By Andy May
Thomas Frederikse and colleagues published a study of sea level data, considering both tide gauges and satellite data in 2020 (Frederikse, et al., 2020). This paper is frequently cited in the Chapter 9 AR6 sea level discussion. They found that there are many causes of global and regional sea level change that need to be considered. Land over much of the Northern Hemisphere is still rebounding from the melting of the massive glaciers they supported during the Last Glacial Maximum. This causes many northern tide gauges to record sea level falling as the land rises. Further, dam construction during the twentieth century caused water to be withheld from the oceans and stored in reservoirs on land, especially between 1960 and 1980. They also tell us that previous assessments of sea level were unable to reconcile observations with the calculated contributions of ice-mass loss, dam construction, and thermal expansion of water. As mentioned in Part 1 of this series, observed sea level change is very small, so this is not surprising. Yearly changes are below the measurement accuracy of the instruments.
The observations of sea level, ocean temperature, ice-mass loss, water held in man-made reservoirs, and total river discharge to the oceans all have considerable uncertainty, which is why studies have not been able to close the gap between observations. Frederikse and colleagues make another attempt to close the gap. They note that over the past few years much more accurate estimates of all the critical observations have been made available and they collected these in a new estimate.
Their best estimate of the observed sea-level rise trend from 1900 to 2018 is 1.56 ±0.33 mm/year, an error of ±20%. In Part 1 using the NOAA sea level record we derived a slope of 1.74 mm/year, with an R2 of 0.97, this value falls within the 90% confidence limits given by Frederikse and colleagues. The observed sea level change estimate is shown in dark blue in Figure 1. The sum of sea level change components is shown in black. The two major components of sea level change are also shown for comparison. Barystatic (ocean volume, excluding thermal expansion) changes are shown in red and thermosteric (ocean volume changes due to thermal expansion) are shown in orange. All curves are centered on their 2002 to 2018 means. Due mostly to the centering period, the match in the component sum and the sea level observations looks good in the 21st century. Prior to 1990 it is not very good, but both the sum and the observations match within their respective margins of error. The observed sea level uncertainty, prior to 1990, generally exceeds ±10 mm; prior to 1960, it exceeds ±15 mm. Prior to 1940, it exceeds ±20 mm.
The sub-components of barystatic changes examined in the paper are: glacier melting, melting of the Greenland and Antarctic Ice Sheets, and terrestrial water storage (including new dam construction and groundwater depletion). Thermosteric changes are estimated using ocean subsurface temperature measurements. Frederikse, et al. try to reconcile the component total with observed sea level changes as measured by satellite and tide gauges using a model and find modest agreement, within the respective margins of error.
The results of his study increase the previous estimates of GMSL (global mean sea level) rise in the 1960s and 1970s, after excluding the effect of dam construction. His model also increases the uncertainty prior to 1940. The match is quite poor in the 1920s and 1930s, and the steep rise in sea level from 1930 to 1950, nearly as rapid as in the 21st century, is also not matched well.
While the GMSL rate uncertainty narrows for the period 1993 to 2018, it still exceeds ±0.4 mm/year as shown in Figure 2. Both figures are a portion of Frederikse et al.’s Figure 1. Figure 2 shows the 30-year rate of change from his models of barystatic and thermosteric change in red and orange respectively, along with their sum in black. These are compared to the observed 30-year rate of change rate, in blue. Clearly the rate of sea level rise oscillates on a multidecadal scale and probably rose as fast as today in the 1940s, within the margin of error.
In Figure 2, the shaded regions are the 90% confidence intervals. The graph plots the rate of sea level rise in mm/year. The periods where the match between the observations, in blue, and the model, in black do not match are clearer in Figure 2. The match is particularly poor from 1915 to 1950. The rapid slowing of the rate of rise between 1950 and 1965 is not matched well at all. The rapid rise from 1990 to 2005 is only marginally better than the other periods.
Frederikse et al.’s model has a total rate uncertainty of at least one-half mm/year (see black shading in Figure 2) and the uncertainty in the data (blue shading) is even larger. Figure 2 is uncertain, but the roughly 60-year oscillation is significant and matches normal long-term ocean oscillations as described by Wyatt and Curry.[1] Wyatt and Curry’s stadium wave can be seen in Figures 8 and 9 here. Their roughly 60-year cycle can be divided into a 30-year warming cycle and a 30-year cooling cycle. 1918 to 1942 was a warming period and 1942 to 1976 was a cooling period in their analysis, this fits the data shown in Figure 2 fairly well.
Combining the analysis in Wyatt and Curry with Frederikse et al.’s analysis we can see that variations in sea level rise rates in the 20th century are probably, in part, a result of natural ocean oscillations. The Earth went into a natural warming regime in 1976, that probably ended early in the 21st century, perhaps around 2005, and then entered a cooling regime. Judging from Figure 2, it seems possible that the apparent acceleration in sea level rise from the late-1980s to about 2005 was merely a repeat of the acceleration from about 1925 to the early 1940s. Even if this is not true, it is clear that the data shown in Figures 1 and 2 are not accurate enough to conclude that the overall rate of sea level rise is accelerating, in fact it is possible that we will see a deceleration of sea level rise in the near future.
