Global warming, climate change, all these things are just a dream come true for politicians. I deal with evidence and not with frightening computer models because the seeker after truth does not put his faith in any consensus. The road to the truth is long and hard, but this is the road we must follow. People who describe the unprecedented comfort and ease of modern life as a climate disaster, in my opinion have no idea what a real problem is.
President Maumoon Abdul Gayoom had raised sea- level concerns at the UN and Commonwealth meetings around 1987– 88, describing a potential “death of a nation,” but those were political warnings rather than dated quantitative forecasts of total submersion by a specific year.
AI generated by Grok
No, the United Nations did not predict in 1988 that all 1,196 islands of the Maldives would drown between 2008 and 2018.
The UN- UNEP only planned a study at the time.
The specific claim is a misattribution of statements by Maldivian government officials reported in contemporary news coverage.
On 26 September 1988, an AFP report (published in Australia’s The Canberra Times and elsewhere) stated:
“A gradual rise in average sea level is threatening to completely cover this Indian Ocean nation of 1196 small islands within the next 30 years, according to authorities.”
Maldives Environmental Affairs Director Hussein Shihab was quoted saying an estimated rise of 20- 30 cm in the next 20- 40 years “could be ‘catastrophic’ for most of the islands, which were no more than a metre above sea level.”
The same report noted the United Nations Environment Programme was planning a study and mentioned a separate (also unrealized) concern that drinking- water supplies might dry up by 1992.
This was a statement from Maldivian officials (following severe flooding in 1987 that damaged the capital Malé), not a formal UN scientific prediction or IPCC assessment.
President Maumoon Abdul Gayoom had raised sea- level concerns at the UN and Commonwealth meetings around 1987– 88, describing a potential “death of a nation,” but those were political warnings rather than dated quantitative forecasts of total submersion by a specific year.
Early media and some expert commentary in the late 1980s sometimes framed low- lying atolls as potentially disappearing on multi- decade timescales under high- end sea- level scenarios of the era, but the precise “all islands gone by ~2018” wording traces to the 1988 AFP report of local authorities.
The Maldives still exist.
The islands have not been submerged.
Tourism infrastructure has expanded (including airport developments), and studies have documented that many reef islands have remained stable in area or grown through natural accretion and sediment processes, even as relative sea level has risen.
Sea level has risen. Local tide-gauge and other data show relative sea-level rise on the order of roughly 3+ mm/year in recent decades in the region (e.g., ~3.4 mm/year averages cited for periods including 1987- 2018). Global mean sea level has accelerated compared with the early 20th century.
The 20–30 cm rise referenced as potentially catastrophic has not occurred on that short timescale; cumulative rise since the late 1980s is substantially less.
Vulnerability remains real.
Most islands are very low- lying (typically <1–2 m above mean sea level).
Flooding, erosion, saltwater intrusion into freshwater lenses, and storm- related impacts are ongoing concerns. Longer- term projections (e.g., toward 2100) under higher- emission scenarios continue to flag substantial risks of inundation or uninhabitability for large portions of the archipelago without adaptation (seawalls, land reclamation, elevated structures, etc.).
Contemporary scientific estimates circulating in the late 1980s projected global sea- level rise on the order of tens of centimeters to a couple of meters over a century under various scenarios.
Maldivian officials and leaders highlighted the extreme vulnerability of their low- lying islands and used strong language about potential national catastrophe.
UNEP facilitated studies and the UN provided a forum, but there was no official United Nations prediction that the entire archipelago would be underwater by 2018.
The Maldives government has pursued adaptation and international advocacy on the issue for decades.
The Maldives hosted the Small States Conference on Sea Level Rise (14–18 November 1989).
The resulting Male Declaration on Global Warming and Sea Level Rise (transmitted to the UN) expressed deep concern about the threat of sea- level rise to low-lying island states, called for international cooperation, monitoring, and action (including support from UNEP, WMO, and others), and recognized the emerging IPCC process. It did not contain a prediction that the Maldives would drown by 2018.
A 1988 news report of Maldivian officials warning of potential catastrophic effects from modest sea- level rise within a few decades has been widely circulated (especially in skeptic commentary around 2018) as a failed “UN and experts predicted total drowning by 2018” claim.
The islands are still above water, sea level has risen more slowly than the most alarming short-term framing implied, and natural island dynamics plus human adaptation have so far prevented the worst outcomes of that particular warning.
(WARNING – this paper contains images of 5-year projections in sea levels that some readers may find disturbing.)
The next 5 years may be a good opportunity to judge how the “global” sea level rises are changing, especially with respect to the perceived “accelerations”. The terms “global” and “accelerations” are in quotes to infer that that these terms may not be totally appropriate. The satellite coverage is only 95% and the “accelerations” may be caused by the methodology used and not a true physical attribute.
(WARNING – this paper contains images of 5-year projections in sea levels that some readers may find disturbing.)
The next 5 years may be a good opportunity to judge how the “global” sea level rises are changing, especially with respect to the perceived “accelerations”. The terms “global” and “accelerations” are in quotes to infer that that these terms may not be totally appropriate. The satellite coverage is only 95% and the “accelerations” may be caused by the methodology used and not a true physical attribute.
The two papers by Nerem et al have had a major influence about sea level rises. The paper, Nerem et al (2018), introduced the concept of an “acceleration” stemming from the fitting of a quadratic curve. This paper has had over 1500 citations and has greatly influenced the media and frightened all the kids from Boca Chica to Timbuctoo. The second paper, Nerem et al (2022), stated that the “acceleration coefficient becomes stable after 2017”, this being shown in figure 0 below of the paper which reproduces their figure 2.
Figure 0
Contact was made with one of the co-authors, (B. D H), saying that the graph was a very interesting presentation and that it would be informative to keep it up-dated. No reply was received, no update has been seen and no other form of this graph has appeared anywhere else except, I think, in my various papers on WUWT.
Before proceeding a small diversion. I asked the new kid on the block, AI, two questions. The AI used was Google AI.
Question 1
I have 25 years of sea level data to which I fit a quadratic curve. That curve is then extrapolated for another 80 years. Is this acceptable?
AI answered“In general, this approach is highly discouraged. Extrapolating a quadratic curve fitted to just 25 years of data over an 80-year period is statistically problematic, as sea level rise is highly complex and not strictly governed by a simple mathematical parabola.”
Question 2 (Almost the same as question 1 except replacing me with Nerem et al).
In their 2018 paper Nerem et al used 25 years of sea level data to which they fitted a quadratic curve. That curve was then extrapolated for another 80 years. Is this acceptable?
AI answered (although AI seems to have now donned rose tinted glasses) “The quadratic extrapolation used in the 2018 Nerem et al. paper is considered scientifically acceptable as a descriptive diagnostic to detect current acceleration, but it is not intended to be a definitive physical projection of future sea levels.
While fitting a curve to 25 years and extending it for 80 is often criticized in statistics, the authors used it primarily as a ““thought experiment”” to demonstrate that observed satellite data is already tracking with high-end climate models.”
My updated graph based on the NASA data for the end of May 2026, showing how “acceleration” changed with time, is reproduced below (figure 1).
Figure 1
It should be noted that the historic NASA data (as well as the historic University of Colorado data) have changed many times since 2018. The 5-year projections use the NASA data for December 2025 which was slightly different as shown in the “acceleration” plot below (figure 2). The topic of changes in the data over the last 8 years will be addressed in a separate paper. The historic data sets are not readily available, but having retained most of this data it can revisited or reanalysed at any stage. The differences between the December 2025 and May 2026 NASA data have pulled the “accelerations” between 2018 and 2025 generally back up towards the 0.08 mm/year2, which may be just coincidental, but from 2025 there is still a change to a rapidly decreasing “acceleration” values, which in May 2026 are now already below 0.06 mm/year2 as can be seen in Figure 1.
