atmospheric research operationalising coping ranges climate sensitivity, coping ranges and risk...

Atmospheric Research

Operationalising Coping Rangesclimate sensitivity, coping ranges and risk

Roger N. Jones

AIACC Training Workshop on Adaptation and Vulnerability

TWAS, Trieste

June 3-14 2002


Choices

• Roundtable of project needs regards coping ranges, thresholds, climate risk and uncertainty management

• Choosing climate variables/sensitivity relationships (exercise)

• How to construct thresholds

• Structure of climate probabilities (variability and change, short exercise)

• Case studies– hot cows (heat stress and adaptation)– water resources (Monte Carlo uncertainty and

Bayesian analysis– risk as a function of global warming


Sensitivity to what?

Sector Sensitivity to what?

Water Rainfall variability, flood, drought

Agriculture ENSO, flood, drought, cool/hotextremes, storms

Health Hot/wet conditions, temperatureextremes, violent storms, floods, cropand water shortages

Coasts Storm surges, wind/wave climates,pressure extremes, tidal extremes

Biodiversity Fire, flood, drought, storms


Linking climate to impacts

Climate system

Impacted activity

Socio-economicsystem

Current climate

Current adaptations

Future climate

Future adaptations


Cross impacts analysis

Po

ultr

y

Da

iry

Gra

zin

g

Cro

pp

ing

Win

e

Ho

rse

s

Ma

rin

e (

esp

. fis

he

rie

s)

Be

ach

Co

ast

al w

ate

r su

pp

ly

Ha

rbo

ur

Inla

nd

wa

ter

sup

ply

Riv

er

ma

na

ge

me

nt

Dry

lan

d/ir

rig

atio

n s

alin

ity

Fo

rest

& b

iod

ive

rsity

Urb

an

infr

ast

ruct

ure

Air

qu

alit

y

Wa

ste

Ind

ust

ry,

coa

l & p

ow

er

He

alth

Rainfall - average 2 1 2 2 1 3 2 2 2 2 1 1 21Rainfall - extreme 1 2 2 1 1 1 2 2 2 3 3 2 3 2 2 1 30Rainfall - variability 2 3 3 2 2 1 3 3 1 1 2 23Drought 2 3 3 2 1 1 2 3 2 2 3 2 2 28Temperature - average 1 2 1 2 2 2 1 2 1 2 2 1 1 20Temperature - max 3 2 2 2 2 1 3 1 3 2 1 2 24Temperature - min 2 2 2 2 2 1 2 1 1 1 16CO2 2 2 2 2 1 2 11Cloud 1 1 1 3Pressure 1 1Humidity 2 1 2 1 2 1 1 10Wind 1 1 1 1 1 2 2 1 2 2 2 16Evaporation 2 1 2 1 2 1 1 10Soil moisture 3 3 3 3 2 1 2 1 2 20Stream flow 2 1 3 3 2 1 1 3 1 17Flood 2 1 1 1 1 1 3 2 3 2 1 3 3 3 2 29Watertable 2 3 1 1 1 1 1 2 12Water salinity 1 1 1 3 2 2 3 1 2 1 17Irrigation 2 1 2 3 2 2 1 13Sea level 1 3 3 3 2 12Storm surge 3 3 1 7Waves 2 3 1 2 8Lightning 1 1 1 3Hail 2 3 2 7Fire 1 1 1 3 1 2 1 2 12

8 24 23 29 28 11 18 13 17 14 24 27 20 27 30 9 14 18 16

Workshop Report

(example)

Worked example in MS Excel®


Cross impacts analysis


Uncertainty explosion

Global climate sensitivity

Emission scenarios

Regional changes

Biophysical impacts

Socio-economic impacts

Climate variability


Uncertainty explosion

Global climate sensitivity

Emission scenarios

Regional variability

Biophysical impacts

Socio-economic impacts


Likelihood

Probability can be expressed in two ways:

1. Return period / frequency-based(Climate variability)

2. Single event(Mean climate change, one-off events)


Return period / frequency-based probability

Recurrent or simple eventWhere a continuous variable reaches a critical level, or

threshold.

