Site with page
https://www.aviso.altimetry.fr/data/products/ocean-indicators-products/mean-sea-level/products-and-images-selection-without-saral-old.html
is now back working, after an absence of a month or two.
To the eye , the reference plot shows something of a curve, top and
bottom of the trace is subatantially above the linear "fit" and middle
is below the straight line. So what sort of curve is the best RMS
least-square fit,ie curves with zero, constant, decreasing or increasing
acceleration?

Firstly using all 1007 datapoints of the Reference plot from 1993 to
2020.404245 , as a partial check on my processing and as a check for
anyone else repeating this.
Linear "fit" , here y is cm as Aviso and x=0 for year 1990.0000 (to
avoid problems if using an exponential or indicial curve-type, but just
the linear here)
y= 0.341193*x -1.607415
R^2= 0.98572
Agreeing with the rounded to 3.41, as mm/year, of the Aviso plot
As explained below , I prefer to start from 2003 and just the Jason
missions.
Ranking of fit by R*R, ie closest to 1 is best curve fit.
x=0 for year 2000.0000 and for the following curves
Linear
y= 0.3751*x + 1.35
R*R=0.972487
SLR to 2100, 38.9cm

Exponential
y=2.20 -4.47675*(1-exp(0.04735*x))
R^2=0.984027
slr to 2100 = 5.05metres

Quadratic
y=2.38 + 0.15843*x + 0.009259*x^2
R^2= 0.98450
slr to 2100= 110.82cm

Indicial (best fit)
y=2.64 + 0.06310*x^(1.55207)
R^R= 0.984715
SLR to 2100 = 82.87cm

History of these results, ranking by R*R, goodness of fit, for best
curve type each time usual the indicial form and decreasing acceleration
, using 2003 to the latest datapoint to avoid the early altimeter
calibration problem and post-Pinatubo recovery SLR flattening and
including the 1993 to 2003 tranche does not actually make much
difference to projections.
Initially melding together the separate J1,J2 and J3 plots and then
since 2019 using the Aviso Reference data as the small GIA component is
getting less and less significant and less and less confidence in the
mission cross-over/overlap data, going in and out of the filters.
SLR to year 2100 using Dec 2017 data of May2017 , J1+J2 only , 56.2cm
data to 25 May 2018 to 2100 , SLR 57.1 cm
data to 02 Aug 2018 to 2100 , SLR 50.5 cm
Update data to 01 Sep 2018, public output 07 Dec 2018
SLR to year 2100 , 49.0 cm
Update data to 01 Oct 2018, public output 18 Jan 2019
SLR to year 2100 , 50.9 cm
Update data to 29 Nov 2018, public output 02 Feb 2019
SLR to year 2100 , 77.4 cm
Update data to 26 June 2019, public output 07 September 2019
SLR to year 2100 , 80.2 cm

Update to 25 July 2019, 02 Nov 2019 public output,
SLR to 2100, 88.2cm

Update 11 January 2020
for data 2003.002659 to 2019.806999,
SLR to 2100 , 88cm

01 Dec 2019 output to the public 15 Feb 2020
SLR to 2100, 117cm (quadratic was the best fit that time, otherwise
indical was 89cm)

624 datapoints from year 2003.002659 to year 2019.9699
output to the public 29 Feb 2020.
SLR to 2100, 88.5cm

640 datapoints 2003.002659 to 2020.404245 website back working on
20 July 2010
SLR to 2100 = 82.9cm

Emlargement of the above and a few images on my page below
--
Global sea level rise to 2100 from curve-fitted existing altimetry data
http://diverse.4mg.com/slr.htm

On Tuesday, 21 July 2020 at 18:25:34 UTC+1, N_Cook wrote:
Site with page
https://www.aviso.altimetry.fr/data/products/ocean-indicators-products/mean-sea-level/products-and-images-selection-without-saral-old.html
is now back working, after an absence of a month or two.
To the eye , the reference plot shows something of a curve, top and
bottom of the trace is subatantially above the linear "fit" and middle
is below the straight line. So what sort of curve is the best RMS
least-square fit,ie curves with zero, constant, decreasing or increasing
acceleration?