Figure 2
It can be seen that the “acceleration” remains basically constant over 2017 to 2022 then starts to drop and after the beginning of 2025 starts to drop more rapidly. The “red” line is a theoretical line based on the sea levels varying about the best straight line fit in a sinusoidal manner with a period of 29 years. This red curve continues over the next 15 years decaying to near zero values commensurate with those seen in long term Tidal Gauges.
To extend the data to cover the years 2026 to 2030 the following steps were taken.
Use the annual variation through 2025 to generate data for each subsequent year. Due to the small changes during 2025 this generates quite a small increase over the 5 years, and this is deemed to be too low to be meaningful.
Add an extra 2 mm rise per year each year this being deemed a lower bound (data set 1).
Add an extra 3 mm rise per year each year this being deemed an upper bound (data set 2).
To each of the above add a large El Niño set of data based on a rise of 10 mm over 2027 and a following decrease over 2028 (data sets 3 and 4).
The definition of data sets 1 and 2 as lower and upper bounds is only used to give a pair of data sets that are in the right ballpark. Plots of the data are shown below (figures 3 to 6).
Figure 3
Figure 4
Figure 5
Figure 6
The slopes of the best fit straight line for the December 2025 and the 4 simulated data sets were obtained and are listed below.
December 2025 3.09 mm/year
Data set 1 3.07 mm/year Lower Band
Data set 2 3.12 mm/year Upper Band
Data set 3 3.10 mm/year Lower Band plus El Niño
Data set 4 3.15 mm/year Upper Band plus El Niño
The slope for December 2025 falls between data sets 1 and 2 with the slopes involving an El Niño being both higher. The slopes at this period are only increasing at about 0.02mm/year so the simulated data sets are considered to be reasonable or even slightly high.
The “accelerations” for all 4 cases were calculated between 2012 (when the NASA data started to give less erratic values) and 2031and are shown below in figures 7 to 10.
Figure 7
Figure 8
Figure 9
Figure 10
The “accelerations” fall to values of approximately 0.025, 0.035, 0.030 and 0.040 mm/year2.
The conclusion reached is that generally the “acceleration” will continue to fall over the next 10 to 15 years or will reach values commensurate with the Tidal Gauge estimates.
Appendix – Most of the analyses and graphs have involved the perceived “accelerations”. Figure 11 shows the variation of the slope as calculated over the period from 1993 to the year indicated. The 3 curves show the slopes calculated using the actual data, a quadratic variation and a sinusoidal variation. It can be seen that the sinusoidal curve matches the actual data more closely adding more evidence negating the use of a quadratic curve and the associated “acceleration”. The quadratic variation is basically a straight line, whereas the sinusoidal variation shows the drop to about 2008, a climb up to an inflection point at about 2017 at which stage the growth in the curve begins to slow down.
Figure 11
Mission Statement – Being an 88-year OAP a 5-year projection seems overly ambitious, but I hope to make 2031 to see how right (or wrong) I have been.
The rainfall data behind it shows no drying trend. The geology behind it has been stable since before the Romans. And the number the press ran was the one from the dead scenario.
The rainfall data behind it shows no drying trend. The geology behind it has been stable since before the Romans. And the number the press ran was the one from the dead scenario.
Charles Rotter
“Millions of homes in London, Essex and Kent at risk of sinking as climate crisis worsens,” said the Guardian. AOL ran it. The story bounced around the usual outlets, all of them leading with the same number: more than four million British homes at risk from climate-driven subsidence by 2070. The source is the British Geological Survey, which is a serious institution, or used to be, and that is what makes this one worth a few minutes.
Paul Homewood already did the legwork on the rainfall trends, and I will not repeat all of it. He pulled the Met Office’s own spring rainfall series back to 1840 and the summer rainfall for South East England back to 1875, and the short version is that there is no trend. Dry springs were more common a century ago. The last really dry summer in the South East was 1995. If the South of England is drying out, the South of England has not been told. Go read his piece for the charts. They are the kind of charts that end an argument.
What I want to walk through is the foundation the four million number sits on, because once you see it, you cannot unsee it, and it is the same foundation holding up half of British climate policy.
The BGS figure comes from a product called the GeoClimate Shrink-Swell dataset. Shrink-swell is a real thing. Clay soils absorb water and swell, then dry out and shrink, and the ground moves, and houses built on clay crack. London sits on London Clay, which is about as shrink-swell as clay gets. This has been true since before there was a London. It is the reason the Victorians had subsidence and the Edwardians had subsidence and your great-grandmother’s terrace in Camden has a crack over the front door. Subsidence is made worse by the weight of buildings, by water extraction, and by the large trees people insist on planting next to their houses. None of that is new, and none of it is climate.
To turn an old geological fact into a 2070 headline, you need a forecast. And to get the forecast, the BGS took its geological maps and combined them with climate projections. Specifically, per the BGS’s own announcement, climate data derived from the Met Office’s UKCP18 projections.
And there it is. UKCP18.
UKCP18 is the Met Office’s 2018 set of UK climate projections, and its headline-grabbing high-end scenario was built around RCP8.5. Regular readers know where this is going, because we have covered it at length. RCP8.5 is the emissions pathway that assumes a fivefold expansion of global coal use through 2100, no meaningful climate policy, and technological stagnation, none of which is happening or has happened. As of the new ScenarioMIP framework for CMIP7, published this April, RCP8.5 and its successor SSP5-8.5 have been formally retired from the scenarios that feed the IPCC’s next assessment. The climate community itself has stopped using it. We have run several pieces on this in the last few weeks.
So here is the actual structure of the four million homes story. The BGS took its geology, fed it through the Met Office’s UKCP18, which is built on RCP8.5, which the IPCC retired three months ago as implausible, and produced a forecast for 2070.
If this were a single overcooked press release I would not bother. What makes it worth writing about is that it is now the house style of British climate institutions, and it arrives the same week as a related example worth holding up next to it.
Consider sea level, which is the other great engine of British coastal doomcasting. The accelerating-sea-level story rests almost entirely on the satellite altimetry record, which began in 1993 and now runs about thirty years, stitched together across several satellite missions, each handoff requiring a calibration adjustment. The long tide gauges, some running well over a century, tell a quieter story. The Battery gauge in New York has measured a steady rise of roughly 2.8 to 3 millimetres a year since 1856 with no statistically significant acceleration across more than 160 years. Where the short satellite record and the long tide-gauge record diverge in character, there is a real and live argument about which one deserves the greater weight, and it is not the settled question the headlines imply. David Burton has been documenting the tide-gauge side of this at sealevel.info for years, patiently, gauge by gauge.
The two stories rhyme. In both cases a long, boring, directly observed record shows a modest, roughly linear trend that has been going on for as long as we have measured it. In both cases a shorter, model-flavoured construction is used to project an alarming acceleration into the future. And in both cases the alarming version is the one that reaches the public, gets attached to a policy, and becomes the thing “everyone knows.”
What this is for
The BGS announcement is candid about what the dataset is for. It is designed, in its own words, to help local authorities, developers, planners, mortgage lenders and insurers assess their exposure to climate-related risk. In other words, it is intended to flow into planning rules, lending decisions, insurance premiums, and house prices. A forecast built on a retired emissions scenario is going to be used to decide whether your bank will lend against a terrace in Barnet.
