Irina Gorodetskaya *, Hubert Gallée, Gerhard Krinner Laboratoire de Glaciologie et Géophysique de l’Environnement, Grenoble,France * Now at: Katholieke.

Slides:



Advertisements
Similar presentations
Ewan OConnor, Robin Hogan, Anthony Illingworth Drizzle comparisons.
Advertisements

A NUMERICAL PREDICTION OF LOCAL ATMOSPHERIC PROCESSES A.V.Starchenko Tomsk State University.
A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.
ASTR Institute of astronomy and geophysics G. Lemaître –– Université catholique de Louvain U C L Regional climate modelling in Belgium with the Regional.
Lidar-Based Microphysical Retrievals During M-PACE Gijs de Boer Edwin Eloranta The University of Wisconsin - Madison ARM CPMWG Meeting, October 31, 2006.
Climatological Estimates of Greenland Ice Sheet Sea Level Contributions: Recent Past and Future J. E. Box Byrd Polar Research Center Understanding Sea-level.
Low clouds in the atmosphere: Never a dull moment Stephan de Roode (GRS) stratocumulus cumulus.
Climate modeling Current state of climate knowledge – What does the historical data (temperature, CO 2, etc) tell us – What are trends in the current observational.
JAN LENAERTS SNOWDRIFT CLIMATE Snowdrift climate of Greenland and Antarctica Jan Lenaerts Michiel van den Broeke Institute for Marine and Atmospheric Research,
What we have learned about Orographic Precipitation Mechanisms from MAP and IMPROVE-2: MODELING Socorro Medina, Robert Houze, Brad Smull University of.
HYDRANT: The role of clouds in Antarctic hydrologic cycle Project scientist: Irina Gorodetskaya, LGGE (France)/KU-Leuven Project leader: Nicole van Lipzig,
OK team…here is where we left off last time…..with conclusions from ice sheet modelling The most pronounced ice sheet fluctuations occurred in the West.
Lecture ERS 482/682 (Fall 2002) Snow hydrology ERS 482/682 Small Watershed Hydrology.
Hector simulation We found simulation largely depending on: Model initialization scheme Lateral boundary conditions Physical processes represented in the.
Southern Hemisphere Climate Change Professor Matthew England Climate and Environmental Dynamics Laboratory School of Mathematics, Faculty of Science The.
4. Models of the climate system. Earth’s Climate System Sun IceOceanLand Sub-surface Earth Atmosphere Climate model components.
Climate change impacts on water cycle in the Tibetan Plateau: A review Kun Yang Institute of Tibetan Plateau Research Chinese Academy of Sciences The fifth.
MODELING OF COLD SEASON PROCESSES Snow Ablation and Accumulation Frozen Ground Processes.
Climate Change Projections of the Tasman Sea from an Ocean Eddy- resolving Model – the importance of eddies Richard Matear, Matt Chamberlain, Chaojiao.
Mesoscale Modeling Review the tutorial at: –In class.
Modelling surface mass balance and water discharge of tropical glaciers The case study of three glaciers in La Cordillera Blanca of Perú Presented by:
Rick Russotto Dept. of Atmospheric Sciences, Univ. of Washington With: Tom Ackerman Dale Durran ATTREX Science Team Meeting Boulder, CO October 21, 2014.
Arctic sea ice melt in summer 2007: Sunlight, water, and ice NSIDC Sept 2007.
Helgi Björnsson, Institute of Earth Sciences, University of Iceland, Reykjavik, Iceland Contribution of Icelandic ice caps to sea level rise: trends and.
Coupled Climate Models OCEAN-ATMOSPHEREINTERACTIONS.
Atmospheric TU Delft Stephan de Roode, Harm Jonker clouds, climate and weather air quality in the urban environmentenergy.
In this work we present results of cloud electrification obtained with the RAMS model that includes the process of charge separation between ice particles.
Concours CNRS CR2, Section 19. Meudon, 17 Mars 2010 Irina Gorodetskaya Candidate for Laboratoire de Glaciologie et Géophysique de l’Environnement, (UMR.
Irina Gorodetskaya, Michael S. Town, Hubert Gallée Laboratoire de Glaciologie et Géophysique de l’Environnement, Grenoble,France EGU, Vienna 23 Apr
An intercomparison of the surface energy budget over the South Pole between observations, ERA-40, and the Modèle Atmosphérique Régional M. Town 1, I. Gorodetskaya.
Non-hydrostatic Numerical Model Study on Tropical Mesoscale System During SCOUT DARWIN Campaign Wuhu Feng 1 and M.P. Chipperfield 1 IAS, School of Earth.
Production and Export of High Salinity Shelf Water in a Model of the Ross Sea Michael S. Dinniman Y. Sinan Hüsrevoğlu John M. Klinck Center for Coastal.
Soil moisture content at SIRTA ( m 3 /m 3 ) at different depths. SIRTA’s data has been transformed to have the same amplitude as ORCHIDEE’s simulation.
