Apirin good; Advil / Ibuprofen / Tylenol / Acetaminophen bad

Sometimes you notice things…

Sometimes the spouse does…

Sometimes they matter…

In this case I’d noticed that I was particularly lethargic while the spouse had noticed a tiny tinge of jaundice to the eyes (that resolved the same day). A bit of digging…

Some months back I’d swapped the “pain killer of choice” for arthritic moments from aspirin to ibuprofen. No particular reason other than that I had been nagged about stomach bleed risks by the medical establishment to the point of being a bit paranoid about it.

I was unwilling to swap to Tylenol / Acetaminophen for the simple reason that it is the leading cause of liver damage from drug overdose. Mixed with even modest quantities of alcohol you can end up needing a liver transplant; though the package insert tries to make it sound like you have to be a raving alcoholic; that isn’t the case. The two work to lower the threshold for liver damage and the overdose level for acetaminophen is a small distance above the normal dose. It has a narrow range between therapeutic and toxic dose.

http://emedicine.medscape.com/article/820200-overview

Because acetaminophen (APAP) is the most widely used pharmaceutical analgesic and antipyretic agent in the United States and the world (contained in >100 products), it is reported by the American Association of Poison Control Centers to be one of the most common pharmaceuticals associated with both intentional and unintentional poisoning and toxicity. Acetaminophen toxicity is the most common cause of hepatic failure requiring liver transplantation in Great Britain. In the United States, APAP toxicity has replaced viral hepatitis as the most common cause of acute hepatic failure and is the second most common cause of liver failure requiring transplantation.

Acetaminophen is also known as paracetamol and N -acetyl-p-aminophenol (APAP). This agent is available in the United States as 325-mg and 500-mg immediate-release (IR) tablets, and as a 650-mg extended-release (ER) preparation marketed for the treatment of arthritis. Various children’s dissolvable, chewable, suspension, and elixir formulations of APAP are available. Acetaminophen is a component of many over-the-counter (OTC) cold and analgesic medications and prescription combinations, including codeine-acetaminophen (Tylenol #3) and oxycodone-acetaminophen (Percocet).

http://www.bailey-law.com/docs/tylenol-toxicity.htm

Acetaminophen is hepatotoxic if taken in overdoses, and for adults, more than 7.5 – 10g/d are considered an overdose (2002 FDA Advisory Meeting). The currently recommended maximal therapeutic dose is 4 g/d, however, instructions for use are often confusing. One product states that up to two 500 mg extra strength tablets can be taken every 4-6 h as required, but not more than 4 g/d. If the condition for which acetaminophen is taken extends over more than 18 h, even with the longer (every 6 hr) interval, there is a chance to go over the recommended daily dose.

So just a ‘double dose’ can be toxic and sometimes the label directions put you close to that. (the article goes on to show how following those directions and being a bit un-careful about the 4 gm / day can cause an overdose). Add a bit of wine with dinner or some beer at the beach and that toxic threshold drops. How much? That isn’t clear…

So I’ve generally avoided Tylenol / acetaminophen.

Once, when I was at the first aid station at the Sharks Game with a headache asking for an aspirin and specifically told the guy I’d had a couple of beers and did not want acetaminophen due to the liver risk, I was handed a generic acetaminophen and told it didn’t have any Tylenol in it… It is incredibly hard to avoid and it is very easy to take a Tylenol and some cold medicine that also has acetaminophen and get a double dose.

So I’m very sensitive about the issue.

But apparently not quite paranoid enough.

Aches and Pains

I’ve had sporadic arthritis. I’ve pretty much traced it back to foods. Primarily “cow stuff” but lately tomatoes too. It is easy to avoid beef and eat pork or lamb instead, but my weakness is beef burritos and ice cream. Oh, and marinara sauce…

About January I’d not yet figured out the tomato reaction (even though it is listed in The Arthritics Cookbook.) So I’ve had some ‘aches and pains’. Then the weekend marathon of schlepping tool boxes and lead batteries had brought some aches and pains as well.

Due to the aspirin and bleeding paranoia nag, I’d bought a bottle of Ibuprofen about last January. Didn’t think much of it. But I’d “upped” my use of Advil / Ibuprofen from ‘nearly none’ to ‘nearly daily’ due to those kinds of things. But I didn’t particularly worry about the wine every so often nor the occasional beers. ( I probably average about a bottle of wine per week, or less some weeks. Occasionally I’ll down a whole bottle in one day. At 100 kg, that’s not a whole lot.)

But what I had not done was be paranoid enough to check for Advil / Alcohol interactions to see if the same risks exist to the liver from that mix as exist with Tylenol / Acetaminophen.

So it caught me a bit by surprise when the spouse said she thought the whites of my eyes were tiny bit yellow last night. (Today all is fine). WHAT was different about yesterday? I’d had a glass of wine AND ibuprophen. Not much else.

So off I went to look things up…

http://www.livestrong.com/article/444422-advil-and-alcohol-effects-on-the-liver/

Both alcohol use and Advil use may contribute to liver damage. Advil may, rarely, cause abnormal liver functioning and liver damage on its own. Although Advil and other brands of ibuprofen are usually safe when taken as directed and for a short period of time, the risks of liver damage with ibuprofen use increase with long-term use. Elevated liver enzymes, which indicate damaged liver cells, may occur in up to 15 percent of patients who regularly use NSAIDs, including Advil, according to Drugs.com. Alcohol use is also associated with liver damage, and combining alcohol with NSAIDs like Advil may quickly result in significant liver damage as alcohol activates enzymes that cause NSAIDs to be even more liver toxic than usual.

So one wonders if about 1/2 the days out of 4 months is “long term use”… and how much overlap with a glass of wine is an issue…

Liver Disease

Over time, using Advil, alcohol, or especially both substances together may lead to diseases of the liver such as cirrosis, hepatitis, jaundice and liver failure. When used long-term or in higher-than-recommended doses, sustained liver damage from Advil use may result in hepatitis, jaundice and even complete liver failure. Heavy alcohol use may also cause these liver diseases and others without Advil use, but even when used in moderate amounts, such as three drinks nightly, alcohol may contribute to liver damage and disease if you are also taking an NSAID like Advil. Therefore, it is of utmost importance to never use alcohol and NSAIDs such as Advil together.

Oh Great…

Well, the good news is that the liver is very good at regenerating and there have only been one or two times I’ve had ibuprophen with wine in the same day. So most likely whatever “compromise” there was is/was transitory. (Today, everything seems fine.)

Still: You would think folks would be making it a bit more clear that BOTH of those drugs (and how many others?) are potentially lethal at common levels of use and mixed with common levels of alcohol use (and don’t even get me started about the 1 Litre / Day wine consumption in some European countries and what THAT means about pushing Tylenol and Advil there).

That puts me back at Aspirin.

Being a bit paranoid, I decided to do some more “digging” about it. The “Livestrong” article tossed aspirin in the same bucket as the other NSAIDS with respect to alcohol, but was it true?

http://www.news-medical.net/news/2009/01/27/45267.aspx

Aspirin shown to help prevent liver damage

Published on January 27, 2009 at 1:43 AM ·

According to scientists at Yale University ordinary aspirin may help prevent liver damage in millions of people suffering from the side effects of common drugs, alcohol abuse and obesity-related liver disease.

The new study by researchers at Yale School of Medicine suggests that aspirin may help prevent and treat liver damage from a host of non-infectious causes.

Dr. Wajahat Mehal from the Digestive Diseases and Department of Immunobiology, says research with mice has shown that aspirin reduced the number of deaths caused by an overdose of acetaminophen, best known as paracetamol.

Dr. Mehal says many agents such as drugs and alcohol cause liver damage, and they found that aspirin blocks a central pathway responsible for such liver injury.

He says aspirin could be used on a daily basis to prevent liver injury and suggests that promising drugs which have failed clinical trials because of liver toxicity might be resurrected if combined with aspirin.

Dr. Mehal says the strategy offers the exciting possibility of reducing a lot of pain and suffering in patients with liver diseases, using a new and very practical approach.

Aspirin it seems counteracts new mechanisms of acetaminophen or paracetamol-induced liver damage – overdoses of acetaminophen account for most drug overdoses in most Western countries.

Such overdoses cause two waves of liver damage – the first wave of liver cell destruction is a result of the toxic nature of acetaminophen – the second wave is mediated by molecules of the immune system, which is activated in response to the initial acetaminophen-induced liver damage.

A daily aspirin is already recommended to prevent heart attacks in people at high risk of having one and recent research has shown that aspirin can help treat heart attacks – doses of between 75 milligrams and 325 milligrams help thin the blood; it has also been suggested women who take aspirin once a day may slightly reduce their risk of the most common type of breast cancer.

The study is published in the latest issue of the Journal of Clinical Investigation.

Well.

Not only is it not accused of causing liver damage, it is asserted to help prevent damage from drugs like alcohol and Tylenol / Advil.

Needless to say, I’ve taken an aspirin…

In Conclusion

As we all prepare for various celebrations, especially those of us in the USA with Memorial day, I’d like to suggest making sure you have a bottle of aspirin in the medicine cabinet for “the day after” and perhaps also for “the day before and the day of”…

I’m pretty sure I’ve had no long term damage from my ‘couple of months’ of being uncareful and not paranoid enough about Over The Counter medications. I do feel just a bit annoyed at having been “caught” by an effect to which I was already sensitized, simply from being a bit too lax and not paranoid enough.

So today the “energy” level is rising, the eyes are clear and white, and all is well with the world. Oh, and I’ve dumped the ibuprophen from the pill case and put aspirin back in.

With luck, that’s all that will come of it.

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Beer (cans) Will Save The World!

Beverage Can Stove

Original Image. (Yeah, I know, it’s a Pepsi Can not a Beer Can…
what can you expect from Wiki…)

What motivates me can be strange and wondrous. What I find when motivated can be more strange and sometimes more wondrous.

Recently I’d been a bit bummed about the lack of practical skill in many of the most educated being produced by our “educational” system, and thinking it might mean Dire Things for our collective future.

http://chiefio.wordpress.com/2012/05/23/practically-dis-educated/

Then while channel hopping looking for something on TV to distract me from all the things that need doing, I saw a report on Fox (on, I think, The Willis Report) about the Obama war on coal and how something like 53 Coal Fired power plants had already shutdown due to EPA regulations and a whole slew of more draconian regulations would hit (just AFTER the election) that would likely cause all coal to be shutdown (and electricity prices to “Necessarily Skyrocket” – to quote Obama) and was getting just a tiny bit bothered.
As we saw in comments elsewhere, “It’s a bad idea to annoy the hacker. -E.M.Smith”…

Add to that the fact that, against my will, I’ve been afflicted with a “Smart Meter”, despite having zero “smart” appliances ( and I have no intention of buying any. All ANY appliance needs is a power cord and an on / off switch under MY control.) So the net result of the “Smart Meter” is that now I get nagging letters from the power company telling me that I cook dinner at about dinner time and watch TV then. Oh, and I turn the lights on at night… Who knew? But IF I can find a way to not cook dinner at dinner time and not watch TV then and turn the lights off when it gets dark, I could move my major energy usage time to some other time…

Oh, and it informs me that my neighbors who have 4 single guys sharing a house, have cold cereal for breakfast and eat lunch and dinner out, then come home late to crash; they use far less power than I do between 5 pm and 7 pm… and somewhat less all the time. Again, one house is empty almost all the time, my house has one or 2 people here at all times and 6 or 8 when it gets busy. One household has most meals eaten out; while we cook pretty much everything we eat (and not frozen in the microwave). BUT, PG&E is nice enough to provide a guilt nag for not burning up a lot of gasoline to go eat meals out somewhere…

Oh, the joys of an electric stove and oven coupled with a “Smart Meter”…

And knowing that “electricity prices will necessarily skyrocket”.

We’re already at 23.5 cents / kWhr IIRC once past the “lifeline” allotment that is almost enough to run the refrigerator and a couple of curly bulbs. This IS California, after all. Land of Governor Grey (out) Davis, the last Dimocrat to screw around with the electrical utility system here, who brought us rolling blackouts and brownouts on a regular basis (prior to the recall election that tossed him out. Something “Obama And The Dims” might want to remember when screwing around with the utilities…) Now we have Governor Jerry “Moonbeam” Brown. Elected for a second time after nearly ruining the economy the first time by attacking infrastructure (such as stopping all new freeway construction and selling the right of way… that had to be bought back a half dozen years later, have the newly built houses destroyed, and the needed freeway built). He is facing near bankruptcy levels of deficit and debt, so thinks putting more taxes on rich folks and businesses will grow the economy. We have 2 massive tax proposals to hit the ballot “soon”. Even they will not fix the problem even if they DID work. But they won’t. Sigh. Wonder if we’ll have another recall election?

So I’m thinking about all this and realizing that in about a year, maybe two, we’re probably going back to the land of rolling blackouts and most likely looking at $1/2 kW-hr electricity and maybe, just maybe, I ought to think about a way to “fix it”. At least for me.

Well, one thing leads to another, and I’m thinking about the generator I own and maybe putting a natural gas feed to it (or buying one already set up for nat gas) and how that ought to cut costs a lot… when I remember that the 20 CENT per Gallon Of Gas Equivalent is the wholesale price of Natural Gas and on my last PG&E bill it was more like $1.80 / GGE. Still a lot better than the $4.40 at the pump for regular gasoline, but still… Wasn’t there a cheaper way?

Which brought me back to pondering the piles of “yard waste” constantly put out each week. Tons of the stuff. Each week. And once again I dreamed of a “Wood Gassifier” and running that “wood gas” into a generator. Which sent me off looking for any new ideas or designs on making wood gas. ( It occurred to me that I’ve got an old car with about a 40 kW engine in it and a 1 kW alternator that would probably “tick over” in neutral at about 1500 RPM on wood gas without much trouble… make about the 1 kW average the house consumes…) So what could be easier than just making a stationary gassifier and feeding the gas at constant speed operation into an existing bit of stuff I already own?… (I ponder this particular fantasy about once every 2 months… in the past it was for commuting with a mobile gassifier, now it’s about stationary power.)

So I went off to “Duckduckgo.com” and youtube and such.

As you might guess, nothing much has changed in the world of “gasogens” and wood gas making in the last year…

But somehow, along the way, I got to thinking about my PG&E bill and electric kitchen. Surely I could just make a non-electric solution to cooking? Heck, I’ve already got a half dozen different camping stoves. Just add an oven…

Stoves, Rocket Stoves, and More

I’ll skip over the “looking for an oven” phase. Not interested in building a 2 ton “wood burning pizza oven” in the back yard. (What shows up most, along with giant bread beehive ovens. Too much wood usage for daily cooking and not likely to make morning coffee fast enough…) My bamboo makes enough fuel for a small cooking stove, not a large giant masonry thing.

And I remembered the Rocket Stove. This was invented for 3rd Worlders to have a very efficient stove that can be made from local materials. Home made fire bricks (lining a very small burn chamber) and then a larger space where pots are placed to cook. Turns out a few thousand variations have now been made. Some not so bright. Some really spectacular.

What does this have to do with beer cans? Not much. They came up as a sequence of “after the rocket stove”, so we’ll get to that a bit further down. Not really anything to do with bamboo either, just some neat stove tech.

This video does a pretty good job of telling the Rocket Stove story, along with telling you how to make light porous fire brick from which to make the stove.

The key points are an elbow shaped combustion chamber, a flue with some draw, and the wood sits on a shelf so preheated combustion air is applied to the burning wood ends from below. VERY efficient and very easy / cheap to build. Doing more to “save the world” in 3rd world countries than anything our governments have done. (For the simple reason that it empowers folks to do what they want and improve their own lives).

There are much larger ones than shown in the video, including some with a large iron grill plate (with holes for pots) suited for cooking a dozen tortillas at once along with a pot of beans and who knows what all else. One of them even has a box in the flue for an oven.

So I’m thinking maybe I’ll just build a Rocket Stove and “Embrace my inner 3rd Worlder”. It’s where Obama and the Dims have us headed, so why not just get out ahead of the parade and prepare now? I can likely do most of my cooking just from the annual bamboo production of my yard, and then PG&E will think I have a job and don’t eat meals at home anymore and stop nagging me about my nasty eating habit…

A bit of looking at videos and I’ve settled on “what to do”. No, not the super deluxe make your own paver sized bricks and have a 3 foot grill area type… I’m not THAT committed to it (or “not that to be committed’?) and I’m more interested in things that are “portable” anyway. Perhaps something just a bit more, um, “ersatz”… So if the local “You Can’t Burn Anything” police ( on “spare the air days” the local fire department become PC Police and use IR gizmos to make sure you don’t use your fireplace to stay warm. IIRC, there is an exception for a back yard BBQ, but why press your luck?) so if they show up; I’d rather have something that could “go away” fast, and / or who’s cost to replace would not be large.

A couple of more hours of looking at different things, turned up two interesting ideas. One isn’t really quite a “Rocket Stove” as the wood is not on a decent shelf and doesn’t have a good enough flue. The other is nice, small and fast to make, but lacks any insulation on the burner system so leaks a lot of heat. It also isn’t stable at all, being a few bricks stacked on their narrow ends and some flue pipe. BUT…

If you combine the ideas… One uses standard flue pipe to make a rocket stove combustion shelf / chamber and flue. But just a few standard bricks in an unstable layout as ‘support’. The other uses cinder blocks to make a sort of a stove, but with a crappy fire box so it burns a bit smokey and not very efficiently and with decent outside insulation but no inside layer. That is, it’s just a cinder block and doesn’t have the inner fire channel.

Combining the two would have a nice $10 firebox / elbow / flue with good air mix and draft, inside a cinderblock housing providing some thermal insulation and better stability. ( I suppose I could pack it with old “glass wool” or even just pour some sand / vermiculite / whatever around the pipe if I wanted an even more traditional design… but I like the “just some cinderblocks and pipe as parts” idea… More “mobile”…

So here are the two videos. One more true to the design, but not sturdy, aimed at a ‘field expedient solution after the fall’ mind set. The other more “durable” but less true to the actual design goal of efficiency, low fuel use, and cleaner burning.

So make one of these: (with the 3 inch diameter pipe and the ‘already the right length’ 2 foot or so flue pipe)

and place it inside a fixture / shell made more like this one (where he just knocks out a hole: put the fire tube from above in it) but keeping the grill on top ( I also have a large cast iron skillet that would make a decent ‘plancha’ and could even see putting a Dutch Oven on it.).

So I make that about a $15 to $20 ‘all up’ Rocket Stove that ought to work rather well and lets me relatively cleanly grow my own fuel. Besides, it would help me “Save the world” by setting parts of it on fire…

FWIW, one guy with an extreme stove design manages to cook 7 kg of beans (over about 2 hours) using 700 grams of bamboo… As it looks like Beans may be all most of the folks in the USA will be able to afford in a couple of more years, that’s an important demonstration!:

OK, with that out of the way, I went on to ponder a cleaner stove. Perhaps something small with high heat output for making morning coffee. Something that could be run sitting in a cast iron skillet on the indoor electric stove. I have a rather efficient and dirt simple alcohol stove with no moving parts. It his essentially a cup with the inner ‘lip’ curled all the way down ALMOST to the bottom, and with little holes around the outer edge. Just pour in some alcohol, and light it. The inner wall gets hot, causing the alcohol between the two walls to boil, and making burner like flames out the little holes up top. Nice. Got it years ago. And alcohol stove fuel is widely available and not too expensive. (Many boats use it as it can be put out with water and vapors are less of a problem than with propane or gasoline… exploding boats are no fun. My ‘live aboard sail boat’ had an alcohol stove and I used it for a few years without trouble.)

Well, turns out that some folks can’t leave well enough alone, and / or didn’t like spending a bunch of money for a bit of bent metal.

It also looks like my worries about lack of practical arts and ingenuity may be a bit over done. There are a bunch of folks making such stoves out of beer cans. There is even a wiki:

https://en.wikipedia.org/wiki/Beverage-can_stove

Though I find this video to be particularly compelling:

Though this next design is more true to the commercial product with both an inner and outer wall. Also note that he pokes the hole inward so doesn’t have ‘tabs’ to trim off.

But what sent me over the edge (or around the bend…) and into making a posting was simply that someone who wanted alcohol fuel but didn’t want to buy it, had designed a multi-stage fractionation tower still to efficiently distill fuel alcohol. Out of beer cans:

Thinking about it, yes, it makes sense. Temperatures are below those of the stove. That adhesives used in the stove will do fine here. All you need is a tube with plates and holes in it.. Yes, all in all, it makes sense.

As I have a small Mr. Beer that has turned a variety of things into dilute alcohol ( that I “upgraded” via the freezer… where ice forms and can be removed leaving, er, “hard cider”… but that doesn’t reach fuel concentrations) it would be nice to have a small column still to take it all the way. (Though I’d likely keep the freezer as the first step. Much less fuel burn that way.)

Some of these stoves can be quite elegant. This one uses a votive candle as ‘preheater’ and gets a spectacular heat level out. Neat visuals too:

Post Maltem

So now I’m going to get my commercial stove out of wherever it has gone off to, and see if it works OK on E85 (that is modestly cheap at about $3 / gallon around here) and perhaps pick up a bit of sealant and some beer to make a custom stove designed just for it… Perhaps I’ll also work up a beer can based lamp. I could see a whole matching set. Ought to be simple enough. A bit of oil and wick, a beer can and perhaps a glass holder or chimney…

All in all, it looks like creativity and some practical arts are still alive in the world. As long as there is beer, and beer cans, it would seem that there is hope… Carpe cerevisi!!

If beer (cans) can solve the power problems of the world, perhaps it can save the whole world…

Though I think I’ll use an electric drill to make the holes and perhaps my moto tool for the cutting.

Mixing beer, power tools, flammable liquids, and fire; what could possibly go wrong? ;-)

Sometimes I wonder about people… the rest of the time I’m sure…

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SPAWAR Space and Naval Warfare – LENR Proof

Looks like the folks at the Space and Naval Warfare Command have been playing around with Cold Fusion.

This is a long video (about an hour) but well worth it. Hard science and it pretty much answered every “conditional” I’d put on the Cold Fusion field. Repeatability. Neutrons demonstrated. Fast production method. Characterization of operational parameters. Clear excess energy. Just a well done bit of science.

All in all, it looks to me like it’s time to start taking the Cold Fusion / LENR folks seriously.

In following up some videos of the Rossi E-Cat I found one that asserts his nickle powder at temperature method can not be patented due to a prior patent. That prior patent describes substantially the same absorption of hydrogen to inside the atomic radius and eventual fusion as I had described in my musings about how this might work; with two very important distinctions:

1) It has, rather than a direct nuclear fusion, the formation and expulsion of a modestly high energy Proton that then causes fusion when it impacts other parts of the electrode.

2) The physics is much more detailed and much more likely to be accurate.

Of particular note is that it asserts just about any transition metal ought to work. Even Lead.

What caught my eye was that the metal had to be heated to above a critical temperature to work (which explains why Rossi has heaters in the E-Cat) and that temperature varies by metal. The Debye limit.

https://en.wikipedia.org/wiki/Debye_model#Debye_temperature_table

Inspection of the table shows several metals with quite low limits, so ought to work at room temperature.

Aluminium 	428 K
Beryllium      1440 K
Cadmium 	209 K
Carbon 	       2230 K
Cesium 	         38 K
Chromium 	630 K
Copper 	        343.5 K
Gold 	        170 K
Iron 	        470 K
Lead 	        105 K
	
Manganese 	410 K
Nickel 	        450 K
Platinum 	240 K
Silicon 	645 K
Silver 	        215 K
Tantalum 	240 K
Tin (white) 	200 K
Titanium 	420 K
Tungsten 	400 K
Zinc 	        327 K

So anything below about 300 K ought to be fairly easy to ‘make go’. This also explains why the ones using, for example, Tungsten electrodes work best once they are glowing and / or steaming at the electrodes.

The need for lots of surface area, crystal defects, et. al. all imply that bulk metal powders, sponges, and irregular chemical depositions ought to be beneficial. I’d pondered using carbon fiber mats with an electroplate over them to get lots of surface area from not much metal. That would likely still work, but the range of metals usable is likely quite large.

In particular, using a chunk of Tin solder ( Debye Temperature point 200 K ) ought to make a readily available electrode material that is easily plated and / or deposited from solution ( i.e. poor / rough tin plate), cheap, and relatively low toxicity and that ought to start working at below room temperature.

IMHO, it looks like a simple electrolysis cell to make hydrogen and then an equally simple electrochemical cell to react it made from common metals ought to be “doable” as a test case.

But Wait, There’s More

The implication of that video and the patent is that there ought to be all sorts of unexpected places where a mix of hydrogen ions and metal atoms in a crystal might produce neutrons, protons, and fusions; including rocks in the Earth and potentially be part of the process of how the sun works. This could explain some of the “odd” occurrences of neutrons from various rock pressurization / electric discharge processes.

If, as some have proposed, the sun is an Iron Sun at some depth but with a hydrogen atmosphere, that hydrogen ought to be getting fused via the metal hydride formation process. That it would then spit out a load of protons and we have a high energy solar wind full of protons is curiously attractive…

The video states that one of the likely reactions (to make the neutrons vs heat work out) is likely a more direct fusion of Deuterium into Helium. That would depend on other processes in the metal crystal lattice. I found the physics a bit deep as it was discussing something discovered long after I learned the physics I know, but it looks like the vibration modes of crystals may be highly important. While I hate saying “crystals and vibrations make it work” as that sounds so “new age” ;-) the fact is that the Phonon theory looks to be well attested and important to making actual devices that do unexpected things. Like sound driven heat engines and heat pumps and gigahertz sound “lasers” called SASERS (which they talk about as important for a variety of benign uses and I immediately thought “Wouldn’t a sonic welding of your innards make a messy kind of weapon?…)

At any rate, it looks to me like defect heavy and small metal particles lets the H or D easily enter the crystal spaces. The application of an external electric field drives the ionized H or D into the metal crystals and into the metal ions and then vibrational modes of the crystal may cause some atoms to be smashed together while others get whacked with decent energy protons and the odd neutron. Part of “the deal” is heating the metal to the point where the metal crystals start to ‘get sloppy’ (above the Debye temperature) but many metals are at / below room temperature Debye Temps so ought to work better / easier in test cells.

Just get over the vision of a crystal as a static lattice and visualize it more as a mini-destruction derby and it all makes sense ;-) Oh, and the smallest lightest cars get crushed together by the big “cement truck” metal ions ;-)

Oh, one other note, several cell types use K2CO3 as a special facilitating salt in the reactor (the tungsten / potassium carbonate cells http://lenr-canr.org/acrobat/CirilloDtransmutat.pdf ) so I’d bet that the “special secret catalyst” used by Rossi is in that family. Either Potassium Carbonate or perhaps, given his “cheap materials” approach of Nickle and Hydrogen instead of Palladium and Deuterium, he might have tried plain old Sodium Carbonate. I’d give Lithium Carbonate a try too.

If the Tungsten is run hot in those reactors to get above the 400 K ( 260 F ) Debye Temperature and the carbonate helps facilitate things, perhaps it helps other metals too and might be useful in colder cells run with lower Debye Temp metals. So maybe just a bit of sintered / co-deposited tin and some carbonate of soda could make a reactor that works without the external heating needed by Rossi. Put an Iron wire cage around a deposited tin electrode, add carbonate solution and perhaps added hydrogen for prolonged runs. Put a small bias voltage on it and stand back… Ought to be all it takes.

Now if only I had a spare shipping container or an empty garage to try this…

One Rossi video stated a 20 or 30 to one ratio of heat out to electricity in and a 1 Cent / kW (thermal) production cost. Might be nice in winter ;-) While it looks like a poor way to make electricity (due to the low temperature steam ) it does look like a nice heater.

So maybe the future is looking better for the folks who think new technologies can give us all a better life with more energy; and maybe the Green Running Out Paranoia has yet another hurdle in front of it.

When guys from a U.S. Navy lab say they got results, and have material evidence and peer reviewed publications to back it up, I think it is time to be mildly optimistic.

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Hope for NASA Climatology – Joan Feynman

Thanks to a comment by Gail Combs at WUWT I’ve got a bit of hope that NASA is not totally a lost cause on the issue of Climate Science.

While GISS may be toast, it looks like JPL still “has clue”.

Gail posted two links, that I’m going to include here. In fact, I’m just going to “lift” her comment wholesale:

theduke says:
May 20, 2012 at 12:43 pm

I’m with Pamela. Whenever I start hearing about 200-year cycles and such, my skeptic side starts to kick in…..
___________________________
You might try reading what Richard Feynman’s sister Dr. Joan Feynman has to say about her research on that.
http://www.nasa.gov/vision/earth/lookingatearth/nilef-20070319.html

PAPER: http://trs-new.jpl.nasa.gov/dspace/bitstream/2014/39770/1/06-1256.pdf

Yes it is “wiggle matching” but that is how we start our scientific journey. An apple bops us on the head and we take notice and try to figure out “WHY?”

So wiggle matching is a first step. Several scientists such as Alexander Ruzmaikin, Joan Feynman, Yuk Yung, (above) John Eddy, Alexandre Joukoff, physicist Richard Willson, and Henrik Svensmark to name just a few are working on the problem of how the sun varies and what effect it has on climate. The links attached to the name are brief articles of what is being done.

Is the “Jury In” of course not. Climate science is an infant science and that is why CAGW is so bad at this time. It stifles the creative thinking necessary for quick advancement.

The NASA press release says:

NASA Finds Sun-Climate Connection in Old Nile Records
03.19.07

Long-term climate records are a key to understanding how Earth’s climate changed in the past and how it may change in the future. Direct measurements of light energy emitted by the sun, taken by satellites and other modern scientific techniques, suggest variations in the sun’s activity influence Earth’s long-term climate. However, there were no measured climate records of this type until the relatively recent scientific past.

Scientists have traditionally relied upon indirect data gathering methods to study climate in the Earth’s past, such as drilling ice cores in Greenland and Antarctica. Such samples of accumulated snow and ice drilled from deep within ice sheets or glaciers contain trapped air bubbles whose composition can provide a picture of past climate conditions. Now, however, a group of NASA and university scientists has found a convincing link between long-term solar and climate variability in a unique and unexpected source: directly measured ancient water level records of the Nile, Earth’s longest river.

Nile River Image right: Nile River. Image credit: NASA/JPL
+ Browse version of image

Alexander Ruzmaikin and Joan Feynman of NASA’s Jet Propulsion Laboratory, Pasadena, Calif., together with Dr. Yuk Yung of the California Institute of Technology, Pasadena, Calif., have analyzed Egyptian records of annual Nile water levels collected between 622 and 1470 A.D. at Rawdah Island in Cairo. These records were then compared to another well-documented human record from the same time period: observations of the number of auroras reported per decade in the Northern Hemisphere. Auroras are bright glows in the night sky that happen when mass is rapidly ejected from the sun’s corona, or following solar flares. They are an excellent means of tracking variations in the sun’s activity.

Feynman said that while ancient Nile and auroral records are generally “spotty,” that was not the case for the particular 850-year period they studied.

“Since the time of the pharaohs, the water levels of the Nile were accurately measured, since they were critically important for agriculture and the preservation of temples in Egypt,” she said. “These records are highly accurate and were obtained directly, making them a rare and unique resource for climatologists to peer back in time.”

A similarly accurate record exists for auroral activity during the same time period in northern Europe and the Far East. People there routinely and carefully observed and recorded auroral activity, because auroras were believed to portend future disasters, such as droughts and the deaths of kings.

“A great deal of modern scientific effort has gone into collecting these ancient auroral records, inter-comparing them and evaluating their accuracy,” Ruzmaikin said. “They have been successfully used by aurora experts around the world to study longer time scale variations.”

The researchers found some clear links between the sun’s activity and climate variations. The Nile water levels and aurora records had two somewhat regularly occurring variations in common – one with a period of about 88 years and the second with a period of about 200 years.

The researchers said the findings have climate implications that extend far beyond the Nile River basin.

“Our results characterize not just a small region of the upper Nile, but a much more extended part of Africa,” said Ruzmaikin. “The Nile River provides drainage for approximately 10 percent of the African continent. Its two main sources – Lake Tana in Ethiopia and Lake Victoria in Tanzania, Uganda and Kenya – are in equatorial Africa. Since Africa’s climate is interrelated to climate variability in the Indian and Atlantic Oceans, these findings help us better understand climate change on a global basis.”

So what causes these cyclical links between solar variability and the Nile? The authors suggest that variations in the sun’s ultraviolet energy cause adjustments in a climate pattern called the Northern Annular Mode, which affects climate in the atmosphere of the Northern Hemisphere during the winter. At sea level, this mode becomes the North Atlantic Oscillation, a large-scale seesaw in atmospheric mass that affects how air circulates over the Atlantic Ocean. During periods of high solar activity, the North Atlantic Oscillation’s influence extends to the Indian Ocean. These adjustments may affect the distribution of air temperatures, which subsequently influence air circulation and rainfall at the Nile River’s sources in eastern equatorial Africa. When solar activity is high, conditions are drier, and when it is low, conditions are wetter.

Study findings were recently published in the Journal of Geophysical Research.

Media contact: Alan Buis/JPL
818-354-0474

while the paper abstract is:

Alexander Ruzmaikin, Joan Feynman1 and Yuk Yung2
1 Jet Propulsion Laboratory, California Institute of Tachnology, Pasadena, CA 91109, USA
emails: Alexander.Ruzmaikin@jpl.nasa.gov, Joan.Feynman@jpl.nasa.gov
2Department of Geology and Planetary Sciences, California Institute of Technology, Pasadena,
CA 91103, USA
emal: yly@gps.caltech.edu

Abstract. Historical records of the Nile water level provide a unique opportunity to investigate the possibility that solar variability influences the Earth’s climate. Particularly important are the annual records of the water level, which are uninterrupted for the years 622-1470 A.D. These records are non-stationary, so that standard spectral analyses cannot adequately characterize them. Here the Empirical Mode Decomposition technique, which is designed to deal with nonstationary, nonlinear time series, becomes useful. It allows the identification of two characteristic time scales in the water level data that can be linked to solar variability: the 88 year period and a time scale of about 200 years. These time scales are also present in the concurrent aurora data. Auroras are driven by coronal mass ejections and the rate of auroras is an excellent proxy for solar variabiliy. Analysis of auroral data contemporaneous with the Nile data shows peaks at 88 years and about 200 years. This suggests a physical link between solar variability and the lowfrequency variations of the Nile water level. The link involves the influence of solar variability on the North Annual Mode of atmospheric variability and its North Atlantic and Indian Oceans patterns that affect rainfall over Eastren Equatorial Africa where the Nile originates.

Keywords. Sun: activity, Sun: solar-terrestrial relations, methods: statistical

All we need now is for them to match it up with planetary positions and also find the lunar correlation and the 1500-1800 year cycle…

But at least it’s a start…

I’m also quietly pleased that “Real Scientists” are interesting is using what the old Egyptians new too. Makes me feel all warm, fuzzy, and justified ;-)

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Memorial Day Snow?

As this cooling turn progresses, the ‘cold loop’ of the Jet Stream is getting colder. Folks on the US East Coast or elsewhere that a ‘warm loop’ from the tropics sits on your head are benefiting from the hot flow to the pole where the heat leaves. This does not mean that your area is “heating up”, just that the heat from the tropics is leaving by running over you… Here on the West Coast we are under the cold air headed south (to be warmed in the process of sucking heat out of the Tropics.)

This is, IMHO, a direct result of the sun going sleepy, having the UV drop off a cliff, and having the atmospheric height shorten. (All the steps are in evidence, only the proof of causality is ‘a work in progress’.)

We’ve already seen one example in the Snows Of Mount Hamilton that were here much later than in the last several decades. Now we have a “Winter Storm Warning” for Memorial Day up in the Sierra Nevada mountains. No, not unheard of, but just not all that common in the last few decades of an extra hot solar cycle.

From http://www.wunderground.com/US/CA/072.html#SPE

Greater Lake Tahoe Area
Special Weather Statement
Statement as of 2:39 AM PDT on May 25, 2012

… Light snow accumulations possible in the Sierra and northeast
California through early Saturday morning…

An unseasonably cold low pressure system will is moving into the
Sierra today. This storm will produce snow across the higher
elevations of northeast California and the Lake Tahoe basin by
this morning… with additional snow showers through Saturday
morning.

During this time… several inches of snow will be possible in the
Tahoe basin above 7000 feet and in northeastern California above
6000 feet. Up to an inch of snow will be possible down to Lake
Tahoe level and between 5000-6000 feet in northeastern California.

Snow may accumulate on Road surfaces early this morning over
higher passes of the Sierra and again Friday night. Most Road
surfaces will be wet but slick patches will be possible above 5000
feet. Little to no accumulation is expected during the day on
roadways due the late may sun angle, despite the fact that it may
be snowing much of the day. By tonight… .some light accumulations
of snow are possible in the Lake Tahoe basin and highways
89… 49… 36… 44 and 299 in northeast California.

Although heavy snow accumulations are not expected… anyone
planning travel in the Sierra tonight or Saturday morning
should slow down and prepare for chain restrictions and possible
delays.

So about those mountain glaciers around the world… think snow in late May (and maybe even early June in some places) might make a difference?

So about those GHCN biases: Think having places like Tahoe IN the record in the baseline period and into the warm phase but OUT of the record now might, just maybe, be missing something important?

So about that 1200 km “fill in” done by GIStemp: Think calibrating it in the 1980′s and then using it in the 2010s-2020′s might just be giving fake warmer grid cells at altitude? Given that California had 4 GHCN thermometers in v2 (I’ve not checked v3 yet) and ALL of them were at the coast, I think “this matters”.

Here is an historical report for May 1982 ( 30 years or 1/2 cycle back) and the report for May 2012 so far. Notice that the scales are different, so the barometric pressure ‘wobble’ in 1982 is between narrower bounds. The present wind scale is shifted by a wind spike (that I suspect is bogus as it is hurricane type wind). Still, it HAS been more windy in this phase of the cycles. Notice the blue “wind gust” dots make regular “hats” on the wind pattern in 2012, while in 1982 there are just a couple of scattered dots.

IMHO the shift to much more wind that I first noted a couple of years back is a key feature of things. That it is entirely ignored by the Global Warming theories is, IMHO, a lethal omission. In the ’70s and ’80s there were many more incidents of aircraft turbulence injury and more crashes. Then in the ’90s and ’00s there were nearly none. Flying was typically very smooth and folks mostly got used to just not wearing seat belts while in flight. Since 1998 we’ve lost a couple of more aircraft and there have been a few reports of in flight injuries from turbulence. I think this anecdotal evidence indicates a fundamental change in air flow. There’s a lot more of it as the heat leaves.

Tahoe 25May2012

Tahoe May1998

It isn’t just a one month or weather event, either. We’ve got more gusts showing up all year long:

Tahoe May 1981 82

Tahoe May 2011 2012

What does it all mean? Who knows.

My suspicion is just that the lowered atmospheric height comes with a stronger stratospheric flow and stronger decent of cold air at the polar vortex. This, then, shows up as a “loopy jet stream” as the Rossby Waves get deeper. It also shows up, IMHO, as a more ‘gusty’ wind paradigm and as faster air flows.

I’ve seen that pattern of more gusty winds in many graphs from different places. It isn’t just a Tahoe thing. I first noticed it in my own back yard. A tree that I look at every day through a picture window was spending much more time moving in the wind than in prior years.

Why does this matter? Well, pretty simply, fast moving air can transport more heat at the same temperature when compared to slow air. It is that whole “heat vs temperature” thing. The “Global Warming Climate Scientists” constantly harp on temperature when what really matters is heat flow. Any Engineer can tell you that moving more mass through a heat exchanger at a constant temperature moves more heat. Yes, eventually the temperature will shift to reflect the heat changes, but it is heat flow that is important in a calorimetry experiment.

Back in about the late ’50s and early ’60s I would watch the local (Chico California area) weather man do the weather reports. They would have a map of the Jet Stream up pretty much all the time and he would explain what was happening. We had great weather reports as it was Farm Country and it basically was THE major determinant of who would make money and who would not.

I got a brain full of those maps over time. They very frequently had a ‘loopy jet stream’. Then in the ’80s and ’90s the jet stream went more ‘flat’. That ‘flatter jet stream’ was not as interesting, and frankly the “nightly news” became infotainment and the nightly weather became the ‘nightly natural disaster’ report with little weather technology displayed. (A sad day…)

Is there an archive of the Jet Stream location over time? I don’t know. I’d love to have some analytical evidence for my assertion that we’ve shifted regimes from “loopy” to “flat” and back to “loopy”. (And then can connect it to winds historically; and on to precipitation and heat flow. EVENTUALLY having an impact on temperatures…) But other things are demanding attention at the moment. Still, I think that would be a very productive investigation of a 60 year pattern of linkages.

In Conclusion

It ought to be possible to identify other areas under ‘equatorward’ Jet Stream Loops and find similar effects at altitude in their mountains. It ought, too, to be possible to find ‘polarward’ loops and find them warmer and wetter. As my ‘personal database of the mind’ is only stocked with North American Jet Stream maps, perhaps someone in Europe, Asia, South Africa, South America, Australia / New Zealand etc. can identify where they have ‘polarward’ vs ‘equatorward’ persistent loops.

Also remember that last year we even had springtime snow in the Los Angeles area. This isn’t just a one time one year fluke.

I think these will also map to historic periods of drought and flood, but haven’t had the time to get into the precipitation data. Still, when one area gets enhanced wet air from the tropics while an adjacent area gets drier colder area from the pole, it must change the precipitation. So, for example, does Eastern Australia get more rain now, but does Perth get a drought then? I just don’t know how the “loops” behave in the Southern Hemisphere…

But what I can say is that the cold air from “up north” is making it to California and the snows are returned to the mountains. Even on Memorial Day.

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GHCN v3 A Question Of Quality

Sometimes when writing code you make assumptions. Quite valid assumptions. That turn out to be quite wrong.

Sometimes it is not your fault.

Sometimes the data sucks.

I’ve ported my dT/dt code to run on both the v1 and v3 versions of GHCN.

It does a ‘first differences’ anomaly processing on EACH thermometer record before doing ANY combining, so is about as pure and clean an ‘anomaly’ process as you can get. The only real ‘twist’ to it is that I do the ‘anomaly’ creation process starting with the most recent data point and going backwards in time. The most recent data ought to be the best, so this puts any extreme excursion for very old and questionable instruments or processes ‘at the start of time’ for that particular thermometer. In this way, a thermometer at a place that was first being read in 1720 and perhaps even in some entirely different scale, like Reaumur, does not cause all of future readings to be offset by whatever oddity it might have had in the first reading.

First Differences simply takes the first reading you have, and subtracts the next one from it. So if you have 20.1 today and 19.1 is the next reading the difference would be -1 C. No Problem.

I create the report in several steps. This is often an easier way to program and it is frequently very useful to have intermediate data files for things like debugging or for feeding the intermediate form into a another ‘great idea’ that comes along (without needing to reprocess all those first steps). In particular, I combined the Inventory File with the Mean Temperatures data to make a combined file where each record is identified. (This is how it really ought to be, since the data in the inventory file changes over time in the real world, but only the most recent point in time is captured in the INV file. A land use, for example, may be AIRSTATION today, but I assure you it was not so in 1800…) Then I create a version of that file where all the temperatures have been put through the dT “first differences” processing. At that point the data can be used to make all sorts of interesting reports (and more easily since the meta data are attached to each record).

Why That Matters

Here I was making dT/dt reports by country and by region, comparing v1 with v3, and looking for patterns. I went to do one for “North America” (via using “country code” of “4″ – yes, just “4″. I search on the string and the first digit of country code is the continent area). My program “blows up” with a run time “data type error” on reading input records. It is trying to read character data into an integer variable and that is forbidden in FORTRAN. ( “C” will let you do it, though ;-) In this case the “not letting you do it” is a feature for FORTRAN.)

Now I’m worried. Have I “blown the port”? Is my “FORMAT” statement “off by one”? A common error is to have numbers and letters near each other and to get your ‘framing’ off in matching the “READ” format to the actual data. So you might have “1995LUFKIN 20.1″ and be trying to read it as “995L” and UFKIN by being ‘off by one’ and looking just one character too far to the right. In some cases that kind of bug will run FINE on 99%+ of the input data, yet fail when one extreme valid value comes along. So, for exapmle, “20.1″ being turned into “0.1 ” might never be noticed. Make a temperature of, say, “22 C” into one of “122 C” and a human being might notice, but a computer program only notices if you told it to check. To do “QC” or “QA” for out of range data.

In my program, I had done no ‘range sanity checking’. It is a common choice for programmers. “Bounds Check” the input data (to catch obviously broken cases of “insane” data) or not? Is the party or program handing you data one that can be “trusted”? Has it already done the “sanity checking” and the data are guaranteed to be “in bounds”? One of the very first things I learned in my FORTRAN IV class many decades back. (To this day I marvel at how much ‘that really matters’ about programming I learned in that one class. Problem sets cleverly designed to force you to run into things like out of bounds data and ‘the typical problems’ with the typical bugs.) The question became: “Was it something I did?”.

Was my program “broken” in some subtle way?

So off I went debugging.

It wasn’t about me…

I did discover in one of my intermediate files an “anomaly” that was all asterisks. FORTRAN does that for you when you tell it to print a number into a field that is too small for it. ( “C” will just let you do it. Sometimes it’s a feature to say “just do it” and “C” is a better language. For engineering work, the behaviours of FORTRAN, where it “barfs” on things that are probably an error, helps to discover “boo boos” better. There are times I Like FORTRAN better. This is one of them.)

So WHY did that field have asterisks? Were MY numbers off? Did I have an ‘off by one’ on the size of the numbers and overflowed the size of the field? All the other numbers looked about right.

Swimming further up stream, I found that the record in question from v3 Mean temperature file was in error.

Inside GHCN v3

Cutting to the chase… There were 3 records for North America that had “crazy hot” temperatures in them. They fit in the 5 space long field in the v3.mean type file (that is in 1/100 C without the decimal). The field might say “-5932″ for a negative -59.32 C reading in Antarctica, for example. One would expect values below about ” 5000″ for most of the world. (This is where range checking can be fun… just what IS the highest temperature ever, and how much ‘head room’ do you leave above it for that new record to show up? Knowing that it may let SOME errors come through undetected…)

In doing my “create the anomaly values” step, a Very Large Positive can become a Very Large Negative after the subtraction. Then there may be no room for the minus sign in the 5 digit space. And you get asterisks. And your report “blows up”…

That is exactly what happened.

Looking at the v3.mean data, there are 3 records for North America with “insane” values. Simply not possible.

Yet they made it through whatever passes of Quality Control at NCDC on the “unadjusted” v3 data set. They each have a “1″ in that first data field. Yes, each of them says that it was more than boiling hot. In one case, about 144 C.

You would think they might have noticed.

Here are the records, as extracted from the ghchm.tavg.v3.1.0.20150511.qcu.dat file. Yes, that is the “unadjusted” file. But one might have thought that “insane” values would not be included. I’ve yet to check the .qca.dat “adjusted” one to see if they are removed from it. It would be a heck of a “Hobson’s Choice” to be stuck with either accepting ALL of their “adjustments” or having “insane values”; but that looks like it may be the case. (Welcome to ‘raw’ data…) So it looks like I’ll be needing to add a step of “compare qca to qcu” to see how much changes.

This record is from CHILDRESS. Notice that the seventh temperature field (near the middle of the record so scroll just a touch to the right) is 13810. That’s 138.10 C. The data then go to “missing data flags” of -9999 from that point forward. I suspect it was a ‘keying error’ and the value was supposed to be 13.81 but got shifted ‘off by one’, but it could just as easily be that the sensor simply went nuts. BTW, this is part of the QA process that was lost when we went to automated thermometers instead of having people read them. A person would say “120 F in winter? No way” and go get a new thermometer. Automated systems typically don’t know winter from summer or that it FEELS like it’s about 60 F today so I ought to suspect an error in that 80 F reading… If a sensor goes “a little bit bad”, the data will be blindly accepted. Heck, even a whole lot of “crazy bad” looks to be let through.

425723520021996TAVG-9999     890  G  890  G 1700  G 2530  G 2700  G13810 OG-9999   -9999   -9999   -9999   -9999  

This is the DALLAS/FAA record:

425722590021996TAVG  750  0 1250  0 1280  0 1870  0 2700  0 2860  015440 O0-9999   -9999   -9999   -9999   -9999   

Again we notice that it’s a 1996 record (the first 3 digits are the Country Code – 425 for the USA, then 8 digits of WMO and instrument identifier, then 4 digits of YEAR). Again it is the 7th temperature field (or July) that is in error. 154.40 C in Dallas. Who knew? And again we go to “missing data flags” the rest of the year.

So just how trustworthy are the May and June values? Did the sensor cleanly and suddenly die just in July? Or has it been on a long slow drift to incorrect high readings for a year (or since the last calibration)? How many bogus high values are accepted as “close enough”? Are these sensors prone to “fail high” readings? Or is there a random distribution of “fail high” and “fail low”? I suspect that is a question for folks like Anthony Watts, who knows more about temperature stations than anyone else on the planet, near as I can tell.

At any rate, it might be interesting to compare these stations to “nearby” stations for the several months or years prior to these “data farts” to see if the offset stays contsant into a catastrophic failure, or if there is a long slow drift that is accepted into the record, until the “blow up” happens. Clearly if 154 C makes in it, 48 C would too… and even 28 C when 27 C was the actual temperature.

This one is from LUFKIN:

425747500011996TAVG  950  G 1310  G 1310  G 1920  G 2640  G 2670  G14420 OG-9999   -9999   -9999   -9999   -9999   

Again 1996 and July, followed by missing data flags. 144.2 C.

In another posting comment in this article, DocMartyn found that the temperature “ramp up” matches the onset of electronic thermometers and the use of short RS-232 ( or RS-2s2) connector cables. The typical assumption has been that it was pulling the stations closer to buildings and power sources, but might there also be a ‘cumulative failure mode’ impact over time as well?

BEST vs GHCN cumulative “anomaly”

In earlier work, I’d found that the bulk of the “warming” came from the most recent “Mod Flag” and speculated that something about the processing of the newer data was suspect. There was also the early failure mode of some of the instruments where they would “suck their own exhaust” and pull in hot air from their humidity testing heaters. To that we can now add some suspect data from a non-graceful failure mode.

Basically, one simply must ask: “Just how good, or bad, is the quality checking on these electronic gizmos?”

The same records, extracted from my “combined with Inventory information” file, so you can get more information about them (though you will need to scroll a LOT to the right… it’s a long record ;-)

425722590021996TAVG  750  0 1250  0 1280  0 1870  0 2700  0 2860  015440 O0-9999   -9999   -9999   -9999   -9999      32.8500  -96.8500  134.0 DALLAS/FAA AP                   148U 4037FLxxno-9A 1WARM CROPS      C
317890c317890

425723520021996TAVG-9999     890  G  890  G 1700  G 2530  G 2700  G13810 OG-9999   -9999   -9999   -9999   -9999      34.4300 -100.2800  594.0 CHILDRESS/FCWOS AP              565R   -9FLDEno-9A-9WARM CROPS      B
343396c343396

425747500011996TAVG  950  G 1310  G 1310  G 1920  G 2640  G 2670  G14420 OG-9999   -9999   -9999   -9999   -9999      31.2300  -94.7500   85.0 LUFKIN/FAA AIRPORT               70S   30HIxxno-9A10WARM DECIDUOUS  B

In Conclusion

So that’s where I got to in last night’s “Coding Frenzy”.

I was planning to post up some v1 vs v3 comparison reports (which have been run), then ran into this “bug” and spent until 3 am chasing phantoms only to find that “It isn’t about me” and it was crappy input data.

Yet that discovery points to a very interesting potential issue. IFF 154 C can just flow through until whatever “Magic Sauce” is applied at NCDC to remove it in “QA” processing: We are 100% dependent on their “QA” process to catch such errors and to catch more subtle errors that do not fall into a “sanity check” bucket.

How many thermometers might read 1 C high for a year? Or 0.4 C high for two years? And never be ‘outed’ by the “QA” code?

We just don’t know.

But we do have a very suspicious “onset” of the ramp in warming right at the time the electronic systems are rolled out (found by two folks using entirely different methods) and long after CO2 had been increasing for decades.

IMHO this is more than enough of an “issue” to put some Liquid In Glass thermometers in selected locations to “Guard the Guardians”… ( from Quis custodiet ipsos custodes? )

We are, in essence, fully dependent on some rough sieve computer programs checking some occasionally insane automated data entries to determine if there is Global Warming, or not. I find that inadequate.

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GHCN v1 v2 v3 All Data

There’s something interesting in the GHCN v1 vs v2 vs V3 data. I’m not sure exactly what to make of it yet, but I think it matters.

It is my working hypothesis that it indicates the change in nature and placement of thermometers between the series and that it indicates “where to look’ for ‘issues’.

The basic problem that I see in how a Global Average Temperature is calculated is that the data are very poorly suited to the task. There are huge variations in placement of thermometers over time, and over space. The actual instruments change radically and the technology used has tranches by time. As a calorimetry experiment (measuring heat gain / loss) it violates substantially all the standards of acceptable practice. ( I can still remember my chemistry teacher lecturing about the essential need to never change the thermometers … nor move them.)

What I do as a first step is just “look at the shape of the data”. There is no attempt to “fix it” via things like anomaly processing or grid /box apportionment. Why do this? It tells you how big a problem you have. If you don’t know how much problem you are expecting those processes to fix, you have no real basis for assessing their “fitness for use” in removing that problem. In the case of a Global Average Temperature (GAT) we’re supposed to be highly worried about temperatures that vary in the range of 2/10 C to 5/10 C and absolutely panic over 1 C. Yet the data have far more than that variation just from data set to data set (and more between years and geographies even within the same grid / box). So we are expecting those “gridding” and “grid anomaly” processes to fix rather a lot.

(It is worth reminding folks that the “anomaly” formation process is NOT done on individual temperatures from individual instruments in GIStemp. Temperatures are carried AS temperatures through a large number of processes, including the “QA process” and the infilling / homogenizing steps. Only at the bitter end, when making Grid / Box values, is a Grid-Box Anomaly calculated. So no, using “anomalies” does not “fix” the issue, as it is done too late in the process. I have some code, the Dt/dt code, that does anomalies as the very first step, only on an instrument against itself. That code is the next step to run on my ‘to do’ list.)

With that, back at the comparison.

In this pass we are just looking at how much the basic data shift between these data sets. Is there enough ‘shifting’ here to be worried? Might it indicate an issue that the GIStemp and HADcrut code might fail to fully ‘correct’ out? If, for example, we find the data warmed by 1/2 C in winter between v1 and v3, what assurances do we have that the various GAT creating processes can adequately remove that effect perfectly? Yes, perfectly. For the simple reason that if it is not perfect, then some of the “Global Warming” is due to imperfection in the GAT codes. IFF they fail to remove it at all, then we get 1/2 C of “winter global warming” that is in fact an artifact of the changed data sets. Perhaps even more as the data shift dramatically in location and average temperatures over the years within a given data set.

Comparing the Averages of Averages

The code I run takes each year and averages all the readings in any given month for any given collection of instruments. In this case I said to select all instruments that start with any digit between 0 and 7 in the “Country Code”. That’s basically everything other than “ships at sea” I then calculate an “Annual Average” from those data and keep two running totals.

One running total, the AA, is the Average of Monthly Averages. Each monthly value has the same weight. Doesn’t matter if there is only one thermometer in that month, or 5000. This is somewhat overly influenced by the very early thermometers as they get to have a larger implact, being fewer of them. (Yet we are expecting the GAT codes to remove that effect… so it DOES matter.)

The other running total, the Ad, is the Average of Data items. Each individual thermometer reading has the same weight. This is somewhat over influenced by later data simply because we have so much more of it in later years. Still, it will tend to indicate if one version of the data has shifted significantly when compared to another, especially in those years where we have the most data and care the most (since about 1950).

GHCN v1 v2 v3 AA Ad Chart

What I find particularly interesting about this chart is that it shows how the data themselves have shifted to warmer winters from data set to data set. Even the v2 vs v3 that have almost the same time coverage (2009 to 2012 being the added years in v3). The changes simply must be caused by the changed processing and which thermometers are included in the set, as the small number of added readings are insufficient to shift the averages this much.

We can clearly see that the AA values have a much warmer winter from data set to data set. Interesting to note is that from v1 to v2 the summers COOLED, then in v3 that is “fixed” and the summers are warmed a bit from v2 (though still cooler than in v1).

For the Ad values, the effect is more muted. This implies that the effect may be stronger in earlier years where there are fewer thermometers. Yet we expect the various GAT codes to do exactly that comparison. Few in the past against more in the present. And perfectly remove this bias.

How big is the bias? Just about the same as the supposed Global Warming magnitude.

Here are the data themselves, so you can do individual comparisons:

	Jan	Feb	Mar	Apr	May	June	July	Aug	Sept	Oct	Nov	Dec	Total
v1 AA	2.0	3.4	7.1	11.7	15.9	19.4	21.4	20.8	17.7	13.1	7.6	3.4	12.0
v2 AA	2.5	3.9	7.3	11.8	15.8	18.9	20.8	20.3	17.4	13.1	7.9	3.9	12.0
v3 AA	3.5	4.8	8.2	12.4	16.2	19.2	21.1	20.7	17.9	13.7	8.8	4.8	12.6
													
v1 Ad	0.0	1.4	4.7	9.6	14.3	18.0	19.9	19.2	15.9	10.8	5.4	1.6	10.1
v2 Ad	0.2	1.6	4.8	9.6	14.1	17.7	19.6	19.0	15.8	10.8	5.6	1.9	10.1
v3 Ad	0.7	2.1	5.2	9.8	14.2	17.7	19.5	19.0	15.8	11.0	5.9	2.3	10.3

I find it interesting that the AA change for v1 vs v2 overall was nil, yet by v3 it is 6/10 C. The total change in the data is “only” 3/10 C (but it is the changes averaged within months and years that must be removed by the GAT codes). Still, this indicates that overall the data set has warmed, and that the warmth is distributed in such a way as to have a stronger effect over time.

That the AA values are all higher than the Ad values is interesting. It implies that much of the data are relatively cooler than the averages, and that the cooler data get ‘submerged’ in an average. Odd thing, that. I likely need to chase down what it means but don’t have a working theory at present.

Looking at Jan, for example, we see that the AA changes from 2.0 to 2.5 to 3.5 C between the data sets. We have 1/2 C of “warmer January Averages” between v1 and v2, and a full 1 Degree C between v2 and v3 (for a whopping 1.5 C overall from v1 to v3).

We are expecting the GAT codes (like GIStemp and HADcrut) to succeed at removing 1.5 C of “change from shifting data sets” while finding 1/2 C of “signal” and do so without error.

Looking at July and August, we find the AA changes are “different”. A drop of 0.6 from v1 to v2 and then a rise by 0.3 from v2 to v3, leaving v3 at 0.3 below v1. For August, v3 ends up within 1/10 of v1. For the Ad values, August cools by 2/10 from v1 to either v2 or v3, while July cools by 0.3 to 0.4 C.

So we are expecting these codes to take 1.5 C of warming winters and 1/2 C of cooling summers from changes of data set and manage to not find 1 C of average warming in the data from those changes of instruments.

My question becomes pretty simple:

If we don’t have any stability in the instruments in use, such that we have whole degree and more wandering in the basic data from set to set (and up to several degrees from year to year), just how do we know we are finding tenths of a degree of influence from other effects?

I’d also question just how much ‘global warming’ is actually making things hotter. In the data itself, the only visible effect is a “less brutal winter” and maybe a bit of “nicer summers”. Oh, I supposed one could argue that we need to do the whole ‘anomaly grid / box’ thing before making that assertion… except that the data are of that pattern. So we have to ask the same question about “less brutal winters” as about the GAT creation: How do we know those conversion codes will do a perfect job?

Who has shown that their vetted and proven error bars are less than 1/10 C (that’s 20% of the 1/2 C we are supposed to be worried over, so a generous error target. Yet we have NO evidence at all that the error bars are inside that bounds. There are no published QA tests or validation suites for GIStemp…) There are 2 basic paths from this point: The data are saying something that matters which can be seen in the data (and that is not much absolute warming highly concentrated in winter – most of the data are from the N. Hemisphere so we can make that assertion) or we trust the GIStemp and HADcrut codes to perfectly remove bias in the data that swamps the signal being sought by a factor of 10. (Whole degrees vs tenths).

As there are no published test suite results on GIStemp and HADcrut, if we choose to trust them it is entirely a matter of faith in the codes and nothing more. There are no test suits. There are no “red data” or “pink data” or “white data” tests. There are no stress tests. We could be looking at nothing but the product of computer programming bugs.

That’s the choice as I see it. Blind faith in perfection in computer programming, or concern that the variation in what is supposed to be “the same time series” clearly swamps any signal and even minor errors in the code could leak 1/10th of that noise and result in a false “Global Warming” signal.

The Raw Reports

The rest of this posting is just the raw reports run on the data sets. So folks who wish to play with it can do things like, oh, graph the changes of JAN monthly averages over time…

v1

Thermometer Records, Average of Monthly Data and Yearly Average
by Year Across Month, with a count of thermometer records in that year
--------------------------------------------------------------------------
YEAR  JAN  FEB  MAR  APR  MAY  JUN JULY  AUG SEPT  OCT  NOV  DEC  YR COUNT
--------------------------------------------------------------------------
1701 -4.2 -1.5  1.6-99.0-99.0 15.4 18.9 15.8-99.0-99.0-99.0 -0.5  6.5    1
1702  2.0 -0.5  0.6  2.6 10.9 16.0 16.0 15.8 10.1  7.5  0.2  0.6  6.8    1
1703 -2.8 -0.9  0.6  7.7 14.1 16.1 15.4 16.3 11.4  6.1  2.2  2.5  7.4    1
1704 -4.9 -0.5  3.9  9.4 11.8 14.1 17.1-99.0-99.0-99.0-99.0 -0.9  6.2    1
1705 -7.1-99.0  1.0-99.0-99.0 16.0 18.3 17.8  8.7  7.5  0.7  1.8  7.2    1
1706 -1.2 -1.0  2.8  7.4 12.8 17.2 16.6 15.6 11.8  8.5  3.5  2.5  8.0    2
1707 -0.5  0.8  2.4  6.4 11.7 17.2 18.0 15.6 12.6  6.0  3.8  1.7  8.0    2
1708  3.0  0.9  4.7  7.7 11.1 14.3 13.7 17.9 14.3  4.6  3.3 -1.6  7.8    2
1709 -9.0 -3.9  0.9  9.4 11.7 16.7 16.0 16.0 12.2  8.1  5.6  2.0  7.1    2
1710 -1.1 -0.2  4.1  6.9 12.9 15.2 15.2 16.5 13.8  9.4  7.4  6.5  8.9    2
1711  3.5  0.0  4.7  9.5 12.2 16.9 16.0 15.6 13.3  9.3  6.5  1.5  9.1    2
1712  0.2  2.9  4.1  7.7 12.3 16.3 16.8 14.9 13.1  9.5  5.0  4.2  8.9    2
1713 -0.3  5.0  1.0  5.3 10.5 13.6 14.8 15.4 13.9  9.3  3.4  2.5  7.9    2
1714  1.9  3.8  5.0  7.9 10.2 14.5 18.5 13.8 13.0  9.7  4.6  2.4  8.8    2
1715  0.7  3.5  5.7  9.6 11.6 14.5 15.8 17.0 14.1 10.3  6.3 -1.5  9.0    2
1716 -5.0  1.5  3.3  9.1 11.3 14.0 16.3 15.5 12.4  8.3  3.9  1.2  7.7    2
1717  0.9  0.7  3.4  7.2 10.2 15.3 15.7 15.5 13.9  9.3  3.4  3.5  8.3    2
1718 -1.6 -0.8  4.6  8.3 12.7 16.0 18.0 19.0 15.1  8.9  5.2  3.3  9.1    2
1719  0.5  2.5  3.5  5.6 13.4 16.0 20.1 18.9 14.1  8.2  5.0  1.3  9.1    2
1720  2.9  2.9  3.1  6.8 12.3 12.6 17.2 14.5 14.3  8.1  5.6  3.6  8.7    2
1721  3.5  0.1  0.8  8.9 10.2 15.3 15.2 16.5 14.4  8.6  5.8  1.9  8.4    2
1722  0.9  3.9  5.5  8.6 11.5 15.1 15.8 15.5 14.6 10.4  6.8  3.3  9.3    2
1723  0.3  2.5  6.4  8.4 12.4 15.6 15.6 15.9 13.9 11.0  2.2  4.7  9.1    2
1724  4.8  3.6  3.7  6.7 11.8 16.7 15.1 16.9 14.2  8.3  5.1  2.0  9.1    2
1725  2.3  0.4  3.5  7.0 10.6 14.0 14.6 14.3 12.5  8.0  3.0  2.0  7.7    2
1726 -1.8  0.2  2.5  7.8 14.1 16.2 15.6 14.4 14.2  9.2  5.3  0.1  8.1    2
1727  2.6  3.6  3.7  7.0 15.0 15.4 16.8 17.5 14.7 11.5  3.6  2.5  9.5    2
1728  2.5 -0.6  6.9  8.9 14.8 16.6 16.5 14.6 13.1  9.2  4.3 -0.6  8.8    2
1729 -3.1  0.3  0.2  6.3 11.1 16.2 17.8 17.7 16.9 12.1  5.3  5.3  8.8    2
1730  2.4  2.3  4.3  9.1 12.6 15.6 17.2 16.8 14.4  7.0  7.5  2.2  9.3    2
1731 -0.5 -0.4  3.3  6.4 12.0 15.3 16.4 16.8 14.8 11.9  6.5  3.2  8.8    2
1732 -0.5  3.3  5.3  9.7 12.9 14.2 16.1 16.2 14.1 10.4  4.4 -0.9  8.8    2
1733  4.3  4.5  5.1 10.5 11.5 13.9 18.3 16.5 12.0  8.2  5.5  5.6  9.7    2
1734  1.6  4.4  6.4  9.5 12.3 14.7 17.0 16.3 14.1  9.5  1.9  1.3  9.1    2
1735  3.0  2.4  5.8  9.6 12.2 15.5 16.1 16.5 15.2  7.5  4.0  3.0  9.2    2
1736  1.3  0.8  3.2  9.2 12.2 15.0 17.3 17.7 13.9  9.3  5.6  4.2  9.1    2
1737  4.0  3.0  5.4  7.3 13.7 16.0 16.6 14.5 14.5  8.7  4.3  2.1  9.2    2
1738  0.0  2.5  5.0  9.6 12.8 15.2 16.5 16.0 13.5  9.9  2.0  4.2  8.9    2
1739 -1.4  0.9  3.6  5.2 12.2 14.8 17.7 14.9 13.8  5.8 -0.9  1.9  7.4    3
1740 -6.3 -5.4  0.5  4.8  7.8 13.2 15.9 15.5 13.8  4.3  1.4  0.5  5.5    3
1741 -2.5  2.3  2.5  5.4  9.4 13.8 17.1 15.7 12.9  9.6  5.8  1.3  7.8    3
1742 -2.8  2.2  1.5  4.9  9.7 14.9 15.9 14.5 10.8  8.0  3.6 -2.5  6.7    3
1743  1.1  1.8  2.6  5.0 12.2 17.8 17.6 16.9 14.2  4.6  4.6  0.2  8.2    5
1744 -3.5 -3.0  0.4  7.0 11.9 16.0 18.2 15.1 13.6  7.9  3.8 -1.1  7.2    5
1745 -3.0 -3.7  0.0  6.8 12.6 16.8 17.8 16.8 15.8  8.5  4.6 -0.4  7.7    5
1746 -0.6 -0.6 -0.9  6.3 13.0 15.5 17.8 15.2 13.1  6.3  1.4  3.0  7.5    5
1747 -1.5 -0.2 -0.5  7.0 11.1 18.3 18.0 15.8 15.0  9.3  4.2  1.0  8.1    5
1748 -1.5 -1.5 -2.3  6.4 13.2 17.3 17.6 18.2 14.0  8.4  4.5  3.4  8.1    5
1749 -0.2 -1.4  0.3  6.4 13.3 15.0 17.1 16.9 13.6  7.6  3.4  0.8  7.7    5
1750 -0.6  1.2  4.3  7.1 11.8 15.5 18.9 17.4 13.4  6.0 -0.8 -0.9  7.8    6
1751 -1.5 -3.6  3.3  6.2 12.7 15.9 17.7 17.2 12.0 11.3  0.8-13.7  6.5    6
1752 -5.7 -3.5  1.0  4.5 10.1 15.5 19.7 18.0 11.7  7.3  3.8 -0.7  6.8    6
1753 -3.6 -1.8  3.4  6.8 11.7 16.7 18.4 17.1 13.6  9.1  2.2 -3.6  7.5    8
1754 -2.9 -3.5 -0.5  6.7 12.7 16.5 17.5 17.3 13.1  8.8  3.3 -0.3  7.4    8
1755 -5.5 -4.7  0.4  8.9 11.8 17.8 19.0 16.3 12.6  7.9  2.9 -0.1  7.3    9
1756  0.0  1.5  2.4  5.6 10.6 17.6 18.8 16.3 13.9  7.8  0.7 -2.4  7.7   10
1757 -3.8 -0.3  2.5  8.4 12.2 17.8 21.6 18.4 13.4  5.3  3.9 -1.7  8.1   12
1758 -4.3 -1.2  2.9  7.3 13.8 17.3 17.5 18.1 12.5  6.5  4.0  0.1  7.9   13
1759  0.8  2.2  3.9  8.2 12.4 17.7 20.4 18.8 14.8  9.9  2.1 -2.4  9.1   14
1760 -3.8 -0.5  1.6  8.1 12.8 17.0 19.1 17.5 15.1  8.6  4.0  1.2  8.4   14
1761 -1.9  0.9  5.2  7.4 13.7 18.1 19.1 18.9 15.1  6.7  3.4 -2.4  8.7   15
1762  1.2  0.2  0.9 10.1 13.5 17.0 19.0 16.5 13.5  6.2  3.5 -1.3  8.4   15
1763 -4.3  2.0  2.2  7.3 11.5 16.7 19.2 19.0 13.5  7.8  4.1  1.7  8.4   16
1764  2.1  3.7  3.5  7.7 13.8 16.2 19.7 17.1 13.2  8.4  3.7  0.8  9.2   17
1765  0.7 -1.6  5.3  8.7 12.2 16.6 17.5 18.3 14.0  9.6  4.3 -0.2  8.8   17
1766 -3.4 -0.5  4.1  9.8 13.2 17.6 19.0 18.5 15.0  9.2  5.4 -0.4  9.0   17
1767 -6.0  2.4  4.0  6.8 11.8 16.2 18.5 18.9 14.8  9.2  6.3 -1.2  8.5   18
1768 -3.4  0.1  2.1  7.8 13.0 16.6 19.2 18.3 13.3  8.3  4.5  1.2  8.4   19
1769  0.5  0.2  3.6  8.4 12.7 16.9 19.0 17.5 14.2  5.9  4.1  0.6  8.6   19
1770 -1.5  0.7  0.4  7.0 12.9 16.3 18.4 18.8 15.5  9.2  4.0  1.5  8.6   19
1771 -1.3 -1.7  1.1  5.3 14.5 17.3 18.7 17.7 14.4  9.7  3.0  1.8  8.4   20
1772 -1.3  0.1  3.3  7.5 11.0 17.6 19.0 18.3 14.9 11.0  6.4  2.0  9.2   20
1773  0.5 -0.8  3.5  8.5 13.9 16.4 18.6 18.8 15.2 10.6  4.6  2.2  9.3   20
1774 -2.5  0.8  4.6  9.4 13.0 17.6 18.9 18.7 13.8  8.5 -0.3 -2.7  8.3   21
1775 -1.9  1.6  3.8  6.9 12.0 17.9 19.7 19.3 15.5  9.4  2.7 -1.0  8.8   22
1776 -7.8  0.1  3.3  7.1 10.4 17.2 19.4 18.4 13.4  8.5  3.2 -0.8  7.7   22
1777 -3.4 -2.5  3.2  5.6 12.3 15.9 17.4 18.4 13.6  8.4  3.9 -1.9  7.6   24
1778 -2.9 -1.8  2.2  8.9 13.3 16.1 20.2 18.7 12.7  6.8  3.3  0.9  8.2   24
1779 -5.3  1.3  4.1  9.6 14.2 15.3 18.3 19.0 15.3 10.1  3.2  0.0  8.8   26
1780 -5.7 -3.5  4.8  6.1 13.9 16.5 19.0 18.5 13.8  9.7  2.8 -1.9  7.8   27
1781 -1.9  0.0  4.2  9.5 13.6 17.5 19.0 19.6 14.9  7.5  3.8 -0.9  8.9   31
1782 -1.2 -4.4  0.9  6.1 11.4 16.9 18.9 17.6 13.9  7.2  0.8 -1.2  7.2   31
1783 -1.1  1.7  1.9  8.6 13.9 17.4 19.8 18.4 14.8  9.6  3.3 -1.8  8.9   31
1784 -4.3 -2.5  1.3  5.9 14.4 16.8 18.5 17.6 15.4  6.6  4.1 -1.4  7.7   32
1785 -1.0 -2.6 -2.2  5.8 12.1 16.4 17.8 17.0 15.1  8.1  4.0 -0.8  7.5   32
1786 -1.6 -0.8  1.0  8.8 12.1 17.2 17.1 17.0 13.3  6.7  0.4 -0.6  7.6   32
1787 -1.7  1.1  4.7  6.9 11.9 17.5 18.0 18.3 14.4 10.4  3.9  1.7  8.9   32
1788 -0.7 -0.5  2.3  8.3 13.2 17.8 20.5 17.2 15.2  8.0  2.2 -8.3  7.9   32
1789 -3.9  0.2 -1.3  8.1 15.0 16.3 19.2 18.5 14.5  8.8  3.6  1.4  8.4   32
1790  0.0  2.3  4.2  6.0 14.2 17.2 17.2 18.0 13.4  9.3  3.6  1.0  8.9   32
1791  1.2  0.5  4.2  9.9 12.6 16.6 18.8 19.1 13.6  8.4  2.6  0.5  9.0   32
1792 -2.5 -1.5  3.3  9.1 12.3 16.8 19.3 18.1 13.5  8.3  3.6  0.0  8.4   34
1793 -2.7  1.5  3.1  6.9 12.4 16.0 20.1 18.8 13.5 10.3  4.3  1.0  8.8   34
1794 -0.6  2.2  5.7 10.8 13.4 17.3 20.4 17.2 12.8  8.6  4.1 -0.6  9.3   35
1795 -6.7 -1.7  2.1  9.7 12.5 16.7 17.1 18.2 14.9 11.1  2.7  1.9  8.2   37
1796  3.6  1.3  1.2  8.6 13.3 16.9 18.6 18.5 15.4  8.8  3.2 -2.9  8.9   38
1797 -1.1  1.1  2.5  8.8 14.2 16.4 20.1 19.1 15.6  9.6  4.5  1.9  9.4   38
1798  0.0  1.7  3.9  9.1 14.6 18.2 19.6 19.6 15.7  9.7  3.7 -2.3  9.5   38
1799 -3.2 -1.7  2.2  7.5 12.4 16.5 18.5 18.3 14.7  9.6  5.2 -2.4  8.1   39
1800 -0.3  0.0  1.5 12.3 15.2 15.8 18.7 19.1 15.2  9.9  5.9  1.7  9.6   39
1801  1.6  1.5  6.5  9.0 15.3 16.7 19.1 18.3 16.4 11.5  5.9  1.6 10.3   40
1802 -1.4  1.6  5.6 10.2 13.1 17.7 18.5 20.5 15.7 12.4  5.4  2.1 10.1   40
1803 -2.7 -0.7  4.3 11.3 12.7 17.0 20.4 19.7 13.6  9.7  5.0  1.4  9.3   40
1804  2.3 -0.4  2.0  8.3 15.0 17.5 19.0 18.4 16.2 10.2  3.6 -1.6  9.2   40
1805 -2.5 -0.3  3.4  7.2 12.1 15.6 18.2 17.6 15.4  6.6  2.0  1.0  8.0   40
1806  1.4  2.2  3.7  6.7 14.9 16.4 18.0 18.3 15.8  9.6  6.0  4.2  9.8   41
1807 -0.5  1.8  1.4  6.5 13.7 16.5 20.3 21.6 13.6 10.5  5.2  1.2  9.3   42
1808 -0.8 -1.2 -0.5  6.1 14.7 16.7 20.1 19.4 15.1  8.0  3.7 -2.9  8.2   43
1809 -3.3  1.6  2.3  5.1 13.9 16.5 18.3 18.4 14.2  8.7  2.7  2.0  8.4   43
1810 -2.1 -1.0  3.5  6.9 12.1 15.4 18.2 18.0 15.8  9.1  4.1  1.5  8.5   43
1811 -3.5  0.5  5.6  8.5 15.7 19.1 20.2 18.3 14.6 11.6  5.3  0.8  9.7   44
1812 -3.5  0.4  2.0  5.6 13.1 16.9 18.0 18.3 13.4 10.3  2.4 -4.2  7.7   48
1813 -3.2  2.0  3.4  9.7 14.1 16.5 18.6 17.9 14.8  8.9  4.4  0.5  9.0   49
1814 -3.8 -3.5  1.8  9.7 11.4 16.2 19.7 18.2 13.4  8.5  4.7  1.5  8.1   49
1815 -3.6  1.3  4.7  8.6 13.6 16.4 17.5 17.5 14.1 10.2  2.9 -1.9  8.4   49
1816 -1.2 -2.5  2.4  7.8 12.1 15.9 17.7 16.8 14.0  9.3  3.8  0.2  8.0   53
1817  1.5  2.4  3.8  6.5 13.3 17.6 18.7 18.2 15.1  7.0  4.9 -1.5  9.0   55
1818  0.3  0.4  4.7  8.7 13.3 18.2 20.0 17.8 14.9 10.0  5.5  0.4  9.5   55
1819  0.7  1.0  4.3  9.0 13.7 18.2 19.9 19.5 15.9  9.4  3.4 -1.6  9.5   56
1820 -4.3  0.6  3.2 10.2 14.5 17.0 19.5 19.6 14.5  9.2  2.8 -1.6  8.8   60
1821 -1.2 -0.5  3.1  9.7 13.6 15.9 18.2 18.6 15.7 10.1  5.4  1.6  9.2   63
1822 -0.6  1.9  6.6 10.2 15.3 19.3 20.4 19.1 15.3 10.9  5.9 -1.2 10.3   66
1823 -4.4 -1.1  3.8  8.2 14.2 17.7 19.4 19.6 15.4  9.8  3.5  0.8  8.9   68
1824 -0.5  0.1  3.1  7.9 12.7 16.9 19.7 18.8 16.0  9.5  4.8  2.0  9.2   70
1825 -0.2 -0.1  2.8  9.0 13.9 18.1 19.9 19.0 15.3  9.9  5.1  1.7  9.5   73
1826 -4.3 -0.2  3.7  8.1 14.7 18.7 21.5 20.5 15.6 10.3  3.8  1.2  9.5   74
1827 -2.7 -2.5  4.1 10.1 14.8 18.4 20.6 18.8 15.4 10.2  2.3  0.6  9.2   75
1828 -1.9 -0.4  3.9  8.4 14.4 19.5 20.7 19.4 15.0  9.6  4.7  1.0  9.5   77
1829 -3.9 -3.6  1.3  8.2 14.0 17.6 19.9 18.4 14.3  8.6  1.4 -2.9  7.8   83
1830 -5.5 -2.8  3.4  9.6 13.5 17.8 20.8 19.3 14.4  9.5  5.9  0.0  8.8   87
1831 -4.7 -1.8  3.3  9.3 13.8 18.6 20.6 19.3 14.5 10.8  3.2 -2.9  8.7   88
1832 -2.3 -1.0  2.8  7.9 12.9 17.6 19.3 19.2 14.4 10.1  3.3 -1.1  8.6   95
1833 -2.1  0.3  2.2  8.7 15.7 18.5 19.8 17.7 15.0  9.4  4.2  1.1  9.2   96
1834 -1.6  0.9  4.0  8.5 14.9 18.4 21.7 20.4 16.2  9.7  4.0 -0.1  9.8   99
1835 -0.6 -0.3  3.1  8.0 13.6 18.0 20.2 18.5 14.6  9.7  2.1 -3.3  8.6  101
1836 -2.3 -1.7  4.0  8.0 12.4 17.3 19.5 17.9 14.4  9.1  2.8 -0.3  8.4  109
1837 -2.8 -0.8  1.0  6.7 12.5 17.7 19.2 19.5 14.7  9.6  4.7 -0.2  8.5  114
1838 -4.1 -4.3  3.1  6.9 13.2 18.1 20.4 18.8 15.7  8.9  3.0 -1.1  8.2  117
1839 -2.3 -0.9  0.8  7.3 13.8 17.8 20.8 18.9 15.2 10.3  3.1 -1.9  8.6  120
1840 -2.6  0.0  1.8  9.5 14.0 18.2 20.0 19.7 15.2  9.1  4.5 -2.7  8.9  123
1841 -1.7 -2.1  3.7  8.5 15.1 18.5 19.8 19.6 16.2 10.0  4.3  1.3  9.4  129
1842 -2.2  0.2  4.7  8.7 13.9 17.9 19.9 19.9 15.1  9.0  3.0  0.6  9.2  131
1843  1.0  0.0  1.7  8.7 13.3 17.6 19.8 19.9 16.2  9.7  4.2  1.7  9.5  133
1844 -2.5 -1.1  3.2 10.4 14.8 18.4 19.8 18.9 15.9 10.1  4.3 -1.2  9.2  134
1845  0.1 -2.3  2.0  9.3 13.1 18.7 20.7 19.3 15.4  9.9  5.3 -0.7  9.2  138
1846 -0.3 -0.1  5.0  9.7 14.7 18.9 21.3 21.2 17.2 10.6  4.7 -0.7 10.2  141
1847 -2.0 -0.6  2.1  8.0 14.7 17.7 21.0 20.2 15.6  9.7  5.4  0.0  9.3  143
1848 -4.0  0.4  3.5  9.6 14.7 18.7 20.1 19.3 14.8 10.4  3.5  0.7  9.3  148
1849 -2.2  0.0  3.7  7.9 13.9 18.6 20.3 19.4 15.6 10.7  6.1 -0.5  9.5  152
1850 -2.8  1.5  2.8  8.3 13.3 18.8 20.9 20.1 15.5  9.9  5.6  0.8  9.6  154
1851  0.2  1.0  4.1  9.3 13.7 18.2 20.0 19.5 15.8 11.5  4.0  0.3  9.8  164
1852 -0.6  0.8  3.3  7.6 14.8 18.5 21.1 19.7 16.0 10.9  5.7  3.6 10.1  169
1853  1.2  0.2  3.1  8.8 14.4 18.9 20.9 20.0 16.1 11.1  5.6  0.3 10.1  174
1854 -0.4  0.8  5.1  9.6 15.4 18.6 21.8 20.5 16.9 12.2  5.1  2.0 10.6  183
1855 -0.4 -1.5  3.9 10.2 14.9 18.7 21.3 20.4 16.7 11.9  5.9 -0.2 10.2  189
1856 -0.5  0.7  3.0 10.4 14.0 19.6 20.8 19.8 16.0 11.3  4.2  1.0 10.0  197
1857 -2.2  2.1  4.1  8.4 13.9 18.2 20.9 20.3 16.7 11.7  5.5  3.5 10.3  203
1858  1.0 -0.7  4.6 10.0 14.4 19.9 20.9 20.0 16.9 12.2  3.9  2.2 10.4  206
1859  1.0  2.8  6.4  9.5 15.3 18.6 21.4 20.5 16.0 11.0  6.1  0.0 10.7  211
1860  1.3  0.4  4.1  9.3 14.8 18.7 20.0 19.8 16.2 11.3  5.2  0.6 10.1  214
1861 -1.5  3.0  5.6  8.9 13.0 18.6 20.1 19.9 15.8 11.5  5.7  2.1 10.2  217
1862 -1.1 -0.4  4.2  9.1 14.3 17.3 19.6 18.9 15.8 10.8  4.1  0.6  9.4  222
1863  1.5  1.3  3.7  9.0 14.2 17.4 19.4 19.5 15.0 10.2  5.4  1.0  9.8  223
1864 -1.8  0.9  4.3  8.3 13.5 17.9 19.9 18.7 15.2  9.1  4.2 -0.2  9.2  234
1865  0.1  0.1  3.7 10.3 15.0 18.1 20.2 18.9 17.3 10.7  6.7  1.9 10.2  239
1866  1.7  2.0  4.3 10.4 13.0 18.2 20.0 18.1 16.1 10.8  6.1  2.1 10.2  259
1867 -0.6  3.2  2.8  9.4 12.7 18.1 19.4 19.6 16.3 11.2  5.9  1.0  9.9  263
1868 -0.6  1.3  5.8  9.1 15.1 18.6 21.5 19.9 15.9 10.8  5.3  2.3 10.4  269
1869  2.1  4.1  4.2 10.4 14.5 17.6 20.3 19.6 16.6 10.1  5.7  2.3 10.6  277
1870  2.3  1.4  4.3 10.7 15.6 19.3 21.5 19.7 16.7 11.8  7.2  1.0 11.0  289
1871  0.8  2.2  7.5 11.1 14.8 18.7 20.9 20.8 16.2 11.9  5.3  1.0 10.9  304
1872  1.3  2.5  4.8 10.9 15.5 19.2 21.4 20.6 17.1 11.9  6.0  1.3 11.0  320
1873  1.3  1.7  5.7  9.6 14.4 19.4 21.3 20.5 16.5 11.5  5.9  3.2 10.9  336
1874  2.8  2.6  5.7  9.6 14.9 19.4 21.3 20.1 17.6 12.5  6.6  3.0 11.3  345
1875  0.1  0.5  4.6  9.8 15.6 19.1 20.8 20.2 16.6 11.7  6.3  3.8 10.8  358
1876  3.5  4.3  6.2 11.4 15.1 19.8 21.7 20.8 17.1 12.5  7.2  2.2 11.8  374
1877  2.6  5.4  6.6 11.1 14.9 19.6 21.3 20.9 17.5 12.9  8.7  6.0 12.3  385
1878  3.7  5.9  9.5 13.4 16.2 19.7 21.9 21.4 18.3 13.9  8.8  3.4 13.0  400
1879  2.3  4.2  8.2 11.7 16.2 19.5 21.4 20.9 17.6 14.4  8.0  3.5 12.3  408
1880  5.0  5.0  7.5 12.3 17.0 19.7 21.4 20.9 17.9 12.7  6.6  3.7 12.5  414
1881  0.7  3.2  6.6 11.3 17.0 19.2 21.9 21.3 18.5 12.9  8.0  5.5 12.2  445
1882  3.7  5.3  8.2 11.9 15.6 19.6 21.4 21.2 18.1 13.8  7.4  3.2 12.4  459
1883  0.6  2.7  5.4 11.6 15.8 20.3 21.7 20.8 17.6 13.0  7.9  3.8 11.8  477
1884  1.3  3.5  6.5 10.9 16.1 19.5 21.4 20.9 18.5 13.7  7.1  2.9 11.9  488
1885  0.4  1.8  5.6 11.3 15.6 19.6 22.0 20.6 17.6 12.3  7.6  3.7 11.5  501
1886  0.2  2.2  5.7 12.3 16.6 19.6 21.8 21.3 18.3 13.3  6.9  2.5 11.7  521
1887  0.7  2.7  6.2 11.1 17.1 20.1 22.6 20.8 17.8 12.0  7.2  2.6 11.7  538
1888 -0.4  2.3  4.7 12.1 15.8 20.0 21.8 21.0 17.6 12.3  7.3  3.7 11.5  550
1889  1.7  1.7  7.0 12.1 16.6 19.9 21.9 21.0 17.3 12.2  6.9  5.1 11.9  564
1890  2.4  3.5  5.9 11.9 16.0 20.4 22.1 20.9 17.7 12.5  7.8  2.6 12.0  576
1891  0.9  1.7  4.8 11.3 15.6 19.7 21.1 21.0 18.5 12.3  6.0  3.9 11.4  620
1892 -0.1  3.1  5.1 10.8 15.3 20.1 21.8 21.4 18.2 12.8  6.3  0.9 11.3  639
1893 -2.0  0.6  5.3 10.8 15.4 20.2 22.3 21.2 17.7 12.6  6.2  2.6 11.1  657
1894  0.7  1.0  7.2 11.8 16.3 20.1 22.4 21.6 18.0 12.8  6.4  2.8 11.8  664
1895 -0.8 -0.8  5.5 12.0 16.3 20.1 21.5 21.4 18.7 11.5  6.3  2.2 11.2  675
1896  0.9  2.7  4.8 11.9 17.2 20.5 22.4 21.6 17.3 12.1  5.7  3.0 11.7  681
1897 -0.1  2.3  5.5 11.4 16.3 20.0 22.5 21.2 18.9 13.4  6.2  1.3 11.6  694
1898  1.7  2.5  6.1 10.9 16.0 20.3 22.2 21.9 18.5 11.9  5.7  1.3 11.6  710
1899  0.8 -0.8  4.5 11.3 16.0 20.1 22.1 21.6 17.9 13.2  8.3  1.6 11.4  718
1900  1.6  0.6  5.3 11.5 16.5 20.4 22.2 22.3 18.4 14.2  6.8  3.0 11.9  726
1901  1.1  0.5  6.2 11.0 16.2 20.3 23.5 21.9 17.6 13.3  6.4  1.5 11.6  747
1902  1.0  1.3  6.9 11.1 16.6 19.6 21.7 21.0 17.2 12.9  7.6  1.2 11.5  753
1903  1.1  1.5  7.3 11.1 16.0 18.8 21.5 20.8 17.3 12.7  6.1  1.1 11.3  764
1904 -0.5  1.0  6.1 10.4 16.0 19.4 21.3 20.8 17.9 12.7  7.3  1.9 11.2  771
1905 -0.5 -0.3  7.7 11.1 15.8 19.9 21.7 21.5 18.5 12.0  7.3  2.4 11.4  790
1906  2.3  2.2  4.5 12.3 16.1 19.7 21.8 21.6 18.6 12.4  6.6  3.0 11.8  797
1907  1.0  2.3  7.8  9.6 14.2 18.7 21.8 20.9 17.8 12.7  6.5  3.0 11.4  812
1908  1.6  2.1  7.0 11.8 15.7 19.4 22.0 20.9 18.4 12.2  7.0  2.5 11.7  819
1909  1.2  2.7  5.7 10.4 15.0 19.9 21.6 21.7 17.8 12.3  8.2 -0.1 11.4  831
1910  0.8  0.9  9.3 12.3 15.4 19.6 22.2 20.9 18.0 13.3  6.2  1.6 11.7  842
1911  1.4  2.1  6.9 10.8 16.5 20.5 21.9 21.0 18.3 12.2  5.3  2.5 11.6  849
1912 -1.5  1.3  4.3 11.1 16.0 19.2 21.5 20.4 16.9 12.1  6.9  2.6 10.9  864
1913  1.2  0.7  5.6 11.6 15.6 19.6 21.8 21.8 17.4 11.9  8.2  3.0 11.5  872
1914  2.5  1.0  6.2 11.3 16.3 20.1 22.2 21.2 17.6 13.2  7.2  0.1 11.6  885
1915  0.1  3.3  4.6 12.7 14.9 18.7 21.1 20.5 17.7 13.0  7.0  2.0 11.3  894
1916  0.1  1.8  5.9 10.8 15.3 18.5 22.2 21.2 17.2 11.9  6.1  0.3 10.9  902
1917 -0.1  0.2  4.7 10.0 13.5 18.9 22.2 20.8 17.5 11.0  7.2  0.3 10.5  910
1918 -1.7  2.2  7.7 10.3 15.8 20.2 21.4 21.4 16.7 13.6  6.4  2.9 11.4  917
1919  1.4  1.8  6.0 11.2 15.4 20.0 22.2 21.2 18.2 12.1  5.6  0.4 11.3  918
1920  0.4  2.3  6.2  9.6 15.3 19.2 21.6 20.8 17.9 12.8  6.0  2.3 11.2  921
1921  2.7  3.7  8.6 11.6 15.9 20.5 22.5 21.2 18.4 13.2  6.8  3.1 12.4  946
1922 -0.4  1.8  6.1 11.0 16.2 20.3 21.6 21.4 18.6 13.0  6.9  2.4 11.6  956
1923  2.5  0.8  5.3 10.7 15.4 19.5 21.9 20.9 18.0 12.0  7.5  3.8 11.5  967
1924 -0.4  2.8  5.0 10.9 15.0 19.6 21.3 21.2 17.1 13.2  7.1  0.4 11.1  978
1925  0.6  4.3  7.3 12.5 15.6 20.1 22.0 21.1 18.5 10.7  6.4  2.3 11.8  987
1926  1.2  4.0  6.0 10.9 16.0 19.4 21.9 21.4 17.5 12.9  6.6  1.6 11.6 1009
1927  1.1  3.8  6.7 11.2 15.4 19.1 21.8 20.3 17.9 13.4  7.6  0.4 11.6 1017
1928  1.5  2.6  6.6 10.1 16.1 18.6 22.0 21.2 17.2 13.0  6.9  2.4 11.5 1023
1929 -0.9 -0.6  7.0 11.0 15.3 19.2 21.9 21.4 17.4 12.7  5.9  2.4 11.1 1042
1930 -1.4  4.3  6.1 12.2 15.5 19.7 22.5 21.7 18.2 11.9  6.7  1.7 11.6 1048
1931  2.2  3.8  5.9 11.3 15.5 20.2 22.6 21.1 18.8 13.7  7.7  3.6 12.2 1120
1932  2.3  3.4  4.9 11.5 15.8 19.7 21.8 21.3 17.8 12.4  6.6  1.7 11.6 1137
1933  2.5  1.2  6.3 10.9 15.5 20.4 22.2 21.0 18.5 13.0  7.0  3.2 11.8 1152
1934  2.8  2.9  6.8 12.1 17.4 20.3 22.7 21.5 17.4 13.5  8.4  2.8 12.4 1162
1935  1.2  3.9  7.3 10.6 14.6 19.1 22.2 21.3 17.6 12.7  6.1  1.7 11.5 1177
1936  0.0 -0.7  6.8 10.7 16.7 20.1 22.8 21.8 18.1 12.5  6.2  3.1 11.5 1203
1937 -0.6  1.8  5.1 10.7 16.1 19.6 22.0 21.9 17.9 12.6  6.5  2.0 11.3 1221
1938  1.5  3.2  7.7 11.6 15.5 19.4 21.8 21.6 18.3 13.5  6.5  2.4 11.9 1248
1939  2.0  1.4  6.3 11.1 16.4 19.5 22.0 21.2 18.2 12.5  6.8  4.1 11.8 1263
1940 -1.9  2.0  5.9 10.7 15.6 19.7 21.8 20.9 17.7 13.0  5.8  3.2 11.2 1282
1941  1.6  2.7  5.8 11.8 16.3 19.4 21.9 20.9 17.5 13.3  7.4  3.7 11.9 1363
1942  1.0  1.5  6.5 11.9 15.4 19.2 21.6 20.8 17.4 13.0  7.3  2.5 11.5 1380
1943  0.5  3.6  5.6 11.8 15.6 19.5 21.8 21.2 17.5 12.8  6.7  2.8 11.6 1393
1944  2.2  3.1  5.7 10.6 16.3 19.3 21.3 20.9 17.9 13.1  6.8  1.6 11.6 1408
1945  0.8  2.8  7.8 11.2 14.8 18.4 21.1 21.0 17.6 12.7  6.8  1.0 11.3 1415
1946  1.7  3.2  8.3 12.4 15.2 19.2 21.5 20.5 17.5 12.4  7.0  3.1 11.8 1432
1947  1.4  1.8  6.0 11.4 15.6 18.8 21.2 21.5 18.1 14.3  6.2  2.7 11.6 1453
1948  0.9  2.1  5.8 12.0 15.8 19.6 21.3 20.7 17.9 12.5  7.2  2.5 11.5 1473
1949  0.6  2.3  6.4 11.7 16.2 19.6 21.6 20.9 17.2 12.9  8.1  2.8 11.7 1489
1950  0.9  3.0  5.8 10.6 15.5 19.2 20.7 20.2 17.2 13.6  6.4  2.6 11.3 1493
1951  3.5  4.8  7.5 12.5 16.7 19.3 21.6 21.1 18.2 13.9  8.1  4.9 12.7 2009
1952  4.5  5.8  7.6 13.1 16.7 20.3 21.9 21.3 18.6 13.7  8.5  5.4 13.1 2053
1953  5.4  6.1  9.4 12.6 16.7 20.3 21.8 21.3 18.7 14.8  9.5  5.8 13.5 2074
1954  3.6  6.7  8.2 13.3 16.2 19.9 21.8 21.0 18.7 14.4 10.0  5.7 13.3 2100
1955  4.4  5.0  8.1 13.2 16.9 19.4 21.8 21.6 18.6 14.4  8.2  4.8 13.0 2121
1956  4.2  4.4  8.3 12.3 16.8 20.0 21.2 20.7 18.0 14.5  8.6  6.0 12.9 2139
1957  3.3  6.5  8.8 12.9 16.5 19.9 21.5 20.8 18.1 13.6  9.1  6.6 13.1 2159
1958  4.8  5.4  7.9 12.7 17.2 19.4 21.2 21.1 18.2 14.2  9.5  5.2 13.1 2168
1959  3.9  5.2  8.9 13.0 16.6 19.9 21.5 21.1 18.0 13.6  8.2  6.0 13.0 2176
1960  4.0  5.1  7.0 13.0 16.3 19.8 21.3 20.8 18.4 14.2  9.4  5.0 12.9 2179
1961  4.0  6.5  9.4 12.4 16.3 19.9 21.2 21.0 17.9 14.0  9.0  4.7 13.0 2445
1962  3.7  5.9  7.9 13.0 16.9 19.4 20.8 20.8 17.8 14.5  9.5  5.4 13.0 2457
1963  2.6  5.4  9.0 12.9 16.6 19.6 21.3 20.7 18.4 15.3  9.9  4.2 13.0 2476
1964  4.5  5.0  8.0 12.8 16.8 19.5 21.4 20.2 17.7 13.7  9.2  4.9 12.8 2488
1965  4.4  4.9  7.5 12.6 16.6 19.3 20.9 20.4 17.4 14.1  9.4  6.0 12.8 2488
1966  3.1  5.2  9.1 12.5 16.5 19.6 21.6 20.6 18.0 13.9  9.4  5.2 12.9 2502
1967  4.7  5.1  9.2 12.8 16.1 19.3 21.1 20.7 18.0 14.3  9.1  5.2 13.0 2511
1968  3.4  5.1  9.7 12.9 16.1 19.6 21.1 20.4 17.9 14.1  9.1  4.6 12.8 2518
1969  3.4  4.9  7.6 13.2 17.0 19.3 21.4 21.0 18.2 13.6  9.4  5.7 12.9 2529
1970  3.6  6.2  8.2 12.9 16.9 19.9 21.4 21.0 18.0 13.7  9.2  5.4 13.0 2525
1971  3.5  4.8  7.7 12.2 15.9 19.5 20.9 20.7 18.0 14.1  8.8  5.3 12.6 2444
1972  3.2  4.6  8.8 12.3 16.3 19.3 20.8 20.6 17.5 13.0  7.9  4.1 12.4 2434
1973  3.6  5.3  9.2 12.2 16.2 19.8 21.2 20.8 17.8 13.9  8.3  4.7 12.8 2437
1974  3.5  5.0  9.0 12.7 16.1 19.3 21.2 20.3 17.1 13.1  8.5  4.7 12.5 2445
1975  4.1  4.6  7.5 11.5 16.5 19.2 21.1 20.4 17.3 13.4  8.4  4.5 12.4 2450
1976  3.3  6.1  8.2 12.7 15.8 19.2 20.8 20.1 17.4 12.0  7.3  3.4 12.2 2445
1977  1.5  5.5  9.2 13.4 16.8 19.8 21.3 20.5 17.9 13.3  9.0  4.4 12.7 2444
1978  2.7  3.7  8.4 12.5 16.1 19.4 21.1 20.3 18.0 13.7  8.8  4.4 12.4 2439
1979  1.6  3.2  8.7 12.0 16.1 19.3 21.0 20.5 18.1 14.1  8.5  6.0 12.4 2430
1980  3.6  4.7  7.7 12.7 16.4 19.4 21.6 20.8 18.1 13.2  8.7  4.9 12.6 2420
1981  1.4  3.3  7.3 11.8 15.0 18.9 20.7 20.1 16.9 11.8  6.8  2.6 11.4 3778
1982 -0.5  1.7  6.2 10.9 15.8 18.5 20.7 20.1 17.2 12.5  7.3  4.1 11.2 3765
1983  2.9  3.4  7.1 10.9 15.2 18.6 21.3 20.9 17.5 12.9  7.8  2.1 11.7 3754
1984  1.4  3.2  5.9 10.7 15.3 18.7 20.5 20.3 16.6 12.8  7.0  2.6 11.2 3708
1985 -0.2  1.2  6.4 11.6 15.7 18.3 20.6 20.1 16.7 12.5  6.3  1.8 10.9 3680
1986  2.1  2.1  7.2 11.5 15.9 19.2 20.6 19.9 16.6 12.2  6.9  2.9 11.4 3664
1987  0.9  3.8  6.0 11.5 15.7 19.0 21.0 20.1 17.4 12.3  7.1  3.6 11.5 3598
1988  3.0  3.1  6.8 11.4 15.7 18.8 20.7 20.0 17.0 12.6  7.0  4.3 11.7 3538
1989  3.2  4.2  7.7 11.2 15.4 18.2 20.3 19.6 16.6 12.4  6.8  3.4 11.6 3327
1990  2.0  4.4  8.3 11.2 15.3 18.5 20.5 20.4 16.8 13.2-99.0-99.0 13.1 3225
AA    2.0  3.4  7.1 11.7 15.9 19.4 21.4 20.8 17.7 13.1  7.6  3.4 12.0
Ad   -0.0  1.4  4.7  9.6 14.3 18.0 19.9 19.2 15.9 10.8  5.4  1.6 10.1
 
For Country Code ALL
 
From input file ./data/v1.mean

v2

Thermometer Records, Average of Monthly Data and Yearly Average
by Year Across Month, with a count of thermometer records in that year
--------------------------------------------------------------------------
YEAR  JAN  FEB  MAR  APR  MAY  JUN JULY  AUG SEPT  OCT  NOV  DEC  YR COUNT
--------------------------------------------------------------------------
1701 -4.2 -1.5  1.6-99.0-99.0 15.4 18.9 15.8-99.0-99.0-99.0 -0.5  6.5    1
1702  2.0 -0.5  0.6  2.6 10.9 16.0 16.0 15.8 10.1  7.5  0.2  0.6  6.8    1
1703 -2.8 -0.9  0.6  7.7 14.1 16.1 15.4 16.3 11.4  6.1  2.2  2.5  7.4    1
1704 -4.9 -0.5  3.9  9.4 11.8 14.1 17.1-99.0-99.0-99.0-99.0 -0.9  6.2    1
1705 -7.1-99.0  1.0-99.0-99.0 16.0 18.3 17.8  8.7  7.5  0.7  1.8  7.2    1
1706 -1.2 -1.0  2.8  7.4 12.8 17.2 16.6 15.6 11.8  8.5  3.5  2.5  8.0    2
1707 -0.5  0.8  2.4  6.4 11.7 17.2 18.0 15.6 12.6  6.0  3.8  1.7  8.0    2
1708  3.0  0.9  4.7  7.7 11.1 14.3 15.3 17.9 14.3  7.9  3.3 -1.6  8.2    2
1709 -9.0 -0.9  0.9  9.4 11.7 16.7 16.0 16.0 12.2  8.1  5.6  2.0  7.4    2
1710 -1.1 -0.2  4.1  6.9 12.9 15.2 15.2 16.5 13.8  9.4  7.4  6.5  8.9    2
1711  3.5  0.0  4.7  9.5 12.2 16.9 16.0 15.6 13.3  9.3  6.5  1.5  9.1    1
1712  0.2  2.9  4.1  7.7 12.3 16.3 16.8 14.9 13.1  9.5  5.0  4.2  8.9    1
1713 -0.3  5.0  1.0  5.3 10.5 13.6 14.8 15.4 13.9  9.3  3.4  2.5  7.9    1
1714  1.9  3.8  5.0  7.9 10.2 14.5 18.5 13.8 13.0  9.7  4.6  2.4  8.8    1
1715  0.7  3.5  5.7  9.6 11.6 14.5 15.8 17.0 14.1 10.3  6.3 -1.5  9.0    1
1716 -5.0  1.5  3.3  9.1 11.3 14.0 16.3 15.5 12.4  8.3  3.9  1.2  7.7    1
1717  0.9  0.7  3.4  7.2 10.2 15.3 15.7 15.5 13.9  9.3  3.4  3.5  8.3    1
1718 -1.6 -0.8  4.6  8.3 12.7 16.0 18.0 19.0 15.1  8.9  5.2  3.3  9.1    1
1719  0.5  2.5  3.5  5.6 13.4 16.0 20.1 18.9 14.1  8.2  5.0  1.3  9.1    1
1720  2.9  2.9  3.1  6.8 12.3 12.6 17.2 14.5 14.3  8.1  5.6  3.6  8.7    1
1721  3.5  0.1  0.8  8.9 10.2 15.3 15.2 16.5 14.4  8.6  5.8  1.9  8.4    1
1722  0.9  3.9  5.5  8.6 11.5 15.1 15.8 15.5 14.6 10.4  6.8  3.3  9.3    1
1723  0.3  2.5  6.4  8.4 12.4 15.6 15.6 15.9 13.9 11.0  2.2  4.7  9.1    1
1724  4.8  3.6  3.7  6.7 11.8 16.7 15.1 16.9 14.2  8.3  5.1  2.0  9.1    1
1725  2.3  0.4  3.5  7.0 10.6 14.0 14.6 14.3 12.5  8.0  3.0  2.0  7.7    1
1726 -1.8  0.2  2.5  7.8 14.1 16.2 15.6 14.4 14.2  9.2  5.3  0.1  8.1    1
1727  2.6  3.6  3.7  7.0 15.0 15.4 16.8 17.5 14.7 11.5  3.6  2.5  9.5    1
1728  2.5 -0.6  6.9  8.9 14.8 16.6 16.5 14.6 13.1  9.2  4.3 -0.6  8.8    2
1729 -3.1  0.3  0.2  6.3 11.1 16.2 17.8 17.7 16.9 12.1  5.3  5.3  8.8    2
1730  2.4  2.3  4.3  9.1 12.6 15.6 17.2 16.8 14.4  7.0  7.5  2.2  9.3    2
1731 -0.5 -0.4  3.3  6.4 12.0 15.3 16.4 16.8 14.8 11.9  6.5  3.2  8.8    2
1732 -0.5  3.3  5.3  9.7 12.9 14.2 16.1 16.2 14.1 10.4  4.4 -0.9  8.8    2
1733  4.3  4.5  5.1 10.5 11.5 15.3 18.3 16.5 12.0  8.2  5.5  5.6  9.8    2
1734  1.6  4.4  6.4  9.5 12.3 14.7 17.0 16.3 14.1  9.5  1.9  1.3  9.1    2
1735  3.0  2.4  5.8  9.6 12.2 15.5 16.1 16.5 15.2  7.5  4.0  3.0  9.2    2
1736  1.3  0.8  3.2  9.2 12.2 15.0 17.3 17.7 13.9  9.3  5.6  4.2  9.1    2
1737  4.0  3.0  5.4  7.3 13.7 16.0 16.6 14.5 14.5  8.7  4.3  2.1  9.2    2
1738  0.0  2.5  5.0  9.6 12.8 15.2 16.5 16.0 13.5  9.9  2.0  4.2  8.9    2
1739 -1.4  0.9  3.6  5.2 12.2 14.8 17.7 14.9 13.8  5.8 -0.9  1.9  7.4    3
1740 -6.3 -5.4  0.5  4.8  7.8 13.2 15.9 15.5 13.8  4.3  1.4  0.5  5.5    3
1741 -2.5  2.3  2.5  5.4  9.4 13.8 17.1 15.7 12.9  9.6  5.8  1.3  7.8    3
1742 -2.8  2.2  1.5  4.9  9.7 14.9 15.9 14.5 10.8  8.0  3.6 -2.5  6.7    3
1743  1.1  1.8  2.6  4.9 12.3 17.7 17.8 16.9 14.3  4.4  4.5 -0.1  8.2    5
1744 -3.7 -3.1  0.2  6.9 11.8 15.9 18.1 14.7 13.4  7.9  3.7 -1.3  7.0    5
1745 -3.1 -3.6 -0.1  6.6 12.7 17.0 18.0 16.8 16.0  8.5  4.7 -0.3  7.8    5
1746 -0.4 -0.4 -0.8  6.5 13.0 15.6 17.8 15.2 13.1  6.3  1.4  3.0  7.5    4
1747 -1.5 -0.2 -0.5  7.2 11.1 18.4 18.2 15.8 15.1  9.5  4.3  1.0  8.2    4
1748 -1.5 -1.5 -2.3  6.6 13.3 17.3 17.6 18.2 14.1  8.6  4.6  3.5  8.2    4
1749 -0.1 -1.3  0.4  6.6 13.3 15.2 17.1 17.5 13.7  7.8  3.6  0.9  7.9    4
1750 -0.5  1.4  4.4  7.3 12.0 16.2 19.1 17.5 13.6  6.2  3.1 -0.9  8.3    5
1751 -1.4 -3.5  3.5  6.4 12.7 16.0 18.0 17.4 11.9  7.4  2.0 -0.7  7.5    5
1752 -5.7 -3.6  1.0  4.4  9.7 15.3 19.4 17.6 11.4  7.2  3.7 -0.9  6.6    5
1753 -3.3 -1.7  3.5  6.9 11.7 16.5 18.4 16.9 13.9  9.4  2.4 -3.6  7.6    8
1754 -2.6 -3.3 -0.5  6.5 12.6 16.6 17.2 17.0 13.0  9.2  3.5 -0.1  7.4    8
1755 -5.3 -4.7  0.4  8.8 11.8 18.2 18.9 16.1 12.6  8.3  3.1  0.0  7.3   10
1756  0.1  1.6  2.4  5.4 10.4 17.2 19.0 16.1 13.9  8.1  0.9 -2.2  7.7   11
1757 -3.7 -0.2  2.4  8.4 12.1 17.9 21.8 18.5 13.4  5.4  4.2 -1.6  8.2   13
1758 -4.2 -1.2  2.7  7.0 13.4 17.5 17.4 18.1 12.5  7.6  4.1  0.1  7.9   13
1759  1.0  2.2  3.8  8.1 12.2 17.7 20.4 18.9 14.7 10.0  2.1 -2.4  9.1   14
1760 -3.8 -0.5  1.6  8.0 12.7 17.5 19.0 17.5 15.4  8.7  4.0  1.3  8.4   14
1761 -1.7  1.0  5.2  7.4 13.7 18.1 19.0 19.0 15.3  6.7  3.6 -2.3  8.7   14
1762  1.2  0.2  0.7 10.0 13.3 17.1 18.9 16.2 13.5  6.2  3.6 -1.2  8.3   13
1763 -4.0  2.3  2.3  7.2 11.3 16.5 19.0 18.7 13.4  7.9  4.3  2.2  8.4   15
1764  2.2  3.8  3.5  7.5 13.6 15.9 19.6 16.9 13.1  8.5  3.8  0.8  9.1   16
1765  1.0 -1.4  5.2  8.6 12.0 16.4 17.3 17.8 13.9  9.7  4.4 -0.1  8.7   16
1766 -3.0 -0.4  4.1  9.6 12.9 17.4 18.7 18.3 15.0  9.4  5.7 -0.2  9.0   16
1767 -5.5  2.5  4.0  6.5 11.6 15.6 18.1 18.7 14.9  9.3  6.5 -0.8  8.4   17
1768 -3.2  0.3  2.0  7.7 12.7 16.5 19.0 18.2 12.8  7.9  3.6  0.2  8.1   20
1769 -0.7 -0.9  2.4  7.5 12.0 16.2 18.5 17.0 14.2  6.1  4.1  1.0  8.1   21
1770 -1.2  0.9  0.4  6.8 12.8 16.2 18.3 18.7 15.7  9.3  4.1  1.8  8.7   21
1771 -1.4 -1.6  0.8  5.1 14.5 17.2 18.6 17.4 14.4  9.9  3.2  2.1  8.3   22
1772 -1.0  0.4  3.3  7.4 10.9 17.7 18.6 18.3 15.0 11.2  6.6  2.3  9.2   21
1773  0.9 -0.5  3.6  8.7 14.1 16.6 18.6 18.8 15.3 10.9  4.6  2.4  9.5   23
1774 -2.1  1.4  5.1  9.5 13.1 17.8 18.8 18.7 13.8  8.7 -0.1 -2.3  8.5   21
1775 -1.4  2.3  4.2  7.2 12.1 18.2 19.7 19.3 15.6  9.5  3.0 -0.4  9.1   23
1776 -8.0  0.2  3.0  6.9 10.2 17.2 19.4 18.2 13.2  8.3  3.0 -0.8  7.6   24
1777 -3.5 -2.7  3.0  5.4 12.3 15.9 17.3 18.2 13.3  8.3  4.0 -1.9  7.5   25
1778 -2.9 -1.7  2.1  8.8 13.3 16.3 20.3 18.6 12.5  6.6  3.1  0.9  8.2   24
1779 -5.0  1.5  4.3  9.5 14.1 15.5 18.4 19.1 15.4 10.3  3.3  0.2  8.9   27
1780 -5.4 -3.1  5.1  6.0 13.7 16.6 19.1 18.7 13.9  9.8  2.8 -1.7  8.0   28
1781 -2.0  0.0  4.2  9.5 14.1 17.7 19.1 19.7 15.2  7.6  3.9 -0.8  9.0   32
1782 -0.9 -4.2  1.2  6.4 11.7 17.3 19.3 18.0 14.1  7.2  0.8 -0.5  7.5   33
1783 -0.8  2.0  1.8  9.0 13.7 17.5 20.1 18.3 14.7  9.6  3.1 -2.0  8.9   32
1784 -4.7 -2.6  1.2  5.9 14.5 16.9 18.5 17.5 15.2  6.6  4.2 -1.5  7.6   33
1785 -1.0 -2.5 -2.3  5.7 12.1 16.3 17.9 17.0 15.2  8.2  4.0 -0.7  7.5   34
1786 -1.4 -0.6  1.0  9.0 12.2 17.3 17.2 17.1 13.3  6.8  0.6 -0.5  7.7   34
1787 -1.6  1.4  4.6  7.0 11.8 17.6 18.1 18.0 14.2 10.6  3.8  1.3  8.9   31
1788  0.0  0.3  3.0  8.9 14.0 18.1 20.8 17.5 15.7  8.6  2.4 -7.6  8.5   33
1789 -3.2  1.2 -0.4  8.7 15.7 16.4 19.4 18.7 14.8  9.3  3.8  1.8  8.9   32
1790  0.5  3.0  4.9  6.5 14.7 17.6 17.5 18.4 13.8  9.9  4.0  1.4  9.4   30
1791  1.9  1.3  4.8 10.6 13.1 17.1 19.3 19.7 14.3  9.2  3.5  1.4  9.7   33
1792 -1.3 -0.4  4.3  9.9 12.8 17.2 19.6 18.6 13.9  9.2  4.3  0.9  9.1   34
1793 -1.7  2.4  4.0  7.4 12.8 16.4 20.6 19.2 14.3 10.9  4.9  1.9  9.4   33
1794  0.1  3.3  6.5 11.5 13.9 17.8 21.1 17.7 13.5  9.4  5.0 -0.1 10.0   34
1795 -6.1 -0.7  3.0 10.3 13.1 17.2 17.4 18.7 15.6 11.7  3.4  2.9  8.9   36
1796  4.4  2.1  2.0  8.8 13.6 16.9 18.9 19.0 16.1  9.4  3.8 -2.1  9.4   38
1797 -0.2  1.7  3.1  9.4 14.6 16.5 20.4 19.4 16.0 10.0  5.0  2.6  9.9   36
1798  0.7  2.4  4.4  9.7 15.0 18.5 19.9 19.9 16.1 10.2  4.3 -1.8  9.9   39
1799 -3.0 -0.8  2.8  7.7 12.7 16.7 18.7 18.6 15.1 10.1  5.0 -1.8  8.5   40
1800  0.2  0.1  1.7 12.7 15.6 16.0 19.1 19.6 15.7 10.3  6.3  2.2 10.0   40
1801  1.9  1.8  6.8  9.6 15.8 17.0 19.6 18.9 16.7 11.9  6.0  1.6 10.6   40
1802 -1.6  1.6  5.4 10.0 13.0 17.8 18.6 20.6 15.7 12.4  5.3  1.9 10.1   40
1803 -2.5 -0.9  4.0 11.3 12.8 17.2 20.6 20.0 13.8  9.8  5.0  1.2  9.4   42
1804  2.3 -0.5  2.2  8.5 15.3 18.0 19.5 18.7 16.3 10.6  4.0 -1.3  9.5   41
1805 -2.0 -0.1  3.5  7.2 12.2 15.9 18.4 18.0 15.4  6.9  2.1  0.9  8.2   42
1806  1.7  2.3  3.8  6.8 15.0 16.7 18.3 18.6 15.9  9.8  6.0  4.3  9.9   46
1807 -0.7  1.6  1.1  6.6 13.7 16.6 20.7 21.9 13.9 10.8  5.4  1.2  9.4   47
1808 -0.4 -1.0 -0.1  6.1 14.9 17.0 20.6 19.6 15.3  8.5  4.0 -2.9  8.5   48
1809 -3.0  1.8  2.4  5.2 14.1 16.9 18.7 18.9 14.6  8.9  2.8  2.4  8.6   49
1810 -2.0 -0.8  3.8  7.2 12.4 15.8 18.6 18.3 16.1  9.3  4.2  1.7  8.7   49
1811 -3.1  0.8  5.8  8.7 16.0 19.4 20.6 18.4 14.8 11.4  5.0  0.6  9.9   51
1812 -3.6  0.9  1.7  5.0 12.9 16.5 17.8 18.2 13.2 10.0  2.0 -4.4  7.5   52
1813 -3.6  1.6  3.1  9.3 13.9 16.2 18.5 17.7 14.6  9.1  4.5  0.3  8.8   58
1814 -3.7 -3.5  1.8  9.6 11.1 16.1 19.8 18.1 13.3  8.5  4.7  1.7  8.1   56
1815 -3.9  1.6  4.8  8.7 13.9 16.6 17.6 17.6 14.2 10.0  2.7 -1.6  8.5   55
1816 -1.5 -2.8  1.7  7.7 11.9 15.7 17.5 16.7 13.9  9.1  3.2  0.2  7.8   60
1817  1.0  2.2  3.2  5.5 13.0 17.6 18.5 18.0 15.0  6.8  4.7 -1.6  8.7   68
1818  0.0  0.0  3.9  8.5 12.7 17.7 19.6 17.0 14.3  9.6  5.3  0.0  9.1   73
1819  0.3  0.7  3.7  8.9 13.4 17.7 19.5 19.1 15.5  9.2  3.1 -1.6  9.1   74
1820 -4.6  0.1  2.7  9.9 14.1 16.3 18.8 19.5 14.0  9.1  2.6 -1.4  8.4   83
1821 -0.8 -0.9  3.2  9.6 13.2 15.2 17.5 18.2 15.4 10.0  5.7  1.9  9.0   88
1822 -0.4  2.3  6.7  9.9 15.1 19.2 19.9 18.7 14.8 10.8  5.7 -1.6 10.1   88
1823 -5.4 -0.9  3.6  7.6 13.8 16.9 18.6 19.1 14.9  9.6  3.2  0.7  8.5   95
1824 -0.8  0.2  2.8  7.5 12.4 16.4 18.9 18.4 15.6  9.3  4.6  2.2  9.0  102
1825  0.0 -0.2  2.5  9.2 13.7 17.6 19.5 18.7 15.5  9.6  5.4  2.5  9.5  107
1826 -4.9  0.2  3.6  8.0 13.7 18.2 21.2 20.4 15.5 10.4  3.6  1.3  9.3  105
1827 -2.8 -3.2  4.1  9.7 14.5 18.0 20.1 18.1 14.9 10.1  2.0  1.1  8.9  112
1828 -2.2 -1.0  3.8  8.5 13.9 18.4 20.0 18.2 14.4  9.1  4.1  0.6  9.0  120
1829 -4.6 -3.9  1.3  7.8 13.4 16.9 19.2 17.6 14.0  8.0  0.8 -4.3  7.2  132
1830 -6.1 -3.2  3.5  9.3 13.3 17.2 19.9 18.5 13.7  8.9  5.5 -0.1  8.4  135
1831 -4.5 -1.3  3.0  9.3 13.3 17.5 19.7 18.5 13.7 10.9  3.2 -1.4  8.5  141
1832 -2.2 -0.8  2.4  7.5 11.8 16.6 18.2 18.4 13.7  9.6  2.9 -0.7  8.1  142
1833 -2.9  0.9  2.0  7.7 15.3 17.8 18.6 16.5 14.1  9.0  4.1  1.5  8.7  142
1834 -0.8  0.4  3.5  7.7 14.6 17.8 21.3 20.0 15.7  9.2  3.8 -0.1  9.4  145
1835 -0.5  0.1  2.9  7.3 12.9 17.4 19.5 17.7 14.2  9.1  1.5 -3.2  8.2  139
1836 -2.6 -1.4  4.4  7.8 11.4 16.9 18.6 17.3 13.7  9.0  2.4 -0.3  8.1  149
1837 -2.8 -1.2  0.3  6.1 11.8 17.0 18.3 19.0 13.8  8.8  3.8 -1.0  7.8  157
1838 -6.3 -4.9  2.2  6.0 12.6 17.2 19.3 17.6 15.0  8.2  2.4 -1.5  7.3  158
1839 -2.7 -1.3 -0.1  6.1 13.3 17.6 20.2 18.2 14.8  9.8  3.2 -2.6  8.0  166
1840 -3.0 -1.3  0.9  8.4 13.0 17.3 19.1 18.5 14.4  7.7  3.9 -4.2  7.9  174
1841 -3.0 -3.6  2.8  7.9 14.7 17.5 19.1 18.8 15.3  9.7  3.5  0.6  8.6  178
1842 -3.8 -0.9  3.5  7.4 13.3 17.1 19.0 19.5 14.3  7.9  2.1  0.1  8.3  177
1843  0.0  0.0  1.1  7.9 12.2 16.8 19.0 18.9 15.0  8.9  3.4  0.9  8.7  179
1844 -3.0 -2.1  1.9  9.0 13.9 17.3 18.7 17.8 15.0  9.3  3.3 -2.7  8.2  183
1845 -0.6 -3.7  0.3  8.3 12.2 18.0 20.1 18.2 14.2  9.1  4.8 -0.6  8.4  184
1846 -1.2 -0.5  4.3  8.7 13.8 18.3 20.7 20.6 16.1 10.1  3.6 -1.9  9.4  174
1847 -3.3 -1.6  1.0  6.9 14.1 17.0 20.2 19.6 14.7  8.8  4.7 -0.9  8.4  176
1848 -5.8  0.2  3.2  9.2 13.9 18.1 19.5 18.5 14.1  9.7  2.9 -0.2  8.6  183
1849 -2.8  0.0  2.8  7.0 13.4 17.7 19.3 18.5 14.4  9.9  5.1 -1.2  8.7  191
1850 -4.2  1.2  2.0  8.1 13.0 18.1 20.0 19.5 14.6  8.8  4.9  0.4  8.9  191
1851 -0.2  0.2  3.2  8.8 12.8 17.5 19.2 18.9 15.1 10.8  3.7  0.3  9.2  212
1852 -0.5  0.1  2.6  6.8 14.1 17.9 20.5 19.2 15.5  9.9  5.3  3.2  9.6  219
1853  1.1 -0.4  2.1  8.1 13.8 18.1 20.3 19.2 15.4 10.8  4.9 -0.7  9.4  212
