On simplicity

This is a long screed in response to a reading list posted by Massimo Pigliucci (so he bears no responsibility) where he nominated a post on simplicity in science by Elliot Sober on Aeon.

Why is simplicity better?

As it happens, I am in the midst of an argument with the climate science community over simplicity as applied to statistical inference. A couple of days ago I bought Probability, Confirmation and Simplicity: readings in the the philosophy of inductive logic Foster and Martin 1966, which contains six essays on simplicity. Not as simple as it’s cracked up to be – exactly the ammunition I require.

Accordingly, I disagree with Sober. He refers to the Akaike Information Criterion, which measures simplicity but says that it refers to the same underlying reality. But we see it being repeatedly used for different underlying realities by people who don’t read the small print. They are being simplistic (#OccamsRazor). By mixing probabilities with theory Sober is making a fundamental mistake. I can apply probabilities to an experiment or a test, but I cannot to a theory. At best I can severely test (Mayo) a hypothesis and by attaching it to probative criteria in such a way that the alternatives are as unlikely as the hypothesis is likely, then I have a chance of confirming that theory.

In climate science, simplicity is represented by trend-like change. Under increasing greenhouse gases, forcing leads to warming as the logarithm of the increasing forcing plus feedbacks. In the Earth system, this leads to monotonic warming, linear to forcing. Trouble is, most of this heat is absorbed by the ocean and it is the atmosphere that needs to respond. The atmosphere-ocean relationship is a dissipative system driven by thermodynamics and decidedly nonlinear. So, if I assume the atmosphere warms according to the linear radiative forcing concept, I have a simple model that is predictive over demi-century-long timescales. If I assume that warming obeys the dissipative pathway, then it proceeds via enhanced climate variability as a series of step-like regime changes. Over both pathways, warming reaches close to the same destination but its mode of getting there is very different. One contains more inherent risk than the other.

So, I can represent both pathways statistically. They get similar sum of squares residuals (trend-like change fails the heteroscedasticity test but almost no-one tests for this), but because the pathway of step-like change carries more adjustable parameters, it is penalised (actually that isn’t even true because the detection method is completely different). But they represent different realities – Sober does mention this but few have remembered this before, so why should they now?

Where simplicity works with this example, is that the natural greenhouse effect (average 155 Watts per square metre per year) is distributed through climate variability. The net anthropogenic greenhouse effect is 0.7 Watts per square metre per year and roughly 1% of that is assumed to be stored within the atmosphere (0.07 W m2/yr) producing a trend. So here we have created a very complex physical situation where most of the energy flux is controlled by climate variability and where perturbations in the climate of >1 W m2/y can be brought back to mean within months, but somehow a tiny amount of heat remains in the atmosphere in preference to an ocean with 24 times the heat conductivity and 3,200 the heat capacity.

Whereas we could accept that the simplest thermodynamic solution is for all heat to follow the same pathway, for climate change to behave like enhanced climate variability and for warming to follow a series of regime change producing a long-term, complex trend.

Theoretically and thermodynamically simple, statistically more complex. The problem with the simplicity argument is that it has to be very finely applied, and that confusing methodological simplicity with theoretical parsimony is an issue. In economic, climatology and a number of other disciplines, simplicity is being misapplied to methods rather than theory and this is a problem, because it means we apply simple solutions to complex, real-world problems.

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Published step change paper

Reconciling the signal and noise of atmospheric warming on decadal timescales

Roger N. Jones and James H. Ricketts
Victoria Institute of Strategic Economic Studies, Victoria University, Victoria 8001, Melbourne, Australia
Received: 13 Aug 2016 – Discussion started: 23 Aug 2016
Revised: 20 Feb 2017 – Accepted: 21 Feb 2017 – Published: 16 Mar 2017

