Our Urban “Climate Crisis”

Reblogged from Watts Up With That:

By Jim Steele

Published in Pacifica Tribune May 14, 2019

What’s Natural

Our Urban “Climate Crisis”


Based on a globally averaged statistic, some scientists and several politicians claim we are facing a climate crisis. Although it’s wise to think globally, organisms are never affected by global averages. Never! Organisms only respond to local conditions. Always! Given that weather stations around the globe only record local conditions, it is important to understand over one third of the earth’s weather stations report a cooling trend (i.e. Fig 4 below ) Cooling trends have various local and regional causes, but clearly, areas with cooling trends are not facing a “warming climate crisis”. Unfortunately, by averaging cooling and warming trends, the local factors affecting varied trends have been obscured.

It is well known as human populations grow, landscapes lose increasing amounts of natural vegetation, experience a loss of soil moisture and are increasingly covered by heat absorbing pavement and structures. All those factors raise temperatures so that a city’s downtown area can be 10°F higher than nearby rural areas. Despite urban areas representing less than 3% of the USA’s land surface, 82% of our weather stations are located in urbanized areas. This prompts critical thinkers to ask, “have warmer urbanized landscapes biased the globally averaged temperature?” (Arctic warming also biases the global average, but that dynamic must await a future article.)


Satellite data reveal that in forested areas the maximum surface temperatures are 36°F cooler than in grassy areas, and grassy areas’ maximum surface temperatures can be 36°F cooler than the unvegetated surfaces of deserts and cities. To appreciate the warming effects of altered landscapes, walk barefoot across a cool grassy lawn on a warm sunny day and then step onto a burning asphalt roadway.

In natural areas like Yosemite National Park, maximum air temperatures are cooler now than during the 1930s. In less densely populated and more heavily forested California, maximum air temperatures across the northern two thirds of the state have not exceeded temperatures of the 1930s. In contrast, recently urbanized communities in China report rapid warming of 3°F to 9°F in just 10 years, associated with the loss of vegetation.


Although altered urban landscapes undeniably raise local temperatures, some climate researchers suggest warmer urban temperatures do not bias the globally averaged warming trend. They argue warming trends in rural areas are similar to urbanized areas. So, they theorize a warmer global temperature is simply the result of a stronger greenhouse effect. However, such studies failed to analyze how changes in vegetation and wetness can similarly raise temperatures in both rural and urban areas. For example, researchers reported overgrazing had raised grassland temperatures 7°F higher compared to grassland that had not been grazed. Heat from asphalt will increase temperatures at rural weather stations just as readily as urban stations.

To truly determine the effects of climate change on natural habitats requires observing trends from tree ring data obtained from mostly pristine landscapes. Instrumental data are overwhelmingly measured in disturbed urbanized areas. Thus, the difference between instrumental and tree ring temperature trends can illustrate to what degree landscapes changes have biased natural temperature trends. And those trends are strikingly different!

The latest reconstructions of summer temperature trends from the best tree ring data suggest the warmest 30-year period happened between 1927 and 1956. After 1956, tree rings recorded a period of cooling that lowered global temperatures by over 1°F. In contrast, although tree rings and instrumental temperatures agreed up to 1950, the instrumental temperature trend, as presented in NASA graphs, suggests a temperature plateau from 1950 to 1970 and little or no cooling. So, are these contrasting trends the result of an increased urban warming effect offsetting natural cooling?


After decades of cooling, tree ring data recorded a global warming trend but with temperatures just now reaching a warmth that approaches the 1930s and 40s. In contrast, instrumental data suggests global temperatures have risen by more than 1°F above the 1940s. Some suggest tree rings have suddenly become insensitive to recent warmth? But the different warming trends are again better explained by a growing loss of vegetation and increasing areas covered by asphalt affecting temperatures measured by thermometers compared with temperatures determined from tree ring data in natural habitats.

Humans are increasingly inhabiting urban environments with 66% of humans projected to inhabit urban areas by 2030. High population densities typically reduce cooling vegetation, reduce wetlands and soil moisture, and increase landscape areas covered by heat retaining pavements. Thus, we should expect trends biased from urbanized landscapes to continue to rise. But there is a real solution to this “urban climate crisis.” It requires increasing vegetation, creating more parks and greenbelts, restoring wetlands and streams, and reducing heat absorbing pavements and roofs. Reducing CO2 concentrations will not reduce stifling urban temperatures.

Jim Steele is the retired director of San Francisco State University’s Sierra Nevada Field Campus and authored Landscapes and Cycles: An Environmentalist’s Journey to Climate Skepticism.


Chinese UHI study finds 0.34C/century inflation effect on average temperature estimate.

Tallbloke's Talkshop

New study published by Springer today makes interesting reading. Phil Jones’ ears will be burning brightly.

Historical temperature records are often partially biased by the urban heat island (UHI) effect. However, the exact magnitude of these biases is an ongoing, controversial scientific question, especially in regions like China where urbanization has greatly increased in recent decades. Previous studies have mainly used statistical information and selected static population targets, or urban areas in a particular year, to classify urban-rural stations and estimate the influence of urbanization on observed warming trends. However, there is a lack of consideration for the dynamic processes of urbanization. The Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) are three major urban agglomerations in China which were selected to investigate the spatiotemporal heterogeneity of urban expansion effects on observed warming trends in this study. Based on remote sensing (RS) data, urban area expansion…

View original post 149 more words

What’s Wrong With The Surface Temperature Record? Guest: Dr. Roger Pielke Sr.

Reblogged from Watts Up With That:

Dr. Roger Pielke Sr. joins [Anthony Watts] on a podcast to discuss the surface temperature record, the upcoming IPCC report, and climate science moving forward.

Dr. Roger Pielke Sr. explains how the Intergovernmental Panel on Climate Change (IPCC) is incorrectly explaining climate change to the media and public. Pielke highlights how the IPCC ignores numerous drivers of climate aside from CO2, leading to numerous factual inaccuracies in the IPCC reports.

Climate monitoring station in a parking lot at University of Arizona, Tucson

We also cover what is wrong with the surface temperature record – specifically why many temperature readings are higher than the actual temperature.

Available on Amazon at a special low price – click image

Pielke is currently a Senior Research Scientist in CIRES and a Senior Research Associate at the University of Colorado-Boulder in the Department of Atmospheric and Oceanic Sciences (ATOC) at the University of Colorado in Boulder (November 2005 -present). He is also an Emeritus Professor of Atmospheric Science at Colorado State University.

BIG NEWS – Verified by NOAA – Poor Weather Station Siting Leads To Artificial Long Term Warming

Sierra Foothill Commentary

Based on the data collected for the Surface Station Project and analysis papers describing the results, my friend Anthony Watts has been saying for years that “surface temperature measurements (and long term trends) have been affected by encroachment of urbanization on the placement of weather stations used to measure surface air temperature, and track long term climate.”

When Ellen and I traveled across the country in the RV we visited weather stations in the historical weather network and took photos of the temperature measurement stations and the surrounding environments.

