linking probabilistic climate scenarios with downscaling methods for impact studies dr hayley fowler...

Linking probabilistic climate scenarios with downscaling methods for impact studies

Dr Hayley FowlerSchool of Civil Engineering and GeosciencesUniversity of Newcastle, UK

With Contributions from:Claudia Tebaldi (NCAR)Stephen Blenkinsop, Andy Smith (Newcastle University)

Aim

Develop a framework for the construction of probabilistic climate change scenarios to assess climate change impacts at the:

regional (~100,000 to 250,000 km2)

river basin (~10,000 to ~100,000 km2)

catchment (~1000 to ~5000 km2) scales

Motivation

Different GCMs produce different climate change projections, especially on a regional scale

Therefore no one model provides a true representation

Most probabilistic scenarios to date have been produced for large regions or globally

Regional scale studies more relevant for impacts

How can we combine probabilistic climate scenarios with downscaling methods to study impacts at the catchment scale?

How can we combine probabilistic climate scenarios with downscaling methods to study impacts at the catchment scale?

Examining how well different RCMs simulate different statistical properties of current climate in their control climates

Do different RCM-GCM combinations produce different future projections?

How can we combine the estimates of different models to produce probabilistic scenarios?

Case-study Locations

1 British Isles

2 Eden

3 Ebro

4 Gallego

5 Meuse

6 Dommel

7 Brenta

8 Scandinavia

9 Eastern Europe

Method: RCMs + WG

PRUDENCE

RCMs

Extract CFs (Catchment)

EARWIG

Weather Generator

Tebaldi Bayesian UK Regions

Calibrated Eden R-R model

λMonte-Carlo resampling of flow sections based on λs

Data available for UK

RCM data – 50km x 50kmControl 1961-90Future SRES A2 2070-2100

Interpolated observations – 5km x 5km

Data – Observations & Models

RCM Driving Data PRUDENCE

Acronym AquaTerra Acronym

HadAM3H A2 HC1 HIRHAM-H Danish Meteorological Institute (DMI)

HIRHAM ECHAM4/OPYC (OGCM SSTs)

ecctrl HIRHAM-E

HadAM3H A2 HCCTL RCAO-H Swedish Meteorological and Hydrological Institute (SMHI)

RCAO ECHAM4/OPYC A2

MPICTL RCAO-E

Hadley Centre – UK Met Office

HadRM3P HadAM3P adeha HAD-P

Météo-France, France Arpège Observed SST DA9 ARP-A

Observed series - Aggregated 5km interpolated precipitation dataset

Regional Climate Models – PRUDENCE (http://prudence.dmi.dk/)

How well do RCMs represent the seasonal cycle?

Mean Rainfall Comparison

0

1

2

3

4

5

6

jan feb mar apr may jun jul aug sep oct nov dec

Month

Me

an

Da

ily R

ain

fall

(mm

)

HIRHAM_E

HIRHAM_H

RCAO_E

RCAO_H

HAD_P

ARPEGE_C

OBSERVED


Mean Temperature Comparison

0

2

4

6

8

10

12

14

16


Month

Me

an

Te

mp

era

ture

(D

eg

C)

HIRHAM_E

HIRHAM_H

RCAO_E

RCAO_H

HAD_P

ARPEGE_C

OBSERVED


Daily Rainfall Variance Comparison

0

5

10

15

20

25

30

35

40


Month

Va

ria

nc

e o

f D

aily

Ra

infa

ll

(mm

2 )

HIRHAM_E

HIRHAM_H

RCAO_E

RCAO_H

HAD_P

ARPEGE_C

OBSERVED

Summer Skewness Coefficient

UK Regions

Method: RCMs + WG

PRUDENCE

RCMs


EARWIG

Weather Generator




Model weighting (a la Tebaldi)

Bayesian statistical model delivers a fully probabilistic assessment of the uncertainty of climate change projections at regional scales

Based on: Reliability Ensemble Average method (Giorgi and

Mearns, 2002)

Summary measures of regional climate change, based on a WEIGHTED AVERAGE of different climate model responses


Weights account for: BIAS - the performance of GCMs when

compared to present day climate ( i.e. results from model validation)

CONVERGENCE - the degree of consensus among the various GCMs’ responses/


pdf of change in temperature and precipitation fitted using area-averages of the model output

