The Multivariate ENSO Index (MEI) Captures The Global Temperature Impacts Of ENSO Differently Than SST-Based Indices

I’ve moved to WordPress.  This post can now be found at The Multivariate ENSO Index (MEI) Captures The Global Temperature Impacts Of ENSO Differently Than SST-Based Indices

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But Those Differences Are Subtle

I was recently asked to comment on the Multivariate ENSO Index (MEI). (Thanks, d.) This post compares the MEI to HADSST2-based NINO3.4 SST anomalies. It also removes the linear effects of ENSO from the Global Temperature Record to show the effects of the differences when performing that type of analysis. And since I’d brought the analysis that far, I thought I’d carry the post a step farther and show the opposing effects of ENSO that exist in global temperature anomaly data.

HADISST-based NINO3.4 SST anomalies also show similar results, though I have not included them in this post. I used HADSST2 data here because I will reference this post in an upcoming one about a paper in press, and that paper uses HADCRUT and HADSST-based NINO3.4 data in its analysis. (The paper attempts to perpetuate a myth I’ve discussed before.)

INTRODUCTION

The Multivariate ENSO Index (MEI) is a calculated dataset that illustrates the timing and magnitude of El Niño and La Niña events. Other ENSO indices use Sea Surface Temperature (SST) Anomalies of the central and eastern Equatorial Pacific or the sea level pressure difference between Tahiti and Darwin, Australia. The MEI, on the other hand, uses additional variables that are part of the coupled ocean-atmosphere ENSO processes. Wolter and Timlin (1998) in “Measuring the strength of ENSO events - how does 1997/98 rank?” note in the abstract, “The Multivariate ENSO Index (MEI) is favoured over conventional indices, since it combines the significant features of all observed surface fields in the Tropical Pacific.” Link to Wolter and Timlin (1998):
http://www.esrl.noaa.gov/psd/people/klaus.wolter/MEI/WT2.pdf

The MEI is maintained by Klaus Wolter of NOAA. He explains why he believes the MEI is “better for monitoring ENSO than the SOI or various SST indices” on the NOAA MEI timeseries data webpage. (Scroll down to the FAQs.) He writes, “In brief, the MEI integrates more information than other indices, it reflects the nature of the coupled ocean-atmosphere system better than either component, and it is less vulnerable to occasional data glitches in the monthly update cycles.”

AN OVERVIEW OF THE MEI

The NOAA MEI home page provides a further description of the Multivariate ENSO Index (MEI): “El Niño/Southern Oscillation (ENSO) is the most important coupled ocean-atmosphere phenomenon to cause global climate variability on interannual time scales. Here we attempt to monitor ENSO by basing the Multivariate ENSO Index (MEI) on the six main observed variables over the tropical Pacific. These six variables are: sea-level pressure (P), zonal (U) and meridional (V) components of the surface wind, sea surface temperature (S), surface air temperature (A), and total cloudiness fraction of the sky (C).”

With the exception of the surface wind components, all of the above variables should be self explanatory. NOAA describes the U and V surface wind components in their Transport Winds webpage: “The meridional component of the wind, V, is considered positive when the wind [is] blowing from south to north. A south wind has a positive meridional component while a north wind has a negative meridional component. The zonal component of the wind, U, is considered positive when the wind is blowing from west to east. Thus, a west wind has a positive zonal component and an east wind a negative zonal component.” They continue, “For example, a wind that is blowing from the northeast would have a negative meridional component, V, and a negative zonal component, U. Such a wind would have a direction of 45 degrees.”

COMPARING THE MEI TO AN SST-BASED ENSO INDEX
Figure 1 illustrates the MEI data from January 1950 through July 2010. Like the SST-based ENSO indices, El Niño events are represented by positive values and La Niña events are negative. The 1982/83 El Niño event is shown to peak higher than the 1997/98 event. And the 1997/98 El Niño shows a double peak. The NOAA MEI timeseries data webpage presents the MEI data in bimonthly form.

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Figure 1

Note: The MEI data in this post was downloaded from the KNMI Climate Explorer. I’ve used MEI “anomalies” since they will have the same base years as the NINO3.4 SST anomalies and allow for a more direct comparison in the following. The use of MEI anomalies shifts the MEI data slightly, but that shift has no effect on this post.

Figure 2 compares the MEI data to NINO3.4 SST anomalies based on the HADSST2 dataset. Since the MEI is presented bimonthly, the NINO3.4 SST anomalies were smoothed with a lagging 2-month filter for this illustration. That is, for example, the average of January and February SST anomalies are displayed in February. The major variations in both datasets are similar in timing but they differ in magnitude for each event. Note, also, that the MEI data seems to shift upwards around 1976.
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Figure 2

If we subtract the NINO3.4 SST anomaly data from MEI, that shift becomes more obvious. Refer to Figure 3. From 1976 to 1980, there is additional rise in the MEI that is not present in the NINO3.4 SST anomalies. There is also some obvious additional variability in the MEI.
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Figure 3

Averaging the differences between the MEI and NINO3.4 SST anomalies over the periods before and after 1976, Figure 4, provides an idea of the magnitude of that additional variation in the MEI.
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Figure 4

It appears the MEI should account for some of the rise in global temperatures caused by the 1976/77 Pacific Climate Shift.

