Literature#

Reference Publications

Please see also the list of reference publications.

Tutorials#

ICON Tutorial 2024#: F. Prill, D. Reinert, D. Rieger, G. Zängl (2024). Working with the ICON Model, https://doi.org/10.5676/DWD_pub/nwv/icon_tutorial2024
ICON-ART User Guide#: https://www.icon-art.kit.edu/userguide/index.php?title=Main_Page

Reports on ICON#

The DOI and ISSN referenced series Reports on ICON allows for a fast and straightforward publication of technical and scientific contributions around the numerical weather prediction and climate model ICON. New articles are published when available. There exist no strict specifications on the content of articles by intention. By this, the series can cover a broad spectrum as regards content. The idea behind it is to collect contributions around ICON that are not published in peer-reviewed journals within one publication series.

Technical Documentation#

Scientific Documentation#

Literature for atmosphere modeling in ICON#

Barker et al. 2003#: Barker, H. W., G. L. Stephens, P. T. Partain, and Coauthors (2003): Assessing 1D atmospheric solar radiative transfer models: Interpretation and handling of unresolved clouds. J. Clim., 16 (16), 2676-2699.
Bechtold et al. 2008#: Bechtold, P., M. Köhler, T. Jung, F. Doblas-Reyes, M. Leutbecher, M. J. Rodwell, F. Vitart, and G. Balsamo (2008): Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales. Q. J. R. Meteorol. Soc., 134 (634), 1337-1351, https://doi.org/10.1002/qj.289.
Borchert et al. 2019#: Borchert, S., Zhou, G., Baldauf, M., Schmidt, H., Zängl, G., and Reinert, D. (2019). The upper-atmosphere extension of the ICON general circulation model (version: ua-icon-1.0). Geoscientific Model Development, 12(8), 3541-3569. https://doi.org/10.5194/gmd-12-3541-2019
Doms et al. 2011#: Doms, G., and Coauthors (2011): A Description of the Nonhydrostatic Regional COSMO Model. Part II: Physical Parameterization. Consortium for Small-Scale Modelling, http://www.cosmo-model.org.
Hogan & Bozzo 2018#: Hogan, R. J., and A. Bozzo (2018): A flexible and efficient radiation scheme for the ecmwf model. J. Adv. Model Earth Sy., 10 (8), 1990-2008.
Khain & Sednev 1996#: Khain, A. P., and I. Sednev (1996): Simulation of precipitation formation in the eastern mediterranean coastal zone using a spectral microphysics cloud ensemble model. Atmos. Res., 43 (1), 77-110, https://doi.org/10.1016/S0169-8095(96)00005-1
Khain et al. 2004#: Khain, A., A. Pokrovsky, M. Pinsky, A. Seifert, and V. Phillips (2004): Simulation of Effects of Atmospheric Aerosols on Deep Turbulent Convective Clouds Using a Spectral Microphysics Mixed-Phase Cumulus Cloud Model. Part I: Model Description and Possible Applications. J. Atmos. Sci., 61 (24), 2963-2982, https://doi.org/10.1175/JAS-3350.1.
Lilly 1962#: Lilly, D. K. (1962): On the numerical simulation of buoyant convection. Tellus, 14 (2), 148-172, https://doi.org/10.1111/j.2153-3490.1962.tb00128.x.
Lott & Miller 1997#: Lott, F., and M. J. Miller (1997): A new subgrid-scale orographic drag parametrization: Its formulation and testing. Q. J. R. Meteorol. Soc., 123 (537), 101-127, https://doi.org/10.1002/qj.49712353704.
Mlawer et al. 1997#: Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res.: Atmos., 102 (D14), 663-682, https://doi.org/10.1029/97JD00237.
Orr et al. 2010#: Orr, A., P. Bechtold, J. Scinocca, M. Ern, and M. Janiskova (2010): Improved middle atmosphere climate and forecasts in the ECMWF model through a nonorographic gravity wave drag parameterization. J. Clim., 23 (22), 5905-5926, https://doi.org/10.1175/2010JCLI3490.1.
Raschendorfer 2001#: Raschendorfer, M. (2001): The new turbulence parameterization of LM. COSMO News Letter No. 1, Consortium for Small-Scale Modelling, 89-97, http://www.cosmo-model.org.
Reinert 2020#: Reinert, D. (2020). The tracer transport module Part I: A mass consistent finite volume approach with fractional steps. Reports on ICON, 5, 1-19. https://doi.org/10.5676/DWD_pub/nwv/icon_005
Reinert & Zaengl 2021#: Reinert, D., and Zaengl, G. (2021). The tracer transport module part II: Description and validation of the vertical transport operator. Reports on ICON, 7. https://doi.org/10.5676/DWD_pub/nwv/icon_007
Seifert & Beheng 2006#: Seifert, A., and K. D. Beheng (2006): A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description. Meteorol. Atmos. Phys., 92 (1), 45-66, https://doi.org/10.1007/s00703-005-0112-4.
Smagorinsky 1963#: Smagorinsky, J. (1963): General Circulation Experiments with the Primitive Equations. Mon. Weather Rev., 91, 99.
Thuburn & White 2013#: Thuburn, J., and White, A. A. (2013). A geometrical view of the shallow-atmosphere approximation, with application to the semi-Lagrangian departure point calculation. Quarterly Journal of the Royal Meteorological Society, 139(670), 261-268.
Tiedtke 1989#: Tiedtke, M. (1989): A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Weather Rev., 117 (8), 1779-1800.
Zaengl et al. 2015#: Zaengl, G., Reinert, D., Ripodas, P., and Baldauf, M. (2015). The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core. Quarterly Journal of the Royal Meteorological Society, 141(687), 563-579. https://doi.org/10.1002/qj.2378

Literature for ocean modeling in ICON#

Brüggemann et al. 2024#: Brüggemann, N., and Coauthors, 2024: Parameterized Internal Wave Mixing in Three Ocean General Circulation Models. Journal of Advances in Modeling Earth Systems, 16, e2023MS003768, https://doi.org/10.1029/2023MS003768.
Danilov et al. 2015#: Danilov, S., Q. Wang, R. Timmermann, N. Iakovlev, D. Sidorenko, M. Kimmritz, T. Jung, and J. Schröter, 2015: Finite-Element Sea Ice Model (FESIM), version 2. Geoscientific Model Development, 8, 1747–1761, https://doi.org/10.5194/gmd-8-1747-2015.
Gaspar et al. 1990#: Gaspar, P., Y. Grégoris, and J.-M. Lefevre, 1990: A simple eddy kinetic energy model for simulations of the oceanic vertical mixing: Tests at station Papa and long-term upper ocean study site. J. Geophys. Res., 95, 16179–16193.
Korn 2018#: Korn, P., 2018: A structure-preserving discretization of ocean parametrizations on unstructured grids. Ocean Modelling, 132, 73–90.
Korn et al. 2022#: Korn, P., and Coauthors, 2022: ICON-O: The Ocean Component of the ICON Earth System Model–Global Simulation Characteristics and Local Telescoping Capability. J Adv Model Earth Syst, 14, e2021MS002952, https://doi.org/10.1029/2021MS002952.
Ilyina et al. 2013#: Ilyina, T., K. D. Six, J. Segschneider, E. Maier-Reimer, H. Li, and I. Núñez-Riboni, 2013: Global ocean biogeochemistry model HAMOCC: Model architecture and performance as component of the MPI-Earth system model in different CMIP5 experimental realizations. Journal of Advances in Modeling Earth Systems, 5, 287–315, https://doi.org/10.1029/2012MS000178.
Mehlmann and Korn 2021#: Mehlmann, C., and P. Korn, 2021: Sea-ice dynamics on triangular grids. Journal of Computational Physics, 428, 110086, https://doi.org/10.1016/j.jcp.2020.110086.
Olbers and Eden 2013#: Olbers, D., and C. Eden, 2013: A Global Model for the Diapycnal Diffusivity Induced by Internal Gravity Waves. Journal of Physical Oceanography, 43, 1759–1779, https://doi.org/10.1175/JPO-D-12-0207.1.
Semtner 1976#: Semtner, A. J., 1976: A Model for the Thermodynamic Growth of Sea Ice in Numerical Investigations of Climate. Journal of Physical Oceanography, 6, 379–389, <https://doi.org/10.1175/1520-0485(1976)006<0379:AMFTTG>2.0.CO;2>.
Six and Maier-Reimer 1996#: Six, K. D., and E. Maier-Reimer, 1996: Effects of plankton dynamics on seasonal carbon fluxes in an ocean general circulation model. Global Biogeochemical Cycles, 10, 559–583, https://doi.org/10.1029/96GB02561.