Eg. Extreme temperature (max & min), Extreme rainfall, heat stress, 1 in 100 year flood

Discrete or complex eventAn event caused by a combination of variables (an

extreme weather event)

Eg. tropical cyclone/hurricane/typhoon, ENSO event


Frequency-based probability distributions


Single-event probability

Singular or unique eventAn event likely to occur once only. Probability refers to

the chance of an event occurring, or to a particular state of that event when it occurs.

Eg. Climate change, collapse of the West Antarctic Ice Sheet, hell freezing over


What is the probability of climate change?

1. Will climate change happen?• IPCC (2001) suggests that climate change is occurring with

a confidence of 66% to 90%

2. What form will it take?Uncertainties are due to:

• future rates of greenhouse gas emissions

• sensitivity of global climate to greenhouse gases

• regional variations in climate

• decadal-scale variability

• changes to short-term variability


Range of uncertainty

TOTAL RANGE OF UNCERTAINTY

QUANTIFIABLE RANGE OF UNCERTAINTY

M1 M2 M3 M4

UNQUANTIFIABLEUNCERTAINTY

UNQUANTIFIABLEUNCERTAINTY


CO2 emissions and concentrations


Simulated global warming: A2


Global warming


Group exercise - estimating joint probabilities

• Take a gold coin (preferably 1 pound coin)

• Heads represents low end (1.4°C), tails represents high end (5.8°C)

• Flip coin 7 times and record the number of heads and tails

• Which outcome is most likely?


• Give coin to greedy presenter

Risk exercise - conclusion


Probabilistic structure of climate uncertainties

Critical threshold

Critical threshold

Time

Va

riab

le(s

)

Planning horizon


Placing thresholds within scenario uncertainty

global climatesensitivity

emissionscenarios

regionalvariability

range ofpossible impacts

A

B


Impact thresholds

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Year

Glo

ba

l W

arm

ing

(°C

)

Threshold A

Threshold B

Threshold examples

& workshop synthesis


Metrics for measuring costs

• Monetary losses (gains)• Loss of life• Change in quality of life• Species and habitat loss• Distributional equity


System responses

• Resistance (e.g. seawall)

• Resilience (e.g. regrowth, rebuilding after storm or fire)

• Adaptation (adjustments made in response to stress)

• Transformation (old system stops, new one starts)

• Cessation (activity stops altogether)


Hot cows and heat stress

THI between 72 and 78

mild stress


severe stress DEAD COWS!

THI above 98

moderate stress



Frequency of exceeding heat index threshold

50.0

60.0

70.0

80.0

90.0

1/10/98 31/10/98 30/11/98 30/12/98 29/01/99 28/02/99 30/03/99

Date

TH

I U

nits

THI72

THI78

Threshold examples

& workshop synthesis


Production effects



mild stress no stress

moderate stress mild stress

Powerpoint

Report


Coral bleaching

• Caused by SST above a threshold• Expels xosanthellae algae• Severity related to days above

bleaching threshold• Corals may recover or die


Macquarie River Catchment

Burrendong Dam

Windamere Dam

Major Areas ofAbstraction

Macquarie RContributing Area

Macquarie Marshes

Area ~ 75,000 km2

P = 1000 to <400 mm.

Major dams: Burrendong and Windamere

Water demands: irrigation agriculture; Macquarie Marshes; town supply

Most flow from upper catchment runoff

Most demand in the lower catchment


Ranges of seasonal rainfall change for the MDB

Summer

Autumn

Winter

Spring -40 -20 0 20 40Rainfall Change (%)

-40 -20 0 20 40Rainfall Change (%)

20302070


P and Ep changes for Macquarie catchment

In change per degree global warming

-16.0

-8.0

0.0

8.0

16.0

J F M A M J J A S O N D

Cha

nge

fo

r 1

ºC g

lob

al w

arm

ing

(%

)

Evaporation (Ep) Rainfall (P)


Changes to MAF for 9 models in 2030 (%)Based on IPCC 2001

B1 at 1.7°C0.55°C

A1 at 2.5°C0.91°C

A1T at 4.2°C1.27°C

Low

-16

-8

0

Mid

-24

-16

-8

0

High

-30

-20

-10

0


Climate change – flow relationship

flow = a ( atan (Ep / P ) – b

Standard error < 2%


Sampling strategy

• The range of global warming in 2030 was 0.55–1.27°C with a uniform distribution. The range of change in 2070 was 1.16–3.02°C.

