David, it is very easy to test if two or more entirely different initial states lead to a statistically similar climate. This is one of the points of running ensembles. Another way to see this is that weather states only a few weeks apart bear little correlation to each other. Initial state information is quickly lost among the growing modes that dominate the tropical and midlatitude systems. The analogy is closer to a boiling water pot with continuous energy input than a gently swirling unheated pot. The memory of the initial state is short in these conditions.
Comment on AMS members surveyed on global warming by Jim D
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Comment on Climate policy discussion thread by Pooh, Dixie
I believe that one of the justifications to reconsider a previous decision is false testimony at the first.
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Comment on AMS members surveyed on global warming by David Young
Jim, You are making a statement about the models which I do not doubt is true. It is true of a turbulent simulation with too much dissipation where you get the wrong answer. This is independent of whether its true of the climate system itself and whether the models accurately reflect this system. We need somehow to escape the circular reasoning that because models have a certain characteristic, the real system must have it.
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Comment on AMS members surveyed on global warming by Don Aitkin
Got it! Seeing it again, it is an even more awful question. The way I was taught to develop a question like that was to express the point in an initial statement, then get the respondent to comment on his/her feelings about that statement, either pro or con, along a Likert scale of some kind (VS Agree, S Agree, neutral, S Disagree, VS disagree.
So it might have been as follows:
‘As you know there is much talk today about global warming, and one statement is that human activity, through the burning of fossil fuels, has caused warming. What is your own view? Do you agree, or disagree, with that statement [Interviewer: repeat if necessary]?
‘How strongly do you feel about this, Very strongly, Fairly strongly or Not very strongly?’
Then one could go on to the ‘unprecedented’ bit, again using a strength of feeling follow-up question.
If you did that you would get seven-fold distributions of responses, with ‘no opinion, neutral’ in the middle, and ‘Don’t know’ in the residual category. There shouldn’t be many of those in a group like the AMS.
Of course, for the answers to mean anything, you would need to employ a random stratified sample of responses. The AMS survey did neither, and is worthless, in my opinion.
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Comment on AMS members surveyed on global warming by Stephen Rasey
@NW, Re: "planes that come back"
I prefer the word "historical" rather than "aprocryphal". I heard the story in 1978. Here is another telling from a <a href="http://www.navy.mil/navydata/people/secnav/danzig/speeches/hult000714.txt" rel="nofollow"> 2000 Navy UndSec retirement. </a> Legends get retold and embellished based upon the story teller and setting. Operations Research grew up in WWII. There is no doubt in my mind that the OR Group did go out and study battle damage with recommendations for changes to armor. And no doubt some people thought the returning planes were a reliable sample of damage from attack. But I have no doubt that the leaders quickly realized that the "planes that come back" were NOT a random sample of battle inflicted, but a highly biased one. This is the whole moral of the tale.
It is not possible to inspect the planes shot down. All you have are the planes that come back. You have to work with biased data. Understand the bias. Be aware of it, not blind to it. Make your recommendations accordingly.
In the climate arena, the "planes that come back" are the surviving thermometers; it's The UHI Problem.
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Comment on Gleick’s Testimony on Threats to the Integrity of Science by MajikFireHornet
AGW = GT. GT = Gravy Train.
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Comment on AMS members surveyed on global warming by Jim D
GCMs resolve the main energy transferring modes of motion. Very little of it happens at sub-grid scales, except in the vertical direction, where adequate boundary layer and moist convection schemes are required, and they would quickly know if they did not have sufficient vertical energy transport when comparing with observed profiles globally, which of course is done. You seem to be under the impression that model outputs are not compared to reality as validation, or maybe you think the reality is not known?
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Comment on AMS members surveyed on global warming by Beth Cooper
Don A @ 8/3 12.32.
Doesn’t seem to work like that now, Don. You’re looking for a reasoned response, could there perhaps be another agenda?
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Comment on AMS members surveyed on global warming by David Young
I’m not sure what Chris means when he says that climate “can be understood in terms of forcings.” In a trivial sense this is always true. All systems can be understood in terms of forcings. The problem is that the response to forcings can be very complex and involve all spacial and time scales.
