Outside the Black Box: Back to Basics
17 hours ago Guest Blogger 83 Comments
Ad Huijser, October 2022
Summary. Analyzing the trend in the energy imbalance at the top of the atmosphere as measured by satellites, delivers a “natural” climate sensitivity of 0.3 K/W/m2. That is at, or very close to the inverse of the Planck feedback parameter as could be expected. Starting from the basic energy balance, it is shown that the high climate sensitivities as used by the IPCC are just a result from the invalid assumption that global warming is caused by greenhouse gasses only. Climate feedbacks to explain those high values are no more than necessary artifacts needed to support this mis-conception. At present conditions it is calculated from a simple analytical expression that the IPCC climate sensitivity is 3.2x too high. That implies that the global warming as measured since 1980, is for about 2/3rd the result of an increase in incoming solar power and can only for 1/3rd be attributed to an increase in GHG’s, at max. This analysis is supported by radiation data from NASA’s CERES-project (2000-2020).
A couple of years ago, I made a simple estimate of the temperature effect of the more than 10% brightening over the last 4 decades in The Netherlands [1]. The Royal Dutch Metrological Institute (KNMI) attributed only 0.2oC to that brightening [2], whereas my methodology resulted in about 1oC. That would leave only 1/3 of the observed 1.5 oC warming to the effect of greenhouse gasses (GHG’s). I coupled “brightening” to less clouds, and came to an estimate for the sensitivity to cloud change (cc) of about 0.1 K/%cc.
In the subsequent discussion with the KNMI, the only argument against my approach boiled down to: “sophisticated climate models tell us something different, so your simplistic model must be wrong”. Several other methods to determine this cloud-sensitivity, all delivered similar results. Finally, I concluded that KNMI referred to cloud-feedback results from climate models, whereas I was looking to the effect of an independent change in cloudiness. Next, I compared both views against existing trends in cloudiness, surface temperatures, etc. from satellite data [3]. When matched to trends in cloud-coverage, Global Circulation Model (GCM)-derived cloud-feedbacks delivered a climate condition close to a runaway scenario. Whereas my own idea of an independent forcing due to clouds acting as shutters (modulating solar input) delivered very surprisingly, that the sum of all feedbacks outside the basic Planck feedback parameter, became all of a sudden (almost) zero [3].
https://wattsupwiththat.com/2022/11/07/outside-the-black-box-back-to-basics/
