Group 2 E treme hot spells 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) 2 ASP Summer Colloquium Project#2 23 June 2011

Russia July earthobservatory.nasa.gov/IOTD/view.php?id=47880

Data Sets Barcelona: Oslo: Oxford: Moscow: Trier: Blocking: NAO: ENSO-SST: ASP Summer Colloquium Project#2 23 June 2011

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 -6 m 2 s -1 K kg -1 ] Sillmann, 2009

Atmospheric blocking Potential Vorticity (PV) - based blocking indicator Blocking detection method (Schwierz et al. 2004): Identification of regions with strong negative PV anomalies between hPa PV anomalies which meet time persistence (> 10 days) and spatial criteria (1.8*10 6 km 2 ) are tracked from their genesis to their lysis Sillmann, 2009

Excesses over Thresholds

Stationary Point Process Frequency of Events: Poisson Process Magnitude of excess: GPD

Threshold Selection ASP Summer Colloquium Project#2 23 June

Model fitting ASP Summer Colloquium Project#2 23 June Stationary Model Non-Stationary Model Blocking Non-Stationary Model ENSO Non-Stationary Model NAO

Stationary Point Process Parameters for JJA Maximum temperature location MLE estimates of the GEV parameters transformed to give the parameters of the Poisson model and GPD: σ u = σ + ξ(u – μ) Λ = (t 2 -t 1 )[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 Model choice Deviance Statistic: where nllh 0 (M 0 ) is the neg. log-likelihood of simple model nllh 1 (M 1 ) is the neg. log-likelihood of more complex model * * degrees of freedom ModelµσλDistribution functionsd.f. 0000F(x) ~ GPD(μ,σ) G(x) ~Pois(λ)3 1CAB0 F(x|CAB(t)=z) ~ GPD(μ(z),σ) G(x|CAB(t=z)) ~Pois(λ(z)) 4 2CAB F(x|CAB(t)=z) ~ GPD(μ(z),σ(z)) G(x|CAB(t)=z) ~Pois(λ(z)) 5

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 15 ASP Summer Colloquium Project#2 23 June 2011

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#2 23 June