Guest Post by Willis Eschenbach
In the course of doing the research for my previous post on thunderstorm evaporation, I came across something I’d read about but never had seen. This was the claim that the models showed not one, but two inter-tropical convergence zones (ITCZ).
Please allow me a small digression here, regarding my unusual methods of study and investigation. Faced with information about the possible existence of a dual intertropical convergence zone in the models, most scientists would start by going out and researching the question in the scientific literature. First they would find out everything that is known about the models having two ITCZs. They would study what other people have written about double ITCZs. They would read what people believe are the causes of the dual ITCZ. Then, and only then, would they take up independent research.
Me, I approach a new subject the other way ’round. I don’t want to start out already knowing what other people have written about double ITCZs. I don’t want to know the various theories about them when I begin my investigation. I don’t want to understand what scientists say are the causes before I begin. Instead, I want to start out without preconceptions, without prejudices, without formed ideas of what I will find. The Zen Buddhists have a lovely term for this condition. They call it “beginner’s mind”, and they strive to achieve and maintain it. Starting my investigations with beginner’s mind forces me to invent my own methods. It makes me have to dig in deeply to understand what I’m looking at. And most importantly, it shuts no doors, it rules nothing out, it leaves intact my awe and wonder at the myriad possibilities of the amazing system I’m studying. I like to start out with the clear understanding that I understand nothing. It is a huge advantage if you wish to discover new things.
Once I have formed my own ideas about what is going on, once I’ve graphed and mapped and sliced and diced the data, once I’ve pondered the findings and the graphics to the point where I think I understand the relationships and the implications, then and only then do I go and read what other people have said about the subject.
Now, I understand that there are a lot of folks who do it the other way. And I have no problem with that. However, I do get flack for the way that I do it, people insisting that I should always start by studying what is known before I start my own investigations. Sorry, not my style. Especially in climate science, what is “known” is far too often not true in the slightest. I don’t want to be burdened with that. As Mark Twain, one of my lifelong literary heroes, said:
It’s not what you don’t know that kills you, it’s what you know for sure that ain’t true.
Now, I’m aware that sometimes my way leads me to error … and given the number of folks who notify me whenever such an error occurs, I could hardly be unaware of it.
But the other way often leads to error as well. Worse than that, however, is that it leads to a hidebound view of the subject, one which has already closed a host of doors and ruled out a host of possibilities. If someone doesn’t do independent research into a subject until AFTER they have been totally indoctrinated into whatever the current view of the subject might be, they’ve already put the blinders on. They’ve already taken up the current paradigm and the current frame without even noticing it, and sadly, that means that they are very unlikely to ever discover anything outside of that frame and paradigm … but I digress.
As you might imagine given my method described above, my first move was to go grab the rainfall data and consider the ITCZ:
Figure 1. Annual average rainfall from the Tropical Rainfall Measuring Mission (TRMM).
One of the surprises to me when I first graphed this up were the large areas of ocean in both the northern and southern hemispheres which get little or no rainfall. I never considered that there would be oceanic “deserts” of such size and scope.
The ITCZ (inter-tropical convergence zone) can be seen clearly in the TRMM average rainfall record. It is the yellow/red area that runs along and generally above the Equator. As the name suggests, the ITCZ is the area where the winds of the two hemispheres converge.
Next, I got rainfall data from a random model to see what the ITCZ looked like in the model output. It’s the bcc-csm1 model, chosen because they were listed alphabetically and I took the first one. I’ve trimmed the model output to 40°N to 40°S to match the TRMM data to allow easy and accurate comparison.
Figure 2. Annual average rainfall from the bcc-csm1 model. Data Source: KNMI
Yikes! … I’d sure call that a “double ITCZ”, all right. I can see why this would be very worrisome. The model is claiming extensive rainfall in parts of the central Pacific where in fact there is almost no rain at all. For example, in the observations, the ITCZ in the Pacific has dark blue areas of no rain both above and below it. The model has neither. I also note that the model says that there is about 20% more rainfall in the covered area than the observations show. Finally, I note that this gives a modeled evaporative cooling about 15 W/m2 larger than in the real world. Errors like these make me laugh at the claims that the models can show the results of a change of a few W/m2 in the forcings … but once again I digress …
To gain a better understanding of the two convergence zones, I made a movie loop of the monthly modeled rainfall so I could compare it to the movie loop of actual rainfall that I showed in the last post. Here are those two movie loops.
Yikes again! It’s interesting. Even better, it’s not at all what I expected, which is the most fun part of science. Actually, the model appears to only have one ITCZ … but the model ITCZ spends half the year north of the equator and the other half south of the equator. The net result looks like two ITCZs, but it’s not.
The problem appears to be that the model is too symmetrical. As a result, the ITCZ is pulled evenly first north and then south of the equator. But for some unknown reason, that doesn’t happen in the real world. [Curiously, in both cases the ITCZ never occurs right at the equator. It may have something to do with the existence of the equatorial counter-current, which runs along the equator in the opposite direction to the waters just north and south of it … but that is just speculation.]
I can see that this would be a particularly thorny problem to solve. It’s one of the difficulties with iterative models. You can never be sure in advance what some small change might do. And in particular, this asymmetric oddity would require unknown pressures to maintain itself. Hard to even guess where and why the models are wrong, when as far as I know, we don’t know why the real ITCZ isn’t symmetrical in the same way that the modeled one is. (In passing, to me this is the real value of models—to point out the interesting areas where the world does NOT agree with the models.)
Now, I’m sure there’s more to learn in all of this investigation of modeled rainfall. And normally, this would be nearing the point in my investigation where I would go Google “double ITCZ climate models” or something and read some of the literature … but why? For me the model results are meaningless. Almost all of the active temperature-regulating emergent phenomena are far smaller than the model gridscale. So phenomena like thunderstorms, dust devils, tornadoes, and tropical cumulus are not modeled, they are merely parameterized … and those are the very phenomena which keep the global surface temperature regulated between narrow limits (e.g. ± 0.3°C over the 20th century). In my opinion, the lack of such sub-gridscale phenomena is why the modeled rainfall patterns over the ocean are so bizarre. Without the details of how and where and when the thunderstorms emerge, it would be hopeless to try to model oceanic rainfall.
Since the models don’t explicitly model the very phenomena that keep the world from overheating or excessive cooling, the model results are useless to me. So I try to not waste too much time on them.
Anyhow, that’s my first and likely only look at modeled rainfall. As Demetris Koutsoyiannis pointed out from his study back in 2008 entitled On the credibility of climate predictions,
The results show that models perform poorly, even at a climatic (30-year) scale. Thus local model projections cannot be credible, whereas a common argument that models can perform better at larger spatial scales is unsupported.
Can’t say fairer than that …
My best to everyone,
w.
My Usual Request: If you disagree with me or anyone, please quote the exact words you disagree with. I can defend my own words. I cannot defend someone’s interpretation of my words.
My New Request: If you think that e.g. I’m using the wrong method on the wrong dataset, please educate me and others by demonstrating the proper use of the right method on the right dataset. Simply claiming I’m wrong doesn’t advance the discussion.
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Again, this needs to become a paper…
+1
Where exactly is that red dot in Fig. 1 in the US? Looks like in a generally dry area.
I am not aware of anywhere that gets 4 meters of rain in the US. Of course that could just be my ignorance. I guess the Olympic Rain Forest might get that much, but somewhere on the front range of the Colorado Rockies? That doesn’t seem credible.156 inches is a lot of rain outside of the tropics. Hawaii has some places that get 200+ inches a year, Alaska has some that get 140+ inches a year, some places in Oregon get in the ~120 inches per year range, some places in western Washington ~100 inches per year, a couple of places in New Hampshire average in the 90s inches per year, a couple of places in North Carolina in the low 90s, one place in Northern California gets in the 80s. There is no location in the mountain west with rainfall totals above 35 inches a year. The source for this is http://rainfall.weatherdb.com/
Owen, 400mm is .4M which is 15.8 inches. So 15 inches on the rain side of a mountain is not that unusual.
v/r,
David Riser
David, look at figure 1 and 2. The red is 4m not 400mm. The animations are mm per month and the red is 400mm. Annual versus monthly is a big difference.
Yes, I caught your red dot. There is a small region near Crested Butte, Colorado that receives as much as 1000 inches of snow each year, which might be close to 4 meters of rain.
Pickens, WV gets 70 inches, but that’s very exceptional & a particular situation of mountain “funneling”.
That’s probably RF interference.
Utah, east of Great Salt Lake? Look at a U.S. average precipitation map.
