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OUCE

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Applying probabilistic climate change information to strategic resource assessment and planning Funded by ENVIRONMENT AGENCY TYNDALL CENTRE – PowerPoint PPT presentation

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Title: OUCE

1
Applying probabilistic climate change
information to strategic resource assessment and
planning

Funded by
ENVIRONMENT AGENCY
TYNDALL CENTRE

2
Overall Objective

To develop a risk-based framework for handling
probabilistic climate change information and for
estimating uncertainties inherent to impact
assessments performed by the Agency for strategic
planning (water resources and biodiversity in the
first instance).

3
Specific Objectives

To develop and compare methods for generating
regional/local scale climate change probabilities
from coarse resolution CP.net data.
To trial the application of probabilistic climate
change information to Agency-relevant case
studies (initially for water resources and
biodiversity management).
To explore the added-value of probabilistic
scenarios for strategic planning and practical
lessons learnt from the case studies.
To share the techniques and experience gained
from the exemplar projects with a wider community
of partner organisations and stakeholders.

4
climateprediction.net aims to

Sample uncertainty in climate models across
Physics
Initial conditions
Climate forcing
Provide better understanding of plausible future
climate changes that can be forecast with one GCM
species

5
Experimental Strategy

Distributed public computing port HadCM3 to
windows/linux/mac
Each participant runs a specific experiment
Different model physics, initial conditions,
forcing
Currently 17 million model years

6
Phase 1

2 x CO2 equilibrium experiments
15 years calibration at 1 x CO2
15 years control at 1 x CO2
15 years at 2 x CO2

7
ClimatePrediction.net
8
Data Available

Global mean time series
Eight year seasonal climatologies
Surface air temperature
Precipitation
Cloudiness
Surface heat budget

9
Phase 2

Transient simulations with HadCM3
1920-2000 hindcast
2001-2080 forecast
Launched with BBC in February

10
Data Available in Phase 2

More variables
Global mean monthly time series
Regional monthly time series (Giorgi NAO MOC)
UK grid-box monthly series
Ten-year seasonal climatologies (1920-2080)

11
First Results

Use of CP.Net probabilistic climate change data
for water resource assessment in the Thames basin
CATCHMOD water balance model of River Thames
basin
CP.net data available from Experiment 1
Results and discussion

12
CATCHMOD water balance model of River Thames
basin.
13
River Thames Basin upstream of Kingston gauge and
GCM grid-boxes
14
CATCHMOD parameters

Six key parameters controlling
Direct runoff
Soil WC at which evaporation is reduced
Drying curve gradient
Storage constant for unsaturated zone
Storage constant for saturated zone

Wilby and Harris (2005)
15
CATCHMOD

Inputs daily time series of precipitation (PPT)
and potential evaporation (PET)
Output daily time series of river flow
Parameters chosen as the ones that best
reproduce observed flows for the period 1960-1991

16
CP.net Data

Grand ensemble of 2578 simulations of the HadAM3
GCM
Explores 7 parameter perturbations and perturbed
initial conditions
450 IC ensembles (model versions)

17
CP.net variables and CATCHMOD Inputs

8-year seasonal means for
total cloud amount in LW radiation
surface (1.5m) air temperature
total precipitation rate
Use these to calculate change factors for PPT and
PET over Thames
Change factors used to perturb CATCHMOD daily
time series of PPT PET

18
Results Change Factors
19
Results Standard CATCHMOD
unperturbed HadAM3 present day
20
Results CP.net and CATCHMOD
Q50
Q50
21
Results CP.net and CATCHMOD
Q95
Q95
22
Factors not Considered

Full set of CP.net perturbations
Emissions uncertainty
Downscaling uncertainty
Alternative model structures (GCM and
Hydrological)
Coupled transient climate response

23
Are Probabilistic Approaches Useful?

CP.net provides useful climate information
particularly joint probabilities of key variables
Enable more informed decision making
Issues for Water Utility stakeholders
Understanding the information
Having time and resources to use information
Regulatory constraints
In many cases other (non-climate) factors are
more uncertain

24
CP.net parameters
Parameter Description
VF1(m/s) Ice fall speed.
CT(1/s) Cloud droplet to rain conversion rate.
RHCRIT Threshold of relative humidity for cloud formation.
CW_sea (1/kgm3) CW_land Cloud droplet to rain conversion threshold.
EACF Empirically adjusted cloud fraction.
ENTCOEF Scales rate of mixing between environmental air and convective plume.
25
Potential Evaporation
Penman PET is a function of mean air T, mean
vapour pressure (vp), sunshine and wind speed
Present calculate monthly Penman PET using
observed climate variables for London (monthly
long term means 1961-1990, UK national
grid) 2xCO2 calculate monthly Penman PET
assuming wind speed constant
relative humidity constant thus relative
change in vprelative change in svp relative
change in sunshine - relative change in cloud
amount T at 2xCO2 observed T deltaT vp at
2xCO2 observed vp x (1CF(svp)) sunshine at
2xCO2 observed sunshine x (1-CF(cloud)) CF
calculated using control and 2xCO2 phases for all
the variables.
26
Smoothed frequency distributions and CDFs Q50