Guest Post by Willis Eschenbach
I have put forth the idea for some time now that one of the main climate thermoregulatory mechanisms is a temperature-controlled sharp increase in albedo in the tropical regions. I have explained that this occurs in a stepwise fashion when cumulus clouds first emerge, and that the albedo is further increased when some of the cumulus clouds evolve into thunderstorms.
I’ve demonstrated this with actual observations in a couple of ways. I first showed it by means of average photographs of the “view from the sun” here. I’ve also shown this occurring on a daily basis in the TAO data. So I thought, I should look in the CERES data for evidence of this putative phenomenon that I claim occurs, whereby the albedo is actively controlling the thermal input to the climate system.
Mostly, this thermoregulation appears to be happening over the ocean. And I generally dislike averages, I avoid them when I can. So … I had the idea of making a scatterplot of the total amount of reflected solar energy, versus the sea surface temperature, on a gridcell-by-gridcell basis. No averaging required. I thought well, if I’m correct, I should see the increased reflection of solar energy required by my hypothesis in the scatterplots. Figure 1 shows those results for four individual months in one meteorological year. (The year-to-year variations are surprisingly small, so these months are quite representative.)
Figure 1. Scatterplots showing the relationship between sea surface temperature (horizontal axis, in °C) and total energy reflected by each gridcell (in terawatts). I have used this measurement in preference to watts/square metre because each point on the scatterplot represents a different area. This approach effectively area-averages the data. Colors indicate latitude of the gridcell. Light gray is south pole, shading to black at the equator. Blue is north pole, shading to red at the equator. Click to enlarge
So … what are we looking at here, and what does it mean?
This analysis uses a one-degree by one-degree gridcell size. So each month of data contains 180 rows (latitude) by 360 rows (longitude) of data. Each point in each graph above is one gridcell.That’s 64,800 data points in each of the graphs. Each point is located on the horizontal axis by its temperature, and on the vertical axis by the total energy reflected from that gridcell.
The main feature I want to highlight is what happens when the ocean gets warm. From about 20°C to maybe 26°C, the amount of solar energy reflected by the system is generally dropping. You can see it most clearly in Figure 1’s March and September panels. But from about 26° up to the general oceanic maximum of just above 30°C, the amount of solar energy that is reflected goes through the roof. Reflected energy more than doubles in that short interval.
Note that as the ocean warms, the total energy being reflected first drops, and then reverses direction and increases. This will tend to keep ocean temperatures constant—decreasing reflections allow more energy in. But only up to a certain temperature. Above that temperature, the system rapidly increases the amount reflected to cut down any further warming.
Overall, I’d say that this is some of the strongest evidence that my proposed thermoregulatory system exists. Not only does it exist, but it appears to be a main mechanism governing the total amount of energy that enters the climate system.
It’s very late … my best regards to everyone, hasta luego …
w.
[UPDATE] A commenter asked that I show the northern and southern hemispheres separately. Here is the Southern Hemisphere
And the Northern. The vertical lines are at 30.75°C, nothing magical about that number, I wanted to see the temperature shift over the year and that worked.
Remarkable and very instructive information!
Was it really not shown before?
Thank you Willis, a good analysis that an idiot like me can understand. You do good work.
Dear Wiilis, you nailed it! Thanks
That’s really interesting.
Thanks, Willis, you’re doing a great job!
Damn. Those are works of art in themselves!
Talk about a picture (or four) worth a thousand words. This barely needs words at all.
You could not have a clearer picture painted of your equatorial regulatory mechanism.
I have some problems with your analysis.
You state that cumulus clouds are relatively short lived. Yet you are, if I understand you correctly, using monthly samples of ceres data.
This suggests that you may be using aliased data, in which case your calculations may not be reliable.
Clouds affecting temperatures are well known to us living far north. A cloudless winter night is noticeably colder than a cloudy one.
The Real Earth.
The climate modelers can’t model clouds. Well, it is right there.
It is also says that the cloud feedback is not a linear +0.7 W/m2/C as is assumed in the theory but is a much more complex function depending on temperature that most likely goes strongly negative at the 30C. You now have the short-wave part of the function. Probably easy enough to do the out-going long-wave component now as well. Overall, clouds reflect -54 W/m2 of solar radiation and hold in +32 W/m2 of long-wave for a net -22 W/m2 of impact. One needs both components to arrive a net solution function.
Thanks Willis, graphic presentations are the best.
In the areas concerned:
i) In the tropics with a temperature range of 20C to 26C one generally sees low cloud burning off as the temperature rises.
ii) At about 26C cloudiness is at its minimum and reflectivity at its lowest.
ii) From 26C upward higher level convective clouds build up rapidly and reflectivity increases exponentially.
There is no doubt that Willis’s Thermostat Effect is present and highly effective but one needs to extend the general principle across centuries to deal with events such as the MWP, LIA and Current Warm Period.
In itself it is not an adequate explanation for global climate variability over centuries.
Stephen Wilde says: October 6, 2013 at 4:59 am
In itself it is not an adequate explanation for global climate variability over centuries.
True enough. It is more of a very nice explanation for the amazing stability of the system.
What is always being searched for are the events or drivers that force the system away from the base level to which it eventually returns. From these prognostications arose the GHG line of research, but it is hard to visualize CO2 as ‘the’ driver, now, or more so in the distant past. Willis’ charts would indicate that ‘the system’, in terms of both latitude and sea surface temperature, has a lot more buffer in it yet.
An important point of course is that in ‘recent’ times, (the last half million years or so) glacials are the base level, and interglacials (ie, right now) the exception. Makes you wonder how he prospect warming was put up as a disaster. The alternative is …um … chilling.
Nice work Willis. That!…is a hockey stick.
Do you only count grid-cells that contain water? Why not correct for grid surface area? Seems like the polar grids would be extremely reflective if you did.
Again, nice work. Thanks.
That does look compelling Willis, surely water vapour is the planets thermostat? In getting to where Climate science currently is (or is not!), did someone not already set up a simple model with reasonable resolution of land and ocean albedo, air and water thermodynamics and some generalised ocean currents and atmospheric circulation. Add in incoming solar radiation and parameterization of clouds and ice cover? Ideally they would have elevation in too (because I suspect ice coverage at mid and tropical latitudes will be important, especially over long timescales). Did they discard this model because it failed dismally – ran away to a snowball planet, or boiled off the oceans? It seems to me the planet is naturally buffered to the logical extremes of current poles and tropics. Lovelock did something similar with Daisyworld, but in this model clouds and ice would be the white daisies, vegetated continents and open ocean the black daisies. I’m not suggesting you do this – I know how busy you are and really appreciate your observations and writing, I just wish I was smarter and could run the numbers myself or had better access to the literature.
“And I generally dislike averages, I avoid them when I can”
Hehe, especially since your own work shows an average positive feedback of +0.7 W/m2/C globally. Much better then to try changing the focus to some areas of the tropics where the regulation works. You are only fooling yourself and your hardcore followers.
“But from about 26° up to the general oceanic maximum of just above 30°C, the amount of solar energy that is reflected goes through the roof.”
Thus suggesting a reason for what appears to have been an upper limit to earth’s temperature over million of years: http://geocraft.com/WVFossils/PageMill_Images/image277.gif
i have spent a lot of time in my younger days watching clouds in the tropics…at 7500 ft altitude a lot of them are close by…its fun trying to figure out what they look like…because they change all the time…but trying to calculate this stuff is a fools game
I might remark on the fact that Roy Spencer has a recent blogpost along similar lines. Might be worth the time to check
lgl says:
October 6, 2013 at 5:48 am
your own work shows an average positive feedback of +0.7
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that would be consistent with the earth’s average temperature being lower than the local minimum shown in the graphs at around 25C.
notice that the curves are consistent for the N and S hemispheres during the equinox, and substantially different during the solstice. this argues strongly that feedback is not constant, rather it is a non-linear function of temperature.
this non-linear dynamic feedback is not the mechanism that causes the earth’s temperature to vary, rather it is the mechanism that returns the earth’s temperature to the habitable range for life, in the face of other mechanisms that try and force the temperature outside this range.
Willis – I don’t want to knock this, I think what you are doing is fascinating and a truly worthwhile exercise. But I’m not convinced that it shows what you claim that it shows.
Looking at the southern hemisphere, low latitudes (light grey), there is much more cloud in summer (Dec) than in the other seasons. I assume we are looking at cloud cover not snow and ice. There is the same effect in the NH but less marked. To my mind, we are looking at a seasonal effect, and it does not necessarily reflect – in your words – “what happens when the ocean gets warm“. ie, it shows what happens when the ocean warms seasonally and locally, but it doesn’t show what happens when the planet warms up over a multi-year period. The point is that we don’t know what the drivers of cloud cover are, and the absolute sea surface temperature may be a minor driver. If the drivers include, for example, the relationship between various weather factors in neighbouring regions, then no inference can be drawn from your analysis wrt any multi-year period.
Similarly, looking at the tropics, there is a lot more cloud there than elsewhere. That is why your graph shows that “the amount of solar energy that is reflected goes through the roof” in the tropics. We may simply be looking at a regional effect, driven by things like winds and currents and the relationship to nearby regions. ie, the absolute sea surface temperature may be a minor driver. Again, I don’t think that any inference can be drawn from your analysis wrt any multi-year period.
In summary, you appear to be finding seasonal and regional effects, for which there may be seasonal and regional drivers other than absolute sea-surface temperature, and which therefore may have no implications wrt multi-year global temperature changes.
As Stephen Wilde said, “ In itself it is not an adequate explanation for global climate variability over centuries.“.
Mike M says:
October 6, 2013 at 5:55 am
Thus suggesting a reason for what appears to have been an upper limit to earth’s temperature over million of years: http://geocraft.com/WVFossils/PageMill_Images/image277.gif
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Agreed. Even if the earth’s temperature rises such that more and more regions become tropical, regionally the oceans will keep temperatures locally below 30C except for those areas away from the oceans.
This also shows how averages can be misleading. having one foot in the freezer and the other in the oven is on average comfortable.
What we are seeing globally is that the north pole is warming, while most of the rest of the globe remains largely unchanged. This cannot be due to CO2, because CO2 is reportedly well mixed.
However, it has been strongly argued by climate science that the MWP was not global, that it was mostly a warming of the N hemisphere. This is similar to what we are seeing now – the warming is regional – specific to the N hemisphere.
This suggests that the MWP and the current warming are related by a common cause. This cannot be human created CO2, because the MWP took place during a period of low industrialization.
This is good work – very strong confirmation of the regulatory effect at higher sea temperatures. One expects the Dec and Jun plots to be similar but color-inverted because this pair represents similar orientations of the earth’s axis relative to the sun, but with respect to different poles. And this shows up well in the plots. Also, from the plots the southern hemisphere seems to generally reflect more energy at the lower sea temperatures than does the northern – due perhaps to different ratios of sea area relative to land? This effect is strongest in the Sep plot, but seems evident in all of them.
Hi WIllis.
I share your love for this kind of graphic that organizes a lot of measurements instead of averaging them out.
These graphs are very convincing for your hypothesis of clouds as temperature regulators.
It is great fun to witness your ongoing research. The birth of a theory.
After further thoughts..and playing devils advocate….
Sea surface temp is closely related to latitude. Therefore, one could replace your graphs x axis with latitude and it would likely look fairly similar. This then just shows that the equator has more clouds, then there is a band of fewer clouds followed by a seasonal mid latitude band of variable cloudiness. This pretty much covers standard distribution of atmospheric cell circulation. No causation in other words.
As for thermo regulation, there is nothing in the charts to show that there is regulation going on. Only that it is max.30c at the equator and less as you move away. One could shift the temperature scale 10 degrees and conclude that “see after 35 we get more clouds so it will never go over 40.
I think you need to show tighter causation and remove the latitude bias somehow.
Cheers
kirk
Long term land temperature trends still need to be examined in relation to this work. Have you tried narrowing the area to just the tropical equatorial belt to capture straight on solar irradiance? And how far back can you go with data? I would really like to see this measure of cloud presence through several ENSO events. And especially since 1970. A decrease in clouds (decreased reflectance) allows the oceans to recharge and would predict warming to come, something that Tisdale has explained many times. If La Nina events predominated (as could be deduced from decreased equatorial clouds/reflectance), it would predict eventual long term warming to the degree we have experienced. Additionally, the degree of recharge would diminish as the Sun’s angle varies from 90 degrees, so the “sweet” spot would need to be determined.
So narrow the latitude band and plot reflectance against time over several decades or at least for the length of time we have reflectance data?
October 6, 2013 at 6:28 am
Mike Jonas says: “To my mind, we are looking at a seasonal effect, …”
Hi Mike.
The seasonal effect is very evident at the Poles, and much less so in the Tropics.
Remember, in December the South Pole receives sunlight 24 hrs a day.
In June it is dark 24 hrs a day, ergo: no sunlight to reflect.
It’s pretty evident where the temperature “hits the wall”.
The bifurcation in the northern hemisphere December data plot is a bit odd. Is it possibly due to a a differential between the Atlantic and Pacific basins?
ferd berple says: “… has been strongly argued by climate science that the MWP was not global, that it was mostly a warming of the N hemisphere.”
Operative phrase there is “has been”. They are finding more and more empirical evidence that the MWP was indeed global. Back at the peak of the MWP, Western Europe was clearly one of the most advanced regions of human civilization for science and written language so of course there is preponderance of written evidence that a MWP occurred there.
For example, Incans had no written language. Fortunately though, their climate history was written for them in sedimentation:
“These increasingly warmer conditions allowed the Inca and their predecessors the opportunity to exploit higher altitudes from AD 1150, by constructing agricultural terraces that employed glacial-fed irrigation, in combination with deliberate agroforestry techniques. ”
http://wattsupwiththat.com/2009/07/08/the-medieval-warm-period-linked-to-the-success-of-machu-picchu-inca/
elegant
Dare I say that the factor which determines that maximum temperature is the weight of the atmosphere pressing down on the surface water molecules?
For the phase change to water vapour to occur the energy available has to overcome both the natural attraction between water molecules AND the downward pressure that supplements that attracting force.
The greater the weight of the atmosphere the more energy required and the higher the temperature needs to be.
There then comes a point where the temperature becomes sufficiently high that ALL additional energy added is absorbed in the phase change and that is the maximum temperature achievable.
