Tag Archives: GCCI

Evan Mills Response to My Critique of the Grid Outage Chart

A month or two ago, after Kevin Drum (a leftish supporter of strong AGW theory) posted a chart on his site that looked like BS to me.  I posted my quick reactions to the chart here, and then after talking to the data owner in Washington followed up here.

The gist of my comments were that the trend in the data didn’t make any sense, and upon checking with the data owner, it turns out much of the trend is due to changes in the data collection process.  I stick by that conclusion, though not some of the other suppositions in those posts.

I was excited to see Dr. Mills response (thanks to reader Charlie Allen for the heads up).  I will quote much of it, but to make sure I can’t be accused of cherry-picking, here is his whole post here.  I would comment there, but alas, unlike this site, Dr. Mills chooses not to allow comments.

So here we go:

Two blog entries [1-online | PDF] [2-online | PDF] [Accessed June 18, 2009] mischaracterize analysis in a new report entitled Global Climate Change Impacts in the United States. The blogger (a self-admitted “amateur”) created a straw man argument by asserting that the chart was presented as evidence of global climate change and was not verified with the primary source. The blog’s errors have been propagated to other web sites without further fact checking or due diligence. (The use of profanity in the title of the first entry is additionally unprofessional.)

Uh, oh, the dreaded “amateur.”  Mea Culpa.  I am a trained physicist and engineer.  I don’t remember many colleges handing out “climate” degrees in 1984, so I try not to overstate my knowledge.  As to using “bullsh*t” in the title, the initial post was “I am calling bullsh*t on this chart.”  Sorry, I don’t feel bad about that given the original post was a response to a post on a political blog.

The underlying database—created by the U.S. Department of Energy’s Energy Information Administration—contains approximately 930 grid-disruption events taking place between 1992 and 2008, affecting 135 million electric customers.

As noted in the caption to the figure on page 58 of our report (shown above)—which was masked in the blogger’s critique—

First, I am happy to admit errors where I make them (I wonder if that is why I am still an “amateur”).   It was wrong of me to post the chart without the caption. My only defense was that I copied the chart from, and was responding to its use on, Kevin Drum’s site and he too omitted the caption. I really was not trying to hide what was there.   I am on the road and don’t have the original but here it is from Dr. Mills’ post.



Anyway, to continue…

As noted in the caption to the figure on page 58 of our report (shown above)—which was masked in the blogger’s critique—we expressly state a quite different finding than that imputed by the blogger, noting with care that we do not attribute these events to anthropogenic climate change, but do consider the grid vulnerable to extreme weather today and increasingly so as climate change progresses, i.e.:

“Although the figure does not demonstrate a cause-effect relationship between climate change and grid disruption, it does suggest that weather and climate extremes often have important effects on grid disruptions.”

The associated text in the report states the following, citing a major peer-reviewed federal study on the energy sector’s vulnerability to climate change:

“The electricity grid is also vulnerable to climate change effects, from temperature changes to severe weather events.”

To Dr. Mills’ point that I misinterpreted him — if all he wanted to say was that the electrical grid could be disturbed by weather or was vulnerable to climate change, fine.  I mean, duh.  If there are more tornadoes knocking about, more electrical lines will come down.  But if that was Dr. Mills ONLY point, then why did he write (emphasis added):

The number of incidents caused by extreme weather has increased tenfold since 1992.  The portion of all events that are caused by weather-related phenomena has more than tripled from about 20 percent in the early 1990s to about 65 percent in recent years.  The weather-related events are more severe…

He is saying flat out that the grid IS being disturbed 10x more often and more severely by weather.  It doesn’t even say “reported” incidents or “may have” — it is quite definitive.  So which one of us is trying to create a straw man?   It is these statements that I previously claimed the data did not support, and I stand by my analysis on that.

And it’s not like there is some conspiracy of skeptics to mis-interpret Mr. Mills.  Kevin Drum, a  huge cheerleader for catastrophic AGW, said about this chart:

So here’s your chart of the day: a 15-year history of electrical grid problems caused by increasingly extreme weather.

I will skip the next bit, wherein it appears that Dr. Mills is agreeing with my point that aging and increased capacity utilization on the grid could potentially increase weather-related grid outages without any actual change in the weather  (just from the grid being more sensitive or vulnerable)

OK, so next is where Mr. Mills weighs in on the key issue of the data set being a poor proxy, given the fact that most of the increase in the chart are due to better reporting rather than changes in the underlying phenomenon:

The potential for sampling bias was in fact identified early-on within the author team and—contrary to the blogger’s accusation—contact was in fact made with the person responsible for the data collection project at the US Energy Information Administration on June 10, 2008 (and with the same individual the blogger claims to have spoken to). At that time the material was discussed for an hour with the EIA official, who affirmed the relative growth was in weather-related events and that it could not be construed as an artifact of data collection changes, etc. That, and other points in this response, were re-affirmed through a follow up discussion in June 2009.