Literature for land surface modeling in ICON#

TERRA#

Dickinson 1984#: Dickinson, R. E. (1984). Modeling evapotranspiration for three-dimensional global climate models. Climate processes and climate sensitivity, 29, 58-72.
Jarvis 1976#: Jarvis, P. (1976). The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philosophical Transactions of the Royal Society of London. B, Biological Sciences, 273(927), 593-610.
Schulz & Vogel 2020#: Schulz, J. P., & Vogel, G. (2020). Improving the processes in the land surface scheme TERRA: Bare soil evaporation and skin temperature. Atmosphere, 11(5), 513.
Viterbo & Beljaars 1995#: Viterbo, P., & Beljaars, A. C. (1995). An improved land surface parameterization scheme in the ECMWF model and its validation. Journal of climate, 8(11), 2716-2748.

JSBACH#

de Vrese et al. 2021#: de Vrese, P., Stacke, T., Kleinen, T., Brovkin, V. (2021). Diverging responses of high-latitude CO2 and CH4 emissions in idealized climate change scenarios. The Cryosphere, 15, 1097-1130, https://doi.org/10.5194/tc-15-1097-2021
Ekici et al. 2014#: Ekici, A., Beer , C., Hagemann, S., Boike, J., Langer, M., Hauck, C. (2014). Simulating high-latitude permafrost regions by the JSBACH terrestrial ecosystem model. Geoscientific Model Development, 7, 631-647, https://doi.org/10.5194/gmd-7-631-2014.
Hagemann & Duemenil 1997#: Hagemann, S., & Dümenil, L. (1997). A parametrization of the lateral water flow for the global scale. Climate Dynamics, 14, 17–31, https://doi.org/10.1007/s003820050205.
Reick et al. 2021#: Reick, C. H., Gayler, V., Goll, D., Hagemann, S., Heidkamp, M., Nabel, J. E. M. S., et al. (2021). JSBACH 3 - The land component of the MPI Earth System Model: documentation of version 3.2. Berichte zur Erdsystemforschung, 240. https://doi.org/10.17617/2.3279802.
Riddick et al. 2018#: Riddick, T., Brovkin, V., Hagemann, S. & Mikolajewicz, U. (2018). Dynamic hydrological discharge modelling for coupled climate model simulations of the last glacial cycle: the MPI-DynamicHD model version 3.0. Geoscientific Model Development, 11, 4291-4316, https://doi.org/10.5194/gmd-11-4291-2018.

QUINCY#

Caldararu et al. 2020#: Caldararu, S., Thum, T., Yu, L. and Zaehle, S. (2020), Whole-plant optimality predicts changes in leaf nitrogen under variable CO2 and nutrient availability. New Phytol, 225: 2331-2346. https://doi.org/10.1111/nph.16327.
Caldararu et al. 2021#: Caldararu, S., Thum, T., Yu, L., Kern, M., Nair, R., & Zaehle, S. (2022). Long-term ecosystem nitrogen limitation from foliar δ15N data and a land surface model. Global Change Biology, 28, 493–508. https://doi.org/10.1111/gcb.15933.
Thum et al. 2019#: Thum, T., Caldararu, S., Engel, J., Kern, M., Pallandt, M., Schnur, R., Yu, L., and Zaehle, S.: A new model of the coupled carbon, nitrogen, and phosphorus cycles in the terrestrial biosphere (QUINCY v1.0; revision 1996), Geosci. Model Dev., 12, 4781–4802, https://doi.org/10.5194/gmd-12-4781-2019, 2019.
Yu et al. 2020a#: Yu, L., Ahrens, B., Wutzler, T., Schrumpf, M., Zaehle, S. (2020a). Jena Soil Model (JSM v1.0; revision 1934): a microbial soil organic carbon model integrated with nitrogen and phosphorus processes. Geoscientific Model Development, 13(2), 783-803, https://doi.org/10.5194/gmd-13-783-2020.
Yu et al. 2020b#: Yu, L., Ahrens, B., Wutzler, T., Zaehle, S., Schrumpf, M. (2020b). Modeling soil responses to nitrogen and phosphorus fertilization along a soil phosphorus stock gradient. Frontiers in Forests and Global Change, 3: 543112, https://doi.org/10.3389/ffgc.2020.543112.