White, Green, or Brown? Decisions Decisions
Andes? What Andes?
Looking at South America by altitude is complicated by all that Brazilian low land. You get a better view when you look “Country by Country”. And there are some strange things going on in those countries with The Andes running through them. There is a general movement out of the mountains and toward the beach. But is there anything particularly surprising?
Yes.
Bolivia. It is that big block of elevated ‘whitish’ toward the top of the image. A very high elevation country, with no beach. What to do, what to do… How about “Nuke Bolivia?”
Nuke Bolivia?
Weather Underground can still find Bolivia, wonder why GHCN can’t?
http://www.wunderground.com/global/stations/85201.html
Huayna Bolivia - Can't Have That!
The GHCN “By Altitude” report for Bolivia, Country Code 302:
[chiefio@tubularbells Alts]$ cat Therm.by.Alt302.Dec.ALT
Year -MSL 20 50 100 200 300 400 500 1000 2000 Space
DAltPct: 1919 0.0 0.0 0.0 0.0 0.0 0.0 0.0 75.0 0.0 25.0 0.0
DAltPct: 1929 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 0.0 50.0 0.0
DAltPct: 1939 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9.1 0.0 90.9 0.0
DAltPct: 1949 0.0 0.0 0.0 0.0 0.0 0.0 27.6 0.0 0.0 72.4 0.0
DAltPct: 1959 0.0 0.0 0.0 18.5 23.9 0.0 20.8 10.0 0.0 26.6 0.0
DAltPct: 1969 0.0 0.0 0.0 20.5 21.8 0.0 16.2 13.0 2.6 26.0 0.0
DAltPct: 1979 0.0 0.0 0.0 23.1 15.7 0.0 14.0 12.5 6.2 28.4 0.0
DAltPct: 1989 0.0 0.0 0.0 23.3 17.3 0.0 16.4 12.4 3.6 27.0 0.0
DAltPct: 1990 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0100.0 0.0
For COUNTRY CODE: 302
[chiefio@tubularbells Alts]$
Notice that while one thermometer manages to straggle into 1990, it gets shot that year (or the “decade ending” would have had a later year – by default I end the decade counts in years ending in “9” so 0-9 end up in one decade average together; unless you run out of records… )
Guess the easiest thing to do was just Nuke Bolivia. Hey, it’s high and cold… and doesn’t have a single hot tropical beach in the whole place. Heck, I’d bet their airport doesn’t even get much traffic… We’ll keep it in the baseline period though (but GIStemp will fill in the “anomaly map” with thermometers stretched from 1000 km away that are used to fill in the Grids and Boxes from 1200 km away. So we can have Bolivia on the “anomaly map” even if we don’t have any thermometers there…
(Yes, it IS on the anomaly maps from GISS). but with a pattern that looks remarkably just like whatever is happening in the nearby country.
Notice that this chart has asymmetrical altitude bands. The gradations are smaller at the low altitudes than they are at the top. In the middle, I go by 100 m, then a 500 m jump, then 1000 m, then everything above 2000 m.
Even Brazil, Who Knew?
Look at ./Alts/Therm.by.Alt303.Dec.ALT (Y/N)? y
Year -MSL 20 50 100 200 300 400 500 1000 2000 Space
DAltPct: 1879 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
DAltPct: 1889 47.6 0.0 0.0 0.0 0.0 0.0 0.0 52.4 0.0 0.0 0.0
DAltPct: 1899 28.3 0.0 0.0 0.0 15.1 0.0 0.0 56.6 0.0 0.0 0.0
DAltPct: 1909 22.7 0.0 0.0 10.2 22.7 0.0 0.0 44.3 0.0 0.0 0.0
DAltPct: 1919 16.5 12.4 14.9 8.3 16.5 0.0 0.0 31.4 0.0 0.0 0.0
DAltPct: 1929 15.4 15.4 15.4 7.7 15.4 0.0 0.0 30.8 0.0 0.0 0.0
DAltPct: 1939 11.0 12.2 23.2 17.1 12.2 0.0 0.0 24.4 0.0 0.0 0.0
DAltPct: 1949 17.5 12.9 21.7 13.3 9.2 0.0 0.0 25.4 0.0 0.0 0.0
DAltPct: 1959 22.1 12.2 24.1 10.2 9.9 0.0 0.0 21.6 0.0 0.0 0.0
DAltPct: 1969 26.0 10.2 19.2 11.2 8.4 1.3 1.3 20.0 2.5 0.0 0.0
DAltPct: 1979 26.4 6.5 17.3 14.2 7.9 2.0 1.6 19.4 4.7 0.0 0.0
DAltPct: 1989 22.3 5.3 16.7 19.9 6.5 2.9 1.6 20.1 4.6 0.0 0.0
DAltPct: 1999 17.5 6.7 17.5 21.4 5.2 4.0 2.4 20.2 5.0 0.0 0.0
DAltPct: 2009 15.7 7.7 20.3 20.6 5.1 4.9 2.6 18.3 4.9 0.0 0.0
For COUNTRY CODE: 303
So here I was, thinking I was all done, and I decided, just for the heck of it, to look at Brazil “by altitude” even though it already had a heck of a lot of low elevation jungle.
And I found the same pattern you will see below. After the initial spread of thermometers in the early years (and through the GIStemp baseline period) we have the erosion of the “up to 1000 meters” band and the growth in the lower elevations of 100 and 200 meters.
64% below 200 meters. Just look at the 1000 meter band melt and even the 300 meter band being eroded. But we’ve added something over 1000m, surely that’s a sign of good faith coverage?