Willis, I think Anthony is right. Don’t let someone else incorporate your ideas and take the credit. You’re already a published, peer reviewed author. Make this another paper to your credit. That’s just MHO…
“Now, I understand that there are a lot of folks who do it the other way. And I have no problem with that. However, I do get flack for the way that I do it, people insisting that I should always start by studying what is known before I start my own investigations. Sorry, not my style. Especially in climate science, what is “known” is far too often not true in the slightest.”
I remember when some here were giving you a lot of flak about “claiming” that you “discovered” something, then saying you’re an egomaniac that hasn’t bothered to study the literature.
I’m a scientist, and I say just the opposite. The flak is ego driven. They hold themselves in such high regard because they have read so much of what others have done. They haven’t attempted to replicate any of it, but merely reading the literature makes them “real scientists”.
Your work is much more valuable, because you provide an independent validation or contradiction of what others have studied before. That’s real science, not the herd mentality that passes for science these days.
Kudos.
+100
Ciao
John
The amplitude of rainfall over time is also apparently highly exaggerated in the models, from what the movies suggest. I guess that would imply they are far too sensitive compared to reality.
Very interesting, I wonder if their model takes into account the 90+ watt per square meter TSI difference associated with Earths eccentric orbit.
Concur. A nice example of models geting it wrong.
The model double ITCZ you were recalling was from my guest post here earlier this year, The Trouble with Models. DoE’s CAPT program was one of two formal US government initiatives for improving GCM parameterizations. Parameterizing away a ‘true’ double ITCZ (because there isn’t one in the real world) was the first example in NASA’s brag paper about the process, the example cited in the guest post. You can download the paper at http://www.ceres.larc.nasa.gov/documents/STM/2004-11/Xie.pdf.
P.S. the model that was acting up was NCAR CAM2, the US ‘go to’ GCM. The current version is CAM5.3
link returns an error message “Name not resolved.”
Lance, you can get there by googling Xie double ITCZ CAPT, or similar. I thought had copied the link exact.
The period (.) following ‘pdf’ is treated as part of the url, which is an error. Additionally, when I tried copy/pasting the address, the slashes (/) and the hyphen (-) were not carried over leaving spaces in their stead. Fix these copy errors and you’re good to go.
The link is corrected by removing the www. (not all addresses start with www) and the X should be lower case “x”
http://ceres.larc.nasa.gov/documents/STM/2004-11/xie.pdf
So if the Northern Hemisphere behaviour does not, in reality mirror, the Southern Hemisphere behaviour in this respect then it must be due to one of the differences between the two hemispheres.
Either:
A) Width of the Ocean in that hemisphere.
B) Amount of land in that hemisphere.
C) Nutrients from rivers and volcanology in that hemisphere.
D) Proximity of the Earth to the Sun in Summer.
E) Something else.
Can we brainstorm to fill out E?
Can we reject any of A, B C or D?
E) will probably include things related to A) thru D). For instance, albedo relates to A); plankton levels relate to C). E) could include thickness of the ionosphere, possibly related to the Earth’s magnetic field. I can’t rule any of A) through D) out, though B) is obviously a function of A).
Related to D, the sun shines north of the equator for 5 more days than south of the equator each year.
Thanks Willis for yet another thought provoking post which perfectly illustrates the stupidity of using flawed models by our badly advised lawmakers.
Figure one triggered a thought that the main centers of precipitation are probably caused by transpiration from rain forests. These centers being SE Asia, Central Africa and Amazonas. The produced moisture is then distributed far across the globe. Perhaps climate science under-estimates the effects of our biosphere on global precipitation by orders of magnitude.
F) That bloody great mountainous desert of ice. The worlds highest continent, surrounded (As you do say!) by the worlds greatest expanse of open ocean might also have something to to with it but I’m justing thinking out loud! 😉
This is yet another compelling post by Willis.
I’m a very visual person and also have my own idiosyncratic approach to life.
I believe our maps may at times, betray a cultural bias.
I had an interest in space as a kid and I used to say that if you visited the Earth from a direction directly above the Pacific, all you can see is water! (But that is for another projection.)
Meditating on Willis’s animations, I was excited by the idea of seeing them longitudinally, as a Cassini projection. Being a spatial thinker I wished to see them in a way that might help me understand the processes better.
