Haoguo Hu

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

AbstractIn 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 tot...

Development of the Great Lakes Ice-Circulation Model (GLIM): Application to Lake Erie in 2003–2004

ABSTRACT To simulate ice and water circulation in Lake Erie over a yearly cycle, a Great Lakes Ice-circulation Model (GLIM) was developed by applying a Coupled Ice-Ocean Model (CIOM) with a 2-km resolution grid. The hourly surface wind stress and thermodynamic forcings for input into the GLIM are derived from meteorological measurements interpolated onto the 2-km model grids. The seasonal cycles for ice concentration, thickness, velocity, and other variables are well reproduced in the 2003/04 ice season. Satellite measurements of ice cover were used to validate GLIM with a mean bias deviation (MBD) of 7.4%. The seasonal cycle for lake surface temperature is well reproduced in comparison to the satellite measurements with a MBD of 1.5%. Additional sensitivity experiments further confirm the important impacts of ice cover on lake water temperature and water level variations. Furthermore, a period including an extreme cooling (due to a cold air outbreak) and an extreme warming event in February 2004 was examined to test GLIMs response to rapidly-changing synoptic forcing.

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.

Impacts of the Siberian High and Arctic Oscillation on the East Asia winter monsoon: Driving downwelling in the western Bering Sea

The relationships between the wind fields of the East Asia winter monsoon (EAWM), the Siberian High (SH), and the Arctic Oscillation (AO) were investigated using reanalysis products. The winter anomalies of the wind fields were systematically examined from the Bering Sea, the Sea of Okhotsk (SoO), the Sea of Japan (SOJ), the East China Sea (ECS), and all the way to the South China Sea (SCS). The sea-level pressure (SLP) difference between the SH and the Aleutian Low (AL) determines the intensity of the EAWM.Wind field anomalies are controlled directly by the SH and indirectly by the AO that has significant impacts on the SH and AL. It is found that +SH enhances the EAWM, while +AO reduces the intensity of the EAWM by reducing the SLP difference (gradient) between the SH and AL; vice versa for the –SH and –AO, respectively. The surface air temperature (SAT) anomalies caused by the +SH result in a significant cooling in the downstream regions and a warming in the upstream regions; vice versa for the negative phase of the SH. The +AO produces a large warming in northern Eurasian and a cooling in the Bering Sea. Furthermore, using a Coupled Ice-Ocean Model (CIOM), it is found that the EAWM can produce a downwelling and dense water formation along the Siberian coast in the western Bering Sea, and also a significant surface-to-bottom convection over the Bering shelf, forming the winter shelf water, which can survive the summer as the so-called cold pool. The cold pool in the Bering Sea has significant impacts on marine ecosystems and habitat including fisheries, which has much implication to other marginal seas of East Asia.

Abrupt Climate Changes and Emerging Ice-Ocean Processes in the Pacific Arctic Region and the Bering Sea

The purpose of this chapter is to reveal several emerging physical ice-ocean processes associated with the unprecedented sea ice retreat in the Pacific Arctic Region (PAR). These processes are closely interconnected under the scenario of diminishing sea ice, resulting in many detectable changes from physical environment to ecosystems. Some of these changes are unprecedented and have drawn the attention of both scientific and societal communities. More importantly, some mechanisms responsible for the diminishing sea ice cannot be explained by the leading Arctic Oscillation (AO), which has been used to interpret most of the changes in the Arctic for the last several decades. The new challenging questions are: (1) What is the major forcing? (2) Is the AO, the DA, or their combination, contributing to the sea ice minima in recent years? How do we use models to investigate the recent changes in the PAR. Is the heat transport through the Bering Strait associated with the DA? What processes accelerate sea ice melting in the PAR?

Low Primary Productivity in the Chukchi Sea Controlled by Warm Pacific Water: A Data-Model Fusion Study

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) has identified a broad low chlorophyll-a (chl-a) area in the Chukchi Sea since 2002. High sea surface temperature from 2002 (more than 5°C), which resulted in a long duration of open water, was also detected by satellite. An intensified ocean color front at the southwest Chukchi Sea near the Siberian Coast indicates nutrient depletion in the Alaska Coastal Current and its branches. A low chl-a area started to emerge in the Hope Valley in June, and then expanded to the Herald Shoal and Hanna Shoal during July and August. The evolution pattern of low chl-a area is consistent with the variability of the pathway of the Pacific water simulated by a Coupled Ice-Ocean Model (CIOM). These results suggest that the summer phytoplankton bloom from 2002 to 2005 was suppressed by the dominance of warm nutrient-poor water from the Pacific, and by the deepening of the surface mixed layer by strong wind stress. During the summer of 2004, a phytoplankton bloom was detected at the ice edge when the sea surface wind field was relatively calm. Our results imply that the ice-edge bloom was induced due to weak wind speeds, which produce shallower upper mixed layer, favoring the ice-edge bloom.

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.