Now THAT’s Climate Change!
While looking at the videos of anomalies on the Ünür.com site, I’d noticed an odd alternation of blue and red. Here we were used to seeing these consistently RED annual anomaly maps and this video showed alternating RED and BLUE. How can this be?
That reminded me of the pattern I’d seen in the data files of “Winter Warms, Summer Doesn’t” and that it was more in some months than in others.
So I pondered. And when I ponder nothing is safe ;-)
A little bit of change and the dT/dt code, instead of making monthly anomaly ‘Hair” can make monthly Running Total Anomalies and show us the “Global Warming” for each individual month… And that’s what I did. Call it: dMT/dt.
What To Expect?
While I try not to “expect at the data” (or “expect at” anyone if at all possible – it’s more valuable to find out who someone really is than to ‘expect at them’ and be disappointed that they don’t match your expectations… they are who they are and your disappointment is really only over how poorly you set your expectations…). But there are things others are expecting of the data, so…
If we were having consistent “Global Warming” you would expect to see consistent rises to the monthly data. Perhaps with one season showing more warming than another, but mostly with things all ‘rising together’ to some degree. It also ought to have more onset early (as CO2 has log onset with diminishing returns) but with the potential for a mild exponential if there is, in fact, some sort of ‘tipping point’ feedback mechanism.
What you would definitely NOT expect is for 2 similar months in the same season to be going in different directions, nor would you expect different months to have divergent effects where, for example, May warms but April and June cool with July dead flat.
These graphs could likely use a lot of ‘tuning’. As usual, they are quite large and you can get a more readable size by clicking on it. This is just a first exploratory cut. And I’m still trying to decide what they say. It’s not what I expected (more divergent than even I’d thought I’d seen in the reports). So take a look and see what you can see…
So what do we get?
Russia Asian Sector
Russian Asian Sector dMT/dt Monthly Cumulative Anomalies
February falling, headed for an Ice age, while December rises with a wobble. April and May rising rapidly. July and September just lay there… Poor March, plummets then rockets.
France
The color scheme varies by graph as I need to separate some lines of similar color if the defaults land on top of each other, so check the “color key” again…
France dMT/dt Cumulative Monthly Anomalies
December Plunging into an Ice Age … guess it hasn’t talked with the Russian December…
February having massive Global Warming, while March gets in on the act too, but only after dropping into 1845. And it looks to me like July will have none of it, yet August dashes back and forth around zero ending up going nowhere, though having had some cool fits along the way.
The Bahamas
Much less total change. (Notice the narrower scale).
Bahamas dMT/dt Monthly Cumulative Anomalies
January, April and May just On Fire! (though after a bit of a sag…) while December crashes, only to be pulled up a bit in the end. August and November just wanting to be left alone.
Conclusions
I’ll be doing more of these but not at nearly the pace I did for the global dT/dt set. I wanted that baseline done to assure what was seen was shown to be global and to identify “interesting places” for further study. But I’m not ready for another marathon graph session like that one any time soon… So these will come out 2 or 4 at a time. With some discussion as they happen.
To me, these graphs just look very “unphysical”. You can have Jan rising while Feb falls. You can have global cooling in one month and global warming in another, while some months ‘just lay there’. I don’t know if it’s “splice artifacts” (though it isn’t always, I’ve seen these patterns in a single thermometer…) or if it’s a “QA Artifact” (that is an attractive option as some of the impact looks to be in one month but not in a neighbor in a way similar to the odd grouping of months by seasons in some of the climate codes… so if QA were done ‘per season’ the edge effects would follow).
But it sure isn’t what one would expect from ‘steadily rising CO2’ smoothly warming the planet. Or even warming it with a non-linear impact…
“Houston, Global Warming has a Problem! -E.M.Smith ”
So, speculate away… Lord knows I’m at a loss to explain it. All I can do is say: “This is what the data said when I asked them politely – then shut up and listened.” And if anyone has ideas how to better visualize this data, feel free to give it a whirl. I’ll include a couple of the data reports for folks who want to play with it.
UPDATE 16 April 2010
I’ve added a couple of more. Antarctica and Mauritius.
Antarctica
Whoo wee! What happens when you splice a whole bunch of very different equipment in very different places together to try to make a single record out of it. Maybe someone ought to tell GISS and CRU that it’s a bad idea to splice data series together (even if you call it “homogenizing” or “The Reference Station Method”… )
Antarctica Cumulative dT/dt Monthly Anomalies
Notice that May, June and September are climbing like rockets… while August is volatile but flat and December just lays there…
Mauritius
What happens to December bothered me, so I ‘ran the numbers by hand’. In the process I’ve decided I need to change how this code works (slightly).
Mauritius Cumulative dT/dt Monthly Anomalies
Generally just laying there around -1 C until a bit of a squeeze in the mid 1970s. Then flat but squeezed through the 1980’s and we hit 1990. Then all hell breaks loose. What The … happened to December? The range gets dramatically compressed and there is a general rise to the bundle (though May and January don’t want to play).
The same processing that gives the “Bullseye” on the dT/dt graphs makes December jump more than is warranted. I deliberately did not “feather” together the “Duplicate Number” transitions in the dT/dt set as they make a better indicator of when something odd happens that way. But at the monthly detail level, you can get a rather too abrupt transition. (When the new “Duplicate Number” takes over, you get a dropped value, as that’s how the “First Difference Method” did things and dT/dt is modeled on it). In a yearly average that’s not too bad, but in a single month it can be a bit drastic as a month can have an extreme value from time to time. Dropping that extreme event can result in a “jump” like in this graph for December.
I’m going to re-work the dMT/dt code and see if I can smooth out the “Duplicate Number” transition somewhat. Basically, feather together the “Duplicate Number” transitions via averaging the duplicates together in the overlap (rather than taking a ‘line of zeros’).
The Reports
This is the same stuff just as the tabular reports prior to graphing. It’s a csv file (Comma Separated Values) with the fields being:
Year, dT, dT/yr, Thermometer Count, 12 Monthly Anomaly Running Totals Jan – Dec.
Russian Asia
chiefio$ cat RussiaAsian.dMT.csv
2010, 0.23, -0.23, 89, 2.7, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
2009, 1.55, -1.32, 97, 4.3, 3.9, 2.2, -0.7, 0.0, 0.3, 0.8, 0.5, -0.8, 0.9, 3.3, 3.9
2008, 1.82, -0.28, 96, 6.6, 2.3, 0.6, 1.5, 0.0, 0.0, 1.1, 0.7, 0.4, 0.6, 3.0, 5.1
2007, 0.05, 1.77, 95, -2.2, 2.3, 0.0, -4.0, -0.3, 1.1, 0.3, -0.5, 0.1, -1.7, 1.0, 4.5
2006, 1.07, -1.02, 95, 1.7, 2.7, 1.7, -0.7, 0.3, 0.7, 0.9, -0.3, -0.1, 0.1, 2.8, 3.0
2005, 0.55, 0.52, 94, 1.1, 3.4, 1.1, -1.3, -0.2, 0.4, 0.5, -0.6, -0.6, -1.3, 2.8, 1.3
2004, 1.13, -0.58, 67, 0.9, 3.2, 3.2, 0.1, 0.0, 0.6, 0.7, 0.2, -0.3, -0.3, -0.5, 5.8
2003, 0.78, 0.35, 65, 2.0, 5.0, 4.5, -0.4, 0.3, 0.8, 0.5, -0.4, -1.3, -2.1, 0.7, -0.2
2002, 0.15, 0.63, 65, -3.3, 1.0, 0.8, -0.7, 0.5, 0.8, 0.5, 0.2, -1.8, -1.8, 3.6, 2.0
2001, 0.09, 0.06, 68, -2.2, 4.4, 2.0, 1.0, -0.1, 0.8, 0.2, 0.1, -1.1, -2.4, -1.2, -0.4
2000, -0.11, 0.20, 67, -1.3, 4.0, -3.7, -1.4, -0.2, -0.9, 1.1, -1.1, -1.6, -1.3, 0.3, 4.8
1999, -0.23, 0.12, 65, -1.8, 3.0, 2.1, -1.5, -1.6, -0.0, 1.4, 0.5, -2.3, -2.8, -2.9, 3.1
1998, 0.83, -1.06, 65, -0.5, 3.9, 3.5, 3.3, 0.1, -0.4, -0.5, -0.5, -1.0, 0.5, 0.3, 1.2
1997, 0.41, 0.42, 68, 0.4, 4.4, 2.6, -1.1, -0.5, -0.6, 1.0, -1.3, -2.6, -1.7, 2.0, 2.3
1996, 1.49, -1.08, 66, 1.8, 6.5, 2.6, 1.1, -0.5, -0.5, 0.5, 0.3, -0.9, 0.3, 2.7, 4.0
1995, 0.03, 1.46, 65, -1.6, 2.2, 1.1, -1.0, -1.1, 0.5, 0.5, -0.6, -0.9, 0.3, -0.4, 1.4
1994, 0.48, -0.45, 70, 1.6, 5.5, 2.4, -1.2, -0.9, 0.1, 1.0, -0.6, -1.7, -1.6, -0.6, 1.8
1993, 0.11, 0.38, 72, 1.2, 4.6, 1.0, -1.9, 0.3, -0.9, 0.4, -0.9, -2.0, -2.0, -2.0, 3.5
1992, 0.10, 0.01, 74, 1.1, 4.6, 1.0, -1.9, 0.3, -0.9, 0.4, -0.9, -2.0, -2.0, -2.0, 3.5
1991, 0.24, -0.14, 70, -0.2, 4.9, 2.8, -1.9, 0.3, -0.9, 0.6, -0.9, -2.2, -2.0, -2.0, 4.4
1990, 0.19, 0.05, 123, 1.8, 8.1, 1.5, -4.8, -0.3, -1.4, 0.3, -1.2, -2.0, -2.4, -2.0, 4.7
1989, -0.17, 0.36, 137, 2.5, 3.1, -2.3, -4.6, -1.0, -1.3, 0.5, -0.8, -1.0, -1.6, -1.3, 5.8
1988, -1.74, 1.57, 138, -0.7, 3.7, -3.3, -6.2, -1.0, -2.3, -0.3, -1.0, -2.4, -3.1, -6.3, 2.0
1987, -0.58, -1.16, 138, 1.4, 4.9, -1.1, -4.2, -1.6, -1.4, -0.2, -1.3, -2.1, -2.5, -0.6, 1.7
1986, -1.21, 0.62, 136, -0.2, 2.0, -3.1, -4.6, -1.9, -1.6, -0.7, -1.3, -1.9, -2.7, -1.1, 2.6
1985, -1.08, -0.13, 139, 4.1, 3.3, -2.1, -7.0, 0.1, -1.2, 0.2, -1.6, -1.9, -3.4, -4.6, 1.2
1984, 0.08, -1.16, 139, 4.8, 4.5, -0.5, -5.2, -1.8, -2.2, -0.3, -0.9, -1.5, -2.5, 0.1, 6.5
1983, -0.76, 0.84, 139, -0.1, 5.5, -4.1, -2.8, -1.1, -1.7, -0.1, -1.4, -2.1, -4.7, -2.3, 5.8
1982, -0.13, -0.63, 139, 3.9, 4.6, -2.7, -3.3, -0.8, -1.1, -0.2, -0.7, -2.2, -2.5, -2.2, 5.6
1981, -1.06, 0.93, 143, 0.6, 4.0, -3.9, -6.3, -1.4, -1.6, -0.2, -1.1, -1.9, -3.0, -2.7, 4.8
1980, -1.39, 0.33, 139, -1.4, 3.0, -2.9, -7.0, -0.5, -1.8, -0.2, -1.7, -1.9, -4.0, -3.1, 4.8
1979, -0.79, -0.60, 139, 2.4, 3.1, -1.4, -4.7, -1.8, -1.9, -0.4, -1.8, -2.2, -2.5, 1.1, 0.6
1978, -1.30, 0.51, 139, -1.0, 0.0, -3.5, -3.7, 0.1, -0.7, -0.1, -1.5, -2.1, -5.1, -0.8, 2.8
1977, -1.41, 0.11, 139, 3.4, 2.3, -3.5, -4.0, -1.4, -1.7, -0.4, -1.5, -1.9, -6.0, -3.6, 1.4
1976, -0.03, -1.38, 139, 3.3, 3.9, -0.1, -2.9, -0.5, -1.2, -0.3, -1.5, -1.5, -2.7, -1.3, 4.4
1975, -1.43, 1.40, 139, 0.3, 1.1, -1.8, -4.3, -1.3, -2.2, 0.5, -0.7, -1.2, -3.6, -5.4, 1.4
1974, -0.58, -0.85, 139, 0.5, 4.4, -2.2, -4.5, -0.8, -1.1, -0.6, -1.2, -2.0, -3.4, -0.9, 4.8
1973, -1.45, 0.87, 139, -1.6, 4.4, -2.2, -3.5, -1.9, -2.5, -0.8, -1.9, -3.1, -2.9, -5.1, 3.7
1972, -0.71, -0.74, 139, 3.1, 0.9, -3.8, -5.5, -1.1, -1.8, -0.3, -1.6, -1.3, -2.1, 0.9, 4.1
1971, -1.21, 0.50, 139, 0.6, 3.4, -3.2, -4.8, -1.5, -2.1, 0.2, -2.2, -1.3, -4.1, -2.4, 2.9
1970, -2.43, 1.23, 143, -2.7, -2.5, -5.1, -5.8, -2.6, -2.8, 0.4, -1.8, -2.6, -3.9, -1.8, 2.0
1969, -1.12, -1.31, 143, 1.8, 5.3, 1.4, -3.6, -0.3, -2.5, -0.4, -1.6, -3.1, -3.7, -5.9, -0.9
1968, -0.18, -0.94, 144, 1.0, 2.7, -0.4, -2.9, 0.2, -1.7, 0.4, -1.7, -2.6, -0.3, -1.8, 4.9
1967, -2.06, 1.88, 144, -0.1, -0.8, -4.5, -6.9, -1.0, -2.1, -0.7, -1.3, -1.4, -3.7, -3.1, 0.9
1966, -1.58, -0.48, 144, 1.2, 1.8, -1.7, -5.2, -1.4, -2.0, -0.5, -1.8, -1.9, -2.9, -5.3, 0.8
1965, -1.67, 0.09, 144, 1.4, 2.0, -4.7, -7.4, -1.3, -2.1, -0.4, -1.4, -2.3, -4.1, -3.2, 3.5
1964, -0.61, -1.06, 144, 3.5, 6.4, -3.4, -6.0, -1.6, -2.3, -0.2, -1.4, -2.1, -2.2, -1.5, 3.5
1963, -0.25, -0.36, 144, 4.6, 6.5, -1.9, -3.3, -0.0, -2.1, 0.3, -1.7, -1.9, -3.7, -3.5, 3.7
1962, -0.80, 0.55, 144, 1.0, 5.1, -1.2, -4.7, -1.1, -2.3, -0.3, -1.5, -1.4, -3.9, -2.5, 3.2
1961, -1.73, 0.93, 144, -1.0, 3.6, -5.0, -5.3, -2.1, -1.6, -0.6, -1.8, -1.7, -4.2, -4.6, 3.5
1960, -0.54, -1.19, 144, 1.8, 4.9, 0.0, -5.3, -0.9, -1.1, -0.6, -0.7, -0.7, -3.0, -2.5, 1.6
1959, -1.58, 1.04, 144, 1.0, 4.3, -5.4, -6.6, -2.2, -2.2, -0.6, -1.2, -3.0, -3.0, -2.1, 2.0
1958, -1.19, -0.39, 144, 2.4, 2.5, -5.6, -5.2, -0.2, -1.3, -0.6, -1.2, -1.8, -3.0, -3.7, 3.4
1957, -1.44, 0.25, 143, 0.8, 2.8, -3.5, -6.6, -1.2, -1.4, -0.4, -1.6, -2.5, -3.2, -3.2, 2.7
1956, -1.25, -0.19, 142, 3.5, 1.6, -5.9, -5.5, -0.6, -1.2, -0.6, -0.9, -2.4, -2.3, -2.2, 1.5
1955, -1.14, -0.11, 142, -0.1, 2.1, -3.7, -4.0, -2.0, -1.1, 0.1, -0.9, -1.6, -1.7, -2.7, 1.9
1954, -0.73, -0.41, 142, 0.6, 2.7, -2.0, -2.9, -0.5, -1.1, 0.1, -0.8, -1.6, -2.2, -5.0, 3.9
1953, -1.88, 1.14, 142, 1.2, 2.3, -4.2, -5.9, -1.3, -1.7, 0.3, -0.8, -1.0, -3.9, -7.1, -0.4
1952, -0.74, -1.13, 142, -0.8, 0.4, -2.7, -3.8, -0.8, -2.0, -0.5, -1.0, -1.1, -1.5, -1.9, 6.8
1951, -1.13, 0.38, 142, -0.0, 4.5, -2.5, -4.8, -1.3, -1.7, -0.6, -1.1, -2.0, -2.5, -4.4, 2.9
1950, -0.77, -0.36, 137, 4.9, 4.8, -3.3, -4.0, -0.9, -2.3, -1.0, -0.6, -2.2, -1.1, -4.1, 0.6
1949, -0.08, -0.68, 136, 2.8, 4.8, -1.7, -3.3, -0.1, -1.6, -0.1, -0.9, -1.7, -1.4, -0.6, 2.8
1948, -1.30, 1.22, 135, -1.6, 2.8, -3.1, -3.3, -0.9, -2.1, -1.2, -1.7, -1.6, -0.0, -3.4, 0.5
1947, -1.38, 0.08, 134, 1.1, 3.8, -5.1, -4.4, -2.0, -2.2, -0.1, -0.7, -3.2, -2.6, -2.1, 0.9
1946, -1.11, -0.28, 132, -0.0, 2.5, -4.4, -3.0, -0.7, -1.5, -1.0, -0.6, -1.2, -1.3, -2.2, 0.1
1945, -0.30, -0.81, 131, 2.6, 5.4, -0.6, -4.2, -0.5, -1.5, -0.5, -1.0, -0.5, -1.6, -4.1, 2.9
1944, 0.07, -0.38, 133, 1.3, 5.7, -0.6, -2.4, 1.3, -1.5, -0.1, -0.8, -0.9, -1.3, -3.3, 3.5
1943, -1.21, 1.28, 132, 1.7, 2.4, -5.5, -6.1, -1.9, -1.6, -0.4, -1.5, -1.7, -2.8, -2.1, 5.0
1942, -1.99, 0.78, 130, -0.7, 1.5, -5.3, -7.0, -1.7, -1.4, -0.9, -1.0, -1.7, -2.0, -3.7, 0.0
1941, -1.16, -0.83, 127, -1.8, 4.2, -1.8, -3.7, -1.8, -1.6, -0.4, -0.7, -1.8, -4.3, -2.2, 2.0
1940, -0.40, -0.76, 128, -0.9, 6.2, -1.7, -3.7, -1.3, -0.9, -0.4, -1.2, -1.8, -3.0, -1.9, 5.8
1939, -0.54, 0.14, 126, 1.3, 5.3, -1.5, -2.5, -0.8, -1.5, -0.4, -0.6, -1.5, -2.9, -0.7, -0.7
1938, -1.09, 0.55, 124, 2.5, 5.0, -5.9, -5.9, -1.7, -1.3, -0.4, -1.6, -1.7, -1.5, -2.2, 1.6
1937, -0.78, -0.31, 122, -0.0, 4.1, -4.6, -4.7, -1.6, -1.7, -0.1, -0.9, -1.2, -1.6, -2.2, 5.1
1936, -0.87, 0.08, 119, 1.4, 7.5, -2.8, -6.0, -1.6, -1.9, -0.4, -1.1, -1.5, -2.1, -3.8, 1.9
1935, -0.48, -0.39, 108, 3.1, 7.3, -3.6, -6.3, -0.3, -1.7, -1.3, -1.1, -2.8, -1.7, -0.9, 3.6
1934, -1.73, 1.25, 97, -1.1, 0.9, -5.3, -5.2, -2.1, -2.4, -0.1, -1.1, -2.2, -1.6, -3.2, 2.7
1933, -0.63, -1.09, 91, 4.0, 3.3, -3.2, -4.3, -2.1, -2.2, -0.3, -1.1, -0.8, -0.9, -3.0, 3.0
1932, -1.45, 0.82, 84, -1.7, 0.0, -2.9, -6.4, -2.1, -1.4, -0.3, -0.6, -1.1, -1.0, -2.4, 2.5
1931, -1.17, -0.28, 78, 3.2, 2.3, -2.1, -6.3, -2.0, -2.2, -0.6, -0.7, -2.3, -2.4, -1.6, 0.7
1930, -1.77, 0.60, 81, -0.6, 1.4, -3.3, -5.7, -1.9, -2.0, -0.6, -1.5, -2.4, -2.0, -1.6, -1.0
1929, -1.27, -0.49, 78, 1.3, 3.9, -4.2, -5.7, -2.0, -2.1, -0.2, -0.6, -1.6, -2.2, -4.2, 2.3
1928, -1.20, -0.07, 77, -0.4, 3.9, -4.9, -5.0, -0.8, -1.6, -0.3, -1.3, -1.0, -2.0, -1.9, 0.9
1927, -0.97, -0.23, 73, 2.9, 3.7, -0.7, -5.8, -2.9, -2.0, -1.3, -1.2, -1.5, -2.6, -2.0, 1.8
1926, -0.75, -0.22, 72, 3.6, 4.0, -4.4, -5.4, -2.2, -1.8, -0.9, -0.8, -1.3, -1.6, -1.6, 3.4
1925, -1.01, 0.26, 67, -0.5, 2.7, -3.4, -5.3, -1.0, -2.1, 0.2, -1.0, -1.4, -2.8, -1.0, 3.5
1924, -1.02, 0.01, 58, 0.8, 3.0, -2.9, -6.7, -2.3, -0.9, -0.3, -0.5, -1.2, -1.4, -3.4, 3.6
1923, -1.37, 0.35, 57, -1.8, 1.7, -2.7, -5.1, -1.3, -0.6, 0.1, -1.1, -2.2, -3.1, -3.7, 3.4
1922, -0.72, -0.64, 56, 2.6, 4.6, -2.4, -4.5, -1.4, 0.1, 0.4, -1.0, -1.9, -2.5, -3.1, 0.4
1921, -1.12, 0.39, 54, 2.2, 2.7, -0.6, -3.5, -0.1, -1.0, -0.4, -0.4, -2.4, -4.6, -4.9, -0.4
1920, -2.14, 1.02, 55, -3.2, 1.9, -5.2, -5.2, -3.1, -2.5, -0.6, -0.8, -1.5, -1.3, -4.9, 0.7
1919, -1.49, -0.65, 62, 3.3, 3.8, -3.0, -5.0, -3.1, -1.7, -0.9, -1.4, -1.9, -2.6, -3.9, -1.5
1918, -1.42, -0.07, 64, 1.0, 2.5, -4.8, -4.7, -1.5, -2.3, -0.8, -1.9, -1.5, -2.5, -3.4, 2.8
1917, -1.80, 0.38, 64, 1.3, 4.7, -4.7, -6.6, -2.6, -1.9, -0.5, -1.2, -2.4, -2.3, -3.8, -1.6
1916, -1.98, 0.18, 63, -1.8, 4.0, -4.6, -5.6, -1.0, -1.1, -0.2, -1.6, -2.3, -5.1, -4.4, -0.1
1915, -1.44, -0.54, 65, 2.4, 5.7, -4.8, -6.1, -1.2, -2.3, -2.0, -0.6, -2.0, -3.4, -5.3, 2.3
1914, -1.77, 0.33, 66, -0.4, 2.1, -2.4, -5.9, -2.2, -2.3, -1.7, -1.5, -2.9, -3.9, -3.8, 3.6
1913, -2.67, 0.90, 62, 2.0, 2.1, -6.2, -5.5, -1.5, -1.6, -1.7, -2.7, -3.2, -6.8, -5.6, -1.4
1912, -1.58, -1.09, 62, 0.1, 3.3, -5.3, -4.9, -2.3, -1.5, -0.4, -1.6, -2.8, -2.6, -1.8, 0.8
1911, -1.77, 0.18, 59, 0.3, 5.6, -5.1, -5.5, -1.4, -2.4, -0.4, -0.6, -2.2, -3.2, -5.7, -0.6
1910, -1.57, -0.19, 58, -0.6, 4.6, -6.0, -5.6, -2.2, -1.7, -0.6, -0.8, -2.1, -2.6, -2.6, 1.3
1909, -2.01, 0.43, 56, -2.0, 4.4, -5.5, -5.6, -1.2, -1.7, -1.6, -1.2, -1.9, -3.0, -5.1, 0.3
1908, -1.82, -0.18, 54, -1.4, 3.9, -1.8, -3.8, -2.0, -2.4, -1.0, -0.2, -1.7, -2.9, -6.8, -1.8
1907, -1.13, -0.69, 50, -1.3, 1.8, -1.0, -3.2, -1.2, -1.3, -1.4, -0.1, -2.5, -2.5, -5.4, 4.5
1906, -1.25, 0.12, 49, 3.4, 4.5, -3.6, -7.1, -1.8, -3.1, -1.4, -1.4, -2.3, -2.2, -2.6, 2.6
1905, -1.20, -0.05, 49, 1.2, 3.7, -2.4, -5.9, -0.9, -2.0, -1.9, -1.1, -3.0, -2.7, -1.3, 1.9
1904, -1.37, 0.17, 49, 1.3, 8.0, -2.2, -5.6, -2.3, -2.8, -1.1, -1.5, -2.8, -4.5, -3.3, 0.3
1903, -2.63, 1.26, 50, -0.2, 5.3, -4.6, -7.8, -3.3, -2.7, -0.7, -1.5, -2.4, -5.1, -7.7, -0.9
1902, -1.42, -1.22, 49, -0.1, 6.6, -0.8, -4.3, -1.1, -1.8, -1.3, -1.5, -3.1, -4.2, -3.3, -2.1
1901, -1.44, 0.02, 47, -2.1, 4.7, -1.5, -6.6, -0.7, -2.2, -1.3, -1.0, -2.0, -2.1, -3.8, 1.3
1900, -0.99, -0.45, 47, 2.9, 4.8, -3.2, -5.2, -1.7, -2.3, -1.9, -1.1, -1.8, -1.3, -1.2, 0.1
1899, -1.70, 0.71, 45, 2.9, 2.9, -8.1, -6.6, -2.4, -2.0, -0.4, -0.8, -1.9, -4.3, -3.4, 3.7
1898, -1.57, -0.13, 45, -0.1, 3.5, -4.3, -5.7, 0.1, -1.8, -1.1, -1.2, -1.9, -3.1, -3.7, 0.5
1897, -1.68, 0.12, 44, -0.2, 3.7, -4.0, -7.7, -0.6, -1.5, -1.1, -1.0, -2.3, -1.5, -3.9, -0.1
1896, -1.98, 0.30, 44, -1.5, 1.2, -2.7, -7.1, -2.0, -2.9, -0.8, -1.7, -1.9, -2.0, -3.5, 1.1
1895, -1.45, -0.53, 42, 0.2, 6.9, -2.1, -7.0, -0.6, -2.7, -1.0, -0.9, -2.2, -3.5, -5.0, 0.5
1894, -1.57, 0.12, 42, -3.4, 2.9, -1.0, -4.2, -1.7, -2.2, -0.5, -1.8, -2.2, -2.5, -2.6, 0.4
1893, -2.23, 0.66, 38, -0.7, 2.4, -5.0, -7.8, -0.4, -1.4, -0.5, -1.3, -2.5, -3.1, -6.2, -0.2
1892, -2.22, -0.01, 39, -0.6, 4.7, -0.7, -7.9, -1.6, -2.8, -1.1, -1.7, -3.2, -5.5, -7.5, 1.3
1891, -2.00, -0.22, 37, -0.6, 4.6, -2.2, -6.7, -3.0, -2.1, -0.2, -1.4, -2.5, -2.8, -8.6, 1.5
1890, -1.83, -0.17, 31, -1.2, 6.5, -2.9, -5.4, -0.6, -2.3, -1.0, -1.3, -2.5, -6.4, -6.2, 1.3
1889, -1.82, -0.02, 28, 0.6, 1.9, -2.9, -6.3, 0.6, -1.7, -0.2, -1.6, -2.8, -4.6, -3.2, -1.6
1888, -1.28, -0.54, 26, -1.1, 5.8, -1.3, -5.8, 0.4, -1.5, -0.3, -2.5, -2.5, -4.5, -3.3, 1.3
1887, -1.62, 0.34, 24, 1.2, 3.3, -3.2, -5.9, -0.6, -4.0, -0.4, -2.4, -2.2, -6.1, -4.1, 5.0
1886, -2.27, 0.65, 22, -2.3, 3.8, -0.6, -7.7, -1.4, -2.7, -1.7, -3.0, -3.3, -5.1, -5.3, 2.1
1885, -2.05, -0.22, 19, 2.1, 4.1, -4.5, -8.7, -0.7, -3.9, -1.5, -3.1, -3.8, -4.0, -3.1, 2.5
1884, -1.77, -0.28, 18, -0.4, 2.6, -0.9, -6.5, -0.3, -3.3, -0.9, -1.9, -4.0, -4.0, -4.7, 3.1
1883, -1.84, 0.08, 16, 1.9, 6.4, 0.3, -5.8, 0.7, -2.9, -1.6, -1.6, -3.0, -7.4, -4.9, -4.2
1882, -1.88, 0.04, 16, 0.9, 2.4, -1.2, -5.1, 0.2, -3.1, -1.5, -1.4, -3.6, -5.3, -3.5, -1.4
1881, -1.61, -0.28, 11, -1.2, 3.0, -0.2, -6.6, 0.6, -2.4, -1.1, -1.4, -3.8, -6.2, -1.0, 1.0
1880, -1.89, 0.28, 7, 0.2, 3.2, -1.3, -6.7, -1.0, -2.6, -0.0, -2.7, -3.4, -2.8, -4.8, -0.8
1879, -1.29, -0.60, 7, -0.8, 4.8, 2.5, -4.9, -0.2, -1.1, -0.3, -1.3, -2.9, -3.7, -5.6, -2.0
1878, -1.81, 0.52, 7, 0.6, 2.3, 1.0, -6.3, -0.5, -2.4, -1.1, -1.7, -2.9, -3.1, -3.0, -4.6
1877, -2.02, 0.21, 7, 0.7, 4.2, 1.1, -5.1, -1.2, -2.6, -0.7, -2.4, -3.7, -3.5, -5.6, -5.4
1876, -2.16, 0.14, 7, 0.9, 3.6, -0.6, -4.8, -0.5, -1.6, -1.0, -2.7, -3.9, -4.7, -7.1, -3.5
1875, -0.62, -1.53, 12, 3.8, 5.3, 0.1, -3.8, 1.9, -1.4, -1.8, -1.0, -1.9, -4.9, -2.9, -0.9
1874, -1.60, 0.98, 12, -0.1, 5.3, -1.8, -7.7, -0.1, -1.4, -1.8, -1.6, -4.2, -2.7, -4.3, 1.2
1873, -1.96, 0.36, 13, -2.1, 1.6, -0.4, -5.5, 0.6, -2.9, -1.4, -1.2, -3.3, -3.5, -4.4, -1.0
1872, -1.84, -0.12, 11, -2.7, 0.7, -0.5, -3.9, -1.9, -1.9, -0.9, -1.8, -3.1, -3.9, -4.1, 1.9
1871, -2.02, 0.18, 10, -0.4, 3.3, 0.4, -6.1, -0.2, -0.1, -1.3, -2.9, -2.6, -5.4, -5.6, -3.4
1870, -0.72, -1.30, 9, -2.0, 9.7, -3.2, -7.0, -1.1, -0.1, -0.8, 0.2, -1.1, -2.7, -1.4, 0.8
1869, -1.48, 0.76, 8, -0.2, 4.3, -1.7, -4.8, -0.1, -2.0, -1.2, -2.0, -3.1, -2.9, -5.3, 1.2
1868, -0.82, -0.67, 8, 1.5, 4.1, -2.3, -5.4, -1.9, -1.6, -0.6, -2.2, -2.2, -2.7, -0.2, 3.7
1867, -0.55, -0.27, 11, 4.9, 2.9, -0.6, -6.2, 0.9, -2.2, -0.0, -2.2, -2.2, -5.0, -1.1, 4.2
1866, -1.40, 0.85, 11, 4.0, 2.1, -2.9, -5.8, -1.1, -1.2, -1.1, -2.0, -3.4, -4.5, -2.5, 1.6
1865, -1.20, -0.20, 12, -0.9, 5.6, 0.5, -4.3, 1.6, 0.0, -0.7, -0.6, -3.7, -5.1, -5.9, -0.9
1864, -0.30, -0.90, 13, 4.6, 6.3, -0.2, -4.1, 2.4, -2.5, -0.8, -1.1, -2.8, -4.7, -2.9, 2.2
1863, -2.64, 2.34, 12, -2.1, 2.1, -1.1, -5.6, -2.4, -2.8, -0.9, -3.1, -3.2, -5.5, -4.5, -2.6
1862, -1.92, -0.72, 13, -2.1, 2.9, -0.5, -8.0, 2.8, -3.2, -0.7, -1.0, -2.6, -5.4, -4.7, -0.6
1861, -2.52, 0.59, 12, -1.3, 3.2, -6.0, -5.5, 0.5, -1.7, -1.0, -1.7, -2.4, -2.3, -5.2, -6.8
1860, -0.14, -2.37, 12, 2.3, 6.9, -0.4, -2.2, -0.3, 0.2, -1.2, -3.4, -4.3, -2.4, -0.7, 3.8
1859, -1.05, 0.91, 13, 3.2, 4.2, -1.5, -3.4, 2.0, -1.4, -0.8, -4.1, -4.6, -3.1, -3.3, 0.2
1858, -2.02, 0.97, 13, 3.7, 1.6, -0.8, -7.5, -1.8, -2.2, -1.0, -1.8, -4.6, -5.4, -5.3, 0.8
1857, -1.15, -0.87, 13, 5.2, 3.7, -2.3, -6.9, -0.2, -2.0, -0.8, -2.8, -3.8, -5.2, -3.0, 4.3
1856, -0.92, -0.23, 12, 2.7, 6.8, -1.3, -2.3, 2.0, -2.0, -2.9, -1.0, -3.9, -2.1, -4.2, -2.8
1855, -0.43, -0.48, 12, -2.6, 5.0, -0.7, -5.5, 0.5, -1.8, -0.4, -1.0, -1.2, -0.2, -2.5, 5.2
1854, -1.42, 0.98, 13, -0.2, 4.5, -2.4, -7.0, -0.2, -1.8, -0.6, -0.1, -3.1, -3.2, -4.9, 2.0
1853, -1.32, -0.10, 12, 0.8, 3.5, -0.3, -5.3, 1.9, -1.7, -2.8, -2.9, -2.3, -4.3, -6.9, 4.5
1852, -0.63, -0.68, 13, 1.4, 4.3, -2.8, -7.4, 0.9, 0.8, -1.6, -1.2, -0.7, -3.2, -2.7, 4.6
1851, -1.98, 1.34, 12, -5.8, 6.8, 1.0, -5.5, -1.9, -2.0, -1.8, -1.9, -3.8, -6.6, -5.6, 3.4
1850, -0.72, -1.26, 12, 2.8, 7.4, 2.0, -5.8, -2.5, -0.8, -0.7, -2.2, -4.4, -1.5, -3.0, 0.1
1849, -1.33, 0.61, 12, -3.4, 8.3, 0.3, -4.6, -1.2, -1.4, -0.7, -2.1, -3.5, -3.4, -3.4, -0.8
1848, -1.24, -0.08, 12, -1.6, 5.4, -3.3, -5.1, -0.2, -1.7, -1.4, -0.8, -1.3, -2.3, -2.1, -0.5
1847, -0.92, -0.33, 12, 1.3, 6.4, 1.9, -6.8, -0.2, -1.0, -1.4, -3.5, -3.2, -3.4, -4.4, 3.3
1846, -1.32, 0.40, 11, 2.3, 2.3, -1.3, -5.7, -1.8, -1.0, -1.8, -1.7, -2.3, -2.7, -2.8, 0.7
1845, -1.39, 0.07, 11, 3.8, 3.4, 0.1, -6.5, 1.0, 0.4, -1.5, -2.8, -3.3, -2.7, -7.7, -0.9
1844, 0.13, -1.52, 12, 3.8, 10.2, 1.9, -6.5, 0.1, -0.6, -1.3, -3.8, -1.8, 0.6, -4.5, 3.5
1843, -1.34, 1.48, 13, 1.0, 5.7, -0.1, -6.5, -1.0, -1.9, -0.8, -3.8, -4.0, -3.2, -3.6, 2.1
1842, -1.70, 0.36, 12, -1.8, 3.5, -1.9, -6.4, 0.7, -0.4, -1.2, -1.2, -4.6, -1.8, -6.0, 0.7
1841, -2.01, 0.31, 12, 2.5, 1.6, -2.3, -4.7, 1.5, 0.8, -1.1, -2.5, -4.9, -5.9, -8.2, -0.9
1840, -2.15, 0.14, 12, 2.6, 4.5, -3.3, -7.0, 1.0, -0.5, -0.4, -1.9, -3.8, -3.9, -7.1, -6.0
1839, -2.06, -0.09, 12, -2.0, 2.9, -2.4, -6.0, 0.3, -1.6, -2.0, -1.9, -4.8, -3.0, -4.4, 0.2
1838, -2.32, 0.27, 9, 1.2, 4.5, -1.3, -9.2, 0.3, -0.6, -2.9, -3.1, -4.9, -4.2, -5.2, -2.5
1837, -1.66, -0.67, 8, 2.3, 4.0, 1.5, -6.8, -0.2, -0.8, -2.7, -3.6, -4.2, -2.8, -5.9, -0.7
1836, -2.73, 1.07, 5, 1.0, 6.0, -1.2, -7.8, 0.3, -1.8, -3.6, -3.4, -4.7, -3.9, -6.6, -7.1
1835, -1.63, -1.10, 6, -3.0, 3.3, 0.2, -7.8, 1.4, -0.2, -3.5, -1.6, -3.3, -2.2, -2.0, -0.9
1834, -1.98, 0.35, 6, -0.1, 5.3, -4.0, -7.8, -0.3, 0.0, -2.6, -2.1, -4.8, -4.3, 0.2, -3.3
1833, -2.27, 0.29, 6, -0.2, 6.7, -2.5, -7.4, 2.1, -1.4, -0.4, -3.0, -4.0, -3.6, -7.0, -6.6
1832, -2.36, 0.08, 6, 0.3, 5.1, -4.1, -8.6, 2.1, -0.7, -0.4, -3.0, -4.0, -4.1, -7.0, -3.9
1831, -1.25, -1.11, 4, 2.2, 6.9, -2.8, -7.9, 2.1, -0.1, 0.5, -1.5, -4.1, -5.4, -2.5, -2.4
1830, -1.75, 0.50, 3, 0.7, 5.2, -2.9,-10.1, 2.6, -1.2, 1.2, -1.8, -2.9, -1.7, -4.5, -5.6
1829, -1.86, 0.11, 3, -4.1, 3.2, -1.4, -7.3, 2.5, 0.1, 0.2, -1.1, -3.6, -2.2, -4.8, -3.8
1828, -0.23, -1.62, 2, 6.2, 7.7, 1.2, -5.0, 3.9, -0.9, 2.5, -1.1, -2.3, -4.6, -2.2, -8.2
1827, -0.17, -0.06, 2, 3.1, 2.8, -1.4, -5.8, 4.3, -0.3, 2.9, -1.1, -1.4, -2.4, -3.2, 0.4
1826, -0.35, 0.18, 2, 7.9, 6.8, -1.7, -6.8, 1.7, 0.3, 0.2, -1.6, -3.6, -2.9, -1.2, -3.3
1825, -0.37, 0.02, 2, 2.2, 7.7, -0.4, -7.8, 2.0, -2.9, 1.9, -1.7, -1.6, -2.4, -2.7, 1.3
1824, -1.70, 1.33, 2, 0.9, 5.8, -0.5, -8.9, 2.5, 0.8, 1.2, -2.0, -2.3, -4.1, -8.9, -4.9
1823, -0.16, -1.54, 2, 5.7, 11.4, 3.1, -6.1, 0.8, -2.0, 1.1, -2.0, -1.6, -2.6, -6.6, -3.1
1822, -0.64, 0.48, 2, 6.5, 9.0, -1.8, -7.7, 3.3, -2.9, 0.8, -1.5, -3.9, -1.9, -4.3, -3.3
1821, -1.25, 0.61, 2, 2.8, 6.7, 0.5, -6.3, 2.9, -2.0, 0.9, -3.1, -2.1, -2.7, -6.7, -5.9
1820, -1.78, 0.53, 2, 7.4, 6.7, -1.6, -9.2, 0.5, -1.7, 0.2, -3.0, -1.6, -3.0, -9.1, -6.9
1819, -1.42, -0.36, 1, 5.4, 3.8, 0.8, -5.7, 2.5, -3.4, -1.4, -1.5, -2.2, -7.1, -8.8, 0.6
1818, -2.93, 1.51, 1, 5.4, 3.8, 0.8, -5.7, 2.5, -3.4, -1.4, -0.3, -6.6, -8.2, -7.1,-14.9
chiefio$
France
chiefio$ cat France.dMT.csv
2009, -0.28, 0.28, 17, 3.8, 2.5, -0.1, 0.0, -0.3, -0.7, -0.7, -2.0, -2.0, -1.0, -2.2, -0.7
2008, -0.05, -0.23, 17, 4.0, 3.0, 0.2, 2.6, -0.5, -0.6, -1.2, -2.4, -1.7, -1.0, -2.8, -0.2
2007, 0.24, -0.29, 17, 0.6, -0.8, -0.6, -0.4, -0.9, 0.5, 3.6, -2.8, 1.1, 1.9, 0.1, 0.6
2006, -0.50, 0.74, 17, 1.8, -1.8, -0.5, -0.6, -0.9, 1.1, 0.4, -1.9, -0.3, 1.4, -2.9, -1.8
2005, -0.40, -0.10, 17, 2.1, 0.7, -0.9, -1.0, -2.0, -0.1, -0.5, -1.1, -0.0, 0.6, -2.5, -0.1
2004, 0.38, -0.78, 17, 0.6, -0.8, 1.6, -0.0, -0.6, 2.7, 1.4, 2.7, -0.5, -2.1, -0.9, 0.5
2003, 0.03, 0.35, 17, 2.2, 3.0, 1.4, -0.2, -2.0, -0.3, -1.0, -2.2, -1.6, -0.3, -0.6, 2.0
2002, -0.41, 0.44, 18, 2.5, 1.3, 1.4, -1.3, -0.9, -1.1, -0.2, -0.6, -2.7, 2.1, -3.4, -2.0
2001, -0.19, -0.22, 18, 1.2, 2.1, 0.3, -0.8, -0.8, -0.5, -1.4, -1.3, -0.4, -0.7, -1.8, 1.8
2000, -0.40, 0.21, 13, 2.8, -0.3, 0.6, -0.6, -0.8, -1.7, 1.0, -1.4, 0.5, -0.6, -3.8, -0.5
1999, -0.82, 0.42, 13, 2.6, 1.1, 0.7, -1.7, -1.1, -1.3, -0.3, -1.4, -1.4, -1.3, -4.7, -1.0
1998, 0.08, -0.89, 13, 0.4, 2.3, 1.8, -0.5, -1.5, -2.1, -0.4, -0.2, -0.1, 2.6, -1.5, 0.1
1997, -1.26, 1.33, 13, 3.3, -1.3, -1.2, -0.6, -3.3, -0.7, -0.1, -2.8, -3.1, -1.2, -2.7, -1.4
1996, -0.48, -0.78, 13, 2.1, 2.7, -1.0, -0.9, -2.3, -2.2, 1.9, -1.3, -3.0, 1.5, -2.5, -0.8
1995, -0.07, -0.42, 16, 2.7, 0.6, 2.0, -2.1, -2.1, -1.2, 2.1, -1.0, -2.3, -1.0, 0.3, 1.2
1994, -1.12, 1.06, 16, 3.2, -0.0, -0.6, -0.5, -1.9, -1.1, -1.5, -2.2, -2.6, -2.4, -5.0, 1.1
1993, -0.86, -0.27, 16, 0.2, -0.0, 0.1, -1.1, -0.8, -2.6, -0.2, -1.1, -1.9, -3.1, -0.9, 1.1
1992, -0.86, 0.00, 16, 0.2, -0.0, 0.1, -1.1, -0.8, -2.6, -0.2, -1.1, -1.9, -3.1, -0.9, 1.1
1991, -0.52, -0.33, 17, 0.2, -0.0, 0.1, -0.9, 2.6, -1.7, -0.4, -1.6, -3.7, -0.7, -0.6, 0.4
1990, -0.58, 0.06, 17, -0.2, -3.3, 0.8, -1.4, 2.7, -0.9, -0.2, -2.3, -3.5, -1.3, -0.8, 3.4
1989, -1.07, 0.49, 17, 2.2, -3.9, -1.8, 0.3, 0.9, -1.6, -2.2, -3.2, -4.2, -1.2, -1.5, 3.3
1988, -1.86, 0.78, 18, -4.7, -4.7, -3.3, 0.8, -1.5, -2.7, -1.5, -3.2, -1.9, -1.7, -0.9, 3.0
1987, -1.83, -0.02, 18, -0.2, -8.6, -2.1, -2.8, 1.0, -1.0, -1.4, -4.0, -4.3, -1.1, -0.1, 2.6
1986, -2.08, 0.25, 19, -5.5, -4.2, -3.0, 0.1, -0.6, -2.4, -0.7, -4.2, -2.3, -1.9, -3.5, 3.2
1985, -1.83, -0.25, 19, 0.1, -5.0, -3.0, -0.3, -2.4, -2.1, -1.3, -3.7, -5.1, -2.5, 1.1, 2.2
1984, -1.06, -0.77, 20, 1.3, -5.8, -1.1, -0.9, -1.2, -0.7, 1.8, -2.7, -3.2, -2.2, -0.2, 2.2
1983, -0.90, -0.16, 20, 0.9, -3.2, -1.9, -0.5, 0.6, -0.4, 0.2, -4.1, -2.4, -2.9, 0.3, 2.6
1982, -1.51, 0.61, 21, -0.9, -5.6, 0.9, 0.2, -0.4, -1.7, -2.7, -3.0, -3.4, -2.9, -0.7, 2.1
1981, -2.07, 0.56, 22, -1.5, -2.3, -1.6, -1.5, -0.9, -2.8, -3.6, -3.0, -3.0, -3.3, -2.0, 0.7
1980, -1.67, -0.39, 22, -2.1, -4.3, -1.0, -1.6, -0.3, -1.1, -1.3, -4.7, -4.0, -1.8, -1.3, 3.4
1979, -1.92, 0.24, 22, -0.8, -4.5, -0.6, -2.0, -0.6, -2.7, -2.7, -4.6, -3.9, -2.6, -1.2, 3.2
1978, -1.47, -0.44, 22, -0.2, -2.0, 0.5, -1.2, -0.6, -2.8, -2.5, -5.2, -4.9, -1.1, -0.9, 3.2
1977, -1.24, -0.23, 22, -0.0, -3.7, -1.7, -0.9, 1.3, 1.6, -0.2, -3.2, -5.2, -2.7, -1.7, 1.5
1976, -1.51, 0.27, 22, 2.0, -3.1, -2.7, -0.8, -0.6, -1.9, -0.9, -2.3, -3.8, -3.7, -1.3, 1.0
1975, -1.55, 0.04, 22, 2.0, -3.5, -0.6, -0.7, -0.3, -1.8, -2.1, -3.2, -5.3, -6.8, -0.7, 4.4
1974, -1.62, 0.07, 22, -1.0, -5.4, -2.2, -2.2, 1.0, -0.8, -1.7, -1.7, -3.0, -3.4, -0.7, 1.6
1973, -2.00, 0.38, 22, -0.9, -3.1, -0.1, -1.1, -1.0, -3.4, -1.9, -4.9, -6.6, -3.2, -0.3, 2.5
1972, -1.66, -0.34, 22, -0.9, -4.5, -4.4, 0.9, 0.9, -2.9, -0.4, -2.9, -4.1, -1.8, -2.5, 2.7
1971, -1.65, -0.01, 22, 0.2, -4.2, -3.6, -1.9, 0.1, -0.3, -2.0, -3.5, -3.1, -2.8, 0.7, 0.6
1970, -1.86, 0.21, 24, 0.4, -6.4, -1.5, -0.5, 0.4, -3.1, -1.0, -3.6, -4.2, -1.2, -0.9, -0.7
1969, -1.73, -0.12, 24, -0.9, -4.0, -1.3, -0.0, -0.8, -2.0, -2.1, -4.7, -4.8, -0.7, -0.9, 1.4
1968, -1.41, -0.32, 25, -0.8, -3.3, -0.2, -0.9, -0.3, -2.4, -0.2, -3.5, -4.7, -0.7, -0.5, 0.6
1967, -1.41, 0.00, 26, -1.3, -1.0, -1.7, 0.4, 0.4, -1.0, -3.3, -4.6, -3.2, -1.3, -3.1, 2.8
1966, -2.13, 0.73, 30, -0.4, -7.2, -1.4, -1.3, 0.1, -1.8, -3.2, -4.5, -6.0, -1.6, -1.6, 3.3
1965, -1.51, -0.62, 29, -2.2, -3.3, -2.2, -0.5, 1.8, -1.0, -0.3, -3.5, -2.8, -4.3, -0.7, 0.9
1964, -2.61, 1.10, 30, -4.9, -8.7, -1.6, -0.5, -0.5, -2.2, -1.2, -5.2, -4.5, -2.4, 1.0, -0.6
1963, -2.21, -0.40, 30, 1.2, -4.8, -4.1, -0.9, -1.0, -2.1, -2.2, -2.8, -4.1, -1.8, -3.1, -0.8
1962, -0.70, -1.51, 30, -0.4, -0.9, 0.4, 1.7, -0.1, -1.3, -2.2, -3.5, -1.1, -1.7, -1.9, 2.6
1961, -1.42, 0.72, 30, -0.4, -3.3, 0.2, -0.5, 1.8, -0.4, -2.8, -4.3, -5.1, -3.0, -0.2, 0.9
1960, -0.72, -0.71, 30, -0.4, -3.6, 0.6, -0.1, 1.0, -1.0, 0.3, -2.8, -2.5, -2.0, -1.7, 3.6
1959, -1.47, 0.75, 30, -0.2, -2.3, -3.0, -2.4, 1.3, -2.4, -2.2, -3.0, -2.3, -2.5, -1.7, 3.1
1958, -1.34, -0.12, 30, -1.0, -1.6, 1.8, -0.6, -1.3, -1.3, -1.6, -3.9, -4.4, -2.2, -1.6, 1.6
1957, -2.50, 1.16, 29, 0.6,-10.6, -1.4, -2.0, 1.2, -3.7, -2.4, -4.8, -3.0, -3.5, -3.4, 3.0
1956, -1.39, -1.11, 29, 1.0, -4.8, -3.2, 0.3, 0.1, -1.6, -1.0, -2.4, -4.0, -3.6, -1.8, 4.3
1955, -1.88, 0.49, 29, -2.2, -6.3, -0.8, -1.5, -0.1, -1.8, -3.4, -4.4, -4.0, -1.7, -0.5, 4.1
1954, -1.38, -0.51, 29, -2.8, -5.8, -0.8, -0.0, 2.2, -2.7, -2.1, -2.4, -3.6, -1.8, -1.4, 4.7
1953, -1.42, 0.04, 28, -1.4, -5.8, 0.0, 0.9, 1.8, -0.0, 0.2, -2.3, -6.4, -2.8, -2.8, 1.6
1952, -1.60, 0.18, 28, 0.3, -4.4, -2.2, -0.8, -0.6, -1.8, -1.4, -3.7, -3.5, -3.3, -0.3, 2.5
1951, -1.42, -0.17, 25, 0.1, -3.7, -1.4, -1.0, 0.4, -0.7, -0.5, -3.1, -4.2, -2.8, -1.0, 0.8
1950, -0.87, -0.56, 14, 1.3, -4.7, -3.1, 2.6, -1.5, -2.4, -0.5, -2.1, -0.9, -1.2, -2.3, 4.4
1949, -1.03, 0.17, 13, 1.8, -4.6, -1.9, 2.2, -0.9, -2.7, -1.6, -2.7, -2.3, -1.9, -2.2, 4.4
1948, -0.52, -0.52, 4, -3.2, -7.0, -3.6, 3.1, 0.0, -0.4, 1.9, 0.7, 0.4, -0.8, -0.2, 2.9
1947, -1.37, 0.85, 4, -2.7, -2.4, -4.6, 3.2, -1.9, -2.3, 0.3, -2.7, -1.0, -1.9, -1.7, 1.3
1946, -0.48, -0.88, 4, -4.1, -2.2, -3.0, 4.1, 0.2, -1.1, 1.3, -1.9, -0.6, -0.5, -2.5, 4.5
1945, -1.07, 0.58, 4, 0.8, -6.3, -5.5, 3.2, -1.3, -2.2, -0.1, 0.2, -1.6, -1.8, -1.7, 3.5
1944, -0.24, -0.82, 3, 1.8, -3.2, -2.3, 3.8, -0.2, -1.5, 1.1, -2.0, -0.9, 0.5, -3.2, 3.2
1943, -1.37, 1.13, 4, -4.6, -8.9, -2.1, 3.0, -1.4, -1.3, -0.1, -2.1, -0.2, 0.9, -4.0, 4.4
1942, -1.87, 0.50, 4, -3.6, -3.6, -3.1, -0.0, -4.4, -1.6, 1.1, -4.2, -1.8, -1.6, -2.3, 2.7
1941, -1.88, 0.02, 4, -5.3, -3.3, -3.4, 1.5, -1.1, -1.3, -1.2, -3.7, -1.4, -1.7, -1.6, -0.1
1940, -1.38, -0.51, 4, 1.6, -4.0, -5.2, 2.1, -3.3, -1.9, -1.2, -3.3, -2.0, -1.6, 0.0, 2.3
1939, -1.26, -0.12, 4, 0.1, -4.8, -2.0, -0.4, -3.1, -1.4, -0.7, -3.1, -1.4, -1.1, 0.5, 2.3
1938, -0.68, -0.58, 4, 2.0, -1.2, -4.7, 1.8, -0.7, -1.7, 0.6, -1.9, -1.3, -0.6, -3.2, 2.8
1937, -1.18, 0.51, 4, 3.0, -3.5, -2.6, 0.2, -1.5, -2.1, -1.2, -3.1, -0.8, -3.9, -2.5, 3.8
1936, -1.33, 0.15, 4, -1.9, -3.3, -4.4, 1.1, -3.2, -1.0, 1.2, -3.2, -0.8, -2.0, -2.3, 3.8
1935, -0.88, -0.45, 4, -1.0, -6.2, -4.3, 2.2, -1.1, -1.1, 1.7, -3.7, -0.1, -0.7, -3.8, 7.5
1934, -1.47, 0.58, 4, -2.3, -4.3, -2.5, 1.9, -1.5, -3.0, 0.9, -0.8, -0.1, -0.7, -3.8, -1.4
1933, -1.47, 0.00, 4, 0.4, -7.8, -4.9, -0.1, -2.8, -2.6, -1.7, -0.6, 0.3, -1.4, -1.7, 5.3
1932, -1.86, 0.39, 4, -0.4, -5.3, -4.4, 0.7, -1.1, -0.6, -1.0, -4.3, -4.9, -2.0, -1.0, 2.0
1931, -0.89, -0.97, 4, 2.4, -5.1, -3.3, 1.5, -2.9, -0.6, -1.3, -3.2, -1.2, -0.4, -0.5, 3.9
1930, -1.58, 0.68, 4, -3.8, -9.0, -4.1, -0.4, -1.8, -1.8, 0.6, -3.2, 1.5, -0.9, -1.9, 5.9
1929, -0.73, -0.84, 4, 1.0, -2.1, -3.2, 1.1, -3.3, -2.1, 2.0, -2.0, -1.2, -0.9, -1.3, 3.2
1928, -1.34, 0.61, 4, 0.1, -4.1, -3.0, 1.7, -0.9, -2.3, -0.8, -3.9, -2.0, -1.2, -2.5, 2.8
1927, -0.86, -0.48, 4, -0.0, 0.1, -3.5, 1.9, -3.4, -3.6, -0.2, -2.7, 0.3, -0.1, -0.6, 1.5
1926, -1.75, 0.89, 4, 0.8, -3.1, -6.9, 0.8, -2.2, -0.9, -0.7, -3.7, -3.6, -0.6, -4.7, 3.8
1925, -1.49, -0.26, 4, 0.3, -6.4, -4.0, 1.2, -0.6, -2.1, -0.2, -5.7, -1.8, -0.7, -1.9, 4.0
1924, -1.15, -0.34, 4, -0.9, -2.0, -3.3, 1.4, -2.8, -4.3, 1.8, -2.1, -1.8, 0.4, -3.8, 3.6
1923, -1.79, 0.64, 4, -0.3, -3.7, -3.9, -0.1, -0.5, -1.0, -0.9, -3.8, -3.7, -3.2, -4.3, 3.9
1922, -0.64, -1.15, 4, 2.1, -4.6, -4.0, 0.5, -1.3, -0.9, 2.3, -3.3, 0.2, 2.1, -4.7, 3.9
1921, -1.19, 0.55, 4, 0.8, -2.1, -3.9, 1.3, -1.1, -1.8, -0.6, -4.9, -1.7, -0.9, -3.2, 3.8
1920, -1.91, 0.72, 3, -0.9, -4.8, -5.7, -0.6, -1.6, -1.4, -2.0, -2.3, -0.6, -4.4, -4.5, 5.9
1919, -1.41, -0.50, 3, -0.4, -3.3, -5.7, -0.4, -1.6, -2.7, 0.2, -3.2, -1.3, -3.1, -2.5, 7.1
1918, -2.41, 1.00, 3, -3.1, -7.1, -7.6, -1.7, -0.5, -0.2, 0.4, -4.4, -0.6, -3.1, -1.9, 0.9
1917, -1.41, -1.00, 3, 2.5, -3.7, -6.4, 1.1, -1.9, -3.9, -0.5, -2.8, -2.3, -1.3, -2.0, 4.3
1916, -1.71, 0.30, 3, -0.5, -4.1, -6.5, -0.0, -1.4, -0.5, -0.8, -3.9, -2.1, -3.2, -4.6, 7.1
1915, -1.50, -0.21, 3, -3.7, -2.0, -4.7, 2.9, -3.5, -2.7, -0.6, -3.0, -1.6, -2.1, -2.8, 5.8
1914, -1.03, -0.47, 3, 2.2, -3.9, -4.3, 0.5, -2.9, -2.0, -1.1, -3.5, -1.3, 0.0, 0.4, 3.6
1913, -1.55, 0.52, 3, 1.1, -1.5, -3.6, 0.9, -1.9, -2.4, -0.1, -5.9, -4.6, -2.3, -3.8, 5.5
1912, -0.65, -0.90, 3, -2.6, -4.2, -5.6, -0.3, -2.1, -1.4, 3.2, 0.1, 1.0, -0.9, -1.8, 6.8
1911, -1.68, 1.03, 3, 0.2, -3.2, -5.6, -0.1, -4.1, -1.8, -1.5, -3.8, -2.7, -0.7, -2.9, 6.0
1910, -2.30, 0.62, 3, -1.7, -6.7, -7.3, 2.1, -2.8, -3.6, -1.7, -3.1, -2.8, -0.2, -4.5, 4.7
1909, -1.73, -0.57, 3, -2.1, -3.7, -7.1, -0.4, -1.4, -0.9, 0.2, -4.0, -2.1, -0.3, -2.6, 3.6
1908, -1.68, -0.06, 3, -1.4, -6.5, -5.6, -0.1, -3.1, -2.8, -1.2, -3.3, -0.2, -0.8, -1.0, 5.9
1907, -1.40, -0.28, 3, 0.9, -5.3, -6.5, 0.1, -3.1, -1.3, 0.7, -2.1, -1.1, 0.4, -1.5, 2.0
1906, -2.00, 0.60, 3, -2.0, -4.7, -4.6, 0.8, -3.9, -1.3, 2.4, -3.6, -2.1, -5.0, -3.5, 3.5
1905, -1.33, -0.67, 3, -0.5, -3.9, -6.5, 1.8, -2.0, -1.3, 2.7, -2.6, -2.8, -1.5, -4.0, 4.7
1904, -1.55, 0.22, 3, 0.3, -2.7, -4.5, -1.4, -2.6, -2.8, -0.2, -3.9, -1.1, -0.2, -2.1, 2.6
1903, -1.80, 0.25, 3, 0.2, -5.3, -4.3, 2.0, -5.5, -2.6, 0.6, -3.6, -1.7, -2.5, -2.4, 3.5
1902, -1.93, 0.12, 3, -0.7, -8.1, -7.4, 1.6, -2.3, -0.4, 1.5, -2.8, -1.0, -2.2, -4.6, 3.3
1901, -0.94, -0.98, 3, 1.0, -2.8, -7.9, 0.7, -3.3, -1.0, 2.3, -3.5, 0.0, -1.1, -1.5, 5.8
1900, -0.80, -0.14, 6, 2.3, -2.2, -5.5, 1.0, -3.0, -0.9, 1.1, -0.4, -1.1, -0.4, -1.8, 1.3
1899, -0.99, 0.19, 6, 0.7, -4.3, -7.1, 1.1, -3.8, -2.2, -0.0, -1.1, 0.7, -0.2, -0.6, 4.9
1898, -1.05, 0.06, 6, -0.7, -1.9, -3.4, 1.1, -3.7, 0.0, 1.1, -3.2, -2.7, -2.4, -1.2, 4.4
1897, -1.97, 0.92, 12, -0.9, -5.9, -3.4, 0.4, -3.2, -1.2, 0.9, -5.1, -1.9, -3.3, -4.2, 4.2
1896, -1.58, -0.39, 12, -3.3, -9.9, -6.8, 2.0, -2.7, -1.5, 0.6, -3.2, 1.7, -2.4, 1.2, 5.4
1895, -1.44, -0.13, 12, -0.9, -3.7, -4.9, 2.5, -4.2, -1.9, 0.6, -3.7, -2.4, -1.7, -0.7, 3.7
1894, -0.93, -0.52, 12, -4.1, -2.9, -3.5, 4.3, -2.0, -0.4, 0.8, -1.5, -1.3, -0.7, -3.1, 3.3
1893, -1.53, 0.60, 12, -0.5, -4.1, -7.9, 1.3, -2.4, -1.0, 0.3, -2.3, -1.2, -2.6, 0.0, 2.1
1892, -2.22, 0.69, 12, -4.0, -5.9, -6.4, -0.4, -4.0, -1.7, -0.4, -4.6, -1.4, -0.6, -2.5, 5.3
1891, -2.38, 0.17, 12, 2.2, -6.4, -6.1, 0.1, -2.8, -1.9, -1.2, -3.8, -1.9, -3.0, -2.5, -1.3
1890, -2.46, 0.07, 12, -1.7, -6.3, -7.7, -0.4, -2.4, -0.4, -0.3, -4.2, -2.4, -2.5, -2.1, 0.9
1889, -2.55, 0.09, 12, -1.7, -7.9, -7.7, -1.0, -2.4, -1.6, -1.9, -4.8, -1.6, -4.1, -0.4, 4.5
1888, -2.67, 0.12, 12, -2.6, -6.4, -7.3, -0.5, -5.0, -0.2, 1.8, -3.0, -3.0, -5.5, -3.0, 2.7
1887, -1.50, -1.17, 12, -1.0, -6.4, -6.3, 1.4, -2.9, -2.7, 0.6, -3.0, 0.2, -0.2, -1.4, 3.7
1886, -1.73, 0.23, 12, -2.7, -1.6, -6.0, 0.6, -4.5, -0.6, 1.4, -3.4, -2.3, -3.6, -1.3, 3.3
1885, -1.18, -0.55, 12, 1.8, -2.3, -4.5, -0.1, -2.0, -3.4, 1.1, -1.5, -1.2, -2.8, -3.3, 4.1
1884, -1.69, 0.52, 12, 0.8, -2.9, -8.9, 0.4, -2.5, -1.9, -0.8, -2.9, -1.8, -2.1, -1.3, 3.6
1883, -1.34, -0.35, 12, -0.4, -4.4, -3.9, 1.4, -2.5, -2.1, -0.8, -3.6, -3.0, -1.2, -0.8, 5.2
1882, -1.48, 0.14, 13, -3.3, -2.8, -4.1, 1.0, -3.0, -1.9, 2.6, -2.8, -2.4, -4.5, 0.3, 3.1
1881, -1.20, -0.28, 13, -3.6, -3.4, -3.1, 0.8, -3.1, -2.8, 0.8, -2.8, -0.6, -1.5, -2.4, 7.3
1880, -2.90, 1.70, 13, -0.9, -3.7, -5.2, -1.0, -6.0, -1.8, -2.0, -2.3, -1.6, -2.4, -4.1, -3.8
1879, -1.51, -1.39, 13, -0.8, -3.9, -5.7, 2.0, -2.0, -1.1, 0.5, -2.8, -1.6, -0.9, -3.3, 1.5
1878, -1.08, -0.43, 13, 3.1, -2.2, -6.6, 1.1, -4.5, 0.8, 0.1, -2.4, -3.4, -3.0, -0.1, 4.1
1877, -1.06, -0.02, 13, -2.2, -3.6, -5.3, 0.9, -4.5, -1.8, 1.9, -1.9, -2.2, 0.3, -1.4, 7.1
1876, -1.45, 0.39, 13, 2.6, -7.0, -6.5, 0.9, -0.7, -1.4, -1.1, -2.1, 0.4, -2.4, -1.8, 1.7
1875, -1.33, -0.13, 13, 1.1, -4.9, -5.5, 2.3, -4.5, -0.8, 2.3, -3.6, -0.6, -1.1, -2.1, 1.5
1874, -1.27, -0.06, 13, 2.2, -5.7, -3.8, -0.1, -3.8, -1.5, 1.6, -2.0, -2.2, -1.6, -1.3, 3.0
1873, -0.71, -0.56, 13, 1.1, -1.9, -4.2, 1.2, -3.9, -1.5, 1.5, -3.5, -1.2, -2.5, -0.1, 6.5
1872, -2.13, 1.42, 13, -4.1, -2.8, -4.5, 2.4, -2.9, -3.9, 0.6, -2.0, -0.2, -2.6, -4.8, -0.8
1871, -1.73, -0.40, 13, -0.0, -6.1, -6.8, 1.6, -1.5, 0.1, 2.5, -3.9, -2.2, -1.4, -2.3, -0.8
1870, -1.35, -0.38, 13, 0.5, -1.1, -8.5, 2.2, -2.0, -2.9, 2.1, -3.8, -0.7, -3.1, -1.8, 2.9
1869, -0.46, -0.89, 14, -2.0, -3.5, -5.3, 0.9, 0.9, 0.6, 2.4, -2.4, 0.1, -2.3, -3.5, 8.6
1868, -1.30, 0.84, 17, -0.5, -1.0, -5.3, 1.9, -2.1, -1.0, -0.3, -2.5, -1.2, -2.9, -3.2, 2.5
1867, -0.66, -0.64, 17, 2.6, -2.2, -5.7, 2.2, -4.0, 0.5, 0.8, -4.1, -1.7, -1.4, -1.4, 6.5
1866, -0.79, 0.13, 19, 0.4, -6.4, -8.9, 4.4, -0.2, 0.2, 1.7, -3.2, 1.0, -0.6, -1.1, 3.2
1865, -1.87, 1.07, 19, -1.8, -6.4, -4.1, 1.6, -1.6, -1.6, 1.1, -3.6, -2.3, -2.6, -3.1, 2.0
1864, -0.83, -1.04, 19, 1.4, -4.3, -5.2, 1.9, -2.3, -0.8, 1.2, -1.5, -3.7, -0.7, -1.6, 5.7
1863, -0.73, -0.10, 19, -0.1, -3.9, -3.0, 2.8, -0.7, -1.9, 0.9, -3.5, -1.8, -0.3, -3.0, 5.8
1862, -1.10, 0.37, 19, -3.2, -3.2, -4.5, 0.5, -2.8, -0.0, -0.3, -1.1, -1.8, 0.5, -2.0, 4.7
1861, -2.40, 1.30, 19, 2.0, -8.0, -7.0, -1.4, -1.7, -2.0, -1.1, -4.7, -3.6, -2.0, -3.2, 3.9
1860, -0.57, -1.83, 19, -0.3, -3.7, -3.8, 1.5, -2.1, -0.2, 4.6, -0.7, -1.7, -0.2, -2.5, 2.3
1859, -1.40, 0.83, 20, -3.2, -5.7, -5.7, 2.7, -3.5, 2.2, 0.1, -2.8, -0.2, -1.2, -4.3, 4.8
1858, -0.85, -0.55, 20, -1.1, -5.4, -5.7, -0.0, -2.2, -0.2, 2.6, -1.0, -0.6, -0.6, -0.8, 4.8
1857, -1.15, 0.30, 20, 2.1, -3.6, -5.7, 1.4, -4.2, -0.7, 0.8, -0.5, -3.1, -1.0, -3.9, 4.6
1856, -1.97, 0.82, 21, -2.1, -6.2, -6.2, 0.8, -4.2, -1.8, 0.8, -1.5, -1.7, -0.6, -3.4, 2.5
1855, -1.20, -0.77, 18, 1.1, -5.7, -4.6, 2.2, -3.2, -2.1, 1.0, -2.8, -1.5, -1.0, -3.4, 5.6
1854, -2.28, 1.08, 17, 2.9, -7.3, -8.5, -0.3, -3.3, -1.9, 0.4, -2.8, -3.3, -1.6, -2.4, 0.8
1853, -0.81, -1.47, 18, 2.0, -4.8, -6.3, -0.0, -2.2, -1.8, 2.5, -3.1, -2.9, -2.6, 1.2, 8.3
1852, -2.15, 1.34, 17, 1.8, -5.0, -6.1, 0.5, -4.7, -0.1, -0.6, -2.2, -4.2, -1.6, -6.3, 2.7
1851, -1.83, -0.32, 17, -0.7, -3.2, -6.2, 0.8, -4.0, 0.4, -0.1, -2.7, -4.1, -2.7, -3.0, 3.5
1850, -1.21, -0.62, 10, 3.5, -3.8, -5.6, -1.6, -2.0, 0.9, 0.3, -1.9, -2.7, 0.3, -4.6, 2.7
1849, -1.20, -0.01, 11, -2.0, -3.5, -4.3, 1.3, -1.3, -0.5, 0.6, -1.5, -3.0, -0.1, -4.9, 4.8
1848, -1.43, 0.23, 11, 2.2, -7.0, -6.0, -2.0, -0.9, -1.9, 2.0, -0.7, -3.7, -0.1, -2.3, 3.2
1847, -0.58, -0.86, 11, 4.0, -3.6, -3.8, 0.3, -2.6, 2.3, 1.6, -0.0, -0.6, -0.3, -3.8, -0.4
1846, -2.36, 1.78, 10, 1.8,-11.2, -9.5, 0.5, -5.4, -0.4, -0.8, -4.4, -2.6, -0.3, -1.6, 5.6
1845, -1.83, -0.52, 11, 1.6, -8.2, -5.9, 1.8, -3.7, -0.4, -1.4, -4.1, -1.9, -0.3, -2.2, 2.7
1844, -1.24, -0.59, 8, 2.5, -6.1, -4.9, -0.4, -3.2, -2.8, -1.2, -1.5, -1.4, 0.0, -2.4, 6.5
1843, -1.56, 0.32, 8, -1.6, -7.0, -4.6, -0.9, -2.1, 0.5, -0.1, 0.3, -2.7, -2.7, -4.3, 6.5
1842, -1.31, -0.25, 8, 0.7, -7.8, -4.0, -0.6, -0.4, -2.3, -2.1, -2.6, -0.8, -0.3, -2.5, 7.0
1841, -1.78, 0.47, 10, 2.2, -7.8, -7.2, 0.3, -2.3, -1.0, -1.6, -1.4, -2.4, -1.2, -1.8, 2.9
1840, -1.17, -0.61, 8, 1.1, -6.4, -5.1, -2.6, -3.2, -0.3, 0.4, -3.3, -2.2, 0.1, -1.4, 8.9
1839, -2.38, 1.21, 8, -3.7, -8.8, -4.9, -3.4, -3.2, -3.1, -0.3, -3.3, -2.1, 0.0, -2.0, 6.3
1838, -2.21, -0.17, 8, 0.3, -6.7, -8.3, -4.3, -5.3, -1.6, -0.5, -1.3, -3.1, -0.2, -3.3, 7.8
1837, -1.55, -0.66, 7, 0.4, -8.1, -2.0, -2.0, -5.2, -1.9, 0.6, -2.4, -3.0, 0.0, -2.6, 7.6
1836, -1.83, 0.28, 7, 1.5, -6.0, -5.1, -1.9, -3.9, -2.7, 1.2, -2.4, -1.8, -1.3, -4.4, 4.8
1835, -0.43, -1.41, 6, 4.9, -7.8, -4.3, -1.9, -1.3, -2.0, 1.9, -1.3, 0.4, 0.5, -1.6, 7.4
1834, -1.53, 1.11, 6, -2.3, -4.9, -6.0, -1.9, -0.8, -2.2, -1.6, -4.2, -3.5, 0.5, -2.3, 10.8
1833, -1.76, 0.22, 6, 0.4, -8.4, -5.2, -0.7, -4.9, -3.5, 0.4, -1.7, -2.3, 0.1, -2.8, 7.5
1832, -1.09, -0.67, 6, -1.0, -6.5, -3.2, 0.7, -3.4, -3.5, 0.1, -2.5, -2.8, 2.8, -2.4, 8.6
1831, -2.18, 1.09, 7, -4.8,-10.3, -2.4, 1.2, -2.8, -3.8, 0.4, -3.4, -3.9, -0.6, -2.1, 6.3
1830, -3.01, 0.82, 7, -2.5, -9.1, -4.6, -1.0, -2.9, -3.3, -0.5, -4.4, -3.8, -1.0, -4.6, 1.6
1829, -0.93, -2.08, 8, 3.1, -7.0, -4.5, -0.2, -2.9, -2.3, 0.5, -3.5, -1.4, 0.0, -2.4, 9.4
1828, -1.89, 0.96, 7, -2.8,-11.6, -4.5, -0.6, -3.6, -2.8, 1.3, -2.9, -1.6, 0.9, -4.4, 9.9
1827, -1.06, -0.83, 7, -3.4, -6.0, -3.6, -0.6, -4.4, -1.8, 1.8, -0.1, -0.6, 1.3, -4.0, 8.7
1826, -0.62, -0.44, 6, 1.9, -7.7, -6.0, 0.8, -2.9, -2.4, 1.7, -1.9, -0.1, 0.5, -1.4, 10.1
1825, -0.98, 0.36, 7, 1.5, -6.4, -6.1, -1.2, -3.6, -3.5, 0.2, -2.4, -1.0, 0.2, -0.2, 10.8
1824, -0.78, -0.20, 7, 0.4, -5.7, -4.7, -0.2, -0.4, -1.9, -0.4, -1.4, -0.7, -0.2, -2.9, 8.8
1823, 0.96, -1.73, 7, 3.4, -5.3, -0.8, 2.0, 0.9, 3.5, 1.4, -1.8, -0.8, 2.5, 0.4, 6.1
1822, -0.11, 1.07, 7, 3.9, -9.1, -3.5, 2.0, -2.7, -3.1, -0.7, -0.8, -0.1, 0.2, 1.2, 11.4
1821, -1.03, 0.93, 7, 0.2, -7.2, -6.1, 2.3, -1.2, -2.3, 0.8, -0.4, -2.3, -0.6, -3.9, 8.3
1820, -0.05, -0.98, 7, 4.0, -6.4, -3.6, 2.4, -0.6, -1.4, 1.4, -1.1, -0.2, 0.5, -3.6, 8.0
1819, -0.04, -0.01, 7, 3.9, -6.5, -4.1, 1.9, -1.9, 0.7, 1.6, -2.2, -1.5, 0.8, 0.1, 6.7
1818, -0.33, 0.28, 7, 4.4, -4.9, -3.9, -1.2, -2.4, 0.4, 0.2, -2.8, 0.3, -1.1, -0.4, 7.5
1817, -1.48, 1.15, 5, 2.2, -9.1, -4.6, 1.5, -2.4, -2.9, -1.5, -4.1, -2.6, 2.3, -4.7, 8.2
1816, -0.53, -0.94, 5, -1.0, -5.6, -2.5, 1.5, 0.2, -1.2, 0.7, -2.1, -1.6, 2.7, -4.4, 6.9
1815, -1.17, 0.63, 5, -0.8,-11.0, -7.0, 2.3, -2.7, -1.7, 2.1, -2.3, -2.2, 1.1, -2.3, 10.5
1814, -1.25, 0.08, 5, -0.6, -6.7, -5.1, 1.5, -1.1, -2.3, -1.1, -2.9, -2.7, 2.0, -3.4, 7.4
1813, -1.69, 0.44, 5, -0.3, -6.2, -5.1, -1.6, -0.4, -1.7, -0.2, -2.7, -2.3, 1.9, -5.3, 3.6
1812, 0.37, -2.07, 5, -1.2, -5.5, -2.3, 2.9, 1.3, 0.1, 1.9, -2.5, -1.2, 4.5, -1.7, 8.2
1811, -0.86, 1.23, 5, -1.2, -9.8, -1.9, 0.9, -2.3, -1.9, -0.0, -2.5, -0.3, 1.8, -2.2, 9.1
1810, -0.59, -0.27, 5, 3.5, -4.3, -3.6, -2.0, -0.2, -1.7, 0.2, -1.9, -2.3, 0.5, -4.5, 9.2
1809, -1.06, 0.47, 5, 1.7, -9.5, -7.1, -0.9, 1.4, -1.8, 3.3, -0.5, -1.9, 0.0, -2.3, 4.9
1808, -0.52, -0.54, 5, 0.9, -6.9, -7.4, -0.0, -0.1, -1.8, 3.3, 1.6, -3.5, 3.7, -2.6, 6.6
1807, 0.46, -0.98, 5, 4.7, -6.0, -3.2, -0.9, 0.6, -0.3, 1.2, -2.1, -1.0, 1.6, -1.0, 11.9
1806, -1.02, 1.48, 5, 1.5, -7.2, -4.3, -0.4, -2.3, -1.8, 0.4, -2.3, -1.0, 0.8, -4.4, 8.8
1805, 0.53, -1.55, 3, 7.4, -9.0, -4.7, 0.1, 1.8, 1.8, 1.2, -2.1, -0.0, 3.4, -0.6, 7.1
1804, -0.08, 0.62, 3, 0.9,-10.6, -4.5, 2.7, -2.6, -0.3, 3.5, -0.1, -3.4, 2.1, -0.6, 11.9
1803, 0.32, -0.41, 3, -0.8, -6.9, -4.1, 1.1, -0.4, 0.4, -0.0, 1.7, -0.5, 4.1, -1.1, 10.4
1802, 0.74, -0.42, 3, 5.3, -7.5, -2.0, 1.1, -0.4, -0.7, 0.8, -1.3, -0.1, 4.0, -0.9, 10.6
1801, 0.69, 0.05, 3, 5.3, -7.9, -4.8, 2.8, 1.0, -1.8, 1.7, -0.4, -0.6, 2.7, -0.5, 10.8
1800, -1.31, 2.00, 2, -1.7, -6.0, -4.9, -3.2, -2.7, -0.9, 1.1, -2.5, -1.5, 3.0, -1.2, 4.8
1799, 0.74, -2.05, 2, 4.5, -5.7, -3.1, 1.9, -0.5, 2.3, 2.2, -1.1, -1.0, 4.6, -1.5, 6.3
1798, 0.69, 0.05, 2, 4.4, -7.1, -4.3, 1.0, 0.3, -1.9, 3.8, -1.4, -1.7, 2.6, -0.1, 12.7
1797, 0.22, 0.47, 2, 9.0, -6.3, -5.0, 0.1, -1.5, -0.5, 0.8, -1.9, 0.6, 2.4, -2.1, 7.0
1796, -0.18, 0.40, 2, -5.5, -7.4, -3.8, 0.9, -2.1, -0.5, -1.6, -2.6, 1.3, 7.0, -1.8, 13.9
1795, 0.50, -0.68, 2, 1.5, -3.6, -0.4, 2.5, -2.3, 0.7, 5.0, -4.0, -3.3, 2.7, 0.1, 7.1
1794, -0.46, 0.96, 2, 1.5, -5.5, -3.5, -2.3, -3.9, -2.1, 3.8, -2.9, -4.0, 4.0, -1.1, 10.5
1793, -0.13, -0.32, 2, 4.4, -7.5, -2.3, 2.3, -2.5, -1.3, 1.1, -2.4, -4.2, 3.1, -2.6, 10.3
1792, -0.03, -0.11, 2, 5.7, -6.4, -3.5, 2.4, -3.2, -0.5, 0.7, -1.9, -2.0, 1.8, -2.7, 9.3
1791, -0.01, -0.02, 2, 3.9, -4.6, -1.7, -2.2, -1.0, -0.1, -0.6, -3.1, -3.7, 4.0, -1.8, 10.8
1790, -0.82, 0.81, 2, 1.6, -5.1, -7.8, -1.4, 0.6, -2.0, 0.5, -3.2, -2.8, 1.9, -3.1, 11.0
1789, -0.57, -0.25, 2, 4.6, -4.9, -3.7, 1.0, 0.8, 0.8, 1.7, -3.3, -0.9, 2.3, -4.7, -0.5
1788, 0.24, -0.81, 2, 1.6, -5.2, -1.4, -1.2, -3.2, 0.3, 0.6, -2.2, -1.3, 4.8, -1.7, 11.8
1787, -0.51, 0.75, 3, 4.4, -6.2, -6.0, 0.6, -1.3, 1.1, 0.2, -3.3, -3.2, 1.1, -3.8, 10.3
1786, -0.87, 0.36, 3, 4.8, -9.8, -7.7, -2.1, -1.3, -0.4, 0.7, -4.2, -0.5, 3.1, -2.1, 9.1
1785, -1.17, 0.30, 3, 0.4, -9.0, -4.3, -2.7, 0.8, -0.2, 0.6, -5.5, 0.3, 0.0, -1.2, 6.8
1784, -0.59, -0.57, 3, 2.7, -7.3, -4.2, -1.2, -0.2, -0.2, 1.8, -4.8, -0.4, 1.2, -1.1, 6.6
1783, -2.17, 1.57, 1, 2.0,-12.5, -3.2, -4.9, -2.8, 1.4, -1.2, -7.3, -0.2, -1.3, -5.6, 9.6
1782, 0.81, -2.97, 1, -0.2, -7.2, -0.2, 0.2, 3.4, 1.8, 0.6, -2.6, 1.3, 1.0, -1.5, 13.1
1781, -0.48, 1.29, 1, -3.2,-11.2, 0.4, -4.8, 1.9, 1.4, -0.0, -0.4, 1.9, 2.5, -2.0, 7.7
1780, 0.58, -1.07, 1, -3.8, -5.6, -0.1, -2.1, 2.0, -0.9, 0.2, -2.1, 2.5, 4.2, -0.7, 13.4
1779, -0.56, 1.14, 1, -1.1,-11.0, -3.1, -3.0, 1.0, 0.3, 0.6, -3.0, -1.2, 0.1, 0.4, 13.3
1778, -1.28, 0.72, 1, -2.1,-10.6, -0.7, -4.5, -1.0, -1.2, -1.8, -3.4, 1.1, 1.7, -0.8, 7.9
1777, -0.03, -1.25, 1, -6.7, -6.1, 0.3, -1.3, -1.1, 1.5, 1.9, -3.2, 0.0, 3.5, 0.1, 10.7
1776, 1.32, -1.36, 1, 2.5, -5.0, -1.0, -1.1, 0.8, 4.6, 1.9, -2.2, 4.0, 2.3, -2.2, 11.3
1775, 0.57, 0.75, 1, 0.7, -7.5, 0.8, -1.2, 1.0, 2.8, 0.7, -2.5, 1.1, 2.3, -2.8, 11.5
1774, 0.59, -0.02, 1, 2.4, -9.2, -1.6, -2.5, 1.1, 1.6, -0.2, -2.2, 2.3, 3.5, -0.6, 12.5
1773, 0.66, -0.07, 1, -1.6, -6.7, -1.3, -3.0, -0.4, 4.1, 0.5, -2.9, 2.6, 4.6, 0.4, 11.6
1772, -0.23, 0.88, 1, -1.2, -9.2, -4.4, -5.0, 3.9, 0.9, 1.0, -3.9, 1.2, 2.3, -2.1, 13.8
1771, 0.11, -0.33, 1, 0.7, -9.4, -4.1, -4.4, 1.8, 0.8, -0.6, -2.3, 3.7, 1.5, 0.0, 13.6
1770, 0.14, -0.03, 1, 1.1, -8.2, -2.6, -0.9, 2.2, 0.5, 0.9, -3.8, 1.6, -1.0, -0.7, 12.6
1769, 0.63, -0.49, 1, -0.8, -5.3, -3.1, -0.5, 2.6, 1.2, 1.3, -3.0, 1.0, 2.9, -0.2, 11.5
1768, -0.00, 0.63, 1, -3.9, -3.3, -1.2, -3.2, -0.1, 0.3, 0.3, -2.6, 1.6, 3.4, 1.1, 7.6
1767, 0.30, -0.30, 1, -4.4,-10.1, -0.7, -0.5, 3.0, 2.0, 1.2, -2.4, 2.5, 3.7, -0.1, 9.4
1766, 0.28, 0.02, 1, 3.5,-12.0, -0.5, -1.5, 1.3, 2.6, 0.9, -4.5, 3.2, 3.2, -1.5, 8.7
1765, -0.18, 0.47, 1, 3.9, -6.9, -3.9, -2.8, -0.7, 2.6, 1.7, -4.5, -0.3, -0.2, -1.8, 10.7
1764, -0.73, 0.54, 1, -5.2, -5.1, -2.5, -2.2, -0.6, 2.1, 0.4, -1.4, -0.1, 0.5, -1.2, 6.6
1763, 0.47, -1.19, 1, 2.1, -8.8, -4.9, 1.0, 3.7, 3.1, 3.7, -3.5, 1.4, 0.0, -2.1, 9.9
1762, 0.93, -0.47, 1, -1.0, -6.5, -0.2, -2.0, 2.7, 2.7, 1.8, -1.2, 2.9, 0.6, -2.3, 13.7
1761, 0.27, 0.67, 1, -2.6, -9.2, -2.3, -0.5, 2.4, 3.2, 2.0, -3.4, 3.3, 2.0, -0.4, 8.7
1760, 0.28, -0.02, 1, 1.7, -6.8, -1.6, -1.1, 1.7, 2.6, -1.7, -2.7, 2.3, 2.8, -4.2, 10.4
1759, -0.45, 0.73, 1, -1.2, -8.9, -1.0, -2.8, 0.9, 3.5, -1.7, -2.3, 0.0, 0.0, -2.3, 10.4
1758, -0.34, -0.11, 1, -3.1, -9.1, -2.9, -1.6, 0.9, 2.2, 5.0, -3.5, 0.8, -1.6, 1.1, 7.7
chiefio$
The Bahamas
chiefio$ cat Bahamas.dMT.csv
2010, 0.04, -0.04, 1, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
2009, 0.37, -0.33, 1, 1.4, 3.3, 1.5, 0.2, 1.1, 0.7, -0.4, -0.3, -0.1, -0.7, -0.6, -1.6
2008, -0.04, 0.42, 1, 2.0, -0.2, 1.3, -0.9, -0.1, -0.7, -0.4, -0.3, 0.1, -0.7, -0.6, 0.0
2007, -0.24, 0.20, 1, 1.2, -0.2, 0.0, -0.7, -0.1, -0.4, 0.3, -0.1, 0.3, -1.6, -1.0, -0.6
2006, -0.25, 0.01, 1, 0.3, 0.2, 0.5, -0.9, -0.1, 0.0, 0.3, -0.1, 0.3, -1.6, -0.2, -1.7
2005, -0.60, 0.35, 1, 0.0, 1.9, 0.2, -1.3, -1.2, 0.4, -1.4, -0.9, -0.4, -1.8, -0.3, -2.4
2004, -0.08, -0.52, 1, -0.9, 2.4, 3.5, -0.3, 0.6, -0.1, -1.0, -1.0, -0.4, -1.3, -0.1, -2.4
2003, -0.12, 0.03, 1, 1.7, 1.3, 1.3, -0.3, -0.3, -0.6, -1.0, -1.3, -0.5, -1.1, 0.4, -1.0
2002, -0.82, 0.70, 1, -1.5, 2.4, 1.0, -1.0, -1.8, -1.8, -1.5, -1.1, -0.7, -1.8, -1.2, -0.8
2001, -0.74, -0.08, 1, -0.1, 0.5, 0.5, -0.6, -0.6, -0.8, -1.4, -1.7, -0.1, -1.1, -1.4, -2.1
2000, -0.38, -0.37, 1, 2.6, 0.5, -0.5, -0.1, -1.1, -0.9, -0.5, -0.8, -0.2, -1.1, -0.2, -2.2
1999, -0.26, -0.12, 1, 1.4, 1.1, -0.4, -1.0, -0.1, -0.1, -0.5, -1.3, -0.2, -1.6, 0.3, -0.7
1998, -0.21, -0.05, 1, 0.2, 2.8, 1.9, -0.1, 0.2, -0.7, -1.5, -1.3, -0.8, -2.0, 0.4, -1.6
1997, -0.89, 0.68, 1, 0.1, 0.5, 0.2, -1.2, -1.4, -0.1, -1.5, -1.1, -0.2, -2.4, -1.2, -2.4
1996, -0.61, -0.28, 1, 0.5, 0.5, 0.2, 0.3, -1.4, -0.1, -1.3, -1.5, -0.9, -1.8, -0.7, -1.1
1995, -0.56, -0.05, 1, 1.6, 0.5, -0.6, 0.3, -1.4, -0.1, -1.3, -1.9, -0.9, -1.8, -0.0, -1.1
1994, -0.79, 0.23, 1, 2.1, 0.5, -0.6, -1.9, -1.4, -0.8, -1.3, -1.3, -0.5, -2.7, -0.0, -1.6
1993, -0.92, 0.12, 1, 0.2, 0.9, 0.1, -1.9, -2.0, -0.6, -1.4, -1.2, -0.5, -2.7, -0.0, -1.9
1992, -0.92, 0.00, 1, 0.2, 0.9, 0.1, -1.9, -2.0, -0.6, -1.4, -1.2, -0.5, -2.7, -0.0, -1.9
1991, -0.95, 0.03, 1, -0.4, 1.8, -0.0, -3.6, -2.3, -0.6, -1.4, -0.8, -0.1, -2.2, 0.1, -1.9
1990, -1.33, 0.37, 1, -1.3, 0.3, -0.3, -2.7, -2.4, -1.1, -1.5, -1.4, 0.1, -3.1, 0.8, -3.3
1989, -1.53, 0.20, 1, -1.7, 0.1, -1.3, -3.1, -3.4, -1.6, -1.2, -1.2, -0.2, -3.2, 1.1, -2.6
1988, -1.59, 0.07, 1, -1.9, 0.4, -1.1, -5.2, -3.2, -0.8, -1.3, -0.9, -0.2, -2.8, 0.8, -2.9
1987, -1.46, -0.13, 1, -2.3, -0.1, -1.1, -5.0, -3.9, -1.4, -1.2, -0.8, 0.0, -2.8, 1.5, -0.4
1986, -1.92, 0.46, 1, -4.1, -0.8, -1.1, -4.1, -3.2, -1.1, -1.7, -1.2, -1.0, -3.1, 0.8, -2.4
1985, -1.96, 0.04, 1, -1.2, 0.6, -1.4, -4.0, -2.9, -2.4, -2.0, -2.1, -1.3, -3.7, -0.8, -2.3
1984, -1.48, -0.48, 1, -2.0, 0.0, -1.2, -3.8, -3.3, -1.2, -1.2, -1.1, -0.2, -2.8, 0.2, -1.1
1983, -1.25, -0.23, 1, -2.0, 1.3, 0.3, -2.8, -3.6, -0.7, -1.3, -1.0, -0.3, -3.2, 0.4, -2.1
1982, -2.00, 0.75, 1, -4.7, -0.1, -1.8, -3.6, -2.7, -0.7, -1.2, -0.8, -0.5, -3.5, -1.5, -2.9
1981, -2.19, 0.19, 2, -3.8, -1.7, -1.4, -3.5, -3.3, -1.1, -1.8, -1.6, -1.2, -3.5, -0.4, -3.0
1980, -2.17, -0.03, 2, -3.5, -0.9, -2.7, -3.4, -3.7, -1.3, -1.2, -1.8, -0.9, -4.3, -0.3, -2.0
1979, -2.23, 0.07, 2, -4.6, -2.3, -2.5, -4.0, -3.0, -0.9, -1.4, -1.9, -1.7, -3.9, -0.0, -0.6
1978, -2.45, 0.22, 2, -5.7, -0.6, -0.7, -3.9, -4.5, -1.1, -2.0, -1.6, -1.2, -4.9, -1.2, -2.0
1977, -2.53, 0.07, 2, -4.5, -0.4, -1.1, -4.6, -3.3, -2.5, -2.2, -2.3, -1.8, -4.5, -1.3, -1.8
1976, -1.94, -0.58, 2, -2.3, 1.9, -0.9, -2.9, -3.3, -1.5, -2.5, -2.3, -1.3, -4.0, -1.4, -2.8
1975, -2.26, 0.32, 2, -1.9, -0.2, -0.9, -3.4, -3.4, -1.7, -2.6, -2.4, -1.0, -4.8, -1.8, -3.0
1974, -2.36, 0.10, 2, -2.5, -1.4, -1.0, -3.6, -3.4, -1.7, -2.3, -2.4, -1.3, -4.5, -1.2, -3.0
1973, -1.94, -0.42, 2, -1.8, 0.3, -1.5, -3.6, -3.5, -1.7, -2.6, -2.2, -1.4, -4.1, -0.3, -0.9
1972, -2.39, 0.45, 2, -4.1, 0.2, -2.8, -4.6, -3.2, -1.6, -1.8, -2.3, -1.7, -4.2, -1.6, -1.0
1971, -2.53, 0.13, 2, -4.2, -0.9, -2.5, -3.8, -3.4, -1.7, -1.7, -1.9, -1.7, -4.1, -2.5, -1.9
1970, -1.97, -0.56, 2, -3.8, 0.9, -1.1, -3.2, -3.0, -1.7, -1.3, -2.0, -0.9, -3.2, -1.2, -3.1
1969, -1.86, -0.11, 2, -2.6, 0.7, -1.6, -3.6, -3.3, -2.0, -1.8, -2.0, -0.7, -3.2, -0.8, -1.4
1968, -1.67, -0.19, 2, -2.1, 1.5, -1.1, -4.0, -3.3, -2.0, -1.7, -2.0, -0.7, -3.2, -0.8, -0.6
1967, -1.98, 0.31, 2, -3.7, 0.5, -1.8, -4.4, -3.4, -2.0, -1.7, -1.7, -0.3, -3.1, -0.9, -1.2
1966, -2.16, 0.18, 2, -4.4, 0.9, -1.4, -4.3, -4.0, -1.8, -2.1, -1.9, -0.9, -3.6, -1.0, -1.4
1965, -1.87, -0.29, 2, -2.9, 0.0, -0.5, -3.6, -3.2, -1.4, -2.1, -1.8, -0.7, -3.8, -0.9, -1.5
1964, -2.20, 0.33, 2, -4.0, 0.6, -1.0, -4.2, -4.1, -1.8, -1.6, -2.0, -0.5, -4.1, -1.5, -2.2
1963, -2.14, -0.06, 2, -2.6, 0.3, -1.5, -4.1, -3.7, -2.3, -1.4, -2.1, -1.3, -3.7, -1.6, -1.7
1962, -1.86, -0.28, 2, -3.9, 2.3, -0.9, -3.6, -3.2, -1.3, -1.3, -1.7, -1.2, -4.4, -1.9, -1.2
1961, -1.83, -0.03, 2, -3.8, 2.1, -2.5, -4.0, -3.2, -1.6, -1.4, -2.0, -0.9, -2.8, -0.0, -1.8
1960, -1.68, -0.14, 2, -3.2, 1.7, -1.6, -3.5, -3.1, -1.1, -1.9, -2.0, -1.1, -2.7, -0.4, -1.3
1959, -2.24, 0.56, 2, -5.1, -2.3, -2.1, -4.1, -3.6, -1.3, -1.4, -1.6, -1.3, -3.9, -0.1, -0.1
1958, -2.10, -0.14, 2, -3.1, 1.2, -1.7, -3.6, -3.0, -2.2, -2.0, -2.3, -1.9, -4.1, -0.6, -1.9
1957, -2.37, 0.27, 2, -5.5, 1.0, -2.4, -4.6, -3.1, -2.0, -2.1, -2.0, -1.4, -3.7, -1.6, -1.0
1956, -2.85, 0.48, 2, -5.3, -0.8, -3.2, -5.9, -3.9, -1.9, -2.7, -2.0, -1.5, -4.5, -1.2, -1.3
1955, -2.38, -0.48, 2, -2.9, -0.4, -2.4, -3.7, -3.1, -1.9, -2.0, -2.0, -1.6, -4.5, -2.0, -2.0
1954, -2.08, -0.29, 2, -5.5, 0.8, -1.7, -4.5, -2.5, -1.7, -2.3, -1.6, -0.8, -4.0, -0.5, -0.7
1953, -2.40, 0.32, 2, -4.3, -0.4, -1.7, -5.2, -3.3, -1.5, -2.1, -2.2, -1.3, -3.5, -0.9, -2.4
1952, -2.82, 0.42, 2, -5.6, -1.8, -4.4, -4.8, -3.4, -1.9, -2.1, -2.3, -0.9, -4.1, -1.7, -0.9
1951, -2.88, 0.06, 2, -4.5, -1.4, -4.1, -5.6, -3.6, -1.9, -2.2, -2.1, -1.4, -3.8, -2.0, -2.0
1950, -2.24, -0.64, 1, -4.4, 0.0, -3.9, -3.1, -3.2, -2.0, -2.4, -2.7, -0.6, -3.4, -1.8, 0.6
1949, -1.87, -0.37, 1, -4.6, -0.3, -2.0, -4.0, -3.3, -1.7, -1.9, -2.5, -0.6, -3.8, 0.8, 1.4
1948, -2.34, 0.47, 1, -3.5, -3.2, -4.6, -2.5, -2.9, -2.1, -2.2, -2.8, -1.1, -3.8, 0.1, 0.5
1947, -2.17, -0.17, 1, -5.0, -0.8, -2.9, -4.4, -2.2, -1.7, -1.8, -2.9, -1.4, -3.8, -0.3, 1.1
1946, -2.95, 0.78, 1, -7.3, -1.8, -3.5, -3.5, -4.1, -1.3, -2.1, -2.8, -0.9, -4.1, -2.2, -1.8
1945, -2.87, -0.08, 1, -6.9, -2.0, -2.4, -3.4, -4.0, -1.7, -2.1, -3.4, -1.5, -4.3, -1.4, -1.4
1944, -2.97, 0.10, 1, -5.0, -2.4, -3.5, -4.8, -2.9, -2.2, -2.6, -3.1, -1.6, -4.7, -1.4, -1.5
1943, -2.65, -0.32, 1, -5.5, -3.1, -4.2, -5.1, -3.5, -1.7, -1.2, -2.3, -0.7, -4.2, -1.1, 0.8
1942, -2.68, 0.03, 1, -5.9, -3.4, -5.7, -3.6, -4.6, -1.7, -1.9, -2.3, -0.7, -3.3, -0.3, 1.2
1941, -2.35, -0.33, 1, -6.1, -1.7, -3.5, -3.6, -3.3, -0.9, -1.0, -2.1, -1.1, -4.9, -1.0, 1.0
1940, -1.62, -0.72, 1, -3.8, -0.1, -2.3, -3.0, -2.6, -1.9, -2.0, -2.1, -0.1, -1.8, -0.2, 0.4
1939, -1.59, -0.03, 1, -2.4, 1.8, -2.7, -3.1, -1.8, -1.7, -3.1, -2.7, -0.4, -3.5, 0.1, 0.4
1938, -1.41, -0.18, 1, -2.2, -0.1, -2.0, -3.0, -2.4, -1.4, -1.7, -2.1, -0.1, -2.7, -0.2, 1.0
1937, -1.69, 0.28, 1, -3.5, -0.1, -3.7, -3.2, -2.6, -2.2, -1.7, -2.1, -0.7, -2.1, 0.4, 1.2
1936, -2.04, 0.35, 1, -4.3, -1.8, -2.6, -2.2, -1.5, -2.2, -2.3, -2.1, -0.7, -3.4, -0.4, -1.0
1935, -2.13, 0.09, 1, -3.8, -1.0, -2.6, -4.1, -3.0, -1.6, -1.7, -1.8, -0.5, -2.7, -1.9, -0.9
1934, -1.59, -0.54, 1, -5.2, 0.0, -2.0, -2.9, -1.4, -1.5, -3.1, -1.3, -0.2, -2.2, -1.1, 1.8
1933, -2.27, 0.68, 1, -4.3, 0.4, -3.2, -4.1, -2.9, -2.0, -2.0, -2.0, -0.3, -4.9, -2.1, 0.2
1932, -2.14, -0.12, 1, -5.7, -2.0, -4.6, -4.5, -2.9, -1.8, -1.0, -1.5, -0.1, -2.9, -0.3, 1.6
1931, -1.87, -0.27, 1, -3.1, -0.5, -3.2, -2.8, -2.3, -2.0, -1.7, -1.6, -0.0, -3.8, -1.9, 0.4
1930, -1.52, -0.35, 1, -3.7, 0.4, -1.7, -2.8, -1.4, -1.7, -1.7, -2.0, -0.8, -4.4, 0.2, 1.3
1929, -2.10, 0.57, 1, -4.3, 0.2, -3.2, -4.1, -4.0, -1.5, -2.0, -2.4, -0.3, -3.6, -0.2, 0.2
1928, -1.95, -0.15, 1, -6.1, 0.6, -2.3, -2.6, -2.6, -0.4, -1.4, -2.5, -1.7, -3.9, -1.1, 0.6
1927, -1.28, -0.67, 1, -6.2, -0.4, -2.3, -2.6, -1.8, -1.5, -1.4, -0.9, 0.7, -2.9, 1.8, 2.1
1926, -1.22, -0.06, 1, -4.1, 0.6, -1.2, -2.6, -1.3, -1.5, -1.9, -1.9, 0.8, -2.9, 0.2, 1.1
1925, -1.22, -0.01, 1, -4.2, -1.5, -4.3, -1.6, -0.8, -0.7, -0.6, -0.8, 0.1, -2.2, -0.1, 2.1
1924, -2.65, 1.43, 1, -6.2, -3.3, -4.2, -4.8, -3.5, -2.2, -2.6, -3.0, -0.1, -2.8, -0.2, 1.1
1923, -2.80, 0.15, 1, -7.5, -3.2, -4.4, -4.9, -4.1, -0.8, -2.0, -3.5, -0.3, -3.2, -0.4, 0.7
1922, -2.23, -0.57, 1, -7.3, -1.4, -3.5, -2.8, -1.8, -0.8, -2.0, -2.3, -0.3, -3.7, -1.0, 0.1
1921, -2.03, -0.20, 1, -5.5, -2.4, -3.6, -3.1, -1.7, -0.7, -2.1, -1.5, -0.5, -2.4, -0.7, -0.2
1920, -2.03, 0.00, 1, -4.9, -0.8, -2.7, -4.4, -2.5, -1.7, -2.1, -1.5, -0.5, -2.4, -0.7, -0.2
1919, -2.27, 0.23, 1, -6.5, -0.5, -2.5, -3.9, -3.5, -2.2, -2.0, -2.3, -1.5, -3.0, -0.3, 1.0
1918, -2.36, 0.09, 1, -4.3, -1.3, -2.7, -4.3, -3.1, -2.2, -1.9, -2.0, -0.5, -3.0, -2.2, -0.8
1917, -2.47, 0.12, 1, -4.2, -1.0, -4.9, -4.6, -3.1, -1.3, -2.3, -2.9, -0.9, -3.7, -0.7, -0.1
1916, -2.23, -0.24, 1, -4.2, -1.4, -5.6, -6.1, -2.3, -1.3, -1.4, -1.9, 0.2, -2.7, 0.2, -0.3
1915, -2.14, -0.09, 1, -5.7, -0.4, -5.1, -3.4, -2.8, -1.8, -1.8, -1.8, -0.7, -3.0, -0.9, 1.7
1914, -1.98, -0.16, 1, -3.1, 0.8, -1.3, -4.3, -3.5, -2.8, -2.1, -2.6, -0.8, -3.3, -1.1, 0.3
1913, -1.45, -0.53, 1, -3.4, -1.5, -1.4, -2.6, -1.9, -1.6, -1.7, -2.2, -0.3, -2.7, -0.2, 2.1
1912, -1.93, 0.48, 1, -4.1, -0.5, -3.6, -4.2, -3.8, -1.8, -2.1, -2.5, -0.9, -2.5, 1.3, 1.5
1911, -2.75, 0.82, 1, -6.0, -1.4, -4.1, -4.5, -3.1, -1.9, -2.2, -2.5, -1.0, -3.3, -1.5, -1.5
1910, -2.23, -0.52, 1, -4.5, -0.7, -3.0, -3.8, -2.7, -1.5, -1.9, -2.6, -0.8, -3.4, -1.2, -0.7
1909, -1.85, -0.38, 1, -4.9, -1.5, -2.6, -2.1, -2.1, -1.7, -1.8, -2.1, -0.6, -4.0, -0.0, 1.2
1908, -1.78, -0.07, 1, -4.4, -1.0, -2.6, -4.0, -1.8, -1.5, -1.8, -2.3, -0.7, -2.9, 0.4, 1.2
1907, -2.12, 0.33, 1, -3.5, 0.0, -3.2, -4.4, -3.1, -2.0, -1.8, -2.6, -0.4, -3.1, -0.7, -0.6
1906, -1.53, -0.58, 1, -6.2, 0.0, -2.3, -3.6, -1.0, -0.9, -1.9, -1.9, -0.4, -2.5, 0.6, 1.7
1905, -2.21, 0.68, 1, -4.4, 0.1, -2.4, -3.8, -2.8, -2.5, -2.3, -2.7, -1.3, -3.4, -1.5, 0.5
1904, -2.33, 0.12, 1, -4.7, 0.2, -2.7, -4.8, -4.2, -2.1, -1.7, -1.8, -1.3, -4.0, -0.7, -0.2
1903, -2.09, -0.24, 1, -6.1, -2.0, -3.5, -4.1, -2.8, -1.5, -1.4, -2.1, -0.2, -2.4, -0.0, 1.0
1902, -2.22, 0.13, 1, -5.2, -1.6, -3.0, -4.4, -3.1, -1.3, -1.4, -1.7, 0.3, -3.1, -1.9, -0.3
1901, -1.35, -0.88, 1, -5.0, -1.7, -3.1, -3.0, -1.7, -0.2, -0.6, -0.9, 0.0, -1.5, 0.5, 1.0
1900, -0.93, -0.42, 1, -2.8, 0.8, -1.3, -3.4, -1.6, -0.1, -0.1, -0.9, 0.7, -2.7, -0.0, 0.2
1899, -1.25, 0.32, 1, -4.1, -0.8, -2.7, -3.0, -2.5, -0.4, -0.5, -1.0, 0.4, -2.3, 0.7, 1.2
1898, -1.23, -0.02, 1, -5.4, 1.1, -1.6, -3.2, -2.4, 0.3, -0.3, -1.6, -0.4, -3.3, 0.7, 1.3
1897, -1.70, 0.47, 1, -5.5, -1.4, -3.8, -4.8, -2.5, -0.1, -0.8, -1.6, 0.2, -2.3, 1.1, 1.1
1896, -1.75, 0.05, 1, -5.3, 1.2, -2.7, -4.4, -3.3, -1.8, -0.9, -1.6, 0.7, -2.7, 0.2, -0.4
1895, -1.94, 0.19, 1, -5.3, 1.2, -2.7, -4.4, -3.3, -1.8, -0.9, -1.6, -0.4, -3.9, 0.3, -0.5
1891, -1.62, -0.32, 1, -3.5, 0.6, -3.7, -3.5, -1.9, -0.9, -1.2, -2.0, -0.4, -2.8, 0.3, -0.5
1890, -1.79, 0.17, 1, -4.1, -0.4, -4.3, -4.1, -2.4, -0.9, -0.6, -1.8, -0.6, -3.6, 0.5, 0.8
1889, -2.20, 0.41, 1, -4.5, -0.2, -4.2, -4.6, -3.0, -1.9, -1.8, -2.3, -0.4, -3.1, -0.1, -0.3
1888, -2.02, -0.18, 1, -4.8, 0.8, -3.7, -3.6, -3.3, -1.3, -1.3, -2.1, -1.1, -3.0, -1.0, 0.2
1887, -2.19, 0.17, 1, -6.9, -1.7, -3.4, -5.3, -2.3, -0.4, 0.0, -1.5, -0.9, -3.9, -0.4, 0.4
1886, -1.49, -0.70, 1, -3.5, -1.1, -3.6, -3.6, -2.0, -0.1, -0.5, -0.9, 0.1, -3.0, 0.3, -0.0
1885, -1.79, 0.30, 1, -4.9, 0.3, -2.5, -3.4, -2.6, -2.7, -0.2, -1.8, -0.9, -4.3, 0.2, 1.3
1884, -1.32, -0.47, 1, -3.4, 1.3, -3.3, -2.3, -2.5, 0.2, -0.8, -1.8, -0.9, -3.0, -0.2, 0.9
1883, -1.47, 0.15, 1, -3.8, 0.1, -2.7, -2.9, -2.6, -0.7, -0.9, -1.5, -0.2, -2.8, -0.6, 1.0
1882, -1.77, 0.31, 1, -3.7, -0.6, -4.5, -4.3, -3.0, -0.4, -0.6, -1.8, 0.0, -3.7, 0.4, 0.9
1881, -1.52, -0.26, 1, -3.7, 0.6, -2.0, -2.8, -2.8, -1.3, -0.6, -2.3, -1.2, -3.6, 0.8, 0.7
1880, -2.04, 0.52, 1, -4.8, -0.8, -3.2, -3.8, -3.3, -2.1, -1.6, -1.8, -0.7, -3.1, -0.4, 1.1
1879, -1.97, -0.07, 1, -5.5, -0.8, -2.7, -3.3, -2.3, -1.4, -1.3, -1.7, -0.5, -3.3, -1.0, 0.1
1878, -1.74, -0.23, 1, -3.3, -0.8, -3.1, -4.0, -4.1, -1.4, -1.7, -1.6, 0.6, -2.2, 0.4, 0.3
1877, -1.86, 0.12, 1, -3.9, 0.6, -3.5, -3.5, -2.0, -1.4, -1.3, -1.9, -0.9, -2.8, -1.3, -0.4
1876, -1.44, -0.42, 1, -3.3, 0.5, -2.2, -4.5, -2.8, -1.8, -1.3, -1.6, 0.3, -2.9, 1.1, 1.2
1875, -1.88, 0.44, 1, -4.8, 0.3, -1.8, -2.2, -3.0, -1.1, -2.4, -2.7, -0.9, -4.3, -0.3, 0.6
1874, -1.97, 0.09, 1, -3.7, -0.3, -3.7, -4.0, -3.0, -0.2, 0.2, -2.7, 0.1, -4.7, -1.0, -0.7
1871, -1.92, -0.06, 1, -3.7, -0.3, -3.7, -4.0, -3.0, -0.2, -0.8, -1.0, 0.1, -4.7, -1.0, -0.7
1869, -1.48, -0.43, 1, -4.0, 0.5, -3.1, -3.1, -1.1, -0.2, -0.8, -1.0, 0.1, -3.4, -1.0, -0.7
1868, -1.25, -0.23, 1, -4.0, 0.5, -3.1, -3.1, -1.1, -0.2, -0.8, -1.0, 0.1, -3.4, -0.5, 1.6
1866, -1.77, 0.52, 1, -5.8, -2.0, -3.1, -3.4, -2.7, -0.9, 0.0, -1.1, 0.1, -3.4, -0.5, 1.6
1865, -2.80, 1.03, 1, -4.7, -1.8, -4.0, -5.4, -4.4, -2.2, -2.6, -3.4, -1.4, -4.3, -0.4, 1.0
1864, -1.19, -1.61, 1, -4.1, 0.8, -2.7, -3.0, -0.8, -0.8, -0.7, -1.6, 0.5, -2.0, -0.7, 0.8
1862, -1.33, 0.14, 1, -4.4, -0.1, -3.2, -3.0, -0.8, -0.8, -0.7, -1.6, 0.5, -2.0, -0.7, 0.8
1861, -2.13, 0.80, 1, -4.5, -0.6, -4.3, -2.7, -3.3, -0.4, -0.5, -2.3, -0.5, -4.2, -1.6, -0.7
1860, -1.68, -0.45, 1, -4.3, -0.7, -1.8, -2.2, -2.1, -0.9, -1.0, -2.1, -0.6, -3.4, -1.2, 0.1
1859, -2.19, 0.51, 1, -5.0, -0.9, -4.8, -3.8, -2.1, -1.0, -0.7, -2.0, -1.2, -4.2, -1.8, 1.2
1858, -2.23, 0.04, 1, -6.0, -0.0, -2.7, -4.0, -2.6, -0.7, -1.7, -2.6, -1.6, -5.0, -0.6, 0.7
1857, -0.87, -1.37, 1, -4.4, -0.3, -1.3, -2.3, -0.8, -0.4, -0.5, -1.3, 0.1, -2.5, 1.7, 1.6
1856, -0.88, 0.01, 1, -4.4, -0.3, -1.3, -2.3, -0.8, -0.4, -1.7, -1.6, 0.7, -2.8, 1.7, 2.7
chiefio$
Musings from the Chiefio 
It’s interesting that from about 1995 on the variability (ie, the excursions to the extremes) narrows considerably. That that seems to be a gradually increasing trend from the early 1800’s to 1995 and then, BAM, the amplitudes just drop right down suddenly.
I don’t know what that may mean but it’s an interesting and obvious perturbation.
The early narrowing is a simple and essential mathematical side effect of thermometer count. And, as you can see, it is symmetrical to the mean.
As you average an ever larger number of things together, to get an “extreme excursion” requires an ever more unlikely event (all of them moving together). Where at “the start of time” almost universally it is one (or very few) thermometers, so “extreme excursions” only require one place to have something “odd” happen.
What is astounding about the Emeril like “BAM!” is that it happens WHILE COUNTS DROP. We ought to be seeing a re-expansion of range as the counts drop, instead we get more compression? Very non-mathematical and non-physical… And that it’s all the ‘cold anomalies’ that get destroyed is also very odd. That’s not a statistical artifact of large numbers. It’s very non-symmetrical.
As you put it, an “obvious perturbation”. (~”in the force… like a million lives being snuffed out at the same time”… )
And that’s part of why that “count” line is there. So you can see which way the “central limit theorem” and the “law of large numbers” ought to be pushing.
Then there are The Bahamas. All of 2 thermometers? No reason for math to compress that range. IMHO, this graph clearly shows EXACTLY which data are buggered, and when it starts. It also points at “instrument and processing” as the source of “Bahamas Warming” and away from any natural physical process. (Assuming Jet Airport Tarmac is not counted as a “natural physical process” but rather as “instrument issues” ;-)
I’m not sure I know what to make of these. In France, I’d hazard a guess that December could be affected by thermometer additions in cooler places. Bahamas it looks like one added then the other one dropped. Hmm worth a closer look.
A suggestion reflecting an uncertainty I have. You are using anomaly data to extract those informative dMT/dt graphs. This asks, is the baseline period, be it 1951-1990 or 1951-1980, of constant or variable condition? Given the dropping of a large number of stations from some global compilations lately, what is the fate of a station in the reference set that is closed after (say) 1990? Is it also removed from the reference set? (a) If it is removed, then there will be a change in value of the reference temperature which is subtracted to give the anomaly (b) if it is not removed, then the present geographical spread of stations is not the same as in the reference set and so a source of error if too many hot or too many cold stations are left (“The march of the thermometers”).
Would it be appropriate to suggest a subset like France be re-run on actual deg C values (not anomalies) to see if the essence of the graph is retained? The deltas should be equally valid, perhaps more valid.
