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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Dear Wam,
you contribution goes along the IPPC “mankind caused it lets find out how”, rather than the scientific: “does the evidence cause us to reject the null hypothesis of random variation”?
To put it bluntly, the climate “scientists” who work on this stuff are just out of their depth and haven’t a clue how to deal with a real world complex system like this.
As for the author, unfortunately they have fallen into the same trap as the IPCC. 1/f type Random noise has strong low frequency noise components. Because many people are unfamiliar with this kind of noise, they tend to assume they can just transpose interpretation techniques from simple white noise and get a meaningful result. In fact pure 1/f type noise has random components that appear to “trend” or “cycle” in a short sample like this. Not only does this make it extremely difficult to distinguish between noise and signal, it also makes it extremely easy to confuse random fluctuation with “something”.
In a century (if the economic collapse at the end of oil still means we have universities – joke – I hope!) academics will look back at this period and wonder why so much spurious writing occurred on a subject that in retrospect will seem so simple: the world climate varies. If any is reading this from the future, can I first say: “hi” and second I want to say: “look it may seem obvious to you in the future, but sitting here without the end of the graph it sure isn’t obvious it is noise!”.
I was referring (I guess I should have been more explicit) only to “temperatures for the first three months of the year,” during which period the UAH temperatures have also risen sharply, along with GISS.
@ur momisuglyroger Knights (21:15:47) and @ur momisugly Willis Eschenbach (23:09:06) :
I see, the bets are at 77% for 2010 to become the hottest year ever. That’s quite possible if the super dry El-Nino goes on and the sun heats up the Pacific (Lower cloud cover was seemingly higher in 1998, has anybody updated data?).wThen the CO2 line will be above 90% fit and the straight line goes way down, because global warming is indeed accelerating since 1850 albeit at a smaller rate. See
Hadcrut3-global-land-ocean-index.
Now, assuming that correlation is causation of all the warming, this would indeed mean a direct climate sensitivity of 2K for 2xCo2 which according to Arthur Smith is equivalent to 3K equivalent sensitivity and thus the model average of IPCC. See monckton-smith-controversy.
May I suggest to give it a voting tie between the discussed simple model and the IPCC. However, a bit of chilly cloud ionization by Svensmark’s stars and/or a nice volcano will do, and we may find ourselves back in 1970’s conditions.
Regards, Climatepatrol
“The Ghost Of Big Jim Cooley (13:20:37) :
Sorry, I’m not one to usually nitpick, but the Hadley Centre is a noun (proper). So it CANNOT be Hadley Center – you can’t just change it to the American spelling. It is Hadley CenTRE, not Center. Sorry, but we English get a little fed up about the corruption of the language. We accept other countries adapting it, but not when they’re stating something that’s actually here. Hence, the ‘British Tyre Company’ could never be ‘The British Tire Company’.”
Hear, hear. Next thing you know they’ll be calling the Revoutionary War the War of Independence! 😉
cheers David
stevengoddard (12:37:38) :
“Both HadCrut and GISS show a good correlation between CO2 and temperature.
http://docs.google.com/View?id=ddw82wws_616c7qsc3gm”
Whoever made these plots was careful not to apply any filtering. The dots have a large random component that will tend to give the impression of a straight line. If you use, say, a twelve month rolling average, then the dots form a more realistic pattern that looks virtually identical to the standard graphs with temp plotted against time.
It then becomes obvious that there is essentially no correlation at all. All you can say is that both temp and CO2 went up in the 20th century – but then, so did a lot of things.
I would class these graphs as a confidence trick. Apart from anything else, if you plot one variable against another, it’s pretty meaningless unless you can keep all other variables constant, as you would try to do in a laboratory experiment.
Chris
Stephan (22:13:22) :
“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”
We had SSN 70+ in February, and a few years yet till maximum. Expect some very strong solar activity till 2013, and a corresponding rise in temperatures. The risk is also high for damaging solar storms for C24, as there are up to 10 times as many of these events through the maximums following quieter minimums. Archibald is just wiggle matching, like everyone else who is predicting a decline in world temepratures till at least 2030, watch them go very quiet over the next few years of warming! Successfull forecsasting can only be acheived by an understanding of the causation, and not assumptions about perceived trends. I would be very happy to put down money now on 2025 to 2038 being very warm.