I understand that full data coverage may not be available but I think it would be revealing to visualise the worlds climatic zones, “breathing in and out” in a way that doesn’t privilege latitude. I do appreciate that the post was about the equatorial zones, of course! 😉
http://i66.tinypic.com/20rmsg6.jpg
I may have missed something but the annual average plots suggest that you are comparing TRMM data from 1997 – 2015 with simulated data from 1850 – 2012. If that is what we are looking at I don’t think it’s reasonable to expect them to match up terribly closely and I’m not sure it’s possible to draw any reasonable conclusions from the comparison without presenting additional analyses (synoptic structures, etc).
And if the monthly average values are also from the same time periods for each dataset I don’t think that’s an entirely fair or defensible comparison either.
If it’s possible to trim the two datasets so they both show results from 1997-2012 I think this would be considerably more instructive.
Trimming the results to 1997-2012 made little difference in the results, so I decided to use the full dataset.
w.
If I had done this, after finding the first model deficient, I’d have tried a few of the other models as well to see if any did match reality. A sample of one is not likely to be representative of all the models.
I would also like to see a loop of the individual years since an average may hide some effects.
Jeff Mitchell November 12, 2015 at 1:14 pm
Thanks, Jeff. I’d do that myself … if I thought there was any cheese at the end of that particular maze. But I already know from a host of other evidence that the models are totally inadequate for the purposes they’re put to, so I choose not to waste my time demonstrating that one more time.
w.
The TRMM data seem to show a band of rainfall appearing very briefly just below the equator around February or perhaps March. The animation moves too fast for my feeble eyes to make it out clearly just when it occurs. Perhaps that is the phenomena the model is trying to reproduce?
The Fig. 2 legend is marked as W/m^2. Shouldn’t it be m/year instead?
You are right. Thanks, fixed.
w.
Those who ‘play’ at science often labor under the misapprehension that the purpose of a model is to predict the real observations with high accuracy. This is a pitfall of misunderstanding, at the bottom of which one finds only confirmation bias.
The true purpose of a model is to test our understanding of reality. If the model does not match the reality, a scientist assumes the *model* is in error, and by examining the differences seeks to find the disconnect between the two. By developing NEW hypotheses the scientist refines and then further tests the model to learn something NEW.
To say ‘l learned something new’ is much more enriching than to say ‘I was right all the time.’
The problem comes when ideological, such as the Gorestapo, attempt to shut-down actual knowledge by pointing to their proven-failures of models and declare that the data from them are sound, when the opposite has demonstably proven, time and again.
“ideological groups”
“To say ‘l learned something new’ is much more enriching than to say ‘I was right all the time.’”
You mean like something new that doesn’t happen on earth?? As they continuously use the models to try and justify their political allies agenda it is necessary to discover something NEW that actually happens on earth so they can actually model the earth system well enough to make projections relevant to the purpose for which they are primarily used, supporting, or not, mitigation efforts.
Learning something new is relatively pointless as we still don’t know why it does or does not apply to the earth.
“As a result, [in the model]the ITCZ is pulled evenly first north and then south of the equator. But for some unknown reason, that doesn’t happen in the real world”
I believe the N-S assymmetry in the ITCZ is thought to be due to the asymmetrical continental masses N and S of the equator – unfortunately I forget the details.
“[Curiously, in both cases the ITCZ never occurs right at the equator. It may have something to do with the existence of the equatorial counter-current, which runs along the equator in the opposite direction to the waters just north and south of it … but that is just speculation.]”
WR: The Pacific counter current – as far as I can see at maps – runs at 5 degrees north. However, the blue region is at the equator, more to the south. Relative cold water corresponds better with the blue rainless area: see sea water temperature maps. In other oceans the pattern the rainfall area does travel across the equator.
Further: the definition of Wikipedia of ITCZ is about winds and not about rainfall.
https://en.wikipedia.org/wiki/Intertropical_Convergence_Zone
“The Inter Tropical Convergence Zone (ITCZ), known by sailors as the doldrums, is the area encircling the earth near the equator where the northeast and southeast trade winds come together.”
and:
https://en.wikipedia.org/wiki/Doldrums
“The doldrums is a colloquial expression derived from historical maritime usage, in which it refers to those parts of the Atlantic Ocean and the Pacific Ocean affected by the Intertropical Convergence Zone, a low-pressure area around the equator where the prevailing winds are calm.”
WR: interesting again. The doldrums area – where trade winds come together – is known by less or no wind, at least during longer periods. As I read in your post before, Willis, thunder storms create wind at the surface. The situation in the Pacific above the blue zone of (I think) cold water tells once again that relative cold water does not create thunderstorms. And doesn’t have much wind – doldrums.