The problem here is that there is a lot of evidence that climate responds to very subtle changes in distribution of forcings and that feedbacks can be very important. And these feedbacks can be very entangled in details of dynamics, for example clouds. There is also evidence as Chief points out for rapid climate changes. The distinction between “forced variation” and “internal variability” is of no value except as a rhetorical device. Each situation is different in terms of sensitivity to changes in forcing and time scales of the response.
One thing you must understand very clearly. Incorrect dissipation, usually too much dissipation, is the eternal enemy of models. It gives you simulations that appear “too stable” and miss important variations. Absent rigorous efforts to control it, numerical dissipation is likely too large because real dissipation is usually very small. In the real climate system, the dissipation is very small and must be unresolved in models. It can only be incorporated in subgrid models. The numerical dissipation on the grids used must be orders of magnitude larger.
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Comment on AMS members surveyed on global warming by David Young
JimD, I don’t know how to answer you. All scales are important in fluid dynamics. Just capturing the main energy transfers tells you nothing about feedbacks like clouds for example. These depend on the subgrid scale effects. I know models are checked against data. My impression is that the correspondence is not very good at decadal scales. How good it is at century scales is unknown because of lack of data . I do note that the models have changed a lot in terms of their predictions over just the last 30 years. One thing that concerns me is Isaac Held’s recent work on convection that shows that the subgrid details can have a big effect on the larger scales. Generally, it would be a miracle if the subgrid scales did not impact the resolved scales. You can read about Reynolds’ averaging for example to see some of the issues. The effects go both ways.
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Comment on AMS members surveyed on global warming by Jim D
Decadal scale predictability is not difficult because of the models, it is difficult because of the character of the turbulence that makes it even impossible for models that are perfect to machine round-off (Lorenz’s study). GCMs have to get the jet stream, equator-pole heat and moisture transport, annual cycle, continental effects, stratosphere, etc., right to even have a chance at being used for climate. Each model may have different biases in regional climate details, but only when they have generally similar responses to forcing changes is it considered significant in the IPCC report. There are things as pointed out by CK that may not be modeled, e.g. collapses of ice shelfs, slowing or stopping of major ocean currents that are more likely under climate forcing changes, or just solar variations and volcanic activity, so no one can say the prediction is completely reliable, and is just a guidance about the least that could happen in that sense.
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Comment on AMS members surveyed on global warming by Peter Davies
The adoption of income redistribution policies is not necessarily a consequence of Keynesian macro-economics Cwon14, more likely socialism based on simplistic left wing ideology.
The basic problem with socialism is the inbuilt dis-incentives for productive working lives on the part of rapidly decreasing numbers of entrepreneurs and rapidly increasing numbers of mendicants and other non-productive government employees with snouts in the publically funded troughs.
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Comment on AMS members surveyed on global warming by Chris Colose
David, I don’t get the feeling we are getting far in this discussion so I’ll highlight in a few points my main views here, and one that I suspect would be common to the bulk of the climate community. :
1) To me, the whole issue is one of signal vs. noise. That is, can any possible sensitive dependence to initial conditions that may conceivably apply over centennial timescales dampen out the expected changes due to a doubling or quadrupling of CO2? Even if we integrate an essentially perfect atmosphere-ocean model forward in time with no forcings (e.g., volcanoes) it will have variability on all timescales, and the magnitude of that variability will depend on the variable (e.g., temperature or precipitation) and spatial scale. Is that larger or smaller than the size of the signal we expect from a given CO2 or solar increase. Period.
For, say, long-term global mean temperature, there is no evidence that the models are missing anything of strong significance. Moreover, (on a consistent basis) no one has successively observed or simulated internal variability of the magnitude like 2xCO2. That may not be true for other statistics such as rainfall in East Asia, though this should be evaluated independently.
