A model‐data study of the 1999 St. Lawrence Island polynya in the Bering Sea

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95536/1/jgrc12221.pd

Simulation of phytoplankton distribution and variation in the Bering‐Chukchi Sea using a 3‐D physical‐biological model

A three‐dimensional physical‐biological model has been used to simulate seasonal phytoplankton variations in the Bering and Chukchi Seas with a focus on understanding the physical and biogeochemical mechanisms involved in the formation of the Bering Sea Green Belt (GB) and the Subsurface Chlorophyll Maxima (SCM). Model results suggest that the horizontal distribution of the GB is controlled by a combination of light, temperature, and nutrients. Model results indicated that the SCM, frequently seen below the thermocline, exists because of a rich supply of nutrients and sufficient light. The seasonal onset of phytoplankton blooms is controlled by different factors at different locations in the Bering‐Chukchi Sea. In the off‐shelf central region of the Bering Sea, phytoplankton blooms are regulated by available light. On the Bering Sea shelf, sea ice through its influence on light and temperature plays a key role in the formation of blooms, whereas in the Chukchi Sea, bloom formation is largely controlled by ambient seawater temperatures. A numerical experiment conducted as part of this study revealed that plankton sinking is important for simulating the vertical distribution of phytoplankton and the seasonal formation of the SCM. An additional numerical experiment revealed that sea ice algae account for 14.3–36.9% of total phytoplankton production during the melting season, and it cannot be ignored when evaluating primary productivity in the Arctic Ocean.Key PointsSea ice plays a key role in algal bloom in the Bering ShelfSea ice algae account for a signification of phytoplankton biomassPlankton sinking is important for model simulationsPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/133606/1/jgrc21750_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/133606/2/jgrc21750.pd

A modeling study of seasonal variations of sea ice and plankton in the Bering and Chukchi Seas during 2007–2008

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97464/1/jgrc20061.pd

Simulating the 1998 spring bloom in Lake Michigan using a coupled physical‐biological model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95047/1/jgrc12526.pd

Seasonal variations of sea ice and ocean circulation in the Bering Sea: A model‐data fusion study

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94775/1/jgrc11005.pd

Modeling effects of tidal and wave mixing on circulation and thermohaline structures in the Bering Sea: Process studies

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95183/1/jgrc11280.pd

Modeling the ocean circulation in the Bering Sea

With parameterized wave mixing, the circulation and the tidal current in the Bearing Sea were simulated simultaneously using the three-dimensional Princeton Ocean Model. The simulated circulation pattern in the deep basin is relatively stable, cyclonic, and has little seasonal change. The Bering Slope Current between 200-1000 m isobaths was estimated to be 5 Sv in volume transport. The Kamchatka Current was estimated to be 20 Sv off the Kamchatka Peninsula. The Bering shelf circulations vary with season, driven mainly by wind. These features are consistent with historical estimates. A counter current was captured flowing southeastward approximately along the 200 m isobath of the Bering Slope, opposite to the northwestward Bering Slope Current, which needs to be validated by observations. An upwelling current is located in the shelf break (120-1000 m) area, which may imply the vertical advection of nutrients for supporting the Bering Sea Green Belt seasonal plankton blooms in the break slope area. The Bering Slope Current is located in a downwelling area

Temporal and Spatial Variability of Great Lakes Ice Cover, 1973–2010

In this study, temporal and spatial variability of ice cover in the Great Lakes are investigated using historical satellite measurements from 1973 to 2010. The seasonal cycle of ice cover was constructed for all the lakes, including Lake St. Clair. A unique feature found in the seasonal cycle is that the standard deviations (i.e., variability) of ice cover are larger than the climatological means for each lake. This indicates that Great Lakes ice cover experiences large variability in response to predominant natural climate forcing and has poor predictability. Spectral analysis shows that lake ice has both quasi-decadal and interannual periodicities of;8 and ~4 yr. There was a significant downward trend in ice coverage from 1973 to the present for all of the lakes, with Lake Ontario having the largest, and Lakes Erie and St. Clair having the smallest. The translated total loss in lake ice over the entire 38-yr record varies from 37% in Lake St. Clair (least) to 88% in Lake Ontario (most). The total loss for overall Great Lakes ice coverage is 71%, while Lake Superior places second with a 79% loss. An empirical orthogonal function analysis indicates that a major response of ice cover to atmospheric forcing is in phase in all six lakes, accounting for 80.8% of the total variance. The second mode shows an out-of-phase spatial variability between the upper and lower lakes, accounting for 10.7% of the total variance. The regression of the first EOF-mode time series to sea level pressure, surface air temperature, and surface wind shows that lake ice mainly responds to the combined Arctic Oscillation and El Nin˜ o–Southern Oscillation patterns

A modeling study of coastal circulation and landfast ice in the nearshore Beaufort and Chukchi seas using CIOM

This study investigates sea ice and ocean circulation using a 3‐D, 3.8 km CIOM (Coupled Ice‐Ocean Model) under daily atmospheric forcing for the period 1990–2008. The CIOM was validated using both in situ observations and satellite measurements. The CIOM successfully reproduces some observed dynamical processes in the region, including the Bering‐inflow‐originated coastal current that splits into three branches: Alaska Coastal Water (ACW), Central Channel branch, and Herald Valley branch. In addition, the Beaufort Slope Current (BSC), the Beaufort Gyre, the East Siberian Current (ESC), mesoscale eddies, and seasonal landfast ice are well simulated. The CIOM also reproduces reasonable interannual variability in sea ice, such as landfast ice, and anomalous open water (less sea ice) during the positive Dipole Anomaly (DA) years, vice versa during the negative DA years. Sensitivity experiments were conducted with regard to the impacts of the Bering Strait inflow (heat transport), onshore wind stress, and sea ice advection on sea ice change, in particular on the landfast ice. It is found that coastal landfast ice is controlled by the following processes: wind forcing, Bering Strait inflow, and sea ice dynamics. Key Points Modeling landfast ice and nearshore processes Reveal mesoscale eddies using a model and theory Nearshore sea ice responds to both +DA and −DAPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108088/1/jgrc20680.pd