6-weekly update of Jason1+2+3 data from aviso.altimetry.fr data to 16
Mar 2018, publically accessible 13 May 2018. x= year minus 2000, Y= cm
height by Aviso assessment.
Various curve-fit types ranked by R^2 quality of fit, best fit still the
indicial power curve and best estimate so far , of 57cm global mean sea
level rise to year 2100. Officialdom is still showing linear "fits" to
the Jason data, downplaying to about 35cm rise to year 2100
Determinations still falling , but exceedingly unlikely to return to
linear as best fit of curve type. The linear rate here (0.335159
cm/year) does near enough agree with the Aviso reference assessment in
3.32 mm per year considering only subset of 51 datapoints used by me to
cover 2003 to 2018.
Sequence of best-fits of the 4 types, all indicial power curves falling
indices, for the 6-weekly asessments this year, out to 2100 61.2cm,
60.7cm and this latest 57.1cm

linear
Y = 1.419263 + 0.335159*x
R^2= 0.981084
2030 11.474cm
2050 18.177
2100 34.935


exponential
Y = 1.952271 -6.730993*(1-e^(0.033595*x))
R^2=0.984702
2030 13.662 cm
2100 1.889 m


quadratic
Y = 2.029890 + 0.202368*x + 0.005775*x^2
R^2 = 0.984857

2030 13.298 cm
2050 26.585
2100 80.016


Indicial power
Y = 2.263276 + 0.101848*x^1.365590
R^2 = 0.985011

2030 12.858cm
2050 23.547
2100 57.107


Y = year (minus 2000) , x is cm SLR in Aviso.Altimetry terms for Jason-3
output up to 05 April 2018, publically outputed 26 June 2018,
for various optimised curve fits and concattenated 52 datapoint data for
Jason1+2+3

Linear
y= 1.427594 + 0.334124x
r*r = 0.981312
year Sea Level Rise
2020 8.11cm
2050 18.133
2100 34.839

Exponential
Y = 1.926243 -7.467664*(1-Exp(0.031073*x))
r*r = 0.984443
year Sea Level
2020 8.36 cm
2050 29.77 cm
2100 161.4m

Quadratic
Y= 1.998822 +0.210329 * x +0.005367 * x^2
r*r = 0.984598
year Sea Level
2020 8.352cm
2050 25.932
2100 76.701

Best still on R*R goodness, Indicial
Y= 2.232609 +0.109142 * x^1.342432
r*r = 0.984789
about 4/3 power
year Sea Level Rise
2020 8.321 cm
2050 23.065
2100 55.059

On 27/06/2018 15:39, N_Cook wrote:
2100 161.4m

error 1.614m

Y = year (minus 2000) , x is cm SLR in Aviso.Altimetry terms for Jason-3
output up to 05 April 2018, publically outputed 26 June 2018,
for various optimised curve fits and concattenated 52 datapoint data for
Jason1+2+3

Linear
y= 1.427594 + 0.334124x
r*r = 0.981312
year Sea Level Rise
2020 8.11cm
2050 18.133
2100 34.839

Exponential
Y = 1.926243 -7.467664*(1-Exp(0.031073*x))
r*r = 0.984443
year Sea Level
2020 8.36 cm
2050 29.77 cm
2100 161.4m

Quadratic
Y= 1.998822 +0.210329 * x +0.005367 * x^2
r*r = 0.984598
year Sea Level
2020 8.352cm
2050 25.932
2100 76.701

Best still on R*R goodness, Indicial
Y= 2.232609 +0.109142 * x^1.342432
r*r = 0.984789
about 4/3 power
year Sea Level Rise
2020 8.321 cm
2050 23.065
2100 55.059

Y = year (minus 2000) , x is cm SLR in Aviso.Altimetry terms for Jason-3
output up to 25 May 2018, publically outputed approx 23 July 2018,
for various optimised curve fits and concattenated 54 datapoint data for
Jason1+2+3, ranked in terms of R*R

Linear
Y= 1.440160 + 0.332706 * x
R^R=0.981621
year Sea Level Rise , cm
2020 8.094
2050 18.075
2100 34.71

Exponential
Y = 1.885012 -8.885318*(1-Exp(0.027178*x))
r*r = 0.984062
year Sea Level Rise ,cm
2020 8.301
2050 27.58
2100 127.585

Quadratic
Y= 1.949468 +0.222901 * x +0.004728 * x^2
r*r = 0.984207
year Sea Level Rise , cm
2020 8.298
2050 24.914
2100 71.519


Best still on R*R goodness, Indicial
Y= 2.182871 +0.121504 * x^1.306716
r*r = 0.984447
still about 4/3 power, projection still falling
year Sea Level Rise , cm
2020 8.273
2050 22.35
2100 52.073

I'm afraid that I was always taught that curve fitting is good (with
caveats) for interpolation but to be avoided as far as humanly possible
for extrapolation.

(Because there is always a serious danger that a model that appears to
be a good fit over a limited range of data can become - potentially -
absurdly wrong the further the curve is pushed beyond the available
data. This is especially so if the model equation includes some sort of
power function and/or is not grounded in some credible physical hypothesis.)

This obviously presents a real difficulty for forecasts of climate
change and related parameters where - short of becoming time travellers
- there is no choice but to try to extrapolate into the future. But it
needs to be done with real caution if the parameter values are to be at
all useful or credible, whatever the nominal SSR might suggest.

On 26/07/2018 16:55, JGD wrote:
I'm afraid that I was always taught that curve fitting is good (with
caveats) for interpolation but to be avoided as far as humanly possible
for extrapolation.

(Because there is always a serious danger that a model that appears to
be a good fit over a limited range of data can become - potentially -
absurdly wrong the further the curve is pushed beyond the available
data. This is especially so if the model equation includes some sort of
power function and/or is not grounded in some credible physical
hypothesis.)

This obviously presents a real difficulty for forecasts of climate
change and related parameters where - short of becoming time travellers
- there is no choice but to try to extrapolate into the future. But it
needs to be done with real caution if the parameter values are to be at
all useful or credible, whatever the nominal SSR might suggest.


Fair enough and here
https://www.aviso.altimetry.fr/en/da...sea-level.html
they say
"for scientific and statistics reasons, period under 5 years are not
significant."
so what is the point of graphing out this info.
Last year it seemed to me, that concattenating Jason1+2+3 SLR plots,
whatever character SLR had, it was not linear, and so my concattenation,
allowing for filter-effect discontinuites at 1/2 and 2/3 junctions
http://diverse.4mg.com/jason1+2+3r.jpg
I've not even found a proper academic concattenation of J1+2+3 outputs.
They and other academics are still putting a linear "fit " to the curve.
The situation at the end of last year looked a lot worse, due to filter
effects etc, but the curve is still not linear best fit for northern
nemispherw spring+summer months added data.
Just my effort to get a more rational handle on the SLR later this
century. The curve-fit projection will increase again , due to the
filters ,again this autumn/winter, but hopefully it will all
give a better idea than linear, after a few years of doing this

On Thursday, 26 July 2018 18:45:05 UTC+1, N_Cook wrote:
On 26/07/2018 16:55, JGD wrote:
I'm afraid that I was always taught that curve fitting is good (with
caveats) for interpolation but to be avoided as far as humanly possible
for extrapolation.

(Because there is always a serious danger that a model that appears to
be a good fit over a limited range of data can become - potentially -
absurdly wrong the further the curve is pushed beyond the available
data. This is especially so if the model equation includes some sort of
power function and/or is not grounded in some credible physical
hypothesis.)

This obviously presents a real difficulty for forecasts of climate
change and related parameters where - short of becoming time travellers
- there is no choice but to try to extrapolate into the future. But it
needs to be done with real caution if the parameter values are to be at
all useful or credible, whatever the nominal SSR might suggest.