[chiefio@tubularbells analysis]$ cat Temps/303.stns2009 30382024000 BOA VISTA 2.82 -60.65 90 150S 43FLxxno-9A 1WARM GRASS/SHRUBC 22 30382106000 SAO GABRIEL D -0.13 -67.08 90 65R -9HIFOno-9A-9EQ. EVERGREEN B 0 30382191000 BELEM -1.45 -48.47 10 19U 758FLxxCO 1x-9WARM FIELD WOODSC 42 30382280000 SAO LUIZ -2.53 -44.30 51 40U 182FLxxCO10A 3WATER C 42 30382331000 MANAUS -3.13 -60.02 72 50U 613FLxxno-9x-9TROP. SEASONAL C 48 30382397000 FORTALEZA -3.77 -38.60 26 0U 648FLxxCO 1x-9WARM CROPS C 54 30382410000 BENJAMIN CONS -4.38 -70.03 65 90R -9FLFOno-9x-9EQ. EVERGREEN B 0 30382425000 COARI -4.92 -63.08 46 30R -9FLFOLA-9x-9TROP. SEASONAL A 0 30382571000 BARRA DO CORD -5.50 -45.27 153 113R -9HIxxno-9x-9TROP. SEASONAL B 0 30382578000 TERESINA -5.08 -42.82 74 112U 339FLxxno-9A 1WARM CROPS C 52 30382586000 QUIXERAMOBIM -5.20 -39.30 212 207R -9HIxxno-9x-9TROPICAL DRY FORB 9 30382678000 FLORIANO -6.77 -43.02 127 154S 36FLxxno-9x-9WARM GRASS/SHRUBC 15 30382704000 CRUZEIRO DO S -7.63 -72.67 170 180R -9FLFOno-9x-9EQ. EVERGREEN C 9 30382765000 CAROLINA -7.33 -47.47 193 174R -9HIFOno-9x-9TROP. SEASONAL B 0 30382861000 CONCEICAO DO -8.25 -49.28 157 157R -9HIxxno-9A-9WARM GRASS/SHRUBC 12 30382900000 RECIFE -8.05 -34.92 7 18U 1184FLxxCO 3A 1WATER C 100 30382915000 RIO BRANCO -9.97 -67.80 160 150U 87HIxxno-9A 1TROP. SEASONAL C 31 30382983000 PETROLINA -9.38 -40.48 370 372U 73HIxxno-9A 1TROPICAL DRY FORC 49 30383064000 PORTO NACIONA -10.52 -48.72 239 246S 19HIxxno-9A 1WARM GRASS/SHRUBA 0 30383096000 ARACAJU -10.92 -37.05 5 29U 288FLxxCO 2x-9WARM FOR./FIELD C 60 30383229000 SALVADOR -13.02 -38.52 51 3U 1496HIxxCO 2x-9WATER C 41 30383288000 BOM JESUS DA -13.27 -43.42 440 436S 10FLxxno-9A 1SUCCULENT THORNSA 13 30383361000 CUIABA -15.55 -56.12 151 170U 167HIxxno-9x-9MARSH, SWAMP C 20 30383377000 BRASILIA (AER -15.87 -47.93 1061 1128U 411HIxxno-9x-9WARM CROPS C 30 30383423000 GOIANIA -16.67 -49.25 741 765U 703HIxxno-9x-9WARM GRASS/SHRUBC 50 30383437000 MONTES CLAROS -16.72 -43.87 646 849U 152HIxxno-9x-9WARM GRASS/SHRUBC 37 30383498000 CARAVELAS -17.73 -39.25 3 7R -9FLxxCO10A-9WARM FOR./FIELD C 10 30383552000 CORUMBA -19.08 -57.50 130 171U 66FLxxno-9x-9TROP. SAVANNA A 0 30383587000 BELO HORIZONT -19.93 -43.93 850 875U 2542HIxxno-9A 1WARM CROPS C 79 30383618000 TRES LAGOAS -20.78 -51.70 313 353S 45HIxxLA-9x-9WARM FIELD WOODSC 28 30383702000 PONTA PORA -22.53 -55.73 650 629S 20HIxxno-9A 2TROP. SEASONAL C 32 30383766000 LONDRINA (AER -23.33 -51.13 569 504U 258HIxxno-9x-9WARM FIELD WOODSC 23 30383781000 SAO PAULO -23.50 -46.62 792 883U 7034HIxxno-9x-9TROPICAL DRY FORC 87 30383842000 CURITIBA -25.43 -49.27 924 961U 844HIxxno-9x-9WARM CROPS C 73 30383897000 FLORIANOPOLIS -27.58 -48.57 2 10U 154HIxxCO 5x-9WATER C 62 30383967000 PORTO ALEGRE -30.00 -51.18 3 30U 1109FLxxCO 5x-9WARM GRASS/SHRUBC 75 30383997000 ST.VITORIA DO -33.52 -53.35 24 0R -9FLxxCO18A-9WATER B 24 [chiefio@tubularbells analysis]$
30383377000 BRASILIA (AER -15.87 -47.93 1061 1128U 411HIxxno-9x-9WARM CROPS C 30
Brasilia: inland, on the edge of the Amazon, and near the equator…
And when we look at the nature of the sites more southernly than the 20 S mark, we find them in the warm band near the coastal areas.
