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The Weather Channel warns of More Blizzards

The Weather Channel is advising to travel as far south as I-10 to avoid snow and blizzards (one moving eastward now, and another coming next week. That will be 3 in a row.) They are also advising that folks stuck at airports from last weeks blizzard on the East Coast and midwest take any plane they can to a warmer place and then rebook to your final destination (i.e. you will be snowed in again at that prior snowy airport if you don’t get out of there soon.)

They have said it will be moving slowly and dumping a lot of snow for days to come.

This is The Weather Channel “stormwatch” link so I expect content will change over time as the storms come (and go);

http://www.weather.com/newscenter/stormwatch/

Europe Cold Too

There was also a “tease” about a story of 100 dead in Europe from ice and cold.

The BBC seems to have the same story here:

http://news.bbc.co.uk/2/hi/europe/8423442.stm

Be Prepared

For now, just realize it is cold. It is going to be colder, and if you have not already gassed up the car, put food in the pantry, bought some candles and thermal blankets, and set some “Presto logs” near the fireplace: This is the last call before a couple of more very cold snowy weeks

You may find this information helpful:

http://chiefio.wordpress.com/2009/05/27/crisis-kits-and-preparedness-packs/

http://chiefio.wordpress.com/2009/04/06/food-storage-systems/

If you are expecting global warming stuff, it’s here:

http://chiefio.wordpress.com/gistemp/.

This posting is about the other thing I do, looking at investment markets. Prior postings in this series are available here.

The charts in this posting are live charts, so my comments will describe how it is now, but in a week it will be showing new data and a new week. Since I think it’s more important to be in touch with what the market is doing NOW than to preserve the historical chart, this is, IMHO, a reasonble choice. Just don’t be surprised if the chart I describe is not the one you see a few weeks from now! If you would like to see the historical chart, you may enter custom date ranges on the charting tool at www.bigcharts.com

Daily Notes

Wall Street Week – Monday, December 21, 2009

Well, we’ve been in a roughly sideways choppy market since about the time, last month, that I said we would be in a roughly sideways choppy market. What? You expected something more interesting? Sometimes ‘the hard bit’ in trading is the boredom. “Nothing happened” can happen for a fairly long time. OK, yes, some toppy markets have had a bit of a tumble (like gold). Yes, the dollar has had a bit of a rally. But I did say to ‘hold dollars’ and after you have ‘gone to cash’ it is just, well, boring. OK, you could have bought the ‘dollar up’ UUP ticker or the “short gold” ticker. But those are slightly exotic instruments for the average Joe or Jane sixpack. If you want to trade them, you probably don’t need me telling you about them.

So I’m mostly on the sidelines right now. Same as a month ago. You can do some small day trades. You can hold some large dividend payers (I still have my CEL that is paying well along with TGP that is a high dividend LNG tanker company, for example) along with the very long term investments (like Birkshire Hathaway BRKA / BRKB ). But there isn’t much ‘action’ in holding them. And I’m still holding my “oil trusts” as a high dividend hedge on my personal fuel consumption. Again, not a lot of “action” in saying “hold the core holdings”…

Was there money to be made? There is always money to be made, but at what cost and at what risk? It’s the Holidays. A time for family, friends, and excessive demands on your time. Not the best circumstances for making good trades. The market volume drops, you get confusing currents (like “tax loss selling” where folks may sell a lot of some position just to cover taxes on another- not very predictable…) and bored market makers look for games to play in thin individual issues. So, in my humble opinion, trade only the amount that is very comfortable, then take a break and do what really matters: spend time with family and friends.

The Long Term Context

A month or so ago I explained this particular chart. Since it takes such a long time for a 10 year weekly chart to show changes, I’m going to do a bit of a ‘change up’ this time with two variations just to keep it interesting. One as the NYSE, the other as the SPY exchange traded fund (ETF). This week the NYSE Volume is astoundingly light. A bit over a Billion shares, or about 1/10 to 1/5 of ‘typical’. When volume weakens, a trend is running out of gas. This is ‘run out of gas’ AND ‘run into a holiday wall’. There’s just nothing happening on wall street. I’ve also put DMI on the chart. You can see that the ‘black line’ is about 20. That’s ‘very weak trend’ land (and says ‘use Slow Stochastic’). We also see both the red and blue lines dropping. Both up and down tendencies are losing strength. Flat, headed flatter… Slow stochastic is ‘middle of nowhere’ but advising to stay out (‘mouth’ pointed downward, blue on top). It’s a pretty weak market when even the “fast trades in low trend markets” indicator is saying not much of interest is happening. Highest expectation is still to the downside, though.

10 Years, NYSE

10 years, NYSE

When we look at the SPY, it has a bit more trend to the DMI indicator. Still weak, but not quite as week as the broad market. Slow Stochastic is still saying ’sit this one out’. I’ve added the Rate of Change indicator. It is still ‘above zero’ which is technically a ‘be in’ call, but look at how close to zero we are! This is a very tepid ‘be in’! and ROC has been weakening into this point. I’d expect a tiny “Christmas Rally” into Dec 25, but some kind of drop after that until a new trend sets up in January.

10 Years, SPY

10 years, SPY

What Is Our Context

Let’s look at the S&P 500 largest stocks in America compared with some other kinds of assets; a couple of month maturity bond fund, oil, gold, Yen.

Asset Class Races

Asset Class Recent Race

SPY       The S&P 500 ETF
GLD       Gold ETF
USO       Oil ETF
FXY       Japanese Yen currency fund
SHY       1 to 3 year U.S. Treasury Bond fund
FXE       Euro currency ETF
SLV       Silver fund
BZF       Brazilian currency ETF
EWA       Austria ETF
WOOD      A wood and paper products fund

Our basic benchmark SPY has gone into a very shallow ‘flat wobble’ for about a month and a half. No trend and not even much ripple to fast trade. No joy in Mudville to be had there. Notice that DMI even on this daily chart has gone to an incredibly low 10. That is dead flat. MACD is in a very shallow down drift (with a ‘be out’ red on top bias) as we see even the “moving average stack” go flat (MACD approaching zero, but slowly). Even RSI at ‘near 50′ mid-line and flattening is not giving anything to work with. Strange market, isn’t it? Not at all like a live jumping mid-year market… Everyone is just stepping to the side and taking a long pause.

Gold and Silver continue their ‘correction’ to the downside. The Euro and Yen both show a drop due to “dollar strength” as does BZF. Sitting in the US dollar looks like it was a pretty good call. (Whenever it was I called it… about a week into the trend, I think… probably ought to look it up. But like Italian driving: “What’sa behind you, she is a no important!”… (rough paraphrase from the movie “Gum ball Rally” I think…) Cash was even better than bonds where we see the “Fed might raise rates soon” worry putting a downward kink in even the ‘couple of years’ short maturity bond funds.

We also see the impact of the dollar strength on the foreign stock markets in the EWA Australian ETF. Stepped out at a good time, but it would be a good thing to watch for a ‘reentry’ some time next year.

Oil has flattened out after a brief ‘correction’. Not a clear reentry yet, but ok to hold high paying oil trusts. WOOD continues to climb. It would be good to check out other “softs” and ag commodities. ( Sugar SGG and Cotton BAL both are rising, though sugar is very volatile…) PCL is rising with wood. Still has a nice dividend too. It ought to continue rising for quite a while as demand for wood in building recovers.

What about Brazil? A Closer Look.

We stepped out of Brazil when they had a “Ministry of Stupidity Speaks” moment a couple of months back. We ought to keep an eye on them, though. But for now it is still a “watch but don’t touch” market.

Brazil the EWZ ETF vs the BZF currency ETF

Brazil ETF vs Currency Race

I’ve added a couple of other ‘emerging markets’ to this chart so we can see the relative performance. Mexico (EWW) had been doing well, but looks to be having a ‘holiday flat’ as well. Notice that DMI / ADX is now ‘red on top’ confirming the ‘be out of Brazil for now’ call of a while back. (Oct. 20th, 2009). In general, you can see that the (temporarily) strong dollar can take a big bite out of emerging markets. That is why ‘early out’ is especially important for this stocks. They go up a lot faster in good times, but drop more and faster when things are not as good…

In a special posting October 20th, I had said to bail from Brazil as their President had started talking about special taxes on foreign stock trades. Now you can see why I said that.

Running ETFs

I have a new tool that searches chart patterns and finds those that I describe to it as “interesting”. For this section, “interesting” is those that have price over 25 day Simple Moving Average over 50 SMA over 75 SMA. Basically, those that are in a steady up run.

This is most likely to continue, but will at some point each ticker will hit a “dip” and fall off this search, only to return at the next rise. So a high number is good, until it fails, and a low number can mean time for a second bite at the apple. Being ON the list can be as important as rank on the list. Races tell you how to rank them. Realize that these have not been filtered in any way for the quality of the fund, nor for the volume traded, nor for what they hold. Each ticker must be looked at for those qualities before buying anything. This is just a way to find “things of interest” to explore. So what is on the top of the list?


I will be filling these in a bit later.
  

OOTUS – Out Of The U.S.

See the racing stocks tab for currencies and for foreign emerging stock markets for the latest moves.

The currencies are all generally dropping against the US Dollar (with the Canadian FXC and Mexican FXM being mostly flat rolling).

Indonesia Fund 1 Year Chart

Indonesia Fund 1 Year Chart

This chart compares FXI – China 25 big stocks, EWZ – Brazil, EWO – Austria, EPI – Wisdom Tree India fund, and the Indonesia fund.

The “Emerging Market” trades have gone flat or are dropping. Time to be in cash or elsewhere in “watchful waiting”.

VIX the Volatility Index

Volatility Index and Related

Volatility Index and Related

Low, very low. It is saying “time to be sold”. We can buy back in again on one of those little blip / peaks to the upside. I’ve added a couple of tickers for some interesting sectors (such as transports, that are often a leading economic indicator) too.

VIX  - Volatility Index (not a ticker, you can't trade it)
VXX - Short term VIX futures ETN (a ticker you can trade)
VXZ - Medium term VIX futures ETN (a ticker you can trade)
FXY - Japanese Yen
SH - "Short" sell of SPY
SPY - S&P 500 benchmark
IYT - Transports
XHB - Homebuilders
XRT - Retail

Notice that Transports are continuing to rise against the broad market (SPY) flat trend. Retail is doing better (it often runs up through Christmas and into the middle / end of January) and home builders are showing some signs of renewed life. All part of the “We’re Not Dead Yet, We’re Getting Better!” recovery trade.

The Dollar

I’m going to turn this chart around from the prior version. This is now a ‘US Dollar UP” trade chart of UUP instead of UDN.

Dollar Trade -UP

Dollar Trade -UP

Notice we have crossed the SMA stack to the top side. DMI is rising strongly into 20 headed for 25 with ADX+ (blue) on top. For now, it’s US Dollar time. RSI is not yet at 80, so the run has some more room (but remember that this kind of rollover to the topside will return to the SMA stack and that the SMA stack must ‘invert’ or roll over to confirm the run ‘has legs’. MACD is also “blue on top” and is above zero. Again, a bullish call on the US Dollar.

Ideas of the Week

Spend this week doing your ‘end of year’ evaluation. What was right, what was not. Where could you have done better. Plan for the new year. What will the big money dancing elephants be doing then? (And expect that as they spend the US Dollars they are presently sitting on, the US Dollar Up trade will soften or maybe even end.)

What does the 10 day hourly chart say is happening now?

Here’s a 10 day houly chart of the Dow 30 Industrials (DIA), the S&P 500 (SPY), the Nasdaq tech companies (QQQQ), the Russel 2000 (RUT), and both a Brazil fund (EWZ) and an Australia fund (EWA). It also has a ‘short fund’ (SH) on the chart so you can see what being short this market is doing right now. We also have EWO, an emerging Europe Austria fund, EWW for Mexico and IIF for India.

10 Day Hourly Interval Broad Market

10 Day Hourly Interval Broad Market

Shorting the Emerging Markets would have worked (though I’m not fond of shorting thin markets like holidays) and we see that Nasdaq 100 (QQQQ) and Russell 2000 (RUT / IWM ) are both doing better than the broad market. So watch for a Tech / Small Cap outperform trend to develop. If it does, it can be traded. (RUT is an index, IWM is an ETF that follows that index)

Other Asset Classes

The 6 month asset class race:

Asset Class Race

Asset Class Race


SPY  S & P 500 US stocks
GLD  Gold
EEM  Emerging Markets
FXY  Japanese Yen
JJC  Copper
SHY  Short term bonds 1-3 year
USO  U.S. Oil
DBA  Agricultural basket
SLV  Silver
WOOD  Wood / Timber

Not much news here. WOOD rising, stocks flat, everything else in the list weak or falling.

So what happened in the Tech Market relative to world markets?

Tech is beating the US large stock Markets, but weakening. Mid Caps (MDY) and broad market (RUT / IWM) are starting to show a bit more life. Tech is holding up better that foreign markets, for now at least. I still like cash, trees, and shorting bonds better, though. At least for now. You can get some short choppy trades out of it, but that’s a lot like work…

Tech vs Other Markets

Tech vs Other Markets


QQQQ  Nasdaq 100 mostly Tech companies
DIA  Dow Jones 30 Industrials
SPY  S & P 500 largest companies in the U.S.A.
MDY  Midcap  (Middle sized in terms of market capitalization)
RUT  Russel 2000 - a collection of 2000 companies from small to large.
EWZ  Brazil fund
EWA  Australia fund
EWO  Austria fund
EWW  Mexico fund

Were Bonds a good idea?

OK, lets take a peak at the Bonds Race but with TBT (the “long term bonds” short sell ETN – that is, the thing that “shorts bonds”) as the main ticker symbol:

Bonds - TBT to Short Them

Bonds - TBT to Short Them

Clearly, bonds were not a good place to be, especially long term bonds. The “short sell bonds” trade, while not exciting, has an entry call.

What sectors won this week?

10 Best Performing Industries

TO BE ADDED LATER

What About Oils?


XOM  Exxon Mobil - Largest, U.S. / Global
COP  Conoco Philips - U.S.  with Russian exposure
CVX  Chevron Texaco - U.S.
PBR  Petrobras - Brazil
PCZ  Petro Canada HAS NOW MERGED WITH SU SUNCOR
BP   British Petroleum
STO  Norway
E    Eni Italy
TOT  Total - France
RDSA  Royal Dutch Shell
IMO  Imperial Oil - Canada Oil and Oil Sands
SU   Suncor - Canadian Oil Sands
SSL  Sasol - South African Synthetic Oil Company

The action here was Exxon bought XTO, a major US Nat.Gas company. XTO jumped, but that trade is over. Exxon dropped (common when a company spends a bundle of cash) and that probably gives a good entry point for Exxon (especially for fast traders OR very long term investors… “It’s on sale cheap, so buy some” works for both styles.)

Some Near Oil and Oil Related Comparisions

.

Sugar moving up, if in fits and starts, is the interesting thing here.

SEA - A Boatload of Boats ETF

SEA - A Boatload of Boats ETF

Transports in general are a good early indicator. Boats, rail, maybe even some trucking. Anyone for a FDX / UPS Christmas?

The REITS race – Real Estate Investment Trusts

REITS Race

REITS Race


PEI  Pennsylvania Real Estate - Mall REIT
VTR  Ventas - sr. care, nursing homes, hospitals
PSA  Public Storage - junk storage units
BXP  Boston Properties - office REIT on BosWash corridor
HCN  Health Care REIT -  extended care, senior care, medical offices
HCP  Health Care Properties - ex. care, senior living, Dr. offices
PCL  Plum Creek Timber - lumber and trees REIT
SPY  S & P 500 broad stock market benchmark
RPT  Ramco Mall REIT
PLD  Prologis - logistics

PLD is in a very nice run. Mall REITs look nice too. Whole new meaning to ‘Mall Shopping’ eh?

Conclusions and Likely Actions

Holding cash and big dividend paying positions in well capilalized companies (like LNG tankers and oil trusts). Picking up a little realestate cheap. Watching for the “sector rotation” and doing new year planning.

Stock Indicators – what and how


If all this talk of indicators is leaving you wondering what the heck I’m talking about, hit the link in the heading of this section and there is a bit of an explanation.

Click for Disclaimers, Disclosures, and Where To Get Charts

Remember that on any stock or ticker I say I’m looking at, you don’t just go buy it. You wait for a stock entry indication to get the best possible entry into the position.

Graph of USA JetA fuel usage vs. Gobal temps rising together

Jet-A fuel use correlation with Global Temperatures is rather good

Original Image, full sized

From the “Correlation is not Causality” department we have this marvelous little graph. It shows the correlation between USA Jet-A fuel usage and the global temperature. Notice that unlike global CO2 levels, the Jet-A usage rises before the global average temperature.

I’m sure you could try to spin this as jet fuel being a producer of CO2 and so a proxy for CO2 yada yada. I think all that jet exhaust from burning kilotons of kerosene right next to the airport thermometers is a more “proximal causality”… If you want to start assigning causalities, one typically looks at the closest and least diluted causality first.

This graph was originally posted in comments by B. Louis79 on this article:

http://chiefio.wordpress.com/2009/08/26/agw-gistemp-measure-jet-age-airport-growth/#comment-1987

And another related article or three would be:

http://chiefio.wordpress.com/2009/08/23/gistemp-fixes-uhi-using-airports-as-rural/

http://chiefio.wordpress.com/2009/09/04/most-used-rural-airport-for-uhi-adj/

So what happens when your “Rural Reference Station” for Urban Heat Island effect is artificially heated with Kilotons of burning kerosene? Hmmmm? Think that might bias your “global warming” figure higher than reality?

I just love the island airport picture at the top of this one:

http://chiefio.wordpress.com/2009/09/08/gistemp-islands-in-the-sun/

But Wait, There’s More

In the same article comment thread, Verity Jones, of Digging In The Clay, chipped in with this graph:

Plot of Oil with Food Too

Oil With Food Too

Orginal Graph, full sized

Given that the thermometers that are NOT at airports tend to be in towns (near all the cars burning all that oil) it makes sense to look at the fuel BURNING in proximity to the thermometers… And a lot of “fuel” gets consumed on the farm, too. A field of cows has a fairly large amount of oxidation going on.

And, of course, that farm production ends up, eventually, finally, being oxidized as “poo”. Sewage Plant thermometers anyone? Yes, all those sewage plant thermometers found by www.surfacestations.org, where “poo” undergoes a final oxidation, do generate heat. Lots of it.

Some of them even have a ‘methane flare’ where they burn up the methane produced. Others run it through a co-generation plant to make power to run the place. For example, from this article we have:

The plant, which treats 4,500 liters of sewage per second, will use the ultra low-emission microturbines in a combined heat and power (CHP) application. In addition to producing 2.4 megawatts of electricity to power the plant’s equipment and buildings, the excess heat produced by the methane fueled microturbines will help maintain the proper temperature in the plant’s onsite digester used to breakdown sewage.