The statistical methods used in AR6 to show sea level rise acceleration were quite crude, as discussed in Part 1. They simply cherry-picked data and used least squares fits of them to estimate acceleration. In this part we show that the error in estimating sea level rise and its components is so large that showing acceleration definitively is probably not possible. In the next post we will discuss the problems with that approach and provide a more statistically sound projection of the rate of sea level rise.
The bibliography can be downloaded here.
-
(Wyatt & Curry, Role for Eurasian Arctic shelf sea ice in a secularly varying hemispheric climate signal during the 20th century, 2014) and (Wyatt, The “Stadium Wave”, 2014) ↑
Here ya go again with your ARs. .30 is out there beckoning! Answer the call,,,,,,what? Not that kinda AR. OK. Never mind.
Ya know, the AK47 is 7.62×39. 7.62 happens to be 30 cal ( an ideal bullet weight for ‘modern’ gunpowders since oh, about 1890 and the 30-30 carbine).
The AR15 is 5.56, or caliber 0.223. A bit small but fast. So the new Army combat. Rifle is species for 6.7mm, a tweener balancing more bullet mass with better gunpowders for more effective range.
Rud, real facts that put Leftist speculations to shame – additionally 30-06 does fire ’em up.
If one cannot discern the similarity of early 20th Century temperature and SLR trends from late 20th Century trends, one has nothing to say about climate change.
My deer rifle since maybe I was 30 has been a 30-06 bolt action R700 with wood stock. Have gone thru 3 cheap scopes and over 40 deer on my Wisconsin dairy farm. Use only handloads with 162 gr bullets and 40 GR of IMR 3240 powder on a large rifle Federal primer. After fine tuning that and other rifles in my own dairy farm range for decades. Best we ever was 11 deer for 9 guys in three days. Proof way too many deer on my farm.
My long-range weapon is a Winchester synthetic stock 30-06 bolt action rifle. It has only been fired on the range since my moving to Las Vegas in 2000. Before that I only used it to bring down various deer, moose and elk in outings arranged by my acquaintances; very seldom. As a youngster in Oregon I brought down a black bear with an old Savage.
In Vietnam I brought down what would be considered dangerous prey with an M16 and a stolen Ruger 357 Magnum. Since then I’ve only been involved in hunting at the initiation of others; no real excitement in my decisionmaking. While I like moose jerky, I’m not going out of my way to kill any more moose; too much work.
Since being drafted out of the mountains of Oregon and going to college on the GI Bill, I’ve lost the rural orientation over time. I still understand it, but I don’t live it anymore. Without that understanding young people currently growing up have no grounding in the values exhibited in the creation of our nation. We are developing into a hive mind of an omnipresent government. Our Founders would weep.
My preferred long range arm is my Remington 700 chambered for 300 Winchester Magnum. In light of the Ukraine invasion, I have to wonder what the Ukrainians would like to have. With a rudimentary scope, the 300 Win-Mag can take out a troop who can’t even see you, let alone hit you with a 7.62×39. I would send these over. But for close-in, nothing beats the AK-47.
I shoot an Enfield 303 Mark IV. 150 gr spitzer at varmits. Stock 180 gr Rem at bigger stuff.
Uh, yea. I own and shoot several Garands, chambered in .30US and 7.62NATO(built a couple of piece works in 7.62NATO) and I love me some smelly. War Bitch is a Singer manufacture under Lend/Lease for Canda, stamped right on the side of the breech block, “US PROPERTY” . Deer. Killing. Machine.
All that said, for a working gun I prefer AK. Well, that is not entirely true. I prefer my Romanian RPK on single point sling. Technically an AK.
My AR shoots 7.62X51 or better .308 Winchester, but it is an AR10, the AR-15s big brother. Shoots a nice group at 800meters too.
They simply cherry-picked data and used least squares fits of them to estimate acceleration. In this part we show that the error in estimating sea level rise and its components is so large that showing acceleration definitively is probably not possible. In the next post we will discuss the problems with that approach and provide a more statistically sound projection of the rate of sea level rise.
” The statistical methods used in AR6 to show sea level rise acceleration were quite crude, as discussed in Part 1.
They simply cherry-picked data and used least squares fits of them to estimate acceleration.
In this part we show that the error in estimating sea level rise and its components is so large that showing acceleration definitively is probably not possible. ”
**
I’m still waiting for comments by Mr May about the most impressing seal level study of the last 20 years:
Persistent acceleration in global sea-level rise since the 1960s
Sönke Dangendorf, Carling Hay, Francisco M. Calafat, Marta Marcos, Christopher G. Piecuch, Kevin Berk and Jürgen Jensen (2019)
https://drive.google.com/file/d/1-ilhh3ov20tfb03P5ZKDHTzZuJ9rD4P8/view
Was that also cherry-picking?
Here is the data they compiled, processed and published:
And here are, for the Dangendorf data represented above, the five year distant consecutive trends, from 1903-2015 till 1993-2015:
If there was no acceleration anywhere: would the consecutive trends not be all the same, resulting in a straight line?
Moreover, Mr May: if there was no acceleration, why then do the quadratic fits of three different sea level processing series look so similar?
Any idea?
*
I agree: Dangendorf’s article title couldn’t have been more alarmistic.
But that is no reason to ignore their results.
Bindidon,
I’ve not read their paper, but I will. Thanks for the link. As for your questions about acceleration, my next post, on Wednesday should put all that to rest.