If you blew these up to 3m x 3m and put them on the wall in an art gallery I reckon you’d get some good offers.
Willis you ask what else we might get from this data. One thing is (though I love the scatter plots) is to sum the terawatts per latitude and get a plot of reflected energy. This is the same sort of chart that Monckton uses for showing the hotspot is not there: poles on each end, equator in the middle.
I am dying to know if you might demonstrate (by bundling the dots) that the reflection of insolation is the reason the hot spot is not there. There may be, for example, a spike in wattage in the tropics that rises or falls faster than the change in the incident angle of incoming energy.
There could be one terawatt line plotted per deg of average sea temp, or the local grid cell sea temp could be factored in to spot how the reflected energy varies away from the incident angle locally.
The hot spot idea is base on the premise of constant incoming insolation. Maybe it is not so constant. If clouds cool the ground, the IR is simply not there to be captured by the increased CO2. Maybe the CO2 would have created a hot spot after all, but the self-regulation point was reached and heat shedding kicked in.
Thanks for elucidating.
This is why the warmistas always say that the poles heat more than the tropics.
There is a potential problem with the analysis presented in this and earler posts that depends on the temporal scale of the processes involved.
1) Calculations have been on a 1×1 degree grid. Cloud albedo can vary in hours at this scale (60nautical miles). Therefore there is a highly variable downward radiation affecting surface temperatures.
2) The temperature used by historical series have a coarse sampling time and are aliased.
3) Therefore one is trying to resolve processes, i.e.: feedback, that have a scale of hours with data that has much longer sampling intervals.
4) Thuis the temporal scale of the process and the dynamics of the feedback MUST be specified.
5) Calculations of anything related to feedback that uses undersampled data may contain large errors.
Therefore this work needs a clear statement about the exact properties of the data.
Your main point is:
“Note that as the ocean warms, the total energy being reflected first drops, and then reverses direction and increases. This will tend to keep ocean temperatures constant—decreasing reflections allow more energy in. But only up to a certain temperature. Above that temperature, the system rapidly increases the amount reflected to cut down any further warming.”
The graphs don’t show that. At and above that “certain temperature”–say 27 or 28 C–the graphs don’t show a “rapid increase” in the amount reflected, at all; they show a 4- to 5-fold VARIATION in the amount reflected (over the grid), with little or no accompanying variation in sea surface temperature (SST)–that is, no correlation between the two. Similarly, for the two equinoctial graphs (Mar and Sep), in the lower range of sea surface temperatures, below 27 or 28 C, the variation in SST is accompanied by little (or much smaller) variation in the reflected radiation. What you have is basically two straight lines, a horizontal one over most of the SST range, and a vertical one at the high end. That means you have picked the wrong two variables to establish a correlation between, and there are entirely other causes for the variations in both of them. The two solstitial graphs (Dec and Jun) basically show that the north and south hemispheres switch places with respect to the effects of the incident solar energy, at those two points of the year–obviously, the Sun shines more intensely on one hemisphere, then more intensely on the other. And note that the seeming correlation between reflection and STT in those two graphs is in one direction in the north and the other direction in the south–again, no substantial, lasting correlation between the two picked variables at all, over the whole globe.
One can certainly make the case that one is seeing a stable system in these graphs (although they are obviously not the clearest representatives of such global stability, with their swirls and vertical take-off at the highest SSTs), but definitely NOT due the physical cause and effect you are implying.
Again, my Venus/Earth temperatures comparison is the definitive correction to climate science, and it shows no global cloud effect on temperatures, as I just got through commenting upon–again–at Steven Goddard’s site. Clouds and albedo are just another red herring (false “settled science”) in climate science, globally. There are no competent climate scientists, as the above post, and the general belief in false dogma among consensus scientists, once again shows.
Thanks, DavidA “If you blew these up to 3m x 3m and put them on the wall in an art gallery I reckon you’d get some good offers.”
I’m an artist with exhibits scheduled solid for the next 8 months. I was looking for an inspiration for a new abstract piece, I think I’ll use these scatter plots just for that purpose.
Paul says: “This is why the warmistas always say that the poles heat more than the tropics.”
I thought everybody was on board with that? But anyway, in their case, not “always”… not when discussing how tornado and hurricane energy are reliant upon the differential between the temperature / humidity of air masses. More warming in the higher latitudes produces a smaller difference = warming is good!
Negative feedback is definitely observed, as in feedback which relatively opposes the temperature change which would occur from an external forcing change in the absence of feedbacks. Negative feedback is a dampener, resulting in low climate sensitivity.
If ocean temperature increases, it is one of the factors on cloud cover. For example, as a thought experiment, if the oceans were magically cooled all over their surface to just slightly above freezing, evaporation would plummet towards relatively nil; cloud cover would diminish to little; and, with few white clouds, Earth’s albedo would approximately halve towards 0.15 or so, much less than the normal value of around 31% to 35% average reflectivity, which in turn would lead to more sunlight absorption opposing the original magical cooling.
Of course, the preceding is a simplified thought experiment (like cooling still further would cause a different effect of albedo increase by ice growth, “snowball Earth”). But the point is that, if all else was equal, “trying” to cool the ocean surface results in an opposing effect of decreased albedo (increased absorption of sunlight). Conversely, “trying” to warm the ocean surface results in an opposing effect of increased albedo (decreased absorption of sunlight).
Dr. Lindzen once noted how ocean surface temperatures near the equator appear to never have been much higher than now in billions of years of past Earth history, supporting negative feedback as if a thermostat. I forget the exact figure, but it was something like never more than a couple degrees Celsius higher, based on mineral deposits IIRC, mentioned in some early article of his. (Others have observed that even the era of the dinosaurs had its extra warmth primarily by warming further from the equator, including arctic warming).
In a later 2010 report to part of Congress, Dr. Lindzen made a presentation (with the most relevant section on pages 35 through 47) in which negative feedback seen in CERES and ERBE data fit a low estimated climate sensitivity of around 0.22 K per W/m^2 radiative forcing change (as in 0.8 degrees Celsius for a 3.6 W/m^2 change).
A copy of that report is within http://wattsupwiththat.com/2010/11/18/profess-richard-lindzens-congressional-testimony/
at http://wattsupwiththat.files.wordpress.com/2010/11/lindzen_testimony_11-17-2010.pdf
Especially considering the limited number of actually significant digits in the prior example, more a single significant digit then the nominal 0.22 K per W/m^2 written above, it approximately or nearly overlaps the low end of Dr. Shaviv’s approximate estimate of 0.26 to 0.44 K per W/m^2 climate sensitivity (of 0.35 +/- 0.09 K per W/m^2).
The latter is discussed at http://www.sciencebits.com/OnClimateSensitivity . Climate sensitivity, to be most precise, is not exactly a constant over different time scales and magnitudes, not just a simple linear response. Still, the approximation is often not too bad if properly observing low climate sensitivity and not falsely assuming it to be high. A number of illustrations, over time periods of a few years to vast eons, are in figure 2 of the prior link, showing how observation-based climate sensitivity estimates become relatively consistent (and low) once properly considering cosmic rays. That is since the larger forcing of TSI+GCR variation makes low climate sensitivity fit historical records, unlike CAGW dogma of pretending nothing other than near-constant TSI matters. (And illustrations in http://img176.imagevenue.com/img.php?image=81829_expanded_overview_122_424lo.jpg highlight why to consider both).
With low climate sensitivity from negative feedback, a large powerful forcing is required for a small change in temperature, and that is what is observed in the historical record over the centuries.
Brilliant, brilliant work, Willis. You are showing the climate scientists what genuine physical science looks like. Your work on clouds alone, not to mention your thermostatic hypothesis, is the best empirical work on how clouds change in response to temperature and how those changes can affect temperatures. The very existence of your work drives home just how uninterested mainstream climate scientists are in the facts of cloud behavior even though those facts alone could reveal whether increased manmade CO2 causes warming.
For all those who have no idea what a physical hypothesis is nor any idea what evidence for a physical hypothesis looks like, please study Willis’ Thermostatic Hypothesis and then this article for an excellent example of physical evidence.
Once more Willis is corroborating what we here in Panama experience daily. Over the course of a year, average day and night temps generally (8 months) don’t vary by more than one or two degrees F. The maximum variation is no more than ten degrees, and that is a lot. One thing you never see in Panama is a weather thermometer, nor are temps reported on the news.
That a thermostat is working is blindingly obvious to anyone who lives in the tropics. Clouds cover varies wildly, as does rainfall (from 200 to over 300 inches per year) depending apparently on the ENSO cycle. Interestingly, the temps are constant whether we are getting the Norherly trades off the Caribbean between November and May, or the huge thunderstorms off the Pacific when we are sitting under the ITCZ during the rainy season.
What effect the thermostat has on global weather is a matter for exploration, as Willis has started to do. Personally, my hypothesis is that for global climate to get seriously off course, as in warming, you would see changes in the tropical thermostat first. That the water vapor/cloud thermostat has operated over many millions, if not billions of years, neatly explains the stability of Earth’s climate through asteroid strikes, volcanic havoc, continental drift, changing GCRs etc.
Occam’s razor rules.
I love the fact that you have dispensed with averages as well. When trying to identify a real life process such as a thermoregulatory mechanism, averages will lead you away from the mechanism not towards it. More scientists should ask themselves if an average has any physical meaning in the real world.
Willis,
I keep looking for something simple, like a nice large sample size analysis of cloud cover vs surface temperature. There should be some sort of sinusoidal mechanism ie, temp goes up, clouds build, temp goes down, clouds dispate, temp goes back up, etc., etc. Did I miss it somewhere? You have explained all the downdwelling/radiative/reflective/yata yata. What about some direct stuff for us old engineers. The how and why analysis are great, but does it work? Need data showing it working.
Henry Clark says:
“Dr. Lindzen once noted how ocean surface temperatures near the equator appear to never have been much higher than now in billions of years of past Earth history, supporting negative feedback as if a thermostat. I forget the exact figure…”
Prof Lindzen states that the planet’s temperature at the equator has not changed by more than ±1ºC over the past billion years. I can get you the exact quote if you like.
“Stephen Wilde says:
October 6, 2013 at 7:23 am
Dare I say that the factor which determines that maximum temperature is the weight of the atmosphere pressing down on the surface water molecules?”
I think that you are wrong here. If what you say is true it would be impossible to boil water at sea level anywhere.
The maximum temperature is determined by the solar flux which in turn reaches its maximum in the tropics. The observed temperature is the equilibrium point between the solar flux and evaporation. The heat carried away by evaporation just balances the heat input of the solar flux. If you put a pot of water on the stove but set the heat to a very low level, it won’t boil for the same reason, though it will be warmer than room temperature.
The exact temperature of the air will be determined by the humidity of the air, which is determined by air pressure, and by the wind speed across the surface of the water.
Willis,
Idea. What if you take your existing data, pick some specific location, put time on the x-axis and reflective data on the y-axis-left and temp on the y-axis-right. Do this for several specific locations. Same data, different presentation. Do a few different locations again. Assumption you are using now is the same, ie reflective data= more or less clouds. Kind of ignores your other radiative factors in that you are not showing possible effects of cloud composition, altitude and thickness but I’ll bet there is a sinusoidal mechanism going on over time and something we might learn from it regarding your regulatory engine..
Too late Willis, Bob Geldof says our time is up! http://www.dailymail.co.uk/news/article-2446613/The-end-world-nigh-says-Bob-Geldof-predicts-climate-change-wipe-humans.html#comments Here in England, we call people like this a ‘prat’.
Studies have demonstrated that earthshine on the moon is variable not constant. This means that earth’s albedo is not constant. Not only is there a daily and annual variance but a longer term variance which at this time would best be described and categorized as “natural” variance. While not tremendous the effect on climate and climate models is probably important. It is also not an unknown phenomenon being first considered by Galileo himself.
dbstealey says:
October 6, 2013 at 8:50 am
“Prof Lindzen states that the planet’s temperature at the equator has not changed by more than ±1ºC over the past billion years.”
Indeed. Actually that sparked my memory enough to do a keyword search successfully finding the following again:
http://www.cato.org/sites/cato.org/files/serials/files/regulation/1992/4/v15n2-9.pdf
“Global Warming: The Origin and Nature of the Alleged Scientific Consensus” by Dr. Lindzen
noting
“There is ample evidence that the average equatorial sea surface has remained within plus or minus one degree centigrade of its present temperature for billions of years“
If the gridcells are each one-degree by one-degree quadrilaterals, their area varies systematically with latitude from a maximum at the equator to a minimum at the poles. I don’t know how that affects your scatter plot, but I think I’d be happier with an equal-area grid.
I know this is off topic even though related to the contents of the post, but I can’t help but think of two things looking at the Figure 1 scatterplots: the elevator scene from “The Shining” and a Rorschach (or Rorschenbach) test. Top left I see an alligator snapping turtle, hawksbill sea turtle, or any raptor. My work here is done.
Interestingly you can see the difference due to the south pole having a persistent ice cap and the north pole having transient periods of open ocean with less reflective ice in general.
At least that’s the biggest thing that stood out to me as interesting.
Willis,
Since this is only over 10 year time span I would guess that perhaps the positive feedback shown so far is still in the process of changing which may be what is holding temp steady against another feedback trying to drive temperatures down. While this is showing the overall control mechanism whereby once a temperature is reached the positive feedback may move to negative, much like what happens in your example of cruise control. Just a thought. hehe perhaps you need to add in a computer model to play with history where there is no actual data. Just a thought.. I know a lot of folks hate models but they can help in terms of understanding when there is no other way, you just need to be careful not to put your own biases into the model which is hard to do.
v/r,
David Riser
IMHO,cold is a more natural state in the universe,and we should be thankfull for any heat we can get.I also think that clouds are like visible latant heat(maybe sensible too…).We can see the warmer air/water mass rising and expanding and losing its heat up there where it’s really cold,not very far away either.
Source info:
http://en.wikipedia.org/wiki/File:Phase_diagram_of_water.svg
http://www.engineeringtoolbox.com/air-altitude-temperature-d_461.html
http://www.engineeringtoolbox.com/air-psychrometrics-properties-t_8.html
http://chemwiki.ucdavis.edu/Physical_Chemistry/Physical_Properties_of_Matter/Atomic_and_Molecular_Properties/Intermolecular_Forces/Unusual_Properties_of_Water
I think it’s called ‘miniscus’ (not steam,maybe fog)when at lower levels.And the miniscus is thickening!
