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.
Willis,
I echo the sentiments of the others here… your last couple of posts really deserve to become published papers.
Another source that I watch is the worldwide lightning locator network at….
http://wwlln.net/
They use over 50 ground based VLF sensor locations to detect, locate and record lightning strikes from thunderstorm activity around the world. I’m not sure if you are aware of them. If not their data may also prove of some value to you.
Wills wrote: “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.”
Are you missing a sentence that could follow? “Then I try to understand why I came to a different conclusions from others.”
One wanders with such inaccuracy of the models on this scale what the up stream model ramifications are? That is a tremendous amout of missplaced energy in the end.
Nice job W!
“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)
When I saw Willis’ chart on rainfall yesterday I thought “Wow, I would not have expected that.” What I expected is exactly what the model shows. Conclusion: Models simply confirm the ignorant prejudices we have when we don’t actually perform observations.
P. Wayne Townsend: “Models simply confirm the ignorant prejudices we have when we don’t actually perform observations.”
Of course they do.
They reflect the prejudices of the person who hired the programmer, or else the programmer won’t get paid.
Programmers have to make a living too, you know!
What was the programmer’s name – Harry???
Here you go!
;
mknormal,yyy,timey,refperiod=[1881,1940]
;
; Apply a VERY ARTIFICAL correction for decline!!
;
yrloc=[1400,findgen(19)*5.+1904]
valadj=[0.,0.,0.,0.,0.,-0.1,-0.25,-0.3,0.,-0.1,0.3,0.8,1.2,1.7,2.5,2.6,2.6,$
2.6,2.6,2.6]*0.75 ; fudge factor
(…)
;
; APPLY ARTIFICIAL CORRECTION
;
yearlyadj=interpol(valadj,yrloc,x)
densall=densall+yearlyadj
FOIA\documents\osborn-tree6\briffa_sep98_d.pro
Willis wrote: “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.”
And later: “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 grid scale.”
One reason for following up on your observation would be that this climate model must be compensating for the excess evaporative cooling somehow. 15 W/m2 is an error – equivalent to the change in OLR from a 3 degC change in surface temperature – assuming DLR doesn’t increase (which it will). Perhaps an increase in cloud cover compensates, but this should produce an incorrect albedo. All models are tuned so that outgoing OLR and reflected SWR agree with climate models.
thank you for that really great mark twain quote. it has COP21 written all over it.
pd2413 November 12, 2015 at 2:50 pm
I started the paper by saying that I had read about the double ITCZ model, so OBVIOUSLY it is not a new finding, nor did I claim it was a new finding. You’re attacking a straw man.
Since people are claiming on the basis of these same models that we need to totally re-structure the global economy, yes, pd, people ARE “pretending that the models are right”.
Given the number and the extent of the interesting discoveries that I’ve made using exactly this method, I can only conclude that it is an exceptionally good way of doing science. Take my work on extinctions. I never could have found out what I did if I had first absorbed the conventional wisdom on the subject. When I first wrote about extinctions, everyone was convinced we were in the so-called “Sixth Wave of Extinctions”.
Being free of such preconceptions allowed me to question the bogus claims and to determine the underlying truth. The peer-reviewed paper that Dr. Craig Loehle and I wrote on the subject has now garnered over thirty citations in other scientific journals.
So I fear that the reality of my original work disproves your claims that my way is a “poor way”. Look, I’m not recommending it for other people. It might indeed be a very poor way for you to do science, I don’t know. Everyone has to work in the way that they find best. However, this is what works for me, and it has worked exceptionally well for me.
I never said that we should “ignore all previous work on the subject”. I said very clearly that at a certain point in my investigations, I look at what others have written on the subject. I’m just careful not to do it until I’ve taken my own look, free from pre-conceptions, and drawn what insights I can from the data itself.
You see, when I start my investigation, I’m not interested in what PEOPLE have to say on the subject. That comes later. Instead, when I begin my research I’m only interested in what the DATA has to say about the subject.
Regards,
w.
“They didn’t know it was impossible. So they did it.” -Mark Twain
I like this quote. I also like to try things on my own first to see what I can see. For me, if I read anything about what other people have tried it biases my brain somehow. I seem to learn more from their work if I read about it after I try it myself. Not ‘pre-biasing’ oneself is an excellent way to do science. Read what other people have done only after you exhaust your own ideas first. You’ll never get another chance to be totally unbiased after you read their ideas.
