Nakamura Mototaka’s Book, “Confessions of a climate scientist”

Confessions of a climate scientist:
The global warming hypothesis is an unproven hypothesis
– by Nakamura Mototaka

“From 1990 to 2014 he worked on cloud dynamics and forces mixing atmospheric and ocean flows on medium to planetary scales. His bases were MIT (for a Doctor of Science in meteorology), Georgia Institute of Technology, Goddard Space Flight Centre, Jet Propulsion Laboratory, Duke and Hawaii Universities and the Japan Agency for Marine-Earth Science and Technology. He’s published about 20 climate papers on fluid dynamics.”

A Climate Modeller Spills the Beans

This was a fast but very interesting read which reinforced many of my understandings about the relevance of the climate models, given the observational data. But he also clarified several important issues I haven’t explored. (Bold emphasis in quotes added by me.)

“… let me state unambiguously that I am all for environmental conservation, contrary to what some people might think about me. I do support the idea of reducing oil and gas consumption, … and also on a fact that there are human health problems caused by the use of those resources, not based on the unproven hypothesis of the global warming.”

A sample of factors before any feedback loops. Not from the book.

The most obvious and egregious problem is the treatment of incoming solar energy — it is treated as a constant, that is, as a “never changing quantity”. It should not require an expert to explain how absurd this is if “climate forecasting” is the aim of the model use. It has been only several decades since we acquired an ability to accurately monitor the incoming solar energy. In these several decades only, it has varied by 1 to 2 Watts per square meters.”

I’m not sure I’ve ever been aware that solar energy was treated as a constant in the models. This information would’ve ended the debate for me much more quickly. I learned it within days of reading this book/essay, overshadowed by a global climate strike.

I’ve long been skeptical that even the world’s best can sufficiently understand the countless chaotic nonlinear systems which interact to create climates. I still believe it is hubris to believe we can know these things with any actionable certainty. He discusses this in more detail…

“All climate simulation models have many details that become fatal flaws when they are used as climate forecasting tools, especially for mid- to long-term (several years and longer) climate variations and changes. These models completely lack some of critically important climate processes and feedbacks, and represent some other critically important climate processes and feedbacks in grossly distorted manners to the extent that makes these models totally useless for any meaningful climate prediction. I myself used to use climate simulation models for scientific studies, not for predictions, and learned about their problems and limitations in the process. I, with help of some of my former colleagues, even modified some details of these models in attempts to improve them by making some of grossly simplified expressions of physical processes in the models less grossly simplified, based on physical theories. So, I know the internal workings of these models very well. I find it rather bewildering that so many climate researchers … appear to firmly believe in the validity of using these models for climate forecasting.

“By the way, none of the climate simulation models used for predictions can reproduce the current climate accurately despite the heavy tuning and engineering efforts by climate researchers.”

“Climate researchers used to downplay the significance of interactions between the large-scale atmosphere and oceans in the middle and high latitudes, based on many experiments using coarse-resolution climate simulation models that are hopeless in capturing the atmospheric response to the underlying oceanic temperature structures and analyses of these experiments with mostly linear statistical methods in simple frameworks. They had missed important factors in the large-scale atmosphere-ocean interactions in the extra-tropics (middle and high latitudes) — importance of the spatial position of the westerly jet stream with respect to the areas of large horizontal temperature gradient (contrast) in the oceans and high horizontal resolutions required for capturing effects of the oceanic temperature structures — and grossly underestimated the oceanic impacts on the large-scale atmospheric states.

I hate to say this, because I know well how much of serious efforts have been put into improving these parametric representations (I spent hundreds of hours in vain myself), but all of these parametric representations, even the best of them, are Mickey Mouse mockeries when compared with the reality. In the real oceans, just like in the atmosphere, the smaller-scale flows often tend to counteract the effects of the larger-scale flows. So, small- to medium-scale motions exert ensemble effects on the larger-scale state in such a way that they “tighten” the larger-scale fields of flows, temperature, and salinity when and where the larger-scale flows tend to “loosen” the larger-scale fields. I found this situation occurring roughly half of the time in realistic ocean simulation output.”

“Not only is the strictly-diffusive qualitative aspect of the representations wrong, but also the quantitative aspect of the representations, the strength of mixing and transport, is an ad hoc “model tuning tool”. Parameters that determine the strength of mixing and transport by the smaller-scale flows are selected to “tune” the model without adhering to numbers estimated from observations or high-resolution model output. That is, the selection of the parameter values is an engineering process to “make the model work” rather than a scientific process. The models are “tuned” by tinkering around with values of various parameters until the best compromise is obtained. I used to do it myself. It is a necessary and unavoidable procedure and is not a problem so long as the user is aware of its ramifications and is honest about it. But it is a serious and fatal flaw if it is used for climate forecasting/prediction purposes.”

“So, if a model were to have the same relative humidity regardless of changes in other aspects of the atmosphere, then, for a minor warming caused by an increased amount of carbon dioxide, the artificially imposed condition on the relative humidity would generate some extra warming due to an increase in the water vapor amount, which would tend to further raise the atmospheric temperature and water vapor content, creating a vicious cycle between the atmospheric water vapor and temperature. This positive feedback itself is not fictitious. But, in climate models, it is artificially enforced to operate without interference by other feedbacks that exist in the real climate system, and is very likely to be exaggerated. It is this feedback that generates major increases in the surface temperature when the atmospheric carbon dioxide is increased in climate simulation models.”

“The ad hoc treatment of the vertical water vapor distribution is not the only major problem associated with this most important greenhouse gas. Methods to calculate its horizontal distribution are laced with a grave problem also. It is rooted in the treatment of effects of sub-grid (too small to be calculated explicitly in climate models) motions on the water vapor.”

“Reasonably accurate representation of cloud is one of the most difficult and important tasks in climate simulations. Accurate simulation of cloud is simply impossible in climate models, since it requires calculations of processes at scales smaller than 1mm. So, clouds are represented with parametric methods in climate models. Are those methods reasonably accurate? No. If one seriously studies properties of clouds and processes involved in cloud formation and dissipation, and compare them with the cloud treatment in climate models, one would most likely be flabbergasted by the perfunctory treatment of clouds in the models. The parametric representations of clouds are ad hoc and are tuned to produce the average cloud cover that somewhat resembles that seen in the current climate.”

“The take-home message from the above discussion is this: all climate simulation models, even those with the best parametric representation scheme for convective motions and cloud, suffer from a very large degree of arbitrariness in the representation of processes that determine the atmospheric water vapor and cloud fields. Since the climate models are tuned arbitrarily to produce the time-averaged atmospheric water vapor field and cloud coverage that best resemble the observed climatological ones, but still fail to reproduce the observed fields (especially miserably when the instantaneous field and temporal variability are examined), there is no reason to trust their predictions/forecasts. With values of parameters that are supposed to represent many complex processes being held constant, many nonlinear processes in the real climate system are absent or grossly distorted in the models. It is a delusion to believe that simulation models that lack important nonlinear processes in the real climate system can predict at least the sense or direction of the climate change correctly.

So it continues to be my position that there are far better reasons — and less divisive reasons — to hasten the inevitable shift to renewable energies. Meanwhile, more outlets and activists might be more attracted to trying to prevent this alleged catastrophe with even scarier science. And a global movement of environmentalists will help build the next big energy ownership consolidation, keeping it in the big oil family.


Here is a very related video created by one of the few voices that let me know this book was published: