Workshop goals

The major goal of the workshop is to coordinate modeling activities between SEARCH and DAMOCLES programs in conjunction with AOMIP and (C)ARCMIP projects during IPY and beyond. Though the workshop is targeting at modeling activities, observers are strongly encouraged to attend the workshop. Some tasks are specifically designed to stimulate the discussion between modelers and observers.

Tasks

• Improvement of models

Improvement of atmospheric, oceanic, sea ice, terrestrial, and arctic climate
regional models and provision of recommendations to global modeling communities
on how to improve representation of the Arctic in global models. How can we best reduce uncertainties in model predictions?

• Process studies

Are there processes not (well) modeled in present-day Arctic models? Themes that should be discussed are for example: atmospheric boundary layer, tidal mixing in the ocean, multi-layer sea-ice model and coupled feedbacks. This task is also intended to stimulate the exchange between modelers concerned with process studies and regional to global-scale modelers.

• Reliability of reanalyzes in the Arctic

The reanalyzes of NCAR and ECMWF are the most widely used forcing data sets for regional models. However, it is little known about the uncertainties of these data in the Arctic and Subarctic. Especially the hydrological cycle in the Arctic should be discussed.

• Enhance coordination of experiments

Increase of the effectiveness of model development and model validation through coordinated design of numerical experiments and long-term model runs, coordinated forcing, model initialization, and analyses. How to avoid duplication, ensure transfer of knowledge and generally enhance effectiveness of modeling activities?

• Data and Models. (“What are modelers doing with our observations?” - “Which data do we need from the observers for our models?”)

This task should stimulate the exchange of knowledge between modelers and observers. Some modelers are just processing the data gathered by observers without any feedback to the observers. Within this task contributions are welcome which demonstrate how observations are used by modelers and what kind of data modelers need for their activities. Also we will discuss how the data exchange can be facilitated between data collectors and modelers performing data assimilation. This task naturally leads to the next task.

• Enhance synthesis and integration

Enhance synthesis and integration among modeling and observational efforts and develop an optimized scheme for an observational network potentially capable to satisfy needs of both, observational and modeling communities.

Participants

1. Fanny Ardhuin, DOPS/LOS, IFREMER, Plouzane, France, Email: fanny.ardhuin(AT)ifremer.fr, URL: http://cersat.ifremer.fr/about_us/staff/recherche/fanny_ardhuin/

2. David Bromwich, Polar Meteorology Group Byrd Polar Research Center, Ohio State University, Columbus, OH, USA, Email: bromwich(AT)polarmet1.mps.ohio-state.edu

3. Burghard Bruemmer, Meteorological Institute, University Hamburg, Germany, Email: burghard.bruemmer(AT)zmaw.de

4. John Cassano, CIRES / ATOC, University of Colorado, Boulder, CO, USA, Email: john.cassano(AT)colorado.edu

5. Changsheng Chen, School for Marine Science and Technology, University of Massachusetts-Dartmouth, New Bedford, MA, USA, E-mail: c1chen(AT)umassd.edu

6. Bin Cheng, Finnish Institute of Marine Research, Helsinki, Finland, Email: Bin.Cheng(AT)fimr.fi

7. Klaus Dethloff, Alfred Wegener Institute for Polar and Marine Research (AWI), Potsdam, Germany, Email: Klaus.Dethloff(AT)awi.de

8. Ralf Döscher, Research Departement Swedish Meteorological and Hydrological Institute (SMHI), Norrkoping, Sweden, Email: Ralf.Doescher(AT)smhi.se, URL: http://www.smhi.se/sgn0106/if/rc/staff.htm#RD, http://www.smhi.se/sgn0106/if/rc/main.htm

9. Wolfgang Dorn, Alfred Wegener Institute for Polar and Marine Research (AWI), Potsdam, Germany, Email: Wolfgang.Dorn(AT)awi.de

10. Tor Eldevik, Nansen Environmental and Remote Sensing Center,  Bergen, Norway, Email: tor.eldevik(AT)nersc.no, URL: www.nersc.no/~torel/

11. Gao Gao, University of Massachusetts, Dartmouth, NH, USA, Email: ggao(AT)umassd.edu

12. Jean-Claude Gascard, Laboratoire d'Oceanographie Dynamique (LODYC), Laboratoire d'Océanographie et du Climat, Paris, France, Email: Jean-Claude.Gascard(AT)lodyc.jussieu.fr

13. Rüdiger Gerdes, Climate Sciences, Alfred Wegener Institute for Polar and Marine Research (AWI), Bremerhaven, Germany, Email: Ruediger.Gerdes(AT)awi.de

14. Elena Golubeva, Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russian Federation, Email: elen(AT)ommfao.sscc.ru

15. Sirpa Hakkinen, NASA Goddard Space Flight Center, Greenbelt, MD, USA, Email: sirpa(AT)fram.gsfc.nasa.gov

16. Christophe Herbaut, LOCEAN-IPSL, Laboratoire d'Océanographie et du Climat, Paris, France, Email: Christophe.Herbaut(AT)locean-ipsl.upmc.fr

17. W. D. Hibler, III, University of Alaska Fairbanks, USA, Email: hibler(AT)iarc.uaf.edu

18. Marie-Noelle Houssais, LOCEAN-IPSL, Laboratoire d'Océanographie et du Climat, Paris, France, Email: marie-noelle.houssais(AT)locean-ipsl.upmc.fr

19. Elizabeth Hunke, Los Alamos National Laboratory, USA, Email:eclare@lanl.gov

20. Jaromir Jakacki, Institute of Oceanology, Polish Academy of Sciences, Physical Oceanography Department, Ocean Circulation Laboratory,  Sopot, Poland, Email: jjakacki(AT)iopan.gda.pl

21. Erko Jakobson, Environmental Physics Institute, University of Tartu, Estonia, Email: erko.jakobson(AT)ut.ee

22. Per Kållberg, Swedish Meteorological and Hydrological Institute (SMHI), Norrkoping, Sweden, Email: per.kallberg(AT)smhi.se

23. Thomas Kaminski, FastOpt, Hamburg, Germany, Email: Thomas.Kaminski(AT)FastOpt.com, URL: http://www.FastOpt.com

24. Michael Karcher, O.A.Sys - Ocean Atmosphere Systems GbR, Hamburg, Germany, Email: michael(AT)oasys-research.de, URL: http://www.oasys-research.de

25. Frank Kauker, O.A.Sys - Ocean Atmosphere Systems GbR, Hamburg, Germany, Email: frank(AT)oasys-research.de, URL: http://www.oasys-research.de
    

26. Ron Kwok, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA, Email: ron.kwok(AT)jpl.nasa.gov

27. Jean-François Lemieux, Earth System Modeling Group, Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, QC, Canada, Email: lemieux(AT)zephyr.meteo.mcgill.ca

28. Christof Lüpkes, Climate Sciences, Alfred Wegener Institute for Polar and Marine Research (AWI), Bremerhaven, Germany, Email: Christof.Luepkes(AT)awi.de

29. Elena Maksimovich, Laboratoire d'Oceanographie Dynamique (LODYC), Laboratoire d'Océanographie et du Climat, Paris, France, Email: Elena.Maksimovich(AT)locean-ipsl.upmc.fr

30. Wieslaw Maslowski, Naval Postgraduate School, Monterey, CA, USA, Email: maslowsk(AT)nps.edu

31. H.E. Markus Meier, Research Departement Swedish Meteorological and Hydrological Institute (SMHI), Norrkoping, Sweden, Email: Markus.Meier(AT)smhi.se

32. Uwe Mikolajewicz, Max Planck Institute for Meteorology, Hamburg, Germany, Email: uwe.mikolajewicz(AT)zmaw.de

33. An T. Nguyen, Jet Propulsion Lab, California Institute of Technology, Pasadena, CA, USA, Email: atn(AT)jpl.nasa.gov

34. Gleb Panteleev, International Arctic Research Council, Fairbanks, AL, USA, Email: gleb(AT)iarc.uaf.edu

35. Per Pemberton, Research Departement Swedish Meteorological and Hydrological Institute (SMHI), Norrkoping, Sweden, Email: Per.Pemberton(AT)smhi.se

36. Don Perovich, ERDC-Cold Region Research and Engineering Laboratory, Hanover, NH, USA, Email: donald.k.perovich(AT)erdc.usace.army.mil

37. Jan Piechura, Institute of Oceanology, Polish Academy of Sciences, Sopot, Poland, Email: piechura(AT)iopan.gda.pl

38. Gennady Platov, Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russian Federation, Email: plat(AT)ommfao.sscc.ru

39. Andrey Proshutinsky, Woods Hole Oceanographic Institution, MA, USA, Email: aproshutinsky(AT)whoi.edu

40. Tatiana Proshutinsky, Woods Hole Oceanographic Institution, MA, USA, Email: tproshutinsky(AT)whoi.edu

41. Annette Rinke, Alfred Wegener Institute for Polar and Marine Research (AWI), Potsdam, Germany, Email: Annette.Rinke(AT)awi.de

42. WITHDRAWN Bert Rudels, Department of Physical Oceanography, Finnish Institute of Marine Research, Helsinki, Finland, Email: Bert.Rudels(AT)fimr.fi

43. Peter Schlosser, Columbia University, New York, USA, Email: schlosser(AT)ldeo.columbia.edu

44. Joseph Sedlar, Department of Meteorology, Stockholm University, Stockholm, Sweden, Email: michaelt(AT)misu.su.se

45. Jun She, Centre for Ocean and Ice, DMI, Lyngby, Denmark, Email: js(AT)dmi.dk, URL: http://www.noos.cc/ODON

46. Øystein Skagseth, Institute of Marine Research, Bergen, Norway, Email: oystein.skagseth(AT)imr.no

47. Gregory C. Smith, Environmental Systems Science Centre, University of Reading, Reading, UK, Email: gcs(AT)mail.nerc-essc.ac.uk

48. Tara Troy, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA: Email: tjtroy(AT)princeton.edu

49. Timo Vihma, Finnish Meteorological Institute (FMI), Helsinki, Email: timo.vihma(AT)fmi.fi

50. Klaus Wyser, Research Departement Swedish Meteorological and Hydrological Institute (SMHI), Norrkoping, Sweden, Email: Klaus.Wyser(AT)smhi.se