And that is the part that should bother people who do not care one way or the other about climate politics. UKCP18 has not been withdrawn. The Met Office still serves it across its web pages. The BGS has now built a property-risk product on top of it. The scenario underneath has been declared implausible by the body that invented it, and the British institutional response has been to carry on as though nothing happened, because the projections are load-bearing. They hold up the Net Zero policy architecture, and you cannot pull the bottom scenario out without the upper floors getting nervous.
So the British Geological Survey, an organisation with a genuinely distinguished history of mapping the actual rocks under actual Britain, has published a forecast of four million sinking homes built on a climate scenario that the climate community retired in April. The rainfall data behind it shows no drying trend. The geology behind it has been stable since before the Romans. And the number the press ran was the one from the dead scenario.
If a graduate student turned this in, the supervisor would hand it back. But it was not turned in by a graduate student. It was published by the BGS, printed by the Guardian, and is now, somewhere in Whitehall, being quietly folded into the case for a policy that was decided long before the homes were counted.
A recent Daily Mail (DM) article, “Rapidly melting Antarctic ice shelves may cause global sea levels to rise even FASTER than expected – leaving millions at risk of being plunged underwater, study warns,” cites a study by Norwegian researchers examining the East Antarctic Ice Shelf, suggesting it may soon collapse. The claim is unsubstantiated and likely wrong.
A recent Daily Mail (DM) article, “Rapidly melting Antarctic ice shelves may cause global sea levels to rise even FASTER than expected – leaving millions at risk of being plunged underwater, study warns,” cites a study by Norwegian researchers examining the East Antarctic Ice Shelf, suggesting it may soon collapse. The claim is unsubstantiated and likely wrong. Instead of reporting just the facts, DM turned basic scientific uncertainty in the present into a projection far into the future, predicting doomsday by the year 2300 saying “millions at risk of being plunged underwater.” It’s virtually impossible to predict climate conditions 225 years in the future, but even if the ice shelf collapsed 225 years from now, humans have sufficient time to adapt or prepare. There is no present crisis, as is being implied by DM.
“Rapidly melting ice shelves in Antarctica could trigger global sea levels to rise even faster than expected, scientists have warned,” write DM. “Norwegian researchers have discovered that deep channel–like grooves beneath the ice are trapping swirling eddies of relatively warm ocean water.
“That warm water melts ice beneath the surface 10 times faster than normal, threatening the structural integrity of the entire ice shelves.
“If the Antarctic shelves were significantly weakened or even started to collapse, it would release the gigatonnes of ice currently being held back in the ice sheet,” DM writes.
The DM article is a near-perfect specimen of hyperbolic climate reporting. The DM ignored what the science actually said and took genuinely interesting research and reprocessed it into a pure climate scare story, without explaining any of the caveats that came with the science.
The recent discovery of deep channels beneath ice shelves trapping warm ocean eddies and accelerating basal melt are real and scientifically noteworthy. What the DM did with this finding is something else entirely. The leap from “we discovered something we didn’t fully know about” to “sea levels could rise 30 meters by 2150” does not appear in the scientific paper as a confident projection, rather it is a worst case scenario the researchers said they “cannot rule out.”
“Cannot rule out” is not a forecast. It is an admission of large uncertainty meriting further study, but not scary headlines suggesting such outcomes are likely. The fact that none of these projections will likely materialize failed to give DM pause, as it would a serious journalistic analysis. Instead, the DM treated it as a headline generator. Their choice was not based on scientific judgment, rather it was an editorial one, made in the service of audience engagement.
What DM’s coverage buried entirely is the fact that we are only learning about these sub-ice channels right now because we have only recently developed the technology and methodology to observe conditions beneath Antarctic ice shelves at all. At this stage, we have no idea whether or not such channels are and have been a permanent feature of the Fimbulisen Ice Shelf, how long they have been in existence, and how they have affected the ice shelf overtime. The Fimbulisen Ice Shelf case study used a combination of detailed topographical mapping and computer modeling, not decades of accumulated direct observational data, to reach its conclusions. We have Antarctic satellite data going back just about 40 years, which is barely a blink in geological time. Sub-shelf observational records are even shorter. The models generating these projections are built on assumptions that, by the researchers’ own acknowledgment, are still being revised as new processes like these channeled melt eddies come to light. None of that context appeared prominently in the DM’s coverage because context deflates alarmism, and deflated alarmism does not perform well online.
The science-to-media pattern repeats: a study is released suggesting possibly troubling findings, the media announces a crisis, the models get revised or new data, sometimes contradictory data, comes in and the crisis gets walked-back or moderated in the scientific literature (though rarely in the press), and then a new crisis emerges to generate headlines.
Antarctic glaciology is, by the candid admission of scientists within the field, still moving from basic exploration and mapping toward complex predictive modeling. That is an adolescent science being asked by the media to speak with authority on century-scale outcomes. The researchers often resist that framing in their papers. The media imposes it anyway, because the alternative story does not trend or generate as many clicks.
The ice will tell its story over years and decades of careful observation. The media’s job should be to report that story faithfully, uncertainty and all. Instead, outlets like the Daily Mail are writing the ending before the data exists to scare their readers and motivate anti-fossil fuel political action.
That is not a scientific failure. It is click-bait motivated journalistic malfeasance, and it deserves to be named as such.
More propaganda!
We have looked at Fairbourne a few times in the past. Most of the village sits about 3 meter above sea level on a sand spit of land. Until the 1860s, the land was mostly salt marshes.
A TV crime drama was inspired by the “tragic reality” of its real-life filming locations, according to actor Sir Jonathan Pryce.
Under Salt Marsh follows detectives as they race to solve the mystery of a local schoolboy’s death before a once-in-a-generation storm threatens to destroy evidence and the future of the fictional town of Morfa Halen.
Some of the show’s filming locations in Gwynedd and on Anglesey in north Wales are also facing an uncertain future due to severe weather.
The Crown actor Sir Jonathan, who plays farmer Solomon Bevan in the series which also stars Yellowstone’s Kelly Reilly, said: “It’s a thriller with an important message behind it.
“There are real towns on the west coast of Wales under threat from the sea.
“The show reflects that kind of looming ecological crisis and the community’s fight to survive and be heard.”
With most of the filming taking place in north-west Wales, Under Salt Marsh actress Reilly said the environment was “a character in itself”.
As the impending storm adds to the urgency to solve the mystery of young schoolboy Cefin’s death, it becomes an even more difficult task for ex-detective Jackie Ellis played by Reilly and her former partner DC Eric Bull.
Speaking to BBC Radio Wales, the show’s creator Claire Oakley said: “They’re at the mercy of the weather and the tides.
“I was interested in how it might feel to live somewhere where your home could get destroyed by the next big storm and how that might effect things and we started to develop this idea of a police investigation that would help us dig into these landscapes.
“They had to do this under this huge pressure of an encroaching red level storm that had the potential to completely derail the investigation and wash all of the evidence away.”
Barmouth in Gwynedd stands in as the main setting of Morfa Halen on the north-west coast.
With a population of almost 2,500, the seaside town is located on the Mawddach Estuary.
The show’s fictional community lives on the end of a tidal causeway, with high tides cutting them off from the rest of the world.
Some of the seafront scenes were filmed in Fairbourne, which has its own battle with the elements.
Its location and rising sea levels means that the town faces regular severe flooding, with Gwynedd council deciding it will stop maintaining flood defences by the 2050s.
The town could be decommissioned before then to allow over 800 local residents to be re-located elsewhere, making them the country’s first climate refugees.
We have looked at Fairbourne a few times in the past. Most of the village sits about 3 meter above sea level on a sand spit of land. Until the 1860s, the land was mostly salt marshes.
