Weekly Climate and Energy News Roundup #276

By Ken Haapala, President, Science and Environmental Policy Project

The Week That Was: July 8, 2017 – Brought to You by www.SEPP.org,



Quote of the Week. “Whoever is careless with the truth in small matters cannot be trusted with important matters: “– Albert Einstein


Number of the Week: 39%


New Atmospheric Data? Roy Spencer responds to the recent paper by Mears and Wentz, who are principals in Remote Sensing Systems (RSS), competitors with the Earth System Science Center at the University of Alabama in Huntsville (UAH). As speculated in last week’s TWTW, this may be part of an effort to discredit John Christy’s effective testimony on Capitol Hill that global climate models greatly overestimate the warming trend of the atmosphere. Spencer states:

“Before I go into the details, let’s keep all of this in perspective. Our globally-averaged trend is now about +0.12 C/decade, while the new RSS trend has increased to about +0.17 C/decade.

“Note these trends are still well below the average climate model trend for LT [Lower Troposphere], which is +0.27 C/decade.” [Boldface was italic in the original.]

What we see is that extending warming trends for a century, the models calculate a century-long trend of 1 degree C above the RSS calculations and 1.5 degrees C above the UAH calculations. The so-called “corrections” of 0.5 degrees C to the RSS data are not that significant when compared with the overestimates of the average of the global climate models.

Among other points, Spencer discusses the different techniques used by the two groups to adjust for the error in the diurnal cycle (daily pattern) in the climate models. UAH uses empirically derived adjustments, RSS uses model derived adjustments. As Spencer states:

“In general, it is difficult for us to follow the chain of diurnal corrections in the new RSS paper. Using a climate model to make the diurnal drift adjustments, but then adjusting those adjustments with empirical satellite data feels somewhat convoluted to us.”

See links under Challenging the Orthodoxy.


Surface Data: For some years, independent meteorologists such as Joseph D’Aleo have noticed a disturbing trend in historic data reported by certain government entities, such as NOAA, Ashville (previously called the National Climatic Data Center, now called the National Centers for Environmental Information). These historic data are used by NOAA, NASA and the Hadley CRU. Hadley CRU is a dataset developed at the Climatic Research Unit at the University of East Anglia in England and the Hadley Centre (the UK Met Office). In general, multiple adjustments were made to the historic data that reduced past warm periods. The net effect was to give a greater present day warming trend, than in the past.

For example, in the US, many long-term records were set in the 1930s, but the current, adjusted data does not show that decade as particularly hot, when compared to today. And, the US was the world-wide gold standard for temperature measurements.

A new study by Wallace, D’Aleo, and Idso systematically analyzes the Global Average Surface Temperatures reported by NOAA, NASA, and Hadley CRU. The results are striking. For example, Figure IV-1 shows five different plots of 5-year temperature trends by NASA-GISS (Goddard Institute for Space Studies on Broadway) produced from 1980 to 2015. The period around 1940 became progressively cooler in these plots. Similar adjustments have been made to the other datasets as well as to datasets for specific locations.

The study recognizes that adjustments to surface data may need to be changed, but the overall trend reflected in the changes appears to create a bias in the data. Further, strong cyclical patterns that once appeared are muted. A comprehensive review of the adjustments is in order.

Side note: Some of those who established the standards for US weather stations, which became the world-wide gold standard, were members of the oldest science society of Washington. As a past president of that society, this author finds the tarnishing of that standard particularly disturbing. See links under Challenging the Orthodoxy.


Red Team / Blue Team: In several instances in congressional testimony, John Christy has called for a Red Team/Blue Team approach for addressing the US issues regarding climate science. The UN Intergovernmental Panel on Climate Change (IPCC) and its followers such as the US Global Change Research Program (USGCRP) are well funded by government. They attribute climate change to primarily human activities, particularly carbon dioxide emissions.

As Christy points out, what is lacking is a well-funded Red Team:

“…[to] look at issues such as natural variability, the failure of climate models and the huge benefits to society from affordable energy, carbon-based and otherwise. I would expect such a team would offer to congress some very different conclusions regarding the human impacts on climate.”

One can liken this approach to the adversarial arguments in a criminal court of law. (CO2 is a criminal?) The reports of the Nongovernmental International Panel on Climate Change were intended to have a Red Team approach. However, the publisher, The Heartland Institute and other groups, do not have the deep, multi-billion-dollar pockets enjoyed by the IPCC, USGCRP, etc.

