Scientific Hubris and Global Warming

Reblogged from Watts Up With That:

Scientific Hubris and Global Warming

Guest Post by Gregory Sloop

Notwithstanding portrayals in the movies as eccentrics who frantically warn humanity about genetically modified dinosaurs, aliens, and planet-killing asteroids, the popular image of a scientist is probably closer to the humble, bookish Professor, who used his intellect to save the castaways on practically every episode of Gilligan’s Island. The stereotypical scientist is seen as driven by a magnificent call, not some common, base motive. Unquestionably, science progresses unerringly to the truth.

This picture was challenged by the influential twentieth-century philosopher of science Thomas Kuhn, who held that scientific ”truth” is determined not as much by facts as by the consensus of the scientific community. The influence of thought leaders, rewarding of grants, and scorn of dissenters are used to protect mainstream theory. Unfortunately, science only makes genuine progress when the mainstream theory is disproved, what Kuhn called a “paradigm shift.” Data which conflict with the mainstream paradigm are ignored instead of used to develop a better one. Like most people, scientists are ultimately motivated by financial security, career advancement, and the desire for admiration. Thus, nonscientific considerations impact scientific “truth.”

This corruption of a noble pursuit permits scientific hubris to prosper. It can only exist when scientists are less than dispassionate seekers of truth. Scientific hubris condones suppression of criticism, promotes unfounded speculation, and excuses rejection of conflicting data. Consequently, scientific hubris allows errors to persist indefinitely. However, science advances so slowly the public usually has no idea of how often it is wrong.

Reconstructing extinct organisms from fossils requires scientific hubris. The fewer the number of fossils available, the greater the hubris required for reconstruction. The original reconstruction of the peculiar organism Hallucigenia, which lived 505 million years ago, showed it upside down and backwards. This was easily corrected when more fossils were found and no harm was done.

In contrast, scientific hubris causes harm when bad science is used to influence behavior. The 17th century microscopist Nicholas Hartsoeker drew a complete human within the head of a sperm, speculating that this was what might be beneath the “skin” of a sperm. Belief in preformation, the notion that sperm and eggs contain complete humans, was common at the time. His drawing could easily have been used to demonstrate why every sperm is sacred and masturbation is a sin.

Scientific hubris has claimed many. many lives. In the mid 19th century, the medical establishment rejected Ignaz Semmelweis’ recommendation that physicians disinfect their hands prior to examining pregnant women despite his unequivocal demonstration that this practice slashed the death rate due to obstetric infections. Because of scientific hubris, “medicine has a dark history of opposing new ideas and those who proposed them.” It was only when the germ theory of disease was established two decades later that the body of evidence supporting Semmelweis’ work became impossible to ignore. The greatest harm caused by scientific hubris is that it slows progress towards the truth.

Record keeping of earth’s surface temperature began around 1880, so there is less than 150 years of quantitative data about climate, which evolves at a glacial pace. Common sense suggests that quantitative data covering multiple warming and cooling periods is necessary to give perspective about the evolution of climate. Only then will scientists be able to make an educated guess whether the 1.5 degrees Fahrenheit increase in earth’s temperature since 1930 is the beginning of sustained warming which will negatively impact civilization, or a transient blip.

The inconvenient truth is that science is in the data acquisition phase of climate study, which must be completed before there is any chance of predicting climate, if it is predictable [vide infra]. Hubris goads scientists into giving answers even when the data are insufficient.

To put our knowledge about climate in perspective, imagine an investor has the first two weeks of data on the performance of a new stock market. Will those data allow the investor to know where the stock market will be in twenty years? No, because the behavior of the many variables which determine the performance of a stock market is unpredictable. Currently, predicting climate is no different.

Scientists use data from proxies to estimate earth’s surface temperature when the real temperature is unknowable. In medicine, these substitutes are called “surrogate markers.” Because hospital laboratories are rigorously inspected and the reproducibility, accuracy, and precision of their data is verified, hospital laboratory practices provide a useful standard for evaluating the quality of any scientific data.

Surrogate markers must be validated by showing that they correlate with “gold standard” data before they are used clinically. Comparison of data from tree growth rings, a surrogate marker for earth’s surface temperature, with the actual temperature shows that correlation between the two is worsening for unknown reasons. Earth’s temperature is only one factor which determines tree growth. Because soil conditions, genetics, rainfall, competition for nutrients, disease, age, fire, atmospheric carbon dioxide concentrations and consumption by herbivores and insects affect tree growth, the correlation between growth rings and earth’s temperature is imperfect.

Currently, growth rings cannot be regarded as a valid surrogate marker for the temperature of earth’s surface. The cause of the divergence problem must be identified and somehow remedied, and the remedy validated before growth rings are a credible surrogate marker or used to validate other surrogate markers.

