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.
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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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To be frank, this article could have been cut short simply by mentioning that nobody REALLY knows what the world’s median temperature really is or has been since temperature keeping began. All records have biases, are local, and then processed through befuddled computer algorithms to come up with a seemingly sensible average, which it really isn’t. It’s an average of the RECORDS, not the world. When we’re talking about tenths or hundredths of a degree, that matters a lot.
And to make it all the more laughable, nobody really knows how much CO2 is in the atmosphere, where it is all being produced, how much is produced, or has accurate knowledge of the CO2 cycle. All we have is some localised readings which again are parsed to come up with an average without knowing if it is accurate or what the carbon cycle has in store for us in the future.
The skeptics viewpoint is mostly that climate is driven by the sun, leading to roughly 30 year periods of warming and cooling, moderated by a slight overall increase as we are still emerging from the ‘Little Ice Age’. The current view is that 1998 was the warmest year, and global cooling started in 2005. As the sun was unusually quiet for the past 3 years it is expected that the next 20 years of global cooling may be severe.
From this viewpoint the above is par for the course. The question is whether the sun’s behaviour means we are heading for a Dalton minimum, in which case the gradual upward trend will come to a dramatic end!
I was hoping that we would now be in a position to give a comprehensive description of the [possible] mechanism governing these 60 year cycles of cooling and warming. The effects of cosmic rays on climate seems to be gaining credence, more is known about the oceanic oscillations, and our knowledge of the sun is increasing rapidly. Is anyone in a position to put it all together to provide an approximate model of climate change?
Good comments.
Another example is Max Planck and the blackbody radiation curve. His first step was curve-fitting. His second was to see what needed to be changed in the traditional analysis in order to produce the function that he had found. He never really believed his own results, but he was right anyway, and it led to the development of Quantum Theory. 🙂
The first attempts are seldom perfect, but this series of steps is perhaps the most common paradigm in Science. You do, of course, eventually need to back it up with something fundamental.
Some reading here: http://www.daviddarling.info/encyclopedia/Q/quantum_theory_origins.html
/dr.bill
What a wonderful streak of rule breaking by the IPCC:
http://bishophill.squarespace.com/blog/2010/4/25/ipcc-in-trouble-again.html
Roger Sowell (18:32:26) :
“First principles models begin with known physics, and when performed properly, can be used to extrapolate into the future. These must include all variables
Steven mosher (19:23:09) :
physics models do not have to include all variables. They have to include the relevant variables for the purpose at hand>>
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?
The moon has an orbital variation of 18.6 years, sun spots 11 and 22, AMO is about 60. Does it not make sense, given the clear 60 year cycle presented above, that we investigate elements such as these which divide nicely into 60? Do they roughly coincide in some way with the temperature cycle? When we go backward or forward in time do they diverge and is there evidence that when they diverge the short term (60 year) cycle also disappears from the record?
If one is trying to prove a specific principle of physics, then start with a model of that element. But a chaotic system with thousands of elements requires that we narrow the number to the most significant elements and then refine from there. Seems to me this study pretty much shows that CO2 is not one of the elements to consider. But it provides important clues as to what the dominant elements are, and from that perspective, it has value.
Darwin came up with evolution by making observations and then searching for a scientific explanation. Newton supposedly got thinking about gravity upon being struck by an apple to the head. Galileo didn’t just decide one day that the earth circled the sun and then built a telescope to prove it. He made carefull obervations and came up with a theory that fit the data. There is nothing “unscientific” about obervations providing clues as to which physics is important and which less so. Itz when you investigate the physics you THINK is important and it doesn’t match so you discard the data that you are no longer in the realm of science.
Steven mosher (19:23:09):
I’m not sure I understand what you’re saying there. The planet is still emerging from the LIA. There is a small natural warming trend, but there is no solid, empirical evidence showing that CO2 is the primary cause.
In fact, if the CO2 entity is kept out of the equation entirely, the result is the same as the null hypothesis, once again supporting Occam’s Razor: Never increase, beyond what is necessary, the number of entities required to explain anything.
Opinions about the effect of CO2 vary widely, from the IPCC’s preposterously high sensitivity numbers based on a CO2 persistence of over a century, to the other extreme, where CO2 is a negative forcing. But there is no empirical test showing the actual number. Nobody, in fact, knows the specific forcing caused by CO2, if there is any [I assume there is some CO2 forcing, but my assumption is based on physics, not on measurable evidence, because there is none. Please correct me if I’m wrong about that].
We do, however, have charts of past temperature trends showing that the steady rise in CO2 [only about 3% of which is emitted by human activities] does not correlate well at all with global temperatures: click
Richard Telford (13:23:18)
You wrote,
“Your model describes the data passably, but has no predictive power, other than that of predicting your credibility – absolute zero”
The model was valid for 129 years. As a result, it is reasonable to assume that it will be valid at least for the next 20 years.
IPCC is wrong in its projection of 0.2 deg C per decade for the next two decades. But the model I described shows the current plateau followed by cooling until about 2030.
The question is whether we will have a cooling or warming globe in the next couple of decades.
While this is true it is also completely irrelevent for modelling of CO2 and climate. Climate by its nature has a lot of variables that drastically affect the final “result”. Dropping a bomb on a location tends to be affected by only a handful.
For example, you drop a bomb from a low lying plane at 1000 feet. All you need to do is calculate any shear from wind, the slight movement from the moving plane and given the solid science showing how fast an object dropped when it weighs a certain amount and not only can you predict its location down to a few feet, but exactly when in time it will happen.
The analogy is the same for the aircraft example you used. While they sound nice, the original commenter you quoted is correct – in GCM’s not including variables that drastically affect the final result would throw out the result to beyond an acceptable answer. The problem with climate is that multiple small variables come together in a result that is highly susceptible to changes in those variables. Getting one slightly wrong and it is the equivalent of your bomber plane, leaving the carrier then setting a course 5 degrees to the east of his target. After an hour of travel time, using all the best methodology and science predictive power I am sure we could determine the exact location once dropped of the bomb – the only problem being we are several hundred miles in the wrong drection.
You seem to be confusing what is required to get even a close to accurate model for climate and comparing it to the far simplistic models you gave leaving the impression that we do a good job of predicting climate – we dont. We are terrible at predicting climate. We havent even been able to predict what happens just a year or two from now. In addition, the inability to predict short term and the constant complex nature of the climate means 10 and 20 year predictions will be even worse. Hey yes we can get a ball park figure – I can look at the graph and guess a spot and place bets that were well be – and yet the unfortunate aspect of that is that in reality, thats about as useful as most climate models created today are.
I agree with this post.
I did some work on this issue last year, but as an unknown, it is impossible to get heard.
I suggest several minor changes:
Start in 1878 (at least notionally, although the first two years data is absent).
Use NCDC annual data, as this gives a much close fit.
Work with a 65 year cycle (half cycles 1878-1911, 1912-1943, 1944-1975, 1976-2008).
Use zigzags rather than cosine. I produced these from short term linear trends of the above half cycles, then added plus and minus 0.15 degrees to give the upper and lower bounds.
Use degrees C or absolute, not anomalies.
You will find that the annual NCDC data fits into these zigzag boundary lines very well and that even the 1998 El Nino just peeps over the paraphet.
My analysis agrees with the forecast for the 21st century.
But remember this is just a projection of the last 130 years history.
Should the sunspot – cosmic ray- cloud theory be correct, the current cycle may be about to end and we could be in for a much colder, more unpleasant time.
We have to wait some years yet before we can decide.
The bottom line is this if past conditions continue, we will be in for a fairly flat temperature for the remainder of our lifetimes.
If not, it may be too cold for comfort.
Thanks for a great post.
Anthony Watts, and others have shown the Hadcrut data to be artificially warmed. You needed to use a data set.
What if the actual (not sure how we will find out) data is cooler by say 0.5deg C?
Thanks again for the great work.