Thanks for the interesting articles and comments
That’s a great idea Willis, nice work.
If you pick a month, say September, and made one of your charts for every year of CERES data, you could then draw a vertical line where the limit of temperature is and plot that vs concentration of CO2. That might give you a good idea of climate sensitivity for the oceans. It is probable land would have a different sensitivity since the response to the back-radiation from CO2 would be different there.
People who expect the summer and winter solstice to be mirror images of one another are forgetting that the Earth’s eccentricity places the Earth closest to the sun during the southern hemisphere summer and farthest away on the northern hemisphere summer, thus introducing a very distinct bias in observation. It really should be hotter in summer and cooler in winter in the southern hemisphere.
Very elegant presentation, Willis. Kudos for all the thought and sweat you put into it.
One small nit: can we see the hemispheres individually? Since the high temperature area is the “punch-line” of your hypothesis, it would help to not have the black and red laid on top of each other at the right-hand side.
Thanks, again!
Coldish says:
October 6, 2013 at 9:44 am
Thanks, Coldish. I’ve taken that into account by multiplying the reflected solar (in W/m2) for each gridcell by the area of that gridcell. That’s the units I used in the head post, which allow for the difference in areas …
w.
Paul Linsay says, October 6, 2013 at 8:59 am:
“The observed temperature is the equilibrium point between the solar flux and evaporation. The heat carried away by evaporation just balances the heat input of the solar flux.”
Exactly. But a greater atmospheric weight makes it harder for the surface to rid itself of energy through evaporation as fast as it would with a smaller atmospheric weight and at equal surface temperatures (kinetic level).
If you have two initially identical planetary (oceanic) surfaces, same atmospheric weight on top, same rate of solar input and same mean temperature, what would happen if you increased the atmospheric weight on top of one of them? This surface would no longer be able to maintain your equilibrium between solar flux and evaporation. And hence solar energy would pile up at/below the surface (coming in faster than escaping) until the kinetic level (the temperature) became high enough to propel the molecules leaving the surface as efficient against the stronger downward force of the heavier atmosphere as before, with the weaker opposing downward force of the lighter atmosphere.
Increasing the atmospheric weight pressing down on a heated surface makes it harder for the surface to rid itself of absorbed energy through molecular motion/convective processes (convection and evaporation) at a specific temperature/energy level. That’s why, to maintain dynamic equilibrium, mean temperatures will naturally go up/be higher.
As Stephen Wilde said, “ In itself (Willis’s idea) is not an adequate explanation for global climate variability over centuries.“.
—– ——- —-
Having looked through thousands of British weather records back to 1100AD it is evident that the LIA was not a monolithic deep freeze for 500 years but instead it was episodic with many warm periods interspersed (The decade of 1730 was around as warm as today)
What is very noticeable in the records are long periods of blocking highs, which in winter generally provide freezing conditions and the opposite if they occur in summer.
There is also ample evidence of the jet stream being stuck in the ‘wrong’ or ‘right’ position for extended periods. This caused long periods of cold or warmth although the most notable effect in Britain during the LIA was huge amounts of rain and high winds. These events lasted many months or even years.
Combine Blocking highs with jet stream position, wind direction and the Gulf stream/Pdo/Ao etc being especially active or quiet with the resultant impact on levels of cloudiness and that could explain a large part of the periods we know as the LIA/MWP. Perhaps a quiet or active sun also had an effect . There is some correlation with sun spots during the coldest periods.
tonyb
Explain how this ties into abrupt climate changes and inter glacial versus glacial climate regimes?
This makes perfect sense to any sailplane pilot who knows very well how clouds will proliferate on a sunny day and ultimately liberate their energy in the form of rain. The way they rise and spread out so their shadows cut off the thermals is always in the backs of their minds. The climate scientists should spend less time behind their desks and more time actually within the element they profess to study to see how it actually works.
Wilis keeps trying to say the climate system is always stable , which could not be farther from the truth, so all his studies are in a sense of no value when trying to predict future climatic trends.
Are they correct within a particular climate regime, perhaps.
What? Nature has a thermoregulatory system that helps the planet self-regulate temperature and prevents runaway global warming? We can’t have that. Climate alarmists might lose some funding. Besides, this might lead some people to think that nature was intelligently designed or something. So you had better put a lid on it. There are just some things that science has to take a back seat for. /sarc
Willis, now that shows so very clear what you have said in the past of the ~30C apparent ocean temperature limit. This is one topic I have never doubted that you were exactly correct since you brought that matter up.
I spent some time after your first post quite a while ago looking at the surrounding parameters for those with the power to squelch any further rise in temperatures as ~30C was approached and it seems a set. Wind velocity of drier air coupled with a marked increase in evaporation speeding uplift is the one set of factors that can move that much energy so suddenly. But you know that and those are on you list in the previous post. I am assuming this is only over the oceans but the same effect occurs in my neck of the woods though the limit is at a bit warmer here (mid US).
Now that’s some very important work. (but will the GCMs ever program in that clear non-linearity?)
Paul Linsay says:
October 6, 2013 at 8:59 am
“Stephen Wilde says:
October 6, 2013 at 7:23 am
Dare I say that the factor which determines that maximum temperature is the weight of the atmosphere pressing down on the surface water molecules?”
I think that you are wrong here. If what you say is true it would be impossible to boil water at sea level anywhere.
______
HUH?
At 1 bar, the atmospheric pressure today ±, at sea level, pure water boils at 373.15k (100°C) so the adiabatic lapse rate (~-9°C/1000 M change in elevation) is dependent on the reduction in atmos. press. as one travels higher than sea level. Since warm air can hold more moisture than cooler air and since the molecular wt. of water is 18 and air is 28.8 the warm, moist air is much lighter than the cooler, drier air so it rises. As it rises it cools, because it expands (PV=nRT), until it can no longer can hold the dissolved water vapor. At that elevation clouds start to form. That is why cumulus cloud bases are all at a similar altitude at a given time and location.
In the distant past, the atmospheric pressure was considerable higher than the 1 bar today so, as Wilde points out, this would have had a significant effect on the lapse rate and thus the Eschenbach thermoregulation of the Earth’s temp. (and yes the boiling point of water so Paul Linsay your noodles would have cooked much more rapidly back then).
Willis brilliant! What causes the increased reflected radiation when the ocean temp. is 18 – 21°C? Is it at a narrow range of latitudes?
@Willis,
Re: RC Saumarez 4:32 am
You state that cumulus clouds are relatively short lived. Yet you are, …. using monthly samples of ceres data. …. This suggests that you may be using aliased data….
I want to return to the question of what is it that CERES measures.
From : Motivations of merging geostationary 3 hourly data with the polar orbiters
It seems to me that aliasing is indeed a worry. While the orbit my be sun-synchronous, and therefore consistent month to month, it probably under-samples the diurnal period crucial to the thermostatic dynamics of the clouds. What solar time(s) of the day does CERES measure any given Grid cell? Is there greater overlap at the poles then the equator? Is it full areal coverage at the equator? What if the major influence clouds have on the climate peaks 1, 2, or 3 hours before or after the satellite pass?
The geosynchronous readings are not sun-synched. So they give diurnal based data (how often sampled?) but to what areal resolution?
Willis is making an excellent point. My own experience is that at middle latitudes and over a dry land clouds have a cooling effect during a day, a heating effect during a night. Maybe we should average grudgingly not over months, but over daytimes and nighttimes – over a convenient period. Is the source data available with a time resolution better than a day?
Salvatore Del Prete says:
October 6, 2013 at 11:51 am
If you think I’ve said something like that, please QUOTE MY WORDS. I’m not interested in your fantasies about what I never said.
w.
Looks to me as a nice way for the atmospheric pressure gradient to control temperatures.
Once the surface temp becomes more than the atmospheric gradient can hold, the atmosphere finds ways of getting rid of that heat.
Willis:
Also brings me to one of my “classic conundrums” which I face the “easily fooled” public with all the time. Explain to someone that there is a definite, limited amount of ENERGY in a cubic foot of air.
(Sorry, old time neandrethal here..you’ll see why I use ft^3 in a bit.) I then ask them, “Compare a cubic foot of AZ air, 110 F, at 10% RH, and MN summer air, 86 F at 60% RH. Which one has more ENERGY in it?” They, of course, don’t know…and I tell them that there are Psychometric Calculators on the internet which will give 38 BTU’s (British Thermal Units) for the MN cubic foot, and 33 BTU’s for the AZ cubic foot. Then I note that the question of Global Warming has to do with ENERGY in the atmosphere, NOT the “temperature”, per se. So I propose, say the whole world became 86 F, 60% RH, and it went there from 110 F. and 10% RH, I ask – Would you say you have “Global Warming” or “Global Cooling”? When they say, “Cooling” on the basis of temperature, I patiently explain the CO2/Energy retention problem would cause the HIGHER ENERGY per Cubic foot, thus the LOWER TEMPERATURE represents “Global Warming” in the truest sense.
By this time, there are blown fuses in tiny minds…but it brings me to one of my BIGGEST COMPLAINTS ABOUT THE GLOBAL WARMING WONKS!!! Why does NO ONE even ATTEMPT a “global atmospheric energy” calculation based on Radiosone balloon data and satellite data? It would be FAR MORE SIGNIFICANT than the “average temperature” canard, and the data might actually be there to show NO SIGNIFICANT ATM ENERGY CHANGE over 20, 30, 50, 70 years.
Another assignment for the brilliant Willis! (Sorry, I know…do it yourself. Personal reasons, still working as an Engineer, and some family obligations which soak up 100% of my time these days.)
Max
The thing that I find rather odd is that the CERES satellite system was devised to make radiation imbalance measurements and to measure clouds – in other words this type of analysis.
This whole hypothesis seems to stem naturally from these measurements and many people have suggested it; in fact it has been one of the central issues of debate in climate science for many years.and so I would have expected that this analysis would have been done before. After all, the people who devised the CERES system and are responsible for analysing its data are not completely stupid.
I suspect that the data is not good enough to support this type of analysis. One of the problems in CGMs is that if cloud formation is involved, the model has to be run at short time intervals of ~ 20 minutes to capture the spatial and temporal sampling requirements. I am very suspicious of aliasing effects in the data which would make any calculation of feedback unreliable. Having just made a simple feedback model in which sea surface heating has a time constant of 25 days and the cloud feedback has a time constant of 2 days amd both are regarded as 1st order systems, decimation of the data makes huge errors in the calculated feedback. (Yes, I know this is very oversimplified, it is a back of the envelope calculation to look at the effects of sampling).
Barry Cullen says: “….would have had a significant effect on the lapse rate and thus the Eschenbach thermoregulation of the Earth’s temp.”
Why would it affect the lapse rate at all? Wouldn’t the rate itself have been exactly the same as what it is now beginning back then at some higher altitude ‘X’ on up? (i.e living back then at say 5000′ would be like MSL today) And wouldn’t clouds nonetheless still form at roughly the same differential change of pressure altitude to then reflect solar energy as they do today?
Willis Eschenbach says:
October 6, 2013 at 2:02 pm
Salvatore Del Prete says:
October 6, 2013 at 11:51 am
“Wilis keeps trying to say the climate system is always stable …”
Salvatore, you do not understand “feedback mechanism,” the concept of a governor on a car, or the concept of an automatic thermostat.
The clouds that Willis describes are an automatic response to increased temperature and the effect of the clouds is to lower the temperature. That claim does not mean that the clouds keep the temperature constant or that other conditions cannot interfere with them.
RC Saumarez says:
October 6, 2013 at 2:12 pm
“The thing that I find rather odd is that the CERES satellite system was devised to make radiation imbalance measurements and to measure clouds – in other words this type of analysis.
I suspect that the data is not good enough to support this type of analysis.”
Very interesting. So, what does the government use for reports on changes in Earth’s albedo? Are they no better than this?
I’ve updated the head post with graphics of the northern and southern hemispheres respectively.
w.
Curious George says:
October 6, 2013 at 2:00 pm
That is generally true if you are talking about the net effect. However, clouds provide increased downwelling (and upwelling) longwave radiation both day and night.
Clouds increase downwelling longwave over the condition known as “no clouds”. This occurs at any time there are clouds, 24/7. In the TAO data, from memory it’s about a 20 W/m2 jump whenever a cloud comes over.
During the day, however, when a cloud comes over, it also cuts out the sun. This cooling effect is usually a couple hundred W/m2, an order of magnitude larger than the 20W/m2 of extra energy striking the surface from the increased LW radiation.
You might enjoy taking a look at my post “Cloud radiation forcing in the TAO dataset”, there are views of actual data there …
w.
graphics of the northern and southern hemispheres
In which case Southern December should be compared with Northern June for similar solar Zenith Angles.
Likewise,
SH March compares to NH September,
SH June compares to NH December
SH September compares to NH March.
Stephen Rasey:
They compare, but don’t forget that the whole Earth is closer to the sun in December than in June, so the comparison isn’t quite perfect. If the southern hemisphere is reflecting more light in December than the northern hemisphere does in June, it may just be that there is more sunlight to reflect.
@Owen in GA
No reason to expect perfect comparison. The shape and amount of the landmasses in the NH and SH are different. I mearly wanted to make clear that NH December shouldn’t be compared to SH December
The regulation appears to me as currently confined to the tropics, given greater variability of temperature elsewhere. Paleoclimate climate reconstruction suggests that past global temperature regulation against warming was generally at 24 degrees C. Also, a major change in received insolation appears to me as capable of moving the temperature of the tropical tropopause. So far, that has been protecting the convective subset of the tropics from greenhouse gas warming. However, since tropopause-level air over the Intertropical Convergence Zone appears to me as having a net gain of heat by radiation (it is cooler than most tropopause level air elsewhere, and apparently warms as it moves elsewhere), I suspect that increasing greenhouse gases will cause that to warm (or move to a higher altitude) – despite greenhouse gas increase causing cooling of much of the uppermost troposphere elsewhere and in most of the lower stratosphere.
Willis:
What an amazing post. The non-linearity of the feedback mechanism is breathtaking to behold in the actual data – a super-sharp hockey stick. It clearly supports negative feedback, but because of temperature dependence it is quite difficult to model as an average, thus all the confusion.
Although the main point is the sharp cutoff at 30C, it might be clearer if you analyze the data at lower temperatures in a way that produces a more “collapsed” description. Have you tried plotting the data versus angle of the sun at noon instead of latitude? This might properly account for the month of the year and northern/southern latitudes, and allow you to describe the phenomenon with only one graph.
Flydlbee says:
October 6, 2013 at 11:49 am
The climate scientists should spend less time behind their desks and more time actually within the element they profess to study to see how it actually works.
============
Nothing new. The Virtuoso is a comedy from 1676 that satirizes the scientists of the day. Sir Nicholas, who thought himself an expert on everything and the world’s best swimmer, never swims in water. He simply lies on a table and follows the movements of a frog dangled on a string in front of him.
Today’s scientists forgo the table. Now they swim on the computer, still believing themselves to be expert on everything and the worlds best swimmers.
“So … what are we looking at here, and what does it mean?”
I wish most posters would say that early and often in their verbose and impenetrable treatises! Nice clarity, Willis, as always.
The 30C barrier is too evident to ignore.
What is it about this sea surface temp that cause cloud cover to explode? I note that Water itself has a number of unusual properties compared to other chemicals. It just acts differently than other chemicals. And it only covers 71% of the Earth’s surface down to 6 kms in places so it is something that might be the make or break factor in the climate.
Then there is the thermodynamic energy transfer properties of Water in the Earth’s climate. Everything from energy accumulation, to cloud formation, to energy transfer of evaporation, to energy transfer of simple rain. God decided to make Water the most important chemical. Now throw in that Hydrogen and Oxygen (two of the most abundant elements in the universe) have an almost unbreakable affinity for each other once formed (as in Water is almost an eternal chemical) its hard to imagine that this is not the most important factor in the climate.
The atmospheric system especially cloud is a dissipative system – it sheds heat energy. It is also nonlinear-chaotic, and thus it should not be at all surprising for it to exhibit one or more Lyapunov stable attractors.
Willis Eschenbach
An excellent post, thank you. Looking at them by hemisphere, I think we see that sensitivity varies if you go along with, the slope of apparent trendlines is the sensitivity. As the slope passes through the horizontal we may be going from a positive feedback to a negative one. You realize though, some are predisposed to see hockey sticks. I can’t figure that one out? In this case the blades seem to be hard walls of negative feedback.
“So … what are we looking at here, and what does it mean?”‘
Mrs Dai Bread Two is looking into a crystal ball which she holds in the lap of
her dirty yellow petticoat, hard against her hard dark thighs. …
…
MRS DAI BREAD TWO : I can’t see any more. There’s great clouds blowing again.
MRS DAI BREAD ONE: Ach, the mean old clouds!
(Dylan Thomas, Under Milk Wood )
The data displayed on these charts might make a good test of climate models. Do they replicate these results? If not, back to the drawing board.
Willis,
from the TAO data (currently surrounded by orange federal traffic cones) can you plot a cooling curve for SST measured below the skin evaporation layer against varying downwelling LWIR for night only. Are there flat spots observed in the SST cooling curve that correlate with passing cloud?
Salvatore Del Prete says:
October 6, 2013 at 11:48 am
“Explain how this ties into abrupt climate changes and inter glacial versus glacial climate regimes?”
Kind of a grand unifying theory of the climate? I’ll settle for small steps, thank you.
If you look at some of the charts, you may see the typical Z or S curve lines associated with regime changes. Chaos on all time and size scales?
Despite several negative comments, mostly unwarranted, I think the basic theory here is sound. There are negative feedbacks, and not just one or two of them. I used to work and live near the sea. On the way to work, day after day, I’d be amazed at how many ways the sun can shine off the ocean. Any model that doesn’t take into account variations in ocean reflectance will fail. Although solar zenith angles near zero theoretically give an ocean absorbtivity of 0.997, wind chop can lower that figure significantly.
It should also be noted that wind chop amplitude is related to water viscosity and surface tension, both of which drop as surface temperature increases, particularly the viscosity, which drops 23% over a ten degree (C) range. Thus for a given wind velocity, the hotter the water, the greater the albedo, and the more the surface rejects further heating. I would bet that GCMs don’t adjust for this. Wind chop in itself wil cause more loss of heat by convection because of the higher resultant surface area.
Multi dimensional plots are a fantastic instrument of scientific discovery. Asking “what if we plot a against b?” frequently unlocks new insights. Seeing space and time as linked dimensions was key to Einstein’s discovery of general relativity.
Willis: “This approach effectively area-averages the data.”
I don’t understand what you mean by this and why you are not plotting W/m2 .
The result should not depend up on the coordinate system chosen to divide up the world. The mass of water in each grid cell determines how important each watt is. You must look at power density not total power in some variable sized grid cell.
This would mean dividing by cos(latitude) and would boost polar regions.
This would probably accentuate the minimum around 26 degrees making the (non-linear) negative feedback maintaining a moderate temperature even clearer. In fact it appears from this that it is not simply a negative feedback with increasing temperature but rather a non linear negative feedback on the deviation from 26 deg C
You currently have two minima in most of these plots , one around 26 deg. the other at the poles. That will not lead to a stable system
However, if you plot power density I’m pretty sure you will only have one minimum, that is the indication of a stable system. Or perhaps more correctly that this is a feedback that will act to make the system stable at all latitudes not just as global or regional average.
Another thing I notice is that there are two traces in NH especially Dec and March. As a quick guess I suppose that this will reflect differences between land and ocean cells. The similarity being due to bleed-over of the oceanic pattern as persistent winds carry cloud over land in Europe and N. America.
There are grossly similar patterns in SH with some spreading but suggestions a clear split are a lot less clear.
Re-plot this in W/m2 and I think you will have a much more powerful argument.
Salvatore Del Prete says: October 6, 2013 at 11:48 am
Explain how this ties into abrupt climate changes and inter glacial versus glacial climate regimes?
Why? Do you expect (or hope) this could explain everything?
Greg says:
October 6, 2013 at 10:50 pm
Thanks, Greg. Indeed, it does boost polar regions. It makes them look much more important than the equatorial regions … but in fact, in global terms the polar regions are unimportant because they are so small and receive so little sunlight. That’s why I plotted them that way. In any case, here’s the other option, in W/m2 as you wished …
Actually, as mentioned in the head post, this is just the ocean gridcells, no land gridcells are shown.
All the best,
w.
@Theo Goodwin.
Unfortunately the CERES website remains blocked.
The point is that if you want to estimate albedo and get an estimate of radiation imbalance, that is a set of measurements designed with a specific goal.
However, if you want to construct a dynamic model of feedback that incorporates rapidly changing variables, such as cloud, that requires a completely different set of measurements that may require a much higher sampling requirement.
What has been done here, as far as I can understand it, is that the CERES data has been decimated into monthly samples, and has been used with monthly surface measurement data, which has major limitations, see:
http://judithcurry.com/2011/10/18/does-the-aliasing-beast-feed-the-uncertainty-monster/
This leads to major problems in determining the magnitude and even the sign of the feedback as is easily shown by simple modelling.
The reason that the type of analysis, performed here, which is a burning question in climate science, hasn’t been performed is a matter for speculation. It has been tackled, rather poorly in my view, by Spencer et al, see:
http://judithcurry.com/2011/10/10/climate-control-theory-feedback-does-it-make-sense/
but I suspect that the reason is that a few minutes of calculation shows that the problem is almost intractable unless one has very good data.
Yet another fascinating post from Willis. Keep it up.
It looks like a hockey stick – but a nice hockey stick!
Unlike Mann’s fraudulent manipulations, this comes from real data and is not the result of statistical trickery.
Chris
@RC Saumarez
Wow, I wouldn’t even have thought about that.
It seems this is further compounded by the issues another commentator raised in relation to satellite sampling.
So are you saying that the CERES data as used, is pretty useless for this purpose, and what is needed is a global array of light sensors and temperature gauges that can be sampled at c. 10 minute intervals.
If Willis were to state that his grid-measurements were just upscalings of cloud cover (only interested in the total signal over a month) vs upscaled temperature (for the entire month) in order to decipher whether a regulator might exist. Would this be reasonable in light of the issues you raised.
Willis
Looking at the colouration of dots it appears that you are showing that equatorial reflectance is greater than polar reflectance – you are not showing that as the ocean temperature rises so the cloud reflectance rises.
Is this what you intended?
Surely you need to limit the month and the latitude to as small a range as possible (limiting only by sufficient results returned).
The month obviously affects the results (your very broad bands show wildly differing results)
The lattitude obviously affects the results (your same coloured points are very bunched)
are you also taking into account the varying distance from the sun? and the changing obliquity? Both these obviously affect solar input and hebnce the reflected levels. Would it not be better to use a vertical scale of percentage reflectance compared to TOA received solar input?
Also, as a side comment., Even if the thunderstorms are cooling the ocean the global cooling will still be limited by how much upward long wave IR can be passed to a point where it will radiate to space. Then one also has to consider that heat from the deeper ocean layers has to be cooled also, and just how long does it take for deep thermal stores and higher lattitude waters to be cooled by equatorial thunderstorms?
@cdI am saying that if you want to characterise a feedback (or any other) system, the rate at which you have to sample its behaviour is governed by its dynamics.
What is worse, is that if you undersample the signals, the will be irretrievably corrupted, known as aliasing and will give completely misleading results.
A good example of aliasing is wagon wheels in movies. As they start to go round, they appear to move in the correct direction, they then speed up and suddenly appear to reverse direction and then slow down until they appear to stop. This is because the frame rate is too slow to capture the movement and the reversal of motion is aliasing (the true frequency gets represented as a negative frequency).
I really do not know how much aliasing is a problem in the CERES system, but given that cloud dynamics are short lived, if you try to work out was is happening in feedback with poorly sampled data, you will get some very peculiar results.
RC Saumarez
Thanks for your response – I know what aliasing is (hence the reference to the 10 mins: Nyqsuit frequency for the proposed cloud changes c. 20mins). What I was asking, was whether the CERES is any use and if not what was its mission statement. I can’t imagine, even with the limitations you mention, you can’t garner anything from it in relation to the above post.
Really hard to believe this analysis hasn’t been done before now.
RC Saumarez
BTW, and I should have mentioned in the Earth Sciences aliasing is very often something that one has to accept. You cannot exhaustively sample strata for example. Without doing so you never really know whether you’re sampling at a high enough resolution to capture the highest frequency components and whether aliasing is therefore an issue . But it’s quite common to compare wireline responses between different petrophysical logs in order to garner interrelationships. In short, sometimes you have to be a pragmatist. Furthermore, there is also natural processes which smooth some petrophysical properties more so than others. This isn’t laboratory science!
RCS says: “A good example of aliasing is wagon wheels in movies. As they start to go round, they appear to move in the correct direction, they then speed up and suddenly appear to reverse direction and then slow down until they appear to stop. This is because the frame rate is too slow to capture the movement and the reversal of motion is aliasing (the true frequency gets represented as a negative frequency).”
That’s a very good example of how devastating the problem can be. It’s not just a case of not being too pedantic and making do with rough data.
You earlier post showing how it can create false trends is also very relevant.
Climate may not be wagon-wheel bad but this problem seems never to have been addressed in climatology. Probably because they make up most of their own methods and ignore existing systems analysis and sig. processing knowledge.
@cd
I missed one of comments.
Averaging does not eliminate aliasing.
Given high frequency data, there can be high excursions and profound control activity, yet the average over a time record can be near zero – thus wiping out the interesting data. (averaging is a filter with a frequency response of -1/jw).
I don’t know how well these calculations can be performed. The CERES site is down (?Tea party deniers) and I can’t trawl through it. What I am saying is that one has to be really careful with this sort of analysis and absolutely nail down the possible sources of error, which is a pretty non-trivial in this case.
ps: In medicine one usually get good signals, except from images but I can see how the problems arise in Earth sciences. However we have the problem of highly non-linear, non-stationary systems to deal with – generally dismissed as “variability”! I expect you encounter the same problems. in Earth sciences.
Willis, thanks for the flux density plots.
Both the equinox months now look pretty flat for both hemispheres, with a slight max around 10 deg C. Interesting. This would add a little hysteresis to the system.
This does not make the poles more important globally, it just means the effect is more pronounced at the poles in their respective summers and is still nearly nothing when not illuminated. That makes sense to me.
NH has a clear local minimum near the pole near 3-4 deg C, which is interesting.
Also the two separate traces in NH are perhaps even more clearly defined now. (Sorry, I forgot you’d taken out land). It would be interesting to see what this is due to. My instant guess is Atlantic vs Pacific. There must be some useful information in there being two different response curves. Maybe much bigger equatorial component in Pacific.
Bill Illis says:
The 30C barrier is too evident to ignore.
What is it about this sea surface temp that cause cloud cover to explode?
Once positive feedbacks kick in it’s catastrophic change time.
A good example of +ve f/b is a classic household light switch. You push it slowly up to a point, then “snap” it bangs home against the limiting negative feedback: the casing of the switch.
It may that there is a fairly broad range of localised temperature variation (hot and cold zones ) that allows quite fine variations in the percentage of sea area affected. This will give a smooth control. If there was very little variation it would tend to be all-or-nothing, on/off regulation with large hysteresis and poor control.
One way to ensure a feedback control cct provides smooth regulation is to add some noise when comparing the input to the threshold. This adds some jitter and smooths the transition response.
The +/ve f/b would make for a very steep transition which has to happen somewhere along the scale 0 – 100 degrees. The statistical distribution of SST smooths the transition. I don’t think this implies any magical property of water at 30 deg C that we’re missing.
BTW I have seen some temps touch 32 in western Pacific in plotting ARGO this week.
Short answers is +ve feedbacks make things go snap, crack, explode or shatter. Expect rapid change in measured variables.
Willis, could you clarify what you’re plotting here in relation to the the first-cut and second-cut articles. Is this just reflected, reflected incomeing plus effect on outgoing, or that latter with solar correction?
The V shaped summer plots show a +ve feedback in cold and temperate zones leading to -ve f/b in tropics. ie in temperate latitudes warmer days => less cloud => even warmer. Tropics as you’ve detailed before are the opposite.
The cloud feedback would seem to drive SST towards 26 degC, at least in summer. Quite clever, it provides both +ve and -ve f/b to control both extremes of temperature. Neat trick.
