The Neoglacial Period

By Andy May

Most agree that the Milankovitch cycles of eccentricity, obliquity, and precession drive long-term global and hemispheric climate changes, see figure 4 in this post for a brief description of them. The modern climate debate is about short-term climate change. The “consensus” says that human emissions have caused “the most rapid change” or “temperatures are the warmest in X years” (Lecavalier et al., 2017) and (IPCC, 2021, p. 8) with X varying from one thousand years to over 100,000 years. Obviously, we only have global instrumental data for the past 170 years or so, so any global or hemispheric data before then is either local or proxy temperature data.

The mainstream view is to ignore inconvenient data that shows CO2 and methane air concentrations do not correlate with temperature during the Holocene Epoch, or the past 12,000 years as shown in figure 4 here. Correlation is not causation, but the lack of correlation normally precludes causation. If changes in heat storage in the climate system are ignored, as is often done, then only outside forcing can cause climate change. Since recent climate changes (since 1950) have been too rapid to be caused by the Milankovitch orbital cycles, the only outside forces left are the Sun and greenhouse gases (GHGs). Since the oceans and atmosphere change the amount of heat they store, as opposed to emit to space, climate changes as climatic heat storage changes (Irvine, 2014). We can observe this in the 60-70-year climate or ocean oscillations, like the Atlantic Multidecadal Oscillation (AMO, see here and here).

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Holocene Glacier Records

By Andy May

Glacier length changes through time, they advance when the local climate around them is colder and retreat when it is warmer (Bray, 1968). Over century and greater time scales glacier length is considered a highly reliable indicator of both regional and worldwide warming trends according to Olga Solomina, Johannes Oerlemans, and the IPCC (Solomina et al., 2008), (Oerlemans, 2005) & (IPCC, 2001, pp. 127-130). While studying glacier lengths can illuminate long-term warming or cooling trends in glaciated areas is true, the idea that they can reveal hemisphere-wide or global climatic trends is somewhat speculative.

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Holocene Warming

By Andy May

I find it amazing that some papers still state:

“air temperatures in the [Arctic or globally] are now at their warmest in the past 6,800–7,800 y, and that the recent rate of temperature change is unprecedented over the entire Holocene.” (Lecavalier et al., 2017)

While it is remotely possible that current Arctic or global average temperature is higher than any seen in the past 6,800 years, it is very unlikely and can’t be demonstrated with data we have today. It is almost certainly true that the rate of change in global or Arctic temperature observed recently is not unprecedented in the Holocene Epoch. This modern myth has been thoroughly debunked in the literature and seeing it pop up in PNAS and elsewhere is disconcerting. I thought peer-review was supposed to catch such errors.

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Re-evaluating the Concern of Climate Change

By Andy May

I’ve just been made aware of a paper critical of the “consensus” view that man-made climate change is dangerous. It is by Ashutosh Sharma, Vinit Vithalrai Shenvi, and Mohit Sain of the MS Ramaiah Institute of Technology in India (or MSRIT) (Sharma et al., 2024). It was published just two months before “Carbon Dioxide and a Warming Climate are not problems,” by Marcel Crok and myself (May & Crok, 2024) and makes similar points, at least until we reach the paper’s conclusions. The paper argues that: “climate change policies impose unwarranted economic strains on nations and impede technological advancement.”

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The Cost of Wind and Solar Power Backup

By Andy May

There have been many attempts to compare wind and solar power generation to generation with natural gas and other fossil fuels. I have summarized and criticized these attempts before, see here and here. Another excellent discussion of the topic is in this TPPF report by Michael Reed and Brent Bennet. Reed and Bennett estimate that modifying the Texas grid to handle wind and solar output variability cost Texas electricity consumers $2.3 billion in 2023. The purpose of this post is not to cover the whole issue, as Reed and Bennett do, but only discuss how to account for the natural gas swing generation used to backup wind and solar when these sources are unavailable, like on windless nights. The power one can produce using wind turbines and solar panels varies a lot from place to place and from time to time. Figure 1 shows the mix in Texas on 27 January 2026 at 12:21PM.

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Weather Reanalysis Models

By Andy May

My new paper (May, 2025) emphasizes that while many of the underlying observations used to build weather reanalysis datasets, such as ERA5 (European Centre for Medium-Range Weather Forecasts or ECMWF Reanalysis v5) (Soci et al., 2024) or MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) (Gelaro et al., 2017), are from radiosondes, weather reanalysis models are still models and have the same problems that other models have. Thus, they are not observations or measurements, like those in radiosonde data repositories such as IGRA2, and should not be treated as such. The reanalysis models assimilate surface measurements and satellite data in addition to radiosonde data and blend the measurements together into a global or regional grid using a general circulation atmospheric model. Weather reanalysis models produce reasonably consistent, physics-based periodic (usually every 6 to 12 hours) estimates of the global atmospheric state (Bloom et al., 1996), but they are not observations. Dr. Hans Hersbach of ECMWF (European Centre for Medium-Range Weather Forecasts) provides us with figure 1 below which is an illustration of the data assimilation process in ERA5.

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The Monthly ITCZ Central Latitude

By Andy May

The Intertropical Convergence Zone or ITCZ is where the trade winds from the Northern and Southern Hemisphere converge and where the column-integrated meridional (north-south) circulation and the “near-surface meridional mass flux” vanishes according to Adam et al., 2016. For a history of the discovery of the ITCZ see Nicholson, 2018. The ITCZ is not the solar equator, the latitude where the Sun is directly overhead at noon, but it is closely related to it, and they move in a coordinated fashion. The ITCZ is an oceanic phenomenon and doesn’t really exist over land in the same way as described here, except in coastal areas (Nicholson, 2018).

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R Programming tips to process IGRA2 Radiosonde data

By Andy May

In the last two posts (here and here) I showed how to read, plot, and map IGRA2 data. In this post I will discuss how to efficiently process large R data frames and lists to compute useful variables.

One of the more difficult things to do in R is to write readable code. This problem has been around for a long time, but in recent years a very useful tool has appeared, the “pipe” or %>%.

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R Programming tips on mapping IGRA2 Radiosonde data

By Andy May

Our last post was on reading and plotting IGRA2 weather balloon radiosonde data. This post shows how to map the data. The IGRA2 data can be downloaded from here (ftp site: ftp.ncei.noaa.gov/pub/data/igra). Once the data are prepared, some further tricky programming is needed to map it. This post will introduce the key concepts and code structure required. Writing complete programs will require understanding these concepts and working with Grok.

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R Programming tips to read and plot IGRA2 Radiosonde data

By Andy May

R is an extremely powerful programming language for processing, analyzing and displaying data from large datasets. As discussed in the first post of this series on analyzing IGRA2 radiosonde data with R, the language has improved considerably in recent years. Surprisingly it is free and can be downloaded here. This post will cover some necessary R programming techniques for those interested in reading and plotting IGRA2 data. The IGRA2 measurements have had minimal processing and are as close to raw data as possible, unlike RICH or ROABCORE data, thus it is a useful check on climate model output. The IGRA2 data can be downloaded here or from its ftp site: ‘ftp.ncei.noaa.gov/pub/data/igra.’ It is well formed but requires some manipulation to make it useable. Once the data are prepared, some further tricky programming is needed to plot it. I’ll briefly introduce the key programming techniques here. The full suite of complete R programs that I used to analyze the IGRA2 data (May, 2025) can be downloaded here (warning the file is 658 MB and processing it will require > 32 GB of RAM). For a simple and brief list of the programs and what they do, download this pdf.

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