Understanding Change Science:

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Understanding Change Science Results of SEARCH
for DAMOCLES (S4D) Workshop on Coordinated
Modeling Activities October 29-31, 2007, Paris
Andrey Proshutinsky Woods Hole Oceanographic
Institution
SEARCH Science Steering Committee Meeting 57
November 2007 The Westin Grand (Washington
Ballroom) Washington, D.C.
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Workshop goal

• The major goal of the workshop was to
  coordinate modeling activities between SEARCH and
  DAMOCLES programs in conjunction with AOMIP and
  (C)ARCMIP projects during IPY and beyond.
• Though the workshop was targeting at modeling
  activities, observers were strongly encouraged to
  attend the workshop. Some tasks were specifically
  designed to stimulate the discussion between
  modelers and observers.
• AOMIP Arctic Ocean Model Intercomparison
  Project
• (C)ARCMIP (Coupled) Arctic Regional
  Climate Model
• Intercomparison Project

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Workshop participants
52 participants from 11 countries (Canada,
Denmark, Germany, Finland, France, Norway,
Poland, Russia, Sweden, UK, and USA)
ltOctober, 31, Parisgt
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Workshop participants

• USA was represented by AOMIP-related
  modeling and observational teams (ice and ocean)
  and scientists from atmospheric and hydrologic
  communities
• D. Bromwich, Ohio State University (ATMOSPHERE)
• J. Cassano, University of Colorado (ATMOSHERE)
• C. Chen, University of Massachusetts-Dartmouth
  (OCEAN)
• G. Gao, University of Massachusetts, Dartmouth
  (OCEAN)
• S. Hakkinen, Goddard Space Flight Center,
  (ICE/OCEAN)
• W. Hibler, III, University of Alaska Fairbanks
  (ICE)
• E. Hunke, Los Alamos National Laboratory (ICE)
• R. Kwok, Jet Propulsion Laboratory (ICE)
• W. Maslowski, Naval Postgraduate School (OCEAN)
• A. Nguyen, Jet Propulsion Laboratory (ICE)
• G. Panteleev, International Arctic Research
  Center (OCEAN)
• D. Perovich, Cold Region Research and
  Engineering Laboratory (ICE)
• A. Proshutinsky, Woods Hole Oceanographic
  Institution (OCEAN, ICE)
• P. Schlosser, Columbia University, (SEARCH)
• T. Troy, Princeton University (HYDROLOGY)

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Represented teams and activities

• Workshop represented activities of
• AOMIP Arctic Ocean Model Intercomparison
  Project
• ARCMIP Arctic Regional Climate Model
  Intercomparison Project (basic atmospheric
  block)
• (C)ARCMIP Coupled (atmosphere, ocean,
  terrestrial) Arctic Regional Climate Model
  Intercomparison Project
• Global climate modeling teams
• Atmosphere, ice and ocean reanalysis projects
• Observational atmosphere, ice, and ocean teams
  and projects

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Common model domain
The AOMIP grid is defined over a geographic
domain that includes the Arctic Ocean, the Bering
Strait, the Canadian Arctic Archipelago, the Fram
Strait and the Greenland, Iceland, and Norwegian
Seas.
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Regional climate model, Arctic integration
areaHigh horizontal resolution of regional
topographic structures at the surface, Improved
simulation of hydrodynamical instabilities and
baroclinic cyclones
(m)
RCM HIRHAM, 50 km
GCM (ERA40)
Initial boundary conditions for the RCM
provided by ERA40 data
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Workshop themes/sessions

• Improvement of models
• Process studies
• Reliability of reanalyzes in the Arctic
• Data and Models (coordination of work)
• Synthesis and integration

Each session followed by discussions with goals
to identify the important problems needed to be
resolved and formulate recommendations for the
international modeling and observing communities
for future activities and coordination of research
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Workshop Questionnaire

• 1. How to validate arctic models?
• What are the most complete data sets and
  parameters for model validation?
• What is needed to make these data sets and
  parameters available for the entire modeling
  community and how to encourage modelers to carry
  out model validation?
• 2. How to improve arctic models?
• What are the critical areas in model performance
  which need immediate attention for model
  improvement?
• What new mechanisms and parameterizations to be
  introduced in models?
• How to avoid restoring and flux corrections these
  procedures?
• Are we able to identify quantitatively a range
  of uncertainties in model results and
  predictions? How to improve models to reduce
  these uncertainties?