Data from ice cores, boreholes, corals, and lake and ocean sediments are also used as surrogate markers. These are said to correlate with each other. Surrogate marker data are interpreted as showing a warm period between c.950 and c. 1250, which is sometimes called the “Medieval Climate Optimum,” and a cooler period called the “Little Ice Age” between the 16th and 19th centuries. The data from these surrogate markers have not been validated by comparison with a quantitative standard. Therefore, they give qualitative, not quantitative data. In medical terms, qualitative data are considered to be only “suggestive” of a diagnosis, not diagnostic. This level of diagnostic certainty is typically used to justify further diagnostic testing, not definitive therapy.

Anthropogenic global warming is often presented as fact. According to the philosopher Sir Karl Popper, a single conflicting observation is sufficient to disprove a theory. For example, the theory that all swans are white is disproved by one black swan. Therefore, the goal of science is to disprove, not prove a theory. Popper described how science should be practiced, while Kuhn described how science is actually practiced. Few theories satisfy Popper’s criterion. They are highly esteemed and above controversy. These include relativity, quantum mechanics, and plate tectonics. These theories come as close to settled science as is possible.

Data conflict about anthropogenic global warming. Using data from ice cores and lake sediments, Professor Gernot Patzelt argues that over the last 10,000 years, 65% of the time earth’s temperature was warmer than today. If his data are correct, human deforestation and carbon emissions are not required for global warming and intervention to forestall it may be futile.

The definitive test of anthropogenic global warming would be to study a duplicate earth without humans. Realistically, the only way is develop a successful computer model. However, modeling climate may be impossible because climate is a chaotic system. Small changes in the initial state of a chaotic system can cause very different outcomes, making them unpredictable. This is commonly called the “butterfly effect” because of the possibility that an action as fleeting as the beating of a butterfly’s wings can affect distant weather. This phenomenon also limits the predictability of weather.

Between 1880 and 1920, increasing atmospheric carbon dioxide concentrations were not associated with global warming. These variables did correlate between 1920 and 1940 and from around 1970 to today. These associations may appear to be compelling evidence for global warming, but associations cannot prove cause and effect. One example of a misleading association was published in a paper entitled “The prediction of lung cancer in Australia 1939–1981.” According to this paper, “Lung cancer is shown to be predicted from petrol consumption figures for a period of 42 years. The mean time for the disease to develop is discussed and the difference in the mortality rate for male and females is explained.” Obviously, gasoline use does not cause lung cancer.

The idea that an association is due to cause and effect is so attractive that these claims continue to be published. Recently, an implausible association between watching television and chronic inflammation was reported. In their book Follies and Fallacies in Medicine, Skrabanek and McCormick wrote, “As a result of failing to make this distinction [between association and cause], learning from experience may lead to nothing more than learning to make the same mistakes with increasing confidence.” Failure to learn from mistakes is another manifestation of scientific hubris. Those who are old enough to remember the late 1970’s may recall predictions of a global cooling crisis based on transient glacial growth and slight global cooling.

The current situation regarding climate change is similar to that confronting cavemen when facing winter and progressively shorter days. Every day there was less time to hunt and gather food and more cold, useless darkness. Shamans must have desperately called for ever harsher sacrifices to stop what otherwise seemed inevitable. Only when science enabled man to predict the return of longer days was sacrifice no longer necessary.

The mainstream position about anthropogenic global warming is established. The endorsement of the United Nations, U.S. governmental agencies, politicians, and the media buttresses this position. This nonscientific input has contributed to the perception that anthropogenic global warming is settled science. A critical evaluation of the available data about global warming, and anthropogenic global warming in particular, allow only a guess about the future climate. It is scientific hubris not to recognize that guess for what it is.


A New 200‐year Spatial Reconstruction of West Antarctic Surface Mass Balance

Reblogged from Watts Up With That:

Antarctica consisted of a large Eastern ice sheet, a smaller Western ice sheet and the Antarctic peninsula. The Eastern ice sheet has been slightly gaining ice from 1992, but the Western ice sheet mass trend is more uncertain.

A new paper published in Geophysical Research: Atmospheres,  presents a reconstruction of the surface mass balance (SMB) over the West Antarctic Ice Sheet (WAIS) spanning 1800-2010. The study is based on ice core records combined with a European reanalysis model and a regional climate model.

The results show a significant negative trend of -1.9 ± 2.2 Gt/yr over the WAIS during the 19th century but a significant positive trend of 5.4 ± 2.9 Gt/yr between 1900 and 2010. In contrast, the Antarctic Peninsula (AP) shows opposite SMB trends to the WAIS trends with different signs in the 19th and 20th centuries. The study compared the trends to large-scale atmospheric oscillations. The SMB in the AP and WAIS are significantly correlated with the Southern Annular Mode (the north–south movement of the westerly wind belt that circles Antarctica), but the correlations are unstable.


