| HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model
Tang, Q., Delottier, H., Kurtz, W., Nerger, L., Schilling, O. S., & Brunner, P. (2024). HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model. Geoscientific Model Development, 17(8), 3559-3578. https://doi.org/10.5194/gmd-17-3559-2024 |
| Importance of microphysical settings for climate forcing by stratospheric SO2 injections as modeled by SOCOL-AERv2
Vattioni, S., Stenke, A., Luo, B., Chiodo, G., Sukhodolov, T., Wunderlin, E., & Peter, T. (2024). Importance of microphysical settings for climate forcing by stratospheric SO2 injections as modeled by SOCOL-AERv2. Geoscientific Model Development, 17(10), 4181-4197. https://doi.org/10.5194/gmd-17-4181-2024 |
| Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks
Berkemeier, T., Krüger, M., Feinberg, A., Müller, M., Pöschl, U., & Krieger, U. K. (2023). Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks. Geoscientific Model Development, 16(7), 2037-2054. https://doi.org/10.5194/gmd-16-2037-2023 |
| A framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake Sector
Golub, M., Thiery, W., Marcé, R., Pierson, D., Vanderkelen, I., Mercado-Bettin, D., … Zdorovennova, G. (2022). A framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake Sector. Geoscientific Model Development, 15(11), 4597-4623. https://doi.org/10.5194/gmd-15-4597-2022 |
| A map of global peatland extent created using machine learning (Peat-ML)
Melton, J. R., Chan, E., Millard, K., Fortier, M., Winton, R. S., Martín-López, J. M., … Verchot, L. V. (2022). A map of global peatland extent created using machine learning (Peat-ML). Geoscientific Model Development, 15(12), 4709-4738. https://doi.org/10.5194/gmd-15-4709-2022 |
| A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1
Safin, A., Bouffard, D., Ozdemir, F., Ramón, C. L., Runnalls, J., Georgatos, F., … Šukys, J. (2022). A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1. Geoscientific Model Development, 15(20), 7715-7730. https://doi.org/10.5194/gmd-15-7715-2022 |
| SuperflexPy 1.3.0: an open-source Python framework for building, testing, and improving conceptual hydrological models
Dal Molin, M., Kavetski, D., & Fenicia, F. (2021). SuperflexPy 1.3.0: an open-source Python framework for building, testing, and improving conceptual hydrological models. Geoscientific Model Development, 14(11), 7047-7072. https://doi.org/10.5194/gmd-14-7047-2021 |
| Atmosphere-ocean-aerosol-chemistry-climate model SOCOLv4.0: description and evaluation
Sukhodolov, T., Egorova, T., Stenke, A., Ball, W. T., Brodowsky, C., Chiodo, G., … Rozanov, E. (2021). Atmosphere-ocean-aerosol-chemistry-climate model SOCOLv4.0: description and evaluation. Geoscientific Model Development, 14(9), 5525-5560. https://doi.org/10.5194/gmd-14-5525-2021 |
| SELF v1.0: a minimal physical model for predicting time of freeze-up in lakes
Toffolon, M., Cortese, L., & Bouffard, D. (2021). SELF v1.0: a minimal physical model for predicting time of freeze-up in lakes. Geoscientific Model Development, 14(12), 7527-7543. https://doi.org/10.5194/gmd-14-7527-2021 |
| Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4
Baracchini, T., Chu, P. Y., Šukys, J., Lieberherr, G., Wunderle, S., Wüest, A., & Bouffard, D. (2020). Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4. Geoscientific Model Development, 13(3), 1267-1284. https://doi.org/10.5194/gmd-13-1267-2020 |
| The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO<sub>2</sub>, temperature, water, and nitrogen (version 1.0)
