It’s easy to see that sea level rise has **not** been steady. It has *accelerated*.

In fact it has accelerated a lot, especially recently. For most of the 20th century, it rose sometimes faster, sometimes slower, but for the last few decades its rise has picked up speed. The clearest demonstration is the change in *global mean sea level*. There are several different estimates of that based on historical data from tide gauges around the world, which differ on how much and how fast sea level has risen, but they all show — without a doubt — that the rise has **not** been steady.

Let’s look at three different estimates, starting with the one which was, for a decade or more, probably the most trusted: that from Church & White. We’ll also look at a more recent estimate of global sea level since the year 1900, the one that was used in Rutgers University’s report on preparing New Jersey for climate change, that from Dangendorf et al. Finally, we’ll look at my own reconstruction. Here are annual averages of sea level, according to the three different estimates:

If sea level rise had been steady, then over time it would fluctuate about a straight line. Its deviation from that straight line would be just fluctuation. It would *not* show meaningful patterns, just noise.

So for each data set, I computed the best-fit straight line (by least squares regression), then subtracted the straight-line values from the observed values to compute the deviation from steady rise, the residuals:

No, this is not “just noise.” Of particular interest is the rapid rise over the last few decades, clearly shown by all three estimates. They don’t agree precisely how fast sea level has risen, but they do all agree that it has accelerated.

Of course, I’ll apply some heavy-duty math to estimate how fast sea level has been rising, according to all three data sets. First I’ll compute a lowess smooth, not so much to estimate the smooth value, as to estimate the smooth *rate of change* (i.e. how fast sea level is rising). Here are the results since the year 1900, for the data from Chuch & White (in black), Dangendorf (blue), and Foster (red). I’ll add pink shading to show the uncertainty range for the Foster data, but not all three data sets (because then the graph gets confusingly crowded).

I’ll also apply some not-so-heavy-duty math, by fitting a model consisting of connected straight-line segments over 30-year periods. This will estimate the *average rate* of sea level rise during each 30-year period, which I’ll graph like this:

No, the rate of sea level rise has **not** been the same since 1900. Sea level rise has **not** been steady. It has gotten faster: acceleration. The fastest rise is over the last few decades.

**The BIG LIE about Sea Level Rise**

Yes, it’s easy to see that sea level rise has accelerated. Alas, it’s also easy *not* to see it — if you don’t want to.

Judith Curry recently blogged about her presentation to a “*Conference on Energy and Decarbonization — a New Jersey Business Perspective*. One of the issues she addresses, one which is important to New Jersey, is sea level rise.

I don’t know whether or not she believes the BIG LIE about sea level rise, but I do know that she repeats it. It’s the gist of her slide #11:

She even declares that “Since 1910, sea level has been rising at a steady rate of 1.36 feet, or 16 inches, per century.” And that, folks, is the BIG LIE about sea level rise: that it has been steady for the last century or more, that it hasn’t been getting faster (i.e. accelerating). A lot. Especially recently.

Her main advice (a business perspective) about sea level is to ignore the future sea level rise estimated by Rutgers University’s report on the impact of climate change on New Jersey. The only basis I can see for her making this claim is that she believes the BIG LIE about sea level rise (that it has been steady), and that she also believes the unspoken but bigger lie: that it will remain steady for the rest of the century.

Perhaps next, I’ll blog about sea level rise *in New Jersey*. You might be surprised by the result. You might even be worried, if you live in New Jersey.

This blog is made possible by readers like you; join others by donating at My Wee Dragon.

Two quick comments: first, you have rising pinnipeds in a couple of your graphs. Second: it is really frustrating to me that the WattsUp denizens keep posting the NOAA trends plots because they look straight, but never look at the actual data. NOAA even helpfully provides a chart of 50 year trends, which show that last 50 year rate of 4.71 mm/yr is already substantially above the full period trend of 4.14 mm/yr…

-Marcus

Hi, Marcus. I’ve been dealing with people misusing the straight-line chart NOAA leads with for years now – when going to the more informative chart of 50-year trends for a given tide station, you should be able to right-click the “Save image” button and grab a URL to the image that way.