Now, NOAA has validated Anthony’s findings — weather station siting can influence the surface station long temperature record. Here some samples that were taken by other volunteers :


Impacts of Small-Scale Urban Encroachment on Air Temperature Observations

Ronald D. Leeper, John Kochendorfer, Timothy Henderson, and Michael A. Palecki


A field experiment was performed in Oak Ridge, TN, with four…

View original post 248 more words

Climate data shows no recent warming in Antarctica, instead a slight cooling

Reblogged from Watts Up With That:

Below is a plot from a resource we have not used before on WUWT, “RIMFROST“. It depicts the average temperatures for all weather stations in Antarctica. Note that there is some recent cooling in contrast to a steady warming since about 1959.

Data and plot provided by http://rimfrost.no 

Contrast that with claims by Michael Mann, Eric Steig, and others who used statistical tricks to make Antarctica warm up. Fortunately, it wasn’t just falsified by climate skeptics, but rebutted in peer review too.

Data provided by http://rimfrost.no 

H/T to Kjell Arne Høyvik‏  on Twitter


No warming has occurred on the South Pole from 1978 to 2019 according to satellite data (UAH V6). The linear trend is flat!

Analysis of new NASA AIRS study: 80% of U.S. Warming has been at Night

Reblogged from Watts Up With That:

By Dr. Roy Spencer

I have previously addressed the NASA study that concluded the AIRS satellite temperatures “verified global warming trends“. The AIRS is an infrared temperature sounding instrument on the NASA Aqua satellite, providing data since late 2002 (over 16 years). All results in that study, and presented here, are based upon infrared measurements alone, with no microwave temperature sounder data being used in these products.

That reported study addressed only the surface “skin” temperature measurements, but the AIRS is also used to retrieve temperature profiles throughout the troposphere and stratosphere — that’s 99.9% of the total mass of the atmosphere.

Since AIRS data are also used to retrieve a 2 meter temperature (the traditional surface air temperature measurement height), I was curious why that wasn’t used instead of the surface skin temperature. Also, AIRS allows me to compare to our UAH tropospheric deep-layer temperature products.

So, I downloaded the entire archive of monthly average AIRS temperature retrievals on a 1 deg. lat/lon grid (85 GB of data). I’ve been analyzing those data over various regions (global, tropical, land, ocean). While there are a lot of interesting results I could show, today I’m going to focus just on the United States.

AIRS temperature trend profiles averaged over the contiguous United States, Sept. 2002 through March 2019. Gray represents an average of day and night. Trends are based upon monthly departures from the average seasonal cycle during 2003-2018. The UAH LT temperature trend (and it’s approximate vertical extent) is in violet, and NOAA surface air temperature trends (Tmax, Tmin, Tavg) are indicated by triangles. The open circles are the T2m retrievals, which appear to be less trustworthy than the Tskin retrievals.

Because the Aqua satellite observes at nominal local times of 1:30 a.m. and 1:30 p.m., this allows separation of data into “day” and “night”. It is well known that recent warming of surface air temperatures (both in the U.S. and globally) has been stronger at night than during the day, but the AIRS data shows just how dramatic the day-night difference is… keeping in mind this is only the most recent 16.6 years (since September 2002):

The AIRS surface skin temperature trend at night (1:30 a.m.) is a whopping +0.57 C/decade, while the daytime (1:30 p.m.) trend is only +0.15 C/decade. This is a bigger diurnal difference than indicated by the NOAA Tmax and Tmin trends (triangles in the above plot). Admittedly, 1:30 a.m. and 1:30 pm are not when the lowest and highest temperatures of the day occur, but I wouldn’t expect as large a difference in trends as is seen here, at least at night.

Furthermore, these day-night differences extend up through the lower troposphere, to higher than 850 mb (about 5,000 ft altitude), even showing up at 700 mb (about 12,000 ft. altitude).

This behavior also shows up in globally-averaged land areas, and reverses over the ocean (but with a much weaker day-night difference). I will report on this at some point in the future.

If real, these large day-night differences in temperature trends is fascinating behavior. My first suspicion is that it has something to do with a change in moist convection and cloud activity during warming. For instance more clouds would reduce daytime warming but increase nighttime warming. But I looked at the seasonal variations in these signatures and (unexpectedly) the day-night difference is greatest in winter (DJF) when there is the least convective activity and weakest in summer (JJA) when there is the most convective activity.

One possibility is that there is a problem with the AIRS temperature retrievals (now at Version 6). But it seems unlikely that this problem would extend through such a large depth of the lower troposphere. I can’t think of any reason why there would be such a large bias between day and night retrievals when it can be seen in the above figure that there is essentially no difference from the 500 mb level upward.

It should be kept in mind that the lower tropospheric and surface temperatures can only be measured by AIRS in the absence of clouds (or in between clouds). I have no idea how much of an effect this sampling bias would have on the results.

Finally, note how well the AIRS low- to mid-troposphere temperature trends match the bulk trend in our UAH LT product. I will be examining this further for larger areas as well.

GHCN v3.3 vs. v4 Anomaly Australia / Pacific Islands

Reblogged from Musings from the Chiefio:

In prior postings I did a sample of various countries around the world, and a full set of North America, South America and Antarctica. This extends that set with Australia and the Pacific Islands. Note that these are often near the equator and may be on either side of it, so seasonality may vary by graph.

I’m going to group things into Australia, New Zealand, North of Australia (Indonesia, Papua New Gunea, Philipines, etc.), then those islands scattered across the center of the Pacific. Why? Because countries in those areas ought to look a lot more like each other in terms of Anomaly than like those in other groups. The Pacific is dominated by ENSO and tropical conditions. while the countries north of Australia have Indian Ocean influences and share a current flow up the coast of Asia. To some extent New Zealand is “special” in that it is closest to The Southern Ocean so has more cold southern islands and arctic water exposure. Similarly, Singapore is on the Malay Peninsula and north of the equator so it, and Malaysia, ought to reflect some of the Asian Continent; but protruding into the mixed ocean area will also reflect climate similar to Indonesia. Finally, Australia is unique in this group as it has a large hot desert in the center.

Here’s the Koppen Climate graph for the World (from the Wiki) so you have something for comparison.

Köppen-Geiger Climate Map for 1980-2016

From that you can easily see how Australia and New Zealand differ from the tropical ocean group.

What I find fascinating in these graphs is just how much the islands temperature recordings vary (often a lot) while they are in the same climate zone as a nearby neighbor and share a large body of nearly uniform temperature water between them. I expect change between distant islands, but we often see it on neighboring islands. I think that likely is an instrument or siting change issue. Who has the large airport and tourists, and who doesn’t. But that needs an historical retrospective photo essay on each of the places like that and someone else will need to take that “Dig Here!”.

Which countries?

