National Review wins, Michael Mann loses!

By Andy May

The D.C. Superior Court dismissed Michael Mann’s lawsuit against the National Review today in a definitive way. The National Review was sued by Mann over a blog post that Mark Steyn posted in 2012 criticizing Mann’s work. Mark Steyn was not a National Review employee, and no one at the magazine had reviewed the post before he put it up.

Rich Lowry, the NR editor in chief, said: “It’s completely ridiculous that it took us more than eight years to get relief from the courts from this utterly meritless suit.”

Read more here.


Lots of discussion around the details of the lawsuit. Mann’s original complaint against the National Review and Mark Steyn can be downloaded here.

Mark Steyn updates us on the progress of the lawsuit here. One of the previous judges in the case had this to say:

The main idea of Defendant [Steyn]’s article is the inadequate and ineffective investigations conducted by Pennsylvania State University into their employees, including Jerry Sandusky and Plaintiff [Michael E Mann].


Climate Model Failed

By Andy May

On January 22, 2021, John Christy presented a new online talk to the Irish Climate Science Forum. The talk was arranged by Jim O’Brien. A summary of the presentation can be read at here. In this post we present two interesting graphs from the presentation. These compare observations to the IPCC Coupled Model Intercomparison project 5 climate models (CMIP5, 2013) and CMIP6 (current set of IPCC models) climate model projections.

Figure 1. CMIP5 models versus weather balloon observations in green. The details of why the models fail statistically can be seen in a 2018 paper by McKitrick and Christy here.

The next graph compares the newer CMIP6 models to both the weather balloon data (light green) and the weather reanalysis data (dark green).

Figure 2. CMIP6 models versus weather balloon data in light green and weather reanalysis data in dark green.

The difference between the models and the observations is statistically significant and shows that the models have been invalidated. It is also significant that the CMIP6 spread of model results is worse than the CMIP5 spread. Thus, the newer models show less agreement to one another than the previous set.

Wind or Eagles?

Wind turbines are not only ugly, they are also dangerous for Eagles and other large birds. Takes a look at this video. Those blades can travel at 180 mph at the tips!

The CO2 Kink; Firn to Ice Transition

Guest Post by Renee Hannon


This post examines CO2 data collected in Antarctic firn and its journey as firn transitions to ice where CO2 is eventually trapped in bubbles. Atmospheric gases within the firn and trapped in bubbles are smoothed due to gas mixing processes with depth and time. The bubble trapping zone, also known as the Lock-in-Zone (LIZ), is a mysterious thin interval where CO2 concentrations decrease significantly with depth creating a kink in CO2 concentrations.

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Michael Mann’s 2008 Reconstruction

By Andy May

In my last post, it was suggested that Michael Mann’s 2008 reconstruction (Mann, et al., 2008) was similar to Moberg’s 2005 (Moberg, Sonechkin, Holmgren, Datsenko, & Karlen, 2005) and Christiansen’s 2011/2012 reconstructions. The claim was made by a commenter who calls himself “nyolci.” He presents a quote, in this comment, from Christiansen’s co-author: Fredrik Charpentier Ljungqvist:

“Our temperature reconstruction agrees well with the reconstructions by Moberg et al. (2005) and Mann et al. (2008) with regard to the amplitude of the variability as well as the timing of warm and cold periods, except for the period c. AD 300–800, despite significant differences in both data coverage and methodology.” (Ljungqvist, 2010).

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Sea-Surface Temperature Anomalies

By Andy May

My previous post on sea-surface temperature (SST) differences between HadSST and ERSST generated a lively discussion. Some, this author included, asserted that the Hadley Centre HadSST record and NOAA’s ERSST (the Extended Reconstructed Sea Surface Temperature) record could be used as is, and did not need to be turned into anomalies from the mean. Anomalies are constructed by taking a mean value over a specified reference period, for a specific location, and then subtracting this mean from each measurement at that location. For the HadSST dataset, the reference period is 1961-1990. For the ERSST dataset, the reference period is 1971-2000.

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Sea-Surface Temperatures: Hadley Centre v. NOAA

By Andy May

My last post compared actual sea-surface temperature (SST) estimates to one another to see how well they agreed. It was not a pretty sight; the various estimates covered a range of global average SSTs from ~14°C to almost 20°C. In addition, some SSTs were declining with time and others were increasing. While I did check the latitude range of each of the grids I averaged, John Kennedy (HadSST climate scientist in the UK MET Hadley Centre) pointed out that I did not check the cell-by-cell areal coverage of the HadSST grid, relative to the NOAA ERSST grid. He suspected that the results I presented were mostly due to null grid cells in HadSST that were populated by interpolation and extrapolation in the ERSST dataset. The original results were presented in Figure 6 of my previous post which is Figure 1 here.

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The Ocean Mixed Layer, SST, and Climate Change

By Andy May

As described in my previous post, the ocean “mixed layer” is sandwiched between the very thin “skin” layer at the ocean surface and the deep ocean. The skin layer loses thermal energy (“heat”) to the atmosphere primarily through evaporation, gains thermal energy from the Sun during the day, and constantly attempts to come to thermal equilibrium with the atmosphere above it. During the day, with the Sun beating down on the ocean and calm clear conditions, the skin layer might be as thick as ten meters. At night, especially under windy conditions, it can be less than a millimeter thick.

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Sea surface skin temperature

By Andy May

In previous posts, see here and here, I’ve tried to show that because the oceans cover 71% of Earth and they contain 99% of the thermal energy stored on the Earth’s surface, they dominate the speed and magnitude of climate changes. In all my posts the Earth’s surface is defined as everything from the ocean floor to the top of the atmosphere. The details of the calculation of ocean and atmospheric heat content is detailed in this spreadsheet. The ocean’s huge heat capacity prevents large temperature swings and dampens and delays those that do occur.

Attempting to show the direction, speed, and magnitude of climate change by measuring and averaging atmospheric surface temperatures is pointless, in my opinion. The record we have of atmospheric and ocean surface temperatures is too short and far too inaccurate to provide us with useful trends on a climatic (30 years +) time scale. Further, these records are sporadic measurements in a chaotic surface zone that has large temperature swings. In Montana, United States, for example, recent minimum/maximum temperatures have been as low as -70°F (-57°C) and as high as 117°F (47°C). These enormous swings make measuring year-to-year global average differences of 0.1°C exceedingly difficult. Yet, this is the precision demanded if we are to properly characterize a climate that is only warming at a rate of roughly 1.4°C/century, which is 0.014°C per year and 0.14°C/decade.

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Ocean Temperature Update

By Andy May

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.

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