30383618000 TRES LAGOAS -20.78 -51.70 313 353S 45HIxxLA-9x-9WARM FIELD WOODSC 28
30383702000 PONTA PORA -22.53 -55.73 650 629S 20HIxxno-9A 2TROP. SEASONAL C 32
30383766000 LONDRINA (AER -23.33 -51.13 569 504U 258HIxxno-9x-9WARM FIELD WOODSC 23
30383781000 SAO PAULO -23.50 -46.62 792 883U 7034HIxxno-9x-9TROPICAL DRY FORC 87
30383842000 CURITIBA -25.43 -49.27 924 961U 844HIxxno-9x-9WARM CROPS C 73
30383897000 FLORIANOPOLIS -27.58 -48.57 2 10U 154HIxxCO 5x-9WATER C 62
30383967000 PORTO ALEGRE -30.00 -51.18 3 30U 1109FLxxCO 5x-9WARM GRASS/SHRUBC 75
30383997000 ST.VITORIA DO -33.52 -53.35 24 0R -9FLxxCO18A-9WATER B 24
Nothing at all like the snowy and cold parts down there:
Planalto Serrano. Neve Santa Catarina
Fascinating things, these by latitude and by altitude studies…
Argentina – Life on the Pampas
Argentine Pampas, Nice
[chiefio@tubularbells Alts]$ cat Therm.by.Alt301.Dec.ALT
Year -MSL 20 50 100 200 300 400 500 1000 2000 Space
DAltPct: 1859 0.0100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
DAltPct: 1869 0.0 50.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
DAltPct: 1879 0.0 43.3 33.3 0.0 0.0 0.0 23.3 0.0 0.0 0.0 0.0
DAltPct: 1889 0.0 58.8 11.8 0.0 0.0 0.0 29.4 0.0 0.0 0.0 0.0
DAltPct: 1899 0.0 58.8 11.8 0.0 0.0 0.0 29.4 0.0 0.0 0.0 0.0
DAltPct: 1909 8.2 26.4 16.4 16.4 6.4 0.0 14.5 3.6 8.2 0.0 0.0
DAltPct: 1919 7.2 21.6 14.4 14.4 7.2 0.0 14.4 7.2 7.2 6.5 0.0
DAltPct: 1929 7.7 23.1 7.7 15.4 7.7 0.0 15.4 7.7 7.7 7.7 0.0
DAltPct: 1939 9.0 19.4 14.5 15.8 10.5 1.7 8.0 15.6 3.6 1.9 0.0
DAltPct: 1949 8.5 19.2 14.4 16.0 9.6 1.6 6.6 16.3 3.2 4.5 0.0
DAltPct: 1959 7.8 19.6 14.3 16.1 7.9 3.1 8.1 15.8 3.3 3.9 0.0
DAltPct: 1969 9.1 20.0 17.5 10.9 7.7 2.6 9.0 16.7 2.7 3.6 0.0
DAltPct: 1979 8.6 15.1 21.9 13.6 5.3 3.7 10.4 16.2 2.6 2.7 0.0
DAltPct: 1989 9.1 16.2 21.5 13.0 5.2 4.9 9.9 15.4 2.6 2.3 0.0
DAltPct: 1999 9.9 17.1 21.0 11.5 5.1 4.7 11.2 15.0 3.1 1.4 0.0
DAltPct: 2009 10.4 18.1 21.5 10.9 2.0 4.6 12.2 15.3 3.5 1.5 0.0
For COUNTRY CODE: 301
[chiefio@tubularbells Alts]$
The “above” 1000 m is melting away, and the 500 – 1000m is gaining. Leave the mountains, head for the plains. But the “under 100 m” is gaining nicely too. Now adding up to exactly 1/2 the country. Why would you want to be in the mountains if you could be in the nice warm air of Buenos Aires with a beach view?
Look at ./Lats/Therm.by.lat301.Dec.LAT (Y/N)? y
Year SP -65 -60 -55 -50 -45 -40 -35 -30 -25 -NP
DecPct: 1859 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0
DecPct: 1869 0.0 0.0 0.0 0.0 0.0 0.0 50.0 50.0 0.0 0.0
DecPct: 1879 0.0 0.0 0.0 0.0 0.0 0.0 33.3 56.7 10.0 0.0
DecPct: 1889 0.0 0.0 0.0 0.0 0.0 0.0 11.8 58.8 29.4 0.0
DecPct: 1899 0.0 0.0 0.0 0.0 0.0 0.0 11.8 58.8 29.4 0.0
DecPct: 1909 0.0 0.0 0.0 23.6 6.4 16.4 9.1 21.8 14.5 8.2
DecPct: 1919 0.0 0.0 0.0 21.6 7.2 14.4 7.2 21.6 14.4 13.7
DecPct: 1929 0.0 0.0 0.0 15.4 7.7 15.4 7.7 23.1 15.4 15.4
DecPct: 1939 0.0 0.0 0.0 7.2 5.3 7.8 22.1 30.3 20.0 7.2
DecPct: 1949 0.0 0.6 0.0 6.2 4.8 6.6 22.4 33.7 19.2 6.4
DecPct: 1959 0.0 0.9 0.0 7.0 6.5 6.2 20.1 32.9 18.0 8.5
DecPct: 1969 0.0 3.5 0.0 7.5 5.6 10.8 13.5 33.4 17.5 8.2
DecPct: 1979 0.0 1.9 0.0 7.0 5.4 10.6 14.6 32.4 19.7 8.4
DecPct: 1989 0.0 1.6 0.0 7.3 6.4 10.7 15.7 31.5 18.4 8.2
DecPct: 1999 0.0 1.8 0.0 6.8 6.4 11.5 15.4 31.3 19.0 7.9
DecPct: 2009 0.0 1.8 0.0 4.6 6.8 11.7 15.7 32.8 19.7 6.9
For COUNTRY CODE: 301
Look at ./Lats/Therm.by.lat301.per.LAT (Y/N)?
Notice that 68% of the thermometers are from above 40 S and bellow 25 S latitude; with 48.5% from between 40S and 30S or about the latitude of Buenos Aires. Who needs Patagonia, anyway. Nothing down there but cold and glaciers…
Chilly in Chile? Head to the beach!
Mountain or Valley? Decisions Decisions
A more interesting and variable chart for Chile. It starts out at altitude with one thermometer between 400 and 500 m elevation, then jumps to “half near the beach” at 50 m in 1899. Then there is a slow spread to the beach and a little upland from it, while the higher elevation percentage just melts away… But then a sudden jump in the 500 and 1000 m bands as the 1990’s Great Dying of Thermometers hits (and I would speculate, left a couple behind in the mountains while heavily pruning the lower altitudes).
[chiefio@tubularbells Alts]$ cat Therm.by.Alt304.Dec.ALT
Year -MSL 20 50 100 200 300 400 500 1000 2000 Space
DAltPct: 1869 0.0 0.0 0.0 0.0 0.0 0.0100.0 0.0 0.0 0.0 0.0
DAltPct: 1879 0.0 0.0 0.0 0.0 0.0 0.0100.0 0.0 0.0 0.0 0.0
DAltPct: 1889 0.0 16.7 0.0 0.0 0.0 0.0 83.3 0.0 0.0 0.0 0.0
DAltPct: 1899 0.0 52.4 0.0 0.0 0.0 0.0 47.6 0.0 0.0 0.0 0.0
DAltPct: 1909 19.8 61.4 8.9 0.0 0.0 0.0 9.9 0.0 0.0 0.0 0.0
DAltPct: 1919 16.8 58.0 8.4 8.4 0.0 0.0 8.4 0.0 0.0 0.0 0.0
DAltPct: 1929 15.3 59.3 8.5 8.5 0.0 0.0 8.5 0.0 0.0 0.0 0.0
DAltPct: 1939 8.1 56.9 18.7 8.1 0.0 0.0 8.1 0.0 0.0 0.0 0.0
DAltPct: 1949 15.9 37.9 24.7 15.9 0.0 0.0 5.5 0.0 0.0 0.0 0.0
DAltPct: 1959 17.0 30.9 24.2 17.0 6.4 0.0 3.8 0.8 0.0 0.0 0.0
DAltPct: 1969 22.9 20.9 22.9 22.1 4.8 0.0 2.5 4.0 0.0 0.0 0.0
DAltPct: 1979 28.0 23.8 18.5 17.7 4.1 0.0 2.0 5.9 0.0 0.0 0.0
DAltPct: 1989 24.2 25.5 23.9 21.3 1.3 0.0 2.6 1.3 0.0 0.0 0.0
DAltPct: 1999 27.9 13.1 21.8 16.6 7.9 0.0 4.8 7.9 0.0 0.0 0.0
DAltPct: 2009 18.8 13.0 22.1 19.5 7.1 0.0 6.5 13.0 0.0 0.0 0.0
For COUNTRY CODE: 304
[chiefio@tubularbells Alts]$
Of interest in the detail is this: In 2009, even that was not good enough…
ALT pct: 2009 15.4 15.4 23.1 23.1 0.0 0.0 7.7 15.4 0.0 0.0 0.0
Those 200-300 m sites just had to go… Now 30.8% of Chile is below 50 m and another 46.2% is between 50 m and 200 m. That is, 77% of the country is below 200 m in elevation. Last time I looked, there was this large spine of the Andes in Chile. Guess someone thought it would be better ‘spineless’…
Look at ./Lats/Therm.by.lat304.Dec.LAT (Y/N)? y
Year SP -65 -60 -55 -50 -45 -40 -35 -30 -25 -NP
DecPct: 1869 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0
DecPct: 1879 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0
DecPct: 1889 0.0 0.0 0.0 16.7 0.0 0.0 0.0 83.3 0.0 0.0
DecPct: 1899 0.0 0.0 0.0 47.6 0.0 0.0 4.8 47.6 0.0 0.0
DecPct: 1909 0.0 0.0 0.0 27.7 0.0 0.0 9.9 33.7 8.9 19.8
DecPct: 1919 0.0 0.0 0.0 25.2 0.0 8.4 8.4 32.8 8.4 16.8
DecPct: 1929 0.0 0.0 0.0 25.4 0.0 8.5 8.5 33.9 8.5 15.3
DecPct: 1939 0.0 0.0 0.0 20.3 0.0 8.1 8.1 32.5 8.1 22.8
DecPct: 1949 0.0 0.0 0.0 15.4 0.0 5.5 15.4 22.5 28.6 12.6
DecPct: 1959 0.0 0.0 0.0 7.9 3.4 3.8 17.0 27.9 23.0 17.0
DecPct: 1969 0.0 2.5 0.0 5.8 6.3 9.8 16.3 22.4 21.9 15.1
DecPct: 1979 0.0 8.1 0.0 7.9 6.1 6.7 13.2 22.8 19.9 15.2
DecPct: 1989 0.0 12.3 0.0 11.0 1.6 8.1 7.7 21.3 20.3 17.7
DecPct: 1999 0.0 10.9 0.0 6.6 7.0 10.0 14.0 24.0 16.2 11.4
DecPct: 2009 0.0 9.1 0.0 6.5 6.5 9.1 16.2 22.7 16.9 13.0
For COUNTRY CODE: 304
Look at ./Lats/Therm.by.lat304.per.LAT (Y/N)?