This bit of SQL programming gets us a table of countries in Region 5, the Australia & Pacific Islands group (remember that in Linux the command “cat” is “concatenate and print” and with just one file name prints out the contents. In this case the program named “ApacList.sql”):

chiefio@PiM3Devuan2:~/SQL/bin$ cat ApacList.sql 
SELECT cnum, abrev,region, cname 
FROM country WHERE region=5 ORDER BY cname;

So what does that give us? Here’s the result:

MariaDB [temps]> source bin/ApacList.sql
| cnum | abrev | region | cname                                    |
| 521  | AQ    | 5      | American Samoa [United States]           |
| 501  | AS    | 5      | Australia                                |
| 522  | BX    | 5      | Brunei                                   |
| 523  | KT    | 5      | Christmas Island [Australia]             |
| 524  | CK    | 5      | Cocos (Keeling) Islands [Australia]      |
| 525  | CW    | 5      | Cook Islands [New Zealand]               |
| 527  | FM    | 5      | Federated States of Micronesia           |
| 502  | FJ    | 5      | Fiji                                     |
| 528  | FP    | 5      | French Polynesia                         |
| 529  | GQ    | 5      | Guam [United States]                     |
| 503  | ID    | 5      | Indonesia                                |
| 530  | JQ    | 5      | Johnston Atoll [United States]           |
| 504  | KR    | 5      | Kiribati                                 |
| 505  | MY    | 5      | Malaysia                                 |
| 531  | RM    | 5      | Marshall Islands                         |
| 598  | MQ    | 5      | Midway Islands [United States}           |
| 506  | NR    | 5      | Nauru                                    |
| 532  | NC    | 5      | New Caledonia [France]                   |
| 507  | NZ    | 5      | New Zealand                              |
| 533  | NE    | 5      | Niue [New Zealand]                       |
| 534  | NF    | 5      | Norfolk Island [Australia]               |
| 535  | CQ    | 5      | Northern Mariana Islands [United States] |
| 536  | PS    | 5      | Palau                                    |
| 599  | LQ    | 5      | Palmyra Atoll [United States]            |
| 508  | PP    | 5      | Papua New Guinea                         |
| 509  | RP    | 5      | Philippines                              |
| 537  | PC    | 5      | Pitcairn Islands [United Kingdom]        |
| 541  | WS    | 5      | Samoa                                    |
| 511  | SN    | 5      | Singapore                                |
| 512  | BP    | 5      | Solomon Islands                          |
| 597  | TT    | 5      | Timor-Leste                              |
| 538  | TL    | 5      | Tokelau [New Zealand]                    |
| 517  | TN    | 5      | Tonga                                    |
| 518  | TV    | 5      | Tuvalu                                   |
| 520  | NH    | 5      | Vanuatu                                  |
| 539  | WQ    | 5      | Wake Island [United States]              |
| 540  | WF    | 5      | Wallis and Futuna [France]               |
37 rows in set (0.90 sec)

MariaDB [temps]> 

So 37 Countries. 74 total graphs. This is going to take a while…

First I’ll put up Australia and New Zealand as they are the two most different from everything else in terms of climate types.


GHCN v3.3 vs v4 Australia Difference

Interesting that the general trend is a roll off of heat. But a couple of years get a hot bump at the end.

GHCN v3.3 vs v4 Australia Anomaly

New Zealand

GHCN v3.3 vs v4 New Zealand Difference

Abut 1/2 C cooling of the deep past, but not much else.

GHCN v3.3 vs v4 New Zealand Anomaly

North Of Australia

I’m going to start this group with Indonesia, as it is the largest, then work my way around the nearby bits. These all ought to be substantially the same as they all share the same giant bath tub of water and currents.


GHCN v3.3 vs v4 Indonesia Difference

Either the historic Indonesia data are crap and need a lot of fixes, or they can’t decide what their temperature was in the past.. Nice warming jump added at the recent end.

GHCN v3.3 vs v4 Indonesia Anomaly


Looks like these folks are missing data in v3.3:

MariaDB [temps]> SELECT year,AVG(deg_c) FROM anom3 AS A 
INNER JOIN country AS C ON A.country=C.cnum  
WHERE C.abrev='TT' GROUP BY year;
Empty set (0.09 sec)

So the anomaly difference graph report fails:

============ RESTART: /SG500/xfs/chiefio/Py3/Aapac/a3v4deltaTT.py ============
stuffed SQL statement for TT Timor-Leste 
Executed SQL
Got data
This is the exception branch
All Done

So taking num3>0 and num4>0 of of the script (so it accepts years with no data, the result becomes:

============ RESTART: /SG500/xfs/chiefio/Py3/Aapac/a3v4deltaTT.py ============
stuffed SQL statement for TT Timor-Leste 
Executed SQL
[('1917', None), ('1918', None), ('1919', None), ('1920', None),
 ('1927', None), ('1928', None), ('1929', None), ('1930', None),
 ('1931', None), ('1932', None), ('1933', None), ('1934', None),
 ('1936', None), ('1938', None), ('1939', None), ('1940', None),
 ('1941', None), ('1951', None), ('1952', None), ('1953', None),
 ('1954', None), ('1955', None), ('1956', None), ('1957', None),
 ('1958', None), ('1959', None), ('1960', None), ('1961', None),
 ('1962', None), ('1963', None), ('1964', None), ('1965', None),
 ('1966', None), ('1967', None), ('1968', None), ('1969', None),
 ('1970', None), ('1971', None), ('1972', None), ('1973', None),
 ('1974', None), ('1975', None), ('1976', None), ('1977', None),
 ('1978', None), ('1979', None), ('1980', None), ('1981', None),
 ('1982', None), ('1983', None), ('1984', None), ('1985', None),
 ('1990', None)]
Got data
after the transpose

And we get an empty graph. All those “None” for difference data.

Printing the two sets of data has only the v4 data show up on the graph of anomalies:

GHCN v3.3 vs V4 Timor-Leste Anomaly

Where it looks like nobody has got around to molesting the data and making it toe the PC Line. We have a very hot 1930s, a cold 1960s, A return to ALMOST as hot in the 1980s, then a cold dip in the ’90s. Rather like we all experienced and rather like recorded in the history of the times. Golly.

Papua New Guinea

GHCN v3.3 vs V4 Papua New Guinea Difference

Very little change over much of the history, then about 1/4 C cooler in recent years with some ‘fliers’ of 1/2 C higher.

GHCN v3.3 vs V4 Papua New Guinea Anomalies

VERY significant range compression in the GISS/Hadley baseline years (about 1950-1990) then it widens out again with a bit of “higher highs”, then most recently it gets a cold year. Not looking at all like general warming over the years from CO2.


GHCN v3.3 vs V4 Malaysia Differences

Wow! Really cooling the past there in Malaysia. A full degree C colder in many cases; rising to only 1/2 C colder just before the baseline period. Then the baseline period kept at zero. (Remember this is just change between version 3.3 and version 4 for what is supposedly the SAME place and the same instruments recorded at one time in the past…) Then the recent data gets about a 1/3 C “lift” (but freezing the past has already created the slope needed…)

GHCN v3.3 vs V4 Malaysia Anomalies

Here we can see that it is no warmer now than it was in the past in the old version; but only after cooling the past a full degree C does that unfortunate fact go away.


GHCN v3.3 vs V4 Singapore Differences

Interesting cold “adjustment” in the 1870s and then that dip in the late 1990’s is interesting, finally we end with an uptick of only about 1/4 C in the last datapoint.

GHCN v3.3 vs V4 Singapore Anomalies

Then the actual anomaly data shows a nice “dip” in the baseline period, but otherwise the actual temperature change has not been much at all over the years. Other than that one hot dot at the very end…


Lookslike Brunei was also not in GHCN v3.3 so no “difference in anomalies” graph can be made:

MariaDB [temps]> SELECT year,AVG(deg_C) FROM anom3 AS A 
INNER JOIN country AS C ON A.country=C.cnum 
WHERE C.abrev='BX' GROUP BY year;
Empty set (0.48 sec)

So all we’ll get is the v4 anomaly data on the Anomalies graph:

GHCN v3.3 vs v4 Brunei Anomalies

Then this is too short a record to say much at all about climate. It’s about 35 years so only a tiny bit over one half the known 60 year cycle. Fitting a trend to cyclical data is a fools errand. I note in passing that recent years are about the same as the mid 1990s.


