The influence of sea ice and snow cover and nutrient availability on the formation of massive under-ice phytoplankton blooms in the Chukchi Sea

Zhang, Jinlun; Ashjian, Carin J.; Campbell, Robert G.; Spitz, Yvette H.; Steele, Michael; Hill, Victoria

doi:10.1016/j.dsr2.2015.02.008

Cited by 44 publications

(58 citation statements)

References 68 publications

(109 reference statements)

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“…To describe seasonal variation in stage C3, we plotted concentrations (ind./m 3 ) in the upper and deeper layers (defined as samples with a maximum sampling depth of 200 m and samples from depth layers deeper than 200 m, respectively) per sampling day in three regions: north of 85°N and ≥200 km from the shelf (“northern Arctic basins”), 70–85°N and ≥50 km from the shelf (“southern Arctic basins”) and north of 70°N and <50 km from the shelf (“Arctic shelf and slope”). We defined the shelf as areas with bottom depth ≤500 m. We compared C3 concentrations to the growth season using daily mean phytoplankton concentrations in the upper 60 m from a pan‐arctic ice‐ocean‐biogeochemical model (Biology‐Ice‐Ocean Modeling and Assimilation System, BIOMAS) (Zhang et al., , , ). This included both pelagic phytoplankton and sea ice algae released to the water column (hereafter referred to as phytoplankton) given as mmol N/m 3 , which we converted to μg C/m 3 using the Redfield C:N ratio of 106:16.…”

Section: Methodsmentioning

confidence: 99%

Pushing the limit: Resilience of an Arctic copepod to environmental fluctuations

Kvile

Ashjian

Feng

et al. 2018

Global Change Biology

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Life history strategies such as multiyear life cycles, resting stages, and capital breeding allow species to inhabit regions with extreme and fluctuating environmental conditions. One example is the zooplankton species Calanus hyperboreus, whose life history is considered an adaptation to the short and unpredictable growth season in the central Arctic Ocean. This copepod is commonly described as a true Arctic endemic; however, by statistically analyzing compiled observational data, we show that abundances are relatively low and later stages and adults dominate in the central Arctic Ocean basins, indicating expatriation. Combining data analyses with individual-based modeling and energy requirement estimation, we further demonstrate that while C. hyperboreus can reach higher abundances in areas with greater food availability outside the central Arctic basins, the species' resilience to environmental fluctuations enables the life cycle to be completed in the central Arctic basins. Specifically, the energy level required to reach the first overwintering stage-a prerequisite for successful local production-is likely met in some-but not all-years. This fine balance between success and failure indicates that C. hyperboreus functions as a peripheral population in the central Arctic basins and its abundance will likely increase in areas with improved growth conditions in response to climate change. By illustrating a key Arctic species' resilience to extreme and fluctuating environmental conditions, the results of this study have implications for projections of future biogeography and food web dynamics in the Arctic Ocean, a region experiencing rapid warming and sea ice loss.

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Section: Methodsmentioning

confidence: 99%

Pushing the limit: Resilience of an Arctic copepod to environmental fluctuations

Kvile

Ashjian

Feng

et al. 2018

Global Change Biology

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“…The TED sea ice model has eight categories each for ice thickness, ice enthalpy, and snow depth. The centers of the eight ice thickness categories are 0, 0.38, 1.30, 3.07, 5.97, 10.24, 16.02, and 23.41 m [see Zhang et al ., ]. Thus the first category is actually the open water category, while the other seven categories represent ice of various thicknesses.…”

