From listening to Romm, you would think that the US is having a very hot year. He has claimed heat from coast to coast.
But NOAA data shows Romm’s folly.
The map above is based on the NOAA year to date map. I split the country up into regions based on above normal (red,) normal (green,) and below normal (blue) temperatures. Normal is defined as within 1ºF of the mean temperature.
61% of the country has seen normal temperatures, 27% has seen above normal temperatures, and 13% has seen below normal temperatures. Hardly what would be called a hot year
According to NCDC, the year to date is actually the fourth coolest since 1998, with temperatures declining at a rate of nine degrees F per century.
http://www.ncdc.noaa.gov/oa/climate/research/cag3/na.html
So what is special about 1998? That is the year which USHCN/GISS promoted to #1 a few years ago, and is now listed as the hottest year ever in the US.
1934 used to be the hottest year, before GISS did their temperature gymnastics. Below is the US Weather Bureau map for July, 1934. The whole country was hot.
In June, 1934, the whole country had temperatures over 100 degrees.
So how about the East Coast? Is it the hottest year ever? Hardly – 1828 was hotter when CO2 levels were 284 ppm.
Conclusion : Romm’s claims are based on a complete lack of both near and long-term historical perspective.
Steven, you know that the projection of the first map skews data greatly to the southern states, by up to 50% IIRC? I’ll see if I can’t scare up that script I wrote that demonstrated that. -bbt
I see, Texas is actually a small state.
Why is it so hard to accept constructive criticism. Texas is a big state, but the projection you use does alter the perception of the relative sizes of states and thus introduces a bias if the warming ocurrs in the north. Use an equal area projection and your argument would no longer be susceptible to that particular criticism.
If you are right, your argument is stronger if you use a diagram that isn’t biased in favour of your argument, because it shows the argument is strong enough to stand on its own merit without needing the assist.
It is only a small point, maybe, but the flippant dismissal isn’t confidence inspiring.
It is an equal area projection.
then why not merely say so rather than the flippant dismissal?
Do you really think Texas is 50% too large on that map?
not by 50%, but it doesn’t look like an equal area projection (at least not compared to the equal area projections I have found by performing the obvious google searches) and there is no evidence AFAICS that NOAA claim to use equal area projections.
Can you provide evidence that it is an equal area projection?
Funny, I brought this up previously: http://wattsupwiththat.com/2010/08/02/noaa-graphs-62-of-continental-us-below-normal-in-2010/#comment-447081
Perhaps you missed it? Perhaps you didn’t have an answer?
An approximate N-S transect from TX to MT
state area pixels area/pixels
tx 696241 6576 105.876064476886
co 269837 2255 119.661640798226
wy 253348 1978 128.082912032356
mt 381156 2729 139.668743129351
Exageration extends to the E-W it seems as well:
wa 184827 1200 154.0225
Essentially, if you count pixels, that’s what you get pixels. A single pixel does not represent the same area across the map. Assigning the same weight to all pixels over-represents southern states.
It is NOAA’s map (not mine) and I don’t see any reason to believe that it makes any difference to the argument.
NCDC shows the current year as fourth coldest out of the last 13.
Is NOAA pixel counting? No. They construct a (necessarily) pixelated map from raw data.
Doesn’t make a difference to the argument? Lol. Just the small problem of a poor reverse engineering that happens to fit your argument. Nothing wrong with that… 😛
Also, I’ve often wondered what effect the Dust Bowl—a minor man-made catastrophe—had on elevating the temperatures in the 1930’s?
All maps (and everything else) have a tolerance error. Big news there.
Montana (cold) Florida (cold) and Pennsylvania (hot) all have about the same ratio of km^2 per pixel (~130.)
You are going to have a tough time making a case that there is systematic bias which affects the conclusions of this article, and you have not made any attempt to do so.
Using values specific to each color, we get these results:
total pixels: 63043.0
unweighted total: 23196.5
unweighted average: 0.367947274082769
weighted total: 39561.1831971011
weighted average: 0.627526976779358
Using your values (that is to say blue: -1, green: 0, red: +1), we get these results:
total pixels: 63043.0
unweighted total: 1783.0
unweighted average: 0.0282822835207715
weighted total: 2911.72385489186
weighted average: 0.0461863149737776
Averages are in °C, and weighted values normalize pixels to area for each individual state, and then summed.
