Weather vs. Climate

Might want to use the June version of your later graph, NASA now forcasts ENSO below -2.5 later this year, it’s on page 27 of this .pdf: http://www.cpc.noaa.gov/products/analysis_monitoring/lanina/enso_evolution-status-fcsts-web.pdf

Obviously the big guy is Climate, as Climate defines the outliers of daily weather.

jcrabb says: Climate defines the outliers of daily weather
JC not quite sure what that means, however, I have never been someone who is happy to separate climate from weather. To me, climate was always just another word to describe weather but in a very loose, non-descript way. Climate, the word, allowed the formation of another university/government department for creating acedemic, non-productive jobs for school children who never want to grow up and “go out to work” as my dear old mother used to tell me. Although Anthony succinctly describes the difference between weather and climate models in terms of parameters used, the climate parameters he describes also affect weather when then affects the climate. See what I mean?

Thank you for mentioning the compounding error issue.
I try to explain to friends and colleagues about floating point representation and problems arising from that. I dipped into some of the online models and some of the CRU code and it seems to all rely on the standard number handling of the hardware and software platform.
Anyone who needs to know about it should read Knuth’s “Art of Programming”, he deals with the issue with typical humour-tinged clarity. While you’ve got it out, check out his section on random numbers. I believe (not sure) that most of the models use random numbers to some extent, and yet we never learn about the actual algorithms used in these models.

OT:
Don’t worry, eat bananas and all will be well:
http://www.guardian.co.uk/environment/green-living-blog/2010/jul/01/carbon-footprint-banana
Bananas? Quite ….

Several years ago now, I was part of a discussion panel with a local TV meteorologist. I was doing computer modeling of astronomical phenomena at the time; he was discussing weather forecasting models, I was talking about the difficulties of complex, long-time-span models.
The forecaster related the story of how his own mentor, who was a very popular meteorologist on TV, back in probably the late 70’s or early 80’s, resigned his position rather than give a 7-day forecast. He refused to be part of something he felt was a hopeless waste of time.
The guy on the panel said 3 days is believable, 5 is a stretch, but hey, everybody wants a 7-day outlook, right?
Jeff

Steven, Your Good!
It all comes down to what are the rules and what are the boundries that separate the two fighters assuming the arena is the world. What label do you put onto each fighter depends also on what you would like to achieve. Most people would assume climate to be the “big guy” with the power, money and backing of governments and the assumption that they are invincible or supreme in the science. In a short term duration (such as the science now) the energy and power would be a good bet but over a longer span, they tire out and fall apart. Weather will still continue on way past the climate models due to the lack of total understanding ALL the input and not staying “hyper-focused” on one area such as CO2 or strictly temperatures.
This same situation can be said for AGW and the “Deniers”(enlightened ones? truth finders? hmmmm)

The smaller boxer would be climate, because he, being more fit would be able to outlast his opponent, wearing him out essentially.The larger boxer (weather) might seem to dominate at first, but would eventually succumb to the smaller boxer’s speed, agility, and endurance.
In the battle between the climate Alarmists vs Skeptics/Climate Realists, the latter would be represented by the small boxer. The Alarmists appear to be exhausted now, lashing out furiously but ineffectually in their last-ditch efforts to survive.
Man’s effect on climate could be represented by a fly buzzing about climate’s head.

well, here’s hoping the moderator can fix my tags in the previous comment and close out the bold tag at the start (wasn’t trying to shout)… thanks moderators!

‘To punch below one’s weight’ is to achieve or perform at a level lower than should be expected based on one’s preparation, attributes, rank, or past accomplishments.  
The big guy is climate.

Jaime says:
July 1, 2010 at 2:46 am
” … the argument is ridiculous. Similar to saying we can’t know the pressure of a gas is, because we can’t even predict with certainty what the trajectory of a given molecule will be…”
The gas is not a chaotic system, however the atmosphere, oceans, and landmass is.

Jaime
Climate models are basically extended weather models. They have to be, they are modeling the same atmospheric and oceanic processes.

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

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.

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

I do like models….but the fashion ones.

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?

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!

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.

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.

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.

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.

Ocean conveyor theory evolution.
http://www.theresilientearth.com/?q=content/ocean-conveyor-belt-dismissed

@Expat in France
(finally a topic on WUWT I have some expertise in to comment on, heh)
“I think it’s the other way around. Weather is in constant motion, more aggresive, volatile, and packs a punch at short notice. Clearly the heavyweight.”
As a boxer myself my un-peer reviewed “professional” opinion is that the above description much better suits the smaller fighter. That larger fighter is probably only capable of slow haymakers. The smaller boxer will probably take his time, giving the larger fighter a much longer time in the ring that his fighting skills rightly deserve, being carried only through sheer size and momentum (the smaller boxer will want to avoid letting the larger corner him and use his weight against him, though even then the larger fighter’s options are limited). That description of the larger boxer to me accurately represents the nexus of CAGW committed media-political-and activist interests with the smaller boxer representing skilled and sincere scientists and truth-seeking laypeople.
Someone above said the smaller boxer looked like he’s stumbing, he’s not. He has ducked into position to give a left bodyshot and looks like he is about to follow through with a right cross.

In the Sea Ice #11 thread “Gavin” wanted to know what people thought of 45F in Siberia.
http://wattsupwiththat.com/2010/06/28/sea-ice-news-11/#comment-420492
45 F in a city in Siberia means global warming is happening. 45F in a city in Siberia is climate and not weather.
😉

This is an answer given just now by Dr.Roger Pielke Sr. in his website with reference to a question on weather and climate modelling.
http://pielkeclimatesci.wordpress.com/
His view on the subject is quite realistic.

