|Fig. 1 (click to enlarge)|
One of those practices was based on the use of historical experience to determine best practices.
Projects that build computer software models which project future events, as well as the software eventually built, work better when a beneficial dataset can be used to drive the projection logic that will eventually be used to project what seems to be the most likely future scenario.
To be sure, Fig. 1 is an example of that, i.e., dataset that were used to very accurately project CO2 levels from 2009 through 2015 (the model was written before 2009).
|Fig. 2 (click to enlarge)|
It is an example of accurate datasets being used to build robust projection models.
Taking that up a notch, Fig. 2 shows the model projected out much further, some 85 years, to 2100 (see IPCC CO2 projections).
For a background and a source for realizing why the datasets are useful in developing projection models, Fig. 3 shows that our current environment has been rendered into quite a different one from the CO2 environment that has existed for many thousands of years.
|Fig. 3 (click to enlarge)|
The injection of green house gases leads to temperature increases, which in turn lead to SLR, which is a catastrophic danger to civilization (New Climate Catastrophe Policy: Triage - 12).
The factor that makes SLR projection more difficult than temperature or CO2 emission projections is the double lag or delay of impact.
How long does it take, after CO2 is injected into the atmosphere, for a temperature rise to manifest (first lag), and further, how long after the temperature rises does it take for SLR to manifest (second lag)?
I have used another IPCC projection model that anticipated temperature increase, which has also historically been accurate (The IPCC Record on Global Warming Temperature Projections).
The element of the IPCC modelling that has been inaccurate has been SLR projection, which has consistently underestimated SLR.
So, I am not trying to reinvent the wheel here, I am just trying to make that wheel round, like the other IPCC projection elements that are well rounded.
So, by adding another partner (the CO2 model) to my SLR projection model (which already uses the 4 deg. C IPCC projection dataset), I hope to improve the SLR projection model.
The average of CO2 ppm projected by circa 2100, per IPCC, is about 750 ppm, (up from about 400 ppm in the atmosphere now).
The figures I posted recently (before adding the CO2 dataset), from a slice of the data printout, were:
Year, Greenland, Antarctica, Non-polar, Combined(The IPCC Record on Global Warming Temperature Projections - 2). The software model generated similar data with the addition of the CO2 dataset I just added:
2015, 0.106398, 0.0444488, 0.58813, 0.738976
2016, 0.143898, 0.0544488, 0.59063, 0.788976
2017, 0.181398, 0.0644488, 0.59313, 0.838976
2029, 1.61329, 0.446286, 0.688589, 2.74816
2030, 1.89626, 0.521745, 0.707454, 3.12546
2097, 10.1968, 16.2361, -, 27.9129
2098, 10.2911, 16.5003, -, 28.2714
2099, 10.3854, 16.7644, -, 28.6298
Year, Greenland, Antarctica, Non-polar, CombinedIt does not change much, so, if my assumptions and implementation of the dataset has been properly done, it is a reasonable projection.
2015, 0.106551, 0.0444897, 0.58814, 0.739181
2016, 0.144205, 0.0545307, 0.59065, 0.789385
2017, 0.181858, 0.0645716, 0.59316, 0.83959
2029, 1.61961, 0.447972, 0.68901, 2.75659
2030, 1.90374, 0.52374, 0.707952, 3.13543
2097, 10.2382, 16.3025, -, 28.0207
2098, 10.333, 16.5677, -, 28.3806
2099, 10.4277, 16.8329, -, 28.7405
|Fig. 4 (click to enlarge)|
Including it may have caused some confusion concerning SLR "from now on" with that 21 cm. of past history in it.
The graph Fig. 4 shows only SLR beginning at zero SLR, which I think is more clear in the sense that we are now only talking about SLR from now on, rather than including the past.
One impact is that it moves the 3 ft. of SLR somewhat, as shown by comparing the two detail print outs above, with this one:
Year, Greenland, Antarctica, Non-polar, CombinedThe main impact is that the 3 ft. SLR shows up circa 2031-32 rather than at 2029-30, but, it also shows how touchy or delicate SLR projections are.
2015, 0.0376535, 0.0100409, 0.00251024, 0.0502047
2016, 0.0753071, 0.0200819, 0.00502047, 0.100409
2017, 0.112961, 0.0301228, 0.00753071, 0.150614
2029, 1.55071, 0.413523, 0.103381, 2.06762
2030, 1.83484, 0.489292, 0.122323, 2.44646
2031, 2.11897, 0.56506, 0.141265, 2.8253
2032, 2.40311, 0.640828, 0.160207, 3.20414
2097, 10.1694, 16.268,1.39144, 27.8288
2098, 10.2641, 16.5332,1.41038, 28.2077
2099, 10.3588, 16.7984,1.42933, 28.5865
I am going to keep at it until I am satisfied that the assumptions and implementations are correct, then declare how it is done (and share the code with any who would like to check it out).
The next post in this series is here, the previous post in this series is here.