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,

Slides:



Advertisements
Similar presentations
Met Office Hadley Centre, FitzRoy Road, Exeter, Devon, EX1 3PB United Kingdom Tel: +44 (0) Fax: +44 (0)
Advertisements

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.
Sea Ice Thermodynamics and ITD considerations Marika Holland NCAR.
Daniela Flocco, Daniel Feltham, David Schr  eder Centre for Polar Observation and Modelling University College London.
Climate models in (palaeo-) climatic research How can we use climate models as tools for hypothesis testing in (palaeo-) climatic research and how can.
Climate Forcing and Physical Climate Responses Theory of Climate Climate Change (continued)
Sea Ice Presented by: Dorothy Gurgacz.
Sea-ice & the cryosphere
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,
Challenges in Modeling Global Sea Ice in a Changing Environment Marika M Holland National Center for Atmospheric Research Marika M Holland National Center.
© Crown copyright Met Office UK report for GOVST Matt Martin GOVST-V, Beijing, October 2014.
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,
© Crown copyright /0653 Met Office and the Met Office logo are registered trademarks Met Office Hadley Centre, FitzRoy Road, Exeter, Devon, EX1.
Xingren Wu and Robert Grumbine
An Overview of the UK Met Office Weymouth Bay wind model for the 2012 Summer Olympics Mark Weeks 1. INTRODUCTION In the summer of 2012 a very high resolution.
© Crown copyright 03/2014 Met Office and the Met Office logo are registered trademarks Met Office FitzRoy Road, Exeter, Devon, EX1 3PB United Kingdom Tel:
SMHI in the Arctic Lars Axell Oceanographic Research Unit Swedish Meteorological and Hydrological Institute.
Climate Forecasting Unit Arctic Sea Ice Predictability and Prediction on Seasonal-to- Decadal Timescale Virginie Guemas, Edward Blanchard-Wrigglesworth,
Page 1© Crown copyright 2005 Using metrics to assess ocean and sea ice simulations Helene Banks, Cath Senior, Jonathan Gregory Alison McLaren, Michael.
Operational assimilation of dust optical depth Bruce Ingleby, Yaswant Pradhan and Malcolm Brooks © Crown copyright 08/2013 Met Office and the Met Office.
Status of the Sea Ice Model Testing of CICE4.0 in the coupled model context is underway Includes numerous SE improvements, improved ridging formulation,
The dynamic-thermodynamic sea ice module in the Bergen Climate Model Helge Drange and Mats Bentsen Nansen Environmental and Remote Sensing Center Bjerknes.
Page 1© Crown copyright 2004 The Hadley Centre The forcing of sea ice characteristics by the NAO in HadGEM1 UK Sea Ice Workshop, 9 September 2005 Chris.
Hadley Centre Evaluating modelled and observed trends and variability in ocean heat content Jonathan Gregory 1,2, Helene Banks 1, Peter Stott 1, Jason.
Mixed Layer Ocean Model: Model Physics and Climate
ESTIMATION OF SOLAR RADIATIVE IMPACT DUE TO BIOMASS BURNING OVER THE AFRICAN CONTINENT Y. Govaerts (1), G. Myhre (2), J. M. Haywood (3), T. K. Berntsen.
Arctic Sea Ice Mass Budgets We report, you decide. Marika Holland NCAR.
Contribution of MPI to CLIMARES Erich Roeckner, Dirk Notz Max Planck Institute for Meteorology, Hamburg.
© Crown copyright Met Office Uncertainties in the Development of Climate Scenarios Climate Data Analysis for Crop Modelling workshop Kasetsart University,
Physical Feedbacks Mike Steele Polar Science Center University of Washington Steve Vavrus Center for Climatic Research University of Wisconsin Co-Chairs:
Current state of ECHAM5/NEMO coupled model Wonsun Park, Noel Keenlyside, Mojib Latif (IFM-GEOMAR) René Redler (NEC C&C Research Laboratories) DRAKKAR meeting.
The CHIME coupled climate model Alex Megann, SOC 26 January 2005 (with Adrian New, Bablu Sinha, SOC; Shan Sun, NASA GISS; Rainer Bleck, LANL)  Introduction.
Page 1© Crown copyright 2005 Met Office plans for sea ice model development within a flexible modelling framework Helene Banks Martin Best, Ann Keen and.
Atmospheric Circulation Response to Future Arctic Sea Ice Loss Clara Deser, Michael Alexander and Robert Tomas.
Coupled HYCOM in CESM and ESPC Alexandra Bozec, Eric P. Chassignet.
Impact of sea ice dynamics on the Arctic climate variability – a model study H.E. Markus Meier, Sebastian Mårtensson and Per Pemberton Swedish.
Status of CAM, March 2004 Phil Rasch. Differences between CAM2 and CAM3 (standard physics version) Separate liquid and ice phases Advection, sedimentation.
Seasonal Arctic heat budget in CMIP5 models
Quantifying rapid adjustments using radiative kernels
Paleoclimate Models (Chapter 12).
