By Steven Goddard
I recently had the opportunity to attend a meeting of some top weather modelers. Weather models differ from climate models in that they have to work and are verified every hour of every day around the planet. If a weather model is broken, it becomes obvious immediately. By contrast, climate modelers have the advantage that they will be long since retired when their predictions don’t come to pass.
Weather and climate models are at the core very similar, but climate models also consider additional parameters that vary over time, like atmospheric composition. Climate models iterate over very long time periods, and thus compound error. Weather modelers understand that 72 hours is about the limit which they can claim accuracy. Climate modelers on the other hand are happy to run simulations for decades (because they know that they will be retired and no one will remember what they said) and because it provides an excuse to sink money into really cool HPC (High Performance Computing) clusters.
But enough gossip. I learned a few very interesting things at this meeting.
1. Weather modelers consider the realm of climate calculation to be “months to seasons.” Not the 30 year minimum we hear quoted all the time by AGW groupies. That is why NOAA’s “Climate Prediction Center” generates their seasonal forecasts, rather than the National Weather Service.
2. The two most important boundary conditions (inputs) to seasonal forecasts are sea surface temperatures and soil moisture. No one has shown any skill at modeling either of those, so no surprise that The Met Office Seasonal forecasts were consistently wrong.
For example, just a few months ago the odds of La Niña were considered very low. Compare the December forecast with the May version. How quickly things change!
SST modeling capabilities are very limited, and as a result seasonal weather forecasts (climate) are little more than academic exercises.
Oh and by the way, Colorado will be exactly 8.72 degrees warmer in 100 years. But they can’t tell you what the temperature will be next week.
“If I don’t understand it, it must be simple.”
– Dilbert Principle
In the top picture, which boxer is weather and which one is climate? What do readers think?
There is nothing secretive about climate models. You can download one of the more commonly used radiative transfer models here :
http://rtweb.aer.com/rrtm_frame.html
Of course, there’s actually no such thing as climate. It’s an abstraction, a statistical fancy, which amuses those who enjoy torturing numbers but provides nothing useful, except a general guide to what conditions one is likely to experience at any given spot on the earth at a particular time. But unlike weather forecasts, which give fairly accurate predictions that help you plan your clothing layers and umbrella choices for a few days ahead, climate study makes too much of averages, which hide the potentially massive swings either side of the ‘norm’. Lay persons know only too well that the exception is the rule, and bear the scars of ruined vacations. This is also why weather forecasters are more sanguine about the future.
A cynic could conclude that it’s the study of something of such questionable utility that has led so many climate practitioners to forecast catastrophe, otherwise they’d have nothing interesting to say.
If we compare the size of the boxer to the grants climate vs weather receive then the answer is pretty obvious.
However the guy on the right is a little small for a realistic comparison.
Oh and by the way, Colorado will be exactly 8.72 degrees warmer in 100 years. But they can’t tell you what the temperature will be next week.
Really, “Steven Goddard,” would you have written a howler like that under your own name?
Weather is chaotic and unpredictable. Climate is weather averaged out over time. Simple analogy: you can’t predict whether a coin will land heads or tails, but you can predict statistically how thousands or millions of coin tosses will go.
There are dozens and dozens of good arguments which could be made about the inaccuracy of climate models, how they are misused and how poorly the science is understood. This, however, is not one of those arguments.
“I award you no points, and may God have mercy on your soul.”
REPLY: What happened to posting as Paul Daniel Ashe? Why go back into the closet, you were doing so well? – Anthony
In the top picture, which boxer is weather and which one is climate? What do readers think?
I think that “the fight” itself is the climate and both fighters are weather.
Formal 1-on-1 fights have been going on for thousands of years. The the styles and rules change slightly over time (regional differences: sumo, boxing, eastern martial arts, etc.) but these fights still take place under rules that have remained fairly constant over time (current climate steady state or “equilibrium” if you prefer).
The disparate sizes of the fighters merely illustrate weather extremes within the fight game (global climate.)
The “weather” just produced a 46F (7.5C) low for July 1 here in western MD. Just a few degrees above a record low for the date. Last yr I had a record-low for the date of 44F in mid-July.
It’s getting worse than we thought.
‘Climate modelers on the other hand are happy to run simulations for decades (because they know that they will be retired and no one will remember what they said) and because it provides an excuse to sink money into really cool HPC (High Performance Computing) clusters.’
The hilarious paradoxical nature of all that is, of course, that people in the future will literally laugh their collective ass’ off and call those philosophical doctor farts utterly mental for trying to predict future climate 50 and 100 years hence with slow ass retarded LPC clusters.
Think about it, would they not have had to eat their shorts every online day if they’d been on mainframes of PDP-10s in the sixties having tried to predict todays climatic change of an “ice age”. They were stellar back then, helped take people to the moon, so they were seen almost as automagical really, yet today a simple ipod packs more punch in processing power, RAM, storage capacity, programming versatility, et cetera, not even 40 years later.