RC Saumarez
As i said i understand what aliasing is. I write commercial software where signal processing can be an important part of the workflow. And i never suggested you can eliminate aliasing by averaging and hence the point about natural smoothing.
You’re setting laboratory standards to often very extreme analytical conditions it is not a controlled envirnment. If we took your view we wouldnt have seismic imaging.
Dec-Mar-June-Sept-Dec makes a really interesting mental film loop. Would love to see these plots “animated” like that.
Btw, what are the specific areas to the right of the dotted line? They seem to be mostly NH June-Sept. Red Sea?
@cd
I didn’t mean to be condescing or question you. I agree that you have to make all sorts of allowances and approximations in real life. Nevertheless, basic signal processing theory generally appears to be correct and when one ignores it, one can come unstuck, as I have discovered on many occasions
I’m simply saying that you cannot buck basic theory too much. Aliasing is aliasing and the question is does it matter or are its effects negiigable? . Having done a fairly simple-minded simulation of a CS with the dynamics of cloud feedback and ocean heating/coooling (linear only, which the real system isn’t), I would predict l;arge errors, even including the sign of the feedback.
As I pointed out, one has to analyse the process carefully and get an idea what errors are involved. What has not been specified here is the exact structure of the data, but the CERES data appears to have been decimated to 1 monthly samples, as is the HADCRUT data, which I feel is likely to give large errors.if the feedbacks operate on a shorter timescale of hours.
Willis, I find your analyses insightful and compelling. Keep up the good work!
Willis, I just cranked up the colour contrast and saw your plots more clearly.
NH March has two clear lines too.Definitely worth finding if that’s geographical.
The 10 deg max is a bit stronger that I first thought. It looks strong enough to present a barrier. In March there’s negative cloud feedback at high lat. pushing temps back towards just above zero.
Once through the 10 deg C barrier, +ve f/b pushing towards 26.
Even in June the local minimum will give some neg f/b pushing temps down.
This idea definitely reveals some interesting and useful info.
Stephen Wilde: In itself it is not an adequate explanation for global climate variability over centuries.
That’s for sure. It is a partial description of how part of the climate system works now. I doubt that it says anything useful about the last few centuries, but it has a serious implication for how a doubling of CO2 and possible increase in surface temperature might be understood in the next few decades.
Mike Jonas: In summary, you appear to be finding seasonal and regional effects, for which there may be seasonal and regional drivers other than absolute sea-surface temperature, and which therefore may have no implications wrt multi-year global temperature changes.
True enough, but in the classical hypothesis testing tradition, Willis has formulated a hypothesis and performed a series of tests based on the available evidence. So far, the evidence supports/doesn_discredit his hypothesis. In the standard language, “More research is needed to eludicate the mechanisms and explore alternative hypotheses.” No matter how you cut it, these are challenging results for the standard CO2-based theory of future global warming.
Matthew R Marler says:
October 7, 2013 at 10:17 am
A doubling of CO2 from its late LIA level (supposedly) around 285 ppm globally to 570 would have an effectively immeasurable impact on average temperature. We’ve already gotten to 400 ppm with (allegedly) a resulting increase of at most less than a degree C, & the next 170 ppm will have relatively less effect, since it’s logarithmic. A more than negligible effect relies on assumptions about positive feedbacks not in evidence, indeed demonstrably false.
“””””……SasjaL says:
October 6, 2013 at 4:52 am
Clouds affecting temperatures are well known to us living far north. A cloudless winter night is noticeably colder than a cloudy one……..”””””””
Nearly everybody always gets it backwards:- Clouds affecting temperatures are well known to us living far north…. A cold winter night is noticeably less cloudy than a warm one.
And the daytime before the cold night, is cooler than the daytime before the warm night, and in both cases it will be colder just before sunrise, than it was before the sunset.
Warm daytime Temperatures in the presence of some humidity, are THE CAUSE OF both the warmer night, and the clouds. The clouds do NOT cause the warmer night. And the warmer the day, the warmer the night, and the higher will be any clouds that form.
Greg says:
October 7, 2013 at 8:06 am
I have plotted temperature vs amount reflected.
w.
Well I don’t disagree with Willis’ conjecture; seems obvious to me.
But there’s more to it than that.
More cloud formation (and conditions) imply higher atmospheric water vapor concentration. Atmospheric water vapor (H2O) plus O3, and also CO2 to a smaller extent, also absorb significant amounts of incoming solar radiant energy , particularly in the case of H2O and O3.
This absorbed solar energy, never reaches the ground as short wave solar spectrum energy, even in the absence of local clouds, so it is lost from the ocean energy storage sump.
Yes there’s an increased isotropic thermal radiation emission from the warmer atmosphere, but only half of that is directed towards the surface, and when it reaches the ocean surface, which most of it does, it simply promotes more prompt evaporation, rather than warming the bulk of the ocean.
RC Saumarez, you keep talking about the problem with “aliasing”. Perhaps you could explain how that aliasing would affect a scatterplot such as the one that I have done above. What would your putative “aliasing” look like if it were actually there? How might it be detected if it existed in my plot?
So far, you’ve brought up your “suspicions” and your “concerns” and your “potential problems” and what you “feel is likely to give large errors”, which seems to be your forte—lots of worry backed by no facts.
Because what you haven’t brought up is one single scrap of actual evidence that your suspicions and concerns have validity. You just wave your hands and say trust me, I’m a signal analyst …
So you’ll have to excuse me if I’m not as impressed as cd seems to be with your line of patter. Can you show that aliasing is an issue in my figures above?
Because if you can’t, then you’re just another concern troll. Look, aliasing can indeed be an issue, as you point out.
But I doubt greatly whether the rapid rise in reflected sunlight when the ocean approaches 30°C is an artifact or a result of aliasing. And since that’s all I’ve shown in this particular thread … why are you going on and on about aliasing?
As I’ve said before to you more than once, RC, you need to either put up or shut up. Either show that aliasing is in fact a problem in this very analysis, or go be a concern troll somewhere else. I’m getting bored with you playing Banquo’s ghost, full of foreboding. Your schtick is getting old. You need to actually demonstrate that your concerns are valid … because your habit of endlessly repeating that you are so concerned and suspicious and worried ad nauseum? That spiel is not going anywhere at all.
Put up or shut up, RC. Either show that aliasing is a problem with this very analysis, or go peddle your line of grave concern somewhere else.
w.
RC Saumarez
I wasn’t offended but I think we might have been talking cross-purposes. I think you raised an excellent point, and not an obvious one, certainly not the first thing that came to my mind. But, you must admit, at face value there does seem to be something about these plots – even if it could be an artifact. Let’s assume Willis is comparing bulk characteristics. Surely an obvious analogy is biogeographical zones often explained in terms of broad, regional climate. Of course you could argue that if you look at higher temporal and spatial resolution the picture is much more complex and causality is less certain because of other micro-issues interfere. It’s only after upscaling that one sees that there might be a relationship – even if not an exact one. By upscaling, I mean using something akin to a simple summary statistic (I’m not concerned about higher frequency components that cannot be adequately sampled and yes is likely to lead to issues of aliasing; an experimental limitation). Surely, you can see that there might be some value in such an approach with all the stated caveats; as a first pass at least.
Perhaps you could offer a better experimental design – the current practical limitations.
Thank you Willis.
It’s interesting that I can have a reasonable dialogue with people on this blog. I was actually referring to your earlier posts.
I am certainly not a troll. I, and others who contribute to this blog have a highly educated knowledge of signal processing and, while you may be reluctant to accept it, might see some problems with the way you handle data.
I would like you to explain exactly how you handled the CERES data and explain why. If I think there is a problem, I will explain it to you.
RC,
Willis said he plotted the data (no signal processing), in this case there would be no alias issues caused by Willis. The sampling done by the Satellite is nothing like sampling done for digital music or digital film. Additionally the CERES team has taken the time to check their data with other observational systems to eliminate Temporal Sampling errors (which would be aliasing errors in certain types of signal processing).
You can read more about CERES here:
https://climatedataguide.ucar.edu/climate-data/ceres-ebaf-clouds-and-earths-radiant-energy-systems-ceres-energy-balanced-and-filled
v/r,
David Riser
@David Riser,
Thanks, The issue is not with the CERES data itself, but the way that it is used, in conjunction with other data to derive feedback.
Obviously the CERES data is sampled, and I agree that immense precautions are taken to get high quality data. I have huge admiration for the engineers and scientists involved in the program and their work is superb.
@Willis 11:34am, RE: concern on aliasing.
What are the data being plotted?
We have on the Y-axis, Terawatts/grid cell. I didn’t realize it until now but are you normalizing by the size of the grid cell? I suspect not, otherwise why give those units? Therefore the trend we are seeing at the low temperatures is partly convolved with the variable size of high latitude cell sizes as well as temperature.
That aside, The Y-Axis is a measurement from CERES from a sun-synchronized orbit. How many times per day does a grid cell get measured? At what times per day? How wide is the CERES measurement? Possibly in the northern latitudes, we see an average reading from many passes each day, but at the equator, we get an overhead pass on one grid cell out of 20 and the other 19 are oblique. Again, what are the times of the CERES data by latitude? I can envision that 30N and 30S are measured 15 minutes apart in a 90 minute orbit. 45N and 45S are measured 22 minutes apart. If the thunderstorms are blowing up during this hour, it might make a difference.
What is the time of the SST measurement in each grid cell? Do they coincide with the CERES? Is SST and the CERES measured from the same satelite? If so, how does one know the SST under the clouds? Is the SST a broader average reading?
But I doubt greatly whether the rapid rise in reflected sunlight when the ocean approaches 30°C is an artifact or a result of aliasing.
The quantization of the effect, may very well be affected by the sample timing. If CERES is a solar noon pass, then your plots may be underestimating the solar reflectivity from afternoon cloud development. If it is a solar 3 pm pass, then it is overstating the reflectivity. If at 60N we are seeing 40 grid cells on a pass instead of 20 at the equator. So are we getting 3 readings over a 3 hour period at 60N instead of 1 reading at the equator?
The aliasing concern derives from my personal lack of knowledge of what CERES measures, how it measures, and when it measures it compared to the SST measurement.
Along the lines of the strobicopically stopped wagon wheel, what would we know of the Earth’s weather if the GOES-13 satellite only took pictures at 1500Z each day?
@Willis Eschenbach.
You keep on telling me to put up or shut up. I have several times but the problem seems to be that you can’t understand the answer. Your inability to understand Autocorrelation functions is a case in point. Since its very difficult for me to post mathematics and graphs her, why don’t we approach this in a different way and make this an exercise for the reader?
Wtite a program to simulate your hypothesis that increased radiation leads to higher surface temperatures, this leads to higher albedo which has a negative feedback on radiation.
We’ll keep it dead simple and assume that the increase in water temperature is a first order system (which it isn’t but for the purposes of this discussion it doesn’t matter). We will also assume that cloud formation is a first order process (which it isn’t). Make the time constant of the sea, say 20 days and the time constant of cloud formation 2 days. B y keeping everything linear, this makes it very easy to calculate in the frequency domain.
Now simulate the process. Then decimate the data so we have “observations” at monthly intervals.
Now generate a different model which is exactly the same but the clouds do not have a negative feedback effect, i.e. they are just derived variables. Decimate the data in the same way.
Using this “data” show FORMALLY that one model contains feedback and the other doesn’t.
@RC Saumarez at 3:44 pm
Now simulate the process. Then decimate the data so we have “observations” at monthly intervals.
No.
Simulate the sampling process so that only one observation is made each day at the same time each day. 30 days are averaged into a month.
The Nyquist damage is done by the one sample per day at a constant time.
We’ve strobed the wagon wheel into what appears to be a dead stop.
Stephen, there are several problems.
SST is a separate dataset (I presume the same as Willis said in previous articles but not explicitly stated here). SST itself is a massive integrator so sampling is less of an issue but suffers the same aliasing issues as just about everything in climate science.
RC Saumarez did a very good article demonstrating how aliasing can cause false long term trends in SST and showed that hadSST was showing signs of aliasing in frequency domain.
Variations in cloud, if they are not at the right time of day get missed totally. They’ve been, had their radiative effect and gone.
Willis has already detailed in earlier articles that they are not randomly spread through the day, They arrive in late morning. Further he is suggesting a mechanism where the time they appear is the key climate feedback.
Now a sun sync orbit will always sample a give cell at the same time. That means one or two cells may get sampled at about the right time but still only once, the rest miss the event. There is still some information since the cells retain the storm activity (not individual storms) to late afternoon. Some cells get sampled before it all happens, some late afternoon in a sliding pattern.
It is obvious there is plenty of scope for creating false signal.
Willis calls out “show me what it would look like in my plot” . The trouble is, unless you know what the correctly sampled data looks like, you cannot predict what aliasing effects will be produced. This is why the problem is so important : it irretrievably corrupts the data.
However let me try to help. In the last plot in Willis’ article we see June and Sept NH plots. I had already noticed some odd repetitive banding looking rather like an animal’s rib cage in the middle of these plot.
I think it very unlikely that this is a physical phenomenon in the feedback relationship and is almost certainly a sampling issue related to the time of day.
SH March shows similar oddity but in a different temp range , nearer high end. This seems like a clear ‘wagon-wheel’ and is due to repetitive patterns in the data being incorrectly sampled. Irregular patterns will suffer that same sampling issues but will not produce nice identifiable patterns that we can apply external logic to.
I do not think the strong up tick is likely to be sampling artefact but we cannot be sure it is not in the presence of such problems with the data. But that possibility needs to be examined.
I does however put into doubt some of the other interesting tendencies I had noted in comments above.
The whole idea of monthly averages is anathema to good data processing yet is ubiquitous in climatology. It is equivalent to passing a 30 day running mean filter (which itself introduces huge artefacts) then sub-sampling at every 30th point. WRONG. You need to use a clean filter at 60 days before re-sampling at 30.
It may be instructive to look into the rib-cage pattern, for example, in NH Sept plot.
It seems to be temperate latitudes. Is it one ocean basin? Does each band represent a day or week or is it a time of day?
Since there are at least five clearly definable bands we should be able to walk this back by breaking the data down into subsets , spacial and temporal, to find out what is causing it. We may then find that each line collapses to small cluster and is pure artefact of the sun-sync orbital cycle, or that it reflects an underlying phenomenon that has been badly sampled.
