Cooling of the Multidecadal Cyclic GMST until about 2030’s suggests La Nina conditions will dominate in the next twenty years.
Guest post by Girma Orssengo, PhD
IPCC’s climate model prediction for a global warming of about 0.2 deg C per decade for the next two decades is contrary to the observed climate pattern.
In the following graphs that show climate data analysis results, the Observed Global Mean Surface Temperature (GMST) shown in Graph “a” has an oscillating Residual GMST of +/- 0.2 deg C as shown in Graph “b”, and a Multidecadal Cyclic GMST of +/- 0.1 deg C as shown in Graph “e”.
As a result, because of these two oscillating components of the Observed GMST, it is incorrect for the IPCC to claim a constant warming rate of 0.2 deg C per decade that lasts for two decades.
Note that for the parameters of the model given in Equation 1, the Residual GMST from 1885 to 2011 shown in Graph “b” has zero mean and zero trend. The result shown in Graph “e” indicates the cooling of the Multidecadal Cyclic GMST until about 2030s. This result suggests La Nina conditions will dominate in the next twenty years. Finally, Graph “f” demonstrates there was no change in the climate pattern before and after mid-20th century, contrary to IPCC claim.
Observed GMST (Graph a) = Residual GMST (Graph b) + Model Smoothed GMST (Graph c)
Model Smoothed GMST = a*Cos[2*Pi*(Year-1910)/60] + b*(Year-1910)^2 + c*(Year-1910) + d
Where a = -0.1050, b = 3.598*10^(-5), c = 3.27*10^(-3), d = -0.345 (Equation 1)
Secular GMST = b*(Year-1910)^2 + c*(Year-1910) + d (Equation 2)
MultiDecadal Cyclic GMST = a*Cos[2*Pi*(Year-1910)/60] (Equation 3)
I do like the fit, but I would prefer using a Temp minus sine wave vs the log([CO2]) plot.
I’d call this “yet another regression”. What I’m missing is explanation of physical relevance of chosen regression components, particularly the 60-year cycle and quadratic baseline.
Girma: Here we have again empirical statistics of the 60-year
trisynodic Scafetta cycle, which we discussed in January/ February, when
our Willis got mad on “statistical curve fitting”, demanding its astronomical
climate forcing background to be put on the table….
I will do it coming spring, because this cycle is an important decadal
Holocene climate forcing cycle….JS
This doesn’t allow for the sudden and deep solar slowdown which has begun, and is likely to reach a nadir around 2035. It is a non-linear non-sinusoidal interregnum which occurs on a complex cycle. For those who don’t think solar variation affects climate much – keep watching.
I think a little more explanation might be helpful….
The IPCC define what it is to be between a rock and a hard place.
It’s always been a problem for me that the period 1910-1940 showed the same or faster temperature increase than the 1979-1998 period. Fully half the temperature increase of the 20th century happened before 1950. Since we all know that the co2 output of the world greatly accelerated post 1950 and the great industrialization of the world it makes no sense that the temperature increase prior to 1950 should be the same as before 1950. That would imply that co2 is not the cause of the warming but something else. Or that co2 took over after the mysterious unexplained warming from 1910-1940 stopped. Since we now know of a 1000 year cycle where temperatures peak roughly every 1000 years it seems that maybe the warming we are getting now and prior to 1950 is mostly or all related to that phenomenon.
Yes, I and others have been saying this (including on WUWT) for years.
Richard
Now you need to plot CO2 against graph d then you will get something what Dr. Vaughan Pratt did on JC’s Climate etc some time last year. He claims that ‘graph d is CO2 contribution.
Do you have a different attribution?
Dr. Pratt’s problem is the same one you encounter by analyzing, in climatic terms, too short data set.
Now if you turn to the CET and consider 350 instead of 110 year long data set than the ambiguity would disappear:
http://www.vukcevic.talktalk.net/CET-NV.htm
here oscillating curves have true observational (empirical) properties, they are actual spectral components derived from the actual data set.
I do not see anything noteworthy in there that can be attributed to recent CO2 increase that didn’t occur in the low CO2 era.
More La Niña conditions mean more droughts in North America. A return to the Dust Bowl.
That’s bad news. Very bad news if this is true.
It’s much worse than that. You have to include the 20 yr and 9 yr cycles too.
http://virakkraft.com/Temp-future.png
Looking at the graphs, it would appear that the rate of warming in raw data Graph “a” from 1910 to 1940 is steeper than the rate from 1960 to 2010. In Graph “c,” however, after mucking about with residuals and model formation, the latter period’s slope is slightly steeper than the former’s.
Also, I note that, according to the graph titles, Graph “c” is derived from Graph “a” minus Graph “b,” and Graph “b” is derived from Graph “a” minus Graph “c.” A labeling error somewhere. Otherwise interesting, possibly significant. It would be nice if we had a longer set of observations that Hansen hasn’t diddled with.