REMOVING THE LINEAR EFFECTS OF ENSO FROM GLOBAL TEMPERATURES

The common method used by bloggers and climate scientists to remove the effects of ENSO from the global temperature record is to scale the ENSO index data so that the change in the ENSO Index agrees with the resulting change in global temperatures and to lag the ENSO Index data a few months. Then the ENSO Index data is simply subtracted from the global temperature data. It seems to make sense. Unfortunately, it only accounts for the linear effects of ENSO and does not account for the fact that El Niño and La Niña events can warm parts of the global oceans and that these warmings can be cumulative. I’ve discussed this in numerous posts, including “More Detail On The Multiyear Aftereffects Of ENSO - Part 2 – La Nina Events Recharge The Heat Released By El Nino Events AND... ...During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents,” “More Detail On The Multiyear Aftereffects Of ENSO - Part 3 – East Indian & West Pacific Oceans Can Warm In Response To Both El Nino & La Nina Events”, and with animations of numerous datasets in “La Niña Is Not The Opposite Of El Niño – The Videos.”

Putting that aside, let’s use the method to illustrate two points: the additional portion of the aftereffects of the 1976 Pacific Climate Shift accounted for by the MEI, and the opposing effects of ENSO events.

For those who have never attempted to remove the linear effects of ENSO from the global temperature record, I’ll run through the process. As discussed above, first we need to scale the ENSO index data so that the change in the ENSO Index agrees with the resulting change in global temperatures. Figure 5 illustrates the Hadley Centre’s HADCRUT land plus sea surface temperature anomalies from 1950 to present. It also illustrates scaled NINO3.4 SST anomalies, which are being used as the reference ENSO Index in this example. To scale them, the NINO3.4 SST anomalies are simply multiplied by a factor, and in this example, I used a scaling factor of 0.18. I’ve also shifted the NINO3.4 SST anomalies upwards 0.12 deg C so that they will align with Global Temperature anomaly data during the evolution phase of the 1997/98 El Niño. I’ve used the 1997/98 El Niño event as reference since it is the most significant El Niño event that was unaffected by volcanic aerosols, and its SST anomalies were measured by satellites and in situ buoys.

The NINO3.4 SST anomalies have also been shifted back in time (lagged) two months to account for the delayed response of global temperatures to the change in tropical Pacific SST. In other words, it takes global temperatures a few months to respond fully to the ENSO event. As you can see, the leading edges of the two datasets align well. This makes sense since the central and eastern tropical Pacific (20S-20N, 180 to 80W) represent a major portion of the globe, about 9%. The magnitudes of the two variations from trough to peak are also similar during the evolution phase with the scaling factor I’ve used.
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Figure 5

In Figure 6, I’ve started the graph in 1995 to show how well the scaled NINO3.4 SST anomalies and the Global Temperature anomalies align during the ramp up of the 1997/98 El Niño.
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Figure 6

The next step is to subtract the NINO3.4 SST anomalies from the Global Temperature anomaly data. The remainder is shown in Figure 7. I’ve highlighted the periods that include the impacts of the explosive eruptions of El Chichon and Mount Pinatubo.
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Figure 7

A Stratospheric Aerosols dataset was introduced in a 1993 paper (Stratospheric aerosol optical depth, 1850-1990) by Sato et al. It can be used to account for the impacts of these eruptions (and those of other volcanic eruptions from 1950 to 1999). Estimates of the peak impact on global temperatures from the Mount Pinatubo eruption vary from 0.2 to 0.5 deg C. I’ve scaled the Sato Index data so that it accounts for approximately 0.35 deg C.

Figure 8 illustrates the Hadley Centre’s HADCRUT Global Temperature anomaly data after it has been adjusted for the impacts of volcanic aerosols and the linear effects of ENSO (using the NINO3.4 SST anomalies). Most of the dips and rebounds from the eruptions of the El Chichon and Mount Pinatubo eruptions have been eliminated. I’ve highlighted the apparent step changes caused by the multiyear aftereffects of the 1986/87/88 and 1997/98 El Niño events. Also note the gradual ramp up in temperatures after the 1976 Pacific Climate Shift. There are no other 5-year periods with a gradual rise similar to that. Does this represent the time required for global temperatures to respond to the sudden 1976 upward shift in eastern Pacific Sea Surface Temperatures?
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Figure 8

In Figure 9, the average adjusted global temperature anomalies for the periods before and immediately after 1976 are shown. The average adjusted global temperature before 1976 is 0.12 deg lower than it is for period of 1977 to 1986. And for those who are interested, I’ve also illustrate the average temperatures for the two periods after the 1986/87/88 and 1997/98 El Niño events.
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Figure 9