In either case it would be a good example of one of the oddities that aliasing can produce.
This is very analogous to the wagon-wheel turning backwards.
Greg
It is equivalent to passing a 30 day running mean filter
I don’t think this is true. I could be wrong. But as far as I know they use discrete windows (window length = calender month/fixed length). The method involved in producing monthly averages is akin to upscaling rather than as you suggest running a low pass filter. Where this type of thing is required one generally uses a Butterworth filter which by design removes higher frequency components rather than augments the entire signal (yes and I do accept there will be a slight phase-shift with such an approach and yes there strategies to deal with this).
We seem to be getting a lot of comments from people outside the Earth sciences here. There are very challenging experimental issues to deal with and there have been many, many methodologies developed to deal with them. A large part of geophysics and geology is dedicated just to these issues.
Look for upscaling in the Earth science literature.
@Stephen Rasey
Wrong.
2 stages:
1) simulate the process to achieve unaliased model data.
2) Use this data to simulate the actual sampling process.
“…there have been many, many methodologies developed to deal with them.”
Oh there is a huge wealth of techniques used in a range science and engineering fields. They just seem to get ignored. Climatology d.p. seems often completely naive apart from some stats ideas they’ve imported from econometrics. Another field of study that seems to have little success with prediction 😉
Monthly average: It is equivalent to passing a 30 day running mean filter
I’m not saying they do this as two separate steps, they have not realised they need to filter before re sampling.
My point is that taking a simple monthly average to decimate the data is _mathematically_ identical to doing a running mean , then picking every 30th value. ie it is effectively identical to using a filter with a poor freq resp and the wrong period.
The basic error here is that averaging is an effective means to reduce truly random gaussian distributed noise. It is not a valid method in the presence periodic or structured variation.
This may reflect a misconceived assumption that climate is AGW + noise. !
RC Saumarez
Seems like a sound idea. But what happens if you can’t practically achieve 2. Should you abandon such a study altogether?
Greg
It is not a valid method in the presence periodic or structured variation.
Is this true. If your signal is locally stationary then it is quite reasonable to take an average (e.e. average daily temperature) in order to compare periods of time by any type of measurement. This is a type of upscaling; fractal type models are used just about everywhere today in image re-sampling. They work on the assumption that continuous phenomenon can be discretised (which involves upscaling rules – averaging could be one such rule) into ever coarser grids all the while preserving the character of the signal at that scale => semi-continuity. You’re probably quite happy to accept it in the fields of astronomy say but not here; it happens everywhere and yes it probably does produce artifacts but as long as they are acknowledged then so what?
BTW Greg I do accept that if your discrete window is out of phase with the signal then you will lose relative information – but again you got start somewhere.
” You’re probably quite happy to accept it in the fields of astronomy say but not here”
I’d love to have the chance see. So far all I see is “anomalies” instead of filtering, averages instead of proper re-sampling, running mean distortion and bloody straight line regression on everything that is neither straight nor linear.
Maybe some of the complex techniques you cite could be applied. but they won’t help if the data’s already been screwed by improper processing. Someone should probably try what you suggest but not before insisting that any and all processing is done correctly.
Roy Spencer’s commented on this article:
http://www.drroyspencer.com/2013/10/citizen-scientist-willis-and-the-cloud-radiative-effect/
@CD.
I think the key is to ANALYSE what is happening. We all have to accept that our measurements have limitations. The question is what effect do those limitations have on the conclusions we are trying to draw.
For example, consider aliasing. If we have no a-priori knowledge of the system, an aliased signal is an aliased signal. If, on the other hand, we KNOW that the highest frequency in the system is only a small amount above the Nyquist, we know which components of the signal are likely to be degraded, because there is a small degree of spectral overlap near the Nyquist, and we can can filter these out to get reliable low frequency information. This approach presupposes a model of the system.
I would say that we have a model of the system we are investigating and we want to make some deductions about that model using measurements. We can simulate the model and then simulate the effects of the data acquisition chain on the measurements that we can make practically. Given these simulated measurements, we can then determine the likely errors in determining whatever parameter of the model we are interested in. If we can tie down the statistics of the process, we can use Monte-Carlo methods to get the error distribution. Generally this shows the limits of SCALE that we can hope to measure successfully.
I have found this approach very useful in the past; it is really just glorified error analysis. In my discipline, biomedical engineering, I’ve often found that what I hoped I could measure with some precision, has large errors and this effects the experimental approach.
Greg
You’re probably right on all counts. But I think one must also acknowledge that there is sometimes, in the realms of experimentation, a disconnect between best practice and what is practically possible – surely pragmatism should prevail. As for bad stats and inappropriate post-processing, it’s pretty much endemic.
Thanks for the link.
Yeah, sometimes you have to make do but you don’t have to make worse. 😉
RC, you’re way ahead of most of us on this and I’m not challenging you on any of the points you make. And, although it mightn’t seem like it, I appreciate all the points you make.
My own experience of error, and by extension uncertainty, is that most people get completely obsessed with this and very often in the wrong parts of the work-flow; in fact a significant amount of my work deals with modelling uncertainty. I’d say, in this instance, error associated with the apparatus is likely to be the most significant issue here and yet no one has mentioned it.
If I’ve demangled the address OK this is the Ramanthan et al 1989 that Spencer refers to:
http://88.167.97.19/albums/files/TMTisFree/Documents/Climate/The_radiative_forcing_due_to_clouds_and_water_vapor_FCMTheRadiativeForcingDuetoCloudsandWaterVapor.pdf
CD.
If your job is modelling uncertainty, you probably know a lot more about this than I do. I agree that instrument error is an issue that needs to be taken into account.
RC, the problems all started from your claim on this thread:
RC Saumarez says:
October 6, 2013 at 4:32 am
When I asked you to “put up or shut up” regarding that claim, you’ve replied as follows …
RC Saumarez says:
October 7, 2013 at 1:11 pm
RC Saumarez says:
October 7, 2013 at 3:44 pm
So … “several times” you’ve demonstrated that aliasing is a problem in this or another thread of mine? As always, you’re long on claims but short on citations. Where and when have you actually shown (not claimed but shown) that my analyses have suffered from aliasing?
In any case, so far you’ve told us nothing except that you are not referring to this post. You are referring to earlier posts … except that you’ve made the same claims regarding my work here
So, sorry for my lack of clarity. What I meant was, it’s time to put up or shut up about this post. I am using the CERES and Reynolds information directly. You know, directly, as in, take it from the sources and show a scatterplot.
Now, you keep claiming that somehow aliasing comes into play in that process, viz:
and
So here’s your big chance, RC, to show that you are right. What you need to do is to show, not claim but show, that:
a) the CERES data is aliased, and that
b) this somehow makes my scatterplot wrong.
Please do not bother posting anything containing the words “suggests” or “implies”, or that you are “concerned” or “worried”, or about “potential problems”, or that you “suspect” this or that. I have been informed by you, time after time, that you are concerned about aliasing, and that you suspect it is doing bad things to my analysis … sorry, that doesn’t impress me in the slightest.
Because as far as I know, all we have are your fatuous claims—not once have you actually shown that aliasing was an issue in my work. And in particular, in this thread you have given us nothing but your inchoate fears.
So how about it, RC? Look at the head post, and point out to us where the aliasing is actually a problem, and tell us how you know it’s a problem there and not elsewhere. You know, with links and data and all that sciencey stuff you love to skip over in favor of informing us, endlessly, that you suspect that there might possibly be something that could raise a concern a potential problem in some unspecified part of my work. Somewhere.
w.
RE: Stephen Rasey 10/7 9:22 pm
Simulate the sampling process so that only one observation is made each day at the same time each day. 30 days are averaged into a month.
…The Nyquist damage is done by the one sample per day at a constant time.
@RC Saumarez 10/8 4:02 am
… @Stephen Rasey
Wrong. … 2 stages:
1) simulate the process to achieve unaliased model data.
2) Use this data to simulate the actual sampling process.
We are saying the same thing, aren’t we?
@ Willis Eschenbach
You have produced 3 science posts in the last 3 days relating to feedback and its components.
You keep telling me to put up or shut up, which I am unable to do because the medium allows you to advance an argument using graphs and the ability to paste in maths, but not myself.
I note that you have also told another commenter on this blog who does Earth science signal processing for a living that he doesn’t know what he is talking about.
I suggest that you do the little exercise that I set you. It is a variant of an exercise that used when I taught DSP to undergraduates and MSC students. They thought it was informative and it should satisfy your intellectual curiosity.
Then the answer will staring you straight in the eyes and you will have learnt something.
@Willis Eschenbach 9:44 am
Willis, the aliasing is not in your use of the CERES data, it is in the CERES data itself.
How many times per day does the CERES measure an equatorial grid cell?
I think it is once. And that is generous. What was vertical on one pass is on the horizon on the next pass.
Sun-Synchronous orbit parameters (dayling side. same goes for night passes)
http://en.wikipedia.org/wiki/Terra_(satellite)
Perigee (h) = 705. km
Apogee = 725. km
Inclination = 98.1991 °
Period = t = 98.8 min
Earth Radius = r = 6350. km
Distance to Horizon = r/(r+h)*Sqrt(2rh + h^2)
Distance to Horizon a Perigee = 2767. km
Distance around Equator = c = 39898. km
speed of rotation of Earth at equator = Ve = 27.7 km/min
Distance between passes = Ve*t = 2737 km
Width of 1×1 grid cell at equator = 111 km
Grid Cells between passes = 24.7 cells
Grid Cells between 45 deg oblique on each pass = 12.7
So every day, only 1/2 of the grid cells near the equator is within the 45 degree oblique view of TERRA (CERES) satellite. These fortunate cells are sampled only once that day. You have to be up near 60N and 60S before you get full coverage of the cells within 45 deg of vertical.
I still do not know the time of the day of the pass.
Would we have any idea of the Global Average Temperature if we measured half the thermometers only at 11:58 am and 11:58 pm each day? It would be an interesting dataset. It might even make some sense. But it isn’t the min and max. It isn’t the hourly measured average. It is something else.
I think I know where Trenberth’s Missing Heat is…. It is lost in the Nyquist aliasing of the CERES measurement system. He should be thankful that only 0.7 W/m^2 is unaccounted for. The rest is lost between the frames of the “stopped” wagon wheel.
Stephan says: “Would we have any idea of the Global Average Temperature if we measured half the thermometers only at 11:58 am and 11:58 pm each day?”
Then you measure 10N at 10.30; 20N at 10am …. the poles at 6 am and 6 pm….and see whether anything odd happens to GMST.
that kind of orbit is adequate for things that change slowly from day to day but starts to be a problem when you have fast moving changes . Exactly the short of fast moving changes Willis is suggesting controls tropical climate.
I pointed out what looks like an obvious wagon-wheel aliasing defect in the data.
Willis apparently finds more interest telling RC to shut up than addressing the issue he want him to ‘put up’ on, when it is someone other than RC that points it out what he wants to be pointed out.
Grid Cells between passes = 24.7 cells
Grid Cells between 45 deg oblique on each pass = 12.7
I think this flight path has 360/24.7=14.6 orbits per day sampling down one side and up the other. It seems designed to get full coverage (at least one reading per day) at the equator.
This is quite common for polar sun-sync orbital paths. At polar latitudes it gives 6 or 7 readings per day but with at 2 deg black spot at the geographic pole.
If this was done for ERBE it appears to imply that they assumed no regular diurnal pattern in cloud and that variations were normally distributed and would all average out.
where have I seen that before?
Large file 64mb: hhttp://tinyurl.com/k88q3h6
“Radiative Flux Assessment”
from “Appendix A :
Data Product Descriptions and Supporting Detail for
Data Error Sources and Estimates”
In short:
Willis;
The aliasing is not affecting Your good work to show evidence of your claim ” main climate thermoregulatory mechanisms is a temperature-controlled sharp increase in albedo in the tropical regions”
A temperature controlled non-linear albedo feedback hypothesis is refreshing compared to all other simplified linear forcing and feedbacks theories.
lynx, just what part of that document says: “The aliasing is not affecting Your good work”??
This has got nothing to do with aliasing of the CERES data as such.
The problem is decimation and the temporal scaling of the proposed feedback mechanisms.
Time Sampling :
For time sampling errors, the ERBS spacecraft orbit samples the entire 24 hour diurnal cycle every 72 days, or close to 5 times per year. Sampling studies were carried out using 3-hourly geostationary data subsampled over the tropics at the ERBS orbit times to determine diurnal sampling errors for monthly means. Wielicki et al. (2002a,b) showed that the 20°S to 20°N monthly mean ERBS WFOV SW flux error is 1.7 Wm-2 and LW flux diurnal sampling error is 0.4 Wm-2. They also showed that use of orbit precession cycle means of 36 days for the tropics (72 days for 60°S to 60°N) dramatically reduce time sampling errors.
====
So if you’re doing 20°S to 20°N monthly mean , moderate errors. If you’re using individual point values…. expect significant errors.
Also if changing from 30 to 36 means “dramatically reduce time sampling errors” that means that there is a very large diurnal sampling error. Perhaps like the ones I pointed out.
Stephen Rasey says:
October 8, 2013 at 1:33 pm
Thanks, Steven. Man, I don’t find anything like that. Here’s what I find:
Also, it doesn’t pass over a given gridcell at exactly the same time of day each time. I say that because the satellite makes 14.575 complete revolutions in 24 hours …
So far from the situation you envisioned, of a gridcell being sampled every other day, 15 samples per month, and all the samples at the same times …
… in fact we have about 720 samples per gridcell per month, at different times of day.
Your comment is a perfect example of doing it the right way. Unlike RC Saumarez, who never gives us anything remotely falsifiable, you’ve given us actual numbers so we have something to talk about. It turns out your claims weren’t true, but that’s just science. At least you’ve put up your numbers and advanced your argument, well done.
w.
RC Saumarez says:
October 8, 2013 at 10:08 am
Contrary to your claim, latex for putting in mathematical equations is available to everyone. Start it with the dollar sign ($) and the word “latex” with no space in between. Everything from there to the next $ sign will be interpreted as latex.
is what you get from
\displaystyle { \bold{ \beta = \frac {y} {x} } }
Regarding graphs, put them up on the web somewhere, and then post a link to them. As long as you start it with http colon slash slash wordpress will convert it to a link. But I’ll go you one better. You put in the link, and I’ll convert it to an in-line graphic for all to see. Can’t say fairer than that.
