provides a more realistic EBS circulation and Bering Strait throughflow both in
terms of model-data misfits and general dynamical consistency. This allows for a
more accurate analysis of the property exchange processes between the Pacific
Ocean and the Bering Sea, including residence times, pathways and the transformation rates of various water masses on their way to the Arctic Ocean.
The wealth of new information on the Bering Sea dynamics delivered by the
developed DAS illustrates the importance of regional DA systems for further
improvement in understanding and forecasting environmental conditions in the
marginal seas and nearshore areas. Our experience shows that 4dVar reconstruction
of the dynamically consistent regional climatologies is of particular importance for
the reanalysis of the local data sets. In turn, the improved accuracy of the hindcasts
would inevitably result in better forecasting of the regional environmental conditions that are extremely important in many applications.
Acknowledgements This study was supported by the University of Hawaii, the International
Arctic Research Center, NSF grants 1107925, 1203740 and ARC-1107327. G. Panteleev and M.
Yaremchuk were also supported by the ONR core project “Arctic data assimilation” and program
element 0602435N as part of the project “Adjoint-free 4dVar for Navy ocean models”. The authors
are indebted to P. Stabeno for providing drifter and current data.
References
1. Brasseur, P. (1991). A variational method for the reconstruction of general circulation fields in
the Northern Bering Sea. Journal Geophysical Research, 96, 4891–4907.
2. Carlotti, F., & Wolf, K.-U. (1998). A Lagrangian ensemble model of Calanus finmarchicus
coupled with a 1-D ecosystem model. Fisheries Oceanography, 7(3/4), 191–204.
3. Cianelli, L., & Bailey, K. M. (2005). Landscape dynamics and resulting species interactions:
The cod-capelin system in the southeastern Bering sea. Marine Ecology Progress Series, 291,
227–236.
4. Coachman, A. K. (1993). On the flow field in the Chirikov Basin. Continental Shelf Research,
13, 481–492.
5. Forget, G., Campin, J.-M., Heimbach, P., Hill, C. N., Ponte, R. M., & Wunsch, C. (2015).
ECCO version 4: An integrated framework for non-linear inverse modeling and global ocean
state estimation. Geoscientific Model Development Discussions, 8, 3653–3743. https://doi.
org/10.5194/gmdd-8-3653-2015.
6. Janekovich, I., Powell, B. S., Matthews, D., McManus, M. A., & Sevadjian, J. (2013). 4D-Var
data assimilation in a nested, coastal ocean model: A Hawaiian case study. Journal
Geophysical Research, 118, 1–14.
7. Hughes, F. W., Coachman, L. K., 7 Aagard, K. (1974). Circulation transport and water
exchange in the western Bering Sea. In D. W. Hood and E. J. Kelly (Eds.), Oceanography of
the Bering Sea with emphasis on the renewable resources (pp. 59–98). Institute of Marine
Science, University of Alaska, Fairbanks.
8. Kurapov, A. L., Foley, D., Strub, P. T., Egbert, G. D., & Allen, J. S. (2011). Variational
assimilation of satellite observations in a coastal ocean model off Oregon. Journal
Geophysical Research, 116, C05006. https://doi.org/10.1029/2010JC006909.
9. Le Dimet, F. X., & Talagrand, O. (1986). Variational algorithms for analysis and assimilation
of meteorological observations: Theoretical aspects. Tellus Series A, 38, 97–100.
The Bering Sea Regional Data Assimilation System …
515
terms of model-data misfits and general dynamical consistency. This allows for a
more accurate analysis of the property exchange processes between the Pacific
Ocean and the Bering Sea, including residence times, pathways and the transformation rates of various water masses on their way to the Arctic Ocean.
The wealth of new information on the Bering Sea dynamics delivered by the
developed DAS illustrates the importance of regional DA systems for further
improvement in understanding and forecasting environmental conditions in the
marginal seas and nearshore areas. Our experience shows that 4dVar reconstruction
of the dynamically consistent regional climatologies is of particular importance for
the reanalysis of the local data sets. In turn, the improved accuracy of the hindcasts
would inevitably result in better forecasting of the regional environmental conditions that are extremely important in many applications.
Acknowledgements This study was supported by the University of Hawaii, the International
Arctic Research Center, NSF grants 1107925, 1203740 and ARC-1107327. G. Panteleev and M.
Yaremchuk were also supported by the ONR core project “Arctic data assimilation” and program
element 0602435N as part of the project “Adjoint-free 4dVar for Navy ocean models”. The authors
are indebted to P. Stabeno for providing drifter and current data.
References
1. Brasseur, P. (1991). A variational method for the reconstruction of general circulation fields in
the Northern Bering Sea. Journal Geophysical Research, 96, 4891–4907.
2. Carlotti, F., & Wolf, K.-U. (1998). A Lagrangian ensemble model of Calanus finmarchicus
coupled with a 1-D ecosystem model. Fisheries Oceanography, 7(3/4), 191–204.
3. Cianelli, L., & Bailey, K. M. (2005). Landscape dynamics and resulting species interactions:
The cod-capelin system in the southeastern Bering sea. Marine Ecology Progress Series, 291,
227–236.
4. Coachman, A. K. (1993). On the flow field in the Chirikov Basin. Continental Shelf Research,
13, 481–492.
5. Forget, G., Campin, J.-M., Heimbach, P., Hill, C. N., Ponte, R. M., & Wunsch, C. (2015).
ECCO version 4: An integrated framework for non-linear inverse modeling and global ocean
state estimation. Geoscientific Model Development Discussions, 8, 3653–3743. https://doi.
org/10.5194/gmdd-8-3653-2015.
6. Janekovich, I., Powell, B. S., Matthews, D., McManus, M. A., & Sevadjian, J. (2013). 4D-Var
data assimilation in a nested, coastal ocean model: A Hawaiian case study. Journal
Geophysical Research, 118, 1–14.
7. Hughes, F. W., Coachman, L. K., 7 Aagard, K. (1974). Circulation transport and water
exchange in the western Bering Sea. In D. W. Hood and E. J. Kelly (Eds.), Oceanography of
the Bering Sea with emphasis on the renewable resources (pp. 59–98). Institute of Marine
Science, University of Alaska, Fairbanks.
8. Kurapov, A. L., Foley, D., Strub, P. T., Egbert, G. D., & Allen, J. S. (2011). Variational
assimilation of satellite observations in a coastal ocean model off Oregon. Journal
Geophysical Research, 116, C05006. https://doi.org/10.1029/2010JC006909.
9. Le Dimet, F. X., & Talagrand, O. (1986). Variational algorithms for analysis and assimilation
of meteorological observations: Theoretical aspects. Tellus Series A, 38, 97–100.
The Bering Sea Regional Data Assimilation System …
515