This interesting essay by Dr. Spencer is reposted from his blog, link here:
Global Warming Causing Carbon Dioxide Increases: A Simple Model
May 11th, 2009 by Roy W. Spencer, Ph. D.
Global warming theory assumes that the increasing carbon dioxide concentration in the atmosphere comes entirely from anthropogenic sources, and it is that CO2 increase which is causing global warming.
But it is indisputable that the amount of extra CO2 showing up at the monitoring station at Mauna Loa, Hawaii each year (first graph below) is strongly affected by sea surface temperature (SST) variations (second graph below), which are in turn mostly a function of El Nino and La Nina conditions (third graph below):
Click for larger images
Click for larger image
During a warm El Nino year, more CO2 is released by the ocean into the atmosphere (and less is taken up by the ocean from the atmosphere), while during cool La Nina years just the opposite happens. (A graph similar to the first graph also appeared in the IPCC report, so this is not new). Just how much of the Mauna Loa Variations in the first graph are due to the “Coke-fizz” effect is not clear because there is now strong evidence that biological activity also plays a major (possibly dominant) role (Behrenfeld et al., 2006).
The direction of causation is obvious since the CO2 variations lag the sea surface temperature variations by an average of six months, as shown in the following graph:
So, I keep coming back to the question: If warming of the oceans causes an increase in atmospheric CO2 on a year-to-year basis, is it possible that long-term warming of the oceans (say, due to a natural change in cloud cover) might be causing some portion of the long-term increase in atmospheric CO2?
I decided to run a simple model in which the change in atmospheric CO2 with time is a function of sea surface temperature anomaly. The model equation looks like this:
delta[CO2]/delta[t] = a*SST + b*Anthro
Which simply says that the change in atmospheric CO2 with time is proportional to some combination of the SST anomaly and the anthropogenic (manmade) CO2 source. I then ran the model in an Excel spreadsheet and adjusted an “a” and “b” coefficients until the model response looked like the observed record of yearly CO2 accumulation rate at Mauna Loa.
It didn’t take long to find a model that did a pretty good job (a = 4.6 ppm/yr per deg. C; b=0.1), as the following graph shows:
Click for larger image
The best fit (shown) assumed only 10% of the atmospheric CO2 increase is due to human emissions (b=0.1), while the other 90% is simple due to changes in sea surface temperature. The peak correlation between the modeled and observed CO2 fluctuation is now at zero month time lag, supporting the model’s realism. The model explained 50% of the variance of the Mauna Loa observations.
The best model fit assumes that the temperature anomaly at which the ocean switches between a sink and a source of CO2 for the atmosphere is -0.2 deg. C, indicated by the bold line in the SST graph, seen in the second graph in this article. In the context of longer-term changes, it would mean that the ocean became a net source of more atmospheric CO2 around 1930.
A graph of the resulting model versus observed CO2 concentration as a function of time is shown next:
If I increase the anthropogenic portion to 20%, the following graph shows somewhat less agreement:
Click for larger images
There will, of course, be vehement objections to this admittedly simple model. One will be that “we know the atmospheric CO2 increase is manmade because the C13 carbon isotope concentration in the atmosphere is decreasing, which is consistent with a fossil fuel source.” But has been discussed elsewhere, a change in ocean biological activity (or vegetation on land) has a similar signature…so the C13 change is not a unique signature of fossil fuel source.
My primary purpose in presenting all of this is simply to stimulate debate. Are we really sure that ALL of the atmospheric increase in CO2 is from humanity’s emissions? After all, the natural sources and sinks of CO2 are about 20 times the anthropogenic source, so all it would take is a small imbalance in the natural flows to rival the anthropogenic source. And it is clear that there are natural imbalances of that magnitude on a year-to-year basis, as shown in the first graph.
What could be causing long-term warming of the oceans? My first choice for a mechanism would be a slight decrease in oceanic cloud cover. There is no way to rule this out observationally because our measurements of global cloud cover over the last 50 to 100 years are nowhere near good enough.
