Frequently And Never Asked Questions
Questions that people often ask, or never thought to ask, after reading The Climate Demon (or even before reading it).
No. Technically, a Black Swan event should be completely unexpected and most people expect global warming to some degree. (The Antarctic Ozone Hole, on the other hand, was a Black Swan event because it was completely unexpected.)
Q2. Why do climate contrarians often appear to be more trusting of numbers from (certain) economic models than climate models, even though the latter are more scientifically constrained? (p.249)
The answer may lie in the question itself – it may be simply because climate models are more scientifically constrained. As a discipline located at a further deductive distance from physics, and therefore more inductive and empirical than climate science, economic predictions will be more uncertain than climate predictions. (Recall Harry Truman's plea for "a one-handed economist.") It is perhaps easier to find an economic model prediction to confirm one's ideological priors than to find a climate model prediction, and thus craft a narrative that appears to have a quantitative basis. [This is not to discourage the use of economic models. Economic models are essential for making short-term predictions and for long-term scenario construction. One just needs to be aware of the quantified and unquantified uncertainties associated with different types of model predictions at various timescales.]
Q3. What about the IPCC Sixth Assessment (AR6)?
The book does not discuss any IPCC AR6 results because the proofs were completed before the Working Group 1 Report was released. But the IPCC report is based on published literature and it was not hard to guess what some of the important conclusions were likely to be. For example, the AR6 likely climate sensitivity range (2.5–4.0) closely tracks the 2020 WCRP Assessment likely range (2.6–3.9) discussed in the book (p.222, p.240). Also, the AR6 projections constrain model results based on certain criteria that discount models with high simulated climate sensitivies. This ex post facto imposition of what is effectively a retroactive tuning mandate for model acceptability (p.175) ends up shackling the CMIP6 models. The net result is that the AR6 temperature projections (Figure 4.11c of AR6-WG1) look rather similar to the AR5 projections (Figure 7.4c of the book).
Past IPCC assessments relied primarily on comprehensive climate models to estimate key global parameters such as climate sensitivity and the fraction of recent warming attributable to greenhouse gas forcings. The AR6 report relies more on an assortment of statistical models coupled with simple physical models to estimate these global parameters from data. What the book speculated might be "an implicit rebuke of the latest generation of models with higher climate sensitivities" (p.222) by the IPCC turned out to be a rather more explicit rebuke!
If you feel that this book may have been too harsh on comprehensive climate models, the AR6 report is in some ways even harsher (especially if you read between the lines). For instance, the book recommends selecting a set of acceptable comprehensive climate models and aggregating their simulations them to make climate projections (p.298). The AR6 report instead uses a hybrid (and somewhat inelegant) approach to making climate projections that involves averaging emulations based on analysis of past data with constrained simulations from comprehensive models (Figure 4.11d of AR6-WG1). Continuing to read between the lines, the narrowing of uncertainty of climate sensitivity in AR6 around the central value of about 3 degC, in and of itself, could be literally interpreted as reducing the urgency to act to mitigate climate change as compared to AR5 (p.309), because some high-end estimates of climate sensitivity now appear less likely. Of course, the urgency to act is greatly increased by the passage of many years without strong mitigating action since AR5 leading to accumulated carbon emissions.
The broad qualitative conclusions of the book, though, remain consistent with the results of the AR6 report – that's the nice thing about taking climate models seriously but not literally!
Q4. Was the extreme Texas winter storm of February 2021 caused by climate change? (p.112)
It is the wrong question to ask, although some in the media and government were quick to blame climate change for this extreme winter storm. As the book argues, one shouldn't generally try to attribute specific weather events entirely to climate change or global warming. A better question to ask would be "did global warming make the 2021 extreme winter storm more or less likely?" A related question is whether global warming made the storm stonger or weaker (in a statistical sense)? This question was addressed in the 2021 Texas state climatologist's report that assessed trends in extreme weather in Texas using data. The report concludes that "[a]s the climate warms, the likelihood of winter weather decreases. Both extreme cold and snowfall either become less frequent or are expected to do so. Widespread snowfall events in Texas such as the one that took place in February 2021 are extremely rare." The figure below shows the coldest temperature recorded each year in Texas since 1900. We see that the extreme cold temperatures are warming faster than the average temperatures. Therefore, the data suggest that global warming perhaps made the 2021 winter storm just a tiny bit less severe, as one might intuitively expect. It is important to note that the report also says that heat extremes in Texas (see the other figure below) as well as intense rainfall events are made more severe, and more likely, by global warming. The take home message is that although many extreme weather phenomena are made worse by climate change, not all are.
