GMD cover
Executive editors: David Ham, Juan Antonio Añel, Astrid Kerkweg, Min-Hui Lo, Richard Neale, Rolf Sander & Paul Ullrich

Geoscientific Model Development (GMD) is a not-for-profit international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication:

  • geoscientific model descriptions, from statistical models to box models to GCMs;
  • development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results;
  • new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data;
  • papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data;
  • model experiment descriptions, including experimental details and project protocols;
  • full evaluations of previously published models.

More details can be found in manuscript types and the journal editorial (compiled by the executive editors).

"I believe that the time is ripe for significantly better documentation of programs, and that we can best achieve this by considering programs to be works of literature."

(Donald E. Knuth, Literate Programming, 1984)

"Essentially, all models are wrong, but some are useful."

(George E. P. Box, Robustness in the strategy of scientific model building, 1979)

GMD per-paper APC pilot 2021
To help authors know the article processing charges (APCs) levied for their final journal article already from submission, the EGU and Copernicus test a per-paper APC model for manuscripts submitted to GMD from 1 January 2021. The standard fee will be €1,600 net, independent of the article length. Please find further information about this pilot.
IF value: 6.135
IF6.135
IF 5-year value: 7.708
IF 5-year7.708
CiteScore value: 9.9
CiteScore9.9
h5-index value: 55
h5-index55
Highlight articles
12 May 2022
| Highlight paper
Training a supermodel with noisy and sparse observations: a case study with CPT and the synch rule on SPEEDO – v.1
Francine Schevenhoven and Alberto Carrassi
Geosci. Model Dev., 15, 3831–3844, https://doi.org/10.5194/gmd-15-3831-2022,https://doi.org/10.5194/gmd-15-3831-2022, 2022
Short summary
23 Feb 2022
| Highlight paper
Using neural network ensembles to separate ocean biogeochemical and physical drivers of phytoplankton biogeography in Earth system models
Christopher Holder, Anand Gnanadesikan, and Marie Aude-Pradal
Geosci. Model Dev., 15, 1595–1617, https://doi.org/10.5194/gmd-15-1595-2022,https://doi.org/10.5194/gmd-15-1595-2022, 2022
Short summary
17 Feb 2022
| Highlight paper
CSDMS: a community platform for numerical modeling of Earth surface processes
Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Benjamin Campforts, Tian Gan, Katherine R. Barnhart, Albert J. Kettner, Irina Overeem, Scott D. Peckham, Lynn McCready, and Jaia Syvitski
Geosci. Model Dev., 15, 1413–1439, https://doi.org/10.5194/gmd-15-1413-2022,https://doi.org/10.5194/gmd-15-1413-2022, 2022
Short summary
26 Jan 2022
| Highlight paper
The Whole Antarctic Ocean Model (WAOM v1.0): development and evaluation
Ole Richter, David E. Gwyther, Benjamin K. Galton-Fenzi, and Kaitlin A. Naughten
Geosci. Model Dev., 15, 617–647, https://doi.org/10.5194/gmd-15-617-2022,https://doi.org/10.5194/gmd-15-617-2022, 2022
Short summary
26 Jan 2022
| Highlight paper
Numerically consistent budgets of potential temperature, momentum, and moisture in Cartesian coordinates: application to the WRF model
Matthias Göbel, Stefano Serafin, and Mathias W. Rotach
Geosci. Model Dev., 15, 669–681, https://doi.org/10.5194/gmd-15-669-2022,https://doi.org/10.5194/gmd-15-669-2022, 2022
Short summary
Recent papers
20 May 2022
Development of a flexible data assimilation method in a 3D unstructured-grid ocean model under Earth System Modeling Framework
Hao-Cheng Yu, Yinglong Joseph Zhang, Lars Nerger, Carsten Lemmen, Jason C. S. Yu, Tzu-Yin Chou, Chi-Hao Chu, and Chuen-Teyr Terng
EGUsphere, https://doi.org/10.5194/egusphere-2022-114,https://doi.org/10.5194/egusphere-2022-114, 2022
Preprint under review for GMD (discussion: open, 0 comments)
Short summary
19 May 2022
DFN Generator v2.0: A new tool to model the growth of large-scale natural fracture networks using fundamental geomechanics
Michael John Welch, Mikael Lüthje, and Simon John Oldfield
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-22,https://doi.org/10.5194/gmd-2022-22, 2022
Preprint under review for GMD (discussion: open, 0 comments)
Short summary
18 May 2022
Transfer learning for landslide susceptibility modelling using domain adaptation and case-based reasoning
Zhihao Wang, Jason Goetz, and Alexander Brenning
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-119,https://doi.org/10.5194/gmd-2022-119, 2022
Preprint under review for GMD (discussion: open, 0 comments)
Short summary
18 May 2022
Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)
Wentao Zhang, Xiangjun Shi, and Chunsong Lu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-95,https://doi.org/10.5194/gmd-2022-95, 2022
Preprint under review for GMD (discussion: open, 0 comments)
Short summary
18 May 2022
Spatial parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes for deciduous forests in the eastern United States: an efficient model-data fusion method
Rui Ma, Jingfeng Xiao, Shunlin Liang, Han Ma, Tao He, Da Guo, Xiaobang Liu, and Haibo Lu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-96,https://doi.org/10.5194/gmd-2022-96, 2022
Preprint under review for GMD (discussion: open, 0 comments)
Short summary
News
18 Apr 2022 Predicting global terrestrial biomes with the LeNet convolutional neural network

The authors demonstrate an accurate and practical method to construct empirical models for operational biome mapping via a convolutional neural network (CNN) approach.

18 Apr 2022 Predicting global terrestrial biomes with the LeNet convolutional neural network

The authors demonstrate an accurate and practical method to construct empirical models for operational biome mapping via a convolutional neural network (CNN) approach.

07 Apr 2022 KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from mesocosm experiments

By incorporating the domain knowledge into a machine learning model, KGML-ag overcomes the well-known limitations of process-based models due to insufficient representations and constraints, and unlocks the “black box” of machine learning models. Therefore, KGML-ag can outperform existing approaches on capturing the hot moment and complex dynamics of N2O flux.

07 Apr 2022 KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from mesocosm experiments

By incorporating the domain knowledge into a machine learning model, KGML-ag overcomes the well-known limitations of process-based models due to insufficient representations and constraints, and unlocks the “black box” of machine learning models. Therefore, KGML-ag can outperform existing approaches on capturing the hot moment and complex dynamics of N2O flux.

09 Feb 2022 EGUsphere opens for preprints

EGUsphere, the innovative open-access repository created by the European Geosciences Union and Copernicus Publications, is growing. For the first time, authors will be able to upload preprints to the online resource, taking advantage of EGU’s pioneering public peer-review process, whilst preparing their papers for future release.

09 Feb 2022 EGUsphere opens for preprints

EGUsphere, the innovative open-access repository created by the European Geosciences Union and Copernicus Publications, is growing. For the first time, authors will be able to upload preprints to the online resource, taking advantage of EGU’s pioneering public peer-review process, whilst preparing their papers for future release.