0.028°C is only off by a factor of 22 compared with the more accurate calculation of 0.627°C of the US YTD.
FYI, weighted refers to calculations that take into account distortion of the map. Unweighted does not normalize the distortion of area across the map.
I got +0.032 degrees F for the average.
The discrepancy with NCDC is probably due to the fact that NCDC adds more than 0.6F on to their number as a “correction” – and the HPRCC maps use actual thermometer readings.
Thermometer readings are a problem if you believe in global warming.
Ha, I’m having a d’oh moment now. Temps above are not in °C, but rather °F.
‘The discrepancy with NCDC is probably due to the fact that NCDC adds more than 0.6F on to their number as a “correction”’
That’s all? Your methodology which doesn’t take into account actual temperature anomolies (which are skewed towards the warm side—look at the histogram) and doesn’t take into account exaggeration of surface area at lower latitudes, is fine.
Whereas if you assign values to pixels as shown by the original image, and correct for the areal distortion, you get the same value? But … it’s just something they randomly add on? Wow. I expected a little more rigor…
A drop in temperatures from this has to show up soon:
http://img137.imageshack.us/img137/963/nino34.png
Also, Anthony has this:
With a rapid onset – the strongest La Niña since 1955-56
http://wattsupwiththat.com/2010/10/11/with-a-rapid-onset-the-strongest-la-nina-since-1955-56/
Steven,
could this current La Nina still get stronger?
Well, Amino, for the latest on SST’s and all that, I always go to Bob Tisdale’s blog…
http://bobtisdale.blogspot.com/
Now here is an interesting graph showing three recent years of El Nino to big La Nina transitions and how 2010 is stacking up…
http://i54.tinypic.com/rhk286.jpg
Looks like we’ll have to go back a lot farther to find a steeper decline.
Now that I’ve seen Anthony’s post I’d say we ‘ll have to back to 1955! 🙂
This is a lot of magenta:
http://img171.imageshack.us/img171/8695/sstanom.gif
I would love to see a correlation of air conditioner installations vs temperature by region. All I know is the air con businesses here are doing a roaring trade installing cheap Chinese split systems, and the power companies are desperately struggling to keep up with electricity demand on hot days.
The map of air con installation rate just might look like the one at the top of your post.
In other words UHI is probably still going up.
Bruce, UHI doesn’t affect either satellite readings or the USCRN. Both show temp trends that are consistent with GISTEMP and HadCRUT.
Well, you may be correct about UHI in general, but here are the temp trends from the mega- El Nino year, 1998. No, the trends are not in agreement. http://www.woodfortrees.org/plot/hadcrut3vgl/from:1998/trend/offset:0.04/plot/gistemp/from:1998/trend/offset:0.02/plot/rss/from:1998/trend/offset:0.18/plot/uah/from:1998/trend/offset:0.23
They diverge from a 0.02 / 12 yrs(HadCrut) to 0.18 / 12 yrs UAH splits the middle and RSS more closely aligns with HadCrut.
Also, I wouldn’t be sure about the UHI not affecting sat. readings. Heat rises. I would assume heat over a city the size of New York would be noticeable by the satellites even at the heights they measure, but I could be wrong.
I agree with James. If you put 2kW of heat from a cheap air con into the lower troposphere (assuming hot air rises over a city) then it is going to affect satellite temperature in the local area. I was alluding to Steven’s top area map, with a hot zone in the north east. If you average out over the year and over the globe the UHI heat (from aircon’s, tarmac and you name it) will be diluted, but heat is heat – its got to go somewhere. And, no I don’t think it has been properly corrected in USHCN etc, but that is my personal view.
James Sexton
HadCRUT (CRU) is the elephant in the room when people want to make UHI the reason for GISTemp (GISS) diverging from the satellite records in recent years. GISTemp doesn’t just diverge from the satellite readings it also diverges from HadCRUT land readings that are also contaminated by UHI.
What makes it worse for GISS is that CRU is home of ClimateGate. People already are not trusting CRU. So, if GISS is showing something that not even CRU is showing…… well, I’ll let others fill in the rest of the sentence.