On the other hand: colder subsurface water comes up when wind blows the warm surface water away. From this point of view it is logic that you can find the cold water strip in between the two rain zones where the wind is drawn to by the rising air of the thunderstorms in those rainy zones.
Remains the question: why are there in the Pacific TWO rain zones.
Researched this for reasons different than Willis’ starting point. ITCZ is not just rain, it is a very satellite visible ‘permanent’ cloud band. Its seasonal undulations are responsible for the Asian monsoons, the wet and dry seasons in Amazonia, and for the two rainy seasons in Kenya (the longer crop nourishing one being the MAM rain). None of which the CMIP5 archive gets anywhere close to right.
Willis,
You’ve compared a model with a satellite measure, TRMM, and decided the model is wrong. As hipper noted, the time periods are different. But how about some ground truth?
I think Tahiti is among the dots on about a level with Cape York, and East of Hawaii. TRM shows it well beyond the ICTZ, and pretty dry, on the border of blue (.8 m) and dark blue (0). BCC shows it on the southern fringe of a wet region, 2.4 (W/m2?? – are the units right in either plot?).
Papeete gets about 1.8 m/yr.
Nick Stokes November 12, 2015 at 2:01 pm Edit
Nope. I’ve compared a model with everything that I know about the ITCZ from a host of sources including satellite data, and decided the model is wrong. That is, unless you are claiming that the real world actually does have the “dual ITCZ” pattern shown by the model …
w.
Mr Stokes, the resolution of the figure makes it impossible for you to make that statement. Tahiti is not big enough to make a dot.
In the early 80’s I worked for a large consulting firm. My boss was a physicist. When discussing with a client an area in which he did not have extensive knowledge, he had a great line: “I am not burdened by an excess of knowledge in that area.”
Two things, Willis;
First, I had never heard of the “beginners mind” before but the concept is my own approach to anything that I find doesn’t make sense.
Second, Anthony is right, you have to start publishing these things as scientific papers, otherwise someone else will pick up the idea and get the kudos.
We here will know, but lots of other folk won’t have a clue and the thief (not too strong a term) will get the credit.
Willis, “ditto” to Oldseadog. A paper will make it harder to ignore your points too.
Because the stakes are so high I want you to be “heard”. The voting public needs to realize there is no science to support the GHG. Theories explain the data, modern Climatology explains how to steal trillions.
The image for Modeled Average Annual Rainfall bcc-csm1 model, “historical” run 1850-2012 has incorrect units of W/m2 instead of m/yr.
This seems to be consistent with “Manual of Tropical Housing and Building” 1974, pages 10 – 11:
https://www.dropbox.com/s/i1rvs98jiam1xmt/ITCZfromManualTropicalHousingBuilding.pdf?dl=0
https://goo.gl/xKk6Zr
Good one Will. This explains what I saw and referred to in my comment to you in your previous post. Ie the sine wave appearance….frequency and periodicy. I mistakenly took the cycle above and below equtora as true, whereas you took it as an opportunity to test validity. Nice one.
Maybe the below would apply to Willis’ post concerning why it appears there is more rainfall above the equator at the ITCZ. Sounds reasonable.
By thx1138 from Google answers:
The answer is not really the complex atmospheric patterns, it is the
fact that the southern hemisphere has more area of ocean than the
northern hemisphere, thus when the Earth is closer to the sun the
southern oceans absorb that extra energy more than the in the northern
hemisphere where there is more land area.
See:
“There is more land in the Northern Hemisphere, and more water bodies
in the Southern Hemisphere. Now, land has a much lower specific heat
capacity than water; in other words, water can hold a lot of heat
while land cannot. Hence, land gets heated up faster and also cools
faster than water. So, during summer, the greater amount of land in
the northern hemisphere gets heated up quicker, while in the southern
hemisphere, the water soaks a lot of heat and gets warmer by a much
lesser amount. In any case, the result is that northern summers are
hotter than the southern summers”
http://curious.astro.cornell.edu/question.php?number=180
also:
“Even though the difference between the earth?s perihelion and
aphelion distances is less than 3%. The amount of solar energy
striking the earth is 7% greater at perihelion (in January) than at
aphelion (in July). This would lead one to conclude that summer in the
southern hemisphere, which occurs at perihelion, is warmer than summer
in the northern hemisphere. This, however, is not the case. Most of
the land mass of the earth is concentrated in the northern hemisphere.
