IPCC Projections Continue to Falsify
I promised I would check the status of the comparison between IPCC AR4 projection of 2C/century each month and report. The NOAA data came in last week, and I had a chance to update my spread sheet with all data series.
The main result is: Despite an uptick in the temperature, the IPCC projections or 2C/century still falsifies to p=95% (α=5%).
This result is illustrated in the figure below. The mean trend for the temperature calculated based on the average of all five data sets is show in orange. The uncertainty intervals are illustrated in fuzzy orange lines. The IPCC short term projection is shown in brown and lies outside the uncertainty intervals for the trends that are consistent with the data. Additional details are discussed later in this post.
Click for largerThe rest of this post shows:
- A table summarizing results of tests using individual instruments.
- A very brief discussion of Beta Error. The “Beta” or “Type 2″ error is important when interpreting the meaning of “failed to falsify results”
- Links to other bloggers discussing March temperatures.
Table of individual Results
This month, the IPCC 2C/century projection was falsified using the averaged data, and all main data reporting services except GISS. So, GISS failed to falsify. The results are summarized below. I have also added a column quantifying the “beta” (β) or “type 2″ error that would be associated with any fail to falsify result based on the average of all 5 instruments sets. (The beta is higher for individual instruments.)
| Best Fit Trend | Reject 2.0 C/century to confidence of p=95%. (α=5%) | β (beta error) relative to: |
||
| C/century <m> | Result of test. | Denialists Hypothesis 0 C/century |
TAR Hypothesis 1.5 C/century |
|
| Average all, fit T vs time: | -0.7 ± 2.0 | IPCC Projection Rejected | 49% | 95% |
| Average all, fit T vs (time, MEI): (See note 1:) |
0.1 ± 1.7 | IPCC Projection Rejected | Fit T vs time, then average: | -0.7 ± 2.1 | See note 2. |
| Individual Instruments | ||||
| GISS | 0.2 ± 2.1 | Fail to reject | ||
| HadCrut | -1.3 ± 1.8 | IPCC Projection Rejected | ||
| NOOA | 0.0 ± 1.6 | IPCC Projection Rejected | ||
| RSS | -1.5 ± 2.2 | IPCC Projection Rejected | ||
| UHA | -0.9 ± 2.8 | IPCC Projection Rejected | ||
| Note 1: There are known problems associated with using MEI in any correlation including lagged variables, because the MEI include time. However, this is included for now because people are interested in an estimate of the effect. I’m looking for better ways to do this, but have been swamped with real work over the past two weeks. | ||||
|
Note 2: Calculating the trend for the 5 individual instruments and averaging afterwards is shown for illustrative purposes. However, as I noted in an earlier post, this method is poor, as the uncertainty intervals only include the variation due to measurement uncertainty which is expected to be uncorrelated between instruments but also treats weather variability as uncorrelated. |
||||
| Note 3: “Beta” (β or ‘type 2′) error is the probability that a test will “fail to falsify” even though the null hypothesis is false. If we treat type 1 and type 2 error similarly, “failed falsify” means “confirmed” only when β is less than or equal to the chosen α, which is 5% for our tests. | ||||
Graphs of β error relative to alternatives.
What does the “Beta” (β) mean?
The “Beta” (β) error describes the probability that the result of a hypothesis test would be “failed to falsify the null” hypothesis when the null hypothesis is false.
Given the formalism of statistics, this type of error is very frequent when data contain noise or random components of any type and limited quantities of data are available. In fact, when one has not yet taken any data, the β error equals 100 %.
To a large extent, the idea that it takes years of data to formally test hypotheses about climate variables arises from the fact that many hypotheses in climate are proven using “failed to falsify” as a criterion. Failed to falsify only means “confirm”, when β error is low and it turns out that to get empirical confirmation of AGW in the first place too years. I discussed this generally here.
For now: Suffice it to note that the amount of data for my current test of 2C/century is such that:
- If the out and out denialists are correct, and warming is 0C/century is correct, we would expect our test of 2C/century to result in “failed to falsify” 49% of the time. (That is to say, we’d make the mistake of “failing to falsify” nearly 50% of the time, while incorrectly falsifying only 5% of the time.)
