UNITED STATES

DEPARTMENT OF COMMERCE

Carlos M. Gutierrez

Secretary

NATIONAL OCEANIC AND

ATMOSPHERIC ADMINISTRATION

VADM Conrad C. Lautenbacher, Jr.

Under Secretary for Oceans

and Atmosphere/Administrator

Office of Oceanic and 

Atmospheric Research

Richard W. Spinrad

Assistant Administrator

NOAA Technical Memorandum OAR PMEL-140

CHARACTERIZING THE INTERANNUAL VARIABILITY OF THE

EQUATORIAL PACIFIC: AN OLR PERSPECTIVE

Andrew M. Chiodi1,2

D.E. Harrison1,2

1

Joint Institute for the Study of the Atmosphere and Ocean (JISAO)

University of Washington, Seattle, WA

2

Pacific Marine Environmental Laboratory

Seattle, WA

Pacific Marine Environmental Laboratory

Seattle, WA

June 2008
NOTICE from NOAA

Mention of a commercial company or product does not constitute an endorsement by

NOAA/OAR. Use of information from this publication concerning proprietary products

or the tests of such products for publicity or advertising purposes is not authorized. Any

opinions, findings, and conclusions or recommendations expressed in this material are those

of the authors and do not necessarily reflect the views of the National Oceanic and Atmospheric Administration.

Contribution No. 3204 from NOAA/Pacific Marine Environmental Laboratory

Also available from the National Technical Information Service (NTIS)

(http://www.ntis.gov)

ii
Contents iii

Contents

Abstract 1

1 Introduction 1

2 Data and Methods 3

3 Results 4

4 Distribution of Monthly-Average Index Values 9

5 Distribution of Annual Peaks 13

6 Discussion 16

7 Conclusion 17

8 References 19

Appendix A 1950–2007 ENSO Indices 21

Appendix B Gaussian Distribution Confidence Intervals 29

Appendix C Gaussian Annual Peak Rank-Order Confidence

Intervals 29

Characterizing the interannual variability of the equatorial

Pacific: An OLR perspective

A.M. Chiodi1,2 and E.D. Harrison1,2

Abstract. There has been considerable societal importance placed on the ability to distinguish

and predict interannual anomalies of the tropical Pacific’s coupled ocean-atmosphere system. Specific importance has been placed on determining when anomalous conditions follow one of two sets

of conditions first described in detail by Bjerknes and commonly referred to as “El Ni ̃no” and “La

Ni ̃na.” Here, we investigate the use of outgoing-longwave-radiation (OLR) as a means of determining “El Ni ̃no”-type anomalies operationally and retrospectively. We find that OLR, averaged

over regions which are known to play a role in Bjerknes feedbacks over the tropical Pacific, can

be used to unambiguously identify such interannual anomalies and that some OLR-based indices

are advantageous in terms of the degree to which anomalous years are clearly distinguished from

background variability. Thus, OLR offers a useful perspective on the state of the coupled tropical

Pacific system and should be considered when assessing the state of this system, along with other

more traditionally discussed variables such as sea surface temperature (SST) and sea level pressure

(SLP).

1. Introduction

Coupled ocean-atmosphere processes are known to cause variability on a

range of temporal and spatial scales in the equatorial Pacific. Considerable

societal importance, however, has been attached specifically to determining

occasions in which the interannual variability of the system follows a set of

conditions first described in detail by Bjerknes, commonly referred to as El

Ni ̃no-Southern Oscillation (ENSO) events.

The ENSO phenomenon has become familiar around the world because

substantial weather anomalies often occur during periods of extreme “ENSOstate.” The warm and cold phases of ENSO, generally referred to as El Ni ̃no

and La Ni ̃na, respectively, often bring anomalous regional rainfall and sometimes anomalous seasonal temperatures. Lists of years of extreme conditions have been composited and multi-month life cycles of “El Ni ̃no events”

and “La Ni ̃na events,” as well as associated weather anomalies, have been

constructed and evaluated for the robustness of the characteristic weather

anomalies (see Ropelewski and Halpert, 1987, 1996; Trenberth and Caron,

2000; Harrison and Larkin, 1998; Smith et al., 1999) Such composites provide a statistical basis for seasonal weather forecasting in the affected regions, if the statistical linkages are sufficiently strong. The dynamical basis

for such relationships is based on the fact that extremes of ENSO involve

substantially anomalous patterns of deep tropical convection and, hence, of

large-scale atmospheric forcing that can be expected to drive tropical and

extra-tropical atmospheric circulation anomalies.

Traditionally, the occurrence of such anomalies has been agreed upon

based on qualitative considerations. There is, however, a need to unambigu1Joint Institute for the Study of the Atmosphere and Ocean, University of Washington,

Seattle, WA 2NOAA, Pacific Marine Environmental Laboratory, Seattle, WA
2 Chiodi and Harrison

ously determine both previous and future events from simple observational

metrics. In the past decade or two, indices based on sea surface temperature (SST) and sea level pressure (SLP) difference have commonly served

this purpose. Multivariate ENSO indices, e.g., the Bjerknes ENSO Index

(BEI, see Harrison and Larkin, 1996) and the Multivariate ENSO index

(Wolter and Timlin, 1998) have also been proposed but have not gained

wide acceptance (reference indices shown for period 1950–2007 in Appendix

A.) Here, we evaluate a different readily observed variable in terms of its ability to clearly distinguish years in which significantly anomalous conditions

occur on interannual timescales. We find that outgoing-longwave-radiation

(OLR), averaged over a region known to play a role in Bjerknes feedbacks

over the tropical Pacific, can be used to unambiguously identify interannual

anomalies related to warm-ENSO, and that this OLR-based index is advantageous in terms of the (1) degree to which anomalous years are clearly

distinguished from background variability, (2) the ability to directly interpret changes in OLR as significant phenomenological changes in the coupled

tropical Pacific system (i.e., the presence of atmospheric deep convection),

and (3) the usefulness of absolute monthly data, as opposed to longer-term

anomaly averages.

