Understanding Change Science: Understanding Change Science: Results of SEARCH for DAMOCLES (S4D)Results of SEARCH for DAMOCLES (S4D)

Workshop on Coordinated Modeling Workshop on Coordinated Modeling Activities Activities

October 29-31, 2007, ParisOctober 29-31, 2007, Paris

SEARCH Science Steering Committee Meeting5–7 November 2007

The Westin Grand (Washington Ballroom) Washington, D.C.

Andrey ProshutinskyAndrey Proshutinsky

Woods Hole Oceanographic InstitutionWoods Hole Oceanographic Institution

Workshop goalWorkshop 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

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

Workshop participants:Workshop participants:

52 participants from 11 countries (Canada, Denmark, Germany, Finland, France, Norway, Poland, Russia, Sweden, UK, and USA) <October, 31, Paris>

USA was represented by AOMIP-related modeling and observational

teams (ice and ocean) and scientists from atmospheric and hydrologic communities

1. D. Bromwich, Ohio State University (ATMOSPHERE)2. J. Cassano, University of Colorado (ATMOSHERE)3. C. Chen, University of Massachusetts-Dartmouth (OCEAN)4. G. Gao, University of Massachusetts, Dartmouth (OCEAN)5. S. Hakkinen, Goddard Space Flight Center, (ICE/OCEAN)6. W. Hibler, III, University of Alaska Fairbanks (ICE)7. E. Hunke, Los Alamos National Laboratory (ICE)8. R. Kwok, Jet Propulsion Laboratory (ICE)9. W. Maslowski, Naval Postgraduate School (OCEAN)10. A. Nguyen, Jet Propulsion Laboratory (ICE)11. G. Panteleev, International Arctic Research Center (OCEAN)12. D. Perovich, Cold Region Research and Engineering Laboratory (ICE)13. A. Proshutinsky, Woods Hole Oceanographic Institution (OCEAN, ICE)14. P. Schlosser, Columbia University, (SEARCH)15. T. Troy, Princeton University (HYDROLOGY)

USA was represented by AOMIP-related modeling and observational

teams (ice and ocean) and scientists from atmospheric and hydrologic communities

1. D. Bromwich, Ohio State University (ATMOSPHERE)2. J. Cassano, University of Colorado (ATMOSHERE)3. C. Chen, University of Massachusetts-Dartmouth (OCEAN)4. G. Gao, University of Massachusetts, Dartmouth (OCEAN)5. S. Hakkinen, Goddard Space Flight Center, (ICE/OCEAN)6. W. Hibler, III, University of Alaska Fairbanks (ICE)7. E. Hunke, Los Alamos National Laboratory (ICE)8. R. Kwok, Jet Propulsion Laboratory (ICE)9. W. Maslowski, Naval Postgraduate School (OCEAN)10. A. Nguyen, Jet Propulsion Laboratory (ICE)11. G. Panteleev, International Arctic Research Center (OCEAN)12. D. Perovich, Cold Region Research and Engineering Laboratory (ICE)13. A. Proshutinsky, Woods Hole Oceanographic Institution (OCEAN, ICE)14. P. Schlosser, Columbia University, (SEARCH)15. T. Troy, Princeton University (HYDROLOGY)

Workshop participants:Workshop participants:

Represented teams and activitiesRepresented 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

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

The AOMIP grid is The AOMIP grid is defined over a geographic defined over a geographic domain that includes the domain that includes the Arctic Ocean, the Bering Arctic Ocean, the Bering Strait, the Canadian Arctic Strait, the Canadian Arctic Archipelago, the Fram Archipelago, the Fram Strait and the Greenland, Strait and the Greenland, Iceland, and Norwegian Iceland, and Norwegian SeasSeas.

Common model domainCommon model domain

Regional climate model, Arctic integration areaHigh horizontal resolution of regional topographic structures at the surface, Improved

simulation of hydrodynamical instabilities and baroclinic cyclones

GCM (ERA40) RCM HIRHAM, 50 km

Initial & boundary conditionsfor the RCM provided by ERA40 data

(m)

Workshop themes/sessions:Workshop themes/sessions:

• Improvement of models

• Process studies

• Reliability of reanalyzes in the Arctic

• Data and Models (coordination of work)

• Synthesis and integration

• 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

Workshop QuestionnaireWorkshop Questionnaire1. How to validate arctic models?

a) What are the most complete data sets and parameters for model validation?

b) 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?

a) What are the critical areas in model performance which need immediate attention for model improvement?

b) What new mechanisms and parameterizations to be introduced in models?

c) How to avoid restoring and flux corrections these procedures?d) Are we able to identify quantitatively a range of uncertainties in

model results and predictions? How to improve models to reduce these uncertainties?

1. How to validate arctic models?

a) What are the most complete data sets and parameters for model validation?

b) 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?

a) What are the critical areas in model performance which need immediate attention for model improvement?

b) What new mechanisms and parameterizations to be introduced in models?

c) How to avoid restoring and flux corrections these procedures?d) Are we able to identify quantitatively a range of uncertainties in

model results and predictions? How to improve models to reduce these uncertainties?