2) It seems necessary to formulate a common definitional framework here. Negative feedbacks are not examples of the same sensitive dependence to initial conditions that plagues the weather forecasting issue beyond 1-2 weeks, even if they involve clouds. Things may be “unpredictable” in the sense that they are left out of current models, or are parametrized in ways that cannot be extrapolated outside the modern climate, but it’s unclear that they are “unpredictable” in the sense that whether they actually happen is sensitively dependent on initial conditions.
3) It is my impression that there is widespread agreement that bifurcations, tipping points, threshold responses etc can and do occur, and that responses may exist not anticipated by modern models (such as the prospect of much more rapid melt from the Greenland ice sheet). Even in these cases, it is generally the long-term growth/decrease in global mean T which can often set the stage for such events. For unknown ‘switches,’ it is impossible on first principles to evaluate whether it will have sensitive dependence to initial conditions until someone comes up with a specific proposal for what such a switch might be, though constraints exist concerning the realism of various events occurring (e.g., a background state that is conducive to D-O and Heinrich events is unlikely in a warm climate with strong deep water formation and little ice).
4) The theoretical possibility of long-term chaos could conceivably apply when you include the deep ocean, owing to multiple interacting timescales in the system. Even if we stick to shallow ocean dynamics, ENSO emerges as a nonlinear chaotic phenomenon that exhibits predictability loss, and the strength/impacts of ENSO do depend somewhat on the background state, but its projection onto the climate is fairly standard across multiple events (as I mentioned in a previous comments, we see rather predictable responses in the distribution of warm/cool or wet/dry across the U.S/Canada, and its projection onto global mean T is very small relative to the increase in CO2).
5) A lot of this discussion is very academic since on first principles one could concoct some play-around model where the sensitivity of trends in decadal-centennial statistics (to initial conditions) overwhelms the long term sensitivity of that statistic to the increase of GHGs. However, here is where I part ways with several blogospheric dynamical system theorists and mathematicians, because it’s another thing to demonstrate a compelling mechanism that acts like this in the real world.
“As I began to learn meteorology, I found it necessary
to unlearn some mathematics.” –Lorenz
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Comment on AMS members surveyed on global warming by David Young
Chris, I appreaciate your points. I suspect that Lorenz was speaking of the mathematics of linear systems, which it turns out is limited. My point is merely that many of these questions about fluid dynamics are well understood in simpler situations where it is easier to quantify things including the limitations of models. You would see things where the data is a lot more reliable and the models well understood. And you would see systems where sensitivity to initial conditions is indeed present at all time scales. The simple fact of the matter is that we don’t know the answers to a lot of these questions. We have noisy data or no data at all and we have models that we know are wrong. There is a lot of work to do.
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Comment on Should we tell the whole truth about climate change? by cwon14
It’s something NPR and Dr. Curry have in common, delusional belief in their own objectivity.
Go to the huffington post and you can find people who think NPR and the NYTimes are “right wing” and they frame their arguments accordingly.
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Comment on Should we tell the whole truth about climate change? by Beth Cooper
Phil: Hey man, that was a good looking model I saw you with last night!
Mann in street: Between you and me, it didn’t work out. Initial conditions just were’nt right.
Phil: (sigh) Guess we’ve all had that experience with models. Life’s too complex, I reckon.
Mann in street: Yeah. I’m thinking of writing another book. Something post moderne on alienation.
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Comment on Lindzen’s seminar: Part II by Fred Moolten
Matt – I think all models are “tuned… to make better forecasts”. Not to do so would be negligent. My point was that the tuning was not done by consulting the climate trend that the model would eventually try to simulate in order to make the simulated and observed trends match better. The model was not “tuned to the trend”. That certainly seems to be true for CCSM4 (and that has been explicitly stated elsewhere – in the USGCRP Report – for CCSM, GISS, and GFDL models).
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Comment on Should we tell the whole truth about climate change? by Jack Hughes
After 20 years of trying, the climate scientists have not found any compelling evidence of anything unusual.
How much longer do they need?
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Comment on Should we tell the whole truth about climate change? by Fred Moolten
Dan – Thanks.
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Comment on Lindzen’s seminar: Part II by MrE
Not much of climate science is not “dodgy” (your word not mine). You missed the point. Steve, did you even watch the presentation?
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