It’s interesting you mention temp rise looks slightly lower between 1910-1940 vs the time after 1950 because temp charts I’ve seen for years showed that the 1910 -1940 went up faster. However the giss and other source for data keep adjusting the temperature in the past. So now it seems that 1910-1940 is slower than after 1950 and also that the decline in temperature between 1945-1975 seems to have become more of a flat period than a period of decline. Why they would feel the need or how they justify mucking with past temperatures is beyond me. It is interesting the modifications of past temperature 100% of the time reduce those temperatures. How amazingly coincident with the theory that co2 is the cause of all warming. We’ve seen over and over again in other sciences and disciplines that scientists of all types are conciously or subconsciously always destined to make experimental bias that confirms their theory. I have seen numerous times that when there are errors In the temperature data that make it look like temperatures deceased for any period of time intense scrutiny is applied to figure out how to create a way of discarding that data yet when temperatures come out higher they recieve no scrutiny at all. This has been so egregious that at times it’s been hard to believe. For instance a few years ago the temperature of the world was reported hitting a new peak. When someone glanced at the data they found that they had inadvertently copied in the soviet unions temperature from July into August and September. Since the temperature can be 10-20 degrees colder a few months later and since Russia is a very large land mass it had the effect of massively raising temps for the whole planet for that year until someone noticed the fact that Russia was incredibly hotter than it seemed it should be and pointed this out the “scientists” at NASA apparently didn’t notice that the whole country of Russia was 10 degrees Warner than it should be in their data. Talk about extreme evidence of experimenter bias. Normally the argument is that scientists cross check the work of other scientists. That there is a “competition” of scientists that weeds out errors of this type. So then why did it take a non-academic lay person to find this large error? Again proof that the scientific community is not policing itself. This kind of thing happens regularly and is almost never found by other scientists but by lay people. The temperature record would not have to be manipulated much for a trend to become significant or unsignificant. So the need for accuracy of this data is paramMount
TonyB
I think a little more explanation might be helpful….
I agree. I believe there is an unwritten law that links the level of explanation with the level of comprehension. In other words, if you can’t explain your results, then you probably don’t understand them.
Girma, you and I have discussed this before on Climate Etc. It follows from your analysis that there is no CO2 signal in the temperature/time graph that is detectable above the antural noise. Therefore, by definition, the total climate sensitivity for CO2 is indistinguishable from zero; since no signal is detectable
Re: Vaughan Pratt analysis
http://judithcurry.com/2010/12/05/confidence-in-radiative-transfer-models/#comment-19481
http://xkcd.com/687/
I get similar results but use an x^2 factor instead of logarithmic. Cooling until 2030±5 depending on the dataset. The wavelength changes quite a bit depending on whether you are looking at global, regional, or local areas. Mine analyzes until it reaches minimum error using about 9 digits, not that it is that precise.
Check out the one on sea level too – declining until 2019! But this was using January data, before the University of Colorado decided such a result was just a little too much to bear. With Envisat out of the way, it makes their job a lot easier.
http://naturalclimate.wordpress.com/
Girma,
Place realistic error bands on the data and all your graphs disappear in the haze.
Even recording accuracy (+/- 0.5 for most of the data) swamps any trends.
Robbie says:
September 3, 2012 at 1:12 pm
More La Niña conditions mean more droughts in North America. A return to the Dust Bowl.
That’s bad news. Very bad news if this is true.
Well I hate to be the bearer of bad tidings.. but if you look at the Unisys SST anomalies map http://weather.unisys.com/surface/sst_anom_new.gif you will see a plume of upwelling cold water from the coast of Peru out into the Pacific. That looks very much like a La Nina. I think that the Nino 3.4 metrics have been fooled by the huge pool of cold water to the North of the Nino boxes. It certainly does not look like an El Nino.
Steven Mosher says: September 3, 2012 at 2:15 pm
http://xkcd.com/687/
……………………
Hi Steven
Looks very familiar
CET = (pi) f (a, b, c)
a = algorithm for width of English Channel due to the continental drift
b = baroclinic pressure at the Earth core (by proxy of Earth’s magnetic field)
c = ciclo solare
Grima,
extrapolate graph c into a cycle and predict “something else”; predict something that has already happened (tick), then apply it to more data (do it again).
“Make something else come out right, in addition” -Feynman
Other wise it’s just cargo cult crap (like the hockey stick). Time to do something more useful with a PhD.
I disliked many filters
http://www.woodfortrees.org/plot/hadcrut3vgl/from:1885/to:2012/mean:5/isolate:180/mean:60
X anomaly,
Your opinion might carry some weight if you could spell Girma’s name correctly.
Caption check:
Graph d = Graph c – Graph e
Graph e = Graph c – Graph d
Therefore:
Graph e = Graph c – (Graph c – Graph e)
Graph e = Graph e
So Graph e has no relation to Graph c, nor to Graph d, only to itself.
Is that what you were trying to say?
This article, looking at the effects of Hadley “corrections” to SST
http://judithcurry.com/2012/03/15/on-the-adjustments-to-the-hadsst3-data-set-2/
contains this graph:
http://curryja.files.wordpress.com/2012/03/icoads_monthly_adj0_40-triple1.png which shows a similar fit but using several periods not just 60 year period.
The periods and starting years are determined by non-linear regression, and are thus determined by the data.
Exponential increase in CO2 would only produce a linear (or likely less) increase in temp. I see no reason to fit a quadratic.
Looking at rate of change (dT/dt) is most informative, since it removes the constant base line temp , which is arbitrary in the case of “anomalies”. The “constant” 0.42 K/c in dT/dt being a linear rise in temperature.
tallbloke says:
September 3, 2012 at 12:52 pm
>> This doesn’t allow for the sudden and deep solar slowdown which has begun, and is likely to reach a nadir around 2035. It is a non-linear non-sinusoidal interregnum which occurs on a complex cycle. For those who don’t think solar variation affects climate much – keep watching.
>>
Well Girma’s plot doesn’t but the three terms in the dT/dt plot may well catch something like it. The system probalby is “non-linear non-sinusoidal ” in reality but 164 + 64 + 21 year cycles seems to give something near to what may be expected from very low cycles 24 and 25.
The d2T/dt2 plot is also interesting but requires more commentry that I’ll omit for brevity here.