On 19/01/2019 08:48, N_Cook wrote:
On 18/01/2019 20:24, Graham Easterling wrote:
On Friday, January 18, 2019 at 7:43:09 PM UTC, N_Cook wrote:
On 18/01/2019 19:01, JGD wrote:
On 18/01/2019 17:05, N_Cook wrote:

Oceanographers seem to be comfortable with quoting IPCC projections
for
SLR to 2100 but at the same time will only fit a straight line to the
Jason data.

I'm afraid that there's a reason for that, which is that they are
scientists. And a good scientist should never (and usually will never)
try to fit an arbitrary curve to a set of data, especially so when very
considerable extrapolation is involved (which is inevitably the case
when trying to forecast 80 years into the future).

The only pragmatic answer available is to use the most conservative of
assumptions or tools, which in this case is limited to a linear trend.

Obviously this creates a major headache for climate change predictions,
but the only way out is to base the curve-fitting on some sort of
defined model, which will take account of parameters like thermal
expansion of the oceans, estimated melting of land ice (insofar as it
can even be estimated roughly), and so on. I presume that there are
interdiscplinary teams that can try to put this sort of model together
and I'd guess that there this must have been happening already for a
few
years. So the embryonic models must be out there somewhere in the
oceanographic literature.

But please, please, please let's not fit arbitrary functions,
exponential or otherwise, to a set of data and pretend that
extrapolation of such curves way into the future means anything at all.

One of them might be more accurate if it was based on a physical model
of how rising sea level and loss of permanent snow cover and glacier
mass would affect the Earth's albedo for example.

Have you seen the IPCC predictions?
Well above the linear "fit" of 3.34mm per year.
I hope you can admit , that to reach such IPCC predictions, there must
be some sort of up-curve at some point.
Where is the evidence this deviation from the current "scientific"
straight line would be as late as 2080 or 2060 say , it has to happen
sometime, what is wrong in putting it where there is some evidence of
curving upwards, ie before 2020.

I think there's a danger of not seeing the wood for the trees.

AGW is resulting in a rise in sea level, which is overall a very bad
thing. So is chopping down the rain forests, and removing part of the
sink for all the CO2 mankind is emitting.

Filling the oceans with plastic is a bad idea, there is no need to use
formulae to demonstrate what the situation might or might not be in 50
years based on a number of scenarios. Just use less bl**dy plastic! Of
course, it can be recycled, Maylasia takes a lot from the UK now CHina
doesn't. Seems to be working well
http://www.klexpatmalaysia.com/wp-co...ls-933x445.jpg


We need to minimise our impact on the planet wherever possible, that's
what's important, not the endless quoting/production of figures, IMHO.

But stubbornly holding to "fitting" a straight line to Jason data ,
falsely implies to the AGW crowd that sea-level is rising , yes, but ar
a fixed rate. Not that the rate of increase is increasing, can be seen
if you plot out concattenated J1+J2+J3 and look at it , or more
precisely analyse it , as here.
Some media referred to exponential rise a year or 2 ago, but this
analysis shows there is no current evidence for exponential rise, but
does give some sort of justified character to the rise of rise.

I think there might be just enough evidence of a very small quadratic
term in these data but you really need to use ANOVA to see if it is
sufficiently convincingly non-zero to make the grade. The scientific
community is careful not to over-egg their date unless they have a very
clear physical model that describes the process to fit against.

That said too many people (especially engineers) over fit their data.
Adding extra parameters to a fit will always improve if but unless it
explains a worthwhile fraction of remaining variance it is not valid.

Where are the numerical data in a time series form?

Another one which you might like to try is of the form

y = (a+bx)/(c+dx)

a = 1.92 b = 0.24084
c = 1 d =-0.0093

(approx) - 2050 = 26.1 but 2100 = 371.5

It is inclined to extrapolate towards a very worst case scenario.
(there is a pole in the denominator)

BTW I deduce that your x = Y - 2000

--
Regards,
Martin Brown