Firstly using all 1007 datapoints of the Reference plot from 1993 to
2020.404245 , as a partial check on my processing and as a check for
anyone else repeating this.
Linear "fit" , here y is cm as Aviso and x=0 for year 1990.0000 (to
avoid problems if using an exponential or indicial curve-type, but just
the linear here)
y= 0.341193*x -1.607415
R^2= 0.98572
Agreeing with the rounded to 3.41, as mm/year, of the Aviso plot
As explained below , I prefer to start from 2003 and just the Jason
missions.
Ranking of fit by R*R, ie closest to 1 is best curve fit.
x=0 for year 2000.0000 and for the following curves
Linear
y= 0.3751*x + 1.35
R*R=0.972487
SLR to 2100, 38.9cm

Exponential
y=2.20 -4.47675*(1-exp(0.04735*x))
R^2=0.984027
slr to 2100 = 5.05metres

Quadratic
y=2.38 + 0.15843*x + 0.009259*x^2
R^2= 0.98450
slr to 2100= 110.82cm

Indicial (best fit)
y=2.64 + 0.06310*x^(1.55207)
R^R= 0.984715
SLR to 2100 = 82.87cm

History of these results, ranking by R*R, goodness of fit, for best
curve type each time usual the indicial form and decreasing acceleration
, using 2003 to the latest datapoint to avoid the early altimeter
calibration problem and post-Pinatubo recovery SLR flattening and
including the 1993 to 2003 tranche does not actually make much
difference to projections.
Initially melding together the separate J1,J2 and J3 plots and then
since 2019 using the Aviso Reference data as the small GIA component is
getting less and less significant and less and less confidence in the
mission cross-over/overlap data, going in and out of the filters.
SLR to year 2100 using Dec 2017 data of May2017 , J1+J2 only , 56.2cm
data to 25 May 2018 to 2100 , SLR 57.1 cm
data to 02 Aug 2018 to 2100 , SLR 50.5 cm
Update data to 01 Sep 2018, public output 07 Dec 2018
SLR to year 2100 , 49.0 cm
Update data to 01 Oct 2018, public output 18 Jan 2019
SLR to year 2100 , 50.9 cm
Update data to 29 Nov 2018, public output 02 Feb 2019
SLR to year 2100 , 77.4 cm
Update data to 26 June 2019, public output 07 September 2019
SLR to year 2100 , 80.2 cm

Update to 25 July 2019, 02 Nov 2019 public output,
SLR to 2100, 88.2cm

Update 11 January 2020
for data 2003.002659 to 2019.806999,
SLR to 2100 , 88cm

01 Dec 2019 output to the public 15 Feb 2020
SLR to 2100, 117cm (quadratic was the best fit that time, otherwise
indical was 89cm)

624 datapoints from year 2003.002659 to year 2019.9699
output to the public 29 Feb 2020.
SLR to 2100, 88.5cm

640 datapoints 2003.002659 to 2020.404245 website back working on
20 July 2010
SLR to 2100 = 82.9cm

Emlargement of the above and a few images on my page below
--
Global sea level rise to 2100 from curve-fitted existing altimetry data
http://diverse.4mg.com/slr.htm

I have downloaded the data into an Excel spreadsheet and fitted a polynomial trendline which i have extended to 2100. The result was a sea-level rise of ~80 cm which agrees with your indicial (best fit). Nothing for me to worry about at 25m ASL, but bound to have consequences for properties on the coast during storms, e.g. Sandbanks nr Poole.

On 23/07/2020 17:08, Alastair B. McDonald wrote:
On Tuesday, 21 July 2020 at 18:25:34 UTC+1, N_Cook wrote:
Site with page
https://www.aviso.altimetry.fr/data/products/ocean-indicators-products/mean-sea-level/products-and-images-selection-without-saral-old.html
is now back working, after an absence of a month or two.
To the eye , the reference plot shows something of a curve, top and
bottom of the trace is subatantially above the linear "fit" and middle
is below the straight line. So what sort of curve is the best RMS
least-square fit,ie curves with zero, constant, decreasing or increasing
acceleration?

Firstly using all 1007 datapoints of the Reference plot from 1993 to
2020.404245 , as a partial check on my processing and as a check for
anyone else repeating this.
Linear "fit" , here y is cm as Aviso and x=0 for year 1990.0000 (to
avoid problems if using an exponential or indicial curve-type, but just
the linear here)
y= 0.341193*x -1.607415
R^2= 0.98572
Agreeing with the rounded to 3.41, as mm/year, of the Aviso plot
As explained below , I prefer to start from 2003 and just the Jason
missions.
Ranking of fit by R*R, ie closest to 1 is best curve fit.
x=0 for year 2000.0000 and for the following curves
Linear
y= 0.3751*x + 1.35
R*R=0.972487
SLR to 2100, 38.9cm

Exponential
y=2.20 -4.47675*(1-exp(0.04735*x))
R^2=0.984027
slr to 2100 = 5.05metres

Quadratic
y=2.38 + 0.15843*x + 0.009259*x^2
R^2= 0.98450
slr to 2100= 110.82cm

Indicial (best fit)
y=2.64 + 0.06310*x^(1.55207)
R^R= 0.984715
SLR to 2100 = 82.87cm

