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.
"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)
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.
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.