Archive for the ‘Mann’ Category

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Michael Mann’s delusional fever

November 29, 2013
Michael Mann's fevered dream

Michael Mann’s fevered dream

Ever read the New York Times?  Wadda ya think, does it veer persistantly to the left?  Is the Pope Catholic?

Well, Michael Mann has been huffing and puffing at the Huffington Post that the New York Times has strayed from his approved dogmatism concerning global warming.  They dared to run an opinion piece by Richard Muller.

Richard Muller lives happily on the alarmist side of the road, but on occasion he lets his toes cross the dividing line, so that he can claim some credit for being open-minded.

In a recent op-ed he must have touched one of Mann’s extremely frayed nerves when he said…

“I worried that the famous “hockey stick” graph plotted by three American climatologists in the late 1990s portrayed the global warming curve with too much certainty and inappropriate simplicity.”

Ouch.  This was simply Muller’s demure way of stating the obvious. Of course, the main “American climatologist” who made this graph of “inappropriate simplicity” is none other than Michael Mann.  Muller’s soft punch of a statement seems to have left a big bruise on Mann’s sensitive ego.

Mann thinks The New York Times never should have let Muller engage in this attack on his crowning achievement.  But then the Times went even further and let the apostate spread even more heresy in a second op-ed about tornadoes.  Muller wrote

Despite the recent spate of deadly twisters, including those that tore through the Midwest over the weekend, the scientific evidence shows that strong to violent tornadoes have actually been decreasing for the past 58 years, and it is possible that the explanation lies with global warming…

I am not talking about global warming per se, which I am convinced is real and caused by man-made emissions of greenhouse gases. But not everything attributed to global warming has a scientific basis…

So let’s consider only the most violent tornadoes, the ones in categories EF3 to EF5…

NOAA… shows that the number of these storms has been significantly decreasing over the past 58 years, from over 50 per year in the first half to under 40 per year in the second. The statistical significance of this decrease is extremely high: well above 99 percent confidence.

How dare Muller display such an attitude!

Mann is especially incensed that Muller quoted from an earlier HuffPost article which said…

Michael Mann, a climatologist who directs the Earth System Science Center at Pennsylvania State University, agreed that it’s too early to tell.  “If one factor is likely to be favorable and the other is a wild card, it’s still more likely that the product of the two factors will be favorable,” said Mann. “Thus, if you’re a betting person — or the insurance or reinsurance industry, for that matter — you’d probably go with a prediction of greater frequency and intensity of tornadoes as a result of human-caused climate change.”

But Muller wrote

Michael E. Mann, a prominent climatologist, was only slightly more cautious. He said, “If you’re a betting person — or the insurance or reinsurance industry, for that matter — you’d probably go with a prediction of greater frequency and intensity of tornadoes as a result of human-caused climate change.”

Mann called this innocent contraction “sleight of hand.”  Touchy, touchy.

Mann uses his mighty reasoning powers to discern a conspiracy.  You can’t be too careful when even your friends are out to get you.  He warns us that this is ultimately the work of the Koch brothers, just like every other vile conspiracy against the goodness and light of global warming alarmism and the left in general.  (It used to be Dick Cheney and Halliburton, but I guess they must have passed the world control levers over to the Koch brothers.)  You see, Richard Muller now controls the New York Times, and the Koch brothers control Richard Muller.

Mann wraps his tin foil a little tighter and lectures…

The New York Times does a disservice to its readers when it buys into the contrived narrative of the “honest broker”–Muller as the self-styled white knight who must ride in to rescue scientific truth from a corrupt and misguided community of scientists. Especially when that white knight is in fact sitting atop a Trojan Horse–a vehicle for the delivery of disinformation, denial, and systematic downplaying of what might very well be the greatest threat we have yet faced as a civilization, the threat of human-caused climate change.

Shame on you New York Times. You owe us better than this.

You can get the full temperature of Mann’s paranoid delusional fever at his Huffington Post’s article, Something Is Rotten at the New York Times.

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Gordian Knot of Nonsense – Part 3. More Math (Sorry about that.)

