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  • 551.343  (1)
  • Air-sea-ice fluxes  (1)
  • Dissertation  (1)
  • 1
    Book
    Book
    Geesthacht : GKSS-Forschungszentrum
    Keywords: Dissertation ; Hochschulschrift ; Forschungsbericht ; Nordsee ; Klima ; Modell
    Type of Medium: Book
    Pages: IV, 111 S. , graph. Darst., Kt. , 30 cm
    Edition: Als Ms. vervielfältigt
    Series Statement: GKSS 99,06
    RVK:
    Language: English
    Note: Zugl.: Hamburg, Univ., Diss., 1998
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  • 2
    Publication Date: 2021-07-21
    Description: We have equipped the unstructured‐mesh global sea‐ice and ocean model FESOM2 with a set of physical parameterizations derived from the single‐column sea‐ice model Icepack. The update has substantially broadened the range of physical processes that can be represented by the model. The new features are directly implemented on the unstructured FESOM2 mesh, and thereby benefit from the flexibility that comes with it in terms of spatial resolution. A subset of the parameter space of three model configurations, with increasing complexity, has been calibrated with an iterative Green's function optimization method to test the impact of the model update on the sea‐ice representation. Furthermore, to explore the sensitivity of the results to different atmospheric forcings, each model configuration was calibrated separately for the NCEP‐CFSR/CFSv2 and ERA5 forcings. The results suggest that a complex model formulation leads to a better agreement between modeled and the observed sea‐ice concentration and snow thickness, while differences are smaller for sea‐ice thickness and drift speed. However, the choice of the atmospheric forcing also impacts the agreement of the FESOM2 simulations and observations, with NCEP‐CFSR/CFSv2 being particularly beneficial for the simulated sea‐ice concentration and ERA5 for sea‐ice drift speed. In this respect, our results indicate that parameter calibration can better compensate for differences among atmospheric forcings in a simpler model (i.e., sea‐ice has no heat capacity) than in more realistic formulations with a prognostic sea‐ice thickness distribution and sea ice enthalpy.
    Description: Plain Language Summary: The role of model complexity in determining the performance of sea‐ice numerical simulations is still not completely understood. Some studies suggest that a more sophisticated description of the sea‐ice physics leads to simulations that agree better with sea‐ice observations. Others, however, fail to establish a link between complex model formulations and improved model performance. Here, we investigate this open question by analyzing a set of sea‐ice simulations performed with a revised and improved sea‐ice model that features substantial modularity in terms of model complexity. Ten model parameters in three different model configurations are optimized to improve the agreement between model results and observations, allowing a fair comparison between model configurations with varying complexity. The model optimization is repeated for two different atmospheric forcings to shed light on the relationship between model complexity and other sources of uncertainty in the sea‐ice simulations, such as those associated with the atmospheric conditions. The results suggest that a more complex formulation of our model can lead to a more appropriate representation of sea ice concentration and snow thickness, while it is less relevant for sea‐ice thickness and drift.
    Description: Key Points: Increased sea‐ice model complexity can improve the simulated sea‐ice concentration and snow thickness Sea‐ice thickness and drift are only weakly affected by model complexity Parameter calibration can better compensate for differences between atmospheric forcings in a simpler model
    Description: Bundesministerium für Bildung und Forschung (BMBF) http://dx.doi.org/10.13039/501100002347
    Description: European Commission (EC) http://dx.doi.org/10.13039/501100000780
    Description: US Department of Energy (DOE)
    Keywords: 551.343 ; Arctic ; FESOM2 ; Green's function ; parameter optimization ; sea ice ; unstructured mesh
    Type: article
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  • 3
    Publication Date: 2022-10-26
    Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Smith, G. C., Allard, R., Babin, M., Bertino, L., Chevallier, M., Corlett, G., Crout, J., Davidson, F., Delille, B., Gille, S. T., Hebert, D., Hyder, P., Intrieri, J., Lagunas, J., Larnicol, G., Kaminski, T., Kater, B., Kauker, F., Marec, C., Mazloff, M., Metzger, E. J., Mordy, C., O'Carroll, A., Olsen, S. M., Phelps, M., Posey, P., Prandi, P., Rehm, E., Reid, P., Rigor, I., Sandven, S., Shupe, M., Swart, S., Smedstad, O. M., Solomon, A., Storto, A., Thibaut, P., Toole, J., Wood, K., Xie, J., Yang, Q., & WWRP PPP Steering Grp. Polar ocean observations: A critical gap in the observing system and its effect on environmental predictions from hours to a season. Frontiers in Marine Science, 6, (2019): 429, doi:10.3389/fmars.2019.00429.
