Guest post by Girma Orssengo, B. Tech, MASc, PhD
The Intergovernmental Panel on Climate Change (IPCC) claims that human emission of CO2 causes catastrophic global warming. When such extraordinary claim is made, every one with background in science has to look at the data and verify whether the claim is justified or not. In this article, a mathematical model was developed that agrees with observed Global Mean Temperature Anomaly (GMTA), and its prediction shows global cooling by about 0.42 deg C until 2030. Also, comparison of observed increase in human emission of CO2 with increase in GMTA during the 20th century shows no relationship between the two. As a result, the claim by the IPCC of climate catastrophe is not supported by the data.
Fossil fuels allowed man to live his life as a proud human, but the IPCC asserts its use causes catastrophic global warming. Fortunately, the global warming claim by the IPCC that “For the next two decades, a warming of about 0.2°C per decade is projected for a range of SRES emission scenario” [1] is not supported by observations as shown in Figure 1, which shows a plateau for the global mean temperature trend for the last decade.
.”]
Figure 1 also shows that the observed temperatures are even less than the IPCC projections for emission held constant at the 2000 level.
As a result, the statement we often hear from authorities like UN Secretary-General Ban Ki-moon that “climate change is accelerating at a much faster pace than was previously thought by scientists” [3] is incorrect.
Thanks for the release of private emails of climate scientists, we can now learn from their own words whether global warming “is accelerating at a much faster pace” or not. In an email dated 3-Jan-2009, Mike MacCracken wrote to Phil Jones, Folland and Chris [4]:
I think we have been too readily explaining the slow changes over past decade as a result of variability–that explanation is wearing thin. I would just suggest, as a backup to your prediction, that you also do some checking on the sulfate issue, just so you might have a quantified explanation in case the prediction is wrong. Otherwise, the Skeptics will be all over us–the world is really cooling, the models are no good, etc. And all this just as the US is about ready to get serious on the issue.
…
We all, and you all in particular, need to be prepared.
Similarly, in an email dated 24-Oct-2008, Mick Kelly wrote to Phil Jones [5]:
Just updated my global temperature trend graphic for a public talk and noted that the level has really been quite stable since 2000 or so and 2008 doesn’t look too hot.
…
Be awkward if we went through a early 1940s type swing!
The above statements from the climategate emails conclusively prove that the widely used phrase by authorities in public that global warming “is accelerating at a much faster pace” is supported neither by climate scientists in private nor by the observed data.
Thanks also goes to the Climate Research Unit (CRU) of the Hadley Center for daring to publish global mean temperature data that is “quite stable since 2000”, which is contrary to IPCC projections of 0.2 deg C warming per decade. If the CRU had not done this, we would have been forced to swallow the extremely irrational concept that the gas CO2, a plant food, i.e. foundation of life, is a pollutant because it causes catastrophic global warming.
As IPCC’s “models are no good”, it is the objective of this article to develop a valid mathematical global mean temperature model based on observed temperature patterns.
Mathematical Model For The Global Mean Temperature Anomaly (GMTA) Based On Observed Temperature Patterns
The Global Mean Temperature Anomaly (GMTA) data from the Climate Research Unit (CRU) of the Hadley Center shown in Figure 2 will be used to develop the mathematical model. In this article, the observed GMTA data from the CRU are assumed to be valid.
Examination of Figure 2 shows that the globe is warming at a linear rate as shown by the least square trend central line given by the equation
Linear anomaly in deg C = 0.0059*(Year-1880) – 0.52 Equation 1
Figure 2 also shows that superimposed on this linear anomaly line there is an oscillating anomaly that gives the Global Mean Temperature Anomaly (GMTA) the characteristics summarized in Table 1.
Table 1. Characteristics of the observed Global Mean Temperature Anomaly (GMTA) shown in Figure 2.
|
From 1880s to 1910s |
End of warming, plateau at –0.2 deg C & then cooling trend |
|
From 1910s to 1940s |
End of cooling, plateau at –0.6 deg C & then warming trend |
|
From 1940s to 1970s |
End of warming, plateau at 0.1 deg C & then cooling trend |
|
From 1970s to 2000s |
End of cooling, plateau at –0.3 deg C & then warming trend |
|
From 2000s to 2030s |
End of warming, plateau at 0.5 deg C & then ? trend |
A mathematical model can be developed that satisfies the requirements listed in Table 1. If the model to be developed gives good approximation for the GMTA values at its turning points (plateaus) and the GMTA trends between its successive turning points as summarized in Table 1, the model may be used for prediction.
.”]
