Posted by john byatt
Now that I have your attention, The post is about Models, just not the world’s top rating hot chili pepper of the title but rather the IPCC AR4 20C Models. I was prompted to write the post after a denier replied to the ABC Environment Opinion piece by Sarah Clarke, offering up his ignorance of the IPCC Models
18 Dec 2012 5:55:19pm ABC Environment
“Well within the 95% (confidence range of opportunity)”?
What does that even mean? No such concept exists in statistics.
Most of the model ranges now lie outside the error bars, meaning that there is less than a 1 in 20 chance that natural variance is the reason that temps are outside the range predicted by the models. This actually means that it is now the case that there is less than a 5% chance that the models are correct, natural variance can no longer explain their divergence from measured data.
The figures in the actual report itself illustrate this.
The models are physics based not statistical based
statistical models aren’t much good for predictions if you know the underlying system is changing The ability of statistics to interpolate within the range of known behavior is impressive, but extrapolation into unknown territory is very risky business.
• Short term (15 years or less) trends in global temperature are not usefully predictable as a function of current forcings. This means you can’t use such short periods to ‘prove’ that global warming has or hasn’t stopped, or that we are really cooling despite this being the warmest decade in centuries.
• The AR4 model simulations were an ‘ensemble of opportunity’ and vary substantially among themselves with the forcings imposed, the magnitude of the internal variability and of course, the sensitivity. Thus while they do span a large range of possible situations, the average of these simulations is not ‘truth’.
• The model simulations use observed forcings up until 2000 (or 2003 in a couple of cases) and use a business-as-usual scenario subsequently (A1B). The models are not tuned to temperature trends pre-2000.
• Differences between the temperature anomaly products is related to: different selections of input data, different methods for assessing urban heating effects, and (most important) different methodologies for estimating temperatures in data-poor regions like the Arctic. GISTEMP assumes that the Arctic is warming as fast as the stations around the Arctic, while HadCRUT3v and NCDC assume the Arctic is warming as fast as the global mean. The former assumption is more in line with the sea ice results and independent measures from buoys and the reanalysis products.
• Model-data comparisons are best when the metric being compared is calculated the same way in both the models and data. In the comparisons here, that isn’t quite true (mainly related to spatial coverage), and so this adds a little extra structural uncertainty to any conclusions one might draw.
QUESTIONS ABOUT MODELS
What is the difference between a physics-based model and a statistical model?
• Are climate models just a fit to the trend in the global temperature data? (Answer = NO)
• Why are there ‘wiggles’ in the output?
• What is robust in a climate projection and how can I tell?
• How have models changed over the years?
• What is tuning?
• How are models evaluated?
• Are the models complete? That is, do they contain all the processes we know about?
• Do models have global warming built in? (Answer = NO)
• How do I write a paper that proves that models are wrong?
• Can GCMs predict the temperature and precipitation for my home?
• Can I use a climate model myself? (Answer = YES)
• What are parameterisations?
• How are the parameterisations evaluated?
• Are clouds included in models? How are they parameterised?
• What is being done to address the considerable uncertainty associated with cloud and aerosol forcings?
• Do models assume a constant relative humidity? (Answer = NO)
• What are boundary conditions?
• Does the climate change if the boundary conditions are stable?
• Does the climate change if boundary conditions change?
• What is a forcing then?
• What are the differences between climate models and weather models?
• How are solar variations represented in the models?
• What do you mean when you say a model has “skill”?
• How much can we learn from paleoclimate?
So Harry has spent his time following the distortions and nonsense of the false sceptic blogs, had he instead spent an hour or less trying to understand the Model FAQ’s he would never have made such a goose of himself.
Plus I got to use the word Bombshell in a post