- If the IPCC TAR projection of 1.5 C/century is correct, we would expect the 2C/century result to “fail to falsify” 95% of the time. (We really need a lot of data to distinguish between these two values.)
Those who doubt the previous falsifications are now going to think “Hmmm… she wants to have her cake and eat it too!” After, all, I’m telling you fail to falsify means practically nothing, but falsification does mean something!
Well, … this is just the way it is.
The reason it is this way is that we initially gave the 2C/century preferred status. We structured the hypothesis test so its result is “fail to falsify” before we have even one iota of data. We also set bar high for “falsify” high: We must show that if 2C/century the data we get would happen only 1 time in 20 by pure random chance. So, we only say “falsify” when there is strong evidence that 2C/century must be wrong.
If the evidence 2C/century exists but is “medium” rather than “strong”, we say “fail to falsify”. It is the preference from saying 2C/century that results in ‘fail to falsify’ not actually meaning confirm. What “fail to falsify means is, “There is no strong evidence, and I refuse to believe it’s wrong unless the evidence is strong!”
But, once a falsification is logged we really can’t just forget the evidence existed (and materialized rather quickly in this particular case.)
What are we likely to see going forward?
Even though 2C/century has been shown likely false, and it is likely falls, we will probably see some ‘fail to falsifies’ to ‘falsify’ transitions for quite some time. I think this because
- I think it’s nearly impossible that the climate trend is 0C/century (or below). The only way we’d get a permanent string of “falsify” this soon after decreeing the “start” data of 2001 is if the trend were much lower than 0C/century. So, that won’t happen.
- We got a falsify the first time I ran the test. (I was expecting to get “failed to falsify”, but… well.. number are numbers.)
- We know we are in the region where β error is high for all underlying trends I think remotely possible. This means we should get “fail to falsify” quite often, even though the 2C/century is probably false.
So, how will readers know if the “falsification” we are currently getting are incorrect? One way is if we see “fail to falsify” with β<5%. The other way is if we get a result, starting with data from 2001 that falsifies 2 C/century, but on the high side.
In the meantime, “failing to falsify” means rather little, as we expect that result rather often even if 2C/century is flat out wrong.
Other People Discussing March Data
Since this is just the regularly scheduled comparison, I thought I’d bring readers links to what other people are saying about the recent temperature fluctuations:
- David Stockwell Compares Norhtern and Southern Hemisphere temperatures.
- Anthony Watts discusses a German who claimsHadCrut data converging to GISS data, but Anthony finds that claim suspect.
- Chris Colose note GISS Temps are up in March.
- Roger Pielke Sr. discusses some reasons why Northern and Southern Hemisphere temperatures may diverge.
- Craig James compares this March to previous Marches on record.
- Hall of Record talks about recent “coolth”.
- Redstate.com discusses why 10 years is not long enough to be a trend.
- Yid with a Lid also discusses the temperatures.
As a closing note, I observe that most the bloggers talking about the recent temperatures are on the AGW-skeptic end. For the most part, I found these links on Technorati, so presumably I would have found AGW-believers blogs discussing the temperatures if they were discussing the temperatures.
I’ll try to remember to repeat this in a few months when El Nino returns just to see the results. 
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112 Responses to “IPCC Projections Continue to Falsify”
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Raven April 21st, 2008 at 4:22 pm
Lucia,
Does your analysis presume that the datasets are independent? For example, all of the surface measurements use some of the same thermometers and the satellite measurements use the same satellite data.
lucia April 21st, 2008 at 4:45 pm
Raven,
That depends.
Look down 3 rows, and find the analysis with note “See note 2.”. That analysis involves assuming the 5 data sets are independent- and that applies both the the weather and uncertainty noise.. It’s done by first fitting Temp vs. Time and then calculating the slope, and then averaging. Then, I calculate the uncertainty based on the standard deviation around that mean slope, divide by the square root of (N-1) and apply the t-test for 4 degrees of freedom.