Around 1865, Solomon Andrews, a Welsh entrepreneur, purchased the promontory. Over the next few years, he built a seawall for tidal protection and several houses. The wealthy flour-maker Sir Arthur McDougall had been looking for a country estate but when he discovered this area, he soon conceived of it as a seaside resort. In July 1895, Arthur McDougall purchased a substantial acreage from land speculators, which he enlarged by additional lots the following year. He then immediately hired a builder to begin the development of a model seaside resort.
In short, the land never was habitable. It was only the construction of a seawall that made the building of a village feasible.
The BBC’s claim that “climate change” has suddenly made Fairbourne unsustainable is nonsense. It never was sustainable without a seawall. Gwynedd Council has now decided that it is not worth spending any more money on flood defence.
Sea levels at nearby Holyhead have been rising steadily at about 2.3 mm/yr since the start of records in 1938. We know from other UK locations that seas have been rising since the 19thC:
It has nothing to do with “climate change”, not the BBC’s version anyway, which means “man-made global warming”. Sea levels have been rising for perfectly natural reasons for a long time.
As for the decision by Gwynedd to stop maintaining flood defences, it appears that this is a money saving exercise, based on alarmist Met Office projections of sea level rise of 4.7mm a year.
An independent scientific study has concluded that the cost of repairing flood defences is a tiny fraction of the council estimate, and certainly much less than decommissioning the town.
Based on realistic estimates of sea level rise, Fairbourne is under no threat at all for the foreseeable future. As one of the authors, Dr Graham Hall, explained, Fairbourne is mainly protected by a shingle bank spit:
“Fairbourne is protected from the sea by the Ro Wen shingle spit. The flat crest is so wide and solid that in the south of the village there is room on the top for a car park. The natural spit is also strengthened by a core wall of concrete. The shingle spit is not the flimsy structure of the misleading diagram. Recent and more accurate computer modelling has shown that even an extreme storm could not do serious damage to something so solid.
Could waves flow over the top? The spit is a natural construction built by storm waves hurling pebbles and small boulders onto the sloping seaward side. As sea level rises, the spit will naturally grow higher to match the new level of the storm waves.”
As Hall points out, the existing sea wall certainly need repair and maintenance, as all flood defences to do. Other fairly conventional and relatively inexpensive, such as raising levies to protect from flooding from the estuary or landward side, have been recommended too.
But the idea that Fairbourne is soon going to be under the sea is just alarmist claptrap.
Nobody would, of course, dream of building a village in such a place now. Whether it is worth spending money to protect it is one I cannot answer. But according to the scientists, there are cheap solutions which will ensure Fairbourne remains habitable for a long time to come.
This report presents and analyses the sea level data for 2025. It was a roller coaster year for sea level data that turned into a “Hunt the Data” exercise. As a consequence, the analyses main purpose is to link the new data to the old and produce a new methodology so that changes to sea levels in 2026 can be more easily judged.
This report presents and analyses the sea level data for 2025. It was a roller coaster year for sea level data that turned into a “Hunt the Data” exercise. As a consequence, the analyses main purpose is to link the new data to the old and produce a new methodology so that changes to sea levels in 2026 can be more easily judged.
produced data for January, April, July, August, September, November and December but in July 2025 NASA changed dramatically the amount of data being provided. Up to April 2025 the data consisted of 13 columns giving number of readings, standard deviations and results with and without a 60-day Gaussian type filter applied, with and without GIA not applied and with and without annual and semi-annual signal removed. The data analysed was from COLUMN 12 which was for smoothed (60-day Gaussian type filter) GMSL (GIA not applied) variation (mm); annual and semi-annual signal removed.
Whereas in July only 3 columns of data appear:-
HDR 1 year+fraction of year
HDR 2 GMSL (cm)
HDR 3 GMSL with 60-day smoothing applied (cm)
with the 3rd column being used in the analysis.
Also, up to April 2025 data appeared on a roughly 10-day cycle but in July this changed to a 7- day cycle. To add more misery the U.S. Government shut down stopped any data at all being provided for a couple of months.
To show he differences in the data used for the analyses Figures 1 and 2 give the diagrams provided by NASA for April and July 2025.
How to proceed?
Up to April 2025, after 7 years of studying the NASA data, several standard Excel spreadsheets had been created producing plots of the Full Data with best fit Linear, Quadratic and Sinusoidal curves with the associated equations and plots of residuals, measured from the linear line, with best fit Quadratic and Sinusoidal curves. Also, statistical results have been produced and historical plots of how the so called “accelerations” had changed with time.
One approach would be to use the data as it is with the old spreadsheets, but this would lead to very “bumpy” plots. The annual and semi-annual signal vary from -4.33 to 5.46mm which is quite large when compared with an annual average increase of about 3.3mm.
A second method would be to do a moving annual average which would eliminate the bumps but would also lose a lot of the El Niño/La Niña effects which are useful to show.
The annual and semi-annual signal is a regular variation that repeats year in year out with the same shape, so the amount involved only depends on the date in the year. Using this fact the signal’s value at any time in the year can be ascertained from the readings with and without it included. This was done using the difference between columns 11 and 12 of the April 2025 data, creating a list of dates and differences then subtracting the year value from the date. This leaves 2 columns for the partial year date and the annual and semi-annual signal which is shown in figure 3.
An 8-order polynomial curve was fitted to the graph in figure 3 using the LINEST Function in Excel, resulting in
The order and accuracy may look excessive, but a 6-order was found to be too inaccurate at the extreme ends of the graph and although 2 less significant figures only changed the graph slightly it was decided to stay with this equation. Figure 4 shows a plot of the actual and fitted curves indicating only a small inaccuracy at the extreme ends of about 0.1mm.
The new data with annual and semi-annual signals can now be processed by taking the sea level and calculate the part year by using a calculation in Excel
Part year = year – INT(year)
and then calculating the annual and semi-annual signal using Equation 1. This can then be taken off the listed sea level and the normal processing carried out.
I wish life was that easy.
This process was applied to the NASA Aug 2025 data which only had data with the signal included. The result was not satisfactory with the Aug data having a seemingly larger variation than what was shown in previous data releases.
Another source of readings is from our friends at the University of Colardo. Their site https://sealevel.colorado.edu/data is a useful source of information even if you don’t like the conclusions they reach like extrapolated sea levels in 2100. The site has 2 sets of processed data shown in figures 5 and 6. The “acceleration” indicated is 0.071mm/year2 which is line with other estimations. Strangely this site has removed several figures for analyses carried out between 2020 and 2025.
Repeating the process shown in Figure 3 resulted in
The equation for an eight-order polynomial is given by Equation 2.
The comparison of this eight-order polynomial with the actual values is shown in Figure 8.
This shows a similar form with some small differences in shape and is generally about 30% more than in Figure 3. The best option therefore is to use the data as supplied by NASA and accept that any graphs will be affected by the annual signals.
The sets of data analysed are therefore January and April 2025 with the full data and July, August, September , November, and December 2025 with the reduced data. The data for April 2025 and December 2025 will both be processed for comparison.
Figures 9 and 10 show the linear and quadratic best fits of the full data.
The differences in the equation’s coefficients seem larger than would be expected but it is more revealing the change year on year with consistent sets of data so 2026 data will be more revealing.
The residuals are plotted in Figures 11 and 12 together with the quadratic best fit curves and the standard deviations of the errors added. To check this process is carried out correctly the quadratic terms are compared with that for the full data and the linear fits checked as being given by y = 0 x + 0.
Next in Figures 13 and 14, the residuals are plotted together with a sinusoidal curve having an amplitude of 4.2 mm and a period of 29 years. The value of 29 years has been used for a couple of years having been originally eyed in and is probably not quite the best fit curve but not far off.
