From the University of Gothenburg
Climate models are not good enough
Only a few climate models were able to reproduce the observed changes in extreme precipitation in China over the last 50 years. This is the finding of a doctoral thesis from the University of Gothenburg, Sweden.
Climate models are the only means to predict future changes in climate and weather.
“It is therefore extremely important that we investigate global climate models’ own performances in simulating extremes with respect to observations, in order to improve our opportunities to predict future weather changes,” says Tinghai Ou from the University of Gothenburg’s Department of Earth Sciences.
Tinghai has analysed the model simulated extreme precipitation in China over the last 50 years.
“The results show that climate models give a poor reflection of the actual changes in extreme precipitation events that took place in China between 1961 and 2000,” he says. “Only half of the 21 analysed climate models analysed were able to reproduce the changes in some regions of China. Few models can well reproduce the nationwide change.”
China is often affected by extreme climate events. Such as, the flooding of 1998 in southern and north-eastern China caused billions of dollars worth of financial losses, and killed more than 3,000 people. And the drought of 2010-11 in southern China affected 35 million people and also caused billions of dollars worth of financial losses.
“Our research findings show that extreme precipitation events have increased in most areas of China since 1961, while the number of dry days – days on which there is less than one millimetre of precipitation – has increase in eastern China but decreased in the western China.”
Cold surges in south-eastern China often cause severe snow, leading to significant devastation. Snow, ice and storms in January and February 2008 resulted in hundreds of deaths. Studies show that the occurrence of cold surges in southeast China significantly decreased from 1961 to 1980, but the levels have remained stable since 1980 despite global warming.
Let me summarize, Climate models can accurately show the past and they can’t accurately show the future either. However, Climate models are good for shock headlines and demand for more funding.
Climate models not good enough….please send money…
When will they ever get over this tired refrain…
No amount of computing power will ever compensate for
or correct bad data….GIGO…
This reinforces an open secret, namely, AOGCMs are notoriously bad at even hindcasting, especially precipitation, particularly at less than continental scales. As noted here, pp. 12-13, even the IPCC’s AR4 tacitly acknowledged that confidence is low for model projections of temperature at less than continental scales, and is even lower for precipitation — perhaps even at the continental scale. In fact, it didn’t even provide any quantitative estimate of the confidence that should be attached to projections of temperatures at the subcontinental scale. According to the US Climate Change Science Program (CCSP) :
Reference:
CCSP (2008). Climate Models: An Assessment of Strengths and Limitations. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Bader D.C., C. Covey, W.J. Gutowski Jr., I.M. Held, K.E. Kunkel, R.L. Miller, R.T. Tokmakian and M.H. Zhang (Authors)]. Department of Energy, Office of Biological and Environmental Research, Washington, D.C., USA, 124 pp.
So… these models can’t predict forwards or backwards. I’m sorry, what are they good for again? Oh, that’s right, funding and money-wasting. Right. Got it.
How about this just have their models predict 1 month out and see how they do.
Well, I am flabbergasted. Still, that rain could be hiding in the deep ocean.
Is climate change cyclic …. or stoichastic/chaotic? If it is cyclic, the predictive value of a hind casting model may have value. If it is stoichastic/chaotic, a perfect hind casting model will prove ineffective.
MtK
Oops! Typos:
stoichasticstochasticMtK
Sorry, I just can’t accept climate modeling any more than driving a VW Beetle to a drag strip and being happy with a 55 mph top end.
I just can’t do it!
Suggestion to the writer of the report: Delete the following word from the last sentence, “despite global warming.”
Again Leroux MPHs explain very logically why ” extreme precipitation events have increased in most areas of China since 1961, while the number of dry days – days on which there is less than one millimetre of precipitation – has increase in eastern China but decreased in the western China.” since any satellite shows Eastern China being on the path of MPHs, thus more powerful MPHs will bring higher pressure/dryer weather on their path while likely advecting more warm air along the reliefs of western China. Another indication that contrary to warming claims, we see in fact the signs of cooling air masses coming from the pole.
Wow. I don’t usually think it is worth testing the hindcasting ability of these models because I just assume that they have all already been ‘trained’ against historic data.
If they do not even fit historic data then where are they coming from??