September 22, 2011

“Make everything as simple as possible, but not simpler”

Albert Einstein

As usual, I will refer to ”Climate related sea-level variations over the past two millennia” (Andrew C. Kemp, Benjamin P. Horton, Jeffrey P. Donnelly, Michael E. Mann, Martin Vermeer, and Stefan Rahmstorf, PNAS, 2011)  as KMVR2011.

I would like to elaborate on my previous post, in which I presented a simple temperature vs. time function that causes KMVR2011’s model relating sea level rise rate to global temperature behave in a rather peculiar manner.  I am trying to find a balance between simplicity, clarity and thoroughness.  The level of mathematical literacy of my readers may vary widely, but this time around I need to employ some calculus.  If the equations bother you,  just consider the conclusions.

Starting with the conclusions

There exists a simple class of realistic temperature vs. time functions, which when applied to KMVR2011’s model yield results that disqualify it as representing a relationship between global temperature and sea level rise rate.  This class of temperature vs. time functions gives a family of curves for which it is guaranteed that the higher the temperature the lower the sea level rise rate.  This implausible effect is so severe that if forces rejection of the KMVR2011 model.

The Math

Here is the KMVR2011 model


where

Where H is the sea level, T(t) is the global temperature, Too, a1, a2, b and τ are all constants and To(t) is a to-be-determined time varying function related to T(t) as defined by equation Ia.

Now, consider the following temperature evolution.  It is nearly the same as equation II from my previous post, but has an additional unitless constant, γ (a.k.a. “gamma”), in the exponential…

If equation II is inserted into equation I, then…

Rearranging terms in equation III gives…


H is the sea level.  dH/dt, the derivative of the sea level,  is the sea level rise rate.  d2H/dt2, the second derivative of the sea level, is the rate at which the sea level rise rate changes.  That is, d2H/dt2, is the acceleration.  If d2H/dt2, is positive, the sea level rise rate is increasing.  Conversely, if d2H/dt2, is negative, then the sea level rise rate is decreasing.  Taking the time derivative of equation IIIa gives…


Let’s also consider the difference in the sea level rise rates at some time, t, for different values of γ.  We can do this by analyzing the derivative of dH/dt (equation IIIa) with respect to γ.

What does the math tell us?

KMVR2011 does not conclude with specific values for their model constants and their time varying T0(t).  Instead, they present probability density distributions for some constants, or combination of constants.  However, there are some definite constraints that can be noted about the variables and their relationships to each other.  These constraints are key to my conclusions.

Constraints:

  1. a1 + a2 = a, where a1 and a2 are defined in KMVR2011 (see equation I, above) and a is defined in VR2009.  VR2009 found a = 5.6 mm/yr/K.
  2. a1 > 0 mm/yr/K  and a2 > 0 mm/yr/K.  KMVR20011 states that the distribution of a1 for their Bayesian analysis varied between 0.01 and 0.51 mm/yr/K.   Needless to say, if either of these terms were less than zero the KMVR2011 model would make even less sense that it does now.  That would be a road that the KMVR2011 authors do not want to travel.
  3. b < 0 .  VR2009 found b = -49 mm/K.   KMVR2011 varied b about -49 mm/K with σ2 = (10 mm/K)2 for their Bayesian analysis.
  4. C  > 0.  C is a unitless constant that I introduced, and for the purposes of this post I am constraining C to be greater than zero.
  5. γ > 0γ is a unitless constant that I introduced, and for the purposes of this post I am constraining γ to be greater than zero.
  6. Time, t, is restricted to about 1900 and later for my hypothetical temperature (equation II).  This insures that T(t) > T0(t), which in turn insures that dT0(t)/dt > 0.

The equations above, coupled with the listed constraints guarantee the signs of the derivatives shown in table 1, below.

Table 1. Derivatives of temperatures and second derivatives of sea level. Green “up arrows” indicate increasing values and red “down arrows” indicate decreasing values.

