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.

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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 :

clip_image004Detroit_lakes_USHCN

Impacts of Small-Scale Urban Encroachment on Air Temperature Observations

Ronald D. Leeper, John Kochendorfer, Timothy Henderson, and Michael A. Palecki
https://journals.ametsoc.org/doi/10.1175/JAMC-D-19-0002.1

Abstract

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

View original post 248 more words

Fake climate science and scientists

Reblogged from Watts Up With That:

Alarmists game the system to enrich and empower themselves, and hurt everyone else

by Paul Driessen

The multi-colored placard in front of a $2-million home in North Center Chicago proudly proclaimed, “In this house we believe: No human is illegal” – and “Science is real” (plus a few other liberal mantras).

I knew right away where the owners stood on climate change, and other hot-button political issues. They would likely tolerate no dissension or debate on “settled” climate science or any of the other topics.

But they have it exactly backward on the science issue. Real science is not belief – or consensus, 97% or otherwise. Real science constantly asks questions, expresses skepticism, reexamines hypotheses and evidence. If debate, skepticism and empirical evidence are prohibited – it’s pseudo-science, at best.

Real science – and real scientists – seek to understand natural phenomena and processes. They pose hypotheses that they think best explain what they have witnessed, then test them against actual evidence, observations and experimental data. If the hypotheses (and predictions based on them) are borne out by their subsequent findings, the hypotheses become theories, rules, laws of nature – at least until someone finds new evidence that pokes holes in their assessments, or devises better explanations.

Real science does not involve simply declaring that you “believe” something, It’s not immutable doctrine. It doesn’t claim “science is real” – or demand that a particular scientific explanation be carved in stone. Earth-centric concepts gave way to a sun-centered solar system. Miasma disease beliefs surrendered to the germ theory. The certainty that continents are locked in place was replaced by plate tectonics (and the realization that you can’t stop continental drift, any more than you stop climate change).

Real scientists often employ computers to analyze data more quickly and accurately, depict or model complex natural systems, or forecast future events or conditions. But they test their models against real-world evidence. If the models, observations and predictions don’t match up, real scientists modify or discard the models, and the hypotheses behind them. They engage in robust discussion and debate.

They don’t let models or hypotheses become substitutes for real-world evidence and observations. They don’t alter or “homogenize” raw or historic data to make it look like the models actually work. They don’t hide their data and computer algorithms (AlGoreRythms?), restrict peer review to closed circles of like-minded colleagues who protect one another’s reputations and funding, claim “the debate is over,” or try to silence anyone who dares to ask inconvenient questions or find fault with their claims and models. They don’t concoct hockey stick temperature graphs that can be replicated by plugging in random numbers.

In the realm contemplated by the Chicago yard sign, we ought to be doing all we can to understand Earth’s highly complex, largely chaotic, frequently changing climate system – all we can to figure out how the sun and other powerful forces interact with each other. Only in that way can we accurately predict future climate changes, prepare for them, and not waste money and resources chasing goblins.

But instead, we have people in white lab coats masquerading as real scientists. They’re doing what I just explained true scientists don’t do. They also ignore fluctuations in solar energy output and numerous other powerful, interconnected natural forces that have driven climate change throughout Earth’s history. They look only (or 97% of the time) at carbon dioxide as the principle or sole driving force behind current and future climate changes – and blame every weather event, fire and walrus death on manmade CO2.

Even worse, they let their biases drive their research and use their pseudo-science to justify demands that we eliminate all fossil fuel use, and all carbon dioxide and methane emissions, by little more than a decade from now. Otherwise, they claim, we will bring unprecedented cataclysms to people and planet.

Not surprisingly, their bad behavior is applauded, funded and employed by politicians, environmentalists, journalists, celebrities, corporate executives, billionaires and others who have their own axes to grind, their own egos to inflate – and their intense desire to profit from climate alarmism and pseudo-science.

Worst of all, while they get rich and famous, their immoral actions impoverish billions and kill millions, by depriving them of the affordable, reliable fossil fuel energy that powers modern societies.