This idea appears to be gaining attention. In Climate Etc. Judith Curry discusses the idea more fully. We have had decades of spurious claims about the dangers of carbon dioxide, which is essential for life as we generally understand it. Such an approach may help dispel decades of myths such as a 97% consensus, CO2 can be seen from smoke-stacks, etc. It would be important to establish solid rules of evidence, such as unvalidated models are not hard evidence, and to avoid dogmatic participants. See links under Challenging the Orthodoxy and Seeking a Common Ground.


Executive Actions: The Constitution is a practical guide for government, limiting the powers of its branches. From this comes the popular term “checks and balances.” Increasingly, some of the executive actions of the prior administration are being discarded. Since these actions are not law, there is no reason for the current administration to keep them, should it so choose to change them. Increasingly, the Trump administration is reversing executive actions under the Obama administration.

The same can be said for the Paris Accord (Agreement) which the Obama administration sold to the public as an executive action and did not send to the Senate for two-thirds approval, as required by the Constitution for a treaty. The cries of those who expected great sums of money through the Paris agreement, such as Christiana Figueres, formerly Executive Secretary of the UN Framework Convention on Climate Change (UNFCCC), are not significant. They knowingly played a game, and lost. As discussed in last week’s TWTW, Ms. Figueres formed an organization expecting up to One Trillion Dollars a year. See Article # 1 and links under After Paris!


Economic Return on Energy Investment: Writing for the Global Warming Policy Forum, Economics Professor Michael Kelly brings up an important concept that many writers on energy issues fail to consider: Economic Return on Energy Investment.

In the US, following the Civil War, fossil fuels such as coal quickly replaced biomass (wood) and muscle power (animal and human). The economy boomed. People found the care and feeding of a steam engine is much easier than the care and feeding of horses. City streets became much cleaner, and boots were no longer needed. What was important was not the number of people employed in a particular energy sector, but the employment the energy sector created in other economic sectors.

Kelly’s Economic Return on Energy Investment is a measure of the productivity of various energy types. He finds that 9% of the global GDP is tied up in energy, yielding a return of about 11:1. For coal and gas power plants, the return is about 50:1. For nuclear power plants it is about 70:1. The low values of traditional biomass, and other external issues bring the global value down to 11:1.

Applying this analysis to solar photovoltaics, he finds a return of less than 4:1; for wind power, a return of less than 8:1. In brief, there is not much opportunity for solar and wind to lift the third world to modern European standards. See links under Questioning European Green.


Offshore Wind: Often, wind promoters claim offshore wind is reliable, even though it is becoming obvious that onshore wind is not. Writing in Energy Matters, Roger Andrews examines the validity of this claim for the world’s wind nation, Denmark, and finds it wanting – without considering added costs of salt water corrosion.

“Previous Energy Matters posts that highlight the difficulties of integrating intermittent wind power with the grid have been based dominantly on onshore wind data, but claims that offshore wind is significantly less erratic and will therefore be much easier to integrate with the grid have not been checked. This post reviews the question of whether it will. It finds that offshore wind is indeed less erratic than onshore wind but still nowhere near consistent enough to do away with the need for storage or conventional backup generation.”

Finding solid data is always a major problem for such studies, but he succeeds in finding a database for Denmark that separates onshore and offshore production. The analysis covers three years, 2014 to 2016. A small country, Denmark is ringed with offshore wind farms on three sides.

Rogers finds that offshore wind has a capacity factor of 43% as compared with onshore wind of 25%; but, also, that when wind dies onshore it does so offshore as well. Back-up is needed for both. Given that offshore wind costs about twice that of onshore, it is not much of a bargain. See links under Alternative, Green (“Clean”) Solar and Wind.


Number of the Week: 39%: The island of El Hierro in the Canary Islands was to be a show-case of 100% wind power for electricity. Excess electricity would be used for pumped hydro storage, to be used when the wind failed to meet demand. After two full years of operation, the system provided 39.1% of the electricity needed. The balance came from diesel generators. The reservoirs are inadequate for the hydro component. But the circus continues with plans for wind supplying a higher percentage of total energy needs. Have those in the Pentagon who bragged about weather-dependent wind power helping the nation’s energy security heard of this island? See link under Alternative, Green (“Clean”) Solar and Wind.



1. Pruitt’s Clean Water Break

Obama’s legacy of rule by decree is rapidly being undone.