Steven,
Couple points:
1) No models are perfect, and scientists using them will readily admit this. It tends to be the scientific-illiterate media that place an extreme amount of trust in climate model results. The fact is that even simulating evolution of an afternoon thunderstorm is extremely difficult. (Look at cloud-resolving models and single-column models for instance)
2) Models tend to be subjected to rigorous validation. Yes, none are perfect, but there is always one more advanced and accurate than the rest which goes on to beadopted for broad use. If there was a model predicting sea ice decline in Antarctica over the past several decades, and this failed to match the observational record, then any results from this model predicting the next several decades should be regarded with suspicion.
3) The example you give also illustrates the reason that for the majority of serious model results (be it climate, weather, hurricane, or otherwise), an ensemble average is used to determine the most likely outcome. You seed small perturbations in the initial conditions, then use a few different models, and average the results. This also gives you a nice range of possibilities.
4) Just something quick I noticed – in your 2 ENSO model forecasts shown above, several models in the December run did a very good job at predicting the SST anomaly in May and beyond.

Climate (if you consider rainfall an element of climate) sometimes varies almost as much as weather. Look at this graph of prehistoric lake levels of Devils Lake in North Dakota.
http://mapservice.swc.state.nd.us/4dlink9/4dcgi/GetContentPhoto/PB-42/640/480
Devils Lake is a lake in the prairie pothole region of the US that has no outlet and thus gives a picture of how changeable precipitation patterns are in this region. The lake levels are inferred from diatom studies like this http://www.jstor.org/pss/3225026 . If it is true that soil moisture is a key difficulty with creating long term climate models, I have to think that the changeable moisture patterns in the western plains suggest that climate models are robustly intractable.
I also remember hearing Bill Gray remark on C-SPAN that momentum fields that weather forecasters use are less troublesome than the energy fields that have to be used to create more faithful longer term models. I took this to mean that momentum being a product (mv) is less prone to error compounding than energy (mv^2) which involves a squaring.
Is it possible that the enterprise of creating statistical climate averages misleads us into thinking something unwarranted? That this notion that we’ve created called climate is really just an average as it seems from the perspective of a lifetime or decades. To me the existence of the Roman Warm Period and the Medieval Warm Period and the Little Ice Age and for that matter the Younger Dryas all suggest that there is no such thing as a stable global climate. In and of itself there is no reason to be surprised or alarmed that we find we are in trend. What would be more surprising would be to find that with a surface as variable and varying as the earth’s that there should be a rock steady average global temperature.

@ PDA July 1, 2010 at 5:33 am:
You can’t compare this to coin flips. If we were, then you would be talking about someone claiming to predict a million coin flips to the accuracy of 10 +- 15. Unlikely garbage.

Mike,
Your analogy is flawed in several ways – the glaring one is you’re comparing an open system (the river) to a mostly* closed system (the earth). This is why the coffee cup analogy works – its a closed system in everything except energy.
Examining your claim that “Likewise climatic noise follows a 1/f^n type relationship, in that the longer you look at the climate, the more variation there will be,” its true in some sense, but given a broad enough timescale, you can make fairly accurate predictions, because the nature of a long time scale tends to smooth out small-scale effects. Take the Vostok ice core data, for example – http://faculty.gg.uwyo.edu/neil/teaching/Geomorph/lect_images/Vostok-ice-core-petit.png Over a large enough time scale, the temperature follows a fairly regular oscillation, which is driven by the well known Milankovich cycles. Given a sufficient understanding of the oscillation of the past, and the Milankovich cycles, it wouldn’t be unreasonable to give a ballpark prediction for several thousand years from now.
* I say mostly, because the Earth is a closed system in almost all respects except energy, the input/output of which are fairly quantifiable on broad timescales.

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.
Now you’re not even trying. Hint: as weather is not the same as climate, so mere extrapolation is not the same as modeling.
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.
Yes, and a Peterbilt is essentially the same as a bicycle, only with more circular rotational devices, more mechanical motive power and often heated seats. If you can’t haul 160,000 lbs GCW coast to coast with a bicycle, why do you think you can do it with a Peterbilt?

Even knowing the mechanism of a model does not mean you can predict the future to 100% at any time.
No one but you is talking about “predict[ing] the future to 100% at any time.” No one but “Steven” is saying that “Colorado will be exactly 8.72 degrees warmer in 100 years.” It’s the reductio ad absurdum where the wheels fall off you guys’ arguments.
There is no mechanism for climate models to correct themselves, which is why they consistently predict higher than observed temperatures.
Do they “consistently predict higher than observed temperatures?” Do you have a source for that assertion or is it just an article of faith?
Others might disagree.

Suppose that a climate model assumed clouds to be a positive feedback, and it turned out they were actually negative. Each successive iteration of the model would produce increasingly inaccurate results.
Yup, and hindcasting would show whether that was the case: the model would show different results than what was actually observed. And yet they don’t. Why do you suppose that is?

Paul Daniel Ash
Climate models are backfitted with thousands of empirically derived parameters, so not surprising that they work backwards.
I tried a stock forecasting program once which generated a fifth order polynomial to backfit past behaviour of stock prices. It was incredibly accurate looking backwards, and demonstrated zero skill looking forward.
“With four parameters I can fit an elephant, and with five I can make him wiggle his trunk”
Johnny Von Neumann

carrot eater: July 1, 2010 at 6:28 am
……..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.

That’s not necessarily a foregone conclusion. If the Sun suddenly increased its output, there would undoubtedly be a fairly immediate short-term warming response, but that would be accompanied by increased evaporation from the oceans, which would result in greater cloud cover, which would inhibit the inflow of solar energy and counteract the increase in temperature. Similarly, if the Sun’s output were to suddenly drop, there would be short-term cooling, but then the evaporation would decrease, producing fewer clouds, and allowing a higher proportion of the energy from the fainter Sun to penetrate to the ground, thus counteracting the decrease in temperature.
From the geological record, Earth appears to have “hard walls” at 12°C and 22°C, and the temperature has always been somewhere in that range, through many cataclysmic changes of every kind over hundreds of millions of years. In more recent times (mere millions of years), that ±5°C has been more like ±3°C or less. Nobody knows why either of these things is true, but somehow here we are, with all of the other abundant life on the planet, still in one piece, and every single one of our ancestors has managed to survive (at least long enough to procreate).
/dr.bill

“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…..”