Bruce, USCRN and USHCN are different.
ChrisD says:
October 11, 2010 at 10:55 pm
consistent with GISTEMP and HadCRUT.
GISTemp and HadCRUT diverge from each other in recent years. Graphs show that.
GISTemp diverges from the other 3 well known data sets in recent years. You can see GISTemp change characteristics in this video:
James, what happens to your chart if you change the start year from 1998 to 1997 or 1999?
It is true that HadCRUT does tend to show a slightly lower trend than the others, but that’s because it doesn’t include Arctic temps, which are warming faster. Nevertheless, all of the trends are broadly in agreement, i.e., they all show warming. There are many who really try hard to blame this on UHI, but it just doesn’t hold up when you look at it.
As someone wittier than me has said, there are so many nails in the UHI coffin that it would be hard to find anywhere to bang in another one.
Chris, they show essentially the same thing. I would say though, that a rise of 0.02 deg C over 12 years hardly qualifies as showing warming.(it doesn’t reach any margin of error) Further, its been well documented that GIS doesn’t really measure the temps in the arctic either. They just make them up. Extrapolate is the word I believe they use. Well, ok, they measure temps……..both of them when it comes to Nothern Hemisphere of the Western side. 😐 So we don’t really know whether its warming in the arctic or not, much less if its warming faster or not. I’m agreeing with Amino, the GIS divergence is significantly greater than the other 3.
Chris, they show essentially the same thing
No, they don’t. Here’s your chart using 1999 instead of 1998 (I didn’t bother fixing the offsets):
http://www.woodfortrees.org/plot/hadcrut3vgl/from:1999/trend/offset:0.04/plot/gistemp/from:1999/trend/offset:0.02/plot/rss/from:1999/trend/offset:0.18/plot/uah/from:1999/trend/offset:0.23
The UAH, RSS, and GISTEMP trends are nearly the same (0.021, 0.018, and 0.020 respectively). Had CRUT is a bit lower, unsurprisingly.
a rise of 0.02 deg C over 12 years
Not sure where this number came from. The trend is ~0.02 per year, or ~0.2/decade. No, it’s not statistically significant, but that’s because the period is too short, not because the trend is too small.
Finally, I have to note that “interpolating” is not a synonym for “making things up.” 🙂
Why 1999 for a starting point?
ChrisD
Here’s your chart using 1999 instead of 1998
There is a very good reason why there is a difference between starting at 1998 and 1999.
You left out the differences that naturally occur from a El Nino between surface and troposphere. This error (to put it diplomatically) has been done before.
You also did not point out that the difference between GISTemp and HadCRUT, but tried to make it sound that it is correct that the trend between GISTemp, RSS, and UAH are almost the same. But that similarity is not supposed to happen because of the difference between troposphere warming and surface warming during El Nino.
The thing that matters in the graph that you posted is the difference GISTemp and HadCRUT not the similarities between GISTemp and satellites. There is not supposed to be similarity in that time frame between surface and satellite. That’s why HadCRUT is different that the satellite records. There is supposed to be similarity between surface and surface, and between satellite and satellite.
The fact that GISTemp shows similarity with the satellite record in your graph show something could be wrong at GISS.
ChrisD – first you dismissed my post without reading it (ie if you put heat into the air above a city it’ll affect the local satellite determined temperature) and second your comment dismissing UHI is without any cited basis.
Here are two recent studies which suggest that UHI is poorly or incorrectly accounted for in the surface temperature record (which is what I referred to):
48 states
quantified UHI
You will note we’re comparing good quality rural stations to nearby urban stations in the raw data. This quantifies UHI effect. You will also see the correction in the adjusted data to the rural stations in Ed Long’s study is wrongly applied, since it artificially introduces a spurious warming trend which doesn’t exist in the data.
There are many similar examples. I particularly like the adjustments made by GISS to our local Mackay sugar mill GHCN station which imply the glaciers retreated from tropical Nth Queensland around 800 AD.
Sure there is: It only works if you use 1998. If you use 1997 or 1999, there’s not much difference. So we insist on using 1998. There’s a term for this. It involves a small red fruit.