When you are measuring PART of the heat in megawatts, you can “feel the heat”.

(Disclosure: I own some stock in CPST Capstone Turbine. Not a lot, more of a “toy” position, but it interests me. That’s part of why I know about this topic.)

So I’m left to wonder how many “rural” thermometer are near all the kilotons of hay, silage, and feed corn being “burned” in animals… and sewage. Then again, the number of thermometers at truly rural places and not at airports has been crashing over time:

http://chiefio.wordpress.com/2009/12/08/ncdc-ghcn-airports-by-year-by-latitude/

So maybe “at airports” is the more important place to “Dig Here!” … Then again: megawatts PLUS the thermal component? That sure looks like a “Dig Here!” to me too… “SHI” seems like a suitable acronym for “Sewage Heat Island”. Just don’t put “Thermal effect” on the end of it OK? 8-)

Take just a couple of minutes to visit this site. It is a nice little interactive globe with “spots” you can click on. Each one brings up a very long lived thermometer and lets you see what we know with certainty about the temperature history of the planet. I particularly liked the contrast of NY the City with West Point and the Bahamas, but a lot of other places were fun too. It is a marvelous toy… and just a little bit addictive as you explore the planet. But the good news is that there is not much long lived history to explore so you won’t end up doing it for hours on end… well, not without some repeats ;-).

http://climatereason.com/LittleIceAgeThermometers/

It is a fascinating way to see all the places showing what has not warmed, and those urbanized places where oceans of tarmac have shown warming, but due to the growth of cities and not CO2.

When you are done with that, there is an amusing posting here:

http://diggingintheclay.blogspot.com/2009/11/gistemp-reloaded.html

that leaves me a bit flattered, and with a bit of a giggle. ;-)

That site also has an interesting article about comparing charts of thermometers found in the leaked CRU emails with links to several other postings about them on Strata-sphere.com blog with a nice preamble and it collects the strata-sphere links together in one place.

Are NCDC and CRU really independent?

From 1248902393.txt where there is a letter from Phil Jones to Thomas Peterson quoting an earlier letter from Peterson to Phil. Some uninteresting bits were removed (marked by [...] )


From: Phil Jones

To: Thomas.C.Peterson@noaa.gov
Subject: Re: This and that
Date: Wed Jul 29 17:19:53 2009

Tom,
[...]
At 17:07 29/07/2009, you wrote:

Hi, Phil,
Yes, Friday-Saturday I noticed that ClimateFraudit had renewed their
interest in you. I was thinking about sending an email of sympathy, but
I was busy preparing for a quick trip to Hawaii – I left Monday morning
and flew out Tuesday evening and am now in the Houston airport on my way
home.
Data that we can’t release is a tricky thing here at NCDC. Periodically,
Tom Karl will twist my arm to release data that would violate agreements
and therefore hurt us in the long run, so I would prefer that you don’t
specifically cite me or NCDC in this.

So here we see that NCDC is in bed with Phil. Nice chummy “insider code” between them with the hacked name for the site they don’t like (one presumes http://climateaudit.org/ ).

We also see that NCDC is unwilling / unable to release some data as well. Then he askes for anonymity… Okay… When relationships can’t stand the light of day (even if only because it would put pressure on them to release data they have agreed to keep secret) it is a bit disconcerting…

So much for NCDC being “independent”…

The Undiscovered Data

A bit further down:


So far as far as I know, we have all lived up to that
agreement – myself with the Caribbean data (so that is one example of
data I have that are not released by NCDC), Lucie and Malcolm for South
America, Enric for Central America, Xuebin for Middle Eastern data,
Albert for south/central Asian data, John Ceasar for SE Asia, Enric
again for central Africa, etc. The point being that such agreements are
common and are the only way that we have access to quantitative insights
into climate change in many parts of the world. Many countries don’t
mind the release of derived products such as your gridded field or
Xuebin’s ETCCDI indices, but very much object to the release of actual
data (which they might sell to potential users). Does that help?

So the world is going to spend a few trillion in part because some Banana Republic might want to make a few bucks off of the raw data…

Ooh Kaayy… But we do get a nice list of who most likely cooked each continent / country…

But at least the IPCC is an independent Agent, Right?

Then we get this nice chummy exchange fishing for an appointment:


Regarding AR4, I would like to be part of it. I have no idea what role
would be deemed appropriate. One thing I noticed with the CLAs in my
old chapter is that if one isn’t up to doing his part (too busy, or a
different concept of timeliness, or …) it can make for a difficult
job. You and I have worked well together before (e.g., GSN) so I’d be
delighted to work with you on it and I know you’d hold up your side of
the tasks. We touched on this briefly at the AOPC meeting. If I get an
opportunity, I would say yes.
But I also don’t know what the U.S. IPCC nominating approach would be or
even who decides that. There is an upcoming IPCC report on extremes and
impacts of extremes and I wasn’t privy to any insights into the U.S.
nominations other than when it was over it was announced in NCDC staff
notes that the nominations had been made. However, Kumar had earlier
asked if he could nominate me, so he did (I provided him with the details).
Regards,
Tom

And we again get confirmation that these folks ‘work well together’… and like to pat each other on the back via getting each other appointed to authority positions. So exactly how can NCDC be “independent” of UEA CRU when they are working so “hand in sock puppet” together?

The MIssing Data and The Leaked Emails / data files

Then Phil responds


Tom,

If you look on Climate Audit you will see that I’m all over it!
Our ftp site is regularly trawled as I guess yours is. It seems that
a Canadian along with two Americans copied some files we put there
for MOHC in early 2003. So saying they have the CRU data is not
quite correct. What they have is our raw data for CRUTEM2 which
went into Jones and Moberg (2003) – data through end of 2002.
Anyway enough of my problems – I have a question for you. I’m
going to write a small document for our web site to satisfy (probably the
wrong word) the 50 or so FOI/EIR requests we’ve had over the weekend.
I will put up the various agreements we have with Met Services.

Two things here. First, they had Raw Data through the year 2002 in the year 2003. So much for that “lost the data in the 1980’s building move” story. Now I don’t know what the nuance is between CRU and CRUTEM2, but clearly they didn’t lose everything.

Second, they put FOI/EIR request data on their web server. Yes, the same kind of FOI request that the FOIA “leaked” file seems to be. And the same web server they shut down after the leak. My take on this is that someone messed up the permissions in an FOIA file they were preparing for a request, and it got released when they thought they were locking it down (after the request was denied.) That “hacking” story is just too lame.

Then:

But at least the IPCC is independent, right?


The question – I think you told me one time that you had a file
containing all the data you couldn’t release (i.e. it’s not in GHCN). Presumably
this is not in your gridded datasets? Do you know off hand how much
data is in this category? Would NCDC mind if I mentioned that you
have such data – not the amount/locations/anything, just that there is some?

And not only do NCDC have the (substantially duplicate) same data as CRUT, they have some secret sauce data too…

Then follows a chummy discussion of who ought to cook what part of the IPCC report. Again we find that the IPCC is NOT independent of CRU nor of NCDC. They are all in bed with each other. Phil deciding what part of the IPCC AR5 report he want to write. Soliciting to find out if NCDC wants to write a chunk.

On something positive – attached is the outlines for the proposed Chs in AR5/WG1.
Ch1 is something Thomas thinks he can write himself – well with Qin Dahe, so
only 13 chapters. There are a lot of issues with overlaps between some of the
data chapters 2 with 3, 2 with 5 and 2 with 14.
I’m still thinking about whether to get involved. It would be 2 if I decide. At the
moment I’d say yes, but I might change my mind tomorrow! Nominations are
from Nov09 thru Jan10 with the selection made in April 10. Are you considering
getting involved?

And then it gets nicely juicy. A tiny conspiracy to figure out how to shield Phil and “others” from FOI requests with the collusion of the IPCC.


I have got the IPCC Secretariat and Thomas to raise the FOI issues with
the full IPCC Plenary, which meets in Bali in September or October. Thomas
is fully aware of all the issues we’ve had here wrt Ch 6 last time, and others in
the US have had.
Cheers
Phil
Prof. Phil Jones
Climatic Research Unit Telephone +44 (0) 1603 592090
School of Environmental Sciences Fax +44 (0) 1603 507784
University of East Anglia
Norwich Email p.jones@uea.ac.uk
NR4 7TJ

Oh. I guess not…

Watch The Pea, Lose the Purse

Watch The Pea, Lose the Purse

It’s just a little bit of “magic” from an Illusionist… You know, the “old shell game”.

Original Image

So, They Released the Data, Right?

Well, sort of…

When you read the press reports, it sounds like there was a release of the 1500 or so sites that were well sited, yet not subject to legal restrictions on data sharing. It also sounds like they are releasing the basic data for all of us to look at it.

The web page that gives you the data:

http://www.metoffice.gov.uk/climatechange/science/monitoring/subsets.html

says:


The data subset consists of a network of individual land stations that has been designated by the World Meteorological Organization for use in climate monitoring. The data show monthly average temperature values for over 1,500 land stations.

“The data” “individual land stations” “monthly average temperature values”. It all sounds like they are releasing the temperature data…

But…

There is a link near the top of that page that mentions this is a subset of the HadCRUT3 data set… “But I thought HadCRUT3 was a product, not the “raw” data?”

And right you are… When you click through that link, you find that the “data” they have released is a partial subset of the output of the CRU Code, not the input. From:

http://www.metoffice.gov.uk/climatechange/science/monitoring/hadcrut3.html

I’ve bolded the key words:


HadCRUT3: Global surface temperatures

HadCRUT3 is a globally gridded product of near-surface temperatures, consisting of annual differences from 1961-90 normals. It covers the period 1850 to present and is updated monthly.