High‐spatial resolution surface mass balance (SMB) over the West Antarctic Ice Sheet (WAIS) spanning 1800‐2010 is reconstructed by means of ice core records combined with the outputs of the European Centre for Medium‐range Weather Forecasts “Interim” reanalysis (ERA‐Interim) and the latest polar version of the Regional Atmospheric Climate Model (RACMO2.3p2). The reconstruction reveals a significant negative trend (‐1.9 ± 2.2 Gt yr‐1 decade‐1) in the SMB over the entire WAIS during the 19th century, but a statistically significant positive trend of 5.4 ± 2.9 Gt yr‐1 decade‐1 between 1900 and 2010, in contrast to insignificant WAIS SMB changes during the 20th century reported earlier. At regional scales, the Antarctic Peninsula (AP) and western WAIS show opposite SMB trends, with different signs in the 19th and 20th centuries. The annual resolution reconstruction allows us to examine the relationships between SMB and large‐scale atmospheric oscillations. Although SMB over the AP and western WAIS correlates significantly with the Southern Annular Mode (SAM) due to the influence of the Amundsen Sea Low (ASL) and El Niño/Southern Oscillation (ENSO) during 1800‐2010, the significant correlations are temporally unstable, associated with the phase of SAM, ENSO and the Pacific decadal oscillation (PDO). In addition, the two climate modes seem to contribute little to variability in SMB over the whole WAIS on decadal‐centennial time scales. This new reconstruction also serves to identify unreliable precipitation trends in ERA‐Interim, and thus has potential for assessing the skill of other reanalyses or climate models to capture precipitation trends and variability.

Comparison of global climatologies confirms warming of the global ocean

Reblogged from Watts Up With That:

Institute of Atmospheric Physics, Chinese Academy of Sciences


IMAGE: Deployment of an APEX float from a German research ship.

Credit: Argo

The global ocean represents the most important component of the Earth climate system. The oceans accumulate heat energy and transport heat from the tropics to higher latitudes, responding very slowly to changes in the atmosphere. Digital gridded climatologies of the global ocean provide helpful background information for many oceanographic, geochemical and biological applications. Because both the global ocean and the observational basis are changing, periodic updates of ocean climatologies are needed, which is in line with the World Meteorological Organization’s recommendations to provide decadal updates of atmospheric climatologies.

“Constructing ocean climatologies consists of several steps, including data quality control, adjustments for instrumental biases, and filling the data gaps by means of a suitable interpolation method”, says Professor Viktor Gouretski of the University of Hamburg and a scholarship holder of the Chinese Academy of Sciences’ President’s International Fellowship Initiative (PIFI) at the Institute of Atmospheric Physics, Chinese Academy of Sciences, and the author of a report recently published in Atmospheric and Oceanic Science Letters.

“Sea water is essentially a two-component system, with a nonlinear dependency of density on temperature and salinity, with the mixing in the ocean interior taking place predominantly along isopycnal surfaces. Therefore, interpolation of oceanic parameters should be performed on isopycnals rather than on isobaric levels, to minimize production of artificial water masses. The differences between these two methods of data interpolation are most pronounced in the high-gradient regions like the Gulf Stream, Kuroshio, and Antarctic Circumpolar Current,” continues Professor Gouretski.

In his recent report, Professor Gouretski presents a new World Ocean Circulation Experiment/ARGO Global Hydrographic Climatology (WAGHC), with temperature and salinity averaged on local isopycnal surfaces. Based on high-quality ship-board data and temperature and salinity profiles from ARGO floats, the new climatology has a monthly resolution and is available on a 1/4° latitude-longitude grid.

“We have compared the WAGHC climatology with NOAA’s WOA13 gridded climatology. These climatologies represent alternative digital products, but the WAGHC has benefited from the addition of new ARGO float data and hydrographic data from the North Polar regions”, says Professor Gourteski. “The two climatologies characterize mean ocean states that are 25 years apart, and the zonally averaged section of the WAGHC-minus-WOA13 temperature difference clearly shows the ocean warming signal, with a mean temperature increase of 0.05°C for the upper 1500-m layer since 1984”.

SVENSMARK’s Force Majeure, The Sun’s Large Role in Climate Change

Reblogged from Watts Up With That:


By H. Sterling Burnett

By bombarding the Earth with cosmic rays and being a driving force behind cloud formations, the sun plays a much larger role on climate than “consensus scientists” care to admit.

The Danish National Space Institute’s Dr. Henrik Svensmark has assembled a powerful array of data and evidence in his recent study, Force Majeure the Sun’s Large Role in Climate Change.

The study shows that throughout history and now, the sun plays a powerful role in climate change. Solar activity impacts cosmic rays which are tied to cloud formation. Clouds, their abundance or dearth, directly affects the earth’s climate.

Climate models don’t accurately account for the role of clouds or solar activity in climate change, with the result they assume the earth is much more sensitive to greenhouse gas levels than it is. Unfortunately, the impact of clouds and the sun on climate are understudied because climate science has become so politicized.

