Sea LevelNiño

The Sea Level site of the University of Colorado has an interesting graph which shows quite plainly that there’s a relationship between global sea level and the el Niño southern oscillation:


I decided to see how el Niño might be related, not to global sea level, but to that of individual tide gauge stations. Like the U.Colorado graph, I too will use MEI (Multivariate el Niño Index) to characterize the el Niño southern oscillation.

It occurred to me that the strongest influence would probably be at tide gauges in the Pacific ocean and surrounding areas — after all, that’s where el Niño really happens. First I looked at a U.S. Pacific coast station, San Diego (Quarantine Station). I took the data from PSMSL (Permanent Service for Mean Sea Level), which goes through the end of 2015, and first removed the annual cycle. Then I de-trended the result, to remove the long-term sea level rise. Finally, I regressed the de-trended de-seasonalized sea level against MEI.

For San Diego, this indicated that a single unit of MEI causes sea level to rise by +28 ± 2 mm. The statistical significance is overwhelming. It’s also no surprise, since el Niño is Pacific ocean waters piling up in the east Pacific.

I could then remove the el Niño influence, to define a revised tide gauge record for San Diego which not only has the seasonal cycle removed, it has the el Niño influence out of the way as well (but the long-term trend is left in place):


The red line is a modified lowess smooth. It not only enables me to estimated the smoothed sea level, it also returns the estimated rate of change, and the uncertainty level of that (an autocorrelation correction is included in uncertainty estimates).

I also wanted to see how fast it was rising using a method simpler than my “modified lowess smooth,” so I decided to simply fit straight lines. One giant problem with doing that is that if you just fit a straight line to some time span in isolation, you’re essentially fitting a model with a broken trend, and that can really gum up the works of trend estimation.

I wanted to get at the rate of sea level rise on about a 30-year time scale, so I took the data and split it into sections 30 years long. Approximately: I actually split it into section 360 data points long, to avoid the difficulty when a big gap in the data makes some particular 30-year time span too sparse to give reliable results. I then fit a piecewise linear model, allowing a different slope in each 30-year segment, but to avoid the “broken trend” issue I constrained the model to be continuous, i.e. the endpoints of the 30-year time spans have to meet. For the San Diego data we get this:


I’m also able to compute the rate for each time span and its uncertainty (again including an autocorrelation correction). Finally, I can graph the estimated rate from the modified lowess smooth, and that from the continuous (unbroken) piecewise linear fit, on the same graph:


The very thick black line (actually, it’s closely spaced small circle) shows the estimate rate of sea level rise from the piecewise linear fit, the dashed black lines the uncertainty level (95% confidence) of that. The solid red line is the estimate from the modified lowess smooth, the dashed red lines its uncertainty envelope. Finally, the solid blue line shows the linear rate of change using the entire data set in one fell swoop. That’s what’s usually reported for a tide gauge station, but it assumes that the rise rate doesn’t change, which I regard as a capital mistake.

It’s abundantly clear that the rate of sea level rise at this station is not constant. In fact it shows a pattern which is quite common for long records: acceleration followed by deceleration followed by acceleration. The fastest rise occurred in the 1925-1955 period according to the linear fit, in the 1985-2015 period according to the lowess smooth, but neither outpaces the other with statistical significance. They do, however, outpace the remainder of the time span.

San Diego is on the eastern edge of the Pacific, but I also wanted to look at a station in the West Pacific. I chose another with long time coverage, Fremantle in Australia. This too shows a very strong response to el Niño, but out of phase with the east Pacific. An increase of MEI by 1 unit causes a drop in sea level at Fremantle, by about -40 ± 3.7 mm. Again, statistical significance is beyond question.

Again, I can remove the el Niño influence and estimate how the rate of sea level rise has changed over time, using the same methods as for San Diego. I get this:


We see the same pattern of acceleration followed by deceleration followed by acceleration, although at Fremantle the first acceleration doesn’t make statistical significance. The last one does; the fastest rise is definitely in the most recent 30-year period.

Finally, I wondered about stations in the Atlantic, so I chose a place in the western Atlantic ocean with a long record, Key West in Florida. This time, the estimated response to a single unit of change of MEI is a mere -0.15 ± 2.8 mm, which is definitely not statistically significant. Effectively, it’s zero. There’s no sign that el Niño is having an impact on sea level at Key West.

I did, however, repeat the computation of the rate of sea level rise by both methods, giving this:


We no longer see an initial acceleration, but we do see deceleration followed by acceleration. We do see once again, however, that the fastest rise has been during the most recent 30-year period.

