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Understanding Change Science:

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Process studies. Reliability of reanalyzes in the Arctic. Data and Models (coordination of work) ... What new mechanisms and parameterizations to be introduced ... – PowerPoint PPT presentation

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Title: Understanding Change Science:


1
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.
2
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

3
Workshop participants
52 participants from 11 countries (Canada,
Denmark, Germany, Finland, France, Norway,
Poland, Russia, Sweden, UK, and USA)
ltOctober, 31, Parisgt
4
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)

5
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

6
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.
7
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
8
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
9
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?

10
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?

11
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?

12
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"

13
Model improvements
AOMIP/OCEAN/ICE
14
Model improvements
Atmosphere
15
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?

16
Process studies
ICE/OCEAN
17
Process studies
Atmosphere
18
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

19
Reliability of Arctic reanalyzes
20
2 m air temperature
winter
Summer
Autumn
Spring
Winter
21
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.

22
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)

23
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

24
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)
25
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26
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.

27
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

28

Enhance synthesis and coordination
Synthesis between observational and modeling
products could be done based on reanalysis which
combines modeling with data assimilation
29
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.

30
Model Domains
SIOM
PIOMAS
31
Table 1 AOMIP Project participants.
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38
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.

39
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
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