GHCN v3.3 Anomaly By Continent

This is to some extent about anomalies in general. Basically, subject to a bit more detailed QA work (i.e. taking a significant sample of station data and computing the anomalies by hand then comparing with the result of my SQL script) I think I’m gotten a nice table of all the GHNC v3.3 data into the form of anomalies.

In this case, the anomalies are NOT based on some hypothetical baseline grid cell box made up of thermometers that don’t exist in the present. For each thermometer (station) I compute the average of all readings from that thermometer in each month of the calendar over all years. That is all the data for that thermometer for that month. Pretty much by definition that’s the average of the data. Then each temperature datum has that average subtracted from it to make the “anomaly”. Again, pretty much by definition an anomaly is the departure from the average of the data.

Then all that gets loaded into a table for processing as desired.

Initially I just did a quick “give me the average of all anomalies in each year” up to 2015 (the last year in GHCN v3.3) as that ought to be how warm 2015 was compared to the prior history (for those thermometers that survive into 2015). Then I did a similar report for each continent (“region” in GHCN terms) for the years since 1999. (Since that’s already 112 rows and a bit long for a posting anything more would be way too long).

I’m going to include the table schema and the various scripts after these sample results, so this will be a very long posting, but most of it just a table of numbers and some code down below.

This is the table description for the reports:

MariaDB [temps]> describe anom3
    -> ;
+----------+--------------+------+-----+---------+-------+
| Field    | Type         | Null | Key | Default | Extra |
+----------+--------------+------+-----+---------+-------+
| stnID    | char(11)     | NO   | PRI | NULL    |       |
| region   | char(1)      | NO   |     | NULL    |       |
| country  | char(3)      | NO   |     | NULL    |       |
| wmo      | char(5)      | NO   |     | NULL    |       |
| near_wmo | char(3)      | YES  |     | NULL    |       |
| year     | char(4)      | NO   | PRI | NULL    |       |
| month    | char(4)      | NO   | PRI | NULL    |       |
| deg_c    | decimal(5,2) | NO   |     | NULL    |       |
+----------+--------------+------+-----+---------+-------+
8 rows in set (0.00 sec)

Anomaly Average By Year

As this report is 315 lines long, I’m chopping out big chunks of it. This will give you an idea how much the anomaly can change in any given year, and it can give you a sense of what we are at now.

I’ve bolded a few interesting years. Note that in the 1700s there’s some that are almost average. Then note how some years in the ’30s were quite hot.

MariaDB [temps]> source bin/anombyyr.sql
+------+------------+
| year | AVG(deg_C) |
+------+------------+
| 1701 |  -1.861429 |
| 1702 |  -2.210000 |
| 1703 |  -1.635000 |
| 1704 |  -1.590000 |
| 1705 |  -2.298889 |
| 1706 |  -1.139167 |
| 1707 |  -1.189167 |
| 1708 |  -1.330833 |
| 1709 |  -2.026667 |
| 1710 |  -0.437500 |
| 1711 |  -0.260000 |
| 1712 |  -0.426667 |
| 1713 |  -1.476667 |
| 1714 |  -0.568333 |
| 1715 |  -0.376667 |
| 1716 |  -1.693333 |
| 1717 |  -1.093333 |
| 1718 |  -0.285000 |
| 1719 |  -0.251667 |
| 1720 |  -0.685000 |
| 1721 |  -0.910000 |
| 1722 |  -0.018333 |
| 1723 |  -0.268333 |
[...]
| 1928 |   0.040689 |
| 1929 |  -0.477446 |
| 1930 |   0.177120 |
| 1931 |   0.599580 |
| 1932 |   0.043135 |
| 1933 |   0.150668 |
| 1934 |   0.525242 |
| 1935 |  -0.096415 |
| 1936 |  -0.020545 |
| 1937 |  -0.010578 |
| 1938 |   0.559982 |
| 1939 |   0.401496 |
| 1940 |  -0.040652 |
| 1941 |   0.260952 |
| 1942 |   0.057411 |
| 1943 |   0.005099 |
| 1944 |   0.152911 |
| 1945 |  -0.106840 |
| 1946 |   0.283012 |

[...]
| 2008 |   0.186768 |
| 2009 |   0.220831 |
| 2010 |   0.455874 |
| 2011 |   0.418812 |
| 2012 |   0.900264 |
| 2013 |   0.267939 |
| 2014 |   0.347400 |
| 2015 |   0.646083 |
+------+------------+
315 rows in set (0.00 sec)

Now when I look at those, I don’t see a whole lot of difference between the ’30s and now. The recent years were a bit more consistently warm, but not excessively warm; and certainly inside the range of natural variation of the ’30s.

Anomaly By Continent

I ought to have done a JOIN and put the name in the below report, but this is the “first fire” version… Here’s a description of the continent table and a report of which number is what continent:

MariaDB [temps]> DESCRIBE continent;
+-------------+----------+------+-----+---------+-------+
| Field       | Type     | Null | Key | Default | Extra |
+-------------+----------+------+-----+---------+-------+
| version     | char(5)  | NO   |     | NULL    |       |
| ascension   | char(10) | YES  |     | NULL    |       |
| region      | char(1)  | NO   |     | NULL    |       |
| region_name | char(25) | YES  |     | NULL    |       |
+-------------+----------+------+-----+---------+-------+
4 rows in set (0.18 sec)

MariaDB [temps]> SELECT region, region_name FROM continent;
+--------+---------------------------+
| region | region_name               |
+--------+---------------------------+
| 1      | Africa                    |
| 2      | Asia                      |
| 3      | South America             |
| 4      | North America             |
| 5      | Australia Pacific Islands |
| 6      | Europe                    |
| 7      | Antarctica                |
| 8      | Ship Stations Ocean       |
+--------+---------------------------+
8 rows in set (0.71 sec)

I’ve also bolded a couple of bits below. Here we are seeing some of the influence of larger number of thermometers on the overall average above. In particular, the USA has a very modest “anomaly” that due to the large number of records, dilutes the much larger number in some other continents in the overall average.

What I find striking is just how much the anomaly VARIES by continent. For a “well mixed gas” it sure does have significantly more impact in some places than in others… /snark; Such bits bolded.