Full audio interview here:  Interview with Dr. Henrick Svensmark


H. Sterling Burnett, Ph.D. is a Heartland senior fellow on environmental policy and the managing editor of Environment & Climate News.

UAH, RSS, NOAA, UW: Which Satellite Dataset Should We Believe?

Reblogged from

April 23rd, 2019 by Roy W. Spencer, Ph. D.

NOTE: See the update from John Christy below, addressing the use of RATPAC radiosonde data.

This post has two related parts. The first has to do with the recently published study of AIRS satellite-based surface skin temperature trends. The second is our response to a rather nasty Twitter comment maligning our UAH global temperature dataset that was a response to that study.

The AIRS Study

NASA’s Atmospheric InfraRed Sounder (AIRS) has thousands of infrared channels and has provided a large quantity of new remote sensing information since the launch of the Aqua satellite in early 2002. AIRS has even demonstrated how increasing CO2 in the last 15+ years has reduced the infrared cooling to outer space at the wavelengths impacted by CO2 emission and absorption, the first observational evidence I am aware of that increasing CO2 can alter — however minimally — the global energy budget.

The challenge for AIRS as a global warming monitoring instrument is that it is cloud-limited, a problem that worsens as one gets closer to the surface of the Earth. It can only measure surface skin temperatures when there are essentially no clouds present. The skin temperature is still “retrieved” in partly- (and even mostly-) cloudy conditions from other channels higher up in the atmosphere, and with “cloud clearing” algorithms, but these exotic numerical exercises can never get around the fact that the surface skin temperature can only be observed with satellite infrared measurements when no clouds are present.

Then there is the additional problem of comparing surface skin temperatures to traditional 2 meter air temperatures, especially over land. There will be large biases at the 1:30 a.m./p.m. observation times of AIRS. But I would think that climate trends in skin temperature should be reasonably close to trends in air temperature, so this is not a serious concern with me (although Roger Pielke, Sr. disagrees with me on this).

The new paper by Susskind et al. describes a 15-year dataset of global surface skin temperatures from the AIRS instrument on NASA’s Aqua satellite. ScienceDaily proclaimed that the study “verified global warming trends“, even though the period addressed (15 years) is too short to say much of anything much of value about global warming trends, especially since there was a record-setting warm El Nino near the end of that period.

Furthermore, that period (January 2003 through December 2017) shows significant warming even in our UAH lower tropospheric temperature (LT) data, with a trend 0.01 warmer than the “gold standard” HadCRUT4 surface temperature dataset (all deg. C/decade):

AIRS: +0.24
GISTEMP: +0.22
ECMWF: +0.20
Cowtan & Way: +0.19
UAH LT: +0.18
HadCRUT4: +0.17

I’m pretty sure the Susskind et al. paper was meant to prop up Gavin Schmidt’s GISTEMP dataset, which generally shows greater warming trends than the HadCRUT4 dataset that the IPCC tends to favor more. It remains to be seen whether the AIRS skin temperature dataset, with its “clear sky bias”, will be accepted as a way to monitor global temperature trends into the future.

What Satellite Dataset Should We Believe?

Of course, the short period of record of the AIRS dataset means that it really can’t address the pre-2003 adjustments made to the various global temperature datasets which significantly impact temperature trends computed with 40+ years of data.

What I want to specifically address here is a public comment made by Dr. Scott Denning on Twitter, maligning our (UAH) satellite dataset. He was responding to someone who objected to the new study, claiming our UAH satellite data shows minimal warming. While the person posting this objection didn’t have his numbers right (and as seen above, our trend even agrees with HadCRUT4 over the 2003-2017 period), Denning took it upon himself to take a swipe at us (see his large-font response, below):


First of all, I have no idea what Scott is talking about when he lists “towers” and “aircraft”…there has been no comprehensive comparisons of such data sources to global satellite data, mainly because there isn’t nearly enough geographic coverage by towers and aircraft.

Secondly, in the 25+ years that John Christy and I have pioneered the methods that others now use, we made only one “error” (found by RSS, and which we promptly fixed, having to do with an early diurnal drift adjustment). The additional finding by RSS of the orbit decay effect was not an “error” on our part any more than our finding of the “instrument body temperature effect” was an error on their part. All satellite datasets now include adjustments for both of these effects.

Nevertheless, as many of you know, our UAH dataset is now considered the “outlier” among the satellite datasets (which also include RSS, NOAA, and U. of Washington), with the least amount of global-average warming since 1979 (although we agree better in the tropics, where little warming has occurred). So let’s address the remaining claim of Scott Denning’s: that we disagree with independent data.

The only direct comparisons to satellite-based deep-layer temperatures are from radiosondes and global reanalysis datasets (which include all meteorological observations in a physically consistent fashion). What we will find is that RSS, NOAA, and UW have remaining errors in their datasets which they refuse to make adjustments for.

