UPDATE from Girma: “My title should have been ‘How to arrive at IPCC’s climate sensitivity estimate’ instead of the original”
Guest essay by Girma Orssengo, PhD
1) IPCC’s 0.2 deg C/decade warming rate gives a change in temperature of dT = 0.6 deg C in 30 years
IPCC:
“Since IPCC’s first report in 1990, assessed projections have suggested global average temperature increases between about 0.15°C and 0.3°C per decade for 1990 to 2005. This can now be compared with observed values of about 0.2°C per decade, strengthening confidence in near-term projections.”
Source: http://www.ipcc.ch/publications_and_data/ar4/wg1/en/spmsspm-projections-of.html
2) The HadCRUT4 global mean surface temperature dataset shows a warming of 0.6 deg C from 1974 to 2004 as shown in the following graph.
Source: http://www.woodfortrees.org/plot/hadcrut4gl/from:1974/to:2004/trend/plot/hadcrut4gl/from:1974/to:2005/compress:12
3) From the following Mauna Loa data for CO2 concentration in the atmosphere, we have CO2 concentration for 1974 of C1 = 330 ppm and for 2004 of C2=378 ppm
Source: http://www.woodfortrees.org/plot/esrl-co2/compress:12
Using the above data, the climate sensitivity (CS) can be calculated using the following proportionality formula for the period from 1974 to 2004
CS = (ln (2)/ln(C2/C1))*dT = (0.693/ln(378/330))*dT = (0.693/0.136)*dT = 5.1*dT
For change in temperature of dT = 0.6 deg C from 1974 to 2004, the above relation gives
CS = 5.1 * 0.6 = 3.1 deg C, which is IPCC’s estimate of climate sensitivity and requires a warming rate of 0.2 deg C/decade.
IPCC’s warming rate of 0.2 deg C/decade is not the climate signal as it includes the warming rate due to the warming phase of the multidecadal oscillation.
To remove the warming rate due to the multidecadal oscillation of about 60 years cycle, least squares trend of 60 years period from 1945 to 2004 is calculated as shown in the following link:
Source: http://www.woodfortrees.org/plot/hadcrut4gl/from:1945/to:2004/trend/plot/hadcrut4gl/from:1945/to:2005/compress:12
This result gives a long-term warming rate of 0.08 deg C/decade. From this, for the three decades from 1974 to 2004, dT = 0.08* 3 = 0.24 deg C.
Substituting dT=0.24 deg C in the equation for Climate sensitivity for the period from 1974 to 2004 gives
CS = 5.1* dT = 5.1* 0.24 = 1.2 deg C.
IPCC’s climate sensitivity of about 3 deg C is incorrect because it includes the warming rate due to the warming phase of the multidecadal oscillation. The true climate sensitivity is only about 1.2 deg C, which is identical to the climate sensitivity with net zero-feedback, where the positive and negative climate feedbacks cancel each other.
Positive feedback of the climate is not supported by the data.
UPDATE:
To respond to the comments, I have included the following graph
Source: http://www.woodfortrees.org/plot/hadcrut4gl/mean:756/plot/hadcrut4gl/compress:12/from:1870/plot/hadcrut4gl/from:1974/to:2004/trend/plot/esrl-co2/scale:0.005/offset:-1.62/detrend:-0.1/plot/esrl-co2/scale:0.005/offset:-1.35/detrend:-0.1/plot/esrl-co2/scale:0.005/offset:-1.89/detrend:-0.1/plot/hadcrut4gl/mean:756/offset:-0.27/plot/hadcrut4gl/mean:756/offset:0.27/plot/hadcrut3sh/scale:0.00001/offset:2/from:1870/plot/hadcrut4gl/from:1949/to:2005/trend/offset:0.025/plot/hadcrut4gl/from:1949/to:2005/trend/offset:0.01
I have got a better estimate of the warming of the long-term smoothed GMST using least squares trend from 1949 to 2005 as shown in the above graph, which shows the least squares trend coincides with the Secular GMST curve for the period from 1974 to 2005. For this case, the warming rate of the least squares trend for the period from 1949 to 2005 is 0.09 deg C/decade.