MariaDB [temps]> source bin/anombyr.sql
+--------+------+------------+
| region | year | AVG(deg_C) |
+--------+------+------------+
| 1      | 2000 |   0.231018 |
| 1      | 2001 |   0.541299 |
| 1      | 2002 |   0.549131 |
| 1      | 2003 |   0.661826 |
| 1      | 2004 |   0.491127 |
| 1      | 2005 |   0.634152 |
| 1      | 2006 |   0.570306 |
| 1      | 2007 |   0.488188 |
| 1      | 2008 |   0.503186 |
| 1      | 2009 |   0.752915 |
| 1      | 2010 |   1.059639 |
| 1      | 2011 |   0.595951 |
| 1      | 2012 |   0.626261 |
| 1      | 2013 |   0.652745 |
| 1      | 2014 |   0.749250 |
| 1      | 2015 |   0.774511 |
| 2      | 2000 |   0.438844 |
| 2      | 2001 |   0.726937 |
| 2      | 2002 |   0.871634 |
| 2      | 2003 |   0.617491 |
| 2      | 2004 |   0.881025 |
| 2      | 2005 |   0.690497 |
| 2      | 2006 |   0.619442 |
| 2      | 2007 |   1.297323 |
| 2      | 2008 |   0.886196 |
| 2      | 2009 |   0.661257 |
| 2      | 2010 |   0.743939 |
| 2      | 2011 |   0.631966 |
| 2      | 2012 |   0.429281 |
| 2      | 2013 |   0.870530 |
| 2      | 2014 |   0.808842 |
| 2      | 2015 |   1.487012 |
| 3      | 2000 |  -0.205592 |
| 3      | 2001 |   0.298580 |
| 3      | 2002 |   0.258226 |
| 3      | 2003 |   0.293338 |
| 3      | 2004 |   0.271234 |
| 3      | 2005 |   0.223696 |
| 3      | 2006 |   0.358147 |
| 3      | 2007 |  -0.132888 |
| 3      | 2008 |   0.152775 |
| 3      | 2009 |   0.323636 |
| 3      | 2010 |   0.274894 |
| 3      | 2011 |   0.191836 |
| 3      | 2012 |   0.446172 |
| 3      | 2013 |   0.244417 |
| 3      | 2014 |   0.440813 |
| 3      | 2015 |   0.798713 |
| 4      | 2000 |   0.209868 |
| 4      | 2001 |   0.521326 |
| 4      | 2002 |   0.339425 |
| 4      | 2003 |   0.170955 |
| 4      | 2004 |   0.191346 |
| 4      | 2005 |   0.501150 |
| 4      | 2006 |   0.872367 |
| 4      | 2007 |   0.460908 |
| 4      | 2008 |  -0.278376 |
| 4      | 2009 |  -0.224088 |
| 4      | 2010 |   0.281923 |
| 4      | 2011 |   0.298461 |
| 4      | 2012 |   1.340371 |
| 4      | 2013 |  -0.183151 |
| 4      | 2014 |  -0.216159 |
| 4      | 2015 |   0.281730 |
| 5      | 2000 |   0.229702 |
| 5      | 2001 |   0.082406 |
| 5      | 2002 |   0.295626 |
| 5      | 2003 |   0.307763 |
| 5      | 2004 |   0.278196 |
| 5      | 2005 |   0.495219 |
| 5      | 2006 |   0.263329 |
| 5      | 2007 |   0.351872 |
| 5      | 2008 |   0.095123 |
| 5      | 2009 |   0.429328 |
| 5      | 2010 |   0.402589 |
| 5      | 2011 |   0.123205 |
| 5      | 2012 |   0.179120 |
| 5      | 2013 |   0.612238 |
| 5      | 2014 |   0.552790 |
| 5      | 2015 |   0.306413 |
| 6      | 2000 |   0.943494 |
| 6      | 2001 |   0.547935 |
| 6      | 2002 |   0.748425 |
| 6      | 2003 |   0.664276 |
| 6      | 2004 |   0.495172 |
| 6      | 2005 |   0.469251 |
| 6      | 2006 |   0.742229 |
| 6      | 2007 |   1.062672 |
| 6      | 2008 |   0.912470 |
| 6      | 2009 |   0.802050 |
| 6      | 2010 |   0.467736 |
| 6      | 2011 |   0.773569 |
| 6      | 2012 |   0.716851 |
| 6      | 2013 |   0.727256 |
| 6      | 2014 |   1.470550 |
| 6      | 2015 |   1.135180 |
| 7      | 2000 |  -0.007846 |
| 7      | 2001 |   0.353267 |
| 7      | 2002 |   0.547419 |
| 7      | 2003 |   0.337626 |
| 7      | 2004 |   0.276066 |
| 7      | 2005 |   0.187214 |
| 7      | 2006 |   0.380047 |
| 7      | 2007 |   0.610092 |
| 7      | 2008 |   0.541765 |
| 7      | 2009 |   0.626320 |
| 7      | 2010 |   0.555957 |
| 7      | 2011 |   0.329696 |
| 7      | 2012 |  -0.050348 |
| 7      | 2013 |   0.486780 |
| 7      | 2014 |   0.090290 |
| 7      | 2015 |  -0.771985 |
+--------+------+------------+
112 rows in set (31.18 sec)

MariaDB [temps]> 

So Asia and Europe are darned near burning up!! While the USA is quite modest and well inside normal especially when compared with the ’30s. Antarctica took a real cold plunge in 2015(!) – so much for melting Antarctica.

Australia / New Zealand and the Pacific Ocean are almost normal and were in fact normal in 2008 (well inside any error bands that might apply). While South America was cold in 2000 and about normal in 2008 (Guess CO2 was vacationing in Europe those years…). It also looks like Africa had a cool 2000 but then popped up 1/2 C and more or less stayed there since.

Now when I look at this, I don’t see CO2 induced “Global Warming”. I see lousy data from Asia and likely heavily adjusted or Urban Heat Island corrupted data from Europe. I certainly don’t see anything you could call “global” going on.

With that, I’m going to move on to GHCH v4 data. I’ll still work on getting some graphs of anomalies and see what other fun bits might be hiding in this anomaly processed data; but the major reason I was using GHCN v3 was just the smaller size of the data set. This lets me build & debug & test things with a faster cycle time. At this point I think I’m far enough along it’s time to start loading up the Full Monty most recent “version” of the data. (Since when did data, real data, come in versions?…)

It will likely be a few days / week+ before any GHCN v4 stuff shows up, just because I’ve cranked on this one for a while and the family will be wanting to see me for a day or two ;-) But that’s the next thing in this series. Between now and then, I’ll putter around with this version of the data and see if anything interesting pops out at me.

Tech Talk

I put up some notes as I was doing the development on this in comments in a posting starting about here:

https://chiefio.wordpress.com/2019/03/11/thermometers-over-time/#comment-109469

I also got tired of typing “stationID” all the time so dumped all the tables and remade them with “stnID”, plus updated all the MySQL scripts. (Python programs that use SQL to be done another day…).

I spent a good chunk of a couple of days trying to get the anomaly calculated all in one SQL query. I could see ways it ought to work, but just didn’t get it pulled together (usually with complaints about subqueries with more than one record being returned). Eventually I just made a statistics table. Something I’d intended to do as a next step, but that makes the anomaly calculating MUCH easier. So this first bit is how to make the statistics by station by month:

chiefio@PiM3Devuan2:~/SQL/tables$ cat mstats3 
CREATE TABLE mstats3 (
    stnID CHAR(11) NOT NULL,
    month CHAR(4) NOT NULL,
    mean  FLOAT NOT NULL,
    big   DECIMAL(7,2) NOT NULL,
    small DECIMAL(7,2) NOT NULL,
    num   INTEGER NOT NULL,
    trang FLOAT NOT NULL,
    pctd  FLOAT,
    pctm  FLOAT,
    PRIMARY KEY (stnID,month)
    ) ;

The name “mstats3” is for monthly statistics GHCN version 3. Thus the key being station and month. What are the Average (mean), MAX (big), MIN (small) and COUNT (num) of the temperatures for a station in a month. As SQL uses MAX, MIN, and COUNT as key words, I used big, small, and num for field names. Then trang is because RANGE is a key word too… so that’s the temperature range found by subtracting the MIN from the MAX value. Pctd and pctm are for future use as “percent valid data” and “percent missing flags”… To be calculated as an update process later…

(You may notice I got tired of typing Schemas with a cap S so renamed that directory “tables”)

Here’s the code that creates the statistics:

chiefio@PiM3Devuan2:~/SQL/bin$ cat lmstats3.sql 
INSERT INTO  mstats3 (stnID,month,mean,big,small,num,trang)
SELECT stnID,month,
AVG(deg_C),MAX(deg_C),MIN(deg_C),COUNT(deg_C), MAX(deg_C)-MIN(deg_C)
FROM temps3 
WHERE deg_C>-90 
GROUP BY stnID,month;

L is for load so lmstats3 is “load monthly stats for GHCN v3 table”… Doesn’t look like it would take all day for that… and it didn’t. About a 1/2 hour after I gave up on “all in one shot of SQL”, this was running…

So it loads those statistics values, that it computes from the temps3 table where it doesn’t have a missing data flag and it groups them by station ID and by month. I could likely have used the RANGE function for the trang calculation but had already done it long hand…

Once that table is built, you can then JOIN it into the data load for the anomaly table and use the average from it to calculate the anomalies.

