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Attributing Variation in a Regional Climate-change Modelling Experiment. Chris Ferro Centre for Global Atmospheric Modelling Department of Meteorology University of Reading, UK. CRU Seminar, Norwich, 16 December 2004. PRUDENCE Statistical model Illustrative example Grid-point analysis - PowerPoint PPT Presentation
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Attributing Variation in a Regional Climate-change
Modelling Experiment
Chris Ferro
Centre for Global Atmospheric Modelling
Department of MeteorologyUniversity of Reading, UK
CRU Seminar, Norwich, 16 December 2004
PRUDENCEStatistical modelIllustrative exampleGrid-point analysisDiscussion
The PRUDENCE Project
European climate simulations
9 limited-area RCMs, drivenby various global GCMs
Control 1961–1990Scenarios 2071–2100
http://prudence.dmi.dk
Orography at 50km resolution
Experimental Design
ObservedSST/SICE
AGCM
RCM
ObservedEmissions
SRES A2Emissions
AOGCM AOGCM
RCM RCM
AGCM
RCM
Control Scenario
Aims
Characterise
projected climate change
effects of model differences
relative importance of different model components: emissions, GCM, RCM and initial conditions
HADCM3 / OPYC3 /HADAM3H ECHAM4
RCM C A C A GroupCHRM 1 1 ETHZCLM 1 1 GKSSHADRM 1 1 HCHIRHAM 1 1 1 1 DMIPROMES 1 1 UCMRACMO 1 1 KNMIRCAO 1 1 1 1 SMHIREGCM 1 1 ICTPREMO 1 1 MPI
Selected Experiments
Land-averaged annual mean 2m air temperature
interpolated to CRU gridECHAM HADAM
HIRHAMRCAO
HIRHAMRCAO
Tem
pera
ture
(°C
)
Tem
pera
ture
(°C
)
Year Year
General Linear Model
Ti j k l is temperature for GCM i, RCM j, Period k, Year l
Ti j k l = i j k + i j k t l + i j k t l2 + Zi j k l
Residuals Zi j k l ∼ N(0, 2i j k), cor(Zi j k l, Zi j k l)
= i k (j, j)
Model Components
Ti j k l = i j k + i j k t l + i j k t l2 + Zi j k l
i j k = + iG + j
R + kP + i j
GR + i kGP + j k
RP +
i j kGRP
overall mean
iG effect of GCM i
jR effect of RCM j
kP effect of Period k
i jGR effect of combining GCM i
with RCM ji k
GP effect of GCM i in Period k
j kRP effect of RCM j in Period k
i j kGRP effect of combining GCM i
with RCM j in Period k
Simplified Model
Ti j k l = + iG + j
R + kP + i j
GR + i kGP
+ ( + iG + j
R + kP) t l + ( + j
R ) t l
2 + Zi j k l
12.1(0.03
) -0.3
(0.15)
1G 0.07
(0.04)
1G 0.10
(0.04)
1R -
0.33(0.01
)1
R 0.11(0.05
)
1P -
2.22(0.04
)1
P -0.26
(0.04)
11GR -
0.13(0.02
) 0.21
(0.05)
11GP -
0.31(0.05
)1
R -0.03
(0.02)
i j k C AHIRHAM-
E0.56 0.62
RCAO-E0.55 0.51HIRHAM-
H0.50 0.43
RCAO-H0.46 0.52i k C A
ECHAM
0.92-
0.10HADA
M0.880.82
Diagnostic PlotsECHAM HADAM
HIRHAMRCAO QQ PP
Variance Partition
%
Trend 91.0
GCM 0.1
RCM 2.0
G x T 1.8
R x T 0.0
G x R 0.3
G x R x T
–
Z 4.7
ECHAM HADAM
HIRHAMRCAO
Temperature Contrasts (C)
HIRHAM
RCAO
HADAM
ECHAM
HIRHAM
RCAO
HADAM warmer than ECHAM at 1961 becomes progressively cooler– warm bias greater for HIRHAM
RCAO warmer than HIRHAM– warm bias greater for ECHAM
Response Contrasts
HADAM – ECHAM response-0.19C/decade (-0.37, -0.02)
HADAM response lower thanECHAM independently of RCM,Period, Year
RCAO response lower thanHIRHAM at start of integration,becomes progressively higherindependently of Period, GCM
Year
C /
decad
e
Grid-point Analysis
Fit model separately at each grid point and plot maps:
Proportion of variation explained by each model term
Evolution of GCM temperature contrasts for each RCM
Evolution of RCM temperature contrasts for each GCM
Evolution of GCM response contrasts for each RCM
Evolution of RCM response contrasts for each GCM
Variation Explained (%)model T Z
G R GR
GT RT GRT
Changes in Mean Temperature (°C)
HIR
HA
MR
CA
OECHAM HADAM
Temperature Contrasts: HADAM – ECHAM
19901961 2071 2100
HIR
HA
MR
CA
O
Response Contrasts: HADAM – ECHAM
19901961 2071 2100
HIR
HA
MR
CA
O
Temperature Contrasts: RCAO – HIRHAM19901961 2071 2100
EC
HA
MH
AD
AM
Response Contrasts: RCAO - HIRHAM19901961 2071 2100
EC
HA
MH
AD
AM
HADCM3 / OPYC3 /HADAM3H ECHAM4
RCM C A C A GroupCHRM 1 1 ETHZCLM 1 1 GKSSHADRM 1 1 HCHIRHAM 1 1 1 1 DMIPROMES 1 1 UCMRACMO 1 1 KNMIRCAO 1 1 1 1 SMHIREGCM 1 1 ICTPREMO 1 1 MPI
Selected Experiments
Residual Standard Deviation (⁰C)
CLM RACMOCHRMREMOPROMESHIRHAM
RCAO REGCM HADRM
0.38 0.39 0.39 0.40 0.44 0.45 0.46 0.48 0.48
Temperature Comparisons (⁰C)
Response Comparisons (⁰C/century)
Review
Method: statistical model synthesises output and enables inference on climate changes, model differences, and the relative importance of model components.
Results: warming signal dominates, choice of RCM affects temperature in mountainous regions, choice of GCM affects temperature and response in Atlantic and response over land, inter-annual variation is greater than GCM and RCM effects over land, effects of model differences change over space and time, other RCMs have different behaviour
Extensions: RCMs, annual cycle, model resolution, EOFs, multivariate, spatial, ensemble generation
Discussion
Danger of over-simplified summaries of model output
Features can be hidden by poor experimental design
If you were a politician…
http://www.met.rdg.ac.uk/~sws02caf