• Changes in P were taken from the full range of change for each quarter from the sample of nine climate models.

• Changes in P for each quarter were assumed to be independent of each other

• The difference between samples in any consecutive quarter could not exceed the largest difference observed in the sample of nine climate models.

• Ep was partially dependent on P (dEp = 5.75 – 0.53dP, standard error = 2.00, randomly sampled using a Gaussian distribution)


Changes to Burrendong Dam storage 2030

<60

<70

<80

<90

<95

<100

<50

Cumulative Probability (%)

0

10

20

-10-20-30-40

0 5-5-5

0

5

10

10

15

-10

Rainfall change (%)

Po

ten

tial

eva

po

rati

on

ch

an

ge

(%)


0

10

-10-20-30

0 5-5-5

0

5

10

10

15

-10

Rainfall change (%)

Po

ten

tial

eva

po

rati

on

ch

an

ge

(%)

Changes to bulk allocations for irrigation 2030

<60

<70

<80

<90

<95

<100

<50



0

10

20

-10-20-30-40

0 5-5-5

0

5

10

10

15

-10

Rainfall change (%)

Po

ten

tial

eva

po

rati

on

ch

an

ge

(%)

Changes to Macquarie Marsh inflows 2030

<60

<70

<80

<90

<95

<100

<50



Probabilities of flow changes - impacts view

0

10

20

30

40

50

60

70

80

90

100

-40-30-20-1001020

Change in supply (%)

Cu

mu

lativ

e P

rob

ab

ility

Burrendong Marshes Irrigation

Likeliest outcome

Range of possible outcomes


Critical thresholdsMacquarie River Catchment

Irrigation5 consecutive years below 50% allocation of

water right

Wetlands10 consecutive years below bird breeding

events


Irrigation allocations and wetland inflows- historical climate and 1996 rules

10,000

100,000

1,000,000

10,000,000

1890 1910 1930 1950 1970 1990

Year

Flo

w (

Gl x

10)

0

20

40

60

80

100

Irrig

atio

n al

loca

tion

(%)

Allocations Marshes


Threshold exceedance as a function of change in flow (irrigation)

Change in mean average allocationSequences belowthreshold (years) +5% 0 -10% -15% -30% -40% -45%

1615 114 113 112 111 1 010 1 1987 16 1 15 2 2 14 2 2 4 53 1 1 1 2 4 1 12 5 4 6 6 5 6 21 10 13 11 12 7 4 4

Percent of total yearsbelow threshold

22 23 34 38 50 58 64


Threshold exceedance as a function of change in flow (bird breeding)

Change in MAFSequences belowthreshold (years) +5% 0 -10% -15% -30% -40% -50%

16 115 1 1 214 1 1 213 1 2 312 1 2 311 1 110 1 1 1 198 17 1 1 1 16 2 15 1 1 1 1 2 14 3 2 2 3 4 2 33 2 1 3 4 3 3 12 4 7 4 2 2 11 4 3 7 5 3 3

Percent of total yearsbelow threshold 40 45 52 56 63 71 79


Risk analysis resultsMacquarie 2030

0

10

20

30

40

50

60

70

80

90

100

-40-30-20-1001020

C ha nge in sup ply (% )