In that analysis, the assumption the uncertainties are indepenednet is clearly false, and that’s why I don’t show any conclusion based on that. However, I post the answer, just so people can see. (It also lets me explain that that is not what I do for my main method should they ask.
)
In contrast, the “main” method, which is listed in the top result row doesn’t really assume anything about the dependence of independence. The calculation is done in this order:
The average temperature for the month is the simple average of the five sets. This calculation involves no computation of uncertainty, because a) we don’t know the uncertainty for any individual temperature and b) we don’t need that information at this point.
Had we needed it, then I would have had to make an assumption about independence. But I don’t need to make any assumption, so I don’t.
After averaging, I apply the linear regression to that set. The uncertainty is whatever it is. (If they were truly independent, we would have certain expectations about how that dropped.)
Then, I compute the uncertainty in the fit according to the standard method for uncertainties in fits. That also makes no assumption about dependence of independence.
So, the only case that makes an assumption is listed in row three. That’s noted in “note 2″, and I don’t use it!
The place dependence of independece does come in is indirectly. If the uncertainties due to measurement in the five sets were independent, we’d see the uncertainty obtained by averaging be lower than the those obtained with each instrument. In contrast, if they were all totally dependent with each other, we’d see no reduction by averaging.
But whatever happened, it would fall out naturally– making no assumption.
I hope this answers without being too confusing.
James A. Donald (3 comments.) April 21st, 2008 at 10:15 pm
Every so often, the IPCC issues a graph showing the doom that will come upon us if we do not repent of our sinful ways, but they all seem to have vanished, which leads me to suspect it would be interesting to dig out one of these graphs issued around 2001, and plot the recent satellite data on top of it. Can anyone point me to one such graph of doom, or recollect a website that carried them back in 2001?
lucia April 22nd, 2008 at 3:39 am
James,
The Third Assessment Report (TAR) was issued in 2001. You can flip through the pages here:
http://www.ipcc.ch/ipccreports.....sh/080.htm
Boris April 22nd, 2008 at 4:23 am
Political blogs should not write about global warming. Yikes.
terry (26 comments.) April 22nd, 2008 at 5:46 am
I agree Boris, they shouldn’t but they do, myself included.
This would include all sides of the political spectrum.
terry (26 comments.) April 22nd, 2008 at 5:52 am
Lucia I got a little bit lost trying to understand beta. is there a formula you used to run the test? I think I can get it if I can have something to plug numbers into and then chug away. (that’s how I learned calculus…plug and chug.)
lucia April 22nd, 2008 at 6:14 am
Terry,
There isn’t a formula so much as a procedure. I describe it here:
http://rankexploits.com/musing...../#comments
What you need to do is:
Figure out how to do the hypothesis test for 2C/century.
In the process, figure out the values of the slope that would “falsify” 2C/century. For the period of time since 2001, these happen to be a bit below 0 on the low side and a bit above 4 c/century on this high side for our current tests. The calculation of these values uses the standard error for the slope, which is calculated based on the magnitude of the “weather noise” we have actually experienced. Lets’ call these “Low bound” = 0C/century; “high bound” = 4C/century. (I’m rounding to make the explanation easier to type– but the real values aren’t 0 or 4).
So, you know you would falsify 2C/century if the trend “m”<0 C/century=mlow or if 4 C/century < mhigh.
So, given you will say falsify for these cases, here is how you figure out the likelyhood you would falsify if the trend were really 0C/century.
For the purpose of calculation, assume the trend is 0C/century.
2) Assume that the weather variability is still what we have experienced. So, you can just take the standard errors on the slope you got from the previous calculation.
Recall you will falsify 2C/century if m<mlow=0C/century or if 4C/century=mhigh<m.
Calculate two things, but assuming the true mean is 0C/century.
a) Probability you will falsify, because m is less than the lower bound for 2C/century.
The probability that the weather would exhibit a slope less than mlow (0C/century). You use the gaussian distribution for this. (It’s programed in Excel). Note that in this example, 0C/century happens to equal the mean, so if the true mean were 0C/century, we would determine there is a 50% chance that the weather would have a trend with a slope less than 0C/century. (The other 1/2 the time, the slope would be greater than 0C/century.