As you can see from table 1, it gets little confusing for 0<γ<1.  When a1, a2, b, C, and γ conform to the listed constraints, the signs of the various derivatives are known with certainty as long as…


But when …


at some point in time t- t’ will become large enough that d2H/dγdt will become positive.  When that time occurs depends on the choices of a1, a2, b  and γ.  If we choose a1, a2 and b to agree with VR2009 (recall a+a2 = a = 5.6 mm/yr/K, and b = -49 mm/K) and γ = 0.8, then d2H/dγdt will continue to be negative until t – t’ = 44 years.

The conclusion, again.

Equation 2, above, can be used to build realistic hypothetical temperature evolutions.  See figure 1, here, for some examples.  Remember, KMR2011’s model relates sea level rise to temperature, and when applied to these hypothetical temperatures it must yield realistic sea level rises.  It does not. 

Table 1 shows various aspects of temperature and sea level using my hypothetical temperature evolution and KMVR2011’s resulting sea levels.  Summed up succinctly, the table shows that with this combination the greater temperatures result in lower sea levels.  This implausible situation disqualifies KMVR2011’s model. 

Coming soon

I realize that a bunch of equations and a table do not give visceral understanding of this effect.  A graphical illustration of these points will be coming soon.

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Update (9/30/11)
Table 1 corrected.  Change makes no difference to conclusions.

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Gordian Knot of Nonsense – Part 1. Rahmstorf and company strike again.

August 28, 2011

Rahmstorf and friends are at it again, but this time they have signed on a bigger fish: Michael Mann of hockey stick infamy.  Somehow it does not surprise me that this new serving of dribble comes to us via the Proceedings of the National Academy of Sciences.  Frankly, it grieves me to know that this is the state of the scientific culture in the US. 123

I will refer to “Climate related sea-level variations over the past two millennia” (Andrew C. Kemp, Benjamin P. Horton, Jeffrey P. Donnelly, Michael E. Mann, Martin Vermeer, and Stefan Rahmstorf, PNAS, 2011)  as KMVR2011.  This paper dishes up a third generation model relating sea level rise rate to temperature whose immediate ancestors are Rahmstorf’s 2007 model and Vermeer’s and Rahmstorf’s 2009 model.

With H being sea level and T being global temperature the models have evolved as follows.

Generation 1, form Rahmstorf’s 2007 “A Semi-Empirical Approach to Projecting Future Sea-Level Rise

Generation 2, from Vermeer and Rahmstorf’s 2009 “Global sea level linked to global temperature


And now, Generation 3, from KMVR2011


where


A cursory examination of equation I makes it plain the this new model is simply the cobbling together of  the VR2009 model (with a1 and Too in this model being the same as a and To  respectively in VR2009) with an additional term,  a2[T(t) - T0(t)], taken from Jevrejeva (GRL, 37, 2010).  KMVR2011 sum up the meanings of each term in equation I as follows…

The first term captures a slow response compared to the time scale of interest (now one or two millennia, rather than one or two centuries as in [VR2009]). The second term represents intermediate time scales, where an initial linear rise gradually saturates with time scale τ as the base temperature (T0) catches up with T. In [VR2009], T0 was assumed to be constant. The third term is the immediate response term introduced by [VR2009]; it is of little consequence for the slower sea-level changes considered in this paper.

 In Rahmstorf’s 2007 model linking sea level rise rate to temperature there were only two constants (a and To) that needed to be determined.  The 2009 Vermeer and Rahmstorf (VR2009) model went a step further with three constants (a, To, and b) that needed to be determined.  The new KMVR2011 model advances the science with four constants (a1, a2, Too and b).  Count them!  But even more astonishing: this model requires not just solving for the four constants, but also a time varying function (To(t) )!

Back at the keyboard

I have had a leisurely summer, and have not written any blog posts for several months, but my eyes and ears have been open, and my pencil has scratched out a few equations.   This post represents the beginning of a new series on KMVR2011, which I will call the “Gordian Knot of Nonsense.”

This series will be interspersed with posts on other topics, so please check back occasionally for updates.

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Tree-rings: Proxies for Temperature or CO2?

February 15, 2010

Recall step 2 of the  five super-simple steps for building a hockey stick:

Step 2. Select those time series that fit the instrumental (measured) temperature record of choice. Assume that since these time series match the measured temperature in some way, then they are, in fact, temperature proxies.

This step begs the question (in the classical logical sense) about the usefulness of tree-rings as proxies for temperature.  Surely tree-ring width is not solely dependent on temperature, is it? 

What about drought conditions?  Usually we think of droughts as coinciding with high temperatures.  Would higher temperatures cause tree-rings to be thicker even when the tree is being stressed or dying due to lack of water?  Of course not.

What about the abundance of CO2? 

Back when I was a college student I worked at Phytofarms of America in DeKalb, Illinois, USA, which grew the highest quality leafy vegetables in a giant indoor,  innovative, artificially lighted, hydroponic facility.  One of the keys to this industrial sized facility was elevated CO2.  Huge tanks of CO2 pumped up the indoor level to about 1000 ppm, or about 4 times the pre-industrial level.  The resulting veggies were expensive, but they were the best money could buy.

Is it possible that tree-rings are better proxies for atmospheric CO2 than for temperature?   As a simple test, I selected all the tree-ring proxies used for Mann’s 2008 version of the hockey stick and did a simple correlation to the Northern Hemisphere instrumental temperature record and to the atmospheric CO2 record.  The tree-ring data and the instrumental temperature record came from the NCDC archive for Mann’s paper

 The CO2 data is a combination of Mauna Loa data (1959 to present) and the Siple Station ice Core (1744 to 1953).  The Siple data was not in yearly increments, so I interpolated.   I also interpolated between the end of the Siple data (1953) and the beginning of the Mauna Loa date (1959).  The Mauna Loa data was truncated beyond 2006 so that the CO2 data would cover the same time domain as the instrumental record used by Mann.

Results

The first graph below (click to enlarge) shows the 30 tree-ring time series with the best correlations to the instrumental temperature record.  Each of these correlations has been matched with the correlation to the CO2 level.  The first thing to jump out is that for 23 out of 30 cases, the CO2 correlation is better than the temperature correlation!

The next graph is the reverse situation.  It shows the 30 tree-ring time series with the best correlations to CO2 in descending order.  As above, each of these correlations is matched with the correlation to the instrumental temperature record.  This time the thing that jumps out is that the correlation to CO2 is better in every single case.  And these correlations to CO2 are not just a little better, they are a lot better.

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Amazing multiplying hockey stick proxies

February 3, 2010

In my previous post I wrote about the five super-simple steps for building a hockey stick:   

Step 1. Gather time series.
Step 2. Select those time series that fit the instrumental (measured) temperature record of choice. Assume that since these time series match the measured temperature in some way, then they are, in fact, temperature proxies.
Step 3. Combine the chosen proxies in some fashion and note, not surprisingly, that the combined proxies match the temperature record. (duh) Call this your temperature reconstruction.
Step 4. Call this thing made from the combined proxies your temperature reconstruction, and therefore assume that the combined proxies are also a match for the temperature that occurred prior to the temperature measurement records.
Step 5. Note that the reconstruction shows that the temperature prior to the instrumental data is relatively flat, and conclude that the temperature prior to the instrumental record changed very little.   

This post is about a little subplot in gathering of time series for the Michael Mann’s 2008 version of the hockey stick (Proxy based reconstructions of hemispheric and global surface temperature variations over the past two millenia, PNAS, 2008)   

Mann used 1209 proxies for this reconstruction.  He explains the breakdown as follows…   

We made use of a multiple proxy (‘‘multiproxy’’) database consisting of a diverse (1,209) set of annually(1,158) and decadally (51) resolved proxy series … including tree-ring, marine sediment, speleothem, lacustrine, ice core, coral, and historical documentary series. All 1,209 series were available back to at least A.D. 1800, 460 extend back to A.D. 1600, 177 back to A.D. 1400, 59 back to A.D. 1000, 36 back to A.D. 500, and 25 back to year ‘‘0’’ (i.e., 1 B.C.).   

Figure 1. Northern Hemisphere proxies in alphabetical order

 

Mann split his analysis between the Northern and Southern hemispheres.  I am going to talk about the 1,036 of the 1,209 proxies that applied to the North.  The following two images show the plots of the these 1,036 proxies, just click on them to enlarge.  The file sizes are less than a megabyte each and should open quickly in your browser.  Figure 1 is the plots arranged in alphabetical order.  If you scroll through this image you will see a lot of proxies that don’t look much like a hockey stick, and a few scattered here and there that do.  However, there is a series of 71 proxies named lutannt1 through lutannt71 that look very much like hockey sticks.    

