Haoguo Hu - Academia.edu (original) (raw)

Papers by Haoguo Hu

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

Journal of Geophysical Research: Oceans, 2014

This study investigates sea ice and ocean circulation using a 3-D, 3.8 km CIOM (Coupled Ice-Ocean... more 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.

Research paper thumbnail of Predicting Lake Erie Wave Heights using XGBoost

ArXiv, 2019

Dangerous large wave put the coastal communities and vessels operating under threats and wave pre... more Dangerous large wave put the coastal communities and vessels operating under threats and wave predictions are strongly needed for early warnings. While numerical wave models, such as WAVEWATCH III (WW3), are useful to provide spatially continuous information to supplement in situ observations, however, they often require intensive computational costs. An attractive alternative is machine-learning method, which can potentially provide comparable performance of numerical wave models but only requires a small fraction of computational costs. In this study, we applied and tested a novel machine learning method based on XGBoost for predicting waves in Lake Erie in 2016-2017. In this study, buoy data from 1994 to 2017 were processed for model training and testing. We trained the model with data from 1994-2015, then used the trained model to predict 2016 and 2017 wave features. The mean absolute error of wave height is about 0.11-0.18 m and the maximum error is 1.14-1.95 m, depending on lo...

Research paper thumbnail of Great Lakes ice duration, winter severity index, cumulative freezing degree days, and atmospheric teleconnection patterns, 1973 – 2018

Research paper thumbnail of Responses of surface heat flux, sea ice and ocean dynamics in the Chukchi–Beaufort sea to storm passages during winter 2006/2007: A numerical study

Deep Sea Research Part I: Oceanographic Research Papers, 2015

Research paper thumbnail of A modeling study of seasonal variations of sea ice and plankton in the Bering and Chukchi Seas during 2007-2008

Journal of Geophysical Research: Oceans, 2013

A nutrient (N), phytoplankton (P), zooplankton (Z), and detritus (D) ecosystem model coupled to a... more A nutrient (N), phytoplankton (P), zooplankton (Z), and detritus (D) ecosystem model coupled to an ice-ocean model was applied to the Bering and Chukchi Seas for 2007-2008. The model reasonably reproduces the seasonal cycles of sea ice, phytoplankton, and zooplankton in the Bering-Chukchi Seas. The spatial variation of the phytoplankton bloom was predominantly controlled by the retreat of sea ice and the increased gradient of the water temperature from the south to the north. The model captures the basic structure of the measured nutrients and chl-a along the Bering shelf during 4-23 July 2008, and along the Chukchi shelf during 5-12 August 2007. In summer 2008, the Green Belt bloom was not observed by either the satellite measurements or the model. The model-data comparison and analysis reveal the complexity of the lower trophic dynamics in the Bering and Chukchi Seas. The complexity is due to the nature that the physical and biological components interact at different manners in time and space, even in response to a same climate forcing, over the physically distinct geographic settings such as in the Bering and North Aleutian Slopes, deep Bering basins, Bering shelf, and Chukchi Sea. Sensitivity studies were conducted to reveal the underlying mechanisms (i.e., the bottom-up effects) of the Bering-Chukchi ecosystem in response to changes in light intensity, nutrient input from open boundaries, and air temperature. It was found that (1) a 10% increase in solar radiation or light intensity for the entire year has a small impact on the intensity and timing of the bloom in the physical-biological system since the light is not a limiting factor in the study region; (2) a 20% increase in nutrients from all the open boundaries results in an overall 7% increase in phytoplankton, with the Slope region being the largest, and the Bering shelf and Chukchi being the smallest; and (3) an increase in air temperature by 2 C over the entire calculation period can result in an overall increase in phytoplankton by 11%.

Research paper thumbnail of A modeling study of ice–water processes for Lake Erie applying coupled ice-circulation models

Journal of Great Lakes Research, 2012

ABSTRACT A hydrodynamic model that includes ice processes and is optimized for parallel processin... more ABSTRACT A hydrodynamic model that includes ice processes and is optimized for parallel processing was configured for Lake Erie in order to study the ice–water coupling processes in the lake. A hindcast from April 2003 to December 2004 with hourly atmospheric forcing was conducted. The model reproduced the seasonal variation of ice cover, but the development of ice extent in January and its decay inMarch somewhat preceded the observations. Modeled lake circulation in ice-free seasons is consistent with previous studies for Lake Erie. Thermal structure of the lake was reasonably comparable to both satellite-derived observations and in-situ measurements, with mean differences ranging from−2 °C to 4 °C, depending on the season. The impacts of ice–water stress coupling and basal melting of ice were examined based on numerical experiments. The results show that: 1) ice–water stress coupling significantly dampens the subjacent lake circulation in winter due to packed ice cover that slows down the surface water, and 2) basal melting of ice contributes to widespread ice cover in the lake. The demonstrated model validity could lead to further studies of ice–water processes in the lake, including interannual variation and impacts on ecosystems.

Research paper thumbnail of Seasonal variations of sea ice and ocean circulation in the Bering Sea: A model-data fusion study

Journal of Geophysical Research, 2009

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

Journal of Geophysical Research, 2010

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

Journal of Climate, 2012

In this study, temporal and spatial variability of ice cover in the Great Lakes are investigated ... more 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 ...