And that’s how the climatological nutties will be remembered, whereas the rational skeptical ones probably’ll be remembered as the both feet on the ground stoic’s that stood against the flood of crazies–for an age. :p
Hi Steven,
Let’s make a bet. I’ll bet you that the average temperature in the United States in July 2100 will be warmer than the average temperature in January 2100. If I’m wrong, I’ll give you $100. If I’m right, you give me $1. (What a deal!)
Do you accept? If you’d like this bet to be a bit more tangible, we can change the year to 2015, or any other year of your choosing. According to your logic, accepting this deal should be a no-brainer, since you’ve claimed that prediction of climate variables and weather variables are mathematically equivalent.
Let me know. Thanks!
Dan
In my efforts to obtain a degree in electrical engineering I took some courses in computational analysis. In some of the last classes on multivariable analysis, one of the topics was on picking the right (independent) variables, and how would one know they are correct, and independent. That was a long time ago, and undoubtedly I have lost a lot of the knowledge.
But one thing still stands out. That is, over a defined set, one can model, or at least try to model, just about anything that shows some correlation over a defined set. I’m surprised that someone hasn’t modeled AGW (assuming existence) directly to population –human, or chicken, or even mammoth (oops they have in the case of the last).
Such a model might even appear valid over a defined data set. However, to go from there to declaring that humans, or chickens, or mammoth are responsible for some warming leaves a huge void in science, and obviously isn’t valid.
I was listening to a CBC science show about 8 or 10 years ago where a “scientist” was running 20 years (or something like that) of data through climate models and declared them valid. Also, claiming validation of C02 link to global warming.
I immediately thought to myself Bull! Of course the models align, the same data used to develop the models was used to prove the models!!! And yes, I realize someone is getting paid big bucks to constantly refine these. But I still don’t believe everyone is aware (or even honest about) about the correct independent variables.
The last paragraph of this article says it all:
http://news.bbc.co.uk/2/hi/world/us_and_canada/10472152.stm
Anthony, I have two computers, and I just cleared the cache on the one downstairs. My bad, I’ll straighten that out right away.
And I’m assuming you’re giving “Steven” just as hard a time for being “in the closet” as you do everyone else who writes under a pseudonym. Right?
I do like models….but the fashion ones.
PDA says:
July 1, 2010 at 5:33 am
[…]
“Really, “Steven Goddard,” would you have written a howler like that under your own name?
Weather is chaotic and unpredictable. Climate is weather averaged out over time. Simple analogy: you can’t predict whether a coin will land heads or tails, but you can predict statistically how thousands or millions of coin tosses will go.”
Assuming a fair coin, assuming a fair landing surface, assuming a fair coin-flip method…
==> Assuming one knows the mechanisms producing the conditions that allowed Otzi the Iceman to cross the alps, then get burried in (I forget how many) meters of ice, then have his remains discovered discovered 5,300 years later when the ice melted, and assuming one can predict within a few years, based on long term averages, when those mechanisms will act…
… well then I’d say that stochastic climate models are pretty darn good and I’d have to concede your point.
But I don’t think so.
After a couple of days of high heat in D.C. Climate Progress declared climate change last week. After a heavy rain in Tennessee, they declared the climate was changed. In January 26, 2009 they declared the southwest would have a permanent drought.
It is warm enough to raise oranges in Manitoba today. Actually both June and July. Shall we turn that into a trend line?
The Met Office has provided annual temperature forecasts and 10 consecutive years have been wrong. 9 of the years they predicted hotter than actual. How are those forecasts working for the warmists?
Steven at the public govt questioning of Phil Jones representatives of the Hadley Centre were questioned. The question was asked how the Climate Models were tested. The woman who seemed to have some authority answered that the same code was used for short term forecasting as for the GCMs. For that reason she claimed they had been thoroughly tested! I wonder how many people would believe such rubbish. From personal experience it is quite difficult to make sure large computer systems to work correctly even when the correct output can be easily calculated. It takes dedicated meticulous testing by a number of testers. In system I worked on ten to twenty people were tasked for this.
Surferdave your reference to Knuth’s “Art of Programming” was a surprise I thought everyone had forgotten about it. How about we create a fake GCM using RNGs would anyone notice? Then again perhaps it has already been done? Another test is to say if GCMs produce the same results then they must be correct!
stevengoddard replied to Jaime, “Climate models are basically extended weather models. They have to be, they are modeling the same atmospheric and oceanic processes.”
Your assumption is that climate models model oceanic processes. Many do not, including the GISS Model E.