And still these slippery characters endlessly repeat the tired trope that they “believe in science” – and anyone who doesn’t agree to “keep fossil fuels in the ground” to stop climate change is a “science denier.”

When these folks and the yard sign crowd brandish the term “science,” political analyst Robert Tracinski suggests, it is primarily to “provide a badge of tribal identity” – while ironically demonstrating that they have no real understanding of or interest in “the guiding principles of actual science.”

Genuine climate scientist (and former chair of the School of Earth and Atmospheric Sciences at the Georgia Institute of Technology) Dr. Judith Curry echoes Tracinski. Politicians like Senator Elizabeth Warren use “science” as a way of “declaring belief in a proposition which is outside their knowledge and which they do not understand…. The purpose of the trope is to bypass any meaningful discussion of these separate questions, rolling them all into one package deal – and one political party ticket,” she explains.

The ultimate purpose of all this, of course, is to silence the dissenting voices of evidence- and reality-based climate science, block creation of a Presidential Committee on Climate Science, and ensure that the only debate is over which actions to take first to end fossil fuel use … and upend modern economies.

The last thing fake/alarmist climate scientists want is a full-throated debate with real climate scientists – a debate that forces them to defend their doomsday assertions, methodologies, data manipulation … and claims that solar and other powerful natural forces are minuscule or irrelevant compared to manmade carbon dioxide that constitutes less that 0.02% of Earth’s atmosphere (natural CO2 adds another 0.02%).

Thankfully, there are many reasons for hope. For recognizing that we do not face a climate crisis, much less threats to our very existence. For realizing there is no need to subject ourselves to punitive carbon taxes or the misery, poverty, deprivation, disease and death that banning fossil fuels would cause.

Between the peak of the great global cooling scare in 1975 until around 1998, atmospheric carbon dioxide levels and temperatures did rise in rough conjunction. But then temperatures mostly flat-lined, while CO2 levels kept climbing. Now actual average global temperatures are already 1 degree F below the Garbage In-Garbage Out computer model predictions. Other alarmist forecasts are also out of touch with reality.

Instead of fearing rising CO2, we should thank it for making crop, forest and grassland plants grow faster and better, benefitting nature and humanity – especially in conjunction with slightly warmer temperatures that extend growing seasons, expand arable land and increase crop production.

The rate of sea level rise has not changed for over a century – and much of what alarmists attribute to climate change and rising seas is actually due to land subsidence and other factors.

Weather is not becoming more extreme. In fact, Harvey was the first Category 3-5 hurricane to make US landfall in a record 12 years – and the number of violent F3 to F5 tornadoes has fallen from an average of 56 per year from 1950 to 1985 to only 34 per year since then.

Human ingenuity and adaptability have enabled humans to survive and thrive in all sorts of climates, even during our far more primitive past. Allowed to use our brains, fossil fuels and technologies, we will deal just fine with whatever climate changes might confront us in the future. (Of course, another nature-driven Pleistocene-style glacier pulling 400 feet of water out of our oceans and crushing Northern Hemisphere forests and cities under mile-high walls of ice truly would be an existential threat to life as we know it.)

So if NYC Mayor Bill De Blasio and other egotistical grand-standing politicians and fake climate scientists want to ban fossil fuels, glass-and-steel buildings, cows and even hotdogs – in the name of preventing “dangerous manmade climate change” – let them impose their schemes on themselves and their own families. The rest of us are tired of being made guinea pigs in their fake-science experiments.

Paul Driessen is senior policy advisor for the Committee For A Constructive Tomorrow (CFACT) and author of articles and books on energy, environmental and human rights issues.

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.

Australia

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.

Indonesia

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

Timor-Leste

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.

Malaysia

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.

Singapore

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…

Brunei

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.

Philippines

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.

Palau

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

Guam

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.

Nauru

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?

Kiribati

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 😉

Tuvalu

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).

Tokelau

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.

Samoa

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

Vanuatu

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

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?

Tonga

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.

Niue

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:

mwr-035-01-0007b.pdf

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.

RSSChanges

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.

-Roy”

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.

Spreadsheet

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.

Cooling Down the Hysteria About Global Warming

Reblogged from Watts Up With That:

Guest essay by Rich Enthoven

Recently, NASA released its annual report on global temperatures and reported that 2018 was the fourth hottest year on record, surpassed only by three recent years. This claim was accompanied by dire predictions of climate change and for immediate action to dramatically curtail CO2 emissions around the globe. Like every concerned citizen read this report with interest. I also read it as an informed and trained climate analyst – and I can tell that there are some serious problems with the report and its conclusions.

For starters, I can assure my readers that I am not a climate change “denier.” No one doubts the climate changed when it experienced the Ice Age that ended 12,000 years ago. I have read enough scientific literature to believe the well documented view that the planet experienced the Medieval Warm Period (950 – 1250 AD) and Little Ice Age (1550 – 1850 AD) when global temperatures changed materially. I have also read enough scientific literature to understand that solar and ocean cycles affect global climate.

NASA is now reporting significant changes to the global temperature. According to NASA (and others) the entire globe experienced a persistent warming trend in the early part of the 20th century (1911 – 1940). Then, this trend reversed, and the globe cooled until the 1970’s.[1] Now, NASA is reporting that the global temperature increased .31° C in the last 10 years and that this trend is different than the .31° C increase NASA reports for the 1930’s[2]. But, a closer look at the data and methods used by NASA should make any reader skeptical of their results.

image

Land Temperatures

It turns out, that over long periods of time it is actually quite difficult to measure temperature changes from climate consistently. The problems arise from changes in measurement technology (mercury bulbs then, semiconductors now) and changes in the sites surrounding the measurement locations. A good way to think about this problem is to consider Dallas Love Field Airport where average temperatures have been reported monthly since 1940. During that time Love Field transformed from a tiny airport near a small city[3] – to large urban airport with 200 daily flights. These changes have generated massive heat at the airport. It is no wonder that the reported temperatures at Love Field have trended up by approximately 2.9 ° F since 1940. [4]

image

But, when we look at the temperatures in Centerville, TX – much less affected by land use changes – we see the opposite trend. The average reported temperature in Centerville has been on a declining trend and now averages (on trend) .3 °F less than it was in 1940.[5]

As a result of this urban heat effect, scientists around the world have been identifying (or constructing) ‘pristine’ weather monitoring stations to get a clearer look at temperature changes. These stations are located in areas where urban development has not occurred and is not expected. These locations do not show any meaningful change in reported land temperatures. The best data comes from the National Oceanic and Atmospheric Administration (NOAA) which set up 114 rural temperature monitoring stations in the US in 2002 (USCRN). When we look at these, we see no persistent increase in US temperatures.[6] In fact, 2018 was .3°F colder than the first two years measured. February and March 2019 combined to be the coldest two-month period (temperature anomaly) ever recorded by the USCRN.

MONTHLY TEMPERATURE CHANGES AT USCRN STATIONS

image

And it is not just the US rural temperatures that are stable – all around the globe, temperature growth is eliminated once land use changes are eliminated. Shown below are temperature graphs from rural areas in Netherlands, Ireland, Chile, Antarctica, Japan[7], and China[8].

image

image

Further calling into question the global land temperature data used by NASA are climate scientists themselves. Seventeen leading climate scientists (including scientists at NOAA) recently co-authored a paper calling for a new network of global weather stations in which they lamented the “imperfect measurements and ubiquitous changes in measurement networks and techniques.”[9]

Even these efforts to measure temperature change may not be enough – even the ‘pristine’ USCRN temperature measurement locations continue to biased towards warmer temperatures from land use changes. For example, a parking area and road was built next to the USCRN weather station[10] at the University of Rhode Island leading to a .34 ° C increase in measured temperatures at that location.[11][12]

image

Ocean and Satellite Temperature Measurement

The NASA global temperature estimate also relies heavily on estimates of temperatures in the ocean and air above it. Ocean temperatures have been measured over the years with highly inconsistent methods (buckets off ships; water flowing through ship engine rooms; buoys; and lately, satellites). In addition to technology changes, there are short term annual ocean cycles such as the well-publicized El Nino/La Nina and long term (multi decade) cycles such as the Pacific (and Atlantic) Decadal Oscillations which affect ocean temperatures at many depths over decades. A recent report out of UC San Diego described the problem “Determining changes in the average temperature of the entire world’s ocean has proven to be a nearly impossible task due to the distribution of different water masses.”[13]