GHCN v3.3 vs v4 Philippines Differences

Not much changed between the two data set versions. Looks like the W.W.II data changed a bit more.

GHCN v3.3 vs v4 Philippines Anomalies

So the 1800s were a bit of cold, then we see about 1-1.5 C of range in the Yr/Yr data until the “Baseline period” where the range narrows (closer to 3/4 C though near 1980 things are remarkably constant. In more recent years we have the return of some range (though it looks like minus some cold excursions) and the final temperature is very much like about 1965, 1942 or so, and around 1932. So while the slope of a fit line might well show a trend, the present temperature is not out of line with hot periods in the past. My best guess would be a bit of growth of the airport, UHI, and jet exhaust.


Oddly, Pelau right nearby the Philippines, has a different shape to their data…

GHCN v3.3 vs v4 Palau Differences

The deep past gets changed to a little cooler, then the present has a 1.5 C range to the CHANGES between version 3.3 and version 4 of what is supposedly the same place and data. Now Pelau isn’t big enough to have a whole lot of thermometers to chose between and among, so just why is the data that “mailable”? Eh?

GHCN v3.3 vs v4 Palau Differences

The actual anomaly graphs have the usual compressed “waistline” with reduced range in the “Baseline Years”, then with an otherwise almost constant spread and range of data from about 1950 to 1995, when suddenly the low ranges start to pull up. The spectacular bit, though, is the spike of roughly 2 C in the last few years. I’m sorry, but CO2 effects to not lurk for 40 years doing not much then suddenly show up in one year and stay for 3 or 4. That’s something else. Jet exhaust maybe? Isn’t that a big US Military spot?

Pacific Island Arc

This set is all those islands and atolls scattered around the Pacific Ocean toward North America (compared to the prior set). As ENSO tends to make an oscillator between the E and W sides of this basin, and some of these are N of the equator while most are S, I’m generally going to lay them out from near New Zealand over toward the Americas, but with those North of the Equator near the middle (some US owned Atolls mostly) set out separately. (Provided I can keep straight which of these rocks is classified as a what and who has had which name change and…)

Up North & Scattered

Here’s a few islands and atolls in the more northern part of the Pacific and scattered around a bit in the Big Empty.

Midway Islands

Midway also has no data in the GHCN v3.3 set:

MariaDB [temps]> SELECT year,AVG(deg_C) FROM anom3 AS A 
INNER JOIN country AS C ON A.country=C.cnum 
WHERE C.abrev='MQ' GROUP BY year;
Empty set (0.06 sec)

MariaDB [temps]>

So once again all we will get is the GHCN v4 Anomalies graph:

GHCN v3.3 vs v4 Midway Islands Anomalies

Other than a couple of “fliers” recently, the temperatures are rather like the hot points in the 1930s-50s. I note that the W.W.II years are missing. Low excursions are about the same in the 1920s-1940 and in the 1955-1975 range, then just “go away”. Rather like a 1/2 C “step function” happened in 1979. Very strange. Wonder if there was any equipment change then?

Johnston Atoll

GHCN v3.3 vs v4 Johnston Atoll Difference

Not much going on with Johnston Atoll. Then again they already have 2 C range in the anomaly (see next graph) so maybe nothing more was needed…

GHCN v3.3 vs v4 Johnston Atoll Anomaly

Once again almost nothing really happening until 1980, then a jump up; followed by another big jump up in about 1995. Odd little atoll. Wonder what was going on then… From the Wiki:

Chemical weapon demilitarization mission 1990–2000
Johnston Atoll Chemical Agent Disposal System (JACADS) building
Main article: JACADS

The Army’s Johnston Atoll Chemical Agent Disposal System (JACADS) was the first full-scale chemical weapons disposal facility. Built to incinerate chemical munitions on the island, planning started in 1981, construction began in 1985, and was completed five years later. Following completion of construction and facility characterization, JACADS began operational verification testing (OVT) in June 1990. From 1990 until 1993, the Army conducted four planned periods of Operational Verification Testing (OVT), required by Public Law 100-456. OVT was completed in March 1993, having demonstrated that the reverse assembly incineration technology was effective and that JACADS operations met all environmental parameters. The OVT process enabled the Army to gain critical insight into the factors that establish a safe and effective rate of destruction for all munitions and agent types. Only after this critical testing period did the Army proceed with full-scale disposal operations at JACADS. Transition to full-scale operations started in May 1993 but the facility did not begin full-scale operations until August 1993.

All of the chemical weapons once stored on Johnston Island were demilitarized and the agents incinerated at JACADS with the process completing in 2000 followed by the destruction of legacy hazardous waste material associated with chemical weapon storage and cleanup. JACADS was demolished by 2003 and the island was stripped of its remaining infrastructure and environmentally remediated.

Oh… So a lot of stuff shipped in, big construction, then years of running an incinerator… a BIG incinerator. I’m sure that had nothing to do with it… /sarc;

Wake Island

GHCN v3.3 vs v4 Wake Island Differences

Nothing changed much for years other than a roughly 1/4 C cooling of the past then BAM a 1.5 C range of changes in a few year and then back to not much change.

GHCN v3.3 vs v4 Wake Island Anomalies

Looks to me like another “Step function” of about 1 C in 1980 with a sight cooling trend over a cyclical spike of about 15 to 20 years. Not at all what steady increases in a warming gas would cause.

The more Southern Group

These are the islands that make an equatorial to South Pacific arc.

North Marianas Islands

GHCN v3.3 vs v4 Northern Mariana Islands Differences

Gosh, a 2.5 C range in teh anomalies just from variation in instrument chosen or processing. When you can get that much essentially “random” variation from what is supposedly the same small place and the same data / instruments, where are the error bars on that 1/2 C of “Global Warming” fantasy?

Interesting that this pushes up the “New Ice Age Comming” 1970s and pulls down the present. Just how crazy bad was this “Global Warming” chart that they needed to take 2.3 C out of it?

GHCN v3.3 vs v4 Northern Mariana Islands Anomalies

Gee from warming black dots to dead flat red dots in one “fix”. I wonder who got caught doing what and had to fix it? ;-0


GHCN v3.3 vs v4 Guam Differences

Another small island with big changes in their “historical” data. Looks like a tiny rise in the early ’80s, then a big 1 C cut around 2000.

New data looks to be about as high as what was reduced. I guess it would look bad to have a “Halt” to “Global Warming”, so need to take a tuck in that older “hot” time and preserve the warming “trend” that way… I note in passing that the 1920s to 1940s are about as hot as “whichever hot now is really now”, and only the “baseline period” is nominally cool.

Fed. Islands Of Micronesia

GHCN v3.3 vs v4 Federated States Of Micronesia Differences

Another one with a change “dip” around 2000.

GHCN v3.3 vs v4 Federated States Of Micronesia Anomalies

Oddly, even though in the same giant bathtub of warm water as Guam, these Islands have a cold 1920s to 1940s. Then essentially dead flat from 1950 to about 2000-2005 and only then a jump up (or smooth rise depending on version). Doesn’t look at all like a gentle persistent rise of 1/2 C due to CO2 and looks a whole lot more like ENSO, cyclical changes with step functions, or diddled data / bad measuring.

