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

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 5–7 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 DAMOCLESprograms 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) <October, 31, Paris>

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. AOMIP/OCEAN/ICE Model improvements

14. Atmosphere Model improvements

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 1.2°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.

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)

26. Data/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.

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 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.

30. Model Domains SIOM PIOMAS

31. Table 1 AOMIP Project participants.

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