Global Impacts and Consequences of Climate Change
Yinghui Liu1, Jeff Key2, and Xuanji Wang1
Climate Change Climate change scenarios of the
Mid Term II Review.
FORECASTING HEATWAVE, DROUGHT, FLOOD and FROST DURATION Bernd Becker
Schematic framework of anthropogenic climate change drivers, impacts and responses to climate change, and their linkages (IPCC, 2007; 2014).
Evaluation of a scheme representing cloud inhomogeneous structure in the Australian Community Climate and Earth System Simulator (ACCESS)
Nguyen, An T. , D. Menemenlis, R
Intelligent pricing plans for de-icing companies
Radiation Balance and Feedbacks
Sample Global Climate Change Issues
The ACCIMA Project - Coupled Modeling of the High Southern Latitudes
GFDL Climate Model Status and Plans for Product Generation
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing
Chapter 3 Atmospheric Radiative Transfer and Climate
Weather forecasting in a coupled world
Jeff Key*, Aaron Letterly+, Yinghui Liu+
Climate Science Centre, CSIRO Ocean and Atmosphere
Antarctic Sea Ice Variability in the CCSM2 Control Simulation
WP3: Linkages of Arctic climate changes to lower latitudes
Large-scale Modeling of Primary Production and Related Dimethylsulfide (DMS) Production in the Sea Ice Environment Clara Deal and Meibing Jin, International.
Modeling the Atmos.-Ocean System
Tore Furevik Geophysical Institute, University of Bergen
4th WGNE Workshop on Systematic Errors in Weather and Climate Models
Climate change mash-ups
A Model View of Arctic Sea Ice During Summer 2007 and Beyond
Surface Fluxes and Model Error An introduction
Fig. 2. Annual sea ice extent (107 km2) cycle for all 30 ensemble members. Each panel shows the five orbital simulations with the same pCO2 and minimum.
ROMS+WRF+Budgell Jeff Willison1, Ruoying He1, Michael S. Dinniman2, Xiaojun Yuan3 1North Carolina State University 2Old Dominion University 3Lamont-Doherty.
Presentation transcript:

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, and Chris Harris 5.0 2. Model 3. What parameters can/should we adjust? 1. Motivation Coupler Test sensitivity to downward SW radiation and surface effects by adjusting albedo. αb: Bare ice albedo. αc, αm: Cold snow and melting snow albedos. Parameterisations for: αc to αm transition (function of T). Effect of meltponds on αb (function of T). Scattering effects in zero-layer model (Semtner, 1976). Test sensitivity to ocean-ice heat transfer by adjusting transfer coefficient, cH (McPhee, 1992). Ice salinity, S, which affects brine rejection, ocean stability, and advection of oceanic heat to ice base. Ice dynamics/ridging: test sensitivity to ridging parameter μrdg (Hunke, 2010) Snow and ice thermodynamics: test sensitivity to thermal conductivities of ice (κi) and snow (κs). Also tested sensitivity to resolutions of ocean-ice and atmosphere-surface models. Arctic summer sea ice extent and thickness too low in previous model version. Desire to improve this by adjusting sea ice parameters within the range of observational uncertainty. Desire to inform future model enhancements through studying sensitivity to parameter perturbations. Extent seasonal cycle Volume seasonal cycle CICE The Los Alamos Sea Ice Model Surface Ocean Atmosphere Sea ice HadISST Sept ice concentration Model Sept ice concentration Model Model HadISST ± 20% PIOMAS HadGEM3: Fully-coupled global atmosphere-ocean-ice model. Sea ice component is Los Alamos CICE model. CICE currently run in zero-layer configuration. Atmosphere-surface and ocean-ice components run at different resolutions. (Model output plotted here is for the mean of years 16-30 of a 30-year coupled HadGEM3 run with constant year-2000 greenhouse gas concentrations and aerosol emissions. HadISST and PIOMAS data are means for the period 1995-2004.) 4. Model experiments (all parameters defined in box 3) Parameters perturbed separately and in combination. Selected experiments: Control experiment: αb = 0.61 αc = 0.80 αm = 0.65 : κi = 2.09 Wm-1K-1 κs = 0.31 Wm-1K-1 Ocean-ice res: ORCA1 (~1°) Atmosphere res: N96 (~130 km) αb = 0.58 αc = 0.85; αm = 0.72 cH = 0.003 S = 8 ppt All experiments ran for 30 years with year-2000 greenhouse gas concentrations μrdg = 3 m1/2 κi = 2.63 Wm-1s-1 κi = 2.63 Wm-1s-1; κs = 0.50 Wm-1s-1 cH = 0.006 S = 4 ppt μrdg = 4 m1/2 Colour-highlighted experiments are discussed in box 6. 