Trends of cold and hot extreme weather in Texas (from Nielsen-Gammon et al., 2021)
Q5. Can you explain stochastic uncertainty ("certain uncertainty") some more? (p.115, 124, 297)
Consider weather prediction in the atmosphere (for starters). We use a weather model to make an ensemble of forecasts, starting from slightly different initial conditions to represent the error in our knowledge of the initial atmospheric state (p.43). For the first few hours, the forecasts will be very similar to each other, but then they will start diverging due to chaotic error growth or the Butterfly Effect (p.40; see this TED-Ed video). After about 2–4 weeks, all memory of the initial condition will be lost and each forecast can be considered to be a random (or stochastic) sample from all possible weather states. For climate prediction, we also need to include initial conditions in the ocean (also land and sea-ice). Since the ocean evolves much more slowly, it takes a decade or longer for the memory of the initial condition to be lost (p.116). But eventually, after a few decades, the ensemble of forecasts will end up being a collection of random samples from all possible climate states. The spread of among these states is the natural (or internal) climate variability, and can be considered unpredictable "climate noise."
Suppose we add a long-term climate trend, such as global warming, to this system. The actual climate in the year 2060 (say) will be the sum of the predicted long-term trend ("climate signal") and the unpredictable climate noise. As NCAR climate scientist Clara Deser explains in her 2020 paper on climate variability: “anthropogenic climate change is what you expect; anthropogenic climate change and natural climate variability is what you get.” The figure below from the paper shows two samples from a 40-member ensemble of 50-year (2010-2050) predictions using the NCAR global climate model for a high carbon emission scenario. The top panel shows the expected (ensemble-average) change in winter precipitation over North America. i.e., the signal. We note that precipitation is expected to increase both in the East and the West Coast of the US. The bottom two panels show two individual ensemble members, which span the 5th to 95th percentile range of the actual climate states we may get, i.e., the signal plus two extreme realizations of noise. In the left panel, we see even stronger increase in West Coast precipitation, whereas the right panel actually shows a decrease in precipitation over the same region. When planning for the future, we will need to take into account the whole range of possibilities associated with this "certain uncertainty," especially for regional climates (p.124,297)
Predicted change in winter precipitation between 2010 and 2060, measured in millimeters/day. Brownish colors indicate decreased rain/snow and the greenish/bluish colors indicate increased rain/snow (From Deser, 2020)
Q6. Can you provide another example of the "pitfalls of the uncertainty trough?" (p.244)
This recent twitter thread provides a great example. At the end of the 2021 COP26 climate conference in Glasgow, it was widely reported that the conference had managed to keep the goal of limiting to global warming 1.5 degC alive. It turns out that this statement relies upon certain carbon sinks persisting after zero emissions is reached. Although some experts on carbon sinks believe this will be the case, and carbon sink models support this idea, other experts weren't so sure because we have no actual experience of how carbon sinks will behave in a "zero emission" world. (When relying on inductive models, one may not be able to confidently extrapolate them to unprecedented scenarios.)
Q7. The age-old question: Star Trek or Star Wars, which is better? (p.37)
The book clearly prefers the episodic Star Trek (nondeterministic) over the epic Star Wars (too deterministic). Spock will likely provide better advice to deal with climate change than Darth Vader (or Han or Luke or Leia, for that matter – although older Obi Wan may do the job), even though Spock has been criticized for assuming that others will act rationally like him when picking the optimal solution. [See also the Seriously, not literally Visual.]
Q8. The book's Epilogue references the novel Dune. The book was published on the same day that the new Dune movie premiered in the US (Oct. 21, 2021). Coincidence? (p.314)
Lacking spice, I could not have foreseen it. The style of the book, with its numerous epigraphs – some of them made-up – is indeed influenced by the science-fiction classic Dune, which explores the power of prophecies and the theme of planetary climate change. The Bene Gesserit Litany from Dune may even be good psychological advice for coping with climate change anxiety!
No. The book was sent to the printers a month before the Nobel announcement. I didn't expect that climate modeling would be recognized with a Physics Nobel, as you can tell from how Manabe is introduced in Chapter 4 of the book:
Q11. Can you really buy insurance to protect against the zombie apocalypse and/or alien abductions? (p.291)
Q12. Shouldn’t this book be called The Climate Demon Demon, to be consistent with its subtitle?
Yes, and if you have figured this out, you must be a logic fiend as well as a Laplace Demon aficionado – I didn’t realize this meta inconsistency until the book had been sent to the printers. In any case, I’m sure the editor would have rejected this accurately recursive but obviously clunky title. [A bit of history: I kept a few random notes over the years under a file labeled Occam’s Beard for a book with a broad scope dealing with general issues of complexity. By the time of the first draft, the scope had narrowed to a topic that I actually knew something about, i.e., climate prediction, and the working title became Butterflies in the Greenhouse. Worried that the book may end up being shelved in the gardening section, I used a forgettable working title when submitting it to the Cambridge editor, Matt Lloyd, who, after reading the draft, suggested The Climate Demon.]