Epistemic Caution and Climate Change

Written by: Stephen Hsu

Primary Source:  Information Processing

I have not, until recently, invested significant time in trying to understand climate modeling. These notes are primarily for my own use, however I welcome comments from readers who have studied this issue in more depth.

I take a dim view of people who express strong opinions about complex phenomena without having understood the underlying uncertainties. I have yet to personally encounter anyone who claims to understand all of the issues discussed below, but I constantly meet people with strong views about climate change.

See my old post on epistemic caution Intellectual honesty: how much do we know?

… when it comes to complex systems like society or economy (and perhaps even climate), experts have demonstrably little predictive power. In rigorous studies, expert performance is often no better than random.

… worse, experts are usually wildly overconfident about their capabilities. … researchers themselves often have beliefs whose strength is entirely unsupported by available data.

Now to climate and CO2. AFAIU, the heating effect due to a increasing CO2 concentration is only a logarithmic function (all the absorption is in a narrow frequency band). The main heating effects in climate models come from secondary effects such as water vapor distribution in the atmosphere, which are not calculable from first principles, nor under good experimental/observational control. Certainly any “catastrophic” outcomes would have to result from these secondary feedback effects.

The first paper below gives an elementary calculation of direct effects from atmospheric CO2. This is the “settled science” part of climate change — it depends on relatively simple physics. The prediction is about 1 degree Celsius of warming from a doubling of CO2 concentration. Effects beyond this are due to secondary effects which, in their totality, are not well understood — see second paper below, about model tuning, which discusses rather explicitly how these unknowns are dealt with.

Simple model to estimate the contribution of atmospheric CO2 to the Earth’s greenhouse effect
Am. J. Phys. 80, 306 (2012)
http://dx.doi.org/10.1119/1.3681188

We show how the CO2 contribution to the Earth’s greenhouse effect can be estimated from relatively simple physical considerations and readily available spectroscopic data. In particular, we present a calculation of the “climate sensitivity” (that is, the increase in temperature caused by a doubling of the concentration of CO2) in the absence of feedbacks. Our treatment highlights the important role played by the frequency dependence of the CO2 absorption spectrum. For pedagogical purposes, we provide two simple models to visualize different ways in which the atmosphere might return infrared radiation back to the Earth. The more physically realistic model, based on the Schwarzschild radiative transfer equations, uses as input an approximate form of the atmosphere’s temperature profile, and thus includes implicitly the effect of heat transfer mechanisms other than radiation.

From Conclusions:

The question of feedbacks, in its broadest sense, is the whole question of climate change: namely, how much and in which way can we expect the Earth to respond to an increase of the average surface temperature of the order of 1 degree, arising from an eventual doubling of the concentration of CO2 in the atmosphere? And what further changes in temperature may result from this response? These are, of course, questions for climate scientists to resolve. …

The paper below concerns model tuning. It should be apparent that there are many adjustable parameters hidden in any climate model. One wonders whether the available data, given its own uncertainties, can constrain this high dimensional parameter space sufficiently to produce predictive power in a rigorous statistical sense.

The first figure below illustrates how different choices of these parameters can affect model predictions. Note the huge range of possible outcomes! The second figure below illustrates some of the complex physical processes which are subsumed in the parameter choices. Over longer timescales, (e.g., decades) uncertainties such as the response of ecosystems (e.g., plant growth rates) to increased CO2 would play a role in the models. It is obvious that we do not (may never?) have control over these unknowns.

THE ART AND SCIENCE OF CLIMATE MODEL TUNING

AMERICAN METEOROLOGICAL SOCIETY MARCH 2017 | 589

… Climate model development is founded on well-understood physics combined with a number of heuristic process representations. The fluid motions in the atmosphere and ocean are resolved by the so-called dynamical core down to a grid spacing of typically 25–300 km for global models, based on numerical formulations of the equations of motion from fluid mechanics. Subgrid-scale turbulent and convective motions must be represented through approximate subgrid-scale parameterizations (Smagorinsky 1963; Arakawa and Schubert 1974; Edwards 2001). These subgrid-scale parameterizations include coupling with thermodynamics; radiation; continental hydrology; and, optionally, chemistry, aerosol microphysics, or biology.

Parameterizations are often based on a mixed, physical, phenomenological and statistical view. For example, the cloud fraction needed to represent the mean effect of a field of clouds on radiation may be related to the resolved humidity and temperature through an empirical relationship. But the same cloud fraction can also be obtained from a more elaborate description of processes governing cloud formation and evolution. For instance, for an ensemble of cumulus clouds within a horizontal grid cell, clouds can be represented with a single-mean plume of warm and moist air rising from the surface (Tiedtke 1989; Jam et al. 2013) or with an ensemble of such plumes (Arakawa and Schubert 1974). Similar parameterizations are needed for many components not amenable to first-principle approaches at the grid scale of a global model, including boundary layers, surface hydrology, and ecosystem dynamics. Each parameterization, in turn, typically depends on one or more parameters whose numerical values are poorly constrained by first principles or observations at the grid scale of global models. Being approximate descriptions of unresolved processes, there exist different possibilities for the representation of many processes. The development of competing approaches to different processes is one of the most active areas of climate research. The diversity of possible approaches and parameter values is one of the main motivations for model inter-comparison projects in which a strict protocol is shared by various modeling groups in order to better isolate the uncertainty in climate simulations that arises from the diversity of models (model uncertainty). …

… All groups agreed or somewhat agreed that tuning was justified; 91% thought that tuning global-mean temperature or the global radiation balance was justified (agreed or somewhat agreed). … the following were considered acceptable for tuning by over half the respondents: atmospheric circulation (74%), sea ice volume or extent (70%), and cloud radiative effects by regime and tuning for variability (both 52%).

 

Here is Steve Koonin, formerly Obama’s Undersecretary for Science at DOE and a Caltech theoretical physicist, calling for a “Red Team” analysis of climate science, just a few months ago (un-gated link):

WSJ: … The outcome of a Red/Blue exercise for climate science is not preordained, which makes such a process all the more valuable. It could reveal the current consensus as weaker than claimed. Alternatively, the consensus could emerge strengthened if Red Team criticisms were countered effectively. But whatever the outcome, we scientists would have better fulfilled our responsibilities to society, and climate policy discussions would be better informed.

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Stephen Hsu
Stephen Hsu is vice president for Research and Graduate Studies at Michigan State University. He also serves as scientific adviser to BGI (formerly Beijing Genomics Institute) and as a member of its Cognitive Genomics Lab. Hsu’s primary work has been in applications of quantum field theory, particularly to problems in quantum chromodynamics, dark energy, black holes, entropy bounds, and particle physics beyond the standard model. He has also made contributions to genomics and bioinformatics, the theory of modern finance, and in encryption and information security. Founder of two Silicon Valley companies—SafeWeb, a pioneer in SSL VPN (Secure Sockets Layer Virtual Private Networks) appliances, which was acquired by Symantec in 2003, and Robot Genius Inc., which developed anti-malware technologies—Hsu has given invited research seminars and colloquia at leading research universities and laboratories around the world.
Stephen Hsu

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