Steve Goddard – you said
“Both HadCrut and GISS show a good correlation between CO2 and temperature”
I suggest you try a correlation between say the Central England long term series which started in the 1600’s or the even earlier Central Europe (in 1525, I think).
You will find both of these have small strictly linear increases, while CO2 flatlined untill after 1850.
There is no cause and effect.
You are just looking at a meaningless short term correlation (130 years in this context is short term).
In haste as I have to go out to collect grandchildren, which is more urgent than (not) saving the world.
Regards.
Observing a sequence and derive a mathematical model behind is much better than
– have a single factor in a chaotic system
– which does not fit reality
– and then consider reality a travesty,
– because it does not follow a wrong theory.
And hey, the others don’t consider all factors either.
Not A Carbon Cow (19:02:59) :
“As others have stated, though, this can’t be the true picture, as not too long ago absolute zero is reached, and not too far from now everything would be a plasma.”
However, if the model was encoded in computer language (which was kept secret); and the baseline data for the model was lost or misplaced; and the model was only run back so far as to be plausible; and the model was only run forward, to just beyond your retirement age, on a multi-million dollar super confuser:
Would it work then?
Could you sell it?
A world turning into plasma is actually a great end state.
Leave the lead time a bit obscure, add a tipping point, find two or three other planets in the universe that are more or less all plasma to use as examples. Hey you could say it was caused by the sudden re-emergence of the hidden heat that was wrapped up in the dark mater in the seventh dimension but must have been unleashed when the hidden heat density exceeded that carrying capacity of the dark matters inner dimensions.
It might also make a pretty good movie.
PS.
The problem with Girma Orssengos explanation is that the author has shown their work.
astonerii (13:28:08) :
As long as your using bogus temperature readings to make your hypothesis you will never have a valid hypothesis. 1934 was warmer than 1998, thus the whole idea is bunk and garbage. It seems strange that the skeptics would indulge the alarmist warmists by using garbage data.
_________________________________________________________________________________
Lighten up. The person had an idea and has the intestinal fortitude and humility to toss it into the WUWT arena for a critique. So please comment on the idea and do not attack the person. Leave that type of behavior to Mr. Mann.
And yes you are correct that he has used a “Mann-made” data set with added warming. That is the type of information he needs to go forward and modify his ideas.
Mann Made Data set
http://i49.tinypic.com/mk8113.jpg
The difference between raw and “adjusted” data sets USHCN
http://cdiac.ornl.gov/epubs/ndp/ushcn/ts.ushcn_anom25_diffs_urb-raw_pg.gif
Hansen Changing graphs
http://i31.tinypic.com/2149sg0.gif
US temp Raw vs “Adjusted”
http://i31.tinypic.com/5vov3p.jpg
European data:
http://i50.tinypic.com/301j8kh.jpg
Avg of thirty stations – raw data: (All 100 yr stations in Australia)
http://wattsupwiththat.files.wordpress.com/2009/12/darwin_zero3.png
This is the information he actually needs: http://www.john-daly.com/stations/stations.htm
Exactly, Girma (20:13:39)
The mathematical model fits for the last 100 years and so can be used usefully to predict the next decade or so. Sure a meteorite might land on New York, a volcano may errupt here or there but even so the long term climate trends look pretty robust. If you look back the last 1000 or so years there are no huge dislocations in the trend so one might consider that in the next decade or so will also follow trend too. The point is, has this model got more credibility than the IPCC models in terms of their respective predictve powers in the light of the X trillion dollar main game that is being played.
Steven mosher:
You are correct that I should have said all “relevant” variables, or perhaps “important” variables. There are a large number of irrelevant variables to most problems of interest, and it makes no sense to include irrelevant variables.
Still, my point is valid. When one omits variables such as volcanoes and clouds from a climate model, even one based on first principles, the effort is doomed to failure. Another very important variable is wind, including direction, strength, humidity, and duration. It also appears that dust-laden wind blowing into the Atlantic from Africa impedes hurricane formation. To my knowledge, wind is omitted also from GCMs.