Fair enough and here
https://www.aviso.altimetry.fr/en/da...sea-level.html
they say
"for scientific and statistics reasons, period under 5 years are not
significant."
so what is the point of graphing out this info.
Last year it seemed to me, that concattenating Jason1+2+3 SLR plots,
whatever character SLR had, it was not linear, and so my concattenation,
allowing for filter-effect discontinuites at 1/2 and 2/3 junctions
http://diverse.4mg.com/jason1+2+3r.jpg
I've not even found a proper academic concattenation of J1+2+3 outputs.
They and other academics are still putting a linear "fit " to the curve.
The situation at the end of last year looked a lot worse, due to filter
effects etc, but the curve is still not linear best fit for northern
nemispherw spring+summer months added data.
Just my effort to get a more rational handle on the SLR later this
century. The curve-fit projection will increase again , due to the
filters ,again this autumn/winter, but hopefully it will all
give a better idea than linear, after a few years of doing this

Nick,

I think you are doing sterling job. As you say, the conventional approach is to use a linear fit which has all the drawbacks explained by JGD. By presenting several projections you are describing various alternatives, only one at most of which can be correct. Keep up the good work!

Cheers, Alastair.

On Thursday, 26 July 2018 21:55:47 UTC+1, Alastair wrote:

Come away from here Alistair they are making you look stupid by association. How much water is available to raise the level of the sea by one linear millimetre?

Leave the dead lie. They are beyond recall. Pay attention to what the rotting corpse is going to be washed away by:
https://gab.ai/NortonIceman/posts/30229877

These people are owls
Their machine is geared to the production of words, are you going to remain trapped in their gears or going to break out of their spell?

On 26/07/2018 21:55, Alastair wrote:
On Thursday, 26 July 2018 18:45:05 UTC+1, N_Cook wrote:
On 26/07/2018 16:55, JGD wrote:
I'm afraid that I was always taught that curve fitting is good (with
caveats) for interpolation but to be avoided as far as humanly possible
for extrapolation.

(Because there is always a serious danger that a model that appears to
be a good fit over a limited range of data can become - potentially -
absurdly wrong the further the curve is pushed beyond the available
data. This is especially so if the model equation includes some sort of
power function and/or is not grounded in some credible physical
hypothesis.)

This obviously presents a real difficulty for forecasts of climate
change and related parameters where - short of becoming time travellers
- there is no choice but to try to extrapolate into the future. But it
needs to be done with real caution if the parameter values are to be at
all useful or credible, whatever the nominal SSR might suggest.


Fair enough and here
https://www.aviso.altimetry.fr/en/da...sea-level.html
they say
"for scientific and statistics reasons, period under 5 years are not
significant."
so what is the point of graphing out this info.
Last year it seemed to me, that concattenating Jason1+2+3 SLR plots,
whatever character SLR had, it was not linear, and so my concattenation,
allowing for filter-effect discontinuites at 1/2 and 2/3 junctions
http://diverse.4mg.com/jason1+2+3r.jpg
I've not even found a proper academic concattenation of J1+2+3 outputs.
They and other academics are still putting a linear "fit " to the curve.
The situation at the end of last year looked a lot worse, due to filter
effects etc, but the curve is still not linear best fit for northern
nemispherw spring+summer months added data.
Just my effort to get a more rational handle on the SLR later this
century. The curve-fit projection will increase again , due to the
filters ,again this autumn/winter, but hopefully it will all
give a better idea than linear, after a few years of doing this

Nick,

I think you are doing sterling job. As you say, the conventional approach is to use a linear fit which has all the drawbacks explained by JGD. By presenting several projections you are describing various alternatives, only one at most of which can be correct. Keep up the good work!

Cheers, Alastair.