Willis: On the subject of “Zen Mind, Beginner ‘s Mind”, your method of learning and approach to scientific research is also the one I’ve used very successfully myself for over 30 years and it’s resulted in several papers that changed the field I worked in.
Quite a few scientists brought up in academia (as opposed to being born that way) end up being what I call “puzzle solvers” who take the existing body of work and expend great effort filling in blanks. It’s a necessary activity in mature disciplines I suppose, but I can’t be sure since I’ve never worked in a mature discipline 🙂 I don’t think it’s very useful in the development of new fields if for no other reason than there’s far too much cruft and wild speculation going on so spending a great amount of time reading detailed accounts of other failures isn’t very productive; like you I prefer a fast overview of current thinking to make sure I’m not replicating one of those failures, then set off on my own line of inquiry. After I’m done, I search for other work that might either reproduce what I think I’ve discovered or refute it in a believable way. I find that approach most economical and also more fun.
Good article BTW.
Willis Eschenbach:
My guess would be that the model doesn’t get right the Milankovitch precessional cycle forcing. The position of the ITCZ is determined by the thermal latitudinal gradient, and moves slightly North and South with the seasons producing the summer and winter monsoons. The thermal latitudinal gradient that determines the position of the ITCZ is affected by the different insolation at both hemispheres due mainly to the precessional cycle. As the Holocene has advanced, the northern summer insolation has decreased and the northern summer position of the ITCZ has moved southward putting an end to the African Humid Period. But it still favors that the ITCZ spends more time in the northern hemisphere.
All models appear to underestimate effective solar forcing and the incorrect position of the ITCZ is just one of many indications of that. The modeled ITCZ is moving southward too much during the austral summer.
Willis:
Keep publishing any way you desire. Do keep copyrights though so when the plagiarists forget to assign proper authorship, they can be set straight.
I couldn’t help thinking while watching the modeled run that it bore a strong resemblance to the claims last January by Sherwood that he had found the missing hotspot. http://iopscience.iop.org/article/10.1088/1748-9326/10/5/054007/pdf
Though Sherwood alleges to have used radiosonde data in his model… From Sherwood’s model.
Willis
Did you check the TRMM average rainfall record? It does not look right to me.
For example, India has on average just under 1.8m of rainfall, and yet on the TRMM plot it is shown dark blue (ie., 0m, at any rate less than 0.8m). Putting more detail, the South such as Goa and Mangalore have some 2.8m to ~3.5m of rainfall, ie., the South should appear yellow and orange, and at best one can see a little bit of green. See for example the detailed summary on India:
http://www.india.climatemps.com/
I consider that the TRMM plot needs to be cross checked for accuracy.
Richard, India is not “dark blue” on the map above (figure 1). Not sure what you’re talking about.
w.
I am talking about this map [Figure 1. Annual average rainfall from the Tropical Rainfall Measuring Mission (TRMM)], which I understand represents annual rainfall. India is the triangular shaped country, which in my eyes is shaded in dark blue variably light blue, with the little island (Sri Lanka) at its southern most tip, and is situated to the right of Africa. The Indian ocean is shown in green.
?w=720
South India has 3 m rainfall only on the East coast strip where the summer monsoon hits the coastal mountains. Central and East South India have much less, between 0.6 m and 1.3 m at most.
Willis
if you look at ClimaTemps (http://www.india.climatemps.com/) , it lists 42 locations in India in which rainfall is actually measured. The wettest place (Cherrapunji) has a subtropical rainforest climate with over 11m of rainfallannually! The average for India is 1.8m, and given that average, one might expect that there would be plenty of places that have an annual rainfall of about 1.6m annually (green), and the data at ClimaTemp confirms that there are many such places.
No doubt the TRMM plot is low resolution, but to my eyes it does not seem to correlate well with the actual measurement data set out at ClimaTemps.