” … should put all that to rest. ”
Well, Herr May: I apologize, but… that sounds a bit overconfident, based on what I’ve seen from you so far.
I therefore hope that then your third contribution to the sea level data processing will be a lot more convincing.
Comrade Bindidon, your intended slur against Mr May, addressing him as “Herr” did not go unnoticed … I am, by the way, of German heritage.
I didn’t even notice that.
No idea how this ‘Herr’ came into play; I wanted to write ‘Mr’.
My native tongue is… French, but indeed I live in Germany since a few decades.
Moreover, I read:
” The observed sea level uncertainty, prior to 1990, generally exceeds ±10 mm; prior to 1960, it exceeds ±15 mm. Prior to 1940, it exceeds ±20 mm. ”
Some valuable source for your allegation?
I’m interested.
The source is Frederiske, cited in the post and in the bibliography. The error is apparent in Figure 1.
What?
For such a crazy contradiction, only three downvotes? I’m disappointed.
Try harder, anonymoosies!
I love the use of quadratics without limits in a constrained system … so physically realistic 🙂
Hint an S curve is the most likely outcome even if you had acceleration.
Addendum: what I don’t appreciate at all in comments like yours is this condescending, discrediting attitude of the typical better-knower who never and never would be willing (able?) to scientifically and technically contradict what is shown.
Do that work, LdB, and come back when you have something more substantial to say.
Sorry I can’t help that you didn’t graduate past primary school … I don’t publish dummies guide to books I assume a reasonable IQ.
Wow. Your fig 1 shows that satellite altimetry must be real good. /sarc. The instrument accuracy is +/- 33mm.
What about you doing the sat people’s work better, and showing us what they exactly did wrong?
lee
It seems that you confound the accuracy of measurements (indeed: 33 mm) with the average over all measurements.
Exactly as one should not confound the accuracy of O2 microwave emissions at a given time and in a given place in space with the global time series generated when processing all the data.
If all measurements have an uncertainty of ± 33 then the average also has an uncertainty of ± 33.
You know nothing of metrology.
https://tidesandcurrents.noaa.gov/sltrends/sltrends_station.shtml?plot=50yr&id=140-012
Just have a look!
Trend accuracy of +-1 mm/year, a little better than Figure 2. Interesting, but it is only one location.
OK. If that is the mean anomaly, then there must be at least one 120 year tide gauge whose graph depicts acceleration greater than your first two graphs.
If you want to change my mind, then find me that tide gauge.
OK Mr Lee…
But… the data I show the quad fits of are the results of runs over PSMSL gauges made by (1) the Dangendorf group and (2) Grant Foster.
Maybe you ask them?
I’m asking you because you were the one to post the graphs here. I was assuming that your concern was genuine.d
Grant Foster — ROFLMAO. One of the original climate-buffoons. Maybe you should check for Michael Mann’s opinion.
You might select some gauges out of these two lists: trends over (1) lifetime vs. (2) over 1993-2018
(1) https://drive.google.com/file/d/1jIAhx1OifHrLF4Pf5YUqCwRenw26Ev3u/view
(2) https://drive.google.com/file/d/19dXIBq8Q7_ZtQm_V7tfcAPCmvEvHiY1P/view
When I have some idle time, I’ll compute standard error and quad fit out of the linear fit’s covariance matrix for each station separately.
Or you could just admit there isn’t one. In which case you are claiming that the acceleration is everywhere but nowhere.
Again, find me a tide a gauge that should worry me. I’ve looked at all the long term tide gauges and can’t find one myself.
Then you ‘ll have to wait until I manage to compute quadratic fits over the PSMSL tide gauge data.
Anyway, you might have misunderstood what I wrote.
The acceleration identified in a full lifetime series of gauges older than 100 or 120 years is so tiny that it is not worth any discussion.
What I’m talking about is the similarity of the quadratic fits for gauge averages and sat altimetry when you consider the altimetry period, i.e. 1993-now.
Lasse
It’s so terribly simple to cherry-pick one only station of about 1,500.
LdB
How strange that all quadratic fits are so similar, isn’t it?
And that though they were computed out of thoroughly, completely different data.
Lets give you a hint do you know how to solve the roots of the above quadratics .. do it.
Now do the derivative of the equations .. do you know what that is?
Something should be obvious if you look at the two results lets see if you are smarter than a 4th grader.
If you still don’t get it enter the function co-ords into the app
https://www.desmos.com/calculator/pfdwlq5qht
Now zoom out using the minus sign
Given the claim that CO2/Temperature is causing that walk back several hundred years does the history match that quadratic 🙂
Remember for any match you are supposed to be able to hindcast as far as you can predict the future .. that establishes confidence.
So how confident are you with the quadratics .. hence why I laugh at that crap 🙂
Feel free to laugh ‘at that crap’.
There are so many arrogant people like you, after all.
One more or less doesn’t make any difference.
That is called basic science and “look elsewhere effect” the quadratic is nothing more than a statistical randomness because you can’t hindcast it.
There is acceleration. Figure 2 shows it, as the curves are above zero all time, indicating positive but variable acceleration. Every study finds it, Church & White 2011, Jevrejeva et al. 2014, Hogarth 2014. It varies with the multidecadal oscillation (aka stadium-wave). THE PROBLEM IS:
a) The acceleration is very small, c. 0.01 mm yr-2, so it does not support any scary scenario.
b) The acceleration does not respond to anthropogenic forcing, as it should according to the CO2-hypothesis. It does not support the hypothesis.
