Biblio

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2020
Shepherd, A., et al. Mass balance of the Greenland Ice Sheet from 1992 to 2018. Nature 579, (2020).
Braby, L., Backeberg, B., Krug, M. & Reason, C. Quantifying the Impact of Wind‐Current Feedback on Mesoscale Variability in Forced Simulation Experiments of the Agulhas Current Using an Eddy‐Tracking Algorithm. Journal of Geophysical Research (JGR): Oceans 125, (2020).
Gnatiuk, N., Radchenko, I., Davy, R., Morozov, E. & Bobylev, L. Simulation of factors affecting Emiliania huxleyi blooms in Arctic and sub-Arctic seas by CMIP5 climate models: model validation and selection. Biogeosciences 17, (2020).
2019
Jackson, L.C., et al. The mean state and variability of the North Atlantic circulation: a perspective from ocean reanalyses. Journal of Geophysical Research: Oceans (2019).doi:10.1029/2019JC015210
Raanes, P.N., Bocquet, M. & Carrassi, A. Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures. Quarterly Journal of the Royal Meteorological Society 145, (2019).
Palmer, M.D., et al. Adequacy of the ocean observation system for quantifying regional heat and freshwater storage and change. 6:416, (2019).
Raj, R.P., Chatterjee, S., Bertino, L., Turiel, A. & Portabella, M. The Arctic Front and its variability in the Norwegian Sea. Ocean Science 15, (2019).
Ali, A., et al. A comparison of Langmuir turbulence parameterizations and key wave effects in a numerical model of the North Atlantic and Arctic Oceans. Ocean Modelling 137, (2019).
Aaboe, S., et al. Copernicus Marine Service Ocean State Report, Issue 3. Journal of operational oceanography. Publisher: The Institute of Marine Engineering, Science & Technology 12, (2019).
Aydogdu, A., Carrassi, A., Guider, C.T., Jones, C.K.R.T. & Rampal, P. Data assimilation using adaptive, non-conservative, moving mesh models. Nonlinear processes in geophysics 26, (2019).
Evensen, G., Raanes, P.N., Stordal, A.S. & Hove, J. Efficient Implementation of an Iterative Ensemble Smoother for Data Assimilation and Reservoir History Matching. Frontiers in Applied Mathematics and Statistics 5, (2019).
Metref, S., et al. Estimating model evidence using ensemble‐based data assimilation with localization – The model selection problem. Quarterly Journal of the Royal Meteorological Society 145, (2019).
Xie, J., Raj, R.P., Bertino, L., Samuelsen, A. & Wakamatsu, T. Evaluation of Arctic Ocean surface salinities from the Soil Moisture and Ocean Salinity (SMOS) mission against a regional reanalysis and in situ data. Ocean Science 15, (2019).
Sloyan, B.M., et al. Evolving the global ocean observing system for research and application services through international coordinatio. Frontiers in Marine Science 6, (2019).
Lee, C.M., et al. A Framework for the Development, Design and Implementation of a Sustained Arctic Ocean Observing System. 6, (2019). Abstract
Lee, C.M., et al. A Framework for the Development, Design and Implementation of a Sustained Arctic Ocean Observing System. Frontiers in Marine Science (2019).
Traon, P.-Y.L., et al. From observation to information and users: The Copernicus Marine Service Perspective. 6:234, (2019).
Box, J.E., et al. Key indicators of Arctic climate change: 1971–2017. Environmental Research Letters 14, (2019). Abstract
Nkwinkwa, A.S.I., Johannessen, J.A. & Rouault, M. Latent Heat Flux in the Agulhas Current. Remote Sensing 11, (2019).
Jackson, L.C., et al. The mean state and variability of the North Atlantic circulation: A perspective from ocean reanalyses. Journal of Geophysical Research (JGR): Oceans 124, (2019).
Rampal, P., et al. On the multi-fractal scaling properties of sea ice deformation. The Cryosphere 13, (2019).
Chafik, L.M., Nilsen, J.E.Ø., Dangendorf, S., Reverdin, G. & Frederikse, T. North Atlantic ocean circulation and decadal sea level change during the Altimetry era. Scientific Reports 9:1041, (2019).
Penny, S.G., et al. Observational needs for improving ocean and coupled reanalysis, S2S Prediction, and decadal prediction. Frontiers in Marine Science 6:391, (2019).
Fujii, Y., et al. Observing System Evaluation Based on Ocean Data Assimilation and Prediction Systems: On-Going Challenges and a Future Vision for Designing and Supporting Ocean Observational Networks. Frontiers in Marine Science (2019).doi:10.3389/fmars.2019.00417
Fujii, Y., et al. Observing system evaluation based on ocean data assimilation and prediction systems: On-going challenges and future vision for designing/supporting ocean observational networks. 6:417, (2019).
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