[1] As a part of the Arctic Ocean Model Intercomparison Project, results from 10 Arctic ocean/ice models are intercompared over the period 1970 through 1999. Models' monthly mean outputs are laterally integrated over two subdomains (Amerasian and Eurasian basins), then examined as functions of depth and time. Differences in such fields as averaged temperature and salinity arise from models' differences in parameterizations and numerical methods and from different domain sizes, with anomalies that develop at lower latitudes carried into the Arctic. A systematic deficiency is seen as AOMIP models tend to produce thermally stratified upper layers rather than the ''cold halocline'', suggesting missing physics perhaps related to vertical mixing or to shelf-basin exchanges. Flow fields pose a challenge for intercomparison. We introduce topostrophy, the vertical component of VÂr r r rD where V is monthly mean velocity and r r r rD is the gradient of total depth, characterizing the tendency to follow topographic slopes. Positive topostrophy expresses a tendency for cyclonic ''rim currents''. Systematic differences of models' circulations are found to depend strongly upon assumed roles of unresolved eddies.

Based on biophysical ice-core data collected in the landfast ice off Barrow, Alaska, USA, in 2002 and 2003, a one-dimensional ice–ocean ecosystem model was developed to determine the factors controlling the bottom-ice algal community. The data and model results revealed a three-stage ice-algal bloom: (1) onset and early slow growth stage before mid-March, when growth is limited by light; (2) fast growth stage with increased light and sufficient nutrients; and (3) decline stage after late May as ice algae are flushed out of the ice bottom. Stages 2 and 3 are either separated by a transition period as in 2002 or directly connected by ice melting as in 2003, when in situ light and nutrient enrichment experiments showed only light limitations. The modeled net primary production of ice algae (NPPAi) from March to June is 1.2 and 1.7 g Cm–2 for 2002 and 2003, respectively, within the range of previous observations. Model sensitivity studies found that overall NPPAi increased almost proportionally to the initial nutrient concentrations in the water column. A phytoplankton bloom (if it occurs as in 2002) would compete with ice algae for nutrients and lead to reduced NPPAi. About 45% of the NPPAi was exported to the shallow benthos.

[1] As a part of Arctic Ocean Intercomparison Project, results from five coupled physical and biological ocean models were compared for the Arctic domain, defined here as north of 66.6°N. The global and regional (Arctic Ocean (AO)-only) models included in the intercomparison show similar features in terms of the distribution of present-day water column-integrated primary production and are broadly in agreement with in situ and satellite-derived data. However, the physical factors controlling this distribution differ between the models. The intercomparison between models finds substantial variation in the depth of winter mixing, one of the main mechanisms supplying inorganic nutrients over the majority of the AO. Although all models manifest similar level of light limitation owing to general agreement on the ice distribution, the amount of nutrients available for plankton utilization is different between models. Thus the participating models disagree on a fundamental question: which factor, light or nutrients, controls present-day Arctic productivity. These differences between models may not be detrimental in determining present-day AO primary production since both light and nutrient limitation are tightly coupled to the presence of sea ice. Essentially, as long as at least one of the two limiting factors is reproduced correctly, simulated total primary production will be close to that observed. However, if the retreat of Arctic sea ice continues into the future as expected, a decoupling between sea ice and nutrient limitation will occur, and the predictive capabilities of the models may potentially diminish unless more effort is spent on verifying the mechanisms of nutrient supply. Our study once again emphasizes the importance of a realistic representation of ocean physics, in particular vertical mixing, as a necessary foundation for ecosystem modeling and predictions.

Arctic organisms are adapted to the strong seasonality of environmental forcing. A small timing mismatch between biological processes and the environment could potentially have significant consequences for the entire food web. Climate warming causes shrinking ice coverage and earlier ice retreat in the Arctic, which is likely to change the timing of primary production. In this study, we test predictions on the interactions among sea ice phenology and production timing of ice algae and pelagic phytoplankton. We do so using the following (1) a synthesis of available satellite observation data; and (2) the application of a coupled ice-ocean ecosystem model. The data and model results suggest that, over a large portion of the Arctic marginal seas, the timing variability in ice retreat at a specific location has a strong impact on the timing variability in pelagic phytoplankton peaks, but weak or no impact on the timing of ice-algae peaks in those regions. The model predicts latitudinal and regional differences in the timing of ice algae biomass peak (varying from April to May) and the time lags between ice algae and pelagic phytoplankton peaks (varying from 45 to 90 days). The correlation between the time lag and ice retreat is significant in areas where ice retreat has no significant impact on ice-algae peak timing, suggesting that changes in pelagic phytoplankton peak timing control the variability in time lags. Phenological variability in primary production is likely to have consequences for higher trophic levels, particularly for the zooplankton grazers, whose main food source is composed of the dually pulsed algae production of the Arctic.

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