It turns out that removing the el Niño influence doesn’t have much impact on estimates of the rate of sea level rise on 30-year time scales, even for those stations with a strong el Niño response (San Diego and Fremantle). One could say that it makes estimates a bit more precise, but doesn’t change them substantively. It doesn’t affect the Atlantic ocean station at all, although so far I’ve only looked at one of them. All in all, the response to el Niño by individual tide gauge stations is exactly as expected: an increase in MEI is accompanied by rise in the east Pacific, fall in the west Pacific, and no discernable change in the Atlantic.

Of course there’s a lot more to investigate because there are many, many more tide gauges than just three! Give it time …

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34 responses to “Sea LevelNiño

  1. Sorry – Great post but Fremantle is on the west coast – i.e. its on the Indian ocean. Brisbane or Cairns would be better choices.

  2. Fremantle IS a good choice for this! The El Nino signal reaches past/through Indonesia and right around to the west coast of Australia. Brisbane and Cairns do not see a strong El NIno signal. There is more on the Australian end of this in this paper:

    Tamino does say, incorrectly, that Fremantle is somewhere it is not, but the El Nino influence is clear.


    • And this is interesting. That Fremantle sea level is related to El Nino in the way that it is. Incidentally, I had breakfast in Fremantle this morning, at a nice coffee shop named “Duck Duck Bruce” :-)

  3. Last time I looked Fremantle was on the east coast of the Indian Ocean . Not that it affects the gist of your post.0

  4. My parents used to say the weather would change after a large bombing raid left Eastern England during WWII and so many graphs I see have a “WWII bump” (although the Freemantle example above has the bump in the 1930s). BUt was there a WWII effect on climate?

    Are there any data sources (e.g. aircraft and ship movement) that might be used to test this?

    [Response: There’s a “WW2 bump” in sea surface temperature data because the method of measuring it by ships at sea was changed. It’s probably spurious, and it doesn’t show in land-station data.]

    • I spent a couple of months collecting information about WW2… number of buildings burned… number of ships sunk… amount of ammunition expended… amount fuel burned, so it’s very interesting as it was a gigantic combustion event, but I don’t think it goes anywhere because the amount of fuel burned appeared to go down. Later I found a paper about the possibility that atmospheric testing, started during WW2, which claimed some small effect from that, but the bulk of that was much later.

      • I think it may be interesting to look at the effect of dust kicked up and smoke etc. emitted by e.g. vehicle movements in North Africa, or bombing raids in Northern Europe when WW2 was active there.

        There could be counteracting effects from fuel consumption and industrial activity vs dust/smoke production that would then decay at different rates.

        Of course, there were also lots of energy saving measures put in place, some to save fuel for the war economy, some just to produce blackouts, etc. I do wonder if the total fuel consumption went down mostly due to delivery and production issues caused by the war, with the main source of fuel for industry and the armed forces being savings elsewhere in the economies.

      • I would suspect WW2 to be little more than a blip. When one thinks of worldwide air traffic back then, compared to now, it wouldn’t even register. Chinese factories crank out a Dresden’s worth of air pollution and enough new concrete to rebuild it all over again in a couple hours I’d figure.

  5. I presume the principle* mechanism by which El Niño affects GMSL has to do with the transfer of heat into/out of seawater. Can anyone point, off the tops of their heads, to any recent work that might quantify the magnitude and global distribution pattern of the heat flux that might cause such a thermal expansion response?

    [*I would guess that the second most important impact on sea level might be shifts in precipitation location and subsequent impoundment/retention on terrestrial sinks. I’d also be curious to know to what extent this might be a factor, compared to any thermal expansion phenomenon – after five minutes of poking around I didn’t find anything particarly relevant that might shed any light… :-( ]

    • I would doubt that your presumption is correct. The implied heat flux from the relatively large short-term changes in sea-level rise created by ENSO would be huge.

      The precipitation is a more plausible explanation.

      The other possibility is due to changes of surface pressure. Low pressure over the ocean causes the sea level to rise – this is one of the effects that can create a storm surge. It’s more plausible to me that ENSO could cause pressure changes over the land and oceans that would lower pressure on average over the oceans (and consequently increase pressure over land to keep in balance) and that this would cause an increase in global mean sea level.