The standard deviations of errors with the sinusoidal curve are lower than the quadratic for both the April and December analyses. The inclusion of the annual and semi-annual signal greatly increases the standard deviation for both curves.
Figures 15 to 18 show histogram plots of errors for the two analyses.
Figures 15 and 17 show more normal distributions whereas in Figures 16 and 18 the shape of the annual and semi-annual signal makes them slightly skew.
Figures 19 and 20 shows the result of the spectral analysis for the full data.
Both show that there is a very long-term variation and in figure 20 the annual and semi-annual signal appears as a spike at a period of 1 year. On this plot it appears very small compared with other peaks due to Solar and Lunar cycles but is better judged on the residual spectral analysis shown in figure 22 below.
Figures 21 and 22 shows the result of the spectral analysis for the residuals in the NASA analysis.
Figures 23 and 24 plot the “acceleration” values against the date they were determined based on the data from the start of 1993 up to that date. The effect of El Niños and the annual and semi-annual signal can be seen. If the residuals had followed a sinusoidal variation based on the 29-year sinusoidal curve an equivalent set of “accelerations” could have been determined. Remember the curve labelled “sinusoidal” is NOT a sinusoidal curve but a curve of “accelerations” based on the residuals having a sinusoidal variation.
Figures 25 and 26 show the “accelerations” as at the end of Jan 2025 with the long term predicted “accelerations” up to the 2060’s calculated using quadratic curve fitting to a set of data obtained from a 29-year curve. The validity or not of the curve will be seen earlier by about 2034 when, if correct, “accelerations” will be approaching values seen in the long-term Tidal Gauges i.e. values of about 0.01 mm/year2.
No conclusions about sea levels during 2025 have been deduced. The comparisons between April 2025 and December 2025 show no major differences and so the ongoing work must turn to the changes during 2026. Figure 26 is considered the most revealing graph, now somewhat complicated by the annual and semi-annual signal, which masks the El Niño variations, but assuming no more complications with data presentation by NASA there should be a steady drop in “accelerations” throughout 2026.
If any readers have stayed the course, I wish them all the best for 2026 and hopefully I’ll be reporting on 2026 in twelve months’ time .
Today’s sea levels are the lowest of the last several thousand years.
Carbon dating evidence from the elevation of abandoned penguin rookeries (and other proxies) reveal relative sea level (RSL) was ~30 m higher than today across East Antarctica about 8000 years ago (Small et al., 2025).
Following that high stand RSL fell rapidly at rates of 4 to 10 m per 1000 years. RSL was 24 m above present sea level (ASL) by 7200 years ago, 15 m ASL by 5700 years ago, 5 m ASL by 3200 years ago, and still 1 m ASL about 800 years ago.
Another study from Antarctica’s South Shetland Islands suggests RSL has plummeted by 10 meters just in the last 2000 years after a 15 m highstand 9000 years ago.
There are regions in the northern hemisphere where RSL reached similarly high elevations as they did across East Antarctica. The southeast coast of Sweden, the southern Baltic Sea, records RSL 22 m higher than today from approximately 7500 to 6200 years ago (Katrantsiotis et al., 2023).
The question “Why does the Global Sea Level Rise have a sinusoidal variation with a period of about 26 years?” was asked in the comments to one of my papers in 2023. My reply pointed out that the satellite coverage was only 95% and so what was happening in the other 5% may be relevant. In my follow up paper “Measuring and Analysing Sea Levels using Satellites during 2023 – Part 2”. I analysed the Tidal Gauge results of 9 ports in the northern regions of the Atlantic Ocean and above up to the Arctic Ocean.
An Investigation using Tidal Gauge Data Part 1 – Preliminary Analysis
by Dr. Alan Welch FBIS FRAS — 2 December 2025
Note: This paper will be published in two mutually-dependent parts, this part and the second to follow in day or two, depending on the publishing schedule here.
Introduction
The question “Why does the Global Sea Level Rise have a sinusoidal variation with a period of about 26 years?” was asked in the comments to one of my papers in 2023. My reply pointed out that the satellite coverage was only 95% and so what was happening in the other 5% may be relevant. In my follow up paper “Measuring and Analysing Sea Levels using Satellites during 2023 – Part 2”. I analysed the Tidal Gauge results of 9 ports in the northern regions of the Atlantic Ocean and above up to the Arctic Ocean. The analysis was very simplistic but showed promise so now a more detailed analysis will be carried out using procedures and methods developed more recently.
Before proceeding another question could be “so what?”. The graph is basically almost linear (R2 = 0.99) with fits using quadratic or sinusoidal adding very little on this. But in 2018 Nerem et al produced their paper concentrating on a quadratic curve, using the small quadratic coefficient to be representative of an acceleration. If they had contained this to the confines of the data all would have been acceptable, but they proceeded to extrapolate for over 80 years and release their paper. The paper as still being used as a reference in many papers each month. Over the 8 years it has been used to create dramatic scenarios of flooded cities and frighten all the children. The investigation of a sinusoidal curve has tried to balance the picture. A point overlooked in using the quadratic curve is that if the calculations had been started in 2008, they would now show a deceleration. What paper would Nerem et al then write?
The paper is in two parts.
Part 1 contains preliminary analyses in which each Tidal Gauge is processed up to the Spectral Analysis stage.
Part 2 carries out Curve Fitting in which residual values (Actual values minus values on a best fit curve) are judged against a curve that is the combination of 2 or 3 sinusoidal curves using peak periods derived from the spectral analyes.
Preliminary Analysis
This study makes more use of Tidal Gauge data than NOAA data but uses Spectral Analysis.
The 9 locations are Reykjavik – Iceland Torshavn – Faroe Islands
Only Aberdeen and Bergen have datasets spanning more than 100 years, with Torshavn covering only about 50 years.
Before analysing these 9 ports the results for Brest will be studied as this covers over 210 years although there are a couple of sizeable breaks in data. This work was carried out earlier, but some aspects will be of interest. The data are shown in Figure 1.
Figure 1
The usual curve fitting is carried out as shown in Figure 2. Again, possibly excessive precision has been used to be on the safe side. This is not in order to indicate accurate fitting but with possible high values on the “x” axis power terms might need this extra precision.
Figure 2
A Spectral Analysis was carried out on the data and Figures 3 and 4 show the outcome for long and short periods.
Figure 3
Figure 4
Figure 3 indicates a peak (barely a peak) at a period of 1429 years. Most other Tidal Gauges show much higher periods, but it would be interesting to see how sinusoidal curves with periods in the region of 1000 years would compare with the quadratic fit.
Curve fitting was carried out for curves with periods of 1000, 1100 and 1200 years. The equations are shown in Excel Format.
Figures 5,6 and 7 compare the quadratic curve with the 3 sinusoidal curves, together with comparisons of slope and acceleration.
Figure 5
Figure 6
Figure 7
The next part may be good lateral thinking or a bit more La La Land!!
Using the 1200-year curve and extrapolating from 500 BC to 2500 AD results in the following.
Figure 8
The indicated periods may be arguable. It surprised me that the last Thames Ice Fair occurred as late as 1814. A more worrying interpretation of this graph, if it is remotely indicative, would be that the sea levels (and associated Temperatures) do not peak until about 2450. Could events be as bad as that? How hot were the Roman and Medieval Warm Periods?
Finally with respect to Brest Figure 9 shows the residuals, that is the actual values minus the values on the quadratic curve and Figure 10 the spectral analysis of these residuals.
