Posts tagged ‘temperature’

Immigrants and Crime

Virtually every study done points to the fact the immigrants, even illegal immigrants, are less prone to crime than American citizens.  That is why immigration opponents must rely on repetition of lurid single examples to try to make their case, a bit like global warming advocates point to individual heat waves as a substitute for having any warming show up in the recent global temperature metrics.

From the Foundation for Economic Education

With few exceptions, immigrants are less crime prone than natives or have no effect on crime rates. As described below, the research is fairly one-sided.

There are two broad types of studies that investigate immigrant criminality. The first type uses Census and American Community Survey (ACS) data from the institutionalized population and broadly concludes that immigrants are less crime prone than the native-born population. It is important to note that immigrants convicted of crimes serve their sentences before being deported with few exceptions.

However, there are some potential problems with Census-based studies that could lead to inaccurate results. That’s where the second type of study comes in. The second type is a macro level analysis to judge the impact of immigration on crime rates, generally finding that increased immigration does not increase crime and sometimes even causes crime rates to fall.

Butcher and Piehl examine the incarceration rates for men aged 18-40 in the 1980, 1990, and 2000 Censuses. In each year, immigrants are less likely to be incarcerated than natives with the gap widening each decade. By 2000, immigrants have incarceration rates that are one-fifth those of the native-born

There is a lot more at the link.

"Man-Made" Climate Change

Man has almost certainly warmed the world by some tenths of a degree C with his CO2, though much of this warming has hit night-time lows rather than daily highs.  Anyway, while future temperature rise forecasts are often grossly exaggerated by absurdly high assumptions of positive feedback, there is at least a kernel of fact in there that CO2 is likely warming the world somewhat.

However, the popular "science" on climate change is often awful, positing, for example, that hurricanes are being increased by man right in the midst of the longest hurricane drought we have seen in the US for a hundred years.

Inevitably, the recent severe California droughts have been blamed on manmade CO2.  As a hopefully useful adjunct to this debate, I have annotated a recent chart from the San Jose Mercury News on the history of California droughts to reflect the popular global warming / climate change narrative.  You be the judge of the reasonableness:

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LMAO -- My Kid Learns About the Cold

My Arizona-raised, thin-blooded son was convinced that he had no problem with cold weather when he departed for Amherst College several years ago.  That, of course, was based on exposure to cold via a couple of ski trips.  What he likely underestimated was the impact of cold that lasts for like 6 freaking months.

So it was with good-natured parental fondness for my child that I was LMAO when I read this:

Amherst, MA has coldest February in recorded history.  or here if you hit a paywall.

The average temperature in Amherst in the past month was 11.2 degrees, the lowest average monthly temperature since records were first kept in town in 1835. It broke the previous record of 11.6 degrees set in 1934, according to Michael A. Rawlins, an assistant professor in the department of geosciences and manager of the Climate System Research Center at the University of Massachusetts.

As it turns out, I have made a climate presentation in Amherst so I actually have historic temperature charts.  It is a good example of two things:

  1. While Amherst has been warming, it was warming as much or more before 1940 (or before the era of substantial CO2 emissions) as much as after
  2. Much of the recent warming has manifested as increases in daily minimum temperatures, rather in an increase in daily maximum temperatures.  This is as predicted by warming models, but poorly communicated and understood.  Possibly because fewer people would be bent out of shape if they knew that warming translated into warmer nights rather than higher highs in the daytime.

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Skeptics: Please Relax on the Whole "Greatest Scientific Fraud of All Time" Thing

Climate skeptics are at risk of falling into the same exaggeration-trap as do alarmists.

I have written about the exaggeration of past warming by questionable manual adjustments to temperature records for almost a decade.  So I don't need to be convinced that these adjustments 1) need to be cleaned up and 2) likely exaggerate past warming.

However, this talk of the "Greatest Scientific Fraud of All Time" is just crazy.  If you are interested, I urge you read my piece from the other day for a more balanced view.  Don't stop reading without checking out #4.

These recent articles are making it sound like alarmist scientists are simply adding adjustments to past temperatures for no reason.  But there are many perfectly valid reasons surface temperature measurements have to be manually adjusted.  It is a required part of the process.  Just as the satellite data must be adjusted as well, though for different things.

So we should not be suspicious of adjustments per se.  We should be concerned about them, though, for a number of reasons:

  • In many parts of the world, like in the US, the manual adjustments equal or exceed the measured warming trend.  That means the"signal" we are measuring comes entirely from the adjustments.  That is, to put it lightly, not ideal.
  • The adjustments are extremely poorly documented and impossible for any third party to replicate (one reason the satellite record may be more trustworthy is all the adjustment code for the satellites is open source).
  • The adjustments may have a bias.  After all, most of the people doing the adjustments expect to see a warming trend historically, and so consider lack of such a trend to be an indicator the data is wrong and in need of adjustment.  This is not a conspiracy, but a normal human failing and the reason why the ability to replicate such work is important.
  • The adjustments do seem to be very aggressive in identifying any effects that might have artificially created a cooling trend but lax in finding and correcting effects that might have artificially created a warming trend.  First and foremost, the changing urban heat island effect in growing cities seems to be under-corrected  (Again there is debate on this -- the proprietors of the model believe they have fixed this with a geographic normalizing, correcting biases from nearby thermometers.  I and others believe all they are doing is mathematically smearing the error over a larger geography).

Again, I discussed all the pros and cons here.  If pushed to the wall, I would say perhaps half of the past warming in the surface temperature record is due to undercorrection of warming biases or overcorrection of cooling biases.

Adjusting the Temperature Records

I have been getting inquiries from folks asking me what I think about stories like this one, where Paul Homewood has been looking at the manual adjustments to raw temperature data and finding that the adjustments actually reverse the trends from cooling to warming.  Here is an example of the comparisons he did:

Raw, before adjustments;

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After manual adjustments

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I actually wrote about this topic a few months back, and rather than rewrite the post I will excerpt it below:

I believe that there is both wheat and chaff in this claim [that manual temperature adjustments are exaggerating past warming], and I would like to try to separate the two as best I can.  I don't have time to write a well-organized article, so here is just a list of thoughts

  1. At some level it is surprising that this is suddenly news.  Skeptics have criticized the adjustments in the surface temperature database for years.
  2. There is certainly a signal to noise ratio issue here that mainstream climate scientists have always seemed insufficiently concerned about.  For example, the raw data for US temperatures is mostly flat, such that the manual adjustments to the temperature data set are about equal in magnitude to the total warming signal.  When the entire signal one is trying to measure is equal to the manual adjustments one is making to measurements, it probably makes sense to put a LOT of scrutiny on the adjustments.  (This is a post from 7 years ago discussing these adjustments.  Note that these adjustments are less than current ones in the data base as they have been increased, though I cannot find a similar chart any more from the NOAA discussing the adjustments)
  3. The NOAA HAS made adjustments to US temperature data over the last few years that has increased the apparent warming trend.  These changes in adjustments have not been well-explained.  In fact, they have not really be explained at all, and have only been detected by skeptics who happened to archive old NOAA charts and created comparisons like the one below.  Here is the before and after animation (pre-2000 NOAA US temperature history vs. post-2000).  History has been cooled and modern temperatures have been warmed from where they were being shown previously by the NOAA.  This does not mean the current version  is wrong, but since the entire US warming signal was effectively created by these changes, it is not unreasonable to act for a detailed reconciliation (particularly when those folks preparing the chart all believe that temperatures are going up, so would be predisposed to treating a flat temperature chart like the earlier version as wrong and in need of correction. 1998changesannotated
  4. However, manual adjustments are not, as some skeptics seem to argue, wrong or biased in all cases.  There are real reasons for manual adjustments to data -- for example, if GPS signal data was not adjusted for relativistic effects, the position data would quickly get out of whack.  In the case of temperature data:
    • Data is adjusted for shifts in the start/end time for a day of measurement away from local midnight (ie if you average 24 hours starting and stopping at noon).  This is called Time of Observation or TOBS.  When I first encountered this, I was just sure it had to be BS.  For a month of data, you are only shifting the data set by 12 hours or about 1/60 of the month.  Fortunately for my self-respect, before I embarrassed myself I created a spreadsheet to monte carlo some temperature data and play around with this issue.  I convinced myself the Time of Observation adjustment is valid in theory, though I have no way to validate its magnitude  (one of the problems with all of these adjustments is that NOAA and other data authorities do not release the source code or raw data to show how they come up with these adjustments).   I do think it is valid in science to question a finding, even without proof that it is wrong, when the authors of the finding refuse to share replication data.  Steven Goddard, by the way, believes time of observation adjustments are exaggerated and do not follow NOAA's own specification.
    • Stations move over time.  A simple example is if it is on the roof of a building and that building is demolished, it has to move somewhere else.  In an extreme example the station might move to a new altitude or a slightly different micro-climate.  There are adjustments in the data base for these sort of changes.  Skeptics have occasionally challenged these, but I have no reason to believe that the authors are not using best efforts to correct for these effects (though again the authors of these adjustments bring criticism on themselves for not sharing replication data).
    • The technology the station uses for measurement changes (e.g. thermometers to electronic devices, one type of electronic device to another, etc.)   These measurement technologies sometimes have known biases.  Correcting for such biases is perfectly reasonable  (though a frustrated skeptic could argue that the government is diligent in correcting for new cooling biases but seldom corrects for warming biases, such as in the switch from bucket to water intake measurement of sea surface temperatures).
    • Even if the temperature station does not move, the location can degrade.  The clearest example is a measurement point that once was in the country but has been engulfed by development  (here is one example -- this at one time was the USHCN measurement point with the most warming since 1900, but it was located in an open field in 1900 and ended up in an asphalt parking lot in the middle of Tucson.)   Since urban heat islands can add as much as 10 degrees F to nighttime temperatures, this can create a warming signal over time that is related to a particular location, and not the climate as a whole.  The effect is undeniable -- my son easily measured it in a science fair project.  The effect it has on temperature measurement is hotly debated between warmists and skeptics.  Al Gore originally argued that there was no bias because all measurement points were in parks, which led Anthony Watts to pursue the surface station project where every USHCN station was photographed and documented.  The net result was that most of the sites were pretty poor.  Whatever the case, there is almost no correction in the official measurement numbers for urban heat island effects, and in fact last time I looked at it the adjustment went the other way, implying urban heat islands have become less of an issue since 1930.  The folks who put together the indexes argue that they have smoothing algorithms that find and remove these biases.  Skeptics argue that they just smear the bias around over multiple stations.  The debate continues.
  5. Overall, many mainstream skeptics believe that actual surface warming in the US and the world has been about half what is shown in traditional indices, an amount that is then exaggerated by poorly crafted adjustments and uncorrected heat island effects.  But note that almost no skeptic I know believes that the Earth has not actually warmed over the last 100 years.  Further, warming since about 1980 is hard to deny because we have a second, independent way to measure global temperatures in satellites.  These devices may have their own issues, but they are not subject to urban heat biases or location biases and further actually measure most of the Earth's surface, rather than just individual points that are sometimes scores or hundreds of miles apart.  This independent method of measurement has shown undoubted warming since 1979, though not since the late 1990's.
  6. As is usual in such debates, I find words like "fabrication", "lies",  and "myth" to be less than helpful.  People can be totally wrong, and refuse to confront their biases, without being evil or nefarious.

To these I will add a #7:  The notion that satellite results are somehow pure and unadjusted is just plain wrong.  The satellite data set takes a lot of mathematical effort to get right, something that Roy Spencer who does this work (and is considered in the skeptic camp) will be the first to tell you.  Satellites have to be adjusted for different things.  They have advantages over ground measurement because they cover most all the Earth, they are not subject to urban heat biases, and bring some technological consistency to the measurement.  However, the satellites used are constantly dieing off and being replaced, orbits decay and change, and thus times of observation of different parts of the globe change [to their credit, the satellite folks release all their source code for correcting these things].   I have become convinced the satellites, net of all the issues with both technologies, provide a better estimate but neither are perfect.

The 2014 Temperature Record No One Is Talking About

Depending on what temperature data set you look at **, or on your trust in various manual adjustments in these data sets ***, 2014 may have beaten the previous world temperature record by 0.02C.  Interestingly, the 0.02C rise over the prior record set four years ago would imply (using only these two data points which warmists seem to want to focus on) a temperature increase of 0.5C per century, a few tenths below my prediction but an order of magnitude below the alarmists' predictions for future trends.

Anyway, whether there was an absolute record or not, there was almost certainly a different temperature record set -- the highest divergence to date in the modern measured temperatures from what the computer models predicted.  The temperature increase for the past 5 years was a full 0.17C less than predicted, the largest gap yet for the models in forward-prediction mode (as opposed to when they are used to backcast history).

 

** There are four or five or more data sets, depending on how you count them.   There are 2 major satellite data sets and 2-3 ground based data sets.  The GISS ground data set generally gives the largest warming trends, while the satellite data sets give the least, but all show some warming over the last 30 or so years (though most of this warming was before 1999).

*** The data sets are all full of manual adjustments of various sorts.  All of these are necessary.  For surface stations, the measurement points move and change technology.  For the satellites, orbits and instruments shift over time.  The worrisome feature of all these adjustments is that they are large as compared to the underlying warming signal being measured, so small changes in the adjustments can lead to large changes in the apparent trend.  Skeptics often charge that the proprietors of land data sets are aggressive about including adjustments that increase the apparent trend but reluctant to add similar adjustments (eg for urban heat islands) that might reduce the trend.  As a result, most of the manual adjustments increase the trend.  There is actually little warming trend in the raw data, and it only shows up after the adjustments.  It may be total coincidence, but the database run by the most ardent warmist is the GISS and it has the highest trend.   The database run by the most skeptical is the UAH satellite database and it shows the smallest trend.  Hmm.

