1ST Quarter 2011 Sea Level Anomaly Update And An Initial Look At The Impacts Of ENSO On Global Sea Level

It’s been more than two years since my last Sea Level anomaly update using the data from the University of Colorado Sea Level Research Group . I visit their website regularly, but each update seems to be an extension of the monotonous 3.22 mm per year linear trend with another wiggle or correction that keeps it at or near that trend. That aside, since it has been two years and since there have been significant El Niño and La Niña events since then, I felt it would be good to update the Sea Level anomaly graphs at my blog.

There’s another topic that prompted this post: The University of Colorado’s recently updated webpage included a discussion of how sea levels should start to rise again in response to the ebbing of La Niña conditions in the tropic Pacific, 2011_rel2: GMSL and Multivariate ENSO IndexBut the graph they included did not appear to go along with the description, so I’ve also discussed detrended sea level and the Multivariate ENSO Index (MEI) in this post.

Let’s get the Sea Level update portion out of the way first.

SEA LEVEL UPDATE – MONTHLY DATA

Figure 1 illustrates the global Sea Level anomalies on a monthly basis, from January 1993 to March 2011. I started with the Global Sea Level (2001 rel2) with the seasonal signal included. The data also includes Inverse Barometer and Glacial Isostatic Adjustments. I converted it to monthly data, then determined anomalies from the monthly averages of the base period, which was the entire term of the data, 1993 to 2010. And as discussed earlier and illustrated in Figure 1, the global sea level anomaly data seems simply to follow the linear trend with some minor multiyear divergences.

Figure 1

I followed the same routine for the Atlantic, Indian, and PacificOcean data, Figures 2, 3, and 4, respectively. The Atlantic data appeared to have flattened from 2005 through 2008, but it swung back up in 2009. The Indian Ocean data is noisy, being impacted by ENSO and the phenomenon known as the Indian Ocean dipole, and it seems to be continuing its rise without any multiyear decreases in trend. The Pacific Ocean Sea Level data, however, appears to have flattened since 2006, though it does make a rise and fall in response to the 2009/10 El Niño and the 2010/11 La Niña. How long will it continue to rise at the reduced rate?

Figure 2

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Figure 3

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Figure 4

And, for those interested, Figure 5 is a spaghetti graph that compares the Global Sea Level anomalies and the data for the three major basins. All have been smoothed with 12-month running-average filters to reduce the noise.

Figure 5

SEA LEVEL UPDATE – ANNUAL DATA

Some readers prefer annual data. I’ve presented the Global, Atlantic Ocean, Indian Ocean, and Pacific Ocean data on an annual basis in Figures 6 though 9.

Figure 6

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Figure 7

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Figure 8

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Figure 9

NOTE ABOUT KNMI CLIMATE EXPLORER

KNMI has added the University of Colorado Global Sea Level anomaly data to its Climate Explorer on the Monthly climate indices webpage. They also have the ocean basin and sea subsets that are presently available through the University of Colorado’s Regional Sea Levelwebpage. The updating at the Climate Explorer can occasionally lag the University of Colorado, so the data at KNMI as of this writing is still 2011_rel1. But there is a wonderful benefit to using the KNMI Explorer for that sea level data: KNMI presents it on a monthly basis.

DETRENDED GLOBAL MEAN SEA LEVEL VERSUS ENSO INDEX

Before we begin, I want to clarify two things. I am not questioning the University of Colorado’s prediction that Sea Levels will rise again shortly in response to the ebbing La Niña event in the following discussion. And I am also not implying that my findings show an error with the Sea Level data. This discussion presents a multiyear divergence between an ENSO index and the detrended Global Sea Level anomalies that I find interesting.

The University of Colorado Sea Level Research Group has recently added a discussion of the impact of ENSO on Sea Level. Refer to their 2011_rel2: GMSL and Multivariate ENSO Index webpage. To explain the recent decline in Sea Level, they provide the following illustration, Figure 10, and discussion:

The Multivariate ENSO Index (MEI) is the unrotated, first principal component of six observables measured over the tropical Pacific (see NOAA ESRL MEI, Wolter & Timlin, 1993,1998).  To compare the global mean sea level to the MEI time series, we removed the mean, linear trend, and seasonal signals from the 60-day smoothed global mean sea level estimates and normalized each time series by its standard deviation. The normalized values plotted above show a strong correlation between the global mean sea level and the MEI, with the global mean sea level often lagging changes in the MEI. Since the MEI has recently sharply increased (coming out of a strong La Niña), we expect the global mean sea level estimates to also reverse their recent downward trend and begin to increase as the La Niña effects wane.

Figure 10

Detrended Global Sea Level Anomalies in Figure 10 mimic the MEI data, but I don’t know that I’d call it a strong correlation. In fact, the correlation coefficient for those two datasets is only 0.44. So let’s detrend the monthly Global Sea Level anomalies, standardize the data, and compare them to the MEI data, Figure 11. (Note that the MEI is a standardized dataset, but the University of Colorado standardized it again for their graph, so I did too.) My Figure 11 is a reasonable reproduction of the University of Colorado graph, Figure 10. They presented 6-week averages of the sea level data, and I’ve presented it on a monthly basis.

Figure 11

Now let’s smooth both datasets with a 12-month running average filter, Figure 12. The detrended and standardized Global Sea Level anomalies definitely do not always follow the ENSO index. And it doesn’t appear that any other method of scaling the two datasets will provide better results, but let’s try two more.

Figure 12

For Figure 13, I did not standardize the detrended Global Sea Level anomalies, but I scaled the MEI data based on a linear regression analysis. That doesn’t help. All that seems to do is emphasize the differences between the two datasets, especially the two Bactrian camel-like humps in the detrended Sea Level data compared to the three moderate El Niño events between 2002 and 2007.

Figure 13

Last, for Figure 14, let’s assume that the “Super” 1997/98 El Niño was the only ENSO event during the period that was strong enough to overcome the year-to-year noise in the Sea Level data, and that the evolution phase of that El Niño event should be “cleanest” since the decay phase in the sea level data includes the aftereffects of the El Niño. Then we can scale the MEI data and shift it down so that the leading edges of the two datasets align during the evolution of the 1997/98 El Niño. Now, note how the Detrended Global Sea Level anomalies diverge from the MEI data during the decay phase of the 1997/98 El Niño. Then they rise, remaining well above the ENSO index data through 2005, when they start to drop until they realign again during the decay phase of the 2009/10 El Niño. Interesting, isn’t it? It’s something that needs to be investigated further.

Figure 14

Detrending the Atlantic and Indian Ocean datasets and comparing them to the MEI data that has been scaled to the response to the 1997/98 El Niño does not seem to shed any light. Refer to Figure 15 for the Atlantic Ocean data and Figure 16 for the Indian Ocean data. But the detrended Pacific Ocean data, Figure 17, has a response that’s similar to Global data, so it might hold the key.

Figure 15

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Figure 16

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Figure 17

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A NOTE ABOUT THE ENSO INDEX

Someone is bound to ask why the detrended Pacific sea level data precedes the MEI data. Let’s replace the MEI data with scaled NINO3.4 Sea Level (not Sea Surface Temperature) Anomalies, Figure 18. The detrended Pacific Sea Level anomalies do not lead the NINO3.4 Sea Level Anomalies. Keep in mind that I used the MEI data because the University of Colorado used it, not because it was the right ENSO index to use with Sea Level data.

Figure 18

As illustrated in Figure 19, the NINO3.4 region Sea Level anomalies precede the NINO3.4 SST anomalies and the Multivariate ENSO Index data. And they should. The NINO3.4 Sea Level data captures the Kelvin waves and the subsurface temperature anomalies traveling from west to east across the equatorial Pacific, which lead the response of the NINO3.4 Sea Surface Temperatures and many of the additional variables used in the Multivariate ENSO Index.

Figure 19

CLOSING

The answer to what causes the multiyear divergence of the detrended global sea level anomalies from the ENSO index might rest in the process of ENSO and the significant redistribution of warm waters from the tropical Pacific following the 1997/98 El Niño event. Then again, mass from glacial runoff is also a major contributor to Sea Level. Did it temporarily increase for a few years after the 1997/98 El Niño? For now, I’ll treat the decade-long divergence as a curiosity, but I’ll keep looking for an explanation.