Some will have been long dead as well. We had a 10 year tipping point from the UN back in 1989 and in 2010 we have a pasturised tipping point of 2200.
Doesn’t the Independent ever get tired of reporting utter rubbish.
20 March 2000 “Snowfalls are now just a thing of the past”
Verified as rubbish

The Independent – 28 June 2010
‘Scientists ‘expect climate tipping point’ by 2200′
“The global climate is more than likely to slip into an unpredictable state with unknown consequences for human societies if carbon dioxide emissions continue on their present course, a survey of leading climate scientists has found. “

Unverified rubbish

the plain fact is that climate models fail to accurately predict anything worthwhile
Smokey,
I know that you tend to be a “belief” rather than “evidence” guy, but that’s not a factual statement. It’s just not.
Wake up.
Wake up.
Wake up.
http://journals.ametsoc.org/doi/abs/10.1175/1520-0442%281998%29011%3C0109%3AAPSPWA%3E2.0.CO%3B2“>Wake up.
Models have been shown to be accurate – not perfect, but accurate within reason, and improving – so the “fail to accurately predict” claim is false on its face.
Again: overreach. It is possible to make meaningful objections and critiques without going overboard into “ITZ ALL A HOKES!!1!” territory.

Steven:
“If [GCMs] 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.”
Are you saying that if you run a weather model out over many years, it won’t capture the seasonal cycle? Of course it does, because we understand very well how solar insolation varies by latitude during the course of a year. To leading order, predictability of the seasonal cycle is not dependent upon the chaotic dynamics that make weather prediction localized in space and time so difficult, but instead is simply dependent upon the predictability of the orientation and position of the Earth relative to the Sun.
Similarly, when I blow air onto my hand, the dynamics of the individual particles moving out of my mouth are chaotic and thus totally unpredictable beyond some very short time-frame, but predicting the aggregate–that I will feel air hit my hand–is highly predictable.
In this way, then, the logic of this article is incorrect. If you disagree, please feel free to respond.
Cheers,
Dan

As a follow up to my recent post, I myself do not particularly like our climate models, especially in any context involving regional climate change projections (the scales for which predictability is indeed highly questionable). The models may indeed be very wrong even in prediction of global temperature, perhaps even for reasons given in this article, since the climate sensitivity is dependent upon boundary conditions, which themselves may have limits of predictability due to the internal dynamics of the system. However, this article does not follow that logic, but instead applies the incorrect logic that climate predictability is de facto impossible because weather predictability decreases to zero after only a few days.

vukcevic says:
July 1, 2010 at 10:20 am
That page of yours hope to see it in science books texts, after the collapse of current paradigm and holy creed along with the disappearance of the funds which artificially support it. No money=No self indulging post normal science.

Coin flipping isn’t a good model to use when thinking about climate.
Agreed, which is why I wasn’t using it as “model” for “thinking about climate” but rather to highlight the clear and trivially obvious difference between predicting discrete events (a single coin-flip, next Tuesday’s weather) and statistically modelling a series of such events (10,000 coin flips, long-term climate).
You get that. I get it. It’s not at all clear that “Steven” gets it.

Dan
People run weather models out to two weeks, but they are pretty useless after 72 hours. I check the accuweather two week forecast almost every day – it changes radically from day to day.

Climate models are deterministic, not random. They use random numbers for appropriate Monte Carlo issues, like locations of clouds within a grid cell. But that is a far cry from being based on randomness.

There is a basic flaw in the climate forecast models and the defense of them here.
We have the coin toss comparison:
““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.”
and the similar but more complex argument:
“Climate is maybe more like the ring than the fighters who stumble about, inside the boundaries… Actually, seriously, “climate” is more like predicting the probability distribution of the outcomes of 1 million fights, whereas “weather” is predicting the outcome of the next round of a single fight.
Both of these make the same mistake – climate is not a set of “Markov” daily weather models – climate has no Markov properties a cold day affects the following day, heavy rain one day leads to higher soil moisture the next etc. As forecasters know there is a stochastic property in weather that can at times appear to be a Levy Flight although the likely bounds of the dispersion about the expectation are known (the boxing ring) where the boxer will next step may be chaotic and the step after that depends on the previous step. The ring will move to be the likely Levy Flight bounds from each boxer and could be said to represent the climate. After a round lasting a century it is difficult to forecast the position of the ring – the climate – with any accuracy.
Added to the problem of the non-Markov Levy Flight is the lack of full understanding of the inputs that affect the weather or even knowledge that they exist. It is difficult enough attempting to forecast the behavior of a chaotic system of chaotic systems but when not all the variables are known and the effects of the known variables are uncertain it becomes gifted guesswork at best.

Climate is not exactly an average over weather, is it a wise average over weather.
In the case of the flip of a coin, it is easy to do a wise average because the probability is 50/50 for each flip. In the case of climate, the probability to have a temperature under 40F in New-York is not the same all the time. Meteorologists are aware of several cycles wich modulate the temperature: day-night, summer-winter, el nino-la nina. Now how many other cycles are present in the system? If you calibrate a model during a positive phase of one of these cycles, you will automatically predict a positive trend that does not exist.
I find it very difficult to believe a wise average of climate can be done when the existence or nonexistence of the medieval warm period is not supposed to have any effect on the predictions of the model.

Global warming/climate change has splitted the personalities of many former scientists, who in order to survive in such a competitive environment have surrendered their science replacing it for a creed. This is why some show two or more personalities at the same time, playing the eternal drama of good vs. evil, appearing like Dr.Jekyll and Mr.Hyde.

R.Gates
Both have chaotic dynamic elements
Chaos’ explanation, or rather, justification, appears where knowledge is lacking.
Knowledge is not so hard to attain than hard to accept.
Pitagoras with his simple monochord attained it, how about you?