As an academic writing from the future, thanks for the “hi”. As for your other comment, it’s only a few hours into the future, and we don’t know the answer yet. ☺
/dr.bill
Girma (22:56:56) :
“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?”
That’s the trouble with assuming that this curve fitting has any predictive value and for how long if it has.
What do we absolutely know to be true about this ‘model’?
It didn’t correlate (work) looking backwards for any significant time. We haven’t seen sub zero average temperatures on the Earth very much!
We know it will stop correlating (working) some time in ther future. The Earth is not going to melt any time soon!
However, for its predictive value, when is it going to fail? How do we know it is not tomorrow? How do we know it wasn’t yesterday for that matter?
That’s the trouble with curve fitting without a clear understanding of all the significant processess involved, they don’t have much predictive value and the longer you run them the less value they have.
Just like the current GCMs actually!
Alan
I did a very similar analysis in 2008 by using sine waves and solved for best RMS error, and reached very similar conclusions, except I used PDO, AMO and CO2. My R^2 was 0.89, and like yours, I projected max cooling at year 2028.
The influence of CO2 was in agreement with studies using negative feedback (almost zero influence). I don’t remember R^2 but I think it was around 0.2 once other factors are eliminated.
I’ll try to dig out that analysis and post the results.
I understand the few comments regarding physics, but honestly, if $70Billion isn’t enough to even remotely understand the physics, isn’t showing that natural factors dominate the correct first step? These cycles may have chaotic components, but since they do seem repeatable over the short term, I think they are highly useful and provide a much more plausible predictive ability than anything I’ve seen come out of the IPCC.
Tenuc
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.>>
No. But it seems we have enough for short term (10 to 20 years). As the article author points out, his model is a fit for the last 129 years. I only started looking into climate in detail a few months ago, but the 60 year (give or take) cycle jumped out at me a long time ago, suggesting a peak about now followed by a cooling trend for a decade or two. With ice extent increasing, ocean hot content dropping, land temperatures flat, solar activity down, etc etc etc it seems like that is exactly what is happening. Does this mean the author’s model is valud for the next century or two? No. Does it mean that climate is far less connected to CO2 than other factors? Looks that way. Does it mean that decisions aren’t urgent, stakes high after all? Looks to me like the trend of the last 129 years continues and the only urgency is that we stop panicking about what “we” are doing to the climate, calm down, and start studying what the climate is actually doing and why. There may in fact be reason to panic for all I know, but the dominant factors if there are any will be something other than CO2. Would be nice to know what they are, and which direction they are going to go a couple of decades from now.
Grumbler says:
“Sorry, but we English get a little fed up about the corruption of the language.”
Logically Grumbler, Centre should of course be pronounced Centree
and what about your lieutenant pronounced Leftenant ??? (from M English levetenant) Yet you say “in lieu” of something (LOL). Sorry buddy not logical and a corruption (get up to date) and I am not English or American.
Back the subject:
Some have said this is not a model. I disagree its for me an analytical model as opposed to a numerical model and probably quite useful. It just needs a few years of validation.
Quite a bit of work, but Akasofu’s work isn’t in the references. Hasn’t that reached India yet? His model is a continuing recovery from the Little Ice Age, linear over the time range considered, and a multi-decadal oscillation that is not a pure sinusoid, like the PDO.
More references to the mix:
http://wattsupwiththat.com/2009/03/20/dr-syun-akasofu-on-ipccs-forecast-accuracy/
http://people.iarc.uaf.edu/~sakasofu/pdf/two_natural_components_recent_climate_change.pdf
http://people.iarc.uaf.edu/~sakasofu/
It would be nice if Orssengo writes an addendum comparing his model with Akasofu’s. One thing I really like about Akasofu’s model is the two components. As one includes more and more components, you can adjust the weightings of each and describe almost anything. However, the result often has little predictive power, as many Wall Street investors have discovered.