This gives dT = 0.09 * 3 = 0.27 deg C, and the improved climate sensitivity estimate is
CS = 5.1*0.27 = 1.4 deg C.
That is an increase in Secular GMST of 1.4 deg C for doubling of CO2 based on the instrumental records.
“Guest essay by Girma Orssengo, PhD”
Dr Orssengo, since you are using your title in a way that would tend to lend authority to your views, would you be so kind as to reply to enquiry as to the field of study in which you gained your doctorate?
That is not intended to be provocative so please do not take it the wrong way but since you make a point of waving you qualification, it would seem proper to state your area of competence.
Mike sez: “His unspoken argument is that you have to take a temperature trend over complete cycle(s) in order to remove the cyclical effect”
Just a caution here; the Cosine Warming effect can occur with a single full cycle. It is mainly dependent on the starting and end points. If they aren’t the same, an apparent linear slope occurs.
Greg Goodman
just google him and you find this:
http://www.geocities.ws/girmao/resume.htm
Thanks Alexej . I tried googling his name some time last year a drew a blank.
Chris Schoneveld, are you Mr. Eschenbach’s grandmother? You are, at least, on a first name basis with him. Hm?
Correlation is not causation.
From Greg Goodman on May 18, 2013 at 11:40 am:
But what purpose can the dual “derivative” subtractions serve?
Start: A, B, C, D, E, F
Step1: A, B-A, C-B, D-C, E-D, F-E
Step2: A, B-A-A, C-B-(B-A), D-C-(C-B), E-D-(D-C), F-E-(E-D)
-equaling: A, B-2A, C-2B+A, D-2C+B, E-2D+C, F-2E+D
What’s the justification?
O RYL?
Stick the trends on it.
http://www.woodfortrees.org/plot/rss/from:1980/trend/plot/rss/from:1980/mean:60/trend/plot/rss/from:1980/mean:30/mean:22/mean:17/trend
Original data: 0.0131384°C per year
60-mo running mean: 0.0160371°C per year, a 22% increase.
Your 30,22, then 17 sample “filters”: 0.0167072 per year, a 27% increase. You keep making the warming worse!
Of course that comes from shortening the dataset. The 60-mo “filter” takes off 30 months on each end, you drop from currently 400 data points to 340. Your ‘triple filter’ is down to 332, you lost 34 per end, int(30/2) + int(22/2) + int(17/2). Same rise, but you keep shortening the run.
Greg Goodman said on May 18, 2013 at 11:42 am:
It’s three sequential running means. One running mean smooths things out, is called a filter. Three in a row is data distortion. You don’t take a running mean of a running mean, you certainly don’t do it twice, and you never pretend afterward that you still have data.
@Greg Goodman
Would this help?
http://www.geocities.ws/girmao/resume.htm
Darn, Alexej beat me to it….
Recalling that I characterized “Girma” as a “Snake Oil Salesman” in my Jane Austen parody post in the “Sense about Sensitivity Thread” in April, 2013, I looked up why I did and discovered why I still DO think Girma is a slick operator:
Girma says [citing the following article by M. Latif favorably]:
April 24, 2013 at 7:44 pm
“Communicating Climate Science
***
There is a broad scientific consensus that the climate of the 21st century will warm in response to the anthropogenic emission of greenhouse gases (GHGs) into the atmosphere, but by how much remains highly uncertain. This is due to three factors: natural variability, model error, and emission scenario uncertainty.
***There is overwhelming scientific evidence that a significant share of 20th century warming is driven by the increase of GHGs. They will continue to accumulate in the atmosphere over the next years and possibly even decades, which together with the inertia of the climate system will support further warming. But what else do we really know about the climate of the 20th and 21st century?
Surface air temperature (SAT) during the 20th century displays a gradual warming and superimposed short-term fluctuations (the figure shows observed annual Northern Hemisphere and Arctic SAT as red lines). The upward trend contains the climate response to enhanced atmospheric GHG levels but also a natural component.