Previously I would have considered it a bit rude on my part to accuse climastrologers of plucking models out of bodily orifices …
but now…
Few models can well reproduce the nationwide change
=====
what are the odds when you flip a coin………….
The headline could use the addition of some important detail:
Climate models aren’t good enough to hindcast certain parameters at smaller regional scales.
Not exactly something that wasn’t known already, but interesting to see it investigated in this particular region. In particular it is noted that
Just so no one gets the impression that they are saying climate models are useless, or anything remotely along those lines.
We only have 50 years of good data from China! How can we determine if these “extreme” events are more common than before when we don’t know what “normal” variation is?
In the continental US, extreme events are announced OUTSIDE OF the 1930s. It is the same everywhere: we don’t know the style of weather variability within a climatic cycle because we don’t even know what is a climatic cycle. The LIA is treated as an aberration, and so the warming from the LIA is a reversal of an aberration, and not a legitimate part of the climate pattern. Which means that only the post 1945 time is “normal”, and since the CAGW was supposed to start about 1965, latest 1975, the only “normal” weather we have for comparison is the 20 -30 years post WWII.
Of course we have an increase in extreme events. We could easily have had a dearth of extreme events, itself another proof of a warmer world (less equator-pole temperature gradient). Any climatic cycle greater than 30 years will have a pattern different from that 30 years and be “abnormal”.
If you narrow the definition of “normal” to the weather we had last Tuesday, we are in the throes of an unprecedented, unpredictable, threatening heatwave, precipitation variation and, indeed, frightening change in light levels.
We are all doomed.
The illustration from Wikipedia is out of date.
Sorry to be slightly OT but, according to new research recently cited on SkS, AGW is accelerating. Whilst we’ve all been concerning ourselves with absence of atmospheric warming, the AGW signal has appeared strongly in the depths of the oceans. This is fascinating stuff and it will be interesting to see how the models will have to change to accommodate this new reality:
http://www.skepticalscience.com/new-research-confirms-global-warming-has-accelerated.html
My imagination isn’t good enough to come-up with an explanation for the AGW signal to bypass the atmosphere, sea surface and upper oceans and so convincingly manifest itself in the deep oceans. How can man-made CO2 emissions create enough energy to heat the deep oceans without creating an increase of a similar magnitude in the other more commonly used metrics? For this research to have any merit there must be a physical mechanism which explains the heat transport i.e. energy being sucked out of the atmosphere, past the top part of the ocean and then building-up way below. Maybe the subject of a new post?
We have nominated you for the Dragon’s Loyalty
award. Please go here for details.
OK. Let me see if I got this straight. Even on things which actually have happened, the science is not all that particularly well-settled.
Which makes consideration of the thing which have not happened yet………
A bit unsettling.
The models produce what they are programmed to produce. It has been claimed that more than 90% of the climate science is scientifically either not known or badly known. How can they make a model of something they don’t understand?
The oracle in delfi Mk II. ?
Cue Yogi Berra: “Predictions are hard, especially about the future!”
and in this case, about the past as well.
Malcolm says: “My imagination isn’t good enough to come-up with an explanation for the AGW signal to bypass the atmosphere, sea surface and upper oceans and so convincingly manifest itself in the deep oceans. How can man-made CO2 emissions create enough energy to heat the deep oceans without creating an increase of a similar magnitude in the other more commonly used metrics? For this research to have any merit there must be a physical mechanism which explains the heat transport i.e. energy being sucked out of the atmosphere, past the top part of the ocean and then building-up way below. Maybe the subject of a new post?”
It will be new physics if it can happen in reverse. Considering that the temperature of oceans at depths below 700 m are generally only a few degrees C, there aren’t many places on earth that could be warmed by transport of this hypothetical heat. Last I heard, heat doesn’t flow from colder to hotter objects.
I am amused at the logic behind the so-called science relating to deep ocean warming. One of the arguments used for the lack of warming in the lower troposphere is the extra volcanic activity currently being experienced has caused abnormal SO2 and other gases and particles that have in turn reduced the level of longwave radiation reaching the Earth’s surface. If this is correct then there will be a similar increase in volcanic activity under the oceans. I understand that 70% of volcanoes are underwater and the percentage may be higher than that because of their concentration along tectonic plate rift boundaries. So I wonder what happens to all the heat emitted by these extra volcanoes? Surely it would heat up the surrounding water when they are underwater and the surrounding air when they are above ground.