Prior pdfs are assumed to be uninformative Data from regional models/observation incorporated

through Bayes’ theorem, to derive posterior pdfs Model-specific “reliabilities parameters” estimated as a

function of model performance in reproducing current climate (1961-1990) and agreement with the ensemble consensus for future projections

These are standardised and applied as weights in the downscaling step

NWE Seasonal Mean λ

ARP_C HAD_P HIRH_E HIRH_H RCAO_E RCAO_H

DJF 0.07 0.19 0.25 0.26 0.08 0.15

MAM 0.08 0.05 0.11 0.23 0.26 0.27

JJA 0.15 0.06 0.16 0.23 0.18 0.22

SON 0.11 0.11 0.21 0.20 0.14 0.23

Precipitation

Temperature


DJF 0.23 0.22 0.12 0.19 0.11 0.13

MAM 0.17 0.22 0.15 0.26 0.09 0.1

JJA 0.08 0.18 0.09 0.25 0.16 0.25

SON 0.12 0.23 0.16 0.24 0.13 0.12

Method: RCMs + WG

PRUDENCE

RCMs


EARWIG

Weather Generator




EArWiG

EA Weather Generator

Developed for EA for catchment scale Decision Support Tool models

Generates series of daily rainfall, T, RH, wind, sunshine and PET on 5km UK grid

Observed and climate change based on UKCIP02 scenarios

Collaborative with CRU, UEA

EArWiG

Map viewer interface developed Can select catchments, time periods and

different UKCIP02 scenarios

Catchments tab

Model tab

Catchment finder

OSGB locator

OSGB pointer coords

Toolbar

Map window

Neyman-Scott Rectangular Pulses Rainfall Model

time

time

time

inte

nsit

y

time

tota

l int

ensi

ty

• Storm origins arrive in a Poisson process with arrival rate λ

• Each storm origin generates C raincells separated from the storm origin by time intervals exponentially distributed with parameter β

• Raincell duration is exponentially distributed with parameter η

• Raincell intensity is exponentially distributed with parameter ξ

• Rainfall intensity is equal to the sum of the intensities of all the active cells at that instant

Weather Generator

Depending on whether the day is wet or dry, other meteorological variables are determined by regression relationships with precipitation and values of the variables on the previous day

Regression relationships maintain both the cross- and auto-correlations between and within each of the variables

Change factor fields

Change factor fields are applied to the fitted rainfall model statistics: Mean Variance PD Skewness Coefficient Lag 1 Autocorrelation

Change factor fields are applied to the weather generator statistics: Mean temperature Temperature SD

CF Summer mean temperature

CF Winter mean precipitation

CF Spring PD

Method: RCMs + WG

PRUDENCE

RCMs


EARWIG

Weather Generator




Rainfall-runoff model

ADM model, simplified version of Arno Calibrated for Eden catchment on observed

data R2=0.73, 0.78 Each simulated climate used to produce

simulated flow series (30 years) for each climate model using P and PET

EARWIG run for each RCM

Had_P

RCAO_E

Control

Each series is 30 years in length

1 32 4 … 1000

2071-2100

1961-1990

NWE Seasonal Mean λ


DJF 0.07 0.19 0.25 0.26 0.08 0.15

MAM 0.08 0.05 0.11 0.23 0.26 0.27

JJA 0.15 0.06 0.16 0.23 0.18 0.22

SON 0.11 0.11 0.21 0.20 0.14 0.23

Precipitation

Temperature


DJF 0.23 0.22 0.12 0.19 0.11 0.13

MAM 0.17 0.22 0.15 0.26 0.09 0.1

JJA 0.08 0.18 0.09 0.25 0.16 0.25

SON 0.12 0.23 0.16 0.24 0.13 0.12

Re-sampling

Monte-Carlo re-sampling technique used to weight models according to λ values from Bayesian weighting

Random numbers used to choose a control and future run for a particular RCM, then seasonal statistics of change in mean flow, SD flow, 5th and 95th percentiles calculated.

If seasonal λ=0.14 then random number generator produces 140 resamples from a particular RCM

Generates total of 1000 change statistics for each season – pdf fitted used kernel density

Questions for the audience

Should we weight models (CG)? Should we be weighting on statistics other than

mean? If so, what? Should we be looking at weighting by some

spatial bias measure rather than a simple regional average? Makes the statistics harder…

Models may produce reasonable mean statistics and get higher order statistics important for impact studies wrong

linking probabilistic climate scenarios with downscaling methods for impact studies dr hayley fowler...

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