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

Abstract

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.

Spatiotemporal Variability in Modeled Bottom Ice and Sea Surface Dimethylsulfide Concentrations and Fluxes in the Arctic During 1979–2015

“…The seasonal ice zone is defined here as the area where sea ice is present seasonally. The spatial pattern of the peak concentration shows a shelf basin contrast similar to modeled ice algal biomass distribution (Watanabe et al, 2019); the peak concentration range is 100–1,000 μmol S m −3 in shelf regions, while its range is much lower (1–10 μmol m −3 ) in the basins. There are only a few measurements of DMS within Arctic sea ice reported in Levasseur (2013): 30 μmol S m −3 in the Central Arctic in August 1994, 769 μmol S m −3 in the Beaufort Sea (year unknown), and 2,000 μmol S m −3 in the Resolute Bay in June 2012.…”

Relative Impact of Sea Ice and Temperature Changes on Arctic Marine Production

“…Thus, fully comprehending HiLAT model shortcomings with respect to mixed layer nutrient concentrations will be important for understanding not only the overall response of production to climate change but its partitioning between the ice and ocean. HiLAT model estimate for annual Arctic (all analysis regions except the Bering Sea) ice‐algae primary production (2.4 Tg C) were in line with estimates from the UAF‐G model but significantly less than estimates from the other four model configurations (Watanabe et al, 2019). The HiLAT model and the UAF‐G model are both based on the biogeochemical module (Jin et al, 2012; Moore et al, 2004), which has been incorporated into CESM.…”

“…A reduction in snow and ice thickness will allow increased light penetration and thus a more suitable habitat for the growth of ice algae. However, a substantial reduction in ice cover will inevitability reduce the viable substrate and thus the contribution of ice algae to total production (Gosselin et al, 1997; Watanabe et al, 2019).…”

Increasing riverine heat influx triggers Arctic sea ice decline and oceanic and atmospheric warming

“…Discharge and river water temperatures, simulated using the CHANGE model forced by three different meteorological datasets (Supplementary Materials), were used as riverine freshwater and heat fluxes in the COCO model experiments, which makes it possible to quantify the sensitivity of sea ice to the associated fluxes. Both COCO and CHANGE have been extensively used to simulate changes in sea ice–ocean processes associated with the Arctic sea-ice retreat ( 12 ) and long-term changes in river discharge and water temperatures from the pan-Arctic river system ( 1 , 17 ), respectively.…”