In fact, the analysis understates the scale of weather-related events in at least three ways:

  • EIA noted that there are probably a higher proportion of weather events missing from their time series than non-weather ones (due to minimum threshold impacts required for inclusion, and under-reporting in thunderstorm-prone regions of the heartland).
  • There was at least one change in EIA’s methodology that would have over-stated the growth in non-weather events, i.e., they added cyber attacks and islanding in 2001, which are both “non-weather-related”.
  • Many of the events are described in ways that could be weather-related (e.g. “transmission interruption”) but not enough information is provided. We code such events as non-weather-related.

Dr. Mills does not like me using the “BS” word, so I will just say this is the purest caca. I want a single disinterested scientist to defend what Dr. Mills is saying. Remember:

  • Prior to 1998, just about all the data is missing. There were pushes in 2001 and 2008 to try to fix under reporting.  Far from denying this, Dr. Mills reports the same facts.  So no matter how much dancing he does, much of the trend here is driven by increased reporting, not the underlying phenomenon.  Again, the underlying phenomenon may exist, but it certainly is not a 10x increase as reported in the caption.
  • The fact that a higher proportion of the missing data is weather-related just underlines the point that the historic weather-related outage data is a nearly meaningless source of trend data for weather-related outages.
  • His bullet points are written as if the totals matter, but the point of the chart was never totals.  I never said he was overstating weather related outages today.   The numbers in 2008 may still be (and probably are) understated.  And I have no idea even if 50 or 80 is high or low,  so absolute values have no meaning to me anyway.  The chart was designed to portray a trend — remember that first line of the caption “The number of incidents caused by extreme weather has increased tenfold since 1992. ” — not a point on absolute values.   What matters is therefore not how much is missing, but how much is missing in the early years as compared to the later years.
  • In my original post I wrote, as Dr. Mills does, that the EIA data owner thinks there is a weather trend in the data if you really had quality data.  Fine.  But it is way, way less of a trend than shown in this chart.  And besides, when did the standards of “peer reviewed science” stoop to include estimates of government data analysts as to what the trend in the data would be if the data weren’t corrupted so badly.   (Also, the data analyst was only familiar with the data back to 1998 — the chart started in 1992.
  • Dr. Mills was aware that the data had huge gaps before publication.  Where was the disclosure?  I didn’t see any disclosure.  I wonder if there was such disclosure in the peer reviewed study that used this data (my understanding is that there must have been one, because the rules of this report is that everything had to come from peer-reviewed sources).
  • I don’t think any reasonable person could use this data set in a serious study knowing what the authors knew.  But reasonable people can disagree, though I will say that I think there is no ethical way anyone could have talked to the EIA in detail about this data and then used the 1992-1997 data.


Thanks to the efforts of EIA, after they took over the responsibility of running the Department of Energy (DOE) data-collection process around 1997, it became more effective. Efforts were made in subsequent years to increase the response rate and upgrade the reporting form.

Thanks, you just proved my point about the trend being driven by changes in reporting and data collection intensity.

To adjust for potential response-rate biases, we have separated weather- and non-weather-related trends into indices and found an upward trend only in the weather-related time series.

As confirmed by EIA, if there were a systematic bias one would expect it to be reflected in both data series (especially since any given reporting site would report both types of events).

As an additional precaution, we focused on trends in the number of events (rather than customers affected) to avoid fortuitous differences caused by the population density where events occur. This, however, has the effect of understating the weather impacts because of EIA definitions (see survey methodology notes below).

Well, its possible this is true, though unhappily, this analysis was not published in the original report and was not published in this post.   I presume this means he has a non-weather time series that is flat for this period.  Love to see it, but this is not how the EIA portrayed the data to me.  But it really doesn’t matter – I think the fact that there is more data missing in the early years than the later years is indisputable, and this one fact drives a false trend.

But here is what I think is really funny- — the above analysis does not matter, because he is assuming a reporting bias symmetry, but just  a few paragraphs earlier he stated that there was actually an asymmetry.  Let me quote him again:

EIA noted that there are probably a higher proportion of weather events missing from their time series than non-weather ones (due to minimum threshold impacts required for inclusion, and under-reporting in thunderstorm-prone regions of the heartland).

Look Dr. Mills, I don’t have an axe to grind here.  This is one chart out of bazillions making a minor point.  But the data set you are using is garbage, so why do you stand by it with such tenacity?  Can’t anyone just admit “you know, on thinking about it, there are way to many problems with this data set to declare a trend exists.  Hopefully the EIA has it cleaned up now and we can watch it going forward.”  But I guess only “amateurs” make that kind of statement.

The blogger also speculated that many of the “extreme temperature” events were during cold periods, stating “if this is proof of global warming, why is the damage from cold and ice increasing as fast as other severe weather causes?” The statement is erroneous.

