Drowning by Numbers: Progress Towards CCSM 4.0 David Bailey, NCAR.

Slides:

Advertisements

Similar presentations

Simulating cloud-microphysical processes in CRCM5 Ping Du, Éric Girard, Jean-Pierre Blanchet.

Advertisements

A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.

Ocean Color remote sensing at high latitudes: Impact of sea ice on the retrieval of bio- optical properties of water surface Simon Bélanger 1 Jens Ehn.

Accelerating Change in the Arctic? Perspectives from Observations and Global Climate Models David Lawrence NCAR With contributions from Marika Holland,

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

Collaborative Research on Sunlight and the Arctic Atmosphere-Ice-Ocean System (AIOS) Hajo Eicken Univ. of Alaska Fairbanks Ron Lindsay Univ. of Washington.

Comparison of the CAM and RRTMG Radiation Schemes in WRF and coupled WRF-CMAQ models Aijun Xiu, Zac Adelman, Frank Binkowski, Adel Hanna, Limei Ran Center.

Development of the Regional Arctic Climate System Model (RACM) --- Department of Civil and Environmental Engineering University of Washington Dec, 2009.

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.

A Coupled Ice-Ocean Model Based on ROMS/TOMS 2.0 W. Paul Budgell Institute of Marine Research and Bjerknes Centre for Climate Research Bergen, Norway Terrain-Following.

Air Quality-Climate Interactions Aijun Xiu Carolina Environmental Program.

Challenges in Modeling Global Sea Ice in a Changing Environment Marika M Holland National Center for Atmospheric Research Marika M Holland National Center.

1 An Overview of the NARCCAP WRF Simulations L. Ruby Leung Pacific Northwest National Laboratory NARCCAP Users Meeting NCAR, Boulder, CO April ,

MODELING OF COLD SEASON PROCESSES Snow Ablation and Accumulation Frozen Ground Processes.

Ensemble simulations with a Regional Earth System Model of the Arctic

Black Carbon in Snow: Treatment and Results Mark Flanner 1 Charlie Zender 2 Jim Randerson 2 Phil Rasch 1 1 NCAR 2 University of California at Irvine.

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,

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

Climate Models: Everything You Ever Wanted to Know, Ask, and Teach Randy Russell and Lisa Gardiner Spark – science education at NCAR All materials from.

Toward GELATO6 in EC-Earth3 V. Guemas, D. Salas-Mélia, M. Chevallier

Thermohaline Circulation in the Coupled GISS/HYCOM Climate Simulation Shan Sun NASA/GISS Rainer Bleck Los Alamos National Laboratory.

An empirical formulation of soil ice fraction based on in situ observations Mark Decker, Xubin Zeng Department of Atmospheric Sciences, the University.

Atmospheric TU Delft Stephan de Roode, Harm Jonker clouds, climate and weather air quality in the urban environmentenergy.

Development of the Regional Arctic Climate System Model (RACM) --- Initial Implementation and Simulation of VIC within CCSM System Department of Civil.

6-month Plan: May-Oct 2013 RASM 5 th Workshop Seattle 04/13 red == high priority tasks.

Lessons learned from building and managing the Community Climate System Model David Bailey PCWG liaison (NCAR) Marika Holland PCWG co-chair (NCAR) Elizabeth.

Land Surface Processes in Global Climate Models (1)

Coupling of the Common Land Model (CLM) to RegCM in a Simulation over East Asia Allison Steiner, Bill Chameides, Bob Dickinson Georgia Institute of Technology.

Status report from the Lead Centre for Surface Processes and Assimilation E. Rodríguez-Camino (INM) and S. Gollvik (SMHI)

Future Climate Projections. Lewis Richardson ( ) In the 1920s, he proposed solving the weather prediction equations using numerical methods. Worked.

Improve Noah snow model treatment based on SNOTEL data Michael Barlage, Fei Chen, Mukul Tewari, and Kyoko Ikeda.

Lessons from ARCSyM David Bailey 1 and John Cassano 2 Amanda Lynch, William Chapman, John Walsh, Gunter Weller, Wanli Wu, Andrew Slater, Richard Cullather,

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

Erik Crosman 1, John Horel 1, Chris Foster 1, Erik Neemann 1 1 University of Utah Department of Atmospheric Sciences Toward Improved NWP Simulations of.

Seasonal albedo and snow cover evolution of CMIP5 models in boreal forest regions Chad Thackeray CanSISE East Meeting July 25, 2014.

Problems modeling CAPs Clouds Type (ice or liquid) Extent Duration Winds Too strong in surface layer Over- dispersive, end CAP too soon Proposed possible.

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

Seasonal Cycle of Climate Feedbacks in the NCAR CCSM3.0 Patrick Taylor CLARREO Science Team Meeting July 7, 2010 Patrick Taylor CLARREO Science Team Meeting.

Towards development of a Regional Arctic Climate System Model --- Coupling WRF with the Variable Infiltration Capacity land model via a flux coupler Chunmei.

Features and performance of the NCAR Community Land Model (CLM): Permafrost, snow, and hydrology David Lawrence NCAR / CGD Boulder, CO.

Arctic Sea Ice – Now and in the Future. J. Stroeve National Snow and Ice Data Center (NSIDC), Cooperative Institute for Research in Environmental Sciences.

Changing the albedo in CSIM4 Julie Schramm CCSM Software Engineering Group

Mixed Layer Ocean Model: Model Physics and Climate

Arctic Sea Ice Mass Budgets We report, you decide. Marika Holland NCAR.

Progress of operational processing chain for sea ice albedo and melt pond fraction L. Istomina, G. Heygster.

An Interactive Aerosol-Climate Model based on CAM/CCSM: Progress and challenging issues Chien Wang and Dongchul Kim (MIT) Annica Ekman (U. Stockholm) Mary.