Editorial, WSJ, July 2, 2017


The editorial states:

“President Trump is having a hard time getting legislation through Congress, but his Administration is moving fast to roll back Barack Obama’s pen- and-a-phone lawmaking. The latest example, which barely registered in the press, is the Environmental Protection Agency’s decision last week to rescind the unilateral rewrite of the Clean Water Act.

“The Obama EPA in 2015 redefined “waters of the United States” under the Clean Water Act to include any land with a “significant nexus” to a navigable waterway. Several arbitrary thresholds were used to determine significance, such as land within a 100-year floodplain and 1,500 feet of the high-water mark of waters under government jurisdiction. The rule extended the government’s writ to prairie potholes, vernal pools and backyard creeks.

“Thirty-one states sued the feds for violating the Administrative Procedure Act, and the Sixth Circuit Court of Appeals enjoined the rule nationwide. Now Administrator Scott Pruitt is putting the rule on ice while the EPA works up a replacement. Supreme Court Justice Anthony Kennedy muddied the waters with his controlling opinion in the 2006 Rapanos v. U.S. case that conceived the new “significant nexus” standard, which the Obama EPA used as a pretext to pursue its water land grab.

Side comment: Piles of wet leaves have been arbitrarily been considered proof of “waters of the United States”, leaving the landowner with no recourse but seeking relief by expensive litigation.

“Mr. Pruitt said the EPA will propose a new rule ‘in accordance with Supreme Court decisions, agency guidance, and longstanding practice’ that would ‘return power to the states and provide regulatory certainty.’ Consider it another lesson in the limits of pen-and-phone rule by decree.”


Are Claimed Global Record-Temperatures Valid?

[This excellent article on accuracy and precision of temperature data under-exagerates. Less than 10% of official NWS stations meet required standards on siting – the best stations have a precision of only +/- 1 degree Celcius! The worst are over +/- 5 degrees. -Bob]
By Clyde Spencer – Re-Blogged F5rom http://www.WattsUpWithThat.com

The New York Times claims 2016 was the hottest year on record. Click for article.

Guest essay by Clyde Spencer


The point of this article is that one should not ascribe more accuracy and precision to available global temperature data than is warranted, after examination of the limitations of the data set(s). One regularly sees news stories claiming that the recent year/month was the (first, or second, etc.) warmest in recorded history. This claim is reinforced with a stated temperature difference or anomaly that is some hundredths of a degree warmer than some reference, such as the previous year(s). I’d like to draw the reader’s attention to the following quote from Taylor (1982):

“The most important point about our two experts’ measurements is this: like most scientific measurements, they would both have been useless, if they had not included reliable statements of their uncertainties.”

Before going any further, it is important that the reader understand the difference between accuracy and precision. Accuracy is how close a measurement (or series of repeated measurements) is to the actual value, and precision is the resolution with which the measurement can be stated. Another way of looking at it is provided by the following graphic:


The illustration implies that repeatability, or decreased variance, is a part of precision. It is, but more importantly, it is the ability to record, with greater certainty, where a measurement is located on the continuum of a measurement scale. Low accuracy is commonly the result of systematic errors; however, very low precision, which can result from random errors or inappropriate instrumentation, can contribute to individual measurements having low accuracy.


For the sake of the following discussion, I’ll ignore issues with weather station siting problems potentially corrupting representative temperatures and introducing bias. However, see this link for a review of problems. Similarly, I’ll ignore the issue of sampling protocol, which has been a major criticism of historical ocean pH measurements, but is no less of a problem for temperature measurements. Fundamentally, temperatures are spatially-biased to over-represent industrialized, urban areas in the mid-latitudes, yet claims are made for the entire globe.

There are two major issues with regard to the trustworthiness of current and historical temperature data. One is the accuracy of recorded temperatures over the useable temperature range, as described in Table 4.1 at the following link:


Section 4.1.3 at the above link states:

“4.1.3 General Instruments. The WMO suggests ordinary thermometers be able to measure with high certainty in the range of -20°F to 115°F, with maximum error less than 0.4°F…”

In general, modern temperature-measuring devices are required to be able to provide a temperature accurate to about ±1.0° F (0.56° C) at its reference temperature, and not be in error by more than ±2.0° F (1.1° C) over their operational range. Table 4.2 requires that the resolution (precision) be 0.1° F (0.06° C) with an accuracy of 0.4° F (0.2° C).

The US has one of the best weather monitoring programs in the world. However, the accuracy and precision should be viewed in the context of how global averages and historical temperatures are calculated from records, particularly those with less accuracy and precision. It is extremely difficult to assess the accuracy of historical temperature records; the original instruments are rarely available to check for calibration.