51. Nikolai Yakovlev, Institue for Numerical Mathematics, Russian Academy of Sciences, Moskow, Russian Federation, Email:  iakovlev(AT)inm.ras.ru

52. Jinlun Zhang, Applied Physics Lap, Polar Science Center, University of Washington, Seattle, WA, USA, Email: zhang(AT)apl.washington.edu

Abstracts

(Alphabetic order)

1. David H. Bromwich, Ohio State University, “An Evaluation of Global Reanalyses in the Polar Regions”: In the polar regions, it is difficult to place current weather and climate trends in a long-term climatological perspective, primarily because the meteorological records there are limited in time and space in comparison with other regions of the globe. The low spatial density of polar meteorological data makes it challenging to separate local changes from regional or even continental-scale changes. Reanalyses, which assimilate all available observations into physically-consistent, regularly-spaced and comprehensive datasets, can be especially helpful in the Arctic and Antarctic regions. On the other hand, the unique characteristics of the polar regions present a challenge for the production of the reliable, high-quaility reanalyses. Therefore, an evaluation of the recent and well-known and reanalyses is presented for the Arctic and Antarctic regions.
   Overall, the skill of the reanalyses is found to be much higher in the Arctic than the Antarctic, where the reanalyses are only reliable during the summer months prior to the modern satellite era. In the Arctic, the largest differences are related to the reanalyses' depiction of clouds and their associated radiation impacts. ERA-40 captures the cloud variability much better than NCEP1 and JRA-25, but the ERA-40 and JRA-25 clouds are too optically thin for shortwave radiation. Over the central Arctic Ocean, there is a cold bias for ERA-40 related to the assimilation of the HIRS data. While there is good agreement between the reanalyses on various fluxes across 70 N, NCEP1 produces excessive summer precipitation over the Arctic landmasses. In the Antarctic, large circulation differences between the reanalyses are found primarily before 1979, when vast quantities of satellite sounding data started to be assimilated. An evaluation based upon the results of a cyclone tracking method again shows superior skill in the Northern Hemisphere for 1958-2001.

2. David H. Bromwich and Keith M. Hines, Ohio State University, “Polar-Optimized WRF”: Work with the 5th generation Penn State/National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) demonstrated that the polar-optimized version of the mesoscale model (Polar MM5) achieved a much improved performance for both the Arctic and Antarctic regions. Therefore, a polar-optimized version of the state-of-the-art Weather Research and Forecasting model (WRF) was very recently developed by the Polar Meteorology Group of Ohio State University's Byrd Polar Research Center. "Polar WRF" will serve as the base model for the upcoming Arctic System Reanalysis (ASR). The testing and development work for Polar WRF began with both winter and summer simulations for ice sheet surface conditions using Greenland area domains with 24-km and 40-km resolutions. The simulations facilitated improvements to ice sheet surface energy balance and snow firn energy transfer for the Noah land surface model (LSM). Evaluations with automatic weather station (AWS) data show that Polar WRF simulations are improved over those of Polar MM5 for the Greenland winter, and of slightly less quality than those of Polar MM5 for the Greenland summer. The summertime surface energy balance, however, is improved in the Polar WRF simulations. Polar WRF is also being evaulated with Antarctic Mesoscale Prediction System (AMPS) forecasts for Antarctica at the National Center for Atmospheric Research (NCAR) in preperation for the AMPS transition to WRF. Current developmental work for Polar WRF includes Arctic simulations of land, open ocean and sea ice surfaces. The Surface Heat Budget of the Arctic Ocean (SHEBA) during 1997/98 and other recent observational studies provide excellent opportunities to test Polar WRF for various Arctic conditions. A new treatment for grid points containing both open water and sea ice was very recently added to Polar WRF. Furthermore, Hugh Morrison's fully 2-moment microphysics scheme is being added as a physics option. These new capabilities are currently under evaluation.

3. David H. Bromwich and Keith M. Hines, Ohio State University: “A High-Resolution Arctic System Reanalysis” A physically-consistent integration of Arctic data, including the enhanced observations through the Sustained Arctic Observing Network (SAON), with be achieved through the high-resolution Arctic System Reanalysis (ASR) of the northern high latitude region. The ASR is a collaboration of the Ohio State University's Byrd Polar Research Center (BPRC) and Ohio Supercomputer Center (OSC)with the National Center Atmospheric Research (NCAR), the University of Colorado, and the University of Illinois. The project will provide a high resolution description in space (20 km) and time (3 h) of the atmosphere-sea ice-land surface system of the Arctic. Widespread applications of existing global reanalyses (e.g., ERA-40, NCEP/NCAR, JRA-25, NASA DAS/MERRA) demonstrate the high impact that can be expected on Arctic research. The ASR will ingest historical data streams along with measurements of the physical components of the Arctic Observing Network being developed as part of the International Polar Year (IPY 2007-2009). Gridded fields from the ASR will serve a variety of uses such drivers for coupled ice-ocean, land surface and other models, and will offer a focal point for coordinated model inter-comparison efforts. The ASR will permit detailed reconstructions of the Arctic system's variability and change, thereby complementing efforts of the global reanalyses. The project will also shape the legacy observing network of the IPY by providing a vehicle for observing system sensitivity studies of the integrated Arctic Observing Network. The first generation ASR will span the years 2000-2010. It will then be relatively straightforward to extend the reanalysis to earlier years and provide systematic updates. The ASR will capitalize on prior investment in large Arctic field programs (e.g., SHEBA, LAII/ATLAS, ARM) that provide information for testing improved high-latitude physics and parameterizations. The ASR will be based on a polar-optimized version of the state-of-the-art Weather Research and Forecasting (WRF) model and the WRF data assimilation capabilities being developed at NCAR.

4. John Cassano, CIRES / ATOC, University of Colorado at Boulder, “Development of an Arctic System Model: Atmospheric Model Issues": Participants classified into the workshop task (preliminary)This presentation will focus on issues related to the atmospheric component of an Arctic system model (ASM). First, we will discuss the motivation for developing an ASM, with an emphasis on atmospheric processes. The second topic of discussion will deal with scientific questions that will be addressed with an ASM. Questions to be highlighted will include feedback studies, differences between high-resolution regional simulations and global climate models, and the role of low latitude variability on the Arctic climate system. The final topic will address the requirements for the atmospheric component of an ASM. This discussion will focus on the Weather Research and Forecasting model (WRF) and the ongoing efforts to develop a polar version of WRF.

5. Changsheng Chen¹, Guoping Gao¹, Andrey Proshutinsky² and Robert C. Beardsley², ¹School for Marine Science and Technology, Univ Mass, WHOI, “A High-Resolution, Unstructured-Grid, Finite-Volume Pan-Arctic Ocean Model (FVCOM-Arctic)”: A high-resolution prognostic unstructured-grid finite-volume free-surface 3-D primitive equation model for the Arctic Ocean (FVCOM-Arctic) has been developed and tested by our UMASS-WHOI team. The unstructured triangular grid makes this model capable of resolving accurately the complex coastal geometry of the Arctic Ocean including the narrow straits in the Canadian Archipelago and steep bottom topography over continental slopes and ridges. The finite-volume discrete method solves the integral form of the governing equations using control volumes, which ensures mass conservation in both individual volumes and the entire domain. FVCOM-Arctic is configured in terrain-following spherical coordinates, with uniform layers near the surface and bottom. It is driven by atmospheric forcing, river discharge, water exchange with the Pacific and Atlantic oceans, and tides. The open boundary conditions are specified using an global ocean model through a one-way nesting approach. The ocean model is coupled with the Community Ice Model (CICE) modified for the FVCOM-Arctic unstructured grid. The FVCOM-Arctic model has been validated for tidal simulations and now is being tested for a 50-year spin up run with climatologic forcing. The model results and future plans will be presented, including recommendations for the coupling of FVCOM-Arctic with a coupled atmosphere-ice-ocean system model.

6. Bin Cheng⁽¹⁾ and Timo Vihma⁽²⁾, (1)FIMR, (2)FMI, "Snow and sea ice thermodynamics in the Arctic: Model validation against CHINARE and SHEBA data": The characteristics of snow and sea ice are under continuous change, especially during the melting season. Numerical simulations of snow and sea ice have been carried out with a high-resolution thermodynamic snow/ice model (HIGHTSI). The results are compared against the CHINARE (Ice Camp) and SHEBA field measurements. The HIGHTSI is a process model under continuous development. In this study, the focus is on processes during the melt season, i.e. superimposed ice formation (ice refrozen from melt water), snow/ice sub-surface melting and the variability of snow. The effect of surface albedo and model vertical resolution was also investigated. HIGHTSI provides good results compared with the CHINARE Ice Camp measurements, especially for a model run with a high vertical resolution. For the model run with coarse resolution, the temperature profile shows much less spatial variability than the measurements. The precipitation has a strong impact on modelling snow thickness. A time dependent surface albedo is critical for a seasonal sea ice modelling. A good albedo parameterisation scheme is even more essential than high accuracy in the external forcing. Applying the standard thermodynamic sea ice forcing data proposed by SIMPI2, the HIGHSTI yielded good results compared with the SHEBA measurement. The evolution of modelled snow thickness is in line with the SHEBA observations. The ice mass balance was also modelled well in particular when taking the superimposed ice formation into account.