6. Sensitivity to sea ice parameters 5. Sensitivity to ocean-ice model resolution Arctic extent seasonal cycle Arctic volume seasonal cycle March ice concentration All experiments performed at ORCA1 (~1°) ocean-ice model resolution. Arctic extent seasonal cycle Arctic volume seasonal cycle HadISST ORCA1 (~1°) ORCA0.25 (~0.25°) PIOMAS Control Change αc, αm Change κi only Change κi, κs Control Change αc, αm Change κi only Change κi, κs Sept Arctic ice concentration HadISST Control Change αc, αm Change κi, κs HadISST±20% PIOMAS HadISST±20% ORCA1 (~1°) ORCA0.25 (~0.25°) ORCA1 (~1°) ORCA0.25 (~0.25°) Antarctic extent seasonal cycle Antarctic extent seasonal cycle Antarctic volume seasonal cycle Antarctic extent seasonal cycle Antarctic volume seasonal cycle Increased ocean-ice model resolution from ORCA1 (~1°) to ORCA025 (~0.25°) Leads to disappearance of winter ice in Labrador Sea, and so to closer agreement with HadISST Linked to increased SSTs. Control Change αc, αm Change κi, κs Arctic sea ice found to be most sensitive to snow albedo (αc, αm – see box 3 above) and ice and snow thermal conductivities (κi, κs). Sensitivity to snow albedo is linked to top melt in early summer before snow layer has melted completely. Sensitivity to conductivity is linked to ocean-atmosphere heat flux through ice in autumn, and basal ice growth. Antarctic sea ice more sensitive to changes in atmosphere and ocean, but is sensitive to ice salinity (not shown here). Sept Arctic ice thickness In the Antarctic, increased resolution leads to exacerbation of existing warm bias in ocean. This causes a large negative bias in sea ice extent and volume. Some model development work is focussed on solving this Southern Ocean warm bias. Annual mean SSTs: ORCA025 minus ORCA1. Note increase in Labrador Sea. March Arctic ice thickness metres Last 15 years of 30-year simulation are used in all analysis. 7. Effect of combining the perturbations and sensitivity to atmosphere resolution Ran at two atmosphere model resolutions: N96 (~130 km) N216 (~60 km) Combined-N96, compared to Control-N96: Summer (Sept) Arctic ice concentration has increased, although now larger than HadISST. Arctic ice now thicker in all seasons (but volume now greater than PIOMAS). Antarctic ice extent too small because of Southern Ocean warm bias (see box 5). N216 (~60 km), compared to N96 (~130 km): Arctic ice thinner and less extensive because of increased poleward heat transport. Antarctic ice shows similar bias, because of clouds / SW radiation effects. 8. Summary, conclusions, and future work Performed simulations with a coupled atmosphere-ocean-ice model. Perturbed various sea ice parameters within the range of observational uncertainty. Also studied sensitivity of sea ice to changes in atmosphere and ocean. Arctic sea ice most sensitive to snow albedo, and to ice and snow thermal conductivities. Antarctic sea ice most sensitive to salinity, and to changes in atmosphere and ocean. Both Arctic and Antarctic ice are sensitive to ocean-ice and atmosphere model resolutions. Paper (Rae et al., 2013) submitted to Ocean Modelling. Parameter changes reflect future model enhancements (e.g., improved albedo scheme, impact of meltponds on albedo, multi-layer ice model). Perturbed the following parameters in combination: αc: 0.80  0.85 αm: 0.65  0.72 Scattering fraction: 0.068  0.12 S: 4  8 ppt μrdg: 4  3 m1/2 κi: 2.09  2.63 Wm-1K-1 κs: 0.31  0.50 Wm-1K-1 Changes to atmospheric physics and dynamics, and to ocean model, also included. ORCA-025 (0.25°) ocean-ice resolution. Arctic extent seasonal cycle Arctic extent seasonal cycle Arctic volume seasonal cycle Arctic volume seasonal cycle HadISST±20% PIOMAS Control-N96 Combined-N96 Combined-N216 Control-N96 Combined-N96 Combined-N216 Antarctic extent seasonal cycle Antarctic volume seasonal cycle HadISST Control-N96 Combined-N96 Combined-N216 Control-N96 Combined-N96 Combined-N216 Sept Arctic ice concentration Sept Arctic ice thickness References Hunke, E.C., 2010, Thickness sensitivities in the CICE sea ice model, Ocean Modelling, 34, 137-149. McPhee, M.G., 1992, Turbulent heat flux in the upper ocean under the sea ice, J.Geophys.Res., 97 (C4), 5365-5379. Semtner, A.J., 1976, A model for the thermodynamic growth of sea ice in numerical investigations of climate, J.Phys.Oceanog., 6, 379-389. March Arctic ice concentration March Arctic ice thickness metres Met Office Hadley Centre, FitzRoy Road, Exeter, Devon, EX1 3PB United Kingdom Tel: +44 1392 884442 Fax: +44 1392 885681 Email: jamie.rae@metoffice.gov.uk © Crown copyright 07/0XXX Met Office and the Met Office logo are registered trademarks