We can run through the same process using the Multivariate ENSO Index (MEI) as the ENSO Index. Refer to Figures 10, 11, 12 and 13. The scaling factor used with MEI data was 0.16, as shown in Figure 10.
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Figure 10
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Figure 11
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Figure 12
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Figure 13
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Figure 14 shows the averages of the adjusted global temperature anomalies for the periods before and immediately after 1976. Recall that the shift was 0.12 deg C, using the NINO3.4 SST anomalies to remove the linear effects of ENSO events. Using the MEI dataset, that shift decreases to 0.05 deg C. This was a very simple comparison. Referring back to Figure 3, the period averages are strongly impacted by how the MEI addresses the 1982/83 El Niño. But it’s a starting point for anyone interested in evaluating this further.
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Figure 14

Figure 15 compares the HADCRUT global temperature anomaly datasets after they have been adjusted for ENSO using the MEI and NINO3.4 SST anomalies. Both datasets have also been adjusted for volcanic aerosols using the same Sato Index scaling factors. Another major difference appears to be how the MEI removes the extra El Niño peaks. Both datasets show the ENSO-induced shifts in global temperature anomalies. And both show the multiyear ramp-up from 1976 to 1981, but as illustrated in Figures 4, 9, and 14, the MEI accounts for more of that ramp-up than NINO3.4 SST anomalies.
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Figure 15

And now, the second reason for this post.

WHAT CAUSES THE ADDITIONAL VARIATIONS?

If we look again at Figure 15, there are still large year-to-year and multiyear variations in the global temperature anomaly datasets after they’ve been adjusted for ENSO and volcanic aerosols. What causes those additional variations?

Detailed analyses of ENSO, like Trenberth et al (2002) “Evolution of El Nino–Southern Oscillation and global atmospheric surface temperatures", have shown that parts of the globe warm with a rise in NINO3.4 SST anomalies and others cool. Refer to Figure 16, which is the color version of Figure 8 from Trenberth et al (2002). The correlations with a 0 month lag is shown highlighted in red. Link to Trenberth et al:
http://www.cgd.ucar.edu/cas/papers/2000JD000298.pdf

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Figure 16 (Figure 8 from Trenberth et al 2002)

One would think that after the positively correlated impacts of the ENSO events are removed from the global surface temperature record, as we’ve just done, the remainder would include variations from those areas that are negatively correlated.

This can be shown if we invert either ENSO Index dataset and compare it to what’s left over after the linear effects of ENSO and the volcanic eruptions have been removed from global temperature anomalies. Refer to Figures 17 and 18. Much of the additional yearly and multiyear variability can be explained as warming during La Niña events, and cooling during El Niño events. Note how some of the global responses to the variations in the inverted NINO3.4 SST anomalies are exaggerated while others are suppressed. Why?
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Figure 17
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Figure 18

CLOSING
In summary, the MEI accounts for more of the 1976 Pacific Climate Shift than the HADSST2-based NINO3.4 SST anomalies. This also holds true for the NINO3.4 SST anomalies based on other SST datasets (HADISST and ERSST.v3b) as shown in Figure 19. Note: The Oceanic NINO Index (ONI) is based on ERSST.v3b data.
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Figure 19

Using the simple analysis in this post, the MEI appears to account for more of the aftereffects of the 1976 Pacific Climate Shift (0.07 deg C) than the SST-based ENSO Indices. And there are some additional subtle differences in the MEI data.

And as shown in Figures 17 and 18, when the linear effects of ENSO are removed from Global Temperature anomalies, the remainder logically shows variations that reflect the opposing effects of ENSO.

The post title is The Multivariate ENSO Index (MEI) Captures The Global Temperature Impacts Of ENSO Differently Than SST-Based Indices. It would be up to you as a user of the MEI to determine if the subtle differences mean it’s better.

Regarding the methods used to remove the linear effects of ENSO, Trenberth et al (2002) write in the paper linked above, “Although it is possible to use regression to eliminate the linear portion of the global mean temperature signal associated with ENSO, the processes that contribute regionally to the global mean differ considerably, and the linear approach likely leaves an ENSO residual.”

And as shown in the posts linked earlier, those residuals can be considerable.

SOURCE

The data presented in this post are available through the KNMI Climate Explorer:http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

August 2010 SST Anomaly Update

I’ve moved to WordPress.  This post can now be found at August 2010 SST Anomaly Update

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MONTHLY SST ANOMALY MAP

The map of Global OI.v2 SST anomalies for August 2010 downloaded from the NOMADS website is shown below. The central equatorial Pacific SST anomalies are continuing their drop.

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August 2010 SST Anomalies Map (Global SST Anomaly = +0.22 deg C)

MONTHLY OVERVIEW

Monthly NINO3.4 SST anomalies are well below the -0.5 deg C threshold of a La Niña. The Monthly NINO3.4 SST Anomaly is -1.2 deg C. Weekly data has dropped below -1.5 deg C (-1.58 deg C).

