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“To Measure Is To Know” How many breakthroughs occurred only after conceptual discussions were replaced with actual measurement?
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Replying to @TimOsbornClim @MLiebreich and 34 others
It would be nice to get more actual empirical data to validate forcing values that are used as input in models. This Dutch Nobel Laureate who has given his name to the physics institute I got my PhD from said this famous creed “to measure is to know”
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. is updating economics with measurable quantities.
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1/ Aristotle was a formidable researcher, the grandfather of science. But really only the grandfather. He was empirical: he did look. But -- crucially -- he didn't measure. Aristotle is observation, but not quantification. Science proper starts with Galileo. With quantity.
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I’ve always liked the John Snow story. The simple (but arduous) act of measuring location of cholera outbreaks led to the identification of the tainted well.
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Here's a look at John Snow's map of cholera outbreaks from nineteenth century London in 2D and #3D p.ctx.ly/r/9kmk #GIS #ArcGIS
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Few of us: IR measurement 🛰 = Climate people: 🛰= Hasn’t it been done? Us: no, those are: •from models •broadband •not quantitative Them: ...pretty sure it’s done? Us: Surprisingly no, global warming has not been causally linked to CO₂ via 🛰 IR measurement.
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Analyzing the satellite data has been my side-project:
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This GIF took 3 years to make. Python/Jupyter #dataviz of 17 years (1,400 GB) of 🛰️ measurements, visualized in 17 seconds. This is the first analysis of 🌎 outgoing IR declining at 13-15μm due to rising CO₂. Warming at 10-13μm is also evident.
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Also, do your results match theoretical predictions based on % CO2 in atmosphere? Ie does the absorption effect track the predictions from ppm measurements over the same period correctly?
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And if I understand your work correctly, the logical research program should be 1. Get the absorption data right with more satellites/more conditions 2. Build robust correlation to ppm data 3. Use those to replace a few model parameters 4. Redo calculations of temperature rise
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Parameter: Clear-sky (or all-sky) radiative forcing. Currently calculated as: (constant)*log(CO₂/CO₂[start] Where (constant) = 6.3 in the 1990’s 5.35 in 1998-2016 And now this, 2016-present:
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