COUNTERFACTUALTHINKINGIN CLIMATE SCIENCE

Dr. Gordon WooCatastrophist

Mathematics of Planet Earth Jamboree

21st March 2018

‘Science predicts that many different

kinds of universe will be spontaneously

created out of nothing.

It is a matter of chance which we are in’.

Stephen Hawking

Disaster: an unfavourable aspect of a star

The past cannot

be changed.

Hazard analysts

treat the past

as fixed and

determined,

like eclipses.

This is an

anthropocentric

bias.

Downward counterfactuals

If Things

Had

Gone

Wrong

If Things

Had

Turned

for the

Worse

Fritz

Leiber

Fritz

Leiber

‘Intelligence

without imagination:

a deadly combination’

Downward counterfactual

thought experiment

Consider a historical weather system state S[0] that led to

a major economic loss of L.

Construct an alternative weather system state S[k] that would

have led to a major economic loss of L + k* Δ*L,

where Δ = 5% , and k = 1.

Repeat for ever increasing integer values of k = 0, 1, 2,….n

Estimate the relative likelihoods of the alternative

weather system states S[k] for k = 0,1,2…n

State transition path to a Black Swan

S[0] S[1] S[2] S[n]

The Counterfactual Class of Black Swans:

The set of extreme events that can be explicitly constructed by

progressive downward counterfactual simulation of a historical event.

If there are M historical events, how complete is ? 1

M

j

j

B

Early on September 13, just 50 miles offshore, Ike shifted course.

The wall of water the storm was projected to push into the

Houston area was far smaller than predicted.

ALL NEIGHBORHOODS, AND POSSIBLY ENTIRE COASTAL COMMUNITIES,

WILL BE INUNDATED, PERSONS NOT HEEDING EVACUATION ORDERS

IN SINGLE FAMILY 1 OR 2 STORY HOMES WILL FACE CERTAIN DEATH.

Hurricane Ike: September 2008

$38 billion property damage

A 24ft storm surge along the

Houston Ship Channel would

have released about 90

million gallons of crude oil

and chemicals into Houston

neighbourhoods and

Galveston Bay.

It would have been the worst

environmental disaster in US

history, and caused massive

economic disruption.

‘Perfect storm’ tipping point in consequential loss

Cascading Economic Loss

Black Swan as an

economic black hole

Property Damage

Regional

Environmental

Pollution

Global

Economic

Disruption

Infrastructure

Dysfunction

Supply Chain Failure

Attribution of extreme weather events in the context of climate changeNational Academies Press (2016)

The likelihood

of specified events

resulting from a

given temperature

distribution

and its changes

What emergent

Black Swan

events might

lie in these tails?

How can emergent

Black Swan

events be

better imagined?

Central European flooding

S[0] S[1] S[2] S[n]

Central Europe flooding of August 2002

The severity of the flood was due to the

unusual confluence and stalling of two

continental weather systems.

Downward counterfactual:

Slowing of the Jet Stream

might enable weather patterns

to persist over several weeks.

January 2005 and December 2015 Carlisle floods

The Carlisle flood alleviation

scheme was completed in 2010.

What was the chance of rainfall

causing the river defences

to be overtopped in December 2015?

The January 2005 flood had an

estimated annual exceedance

probability of 1:170.

S[2005] S[2015]

Probabilistic definitions of causation

PS is the probability of sufficient causation.

It is defined to be the probability that Y would have occurred

in the presence of X, given that Y and X did not occur.

PNS is the probability of necessary and sufficient causation.

It is defined as the probability that Y would have occurred in the

presence of X, and that Y would not have occurred in the absence of X.

PN is the probability of necessary causation.

It is defined as the probability that the event Y

would not have occurred in the absence of the event X,

given that both events Y and X did in fact occur.

Hannart et al., 2016 DOI:10.1175/BAMS-D-14-00034.1

2003 European heat wave attribution

For the 2003 European heat wave,

PN = 0.9 whereas PS =0.0072

(Hannart, Pearl et al. 2016)

70,000 died in the European

heat wave.

Between 2030 and 2050, climate change is

projected to cause about 250,000 additional

deaths per year from heat stress, malnutrition

and the spread of infectious diseases.