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Workshop Questionnaire

• 3. Model forcing
• a) Can we quantify the errors of the model
  forcing? How to improve model forcing?
• 4. Observational Network design and modeling
• Are state-of-the-art Arctic models able to assist
  in the design of observational networks. If not,
  what is needed?
• Do the present and planned observational
  activities (IPY, DAMOCLES, AON) satisfy the needs
  of model validation, improvement and data
  assimilation?

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Workshop Questionnaire

• 5. Organizational Issues
• What can we do to encourage modelers and
  observers to collaborate?
• b) What is the role of AOMIP, (C)ARCMIP,
  DAMOCLES, SEARCH in these activities?
• c) How to integrate AOMIP/ARCMIP/CARCMIP
  numerical studies with IPCC global models in
  order to participate in IPCC model improvements
  for the polar regions?
• d) Do we need additional organizational
  structures to facilitate modeling observational
  collaboration and coordination?

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Improvement of models (15 talks)

• Proshutinsky AOMIP sea ice-ocean model
  improvement recommendations
• Rinke ARCMIP results and HIRHAM sensitivity
  studies and further model development
• Gerdes "Long term changes of Arctic fresh water
  reservoirs
• Hibler Toward Improved Ice-Ocean Dynamics
• Dethloff Arctic climate feedbacks and global
  links
• Maslowski Oceanic Heat Fluxes, Arctic Sea Ice
  Melt, and Climate Change
• Hunke A GCM perspective on the Arctic
• Golubeva Modeling variability of the Atlantic
  water circulation
• Doescher Predictability studies in a regional
  coupled model of the Arctic
• Bromwich Polar-Optimized WRF
• Chen A FVCOM-Arctic model
• Hakkinen Model hindcasts from sigma and
  z-coordinate models of the Arctic-Atlantic
  Oceans
• Cassano Development of an Arctic System Model
  Atmospheric Model Issues"
• Mikolajewicz "Modelling Arctic climate
  variability
• Jean-François Lemieux "Using the RESidual method
  to solve the sea ice momentum equation"

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Model improvements
AOMIP/OCEAN/ICE
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Model improvements
Atmosphere
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Process studies (10 talks)

• Wyser Impact of an improved radiation
  parameterization for the Arctic
• Luepkes Impact of leads on processes in the
  polar atmospheric boundary layer
• Vihma and Joseph Sedlar Stable boundary layer
  and cloud-capped boundary layer as challenges for
  modelling in the Arctic
• Meier and Per Pemberton On the parameterization
  of mixing in regional circulation models for the
  Arctic Ocean
• Nguyen Salt rejection, advection, and mixing in
  the MITgcm coupled ocean and sea ice model
• Dorn Uncertain descriptions of Arctic climate
  processes in coupled models and their impact on
  the simulation of Arctic sea ice
• Zhang Some Considerations in Modeling the Arctic
  Ocean and Its Ice Cover
• Maksimovich "Atmospheric warming over the Arctic
  Ocean during the past 20 years"
• Yakovlev FEMAO (Finite-Element Model of the
  Arctic Ocean) Towards the understanding of the
  role of tides in the Arctic Ocean climate
  formation
• Platov Can a polynya effect be resolved in
  coarse resolution model?

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Process studies
ICE/OCEAN
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Process studies
Atmosphere
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Reliability of Arctic reanalyzes (5 talks)

• Bromwich An Evaluation of Global Reanalyses in
  the Polar Regions
• Kalberg The ECMWF ERA-40 reanalysis and beyond
• Troy Reconstructing the Land Surface Water and
  energy Budgets of Northern Eurasia
• Proshutinsky NCAR reanalysis validation in the
  Central Arctic
• Tjernstroem Large-scale model reanalyses for
  the Arctic validation, temperature trends, and
  applicability as forcing for sea ice models

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Reliability of Arctic reanalyzes
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2 m air temperature
winter
Summer
Autumn
Spring
Winter
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Reliability of Arctic reanalyzes

• The NCAR data are in good agreement with
  observations data only in winter. In autumn, the
  NCEP air temperature is lower than observed but
  in spring it is higher than observed. In summer,
  the NCEP air temperature is 1.2C higher than
  observed. Similarly, NCAR humidity data are in
  good agreement with observations only in winter.
  In other seasons, especially summer, the NCAR
  humidity is significantly higher than observed
• Sensitivity experiments run on a thermodynamic
  sea-ice model indicate that both of these
  discrepancies strongly influence accuracy of
  simulated surface sea-ice thickness results (it
  is thinner in the model results)
• The observed and NCEP SLP data are in good
  agreement in all periods. On the other hand, the
  NCEP SLP is usually a bit lower than observed.