For the oscillating anomaly, the sinusoidal function cosine meets the requirements listed in Table 1. From Figure 2, the amplitude of the oscillating anomaly is given by the vertical distance in deg C from the central linear anomaly line to either the top or bottom parallel lines, and it is about 0.3 deg C. From Figure 2, the oscillating anomaly was at its maximum in the 1880s, 1940s, & 2000s; it was at its minimum in the 1910s and 1970s. The years between successive maxima or minima of the oscillating anomaly is the period of the cosine function, and it is about 1940–1880=1970–1910=60 years. For the cosine function, once its amplitude of 0.3 deg C and its period of 60 years are determined, the mathematical equation for the oscillating anomaly, for the years starting from 1880, can be written as
Oscillating anomaly in deg C = 0.3*Cos(((Year-1880)/60)*2*3.1416) Equation 2
In the above equation, the factor 2*3.1416 is used to convert the argument of the cosine function to radians, which is required for computation in Microsoft Excel. If the angle required is in degrees, replace 2*3.1416 with 360.
Combining the linear anomaly given by Equation 1 and the oscillating anomaly given by Equation 2 gives the equation for the Global Mean Temperature Anomaly (GMTA) in deg C for the years since 1880 as
GMTA = 0.0059*(Year-1880) – 0.52 + 0.3*Cos(((Year-1880)/60)*2*3.1416) Equation 3
The validity of this model may be verified by comparing its estimate with observed values at the GMTA turning points as summarized in Table 2.
Table 2. Comparison of the model with observations for GMTA in deg C at its turning points.
|
Year |
Observed (Table 1) |
Model (Equation 3) |
|
Warming plateau for the 1880s |
-0.2 |
-0.22 |
|
Cooling plateau for the 1910s |
-0.6 |
-0.64 |
|
Warming plateau for the 1940s |
+0.1 |
+0.13 |
|
Cooling plateau for the 1970s |
-0.3 |
-0.29 |
|
Warming plateau for the 2000s |
+0.5 |
+0.48 |
Table 2 shows excellent agreement for the GMTA values between observation and mathematical model for all observed GMTA turning points.
A graph of the GMTA model given by Equation 3 is shown in Figure 3, which includes the observed GMTA and short-term IPCC projections for GMTA from 2000 to 2025. In addition to the verification shown in Table 2, Figure 3 shows good agreement for the GMTA trends throughout observed temperature records, so the model may be used for prediction. As a result, Figure 3 includes GMTA predictions until 2100, where the year and the corresponding GMTA values are given in parentheses for all the GMTA turning points.
As shown in Figure 3, a slight discrepancy exist between observed and model GMTA values at the end of the 1890s when the observed values were significantly warmer than the model pattern, and in the 1950s when the observed values were significantly colder than the model pattern.
From the model in Figure 3, during the observed temperature record, there were two global warming phases. The first was from 1910 to 1940 with a warming of 0.13+0.64=0.77 deg C in 30 years. The second was from 1970 to 2000 with a warming of 0.48+0.29=0.77 deg C in 30 years. Note that both warming phases have an identical increase in GMTA of 0.77 deg C in 30 years, which gives an average warming rate of (0.77/30)*10=0.26 deg C per decade.
From the model in Figure 3, during the observed temperature record, there were two global cooling phases. The first was from 1880 to 1910 with a cooling of 0.64-0.22=0.42 deg C in 30 years. The second was from 1940 to 1970 with a cooling of 0.13+0.29=0.42 deg C in 30 years. Note that both cooling phases have an identical decrease in GMTA of 0.42 deg C in 30 years, which gives an average cooling rate of (0.42/30)*10=0.14 deg C per decade.
The above results for the normal ranges of GMTA determined from the model can also be calculated using simple geometry in Figure 2. In this figure, almost all observed GMTA values are enveloped by the two parallel lines that are 0.6 deg C apart. Therefore, as a first approximation, the normal range of GMTA is 0.6 deg C. From Figure 2, the period for a global warming or cooling phase is about 30 years. Therefore, as a first approximation, the normal rate of global warming or cooling is (0.6/30)*10=0.2 deg C per decade.
The above approximation of 0.6 deg C for the normal range of GMTA should be refined by including the effect of the linear warming anomaly given by Equation 1 of 0.006 deg C per year, which is the slope of the two envelope parallel lines in Figure 2. As the oscillating anomaly changes by 0.6 deg C in 30 years between its turning points, the linear anomaly increases by 0.006*30=0.18 deg C. Due to this persistent warming, instead of the GMTA increasing or decreasing by the same 0.6 deg C, it increases by 0.6+0.18=0.78 deg C during its warming phase, and decreases by 0.6–0.18=0.42 deg C during its cooling phase. As a result, the refined normal ranges of GMTA are 0.77 deg C in 30 years during its warming phase, and 0.42 deg C in 30 years during its cooling phase. These results for the normal ranges of GMTA obtained using simple geometry in Figure 2 agree with those obtained from the model in Figure 3.
Correlation of Model and Observed Global Mean Temperature Anomaly (GMTA)
In Table 2, data points for only five years were used to verify the validity of Equation 3 to model the observed data. However, it is important to verify how well the observed GMTA is modeled for any year.