Your move,
w.
@Willis Eschenbach 5:29 pm
From your SOURCE
[!!!] 10:30 am! The day is just warming up! This is the “CLOUDS” Experiment !?!?
It turns out your claims weren’t true, ….
You think so, huh?
Let’s go back to your pasted quote:
That came from the paragraph on ERBE, a low earth orbit (576-589 km), inclination of 57 degs, period 96.30 minutes, from 1984-2005.
How can that that system give “complete global coverage every one hour?”
Stephen Rasey says:
October 8, 2013 at 11:19 pm
Not so. The quote has nothing to do with ERBE. It came from the site I linked to above, and it refers to the CERES instrumentation. Here’s the full quote (emphasis mine:
Regards,
w.
RC Saumarez says:
October 8, 2013 at 3:26 pm
“Temporal scaling of the proposed feedback mechanisms?” I’ve said over and over that this is not about feedback. Nor have I discussed “feedback mechanisms”. As a result, I don’t have a clue what you mean by “temporal scaling” of “feedback mechanisms”.
Look, RC, all I’ve done here is taken the reflected shortwave data from the satellite and plotted it against the Reynolds SST data. What “feedback mechanisms” are getting temporally scaled in that process, and where?
w.
“It has a low 20-km resolution with a limb-to-limb swath width and complete global coverage every one hour.”
Since the US govt. is “down” at the moment we’ll have to rely on Wankypedia. It tells us 98.8min orbital period and 14.5 orbits per day. Not quite “every hour”.
It goes down one side and up the back. It has an instrument scanning limb to limb in longitude so, in some sense it is scanning nearly the whole surface but clearly not in an even vaguely consistent way. How does it’s image of the limb compare to what it sees overhead?
“Complete coverage” has to taken in context, and it’s a pretty distorting context.
It always passed the equator at 10.30 local time. So each orbit it passes a bit further west , that is what is meant by sun synchronous.
So scan ahead longitudes are seeing earlier in the day; behind scanning is seeing later. Now we start to understand rib cage pattern. It’s the scan angle in longitude aliasing the daily temporal build up of cloud.
As I said above the data needs breaking down to see where these patterns come from. Once we understand these artefacts they may hold useful information.
So each latitude has hourly snap-shots of longitude scan that goes back and forth in local time. Within a reasonable range before it becomes too oblique, this gives a time series that covers the onset period Willis is saying is the key to his governor.
The tropical ‘uptick’ is almost certainly similar lines just very compact and unreadable as shown because of the reduced range of temperature in the tropics.
Now if Willis would deal with the data issues and investigate the aliasing patterns I’ve pointed him too, rather than regarding the whole thing as a ego contest, he may be able to extract some very useful information that may have a very direct bearing on his hypothesis.
I don’t know what he hopes to gain from his did-didn’t argument with RC but I’d suggest his time better spend on the data.
Greg,
You don’t need to rely on wiki. Read the link below. This is from a reputable university. The folks responsible for CERES take great pains to ensure the accuracy of their data and unless your willing to replicate their ground truth experiments you don’t have a leg to stand on as far as “alias” errors go. Read the part under the guidance tab.
Also this is not a signal processing issue, Temperature is a relatively slowly changing metric at best and hourly sampling of such a low frequency event is more than sufficient I would think. This was born out by several comparison studies.
RC also seems to think the CERES folks are very careful with their data.
https://climatedataguide.ucar.edu/climate-data/ceres-ebaf-clouds-and-earths-radiant-energy-systems-ceres-energy-balanced-and-filled
v/r,
David Riser
Willis,
No you don’t have a clue what any of this means. That is the problem.
The al;iasing problem, in a nutshell, is that by averaging and decimating data, you destroy phase information. Without this you cannot tell whether you are dealing with a closed or open loop system.
You appear to think that proper analytic theory doesn’t matter. In fact it does because toy start doing things that lead to wrong conclusions.
If you did my little exercise, assuming that you can, you would understand this immediately.
@David Riser,
I am certainly not saying that the CERES data is bad data. I have the greatest admiration for the people behind it. Nevertheless, data which is sampled, which the CERES data is, is potentially aliased, simply because there are things going on in clouds with a higher frequency content. You get the same problems in imaging. Everybody knows this.
The issue here, in the last three posts. is that the data has been decimated and used with decimated data of land and sea temperature to make deductions about feedback. This is where the aliasing problem comes in because it mangles the phase information, without which you cannot determine whether you are dealing with a closed loop or open loop.situation.
The problem is that our author, by his own admission, doesn’t know any maths and certainly doesn’t know anything about signal processing, so trying to have a discussion on any of these problems is like talking to a brick wall because he simply doesn’t understand what the issues are.
RC Saumarez:
re your post at October 9, 2013 at 4:05 am
This is the fourth WUWT thread where you have attacked Willis with bombast and insults instead of reasoned arguments and/ or evidence. Furthermore, you persist in making assertions which you refuse to justify, for example this
But Willis has repeatedly demanded that you justify that assertion or stop wasting space on threads by iterating it. For example, he demanded it here
http://wattsupwiththat.com/2013/10/06/evidence-that-clouds-actively-regulate-the-temperature/#comment-1440695
But you continue with insults, assertions and nothing else. Please desist.
Richard
richardscourtney
RC’s point is rather elementary – do a search for sampling theory/Nyquist frequency. Willis is sampling something with a temporal signal so RC’s point is not open to debate.
Whether or not it renders Willis’ post useless is a different matter. But it does require that caveats are added. I think experimental issues such as whether one uses interpolated data points (with associated uncertainty) as actual control data could be added to the list of caveats. Though I don’t think this should be used to dismiss Willis’ recent work given the limitation imposed on him by the experimental setup. I think everyone needs to calm down and recognise that this isn’t a peer-reviewed article and it isn’t an experiment carried out in a controlled environment.
richard I forgot make clear in relation to the Nyquist frequency, that Willis, and by his own admission, his sampling frequency is lower than the frequency of variation in the phenomenon he wishes to measure (akin to cloud cover variation). In short, he’s way under the appropriate sampling rate and he will have an issue with aliasing…after which all RC’s points follow.
@richard Courtney
How do you suggest I justify this? I can do so easily except that this is a medium that does not allow one to do so.
It is well known that the processes involved have relatively high frequency content. This is lost when processed. In particular, phase information is lost. Without phase information, one cannot distinguish between open and closed looped systems.
This might be assertions to you, but it is elementary signal prrocessing and system theory and should be part of the background of anyone who tries to analyse signals.
For example I have written a very simple simulation to illustrate this point. However I cannot present the results as part of a scientific discussion. Since I am unable to make my point, I have suggested that Eschenbach reproduces these calculations and, if he were to do so, the nature of the problem would be immediately apparent. It is not particularly difficult.
These are reasonable scientific points of criticism and science depends on critical thinking. You will note that there are other professional signal processers on this thread who also have reservations about the approach taken here. If we are correct, then WE’s model of feedback is wrong or certainly incomplete.
Cloud feedback is a problem that has exercised many good scientists in the CERES program and elsewhere. It is acknowledged as being exceptionally difficult to analyse with current data. Any claims to have done so should be treated with utmost scepticism.
@richard Courtney.
Since this is an argument that will go nowhere, Since WE has claimed groundbreaking “science” in his analysis, I suggests that he writes the whole thing up as a proper scientific paper and attempts to publish it in a respectable journal;.
cd:
Sincere thanks for your two posts addressed to me at October 9, 2013 at 6:35 am and October 9, 2013 at 6:46 am.
To be clear, I am not disputing your interpretation of what RC Saumarez intends to say. As I said to you in another thread, if he has an explicit criticism of Willis’ argument then he should state it so the criticism can be addressed. But RC Saumarez does not do that and, instead, he flames Willis.
In this thread the nearest RC Saumarez comes to making the criticism you suggest is in his answer to you at October 8, 2013 at 5:54 am
http://wattsupwiththat.com/2013/10/06/evidence-that-clouds-actively-regulate-the-temperature/#comment-1440432
Prior to that, at October 7, 2013 at 11:34 am, Willis had replied to him by asking him for specific explanation of what he meant by “aliasing” and to justify its putative effect.
But RC Saumarez did not provide the needed clarification. Instead, he provided more arm-waving, bombast and insults. Your explanation of what he meant may be right, or not.
Perhaps he does see a Nyquist violation in what Willis has done. If so, then why does he not show the violation, and why did it require you to prompt him for him to mention it?
This is the second thread where you have provided your interpretation of what RC Saumarez means (and your suggestion was of a different meaning in the previous thread). All I am asking is that when RC Saumarez has a specific criticism of Willis’ work then
(a) he states the specific criticism because that would be useful
and
(b) he stops the inane insults because they are annoying and not useful.
Richard
RC Saumarez:
I was writing my reply to cd when you wrote your posts addressed to me atOctober 9, 2013 at 7:07 am and October 9, 2013 at 7:07 am. I apologise that this may have given the impression that I ignored your posts: in fact I did not see them and I am replying to them now.
I ask you to note my request to you in my post at October 9, 2013 at 4:25 am which you have replied; i.e.
And please read my explanation to cd of why I made that request for you to desist.
As for your saying to me
Well, that is another assertion.
You say you have done a “very simple simulation” which shows Willis is wrong but you have not described it, you have not linked to it, and you have not here explained its relevance to the actual study Willis has conducted. Furthermore, you claim that doing those things is beyond your capabilities.
Sorry, but if you think that is cogent then you need to get a friend whom you trust to explain some basic ‘home truths’.
And it is for Willis to decide when, how and where he publishes his work. I feel sure he will give your suggestion all the consideration it deserves.
Richard
richard
I agree that RC can be quite adversarial, but he has stated his case quite clearly in this instance. I don’t think he needs explain himself any further.
If so, then why does he not show the violation
From what Willis has stated, and for the above case, it is quite clear that his data is was sampled below the Nyquist frequency; and hence the issue with aliasing; and hence all the issues that follow.
Where I think RC and others have got this wrong, is that somehow they think that such shortcomings render the complete exercise a waste of time. I think with the right caveats the work is fine for a first pass. But as Roy Spencer pointed out there is plenty of work on this.
All I am asking is that when RC Saumarez has a specific criticism of Willis’ work then
(a) he states the specific criticism because that would be useful
and
He has done so time and again. You need only do a search on sampling theory and you’ll get plenty of articles explaining why you must sample at or above the Nyquist Frequency. It doesn’t take a great leap to see that RC has made valid points. In short, it shouldn’t need any further qualification.
@richard Courtney.
Set up a model. It has a forward sea heating transfer function A(s). It had a feedback of clouds with a transfer function B(s). Assume that these are of the form gain/(1/tau+s).. Let tau sea=20 days and tau clouds=2 days.
Take a band-linited input X(s), with periodic inputs if you so wish. Calulate the sea temperature, Y(s), as
Y(s)= x(s).a(s)/(1+A(s)B(s))
Solve this by substituting s=-jw and form the complex exponential discrete Fourier Transforms of the transfer function and input. Do this at say 2 hourly intervals.
Now take the input, output and feedback signals and decimate them. Compute the spectrum and spectral overlap imposed by decimation.
Perform a further simulation with the feedback removed, i.e.: the clouds do not modulate temperature.
I realise that this is grossly over-simplified but it allows easy solution in the frequency domain. The thermal capacity of tthe sea cannot in reality be represented by a single phase process and cloud formation is non-linear as is its saturation effects. However, analysis of such a model is more difficult. This simulation is of course error free and completely analytic. If there are measurement errors the problem becomes much worse. However, the model is sufficient to illustrate the problem.
Given the two sets of simulated signals attempt to establsh which one is open loop and which one is closed loop. This is inherent in the transfer function. Because you have destroyed the phase relationships due to decimation , this is not possible.
If the choice of time constants in this model is reasonable, it is clear that all phase relations which determine feedback are obliterated.
This is not an assertion, as you constantly assert, it is analysis of the problem and uses well established theory to calculate possible errors, which should be a standard component of experimental work.
I have now laid out the problem clearly and it is a reasonable criticism. If I am wrong refute it.
cd says:
October 9, 2013 at 7:48 am
First off, the “Nyquist frequency” is half the sampling rate. As a result, it is impossible to sample a data set “below the Nyquist frequency”. I suspect that you are conflating the Nyquist frequency with the Nyquist rate. The Nyquist rate is approximately twice the highest frequency of interest.
Now, in this case, the frequency of interest to me is monthly. Sure, I’d love to have samples every ten minutes … but I’m happy to concern myself with monthly variations for the moment.
That means that the Nyquist rate is about two samples per month … and CERES takes about 720 samples per month.
So no, I’m not sampling below the Nyquist rate as you claim.
Next, I’ve asked Richard to provide evidence that aliasing is an issue in this dataset. I know it’s an issue in theory, and can easily be a problem in certain kinds of analysis … but is it an issue in this dataset for this particular analysis?
Both he and you have returned with nothing but theory and handwaving. I don’t give a rat’s patootie about your theories. I’m interested in one question—can you show that this particular analysis is affected by aliasing? In other words, if there is aliasing in a movie of wagon wheels, we can see it and measure it. So … where is the aliasing going on in my analysis? How large is it when you measure it? How can we detect if it is happening? Those are the kinds of questions that need answers, and you’ve not given even one of them.
To date, all either of you have done is flapped your gums. To date, neither you nor RC has shown anything at all about my analysis. To date, neither one of you has demonstrated that sampling hourly and averaging monthly makes the slightest difference IN THE REAL WORLD IN THIS PARTICULAR ANALYSIS. Yes, you both keep saying over that your theories say it should make a difference … but you haven’t said what difference it might be making.
Sorry, not impressed.
w.
RC Saumarez:
Thankyou for your post addressed to me at October 9, 2013 at 8:51 am. It – at last – attempts to say what you mean, and it concludes saying
Firstly, I didn’t “constantly assert” anything about the “analysis” you have now provided. Indeed, I could not have done that because until now you have persistently refused to present it and have provided bombast and assertions instead.
I agree that error analysis is an essential part of any work. Unfortunately your “analysis” does not include a result to which a confidence level can be applied so such error analysis is not possible.