Here’s the anomaly schema:

chiefio@PiM3Devuan2:~/SQL/tables$ cat anom3 
CREATE TABLE anom3 (
    stnID CHAR(11) NOT NULL,
    region CHAR(1) NOT NULL,
    country CHAR(3) NOT NULL,
    wmo CHAR(5) NOT NULL,
    near_wmo CHAR(3),
    year CHAR(4) NOT NULL,
    month CHAR(4) NOT NULL,
    deg_C DECIMAL(5,2) NOT NULL,
    PRIMARY KEY (stnID,year,month)
    ) ;

Like a cut down version of temps3. I’ll likely change deg_C to a different name (like maybe anom) in a future reload. It is just a bit confusing to have it named that same as the deg_C in the temps3 table. But it is a nice reminder that it’s degrees of C. Maybe anom_C?

Here’s the magic sauce that creates the anomaly data and loads it. Vastly simpler than what I was trying to do before:

chiefio@PiM3Devuan2:~/SQL/bin$ cat lanom3.sql 
INSERT INTO  anom3 (stnID,region,country,
             wmo,near_wmo,year,month,deg_C)
SELECT T.stnID,T.region,T.country,
       T.wmo,T.near_wmo,T.year,T.month,T.deg_C-ST.mean
FROM temps3 AS T
INNER JOIN mstats3 AS ST 
ON
        ST.stnID=T.stnID
	AND ST.month=T.month 
WHERE T.deg_C > -90 
;

yes, lanom3 is for “load anomaly table for GHCN v3” ;-)

It loads into the anom3 table the referenced fields from the temps3 table via a SELECT statement. That SELECT does an INNER JOIN on temps3 and mstats3 to make available the monthly average of all the data for each station in each month over the whole history of years for that station. Where the STatistics station ID matches the Temperatures station ID and where the STatistics month matches the Temperatures month. And where it is not a missing data flag in the degrees field.

The only keen bit after that is at the end of the SELECT statement where I have “T.deg_C-ST.mean” to subtract the STatistics table “mean” for that station for that month from the actual Temperture.deg_C for that month for that station in that year. Creating the anomaly value.

Doesn’t look like much for a couple of days of noodling around… but this is with all the dead ends removed and ignoring all the “lap time” the dog wanted because it was windy today…

So, there you have it. How to make an anomaly table from the temperatures table.

And no, I have ZERO desire to do the “box grid” anomalies based on a “baseline” set of thermometers that are different from the present thermometers. IMHO this is a superior method anyway. If, for example, a thermometer is trending up over 20 years, it will have positive values more recently and negative in the past. Average that in with other thermometers, you ought to get lower in the past and higher in the future in that segment of time, reflecting the reality measured.

Also, I intend (probably after evening tea) to make a report, much like those above, of the average anomaly over years BUT, only for stations still around in 2015. Another one just for long lived stations. Between those two, and the one above, we ought to be able to see just how sensitive the conclusions are to the selection of thermometers to include in the test… and potentially put the lie to the notion that thermometer selection does nothing if you are using anomalies. But, as always, we’ll see…

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About E.M.Smith

A technical managerial sort interested in things from Stonehenge to computer science. My present "hot buttons' are the mythology of Climate Change and ancient metrology; but things change...
This entry was posted in AGW Science and Background, Global Cooling, Global Warming General, NCDC - GHCN Issues, Tech Bits and tagged , , , . Bookmark the permalink.

17 Responses to GHCN v3.3 Anomaly By Continent

  1. E.M.Smith says:

    There’s that wonderful moment in learning a new language or system where it just starts to come together and work. I’m reaching that point with SQL. Still need some cheat sheet time from my SQL Pocket Guide or the occasional web site, but managing to make ever more interesting commands in any case.

    This one joins 3 tables together to make a report. It reports on only those stations with over 100 years of data and does it by region (continent). For Antarctica (yes, there was data for over 100 years!) that is only ONE station.
    “| 7 | BASE ORCADAS | -2.58545 | 110 |”
    With 110 years of data. So don’t read too much into the places with “thin” coverage.

    With that, here’s the report, by region, for stations over 100 years, of their average anomaly by year.

    The Code:

    chiefio@PiM3Devuan2:~/SQL/bin$ cat anomlreg.sql 
    SELECT A.region,C.region_name,A.year, AVG(A.deg_C) 
    AS "over 100yr Anom"  
    FROM anom3 AS A
    INNER JOIN mstats3 AS ST
    ON
      	ST.stnID=A.stnID
    	AND ST.month=A.month
    INNER JOIN continent AS C
    ON      C.region=A.region
    WHERE ST.num > 100 AND year>1920
    GROUP BY A.region,A.year
    ORDER BY A.region,A.year
    ;
    

    As this report is 665 lines, I’m going to cut out chunks. Bold some interesting bits.

    First off, Africa is remarkably stable for most of the history. I note that the ’30s are not abnormally hot there. Even into the ’80s not warming. It is only after the ’90s that things suddenly get hot.

    MariaDB [temps]> source bin/anomlreg.sql
    +--------+---------------------------+------+-----------------+
    | region | region_name               | year | over 100yr Anom |
    +--------+---------------------------+------+-----------------+
    | 1      | Africa                    | 1921 |       -0.282841 |
    | 1      | Africa                    | 1922 |       -0.015265 |
    | 1      | Africa                    | 1923 |       -0.144270 |
    | 1      | Africa                    | 1924 |        0.004640 |
    | 1      | Africa                    | 1925 |       -0.139114 |
    | 1      | Africa                    | 1926 |        0.085754 |
    | 1      | Africa                    | 1927 |        0.212351 |
    | 1      | Africa                    | 1928 |        0.096381 |
    | 1      | Africa                    | 1929 |       -0.202872 |
    | 1      | Africa                    | 1930 |       -0.174632 |
    | 1      | Africa                    | 1931 |        0.163333 |
    | 1      | Africa                    | 1932 |       -0.045882 |
    | 1      | Africa                    | 1933 |       -0.054007 |
    | 1      | Africa                    | 1934 |       -0.131615 |
    | 1      | Africa                    | 1935 |       -0.246237 |
    | 1      | Africa                    | 1936 |        0.027282 |
    | 1      | Africa                    | 1937 |        0.268648 |
    | 1      | Africa                    | 1938 |       -0.191189 |
    | 1      | Africa                    | 1939 |       -0.064432 |
    | 1      | Africa                    | 1940 |        0.280570 |
    [...]
    | 1      | Africa                    | 1970 |        0.105240 |
    | 1      | Africa                    | 1971 |       -0.526500 |
    | 1      | Africa                    | 1972 |       -0.299652 |
    | 1      | Africa                    | 1973 |       -0.036996 |
    | 1      | Africa                    | 1974 |       -0.553927 |
    | 1      | Africa                    | 1975 |       -0.500036 |
    | 1      | Africa                    | 1976 |       -0.470037 |
    | 1      | Africa                    | 1977 |       -0.085207 |
    | 1      | Africa                    | 1978 |       -0.163972 |
    | 1      | Africa                    | 1979 |        0.152165 |
    | 1      | Africa                    | 1980 |       -0.117083 |
    | 1      | Africa                    | 1981 |       -0.151698 |
    | 1      | Africa                    | 1982 |       -0.205803 |
    | 1      | Africa                    | 1983 |        0.003109 |
    | 1      | Africa                    | 1984 |       -0.026170 |
    | 1      | Africa                    | 1985 |       -0.027986 |
    | 1      | Africa                    | 1986 |       -0.042810 |
    | 1      | Africa                    | 1987 |        0.388112 |
    | 1      | Africa                    | 1988 |        0.230504 |
    | 1      | Africa                    | 1989 |       -0.011018 |
    | 1      | Africa                    | 1990 |        0.136214 |
    | 1      | Africa                    | 1991 |        0.139671 |
    | 1      | Africa                    | 1992 |       -0.042535 |
    | 1      | Africa                    | 1993 |        0.275138 |
    | 1      | Africa                    | 1994 |        0.242050 |
    | 1      | Africa                    | 1995 |        0.398483 |
    | 1      | Africa                    | 1996 |        0.215862 |
    | 1      | Africa                    | 1997 |        0.472137 |
    | 1      | Africa                    | 1998 |        0.712645 |
    | 1      | Africa                    | 1999 |        0.701498 |
    | 1      | Africa                    | 2000 |        0.290611 |
    | 1      | Africa                    | 2001 |        0.566623 |
    | 1      | Africa                    | 2002 |        0.550945 |
    | 1      | Africa                    | 2003 |        0.602600 |
    | 1      | Africa                    | 2004 |        0.548987 |
    | 1      | Africa                    | 2005 |        0.646695 |
    | 1      | Africa                    | 2006 |        0.628348 |
    | 1      | Africa                    | 2007 |        0.520699 |
    | 1      | Africa                    | 2008 |        0.729075 |
    | 1      | Africa                    | 2009 |        0.936681 |
    | 1      | Africa                    | 2010 |        1.238326 |
    | 1      | Africa                    | 2011 |        0.634934 |
    | 1      | Africa                    | 2012 |        0.703771 |
    | 1      | Africa                    | 2013 |        0.548608 |
    | 1      | Africa                    | 2014 |        0.914393 |
    | 1      | Africa                    | 2015 |        0.944766 |
    