Marshal Islands

GHCN v3.3 vs v4 Marshal Islands Difference

Not much in the change department. Bit of a minor down tweak at the end.

GHCN v3.3 vs v4 Marshal Islands Anomalies

Other than the “dip” or “sag” in the “basline period” of about 1950 to 1990, not much in the anomalies either. OTOH, they have a nice 1 C range from bottom of the baseline to now pretty much baked in, so why change anything? Just ignore that pesky pre-baseline data and call it a warming trend.


GHCN v3.3 vs v4 Nauru Differences

Changes all over the place and with a 2 C range. Big dropout from 1940 to 1960. Huge cooling of the hot 1930s.

GHCN v3.3 vs v4 Nauru Anomalies

Ah, that’s why. Turn a cooling down trend into a slight warming then throw away any recent data and anything newer than 1970. Can’t keep a place that’s getting cooler in the data now can we?


GHCN v3.3 vs v4 Kiribati Differences

Another “dogs breakfast” of changes. Almost 3 C of “fix ‘er up” done with cooling the 1920s to ’30s. Got to erase that pesky hot ’30s somehow. Then pull down the ’80s a little to erase the “pause” and make it a smoother trend.

GHCN v3.3 vs v4 Kiribati Anomalies

And “Bob’s Yer Uncle” a flat to cooling trend becomes a “warming out of the baseline period”. (Even though over all the data it isn’t warming, but no worries, nobody cares about data older than W.W.II).

Christmas Island

GHCN v3.3 vs v4 Christmas Island Differences

Again a big dropout of data in the baseline, then a nice 1/2 C of “Pop” added in 2000-2010.

GHCN v3.3 vs v4 Christmas Island Anomalies

So not erased the hot 30s & 40s here yet (which just begs the question how they could vary so much from nearby island to nearby island…) but did get rid of that annoying cold dip after 2000. Add a couple of juiced up hot years in the recent data and you too can turn a dead flat trendless Island into a Global Warming place. Just ignore that 30s & 40s data (don’t worry, it will be taken care of in v5, I’m sure… /sarc;)

Solomon Islands

GHCN v3.3 vs v4 Solomon Islands Differences

Another place with a 2 C range in the ‘fix up’ differences. Makes one wonder how bad the recent data are to need to much changing.

GHCN v3.3 vs v4 Solomon Islands Anomalies

Essentially trendless until after 2000. Even then not much (and mostly from removing low going excursions). Wonder if they moved the thermometer closer to a cement runway 😉


GHCN v3.3 vs v4 Tuvalu Differences

About a 1 C range of what looks like a few semi-random changes.

GHCN v3.3 vs v4 Tuvalu Anomalies

Nothing much at all going on until the year 2000 then a sudden jump up of about 1/2 C consistent with the 1940 temperatures. This will create a false trend if you plot a trend line from the “baseline period” to the present when at best there’s a cyclical thing happening (and at worst it is an instrumentation issue).


Oh man is this one a challenge / amusing:

That big pop up of up to 1.5 C in 1965-70 range shows that somebody did go back and get different data, yet the result (graph below) is still just crazy time.

GHCN v3.3 vs v4 Tokelau Anomalies

A full 4 C+ of range, all over the place, with the most recent data quite cool. No trend until the late 1970s, then a massive pop up of 1 C for near a decade+, a drop of 4 C, and then it returns with mostly cooler data but still bouncing around by 2 C. This one is a real “Dig Here!” issue.

Wallis & Fortuna

GHCN v3.3 vs v4 Wallis & Fortuna Differences

One degree C of changes in the data with no clear pattern nor reason. So one full degree C of “jitter” can be in the data with no connection at all to CO2 (By Definition – since this is only the result of change in instruments or processing – and I doubt there were many instruments to change in Wallis & Fortuna).

GHCN v3.3 vs v4 Wallis & Fortuna Anomalies

Other than a “dip” in the baseline period (that rughly 1965-1985 low) it is essentially flat. Present temperatures essentially the same as around 1960.


GHCN v3.3 vs v4 Samoa Differences

How unusual. the past is warmed in the v4 data and the present is cooled. I guess having 2 C of warming in Samoa didn’t look very CO2 physical as it was only supposed to be about 1/2 C.

GHCN v3.3 vs v4 Samoa Anomalies

We still have a nice 2 C of range, rising from -1 C in 1900 through 0 C (or equal to the average) in 1920 to 1980, then finally a bit of “lift” in the end with one year at +1 C and another at closer to +1.5 C. Yet the low years are about normal. Wonder what was in the missing years (and why “modern” data is missing but we have full data prior to 1995 or so…

American Samoa

GHCN v3.3 vs v4 American Samoa Differences

GHCN v3.3 vs v4 American Samoa Anomalies


GHCN v3.3 vs v4 Vanuatu Differences

Vanuatu looks like another of those “too hot to be CO2 physical need to cool it” charts. Nothing much changes in the past, but the recent (“highest quality”) data gets cooled up to 3/4 of a degree C.

GHCN v3.3 vs v4 Vanuatu Anomalies

Basically a flat chunk from about 1950 to 1990 then a sudden jump up by about 3/4 C to 1.5 C. Anyone want to bet it became a “destination” then and the airport got bigger with more jet traffic and tarmac / concrete? But I can see where you would want to blend down that big jump into a more gentile rise. Doesn’t stand out as so odd then.

New Caledonia

GHCN v3.3 vs v4 New Caledonia Differences

A gentle cooling of the 1940s so they blend in with each side (can’t have them being about the same as now, can we?)

GHCN v3.3 vs v4 New Caledonia Anomalies

So now it looks like a steady flat period from about 1940 to 1965, then warming. Except most of the recent years data looks a lot like the 1930s.

Norfolk Island

GHCN v3.3 vs v4 Norfolk Island Anomalies

Nice little 1/2 C “POP” up in the recent years there. Wonder what that does?

GHCN v3.3 vs v4 Norfolk Island Differences

Oh, erases that cold dip… Realistically, this isn’t warming. A couple of recent years have a warm spike, but about the same as 1998 and the 19-teens, and with a (pre-erasure) cold dip in the 2010’s about like prior years too.


Fiji is a bit of a trip. They change the recent data to about 1 C warmer and it is still cooling.

GHCN v3.3 vs v4 Fiji Differences

So about 1/2 C cooler in 1990 to 1/2 C warmer in the early 2000s. Looking at the graph below, it seems to have taken a “rolling off to cooler” in the black dots and turned it into a “continuing to warm”… Wonder if they manicure fingernails as well? /snark;

GHCN v3.3 vs v4 Fiji Anomalies

While it does look like a trend line from the “Baseline” years to the present would have a warming trend, the data overall do not. “Now” is no warmer than 1900 or 1930 or 1980. It does look like some low going excursions might be being clipped off. Airport cement anyone?


GHCN v3.3 vs v4 Tonga Difference

Looks like about a 3/4 C range of mindless changes.

GHCN v3.3 vs v4 Tonga Anomaly

And more random coin toss than trend in the anomalies.


GHCN v3.3 vs v4 Niue Differences

Nobody changing much n Niue.