Respected climate scientists are tackling the ocean measurement challenge and come up with results very different than the NASA report. Satellite measurements from University of Alabama show atmosphere temperatures over the ocean increasing since 1980 (end of the last cooling period per NASA) but only at .13 ° C per decade.[14] Both major satellite measurement groups report temperatures are lower now than they were in 1998, although by different amounts.[15] Harvard University oceanographer Carl Wunsch estimated the average temperature of the ocean grew by .02 degrees during 1994 – 2013.[16] Scripps Institute of Oceanography recently estimated the ocean temperature growth at .1 ° C total over the last 50 years. The science and history of measuring ocean temperatures is far from ‘settled’ and there are plenty of credible estimates that ocean temperatures are not changing rapidly or at anywhere near the rate that NASA is estimating.

Back to the NASA Temperature Estimate

To come up with their global temperature assessments, NASA faces all these problems and more. For starters, there is very little reliable global scale land data before 1940, and there are still shortages of reliable data in many parts of the world. (Africa, Middle East). Most of the historical data has been affected by land use changes and measurement technology changes. As they have tried to deal with these problems, NASA has dramatically changed the locations and methods that they use to assess temperatures over the last several decades.[17] Some observers question whether the new locations and technologies have the same pattern as the old ones would have had.

Not only have they adjusted the locations they take land measurements from, NASA adjusts the data that goes into their estimates[18]. Here are examples from the NASA website for Darwin Airport, Australia and Reykjavik, Iceland that show the liberal data changes adopted by NASA.[19]

image

image

Readers should note several problematic elements of these graphs:

1) The unadjusted data does not indicate warming at these locations over the last 80 years.

2) The unadjusted data is shown in such a faint outline that its hard to see. Why would NASA present it this way?

3) As NASA changed each data set, they made the past appear cooler – the “adjusted, cleaned” data is cooler than the “unadjusted” data – and the “homogenized” data is cooler still. A cooler past allows NASA to claim current temperatures are dramatically higher.

The NASA has “adjusted, cleaned, and homogenized” the data from these locations along with thousands of others to make up the data set that NASA uses. They then add data from satellites and use data grid methodology to come up with a final temperature change result.

Needless to say, the NASA changes have been the subject of considerable debate – within the climate scientist community, the climate “skeptic” community, and even NASA itself.[20] The “unadjusted” raw data has been adjusted meaningfully over the years as NASA recalculates.[21] The satellite measurements are very controversial according Zeke Hausfather, climate researcher at Berkley Earth – “If you don’t like adjustments, you really shouldn’t use the satellite record.”[22] A major problem is that the average adjustments between raw and final data average strongly in one direction – the adjustments tend to cool the past – which makes the present temperatures seem warmer by comparison.[23] NASA itself is apparently unhappy with their current formulas and plans to release version four of their “adjustments” soon.[24]

Other Indicators of Global Temperatures

The debate about the temperatures adjustments and estimates used by NASA can quickly get in to mathematical manipulations that are well beyond the level of this article. Scientists are arguing about changes in the global temperature that are on the order of one percent of one degree centigrade. Fortunately, we can look at a variety of other climate indicators in an effort to verify whether temperatures are changing. According to the theory endorsed by NASA, humans have been increasing carbon dioxide (CO2) in the atmosphere for more than 70 years[25] – and this increased CO2 has led to demonstrably higher global temperatures which affect major aspects of global climate.

