Bishop Hill Good reads on climate models
Jun 14, 2012 Climate: Models Two excellent and accessible papers on climate models have appeared today. Ross McKitrick, writing in the Financial Post, wonders about model evaluation:
So how do models do at predicting the spatial pattern of warming over land? Though the 2007 report of the Intergovernmental Panel on Climate Change (IPCC) devoted a whole chapter to model evaluation, it said almost nothing about this question. The IPCC talked mainly about static features, such as whether the model can make the tropics hot and poles cold, and so forth. But it was mostly silent on the spatial changes. A 2008 report of the U.S. Climate Change Science Program went a bit deeper, but only to report on tests of how daily and seasonal variations in models matched the real world (is winter a suitable amount colder than summer, etc.).
The reports weren’t ignoring anything: There just hasn’t been much work on the topic.
Meanwhile, Tamsin Edwards has this:
Models are always wrong, but what is more important is to know how wrong they are: to have a good estimate of the uncertainty about the prediction. Mark and Patrick explain that our uncertainties are so large because climate prediction is a chain of very many links. The results of global simulators are fed into regional simulators (for example, covering only Europe), and the results of these are fed into another set of simulators to predict the impacts of climate change on sea level, or crops, or humans. At each stage in the chain the range of possibilities branches out like a tree: there are many global and regional climate simulators, and several different simulators of impacts, and each simulator may be used to make multiple predictions if they have parameters (which can be thought of as “control dials”) for which the best settings are not known. And all of this is repeated for several different “possible futures” of greenhouse gas emissions, in the hope of distinguishing the effect of different actions.
Reader Comments (17)
So if I summed up by saying they were interesting tools whose development may lead to better understanding, but not ready for policy prime time, would anyone from the modelling community dispute the summation? And if that is the case, who exactly does want to use them to support policy?
Did anyone notice the Scottish Renewables: crying to mummy because someone is threatening to take their grants away?
And please ... please just for me, on this one, could someone please make a real sarky comment on SCEF article.
Nice piece from Ms Edwards. The following is succinctly phrased and captures perfectly the nature of the problem:
Bish - OT, but you will be interested in this one....
http://blackswhitewash.com/2012/06/14/stakeholder-forums-rio20-budget-makes-heartlands-budget-look-like-a-childs-pocket-money/
Did anyone notice the Scottish Renewables: crying to mummy because someone is threatening to take their grants away?
Yes I saw that and also the strange comment in the same papers article on the Judge allowing a wind farm to go ahead that onshore wind was now on a level playing field as conventional generation cost wise per kwh.
So why the subsidies ?
Thanks Bish, thanks Geckko. My research is in 'detuning' the model parameters to see how different the resulting predictions are. Just like the UK Climate Projections and climateprediction.net that I linked to in my post (in fact my work has been very closely linked to the first).
Tamsin: the problem with the models is that the TOA and BOA boundary conditions are wrong because of basic mistakes in the heat transfer originating in the Schwarzchild approximation. According to the 2009 Trenberth Energy budget, It gives ~40% extra energy input, ~400% in the IR.
Detuning the models won't help when they are so wrong in the first place. For further details read this: http://tallbloke.wordpress.com/2012/06/07/mdgnn-limits-on-the-co2-greenhouse-effect/#more-6600
These faults are obvious to any engineer who has dealt with complex heat transfer using gases.
Breath of Fresh Air : Yes I saw that and also the strange comment in the same papers article on the Judge allowing a wind farm to go ahead that onshore wind was now on a level playing field as conventional generation cost wise per kwh.
So why the subsidies ?
Breath of Fresh Air, I couldn't find it. There's
Judge rejects attempt to block Speyside wind farm
which didn't seem to cover costs &
Giant turbines could be key to reducing renewables costs
Which was not a judgement ... "reducing costs" ... a bit like "not beating your children so much".
"At each stage in the chain the range of possibilities branches out like a tree: there are many global and regional climate simulators, and several different simulators of impacts, and each simulator may be used to make multiple predictions"
What is the betting (pun intended) that the outputs that best match the preferred hypothesis are the ones selected?
Over to you Richard (Betts) :-)
Scottish Sceptic.
In the comments for the piece
4
fred bloggs
Wednesday, June 13, 2012 at 06:17 PM
Wind turbines are already at 5-7 cents per kWH or grid parity in the best sites and the majority of installations in 2016 are expected to be at grid parity. Costs have dropped 14% for every doubling of installed capacity over the past 30 years. Projections are for another 20-30% drop over the next 20 years before levelling off.
BS
Models are part of reality, reality is not a part of your model.
Breath of fresh air: this report** shows that windmills cannot be considered as a sensible part of a synchronous grid unless they are integrated into local storage systems - pumped or natural hydro.
Otherwise they cause more CO2 to be generated than without them so the costs you quote are entirely artificial.
**www.europhysicsnews.org/articles/epn/pdf/2012/02/epn2012432p22.pdf
All astrological charts are wrong but some are useful.
[ ... ] At each stage in the chain the range of possibilities branches out like a tree [ ... ]
And as a consequence the resulting output from each branch becomes more and more unrealistic ... GIGO !
Models are worse at predicting than a random walk, apparently
http://wattsupwiththat.com/2009/08/12/is-global-temperature-a-random-walk/
http://opinion.financialpost.com/2012/06/13/junk-science-week-climate-models-fail-reality-test/
How is truncation error controlled in models which run for days or months on very fast computers?
There's a nice piece on WUWT about current climate model 'predictions' versus Random Walk modelling. A perfect model would score 0 deviation from reality. Random walk approach scores 1: ie just guessing likely to be right or wrong 50% of the time.. Climate models score above 2 - much worse than just guessing. It figures!