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  • Löb, J., Köhler, J., Mertens, C., Walter, M., Li, Z., von Storch, J.‐S., et al. (2020). Observations of the low‐mode internal tide and its interaction with mesoscale flow south of the Azores. J. Geophys. Res. - Oceans 125, e2019JC015879, doi: https://doi.org/10.1029/2019JC015879.

  • Kutsenko, A. (2020). Isomorphism between one-Dimensional and multidimensional finite difference operators. Commun. Pure. Appl. Ana., doi: 10.3934/cpaa.2020270.

  • Sidorenko, D., Danilov, S., Koldunov, N. et al. (2020). AMOC, Water Mass Transformations, and Their Responses to Changing Resolution in the Finite-VolumE Sea Ice-Ocean Model. J. Adv. Model Earth Sy. 12(12), e2020MS00231, doi: https://doi.org/10.1029/2020MS002317. 

  • Buckley, M., Veron, F. & Yousef, K. (2020). Surface viscous stress over wind-driven waves with intermittent airflow separation, J. Fluid Mech. 905(31), doi: https://doi.org/10.1017/jfm.2020.760. 

  • Kerimoglu, M., Voynova, Y.G., Klingbeil, K. et al. (2020). Interactive impacts of meteorological and hydrological conditions on the physical and biogeochemical structure of a coastal system. Biogeosciences 17(20), doi: https://doi.org/10.5194/bg-17-5097-2020. 

  • Önskog, T., Franzke, C.L.E. & Hannachi, A. (2020). Nonlinear time series models for the North Atlantic Oscillation. Adv. Stat. Clim. Meteorol. Oceanogr. 6(2), 141–157, doi: https://doi.org/10.5194/ascmo-6-141-2020. 

  • Quinn, B., Eden, C. & Olbers, D. (2020). Application of the IDEMIX Concept for Internal Gravity Waves in the Atmosphere. J. Atmos. Sci. 77(10), 3601–3618, doi: https://doi.org/10.1175/JAS-D-20-0107.1. 

  • Chassignet, E.P., Yeager, S.G., Koldunov, N. et al. (2020). Impact of horizontal resolution on global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2). Geosci. Model Dev. 13, 4595–4637, doi: https://doi.org/10.5194/gmd-2019-374. 

  • Franzke, C.L.E. & Torelló i Sentelles, H. (2020). Risk of extreme high fatalities due to weather and climate hazards and its connection to large-scale climate variability. Climatic Change 162,507–525, doi: https://doi.org/10.1007/s10584-020-02825-z. 

  • Nian, D., Franzke, C.L.E., Yuan, N. et al. (2020). Identifying the sources of seasonal predictability based on climate memory analysis and variance decomposition. Clim. Dyn. 55, 3239–3252. doi: https://doi.org/10.1007/s00382-020-05444-7.