All you have provided are these assertions
Also, it is not possible for anybody to “refute” an analysis which has no result and relies on assumptions of time constants. Please note that all one needs to say is the sample frequency must be at least twice the frequency of the signal to be detected for Nyquist compatibility. So, the only argument required is to show that the signal Willis attempts to determine is less than double the sample rate adopted by Willis.
You have described a reasonable method to determine Nyquist compatibility and there is no reason to refute it. However, it would be reasonable to assess the application of your method to Willis’ work if you were to state that. Unfortunately, you have not provided
(a) the indication of your suggested analysis,
(b) the applicability of that analysis to Willis’ work
and
(c) the effect of that assessment on Willis results.
When you correct those oversights then anybody will be able to assess the validity of your criticism. Until then you have merely added to your arm-waving.
Please note that none of this would have arisen if you had started by asking Willis if he had checked for Nyquist compatibility.
Richard
PS I ponder what grade you would give one of your students whose work included similar oversights.
RC Saumarez says:
October 9, 2013 at 8:51 am
Actually, RC, the problem was already laid out for you—it was to demonstrate how and where your claimed aliasing is affecting my analysis above. All you’ve done is ignore that very real problem, and make up your own problem to analyze.
As a result, all of your claims about your lovely model are meaningless. I don’t care what your model does. Either you can show whether and where aliasing is affecting my analysis, or you can’t—showing that aliasing does or doesn’t occur in your wonderful model complete with a “forward sea heating transfer function” means nothing about my analysis.
w.
PS—I did love your “transfer function B(s)”, it’s so much more refined than your usual BS …
@ Willis Eschenbach.
Thank you for this well reasoned and polite response.
You claim to do “science”, When people make scientific criticisms of your work, which is completely reasonable as you are publishing your ideas on the web, You never address their questions. .
As a case in point, I an another professional signal processer have questioned your work on a basis that seems to us, based on our training and experience, to be sound.
If you had a genuine scientific interest in the problems that you try to tackle, you would take our remarks much more seriously and try to understand them.
I have reviewed some of your “mathematical” posts. The effects of filtering on correlation, the periodicity transform, your first oder model of heat transfer. The intellectual standard is lamentable. You really haven’t got a clue.
As regards your feedback hypothesis, do you really think that other people haven’t explored this? It is after all, one of the burning topics in climatology and has proved very difficult. You seem to think that you’ve cracked it in about a day. Well, forgive me, I’m sceptical. Any scientist, would trest this claim with caution and the first step is criticism. The demonstration of negative feedback, particularly with non-ideal data, is extremely difficult and I don’t think that you have demonstrated this. I would remind you that if you make these claims the onus is on you to defend them. The purpose of my “model” is to demonstrate that the processing methods involved makes it almost impossible to distinguish between a feedback and a non-feedback system. If you were to attempt this, you would discover this and you might learn something. If you can’t do it, I would suggest that your knowledge is too limited to perform reliable processing and analysis or even to understand the issues that I have raised.
WUWT is worth visiting because there are people who post on it who are genuine scientists and what they write is sensible. You do not fall into that category.
Willis: “Next, I’ve asked Richard to provide evidence that aliasing is an issue in this dataset. I know it’s an issue in theory, and can easily be a problem in certain kinds of analysis … but is it an issue in this dataset for this particular analysis?”
So it doesn’t count unless it Richard that says it , is that the game?
I have pointed to artefacts in the rad variable that are at least 30% across large areas of the data. In fact if you look closely there are many more such artefacts.
I have also suggested a possible cause, that they are time of day changes in cloud cover being aliased into the rad/temp data because of longitudinal scanning of the instrument.
This may be exactly the evidence you are looking for to test your hypothesis. Instead of looking at what I pointed out you seem more interested in continuing your pissing contest with professional engineers , presumably in the hope that you can prove untrained you is smarter than they are.
Your ill-mannered snarky comments do not flatter you, How about you put your ego in the fridge for the evening and get back to doing some science.
@Willis Eschenbach 12:45 am
Here’s the full quote (emphasis mine):
Had it said, every one day it would be technically true, if deceptive because it would include highly oblique coverage grid cells of almost half the planet each day.
But “one hour”? That can’t be done from any one satellite.
What is the minimum number of satellites to give “complete global coverage every one hour?”
It can easily be done with five: 3 geosynchronous 120 degrees apart and 2 low polar orbiters with orbital periods in the 90-120 minute range.
It might be done with four: 2 Lagrangian L1 and L2 satellites and two low polar orbiters, and you fudge the “one hour” by letting the earth’s grazing limb rotate into view during the hour.
But you cannot do it with one, no matter how close or far away.
On what satellite(s) does CERES ride? It is on Terra. Is it on any others?
From My 10/8 11:18 pm
The Terra satellite was launched on December 18, 1999 and began collecting data on February 24, 2000. It operates in a polar sun-synchronous orbit at 705 km above the Earth’s surface, crossing the equator on descending passes at 10:30 AM, when daily cloud cover is typically at a minimum over land. Because of this morning equatorial crossing time, “Terra” (a mythical name for “Mother Earth”) was originally named EOS-AM-1. Terra has a repeat cycle of 16 days, meaning every 16 days it crosses the same spot on the Earth. It is roughly the size of a small school bus. Follow-on missions are planned to continue key measurements made by the five instruments aboard Terra: ASTER, CERES, MISR, MODIS, and MOPITT.
Back on 10/8 1:33 pm I ran some orbit numbers on Terra. That where I got the distance on the ground between consecutive passes: 2737 km or 25.83 degrees longitude at the equator. The distance on the ground from the overhead point to the satellite’s horizon 2767 km, almost exactly the spacing of the ground track. So from the satellite’s horizon to horizon it is 5334 km or 51.66 deg of longitude. Once per day. Won’t be back until 24 hours and 42 minutes on a track 1163 km further west.
So. “complete global coverage every one ” day. Provided you will accept readings 1400 km off track from an altitude of 700 km which means the satellite is only about 15 degrees above the horizon at that grid cell midway between the day’s passes. I don’t know how precise those measurements will be.
CERES is not giving you 720 samples per grid cell per month. At the tropics, Terra/CERES is giving you 30 daylight passes per month confined to between solar 9:40 am and 11:20 am, and 30 night time passes at 9:40 pm to 11:20 pm. with half of these measurements at an oblique 15-30 degrees above the horizon.
All,
Well obviously some of you didn’t read my post or review the link I posted. There is of course more than one satellite that has CERES instruments on board. The Satellites that have CERES instruments on board have 3 each. Currently there are 3 Satellites in orbit which allow for the hourly sample rate. As far as signal processing goes the CERES data is double checked with land based instruments to ensure there is no Temporal (alias type) errors. This type of data collection, while there are obviously electronic circuits in the instruments, is not signal processing! It is measurement of physical process which change relatively slowly.
By Changing slowly I mean that on average it takes around 24 hours to achieve an entire cycle of temperature change and while cloud cover is somewhat chaotic its more about moving than creating and clouds tend to persist for periods of time greater than 12 hours. A sample rate of 24 times a day is more than enough to capture those changes. The data is then averaged by the CERES folks for the monthly mean. Once again the base data is sound.
So Willis has done nothing to the data, other than plot it. I refuse to think that direct plotting data creates errors in and of itself. I find that Willis logic and thoughts on this are sound. Arm waving and calling names are not helpful. If you think there are true errors with the data or the plotting, put your math where you mouth is and explain it mathematically or graphically or explain it so a non electronics technician can understand. Oh, and do a little better research on the CERES project, quite a few EDU sites have information on it, even though the official site is down.
v/r,
David Riser
@David Riser 1:26 pm
Burried in your 10/7 1:59 pm linked reference
– To improve scene identification, the CERES data products make full use of coincident 1-km imager data (e.g., MODIS on Terra and Aqua) to provide detailed information about cloud and aerosol properties within the larger 20-km CERES footprints.
Oh, so there is another satellite that carries CERES. Nice to know. Any others?
BTW, this is the first use of the word “Aqua” on this thread.
Willis
I didn’t mean to devalue anything you’ve done. It’s easy to nit-pick with hindsight.
the “Nyquist frequency” is half the sampling rate
OK yes – fair point. Talking too loosely.
But then being pedantic…
The Nyquist rate is approximately twice the highest frequency of interest.
Should be…”the sampling rate should be at least twice the highest frequency component.”
That means that the Nyquist rate is about two samples per month …
But the variable of interest changes at a much higher rate than this ~20mins. You’re sampling way below this. Perhaps I’ve missed something.
Both he and you have returned with nothing but theory and handwaving
I think an acknowledgement that there might be an issue is all that is needed. I’m not as dogmatic as others who seem to be suggesting that unless you have the perfect experimental setup everything that follows has no value. Yes there does look to be artifacts, but the sheer weight of data would suggest you have something.
IN THE REAL WORLD IN THIS PARTICULAR ANALYSIS
I guess I’m just suggesting that from what you have described, it would appear that aliasing is an issue. But again, even if RC is right, I’m not suggesting that this falsifies anything you’ve stated. I think RC is being too dismissive and you’re being a bit to obstinate.
Willis oops:
”the sampling rate should be at least twice the highest frequency component.”
Should’ve been:
“The sampling frequency should be at least twice the highest frequency contained in the signal”
CERES is apparently on Terra, Aura, and Aqua All are in sun-synchronous polar orbits, with very similar periods, 98.8, min, 98.82 minutes and 98.4 min. (differences are probably due to Wike edits.) So they have the same coverage parameters. They each measure one tropical grid cell once in the day, once at night.
Wikipedia says Aura crosses Equator about 1:30 pm. But Aqua and Aura are in the “A-train” a formation of 5 satellites. So Aqua and Aura are only 8 minutes apart at the equator on the same track. Puzzling. Instead of three tracks, it is two with duplication/redundancy.
So, we are up to 2 daylight measurements per grid cell per day. Where is CERES getting it’s every hour coverage? Is it from the GOES geostationary satellites? I cannot find a reference.
Greg says:
October 9, 2013 at 11:41 am
Oh, please. I’ve asked Richard because he is the one who always promises and never delivers. I see you want to join him. OK.
So you, like RC, think there might be a problem. Sure, there might be … but the sampling rate is way above the corresponding sampling rate for e.g. temperature data, and monthly averages of temperature are used around the world. You claim that there is aliasing from a monthly average of ~ 720 samples per month … sorry, bub, but you’ve gotta demonstrate that, not just claim it. Because I don’t believe it.
But instead of demonstrating it, what you’ve done is point at some flyspecks or other and pompously declaimed that they might be showing signs of aliasing …
… just what part of my request to “provide evidence that aliasing is an issue in this dataset” has escaped you? You, like RC, just issue claim after claim. I don’t care in the slightest that you are worried that there’s a chance that some aspect of my graph might possibly be showing the effects of aliasing. That is an OPINION. What you need to support your OPINION is some EVIDENCE.
And that’s why I didn’t bother answering you, Greg. You provided nothing more than RC, just another opinion unsupported by any evidence.
w.
RC Saumarez says:
October 9, 2013 at 10:59 am
You’re welcome.
Yeah, that’s the ticket. I’ve “never” addressed anyone’s questions … RC, that’s total hogwash. I answer more questions than just about any climate blogger you can name. That claim is a joke.
Do I answer dumb questions? Often not. Do I answer spiteful or “gotcha” questions? I try not to. Do I answer questions designed to deflect or to troll rather than to actually find something out? Not when I see them first.
So if your question didn’t get answered, RC, you might consider all of the above. At that point, what you need to do is what I do: point out the question, and ask it again. Because you see … no one here but you has any idea what question or questions you are referring to.
This is because, once again, your rant is without a single example, link, citation, or number. You wave your hands and say I don’t answer questions … but you don’t identify the questions.
Near as I can tell, your whole post a valiant but futile effort to avoid answering my question, viz: what evidence do you have that aliasing is affecting my analysis?
You made the claim that aliasing was an issue. When I asked you to back it up, you erupted with fantasies about my never answering questions, and a host of other equally fanciful objections … but the question is still there, RC.
Where’s your evidence that aliasing is affecting this analysis?
w.
.
Ok. That gives the hourly coverage in grid cells outside the 10:30am and 1:30 pm CERES passes.
In the same paragraph….
It seems to me that the paragraph was badly worded to confuse CERES and ERBE processes.
David Riser, thanks for your contribution. You say:
There’s a good explanation of the whole sample rate question on page 863 here.
It says in part:
See the original document for more details.
Given that there is “excellent temporal coverage of the diurnal radiative cycle for most of the globe”, I’m not seeing how that would cause aliasing. Even if it’s only four samples per day, that’s plenty to give us accurate monthly data.
w.
RE: Oct 9, 3:21 pm
That gives the “CERES dataset” the hourly coverage in grid cells outside the 10:30am and 1:30 pm CERES passes. But calling it CERES data isn’t exactly “truth in lending” since 85% of it is GOES imager data “calibrated” to look like CERES.
I am quite curious what the GOES data looks like before and after the CERES calibration.
How much does the calibration change day to day? Month to Month?
How much does it change from 10:30am to 1:30pm? from 40N to 20N to 40S?
You see, if it is a rock steady calibration, and we can use GOES reliably for 8:30 am, 3:30 pm and 5:30 pm estimates of cloud emissions…. why do we need the CERES instrumentation at all?
Well, one answer is resolution. CERES is capable of 1 km x 1km studies of clouds at two snapshots at 10:30am, 1:30pm and 1:38pm each day (time approx at equator. an Equal number on the night side).
So, in principle, you take 400 1km x 1km CERES measurements and match them to one 20 km x 20 km reading from GOES. Calibrate GOES from that. Repeat for every 20 km x 20 km GOES cell that you have simultaneous CERES pass. Cross plot GOES vs. CERES. There is your calibration. Take 5 to 25 GOES cells, convert by the calibration cross plot into a 1×1 degree grid cell. Presto! A 1×1 degree CERES dataset.
Would it not be lovely to see those CERES vs GOES cross plots by time, by latitude, by season? I’d settle for knowing the error bars. If we calibrate on the10:30 data, how far are we off on the 1:30 data? And vice versa. Only then would we know how good the GOES in-fill in the CERES dataset.
BTW, a cross plot of Aqua and Aura CERES data would be interesting too. How wide is that cloud of data points measured 8-15 minutes apart?