Re: the boxers, the guy on the left is me, the guy on the right is the IPCC. 🙂

“Climate models iterate over very long time periods, and thus compound error. ”
Non sequitur.
“Weather modelers consider the realm of climate calculation to be “months to seasons.””
Whichever arbitrary and unspecified subset of weather modellers you spoke to were gravely mistaken. You can no more measure climate over months or seasons than you can measure continental drift in a minute.

“That assumes that the feedbacks are an invariant set. ”
At this point, time scales matter. There are some feedbacks that operate on the shorter term. There are some feedbacks that operate over the longer term.
For example, for a sloooooow feedback, there’s the volcano/rock weathering one. Or, similarly, all the carbon dioxide being introduced into the climate system now will eventually end up in rocks somewhere. But just because all the carbon dioxide will eventually go away if you just wait many thousands of years does not mean you can’t predict it will warm in the meantime.
Just as there might be some feedback, perhaps now unimagined, that would over a long time counteract a huge change in the sun, it doesn’t mean you can’t predict that a huge change in the sun would have a climate impact in the meanwhile.
To make an extreme example to prove the point: if the sun magically ceased to exist tomorrow, I really don’t care that we can’t predict weather more than a week ahead of time. I know that I’m going to be cold and dead. Actually, if we wait long enough, we’re going the other way with the sun, but you get the point.
“you obtain a steady-state system that can be almost impervious to change. ”
You can write whatever you want into your equation, but the actual Earth clearly isn’t impervious to change. There are ice age cycles, after all. There may have been a snowball earth or two. There was the PETM. And so on.

“I have an excellent understanding of climate models. ”
Not once have you shown that you understand that weather prediction is an initial value problem, and climate is a boundary value problem.

vukcevic: July 1, 2010 at 1:21 pm
dr.bill says: July 1, 2010 at 12:28 pm
“…..But feel free to rave on.”
If you are real Dr. (someone ?), then for the above choice of words, the real name rather than the ellipsis would be more appropriate, so readers can judge the real command of authority you articulate.

Translate, please?
/dr.bill

When your model does not get the cloud distribution right but in the end the temperature fits, does this not give you the feeling that it might be right for the wrong reasons? And has there ever been a climate model that gets cloud distribution over latitudes halfway right? I think it has yet to be invented.
First off, they’re not my models, and secondly yes, as I’ve been saying throughout the thread, there’s lots to criticize in how models are run and in the certainty ranges. And absolutely: parameterization of clouds and aerosols go near the top of that list. “Failing to accurately predict anything worthwhile” doesn’t even make the list.
Past a certain point, as the goalposts get moved from “wrong” to “right but for the wrong reasons,” one has to wonder if we’re talking about science or belief. And i don’t get into religious arguments, on principle.

When your model does not get the cloud distribution right but in the end the temperature fits, does this not give you the feeling that it might be right for the wrong reasons?
Maybe it means clouds are not as important as you think.

blockquote cite=”Dan says:
July 1, 2010 at 5:56 am
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
“>
I think you’ll find that January colder than July in the northern hemisphere due to Earth’s orbit around the Sun and the axial tilt of the Earth WRT the orbital plane.

Found in: PDA on July 1, 2010 at 5:33 am

REPLY: What happened to posting as Paul Daniel Ashe? Why go back into the closet, you were doing so well? – Anthony

By the circumstantial evidence I’d say the word has gone out to take out Steven Goddard. Since “PDA” is known here, time to break out a mask, which turned out to be a lousy one. You recognized him so now the mask is off but he’s going ahead anyway.
Seems like they’re using the plan they started using at the “Amazing Grace” thread, complain about his credentials, complain about his posting name being fake, and keep working him down until he feels he has to defend himself by declaring where his degrees are from, where he works, maybe they’re hoping he’ll skip to the chase and just give his real name and save them the effort of the tracking down. Then they can stick his name on the list and destroy him professionally.
Heh, “PDA” linked to a one-entry new blog where he doesn’t even use the name found here, of which the domain has an anonymous registration. And while there is complaining about Steven being “anonymous”… Hey look, it’s carrot eater! Yup, got a real party going on here now. 🙂

The Climate Model Paradox:
If climate physics is “settled” science why are all climate models not identical? If it is not settled how can a computer projection based on unknown climate physics give remotely usable output?
Where is Leif when you need him, he thinks computer models cannot be programmed to get the results that you want. If most climate scientists think like him it is likely they are all computer illiterate.
RE: The silly seasonal argument,
Saying that the winter months will be colder than the summer months in the northern hemisphere is based on observational data since man recorded such things. No need to model something you can look up in the farmers almanac. Yes of course we now know why based on the orientation of the Earth to the Sun, none of which has anything to do with being able to correctly predict or project the temperature of those months in the future.
The problem with those who never studies computer science is they think “close-enough” results from a model are accurate, they are not they wrong (right in the sense that the model did exactly what it was supposed to, wrong in that they do not match reality). Why they are wrong (do not match reality) is a guessing game and cannot be determined on a computer. Wrong results (do not match reality) are worthless.
Virtual reality can be whatever you want it to be and computer climate models are just that, they are the code based on the subjective opinions of the scientists creating them. The real world has no such bias.

RW
You should get on the phone with NOAA’s “Climate Prediction Center” right away and tell them that based on your understanding of the definition of climate, they have no business doing seasonal forecasts.
I bet you will get a really positive response.

I feel that Climate pushes Weather around, so I go with the big guy being Climate.
The Earth isn’t the center of the universe? Things look pretty spread out in all directions to me. What would Dr Hawking say?

PDA says:
July 1, 2010 at 5:33 am
[…]
Weather is chaotic and unpredictable. Climate is weather averaged out over time.
Common quality of chaotic systems is they tend to be self-similar at any scale. If we Define weather a a phenomena lasting 15 minutes and climate as a phenomena lasting 30 years, weather seems erratic when viewed over a period of a week, like wise climate will seem erratic when viewed over a period of a millenia. Additionally whether a phenomena erratic and unpredictable, or it is chaotic are two separate issues.