)
This image may help (Though it’s backwards for the current discussion):
b) Probability you will falsify, because m is greater than the lower bound for 2C/century. The probability the weather would exhibit a slope greater than 4 C/century. (This ends up being really, really small for our case.)
Add the probabilities from (a) and (b). You’ll get something just above 50%. That’s the total probability you will falsify 2 C/century when the real value is 0 C/century. This is called “The Power”.
So, the probability you fail to falsify is 1-Power= β. This is “beta” error contingent on m=0C/century, because in this hypothetical, we assumed m is really 0C/century.
Obviously, this can all be repeated for any alternate hypothesis. In the table, I used 0C/century and 1.5C/century, because those seemed like reasonable ones. The first value is the one the denialists insist on. The second one would be the TAR; so that would interest us if we wanted to figure out if the AR4 values are any better than the TAR values.
John V April 22nd, 2008 at 7:28 am
lucia,
I put together my own spreadsheet of temperature trends, and I’m happy to say that our results agree (within 0.1K/century) on all trends. The small differences are probably due to different values of rho in the Cochrane-Orcutt regression. It’s pretty sensitive.
The C-O trends are still more negative than the OLS trends, but the gap is closing. I looked at different time periods and the C-O trends are at times more positive than the OLS trends. That gives me confidence that C-O is not inherently biased towards negative trends. (I couldn’t see how it would be, but it never hurts to check). In fact, C-O seems like a sensible technique IMHO.
Basically, I agree that the 7-year trend from Jan2001 to Mar2008 is not consistent with a century-long average trend of 2.0K/century.
So, why is the current short-term trend different than the longer-term trend? Why is it basically flat since 2001? You have discussed ENSO, and your correlation with MEI makes sense to me. As you’ve shown, it’s not enough to explain the trend.
You have hypothesized that a PDO switch in 2001 would explain the change. In comments, you have also expressed some interest in the solar cycle vs temperature correlations from WattsUpWithThat.com. I’m surprised you haven’t yet looked at the influence of the current solar cycle on the short-term temperature trend.
The correlation between solar cycle and temperature is not well defined. At the low end, Tamino has shown that the correlation is weak if it exists at all. At the high end, Camp and Tung 2007 show an effect of ~0.16K between solar min and solar max. In the middle, other empircal analyses have found ~0.1K and model simulations have predicted ~0.06K.
In Jan2001 we were near solar maximum. In early 2008 we are at solar minimum. If we assume a short lag time between solar cycle and temperature (consistent with the 2 month lag between MEI and temperature), then we can expect the temperature signal to be near full amplitude.
Using rough numbers, the trend from the solar cycle over the last 7 years is between 0.06K/7years (0.9K/century) and 0.16K/7years (2.4K/century). It could actually be 50% higher or it could be zero using the published 95% confidence limits.
Adding the range of solar cycle trends to your MEI-adjusted trend:
MEI-Adjusted + Small Solar Trend: 1.0 +- 1.7 K/century
MEI-Adjusted + Large Solar Trend: 2.4 +- 1.7 K/century
These trends definitely fail to falsify 2.0K/century. The same is true of the average trends that are not MEI-adjusted. Have I missed something obvious?
lucia April 22nd, 2008 at 8:47 am
Hi JohnV,
I think it’s worth while for everyone to look at various possible reasons. But, I’m not going to look at solar partly because others are. Either Anthony and Basil will find a smoking gun there, or they won’t. I’m currently looking try to find how much ‘energy’ people believe exists in other long cycles– like the PDO and the AMO.
In those cases, when I find literature, I’ll try to do a back of the envelope calculation to see if those explain the slow down, and also, to see what that same explanation says about the initial empirical support for warming during the 1970-now run up.
I’m also trying to learn better statistics to deal with ENSO. (Because the method I’m using isn’t quite up to snuff.)
terry (26 comments.) April 22nd, 2008 at 9:13 am
ok, thanks Lucia, I think I have it now.