1854 -1.1  0.2  4.4  8.9 14.6 17.7 21.0 19.7 15.9 11.4  4.2  1.8  9.9  223
1855 -1.3 -2.5  3.1  9.3 14.0 18.1 20.5 19.8 15.8 11.4  4.9 -1.4  9.3  232
1856 -0.6  0.3  2.0  9.4 13.2 18.6 19.8 19.2 15.2 10.6  3.3  0.9  9.3  256
1857 -2.0  1.3  3.7  8.0 13.4 17.7 20.2 20.0 16.1 11.3  5.0  3.2  9.8  266
1858  0.1 -1.2  3.8  9.3 13.7 19.3 20.1 19.3 16.5 11.6  3.1  2.0  9.8  265
1859  1.0  2.8  6.2  9.1 14.6 18.1 20.8 19.9 15.4 10.8  5.7  0.1 10.4  266
1860  1.1 -0.1  3.3  8.6 14.2 17.8 19.1 18.8 15.3 10.5  4.4 -0.1  9.4  245
1861 -2.3  2.7  5.3  8.2 12.5 18.0 19.5 19.2 15.1 11.0  5.2  1.8  9.7  259
1862 -1.1 -0.4  4.4  9.0 14.0 16.6 18.7 18.1 15.2 10.6  4.0  0.7  9.1  252
1863  2.1  1.8  4.0  8.8 13.7 16.8 18.5 18.8 14.6 10.3  5.5  1.8  9.7  253
1864 -1.5  0.9  4.5  8.2 13.0 17.6 19.3 18.0 14.9  9.0  3.9 -0.2  9.0  276
1865  0.5 -0.3  3.0 10.1 14.7 17.2 19.8 18.3 16.8 10.5  6.6  1.9  9.9  280
1866  2.3  2.2  4.1 10.0 12.5 17.9 19.4 17.8 15.8 10.6  6.1  2.2 10.1  313
1867 -0.3  3.4  2.7  9.1 12.2 17.5 18.8 19.1 15.8 10.9  5.8  0.8  9.7  317
1868 -0.5  1.6  5.6  8.7 14.8 17.9 20.7 19.4 15.6 10.6  5.2  2.8 10.2  327
1869  2.0  4.1  3.9 10.0 13.7 16.9 19.7 19.0 16.1  9.9  5.5  2.3 10.3  320
1870  1.8  0.8  4.0 10.1 14.9 18.5 20.7 19.0 16.1 11.3  6.7  0.5 10.4  350
1871  0.2  1.5  7.0 10.4 14.0 17.6 20.1 20.0 15.5 11.4  4.9  0.7 10.3  388
1872  1.4  2.2  4.5 10.5 14.9 18.6 20.7 19.9 16.6 11.5  6.0  1.5 10.7  419
1873  1.1  1.3  5.2  9.0 13.7 18.7 20.7 20.1 16.0 11.2  5.6  3.2 10.5  434
1874  2.5  2.0  4.9  9.1 14.1 18.5 20.7 19.5 17.0 12.1  6.1  2.3 10.7  444
1875 -0.3 -0.2  3.8  9.1 15.0 18.6 20.3 19.8 16.2 11.1  5.5  2.7 10.1  459
1876  2.5  3.3  5.4 10.5 14.2 19.1 21.1 20.3 16.5 11.9  6.5  1.5 11.1  463
1877  1.6  4.6  5.4 10.2 14.0 18.8 20.7 20.3 16.6 12.0  8.0  5.3 11.5  485
1878  2.9  5.0  8.5 12.6 15.3 18.9 21.4 20.9 17.8 13.2  8.0  2.7 12.3  533
1879  1.4  2.9  6.8 10.6 15.2 18.6 20.7 20.4 17.0 13.7  7.1  2.3 11.4  554
1880  3.9  4.1  6.4 11.1 15.9 18.9 20.9 20.4 17.5 12.1  6.0  3.0 11.7  562
1881  0.0  2.4  5.9 10.2 15.9 18.2 21.2 20.6 17.6 11.9  7.1  4.5 11.3  605
1882  2.8  4.1  6.9 10.7 14.6 18.8 20.7 20.6 17.3 12.7  6.5  2.0 11.5  674
1883 -0.3  1.6  4.2 10.5 14.7 19.4 20.9 20.1 16.9 12.0  6.9  2.6 10.8  704
1884  0.1  2.1  5.1  9.8 14.9 18.6 20.5 20.1 17.5 12.5  6.1  1.7 10.8  753
1885 -1.0  0.6  4.2 10.1 14.5 18.6 21.1 19.7 16.7 11.4  6.5  2.7 10.4  785
1886 -0.9  0.8  4.5 11.2 15.4 18.7 21.1 20.6 17.4 12.4  6.1  1.5 10.7  827
1887 -0.3  1.4  5.0 10.1 16.0 19.1 21.8 20.1 17.1 11.3  6.4  1.9 10.8  871
1888 -1.0  1.3  3.9 10.9 14.9 19.0 21.0 20.3 17.0 11.7  6.8  3.0 10.7  946
1889  1.0  1.0  6.0 11.2 15.8 19.1 21.2 20.4 16.8 11.8  6.6  4.0 11.2 1037
1890  1.5  2.5  5.2 11.0 15.1 19.5 21.4 20.4 17.2 12.0  7.1  1.8 11.2 1078
1891 -0.1  0.6  4.1 10.3 14.8 18.9 20.6 20.4 17.7 11.7  5.3  3.1 10.6 1169
1892 -0.8  1.9  4.1  9.9 14.6 19.4 21.2 20.9 17.6 12.1  5.6  0.2 10.6 1259
1893 -2.9 -0.2  4.7 10.0 14.7 19.5 21.7 20.7 17.1 12.2  5.7  1.7 10.4 1340
1894  0.0  0.6  6.4 11.0 15.5 19.5 21.8 21.0 17.3 12.2  5.9  2.1 11.1 1394
1895 -1.3 -1.2  4.6 11.2 15.5 19.4 20.9 20.8 18.0 11.3  5.9  1.7 10.6 1474
1896  0.0  1.9  4.1 10.9 16.3 19.7 21.7 21.0 16.8 11.7  5.2  2.3 11.0 1512
1897 -0.5  1.5  4.8 10.8 15.6 19.3 21.9 20.8 18.2 12.7  5.7  0.9 11.0 1582
1898  1.5  1.9  5.2 10.2 15.4 19.5 21.6 21.3 17.9 11.6  5.7  1.4 11.1 1623
1899  0.5 -0.7  4.0 10.8 15.4 19.3 21.4 20.9 17.3 12.7  7.9  1.4 10.9 1656
1900  0.9  0.6  4.7 10.8 15.7 19.5 21.3 21.5 17.8 13.7  6.4  2.6 11.3 1697
1901  0.4  0.2  5.7 10.6 15.5 19.5 22.4 21.1 17.0 12.6  6.1  1.4 11.0 1713
1902  0.8  1.1  6.3 10.4 15.6 18.7 20.9 20.3 16.7 12.2  7.1  0.9 10.9 1756
1903  0.8  1.5  6.7 10.5 15.2 18.1 20.7 20.0 16.8 12.2  5.8  0.9 10.8 1807
1904 -0.7  0.5  5.3 10.0 15.3 18.7 20.7 20.2 17.1 12.3  7.1  1.8 10.7 1847
1905 -0.5 -0.3  6.8 10.5 15.3 19.2 21.1 20.8 17.8 11.7  7.2  2.6 11.0 1885
1906  2.3  1.9  4.5 11.9 15.7 19.2 21.2 21.0 17.9 12.3  6.5  2.8 11.4 1926
1907  1.2  2.3  7.4  9.6 14.0 18.1 20.7 20.1 17.3 12.6  6.9  3.4 11.1 2056
1908  2.1  2.4  6.5 11.4 15.3 18.6 20.9 20.1 17.7 12.1  7.2  3.0 11.4 2082
1909  1.2  2.8  5.7 10.2 14.6 18.9 20.5 20.8 17.3 12.3  8.2  1.0 11.1 2128
1910  1.6  1.6  8.7 12.0 14.9 18.7 21.1 20.1 17.4 13.0  6.4  2.1 11.5 2177
1911  1.3  2.4  6.6 10.7 15.9 19.4 20.9 20.2 17.7 12.3  6.2  3.3 11.4 2229
1912 -0.6  2.1  4.8 11.1 15.5 18.5 20.5 19.6 16.5 12.0  7.1  3.2 10.9 2262
1913  1.7  1.5  5.9 11.6 14.9 18.6 20.7 20.7 16.9 12.0  8.4  3.9 11.4 2326
1914  2.8  1.8  6.5 11.0 15.8 19.1 21.1 20.3 17.0 13.1  7.4  1.3 11.4 2377
1915  0.8  3.6  5.0 12.3 14.5 18.0 20.2 19.6 17.1 12.5  7.3  2.6 11.1 2392
1916  0.6  2.0  5.4 10.6 14.7 17.8 21.1 20.2 16.5 11.7  6.4  0.9 10.7 2405
1917  0.0  0.1  4.8  9.9 13.2 18.0 20.9 19.8 16.7 10.8  7.2  0.2 10.1 2426
1918 -1.2  1.9  7.1 10.2 15.0 18.8 20.1 20.3 16.1 13.2  6.9  3.2 11.0 2448
1919  1.9  2.0  5.8 11.0 14.8 19.0 20.9 20.1 17.5 12.1  6.0  1.3 11.0 2449
1920  0.7  2.6  6.4  9.8 14.9 18.4 20.5 20.0 17.3 12.8  6.5  2.9 11.1 2459
1921  3.0  3.8  8.1 11.6 15.6 19.5 21.5 20.1 17.8 12.9  6.9  3.5 12.0 2523
1922  0.3  2.2  6.4 11.2 15.8 19.2 20.5 20.4 17.8 12.8  7.4  2.7 11.4 2548
1923  2.3  1.2  5.4 10.4 14.9 18.6 20.7 19.9 17.3 12.2  7.7  4.3 11.2 2590
1924  0.0  2.6  5.2 10.5 14.4 18.5 20.4 20.2 16.6 12.9  7.2  0.9 10.8 2624
1925  0.7  3.8  6.8 12.0 14.9 19.0 20.7 20.2 17.6 10.7  6.7  2.8 11.3 2660
1926  1.7  3.8  5.8 10.3 15.2 18.3 20.6 20.3 16.8 12.3  6.6  1.7 11.1 2712
1927  0.9  3.1  6.3 10.7 14.6 18.2 20.7 19.5 17.2 13.1  7.2  0.6 11.0 2713
1928  1.5  2.4  5.9  9.9 15.3 17.7 20.8 20.2 16.7 12.5  7.1  2.8 11.1 2718
1929 -0.8 -0.5  6.4 10.4 14.6 18.2 20.6 20.3 16.6 12.4  6.2  2.0 10.5 2752
1930 -0.7  3.6  6.0 11.5 15.0 18.7 21.3 20.7 17.2 11.7  6.9  2.3 11.2 2779
1931  2.1  3.3  5.9 11.0 15.0 19.3 21.4 20.4 18.1 13.3  7.9  3.8 11.8 2875
1932  2.8  2.8  4.9 11.3 15.4 18.9 20.8 20.6 17.3 12.3  6.5  2.3 11.3 2911
1933  2.1  1.5  6.0 10.8 15.3 19.5 21.2 20.3 17.8 12.5  6.8  2.4 11.3 2946
1934  2.3  2.6  6.2 11.4 16.6 19.3 21.5 20.5 16.9 13.0  8.1  2.7 11.8 2965
1935  0.7  3.8  6.8 10.4 14.3 18.4 21.3 20.5 17.0 12.6  6.0  1.9 11.1 2980
1936 -0.3 -1.0  5.9 10.1 15.8 19.1 21.7 20.8 17.3 12.0  6.2  2.8 10.9 3045
1937 -0.4  1.5  4.7 10.3 15.5 18.9 21.1 21.0 17.3 12.2  6.2  1.5 10.8 3074
1938  0.8  2.2  7.0 11.3 15.2 18.6 20.9 20.7 17.5 13.0  6.5  1.7 11.3 3103
1939  1.3  1.2  5.4 10.6 15.6 18.7 21.0 20.3 17.2 11.7  6.5  3.7 11.1 3140
1940 -1.8  1.5  5.4 10.4 14.8 18.7 20.9 20.1 17.1 12.3  5.8  2.8 10.7 3176
1941  0.7  2.0  5.3 11.2 15.4 18.6 20.8 19.9 16.7 12.6  6.8  2.8 11.1 3273
1942  0.8  1.1  6.0 11.2 14.9 18.5 20.6 19.9 16.7 12.4  6.7  1.8 10.9 3283
1943 -0.2  2.8  5.1 11.0 15.0 18.4 20.7 20.1 16.7 12.4  6.4  2.6 10.9 3322
1944  2.0  2.5  5.3 10.1 15.5 18.5 20.4 19.9 17.1 12.4  6.5  1.2 11.0 3330
1945  0.1  1.8  6.8 10.7 14.1 17.8 20.0 20.1 16.8 12.1  6.3  0.9 10.6 3378
1946  1.4  2.6  7.1 11.5 14.6 18.2 20.5 19.7 16.8 11.9  6.6  2.1 11.1 3430
1947  0.8  1.1  5.5 10.8 14.8 18.0 20.3 20.4 17.1 13.3  6.1  2.2 10.9 3492
1948  0.8  1.6  5.2 11.2 15.2 18.7 20.4 19.9 17.1 12.1  7.0  2.2 10.9 3652
1949  1.5  2.3  6.2 11.3 15.7 18.8 20.7 20.3 16.9 12.9  7.9  3.0 11.5 3940
1950  1.2  3.1  6.2 10.8 15.5 18.7 20.2 19.7 17.0 13.2  6.8  3.2 11.3 4029
1951  2.8  3.9  7.0 12.1 16.2 18.8 20.9 20.6 17.8 13.5  8.0  4.9 12.2 4701
1952  4.0  5.1  7.1 12.7 16.2 19.6 21.3 20.7 18.0 13.2  8.2  4.8 12.6 4865
1953  4.2  5.2  8.8 12.5 16.4 19.8 21.2 20.8 18.1 14.3  8.9  5.5 13.0 4963
1954  2.7  5.3  7.6 12.6 15.8 19.4 21.0 20.6 18.2 13.8  9.4  4.9 12.6 5057
1955  3.9  4.6  7.5 12.6 16.4 19.1 21.2 20.9 18.0 13.9  7.8  4.3 12.5 5053
1956  3.3  3.6  7.5 11.9 16.1 19.3 20.6 20.1 17.4 13.7  8.0  4.9 12.2 5095
1957  2.6  4.9  7.6 12.3 16.1 19.4 20.9 20.3 17.5 13.0  8.5  5.4 12.4 5091
1958  3.6  4.3  7.2 12.2 16.4 18.9 20.7 20.3 17.4 13.3  8.6  4.3 12.3 5114
1959  2.9  4.3  8.2 12.4 16.1 19.3 21.0 20.5 17.4 13.0  7.6  4.9 12.3 5157
1960  3.2  4.8  6.6 12.2 15.8 19.2 20.7 20.3 17.7 13.5  8.6  4.7 12.3 5241
1961  3.9  6.0  9.1 12.6 16.2 19.5 20.7 20.5 17.8 13.8  9.1  4.9 12.8 5439
1962  4.1  5.6  8.1 12.8 16.5 19.0 20.5 20.3 17.6 14.2  9.3  5.4 12.8 5556
1963  2.9  5.2  8.5 12.7 16.4 19.1 20.9 20.4 18.1 14.7  9.7  4.5 12.8 5657
1964  4.2  4.7  7.9 12.6 16.5 19.1 20.8 19.9 17.4 13.5  9.1  4.9 12.6 5692
1965  4.3  4.8  7.8 12.3 16.2 18.9 20.2 19.9 17.3 13.8  9.1  6.0 12.6 5852
1966  3.5  5.5  8.9 12.5 16.0 19.1 20.8 20.1 17.5 13.8  9.3  5.2 12.7 5912
1967  4.2  4.9  8.8 12.7 16.1 18.9 20.5 20.2 17.5 14.2  9.1  5.3 12.7 5926
1968  3.7  5.0  9.6 12.9 15.9 18.9 20.4 19.8 17.5 13.8  9.2  5.0 12.6 5943
1969  3.3  4.6  7.8 12.8 16.4 18.7 20.5 20.2 17.6 13.6  9.4  5.7 12.5 5977
1970  3.6  5.9  8.1 12.8 16.3 19.1 20.7 20.2 17.6 13.5  9.1  5.2 12.7 5984
1971  4.0  5.2  7.9 12.3 15.8 18.7 20.2 19.9 17.5 13.8  9.1  5.6 12.5 5874
1972  3.3  4.5  8.7 12.4 16.1 18.8 20.3 19.9 17.1 13.1  8.5  5.2 12.3 5871
1973  4.3  5.9  9.2 12.6 16.1 19.1 20.6 20.2 17.5 13.7  8.6  5.2 12.8 5929
1974  3.8  5.2  8.7 12.6 15.8 18.6 20.4 19.8 17.0 13.2  8.8  5.2 12.4 5935
1975  4.5  5.0  8.2 12.2 16.2 18.8 20.5 19.9 17.4 13.5  8.8  4.8 12.5 5951
1976  3.4  5.3  7.7 12.3 15.5 18.4 19.9 19.3 16.8 12.1  7.8  4.1 11.9 5797
1977  2.0  5.4  9.0 12.9 16.2 18.8 20.4 19.7 17.2 13.2  9.1  4.6 12.4 5776
1978  3.3  4.2  8.3 12.2 15.8 18.5 20.2 19.6 17.3 13.3  8.6  4.6 12.2 5776
1979  2.8  3.8  8.5 11.7 15.7 18.7 20.1 19.8 17.4 13.6  8.7  6.0 12.2 5726
1980  3.3  4.6  7.6 12.2 15.9 18.7 20.4 19.9 17.2 13.2  9.0  4.7 12.2 5704
1981  3.2  4.9  8.4 12.4 15.3 18.6 20.3 19.8 17.0 12.7  8.0  4.5 12.1 5440
1982  1.1  3.3  6.7 11.1 15.6 17.8 19.9 19.5 16.9 12.7  7.5  4.7 11.4 5171
1983  3.2  4.4  7.7 11.4 15.2 18.0 20.3 20.3 17.2 13.1  8.3  2.7 11.8 5130
1984  2.4  4.2  6.7 11.1 15.3 18.4 20.1 19.9 16.4 12.7  7.3  3.0 11.5 5040
1985  1.0  2.5  6.9 12.1 15.7 18.0 20.1 19.8 16.5 12.5  6.8  2.2 11.2 4982
1986  3.0  3.1  7.7 12.1 15.8 18.8 20.2 19.7 16.9 12.5  7.0  3.7 11.7 4928
1987  2.9  5.1  7.1 12.2 16.1 19.1 20.8 20.0 17.5 12.8  8.3  4.8 12.2 4846
1988  3.2  3.7  7.8 12.2 16.1 19.2 21.1 20.6 17.4 12.9  8.1  4.6 12.2 4805
1989  3.9  4.2  8.0 12.4 15.9 18.6 20.6 20.0 17.1 13.1  7.8  3.2 12.1 4723
1990  5.7  6.9 10.5 13.3 16.4 19.5 21.3 21.0 18.7 14.9 11.3  6.9 13.9 4482
1991  6.8  8.8 11.1 14.1 17.2 19.6 20.9 20.6 18.4 14.8  9.4  6.6 14.0 3494
1992  6.6  8.1 10.2 13.5 16.8 19.0 20.7 20.1 18.1 14.1  8.5  4.6 13.4 3372
1993  3.5  3.8  7.8 12.4 17.1 19.8 22.0 21.7 18.1 13.8  7.5  5.4 12.7 2945
1994  3.6  4.5  9.4 13.7 16.9 21.2 22.2 21.6 19.2 14.8 10.1  6.8 13.7 2872
1995  4.8  5.7  9.6 12.6 16.6 20.1 22.8 22.7 18.7 14.7  8.8  5.1 13.5 2711
1996  4.2  5.9  7.7 12.6 17.2 20.5 22.0 21.7 18.2 14.0  8.0  5.6 13.1 2815
1997  3.2  6.2  9.7 11.9 16.5 20.2 22.0 21.6 18.9 14.1  8.7  5.5 13.2 2766
1998  5.2  7.4  8.7 13.3 17.9 20.4 22.7 22.2 19.9 14.6  9.7  5.8 14.0 2731
1999  4.6  6.7  8.6 13.3 17.1 20.3 22.6 21.9 18.7 14.1 10.1  5.7 13.6 2741
2000  3.8  6.5  9.7 13.1 17.4 20.3 21.8 22.0 18.6 14.1  7.4  3.2 13.2 2702
2001  3.3  4.6  8.5 13.1 17.2 20.3 22.2 22.4 18.6 14.5 10.4  5.7 13.4 2727
2002  5.1  6.1  8.6 13.6 16.6 20.9 22.9 21.9 19.4 13.5  9.1  5.5 13.6 2698
2003  4.0  4.4  8.9 13.1 17.2 20.3 22.5 22.4 18.5 14.7  9.4  5.6 13.4 2669
2004  3.0  5.3 10.1 13.4 17.4 20.2 21.9 21.2 19.1 14.7 10.0  5.9 13.5 2663
2005  5.0  6.3  8.9 13.9 16.9 20.8 22.7 22.1 19.7 14.9 10.3  4.4 13.8 2591
2006  5.3  5.1  8.8 14.3 17.1 19.7 21.4 20.5 18.7 15.6 11.3  8.7 13.9 2538
2007  8.3  8.2 11.2 14.6 17.5 19.9 20.9 20.8 18.8 15.5 10.8  8.5 14.6 1491
2008  8.0  9.1 12.7 15.3 17.7 20.0 21.5 21.0 18.5 15.4 11.6  8.2 14.9 1612
2009  7.2  8.8 11.3 15.1 17.8 20.1 21.2 20.9 18.4 14.8 11.5-99.0 15.2 1595
AA    2.5  3.9  7.3 11.8 15.8 18.9 20.8 20.3 17.4 13.1  7.9  3.9 12.0
Ad    0.2  1.6  4.8  9.6 14.1 17.7 19.6 19.0 15.8 10.8  5.6  1.9 10.1
 
For Country Code ALL
 
From input file /gnuit/GIStemp/STEP0/to_next_step/v2.mean_comb

v3

Thermometer Records, Average of Monthly Data and Yearly Average
by Year Across Month, with a count of thermometer records in that year
--------------------------------------------------------------------------
YEAR  JAN  FEB  MAR  APR  MAY  JUN JULY  AUG SEPT  OCT  NOV  DEC  YR COUNT
--------------------------------------------------------------------------
1701 -4.2 -1.5  1.6-99.0-99.0 15.4 18.9 15.8-99.0-99.0-99.0 -0.5  6.5    1
1702  2.0 -0.5  0.6  2.6 10.9 16.0 16.0 15.8 10.1  7.5  0.2  0.6  6.8    1
1703 -2.8 -0.9  0.6  7.7 14.1 16.1 15.4 16.3 11.4  6.1  2.2  2.5  7.4    1
1704 -4.9 -0.5  3.9  9.4 11.8 14.1 17.1-99.0-99.0-99.0-99.0 -0.9  6.2    1
1705 -7.1-99.0  1.0-99.0-99.0 16.0 18.3 17.8  8.7  7.5  0.7  1.8  7.2    1
1706 -1.2 -1.0  2.8  7.4 12.8 17.2 16.6 15.6 11.8  8.5  3.5  2.5  8.0    2
1707 -0.5  0.9  2.5  6.4 11.7 17.2 18.0 15.6 12.6  6.0  3.8  1.8  8.0    2
1708  3.0  0.9  4.8  7.8 11.1 14.3 13.7 18.0 14.3  4.7  3.3 -1.6  7.9    2
1709 -9.0 -3.9  0.9  9.4 11.7 16.7 16.0 16.0 12.2  8.1  5.6  2.0  7.2    2
1710 -1.1 -0.2  4.2  6.9 12.9 15.2 15.2 16.5 13.8  9.4  7.4  6.5  8.9    2
1711  3.5  0.0  4.7  9.5 12.2 16.9 16.0 15.6 13.3  9.3  6.5  1.5  9.1    1
1712  0.2  2.9  4.1  7.7 12.3 16.3 16.8 14.9 13.1  9.5  5.0  4.2  8.9    1
1713 -0.3  5.0  1.0  5.3 10.5 13.6 14.8 15.4 13.9  9.3  3.4  2.5  7.9    1
1714  1.9  3.8  5.0  7.9 10.2 14.5 18.5 13.8 13.0  9.7  4.6  2.4  8.8    1
1715  0.7  3.5  5.7  9.6 11.6 14.5 15.8 17.0 14.1 10.3  6.3 -1.5  9.0    1
1716 -5.0  1.5  3.3  9.1 11.3 14.0 16.3 15.5 12.4  8.3  3.9  1.2  7.7    1
1717  0.9  0.7  3.4  7.2 10.2 15.3 15.7 15.5 13.9  9.3  3.4  3.5  8.3    1
1718 -1.6 -0.8  4.6  8.3 12.7 16.0 18.0 19.0 15.1  8.9  5.2  3.3  9.1    1
1719  0.5  2.5  3.5  5.6 13.4 16.0 20.1 18.9 14.1  8.2  5.0  1.3  9.1    1
1720  2.9  2.9  3.1  6.8 12.3 12.6 17.2 14.5 14.3  8.1  5.6  3.6  8.7    1
1721  3.5  0.1  0.8  8.9 10.2 15.3 15.2 16.5 14.4  8.6  5.8  1.9  8.4    1
1722  0.9  3.9  5.5  8.6 11.5 15.1 15.8 15.5 14.6 10.4  6.8  3.3  9.3    1
1723  0.3  2.5  6.4  8.4 12.4 15.6 15.6 15.9 13.9 11.0  2.2  4.7  9.1    1
1724  4.8  3.6  3.7  6.7 11.8 16.7 15.1 16.9 14.2  8.3  5.1  2.0  9.1    1
1725  2.3  0.4  3.5  7.0 10.6 14.0 14.6 14.3 12.5  8.0  3.0  2.0  7.7    1
1726 -1.8  0.2  2.5  7.8 14.1 16.2 15.6 14.4 14.2  9.2  5.3  0.1  8.1    1
1727  2.6  3.6  3.7  7.0 15.0 15.4 16.8 17.5 14.7 11.5  3.6  2.5  9.5    1
1728  2.5 -0.6  6.9  8.9 14.8 16.6 16.5 14.6 13.1  9.2  4.3 -0.6  8.9    2
1729 -3.1  0.3  0.2  6.3 11.1 16.2 17.8 17.7 16.9 12.1  5.3  5.3  8.9    2
1730  2.4  2.3  4.3  9.1 12.6 15.6 17.2 16.8 14.4  7.0  7.5  2.2  9.3    2
1731 -0.5 -0.4  3.3  6.4 12.0 15.4 16.5 16.9 14.8 11.9  6.6  3.2  8.8    2
1732 -0.6  3.3  5.3  9.8 12.9 14.2 16.1 16.2 14.1 10.4  4.4 -0.9  8.8    2
1733  4.3  4.5  5.1 10.5 11.5 13.9 18.4 16.5 12.1  8.2  5.5  5.7  9.7    2
1734  1.6  4.4  6.4  9.5 12.4 14.7 17.0 16.4 14.1  9.6  2.0  1.4  9.1    2
1735  3.0  2.5  5.8  9.6 12.2 15.6 16.1 16.5 15.2  7.6  4.1  3.0  9.3    2
1736  1.4  0.8  3.2  9.2 12.2 15.1 17.4 17.7 13.9  9.4  5.7  4.2  9.2    2
1737  4.0  3.0  5.4  7.3 13.8 16.0 16.6 14.5 14.5  8.7  4.3  2.1  9.2    2
1738  0.0  2.5  5.1  9.6 12.8 15.2 16.5 16.0 13.6  9.9  2.0  4.2  9.0    2
1739 -1.4  1.0  3.6  5.2 12.3 14.9 17.7 15.0 13.8  5.8 -0.9  1.9  7.4    3
1740 -6.4 -5.4  0.6  4.9  7.8 13.2 16.0 15.5 13.9  4.3  1.5  0.5  5.5    3
1741 -2.6  2.3  2.5  5.4  9.4 13.8 17.1 15.8 12.9  9.7  5.8  1.4  7.8    3
1742 -2.8  2.2  1.5  4.9  9.7 14.9 15.9 14.5 10.9  8.1  3.6 -2.5  6.7    3
1743  1.2  1.8  2.6  4.9 12.4 18.0 17.8 17.0 14.4  4.4  4.6 -0.1  8.2    5
1744 -3.8 -3.1  0.2  6.9 11.8 16.0 18.2 14.7 13.5  7.9  3.8 -1.4  7.1    5
1745 -3.1 -3.7 -0.1  6.7 12.8 17.0 18.0 16.8 16.1  8.5  4.8 -0.3  7.8    5
1746 -0.5 -0.4 -0.8  6.6 13.0 15.6 17.9 15.2 13.2  6.4  1.5  3.1  7.6    4
1747 -1.5 -0.3 -0.5  7.2 11.2 18.5 18.2 15.8 15.2  9.5  4.4  1.0  8.2    4
1748 -1.5 -1.5 -2.3  6.7 13.4 17.3 17.6 18.2 14.2  8.6  4.7  3.5  8.2    4
1749 -0.1 -1.3  0.5  6.6 13.3 15.2 17.1 17.1 13.8  7.8  3.6  0.9  7.9    4
1750 -0.5  1.4  4.4  7.3 12.1 15.7 19.1 17.6 13.6  6.2 -0.6 -0.9  8.0    5
1751 -1.5 -3.5  3.5  6.4 12.8 16.0 17.9 17.4 12.2 11.1 -5.0-14.1  6.1    6
1752 -5.8 -3.7  1.0  4.4  9.8 15.3 19.4 17.6 11.4  7.3  3.8 -0.9  6.6    5
1753 -3.3 -1.7  3.5  7.0 11.7 16.5 18.3 17.0 13.7  9.4  2.4 -3.7  7.6    8
1754 -2.6 -3.1 -0.6  6.5 12.6 16.5 17.3 17.1 13.1  9.2  3.5 -0.2  7.4    8
1755 -5.3 -4.7  0.5  8.9 11.9 17.9 18.9 16.1 12.7  8.1  3.1 -0.0  7.3   10
1756  0.2  1.6  2.4  5.5 10.5 17.7 18.9 16.3 14.0  8.1  0.9 -2.2  7.8   11
1757 -3.7 -0.2  2.5  8.4 12.1 18.0 21.6 18.4 13.5  5.5  4.2 -1.6  8.2   13
1758 -4.2 -1.2  2.8  7.0 13.8 17.3 17.5 18.0 12.5  6.7  4.2  0.1  7.9   13
1759  1.0  2.3  3.9  8.1 12.2 17.7 20.4 18.8 14.7 10.0  2.2 -2.5  9.1   14
1760 -3.8 -0.5  1.6  8.0 12.8 17.3 19.0 17.5 15.2  8.8  4.0  1.3  8.4   14
1761 -1.7  1.0  5.2  7.4 13.7 18.1 19.0 18.9 15.2  6.8  3.6 -2.3  8.7   14
1762  1.2  0.2  0.8 10.0 13.3 17.1 19.0 16.3 13.6  6.2  3.6 -1.2  8.3   13
1763 -4.1  2.3  2.4  7.2 11.4 16.4 18.9 18.7 13.4  7.9  4.3  2.3  8.4   15
1764  2.3  3.8  3.5  7.6 13.6 15.9 19.6 17.0 13.1  8.5  3.8  0.9  9.1   16
1765  1.1 -1.4  5.3  8.6 12.0 16.4 17.3 18.1 13.9  9.7  4.4 -0.2  8.8   16
1766 -3.0 -0.4  4.1  9.7 13.0 17.5 18.9 18.4 15.0  9.4  5.7 -0.2  9.0   16
1767 -5.6  2.6  4.1  6.6 11.6 15.9 18.1 18.7 14.9  9.4  6.5 -0.9  8.5   17
1768 -3.2  0.4  2.1  7.8 12.8 16.5 19.0 18.2 12.9  7.9  3.7  0.2  8.2   20
1769 -0.8 -0.9  2.4  7.5 12.1 16.3 18.5 17.0 14.2  6.1  4.2  1.0  8.1   21
1770 -1.3  0.9  0.5  6.8 12.8 16.3 18.4 18.7 15.6  9.4  4.1  1.8  8.7   21
1771 -1.4 -1.6  0.8  5.1 14.6 17.3 18.7 17.5 14.4  9.9  3.2  2.2  8.4   22
1772 -1.1  0.5  3.3  7.4 11.0 17.8 18.9 18.3 15.1 11.3  6.7  2.3  9.3   21
1773  0.9 -0.5  3.6  8.7 14.1 16.6 18.6 18.8 15.3 10.9  4.7  2.5  9.5   23
1774 -2.1  1.4  5.1  9.8 13.2 17.9 18.9 18.8 13.8  8.8 -0.1 -2.3  8.6   21
1775 -1.5  2.3  4.2  7.2 12.2 18.2 19.8 19.4 15.7  9.5  3.0 -0.4  9.1   23
1776 -8.0  0.3  3.1  7.0 10.3 17.2 19.5 18.2 13.2  8.4  3.0 -0.9  7.6   24
1777 -3.5 -2.8  3.0  5.5 12.4 15.9 17.4 18.3 13.4  8.4  4.0 -1.9  7.5   25
1778 -2.9 -1.7  2.1  8.8 13.4 16.3 20.3 18.6 12.6  6.6  3.2  1.0  8.2   24
1779 -5.0  1.6  4.4  9.6 14.1 15.5 18.5 19.1 15.4 10.4  3.3  0.2  8.9   27
1780 -5.4 -3.2  5.1  6.1 13.9 16.7 19.1 18.7 13.9  9.9  2.8 -1.7  8.0   28
1781 -2.0  0.1  4.2  9.5 13.6 17.8 19.1 19.8 15.2  7.6  4.0 -0.8  9.0   32
1782 -0.9 -4.2  1.2  6.4 11.7 17.4 19.3 17.8 14.1  7.3  0.8 -1.0  7.5   33
1783 -0.8  2.0  1.8  8.4 13.8 17.5 20.1 18.4 14.8  9.6  3.2 -2.0  8.9   32
1784 -4.8 -2.6  1.2  6.0 14.6 16.9 18.6 17.5 15.5  6.7  4.2 -1.6  7.7   33
1785 -1.1 -2.6 -2.4  5.7 12.1 16.5 18.0 17.0 15.2  8.3  4.1 -0.7  7.5   34
1786 -1.5 -0.6  1.0  9.0 12.2 17.4 17.3 17.1 13.3  6.8  0.6 -0.6  7.7   34
1787 -1.7  1.5  4.7  7.0 11.9 17.6 18.1 18.3 14.4 10.7  3.8  1.7  9.0   31
1788  0.1  0.4  3.0  9.0 14.0 18.2 20.9 17.6 15.7  8.6  2.4 -7.9  8.5   33
1789 -3.2  1.3 -0.5  8.7 15.7 16.5 19.4 18.7 14.8  9.3  3.8  1.9  8.9   32
1790  0.6  3.0  4.9  6.6 14.7 17.7 17.6 18.4 13.9  9.9  4.1  1.5  9.4   30
1791  2.0  1.3  4.8 10.6 13.1 17.2 19.3 19.8 14.3  9.3  3.6  1.4  9.7   33
1792 -1.3 -0.4  4.3  9.9 12.9 17.3 19.7 18.6 14.0  9.2  4.3  0.9  9.1   34
1793 -1.9  2.5  4.0  7.4 12.8 16.4 20.7 19.3 14.2 11.1  5.1  2.0  9.5   33
1794  0.2  3.3  6.6 11.6 13.9 17.9 21.1 17.8 13.4  9.4  5.0 -0.1 10.0   34
1795 -6.2 -0.6  3.0 10.3 13.0 17.2 17.3 18.6 15.6 11.8  3.5  3.0  8.9   35
1796  4.7  2.3  2.1  9.0 13.7 17.2 18.9 19.0 16.1  9.5  3.9 -2.0  9.5   37
1797 -0.3  1.8  3.1  9.4 14.7 16.6 20.5 19.5 15.9 10.0  5.1  2.7  9.9   36
1798  0.8  2.5  4.5  9.8 15.0 18.6 20.0 19.9 16.2 10.2  4.3 -1.8 10.0   39
1799 -3.4 -0.9  2.7  7.5 12.5 16.5 18.6 18.5 14.9  9.9  5.3 -2.1  8.3   39
1800  0.1  0.4  1.5 12.7 15.6 15.8 19.0 19.5 15.5 10.1  6.1  2.0  9.9   39
1801  1.8  1.6  6.7  9.5 15.6 16.8 19.4 18.6 16.5 11.6  5.8  1.4 10.4   39
1802 -2.0  1.3  5.2  9.9 12.9 17.9 18.5 20.6 15.6 12.3  5.2  1.8  9.9   39
1803 -2.8 -1.2  3.8 11.2 12.7 17.1 20.5 19.9 13.6  9.6  4.9  1.1  9.2   41
1804  2.4 -0.4  2.4  8.6 15.4 18.1 19.6 18.8 16.5 10.7  4.1 -1.3  9.6   41
1805 -2.0 -0.1  3.6  7.4 12.3 16.0 18.5 18.1 15.5  7.0  2.1  0.9  8.3   42
1806  1.8  2.4  3.9  6.9 15.2 16.9 18.5 18.6 15.9  9.8  6.1  4.4 10.0   46
1807 -0.8  2.1  1.6  6.7 14.1 16.6 20.8 22.0 13.9 10.9  5.5  1.2  9.5   47
1808 -0.5 -1.0 -0.2  6.2 15.1 17.0 20.6 19.6 15.4  8.5  4.1 -3.0  8.5   48
1809 -3.0  1.9  2.5  5.3 14.2 17.0 18.8 18.9 14.6  8.9  2.8  2.4  8.7   49
1810 -2.1 -0.8  3.9  7.3 12.5 16.0 18.7 18.4 16.1  9.4  4.3  1.7  8.8   49
1811 -3.2  0.9  5.9  8.9 16.1 19.5 20.7 18.5 14.9 11.4  5.1  0.6  9.9   51
1812 -3.7  0.3  1.8  5.1 13.0 16.6 17.9 18.3 13.3 10.0  2.0 -4.6  7.5   51
1813 -3.6  1.8  3.3  9.5 14.1 16.1 19.2 17.6 14.5  9.2  4.5  0.7  8.9   56
1814 -3.7 -3.6  1.9  9.8 11.3 16.1 19.9 18.1 13.3  8.5  4.7  1.8  8.2   54
1815 -3.5  1.8  4.9  8.9 14.0 16.8 17.6 17.7 14.2 10.0  2.6 -1.7  8.6   54
1816 -1.1 -2.8  2.2  7.8 12.0 15.8 17.6 16.7 14.0  9.1  3.2  0.2  7.9   59
1817  1.1  2.4  3.3  5.7 13.1 17.7 18.5 18.0 15.1  6.7  4.7 -1.8  8.7   66
1818 -0.1  0.1  4.0  8.7 12.8 17.6 19.5 17.0 14.5  9.6  5.3 -0.1  9.1   71
1819  0.3  0.9  4.0  9.1 13.6 17.7 19.6 19.1 15.6  9.4  3.4 -1.4  9.3   71
1820 -4.2  0.6  3.0 10.1 14.3 16.1 18.6 19.4 13.9  9.1  2.7 -1.1  8.5   77
1821 -0.2 -0.8  3.4 10.3 13.2 14.9 17.4 18.1 15.4 10.1  5.8  2.4  9.2   81
1822 -0.1  2.6  6.8  9.9 14.8 18.9 19.4 18.2 14.2 10.6  5.5 -1.5  9.9   77
1823 -5.7 -0.3  3.7  7.2 13.8 16.6 18.1 18.9 14.7  9.6  3.4  1.0  8.4   85
1824 -0.9  0.6  2.6  7.2 12.0 15.9 18.6 18.0 15.5  9.0  4.8  2.4  8.8   89
1825  0.2 -0.2  1.9  9.0 13.5 17.2 18.9 18.3 15.3  9.4  5.4  2.7  9.3   95
1826 -5.1  0.3  3.8  7.9 13.1 17.8 20.9 20.2 15.1 10.2  3.6  1.6  9.1   94
1827 -2.5 -4.0  4.1  9.6 14.5 18.0 19.9 17.9 14.7 10.0  2.0  1.5  8.8   97
1828 -2.4 -1.5  3.6  8.8 13.8 18.1 19.8 17.6 14.0  8.8  4.0  0.4  8.7  103
1829 -4.9 -3.8  1.3  7.7 12.9 16.4 19.0 16.9 13.8  7.5  0.5 -5.6  6.8  111
1830 -7.0 -3.8  3.1  8.7 12.9 16.6 19.1 17.8 13.0  8.1  4.6 -0.6  7.7  112
1831 -4.7 -0.9  2.7  9.1 12.7 16.6 19.1 17.7 12.9 10.5  2.6 -0.7  8.1  117
1832 -2.5 -0.8  2.3  7.4 11.6 16.0 17.3 17.8 12.7  8.7  1.9 -1.6  7.6  114
1833 -4.1  1.1  1.6  6.8 15.0 17.8 17.7 15.5 13.3  8.5  3.6  1.3  8.2  115
1834 -0.7 -0.6  3.0  6.8 14.6 17.4 20.7 19.5 15.2  8.8  3.3 -0.1  9.0  116
1835 -0.6  0.9  3.0  6.9 12.2 16.7 19.0 17.0 13.8  8.2  0.5 -3.6  7.8  112
1836 -2.9 -0.9  5.3  7.8 10.9 16.6 18.0 16.8 13.0  9.6  2.4  0.2  8.1  117
1837 -2.5 -1.2  0.1  5.9 11.4 16.6 17.4 18.6 13.2  8.5  3.3 -1.4  7.5  123
1838 -8.0 -4.6  1.7  5.9 12.6 16.3 18.4 16.8 14.5  7.9  2.5 -1.3  6.9  128
1839 -2.9 -1.6 -0.9  5.0 13.0 17.6 19.7 17.8 14.5  9.2  3.2 -3.0  7.6  137
1840 -2.4 -2.0  0.1  7.8 12.3 16.8 18.3 17.8 13.9  7.2  3.8 -5.0  7.4  141
1841 -3.5 -3.8  2.8  8.1 14.9 16.9 18.5 18.2 14.8  9.9  3.4  0.6  8.4  144
1842 -4.6 -1.5  2.8  6.5 13.3 16.8 18.3 19.2 13.8  7.2  1.9  0.6  7.9  142
1843 -0.6  1.0  1.9  7.4 11.6 16.1 18.1 18.1 14.1  8.7  3.3  0.9  8.4  139
1844 -2.8 -2.6  1.2  7.9 13.2 16.7 17.8 17.0 14.5  8.9  2.8 -3.6  7.6  142
1845 -1.0 -4.9 -1.0  7.8 11.6 17.5 19.5 17.4 13.9  8.8  4.6 -0.0  7.9  145
1846 -1.2  0.2  4.5  8.3 13.0 18.1 20.2 20.0 15.1  9.9  2.7 -2.5  9.0  139
1847 -3.8 -1.7  1.0  6.6 13.8 16.5 19.5 19.4 14.2  8.7  4.2 -1.6  8.1  143
1848 -7.1  0.5  3.4  9.5 13.5 17.8 19.1 17.9 13.8  9.5  3.0 -1.0  8.3  151
1849 -2.8  0.7  2.5  6.9 13.2 17.2 18.8 17.8 13.9  9.5  4.0 -2.0  8.3  153
1850 -5.8  0.9  1.2  7.8 12.6 17.6 19.2 18.6 13.6  7.8  4.2  0.5  8.2  153
1851 -0.7 -0.6  2.4  8.3 12.1 17.0 18.5 18.4 14.3 10.3  3.2  0.5  8.6  169
1852 -0.1 -0.4  2.0  6.3 13.5 17.1 19.8 18.5 14.7  8.9  5.1  2.9  9.0  174
1853  1.0 -1.1  1.1  7.2 13.1 17.4 19.8 18.7 14.5 10.3  4.0 -1.6  8.7  178
1854 -1.7 -0.4  3.7  8.2 13.9 16.7 19.9 18.6 14.8 10.4  3.3  2.0  9.1  177
1855 -2.4 -3.0  2.6  8.4 13.1 17.4 19.5 19.0 14.6 11.1  3.9 -2.3  8.5  181