Franke, J. A., Müller, C., Elliott, J., Ruane, A. C., Jägermeyr, J., Snyder, A., … Moyer, E. J. (2020). The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0). Geoscientific Model Development, 13(9), 3995-4018. https://doi.org/10.5194/gmd-13-3995-2020 |
| The GGCMI phase 2 experiment: global gridded crop model simulations under uniform changes in CO<sub>2</sub>, temperature, water, and nitrogen levels (protocol version 1.0)
Franke, J. A., Müller, C., Elliott, J., Ruane, A. C., Jägermeyr, J., Balkovic, J., … Moyer, E. J. (2020). The GGCMI phase 2 experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0). Geoscientific Model Development, 13(5), 2315-2336. https://doi.org/10.5194/gmd-13-2315-2020 |
| Improved tropospheric and stratospheric sulfur cycle in the aerosol-chemistry-climate model SOCOL-AERv2
Feinberg, A., Sukhodolov, T., Luo, B. P., Rozanov, E., Winkel, L. H. E., Peter, T., & Stenke, A. (2019). Improved tropospheric and stratospheric sulfur cycle in the aerosol-chemistry-climate model SOCOL-AERv2. Geoscientific Model Development, 12(9), 3863-3887. https://doi.org/10.5194/gmd-12-3863-2019 |
| Toward an open access to high-frequency lake modeling and statistics data for scientists and practitioners - the case of Swiss lakes using Simstrat v2.1
Gaudard, A., Råman Vinnå, L., Bärenbold, F., Schmid, M., & Bouffard, D. (2019). Toward an open access to high-frequency lake modeling and statistics data for scientists and practitioners - the case of Swiss lakes using Simstrat v2.1. Geoscientific Model Development, 12(9), 3955-3974. https://doi.org/10.5194/gmd-12-3955-2019 |
| Optimizing the parameterization of deep mixing and internal seiches in one-dimensional hydrodynamic models: a case study with Simstrat v1.3
Gaudard, A., Schwefel, R., Vinnå, L. R., Schmid, M., Wüest, A., & Bouffard, D. (2017). Optimizing the parameterization of deep mixing and internal seiches in one-dimensional hydrodynamic models: a case study with Simstrat v1.3. Geoscientific Model Development, 10(9), 3411-3423. https://doi.org/10.5194/gmd-10-3411-2017 |
| Solar forcing for CMIP6 (v3.2)
Matthes, K., Funke, B., Andersson, M. E., Barnard, L., Beer, J., Charbonneau, P., … Versick, S. (2017). Solar forcing for CMIP6 (v3.2). Geoscientific Model Development, 10(6), 2247-2302. https://doi.org/10.5194/gmd-10-2247-2017 |
| Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications
Müller, C., Elliott, J., Chryssanthacopoulos, J., Arneth, A., Balkovic, J., Ciais, P., … Yang, H. (2017). Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications. Geoscientific Model Development, 10(4), 1403-1422. https://doi.org/10.5194/gmd-10-1403-2017 |
| The coupled atmosphere-chemistry-ocean model SOCOL-MPIOM
Muthers, S., Anet, J. G., Stenke, A., Raible, C. C., Rozanov, E., Brönnimann, S., … Schmutz, W. (2014). The coupled atmosphere-chemistry-ocean model SOCOL-MPIOM. Geoscientific Model Development, 7(5), 2157-2179. https://doi.org/10.5194/gmd-7-2157-2014 |
| Climate forcing reconstructions for use in PMIP simulations of the Last Millennium (v1.1)
Schmidt, G. A., Jungclaus, J. H., Ammann, C. M., Bard, E., Braconnot, P., Crowley, T. J., … Vieira, L. E. A. (2012). Climate forcing reconstructions for use in PMIP simulations of the Last Millennium (v1.1). Geoscientific Model Development, 5(1), 185-191. https://doi.org/10.5194/gmd-5-185-2012 |
| Climate forcing reconstructions for use in PMIP simulations of the last millennium (v1.0)
Schmidt, G. A., Jungclaus, J. H., Ammann, C. M., Bard, E., Braconnot, P., Crowley, T. J., … Vieira, L. E. A. (2011). Climate forcing reconstructions for use in PMIP simulations of the last millennium (v1.0). Geoscientific Model Development, 4(1), 33-45. https://doi.org/10.5194/gmd-4-33-2011 |