This was pure supposition in my first reaction to the chart.  I later admitted that I was wrong.  Most of the “temperature” effects are higher temperature.  But I will admit it again here – that supposition was incorrect.  He has a nice monthly distribution of the data to prove his point.

I am ready to leave this behind, though I will admit that Dr. Mills response leaves me more rather than less worried about the quality of the science here.  But to summarize, everything is minor compared to this point:  The caption says “The number of incidents caused by extreme weather has increased tenfold since 1992.”  I don’t think anyone, knowing about the huge underreporting in early years, and better reporting in later years, thinks that statement is correct.  Dr. Mills should be willing to admit it was incorrect.

Update: In case I am not explaining the issue well, here is a conceptual drawing of what is going on:


Update #2: One other thing I meant to post.  I want to thank Dr. Mills — this is the first time in quite a while I have received a critique of one of my posts without a single ad hominem attack, question about my source of funding, hypothesized links to evil forces, etc.  Also I am sorry I wrote “Mr.” rather than “Dr.” Mills.  Correction has been made.

GCCI #12: Ignoring the Data That Doesn’t Fit the Narrative

Page 39 of the GCCI  Report discusses retreating Arctic sea ice.  It includes this chart:


The first thing I would observe is that the decline seems exaggerated through some scaling and smoothing gains.    The raw data, from the Cyrosphere Today site   (note different units, a square mile = about 2.6 sq. km).


But the most interesting part is what is not mentioned, even once, in this section of the report:  The Earth has two poles.  And it turns out that the south pole has actually been gaining sea ice, such that the total combined sea ice extent of the entire globe is fairly stable (click for larger version).


Now, there are folks who are willing to posit a model that allows for global warming and this kind of divergence between the poles.  But the report does not even go there.  It demonstrates an inferiority complex I see in many places of the report, refusing to even hint that reality is messy in fear that it might cloud their story.

Update #2 On GCCI Electrical Grid Disruption Chart

Update: Evan Mills, apparently one author of the analysis, responds and I respond back.

Steve McIntyre picks up my critique on the electrical grid disruption chart (here and here) and takes it further.  Apparently, this report (which I guess I should be calling the Climate Change Synthesis Report or CCSP) set rules for itself that all the work in the report had to be from peer-reviewed literature.  McIntyre makes a grab at the footnotes for this section of the report for any peer-reviewed basis, but comes up only with air.   He also references a hurricane chart in the report apparently compiled by the same person who compiled the grid outage report.  Roger Pielke rips up this hurricane report, and I have it on my list to address in a future post as well.

GCCI #11: Changing Wet and Dry Weather

From the GCCI report on page 24:

Increased extremes of summer dryness and winter wetness are projected for much of the globe, meaning a generally greater risk of droughts and floods. This has already been observed, and is projected to continue. In a warmer world, precipitation tends to be concentrated into heavier events, with longer dry periods in between.

Later in the report they make the same claims for the US only.  I can’t speak for the rest of the world, but I don’t know what data they are using.  This is from the National Climate Data Center, run by the same folks who wrote this report:



Maybe my Mark I eyeball is off, but it sure doesn’t look like any trend here, or that there we are currently at any particularly unprecedented levels today.  Of course, the main evidence they have of increasing extreme rainfall is in this chart — but of course this is “simulated” history, rather than actual, you know, observations.

GCCI #10: Extreme Example of Forcing Observation to Fit the Theory

In the last post, I discussed forcing observations to fit the theory.  Generally, this takes the form either of ignoring observations or adding “adjustment” factors to the data.  But here is an even more extreme example from page 25:


A quick glance at this chart, and what do we see?  A line historically rising surprisingly in parallel with global temperature history, and then increasing in the future.

But let’s look at that chart legend carefully.  The green “historic” data is actually nothing of the sort – it is simulation!  The authors have created their own history.  This entire chart is the output of some computer model programmed to deliver the result that temperature drives heavy precipitation, and so it does.

GCCI #9: Forcing Observation to Fit the Theory

Let me digress a bit.  Just over 500 years ago, Aristotelian physics and mechanical models still dominated science.  The odd part about this was not that people were still using his theories nearly 2000 years after his death — after all, won’t people still know Isaac Newton’s contributions a thousand years hence?  The strange part was that people had been observing natural effects for centuries that were entirely inconsistent with Aristotle’s mechanics, but no one really questioned the underlying theory.

But folks found it really hard to question Aristotle.  The world had gone all-in on Aristotle.  Even the Church had adopted Aristotle’s description of the universe as the one true and correct model.  So folks assumed the observations were wrong, or spent their time shoe-horning the observations into Aristotle’s theories, or just ignored the offending observations altogether.  The Enlightenment is a complex phenomenon, but for me the key first step was the willingness of people to start questioning traditional authorities (Aristotle and the church) in the light of new observations.