That has to be a problem with the way TRMM collected and processed the returns (mission ended April 2015). The data I posted earlier shows the Hawaiian mountains getting about 5 meters per year and they show up in the 0.8-1.6 m/year color in the data as well. Though the color for Guam may be close to its recorded rainfall. I have a feeling the pixel resolution for TRMM may be even larger than the model’s resolution.(I looked it up – it is 0.25X0.25 degrees, or 15X15 minutes.) The problem we are noting is likely associated with the note in the mission statement below. (from https://climatedataguide.ucar.edu/climate-data/trmm-tropical-rainfall-measuring-mission)
Both the Indian coastal mountains and the area of the Hawaiian Islands would fit that description well, but they seem to think the ocean areas should be fairly useful. We’d probably have to compare to the rainfall measurement capable TAO buoys to see if that assumption is close. I would hope the program office had checked their rainfall model against a few rain gauges to verify the assumptions, but I have seen too many models divorced from reality in climate science to make that assumption. OK, I researched that and discovered that from 1997-2000 they did that experiment to calibrate TRMM (details at http://www.pmel.noaa.gov/tao/proj_over/trmm/trmm.html),so open ocean areas should be good data.
Funny what you learn as you look into problems.
Makes sense, but it goes to show the difficulty in modelling the real world.
One significant problem is that in any grid cell, the energy in does not equal the energy out since much energy is transported all around, indeed actually in 3D since the energy is not simply being distributed horizontally, but also vertically.
One only has to look at the equatorial and tropical oceans that are powerhouse of the planet’s heat pump to see that there must be massive amounts of evaporation over these oceans but over large areas of the ocean from where the evaporation takes place, there is very little rainfall, so one can see how energy and heat is being both dissipated and distributed from one p[lace to another.
Given the importance of the latent heat exchange and the contrast between dry and wet adiabatic lapse rate, and the manner in which evaporation drives cloud formation and indeed varying degrees of cloud albedo (which varies according to the nature of the water vapour, droplets, crystals that are held) getting the water cycle right at small grid cell basis is an imperative if there is to be any prospect of modelling the real world.
There are some issues with the TRMM Rainfall Map image in your post.
Western Indian Peninsula receives much higher rainfall than depicted. North India receives much less rainfall than 3.2 to 4 m/yr shown in the image.
Here is a time average map of precipitation rate monthly in mm/month (40N to 40 S for the world)
This map uses precipitation rate TRMM 3B43 v7
http://www.gujaratweather.com/wordpress/wp-content/uploads/2015/04/GIOVANNI-outputmdJiNsMq.png
“Analyses and visualizations of the image used for this comment was produced with the Giovanni online data system, developed and maintained by the NASA GES DISC.”
I don’t know Ashok Patel:
Your map has a great many similarities except the coastline and high Himalaya’s.
Another difference is that the map Willis provides is Average annual rainfall. The map you provide is a monthly average. I would expect that your map summed into an annual would look just like the one Willis provides.
On a water world whose oceans drive the climate, if one cannot correctly model the water cycle (which in turn will require modelling clouds), one cannot begin to expect to be able to model climate.
When the CGMs get the water cycle right not simply globally but on small scale regional basis, then it might be time to look at them and consider what they project. Until then, it is obvious that their output is simply garbage.
Finally, that “extra heating” signature 18 km above the tropics appears … in the models
I am just as amazed as I am amused!
Throughout the year of 1959 I was on Christmas Island [1200 miles due south of Honolulu and 2deg N of the equator ] with my engineer regiment preparing for a series of H bomb tests that did not take place !
Prior to travelling I did a little research :
In 1892 James Morrison & Co,London obtained 21 year occupancy rights for coconut planting and pearl fishing .
This was not a success ,as in 1902 – 10 years into the lease – Lever Bros acquired the island and planted huge numbers of coconut palms .
In 1905 a Lever Bros expert seeded the lagoon with gold-lipped oysters from Thursday Island in the Torres Straits. However later that year there was a drought and 75% of the coconut was destroyed.
By 1906 the island was deserted .
If only they had had the benefit of your rainfall maps they would have seen that they were on the very edge of the ITCZ and developed accordingly , or not at all.
Incidentally by 1959 the oysters were doing well .I bought an aqualung and sold the shells I found in Honolulu while on leave . The income paid for the aqualung !
Now-days the Honolulu shoreline is higher:
-6 inches or 0,15 m since 1905.
And the trend is stable.
http://tidesandcurrents.noaa.gov/sltrends/50yr.htm?stnid=1612340
I do not want to derail or side track this post where Willis comments upon a known problem with the models
which models are hopeless at reproducing the water cycle on this water world which we inhabit, but apparently UAH is not reading the current El NIno as pushing up November temperatures.
