UNCERTAINTY IN CLIMATE CHANGEAndrew Levan

For fans of probability, confidence intervals and margins of error, climate change is a dream come true. For everyone else, the fact that uncertainty (inherent in any complex area of science) has gradually become one of climate change's defining features is a constant headache. Because uncertainty – real or manufactured – is a well-rehearsed reason for inaction.The Guardian – 31 Jan 2014

Uncertainty in measurement

Uncertainty in prediction

Uncertainty in action

Uncertainty in measurement

Uncertainty in prediction

Uncertainty in action

It is extremely likely [95 percent confidence] more than half of the observed increase in global average surface temperature from 1951 to 2010 was caused by the anthropogenic increase in greenhouse gas concentrations and other anthropogenic forcings together. – IPCC 2013

It is more likely than not that the mean global mean surface air temperature for the period 2016–2035 will be more than 1°C above the mean for 1850–1900, and very unlikely that it will be more than 1.5°C above the 1850–1900 mean (medium confidence). - IPCC 2013

to preserve a livable planet, scientists tell us we must reduce the amount of CO2 in the atmosphere from its current level of 400 parts per million to below 350 ppm. – 350.org

Uncertainty in temperature measurements

A simple example:

I just took my own temperature using 3 different thermometers (all good and the type you might find in hospital).

Oral : 36.6

Forehead : 36.3

Right ear: 36.9

Left ear 36.5

Why are these different? Does it matter?

1) Different calibration between different types of thermometer

2) Different locations on the body measured (not all the same temperature)

3) Actually, temperature at any of these location is a proxy for core temperature, which can be a sign of serious illness. The important thing is the illness (and its consequence), not the temperature.

Measuring global mean temperature is attempt to describe a complex multi-dimensional problem with a single number.

It may be a useful number, but it doesn’t encapsulate the full complexity of the problem.

Creating a temperature history• Use direct measurements of temperature (thermometers) going back

as far as possible (~200 years).

• Chose proxies for global climate going back further (dendrochronology has proved especially popular for the hockey stick).

• Remove systematic offsets between different methods to bring them to a common temperature scale.

• “Average” them together across the planet.

• Plot them

Error bars - exercise• Measure the size of the LEGO block and the piece of

string.

• Write down the length and an error

Likelihood distributions

Probability extends well beyond the “error bars” of a measurement. Standard deviation (sigma)

Likelihood distributions

That uncertainty is “normal” (alpha = 2 in this plot) a common assumption, but is often untested. Fat tails mean extremes are much more likely than we expect.

Likelihood distributions

Small earthquakes are exceptionally common, but almost all the damage is caused by large events. In regions prone to earthquakes buildings must be designed for

worst case scenarios.Are these the natural considerations in climate related disasters?

Worry about the extremes (but be careful of false predictions)

Correlation and causation

http://www.tylervigen.com/spurious-correlations

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H HProbability of 8 heads in a row = 1/28 = 1/256

Trials in climate

Cited 3104 times

There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.

When is something true?• Many studies in medicine, economics and humanities take a

significance level of 95% (2-sigma) to mean something is true.

• Many physicists will use 99.7% (3-sigma) as their threshold.

• Teams using the Large Hadron Collider announced the discovery of the Higgs Boson when it was detected at the 5-sigma level (99.9999426697%).

• New gravitational wave telescopes are using a 7-sigma threshold for detection (99.9999999997440%).

When is something true?• Would you put a loved one on a plane if there was a 95%

chance of it arriving safely?

• If I told you there was a 95% chance you would fail your degree unless you took some action, would you take it?

So why should we believe this?

Answer: Physical basis, direct measurements, predictive power, number of trials

The physical basis for warming• https://www.youtube.com/watch?v=kwtt51gvaJQ

95% confidence in warming

• IPCC statement is 95% confidence that at least half of the observed warming arises from anthropogenic influence.

Out of equilibriumStratospheric cooling, its not all about surface temperature

IPCC approachConfidence

IPCC approachLikelihood

Discussion point

• What is the role of expert opinion?

Conditional probability

Conditional probability

CO2 is a known greenhouse gas. It traps IR radiation. Arguing that you don’t expect a temperature increase from higher greenhouse gas concentrations is a bit not changing

your risk model for lightning strikes while standing in a thunderstorm

The null hypothesis• The null hypothesis refers to a thing that you adopt as

your baseline and then try to disprove.

• Write down your null hypothesis for climate change (or in particular global warming).

What are you really saying? • “Sunday, May 11, 2014. Europe’s biggest clean-energy

market reached almost 75% renewable power market share noon on that day.” – cleantechnia.com• But electricity is roughly half of primary energy consumption in

Germany, less if you consider e.g. imports.

• “In the next 20 years we will cut our emissions by 30% (relative to a business as usual approach)” – generic statement• But if business as usual is actually a 3% per year increase then this

is 80% over 20 years, so a 30% cut relative to business as usual is asking to increase your emissions by ~25%.

Future scenarios

IPCC 2007

When to act

Costs and benefits• Costs to acting are real. e.g. NHS is ~4% of UK GDP, c.f.

some estimates of cost of climate change mitigation.

• Indeed, NHS costs would be much lower if we mitigated against many health risks rather than adapting, but most of us don’t.

• How do we value money today vs money tomorrow – discounting?

Principles of dealing with uncertainty

• It is a huge and complex area, and nobody gets it right all the time.

• Think carefully about what you are being told.• Have you been told everything you need to know? • Could the data have been cherry picked somehow? • What is the actual impact of a given result if correct? • Be aware of your biases

• In climate change a multitude of independent studies offer support for a a similar picture. Combined with a physical expectation of this result it offers high confidence in anthropogenic changes.

• Trying to express that confidence as some numerical value is fraught with difficulty.