The southern hemisphere, by contrast, is 80% covered by water. Water
has the ability to absorb large amounts of heat. The additional solar
energy supplied by the sun at perihelion is absorbed by the large
bodies of water in the southern hemisphere. The result is that
temperatures are actually more moderate during summers in the southern
hemisphere. On Mars, which does not have any oceans to absorb heat,
the temperature fluctuations are much greater due to perihelion and
aphelion.”
http://www.physics.isu.edu/~hackmart/astlbsol.pdf
and:
“But that’s not the whole story. Earth is warmer overall in July due
to the unequal distribution of land on the planet. Oceans and
continents are not distributed evenly around the globe, so the summer
sun beating down on the extensive land in the Northern Hemisphere
raises the temperature more than it does in the Southern Hemisphere
six months later.”
http://starbulletin.com/2002/07/07/business/brill.html
Before you try to write a paper you might want to actually read the literature, because there are a number of papers that do discuss the double ITCZ model issue. This is not a new finding, and no one is pretending that the models are right and the observations are wrong. I understand the desire to not pre-bias yourself, but this is an exceptionally poor way of doing science. We’d never get anywhere if everyone just ignored all previous work on the subject.
If the models are known to be “not right” then why all this dependence on the models? Because the facts don’t agree with the agenda?
I’m not sure why you would put “not right” in quotes when I didn’t even say “not right.” When models and observations do not match up, scientists try to figure out why. My point was that they don’t throw out the observations (assuming they were properly collected) and just go with the models, while ignoring the real world. Models help to teach us what we don’t understand. And we depend on models for future decision making because unfortunately we don’t have time machines to collect future observations. Just because a model is not perfect does not mean it’s completely wrong or we shouldn’t take any action. I’m sure you’ll continue to twist my words on that topic, but my main point was that the double ITCZ is not a new feature that is just being discovered. Reading the past literature is necessary to understanding any scientific process.
pd2413: “My point was that they don’t throw out the observations (assuming they were properly collected) and just go with the models, while ignoring the real world.”
Unfortunately, in the field of climate “science”, that is exactly what they do.
I mean what climate “scientist’ would believe a $10 thermometer when it disagrees with a $100,000,000
computer gameclimate model?Here you go, straight from the horse’s mouth.
“The data doesn’t matter. We’re not basing our recommendations on the data. We’re basing them on the climate models.”
~ Prof. Chris Folland ~ (Hadley Centre for Climate Prediction and Research)
Don’t forget, we’re talking climate “scientists” here, not engineers.
@pd2413 “When models and observations do not match up, scientists try to figure out why.”
Kevin Trenberth here:
http://blogs.nature.com/climatefeedback/2007/06/predictions_of_climate.html
“…no one is pretending that the models are right and the observations are wrong….”
Ah, that’s not so. The models are being used to sell political action, while observations are being explained away with specious mathematics and statistical methods.
pd2413
pee dee writes, … “This is not a new finding, and no one is pretending that the models are right and the observations are wrong. ” ……. Did you just wake up from a long induced coma or sleep?
So we’re at the point where you just mock rather than use facts. But apparently, while I was in my long coma, multiple climate scientists were studying why the models do not properly simulate the ITCZ and instead show a double ITCZ. They know that the ITCZ is an incorrect output and are attempting to determine what is causing the discrepancy and improve the next iteration.
Please identify the multiple scientists you speak of. Links please.
http://www.atmos.washington.edu/~dargan/papers/hf13.pdf
http://journals.ametsoc.org/doi/abs/10.1175/jcli4272.1
http://journals.ametsoc.org/doi/abs/10.1175/jcli4272.1
The reference sections provide more.
pd2413 – “Just because a model is not perfect does not mean it’s completely wrong or we shouldn’t take any action.”
Oh dear. “Precautionary Principle”?? Doubled down?
Instead, I think we should follow “Darwin’s Principle” and adapt. If the seas rise 10 metres in 10,000 years, I think I can live with that. 🙂
pd2413 writes
I looked at the first linked paper in your list and there were no solutions. Nothing definitive, just correlations and theories about components likely to be at fault. Clouds mostly..surprise, surprise… and then statements along the lines of needing to do more work to narrow the causes down.