Another in a long list of model failures. The only way CO2 is attributed to global warming is because the models can’t reproduce the current warming without CO2, however they can not reproduce natural cycles either. The models fail to get spatial change correct. Global models fail to capture the Pacific Decadal Oscillation keeping the ocean uniformly warm and they fail to recreate the frequency of El Nino events and thus can not recreate dry and wet periods and locations. Dai’s 2012 drought paper predicted the Sahel would get wetter in the 80’s and the western USA would dry and the exact opposite occurred. My favorite is Johannessen’ 2004 paper Arctic climate change— Observed and modeled temperature and sea ice variability. He argued that the models could only simulate the recent Arctic warming by adding CO2 and sulfates. However in so doing the model turned the peaks of the 1930’s and 40’s with Arctic temperatures as warm as the late 1990’s into cold periods, yet because adding Co2 warmed the last 2 decades, that was proof.
Modeling 1000.
Welcome to my class, please sign the class list before you leave.
First thing I want all you new budding scientists to know is the scientific method.
Here is how it works.
1) you observe the world in some respect
2) you get inspired to have some notion about how an aspect of the world operates
3) you calculate what the results might be based on an assumed model
4) you test the model with an experiment by getting data
5) you then compare the experimental results against the model
6) if the experimental results are different from the model, the model is wrong.
7) change the model, go to 3)
It seems many budding scientists do not know this 900 year old process.
If a climate model can’t reproduce historical events, it is wrong.
” Paul Westhaver says:
March 25, 2013 at 10:56 pm
7) change the model, go to 3)”
That’s what a modeller would say. A physicist would say
‘7) identify where the physics of the model is incorrect, change the model and then go to 3).’
That way the use of fudge factors is eliminated.
But a mathematician would ask ‘Is the system in question intrinsically chaotic and if so is it realistic to use the model to try to support my hypothesis? If no then go to the pub, if yes then correct model and go to 3).
Climate models will always be wrong if the wrong basics are factored in. It is also difficult/impossible to model a chaotic system.
What I find amazing is that for the past 15-17 years, “natural factors” have managed to cancel out, with exquisite precision, the increase in global temperature purported to result from increased CO2 in the atmosphere. In fact these “natural factors” have managed to increase their effect, year by year, to exactly match the increased radiative effect of this CO2.
What do you think the odds of this are (assuming independence and no-autocorrelation- which is what climate psientists do in their reconstructions)?
A not unreasonable assumption is to say that the probability that for any one year the chances of “natural factors” exactly matching the radiative effects of increased CO2 is 50%, then the binomial outcome culmulative probability = (0.5)^15 = 0.00003, or 1 chance in 33,333
If we are really generous and say the probability is 70% then (0.70)^15 = 0.0047, or 1 in 212.
Compare the “mighty” GCMs that can’t forecast or hindcast, with Scafetta’s “lowly” model that can both forecast and hindcast.
Scafetta N., 2012. Testing an astronomically based decadal-scale empirical harmonic climate model versus the IPCC (2007) general circulation climate models. Journal of Atmospheric and Solar-Terrestrial Physics 80, 124-137. DOI: 10.1016/j.jastp.2011.12.005. PDF – Supplement
Harmonic model for solar and climate cyclicalvariation throughout the Holocene based on Jupiter–Saturn tidal frequencies plus the 11-year solar dynamo cycle. Presented at 2012 Fall Meeting, AGU, San Francisco, Calif., 3-7 Dec, 2012.
Press release @ Eurekalert
Regional Climate Group – University of Gothenberg Regional Climate Group Website
PhD defense 2013-02-25
Thank you Dr. Leif.
Indur M. Goklany says:
March 25, 2013 at 5:54 pm
“Climate model simulation of precipitation has improved over time but is still problematic. Correlation between models and observations is 50 to 60% for seasonal means on scales of a few hundred kilometers.” (CCSP 2008:3).
I know this is not a quote from you Indur but correlations of 50 to 60% on precipitation? What is the the standard deviation? Use it alone and you will beat the hell out of the models 9 times out of 10.
Would a link URL to Mr. Ou’s paper not be appropriate? It is available from his university’s dissertation bank.
