Programing Skill, Software I'm using
- Fortran 77/90/95, IDL (Interactive Data Language), Keyhole Markup Language (for Google Earth)
- SeaDAS(SeaWiFS Data Analysis System), AVS/Express, GMT(Generic Mapping Tool), GrADS, Ferret, Ocean Data View
Left figure: Merged (SeaWiFS+MODIS) chl-a image, which is distributed by the NASA/GSFC Ocean Color website, is imported to the Google Earth; Chl-a image was procced by using IDL and SeaDAS.
Publication
- Journal
- Mizobata, K. and J. Wang, A summer oceanic desert triggered by the warm Pacific water intrusion into the Chukchi Sea, in preparation.
- Mizobata, K., S. Saitoh, J. Wang, Interannual variability of summer biochemical enhancement in relation to the mesoscale eddy at the shelf break in the vicinity of the Pribilof Islands, Bering Sea. Deep Sea Research U , accepted.
- Mizobata, K., J. Wang, and S. Saitoh (2006), Eddy-induced cross-slope exchange maintaining summer high productivity of the Bering Sea shelf break, Journal of Geophysical Research, 111, C10017, doi:10.1029/2005JC003335.
- Mizobata, K., S. Saitoh, 2004. Variability of Bering Sea eddies and primary productivity along the shelf edge during 1998-2000 using satellite multi-sensor remote sensing. Journal of Marine Systems., Vol.50, pp101-111.
- Mizobata, K., S. Saitoh, A. Shiomoto, T. Miyamura, N. Shiga, K. Imai, M. Toratani, Y. Kajiwara, and K. Sasaoka, Bering Sea Cyclonic and Anticyclonic Eddies observed during Summer 2000 and 2001., Progress in Oceanography, Vol 55, Numbers 1-2, pp65-75, 2002
- Proceedings
- Mizobata, K., S. Saitoh, Characteristics of cyclonic and anticyclonic eddies in the southeastern Bering Sea 1998-2000 using TOPEX/Poseidon. SPIE Journal, 2003
- Iida, T., S. Saitoh, K. Mizobata, Phytoplankton distribution as observed from bio-optical drifters and SeaWiFS images in the Bering Sea green belt. SPIE Journal, 2003
- Mizobata, K., S. Saitoh, J. Wang, The influence of Bering Sea eddy field on the shelf slope exchange in the southeastern Bering Sea. Proceedings of The 4th international workshop on global change: Connection to the Arctic 2003 (GCCA4)
Award, Grant
Oct.24.2002: Best paper at the BIO Topic Session, North Pacific Marine Science Organization 11th Meeting, Qingdao, China
Phytoplankton Dynamics and Ice-Ocean Circulation in the Chukchi and Beaufort Seas
In the high latitude ocean, like the Chukchi/Beaufort Sea, oceanic environments is complicated because of sea ice and intrusion of the Pacific water. Ice-ocean circulation should affect the phtoplankton dynamics. Phytoplakton biomass is tightly connected to benthic biomass, which is needed for sea bird and marine mammal (Grebmeier and Dunton, 2000). But the mechanisms controlling the phytoplaknton dynamics is poorly known. It is hard to conduct sufficient ship surveys to observe ecosystem change due to heavy sea ice situation. In the Arctic/Subarctic Sea, it is very cloudy but satellite remote sensing is still powerful tool to understand what's going on there. Also the ice-ocean modeling is useful to describe the temporal and spatial changes of ice-ocean circulation. Our analyses shows that satellite images captured wide low-chl area in the Chukchi Sea between 2002 and 2005, when water temperature was relatively high. In the meantime, ice-edge bloom was found only in July , 2004. Currently I am applying both two methods (Remote sensing and Ice-ocean model) to the western Arctic Ocean to explore the linkage between phytoplankton and ice-ocean circurlation ( Mizobata, Wang and Hu, submitted to GRL ).
Left figure: Simulated ice-ocean circulation (temperature and sea ice concentration, top) and SeaWiFS OC4L chl-a and SSM-I sea ice cover (bottom) in the Chukchi Sea in 2002 in June, July and August. Seasonal sea ice variation is roughly consistent with satellite measurements.