California Drought Update -- Not Even Close to Worst Drought Ever

There is little trend evidence anywhere that climate is getting -- pick the world -- weirder, more extreme, out of whack, whatever.  In particular, name any severe weather category you can imagine, and actual data in trend charts likely will not show any recent trend.

The reasons the average person on the street will swear you are a crazy denier for pointing such a thing out to them is that the media bombards them with news of nearly every 2+ sigma weather event, calling most of these relatively normal episodes as "the worst ever".

A great example is the California drought.  Here is the rolling average 5-year precipitation chart for California.  Find the worst drought "ever".

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I know no one trusts anyone else's data in public debates, but you can make these charts yourself at the NOAA site, just go here:  http://www.ncdc.noaa.gov/cag/.  The one record set was that 2013 had the lowest measured CA precipitation in the last century plus, so that was indeed a record bad year, but droughts are typically made up of multiple years of below average precipitation and by that measure the recent CA drought is the fourth or fifth worst.

By the way, Paul Homewood points out something that even surprised me and I try not to be susceptible to the mindless media bad news stampeded:  California rainfall this year was close to normal.  And, as you can see, there is pretty much no trend over the last century plus in California rainfall:

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As discussed previously, let's add the proviso that rainfall is not necessarily the best metric of drought.  The Palmer drought index looks at moisture in soil and takes into account other factors like temperature and evaporation, and by that metric this CA drought is closer to the worst of the century, though certainly not what one would call unprecedented.  Also, there is a worsening trend in the Palmer data.

multigraph_palmer

 

Update:  By the way, the fact that two measures of drought give us two different answers on the relative severity of the drought and on the trend in droughts is typical.   It makes a mockery of the pretense to certainty on these topics in the media.  Fortunately, I am not so invested in the whole thing that I can't include data that doesn't support my thesis.

Geeky Reflections -- Simulated Annealing

When I was an undergrad, my interest was in interfacing microcomputers with mechanical devices.  Most of what we did would be labelled "robotics" today, or at least proto-robotics (e.g. ripping the ultrasonic rangefinder out of a Polaroid camera, putting it on a stepper motor, and trying to paint a radar image of the room on a computer screen).

In doing this, we were playing around with S-100 bus computers (PC's were a bit in the future at that point) and I got interested in brute force approaches to solving the traveling salesman problem.  The way this is done is to establish some random points in x,y space and then connect them with a random path and measure the length of that path.  The initial random path is obviously going to be a terrible solution.  So you have the computer randomly flip flop two segments, and then you see if the resulting total distance is reduced.  If it is, then you keep the change and try another.

This will lead to a much shorter path, but often will not lead to the optimally shortest path.  The reason is that the result can get stuck in a local minimum that is not the optimum.  Essentially, to break out of this, you have to allow the solution to get worse first before it can get better.

The approach I was playing with was called simulated annealing.  Everything I said above is the same in this approach, but sometimes you let the program accept flip-flopped segments that yield a worse (ie longer) rather than better path.  The allowed amount worse is governed by a "temperature" that is slowly lowered.  Initially, at high temperatures, the solution can jump into most any solution, better or worse.  But as the "temperature" is lowered, the allowed amount of jumping into worse solutions is reduced.  Essentially, the system is much, much more likely than the previous approach to settle closer to the actual optimum.  This is roughly an analog of how annealing works in metals.  The code is ridiculously simple.   I don't remember it being much more than 100 lines in Pascal.

Anyway, if you lived through the above without falling asleep, the payoff is this site.  After 30 years of pretty much never thinking about simulated annealing again, I found Todd Schneider's blog which has a great visual overview of solving the travelling salesman problem with simulated annealing.  If you really want to visually see it work, go to the customizable examples at the bottom and set the iterations per map draw for about 100.  Then watch.  It really does look a bit like a large excited molecule slowly cooling.  Here is an example below but check out his site.

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What is Normal?

I titled my very first climate video "What is Normal," alluding to the fact that climate doomsayers argue that we have shifted aspects of the climate (temperature, hurricanes, etc.) from "normal" without us even having enough historical perspective to say what "normal" is.

A more sophisticated way to restate this same point would be to say that natural phenomenon tend to show various periodicities, and without observing nature through the whole of these cycles, it is easy to mistake short term cyclical variations for long-term trends.

A paper in the journal Water Resources Research makes just this point using over 200 years of precipitation data:

We analyze long-term fluctuations of rainfall extremes in 268 years of daily observations (Padova, Italy, 1725-2006), to our knowledge the longest existing instrumental time series of its kind. We identify multidecadal oscillations in extremes estimated by fitting the GEV distribution, with approximate periodicities of about 17-21 years, 30-38 years, 49-68 years, 85-94 years, and 145-172 years. The amplitudes of these oscillations far exceed the changes associated with the observed trend in intensity. This finding implies that, even if climatic trends are absent or negligible, rainfall and its extremes exhibit an apparent non-stationarity if analyzed over time intervals shorter than the longest periodicity in the data (about 170 years for the case analyzed here). These results suggest that, because long-term periodicities may likely be present elsewhere, in the absence of observational time series with length comparable to such periodicities (possibly exceeding one century), past observations cannot be considered to be representative of future extremes. We also find that observed fluctuations in extreme events in Padova are linked to the North Atlantic Oscillation: increases in the NAO Index are on average associated with an intensification of daily extreme rainfall events. This link with the NAO global pattern is highly suggestive of implications of general relevance: long-term fluctuations in rainfall extremes connected with large-scale oscillating atmospheric patterns are likely to be widely present, and undermine the very basic idea of using a single stationary distribution to infer future extremes from past observations.

Trying to work with data series that are too short is simply a fact of life -- everyone in climate would love a 1000-year detailed data set, but we don't have it.  We use what we have, but it is important to understand the limitations.  There is less excuse for the media that likes to use single data points, e.g. one storm, to "prove" long term climate trends.

A good example of why this is relevant is the global temperature trend.  This chart is a year or so old and has not been updated in that time, but it shows the global temperature trend using the most popular surface temperature data set.  The global warming movement really got fired up around 1998, at the end of the twenty year temperature trend circled in red.

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They then took the trends from these 20 years and extrapolated them into the future:

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But what if that 20 years was merely the upward leg of a 40-60 year cyclic variation?  Ignoring the cyclic functions would cause one to overestimate the long term trend.  This is exactly what climate models do, ignoring important cyclic functions like the AMO and PDO.

In fact, you can get a very good fit with actual temperature by modeling them as three functions:  A 63-year sine wave, a 0.4C per century long-term linear trend  (e.g. recovery from the little ice age) and a new trend starting in 1945 of an additional 0.35C, possibly from manmade CO2.Slide52

In this case, a long-term trend still appears to exist but it is exaggerated by only trying to measure it in the upward part of the cycle (e.g. from 1978-1998).