While we start with the typical one thermometer that spreads out, we have a more complex picture recently. It looks like the southern tip was “discovered” thermally in the 1970s while the northern desert wanders around in the 30-something% total range, but sometimes above 25 S and sometimes below. The interesting feature to me is the way the ‘below 50 S’ band shrinks while the ‘below 35 S’ gains. We are once again seeing a run away from the Patagonia latitude and toward Santiago latitudes. I am left wondering, though, if those northern deserts were prone to cold nights…
From the wiki: http://en.wikipedia.org/wiki/Andes
Border between Argentina and Chile
• Cerro Bayo, 5,401 m (17,720 ft)
• Cerro Chaltén, 3,375 m (11,073 ft) or 3,405 m,
Patagonia, also known as Cerro Fitz Roy
• Cerro Escorial, 5,447 m (17,871 ft)
• Cordón del Azufre, 5,463 m (17,923 ft)
• Falso Azufre, 5,890 m (19,324 ft)
• Incahuasi, 6,620 m (21,719 ft)
• Lastarria, 5,697 m (18,691 ft)
• Llullaillaco, 6,739 m (22,110 ft)
• Maipo, 5,264 m (17,270 ft)
• Marmolejo, 6,110 m (20,046 ft)
• Ojos del Salado, 6,893 m (22,615 ft)
• Olca, 5,407 m (17,740 ft)
• Sierra Nevada de Lagunas Bravas, 6,127 m (20,102 ft)
• Socompa, 6,051 m (19,852 ft)
• Nevado Tres Cruces, 6,749 m (south summit) (III Region)
• Tronador, 3,491 m (11,453 ft)
• Tupungato, 6,570 m (21,555 ft)
• Nacimiento, 6,492 m (21,299 ft)
I guess with all that real estate above 3000 – 6000 m they needed to have at least a couple of thermometers below 1000m but above 400.. otherwise it might start to look like it was an unrepresentative sample of the country…
Peru too
Alpamayo Snow - Gotta Go.
[chiefio@tubularbells Alts]$ cat Therm.by.Alt309.Dec.ALT
Year -MSL 20 50 100 200 300 400 500 1000 2000 Space
DAltPct: 1939 0.0 42.9 0.0 42.9 0.0 0.0 0.0 0.0 0.0 14.3 0.0
DAltPct: 1949 2.1 38.3 4.3 31.9 0.0 0.0 4.3 0.0 0.0 19.1 0.0
DAltPct: 1959 6.2 17.2 13.8 23.8 9.0 3.1 6.9 9.7 0.0 10.3 0.0
DAltPct: 1969 9.3 9.5 12.0 17.1 10.5 4.0 2.8 10.8 2.2 21.8 0.0
DAltPct: 1979 8.2 12.2 10.9 14.2 9.7 4.1 2.7 10.8 2.7 24.5 0.0
DAltPct: 1989 8.2 16.1 12.2 12.7 8.4 4.2 3.9 8.7 2.5 23.1 0.0
DAltPct: 1999 8.6 16.8 11.8 12.5 8.6 4.3 4.3 4.3 3.9 25.0 0.0
DAltPct: 2009 14.3 22.9 8.6 17.1 7.9 1.4 6.4 1.4 1.4 18.6 0.0
For COUNTRY CODE: 309
[chiefio@tubularbells Alts]$
So here we have a staggering 70.8% of Peru below 300 m elevation. I can hardly say “Peruvian” without the word “Andes” queued up and ready to follow. Yet from a thermometers point of view, the country is mostly lowlands.
So is anything happening in that “above 2000m to Space” band?
Look at ./Alts/Therm.by.Alt309.Dec.ALT (Y/N)? y
Year -MSL 50 200 500 1000 2000 2500 3000 3500 4000 Space
DAltPct: 1939 42.9 42.9 0.0 0.0 0.0 0.0 0.0 14.3 0.0 0.0 0.0
DAltPct: 1949 40.4 36.2 4.3 0.0 0.0 0.0 0.0 19.1 0.0 0.0 0.0
DAltPct: 1959 23.4 37.6 19.0 9.7 0.0 0.7 2.1 7.6 0.0 0.0 0.0
DAltPct: 1969 18.8 29.1 17.3 10.8 2.2 4.0 6.5 7.9 3.5 0.0 0.0
DAltPct: 1979 20.4 25.1 16.5 10.8 2.7 4.1 10.5 5.8 4.1 0.0 0.0
DAltPct: 1989 24.3 25.0 16.4 8.7 2.5 4.2 10.7 4.0 4.2 0.0 0.0
DAltPct: 1999 25.3 24.3 17.1 4.3 3.9 3.9 12.5 4.3 4.3 0.0 0.0
DAltPct: 2009 37.1 25.7 15.7 1.4 1.4 1.4 3.6 7.1 6.4 0.0 0.0
For COUNTRY CODE: 309
Look at ./Alts/Therm.by.Alt309.per.ALT (Y/N)?
Even here we see a “drift lower”. The “up to 3000 m” bands being slowly drained. There is a small uplift in percentages in the 3000-4000m range, but I’m sure that is “survivor bias” due to the generally Great Dying of Thermometers and will be “corrected” soon enough…
Though looking into the detail of the last year:
ALT pct: 2009 41.7 25.0 16.7 0.0 0.0 0.0 0.0 8.3 8.3 0.0 0.0
Shows that they ‘drained the swamp’ of the middle altitudes first. We now have 83.4% below 500 meters. That the two percentages at altitude match exactly leads me to suspect a single thermometer in each altitude. Easy enough to drop one each year in a couple of years. Nobody would notice a single thermometer being dropped… And when we look, what do we find?
