Before the satellite era, the best data we have about sea level comes from tide gauges. They give local sea level, which is the difference between the height of the sea surface and the height of the land (it can move up and down too). It is possible — but very complicated — to combine data from tide gauges around the world in order to estimate how global mean sea level (GMSL) has changed over the past century-and-a-half or so.
The two best-known such estimates come from two different teams of researchers; one from Church & White (which I’ll refer to as “cw”), the other from Jevrejeva et al. (which I’ll refer to as “jev”). Let’s compare them. Here they are:
I’ll draw your attention to how they compare from 1930 through 1990 (the end of 1989), in particular how they show very different trends. For each, I’ll fit a piecewise-linear model allowing for a trend change in 1960. Doing so gives this:
The cw data (Church & White) show very little trend change at 1960; the “before” and “after” rates of increase are 1.95 and 1.58 mm/yr. But the jev data (Jevrejeva et al.) show a dramatic trend change, rising a whopping 3.26 mm/yr before 1960 but only 0.94 mm/yr after. Which is more correct?
To investigate, I’ve been looking at individual tide gauge stations. The cw data suggest that when comparing the 1930-1960 time span with 1960-1990, the rate of increase goes down slightly, by 0.36 mm/yr, while the jev data suggest a very large drop of 2.32 mm/yr (over six times as large). What do individual tide gauge stations say about that?
I scanned the data available from PSMSL (the Permanent Service for Mean Sea Level) to determine which of them have sufficient data to compare those two time intervals properly. The total span of 60 years (1930 to 1990) covers 720 months, so I identified those stations with at least 660 months’ data during that time span. There are 102 of them, located like this:
Most of them are in Europe and North America, simply because most tide gauge stations (and especially those with long enough records) are there, but there’s a smattering of stations in other parts of the world.
For each of the 102 “enough-data” stations I computed the difference between the 1930-1960 trend and the 1960-1990 trend. Recall that the cw data say the global average decrease was 0.36 mm/yr while the jev data suggest 2.32 mm/yr. Here’s a histogram of the decrease as estimated at individual tide gauges:
Two of the stations show extreme rate changes; examination of their individual graphs shows clear discontinuities such as come from earthquakes which can dramatically alter the height of the land. So I eliminated those two from consideration, leaving 100 tide gauge stations with at least 660 out of 720 months’ data during the 1930-1990 period, and without obvious discontinuities which invalidate their use for global sea level estimation.
And what do those 100 stations say? The average trend rate decrease for all 100 stations is 0.32 mm/yr. That’s quite close to the estimate from the cw global data of 0.36 mm/yr but nowhere near the estimate from jev data of 2.32 mm/yr. That fact argues very strongly that the cw global estimate is doing a much, much better job than the jev global estimate.
What about their locations? Here are the locations of the 100 tide gauge stations, with those showing a change bigger than 2.32 mm/yr in blue and those showing less change in red:
By far most of the stations (86 out of 100) show less change than the jev data, which argues that the jev data are giving a false impression of the change in the rate of sea level rise around 1960. Their locations don’t contradict that conclusion either; those stations are isolated, apparently at random, except for the fact that there are a number of them along the northeast coast of the U.S.
It seems to me, this is nearly conclusive evidence that the jev data are seriously flawed. In particular, they point to a large decrease in sea level rise which is not only contradicted by the cw data, it is also contradicted by close examination of individual tide gauge stations.
How then did the jev data reach this conclusion? In my opinion, it’s because of two serious analytical flaws. First, the “virtual station method” puts way too much emphasis on a small number of individual stations, enabling those very few which show the large change to dominate the vast majority which don’t. Second, using the “first-difference method” (actually a modified form of it) so greatly increases the influence of random noise that it alone makes the jev reconstruction unreliable (see this).
All of this means that we should be using the cw data, not the jev data. Let me make one thing clear: that does not mean that Jevrejeva et al. are incompetent. The “virtual station method” was an ingenious solution to a problem that needed addressing (essentially, area-weighting). The fact that it can overemphasize a small number of stations is a flaw, but when smart people invent new methods it’s all too easy for honest and intelligent researchers not to grasp all its implications right off the bat. The fact that the “first difference method” (which was well known even before their research) is tremendously flawed is something that was missed by nearly everybody. I myself considered it one of the best way to align stations’ data until I looked very closely into the matter.
Jevrejeva et al. aren’t fools, and in no way are they dishonest, in fact they did a great deal of work and identified important issues which we can’t ignore, making great progress in advancing our understanding of historical sea level rise. The fact that there were unknown flaws in some of their methods and that subsequent research has done a better job of it — that’s just science.
Unfortunately, climate deniers seem to know only two possible explanations for scientific data: either it supports their world-view, or it’s some kind of fraud. Real scientists know that research can arrive at mistaken conclusions, not because of some global conspiracy to destroy America, but because science is difficult, complex, intricate, and we don’t always get everything right the first time.
I suspect that among scientists the Jevrejeva et al. data will fall out of favor because it has demonstrable flaws. Among climate deniers, it will remain a favorite because it supports a tiny part of their climate-denier worldview. In my opinion, their support for purely ideological reasons is a genuine insult to the efforts of Jevrejeva et al. Criticism of their work for purely scientific reasons is how real science works, and I strongly suspect that Jevrejeva and colleagues would agree.
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