From an operational perspective, the detection and identification of significant shifts in the coupled tropical Pacific system depends upon first quantifying the “normal” or background variability of the system. In this context, an ideal index will be one in which the events of main interest (significant phenomenological anomalies) are clearly distinct from the observed

background variability. Furthermore, these outliers should be indicative of

fundamental phenomenological shifts in the system’s behavior. Thus, we investigate the extent to which the metrics considered here have unambiguous

(statistically significant) outliers that occur on interannual timescales and

are indicative of fundamental-phenomenological shifts (i.e., Bjerknes conditions) in the system. Herein, we will statistically distinguish between background and outlier by comparing observations to a commensurate “normal”

(Gaussian) system (described below).

First, it is helpful to review some well-known average conditions of the

tropical Pacific. Normally, relatively strong easterlies are present over the

central equatorial Pacific. These easterlies depress the thermocline in the

West, shoal the thermocline in the East, and drive upwelling, which leads to

the formation of a cold-tongue of SST in the central and eastern equatorial

Pacific. This creates a gradient in SST between the warm western and cooler

central and eastern tropical Pacific. This gradient is involved in setting up a

SLP gradient between the eastern (high SLP) and western (low SLP) tropical

Pacific, which in turn is involved in driving the Walker Circulation, in which

air flows west along the equator, rises over the western Pacific and Maritime

Continent, is returned to the east in the upper troposphere, and sinks over

the eastern Pacific. Accordingly, the central and eastern tropical Pacific is

normally arid while heavy rains occur over the (convective) western tropical

Pacific.

Bjerknes (1969) proposed that the strong interannual variability of this

system can be explained by sequences of positive feedbacks which push the
Characterizing interannual equatorial Pacific variability with OLR 3

system into one of two phases. For example, if wind anomalies over the

equatorial Pacific lead to warming of the cold tongue (i.e., anomalously

warm Ni ̃no 3 and Ni ̃no 3.4 SSTAs), then the SST and thus SLP gradients

would weaken (i.e., the Southern Oscillation Index would decrease). In turn,

the equatorial easterlies would subside, leading to more warming of SST

and eventually a shift in the Walker Circulation such that the convection

appears over the now anomalously warm central and possibly eastern Pacific.

Conversely, an initial cooling of SST could lead the system in the opposite

direction, resulting in a state with cooler SST over the cold tongue, a stronger

SLP gradient, and an upward branch of the Walker Circulation that is more

tightly confined to the western Pacific.

Although the existence of these feedbacks is generally well accepted by

the scientific community, assessing the state of the system is complicated

by the fact that observations are subject to modes of variability other than

those described above. This complication is known to lead to ambiguity

in assessing the state of the system; for example, it is well known that the

traditional ENSO indices based on SLP and SST (SOI, Ni ̃no 3, Ni ̃no 3.4)

do not always vary consistently with each other (e.g., Deser and Wallace,

1987).

We here revisit the supra-seasonal variability of the equatorial Pacific

as revealed by OLR observations. OLR data is a known proxy for deep atmospheric convection and has been available reliably since 1979, and as far

back as 1974. OLR observations have confirmed that the equatorial Pacific

undergoes large intraseasonal and interannual variability in deep convection.

By averaging over space and time it is possible to focus on interannual variability. We here offer perspective on interannual anomalies of the equatorial

Pacific by looking at monthly and spatially averaged OLR values over three

regions that collectively span much of the central and western basin. Results

are compared to other traditionally used information (e.g., SSTA and SLP).

Section 2 describes the data sets used. Section 3 discusses various monthly

averaged time series. Section 4 examines the distributions of monthly average values. Section 5 presents statistical analysis of the distribution of

annual peaks. Section 6 discusses the implications of these results. Section

7 presents some conclusions.

2. Data and Methods

We use the daily “Interpolated OLR” data provided by the NOAA/OAR/

ESRL PSD, Boulder, Colorado, USA, from their web site at http://www.

cdc.noaa.gov/. Following several previous studies (e.g., Garreaud and Wallace, 1997; 1998), we consider a convective index (CI) defined as 230-OLR.

Positive values of this index are generally considered to be indicative of deep

convection. We use monthly averaged OLR values, spatially averaged over

the three regions listed in Table 1. The longitude boundaries of the two

central Pacific regions can be varied by 10 degrees without significantly af-
4 Chiodi and Harrison

Table 1: OLR analysis regions.

Region Name Latitude Range Longitude Range

Eastern Central Pacific (ECP) 155◦W to 125◦W 4◦S to 4◦N

Western Central Pacific (WCP) 175◦E to 155◦W 4◦S to 4◦N

Western Pacific (WP) 150◦E to 180◦ 5◦S to 5◦N

fecting the results presented here. Results for the western Pacific region are

more sensitive to such changes, although they remain qualitatively similar.

We also use ENSO indices obtained as follows; SST data was provided

by the Hadley Centre’s HadISST data set (Rayner et al., 2003, available at

http://hadobs.metoffice.com/hadisst/). Anomalies were determined as

the difference between monthly averages and the monthly mean climatology (base period January 1971 to December 2000), consistent with current

practices. SSTA was then averaged spatially over the standard Ni ̃no 3 and

Ni ̃no 3.4 regions (150◦W–90◦W, 5◦S–5◦N and 170◦W–120◦W, 5◦S–5◦N, respectively).

Monthly averaged values of the Troup Southern Oscillation Index, based

on SLP at Tahiti and Darwin, were obtained from the Australian Bureau of

Meteorology. This data is available at http://www.bom.gov.au. We here

use a Jan 1971 to Dec 2000 base period, for consistency with the SST-based

indices.

Monthly averages of the Multivariate ENSO Index (MEI) were obtained

from NOAA’s ESRL website. This index is based on the covariance of six

variables observed over the tropical Pacific (sea-level pressure, zonal and

meridional components of near surface winds, SST, near surface air temperature and cloud fraction, Wolter and Timlin, 1998). Data is presented as

provided, based on a reference period of 1950–1993.

