Guest Post by Willis Eschenbach
A while back in the US there was an ad for a hamburger chain. It featured an old lady who bought a competitor’s hamburger with a great big hamburger bun. But when she opened it up she asked …
I got to thinking about this in the context of whether there is any real danger in a degree or two of average temperature rise, or whether it’s a big bun with no beef. In my previous post, “Lies, Damned Lies, Statistics … and Graphs”, I closed by saying:
My conclusion? Move along, folks, nothing to see here …
A commenter took exception to this, saying
When talking about global average temperatures, tenths of a degree really do matter.
Now, if tenths of a degree changes over a century “matter” for the globe, they certainly must matter for parts of the globe.
So here’s your pop quiz for the day: Which US State warmed the most, which cooled the most, and by how much?
To answer this, I used the USHCN State Temperature Database. Here are my findings:
Figure 1. Temperature trends by state, USHCN data. Seven states cooled, and forty-one warmed.
The state that warmed the most was North Dakota (top center), which warmed 1.4°C per century. The state that cooled the most was Alabama (middle of three dark blue states, lower right). It cooled by 0.3°C/century.
To compare with my previous post, here’s a similar graph, of the decadal changes in North Dakota by month.
Figure 2. North Dakota decadal average temperatures by month, 1900-2009. Red line is the average for the decade 2000-2009. Photo is an old North Dakota farmhouse.
As with the US, for much of the year there is little change, and the warming is in November to February. Note that unlike the US, during that four months, the temperature of North Dakota is below freezing (32°F) …
Now, if tenths of a degree “matter”, if they are as important as the commenter claimed, we should have seen some problems in North Dakota. After all, it has warmed by 1.6°C since 1895. That’s almost three times the global average warming.
But somehow, I must have missed all of the headlines about the temperature calamities that have befallen the poor residents of the benighted state of North Dakota. I haven’t seen stories about them being “climate refugees”. I didn’t catch the newspaper articles about how it has been so hard on the farmers and the frogs. I am unaware of folks moving in droves to Alabama, which has cooled by -0.4° since 1895, and thus should be the natural refuge of those fleeing the thermal holocaust striking North Dakota.
In fact, I don’t remember seeing anything that would support the commenter’s claims that tenths of a degree are so important. North Dakota has warmed near the low end of the range forecast by the IPCC for the coming century, and there have been no problems at all that I can find. So I have to say, as I said before,
My conclusion? Move along, folks, nothing to see here … where’s the beef?
APPENDIX: R Code for the US Map
(I think this is turnkey. Sometimes WordPress puts in extra line breaks. If so, it is also available as a Word document here.)
The code requires that you download the USHCN Temperature Data cited above and save it as a “Comma Separated Values” (CSV) file. I downloaded it, opened it in Excel. I split it using “Text to Columns …” into the following columns, as detailed in the USHCN ReadMe file:
FILE FORMAT:
STATE-CODE 1-3 STATE-CODE as indicated in State Code Table above. Range of values is 001-110.
DIVISION-NUMBER 4 DIVISION NUMBER. Value is 0 which indicates an area-averaged element.
ELEMENT-CODE 5-6
02 = Temperature (adjusted for time of observation bias)
YEAR 7-10 This is the year of record. Range is 1895 to current
year processed.
JAN-VALUE 11-17 Monthly Temperature format: Range of values -50.00 to 140.00 degrees Fahrenheit. Decimals retain a position in the 7-character field. Missing values in the latest year are indicated by -99.90.
FEB-VALUE 18-24
MAR-VALUE 25-31
APR-VALUE 32-38
MAY-VALUE 39-45
JUNE-VALUE 46-52
JULY-VALUE 53-59
AUG-VALUE 60-66
SEPT-VALUE 67-73
OCT-VALUE 74-80
NOV-VALUE 81-87
DEC-VALUE 88-94
If that is too complex, the CSV file is here.
Here’s the R code:
# The code requires that you download
# the USHCN Temperature Data
# and save it as a "Comma Separated Values" (CSV) file.
# I downloaded it, opened it in Excel, and used
# "Save As ..." to save
# it as "USHCN temp.csv"
#Libraries needed
library("mapdata")
library("mapproj")
library("maps")
# Functions
regm =function(x) {lm(x~c(1:length(x)))[[1]][[2]]}
#Read in data
tempmat=read.csv('USHCN temp.csv')
# Replace no data code -99.9 with NA
tempmat[tempmat==-99.9]=NA
# split off actual temps
temps=tempmat[,5:16]
# calculate row averages
tempavg=apply(temps,1,FUN=mean)
# calculate trends in °C by state
temptrends=round(tapply(tempavg,as.factor(tempmat[,1]),regm)*100*5/9,2)
# split off states from regional and national
statetrends=temptrends[1:48]
#calculate ranges for colors
statemax=max(statetrends)
statemin=min(statetrends)
statefract=(statetrends-statemin)/staterange
#set color ramp
myramp=colorRamp(c("blue","white","yellow","orange","darkorange","red"))
# assign state colors
mycol=myramp(statefract)
# names of the states (north michigan is missing for ease of programming)
myregions=c("alabama", "arizona", "arkansas", "california", "colorado", "connecticut", "delaware",
"florida", "georgia", "idaho", "illinois", "indiana", "iowa", "kansas", "kentucky", "louisiana", "maine",
"maryland", "massachusetts:main", "michigan:south", "minnesota", "mississippi", "missouri", "montana", "nebraska",
"nevada", "new hampshire", "new jersey", "new mexico", "new york:main", "north carolina:main", "north dakota",
"ohio", "oklahoma", "oregon", "pennsylvania", "rhode island", "south carolina", "south dakota", "tennessee", "texas",
"utah", "vermont", "virginia:main", "washington:main", "west virginia", "wisconsin", "wyoming")
# draw map
par(mar=c(6.01,2.01,4.01,2.01))
return=map('state',regions=myregions, exact=T,projection='mercator',fill=T,
mar=c(5.01,8.01,4.01,2.01),col=rgb(mycol,maxColorValue=255),ylim=c(10,60))
# set up legend boxes
xlref=-.48
yb=.37
ht=.05
wd=.08
textoff=.025
# assign legend labels
mylabels=round(seq(from=statemin,by=staterange/12,length.out=13),2)
#draw legend
myindex=0
for (i in seq(from=xlref,by=wd,length.out=12)){
xl=i
xr=xl+wd
yt=yb+ht
rectcolor=myramp(myindex/11)
rect(xl,yb,xr,yt,col=rgb(rectcolor,maxColorValue=255))
text(xl,yb-textoff,mylabels[myindex+1],cex=.65)
myindex=myindex+1
}
text(xl+wd,yb-textoff,mylabels[myindex+1],cex=.65)
# add annotations
text(0,1.08,"US Temperature Trends (°C/century)")
text(0,1.03,"USHCN Dataset, 1895-2009",cex=.8)
Willis – You wrote that “pine bark beetles will be back down to low levels in a while, and the forests will regrow, as they have done for millennia, they go up, they go down.”
This is very true in general but the mountain pine beetle epidemic in British Columbia that was so famously used as an AGW poster child is a little different. A series of less cold winters – actually less cold falls are most critical – did allow it to spread further north and northeast than historically known. But what made the recent infestation so huge and rapid was the unnatural lodgepole pine forests created by fire suppression. And that came after earlier widespread fires had effectively planted them.
This beetle lives in the cambium layer under the bark, and that layer is only thick enough in mature trees. (In a hyper-abundant epidemic like this recent one they will attack younger trees, and even non-hosts like spruces, but those trees cannot sustain them.)
In past prehistoric warm periods when it had the same potential to spread it did not have huge swaths of mature even-aged pines because periodic fires created a more patchy and mostly younger type of forest. Lots of lightning and dry summers in most of this tree’s range plus Native North Americans burned them.
So this Canadian AGW poster child is a particularly fraudulent one because while climate enabled these epidemics, the ‘catastrophic’ results were caused by human intervention in forest ecology. Smokey the Bear stomped out all the fires. They would rather log trees than let them burn. And while this epidemic was inevitable, it started in a large provincial park where everybody loves ‘old growth’ even when its not natural.