7. K. Dethloff (1), A. Rinke (1), E. Sokolova (2), A. Benkel (3) W. Dorn (1), S. Brand (1), D. Handorf (1), M. Läuter (1), S. Saha (1), (1) Alfred Wegener Institute for Polar and Marine Research, Research Unit Potsdam, Germany, (2) Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany, (3) Institute of Coastal Research, GKSS, Germany, “Arctic climate feedbacks and global links”: The global influence of two Arctic climate feedbacks connected with sea-ice albedo changes and stratospheric ozone changes have been investigated. A new sea ice surface albedo scheme taking into account three different surface types (snow, pure sea ice, melt ponds) and being dependent on snow cover and surface temperature have been developed andintroduced in a global coupled model. The simulations show the largest temperature and sea-ice cover signal in the Arctic, but also significant changes over the rest of the globe up indicating global connections. This global link is due to changes ofbarotropic planetary wave patterns, which trigger the Arctic Oscillation and influences the sea -ice cover. Changes between high and low sea-ice cover phases of the Arctic ocean exert a strong influence on the northern hemisphere storm tracks. Two seven year long time slices with low and high sea ice cover have been analysed with respect to the feedbacks between the time-mean flow, the quasi-stationary planetary and baroclinic waves. Polar stratospheric ozone is an important factor impacting on climate dynamics and thus on atmospheric variability. A new coupled atmosphere ocean-sea ice general circulation model with interactive stratospheric chemistry and extending up to 80 km height was developed. This model was used to perform two 150-year climate simulations in order to study coupling mechanisms between ozone chemistry and dynamical processes, focusing on interannual to decadal scales. The non-interactive run only considered atmospheric chemistry driven by the simulated dynamics, with prescribed conditions for radiation. The second run with interactive chemistry dynamics feedbacks additionally allowed for the opposite dependency via radiation processes. Here we present results from these simulations, which indicate changes in the circulation in tropo- and stratosphere due to stratospheric ozone feedbacks, and discuss their differences in terms of decadal variability, analysing the Arctic Oscillation (AO) mode. The tropospheric variability within the interactive simulation, which favours the negative phase of the AO, appears to be enhanced, while at the same time the stratospheric variability weakens. To improve the nonlinear multiple-scale feedbacks between regional and global circulation structures an adaptive barotropic atmospheric model has been developed. Sensitivity studies with an adaptive model of the atmosphere based on the discretization of the underlying spherical shallow water equations by a Lagrange-Galerkin method, a combination of the finite element method and the semi-Lagrangian method, have been carried out. Uniform grid experiments as well as adaptive grid experiments have been performed. In the adaptive case the computational grid is adapted at every time step according to a physical error indicator. The comparison of uniform and adaptive grid experiments documents, that the adaptive model leads to a significant reduction of the number of grid points while the numerical error increases only slightly.

8. Ralf Doescher, SMHI, “Predictability studies in a regional coupled model of the Arctic”: This project aims at assessing the natural variability of the Arctic coupled system on timescales from years to decades. The tool to address this topic is Rossby Centres coupled ocean-ice-atmosphere model RCAO, which is run over the ERA40 period 1959-2000. During the development phase, sensitivity studies with respect to snow and ice albedo and river runoff have been carried out. The resulting coupled model shows a realistic sea ice extent decrease in the 1980s and 1990s.
   The goal of the predictability study is to distinguish between the internal and external Arctic variability. The internal variability is that part of the total variability which is generated inside the Arctic model domain due to nonlinear interaction between ocean, ice and atmosphere components of the Arctic system. Knowledge on the internal variability is important for statistical attribution of climate change signals. RCAO has been set up for an ensemble experiment with several runs, only differing by slightly varying initial conditions.
   First results show strongest differences in the variability of sea ice on the 1-5 year timescale, while the longer scale variability is common to all coupled runs. The two-dimensional internal variance is assessed by the mean internal variability among the ensemble members which is the considered noise, not predictable by providing lateral boundary conditions in sub-arctic regions. The external variance is given by the time-variance among the ensemble-averaged anomalies, which is interpreted as the variance due to external lateral forcing at the outer boundaries of the regional model. Generally both external and internal variability show strongest amplitudes at the Arctic coasts. The internal part has a clear stronghold at the Siberian coasts. In coastal regions, the external amplitudes are up to two times stronger than the internal ones. Away from the coast, the external part is often larger by a factor of 1-1.4. Furthermore, the role of large scale atmospheric circulation patterns for the amplitude of Internal variability is examined.
   These results need to be substantiated by further ensemble member. It is hypothesised that this kind of variability and predictability analysis will be even more interesting for transient climate runs, were thinning sea ice possibly allows for stronger internal amplitudes.

9. W. Dorn, K. Dethloff, A. Rinke, R. Gerdes, M. Karcher, F. Kauker, S. Frickenhaus: AWI, “Uncertain descriptions of Arctic climate processes in coupled models and their impact on the simulation of Arctic sea ice” The presence of an ice covered Arctic Ocean plays an important role in the Arctic climate system by its influence on the exchange between atmosphere and ocean. The interactions between atmosphere, ocean, and sea ice are determined by a couple of feedback processes which are not yet completely understood. For this reason, coupled climate modelshave still difficulties in reproducing present-day sea-ice conditions and their variability close to reality. The outcome of this is a large scatter in the simulated sea-ice cover and thickness among different models, which is further amplified when applying the models to climate change scenarios where an Arctic amplification of global warming is expected as a result of diminishing sea-ice cover.
   Sensitivity experiments with the coupled regional climate model HIRHAM-NAOSIM have shown that uncertainties in the description for Arctic clouds, snow and sea-ice albedo, lateral freezing and melting of sea ice, and a snow cover on sea ice are responsible for large deviations in the simulation of Arctic sea ice. While more sophisticated schemes for the albedo, the treatment of lateral freezing and melting, and the snow cover have already been successfully introduced into the model, the parameterization of clouds is still an open issue. Currently the model overestimates the cloud cover during winter associated with a warm temperature bias and too low ice growth during the cold season. The outcome of this is a thinner ice cover at the beginning of the melting period which tends to disappear more quickly than observed. This has strong consequences for the model performance at large, since feedback processes may further amplify biases in the coupled model system.

10. Tor Eldevik^(1,2), Jan Even Ø. Nilsen^(1,2), Doroteaciro Iovino^(1,2), K. Anders Olsson^(2,3) and Anne Britt Sandø^(1,2), (1) Nansen Environmental and Remote Sensing Center, Bergen, Norway, (2) Bjerknes Centre for Climate Research, Bergen, Norway, (3) Gøteborg University, Sweden,  “The Greenland Sea does not control the overflows feeding the Atlantic conveyor”: Open ocean convection in the Greenland Sea is often considered a dominant component in the Nordic Seas’ overturning from warm and buoyant Atlantic inflow to cold and dense overflow waters. Here we test this concept in a most direct sense by combining a unique set of hydrographic observations from 1950 to 2005, a full-scale tracer release experiment, and output from a regional ocean general circulation model. The Greenland Sea is estimated to contribute less than a fifth of the total overflow, and the commonly presumed causality between changes in the Greenland Sea and the overflows is neither evident in the observations nor in the model. There is however a notable co-variability between the Atlantic inflow to the Nordic Seas, its reminiscence in the Fram Strait, and the overflows.

11. Jean-Claude Gascard, Hervé le Goff and Matthieu Weber, UPMC, “Massive frazil ice production during winter 2007 observed during the transpolar drift of Tara across the Arctic Ocean”: During the transpolar drift of Tara across the central Arctic Ocean a massive production of frazil ice was observed during most of the winter season. This process was associated with a very well mixed shallow surface layer (20 to 30m thick) and a super cooled water at the bottom of the mixed layer sitting just above the cold halocline. This process was activated by internal waves (either tidal and/or inertial waves) at the base of the mixed layer. Abundant ice crystals forming at depth and popping up to the surface were observed visually by Tara crew members. This surprising phenomenon could be responsible for the formation of first year thick undeformed sea-ice as observed south of Tara during the following spring season. We will discuss the implication of this process as far as models are concerned and also as far as remote sensing can help us in documenting frazil ice formation in the Arctic since at the moment this process for sea ice formation is completely discarded in the Arctic.

12. Rüdiger Gerdes, AWI, "Long term changes of Arctic Ocean fresh water reservoirs in ocean-sea ice hindcasts and climate model scenario calculations": In recent years, a new round of climate simulations with improved coupled models has been performed. Experiments include simulations of the 20th century with realistic external forcing (solar radiation, sulfate aerosols) and different scenario calculations for the 21st century. This presentations will try to assess the 20th century performance of climate models in high northern latitudes. Ocean-sea ice hindcast simulations will be utilized in this assessment. Some fundamental problems in ocean-sea ice hindcasts will be addressed. The presentation concludes with examples of possible changes in the Arctic fresh water reservoirs over the 21th century according to the A1B scenario.

     

13. Fanny GIRARD-ARDHUIN and Robert EZRATY, IFREMER, “Sea ice drift data at global scale”:Polar orbiting satellites enable daily and global coverage of the polar oceans, providing an unique monitoring capability sea ice dynamics over Arctic and Antarctic. Available geophysical parameters include ice concentration, extent, type and sea ice drift. These parameters are used as observations for climate change and also for ocean models. Sea ice concentration and extent are inferred from radiometer data, the two last parameters are estimated using scatterometer data.
    Backscatter maps from SeaWinds/QuikSCAT and brightness temperature maps from SSM/I are available at a pixel resolution of 12,5 km from which ice drift can be estimated for each sensor during the cold period. IFREMER/CERSAT makes available a “Merged” sea ice drift dataset based on the combination of these drifts at 3 and 6-day lags at the grid resolution of 62,5 km. Combining these drifts increases the number of valid vectors and the time window. From 1992 until 1999, 3 and 6 day-lags drifts are estimated from SSM/I radiometer. Since 1999, drifts are estimated from the combination of QuikSCAT scatterometer and SSM/I radiometer. Monthly merged drift maps are also available. This time series will continue with ASCAT/MetOp scatterometer data. Since 2002, IFREMER/CERSAT provides also higher resolution drift maps from AMSR-E radiometer data, adapted to regional studies. Due to its resolution, drifts can be estimated at 2 day-lag.
    These routinely produced drifts are available for ocean and climate models, for validation or assimilation. Results of AOMIP coupled ocean-ice models and LIM ice model have been compared with these datasets. These data have been also used in the UK MetOffice FOAM model for assimilation with very good results. The presentation will show the IFREMER/CERSAT available datasets and examples of their use.

14. Elena Golubeva, Gennady Platov and Valentina Malakhova, ICMMG, Russia, “Modeling variability of the Atlantic layer circulation in the Arctic Ocean”: A warm (with the temperature exceeding zero) and a salty flow of Atlantic Water penetrates the Arctic via Fram Strait and the Barents Sea. Observational data provide evidence that Atlantic Water spreads in the Arctic at intermediate depth. Following the isobaths along the slopes and ridges it travels cyclonically as a boundary current and exhibits a cyclonic circulation cell in each topographic basin in the Arctic Ocean.
    Here we present model results from sensitivity studies concerning a reproduction of this cyclonic circulation in ICMMG ice-ocean model. We discuss numerical experiments with and without ‘Neptune’ parameterization of eddy-topography interaction. It was detected that basically cyclonic Atlantic Water exhibits different paths depending on the viscosity coefficients used in numerical model. Then we examine the Atlantic Water path variability depending on the forcing conditions which is most evident in the Canadian basin.

