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 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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 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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 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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
Musings from the Chiefio 
Thanks for this.
I’d seen month-by-month average temps and noted the more pleasant winter and more pleasant summer effects. Australia, for example, showed this. I cannot bring myself to tremble in fear over this prospect.
On that interesting graph: I suspect that the three-year add in V3 will be a source of nitpicking. Can you constrain the same date ranges for each and see what it looks like?
===|==============/ Keith DeHavelle
This will take some time to think about.
A thought. Steven Mosher is leaning warmist, having done his own analysis and losing patience with those of us less willing to do the exercise ourselves. Which is fine, the science is better, as is justice generally, when sharp advocates argue on both (or all) sides of a dispute. What each would prefer to overlook is exposed by the other and perhaps a layman — as a juror — might recognize a situation than neither advocate would admit.
Another analogy, though, are traders working both sides in a developing market. One willing buyer believes a number is headed up, and soon. A willing seller believes that number is either stable, or headed down. A trade is made. Or, sometimes a hedge.
Mosher recently has taken to arguing that the various temperature reports are NOT the “T” that is raised to the fourth power in the black body radiation equation. Instead, it is, he says, an index. A proxy, actually. An indicator of temperature, in much the way a sample of 30 company’s stock prices in the Dow Jones Industrial Index are a proxy for the entire S&P 500, or Wiltshire 3000 or US government calculated GDP or whatever else one might use to track the ups and downs of overall “wealth”. We might have quite legitimate quibbles about which companies are in, or out, of the Dow this decade versus last. We might dispute whether the price needs to be adjusted for inflation or commodity pricing or hedonic values. But overall the Dow does seem to track the health and wealth of the market generally, (we think) even though companies come and go and currencies change and the times and volumes of trades change from eighths to tenths and so on and so forth.
I’d like to pick his brain about that notion. But I’d also like to hear from somebody who REALLY understands the notions of financial indexes.
Uhm. Who do I know who makes something of a living understanding financial indexes, AND has a serious hobbyist’s interest in global temperatures?
Another thought. Mao killed a potload of people because he confused the lines on a chart he supposed to represent steel-production with the actual production of steel, and mistakenly believed that steel was a causal factor in market power rather than a measure of it. Proxies, indexes, and causes are different. Or so I think.
Thanks, E.M., for the information.
While I agree the science is better, as is justice generally, when sharp advocates argue on both (or all) sides of a dispute, my own experiences suggest that:
Deception in global temperature data is only a minor, but glaring, example of the pervasive dishonesty that has destroyed the ability of world leaders to formulate policies in the best interest of society.
World leaders do not understand science, and shouldn’t be expected to.
I see social and economic collapse ahead if skeptics cannot quickly find a way to restore integrity to government science.
In this regard, Steve Mosher would be interesting study, if there were not far more important issues at hand.
With kind regards,
Oliver K. Manuel
Former NASA Principal
Investigator for Apollo
http://www.omatumr.com
From Tom Peterson (NOAA) to Phil Jones 4992.txt
4798.txt also discusses GHCNv2 being somewhat useless because of the drop in number of stations.
Just grepping in GHCN through the climategate2 emails yields what looks like a lot of complaints and frustrations with it among the scientists. Seems nobody had much use for v2
Crooked scientists advising crooked politicians how to win more votes from crooked capitalists, communists, liberals, conservatives, environmentalist, etc. The root of the problem: Naked apes are highly manipulative, creative, intelligent and inquisitive.
All religions caution mankind of the inherent dangers that accompany our talents.
G.I.G.O.?
