, golf, and bicycle riding.

Although he had not published any papers on meteorology, in 1904 he was appointed Director of Observations in India, a post he retained

until 1924. Discovered the Southern Oscillation, which turned out to be useful in seasonal forecasting over India. Later became Professor of

Meteorology at the Imperial College of Science, London, where he became interested in the forms of clouds and the conditions that produced

them. He was an accomplished flute player; some modern flutes are made according to his suggestions.

P732951-Ch10.qxd 12/09/2005 09:12 PM Page 431

432 Climate Dynamics

80S

60S

40S

20S

20N

40N

60N

80N

–1 –0.5 0 0.5 1 °C

60E 120E 180 120W 60W 0

Fig. 10.18 Global pattern of sea surface temperature anomalies observed during El Niño years (in °C).

[Based on data from

the U. K. Meteorological Office HadISST. Courtesy of Todd P. Mitchell.]

Figure 10.18 was constructed by linearly regressing the sea surface temperature anomalies at each grid point onto standardized

monthly values of a standardized EI Niño index, shown in Fig. 10.20, which is defined as the average sea surface temperature anomaly over

the region of large equatorial sea-surface temperature anomalies in the eastern equatorial Pacific (6N6S, 18090W) minus the global-

mean sea-surface temperature anomaly.

60E 120E 180 120W 60W 0

80S

60S

40S

20S

20N

40N

60N

80N

–1 –0.5 0 0.5 1

Fig. 10.19 Global pattern of sea-level pressure anomalies observed during El Niño years. This pattern was formed by linearly

correlating monthly-mean sea-level pressure at each grid point with sea-level pressure at Darwin, Australia, indicated by the D on

the map. The Darwin sea-level pressure time series appears on the lower time axis in Fig. 10.20 as an index of the status of the

Southern Oscillation. [Darwin time series from the NCAR Climate and Global Dynamics Division and map-form data from the

NCEP-NCAR Reanalysis and the Darwin time series from the NCAR Data Library. Courtesy of Todd P. Mitchell.]

P732951-Ch10.qxd 12/09/2005 09:12 PM Page 432

10.2 Climate Variability 433

Oscillation), shown together in Fig. 10.20, demonstrate

the remarkable strength of the atmosphere–ocean

coupling on the interannual timescale that is observed

in association with ENSO.

In the long-term climatology (Fig. 1.19), sea-level

pressure is higher on the eastern side of the Pacific

than on the western side and the easterly surface winds

along the equator are directed down the pressure

gradient. It is evident from Fig. 10.20 that during

El Niño events, sea-level pressure at Darwin tends to

be above normal. It follows that El Niño events are

maeked by a weakening of the climatological-mean

pressure gradient. We will now show, by means of a

“case study,” that El Niño events are marked by a

weakening of the easterly surface winds, as well as the

east–west gradients in sea level and thermocline depth.

By most measures, the strongest El Niño event of

the 20th century was the one that began in the boreal

summer of 1997 and lasted about 9 months. The year

that followed was typical of cold events of the ENSO

cycle, referred to in the popular press as La Niña.

Various fields for these contrasting times are shown

in the next few figures. Relative to the cold year or to

normal conditions, the warm (El Niño) year is char-

acterized by:

•a weakening of the easterly trade winds winds

along the equator (Fig. 10.21).

•a weakening of the “equatorial cold tongue” in

the sea-surface temperature field (Fig. 10.21;

compare also with Fig. 2.11).

• reduced productivity of the marine biosphere

in the band of upwelling along the equator

(Fig. 10.22).

•a rise in sea level in the equatorial eastern

Pacific and lowering of sea level in the

western Pacific (Fig. 10.23), which is indicative

of a weakening of the climatological-mean

east-to-west gradient.

These diverse manifestations of El Niño are

physically consistent with each other and with the

patterns of sea surface temperature anomalies and

sea-level pressure anomalies in Figs. 10.18 and

10.19. In accordance with the equation of motion

for the zonal wind component on the equator, a

reduced east–west pressure gradient during El

Niño (i.e., positive sea-level pressure anomalies at

Darwin) is consistent with a weakening of the east-

erly trade winds along the equator. In accordance

with the relationships discussed in Section 7.3.4, the

weakening of the trade winds is consistent with

reduced equatorial upwelling. Reduced equatorial

upwelling, in turn, favors higher sea surface tem-

perature and lower marine productivity. The relax-

ation of the east–west gradient in sea level is a

response to the weakening of the surface easterlies

along the equator.

The consistency in El Niño-induced variations in

wind, sea level, and equatorial upwelling is evident in

the schematics shown in Fig. 10.24. The relaxation of

the slope of the thermocline occurs in response to

The first convincing proof of the link between El Niño and the Southern Oscillation was in a series of papers by Jacob Bjerknes in

the late 1960s. Bjerknes’ conceptual model of the life cycle of extratropical cyclones, published nearly 50 years earlier, was highly influen-

tial in the development of weather forecasting.

In English, “the girl child.”

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

−2

Temperature (°C)

Darwin SLP (hPa)

−2

Year

Fig. 10.20 Time series of indices of El Niño (top) and the Southern Oscillation (bottom). Minus the global-mean sea surface

temperature anomaly. The Southern Oscillation is represented by Darwin sea-level pressure anomalies. The location of Darwin is

indicated by the D in Fig. 10.19. Prominent warm episodes of the ENSO cycle occurred during 1957–1958, 1965–1966,

1972–1973, 1982–1983, 1986–1988, and 1997–1998. [Courtesy of Todd P. Mitchell.]

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