 

Listening to California Parks People Discuss Climate Change

Some random highlights:

  • I watched a 20 minute presentation in which a woman from LA parks talked repeatedly about the urban heat island being a result of global warming
  • I just saw that California State Parks, which is constantly short of money and has perhaps a billion dollars in unfunded maintenance needs, just spent millions of dollars to remove a road from a beachfront park based solely (they claimed) based on projections that 55 inches of sea level rise would cause the road to be a problem.  Sea level has been rising 3-4mm a year for over 150 years and even the IPCC, based on old much higher temperature increase forecasts, predicted about a foot of rise.
  • One presenter said that a 3-5C temperature rise over the next century represent the low end of reasonable forecasts.  Most studies of later are showing a climate sensitivity of 1.5-2.0 C (I still predict 1C) with warming over the rest of the century of about 1C, or about what we saw last century
  • I watched them brag for half an hour about spending tons of extra money on make LEED certified buildings.  As written here any number of times, most LEED savings come through BS gaming of the rules, like putting in dedicated electric vehicle parking sites (that do not even need a charger to get credit).  In a brief moment of honesty, the architect presenting admitted that most of the LEED score for one building came from using used rather than new furniture in the building.
  • They said that LEED buildings were not any more efficient than most other commercial buildings getting built, just a matter of whether you wanted to pay for LEED certification -- it was stated that the certification was mostly for the plaque.  Which I suppose is fine for private businesses looking for PR, but why are cash-strapped public agencies doing it?

Scott Sumner Explains a Lot of Climate Alarmism, Without Discussing Climate

Scott Sumner is actually discussing discrimination, and how discrimination is often "proven" in social studies

The economy operates in very subtle ways, and often when I read academic studies of issues like discrimination, the techniques seem incredibly naive to me. They might put in all the attributes of male and female labor productivity they can think of, and then simply assume than any unexplained residual must be due to "discrimination." And they do this in cases where there is no obvious reason to assume discrimination. It would be like a scientist assuming that magicians created a white rabbit out of thin air, at the snap of their fingers, because they can't think of any other explanation of how it got into the black hat!

Most alarming climate forecasts are based on the period from 1978 to 1998.  During this 20 year period world temperatures rose about a half degree C.  People may say they are talking about temperature increases since 1950, but most if not all of those increases occurred from 1978-1998.  Temperatures were mostly flat or down before and since.

A key, if not the key, argument for CO2-driven catastrophic warming that is based on actual historic data (rather than on theory or models) is that temperatures rose in this 20 year period farther and faster than would be possible by any natural causes, and thus must have been driven by man-made CO2.  Essentially what scientists said was, "we have considered every possible natural cause of warming that we can think of, and these are not enough to cause this warming, so the warming must be unnatural."  I was struck just how similar this process was to what Mr. Sumner describes.  Most skeptics, by the way, agree that some of this warming may have been driven by manmade CO2 but at the same time argue that there were many potential natural effects (e.g. ocean cycles) that were not considered in this original analysis.

Reconciling Seemingly Contradictory Climate Claims

At Real Science, Steven Goddard claims this is the coolest summer on record in the US.

The NOAA reports that both May and June were the hottest on record.

It used to be the the media would reconcile such claims and one might learn something interesting from that reconciliation, but now all we have are mostly-crappy fact checks with Pinocchio counts.  Both these claims have truth on their side, though the NOAA report is more comprehensively correct.  Still, we can learn something by putting these analyses in context and by reconciling them.

The NOAA temperature data for the globe does indeed show May and June as the hottest on record.  However, one should note a couple of things

  • The two monthly records do not change the trend over the last 10-15 years, which has basically been flat.  We are hitting records because we are sitting on a plateau that is higher than the rest of the last century (at least in the NOAA data).  It only takes small positive excursions to reach all-time highs
  • There are a number of different temperature data bases that measure the temperature in different ways (e.g. satellite vs. ground stations) and then adjust those raw readings using different methodologies.  While the NOAA data base is showing all time highs, other data bases, such as satellite-based ones, are not.
  • The NOAA database has been criticized for manual adjustments to temperatures in the past which increase the warming trend.  Without these adjustments, temperatures during certain parts of the 1930's (think: Dust Bowl) would be higher than today.  This was discussed here in more depth.  As is usual when looking at such things, some of these adjustments are absolutely appropriate and some can be questioned.  However, blaming the whole of the warming signal on such adjustments is just wrong -- satellite data bases which have no similar adjustment issues have shown warming, at least between 1979 and 1999.

The Time article linked above illustrated the story of these record months with a video partially on wildfires.  This is a great example of how temperatures are indeed rising but media stories about knock-on effects, such as hurricanes and fires, can be full of it.  2014 has actually been a low fire year so far in the US.

So the world is undeniably on the warm side of average (I won't way warmer than normal because what is "normal"?)  So how does Goddard get this as the coolest summer on record for the US?

Well, the first answer, and it is an important one to remember, is that US temperatures do not have to follow global temperatures, at least not tightly.  While the world warmed 0.5-0.7 degrees C from 1979-1999, the US temperatures moved much less.  Other times, the US has warmed or cooled more than the world has.  The US is well under 5% of the world's surface area.  It is certainly possible to have isolated effects in such an area.  Remember the same holds true the other way -- heat waves in one part of the world don't necessarily mean the world is warming.

But we can also learn something that is seldom discussed in the media by looking at Goddard's chart:

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First, I will say that I am skeptical of any chart that uses "all USHCN" stations because the number of stations and their locations change so much.  At some level this is an apples to oranges comparison -- I would be much more comfortable to see a chart that looks at only USHCN stations with, say, at least 80 years of continuous data.  In other words, this chart may be an artifact of the mess that is the USHCN database.

However, it is possible that this is correct even with a better data set and against a backdrop of warming temperatures.  Why?  Because this is a metric of high temperatures.  It looks at the number of times a data station reads a high temperature over 90F.  At some level this is a clever chart, because it takes advantage of a misconception most people, including most people in the media have -- that global warming plays out in higher daytime high temperatures.

But in fact this does not appear to be the case.  Most of the warming we have seen over the last 50 years has manifested itself as higher nighttime lows and higher winter temperatures.  Both of these raise the average, but neither will change Goddard's metric of days above 90F.  So it is perfectly possible Goddard's chart is right even if the US is seeing a warming trend over the same period.  Which is why we have not seen any more local all-time daily high temperature records set recently than in past decades.  But we have seen a lot of new records for high low temperature, if that term makes sense.  Also, this explains why the ratio of daily high records to daily low records has risen -- not necessarily because there are a lot of new high records, but because we are setting fewer low records.  We can argue about daytime temperatures but nighttime temperatures are certainly warmer.

This chart shows an example with low and high temperatures over time at Amherst, MA  (chosen at random because I was speaking there).  Note that recently, most warming has been at night, rather than in daily highs.

On The Steven Goddard Claim of "Fabricated" Temperature Data

Steven Goddard of the Real Science blog has a study that claims that US real temperature data is being replaced by fabricated data.  Christopher Booker has a sympathetic overview of the claims.

I believe that there is both wheat and chaff in this claim, and I would like to try to separate the two as best I can.  I don't have time to write a well-organized article, so here is just a list of thoughts

  1. At some level it is surprising that this is suddenly news.  Skeptics have criticized the adjustments in the surface temperature database for years
  2. There is certainly a signal to noise ratio issue here that mainstream climate scientists have always seemed insufficiently concerned about.  Specifically, the raw data for US temperatures is mostly flat, such that the manual adjustments to the temperature data set are about equal in magnitude to the total warming signal.  When the entire signal one is trying to measure is equal to the manual adjustments one is making to measurements, it probably makes sense to put a LOT of scrutiny on the adjustments.  (This is a post from 7 years ago discussing these adjustments.  Note that these adjustments are less than current ones in the data base as they have been increased, though I cannot find a similar chart any more from the NOAA discussing the adjustments)
  3. The NOAA HAS made adjustments to US temperature data over the last few years that has increased the apparent warming trend.  These changes in adjustments have not been well-explained.  In fact, they have not really be explained at all, and have only been detected by skeptics who happened to archive old NOAA charts and created comparisons like the one below.  Here is the before and after animation (pre-2000 NOAA US temperature history vs. post-2000).  History has been cooled and modern temperatures have been warmed from where they were being shown previously by the NOAA.  This does not mean the current version  is wrong, but since the entire US warming signal was effectively created by these changes, it is not unreasonable to act for a detailed reconciliation (particularly when those folks preparing the chart all believe that temperatures are going up, so would be predisposed to treating a flat temperature chart like the earlier version as wrong and in need of correction.
    1998changesannotated
  4. However, manual adjustments are not, as some skeptics seem to argue, wrong or biased in all cases.  There are real reasons for manual adjustments to data -- for example, if GPS signal data was not adjusted for relativistic effects, the position data would quickly get out of whack.  In the case of temperature data:
    • Data is adjusted for shifts in the start/end time for a day of measurement away from local midnight (ie if you average 24 hours starting and stopping at noon).  This is called Time of Observation or TOBS.  When I first encountered this, I was just sure it had to be BS.  For a month of data, you are only shifting the data set by 12 hours or about 1/60 of the month.  Fortunately for my self-respect, before I embarrassed myself I created a spreadsheet to monte carlo some temperature data and play around with this issue.  I convinced myself the Time of Observation adjustment is valid in theory, though I have no way to validate its magnitude  (one of the problems with all of these adjustments is that NOAA and other data authorities do not release the source code or raw data to show how they come up with these adjustments).   I do think it is valid in science to question a finding, even without proof that it is wrong, when the authors of the finding refuse to share replication data.  Steven Goddard, by the way, believes time of observation adjustments are exaggerated and do not follow NOAA's own specification.
    • Stations move over time.  A simple example is if it is on the roof of a building and that building is demolished, it has to move somewhere else.  In an extreme example the station might move to a new altitude or a slightly different micro-climate.  There are adjustments in the data base for these sort of changes.  Skeptics have occasionally challenged these, but I have no reason to believe that the authors are not using best efforts to correct for these effects (though again the authors of these adjustments bring criticism on themselves for not sharing replication data).
    • The technology the station uses for measurement changes (e.g. thermometers to electronic devices, one type of electronic device to another, etc.)   These measurement technologies sometimes have known biases.  Correcting for such biases is perfectly reasonable  (though a frustrated skeptic could argue that the government is diligent in correcting for new cooling biases but seldom corrects for warming biases, such as in the switch from bucket to water intake measurement of sea surface temperatures).
    • Even if the temperature station does not move, the location can degrade.  The clearest example is a measurement point that once was in the country but has been engulfed by development  (here is one example -- this at one time was the USHCN measurement point with the most warming since 1900, but it was located in an open field in 1900 and ended up in an asphalt parking lot in the middle of Tucson.)   Since urban heat islands can add as much as 10 degrees F to nighttime temperatures, this can create a warming signal over time that is related to a particular location, and not the climate as a whole.  The effect is undeniable -- my son easily measured it in a science fair project.  The effect it has on temperature measurement is hotly debated between warmists and skeptics.  Al Gore originally argued that there was no bias because all measurement points were in parks, which led Anthony Watts to pursue the surface station project where every USHCN station was photographed and documented.  The net results was that most of the sites were pretty poor.  Whatever the case, there is almost no correction in the official measurement numbers for urban heat island effects, and in fact last time I looked at it the adjustment went the other way, implying urban heat islands have become less of an issue since 1930.  The folks who put together the indexes argue that they have smoothing algorithms that find and remove these biases.  Skeptics argue that they just smear the bias around over multiple stations.  The debate continues.
  5. Overall, many mainstream skeptics believe that actual surface warming in the US and the world has been about half what is shown in traditional indices, an amount that is then exaggerated by poorly crafted adjustments and uncorrected heat island effects.  But note that almost no skeptic I know believes that the Earth has not actually warmed over the last 100 years.  Further, warming since about 1980 is hard to deny because we have a second, independent way to measure global temperatures in satellites.  These devices may have their own issues, but they are not subject to urban heat biases or location biases and further actually measure most of the Earth's surface, rather than just individual points that are sometimes scores or hundreds of miles apart.  This independent method of measurement has shown undoubted warming since 1979, though not since the late 1990's.
  6. As is usual in such debates, I find words like "fabrication", "lies",  and "myth" to be less than helpful.  People can be totally wrong, and refuse to confront their biases, without being evil or nefarious.

Postscript:  Not exactly on topic, but one thing that is never, ever mentioned in the press but is generally true about temperature trends -- almost all of the warming we have seen is in nighttime temperatures, rather than day time.  Here is an example from Amherst, MA (because I just presented up there).  This is one reason why, despite claims in the media, we are not hitting any more all time daytime highs than we would expect from a normal distribution.  If you look at temperature stations for which we have 80+ years of data, fewer than 10% of the 100-year highs were set in the last 10 years.  We are setting an unusual number of records for high low temperature, if that makes sense.

click to enlarge

 

Great Moments in "Science"

You know that relative of yours, who last Thanksgiving called you anti-science because you had not fully bought into global warming alarm?

Well, it appears that the reason we keep getting called "anti-science" is because climate scientists have a really funny idea of what exactly "science" is.

Apparently, a number of folks have been trying for years to get articles published in peer reviewed journals comparing the IPCC temperature models to actual measurements, and in the process highlighting the divergence of the two.  And they keep getting rejected.

Now, the publisher of Environmental Research Letters has explained why.  Apparently, in climate science it is "an error" to attempt to compare computer temperature forecasts with the temperatures that actually occurred.  In fact, he says that trying to do so "is harmful as it opens the door for oversimplified claims of 'errors' and worse from the climate sceptics media side".  Apparently, the purpose of scientific inquiry is to win media wars, and not necessarily to discover truth.

Here is something everyone in climate should remember:  The output of models merely represents a hypothesis.  When we have complicated hypotheses in complicated systems, and where such hypotheses may encompass many interrelated assumptions, computer models are an important tool for playing out, computationally, what results those hypotheses might translate to in the physical world.  It is no different than if Newton had had a computer and took his equation Gmm/R^2 and used the computer to project future orbits for the Earth and other planets (which he and others did, but by hand).   But these projections would have no value until they were checked against actual observations.  That is how we knew we liked Newton's models better than Ptolemy's -- because they checked out better against actual measurements.