Here’s Atlantic City showing a good deal of agreement with the last three increments of Tamino’s 30-year-rate chart.

That Curry pretends the Atlantic City trend is steady by using a chart that, by it’s design, guarantees a straight line? I guess she’d defend that deception in a “fair and balanced” way by saying she could have shown downward-plunging Kodiak Island’s linear trend instead.

https://tidesandcurrents.noaa.gov/sltrends/sltrends_station.shtml?id=9457292

Thanks for the “save image” trick! Yes, the pattern for the Atlantic City 50 year trends is very similar to the global mean pattern that Tamino showed in both his Lowess smooth & 30 year charts. The difference between 50 year trends is slightly smaller (about 1.3 mm/yr/yr between the 1960 low and the most recent 50 year segment, compared to 1.8 mm/yr/yr between the 1960 low for the lowess smooth and the most recent trends for the Dangendorf analysis), but that isn’t surprising because 50 year trends will reduce variability in trends relative to more sophisticated measures (or shorter term trends). Plus, one wouldn’t expect Atlantic City to perfectly mirror global trends (it is almost surprising how close it comes to matching!).

https://tidesandcurrents.noaa.gov/sltrends/sltrends_station.shtml?id=8534720

So…the Atlantic City tide gauge corrected for annual cycles (i.e., that “processed data” which we all know is hoaxed) uncorrected for land movements is your proxy for global sea level???!!!

Wow. Nice totally out-of-context/irrelevant to the question factoid!

If it wasn’t clear enough, I had meant to include the NOAA link as a support to my first post (and I picked that particular station because it was the one that Curry highlighted). Unfortunately, one can’t link directly to the 50-year trend tab, or I would have done that. I would also suggest that you shouldn’t be so quick to insult people.

Thanks. I was saddened to see Judith Curry being used to mislead again recently. There just aren’t enough hours in the day to stop falsehood and propaganda from providing a convenient bulwark against reality.

In Boston, my tide table tells me we have some unusually big ‘uns coming in midday:

https://www.boatma.com/tides/print/Nov/Charlestown-Charles-River-entrance-Boston-Harbor.html

Thursday 11.5 feet

Friday 12.0

Sat 12.2

Sun 12.1

Mon 11.8

Tues 11.3

I assum that would be the baseline, on which the observed (and all too obvious) acceleration builds …

Reblogged this on Don't look now.

The NOAA trends plot is suffering a specification error. Unlike Dr Foster’s models, which essential bend the way the data tells them to go, the NOAA trend insists the data is a line. Whenever or whoever did that plot never bothered to determine how good their model was in comparison to other models. While the straight line fit may have been okay once upon a time — although given the work reported above, not clear how it could be okay between 1930 and 1960 — it’s not now. In fact it’s misleading.

Here’s my take for data through 2000, done in 2014.

Thanks much for this post!

Reblogged this on 667 per centimeter : climate science, quantitative biology, statistics, and energy policy and commented:

Nice post. Using different techniques, but an update of what I wrote about in 2014, better and more comprehensive.

In looking at trends are the sea level data adjusted for the lunar orbital wobble cycle?

See https://www.sciencenewsforstudents.org/article/moons-orbital-wobble-can-add-to-sea-level-rise-and-flooding

Or is this irrelevant, as it just affects the size of the tides and not the mean sea level?

The same story here in the Netherlands. For the past 140 years it was just 2mm/j. For the past 30y it was already 3mm/y. There is no longer a need to mix tide gauges and satellite to prove you point. The latest forecast see a rise in sea level of 1,2 to 2m (=6 feet) in 2100. This means the rate of sea level rise could become 10 times as fast as it was.

The global warming cult talk of sea level rise, but conveniently cherry pick the data which suits them. Some data sets have satellite measurements of sea level rise below that of tidal gauges. Some show satellite measurements above tidal gauges. The accuracy of such is beyond any statistical significance and is unreliable, yet the cult peddle this nonsense.