October 2015 was indeed a warm month in the satellite record, and many predicted that due to lag temperatures would remain high (and increase) for the next 4 to 6 months, but so far in November (first 12 days), temperatures have been falling back from the October figure.
ENSO is still showing up strongly in the ENSO meter (just under +2.5C), and, of course, it is too early to say how November will pan out in the UAH and RSS data sets, but it would be ‘useful’ if the November satellite data (which no doubt will be available early December) adds some realism and adds a contrarian standpoint to the claims that 2015 is the warmest year ever on record.
See: http://notrickszone.com/#sthash.kT65AL9n.dpbs
Sorry if I am diverting attention, but I thought that those who are interested in current events and the present cycle might like to know how November is presently shaping up.
No diversion Richard:
More likely that Willis’s warm water causes evaporation cooling is affecting temperatures.
The problem appears to be that the model is garbage. It is not only that its representation of ITCZ is too symmetrical, but in another respect lacks symmetry altogether, in spite of the fact that annual hemispheric average absorbed radiation is the same for North and South. It is not so in models, only in reality.
Looks like an important symmetry can only be established on an asymmetric object by a symmetry breaking process, that is, albedo is a strictly regulated quantity and a single ITCZ is part of this regulation loop. The details of this regulation are not understood at all, so no wonder computational climate models miss it completely.
Journal of Climate, Volume 26, Issue 2 (January 2013) pp. 468–477.
doi: http://dx.doi.org/10.1175/JCLI-D-12-00132.1
The Observed Hemispheric Symmetry in Reflected Shortwave Irradiance
Aiko Voigt, Bjorn Stevens, Jürgen Bader and Thorsten Mauritsen
It is always interesting to see your findings. I have some questions about what model is chosen. It is important for me to know a little more about the model. Every model will have different results, and every model will have its shortcomings. Which of the models are most representative? It would be interesting to see if the same picture came up with a GISS-model (the preferred choice of some of the climatists). I see that modelers are aware of many of the shortcomings of the models , as you can see from the discussion in this paper:
Changes in precipitation extremes over Eastern China simulated by the Beijing Climate Center Climate System Model (BCC_CSM1.0) Li Zhang*, Min Dong, Tongwen Wu Beijing Climate Center, China Meteorological Administration, Beijing, PR China
4. DISCUSSION Based on daily precipitation during 1956−2009 from 349 rain gauge stations in eastern China, the performance of the BCC_CSM1.0 in simulating the regional extreme precipitation change over eastern China (east of 105° E) is evaluated. We also obtain similar results as previous research about the observation (e.g. Pan 2002, Zhai et al. 2005, Dong et al. 2011), such as the frequency and amount of extreme precipitation decreased in Northeast and North China, and increased in South China during the period of 1956 to 2009. The 20th century simulation forced by observed greenhouse gases, aerosols, solar irradiance and vol – canic aerosols shows that (1) BCC_CSM1.0 reproduces the basic feature of observed climatology of annual total and extreme precipitation over the 95th percentile, with spatial correlation of 0.77 for both of them, and (2) BCC_CSM1.0 captures the main change patterns of yearly accumulated extreme precipitations above the 95th percentile, although there are some model biases in the spatial extension and strength. There are also some model biases: (1) the annual to tal and extreme precipitation south of Yangtze River are underestimated. (2) The frequency of heavy precipitation (≥25.0 mm d–1) over eastern China is underestimated, but the frequency of light rain (0.1−10 mm d–1) is overestimated. (3) The longterm trends of annual extreme precipitation amount and frequency in the recent 50 yr are opposite to those from observations in North and South China. The model also has poor performance in simulating their interdecadal variations in North and South China. The regional differences for extreme precipitation change is considerable and there are interdecadal variation signals for extreme heavy precipitation. Although some encouraging results for extreme precipitation simulation are obtained, the performance of BCC_ CSM1.0 needs to be im proved. IPCC AR4 models also have large biases in simulation of precipitation resulting from the East Asian summer monsoon (EASM) (Sun & Ding 2008). It is therefore a common task for current climate system models to improve their performance in simulating the pre – cipitation over East Asia. A good simulation of the EASM is very important for precipitation simulation in East Asia. The probability of climate extremes is also strongly affected by atmo spheric circulation and climate variability, such as Madden−Julian Oscillation, El Niño−Southern Os – cillation, and Pacific interdecadal variability, in the global and/or regional scale (Jones et al. 2004, Curtis et al. 2007, Kenyon & Hegerl 2010). The climate system model working group in BCC are devoting themselves to improve the capability of BCC_CSM.