Figure 9
Figure 10
The periods of the peaks vary from those on the full data spectral analysis, and it is not very easily seen on figure 9 that there may be a decadal oscillation of about 93 years. This was
investigated by creating data files of random values, one set as straightforward random values and one set as random numbers with a standard variation. Many cases were run and figures 11 and 12 show one case from each series.
Figure 11
Figure 12
First impressions are a bit worrying as they indicate curves with periods in the range 10 to100 years but closer inspection shows these all have small amplitude (labelled theta on these plots) of about 2 whereas in the actual tidal gauge plots these are between 10 and 30 for the residuals. These Theta values are relative values and do not indicate actual physical values.
(I may not have described the process clearly, and may not have described it correctly, so if anyone out there can help I would be very grateful)
Turning now to the 9 Tidal Gauge datasets they will be considered starting with the longest period of measurement and then in roughly an order of reducing period. But before proceeding the following 9 small figures (in Figure 13) show the outcome of the earlier curve fitting exercise of a 26-year period curve to plots of the residual values, that is the actual values minus the value on the quadratic fit. Whilst a roughly 26-year period curve fits in many positions there is evidence of other, usually, longer period components as can be seen in the Aberdeen plot where a larger 85ish year variation is obvious. The larger spectral analysis peaks will refer to primary modes, but shorter ones may be other primary modes or secondary modes. One problem with these 9 analyses was that the data plotted is a moving average based on 101 data points. With no breaks in data this equates to just over 8 years but as there are several gaps in the data this may distort matters. Averaging removes most of the short-term frequencies and makes it easier to perceive the general form.
Aberdeen .. Bergen .. Narvik
Reykjavik .. Murmansk .. Barentsburg
Lerwick .. Tiksi .. Torshavn
Figure 13
Each of the 9 Tidal Gauges will now be looked at showing the initial data from NOAA Web Site, processed data showing best fit quadratic curve, residuals (actual value minus value on best fit curve), spectral analysis plots and tables of results. The tables are basically values from the software’s original use, which was variable star analysis. The first column is Frequency
(1/Period) in cycles per year. Time is the Period at which this Frequency occurs in Years. Theta has been referenced in this paper as Amplitude.
At this stage no curve fitting was carried out as in most cases there are 2 or more peaks. The Amplitudes used on the Spectral Analysis (amp) and the Amplitudes of the Sinusoidal Curves (AMP) have been studied for known multi sinusoidal curves and it has been found that the Amplitudes for a pair of curves, 1 and 2, are related by the equation
AMP2 = AMP1 * SQRT(amp2/ amp1) (Equation 2)
Using this it can be estimated the relative dominance of each curve shown on the Spectral Analysis graphs.
Bergen
Narvik
Narvik (reduced data set)
The data before 1947 has been discarded due to the large gap in readings and the suspicious initial data values close to the beginning of the data.
Reykjavik
Murmansk
Barentsburg
Lerwick
Tiksi
Torshavn
Coming Soon:
Having applied spectral analysis to a number of Tidal Gauges, Part 2 will use the findings to derive sinusoidal curves.
These datasets were published at irregular intervals and listed the sea level readings from the 5 individual satellites both globally and for 24 sub-areas of the Oceans, Seas and Gulfs. As each sub-area has generated at least 5 graphs there are well in excess of 130 graphs produced, too much for a paper, but they can all be found on a Google Drive link https://drive.google.com/file/d/1KCYo7iYmVxbEoTubeP7YB06jEVQo82k5/view?usp=sharing.
This paper will not go into much discussion but will act as a depository for the graphs for others to have access to. The following notes describe the study.
1. Both the individual satellite results and the combined results for each sub-sea have been studied but this paper only concerns the latter. Graphs for all sub-seas have been produced. Some aspects of the individual satellite results will be reported in Part 2.
2. The order of the presentation will be different from the NOAA order as follows:-
First the Global results
Next 22 sub-seas listed in increasing value of Easting. The Easting for each area is defined as the number of degrees E an approximate centre of the sub-sea is East of the 180-degree longitude. The reason for doing this is in the off chance that one or more derived parameters varies in a systematic way in the longitudinal direction which would be informative.
Finally, the 2 circum-global areas, namely the Southern Oceans and the Tropics.
3. When analysing the combined results if two satellites have the same date/time then their values are averaged.
4. The data are from Sept 2024. The latest available data are for about the end Feb 2025. At the time of writing no updates have taken place since Feb 2025.
5. The graphs produced for each area are as follows with the graphs for the Yellow Sea being used to illustrate:-
A graph, Figure 1, of all the satellite readings colour coded for each satellite.
Figure 1
A graph, Figure 2, of the full combined data with the linear and quadratic best fit lines shown together with their equations. It may seem rather excessive precision has been used but better to be safe than sorry. Sometimes actual year dates, like 1993, may have been used and when powers of these appear in equations extra precision must be used to guarantee accuracy.
Figure 2
A graph, Figure 3, of residuals defined as the actual value minus the equivalent value on the linear best fit line. Added is a best fit sinusoidal curve calculated via an iterative process along with the parameters of that curve.
Figure 3
In Figure 4 is a spectral analysis of the data indicating any peaks generally between about periods of 10 and 40 years. Usually, peaks with about a 10-year period or less are due to irradiance variation or El Niño events.
Figure 4
A plot, Figure 5, of how the derived “acceleration” changes with the year of determination. In previous papers it was suggested that this plot for analyses of the Global Sea Level was important in the way it changed with time. It seemed that the shape was like a slightly underdamped sinusoidal oscillation, but events change so slowly that repeating it for several other sea areas may prove informative.
Figure 5
At this stage a short thought on the term global sea level. Both Tidal Gauges and Satellite readings are not global. The former cover only a very limited portion of the sea but the length of monitoring can be useful as some stretch back over 200 years. Conversely the satellite coverage is just over 30 years and covers 95% of the seas. In previous work I have suggested that this missing 5% is the cause of the sinusoidal oscillation in the global variation. Also, that the missing 5% may mainly be found in the North Atlantic, North Sea and Arctic Ocean areas. A preliminary investigation some years ago of several Tidal Gauge records of ports in these areas showed promise but a further study making more use of spectral analysis has been carried out that is more comprehensive and will be reported on at a later date.
The results of the various analyses have been collated in several tables. A first preliminary look at the satellite and residual plots was made to ascertain if any data were suspicious. Having calculated all the residuals a histogram was produced for each sea and the standard deviation derived. The table of statistics is shown below, Table 1.
Table 1
The South China Sea and the Tropics each contained a suspicious reading. Calculating the ratio of the maximum absolute value of any residual to the standard deviation showed values of 13.3σ and 11.8σ respectively, which are very large hence they were replaced by the average of the 2 adjacent values. The analyses then proceeded with Table 2 giving the slopes and “accelerations” for each sea. Results for the South China Sea and the Tropics are presented but only using the modified data.
Table 2
The next stage was fitting a sinusoidal curve via an iterative process. The equation is shown in Excel Format.
and PERIOD is a period of a complete oscillation (years)
Table 3 shows the results of the curve fitting, again in order of increasing Eastings.
Table 3
Following the spectral analyses a comparison was made between the Period derived in the equation fitting and the main Period found in the spectral analysis. All agreed within about 5% and most within 2% except for the result for the Persian Gulf which was 50% different. The curve fitting gave a period of 12.2 years, but the spectral analysis showed 2 peaks of 12.3 and 24.5 years as shown in the Figure 6 below. A best fit curve with a longer period is also shown in the file of graphs.
