Contribution of MPI to CLIMARES Erich Roeckner, Dirk Notz Max Planck Institute for Meteorology, Hamburg.

Slides:

Advertisements

Similar presentations

Basics of numerical oceanic and coupled modelling Antonio Navarra Istituto Nazionale di Geofisica e Vulcanologia Italy Simon Mason Scripps Institution.

Advertisements

Coupled modelling - snow Patrick Samuelsson Rossby Centre, SMHI

Coupled & Ocean MPI-M Johann Jungclaus Max-Planck-Institut für Meteorologie.

Global Change Research in Belgium Guy P. Brasseur Max Planck Institute for Meteorology Chair, International Geosphere Biosphere Programme (IGBP)

Global, Regional, and Urban Climate Effects of Air Pollutants Mark Z. Jacobson Dept. of Civil & Environmental Engineering Stanford University.

Implementation of lakes in the Canadian Regional Climate Model (CRCM): towards CRCM 5 Andrey Martynov, Rene Laprise, Laxmi Sushama University of Quebec.

Alan Robock Department of Environmental Sciences Rutgers University, New Brunswick, New Jersey USA

Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London.

Indicators for policy support of atmosphere related environmental problems Robert Koelemeijer National Institute for Public Health and the Environment.

03/06/2015 Modelling of regional CO2 balance Tiina Markkanen with Tuula Aalto, Tea Thum, Jouni Susiluoto and Niina Puttonen.

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.

Climate Forcing and Physical Climate Responses Theory of Climate Climate Change (continued)

Observed Reductions of Surface Solar Radiation at Sites in the United States and Worldwide from 1961 to 1990 Beate Liepert Lamont-Doherty Earth Observatory.

WFM 6311: Climate Risk Management © Dr. Akm Saiful IslamDr. Akm Saiful Islam WFM 6311: Climate Change Risk Management Akm Saiful Islam Lecture-1: Module-1.

Outline Further Reading: Detailed Notes Posted on Class Web Sites Natural Environments: The Atmosphere GG 101 – Spring 2005 Boston University Myneni L31:

REFERENCES Maria Val Martin 1 C. L. Heald 1, J.-F. Lamarque 2, S. Tilmes 2 and L. Emmons 2 1 Colorado State University 2 NCAR.

Sea-ice & the cryosphere

Anthropocene Introduction to Meteorology, spring 2011 Observations –Trace gases –Temperature, land and ocean –Precipitation –Sea level Attribution Models.

Image: NASA ECHAM5/6 projects by Quentin Bourgeois, Junbo Cui, Gabriela Sousa Santos, Tanja Stanelle C2SM’s research group - Isabelle Bey.

Anthropogenic Climate Change The Greenhouse Effect that warms the surface of the Earth occurs because of a few minor constituents of the atmosphere.

4. Models of the climate system. Earth’s Climate System Sun IceOceanLand Sub-surface Earth Atmosphere Climate model components.

ARCTIC SEA ICE COVER September 2005 NASA. SEA ICE EXTENT March 2006 Maximum September 2006 Minimum NEW RECORD! 2006: At or near record minimum in summer.

Climate Response to a very large volcanic eruption: An Earth System Model Approach Claudia Timmreck and Super Volcano Project Group Max Planck Institute.

General comments Need for new observations versus new parameterizations? Should explore what GCOS and others are doing Urgent need for concerted physical.

Climate Change – 1: Background

Ensemble simulations with a Regional Earth System Model of the Arctic

How does it all work? Synthesis of Arctic System Science Discover, clarify, and improve our understanding of linkages, interactions, and feedbacks among.

The Louvain-la-Neuve sea ice model : current status and ongoing developments T. Fichefet, Y. Aksenov, S. Bouillon, A. de Montety, L. Girard, H. Goosse,

Xingren Wu and Robert Grumbine

The Future of Arctic Sea Ice Authors: Wieslaw Maslowski, Jaclyn Clement Kinney, Matthew Higgins, and Andrew Roberts Brian Rosa – Atmospheric Sciences.

CLIMARES, NERSC, October 2009 Arctic climate and future scenarios Ola M. Johannessen and Mats Bentsen Nansen Environmental and Remote Sensing Center.

Bern, 9 November 2007 Annual meeting 2007 Global Warming Lennart Bengtsson Global Warming Professor Lennart Bengtsson Max Planck Institut für Meteorologie,

Helgi Björnsson, Institute of Earth Sciences, University of Iceland, Reykjavik, Iceland Contribution of Icelandic ice caps to sea level rise: trends and.

Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.

SMHI in the Arctic Lars Axell Oceanographic Research Unit Swedish Meteorological and Hydrological Institute.

Outline Further Reading: Detailed Notes Posted on Class Web Sites Natural Environments: The Atmosphere GE 101 – Spring 2007 Boston University Myneni L30:

Clouds, Aerosols and Precipitation GRP Meeting August 2011 Susan C van den Heever Department of Atmospheric Science Colorado State University Fort Collins,

Natural and Anthropogenic Drivers of Arctic Climate Change Gavin Schmidt NASA GISS and Columbia University Jim Hansen, Drew Shindell, David Rind, Ron Miller.

Model and Data Hierarchies for Simulating and Understanding Climate Marco A. Giorgetta Overview of Earth System Modeling and Fluid Dynamical Issue.

The early 20th century warming in the Arctic – A possible mechanism Lennart Bengtsson Max Planck Institute for Meteorology, Hamburg, Germany ESSC, University.

Projection of Global Climate Change. Review of last lecture Rapid increase of greenhouse gases (CO 2, CH 4, N 2 O) since 1750: far exceed pre-industrial.

Status of the Sea Ice Model Testing of CICE4.0 in the coupled model context is underway Includes numerous SE improvements, improved ridging formulation,

ETH On-going and planned projects with ECHAM Martin Wild, Doris Folini, Adeline Bichet, Maria Hakuba, Christoph Schär IACETH.

(Mt/Ag/EnSc/EnSt 404/504 - Global Change) Climate Models (from IPCC WG-I, Chapter 10) Projected Future Changes Primary Source: IPCC WG-I Chapter 10 - Global.

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.

Human fingerprints on our changing climate Neil Leary Changing Planet Study Group June 28 – July 1, 2011 Cooling the Liberal Arts Curriculum A NASA-GCCE.

Research Needs for Decadal to Centennial Climate Prediction: From observations to modelling Julia Slingo, Met Office, Exeter, UK & V. Ramaswamy. GFDL,

The dynamic-thermodynamic sea ice module in the Bergen Climate Model Helge Drange and Mats Bentsen Nansen Environmental and Remote Sensing Center Bjerknes.

LUCID Land-Use and Climate: IDentification of robust impacts Nathalie de Noblet, Andy Pitman

CLIMARES WP 110 Climate model scenarios for the Arctic region for the next decades Current state: Klaus Dethloff, AWI WP Leader: Erich Roeckner, MPI Planing.

The evolution of climate modeling Kevin Hennessy on behalf of CSIRO & the Bureau of Meteorology Tuesday 30 th September 2003 Canberra Short course & Climate.

Scientific Advisory Committee Meeting, November 25-26, 2002 Dr. Daniela Jacob Regional climate modelling Daniela Jacob.

INGV-CMCC contribution to CLIMARES proposal Silvio Gualdi and Elisa Manzini CLIMARES meeting October 2009, Bergen.

1 Hadley Centre for Climate Prediction and Research Vegetation dynamics in simulations of radiatively-forced climate change Richard A. Betts, Chris D.

Modelling the climate system and climate change PRECIS Workshop Tanzania Meteorological Agency, 29 th June – 3 rd July 2015.

The GEOS-5 AOGCM List of co-authors Yury Vikhliaev Max Suarez Michele Rienecker Jelena Marshak, Bin Zhao, Robin Kovack, Yehui Chang, Jossy Jacob, Larry.

Aerosols and climate - a crash course Marianne T. Lund CICERO Nove Mesto 17/9-15.

Climate feedback on the marine carbon cycle in CarboOcean Earth System Models J. Segschneider 1, E. Maier-Reimer 1 L. Bopp 2, J. Orr 2 1 Max-Planck-Institute.

© Crown copyright Met Office Uncertainties in the Development of Climate Scenarios Climate Data Analysis for Crop Modelling workshop Kasetsart University,

Preliminary modeling work simulating N-ICE snow distributions and the associated impacts on: 1)sea ice growth; 2)the formation of melt ponds; and 3)the.

Radiative forcing due to BC on snow and the direct aerosol effect of BC in the Arctic Gunnar Myhre CICERO – Center for International Climate and Environmental.

Chapter 6 Future climate changes Climate system dynamics and modelling Hugues Goosse.

1 MET 112 Global Climate Change MET 112 Global Climate Change - Lecture 12 Future Predictions Eugene Cordero San Jose State University Outline  Scenarios.

