Gross Primary Production of Antarctic Landfast Sea Ice: A Model-Based Estimate

Gross Primary Production of Antarctic Landfast Sea Ice: A Model-Based Estimate
These are the input and outputs containing gross primary production of Antarctic landfast sea ice (fast ice) data used in the paper " Gross Primary Production of Antarctic Landfast Sea Ice: A Model-Based Estimate" by Wongpan et al.
There are required inputs and processed outputs from the 1-dimensional Louvain-la-Neuve Sea Ice Model (LIM1D). The model was configured to model Antarctic fast ice, assumed to form in situ with its spatial distribution prescribed from the recent satellite-derived fast ice product of Fraser et al. (2020), with an initial thickness of 0.05 m (Wongpan et al., 2021), and evolving with a 1–h time step. LIM1D represents the ice column as ten layers with equal thickness plus one additional snow layer. Four categories of physical processes are implemented: sea-ice growth and melt, thermal diffusion, brine dynamics, and radiative transfer. Photosynthesis was limited by light and macro-nutrient availability, temperature, and brine salinity. A full description of LIM1D is given in Vancoppenolle et al. (2010), Moreau et al. (2015) and Vancoppenolle and Tedesco (2016). We followed initialization and parameterizations as described in Lim et al. (2019) suggesting that ice algae (represented as diatoms) have higher silicate half-saturation constants (KSi) than pelagic diatom species. The model can be downloaded from http://forge.ipsl.jussieu.fr/lim1d revision #3.26.
The Japanese 55-year atmospheric reanalysis product for driving ocean-sea ice models (JRA55-do; Tsujino et al., 2018) was used as surface forcing, selected because of its high resolution and development for forcing ocean and sea-ice models of the Ocean Model Intercomparison Project phase 2 (OMIP-2; Tsujino et al., 2020). To avoid truncation of extremely low air temperatures applied in JRA55-do around the Antarctic coast as a function of time and latitude(Large and Yeager, 2004; Tsujino et al., 2018), JRA55-do temperatures were replaced with data from the fifth-generation European Centre for Medium-Range Weather Forecasts re-analysis (ERA5; Hersbach et al., 2018). A simulation (JRATEMP, Table 1) was also performed using the JRA55-do temperatures, despite their unrealistic truncation described above.
For 2005-2006, fast-ice pixels at a native resolution of 1 km from the satellite-based dataset of Fraser et al. (2020) were distributed into 1690 grid cells, matching JRA55-do’s 0.5625° grid. The details for each run are
CONTROL = RUN006
For all runs, each grid cell is divided into nine equal-area snow depth categories. For each category, snowfall is multiplied by a log-normally distributed, category-specific factor (0.102, 0.272, 0.427, 0.532, 0.721, 0.952, 1.310, 1.740, 3.310), in order to approach a log-normal snow depth distribution (see Table 1 in Saenz and Arrigo, 2014). Finally, primary production in the grid cell is the average of the nine equal-area productivities calculated with different snow depths. This experiment is considered the most realistic approach and is named CONTROL;
OHF = RUN004
This run used an oceanic heat flux of 30 W m–2 during summer which was derived from observations at Davis Station (Swadling, 1998).
KSI = RUN005
   An experiment to test the effect of modified silicate half-saturation constants (KSI) from KSi = 50 μM in the CONTROL experiment (after Lim et al., 2019) to KSi = 3.9 μM in 176 the KSI experiment (after Sarthou et al., 2005).
OHF_KSI = RUN007
    Combining OHF and KSI changed as above.
JRASNOW = RUN009
   This run includes a prescribed subgrid scale snow thickness distribution, as CONTROL but using snow input directly from JRA55-do.
JRATEMP = RUN010
   This run uses the JRA55-do temperatures
NOSUB = RUN006 without sub-grid-scale snow
   Spatially-uniform snow cover increasing linearly throughout the year at a rate of 0.29 m y–1 (as with CONTROL), but without subgrid-scale snow thickness distribution.