Global SST anomalies dropped very little this month, -0.006 deg C. For all intents and purposes, there was no change. The slight decline in the Southern Hemisphere (-0.022 deg C) was greater than the rise in the Northern Hemisphere (+0.014 deg C).
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Global
Monthly Change = -0.006 deg C
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NINO3.4 SST Anomaly
Monthly Change = -0.22 deg C

EAST INDIAN-WEST PACIFIC

The SST anomalies in the East Indian and West Pacific made a slight rise this month. Will they continue to rise, noticeably, in response to the La Niña as they have in the past?

I’ve added this dataset in an attempt to draw attention to what appears to be the upward step responses. Using the 1986/87/88 and 1997/98 El Niño events as references, East Indian-West Pacific SST Anomalies peak about 7 to 9 months after the peak of the NINO3.4 SST anomalies, so we shouldn’t expect any visible sign of a step change for almost 18 to 24 months. We’ll just have to watch and see.
http://i53.tinypic.com/2ijrae1.jpg
East Indian-West Pacific (60S-65N, 80E-180)
Monthly Change = +0.029 deg C

Further information on the upward “step changes” that result from strong El Niño events, refer to my posts from a year ago Can El Niño Events Explain All of the Global Warming Since 1976? – Part 1 and Can El Niño Events Explain All of the Global Warming Since 1976? – Part 2

And for the discussions of the processes that cause the rise, refer to More Detail On The Multiyear Aftereffects Of ENSO - Part 2 – La Niña Events Recharge The Heat Released By El Niño Events AND...During Major Traditional ENSO Events, Warm Water Is Redistributed Via Ocean Currents -AND- More Detail On The Multiyear Aftereffects Of ENSO - Part 3 – East Indian & West Pacific Oceans Can Warm In Response To Both El Niño & La Niña Events

The animations included in post La Niña Is Not The Opposite Of El Niño – The Videos further help explain the reasons why East Indian and West Pacific SST anomalies can rise in response to both El Niño and La Niña events.

NOTE ABOUT THE DATA

The MONTHLY graphs illustrate raw monthly OI.v2 SST anomaly data from November 1981 to August 2010.

MONTHLY INDIVIDUAL OCEAN AND HEMISPHERIC SST UPDATES
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Northern Hemisphere
Monthly Change = +0.014 deg C
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Southern Hemisphere
Monthly Change = -0.022 deg C
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http://i55.tinypic.com/2dt501i.jpg
North Atlantic (0 to 75N, 78W to 10E)
Monthly Change = +0.120 deg C
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South Atlantic (0 to 60S, 70W to 20E)
Monthly Change = -0.120 deg C

Note: I discussed the upward shift in the South Atlantic SST anomalies in the post The 2009/10 Warming Of The South Atlantic.

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North Pacific (0 to 65N, 100 to 270E, where 270E=90W)
Monthly Change = -0.030 Deg C
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South Pacific (0 to 60S, 145 to 290E, where 290E=70W)
Monthly Change = -0.036 deg C
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Indian Ocean (30N to 60S, 20 to 145E)
Monthly Change = +0.023 deg C
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Arctic Ocean (65 to 90N)
Monthly Change = +0.137 deg C
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Southern Ocean (60 to 90S)
Monthly Change = +0.044 deg C

WEEKLY NINO3.4 SST ANOMALIES

The weekly NINO3.4 SST anomaly data illustrate OI.v2 data centered on Wednesdays. The latest weekly NINO3.4 SST anomalies are -1.158 deg C.
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Weekly NINO3.4 (5S-5N, 170W-120W)

Weekly NINO3.4 SST anomalies are now lower than the values for the same week during the previous transitions to major satellite-era La Niña events.
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La Niña Evolution Comparison

SOURCE

The Optimally Interpolated Sea Surface Temperature Data (OISST) are available through the NOAA National Operational Model Archive & Distribution System (NOMADS).
http://nomad1.ncep.noaa.gov/cgi-bin/pdisp_sst.sh
or
http://nomad3.ncep.noaa.gov/cgi-bin/pdisp_sst.sh

An Introduction To ENSO, AMO, and PDO -- Part 3

I’ve moved to WordPress.  This post can now be found at An Introduction To ENSO, AMO, and PDO — Part 3

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UPDATE (September 5, 2010): I’ve added a comparison of Detrended North Pacific SST anomalies (north of 20N) to detrended North Atlantic SST anomalies (the AMO) at the end of the post. And I corrected the title of the subheading in the following to read “The PDO Is Not Calculated Similarly To The Atlantic Multidecadal Oscillation (AMO)”. It was missing the word calculated.

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This post presents an overview of the Pacific Decadal Oscillation (PDO) and is intended to provide the reader with a basic understanding of what the PDO represents, and, just as important, what it does not represent. The Sea Surface Temperature (SST) side of the El Niño – Southern Oscillation (ENSO) was discussed in An Introduction To ENSO, AMO, and PDO – Part 1, and An Introduction To ENSO, AMO, and PDO -- Part 2 presented the Atlantic Multidecadal Oscillation (AMO).

