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March 13th 2014 print

Michael Kile

Guessing Games And Climate ‘Science’

They seem so certain when predicting what long-term weather has in store for us, but don't be fooled. Read the fine print in their pronouncements and the truth is revealed: those much-quoted experts admit they don't have a clue

predictionsThe smart money is on Skeptic Lad to win the 2014 Oz Climate Cup by a length from Miss Model and ENSO-Joe. Expect a strong finish from Heat Wave, Naughty Nina and Hot Stuff. Angry Summer, Flim Flan, Gaia Girl and Fry Baby could surprise, but will struggle in a moist extreme weather event.  Scorcher is the dark filly.

The Bureau of Meteorology had three entrants in last month’s Sizzling Summer Stakes: Hot Stuff, Whatever the Weather and Miss Model. First out of the stalls was ABC and Fairfax Media favourite, the Annual Climate Report 2013, a sequel to BOM’s Annual Climate Statement of 3 January.

Its analysis of national temperature and rainfall patterns concluded that 2013 was “Australia’s warmest on record”, with record-breaking temperatures, “several” severe bushfires and national rainfall slightly below average.

The second entrant was State of the Climate 2014. A joint BOM/CSIRO exercise, this was a more interpretative document. Emphatic statements were made about “long-term trends in Australia’s climate” and “future climate scenarios”.

“It is extremely likely that the dominant cause of recent warming is human-induced greenhouse gas emissions and not natural climate variability.”

“Atmospheric greenhouse gas concentrations continue to rise and continued emissions will cause further warming over this century. Limiting the magnitude of future climate change requires large and sustained net global reductions in greenhouse gases [presumably also including the most abundant GHG, water vapour].”

No mention was made of the record snowfalls in the northern hemisphere, the two-decade record for Great Lakes ice cover, 17-year pause in global surface temperature  and so on.

If these government-owned – and -funded  agencies were ASX-listed companies, there would be compliance with Corporations Act 2001 (section 728) regulations on omissions and misleading statements, with mandatory disclosures about risks and uncertainties; perhaps even a riff on the “epistemic opacity of knowledge codified in complex [climate] models” and whether the catastrophist warming hypothesis is falsifiable .

Inclusion of this Cautionary Statement on Forward-Looking Information also would reassure readers about the status of current expert climate-prognostications. For example:

All statements, other than statements of historical fact, contained or incorporated in this report, including any and all projections as to the planet’s climate-future, constitute “forward-looking statements” (FLS). Such statements include, without limitation, possible or future events, and statements with respect to possible or future events. The words “expects”, or “is expected”, ”projection”, “scenario”, “target”, or “believes”, or variations of such words and phrases or statements that certain actions, events or results “may”, “could”, “would”, “should”, “might”, or “will”, “occur” or “be achieved” and similar expressions identify FLS. FLS made here are necessarily based upon a number of (often dodgy) assumptions and hypotheses yet to be accepted as verifiable laws of Nature. While considered reasonable by the authors and members of the international multi-model community as of the date of such statements, they are nevertheless inherently subject to significant “uncertainties in model parameters and structural uncertainties resulting from the fact that some processes in the climate system are not fully understood or are impossible to resolve due to computational constraints” (Reno Knutti, et al, IPCC Expert Meeting 25 January 2010). These uncertainties can affect, and could cause, actual outcomes to differ materially from those expressed or implied in any FLS. Hence there can be no assurance that these FLS will prove to be accurate, as actual results and future events could differ materially from those anticipated in such statements.

There is another issue here. Protecting the public is paramount. New research confirms that gloomy, forward-looking “projections” and “scenarios” are behind recent outbreaks of Climate Change Traumatic Stress Disorder (CCTSD), especially in the under-30 demographic. CCTSD constellates after prolonged exposure to atmospheric-ocean alarmism (AOA). Any change (or feared change) in the Earth’s climate or weather spontaneously produces psychological trauma.  A severe disorder, it may involve a perceived threat of death to oneself or to someone else, either now or in the future, or to one’s own or someone else’s physical or psychological integrity; or it may present as persistent anxiety about “missing heat”, synchronous with a debilitating “whatever-the-weather” vacuity and marked reduction in a person’s ability to cope with meteorological variability.