***
To some extent, we need to “ignore” the natural fluctuations, if we want to “see” the human influence on climate. ***
The uncertainty in climate sensitivity itself is in my opinion a good reason to demand reductions of global GHG emissions, because the possibility of ‘a dangerous interference with the climate system’ cannot be ruled out with high confidence.
To predict the future climate we have to consider both natural variability and anthropogenic forcing. The latter is taken into account by assuming scenarios about future GHG and aerosol emissions. The scenarios cover a wide range of the main driving forces of future emissions, from demographic to technological and economic developments. IPCC AR4 published only climate projections based on such scenarios with no attempt to take account of the likely evolution of the natural variability. *** In the real world, the natural variations will introduce a large degree of irregularity, and even short-term cooling may occur during the next years .
This could have been explained better to the public, as in some media reports the existence of Global Warming has been questioned after for more than ten years no global SAT record has been observed. Had we emphasized more the uncertainty, that debate which confused many people could have been avoided.
Mojib Latif is a Professor of Climate Physics at Kiel University and Head of the Ocean Circulation and Climate Dynamics Division of the Helmholtz Centre for Ocean Research, Germany. He is Contributing Author of the IPCC Reports 2001 (TAR) and 2007 (AR4).”
[End Girma’s post.]
Geocities still exists? And from Western Samoa?
Your end point is almost a decade old.
Determining climate sensitivity to a given forcing via observed temperature change over the past 60yrs is silly for obvious reasons.
You need to look at spectral dampening through the TOA boundary (since all radiative forcings are derived at the TOA). The data in that regard (CERES) makes clear the fact that there has indeed been a minor dampening in the CO2 spectrum, yet total OLWR has actually increased from 1979-present, by over 1W/m^2. It can be determined that at least 94% of the warming observed since 1979 was naturally forced via a slight reduction in low level cloud cover, of the cumulus/cumulonimbus type, and depleted upper tropospheric/stratospheric H2O/O^3, possibly stemming from a weakening magnetic field or increase in the ferocity of the solar wind up until solar cycle 23.
kadaka (KD Knoebel) says: You keep making the warming worse!
No I don’t because I did not fit trends. That would be a stupid thing to do and is certainly not a test of quality of a frequency filter. No cookie. Try again.
“It’s three sequential running means. One running mean smooths things out, is called a filter. Three in a row is data distortion. You don’t take a running mean of a running mean, you certainly don’t do it twice, and you never pretend afterward that you still have data.”
You argue from ignorance . Go and inform yourself about filter design ,frequency response, and phase distortion. Look at the response of a single running mean, a gaussian and the triple running mean.
Then you may be able to explain to me why the running mean gets nearly all the peaks and troughs perfectly upside down in the RSS data that I posted earlier and that you chose to ignore.
http://www.woodfortrees.org/plot/rss/from:1980/plot/rss/from:1980/mean:60/plot/rss/from:1980/mean:30/mean:22/mean:17
Once you have learnt enough to understand that, come back and admit you were talking rather too loudly about something of which you have little understanding or knowledge.
Willis
But no, now that I’ve shown that your “multidecadal oscillation” pattern doesn’t exist further back than 1869, now you are saying it’s just a temporary pattern, might disappear tomorrow … but if so, what is the justification for removing it?
The quality of the instrumental temperature records is poor before 1880s as stated by Phil Jones here:
Temperature data for the period 1860-1880 are more uncertain, because of sparser coverage, than for later periods in the 20th Century.
http://news.bbc.co.uk/2/hi/8511670.stm
However, here is a published paper that shows the multidecadal oscillation extends back to 1400 years:
A signature of persistent natural thermohaline circulation cycles in
observed climate
Knight et al.
Analyses of global climate from measurements dating
back to the nineteenth century show an ‘Atlantic
Multidecadal Oscillation’ (AMO) as a leading large-scale
pattern of multidecadal variability in surface temperature.