And just how strenuous and vehement the resulting objections are to what I have presented above will be a good indication of how politicized the science of global warming has become.
REFERENCES
Michael J. Behrenfeld et al., “Climate-Driven Trends in Contemporary Ocean Productivity,” Nature 444 (2006): 752-755.
Discover more from Watts Up With That?
Subscribe to get the latest posts sent to your email.
To understand the minuscule effect of even doubling atmospheric CO2, check out the graph on Page 22 of David Archibald’s paper: click
That Page 22 graph is another nail in the coffin of the repeatedly discredited, falsified and ridiculous notion that CO2 = AGW.
RE Joel Shore (06:49:08) :
*** Gerald Machnee:
“if CO2 is the main factor in temperature increase, then the global temperature must increase EVERY year”
Not true at all. Why would CO2 stop weather?
Indeed…and a simple way to see this is to consider the seasonal cycle in a place such as here in Rochester. I think everyone would agree that it is a very significant feature of the climate here…and yet as we go from winter to summer, we do not see a steady temperature increase each day. I haven’t looked at the actual data but my guess would be, for example, that week-long periods in the spring with negative temperature trends are not at all uncommon.***
Please read what I said, “Then the global temperature must increase EVERY year.”
I was not talking about daily or seasonal variations or local at your house!! I said GLOBAL and YEAR!
RE Joel Shore (06:49:08) :
***It means exactly what it says: For a function like y = A + log(x), each doubling of x produces the same amount of change in y. So, going from 280ppm to 560ppm produced the same amount of change in y as going from 140ppm to 280ppm. This is in contrast to a linear function where the same amount of change in x produces the same amount of change in y…In particular, going from 280ppm to 560ppm would produce the same amount of change in y as going from 0ppm to 280ppm.***
No.
I think you should review the mathematics. Each successive doubling has a decreased effect. Only the first doubling of CO2 had a significant result on the temperature. any doubling now is a fraction of a degree. Please check slide 22.
Bill:
I suspect that CO2 changes on all time scales…seasonal, interannual, decadal, centennial, etc. The time-smoothing inherent in the ice core record from long ago means you will only see the longest time scale relationships in the ice cores. I also suspect the different time scales correspond to different physical (or biological) mechanisms.
In my blog posting I’m showing a clear interannual relationship, and hypothesizing that it might also extend to decadal and longer time scales. If it does, then some significant part of the observed CO2 increase in the last century is likely natural. Since mankind emits more than is needed to explain the observed increase, this also means the “missing sink” of carbon dioxide is bigger than is currently appreciated.
Roy W. Spencer (07:53:15) :
The time-smoothing inherent in the ice core record from long ago means you will only see the longest time scale relationships in the ice cores.
Are you also suggesting that the fast response is transient? Could you please explain why because I just cannot see why that would be. Thanks
If transient then after 30 years of increase are we now due for an unexpected fall?
Gerald MacNhee says, in responding to Joel Shore:
Y = A + log2(X): A = 0. At X = 1, Y = 0, at, at X = 2, Y = 1, at X = 4, Y = 2, at X = 8, Y = 3!
Thus, as Joel says, each doubling of X produces a linear change in Y, a unit increase in Y. Note, I chose log2(X) to make the numbers easier.
However, for Y = A + nX, where A = 0, and N = 1, each doubling in X produces a doubling in Y.
I think what you actually meant to say when you said:
Is: Each successive increase has a decreased effect.
However, I don’t want to put words in your mouth.
To achieve what you said would require a function with history or scaling it with some factor that gets smaller the further along the X axis you go.
Joel Shore:
“I haven’t looked at the actual data but my guess would be, for example, that week-long periods in the spring with negative temperature trends are not at all uncommon.”
And these are all explainable by weather systems (primarily the jet stream movement). Exactly how to do you explain the lack of increasing temps for the last few years. Please provide the exact mechanism. Thanks.