John V April 22nd, 2008 at 10:50 am
I’m disappointed that you’re not going to look at the solar cycle. Next to the seasons and ENSO it’s the clearest cycle in the climate system. Its timing from max to min matches your 7-year trend very well. Even without correcting for ENSO, the solar cycle amplitude need be only ~0.02K to de-falsify (is that a word?) the “2C/century” trend. With your ENSO compensation, a solar cycle amplitude of ~0.11K would be sufficient to falsify the “no-warming” hypothesis.
From my back-of-envelope calculations, it is clear that even a modest solar cycle effect on temperature can make a substantial difference to a 7-year trend. It would also have very little effect on the 38-year trend preceding 2008 (the “1970-now run up”).
None of this necessarily means that PDO and AMO are not factors — but surely an explanation as simple (and seemingly complete) as the solar cycle is worthy of investigation.
lucia April 22nd, 2008 at 10:57 am
John,
Are you saying that if I account for the decline in temperature expected due to a drop in the solar intensity from 2001 to now, then we would see the AGW signal?
John V April 22nd, 2008 at 11:20 am
lucia,
If I understand your question, then my answer is yes. Allow me to clarify that I’m answering the right question:
The trend from Jan2001 to Mar2008 can be written as:
(1)
T = A + E + S + O + W
where
A is the AGW trend,
E is the ENSO trend,
S is the solar-cycle trend,
O is the trend from other sources (AMO, PDO, etc)
W is weather noise
The IPCC trend is basically just A. You have shown that E is fairly small and have attempted to correct for it. Your ENSO-corrected trend can be written as:
(2)
Te = A + S + O + W
W is the remaining error bars, which you have estimated as plus or minus 1.7K/century.
That leaves S and O (plus the W noise). I can’t say anything intelligent about O, but S has been estimated. If the solar cycle temperature amplitude is between 0.06K and 0.16K (references that I found from a quick search), then S is between -0.9K/century and -2.3K/century for the last 7 years. To keep it simple, I’ll define S = -1.5K/century (a little less than the average of the range).
Re-writing (2) to solve for A:
(3)
A = Te - S - O - W
Substituting your computed trend of 0.1K/century:
(4)
A = 0.1 + 1.5 - O - W
Neglecting O and expanding W as error bars at plus or minus 1.7K/century:
(5)
-0.1 K/century < A < 3.3K/century
or,
(6)
A = 1.6 K/century plus or minus 1.7K/century
This is very close to the IPCC trend of 2.0K/century. The results are similar using the temperature trends without ENSO correction.
lucia April 22nd, 2008 at 11:44 am
JohnV–
Hmmm…Do you have references on the magnitude of those effects?
I’ll look a bit through the SRES, but the normal 11 year solar cycle, being entirely predictable, should in principle already be included in the IPCC projections and incorporated into the mean trend. (Unlike ENSO, PDO, AMO, it’s should be something in the error bars. But, I’ll need to read a bit on the forcings used in the IPCC document.
So, basically, if the 2C/century was projected with full accounting for the 11 year solar cycle at the strength the modelers think it has, a correction to give credit for that would be inappropriate.
With respect to the IPCC ‘falsification’ it matters who says that effect exists. Because, if I understand those at Real Climate, NASA etc. at this point, the solar cycle is supposed to be buried in the noise. So, in a sense, if their projection is only ’saved’ by the impact of the solar cycle, then their models are…well… wrong!
Still, if you can say who suggests those numbers, it might still be interesting to examine.
BenSolar April 22nd, 2008 at 12:24 pm
I am no statistician, but I will comment that I think the window you are taking data from is way too small to make the claims you are making, lucia. At least include a full solar cycle, preferably two. You aren’t capturing the full amount of noise in the system when you only sample from solar maximum to solar minimum, and so your error bars on your trend line are not nearly wide enough, IMO.
I’d have a look at what the variance is in the temperature record going back to the introduction of satellite records, I think you’ll find that 7 year trends vary a lot more than you are estimating.
The IPCC never issued a forecast for what the trend would be 7 years out, IINM, for good reason.
John V April 22nd, 2008 at 12:25 pm
The 11-year solar cycle averages out so does not need to be considered in a multi-decade trend. That is, it has little effect on the *mean* trend just as ENSO has little effect on the *mean* trend. It only affects the trend over short time scales. I doubt there is any discussion of the solar cycle in SRES.