The pathway of the Pacific Summer Water (the Alaska Coastal Curent) is well reproduced. In June, low chl-a started to appear at the Hope Valley where relatively high SST was simulated by the CIOM and captured by the AVHRR (not shown). The comparison between CIOM and satellite images allows us to infer that high SST water covering the Chukchi Sea results in low chl-a because of less oppotunity to utilize nutrients due to suppression of vertical mixing, except for near the Bering Strait.
Bering Sea Eddy Field and Primary Productivity
High amount of phytoplankton biomass is sustaining rich marine ecosystem in the Bering Sea and the western Arctic Ocean. In the Bering Sea, I have studied chl-rich area along 200-m isobaths, so-called "Green Belt (Springer et al., 1996)". Using in-situ data, satellite imagery and numerical model, we found that the mechanisms controlling phytoplankton dynamics can be explained by the summer mesoscale eddy field in the Bering Sea Green Belt area (Mizobata et al., 2002, Mizobata and Saitoh, 2004, JMS: Prog. Oceanogr.; Mizobata et al., 2006, JGR; Mizobata et al., DSR-2, in revision).
Left figure: Schematic image of the eddy field and the mechanism controlling primary productivity by mesoscale eddies along the shelf break area (Mizobata et al., 2006, JGR).
Mesoscale eddy field is induced by the intrusion of the warm/saline Pacific Water through the Aleutian passes, resulting in direct input of barotropic instability and baroclinic instability between the shelf (fresh) and the basin (saline). Eddies along the shelf break contributes to on-shelf nutrient flux into the continental shelf, at least 200m isobath, and bring chl-rich water from the shelf break to the basin area. Thus eddies is quite important for maintaining the high productive area along the shelf break.
Orbview2/SeaWiFS, AQUA/MODIS and ADEOS2/GLI - Ocean colour (nLw, chl-a, K490..)
Chlorophyll-a (Chl-a) concentration, which any kind of phytoplankton have, is usually used as an index of phytoplankton abundance. Chl-a value can be estimated from ocean colour data (normalized water-leaving irradiance) acquired by the ocean colour sensors, such as SeaWiFS. Basically the Ocean Color 4 Version 4 (OC4V4) developped by the NASA/GSFC is applied to estimate chl-a for Global Ocean, while the OC4 Linear algorithm, which is proposed by Wang and Cota (2003), is used for the western Arctic Ocean area in my study. Right now, I am using chl-a value derived from GLI (Global Imager), which is one of the sensors of ADEOS-2 (JAXA's satellite). Althoug the observation period was short ( 9-10 month), GLI obtained much dataset in the western Arctic Ocean. Good thing is that GLI and MODIS has corrected temperature image and ocean colour maps simultaneously.
NOAA/AVHRR, MODIS - Sea surface temperature
In my study, I have used the AVHRR Pathfinder SST map (4km, Daily) provided by the NASA/JPL/PO.DAAC ( ). SST image is used not only to understand temperature and SST front but also to estimate primary production. Primary Production can be estimated by Behrenfeld and Falkowski Model (1997) or Kameda and Ishizaka Model (2005).
TOPEX/Poseidon, ERS-1/2, Jason-1 and Envisat - Sea level anomaly, geostrophic velocity, eddy kinetic energy
To reveal mesoscale eddy field, Altimeter-derived sea level anomaly (SLA) can be utilized. In my study, SLA maps is also used for calculating the eddy kinetic energy and interannual variabiliry of eddy field in the Bering Sea basin area. Those dataset is destributed by the NASA/JPL/PO.DAAC or AVISO. Mizobata and Saitoh (2004) used TOPEX/poseidon orbital dataset along the Bering Sea shelf break area to reveal the eddy field and compare to the primary production. Currently, AVISO is distributing high quality 1/3 degree grid SLA maps, which Mizobata et al. (DSR2, in revision) used to reveal the interannual variability of the eddy field.
