How Cold Air Caused a Heatwave

Reblogged from Watts Up With That:

Guest Post from Jim Steele

From What’s Natural? Column published in Pacifica Tribune June 26, 2019

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I was recently asked if the record June 2019 heat in the San Francisco Bay Area validated CO2 driven climate models. Surprisingly climate scientists have now demonstrated the heat wave was largely due to an intrusion of record cold air into the Pacific Northwest. How?

Basically, the winds’ direction controls the San Francisco Bay Area’s weather. In summer, California’s inland regions heat faster than the ocean, so the winds blow inland from the cooler ocean. Those onshore winds bring cooling fog, our natural air conditioner. Later, as the sun retreats southward in the fall, the land cools faster than the ocean. Seasonal winds then reverse and blow from the cooling land out to sea. Those winds keep the fog offshore. Without fog, San Franciscans finally enjoy pleasantly warm days in September and October. In northern California those strong offshore winds are called the Diablo winds. Although Diablo winds bring welcome warmth, those winds also increase wildfire danger.

Typically, inland California heats up in June drawing in the fog. But that temporarily changed when a surge of record cold air briefly entered Washington state and then moved down into northeastern California and Nevada. Dr. Cliff Mass, a climate scientist at the University of Washington, studies the Diablo winds. On his popular weather blog, he discussed how that intruding cold air created an unseasonal burst of Diablo winds that then kept the fog offshore. Without cooling fog, solar heating increased temperatures dramatically. According to Accuweather, San Francisco’s maximum temperature on Friday June 7th was 67 °F, skyrocketed to a record 97 °F by Monday and then fell to 61 °F three days later as onshore winds returned.

Such rapid temperature change is never caused by a slowly changing greenhouse effect. Nevertheless, the media asks if rising CO2 concentrations could have contributed to the higher temperatures or made the heatwave more likely?

Although definitions vary, the World Meteorological Organization defines a heat wave as 5 or more consecutive days of prolonged heat in which daily maximum temperatures are 9+ °F higher than average. Assuming the rise in CO2 concentration increased all temperatures relative to the 20th century average, it is believed maximum temperatures are more likely to exceed that 9 °F threshold. But heatwaves are not caused by increasing greenhouse gases.

The science is solid that greenhouse gases can intercept escaping heat and re-direct a portion of that heat back to earth. That downward directed heat reduces how quickly the earth cools, and thus the earth warms. However, heat waves typically occur when greenhouse gas concentrations are greatly reduced. Eighty percent or more of our greenhouse effect is caused not by CO2, but by water vapor. Satellite data shows the dry conditions that accompany a heat wave actually reduce the greenhouse effect because drier air allows more infrared heat to escape back to space. However, like less fog, less water vapor and less clouds allow more solar heating. So despite the increase in escaping heat, increased solar heating dominates the weather and temperatures rise.

The important contribution of dryness to heat waves helps explain why the USA experienced its worst heat waves during the 1930s Dust Bowl years (see EPA Heatwave Index above). Furthermore, the EPA’s heat wave index appears totally independent of rising CO2 concentrations. Dryness also helps to explain why the hottest air temperature ever recorded anywhere in the world happened over a century ago in Death Valley on July 10, 1913; a time of much lower CO2 concentrations.

To summarize, an intrusion of record cold air into the Pacific Northwest generated unseasonal Diablo winds in northern California. Those offshore winds prevented the fog from reaching and cooling the land. In addition, because the Diablo winds are abnormally dry, solar heating of the land increased. Those combined effects caused temperatures to temporarily jump by 30 °F.

Lastly, not only can Diablo winds cause heatwaves, Diablo winds will fan small fires into huge devastating infernos such as the one that destroyed Paradise, California. Fortunately, there were few wildfire ignitions during this heat wave. To be safe, Pacific Gas and Electric had shut off electricity to areas predicted to have high wind speeds. So Dr. Mass mused, that because colder temperatures generate the destructive Diablo winds, climate warming may have some benefits.

Jim Steele is retired director of the Sierra Nevada Field Campus, SFSU

and authored Landscapes and Cycles: An Environmentalist’s Journey to Climate Skepticism

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Whatever happened to the Global Warming Hiatus?

Reblogged from Clive Best:

The last IPCC assessment in 2013 showed a clear pause in global warming lasting 16 years from  1998 to 2012 – the notorious hiatus. As a direct consequence of this  AR5 estimates of climate sensitivity were reduced and CMIP5 models appeared to clearly overestimate trends. Following the first release of HadCRUT4 in 2014  the ‘headline’ then was that 2005 and 2010 were marginally warmer than 1998. This was the first dent in removing the hiatus. Since then each new version of H4 has showed further incremental warming trends, such that by 2019 the hiatus has now completely vanished. Anyone mentioning it today is likely to be ridiculed by the climate science community. So how did this reversal happen within just 5 years? I decided to find out exactly why the post 1998 temperature record changed so dramatically in such a short period of time.

In what follows I always use the same algorithm as CRU for the station data and then blend that with the Hadley SST data. I have checked that I can reproduce exactly the latest HadCRUT4.6 results based on the current 7820 stations from CRU merged with  HadSST3. Back in 2012 I downloaded the original station data from CRU –  CRUTEM3. I have also downloaded the latest CRUTEM4 station data.

Figure 1 compares the latest HadCRUT4.6 results with the last version of HadCRUT3.

Fig1-768x452

I had assumed that the reason for the apparent trend change was because CRUTEM4 had added many new weather stations in the Arctic (removing some in S.America as well), while additionally the SST data had also been updated (HadSST2 moved to HADSST3). However, as I show below, my assumption simply isn’t true.

To investigate I recalculated a ‘modern’ version of HadCRUT3 by using only the original 4100 stations (used by CRUTEM3) from CRUTEM4 station data.  The list of these stations are defined here. I then merged these with  both the older HadSST2 and HADSST3 to derive annual global temperature anomalies. Figure 2 shows the result. I get almost exactly the same values as the full 7820 stations in HadCRUT4. It certainly does not reproduce HadCRUT3 !

Fig2-768x452

This result provides two conclusions.

  1. Modern CRUTEM3 stations give a different result to the original CRUTEM3 stations.
  2. SST data is not responsible  for the difference between HadCRUT4 and HadCRUT3

To confirm point 1) I used exactly the same code to regenerate HadCRUT3 temperature series using the original CRUTEM3 station data as opposed to the ‘modern’ values based on CRUTEM4.

Fig3-768x452

The original CRUTEM3 station data I had previously downloaded in 2012. These are combined with HADSST2 data. Now we see that  the agreement with the H3 annual temperatures is very good, and indeed reproduces the hiatus.

So the conclusion is very simple. The monthly temperature values in over 4000 CRUTEM3 stations have all been continuously changed, and it is these changes alone that have resulted in transforming the 16 year long hiatus in global warming into a rising temperature trend. Furthermore all these updates have only affected temperatures AFTER 1998! Temperatures before 1998 have hardly changed at all, which is the second requirement needed to eliminate the hiatus.

P.S. I am sure there are excellent arguments as to why pair-wise ‘homogenisation’ is wonderful but why then does it only affect data after 1998 ?

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”

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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.)

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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.

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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?

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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.

Abstract:
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 :

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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

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

ADDED:

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.

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.