Global Mean Surface Temperature: Early 20th Century Warming Period – Models versus Models & Models versus Data

Bob Tisdale - Climate Observations

This is a long post: 3500+ words and 22 illustrations. Regardless, heretics of the church of human-induced global warming who frequent this blog should enjoy it.  Additionally, I’ve uncovered something about the climate models stored in the CMIP5 archive that I hadn’t heard mentioned or seen presented before.  It amazed even me, and I know how poorly these climate models perform.  It’s yet another level of inconsistency between models, and it’s something very basic. It should help put to rest the laughable argument that climate models are based on well-documented physical processes.

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Events That Causes Long-Term Global Warming: Does The Climate-Science Industry Purposely Ignore A Simple Aspect of Strong El Niño?

Bob Tisdale - Climate Observations


It was a little more than 10 years ago that I published my first blog posts on the obvious upward steps in the sea surface temperatures of a large portion of the global oceans…upward steps that are caused by El Niño events…upward steps that lead to sunlight-fueled, naturally occurring global warming.

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A Synthesis of Papers about the Madden-Julian Oscillation under Anthropogenic Warming

Reblogged from Watts Up With That:

Maloney, et al. (2019) Madden–Julian oscillation changes under anthropogenic warming (paywalled) is a summary of a gazillion papers (well, that may be an exaggeration) about the future of the Madden-Julian oscillation in an anthropogenic-warming world. There’s an assumption there, isn’t there?

The MJO is of great interest to many weather forecasters.

The abstract of Maloney, et al. (2019) reads:

The Madden–Julian oscillation (MJO) produces a region of enhanced precipitation that travels eastwards along the Equator in a 40–50 day cycle, perturbing tropical and high-latitude winds, and thereby modulating extreme weather events such as flooding, hurricanes and heat waves. Here, we synthesize current understanding on projected changes in the MJO under anthropogenic warming, demonstrating that MJO-related precipitation variations are likely to increase in intensity, whereas wind variations are likely to increase at a slower rate or even decrease. Nevertheless, future work should address uncertainties in the amplitude of precipitation and wind changes and the impacts of projected SST patterns, with the aim of improving predictions of the MJO and its associated extreme weather.

Hmmm, “…wind variations are likely to increase at a slower rate or even decrease…” That narrows it down.

Also (with my boldface), “…MJO-related precipitation variations are likely to increase in intensity…” If that’s a “likely” based on the IPCC likely scale, then that’s about a 66% likelihood, or so they say.

Examples of How the Use of Temperature ANOMALY Data Instead of Temperature Data Can Result in WRONG Answers

“…it is the change in temperature compared to what we’ve been used to that matters.” – Part 2

Bob Tisdale - Climate Observations

In this post, we’re going to present graphs that show the annual lowest TMIN and highest TMAX Near-Land Surface Air Temperatures (not in anomaly form) for ten (10) Countries in an effort to add some perspective to global warming.  The list of countries, which follows, includes the countries with the highest populations.

And, as always with my posts, as part of the text, there are hyperlinks to the data that were used to prepare the graphs. Just click on the links if you’re looking for the data.

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“…it is the change in temperature compared to what we’ve been used to that matters.” – Part 1

Bob Tisdale - Climate Observations

In this post, we’re going to present monthly TMIN and TMAX Near-Land Surface Air Temperature data for the Northern and Southern Hemispheres (not in anomaly form) in an effort to add a little perspective to global warming. And at the end of this post, I’m asking for your assistance in preparing a post especially for you, the visitors to this blog.

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Bob Tisdale - Climate Observations

Alternate Title: An Average of Climate Models, Which Individually Give Wrong Answers, Cannot, By Averaging Them, Give the Right Answer, So A Model Mean Can Be Very Misleading. And The Use of Anomalies in Model-Data Comparisons, When Absolute Values Are Known, Can Also Be Very Misleading

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