    Description: There is a growing need for operational oceanographic predictions in both the Arctic and Antarctic polar regions. In the former, this is driven by a declining ice cover accompanied by an increase in maritime traffic and exploitation of marine resources. Oceanographic predictions in the Antarctic are also important, both to support Antarctic operations and also to help elucidate processes governing sea ice and ice shelf stability. However, a significant gap exists in the ocean observing system in polar regions, compared to most areas of the global ocean, hindering the reliability of ocean and sea ice forecasts. This gap can also be seen from the spread in ocean and sea ice reanalyses for polar regions which provide an estimate of their uncertainty. The reduced reliability of polar predictions may affect the quality of various applications including search and rescue, coupling with numerical weather and seasonal predictions, historical reconstructions (reanalysis), aquaculture and environmental management including environmental emergency response. Here, we outline the status of existing near-real time ocean observational efforts in polar regions, discuss gaps, and explore perspectives for the future. Specific recommendations include a renewed call for open access to data, especially real-time data, as a critical capability for improved sea ice and weather forecasting and other environmental prediction needs. Dedicated efforts are also needed to make use of additional observations made as part of the Year of Polar Prediction (YOPP; 2017–2019) to inform optimal observing system design. To provide a polar extension to the Argo network, it is recommended that a network of ice-borne sea ice and upper-ocean observing buoys be deployed and supported operationally in ice-covered areas together with autonomous profiling floats and gliders (potentially with ice detection capability) in seasonally ice covered seas. Finally, additional efforts to better measure and parameterize surface exchanges in polar regions are much needed to improve coupled environmental prediction.
    Description: The development of the new generation of floats (PRO-ICE) to be operated under ice was funded by the French project NAOS. Twelve PRO-ICE were funded by NAOS and nine by the Canadian Foundation for Innovation (FCI-30124). The GreenEdge project is funded by the following French and Canadian programs and agencies: ANR (Contract #111112), CNES (project #131425), IPEV (project #1164), CSA, Fondation Total, ArcticNet, LEFE and the French Arctic Initiative (GreenEdge project). The INTAROS project has received funding from the European Union’s Horizon 2020 Research and Innovation Program under grant agreement No. 727890. The setup of the ArcMBA system and the experiment described in section “Quantitative Network Design” were funded by the European Space Agency through its support to science element (contract #4000117710/16/I-NB). SSw was supported by a Wallenberg Academy Fellowship (WAF 2015.0186). The work at CLS (GL, PPr, and PT) has been funded by internal investment, in relation with on-going CNES and ESA funded studies making use of radar data over Polar regions. EMODNET (BK) is funded by the European Commission. NRL Funding (for RA, JC, DH, EM, PPo, OS) provided by NRL Research Option “Determining the Impact of Sea Ice Thickness on the Arctic’s Naturally Changing Environment (DISTANCE), ONR 6.2 Data Assimilation and under program element 0602435N (JC, RA, DH). JT’s Arctic research activities are supported by the U.S. National Science Foundation and ONR. SG was funded by NSF grants/awards PLR-1425989 and OCE 1658001. IR is funded by contributors to the US IABP (including CG, DOE, NASA, NIC, NOAA, NSF, ONR). CAFS is supported by the NOAA ESRL Physical Sciences Division (AS and JI). LB and JX are funded by CMEMS. The WWRP PPP Steering Group is funded by a WMO trust fund with support from AWI for the ICO. The publication fee is provided by ECCC.
    Keywords: Polar observations ; Operational oceanography ; Ocean data assimilation ; Ocean modeling ; Forecasting ; Sea ice ; Air-sea-ice fluxes ; YOPP
    Repository Name: Woods Hole Open Access Server
    Type: Article
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