Hi Javier,
It is true that every study and every dataset shows apparent acceleration over, at least the past 50 years or so. Acceleration was negative from 1930 to the late 1950s. Neither the acceleration nor the deceleration seen in the 20th century were significant given the accuracy of the data. But, if they are true, it is much more likely to be driven by natural ocean oscillations than GHGs. Here is a plot of 20th century apparent acceleration and rate from Dangendorf, 2019, Nature Climate Change. You can see from the -0.1 to 0.1 scale that it is way below the accuracy of the measurements, particularly prior to 1990.
Thanks Javier for your reply, though all of it is known to me.
First, though most people here view me as an alarmist, I have nothing in mind with such trivial behavior. But the inverse of it – persistent denial of evidence – is in my opinion far worse.
I have no primary interest in showing SLR acceleration in any political context à la agenda, CO2-hypothesis, GHG, anthro forcing etc etc. That is simply too boring for me.
*
I saw traces of SLR in the data I processed, as well as in data processed by others
looked at how the consecutive trends varied (even in the trend sequence for 61 gauges with a lifetime greater than 100 years):
and that’s all.
It is evident that if there was no increase visible in the trend lists plotted above, all plots would look desperately flat.
*
Second: that SLRA is a very tiny factor, anyone evaluating the quadratic functions in the graph above inevitably sees it.
Even under the absolutely unrealistic ‘all things remaining equal’, the quadratic fit suggests for 2100 a rise equal to the double of what we obtain when using a linear fit, i.e. about 55 cm on average worldwide.
No one knows what it means for specific places, unless someone sees how linear trends already vary during the recent period
https://drive.google.com/file/d/19dXIBq8Q7_ZtQm_V7tfcAPCmvEvHiY1P/view
and thinks it might be worthwhile to compute the quadratic fits for all gauges selectively, in order to check what happens everywhere.
*
But one thing you won’t change with a discourse based on ‘no relevant SLR acceleration’.
Namely that groups working in great (re)insurance companies, who are responsible for calculating the insurance premiums for seafront real estate and infrastructure over the next 30 years, won’t believe you, let alone would they trust what Andy May & Co write, especially things like ‘sea level was higher 6,000 years ago’ !
Gracias
I can’t stand graphs like these. The vertical axis says “sea level”. What does that mean? There is no “sea level measurer” so what is it? How can I even discuss the graph if I don’t even know what I’m looking at?
Check the reference Jevrejeva et al. 2008.
Jevrejeva, S., Moore, J.C., Grinsted, A. and Woodworth, P.L., 2008. Recent global sea level acceleration started over 200 years ago?. Geophysical Research Letters, 35 (8).
Bindidon,
Dangendorf, et al. destroy their own thesis in the first few paragraphs. They claim that “Satellite altimetry shows that GMSL has been rising at an average rate of 3.1 +-0.3 mm/yr since 1993.” A little later they repeat that the accuracy is “~0.3 mm/yr”
NASA disagrees, they claim an accuracy of 3.3 cm or 33 mm for the OSTM.
1671_Spacecraft-OSTM_Fact_Sheet_Final.pdf (nasa.gov)
The latest Sentinel 6 satellite has an accuracy of 3.5 cm. Some day with processing, it might reach a 1 cm accuracy, in no way can it achieve 0.3 mm accuracy.
Sentinel-6 and Sea Level Rise – Watts Up With That?
NOAA claims to improve this accuracy for longer term measurements by assuming that the true sea level value is the average of many measurements (probably not true) and that the errors are random and normally distributed (probably not true) and claim an accuracy of 3-4mm for a 10-day average.
This is dubious, but even if true, it is 10 times the accuracy claimed by Dangendorf, et al. Further he only claims this accuracy since 1993, way too short a time to prove acceleration, as shown in Figures 1 and 2. His satellite rates are much higher than tide gauge rates used in this post that are considered to be more accurate.
Note Dangendorf, et al.’s Figure 2. He shows negative acceleration from 1930 to 1955. Constant acceleration since 1970 and the scale is -0.1 to 0.1 mm/yr^2, he is just plotting noise at that scale. The paper never should have passed peer review. It is far less comprehensive than Frederikse, cited in my post.
Even if Dangendorf’s satellite accuracy estimates are correct (they aren’t) 1993 to 2020 is not long enough a period. Sea level trends vary with ocean oscillations (see Figure 2) and the oscillations are ~60+ years, he’s only looking at half an oscillation.
I think Prof Humlum’s estimate for SLR is about 1.5 mm a year or about 150 mm/ century or 6 inches by 2122.
And the adjusted satellite data is about 3.2 mm/ year or about 320 mm /century or about 12.8 inches by 2122.
But we also know that coral islands are mostly stable or growing today, see the Prof Kench studies over the last 35 years.
Neville, please check out SLR expert Nils Axel Moerner’s last estimate before he passed. 2.2mm/yr. I provided a (then) hot link in guest post here ‘SLR. Acceleration, and closure’.
Neville
Typically mistaken comparison:
What must be compared however is 1993-now for both gauge and satellite data.