I've not updated that plot I referred to above, probably even less
difference in recent months. What I did not expect was only 2 pixels of
vertical difference between those 3 curved plots, almost imperceptible,
but of course project on through the century and very different outcomes.
It looks as though the next but one Jason3 public update may return to
the more elevated cycle again. It does not look as though the R*R
ranking order is going to change before that return to more positive
territory and 4/3 indicial power law will continue to be the best fit.

On Friday, 27 July 2018 08:14:07 UTC+1, N_Cook wrote:
On 26/07/2018 21:55, Alastair wrote:
On Thursday, 26 July 2018 18:45:05 UTC+1, N_Cook wrote:
On 26/07/2018 16:55, JGD wrote:
I'm afraid that I was always taught that curve fitting is good (with
caveats) for interpolation but to be avoided as far as humanly possible
for extrapolation.

(Because there is always a serious danger that a model that appears to
be a good fit over a limited range of data can become - potentially -
absurdly wrong the further the curve is pushed beyond the available
data. This is especially so if the model equation includes some sort of
power function and/or is not grounded in some credible physical
hypothesis.)

This obviously presents a real difficulty for forecasts of climate
change and related parameters where - short of becoming time travellers
- there is no choice but to try to extrapolate into the future. But it
needs to be done with real caution if the parameter values are to be at
all useful or credible, whatever the nominal SSR might suggest.


Fair enough and here
https://www.aviso.altimetry.fr/en/da...sea-level.html
they say
"for scientific and statistics reasons, period under 5 years are not
significant."
so what is the point of graphing out this info.
Last year it seemed to me, that concattenating Jason1+2+3 SLR plots,
whatever character SLR had, it was not linear, and so my concattenation,
allowing for filter-effect discontinuites at 1/2 and 2/3 junctions
http://diverse.4mg.com/jason1+2+3r.jpg
I've not even found a proper academic concattenation of J1+2+3 outputs..
They and other academics are still putting a linear "fit " to the curve.
The situation at the end of last year looked a lot worse, due to filter
effects etc, but the curve is still not linear best fit for northern
nemispherw spring+summer months added data.
Just my effort to get a more rational handle on the SLR later this
century. The curve-fit projection will increase again , due to the
filters ,again this autumn/winter, but hopefully it will all
give a better idea than linear, after a few years of doing this

Nick,

I think you are doing sterling job. As you say, the conventional approach is to use a linear fit which has all the drawbacks explained by JGD. By presenting several projections you are describing various alternatives, only one at most of which can be correct. Keep up the good work!

Cheers, Alastair.


I've not updated that plot I referred to above, probably even less
difference in recent months. What I did not expect was only 2 pixels of
vertical difference between those 3 curved plots, almost imperceptible,
but of course project on through the century and very different outcomes.
It looks as though the next but one Jason3 public update may return to
the more elevated cycle again. It does not look as though the R*R
ranking order is going to change before that return to more positive
territory and 4/3 indicial power law will continue to be the best fit.

The surface melt from Greenland is well down this summer, so the next Jason plot may be anomalously low. https://www.dmi.dk/uploads/tx_dmidat...mulatedsmb.png

See also https://www.dmi.dk/uploads/tx_dmidat...mulatedsmb.png

But there is also a contribution to sea level rise from Greenland mass flow and from Antarctica.

The surface melt from Greenland is well down this summer, so the next Jason plot may be anomalously low. https://www.dmi.dk/uploads/tx_dmidat...mulatedsmb.png

See also https://www.dmi.dk/uploads/tx_dmidat...mulatedsmb.png

But there is also a contribution to sea level rise from Greenland mass flow and from Antarctica.


But I get the impression this was an unexpected surprise

https://www.leeds.ac.uk/news/article...sea_level_rise

and nothing like that had generally been factored in for global sea
level rise.

"Between 2012 and 2017 the continent lost 219 billion tonnes of ice per
year – a 0.6 mm per year sea level contribution. "
or about 1/4 to 1/5 of the total.