The data set is based on regular measurements of air temperature at a global network of long-term land stations and on sea-surface temperatures measured from ships and buoys. Global near-surface temperatures may also be reported as the differences from the average values at the beginning of the 20th century.

So this is the product and not the data. It has the HadCRUt 1850 cutoff in it. It is based on measurements and it not itself a measurement of anything. This is not the temperature data, this is the homogenized pasteurized processed data food product.

Nice deflection. Nice packaging that looks like it is releasing the data while not quite lying. Bad form. Very Bad Form.

Try again, please…

One is left to wonder if they subscribe to the broken behaviours of “Post Normal Science”. It looks like it… A good read on that subject (that does seem to explain the broken moral compass at CRU and Met Office) is here:

http://i-squared.blogspot.com/2009/12/green-snake-in-grass.html

The Bright Idea

I re-hacked the ‘by latitude’ program to figure out what percentage of stations are at airports and report that. This is a bit hobbled by the primitive data structure of the “station inventory” file. It only stores an “Airstation” flag for the current state. Because of this, any given location that was an open field in 1890 but became an airport in 1970 will show up as an airport in 1890. Basically, any trend to “more airports” is understated. Many of the early “airports” are likely old military army fields that eventually got an airport added in later years.

With that caveat, the charts are rather interesting. I’ll be adding more of these over time. This is just the first cut. Well, not completely the first cut, we did look at airports in a different way in 3 earlier postings.

This one does an aggregate of all the data by a few limited climate bands and as a total of all data.

http://chiefio.wordpress.com/2009/08/26/agw-gistemp-measure-jet-age-airport-growth/

And these two look at how GIStemp uses those airports as pristine “rural” areas to adjust other areas for Urban Heat Island Effect. A completely broken behaviour.

http://chiefio.wordpress.com/2009/08/23/gistemp-fixes-uhi-using-airports-as-rural/

http://chiefio.wordpress.com/2009/09/04/most-used-rural-airport-for-uhi-adj/

But it is the first time for this particular report.

The Pacific, New Zealand, and Australia

One would expect to find a lot of airports on islands. Much more than in, say, Europe where there are many passenger rail services. The actual numbers were a bit startling anyway. Here is the entire “5″ region, that includes the Pacific basin (exclusive of Hawaii and Japan). Australia, New Zealand, Indonesia, Philippines, and all those lovely pacific islands.

      Year SP -35  -30  -25  -20  -15  -10   -5    5   10  -NP
DArPct: 1839  0.0  0.0  0.0  0.0  0.0  0.0  0.0100.0  0.0  0.0100.0
DArPct: 1849 81.8  0.0  0.0  0.0  0.0  0.0  0.0 18.2  0.0  0.0100.0
DArPct: 1859 27.8 16.7  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 44.4
DArPct: 1869 13.3  8.3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 21.7
DArPct: 1879 12.9  4.0  0.0  0.4  0.0  0.0  0.0  2.4  0.0  0.0 19.7
DArPct: 1889  8.4  2.1  0.2  2.9  0.0  1.7  0.0  1.5  2.1  1.5 20.4
DArPct: 1899  6.8  1.5  0.4  3.5  0.0  1.5  0.0  1.3  0.9  2.6 18.5
DArPct: 1909  4.9  1.2  0.4  1.9  0.7  0.7  0.5  0.7  0.4  1.2 12.6
DArPct: 1919  3.1  2.1  1.3  2.6  1.2  0.4  0.4  0.7  0.4  0.8 13.0
DArPct: 1929  2.5  2.2  1.3  2.5  1.5  0.4  0.4  0.4  0.9  1.0 13.1
DArPct: 1939  2.4  2.4  1.4  2.3  2.4  0.6  0.7  1.1  1.8  1.0 16.1
DArPct: 1949  3.9  3.8  2.6  3.1  3.1  1.0  1.0  1.3  1.1  1.0 21.8
DArPct: 1959  4.6  4.1  3.1  3.9  3.1  1.8  2.8  3.8  4.5  4.5 36.1
DArPct: 1969  4.9  4.5  2.8  4.0  2.9  2.6  5.8  8.3  4.1  4.2 44.2
DArPct: 1979  5.6  5.3  2.9  4.0  3.7  3.3  3.6  5.4  3.6  3.4 40.8
DArPct: 1989  5.9  6.3  3.4  4.6  4.5  3.8  3.6  4.3  3.6  2.6 42.7
DArPct: 1999  7.6  7.2  4.0  6.3  5.1  3.9  3.2  5.3  6.1  3.5 52.2
DArPct: 2009 10.7  8.3  5.1  7.8  5.6  4.8  4.1 11.1  9.4  4.5 71.3

For COUNTRY CODE: 5
From source ./vetted/v2.inv.id.withlat

The right most column is the percentage of total thermometer stations that had the “A” for Airstn flag set for the stations present in that year. (Some airports do not have the flag set, so these numbers will be low to some extent.) We can see the bogus value of 100% of stations being Airports in the decade ending in 1839. That just means that that particular station is an airport NOW, but was something else then. In 1909 we have a bit over 12% Airports. So we’ve added a lot of thermometers at places that never become an airport. From that point on, the percentage of thermometers at airports climbs. We end at 71.3% of all thermometers in the Pacific Basin are at airports. Kind of hard to find a “rural reference station” for urban heat island correction when everything is tarmac, parking lots, jet exhaust, and terminal buildings…

The other values show the percentage of the total thermometers for that region that are airports. This lets us see if there is any particular geographic bias to the airports. In this case, the cooler bottom band has a higher percentage (most likely due to Australia and New Zealand) and the ‘at the equator’ is a bit high too (that “more than -5 and less than 5 band at 11.1% )

We’ll zoom in a little on two countries in particular.

First, New Zealand:

This chart is by latitude bands from “South Pole up to 44 S” latitude to “above 36 S” labeled “NP” (as in “everything to the North Pole…)

      Year SP -44  -43  -42  -41  -40  -39  -38  -37  -36  -NP
DArPct: 1869  0.0 21.4 14.3  0.0  0.0  0.0  0.0 21.4  0.0  0.0 57.1
DArPct: 1879  0.0 23.1 19.2  0.0  0.0  0.0  0.0 19.2  0.0  0.0 61.5
DArPct: 1889  0.0 26.2  2.4  0.0  0.0  0.0  0.0 23.8  0.0  0.0 52.4
DArPct: 1899  0.0 21.7 13.0  0.0  0.0  0.0  0.0 21.7  0.0  0.0 56.5
DArPct: 1909  0.0 37.5 15.6  0.0  0.0  0.0  0.0 15.6  0.0  0.0 68.8
DArPct: 1919  0.0 33.3 16.7  0.0  0.0  0.0  0.0 16.7  0.0  0.0 66.7
DArPct: 1929  0.0 20.0 20.0  0.0  0.0  0.0  0.0 20.0  0.0  0.0 60.0
DArPct: 1939  0.0 23.1 19.2  0.0  0.0  0.0  0.0 19.2  0.0  0.0 61.5
DArPct: 1949  0.0 27.8  9.3  0.0  0.0  0.0  0.0  9.3  0.0  0.0 46.3
DArPct: 1959  6.1 23.5  4.7  4.2  0.0  4.2  0.0  4.7  4.2  0.0 51.6
DArPct: 1969  9.8 20.6  3.5  3.5  0.0  3.5  2.8  4.9  3.5  3.1 55.2
DArPct: 1979 11.6 20.3  5.7  3.0  0.0  5.7  3.0  6.0  3.0  5.7 63.9
DArPct: 1989 11.3 24.8  5.0  0.9  0.0  5.0  4.5 10.4  0.5  4.5 66.7
DArPct: 1999 12.3 23.6  9.4  0.0  0.0  9.4  9.4  6.6  0.0  9.4 80.2
DArPct: 2009 12.0 24.1 12.0  0.0  0.0 12.0 12.0  0.0  0.0 12.0 84.3

For COUNTRY CODE: 507
From source ./vetted/v2.inv.id.withlat

Clearly the 50 percent early values are places that started life as “flat but not an airport” fields and later got tarmac.