Cu

mu

lati

ve

Pro

ba

bili

ty

B urrend ong M arsh es Irr igat ion

DDR Nor mal FD R

Report


Risk analysis resultsMacquarie 2070

0

10

20

30

40

50

60

70

80

90

100

-80-60-40-2002040


Cu

mu

lativ

e P

rob

ab

ility

Burrendong Marshes Irrigation

DDR Normal FDR


Bayesian analysis resultsMacquarie 2030

0

10

20

30

40

50

60

70

80

90

100

-40-30-20-1001020


Cu

mu

lativ

e P

rob

ab

ility

Standard W&R warming All


Bayesian analysis resultsMacquarie 2070

0

10

20

30

40

50

60

70

80

90

100

-80-60-40-2002040


Cu

mu

lativ

e P

rob

ab

ility

Standard W&R warming All


Characterising risk as a function of global warming

The standard “7 step method” of impact assessment progresses from climate to impacts to adaptation. This infers that we must predict the likeliest climate before we can predict the likeliest impacts.

Can we get around this limitation?


Characterising risk

There is another way.

Impacts = function(Global warming)

Impacts = function(global, local CC & CV )

p(impacts) = no. of scenarios > threshold = risk


Risk exercise - estimating threshold exceedance: sea level rise

• Recover coin from greedy presenter

• Heads represents low end (9 cm), tails represents high end (88cm)

• The group chooses two critical thresholds

• Flip coin 7 times and record the number of heads and tails

• Which outcome is most likely?


0 1 2 3 4 5

0

1

2

3

4

5

6

Glo

bal w

arm

ing

(°C

)

Frequency (%)

Increasing likelihood of global warming

0 50 100

0

1

2

3

4

5

6

Glo

bal w

arm

ing

(°C

)Frequency (%)

Pro

bab

ility

of t

hre

sho

ld

exc

eed

anc

e

Characterising the risk of global warming

0

20

40

60

80

100

0 100

Probability (%)

Sea

Lev

el R

ise

(cm

)

25 cm

50 cm

75 cm

0

20

40

60

80

100

0 100

Probability (%)

Sea

Lev

el R

ise

(cm

)

25 cm

50 cm

75 cm

0

20

40

60

80

100

0 100

Probability (%)

Se

a L

eve

l Ris

e (

cm)

25 cm

50 cm

75 cm

0

20

40

60

80

100

0 5 10

Probability (%)

Se

a L

eve

l Ris

e (

cm)

25 cm

50 cm

75 cm

0

20

40

60

80

100

0 5 10

Probability (%)

Se

a L

eve

l Ris

e (

cm)

25 cm

50 cm

75 cm

0

20

40

60

80

100

0 100

Probability (%)

Se

a L

eve

l Ris

e (

cm)

25 cm

50 cm

75 cm

25 cm

50 cm

75 cm


Characterising the risk of global warming

Risks to Many

Risks to Some

I

I Risks to unique and threatened systems

II

II Risks from extreme climate events

Large Increase

Increase

III Distribution of impacts

III

Negative for most regions

Negative for some regions

IV Aggregate impacts

IV

Net Negative

in all metrics

Markets + and -

Most people

worse off

V Risks from large-scale discontinuities

V

Very low

Higher

0 50 100

0

1

2

3

4

5

6

Glo

ba

l warm

ing (

°C)

Frequency (%)

Pro

bab

ility

of t

hre

sho

ld

exc

eed

anc

e


Long-term planning Short-term policy response

1. Enhance adaptive capacity so that the current coping range expands, reducing present vulnerability.

2. Develop this capacity in such a way that the longer-term risks to climate change are also reduced.


Basic principles

• Pay greater attention to recent climate experience. Link climate, impacts and outcomes to describe the coping range.

• Address adaptation to climate variability and extremes as part of reducing vulnerability to longer-term climate change.

• Assess risk according to how far climate change, in conjunction with other drivers of change, may drive activities beyond their coping range.

• Focus on present and future vulnerability to ground future adaptation policy development in present-day experience.

• Consider current development policies and proposed future activities and investments, especially those that may increase vulnerability.


Foresighting your project

• Visualise how you will present the results (graph, text, table, animation)

• Rehearse how you will communicate the uncertainties

• Anticipate questions upon presentation or review

• How will you engage different stakeholders?

atmospheric research operationalising coping ranges climate sensitivity, coping ranges and risk...

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