If you read the IPCC report most effects of climate change are specified with medium or low confidence. There is no hard evidence for doomsday scenarios of climate change beyond faith. Yet this is supposed to be an existential threat! What a farce!

Never mind the sheer inaccuracy of the models. We cannot model cloud formation to any degree, which is important because we need to understand how this interacts with the climate, yet ‘scientists’ make wild predictions based on CO2 without incorporating such cloud modelling. If anyone thinks we can accurately model cloud formation over time, so this can be accounted for, then their heads are in the clouds. I seriously doubt anyone here will be able to understand the importance of this.

It’s relatively recent, but cloud affects on climate has been solved as a problem. This was reported in the last few years. I don’t know if it made the cut to their recent IPCC report, but I suspect it did, as they allowed the cutoff to run relatively late, partly as a result of the pandemic.

As far as sea level projections go, you’re never going to see where sea level is headed simply by using past history. Projections rely on understanding processes, such as glacier melt. Also, you’re mixing apples and oranges. The GCMs don’t model ice melts in any kind of detail. In fact ice melt models feed into the GCMs.

I seriously doubt anyone here will be able to understand the importance of this.That is the giveaway regarding the seriousness of the comment.

MW: The global warming cult talk of sea level rise, but conveniently cherry pick the data which suits them.

BPL: You have no idea what “cherry pick” actually means, do you? You just throw words together that you think sound profound. Word salad.

MW: We cannot model cloud formation to any degree

BPL: Yes we can, we have for years. You’re making stuff up.

Just shut up. You add nothing to the discussion. Butt out until you’ve done some studying and know what you’re talking about.

Mark, help us get up to speed with your effort to expose the global warming cult. It would help for you to link the papers you know of where the authors cherrypicked the sea level data that suits them. And point us to the locations where you satellite sea level data are lower than tide gauge data. You’ve obviously done the research so help us catch up with you. Post those links too.

I like this new phrase you thought up: “beyond any statistical significance.” I can use it anywhere. It obviously comes from knowing your ass from third base, but how can the global warming cult hold you to anything? The won’t know WTF you’re talking about.

“If you read the IPCC report most effects of climate change are specified with medium or low confidence.” Your strategy of assuming no one will read the WG1 report might backfire on you, though. It’s really long, so I bet you didn’t read it yourself. Most people will read the “Summary for Policymakers,” where quite a lot of statements are qualified with words like unequivocal, unequivocally, likely, very likely, unprecedented, words like that. Sounds more like high confidence. List the points you found where there is only medium or low confidence.

“There is no hard evidence for doomsday scenarios of climate change beyond faith. Yet this is supposed to be an existential threat! What a farce!” What are the doomsday scenarios that defy all the hard evidence of global temperature rise and global sea level rise?

“Never mind the sheer inaccuracy of the models.” Which models, Mark? Have you found some evidence the global climate models are wrong? I thought the current global average temperature was pretty much where the models said it would be. But “sheer inaccuracy!” You must be withholding some real, smoking gun proof. Let us see it. Please!

“I seriously doubt anyone here will be able to understand the importance of this.” Yes, Mark! Lead us!

Mark West wrote: “The global warming cult…”

At which point I stopped reading, knowing that it would be just another ignorant, evidence-free rant.

Ross McKitrick has a new YouTube video out:

UN’s IPCC Gets it Wrong on Greenhouse Gas Effect

I don’t know much about anything, but this sounds totally bonkers and delusional. Can somebody please go over there to see what he’s on about and challenge his claims before too many gullible people fall for it. Lately, there seems to be an increase in activity online from the denialati, I guess in response to the Glasgow climate talks.

As best I understand what McKittrick is trying to assert is that modeling falling off a cliff by using Newton’s equations for impact speed is somehow “assuming” that falling is dangerous as opposed to noticing that colliding with rocks as the “silly” model predicts at x mph is, well, dangerous.