I am reminded of this story a bit when I read about “fingerprint” analyses for anthropogenic warming.  These analyses propose to identify certain events in current climate (or weather) that are somehow distinctive features of anthropogenic rather than natural warming.  From the GCCI:

The earliest fingerprint work focused on changes in surface and atmospheric temperature. Scientists then applied fingerprint methods to a whole range of climate variables, identifying human-caused climate signals in the heat content of the oceans, the height of the tropopause (the boundary between the troposphere and stratosphere, which has shifted upward by hundreds of feet in recent decades), the geographical patterns of precipitation, drought, surface pressure, and the runoff from major river basins.

Studies published after the appearance of the IPCC Fourth Assessment Report in 2007 have also found human fingerprints in the increased levels of atmospheric moisture (both close to the surface and over the full extent of the atmosphere), in the decline of Arctic sea ice extent, and in the patterns of changes in Arctic and Antarctic surface temperatures.

This is absolute caca.  Given the complexity of the climate system, it is outright hubris to say that things like the “geographical patterns of precipitation” can be linked to half-degree changes  in world average temperatures.  But it is a lie to say that it can be linked specifically to human-caused warming, vs. warming from other causes, as implied in this statement.   A better name for fingerprint analysis would be Rorschach analysis, because they tend to result in alarmist scientists reading their expectation to find anthropogenic causes into every single weather event.

But there is one fingerprint prediction that was among the first to be made and is still probably the most robust of this genre:  that warming from greenhouse gasses will be greatest in the upper troposphere above the tropics.  This is demonstrated by this graph on page 21 of the GCCI


This has always been a stumbling block, because satellites, the best measures we have on the troposphere, and weather balloons have never seen this heat bubble over the tropics.  Here is the UAH data for the mid-troposphere temperature — one can see absolutely no warming in a zone where the warming should, by the models, be high:


Angell in 2005 and Sterin in 2001 similarly found from Radiosonde records about 0.2C of warming since the early 1960s, below the global surface average warming when models say it should be well above.

But fortunately, the GCCI solves this conundrum:

For over a decade, one aspect of the climate change story seemed to show a significant difference between models and observations.

In the tropics, all models predicted that with a rise in greenhouse gases, the troposphere would be expected to warm more rapidly than the surface. Observations from weather balloons, satellites, and surface thermometers seemed to show the opposite behavior (more rapid warming of the surface than the troposphere). This issue was a stumbling block in our understanding of the causes of climate change. It is now largely resolved.   Research showed that there were large uncertainties in the satellite and weather balloon data. When uncertainties in models and observations are properly accounted for, newer observational data sets (with better treatment of known problems) are in agreement with climate model results.

What does this mean?  It means that if we throw in some correction factors that make observations match the theory, then the observations will match the theory.  This statement is a pure out and out wishful thinking.  The charts above predict a 2+ degree F warming in the troposphere from 1958-1999, or nearly 0.3C per decade.  No study has measured anything close to this  – Satellites show 0.0C per decade and radiosondes about 0.05C per decade.    The correction factors to make reality match the theory would have to be 10 times the measured anomaly.  Even if this were the case, the implied signal to noise ratio would be so low as to render the analysis meaningless.

Frankly, the statement by these folks that weather balloon data and satellites have large uncertainties is hilarious.  While this is probably true, these uncertainties and inherent biases are DWARFED by the biases, issues, uncertainties and outright errors in the surface temperature record.  Of course, the report uses this surface temperature record absolutely uncritically, ignoring a myriad of problems such as these and these.  Why the difference?  Because observations from the flawed surface temperature record better fit their theories and models.  Sometimes I think these guys should have put a picture of Ptolemy on their cover.

GCCI #8: A Sense of Scale

In this post I want to address a minor point on chartsmanship.  Everyone plays this game with scaling and other factors to try to make his or her point more effective, so I don’t want to make too big of a deal about it.   But at some point the effort becomes so absurd it simply begs to be highlighted.

Page 13 of the GCCI report has this chart I have already seen circulating around the alarmist side of the web:


There are two problems here.

One, the compression of the X axis puts the lower and upper scenario lines right on top of each other.  This really causes the higher scenario  (which, at 900ppm, really represents a number higher than we are likely to see even in a do-nothing case) to visually dominate.

The other issue is that the Y-axis covers a very, very small range, such that small changes are magnified visually.  The scale runs from 0% of the atmosphere up to 0.09% of the atmosphere.  If one were to run the scale to cover a more reasonable range, he would get this  (with orange being the high emissions case and blue being the lower case):


Even this caps out at just 1% of the atmosphere.  If we were to look at the total composition of the atmosphere, we would get this:


GCCI #7: A Ridiculously Narrow Time Window – The Sun

In a number of portions of the report, graphs appear trying to show climate variations in absurdly narrow time windows.  This helps the authors  either a) blame long-term climate trends on recent manmade actions or b) convert natural variation on decadal cycles into a constant one-way trend.  In a previous post I showed an example, with glaciers, of the former.  In this post I want to discuss the latter.

Remember that the report leaps out of the starting gate by making the amazingly unequivocal statement:

1. Global warming is unequivocal and primarily human-induced. Global temperature has increased over the past 50 years. This observed increase is due primarily to human induced emissions of heat-trapping gases.

To make this statement, they must dispose of other possible causes, with variations in the sun being the most obvious.  Here is the chart they use on page 20:


Wow, this one is even shorter than the glacier chart.  I suppose they can argue that it is necessarily so, as they only have satellite data since 1978.  But there are other sources of data prior to 1978 they could have used**.

I will show the longer view of solar activity in a minute, but let’s take a minute to think about the report’s logic.  The chart tries to say that the lack of a trend in the rate of solar energy reaching Earth is not consistent with rising temperatures.  They are saying – See everyone, flat solar output, rising temperatures.  There can’t be a relationship.

Really?  Did any of these guys take basic thermodynamics?  Let’s consider a simple example from everyone’s home — a pot on a stove.  The stove is on low, and the water has reached an equilibrium temperature, well below boiling.  Now we turn the stove up — what happens?


In this chart, the red is the stove setting, and we see it go from low to high.  Prior to the change in stove setting, the water temperature in the pot, shown in blue, was stable.  After the change in burner setting, the water temperature begins to increase over time.

If we were to truncate this chart, so we only saw the far right side, as the climate report has done with the sun chart, we would see this:


Doesn’t this look just a little like the solar chart in the report?  The fact is that the chart from the report is entirely consistent both with a model where the sun is causing most of the warming and one where it is not.  The key is whether the level of the sun’s output from 1987 to present is a new, higher plateau that is driving temperature increases over time (like the higher burner setting) or whether the sun’s output recently is consistent with, and no higher than, its level over the last 100 years.  What we want to look for, in seeking the impact of the sun, is a step-change in output near when temperature increases of the last 50 years began.

Does such a step-change exist?  Yes.  One way to look at the sun’s output is to use sunspots as a proxy for output – the more spots in a given 11 year cycle, the greater the sun’s activity and likely output.  Here is what we see for this metric:


And here is the chart for total solar irradiance (sent to me, ironically, by someone trying to disprove the influence of the sun).


Clearly the sun’s activity and output experienced an upwards step-change around 1950.  The average monthly sunspots in the second half of the century were, for example, 50% higher than in the first half of the century.

The real question, of course, is whether these changes result in large or small rates of temperature increase.  And that is still open for debate, with issues like cloud formation thrown in for complexity.  But it is totally disingenuous, and counts on readers to be scientifically illiterate, to propose that the chart in the report “proves” that the sun is not driving temperature changes.

**By this logic, they should only have temperature data since 1978 for the same reason, though by one of those ironies I am starting to find frequent in this report, all the charts, including this one, use flawed surface temperature records rather than satellite data.  Why didn’t they use satellite data for the temperature as well as the solar output for this chart?  Probably because the satellite data does not include upward biases and thus shows less warming.  Having four or five major temperature indices to choose from, the team writing this paper chose the one that gives the highest modern warming number.

GCCI #6: A Ridiculously Narrow Time Window – Glaciers

In a number of portions of the report, graphs appear trying to show climate variations in absurdly narrow time windows.  This helps the authors of this scientific report advocacy press release either a) blame long-term climate trends on recent manmade actions or b) convert natural variation on decadal cycles into a constant one-way trend.  In this post we will look at an example of the former, while in the next post we will look at the latter.

Here is the melting glacier chart from right up front on page 18, in the section on sea level rise (ironic, since if you really read the IPCC report closely, sea level rise comes mainly from thermal expansion of the oceans – glacier melting is offset in most models by increased snow in Antarctica**).


Wow, this looks scary.  Of course, it is clever chartsmanship, making it look like they have melted down to zero by choice of scale.   How large is this compared to the total area of glaciers?  We don’t know from the chart — percentages would have been more helpful.

Anyway, I could criticize these minor chartsmanship games throughout the paper, but on glaciers I want to focus on the selected time frame.  What, one might ask, were glaciers doing before 1960?  Well, if we accept the logic of the caption that losses are driven by temperature, then I guess it must have been flat.  But why didn’t they show that?  Wouldn’t that be a powerful chart, showing flat glacier size with this falloff to the right?

Well, as you may have guessed, the truncated time frame on the left side of this chart is not flat.  I can’t find evidence that Meier et al looted back further than 1960, but others have, including Oerlemans as published in Science in 2005.  (The far right hand side really should be truncated by 5-10 years, as they are missing a lot of datapoints in the last 5 years, making the results odd and unreliable).