Tom in South Jersey
Yes, team AGW has convinced themselves that the world depends on their belief system. The same delusions as the fanatics of every generation and every culture.

Ulric Lyons said:
“The energy from the Sun is certainly the only supply of energy on earth…”
_________________
Quite untrue. There are several more, and so I don’t think you’ve thought this through very well, but let me give you a hint: Think Madame Curie. Here’s another hint: Think of Eyjafjallajokull. So when our spacecraft leave the Earth and travel deep into space where the influence of the sun’s energy is even less, or non-existent, then we rely on the first of these non-solar sources of energy (it being too impractical to take small volcanoes onboard a spacecraft)
But in general, I don’t deny the major role that the sun plays in the Earth’s climate, but I also am not blind to the other factors. The weathering of rock for example and geological processes such as volcanoes are completely independent of solar output but have major influences on the composition of our atmosphere and in creating the GHG forcing that we enjoy and the subsequent climate, without which, we’d all be, well…nonexistent.

This is what Kerry and Waxman said about GRACE. I’m surprised all the GRACE apologists here didn’t rush to Washington to straighten them out.

Congressional Democrats, including Sen. John F. Kerry (Mass.) and Rep. Henry A. Waxman (Calif.) said yesterday that the two new papers show that the United States must act quickly to impose mandatory limits on carbon dioxide and other greenhouse gases.
“Climate change is not just someone else’s concern but a very real threat to the lives and livelihood of people across the globe,” Kerry said.

winterkorn July 1, 2010 at 7:51 am:
However, when the agenda-driven get hold of it, and like so many Cassandras, cry “Doom, doom is nigh….doom deserved because we are eco-evil!”

At the risk of being accused of pedantry, I’ll point out that Cassandra was blessed with the gift of prophecy, and cursed so that her predictions, though correct, would not be believed. So I certainly hope that the doom-sayers are not Cassandras.
I wouldn’t be disappointed, though, if the not-believed part were accurate.

CarrotEater declares: “Not once have you shown that you understand that weather prediction is an initial value problem, and climate is a boundary value problem.”
This primitive authoritarianism is really annoying. I am tired of repetition of this meaningless AGW gobbledygook.
There is no “climate boundary or initial or whatever problem”, there is only one coupled dynamical system that can be considered as two coupled boundary value problems, oceans and atmosphere, with non-trivial (and non-stationary) boundary conditions and with uncountable set of possible initial states (“values”) of the system. There is only one (and fully deterministic) evolution of these initial states called “weather”, which appears to be quite irregular and chaotic.
One can apply various spatio-temporal filters over these solutions and call them whatever you wish, “regional climate”, “global seasonal (or yearly) temperature”, whatever suite your needs. However, the science (classical mechanics) of condensed media has come to conclusion that averaged equations of this turbulent motion are not closed, and therefore no new physical abstraction can be identified (that can be called “climate”) and governed by some special physical equations. Therefore, one cannot “calculate climate” without calculating physical weather first.
This does not mean that the weather must be calculated precisely to reproduce every hurricane or location and time of every tornado in Texas. Since topological properties of phase flow on weather attractor are known to have hyperbolic qualities, the weather seems to be pretty ergodic, and therefore small computational imperfections should keep the [correctly designed] system on the attractor, such that statistically speaking the GCMs should be capable of predicting climate. The keyword here is “correctly designed”, especially with regard to slow-changing boundary conditions and long-distance weak interactions.
Cheers to audacious climate modelers – Al

Tonyb,
I’m glad at least someone else here gets that models are used to predict “the probability distribution of the system’s future possible states by the generation of ensembles of model solutions.” The highlighted words are important: using output from a range of different models – rather than relying on just one – to estimate the likely ranges of a host of variables. Very different than a specific forecast.
The idea that models are thought of as perfect predictors in part comes from the popular press, no doubt, who treat big computers as some sort of time machines. It’s also a useful straw man to some, obviously, as evidenced by this whole nonsense about “100%” and “exactly 8.72 degrees warmer.”

Smokey says:
July 1, 2010 at 8:29 pm
R. Gates, you should listen to Bobbie Burns: “To see oursels as ithers see us!”
You claim that “no matter what facts are presented to them [skeptics – the only honest kind of scientists] because it is an emotional bond they have with their beliefs, not a rational one. I have no such attachment…”
Who are you trying to kid? You constantly call yourself a 75% climate alarmist [while heading a 2-car household], and pretend that you have an open mind??
Please.
Admit what you are: a CAGW advocate pushing an agenda.
And for my skeptical part, simply show me where the climate has exceeded its past parameters, and you will have a CAGW convert.
Word up.
_____________________
Actually Sparky, you once more really twist my words as I was quite clearly talking about the true believers on both side of the issue, “warmist” and “skeptic” alike. I may be on the side of believing the AGW is likely happening, but I’m hardly an alarmist nor do I put the “C” in front of my AGW beliefs.
I’m not out to convert anyone, but simply try to present alternative views to plainly wrong or completely cherry-picked data, though it may be hard for some so-called skeptics to imagine that there are those who think that AGW could be happening without being alarmist, wild-eyed radicals. True believers want to see the world in extreme terms, and in fact can ONLY see the world in extreme terms– everyone is a saint or a sinner, whereas in reality most people are neither.

“This idea that the solutions do not depend on the initial conditions is simply bogus and demonstrates a fundamental lack of understanding of mathematical physics…”
The solution does depend on the initial condition. That’s why you run the model several times at different initial conditions, to get an ensemble of results.
But given the same boundary conditions (the external forcings), the long term trends are going to be about the same in each case. It’s the short term and decadal wiggles that differ.
Same as my point with the sun. Crank up the sun by 20%, and I won’t know if it’s El Nino or La Nina in Jan 2100, but I know on average it’s going to warm up over time.

From: Paul Daniel Ash on July 2, 2010 at 5:38 am

I’m glad at least someone else here gets that models are used to predict “the probability distribution of the system’s future possible states by the generation of ensembles of model solutions.” The highlighted words are important: using output from a range of different models – rather than relying on just one – to estimate the likely ranges of a host of variables. Very different than a specific forecast.

Major problem being, that might not be how “ensemble” is used.
From Zhang’s (and Lindsay’s) “Introduction” section on the “Seasonal Ensemble Forecasts of Arctic Sea Ice” page:

The model is the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS, Zhang and Rothrock, 2003). The ensemble predictions are constructed by using the NCEP/NCAR atmospheric forcing from the previous 7 years (corresponding to 7 ensemble members) and the PIOMAS retrospectively estimated, with assimilation of satellite ice concentration data, ice and ocean conditions at a given date at which ensemble predictions start. Details about the ensemble prediction procedure may be found in Zhang et al., 2008.

In the 2008 paper we find:

The ensemble predictions consist of seven numerical experiments with PIOMAS. Each of these seven individual ensemble members is associated with a unique set of forcing fields that are used to drive the model from 1 October 2007 to 30 September 2008. We use daily forcing fields from the NCEP/NCAR reanalysis such that ensemble member 1 uses the reanalysis forcing over the period 1 October 2000 through 30 September 2001, member 2 over the period 1 October 2001 through September 30, 2002, etc., and member 7 over the period 1 October 2006 through 30 September 2007. To our knowledge this is the first time ensemble prediction methods have been applied to forecasts of Arctic sea ice.

Same model, different input data. You can see what the output looks like in the 2008 SEARCH Sea Ice Outlook (Full Report tab), Figure 6. From the ensemble came a median map.
As you quoted, “…by the generation of ensembles of model solutions.” This is what Zhang does. What you interpreted: “The highlighted words are important: using output from a range of different models – rather than relying on just one – to estimate the likely ranges of a host of variables.” Sorry, but only one model could be used for an ensemble forecast. Thus the accuracy of the ensemble forecast is dependent on the accuracy of that single model.

For example, if your bank gives you -5% interest and you are expecting +5% interest, you may come up with different compounded numbers after a few years. ;^)

I think what you have failed to appreciate is that there are two main types of ‘disbelievers’.
No, I allowed for that. I don’t have any way of knowing how large each group is, but I’m sure that the blind skeptics and blind believers well outweigh their counterparts when it comes to internet postings.
Or at least provide real world, verifiable evidence showing that an increase in a minor trace gas controls the planet’s temperature.
Smokey, you keep saying that over and over again, but you never indicate what it could possibly mean. What would this “real world, verifiable evidence” look like? Can we tag individual molecules of CO2 and measure the forcing of that molecule and no other? Obviously not. What, then, would constitute the evidence you claim to be searching for? Give an example, or let go of the talking point: you can’t look for something without any way to recognize when you’ve found it.
[REPLY – At a guess, I think perhaps he means something that would refute Lindzen’s study on radiative forcing observations. However, you may be correct about the blindness of most on both sides as expressed on the internet. (Not in this forum, of course!) ~ Evan]

carrot eater says:
July 2, 2010 at 8:08 am
“The solution does depend on the initial condition. ”
Yes it does! The PDEs demand it :^)
“That’s why you run the model several times at different initial conditions, to get an ensemble of results.”
So the climate solutions don’t “forget” their initial conditions like some claim. Excellent! :^)
“But given the same boundary conditions (the external forcings), the long term trends are going to be about the same in each case. It’s the short term and decadal wiggles that differ.”
However, the problem is that the boundary conditions depend non-linearly on the evolving solution. And the external “forcings” are not known perfectly. In the end, the “trends” generated by these models are going to be highly dependent on the behavior of the submodels (which are themselves imperfect) and the prescribed forcings. By the way, if everyone uses the same forcings, do you think the trends will be similar?? …hmmmm…
All of this begs another question – if the short term climate solutions are not accurate, why run them with high resolution grids? For example:
http://www.hpcwire.com/offthewire/NASA-Center-for-Climate-Simulation-Debuts-Spring-2010-95542709.html
“With the new augmentations of Discover we probably have a 3 to 4x increase in the amount of work that we can push through the computer in a day,” Webster said. “You can run more simulations at the same resolutions you’ve had, but the thing that really excites us is that we can run much higher resolution simulations.”
By the way, the multi-million dollar “new augmentations” of the Discover computing facility (referred to above) were paid for by US government “stimulus” money…just in case anyone was wondering who’s getting the climate ca$h…

Apparently things evolved this way: Weather, then Climate, then Climaterium……

Excerpts from: Paul Daniel Ash on July 1, 2010 at 7:27 pm

I’m “known here?” Under a name I’ve never posted with before?

Near as I could tell since I wasn’t here when it was posted, Anthony had you pegged as “PDA” right away. Afterwards you went back to the full name. BTW, your “explanation” doesn’t make sense. Purging the cache, actually the “personal data” like the cookies, leaves a blank comment form. Since “Name” is a required field then automatically hitting “Post Comment” while assuming the fields (which includes “Email” and “Website”) were auto-filled should have caused the post to be rejected. After such purging, I know of no mechanism by which “PDA” would have been auto-filled in that field. Therefore for “PDA” to be there, someone typed it in. Most likely, you typed it in.
If you have an alternate explanation of how “PDA” got in the “Name” field, perhaps some funky weird Windoze auto-fill thing which would seem to indicate you have used “PDA” before, that actually makes sense, or can properly explain how your previous explanation really does make sense, feel free to supply it.
BTW, from your “explanation”:

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?