Figure 6
The Indian Ocean and the adjacent seas and gulfs show multiple peaks or short periods in the spectral analysis. The Indonesian Sea has a single peak of 10.9 years, but this may be due to irradiance. The Indonesian Sea is studded with 100’s of islands with its own particular flow pattern. But what is of interest is the convergence plot that shows more clearly the damped oscillations pattern as is seen in Figure 7.
Figure 7
Finally, the Eastings order was used to see if there was any variation with longitude with any parameter. No totally clear evidence was found but below in Figure 8 is a plot of “acceleration” against Easting for the 24 areas which does not appear to be a random graph .
Figure 8
If there was a decadal oscillation around the Earth it would show up in the phase shifts which would affect the “acceleration” calculation. This effect was investigated by repeating the Indian Ocean calculation but starting the data in 2000 instead of 1993. Starting in 1993 it now registers an “acceleration” but starting in 2000 it registers a “deceleration”. Both analyses are shown in the file of graphs. The corresponding sinusoidal curves and spectral analyses are little changed.
At the start it was stated that this work was not a thorough analysis of all the NOAA sub-sea datasets but more a systematic presentation of a large amount of analysis and graphical presentation for others to comment upon. It is hoped readers may find it informative. I know many are sceptical of any curve fitting be it quadratic or sinusoidal. Many reject the data totally, but with such a large database of readings it seemed a waste not to carry out some investigations. If you don’t look you don’t find.
Part 2 –NOAA Sea Levels – Variation based on individual satellites
Following on from Part 1 when the averaged sea levels were studied, Part 2 investigates the individual satellite readings. Little has been produced studying the separate satellites although one example has been shown a number of times, namely the graph by Willis Eschenbach shown in Figure 9.
Figure 9
This has been used to help discredit the findings that an acceleration in sea level rises exists – a worthy use. To a lesser extent the graph has been used to discredit the sinusoidal curve fitting but as part of this analysis the equivalent graph has been produced and is shown in Figure 10.
Figure 10
It can be seen that we are all singing from the same hymn sheet. But Nature and NASA may have combined to mislead somewhat. Figure 10 can be modified by subtracting the linear fit values from all the readings. This results in Figure 11. On this are marked 2 points of inflection where the curvature changes sign. These are at a residual value of -0. 2 mm.
Figure 11
Each half of the sinusoidal curve is skew symmetric about an inflection point and in this case the 2 sets of satellite data are roughly skew symmetric about an inflection point. If they had been exactly skew symmetric then each pair of satellites would have had the same slope. Hence having 2 pairs of satellite data with almost the same slope is a sign that the data may follow approximately a sinusoidal curve. Plots similar to Figure 11 are produced for all 24 sub sea areas and most are different from the Global graph as they have different phase shifts and/or different periods. The only similar case is that of the Atlantic Ocean which hints that this has some input to why the Global variation has a sinusoidal variation.
Each region of sea will generally have 3 plots which are described by reference to the Yellow Sea again.
The first graph, Figure 12, shows the linear fit to all the data together with the linear line for each satellite. All the lines for the Sentinel-6MF satellite are more erratic due to the short period involved and the domination of the latest El Niño during this period.
Figure 12
Figure 13 shows the 5 satellite linear lines superimposed on a best fit sinusoidal curve.
Figure 13
Finally, Figure 14, shows all the individual data with the best fit sinusoidal curve superimposed.
Figure 14
The 2 sets of graphs are arranged to have a similar outlay with each sub area occupying one page. If possible, they can be viewed side by side on a split screen. Figure 15 shows a split screen presentation for the Indonesian Sea. Some computers/systems may not be able to accomplish this.
Figure 15
Comparing the first figure of each sub area with the “global” plot only the Atlantic plot is similar except for the 2 special areas namely the Southern Seas and the Tropics. The North Atlantic and North Sea do not compare with the “global” so no definitive conclusion can be made. Having said that the Atlantic Ocean, North Atlantic Ocean and North Sea all have a period in the range 24 to 27 years according to the fitted sinusoidal curves. It is planned to revisit the question of why the “global” sea level has a variation by using later procedures, like spectral analysis, on the Tidal Gauges in the northern reaches of the Atlantic Ocean.
Every few months, a new study emerges claiming to have finally cracked the code on why the Maldives—or Kiribati, Tuvalu, or whichever island chain is the fashionable climate casualty of the moment—are supposedly on the verge of disappearing under rising seas. They never quite do, of course, but that fact rarely stops the press releases. The latest entry comes from the University of Plymouth and Deltares, announcing with great fanfare that the Maldives may soon face flooding events “every two to three years by 2050,” based on modeling of a rare July 2022 swell-driven flood.
Every few months, a new study emerges claiming to have finally cracked the code on why the Maldives—or Kiribati, Tuvalu, or whichever island chain is the fashionable climate casualty of the moment—are supposedly on the verge of disappearing under rising seas. They never quite do, of course, but that fact rarely stops the press releases. The latest entry comes from the University of Plymouth and Deltares, announcing with great fanfare that the Maldives may soon face flooding events “every two to three years by 2050,” based on modeling of a rare July 2022 swell-driven flood.
You can read the press release yourself here: Study provides new forecasts of remote islands’ vulnerability to sea level rise (EurekAlert!, University of Plymouth). I encourage you to read it closely, because what is buried inside is a story less about island vulnerability and more about modeling overreach—yet again.
The narrative is predictable: a single unusual event is simulated, extrapolated, and then turned into a headline-worthy forecast of future doom. What goes largely unmentioned is the inconvenient fact that the peer-reviewed literature overwhelmingly shows that reef islands—including those in the Maldives—have been stable or growing for decades, and that their natural geomorphic processes often make them more resilient in the face of rising seas, not less.
Here at Watts Up With That, I’ve spent nearly two decades highlighting these uncomfortable facts. And because the press release slyly acknowledges some of those same processes but then stuffs them behind the modeling curtain, it’s worth digging into what the science says, what the models assume, and what real-world data continues to show: the Maldives are not drowning. Look at the data:
A Rare Flooding Event—But a Convenient One
Let’s start with what the press release says upfront:
“In the summer of 2022, 20 islands in the Maldives were flooded when a distant swell event in the Indian Ocean coincided with an extremely high tide level.”
The authors admit this was “a relatively rare occurrence” and the worst flooding since the 2004 tsunami. That should be our first clue: linking a common swell event with an extreme high-tide coincidence is not a harbinger of climate-driven catastrophe. It’s simply a rare confluence of natural factors. Yet this single event becomes the backbone of the model’s prediction that such events will happen “every two to three years by around 2050.” That phrase—by around 2050—deserves its own eyebrow raise. It’s not a measurement. It’s not even a trend. It’s a scenario extrapolated from a model driven by assumed sea-level rise rates and closed-box geomorphic behavior. And that’s the central flaw: models can only produce results consistent with the assumptions embedded into them. If you assume islands are static sandpiles in a rising bathtub, you shouldn’t be surprised when the model says they get wetter.
The Press Release Accidentally Tells the Real Story
Buried in the release is a short sentence that contradicts the entire catastrophe narrative:
“Waves washing over an island can deposit coral sand and rubble… raising its elevation and potentially making it more resilient to future flooding events and sea-level rise.”
That single line—almost tossed in as an afterthought—summarizes two decades of geomorphological research on reef islands. It confirms what real measurements, aerial surveys, and long-term shoreline studies have shown: atolls are dynamic landforms that respond to environmental forces by adjusting, migrating, accreting, and sometimes even expanding vertically. These are not static mounds of sand the way modelers often treat them. They are living systems connected to coral reefs, sediment supply, storm dynamics, and wave climates.