Abstract
Interactions between externally forced and internally generated climate variations on decadal timescales is a major determinant of changing climate risk. Severe testing is applied to observed global and regional surface and satellite temperatures and modelled surface temperatures to determine whether these interactions are independent, as in the traditional signal-to-noise model, or whether they interact, resulting in step-like warming. The multistep bivariate test is used to detect step changes in temperature data. The resulting data are then subject to six tests designed to distinguish between the two statistical hypotheses, h_step and h_trend. Test 1: since the mid-20th century, most observed warming has taken place in four events: in 1979/80 and 1997/98 at the global scale, 1988/89 in the Northern Hemisphere and 1968–70 in the Southern Hemisphere. Temperature is more step-like than trend-like on a regional basis. Satellite temperature is more step-like than surface temperature. Warming from internal trends is less than 40 % of the total for four of five global records tested (1880–2013/14). Test 2: correlations between step-change frequency in observations and models (1880–2005) are 0.32 (CMIP3) and 0.34 (CMIP5). For the period 1950–2005, grouping selected events (1963/64, 1968–70, 1976/77, 1979/80, 1987/88 and 1996–98), the correlation increases to 0.78. Test 3: steps and shifts (steps minus internal trends) from a 107-member climate model ensemble (2006–2095) explain total warming and equilibrium climate sensitivity better than internal trends. Test 4: in three regions tested, the change between stationary and non-stationary temperatures is step-like and attributable to external forcing. Test 5: step-like changes are also present in tide gauge observations, rainfall, ocean heat content and related variables. Test 6: across a selection of tests, a simple stepladder model better represents the internal structures of warming than a simple trend, providing strong evidence that the climate system is exhibiting complex system behaviour on decadal timescales. This model indicates that in situ warming of the atmosphere does not occur; instead, a store-and-release mechanism from the ocean to the atmosphere is proposed. It is physically plausible and theoretically sound. The presence of step-like – rather than gradual – warming is important information for characterising and managing future climate risk.

Earth Syst. Dynam., 8, 177-210, 2017
http://www.earth-syst-dynam.net/8/177/2017/
doi:10.5194/esd-8-177-2017

Download the full paper

Step change hypothesis and working paper

Imagine you didn’t know anything about climate change and the greenhouse effect but were interested and you know a bit about general science. Would you accept the following story?

“Earth’s climate is a large, complex system, affected by forces that produce both linear and nonlinear responses. Shortwave radiation – basically UV – from the sun comes in and heats up the planet, producing infrared radiation. Some UV gets reflected straight back out by clouds, snow and ice and stuff. The land can heat up quite a lot, but it cools back down again and doesn’t store much. If a forest is cleared and replaced by buildings, it will warm up a bit but the effect is only local.”

“But the ocean – that’s another story. It absorbs a lot of radiation, so is taking up heat all the time. Huge streams of energy are entering and leaving the ocean store each year. Some is ‘dry’ or sensible heat, which is ordinary warmth. Some is ‘wet heat’ or evaporated moisture. Energy gets taken up when the moisture is evaporated and it will be released again when the moisture cools, condenses and then gets rained out. In this way, the oceans provide a lot of heat to the land every year, largely as rainfall and a bit of snow.”

End of the hiatus

Understanding Climate Risk has been in something of a hiatus, or a pause for the last couple of years due your host being almost fully submerged, but maybe it’s time to rise to the surface and get things going again.

This is for a few reasons. One is that research, especially public good research and especially in CSIRO, is under serious threat in Australia. We have a government who tout innovation, but who wilfully ignore the role of the generation of underpinning knowledge in fuelling such innovation. They are interested only in commercial innovation – public-good innovation is not only being ignored, it is being excluded from processes such as the Cooperative Research Centre bids currently under way. Having sustainable cities, catchments and ecosystems is impossible without public good research and social innovation, with funding that extends across the sciences, the humanities and the arts. With an election going on, these harms need to be publicised.

Frontiers retraction controversy

The following is a long post, but on an important issue.

Frontiers is an open source science publisher based in Switzerland. Their aim is to provide an open access, open science platform that empowers researchers in their daily work and where everybody has equal opportunity to seek, share and generate knowledge. They have started up a whole host of “Frontiers in” journals covering a wide range of subjects. They have also been linked with the Nature publishing group who is interested in the open access model Frontiers is developing.