RE: Richard Sharpe (08:25:38) :
**I think what you actually meant to say when you said:
Each successive doubling has a decreased effect
Is: Each successive increase has a decreased effect.
However, I don’t want to put words in your mouth.
To achieve what you said would require a function with history or scaling it with some factor that gets smaller the further along the X axis you go.**
I think that what I said is correct. Anyway, I will not quibble over wording. I have not worked with logs (either type) for a while. That is why I referred to David Archibald’s chart on slide 22 of his presentation. Now that is not his originally, but he used the calculations from MODTRAN facility in Chicago.
Your calculations above are still linear in result.
The MODTRAN calculations show that the first 20 ppm increase in CO2 gave a temperature increase of about 1.55 deg C. the next 20 ppm give an increase of about 0.3 deg C. The next 20 ppm give an increase of less than 0.2 deg C. So by the time the ppm reached 280, we had achieved most of the temperature increase due to CO2.
There are also others who talk about the logarithmic effect, but too many are ignoring it.
“”” RW (01:11:44) :
Gerald Machnee:
“if CO2 is the main factor in temperature increase, then the global temperature must increase EVERY year”
Not true at all. Why would CO2 stop weather?
“Check the latest studies – The 13C is not only from fossil fuels”
Link to those ‘latest studies’ please.
George E. Smith:
“And yes I do believe that CO2 molecules can and do absorb long wave infra red radiation; that does not equate to raising the global temperature.”
That is a horrible self-contradiction. Whatever absorbs more radiation, gets warmer. There is more CO2, hence more absorption, hence…
“””
Sorry RW; not contradictory at all. It certainly might raise a local temperature; specifically the temperature of the atmosphere where the CO2 is but that in turn sets in motion other effects; some of which lead to increases in cloud cover (globally); and increases in cloud cover lead to cooling (locally); but the overall global effect may be quite negligible. And in fact we know from measured data that increases in CO2 have not caused any significant increases in global temperature; which incidently we don’t even have any reliable way of measuring.
So we have had a 37.5 % increase in atmospheric CO2 since the end of the 19th century; which continues apace to this day, and essentially no credible evidence of a significant temperature increase.
Sure some temperature anomaly data sets suggest that those data sets have recorded an increase; but they don’t reflect any scientific measure of a global temperature.
And prior to around 1980, when oceanic buoys first started to measure both air and water temperature; we now know that all that data taken from 70% of the earth’s surface; namely the oceans, is useless, since water and atmospheric temperatures aren’t correlated; so the air temperatures cannot be reconstructed from the water temperatures that were historically measured.
When the computer climate models properly include the full physical effects of the most important GHG namely water; then come and tell us that CO2 increases cause GLOBAL warming.
Right now we appear to have global cooling; yet we still have increasing CO2 in the atmosphere; obviously something else is seeing to it that the effect of the CO2 increase is completely squashed.
“”” Nick Stokes (20:55:14) :
Paul Vaughan (and George Smith)
Here is a good set of notes on dissolved carbonate chemistry. The last page (sec 6.2) is quite relevant here. “””
Hey Nick; so you want to put this old codger to work relearning some chemistry ?
That is a very nice piece of relevent information; more than I ever expected to get my hands on. And yes I am going to have to hit the books to freshen up what chemistry I learned in High school; but I can at least read it now.
And yes like a good Sherlock Holmes you unmasked my lazy shift thumb; pretty soon I could get to just remember that (5) is Bunglese for percent(%).
Thanks for the help; I’m determined to become very friendly; even intimate with the ocean/atmosphere carbon exchange; although I sometimes wonder why; because I’m pretty darn certain that CO2 doesn’t have much to do with the earth’s temperature; well at least as far as causing it; not with all that water around to veto the CO2.
Thanks for the help.