From late 1998 through 2004, there were two satellites operating: NOAA-14 with the last of the old MSU series of instruments on it, and NOAA-15 with the first new AMSU instrument on it. In the latter half of this overlap period there was considerable disagreement that developed between the two satellites. Since the older MSU was known to have a substantial measurement dependence on the physical temperature of the instrument (a problem fixed on the AMSU), and the NOAA-14 satellite carrying that MSU had drifted much farther in local observation time than any of the previous satellites, we chose to cut off the NOAA-14 processing when it started disagreeing substantially with AMSU. (Engineer James Shiue at NASA/Goddard once described the new AMSU as the “Cadillac” of well-calibrated microwave temperature sounders).

Despite the most obvious explanation that the NOAA-14 MSU was no longer usable, RSS, NOAA, and UW continue to use all of the NOAA-14 data through its entire lifetime and treat it as just as accurate as NOAA-15 AMSU data. Since NOAA-14 was warming significantly relative to NOAA-15, this puts a stronger warming trend into their satellite datasets, raising the temperature of all subsequent satellites’ measurements after about 2000.

But rather than just asserting the new AMSU should be believed over the old (drifting) MSU, let’s look at some data. Since Scott Denning mentions weather balloon (radiosonde) data, let’s look at our published comparisons between the 4 satellite datasets and radiosondes (as well as global reanalysis datasets) and see who agrees with independent data the best:

Trend differences 1979-2005 between 4 satellite datasets and either radiosondes (blue) or reanalyses (red) for the MSU2/AMSU5 tropospheric channel in the tropics. The balloon trends are calculated from the subset of gripoints where the radiosonde stations are located, whereas the reanalyses contain complete coverage of the tropics. For direct comparisons of full versus station-only grids see the paper.

Clearly, the RSS, NOAA, and UW satellite datasets are the outliers when it comes to comparisons to radiosondes and reanalyses, having too much warming compared to independent data.

But you might ask, why do those 3 satellite datasets agree so well with each other? Mainly because UW and NOAA have largely followed the RSS lead… using NOAA-14 data even when its calibration was drifting, and using similar strategies for diurnal drift adjustments. Thus, NOAA and UW are, to a first approximation, slightly altered versions of the RSS dataset.

Maybe Scott Denning was just having a bad day. In the past, he has been reasonable, being the only climate “alarmist” willing to speak at a Heartland climate conference. Or maybe he has since been pressured into toeing the alarmist line, and not being allowed to wander off the reservation.

In any event, I felt compelled to defend our work in response to what I consider (and the evidence shows) to be an unfair and inaccurate attack in social media of our UAH dataset.

UPDATE from John Christy (11:10 CDT April 26, 2019):

In response to comments about the RATPAC radiosonde data having more warming, John Christy provides the following:

The comparison with RATPAC-A referred to in the comments below is unclear (no area mentioned, no time frame).  But be that as it may, if you read our paper, RATPAC-A2 was one of the radiosonde datasets we used.  RATPAC-A2 has virtually no adjustments after 1998, so contains warming shifts known to have occurred in the Australian and U.S. VIZ sondes for example.  The IGRA dataset used in Christy et al. 2018 utilized 564 stations, whereas RATPAC uses about 85 globally, and far fewer just in the tropics where this comparison shown in the post was made.  RATPAC-A warms relative to the other radiosonde/reanalyses datasets since 1998 (which use over 500 sondes), but was included anyway in the comparisons in our paper. The warming bias relative to 7 other radiosonde and reanalysis datasets can be seen in the following plot:


Adjusting Good Data To Make It Match Bad Data

Reblogged from


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

Gavin Schmidt Promises To Resign | The Deplorable Climate Science Blog

This is what the same graph looks like now.

Wood for Trees: Interactive Graphs

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

I predicted this would happen on

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


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

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

Here’s what I’m predicting:

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

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

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

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


Wood for Trees: Interactive Graphs

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

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

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

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

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

Climate Analysis | Remote Sensing Systems

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

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

Remote Sensing Systems

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

RSS V3 shows no warming since 2002.

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


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

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

NASA 1999   NASA 2016

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

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.


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]


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.



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].



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]


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]



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]


· 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.



· 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.


· 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.


· 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]


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.


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.


[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 Authors trend analysis – least squares regression.

[5] Authors trend analysis – least squares regression.

[6] See also 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.


[9] Journal of Climatology 3/1/18 –

[10] Data available at:


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


[14] Note this is closer to one third of the NASA estimated increase.






[20] Sample paper on the debate from Journal of Geophysical Research – “There remain important inconsistencies between surface and satellite records.”