This beetle does attack other western pines, notably the Ponderosa, but the stand characteristics of the lodgepole pine create the most ‘catastrophic’ looking results. That is because under natural conditions, they are short-lived species adapted to regular burning, and when a fire hits it pops all their cones and simultaneously replants whole even-aged stands. Until the next fire or, if they live long enough, the next pine beetle epidemic.
And after a beetle kill, voila, a forest of dead wood ready to burn and pop those pine cones lying on the ground. Without fire, spruce forests typically take over… and the pines lose out… until the next fire which kills the spruce, pops the pine cones and the pines take over again.
So, yes, its a cycle, and the pines with with fire. But what happened was a disturbed cycle and what they do next will determine whether it will just repeat itself.
On the bright side, that epidemic is all but over because the beetle killed off most of the available mature pine stands. Now the beetle will go back to its usual mode of just infesting scattered mature trees, which can survive those infestations if there are relatively low numbers of beetles and the trees are not otherwise stressed.
The AGW hype about this was spectacular. Some stories left the impression that this beetle was going to spread across Canada and kill every tree, when in fact it only attacks certain host species and they are all out west.
Sorry for rambling on about what is now an old story. But its such a whopper. They hype about it was one of the things that confirmed to me that the AGW crowd was telling tall tales to flog their story. And polar bears!!!
———————-
VicV (12:21:16) :
sphaerica (11:39:18)
It nice to see a dispassionate contribution from your side, the side where you “don’t care what people choose to believe about AGW (well, I do, but I’m not touching that here and now at all).”
But on your blogsite, as is typical for True Believers, you paint all skeptics with a broad stroke of hyperbole, then proclaim your prescience by telling us how it’s going to be.
———————–
sphaerica (14:59:31) :
VicV,
I’m not sure what your point is. I explicitly said “but I’m not touching that here and now at all.” What part of that sentence is unclear, or untrue?
———————–
Didn’t claim anything to be unclear or untrue. Anything unclear or untrue about what I said?
Regardless of how ‘objective’ you’d like to present yourself, you’re in the tank for CAGW, right?, and you have little respect for CAGW falsification efforts (i.e., finding out what’s really true).
slightly OT.
A psychoanalyst at a reputable institute is now using AGW “denial” as an example for training mental health professionals.
http://www.tavi-port.org/Climatechange
How many questionable assertions and ignored facts in this short paragraph?
“The overwhelming consensus of scientific opinion is that the warming the Earth has observed over the last 50 years has been due to an increase in greenhouse gases directly caused by human activities. Predictions for the likely effects of this warming vary from the disastrous to the apocalyptic.”
Phil M. has a problem with looking at North Dakota in isolation for a very simple reason: it reduces the issue of climate change to a scale where it can be realistically examined. With the issue yielding to inspection it becomes possible for us to ask a precise question: “we have seen warming (well, maybe). Do we see any consequences?” The exercise is repeatable and concrete, and claims made in either direction can be reviewed and falsified.
When no ill consequences can be found Phil wants to look elsewhere. He and other AGW proponents are much more comfortable in the vague and ambiguous global sphere, where there is always a drought, a coral die-off, a hurricane or heat wave somewhere that can be used to claim confirmation of looming catastrophe.
Meanwhile the local landscape with its flora and fauna that is influenced but not dominated by a slightly changing climate, but for all intents and purposes is repeating its yearly cycles without much attention to these minor changes. Such is apparently the case in a place we know as North Dakota.
Sort of like Aztecs in a solar eclipse, Phil and many others want to atone. It makes them feel better.
Nothing wrong with that, I suppose. It’s the evangelizing that gets annoying.
{Al Gored (20:58:47) :…}
sensational summary of the pine beetle infestation. I read about the lack of fire cause in Canadian Goegraphic about 6 years ago. I have tried to point this out on certain other blogs, but of course, no one wanted to hear it.
sigh..
I don’t have Willis’ capacity to make all this real. Kudos to Willis.
I’ve thought so many timers about what he says here, but don’t have the knowledge he has, just my own real world experience.
But part of that real world experience is that I can’t see HOW a +1C or +2C can cause the MWP and a -1C or -2C can cause a Little Ice Age.
