Worst case scenarios versus fat tails: a discussion about climate change

Summary: Threat assessment requires understanding not just of worst case scenarios, but their odds of occurrence. Yesterday’s post looked at the math: A guide into the weird numbers that run our world, describing both financial bubbles & climate change — power laws, Black Swans, and Dragon Kings. If the worst case scenarios come true, we’ll all become too familiar with these terms. Today Professor Judith Curry discusses how can determine the likelihood of one of these scenarios happening. It’s one of the great questions in the public policy debate about climate change.

Fat Tails

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Contents

  1. Worst case scenarios vs fat tails
  2. About Judith Curry
  3. Important things to know about climate change
  4. For More Information
  5. Other worst-case scenarios

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Worst case scenarios versus fat tails

by Judith Curry, at her website Climate Etc
18 April 2014
Posted here under her Creative Commons license

.If we omit discussion of tail risk, are we really telling the whole truth? 

(a)  Kerry Emanuel

This post is motivated by an essay by Kerry Emanuel published at the Climate Change National Forum, entitled Tail Risk vs. Alarmism, which is in part motivated by my previous post AAAS: What we know. Excerpts:

In assessing the event risk component of climate change, we have, I would argue, a strong professional obligation to estimate and portray the entire probability distribution to the best of our ability. This means talking not just about the most probable middle of the distribution, but also the lower probability high-end risk tail, because the outcome function is very high there.

Do we not have a professional obligation to talk about the whole probability distribution, given the tough consequences at the tail of the distribution? I think we do, in spite of the fact that we open ourselves to the accusation of alarmism and thereby risk reducing our credibility. A case could be made that we should keep quiet about tail risk and preserve our credibility as a hedge against the possibility that someday the ability to speak with credibility will be absolutely critical to avoid disaster.

(b)  Uncertainty monster simplification

In my paper Climate Science and the Uncertainty Monster, I described 5 ways of coping with the monster. Monster Simplification is particularly relevant here:  Monster simplifiers attempt to transform the monster by subjectively quantifying or simplifying the assessment of uncertainty.

The uncertainty monster paper distinguished between statistical uncertainty and scenario uncertainty:

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Statistical uncertainty is the aspect of uncertainty that is described in statistical terms. An example of statistical uncertainty is measurement uncertainty, which can be due to sampling error or inaccuracy or imprecision in measurements.

Scenario uncertainty implies that it is not possible to formulate the probability of occurrence of one particular outcome. A scenario is a plausible but unverifiable description of how the system and/or its driving forces may develop over time. Scenarios may be regarded as a range of discrete possibilities with no a priori allocation of likelihood.

Given our uncertainty and ignorance surrounding climate sensitivity, I have discussed the folly of attempting probabilistic estimates of climate sensitivity, and to create a pdf {probability distribution function} (see this previous post: Probabilistic estimates of climate sensitivity).  In my opinion, the most significant point in the IPCC AR5 WG1 report is their acknowledgment that they cannot create a meaningful pdf of climate sensitivity with a central tendency, and hence they only provide ranges with confidence levels (and they avoid identifying a best estimate of 3C as they did in the AR4).   The strategy used in the AR5 is appropriate in context of scenario uncertainty, where they identify some bounds for sensitivity, and present some assessment of likelihood (values less than 1C are extreme unlikely, and values greater than 6C are very unlikely).

So I starkly disagree with this statement by Emanuel:

We have a strong professional obligation to estimate and portray the entire probability distribution to the best of our ability.

In my opinion, we have a strong profession obligation NOT to simply the uncertainty by portraying it as a pdf, when the situation is characterized by substantial uncertainty that is not statistical in nature.  This  issue is discussed in a practical way with regards to climate science in a paper by Risbey and Kandlikar (2007), see especially Table 5:

Risbey & Kandlikar
Risbey & Kandlikar, Climatic Change (2007)

Climate sensitivity is definitely not characterized by #1, rather it is characterized by #2 or #4.  The lower bound is arguably well defined; the upper bound is not.  The problem at the upper bound is what concerns Emanuel; I am arguing that the way to address this is NOT through considering a fat tail that extends out to infinity of a mythical probability distribution.

Nicholas Taleb’s black swan arguments emphasize the non-computatability of the consequential rare events using scientific methods (owing to the very nature of small probabilities)

Certainty Channel
The IPCC has switches away from this channel

(c)  What’s the worst case?

I have spent considerable effort in identifying possible/plausible worst case scenarios, black swans and dragon kings:

  1. Anticipating the climate black swan
  2. My 2010 AGU talk Climate surprises, catastrophes and fat tails (end of the post)
  3. My presentation at the NOAA Water Cycle Challenges Workshop
  4. My recent presentation Generating possibility distributions of scenarios for regional climate change

Identifying possible/plausible worst case scenarios is much more useful in my opinion  than the fat tail approach to identifying possible black swans and dragon kings in the climate system.