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




[29] NOAA Tides & Currents –

[30] US Hurricanes:

[31]Global Cyclone activity:



[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 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)


GHCN v3.3 vs v4 Greenland Difference

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

GHCN v3.3 vs v4 Greenland Anomaly


GHCN v3.3 vs v4 Canada Difference

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

GHCN v3.3 vs v4 Canada Anomaly


GHCN v3.3 vs v4 USA Anomaly Difference

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

GHCN v3.3 vs v4 USA Anomaly

The Caribbean & Bermuda

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

Antigua & Barbuda

Antigua has no data in GHCN v3.3:

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

So all we get is the v4 anomaly graph:

GHCN v4 Antigua & Barbuda Anomaly


GHCN v3.3 vs v4 Bermuda Difference

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

GHCN v3.3 vs v4 Bermuda Anomaly

The Bahamas

GHCN v3.3 vs v4 Bahamas Difference

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

GHCN v3.3 vs v4 Bahamas Anomaly


GHCN v3.3 vs v4 Barbados Difference

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

GHCN v3.3 vs v4 Barbados Anomaly

Cayman Islands

GHCN v3.3 vs v4 Cayman Islands Difference

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

GHCN v3.3 vs v4 Cayman Islands Anomaly


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

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

GHCN v4 Dominica Anomaly

Dominican Republic

GHCN v3.3 vs v4 Dominican Republic Difference

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

GHCN v3.3 vs v4 Dominican Republic Anomaly


GHCN v3.3 vs v4 Grenada Difference

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

GHCN v3.3 vs v4 Grenada Anomaly


GHCN v3.3 vs v4 Guadeloupe Difference

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

GHCN v3.3 vs v4 Guadeloupe Anomaly


GHCN v3.3 vs v4 Haiti Difference

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

GHCN v3.3 vs v4 Haiti Anomaly


GHCN v3.3 vs v4 Jamaica Difference

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

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

GHCN v3.3 vs v4 Jamaica Anomaly


GHCN v3.3 vs v4 Martinique Difference

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

GHCN v3.3 vs v4 Martinique Anomaly

Netherlands Antilles

GHCN v3.3 vs v4 Netherlands Antilles Difference

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

GHCN v3.3 vs v4 Netherlands Antilles Anomaly

Puerto Rico

GHCN v3.3 vs v4 Puerto Rico Difference

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

GHCN v3.3 vs v4 Puerto Rico Anomaly

St. Kits & Nevis

GHCN v3.3 vs v4 Saint Kits & Nevis Difference

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

GHCN v3.3 vs v4 Saint Kits & Nevis Anomaly

St. Lucia

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

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

So all we get is this v4 anomaly graph:

GHCN v4 Saint Vincent & The Grenadines Anomaly

Trinidad & Tobago

GHCN v4 Trinidad & Tobago Difference

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

GHCN v4 Trinidad & Tobago Anomaly

Virgin Islands (US)

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

GHCN v4 U.S. Virgin islands Difference

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

GHCN v4 U.S. Virgin islands Anomaly

Mexico & Central America


GHCN v3.3 vs v4 Mexico Difference

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

GHCN v3.3 vs v4 Mexico Anomaly


GHCN v3.3 vs v4 Belize Difference

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

GHCN v3.3 vs v4 Belize Anomaly


GHCN v3.3 vs v4 Guatemala Difference

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

GHCN v3.3 vs v4 Guatemala Anomaly

El Salvador

GHCN v3.3 vs v4 El Salvador Difference

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

GHCN v3.3 vs v4 El Salvador Anomaly


GHCN v3.3 vs v4 Honduras Difference

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

GHCN v3.3 vs v4 Honduras Anomaly


GHCN v3.3 vs v4 Nicaragua Difference

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

GHCN v3.3 vs v4 Nicaragua Anomaly

Coasta Rica

GHCN v3.3 vs v4 Costa Rica Difference

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

GHCN v3.3 vs v4 Costa Rica Anomaly


GHCN v3.3 vs v4 Panama Difference

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

GHCN v3.3 vs v4 Panama Anomaly

Tech Talk

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

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

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

Here’s the list:

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

In Conclusion

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

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

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

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

Inconvenient stumps

Reblogged from Watts Up With That:

Climate alarmists tell us that the Earth has never been warmer, and that we can tell by looking at tree rings, treelines, and other proxy indicators of climate.

Climate scientists claim the warmth is unprecedented.

We’ve been told it is warming so fast, we have only 12 years left!

Yet nature seems to not be paying attention to such pronouncements, as this discovery shows.

This photo shows a tree stump of White Spruce that was radiocarbon dated at 5000 years old. It was located 100 km north of the current tree line in extreme Northwest Canada.

The area is now frozen tundra, but it was once warm enough to support significant tree growth like this.

If climate was this warm in the past, how did that happen before we started using the fossil fuels that supposedly made our current climate unprecedentedly warm?

The noble corruption of climate science

Reblogged from Fabius Maximus Website:

Science & Nature

Summary:  This is a story of climate science, tracing from its enthusiastic beginnings as small field – warning of a global threat –to its rich and increasingly desperate present. It is a long story, with a climax at the end.

Sign of "Corruption above" - dreamstime_105297867
ID 105297867 © Adonis1969 | Dreamstime.

