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  • 1
    Publication Date: 2021-07-20
    Description: We present an Arctic ocean–sea ice reanalysis covering the period 2007–2016 based on the adjoint approach of the Estimating the Circulation and Climate of the Ocean (ECCO) consortium. The spatiotemporal variation of Arctic sea surface temperature (SST), sea ice concentration (SIC), and sea ice thickness (SIT) is substantially improved after the assimilation of ocean and sea ice observations. By assimilating additional World Ocean Atlas 2018 (WOA18) hydrographic data, the freshwater content of the Canadian Basin becomes closer to the observations and translates into changes of the ocean circulation and of transports through the Fram and Davis straits. This new reanalysis compares well with previous filter‐based (TOPAZ4) and nudging‐based (PIOMAS) reanalyses regarding SIC and SST. Benefiting from using the adjoint of the sea ice model, our reanalysis is superior to the ECCOv4r4 product considering sea ice parameters. However, the mean state and variability of the freshwater content and the transport properties of our reanalysis remain different from TOPAZ4 and ECCOv4r4, likely because of a lack of hydrographic observations.
    Description: Arctic sea ice has declined rapidly and reached a record minimum in September, 2012. Arctic ocean–sea ice reanalyses are invaluable sources for understanding the Arctic sea ice changes. We produce an Arctic ocean–sea ice reanalysis of the years 2007–2016 using the adjoint method. The reanalysis is dynamically consistent without introducing unphysical mass and energy discontinuities as in filter‐based data assimilation methods.
    Keywords: 551 ; adjoint method ; data assimilation ; ocean–sea ice reanalysis
    Type: article
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  • 2
    Publication Date: 2020-05-15
    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.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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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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  • 4
    Publication Date: 2014-03-29
    Print ISSN: 0253-505X
    Electronic ISSN: 1869-1099
    Topics: Geosciences , Physics
    Published by Springer
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  • 5
    Publication Date: 2014-07-16
    Print ISSN: 0256-1530
    Electronic ISSN: 1861-9533
    Topics: Geosciences , Physics
    Published by Springer
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  • 6
    Publication Date: 2015-09-09
    Print ISSN: 0256-1530
    Electronic ISSN: 1861-9533
    Topics: Geosciences , Physics
    Published by Springer
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  • 7
    Publication Date: 2018-05-01
    Print ISSN: 0034-4257
    Electronic ISSN: 1879-0704
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by Elsevier
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  • 8
    Publication Date: 2019-12-14
    Print ISSN: 0280-6495
    Electronic ISSN: 1600-0870
    Topics: Geography , Geosciences , Physics
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  • 9
    Publication Date: 2019-01-18
    Description: Although the stratification of the upper Arctic Ocean is mostly salinity-driven, the sea surface salinity (SSS) is still poorly known in the Arctic, due to its strong variability and the sparseness of in-situ observations. Recently, two gridded SSS products have been derived from the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, independently developed by the Barcelona Expert Centre (BEC) in Spain and the Ocean Salinity Expertise Center (CECOS) of the Centre Aval de Traitemenent des Donnees SMOS (CATDS) in France, respectively. In parallel, there are two reanalysis products providing the Arctic SSS in the framework of the Copernicus Marine Environment Monitoring Services (CMEMS), one global, and another regional product. While the regional Arctic TOPAZ4 system assimilates a large set of sea-ice and ocean observations with an Ensemble Kalman Filter, the global reanalysis combines in-situ and satellite data using a multivariate ensemble optimal interpolation method. In this study, focused on the Arctic Ocean, these four salinity products, together with the climatology both World Ocean Atlas (WOA) of 2013 and Polar science center Hydrographic Climatology (PHC), are evaluated against in-situ datasets during 2011–2013. For the validation the in-situ observations are divided in two; those that have been assimilated and those that have not. The deviations of SSS between the different products and against the in-situ observations show largest disagreements below the sea-ice and in the marginal ice zone (MIZ), especially during the summer months. In the Beaufort Sea, the summer SSS from the BEC product has the smallest – saline – bias (~0.6 psu) with the smallest root mean squared difference (RSMD) of 2.6 psu. This suggests a potential value of assimilating of this product into the forthcoming Arctic reanalyses. Keywords: Arctic Ocean; sea surface salinity; SMOS; reanalysis; absolute deviation;
    Print ISSN: 1812-0806
    Electronic ISSN: 1812-0822
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 10
    Publication Date: 2017-02-13
    Description: Long dynamical atmospheric reanalyses are widely used for climate studies, but data-assimilative reanalyses of ocean and sea ice in the Arctic are less common. TOPAZ4 is a coupled ocean and sea ice data assimilation system for the North Atlantic and the Arctic that is based on the HYCOM ocean model and the ensemble Kalman filter data assimilation method using 100 dynamical members. A 23-year reanalysis has been completed for the period 1991–2013 and is the multi-year physical product in the Copernicus Marine Environment Monitoring Service (CMEMS) Arctic Marine Forecasting Center (ARC MFC). This study presents its quantitative quality assessment, compared to both assimilated and unassimilated observations available in the whole Arctic region, in order to document the strengths and weaknesses of the system for potential users. It is found that TOPAZ4 performs well with respect to near-surface ocean variables, but some limitations appear in the interior of the ocean and for ice thickness, where observations are sparse. In the course of the reanalysis, the skills of the system are improving as the observation network becomes denser, in particular during the International Polar Year. The online bias estimation successfully maintains a low bias in our system. In addition, statistics of the reduced centered random variables (RCRVs) confirm the reliability of the ensemble for most of the assimilated variables. Occasional discontinuities of these statistics are caused by the changes of the input data sets or the data assimilation settings, but the statistics remain otherwise stable throughout the reanalysis, regardless of the density of observations. Furthermore, no data type is severely less dispersed than the others, even though the lack of consistently reprocessed observation time series at the beginning of the reanalysis has proven challenging.
    Print ISSN: 1812-0784
    Electronic ISSN: 1812-0792
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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