This process—overwash deposition—is not a theory. It is a documented mechanism shaping reef islands globally, including the Maldives. And yet, after admitting this, the press release immediately pivots back to the model’s forecast of increased flooding. The contradiction is obvious: if overwash can raise island elevation, and if atoll islands have repeatedly done so over decades, then a model that excludes island adaptation cannot validly predict their future vulnerability.
The Models Used Don’t Include Island Adaptation—And the Authors Admit It
The key sentence in the release is this:
“The model did not factor in natural or anthropogenic adjustments over the next quarter of a century…”
That is a polite way of saying: We built a model that assumes the islands will not change at all, then used that assumption to claim that they won’t be able to change enough to keep up with rising seas. This is like modeling the future of forests but disallowing tree growth. Or predicting the trajectory of a living reef while assuming coral calcification ceases tomorrow. In the real world, reef islands adjust continually through: sediment overwash, lagoon infilling, seasonal shoreline migration, vertical accretion, and reef-supplied sediment feedback loops.
None of these mechanisms are speculative. They are documented in field studies across multiple ocean basins. But because they are difficult to model—especially at the high resolution required to simulate multi-decadal morphology—most coastal inundation models simply omit them. That’s not a criticism of XBeach itself; it’s a powerful tool for short-term storm simulation and coastal erosion. But it is not designed to model 25-year geomorphic island evolution. Yet here it is used to do exactly that.
The Peer-Reviewed Literature Paints a Very Different Picture
Let’s turn to what the research actually shows about island stability and growth. I’ll cite just a few of the most comprehensive studies.
Webb & Kench (2010), Global and Planetary Change
In one of the most influential papers on atoll dynamics, Paul Kench and Arthur Webb examined 27 reef islands over several decades using historic aerial imagery and modern satellite data. Their findings? 86 percent of islands were either stable or increased in size. Not a single island was found to be contracting rapidly enough to justify claims of imminent disappearance. Link: https://doi.org/10.1016/j.gloplacha.2010.03.005
Duvat (2019), “A Global Assessment of Atoll Island Planform Change”
Virginie Duvat undertook the largest global analysis of atoll islands ever attempted—709 islands across 30 atolls over multi-decadal timescales. Her findings: 89 percent of islands were stable or increasing in size. Only 11 percent showed any decrease, and those were primarily linked to human modifications. Link: https://doi.org/10.1016/j.earscirev.2018.10.001
Kench et al. (2018), Nature Communications
This landmark study examined all 101 islands of Tuvalu over four decades and found that 73 percent grew in land area, some by as much as 30 percent. This occurred during a period of significant regional sea-level rise. The conclusion: “Tuvalu’s islands are not simply drowning.” Link: https://www.nature.com/articles/s41467-018-02954-1
Ford & Kench (2015), Geology
This paper documented how overwash processes on atoll islands increase island elevation, enabling islands to “keep pace” with sea-level changes. Link: https://doi.org/10.1130/G36555.1
Kench, Owen & Ford (2014), Geophysical Research Letters
These studies do not support the idea that rising seas doom atolls. They support the exact opposite: atoll islands are remarkably resilient natural structures that evolve dynamically in response to environmental changes. This is why the Maldives—despite decades of dire predictions—have not only survived but grown in total land area.
Yes, You Read That Correctly: The Maldives Are Growing
Between 2000 and 2017, the Maldives added roughly 3 percent to its land area. Some islands expanded naturally; others expanded through reclamation. The point is not whether growth is natural or engineered, but that the commonly repeated narrative—that these islands are teetering on the edge of inundation—is contradicted by their actual behavior.
Meanwhile, Maldivian development tells its own story. This is not a nation behaving like it expects to drown. It is building airports, hotels, resorts, sea walls, harbors, luxury properties, and commercial centers. Among the most striking examples is the expansion of Hulhumalé, a massive reclamation project creating entirely new land that is being developed with full confidence in long-term habitability. If Maldivian officials truly believed their country was doomed to drown in the next few decades, their financial and infrastructure behavior would not look like this.
Why This Matters: Atolls Are Dynamic Landforms, Not Bathtub Islands
This is the crux of the issue. Nearly all modeling studies—including the one in the Plymouth/Deltares press release—treat atolls as static. They behave as if islands were poured out of a concrete mold in 1950 and left to erode. But as our own Willis Eschenbach points out, reef islands are the opposite of static: The Unsinkable “Sinking Atolls” Meme
Their shape, size, elevation, and position change continually. They grow vertically through overwash deposition. They migrate laterally with longshore transport. They expand with sediment from reef production. They respond to storms in ways that sometimes look destructive but often turn out to be constructive. Kench and colleagues have shown repeatedly that storm activity can carry sediment inland, thickening island cores and raising overall elevation. Islands that “flood” in one event may actually be gaining elevation through the same process. In other words, the mechanism that causes flooding can also be the mechanism that makes future flooding less likely. This is precisely what the Plymouth press release acknowledges—then ignores when it comes time to present its model results.
Models like XBeach are useful for simulating the short-term hydrodynamic response of coastlines to storm events. I have no quarrel with XBeach as a research tool. But it is not an island evolution model. It is not designed to handle multi-decadal vertical accretion, sediment sorting, coral reef sediment production, reef slope changes, or lagoon infilling. It is certainly not designed to include anthropogenic interventions like sea walls, groynes, reclamation, or harbors. And crucially, as the press release itself admits, the model includes no natural adaptation processes. Yet this omission is not a minor technical detail—it’s the entire story. Imagine modeling the future of a coral reef ecosystem while excluding coral growth. That’s what’s happening here.
The Problem With Extrapolating From a Single Rare Event
The July 2022 event was the only one of 158 storm events (from 1990 to 2023) that produced flooding at the study site under current sea-level conditions. That alone gives us a baseline frequency: roughly one event every 33 years. The model then says that with assumed sea-level rise by 2050, an additional nine storms would produce flooding. This is how we get the “every 2–3 years” projection. But this projection ignores: island elevation increases, shoreline adjustments, wave climate shifts, ENSO and Indian Ocean Dipole variability, storm track variability, and coral reef changes affecting wave attenuation.
In other words, the model’s result is not a forecast; it’s an assumption cascade. Feed the model sea-level rise. Freeze the islands in place. Press “Run.” Report the output as fact.
Atoll islands grew out of rising seas during the Holocene. They adapted to rapid sea-level changes far greater than today’s modest rise. If atolls were as fragile and static as the models assume, they would have drowned thousands of years ago. But they didn’t. They formed, expanded, and stabilized. Today’s sea-level rise is not unprecedented in magnitude or rate. Yet today’s models behave as if every inch of sea-level rise must translate to inundation because the island cannot adapt. It can. It does. It will.
Conclusion: The Larger Narrative Is Politicized—And the Maldives Know It
The Maldives government has long played a dual game:
• Presenting itself internationally as a victim of sea-level rise to attract climate finance. • Presenting itself domestically and to tourists as a booming island destination full of new development.
These two narratives cannot both be true. And as always, money follows the doom narrative. Meanwhile, concrete, steel, dredging, reclamation, and resort construction follow the island-growth narrative. Which of these two behaviors reflects what the Maldives truly believe about their future?
The Plymouth/Deltares press release is a case study in the gap between model-based forecasting and real-world evidence. The real scientific story, supported by hundreds of surveyed islands and decades of research, is this:
• Atoll islands are not drowning. • Most islands are stable or growing. • Natural processes raise island elevation. • Models that assume static islands cannot predict future vulnerability. • Sea-level rise in the Maldives is not behaving catastrophically. • The Maldives continue to expand and develop aggressively.
And when a press release acknowledges that overwash raises islands but then uses a model that omits that process, you know you’re reading a narrative, not a neutral scientific assessment.
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