A better approach when one is faced with numerous variables is to use neural networks. These can quickly and easily evaluate thousands of variables, and identify those that are of interest because they have a significant impact on the outcome. Modelers in the continuous process industries have done exactly this with good success.
My conclusion remains, though, that correlation models are not useful as predictors when they lack the correct variables.
There is an over-arching field of science that performs and publishes research into these matters, and that is Operations Research, with a website at http://www.informs.org/
stevengoddard (12:37:38)
Man, I thought the “But CO2 correlates with temperature” argument had been relegated to the museums. Two points:
1. Correlation is not causation. The canonical example of this is two clocks that strike the hour. One is five minutes fast. They are perfectly correlated … but does that mean that clock A causes clock B to strike five minutes later?
For another example of why correlation is not causation, see point two.
2. What you say is true … but there is also a good correlation between a straight line and temperature. Here’s a comparison.
If the red confidence intervals overlap, then we cannot say that they are different. In other words, there is no statistical difference between the correlation of CO2 with temperature and the correlation of a straight line with temperature.
So perhaps you are impressed with the CO2 corrrelation. For me, something has to outperform a straight line before I pay any attention to it. Your claim about correlation is true … but it is as meaningless as claiming that temperatures will increase in a straight line because of that correlation.
People have been predicting an “acceleration in temperature growth” for a quarter century now. There has been no acceleration since Hansen made his predictions of imminent thermal doom in 1988. None.
Instead, what we’ve gotten is deceleration, there is no significant temperature change since 1995 … at what point are people going to notice that acceleration is not happening?
AusieDan (20:40:09) :
Please feel free to make a plot of CET vs. CO2 to demonstrate your claim.
If CO2 doesn’t cause warming, and in the absence of the Trenberth’s “hidden heat” that appears to be the case, then we are back to the status quo – which is what the mathematical model describes here.
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.
Unfortunately, based on the GISS values (which are in-line with the UAH satellite-based values), temperatures for the first three months of the year have set record highs, and temperatures for the last half-year of 2009 was high as well, due to an El Nino. The GISS records can be viewed here:
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt
The “anomaly” for calendar-year 2009 was .57, tieing it with 2007 as the 2nd hottest year. (2005 was the hottest.) The anomalies for the first three months of 2010 are 70 72 83; these high values need to be offset by a reversal of the Southern Oceanic Index. A reversal seems to be occurring, but whether it will reduce the anomalies of subsequent months sufficiently to lower the average for 2010 is uncertain.
As a result, the betting odds of 2010 being the hottest year on the temperature record are now 75% on https://www.intrade.com. (See under the “Climate and Weather” market. I “sold short” at 80%, so I’m ahead at the moment.)
There are bets for that on Intrade (see link above).
Just skimmed this, but Figure 1 is the money figure. If you support that thoroughly, the rest is just background. In terms of public persuasion, the critical argument is “Look what they predicted compared to what actually happened. Now they want us to reorganize the global economy based on this level of predictive ability.”
stevengoddard (21:09:58) :”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”. Seems to me that Co2 follows temperature, and that is what we are seeing right now following the warming to ’98. Co2 has gone up , slightly lagging the temperature. The link to man has yet to be shown, IMHO.
Ummm…is there any more to this than fitting a curve to some data? If so, I have missed it.
I vaguely remember from maths classes that there are a large number of polynomials that can fit any known set of data (maybe an infinite number??). Any reason to believe that this one is better than the others? Or that just curve-fitting has any form of predictive power? I
I’m as sceptical as the next man of the Climo’s claims, but this work does not seem to show much more than mathematical dexterity. It would take the RC guys no more that five minutes to destroy. Sorry.
In case my previous remark is taken as completely negative. I agree with Robert Kraal above that the Fig1 graph is great….a good and immediate illustration of the way in which the Climos exaggerate their case and certainty. Its just the reliance on a purely mathematical method below that I dislike.
Way back I was a theoretician until the pesky experiments kept on not doing what I expected. So I learnt not to rely on clever ‘tricks’, but to make the theory fit the observations…not try to do things the other way round.