But I digress. While the above two agency reports may have been economical with the truth (as Joanne Nova explains here), my focus is on BOM’s third report – Climate Model Summary for March to July – released on February 17.  For if the future of humankind hangs in the balance, we have to rend-the-veil and venture into the sanctum sanctorum. There must be more scrutiny of climate models – and especially climate (model) ensembles – as they underpin all climate-scenarios.

Consider the case of the El Nino-Southern Oscillation. Australia’s climate is influenced by temperature patterns in the Pacific and Indian Oceans. BOM provides a monthly outlook for the next six months, based on outputs generated by this selection of climate models:

Model

Group

Description

POAMA Bureau of Meteorology (CAWCR) 30 ensemble members
System4 ECMWF (EU) 51 ensemble members
GloSea5 UK Met Office 42 ensemble members
CSFv2 NCEP (US) 40 ensemble members
GEOS5 NASA Goddard GMAO (US) 20 ensemble members
ARPEGE MeteoFrance 51 ensemble members
JMA.MRI-CGCM Japan Met Agency 51 ensemble members

Models are seductive, especially when they claim to have predictive power. BOM’s POAMA, modestly described as a Predictive Ocean Atmosphere Model for Australia, generates eight-monthly forecasts on the first day of each month. The most recent model run (started in February) predicts NINO3.4 is most likely to remain within neutral values throughout the forecast period.  (The National Climate Centre uses the “NINO3.4 index” to classify ENSO conditions.)

POAMA’s “predictive” capability, however, has this intriguing qualification:

The fraction or proportion of models categorized as warm, neutral or cold, should not be taken as an official Bureau or National Climate Centre view as to the likelihood of these various outcomes.

In Warmerland, the Devil invariably lurks in the detail. BOM claims its “prediction systems” have “a respectable level of skill in predicting ENSO”.

Nearly all the forecast systems whose results are included in the ENSO forecast have been documented in the reputable peer-reviewed international scientific literature and have a respectable level of skill in predicting ENSO…All models use an ensemble method, where several model forecasts are run at the same time with minor tweaks to the initial conditions and the model parameters.

All models apparently “represent the state-of-the-art as far as dynamical ENSO prediction systems are concerned”; yet they provide only “useful guidance up to about nine months”.

What was POAMA’s “useful guidance” in early March 2014? BOM’s climate-model ensemble indicated ENSO “is likely to remain neutral into the austral autumn” with all models suggesting

warming of the tropical Pacific Ocean will occur leading into winter, with some (but not all) models either approaching or exceeding El Niño thresholds by July. Hence neither neutral nor El Niño states can be discounted for the second half of 2014, while La Niña appears unlikely.

There is a two-way bet here, acknowledged in the statement that model outlooks spanning autumn “tend to have lower skill than outlooks issued at other times of the year, therefore more or less warming than indicated remains possible.” What value is a “forecast” where any outcome is possible?

Another BOM-CSIRO partnership — the Centre for Australian Weather and Climate Research –- has been somewhat less emphatic about climate/ENSO “prediction”. In a 2011 Technical Report 036, it noted, “the degree to which global warming may have enhanced heavy rainfall in some parts of eastern Australia remains uncertain”.

As for the “current generation of climate models”, they gave “no clear guidance” as to whether ENSO – one of the most important drivers of the continent’s weather and climate – “will change in response to global warming. Some models have strengthened ENSOs, some weaker, and others exhibit little if any change” (page 14).

For Professor Reto Knutti, (here), a leading researcher at Zurich’s Institute for Atmospheric and Climate Science, future climate change will be the sum of (i) an externally forced response, due to changes in radiative forcing arising from human activity, variations in the sun and major volcanic eruptions; and (ii) internal variability, e.g. ENSO and other patterns, and year-to-year and decade-to-decade fluctuations in winds, precipitation, temperature, etc.

He is sceptical of attempts to forecast ENSO. “It is an initial condition problem much like the weather forecast. There are inherent limits of predictability on those timescales.” So why are we told Australia’s future MCSs – and ENSOs – are already predictable?

Knutti recently reaffirmed this view (here): “We’re less certain than many would hope about the local impacts.” So we are left with yet another counter-intuitive paradox: as the causes of climate change allegedly become more certain, the ability to predict regional and local changes becomes more difficult.

