GHCN v3.3 vs v4 Anomaly South America

Earlier I looked at a subset of countries globally, comparing their “change of temperature from the average” (anomaly) for a given set of thermometers in a given country as it is found in the Global Historical Climate Network version 3.3 when compared to version 4. Now you might think that since this is “historical” it ought not change. And since it is based on “anomalies”, if you have a couple of different thermometers in the set (as the Warmers constantly insist) it would not cause a change. (After all, if it’s 1 C warmer in your front yard it’s also 1 C warmer in your back yard…) So you would expect that there ought to either be NO change, or any change will be due to the application of changed “adjustments” to the temperatures (that just happen to exactly match ALL global warming found…) But these are the “unadjusted” data sets, so ought not to have any of that.

Yet they are different. Often for what ought to be the SAME thermometer in the same time and place in history. (You can’t go back to 1850 and add a new thermometer in Cuba…)

I’ll be presenting two graphs for each country. One has black spots for the Anomaly in a given year for GHCN v3.3 in that country, and red spots for what is the same country, year, and anomaly process but from GHCN v4. Often they are different (almost always). Sometimes up to whole degrees C. Now if your thermometer selection and processing can change THE SAME PLACE AND TIME in history by 1 C, what are the odds that 1/2 C of “global warming” comes from just that sort of instrument change? I’d rank it at about 100%.

For reference, here’s the climate zones from the Wiki on South America

South America Koppen – Geiger Climate Zones

The Graphs

Since we saw Argentina and Brazil in the earlier posting, I’ll Start with Brazil, then add the countries near it to the north and away from the Andes. Then we’ll travel down the spine of the Andes ending with Chile and Argentina, then finally fill in Paraguay and Uruguay on the south side of Brazil up against Argentina. Ending with the two island clusters of the Falkland islands and South Georgia & Sandwich Islands.

Brazil

GHCN v3.3 vs v4 Brazil Difference

GHCN v3.3 vs v4 Brazil Anomaly

Cuba

Cuba is included in South America, but personally I’d have accounted for it in with all the other Caribbean islands.

GHCN v3.3 vs v4 Cuba Difference

Mostly the old data is “cooled” and the recent data given a bit of a “lift”. Looking at the raw anomalies below, it looks like Cuba has some cycles in it, and like it was “way hot” in the long ago past.

GHCN v3.3 vs v4 Cuba Anomaly

Venezuela

GHCN v3.3 vs v4 Venezuela Difference

The past cooled by 1/4 to 3/4 C in a nice general slope. Has the past of Venezuela really cooled?…

GHCN v3.3 vs v4 Venezuela Anomaly

French Guiana

GHCN v3.3 vs v4 French Guiana Difference

Looking at the anomalies down below, not much to work with. So we get that tail in the present being changed a lot higher. But hey, what’s a full degree C of “fixing it up” anomaly change when you need to get a global 1/2 C of “warming’ out of stable actual data… But really, what a “dogs breakfast” that is. A “dip” of 1/2 C in the “baseline period” and then an added almost a full C in the most recent common data? What can possibly justify that? Remember, this is supposed to be the same place same times.. and many of the same instruments if not all of them the same.

GHCN v3.3 vs v4 French Guiana Anomaly

Guyana

GHCN v3.3 vs v4 Guyana Difference

Oh wow. A full degree C of “dip” in the baseline near 1980, then a 2 c “FLYER” negative before 2000, then up to 1 C of “uplift” in the recent tail. Sheesh.

GHCN v3.3 vs v4 Guyana Anomaly

Suriname

GHCN v3.3 vs v4 Suriname Difference

1.5 C of “rise” added recently. Really? WT?… Nice 1/2 C “dip” in the tail of the baseline period.

GHCN v3.3 vs v4 Suriname Anomaly

Colombia

GHCN v3.3 vs v4 Colombia Difference

Then we hit Colombia and it’s just not happening. Looks rather flat and dull. Guess they were too busy with the cocaine trade to give a fig about the UN Climate Graft money… Or maybe CO2 got kinda high and forgot to warm things up… Well, someone got high…

GHCN v3.3 vs v4 Colombia Anomaly

Ecuador

GHCN v3.3 vs v4 Ecuador Difference

Half a degree down in the baseline, to 3/4 C down, then 1/2 C up recently in the “fixing”. Now that’s someone on board with the agenda!

GHCN v3.3 vs v4 Ecuador Anomaly

Peru

GHCN v3.3 vs v4 Peru Difference

Actual anomalies (below) not going anywhere… Short record. What to do, what to do… How about dip it 0.4 C in the baseline and lift the near end another 1/2 C?

GHCN v3.3 vs v4 Peru Anomaly

Bolivia

GHCN v3.3 vs v4 Bolivia Difference

Highly volatile (see below) and not much trend, then the “fix” being all over the place. What a mess. On this we bet the global economy?

GHCN v3.3 vs v4 Bolivia Anomaly

Chile

GHCN v3.3 vs v4 Chile Difference

Not much really happening below, so what’s our “Go To” thing? Dip the baseilne around the ’50s and bump up the present by 1/4 C to 1/2 C.

GHCN v3.3 vs v4 Chile Anomaly

Argentina

GHCN v3.3 vs v4 Argentina Difference

GHCN v3.3 vs v4 Argentina Anomaly

Paraguay

GHCN v3.3 vs v4 Paraguay Difference

Just WOW. Drop the WHOLE past by 1/4 C, then pop up 1/5 C to a full 1.5 C in the recent data. Just WOW.

GHCN v3.3 vs v4 Paraguay Anomaly

Uruguay

GHCN v3.3 vs v4 Uruguay Difference

Guess Uruguay is not all that interesting. Not a team players. Only gets about .4 C of dip at the very end of the “baseline period” and can’t get more than 1/4 C of “lift” in the recent data.

GHCN v3.3 vs v4 Uruguay Anomaly

Then we once again leave the mainland for two groups of Islands in the Southern Ocean.

The Falkland Islands

GHCN v3.3 vs v4 Falkland Islands Difference

Nothing really happening in the Falklands. (In more ways than one). Gee, think a stable station in the middle of the south Atlantic Gyre might mean not much is happening? (Someone will need to fix that in v5… I’d /sarc; it but I’m not sure that’s valid…)

GHCN v3.3 vs v4 Falkland Islands Anomaly

South Georgia and Sandwich Island

GHCN v3.3 vs v4 South Georgia & Sandwich Islands Difference

This one is interesting. Some “High Fliers” in years where the prior data set had no data. How’d they do that? Go back and put data in where none was reported? Overall, another flat island in the ocean. But with mystery fliers. Though they did manage to cool almost the entire history by about 1/3 C, so there’s that…

GHCN v3.3 vs v4 South Georgia & Sandwich Islands Anomaly

Tech Talk

This would basically be a repeat of the tech stuff in the prior posting, so take a look there for example code and the hows / whys / and designs.

https://chiefio.wordpress.com/2019/04/09/ghcn-v3-3-vs-v4-selected-country-anomaly-differences

What can be added here is the SQL that gets a list of South American countries from my database layout:

chiefio@PiM3Devuan2:~/SQL/bin$ cat Samerica.sql 
SELECT cnum, abrev,region, cname FROM country WHERE region=3 ORDER BY cname;

And the result:

MariaDB [temps]> source bin/Samerica.sql
+------+-------+--------+--------------------------------------------------------------+
| cnum | abrev | region | cname                                                        |
+------+-------+--------+--------------------------------------------------------------+
| 301  | AR    | 3      | Argentina                                                    |
| 302  | BL    | 3      | Bolivia                                                      |
| 303  | BR    | 3      | Brazil                                                       |
| 304  | CI    | 3      | Chile                                                        |
| 305  | CO    | 3      | Colombia                                                     |
| 406  | CU    | 3      | Cuba                                                         |
| 306  | EC    | 3      | Ecuador                                                      |
| 316  | FK    | 3      | Falkland Islands (Islas Malvinas) [United Kingdom]           |
| 315  | FG    | 3      | French Guiana [France]                                       |
| 307  | GY    | 3      | Guyana                                                       |
| 308  | PA    | 3      | Paraguay                                                     |
| 309  | PE    | 3      | Peru                                                         |
| 317  | SX    | 3      | South Georgia and the South Sandwich Islands [United Kingdom |
| 312  | NS    | 3      | Suriname                                                     |
| 313  | UY    | 3      | Uruguay                                                      |
| 314  | VE    | 3      | Venezuela                                                    |
+------+-------+--------+--------------------------------------------------------------+
16 rows in set (0.00 sec)

In Conclusion

I’ve scattered some detail comments through the graphs and as I get time to stare at them a bit, if I see something else I’ll add it in comments. You may well see something I’ve not seen, so stare at ’em and ponder…

In general, I’ve noticed some places hardly change at all. Often very minor places like an island somewhere. Larger places look more “manicured” with loss of low going excursions in the data lately. Then there’s the general tendency to cool the past, and put “dips” in the “baseline” period used by GISStemp and Hadley (1950 to 1990). Is it really the case that all those places had just those same needs to cool the past, dip the baseline and juice up the recent highs while clipping recent lows? What physicality could possible account for that? What systematic failure of thermometer tech Globally can account for those “errors”?

To me it looks like deliberately cooking the books.

Update: Adding Antarctica

I “moved on” to Antarctica, and as it is only two graphs, I’m putting them here as an update.

Remember that this data comes from professionally staffed scientific stations.

GHCN v3.3 vs v4 Antarctica Difference

So there are 2 C of range in the changes. There are only 2 choices. Using anomaly processing does not prevent thermometer change from dramatically shifting the results when the thermometers change, or, “What on earth justifies 2 C of diddle?”. Simple as that, IMHO.

Then the basic anomaly A/B graph looks like the range is compressing, but not warming. So WUWT?

GHCN v3.3 vs v4 Antarctica Anomaly

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