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Reliability of Arctic reanalyzes (activities,
recommendations)

• It is recommended to continue validation
  reanalysis product because it is important to
  know model errors associated with forcing
  uncertainties
• It is recommended to extend reanalysis efforts to
  involve other disciplines (hydrology, permafrost,
  etc)

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Data and Models (9 talks)

• Perovich The Mass and Heat Balance of Ice
• Cheng "Snow and sea ice thermodynamics in the
  Arctic Model validation against CHINARE and
  SHEBA data"
• Girard-Ardhuin Sea ice drift data at global
  scale
• Kwok Assessment of sea ice simulations using
  high-resolution kinematics from RADARSAT
• Houssais Validation of a regional Arctic-North
  Atlantic model based on the ORCALIM sea ice-ocean
  model
• Jakobson Tethered balloon measurements in the
  Arctic
• Michael Karcher The Arctic ocean in the 20th
  century - first results from an AOMIP experiment
  driven with 100 years of reconstructed forcing
  fields
• Skagseth On the Atlantic water through the
  Norwegian and Barents Seas
• Eldevik The Greenland Sea does not control the
  overflows feeding the Atlantic conveyor

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This is October 23 sea ice coverage of the Arctic
Ocean. From here you can see very well where
Atlantic water penetrates to the Arctic Basin
(ice is melted in these regions)
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Data/model recommendations

• We cant well understand/explain/construct
  global picture based on observational data
  without modeling
• We cant use models for understanding or
  predicting of arctic change without model
  validation, data assimilation, initial
  conditions, model forcing (observations are
  needed)
• Strong coordination between observing and
  modeling programs is needed.

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Enhance synthesis and coordination (6 talks)

• David Bromwich A High-Resolution Arctic System
  Reanalysis
• Andrey Proshutinsky Toward reconstruction of the
  Arctic climate system Sea ice and ocean
  reconstruction with data assimilation
• Gregory Smith Using ocean reanalysis to study
  water mass variability with the help of a new
  Java web application
• Frank Kauker ADNAOSIM and NAOSIMDAS
• Jun She (keynote) Optimal Design of Observing
  Networks (ODON)
• Thomas Kaminski Quantitative Design of
  Observational Networks

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Enhance synthesis and coordination
Synthesis between observational and modeling
products could be done based on reanalysis which
combines modeling with data assimilation
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Motivation and goals

• An Integrative Data Assimilation for the Arctic
  System (IDAAS) has been recommended for
  development by SEARCH in 2005. While existing
  operational reanalyses assimilate only
  atmospheric measurements, an IDAAS activity would
  include non-atmospheric components sea ice,
  oceanic, terrestrial geophysical and
  biogeochemical parameters and human dimensions
  data.
• Atmospheric reanalysis products play a major
  role in the arctic system studies and are used to
  force sea ice, ocean and terrestrial models, and
  to analyze the climate systems variability and
  to explain and understand the interrelationships
  of the systems components and the causes of
  their change.
• Motivated by this success and the major goals and
  recommendations of SEARCH, we work to develop an
  integrated set of assimilation procedures for the
  iceocean system that is able to provide gridded
  data sets that are physically consistent and
  constrained to the observations of sea ice and
  ocean parameters.

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Model Domains
SIOM
PIOMAS
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Table 1 AOMIP Project participants.
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Challenges

• The major challenge of the MIPs is to improve
  existing regional Arctic atmosphere, ice, ocean
  and terrestrial models and, respectively, global
  climate models
• This work is expensive and requires significant
  financial and labor resources.
• In order to develop a comprehensive arctic model
  it is necessary to involve the entire community
  of arctic researches including modelers and
  observers, scientists and engineers from
  different disciplines.

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Concerns

• There are not enough observational data for
  model initialization, forcing, validation and
  assimilation.
• A comprehensive AON is urgently needed to
  satisfy needs of both observational and modeling
  communities