Gerald Machnee (09:49:26) :
Gerald, also keep in mind that the log relationship for radiative forcing is between the mixing ratio and the forcing. The first 20 ppm will affect the mixing ratio much more than the last 20 ppm.
Gerald MacNhee says:
What you have just described is the increase in the output of a function decreases for every linear increase in the input.
X + 20ppm ==> Y + 1.55C
X + 20ppm + 20ppm ==> Y + 1.55C + 0.3C
X + 20ppm + 20ppm + 20ppm ==> Y + 1.55C + 0.3C + 0.2C
So, at some point we will get to:
X + X ppm ===> Y + aC (here a is a constant).
X + X + X + X ppm ===> Y + 2aC
This is what a logarithmic function does.
“”” Nick Stokes (16:26:17) : “””
Nick, I noticed the book chapter referred to “hydrated” CO2, rather than CO2.
I’m guessing that they are implying that the 1% CO2 is actually in some complex of the CO2n(H2O) variety. Those crazy water molecules do have a habit of grabbing onto other things.
Almost makes me wish I had studied more chemistry.
And yes I understand how the CO2 (aqua)/CO2 (atm) Henry’s law equilibrium will “trickle down” to the HCO3- and CO3- – concentrations as well (roughly); and I’m not surprised that the calcium carbonate shells will also play a role. It’s a wonder we can ever get anything straight with all these interractions.
Most immediaterly, I’m trying to figure out; if freezing sea water ejects the CO2/HCO3-/CO3–species from the solid, into the water, how much extra CO2 should be emitted into the atmosphere for each cubic metre of new ice formed; and how that compares with the 18 ppm change over the arctic annual cycle. It seems to me the amount should be pretty large, but maybe not so when compared to the land based seasonal emissions (tundra and permafrost bogs)
George
Suggested exercise for bill, motivated by bill (05:25:02):
1) Create a sinusoidal wave – call it wave A.
2) Create a second sinusoidal wave (wave B) with the same period but half-a-cycle out-of-phase with wave A.
3) Apply a moving-average of bandwidth 1 cycle to both waves A & B.
4) Note the cross-correlation best-lag for cycle-integrated-A with cycle-integrated-B.
5) Create a longer wave of period p where p is much greater than 1 (i.e. much greater than the period of waves A & B) – call it wave C.
6) Translate wave C in time by some amount h where h is less than p – call the result D.
7) Add wave C to (unsmoothed) wave A – call the result wave E.
8) Add wave D to (unsmoothed) wave B – call the result wave F.
9) Apply a moving-average of bandwidth 1 cycle (i.e. a moving-average with bandwidth equal to the period of waves A & B) to both waves E & F.
10) Note the cross-correlation best-lag for A&B-cycle-integrated-E with A&B-cycle-integrated-F.
11) Repeat steps 1 to 10 introducing random amplitude variations.
12) Repeat steps 5 to 10 introducing random phase variations.
13) Explore steps 11 & 12 in concert.
Questions:
a) Do you understand that if temporal sampling resolution is 1 year, seasonal variations will be smoothed over?
b) What was your main insight from the exercise I suggested earlier? [See Paul Vaughan (17:29:55) – latter portion, addressed to bill (14:46:30).]
– – –
Re: Lucy Skywalker (01:06:41)
Can you share the standard explanation that is offered for the winter dCO2/dt dip at Alert (in northern Canada)? [Contextual note for anyone following along: The main dip at that location occurs in summer, but there is a smaller, narrower (but striking & substantial) dip in winter.]
Roy W. Spencer (04:57:10) “[…] It is a perfectly legitimate scientific hypothesis, and the fact that it is avoided like the plague illustrates how political climate science has become.”
Thank you for your comments Dr. Spencer.
Can you point out some publications related to your above presentation? Although I have not been down that branch of the literature, I am left with the impression that it might not exist – and if that is the case, I plan to launch an investigation.
By the way: I never found CO2 to be a very interesting variable to study before seeing your WUWT post (above) and the resulting discussion.