Fortunately for the planet, there is no evidence of change in large scale climate indicators that should be changing with the temperature. Here are some notable examples:

· US Land Temperatures: In 1986, James Hansen testified to congress that rising CO2 levels would cause US temperatures to rise by three to four degrees by 2020. [26] This prediction was spectacularly wrong – US land temperatures have moved at most a fraction of that amount since 1986.[27]

image

· Sea Level Rise: NASA (and later Al Gore) have made it clear that a warmer planet would cause ice to melt and the seas to expand – rising by up to four feet in 2050[28]. An accelerating trend in sea levels would potentially inundate lower elevation cities. But, NOAA data makes it clear that there is no change in the rate of sea level increase since measurements began.[29] If the warming globe would accelerate sea level changes, and we don’t see acceleration – it seems reasonable to suggest the globe isn’t warming.

image

image

· Hurricanes and Other Adverse Weather Events: By the early 2000s climate scientists told us to expect an increase in hurricanes due to higher temperatures in the ocean. Instead, the US experienced a major hurricane drought from 2006 – 2016.[30] In fact, global hurricanes/typhoon activity have shown no up trend in frequency or severity for the last fifty years.[31] The IPCC also reported in 2013 that there was no change in frequency of other adverse events such as droughts, floods, and tornados.

image

· Glaciers: Observers often become concerned as they see glaciers melting and blame it on global warming. It is certainly true that on average glaciers in the northern hemisphere have been retreating lately. But, glaciers have been retreating since the end of the Little Ice Age (1850) and numerous studies point out that many glaciers were actually melting faster during early 1900’s than they are today.[32] Glacier Bay in Alaska is a good example of the long term melting trend.

image

· Snowfall: In 2001, the scientists at IPCC (worlds global authority on climate change) said that rising global temperatures would result in a reduction in snowfall and even the end of skiing industry.[33] However, according to both NOAA and Rutgers University, snowfall has been trending up across the northern hemisphere since 1970. If less snow is expected from higher temperatures – is more snow an indicator of lower temperatures?[34]

image

These are large scale indicators that should not be subject to much measurement debate. They are not subject to “adjustments.” They all tell me that the NASA report is hopelessly biased in favor of reporting a temperature increase that is not happening.

Motivation for NASA to Report Higher Temperatures

Why would NASA come up with results so different from those of other climate observations? Consider the history of the NASA global temperature estimates. In 1986, James Hansen broadly publicized his global warming theory in testimony before the US Senate. For the next 27 years, Mr. Hansen was the chief scientist at NASA in charge of preparing and presenting those estimates. Is it unreasonable to suggest that the “adjustments” and formulas he used after his Senate testimony were biased with an effort to make his predictions turn out to be correct? How much of the NASA estimate is a simple self-fulfilling prophesy?

It’s not just NASA that is subject to significant pressure which likely introduces bias into their results. Climate scientists may be in the same position as those in other fields (i.e. nutrition, pharmaceuticals, psychology) where the desire to produce a pre-selected result influences the inputs, methods, and findings of their science. Alarming results (“hottest ever!” “disaster predicted” “urgent action needed”) all generate headlines; speaking engagements; trips to climate conferences (IPCC); and additional funding for more research. When scientists find opposite results (“nothing is really changing” “it’s just weather” “random events as usual”) they get no publicity; no funding; and instead are attacked (“pro big oil” “anti-environment” or worst of all, a “climate change denier.”)[35] There are indeed thousands of scientific papers that are at odds with NASA, but they don’t get nearly the media coverage and they are not included in NASA’s estimates.

Summary

It is time for a much more open and fair reporting and debate about global temperatures and climate change. Every time an adverse weather event occurs, we have news media blaming it on climate change that isn’t happening. We now have people marching in the streets over a non-existent crisis. All around the globe, trillions of dollars are being spent to avert a perceived global temperature crisis that is not happening. These energies and funds could be spent on far better uses to protect our environment, educate our people, and actually help the planet. We could be spending money on keeping toxins out of our ecosystems; keeping our oceans clean and healthy; improving sustainable farming techniques; expanding and protecting our natural habitats. Its time to take real action to protect and improve our planet – and stop the misplaced worry about climate change.


[1].https://climate.nasa.gov/vital-signs/global-temperature/

[2] Temp anomalies per NASA site: 2018 +.82 ° C less 2008 +.51 ° C =+.31 ° C. 1939 -.03 ° C – 1929 -.34 ° C =+.31 ° C

[3] Dallas population 400,000. Love Field had three daily flights. Wikipedia

[4] Data per iweathernet.com. Authors trend analysis – least squares regression.