As for the IPCC error bars, I don’t believe you are using them. Instead, you have been using the IPCC mean trend and deriving the error bars from observations. That’s a valid choice but means that the size of the IPCC error bars is irrelevant to your analysis.
Camp&Tung (2007) is the most recent study that I know of (link below). They found 0.16K (0.06K to 0.26K) for the solar cycle. At the low end, they reference Stevens & North (1996) which finds a model-predicted cycle of 0.06K. (My results for the underlying AGW trends for 0.06K and 0.16K are in comment #2046).
http://www.amath.washington.ed....._2007b.pdf
Using NASA’s numbers from one of your earlier posts (link below), the solar cycle has a peak-peak amplitude of ~0.3W/m2. Coincidentally, the GHG forcing is increasing at roughly 0.3W/m2/decade or ~0.2W/m2 in 7 years. The net change in forcing from GHG and solar effects over the last 7 years is thus about -0.1W/m2.
http://rankexploits.com/musing.....nt-matter/
Ironically, those who are most convinced that the IPCC is wrong tend to argue for a strong solar cycle effect on temperature (and vice-versa). In this case, the two arguments negate each other. If the solar effect is weak, the IPCC trend is falsified. If the solar effect is strong, the IPCC trend for AGW is validated. Nobody can have it both ways.
lucia April 22nd, 2008 at 1:12 pm
JohnV–
In the first post, I showed the IPCC error bars for the mean trend. The IPCC doesn’t communicate these in words, or even very well. So, unfortunately, it’s a bit difficult to talk about them. Here’s the graph:
There are other graphs for other SRES, but they are all similar in the short term projection.
What I found then is, given the data at that point, the climate trends predicted were not consistent with the trends in the data. The range of trend consistent with the data are based on the data. The climate trends the IPCC projects were those discussed in the report.
But one must always compare trends to trends, and that’s what I do.
But yes, at this point, in my table, I’m focusing on talking about the central tendency for the trend, and seeing if that is consistent with the data. The central tendency is important in and of itself. From time to time, I’ll show the full uncertainty intervals, but admittedly not in the table.
With respect to the solar cycle: When I said I needed to read, that’s what I meant.
Either they included those when running the “simple tuned models” used to project or they didn’t. I’m reading table 10.1 on page 756 of the WG1 part of the AR4, and solar is listed as “C”. Reading sideway, the seem to let it vary in AOGCM’s but treat it as a constant or annually cyclic in scenario integrations. This would mean you are correct and they don’t include the 11 year cycle in scenario integrations– which I think is what they ultimately use for their projections. I think they do this because they consider the variation over the 11 year cycle weak. So…
Yes, I agree that it is ironic that those who disagree the IPCC modeling efforts suggest the strong effect, while those who think the IPCC models are great think the solar effect is weak. (In fact, evidently sufficiently weak as to neglect it when projecting trends!? If we are both reading correctly?)
Still, it seems to me that if the only way to “redeem” the IPCC models is the solar cycle… well… (But I think we agree on this?)
Ben:
If you read the IPCC AR4, it’s not clear what the time scale for their projections are. They never discuss how one would verify or falsify, what time scales etc. That’s one of the reasons Roger Pielke Jr. recently advised they should state these things more clearly.
The issue of needing several solar cycles is puzzling since those who make the projections seem to insist the solar cycle doesn’t matter. So, in a sense, if it matters, the basis for their projections is somewhat undermined! (You can see that’s why John V and I are trying to puzzle this out.)
I’d be willing to agree on the need for more data if anyone brought up a relevant phenomena with a large time scale with sufficient “energy” in terms of temperature variation. But so far, no one does. They just decree things in terms of numbers of years. When analyzing data, there are actually quantifiable things one can state, and for some reason, no one will suggest particular known cycles.