15. Sirpa Hakkinen, NASA Goddard SFC, “Model hindcasts from sigma and z-coordinate models of the Arctic-Atlantic Oceans”: The variability of the Arctic sea ice-ocean system will be analyzed from 50+ year runs using NCEP/NCAR Reanalysis data as surface forcing. The ice-ocean model versions include a sigma-coordinate model, a z-level model, and a z-level model with imbedded sea ice. All versions use Mellor-Yamada 2.5 turbulence scheme for vertical mixing, and the parameterization for the ice-ocean heat and salt exchange and bottom boundary layer are the same. The analysis of oceanic quantities focuses on surface height, stream function and heat and fresh water content, their spatial and temporal variations. The seasonal and interannual variability of the Beaufort Sea fresh water and heat content will be discussed in light of the 2002-2006 observations in this region. Recommendations for model improvements will be formulated based on model performances estimated by model validation against observational data.

16. W. D. Hibler, III, University of Alaska Fairbanks, “Toward Improved Ice-Ocean Dynamics in Atmosphere-Ice-Ocean Models”: While considerable progress has been made over the last decade on the treatment of sea ice in coupled atmosphere-ice-ocean models there are still a number of deficiencies that need to be addressed, especially in the realm of ice-ocean circulation models. A number of these issues are discussed in this overview presentation which also includes recent work (together with a preprint of a manuscript submitted for publication) on ice-ocean-tidal modeling. In terms of sea ice mechanics important effects not included in most current models are strict energy dissipation (both theoretically and numerically) in ice rheology formulations, utilization of non-linear plastic ice rheologies consistent with laboratory
    measurements in both shape and flow rule and capable of producing realistic linear kinematic features in simulated sea ice deformation fields.

17. Marie-Noelle Houssais and Christophe Herbaut, LOCEAN, "Validation of a regional Arctic-North Atlantic model based on the ORCALIM sea ice-ocean model": A regional version of the global ORCA05 coupled sea ice-ocean model has been developed to investigate the dynamics of the Arctic-North Atlantic oceans. In addition to the Arctic Ocean, the model domain encompasses the Bering Sea and the Atlantic Ocean down to 30 °S. At the Pacific and Atlantic open boundaries, the velocity and tracer distributions must be prescribed. The variable horizontal grid provides enhanced resolution (25 km) at high latitude and lower resolution (50 km) at the equator. Results of a simulation in which the model has been forced by repeated cycles of the ERA40 atmospheric fields over the period 1958-2001 with open boundary conditions taken from a global simulation of the ORCA05 model are presented. The analysis focuses on the Atlantic water pathways from the Greenland Scotland ridge toward the Arctic Ocean. Some sensitivity analyses show the great sensitivity of the simulated fields to the parameterization of the subgrid scale processes and to the model forcing.

18. Elizabeth Hunke, Los Alamos National Laboratory, "A GCM Perspective on the Arctic": Global Climate Models (GCMs) represent one set of customers for both observations and modeling improvements, particularly those that provide better fidelity or understanding of physical processes.  GCMs must balance limitations in computing resources and manpower with the need to simulate all aspects of the climate system accurately and in detail.  As a result, polar simulations of GCMs often receive less attention than do the lower latitudes.  This talk will provide a global climate modeling perspective on the Arctic from the point of view of a GCM/sea-ice modeler, including an overview of the sea ice components in current IPCC-class GCMs.
    One of these models features some striking advances in its simulation of the Arctic since the recent IPCC modeling cycle;  I will discuss the current status of this model and our plans for further improving the cryospheric component of GCMs.

19. Erko Jakobson, Environmental Physics Institute, University of Tartu, “Tethered balloon measurements in Arctic”: In task "Data and models", I could introduce the results of tethered balloon measurements made this summer in Arctic. The headline is "Tethered balloon measurements made in Arctic at drifting base TARA". The system measures temperature, humidity, wind velocity and direction profiles up to 2 km height. Measurements started in April 2007 and will end in September. I participated in the measuring work there at the beginning for 1 week, an other Estonian scientist is running this experiment till the end of September.

20. Per Kållberg, SMHI, “The ECMWF ERA-40 reanalysis and beyond”: These days ERA-40, the global atmospheric reanalysis carried out by ECMWF, (European Centre for Medium-range Weather Forecasts) is a major and important resource for most aspects of atmospheric general circulation research. As a former member of the ERA team, I will here give a general description of the ERA-40 data-assimilation system. The availability and treatment of the observations – both ‘conventional’ and satellite radiances – as well as their temporal variation will be discussed. The 3-dimensional variational analysis scheme and the assimilating forecast model will be summarized. Known problems and limitations in ERA-40 will also be addressed, with special emphasis on the arctic regions.
    Since the finalization of its previous two reanalysis projects, ERA-15 and ERA-40, ECMWF has now embarked on a third ‘interim’ reanalysis. It is intended to cover the period from 1989 to ‘today’. So far only a couple of years have been completed, but already the new reanalyses demonstrate the continuing and major improvements in the ECMWF data assimilation system. The new ‘interim’ analyses are made with a higher spatial resolution, with a more advanced 4-dimensional variational analysis system, with a new technique for bias correction of satellite radiances and with several revisions to the physical parameterization in the assimilating forecast model. Some examples of the new reanalyses will be shown.

21. Thomas Kaminski⁽¹⁾, Ralf Giering⁽¹⁾, Ernest Koffi⁽²⁾, Peter Rayner⁽²⁾, Marko Scholze⁽³⁾, Michael Vossbeck ⁽¹⁾, (1) FastOpt, (2) LSCE, (3) QUEST, “Quantitative Design of Observational Networks”: Quantitative network design aims at evaluating a set of observational networks in terms of their ability to constrain a target quantity of interest. Network design thus relies on a numerical model that can simulate the quantities observed by the (candidate) networks as well as the target quantity. It also relies on an assimilation system that can quantify the observational constraint in terms of an uncertainty in the target quantity. The presentation will provide the methodological background and examples. It will also demonstrate an interactive software tool for the design of an observational network for the terrestrial carbon cycle.

22. M. Karcher^(1,2), F. Kauker^(1,2) , R. Gerdes⁽²⁾ , (1) Ocean Atmosphere Systems, Germany; (2) Alfred Wegener Institute for Polar and Marine Research: The Arctic ocean in the 20^th century - first results from an AOMIP experiment driven with 100 years of reconstructed forcing fields”: The recent decade has seen grave changes of ice and ocean characteristics in the Arctic. The bulk of simulation experiments which aimed at an analysis of these changes has been limited to the application of atmospheric forcing fields from the ECMWF or NCEP reanalysis data sets. Due to a lack of observational data for assimilation in the early 20^th century these data sets were limited to the second half of the century.
    A reconstructed atmospheric forcing data set developed recently at the Alfred Wegener Institute for Polar and Marine Research now allows to extend the analysis of the ice-ocean dynamics of the northern oceans to cover the fully 20^th century.
    As a contribution to the Arctic Ocean Model Intercomparison Project (AOMIP) the medium resolution (0.25 degree) model version of NAOSIM (North Atlantic/Arctic Ocean Sea Ice Model) has been driven with the reconstructed 100-year Arctic forcing data. We present first results focussing on the ice extend and volume, as well as on characteristics of the inflowing water of Atlantic origin to evaluate the model experiment in comparison with observations.

23. R. Kwok, JPL, “Assessment of sea ice simulations using high-resolution kinematics from RADARSAT”: Sea ice motion products from RADARSAT Geophysical Processor System (RGPS) provide high-resolution kinematics and deformation estimates at length scales of ~10 km covering more than half the Arctic Basin. The RGPS sea ice trajectories are of the same quality as those motion estimates from the drifting buoys. We use these estimates to assess the Arctic sea ice simulations from four different coupled ice-ocean models with results made available by the: University of Washington (PIOMAS), Naval Postgraduate School, Los Alamos National Laboratory, and Jet Propulsion Laboratory (ECCO2). The following time-varying parameters are examined: ice export, regional ice deformation, and ice volume production. We also examine the spatial correspondence in the patterns of ice ‘fractures’ or linear kinematic features that seem to be an emerging characteristic of high-resolution simulations of the ice cover. Even though these models use the essentially the same ice mechanics (i.e. viscous-plastic) and some with the same forcing fields, these model-data comparisons illustrate the range behavior of the ice cover depending on the parameters used in these models and their implementation. We summarize the results in this talk.

24. W. Maslowski⁽¹⁾, J. Jakacki⁽²⁾, (1) NPS, (2) Institute of Oceanology, Polish Academy of Sciences, “Oceanic Heat Fluxes, Arctic Sea Ice Melt, and Climate Change”: The recent warming in the Arctic Ocean has received a lot of attention within the science community and general public. The main manifestation of this warming has been the dramatic reduction of summer sea ice cover. This trend, superimposed over large seasonal and interannual variability, has been coincident with the Northern Hemisphere Annular Mode (NAM also known as the Arctic Oscillation (AO) or the North Atlantic Oscillation (NAO)). The reduction of the Arctic ice pack has been typically associated with anomalies of surface air temperature and circulation over the Arctic and those in turn have been linked to the Arctic Oscillation (AO). Such studies typically assume the dominant role of external atmospheric forcing and neglect effects of processes internal to the Arctic Ocean. However, some of the recent variability clearly points to other modes of large scale climate forcing than NAM/AO/NAO. Analyses of our combined sea ice and ocean model results suggests that the oceanic heat, in addition to atmospheric radiative and sensible heat input, can significantly contribute to sea ice melt, especially in regions coincident directly downstream of oceanic heat advection from the Pacific and Atlantic oceans.
    We will present results from a high resolution coupled ice-ocean model of the Pan-Arctic region forced with realistic atmospheric data to demonstrate the role of oceanic forcing in recent melting of the Arctic sea ice cover. In particular, the influence of summer Pacific Water on the western Arctic sea ice retreat and a mechanism for the removal of sea ice cover over the Greenland shelf in recent years will be discussed. In addition, we will argue that a high resolution regional Arctic climate system model consisting of state-of-the-art land, atmosphere, sea ice, and ocean components is needed to address some of the main limitations of present-day global climate models. Such a regional model will advance understanding of past and present states of Arctic climate system and will improve prediction of its future regimes and its potential effect on global climate.

