Sea Ice Deformation Studies and Model Development

Slides:



Advertisements
Similar presentations
Salt rejection, advection, and mixing in the MITgcm coupled ocean and sea-ice model AOMIP/(C)ARCMIP / SEARCH for DAMOCLES Workshop, Paris Oct 29-31, 2007.
Advertisements

Satellite observations of the meso- and submesoscale eddies in the Baltic and Black Seas Svetlana Karimova M.Sc. in oceanography – Lomonosov.
Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London.
David Prado Oct Antarctic Sea Ice: John N. Rayner and David A. Howarth 1979.
Preliminary results on Formation and variability of North Atlantic sea surface salinity maximum in a global GCM Tangdong Qu International Pacific Research.
Chukchi/Beaufort Seas Surface Wind Climatology, Variability, and Extremes from Reanalysis Data: Xiangdong Zhang, Jeremy Krieger, Paula Moreira,
Dynamical Downscaling of CCSM Using WRF Yang Gao 1, Joshua S. Fu 1, Yun-Fat Lam 1, John Drake 1, Kate Evans 2 1 University of Tennessee, USA 2 Oak Ridge.
Indirect Determination of Surface Heat Fluxes in the Northern Adriatic Sea via the Heat Budget R. P. Signell, A. Russo, J. W. Book, S. Carniel, J. Chiggiato,
Bow Echo Sensitivity to Ambient Moisture and Cold Pool Strength Richard P. James, Paul M. Markowski, and J. Michael Fritsch, 2006: Mon. Wea. Rev., 134,
Ron Kwok Jet Propulsion Laboratory California Institute of Technology Critically Needed: Continued 3-day RADARSAT coverage of the Western Arctic Ocean.
A Regional Ice-Ocean Simulation Of the Barents and Kara Seas W. Paul Budgell Institute of Marine Research and Bjerknes Centre for Climate Research Bergen,
The Role of Surface Freshwater Flux Boundary Conditions in Arctic Ocean/Sea-Ice Models EGU General Assembly, Nice, April 2004 Matthias Prange and Rüdiger.
Challenges in Modeling Global Sea Ice in a Changing Environment Marika M Holland National Center for Atmospheric Research Marika M Holland National Center.
A Multi-Sensor, Multi-Parameter Approach to Studying Sea Ice: A Case-Study with EOS Data Walt Meier 2 March 2005IGOS Cryosphere Theme Workshop.
Ankur R Desai, UW-Madison AGU Fall 2007 B41F-03 Ankur Desai AOS 405, Spring 2010 Why Has Wind.
Regional Climate Modeling in the Source Region of Yellow River with complex topography using the RegCM3: Model validation Pinhong Hui, Jianping Tang School.
EGU 2012, Kristine S. Madsen, High resolution modelling of the decreasing Arctic sea ice Kristine S. Madsen, T.A.S. Rasmussen, J. Blüthgen and.
Ensemble-variational sea ice data assimilation Anna Shlyaeva, Mark Buehner, Alain Caya, Data Assimilation and Satellite Meteorology Research Jean-Francois.
Jonathan Whitefield Peter Winsor Tom Weingartner USING IN SITU OBSERVATIONS TO VALIDATE THE PERFORMANCE OF ECCO IN THE ARCTIC SEAS.
Opening and closing of the Storfjorden polynya. Coastal Polynya Skogseth (2003), PhD thesis Storfjorden is estimated to supply 5-10% of the newly formed.
Climate Forecasting Unit Arctic Sea Ice Predictability and Prediction on Seasonal-to- Decadal Timescale Virginie Guemas, Edward Blanchard-Wrigglesworth,
Dr. Frank Herr Ocean Battlespace Sensing S&T Department Head Dr. Scott L. Harper Program Officer Team Lead, 322AGP Dr. Martin O. Jeffries Program Officer.