History of these results, ranking by R*R, goodness of fit, for best
curve type each time usual the indicial form and decreasing acceleration
, using 2003 to the latest datapoint to avoid the early altimeter
calibration problem and post-Pinatubo recovery SLR flattening and
including the 1993 to 2003 tranche does not actually make much
difference to projections.
Initially melding together the separate J1,J2 and J3 plots and then
since 2019 using the Aviso Reference data as the small GIA component is
getting less and less significant and less and less confidence in the
mission cross-over/overlap data, going in and out of the filters.
SLR to year 2100 using Dec 2017 data of May2017 , J1+J2 only , 56.2cm
data to 25 May 2018 to 2100 , SLR 57.1 cm
data to 02 Aug 2018 to 2100 , SLR 50.5 cm
Update data to 01 Sep 2018, public output 07 Dec 2018
SLR to year 2100 , 49.0 cm
Update data to 01 Oct 2018, public output 18 Jan 2019
SLR to year 2100 , 50.9 cm
Update data to 29 Nov 2018, public output 02 Feb 2019
SLR to year 2100 , 77.4 cm
Update data to 26 June 2019, public output 07 September 2019
SLR to year 2100 , 80.2 cm

Update to 25 July 2019, 02 Nov 2019 public output,
SLR to 2100, 88.2cm

Update 11 January 2020
for data 2003.002659 to 2019.806999,
SLR to 2100 , 88cm

01 Dec 2019 output to the public 15 Feb 2020
SLR to 2100, 117cm (quadratic was the best fit that time, otherwise
indical was 89cm)

624 datapoints from year 2003.002659 to year 2019.9699
output to the public 29 Feb 2020.
SLR to 2100, 88.5cm

640 datapoints 2003.002659 to 2020.404245 website back working on
20 July 2010
SLR to 2100 = 82.9cm

Emlargement of the above and a few images on my page below
--
Global sea level rise to 2100 from curve-fitted existing altimetry data
http://diverse.4mg.com/slr.htm

I have downloaded the data into an Excel spreadsheet and fitted a polynomial trendline which i have extended to 2100. The result was a sea-level rise of ~80 cm which agrees with your indicial (best fit). Nothing for me to worry about at 25m ASL, but bound to have consequences for properties on the coast during storms, e.g. Sandbanks nr Poole.


For completeness was that using the full 1993 to 2020 Reference dataset
or just 2003.0 to 2020 ?




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

Alastair B. McDonald wrote:

On Tuesday, 21 July 2020 at 18:25:34 UTC+1, N_Cook wrote:

snip


History of these results, ranking by R*R, goodness of fit, for best
curve type each time usual the indicial form and decreasing
acceleration , using 2003 to the latest datapoint to avoid the
early altimeter calibration problem and post-Pinatubo recovery SLR
flattening and including the 1993 to 2003 tranche does not actually
make much difference to projections.
Initially melding together the separate J1,J2 and J3 plots and then
since 2019 using the Aviso Reference data as the small GIA
component is getting less and less significant and less and less
confidence in the mission cross-over/overlap data, going in and out
of the filters. SLR to year 2100 using Dec 2017 data of May2017 ,
J1+J2 only , 56.2cm data to 25 May 2018 to 2100 , SLR 57.1 cm
data to 02 Aug 2018 to 2100 , SLR 50.5 cm
Update data to 01 Sep 2018, public output 07 Dec 2018
SLR to year 2100 , 49.0 cm
Update data to 01 Oct 2018, public output 18 Jan 2019
SLR to year 2100 , 50.9 cm
Update data to 29 Nov 2018, public output 02 Feb 2019
SLR to year 2100 , 77.4 cm
Update data to 26 June 2019, public output 07 September 2019
SLR to year 2100 , 80.2 cm

Update to 25 July 2019, 02 Nov 2019 public output,
SLR to 2100, 88.2cm

Update 11 January 2020
for data 2003.002659 to 2019.806999,
SLR to 2100 , 88cm

01 Dec 2019 output to the public 15 Feb 2020
SLR to 2100, 117cm (quadratic was the best fit that time, otherwise
indical was 89cm)

624 datapoints from year 2003.002659 to year 2019.9699
output to the public 29 Feb 2020.
SLR to 2100, 88.5cm

640 datapoints 2003.002659 to 2020.404245 website back working on
20 July 2010
SLR to 2100 = 82.9cm

Emlargement of the above and a few images on my page below
--
Global sea level rise to 2100 from curve-fitted existing altimetry
data http://diverse.4mg.com/slr.htm

I have downloaded the data into an Excel spreadsheet and fitted a
polynomial trendline which i have extended to 2100. The result was a
sea-level rise of ~80 cm which agrees with your indicial (best fit).
Nothing for me to worry about at 25m ASL, but bound to have
consequences for properties on the coast during storms, e.g.
Sandbanks nr Poole.

