On 23/07/2020 20:48, Norman Lynagh wrote:


I would be very wary about fitting trendlines to processes that are
likely to be highly non-linear and which may have step-changes.


I agree 110%. I have made this point on more than one occasion when the
same topic has been posted in the past. It is unscientific in the
extreme to fit arbitrary functions to a set of data and then use the
resulting parameters to extrapolate likely sea level way into the future.

I think everyone accepts that climate change will cause very significant
rises in sea level in the next eg 50-100 years but estimating the likely
extent is very tricky. The only approach I can see with any credibility
involves a proper combined climate and oceanographic model. (Which
clearly is being done at various academic institutions. Why not leave
this technically very challenging problem to the professionals?)

On 23/07/2020 21:34, JGD wrote:
I think everyone accepts that climate change will cause very significant
rises in sea level in the next eg 50-100 years


But obviously not with the likes of Aviso implying , by continuing to
"fit" straight lines, that everything is hunky-dory.
With 3mm/year or even 4mm/year of the Aviso Jason3 plots, you are never
going to reach the IPCC levels predicted median SLR for 2100 of 72cm.
At least trying out different curves to the the very initial signals of
accelerating global SLR, I can see, so far, that any reference to
exponential SLR is fallacious. At least my results from existing data
are ball-park consistent with IPCC expectations, unlike the straight
line nonsense.

--
Global sea level rise to 2100 from curve-fitted existing altimetry data
http://diverse.4mg.com/slr.htm

On 24/07/2020 08:31, N_Cook wrote:

But obviously not with the likes of Aviso implying , by continuing to
"fit" straight lines, that everything is hunky-dory.

No-one is remotely suggesting that as far as I'm aware (though linearity
is probably the least-worst generic option unless you have a better
_model_ (not arbitrary function) that the data can be fitted to).

But compounding one piece of arguably bad science (the linear model)
with another piece of bad or worse science (wild extrapolation of a
model with no justifiable connection to the data) is not good, to put it
mildly and lays the results wide open to exactly the criticism I'm making.

It's the huge extrapolation which is the especially bad part of this.
Different data fits can be tried if you're _interpolating_ values within
the approximate range of the dataset but that's clearly irrelevant here
if the aim is to estimate sea level in eg 2100.

What I'm slightly puzzled about is that there clearly must be
professional estimates of future sea level based on a range of carefully
researched models and which are presumably updated at intervals. Why not
devote your energies to publicising and explaining these as new updates
become available - that would be really interesting?

JGD wrote:

On 24/07/2020 08:31, N_Cook wrote:

But obviously not with the likes of Aviso implying , by continuing
to "fit" straight lines, that everything is hunky-dory.

No-one is remotely suggesting that as far as I'm aware (though
linearity is probably the least-worst generic option unless you have
a better model (not arbitrary function) that the data can be fitted
to).

But compounding one piece of arguably bad science (the linear model)
with another piece of bad or worse science (wild extrapolation of a
model with no justifiable connection to the data) is not good, to put
it mildly and lays the results wide open to exactly the criticism I'm
making.

It's the huge extrapolation which is the especially bad part of this.
Different data fits can be tried if you're interpolating values
within the approximate range of the dataset but that's clearly
irrelevant here if the aim is to estimate sea level in eg 2100.

What I'm slightly puzzled about is that there clearly must be
professional estimates of future sea level based on a range of
carefully researched models and which are presumably updated at
intervals. Why not devote your energies to publicising and explaining
these as new updates become available - that would be really
interesting?

Here is one example of the sort of problems that I encounter:

If you have 20 years of hourly wind data from what is normally a
relatively benign location, but the data includes 6 hours of high winds
resulting from the passage of a hurricane, how do you extrapolate the
distribution to predict the once in 50-year wind event? The short
answer is that it is impossible.

The problem is that the winds produced by the passage of the hurricane
are not part of the same population as all of the other (benign) winds
and therefore the total wind environment cannot be described by any
single statistical function. In this case, what is necessary is to
determine the long-term hurricane climate of the location and work
backwards from that to produce an estimate of the once in 50-year wind
event.

In predicting future sea-level rise what is first of all needed is to
determine the 'climate' of the events responsible for sea level rise.
These include, but are not limited to, simple melting of land-based
ice, expansion of warming sea water, ice-sheet/glacier collapse. In
order to quantify the effects of each of these it is necessary to
predict their frequency and/or rate of occurrence and the range of
their effects. This is not a trivial task! Taking measurements of the
past combined effects of these causes and trying to fit them to some
statistical function and then extrapolating that far into the future is
not sound science. By trial and error it is possible to find a function
that appears to fit the data well and which give the prediction that
you would like to see!