Satellite altimetry is broken into 4 satellite ages each different and blended together in a mash-up. Interestingly Jason 4 was showing markedly lower rates until it was “adjusted” mainly around it’s wave cancellation. Like all things in Climate Change it’s hard to know what to trust but in some ways no-one cares because nothing is going to happen to emissions.
What about writing a paper about what you think being done wrong?
Posting a short comment at WUWT: that’s so pretty easy.
Write a paper about a fact that is easily checked .. you really are handicapped.
Bindidon,
1993 to now is only 29 years, this is half an ocean oscillation, too short a time frame to show anything.
Now you are cooking and look at the quadratics they produce as answers 🙂
Everybody knows that, Andy May – even the Bindidon dumbie.
The reason to choose 1993-now has only to do with the necessity for period alignment when comparing altimetry and gauge data.
Looking forward to part 3.
Andy, this post’s figure 2 triggered a ‘memory’ from long ago math modeling generally. So I did a quick refresh on terms and definitions and want to make a generalized ‘mathy’ observation followed by two specific generalized (no math) observational applications: AGW, SLR acceleration.
In information theory, one general problem is to extract a valid signal from the background noise. This is denoted the signal to noise ratio (SNR) problem. In ‘audio’ continuous stuff, it is defined as valid SNR > 0db (0db is 1:1 SNR). The greater than 0, the stronger and more reliable the signal. In digital pixelated ‘optical’ stuff, it is ~ SNR=mu/sigma, where mu is the mean expected signal value and sigma is the standard deviation of the noise. (This is the math basis for lossy digital video compression for the ‘fast’ internet. Lossless is possible, but ‘slow’.)
Applied to AGW, the SNR problem is just a restatement of the attribution problem I have posted on here several times previously (e.g. ‘Why models run hot’). We know in the warming from ~1925-1950 the natural variation ‘noise’ had to overwhelm the then small AGW signal. We don’t know that for the virtually ‘equivalent’ rise 1975-2000. Alarmists assume we do, when we don’t. Thus their model tuning forces models to run hot by ignoring natural variation ‘noise’.
Applied to SLR acceleration, your post figure 2 shows a natural ‘noise’ cycle over about 60 years on the order of +/- 1mm. Yet in the SLR data IPCC says we should be seeing now just a small fraction of 1mm/yr AGW caused SLR acceleration. Not possible from first SNR principles, as your post #1 nicely showed otherwise.
SNR science IS settled. Climate science most definitely is NOT.
Thanks Rud,
Very astute and important points.
Rud let’s say you were standing at the tide gauge in New York or Sydney in 1922 and forward to 2122.
Would you really be able to see or even notice the difference in SLR? Just asking and if you could see or measure a difference tell us how you could stop it? Or even begin to stop it now in 2022 or back in 1922?
Well, I previously wrote about this in essay PseudoPrecision in ebook Blowing Smoke. The simple answer was and remains, NOPE.
None of these “complexities” stopped you from doing evaluations at multiple time periods in part 1, for the quarterly data you referenced. Per my request then, would you please provide a link to that quarterly data, complete with any quantitative confidence intervals for it?
bigoilbob,
Part three contains a link to a zip file that you can download. It contains a spreadsheet with the quarter-by-quarter NOAA data and a lot of analysis of it. It also contains an R program with a thorough statistical analysis of the data. Be patient. I plan to upload part three on my site and here on Wednesday morning. Too early to make it available right now. I want you to read my analysis first.
Thanks so much. I’ll be patient and read through your posts first, with no actual data to compare it to.
You’re still getting a lot more than Mann gave to McIntyre.
BigOilyBoob,
You are constantly making proclamations without a shred of data, logic, or anything else to back them up. Constantly. How is it that you feel you can demand things that you, yourself, don’t provide?
Let me point out that if Andy and literally hundreds of others can find the basic data, you can too. I’ve found it, it was easy. The fact that you don’t lift a finger speaks volumes – you just sit on the sidelines and toss grenades. Too bad for you that they’re all duds. I, for one, have come to expect that your comments will be unintelliblabber.
“I’ve found it, it was easy. The fact that you don’t lift a finger speaks volumes “
I’m referring to Andy’s quarterly data. And I’m too embarrassed to tell you how much time I spent looking for it. If you found it, then please link me to it’s source.
But FYI, since Andy already said that the quarterly data was not yet available, your claim to have found it is, er, not that credible. He claims that he will release a zip file with his next post. I would rather have the data now, but if this is the way Andy wants to roll, ok.
bigoilbob,
I did not realize that you did not have NOAA’s data, I thought you were after the statistical analysis I did. The NOAA data can be downloaded here:
Climate Change: Global Sea Level | NOAA Climate.gov
The direct link to the data in txt format is here:
https://www.climate.gov/sites/default/files/Climate_dot_gov_dashboard_SeaLevel_Jan2021update.txt
Note after 1/15/70 there are two columns of numbers.
Tomorrow I will make the data and R code for analyzing it available.
Thanks Andy. I’m still guessing that you had the data in your biblio, but I that could not find the table you extracted for me. I have sought this data before, but obviously in the wrong places.
As expected, no confidence intervals, so your evaluations were of expected values. My look will be as well. I hope this doesn’t provide an opening to goal post movers who diss inconvenient analyses because “The confidence intervals are so large we can’t even quantify them.”
Thx again
It is raining here this morning, so I doubled checked post three earlier than expected, since I can’t play golf. It will appear at 10AM Pacific here.
