Transcript Slide 1

E
treme
hot spells
Group 2
Mari Jones
Christiana Photiadou
David Keelings
Candida Dewes
Merce Castella
Motivation
• Examine extreme hot temperatures in Europe
and their drivers:
– Blocking Index
– North Atlantic Oscillation
– El Niño-Southern Oscillation (BEST index)
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Russia July 2010
http://earthobservatory.nasa.gov/IOTD/view.php?id=47880
60
Oslo
Moscow
Oxford
Trier
50
Barcelona: 1926-2010
Oslo: 1937-2010
Oxford: 1853-2010
Moscow: 1949-2010
Trier: 1948-2010
70
Data Sets
Barcelona
40
Blocking: 1961-2000
NAO: 1848-2010
ENSO-SST: 1871-2010
-20
0
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Atmospheric blocking
… sustained, quasi-stationary, high-pressure systems
that disrupt the prevailing westerly circumpolar flow
Height of tropopause (2 pvu *):
• elevated tropopause
associated with strong
negative potential
vorticity anomalies
( > -1.3 pvu )
Sillmann, 2009
 relationship between temperature and precipitation anomalies
(Rex 1951, Trigo et al. 2004)
* [10-6m2s-1K kg-1]
Atmospheric blocking
Potential Vorticity (PV) - based blocking indicator
Blocking detection method (Schwierz et al. 2004):
• Identification of regions with strong negative PV
anomalies between 500-150hPa
• PV anomalies which meet time persistence (> 10 days)
and spatial criteria (1.8*106km2) are tracked from their
genesis to their lysis
Sillmann, 2009
20
5
10
15
u = 16.8mm
v = 1mm
0
Precipitation [mm]
25
Excesses over Thresholds
0
20
40
60
Days
80
100
120
Stationary Point Process
• Frequency of Events: Poisson Process
• Magnitude of excess: GPD
30
10
-10
Modified Scale
Threshold Selection
31
32
33
34
35
34
35
0.0
-1.0
Shape
0.5
Threshold
31
32
33
Threshold
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Model fitting
Stationary Model
Non-Stationary Model NAO
Non-Stationary Model ENSO
Non-Stationary Model Blocking
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Stationary Point
Process
location
Parameters for JJA
Maximum temperature
MLE estimates of the GEV
parameters transformed to give the
parameters of the Poisson model
and GPD:
σu = σ + ξ(u – μ)
Λ = (t2-t1)[1+ξ (z-μ)/σ ]-1/ξ
Non-stationary Point Process
Do the atmospheric driving conditions improve the
statistical mode fits?
stationary Point Process  non-stationary Point Process
COV – time dependent covariate
As before derive GPD parameters from GEV estimates
e.g. Atmospheric blocking as covariate (CAB)
Statistical modeling
Model selection
*d.f.
Model
µ
σ
λ
Distribution functions
0
0
0
0
F(x) ~ GPD(μ,σ) G(x) ~Pois(λ)
3
1
CAB
0
CAB
F(x|CAB(t)=z) ~ GPD(μ(z),σ)
G(x|CAB(t=z)) ~Pois(λ(z))
4
CAB
F(x|CAB(t)=z) ~ GPD(μ(z),σ(z))
Model choice
G(x|CAB(t)=z)
~Pois(λ(z))
5
* degrees of freedom
2
CAB
CAB
Deviance Statistic:
where nllh0(M0) is the neg. log-likelihood of simple model
nllh1(M1) is the neg. log-likelihood of more complex model
Non-stationary Point Process
Comparison of models
Discussion
• Resolution of Blocking index is too low
• JJA Summer only may miss some events
• Attributing excess temperatures to one driver
alone is too simplistic  multiple covariates?
• Hot spells (consecutive days of excess) may be
more interesting
• Similarly considering relative importance of
minimum temperatures and relative humidity
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Issues…
• Data limitations (blocking only available JJA)
• Familiarity with R packages
– Fitting covariates
– Calculating return levels under non-stationarity
– Mapping
• Time!
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