But climate scientists are trying to create some kind of weird world where model results have some sort of independent reality, where in fact the model results should be trusted over measurements when the two diverge.  If this is science -- which it is not -- but if it were, then I would be anti-science.

California Food Sales Tax Rules Are Madness

We have invested a fair amount of time to try to get sales tax treatment on food items in our California stores correct.  But the rules are insane.   Beyond all the crazy rules (e.g. if a customer buys a refrigerated burrito it may be non-taxable, but if he puts it in the microwave in the store to heat it up it becomes taxable for sure) is the fact that sometimes customer intent matters (e.g. will they consume it at one of the picnic tables on site, or take it back to their home or camp site)

When searching for more resources on the topic, I found this flow chart on deciding if CA sales tax applies to food

click to enlarge

Here is more, from the same article

Under California law, foods eaten on the premise of an eatery is taxed while the same item taken to go is not: "Sales of food for human consumption are generally exempt from tax unless sold in a heated condition (except hot bakery items or hot beverages, such as coffee, sold for a separate price), served as meals, consumed at or on the seller's facilities, ordinarily sold for consumption on or near the seller's parking facility, or sold for consumption where there is an admission charge." Exactly which type of foods do and do not fall under the scope of this provision is the frustrating devil in the detail.

Eskenazi notes a few of the ridiculous results of drawing an artificial distinction between hot and cold foods. "A hot sandwich to go would be taxable," for example, "While a prepackaged, cold one would not -- but a cold sandwich becomes taxable if it has hot gravy poured onto it. Cold foods to go are generally not taxable -- but hot foods that have cooled are taxable (meaning a cold sandwich slathered in "hot" gravy that has cooled to room temperature is taxable)."

 

Climate Alarmists Coming Around to At Least One Skeptic Position

As early as 2009 (and many other more prominent skeptics were discussing it much earlier) I reported on why measuring ocean heat content was a potentially much better measure of greenhouse gas changes to the Earth rather than measuring surface air temperatures.  Roger Pielke, in particular, has been arguing this for as long as I can remember.

The simplest explanation for why this is true is that greenhouse gasses increase the energy added to the surface of the Earth, so that is what we would really like to measure, that extra energy.  But in fact the vast, vast majority of the heat retention capacity of the Earth's surface is in the oceans, not in the air.  Air temperatures may be more immediately sensitive to changes in heat flux, but they are also sensitive to a lot of other noise that tends to mask long-term signals.    The best analog I can think of is to imagine that you have two assets, a checking account and your investment portfolio.  Looking at surface air temperatures to measure long-term changes in surface heat content is a bit like trying to infer long-term changes in your net worth by looking only at your checking account, whose balance is very volatile, vs. looking at the changing size of your investment portfolio.

Apparently, the alarmists are coming around to this point

Has global warming come to a halt? For the last decade or so the average global surface temperature has been stabilising at around 0.5°C above the long-term average. Can we all relax and assume global warming isn't going to be so bad after all?

Unfortunately not. Instead we appear to be measuring the wrong thing. Doug McNeall and Matthew Palmer, both from the Met Office Hadley Centre in Exeter, have analysed climate simulations and shown that both ocean heat content and net radiation (at the top of the atmosphere) continue to rise, while surface temperature goes in fits and starts. "In my view net radiation is the most fundamental measure of global warming since it directly represents the accumulation of excess solar energy in the Earth system," says Palmer, whose findings are published in the journal Environmental Research Letters.

First, of course, we welcome past ocean heat content deniers to the club.  But second, those betting on ocean heat content to save their bacon and keep alarmism alive should consider why skeptics latched onto the metric with such passion.   In fact, ocean heat content may be rising more than surface air temperatures, but it has been rising MUCH less than would be predicted from high-sensitivity climate models.

Just When You Thought You Would Never See Any Of That Stuff From Science Fiction Novels...

Via the New Scientist

NEITHER dead or alive, knife-wound or gunshot victims will be cooled down and placed in suspended animation later this month, as a groundbreaking emergency technique is tested out for the first time....

The technique involves replacing all of a patient's blood with a cold saline solution, which rapidly cools the body and stops almost all cellular activity. "If a patient comes to us two hours after dying you can't bring them back to life. But if they're dying and you suspend them, you have a chance to bring them back after their structural problems have been fixed," says surgeon Peter Rhee at the University of Arizona in Tucson, who helped develop the technique.

The benefits of cooling, or induced hypothermia, have been known for decades. At normal body temperature – around 37 °C – cells need a regular oxygen supply to produce energy. When the heart stops beating, blood no longer carries oxygen to cells. Without oxygen the brain can only survive for about 5 minutes before the damage is irreversible.

However, at lower temperatures, cells need less oxygen because all chemical reactions slow down. This explains why people who fall into icy lakes can sometimes be revived more than half an hour after they have stopped breathing.

via Alex Tabarrok

A Bad Chart From My Allies

I try to make it a habit to criticize bad analyses from "my side" of certain debates.  I find this to be a good habit that keeps one from falling for poorly constructed but ideologically tempting arguments.

Here is my example this week, from climate skeptic Steven Goddard.  I generally enjoy his work, and have quoted him before, but this is a bad chart (this is global temperatures as measured by satellite and aggregated by RSS).

click to enlarge

 

 

He is trying to show that the last 17+ years has no temperature trend.  Fine.  But by trying to put a trend line on the earlier period, it results in a mess that understates warming in earlier years.    He ends up with 17 years with a zero trend and 20 years with a 0.05 per decade trend.  Add these up and one would expect 0.1C total warming.   But in fact over this entire period there was, by this data set, 0.3C-0.4C of warming.  He left most of the warming out in the the step between the two lines.

Now there are times this might be appropriate.  For example, in the measurement of ocean heat content, there is a step change that occurs right at the point where the measurement approach changed from ship records to the ARGO floats.  One might argue that it is wrong to make a trend through the transition point because the step change was an artifact of the measurement change.  But in this case there was no such measurement change.  And while there was a crazy El Nino year in 1998, I have heard no argument from any quarter as to why there might have been some fundamental change in the climate system around 1997.

So I call foul.  Take the trend line off the blue portion and the graph is much better.