The startling value is that ending value of 84.3% Airports in New Zealand. This is one of the highest I’ve seen so far. The temperature in New Zealand IS the temperature at the airports. Especially the most southern, cold, latitudes. In fact, if we zoom in on that last year:

LATpct: 2009 12.5 25.0 12.5  0.0  0.0 12.5 12.5  0.0  0.0 25.0 100.0
AIRpct:      12.5 25.0 12.5  0.0  0.0 12.5 12.5  0.0  0.0 12.5 87.5

That LATpct is the percentage of total thermometers in a given latitude band. There is only one value, the most northernly, that has a higher percentage than the airports percentage. It looks like there is ONE non-airport thermometer in New Zealand. Looking at those stations still active in 2009, we find it is Raoul Island:

[chiefio@tubularbells analysis]$ more Temps/507.stns2009
50793012000 KAITAIA -35.13 173.27 87 91R -9FLxxCO 7A-9WARM DECIDUOUS A 0
50793292000 GISBORNE AERO -38.65 177.98 5 65S 30FLxxCO 2A 2WATER C 22
50793309000 NEW PLYMOUTH -39.02 174.18 32 0S 44FLxxCO 2A10WARM FIELD WOODSB 0
50793615000 HOKITIKA AERO -42.72 170.98 40 0R -9HIxxCO 1A-9WARM MIXED B 0
50793780000 CHRISTCHURCH -43.48 172.52 37 47U 165FLxxCO15A 2WARM CROPS C 13
50793844000 INVERCARGILL -46.70 168.55 4 28S 49FLxxCO 1A 1WARM MIXED A 0
50793987000 CHATHAM ISLAN -43.95 -176.57 49 0R -9HIxxCO 1A-9WATER A 0
50793994000 RAOUL ISLAND, -29.25 -177.92 49 0R -9HIxxCO 1x-9WATER A 0
[chiefio@tubularbells analysis]$

The only record with an “x” instead of an “A” in the Airstn field. Half way to Tonga and not what most folks think of when talking about New Zealand…

Australia

Not much better:

      Year SP -50  -45  -40  -35  -30  -25  -20  -15  -10  -NP
DArPct: 1849  0.0  0.0100.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0100.0
DArPct: 1859  0.0  0.0 27.8  0.0 16.7  0.0  0.0  0.0  0.0  0.0 44.4
DArPct: 1869  0.0  0.0  0.0  0.0 11.9  0.0  0.0  0.0  0.0  0.0 11.9
DArPct: 1879  0.0  0.0  0.0  0.0  5.8  0.0  0.6  0.0  0.0  0.0  6.4
DArPct: 1889  0.0  0.0  0.0  4.7  2.6  0.3  3.6  0.0  2.1  0.0 13.2
DArPct: 1899  0.0  0.0  0.0  3.5  1.8  0.5  4.2  0.0  1.8  0.0 11.8
DArPct: 1909  0.0  0.0  0.0  1.9  1.3  0.5  1.6  0.7  0.8  0.0  6.8
DArPct: 1919  0.0  0.0  0.0  1.7  2.3  1.2  2.0  1.3  0.4  0.0  8.8
DArPct: 1929  0.0  0.0  0.0  1.6  2.4  1.2  2.0  1.3  0.4  0.0  8.9
DArPct: 1939  0.0  0.0  0.0  1.5  2.8  1.2  1.9  1.5  0.4  0.0  9.2
DArPct: 1949  0.0  0.0  0.3  2.6  4.4  2.4  2.4  1.8  0.9  0.0 14.9
DArPct: 1959  0.0  0.0  0.8  3.6  6.5  4.0  3.5  2.4  1.0  0.0 21.9
DArPct: 1969  0.0  0.0  1.2  4.4  8.3  4.4  4.1  2.8  1.9  0.0 27.0
DArPct: 1979  0.0  0.0  1.2  4.1  8.2  3.8  3.6  3.2  2.2  0.0 26.3
DArPct: 1989  0.0  0.0  0.9  4.8  9.1  4.2  4.0  3.8  2.7  0.0 29.4
DArPct: 1999  0.0  0.0  1.8  5.9 11.5  5.5  5.6  4.4  3.0  0.0 37.7
DArPct: 2009  0.0  0.0  3.8 11.0 21.9 11.4 11.4  7.6  3.8  0.0 71.0

For COUNTRY CODE: 501
From source ./vetted/v2.inv.id.withlat

We once again have the “bogus high” as the first place with a thermometer became an airport. We drop to about 8% then start the climb to modernity. The “Odd Duck” here is that jump from 29% in decade ending 1989 to 71% today. “The Great Dying of Thermometers” seems to have spared those at airports in Australia…

South America

Has the migration of thermometers from the mountains to the beach also put them at airports?

      Year SP -50  -40  -35  -30  -25  -20  -15  -10   10  -NP
DArPct: 1849  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 55.6  0.0 55.6
DArPct: 1859  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 21.4  0.0 21.4
DArPct: 1869  0.0  0.0 34.5 31.0  0.0  0.0  0.0  0.0  0.0  0.0 65.5
DArPct: 1879  0.0  0.0 20.4 20.4  6.1  0.0  0.0  0.0  0.0  0.0 46.9
DArPct: 1889  2.6  0.0  5.2 13.0 13.0  0.0  0.0  0.0  0.0  0.0 33.8
DArPct: 1899  6.1  0.0  2.4  6.1 10.3  3.0  0.0  0.0  9.7  0.0 37.6
DArPct: 1909  9.8  6.6  2.7  2.7  6.4  5.0  2.4  0.0  5.3  0.0 40.8
DArPct: 1919  8.5  6.4  2.1  2.1  6.0  4.3  2.6  0.0  3.6  0.0 35.5
DArPct: 1929  8.1  6.1  2.0  2.0  6.1  4.0  4.0  0.0  4.0  0.0 36.4
DArPct: 1939  6.1  6.3  6.5  7.7  9.1  2.8  3.1  0.9  5.9  0.0 48.5
DArPct: 1949  4.4  4.6  7.4  7.0 10.7  4.4  3.5  0.9  8.5  0.0 51.4
DArPct: 1959  2.9  4.2  5.5  8.6  7.1  4.9  7.4  3.8 13.0  3.9 61.3
DArPct: 1969  2.3  5.1  4.2  8.6  5.7  4.7  6.1  6.1 18.3  3.3 64.4
DArPct: 1979  2.5  5.0  4.9  7.8  6.8  4.7  5.3  6.2 18.8  3.4 65.4
DArPct: 1989  3.0  5.5  5.4  9.6  6.8  4.6  5.7  5.4 18.4  3.5 67.9
DArPct: 1999  2.7  6.6  6.0 11.7  7.1  4.8  3.1  3.0 19.8  4.9 69.8
DArPct: 2009  1.9  6.5  6.1 12.1  7.6  4.9  2.6  2.3 18.4  5.4 67.9

For COUNTRY CODE: 3
From source ./vetted/v2.inv.id.withlat

Why, yes! About 68% now.

Africa

Well, at least Africa fares a bit better.

      Year SP -40  -30  -20  -10    0   10   20   30   40  -NP
DArPct: 1849  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
DArPct: 1859  0.0  9.7  0.0  0.0  0.0  0.0  0.0  0.0 64.5  0.0 74.2
DArPct: 1869  0.0 25.6  0.0  0.0  0.0  0.0  7.7  0.0 46.2  0.0 79.5
DArPct: 1879  0.0 11.9  0.0  0.0  0.0  7.1  8.3  1.2 14.3  0.0 42.9
DArPct: 1889  0.0  9.9  3.4  0.3  0.0  6.2  0.0  3.4 30.8  0.0 54.1
DArPct: 1899  0.0  7.1  4.1  3.0  1.6  8.3  2.5  3.2 23.1  0.0 52.7
DArPct: 1909  0.0  4.6  3.5  4.5  3.0  5.5  6.9  3.3 13.3  0.0 44.6
DArPct: 1919  0.0  3.8  4.1  4.7  2.2  4.2 10.0  5.0  7.6  0.0 41.7
DArPct: 1929  0.0  3.2  5.5  7.9  2.1  5.8 10.6  3.8  6.4  0.0 45.3
DArPct: 1939  0.0  2.7  6.5  7.3  2.7  4.6  8.9  4.3  7.7  0.0 44.7
DArPct: 1949  0.0  4.4  7.0  8.3  4.5  6.6 11.2  4.7  7.9  0.0 54.6
DArPct: 1959  0.0  2.3  5.2  8.5  7.1 14.5 14.6  3.3  5.2  0.0 60.7
DArPct: 1969  0.0  1.8  5.4  8.6  7.6 14.5 13.4  3.5  4.3  0.0 59.1
DArPct: 1979  0.0  2.0  6.1  7.7  6.4 14.5 16.2  2.9  5.0  0.0 60.8
DArPct: 1989  0.0  2.6  6.7  6.4  6.0 13.4 15.5  2.6  7.0  0.0 60.2
DArPct: 1999  0.0  1.8  5.2  5.6  4.8 11.0 16.6  3.6  9.7  0.0 58.2
DArPct: 2009  0.0  1.6  4.2  4.6  5.1 10.0 16.2  4.8 10.9  0.0 57.5

For COUNTRY CODE: 1
From source ./vetted/v2.inv.id.withlat

We peak at about 60% after the aviation age actually begins, then drift back down to 57.5% now. Still rather a lot. And we know that 1909 decade ending value was really a zero, so I think what we really have here is the fact that a lot of the African thermometers are at fields left over from the Colonial Occupation era and bases that became airports later.

Asia

Strangely, Asia is rather low on Airports.