Nevertheless, Knutti — a modeler himself — still seems confident the (currently non-existent) forecasting ability of climate general circulation models (GCMs) “will improve” over time. But as he emailed me, it will “do so more slowly than some people are hoping”. It could “easily take another 20 years or more to get close to that goal”. Yes, another two decades or more — if ever — to “predict” a future mean climatological state (MCS).

Another worry is BOM’s lack of disclosure about ensemble-modelling. Perhaps the infamously tangle-tongued US baseball legend Yogi Berra had this black art in mind when he said: “In theory, there is no difference between theory and practice. In practice there is.” When the subject is weather, model projections are not predictions.

Something is rotten at the Climate Carbon Cargo Cult Club (CCCCC). While the practice’s flaws are well-known within the international climate-modelling community, they continue to be protected by a cordon of complexity and code of silence, and rarely rate mention in the warmist media or public square.

Knutti, fortunately, has written papers on the discipline’s core dilemmas. In 2008, he published one in the Philosophical Transactions of the Royal Society:Should we believe model predictions of future climate change?” (A full list here.)

When we look at climate projections for the next century, in fact, from similar models, we are torn between believing, questioning and ignoring them. Why is it so difficult to communicate what we know and what is uncertain about future climate change? Why are climate model projections uncertain anyway? How can we be sure that a model that performs well in simulating past or present climate will be reliable in simulating future climate? (page 4647) (They don’t and we can’t).

Knutti did not provide “a final answer (which is unlikely to exist)“ to these questions, or attempt to  put a (“good“ or “bad“) skill score on a climate model. He tried instead to “outline how climate scientists think about their models“, and to “explain why it is so difficult to quantify model performance.“  As for the need for ‘near operational climate projections‘, that “may require a rethink“ of the whole model development and evaluation process. Yet it still rolls merrily on, fueling the faltering – but still powerful – international alarmist juggernaut.

Two years later, he restated his position in a peer-reviewed Journal of Climate paper: Challenges in combining projections from multiple climate models. It will be a revelation to some, but not to others, to learn that “by far the largest contribution to uncertainty stems from the fact that climate models are imperfect and therefore their projections are uncertain“. (p2740)

Strictly, the calibration and evaluation of climate model predictions is impossible, as projections of climate change relate to a state never before observed”. (p2746)

Furthermore, there was still “little agreement on metrics to separate “good” and “bad” models”. It was merely assumed that “individual model biases would partly cancel and that a model average prediction is more likely to be correct” than one from a single model. (In what other occupation – excluding politics and garbage collection – could one suggest that because of an inability to measure individual model performance, just mix them together, take the average output and sell it as a valid “prediction”.)

“While the multi-model average appears to still be useful in some situations, these results show that more quantitative methods to evaluate model performance are critical to maximize the value of climate change projections from global models.”

Another concern was that “model development, evaluation and posterior weighting or ranking are all using the same data sets.” (p2739)

But the key point, philosophically and statistically, as Dr David Whitehouse explains in The Very Model of a Modern Major Problem, “is that the various outputs of computer models are not independent samples in the same way that repeated measurements of a physical parameter could be. They are not independent measurements centred on what is the “truth” or reality.”

Given this, does the addition of more models and “experiments” force the mean of a multi-model ensemble to converge on reality? Some, such as the work by Professor Reto Knutti believe it doesn’t. I agree, and think it is a precarious step to take to decide that reality and models are drawn from the same population. How can uncertainty in parameterisation of climatic variables and numeric calculations reproduce uncertainty in the climate system? The spread of models is not necessarily related to uncertainty in climate predictions.

My overall impression is that computer climate models, useful as they can be, have been oversold and that they have been often used without interpreting their results in terms of known processes and linked to observations – the recent standstill in the annual average global temperatures is an example.

Little wonder punters are confused – and angry – when some agencies insist the fundamental drivers of global atmospheric circulation – and our national climate change future — are known with high certainty; and others promote each-way-bets and their speculative thought-experiments as genuine “predictions”.

As Dr Whitehouse warns, one just has to be careful not to get carried away with models.  For as they say down at the track: “never bet on anything that talks” – or drives a climate-model.

Michael Kile, March 2014