The second issue is the precision with which temperatures are recorded, and the resulting number of significant figures retained when calculations are performed, such as when deriving averages and anomalies. This is the most important part of this critique.

If a temperature is recorded to the nearest tenth (0.1) of a degree, the convention is that it has been rounded or estimated. That is, a temperature reported as 98.6° F could have been as low as 98.55 or as high as 98.64° F.

The general rule of thumb for addition/subtraction is that no more significant figures to the right of the decimal point should be retained in the sum, than the number of significant figures in the least precise measurement. When multiplying/dividing numbers, the conservative rule of thumb is that, at most, no more than one additional significant figure should be retained in the product than that which the multiplicand with the least significant figures contains. Although, the rule usually followed is to retain only as many significant figures as that which the least precise multiplicand had. [For an expanded explanation of the rules of significant figures and mathematical operations with them, go to this Purdue site.]

Unlike a case with exact integers, a reduction in the number of significant figures in even one of the measurements in a series increases uncertainty in an average. Intuitively, one should anticipate that degrading the precision of one or more measurements in a set should degrade the precision of the result of mathematical operations. As an example, assume that one wants the arithmetic mean of the numbers 50., 40.0, and 30.0, where the trailing zeros are the last significant figure. The sum of the three numbers is 120., with three significant figures. Dividing by the integer 3 (exact) yields 40.0, with an uncertainty in the next position of ±0.05 implied.

Now, what if we take into account the implicit uncertainty of all the measurements? For example, consider that, in the previously examined set, all the measurements have an implied uncertainty. The sum of 50. ±0.5 + 40.0 ±0.05 + 30.0 ±0.05 becomes 120. ±0.6. While not highly probable, it is possible that all of the errors could have the same sign. That means, the average could be as small as 39.80 (119.4/3), or as large as 40.20 (120.6/3). That is, 40.00 ±0.20; this number should be rounded down to 40.0 ±0.2. Comparing these results, with what was obtained previously, it can be seen that there is an increase in the uncertainty. The potential difference between the bounds of the mean value may increase as more data are averaged.

It is generally well known, especially amongst surveyors, that the precision of multiple, averaged measurements varies inversely with the square-root of the number of readings that are taken. Averaging tends to remove the random error in rounding when measuring a fixed value. However, the caveats here are that the measurements have to be taken with the same instrument, on the same fixed parameter, such as an angle turned with a transit. Furthermore, Smirnoff (1961) cautions, ”… at a low order of precision no increase in accuracy will result from repeated measurements.” He expands on this with the remark, “…the prerequisite condition for improving the accuracy is that measurements must be of such an order of precision that there will be some variations in recorded values.” The implication here is that there is a limit to how much the precision can be increased. Thus, while the definition of the Standard Error of the Mean is the Standard Deviation of samples divided by the square-root of the number of samples, the process cannot be repeated indefinitely to obtain any precision desired!1

While multiple observers may eliminate systematic error resulting from observer bias, the other requirements are less forgiving. Different instruments will have different accuracies and may introduce greater imprecision in averaged values.

Similarly, measuring different angles tells one nothing about the accuracy or precision of a particular angle of interest. Thus, measuring multiple temperatures, over a series of hours or days, tells one nothing about the uncertainty in temperature, at a given location, at a particular time, and can do nothing to eliminate rounding errors. A physical object has intrinsic properties such as density or specific heat. However, temperatures are ephemeral and one cannot return and measure the temperature again at some later time. Fundamentally, one only has one chance to determine the precise temperature at a site, at a particular time.

The NOAA Automated Surface Observing System (ASOS) has an unconventional way of handling ambient temperature data. The User’s Guide says the following in section 3.1.2:

“Once each minute the ACU calculates the 5-minute average ambient temperature and dew point temperature from the 1-minute average observations… These 5-minute averages are rounded to the nearest degree Fahrenheit, converted to the nearest 0.1 degree Celsius, and reported once each minute as the 5-minute average ambient and dew point temperatures…”

This automated procedure is performed with temperature sensors specified to have an RMS error of 0.9° F (0.5° C), a maximum error of ±1.8° F (±1.0° C), and a resolution of 0.1° F (0.06° C) in the most likely temperature ranges encountered in the continental USA. [See Table 1 in the User’s Guide.] One (1. ±0.5) degree Fahrenheit is equivalent to 0.6 ±0.3 degrees Celsius. Reporting the rounded Celsius temperature, as specified above in the quote, implies a precision of 0.1° C when only 0.6 ±0.3° C is justified, thus implying a precision 3 to 9-times greater than what it is. In any event, even using modern temperature data that are commonly available, reporting temperature anomalies with two or more significant figures to the right of the decimal point is not warranted!