--
Norman Lynagh
Tideswell, Derbyshire
303m a.s.l.
https://peakdistrictweather.org
twitter: @TideswellWeathr

On Friday, July 24, 2020 at 10:39:51 AM UTC+1, Norman Lynagh wrote:
Here is one example of the sort of problems that I encounter:

If you have 20 years of hourly wind data from what is normally a
relatively benign location, but the data includes 6 hours of high winds
resulting from the passage of a hurricane, how do you extrapolate the
distribution to predict the once in 50-year wind event? The short
answer is that it is impossible.

The problem is that the winds produced by the passage of the hurricane
are not part of the same population as all of the other (benign) winds
and therefore the total wind environment cannot be described by any
single statistical function. In this case, what is necessary is to
determine the long-term hurricane climate of the location and work
backwards from that to produce an estimate of the once in 50-year wind
event.

Have you not considered using a technique similar to that used by Nigel Cook. He uses several methods to project sea level rise. You could use several databases to predict your 50 year wind event. The first would be the total record, the second would be the periods during hurricanes, and the third the total record less those during hurricane periods. So long as you make it clear which set you are using.

In predicting future sea-level rise what is first of all needed is to
determine the 'climate' of the events responsible for sea level rise.

I was not predicting that sea level will be 82 cm. I was saying that if you extend the trend until 2100 then you get an 82 cm rise. Nigel extended using four methods, so he can’t be accused of predicting a value either.

On 25/07/2020 22:51, wrote:
I am not claiming it is sound science. I just find it interesting.

It might have been interesting to be a fly-on-the-wall of government
S.A.G.E. meetings earlier this year. I wonder if the scientific
arguments ever broke down into fisty-cuffs at any point, arguing about
"THE" science , when there is usually more than one scientific view on
the same subject and entrenched views come to the fore.


--
Global sea level rise to 2100 from curve-fitted existing altimetry data
http://diverse.4mg.com/slr.htm

wrote:

On Friday, July 24, 2020 at 10:39:51 AM UTC+1, Norman Lynagh wrote:
Here is one example of the sort of problems that I encounter:

If you have 20 years of hourly wind data from what is normally a
relatively benign location, but the data includes 6 hours of high
winds resulting from the passage of a hurricane, how do you
extrapolate the distribution to predict the once in 50-year wind
event? The short answer is that it is impossible.

The problem is that the winds produced by the passage of the
hurricane are not part of the same population as all of the other
(benign) winds and therefore the total wind environment cannot be
described by any single statistical function. In this case, what is
necessary is to determine the long-term hurricane climate of the
location and work backwards from that to produce an estimate of the
once in 50-year wind event.

Have you not considered using a technique similar to that used by
Nigel Cook. He uses several methods to project sea level rise. You
could use several databases to predict your 50 year wind event. The
first would be the total record, the second would be the periods
during hurricanes, and the third the total record less those during
hurricane periods. So long as you make it clear which set you are
using.

Unfortunately, this is not an academic exercise. My requirement is to
provided wind and wave data for offshore design purposes. The engineers
require a single number for each. They are not particularly bothered
how it is calculated. They most certainly do not want a range of
numbers. If that is what they are given they have to design to the
highest.


--
Norman Lynagh
Tideswell, Derbyshire
303m a.s.l.
https://peakdistrictweather.org
twitter: @TideswellWeathr

On 26/07/2020 12:05, Norman Lynagh wrote:
Unfortunately, this is not an academic exercise. My requirement is to
provided wind and wave data for offshore design purposes. The engineers
require a single number for each. They are not particularly bothered
how it is calculated. They most certainly do not want a range of
numbers. If that is what they are given they have to design to the
highest.

There is a very similar problem in coastal marine flood defense engineering.
I got into a soft,non-pugilistic, argument with a proper academic
oceanographer.
About the repeated use of ,IMHO, erroneous stratistical marine flooding
return period calculations, because of GIGO, garbage in , garbage out.
Proper academic oceanographers , if highly relevant data is missing or
questionable , and they know its iffy/missing, then they just exclude
any reference to it being missing or questionable in their inputs .
Include missing record-breakers and it makes a lot of difference to
these return-period calculations, and so heights/visual
intrusions/strengths/costs of flood walls etc.
For local to me marine flooding , the tide gauge broke in 1924 for the,
my research IMHO record breaker , century long period and twice in the
1990s , one of those missing ones the highest in 50 years,
IMHO/research. At least the paper record of the 1924 tide-gauge survived
and the exact fault and slippage dould be determined a century later,
unlike modern electronic tide gauge crapouts, where you have to rely on
newspaper reports, or witness recollections/photos and surveying, to
reconstitute.
My usual rambling stuff around the phrase
"erroneous flood event return periods in multi-million pound flood
prevention schemes"
on historical marine flooding and lots of ancilliary stuff
http://diverse.4mg.com/solent.htm