These lutannt# proxies are from Pauling A Luterbacher, the researcher who “provided” them.  More on this important point later   

Figure 2. All Northern Hemisphere proxies in order of correlation with Northern Hemisphere instrumental temperature record.

 

As explained in the five easy steps for hockey stick construction, the proxies that look much like a hockey stick are likely to be weighted heavily in the final hockey stick construction.  If all the 1,036 proxies are correlated (For the math inclined: see correlation formula below) with the northern hemisphere instrumental temperature record, and the plots laid out from the worst correlation to the best, it will look like figure 2.  Scroll through this figure from top to bottom.  You will see the worst correlations at the top and the best on the bottom.  Note that the Luterbacher proxies are among the best correlated, and show up near the bottom.   

Figure 3. All Northern Hemisphere proxies, except Luterbacher proxies, in order of correlation with Northern Hemisphere instrumental temperature record.

 

Figure 3 is the same as figure 2, but with the Luterbacher proxies removed.  Scroll through, and it is quite clear that there are far fewer hockey stick-like proxies now.   

The Amazing Multiplying Proxies

Remember, the point of a hockey stick is not that it goes up in the 20th century – this is a given because the hockey stick is deliberately constructed from proxies that go up in the 20th century.  The real point is that it is more or less flat prior to the 20th century. (See step 5 of the super-simple steps for building a hockey stick.)  The 71 Luterbacher time series are tailor-made for this purpose, because they tend to show temperature rising in the 20th century but flat prior to that.  The problem with the 71 Luterbacher proxies is that they are actually not 71 separate proxies at all.    

Luterbacher, et.al., (European Seasonal and Annual Temperature Variability, Trends, and Extremes Since 1500, Science, 2004) used about 150 “predictors” spread out over Europe to reconstruct European surface temperature fields.  These predictors consisted of “instrumental temperature and pressure data and documentary proxy evidence.”    Figure 4, taken from Luterbacher’s  supplemental material, shows the geographical distribution of these predictors.   

Figure 4. Luterbacher's original caption: (A) station pressure locations (red triangles) and surface temperature sites (B, red circles) used to reconstruct the monthly European temperature fields (25°W-40°E; 35°N-70°N given by the rectangular blue box). Blue circles indicate documentary monthly-resolved data, blue dots represent documentary information with seasonal resolution back to 1500. Green dots stand for seasonally resolved temperature proxy reconstructions from tree-ring and ice core evidence.

 

 Lutenbacher used combinations of the predictors to interpolate the data to…   

“a new gridded (0.5° x 0.5° resolution) reconstruction of monthly (back to 1659) and seasonal (from 1500 to 1658) temperature fields for European land areas (25°W to 40°E and 35°N to 70°N).”    

Each of these grid points in the reconstruction is like one of the lutannt# graphs that show up in the list of proxies for Mann’s 2008 version of the hockey stick.  Mann ends up with 71 lutannt# “proxies” by simply taking 71 points using 5° x 5° resolution from Luterbacher’s temperature field reconstruction.   

Here’s  the rub: Not all the predictors used to make Luterbacher’s temprature field reconstruction go all the way back to 1500.  In fact, prior to about 1760 only about 10 of the total 150 predictors are used, and these predictors are primarily “documentary information.”  Prior to about 1660, only about 7 are used.   Figure 5, which also comes from Luterbacher’s supplementary material, shows the number of predictors used for each year to reconstruct his surface temperature fields.   

Figure 5. Luterbacher's original caption: Number of predictors through time.

 

Figure 6 shows the location of Mann’s 71 selected “proxies” and the location of the “documentary information” sources.  Not the best match in the world, is it?  Amazingly, the construction of some of the proxies prior to 1750 is based on data from sources over 1000 kilometers away!  

Figure 6. Blue dot show the location of Mann lutannt# "proxies." Red dots show the location of Luterbachers early "documentary information" sources.

