Why Did IMF Disinvite Nobel Laureate?

CO2 Coalition explains.  Nobel Laureate (Physics 2022) Dr. John Clauser was to present a seminar on climate models to the IMF on Thursday and now his talk has been summarily cancelled. According to an email he received last evening, the Director of the Independent Evaluation Office of the International Monetary Fund, Pablo Moreno, had read the flyer for John’s July 25 zoom talk and summarily and immediately canceled the talk. Technically, it was “postponed.”

Dr. Clauser had previously criticized the awarding of the 2021 Nobel Prize for work in the development of computer models predicting global warming and told President Biden that he disagreed with his climate policies. Dr. Clauser has developed a climate model that adds a new significant dominant process to existing models. The process involves the visible light reflected by cumulus clouds that cover, on average, half of the Earth. Existing models greatly underestimate this cloud feedback, which provides a very powerful, dominant thermostatic control of the Earth’s temperature.

More recently, he addressed the Korea Quantum Conference where he stated, “I don’t believe there is a climate crisis” and expressed his belief that “key processes are exaggerated and misunderstood by approximately 200 times.” Dr. Clauser, who is recognized as a climate change skeptic, also became a member of the board of directors of the CO2 Coalition last month, an organization that argues that carbon dioxide emissions are beneficial to life on Earth.

What Difference Clouds Make in Climate Models

Obviously the Clauser presentation is not accessible and I don’t find a link to a publication concerning his treatment of clouds in climate models.  But we can see how the models react to clouds by means of an important paper The Mechanisms of Cloudiness Evolution Responsible for Equilibrium Climate Sensitivity in Climate Model INM-CM4-8 by Evgeny Volodin AGU 03/12/2021.  Excerpts in italics with my bolds.

Abstract

Current climate models demonstrate large discrepancy in equilibrium climate sensitivity (ECS). The effects of cloudiness parameterization changes on the ECS of the INM-CM4-8 climate model were investigated. This model shows the lowest ECS among CMIP6 models. Reasonable changes in the parameterization of the degree of cloudiness yielded ECS variability of 1.8–4.1 K in INM-CM4-8, which was more than half of the interval for the CMIP6 models.

The three principal mechanisms responsible for the increased ECS were increased cloudiness dissipation in warmer climates due to the increased water vapor deficit in the non-cloud fraction of a cell, decreased cloudiness generation in the atmospheric boundary layer in warm climates, and the instantaneous cloud response to CO2 increases due to stratification changes.

Introduction

In CMIP6 the lowest and highest ECS (Equilibrium Climate Sensitivity) values are 1.8 and 5.6 K, respectively (Zelinka et al., 2020). Climate response to some external forcing produces feedbacks. Positive feedback enhances the response to forcing, negative feedback weakens it. Analysis of climate feedback shows that cloud feedback is the principal reason for the broad range of ECS (Zelinka et al., 2020). Clouds (especially low clouds) are significantly reduced with global warming in models with high ECS, resulting in positive feedback. Models with low sensitivity show small cloudiness changes with global warming; some models feature an increase in low clouds in warmer climates, creating a negative feedback.

Clouds produce shortwave and longwave radiative effects. The shortwave cloud radiative effect (SW CRE) is generally negative, because cloudiness reflects solar radiation that would otherwise be absorbed by the climate system. The shortwave effect is usually strongest for low clouds that have high amounts of liquid water and high albedos. The longwave cloud radiative effect (LW CRE) is generally positive, because cloud tops are usually much colder than the surface of the Earth; thus, thermal radiation from the cloud top is much lower than that from the surface. Negative/positive CRE produces cooling/warming from clouds.

The goal of this study is that we turn off some mechanisms responsible for large-scale cloud evolution that lead to increase or decrease ECS, and ECS is changed by the factor of more than 2. The role of a chosen mechanism in decrease or increase of ECS can be clearly seen. At the same time, all model versions show preindustrial climate with systematic biases compared to that for the version used in CMIP6. A realistic way of estimating the impact of change in parameterization on cloud feedback by keeping the cloud mean state realistic in all model versions and running 4xCO2 experiments rather than uniform +4K experiments are used in this study.