Leo Geiger says:
March 25, 2013 at 7:19 pm
Just so no one gets the impression that they are saying climate models are useless, or anything remotely along those lines.
When you arrive back on this planet give us a call !!
The key design aspect of all successful fishing lures is that they are designed first and foremost to catch fishermen. When the fisherman is looking over a series of lures on display on the wall, the successful fishing lure is the one that catches the fisherman’s eye and ends up being purchased.
The same process is at work in climate models. Whether a climate model can successfully model the future is only a secondary design requirement, because it will be many years at best before a model will be proven to actually be fit for purpose.
The primary purpose of all successful climate models is to attract scientists, and through their activities to attract funding. Whether the model is any good at predicting the future is largely irrelevant to the success of the model, because it lags the funding.
One thing to keep in mind is that models are generally created from weather models. As such they do have a certain existing weather knowledge within them. For example, the Sahara will not get vast amounts of rain. It should not be surprising then that they will some answers correct. It will happen automatically. That doesn’t mean they have any usefulness relative to climate. After all, a stopped clock is right twice a day and even a blind squirrel finds a nut once in awhile.
David L. Hagen says:
March 26, 2013 at 4:50 am
Compare the “mighty” GCMs that can’t forecast or hindcast, with Scafetta’s “lowly” model that can both forecast and hindcast.
=============
Early humans learned to predict the future in just this fashion, by observing the location of the planets and stars in the sky. We learned to predict the seasons long before we understood the cause.
We accurately predict the tides in much the same fashion, by observing the position of the sun, moon and major planets in the sky. We understand the cause of the tides rather well, but if you try and model the tides using first principles as we do in climate models, the predictions fail horribly.
Loosely, the future defies all attempts at prediction except in very simple cases. For example, we can pedict the orbits of 2 planets around each other, but if you introduce a third the future becomes murky. The problem is that while first principles work in principle, they do not work in practice becuase the computational problem size grows exponentiallly. This exponential growth in problem size makes first principles impossible to solve using computers.
Thus, until we solve the problem of computational problem size, we need a different method of predicting the future. While Astrology is a dirty word in science, we do in fact use the position of the moon, planets and stars in the sky that accurately predicts the seasons and the tides.
Given that climate is computationally impossible to solve from fist principles on any modern computer, it should not be a surprise to anyone that the same techniques that led to accurate predictions of the seasons and tides show greater accuracy than computers when applied to cliamte prediction.
Thank you all for the interesting discussion and some of the good suggestions and comments. It is a new start for me and my research work. Please refer to https://gupea.ub.gu.se/handle/2077/31816 or the home page of our group (http://rcg.gvc.gu.se/) if you want to find more detail of the work.
We can’t 100% accurately predict the future for both theoretical and practical reason. We just hope the models can be better and better which is true from the developing history of the global climate models from 1D to 4D. It is very important for the model is the only means to project the future.
Paul Westhaver said: on the 25th at 10:56 pm
First thing I want all you new budding scientists to know is the scientific method.
Paul, you forgot 3 steps:
2A) you get a $5 million grant from the gove’mnt to pay for your existence for a year (and 4 post-docs)
6A) you write and have published in a “science” mag a report with a title such as “Global Warming to Accelerate do to Human Existence!” which at the end calls for more study
6B) you get another $5 million grant from the gove’mnt for (same reason above) “more study”…
opps, that’s “due” in 6A above…
‘Climate models aren’t good enough to hindcast, says new study’
Tinghai just kissed any idea of a career idea in climate science good bye , ‘the Team nether forgives nor forgets .
Tinghai says:
March 26, 2013 at 6:33 am
“We just hope the models can be better and better which is true from the developing history of the global climate models from 1D to 4D. It is very important for the model is the only means to project the future.”
As someone who has been practicing CFD for over 20 years, I wish you the best in your research. Please continue to strive for accuracy and robustness, while clearly identifying and documenting your methods and formulations, something which is clearly lost on low quality/accuracy codes such as Model E from the controversial NASA/GISS. (I urge everyone who has an interest in improving the quality of climate prediction software to visit the NASA/GISS Model E site to see how NOT to program in FORTRAN and how NOT to document you methods clearly).
Thank you for the encouraging and suggestions. I need to learn more so as to prepare for the future.
Welcome Tinghai Ou.