You sent me the quarterly data you graphed and tabulated some results for, in part 1. And I thank you again for it. I never meant for you to prioritize the rest of your treatment. AFAIC, take as long as you like.
“unintelliblabber”
Trans:
Whatever I either don’t have the skills to understand or/and am too lazy to ponder.
BigOilyBoob,
I used to think that you had trouble writing and could work on that just by focusing on one or two messages and paying more attention. I don’t believe that anymore. I now believe that you are unable to think. You can’t fix stupid.
BOB, ask here and ye shall receive.
I bet that was NOT the Andy May reply you were expecting. Because now I will forever pester you for your analysis of hisdata that you here provably requested.
Like my suggestion to Griff’s recent comment on 2021’s severe weather rainfall events ‘proving’ climate change—NOT—if you want to play here best up your game. Alinsky’s Rules for Radicals said it best—ridicule. (See a previous ‘climate wars’ post here in last year for specific details.) You are likely destined for some.
Aksed and will be answered. No pestering needed. FYI, Andy replied that the quarterly data was not available. Yet. Big rollout next post.
Also FYI, since Andy is ratholing data – even “temporarily” – then your Rules for Radicals strawman about ridicule is inapplcable here. After all it is ridiculous to OTOH evaluate data, and OTOH to tell us that the data confidence intervals are SO BIG (but I can’t tell you how big) that the data is not evaluable.
Sounds just like the Hockey Team.
“Because now I will forever pester you for your analysis of hisdata that you here provably requested.”
If you read for comprehension, you will find that I did not predict any flaws in his “analysis”. I just wanted to see for myself. I’m surprised that you have not made a similar request.
I’m easier than Bindidon. He found even better curated data, and his findings have yet to be technically challenged. I merely want to look at Andy’s data. Unlike so much of the demo here, I am not yet writing during assisted living free time, and have **** to do. So, I will check it out as is.
I’d imagine that Andy May and many others here also have s..t to do. Me? I don’t give a s..t.
It is perfectly valid to ask for data. No problem with that. The details of my analysis are completed, but I do want to time to check it over today before I release it. Especially my comments in the R program I wrote.
“Prior to 1990 it is not very good, but both the sum and the observations match within their respective margins of error. The observed sea level uncertainty, prior to 1990, generally exceeds ±10 mm; prior to 1960, it exceeds ±15 mm. Prior to 1940, it exceeds ±20 mm.”
Similar to “global temperature”, and about as useful.
All variables must be quantified, or you have to stop making inferences.
It is plausible that there is change in the shape and volume of the geological basins that hold the oceans. It has never been quantified, with its error envelopes.
It is wrong, wrong, wrong in the hard science of metrology, to draw conclusions about sea level change while this variable of basin volume remains unquantified .
Research can continue, but the results of that research should never, never be used for making decisions like the politics of seaside land use.
Tough, but you do either proper metrology, or dummy metrology. Principles are principles for a reason. Geoff S
” Tough, but you do either proper metrology, or dummy metrology. ”
Thus, if I understand you well, the job done by people who analyzed tide gauge data, satellite altimetry, winds and their resulting ocean currents: that’s all based on “dummy metrology” ?
Don’t you think, Mr Sherrington, that before you write such a claim, you should first collect lots and lots of data sustaining that claim?
What exactly are the weather forecasting success rates for say 24 hours ahead ? … you’d be lucky to get 20% correct across the board. So much for “these people”.
bindidon
The classical procedure is for the authors of scientific works to identify and measure the size and accuracy of all of the significant variables in a multi variate study as this is for sea level change.
Authors who publish, while knowing there are further variables but not quantifying them, are not doing proper, hard metrology.
If authors did a proper analysis of errors using well- documented methods, they would be proper to note that present satellite methods to measure sea level change are not accurate enough to be useful. We can still thank them for their efforts in production by data to show this.
But, the results of this work, at its present performance, should NEVER be used as if it was credible enough to be used in future bureaucratic regulation.
That, in a nutshell, is a criticism that applies to the bulk of climate research.
Authors have perverted standards with the lame excuse that we have no duplicate Earth to act as a control.
Stiff. Then do not publish papers that are incomplete and use guesswork to infill gaps.
It really is that simple. Geoff S
Bindidon,
Geoff’s point is well taken. Plate tectonic movements are very slow, but they do change the basin volumes and the changes in GMSL are exceedingly small. Basin volume changes cannot be measured, there are way too many places where continuous changes are taking place, and they are independent of one another. The sea floor changes, the margins change, etc.
Dude, complete metrology requires identifying the functional variables in a measurement before identifying a “cause”. Temperature is not the only variable in sea level rise. There are other variables, know it or not.
Geoff Sherrington
” It is plausible that there is change in the shape and volume of the geological basins that hold the oceans. It has never been quantified, with its error envelopes. ”
How do you know that? Did you compile numerous publications in that corner?
Apart from submarine volcanic output which manifestly can lead to abrupt, not quantifiable basin changes at any time: which are the ocean basin modification factors playing a role in the very short history of tide gauges?
Until now, all papers I scanned for this geological ocean basin topic dealt about sea level change in million year ranges. A link to the present situation I could not find.
Maybe you have some better sources?
There is no apparent overall acceleration in the tide gauge data in this analysis.