So, in the meanwhile, I’m trying to read the literature to see if estimates of the amount of variability due to the PDO or AMO matter. (If they matter a lot, that has the potential to affect the issue of empirical proofs of AGW in the first place, because these cycles are L-O-N-G. Long enough to encompass the full recent run up! But, based on most my reading, the estimates are that they are not strong enough to impact that assessment. If so, they aren’t strong enough to affect this one. But, I admit, I’m still not sure– as you can see from my comment above.)
lucia April 22nd, 2008 at 1:21 pm
John V– OOpps.. I didn’t read everything.
First: Thanks for the numbers and papers.
The problem with the amplitude of the forcing from my previous post is it doesn’t tell us the amplitude of the response. One of the difficulties in the theory is the “in the pipeline” issue. Based on that, about 1C/century (roughly) of the projected run up was due to the GHG’s already being too high, and the planet being low relative to equilibrium for that current level of GHG’s. The second +1C/century was sort of due to the increase after 2001.
So, you can’t just say: we expected GHG’s to go up an amount ‘A’ resulting in a temperature rise of ‘dT’. So, if solar goes up ‘B’, but we neglected that, we would expect the temperature to rise an amount (1 + B/A) dT.
So, I’m a bit puzzled as to how to estimate that. But the numbers in the paper you suggest should help me do order of magnitude calculations (after I read them!
)
John V April 22nd, 2008 at 2:00 pm
Ah yes, I remember that graph now.
Care must be used when stating that the solar cycle “doesn’t matter”. It’s true that it has little effect on long-term trends. It’s true that even an extended solar minimum would make little difference on a multi-decade trend. But it does have the potential to *substantially* impact a 7-year trend that starts at solar max and ends at solar min.
Just for fun, I did OLS and C-O fits going back to the last solar minimum (June 1996). I used the average temp from Atmoz’s data file. The trends with 95% confidence intervals are:
OLS: +1.1K/century (+0.5 to +1.8 K/century)
C-O: +0.9K/century (-0.9 to +2.7 K/century)
I made no attempt to correct for ENSO. I realize that I cherry-picked the starting point so I’m not going to make any claims about these trends.
Although the Jan2001 starting date for the IPCC falsification was justified by publication dates, it starts at a solar maximum and ends at a solar minimum. Is an un-intentional cherry-pick still a cherry-pick?
=====
Re-reading my previous post, I see that I left the impression that the negative trend in forcing would give a negative trend in temperature. That was a mistake. You are right to say that there is warming “in the pipeline”.
My gut feeling is that the “pipeline” warming is mostly in the oceans. The atmosphere should respond quickly to any forcing. The atmosphere’s response is damped by the slower ocean response.
Raven April 22nd, 2008 at 2:06 pm
It pretty obvious from the beginning that the 7 years coincided with falling edge of the solar cycle. I am pretty suprised that it took so long for someone like John V. to point that out. It seems like many in the warmer camp are afraid of admiting that the sun does have an observable influence on climate. Is suspect it is because they know that a strong cooling effect on the trailing edge also means a strong warming effect on the leading edge.
I used the solar data here: http://solarscience.auditblogs.....r-minimum/
And I eyeballed the temperature trends for the falling edge of the last three solar cycles using the graphs here: http://junkscience.com/MSU_Temps/Warming_Look.html
It is clear that there is a roughly 0.2 degC drop for the surface and satellite datasets and this drop is consistent with the drop observed since 2001. This is consistent with the conclusions made in the Camp and Tung paper.
However, It seems to me that removing a 0.2 degC oscillation from the temperature dataset over the last 30 years would also result in a trend much lower than what IPCC claims. For example, eyeballing from the 1982 max to the 2002 max gives a trend around 0.05 degC/decade. Similarily, eyeballing from the 1987 min to the 2008 min gives a trend of 0.05 degC/decade too.
A net trend of 0.05 degC/decade over 20 years would invalidate many of the predictions made by the IPCC.
Raven April 22nd, 2008 at 2:15 pm
I also eyeballed the MEI index here: http://www.cdc.noaa.gov/people/klaus.wolter/MEI/
I noticed that every solar min coincides with a La Nina which and the rising edge of the solar cycle always corresponds to a El Nino.
This observation could imply that ENSO and the solar effect are one in the same.