“ New Ocean Circulation Patterns from Combined Drifter and Satellite Data ” Peter Niiler Scripps Institution of Oceanography with original material from.
Russ Bullock 11 th Annual CMAS Conference October 17, 2012 Development of Methodology to Downscale Global Climate Fields to 12km Resolution.
“ Combining Ocean Velocity Observations and Altimeter Data for OGCM Verification ” Peter Niiler Scripps Institution of Oceanography with original material.
1.Introduction 2.Description of model 3.Experimental design 4.Ocean ciruculation on an aquaplanet represented in the model depth latitude depth latitude.
1 1. Motivation and initial objectives 2. Overview of FOAM system 3. Data to be assimilated 4. Sea-ice model and configurations being used 5. Assimilation.
Improving the modeling of Arctic sea-ice dynamics through high-resolution satellite data retrievals Principal Investigator: Ronald Kwok (334) Patrick Heimbach,
1 Climate Ensemble Simulations and Projections for Vietnam using PRECIS Model Presented by Hiep Van Nguyen Main contributors: Mai Van Khiem, Tran Thuc,
Stratification on the Eastern Bering Sea Shelf, Revisited C. Ladd 1, G. Hunt 2, F. Mueter 3, C. Mordy 2, and P. Stabeno 1 1 Pacific Marine Environmental.
ECCO (Estimating the Circulation and Climate of the Ocean) Initially funded by NOPP (National Oceanographic Partnership Program) to demonstrate practicality.
Steffen M. Olsen, DMI, Copenhagen DK Center for Ocean and Ice Interpretation of simulated exchange across the Iceland Faroe Ridge in a global.
WHOI -- AOMIP 10/20/2009 Formation of the Arctic Upper Halocline in a Coupled Ocean and Sea-ice Model Nguyen, An T., D. Menemenlis, R. Kwok, Jet Propulsion.
Evaluation of climate models, Attribution of climate change IPCC Chpts 7,8 and 12. John F B Mitchell Hadley Centre How well do models simulate present.
Modern Era Retrospective-analysis for Research and Applications: Introduction to NASA’s Modern Era Retrospective-analysis for Research and Applications:
GP33A-06 / Fall AGU Meeting, San Francisco, December 2004 Magnetic signals generated by the ocean circulation and their variability. Manoj,
2nd GODAE Observing System Evaluation Workshop - June Ocean state estimates from the observations Contributions and complementarities of Argo,
AOMIP workshop #12 Jan 14-16, 2009 WHOI Improved modeling of the Arctic halocline with a sub-grid-scale brine rejection parameterization Nguyen, An T.,
“Very high resolution global ocean and Arctic ocean-ice models being developed for climate study” by Albert Semtner Extremely high resolution is required.
For more information about this poster please contact Gerard Devine, School of Earth and Environment, Environment, University of Leeds, Leeds, LS2 9JT.
WP3.10 Cross-assessment of CCI-ECVs over the Mediterranean domain.
Assessment of CCI Glacier and CCI Land cover data for hydrological modeling of the Arctic ocean drainage basin David Gustafsson, Kristina Isberg, Jörgen.
Scientific Advisory Committee Meeting, November 25-26, 2002 Dr. Daniela Jacob Regional climate modelling Daniela Jacob.
UK Sea Ice Meeting, 8-9th Sept 2005
