Transcript MICE talk for PRUDENCE meeting in Trieste

MICE
Modelling the Impact of Climate Extremes
www.cru.uea.ac.uk/projects/mice
The MICE study area
HadCM3 land gridbox
Selected landmass
Other land areas
MICE has six work packages:
(a) Analysis of extremes in model data
1.
2.
3.
Extraction of climate extremes for analysis
Time series characteristics of extremes under climate
change
Spatial patterns of extremes under climate change
(b) Impacts evaluation
4.
5.
6.
Quantitatively modelling impacts of changes in climate
extremes on activity sectors
Expert-judgement based approaches to understanding
impacts of changes in climate extremes on activity sectors
Co-ordination and dissemination
Types of extremes to be analysed:
Temperature
–
–
exceedences of percentiles and fixed thresholds
highest TMAX and lowest TMIN
Precipitation
–
exceedences of percentiles and fixed thresholds
Wind
–
–
exceedences of percentiles and fixed thresholds
highest maximum and mean windspeed per year
MICE impact categories:
IMPACT CATEGORY
a. Forestry
CLIMATE EXTREME
PARENT VARIABLE
Storm tracking
i.
Wind throw
Windstorm
ii.
iii.
Forest fire
Ecological damage
Heat stress, drought
Temperature, rainfall
Flood, drought, heat stress
b. Mediterranean agriculture
Heat stress, drought
Temperature, rainfall
c. Energy use
Summer heat waves
Temperature
d. Tourism
Heat stress & human
comfort
Deficit or excess of snow
Temperature
Precipitation, temperature
Wind storm
Storm tracking
Floods
Rainfall, snow
e. Insurance & civil protection
i.
Property damage )
ii.
Loss of life
)
Core data currently being used in MICE
Model Data
HadCM3 scenarios A1f, A2a, A2b, A2c, B2a, B2b
HadAM3H scenarios A2a, A2b, A2c, B2a (Early 2003)
HadRM3H scenarios A2a, A2b, A2c, B2a
variables include:
temp, tmax, tmin, mslp, prec, rhum, wind, wmax, tclw, dswf
“Observed” Data
NCEP Reanalysis data for the above variables, 1961-1990
Indices of extremes have been extracted from the above, at all grid
points, for further analysis.
Sample Analysis 1: PCA of monthly
tmax from three models
• data - monthly tmax from 1961-2099, using HADCM3,
CGCM2, and CSIRO Mk2 data (A2 and B2 scenarios)
• objectives – provide background climatology for
extremes, using PCA to:
• identify the main modes of variability
• examine effects of different scenarios
• identify inconsistencies in the models
All models give similar results in this PCA.
(Results courtesy of Tom Holt)
Factor 6 of the HADCM3 PCA, explaining
5.9% of the overall variance.
A2
B2
This relatively
unimportant
factor would be
expected to show
differences
between the
scenarios. Yet the
spatial patterns
are almost
identical.
Conversely, the
time series are
quite different.
This is typical for
all factors.
Sample Analysis 2: The behaviour of
extremes in HadCM3 and HadRM3H
• data – daily 1961-1990, using HadCM3, HadAM3H (early
2003), and HadRM3H data (A2 scenario)
• objectives – provide a better understanding of the
behaviour of extremes (fixed thresholds and percentiles)
prior to impact modelling.
• results – models appear to simulate extreme
temperatures relatively well in mid-latitude regions with mild
winters and summers, but significant season-long biases
are present at in polar and Mediterranean regions.
(Results courtesy of Matt Livermore)
Example 1: Extremes in Central
England
50
HadCM3 Tmax (a)
HadCM3 Tmin (a)
40
HadRM3 Tmax (a)
HadRM3 Tmin (a)
Obs Tmax
Obs Tmin
30
Temperature (Deg. C)
NCEP Tmax
NCEP Tmin
20
10
0
-10
-20
1
31
61
91
121
151
181
Day No.
211
241
271
301
331
361
Example 2: Extremes in Northern
Norway
40
HadCM3 Tmax (a)
30
HadCM3 Tmin (a)
HadRM3 Tmax (a)
20
HadRM3 Tmin (a)
NCEP Tmax
NCEP Tmin
Temperature (Deg. C)
10
0
-10
-20
-30
-40
-50
-60
-70
1
31
61
91
121
151
181
Day No.
211
241
271
301
331
361
Example 2: Extremes in Southern
Spain
60
HadCM3 Tmax (a)
HadCM3 Tmin (a)
50
HadRM3 Tmax (a)
HadRM3 Tmin (a)
Obs Tmax
40
Obs Tmin
Temperature (Deg C.)
NCEP Tmax
NCEP Tmin
30
20
10
0
-10
-20
1
31
61
91
121
151
181
Day No.
211
241
271
301
331
361
Sample Analysis 3: Identification of
Cyclones for the European Sector
winter (=ONDJFM)
• data – daily 1961-1990 and 2070-99, using HadCM3 (A2
and B2 scenarios)
• objectives – provide a better understanding of cyclone
behaviour and wind fields prior to impact modelling.
• results – model appear to simulate too many weak
centres and not enough deep cyclones – a scale issue.
(Results courtesy of Uwe Ulbrich - UKoeln)
Example 1: “Closed Systems” with
p < 1010hPa
1961-90
A2a (2070-99) – 1961-90
Unit: Cyclone days per month per 5x5 degree grid box
Sample Analysis 4: Forest Fires
Typical high
values for
the above
codes and
indices.
(Results courtesy of Marco Bindi – FMA)
These results apply to a single HadRM3 grid cell in Tuscany.
MICE over the next few months: Climate Model
Analysis (1)
Time series characteristics of extremes under climate change
• Complete validation of HadCM3 and HaDRM3
• Changes in extremes in model time
• compare extremes for 1961-1990 with 2070-2099
• highest and lowest per year
• three/four independent extremes above a threshold per year
• analysis techniques for extremes
• stationarity, trends, periodicity, return periods
• clustering and Poisson processes
• GEV analysis
• GPD analysis
MICE over the next few months: Climate Model
Analysis (2)
Spatial patterns of extremes under climate change
• Map event occurrence for overlapping 10-year periods
• Use PCA to identify major modes of extreme events
• relate the modes to NAO and AO using regression
• Use CCA to examine slp and 500 hPa height
• Continue analysis of model and NCEP storm tracks
MICE over the next few months: Impact
Modelling
Quantitatively modelling impacts of changes in climate
extremes on activity sectors
• Energy
• Forest Fires
• Insured Losses
• Mediterranean Agriculture
• Scandinavian Forests