Willis:
So I DID see the ITCZ “hop” over the Equator in your earlier post! Inquiring minds wanna know why does it DO that?!
My first thought was “how did they get accurate rainfall data over the ocean,” satellite?
TRMM was a satellite radar transponder and infrared (and microwave) imager that measured rainfall radar returns and the corresponding heat signatures to “measure” rainfall rates. It ran out of fuel in April of this year. There is a follow-on mission called the Global Precipitation Mission (GPM), but I haven’t researched its capabilities. for information on the mission see: https://climatedataguide.ucar.edu/climate-data/trmm-tropical-rainfall-measuring-mission
Thanks so much for the effort of your reply. (To a sophomoric question?)
It can register rainfall. It does have problems with determining whether rainfall actually reaches the surface.
Dry air masses, especially warm ones evaporate a lot of precipitation before it can hit the ground. Either the rain rate overwhelms evaporation or the air reaches moisture saturation.
(While I would never presume to tell you how to spend your time …)
It might be interesting to see the results of a random selection of models. Just to confirm that bcc-csm1wasn’t an outlier.
It’s OK for ‘climate scientists’ to suggest that smaller grids are impossible right now but, as you seem to suggest here, without the detail everything rapidly degenerates into a fantasy world.
If, as you hypothesise, emergent, and relatively local phenomena, govern the the climate then not accurately modelling these features makes a mockery of CMIP5 or any other round of ‘inter-comparison’.
Related is the idea, from your previous post, that there are actually ‘formulae’ available to deal with (global!) evaporation and such. If modellers have simply sat in their ‘offices’ and randomly chosen a formula that they were fed with as under grads then it is little wonder that the models go ‘off the rails’ fairly quickly.
I just wish that they (climate modellers) would be a little more honest about those ‘slight problems’ when they come to brief Politicians and others outside the field.
__________________
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.
While I won’t go into details, I was once involved in a ‘heated discussion’ with a PhD (now tenured) Computer Scientist. “Why on earth would a phone need an IP address?” was the core of his argument. There are none so blind as … well, ‘specialists’.
A real pleasure to understand the primary process that has controlled the planet’s temperature for 100years to closer tolerances than the average home thermostat.
Getting realistic, if one were charged with designing a control system that would keep an Earth sized object within +/-0.3 C for an extended period then if you could do that then I would gladly employ you.
Hey, another person who sounds like they have real-life experience with control systems. The stability of the earth’s climate system is astounding, given that it is only regulated by clouds and such. Thanks for the comment.
w.
It is a water world, and regulated by the water on it; the formation of clouds being one aspect of the water cycle, but it is more than just clouds.
As I see it, the basic problem with the various critics here complaining about Willis’ not reading the extant literature first is one of capacity.
Willis can do in hours or days what many critics here cannot in weeks, months, years or decades and it just urks the bejeezus out of them. If they were to try Willis’ “beginners mind” approach they might never get to the currently discussed issue. For them, reading the extant literature is the only possible way to catch up to the current state of the science and make a relevant contribution or comment (And yes, I cannot even do that as I am only a layman and not a brilliant one either).
Thus in their comments/criticisms they sound like bitter old University Professors.
Them what can do, them what can’t teach.
+1
I see the basic problem as being the climastrologists claim that “The data doesn’t matter”; Willis analyses data and that, in their estimation, is fundamentally wrong. Climastrology is about as relevant as studying the sex habits of pink unicorns. Thus their comments/criticisms make them sound as if they are on potent psychedelic drugs.
Thanks, Willis, for your unusual methods of study and investigation. They should not be unusual, just the opposite, because it is what the scientific method is about; discovery by contradiction of the current paradigm.
See http://www.oarval.org/ClimateChangeBW.htm#ITCZ
The best almost real-time visualization of the ITCZ I have found is from NOAA-NWS OPC – Unified Surface Analysis. Updated every 6 hours, at http://www.oarval.org/ClimateChangeBW.htm#TUSA