Weekly Climate and Energy News Roundup #637
1 hour ago Guest Blogger
The Week That Was: 2025 03-29 (March 29, 2025)
Brought to You by SEPP (
www.SEPP.org)
The Science and Environmental Policy Project
Quote of the Week: “There are more instances of the abridgment of the freedom of the people by gradual and silent encroachments of those in power than by violent and sudden usurpations.” —James Madison (1788)
Number of the Week: 250,000 additional death per year.
THIS WEEK:
By Ken Haapala, President, Science and Environmental Policy Project (SEPP)
Scope: TWTW begins with improvements in numerical weather forecasting using Artificial Intelligence, including limitations. Then, TWTW discusses an AI model for climate change prediction. The use of big numbers that have little meaning is discussed as well as a statistician’s objection to commonly associated links that have little meaning. TWTW concludes with emphasizing the importance of photosynthesis and with a correction from last week.
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Models Forecasting: Improvements in numerical weather forecasting using Artificial Intelligence (AI) promise hope in making accurate forecasts longer than two weeks. Chaos theory and an interpretation of what has been understood in modeling and probability theory since John von Neumann (1903-1957) indicate that weather and climate are too complex to forecast well in advance. When discussing the problems of degrees of freedom in statistical analysis and modeling, von Neumann famously said:
“With four parameters I can fit an elephant, and with five I can make him wiggle his trunk,”
The variables that determine what the weather will be in (say) a week from now in (say) Chicago are all present weather quantities (temperature, humidity, atmospheric pressure, wind speed and direction) as well as weather quantities in regions that are possibly a few thousand kilometers away. The parameters are known in statistics as degrees of freedom. The further into the future we intend to predict the weather, the greater the number of degrees of freedom become, and the more complicated the prediction becomes. A modeler may wish to constrain predicted values by limiting the temperature range to within 10 degrees of the historical average, for example, but doing so does not increase the predictive skill of the model. In particular, climate modelers adjust parameters to conform to the notion that CO2 concentration determines the surface temperature.
https://wattsupwiththat.com/2025/03/31/weekly-climate-and-energy-news-roundup-637/