Now that the “Climate Wars” have escalated to (C)AGW proponents drawing up hit lists, note the last part is not in quotes, anonymity is self-protection from reprisals. Indeed, certain “Doubters” not up to doing Resistance work would be well justified in pulling an Anne Frank for a while.
Another excerpt:

By the way, where’s your blog? Where’s your WHOIS registry, what’s your online record? Or is creepy webstalking only for people you disagree with?

My, aren’t you reaching? You have my first initials and last name, I am in central Pennsylvania “near the river” as I have posted before. I am also in relatively close proximity to at least three (C)AGW-friendly universities, sole caretaker of my mother who is in frail health, as I was to both my parents not long ago, and groups of (C)AGW deluded protesters or even a sole “determined” individual showing up on the property could well be enough stress to kill her. After anywhere from a day to a few years from now that will change, then you will find out the rest.

From: Paul Daniel Ash on July 2, 2010 at 10:49 am

Depends on what you mean by “model.” If you run simulations on the same system (e.g. PIOMAS) with different parameters, different forcings, different initial conditions, I wouldn’t call those the same “model.”

“Model” is a glorified name for a program, with all its subprograms and subroutines etc, which can use some data that is not inputted at the start or during the running (the “tweaking” etc). Your argument boils down to saying since different numbers were used then it’s not the same program.

What you seem to be referring to is meta-ensembles: my understanding is that’s been tried and is tricky but promising:
http://journals.ametsoc.org/doi/pdf/10.1175/BAMS-89-3-303

Tricky but promising, yet acceptable for use by Zhang et al? If the method is good enough to stake a professional reputation on it, then it must be considered acceptable at least by those doing so, don’t you think?
BTW, what is this talk of the Global Climate Models being run with different initial conditions? The initial conditions would be the previous weather records, or “climate records” if you insist, with the related info like CO2 concentrations and ocean temps. You have to run a model from a starting point, so you input the starting point. How can you have different “initial conditions” of the same starting point? You can have different runtime parameters to generate an ensemble of predictions, but it’s still the same starting point thus the same initial conditions for all runs in the ensemble.

kadaka asks: “How can you have different “initial conditions” of the same starting point?”
A model usually has much bigger space of initial conditions than “weather records” can provide. For each “weather record” (usually obtained at ground point) one needs to supply the state of entire atmosphere. For each ground “weather record” there could be a large number of slightly different atmospheric “configurations”. Also, “weather records” have finite (and very lousy) accuracy; therefore small deformations of the same “weather record” within the range of data errors would produce different trajectories in a long run, just as Ed Lorenz has originally discovered in his attractor. However, the space of deformations might not be contiguous, and might have “islands” that belong to different attractors, and climate modelers do not have much of an idea that their initial conditions might provide very different outcomes for the same model. The climateprediction.net is an example.

vukcevic says:
July 2, 2010 at 4:04 am
http://www.vukcevic.talktalk.net/AMOFz.htm
This graph it is astounding, but now you have to relate it to the Sun, as GMF is modulated by it. I would suggest you a real post to decipher all your graphs for us “commoners”.

Dave F said:
July 1, 2010 at 9:15 am
And aside from that, each coin flip is independent. I would not say that each day’s weather is independent, and you certainly cannot say that climate is independent of weather.
OK, how about tossing a coin where you place the coin on your thumb with the previous result facing upwards, i.e. if the last flip landed heads, start the next flip with heads facing upwards.
This way the result of each flip is not independent of the previous one since the system is strictly deterministic (governed by deterministic newtonian mechanics -unless you want to try and argue that quantum effects are somehow significant).
But it doesn’t make any difference. Now you have a system where each outcome depends on the previous outcome, you cannot predict the outcome of any single coin flip, but you can accurately predict the outcome of a large number of flips.
The logic Steve Goddard used in the article was flawed.

Wrong. For that system to be deterministic you would have to know all initial conditions and all energy transfers that would occur during the flip before the flip took place.
I assure you that Newtonian mechanics are deterministic. The coin does not magically move itself for no reason. It’s motions are governed entirely by the forces acting upon it.
To keep it simple, if you can simply crank a set of numbers through the equations and always get the same repeatable result, then a system is deterministic. If you can’t, there is some amount of randomness involved
Yes, but an important characteristic of chaotic system is that they are deterministic systems which are observationally indistinguishable from indeterministic systems. If you take a simple non-linear set of equations (for example see Lorenz’s equations), then the outcome is deterministic (i.e. it depends on the initial conditions), but minute differences in those initial conditions (below observational error) will get larger with time and make the two solutions so different that they may as well be random. But crucially, in the Lorenz system you can predict the bounds of the attractor (the climate) even though the internal fluctuations of the system within those bounds are, to all intents and purposes, random.
I don’t want this discussion to degenerate into a metaphysical argument over whether or not humans have free will so, for the sake of argument, why not let a robot arm flip the coin? If the coin is thrown sufficiently high then I am almost certain that the results will not always be repeatable.

Bemused, you are confusing statistics of a relatively short-term events with individual (unique) trajectory of a long evolving object.
To apply your line of reasoning more coherently, yes, billions of colliding molecules on a billiard with negative curvature gives you a very good ergodic system, such that applying mathematics of big number one can get a clean thermodynamical state of continuous matter (in the limit of thermodynamic equilibrium).
However, when this system is subject to variable forces or is far from equilibrium under some uniform field, the media becomes unstable, and dynamics of individual parcels becomes unpredictable again, as Navier-Stokes equations demonstrate. Again, you need to resort to statistics (of turbulence, invariant measure of a chaotic attractor) to be able to predict dynamics, but only for a time scale that is characteristic for this eddy motion or whatever.
Furthemore, to do so you either need a coherent theory of eddy interactions (which you don’t have due to divergence in Reynolds averaging process), or use experiment to establish these probabilities (eddy viscosity, “universal logarithmic profiles”, Obukhov mixing length, etc.) from observations. To get this statistics for a minute-long process, people run various tanks and wind tunnels for months collecting zillions of spatio-temoral data points, all under very well controlled conditions.
Now, the key point is that when you combine all this stuff into a much bigger system, you have unspecified strange attractor of weather that, according to known facts from nonlinear dynamics, would certainly have long tails of slow-evolving fluctuations. Yes, theoretically one can find invariant measure of this attractor and construct various PDFs out of it, but practically it would require experimental verification. This verification, as usual, should be conducted over time scales that are characteristic for this type of motion.
Typically, even in studies of well-defined strange attractors people would take time series of thousands of characteristic cycles and billions of data points to characterize the attractor statistically. Since we are talking about climate, so far we have only a fraction of climate trajectory. Even to get a single turn around climate attractor one need to get full data for entire atmosphere (and all boundary conditions as well) for at least 100,000 years. Only then you can have some basis for climate prediction.