I would also add that “null hypothesis” is a concept from experimental design, not non-experimental correlational analyses. One would think an econometrics expert would be aware of this fact.

MW: “The accuracy of such is beyond any statistical significance and is unreliable, yet the cult peddle this nonsense.”

This sounds like a non-scientist trying to use big words, or a scientist who smoked a joint or three before they posted. What is it even supposed to mean? Please explain what is going on when accuracy is “beyond any statistical significance”, and why this is supposed to be a bad thing for warmists.

It’s astounding that a so called scientist needs to clarify the necessity for statistical significance. We need statistical significance of CO2 causing global warming to show that it is definitely humans causing climate change. If the relationship is not statistically significant then it means that it needs to demonstrate that there is a relationship which can be demonstrated beyond that of random chance.

The errors of the models are HUGE and do not demonstrate any statistical significance between that of CO2 and temperature, yet the ‘scientists’ make bold claims of how we know how the climate will change. Climate change has happened and always will. The null hypothesis is that it’s natural and until ‘scientists’ can disprove this null hypothesis with statistically significant evidence for human driven climate change. They haven’t because they can’t.

MW: “It’s astounding that a so called scientist needs to clarify the necessity for statistical significance.”

Stop avoiding the issue, Mark. We understand statistical significance. You were called out for your nonsensical remark. You wrote: “The accuracy of such is beyond any statistical significance and is unreliable, yet the cult peddle this nonsense.” What did you mean by “beyond any statistical significance?” If a measure is beyond statistical significance, then it is more than statistically significant, i.e. it is statistically significant. But you must have meant the opposite, yes? If you meant the opposite, you provided no proof. You will know, having read Grant Foster’s work analyses here, that when he says a measure is or is not statistically significant, he shows the proof that it is or is not statistically significant. You have to do that too. That was the point, Mark. Your bloviations are no good without proof.

Now you are trying to do it again. You write: “The errors of the models are HUGE and do not demonstrate any statistical significance between that of CO2 and temperature,” What huge errors are you referring to? What is the “that” of CO2 and temperature you are referring to? Your remark has no meaning, let alone no proof.

This assertion erroneously assumes that “statistical significance” has something to do with a demonstration of causation, and that if an effect is statistically significance, the link is causal. Causation is a completely different thing to demonstrate, but, in fact, the causal link between excess CO2 and warming has been demonstrated in a statistical manner:

Van Nes, Egbert H., Marten Scheffer, Victor Brovkin, Timothy M. Lenton, Hao Ye, Ethan Deyle, and George Sugihara. “Causal feedbacks in climate change.”

Nature Climate Change5, no. 5 (2015): 445-448.There are multiple lines of reasoning which establish that thermal energy is trapped in the climate system due to elevating greenhouse gases, with Carbon Dioxide being the primary one resulting from human choices and activities.

The principle argument is that greater concentrations of CO2 impede escape of thermal radiation through the

Blackbody Effect, raising the thermaltop of atmosphere. That and thelapse rateassures surface temperature must rise. The elevated concentrations of CO2 are known to be derived from human activity due to their isotopic chemistry, which ties them back to plant-produced activity of great age.These mechanisms are far better established in Chemistry and Physics than a mere

analysis of variance-like test, and have been since 1896. I have linked imaged non-contiguous portions of this text below so readers can understand how far along establishing the link between CO2 and Earth temperatures had come in 1896:“Astronomers” make bold claims like that most of those bright twinkly things seen in the sky at night are giant balls of fusing hydrogen. The null hypothesis is that those twinkly things are “natural”. Until ‘astonomers’ can disprove the null hypothesis with statistically significant evidence for this bold notion. They haven’t because they simply cannot. Nor will they be able to for the foreseeable future.

Your understanding of science and the nature of scientific inference is rather lacking. I suggest you study the subject a bit before pontificating.

Hell, PROVE to us that the sun will come up tomorrow. You cannot. It’s simply a model–or perhaps a naive expectation–that says that it will. And in truth the model could be wrong or not account for “natural” factors not yet thought of that will prevent it from happening.

