ICON-Land is a framework developed at MPI-M for the modelling of land processes in ICON and can be used as a stand-alone land-surface model (Nabel et al., 2020) as well as in the fully coupled ICON Earth System Model (Jungclaus et al., JAMES, in review) and in the ICON-A atmosphere-only model (Schneck et al., 2021, in preparation). It is specifically designed in a modular way for the integration of concurrent and alternative process and surface descriptions in a flexible and easy-to-use way. Currently, specific process implementations include the JSBACHv4 (JSBACH version 4) and the QUINCY model configurations. ICON-Land development is governed by the ICON-Land Steering Group and the ICON-Land Management Team.

ICON-Land Framework

The ICON-Land framework has been designed to systematically separate the infrastructure necessary to implement physical, biogeophysical and biogeochemical land processes from the concrete process implementations which are accessed by abstract interfaces. A further goal was the ability to support different experimental configurations of varying scope and complexity to be used in different global, regional or single-site applications, coupled online to an atmosphere model or driven offline by atmospheric observations.

ICON-Land is implemented in an object-oriented and modular way in Fortran2003/2008. Hierarchical trees are used for the flexible description of surface characteristics (tiles; see figure below for an illustration) and matter cycles (pools). Scientific code (processes) is clearly separated from the infrastructure. ICON-Land is a self-contained package while using only a basic infrastructure for I/O, parallel domain decomposition, time control, etc. from the ICON model via a relatively small number of interface routines. It has been ported to accelerated architectures (GPUs) by using the CLAW single column abstraction (Clement et al., 2019).


JSBACHv4 is a dynamic global vegetation model (DGVM) implemented within the ICON-Land framework, but is also implemented as the land surface model used in the ICON-ESM 1.0 Earth System Model, where it provides the lower boundary conditions to the atmosphere (Jungclaus et al., 2021). JSBACHv4 is a re-implementation of JSBACHv3 in the new framework. JSBACHv3 has been the land component of the MPI-M ECHAM and MPI-ESM models used in many modelling studies over the last decades (Giorgetta et al., 2013; Mauritsen et al., 2019) including numerous model intercomparisons such as TRENDY (Friedlingstein et al., 2020), C4MIP (Arora et al., 2020) and LUMIP (Boysen et al., 2020). JSBACHv4 in ICON-ESM 1.0 currently includes the fast physical processes as well as biogeochemical processes to simulate the natural land carbon cycle, disturbances and anthropogenic land cover change, a subset of the processes described in Reick et al. (2021).

The surface energy balance and the soil thermal layers on land are coupled implicitly to the vertical diffusion scheme of ICON-A, which can be applied in a simplified manner to be run with observed meteorology for stand-alone applications. Plant productivity by photosynthesis, phenology (leaf area index), roughness lengths for momentum and heat, and visible and near-infrared albedos are computed on a flexible number of tiles representing sub-grid heterogeneity by plant functional types. Heat and water dynamics in the soil are described to a depth of about 10 m and are coupled to calculations of surface runoff and sub-surface drainage and the resulting river discharge through a hydrologic discharge model (Hagemann & Dümenil, 1997; Riddick et al., 2018). A five-layer snow model is applied and the soil dynamics include freezing water and phase changes between liquid and frozen water (Ekici et al., 2014, de Vrese et al., 2021).

JSBACHv4 also features a new scheme of forest age classes combining computationally efficient age-dependent simulation of all relevant processes with a tracking of the exact forest age (Nabel et al., 2020). This makes the representation of age-based forest management and its biogeophysical as well as carbon cycle impacts possible.


QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system, Thum et al. 2019) has been developed at the MPI for Biogeochemistry as an alternative, more comprehensive representation of biogeochemical processes for ICON-Land. QUINCY is integrated with ICON-Land through a set of defined interfaces that affect the biological control of the land surface’s radiation balance, momentum and heat transfer as well as its water balance. It follows a modular design that allows to integrate fundamental, fast biospheric processes with increasing degree of complexity from a prescribed phenology mode, a vegetation dynamics mode to a fully coupled BGC vegetation-soil model. QUINCY  also serves as test-bed for novel model components, e.g. representations of soil biogeochemical processes (Yu et al. 2020a,b).

QUINCY simulates the element cycling of carbon, nitrogen, and phosphorus in terrestrial ecosystems, and includes novel representations of source/sink limited plant growth; the acclimation of many ecophysiological processes; an explicit representation of vertical soil processes to separate litter and soil organic matter dynamics; a range of new diagnostics (leaf chlorophyll; isotope tracers) to allow for a more in-depth model evaluation. Case-studies include the evaluation against standard biosphere benchmarks (Thum et al. 2019), in particular ecosystem manipulation experiments (Caldararu et al. 2020) as well as long-term monitoring data (Caldararu et al. 2021).

ICON-Land is governed by the ICON-Land Steering Group and the ICON-Land Management Team. The steering group includes the representatives from several groups contributing to development of models of the land processes within the ICON-Land infrastructure, and is headed by the ICON-Land Management Team. The steering group meets two to three times a year in order to coordinate and facilitate ICON-Land development across the different scientific institutions contributing to ICON-Land, and to decide on strategic developments.

The ICON-Land Management Team is composed of six persons and meets monthly to make operational decisions. Please contact the ICON-Land Management Group to discuss opportunities for collaboration as well as contributions to model inter-comparison studies. As our resources are limited, we cannot promise support on a regular basis, but are interested in supporting and contributing to novel research on the land's role in the Earth System.


ICON-Land Management Team





E-mail contact

Sönke Zaehle

Scientific lead


Biogeochemical cycles, coupling to energy and water cycles


Victor Brovkin

Scientific lead


Land-climate interactions, esp. in high latitudes


Julia Pongratz

Scientific lead


Land use and land management, climate-carbon cycle interactions


Linda Schlemmer

Scientific lead


Land-atmosphere coupling


Cathy Hohenegger

Scientific lead


Land-atmosphere interactions


Reiner Schnur

Technical lead


Code development



Publications with JSBACH



Arora, V. K., Katavouta, A., Williams, R. G., Jones, C. D., Brovkin, V., Friedlingstein, P., Schwinger, J., Bopp, L., Boucher, O., Cadule, P., Chamberlain, M. A., Christian, J. R., Delire, C., Fisher, R. A., Hajima, T., Ilyina, T., Joetzjer, E., Kawamiya, M., Koven, C. D., Krasting, J. P., Law, R. M., Lawrence, D. M., Lenton, A., Lindsay, K., Pongratz, J., Raddatz, T., Séférian, R., Tachiiri, K., Tjiputra, J. F., Wiltshire, A., Wu, T., and Ziehn, T. (2020). Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models, Biogeosciences, 17, 4173–4222, https://doi.org/10.5194/bg-17-4173-2020.

Boysen, L., Brovkin, V., Pongratz, J., Lawrence, D., Lawrence, P., Vuichards, N., Peylin, P., Liddicoat, S., Hajima, T., Zhang, Y., Rocher, M., Delire, C., Séférian, R., Arora, V., Nieradzik, L., Anthoni, P., Thiery, W., Lague, M., Lawrence, D. & Lo, M.-H. (2020). Global climate response to idealized deforestation in CMIP6 models. Biogeosciences, 17, 5615-5638. doi:10.5194/bg-17-5615-2020

Caldararu, S., Thum, T., Yu, L., Zaehle, S. (2020). Whole-plant optimality predicts changes in leaf nitrogen under variable CO2 and nutrient availability. New Phytologist, 225(6), 2331-2346. doi:10.1111/nph.16327.

Caldararu, S., Thum, T., Yu, L., Kern, M., Nair, R., Zaehle, S. (2021). Long-term ecosystem nitrogen limitation from foliar delta15N data and a land surface model. Global Change Biology. doi:10.1111/gcb.15933.

Clement, V., et al. (2019). Automatic Port to OpenACC/OpenMP for Physical Parameterization in Climate and Weather Code Using the CLAW Compiler. Supercomputing Frontiers And Innovations, 6(3), 51-63.

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. doi:10.5194/tc-15-1097-2021.

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. doi:10.5194/gmd-7-631-2014.

Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Hauck, J., Olsen, A., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Le Quéré, C., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S., Aragão, L. E. O. C., Arneth, A., Arora, V., Bates, N. R., Becker, M., Benoit-Cattin, A., Bittig, H. C., Bopp, L., Bultan, S., Chandra, N., Chevallier, F., Chini, L. P., Evans, W., Florentie, L., Forster, P. M., Gasser, T., Gehlen, M., Gilfillan, D., Gkritzalis, T., Gregor, L., Gruber, N., Harris, I., Hartung, K., Haverd, V., Houghton, R. A., Ilyina, T., Jain, A. K., Joetzjer, E., Kadono, K., Kato, E., Kitidis, V., Korsbakken, J. I., Landschützer, P., Lefèvre, N., Lenton, A., Lienert, S., Liu, Z., Lombardozzi, D., Marland, G., Metzl, N., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I., Niwa, Y., O'Brien, K., Ono, T., Palmer, P. I., Pierrot, D., Poulter, B., Resplandy, L., Robertson, E., Rödenbeck, C., Schwinger, J., Séférian, R., Skjelvan, I., Smith, A. J. P., Sutton, A. J., Tanhua, T., Tans, P. P., Tian, H., Tilbrook, B., van der Werf, G., Vuichard, N., Walker, A. P., Wanninkhof, R., Watson, A. J., Willis, D., Wiltshire, A. J., Yuan, W., Yue, X., and Zaehle, S. (2020). Global Carbon Budget 2020, Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020.

Giorgetta, M. A., et al. (2013), Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5, J. Adv. Model. Earth Syst., 5, 572– 597, doi:10.1002/jame.20038.

Hagemann, S., & Dümenil, L. (1997). A parametrization of the lateral water flow for the global scale. Climate Dynamics, 14, 17–31. doi: 10.1007/s003820050205.

Jungclaus, J. H., Lorenz, S. J., Schmidt, H., Gutjahr, O., Haak, H., Mehlmann, C., Mikolajewicz, U., Notz, D., Putrashan, D.,von Storch, J.-S., and et al. (2021). The ICON Earth System Model Version 1.0. Earth and Space Science Open Archive, https://doi.org/10.1002/essoar.10507989.1.

Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., et al. (2019). Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and its response to increasing CO2. Journal of Advances in Modeling Earth Systems, 11, 998-1038. doi:10.1029/2018MS001400.

Nabel, Julia E.M.S., Naudts., Pongratz, J.(2020).  Accounting for forest age in the tile-based dynamic global vegetation model JSBACH4 (4.20p7; git feature/forests) – a land surface modelfor the ICON-ESM. Geosci. Model Dev. Vol. 13, 185–200, https://doi.org/10.5194/gmd-13-185-2020.

Reick, C. H., Gayler, V., Goll, D., Hagemann, S., Heidkamp, M., Nabel, J. E. M. S., Raddatz, T., Roeckner, E., Schnur, R., Wilkenskjeld, S. (2021). JSBACH 3 - The land component of the MPI Earth System Model: documentation of version 3.2. Hamburg: MPI für Meteorologie. doi:10.17617/2.3279802.

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. doi:10.5194/gmd-11-4291-2018

Thum, T., Caldararu, S., Engel, J., Kern, M., Pallandt, M., Schnur, R., Yu, L., Zaehle, S. (2019). A new terrestrial biosphere model with coupled carbon, nitrogen, and phosphorus cycles (QUINCY v1.0; revision 1772). Geoscientific Model Development, 12(11), 4781-4802. doi:10.5194/gmd-12-4781-2019.

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. doi:10.5194/gmd-13-783-2020.

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. doi:10.3389/ffgc.2020.543112.