WHAT THE JISAO PDO WEBPAGE SAYS
Someone new to discussions of climate and weather who is looking for information about the Pacific Decadal Oscillation (PDO) would find a link the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) PDO webpage at or near the top of their search engine results. (JISAO "is a Cooperative Institute between the National Oceanic and Atmospheric Administration and the University of Washington…”) The JISAO PDO webpage introduces the PDO as, “The ‘Pacific Decadal Oscillation’ (PDO) is a long-lived El Niño-like pattern of Pacific climate variability. While the two climate oscillations have similar spatial climate fingerprints, they have very different behavior in time.” Figure 1 is the first illustration on the JISAO PDO webpage. It is described as “Typical wintertime Sea Surface Temperature (colors), Sea Level Pressure (contours) and surface windstress (arrows) anomaly patterns during warm and cool phases of PDO.” With the Sea Level Pressure and windstress representations, the maps are busy. If you were to follow the PDO Index Monthly Values link at the top of the PDO webpage you’d discover the following description of how the PDO is calculated: “Updated standardized values for the PDO index, derived as the leading PC [Principle Component] of monthly SST anomalies in the North Pacific Ocean, poleward of 20N. The monthly mean global average SST anomalies are removed to separate this pattern of variability from any ‘global warming’ signal that may be present in the data.”

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Figure 1 (PDO maps from JISAO webpage)

WHAT DID THAT MEAN?
In the first quote above, the “long-lived El Niño-like pattern of Pacific climate variability” does NOT mean that the North Pacific (north of 20N) has a separate El Niño-like event.

A typical El Niño event creates a PATTERN in the North Pacific where it is warmer in the east than it is in the central and western portions, and a typical La Niña event will create the opposite pattern, cooler in the east than it is toward the center and west of the North Pacific. These can be seen in the two maps of the North Pacific Sea Surface Temperature (SST) anomalies in Figure 2. The top map presents the average SST anomalies during the 11-month period of May 1997 to March 1998. It captures the development and decay of the 1997/98 El Niño event. Again, during an El Niño, the PATTERN in the North Pacific typically has warmer SST anomalies in the east and cooler SST anomalies in the central and western portions. (There are a number of interacting ocean-atmosphere processes that cause the pattern, but that discussion is beyond this scope of this post.) The opposite holds true during the typical La Niña event. This can be seen in the lower map of SST anomalies. That map presents the average SST anomalies during the 11-month period from March 1998 to January 1999, and it captures the development stage of the 1998/99/00/01 La Niña.
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Figure 2

Let’s look at the JISAO introduction to the PDO again in a slightly different way. The “long-lived El Niño-like pattern of Pacific climate variability,” means that the pattern of sea surface temperature anomalies that is normally associated with El Niño and La Niña events lasts longer than those El Niño and La Niña events. This could mean that another variable or process is impacting the pattern or causing the pattern to persist. Ongoing research is attempting to close the “loop” between the ENSO and PDO.

Nate Mantua of JISAO provides a slightly different description of the PDO in his (1999) paper The Pacific Decadal Oscillation and Climate Forecasting for North America.” It adds an important aspect. He writes, “The SST pattern highlights the strong tendency for temperatures in the central North Pacific to be anomalously cool when SSTs along the coast of North America are unusually warm, and vice-versa (Graham 1994, Miller et al 1995, Zhang et al 1997, Mantua et al 1997).” “Strong tendency” is a great choice of words, because it implies that the PDO pattern is not the only pattern of SST anomalies that appears in the North Pacific. Written another way to reinforce the point, the North Pacific SST anomalies tend to have that pattern. The PDO pattern is also said to be the “dominant pattern”.

Another important point to keep in mind: Many times the entire Pacific Ocean is shown during presentations of the PDO. However, JISAO uses only the SST anomaly data for the North Pacific north of 20N to calculate the PDO. The PDO represents nothing more than that. That is, the PDO only represents the pattern of SST anomalies in the area shown in the two maps in Figure 2.

HOW DO RESEARCHERS DETERMINE WHICH PATTERN REPRESENTS THE PDO?
Researchers use a method of statistical analysis called empirical orthogonal function (EOF) analysis to determine the pattern that represents the PDO. Wikipedia describes EOF analysis as “a decomposition of a signal or data set in terms of orthogonal basis functions which are determined from the data. It is the same as performing a principal components analysis on the data, except that the EOF method finds both time series and spatial patterns.” Further discussions of this are well beyond the scope of this post. But I did ask someone on a recent thread at WattsUpWithThat if he could simplify the description of Principle Component Analysis (PCA) and EOF analysis for readers without science backgrounds. And while the description is complex, for those who are interested, it is worth reading. Here’s a link:
http://wattsupwiththat.com/2010/08/19/tisdale-on-liu-and-currys-accelerated-warming-paper/#comment-464129

(Thanks, Tom Vonk.)