Your defence here reminds me of the dendro divergence problem, actually. For years AGW enthusiasts responded to the divergence problem with a reference to a paper discussing it published in, from memory, 1998. With an assumption that it was all explained in there. Except it isn’t and here we are nearly 20 years later and still no explanation for divergence. Just theories.
And the obverse would be? Everyone should always accept prior science and build upon it?
A) All science should be independently replicated, multiple times.
B) Since when are ‘models’ science?
The idea is that models should replicate reality. When models fail to replicate reality, they are definitely not models in a scientific aspect; though they might be models in a childish play model.
Willis never claimed to not read the literature. He didn’t read the literature first. A practice more scientists should try as the climate consensus is a long way from observations.
A reality most engineers can readily inform consensus climate science. Building upon improper or poor foundations is a recipe for disaster. Real world engineers get sued for bad research or foundation designs. It will be nice to see climate science treated equitably. Perhaps more climate scientists will approach science with open minds.
pd2413
Willis’ way, as he explained it, allows him to delve into a subject without prejudice and then, when he understands all he can about the subject on his own, can then look at the prior research and findings, and be better able use the prior information without bias and prejudice and to throw out what is useless and retain what is useful. It is not, as you state, simply “ignoring all previous work”.
When one follows the much used path through the forest, one doesn’t get to see and observe what is happening on the unbeaten trail or the other paths. If you simply follow other peoples trails, without exploring and creating your own, you will probably only see the same thing as the people you are following. When you come out of the forest, you have, not only the information from those others who proceeded you, but also your own, unbiased, firsthand observed, information with which you can then compare and either use or discard the useless and come to your own conclusion, free of anyone elses opinions or conclusions.
That make sense?
fantastic analogy i will remember for the future.
One key facet of science that is too often lost in the modern herd mentality is that nobody should ever have to trust someone else. They should be able to reproduce the study for themselves, using the methods and analysis put forward in scientific papers.
Of all the papers you’ve read, how many have you attempted to replicate for yourself? Half? Far, far less than that?
Willis is a throwback, wanting to settle issues in his own mind and through his own experimentation. Most of the herd can’t be bothered to replicate or even to question the findings put forward by others. There could be a cursory review of the data and methods, but no attempt to replicate or validate them. In almost all cases, the results are accepted unquestioned. I’ve attended two high profile talks by visiting professors who’s work was later discredited. One actually received a standing ovation at the end of this presentation, which is quite unusual. Within a year he was retracting the work and trying to shift blame onto his collaborators. But all the scientists in the room were accepting this highly flawed work unquestioned, because that’s what modern science is.
My 5th grader does more scrutiny and replication of scientific knowledge in his elementary school science fair than happens at Universities. Is he wasting his time re-inventing the wheel? NO, he is being a real scientist, taking the things he has been told about the world around him, and testing it for himself. This integral piece of the scientific process has been lost somewhere along the way, and we see the consequences when so much “peer reviewed” work can’t be replicated.
Right
Yes, yes, yes. This is exactly how one should start to do model validation. Start with large scale phenomena and data that are well documented and make sure you can do that correctly. At the very least, you cannot accept model behavior that does not occur in the physical system. Until you can model the grand parameters, you have no idea whether the fine features are correct. It means that you have bad internal models, and you have to fix them until you get the overall behavior correct. Then you have to iterate on all the rest of the parameters.
This is the smoking gun of climate “science”. It does not work. It is NOT settled.
You’d think that the brainiacs who developed the model and saw its results would then check them against the data, wouldn’t you? /rhetoric
I once would have thought that. No more. Science is dead, descended into a Lysenkoist hell.
That’s exactly what they do, which is why there is literature on that exact problem with the models. Mr. Eschenbach is by no means the first person to have noticed a double ITCZ.
pd2413 November 12, 2015 at 3:54 pm
That’s exactly what they do, which is why there is literature on that exact problem with the models. Mr. Eschenbach is by no means the first person to have noticed a double ITCZ.
Your statement makes no sense.
First Willis never claimed to be the first person to spot the double ITCZ
Willis:
In the course of doing the research for my previous post on thunderstorm evaporation, I came across something I’d read about but never had seen. This was the claim that the models showed not one, but two inter-tropical convergence zones (ITCZ).
note the words” I CAME ACROSS”…
He acknowledged it was known.
Perhaps you may wish to go back to the article re-read it, think about it for a time and then try again.
pd2413 you have a brain use it.
michael