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Concours CNRS CR2, Section 19 . Meudon, 17 Mars 2010

Concours CNRS CR2, Section 19 . Meudon, 17 Mars 2010. Understanding Clouds and Their Effects on Radiative Budget and Precipitation in the Present and Future Polar Climate using model simulations and observations. Irina Gorodetskaya. Clouds. Cryosphere.

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Concours CNRS CR2, Section 19 . Meudon, 17 Mars 2010

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  1. Concours CNRS CR2, Section 19. Meudon, 17 Mars 2010 Understanding Clouds and Their Effects on Radiative Budget and Precipitation in the Present and Future Polar Climateusing model simulations andobservations Irina Gorodetskaya Clouds Cryosphere Candidate for Laboratoire de Glaciologie et Géophysique de l’Environnement, (UMR 5183 CNRS, Université Joseph Fourier-Grenoble)

  2. Arctic sea ice decline! Arctic September sea ice extent Credit: NSIDC x x x 2009 2008 2007 ? CLOUDS Motivation ? Antarctic precipitation increase? Relative annual mean precipitation change on the Antarctic ice sheet during the 21st century Krinner et al. 2007

  3. My Background The role of clouds in the Arctic sea ice decline PhD at Lamont-Doherty Earth Observatory, Columbia University 2002-2007 : CCSM3 A1B CCSM3 A1B Gorodetskaya and Tremblay 2008, AGU monograph “Arctic sea ice decline”

  4. My Background Model simulations of present and future Antarctic climate andsfc mass balance Postdoctorat at Laboratoire de Glaciologie et Géophysique de l’Environnement November 2007 - present: supervisors: H. Gallée and G. Krinner Surface air temperature difference between the two models: MAR nested in LMDZ Large-scale model (LMDZ) Mesoscale model (MAR) 1981-1989 annual mean Gorodetskaya, Gallée, Krinner, in prep

  5. My Background Postdoctorat at K. U. Leuven, Belgium August 2009 - present:Clouds and hydrologic cycle of Antarcticasupervisor:N. van Lipzig x • Phase 1 : meteorological and cloud measurements at the new Belgian Antarctic • Station (Dronning Maud Land) AWS • Phase 2 : use obtained data for regional model validation Cloud height Cloud base temperature Precipitation

  6. Research project : Clouds and Radiative Feedbacks in Present and Future Polar Climate Data and Models : Cloud scheme improvement Model simulations and data analysis Model validation meso-scale (MAR) large-scale (LMDZ) ground-based and satellite data Arctic ocean Greenland/Antarctic Meso => large scale • Understanding climate • change in polar regions • Arctic sea ice loss • Greenland melt • Antarctic precipitation • change

  7. Pr Elis (new!) ARM network SHEBA (1997/98) MPACE (2004) ASTAR (2004/7) ASCOS (2008) Summit (ARM) (spring 2010+) South Pole Dome C DDU Model validation ModèledeLaboratoire de Météorologie Dynamique with Zoom capabilities over the polar regions (LMDZ) Modèle Atmosphérique Régional (MAR) Arctic Ocean: Greenland: Antarctica: + CloudSat and CALIPSO => aerosols-clouds-precipitation

  8. MARvalidation :energy budget and temperature Temperature over Dome C, Antarctica Gallée and Gorodetskaya, Clim Dyn 2008

  9. Princess Elisabeth station potential for model validation :clouds andprecipitation Regional model simulations: snowfall events (g/kg of snow particles) accumulation, cm Observations at Princess Elisabeth: Feb 1, 2010 Snow fall event shown by radar reflectivity

  10. Cloud scheme improvement Model simulations and data analysis Meso => large scale • Understanding climate • change in polar regions • Arctic sea ice loss • Greenland melt • Antarctic precipitation • change Research project : Clouds and Radiative Feedbacks in Present and Future Polar Climate Data and Models : Model validation meso-scale (MAR) large-scale (LMDZ) ground-based and satellite data Arctic ocean Greenld/Antarctic

  11. Cloud schemes improvement LMDZ (IPSL) CCSM3 GISS-Er HadCM3 Cloud ice fraction ocean land Cloud temperature II. Improve cloud phase representation in GCM (LMDZ) I. Improve cloud scheme in regional model: • MAR: • tropospheric clouds • are too thin • ice particle size too large • improve treatment of • ice and snow size spectra

  12. Cloud scheme improvement Model simulations and data analysis Meso => large scale • Understanding climate • change in polar regions • Arctic sea ice loss • Greenland melt • Antarctic precipitation • change Research project : Clouds and Radiative Feedbacks in Present and Future Polar Climate Data and Models : Model validation meso-scale (MAR) large-scale (LMDZ) ground-based and satellite data Arctic ocean Greenland/Antarctic

  13. ? cloud properties ICE MASS BALANCE Application:understanding cloud-ice feedbacks planet warming + ? surface sens and latent heat fluxes - MELT + + +/- precipitation atm temperature and humidity +/- radiative fluxes +/- large-scale advection aerosols

  14. Climate modeling: LMD (LMDZ/IPSL) S. Bony, J.-L. Dufresne MeteoFrance (CNRM) Observational programs: GLACIOCLIM, CESOA (LGGE) ENEA programs (Italy) Collaborations LGGE : “Climat moderne et observations glaciologiques” Cloud modeling: LaMP MeteoFrance in Europe : Polar climate modling: Liege U, KU-Leuven, IMAU-Netherlands Sea ice modeling: Louvain-la-Neuve USA/Canada : Arctic cloud obs and modeling (Rutgers, NCAR,U Montreal) Arctic climate/sea ice (McGill, U Wash) Cloud observations: LaMP NOAA KU-Leuven IFAC (Italy) French/European projects: Ice2Sea, COMBINE, HYDRANT, Arctic Observatory International projects: NOAA’s Arctic Atmospheric Observatory (T. Uttal et al) ICECAP (V. Walden et al/Greenland)

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