GLOBAL SST ANOMALIES ARE REMOVED FROM THE PDO
The JISAO description of the PDO data also includes the following sentence: “The monthly mean global average SST anomalies are removed to separate this pattern of variability from any ‘global warming’ signal that may be present in the data.” Let’s clarify why and how they do that. The PDO was first calculated in Zhang et al (1997) ENSO-like Interdecadal Variability: 1900–93. In that paper, the PDO was identified as “NP”. Zhang et al explain why they remove the global average SST anomalies on page 8, under the heading of “Analysis for the period 1900-93.” They write, “When Parker and Folland (1991) performed conventional EOF/PC analysis on the global SST field based on the longer period of record 1900–90, their leading mode was dominated by the upward trend in global mean SST prior to the 1940s. The mathematical constraint that subsequent PCs be orthogonal to this ‘global warming mode’ seems physically unrealistic.”

To isolate the pattern of variability from the changes in global SST anomalies, Zhang et al subtracted the Global SST anomalies from the SST anomalies of every grid (5 deg latitude by 5 deg longitude) in the global SST dataset. Then they performed the EOF/PC analysis on the residuals.

PDO INDEX DATA
Figure 3 is a time-series graph of the JISOA PDO Index data. In its “raw” form, it is a noisy dataset.
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Figure 3

In Figure 4, the PDO data has been smoothed with a 13-month running-average filter to reduce the noise.
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Figure 4

Let’s look again at the description of data: The PDO Index is “derived as the leading PC [Principle Component] of monthly SST anomalies in the North Pacific Ocean, poleward of 20N. The monthly mean global average SST anomalies are removed to separate this pattern of variability from any ‘global warming’ signal that may be present in the data.” In simple words, the PDO is a statistically prepared dataset. It does not represent the Sea Surface Temperature (SST) or SST anomalies of the North Pacific, north of 20N. The differences and the importance of those differences will be discussed later in this post.

Referring to Figure 5, if we compare the PDO data to NINO3.4 SST anomalies, which are commonly used to represent the frequency and magnitude of El Niño and La Niña events, we can see that the magnitude and timing of the major short-term swings in the two datasets are similar. (Refer to An Introduction To ENSO, AMO, and PDO – Part 1 for a discussion of NINO3.4 SST anomalies and ENSO events.) This shows that the El Niño and La Niña events impact the strength of the PDO pattern. But there are differences between the two datasets. There is an additional long-term (low frequency) variation in the PDO data.
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Figure 5

If we recall the discussion of the Atlantic Multidecadal Oscillation (AMO) (Refer to An Introduction To ENSO, AMO, and PDO -- Part 2), the NOAA Earth System Research Laboratory (ESRL) presents the AMO data smoothed with a 121-month filter to highlight the low frequency variations in that dataset. So we’ll also use a 121-month filter to show the differences in the low frequency variations between the PDO and NINO3.4 SST anomalies, Figure 6. While the NINO3.4 SST anomalies do exhibit multidecadal variability, the magnitude of the variations in the PDO data is much greater. Recall, however, that the NINO3.4 SST anomalies represent exactly that, the SST anomalies of an area of the tropical Pacific, while the PDO is a statistically manufactured dataset.
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Figure 6

A REMINDER ABOUT SST OBSERVATIONS
Always keep in mind that the source SST data can be very sparse in early parts of the instrument temperature record. ICOADS data is the source for long-term SST datasets before the satellite era. Figure 7 shows six maps of ICOADS SST observation locations in the tropical and North Pacific. It presents Januarys every ten years from 1900 to 1950. The contours were set to emphasize the reading locations not the values. This sparseness of readings should be considered when examining any early SST-based dataset.
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Figure 7

In an effort to dispel some existing misunderstandings, let’s look at what the PDO does not represent.

THE PDO DOES NOT REPRESENT SST ANOMALIES OF THE NORTH PACIFIC
Figure 8 compares the PDO data to the SST anomalies of the North Pacific, north of 20N. The North Pacific SST anomalies have much less year-to-year and long-term variability than the statistically manufactured PDO.
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Figure 8

If we scale the PDO data by multiplying it by a factor of 0.2, Figure 9, we can see that the year-to-year variations are not similar. Also, the linear trend of the PDO is flat while the North Pacific SST anomalies have a positive linear trend as one would expect.
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Figure 9

THE PDO IS NOT CALCULATED SIMILARLY TO THE ATLANTIC MULTIDECADAL OSCILLATION (AMO)
As discussed in An Introduction To ENSO, AMO, and PDO -- Part 2 , the NOAA Earth System Research Laboratory (ESRL) Atlantic Multidecadal Oscillation webpage describes the calculation of the AMO as, “Compute the area weighted average over the N Atlantic, basically 0 to 70N,” and “Detrend that time series.” To detrend the North Atlantic Sea Surface Temperature anomalies, the monthly values of the linear trend are subtracted from the North Atlantic SST anomalies.