GHCN v3.3 vs v4 Niue Differences

And no “Global Warming” either… Guess that’s why the data get sparse after 1990, so it can be “re-imagined” and infilled via homogenizing from somewhere else.

French Polynesia

GHCN v3.3 vs v4 French Polynesia Difference

Again with the cooling of the baseline window… I think we’re getting a trend here… but not in the climate.

GHCN v3.3 vs v4 French Polynesia Anomaly

Pitcairn Islands

Poor Pitcairn Islands. Off near nowhere. Not important enough for anyone to diddle the data…

GHCN v3.3 vs v4 Pitcairn Islands Differences

Nearly nothing changed.

GHCN v3.3 vs v4 Pitcairn Islands Anomalies

No discernable trend to the anomalies / data… Guess “Global Warming” isn’t very global after all…

In Conclusion

IMHO the degree of change of what ought to be the same data from the same instruments between these “versions” of the “same” data indicate that any warming found is as likely to be error, or more likely to be error, than anything real.

Just looking at the anomaly profiles shows that islands located in the same body of water with nearly constant sea surface temperatures have very different profiles, or shapes of the plotted data. How do you do that when the environment is the same from island to island?

My best guess is that it is local siting issues (in particular measuring at airports with changes of size, materials, and traffic – from grass shack by a Pan Am Clipper seaport to 10,000 foot of concrete and Jet Age Vacationing), or just flat out lousy measuring.

What I do NOT see in the data is a general and steady increase in warming, year over year, across many stations; the kind of thing CO2 and radiative blocking ought to cause.

There will not be a Tech Talk in this posting as it is in the prior postings and all that changes is the letter code used to select for the countries. If you want to know more about the data base used, the codes, and the processing done, see the prior postings.

Adjusting Good Data To Make It Match Bad Data

Reblogged from RealClimateScience.com:


On election day in 2016, both satellite data sets (UAH and RSS) showed a 15 year long hiatus in global warming, and bore no resemblance to the warming trend being generated by NOAA and NASA.  I captured this image in a November 16, 2016 blog post.

Gavin Schmidt Promises To Resign | The Deplorable Climate Science Blog

This is what the same graph looks like now.

Wood for Trees: Interactive Graphs

In the next image, I overlaid the current RSS graph on the 2016 image.  You can see how RSS was adjusted to match the NASA data.

I predicted this would happen on

Look for the satellite data to be adjusted to bring it into compliance with the fully fraudulent surface temperatures. The Guardian is now working to discredit UAH, so it seems likely that RSS will soon be making big changes – to match the needs of the climate mafia. Bookmark this post.


Roy Spencer at UAH made the same prediction on January 9, 2017

“I expect there will soon be a revised TLT product from RSS which shows enhanced warming, too.

Here’s what I’m predicting:

1) neither John Christy nor I will be asked to review the paper

2) it will quickly sail through peer review (our UAH V6 paper is still not in print nearly 1 year after submission)

3) it will have many authors, including climate model people and the usual model pundits (e.g. Santer), which will supposedly lend legitimacy to the new data adjustments.

Let’s see how many of my 3 predictions come true.


Wood for Trees: Interactive Graphs

The reason I made this prediction was because Ted Cruz used an RSS graph in a Senate hearing in March of 2015. Carl Mears at RSS then came under intense pressure to make his data match the surface temperature data.

My particular dataset (RSS tropospheric temperatures from MSU/AMSU satellites) show less warming than would be expected when compared to the surface temperatures. All datasets contain errors. In this case, I would trust the surface data a little more because the difference between the long term trends in the various surface datasets (NOAA, NASA GISS, HADCRUT, Berkeley etc) are closer to each other than the long term trends from the different satellite datasets. This suggests that the satellite datasets contain more “structural uncertainty” than the surface dataset.

Ted Cruz says satellite data show the globe isn’t warming

You can see what Mears did to bring his data into compliance. This was his web page in November 2016.

Note that after 1998, the observations are likely to be below the simulated values, indicating that the simulation as a whole are predicting too much warming.

Climate Analysis | Remote Sensing Systems

But under intense pressure,  Mears altered his own data to bring it into compliance.  The large discrepancy became a small discrepancy.

there is a small discrepancy between the model predictions and the satellite observations.

Remote Sensing Systems

The image below overlays Mears’ old graph (V3) on his new one (V4.) It is clear what he did – he  eliminated the blue error interval, and started using the high side of the interval as his temperature.

RSS V3 shows no warming since 2002.

The warming was all created by tampering with the data to eliminate the error interval.


The corruption is now complete.  NASA has announced that new satellite data matches their surface temperature data. This was done to keep the President’s Commission on Climate Security from having accurate data to work with.

All government climate data goes through the same transition in support of global warming alarm. The past keeps getting cooler, and recent years keep getting warmer.

NASA 1999   NASA 2016

Government climate agencies appear to be using Orwell’s 1984 as Standard Operating Procedure.

Greenland Temperature Data For 2018


By Paul Homewood


Greenland yemps

The DMI has just published its Greenland Climate Data Collection for last year, and it is worth looking at the temperature data:

There are six stations with long records, Upernavik, Nuuk, Ilulissat, Qaqortoq, Narsarsuaq and Tasilaq.








Throughout Greenland we find that temperatures in the last two decades are little different to the 1920s to 60s.

The only exceptions were 2010 on the west coast sites, which was an unusually warm year, and 2016 on the east coast at Tasilaq, another warm year there.

Noticeably, last year was actually colder than the 1981-2010 average at all of the west and south coast stations.

It is also noticeable that temperatures during the very cold interval at all sites during the 1970s and 80s were comparable to the late 19thC, when Greenland beginning to struggle out of the Little Ice Age. This can be seen best in the…

View original post 22 more words

GHCN v3.3 vs v4 Anomaly North America

Reblogged from Musings from the Chiefio:

GHCN v3.3 vs v4 Anomaly North America

In prior postings I did a sample of various countries around the world, and a full set of South America and Antarctica. This extends that set with North America.

I’m goiing to group things into North Continental (USA, Canada, Greenland), Central (Mexico and Central American countries) and Carib, those islands in the Caribbean Sea. Why? Because countries in those areas ought to look a lot more like each other in terms of Anomaly than like those in other groups. The Caribbean is dominated by water and tropical conditions. Central America and Mexico are about the same but with a bit less water influence. The USA, Canada and to some extent Greenland are large land masses prone to cold winters and significantly further north (so more summer / winter sun changes). This also lets me group making the graphs into smaller work units and it is less oppressive 😉

Here’s the Koppen Climate graph for North America so you have something for comparison.

Koppen Climate Zones for North America

From that I think you can see why I’d put Cuba into the Caribbean rather than count it in South America… like GHCN did…

The Graphs

Northern Big 3 (Canada, USA, Greenland)


GHCN v3.3 vs v4 Greenland Difference

So about 1 C range of changes both ways. So an error band of about 1 C? Is that what this means? So we can’t know if there is 1/2 C of warming… The the actual anomalies below bounce around by a 3 C range (that might have a flier off the graph – I didn’t check). Looks to me like Greenland data is just chaotic weather. I do note that the really big DIP happens right on the “baseline period” used by GISStemp (1950-1980) or Hadley (1960-1990)…

GHCN v3.3 vs v4 Greenland Anomaly


GHCN v3.3 vs v4 Canada Difference

Interesting that it’s a roll off to colder. The anomalies (below) look a mess, but without evident warming. More like the loss of some very low going extremes recently. (One wonders if the big freeze in the last couple of years will show up in future data?)