OK, this is length rather than volume, but they should be closely related.  The conclusion is that glaciers have been receding since the early 19th century, LONG before any build-up of CO2, and coincident with a series of cold decades in the last 18th century  (think Valley Forge and Napoleon in Russia).

I hope you can see why it is unbelievably disingenuous to truncate the whole period from 1800-1960 and call this trend a) recent and b) due to man-made global warming.  If it is indeed due to man-made global warming since 1960, then there must have been some other natural effect shrinking glaciers since 1825 that fortuitously shut off at the precise moment anthropogenic warming took over.  Paging William of Occam, call your office please.

Similarly, sea levels have been rising steadily for hundreds, even thousands of years, and current sea level increases are not far off their average pace for the last 200 years.

** The climate models show warming of the waters around Antarctica, creating more precipitation over the climate.  This precipitation falls and remains as snow or ice, and is unlikely to melt even at very high numbers for global warming as Antarctica is so freaking cold to begin with.

Update on GCCI Post #4: Grid Outage Chart

Update: Evan Mills, apparently one author of the analysis, responds and I respond back.

Yesterday I called into question the interpretation of this chart from the GCCI report where the report used electrical grid outages as a proxy for severe weather frequency:


I hypothesized:

This chart screams one thing at me:  Basis change.  Somehow, the basis for the data is changing in the period.  Either reporting has been increased, or definitions have changed, or there is something about the grid that makes it more sensitive to weather, or whatever  (this is a problem in tornado charts, as improving detection technologies seem to create an upward incidence trend in smaller tornadoes where one probably does not exist).   But there is NO WAY the weather is changing this fast, and readers should treat this whole report as a pile of garbage if it is written by people who uncritically accept this chart.

I had contacted John Makins of the EIA who owns this data set yesterday, but I was too late to catch him in the office.  He was nice enough to call me today.

He said that there may be an underlying upward trend out there (particularly in thunderstorms) but that most of the increase in this chart is from improvements in data gathering.  In 1997, the EIA (and Makins himself) took over the compilation of this data, which had previously been haphazard, and made a big push to get all utilities to report as required.  They made a second change and push for reporting in 2001, and again in 2007/2008.  He told me that most of this slope is due to better reporting, and not necessarily any underlying trend.   In fact, he said there still is some under-reporting by smaller utilities he wants to improve so that the graph will likely go higher in the future.

Further, it is important to understand the nature of this data.  The vast majority of weather disturbances are not reported to the EIA.  If the disturbance or outage remains local with no impact on any of the national grids, then it does not need to be reported.  Because of this definitional issue, reported incidents can also change over time due to the nature of the national grid.  For example, as usage of the national grid changes or gets closer to capacity, local disturbances might cascade to national issues where they would not have done so 20 years ago.  Or vice versa – better grid management technologies might keep problems local that would have cascaded regionally or nationally before.  Either of these would drive trends in this data that have no relation to underlying weather patterns.

At the end of the day, this disturbance data is not a good proxy for severe weather.  And I am left wondering at this whole “peer-reviewed science” thing, where errors like this pass into publication of major reports — an error that an amateur like myself can identify with one phone call to the guy listed by this data set on the web site.  Forget peer review, this isn’t even good basic editorial control  (apparently no one who compiled the report called Makins, and he was surprised today at the number of calls he was suddenly getting).

Postscript: Makins was kind enough to suggest some other data bases that might show what he believes to be a real increase in thunderstorm disturbances of electrical distribution grids.  He suggested that a number of state PUC’s keep such data, including the California PUC under their reliability section.  I will check those out, though it is hard to infer global climate trends from one state.

GCCI #5: The Dog That Didn’t Bark

The GCCI is mainly focused on creating a variety of future apocalyptic narratives.  However, it was interesting none-the-less for what was missing:  No hockey stick, and no Co2/temperature 600,000 year ice core chart.  Have we finally buried these chestnuts, or were they thought unnecessary as the report really expends no effort defending the existence of warming.

GCCI #4: I Am Calling Bullsh*t on this Chart

Update#2: Evan Mills, apparently one author of the analysis, responds and I respond back.

UPDATE: I obtained more information from the EIA.  My hypothesis below is correct.   Update here.

For this next post, I skip kind of deep into the report because Kevin Drum was particularly taken with the power of this chart from page 58.


I know that skepticism is a lost art in journalism, so I will forgive Mr. Drum.  But in running a business, people put all kinds of BS analyses in front of me trying to get me to spend my money one way or another.  And so for those of us for whom data analysis actually has financial consequences, it is a useful skill to be able to recognize a steaming pile of BS when one sees it.  (Update: I regret the snarky comment about Kevin Drum — though I disagree with him a lot, he is one of the few folks on either side of the political aisle who is willing to express skepticism for studies and polls even when they support his position.  Mr. Drum has posted an update to his original post after I emailed him this information).