– – –
anna v (05:40:30) “[…] anybody else who has access to the data […]”
Clarification:
The data are available publicly in text-format via:
http://cdiac.ornl.gov/trends/co2/sio-keel.html
ftp://ftp.cmdl.noaa.gov/ccg/co2/in-situ/
RE: Richard Sharpe (11:18:01) :
Are you saying that:
**What you have just described is the increase in the output of a function decreases for every linear increase in the input.
X + 20ppm ==> Y + 1.55C
X + 20ppm + 20ppm ==> Y + 1.55C + 0.3C
X + 20ppm + 20ppm + 20ppm ==> Y + 1.55C + 0.3C + 0.2C
So, at some point we will get to:
X + X ppm ===> Y + aC (here a is a constant).
X + X + X + X ppm ===> Y + 2aC
This is what a logarithmic function does.**
is the same as below?
Y = A + log2(X): A = 0. At X = 1, Y = 0, at, at X = 2, Y = 1, at X = 4, Y = 2, at X = 8, Y = 3!
Gerald Machnee:
Actually, the technically-correct statement is that MODTRAN gives a certain value for the radiative forcing, which Archibald has then converted to a temperature rise by assuming a climate sensitivity of 0.1°C per watt/m2. This is well lower than the generally-accepted range of ~0.5-1.0°C per watt/m2. (Even in the absence of feedbacks, it would be ~0.25°C per watt/m2, so 0.1°C per watt/m2 assumes strong negative feedbacks.
I don’t know who you speak of ignoring it. As I noted above, the whole reason that scientists talk about the climate sensitivity to a doubling of CO2, rather than an increase by some constant increment, is because of this logarithmic dependence. However, it is also worth noting that the difference between a logarithmic and linear function is only very noticeable if the change in concentration is a large fraction of the initial concentration. So, when we go from 300ppm to 320ppm, the increase is almost as great as going from 280ppm to 300ppm. (For example, if the 280 to 300ppm caused an increase of 0.3°C then the 300 to 320ppm would cause an increase of ~0.28°C.)
RE: Joel Shore (13:52:18) :
Gerald Machnee:
**Actually, the technically-correct statement is that MODTRAN gives a certain value for the radiative forcing, which Archibald has then converted to a temperature rise by assuming a climate sensitivity of 0.1°C per watt/m2. This is well lower than the generally-accepted range of ~0.5-1.0°C per watt/m2. (Even in the absence of feedbacks, it would be ~0.25°C per watt/m2, so 0.1°C per watt/m2 assumes strong negative feedbacks.**
Archibald’s figure is a calculation based on what he wrote.
The “generally-accepted range” is based on feedbacks which are assumed or modelled, and which are being questioned now. More study is required to decide if the feedbacks are negative or positive.
**I don’t know who you speak of ignoring it. As I noted above, the whole reason that scientists talk about the climate sensitivity to a doubling of CO2, rather than an increase by some constant increment, is because of this logarithmic dependence. However, it is also worth noting that the difference between a logarithmic and linear function is only very noticeable if the change in concentration is a large fraction of the initial concentration. So, when we go from 300ppm to 320ppm, the increase is almost as great as going from 280ppm to 300ppm. (For example, if the 280 to 300ppm caused an increase of 0.3°C then the 300 to 320ppm would cause an increase of ~0.28°C.)**
Ignoring – the mainstream does not even discuss it.
You note that when you go from 300 to 320 the increase is the same as from 280 to 300. That is correct. However, your figures of 0.3 and 0.28 are much too large and are based on too high a positive feedback..
As I noted in another post, Steve McIntyre has an open question to IPCC or anyone else to provide a detailed engineering quality derivation of the change or sensitivty that a doubling of CO2 will produce. To date there have been no responses other than quoting IPCC which is based on Charney(1979) and lacks the detail.