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

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

On 23/07/2020 20:48, Norman Lynagh wrote:
Alastair B. McDonald wrote:

On Tuesday, 21 July 2020 at 18:25:34 UTC+1, N_Cook wrote:

snip


History of these results, ranking by R*R, goodness of fit, for best
curve type each time usual the indicial form and decreasing
acceleration , using 2003 to the latest datapoint to avoid the
early altimeter calibration problem and post-Pinatubo recovery SLR
flattening and including the 1993 to 2003 tranche does not actually
make much difference to projections.
Initially melding together the separate J1,J2 and J3 plots and then
since 2019 using the Aviso Reference data as the small GIA
component is getting less and less significant and less and less
confidence in the mission cross-over/overlap data, going in and out
of the filters. SLR to year 2100 using Dec 2017 data of May2017 ,
J1+J2 only , 56.2cm data to 25 May 2018 to 2100 , SLR 57.1 cm
data to 02 Aug 2018 to 2100 , SLR 50.5 cm
Update data to 01 Sep 2018, public output 07 Dec 2018
SLR to year 2100 , 49.0 cm
Update data to 01 Oct 2018, public output 18 Jan 2019
SLR to year 2100 , 50.9 cm
Update data to 29 Nov 2018, public output 02 Feb 2019
SLR to year 2100 , 77.4 cm
Update data to 26 June 2019, public output 07 September 2019
SLR to year 2100 , 80.2 cm

Update to 25 July 2019, 02 Nov 2019 public output,
SLR to 2100, 88.2cm

Update 11 January 2020
for data 2003.002659 to 2019.806999,
SLR to 2100 , 88cm

01 Dec 2019 output to the public 15 Feb 2020
SLR to 2100, 117cm (quadratic was the best fit that time, otherwise
indical was 89cm)

624 datapoints from year 2003.002659 to year 2019.9699
output to the public 29 Feb 2020.
SLR to 2100, 88.5cm

640 datapoints 2003.002659 to 2020.404245 website back working on
20 July 2010
SLR to 2100 = 82.9cm

Emlargement of the above and a few images on my page below
--
Global sea level rise to 2100 from curve-fitted existing altimetry
data http://diverse.4mg.com/slr.htm

I have downloaded the data into an Excel spreadsheet and fitted a
polynomial trendline which i have extended to 2100. The result was a
sea-level rise of ~80 cm which agrees with your indicial (best fit).
Nothing for me to worry about at 25m ASL, but bound to have
consequences for properties on the coast during storms, e.g.
Sandbanks nr Poole.

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


Which equally applies to people like those at Aviso who continue to
"fit" a straight line to a situation that is patently not a linear
process. Not even much disturbamce from el Nino/La Nina cycles that has
been effectively neutral for ages now.
When will they get around to being sensible? Do they not believe the
IPCC assesments from multiple global modelling producing far more than
any linear SLR up to 2100


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

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 Thursday, 23 July 2020 at 17:15:37 UTC+1, N_Cook wrote:
On 23/07/2020 17:08, Alastair B. McDonald wrote:

I have downloaded the data into an Excel spreadsheet and fitted a polynomial trendline which i have extended to 2100. The result was a sea-level rise of ~80 cm which agrees with your indicial (best fit). Nothing for me to worry about at 25m ASL, but bound to have consequences for properties on the coast during storms, e.g. Sandbanks nr Poole.

For completeness was that using the full 1993 to 2020 Reference dataset
or just 2003.0 to 2020 ?

It was the full set.

I agree with you that the AVISO straight trend line is ridiculous. But unfortunately, I will not be around in 2100 to find out who is right.

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?

On 24/07/2020 08:45, Alastair B. McDonald wrote:
On Thursday, 23 July 2020 at 17:15:37 UTC+1, N_Cook wrote:
On 23/07/2020 17:08, Alastair B. McDonald wrote:

I have downloaded the data into an Excel spreadsheet and fitted a polynomial trendline which i have extended to 2100. The result was a sea-level rise of ~80 cm which agrees with your indicial (best fit). Nothing for me to worry about at 25m ASL, but bound to have consequences for properties on the coast during storms, e.g. Sandbanks nr Poole.

For completeness was that using the full 1993 to 2020 Reference dataset
or just 2003.0 to 2020 ?

It was the full set.

I agree with you that the AVISO straight trend line is ridiculous. But unfortunately, I will not be around in 2100 to find out who is right.


I know that to the scale of a pc monitor the difference between a
quadratic and indicial power curve best fit is irresolvable, just 1 or 2
pixels in it, best fit exponential is resolvable compared to those.
Aviso could fit a representative best fit smooth curve to that data ,
without even stating the equation or projecting on, and ditching the
straight line, for the 1993 to 2020 plot anyway.


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