GHCN v3.3 vs v4 Canada Anomaly


GHCN v3.3 vs v4 USA Anomaly Difference

Again with recent data cooled. Wonder if they were embarrassed by all the attention over the last few years. We also again see no real warming tops, only loss of cold excursions and a general narrowing of the range. Or perhaps being early to the party, they had already “cooked” the v2 data so no more needed here. /snark;

GHCN v3.3 vs v4 USA Anomaly

The Caribbean & Bermuda

These are all in one large shallow water basin with common weather. They ought to be nearly identical.

Antigua & Barbuda

Antigua has no data in GHCN v3.3:

MariaDB [temps]> SELECT COUNT(deg_C) 
FROM anom3 AS A 
INNER JOIN country AS C on A.country=C.cnum 
WHERE C.abrev="AC";
| COUNT(deg_C) |
|            0 |
1 row in set (0.00 sec)

So all we get is the v4 anomaly graph:

GHCN v4 Antigua & Barbuda Anomaly


GHCN v3.3 vs v4 Bermuda Difference

Not a lot of change in the data. They seem to have cooled since 1980 (see graph below) and were hotter back around 1880 – 1860.

GHCN v3.3 vs v4 Bermuda Anomaly

The Bahamas

GHCN v3.3 vs v4 Bahamas Difference

About 1/2 of recent data warmed about 1/2 C, and a cool dip about 1940-1980; but now about the same as the mid 1800s. Cyclical changes?

GHCN v3.3 vs v4 Bahamas Anomaly


GHCN v3.3 vs v4 Barbados Difference

Drop the past about 0.4 C and warm the recent about 1/4 C – Presto! a trend! Except that those pesky 1800s look about the same as now. Better use that cold snap from 1940 to 1980 as the “baseline period” and ony measure warming against it.

GHCN v3.3 vs v4 Barbados Anomaly

Cayman Islands

GHCN v3.3 vs v4 Cayman Islands Difference

Again with cooling the past a bit. But from 1990 to date is basically flat. Ought not the “warming from CO2” be more now and less in 1970?

GHCN v3.3 vs v4 Cayman Islands Anomaly


Dominica has no data in v3.3, so all we get is the v4 anomaly graph.

MariaDB [temps]> SELECT COUNT(deg_C) 
FROM anom3 AS A 
INNER JOIN country AS C on A.country=C.cnum 
WHERE C.abrev="DO";
| COUNT(deg_C) |
|            0 |
1 row in set (0.06 sec)

GHCN v4 Dominica Anomaly

Dominican Republic

GHCN v3.3 vs v4 Dominican Republic Difference

So they had a crazy high 2 C “warming” rate, chop 1 C out of it and get a more respectable 1 C rate. Or is there just 1.5 C of “random” in temperature measuring on islands?

GHCN v3.3 vs v4 Dominican Republic Anomaly


GHCN v3.3 vs v4 Grenada Difference

Again with 2 C of warming, so chop a degree C out of some of it, yet the most recent data points (see below graph) are below normal. But that’s just weather, right?

GHCN v3.3 vs v4 Grenada Anomaly


GHCN v3.3 vs v4 Guadeloupe Difference

A whopping 5 C range of temperatures, but currently about the same as 1940. Natural variability or error band, one of the other, is way higher than that 1/2 C of Project Fear Warming.

GHCN v3.3 vs v4 Guadeloupe Anomaly


GHCN v3.3 vs v4 Haiti Difference

Looks like nobody fooled with the temperatures between versions. Guess a major hurricane will stop that kind of thing for a while. Anomaly graph below is rather flat too. Had a rise in to 1960, then was ignored for about 20 years, and now the temperatures are like “1940 all over again”.

GHCN v3.3 vs v4 Haiti Anomaly


GHCN v3.3 vs v4 Jamaica Difference

Push up the 40s (outside the baseline interval) a 1/2 C and drop the baseline spots about 1/2 C but with some noise in it, then lift the very recent by about 1/2 C. I think we’ve seen that before.

Per the anomaly graph below, Jamaica has warmed about 3 C. All that without setting regular record temperatures and with nearby islands not having the same rise. Airport tarmac anyone? Didn’t Jamaica have a big pop in tourism with the whole Bob Marley / Reggae thing? I know I went. Locals were complaining about added traffic, all the airplanes and hotels… I note in passing the hot late 1800s highs are about the same as now.

GHCN v3.3 vs v4 Jamaica Anomaly


GHCN v3.3 vs v4 Martinique Difference

Differences look like about 1 C range of random. Anomaly trend looks like it was cold in the ’60s and recovered.

GHCN v3.3 vs v4 Martinique Anomaly

Netherlands Antilles

GHCN v3.3 vs v4 Netherlands Antilles Difference

A nice 1/4 C or so cooling of the past, and another 1/4 C to 1/2 C warming of the recent data, pretty soon you got yourself a trend. Except now is about the same as 1980.

GHCN v3.3 vs v4 Netherlands Antilles Anomaly

Puerto Rico

GHCN v3.3 vs v4 Puerto Rico Difference

Well that’s interesting. In v3.3 it was heating up by 1 C in Puerto Rico, so now the change is a 1 C cooling, and Puerto Rico is showing mostly flat trend in the red anomaly spots below. Almost like a decade of scrutiny and being pulled before congress might have caused some folks to fear being caught…

GHCN v3.3 vs v4 Puerto Rico Anomaly

St. Kits & Nevis

GHCN v3.3 vs v4 Saint Kits & Nevis Difference

Nothing goning on in St. Ktis & Nevis. Oddly, as all these islands are in the bathtub together, their anomaly graphs ought to all look alike. Yet they don’t…

GHCN v3.3 vs v4 Saint Kits & Nevis Anomaly

St. Lucia

Saint Lucia has no data in either version, so no graphs at all:

MariaDB [temps]> SELECT COUNT(deg_C) 
FROM anom3 AS A 
INNER JOIN country AS C on A.country=C.cnum 
WHERE C.abrev="DO";
| COUNT(deg_C) |
|            0 |
1 row in set (0.06 sec)

MariaDB [temps]> SELECT COUNT(deg_C) 
FROM anom4 WHERE abrev="ST";
| COUNT(deg_C) |
|            0 |
1 row in set (0.31 sec)

Kinda makes you wonder why it is in the inventory at all.

St. Pierre & Miquelon

GHCN v3.3 vs v4 Saint Pierre & Miquelon Difference

Nice gentle almost unnoticed 1/4 C cooling of the past, a narrow “dip” in the baseline interval. All in all, nicely done sculpting.

GHCN v3.3 vs v4 Saint Pierre & Miquelon Anomaly

ST. Vincent & The Grenadines

Saint Vincent & The Grenadines have no data in version v3.3.