The philosophical foundation for thinking about ‘worst case scenarios’ is laid out in the work of Gregor Betz, see especially his paper “What‘s the Worst Case? The Methodology of Possibilistic Prediction“.  This paper deserves a thread of its own (I regard it as hugely important), but I want to at least introduce the relevant concepts here. Excerpts

Where even probabilistic prediction fails, foreknowledge is (at most) possibilistic in kind; i.e. we know some future events to be possible, and some other events to be impossible.

Gardiner, in defence of the precautionary principle, rightly notes that (i) the application of the precautionary principle demands that a range of realistic possibilities be established, and that (ii) this is required by any principle for decision making under uncertainty whatsoever.

Accepting the limits of probabilistic methods and refusing to make probabilistic forecasts where those limits are exceeded, originates, ultimately, from the virtue of truthfulness, and from the requirements of scientic policy advice in a democratic society.

‘Possibility’, here, means neither logical nor metaphysical possibility, but simply (logical and statistical) consistency with our relevant background knowledge.

A surprise of the first type occurs if a possibility that had not even been articulated becomes true. Hypothesis articulation is, essentially, the business of avoiding surprises (of this 1st type). There is, however, a second type of surprise that does not simply extend the picture we’ve drawn so far, but rather shakes it. Our scentific knowledge is constantly changing, whereas that change is not cumulative: scientific progress also comprises refuting, correcting, and abandoning previous scientificc results. Now a readjustment of the background knowledge questions the entire former assessment of possibilistic hypotheses.

I take this brief discussion to indicate that the decision deliberation starts to become messy and complicated. It is not clear to me whether there are general principles which can guide rational decisions in such situations at all. This, however, must not serve as an excuse for simplifying the epistemic situation we face! If a policy decision requires a complex normative judgement, then democratically legitimised policy makers have arguably a hard job; it is, nevertheless, their job to balance and weigh the diverse risks of the alternative options. That is not the job of scientic policy advisers who might be tempted to simplify the situation, thereby pre-determining the complex value judgements.

(d)  Alarmism

I have written two previous posts that address the idea that uncertainty increases the argument for action

  1. Uncertainty, risk and (in)action
  2. The case(?) for climate change alarmism

As Betz points out, there is no simple decision rule for dealing with this kind of deep uncertainty.

Alarmism occurs when possible, unverified worst case scenarios are touted as almost certain to occur.  U.S. Secretary of State John Kerry frequently does this, as does Joe Romm (and Rachendra Pachauri).  A recent example from Dana Nuccitelli, John Cook and Stephen Lewandowsky: The climate change uncertainty monster – more uncertainty means more urgency to tackle global warming

The problems with this kind of thinking is summarized in my two previous posts (cited a few paragraphs above); in summary this is a stark and potentially dangerous oversimplification of how to approach decision making about this complex problem.

(e)  Summary

Back to the AAAS statement What We Know. Unverified hypotheses about fat tail events are NOT what we KNOW.  Presenting this as knowledge rather than speculation, and unduly focusing on it for policy decisions, is alarmist.

My biggest concern is that by unduly (and almost exclusively) focusing on AGW that we are making a type 1 error:  a possibility that has not been articulated might come true.  These possibilities (e.g. abrupt climate change) are associated with natural climate variability, and possibly its interaction with AGW.

Pretending that all this can be characterized by a fat tail derived from estimates of climate sensitivity is highly misleading, in my opinion.

So I agree with Emanuel that we should think about worst cases (e.g. black swans and dragon kings); I disagree with him regarding how this should be approached scientifically and mathematically.  However, undue focus on on unverified worst case scenarios as a strategy for building political will for a particular policy option constitutes undesirable alarmism.

—————————–  End of Professor Curry’s article  —————————–

Judith Curry

(2) About Judith Curry

Judith Curry is Professor and Chair of the School of Earth and Atmospheric Sciences at the Georgia Institute of Technology. She is also President and co-owner of Climate Forecast Applications Network (CFAN). Prior to joining the faculty at Georgia Tech, she served on the faculty of the University of Colorado, Penn State University and Purdue University.

She serves on the NASA Advisory Council Earth Science Subcommittee and the DOE Biological and Environmental Science Advisory Committee. She recently served on the National Academies Climate Research Committee and the Space Studies Board, and the NOAA Climate Working Group.

She is a Fellow of the American Meteorological Society, the American Association for the Advancement of Science, and the American Geophysical Union.

Her views on climate change are best summarized by her recent Congressional Testimony:

  1. Policy Relevant Climate Issues in Context, April 2013
  2. Rational Discussion of Climate Change: the Science, the Evidence, the Response, November 2010

(3) A few important things to remember about global warming

Please read this before commenting about my views about global warming and climate change. It also has links to the key posts on the FM website on this topic.

Truth Will Make You Free

(4) For More Information

(a) Reference Pages about climate on the FM sites:

  1. The important things to know about global warming
  2. My posts
  3. Studies & reports, by subject
  4. The history of climate fears

(b)  Posts asking if we’re prepared for past weather?

  1. Have we prepared for normal climate change and non-extreme weather?, 11 February 2014
  2. Droughts are coming. Are we ready for the past to repeat?, 12 March 2014

(c)  Posts about the extreme weather:

  1. Ignorance and propaganda about extreme climate change, 10 July 2012
  2. A look behind the curtain at the news of extreme climate events in the US, 22 August 2012
  3. Hurricane Sandy asks when did weather become exceptional? (plus important info about US hurricanes), 28 October 2012
  4. Has global warming increased the frequency & virulence of extreme weather events?, 10 February 2013
  5. The Oklahoma tornadoes can teach us about our climate, and ourselves, 22 May 2013
  6. The IPCC gives us straight talk about Extreme Weather, 4 October 2013
  7. The IPCC rebukes the climate doomsters. Will we listen?, 15 October 2013
  8. A summary of the state of climate change and extreme weather, 12 December 2013

(5)  Other worst-case scenarios

The Worst Case Scenario is World War III
World War III is a Worst Case Scenario

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Impact on Earth
Asteroid Impact on Earth: a Worst Case Scenario

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6 thoughts on “Worst case scenarios versus fat tails: a discussion about climate change

  1. F Max:  

    I’ve written once or twice in the several years I’ve been following your postings. I’ve agreed on over 80% of what you post, but have always thought it strange that you were denying that global warming is a major problem.  I had many concerns, but last Dec. when I read the article by Dar Jamail in “The Nation” magazine and at TomDispatch, I discovered it’s way worse than I thought. (The Nation version is abridged; see the longer one at http://www.tomdispatch.com/post/175785/tomgram%3A_dahr_jamail,_the_climate_change_scorecard/  

    Briefly, in about 2008 scientists became aware that methane hydrates under the Arctic Ocean have started to dissociate as the temperature of that body rises, releasing the methane. Methane is about 50 times as dangerous a greenhouse gas, and the the gas will rise exponentially over time, with no known mechanism to slow it.  PLEASE, go to the blog site of AMEG- the Arctic Methane Emergency Group-  which formed in 2010 to investigate. It is comprised of scientists from a number of countries, and has a  long list of papers on aspects of the problem.  Myself, I think we probably passed the point of no return a decade or several decades ago. The global mechanisms are so interrelated that it’s difficult to see the change in its systems, but the release of methane that had been bound up in a hydrate lattice for thousands of millenia as part of a feedback loop promise severe difficulties for life on earth.    I hope you will look at the site:   Arctic Methane Emergency Group (AMEG)   and see if you don’t think that this 2008 discovery doesn’t change your mind, and your life. -Chris Hager Arctic Methane Emergency Group (AMEG) ARCTIC SEA ICE CRASHES: GLOBAL FOOD AND METHANE FEEDBACK EMERGENCY – AMEG WARNS ACTION NOW IMPERATIVE AMEG press releas… View on a-m-e-g.blogspot.com Preview by Yahoo    

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    1. Ichinfcv,

      Here’s the difference between us. You rely on articles in The Nation. I rely on the IPCC, the major climate agencies, and the peer-reviewed literature. You’re another of the long long list of people posting comments who have gone hysterical about one of many many threats facing us, fed by activists. Fear makes us easy to manipulate.

      Let’s turn to the most recent IPCC report: AR5, Working Group I, looking at the result of 2 centuries of warming (over half of the warming since 1950 is anthropogenic):

      2.2.1.1.2 Methane

      Globally averaged CH4 in 1750 was 722 ± 25 ppb (after correction to the NOAA-2004 CH4 standard scale) (Etheridge et al., 1998; Dlugokencky et al., 2005), although human influences on the global CH4 budget may have begun thousands of years earlier than this time that is normally considered ‘pre-industrial’ (Ruddiman, 2003; Ferretti et al., 2005; Ruddiman, 2007).

      In 2011, the global annual mean was 1803 ± 2 ppb. Direct atmospheric measurements of CH4 of sufficient spatial coverage to calculate global annual means began in 1978 and are plotted through 2011 in Figure 2.2a.