We also determine the monthly average Bjerknes ENSO Index (BEI,

Harrison and Larkin, 1998) from the near real-time International Comprehensive Ocean-Atmosphere Data Set (ICOADS) data provided by the

NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their web site at

http://www.cdc.noaa.gov/. BEI is based on the sum of (–1 × normalized eastern equatorial Pacific SLP anomaly), (normalized eastern central

equatorial zonal wind anomaly), and (–1 × normalized eastern Inter Tropical Convergence Zone meridional wind anomaly). A base period of January 1971–December 2000 was used for anomaly determination. BEI3, the

3-month running average version, is also considered. In this case, normalization is done after applying the running-mean filter.

3. Results

We focus first on monthly averaged CI over the Eastern Central Pacific

(ECP), where CI values are most often (about 85% of the time) below

−20 W m−2, indicating that arid conditions persist in the ECP through-
Characterizing interannual equatorial Pacific variability with OLR 5

out most years (Fig. 1a). ECP CI typically shows a strong seasonal cycle

(see Fig. 1a), in which seasonal minima of about −50 W/m2 are usually seen

in the months of August or September and maxima, ranging from −35 to

−20 W m−2, occur in either March or April of each year. Convective activity

during four interannual periods (the large events of 1982/1983, 1986/1987,

1991/1992, and 1997/1998), however, was quite different from this usual

behavior. In these four events, ECP CI reached levels more typical of the

west Pacific (discussed below), and even crossed into the deep convective

(CI > 0) regime during the three largest events of 1982/1983, 1991/1992,

and 1997/1998 (monthly average values during the 1986/1987 event fall just

shy of 0, though weekly averages (not shown) do cross this threshold).

It is notable that these four years are commonly agreed to be warmENSO years, and that the areas under these four peaks are qualitatively

consistent with the generally agreed upon relative size of the ENSO events

(e.g., the 1982/1983 and 1997/1998 events are larger than the 1986/1987 and

1991/1992 events.) The peak of the smallest of these events (1986/1987) is

about 1 standard deviation (σ) above the next highest peak (observed in

1992/1993). Thus, these four events can be clearly identified by visual inspection and a wide range of threshold values can be used to unambiguously

distinguish these events from others. We will further examine the statistical

significance of these events below, but first compare CI and SSTA indices

from other regions.

The convective behavior of the Western Central Pacific (WCP) is similar

in many respects to that of the Eastern Central Pacific, but shows some

important differences on interannual timescales (cf., Figs. 1a,b). WCP and

ECP CI are highly correlated (see Table 2) and, like ECP CI, WCP CI

shows a clear seasonal cycle, although the timing and amplitudes of the

annual peaks are more variable in the WCP (cf., Figs. 1a,b). Like the ECP,

CI averaged over the WCP generally remains negative, indicating that deep

convection is unusual, in a regionally averaged sense. Convective events,

however, are more common in the WCP than the ECP. For example, the

four largest WCP CI peaks, which are roughly coincident with those seen in

the ECP, are not the only WCP CI peaks that reach the convective regime

(CI > 0Wm−2). Several others with nearly equal amplitude were observed

during years in which the ECP exhibited mostly normal behavior. CI peaks

in the –10 to +10 W m−2 range can be seen in 1989/1990, 1992/1993,

1994/1995, 2002/2003, 2004/2005, and 2006/2007.

Compared to the regions discussed above, a considerably different sort

of behavior has been observed over the western Pacific (WP), where CI

shifts between the convective (CI > 0) and non-convective (CI < 0) regimes,

spending roughly equal time in each, during the period considered (Fig. 1).

Although there are numerous occasions when CI is more than ±1 standard

deviation from the mean (dashed lines in Fig. 1), it is difficult to determine

if and when anomalies are of interannual timescale because of the relatively

strong variability at shorter timescales. For this index, visual inspection

alone leads to no clear means of unambiguously detecting strong interannual

anomalies of the type seen in ECP CI.
6 Chiodi and Harrison

1980 1990 2000

40

20

0

-20

-40

-60

1985 1995 2005

E. Central Pacific

W m-2

40

20

0

-20

-40

-60

W m-2

W. Central Pacific

1980 1990 2000 1985 1995 2005

40

20

0

-20

-40

-60

W m-2 1980 1990 2000 1985 1995 2005

W. Pacific

a)

b)

c)

Figure 1: Tropical Pacific OLR-based convective indices.
Characterizing interannual equatorial Pacific variability with OLR 7

Table 2: Cross-correlation coefficients of the monthly-average indices

considered here.

WCP WP Ni ̃no 3 Ni ̃no 3.4 MEI BEI SOI

ECP 0.68 0.15 0.69 0.56 0.64 0.68 –0.47

WCP — 0.63 0.74 0.77 0.76 0.58 –0.63

WP — — 0.35 0.45 0.44 0.18 –0.43

Ni ̃no 3 — — — 0.94 0.91 0.78 –0.64

Ni ̃no 3.4 — — — — 0.93 0.65 –0.72

MEI — — — — — 0.71 –0.79

BEI — — — — — — –0.51

In some respects, there is considerable agreement between cold-tongue

SSTA and the central Pacific CI indices discussed above (cf., Figs. 1 and

2a,b). Of the time series considered, the highest OLR/SSTA correlation

is between Ni ̃no 3.4 and WCP CI (see Table 2). There is generally good

agreement between the timing and amplitudes of peak events seen in these

two time series (cf., Figs. 2a and 1b). Thus, a relatively consistent picture

of coupled tropical Pacific variability develops from the comparison of WCP

CI and Ni ̃no 3.4 alone.

There is still also much agreement between ECP CI and Ni ̃no 3.4 (Fig. 2b),

though, on closer inspection, it is clear that the relationship is not a perfect match. Similarities include the fact that the four largest ECP convective events coincide with the largest Ni ̃no 3.4 peaks (times when ECP CI

> –15 are shown by red shading in Fig. 2). There are, however, several

other intervals in which Ni ̃no 3.4 is 0.5 to ≈1.8 standard deviations warmer

than average, yet ECP CI values remain nearly normal (e.g., 1979/1980,

1993/1994, 1994/1994, 2002/2003, 2004/2005, and 2006/2007). Thus, while

it is true that all four ECP convective events occur during times of warm

SST, the converse does not hold; warm Ni ̃no 3.4 SSTA does not necessarily

lead to convection in the ECP. This suggests that warm Ni ̃no 3.4 SSTA is a

necessary but insufficient condition for convection to reach the ECP.

ECP CI and Ni ̃no 3.4 also show significant differences in the degree of

separation between the largest and more “normal” amplitude peaks. Referenced to their respective σ values, ECP anomalies are larger; the largest four

ECP peaks exceed the mean by 2.5 σ and the largest by more than 5 σ, while

only two Ni ̃no 3.4 peaks are in the 2.5–3 σ range. The number of events that

can be unambiguously distinguished by a threshold value is also different for

Ni ̃no 3.4. Ni ̃no 3.4 shows only two peaks (1982/1983 and 1997/1998) that

are approximately 1 σ or more above the next highest (1991/1992). There

are, however, several other peaks within 1 σ of this third-highest peak (at

about 2 σ) in addition to several others above 0.5 σ. Thus, using 1 σ as a

rough measure of sufficient separation, only two events can be distinguished

from Ni ̃no 3.4 unambiguously.

From the perspective taken here, Ni ̃no 3 variability (Fig. 2b) resembles

that seen in Ni ̃no 3.4, and is thus related similarly to central Pacific CI. For

instance, the largest Ni ̃no 3 and Ni ̃no 3.4 peaks occur nearly simultaneously.

It is notable, however, that more separation exists between the largest Ni ̃no 3
8 Chiodi and Harrison

3

2

1

0

-1

-2

-3

30

20

10

0

-10

-20

-30

3

2

1

0

-1

-2

-3

1980 1990 2000 1985 1995 2005

Nino 3.4 ~

Nino 3 ~

SOI

o

C

o

C

a)

b)

c)

Common ENSO Indices

Figure 2: Tropical Pacific SST and SLP based ENSO indices.
Characterizing interannual equatorial Pacific variability with OLR 9

peaks and the background variability; however, this separation is not as large

as that observed for ECP CI. We will further discuss the distribution of peak

Ni ̃no 3 values in Sections 4 and 5.

Monthly mean Troup SOI (Fig. 2c) is comparatively noisy, showing more

variability at high frequency (near-monthly) than the other time series discussed here. Thus, distinguishing interannual anomalies based on peak (or

trough) values is complicated by this relatively strong variability at shorter

timescales. Some of the lowest minima in SOI are seen at the times of the

four largest ECP peaks discussed above, although other troughs of roughly

the same magnitude are also present during other intervals (e.g., 1992/1993,

1993/1994, 2004/2005). SOI is significantly (negatively) correlated with the

other time series considered here (Table 2), though discrepancies are apparent and can lead to interpretations of interannual anomalies different from

those of SSTA, as has been discussed in previous work (Deser and Wallace,

1987).

Variability of the BEI index (Fig. 3a) appears most closely related to

that of Ni ̃no 3, and confirms that the events of 1982/1983 and 1997/1998

were the largest warm-ENSO events in the recent past, with substantial

events also during 1986/1987 and 1991/1992. It is notable that the BEI is

based on surface atmospheric information rather than SST. This agreement

thus confirms that, during these intervals, the surface co-varies in a manner

similar to that first discussed by Bjerknes.

Variability of the the MEI index (Fig. 3b) closely matches that of the

SSTA time series discussed above (see SSTA/MEI correlations in Table 2).

The timing and relative size of the four largest MEI peaks are consistent

with those of SSTA and the secondary peaks are also qualitatively consistent.

The relative peak-to-background separation is also very similar to that of

the SSTA indices. For our purposes, therefore, this index presents a view of

tropical Pacific variability that is similar to that of the SSTA indices.

4. Distribution of Monthly-Average Index

Values

We next examine the distribution of monthly averages about the means of

each time series discussed above. To do this, we tabulated the percentage of

monthly average values falling into 1-σ-wide bins, centered on the mean. We

compare observational results to those expected for a Gaussian-distributed

variable with equivalent mean, σ, and (estimated) degrees of freedom. To aid

comparisons, the 95% and 5% confidence levels of the respective Gaussian

distributions were estimated from Monte Carlo methods for each bin (details

given in Appendix A.) Thus, we are able to determine whether or not the

number of monthly averages in a given bin is consistent with a Gaussian

distribution, and therefore consistent with being driven by the superposition

of many random processes.
10 Chiodi and Harrison

BEI

MEI

1980 1990 2000 1985 1995 2005

1980 1990 2000 1985 1995 2005

3

2

1

0

-1

-2

6

4

2

0

-2

-4

-6

8

a)

b)

Multivariate ENSO Indices

Figure 3: Multivariate ENSO indices (see text.)

It is clear that the distribution of monthly-mean values of ECP CI is nonGaussian in many respects (cf., Fig. 4a). A significantly larger percentage

(about 30%) of ECP CI values are within ±0.5 σ of the mean, compared

to a Gaussian distribution. The percentage of values in the 0.5- to 1.5-σ

range, however, falls short of the Gaussian expectation by about a factor

of 3. This relative abundance of values close to the mean aids in making

the larger peaks (> about –20 W m−2, or roughly +1.5 σ) clearly stand out

from background behavior.

Perhaps the most striking features of ECP CI, however, are the amplitudes of the four peaks that extend more than 2.5 σ above the mean (i.e.,

the events of 1982/1983, 1986/1987, 1991/1992, and 1997/1998). Among

these events, there are 18 monthly averages above 2.5 σ. This is more than

10 times the number expected from a Gaussian distribution with equivalent

mean and σ. Two observed peaks (a total of 4 months) even exceed the
Characterizing interannual equatorial Pacific variability with OLR 11

W. Central Pacific

-1 0123

0

0.1

0.2

0.3

0.4

0.5

0

0.05

3

b)

W. Pacific

-2 -1 0123

0

0.1

0.2

0.3

0.4

0.5 a)

0

0.1

0.2

0.3

0.4

0.5

-2 -1 012345

a) E. Central Pacific

0

0.05

345

Figure 4: Distributions of monthly-average index values. Bin spacing is 1 σ. Hash marks at expected value

and p = 0.05, p = 0.95 levels for a Gaussian-distributed variable with mean and σ equivalent to observations.

mean by 4.5 σ. This is highly inconsistent with a Gaussian distribution;

a time series that follows a commensurate Gaussian-distributed time series

would have to be about 7000 times as long as the one considered here to be

expected to include just two values above 4.5 σ.

