CSIB v1 (Canadian Sea-ice Biogeochemistry): a sea-ice biogeochemical model for the NEMO community ocean modelling framework

Hayashida, Hakase; Christian, James R.; Holdsworth, Amber M.; Hu, Xianmin; Monahan, Adam H.; Mortenson, Eric; Myers, Paul G.; Riche, Olivier G. J.; Sou, Tessa; Steiner, Nadja

doi:10.5194/gmd-12-1965-2019

Cited by 17 publications

(31 citation statements)

References 70 publications

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“…The sea-ice ecosystem model used at the UVic is the Canadian Sea-ice Biogeochemistry model version 1 (CSIB v1) coupled with a modified version from the Canadian Ocean Ecosystem model (CanOE) (Hayashida et al, 2019). The sea-ice ecosystem consists of ice algae, nitrate, and ammonium Mortenson et al, 2017).…”

Section: The Uvic Modelmentioning

confidence: 99%

“…According to these studies, the complicated ice-algal processes have been numerically formulated in various ways. In recent years, the model domains extended from a single landfast ice station to the pan-Arctic scale (Castellani et al, 2017;Deal et al, 2011;Hayashida et al, 2019;Watanabe et al, 2015). Decadal simulations in the three-dimensional framework are also performed by several research groups.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐Model Intercomparison of the Pan‐Arctic Ice‐Algal Productivity on Seasonal, Interannual, and Decadal Timescales

et al. 2019

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Seasonal, interannual, and decadal variations in the Arctic ice‐algal productivity for 1980–2009 are investigated using daily outputs from five sea ice‐ocean ecosystem models participating in the Forum for Arctic Modeling and Observational Synthesis project. The models show a shelf‐basin contrast in the spatial distribution of ice‐algal productivity (ice‐PP). The simulated ice‐PP substantially varies among the four subregions (Chukchi Sea, Canada Basin, Eurasian Basin, and Barents Sea) and among the five models, respectively. The simulated annual total ice‐PP has no common decadal trend at least for 1980–2009 among the five models in any of the four subregions, although the simulated snow depth and sea‐ice thickness in spring are mostly declining. The model intercomparison indicates that an appropriate balance of stable ice‐algal habitat (i.e., sea‐ice cover) and enough light availability is necessary to retain the ice‐PP. The multi‐model averages show that the ice‐algal bloom timing shifts to an earlier date and that the bloom duration shortens in the four subregions. However, both the positive and negative decadal trends in the timing and duration are simulated. This difference in trends are attributed to temporal shifts among different types of ice‐algal blooms: long‐massive, short‐massive, long‐gentle, and short‐gentle bloom. The selected value for the maximum growth rate of the ice‐algal photosynthesis term is a key source for the inter‐model spreads. Understanding the simulated uncertainties on the pan‐Arctic and decadal scales is expected to improve coupled sea ice‐ocean ecosystem models. This step will be a baseline for further modeling/field studies and future projections.

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Section: The Uvic Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi‐Model Intercomparison of the Pan‐Arctic Ice‐Algal Productivity on Seasonal, Interannual, and Decadal Timescales

et al. 2019

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“…The period of the standard run is 1980-2015, with a spin-up period from 1969-1979. Detailed sensitivity analyses regarding various model settings related to the Arctic sea ice and forcing were carried out in Hayashida et al (2019).…”

Section: Methodsmentioning

confidence: 99%

“…The default setting for LIM2 does not allow any light penetration when there is snow cover. In order to provide adequate light penetration at the time of observed ice algae blooms, the default setting for LIM2 was modified to allow some light penetration through a thin snow cover, as described in Hayashida et al (2019). Documentation and source files for both sea ice and ocean physical models (OPA and LIM2) can be found online (at https://www.nemo-ocean.eu).…”

Section: Ocean and Sea Ice Physics Modelsmentioning

confidence: 99%

“…In addition to the sea ice carbon pump, representations of both the pelagic and sympagic (sea ice associated) ecosystems are also implemented in the model (Hayashida et al, 2019;Mortenson, 2019;Mortenson et al, 2017). The ice algae, along with the simulated pelagic ecosystem, act as a sink of DIC associated with primary production, and a source of DIC associated with remineralization of detritus.…”

Section: Introductionmentioning

confidence: 99%

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Modeled Impacts of Sea Ice Exchange Processes on Arctic Ocean Carbon Uptake and Acidification (1980–2015)

et al. 2020

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A regional Arctic ice ocean model incorporating biogeochemical processes occurring inside the sea ice and water column is used to assess changes to the Arctic Ocean's carbon system, including oceanic carbon uptake and ocean acidification, over the recent period of Arctic sea ice decline (1980–2015). Two novel modifications are the following: (1) incorporation of carbon uptake by sea ice algae and (2) modification of the sea ice carbon pump to allow for vertical transport by brine plumes with high concentrations of dissolved inorganic carbon (DIC) and total alkalinity (TA) to the bottom of the mixed layer. The simulated carbon uptake north of 66.5°N increases by 20%, from 110 to 135 Tg C yr−1 from 1980–2015, and the mean pan‐Arctic sea surface pH decreases from 8.1 to 8.0. There is substantial regional and seasonal variability, highlighting potential problems with interpolating sparse measurements. Two sensitivity studies assess the effects of modifications to the near‐surface carbonate system. First, excluding the sea ice carbon pump results in a marked decrease in seasonal variability of pan‐Arctic‐mean sea surface DIC (∼25% less than the standard run) and TA (∼10% less), suggesting that neglecting the ice carbon pump results in models overestimating the saturation state in summer. Second, neglecting the sea ice algae results in an underestimation of the Arctic Ocean's annual carbon uptake (∼3% per year), indicating that the accumulating year‐on‐year underestimation of annual carbon uptake, as a result of neglecting sea ice algae, would lead to increasing error over multidecadal runs of polar ocean models.

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Sediments in sea ice drive the Canada Basin surface Mn maximum: insights from an Arctic Mn ocean model

Rogalla

Allen

Colombo

et al. 2021

Preprint

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CSIB v1 (Canadian Sea-ice Biogeochemistry): a sea-ice biogeochemical model for the NEMO community ocean modelling framework

Cited by 17 publications

References 70 publications

Multi‐Model Intercomparison of the Pan‐Arctic Ice‐Algal Productivity on Seasonal, Interannual, and Decadal Timescales

Multi‐Model Intercomparison of the Pan‐Arctic Ice‐Algal Productivity on Seasonal, Interannual, and Decadal Timescales

Modeled Impacts of Sea Ice Exchange Processes on Arctic Ocean Carbon Uptake and Acidification (1980–2015)

Sediments in sea ice drive the Canada Basin surface Mn maximum: insights from an Arctic Mn ocean model

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