WUWT readers may recall the story by the Daily Mail about the new supercomputer.
The Met Office has caused a storm of controversy after it was revealed their £30million supercomputer designed to predict climate change is one of Britain’s worst polluters.
The massive machine – the UK’s most powerful computer with a whopping 15 million megabytes of memory – was installed in the Met Office’s headquarters in Exeter, Devon.
With a total peak performance approaching 1 PetaFlop — equivalent to over 100,000 PCs and over 30 times more powerful than what is in place today. It is capable of 1,000 billion calculations every second to feed data to 400 scientists and uses 1.2 megawatts of energy to run – enough to power more than 1,000 homes.
With all that power, surely it must produce some quality digital reckoning.
Bishop Hill has located the “supposedly secret” winter forecast sent to the British government. The details of the forecast produced are nothing short of astounding.
When the kerfuffle over the Met Office’s winter forecast blew up, I wrote to the Quarmby team to see if they had actually received a copy of the Met Office’s cold-winter forecast, which was apparently sent to the Cabinet Office. It is alleged that the forecast should have provided sufficient warning to the government machine to ensure that everyone was ready for what happened in December.
Today, rather later than I expected, the Quarmby team have responded and have helpfully provided a copy of the forecast:
Met Office Initial Assessment of Risk for Winter 2010/11
This covers the months of November, December and January 2010/11, this will be updated monthly through the winter and so probabilities will change.
Temperature
3 in 10 chance of a mild start
3 in 10 chance of an average start
4 in 10 chance of a cold start
Precipitation
3 in 10 chance of a wet start
3 in 10 chance of an average start
4 in 10 chance of a dry start
Summary: There is an increased risk for a cold and wintry start to the winter season.
Looking further ahead beyond this assessment there are some indications of an increased risk of a mild end to the winter season.
Yes that seems clear, doesn’t it? Seeing the numbers produced, personally, I think this less expensive computer, using Digital Advanced Reckoning Technology (DART) can do the job of making odds equally well, using less power, less space, and less money:
I really love this one:
Looking further ahead beyond this assessment there are some indications of an increased risk of a mild end to the winter season.
I think its been done, something about “March coming in like a lion and out like a lamb” IIRC. But really, I never thought that a “mild end to winter” could be categorized as a “risk”.
But this forecast for the start of winter still doesn’t square with the Met Office map output.
Here’s the Met Office supercomputer enhanced model output forecast from October 2010: 
See the story about that controversy here and here
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My baloney radar is off the charts. I would so be asking for a FOIA request for the actual ticker tape that came out of that big black bank of computers. The 3/3/4 looks contrived to me and completely at odds with what the ‘puter actually spit out. This has “fudge factor” written all over it.
It must be terrible having to make real world predictions compliant with un proven theory.
Parlimentary question and answer:
Even if the prediction is 100% that it is going to rain, it could rain for 5 minutes or 5 hours and still be right.
War is peace
Freedom is slavery
Ignorance is strength
Wet is dry
Warm is cold
Pink is the new black
40 is the new 30
Bankers steal money from taxpayers and it is good.
Little people fail to file a 1099 and they go to jail.
I just want to know what my chocolate ration is.
r says:
January 22, 2011 at 9:53 am
Even if the prediction is 100% that it is going to rain, it could rain for 5 minutes or 5 hours and still be right.
_____
Well, let’s be clear about this…GCM’s give probability ranges as to when events will occur. Each indivudual GCM does this, and then of course we can take the average of all the GCM’s combined to give a general range. For example, the Arctic is projected to be ice free in the summer sometime this century, say from 2030-2100 approximately. If the Arctic became ice free in the summer in 2020 or in 2200, you could make the case that because it was due to the chaotic nature of the system that the event fell outside that original projectced range, just as if the forecast was for rain to start between noon and 2 p.m. and it started at 11:30 or 2:30…was the forecast wrong, or is this simply the nature of trying to model a chaotic system? Likewise, if it rained at 7 p.m. that night, you could easily make the case that that rain had nothing to do with the original forecast as it was too far out of the range.
I think, in general, GCM’s have the trends pinned down pretty good (as far as the current forcings added to the models go). I think the GCM’s will get better as the dynamics of more forcings are better understood, i.e. additional solar, cosmic rays, longer frequency ocean cycles, etc.. I also think that as in any chaotic system there will be “tipping points” that simply can’t be known, are impossible to model, and will cause the models to be grossly inaccurate once those tipping points are passed. This is exactly the nature of any chaotic system. The much faster rapid decline in Arctic Sea ice for example, could be one such tipping point. If it continues to decline faster than any GCM projected just a few years ago, this represents a tipping point that was not predictable and means the models will get further and further off thereafter.
@ur momisugly Dave: George Carlin’s ‘hippy dippy weather man’ also came immediately to mind for me as well.
The Met’s hippy-dippy winter forecast: cooling temperatures becoming warm towards spring.
The name “Chaos Theory” was and is extremely unfortunate. It it no less unfortunate that one of the first applications was to weather or climate.