Global Warming Updates

I have not been blogging climate much because none of the debates ever change.  So here are some quick updates

  • 67% to 90% of all warming in climate forecasts still from assumptions of strong positive feedback in the climate system, rather than from CO2 warming per se (ie models still assuming about 1 degree in CO2 warming is multiplied 3-10 times by positive feedbacks)
  • Studies are still mixed about the direction of feedbacks, with as many showing negative as positive feedback.  No study that I have seen supports positive feedbacks as large as those used in many climate models
  • As a result, climate models are systematically exaggerating warming (from Roy Spenser, click to enlarge).  Note that the conformance through 1998 is nothing to get excited about -- most models were rewritten after that date and likely had plugs and adjustments to force the historical match.

click to enlarge

 

  • To defend the forecasts, modellers are increasingly blaming natural effects like solar cycles on the miss, natural effects that the same modellers insisted were inherently trivial contributions when skeptics used them to explain part of the temperature rise from 1978-1998.
  • By the way, 1978-1998 is still the only period since 1940 when temperatures actually rose, such that increasingly all catastrophic forecasts rely on extrapolations from this one 20-year period. Seriously, look for yourself.
  • Alarmists are still blaming every two or three sigma weather pattern on CO2 on global warming (polar vortex, sigh).
  • Even when weather is moderate, media hyping of weather events has everyone convinced weather is more extreme, when it is not. (effect explained in context of Summer of the Shark)
  • My temperature forecast from 2007 still is doing well.   Back in '07 I regressed temperature history to a linear trend plus a sine wave.

click to enlarge

Temperature Trends and the Farmer Error

My dad grew up in farm country in Iowa.  He told me stories of the early days of commodity futures when a number of farmers lost a lot of money betting the wrong way.  The error they made is that they would look at their local weather and assume everyone was experiencing the same.  For example, some guy in Iowa would be experiencing a drought and facing a poor corn crop, and would buy corn futures assuming the crop would be bad everywhere.  Unfortunately, this was often not the case.

A few climate sites have monthly contests to predict the next month's average global temperature anomaly.   Apparently, everyone really missed in the January betting.  Since most of the participants were American, they assumed that really cold weather in the US would translate to falling global temperatures.  They were wrong.  The global temperature anomaly in January actually rose a bit.

This is a variation of the same effect I often point out in the opposite direction -- that heat waves in even seemingly large areas do not necessarily mean anything for global temperatures.  The US is only about 2% of the global surface area (land and ocean) and since the cold spell was in the eastern half of the US, it therefore affected perhaps 1% of the globe.  And remember, on average, some area representing 1% of the globe should constantly be seeing a 100-year high or low for that particular day.  It's just how averages work.

No particularly point here, except to emphasize just how facile it is to try to draw conclusions about global temperature trends from regional weather events.

Congratulations to Nature Magazine for Catching up to Bloggers

The journal Nature has finally caught up to the fact that ocean cycles may influence global surface temperature trends.  Climate alarmists refused to acknowledge this when temperatures were rising and the cycles were in their warm phase, but now are grasping at these cycles for an explanation of the 15+ year hiatus in warming as a way to avoid abandoning high climate sensitivity assumptions  (ie the sensitivity of global temperatures to CO2 concentrations, which IMO are exaggerated by implausible assumptions of positive feedback).

Here is the chart from Nature:

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I cannot find my first use of this chart, but here is a version I was using over 5 years ago.  I know I was using it long before that

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It will be interesting to see if they find a way to blame cycles for cooling in the last 10-15 years but not for the warming in the 80's and 90's.

Next step -- alarmists have the same epiphany about the sun, and blame non-warming on a low solar cycle without simultaneously giving previous high solar cycles any credit for warming.  For Nature's benefit, here is another chart they might use (from the same 2008 blog post).  The number 50 below is selected arbitrarily, but does a good job of highlighting solar activity in the second half of the 20th century vs. the first half.

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Explaining the Flaw in Kevin Drum's (and Apparently Science Magazine's) Climate Chart

I won't repeat the analysis, you need to see it here.  Here is the chart in question:

la-sci-climate-warming

My argument is that the smoothing and relatively low sampling intervals in the early data very likely mask variations similar to what we are seeing in the last 100 years -- ie they greatly exaggerate the smoothness of history and create a false impression that recent temperature changes are unprecedented (also the grey range bands are self-evidently garbage, but that is another story).

Drum's response was that "it was published in Science."  Apparently, this sort of appeal to authority is what passes for data analysis in the climate world.

Well, maybe I did not explain the issue well.  So I found a political analysis that may help Kevin Drum see the problem.  This is from an actual blog post by Dave Manuel (this seems to be such a common data analysis fallacy that I found an example on the first page of my first Google search).  It is an analysis of average GDP growth by President.  I don't know this Dave Manuel guy and can't comment on the data quality, but let's assume the data is correct for a moment.  Quoting from his post:

Here are the individual performances of each president since 1948:

1948-1952 (Harry S. Truman, Democrat), +4.82%

1953-1960 (Dwight D. Eisenhower, Republican), +3%

1961-1964 (John F. Kennedy / Lyndon B. Johnson, Democrat), +4.65%

1965-1968 (Lyndon B. Johnson, Democrat), +5.05%

1969-1972 (Richard Nixon, Republican), +3%

1973-1976 (Richard Nixon / Gerald Ford, Republican), +2.6%

1977-1980 (Jimmy Carter, Democrat), +3.25%

1981-1988 (Ronald Reagan, Republican), 3.4%

1989-1992 (George H. W. Bush, Republican), 2.17%

1993-2000 (Bill Clinton, Democrat), 3.88%

2001-2008 (George W. Bush, Republican), +2.09%

2009 (Barack Obama, Democrat), -2.6%

Let's put this data in a chart:

click to enlarge

 

Look, a hockey stick , right?   Obama is the worst, right?

In fact there is a big problem with this analysis, even if the data is correct.  And I bet Kevin Drum can get it right away, even though it is the exact same problem as on his climate chart.

The problem is that a single year of Obama's is compared to four or eight years for other presidents.  These earlier presidents may well have had individual down economic years - in fact, Reagan's first year was almost certainly a down year for GDP.  But that kind of volatility is masked because the data points for the other presidents represent much more time, effectively smoothing variability.

Now, this chart has a difference in sampling frequency of 4-8x between the previous presidents and Obama.  This made a huge difference here, but it is a trivial difference compared to the 1 million times greater sampling frequency of modern temperature data vs. historical data obtained by looking at proxies (such as ice cores and tree rings).  And, unlike this chart, the method of sampling is very different across time with temperature - thermometers today are far more reliable and linear measurement devices than trees or ice.  In our GDP example, this problem roughly equates to trying to compare the GDP under Obama (with all the economic data we collate today) to, say, the economic growth rate under Henry the VIII.  Or perhaps under Ramses II.   If I showed that GDP growth in a single month under Obama was less than the average over 66 years under Ramses II, and tried to draw some conclusion from that, I think someone might challenge my analysis.  Unless of course it appears in Science, then it must be beyond question.

If You Don't Like People Saying That Climate Science is Absurd, Stop Publishing Absurd Un-Scientific Charts

Kevin Drum can't believe the folks at the National Review are still calling global warming science a "myth".  As is usual for global warming supporters, he wraps himself in the mantle of science while implying that those who don't toe the line on the declared consensus are somehow anti-science.