First, does anyone here really think that we have seen a 20-fold increase in electrical grid outages over the last 15 years but no one noticed?  Really?

Second, let’s just look at some of the numbers.  Is there anyone here who thinks that if we are seeing 10-20 major outages from thunderstorms and tornadoes (the yellow bar) in the last few years, we really saw ZERO by the same definition in 1992?  And 1995?  And 1996?  Seriously?  This implies there has been something like a 20-fold increase in outages from thunderstorms and tornadoes since the early 1990’s.  But tornado activity, for example, has certainly not increased since the early 1990’s and has probably decreased (from the NOAA, a co-author of the report):


All the other bars have the same believability problem.  Take “temperature extremes.”  Anyone want to bet that is mostly cold rather than mostly hot extremes?  I don’t know if that is the case, but my bet is the authors would have said “hot” if the data had been driven by “hot.”  And if this is proof of global warming, then why is the damage from cold and ice increasing as fast as other severe weather causes?

This chart screams one thing at me:  Basis change.  Somehow, the basis for the data is changing in the period.  Either reporting has been increased, or definitions have changed, or there is something about the grid that makes it more sensitive to weather, or whatever  (this is a problem in tornado charts, as improving detection technologies seem to create an upward incidence trend in smaller tornadoes where one probably does not exist).   But there is NO WAY the weather is changing this fast, and readers should treat this whole report as a pile of garbage if it is written by people who uncritically accept this chart.

Postscript: By the way, if I want to be snarky, I should just accept this chart.  Why?  Because here is the US temperature anomaly over the same time period (using the UAH satellite data as graphed by Anthony Watt, degrees C):


From 1998 to today, when the electrical outage chart was shooting up, the US was actually cooling slightly!

This goes back to the reason why alarmists abandoned the “global warming” term in favor of climate change.   They can play this bait and switch, showing changes in climate (which always exist) and then blaming them on CO2.  But there is no mechanism ever proposed by anyone where CO2 can change the climate directly without going through the intermediate step of warming.  If climate is changing but we are not seeing warming, then the change can’t be due to CO2. But you will never see that fact in this helpful government propaganda piece.

GCCI Report #3: Warming and Feedback

One frequent topic on this blog is that the theory of catastrophic anthropogenic global warming actually rests on two separate, unrelated propositions.  One, that increasing CO2 in the atmosphere increases temperatures.  And two, that the Earth’s climate is dominated by positive feedbacks that multiply the warming from Co2 alone by 3x or more.  Proposition one is well-grounded, and according to the IPCC (which this report does not dispute) the warming from Co2 alone is about 1.2C per doubling of Co2 concentrations.  Proposition two is much, much iffier, which is all the more problematic since 2/3 or more of the hypothesized future warming typically comes from the feedback.

We have to do a little legwork, because this report bends over backwards to not include any actual science.  For example, as far as I can tell, it does not actually establish a range of likely climate sensitivity numbers, but we can back into them.

The report uses CO2 concentrations numbers for “do nothing” scenarios (no global warming legislation) of between 850 and 950 ppm in 2100.  These are labeled as the IPCC A2 and A1F1 scenarios.  For these scenarios, between 2000 and 2100 they show warming of 6F and 7F respectively.   Now, I need to do some conversions.  850 and 950 ppm represent about 1.25 and 1.5 doublings from 2000 levels.  The temperatures for these are 3.3C and 3.9C.  This means that the assumed sensitivity in these charts (as degrees Celsius per doubling) is around 2.6, though my guess is that there are time delays in the model and the actual number is closer to 3.  This is entirely consistent with the last IPCC report.

OK, that seems straight forward.  Except having used these IPCC numbers on pages 23-25, they quickly abandon them in favor of higher numbers.    Here for example, is a chart from page 29:


Note the map on the right, which is the end of century projection for the US.  The chart shows a range of warming of 7-11 degrees F for a time period centered on 2090  (they boxed that range on the thermometer, not me), but the chart on page 25 shows average warming in the max emissions case in 2090 to be about 7.5F against the same baseline (you have to be careful, they keep moving the baseline around on these charts).  It could be that my Mark I integrating eyeball is wrong, but that map sure looks like more than an average 7.5F increase.  It could be that the US is supposed to warm more than the world average, but the report never says so that I can find, and the US (even by the by the numbers in the report) has warmed less than the rest of the globe over the last 50 years.

The solution to this conundrum may be on page 24 when they say:

Based on scenarios that do not assume explicit climate policies to reduce greenhouse gas emissions, global average temperature is projected to rise by 2 to 11.5°F by the end of this century90 (relative to the 1980-1999 time period).

Oddly enough (well, oddly for a science document but absolutely predictably for an advocacy paper) the high end of this range, rather than the median, seems to be the number used through the rest of the report.  This 11.5F probably implies a climate sensitivity around 5 C/doubling.  Using the IPCC numaber of 1.2 for CO2 alone, means that this report is assuming that as much as 75% of the warming comes from positive feedback effects.