The distribution of monthly averages of WCP CI (Fig. 4b) is similar, in

some respects, to the ECP distribution. Like the ECP, there is a relative

(to Gaussian) enhancement of values in the –1 and 0 σ bins and a relative

scarcity of values in the +1 σ bin, though this is more moderate in the WCP

case (cf., Figs. 4a,b). The abundance of values in the +3 σ bin slightly

exceeds the 95% limit in the WCP case, as it nearly does in the ECP case.

Unlike ECP, however, no values are seen above this bin in the WCP case.

Thus, while the asymmetry of this distribution is clearly non-Gaussian, this

result suggests that only moderate statistical significance can be attached to

any specific feature.

The distribution of monthly averages of WP CI (Fig. 4c) is consistent

with results from a Gaussian distribution for each bin.

The Ni ̃no 3 SSTA and WCP CI distributions share many of the same

characteristics, although the Ni ̃no 3 distribution is somewhat closer to a

Gaussian, statistically (cf., Figs. 4b and 5a). Like WCP, the Ni ̃no 3 distribution is asymmetrical, with a relative (though less than 95% significant) abundance of values in the –1 and 0 σ bins. There is also a relative,

marginally significant deficit of values in the +1 bin. The +2 and +3 σ
12 Chiodi and Harrison

0

0.1

0.2

0.3

0.4

0.5

0

0.1

0.2

0.3

0.4

0.5

Nino 3.4

SOI

-3 -2 -1 0123

-3 -2 -1 012

a)

c)

0

0.1

0.2

0.3

0.4

0.5 Nino 3

-2 -1 01234

0

0.05

4

b) 

Figure 5: Same as in Fig. 4, except for indices as listed.

bins are filled, respectively, less and more than a pure Gaussian distribution

would have them, but these discrepancies are within the 5% and 95% confidence intervals (Fig. 5a). The lone Ni ̃no 3 result, from this analysis, that

exceeds the 95% confidence interval is the percentage of values in the +4 σ

bin. This threshold (3.5 σ) was crossed in only 2 months, both during the

1997/1998 event (the peak of the 1982/1983 event is between 3.3 and 3.4 σ).

It is interesting that the remaining distributions of monthly average values Ni ̃no 3.4 and Troup SOI are consistent with results from a Gaussian

distribution for each bin/data set combination considered here (Figs. 5b,c),

as is the MEI index (Fig. 6b). This suggests that detecting any particular

signal of interest from these indices will be difficult because of the relatively

high level of variance contributed by other processes.

The BEI index shares some of the characteristics of the ECP and WCP

indices. For example, the number of values falling in the 0 σ bin is relatively

high, while the +1 σ bin is rather sparsely populated. The statistical significance of these features, however, is considerably less than that of the ECP

CI case. Still, as for the ECP and WCP indices, some BEI values exceed

the +3 σ bin, which is inconsistent with BEI being a Gaussian-distributed

variable.

In summary, in this section we effectively examined the potential signalto-noise relationship for each of the time series considered here by comparing

the distributions of observed monthly averages to expectations for commensurate Gaussian distributions. Results show that the prospects for detec-
Characterizing interannual equatorial Pacific variability with OLR 13

0

0.1

0.2

0.3

0.4

0.5

0

0.1

0.2

0.3

0.4

0.5

Nino 3.4

SOI

-3 -2 -1 0123

-3 -2 -1 012

b)

c)

0

0.1

0.2

0.3

0.4

0.5 Nino 3

-2 -1 01234

0

0.05

4

a) 

Figure 6: Same as in Fig. 4, except for indices as listed.

tion of significant peak-events are good for ECP CI, moderate for WCP CI,

Ni ̃no 3, and BEI, and poor for the other time series, including Ni ̃no 3.4.

5. Distribution of Annual Peaks

Because of the current focus on interannual anomalies of the tropical Pacific,

in this section we consider the distribution of annual peaks among several of

the indices discussed here; the four that showed good or moderate potential

for event detection in the analysis presented above (ECP CI, WCP CI, BEI,

and Ni ̃no 3.4), and two others (Ni ̃no 3 and SOI) that did not, but are

generally referred to when considering interannual anomalies of the tropical

Pacific system. The analysis discussed here expands upon results presented

above by including results that are dependent on the temporal distribution

of peaks on interannual timescales.

Here, we analyze rank-ordered profiles of annual peaks from each index.

Annual peaks were found by selecting the maximum monthly average value

within each October (Year 0) to September (Year +1) annum of the 1979

to 2007 period (28 peaks in all.) This October to September annum was

chosen to be consistent with the climatological minimum in tropical Pacific

CI, though shifting the interval by a few months does not significantly affect

the results.

When plotted in rank order (see Fig. 7a), it can be seen that most of the

ECP CI annual maxima (rank order 1–23) fall within 1.4 σ of the index mean

(the average over all months in the January 1979–September 2007 period).
14 Chiodi and Harrison

10th 20th

σ

σ σ

σ

σ σ

Rank-Order Annual Peaks (Oct 1979 to Oct 2007) 

-6

-4

-2

0

2

6

-2

0

2

4

E. Central Pacific CI W. Central Pacific CI

Nino 3.4

Nino 3

SOI (troughs)

a) b)

c)

e) f )

6

-2

0

2

4

6

-2

0

2

4

6

-2

0

2

4

10th 20th

d) BEI 6

-2

0

2

4

Figure 7: Rank-order annual peaks (October year 0 to September year +1) of OLR/ENSO indices (black

circles). Gray shading is for the 0.05 < probability < 0.95 levels for a Gaussian-distributed variable with the

same mean and σ as observations. The white line is the expected value for a Gaussian-distributed variable.

In this range, peak-to-peak increments are relatively small and uniform,

especially between the 2nd- and 20th-ranked peaks, which are separated in

a nearly linear manner by increments of <0.1 σ. A break from this uniformity

is seen between the 23rd- and 24th-highest peaks, which are separated by

nearly 1 σ. Each of the next four highest peaks are more than 2.5 σ above

the index mean and the highest extends to more than 5 σ (as discussed

above).

In order to gage the significance of any given annual peak ECP CI value,

we have calculated the 5% and 95% confidence intervals for the rank-order of
Characterizing interannual equatorial Pacific variability with OLR 15

Table 3: Statistics of 1979–2007 OLR/ENSO indices.

Region Mean σ Deg. Freedom/Year

ECP –38.21 (W m−2) 14.6 (W m−2) 3

WCP –25.2 (W m−2) 18.16 (W m−2) 3

Ni ̃no 3 0.04 (◦C) 0.91 (◦C) 2

Ni ̃no 3.4 0.10 (◦C) 0.85 (◦C) 2

SOI –1.33 8.85 5

BEI –0.33 2.45 4

annual peaks for a Gaussian-distributed variable with mean and σ equivalent

to observations (details given in Appendix, values in Table 3).

When plotted against these confidence intervals, it is clear that the values

of the five largest annual peaks in ECP are significantly above the 95%

confidence interval. It is also interesting that the remaining, lower ranked,

annual peak values are distributed somewhat more linearly that predicted

by the Gaussian distribution. This highlights the remarkable consistency of

the seasonal cycle of ECP CI that is seen during most years.

It is notable that the 5th-highest ECP CI peak occurs in the October

1996 to September 1997 interval, as part of the run-up to the large 1997/1998

event (i.e., all monthly-mean values between the 1996/1997 and 1997/1998

peaks are more than 1 σ above the mean). Thus, the five largest peaks

selected by the methods used here are each associated with the four large

ECP CI events discussed above.

The four largest WCP CI peaks roughly coincide with the four largest

ECP CI peaks. In addition to these, several of the next largest WCP CI

peaks exceed the 95% confidence interval, although by a smaller margin than

seen in the ECP (Fig. 7b). This is broadly consistent with the results of the

distribution analysis, presented above, which showed that, compared to the

ECP, WCP CI has a relative abundance of values in the 2- and 3-σ bins, but

no values that exceed 3.5 σ. It is notable that this result, which accounts

for the temporal spacing of the WCP CI peaks, confers more significance on

these secondary WCP peaks (those that do not coincide with the four largest

ECP events) than is evident from the results of Section 4 alone. In this case,

all peaks that exceed the mean by about 1.5 σ are statistically significant. It

is interesting that this corresponds roughly to the deep convection regime,

and thus suggests that events in which convection is seen over the WCP

region (at monthly or longer timescales and in a regionally averaged sense)

are statistically significant anomalies of the coupled tropical Pacific system.

In the case of Ni ̃no 3 SSTA, only the three largest annual peaks selected

by the method used here exceed the 95% confidence interval (Fig. 7c). Of

these, two (the highest and third highest) occur during the large 1997/1998

event that peaks in December of 1997. Thus, we find that only two interannual timescale Ni ̃no 3 SSTA events are statistically significant (1982/1983

and 1997/1998.) This result is similar in many respects to that of the BEI

index, which also shows the three highest-ranked peaks to be above the 95%
16 Chiodi and Harrison

level. Also in this case, these three peaks are associated with what are

nominally the 1982/1983 and 1997/1998 events.

It is interesting that the largest annual peak (that of the 1997/1998 event)

in the Ni ̃no 3.4 record (see Fig. 7e) is not found to be statistically significant

by the methods used here, although marginal significance is obtained by

the annual peak associated with the second largest event (1982/1983). This

result is consistent with results of the distribution analysis described above,

which showed that the behavior of Ni ̃no 3.4 SSTA is generally consistent

with a Gaussian distribution. This confirms results presented above which

suggested that using Ni ̃no 3.4 SSTA to detect significant and unambiguous

interannual anomalies is, at best, problematic because the variability of this

index is largely consistent with Gaussian-type behavior, i.e., being driven by

the super-position of many random processes.

The behavior of the SOI index is also very close to that expected from a

variable that follows a Gaussian distribution (Fig. 7e). Thus it is difficult to

attach statistical significance to any given feature of this time series. Note

that in this case we have chosen to plot the annual minima rather than

maxima, due to the inverse relationship between SOI and the other indices

considered.

6. Discussion

By comparing regional averages of monthly OLR and SSTA, we find that

years with anomalously warm equatorial SST have three distinctive types

of OLR behavior. The first class involves eastern central Pacific OLR values characteristic of the western Pacific. The second class involves western

central Pacific events with peak values about half as anomalous and of substantially shorter duration. The third class involves only very weak OLR

anomalies over these regions. In fact, the third class of events does not

stand out above the normal year-to-year variability in any clear way. The

first class of events corresponds with the subset of years most commonly

identified as El Ni ̃no years (1982–83, 1986–87, 1991–92, 1997–98). The Second Class events (1989–90, 1992–93, 1994–95, 2002–03, 2004–05, 2006–07)

have been noted as anomalous relative to particular ENSO indices but are

less widely agreed to be El Ni ̃no years. The Third Class events (1979–80,

1980–81, 1987–88, 2001–02, 2003–04) include some years that have not been

considered El Ni ̃no years.

The second class of events, which have many characteristics of a fullblown El Ni ̃no, but do not evince convective conditions in the eastern central

Pacific, are intrinsically interesting and may warrant special consideration

in their own right, as a better understanding is formed of the affects that

this second class of anomalies may have on global weather anomalies. Unfortunately, rigorous studies along these lines are hampered because only a

few such events have been observed in the recent, satellite era.