Willis, would you be capable of putting that in a great concise way some time, and in the same vein?
nuclear contributes about 0.2 W/m2 of heating to each country.
That, presumably, is the energy usefully generated. We can multiply this many times for the heat generated that isn’t transformed into electricity.
I would guess France generates a fair bit of nuclear. Have to add in non-power nuclear too, mainly naval vessels: it is still heat into the system.
Mike Borgelt (15:37:56) :
You need a refresher course in statistics.
Dr Burns
Regarding the thermometer error… the idea is that even if not absolutely accurate, the thermometer will still be accurate to itself and can be used to demonstrate changes over time. The problem is more when a new thermometer replaces an old one – then, considering the error range, a step change of some amunt should be expected unless they get lucky. Allegedly this is all addressed by the Quality Controls (and adjustments).
I’m not saying I necessarilly agree with all that, but that seems to be the explanation I heard.
Sphaerica
Your example of estimating the average age of populations is a good example of estimation, not accuracy. We ASSUME that the normal distribution of error around a claimed age is random. It would be interesting to find out if a study has ever actually verified that. However, it would be unwise to use that precision of 0.1 year to justify a trillion dollar effort to change the world’s economy.
Remember, there is a difference between accuracy and precision. Accuracy is how close we are to the real value. Precision is how fine a division we can apply to a value. High precision does not grant high accuracy.
Likewise, averaging one degree temperature values and displaying them to a precision of one tenth of a degree does not meet instrumentation standards for tenth of a degree accuracy. This may seem counter intuitive. Were all the instruments from the same manufacturer with the same errors at the same temperatures? That would produce a bias in the averaged value. That biased value would still be accurate to the instruments specifications but not necessarily to a tenth of that claimed accuracy. Of course, we have not even brought into the discussion, thermometer siting issues.
For the example of population age, we assumed absolute accuracy. That is to say we assume everyone surveyed actually knew their age. In instrumentation terms, that means an instrument error tolerance of 0.0%. Next, we assume that distribution of birth dates throughout a year is random. So basically, again in instrument terms, we are using an extremely accurate instrument with a well known and predictable precision and error characteristic. Neither of those characteristics are present in our temperature records.
“No, you missed the record floods of earlier years. See my post and citation above.”
2009 was the record flood for the Red river. Also, your link notes that increased temperatures during snowmelt and increased precipitation are causes of flooding. Moreover, there is a clear upward trend in peak streamflow:
http://img404.imageshack.us/img404/3305/redriverfargopeak.png
This seems to be exactly what you are looking for: a problem in North Dakota clearly associated with warmer temperatures.
Regarding Anthropogenic Heating… ultimately all power consumption that is not sourced from hydro, wind, solar is adding energy into the system that was not there prior.
From a climate standpoint (meaning atmosphere, ocean, and land) the only energy that is lost is that which is stored in, more or less, permanent chemical bonds.
In 2006 (again, the latest number I could find) average annual global power consumption was 15.8 TW – that works out to 0.03 W/m2 globally.
That said, consumption should be expected to correlate, more or less, with population and economic level (1 average person in the US, for example, will use more gasoline and electricity than someone else). This means the greater the population density, the greater local power consumption forcing.
As stated earlier, power consumption is a smaller factor for UHI than paving, structures, and other LULC changes, but it explains Roy Spencer’s latest UHI post here – where he demonstrated a log correlation between population density and surface temperature trend.
Willis
You have my e-mail, I would love to help you out with a write-up if you’d like.
Best Regards.
@ur momisugly Mooloo (02:21:08) :
Just curious if you are pointing out nuclear as a particularly bad heat generator, since the power would have been generated somehow, since the nuclear power is essentially a substitute for coal, oil or gas.
So, nuclear has serious drawbacks, and it IS putting heat into the system, perhaps of 0.2 kWm^-2, or even more, as you suggest, but it is not all a net increase.
Even the environmentalists’ beloved Mother Earth News wood burning stoves and fireplaces are “still heat into the system.” And collectively it is no small effect. The enviros were shocked back in the 1970s when it was found that the inversion layer “brown smog” over Denver every winter was coming from their wood burning stoves and fireplaces.