    Asia has cool 1930s too. Things are more or less unchanged through the ’60s (I’ve deleted a lot of it. If you really want to see it let me know and I’l post the whole thing). Then again sudden warming late in the record just about the time the new electronic thermometers get installed.

    | 2      | Asia                      | 1921 |       -0.039922 |
    | 2      | Asia                      | 1922 |       -0.070612 |
    | 2      | Asia                      | 1923 |       -0.096874 |
    | 2      | Asia                      | 1924 |       -0.151947 |
    | 2      | Asia                      | 1925 |       -0.031273 |
    | 2      | Asia                      | 1926 |       -0.272132 |
    | 2      | Asia                      | 1927 |       -0.262880 |
    | 2      | Asia                      | 1928 |       -0.250838 |
    | 2      | Asia                      | 1929 |       -0.545920 |
    | 2      | Asia                      | 1930 |       -0.177835 |
    | 2      | Asia                      | 1931 |       -0.368585 |
    | 2      | Asia                      | 1932 |        0.052883 |
    | 2      | Asia                      | 1933 |       -0.505117 |
    | 2      | Asia                      | 1934 |       -0.387550 |
    | 2      | Asia                      | 1935 |       -0.151411 |
    | 2      | Asia                      | 1936 |       -0.229723 |
    | 2      | Asia                      | 1937 |       -0.207631 |
    | 2      | Asia                      | 1938 |        0.030534 |
    | 2      | Asia                      | 1939 |        0.066180 |
    | 2      | Asia                      | 1940 |       -0.124013 |
    | 2      | Asia                      | 1941 |       -0.168298 |
    |[...]
    | 2      | Asia                      | 1970 |       -0.116421 |
    | 2      | Asia                      | 1971 |       -0.095781 |
    | 2      | Asia                      | 1972 |       -0.241686 |
    | 2      | Asia                      | 1973 |        0.257804 |
    | 2      | Asia                      | 1974 |       -0.317544 |
    | 2      | Asia                      | 1975 |        0.329440 |
    | 2      | Asia                      | 1976 |       -0.377881 |
    | 2      | Asia                      | 1977 |        0.076510 |
    | 2      | Asia                      | 1978 |        0.196444 |
    | 2      | Asia                      | 1979 |        0.357272 |
    | 2      | Asia                      | 1980 |        0.000896 |
    | 2      | Asia                      | 1981 |        0.303136 |
    | 2      | Asia                      | 1982 |        0.256373 |
    | 2      | Asia                      | 1983 |        0.632260 |
    | 2      | Asia                      | 1984 |       -0.290683 |
    | 2      | Asia                      | 1985 |        0.059045 |
    | 2      | Asia                      | 1986 |        0.014064 |
    | 2      | Asia                      | 1987 |        0.171013 |
    | 2      | Asia                      | 1988 |        0.543450 |
    | 2      | Asia                      | 1989 |        0.750285 |
    | 2      | Asia                      | 1990 |        1.061819 |
    | 2      | Asia                      | 1991 |        0.656090 |
    | 2      | Asia                      | 1992 |        0.351600 |
    | 2      | Asia                      | 1993 |        0.219129 |
    | 2      | Asia                      | 1994 |        0.579509 |
    | 2      | Asia                      | 1995 |        0.953001 |
    | 2      | Asia                      | 1996 |        0.279858 |
    | 2      | Asia                      | 1997 |        0.764459 |
    | 2      | Asia                      | 1998 |        0.873116 |
    | 2      | Asia                      | 1999 |        0.820466 |
    | 2      | Asia                      | 2000 |        0.736648 |
    | 2      | Asia                      | 2001 |        0.700938 |
    | 2      | Asia                      | 2002 |        1.101533 |
    | 2      | Asia                      | 2003 |        0.796921 |
    | 2      | Asia                      | 2004 |        1.167430 |
    | 2      | Asia                      | 2005 |        0.900436 |
    | 2      | Asia                      | 2006 |        0.737403 |
    | 2      | Asia                      | 2007 |        1.369398 |
    | 2      | Asia                      | 2008 |        1.071458 |
    | 2      | Asia                      | 2009 |        0.833751 |
    | 2      | Asia                      | 2010 |        0.923273 |
    | 2      | Asia                      | 2011 |        0.798239 |
    | 2      | Asia                      | 2012 |        0.710491 |
    | 2      | Asia                      | 2013 |        1.053814 |
    | 2      | Asia                      | 2014 |        0.836460 |
    | 2      | Asia                      | 2015 |        1.558689 |
    

    I don’t see how that is in any way reflective of the gradual accumulation of CO2. Looks a lot more like instrument change and UHI.

    South America is the same basic story. Nothing much to see. No hot ’30s. Then BAM! hots up just at the end.

    | 3      | South America             | 1921 |       -0.434667 |
    | 3      | South America             | 1922 |       -0.388952 |
    | 3      | South America             | 1923 |       -0.500381 |
    | 3      | South America             | 1924 |       -0.559429 |
    | 3      | South America             | 1925 |       -0.194667 |
    | 3      | South America             | 1926 |        0.095333 |
    | 3      | South America             | 1927 |       -0.169429 |
    | 3      | South America             | 1928 |       -0.295619 |
    | 3      | South America             | 1929 |       -0.067524 |
    | 3      | South America             | 1930 |        0.113333 |
    | 3      | South America             | 1931 |       -0.295282 |
    | 3      | South America             | 1932 |        0.170833 |
    | 3      | South America             | 1933 |       -0.207656 |
    | 3      | South America             | 1934 |       -0.296946 |
    | 3      | South America             | 1935 |       -0.046095 |
    | 3      | South America             | 1936 |        0.035981 |
    | 3      | South America             | 1937 |       -0.076364 |
    | 3      | South America             | 1938 |       -0.101111 |
    | 3      | South America             | 1939 |       -0.018738 |
    [...]
    | 3      | South America             | 1982 |        0.136106 |
    | 3      | South America             | 1983 |        0.061346 |
    | 3      | South America             | 1984 |       -0.244752 |
    | 3      | South America             | 1985 |        0.031902 |
    | 3      | South America             | 1986 |        0.241373 |
    | 3      | South America             | 1987 |        0.310974 |
    | 3      | South America             | 1988 |       -0.142183 |
    | 3      | South America             | 1989 |        0.100663 |
    | 3      | South America             | 1990 |        0.202031 |
    | 3      | South America             | 1991 |        0.066077 |
    | 3      | South America             | 1992 |       -0.094813 |
    | 3      | South America             | 1993 |        0.078857 |
    | 3      | South America             | 1994 |        0.341598 |
    | 3      | South America             | 1995 |        0.233333 |
    | 3      | South America             | 1996 |        0.163107 |
    | 3      | South America             | 1997 |        0.584045 |
    | 3      | South America             | 1998 |        0.461236 |
    | 3      | South America             | 1999 |        0.032295 |
    | 3      | South America             | 2000 |       -0.020730 |
    | 3      | South America             | 2001 |        0.413412 |
    | 3      | South America             | 2002 |        0.361834 |
    | 3      | South America             | 2003 |        0.547052 |
    | 3      | South America             | 2004 |        0.318101 |
    | 3      | South America             | 2005 |        0.385872 |
    | 3      | South America             | 2006 |        0.567877 |
    | 3      | South America             | 2007 |        0.025503 |
    | 3      | South America             | 2008 |        0.255817 |
    | 3      | South America             | 2009 |        0.559051 |
    | 3      | South America             | 2010 |        0.290637 |
    | 3      | South America             | 2011 |        0.272716 |
    | 3      | South America             | 2012 |        0.727205 |
    | 3      | South America             | 2013 |        0.501688 |
    | 3      | South America             | 2014 |        0.653580 |
    | 3      | South America             | 2015 |        0.955789 |
    

    Sure looks like manicured for effect to me. Or else the instrument changes were horridly badly done.