Reminds me this:
Chris Hanley’s graph is excellent! This picture captures the uncertainty in the apparent acceleration perfectly. A picture can be worth 10,000 words.
Chris Hanley & Andy May
” There is no apparent overall acceleration in the tide gauge data in this analysis.”
First remarks
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Now let us have a little look at the data.
It seems that the person who created the graph at Humlum’s C4U either has no test output, or is not aware of the PSMSL inventory. Only 3 gauges were active between 1900 and 1910, I started thus 1 decade later:
The data corresponding to C4U’s graph is shown by the red plot. It is raw data including the land movement around the gauges.
It has, starting with 1910, a linear trend of 2.07 ± 0.04 mm/yr.
The trend since 1990 (near 1993, the start of the satellite altimetry era): 3.52 ± 0.39 mm/yr.
For those who, like me, prefer to use SONEL’s data for corrections needed by vertical land movement – despite the evident fact that this GPS data is valid for recent years only – the blue plot is the good one.
Due to the land subsidence at the majority of the stations (8 of 9), the trend for the blue data is lower than that for the red data: 1.14 ± 0.04 mm/yr.
The trend since 1990: 2.56 ± 0.39 mm/yr.
{ I apologize for the superfluous digits after the decimal points: see the C4U chart. }
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It’s nice to plot an annual sea level change for the gauges, sounds pretty good flat of course, but I prefer to use consecutive linear trends instead, each starting a decade later:
1910-2020: 1.14
1920-2020: 1.17
1930-2020: 1.16
1940-2020: 1.16
1950-2020: 1.28
1960-2020: 1.46
1970-2020: 1.85
1980-2020: 2.12
1990-2020: 2.58
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Again: it is not my aim to endlessly discuss about sea level acceleration.
But I don’t like when people simply feel the need to ignore what is clearly visible.
If there was no acceleration, the trend would be the same for all periods.
For me, the major point remains that when you process all available PSMSL gauge data in anomaly form, the result is that the gauge time series looks pretty good like that constructed out of the altimetry data.
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The 360° critique expressed by some commenters about gauge and satellite altimetry data is very nice, but… to that I reply: stop criticizing, start working 🙂
Very good.
Andy states that:
“The Earth went into a natural warming regime in 1976, that probably ended early in the 21st century, perhaps around 2005, and then entered a cooling regime. “
I would be interested to know what is meant by a “cooling regime” given that the last decade was the warmest on record and out of the 10 warmest years on record 8 occurred in the last 10 years. Have a look at
http://berkeleyearth.org/global-temperature-report-for-2021/
Like spring to summer to autumn to winter except on a massive scale. Any questions ?
The most recent (February 2022) MSU temperature is at exactly baseline (yes, I know that the base period has changed, and the 1980s were a little cooler), so in a generally warming climate since the end of the LIA, we may be seeing stair-step increases in temperature, when a “cooling regime” manifests as a “pause” or flat trend, followed by a warming period that bumps the average up slightly (+0.10-+0.15 C give or take). Either way, this is at the very low end of what climate models have been predicting, if that, and you would have to ignore the fact that solar activity over the past century has been much higher than in the preceding centuries. Even if all of the observed warming since 1979 has been anthropogenic…it’s a mountain out of a molehill.
Andy refers to the multidecadal oscillation climate regime shifts. They are known in fisheries as warm sardine regime and cool anchovie regime. See for example:
Tsonis, A.A., Swanson, K. and Kravtsov, S., 2007. A new dynamical mechanism for major climate shifts. Geophysical Research Letters, 34 (13).
Chavez, F.P., Ryan, J., Lluch-Cota, S.E. and Ñiquen C, M., 2003.
From anchovies to sardines and back: multidecadal change in the Pacific Ocean. Science, 299 (5604), pp.217-221.
They have huge repercussions on global climate. They show their warming and cooling nature with respect to the trend, and thus very clearly seen over detrended data.
Javier,
Interesting papers, thanks. This stood out:
Izaak,
I’m not a fan of Berkeleyearth, but you can see the slowdown in the rate of warming, even in their corrupted data. There was a major shift in the warming rate in the late 1990s to ~2005 that continues. This was probably from a shift in solar activity as we come out of the modern maximum. Due to thermal inertia it may not show up clearly in the weather for another 10 to 20 years, hard to say how long or how much. When looking at temperature records mentally take out the La Ninas and El Ninos. This is not for sure, and I cannot prove any of it, but I consider it likely.
See this short post by Javier:
The planet is no longer warming – Andy May Petrophysicist
Thanks Andy, another very useful article.
Andy, I note the graphs on this post only go back to 1900.
Jevrejeva 2014 goes back much earlier and shows a third cycle of faster sea-level rise post 1850.
Glacial retreat data confirms this very convincingly. Peak glacial retreat rates for long glacier records are similar for all three potential warming periods, with late c20th possibly being the slowest.
Recall that glacier length data lag temps by up to 20 years. This possibly indicates a warming onset as early as 1830. If correct, this directly contradicts HadCRUT, AR5 and AR6 models which show no warming until around 1900-1910.
Ultimately the question should boil down to simple economics. If you really believe using fossil fuels is going to cause the sea level to rise enough to flood all coastal cities, what action is going to be more disruptive to society:
They should just build/improve some seawalls if they are so worried.
The rate of rise is so slow, adaptation is the only sensible thing to do.