How well the observed data is modeled can be established from a scatter plot of the observed and model GMTA values as shown in Figure 4. For example, for year 1998, the observed GMTA was 0.53 deg C and the model GMTA is 0.47 deg C. In Figure 4, for year 1998, the pair (0.47,0.53) is plotted as a dot. In a similar manner, all the paired data for model and observed GMTA values for years from 1880 to 2009 are plotted as shown in Figure 4.
Figure 4 shows a strong linear relationship (correlation coefficient, r=0.88) between the model and observed GMTA. With high correlation coefficient of 0.88, Figure 4 shows the important result that the observed GMTA can be modeled by a combination of a linear and sinusoidal pattern given by Equation 3. The positive slope of the trend line indicates a positive relationship between model and observed GMTA. That is, global cooling from the model indicates observed global cooling, and global warming from the model indicates observed global warming.
Global Mean Temperature Prediction Calculations
The following patterns may be inferred from the graph of the Global Mean Temperature Anomaly (GMTA) model shown in Figure 3 for the data from the Climate Research Unit of the Hadley Center [2]:
-
Year 1880 was the start of a cooling phase and had a GMTA of –0.22 deg C.
-
During the global cooling phase, the GMTA decreases by 0.42 deg C in 30 years.
-
Global cooling and warming phases alternate with each other.
-
During the global warming phase, the GMTA increases by 0.77 deg C in 30 years.
The patterns in the list above are sufficient to estimate the GMTA values at all of its turning points since 1880.
For example, as year 1880 with GMTA of –0.22 deg C was the start of a cooling phase of 0.42 deg C in 30 years, the next GMTA turning point was near 1880+30=1910 with GMTA of –0.22–0.42=-0.64 deg C. This GMTA value for 1910 is shown as (1910,-0.64) in Figure 3.
As year 1910 with GMTA of –0.64 deg C was the end of a global cooling phase, it is also the start of a global warming phase of 0.77 deg C in 30 years. As a result, the next GMTA turning point was near 1910+30=1940 with GMTA of 0.77–0.64=0.13 deg C. This GMTA value for 1940 is shown as (1940,0.13) in Figure 3.
As year 1940 with GMTA of 0.13 deg C was the end of a global warming phase, it is also the start of a global cooling phase of 0.42 deg C in 30 years. As a result, the next GMTA turning point was near 1940+30=1970 with GMTA of 0.13–0.42=-0.29 deg C. This GMTA value for 1970 is shown as (1970,-0.29) in Figure 3.
As year 1970 with GMTA of -0.29 deg C was the end of a global cooling phase, it is also the start of a global warming phase of 0.77 deg C in 30 years. As a result, the next GMTA turning point was near 1970+30=2000 with GMTA of 0.77–0.29=0.48 deg C. This GMTA value for 2000 is shown as (2000,0.48) in Figure 3.
As the GMTA values calculated above using the global temperature patterns listed at the beginning of this section give good approximation of observed GMTA values at all GMTA turning points (1880, 1910, 1940, 1970 & 2000), it is reasonable to assume that the patterns may also be used for prediction.
As a result, as year 2000 with GMTA of 0.48 deg C was the end of a global warming phase, it is also the start of a global cooling phase of 0.42 deg C in 30 years. As a result, the next GMTA turning point will be near 2000+30=2030 with GMTA of 0.48–0.42=0.06 deg C. This GMTA value for 2030 is shown as (2030,0.06) in Figure 3.
In a similar manner, the GMTA values for the remaining GMTA turning points for this century can be calculated, and the results are shown in Figure 3.
Figure 3 shows a very interesting result that for the 20th century, the global warming from 1910 to 2000 was 0.48+0.64=1.12 deg C. In contrast, for the 21st century, the change in GMTA from 2000 to 2090 will be only 0.41–0.48=-0.07 deg C. This means that there will be little change in the GMTA for the 21st century! Why?
Why Does The Same Model Give A Global Warming Of About 1 deg C For The 20th Century But Nearly None For The 21st Century?
According to the data shown in Figure 3, it is true that the global warming of the 20th century was unprecedented. As a result, it is true that the corresponding sea level rise, melting of sea ice or the corresponding climate change in general were unprecedented. However, this was because the century started when the oscillating anomaly was at its minimum near 1910 with GMTA of –0.64 deg C and ended when it was at its maximum near 2000 with GMTA of 0.48 deg C, giving a large global warming of 0.48+0.64=1.12 deg C. This large warming was due to the rare events of two global warming phases of 0.77 deg C each but only one cooling phase of 0.44 deg C occurring in the 20th century, giving a global warming of 2*0.77-0.42=1.12 deg C.