      Year SP   0   10   20   30   40   50   60   70   80  -NP
DArPct: 1799  0.0  0.0100.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0100.0
DArPct: 1819  0.0  0.0 68.2  0.0  0.0  0.0  0.0  0.0  0.0  0.0 68.2
DArPct: 1829  0.0  0.0 28.6  0.0  0.0  0.0  0.0  0.0  0.0  0.0 28.6
DArPct: 1839  0.0  0.0 11.8  0.0  0.0  0.0  0.0  0.0  0.0  0.0 11.8
DArPct: 1849  0.0  0.0  3.1  0.0  1.5  0.0  0.0  0.0  0.0  0.0  4.6
DArPct: 1859  0.0  0.0  2.9  0.0  4.9  0.0  0.0  0.0  0.0  0.0  7.8
DArPct: 1869  0.0  0.6  1.1  0.0  4.0  4.0  0.0  0.0  0.0  0.0  9.6
DArPct: 1879  0.0  2.5  3.7  4.7 10.3  4.9  0.0  0.0  0.0  0.0 26.1
DArPct: 1889  0.0  0.8  2.4  4.8  6.5  3.8  0.7  3.8  0.0  0.0 22.8
DArPct: 1899  0.0  0.4  1.9  2.9  3.4  2.9  3.7  4.9  0.0  0.0 20.1
DArPct: 1909  0.0  0.3  1.9  3.7  3.3  2.4  3.4  5.6  0.0  0.0 20.5
DArPct: 1919  0.0  0.2  1.8  3.3  2.9  2.7  3.3  4.3  0.2  0.0 18.7
DArPct: 1929  0.0  0.2  1.5  2.9  2.7  2.8  2.9  3.8  0.4  0.0 17.3
DArPct: 1939  0.0  0.1  1.0  3.5  3.4  2.6  3.5  5.2  0.6  0.0 19.8
DArPct: 1949  0.0  0.1  1.3  3.5  3.6  2.9  3.3  5.9  0.8  0.0 21.3
DArPct: 1959  0.0  0.3  2.7  4.7  4.1  2.3  2.5  4.5  0.8  0.0 21.9
DArPct: 1969  0.0  0.3  2.7  4.8  4.4  2.0  2.0  4.0  0.9  0.0 21.0
DArPct: 1979  0.0  0.2  2.2  4.4  4.1  2.0  2.0  4.1  0.8  0.0 19.8
DArPct: 1989  0.0  0.1  1.6  4.3  3.3  2.1  2.0  4.3  0.8  0.0 18.6
DArPct: 1999  0.0  0.2  4.6  8.4  5.3  1.9  1.3  3.2  0.8  0.0 25.8
DArPct: 2009  0.0  0.3  5.5  9.3  5.5  2.3  1.8  3.9  0.8  0.0 29.2

For COUNTRY CODE: 2
From source ./vetted/v2.inv.id.withlat

“Only” 29% at Airports. One is left to wonder to what extent Russian Siberia and China were paranoid about advertising the location of their airports and may have “fudged” the airstation flag (or even the LAT LONG values). But that “Dig Here” will have to wait for another day…

Europe

More than I’d expected, though less than the Pacific.

      Year SP  35   40   45   50   55   60   65   70   75  -NP
DArPct: 1709  0.0  0.0  0.0  0.0 69.2  0.0  0.0  0.0  0.0  0.0 69.2
DArPct: 1719  0.0  0.0  0.0  0.0  9.1  0.0  0.0  0.0  0.0  0.0  9.1
DArPct: 1729  0.0  0.0  0.0  0.0 16.7  0.0  0.0  0.0  0.0  0.0 16.7
DArPct: 1739  0.0  0.0  0.0  0.0 47.6  4.8  0.0  0.0  0.0  0.0 52.4
DArPct: 1749  0.0  0.0  0.0  0.0 30.3 30.3  0.0  0.0  0.0  0.0 60.6
DArPct: 1759  0.0  0.0  0.0  8.8 13.8 10.0 12.5  0.0  0.0  0.0 45.0
DArPct: 1769  0.0  0.0  0.0 11.6 20.4  0.0  1.4  0.0  0.0  0.0 33.3
DArPct: 1779  0.0  0.0  0.0  9.6 17.7  0.0  1.9  0.0  0.0  0.0 29.2
DArPct: 1789  0.0  0.0  1.3 10.0 15.2  0.0  2.6  0.0  0.0  0.0 29.0
DArPct: 1799  0.0  0.0  0.0 12.0 14.2  0.0  0.0  0.0  0.0  0.0 26.2
DArPct: 1809  0.0  0.0  1.0 13.7 11.8  1.0  0.0  0.0  0.0  0.0 27.5
DArPct: 1819  0.0  0.0  3.8 12.9 10.9  2.2  0.0  0.5  0.0  0.0 30.3
DArPct: 1829  0.0  0.0  2.5 12.6  8.5  0.7  0.0  1.2  0.0  0.0 25.5
DArPct: 1839  0.0  0.0  1.3  9.4  9.5  2.7  0.0  0.7  0.0  0.0 23.7
DArPct: 1849  0.0  0.0  3.3  8.3  8.5  3.2  0.0  0.4  0.0  0.0 23.6
DArPct: 1859  0.0  0.6  3.7  9.6  6.7  2.1  0.0  0.7  0.0  0.0 23.4
DArPct: 1869  0.4  1.3  4.0  7.7  6.7  1.8  0.0  0.9  0.0  0.0 22.8
DArPct: 1879  0.6  2.2  4.9  7.6  6.8  1.8  0.5  2.6  0.0  0.0 26.9
DArPct: 1889  0.8  2.2  4.5  8.7  6.1  2.8  0.7  2.2  0.0  0.0 28.1
DArPct: 1899  0.7  2.7  5.0  8.3  6.7  3.5  0.7  1.8  0.0  0.0 29.5
DArPct: 1909  0.7  3.2  4.0  7.7  7.0  3.9  1.1  1.9  0.4  0.0 29.9
DArPct: 1919  0.4  3.0  4.0  7.4  7.1  3.4  1.6  2.2  0.4  0.0 29.4
DArPct: 1929  0.6  3.0  4.0  7.6  7.6  3.1  1.6  2.0  0.9  0.0 30.3
DArPct: 1939  0.6  3.0  3.5  6.8  6.7  3.7  1.5  1.5  0.9  0.0 28.3
DArPct: 1949  0.8  3.7  4.1  6.3  5.8  3.9  1.4  1.3  0.6  0.0 27.9
DArPct: 1959  1.6  5.4  6.2 10.2  7.7  3.1  2.4  0.9  0.4  0.0 37.9
DArPct: 1969  1.7  6.2  6.7  9.1  7.0  2.8  2.5  0.8  0.4  0.0 37.2
DArPct: 1979  1.7  5.9  6.8  8.9  6.1  2.8  2.5  0.5  0.4  0.0 35.6
DArPct: 1989  1.4  5.8  5.5  9.2  6.8  3.4  2.6  0.5  0.4  0.2 35.8
DArPct: 1999  2.8  8.9  5.6 11.0  8.4  4.4  4.1  1.0  0.7  0.3 47.2
DArPct: 2009  2.8  8.7  4.5 11.1  8.2  3.8  4.0  1.1  0.7  0.3 45.2

For COUNTRY CODE: 6
From source ./vetted/v2.inv.id.withlat

We again have the bogus early values. (Airports in 1709? I don’t think so! But that is what GHCN thinks…) The more interesting bit is the rise from 28% in 1949 to 45% today. Almost half the thermometers in Europe are at airports.

North America

To finish out the set, we will look at North America.

      Year SP   0   10   20   30   40   50   60   70   80  -NP
DArPct: 1749  0.0  0.0  0.0  0.0  0.0100.0  0.0  0.0  0.0  0.0100.0
DArPct: 1759  0.0  0.0  0.0  0.0  0.0 83.3  0.0  0.0  0.0  0.0 83.3
DArPct: 1769  0.0  0.0  0.0  0.0  0.0 66.7  0.0  0.0  0.0  0.0 66.7
DArPct: 1779  0.0  0.0  0.0  0.0  0.0 45.5  0.0  0.0  0.0  0.0 45.5
DArPct: 1789  0.0  0.0  0.0  0.0  0.0 75.0  0.0  0.0  0.0  0.0 75.0
DArPct: 1799  0.0  0.0  0.0  0.0  0.0 80.8  0.0  0.0  0.0  0.0 80.8
DArPct: 1809  0.0  0.0  0.0  0.0  0.0 69.0  0.0  0.0  0.0  0.0 69.0
DArPct: 1819  0.0  0.0  0.0  0.0  0.0 46.3  0.0  0.0  0.0  0.0 46.3
DArPct: 1829  0.0  0.0  0.0  2.8  7.7 33.1  0.0  0.0  0.0  0.0 43.6
DArPct: 1839  0.0  0.0  0.0  4.0  7.7 25.1  0.3  0.0  0.0  0.0 37.0
DArPct: 1849  0.0  0.0  0.0  3.0  7.6 23.0  1.0  0.0  0.0  0.0 34.6
DArPct: 1859  0.0  0.0  1.0  4.5 15.2 15.6  0.0  0.0  0.0  0.0 36.3
DArPct: 1869  0.0  0.0  1.0  1.6 12.2 16.7  0.5  0.4  0.0  0.0 32.3
DArPct: 1879  0.0  0.0  0.1  1.5 15.1 19.6  1.2  0.6  0.3  0.0 38.4
DArPct: 1889  0.0  0.1  0.2  1.3 14.6 16.8  3.3  0.5  0.2  0.0 37.1
DArPct: 1899  0.0  0.0  0.4  1.0 11.0 13.1  3.2  0.5  0.1  0.0 29.3
DArPct: 1909  0.0  0.0  0.4  0.9  9.2 10.7  3.2  1.0  0.1  0.0 25.5
DArPct: 1919  0.0  0.1  0.3  0.8  8.2  9.9  4.1  1.6  0.1  0.0 25.0
DArPct: 1929  0.0  0.1  0.4  0.9  7.6  9.4  4.5  1.8  0.2  0.0 24.9
DArPct: 1939  0.0  0.1  0.5  0.9  7.4  9.1  4.5  2.6  0.3  0.0 25.4
DArPct: 1949  0.0  0.1  0.6  1.2  8.6 10.1  5.6  3.6  0.5  0.0 30.3
DArPct: 1959  0.0  0.1  1.9  2.2 12.8 11.9  5.2  4.1  1.0  0.1 39.4
DArPct: 1969  0.0  0.1  2.4  2.4 13.0 12.6  5.6  4.5  1.1  0.2 41.8
DArPct: 1979  0.0  0.1  2.7  2.1 11.0 12.2  6.2  4.2  1.1  0.2 39.8
DArPct: 1989  0.0  0.0  1.8  2.1 12.7 13.4  5.5  4.0  1.0  0.1 40.7
DArPct: 1999  0.0  0.0  1.3  2.3 15.3 13.6  1.6  1.8  0.4  0.0 36.3
DArPct: 2009  0.0  0.0  1.6  2.0 13.2 11.9  2.0  1.6  0.4  0.0 32.5

For COUNTRY CODE: 4
From source ./vetted/v2.inv.id.withlat

Surprisingly stable at about 1/3 more or less.

