Group 2 E treme hot spells Mari Jones Christiana Photiadou David Keelings Candida Dewes Merce...
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Transcript of Group 2 E treme hot spells Mari Jones Christiana Photiadou David Keelings Candida Dewes Merce...
Group 2
E treme
hot spells
Mari JonesChristiana PhotiadouDavid KeelingsCandida DewesMerce Castella
2
Motivation
• Examine extreme hot temperatures in Europe and their drivers:– Blocking Index– North Atlantic Oscillation– El Niño-Southern Oscillation (BEST index)
ASP Summer Colloquium Project#223 June 2011
Russia July 2010
http://earthobservatory.nasa.gov/IOTD/view.php?id=47880
4
Data Sets
Barcelona: 1926-2010Oslo: 1937-2010Oxford: 1853-2010Moscow: 1949-2010Trier: 1948-2010
Blocking: 1961-2000NAO: 1848-2010ENSO-SST: 1871-2010
ASP Summer Colloquium Project#223 June 2011
-20 0 20 40
40
50
60
70
Oxford
Oslo
Trier
Barcelona
Moscow
Atmospheric blocking
relationship between temperature and precipitation anomalies (Rex 1951, Trigo et al. 2004)
… 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 )
* [10-6m2s-1K kg-1]
Sillmann, 2009
Atmospheric blockingPotential 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
Excesses over Thresholds
0 20 40 60 80 100 120
05
1015
2025
Days
Pre
cip
itatio
n [
mm
]
v = 1mm
u = 16.8mm
Stationary Point Process
• Frequency of Events: Poisson Process
• Magnitude of excess: GPD
9
Threshold Selection
ASP Summer Colloquium Project#223 June 2011
31 32 33 34 35
-10
10
30
Threshold
Mo
difi
ed
Sca
le
31 32 33 34 35
-1.0
0.0
0.5
Threshold
Sh
ap
e
10
Model fitting
ASP Summer Colloquium Project#223 June 2011
Stationary Model
Non-Stationary Model Blocking Non-Stationary Model ENSO
Non-Stationary Model NAO
Stationary Point Process
Parameters for JJA Maximum temperaturelocation
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 ProcessDo 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 modelingModel selection
Model choice
Deviance Statistic:
where nllh0(M0) is the neg. log-likelihood of simple model
nllh1(M1) is the neg. log-likelihood of more complex model
*
* degrees of freedom
Model µ σ λ Distribution functions d.f.
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
2 CAB CAB CAB F(x|CAB(t)=z) ~ GPD(μ(z),σ(z)) G(x|CAB(t)=z) ~Pois(λ(z))
5
Non-stationary Point ProcessComparison of models
15
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
ASP Summer Colloquium Project#223 June 2011
16
Issues…
• Data limitations (blocking only available JJA)• Familiarity with R packages
– Fitting covariates– Calculating return levels under non-stationarity– Mapping
• Time!
ASP Summer Colloquium Project#223 June 2011