A common characteristic of the first class of events is a shift in the climate

of the eastern central Pacific from arid to convectively active regimes. This
Characterizing interannual equatorial Pacific variability with OLR 17

shift is a significant phenomenological change in the air-sea coupling of the

tropical Pacific that has implications for global weather anomalies. It is

also straightforward to detect operationally and stands out unambiguously

from the normal levels of variability inherent to OLR measured over the

eastern central Pacific. Since Bjerknes feedbacks are clearly active in these

events (e.g., anomalously low SOI and warm cold-tongue SSTA), and since

they occur during the years most commonly agreed to be El Ni ̃no years, we

propose that these events be distinguished from the others.

We believe it is in the interest of the broader scientific community to

reserve the name “El Ni ̃no” for years in which the monthly averaged eastern central Pacific convective index (230-OLR) is observed to exceed –15

W m−2. This is because it is the global weather anomalies associated with

ENSO variability, not the state of the tropical Pacific itself, which is of the

broadest importance to society. Because the teleconnections that sustain

such weather anomalies are triggered by the shift of convection from the

west to the central Pacific, we believe this convention is best ensured to

preserve the relationship between weather anomalies and El Ni ̃no that has

been documented previously (see Larkin and Harrison, 2005a;b).

7. Conclusion

Our analysis of various monthly mean ENSO-related indices has shown that

OLR averaged over the Eastern Central Pacific is unique in terms of its

ability to clearly and unambiguously distinguish anomalous behavior of the

coupled ocean-atmosphere tropical Pacific system on interannual timescales.

This metric clearly shows that the years most commonly agreed to be El Ni ̃no

years, 1982/1983, 1991/1992, 1986/1987 and 1997/1998 stand out from the

rest in terms of convective phenomenology.

We believe it makes good sense to distinguish these years from others

that have been related to ENSO by some indices, but do not show convection

over the eastern central Pacific. Such practice, we believe, is most likely to

preserve the previously documented relationship between ENSO variability

and global weather anomalies that has become well known outside of the

scientific literature. This is because the teleconnections involved in allowing

the weather anomalies to persist are driven by the shift in convection from

the west to the eastern Pacific.

Since the OLR-based indices discussed here are defined in an absolute

sense, and are little affected by long-term surface temperature trends, the use

of these indices would alleviate biases due to changes in the mean climate of

the tropical Pacific, as may occur due to the increased levels of anthropogenic

CO2 in the atmosphere, or may stem from decadal or longer-term natural

variability.

Because the OLR-based El Ni ̃no index discussed here is simple to use

and evaluate, does not depend on specification of climatological conditions,

and is derived from monthly means, it is suitable for use as an operational

definition of this phenomena. In some cases, the SSTA-based definitions
18 Chiodi and Harrison

currently in place give an earlier alert that an ENSO event is likely to occur.

Since this is useful information, but warm SSTA does not always lead to a

shift in convection over the cold tongue, we propose that these SSTA (and

SLP) indices be retained and monitored. These can provide an ENSO alert

that will persist until either anomalous conditions subside, or an El Ni ̃no

event is confirmed by OLR information.
Characterizing interannual equatorial Pacific variability with OLR 19

8. References

Bjerknes, J. (1969): Atmospheric teleconnections from the equatorial Pacific. Mon.

Weather Rev., 97, 163–172.

Deser, C., and J.M. Wallace (1987): El Ni ̃no events and their relationship to the

Southern Oscillation: 1925–1986. J. Geophys. Res., 92(C13), 14,189–14,196.

Garreaud, R.D., and J.M. Wallace (1997): The diurnal march of convective cloudiness over the Americas. Mon. Weather Rev., 125, 3157–3171.

Garreaud, R.D., and J.M. Wallace (1998): Summertime incursions of Midlatitude

air into subtropical and tropical South America. Mon. Weather Rev., 126, 2713–

2733.

Harrison, D.E., and N.K. Larkin (1996): The COADS sea level pressure signal: A

near-global El Ni ̃no composite and time series view, 1946–1993. J. Climate, 9,

3025–3055.

Harrison, D.E., and N.K. Larkin (1998): El Ni ̃no-Southern Oscillation sea surface

temperature and wind anomalies, 1946–1993. Rev. Geophys., 36(3), 353–399.

Larkin, N.K., and D.E. Harrison (2005a): Global seasonal temperature and precipitation anomalies during El Ni ̃no autumn and winter. Geophys. Res. Lett., 32,

L16705,doi:10.1029/2005GL022860.

Larkin, N.K., and D.E. Harrison (2005b): On the definition of El Ni ̃no and associated seasonal average U.S. weather anomalies. Geophys. Res. Lett., 32(13),

L13705,doi:10.1024/2005GL022738.

Rayner, N.A., D.E. Parker, E.B. Horton, C.K. Folland, L.V. Alexander, D.P. Rowell, E.C. Kent, and A. Kaplan (2003): Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century.

J. Geophys. Res., 108(D14), 4407,10.1029/2002JD002670.

Ropelewski, C.F., and M.S. Halpert (1987): Global and regional scale temperature

patterns associated with the El Ni ̃no/Southern Oscillation. Mon. Weather Rev.,

115, 1606–1626.

Ropelewski, C.F., and M.S. Halpert (1996): Quantifying Southern Oscillationprecipitation relationships. J. Climate, 9, 1043–1059.

Smith, S.R., D.M. Legler, M.J. Remigio, and J.J. O’Brien (1999): Comparison of

1997–98 U.S. temperature and precipitation anomalies to historical ENSO warm

phases. J. Climate, 12, 3507–3515.

Trenberth, K.E., and J.M. Caron (2000): The Southern Oscillation revisited: Sea

level pressures, surface temperatures and precipitation. J. Climate, 13, 4358–

4365.

Wolter, K., and M.S. Timlin (1998): Measuring the strength of ENSO events—how

does 1997/98 rank? 53, 315–324.