MW: “It’s astounding that a so called scientist needs to clarify the necessity for statistical significance.”

That was not what I asked. Not even close. Try reading. It still sounds as though you are using words you only have distant familiarity with.

MW: “If the relationship is not statistically significant then it means that it needs to demonstrate that there is a relationship which can be demonstrated beyond that of random chance.”

Could you have produced a more effed-up sentence? You sound so completely out of your depth it is embarrassing.

War on Entropy:

I meant that the results need to show a statistical significance that shows any correlation goes beyond that of random chance. They don’t for CO2 and temperature. Therefore, we can assume that climate change is natural and the null hypothesis that it is is not disproven.

Rather than resorting to snide, I suggest you look at the data properly and learn the importance of not promising what we can’t guarantee. Some statistical understanding would be of huge benefit for you.

These are

time seriesso clearly it’s invalid to calculate something like a sample correlation coefficient because they are both correlated with a third quantity, time. That of course betrays your assertion that they have “correlation … of random chance” is false. They are likely to have some correlation because of their temporal dependency, yet that means they aren’t necessarily correlated with one another. To quote a textbook (*),No attention paid to the Van Nes, Egbert,

et alpaper, did you? As noted, it demonstrates the correlation, even if it is non-linear. In general the sample covariance and correlation techniques you’re spouting on about are inapplicable for these data.For the readership which might be distracted by your

faux statistical knowledge(**), they might be interested in discussions ofGranger CausalityandConvergent Cross Mapping. Note Sugihara and colleagues developed and published CCM in 2012, he’s a co-author of the Van Nes,et alpaper.—

(*) That textbook is:

Ramsey, Fred, and Daniel Schafer.

The Statistical sleuth: A Course in Methods of Data Analysis. Cengage Learning, 2002.(**) I’m sure it’s

faux. Is @Mark West some kind of engineer perhaps? Some act like they know more statistics than they really do. (Experience in 20 years of working as quant and statistician with engineers.) Also,@Jim eager, as noted this isn’t about @Mark West, who I’m sure is going to lodge objection after irrelevant objection. It’s about educating the readership regarding the lengths to which some people will go to confuse everyone and waste their time. Rather like Lucy in “Peanuts.”And why am I so sure of the

faux-ness? Because if Mr West were to check out the above reference, which is about the STATS 201 level, he’ll find on pages 438-439 of the 2002 2nd edition a section 15.1.2 titled “Measuring Global Warming — An Observational Study.” This is one of two case studies used in Chapter 15 to teach techniques of treating data having serial correlation, that is, correlation with themselves. I’d recommend the chapter as reading for anyone who thinks @Mark West is even close to spot. The act of reaching for a correlation between temperature and CO2 concentrations is the tell. He didn’t even trudge out a variable-window FFT, which would be as dubious, but at least more sophisticated.Once again, I rely upon Van Nes,

et alto summarize by quoting their Abstract, and leave it at that:Is the land rising from post glacial rebound in New Jersey?

[

Response:It’s falling, rebounding from uplift due to being on the bulge outside the edge of the ice sheets. It’s also sinking due to sediment compaction, exaggerated by groundwater extraction. Both factors make relative sea level (what you see compared to the land) rise faster in NJ. But that’s the subject of an upcoming post.]ecoquant. Forgive me, but none of that mentions MAGNITUDE. No-one denies the greenhouse effect. What matters is magnitude. The ONLY way we can see whether increased CO2 has attributed temperature rise is through robust statistical analysis and modelling. NONE of the models show that there is a statistically significant trend between CO2 and temperature, so we must assume any climate change today is natural.

Arrhenius has estimates of magnitude in his 1896 paper.

Guy Stewart Callendar also had estimates of magnitude in 1938.

Could you please supply us with the strings of time series data upon which actually you base your statement “NONE of the models show that there is a statistically significant trend between CO2 and temperature” on? You keep making explicit quantitative statements implying that you know more than thousands of actual experts who make this field their profession without providing any, well, explicit quantities. Not very impressive.