Where these issues become particularly important is when temperature data from different sources, which use different instrumentation with varying accuracy and precision, are used to consolidate or aggregate all available global temperatures. Also, it becomes an issue in comparing historical data with modern data, and particularly in computing anomalies. A significant problem with historical data is that, typically, temperatures were only measured to the nearest degree (As with modern ASOS temperatures!). Hence, the historical data have low precision (and unknown accuracy), and the rule given above for subtraction comes into play when calculating what are called temperature anomalies. That is, data are averaged to determine a so-called temperature baseline, typically for a 30-year period. That baseline is subtracted from modern data to define an anomaly. A way around the subtraction issue is to calculate the best historical average available, and then define it as having as many significant figures as modern data. Then, there is no requirement to truncate or round modern data. One can then legitimately say what the modern anomalies are with respect to the defined baseline, although it will not be obvious if the difference is statistically significant. Unfortunately, one is just deluding themselves if they think that they can say anything about how modern temperature readings compare to historical temperatures when the variations are to the right of the decimal point!

Indicative of the problem is that data published by NASA show the same implied precision (±0.005° C) for the late-1800s as for modern anomaly data. The character of the data table, with entries of 1 to 3 digits with no decimal points, suggests that attention to significant figures received little consideration. Even more egregious is the representation of precision of ±0.0005° C for anomalies in a Wikipedia article wherein NASA is attributed as the source.

Ideally, one should have a continuous record of temperatures throughout a 24-hour period and integrate the area under the temperature/time graph to obtain a true, average daily temperature. However, one rarely has that kind of temperature record, especially for older data. Thus, we have to do the best we can with the data that we have, which is often a diurnal range. Taking a daily high and low temperature, and averaging them separately, gives one insight on how station temperatures change over time. Evidence indicates that the high and low temperatures are not changing in parallel over the last 100 years; until recently, the low temperatures were increasing faster than the highs. That means, even for long-term, well-maintained weather stations, we don’t have a true average of temperatures over time. At best, we have an average of the daily high and low temperatures. Averaging them creates an artifact that loses information.

When one computes an average for purposes of scientific analysis, conventionally, it is presented with a standard deviation, a measure of variability of the individual samples of the average. I have not seen any published standard deviations associated with annual global-temperature averages. However, utilizing Tchebysheff’s Theorem and the Empirical Rule (Mendenhall, 1975), we can come up with a conservative estimate of the standard deviation for global averages. That is, the range in global temperatures should be approximately four times the standard deviation (Range ≈ ±4s). For Summer desert temperatures reaching about 130° F and Winter Antarctic temperatures reaching -120° F, that gives Earth an annual range in temperature of at least 250° F; thus, an estimated standard deviation of about 31° F! Because deserts and the polar regions are so poorly monitored, it is likely that the range (and thus the standard deviation) is larger than my assumptions. One should intuitively suspect that since few of the global measurements are close to the average, the standard deviation for the average is high! Yet, global annual anomalies are commonly reported with significant figures to the right of the decimal point. Averaging the annual high temperatures separately from the annual lows would considerably reduce the estimated standard deviation, but it still would not justify the precision that is reported commonly. This estimated standard deviation is probably telling us more about the frequency distribution of temperatures than the precision with which the mean is known. It says that probably a little more than 2/3rds of the recorded surface temperatures are between -26. and +36.° F. Because the median of this range is 5.0° F, and the generally accepted mean global temperature is about 59° F, it suggests that there is a long tail on the distribution, biasing the estimate of the median to a lower temperature.


In summary, there are numerous data handling practices, which climatologists generally ignore, that seriously compromise the veracity of the claims of record average-temperatures, and are reflective of poor science. The statistical significance of temperature differences with 3 or even 2 significant figures to the right of the decimal point is highly questionable. One is not justified in using the approach of calculating the Standard Error of the Mean to improve precision, by removing random errors, because there is no fixed, single value that random errors cluster about. The global average is a hypothetical construct that doesn’t exist in Nature. Instead, temperatures are changing, creating variable, systematic-like errors. Real scientists are concerned about the magnitude and origin of the inevitable errors in their measurements.