If we detrend the North Pacific SST anomalies and scale the PDO, Figure 10, we can see that the detrended North Pacific SST anomalies (north of 20N) have no short-term or long-term relationship to the PDO.
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Figure 10

DO GLOBAL TEMPERATURES RISE AND FALL IN RESPONSE TO A POSITIVE AND NEGATIVE PDO?
No. Yes. (The text has been updated between Figures 12 and 13.) We can illustrate this by examining the SST anomalies of the North Pacific and comparing them to Global SST anomalies. Refer to Figure 11. Keep in mind the PDO represents a pattern of SST anomalies, not the SST anomalies of the North Pacific, north of 20N. In order for the SST anomalies of the North Pacific to be contributing to the rise in Global SST anomalies, the North Pacific SST anomalies have to be rising faster than the Global SST anomalies. (Note that in Figures 11 through 13 the data has been smoothed with a 37-month filter.) Or during periods when the North Pacific SST anomalies are above the Global SST anomalies, they are adding from the Global average, and the opposite is true when the SST anomalies of the North Pacific are below the Global SST anomalies. At those times they are subtracting to the Global average.
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Figure 11

And we can illustrate the relationship between the North Pacific and Global SST anomalies by subtracting Global SST anomalies from the North Pacific SST anomalies, Figure 12. I’ve identified this as the North Pacific Residual.
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Figure 12

Comparing the North Pacific Residual to the PDO, Figure 13, the two datasets have no relationship with one another. This means that the contribution of the North Pacific (north of 20N) to Global SST anomalies is independent of the PDO.
UPDATE (September 14, 2010): It was recently pointed out to me that the two curves in Figure 13 appear to be negatively correlated. In other words, while one curve rises, the other falls, and vice versa. I confirmed this is true, so the two curves are related. I discussed this in the follow-up post An Inverse Relationship Between The PDO And North Pacific SST Anomaly Residuals.
(Thanks, TallBloke.)
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Figure 13

FOR THE PDO TO BE POSITIVE, MUST THE SST ANOMALIES BE WARM IN THE EASTERN NORTH PACIFIC?
No. During a positive PDO, the North Pacific SST anomalies are warmer (comparatively) in the east than they are in the central and western portions, and vice versa, but that does not mean North Pacific SST anomalies are warm or cool as discussed earlier. Just in case you’re not convinced, Figure 14 shows the PDO data from November 1981 to June 2010. The data has been smoothed with an 11-month filter to reduce the impact of seasonal variations in the SST anomaly maps that follow (Figure 15 and 16). And I used an 11-month filter so that I could “center” the maps on an individual month. I also noted the positive and negative PDO peaks on Figure 14.
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Figure 14

Figure 15 shows the SST anomaly maps for the North Pacific that correspond to the four positive PDO peaks shown above in Figure 14. The lower right-hand corner map shows what some might consider the typical PDO pattern: warm in the east and cool in the center and west. Note, however, that the upper right-hand corner map has the highest peak PDO (Figure 14), but of the four maps, it shows the lowest SST anomalies in the east, and the coolest SST anomalies in the center and west. So, to reinforce earlier observations, a positive PDO shows the SST anomalies are warmer in the east than in the center and west, not that it’s warm in the North Pacific.
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Figure 15

And we can see similar results in the SST anomaly maps in Figure 16. They show the three negative PDO peaks from Figure 14. The SST anomalies in the upper left-hand map appear warmer than the other two maps, yet that map illustrates a peak negative value of the PDO. In fact, if you were to cycle between Figure 15 (Positive PDO Peaks) and Figure 16 (Negative PDO Peaks), the basin-wide SST anomalies appear higher at the Negative PDO peaks than they do at the Positive Peaks. The exception is the map in the lower right-hand corner of Figure 15 (the map with the anomalies centered on October 1997).
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Figure 16

There is good reason for that. The SST anomalies for the North Pacific (north of 20N) in the short term, like the long term, do not correlate with the PDO. I’ve also highlighted the positive and negative peak months of the PDO with red and blue, respectively. It makes the graph “busy”, but it does help to reinforce the point. (Let me know if it’s too confusing, to the point that it detracts from the post. If it does, I’ll delete it.)
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Figure 17

A SIDE NOTE
For those who are wondering what North Pacific SST anomaly patterns might look like when the PDO is not strongly positive or negative, I’ve created Figure 18. The PDO curve in Figure 14 crosses “zero” a number of times between 1981 and now. The maps in Figure 18 show the SST anomaly patterns the first and last two times the PDO data (smoothed with an 11-month filter) crossed “zero.” And as in the similar Figures, the maps represent the average SST anomalies for the periods shown. As illustrated, there are few to no similarities between SST anomaly patterns shown in the four maps.
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Figure 18

DOES THE PDO DRIVE ENSO?
There are posts and comments around the blogosphere that state something to the effect of “when the PDO is positive, El Niño events are more frequent, and the PDO is negative, there are more La Niña events.” The authors of those comments have cause and effect reversed. Keep in mind, the PDO represents the El Niño-like pattern of the SST anomalies in the North Pacific north of 20N. So during periods when the frequency and amplitude of El Niño events outweigh those of La Niña events, the positive PDO pattern (warmer in the east and cooler in the central and west) will tend to appear more frequently and the PDO will be positive. The reverse occurs when the frequency and amplitude of La Niña events outweigh those of El Niño events.

