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Extreme events and Euro-Atlantic atmospheric blocking in present and future climate simulations

Extreme events and Euro-Atlantic atmospheric blocking in present and future climate simulations Jana Sillmann Max Planck Institute for Meteorology, Hamburg International Max Planck Research School on Earth System Modelling Paris, SAMA seminar, 20 th January 2009. Motivation.

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Extreme events and Euro-Atlantic atmospheric blocking in present and future climate simulations

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  1. Extreme events and Euro-Atlantic atmospheric blocking in present and future climate simulations Jana Sillmann Max Planck Institute for Meteorology, Hamburg International Max Planck Research School on Earth System Modelling Paris, SAMA seminar, 20th January 2009

  2. Motivation http://www.srh.noaa.gov http://nedies.jrc.it http://www.conserveafrica.org.uk ournewsbrooklyn.wordpress.com heat waves cold waves floods droughts IPCC 2007: ”Climate change may be perceived most through the impacts of extremes…” Munich Re 2005: Increase of climate related catastrophes and associated material and human losses since 1950

  3. Outline Theory • Climate model and data • Defining extreme climate events and atmospheric blocking Questions • Is the model able to capture observed patterns of climate extremes? • What changes in extremes can we expect under anthropogenic climate change? • Can we find associations between climate extremes and atmospheric blocking? • Can we use these associations in the statistical modeling of extreme events?

  4. Model & Data Model & Data Coupled general circulation model ECHAM5/MPI-OM Atmosphere Ocean T63 (1.875° x 1.875°) 31 vertical levels 1.5° horizontal resolution 40 vertical levels 20C, A1B and B1 – each with 3 ensemble members

  5. Extreme events Definition of extreme climate events Extreme event … very rare and very intense event with severe impacts on society and biophysical systems.

  6. Extreme events Indices for climate extremes ¹ Statistical modeling of extreme values Identification of extreme events in climate data Methods for extreme value analysis • based on daily temperature and • precipitation data • describe moderate and statistically • robust extremes • easily understandable and • manageable for impact studies Yearly/monthly indices: • Minimum of daily minimum temperature • Maximum of daily maximum temperature • Maximum 5 day precipitation • Maximum number of consecutive dry days ¹ Expert Team on Climate Change Detection Monitoring and Indices

  7. Indices for extremes What changes can we expect under anthropogenic climate change? 1971-2000 2071-2100

  8. Changes in extremes Annual maximum temperature Annual max. 5-day precipitation [ ºC ] [ mm ] Annual max. consecutive dry days Annual minimum temperature [ days ] [ ºC ] Difference A1B scenario – present climate

  9. Atmospheric blocking 1961-2000 2160-2199 Can we find associations between climate extremes and atmospheric blocking? Winter climate of the Euro-Atlantic domain Minimum Temperature

  10. 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 )  relationship between temperature and precipitation anomalies (Rex 1951, Trigo et al. 2004) * [10-6m2s-1K kg-1]

  11. 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

  12. Atmospheric blocking 2160-2199 Representation in present and future climate Blocking events > 10days DJF 1961-2000 model ERA-40 re-analysis Blocking frequency in %

  13. Atmospheric blocking Blocking frequency for DJF % 1961-2000 European blockings (15°W-30°E,50°N-70°N) Blocking frequency %

  14. Atmospheric blocking Correlation of European blockings with winter (DJF) minimum temperature 1961-2000 2160-2199 Significant Spearman’s rank correlation coefficient to the 5% significance level

  15. Extreme events Indices for climate extremes Statistical modeling of extreme values • GEV – Generalized Extreme • Value distribution • parametric approach to • characterize the distribution of • extreme events • calculation of return values Identification of extreme events in climate data Methods for extreme value analysis

  16. Stationary GEV Generalized Extreme Value (GEV) distribution with parameters  (location)‏,  (scale) and  (shape)

  17. Stationary GEV A1B –20C Parameters for DJF minimum temperature location scale shape ERA-40 20C

  18. Non-stationary GEV stationary GEV  non-stationary GEV COV – time dependent covariate Atmospheric blocking as covariate derived from the PV-based blocking indicator (CAB) Can we use the association between extreme events and atmospheric blocking in the statistical modeling of extreme events?

  19. Covariate atmospheric blocking European blockings Euro-Atlantic blockings Euro-Atlantic domain Blocking frequency %

  20. Statistical modeling example nllh 353 349 348 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

  21. Non-stationary GEV model model model Model selection for minimum temperature extremes in winter

  22. Non-stationary GEV ºC/blocking freq. % Slope of the location parameter

  23. Non-stationary GEV Grid-point example at 9ºE, 53ºW GEV distribution for the stationary and non-stationary model 1

  24. Non-stationary GEV Return values at grid point 9ºE, 53ºW T-year return value … is the (1-1/T)th quantile of the GEV distribution median 90% confidence interval 20-year return value

  25. Non-stationary GEV A1B ERA40 20C 20-yr return values for minimum temperature extremes in winter Significant differences between RV20 of stationary and non-stationary GEV distribution

  26. Summary • Is the model able to capture observed patterns of climate extremes? • What changes in extremes can we expect under anthropogenic climate change? • increase of temperature and precipitation extremes as well as dry periods • regional and seasonal distinguished changes of extremes in future climate

  27. Summary • Can we find associations between climate extremes and atmospheric blocking? • atmospheric blocking favors extreme cold nighttime temperatures in Europe • association remains robust in future climate, but influence of blocking events diminishes due to decreasing blocking frequency • Can we use these associations in the statistical modeling of extreme events? • atmospheric blocking implemented as covariate in the GEV can explain more of the variability in the underlying data • modeling of colder return values possible

  28. Outlook • Improvement of the statistical modeling: • longer climate simulations (500-year control run) to further test the statistical robustness of the results • apply Generalized Pareto distribution • use other or more covariates • Usage of this methodology for statistical downscaling: • limit region of interest, e.g. to northern, southern Europe • find appropriate covariate for that region • test method with observations

  29. Thank you very much!

  30. Indices for extremes Is the model able to capture observed patterns of climate extremes? HadEX dataset: indices for extreme events calculated on the basis of a worldwide weather observational dataset from the Hadley Centre (3.75° x 2.5° horizontal resolution) (Alexander et al. 2006) Time coverage: 1951-2001

  31. Present climate Temperature indices - global

  32. Present climate Precipitation indices - global

  33. Present climate Temperature indices - regional

  34. Present climate Precipitation indices - regional

  35. Indices for extremes Temperature indices - global

  36. tropopause = 2pvu [10-6 m2 s-1 K kg-1] [hPa] 150 500 800 climatological tropopause PV anomaly [pvu] instantaneous tropopause PV anomaly < -1.3 pvu 25 35 45 55 65 75 latitude [°N] Atmospheric Blocking Pot. Vorticity (PV)-based Blocking indicator … captures the block at the core PV-anomaly at tropopause level (Croci-Maspoli 2007)

  37. Atmospheric Blocking PV-based Blocking identification averaged PV-anomaly between 500 and 150hPa (Schwierz et al. 2004, GRL)‏

  38. Atmospheric Blocking PV-based Blocking identification filled contours indicate vertically-averaged PV anomalies (0.7pvu steps)‏ red = APV* blocking location (Schwierz et al. 2004, GRL)‏

  39. Atmospheric blocking Composite maps

  40. xCOV(t) Best model (mm)‏ 5 4 3 2 1 0 -1 -2 -3 -4 0 1 2 3 4 5 model # Modeling Diagnostic Testing the method for El Nino and its impact on precipitation for 1961-2000winter (ONDJFM)

  41. Probability Plot model 0.0 0.2 0.4 0.6 0.8 1.0 Quantile Plot 0.0 0.2 0.4 0.6 0.8 1.0 empirical model 0 2 4 -1 0 1 2 3 empirical Model Diagnostic Model Diagnostic at Grid Point [9E, 53N] for min.Tmin (ONDFM)‏

  42. Statistical modeling Generalized Extreme Value (GEV) distribution Block maxima approach Daily minimum temperature data are blocked into sequences of length n, generating a sequence of block minima to which the GEV distribution can be fitted • select block size (e.g., 1 season, 1 month)‏ • choose smallest event in each block (month or season) • fit GEV distribution to selected extreme events • estimation of GEV parameters for each global grid point via Maximum-Likelihood

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