Southern Hemisphere Storminess under recent and anthropogenic climate conditions based on a multi model ensemble analysis Jens Grieger G.C. Leckebusch, M. Schuster, U. Ulbrich (contact: jens.grieger@met.fu-berlin.de) Institut für Meteorologie, Freie Universität Berlin 9th EMS Annual Meeting 02.10.2009 Jens Grieger (FU Berlin) 02.10.2009 1 / 17
Motivation one of the largest freshwater reservoirs: Antarctic ice sheets mass balance could have a major impact on global sea level variations multi model ensemble analysis problems with precipitation modelling atmospheric freshwater fluxes are related to cyclones change of cyclonic activity in an antropogenic changed climate influence on poleward fluxes (e.g. moisture) Jens Grieger (FU Berlin) 02.10.2009 2 / 17
Motivation one of the largest freshwater reservoirs: Antarctic ice sheets mass balance could have a major impact on global sea level variations multi model ensemble analysis problems with precipitation modelling atmospheric freshwater fluxes are related to cyclones change of cyclonic activity in an antropogenic changed climate influence on poleward fluxes (e.g. moisture) Jens Grieger (FU Berlin) 02.10.2009 2 / 17
Motivation one of the largest freshwater reservoirs: Antarctic ice sheets mass balance could have a major impact on global sea level variations multi model ensemble analysis problems with precipitation modelling atmospheric freshwater fluxes are related to cyclones change of cyclonic activity in an antropogenic changed climate influence on poleward fluxes (e.g. moisture) Jens Grieger (FU Berlin) 02.10.2009 2 / 17
Data Reanalysis Resolution years ERA 40 2.5 x 2.5 1961-2000 NCEP 2.5 x 2.5 1958-2006 AOGCM Resolution control period: SRES A1B 20C MPI-ECHAM5-Run1 T63 (ca. 1.9 ) 1981-1999 2081-2099 MPI-ECHAM5-Run2 T63 (ca. 1.9 ) 1981-1999 2081-2099 MPI-ECHAM5-Run3 T63 (ca. 1.9 ) 1981-1999 2081-2099 DMI-ECHAM5 T63 (ca. 1.9 ) 1961-2000 2071-2100 IPSL-CM4 2.5 x 3.75 1961-2000 2071-2100 FUB-EGMAM T30 (ca. 4 ) 1961-2000 2081-2100 CNRM-CM3 T63 (stored at 2.85 ) 1981-2000 2081-2100 BCCR-BCM2 T63 (stored at 2.85 ) 1961-1999 2080-2099 HadGEM1 1.25 x 1.875 1981-1999 2081-2099 Jens Grieger (FU Berlin) 02.10.2009 3 / 17
Methods Cyclone Identification and Tracking Algorithm [Murray and Simmonds(1991)] Identification using mean sea level pressure p maximum of 2 p related minimum of p quasi-geostrophic relative vorticity ξ = 1 ρ f 2 p Tracking for each identified cyclone a subsequent position and core pressure is predicted comparison between these predicted position and identified cyclones in the following timestep Jens Grieger (FU Berlin) 02.10.2009 4 / 17
Methods Cyclone Identification and Tracking Algorithm [Murray and Simmonds(1991)] Identification using mean sea level pressure p maximum of 2 p related minimum of p quasi-geostrophic relative vorticity ξ = 1 ρ f 2 p Tracking for each identified cyclone a subsequent position and core pressure is predicted comparison between these predicted position and identified cyclones in the following timestep Jens Grieger (FU Berlin) 02.10.2009 4 / 17
Reanalysis Data on the SH Skill of Reanalysis Data on SH [Bromwich and Fogt(2004)] Southern Ocean and Antarctica represent one of the largest spatial meteorological data void on globe both ERA40 and NCEP Reanalysis have deficits before 1970 high performance of ERA40 Reanalysis after 1978 Jens Grieger (FU Berlin) 02.10.2009 5 / 17
Track Density of ERA 40 Reanalysis Track Density 1961-2000 Difference: (1981-2000) - (1961-1980) [%] Isolines: track density per season (04-09) Isolines: differences of track density [%] Color: 95th and 99th confidence level Jens Grieger (FU Berlin) 02.10.2009 6 / 17
Track Density of NCEP Reanalysis Track Density 1958-2006 Difference: (1981-2006) - (1958-1980) [%] Isolines: track density per season (04-09) Isolines: differences of track density [%] Color: 95th and 99th confidence level Jens Grieger (FU Berlin) 02.10.2009 7 / 17
Ensemble Mean number of years scaling models have different numbers of years altogether 255 years of control period (20C) altogether 196 years of SRES A1B scenario calculating the mean of these years different resolution of the models different total numbers of cyclone tracks although the spatial patterns are well represented scaling with number of tracks in the control period (20C) all models have the same number of cyclone tracks in the 20C simulation Jens Grieger (FU Berlin) 02.10.2009 8 / 17
Ensemble Mean number of years scaling models have different numbers of years altogether 255 years of control period (20C) altogether 196 years of SRES A1B scenario calculating the mean of these years different resolution of the models different total numbers of cyclone tracks although the spatial patterns are well represented scaling with number of tracks in the control period (20C) all models have the same number of cyclone tracks in the 20C simulation Jens Grieger (FU Berlin) 02.10.2009 8 / 17
Model Ensemble Validation with ERA 40 Track Density Ensemble Mean scaled with ERA40 Track Density ERA40 1961-2000 Isolines: track density per season (04-09) Isolines: track density per season (04-09) Jens Grieger (FU Berlin) 02.10.2009 9 / 17
Climate Change Signal Ensemble mean comparison between control run (20C) and SRES A1B all cyclonic systems strong cyclones are defined by the 5% largest values of the Laplacian of p Jens Grieger (FU Berlin) 02.10.2009 10 / 17
Climate Change Signal of Track Density all cyclones [%] A1B - 20C strongest 5% [%] Isolines: climate signal of track density [%] Color: 95th and 99th confidence level Isolines: climate signal of track density [%] Color: 95th and 99th confidence level Jens Grieger (FU Berlin) 02.10.2009 11 / 17
Climate Change Signal of Core Pressure all cyclones [hpa] A1B - 20C strongest 5% [hpa] Isolines: climate signal of Core Pressure [hpa] Color: 95th and 99th confidence level Isolines: climate signal of Core Pressure [hpa] Color: 95th and 99th confidence level Jens Grieger (FU Berlin) 02.10.2009 12 / 17
Climate Change Signal of 2 p all cyclones [%] A1B - 20C strongest 5% [%] Isolines: climate signal of 2 p [%] Color: 95th and 99th confidence level Isolines: climate signal of 2 p [%] Color: 95th and 99th confidence level Jens Grieger (FU Berlin) 02.10.2009 13 / 17
Summary agreement of 20C model ensemble and reanalysis data significant poleward shift of cyclone track density no significant increase in the pacific sector of the Southern Ocean significant decrease of core pressure south of 50 S significant increase of cyclone intensity no significant increase in the western pacific sector of the Southern Ocean core pressure and 2 p show similar spatial patterns of all cyclones and strongest 5% potential for higher moisture fluxes into Antarctica especially strong cyclones can reach deep into Antarctica Jens Grieger (FU Berlin) 02.10.2009 14 / 17
Summary agreement of 20C model ensemble and reanalysis data significant poleward shift of cyclone track density no significant increase in the pacific sector of the Southern Ocean significant decrease of core pressure south of 50 S significant increase of cyclone intensity no significant increase in the western pacific sector of the Southern Ocean core pressure and 2 p show similar spatial patterns of all cyclones and strongest 5% potential for higher moisture fluxes into Antarctica especially strong cyclones can reach deep into Antarctica Jens Grieger (FU Berlin) 02.10.2009 14 / 17
Summary agreement of 20C model ensemble and reanalysis data significant poleward shift of cyclone track density no significant increase in the pacific sector of the Southern Ocean significant decrease of core pressure south of 50 S significant increase of cyclone intensity no significant increase in the western pacific sector of the Southern Ocean core pressure and 2 p show similar spatial patterns of all cyclones and strongest 5% potential for higher moisture fluxes into Antarctica especially strong cyclones can reach deep into Antarctica Jens Grieger (FU Berlin) 02.10.2009 14 / 17
Outlook - Moisture Flux Moisture Flux Vector Q = 1 g Net Precipitation psfc p 0 q (p) v (p) dp p sfc : surface pressure p 0 = effective pressure at top of atmosphere q : specific humidity v : wind vector { divq } = {P E} Jens Grieger (FU Berlin) 02.10.2009 15 / 17
Outlook - Moisture Flux Moisture Flux Vector Q = 1 g Net Precipitation psfc p 0 q (p) v (p) dp p sfc : surface pressure p 0 = effective pressure at top of atmosphere q : specific humidity v : wind vector { divq } = {P E} Jens Grieger (FU Berlin) 02.10.2009 15 / 17
Thank you Jens Grieger (FU Berlin) 02.10.2009 16 / 17
Bibliography RJ Murray and I Simmonds. A numerical scheme for tracking cyclone centres from digital data. part i: developement and operation of the scheme. Australian Meteorological Magazine, 39:155, 1991. DH Bromwich and RL Fogt. Strong trends in the skill of the era-40 and ncep-ncar reanalyses in the high and midlatitudes of the southern hemisphere. American Meteorological Society, 17:4603, 2004. Jens Grieger (FU Berlin) 02.10.2009 17 / 17