The climate change campaign hits a dead end

On 24 June 1988, James Hansen’s testimony to the Senate began the campaign to fight anthropogenic global warming. During the following 31 years we have heard increasingly dire forecasts of doom. Some describe the distant future, beyond any reasonable forecasting horizon (due to both technical and social uncertainties). Some describe the near future. Many attribute almost all current extreme weather to our emissions of greenhouse gases (GHG) – using impossible to validate methods.

Karl Popper said that successful predictions, especially of the unexpected, were the gold standard of science (see here). That is a problem for climate activists. The Earth has been warming since the mid-19th century, when the Little Ice Age ended. The rate of warming in the past four decades (since 1977) is roughly the same as that during the four decades up to 1945. Anthropogenic GHG became a major factor only after WWII. So warming has occurred as predicted, but a naive forecast (without considering GHG) would have also predicted warming. There are explanations for this, but it makes model validation difficult (perhaps why it is seldom attempted: see links in section f in the For More Info section of this post).

Worse, the weather has not cooperated. Major hurricanes avoided America for 11 years, ending in 2017. Warming slowed during what climate scientists called the “pause” or “hiatus” (see links about its causes). And most forms of extreme weather have no obvious increasing trend. So surveys show little public support in America for expensive measures to fight climate change.

Activists grow desperate.

The Uninhabitable Earth” by David Wallace-Wells in New York Magazine
“Famine, economic collapse, a sun that cooks us: what climate change could wreak
– sooner than you think.”
Expanded into a book: The Uninhabitable Earth: Life After Warming.

The five ways the human race could be WIPED OUT because of global warming.”
By Rod Ardehali at the Daily Mail. H/t to the daily links at Naked Capitalism.
Promo for Falter: Has the Human Game Begun to Play Itself Out?, a book by Bill McKibben.

Activists responded to the uncooperative weather by making ever-more dire predictions (many of which have passed their due date and been proven false).  All extreme weather was “climate change.” They made more vivid propaganda (e.g., the 10:10 video, showing a teacher exploding the heads of students who do not accept her propaganda). They increased the volume of their claims, with more 2-minute hate sessions for dissenters (with lies about even eminent climate scientists). The long-term effects of this are (hopefully) small, since these fear barrages have been the Left’s go-to tactic since the 1960s (see some classics of the genre).

But one tactic might have awful long-term consequences. Many activists are climate scientists (see the many stories about depression among them, overcome by fears about their worst-case scenarios, such as this and this). Some have reacted with noble lie corruption (from Plato’s The Republic). However well-intended, it might weaken the public’s trust in science (as might the replication crisis, of which this is an example, if they learn about it).

Broken stone with "Trust" carved in it.
ID 37813605 © Lane Erickson | Dreamstime.

The Noble Lie in action

Obvious evidence of this is climate scientists’ relentless focus on RCP8.5, the worst-case scenario in the IPCC’s Fifth Assessment Report. As a good worst-case should be, it is almost impossible to happen without unlikely assumptions (details here; also see Dr. Curry’s articles). Yet it receives the majority of mentions in the climate science literature – usually with no mention of its improbable nature (see this history). Activists exaggerate these papers, whose stories are uncritically reported by journalists. A decade of this bombardment has a fraction of the Left terrified, certain that we are doomed.

For a recent example, see “A glacier the size of Florida is on track to change the course of human civilization” by “Pakalolo” at the Daily Kos. Widely reposted, quite bonkers. See the details here.

Worse, climate scientists remain silent when activists exaggerate their work, even when they materially misrepresenting it. The most extreme doomster predictions are greeted by silence. Even over-top climate doomster claims receive only mild push-back. For example, see the reactions to “The Uninhabitable Earth” by David Wallace-Wells. WaPo: “Scientists challenge magazine story about ‘uninhabitable Earth’.” Climate Feedback: “Scientists explain what New York Magazine article on “The Uninhabitable Earth” gets wrong.” It was too much even for Michael Mann.

Yet leading climate scientists are quick to loudly condemn skeptics – even fellow climate scientists – for questioning aggressive claims about climate change. Allowing activists to call scientists “deniers” for challenging the current paradigm is imo among the most irresponsible actions by leaders of science, ever. By ancient law, silence means assent to activists’ behavior. They are guilty of “aiding and abetting.” For more about this, see About the corruption of climate science.

But in the past few years, activist scientists’ desperation appears to have pushed them to take another step away from science.

Papers to generate alarmist news!