REPLY: [ There are anomalies… and then there are anomalies… One of my goals was to use a non-baseline anomaly method. The dT/dt method is very similar to First Differences in that it does not use a baseline interval. (GIStemp and CRUtem do use baselines. All of them are still ‘anomaly processes’.) However, First Differences ‘takes a reset’ on data gaps. A gap in data starts over with a zero anomaly on the next valid data series in FD. I also wanted to ‘gracefully bridge data dropouts’, so I changed that. Finally, I wanted to avoid as absolutely far as possible doing ANY averaging prior to calculating an anomaly (as averaging temperatures is not going to give you a temperature…). So to that end I don’t find an ‘annual average temperature’ prior do doing anomalies. I do them on each month datum. No average need apply to temperatures. (Though I do average AFTER I’ve made them into anomalies…)
So the dT/dt method does not take a reset on data dropouts. It also simply compares one month of one thermometer to itself in another year (whenever the next valid data for that thermometer for that month comes along) and does not make an annual average of temperatures.
Finally, the first form of dT/dt I wrote used ‘the past to the present’ as the time axis. This had the present subject to showing wild anomalies based on a ‘first datum’ being a randomly extreme event. So I ‘reversed time’ in the ‘create running totals’ step. So a thermometer ANOMALY is calculated month to month going forward in time for each thermometer, but the running total of a set is calculated going backward in time. This puts the ‘line of zeros’ that happens at the first year in a ‘long ago time’. One could ‘run time backwards’ in this step too, but I don’t think it will matter much. As it is, you end up with a set of annual anomalies that run all the way to “now” (or whenever the thermometer dies). Then I add these up going backward in time. This puts “NOW” as “normal” and shows how the past was ‘relative to now’. So, in some ways, you could think of “now” as the baseline (though it really isn’t a baseline, it’s just a starting point for accumulating change metrics).
This is done for ALL thermometer records. Every Single One.
So there is “no baseline” and “no baseline set”. Adding or dropping a thermometer will only change its own anomalies (though what value those anomalies contributes to any running total would also change).
So, what happens when a thermometer is dropped?
Say there were two thermometers. One running from 1981 to 1990 inclusive (10 values) and the other from 1980 to 2010. I’ll just give one set of numbers, but this would be done for every month of the year. So think of this as, oh, January temps.
Therm1: 10 11 11.4 10.5 11 12 10 9 12 11
Therm2: 11 12 12.2 11.4 12.1 13 11 10 12 13
( 1991) 12 13 14 13 12 11 10 11 12 13
( 2001) 14 12 14 15 13 13 13.5 15 14 15.5
this would become, as anomalies: (the first term is by definition zero as it matches itself)
Therm1: 0 1 0.4 -0.9 0.5 1.0 -2.0 -1 3.0 -1
Therm2: 0 1 0.2 -0.8 0.7 0.9 -2.0 -1 2.0 1.0
(1991) -1 1.0 1.0 -1 -1 -1 -1 1.0 1.0 1.0
(2001) 1.0 -2.0 2.0 -1.0 -2.0 0.0 0.5 1.5 -1.0 1.5
You can see that I’ve made the post 1990 anomalies for Therm2 simple as they are all a one or a 0.5 multiple. This is just to make the math obvious.
OK, so now we go “backwards in time” to find the running total of “change to date”. We start with that final 1.5. So we have to “drop by 1.5” to get the prior year. (it’s a subtraction going back in time). Then we ‘add 1.0’ (as subtracting a minus 1 is the same as adding one). Then we subtract another 1.5, then subtract 0.5, then “nothing”. This would make our anomaly numbers for the January “anomaly line” toward the end (from 2001 to 2010):
-0.5 0.5 -1.5 0.5 -0.5 -2.5 -2.5 -2.0 -0.5 -1.5
(I sure hope I did that right… on the fly in my head and all…)
You would continue this process all the way back to 1990. At that point, the anomalies for Thermometer 1 begin. Now you have a 1.0 for one thermometer and a -1.0 for the other. At this point, I average the two thermometers numbers and use THAT as the thing that is “accumulated”. So we would have “0” as the average of 1.0 and -1.0. The next value would be “(3.0 + 2.0) / 2” or 2.5. Then (-1 + -1) / 2 = -1. Then -2, then 0.95 then 0.6 etc.
THOSE values are what would be used to continue the running total of anomalies ‘backward in time’. Basically we are averaging the two thermometers “changes” together to get the composite change to date (going backward in time).
This has the many benefits.
In theory, the “present time” has the best thermometers, so we start our series with where we are now with “the best we have now”. As we go back in time, adding thermometers increases the data precision (how accurately the world is sampled) as The Great Dying becomes The Great Birth of Thermometers. Then finally at the very beginning of time you get the “lone old thermometer” that might have had questionable calibration. So, in theory, all the worst measurements are accumulated into the running total at the furthest distance back in time. Basically, 1720 is more prone to error, but 1880 ought to be just fine.
So, we don’t “drop” Thermometer One in 1990, we ADD it in. You can see that it does influence the running total of the anomaly (that “splice artifact” where we had a 0.0 in 1990 when neither one of the thermometers was that value) but most of the anomaly values are more or less in agreement about the direction of change overall; though with a bit of site to site variation). If you take that thermometer out, you remove it’s influence (and 1990 would change the most as that’s where the divergence was greatest) but it is not CONTROLLING. It gets the same “one vote” that all the others get in that year…
Basically, one of my “design goals” was robustness to station changes. Minimal impact from any individual station and no stations with a “key or special role”.
Further, if you want to, you can MEASURE the impact of any station or set of stations. Simply run with the report with them both in, and out, of a set. While I’ve not used this “benchmarkable” feature yet, it was a design goal.
So, my intent was to make the most clear, straight forward, and unambiguous “anomaly method” possible and with the minimum of “sensitivities” to station issues or data issues such as dropouts. (a missing datum just gets a zero anomaly until a valid datum shows up for that month, then the delta just gets calculated and that value put in as the anomaly at that time. Missing data largely self heals when a valid datum shows up.)
This means that there is NO baseline sensitivity. NO key stations or time. Not even a need for “fill in” or “interpolation” to make data “complete enough” for various calculations. If you have no data, you get no anomaly (as that is all you really know), but if, after 5 years, you get a datum, you can find the “change over that time” and fill in the anomaly. The only ‘odd bit’ is that it shows up in one year. But that doesn’t really effect much.
So hopefully you can see that this method obviates your worries. It was designed so those things ought not to matter…
BTW, for France I did do a “rainbow graph” of actual temperatures under the “France, Hide The Decline” posting. They show very similar things. So yes, I do like to anchor myself back to real temperatures from time to time… In fact, this effect shows up very well in the temperature rainbow graph by month. The dMT/dt method is just an attempt at a better visualization of it.
BTW, the dT/dt and the dMT/dt methods are not perfect. They are subject to ‘splice artifacts’ and if the end of a series has lots of data dropouts, you could have a dangling end where the bias was not ‘closed’ from leaving, oh, December out for 3 years if the thermometer dies before a December datum showed up.
Say the two thermometers did NOT overlap much, one rose for 10 years of UHI then the next one picked up for another 20 years of UHI. If the first town had booked all the UHI it was going to get, the SERIES would have “double booked” UHI by splicing the two sets into one anomaly series. I’m “ok with that” as I’m not trying to calculate some fictional Global Average Temperature. I’m trying to measure what GIStemp (and related like CRUTem) do and find a better benchmark. So I want to see the splice artifacts so I know where to go digging.
A future effort will go into making a “non-splice artifact sensitive” tool (when I need it). I showed an example of “splice artifacts” in the “Marble Bar” posting. The dT methods are also sensitive to the sparsity of early thermometers. (The ‘start of time’ values can be ‘off’ if the lone thermometer then was reading an odd quirky moment of weather). So the leading edge of graphs ought to be taken with a grain of salt… but I think folks know to be suspicious of what was going in in the 1700s with thermometer data…
But they ARE pretty darned good methods. From about 1820 to date the graphs do a dandy job of reflecting known real events. Basically the “reality check” is that they do a pretty good job of matching recorded objective reality.
OK, with all that said: One of my “someday” things is to run dT/dt (and now dMT/dt) on somewhere like France with “all thermometers” and with “survivors only” and see what is the difference in using “all data” vs just using “the present thermometers history”. It ought to enlighten in some way. (either showing ‘survivor bias’ in the present set or perhaps ‘splice artifacts’ in the all data set. We’ll see…) I also want to do it for each of the survivor thermometers as a singleton (so we can clearly see ANY splice artifacts in the set) and compare that to “temperature rainbow graphs” for each thermometer and for France as a whole.
I’d really like to do that for the whole world, but it would take a couple of staff folks to do it in any reasonable time, and it’s only me… so it will have to wait. (Unless someone else wants to start doing it too… Hint Hint… 8-)
(Or I can learn enough graphics coding and languages to automate the whole thing… decisions decisions…)
Eventually I’ll even get to the point of comparing those to the infilled and interpolated sets from intermediate points in GIStemp. Then we’ll be able to see exactly what it’s doing to the data, STEP by STEP. (Sadly, I think that will happen about a year after we’re already crushed by Cap and Tax… but we’ll see… I’ve no plans to stop until AGW is dead and buried or I am. Nope, no smiley on that one.)
Hope that clarifies things more than it muddies them up. -E.M.Smith ]
Gah! you hurt my eyes. How about a trend-line(s) only version?
REPLY[ Hurrumph! Why I … I um… er. You know, you might have something there… -E.M.Smith ]
Mea culpa, I did a quick take without adequate catch-up reading earlier today and then after I posted I remembered earlier descriptions of your method. I had wrongly jumped to the conclusion that your starting set was the difference from a 1951-1990 base, my error.
I cannot add to your description – the working backwards method has sense, the avoidance of infilling has a lot of sense and you are open about joining different thermometers. Your reality check of matching recorded data can hardly be criticised.
For some days I’ve been trying to reconcile some of the comments in the UK House of Commons Inquiry and there is enough in there to befuddle any brain.
In Australia, there was a change from thermometers to telemetry devices like thermostats, mostly around 1990-5. I’ve often wondered if that effect of that instrumental change shows up in country data, but I suspect the data I have might have been adjusted to remove step changes. Not sure, just looks a bit that way.
Thank you for your patience in your long reply. I hope it helps others as well. Thanks, Geoff.
REPLY: [ The whole idea is that if one person asks, 10 are wanting and not asking. So I answer. I hope it helps too. -E.M.Smith ]
Very interesting, especially Asian part of Russia. Interesting to see how autumn and spring temperatures jump around the time of the thermometer count drop (and the perestroyka). Thermometers moved to places with shorter snow covered period?
REPLY: [ Yeah, you get site change artifacts. Sometimes it’s a group that exits (or enters) on a high / low point and you get a ‘splice artifact’. Then there is that 1990 process change that just screams at you… Not much left over for CO2 after all that ;-) -E.M.Smith ]
In reply to earlier queries you have said that only about 200 thermometers carried over from the pre 1990 period to the post 1990 period within the GHCN dataset. Did the technology change at the 1990 pivot point either in the actual instrumentation used to record temperatures and in the ways used to record those temperatures? In the Bahamas, for example, there cannot be many, if any, opportunities to relocate to significantly lower altitudes or shift any great distance to be closer to the equator.
REPLY[ The nearest I can tell, it was a mix of two things. At many places (like airports) they rolled out the ASOS electronic stuff somewhere between the 1980’s “step up” and the 1990’s “The Pivot”. There was also a change of ‘processing’ applied to the data (that “QA process” posting…)
So, for example, they still take the temperature at the SFO San Francisco Airport. But with different equipment and with a different process applied. Very few places that are still using the original equipment and process “survived” the “upgrades”.
In:
https://chiefio.wordpress.com/2009/08/13/gistemp-quartiles-of-age-bolus-of-heat/
I looked at thermometers grouped by “length of record”. At that time I didn’t realize that the 1990 Pivot existed or was tied to a change of Duplicate Number ( or “modification history flag”). If you scroll to the bottom of the article, you find the 10% of ‘longest lived records’ with over 100 years history has very little “global warming”. But only 80 of them were active during the final decade… I didn’t realize exactly what it meant, but it was pretty clear that if stable long lived instruments were not warming up, it was the short lived changing ones that “were the issue”. Much of the work since has been to sort out more detailed views of “what, why, when, how…”.
Also, this posting:
https://chiefio.wordpress.com/2009/08/05/agw-is-a-thermometer-count-artifact/
is also a decent entry point to some of that earlier work.
The ‘few hundred’ number is for the top 3000 thermometers and it’s shown as 209 of them survive into the 2009 decade ending… Not a very good thing…
So, IMHO, folks have just screwed up the instrumentation and the processing done to it too much for it to be usable after 1990. Best you can do with it is use it to illustrate how screwed up it is…
But the places are still there, and data are still recorded. And there is some small hope that the original really raw data might be recoverable and we might be able to reconstruct a proper temperature history for those “dark ages”…
-E.M.Smith ]
I’ve added a couple of more graphs… I think Australia will end up in its own posting rather than here…
E.M. Pleased to hear you’re thinking of an “own posting” for Australia. It’s a real goldmine IMHO ! Also good news Sinan Unur is warming up for another look at his work. I’m sure we all look forward to his updates.
I’m continuing with a closer look at Tasmania and will hopefully have an interesting post coming up soon.
As a titillation, have a look at the Australian BoM/CSIRO graphs of methane/CO2 covering the last century. It’s from their March joint “Climate Report”. The measuring station is at Cape Grim Lat.= -40.6828; Long = 144.69.
Link: http://www.bom.gov.au/inside/eiab/State-of-Climate-2010-updated.pdf
Now check Smithton WMO 94952, a station with over 100 years records.
Lat = -40.85 Long = 145.12, 37kms east of Cape Grim.
Link: http://www.unur.com/climate/ghcn-v2/501/94952.htm
Sinan Unur’s graph shows records from 1892 but according to BoM there must have been a site change to Grant Street, Smithton in 1911. It has made no difference.
Unfortunately the station closed Nov.1 1997 and Smithton Aerodrome station 2.8 kms away was started in 1996.
Wynyard WMO 94952, about 50kms east of Smithton and Burnie WMO 94958 a further 10-12kms east are worth a look to prevent any possible warmist cry of “cherrypicking”.
I’ll have more to say on Burnie in a later post.
In the understatement of the year I think you’ll agree that CO2/methane greenhouses gases in Tasmania certainly don’t appear to show any linear effect on temperatures !
(Apologies if this is OT, but it’s all getting inter-related) !!!
Uh-Oh ! Something wrong with my links but you will find the stations on the unur.com site. I’ll try and find other links to the BoM graph. Guess I must have got too “cocky”!
Another try at the links for the posts above.
Link: http://www.bom.gov.au/announcements/media_releases/ho/20100315a.pdf
The CO2/methane graph is on page 5.
The stations are at
Link: http://www.unur.com/climate/ghcn-v2/
Re temperature records the WMO says here:
http://www.wmo.int/pages/prog/wcp/wcdmp/wwr/index_en.html
that they are now available on cdrom by contacting this address: wcdmp@wmo.int
I emailed them two weeks ago, so far with no reply.
They describe the data content but not the format. Perhaps a reader here can shed more light on this and on how to get a copy.
A CD of temperature records! Do you think they read them to us?
Sounds almost as exciting as listening to the Stock Exchange Report or the BBC Weather Report for Shipping on the radio?
Gentlemen,
in case you do not want to miss it:
Conferencia Mundial de los Pueblos sobre el Cambio Climático y los Derechos de la Madre Tierra
Cochabamba, Bolivia 19 al 22 de Abril 2010 Cambio Climático Bolivia
http://cmpcc.org/
Saludos,
don Matías.
Chiefo,
Have you seen Anthony Watt’s post at http://wattsupwiththat.com/2010/04/17/giss-metar-dial-m-for-missing-minus-signs-its-worse-than-we-thought/#comments
The missing -sign at air ports with the introduction of METAR from 1989 could be an answer to your finding of the change in dT/dt around 1990.
It seems the missing – sign also applies to floating buoys. Don’t know when they were introduced.
The missing -sign applies to mainly to minimum temperatures in higher latitudes and in winter. It should not affect Australia much but has a big affect in the Arctic, Antarctic, Siberia. Northern Canada etc.
It was shown up in recent data for Finland (GISS showing warming when Finnish data showed cooling)
I think your work and Anthony’s sleuthing would make a good paper to force a complete review of all the databases and all papers which have relied on these databases without noting errors.
I was just going to post about Anthony’s METAR post too. If METAR was introduced in Russia in 1989, it could be a possible explanation for the strange change in Russian spring and autumn temperatures (since temperatures will be both above an below zero in this period, errors are much more likely to slip by quality control than in the cold winter months).
I had helped Anthony in a minor way on the posting (some time ago I’d sent the Airport percentages and some specific GHCN monthly values, when needed). So I’ve actually seen it from earliest days…
And yes, I suspect this could explains some of the patterns of adjoining months going in different directions…