AOMIP status Experiments 1. Season Cycle 2. Coordinated - Spinup Coordinated - Analysis Coordinated 100-Year Run.
Assimilation of Sea Ice Concentration Observations in a Coupled Ocean-Sea Ice Model using the Adjoint Method.
Observed and projected changes to the ocean, Part 1 Climate models, pitfalls and historical observations (Chapter 3, Ganachaud et al., 2012) Alex Sen Gupta.
Assessment of the ECCO2 optimized solution in the Arctic An T. Nguyen, R. Kwok, D. Menemenlis JPL/Caltech ECCO-2 Team Meeting, MIT Sep 23-24, 2008.
A Green’s function optimization on the CS510 grid - develop and calibrate model configuration and parameterizations - experiment with cost function terms.
Adjoint modeling in cryosphere Patrick Heimbach MIT/EAPS, Cambridge, MA, USA
Evaluation of Upper Ocean Mixing Parameterizations S. Daniel Jacob 1, Lynn K. Shay 2 and George R. Halliwell 2 1 GEST, UMBC/ NASA GSFC, Greenbelt, MD
Arctic climate simulations by coupled models - an overview - Annette Rinke and Klaus Dethloff Alfred Wegener Institute for Polar and Marine Research, Research.
Tropical Atlantic SST in coupled models; sensitivity to vertical mixing Wilco Hazeleger Rein Haarsma KNMI Oceanographic Research The Netherlands.
15 Annual AOMIP Meeting. WHOI, 1- 4 November 2011 Numerical modeling of the Atlantic Water distribution in the upper Arctic Ocean: Sensitivity studies.
The effect of tides on the hydrophysical fields in the NEMO-shelf Arctic Ocean model. Maria Luneva National Oceanography Centre, Liverpool 2011 AOMIP meeting.
Alexandra Jahn 1, Bruno Tremblay 1,3, Marika Holland 2, Robert Newton 3, Lawrence Mysak 1 1 McGill University, Montreal, Canada 2 NCAR, Boulder, USA 3.
DLR, 2016 Seasonal forecasting Omar Bellprat, Francois Massonet, Chloé Prodhomme, Virginie Guemas, Francisco Doblas-Reyes (BSC) Mathias Gröner.
Toward improved understanding of mass and property fluxes through Bering Strait Jaclyn Clement Kinney 1, Wieslaw Maslowski 1, Mike Steele 2, Jinlun Zhang.
Acknowledgments: The study is funded by the Deep-C consortium and a grant from BOEM. Model experiments were performed at the Navy DoD HPC, NRL SSC and.
Seasonal Variations of MOC in the South Atlantic from Observations and Numerical Models Shenfu Dong CIMAS, University of Miami, and NOAA/AOML Coauthors:
Towards the utilization of GHRSST data for improving estimates of the global ocean circulation Dimitris Menemenlis 1, Hong Zhang 1, Gael Forget 2, Patrick.
Climate System Research Center, Geosciences Alan Condron Peter Winsor, Chris Hill and Dimitris Menemenlis Changes in the Arctic freshwater budget in response.
Impact of sea ice dynamics on the Arctic climate variability – a model study H.E. Markus Meier, Sebastian Mårtensson and Per Pemberton Swedish.
Intercomparison of ocean circulation in regional Arctic Ocean models at increasing spatial resolution – Preliminary Results Robert Osinski, Wieslaw Maslowski.
Wind-driven halocline variability in the western Arctic Ocean
Nguyen, An T. , D. Menemenlis, R
Coupled atmosphere-ocean simulation on hurricane forecast
Double tropopauses during idealized baroclinic life cycles
Mark A. Bourassa and Qi Shi
Xuezhu Wang, Qiang Wang, Sergey Danilov, Thomas Jung,
Presentation transcript:

Sea Ice Deformation Studies and Model Development Gunnar Spreen, Han Tran, Ron Kwok, and Dimitris Menemenlis

Importance of sea ice deformation for model mass balance Overview Importance of sea ice deformation for model mass balance Sea ice deformation fields in model and satellite observations Regional Arctic setup with optimized parameters from 18 km integration (Green's function approach):  Nguyen et al. (2011), JGR Regional Arctic solution 4.5, 9 and 18 km horizontal grid spacing. Time: 1992 – 2011 (20 years) Surface boundary conditions: JRA-25 Viscous plastic dynamics [Hibler, 1979] Regional Arctic solution

Model Sensitivity to Ice Strength Pmax Change of sea ice strength parameter P* to simulate changes in ice deformation 18 km grid spacing 1992–2009 Ice strength 3 experiments Baseline P*: optimized Arctic solution (Nguyen et al., 2011; P*=2.3·104) 0.7 P*: P* reduced by 30% (P* =1.6·104) 0.3 P*: P* reduced by 70% (P* =0.7·104) Sensitivity Experiment: Model Domain Arctic Basin

Sea Ice Deformation Rate P* 0.7 P* 0.3 P* Seasonal Cycle Sea Ice Deformation Rate Difference: experiment - baseline (0.7 P*) – P* (0.3 P*) – P* Deformation rate As expected sea ice deformation (and speed) increases for lower ice strength

Influence on Sea Ice Volume P* 0.7 P* 0.3 P* Arctic Basin Sea Ice Volume 1992 1995 1998 2001 2004 2007 2010 1 2 3 x 104 km3 Difference to Baseline km3 (0.7 P*) – P* 8000 (0.3 P*) – P* 6000 4000 2000 92 94 96 98 00 02 04 06 08 10 Total sea ice volume within the Arctic Basin is higher for weaker ice, i.e., higher deformation rates.

Arctic Basin Ice Volume Export Sea Ice Volume Export Arctic Basin Ice Volume Export Sea ice volume export out of the Arctic Basin (combined Fram Strait, CAA, Bering Strait, etc.) all three experiments are highly correlated Ice Volume Export: Difference to baseline (0.7 P*) – P* (0.3 P*) – P* P* 0.7 P* 0.3 P* However, the weaker ice experiments show an enhanced seasonal cycle and “0.3 P*” a negative bias of 43 km3/month or ~20%.

Influence on Mixed Layer Depth Seasonal Cycle Mixed Layer Depth (92-09) Standard Deviation Mixed Layer Depth P* 0.7 P* 0.3 P* P* 0.7 P* 0.3 P* The winter mixed layer depth increases for higher deformation rates, i.e., lower ice strength. Also the variability of the mixed layer depth increases.

Conclusions Model Sea Ice Strength Sensitivity Sea strength and ice deformation processes strongly influence the sea ice mass balance in a coupled ocean-sea ice model using a viscous-plastic rheology. A new, higher equilibrium ice mass is established Sea ice export shows stronger seasonal cycle Reduced sea ice export for very low ice strength (probably caused by thicker ice) Deeper winter-time mixed layer depth Changes can be attributed to enhanced sea ice dynamics Sea ice deformation processes should be adequately represented in the model for realistic sea ice mass balance simulations → next topic

Comparison to RGPS Satellite Data RADARSAT Synthetic Aperture Radar (SAR) data Spatial cross-correlation of patterns → ice movement divergence vorticity shear multiyear ice fraction 20-23 Feb. 2005 1996 Calculate strain rates (divergence, vorticity, shear) from Lagrangian cells 3-daily on 12.5 km grid

RGPS and Model Sea Ice Deformation Example: November 1999 black line: perennial ice Sea ice deformation parameters: divergence, vorticity and shear Number and concentration of linear kinematic features (LKF) increase with decreasing model grid spacing.

Difference in Deformation Rate RGPS data reconstructed from model output RGPS D is by about 50% higher Model and observations highly correlated and show similar trends

Difference in Ice Deformation Distribution Biggest difference between RGPS and model in the seasonal ice zone  suggests thin ice is too strong in model Change of sea ice strength parameterization needed Difference distribution similar for all three model resolution

Way Forward: Material-Point Method Deborah Sulsky, Han Tran, Kara Peterson University New Mexico New sea ice rheology: material-point method Sulsky et al. (2007), JGR Coupled to MITgcm ocean model

Last night’s results 

Conclusions Deformation of sea ice play an important role in viscous- plastic ice models. Small changes in the ice strength change the sea ice mass balance. Compared to RGPS observations, our three model solutions do not adequately reproduce small scale deformation and linear kinematic features (LKFs). Also the overall modeled deformation rate is about 50% lower than the observed one. Increase in model resolution produces a higher density and more localized ice deformation features. A new sea ice rheology might be necessary to reduce differences between modeled and observed ice kinematics.