3. Model forcing

a) Can we quantify the errors of the model forcing? How to improve model forcing?

4. Observational Network design and modeling

a) Are state-of-the-art Arctic models able to assist in the design of observational networks. If not, what is needed?

b) Do the present and planned observational activities (IPY, DAMOCLES, AON) satisfy the needs of model validation, improvement and data assimilation?

3. Model forcing

a) Can we quantify the errors of the model forcing? How to improve model forcing?

4. Observational Network design and modeling

a) Are state-of-the-art Arctic models able to assist in the design of observational networks. If not, what is needed?

b) Do the present and planned observational activities (IPY, DAMOCLES, AON) satisfy the needs of model validation, improvement and data assimilation?

Workshop QuestionnaireWorkshop Questionnaire

5. Organizational Issues

a) 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?

5. Organizational Issues

a) 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?

Workshop QuestionnaireWorkshop Questionnaire

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"

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"

Improvement of models (15 talks)Improvement of models (15 talks)

ModelModel improvements improvementsModelModel improvements improvements

Restoring andRestoring andFlux correctionFlux correction

Atmospheric loadingAtmospheric loading

Vertical and lateralVertical and lateralmixing mixing

Tidal ocean & iceTidal ocean & iceeffectseffects

Bering Strait Inflow Bering Strait Inflow and river runoffand river runoff

Neptune effectNeptune effect

New advection New advection schemesschemes

Data assimilationassimilationtechnology

Vertical and Vertical and lateral resolutionlateral resolution

Forcing biasesForcing biases

Land-fast iceLand-fast ice

AOMIP/OCEAN/ICE

ModelModel improvements improvementsModelModel improvements improvements

Cloud propertiesCloud properties

Arctic Haze and climatic Arctic Haze and climatic

effects of aerosolseffects of aerosols

Troposphere aerosols, Troposphere aerosols, clouds, water vaporclouds, water vapor

Precipitation and humidity fluxesPrecipitation and humidity fluxes

Convective plums due to leadsConvective plums due to leads

Surface turbulent Surface turbulent fluxesfluxes

Snow and ice albedo

Surface radiative Surface radiative fluxesfluxes

Stable boundary Stable boundary layerlayer

Cloud-radiation Cloud-radiation interactioninteraction

Atmosphere

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?

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?

Process studies (10 talks)Process studies (10 talks)

Wind- and Wind- and convection-convection-

drivendrivenmixing mixing

Wind- and Wind- and convection-convection-

drivendrivenmixing mixing

Heat contentHeat contentvariability variability and role of and role of

differentdifferentfactorsfactors

Heat contentHeat contentvariability variability and role of and role of

differentdifferentfactorsfactors

FreshwaterFreshwatercontentcontent

variability andvariability androle of differentrole of different

factorsfactors

FreshwaterFreshwatercontentcontent

variability andvariability androle of differentrole of different

factorsfactors

Reconstruction Reconstruction of hydrography of hydrography and circulationand circulation

based on based on modeling with modeling with

data data assimilationassimilation

Reconstruction Reconstruction of hydrography of hydrography and circulationand circulation

based on based on modeling with modeling with

data data assimilationassimilation

Atlantic water Atlantic water circulationcirculation

origin, origin, variability , variability , sense of sense of rotationrotation

Atlantic water Atlantic water circulationcirculation

origin, origin, variability , variability , sense of sense of rotationrotation

Investigation of Investigation of sea level rise: sea level rise: its rate, role of its rate, role of different factors,different factors,

model errorsmodel errors

Investigation of Investigation of sea level rise: sea level rise: its rate, role of its rate, role of different factors,different factors,

model errorsmodel errors

ICE/OCEANProcess studiesProcess studies

AtmosphericAtmosphericboundary layer boundary layer specifically for specifically for

stable stable stratification stratification

AtmosphericAtmosphericboundary layer boundary layer specifically for specifically for

stable stable stratification stratification

Atmosphere-ice-Atmosphere-ice-OceanOcean

interactionsinteractions

Atmosphere-ice-Atmosphere-ice-OceanOcean

interactionsinteractionsAtmosphere-LandAtmosphere-Land

interactionsinteractions

Atmosphere-LandAtmosphere-Landinteractionsinteractions

High resolutionHigh resolutionregional regional

atmosphericatmosphericreanalysisreanalysis

High resolutionHigh resolutionregional regional

atmosphericatmosphericreanalysisreanalysis

TroposphereTroposphereprocesses andprocesses and

Ozone roleOzone role

TroposphereTroposphereprocesses andprocesses and

Ozone roleOzone role

Turbulence Turbulence parameterizationparameterization

Turbulence Turbulence parameterizationparameterization

AtmosphereProcess studiesProcess studies

Reliability of Arctic reanalyzes (5 talks)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”

Atmospheric Reanalysis

projects

NCAR/NCEP

European:ERA-15, ERA-40ERA-75 (underDevelopment)

Reliability of Arctic reanalyzes Reliability of Arctic reanalyzes

Air temperature mean seasonal variability

0 50 100 150 200 250 300 350

Day

-40

-35

-30

-25

-20

-15

-10

-5

0

5

Air

te

mp

era

ture

, 0 C

NP NCEP NC-NP

winter Spring Summer Autumn Winter

2 m air temperature2 m air temperature

• 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 1.2°C 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.