Even the solar arrays in the deserts are “still heat into the system.” Whatever is collected from them and turned into energy is not available to re-radiate into the desert night sky. If it doesn’t go OUT, it must be staying IN. Ask the AGW folks about that argument.
…And BTW, France produces 78% of its power from nuclear, per Wikipedia.
I am with Mike in this one.
“Mooloo (02:21:08) :
nuclear contributes about 0.2 W/m2 of heating to each country.
That, presumably, is the energy usefully generated. We can multiply this many times for the heat generated that isn’t transformed into electricity.”
Nuclear plants are thermal baseload, you run a turbine and the efficiency of such a turbine is about 35% to 40%. So your “many times” boils down to a factor of 3 at most.
“I would guess France generates a fair bit of nuclear. Have to add in non-power nuclear too, mainly naval vessels: it is still heat into the system.”
Naval vessels would heat up the ocean in which they are operating, not France, so we would have to divide the heat they’re generating by the area of the oceans… which are much larger than France.
“Feet2theFire (10:05:34) :
[…]
Even the solar arrays in the deserts are “still heat into the system.” Whatever is collected from them and turned into energy is not available to re-radiate into the desert night sky. If it doesn’t go OUT, it must be staying IN. Ask the AGW folks about that argument.”
Like photosynthetic plants, PV is endothermic. So a PV cell driving a load will be cooler than an unconnected PV cell; the load in turn will get warm when it performs its work – think about a resistor heating up. Basically, you move heat around.
A few years ago newspapers were publishing articles about the increased severity of flooding in southern states bordering the Mississippi river. From what I could gather it was pretty well understood that sandbagging of rivers and tributaries upstream was the culprit. Man is the culprit – just not in the way AGW-folks want to claim. By preventing spring stream and river uplands from overflowing they’re channelling larger amounts of water every year into narrower river courses downstream. Nowhere for the water to go but up – and into the annual streamflow history books. This year they reported the Red River cresting some 40′ above its banks at Fargo (“A record!”. The local schools let out so the schoolkids can go fill sandbags in a great community effort to save Fargo again. This year officials planned to fill 2 million bags against the river’s onslaught.
Willis,
Trying to learn R so naturally I tried your script:
R has a problem with the line:
statefract=(statetrends-statemin)/staterange
There seems no “definition” of staterange?
ie R Console comes up with:
Error in eval.with.vis(expr, envir, enclos) :
object ‘staterange’ not found
By the way commentors here. There is some good work out there in blogland and people like Jeff Id, Nick Stokes, Zeke, Roman, Eugen etc,etc are putting out some neat stuff to investigate global temperatures. Fun way to learn R and to do your own unique temperature investigations.
Willis,
Couple of other things in case novices like me are trying to run the code:
I needed to download mapdata_2.1-1.zip from r-project.org.
And if people download your cvs file the name needs to be changed from:
USHCN_temp.cvs to USHCN temp.cvs to run the scripts as is.
That’s adjusted data. Raw data shows much less warming (and much greater cooling in the SE).
I ran the USHCN station stats state by state myself.
Steve Hempell (15:34:20)
My bad, appears a line got lost …
Needs to be immediately before the line that gives the error.
Steve Hempell (16:45:40) : edit
Yes, you’ll need to download the three libraries listed in the start of the program.
And the name of the data needs to match the name in the program. I changed it half way through so I could put it on the web.
Thanks, any other questions, please ask.
w.
Willis Eschenbach (14:57:15) :
Good stuff, C3. A small point, my data is from USHCN.
Thanks, Willis. I do have another question for you that is not germane to this posting. If you ever have any free time, send me an email: c3headlines@gmail.com
‘JP’, C3 Editor
Willis,
That fixed it .
Like to see this for the European countries. Any hints on how to do this if I want to tackle it?
Steve Hempell (20:32:14)
I don’t know if there is anything comparable to the USHCN that gives the European climate records country by country … let me know if you find something.
Plotting Europe in R is a bit trickier than plotting the US, you need to use the “regions” variable in the mapping program and spell out (exactly) the names of the countries you are wanting to plot.
w.