Why should the surface and lower tropo trends be radically different? Where did you get this from?
No, I read it. The thing that I think a lot of people miss is that it’s not the absolute temperature that matters so much as the trend in temperature. This is why climate science works with temp anomalies and not actual temps. Stations that are affected by UHI may report a higher temp, but that doesn’t mean that they’ll report a higher trend. They have to be not just warmer, but increasingly warmer over time. This just isn’t the case for most of them.
One way to show that this is not happening is to just look at the maps that show temp anomalies. If UHI were a real influence, the reddest areas should be where the most people live–but it isn’t. It tends to be in places like Canada, Russia, Greenland, central Africa.
Another way is to compare trends in stations in urban and rural areas. This has been done a number of times, and no significant difference has been found.
One thing that’s bothersome is that Watts et al found all those “bad” stations and simply assumed that it wrecks the data. As far as I know, they never took the next step, as any scientist would: remove those stations from the data set and see what happens. Fortunately, one scientist did exactly this and found that the trends didn’t change when the “bad” stations were removed. (I don’t remember offhand who it was, but I can probably dig it up if necessary.)
Then we have USCRN, which is NOAA’s newer network of very carefully sited and maintained stations. The temp trends from these stations match the full US temp record very well.
Neither of those are “studies,” Bruce. One is a blog post and the other is an unrefereed report written for SPPI, which is essentially a conservative think tank. If there is anything to either of them, they should write a paper and submit it to a journal. That’s the way science is supposed to work.
ChrisD,
“On the reliability of the U.S. surface temperature record”
http://www1.ncdc.noaa.gov/pub/data/ushcn/v2/monthly/menne-etal2010.pdf
Oddly enough, the study found that poorly sited stations had a cooling effect compared to properly sited ones.
Yes, that’s it. Thanks.
IIRC, the explanation for the cooling effect was that urban stations tend to use a different sensing system than rural stations, and it tended to produce slightly lower temps. Therefore, removing them resulted in a slight warming of the output numbers. Or something like that.
In any event, removing the “bad” sites produced the opposite effect of what the UHI advocates claim.
“Neither of those are “studies,” Bruce. One is a blog post and the other is an unrefereed report written for SPPI”
ChrisD – that is very dismissive. A study is a study. A peer reviewed paper is a peer reviewed paper. I did not say they were peer reviewed. However as a scientist who left the peer review tribalism of academia for the private sector over 20 years ago I know what a study is.
You cannot dismiss data so easily. When you look at quality rural stations, such as those US stations examined in the twoo studies I linked, often they have low to zero trends in the anomaly data (and I do know what and whyfore regarding anomalies, I also have a peer reviewed statistical paper and 20 years of thermodynamic modelling experience). It is the burden of climate science that this data needs explaining, and the current hypothesis of largely CO2 driven warming cannot explain this dataset. However Pielke Snr’s hypothesis 2a, with low forcing by CO2 does explain it. UHI causes a rising trend vs the baseline because of influences like personal air cons, which are a new and increasing phenomenon. There are other similar examples, such as increasing area of tarmac. If you ignore such variables, and the amount of warming that they statistically explain, you are scientifically sticking your head in the sand at your peril.
I’m not ignoring it, and the climate scientists certainly aren’t. The issue has been studied repeatedly; there’s no evidence that UHI has any significant effects on the trends of the larger data sets, and ample evidence that it does not.
Chris, you don’t see the divergence of GIS from HadCrut in the graph you made? Perhaps its in the eyes of the beholder, but it seems a bit extreme to me. About the 0.02…….sorry, carrying on too many conversations. Yes, the time frame is too small of a sample to state much other than to observe the divergence of GIS from HadCrut. Personally, I don’t like to compare the sats with ground temps, mainly because they aren’t the same. Basically an apples and oranges comparison.
And finally, I have to disagree with your last statement. It is making things up. Our very own Steve Goddard made this video to illustrate the GIS arctic coverage…….now tell me interpolating isn’t “making things up”.
Sigh, should read….
They diverge from a 0.02 / 12 yrs(HadCrut) to 0.18 / 12 yrs(GIS), UAH………
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