    North America is more interesting. It had some Big Heat early, hot ’30s, and not so hot now. In the middle, a long stretch of “nothing much” minor wobble. Though 1953-54 was hot.

    | 4      | North America             | 1921 |        1.144498 |
    | 4      | North America             | 1922 |        0.229222 |
    | 4      | North America             | 1923 |       -0.050820 |
    | 4      | North America             | 1924 |       -0.797424 |
    | 4      | North America             | 1925 |        0.309411 |
    | 4      | North America             | 1926 |       -0.114628 |
    | 4      | North America             | 1927 |        0.147007 |
    | 4      | North America             | 1928 |        0.022979 |
    | 4      | North America             | 1929 |       -0.519413 |
    | 4      | North America             | 1930 |        0.229110 |
    | 4      | North America             | 1931 |        1.163438 |
    | 4      | North America             | 1932 |        0.132717 |
    | 4      | North America             | 1933 |        0.701669 |
    | 4      | North America             | 1934 |        1.021117 |
    | 4      | North America             | 1935 |        0.008070 |
    | 4      | North America             | 1936 |        0.139940 |
    | 4      | North America             | 1937 |       -0.111764 |
    | 4      | North America             | 1938 |        0.884075 |
    | 4      | North America             | 1939 |        0.809877 |
    | 4      | North America             | 1940 |       -0.172762 |
    | 4      | North America             | 1941 |        0.653542 |
    | 4      | North America             | 1942 |        0.104304 |
    | 4      | North America             | 1943 |        0.025353 |
    | 4      | North America             | 1944 |        0.188419 |
    | 4      | North America             | 1945 |       -0.047806 |
    | 4      | North America             | 1946 |        0.706521 |
    | 4      | North America             | 1947 |        0.044116 |
    | 4      | North America             | 1948 |       -0.080369 |
    | 4      | North America             | 1949 |        0.314220 |
    | 4      | North America             | 1950 |       -0.444216 |
    | 4      | North America             | 1951 |       -0.438063 |
    | 4      | North America             | 1952 |        0.353117 |
    | 4      | North America             | 1953 |        0.902517 |
    | 4      | North America             | 1954 |        0.704919 |
    | 4      | North America             | 1955 |       -0.004667 |
    | 4      | North America             | 1956 |        0.194689 |
    | 4      | North America             | 1957 |        0.155117 |
    | 4      | North America             | 1958 |       -0.147153 |
    | 4      | North America             | 1959 |        0.133057 |
    | 4      | North America             | 1960 |       -0.358512 |
    [...]
    | 4      | North America             | 1988 |        0.022971 |
    | 4      | North America             | 1989 |       -0.505219 |
    | 4      | North America             | 1990 |        0.704902 |
    | 4      | North America             | 1991 |        0.535540 |
    | 4      | North America             | 1992 |       -0.063887 |
    | 4      | North America             | 1993 |       -0.652870 |
    | 4      | North America             | 1994 |        0.105304 |
    | 4      | North America             | 1995 |        0.007263 |
    | 4      | North America             | 1996 |       -0.589987 |
    | 4      | North America             | 1997 |       -0.268813 |
    | 4      | North America             | 1998 |        1.098583 |
    | 4      | North America             | 1999 |        0.696069 |
    | 4      | North America             | 2000 |        0.175484 |
    | 4      | North America             | 2001 |        0.507861 |
    | 4      | North America             | 2002 |        0.329042 |
    | 4      | North America             | 2003 |        0.096930 |
    | 4      | North America             | 2004 |        0.185643 |
    | 4      | North America             | 2005 |        0.472478 |
    | 4      | North America             | 2006 |        0.855810 |
    | 4      | North America             | 2007 |        0.467162 |
    | 4      | North America             | 2008 |       -0.352753 |
    | 4      | North America             | 2009 |       -0.309025 |
    | 4      | North America             | 2010 |        0.207154 |
    | 4      | North America             | 2011 |        0.252257 |
    | 4      | North America             | 2012 |        1.419999 |
    | 4      | North America             | 2013 |       -0.273203 |
    | 4      | North America             | 2014 |       -0.377715 |
    | 4      | North America             | 2015 |        0.239201 |
    

    Don’t know how a “well mixed gas” can make some places hotter and others colder… nor how it can wait 20 years to start having an effect. Or 40… Or have such a vastly different effect in North America when compared to Asia…

    Australia / Pacific Islands is just “nothing” +/1 a few 1/10 C, up until about 1988. I’m going to chop out a lot of the middle as it is just repetitive.

    | 5      | Australia Pacific Islands | 1921 |        0.153069 |
    | 5      | Australia Pacific Islands | 1922 |        0.019040 |
    | 5      | Australia Pacific Islands | 1923 |       -0.015239 |
    | 5      | Australia Pacific Islands | 1924 |       -0.299439 |
    | 5      | Australia Pacific Islands | 1925 |       -0.436639 |
    | 5      | Australia Pacific Islands | 1926 |        0.052536 |
    | 5      | Australia Pacific Islands | 1927 |       -0.243929 |
    | 5      | Australia Pacific Islands | 1928 |        0.273326 |
    | 5      | Australia Pacific Islands | 1929 |       -0.407963 |
    | 5      | Australia Pacific Islands | 1930 |        0.014283 |
    | 5      | Australia Pacific Islands | 1931 |       -0.321892 |
    | 5      | Australia Pacific Islands | 1932 |       -0.228854 |
    | 5      | Australia Pacific Islands | 1933 |       -0.220644 |
    | 5      | Australia Pacific Islands | 1934 |       -0.133742 |
    | 5      | Australia Pacific Islands | 1935 |       -0.223808 |
    | 5      | Australia Pacific Islands | 1936 |       -0.216071 |
    | 5      | Australia Pacific Islands | 1937 |       -0.113784 |
    [...]
    | 5      | Australia Pacific Islands | 1976 |       -0.372704 |
    | 5      | Australia Pacific Islands | 1977 |       -0.021043 |
    | 5      | Australia Pacific Islands | 1978 |       -0.078922 |
    | 5      | Australia Pacific Islands | 1979 |        0.302278 |
    | 5      | Australia Pacific Islands | 1980 |        0.413376 |
    | 5      | Australia Pacific Islands | 1981 |        0.351128 |
    | 5      | Australia Pacific Islands | 1982 |        0.136065 |
    | 5      | Australia Pacific Islands | 1983 |        0.226291 |
    | 5      | Australia Pacific Islands | 1984 |       -0.159107 |
    | 5      | Australia Pacific Islands | 1985 |        0.119743 |
    | 5      | Australia Pacific Islands | 1986 |       -0.047149 |
    | 5      | Australia Pacific Islands | 1987 |        0.159698 |
    | 5      | Australia Pacific Islands | 1988 |        0.619907 |
    | 5      | Australia Pacific Islands | 1989 |        0.163781 |
    | 5      | Australia Pacific Islands | 1990 |        0.407919 |
    | 5      | Australia Pacific Islands | 1991 |        0.360376 |
    | 5      | Australia Pacific Islands | 1992 |       -0.125059 |
    | 5      | Australia Pacific Islands | 1993 |        0.007583 |
    | 5      | Australia Pacific Islands | 1994 |        0.027237 |
    | 5      | Australia Pacific Islands | 1995 |        0.053304 |
    | 5      | Australia Pacific Islands | 1996 |        0.157056 |
    | 5      | Australia Pacific Islands | 1997 |        0.112403 |
    | 5      | Australia Pacific Islands | 1998 |        0.652043 |
    | 5      | Australia Pacific Islands | 1999 |        0.434652 |
    | 5      | Australia Pacific Islands | 2000 |        0.328996 |
    | 5      | Australia Pacific Islands | 2001 |        0.097226 |
    | 5      | Australia Pacific Islands | 2002 |        0.160979 |
    | 5      | Australia Pacific Islands | 2003 |        0.271907 |
    | 5      | Australia Pacific Islands | 2004 |        0.279388 |
    | 5      | Australia Pacific Islands | 2005 |        0.534890 |
    | 5      | Australia Pacific Islands | 2006 |        0.274341 |
    | 5      | Australia Pacific Islands | 2007 |        0.536736 |
    | 5      | Australia Pacific Islands | 2008 |        0.309590 |
    | 5      | Australia Pacific Islands | 2009 |        0.504608 |
    | 5      | Australia Pacific Islands | 2010 |        0.584929 |
    | 5      | Australia Pacific Islands | 2011 |        0.373447 |
    | 5      | Australia Pacific Islands | 2012 |        0.391818 |
    | 5      | Australia Pacific Islands | 2013 |        0.884249 |
    | 5      | Australia Pacific Islands | 2014 |        0.694948 |
    | 5      | Australia Pacific Islands | 2015 |        0.385000 |
    

