Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
GMD cover
Executive editors:
Julia
 
Hargreaves
,
Lutz
 
Gross
,
David
 
Ham
,
Astrid
 
Kerkweg
,
Didier
 
Roche
 &
Rolf
 
Sander

Geoscientific Model Development (GMD) is an 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)

Highlight articles

Earth's terrestrial surface influences climate by exchanging carbon and water with the atmosphere through stomatal pores. However, most land-surface models, used to predict global carbon and water fluxes, estimate that water lost through stomata is less than what observations show. In this study, we integrate plant water loss data from 204 species into a global land surface model, finding that global estimates of plant water loss increase, soil moisture decreases, and carbon gain also decreases.

Danica L. Lombardozzi, Melanie J. B. Zeppel, Rosie A. Fisher, and Ahmed Tawfik

We developed a plant hydraulics model for tropical forests based on established plant physiological theory, and parameterized it by conducting a pantropical hydraulic trait survey. We show that a substantial amount of trait diversity can be represented in the model by a reduced set of trait dimensions. The fully parameterized model is able capture tree-level variation in water status and improves simulations of total ecosystem transpiration, showing how to incorporate hydraulic traits in models.

Bradley O. Christoffersen, Manuel Gloor, Sophie Fauset, Nikolaos M. Fyllas, David R. Galbraith, Timothy R. Baker, Bart Kruijt, Lucy Rowland, Rosie A. Fisher, Oliver J. Binks, Sanna Sevanto, Chonggang Xu, Steven Jansen, Brendan Choat, Maurizio Mencuccini, Nate G. McDowell, and Patrick Meir

This study compares the 20th century multi-annual climate variability modes in reanalysis data sets (ERA-20C and 20CR) and 12 climate model simulations using the randomised multi-channel singular spectrum analysis. The reanalysis data sets are remarkably similar on all timescales, except that the spectral power in ERA-20C is systematically slightly higher than in 20CR. None of the climate models closely reproduce all aspects of the reanalysis spectra, although many aspects are represented well.

Heikki Järvinen, Teija Seitola, Johan Silén, and Jouni Räisänen

This paper analyses methods to assimilate chemical measurements in air quality models. We developed a reduced-order atmospheric chemistry model, which was used to compare results from different assimilation algorithms. Using an ensemble variational method (4DEnVar), we exploited the dynamical information provided by hourly measurements of chemical concentrations to diagnose model biases and improve next-day forecasts for several species of interest for air quality.

Emanuele Emili, Selime Gürol, and Daniel Cariolle

This paper tells why to launch the Global Monsoons Model Inter-comparison Project (GMMIP) and how to achieve its scientific goals on monsoon variability. It addresses the scientific questions to be answered, describes three tiered experiments comprehensively and proposes a basic analysis framework to guide future research. It will help the monsoon research communities to understand the objectives of the GMMIP and the modelling groups involved in the GMMIP conduct the experiments successfully.

Tianjun Zhou, Andrew G. Turner, James L. Kinter, Bin Wang, Yun Qian, Xiaolong Chen, Bo Wu, Bin Wang, Bo Liu, Liwei Zou, and Bian He

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