      • Zebraphile, the problem with the pressure idea is that water is effectively incompressible, so pressure changes should make little difference. Are you implying that it is ‘squeezed’ off land into the oceans? This seems implausible. Even a straightforward redistribution from certain parts of the oceanic surface to other parts should be smoothed out as the metric is global mean sea level.

        Impounding that much water terrestrially is itself a huge phenomenon in terms of energy involved and quantities of water shifted (and time taken to do so), which is why I am wondering if simply thermal expansion is the primary mechanism.

        I remain curious…

      • Is there not an extraordinarily clear link between
        air pressure and sea levels in
        the case of cyclone induced tidal

      • Li D,

        The changes in sea level from Tropical storms (or other storms) are temporary, local events and do not affect the measure of overall global sea level. There are a lot of events that change local sea level.

    • Bernard

      See this paper: which explains the large change in GMSL in the large 2010-11 La Nina. Yes, this is La Nina, not El Nino, but it illustrates the size of precipitation signals.

      On the subject of pressure, yes, local changes in atmospheric pressure cause changes in local sea level (e.g. storm urges) as water gets sloshed around due to the changes in local/regional atmospheric pressure. However, they will not change the global-mean sea level. Likewise, changes in global over-ocean atmospheric pressure will not change GMSL.


      • Neil, thanks for the link to the Fasulloet al paper – althougn it had slipped off my radar I recall it now from around the time of its publication when I was locking horns with a sea level denier (probably at Deltoid) and made exactly the point that it makes. It’s interesting to review it in the context of this thread; although I do still wonder at the relative rates of contribution of impoundment and thermal expansion on sea level. I’ll have to have a careful read of Fasulloet al to nut out whether the magnitudes in the paper might account for those implied in the University of Colorado graph.

        The thing that strikes me in all this is that, with our understanding and techology, we can actually detect the subtle influences of components of climate, and yet there’s a whole swath of society that wants to selectively ignore/deny any impact that antropogenic factors have on climate. It’s an extraordinarily dangerous cousin to Maxwell’s demon…

  6. Side note: the Australian CSIRO compares the Southern Oscillation Index – an indicator of ENSO events – with GMSL and gets similar results.

    Bottom of the page; you can also get SOI data at a link there.

  7. Thanks. I think this is well a known effect (as Barry showed as well) but it hasn’t been analyzed enough IMO. For example, if more sea-level gauges are included, we can potentially get better estimates of ENSO. Played around myself with this a couple of years ago

  8. David B. Benson

    Over simplistic: the trade winds push sea water to the western equatorial Pacific so the sea level is higher there. During El Nino the trade winds stop blowing and the water returns eastward along the narrow equatorial counter-current.

    • Similarly simplistic: thermal expansion of the NINO region during el Nino (+flow-on effects further afield) is significant enough to affect global average.

      MEI is based on 6 components. Perhaps some or all work in concert to change GMSL during ENSO events.

      …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).

  9. The spatial correlation between SL and MEI:

    thanks to “Climate explorer”

    • Is that to the end of 2004? Anyway, that looks like dominance of El Nino at the end of the period. If you did it 1993 to the end of 2001, I suspect you would see dark orange in the Western Pacific and blue in the Eastern Pacific.

  10. Fremantle not in west pacific, sea level changes ? linked to subsidence according to some. Disputed. Darwin, Indian Ocean is part of how El Niño is measured and shows some rise but might be temporally challenged

  11. JCH: It’s what the title says: The correlation between MEI and SSH from 1993…2004 from -0.6 (blue) to 0.6 (red). It’s not the MEI index !!

  12. OT, but clearly the denialosphere is trying to manufacture another Climategate moment out of the comments of John Bates on Karl et al, 2015, as reported by David Rose. Numerous misrepresentations about various things, from AR5 to Karl itself to the Paris Accord. I think it’s pretty clear that this is the opening salvo in campaigns to 1) withdraw the US from the Accord, and 2) neuter, defund or reorganize NOAA and particularly NCEI. The latter will likely be part of a wider attack on the institutional capacity of the US research establishment WRT climate change.

  13. Tamino – it’d be exceptionally useful if you’d post your R code at the end of each blog piece. Not that I have a single doubt in your rational or data, but because your posts always seem like a great learning curve for those of us who want to get into understanding how to model sea level rise! I’d be happy to donate for this feature!

  14. Looks like the AVISO rate of SLR has started increasing again. The OHC update indicates OHC has also resumed going back up. Looking at the AVISO graph, I do not it has ever remained above the long term trend for this length of time.