25. Jean-François Lemieux, Bruno Tremblay, Stephen Thomas, Jan Sedlacek, and Lawrence Mysak, McGill University, "Using the preconditioned Generalized Minimum RESidual method to solve the sea ice momentum equation":We have developed a high resolution (10 km) dynamic/thermodynamic sea-ice model of the Arctic to study energy dissipation associated with sea-ice deformations. This new platform can be used with various yield curves and flow rules defining many viscous-plastic formulations. To solve the non-linear momentum equation, the equation is first linearized and solved using the preconditioned Generalized Minimum RESidual method (GMRES). These two steps are repeated multiple times (outer loop) until a convergence criterion is met. The GMRES method has low storage requirements, is computationally efficient and parallelizable. We investigate the convergence properties of the GMRES method using point and line Successive Over-Relaxation (SOR) preconditioners. We compare the computational efficiency of the preconditioned GMRES with that of a stand-alone point SOR. It is observed that GMRES preconditioned by line SOR is about five times faster than the stand-alone point SOR. GMRES allows for the solution of a non-symmetric system. The Coriolis term and off-diagonal part of the water drag term can then be treated implicitly. It was observed that the implicit treatment eliminates a residual oscillation in the total kinetic energy of the ice pack that is present when these terms are handled explicitly. Finally, we discuss the convergence criteria employed for sea-ice dynamic models. As ongoing work, we are currently implementing Newton’s method to replace the outer loop and plan to parallelize the model.

26. C. Lüpkes⁽¹⁾, A. Herold⁽¹⁾, T. Vihma⁽²⁾, (1) AWI, (2) FMI, “Impact of leads on processes in the polar atmospheric boundary layer”: It is essential for climate and weather prediction models that the processes contributing to the surface energy budget are well represented. This has been shown by many modelling and observational studies also in polar regions, where the near-surface processes are strongly influenced by the sea ice concentration. However, the accuracy of sea ice concentration data derived from satellite images is still very limited which might have a strong influence on the results of polar climate and weather prediction models. We present idealized modelling studies on the effect of an inaccurate knowledge of the sea ice concentration during polar winter on the atmospheric boundary layer. First, results of a 3D stand-alone mesoscale atmospheric model are discussed which uses a prescribed surface temperature and sea ice concentration. Differences in the domain averaged profiles of temperature and wind are discussed which result from small differences in the sea ice concentration between different model runs. In a second step the coupled system atmosphere, sea ice and ocean is considered using a 1D mesoscale atmospheric model which is coupled with a thermodynamic snow/sea ice model. It is used to estimate the maximum possible effect of convective heat transport above open leads in a compact ice cover on the atmospheric boundary layer temperature during polar night. After initialisation with observed temperature profiles, the model results are considered at different integration times as a function of geostrophic wind and sea ice concentration A. The results document that for A > 90 % small changes of A may have a strong effect on the ABL temperatures. A change by only 1 % may cause a temperature signal of 1 – 3 K dependent on the time of integration. This shows that modelling of lead formation and parameterization of lead effects on the atmosphere and ocean are crucial issues for climate modelling.

27. Elena Maksimovich and Jean-Claude Gascard, UPMC, “Significant atmospheric warming deduced from Surface Air Temperature in the central Arctic Ocean during recent years compare to late 90s”: Using NCEP reanalysis data we have been comparing different years during the past 10 years in terms of cumulative number of freezing degree days starting during the fall (September) and ending during the spring (May) of each year. Surprisingly an abrupt drop of nearly 1000 cumulative freezing degrees days appears when comparing late 90s with more recent years. Such a drop could easily be responsible for a significant decrease in mean sea ice thickness. We are investigating potential reasons for such a decrease in the number of freezing degrees days and will also expand the analysis further in the past to check for the stability and significance of these variations.

28. H.E. Markus Meier and Per Pemberton, SMHI, “On the parameterization of mixing in regional circulation models for the Arctic Ocean”: In this study we will investigate the role of mixing parameterizations in three-dimensional modelling for the Arctic Ocean. We will show the impact on the ice cover, temperature and salinity fields, and on the circulation using the regional coupled ice-ocean model RCO, the Rossby Centre Ocean model, developed at SMHI. RCO is a regional version of the global OCCAM model coupled with a Hibler-type two-level (open water and ice) dynamic-thermo-dynamic sea-ice model using viscous-plastic rheology. The model domain of RCO covers the central Arctic Ocean, the Nordic Seas, and the North Atlantic roughly to 50°N. Open boundary conditions are implemented at 50°N. The horizontal resolution is rather coarse and amounts to 0.5° or approximately 50 km in a rotated coordinate system centered over the North Pole. Especially we will focus in this study on the ice cover and on the Atlantic Water circulation. A result of the Arctic Ocean Model Intercomparison Project (AOMIP) was that even regional models with higher horizontal grid resolution than RCO can have problems to reproduce the circulation of the Atlantic Water realistically. Approximately half of the models investigated within AOMIP showed cyclonic circulation of Atlantic Water (as observed) and the other half showed the opposite circulation. We will summarize existing articles on the topic and will present sensitivity results using our Arctic Ocean model.

29. U. Mikolajewicz, T. Koenigk and D.V. Sein, Max-Planck-Institute for Meteorology, Hamburg, "Modelling Arctic climate variability": Variabilility of the Arctic climate system on interannual and decadal time scales is investigated using multi-century simulations with coupled atmosphere ocean models. One prominent mode of variability is characterized by accumulation of sea ice at the Siberian coast, its subsequent advection in the transpolar drift towards Fram Strait, where it leads to positive ice export events. The anomaly is advected in the East Greenland current and finally reaches the Labrador Sea as negative salinity anomaly, affecting local convection and atmospheric circulation. At least when strong, this mode offers the potential for some predictability on interannual time scale.
    Experiments with a regionally coupled model of the Arctic and the northern North Atlantic allow to discriminate between forced variability from low latitudes and variability generated in the Arctic. It turns out that the latter substantially modifies the Arctic sea ice export.

30. An Nguyen, MIT: “Salt rejection, advection, and mixing in the MITgcm coupled ocean and sea ice model”: State-of-the-art coupled ocean and sea ice models in the Arctic tend to misrepresent the upper ocean stratification due to inadequate representation of physical processes and due to resolution limitations. Specifically, results from the Arctic Ocean Model Intercomparison Project (AOMIP) showed that participating ocean models consistently failed to either produce and/or maintain the cold halocline layer at depth ~ 50-200 meters. Without a cold halocline, excess heat flux from the warm Atlantic water source at greater depths can inhibit production of realistic sea ice extent and thickness in a sea ice model.  To address possible sources of model deficiencies in reproducing the halocline, we investigate the effects of modified KPP mixing and salt rejection schemes on the upper ocean vertical stratification. We will show preliminary results of the MITgcm coupled ocean and sea ice model with an emphasis on salt rejection, advection, and vertical mixing in the upper 200 meters. We will also assess the model's monthly output for circulation, fresh-water balance, and sea ice production and fluxes in the Arctic using satellite and in-situ data and AOMIP outputs.

31. Don Perovich, Jacqueline Richter-Menge, Bruce Elder, and Keran Claffey, ERDC-CRREL, „The Mass and Heat Balance of Ice”: The Arctic sea ice cover plays an important role in the global climate system as an indicator and an amplifier of climate change. The amount of ice growth and ablation are key measures of the thermodynamic state of the ice cover. This information is needed to understand the causes and the implications of the observed reduction in ice volume. A network of autonomous ice mass balance buoys (IMB) is currently monitoring snow accumulation and ablation, ice growth and melt, and internal ice temperature. Point measurements from IMBs provide reasonable estimates for undeformed and unponded ice, which is the dominant type in the ice pack. IMBs are collocated with other autonomous platforms measuring atmosphere and ocean properties. In addition to mass balance information, the IMBs provide time-averaged values of surface energy budget and the ocean heat flux. IMB results can be combined with surface-based surveys, integrated with satellite data, and assimilated into models to extend mass balance findings to larger scales, verify satellite findings, and evaluate model results.

32. Gennady Platov and Elena Golubeva, ICMMG, Russia, “Can polynya effect be resolved in coarse resolution model?”: The areas with open water surface, free from ice, or polynyas may result from both a strong offshore wind and an upwelling of warm water. The wind-driven coastal polynya results in intense ice production and heat loss. Therefore it is known to be an effective mechanism responsible for deep dense water formation and for the Arctic halocline. The coupled ice-ocean Arctic model was running along with nested regional model of Barents and Kara Seas shelf to investigate whether this mechanism could be resolved in a coarse resolution models. According to previous results (Blindheim, 1989; Quadfasel et al., 1988; Schauer and Fahrbach, 1999) the ability of dense water plume formed in shelf polynyas to sink to deeper layers depends on its initial properties and the properties of ambient shelf water all the way before it reaches the slope. The considerable amounts of ambient water could be entrained into the plume and hence could control the depth to which the plume will sink. It was found that in coarse resolution model this water mass becomes more affected by ambient water and loses its uniqueness too fast. Only those polynyas located close to the shelf break could contribute to deep halocline water.

33. Proshutinsky, A., D. Nechaev, G. Panteleev, J. Zhang and R. Lindsay: “Toward reconstruction of the Arctic climate system: Sea ice and ocean reconstruction with data assimilation”: Motivated by the SEARCH implementation-plan recommendations (SEARCH, 2005), we attempt to develop an integrated set of assimilation procedures for the arctic ice-ocean system that are able to provide gridded data sets that are physically consistent and constrained to the historical observations of sea ice and ocean parameters. Building on our past research activities in sea ice and ocean data assimilation, we make some first steps toward the creation of an Arctic Climate System Reanalysis that uses modern four-dimensional variational data assimilation methods. We employ new data assimilation procedures to maximize the integration of model results with observations and thus provide the arctic research community with complete and accurate data sets, ultimately for at least the last three decades. Because this is a first attempt at constructing a 4D-Var reanalysis of the Arctic Ocean system we focus our attention on three distinct periods, each representing a different state of the Arctic climate. The first period is 1972-1978 when the Arctic was relatively cold and there is a large quantity of hydrographic data available, the second is 1989-1996 when large changes begin in the Arctic Ocean circulation, in its hydrographic structure, and in sea ice conditions, and the third is 1997- present when substantial amounts of open water begin to appear in the late summer. We will also compare our new reanalysis products with fields obtained by AOMIP models to validate AOMIP model results.

