2019 · 20 citations · 30 references
Natural SciencesEarth SciencesEnvironmental SciencesBiogeochemistryClimate ScienceTerrestrial EcologyBiogeochemical Cycle ModelingCarbon CycleClimate ChangeEcological ModelingLand Surface ModelingLand Surface ProcessesBiogeochemical DynamicsBiogeochemical ProcessesCarbon Cycle ModelingLand Cover Change ModelingLand-use Change ModelingNutrient Co-limitationTerrestrial Carbon CycleTerrestrial Ecosystem Dynamics
Abstract. Recent reports by the Global Carbon Project highlight large uncertainties around land surface processes such as land use change, strength of CO2 fertilization, nutrient limitation and supply, and response to variability in climate. Process-based land surface models are well-suited to address these complex and emerging global change problems, but will require extensive development and evaluation. The coupled Canadian Land Surface Scheme and Canadian Terrestrial Ecosystem Model (CLASS-CTEM) framework has been under continuous development by Environment and Climate Change Canada since 1987. As the open-source model of code development has revolutionized the software industry, scientific software is experiencing a similar evolution. Given the scale of the challenge facing land surface modellers, and the benefits of open-source, or community model, development, we have transitioned CLASS-CTEM from an internally developed model to an open-source community model, which we call the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC) v. 1.0. CLASSIC contains many technical features specifically designed to encourage community use including software containerization for serial and parallel simulations, extensive benchmarking software and data (Automated Model Benchmarking; AMBER), self-documenting code, community standard formats for model inputs and outputs, amongst others. Here we evaluate and benchmark CLASSIC against 31 FLUXNET sites where the model has been tailored to the site-level conditions and driven with observed meteorology. Future versions of CLASSIC will be developed using AMBER and these initial benchmark results to evaluate model performance over time. CLASSIC remains under active development and the code, site-level benchmarking data, software container and AMBER are freely available for community use.
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Scikit-learn: Machine Learning in Python
Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · arXiv (Cornell University) · 2012 · 63.7K citations · Full text
Guo‐Yue Niu, Zong‐Liang Yang, Kenneth E. Mitchell et al. · Journal of Geophysical Research Atmospheres · 2011 · 3.1K citations · Full text
Energy balance closure at FLUXNET sites
Kell Wilson, Allen H. Goldstein, Eva Falge et al. · Agricultural and Forest Meteorology · 2002 · 2.4K citations
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