Roy Spencer:
“isn’t it possible that *some* of the warming and CO2 increase during that time is natural?”
Of course. It’s not just possible but certain. But natural mechanisms can only account for an extremely small part of the CO2 rise. Once again, it’s really very very simple – humans have burned a lot of fossil fuels, and this has resulted in the emission of a lot of CO2; fossil fuels have a lower 13C content than either oceanic or atmospheric CO2; both oceanic and atmospheric CO2 is going up, and both oceanic and atmospheric 13C content is going down; and these changes have been dramatic and have happened exactly synchronously with rising CO2 emissions from fossil fuels. Your ‘models’ cannot account for the changes in isotopic ratios; they do not even match the observed rise in CO2 itself. If a model doesn’t reproduce the data, then either the model is wrong, or the data is wrong. Which would you say it is, in this case?
“It is a perfectly legitimate scientific hypothesis, and the fact that it is avoided like the plague illustrates how political climate science has become.”
The hypothesis that some of the temperature and CO2 increases are not due to humans is not even a hypothesis – it’s obvious and well known. The suggestion that ‘natural’ factors are dominant is not a perfectly legitimate scientific hypothesis, because it’s contradicted by the observations. No-one is avoiding anything like the plague.
anna v:
“200 years ago the temperatures started rising because of getting out of the little ice age”
Rising temperatures do not explain rising temperatures.
“and the flora responded accordingly. One can imagine many reasons why a C13 rich strain might flower better with heat.”
Can one? If one imagines what you imagine, one has to imagine that present day temperatures are vastly hotter than they were at any time in the past 1000 years. Otherwise, how can one explain the data?
“The argument about fossil fuel origin of C13 can hold only if there are no other sources of C13 in the game.”
Incorrect. 13C is found in all sources of CO2. It is the proportion that matters. As you see from my link, the 13C proportion of atmospheric CO2 began dropping sharply, about 200 years ago. Fossil fuel CO2 has been emitted in sufficient quantity, and has the right 13C content to explain the observations. What more is there to say?
George E. Smith:
“And in fact we know from measured data that increases in CO2 have not caused any significant increases in global temperature; which incidently we don’t even have any reliable way of measuring.”
You contradicted yourself before, and you’re contradicting yourself now. What can you possibly know from measured data that measures things you believe can’t be reliably measured?
Paul Vaughan (13:08:57) :
You now have me totally confused!
If you are trying to tell me that sampling data at the yearly rate will remove any seasonal data? If so that is obvious even to me!
As to the previous reference to woodfortrees I do not even know what isolate samples (first box) is !! (it appears to be a derivative type of function)
If you told me what you are trying to prove it could help.
All on this blog I think would agree that global temperature is a difficult thing to obtain. Even good data such as central england temperature is subject to weather and so will not represent the temperature of England let alone the world.
CO2 is similar – how do you get a global average from only a few stations with incomplete records.
CO2 from ice cores is a differnt matter – is it an average or spot measurement. (it takes 30 years to be sealed into the ice)
So looking at derivatives of CO2 or temperature on a montly series does not proove much in my mind.
IF it is expected that CO2 affect the temperature on a monthly basis I think it will be very unlikely that this will appear in any comparison plots – there is just too much noise.
Most plots I make take an average of jan temps/co2 over a reference period and then subtract the current jan value to give a deviation from the mean – (this is repeated for each month).
Doing it this way tends to remove the seasonal differences. before doing any sort of 12 month running average.
And again RW you are wrong.
The increases in CO2 we can and have measured; nobody disputes that; so that takes care of that.
But increases or any changes in global temperature (which I said we can’t reliably measure anyway) have not risen to the point where they can be reliably measured.
We don’t even try to measure the true mean global (surface (or lower troposphere)) temperatures, since that would require an astronomical number of simultaneous measurements to satisfy the rules of sampled data systems; so we settle for a rudimentary observation of “anomalies” which aren’t the same as global temperature measurments.