So, since most of the warming, and all of the catastrophe, comes from the assumption that the climate system is dominated by net positive feedback, one would assume the report would address itself to this issue.  Wrong.

I did a search for the word “feedback” in the document just to make sure I didn’t miss anything.  Here are all the references in the main document (outside of footnotes) to feedback used in this context:

  • P15:  “However, the surface warming caused by human-produced increases in other greenhouse gases leads to an increase in atmospheric water vapor, since a warmer climate increases evaporation and allows the atmosphere to hold more moisture. This creates an amplifying “feedback loop,” leading to more warming.”
  • P16:  “For example, it is known from long records of Earth’s climate history that under warmer conditions, carbon tends to be released, for instance, from thawing permafrost, initiating a feedback loop in which more carbon release leads to more warming which leads to further release, and so on.”
  • P17:  “For example, it is known from long records of Earth’s climate history that under warmer conditions, carbon tends to be released, for instance, from thawing permafrost, initiating a feedback loop in which more carbon release leads to more warming which leads to further release, and so on.

That’s it – the entire sum text of feedbacks.  All positive, no discussion of negative feedbacks, and no discussion of the evidence how we know positive feedbacks outweight negative feedbacks.  The first one of the three is particularly disengenuous, since most serious scientists will admit that we don’t even know the sign of the water vapor feedback loop, and there is good evidence the sign is actually negative (due to albedo effects from increased cloud formation).

GCCI Report #2: Climate Must Be Dead Stable Without Man

The other underlying assumption in the GCCI report is that without man, climate would be dead stable.  Year in and year out, decade after decade, every location would get the same rain it got the year before and the decade before, the same number of storms, the same number of tornadoes, the same start date for Spring, etc.

Now, the authors might object to that and say, “we don’t believe that.”  But in fact they must, since in the report, any US climate trend in the last 20 years (more rain, less rain, more storms, fewer storms, more snow, less snow, etc) is all blamed on man.   Why else discuss a given trend in climate in a report on man-made climate change except to create the impression that each and every trend in climate is due to man, and can therefore be extrapolated a hundred years in to the future?

I am going to take on many of these charts in this series, but here is an example from page 30:


So what?   Do you really think there is a single 50-year period in the history of North America where you wouldn’t see this kind of effect?  Where, sans man, the chart would be all white with no changes?  And even trying to pull regional conclusions out of this is almost impossible — for example, the brown in the Southeast is heavily driven by the 2008 endpoint with a big drought.  Shift the period by even a few years and the chart has the same mixture of blue and brown, but distributed differently.

Of course,this assumption of underlying stability is absurd.  History is full of short, medium, and long-term climate cycles.  An honest scientific discussion would look at the degree of variation over time, say hundreds or thousands of years, and then put recent variations in this context.  Are recent changes unprecedented, or not?  Well, we’ll never know from this report.

GCCI Report #1: Overall Tone

The first thing one needs to recognize about the GCCI report is that it is not a scientific document — it is a 170-page long press release from an advocacy group, with all the detailed, thorough science one might expect in a press release from the Center of Science and Public Interest writing about the threat to mankind from Twinkies.  By the admission of the Obama administration, this is a document that has been stripped of its scientific discussion and rewritten by a paid PR firm that specialized in environmental advocacy.

I have not read every word, but it is pretty clear that there is no new science here on the causes or magnitude of warming.  In fact, if I had to describe the process used to prepare the first part of the report, it was to take past IPCC reports, strip out any wording indicating uncertainty, and then portray future forecasts using the IPCC mean forecasts as the lowest possible warming and whatever model they could find that spit out the highest forecast as the “worst case scenario.”  Then, the rest of the report (about 90%) creates a variety of hypothesized disaster movie plots based on this worst case scenario.

You know that this is an advocacy document and not science right of the bat when they write this:

1. Global warming is unequivocal and primarily human-induced.
Global temperature has increased over the past 50 years. This observed increase is due primarily to humaninduced
emissions of heat-trapping gases.

Just look in wonder at the false religion-like certainty.  Name three other scientific findings about horribly complex natural processes that have been studied in depth for only 20 years or so that one would use the word “unequivocal” for.  OK, name even one.

If you can’t read the whole report, read the list of disasters on page 12.  If I had shown this to you blind, and told you it from from a Paul Ehrlich the-world-will-end-in-a-decade book from the 1970s, you would probably have believed me.

This entire report assumes global warming to exist, assumes it is man-made, and assumes its future levels are as large or larger than those projected in the last IPCC report.  The first four or five pages merely restate this finding with no new evidence.  The majority of the report then takes this assumption, cranks it through various models, and generates scary potential scenarios about the US and it would be like if temperatures really rose 11F over the next century.