In contrast to the 20th century, from Figure 3, there will be nearly no change in GMTA in the 21st century. This is because the century started when the oscillating anomaly was at its maximum near 2000 with GMTA of 0.48 deg C and will end when it is at its minimum near 2090 with GMTA of 0.41 deg C, giving a negligible change in GMTA of 0.41-0.48=-0.07 deg C. This negligible change in GMTA is due to the rare events of two global cooling phases of 0.42 deg C each but only one warming phase of 0.77 deg C occurring in the 21st century, giving the negligible change in GMTA of 0.77-2*0.42=-0.07 deg C. Note that this little change in GMTA for the 21st century is identical to that from 1880 to 1970, which makes the global warming from 1970 to 2000 by 0.77 deg C appear to be abnormally high.
If the period for a century had been 120 years, we wouldn’t have this conundrum of nearly 1 deg C warming in the 20th century but nearly none in the next!
Ocean Current Cycles
One of the most important variables that affect global mean surface temperature is ocean current cycles. The rising of cold water from the bottom of the sea to its surface results in colder global mean surface temperature; weakening of this movement results in warmer global mean surface temperature. Various ocean cycles have been identified. The most relevant to global mean temperature turning points is the 20 to 30 years long ocean cycle called Pacific Decadal Oscillation (PDO) [6]:
Several independent studies find evidence for just two full PDO cycles in the past century: “cool” PDO regimes prevailed from 1890-1924 and again from 1947-1976, while “warm” PDO regimes dominated from 1925-1946 and from 1977 through (at least) the mid-1990’s (Mantua et al. 1997, Minobe 1997).
These cool and warm PDO regimes correlate well with the cooling and warming phases of GMTA shown in Figure 3.
The model in Figure 3 predicts global cooling until 2030. This result is also supported by shifts in PDO that occurred at the end of the last century, which is expected to result in global cooling until about 2030 [7].
Effect Of CO2 Emission On Global Mean Temperature
Examination of Figure 3 shows that the Global Mean Temperature Anomaly (GMTA) for 1940 of 0.13 deg C is greater than that for 1880 of –0.22 deg C. Also, the GMTA for 2000 of 0.48 deg C is greater than that for 1940 of 0.13 deg C. This means that the GMTA value, when the oscillating anomaly is at its maximum, increases in every new cycle. Is this global warming caused by human emission of CO2?
The data required to establish the effect of CO2 emission on global mean temperature already exist. The global mean temperature data are available from the Climate Research Unit of the Hadley Centre shown in Figure 3, and the CO2 emission data are available from the Carbon Dioxide Information Analysis Centre [8]. For the period from 1880 to 1940, the average emission of CO2 was about 0.8 G-ton, and the increase in the GMTA was 0.13+0.22=0.35 deg C. For the period from 1940 to 2000, the average emission of CO2 was about 4 G-ton, but the increase in GMTA was the same 0.48-0.13=0.35 deg C. This means that an increase in CO2 emission by 4/0.8=5-fold has no effect in the increase in the GMTA. This conclusively proves that the effect of 20th century human emission of CO2 on global mean temperature is nil.
Note that the increase in GMTA of 0.35 deg C from 1880 to 1940 (or from 1940 to 2000) in a 60 year period has a warming rate of 0.35/60=0.0058 deg per year, which is the slope of the linear anomaly given by Equation 1. As a result, the linear anomaly is not affected by CO2 emission. Obviously, as the oscillating anomaly is cyclic, it is not related to the 5-fold increase in human emission of CO2.
Figure 4, with high correlation coefficient of 0.88, shows the important result that the observed GMTA can be modeled by a combination of a linear and sinusoidal pattern given by Equation 3. This single GMTA pattern that was valid in the period from 1880 to 1940 was also valid in the period from 1940 to 2000 after about 5-fold increase in human emission of CO2. As a result, the effect of human emission of CO2 on GMTA is nil.
Further evidence for the non-existent relationship between CO2 and GMTA is IPCC’s projection of a global warming of 0.2 deg C per decade, while the observed GMTA trend was “quite stable since 2000” [5]. The evidence will be “unequivocal” if global cooling by about 0.42 deg C starts soon and continues until about 2030, as shown by the model in Figure 3. The IPCC projection for the GMTA for 2020 is 0.8 deg C, while the prediction from the model for this value is 0.2 deg C, a large discrepancy of 0.6 deg C. If this global cooling is confirmed, it will then be time to bury the theory that CO2, a plant food, causes catastrophic global warming. Fortunately, we don’t have to wait too long for the burial. Less than ten years. It will be cheering news!
IPCC Projections
According to the IPCC [1], “For the next two decades, a warming of about 0.2°C per decade is projected for a range of SRES emission scenario.”
IPCC explains this projection as shown in Figure 5 where GMTA trend lines were drawn for four periods from 2005 to 1856, 1906, 1956 & 1981. These trend lines give increasing warming rate from a low value of 0.045 deg C per decade for the RED trend line for the first period from 1856 to 2005, to a greater value of 0.074 deg C per decade for the PURPLE trend line for the second period from 1906 to 2005, to a still greater value of 0.128 deg C per decade for the ORANGE trend line for the third period from 1956 to 2005, and to a maximum value of 0.177 deg C per decade for the YELLOW trend line for the fourth period from 1981 to 2005. IPCC then concludes, “Note that for shorter recent periods, the slope is greater, indicating accelerated warming” [9].
