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  • Shi, J., Stepanek, C., Sein, D., Streffing, J., & Lohmann, G. (2023). East Asian summer precipitation in AWI-CM3: Comparison with observations and CMIP6 models. International Journal of Climatology, 1– 16, doi: https://doi.org/10.1002/joc.8075. 

  • Pithan, F., Athanase, M., Dahlke, S., Sánchez-Benítez, A., Shupe, M. D., Sledd, A., Streffing, J., Svensson, G., & Jung, T. (2023). Nudging allows direct evaluation of coupled climate models with in situ observations: a case study from the MOSAiC expedition. Geosci. Model Dev. 16(7), 1857–1873, doi: https://doi.org/10.5194/gmd-16-1857-2023. 

  • Hohenegger, C., Korn, P., Brüggemann, N., Gutjahr, O., Jungclaus, J., Shevchenko, R., von Storch, J.S. et al. (2023). ICON-Sapphire: simulating the components of the Earth system and their interactions at kilometer and subkilometer scales. Geosci. Model Dev. 16, 779–811, doi: https://doi.org/10.5194/gmd-16-779-2023.

  • Denamiel, C., Vasylkevych, S., Žagar, N., Zemunik, P. & Vilibić, I. (2023). Destructive potential of planetary meteotsunami waves beyond the Hunga Tonga–Hunga Ha’apai volcano eruption. B. Am. Meteorol. Soc. 104(1), E178–E191, doi: https://doi.org/10.1175/BAMS-D-22-0164.1.

  • Masur, G.T., Mohamad, H. & Oliver, M. (2023). Quasi-convergence of an implementation of optimal balance by backward-forward nudging. Multiscale Model. Simul. 21, 624–640, doi: https://doi.org/10.1137/22M1506018. 

  • Chrysagi, E., Basdurak, N.B., Umlauf, L., Gräwe, U. & Burchard, H. (2022). Thermocline Salinity Minima Due To Wind-Driven Differential Advection. J. Geophys. Res.- Oceans 127(11), doi: https://doi.org/10.1029/2022JC018904. 

  • Streffing, J., Scholz, P., Koldunov, N., Danilov, S., Juricke, S., Jung, T. et al. (2022). AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model. Geosci. Model Dev. 15, 6399–6427, doi: https://doi.org/10.5194/gmd-15-6399-2022. 

  • Strommen, K., Juricke, S. & Cooper, F. (2022). Improved teleconnection between Arctic sea ice and the North Atlantic Oscillation through stochastic process representation. Weather Clim. Dynam. 3(3), 951–975, doi: https://doi.org/10.5194/wcd-3-951-2022.

  • Franzke, C.L.E., Gugole, F. & Juricke, S. (2022). Systematic multi-scale decomposition of ocean variability using machine learning. Chaos: An Interdisciplinary Journal of Nonlinear Science 32(7), 073122, doi: https://doi.org/10.1063/5.0090064. 

  • Chouksey, M., Griesel, A., Eden, C. & Steinfeldt, R. (2022). Transit Time Distributions and Ventilation Pathways Using CFCs and Lagrangian Backtracking in the South Atlantic of an Eddying Ocean Model. J. Phys. Oceanogr. 52(7), 1531–1548, doi: https://doi.org/10.1175/JPO-D-21-0070.1.