I have been influenced by N. N. Taleb (Black Swan, and Antifragile) on forecasting in finance and meteorology and the demarcation problem.
Dr. Tinghai Ou
Congratulations. Thanks for the links to your thesis: Observed and simulated changes in extreme precipitation and cold surges in China: 1961–2005
6 Conclusions
fred berple
Good observation. Re ” Astrology is a dirty word” – try “Astronomy” & “Planetary Physics”.
Cold surges may be increasing again.
Flagship Daily DIE WELT Stuns Germany: “Scientists Warn Of Ice Age”, Cites New Peer-Reviewed Russian Study
“Latitude says:
March 25, 2013 at 7:01 pm
Few models can well reproduce the nationwide change
=====
what are the odds when you flip a coin………….”
50/50 odds,which is better then the models,which are 100% wrong.
Thank you for your interests with this topic. Precipitation is one of the variables which is very difficult to be reproduced by the models. East Asian summer rainfall is affected by the East Asian Summer Monsoon, the precipitation pattern is poorly reproduced even if the model can well simulate the atmosphere circulation. There are many reasons for this, such as the model physics to simulate the precipitation. And some factors like Pacific Decadal Oscillation (PDO) is not well resolved in the global climate models. There is hope that the models can be better. The models are improving.
This is not like flipping a coin, which is a random number, and this can also be 100% wrong if you are not lucky. Hope you will not loose you confidence on the models based on my work which is not my initial opinion related to this.
Garbage assumptions In : Garbage Gospel Out.
When will we replace the current politicians, who are bedazzled by the shiny, cannot comprehend the limitations of assumptions dressed up as computed models?
As many here have pointed out, the IPCC grade projections are rubbish, were rubbish yet were considered good enough for government, government mandated robbery that is.
Too much incompetence and avoidance of responsibility here to be accidental.
There is clearly something wrong with the past! Historic data should be corrected in line with climate models.
“Whoever watches the wind will not plant; whoever looks at the clouds will not reap.”
-You can’t predict the weather that precisely. Well, at least according to the Good Book.
So his analysis showed a small minority of models actually were good in hindcasting?
I’d be shocked if everybody was right. But if a few people are, that’s all it takes. I wonder if that was coincidence or skill.
Thanks for gracing us with your presence, Tinghai.
In my last comment, which I wrote before reading your comment, I noted that not every model seemed to perform badly. In your opinion, did the few models that worked better do so mainly from random chance or was there something within the models that made them an effective representation of what is happening in the real world?
So far I’m not clear about why some models are better than the others. We are still try to find out the truth behind this. By doing so, we can provide suggestions to the people who are working with the improvement of models. My guess is that this could be affected by both the random part (especially when comes to the small regions) and the dynamic part of the model. There are some part of the model dynamics can not be resolved theoretically and this can only be resolved mathematically, which means this is close to the truth not the truth. And this make the model simulation very difficult to reproduce the observation. On the other hand, you may know that there is error in the observation so that we can not get the truth from the observation. Back to the second part, some of the model can well simulation the basic pattern of the circulation field while some models cannot. This can be link to the model dynamic which I’m not familiar with. The topography in east Asia is very complicate which is a big change to the models. How does the topography be represented in the model and how to resolve the topography effect is very difficult.
Throughout China’s recorded history there have been catastrophic floods and droughts. Therefore anything that can help predict extreme wet or dry years will be helpful. Let us not forget that in the case of China, we’re talking of hundreds of thousands, if not millions, of lives being adversely affected by these events. Since we are talking real life scenarios here, funding for this kind of research (this includes models and their validation) is much more important than the hundreds of billions we have been pissing away on the CAGW/Climate Change hoax.
Where it the link to the thesis so we can see for ourselves what it says?
Where is the link to the thesis so we can see for ourselves what it says?
You can find the link to the thesis here (https://gupea.ub.gu.se/handle/2077/31816).
The climate models are only good for predicting the climate of models – like Railways in the basement of Hansen and Mann
I spent several years verifying the computer model for a nuclear reactor for accident analysis’s (of design accidents.) This entailed many days of multiple runs expending hundreds of dollars per run (in 1975 dollars), fixing one or two things (never more, as that just confused things) and then doing the same the next day. Dumpsters full of printouts (this was before recycling was in vogue) were generated. (we used to joke “they could heat the building with the paper.”They let me take as much used paper home as I wanted. the kids loved it.) For over two years. And this was something that had a limited, known set of measurable, quantifiable parameter’s. I would hazard a guess that it took over a hundred runs per parameter to get a model that was acceptable, but in no way would be considered accurate by todays standards. Over the years since that model was completed it has been tweaked and modified to get it more accurate on the order of 6 to 10 times per year.