I work in the area of computer modeling but not with weather or climate. Feedback on the goodness of my models is more or less immediate and has economic consequences. I learned many years ago that models not carefully tested on “out of sample” data (i.e. data not included in building the models) are virtually useless in prediction of future events or conditions. Computer models can be made to fit any set of data yet have no ability to accurately predict for new data. Climate modelers engage in the fantasy that their untested models are somehow valid and the delusion that it would be good for the world to turn its energy economy inside out based upon them. This is the height of folly and arrogance.
People (including climate modelers apparently) have a naive view that just because a prediction comes out of a computer it must somehow be accurate. Not so. If I relied on untested models I would have been out of business a long time ago. I hope the world wakes up on this computer climate modeling scam before it enacts cap-and-trade. My prediction on that (not computer generated) is that it will destroy our economy and reduce our standard of living to underdeveloped country levels.
PDA says,
“Weather is chaotic and unpredictable. Climate is weather averaged out over time. Simple analogy: you can’t predict whether a coin will land heads or tails, but you can predict statistically how thousands or millions of coin tosses will go.”
Heard it before. Same old false analogy, but you’d be surprised how many people bring it up. The obvious fallacy of logic is that we know before we start what the probability of tossing a head or a tail is – it is 0.5. Given this data, it can be proved mathematicaly and empirically that as the number of tosses becomes very high, the ratio of heads to tails approaches their individual probability, namely 0.5. Climate however, is modelling uncertainty right from the off. Nobody knows the feedbacks, or even whether they are net positive or negative. It can be demonstrated that when the probability of an event is uncertain, then the more iterations there are, the further the possible outcomes diverge from the mean.
There’s a rather big difference between weather and then seasonal forecasts, and long range climate projections.
The former are very sensitive to the initial conditions. Long-range climate, less so.
Downscaling it a bit: I can’t tell you the exact temperature and precipitation for your hometown in three week’s time. Butterflies and chaos and all that.
But I can sure tell you that the average temperature in your summertime month will be more than in your wintertime month. For that, the initial conditions don’t much matter; the boundary conditions are felt such that we can pretty much count on the seasons to appear.
Similarly, if the irradiation from the sun went up by 20% for some reason by 2100, I can sure tell you that on average, it’s going to be warmer here on Earth. I don’t have a chance at telling you if there’ll be an El Nino or a La Nina in Jan 2100, and I don’t have a chance at telling you if it’ll rain on Jan 21, 2100, but you can still be sure that the Earth will be warmer on average due to the active sun.
@Rhys Jaggar says:
July 1, 2010 at 5:17 am
“Piers Corbyn seems quite good at predicting WEATHER a season or so ahead.
By which I mean extreme weather rather than ‘it’ll rain a bit today and be sunny tomorrow’.
His models use solar, geomagnetic and atmospheric parameters and I didn’t see him document anything about oceanic contributions yet. He may do, I may just not pick it up.”
Weather Action forecast for the UK, daily rainfall with a lattitude of 0.5 t0 1.0 days in timing, with often very good regional variation in the specification. A very high percentage of the total number of active weather fronts hitting the UK will be forecast at this level of timing accuracy, and forecasting at this level can be acheived at well over a year in advance. Similar levels of success are acheived with tropical cyclone forecasts, in formation periods and locations, tracks and intensity.
SST`s are considered, as are Lunar modulations of the solar wind and atmospheric circulation patterns.
Dan
From your comments, it appears that you probably haven’t worked with GCMs. If they were simple statistical models as you imagine, there wouldn’t be need for running them on supercomputers. Your view of a climate model could be run on an abacus. Even simpler, you could make a graph of CO2 vs. temperature and just extrapolate it out to some future CO2 value. That would probably be a fairly accurate model.
As I stated in the article, a climate model run is essentially the same as a weather model run, only with more iterations, more input parameters and often a coarser spatial and temporal granularity.
I think an analogy between predicting weather and climate would help here – imagine you have a hot, fresh cup of coffee in front of you. You pour in some cold cream, and stir it up with a spoon. Now, tell me, which is easier, predicting the temperature and cream content at a given spot in the coffee cup in 30 seconds, or predicting the ensemble temperature of the coffee in 20 minutes (given you know the room temperature, etc)? Its clear that it is much easier to predict the coffee temperature in 20 minutes.
Why then, is it so difficult to understand how creating/using a climate model, working on averages and long trends with small changes in each variable is nowhere near the same thing as trying to model the temperature in my backyard 3 days from now at 6h23?
PDA
I can tell you what the temperature will be like in July 100 years from now. Most likely very similar to the present. Over the last 80 years, summer temperatures in the US have hardly changed at all.
Dan @ur momisugly July 1, 2010 at 5:56 am
You utter, deluded fool. Everybody knows that by 2100, the United States will have been abolished and then reconstituted as a dining club in Sidney Harbour. Therefore, it is a racing certainty that January will be warmer than July .. except in the kitchen, when I predict the air conditioning will break down in July.