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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
How well do RCMs represent the seasonal cycle?
Mean Temperature Comparison
0
2
4
6
8
10
12
14
16
jan feb mar apr may jun jul aug sep oct nov dec
Month
Me
an
Te
mp
era
ture
(D
eg
C)
HIRHAM_E
HIRHAM_H
RCAO_E
RCAO_H
HAD_P
ARPEGE_C
OBSERVED
How well do RCMs represent the seasonal cycle?
Daily Rainfall Variance Comparison
0
5
10
15
20
25
30
35
40
jan feb mar apr may jun jul aug sep oct nov dec
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
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
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
Model weighting (a la Tebaldi)
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/
Model weighting (a la Tebaldi)
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
ARP_C HAD_P HIRH_E HIRH_H RCAO_E RCAO_H
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
Extract CFs (Catchment)
EARWIG
Weather Generator
Tebaldi Bayesian UK Regions
Calibrated Eden R-R model
λMonte-Carlo resampling of flow sections based on λs
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
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
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
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
ARP_C HAD_P HIRH_E HIRH_H RCAO_E RCAO_H
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