Table 1. Summary of Model Versions

The ECS estimation method is commonly used in CMIP5 and CMIP6 and was proposed by Gregory et al. (2004). The two model runs performed were the control run, in which all forcings were fixed at preindustrial levels, and the run where the concentration of CO2 in the atmosphere was four times higher than in the control run (4CO2 run). The initial state for both runs was the same and taken from a sufficiently long control run. Each run had a length of 150 years. Subsequently, the global mean difference of GMST and the heat balance at the top of atmosphere (THB) for 4CO2 and the control run were calculated for each model year.

Results of the sensitivity experiments performed with the five climate model versions.

Version 1 shows a very low ECS of 1.8 K due to a low negative climate feedback parameter value of −1.46 W m−2 K−1 (interval from −0.6 to −1.8 W m−2 K−1 for CMIP5 and CMIP6) and a low ERF value of 2.7 W m−2 (intervals of 2.6–4.4 and 2.7–4.3 W m−2 for CMIP5 and CMIP6, respectively, Zelinka et al., 2020). The low ECS was accompanied by mostly negative CRF in both the SW and LW spectral intervals (Figure 2 below).

The parameterization replacement scheme for cloudiness in version 2 dramatically changed all the parameters, and the ECS more than doubled to 3.8 K. ERF increased to 3.8 W m−2, without changes to the radiation code because ΔCREINST changed from −0.88 W m−2 to −0.13 W m−2. Additionally, the climate feedback parameter increased from −1.46 W m−2 K−1 to −1.0 W m−2 K−1. In version 2, global warming was associated with decreased cloudiness at all levels. The net cloud radiative feedback became positive. Version 2 yielded significantly increased net and SW cloud radiative feedbacks at all latitudes compared with version 1. Analysis of the sensitivity experiment results of versions 3–5 helps understand the mechanisms of these significant changes.

Version 3 features a suppressed mechanism of high tropical cloudiness due to decreased convective mass flux and higher ECS than version 2 (4.1 K); however, the change is not very pronounced. The LW CRF in the tropics increases in version 3 compared to version 2. The decrease in SW CRE is not very pronounced; therefore, increased net CRF increases ECS. This confirms our hypothesis that suppressing the decrease in tropical cloudiness should increase ECS and that the impact of this mechanism on ECS is noticeable but not very strong.

ECS is noticeably lower in version 4 (2.9 K) than in version 2. Thus, the mechanism of the decrease in boundary layer cloudiness due to decreased cloudiness generation by boundary layer turbulence is crucial for ECS. SW and LW CRF decreased in version 4 compared to version 2, primarily in the tropics and subtropics.

The mechanism of increased cloud dissipation under global warming conditions was suppressed in version 5, ECS was reduced to 2.5 K, and the climate feedback parameter decreased to −1.56 W m−2 K−1. Additionally, the SW CRF decreased in version 5 compared with version 4, primarily in the tropics and subtropics. In this version, all the mechanisms that decrease clouds with increased temperature, as raised in the previous section, are suppressed. The principal reason for the ECS difference between versions 1 and 5 is the instantaneous adjustment rather than the feedback (see Table 1). ΔCREINST values in versions 1 and 5 were −0.88 and 0.16 W m−2, respectively.

Conclusion

All model versions demonstrate similar model bias values for annual mean CRE, near-surface temperature, and precipitation; thus, determining a relation between present-day climate simulation model quality and ECS is difficult. Version 1 has slightly better quality (because it is a CMIP6 version) due to extensive tuning. The cloudiness scheme used in version 1 contained the dependence of low clouds on stratification. An increase in CO2 leads to more stable stratification and more low clouds and may be the primary cause of the low ECS. Bretherton (2015) and Geoffroy et al. (2017) obtained similar results. The decrease in clouds in warmer climates due to the mixing of cloud air with the unsaturated environment was also stated in a review by Gettelman and Sherwood (2016). Our results confirm those by Bony et al. (2006), Brient and Bony (2012), and others that a significant change in the response of low clouds to global warming leads to significant changes in cloud radiative feedback and ECS.

Comment

The main finding is: If warming increases low clouds, then SW (incoming solar radiation) is reduced, counteracting the warming, in effect a negative feedback.  That is consistent with Clauser’s position.