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Group 2

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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Group 2

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  1. E treme hot spells Group 2 Mari Jones Christiana Photiadou David Keelings Candida Dewes MerceCastella

  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#2 23 June 2011

  3. Russia July 2010 http://earthobservatory.nasa.gov/IOTD/view.php?id=47880

  4. Data Sets Barcelona: 1926-2010 Oslo: 1937-2010 Oxford: 1853-2010 Moscow: 1949-2010 Trier: 1948-2010 Blocking: 1961-2000 NAO: 1848-2010 ENSO-SST: 1871-2010 ASP Summer Colloquium Project#2 23 June 2011

  5. 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]

  6. 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

  7. Excesses over Thresholds

  8. Stationary Point Process • Frequency of Events: Poisson Process • Magnitude of excess: GPD

  9. Threshold Selection ASP Summer Colloquium Project#2 23 June 2011

  10. Model fitting Stationary Model Non-Stationary Model NAO Non-Stationary Model ENSO Non-Stationary Model Blocking ASP Summer Colloquium Project#2 23 June 2011

  11. 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: σu= σ + ξ(u – μ) Λ = (t2-t1)[1+ξ (z-μ)/σ ]-1/ξ

  12. Non-stationary Point Process 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) Do the atmospheric driving conditions improve the statistical mode fits?

  13. Statistical modeling 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 Model selection * * degrees of freedom

  14. Non-stationary Point Process Comparison 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#2 23 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#2 23 June 2011

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