From http://www.sciencebits.com/FittingElephants:
One of William Gray’s changes to his hurricane predictions has been to reduce the number of parameters. I think he noted that as they found dependencies between them, they could replace them with predictors closer to the important parameters.
stevengoddard (12:37:38) :
Joe D’Aleo found a better correlation with “PDO And Solar Correlate Better Than CO2.”
http://wattsupwiththat.com/2008/01/25/warming-trend-pdo-and-solar-correlate-better-than-co2/
So we might see a decelleration in temperature growth over the next 30 years and that matching to the accelleration during the last warm PDO raised more handwring than was warranted.
If CO2 does have an effect based on the logarithm of its concentration (per the approximation of it absorption curve), and if CO2 is increasing exponentially, then we can’t expect much more than a linear increase due to CO2.
CO2’s increase is more like an exponential added to a constant baseline, which would translate to an acceleration, but given CO2’s relatively poor performance of late, and the uncertainities (curse you Leif!) of solar effects, my expectation is with Akasofu.
A thought came to me after scanning through all this. It doesn’t relate to anything anyone said, as far as I recall.
‘Scientific and Technical papers should be published using pen names, with no reference to the author’s identity or education background.’
No need to applause.
It is a model of sorts not just a curve fit. It assumes some of the force driving climate is cyclical in nature and some is a secular upwards force. It seems to me that since it fits the observations better than the models based on the idea that CO2 is causing the warming then the CO2 model is discredited.
All the Y axis equals 1°C. HADCRUT 3 disappears if Y axis would be considered in 1°C increases (less than 1°C cannot be perceived by any of us).
foward this to tom friedman at the new york times. he’s still pushing the carbon tax hustle.
An interesting article, but rather than rehash some of the main points brought up by other posters, I am going to dwell on three. 1) IPCC is not ‘just concerned about global warming’. It’s climate change; they are concerned about erratic and severe changes in climates around the world, including but not limited to cooling in tropical areas, loss or change in dominant vegetation stands and changes in natural cycling. The media usually likes to portray only ‘GLOBAL WARMING’ because it has been a catch phrase since the mid 1990s and it is a lot easier for a lay person to grasp the basics and remember, versus ‘OSCILLATING OCEAN CURRENTS’ or ‘DENDROCLIMATOLOGY’ or ‘DESERTIFICATION’. 2) The results and discussion are interesting, but one of the cons of shorter articles is a lack of methods. How many times were the simulations ran for the above results? How many data sets were used, i.e. was it a data set from 2001 or were the up dated sets from 2002 -present included as well? Did the analysis incorporate results for the st. deviations, mode, medians? Was any data removed as an outlier and why? 3) I read through this a couple times, was the model set up in microsoft excel? “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”. I hope the model was created on software OTHER than Excel. Excel is a great preliminary tool and keen for simple stats. But the sheer number of variables involved in this topic cannot feasibly be incorporated and simulated in Excel without error. I am willing to bet scientists under IBCC and other statistical institutions are not publishing their results in peer edited journals using Excel. Any thoughts on this?
The only suggestion I could make to this incredible site is: please recognise readers are not all academics or scientists. A “conclusion for general information”, or something similar, would help us to get a precis…and maybe distribute the good work to the media in a popular and easily digestible format.
stevengoddard (21:09:58) :
Willis Eschenbach (21:04:18) :
In order to have a “straight line” you need to have two axes. What are the two axes in your model?
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.
________________________________________________________________________________
Of course the correlation is good. Approximately 70 percent of the planet is ocean, and the average depth is about 1,000 meters. CO2 is more soluble in cold water and less soluble in warm water. The more the oceans warm the higher the amount of CO2 that out gases. Also as it warms and CO2 increases the biosphere revs up. Plants expire CO2 at night, as well as animals and then there is all those microbes decaying bio matter and emitting CO2. (think beer)
kwik (15:06:14) :
“Isnt this post missing a plot on how the formula is showing the temperature to unfold, say, the next 10 years?”
Okay, found it in figure 3.Sorry.
Well, after reading it once more, slowly, my comment is;
This is the way a guy dealing with electronics, process control, spectral analysis etc would do it.
It makes common sense? Cannot see any problem with it.