The PDO also lags ENSO, so it would be difficult for the PDO to initiate the variations in ENSO. Recall that Zhang et al refer to the PDO as “NP”. For an ENSO index, they use the Cold Tongue Index (CT) in place of NINO3.4 SST anomalies, which are used more frequently now. The Cold Tongue Index represents SST Anomalies of 6S-6N, 180-90W, where NINO3.4 SST Anomalies represent the area of 5S-5N, 170W-120W. In Figure 7 of Zhang et al, shown here as Figure 19, they illustrate the cross-correlation functions between the Cold Tongue and the other time series they examined. Note how in the bottom cell NP (PDO) lags (CT) ENSO by approximately 3 months.
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Figure 19 (Zhang et al Figure 7)

Confirmation of the lag: In ENSO-Forced Variability of the Pacific Decadal Oscillation, Newman et al (2004) also found that the PDO lags ENSO. Figure 20 is cell d of Figure 1 from Newman et al. They describe it in text as, “ENSO also leads the PDO index by a few months throughout the year (Fig. 1d), most notably in winter and summer. Simultaneous correlation is lowest in November– March, consistent with Mantua et al. (1997). The lag of maximum correlation ranges from two months in summer (r ~ 0.7) to as much as five months by late winter (r ~ 0.6). During winter and spring, ENSO leads the PDO for well over a year, consistent with reemergence of prior ENSO-forced PDO anomalies. Summer PDO appears to lead ENSO the following winter, but this could be an artifact of the strong persistence of ENSO from summer to winter (r = 0.8), combined with ENSO forcing of the PDO in both summer and winter. Note also that for intervals less than 1yr the lag autocorrelation of the PDO is low when the lag autocorrelation of ENSO (not shown) is also low, through the so-called spring persistence barrier (Torrence and Webster 1998).”

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Figure 20 (Figure 1 from Newman et al)

THERE ARE OTHER USES (MISUSES?) OF THE TERM PDO?
Many times bloggers, climate scientists and meteorologists will use the term Pacific Decadal Oscillation (PDO) to refer to the decadal and multidecadal SST variability in the Pacific as a whole. Unfortunately, this practice is becoming common practice. This use of PDO is very confusing to those who are new to the term, who would then check references on the internet and discover the original definition. It’s also confusing to those who understand the original definition and can lead to drawn out debates when the non-classical use of PDO is used by one of the parties.

PACIFIC DECADAL VARIABILITY?
My use of the phrase “the decadal and multidecadal SST variability in the Pacific” will raise obvious questions.

Keep in mind that El Niño and La Niña events are the second most dominant causes of year-to-year changes in global temperatures. The most dominant natural factors are explosive volcanic eruptions. They can easily offset the impacts of the strongest El Niño. Refer to the discussion of El Niño – Southern Oscillation (ENSO) in An Introduction To ENSO, AMO, and PDO – Part 1.

And when we look at a long-term graph of “raw” NINO3.4 SST anomalies (commonly used to represent the frequency and magnitude of ENSO events), Figure 21, we see a noisy dataset.
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Figure 21

Earlier in this post we compared the PDO to NINO3.4 SST anomalies where both datasets were smoothed with 121-month filters, Figure 6. The additional magnitude of the variations in the PDO may have detracted from the variability of the NINO3.4 SST anomaly data. So in Figure 22, I’ve presented the NINO3.4 SST anomalies alone. The red line simply highlights “zero deg C”. We can see that there are decadal and multidecadal periods when El Niño events are dominant, and periods when La Niña events are dominant.
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Figure 22

Those using PDO may also be referring to the multidecadal variations in detrended North Pacific SST anomalies. When smoothed with a 121-month filter, the detrended North Pacific SST anomalies show variability that runs in and out of phase with the AMO. I’ve also included NINO3.4 SST anomalies smoothed with the same filter so show that they too can run in and out of phase.
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Figure 23
ADDITIONAL DISCUSSIONS
Many of the topics covered in this post were also presented in Misunderstandings about the PDO – REVISED and in Revisiting “Misunderstandings About The PDO – Revised”. Additionally, I examined the difference between NINO3.4 SST anomalies and the PDO in the post Is The Difference Between NINO3.4 SST Anomalies And The PDO A Function Of Sea Level Pressure? and showed that a North Pacific sea level pressure dataset appears to correlate with the difference between the PDO and NINO3.4 SST anomalies. This very simple analysis indicates that the additional natural factor that exaggerates the decadal variability of the PDO MAY BE sea level pressure.

SOURCE
All data used in this post is available through the KNMI Climate Explorer:
http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

I also used the KNMI Climate Explorer to create the maps.

The PDO data from JISAO is available through the KNMI Climate Explorer "Climate Indices" webpage, but I used the data directly from the JISAO website for this post:
http://jisao.washington.edu/pdo/PDO.latest