Meanwhile, in the CAGW world they haven’t even run one projection that has proven that the model predicts what it is supposed to predict with an accuracy of assuring that if you shoot a pistol in a barn, it will hit a wall. They haven’t identified all of the parameters involved, nor the polarity (feedback/forcing) of some of the known parameters and still ignore parameters that they don’t like or understand, or don’t fit their ideology.
Good Luck.
Many years ago I worked on the development of a computer model for a nuclear reactor that was used for analyzing design basis accidents. This involved many daily runs verifying the accuracy and correcting the model. After each run we checked the output, fixed the errors and tried again. The standing joke was that we could heat the building with the paper we hauled to the dumpster. They let me take as much home as I wanted. The kids loved it. This took over two years and we had a known, identifiable set of parameters. Still, I would guess, it took over a hundred runs per parameter (times the 20 – 30 power levels) to get a model that was acceptable. Over the years since then that model was tweaked and modified at least 6 times a year to improve it’s accuracy and correct mistakes.
However, in the CAGW world, they have an unidentifiable number of parameters, with some having unknown polarity (feedback/forcing), parameters that are not identified, not included, and even those that are ignored because of ideology. And to my knowledge they only predict the past/future with the accuracy (as stated by them) that is equivalent to saying that if you shoot a pistol in a barn it will hit a wall. And for this new found knowledge we are to spend thousands of trillions of dollars? Shut down all coal plants, and cover the earth with wind turbines? And we have already shown that the bullet went in the ground (the 15 years with no warming) and didn’t hit a wall ( their error band that is large enough to cover even the accuracy of the worst model e.g. 75% accurate with a 95% confidence level) as they projected.
What are GCMs good for, except wasting money?
There is a phenomenon, ENSO, that seems to ‘model’ the climate pretty well, or is it ‘modulate’ the climate? I think trying to model ENSO might teach us more, today, and in the short term.
Thanks, Tinghai, for your reply to my question.
I can tell you’ve given a lot of thought to these issues. Keep up the thinking, including maintaining an open mind and acknowledging such realities as:
This is not surprising. The global climate models have been shown, again and again, to not “be skilful” at prediction or hindcast, at regional scales.
Yet we are supposed to believe that they magically ARE skilful on a global
scale. RIIIIIIGHHTTT!!
Tinghai says: March 26, 2013 at 10:30 am
I am glad you said that. I think many people were misreading your opinion related to this, based on some of the comments here.
Oh I don’t think they’ve taken any particular effort to absorb Tinghai’s position in the first place. I think most people here engaged in a highly-selective reading in something like a mass exercise in confirmation bias.
According to Tinghai, a small minority of models were either fairly accurate in hindcast or got lucky. He also believes the models are getting better over time, or at least that some of them are.
Tinghai seems like an open-minded analytical sort to me, neither too credulous nor too conspirationally-minded. I suspect most of the attempts to model the climate are honest, but crap. However, some are less crap than others, is one of Tinghai’s points.
As someone who has been practicing CFD for over 20 years, I wish you the best in your research.
If Frank K. has been practicing CFD (computational fluid dynamics) modeling for over 20 years, I would like his opinion on the influence of grid sizes on results of climate models. I have done some atmospheric dispersion modeling (of air pollutants) using CFD, and have seen that weather-forecasting models are usually fairly good up to five days into the future, then observed weather tends to diverge from what the models predicted more than six days ago.
Weather forecasting models can usually use fairly small grid sizes (on the order of 1 km horizontally and 100 m vertically) because they don’t need too many time steps to predict weather 5 to 10 days later than the time the model is run. But errors tend to propagate when the model only calculates flows, temperatures, and pressures through the boundaries of each grid cell, without knowing what happens inside a grid cell.