Though when we peek inside the last years when USHCN was not added in by GIStemp, we find a more interesting story:

LATpct: 2006  0.0  0.0  2.4  4.9 44.2 44.4  2.0  1.6  0.4  0.1 100.0
AIRpct:       0.0  0.0  1.3  1.3  9.5  9.3  1.6  1.1  0.3  0.0 24.4
LATpct: 2007  0.0  0.0 13.2 12.8 26.0 25.1 11.9  8.5  2.1  0.4 100.0
AIRpct:       0.0  0.0  6.8  5.5 24.7 19.6  8.9  6.0  1.7  0.0 73.2 
LATpct: 2008  0.0  0.0 14.1 13.7 25.0 25.0 11.7  8.1  2.0  0.4 100.0
AIRpct:       0.0  0.0  7.7  5.6 23.8 19.0  9.3  5.6  1.6  0.0 72.6 
LATpct: 2009  0.0  0.0 14.7 13.8 26.7 26.3 11.6  5.2  1.3  0.4 100.0
AIRpct:       0.0  0.0  7.8  5.6 25.4 19.8  9.5  3.0  0.9  0.0 72.0 

DLaPct: 2009  0.0  0.0  2.7  5.5 43.7 42.9  2.5  2.2  0.4  0.1 100.0
DArPct:       0.0  0.0  1.6  2.0 13.2 11.9  2.0  1.6  0.4  0.0 32.5

For COUNTRY CODE: 4

So GHCN by itself is at 72% of thermometers at Airports. Only the addition of the USHCN data set in the USA, that ended in 2007, keeps the decade average fairly low. As an interesting “someday” report I may go back and make a “without USHCN” report (but that takes a new input data set and will have to wait…)

Mexico

Interesting to see is that Mexico is fairly low in airports:

      Year SP  15   17   19   21   23   25   27   29   31  -NP
DArPct: 1879  0.0  0.0  0.0 50.0  0.0  0.0  0.0  0.0  0.0  0.0 50.0
DArPct: 1889  0.0  0.0  0.0 34.5  0.0  0.0  0.0  0.0  0.0  0.0 34.5
DArPct: 1899  0.0  0.0  0.0 34.1  0.0  0.0  0.0  0.0  0.0  0.0 34.1
DArPct: 1909  0.0  0.0  0.0 34.0  0.0  0.0  0.0  0.0  0.0  0.0 34.0
DArPct: 1919  0.0  0.0  0.0 40.4  0.0  0.0  0.0  0.0  0.0  0.0 40.4
DArPct: 1929  0.0  0.0  0.0 17.4  2.0  0.0  0.0  1.0  0.0  0.0 20.5
DArPct: 1939  0.0  0.0  0.0 13.6  2.0  0.0  0.0  2.3  0.0  0.0 17.9
DArPct: 1949  0.1  0.0  0.0  8.0  1.3  1.0  0.0  1.9  1.0  0.0 13.3
DArPct: 1959  1.4  0.6  0.0  6.6  0.7  0.7  0.0  2.1  0.8  0.0 13.0
DArPct: 1969  1.3  0.7  0.0  6.8  0.7  0.7  0.0  2.0  1.3  0.0 13.5
DArPct: 1979  1.8  0.6  0.0  7.6  0.6  0.6  0.0  1.3  1.2  0.0 13.8
DArPct: 1989  2.2  0.8  0.0  5.8  0.5  0.6  0.0  1.2  1.1  0.0 12.2
DArPct: 1999  3.3  0.0  0.0  5.4  0.0  0.0  0.0  0.0  0.0  0.0  8.8
DArPct: 2009  3.5  0.0  0.0  4.2  0.0  0.0  0.0  1.7  0.0  0.0  9.4

For COUNTRY CODE: 414
From source ./vetted/v2.inv.id.withlat

I have to wonder if the Mexican airports are properly flagged. Here are the 2009 Mexican Stations. Anyone know?

[chiefio@tubularbells analysis]$ more Temps/414.stns2009
41476160000 HERMOSILLO,SO                   29.07 -110.95  211  225U  233HIxxno-9x-9WARM GRASS/SHRUBC3  77
41476220000 TEMOSACHIC,CH                   28.95 -107.83 1870 1944R   -9MVxxno-9x-9WARM DECIDUOUS  B2   0
41476225000 UNIV. DE CHIH                   28.63 -106.08 1435 1528U  327MVxxno-9x-9WARM GRASS/SHRUBC3  78
41476243000 PIEDRAS NEGRA                   28.70 -100.52  250  227S   21FLxxno-9A 1WARM GRASS/SHRUBC3  44
41476311000 CHOIX,SIN.                      26.72 -108.28  238  403R   -9HIxxno-9x-9TROP. SAVANNA   A2   0
41476342000 MONCLOVA,COAH                   26.88 -101.42  615  768U   78MVxxno-9x-9SUCCULENT THORNSC3  46
41476373000 TEPEHUANES,DG                   25.35 -105.75 1810 2061R   -9MVxxno-9x-9WARM DECIDUOUS  B2   0
41476382000 TORREON,COAH.                   25.53 -103.45 1124 1339U  244HIxxno-9x-9WARM GRASS/SHRUBC3  51
41476390000 SALTILLO,COAH                   25.45 -100.98 1790 1594U  201MVxxno-9x-9SUCCULENT THORNSC3  72
41476393000 MONTERREY,N.L                   25.87 -100.20  512  548U 1923MVxxno-9x-9WARM IRRIGATED  C2  14
41476405000 LA PAZ, B.C.S                   24.27 -110.42   18   71S   46HIxxCO 3x-9WATER           A1   0
41476458000 MAZATLAN                        23.20 -105.40    3 1642U  147FLxxCO 1x-9TROP. SAVANNA   A    0
41476525000 ZACATECAS,ZAC                   22.78 -102.57 2612 2421U   50HIxxno-9x-9WARM DECIDUOUS  C   57
41476548000 TAMPICO, TAMP                   22.22  -97.85    9   32U  212FLxxCO 5x-9COASTAL EDGES   C   36
41476556000 TEPIC,NAY.                      21.52 -104.90  922  927U  109MVxxCO30x-9WARM CROPS      C   66
41476577000 GUANAJUATO,GT                   21.02 -101.25 1999 2244S   37HIxxno-9x-9WARM FIELD WOODSC   42
41476581000 RIO VERDE,S.L                   21.85 -100.00  990 1038S   17HIxxno-9x-9COOL DESERT     A    0
41476632000 PACHUCA,HGO.                    20.13  -98.73 2417 2508U   84MVxxno-9x-9WARM CROPS      C   72
41476640000 TUXPAN.VER.                     20.95  -97.40   28   27S   34FLxxCO 7x-9WARM CROPS      C   25
41476644000 AEROP.INTERNA                   20.98  -89.65    9   10U  234FLxxno-9A 2WARM CROPS      C   68
41476647000 VALLADOLID,YU                   20.70  -88.22   22   15S   15FLxxno-9x-9TROP. SAVANNA   C   24
41476654000 MANZANILLO,CO                   19.05 -104.33    3   30S   21HIxxCO 1x-9TROPICAL DRY FORC   21
41476662000 ZAMORA,MICH.                    19.98 -102.32 1562 1733R   -9MVxxno-9A-9WARM CROPS      C   22
41476665000 MORELIA,MICH.                   19.70 -101.18 1913 1979U  199MVxxno-9x-9WARM FIELD WOODSC   63
41476680000 MEXICO (CENTR                   19.40  -99.20 2303 2307U13994MVxxno-9x-9WARM CROPS      C  118
41476683000 TLAXCALA,TLAX                   19.32  -98.23 2248 2342S   10HIxxno-9x-9TROP. MONTANE   C   44
41476685000 PUEBLA,PUE.                     19.05  -98.17 2179 2151U  466MVxxno-9x-9TROP. MONTANE   C   94
41476687000 JALAPA,VER.                     19.53  -96.92 1389 1423U  161MVxxno-9x-9WARM CROPS      C   32
41476692000 HACIENDA YLAN                   19.15  -96.12   13    6U  256FLxxCO 1x-9TROP. SEASONAL  C   56
41476695000 CAMPECHE,CAMP                   19.85  -90.55    5    8U   70FLxxCO 1x-9WATER           C   47
41476726000 CUERNAVACA,MO                   18.88  -99.23 1618 1720U  240MVxxno-9x-9WARM CROPS      C   34
41476741000 COATZACOALCOS                   18.15  -94.42   23    3U   70FLxxCO 1x-9WATER           C   69
41476750000 CHETUMAL,Q.RO                   18.48  -88.30    9    3S   24FLxxCO 1x-9TROP. SEASONAL  C    0
41476775000 OAXACA,OAX.                     17.07  -96.72 1550 1858U  115MVxxno-9x-9TROP. SAVANNA   C   65
41476805000 ACAPULCO,GRO.                   16.83  -99.93   13  113U  309MVxxCO 1x-9COASTAL EDGES   C   19
41476845000 SN. CRISTOBAL                   16.73  -92.63 2276 2336S   26MVxxno-9x-9TROP. SEASONAL  C   35
41476903000 TAPACHULA, CH                   14.92  -92.27  118  281U   60MVxxCO20A 3TROPICAL DRY FORC   55
[chiefio@tubularbells analysis]$

Of these, the only ones flagged as Airports are: Piedras Negra, Aerop Interna, Zamora MICH, and Tapachula CH. Somehow I think there are more airports than that in Mexico.