Climate change as a necessary cause

of catastrophic societal losses

External

Weather

Hazard

Critical

Infrastructure

Failure

Catastrophic

Societal

Losses

Climate

ChangeHuman

Error

What is the most lethal

infrastructure catastrophe

of which climate change

would be a necessary

cause?

Taxonomy of emergent disasters

Complex system phenomena

e.g. trigger events, tipping points

Multi-hazard combinations e.g. natural and man-made hazards, cyber, pandemics etc.

Cascading disasters: e.g. drought; wildfire; mudslide

Historical event Compound disaster

Extreme windstorm impact on

nuclear plant in Massachusetts

On January 27, 2015, Winter Storm

Nor’easter Juno knocked out both of

the 345,000 volt transmission lines

connecting the ageing Pilgrim nuclear

plant in Plymouth, Mass..

The reactor shut down when the

second offsite power line was lost.

Equipment problems and operator

errors complicated the intended

response.

Nor’easter

12 March 2018

High sensitivity to

the coastal

Nor’easter track

shortens the

time horizon for

forecasting

severe weather,

(e.g. snow depth),

and consequent

economic impact.

On 7 February 2017,

water from powerful

winter storms rushed

under the spillway,

which forced up giant

slabs and ripped a

huge hole in

the structure.

The spillway was built

in the late 1960s.

The designer was

just two years out of

college with no prior

professional

experience designing

spillways.

Oroville dam,

Northern California

Greater fragility of critical infrastructure

Critical infrastructure is designed for external hazard levels

prescribed by civil engineers.

This infrastructure is vulnerable to external loading

exceeding these design levels.

These may give rise to tipping point effects.

Extreme November 2009

rainfall in Jeddah, Saudi Arabia

Standard Deviation

Average for November

mm

3.3 Standard Deviations

Just how harmful can

2.8 inches of rain be?

Flash flooding deaths in the desert kingdom

The level of rainfall over 6 hours

on 25 November 2009 in Jeddah

and surrounding areas reached

about 72mm.

Hundreds were swept away in one of

the most deadly floods that Saudi

Arabia has ever experienced. Many

were drowned in their cars.

The increased risk of flash flooding requires

reconsideration of the standards used in the

construction of roads, bridges, tunnels,

and drainage networks.

Saudi cities lack the infrastructure to deal

with heavy rain, such as having effective

rain sewers and drainage channels.

Climate change in Saudi Arabia

Stochastic modelling of the past

‘There is an infinitude of pasts,

all equally valid’André Maurois

Dynamic

Stochastic Process

Historical

Catalogue

Alternative

Catalogue

Alternative

Catalogue

The historical event loss catalogue

is just ONE realization of what

could have happened.

....Alternative catalogues might have been generated.

Stochastic

event sets

could be

benchmarked

against

these

alternative

catalogues.

Focusing more on the process than the events

Historical luck: zero event loss

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

zero $1bn $5bn $10bn $15bn $20bn

Loss Probability

Column2

Column1

Probability

Loss

Actual

loss

2016

The British navy

was fortunate:

the expected

result would have

been the

destruction of at

least one ship,

and possibly as

many as three.

The belief of military historians that this

was a squandered British victory

on January 24, 1915 - IS FALSE.

Results from Approximate Bayesian Computation

Gauging the volatility of historical losses

0

200

400

600

800

1000

Event A Event B Event C Event D

Extreme alternative

Plausible alternative

Actual historical

Unprecedented Simulated Extremes

using Ensembles

Thompson V. et al. , Nature Communications, July 2017: High risk of unprecedented UK rainfall

In the current climate. [Met Office Unified Model simulating 100x more winters than in 1981-2015.]

Southeast England January rainfall

D(t)

{ X(1), X(2),….X(n)}

Perturbation of a state into a more dangerous domain

System State

Major Disaster Domain

NEAR-MISS ZONE

East Bay Hills wildfire

October 1991

34

The Oakland Hills fire, killed 25

people, injured 150, and destroyed

more than 3,800 homes.

“It’s hard to get organized and run for

your life at the same time!”

The fire was contained only when

the Santa Ana wind changed.

Counterfactual wildfire loss distribution

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

$0.5bn $1.5bn $3bn $5bn $10bn $15bn

Loss Probability

Column2

Column1

Probability

Loss

Actual

loss

Climate change cliff-edge

"Climate change

is one of the

great dangers

we face, and it's

one we can

prevent.”