Yes we have a lot of anecdotal observations of local temperature changes (for short periods of time) such as glaciers retreating (which could be precipitation changes rather than temperature changes); but any accurate sampling of the complete global surface temperature map in compliance with the Nyquist criterion would require orders of magnitude more samples to reflect the minute claimed temperature changes that people infer from anomalies; like a few millidegrees out of a global temprature range of around 150 deg C.
By all accounts the recent CO2 changes are unprecedented in the last 3/4 million years; so we are told; yet the expected temperature changes that models predict can’t poke themselves above the noise level.
So I stand by my statement (within the usual 3:1 fudge factor that accompanies all climate modelling)
Roy W. Spencer (07:53:15) :
Here’s a graph [by Bill Illis] showing a 5-month lag: click. There are lots of different cycles where CO2 lags temperature. The 800± year lag is only one of them.
The problem, though, is the fact that CO2 forcing has been endlessly hyped, and its effect has been hugely overestimated. To get some perspective of what the CO2=AGW crowd is trying to sell, Dr. Spencer has a chart that shows atmospheric CO2 in percentage terms, using the correct y-axis: click. This chart is much more honest about CO2 levels that the steeply rising Mauna Loa graph that the alarmists regularly trot out.
The climate isn’t bothered a bit by a desirable increase in this benign trace gas. In fact, the Earth seems to like it: click
And the human contribution of CO2 is very small compared to what nature emits: click.
Finally, I would like to commend Dr. Spencer for admitting he made a mistake. Everyone makes mistakes. It is to the credit of scientific skeptics that when mistakes are made they are corrected. The CO2=AGW side will not stand up and admit they are mistaken about CO2 causing runaway global warming, despite a monumental tower of evidence discrediting that claim. WUWT alone contains reams of information showing that the CO2 scare is wildly overblown.
Gerald Machnee says:
The IPCC number is based on much more than the original Charney estimate. It is based on a lot of observational data, such as the difference in temperature between the last glacial maximum (LGM) and now, the response of the global temperature to the Mt Pinatubo eruption, and the 20th century temperature record (although the latter does not provide very strong constraints because of the uncertainty in the climate forcing).
In addition to this is the fact that the climate models containing our current understanding of all of the relevant physics seem to converge to the same range of values as these observational data give … and while there are certainly uncertainties with regards to clouds, noone has yet demonstrated a model with a significant negative cloud feedback that can successfully reproduce the basics of the current climatology as well as the current models can.
What McIntyre’s gimmick amounts to is a statement that the climate sensitivity is not easy to calculate in any direct and simple way. Well, yes, that is true. But that hardly means that we should ignore the best evidence from the observational data and from modeling of the climate system in favor of hopes that it is much lower.
Joel Shore:
Have to call you on that one, Joel. I doubt if you’re brave enough to go on Climate Audit and call Steve McIntyre’s position a “gimmick” to his face. But I’d be interested in seeing you you try.
REPLY: What Smokey said. Joel, please be my guest, go to CA and make the same claim. And while I’m at it let me add that MciNtyre does things. He investigates, analyses, publishes, makes FOI requests to agancies that aren’t cooperative in sharing data, and suffers fools gladly. Other than whine and complain about others here, what have you done of merit? – Anthony
George E. Smith (16:15:32) :
Astronomical is exactly what satellite remote sensing is about. We care so much about the sparse ground stations because they’ve been around for much, much longer (hence are important for climate trends).
Anomalies usually mean the value in relation to a reference mean value. It’s a lot easier to look at small differences in an anomaly map than to visually compare two maps side by side of T0(x,y) and T1(x,y). You can also try to remove some absolute offset this way. Anomalies aren’t inherently different than the absolute values with relation to the sampling grid.
This does not seem to be a correct application of the Nyquist theorem. Is there some clear evidence of spatial frequency information in the surface temperatures that would bias the temperature integrated over the footprint of a microwave satellite?