Atmospheric Circulation Response to Future Arctic Sea Ice Loss Clara Deser, Michael Alexander and Robert Tomas.

Earth as a System Exploring Connections in Data from 2007 Globe Data Adapted from science/globe-investigations.

Towards development of a Regional Arctic Climate System Model ---

Climate Change Climate change scenarios of the

A sensitivity study of the sea ice simulation in the global coupled climate model, HadGEM3 Jamie Rae, Helene Hewitt, Ann Keen, Jeff Ridley, John Edwards,

Global Climate Modelling Department “Physical Climate System” Mojib Latif, Erich Roeckner and Uwe Mikolajewicz.

Presentation transcript:

Contribution of MPI to CLIMARES Erich Roeckner, Dirk Notz Max Planck Institute for Meteorology, Hamburg

Suggestion: Apply procedure analogous to that of the FP6 ENSEMBLES project ‘Stream 1‘: Global climate simulations to be done for the IPCC AR5 (data available for impact studies by the end of year 2010) ‘Stream 2‘ Additional simulations with improved model - focus on sea ice (data available by the end of year 2011)

Model to be used for stream1 Atmosphere: ECHAM6 (T159L95) including the stratosphere (top at 80km height) Ocean: MPI-OM (0.4°,L80) Sea ice: dynamic/thermodynamic (zero-layer) Aerosols: interactive or prescribed (not yet decided) Carbon cycle: included

The IPCC AR5 Earth System Model ECHam6 (Roeckner et al., 2003), interactive runoff and glacier calving scheme. Land surface JSBACH (Raddatz et al., 2007), Dynamic Vegetation (Brovkin et al., 2009) New Radiation Resolution: T63L47 and T159/L95 OASIS 3.0 coupler MPIOM (Marsland et al., 2003), C-Grid, z-level, partial cells, BBL parameterization Hibler-type sea ice model incl. snow and fractional ice cover Conformal mapping grid: Tri-polar: Resolution: 1°L40, 0.4°L80 Ocean biogeochemistry module HAMOCC5 (Wetzel et al., 2007)

Model experiments (stream 1 = AR5) Hindcasts (1960 to 2005) using observed GHG and aerosol concentrations (or emissions) Forecasts until 2035 (RCP4.5*) starting from different observed (assimilated) ocean initial states Projections until 2100 and beyond (”centennial”) Number of realizations envisaged: ≥ 5 * Representative concentration pathways reaching 4.5 W/m 2 radiative forcing by year 2100

Model experiments (stream 2) Repeat some of the stream 1 simulations with an updated model, including new components Multi-layer sea-ice model (see contribution D. Notz) New parameterization of sea-ice albedo (Pedersen et al. JGR 2009)

New sea-ice model An improved representation of first- year sea-ice formation An improved representation of salt fluxes from ice Multi-layer, multi-category sea-ice thermodynamics Improved albedo scheme (Pedersen et al., 2009) At MPI, we are currently developing a new sea-ice model. It will include:

Sea-ice albedo defined separately for snow on ice (depends on snow aging) bare ice (function of ice thickness) melt ponds (depth, fractional area for FYI, MYI) and specified differently for direct, diffuse, visible, near-infrared radiation Few results from an earlier model version...

Annual cycle of Arctic sea-ice fractions ECHAM5/MPI-OM (T31L19) Snow on ice ponds bare ice

Simulated (min, max, mean) and observed melt pond fractions (f) obs1 for whole Arctic (1998)... SSM/I (IARC) obs2 for Beaufort and Chukchi Seas (Tschudi et al, 2004)

Mean Arctic surface albedo (including sea-ice and leads) Obs: Laine JGR 2004 (AVHRR ) %

Expected outcome in climate change experiments (hypothesis to be checked) Enhanced sea-ice albedo feedback as a result of extended melt ponds simulated (and observed) on first year ice (FYI) Climate warming ==> MYI decreases, FYI increases ==> melt pond area increases ==> summer albedo decreases ==> enhanced absorption of sunlight ==> further warming

Planned work within WP 110 Simulations with the new coupled model will be used within WP 110 to provide all partners with ● Distribution of ice thickness and ice concentration throughout the entire Arctic Ocean ● Uncertainty ranges of future sea-ice evolution ● Impact of changes in future anthropogenic GHG emissions on the expected sea-ice evolution ● Future changes within the Arctic climate system (Air and water temperature, precipitation, wave patterns (-> coastal erosion), changes in storm activity etc.) ● Manpower: 1 Post Doc for 2 years and 1 PhD for 3 years