As Marc Morano of Climate Depot says, recent studies often appear designed to produce media stories for alarmists. I see several of these every week. The most recent is “Key indicators of Arctic climate change: 1971–2017” in Environmental Research Letters (April 2019), by scientists at the International Arctic Research Center at the University of Alaska-Fairbanks and the Geological Survey of Denmark and Greenland in Copenhagen. Abstract:

“Key observational indicators of climate change in the Arctic, most spanning a 47 year period (1971–2017) demonstrate fundamental changes among nine key elements of the Arctic system. …Downward trends continue in sea ice thickness (and extent) and spring snow cover extent and duration, while near-surface permafrost continues to warm. Several of the climate indicators exhibit a significant statistical correlation with air temperature or precipitation, reinforcing the notion that increasing air temperatures and precipitation are drivers of major changes in various components of the Arctic system. …

“The Arctic biophysical system is now clearly trending away from its 20th Century state and into an unprecedented state, with implications not only within but beyond the Arctic. The indicator time series of this study are freely downloadable at”

Ecowatch describes it in their usual apocalyptic fashion: “Researchers Warn Arctic Has Entered ‘Unprecedented State’ That Threatens Global Climate Stability.

The paper is odd in several ways. It is evidence showing the broken peer-review process. Five times they describe conditions in the arctic as “unprecedented.” But they start their analysis with data from the 1970’s. Given the various kinds of long-term natural fluctuations, five decades of data is too brief a period to draw such a bold conclusion.

The authors neglect to mention that the Arctic was also warm in the 1930’s. Which is strange since one of the authors, Uma S. Bhatt, was also a co-author of a major paper on the subject: “Variability and Trends of Air Temperature and Pressure in the Maritime Arctic, 1875–2000” in the Journal of Climate, June 2003. She did not even cite it in their new paper. Abstract …

“Arctic atmospheric variability during the industrial era (1875–2000) is assessed using spatially averaged surface air temperature (SAT) and sea level pressure (SLP) records. Air temperature and pressure display strong multidecadal variability on timescales of 50–80 yr [termed low-frequency oscillation (LFO)]. Associated with this variability, the Arctic SAT record shows two maxima: in the 1930s–40s and in recent decades, with two colder periods in between.

“In contrast to the global and hemispheric temperature, the maritime Arctic temperature was higher in the late 1930s through the early 1940s than in the 1990s. …Thus, the large-amplitude multidecadal climate variability impacting the maritime Arctic may confound the detection of the true underlying climate trend over the past century. LFO-modulated trends for short records are not indicative of the long-term behavior of the Arctic climate system.

“The accelerated warming and a shift of the atmospheric pressure pattern from anticyclonic to cyclonic in recent decades can be attributed to a positive LFO phase. It is speculated that this LFO-driven shift was crucial to the recent reduction in Arctic ice cover. Joint examination of air temperature and pressure records suggests that peaks in temperature associated with the LFO follow pressure minima after 5–15 yr. Elucidating the mechanisms behind this relationship will be critical to understanding the complex nature of low-frequency variability.”

Starting their analysis in the 1970s is misleading without disclosing that was a cold spell. There was concern then about global cooling (but not a consensus). See here and here for details. Starting in the 1970’s makes current conditions look extraordinary. Since we are in the warming period following the Little Ice Age, robust comparisons should include previous warm periods, such as the Medieval Warm Period and the Holocene climatic optimum.

A later paper provides more detail, showing the temperature anomaly in 2008 was aprox. 1°C warmer than the ~1940 peak: “Role of Polar Amplification in Long-Term Surface Air Temperature Variations and Modern Arctic Warming” by Roman V. Bekryaev et al. in Journal of Climate, 15 July 2010. Is that a one standard deviation from the long-term mean? Three? Are temperatures a normal distribution? They do not say. Climate science papers often use arcane statistics, but usually ignore the basics. (Here is an as yet unpublished estimate of arctic sea ice back to the 1880s. Here is a 2017 paper with arctic temperatures and sea ice extent back to 1900)

Two comments from climate scientists on this paper.

“It is normalization of data cherry picking.”
— Dr. Judith Curry (bio). She her analysis of arctic sea ice trends here and here. She writes at Climate Etc.

“Of course, if these changes are predominantly due to the Arctic Oscillation (AO) and/or the LFO, we should see a reversal. If not, the trend would continue. Time will eventually sort this out. But a proper literature summary should still be provided with papers that might disagree with the theme of a newer paper. All peer-reviewed perspectives on this subject should be given.”
— Dr. Roger Pielke Sr. (bio).

See other examples in the comments. These kind of stories are coming along like trolleys.

This is a follow-up to About the corruption of climate science.

Bleeding eye
“Bleeding Eye” by C. Bayraktaroglu.


Science has been politicized, distorting its results, before. It will be again. But climate science provides essential insights on several major public policy issues. Losing reliable guidance from it could have disastrous consequences. Worse, the high public profile of climate science means that a loss of public confidence in it might affect science as a whole.

Let’s hope that the leaders of climate science come to their senses soon, despite their personal, institutional, and ideological reasons to continue on this dark path.

For More Information

Hat tip on the ERL 2019 paper to Naked Capitalism’s daily links, who uncritically run climate alarmist articles, a one-side flow of information without context – terrifying their Leftist readers (other than that, their daily links are a valuable resource – which read every morning). Hat tip on the JoC 2003 paper to Marc Morano at Climate Depot; see his article about it.

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