Canada

And then there is Canada:

      Year SP  45   50   55   60   65   70   75   80   85  -NP
DArPct: 1779  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
DArPct: 1829  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
DArPct: 1839  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
DArPct: 1849  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
DArPct: 1859  9.8  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  9.8
DArPct: 1869 12.2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 12.2
DArPct: 1879  3.8 10.8  2.1  0.0  0.0  0.0  0.0  0.0  0.0  0.0 16.7
DArPct: 1889  2.6  6.8 12.8  2.3  0.0  0.0  0.0  0.0  0.0  0.0 24.6
DArPct: 1899  1.7  6.4 18.5  2.1  0.4  0.7  0.0  0.0  0.0  0.0 29.8
DArPct: 1909  1.6  5.0 18.7  2.0  1.3  0.6  0.0  0.0  0.0  0.0 29.1
DArPct: 1919  0.8  5.4 18.5  2.3  2.0  1.1  0.0  0.0  0.0  0.0 30.1
DArPct: 1929  0.5  5.8 16.9  2.3  1.9  1.2  0.1  0.0  0.0  0.0 28.7
DArPct: 1939  0.1  5.9 15.3  2.5  3.5  2.1  0.3  0.0  0.0  0.0 29.7
DArPct: 1949  1.3  8.3 16.5  2.9  4.2  2.2  0.9  0.3  0.0  0.0 36.6
DArPct: 1959  1.8  8.8 15.7  2.9  4.4  3.4  1.7  1.2  0.6  0.0 40.5
DArPct: 1969  1.9  9.5 15.3  3.2  4.1  5.2  2.0  1.3  0.7  0.0 43.2
DArPct: 1979  1.9  9.7 15.3  3.9  3.8  5.0  1.9  1.4  0.6  0.0 43.4
DArPct: 1989  2.2 10.0 14.4  3.7  3.7  5.5  2.0  1.2  0.5  0.0 43.1
DArPct: 1999  4.2 14.9 17.5  3.4  4.0  8.3  3.6  3.1  0.5  0.0 59.5
DArPct: 2009  2.9  6.9 29.2  7.2  5.5 10.8  4.3  2.4  0.0  0.0 69.1

For COUNTRY CODE: 403
From source ./vetted/v2.inv.id.withlat

Wow, another nice climb to 69% airports in Canada. Wonder if the Airport Heat Island effect is stronger in colder places?

And finally, the USA.

The United States of America

Again we have the apparent stable numbers due to USHCN being blended in

      Year SP  30   35   40   45   50   55   60   65   70  -NP
DArPct: 1749  0.0  0.0  0.0100.0  0.0  0.0  0.0  0.0  0.0  0.0100.0
DArPct: 1759  0.0  0.0  0.0 83.3  0.0  0.0  0.0  0.0  0.0  0.0 83.3
DArPct: 1769  0.0  0.0  0.0 76.9  0.0  0.0  0.0  0.0  0.0  0.0 76.9
DArPct: 1789  0.0  0.0  0.0 76.0  0.0  0.0  0.0  0.0  0.0  0.0 76.0
DArPct: 1799  0.0  0.0  0.0 87.5  0.0  0.0  0.0  0.0  0.0  0.0 87.5
DArPct: 1809  0.0  0.0  0.0 69.0  0.0  0.0  0.0  0.0  0.0  0.0 69.0
DArPct: 1819  0.0  0.0  0.0 63.3  0.0  0.0  0.0  0.0  0.0  0.0 63.3
DArPct: 1829  2.9  2.9  5.2 34.9  0.0  0.0  0.0  0.0  0.0  0.0 45.9
DArPct: 1839  4.1  4.7  3.3 26.0  0.0  0.0  0.3  0.0  0.0  0.0 38.4
DArPct: 1849  3.3  4.1  4.1 25.1  0.0  0.0  1.1  0.0  0.0  0.0 37.7
DArPct: 1859  4.2  8.6  8.2 16.5  0.0  0.0  0.0  0.0  0.0  0.0 37.6
DArPct: 1869  0.9  5.4  8.7 17.7  0.3  0.0  0.6  0.0  0.0  0.0 33.7
DArPct: 1879  1.5  5.8 13.6 18.2  3.5  0.1  0.9  0.0  0.0  0.0 43.7
DArPct: 1889  1.4  8.0 10.7 15.2  4.0  0.2  0.4  0.1  0.0  0.0 39.9
DArPct: 1899  1.0  5.0  8.2 10.5  3.8  0.0  0.2  0.1  0.1  0.0 28.9
DArPct: 1909  1.0  3.9  7.0  8.4  3.2  0.1  0.3  0.5  0.2  0.0 24.6
DArPct: 1919  1.0  3.2  6.7  7.3  3.5  0.2  0.6  0.9  0.2  0.0 23.7
DArPct: 1929  0.9  3.1  6.5  7.0  3.4  0.2  1.0  1.1  0.3  0.2 23.8
DArPct: 1939  0.9  3.3  6.4  7.0  3.3  0.1  1.1  1.3  0.5  0.2 24.2
DArPct: 1949  1.2  4.3  7.3  7.7  3.3  0.3  1.5  2.3  0.6  0.2 28.7
DArPct: 1959  2.6  7.6 10.5  9.9  3.9  0.3  1.5  2.8  0.6  0.3 40.0
DArPct: 1969  2.9  8.1 11.2 10.5  4.4  0.3  1.6  2.6  0.6  0.3 42.4
DArPct: 1979  2.5  6.8 10.4 10.1  4.3  0.3  1.8  2.5  0.5  0.4 39.5
DArPct: 1989  2.6  6.9 11.1 10.6  4.3  0.2  1.5  2.1  0.4  0.3 40.1
DArPct: 1999  2.4  6.7  9.9  9.9  3.9  0.0  0.7  1.1  0.2  0.1 34.9
DArPct: 2009  2.1  5.7  8.8  8.9  3.7  0.0  0.6  0.8  0.2  0.1 30.7

For COUNTRY CODE: 425

But it masks the rather astounding effect of deletions in GHCN without the USHCN set added in:

LATpct: 2006  3.7 18.3 29.5 33.2 14.4  0.0  0.4  0.3  0.1  0.1 100.0
AIRpct:       1.3  4.0  6.3  6.7  3.2  0.0  0.4  0.3  0.1  0.1 22.4
LATpct: 2007  8.2 17.2 28.4 26.9 11.2  0.0  3.7  3.0  0.7  0.7 100.0
AIRpct:       8.2 15.7 27.6 23.1  9.0  0.0  3.7  3.0  0.7  0.7 91.8
LATpct: 2008  8.8 16.9 28.7 26.5 11.0  0.0  3.7  2.9  0.7  0.7 100.0
AIRpct:       8.8 15.4 27.9 22.8  8.8  0.0  3.7  2.9  0.7  0.7 91.9
LATpct: 2009  8.1 17.8 28.1 26.7 11.1  0.0  3.7  3.0  0.7  0.7 100.0
AIRpct:       8.1 16.3 27.4 23.0  8.9  0.0  3.7  3.0  0.7  0.7 91.9

DLaPct: 2009  4.3 18.4 29.5 32.5 13.6  0.0  0.7  0.9  0.2  0.1 100.0
DArPct:       2.1  5.7  8.8  8.9  3.7  0.0  0.6  0.8  0.2  0.1 30.7

For COUNTRY CODE: 425

Yup, just shy of 92% of all GHCN thermometers in the USA are at airports.

I’d call that a problem…

Sacramento California Snow

Sacramento California Snow

Today the EPA declared CO2 an environmental threat.

They could not have had more exquisitely bad timing I’ve they had let me set the date.

Sure, timed to coincide with the Copenhagen treaty opening. So they are going to double dip the fiasco…

Why a poor choice? Well, the title of this posting is one reason. They have no sense of history. Another? Current weather predictions are for the center of the USA to be 20 Degrees F below average for the next few weeks. It has even snowed today in Sacramento, California.

For those not in California, Sacramento gets to 110 F in the shade (and there ain’t no shade!) many summers. It is often over 100F. I’ve lived in or near it for most of the last 50 years+ and I can remember 3 or maybe 4 days with snow. (There were probably more, I was not always paying attention to the weather, but not many more…) Snow is almost unheard of on the Central Valley floor in most years. Certainly not in years of supposedly record warmth.

These folks have set them selves up for the most spectacular “belly flop” in living memory. They are parading naked down the street and don’t even know it.

There is a nice little set of pictures of Sacramento snow down the side of this link:

http://ulocal.kcra.com/_Snow-in-Sacramento-County/photo/6877619/62973.html?b=

BTW, it also snowed in the hills around the San Francisco Bay Area. This is an area significantly further south, Morgan Hill, even south of San Jose. They usually do not get snow covered hills to look at:

http://www.city-data.com/picfilesc/picc34643.php

Just heard on the Weather Channel that I-5 is closed at the Tejon Pass on the approach to L.A. due to snow and ice.

A Modest Suggestion

From this day forward, EPA has taken responsibility for the climate with a goal of making it colder.

Every time there is a cold excursion that causes loss or damage, the EPA ought to be presented the bill. They clearly have stated that they can make the world colder and that their desire and actions are designed to make it colder. The OWN the cold.

So let them pay for what they own…

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