--
Global sea level rise to 2100 from curve-fitted existing altimetry data
http://diverse.4mg.com/slr.htm

In predicting future sea-level rise what is first of all needed is to
determine the 'climate' of the events responsible for sea level rise.
These include, but are not limited to, simple melting of land-based
ice, expansion of warming sea water, ice-sheet/glacier collapse. In
order to quantify the effects of each of these it is necessary to
predict their frequency and/or rate of occurrence and the range of
their effects. This is not a trivial task! Taking measurements of the
past combined effects of these causes and trying to fit them to some
statistical function and then extrapolating that far into the future is
not sound science. By trial and error it is possible to find a function
that appears to fit the data well and which give the prediction that
you would like to see!

--
Norman Lynagh
Tideswell, Derbyshire
303m a.s.l.
https://peakdistrictweather.org
twitter: @TideswellWeathr

I'd agree with all of that.

I once saw James May (of Top Gear fame) being interviewed about climate change, say, rather in opposition of climate change activists
"I put ice cubes in my glass, and when they melted the glass was no fuller."

The annoying thing is, he must have known what he was saying was totally misleading, but he was still happy to say it.

Graham
Penzance

On 24/07/2020 10:39, Norman Lynagh wrote:
JGD wrote:

On 24/07/2020 08:31, N_Cook wrote:

But obviously not with the likes of Aviso implying , by continuing
to "fit" straight lines, that everything is hunky-dory.

No-one is remotely suggesting that as far as I'm aware (though
linearity is probably the least-worst generic option unless you have
a better model (not arbitrary function) that the data can be fitted
to).

But compounding one piece of arguably bad science (the linear model)
with another piece of bad or worse science (wild extrapolation of a
model with no justifiable connection to the data) is not good, to put
it mildly and lays the results wide open to exactly the criticism I'm
making.

It's the huge extrapolation which is the especially bad part of this.
Different data fits can be tried if you're interpolating values
within the approximate range of the dataset but that's clearly
irrelevant here if the aim is to estimate sea level in eg 2100.

What I'm slightly puzzled about is that there clearly must be
professional estimates of future sea level based on a range of
carefully researched models and which are presumably updated at
intervals. Why not devote your energies to publicising and explaining
these as new updates become available - that would be really
interesting?

Here is one example of the sort of problems that I encounter:

If you have 20 years of hourly wind data from what is normally a
relatively benign location, but the data includes 6 hours of high winds
resulting from the passage of a hurricane, how do you extrapolate the
distribution to predict the once in 50-year wind event? The short
answer is that it is impossible.

Bayesian analysis will get you the best answer provided that you are
able to specify *exactly* what your question is, what prior knowledge
and how much data you have.

The problem is that the winds produced by the passage of the hurricane
are not part of the same population as all of the other (benign) winds
and therefore the total wind environment cannot be described by any
single statistical function. In this case, what is necessary is to
determine the long-term hurricane climate of the location and work
backwards from that to produce an estimate of the once in 50-year wind
event.

Same sort of problem applies to the in service failure of things subject
to "preventative" maintenance but sometimes also expire on replacement
due to infant mortality. The decision of when to replace them to obtain
maximum efficiency is a distinctly non-trivial problem.

Filament light bulbs in awkward locations is the canonical example.

In predicting future sea-level rise what is first of all needed is to
determine the 'climate' of the events responsible for sea level rise.
These include, but are not limited to, simple melting of land-based
ice, expansion of warming sea water, ice-sheet/glacier collapse. In
order to quantify the effects of each of these it is necessary to
predict their frequency and/or rate of occurrence and the range of
their effects. This is not a trivial task! Taking measurements of the
past combined effects of these causes and trying to fit them to some
statistical function and then extrapolating that far into the future is
not sound science. By trial and error it is possible to find a function
that appears to fit the data well and which give the prediction that
you would like to see!

There is unlikely to be enough data to go anything beyond a quadratic
fit and there will be a huge uncertainty in the second order term.

--
Regards,
Martin Brown