Welcome! This repository contains the model and the scripts used to generate the figures in Benot-Gagn et al., "Exploring controls on the timing of the phytoplankton bloom in western Baffin Bay, Canadian Arctic".

Directory structure:

• The model itself is contained in the following directories: doc, eesupp, jobs, lsopt, model, optim, pkg, tools, utils and verification.
• The configuration of the model is in the directory gud1d35+16.
• The jupyter notebook timing.ipynb generates the figures in the directory figurestimingprogress.
• The jupyter notebook timingsupmat.ipynb generates the figures in the directory figurestimingsupprogress.
• Data for the generation of the figures (Data S1 to S9) including model output are openly accessible in the directory data.

Model:

• The model is a one-dimensional configuration of the biogeochemical/ecosystem model of Dutkiewicz et al. (2021) in Glob. change biol. The tracers are mixed by the MIT general circulation model (MITgcm, Marshall et al., 1997 in JGR). The paper Benot-Gagn et al. describes some modifications relative to Dutkiewicz et al. (2021).

Datasets:

• data/DataS1observationsIBCAO1minbathy.mat: Bathymetry from International BAthymetric Chart of the Arctic Ocean (IBCAO) Version 3.0, Jakobsson et al. (2012). https://doi.org/10.1029/2012GL052219, details in MetadataS1.pdf.
• data/DataS2forcingfields_nutrients: Forcing fields of nutrient concentrations between January 1 to May 15 for the reference simulation (EXP-0). They are also the in situ nutrient concentrations at the Qikiqtarjuaq sea ice camps between mid-April and the end of May in 2015 and 2016 (67.4797N, -63.7895E). It contains a subset of the files available in the dataset Massicotte et al. (2019). https://doi.org/10.17882/59892. The paper related to this dataset is Massicotte et al. (2020). https://doi.org/10.5194/essd-12-151-2020, details in MetadataS2.pdf.
• data/DataS3observationsQikiqtarjuaq: In situ observations from the Qikiqtarjuaq sea ice camps 2015 and 2016 (67.4797N,-63.7895E). It contains a subset of the files available in the dataset Massicotte et al. (2019). https://doi.org/10.17882/59892. The paper related to this dataset is Massicotte et al. (2020). https://doi.org/10.5194/essd-12-151-2020, details in MetadataS3.pdf.
• data/DataS4outputnemo_lim3: Model output generated for this study using NEMO 3.6 (Madec et al., 2017, https://doi.org/10.5281/zenodo.3248739) coupled with LIM 3.6 (Rousset et al., 2015, https://doi.org/10.5194/gmd-8-2991-2015), details in MetadataS4.pdf.
• data/DataS5forcingfields_light: Forcing fields of light for the reference simulation (EXP-0), details in MetadataS5.pdf.
• data/DataS6outputmitgcm: Simulated data generated for this study, details in MetadataS6.pdf.
• data/DataS7_literature.csv: Observations from literature (Lacour et al., 2017. https:doi.org/10.1002/lno.10369), details in MetadataS7.pdf.
• data/DataS8outputcgrf: Model output generated using CGRF (Smith et al., 2014, https:doi.org/10.1002/qj.2194). For this study, data was selected for the location of the Qikiqtarjuaq sea ice camp 2016 (67.4797N,-63.7895E) and for the year 2016, details in MetadataS8.pdf.
• data/DataS9observationsCCGS_Amundsen: In situ data sampled onboard the CCGS Amundsen, details in MetadataS9.pdf.

Notes:

Some data files are larger than 100 GB. Hence, if Git LFS is not installed, the files larger than 100 GB will be replaced with placeholders after cloning the project.

The exact procedure I used to deploy the code on a supercomputer of the Digital Research Alliance of Canada with the SLURM workload manager is

module load git-lfs git_lfs clone git@github.com:maximebenoitgagne/timing.git

The procedure to run the model is in the README of the directory gud1d35+16.

Let me know if you have any requests or comments. You can contact me via ResearchGate.

How to cite this material: