Irina Gorodetskaya, Michael S. Town, Hubert Gall é e Laboratoire de Glaciologie et G é ophysique

1. Mechanisms behind synoptic-scale variability in South Pole meteorologyfrom observations and a regional model Irina Gorodetskaya, Michael S. Town, Hubert Gallée Laboratoire de Glaciologie et Géophysique de l’Environnement, Grenoble,France EGU, Vienna 23 Apr. 2009

2. acknowledgements: Gerhard Krinner for support and discussions Von P. Walden for providing computer time and space Stephen G. Warren for antarcticcloud discussions Ells Duttonand Tom Meffordof NOAA-GMD, and BSRN for radiation and meteorology data and advice. Gorodetskaya, Town, Gallée, LGGE : EGU 2009

3. importance of synoptic activity over Antarctica • data sets and model description • the climate of the South Pole • model evaluation: wavelets • cluster analysis • conclusions Outline Gorodetskaya, Town, Gallée, LGGE : EGU 2009

4. South Pole climate data set: A review 1975 2003 1957 1911 1950 1960 1970 1980 1990 2000 surface meteorology/observations radiosondes radiation accumulation clouds snow temperatures NOAA CMDL/GMD See poster M. Town and V. Walden, Session AS2.4, XY105

5. Snow HCond FL T4 HLat FS HSen coupling to sea ice, land ice, vegetation...  HMelt HFreez Blowingsnow     FS        Tsfc Liquid water Percolation Atmospheric model: mesoscale hydrostatic primitive equation model (Gallée 1994, 1995) • Terrain following vertical coordinates (normalized pressure) • Turbulence: 1 1/2 closure (Duynkerke 1988) • Bulk cloud microphysics (Kessler 1962 and Lin et al 1983 + improvements of Meyers et al. 1992 and Levkov et al. 1992) • Solar and infrared radiative transfer scheme (Morcrette 2002, Ebert and Curry 1992) • Snow fall included into infrared radiation scheme Snow model: conservation of heat and water (solid and liquid), description of snow properties (density, dendricity, sphericity and size of the grains), melting/freezing Blowing snow model (Gallée et al, 2001) Modèle Atmosphérique Régional (MAR) • Horizontal resolution 80 km • 33 vertical levels (lowest ~9m, one level each 10 m below 50 m; top = 10hPa) • Initial and boundary conditions: ECMWF ERA-40 Gorodetskaya, Town, Gallée, LGGE : EGU 2009

6. altitude = 2835 m accumulation rate = 8 cm yr-1 mean temperature = -50oC The climate of the South Pole Gorodetskaya, Town, Gallée, LGGE : EGU 2009

7. Sfc air temperature MAR. ERA40. South Pole. 1994 Gorodetskaya, Town, Gallée, LGGE : EGU 2009 ..-45oC a ..-65oC

8. Normalized w.e. Accumulation (10-3 m) Importance of synoptic activity over Antarctic interior Gorodetskaya, Town, Gallée, LGGE : EGU 2009 Time series of the five snow accumulation events close to the South Pole (860S, 460W) from acoustic depth gauge Braaten 2000

9. Importance of synoptic activity over Antarctic interior The 700hPa height and 500hPa wind field at 1200 UTC on Nov 5, 1997 Noone, Turner, Mulvaney 1999 Gorodetskaya, Town, Gallée, LGGE : EGU 2009

10. Isobaric temperature advection when 300 hPa wind is from SW or NW (warm events) and from SE (cold events) Directional distribution of hourly near surface winds during warm and cold events Neff, JGR (104) 1999 Along-slope (“North”) Down-slope (“East”) Warming Cooling Height, m Number in interval Gorodetskaya, Town, Gallée, LGGE : EGU 2009 Warming events Cooling events SW SE NW Direction Class Intervals Thermal advection (0C/day)

11. Wavelets applied to time series: Convolve wavelets of increasing size with time series to obtain scaling coefficients. T(a,b) = w(a) x(t)  dt t-b a power spectrum T(a,b) a time b Wavelets give information in temporal and frequency domains. Gorodetskaya, Town, Gallée, LGGE : EGU 2009

12. Model validation : wavelet analysis Power spectrum (units2/time) Gorodetskaya, Town, Gallée, LGGE : EGU 2009

13. Cluster analysis applied to time series: 5 variables... • Variables measured • at South Pole: • Sfc temperature • Water vapor pressure (from frost point) • Sfc wind speed • downwelling LW flux • downwelling SW flux Gorodetskaya, Town, Gallée, LGGE : EGU 2009 • Sfc air temperature amplitude is good in MAR • (both synoptic and seasonal) • Wind speed underestimated during • some warm events • Increased humidity and LW fluxes • during warm events in obs and MAR 6 hour time step, 1994

14. 12 variables... Cluster analysis applied to time series: • Variables simulated • by MAR: • Sfc temperature • Sfc pressure • tropospheric water vapor • downwelling LW flux • downwelling SW flux • U,V near surface • U,V at 300 hPa • tropospheric cloud liquid • tropospheric cloud ice • stratospheric cloud ice Gorodetskaya, Town, Gallée, LGGE : EGU 2009 6 hour time step, 1994

15. Accum, % 11% 54% 7% 24% 4% MAR : 6 meteorological regimes E SE NW NE SW NE S SE NE NW N E NE E ... E SW Gorodetskaya, Town, Gallée, LGGE : EGU 2009

16. Snow accumulation Snow accumulation, mm.w.e warm events Gorodetskaya, Town, Gallée, LGGE : EGU 2009 Integrated snow,mm.w.e

17. Modèle Atmosphérique Régional (MAR): shows good skill in synoptic-scale simulations Conclusions • Cold events are more or less similar: - low tropospheric humidity, clear sky, low downwelling LW flux - NE-E near surface wind (“inversion” wind) - weak SE wind at 300 hPa 11% snow accumulation • Warm events happen for a variety of reasons: I. warm air advection from W-SW (West Antarctica) withincrease in tropospheric humidity and tropospheric cloud liquid 54% snow accumulation Gorodetskaya, Town, Gallée, LGGE : EGU 2009 II. warm air advection from N-NW (Weddell Sea) - slight increase in tropospheric moisture content - no tropospheric clouds but stratospheric clouds form 24% snow accumulation warm events correlated with high stratospheric ice content together with slight increase in tropospheric moisture content 7% snow accumulation

18. Plans • Extend cluster analysis to the entire period (1994-2000) • Upper air charts for each type of warm event Gorodetskaya, Town, Gallée, LGGE : EGU 2009