    One wonders how CO2 can just stop in 1992 having gotten such a good start in 1988… only to pick up again in 1998. Wonder if it has a thin for years ending in 8 /sarc;

    Then there is Europe. The ’30s were definitely hot, then the ’40s went cold. The eary ’30s were opposite of North America. Then, right on queue, 1989 spikes hot. How does CO2 hit in one year?

    | 6      | Europe                    | 1921 |        0.368124 |
    | 6      | Europe                    | 1922 |       -0.427901 |
    | 6      | Europe                    | 1923 |        0.016915 |
    | 6      | Europe                    | 1924 |       -0.238457 |
    | 6      | Europe                    | 1925 |        0.247679 |
    | 6      | Europe                    | 1926 |        0.193038 |
    | 6      | Europe                    | 1927 |       -0.047886 |
    | 6      | Europe                    | 1928 |       -0.092975 |
    | 6      | Europe                    | 1929 |       -0.549628 |
    | 6      | Europe                    | 1930 |        0.577253 |
    | 6      | Europe                    | 1931 |       -0.398775 |
    | 6      | Europe                    | 1932 |        0.248477 |
    | 6      | Europe                    | 1933 |       -0.407663 |
    | 6      | Europe                    | 1934 |        0.932608 |
    | 6      | Europe                    | 1935 |        0.262478 |
    | 6      | Europe                    | 1936 |        0.528919 |
    | 6      | Europe                    | 1937 |        0.662623 |
    | 6      | Europe                    | 1938 |        0.899174 |
    | 6      | Europe                    | 1939 |        0.402634 |
    | 6      | Europe                    | 1940 |       -0.788077 |
    | 6      | Europe                    | 1941 |       -0.907682 |
    | 6      | Europe                    | 1942 |       -0.813564 |
    | 6      | Europe                    | 1943 |        0.571329 |
    | 6      | Europe                    | 1944 |        0.254085 |
    | 6      | Europe                    | 1945 |        0.052580 |
    | 6      | Europe                    | 1946 |        0.264931 |
    | 6      | Europe                    | 1947 |        0.120426 |
    | 6      | Europe                    | 1948 |        0.472951 |
    | 6      | Europe                    | 1949 |        0.675075 |
    | 6      | Europe                    | 1950 |        0.319484 |
    | 6      | Europe                    | 1951 |        0.217246 |
    | 6      | Europe                    | 1952 |       -0.034499 |
    | 6      | Europe                    | 1953 |        0.319264 |
    | 6      | Europe                    | 1954 |       -0.275818 |
    | 6      | Europe                    | 1955 |       -0.177057 |
    | 6      | Europe                    | 1956 |       -1.099989 |
    | 6      | Europe                    | 1957 |        0.400539 |
    | 6      | Europe                    | 1958 |       -0.074558 |
    | 6      | Europe                    | 1959 |        0.370775 |
    | 6      | Europe                    | 1960 |        0.230653 |
    | 6      | Europe                    | 1961 |        0.675760 |
    | 6      | Europe                    | 1962 |       -0.245229 |
    | 6      | Europe                    | 1963 |       -0.569290 |
    | 6      | Europe                    | 1964 |       -0.107076 |
    [...]
    | 6      | Europe                    | 1984 |       -0.023037 |
    | 6      | Europe                    | 1985 |       -0.793367 |
    | 6      | Europe                    | 1986 |       -0.166610 |
    | 6      | Europe                    | 1987 |       -0.762523 |
    | 6      | Europe                    | 1988 |        0.327267 |
    | 6      | Europe                    | 1989 |        1.203366 |
    | 6      | Europe                    | 1990 |        1.230652 |
    | 6      | Europe                    | 1991 |        0.293514 |
    | 6      | Europe                    | 1992 |        0.553333 |
    | 6      | Europe                    | 1993 |       -0.021105 |
    | 6      | Europe                    | 1994 |        0.804429 |
    | 6      | Europe                    | 1995 |        0.634272 |
    | 6      | Europe                    | 1996 |       -0.165997 |
    | 6      | Europe                    | 1997 |        0.434265 |
    | 6      | Europe                    | 1998 |        0.514259 |
    | 6      | Europe                    | 1999 |        1.013055 |
    | 6      | Europe                    | 2000 |        1.263737 |
    | 6      | Europe                    | 2001 |        0.718127 |
    | 6      | Europe                    | 2002 |        1.026156 |
    | 6      | Europe                    | 2003 |        0.795846 |
    | 6      | Europe                    | 2004 |        0.780200 |
    | 6      | Europe                    | 2005 |        0.742364 |
    | 6      | Europe                    | 2006 |        0.963633 |
    | 6      | Europe                    | 2007 |        1.418819 |
    | 6      | Europe                    | 2008 |        1.283514 |
    | 6      | Europe                    | 2009 |        1.081057 |
    | 6      | Europe                    | 2010 |        0.395520 |
    | 6      | Europe                    | 2011 |        1.173554 |
    | 6      | Europe                    | 2012 |        0.910530 |
    | 6      | Europe                    | 2013 |        1.030513 |
    | 6      | Europe                    | 2014 |        1.716961 |
    | 6      | Europe                    | 2015 |        1.576651 |
    

    It sure looks to me like a big “step function” in one go, not at all like a slowly accumulating gas with steady increases in heating. Perhaps the onset of the Jet Age at all those European airport thermometers?