So I jumped at the opportunity to be an associate editor of the newly established area of Interdisciplinary Climate Studies. The Editor in Chief is the Swiss climatologist, Professor Martin Beniston. An associate editor invites a panel of reviewers who review a collection of articles each year. The associate editor establishes their interdisciplinary area with a “challenges” paper to set the ball rolling. Their task is to encourage researchers to submit innovative papers exploring the frontiers of knowledge.

McLean follow up

Elaine McKewon book-ended my letter to the editors of Fairfax papers The Age and Sydney Morning Herald regarding the publishing of John McLean’s error-ridden piece on the IPCC (the editors, by the way, have not responded) with a terrific take down of McLean in Crikey.

She questioned McLean’s byline on the original article, to whit:

“John McLean is the author of three peer-reviewed papers on climate and an expert reviewer for the latest IPCC report. He is also a climate data analyst and a member of the International Climate Science Coalition.”

asking “But is that accurate? Who is John McLean? What qualifications entitle him to speak as an expert on climate science? What is the ICSC, and which groups, interests and agendas do McLean and the ICSC represent? What exactly does it mean to be an “expert reviewer” of IPCC reports?”

The scientific origins of the gradualist adaptation narrative and how to move beyond it

The following statements are typical of the gradualist adaptation narrative:

1. Within limits, the impacts of gradual climate change should be manageable.
2. Therefore, climate change adaptation can be understood as: (a) adapting to gradual changes in average temperature, sea level and precipitation.
3. Gradual climate change allows for a gradual shift in the mix of crops and to alternative farming systems.

So why are Gauss and Newton in the bath and Ed Lorenz in the hot tub?

Wicked, epic fail problem

Two strategies used in problem solving are reframing and rebadging. A problem can often be reframed or looked at in a different way. Any parent who uses reverse psychology to turn a chore into a game knows that one – Tom Sawyer getting his friends to whitewash the fence for fun is a classic example.

Expert and operating languages around a problem-solution sequence has one or more specific grammars. These terms become the labels for a particular narrative that contains a problem, a process and an outcome. So when Paul Harris, Deputy Director of the HC Coombs Policy Forum at the ANU has a go at wicked problems (Rittel and Webber, 1973) for being, well, wicked – is he rebadging, reframing or is a he just erecting a straw man and flaying it to bits in an attempt to change a prevailing narrative?

Harris’ complaint is about wicked problems. They are everywhere and their number is constantly growing:

Et tu, Chief Scientist?

Less than a fortnight ago, I wrote that those barracking for conventional scientific theories often maintain that science is not a matter of belief. Sorry guys, but assessing the probability of t (scientific truth) being T (absolute truth), is a matter of belief, as is anything that goes on in the mind regarding external evidence. But there is a difference between belief and true belief.

And then in a conversation with the Chief Scientist Ian Chubb on communicating science, with specific reference to climate science, The Conversation quotes him as saying:

“We scientists need to talk about evidence, and without being cornered into answering questions like ‘do you believe?’,” Professor Chubb said.

“I get asked that every day and every now and then I make a mistake and say yes or no…It’s not a belief, it’s an understanding and an encapsulation and interpretation of the evidence.”

Aaaaaaaaaaaaarrrrrggghhh!!!

Ian Chubb was speaking to the Royal Society of Victoria, which launched on August 30th a three-year program aimed at increasing the awareness of science among primary school children. And while I agree with most of what Ian Chubb says in his interview with The Conversation, the belief thing should be called for what it is – a full-blown fallacy.

Grauniad on Kuhn

No, not an exotic Hungarian sandwich, but The Grauniad has an excellent 50th anniversary review of Kuhn’s The Structure of Scientific Revolutions written by John Naughton in their Sundy paper. In a total paradigm shift, the comments aren’t totally trolltown, either. See also, Howard Sankey on the The Conversation.

Before Kuhn, our view of science was dominated by philosophical ideas about how it ought to develop (“the scientific method”), together with a heroic narrative of scientific progress as “the addition of new truths to the stock of old truths, or the increasing approximation of theories to the truth, and in the odd case, the correction of past errors”