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

Modeling the Model The Semantics of the CCSM4 Sea Ice Model Tetherless World Constellation Rensselaer Polytechnic Institute.

The Sea Ice-Albedo Feedback in a Warming Climate: Albedos from Today and Reflections on Tomorrow Don Perovich 1 and Tom Grenfell 2 1 Cold Regions Research.

Observational Constraints on Aerosol Deposition and Optical Depth Mark Flanner 1 Phil Rasch 1 Jim Randerson 2 Joe McConnell 3 Tami Bond 4 1 NCAR 2 University.

Climatic implications of changes in O 3 Loretta J. Mickley, Daniel J. Jacob Harvard University David Rind Goddard Institute for Space Studies How well.

Ocean Climate Simulations with Uncoupled HYCOM and Fully Coupled CCSM3/HYCOM Jianjun Yin and Eric Chassignet Center for Ocean-Atmospheric Prediction Studies.

Snow measurements during N_ICE JC Gallet 1, G. Liston 2 and S. Gerland 1 1 Norwegian Polar Institute, Tromsø, Norway 2 Colorado State University 17 November.

Update on the 2-moment stratiform cloud microphysics scheme in CAM Hugh Morrison and Andrew Gettelman National Center for Atmospheric Research CCSM Atmospheric.

Update on progress with the implementation of a new two-moment microphysics scheme: Model description and single-column tests Hugh Morrison, Andrew Gettelman,

Nansen Environmental and Remote Sensing Center Modifications of the MICOM version used in the Bergen Climate Model Mats Bentsen and Helge Drange Nansen.

Sea Ice, Solar Radiation, and SH High-latitude Climate Sensitivity Alex Hall UCLA Department of Atmospheric and Oceanic Sciences SOWG meeting January 13-14,

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.

The Solar Radiation Budget, and High-latitude Climate Sensitivity Alex Hall UCLA Department of Atmospheric and Oceanic Sciences University of Arizona October.

Development of the Regional Arctic Climate System Model (RACM) --- Department of Civil and Environmental Engineering University of Washington May, 2010.

Slide 1 Robin Hogan, APRIL-CLARA-DORSY meeting 2016 ©ECMWF Towards a fast shortwave radiance forward model for exploiting MSI measurements Robin Hogan.

Coupling ROMS and CSIM in the Okhotsk Sea Rebecca Zanzig University of Washington November 7, 2006.

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

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,

Single column models Gunilla Svensson and Frank Kauker

October 23-26, 2012: AOMIP/FAMOS meetings

Performance of the VIC land surface model in coupled simulations

Climate Modeling In-Class Discussion: Radiation & Mars.

Presentation transcript:

Drowning by Numbers: Progress Towards CCSM 4.0 David Bailey, NCAR

9/30/08! PCWG plans for CCSM 4.0 Science frozen by September 30, CICE 4.0 Delta-Eddington shortwave Melt ponds Snow-aging / snow model ? Scene from the film Drowning by Numbers (1988).

Sea Ice Melt Ponds Ponds are prevalent on sea ice Influence surface albedo and ice mass budget Code has been added to explicitly simulate melt ponds and their albedo effects Albedo Evolution During SHEBA (Perovich et al. 2002)

Melt Pond Parameterization Accumulate 15-65% of snow, surface ice melt, and rain into pond volume, depending on ice fraction. Pond fraction is reduced by snow fraction. Compute pond area/depth from simple empirically-based relationship. Currently no change in fresh-water exchange. Pond volume is advected as a CICE tracer. Change in albedo depends on pond fraction and / or depth.

Pond Volume = Pond Fraction X Pond Depth Pond Depth = 0.8 X Pond Fraction Runoff fraction = * Ice Fraction Perovich et al SHEBA observations

Delta-Eddington Shortwave Radiation Briegleb and Light, New shortwave radiation scheme that computes albedos based on inherent optical properties of sea ice, snow, and ponds. Albedos are “tuned” by adjusting snow and ice properties based on a standard deviation from SHEBA observations and offline RT calculations.

Previous Experiments CCSM 3.0 experiments with and without melt ponds showed a strong sensitivity to the prescribed runoff fraction. DE requires a melt pond fraction and depth (prescribed by default). Experiments comparing DE vs CCSM shortwave (without explicit ponds) showed generally thinner ice. Need to include rain.

CCSM 3.5 Experiments (atm-ice-som) 1 x CO22 x CO2 No MPf35.002f35.002co2 MPf35.002mp f35.002de f35.002mpco2 f35.002deco2

Results (Radiation / Melt Ponds - Present Day)

Results (Radiation / Melt Ponds - Future)

Results (Radiation / Melt Ponds - Present vs Future) Melt Pond Area (%) f35.002deco2-f35.002de f35.002de f35.002deco2

Results (Radiation / Melt Ponds - Present vs Future)

Summary Implicit melt ponds in CCSM3 essentially captured the basic albedo effects compared to this simple explicit melt pond formulation. While DE and CCSM shortwave can be tuned to give similar results, DE provides more generality. Climate sensitivity in all configurations is similar. In a 2 x CO2 world, the shift to more rain and less snow along with enhanced surface ice melt appears to mostly compensate the pond accumulation. Generally fewer ponds in the future, likely due to ice- fraction dependent runoff.

Why use Delta-Eddington and Melt Ponds? More physical. Allows for addition of soot, algae, etc. Handles multiple snow layers. Addition of snow-aging easily works with radiation. Interaction with more complicated melt ponds?

Work in Progress CICE 4.0 numerical / software engineering enhancements. DE performance and tuning. Melt ponds and DE soon to be the default in CCSM4 fully-coupled runs. Snow aging / snow model?