The folder tree is
├── INPUT
│   ├── RUN004
│   ├── RUN005
│   ├── RUN006
│   ├── RUN007
│   ├── RUN009
│   └── RUN010
└── OUTPUT
    ├── RUN004
    ├── RUN005
    ├── RUN006
    ├── RUN007
    ├── RUN009
    └── RUN010
References
Fraser, A. D., Massom, R. A., Ohshima, K. I., Willmes, S., Kappes, P. J., Cartwright, J., &amp; Porter-Smith, R. (2020). High-resolution mapping of circum-Antarctic landfast sea ice distribution, 2000–2018. Earth System Science Data, 12(4), 2987-2999. http://doi.org/10.5194/essd-12-2987-2020
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., et al. (2018). ERA5 hourly data on single levels from 1979 to present. Copernicus climate change service (c3s) climate data store (cds). Retrieved from https://doi.org/10.24381/cds.adbb2d47
Large, W. G., and Yeager, S. G. (2004). Diurnal to decadal global forcing for ocean and sea-ice models: The data sets and flux climatologies. In: National Center for Atmospheric Research Boulder.
Lim, S. M., Moreau, S., Vancoppenolle, M., Deman, F., Roukaerts, A., Meiners, K. M., et al. (2019). Field Observations and Physical‐Biogeochemical Modeling Suggest Low Silicon Affinity for Antarctic Fast Ice Diatoms. Journal of Geophysical Research: Oceans, 124(11), 7837-7853. http://doi.org/10.1029/2018jc014458
Moreau, S., Vancoppenolle, M., Delille, B., Tison, J.-L., Zhou, J., Kotovitch, M., et al. (2015). Drivers of inorganic carbon dynamics in first-year sea ice: A model study. Journal of Geophysical Research: Oceans, 120(1), 471-495. http://doi.org/10.1002/2014jc010388
Saenz, B. T., and Arrigo, K. R. (2014). Annual primary production in Antarctic sea ice during 2005-2006 from a sea ice state estimate. Journal of Geophysical Research: Oceans, 119(6), 3645-3678. http://doi.org/10.1002/2013jc009677
Sarthou, G., Timmermans, K. R., Blain, S., and Tréguer, P. (2005). Growth physiology and fate of diatoms in the ocean: a review. Journal of Sea Research, 53(1-2), 25-42. http://doi.org/10.1016/j.seares.2004.01.007
Swadling, K. M. (1998). Influence of seasonal ice formation on life cycle strategies of Antarctic copepods. University of Tasmania, Retrieved from https://eprints.utas.edu.au/22306/ 
Tsujino, H., Urakawa, L. S., Griffies, S. M., Danabasoglu, G., Adcroft, A. J., Amaral, A. E., et al. (2020). Evaluation of global ocean–sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2). Geoscientific Model Development, 13(8), 3643-3708. http://doi.org/10.5194/gmd-13-3643-2020
Tsujino, H., Urakawa, S., Nakano, H., Small, R. J., Kim, W. M., Yeager, S. G., et al. (2018). JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do). Ocean Modelling, 130, 79-139. http://doi.org/10.1016/j.ocemod.2018.07.002
Vancoppenolle, M., Goosse, H., de Montety, A., Fichefet, T., Tremblay, B., and Tison, J.-L. (2010). Modeling brine and nutrient dynamics in Antarctic sea ice: The case of dissolved silica. Journal of Geophysical Research, 115(C2). http://doi.org/10.1029/2009jc005369
Vancoppenolle, M., and Tedesco, L. (2016). Numerical models of sea ice biogeochemistry. In Sea Ice (pp. 492-515). http://doi.org/10.1002/9781118778371.ch20
Wongpan, P., Vancoppenolle, M., Langhorne, P. J., Smith, I. J., Madec, G., Gough, A. J., et al. (2021). Sub‐Ice Platelet Layer Physics: Insights From a Mushy‐Layer Sea Ice Model. Journal of Geophysical Research: Oceans, 126(6). http://doi.org/10.1029/2019jc015918

Tags: AMD, AMD/AU, CEOS, CONTINENT > ANTARCTICA, Computer > Computer, EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > PLANTS > MICROALGAE, EARTH SCIENCE > CRYOSPHERE > SEA ICE > ICE EDGES, EARTH SCIENCE > CRYOSPHERE > SEA ICE > ICE EXTENT, EARTH SCIENCE > CRYOSPHERE > SEA ICE > ICE TYPES, EARTH SCIENCE > OCEANS > SEA ICE > ICE EDGES, EARTH SCIENCE > OCEANS > SEA ICE > ICE EXTENT, EARTH SCIENCE > OCEANS > SEA ICE > ICE TYPES, GEOGRAPHIC REGION > POLAR, LANDFAST SEA ICE, MODELS > MODELS, OCEAN > SOUTHERN OCEAN, SEA ICE ALGAE, SEA ICE SIMULATION
Organization: Australian Ocean Data Network