MariaDB [temps]> SELECT COUNT(deg_C) 
FROM anom3 AS A 
INNER JOIN country AS C on A.country=C.cnum 
WHERE C.abrev="VC";
| COUNT(deg_C) |
|            0 |
1 row in set (0.01 sec)

So all we get is this v4 anomaly graph:

GHCN v4 Saint Vincent & The Grenadines Anomaly

Trinidad & Tobago

GHCN v4 Trinidad & Tobago Difference

WOW, that 2 C of change down in the “baseline interval” is about the same as the dip in the anomaly graph then… Without that 2 C of down, Trinidad & Tobago would look sort of flat; with “now” temps about the same as the 1920-1930 temps.

GHCN v4 Trinidad & Tobago Anomaly

Virgin Islands (US)

One wonders what happened to the British Virgin Islands in terms of temperatures… but moving on…

GHCN v4 U.S. Virgin islands Difference

Sure doesn’t look like warming to me. Given some islands flat, and some with 3 C of warming, I think we’re looking at land use issues, thermometer location, or error bands / data fudging.

GHCN v4 U.S. Virgin islands Anomaly

Mexico & Central America


GHCN v3.3 vs v4 Mexico Difference

Gee, that recent rise at the tail of the anomaly data (below) seems to match in shape the rise in the difference graph (above).

GHCN v3.3 vs v4 Mexico Anomaly


GHCN v3.3 vs v4 Belize Difference

The actual anomaly data (below) isn’t warming on the top end, though we do see the loss of low going anomalies in the recent data. As though tons of concrete at the airport held heat over night… The change graph isn’t doing much either, though around 1980 got a spike. Sleepy little Belize…

GHCN v3.3 vs v4 Belize Anomaly


GHCN v3.3 vs v4 Guatemala Difference

Not really warming though we lose some of the low going anomalies in recent data (see graph below) and the changes between v3.3 and v4 are not much. Looks like nobody is paying attention to Guatemala.

GHCN v3.3 vs v4 Guatemala Anomaly

El Salvador

GHCN v3.3 vs v4 El Salvador Difference

Looking at the anomaly plot below, there’s a nice “dip” in the baseline interval, and a gap about 1990-2005, but then some crazy changes in the above difference graph. Looks to me like +/- 1 C of error band and nobody really knowing just what the temperature is in tenths of C.

GHCN v3.3 vs v4 El Salvador Anomaly


GHCN v3.3 vs v4 Honduras Difference

Already has a nice “dip” in the baseline interval, but those changes in the recent time are just wild.

GHCN v3.3 vs v4 Honduras Anomaly


GHCN v3.3 vs v4 Nicaragua Difference

We have 1/2 C drop added to the baseline, and 1 C of rise added in recent data. Just about the same as the “warming” found. Looks like someone found a way to get Nicaragua data in line with goals.

GHCN v3.3 vs v4 Nicaragua Anomaly

Coasta Rica

GHCN v3.3 vs v4 Costa Rica Difference

Nice dip in the baseline interval, but what’s this with the cooing at the end? Costa Rica just not getting hot? And right next to Nicaragua too. Ought to be nearly identical, but isn’t.

GHCN v3.3 vs v4 Costa Rica Anomaly


GHCN v3.3 vs v4 Panama Difference

Gee.. Looks to me like Panama is being flat to slightly cooling (see below) and not much change at all between v3.3 and v4. I wonder if this is at a US Military Base and they are grumpy if you play with their data? I wonder if that’s why it cuts off in 1980… Seems to me someone ought to know the temperature in Panama right now.

GHCN v3.3 vs v4 Panama Anomaly

Tech Talk

The specifics on the report / graph making programs is in the first “by country” posting so will not be repeated here.

This SQL produced the list of countries in North America (Region 4):

SELECT cnum, abrev,region, cname 
FROM country WHERE region=4 ORDER BY cname;

Here’s the list:

MariaDB [temps]> source bin/Namerica.sql
| cnum | abrev | region | cname                              |
| 426  | AC    | 4      | Antigua and Barbuda                |
| 423  | BF    | 4      | Bahamas, The                       |
| 401  | BB    | 4      | Barbados                           |
| 402  | BH    | 4      | Belize                             |
| 427  | BD    | 4      | Bermuda [United Kingdom]           |
| 403  | CA    | 4      | Canada                             |
| 429  | CJ    | 4      | Cayman Islands [United Kingdom]    |
| 405  | CS    | 4      | Costa Rica                         |
| 430  | DO    | 4      | Dominica                           |
| 407  | DR    | 4      | Dominican Republic                 |
| 408  | ES    | 4      | El Salvador                        |
| 431  | GL    | 4      | Greenland [Denmark]                |
| 409  | GJ    | 4      | Grenada                            |
| 432  | GP    | 4      | Guadeloupe [France]                |
| 410  | GT    | 4      | Guatemala                          |
| 411  | HA    | 4      | Haiti                              |
| 412  | HO    | 4      | Honduras                           |
| 413  | JM    | 4      | Jamaica                            |
| 433  | MB    | 4      | Martinique [France]                |
| 414  | MX    | 4      | Mexico                             |
| 434  | NT    | 4      | Netherlands Antilles [Netherlands] |
| 415  | NU    | 4      | Nicaragua                          |
| 416  | PM    | 4      | Panama                             |
| 435  | RQ    | 4      | Puerto Rico [United States]        |
| 417  | SC    | 4      | Saint Kitts and Nevis              |
| 436  | ST    | 4      | Saint Lucia                        |
| 438  | SB    | 4      | Saint Pierre and Miquelon [France] |
| 437  | VC    | 4      | Saint Vincent and the Grenadines   |
| 424  | TD    | 4      | Trinidad and Tobago                |
| 425  | US    | 4      | United States                      |
| 440  | VQ    | 4      | Virgin Islands [United States]     |
31 rows in set (0.00 sec)

In Conclusion

So there you have it. A few days of “seat time” at the computer. Hopefully it is of use to someone. I think it does point out what country’s data needs more scrutiny. Then there’s also the question of why trends in some nations are different from another very nearby and in the same body of water. Furthermore, there’s a great “cross check” for this in that there is surface water temperature data from decades of hurricane tracking. On these small islands, air temperature never strays far from water temperature. I was swimming in Jamaica once when rain started. The ocean, air, and rain all at 86 F.

It does look like there was a cyclical “dip” after the hot 1930s-40s into a cool ’50s-70s and we have newspaper and magazine articles from then shouting about a New Little Ice Age (and I personally remember it – IT Happened.) So despite the folks saying that picking it for a “baseline” doesn’t matter, I think it’s just too convenient. The method I used to calculate anomaly has no baseline. A given thermometer is only compared to itself over a selected month across the years. (So, for example, the Jamaica Airport thermometer would have all of its Januaries averaged then the January anomaly computed against that. Repeat for each other month of the year for each instrument.) Then there is just the shortness of most records. Many just start in that cold period and rise out of it, not having an old 1870 hot sample to see in their past.

Finally, with that much change showing up in some very small countries, you know they didn’t have a dozen thermometers to choose from in 1890. It must be some kind of “intervention” when decades of data all move by the same amount. It just screams “Fudging the data” (though I’m sure they would call it fixing errors in the past). But when all the “warming” comes from the “fixing up” and “adjusting” (even of this “unadjusted” data) or from splicing a 1/2 cycle and calling it a trend: Just where is the room for CO2?

It took me a few days of “seat time” to make this set and post it, so don’t worry if you just want to look at a few each day over time. After the first dozen even my eyes started to glaze over 😉 But these will be here for months or years to come, for your pondering.