If this IPCC interpretation is correct, catastrophic global warming is imminent, and it is justified for the world to be griped by fear of global warming. However, is IPCC’s “accelerated warming” conclusion shown in Figure 5 correct?
What the GMTA pattern in Figure 3 shows is that it has cooling and warming phases. As a result, in Figure 5, comparing the warming rate of one period that has only one warming phase with another period that has a combination of warming and cooling phases will obviously show the maximum warming rate for the first period. This is comparing apples to oranges.
Comparing apples to apples is to compare two periods that have the same number of cooling and/or warming phases.
.”]
One example of comparing apples to apples is to compare one period that has one warming phase with another that also has one warming phase. From Figure 3, two 30-year periods that have only one warming phase are the periods from 1910 to 1940 and from 1970 to 2000. For the period from 1910 to 1940, the increase in GMTA was 0.13+0.64=0.77 deg C, giving a warming rate of (0.77/30)*10=0.26 deg C per decade. Similarly, for the period from 1970 to 2000, the increase in GMTA was 0.48+0.29=0.77 deg C, giving an identical warming rate of 0.26 deg C per decade. Therefore, there is no “accelerated warming” in the period from 1970 to 2000 compared to the period from 1910 to 1940.
A second example of comparing apples to apples is to compare one period that has one cooling and warming phases with another that also has one cooling and warming phases. From Figure 3, two 60-year periods that have only one cooling and warming phases are the periods from 1880 to 1940 and from 1940 to 2000. For the period from 1880 to 1940, the increase in GMTA was 0.13+0.22=0.35 deg C, giving a warming rate of (0.35/60)*10=0.06 deg C per decade. Similarly, for the period from 1940 to 2000, the increase in GMTA was 0.48-0.13=0.35 deg C, giving an identical warming rate of 0.06 deg C per decade. Therefore, there is no “accelerated warming” in the period from 1940 to 2000 compared to the period from 1880 to 1940.
From the above analysis, IPCC’s conclusion of “accelerated warming” is incorrect, and its graph shown in Figure 5 is an incorrect interpretation of the data.
Based on observed GMTA pattern shown in Figure 3, a global warming phase lasts for 30 years, and it is followed by global cooling. As a result, the recent global warming phase that started in the 1970s ended in the 2000s as shown by the current GMTA plateau, and global cooling should follow. Therefore, IPCC’s projection for global warming of 0.2 deg C per decade for the next two decades is incorrect. Also, divergence between IPCC projections and observed values for the GMTA has started to be “discernible” since 2005 as shown in Figure 3.
According to the Occam’s Razor principle, given a choice between two explanations, choose the simplest one that requires the fewest assumptions. Instead of applying the Occam’s Razor principle by assuming the cause of GMTA turning points to be natural, the IPCC assumed the cause to be man-made [9]:
From about 1940 to 1970 the increasing industrialisation following World War II increased pollution in the Northern Hemisphere, contributing to cooling, and increases in carbon dioxide and other greenhouse gases dominate the observed warming after the mid-1970s.
Like in the 1880s & 1910s, what if the causes of the GMTA turning points in the 1940s and 1970s were also natural?
Figure 4, with high correlation coefficient of 0.88, shows the important result that the observed GMTA can be modeled by a combination of a linear and sinusoidal pattern given by Equation 3. This single GMTA pattern that was valid in the period from 1880 to 1940 was also valid in the period from 1940 to 2000 after about 5-fold increase in human emission of CO2. As a result, the effect of human emission of CO2 on GMTA is nil. Also, IPCC’s conclusion of “accelerated warming” shown in Figure 5 is incorrect.
What is the cause of the GMTA turning point from warming to plateau in the 2000s? Here is the suggestion by Mike MacCracken [4]:
I think we have been too readily explaining the slow changes over past decade as a result of variability–that explanation is wearing thin. I would just suggest, as a backup to your prediction, that you also do some checking on the sulfate issue, just so you might have a quantified explanation in case the prediction is wrong.
According to the IPCC and the above suggestion, the 1940 GMTA turning point from global warming to cooling was caused by sulfates, the 1970 GMTA turning point from cooling to warming was caused by carbon dioxide, and the 2000 GMTA turning point from warming to plateau was caused by sulfates. It is interesting to note that sulfate and carbon dioxide gave the globe a 30-year alternate cooling and warming phases from 1940 to 2000. This is just absurd.
Instead of saying, “Be awkward if we went through a early 1940s type swing!” in private, but global warming “is accelerating at a much faster pace” in public, please release the world from the fear of climate catastrophe from use of fossil fuels, as this catastrophe is not supported by your own data. It is extremely callous not to do so.
Is the theory that “human emission of CO2 causes catastrophic global warming” one of the greatest blunders or something worse of “science”? We will find the unambiguous answer within the next ten years. Hope they don’t succeed in calling the plant food a pollutant and tax us before then.
==========================================
This document is also available as a PDF file, link below:
For any criticism, please leave a comment below, or contact me at orssengo@lycos.com
Girma J Orssengo
Bachelor of Technology in Mechanical Engineering, University of Calicut, Calicut, India
Master of Applied Science, University of British Columbia, Vancouver, Canada
Doctor of Philosophy, University of New South Wales, Sydney, Australia
===========================================
REFERENCES
[1] IPCC Fourth Assessment Report: Climate Change 2007
“a warming of about 0.2°C per decade is projected”
http://www.ipcc.ch/publications_and_data/ar4/wg1/en/spmsspm-projections-of.html
[2] Observed Global Mean Surface Temperatures from the Climate Research Unit of the Hadley Center.
[3] Climate Change Science Compendium 2009
“is accelerating at a much faster pace”
http://www.unep.org/pdf/ccScienceCompendium2009/cc_ScienceCompendium2009_full_en.pdf
[4] Climategate Email from Mike MacCracken to Phil Jones, Folland and Chris
“that explanation is wearing thin”
http://www.eastangliaemails.com/emails.php?eid=947&filename=1231166089.txt
[5] Climategate Email from Mick Kelly to Phil Jones
“Be awkward if we went through a early 1940s type swing!”
http://www.eastangliaemails.com/emails.php?eid=927&filename=1225026120.txt
[6] The Pacific Decadal Oscillation (PDO)
http://jisao.washington.edu/pdo/
[7] Pacific Ocean Showing Signs of Major Shifts in the Climate
http://www.nytimes.com/library/national/science/012000sci-environ-climate.html
[8] Carbon Dioxide Information Analysis Center
Global CO2 Emissions from Fossil-Fuel Burning, Cement Manufacture, and Gas Flaring
http://cdiac.ornl.gov/ftp/ndp030/global.1751_2006.ems
[9] Climate Change 2007: Working Group I: The Physical Science Basis
How are Temperatures on Earth Changing?
http://www.ipcc.ch/publications_and_data/ar4/wg1/en/faq-3-1.html
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Interesting. A engineering approach. OK Its not going to work for ” long term- 100 year” predictions but it puts the data in perspective (even if the temp data is not all that correct).
Also I’d reckon its a much better bet on shorter term predictions ( 10 to 15 perhaps even 30 years) than any of the billion dollar models have come up with. So its got more credibility than the physically CO2 driven variety in my opinion.
Lets see what happens over the next 10 years.
stevengoddard,
You said “The Temperature vs. CO2 plot shows very good correlation. You might argue over which one is the independent axis (cause and effect) but the correlation is quite good.”
That in combination with you plot of CO2 and temp has to be one of the most unintelligent things I’ve ever seen you post on here. You took 2 variables which were both know to be increasing and you plotted them together and, most importantly, you set the scale. Of course they correlate!!! I could do the same exact thing with temperature and the number of shoes in my wife’s closet. When you set the scales, the correlation is absolutley meaningless. In fact, it wouldn’t even have to be 2 quantities that were both increasing. You could reverse one of the scales and still show perfect correlation. I could replot your own graph, with your exact data yet show CO2 going off the chart and temperature remaining relatively flat. Or vice versa.
Richard Telford (13:23:18)
You wrote,
“Your model is very useful. Extrapolating back in time to the early part of the last ice age, your model predicts temperatures a below absolute zero. I knew the ice ages were cold, but that is perhaps a tad excessive. Predicting forwards, we can determine how long it will be before the oceans boil”
Nowhere in my article have I claimed that the linear warming of 0.06 deg C per decade is a constant. It is like in calculus we assume a curve by consecutive small straight lines with varying slopes. At the moment, we are at one of this lines and it has a slope of 0.06 deg C per decade.
stevengoddard (21:09:58)
Not sure what you mean, Steven. For simplicity, I took the correlation between the year of observation and temperature, but any straight line which is not horizontal would give the same answer.
Next, you say:
So does the temperature vs. a straight line, it’s just as good … so what? If you hold that one is important, you have to hold the other is equally important.
You must read Lawrence Durrell’s amusing short story, “Case history,” in Esprit de Corps. Here is an extract to give you a hint:
OT but from solar 24 quote: “Solar Update – The spotless streak continues and now sits at 11 days in a row without a sunspot. Solar activity will continue at very low levels for the next 24 hours. ” So it looks like David Archibald’s prediction of SSN 40 max may turn out to be correct from current trends anyway
Quoting from the paper again: “According to the Occam’s Razor principle, given a choice between two explanations, choose the simplest one that requires the fewest assumptions. Instead of applying the Occam’s Razor principle by assuming the cause of GMTA turning points to be natural, the IPCC assumed the cause to be man-made [9]:”
This is not quite what Occam’s Razon says. I submit the following discussion that I prepared for one of my essays.