Readers will know that as a lukewarmer, I have as little patience with outright CO2 warming deniers as I do with those declaring a catastrophe  (for my views read this and this).  But if you are going to simply be thunderstruck that some people don't trust climate scientists, then don't post a chart that is a great example of why people think that a lot of global warming science is garbage.  Here is Drum's chart:

la-sci-climate-warming

 

The problem is that his chart is a splice of multiple data series with very different time resolutions.  The series up to about 1850 has data points taken at best every 50 years and likely at 100-200 year or more intervals.  It is smoothed so that temperature shifts less than 200 years or so in length won't show up and are smoothed out.

In contrast, the data series after 1850 has data sampled every day or even hour.  It has a sampling interval 6 orders of magnitude (over a million times) more frequent.  It by definition is smoothed on a time scale substantially shorter than the rest of the data.

In addition, these two data sets use entirely different measurement techniques.  The modern data comes from thermometers and satellites, measurement approaches that we understand fairly well.  The earlier data comes from some sort of proxy analysis (ice cores, tree rings, sediments, etc.)  While we know these proxies generally change with temperature, there are still a lot of questions as to their accuracy and, perhaps more importantly for us here, whether they vary linearly or have any sort of attenuation of the peaks.  For example, recent warming has not shown up as strongly in tree ring proxies, raising the question of whether they may also be missing rapid temperature changes or peaks in earlier data for which we don't have thermometers to back-check them (this is an oft-discussed problem called proxy divergence).

The problem is not the accuracy of the data for the last 100 years, though we could quibble this it is perhaps exaggerated by a few tenths of a degree.  The problem is with the historic data and using it as a valid comparison to recent data.  Even a 100 year increase of about a degree would, in the data series before 1850, be at most a single data point.  If the sampling is on 200 year intervals, there is a 50-50 chance a 100 year spike would be missed entirely in the historic data.  And even if it were in the data as a single data point, it would be smoothed out at this data scale.

Do you really think that there was never a 100-year period in those last 10,000 years where the temperatures varied by more than 0.1F, as implied by this chart?  This chart has a data set that is smoothed to signals no finer than about 200 years and compares it to recent data with no such filter.  It is like comparing the annualized GDP increase for the last quarter to the average annual GDP increase for the entire 19th century.   It is easy to demonstrate how silly this is.  If you cut the chart off at say 1950, before much anthropogenic effect will have occurred, it would still look like this, with an anomalous spike at the right (just a bit shorter).  If you believe this analysis, you have to believe that there is an unprecedented spike at the end even without anthropogenic effects.

There are several other issues with this chart that makes it laughably bad for someone to use in the context of arguing that he is the true defender of scientific integrity

  • The grey range band is if anything an even bigger scientific absurdity than the main data line.  Are they really trying to argue that there were no years, or decades, or even whole centuries that never deviated from a 0.7F baseline anomaly by more than 0.3F for the entire 4000 year period from 7500 years ago to 3500 years ago?  I will bet just about anything that the error bars on this analysis should be more than 0.3F, much less the range of variability around the mean.  Any natural scientist worth his or her salt would laugh this out of the room.  It is absurd.  But here it is presented as climate science in the exact same article that the author expresses dismay that anyone would distrust climate science.
  • A more minor point, but one that disguises the sampling frequency problem a bit, is that the last dark brown shaded area on the right that is labelled "the last 100 years" is actually at least 300 years wide.  Based on the scale, a hundred years should be about one dot on the x axis.  This means that 100 years is less than the width of the red line, and the last 60 years or the real anthropogenic period is less than half the width of the red line.  We are talking about a temperature change whose duration is half the width of the red line, which hopefully gives you some idea why I say the data sampling and smoothing processes would disguise any past periods similar to the most recent one.

Update:  Kevin Drum posted a defense of this chart on Twitter.  Here it is:  "It was published in Science."   Well folks, there is climate debate in a nutshell.   An 1000-word dissection of what appears to be wrong with a particular analysis retorted by a five-word appeal to authority.

Update #2:  I have explained the issue with a parallel flawed analysis from politics where Drum is more likely to see the flaws.

Climate Humor from the New York Times

Though this is hilarious, I am pretty sure Thomas Lovejoy is serious when he writes

But the complete candor and transparency of the [IPCC] panel’s findings should be recognized and applauded. This is science sticking with the facts. It does not mean that global warming is not a problem; indeed it is a really big problem.

This is a howler.  Two quick examples.  First, every past IPCC report summary has had estimates for climate sensitivity, ie the amount of temperature increase they expect for a doubling of CO2 levels.  Coming into this IPCC report, emerging evidence from recent studies has been that the climate sensitivity is much lower than previous estimates.  So what did the "transparent" IPCC do?  They, for the first time, just left out the estimate rather than be forced to publish one that was lower than the last report.

The second example relates to the fact that temperatures have been flat over the last 15-17 years and as a result, every single climate model has overestimated temperatures.  By a lot. In a draft version, the IPCC created this chart (the red dots were added by Steve McIntyre after the chart was made as the new data came in).

figure-1-4-models-vs-observations-annotated (1)

 

This chart was consistent with a number of peer-reviewed studies that assessed the performance of climate models.  Well, this chart was a little too much "candor" for the transparent IPCC, so they replaced it with this chart in the final draft:

figure-1-4-final-models-vs-observations

 

What a mess!  They have made the area we want to look at between 1990 and the present really tiny, and then they have somehow shifted the forecast envelopes down on several of the past reports so that suddenly current measurements are within the bands.   They also hide the bottom of the fourth assessment band (orange FAR) so you can't see that observations are out of the envelope of the last report.  No one so far can figure out how they got the numbers in this chart, and it does not match any peer-reviewed work.  Steve McIntyre is trying to figure it out.

OK, so now that we are on the subject of climate models, here is the second hilarious thing Lovejoy said:

Does the leveling-off of temperatures mean that the climate models used to track them are seriously flawed? Not really. It is important to remember that models are used so that we can understand where the Earth system is headed.

Does this make any sense at all?  Try it in a different context:  The Fed said the fact that their economic models failed to predict what actually happened over the last 15 years is irrelevant because the models are only used to see where the economy is headed.

The consistent theme of this report is declining certainty and declining chances of catastrophe, two facts that the IPCC works as hard as possible to obfuscate but which still come out pretty clearly as one reads the report.

Hearing What You Want to Hear from the Climate Report

After over 15 years of no warming, which the IPCC still cannot explain, and with climate sensitivity numbers dropping so much in recent studies that the IPCC left climate sensitivity estimates out of their summary report rather than address the drop, the Weather Channel is running this headline on their site:

weatherch

 

The IPCC does claim more confidence that warming over the past 60 years is partly or mostly due to man (I have not yet seen the exact wording they landed on), from 90% to 95%.  But this is odd given that the warming all came from 1978 to 1998 (see for yourself in temperature data about halfway through this post).  Temperatures are flat or cooling for the other 40 years of the period.  The IPCC cannot explain these 40 years of no warming in the context of high temperature sensitivities to CO2.  And, they can't explain why they can be 95% confident of what drove temperatures in the 20 year period of 1978-1998 but simultaneously have no clue what drove temperatures in the other years.

At some point I will read the thing and comment further.