34. Andrey Proshutinsky, Woods Hole Oceanographic Institution, “AOMIP sea ice – ocean model improvement recommendations“: AOMIP was initiated in 2000. Briefly, this project has created a broad based "community" of directly involved arctic modelers from U.S.A., Canada, Germany, United Kingdom and Russia. The community-based modeling approach provides the unique opportunity to coordinate the investigation of different aspects of Arctic Ocean dynamics and thermodynamics because it allows the group to design a set of carefully-planned numerical experiments covering the most important processes and interactions. The main contributions from AOMIP are: (a) identification of model errors and causes of these errors and model discrepancies; (b) recommendations for improving existing regional and global coupled ice-ocean models by implementing new physics and parameterizations for the Arctic processes; and (c) to assess the degree of uncertainty in the results and conclusions made by different modelers, scientific groups and institutions.
    Model improvement includes several phases: (a) identification of problems; (b) search for solutions/improvements; (c) testing improvements based on one or two models; (d) recommendations to others; and (e) introduction and testing of new ideas. Following this scheme, several mechanisms and parameterizations have been applied and analyzed to improve models and model outputs. The results of these studies will be presented in this talk.

35. Proshutinsky A., A. Makshtas, D. Atkinson, “NCAR reanalysis validation in the Central Arctis”: We compare daily data from the for Atmospheric Research and for Environmental Prediction “Reanalysis I” project with observational data obtained from the North Pole drifting stations in order to validate the atmospheric forcing data used in coupled ice-ocean models. This analysis is conducted to assess the role of errors associated with model forcing before performing model verifications against observed ocean variables. Our analysis shows an excellent agreement between observed and reanalysis sea level pressures and a relatively good correlation between observed and reanalysis surface winds. The observed temperature is in good agreement with reanalysis data only in winter. Specific air humidity and cloudiness are not reproduced well by reanalysis and are not recommended for model forcing. An example sensitivity study demonstrates that the equilibrium ice thickness obtained using NP forcing is two times thicker than using reanalysis forcing.

36. Andrey Proshutinsky, Woods Hole Oceanographic Institution, “Integration and synthesis: the Beaufort Gyre study based observations and modeling”: The major goal of this study is to understand the structure of the BG system, its regulating mechanisms, and impact on Arctic climate. We accomplish this by investigating the composition and variability (at synoptic to decadal time scales) of the atmospheric, cryospheric and oceanic components of this system based on a specially designed observational program, improved modeling studies, and analyses of historical data sets. We speculate and provide observational evidence that the BG is a flywheel of the circulation that smoothly regulates variability of sea ice drift, thickness and concentration; accumulates and releases freshwater and heat of the ; and remotely interacts with the Greenland Sea Gyre promoting decadal variability of the Arctic climate. Among major conclusions of this approach is that the modeling represents the highest level of integration and synthesis and could be recommended for the implementation by the SEARCH and DAMOCLES programs. This approach naturally unites numerous goals and objectives of the observers and modelers allowing them to coordinate their activities, design numerical and field experiments and to enhance our understanding of Arctic change.

37. Rinke, A., K. Dethloff, W. Dorn, S. Saha & ARCMIP group, AWI-Potsdam: “ARCMIP results and HIRHAM sensitivity studies and further model development”: Selected results of the 1st ARCMIP experiment will be presented. Here, the intercomparison of the temperature and circulation patterns among the 8 models and with ERA40 data are discussed. The scatter among the models has been quantified, and found to be largest for air temperature over land, surface radiation fluxes, and cloud cover. The model intercomparison showed that the performance biases as well as the across-model scatter are largest in the lowest height levels and near the surface. There, the individual physical model parameterizations come into play from which the land-surface and boundary layer parameterizations, radiative transfer, and treatment of clouds seem to be of primary importance.
    As a second topic, the sensitivity of the Arctic atmospheric simulation to the given sea ice and landsurface/soil descriptions will be discussed. During winter, the realistic representation of the marginal sea ice zone is important as it contributes to the simulation of regional atmospheric circulation patterns and temperature profiles. During summer the direct thermodynamic effect of sea ice changes is small, while the dynamic response is still of importance but smaller than in winter.  A more complex landsurface/soil scheme which takes freezing/thawing processes into account shows significant influences on soil and air temperature simulations. The influences of land surface schemes and their coupling on the atmospheric conditions not only affect climate simulations on land but also in remote Arctic Ocean regions. Finally, our plans of the further HIRHAM model development will be presented.

38. Andrew Roberts, IARC: "Plans for using an Arctic System Model to investigate high-frequency interactions between the atmospheric and ice-ocean boundary layers": This talk will present plans to investigate 'high-frequency' oscillatory exchanges between the ice-ocean and atmospheric Arctic boundary layers excited via tidal and semi-diurnal dynamics of the ice-ocean boundary layer. A hierarchy of models is planned for the project, ranging from a mechanistic ice-tide model of the Arctic to a fully coupled regional Arctic System Model. Up to now, high-frequency sea ice dynamics has been investigated in the ice-ocean system without an interactive atmosphere, and typically without including a oceanic baroclinic mode. This project aims to include these factors to gain a better understanding of how semi-diurnal sea ice-ocean dynamics may scale to affect large temporal and spatial scale processes of the Arctic System.

39. Øystein Skagseth, Institute of Marine Research, Norway, “On the Atlantic water through the Norwegian and Barents Seas”: The Atlantic water (AW) flow northward through the Norwegian and the Barents Sea is observed by a number different measurements; including repeated hydrographic sections and - spatial surveys, and over the last 10 years arrays of moored current meters. The purpose of this contribution is to point to some important and robust characteristics of the AW flow based on these data.
    The focus will be on 1) the long-term variations in the hydrographic properties in the Norwegian and Barents Sea, 2) the circulation and possible recirculation in the section between Norway and Bear Island, and 3) the different forcing effects of the Norwegian Atlantic Current and the Atlantic inflow to the Barents Sea to similar atmospheric systems. The presentation will aim at identifying important, but not fully understood, issues under these points that could be further analyses in a model framework.

40. Jun She, DMI ”Optimal Design of Observing Networks (ODON)”: ODON is a fundamental research aiming to develop quantitative and practical methods for assessing the gaps and redundancies in existing observing networks and design optimal (cost-effective) monitoring strategies for future observing networks. Statistical optimal design method (a combination of characteristic scale analysis, sampling error analysis, information analysis and optimal control theory), Observing System Simulation Experiments (OSSEs) via data assimilation and cost-benefit analysis will be used to archive the goal. The developed method will be applied to assess and design the spatial-temporal sampling strategies for water temperature and salinity observing system in North Sea and Baltic Sea.
    ODON was supported by European Commission Fifth Framework Programme for 3years (2003-2005), as a part of the EC FP5 Operational Forecasting Cluster. The consortium consists of five major marine research institutions/centres in Northern Europe (DMI, BSH, POL, MUMM and SMHI), coordinated by DMI.

41. Gregory C. Smith¹, Keith Haines¹, Jon Blower¹, Alastair Gemmell¹ and Dan Lea^1,2, ¹ University of Reading, ESSC, ² UK Met Office, “Using ocean reanalysis to study water mass variability with the help of a new Java web application”: Our main aim is to produce a high-resolution reconstruction of the global ocean over the last 50 years suitable for the study of ocean climate signals. The relative abundance of collocated temperature and salinity observations provided by Argo are used to develop an assimilation scheme whereby temperature and salinity profiles are assimilated on isotherms and isopycnals. This allows us to exploit the larger spatial and temporal decorrelations of these quantities, compared with assimilation on geopotential surfaces, allowing flow dependent assimilation and recovery of water mass information.
    Early results from a series of assimilation experiments using a ¼ degree resolution global ocean model over the last few years will be presented. The analysis of model – data misfits from these simulations on depth, temperature and density levels permits a better evaluation of water mass properties by separating errors due to model dynamics and representivity from those in water mass properties.
    In order to support this work, we have developed a tool for simultaneously visualizing model results and observations called OceanDIVA (Ocean Data Inter-comparison and Visualization Application). OceanDIVA is a Java web application which is capable of reading in both observed and model ocean data (in NetCDF format) and outputting information on how they compare. One option is to request KML output which can be read by Google Earth and shows the location of all observed temperature and/or salinity profile data, colour-coded by RMS misfit between observed and model data. Upon clicking the profile icon, a plot of model and observed data on depth or temperature levels is generated on the fly. Other options include statistical measures over predefined (or user-defined) regions. One of the most powerful aspects or OceanDIVA is that the aforementioned capabilities can be used on data from anywhere in the world providing it is accessible via the OPeNDAP protocol.

42. Michael Tjernström⁽¹⁾, Rune Grand Graversen⁽²⁾, Bin Cheng⁽³⁾, and Timo Vihma⁽⁴⁾, (1) Univ Stockholm, (2) FIMR, (3)  FMI, “Large-scale model reanalyses for the Arctic: validation, temperature trends, and applicability as forcing for sea ice models”: The ERA40, NCEP and JRA reanalyses have been validated against some 20 Arctic rawinsonde soundings stations over 20 years. ERA40 and JRA have similar but significantly lower root-mean-square error than NCEP. According to ERA40 and JRA, the enhanced Arctic warming is not confined to the surface. For example, summer warming is almost as large as winter warming, but is found isolated around 700 hPa, while no surface warming is observed in summer. In autumn the warming goes through most of the troposphere. This enhanced warming is not continuous but realizes on distinct events of meridional heat flux associated with cyclones; the lag between a flux event at 60N and a an excessive warming in the Arctic is about 5 days on average. Both ERA40 and JRA feature the elevated warming layer, while NCEP does not.
    A high-resolution thermodynamic snow/ice model (HIGHTSI) was run through summer (May - September) 2003 with forcing from ECMWF operational analyses and NCEP/NCAR reanalyses. Differences in the forcing data sets yielded large differences in modelled snow and ice thermodynamics. The ECMWF sea ice scheme yielded higher surface temperatures than HIGHTSI with the ECMWF forcing data. According to NCEP/NCAR the melting period was much longer than according to (a) HIGHTSI with NCEP/NCAR forcing and (b) the ECMWF. The ECMWF operational precipitation forecasts (not including the first six hours, which suffered from spin-up problems) yielded realistic snow accumulation in the seasonal scale, while the precipitation in NCEP/NCAR reanalysis was unrealistically large.

43. Tara Troy and Eric F Wood, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ USA: "Reconstructing the Land Surface Water and Energy Budgets of Northern Eurasia": Northern Eurasia represents a large portion of the global land surface area and has shown stronger evidence over the past half century of climate change than other regions, yet the state of the water and energy budgets at the land surface are poorly defined. The region provides a challenge to study with its sparse precipitation network, often with a relatively short record, and cold season processes that are difficult to parameterize at large scales. To move beyond these challenges, we synthesize information about the water and energy budgets from all available sources: in situ observations, remote sensing, reanalysis, and land surface modeling. Using precipitation datasets that extend back to 1950, we reconstruct the land surface's energy and water budgets over Northern Eurasia using the Variable Infiltration Capacity (VIC) macro-scale hydrologic model, comparing the results to in situ observations. For later decades, we include remote sensing data, such as NVAP, and the ERA-40 re-analysis model output to compare the land surface budgets from a variety of sources. Challenges to accurate simulation of the water and energy budgets include using accurate soil and land use databases and correctly representing hydrologic processes across a range of spatial and temporal scales, including the effects of water management and land cover change. We will tie our research into past studies, identifying continuing gaps in our knowledge and progress in our understanding of the water and energy budgets across Northern Eurasia.

44. Timo Vihma, Michael Tjernström, Joseph Sedlar, and Matthew D. Shupe, FMI and Univ Stockholm, “Stable boundary layer and cloud-capped boundary layer as challenges for modelling in the Arctic”: The main challenges in modelling of the Arctic atmospheric boundary layer are related to processes in the stably stratified boundary layer and cloud-capped boundary layer. The problems in modelling of the stable boundary layer (SBL) are reviewed with a particular focus on the following aspects: (a) differences between the Arctic long-lived SBL and the nocturnal SBL typical for middle-latitudes, (b) maximum sensible heat flux from the atmosphere to snow/ice surface and their decoupling, and (c) vertical divergence of turbulent fluxes of momentum, sensible heat and latent heat, and its implications for model parameterizations.
    New results on modelling of the cloud-capped boundary layer are presented. They are based on comparisons of six regional climate models validated against SHEBA observations. While some modeled cloud properties, such as the cloud-water paths, are reasonable in a climatological sense, the temporal correlation of model cloud properties with observations is poor. Most models underestimate the presence of high clouds, and the modeled low clouds are too thin and displaced downward. Practically all models show a preference to locate the lowest cloud base at the lowest model grid point. For longwave radiation in summer a negative bias is present both for cloudy and clear conditions and inter-model differences are smaller when clouds are present. In winter, the clear cases are modeled reasonably well, while the cloudy cases show a very large inter-model scatter with a significant bias. This bias is likely due to a complete failure in all the models to maintain liquid water in cold winter clouds. All models overestimate the cloud attenuation of summer solar radiation for thin and intermediate clouds, and some models maintain this behavior also for thick clouds.

45. Klaus Wyser and Ralf Döscher, Rossby Centre, SMHI, “Impact of an improved radiation parameterization for the Arctic”: Climate model simulations tend to overestimate the pressure in the Central Arctic, leading to a wrong circulation pattern with a too low transport of heat and momentum to high latitudes. The regional climate model RCA has suffered from the same shortcomings as GCMs in this aspect. We now show how a small change in the parameterization of the interaction between SW radiation and clouds can help to reduce the pressure field. The effects from the radiation change are amplified through a feedback between clouds, radiation and the atmospheric circulation. The finding is important for other climate models as well as many radiation parameterizations have been developed and tuned for mid-latitude (or even tropical) conditions while the properties of Arctic clouds are quite different. We will demonstrate the impact of an improved parameterization of the optical properties of Arctic clouds in both coupled and un-coupled simulations.

46. Nikolay Yakovlev, INM-RAS, “FEMAO (Finite-Element Model of the Arctic Ocean): Towards the understanding of the role of tides in the Arctic Ocean climate formation”: The implementation of some programs of the Arctic Ocean modeling (e.g. AOMIP) reveals the striking differences in the model output for long-term simulations of the deep-water circulation. There are several problems concerning the role of tides, the physical mechanisms of narrow coastal jets generation, the role of small eddies in the transport of heat, salt and freshwater, and the ice-ocean dynamical coupling.
    To solve some the problems in the frame of DAMOCLES the modified version of the coupled ice-ocean general circulation model FEMAO was implemented.
    The preliminary numerical experiments with the explicit quantitative estimation of the role of tides in the formation of the climate system of the Arctic Ocean (water and sea ice) were carried out. The tidal forcing is specified as the incident M2 wave, similar to the approach by Kowalik and Proshutinsky, 1994.  These runs revealed the problem of the ocean-ice dynamical coupling. The problem is especially important in a case of ice drift under high frequency atmosphere and ocean forcing and in a case of summer shallow halocline. Some general considerations on the ocean-ice drag parameterization will be presented.
    Some aspects of the model improvement during the DAMOCLES implementation period such as bottom topography approximation by analogue of “partial cells” and “shaved cells”, free-surface formulation in “z” and “z-star” vertical coordinate systems, also will be under short discussion.

47. Jinlun Zhang, APL, “On high resolution modeling of sea ice dynamics with ridging calculations”: High-resolution sea ice modeling that involves explicit ridging calculations has become more and more common within the climate modeling community. This poses new challenges and opportunities. Focus will be on the challenges in high-resolution modeling of sea ice dynamics when ridging processes are explicitly represented, which is particularly vulnerable to model instability. Several issues will be discussed: plastic sea ice rheologies and their applicability in high-resolution modeling; time stepping with various ice dynamics models; and ridging calculations that may or may not lead to model instability. Mention will also be made of the possible opportunities in high resolution modeling, such as simulating land fast ice that is important for Arctic marine ecosystem. A brief description will be given of our effort in coupling Arctic sea ice, ocean, and ecosystem models and some preliminary results from the coupled model, including the effect of changing sea ice conditions on the arctic marine ecosystem.

Agenda

29^th October:

830-900: Registration

900-915: Greeting: Jean-Claude Gascard (DAMOCLES)

915-930: Greeting: Peter Schlosser (SEARCH)

Model Improvements: Chairs: Andrey Proshutinsky and Klaus Dethloff

930-950: Andrey Proshutinsky “AOMIP sea ice-ocean model improvement recommendations” (ppt ~7MB)

950-1010: Anette Rinke “ARCMIP results and HIRHAM sensitivity studies and further model development” (ppt ~6MB)

1010-1040: Coffee break

1040-1110: Ruediger Gerdes (keynote) "Long term changes of Arctic Ocean fresh water reservoirs in ocean-sea ice hindcasts and climate model scenario calculations" (ppt ~23MB)

1110-1140: Bill Hibler (keynote) “Toward Improved Ice-Ocean Dynamics in Atmosphere-Ice-Ocean Models” (ppt ~12MB)

1140-1200: Klaus Dethloff “Arctic climate feedbacks and global links” (ppt ~6MB)

1200-1220: Wieslaw Maslowski “Oceanic Heat Fluxes, Arctic Sea Ice Melt, and Climate Change” (ppt ~8MB)

1220-1240: Elisabeth Hunke “A GCM perspective on the Arctic” (pdf ~3MB)

1240-1300: Elena Golubeva “Modeling variability of the Atlantic layer circulation in the Arctic Ocean” (ppt ~2MB)

1300-1400: Lunch

1400-1420: Ralf Doescher “Predictability studies in a regional coupled model of the Arctic” (ppt ~3MB)

1420-1440: David Bromwich (presented by John Cassano) “Polar-Optimized WRF” (pdf ~2MB)

1440-1500: Changsheng Chen “A High-Resolution, Unstructured-Grid, Finite-Volume Pan-Arctic Ocean Model (FVCOM-Arctic)”

1500-1520: Sirpa Hakkinen “Model hindcasts from sigma and z-coordinate models of the Arctic-Atlantic Oceans” (ppt ~3MB)

1520-1540: John Cassano “Development of an Arctic System Model: Atmospheric Model Issues" (ppt ~ 1MB)

1540-1600: Uwe Mikolajewicz "Modelling Arctic climate variability” (ppt ~9MB)

1600-1630: Coffee Break

1630-1650: Jean-François Lemieux "Using the preconditioned Generalized Minimum RESidual method to solve the sea ice momentum equation" (pdf ~3MB)

1650-1730: RTP (right to the point) discussion on Model improvements

Process Studies: Chair: Annette Rinke

1730-1750: Klaus Wyser “Impact of an improved radiation parameterization for the Arctic” (pps ~1MB)

1750-1810: Christoph Luepkes “Impact of leads on processes in the polar atmospheric boundary layer” (ppt ~2MB)

1810-1830: Timo Vihma and Joseph Sedlar “Stable boundary layer and cloud-capped boundary layer as challenges for modelling in the Arctic” (ppt ~1MB)

1830: Closing words first day.