• 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 1.2°C 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.

Reliability of Arctic reanalyzes Reliability of Arctic reanalyzes

Reliability of Arctic reanalyzes (activities, recommendations)

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)

Data and Models (9 talks)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”

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”

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)

Data/model recommendationsData/model recommendations

• We can’t well understand/explain/construct “global” picture based on observational data without modeling;

• We can’t 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.

• We can’t well understand/explain/construct “global” picture based on observational data without modeling;

• We can’t 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.

Enhance synthesis and coordination (6 talks)

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”

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”

Atmospheric Reanalysis

projects

NCAR/NCEP

European:ERA-15, ERA-40ERA-75 (underDevelopment)

High-resolutionArctic

(Bromwich et al)

Arctic ocean and ice monthly reanalysis system

(Proshutinsky et al.

Enhance synthesis and coordinationSynthesis between observational and modeling products could be done based on reanalysis which combines modeling with data assimilation

Motivation and goalsMotivation 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 system’s variability and to explain and understand the interrelationships of the system’s 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 ice–ocean system that is able to provide gridded data sets that are physically consistent and constrained to the observations of sea ice and ocean parameters.

• 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 system’s variability and to explain and understand the interrelationships of the system’s 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 ice–ocean system that is able to provide gridded data sets that are physically consistent and constrained to the observations of sea ice and ocean parameters.

Model DomainsModel Domains

PIOMASPIOMASSIOMSIOM

Table 1 AOMIP Project participants.

Institute, PI(s)Country Abbreviation Support type

Arctic and Antarctic Research Institute, A. Makshtas Russia AARI B

Alfred Wegener Institute, R. Gerdes and C. Koeberle Germany AWI B

Florida State University, E. Chassignet and D. Dukhovskoy USA FSU A

Geophysical Fluid Dynamics Laboratory, S. Griffies, M. Winton USA GFDL B

Goddard Space Flight Center, S. Hakkinen USA GSFC B

International Arctic Research Center, B. Hibler, G. Panteleev USA IARC B

Institute of Marine Sciences, UAF, M. Johnson USA IMS A

Institute of Ocean Sciences, G. Holloway Canada IOS B

Jet Propulsion Laboratory, R. Kwok, A. Nguyen USA JPL B

Los Alamos National Laboratory, E. Hunke USA LANL B

Massachusetts Institute of Technology, C. Hill USA MIT A

Naval Postgraduate School, W. Maslowski USA NPS A

National Center for Atmospheric Research, M. Holland USA NCAR B

New York University, D. Holland USA NYU A

Norwegian Polar Institute, Ole Anders Nøst Norway NPI B

Ocean and Atmosphere Systems, M. Karcher and F. Kauker Germany OASYS B

Proudman Oceanographic Laboratory, M. Maqueda UK POL B

Russian Academy of Science, Moscow, N. Yakovlev Russia RASM B

Russian Academy of Science, Novosibirsk, E. Golubeva Russia RASN B

Swedish Meteorological and Hydrological Institute, M. Meir Sweden SMHI B

University College London, S. Laxon UK UCL B

University of Massachusetts, Dartmouth, C. Chen USA UMAS A

University of Washington, M. Steele, J. Zhang USA UW A

Woods Hole Oceanogr. Ins, A. Proshutinsky, P. Winsor, A. Condron

USA WHOI A

ChallengesChallengesChallengesChallenges

The major challenge of the MIPs is to improve The major challenge of the MIPs is to improve existing regional Arctic atmosphere, ice, ocean existing regional Arctic atmosphere, ice, ocean and terrestrial models and, respectively, global and terrestrial models and, respectively, global climate models:climate models:

This work is expensive and requires significant This work is expensive and requires significant financial and labor resources. financial and labor resources.

In order to develop a comprehensive arctic In order to develop a comprehensive arctic model it is necessary to involve the entire model it is necessary to involve the entire community of arctic researches including community of arctic researches including modelers and observers, scientists and modelers and observers, scientists and engineers from different disciplines. engineers from different disciplines.

Concerns Concerns Concerns Concerns

There are not enough observational There are not enough observational data for model initialization, forcing, data for model initialization, forcing, validation and assimilation.validation and assimilation.

A comprehensive AON is urgently A comprehensive AON is urgently needed to satisfy needs of both needed to satisfy needs of both observational and modeling observational and modeling communitiescommunities

There are not enough observational There are not enough observational data for model initialization, forcing, data for model initialization, forcing, validation and assimilation.validation and assimilation.

A comprehensive AON is urgently A comprehensive AON is urgently needed to satisfy needs of both needed to satisfy needs of both observational and modeling observational and modeling communitiescommunities