Section: Methodsmentioning

confidence: 99%

Accuracy of short‐term sea ice drift forecasts using a coupled ice‐ocean model

Schweiger

Zhang

2015

JGR Oceans

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Arctic sea ice drift forecasts of 6 h–9 days for the summer of 2014 are generated using the Marginal Ice Zone Modeling and Assimilation System (MIZMAS); the model is driven by 6 h atmospheric forecasts from the Climate Forecast System (CFSv2). Forecast ice drift speed is compared to drifting buoys and other observational platforms. Forecast positions are compared with actual positions 24 h–8 days since forecast. Forecast results are further compared to those from the forecasts generated using an ice velocity climatology driven by multiyear integrations of the same model. The results are presented in the context of scheduling the acquisition of high‐resolution images that need to follow buoys or scientific research platforms. RMS errors for ice speed are on the order of 5 km/d for 24–48 h since forecast using the sea ice model compared with 9 km/d using climatology. Predicted buoy position RMS errors are 6.3 km for 24 h and 14 km for 72 h since forecast. Model biases in ice speed and direction can be reduced by adjusting the air drag coefficient and water turning angle, but the adjustments do not affect verification statistics. This suggests that improved atmospheric forecast forcing may further reduce the forecast errors. The model remains skillful for 8 days. Using the forecast model increases the probability of tracking a target drifting in sea ice with a 10 km × 10 km image from 60 to 95% for a 24 h forecast and from 27 to 73% for a 48 h forecast.

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“…Using a varying resolution regional model (minimum 4 km near the Alaska's coast), Zhang et al () found that under‐ice blooms in the Chukchi Sea increased in recent decades due to both increasing light availability related to reducing sea‐ice cover, and nutrient supply from the shelf break due to an intensifying circulation, which is weaker in coarse resolution models. Arctic ecosystem model intercomparison studies (Jin et al, ; Lee et al, ) also show that models with horizontal resolutions from 1 to 0.25°, and even higher for regional models, could simulate the mean states of the seasonal cycle of PP but with less variability compared to in situ observations.…”

Section: Introductionmentioning

confidence: 99%

Effects of Model Resolution and Ocean Mixing on Forced Ice‐Ocean Physical and Biogeochemical Simulations Using Global and Regional System Models

et al. 2018

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The current coarse‐resolution global Community Earth System Model (CESM) can reproduce major and large‐scale patterns but is still missing some key biogeochemical features in the Arctic Ocean, e.g., low surface nutrients in the Canada Basin. We incorporated the CESM Version 1 ocean biogeochemical code into the Regional Arctic System Model (RASM) and coupled it with a sea‐ice algal module to investigate model limitations. Four ice‐ocean hindcast cases are compared with various observations: two in a global 1° (40∼60 km in the Arctic) grid: G1deg and G1deg‐OLD with/without new sea‐ice processes incorporated; two on RASM's 1/12° (∼9 km) grid R9km and R9km‐NB with/without a subgrid scale brine rejection parameterization which improves ocean vertical mixing under sea ice. Higher‐resolution and new sea‐ice processes contributed to lower model errors in sea‐ice extent, ice thickness, and ice algae. In the Bering Sea shelf, only higher resolution contributed to lower model errors in salinity, nitrate (NO3), and chlorophyll‐a (Chl‐a). In the Arctic Basin, model errors in mixed layer depth (MLD) were reduced 36% by brine rejection parameterization, 20% by new sea‐ice processes, and 6% by higher resolution. The NO3 concentration biases were caused by both MLD bias and coarse resolution, because of excessive horizontal mixing of high NO3 from the Chukchi Sea into the Canada Basin in coarse resolution models. R9km showed improvements over G1deg on NO3, but not on Chl‐a, likely due to light limitation under snow and ice cover in the Arctic Basin.

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The influence of sea ice and snow cover and nutrient availability on the formation of massive under-ice phytoplankton blooms in the Chukchi Sea

Cited by 44 publications

References 68 publications

Pushing the limit: Resilience of an Arctic copepod to environmental fluctuations

Pushing the limit: Resilience of an Arctic copepod to environmental fluctuations

Accuracy of short‐term sea ice drift forecasts using a coupled ice‐ocean model

Effects of Model Resolution and Ocean Mixing on Forced Ice‐Ocean Physical and Biogeochemical Simulations Using Global and Regional System Models

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