Yet it is not possible to determine whether these fluctuations
are genuinely oscillatory from the relatively short
observational record alone. Using a 1400 year climate
model calculation, we are able to simulate the observed
pattern and amplitude of the AMO. The results imply the
AMO is a genuine quasi-periodic cycle of internal climate
variability persisting for many centuries, and is related to
variability in the oceanic thermohaline circulation (THC).
This relationship suggests we can attempt to reconstruct
past THC changes, and we infer an increase in THC
strength over the last 25 years. Potential predictability
associated with the mode implies natural THC and
AMO decreases over the next few decades independent
of anthropogenic climate change.
….
The quasi-periodic nature of the model’s AMO
suggests that in the absence of external forcings at least,
there is some predictability of the THC, AMO and global
and Northern Hemisphere mean temperatures for several
decades into the future. We utilise this to forecast decreasing
THC strength in the next few decades. This natural
reduction would accelerate anticipated anthropogenic THC
weakening, and the associated AMO change would partially
offset expected Northern Hemisphere warming. This effect
needs to be taken into account in producing more realistic
predictions of future climate change.
Been saying it for years and will say it again.
What climate science has re-discovered is the PDO/AMDO. Period. Since we already discovered it in 1996, I perceive no value in re-discovering it at a cost of hundreds of billions of dollars.
Maybe I just misunderstood something in the math here, but something seems amiss:
The 5.1 in the climate-sensitivity calculation comes from using CO2 concentrations from 1974 and 2004, but it is used with the temperature change from 1945 to 2004 at the bottom. When looking at the response of temperature to CO2, shouldn’t the changes in each over the same time-period be used?
“This result gives a long-term warming rate of 0.08 deg C/decade. From this, for the three decades from 1974 to 2004, dT = 0.08* 3 = 0.24 deg C.”
No, it seems he is using the dT of three decades in each case.
Stephen
When looking at the response of temperature to CO2, shouldn’t the changes in each over the same time-period be used?
The problem is the CO2 concentration is a smooth monotonic curve. However, the Annual GMST data is not. It has a clear oscillation of about 60 years. To remove this oscillation, you need to smooth the Annual GMST curve with longer period of about 60 years. Here is the annual global mean temperature smoothed with a 63-years moving average showing the pattern.
http://bit.ly/1109qyb
This graph shows the warming based on the 30-years least squares trend of 0.6 deg C is greater than that based on the long-term trend of 0.24 deg C.
My main point is that the long-term trend should be used to estimate climate sensitivity, not the 30-years least squares trend.
Wills
I am estimating climate sensitivity for the period 1974 to 2004. Is not the following pattern sufficient to do that?
http://bit.ly/1109qyb
Why not?
“My main point is that the long-term trend should be used to estimate climate sensitivity, not the 30-years least squares trend.”
That is a very valid point.
Now if you want tp propose a cos+linear model, why not fit one directly using OLS, rather than doing a questionable filter?
In fact quadratic + cosine would be a lot closer, the linear part isn’t linear. That is what Scaffeta does (but with multiple cosines.
Greg
I agree the method is an approximate one, but I have checked it agrees with complicated published results.
Here is my argument.
I have shown below how the 60-years least squares trend relates to the long-term secular GMST. It shows the 60-years least squares trend is tangent to the secular GMST.
http://bit.ly/110bM02
As a result, the change in temperature from 1974 to 2004 may be estimated from the 0.08 deg C/decade warming rate of the 60-years least squares trend.
Mathematical Softwares: Mathematica, MatLab, MathCAD, and Microsoft Excel
Programming Languages: Visual Basic, ASP, HTML, Crystal Report, Fortran, C, and Pascal
With that kind of baggage you should be able to fit a simple model without relying upon trivial detrending and runny mean filters available at WTF.org.
before calling CO2 “monotonic” you ought to have a look at:
http://climategrog.wordpress.com/?attachment_id=233
The formula shown, does that work with actual temperature, in Kelvin?