 

 The important point is that all the data for Mann’s 71 lutannt# “proxies” prior to about 1760 is made up of some combination of the same 10 or so “documentary information” predictors.  This short list of predictors are the “Amazing Multiplying Hockey Stick Proxies.”  These 10 predictors are multiplied into 71 proxies, and these proxies all rank high for correlation to the instrumental temperature record from 1850 to the present.  Consequently, these 71 “proxies” likely weigh heavily in Mann’s 2008 hockey stick, and these 10 “documentary information” predictors, sometimes folded into “proxies” over a thousand kilometers away, have an undeserved multiplied effect in making the flat part of the hockey stick prior to the instrumental temperature record. 

****************************************** 

correlation coefficient: 

 

where P is the proxy, and Pi is the ith year of the proxy
T is the temperature, and Ti is the ith year of the temperature

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Super-simple hockey stick explanation.

January 31, 2010

I have been reading over the blog posts of Steve McIntryre and Jeff Id and others about the nuances of various constructions of the hockey stick.  I’ve been examining the archived Mann08 data at the NCDC.  This is my attempt to boil down hockey stick construction to its bare-bones, expressed as five essential steps:

Step 1. Gather time series.
|

Step 2. Select those time series that fit the instrumental (measured) temperature record of choice. Assume that since these time series match the instrumental temperature record in some way, then they are, in fact, temperature proxies.
|

Step 3. Combine the chosen proxies in some fashion and note, not surprisingly, that the combined proxies match the temperature record (duh!).
|

Step 4. Call this thing made from the combined proxies your temperature reconstruction, and therefore assume that the combined proxies are also a match for the temperature that occurred prior to the temperature measurement records.
|

Step 5.  Note that the reconstruction shows that the temperature prior to the instrumental data is relatively flat.  Make the important conclusion that the temperature prior to the instrumental record changed very little.

Defenders of hockey stick constructions will point out my naïvety with an endless list of nuances and subtleties involved in each of these steps.  But keep your eye on the puck.  The following picture illustrates the difference between my simple steps and their more nuanced approaches…

It’s important to examine those details and nuances at some time and place.  But sometimes they are simply a smokescreen.  Keep your eye on the hockey puck.

Coming soon to ClimateSanity: the Amazing Multiplying Proxies.

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Mann’s 1209 temperature proxies

January 21, 2010

I believe that data used temperature reconstructions, which are in turn used to push for re-structuring the economy of the world, should be easily accessible to everybody.

You can view the plots of all 1,209 proxies used by Michael Mann for his 2008 hockey stick temperature reconstruction joined together into one giant image. Amazingly, the images are relatively small (less than one megabyte) and will download quite quickly. The proxies are arranged in alphabetical order, right to left and up to down.

Steve McIntyre, Jeff Id, and others have done a vast amount of work analyzing how Mann, et. al., used this data in hockey stick construction.  I am just starting to look at it in my own ignorant fashion, and expect to have a lot a fun

1000AD to 2000AD

Click on the graph at the left to see the 1209 proxies from which Michael Mann created his 2008 hockey stick.  This graph plots the data from 1000AD to 2000AD.  Note that most of these proxies cover only part of that year range.  There are some proxies that extend back further that 1000AD, but the pre 1000AD portion of the data has been truncated in these plots.

1500AD to 2000AD

Click on the graph on the left to see plots the data from 1500AD to 2000AD.  This shows the last 500 year a little closer up.

Download data

If you see a proxy of particular interest, note the name at the top of the graph.  Then go here to download the corresponding text file.  The file is tab delimited and will open nicely  in any spreadsheet.  The first column is the year, the second column is the proxy value.

Banners

Finally, you can get banner type versions of the graphical data.   The following banners are each 3 feet by 6 feet, each showing half of the proxy data (1000AD to 2000AD).   These versions will print out on a large format printer at Fedex-Kinko’s stores for about $14 each.  They  might make interesting conversation pieces in the classroom or office.  Left click on the links to see the banners, right-click on the links to download the files (only about 300 kilobytes each).

Part 1

Part 2

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