[chiefio@tubularbells analysis]$ cat Temps/309.stns2009 30984370000 TUMBES -3.55 -80.40 27 35S 48FLxxCO 5A 5WARM GRASS/SHRUBA 0 30984377000 IQUITOS -3.75 -73.25 126 90R -9FLFOno-9x-9EQ. EVERGREEN A 19 30984401000 PIURA -5.18 -80.60 55 67U 186HIxxno-9A 1HOT DESERT C 14 30984452000 CHICLAYO -6.78 -79.83 34 43U 280FLxxCO15A 1WARM IRRIGATED C 27 30984455000 TARAPOTO -6.45 -76.38 282 995R -9HIFOno-9A-9EQ. EVERGREEN B 0 30984501000 TRUJILLO -8.10 -79.03 30 231U 355MVxxCO 6x-9WATER C 63 30984515000 PUCALLPA -8.42 -74.60 149 180U 92HIxxLA-9A 3EQ. EVERGREEN B 0 30984628000 LIMA-CALLAO/A -12.00 -77.12 13 20U 376MVxxCO 2A 1WATER C 52 30984686000 CUZCO -13.55 -71.98 3249 3693U 181MVxxno-9x-9TUNDRA B 18 30984691000 PISCO -13.75 -76.28 7 5U 53FLxxCO 1A 1WATER B 0 30984735000 JULIACA -15.48 -70.15 3827 3833U 78MVxxno-9A 1COOL CROPS C 26 30984782000 TACNA -18.07 -70.30 469 385U 93MVxxCO30A 2HOT DESERT B 16 [chiefio@tubularbells analysis]$
Two stations with an altitude in the 3xxx range. Cuzco and Juliaca.
To represent all the parts of Peru that are not at low elevation…
Ecuador?
So on the equator is already hot enough, right?
[chiefio@tubularbells Alts]$ more Therm.by.Alt306.Dec.ALT
Year -MSL 20 50 100 200 300 400 500 1000 2000 Space
DAltPct: 1898 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0100.0 0.0
DAltPct: 1909 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0100.0 0.0
DAltPct: 1919 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0100.0 0.0
DAltPct: 1929 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0100.0 0.0
DAltPct: 1939 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0100.0 0.0
DAltPct: 1949 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0100.0 0.0
DAltPct: 1959 64.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 35.7 0.0
DAltPct: 1969 34.8 8.7 4.3 0.0 0.0 0.0 0.0 0.0 16.9 35.3 0.0
DAltPct: 1979 31.8 6.3 5.9 0.0 4.3 0.0 0.0 3.1 12.2 36.5 0.0
DAltPct: 1989 24.8 7.8 7.8 0.0 7.8 0.0 0.0 14.2 2.8 34.8 0.0
DAltPct: 2009 31.8 4.5 20.5 0.0 4.5 0.0 0.0 6.8 0.0 31.8 0.0
For COUNTRY CODE: 306
[chiefio@tubularbells Alts]$
Same story. The erosion starts mid mountains, then undermines the peaks and it all ends up headed for the beach. 56.8% of Ecuador thermometers are below 100 m in elevation. Guess when you ARE the equator, it is hard to get closer to it, and the only choice is to head down slope.
But again, even the decade averages don’t capture the whole story:
ALT pct: 2009 33.3 0.0 33.3 0.0 0.0 0.0 0.0 0.0 0.0 33.3 0.0
Gee, now 66.6% are below 100 m. That all three are identical causes me to expect 3 surviving stations. What do we find?
[chiefio@tubularbells analysis]$ more Temps/306.stns2009 30684008000 SAN CRISTOBAL -0.90 -89.60 6 27R -9HIxxCO 1A-9WATER B 11 30684140000 PICHILINGUE -1.10 -79.47 73 123R -9HIxxno-9x-9WARM CROPS A 0 30684270000 LOJA/LA ARGEL -4.03 -79.20 2040 2632S 48MVxxno-9x-9WARM GRASS/SHRUBC 12 [chiefio@tubularbells analysis]$
30684270000 LOJA/LA ARGEL -4.03 -79.20 2040 2632S 48MVxxno-9x-9WARM GRASS/SHRUBC 12
Who needs Quito? It’s only the capital…
I’m sure that Loja (described as “WARM GRASS/SHRUB”) can somehow be made to stand in for the colder parts of the mountains of Ecuador:
Mountain or Desert? Decisions Decisions
Columbia?
Yes, them too. You know, when a pattern hits 100% over 1/2 dozen countries, it is not an accident. This is not an accidental statistical artifact of thermometer change in Latin America. This, IMHO, must be a deliberate removal of mountains from the record so that warmer lowlands and coastal areas dominate. I can think of no other way for the pattern to arise where the mountains are removed, but the tropical beaches kept. Every Time.
Look at ./Alts/Therm.by.Alt305.Dec.ALT (Y/N)? y
Year -MSL 20 50 100 200 300 400 500 1000 2000 Space
DAltPct: 1949 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 50.0 0.0
DAltPct: 1959 7.7 0.0 0.0 0.0 0.0 0.0 0.0 7.7 75.2 9.4 0.0
DAltPct: 1969 13.6 0.7 0.0 0.7 0.7 7.5 0.7 6.8 55.8 13.6 0.0
DAltPct: 1979 20.2 4.0 3.6 7.3 3.2 10.5 7.3 4.0 27.0 12.9 0.0
DAltPct: 1989 25.5 5.5 5.5 11.1 2.6 6.8 10.6 5.5 11.9 14.9 0.0
DAltPct: 1999 22.1 7.0 7.0 14.0 0.0 7.0 14.0 7.0 7.6 14.5 0.0
DAltPct: 2009 22.2 7.4 7.4 11.1 0.0 7.4 14.8 7.4 7.4 14.8 0.0
For COUNTRY CODE: 305
We have 70.3% below 500 m and 29.6% below 50 m and 22.2% of them at below 20 meters and down to the beach.
What stations survive into 2009?
[chiefio@tubularbells analysis]$ more Temps/305.stns2009
30580001000 SAN ANDRES (I 12.58 -81.72 6 0R -9FLxxCO 1A-9WATER C 23
30580009000 SANTA MARTA/S 11.13 -74.23 14 95U 102MVxxCO 1A 7WATER C 19
30580022000 CARTAGENA/RAF 10.45 -75.52 12 2U 293FLxxCO 1A 1WATER C 56
30580028000 BARRANQUILLA/SOLEDAD 10.90 -74.80 21 107U 661FLxxCO20A 3MARSH, SWAMP C 28
30580091000 BARRANCABERMEJA 7.00 -73.80 134 115U 661FLxxno-9A 5TROP. MONTANE C 9
30580094000 BUCARAMANGA/ 7.10 -73.20 1189 773U 292MVxxno-9A 5TROP. MONTANE C 15
30580097000 CUCUTA/CAMILO DAZA 7.90 -72.60 309 686U 220MVxxno-9A 2TROP. MONTANE B 11
30580222000 BOGOTA/ELDORA 4.72 -74.15 2548 2554U 2696MVxxno-9A 2WARM CROPS C 30
30580234000 VILLAVICENCIO 4.17 -73.62 431 1305U 83MVxxno-9A 3TROP. MONTANE C 20
30580259000 CALI/CALIPUERTO 3.40 -76.40 964 1061U 898MVxxno-9A 1EQ. EVERGREEN C 9
30580315000 NEIVA 3.00 -75.30 443 753U 105MVxxno-9A 1TROP. SAVANNA C 10
30580370000 IPIALES 0.80 -77.70 2960 2985S 31MVxxno-9A 5TROP. MONTANE C 9
30580398000 LETICIA/VASQU -4.17 -69.95 84 74R -9FLFOno-9A-9EQ. EVERGREEN B 0
[chiefio@tubularbells analysis]$
Notice the descriptions of “TROP.” Tropical and “EQ.” Equatorial along with warm marshes and water. Not a lot of room for snow …
Mountain or Swamp? Decisions Decisions
Sierra Nevada de Santa Marta – Colombia Not exactly the Andes, but you get the idea… What a difference a few dozen km can make…
Venezuela?
Look at ./Alts/Therm.by.Alt314.Dec.ALT (Y/N)? y
Year -MSL 20 50 100 200 300 400 500 1000 2000 Space
DAltPct: 1899 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0100.0 0.0 0.0
DAltPct: 1909 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0100.0 0.0 0.0
DAltPct: 1919 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0100.0 0.0 0.0
DAltPct: 1929 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0100.0 0.0 0.0
DAltPct: 1939 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0100.0 0.0 0.0
DAltPct: 1949 0.0 0.0 0.0 0.0 0.0 36.8 0.0 18.4 44.7 0.0 0.0
DAltPct: 1959 18.8 21.1 14.7 6.8 0.0 6.8 7.1 13.9 10.9 0.0 0.0
DAltPct: 1969 19.6 16.8 19.0 5.6 0.0 7.3 5.6 15.4 10.9 0.0 0.0
DAltPct: 1979 16.9 17.8 18.0 4.6 0.0 8.9 4.6 18.0 11.2 0.0 0.0
DAltPct: 1989 14.6 17.8 18.4 2.3 0.0 8.7 7.8 18.8 11.7 0.0 0.0
DAltPct: 1999 19.1 18.6 19.6 5.2 0.0 6.7 6.7 17.5 6.7 0.0 0.0
DAltPct: 2009 19.0 19.0 17.7 6.3 0.0 6.3 6.3 19.0 6.3 0.0 0.0
For COUNTRY CODE: 314
A familiar story. Though this time it starts at elevation and only after W.W.II does it make a run for the beaches. We now have 55.7% below 100 m and 62% below 200 m elevation. And 93.7% below 1000 m.
Not like there is anything in Venezuela that is high and cold:
Snow or Beach? Decisions Decisions
Carretera Transandina – Venezuela
What station can be used to “stand in” for these snowy mountain passes? Pick one:
[chiefio@tubularbells analysis]$ cat Temps/314.stns2009
31480403000 CORO 11.42 -69.68 17 19U 69HIxxCO 6A 1TROPICAL DRY FORC 53
31480410000 BARQUISIMETO 10.07 -69.32 614 551U 331HIxxno-9x-9WARM GRASS/SHRUBC 58
31480413000 MARACAY – B.A 10.25 -67.65 437 640U 255MVxxLA-9A 1WARM GRASS/SHRUBC 31
31480415000 CARACAS/MAIQU 10.60 -66.98 48 239U 1035MVxxCO 1A10WATER C 38
31480416000 CARACAS/LA CARLOTA 10.50 -66.90 865 1135U 1035MVxxCO12x-9WARM CROPS C 107
31480419000 BARCELONA 10.12 -64.68 7 62U 78HIxxCO 3A 1WATER C 45
31480423000 LA GUIRIA VENEZUE 10.58 -62.30 8 136S 15HIxxCO 1A 1COASTAL EDGES C 19
31480435000 MATURIN 9.75 -63.18 66 70U 98FLxxno-9x-9WARM GRASS/SHRUBC 53
31480438000 MERIDA 8.60 -71.18 1498 2555U 74MVxxno-9x-9WARM GRASS/SHRUBC 17
31480444000 CIUDAD BOLIVA 8.15 -63.55 48 62U 104FLxxno-9A 1TROP. SAVANNA C 45
31480447000 SAN ANTONIO D 7.85 -72.45 378 474U 220MVxxno-9A 2TROP. MONTANE C 21
31480450000 SAN FERNANDO 7.90 -67.42 48 55S 39FLxxno-9A 5WARM GRASS/SHRUBC 19
31480453000 TUMEREMO 7.30 -61.45 181 183R -9FLxxno-9A-9WARM GRASS/SHRUBA 0
31480457000 PUERTO AYACUC 5.60 -67.50 74 162S 10FLxxno-9A10TROP. SAVANNA A 0
31480462000 SANTA ELENA D 4.60 -61.12 907 934R -9HIxxno-9x-9TROP. MONTANE C 10
[chiefio@tubularbells analysis]$
Conclusion
So I’m sitting here asking myself, just where does GIStemp look to find a “nearby rural reference station” for all the cold snowy parts of the Andes when they have been substantially removed from the record? Where does it find the 10+ it tries to find to average together?
FWIW, if there are less than THREE it just gives up… From PApars.f that does the UHI “adjustment”:
C**** The homogeneity adjustment parameters
C**** =====================================
C**** To minimize the impact of the natural local variability, only
C**** that part of the combined rural record is actually used that is
C**** supported by at least 3 stations, i.e. heads and tails of the
C**** record that are based on only 1 or 2 stations are dropped. The
C**** difference between that truncated combination and the non-rural
C**** record is found and the best linear fit and best fit by a broken
C**** line (with a variable “knee”) to that difference series are found.
C**** The parameters defining those 2 approximations are tabulated.
Without at least 3 “nearby rural” and representative COLD stations, at best it can fabricate a complete fantasy. At worst, it can spread tropical warmth into frozen mountain peaks. What good is a UHI adjustment based only on other cities, airports, and tropical swamps or beaches?
How can you compute the snowy mountains from a Tropical Jungle or Hot Desert?
Simply put: You can’t.
Musings from the Chiefio 
What you are doing is Science in the traditional sense: following the data, trying to make sense of it, positing a theory or three. Great, important stuff.
And the conclusion is that the Temperature Record is a bit like a hospital survey, conducted only amongst the elderly. The elderly like to chat, don’t move around much, and have extended stays. So they are ideal survey respondents.
But you wouldn’t gauge your view of the entire medical establishment from such a narrow base….
Have you asked SteveMc to let you post of CA?
Your stuff deserves wider exposure.
REPLY: [ Thanks for the compliment! He’s seen it and knows where I’m at. There have been links from there to here (and vers vica). If he wants to repost anything from here he can, and if he wants something custom, he knows where I’m at. Not going to force myself on anyone. Volume is growing already anyway. Some folks have started putting in links… -ems ]
Thank you!
Just amazed to see how the books filleted and cooked…
They sure are having some BBQ on the beach…
This is amazing: I found your site just digging around looking for info on Climategate. I grew up with great respect for scientists and it is disheartening to see that it’s more than group think or carelessness. This is intentional. Please keep up the good work.
Wow … this is another piece of the puzzle
What is now needed, is a compilation of the various issues found with the temps and measuring devices. Something that an average Media guy could read, and have links to sources
We have been piecemealing the stuff, and learning as we go, but in the wake of Climategate, we really need a 30,000 foot synopsis of the problem and errors. Maybe one of the professors on the Nature side of the AGW could write it up. Or if we have a group of climate historian types ? …. hmm
Grad School project anyone?
I’ve got a high level intro to GIStemp issues here:
https://chiefio.wordpress.com/2009/11/09/gistemp-a-human-view/
and an overview in bullet list form of general AGW “basic issues” here:
https://chiefio.wordpress.com/2009/07/30/agw-basics-of-whats-wrong/
Though it’s a bit ‘terse’.
I’ll look at putting together an AGW general issues posting similar to the GIStemp Human View … unless someone else beats me to it ;-)
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For us tyros, could you put in some explanation as to what these tables mean?
What is Dalt PCt?
What is -MSL?
What are the units of measurement?
You may have something very useful here, but you need to explain yourself more fully.
This would seem to add to the fraudulent data coming out of our own Bureau of Met. in Australia. NZ data is is also corrupted.
REPLY: [ I’ll look at it to see what I can do. DAltPct is “Decade ending Altitude Percentage”. MSL is the standard abbreviation of Mean Sea Level. The “units” are the percentage of thermometers at that altitude band, in meters. -E.M.Smith ]
Thanks for the clarification. Makes sense now. May need to change column width in last col as some digits don’t appear.
The extent of this distortion is nothing short of breath-taking.
Some further questions.
What is SP?
What is DecPct?
REPLY: [ DecPct shows up on a “by latitude” report. It is the percentage for that decade that are in each lattitude band. It started life as “DecLatPct” but got shortened so the columns would fit in the small space available on WordPress…
Also, I’ve just learned that what I see is not what you see. My (about a decade old?) Mac just died. (The machine is still fine. The “issue” involved feet and power connectors… so a new power brick is needed…) In the mean time, I’ve moved over to my old Toyshiba. And discovered that the whole table does not show up on the PC…
I’m trying to decide on “shave out some white space in this one report” or “swap themes to one that has a ‘preformatted’ table with scroll bars”. But I have no idea if I can just “swap a theme” in a running wordpress site with impunity or not. If I swap, then swap back, have I just changed the formatting on 1000 pages that all have to be fixed by hand?… So I’m just pondering for now….
Fix coming “Real Soon Now” ;-)
If “Sp” is on the far right of the column headings, it is the first letters of “Space”, meaning everything up to outer space. A cute little way of saying “above the top numerical band parameter”. And yes, that implies “ace” is off the right edge of your screen…
-E.M.Smith
wow nice
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