No natural system is more or less chaotic than any other natural system. The adjective “chaotic” does not apply to nature. The adjective applies to the set of mathematical techniques known as Chaos Theory. Get it. Nature is one thing but the languages and tools used to describe and investigate it are entirely another thing.
Chaos theory is a mathematical technique that can be applied to any natural system. If you do not believe that, just google “chaos theory application.” Chaos theory has been applied in many areas of research including heart rhythms. No one believes that ordinary heart rhythms are chaotic, right? You are correct. They are not. But Chaos Theory is useful in understanding them.
So, could we please stop pleading that climate science and climate modeling are so dang difficult because climate and/or weather are chaotic? If some genuine appeal can be made on the basis of the difficulty, it would be that only Chaos Theory offers mathematical techniques of the sort needed in climate/weather science AND THAT IT IS ESPECIALLY DIFFICULT TO APPLY CHAOS THEORY IN THIS ARENA.
So, if you are a climate scientist who makes excuses on the basis of Chaos Theory, what you must explain to justify your excuses is why it is that Chaos Theory is so dang difficult to apply when it applies darn well in many other areas of science.
Theo Goodwin says:
January 22, 2011 at 3:13 pm
The name “Chaos Theory” was and is extremely unfortunate. It it no less unfortunate that one of the first applications was to weather or climate.
No natural system is more or less chaotic than any other natural system. The adjective “chaotic” does not apply to nature. The adjective applies to the set of mathematical techniques known as Chaos Theory. Get it. Nature is one thing but the languages and tools used to describe and investigate it are entirely another thing.
Chaos theory is a mathematical technique that can be applied to any natural system. If you do not believe that, just google “chaos theory application.” Chaos theory has been applied in many areas of research including heart rhythms. No one believes that ordinary heart rhythms are chaotic, right? You are correct. They are not. But Chaos Theory is useful in understanding them.
So, could we please stop pleading that climate science and climate modeling are so dang difficult because climate and/or weather are chaotic? If some genuine appeal can be made on the basis of the difficulty, it would be that only Chaos Theory offers mathematical techniques of the sort needed in climate/weather science AND THAT IT IS ESPECIALLY DIFFICULT TO APPLY CHAOS THEORY IN THIS ARENA.
So, if you are a climate scientist who makes excuses on the basis of Chaos Theory, what you must explain to justify your excuses is why it is that Chaos Theory is so dang difficult to apply when it applies darn well in many other areas of science.
______
Hmmm…well, for those who would like a real brief introduction to Chaos Theory as it applies to both weather and climate models and forecasting, I would offer up this:
http://climlab02.meas.ncsu.edu/mea719/MEA_719_lectureset-5-Feb03-chaos_and_ensemble-predictability.ppt#296,25,Lorenz Attractor
No climate scientist makes “excuses” on the basis of Chaos Theory. That is simply an absurd statement. It is well understood that there are far to many variables interacting in far too many ways, with unpredictable “tipping points” that can change a system very very fast. We know that the climate can and does “tip” into new regimes rapidly, as evidenced in the paleoclimate record. This “tipping” is a hallmark of a chaotic system.
The most important research right now is trying to really pin down how sensitive the climate really is to CO2 (and any related postive feedback such as increased water vapor, polar amplfication, methane release). Climate scientists know that the GCM’s are only going to indicate trends and not specifics and there will be inevitable surprizes along the way because it is a chaotic system that is quite deterministic but also quite unpredictable in specifics.
Another excellent link to a recent presentation of Chaos Theory as applied to both weather and climate models and forecasting:
http://www.atmos.umd.edu/~ekalnay/Chaos-Predictability-EnKF-WMOtalk.pdf
A great read for those who would like to begin to understand both Chaos Theory and how it relates to both GCM’s and ensemble forecasting.
R. Gates says: January 22, 2011 at 5:34 am (Edit)
“I do have faith that the models are giving me general information about trends, though not about the exact timing and particulars.”
What is this “faith” and what part does it play in science?
“I think in fact they might be decent at giving us pretty good clues about general trends”
Why would you think that flawed models [insert equivocations here, e.g. might, decent pretty good, general, etc.] give us with directionally accurate results?
“I just happen to think that the chaos in the system from the many excellent other influences such as you’ve pointed add way to much unknown, such that no matter how big the Met office computer gets, and how refined the GCM’s get, they are still inherently dealing with a chaotic system, and”
Agree, at present we cannot accurately model Earth’s Climate System, much less predict its behavior decades into the future.
“as such, they might get better at telling more about the trends,”
I don’t understand this at all, and the snowstorm analogy doesn’t help. Snowstorm predictions are based on relatively few variables and over a very short period of time, and it’s still an inaccurate science. Why would introducing a plethora of additional variables and increasing the prediction time period by 3 or 4 orders of magnitude, be likely to tell us anything about long term trends?
[youtube=http://www.youtube.com/watch?v=wCKN4IJTlts&w=640&h=390]