For example, you cannot use the NASA CO2 and GISTemp series data from 1959-2017 aggregated annually…

Pearson’s product-moment correlation:

data: CO2 and GISTemp

t = 23.082, df = 57, p-value < 2.2e-16

alternative hypothesis: true correlation is not equal to 0

95 percent confidence interval:

0.9177401 0.9703546

sample estimates:

cor

0.9504504

as that correlation is .95 which as you can see is astronomically significant.

Any model which even moderately tracks GISTemp–and current models most certainly do–will also show much the same highly significant correlation.

Now since there is no "control" Earth, we cannot rule out some "natural" but unknown source moderating/mediating/confounding (in the regression senses of these terms) this relationship, but we certainly have an observation quite consistent with reality. You are free to propose other "natural" forces. [Hint: No known natural force works. See the first 10 pages and Figure SPM.2 "Assessed contributions to observed warming in 2010–2019 relative to 1850–1900" of the most recent IPCC summary report: https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM_final.pdf ] As an aside, the most recent conclusion on human influence is that it is "unequivocal" NOT as you say above of low or medium confidence.I guess you missed that part. It's on page 3.

——

Also, it might be nice if you could provide us with an actual experimental, analysis of variance null/alternative tests using actual data "proving" that stars are giant balls of fusing hydrogen rather than just being "natural" lights in the sky glowing for no known–but natural–reason. Just to show us you understand how sciences seeking to explain naturally-occurring phenomena actually work. I'd be curious as to the specifics of your treatment and control "stars" in your experimental setup as well.

eq and jgnfld, you do realize that you are conversing with a sack of hammers, right?

Actually, not “conversing” at all. That would imply an interested and capable conversant. As you note, hammers are not all that capable at science.

But yes.

MW:

“I meant that the results need to show a statistical significance that shows any correlation goes beyond that of random chance. They don’t for CO2 and temperature. ”

This does not correspond to the sentence I quoted. You were talking about the accuracy of seal level measurements, and you said the accuracy was beyond statistical significance.

Along the way, you have made the assumption I am a scientist, which shows you don’t read carefully or consider things before making assumptions. You also misrepresented my original query as not understanding the need for statistical significance. At this stage, I am simply trying to make sense of what you posted. It is not coherent enough for me to reach the conclusion that you are making unreasonable demands on the data, though I suspect that is what I would conclude had you come up with a coherent statement in the first place.

Twice now you seem to have forgotten the sentence I quoted, so I will quote it again, complete with its original context:

MW: “The global warming cult talk of sea level rise, but conveniently cherry pick the data which suits them. Some data sets have satellite measurements of sea level rise below that of tidal gauges. Some show satellite measurements above tidal gauges. The accuracy of such is beyond any statistical significance and is unreliable, yet the cult peddle this nonsense.”

I ask again, what does it mean to be “beyond” statistical significance? Usually, results reach statistical significance or fail to reach significance. A significance threshold somewhat arbitrarily divides the set of possible results into significant and non-significant. Have you invented a third category of “beyond statistical significance”? If the variable of interest is one dimensional, and we use 95% confidence intervals, we could get a result that is within, below or above that confidence interval, and that would create three potential spaces, but none of them would be described as “beyond significance”. In relation to what you were actually talking about at the time, how does accuracy go “beyond any statistical significance”, and why is this a bad thing for warmists? Your explanation, quoted at the top of this post, does not address your original sentence in the slightest, and switches to a discussion of CO2 and temperature. It sounds as though you are trying to say that sea level rise estimates from tidal gauges do not correlate with those from satellite measurements, but you have not made a statement as clear as that, so we can’t even begin to address your claim. It sounds awfully like you wanted to criticise the accuracy of sea level estimates, but you wanted to sound scientific while doing so and didn’t quite know how. When challenged, you switched the topic, threw in a lot of assumptions, and carried on without taking responsibility for what you posted.

If this is how you usually read climate literature, it is no surprise you are confused.