A considerable amount of new information on ocean temperature has been gathered since I last wrote about the subject in 2016 here. In my last post on GHCN and the National Temperature Index, it appeared that ocean temperature trends and the thermal energy distribution in oceans dominate climate change. Land-based weather stations are invaluable for weather prediction, but they tell us very little about climate change. The common definition of climate is an overall change in temperature or precipitation over a period longer than 30 years. But even 30 years is a short timeframe, 100 years might be better. On this timescale, ocean temperature trends are more significant.
The United States has a very dense population of weather stations, data from them is collected and processed by NOAA/NCEI to compute the National Temperature Index. The index is an average temperature for the nation and used to show if the U.S. is warming. The data is stored by NOAA/NCEI in their GHCN or “Global Historical Climatology Network” database. GHCN-Daily contains the quality-controlled raw data, which is subsequently corrected and then used to populate GHCN-Monthly, a database of monthly averages, both raw and final. I downloaded version 4.0.1 of the GHCN-Monthly database on October 10, 2020. At that time, it had 27,519 stations globally and 12,514 (45%) of them were in the United States, including Alaska and Hawaii. Of the 12,514 U.S. stations, 11,969 of them are in “CONUS,” the conterminous lower 48 states. The current station coverage is shown in Figure 1.
Figure 1. The GHCN weather station coverage in the United States is very good, except for northern Alaska. There are two stations in the western Pacific that are not shown.
We have several questions about the land-based temperature record, which dominates the long-term (~170-year) global surface temperature record. The land-based measurements dominate because sea-surface temperatures are very sparse until around 2004 to 2007, when the ARGO network of floats became complete enough to provide good data. Even in 2007, the sea-surface gridding error was larger than the detected ocean warming.
I wrote my latest book, Politics and Climate Change: A History, because I recognized that government funding of scientific research was corrupting science. We were warned this might happen by President Eisenhower in his farewell address to the public, where he said:
“The prospect of domination of the nation’s scholars by Federal employment, project allocation, and the power of money is ever present and is gravely to be regarded.” (Eisenhower, 1961)
“If you can’t dazzle them with brilliance, baffle them with Bull…” W. C. Fields
In late February 2015, Willie Soon was accused in a front-page New York Times article by Kert Davies (Gillis & Schwartz, 2015) of failing to disclose conflicts of interest in his academic journal articles. It isn’t mentioned in the Gillis and Schwartz article, but the timing suggests that a Science Bulletin article, “Why Models run hot: results from an irreducibly simple climate model” (Monckton, Soon, Legates, & Briggs, 2015) was Davies’ concern. We will abbreviate this paper as MSLB15. Besides Soon, the other authors of the paper are Christopher Monckton (senior author, Lord Monckton, Viscount of Brenchley), David Legates (Professor of Geography and Climatology, University of Delaware), and William Briggs (Mathematician and statistician, former professor of statistics at Cornell Medical School). In the January 2015 article, the authors “declare that they have no conflict of interest.”
The first modern theoretical estimates of ECS, the equilibrium climate sensitivity to carbon dioxide, were reported in 1979 in the so-called “Charney Report” (Charney, et al., 1979). They reported, on page 2, a theoretical ECS of 1.5°C to 4.5°C per doubling of the CO2 atmospheric concentration. This estimate included an estimate of water vapor feedbacks, the effect of ice and their assumed uncertainties. Absent any water vapor feedback their computed value was 1°C per doubling of CO2. They also supply a likely value of 2.4°C on page 9, although on page 2 they offer a value “near 3.0.” The page 9 value is not far off from the empirical estimate of 2°C made by Guy Callendar in 1938, but significantly higher than the 1.2°C to 1.95°C (17% to 83% range, best estimate 1.5°C) given by Nic Lewis and Judith Curry (Lewis & Curry, 2018).
In 2016, I published a post entitled “Facts and Theories.” It has been one of my most popular posts and often reblogged. I updated the post extensively for my new book, Politics and Climate Change: A History. This post is a condensed version of what is in the book.
Sometimes people ask climate skeptics if they believe in evolution or gravity. They want to ridicule our skepticism by equating human-caused, aka anthropogenic, climate change to evolution or gravity. Evolution and gravity are facts and anthropogenic climate change is a hypothesis. Equating “climate change” to gravity or evolution is valid, as all three are facts. Climate changes, gravity holds us to Earth’s surface and species evolve.
By Andy May SAR is an abbreviation for the second IPCC assessment report, Climate Change 1995 (IPCC, 1996). As explained in my new book, Politics and Climate Change: A History, this IPCC report was a turning point in the debate over catastrophic human-caused climate change. The first IPCC report, “FAR,” was written under the chairmanship of Bert Bolin. At the time FAR was completed and published, circa 1990, Margaret Thatcher, the “Iron Lady,” was Prime Minister of the U.K. and a fervent climate change alarmist. Bert Bolin thought she was “seriously misinformed.” The conclusion of FAR was:
“global-mean surface air temperature has increased by 0.3°C to 0.6°C over the last 100 years … The size of this warming is broadly consistent with predictions of climate models, but it is also of the same magnitude as natural climate variability. … The unequivocal detection of the enhanced greenhouse effect from observations is not likely for a decade or more.” (IPCC, 1992, p. 6)
Steven Mosher complained about my previous post on the difference between the final and raw temperatures in the conterminous 48 states (CONUS) as measured by NOAA’s USCHN. That post can be found here. Mosher’s comment is here. Mosher said the USHCN is no longer the official record of the CONUS temperatures. This is correct as far as NOAA/NCEI is concerned. They switched to a dataset they call nClimDiv in March 2014. Where USHCN had a maximum of 1218 stations, the new nClimDiv network has over 10,000 stations and is gridded to a much finer grid, called nClimGrid. The nClimGrid gridding algorithm is new, it is called “climatological aided interpolation” (Willmott & Robeson, 1995). The new grid has 5 km resolution, much better than the USCHN grid.
As Angus McFarlane shows in a 2018 well researched wattsupwiththat.com web post (McFarlane, 2018), some 65% of the peer-reviewed climate papers, that offered an opinion, published between 1965 and 1979 predicted that the global cooling seen at the time would continue. He references and is supported by a Notrickszone.com post by Kenneth Richard (Richard, 2016).
Attempts to erase the “global cooling scare” from the internet by the notorious William Connolley, who has rewritten 5,428 Wikipedia articles in a vain attempt to change history, failed. As James Delingpole explains in The Telegraph, Connolley systematically turned Wikipedia into a man-made global warming advocacy machine (Delingpole, 2009). He rewrote articles on global warming, the greenhouse effect, climate models and on global cooling. He tried to erase the Medieval Warm Period and the Little Ice Age. In the Wikipedia pages he trashed famous climate scientists who were skeptical of man-made global warming like Richard Lindzen, Fred Singer, Willie Soon and Sallie Baliunas. He also blocked people from correcting his lies.
While studying the NOAA USHCN (United States Historical Climate Network) data I noticed the recent differences between the raw and final average annual temperatures were anomalous. The plots in this post are computed from the USHCN monthly averages. The most recent version of the data can be downloaded here. The data shown in this post was downloaded in October 2020 and was complete through September 2020.