    Finally, Antarctica. Remember this is just one station. Starts cold. Ends cold. Cold in the middle. But in the ’70s it suddenly steps warm. Wonder if they did a base build out then…

    | 7      | Antarctica                | 1921 |       -0.665833 |
    | 7      | Antarctica                | 1922 |       -0.249167 |
    | 7      | Antarctica                | 1923 |       -0.274167 |
    | 7      | Antarctica                | 1924 |       -1.165833 |
    | 7      | Antarctica                | 1925 |       -0.540833 |
    | 7      | Antarctica                | 1926 |       -0.424167 |
    | 7      | Antarctica                | 1927 |       -1.540833 |
    | 7      | Antarctica                | 1928 |       -2.732500 |
    | 7      | Antarctica                | 1929 |       -1.549167 |
    | 7      | Antarctica                | 1930 |       -3.015833 |
    | 7      | Antarctica                | 1931 |       -0.557500 |
    | 7      | Antarctica                | 1932 |       -1.132500 |
    | 7      | Antarctica                | 1933 |       -1.199167 |
    | 7      | Antarctica                | 1934 |       -0.607500 |
    | 7      | Antarctica                | 1935 |       -1.415833 |
    | 7      | Antarctica                | 1936 |        0.359167 |
    | 7      | Antarctica                | 1937 |        1.025833 |
    | 7      | Antarctica                | 1938 |       -0.965833 |
    | 7      | Antarctica                | 1939 |       -1.807500 |
    [...]
    | 7      | Antarctica                | 1976 |        0.717500 |
    | 7      | Antarctica                | 1977 |        1.559167 |
    | 7      | Antarctica                | 1978 |        0.967500 |
    | 7      | Antarctica                | 1979 |        0.175833 |
    | 7      | Antarctica                | 1980 |       -1.832500 |
    | 7      | Antarctica                | 1981 |        0.217500 |
    | 7      | Antarctica                | 1982 |        0.900833 |
    | 7      | Antarctica                | 1983 |        0.734167 |
    | 7      | Antarctica                | 1984 |        0.759167 |
    | 7      | Antarctica                | 1985 |        1.775833 |
    | 7      | Antarctica                | 1986 |        0.834167 |
    | 7      | Antarctica                | 1987 |       -0.165833 |
    | 7      | Antarctica                | 1988 |       -0.057500 |
    | 7      | Antarctica                | 1989 |        2.259167 |
    | 7      | Antarctica                | 1990 |        1.250833 |
    | 7      | Antarctica                | 1991 |       -0.324167 |
    | 7      | Antarctica                | 1992 |        1.210000 |
    | 7      | Antarctica                | 1993 |        0.820000 |
    | 7      | Antarctica                | 1994 |        1.485556 |
    | 7      | Antarctica                | 1995 |        0.681250 |
    | 7      | Antarctica                | 1996 |        1.267500 |
    | 7      | Antarctica                | 1997 |        0.050000 |
    | 7      | Antarctica                | 1998 |        0.867500 |
    | 7      | Antarctica                | 1999 |        0.940000 |
    | 7      | Antarctica                | 2000 |        1.350833 |
    | 7      | Antarctica                | 2001 |        0.717500 |
    | 7      | Antarctica                | 2002 |        1.054545 |
    | 7      | Antarctica                | 2003 |        0.867500 |
    | 7      | Antarctica                | 2004 |        1.167500 |
    | 7      | Antarctica                | 2005 |        0.750833 |
    | 7      | Antarctica                | 2006 |        1.175833 |
    | 7      | Antarctica                | 2007 |        0.475833 |
    | 7      | Antarctica                | 2008 |        1.650833 |
    | 7      | Antarctica                | 2009 |        1.267500 |
    | 7      | Antarctica                | 2010 |        1.959167 |
    | 7      | Antarctica                | 2011 |        1.409167 |
    | 7      | Antarctica                | 2012 |        0.242500 |
    | 7      | Antarctica                | 2013 |        1.125833 |
    | 7      | Antarctica                | 2014 |        0.534167 |
    | 7      | Antarctica                | 2015 |       -0.048571 |
    +--------+---------------------------+------+-----------------+
    665 rows in set (11 min 12.43 sec)
    

    So overall it ends up going nowhere (negative anomaly in 2015), but along the way there’s some odd excursions.

    My Big Takeaway: Any “warming” is strongly regional and likely due to local environmental changes or local instrument changes. I’d most strongly suspect Urban Heat Island effects, expansion of airport tarmac and acerage of cars + tons of jet fuel burn in the Jet Age, and the change to electronic thermometers on a cable to a building where many, even in the USA, were sited very badly and brought closer to buildings and heat sources. If WE did it wrong, what do you think they did in China and recession whacked Europe (especially the southern warmer areas with 25% unemployment in about the relevant years).

    What I do NOT see is a steady increase of temperatures, evenly distributed, with a linearly increasing “greenhouse gas” warming profile. I see a LOT of “step functions” just about the time the Jet Age took off (and “Global Warming” started to be pushed by the WMO UN folks… Just sayin’…)

    That is from the only stations we have in the GHCN with a long enough record to really have a hope of capturing natural climate cycles. There’s about 1500 of them, all told. That ought to be enough to say something useful.

  2. Larry Ledwick says:

    I think a lot of the difference is the speed of response for the modern electrical thermometers vs the thermal lag of the old mercury column in glass which cannot respond near as fast.

    I would bet 95% of the “warming” is just the ability to capture and hold very short temp peaks that were totally overlooked by traditional thermometers.

    It would be interesting if any metrologists have worked out the effective response time and shortest temperature excursion that mechanical thermometers can reliably record.

  3. Another Ian says:

    E.M.

    Seems to fit around here

    https://dilbert.com/strip/2019-03-10

    Via a comment at Jo Nova

  4. Another Ian says:

    Larry

    IIRC there is a protocol for how long electronic thermometers have to be averaged before claiming a reading.
    Also IIRC the BOM in Oz claims the 1 second ones

  5. A C Osborn says:

    Don’t forget, there must be some natural warming, you can’t come out of a mini Ice Age without it.
    Which means that when you add in UHI, the change to Electronic thermometers, the changes in locations to Airports and the reduction in Tropical cloud cover there is not much left for CO2 induced warming.

  6. Steven Fraser says:

    E.M.: since you are headed v4-way, I encourage you to build some persistent tables that facilitate cross-version comparisons of the baselines and anomalies at the instrument and regional levels. This would give some insight into the interversion differences, or put in a more fun way, the anomaly of anomalies.

  7. E.M.Smith says:

    @Steven Fraser:

    I actually started this whole SQL / Python based approach for exactly that reason, to compare versions. That’s why I put a “version” and “ascension” field in the tables (and a “type” for MIN vs MAX vs AVE).

    It was just when I started trying to actually DO things that I realized I needed to get over a devo hump and ought to just work with one while I did that, so shifted to the names ending in “3” (that still have a version field…) to flag that I only had GHCN v3 going so far.

    Adding V2 ought to be darned easy (it has the same inventory and layout as V3). It is V1 that’s a pain as it is all way different. Different fields, StationID numbers, even different countries. Then v4 is a bit of a black box right now.

    Yeah, I hae months of this ahead of me 8-}

    @A.C.Osborn:

    Yes that’s true, but the problem there is in the early just post L.I.A. years the only thermometers were in Europe… It does show up as a large negative anomaly in the 1700s, but only in a few thermometers.

    Since most thermometers start after 1850 they don’t show much of the L.I.A., instead they tend to capture the other things you pointed out: UHI, grass marching fields turned into acres of military airports, growth of public aviation, etc.

    @Another Ian:

    That Dilbert does capture the essence. The data are just so crappy, sparse, and short IMHO you would do much better to just use a good proxy. The thermometers are great for local weather, climate not so much…

    @Larry L:

    It isn’t just speed that’s the issue. The electronic ones need power and often communications. LOTS of them were pulled closer to sources of that; i.e. buildings, tarmac, cars, jet exhaust… Then you add in that they can catch the few seconds that a 757 shoves a few tons of hot exhaust at them …

  8. Pouncer says:

    Mr Smith: ” My Big Takeaway: Any ‘warming’ is strongly regional and likely due to local environmental changes or local instrument changes. ”

    Any freshman physics student would point out that when the data derived from the experiment varies this wildly far from the textbook theory, the data (or the experimental set up) must be wrong.

    It takes a host of PhD physics practitioners to explain how the textbook theory, developed from this data, can be used to CORRECT what all is wrong with the same data.