If a “global climate model” is trying to forecast overall weather trends 50 or 100 years into the future, over the entire globe, the length and/or number of time steps must be much greater than those for a weather-forecasting model designed for a 5-to-10-day prediction. If the modeler attempts to maintain short time steps, he may need to use a smaller number of larger grid cells in order to obtain a result within a reasonable computation time.
Does Frank K. know the grid cell sizes used in most of the “global climate models”? Is it possible that the use of large grid cells may overlook local effects within a grid cell that propagate more errors than a relatively short-range weather forecasting model? In particular, do some of the larger grid cells in GCM’s include topographic features such as mountains or coastlines, which can “generate their own weather” from the sea-breeze effect, lake-effect snow, or orographic lifting over mountains, which can generate local precipitation on the windward side, and drying (Foehn effect) on the lee side? For example, could a model really calculate the “average” weather in a grid cell that included both Mount Rainier and Seattle ?
Also, what are the time steps used in “global climate models”? Are they on the order of seconds, hours, or longer? If a weather-forecasting model starts to diverge from reality after five days or so, how many time steps does that represent, and what would be the effect of longer time steps in a GCM? Are there errors within time steps and/or grid cells which can propagate and become much larger than any effect of infrared absorption by CO2?
Hi Steve Zell,
The first thing to note about climate (and weather) modeling is that it is an initial value problem (though some in the climate modeling community claim it is a boundary value problem, which is total B.S.). For initial value problems, numerical solutions must at least satisfy Lax’s Equivalence Theorem:
Given a properly-posed initial value problem, the necessary and sufficient condition for convergence (i.e. a valid numerical representation of the IVP) is stability.
Stability, unfortunately, can only be proven for linear equations, and the equations which form the basis of weather and climate models are highly non-linear. Moreover, we aren’t talking about a single equation but a system of coupled equations with significant source terms (i.e. the “forcings” that modelers like to talk about),
And also remember that coupled GCMs are solving equations which represent the evolution of the BOTH the ocean and atmosphere. So this means, continuity, momentum, energy, species, etc. equations for both atmosphere and ocean (along with special modeling of polar icecaps and sea ice etc. etc.).
In the end, the number of non-linear, coupled differential equations with source terms (fed my sub-models for radiation thermal energy transfer, tracers for aerosol transport, clouds and precipitation in all forms) is very large! And there is no way to guarantee that: (1) the system is well-posed mathematically or numerically, and (2) the numerical scheme is stable. Which means that errors in the initial state can grow unbounded and swamp the solution with garbage.
For most numerical schemes, the errors will be proportional to size of the mesh and the time step. Errors can be reduced by reducing both the cell sizes and time step. Usually, you have to do both, since finer meshes reduce the stability through what is known as the CFL number: CFL = dt*U/dx, where dt is the time step and dx is the spatial discretization size. For time marching schemes you typically want CFL to be about 1.
Finally, getting to your question, climate models are usually more coarsely resolved than weather models, and so use meshes on the order of 100 km cell size. Time steps can be as low as 30 minutes or as much as several hours, depending on the cell size and stability. Here is a good resource for you:
http://www.windows2universe.org/earth/climate/climate_modeling.html
Everything I have mentioned above (and more) is the reason I hammer on these climate modelers to make sure they properly document everything they do, and not just the superficial “fluffy” junk that you see in their papers. I mean: source code, extensive documents on EVERY equation solved and EVERY numerical method employed, a description and results for unit tests and more extensive test cases. And all in one place (not just a reference list of papers which typically do NOT provide all of the details).
Hope this helps…
Jonathan Carter and others wrote a paper in Reliability Engineering & System Safety entitled Our calibrated model has poor predictive value: An example from the petroleum industry (2004). Essentially, this paper, plus follow-up work by Carter and others, has demonstrated that in many complex models, even if one is able to develop a model that can simulate past results quite well (which rarely happens), the model has no capability to predict the future of the system. This is, potentially, a very important result that may have application in many disciplines, both in the physical sciences and the social sciences (I realize the latter phrase is an oxymoron, but I’ll use it for clarity). I’m not sure why I’ve not seen this paper referenced in any discussion of AGW. Perhaps I’ve simply overlooked such.
Reblogged this on Truth, Lies and In Between and commented:
Seems more like guesswork than solid science.
Time to stop the global warming scam. It’s just that. A scam. Look who makes money off of the lies and you can pretty much figure it out.