Of course noone knows when that sloooow increase will flatten out, and why. In any case we cannot do anything about coming out of the last ice age.Just be happy for every day passing by where that slow slope doesnt start turning downhill again.
Only problem for the politicians is that the slowly increase out of the last ice age cannot be taxed. Who to tax? The galaxy?
A great post, and many interesting comments!
VictorianModesty (06:23:17) :
1) IPCC is not ‘just concerned about global warming’. It’s climate change; they are concerned about erratic and severe changes in climates around the world, including but not limited to cooling in tropical areas, loss or change in dominant vegetation stands and changes in natural cycling.>>
BULL. They have been hyping global warming, screaming about runaway temperature increases, hollering about tipping points and demanding extreme taxation systems to limit the root cause which they say is CO2. What part of the last 20 years of their tripe sounds like “climate change”? Only in the VERY recent few months, faced with how ridiculous their claims have been, have they started to reposition as “climate change”.
VictorianModesty (06:23:17) :
The media usually likes to portray only ‘GLOBAL WARMING’ because it has been a catch phrase since the mid 1990s and it is a lot easier for a lay person to grasp the basics and remember, versus ‘OSCILLATING OCEAN CURRENTS’ or ‘DENDROCLIMATOLOGY’ or ‘DESERTIFICATION’.>>
BULL. It is a scare tactic to justify taxation and an excuse to NOT discuss the science, in fact to IGNORE the science.
VictorianModesty (06:23:17) :
2) The results and discussion are interesting, but one of the cons of shorter articles is a lack of methods. How many times were the simulations ran for the above results?>>
LOL. It is a MATH model. 2+2=4 and it doesn’t matter HOW many times you run it, you get the same answer.
VictorianModesty (06:23:17) :
How many data sets were used, i.e. was it a data set from 2001 or were the up dated sets from 2002 -present included as well? Did the analysis incorporate results for the st. deviations, mode, medians? Was any data removed as an outlier and why? 3) I read through this a couple times, was the model set up in microsoft excel? “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″. I hope the model was created on software OTHER than Excel. Excel is a great preliminary tool and keen for simple stats. But the sheer number of variables involved in this topic cannot feasibly be incorporated and simulated in Excel without error.>>
The answers to your questions are pretty much in the article but that last piece is interesting. What mathematical error is it that you think Excel introduces? Can you demonstrate this? Have you told Microsoft?
VictorianModesty (06:23:17) :
I am willing to bet scientists under IBCC and other statistical institutions are not publishing their results in peer edited journals using Excel. Any thoughts on this?>>
How much are you willing to bet? Not that this invalidates the use of Excel even if it was true. You may as well complain that slide rules aren’t accurate enough for science and that an abbaccus can’t be used for math. Either the numbers are right or they are wrong and it makes zero difference what they were calculated with. But put some money where your mouth is, I’m a bit short this month.
Yes, I have a thought: Get over yourself!
Any tool adequate for the purpose is adequate for the purpose. There’s nothing wrong with Excel (at least nothing that Open Office can’t fix), and most of the world’s climate data has been processed with Fortran, which has been around in one form or another since the 1950’s.
/dr.bill
Chris Wright (03:24:11) :
There are no tricks in the plots. They are straight up Temperature vs. CO2 and show good correlation. The fact that you don’t like the results doesn’t invalidate them.
After 7 iterations the fit converged.
final sum of squares of residuals : 12070.5
rel. change during last iteration : -4.17129e-06
degrees of freedom (FIT_NDF) : 127
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 9.74901
variance of residuals (reduced chisquare) = WSSR/ndf : 95.0431
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = 0.641343 +/- 0.02305 (3.594%)
b = -927.309 +/- 44.84 (4.836%)
Ric Werme (05:26:49) :
Joe’s analysis plotted USHCN vs. CO2, not global temps. Global temps correlate much better.
There are four possibilities concerning the GISS/CRU vs. CO2 graphs
1. Coincidence. Not very likely.
2. CO2 drives temperature
3. Temperature drives CO2
4. Temperature data has been adjusted to fit the CO2 data
One or more of the last three are true, and can’t be ignored.