  9. Phil Younger says:

    Another adder to regional temperature variations is drought/soil moisture. Since I live in an area where the soil is like a sandbox that holds less than 1″/foot of profile vs some soils that hold 3-4x that I watch weather channels with interest as they publish actual and predicted temps. In less than one day after a rain the surface of the sand will be dry and after 3 days even well rooted plants with roots to 6″ will be stressed or wilting- At this point our locality will run 3-8 degrees warmer than the non-sand localities unless strong winds are present.
    If we think back to major drought years (the 1930’s dust bowl) the 1988 Midwest drought, and the 2012 Upper Midwest drought- we had larger areas of dry soils that could not cool by evaporation and thus also heated the air above them. If you look at those years in Mr Smith’s chart- they were warmer. I do not know where the thermometers in question were located nor am i making any claim that dry conditions are often less cloudy. If I wanted to prove that climate is changing more radically then I’d sure want to add thermometers above sand.

  10. cdquarles says:

    @Phil,
    Locally, dry conditions are less cloudy. They are either big dry highs that sit and suppress local convection or big dry highs that displace moisture for a time. And, locally, the trees add lots of water that doesn’t get evaporated by sun or dry air. So, the vegetation does help keep moisture for a time. If the time gets long, the vegetation helps dry it out.

  11. Pingback: Anomaly By Continent Graphs GHCN v3.3 | Musings from the Chiefio

  12. gallopingcamel says:

    Tony Heller has made a cottage industry out of comparing GHCN v2 with GHCN v3. He made some very amusing animated graphs to highlight the adjustments.

    It is beyond amusing that GHCN can “Adjust the Past” without any explanation. Richard Lindzen pithily commented that in “Climate Science” we can’t even predict the past.

    Will you be able to publish plots comparing v2, v3 and v4?

  13. Larry Ledwick says:

    To Phil’s comment, soil water content combines with typical local humidity conditions and evapo-transpiration to moderate temperature and varies a lot by region. Out here in the high plains it is an important variable. In Colorado and other south west arid regions pan evaporation in July can reach numbers over a foot of water of evaporation in a month like July. Where in locations like Florida even though their ambient temperatures are warmer most of the year they have pan evaporation rates only about half as high due to higher local humidity levels.

    http://large.stanford.edu/courses/2010/ph240/harting2/docs/NWS34EvapTables.pdf

    When I lived down in south east Colorado in the Arkansas river valley you also had that sort of high drainage soil that held little water. It was also the area where the dust bowl started, and just a short drive from where I lived in Rocky Ford you could find abandoned farms buried in 4-5 ft of wind blown dust from that era.
    Dust Bowl Map

  14. E.M.Smith says:

    @G.C.:

    Two things:

    Running Excel spreadsheets in Linux means you are faced with Microsoft trying to assure it works in Excel and breaks in Linux, so newest “style” of Excel spreadsheets most likely to break… Using older “style” where the Linux folks have had time to unscramble the things MS did will work better.

    Per v 2,3,4:

    Yes, that’s the intent. At my present rate of progress is will be months away, though, for most of it. Hopefully some bits sooner.

    Then v1 will be the hardest one as “everything changed” – country codes, instruments, methods… I hope to get those eggs unscrambled eventually as I suspect those data are the least “adjusted”.

  15. gallopingcamel says:

    @Chiefio,
    “Running Excel spreadsheets in Linux….”

    This may explain the problems I have found with tax software. Since 2008 I have been using TaxAct to prepare my tax return but this year it is close to useless. Given that I am a retired person the main problem for me relates to Worksheet 195 that calculates the amount of your SSA-1099 income that is taxable. TaxAct enters 85% of my SSA income on form 1040, line 5b. The correct figure for me this year is 45%. A similar error occurred last year so I have some taxes to reclaim.

    I bought a copy of TurboTax but it refused to install. A little research turned up this spreadsheet from Glenn Reeves:
    https://drive.google.com/uc?export=download&id=1ODCw3ZvqiM09iFxFHLfOjJMVSJpuk4wS

    This spreadsheet looks pretty good but it did not have Worksheet 195 and even though I checked the “Married Filing Jointly” it stubbornly refused to enter $26,600 on form 1040, line 8. There is supposed to be a “Manual Over-ride” feature but I could not get it to work. It seems likely that these problems could be avoided by running Windoze.

    Instead I chose to rebuild the Glenn Reeves spreadsheet with no “Protected Fields”. You can over-ride any of the calculations but don’t blame me if the IRS disagrees with your figures.

    In the past “Itemized Deductions” (Schedule “A”) was the biggest headache but I can’t even get close to the $26,600. Thus it makes sense to delete everything related to schedule “A”.

    The resulting tax spreadsheet is nimble……..think Gazelle versus Gallopingcamel.

  16. Bill in Oz says:

    To the point & for the weekend fun guys !
    SAID HANRAHAN
    by John O’Brien

    “We’ll all be rooned,” said Hanrahan,
    In accents most forlorn,
    Outside the church, ‘ere Mass began,
    One frosty Sunday morn.

    The congregation stood about,
    Coat-collars to the ears,
    And talked of stock, and crops, and drought,
    As it had done for years.

    “It’s looking crook,” said Daniel Croke;
    “Begad, it’s crook, me lad,
    For never since the banks went broke
    Has seasons been so bad.”

    “It’s dry, all right,” said young O’Neil,
    With which astute remark
    He squatted down upon his heel
    And chewed a piece of bark.

    And so around the chorus ran
    “It’s keepin’ dry, no doubt.”
    “We’ll all be rooned,” said Hanrahan,
    “Before the year is out.”

    “The crops are done; ye’ll have your work
    To save one bag of grain;
    From here way out to Back-o’-Bourke
    They’re singin’ out for rain.

    “They’re singin’ out for rain,” he said,
    “And all the tanks are dry.”
    The congregation scratched its head,
    And gazed around the sky.

    “There won’t be grass, in any case,
    Enough to feed an ass;
    There’s not a blade on Casey’s place
    As I came down to Mass.”

    “If rain don’t come this month,” said Dan,
    And cleared his throat to speak –
    “We’ll all be rooned,” said Hanrahan,
    “If rain don’t come this week.”

    A heavy silence seemed to steal
    On all at this remark;
    And each man squatted on his heel,
    And chewed a piece of bark.

    “We want an inch of rain, we do,”
    O’Neil observed at last;
    But Croke “maintained” we wanted two
    To put the danger past.

    “If we don’t get three inches, man,
    Or four to break this drought,
    We’ll all be rooned,” said Hanrahan,
    “Before the year is out.”

    In God’s good time down came the rain;
    And all the afternoon
    On iron roof and window-pane
    It drummed a homely tune.

    And through the night it pattered still,
    And lightsome, gladsome elves
    On dripping spout and window-sill
    Kept talking to themselves.

    It pelted, pelted all day long,
    A-singing at its work,
    Till every heart took up the song
    Way out to Back-o’-Bourke.

    And every creek a banker ran,
    And dams filled overtop;
    “We’ll all be rooned,” said Hanrahan,
    “If this rain doesn’t stop.”

    And stop it did, in God’s good time;
    And spring came in to fold
    A mantle o’er the hills sublime
    Of green and pink and gold.

    And days went by on dancing feet,
    With harvest-hopes immense,
    And laughing eyes beheld the wheat
    Nid-nodding o’er the fence.

    And, oh, the smiles on every face,
    As happy lad and lass
    Through grass knee-deep on Casey’s place
    Went riding down to Mass.

    While round the church in clothes genteel
    Discoursed the men of mark,
    And each man squatted on his heel,
    And chewed his piece of bark.

    “There’ll be bush-fires for sure, me man,
    There will, without a doubt;
    We’ll all be rooned,” said Hanrahan,
    “Before the year is out.”

    From “Around the Boree Log and Other Verses”.
    Published in 1921
    Old poetry tells us about our Aussie climate !

  17. Pingback: GHCN v3.3 vs v4 – Top Level Entry Point | Musings from the Chiefio

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