_____________________
There appears to be something very healthy and beneficial in the practice of having great minds work in Patent Offices, or some such, and slaving away in universities and wearing the same shirt 3 times a week. I think we’d be much more advanced, and advancing more too, if we did not sponsor mediocrity with government jobs, grants, or research awards –there appears to be something in the numbers that would indicate that it is very counterproductive to spend tax money on such things, and hire people to manage the tax money spent by people doing such things, and account for the money for such things, and write reports quarterly and annual reports to Congress on the money spent for such things, and lobby for more money each year to build a new ‘green’ building and buy new ‘green’ computers to manage such things for the people of the United States of America. You know fellow fools, I think we’re being milked
As one of Anthony Watts’ SurfaceStations surveyors I’ve interviewed a few station managers. One in particular told me that in addition to the temps taken in the Stevenson screen (5 foot level), they also took unofficial ones at ground level and often found these 2-3 degrees colder at the morning reading after the cool air had settled. The station is sited at the bottom of a hill in a field where agricultural trials have been conducted for decades. This points out a concern for trying to use the “official” datasets for climatology — microsite effects are likely to be all over the map and to change with time even if the station doesn’t move. Maybe it doesn’t make much difference to a global average when everything is homogenized, but it certainly expands the confidence intervals. Parsing down to tenths and hundredths of a degree is going beyond what the data support.
@Keith DeHavelle:
Yes, it is on my “to do” list to do all the comparisons with “the same years”. First, though, I wanted to get the dT/dt anomaly code done (and it is now – just pending QA combing) and yes, those reports, too, need a version comparing like years.
But having been up to the wee hours last night chasing bogus data and doubling the size of the project (as I now need to A/B the qca and qcu versions…)
https://chiefio.wordpress.com/2012/05/24/ghcn-v3-a-question-of-quality/
I’m likely to be playing “recover and catch up” for a day or two ;-)
Seems that there were a load of comments come in over the last 24 hours and I’m behind on reading my own blog…
FWIW, part of the “moderation of seasons” may simply be more coverage in the Southern Hemisphere; so also on the “to do” list is the same thing “by hemisphere”. Is the “seasonal moderation” the same when hemispheric effects are removed?
Every time I find an interesting thing, it turns up a half dozen other interesting things to put on the “to do” list…
@Pouncer:
Stock indexes are imperfect. Some of them have imperfections that are an advantage.
“Instrument change” in computing the Global Temperature is a horrible mistake and uncontrolled for quality of impact, near as I can tell. In stock indexes, who is in and who is out is tightly controlled and “for effect”.
So the analogy is ‘reasonable’ in that both are indexes, but seriously flawed in that the fundamental “issue” is strongly addressed in stock indexes and horridly mangled in thermometers. (One of the things that got me going on all this Global Warming stuff to begin with…)
Take, for example, the S&P 500 Index. It has a strong growth bias built in due to it being a “capital weighted index”. Only the 500 largest companies make it in. Companies that are going out of business automatically leave. Fast growing world changers rapidly enter. Great if your goal is growth of your portfolio. Not so good if you want to know “Is the total economy doing well?”.
The “Dow 30 Industrials” was used in Dow Theory to compare to the Dow Transportation Index. The theory was the the time at which industrial production ramped up had a time offset from when goods were transported. But look at the “Dow 30” now. Drug companies. Banks. What happened to the “industrials” in it? So it is far less useful as a Dow Theory tool. (But the numbers have been kept rising even as the industrial capacity of the nation moved to Japan, Korea, and later China…)
So, in short: HOW the index is constructed and managed is critical to it being usable and “suited to purpose”. IMHO the GAT calculated from the random gaggle of thermometers that constantly mutate over time is sorely unfit for purpose. Especially in the small weeds of 1/10 C precision that is needed to ‘discover’ any warming trend.
Now, one other interesting parallel:
BOTH have strong cycles. Look at the SPY for 20 years, you see a strong business cycle about the same as the sunspot cycle. (Loads of literature comments on that starting about Hershel and Jevons) and the folks who trade stock know and allow for the cyclical nature.
The temperature series also has strong cyclicality in it (on several time scales) yet this is ignored by the GAT Keepers. They seem to exploit the cycles to get nice trend lines.
In stock trading one of the classical novice errors is projecting trends out of cycles.
So in the application of the “temperature index” I gross naivety at best (and negligence or fraud at worst).
Hope that helps put it in perspective.