kadaka:
“Now if there is this much difficulty in applying deterministic Newtonian mechanics to accurately predicting something as relatively simple as coin flipping, how can you think that vastly complex global climate models can accurately predict conditions for decades and even centuries past the starting point?”
The point you keep missing is that you can predict that about 50% of coin flips will land on heads. If you run a deterministic prediction for long enough then even if the individual flip outcome sequence is incorrect, the statistics of that system can still be correct.
Nobody is trying to predict the specific weather conditions at a city for a given day in 100 years time -they are just trying to predict the statistics of those weather conditions when averaged globally and over a decade or so. That is a very different problem.

kadaka said: “Heck, with the repeated iterations even the rounding off of the numbers can grow into a significant source of inaccuracy.”
No, this concern is unfounded. The reason is that the weather attractor oscillates within strong bounds, the weather trajectory eventually returns into a very close proximity state space after several iterations. From many known attractors in hydrodynamics, the structure of phase flow on an attractor is a horrible web of homoclinical orbits, but each of this orbit belongs to the same attractor (if it is “structurally stable” one). Therefore, even if the numeric algorithm produces rounding errors and the trajectory jumps to a different orbit of the attractor, it is still on the same attractor. Therefore the system does not go anywhere (it does not “blow up”) and produces (allegedly) the same statistics.
This is in theory of course. In reality of, say, climateprediction.net, evidence is that their model could suddenly produce “outliers”, which they simply discard. They discard about 40% of runs for this reason. To me it means that their dynamical system of pseudo-weather is unphysical and fundamentally wrong, and the results of forward calculations have little to do with reality.

Gail Combs:
“Yes and the models get a straight line. Straight lines are not seen very often in nature, curves and cycles are much more common especially with weather and climate.”
No, the models don’t produce straight lines -in fact they are very wiggly. People often get confused about this as often the time series shown in publications are smoothed with running means or are the average of an ensemble of different model simulations.

“The 100,000 years came form the period of ice ages. I am under an opinion that if the attractor in a climate model does not reproduce this cycle, the entire topology of attractor cannot be trusted”
Well, my understanding of the latest thinking (and I stand to be corrected) is that the ice ages occur primarily from external forcing of the system by Milankovitch cycles. i.e. external forcing of the system changes the shape of the attractor. I don’t think they’re caused by internal variability of the system (within a stationary attractor).
I guess it all depends on how you define the ‘system’ and what you consider to be external to it. If you don’t give a model the external forcing then it won’t reproduce variability on those timescales.
“I was under impression that they can do 5 or even 7 days”
Well, yes. These vague statements are all rather meaningless really unless we define exactly what it is we are trying to predict. Small scale features like thunderstorms may be predictable a few hours in advance, synoptic scale features may be predictable up to 3-5 days (or more in favorable conditions), planetary scale waves are often predictable out to 10 days or longer. In the tropics, seasonal forecasts out to 3 months for certain variables have been demonstrated to have skill.
“but I still think that climate models must reproduce typical weather elements and their individual shapes and dynamics pretty accurately, otherwise statistics of goofy events will result in goofy outcome.”
Absolutely. But you may be surprised by how well the latest GCMs do reproduce atmospheric modes of variability. High resolution models produce very realistic convective storms and tropical cyclones. Synoptic systems and planetary waves have more or less been nailed for some time. Things like El-Nino Southern Oscillation are pretty well represented these days. The Quasi-Bienniel Oscillation in the stratosphere is beginning to appear in the latest models. Many models struggle to produce a fully fledged Madden-Julian Oscillation in the tropics, but there are promising signs from the latest global weather forecast models. The models are becoming less ‘goofy’ as time goes on. It is worth noting that the models spontaneously produce these modes of variability on their own -they are not programmed to do so.

Bemused, I appreciate your posts. It is well known that ENSO parameters correlate with land temperatures. The cause and effect mechanism is fairly well understood as the hydrological cycle. Thus the theory of land temperatures being significantly affected by oceanic temperature is established.
The major underlying assumption of AGW models has to do with added heat from increased greenhouse gas concentration effects. Between land and water, only water has the potential to absorb this heat (land is such a poor candidate for this part of the theory that it can be dismissed). The modeled hypothesis is that the stored heat is then released along with natural heat under warm ENSO parameters, thus adding to the increased land and SST temperatures of warm ENSO parameters. It is also modeled that cool ENSO parameters will not be as cool due to these same greenhouse gasses.
An alternate modeled hypothesis has to do with AGW air temperature changes creating human-induced changes in the natural bimodal atmospheric circulation patterns. This model is less well accepted because air and land do not store heat.
Also, of great importance, the set of models predicting increased warming assume that CO2-AGW is added heat, not overwhelmed heat.
But what if the models have underestimated natural ENSO parameters? And what if the models have not accurately modeled all ENSO parameters, including clouds? Is it possible that these weaknesses in the models produce enough measurement error that any CO2 warming would fall within that error band? Is it possible that what we are seeing as a warming trend, is within the error band of natural variability and weak modeling of this variability?

This article was extremely interesting, especially since I was searching for thoughts on this subject last week.
I have been coming to this blog for a couple of days now and i’m very impressed with the content!
thanks & regards