SSM-I - sea ice concentration
In the Arctic/Subarctic Ocean, the information about sea ice distribution is needed. I am using DMSP/SSM-I F13 sea ice concentration map (Both NASA-team algorithm and Bootstrap algorithm) to compare to simulated results from the IARC Coupled Ice-Ocean Model (CIOM), which has been developed by the Arctic Modeling Group, IARC.
T/S Oshoro-maru summer cruise in the Bering/Chukchi Sea
I have joined the Oshoro-maru (Training Ship @ Hokkaido University) summer cruise in the Bering Sea since 2000. Mizobata et al. (2002, Prog. Oceanogr.) and Mizobata et al. (2006, JGR)) used CTD, nutrients and chl-a data. In 2007 and 2008 (IPY), I will join 07' and 08' cruise in the Chukchi Sea to collect data for the validation of the IARC Coupled Ice-Ocean Model and satellite ocean color image.
Main | Research Activity | Satellite Remote Sensing | Ship Survey | Ice-ocean Modeling
IARC Coupled Ice-Ocean Model (CIOM)
To simulate the ice-ocean circulation, we applied the 3-D CIOM to the Chukchi Sea (Wang et al., 2002; Wang et al., 2005). The sea ice component of the CIOM is a thermodynamic model based on multiple categories of ice thickness distribution function (Throndike et al., 1975; Hibler, 1980) and a dynamic model based on a viscous-plastic sea ice rheology (Hibler, 1979). In this study, ten ice categories (0,0.2,0.5,1,1.5,2,3,4,5 and 6m) were used. The Princeton Ocean Model (Blumberg and Mellor, 1987) was used as the ocean component of the CIOM. The model was spun up with temperature and salinity (PHC) of Steele et al. (2001) for the first four years under monthly atmospheric climatology. After the spin-up integration, we ran the CIOM under daily atmospheric forcing in 2002.
Estuarine Coastal and Ocean Model with semi-implicit scheme (ECOM-si)
To simulate the BSC eddy field and associated on-shelf fluxes, we applied a modified version (Wang and Ikeda, 1997a) of the Estuarine Coastal and Ocean Model with semi-implicit scheme (ECOM-si, Blumberg, 1991). ECOM-si has the following features: horizontal curvilinear coordinates and sigma vertical coordinates, the Arakawa-C grid (Arakawa and Lamb, 1977), a free surface, no time-splitting between the internal and external modes, and a second-order turbulence closure model for vertical viscosity (Mellor and Yamada, 1982). A semi-implicit scheme is introduced for solving the surface elevation in the shallow water equations (Casulli, 1990). The difference between the ECOM-si used here and in the original version (Blumberg, 1991) is a predictor-corrector scheme (Wang and Ikeda, 1995, 1997a). The use of a predictor-corrector scheme allows removal of an inertial instability introduced by the Euler forward scheme in time, and simulation of unstable waves and eddies in a very low viscosity environment (Wang and Ikeda, 1997c).
- Grebmeier, J. M. and K. H. Dunton (2000), Benthic processes in the northern Bering/Chukchi seas: status and global change, pp. 61-71.Impacts of Changes in Sea Ice and other Environmental parameters in the Arctic. Report of the Marine Mammal Commission Workshop,15-17 February 2000, Girdwood, Alaska. Available from the Marine Mammal Commission, Bethesda, Maryland.
- J. Wang and G. F. Cota, "Remote-Sensing Reflectance in the Beaufort and Chukchi Seas: Observations and Models ," Appl. Opt. 42, 2754-2765 (2003)
- Wang, J., Q. Liu and M. Jin (2002), A Userfs Guide for a Coupled Ice-Ocean Model (CIOM) in the Pan-Arctic and North Atlantic Oceans, in International Arctic Research Center-Frontier Research System for Global Change, Tech. Rep. 02-01, 1-65, International Arctic Research Center, Fairbanks, USA, 56pp.
- Wang, J., Q. Liu, M. Jin, M. Ikeda and F. J. Saucier (2005), A coupled ice-ocean model in the pan-Arctic and the northern North Atlantic Ocean: Simulation of seasonal cycles, J. Oceanogr., 61, 213-233.