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

Journal of Geophysical Research: Oceans, 2014

This study investigates sea ice and ocean circulation using a 3-D, 3.8 km CIOM (Coupled Ice-Ocean... more 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.

Research paper thumbnail of Predicting Lake Erie Wave Heights using XGBoost

ArXiv, 2019

Dangerous large wave put the coastal communities and vessels operating under threats and wave pre... more Dangerous large wave put the coastal communities and vessels operating under threats and wave predictions are strongly needed for early warnings. While numerical wave models, such as WAVEWATCH III (WW3), are useful to provide spatially continuous information to supplement in situ observations, however, they often require intensive computational costs. An attractive alternative is machine-learning method, which can potentially provide comparable performance of numerical wave models but only requires a small fraction of computational costs. In this study, we applied and tested a novel machine learning method based on XGBoost for predicting waves in Lake Erie in 2016-2017. In this study, buoy data from 1994 to 2017 were processed for model training and testing. We trained the model with data from 1994-2015, then used the trained model to predict 2016 and 2017 wave features. The mean absolute error of wave height is about 0.11-0.18 m and the maximum error is 1.14-1.95 m, depending on lo...

Research paper thumbnail of Great Lakes ice duration, winter severity index, cumulative freezing degree days, and atmospheric teleconnection patterns, 1973 – 2018

Research paper thumbnail of Responses of surface heat flux, sea ice and ocean dynamics in the Chukchi–Beaufort sea to storm passages during winter 2006/2007: A numerical study

Deep Sea Research Part I: Oceanographic Research Papers, 2015

Research paper thumbnail of A modeling study of seasonal variations of sea ice and plankton in the Bering and Chukchi Seas during 2007-2008

Journal of Geophysical Research: Oceans, 2013

A nutrient (N), phytoplankton (P), zooplankton (Z), and detritus (D) ecosystem model coupled to a... more A nutrient (N), phytoplankton (P), zooplankton (Z), and detritus (D) ecosystem model coupled to an ice-ocean model was applied to the Bering and Chukchi Seas for 2007-2008. The model reasonably reproduces the seasonal cycles of sea ice, phytoplankton, and zooplankton in the Bering-Chukchi Seas. The spatial variation of the phytoplankton bloom was predominantly controlled by the retreat of sea ice and the increased gradient of the water temperature from the south to the north. The model captures the basic structure of the measured nutrients and chl-a along the Bering shelf during 4-23 July 2008, and along the Chukchi shelf during 5-12 August 2007. In summer 2008, the Green Belt bloom was not observed by either the satellite measurements or the model. The model-data comparison and analysis reveal the complexity of the lower trophic dynamics in the Bering and Chukchi Seas. The complexity is due to the nature that the physical and biological components interact at different manners in time and space, even in response to a same climate forcing, over the physically distinct geographic settings such as in the Bering and North Aleutian Slopes, deep Bering basins, Bering shelf, and Chukchi Sea. Sensitivity studies were conducted to reveal the underlying mechanisms (i.e., the bottom-up effects) of the Bering-Chukchi ecosystem in response to changes in light intensity, nutrient input from open boundaries, and air temperature. It was found that (1) a 10% increase in solar radiation or light intensity for the entire year has a small impact on the intensity and timing of the bloom in the physical-biological system since the light is not a limiting factor in the study region; (2) a 20% increase in nutrients from all the open boundaries results in an overall 7% increase in phytoplankton, with the Slope region being the largest, and the Bering shelf and Chukchi being the smallest; and (3) an increase in air temperature by 2 C over the entire calculation period can result in an overall increase in phytoplankton by 11%.

Research paper thumbnail of A modeling study of ice–water processes for Lake Erie applying coupled ice-circulation models

Journal of Great Lakes Research, 2012

ABSTRACT A hydrodynamic model that includes ice processes and is optimized for parallel processin... more ABSTRACT A hydrodynamic model that includes ice processes and is optimized for parallel processing was configured for Lake Erie in order to study the ice–water coupling processes in the lake. A hindcast from April 2003 to December 2004 with hourly atmospheric forcing was conducted. The model reproduced the seasonal variation of ice cover, but the development of ice extent in January and its decay inMarch somewhat preceded the observations. Modeled lake circulation in ice-free seasons is consistent with previous studies for Lake Erie. Thermal structure of the lake was reasonably comparable to both satellite-derived observations and in-situ measurements, with mean differences ranging from−2 °C to 4 °C, depending on the season. The impacts of ice–water stress coupling and basal melting of ice were examined based on numerical experiments. The results show that: 1) ice–water stress coupling significantly dampens the subjacent lake circulation in winter due to packed ice cover that slows down the surface water, and 2) basal melting of ice contributes to widespread ice cover in the lake. The demonstrated model validity could lead to further studies of ice–water processes in the lake, including interannual variation and impacts on ecosystems.

Research paper thumbnail of Seasonal variations of sea ice and ocean circulation in the Bering Sea: A model-data fusion study

Journal of Geophysical Research, 2009

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

Journal of Geophysical Research, 2010

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

Journal of Climate, 2012

In this study, temporal and spatial variability of ice cover in the Great Lakes are investigated ... more 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 ...