1856  0.6  0.9  2.1  8.7 12.6 17.5 18.6 18.5 14.2  9.9  2.4  1.4  8.9  196
1857 -1.1  0.2  3.6  8.0 13.0 17.1 19.4 19.3 15.2 11.0  4.6  2.7  9.4  197
1858 -1.1 -1.4  3.1  8.7 12.9 18.4 18.9 18.2 15.7 10.6  2.4  1.6  9.0  197
1859  1.0  2.5  5.5  8.8 13.7 17.5 20.3 19.3 14.7 10.6  4.9  0.2  9.9  202
1860  1.1 -0.7  2.3  7.9 13.4 16.8 17.9 17.7 14.6  9.8  3.7 -0.6  8.7  195
1861 -3.0  2.4  5.2  7.4 12.0 17.4 19.0 18.8 14.5 10.5  5.0  1.6  9.2  198
1862 -1.1 -0.1  4.7  8.9 13.6 15.9 17.7 16.9 14.3 10.1  3.5  0.2  8.7  199
1863  2.2  2.2  4.4  8.6 13.0 16.3 17.4 18.0 14.0 10.3  5.4  2.2  9.5  197
1864 -1.7  1.0  4.9  8.1 12.1 16.7 18.0 16.7 14.1  8.7  3.6 -0.1  8.5  212
1865  1.5 -0.7  2.2  9.8 14.4 16.0 19.5 17.5 15.7 10.3  6.6  2.4  9.6  218
1866  3.3  2.9  4.4  9.7 12.1 17.5 18.4 17.4 15.5  9.9  5.8  3.2 10.0  245
1867  0.8  4.0  3.4  9.0 12.0 16.3 17.6 17.9 14.9 10.3  5.5  1.2  9.4  243
1868  0.7  2.7  5.5  8.8 14.8 17.2 19.3 18.8 15.4 10.6  5.4  4.6 10.3  245
1869  2.5  5.4  4.5 10.1 13.4 15.9 18.8 17.9 15.6 10.3  6.3  2.9 10.3  234
1870  2.3  0.9  4.6  9.6 13.9 17.1 19.3 17.6 14.9 10.7  6.8  0.9  9.9  243
1871  0.2  1.6  6.7  9.7 12.7 15.9 18.9 18.8 14.9 10.6  5.2  1.2  9.7  274
1872  2.5  2.9  5.4 10.3 14.1 17.4 19.5 18.6 15.7 11.4  6.9  2.9 10.6  297
1873  2.4  1.9  5.4  8.7 12.9 17.5 19.6 19.1 15.1 11.1  6.0  3.8 10.3  314
1874  3.2  2.8  5.3  9.6 13.0 17.2 19.5 18.2 16.1 11.8  6.2  2.5 10.4  325
1875  1.0  0.5  4.0  9.0 14.5 17.8 19.0 19.0 15.5 10.6  5.6  2.3  9.9  340
1876  2.7  3.9  6.2 10.6 13.4 18.1 20.0 19.4 15.9 12.1  6.7  3.1 11.0  343
1877  2.9  4.8  5.6 10.0 13.5 18.1 19.6 19.3 15.7 11.6  8.6  5.5 11.3  365
1878  3.5  5.6  8.2 12.3 15.0 18.1 20.0 19.8 17.3 13.3  8.1  3.6 12.1  403
1879  2.4  3.6  6.6 10.3 14.4 17.7 19.3 19.5 16.4 13.1  7.2  2.4 11.1  419
1880  3.8  4.7  7.1 11.2 15.1 17.9 19.9 19.6 17.1 12.1  7.2  4.4 11.7  426
1881  1.4  3.5  6.8 10.4 15.4 17.5 20.2 19.6 16.9 11.7  7.9  5.2 11.4  468
1882  4.1  5.1  7.8 10.8 14.5 17.7 19.6 19.5 16.6 12.6  7.3  3.3 11.6  492
1883  1.7  3.2  4.9 10.4 14.5 18.3 19.6 19.1 16.2 12.0  7.7  3.8 11.0  505
1884  2.2  3.7  6.2  9.9 14.3 17.5 19.4 19.2 16.6 11.9  6.6  3.2 10.9  515
1885  0.6  2.6  5.2 10.1 13.8 17.4 19.6 18.4 15.9 11.4  7.3  3.9 10.5  527
1886  1.1  1.9  5.5 11.0 14.6 17.5 19.7 19.4 16.7 12.3  7.2  2.9 10.8  555
1887  1.2  2.3  5.6 10.1 14.9 18.0 20.4 19.0 16.3 11.1  7.2  3.0 10.8  576
1888  1.0  2.2  4.9 10.4 14.4 17.9 19.5 19.0 16.4 11.7  7.5  4.1 10.7  599
1889  2.2  2.4  6.3 11.0 15.5 18.3 19.8 19.3 16.1 12.0  7.5  4.2 11.2  630
1890  3.1  3.4  6.4 11.0 14.7 18.2 19.8 19.5 16.7 12.1  7.6  2.8 11.3  652
1891  1.3  2.3  5.8 10.5 14.7 18.0 19.8 19.4 17.2 12.5  6.8  4.8 11.1  691
1892  1.6  3.7  5.7 10.5 14.6 18.4 20.0 20.0 17.2 12.4  7.0  2.2 11.1  722
1893 -0.7  1.6  6.2 10.5 14.7 18.4 20.3 19.9 16.6 12.8  7.1  3.7 10.9  743
1894  1.9  3.1  7.3 11.5 14.8 18.4 20.5 19.8 16.4 12.4  7.4  3.9 11.5  763
1895 -0.4 -0.3  5.4 11.8 15.8 19.5 20.8 20.9 18.3 11.6  6.8  2.7 11.1 1397
1896  1.1  2.8  4.9 11.6 16.6 19.8 21.7 21.0 17.0 12.0  6.0  3.2 11.5 1453
1897  0.5  2.7  5.8 11.3 15.8 19.5 22.0 20.9 18.6 13.3  6.7  2.0 11.6 1547
1898  2.4  2.9  6.4 10.8 15.8 19.9 21.7 21.5 18.4 12.2  6.4  2.2 11.7 1594
1899  1.3 -0.0  4.8 11.3 15.8 19.6 21.6 21.3 17.7 13.3  8.7  2.4 11.5 1632
1900  2.2  1.4  5.4 11.5 16.1 19.9 21.6 21.9 18.3 14.4  7.3  3.6 11.9 1674
1901  1.7  1.0  6.5 11.2 16.0 19.8 22.8 21.5 17.6 13.4  6.9  2.4 11.7 1696
1902  1.9  2.0  7.4 11.2 16.2 19.0 21.2 20.6 17.1 13.0  8.3  2.0 11.7 1747
1903  2.0  2.4  7.8 11.2 15.8 18.3 20.9 20.3 17.2 12.9  6.7  1.9 11.5 1808
1904  0.3  1.5  6.3 10.7 15.9 19.1 20.9 20.4 17.7 13.0  7.9  2.8 11.4 1849
1905  0.3  0.4  8.0 11.3 15.8 19.5 21.3 21.1 18.2 12.2  7.9  3.5 11.6 1891
1906  3.4  3.0  5.1 12.6 16.0 19.4 21.4 21.3 18.5 12.9  7.4  3.8 12.1 1934
1907  2.6  3.5  8.5 10.1 14.3 18.3 20.9 20.3 17.7 13.2  7.8  4.5 11.8 2066
1908  3.4  3.5  7.7 12.1 15.7 18.8 21.1 20.3 18.1 12.7  8.2  4.1 12.2 2088
1909  2.6  3.9  6.8 10.8 15.0 19.2 20.6 21.0 17.6 12.8  9.1  1.7 11.8 2134
1910  2.7  2.5 10.0 12.7 15.3 19.0 21.2 20.3 17.9 13.7  7.3  3.2 12.2 2176
1911  2.8  3.7  7.8 11.3 16.5 19.8 21.1 20.5 18.2 12.9  6.9  4.4 12.1 2227
1912  0.2  3.2  5.7 11.8 16.0 18.7 20.7 19.8 16.9 12.8  8.1  4.4 11.5 2255
1913  3.0  2.6  6.7 12.2 15.4 18.9 20.9 21.0 17.3 12.6  9.3  4.9 12.1 2323
1914  4.0  2.5  7.3 11.7 16.2 19.4 21.3 20.6 17.5 13.8  8.4  2.2 12.1 2379
1915  2.0  4.9  5.8 13.1 14.9 18.2 20.3 19.8 17.5 13.4  8.2  3.7 11.8 2395
1916  1.8  3.0  6.5 11.2 15.2 17.9 21.3 20.5 17.0 12.3  7.3  2.0 11.3 2406
1917  1.3  1.4  5.9 10.4 13.5 18.2 21.1 20.0 17.1 11.3  8.0  1.1 10.8 2426
1918 -0.4  3.1  8.2 10.8 15.7 19.1 20.3 20.7 16.4 13.7  7.7  4.5 11.6 2451
1919  3.2  3.2  6.8 11.6 15.3 19.3 21.1 20.4 17.9 12.6  6.8  2.1 11.7 2450
1920  1.5  3.6  7.1 10.1 15.1 18.6 20.6 20.1 17.8 13.4  7.2  3.8 11.6 2463
1921  4.0  4.8  9.0 12.1 15.9 19.8 21.7 20.3 18.2 13.4  7.6  4.3 12.6 2528
1922  1.2  3.0  7.1 11.7 16.1 19.5 20.6 20.6 18.2 13.3  8.1  3.5 11.9 2553
1923  3.4  2.1  6.1 11.0 15.2 18.9 20.9 20.1 17.6 12.5  8.3  5.3 11.8 2592
1924  0.9  3.7  6.0 11.1 14.7 18.7 20.5 20.5 16.9 13.6  7.9  1.7 11.3 2628
1925  1.7  4.9  7.8 12.7 15.3 19.4 20.9 20.4 18.0 11.1  7.5  3.8 12.0 2666
1926  2.8  5.1  6.7 11.1 15.7 18.5 20.9 20.6 17.3 13.1  7.4  2.9 11.8 2717
1927  2.4  4.6  7.6 11.5 15.1 18.4 20.8 19.7 17.7 13.8  8.3  1.9 11.8 2716
1928  2.9  3.9  7.3 10.6 15.8 18.0 21.1 20.6 17.1 13.3  8.1  4.1 11.9 2726
1929  0.5  0.9  7.8 11.3 15.0 18.5 20.9 20.6 17.2 13.1  7.1  3.5 11.4 2762
1930  0.3  5.3  7.1 12.4 15.5 19.1 21.6 21.0 17.9 12.4  7.9  3.6 12.0 2787
1931  3.7  5.1  7.0 11.8 15.5 19.7 21.7 20.6 18.6 14.1  9.0  5.2 12.7 2883
1932  4.2  4.5  5.9 11.9 15.9 19.2 21.0 20.9 17.8 12.9  7.6  3.4 12.1 2919
1933  3.9  2.9  7.3 11.5 15.8 19.9 21.4 20.5 18.4 13.3  7.9  3.9 12.2 2953
1934  4.0  3.7  7.4 12.3 17.1 19.7 21.8 20.8 17.4 13.7  9.2  4.0 12.6 2973
1935  2.2  4.9  8.0 11.1 14.7 18.6 21.5 20.8 17.5 13.2  7.3  3.1 11.9 2988
1936  1.2  0.3  7.7 11.0 16.5 19.4 22.0 21.2 18.0 12.9  7.4  4.3 11.8 3051
1937  1.1  3.2  6.2 11.3 16.1 19.2 21.4 21.4 17.8 13.1  7.6  3.2 11.8 3082
1938  2.7  4.1  8.7 12.2 15.7 19.0 21.1 21.1 18.2 14.1  7.8  3.8 12.4 3111
1939  3.5  2.8  7.0 11.5 16.3 19.0 21.2 20.7 18.0 12.9  8.0  5.4 12.2 3147
1940 -0.0  3.5  7.0 11.2 15.6 19.1 21.1 20.4 17.8 13.6  7.2  4.7 11.8 3181
1941  2.8  3.9  6.8 12.5 16.2 19.0 21.2 20.3 17.5 13.7  8.6  5.0 12.3 3282
1942  2.7  3.0  7.8 12.4 15.6 18.9 20.9 20.3 17.4 13.5  8.3  3.6 12.0 3290
1943  1.7  4.7  6.5 11.9 15.5 18.9 21.0 20.5 17.4 13.4  7.8  4.2 12.0 3329
1944  3.8  4.2  6.5 11.1 16.2 18.9 20.6 20.4 17.8 13.5  8.2  3.0 12.0 3334
1945  2.0  3.9  8.8 11.7 14.7 18.2 20.3 20.5 17.5 13.1  7.9  2.7 11.8 3380
1946  3.2  4.4  9.0 12.7 15.2 18.6 20.8 20.0 17.5 13.1  8.2  4.3 12.2 3436
1947  3.1  3.0  6.9 11.7 15.5 18.4 20.6 21.0 17.9 14.5  7.4  4.0 12.0 3497
1948  2.3  3.4  6.7 12.3 15.8 19.1 20.7 20.4 17.9 13.0  8.5  4.1 12.0 3658
1949  2.6  3.8  7.4 12.2 16.2 19.2 21.1 20.7 17.4 13.7  9.2  4.4 12.3 3950
1950  2.7  4.4  7.1 11.3 15.9 19.0 20.5 20.0 17.5 14.1  7.8  4.2 12.0 4036
1951  4.1  5.5  8.1 12.6 16.7 19.1 21.2 20.9 18.2 14.2  8.8  5.7 12.9 4709
1952  5.1  6.3  8.1 13.4 16.6 20.0 21.6 21.1 18.5 13.8  9.2  5.9 13.3 4872
1953  5.6  6.4  9.7 12.8 16.7 20.0 21.5 21.1 18.6 15.0 10.0  6.6 13.7 4971
1954  3.9  7.0  8.6 13.3 16.1 19.7 21.4 20.9 18.7 14.5 10.3  6.1 13.4 5066
1955  4.8  5.7  8.5 13.4 16.8 19.3 21.6 21.4 18.5 14.6  8.6  5.3 13.2 5058
1956  4.5  4.9  8.5 12.6 16.6 19.6 20.9 20.5 18.0 14.5  8.9  6.1 13.0 5104
1957  3.4  6.1  8.8 13.0 16.5 19.8 21.4 20.8 18.1 13.7  9.5  6.7 13.2 5100
1958  4.8  5.2  8.2 13.0 17.2 19.4 21.3 21.0 18.3 14.2  9.7  5.3 13.1 5123
1959  3.8  5.3  9.2 13.2 16.8 19.9 21.6 21.2 18.2 13.9  8.5  6.2 13.1 5166
1960  4.3  5.7  7.5 13.1 16.4 19.7 21.2 20.9 18.5 14.5  9.7  5.4 13.1 5250
1961  4.2  6.6  9.7 12.9 16.5 19.9 21.3 21.1 18.4 14.4  9.6  5.2 13.3 5468
1962  4.1  6.0  8.3 13.2 17.0 19.4 20.9 20.9 18.1 14.8  9.9  5.8 13.2 5584
1963  3.0  5.5  9.2 13.2 16.8 19.7 21.4 20.9 18.6 15.5 10.4  4.6 13.2 5681
1964  4.7  5.1  8.4 13.2 17.0 19.5 21.4 20.4 18.0 14.0  9.6  5.2 13.0 5718
1965  4.8  5.4  8.0 12.8 16.7 19.2 20.7 20.4 17.7 14.4  9.9  6.6 13.0 5875
1966  3.9  6.1  9.6 12.8 16.3 19.4 21.3 20.5 18.0 14.2  9.9  5.8 13.2 5937
1967  5.0  5.4  9.3 12.9 16.2 19.2 20.8 20.6 18.0 14.6  9.5  5.8 13.1 5947
1968  4.0  5.4  9.9 13.3 16.1 19.2 20.8 20.2 18.0 14.4  9.8  5.4 13.0 5962
1969  3.9  5.4  8.2 13.3 16.9 19.0 21.0 20.8 18.1 13.9  9.9  6.1 13.0 5993
1970  3.8  6.4  8.4 13.1 16.6 19.5 21.1 20.8 18.0 14.1  9.6  5.8 13.1 6001
1971  4.3  5.9  8.5 12.7 16.1 19.2 20.6 20.5 18.0 14.4  9.6  6.1 13.0 5890
1972  4.1  5.2  9.4 12.9 16.6 19.1 20.6 20.5 17.7 13.7  9.2  5.6 12.9 5887
1973  5.0  6.4 10.1 13.0 16.5 19.4 21.1 20.8 18.1 14.5  9.2  5.9 13.3 5944
1974  4.4  5.8  9.4 13.1 16.2 19.0 20.8 20.2 17.3 13.8  9.6  6.0 13.0 5948
1975  5.1  5.6  8.6 12.4 16.6 19.1 21.1 20.4 18.0 14.2  9.7  5.5 13.0 5968
1976  4.0  6.4  8.5 12.9 15.9 18.9 20.4 19.9 17.5 13.0  8.4  4.9 12.6 5820
1977  2.6  6.4  9.8 13.5 16.8 19.3 21.0 20.4 18.0 14.1  9.9  5.5 13.1 5795
1978  3.7  4.7  9.0 12.8 16.3 19.1 20.8 20.2 18.0 14.0  9.2  5.3 12.8 5797
1979  3.2  4.4  9.3 12.4 16.0 19.2 20.7 20.4 18.1 14.3  9.4  6.8 12.8 5746
1980  4.1  5.2  8.3 12.9 16.5 19.2 21.1 20.6 18.0 13.8  9.9  5.5 12.9 5724
1981  4.5  6.3  9.5 13.4 15.8 19.1 20.8 20.3 17.7 13.4  9.3  5.7 13.0 5532
1982  2.6  4.6  8.2 11.8 16.3 18.3 20.4 20.1 17.6 13.7  8.8  6.2 12.4 5354
1983  4.7  5.9  9.0 12.2 15.8 18.6 20.8 20.9 18.0 13.9  9.4  3.8 12.7 5320
1984  3.8  6.1  7.9 12.1 15.8 18.8 20.4 20.4 17.0 13.7  8.7  4.8 12.5 5251
1985  2.8  4.2  8.6 13.1 16.5 18.4 20.5 20.1 17.1 13.5  8.0  3.8 12.2 5193
1986  4.5  4.7  9.1 12.9 16.3 19.2 20.5 20.1 17.4 13.3  8.0  5.1 12.6 5136
1987  3.6  5.8  7.8 12.8 16.5 19.5 21.2 20.4 18.1 13.3  9.1  5.6 12.8 5045
1988  3.3  4.4  8.2 12.5 16.5 19.6 21.5 21.1 17.8 13.3  8.6  4.8 12.6 4998
1989  4.2  4.0  8.3 12.9 16.2 19.0 21.1 20.5 17.7 13.6  8.4  3.5 12.4 4926
1990  5.6  6.7 10.4 13.3 16.6 20.0 21.6 21.5 19.1 15.0 11.5  6.9 14.0 4701
1991  6.6  8.9 11.3 14.4 17.5 19.9 21.1 20.9 18.5 15.1  9.9  7.6 14.3 3743
1992  7.3  8.9 11.1 14.0 17.1 19.1 20.5 20.2 18.1 14.4  9.2  5.6 13.8 3683
1993  4.6  4.9  9.0 13.2 17.3 19.9 21.8 21.5 18.2 14.2  8.4  6.3 13.3 3283
1994  4.4  5.3 10.2 14.1 17.2 20.9 22.2 21.6 19.1 14.9 10.5  7.3 14.0 3250
1995  5.7  6.9 10.3 13.2 17.0 20.2 22.4 22.4 18.7 15.1  9.6  6.0 14.0 3161
1996  4.9  6.5  8.7 13.1 17.4 20.5 22.0 21.6 18.3 14.4  9.0  6.6 13.6 3226
1997  4.6  7.2 10.3 12.7 16.9 20.3 21.9 21.5 19.1 14.7  9.7  6.6 13.8 3182
1998  6.3  8.4  9.8 14.1 18.1 20.5 22.5 22.2 20.0 15.1 10.5  7.0 14.5 3165
1999  5.7  7.8  9.7 13.9 17.3 20.4 22.5 21.8 18.8 14.7 11.1  6.9 14.2 3173
2000  5.2  7.6 10.7 13.9 17.8 20.4 21.8 22.0 18.8 14.7  8.6  4.5 13.8 3151
2001  3.8  4.9  8.8 13.4 17.3 20.3 22.1 22.3 18.6 14.7 10.8  5.8 13.6 2924
2002  5.3  6.4  8.8 13.6 16.6 20.9 22.9 21.9 19.5 13.6  9.4  5.7 13.7 2960
2003  4.3  4.7  9.1 13.2 17.2 20.3 22.4 22.4 18.7 14.8  9.6  6.0 13.6 2941
2004  3.5  5.7 10.5 13.6 17.4 20.1 21.9 21.1 18.9 14.6  9.7  5.6 13.5 2966
2005  4.8  5.9  8.7 13.7 16.8 20.7 22.6 21.9 19.5 14.6 10.0  4.8 13.7 2814
2006  5.3  5.5  8.9 14.0 17.2 20.6 22.9 21.7 18.4 14.1  9.9  6.4 13.7 2793
2007  5.0  4.9 10.2 13.1 17.6 20.7 22.2 22.3 19.3 15.2  9.3  5.7 13.8 2784
2008  3.9  5.3  9.3 13.2 16.8 20.5 22.3 21.6 18.8 14.4  9.7  5.1 13.4 2774
2009  4.1  6.6  9.4 13.5 17.5 20.3 21.7 21.6 19.0 13.8 10.7  5.1 13.6 2702
2010  4.4  5.2 10.0 14.1 17.4 20.9 22.6 22.2 19.2 15.1 10.0  5.2 13.9 2708
2011  4.0  5.8  9.4 13.8 17.0 20.5 22.5 22.0 19.1 14.5  9.8  6.1 13.7 2682
2012  5.4  6.0 11.1 14.1-99.0-99.0-99.0-99.0-99.0-99.0-99.0-99.0  9.1 2528
AA    3.5  4.8  8.2 12.4 16.2 19.2 21.1 20.7 17.9 13.7  8.8  4.8 12.6
Ad    0.7  2.1  5.2  9.8 14.2 17.7 19.5 19.0 15.8 11.0  5.9  2.3 10.3
 
For Country Code ALL
 
From input file ./data/v3.mean

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