@George:
Interesting idea… “Use the email, Luke!” ;-)
Yet more projects…
FWIW v3 adds some stations,but it’s still way skewed.
Per the “index” point above: IMHO it would be better if they just picked a small number of long lived well tended stations and made an “index” from them. But when I looked at long lived stations I found no warming… Gee, wonder why they didn’t choose them…
@Pascvaks:
Well, one of my “side ponderings” is trying to figure out where in the world one can go to escape it. Not found a great answer yet (though a couple of ‘good ones’ exist… some corners of Latin America at least let you disappear into a reality based local economy…)
@Gary:
The point about precision is one I’ve tried to raise several times (and often been beaten up about by academics who assert you can go to nearly any precision with averaging… when you can’t…)
I’ve got a post or two up with a thermometer on a fence showing over 60 F and steam rising from the melting frost in the sun… or some such. The surface temperature field is fractal in form and the notion we can get ANY certain precision out of it a bit broken. I’ve used the story of a stream visited on a camping trip at about 18 years of age…
It was a nice sunny day. Warm. About 70 F and closer to 80 F sitting in the sun. We got a bit hot and decided a dip in the “mountain stream” was just the ticket. Shucked off the shirt and jumped into the creek… shooting immediately out of the water onto a boulder in the middle… My friend declined my invitation to join me as “the water’s fine!”… something about my blinding translucent white skin having gone sort of bright pink… (The ‘flash headache’ ended after about 5 minutes on the rock in the sun). Seems that just around the bend, which we found upon further exploration, snow was melting ot make the stream…
Yes, eventually I had to make the “plunge” to get back to shore. Decided to ‘ease in’ despite the longer immersion time to avoid the headache effect.
So I like to ask: What is the REAL temperature of that meadow? The 32.x F creek / stream? The 32 F snow in patches of shade? The 70 F in the air in the shade? The 80 F near my skin in the sun? The 100 F of black rocks or tarmac in the sun? The 80 F above them?
We assume that the air does a decent job of averaging those various points in the fractal temperature field and giving us a valid datum. The reality is that it knows nothing about the entropy change of snow to melt, or the specific heat of the water in the stream. It knows nothing about how the parking lot was repaved (or paved the first time) nor about how many trees are transpiring how much.
Even the individual thermometer readings are only an “index” of the actual temperature field and can easily be 20 to 50 F “wrong” compared to particular parts of it.
So what does an “index” made of averaging thousands of constantly variable and inconsistently present indexes mean?
Not much, IMHO.
@Keith DeHavelle:
The v3 results with 2010 onward zeroed has a couple of AA and Ad values that change in the 1/10 C range, but nothing that would make any real difference to the graphs IMHO. Just not significant.
First you have probably seen this:
http://stevengoddard.wordpress.com/2012/05/10/hansen-cheating-in-iceland/
It would appear that quality control and configuration management are totally absent from any of the maintainers of the world ‘temperature’ data.
Then of more interest, I must admit to not understanding what is meant by ‘average temperature’ it would appear to be a totally meaningless coefficient. I have a high temperature at 3pm and a lowest daily temperature at 5am so I add them and divide by two – and what have I really got? its meaningless – how long was the temperature at the 3pm level? How long did the temperature stay at the 5qm level? No information.
Then there is enthalpy. The AGW hypothesis is based on the hypothesis that ‘green house gases’ ‘trap’ (sic) energy leading to a rise in temperature. But temperature is not a measure of energy. It is necessary to know the enthalpy of the air first. A cool misty morning leading to a dry hot summer afternoon – may actually show a DROP in energy in kilojoules per kilogram of the atmosphere over the day. But off go the climate ‘scientists’ measuring and averaging temperature.
This cannot be an accidental use of the incorrect metric – it must be deliberate. It cannot be accidental that updates to the incorrect metric are all moving the value of that incorrect metric in a way that shows increase in that incorrect metric. It cannot be accidental that ‘adjustments’ to the updates of the incorrect metric are also in the same sense. We are watching a deliberate ploy by these ‘scientists’ that the media are too ignorant to understand.