Two points; the ocean basins are more like rubber sheets than bathtubs, so as more water fills them, the more the basin distends. Point two; water disappears into the mantle at sub-oceanic subduction zones, subtracting some water from the global ocean…
Third point of two; when ice melts, its volume decreases as it warms towards maximum density at 4 degrees Celsius, causing it to sink in the ocean, sustaining the smaller volume for many years…
Land over much of the Northern Hemisphere is still rebounding from the melting of the massive glaciers they supported during the Last Glacial Maximum. This causes many northern tide gauges to record sea level falling as the land rises.
I’ve got a bit of a nit pick. Surely the level as recorded on a tide gauge is the sea level at that location? By definition.
You have to decide, are we interested in sea level or are we interested in the volume of water in the oceans? Two different things, and they seem to be confused.
Just a little hint:
https://www.sonel.org/-Vertical-land-movements-
Typical example: Furuögrund, Sweden
PSMSL id, lat, long, name
203; 64.915833; 21.230556; FURUOGRUND
Without VLM correction: -7.8 mm/yr
With VLM correction: +2.3 mm/yr
There does not seem to be mention of the acceleration occurring with the satellite era and also the acceleration within that era coinciding with the change in satellite.
The tidal gauges on average show no acceleration, it’s a straight line increase. There is a discrepancy between tidal gauge and satellite rates of increase. Reportedly, the satellite data has 0.3mm p.a. added on, to compensate for isostasy. In other words the satellite measurements are aimed at measuring the volume of water in the oceans and not sea level per se.
Is this the position or am I out of date with this?
My basic understanding (which is almost certainly too cynical to be considered “true / correct / viable / …”) is that the “logical argument” used to “prove” acceleration is as follows.
1) With a linear regression (polynomial of order one, i.e. “a straight line”) you find the formula “y = Bx + C” that comes closest to your pseudo-random X-Y scatter-plot … sorry, I mean your dataset.
By definition the “acceleration” of your approximation is zero.
2) Find a quadratic regression (polynomial of order two, which “just happens” to be a curved line …) instead, with formula “y = Ax² + Bx + C”.
By definition the “acceleration” of your approximation is 2A.
3) QED.
It is now a “scientific fact” (/ it has been “scientifically proven”) that an acceleration of 2A exists in [ insert dataset here, e.g. “SLR” ].
That is my understanding as well.
The extra 0.3 mm per year merely increases the “acceleration” waiting to be “discovered” using the basic argument above.
Well…yes…that is cynical 🙂
But in reality the acceleration only exists in the satellite data. Not (as far as I can see) in the tide gauge data.
This is my concern, because I can’t see that Andy May has addressed this point. He just believes the acceleration in the satellite era because the satellite data is “more accurate”.
I will add that in modern scientific work, the word accuracy is frowned upon. You should use precision, trueness, bias and their derivatives, as appropriate.
Below you see two charts. In the second one, you see 5-year distant, consecutive trends of sea level data evaluations.
For the Frederikse time series for example, the trend plot is based on the following trend values:
1900-2015: 1.52 (mm/year)
1905-2015: 1.54
1910-2015: 1.53
1915-2015: 1.56
1920-2015: 1.57
1925-2015: 1.60
1930-2015: 1.57
1935-2015: 1.54
1940-2015: 1.50
1945-2015: 1.48
1950-2015: 1.49
1955-2015: 1.61
1960-2015: 1.72
1965-2015: 1.91
1970-2015: 2.06
1975-2015: 2.19
1980-2015: 2.33
1985-2015: 2.70
1990-2015: 3.01
1995-2015: 2.96
What do you think, kzb?
You are assigning to “kzb” the “authority” to decide for the entire world what is (and is not) “frowned upon” by “modern scientists”, as well as what is (and is not) the “appropriate” terminology to use in scientific discourse.
“Mark BLR” does not recognise that particular “authority” for the (anonymous Internet) poster using the identifier “kzb”.
– – – – –
“Precision” = “How many decimal places / significant figures are being used ?”
“Accuracy” = “How close to the ‘true / correct / actual’ answers are the predictions (/ projections) resulting from a logical analysis of your conjecture / hypothesis / theory ?”
If you had bothered to check you would have found that in “modern scientific work” both terms are perfectly valid, their use depending on the context.
– – – – –
PS : “Trueness” [ ?!? ] = “A nonsense ‘derivative’ word made up by the AIP known as ‘kzb’ …”
“Truthiness” = “A nonsense word made up by the comedian Stephen Colbert …”
In a comment upthread, guest editor Andy May clearly discredited the paper written by Sönke Dangendorf & alii, and favored the work of Thomas Frederikse instead.
No problem for me.
Frederikse’s paper unfortunately is behind paywall; thus I can’t read and digest it.
But luckily, the data results were free to access:
https://zenodo.org/record/3862995/files/global_basin_timeseries.xlsx?download=1
so I had at least the opportunity to compare their results (column Global mean) with a yearly average of Dangendorf’s monthly data, and, in addition, of my little layman job.
Here is a comparison of the three time series:
We see that Dangendorf’s series shows a lower trend over the whole period.
And to be honest, I’m happy to see that I can’t be so terribly wrong with my rather unprofessional evaluation 🙂
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A comparison of 5-year distant, consecutive linear trends for the three series, from 1900-2015 till 1995-2015:
Draw your conclusions…