[5] Iweathernet.com Authors trend analysis – least squares regression.

[6] https://www.ncdc.noaa.gov/temp-and-precip/national-temperature-index/time-series?datasets%5B%5D=uscrn&parameter=anom-tavg&time_scale=p12&begyear=2004&endyear=2019&month=3 See also https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2015GL067640 for discussion of this data series. Trend is not significant at any reasonable level of certainty. Measurements themselves are subject to +/-.3°C at source.

[7] Temperatures from Japanese Meteorological Association.

[8] https://www.sciencedirect.com/science/article/pii/S0048969718331978

[9] Journal of Climatology 3/1/18 – https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.5458

[10] Data available at: https://www1.ncdc.noaa.gov/pub/data/uscrn/products/monthly01/CRNM0102-RI_Kingston_1_W.txt

[11] https://iowaclimate.org/2018/04/09/infrastructure

[12] Moose, Wy in Grand Teton National Park is experiencing record park visitors. Are they affecting measured temperatures at the USCRN site there?

[13] https://www.sciencedaily.com/releases/2018/01/180103160129.htm)

[14] https://www.nsstc.uah.edu/climate/2019/february2019/tlt_201902_bar.png Note this is closer to one third of the NASA estimated increase.

[15] http://www.drroyspencer.com/2014/10/why-2014-wont-be-the-warmest-year-on-record/

[16] https://www.tandfonline.com/doi/full/10.1080/16000870.2018.1471911)

[17] https://data.giss.nasa.gov/gistemp/history/

[18] https://data.giss.nasa.gov/gistemp/history/

[19] https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show.cgi?id=501941200000&dt=1&ds=5

[20] Sample paper on the debate from Journal of Geophysical Research – “There remain important inconsistencies between surface and satellite records.” https://pielkeclimatesci.files.wordpress.com/2009/11/r-345.pdf

[21] https://realclimatescience.com/2019/03/nasa-tampering-with-reykjavik-raw-temperature-data/

[22] https://www.carbonbrief.org/explainer-how-surface-and-satellite-temperature-records-compare

[23] https://data.giss.nasa.gov/gistemp/history/

[24] https://data.giss.nasa.gov/gistemp/

[25] CO2 has risen from 315 ppm to 380 ppm per Mauna Loa Observation 1960 – 2018.

[26] https://reason.com/archives/2016/06/17/climate-change-prediction-fail/print).

[27] https://www.ncdc.noaa.gov/temp-and-precip/national-temperature-index/time-series?datasets%5B%5D=uscrn&parameter=anom-tavg&time_scale=p12&begyear=2004&endyear=2019&month=2.

[28] https://www.nytimes.com/1988/06/24/us/global-warming-has-begun-expert-tells-senate.html?/pagewanted=all

[29] NOAA Tides & Currents – https://tidesandcurrents.noaa.gov/sltrends/sltrends_station.shtml?id=9414750

[30] US Hurricanes: https://journals.ametsoc.org/doi/pdf/10.1175/BAMS-D-17-0184.1

[31]Global Cyclone activity: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2011GL047711

[32] http://appinsys.com/globalwarming/gw_4ce_glaciers.htm

https://www.the-cryosphere-discuss.net/tc-2018-22/tc-2018-22.pdf https://www.researchgate.net/publication/312185500_High_sensitivity_of_North_Iceland_Trollaskagi_debris-free_glaciers_to_climatic_change_from_the_%27Little_Ice_Age%27_to_the_present

[33] https://www.theguardian.com/environment/2001/jan/23/globalwarming.climatechange)

[34] In 2019, Mother Nature is making this point emphatically with at or near record snowfall and cold temperatures across North America and Europe.

[35] Prof. Ross McKitrick http://www.rossmckitrick.com/uploads/4/8/0/8/4808045/gatekeeping_chapter.pdf and Judith Curry are well known commentators on this phenomenon.

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)

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

Canada

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

USA

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

Bermuda

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

Barbados

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

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

Grenada

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

Guadaloupe

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

Haiti

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

Jamaica

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

Martinique

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

Mexico

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

Belize

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

Guatemala

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

Honduras

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

Nicaragua

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

Panama

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.

GHCN v3.3 vs v4 Anomaly South America

Reblogged from Musings from the Chiefio:

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

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

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

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

South America Koppen – Geiger Climate Zones

The Graphs

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

Brazil

GHCN v3.3 vs v4 Brazil Difference

GHCN v3.3 vs v4 Brazil Anomaly

Cuba

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

GHCN v3.3 vs v4 Cuba Difference

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

GHCN v3.3 vs v4 Cuba Anomaly

Venezuela

GHCN v3.3 vs v4 Venezuela Difference

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

GHCN v3.3 vs v4 Venezuela Anomaly

French Guiana

GHCN v3.3 vs v4 French Guiana Difference

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

GHCN v3.3 vs v4 French Guiana Anomaly

Guyana

GHCN v3.3 vs v4 Guyana Difference

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

GHCN v3.3 vs v4 Guyana Anomaly

Suriname

GHCN v3.3 vs v4 Suriname Difference

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

GHCN v3.3 vs v4 Suriname Anomaly

Colombia

GHCN v3.3 vs v4 Colombia Difference

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

GHCN v3.3 vs v4 Colombia Anomaly

Ecuador

GHCN v3.3 vs v4 Ecuador Difference

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

GHCN v3.3 vs v4 Ecuador Anomaly

Peru

GHCN v3.3 vs v4 Peru Difference

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

GHCN v3.3 vs v4 Peru Anomaly

Bolivia

GHCN v3.3 vs v4 Bolivia Difference

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

GHCN v3.3 vs v4 Bolivia Anomaly

Chile

GHCN v3.3 vs v4 Chile Difference

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

GHCN v3.3 vs v4 Chile Anomaly

Argentina

GHCN v3.3 vs v4 Argentina Difference

GHCN v3.3 vs v4 Argentina Anomaly

Paraguay

GHCN v3.3 vs v4 Paraguay Difference

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

GHCN v3.3 vs v4 Paraguay Anomaly

Uruguay

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

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

The Falkland Islands

GHCN v3.3 vs v4 Falkland Islands Difference

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

GHCN v3.3 vs v4 Falkland Islands Anomaly

South Georgia and Sandwich Island

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

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

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

Tech Talk

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

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

In Conclusion

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

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

To me it looks like deliberately cooking the books.

Borenstein Tries The Daily Records Con

HiFast Note:

Here’s the Average Mean Temperature and other charts for Wooster, OH:

WOOSTEREXPSTATION_OH_AverageMeanTemperature_Jan_Dec_1895_2018

NOT A LOT OF PEOPLE KNOW THAT

By Paul Homewood

Another remarkably dishonest and deceitful piece, even by Seth Borenstein standards. That it should be aided and abetted by NOAA is shameful:

image

Over the past 20 years, rather than shiver through record-setting cold, a new Associated Press data analysis shows.

View original post 1,520 more words

Bias Or Corruption Of Temperatures

Reblogged from Musings from the Chiefio:

Here are two very good videos per issues in the Temperature Record.

The first, at 15 minutes, is a short overview of motivations of government employee “science” and some of the issues involving just how unimportant any actual warming might be. Touches on the point that government only gets what it pays for, and it pays for alarmist results. By Roy Spencer at “America First Energy Conference”. Titled “Climatologist Roy Spencer – The Bias In Climate Science”.

The second is longer, at about 53 minutes. By Tony Heller and titled “Evaluating The integrity Of The Official Climate Records”; it has a great set of A/B comparisons of what they said then vs now. Demonstrates graphically the way that the past is “mailable” in the hands of NASA / NOAA / IPCC. It is from 2 years ago, and similar to his other presentation from last year, but still good.

In particular, at the Q&A part, he tells how he digs up all those lovely old news articles about the very hot 30’s and the very cold 70’s. Useful information, that.