30^th October:

900-1000: Press conference (ppt ~15MB)

Process Studies (continued): Chair: Michael Karcher

1000-1020: H.E. Markus Meier and Per Pemberton “On the parameterization of mixing in regional circulation models for the Arctic Ocean” (ppt ~3MB)

1020-1040: An Nguyen “Salt rejection, advection, and mixing in the MITgcm coupled ocean and sea ice model” (ppt ~8MB)

1040-1100: Wolgang Dorn (presented by Klaus Dethloff) “Uncertain descriptions of Arctic climate processes in coupled models and their impact on the simulation of Arctic sea ice” (pdf ~1MB)

1100-1120: Jinlung Zhang “Some Considerations in Modeling the Arctic Ocean and Its Ice Cover” (ppt ~11MB)

1120-1140: Elena Maksimovich "Atmospheric warming over the Arctic Ocean during the past 20 years" (ppt ~3MB)

1140-1200: Nick Yakovlev “FEMAO (Finite-Element Model of the Arctic Ocean): Towards the understanding of the role of tides in the Arctic Ocean climate formation” (ppt ~3MB)

1200-1220: Gennady Platov “Can a polynya effect be resolved in coarse resolution model?" (ppt ~1MB)

1220-1320: Lunch

1320-1400: RTP discussion on Process Studies

Reliability of reanalyzes in the Arctic: Chair: Ralf Doescher

1400-1430: David Bromwich (presented by Per Kalberg and John Cassano) (keynote) “An Evaluation of Global Reanalyses in the Polar Regions” (ppt ~4MB)

1430-1450: Per Kalberg “The ECMWF ERA-40 reanalysis and beyond” (ppt ~4MB)

1450-1510: Tara Troy “Reconstructing the Land Surface Water and energy Budgets of Northern Eurasia” (ppt ~8MB)

1510-1530: Andrey Proshutinsky “NCAR reanalysis validation in the Central Arctic” (ppt ~2MB)

1530-1600: Coffee Break

1600-1620: Michael Tjernstroem (presented by Timo Vihma) “Large-scale model reanalyses for the Arctic: validation, temperature trends, and applicability as forcing for sea ice models” (ppt ~1MB)

1620-1650: RTP discussion on Reliability of Reanalyzes in the Arctic

Data and Models: Chair: Jean-Claude Gascard

1650-1720: Don Perovich (keynote) “The Mass and Heat Balance of Ice” (ppt ~19MB)

1720-1740: Bin Cheng "Snow and sea ice thermodynamics in the Arctic: Model validation against CHINARE and SHEBA data" (ppt ~3MB)

1740-1800: Fanny Girard-Ardhuin “Sea ice drift data at global scale” (ppt ~5MB)

1800: Closing words second day.

31^th October:

Data and Models (continued): Chair: Jean-Claude Gascard

900-930: Ron Kwok (keynote) “Assessment of sea ice simulations using high-resolution kinematics from RADARSAT” (pdf ~2MB)

930-950: Marie-Noelle Houssais “Validation of a regional Arctic-North Atlantic model based on the ORCALIM sea ice-ocean model” (ppt ~9MB)

950-1010: Erko Jakobson “Tethered balloon measurements in the Arctic” (ppt ~12MB)

1010-1040: Coffee Break

1040-1110: Michael Karcher (keynote) “The Arctic ocean in the 20th century - first results from an AOMIP experiment driven with 100 years of reconstructed forcing fields” (pdf ~6MB)

1110-1130: Øystein Skagseth “On the Atlantic water through the Norwegian and Barents Seas” (ppt ~5MB)

1130-1150: Tor Eldevik “The Greenland Sea does not control the overflows feeding the Atlantic conveyor”

1150-1230: RTP dicussion on Data and Models

1230-1330: Lunch

Enhance synthesis and integration: Chair: Frank Kauker

1330-1350: David Bromwich (presented by John Cassano) “A High-Resolution Arctic System Reanalysis” (ppt ~4MB)

1350-1410: Andrey Proshutinsky “Toward reconstruction of the Arctic climate system: Sea ice and ocean reconstruction with data assimilation” (ppt ~3MB)

1410-1430: Gregory Smith “Using ocean reanalysis to study water mass variability with the help of a new Java web application”

1430-1450: Frank Kauker “ADNAOSIM and NAOSIMDAS” (pdf ~4MB)

1450-1520: Jun She (keynote) ”Optimal Design of Observing Networks (ODON)” (ppt ~4MB)

1520-1540: Thomas Kaminski “Quantitative Design of Observational Networks” (pdf ~1MB)

1540-1600: Coffee Break

1600-1630: RTP discussion on Enhance synthesis and integration (Chair: Frank Kauker)

1630-1700: RTP discussion on coordination of new modelling experiments (Chair: Klaus Dethloff and Andrey Proshutinsky)

1700-1730: General Discussion/Closing words (Jean-Claude Gascard)

Questionnaire

The intention of this questionnaire is to stimulate the discussion during the meeting. Every participant is encouraged to try to answer some of these questions in her/his talk and in the RTP discussions.

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) Based on your personal experience in both modeling and observational activities identify mechanisms and parameterizations to be introduced.
c) Express your opinion about modeling with restoring, flux corrections, etc. How to avoid these procedures? Is it possible to reconstruct environmental changes in the models with restoring and flux corrections?
d) Are we able to identify quantitatively a range of uncertainties in our model results and in model predictions? How to improve models to reduce these uncertainties?

3. Model forcing
a) (How) Can we quantify the errors of the model forcing?
b) How to improve model forcings?

4. Observational Network design and modelling
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 presently planned) observational efforts (IPY, DAMOCLES, AON) satisfy the needs of the modelers with respect to model validation/improvement and data assimilation?

5. Organizational Issues
a) What can we do to encourage modelers and observers to collaborate in data collection for the entire Arctic ocean based on the results of 2007-2009 IPY field programs?
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) Are we in need of additional organizational structures

• which provides an observational data base with analyzed key variables easily accessible for model validation?

• to facilitate the exchange between observers and modelers (regional modelers and IPCC global modelers)?

Logistics

Venue

The workshop will take place in the Auditorium of the Grande Galerie de l'Evolution in the Museum National d'Histoire Naturelle - MNHN (36, rue Geoffroy Saint Hilaire, 75005, Paris, see the circle in the second jpeg below).

Hotels in Paris

The prices are per night, breakfast included. It is strongly recommend to book a room as soon as possible because of a very low rooms availability in Paris in the month of October. You should have a Credit card number and the expiration date handy for reservations.

WLAN, if not free, costs about 4€/hour. All the *** hotels offer quite similar facilities (with the exception of the last one), the prices differs mostly due to more or less important discounts that they offer to the University Pierre et Marie Curie.

For cancellation condition ask the hotels.

1) Best Western Quartier Latin Panthéon***: single 110€, double 120€

- To book, write an e-mail to Mrs Sandrine Fischer ( sfischer@my-paris-hotel.com ) giving the name of the project SEARCH for DAMOCLES for having these prices, before 15/09 (10 rooms available)

- This very nice “hotel de charme” proposed us very special prices (normal price from 160€): it’s the best quality/price ratio we can propose

- The rooms are very comfortable, a little bit bigger the usual Paris hotel rooms, all for two people, with wi-fi to be paid by credit card.

2) Hotel du Commerce: single 60€

- To book, go to the website www.commerceparishotel.com

- This hotel is completely new and very clean; there’s one free internet point but not wi-fi

- They also offer a dining and food preparation areas for meals.

3) Hotel de l’Espérance**: 78 – 94 €

- To book, write them an e-mail hotel.esperance@wanadoo.fr or go to the website hoteldelesperance.fr (the website is only in French)

- The rooms are quite big for a Paris hotel room.

- The wi-fi is only available in the hotel hall-bar-living room

4) Hotel Moderne Saint Germain ***: 90 - 140€

- To book, go to the website www.hotelmodernesaintgermain.com.

- You can cancel till 2 days before

5) Hotels Sully Saint Germain***: 90 - 140€

- To book, go to the website www.hotelsullysaintgermain.com.

- You can cancel till 2 days before

6) Hotel California Saint Germain***: 90 - 140€

- To book, go to the website www.hotel-california-st-germain-paris.federal-hotel.com

- You can cancel till 2 days before

7) Hotel des Nations Saint Germain***: single 119€, double 135€

- To book, write an e-mail to hoteldesnationssaintgermain@regetel.com giving the name of the project SEARCH for DAMOCLES for having these prices, before 07/09 (10 rooms available).

8) Hotel Elysa-Luxembourg***: single 120€

- To book, write an e-mail to hotel@elysa-luxembourg.fr giving the name of the project SEARCH for DAMOCLES for having these prices, before 20/09 (5-6 rooms available).

- This hotel is the closest to the RER (train from the airports) station and to the beautiful “Parc de Luxembourg”; rooms are a bit bigger then usual.

9) Timhotel Jardin des Plantes***: single 140€

- To book, write an e-mail to Mrs Vanessa PETON vpeton@timhotel.fr giving the name of the project SEARCH for DAMOCLES for having these prices, before 05/09 (10 rooms available).

- This hotel is the closest to the venue; rooms and corridors are quite small.

10) Hotel Serotel Lutèce ***: single 145€, double 155€

- To book, write an e-mail to Mrs Rose Stoyanova lutece@hotelserotel.com giving the name of the project SEARCH for DAMOCLES for having these prices, before 15/09 (10 rooms available).

- This hotel has a very nice garden where is possible to have breakfast and is very quite.

11) Hotel du Panthéon***: standard 189€, superior 219€

- To book, write an e-mail to reservation@hoteldupantheon.com giving the name of the project SEARCH for DAMOCLES for having these prices, before 17/09 (10 rooms available).

- The rooms are decorated in the Louis XVI style; Superior rooms have an exceptional view of the Pantheon

Hotels

- Free wireless (WIFI) internet connection in the entire hotel.

12) Hôtel André Latin**: single 114€
- To book, write an e-mail to hotelandrelatin@wanadoo.fr or send a fax to 0033.1.40.51.77.10 giving the name of the project SEARCH for DAMOCLES for having these prices, before 28/09 (5-10 rooms available). In case of problem you can call the hotel at 0033.1.43.54.76.60

- Complete bathroom, television, safety box, telephone, hairdryer and a wireless Internet access.

Maps, Transportation

When arriving by plane (Roissy-Charles de Gaulles and Orly airports): Take RER (Le Reseau Express Regional - fast regional train) line B (direction: Paris, city center). RER stops at all terminal: where it is not close to the arrival or departure halls, information signs and/or buses allow people to reach it easily.

Find a map of the Paris metro in the first jpg below; RER B is the blue north-south line. According to the chosen hotels, you can either go by feet (or take a taxi) from station "Luxembourg" or change to the metro line 7 at the station "Chatelet-Les Halles" (line 7 is the pink line in front of the RER B line in the map). The metro station closest to each hotel can be found in the jpg showing the hotel locations (below).
The metro stations closest to the meeting place will be on the line 7: Jussieu, Place Monge, Censier Daubenton.

Jpegs showing Paris metro (with RER) and the location of the hotels and the venue (click on the images to enlarge it):

A link to a google map of the area in Paris surrounding the venue.

 Photos