“Occam (of Occam’s razor) expresses a principle; he is an English logician and Franciscan friar (William of Ockham, 1285-1349 CE). Unfortunately I will not be able to meet with him. The principle states that the explanation of any phenomenon should make as few assumptions as possible, eliminating those that make no difference in the observable predictions of the explanatory hypothesis or theory. The principle is often expressed in Latin as the lex parsimoniae (“law of parsimony” or “law of succinctness”): “entia non sunt multiplicanda praeter necessitatem”, (roughly translated to English from Latin as: “entities must not be multiplied beyond necessity”. This is often paraphrased as “All other things being equal, the simplest solution is the best.”) In other words, when multiple competing theories are equal in other respects, the principle recommends selecting the theory that introduces the fewest assumptions and postulates, the fewest entities.”
That does not mean the statement is incorrect just not quite right. I suggest it be slightly reworded. Since the IPCC’s projections and the models used are based on a false primise they are not equal in other respects. Assuming for argument they are equal in other respects then Occam when applied ….
Willis Eschenbach (22:10:02) :
You seem to be trying to make an argument that all linear relationships are meaningless, because they are straight. That is kind of weird.
The CO2 vs. time plot is definitely not a straight line.
Meant to add this link
https://spreadsheets.google.com/oimg?key=0AnKz9p_7fMvBdE9rZ3lzMHRRaGxUb3JHRXZfU0daeWc&oid=1&v=1272259523169
David Mayhew (12:44:50)
You wrote:
Climate Research Unit (CRU) of the Hadley Center”
Surely these are separate institutions ? The CRU is part of the University of East Anglia and the Hadley Centre is part of the UK Meteorological Office.
My source was http://www.woodfortrees.org which states:
#Data processed by http://www.woodfortrees.org
#Please check original source for first-hand data and information:
#
#—————————————————-
#Data from Hadley Centre / UEA CRU
#http://www.cru.uea.ac.uk/cru/data/temperature/
#For terms and conditions of use, please see
#http://hadobs.metoffice.com/hadcrut3/terms_and_conditions.html
#—————————————————-
#
#File: hadcrut3vgl.txt
http://www.woodfortrees.org/data/hadcrut3vgl/compress:12
Roger Knights (21:15:47)
Ummmm … no. The UAH data is definitely not “in-line with” GISS. Here’s the UAH values …
Year, Temp, Rank
1979, -0.07, 24
1980, 0.09, 14
1981, 0.06, 16
1982, -0.15, 28
1983, 0.04, 20
1984, -0.26, 31
1985, -0.21, 30
1986, -0.15, 26
1987, 0.11, 11
1988, 0.11, 13
1989, -0.11, 25
1990, 0.08, 15
1991, 0.12, 10
1992, -0.19, 29
1993, -0.15, 27
1994, -0.01, 23
1995, 0.11, 11
1996, 0.02, 22
1997, 0.05, 18
1998, 0.52, 1
1999, 0.04, 19
2000, 0.04, 20
2001, 0.20, 8
2002, 0.32, 3
2003, 0.28, 5
2004, 0.20, 9
2005, 0.34, 2
2006, 0.27, 6
2007, 0.29, 4
2008, 0.05, 17
2009, 0.26, 7
As you can see, 2005 is almost two tenths of a degree cooler than 1998, which is the warmest in the UAH data. And 2009, far from being tied for second as GISS says, was seventh in the UAH record … not in-line with GISS in any sense.
Girma,
I did a search and found this: click
The comments following that article are also interesting.
The level of attacks from the alarmist contingent show that you are on to something very important. The next few years will show us whether these fluctuations are normal, natural and routine, and are well within past natural climate parameters, or if they are the result of the small [≈3% human fraction of the trace gas CO2], in which case they must perforce exceed the previous temperature parameters.
I think that as always in the past, the climate will revert to its long term trend line, thus falsifying the CO2=CAGW hypothesis.
Maybe this chart makes it more clear (GISS anomaly * 100) + 225 vs. CO2
https://spreadsheets.google.com/oimg?key=0AnKz9p_7fMvBdE9rZ3lzMHRRaGxUb3JHRXZfU0daeWc&oid=2&v=1272260546887
Leif Svalgaard
“John Cooke (13:13:35) :
As far as I can see, this is not really a model, but a mathematical fit to the data. For a physicist, that’s not really a model.
I agree, this is just curve fitting a posteriori. But so is so much that goes for ‘science’ these days.”
Mathematicians seek out patterns. Based on the observed GMTA data, the pattern is a combination of linear and sinusoidal functions. As this pattern was valid for the last 129 years, it is reasonable to assume it will be valid for the next 20 years.
Otherwise, how are you going to tell me whether our globe is going to have further warming or cooling in the coming 20 years?
Ockhams razor applies – if the shown correlation is as good as the elaborate forcing ones then it is better, not worse.
Insisting on the need for every possible influence is idiotic in many instances. Tides can be quite easily predicted using past patterns. That we now know how they work isn’t actually that much use (though interesting). There are so many confounding factors that past patterns is actually the best method, adjusted by measurement after the fact.
Trying to work out tides from first principles would be idiotic. Almost as idiotic as trying to work out all the confounding factors in climate.
stevengoddard (21:05:22)
Same problem as above, but the correlation is much, much worse.
Grima: You have done some first class work here. Lots of good comments from others as well. I strongly suspect the AGW people will attack with all mouths blazing. Since they will have trouble attacking the message they will surly attack the messenger. That is why my concerns as expressed in the other posts. They may seem picky and in the blog world probably are. The thing needs to be so clean that few if any science or logic based arguments can be made against it. Make sure all logic is deductive. The language and semantic implications must be very tight. The reference list as solid and extensive as possible. And remember, models do not produce data, only results. I have written a number of short essays looking at different aspects of the Philosophy of Science. Some of them may be helpful reminders. They are at: http://retreadresources.com/blog
stevengoddard (22:26:39) : edit
Please quote where I made anything like that argument. My point is that a model of a varying phenomenon like temperature should outperform a straight line.
@Girma
Sorry to say again. You have assumed some model simple model (linear trend and harmonic component), based on very weak assumptions pointed by contributors to the discussion (you ommited known long-period components).
Next you did linear LSQ fit to the available data.
Your model can describe (very roughly) the time period used for the approximation. You cannot claim anything for the future.Even for 10 years (eg. compare IPCC linear trend predictions based on years 1980-2000, as for today’s plateau). Reason is unit root and spourious fit (so no way to say anything about your coefficients in terms of “goodness of fit”).
Again, the time series modelling is much more advanced nowadays, read dr Stockwell blog and VS comments (plus long discussion by VS on this subject on Bart’s blog).
And how complex the modelling of temperature might be (due to underlying physical and meteorological processes involved) have a look at book by prof. Marcel Leroux.
And your model just tells us:
the system has constant linear secular term and it is an undamped linear oscillator (“mass-spring” system) – how can you identify and name processess giving such a model (and behaviour).
Sorry, but it looks like an exercise from EXCEL I used to give students when teaching them SOLVER use (finding the period of sinusoidal component and the lag in periodic term).
I completely agree. I think the temptation to think that “the future is a continuation of the present trends” is at the root of the global warming scare and pretty much every apocalyptic movement there has ever been.
There have been a lot of posts on WUWT which claim this or that model has predictive skill but none of them have been particularly impressive or compelling because there is no insight into the underlying forcings or inherent variability of the climate.
They are guesses dressed up as models.
In other news, the Sun has gone into hibernation again – 10 days without sunspots and none on the horizon. I wonder if we’re about find out how much the solar cycle really affects earth’s climate and if so, it won’t be pleasant.
The main problem is, that author tries to adjust his model on wrong HadCRUT, which got its early part statistically cooled down, 1940 warming blip partially reduced/removed and modern period positively affected by UHI and selective station use.
In reality, present decade is just a bit warmer than 40ties and 80ties were almost as cold as 1910 minimum. It is visible on all NH long-term records like Armagh Observatory, even on UHI-affected US record or CET.
Anticlimactic (19:43:19) :
“The skeptics viewpoint is mostly that climate is driven by the sun”
Um .. no. Which skeptic? (not this one) Not-CO2 can mean other things than the sun. The sun is one factor among quite a number.
Willis Eschenbach and stevengoddard (not to mention Girma Orssengo)
You should read http://landshape.org/enm/testing-beenstock/ and http://landshape.org/enm/orders-of-integration/ before bothering to take this conversation any further and explain why this doesn’t apply.
If the correlation is spurious why would you bother drawing any inference from it?
davidmhoffer (20:04:20) :
“…Itz a chaotic system, the possibility of including and correctly modeling ALL variables seems slight. What we need is to understand what the DOMINANT variables are. Since it is a chaotic system, trying to start by making a list of all the variables that we can think of, hope we didn’t miss any, and build a model from there seems futile.
Does it not make sense to evaluate the data for repetitive patterns and then determine if they provide clues as to which processes are dominant?…”
As our climate is driven by deterministic chaos, I agree that looking for repetitive patterns (quasi-cycles) is a useful approach to finding the strange attractors which dictate itz behaviour.
However, moving from this stage to building a predictive model is still fraught with difficulty. We do not have sufficiently accurate climate data of a suitable spacial and temporal granularity to make long-term models viable.
Bob Tisdale (15:55:54) :
Thanks Bob – that is a wonderful resource isn’t it. When you are protected by dykes it behoves one to be considerate of nature. Hadley Met couldn’t give a toss it would seem.
Thanks to all who answered my plea.