The ASTEX Lagrangian model intercomparison case Stephan de Roode and Johan van der Dussen TU Delft, Netherlands.
Modern Era Retrospective-analysis for Research and Applications: Introduction to NASA’s Modern Era Retrospective-analysis for Research and Applications:
Antarctic Climate Response to Ozone Depletion in a Fine Resolution Ocean Climate Mode by Cecilia Bitz 1 and Lorenzo Polvani 2 1 Atmospheric Sciences, University.
The dynamic-thermodynamic sea ice module in the Bergen Climate Model Helge Drange and Mats Bentsen Nansen Environmental and Remote Sensing Center Bjerknes.
HYDRANT: The role of clouds in Antarctic hydrologic cycle Project scientist: Irina Gorodetskaya, LGGE (France)/KU-Leuven Project leader: Nicole van Lipzig,
Class #14 Wednesday, September 30 Class #14: Wednesday, September 30 Chapters 6 and 7 Thermal Circulation, Scales of Motion, Global Winds 1.
Dynamics of Climate Variability & Climate Change Dynamics of Climate Variability & Climate Change EESC W4400x Fall 2006 Instructors: Lisa Goddard, Mark.
LIDAR OBSERVATIONS CONSTRAINT FOR CIRRUS MODELISATION IN Large Eddy Simulations O. Thouron, V. Giraud (LOA - Lille) H. Chepfer, V. Noël(LMD - Palaiseau)
Evapotranspiration Eric Peterson GEO Hydrology.
Impact of solar UV variability on sudden stratospheric warming with LMDz-Reprobus A. Hauchecorne 1, S. Bekki 1, M. Marchand 1, C. Claud 2, P. Keckhut 1,
MOLOCH : ‘MOdello LOCale’ on ‘H’ coordinates. Model description ISTITUTO DI SCIENZE DELL'ATMOSFERA E DEL CLIMA, ISAC-CNR Piero Malguzzi:
Diagnosis of Performance of the Noah LSM Snow Model *Ben Livneh, *D.P. Lettenmaier, and K. E. Mitchell *Dept. of Civil Engineering, University of Washington.
A Thermal Plume Model for the Boundary Layer Convection: Representation of Cumulus Clouds C. RIO, F. HOURDIN Laboratoire de Météorologie Dynamique, CNRS,
Initial Results from the Diurnal Land/Atmosphere Coupling Experiment (DICE) Weizhong Zheng, Michael Ek, Ruiyu Sun, Jongil Han, Jiarui Dong and Helin Wei.
GCM simulations for West Africa: Validation against observations and projections for future change G.Jenkins, A.Gaye, A. Kamga, A. Adedoyin, A. Garba,
Estimating the Surface Mass Balance of the Antarctic coastal area for climate models validation 1 – Coastal area SMB & sea level rise 2 – SMB observation.
Active and passive microwave remote sensing of precipitation at high latitudes R. Bennartz - M. Kulie - C. O’Dell (1) S. Pinori – A. Mugnai (2) (1) University.
An advanced snow parameterization for the models of atmospheric circulation Ekaterina E. Machul’skaya¹, Vasily N. Lykosov ¹Hydrometeorological Centre of.
Atmospheric Circulation Response to Future Arctic Sea Ice Loss Clara Deser, Michael Alexander and Robert Tomas.
(Very) Last updates of the Liu-Penner parametrization in Hirlam. (and of the KF -scheme) Karl-Ivar-Ivarsson, Aladin/ Hirlam all staff meeting Norrköping.
Model calculationsMeasurements An extreme precipitation event during STOPEX I J.Reuder and I. Barstad Geophysical Institute, University of Bergen, Norway.
Chapter 3 Modelling the climate system Climate system dynamics and modelling Hugues Goosse.
Toward Continuous Cloud Microphysics and Cloud Radiative Forcing Using Continuous ARM Data: TWP Darwin Analysis Goal: Characterize the physical properties.
Blizzards! What causes them?. Firstly You Need Cold Air. The air has to be below freezing to make the snow. It has to be cold in the sky where the snowflakes.
Matt Vaughan Class Project ATM 621
B3. Microphysical Processes
Diagnosing latent heating rates from model and in-situ microphysics data: Some (very) early results Chris Dearden University of Manchester DIAMET Project.
GIS in Water Resources Term Project Fall 2004 Michele L. Reba
Grid Point Models Surface Data.
Outlines of NICAM NICAM (Nonhydrostatic ICosahedral Atmospheric Model)
Present and Future Antarctic climate simulations using Modèle Atmosphérique Régional forced with LMDZ GCM Irina Gorodetskaya, Hubert Gallée, Gerhard Krinner.
RegCM3 Lisa C. Sloan, Mark A. Snyder, Travis O’Brien, and Kathleen Hutchison Climate Change and Impacts Laboratory Dept. of Earth and Planetary Sciences.
A CASE STUDY OF GRAVITY WAVE GENERATION BY HECTOR CONVECTION
The case of Artesoncocha subbasin
Kurowski, M .J., K. Suselj, W. W. Grabowski, and J. Teixeira, 2018
Can the increase of Polar Stratospheric Clouds explain the Antarctic Winter Tropospheric warming? Tom Lachlan-Cope (W. M. Connolley, J. Turner, H. Roscoe,
A Coastal Forecasting System
Presentation transcript:

Irina Gorodetskaya *, Hubert Gallée, Gerhard Krinner Laboratoire de Glaciologie et Géophysique de l’Environnement, Grenoble,France * Now at: Katholieke Universiteit Leuven, Belgium CHARMANT, LGGE 19 October, 2009 Comparison of surface mass balance components simulated by LMDZ and MAR forced with LMDZ

SMB compilations Vaughan et al Giovinetto and Bentley 1985 van den Berg et al. 2006: observations van den Berg et al. 2006: calibrated model 166 mmwe 171 mmwe

Changes in precipitation? Linear trends of annual snowfall accumulation (mm yr -1 decade -1 ) for Monaghan et al 2008

Predicted precipitation change: LMDZ (IPSL) Krinner et al. 2007, 2008 Precipitation change: / SIC changes: ( ) - ( )

Large-scale model (ECMWF or GCM) Mesoscale model (MAR) Nesting: MAR forced with LMDZ output

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 improvements of Meyers et al 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) FSFS FSFS FLFL T4T4 H Lat H Sen Snow H Melt H Freez H Cond Tsfc Percolati on Liquid water             Blowing snow coupling to sea ice, land ice, vegetation...  Horizontal resolution 40 km  33 vertical levels (lowest ~9m, one level each 10 m below 50 m; top = 10hPa)  Initial and boundary conditions: LMDZ4 Modèle Atmosphérique Régional (MAR)

Relative annual mean precipitation change: Krinner et al LMDZ (IPSL): / MAR (lmdz forced): 2082 / 1982

Surface mass balance, mm w.e MAR (lmdz forced)LMDZ 175 mmwe 42 mmwe

Ratio between simulated SMB in S20 and estimates by Vaughan et al Ratio between LMDZ-simulated SMB and observed SMB in selected locations Krinner et al. 2007

SMB components: LMDZ Snow fallSublimation surface Total melt units: mmwe Effective melt 220 mmwe 17 mmwe 29 mmwe

SMB components: MAR Snow fall minus erosion Sublimation surface Melt Sublimation drifting snow units: mmwe 62 mmwe 14 mmwe 5 mmwe 7 mmwe

Annual snow fall, mmwe Difference: MAR-LMZ LMDZ: 220 mmwe MAR: 62 mmwe MAR-LMDZ: -128 mmwe

MAR : removal by wind erosion Blowing snow flux Snow fall minus erosion

Surface sublimation/deposition MEAN = 14 mmwe/yr MAR, : ECMWF ERA-15, Déry and Yau, 2002 MEAN = 14 mmwe/yr

Sublimation of drifting snow MEAN = 6 mmwe/yr MAR, , Liu et al 1983 parametrization: ECMWF ERA-15, Déry and Yau, 2002 MEAN = 15 mmwe/yr

Ablation areas MAR SMB, mmwe/yr Ablation areas van den Broeke et al, 2006 Blue = Blue ice areas > 10% (Winther et al. 2001) Red diam = meteorite sites AIS

Conclusions LMDZ and MAR : large differences in SMB LMDZ: - large precipitation and large melt = compensate - only two processes: precip and surface sublimation - melt calculated offline MAR: - snow fall is corrected for erosion = impossible to separate - lack of snow fall or too much erosion by wind - additional ablation processes: snow drift sublimation - melt is simulated  large local differences two models especially over the coasts  need more observations to tell which one is right

Surface mass balance from a GCM: Laboratoire de Meteorologie Dynamique general circulation model (LMDZ) Krinner et al mmwe (S20)

SMB components: LMDZ Melt Precip Sublimation/ deposition Krinner et al mmwe

Annual mean precipitation: MAR(lmdz forced) - LMDZ LMDZ: only snow fall (no erosion) MAR: precip-erosion (blowing snow parameterization) mmwe

Gallée and Gorodetskaya, Clim Dyn 2008 Surface air temperature over Dome C, East Antarctica MAR validation : Dome C (ECMWF forcing)

Model validation : South Pole (ECMWF forcing) Power spectrum (units 2 /time) Town, Gorodetskaya, Walden, Warren, in prep

warm events Snow accumulation, mm.w.e Integrated snow, mm.w.e Snow accumulation at South Pole (MAR forced with ERA-40) 1994 PSCs Gorodetskaya, Town, Gallée, in prep 54% 24% 7% 11%4%

MAR forced with LMDZ vs LMDZ itself : MAR - larger amplitude! r=0.6

SMB changes: from 1982 to 2082 Diff: Ratio: 2082/1982 MAR forced with LMDZ mmwe

Relative annual mean precipitation change: Krinner et al LMDZ (IPSL): / MAR (lmdz forced): 2082 / 1982

Annual mean surface temperature change: Precipitation change: 2082/1982 ratio MAR forced with LMDZ

Annual mean sea ice concentration change LMDZ [ ] - [ ] % Krinner et al. 2007