Characterizing interannual equatorial Pacific variability with OLR 21

Appendix A. 1950–2007 ENSO Indices
22 Chiodi and Harrison

1950 1952 1954 1956 1958 1960 1962 1964 1966 1968

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

2

-2

0

Time series mean (1971-2000 Base Period)

+ 1 Standard Deviation

HadISST Nino 3 Index (Nino3)

Data from NOAA ERSST

(1971-2000 Base Period)

3 mon running-mean

2

-2

0

2

-2

0

o

C

o

C

o

C

~ ~

Figure A1: 3-month running mean Ni ̃no 3 region SSTA. Data from HadISST.
Characterizing interannual equatorial Pacific variability with OLR 23

1950 1952 1954 1956 1958 1960 1962 1964 1966 1968

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

2

-2

0

Time series mean (1971-2000 Base Period)

+ 1 Standard Deviation

HadISST Nino 3 .4 Index (Nino3.4)

Data from NOAA ERSST

(1971-2000 Base Period)

3 mon running-mean

2

-2

0

2

-2

0

o

C

o

C

o

C

~ ~

Figure A2: 3-month running mean Ni ̃no 3.4 region SSTA. Data from HadISST.
24 Chiodi and Harrison

1950 1952 1954 1956 1958 1960 1962 1964 1966 1968

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

2

-2

0

Time series mean (1971-2000 Base Period)

+ 1 Standard Deviation

ERSST Nino 3 Index (Nino3)

Data from NOAA ERSST

(1971-2000 Base Period)

3 mon running-mean

2

-2

0

2

-2

0

o

C

o

C

o

C

~ ~

Figure A3: 3-month running mean Ni ̃no 3 region SSTA. Data from ERSST.
Characterizing interannual equatorial Pacific variability with OLR 25

1950 1952 1954 1956 1958 1960 1962 1964 1966 1968

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

2

-2

0

Time series mean (1971-2000 Base Period)

+ 1 Standard Deviation

ERSST Nino 3.4 Index (Nino3.4)

Data from NOAA ERSST

(1971-2000 Base Period)

3 mon running-mean

2

-2

0

2

-2

0

o

C

o

C

o

C

~ ~

Figure A4: 3-month running mean Ni ̃no 3.4 region SSTA. Data from ERSST.
26 Chiodi and Harrison

1950 1952 1954 1956 1958 1960 1962 1964 1966 1968

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

30

20

-20

-30

0

Time series mean (1971-2000 Base Period)

+ 1 Standard Deviation

Troup Southern Oscillation Index (SOI)

Data from Australian

Bureau of Meteorology

(1971-2000 Base Period)

-10

10

30

20

-20

-30

0

-10

10

30

20

-20

-30

0

-10

10

3 mon running-mean

Figure A5: 3-month running mean SOI.
Characterizing interannual equatorial Pacific variability with OLR 27

1950 1952 1954 1956 1958 1960 1962 1964 1966 1968

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

8

4

-4

-8

0

8

4

-4

-8

0

8

4

-4

-8

0

Time series mean (1971-2000 Base Period)

+ 1 Standard Deviation

Bjerknes Enso Index (BEI3)

Data from COADS

(1971-2000 Base Period)

Figure A6: Bjerknes ENSO Index-3. (See Harrison and Larkin, 1998.)
28 Chiodi and Harrison

1950 1952 1954 1956 1958 1960 1962 1964 1966 1968

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

4

2

-2

-4

0

Time series mean (1971-2000 Base Period)

+ 1 Standard Deviation

Multivariate Enso Index (MEI)

Data from NOAA ESRL

(1950-1993 Base Period)

4

2

-2

-4

0

4

2

-2

-4

0

Figure A7: Multivariate ENSO Index. (See Wolter and Timlin, 1998.)
Characterizing interannual equatorial Pacific variability with OLR 29

Appendix B. Gaussian Distribution

Confidence Intervals

To aid comparison of the distributions of monthly averages discussed above,

we compare observed results to those expected from a commensurate

Gaussian-distributed variable (see hash-marks in Figs. 4–6). We here describe how the most probable value, 5% and 95% confidence intervals were

determined. In this case (for example), the 95% level corresponds to the

value that a fundamentally Gaussian process would exceed, on average, in

only 1 of 20 chances.

We first calculate the mean, σ, and degrees of freedom contained in each

time series (see Table 3). The method for determining the number of degrees

of freedom is discussed in Appendix C, although trials have shown that the

results, in this case, are not highly sensitive to this value (e.g., qualitatively

similar results are obtained when substituting any of the degrees of freedom

values used here for another).

Then we generated multiple, hypothetical time series using a Gaussian

random number generator (this was done using Matlab’s “Randn” function.)

We specified that the hypothetical time series be selected from a Gaussian

distribution with a mean and σ equivalent to observations. The number of

values in each hypothetical time series was set to the number of degrees of

freedom contained in the monthly averaged observations, during the 1979–

2007 period discussed here (about 350 months in all). A library of 100,000

hypothetical time series was generated for each case.

Each member of this library was then binned according to the procedure

used for the observations, then the bin values were sorted in rank order

from 1 to 100,000. The average number of values in each bin determines the

expected value (middle hash). The 5000th value determines the 5% level

and the 95,000th determines the 95% level. Experiment has shown that

these levels are stable over multiple repetitions of this process, relative to

the differences between the observational results.

Appendix C. Gaussian Annual Peak

Rank-Order Confidence Intervals

The confidence intervals for the rank-order profiles discussed herein were

determined as follows. For each year of observations (28 in all), we selected

X number of values at random from a normal distribution, where X is the

number of degrees of freedom per year (listed in Table 3), and the random

distribution was specified to have the same mean and σ as monthly averaged

observations. The largest of these values (smallest in the SOI case) was then
30 Chiodi and Harrison

chosen and saved. Then these “annual peaks” were sorted by magnitude.

We repeated this process 100,000 times, saving each result. These results

were averaged over rank order to determine the most probable result and

sorted by magnitude so that confidence intervals (5th and 95th) could be

determined.

The number of degrees of freedom used here was based on a best fit

to observational results, with the constraint that an integer number of degrees freedom per year be specified. The values used here are consistent with

values estimated from consideration of the time series auto-correlations. Experiment has shown that varying this value by ±1 degree of freedom/year

produces qualitatively similar results, though classification of peaks near the

5% and 95% levels (e.g., ±0.2 σ) are sensitive to this value.