      This time period is characterized by a decreasing growth rate (Figure 2.2b) from the early 1980s until 1998, stabilization from 1999 to 2006, and an increasing atmospheric burden from 2007 to 2011 (Rigby et al., 2008; Dlugokencky et al., 2009). Assuming no long-term trend in hydroxyl radical (OH) concentration, the observed decrease in CH4 growth rate from the early 1980s through 2006 indicates an approach to steady state where total global emissions have been approximately constant at ~550 Tg (CH4) yr–1.

      Superimposed on the long-term pattern is significant interannual variability; studies of this variability are used to improve understanding of the global CH4 budget (Chapter 6). The most likely drivers of increased atmospheric CH4 were anomalously high temperatures in the Arctic in 2007 and greater than average precipitation in the tropics during 2007 and 2008 (Dlugokencky et al., 2009; Bousquet, 2011).

      Observations of the difference in CH4 between zonal averages for northern and southern polar regions (53° to 90°) (Dlugokencky et al., 2009, 2011) suggest that, so far, it is unlikely that there has been a permanent measureable increase in Arctic CH4 emissions from wetlands and shallow sub-sea CH4 clathrates.

      AR5: Methane
      AR5, WGI, 2.2.1.1.2 Methane

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  2. It occurs to me that our political system is similar to the climate system.

    Public opinion, elite opinion, individual events, the focus of journalists and pundits, historical experience, expert knowledge and factual information, policy commitments of politicians, the actual decisions of elected and unelected officials… all these things interact and influence one another in complex ways that are not well understood.

    “It is not clear to me whether there are general principles which can guide rational decisions in such situations at all.” — Gregor Betz as quoted above by Judith Curry

    That surely describes the current operation of our machinery of state. No degree of certainty about the systemic response of some object of policy (e.g., climate, or the economy) can overcome our inability to determine how (or if) we could introduce that information effectively into the political system.

    I cannot escape the conclusion that we can accomplish nothing that requires making collective choices (except, perhaps, by accident) without first “fixing” our political system. Some of our problems—notably, various features of our economy—are deeply entwined with politics. It is probably impossible to address our political dysfunction without simultaneously addressing those problems; this complicates immensely what is already an overwhelming task. (If we were engineers, we might first attempt to isolate the malfunctioning systems from one another, so each could be debugged and repaired independently.)

    Climate policy appears to me to be an effect of politics as it is, but not in any great measure a cause; hence, regardless of its potential urgency, I see it as a problem best deferred until we can achieve a political system capable of addressing it. If we can’t accomplish that, nothing we say or do will have predictable consequences, anyway.

    Certainly, climate scientists must continue their work, and interested laymen may follow along. Concern about how to present the intricacies of what we have learned, though, seems pointless at present: politics is even more inscrutable than climate.

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  3. Not sure how relevant it is, but in the private sector, a well established principle of communicating uncertainty to management is this:

    For any expression of the form “A +/- B”, most of the time, “B” will simply be ignored and it will be interpreted as “A”.

    If the you can’t present a simple message, the message will be simplified for you, without regard for correctness. No amount of explanation can help this.

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  4. This and previous posts make me think that we have largely forgotten points that were well-established long ago.

    1) Statistical and scenario uncertainty correspond to the dichotomy risk (which can be calculated) and uncertainty (which cannot) that were already a bone of contention in economics in the 1930s: Keynes was arguing that uncertainty was not reducible to probabilistic risk functions.

    2) All those discussions about probabilities, fat tails and the like remind me that when I was studying statistics, both lecturers and textbooks insisted that fundamental assumptions be met to ensure the validity of probabilistic computations. It started with the basics, when the Cauchy distribution was introduced to show that a symmetric bell-shaped probability density curve with nicely tapering tails can thoroughly screw the law of large numbers with a mean that is formally uncomputable and an infinite variance. Similar issues affected regressions (heteroscedasticity, normally distributed errors, etc), or testing (independence of variables, etc): fundamental assumptions are not to be glossed over — otherwise conclusions become thoroughly unreliable. Selecting analytical tools appropriate for a specific data set is correspondingly very tricky.

    3) The discussion about policies reminds me of the difference between so-called “tame” and “wicked” problems. Sending a manned mission to Mars is a tame problem: huge and complex, but we know how to proceed, how to decompose the problem, which competencies are required, how to determine success, we can even make tests and prototypes. Dealing with climate change or economic development are wicked problems: too many interactions to decompose the problem in nicely manageable units, many unknown and even unknowable aspects, little or no possibility for trials, not even agreement on how to determine whether the problem is solved at all.

    Reliance upon simplified journalistic metaphors like “black swans” and “dragon kings” is not a good sign, in my opinion.

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