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Liu, Ye
Publications (10 of 12) Show all publications
Raudsepp, U., Uiboupin, R., Maljutenko, I., Hendricks, S., Ricker, R., Liu, Y., . . . Raj, R. P. (2019). Combined analysis of Cryosat-2/SMOS sea ice thickness data with model reanalysis fields over the Baltic Sea. Journal of operational oceanography. Publisher: The Institute of Marine Engineering, Science & Technology, 12, S73-+
Open this publication in new window or tab >>Combined analysis of Cryosat-2/SMOS sea ice thickness data with model reanalysis fields over the Baltic Sea
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2019 (English)In: Journal of operational oceanography. Publisher: The Institute of Marine Engineering, Science & Technology, ISSN 1755-876X, E-ISSN 1755-8778, Vol. 12, p. S73-+Article in journal (Refereed) Published
National Category
Oceanography, Hydrology and Water Resources
Research subject
Oceanography
Identifiers
urn:nbn:se:smhi:diva-5481 (URN)000495675100009 ()
Available from: 2019-11-26 Created: 2019-11-26 Last updated: 2019-11-26Bibliographically approved
Meier, M., Eilola, K., Almroth-Rosell, E., Schimanke, S., Kniebusch, M., Höglund, A., . . . Saraiva, S. (2019). Disentangling the impact of nutrient load and climate changes on Baltic Sea hypoxia and eutrophication since 1850. Climate Dynamics, 53(1-2), 1145-1166
Open this publication in new window or tab >>Disentangling the impact of nutrient load and climate changes on Baltic Sea hypoxia and eutrophication since 1850
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2019 (English)In: Climate Dynamics, ISSN 0930-7575, E-ISSN 1432-0894, Vol. 53, no 1-2, p. 1145-1166Article in journal (Refereed) Published
National Category
Oceanography, Hydrology and Water Resources
Research subject
Oceanography
Identifiers
urn:nbn:se:smhi:diva-5248 (URN)10.1007/s00382-018-4296-y (DOI)000471722400067 ()
Available from: 2019-07-29 Created: 2019-07-29 Last updated: 2019-07-29Bibliographically approved
Meier, M., Eilola, K., Almroth-Rosell, E., Schimanke, S., Kniebusch, M., Höglund, A., . . . Saraiva, S. (2019). Disentangling the impact of nutrient load and climate changes on Baltic Sea hypoxia and eutrophication since 1850 (vol 53, pg 1145, 2019). Climate Dynamics, 53(1-2), 1167-1169
Open this publication in new window or tab >>Disentangling the impact of nutrient load and climate changes on Baltic Sea hypoxia and eutrophication since 1850 (vol 53, pg 1145, 2019)
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2019 (English)In: Climate Dynamics, ISSN 0930-7575, E-ISSN 1432-0894, Vol. 53, no 1-2, p. 1167-1169Article in journal (Refereed) Published
National Category
Oceanography, Hydrology and Water Resources
Research subject
Oceanography
Identifiers
urn:nbn:se:smhi:diva-5249 (URN)10.1007/s00382-018-4483-x (DOI)000471722400068 ()
Available from: 2019-07-29 Created: 2019-07-29 Last updated: 2019-07-29Bibliographically approved
Hordoir, R., Axell, L., Höglund, A., Dieterich, C., Fransner, F., Groger, M., . . . Haapala, J. (2019). Nemo-Nordic 1.0: a NEMO-based ocean model for the Baltic and North seas - research and operational applications. Geoscientific Model Development, 12(1), 363-386
Open this publication in new window or tab >>Nemo-Nordic 1.0: a NEMO-based ocean model for the Baltic and North seas - research and operational applications
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2019 (English)In: Geoscientific Model Development, ISSN 1991-959X, E-ISSN 1991-9603, Vol. 12, no 1, p. 363-386Article in journal (Refereed) Published
National Category
Oceanography, Hydrology and Water Resources
Identifiers
urn:nbn:se:smhi:diva-5159 (URN)10.5194/gmd-12-363-2019 (DOI)000456302000003 ()
Available from: 2019-02-05 Created: 2019-02-05 Last updated: 2019-02-05Bibliographically approved
Dieterich, C., Wang, S., Schimanke, S., Groger, M., Klein, B., Hordoir, R., . . . Meier, M. (2019). Surface Heat Budget over the North Sea in Climate Change Simulations. Atmosphere, 10(5), Article ID 272.
Open this publication in new window or tab >>Surface Heat Budget over the North Sea in Climate Change Simulations
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2019 (English)In: Atmosphere, ISSN 2073-4433, E-ISSN 2073-4433, Vol. 10, no 5, article id 272Article in journal (Refereed) Published
National Category
Oceanography, Hydrology and Water Resources
Research subject
Oceanography
Identifiers
urn:nbn:se:smhi:diva-5253 (URN)10.3390/atmos10050272 (DOI)000472677600046 ()
Available from: 2019-07-29 Created: 2019-07-29 Last updated: 2019-07-29Bibliographically approved
Liu, Y. & Fu, W. (2018). Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea. Ocean Science, 14(3), 525-541
Open this publication in new window or tab >>Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea
2018 (English)In: Ocean Science, ISSN 1812-0784, E-ISSN 1812-0792, Vol. 14, no 3, p. 525-541Article in journal (Refereed) Published
National Category
Oceanography, Hydrology and Water Resources
Research subject
Oceanography
Identifiers
urn:nbn:se:smhi:diva-4803 (URN)10.5194/os-14-525-2018 (DOI)000436122800002 ()
Available from: 2018-08-06 Created: 2018-08-06 Last updated: 2018-08-06Bibliographically approved
Placke, M., Meier, M., Graewe, U., Neumann, T., Frauen, C. & Liu, Y. (2018). Long-Term Mean Circulation of the Baltic Sea as Represented by Various Ocean Circulation Models. Frontiers in Marine Science, 5, Article ID UNSP 287.
Open this publication in new window or tab >>Long-Term Mean Circulation of the Baltic Sea as Represented by Various Ocean Circulation Models
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2018 (English)In: Frontiers in Marine Science, E-ISSN 2296-7745, Vol. 5, article id UNSP 287Article in journal (Refereed) Published
National Category
Oceanography, Hydrology and Water Resources
Research subject
Oceanography
Identifiers
urn:nbn:se:smhi:diva-5165 (URN)10.3389/fmars.2018.00287 (DOI)000457233000001 ()
Available from: 2019-02-12 Created: 2019-02-12 Last updated: 2019-02-12Bibliographically approved
Liu, Y., Meier, M. & Eilola, K. (2017). Nutrient transports in the Baltic Sea - results from a 30-year physical-biogeochemical reanalysis. Biogeosciences, 14(8), 2113-2131
Open this publication in new window or tab >>Nutrient transports in the Baltic Sea - results from a 30-year physical-biogeochemical reanalysis
2017 (English)In: Biogeosciences, ISSN 1726-4170, E-ISSN 1726-4189, Vol. 14, no 8, p. 2113-2131Article in journal (Refereed) Published
National Category
Oceanography, Hydrology and Water Resources
Research subject
Oceanography
Identifiers
urn:nbn:se:smhi:diva-4098 (URN)10.5194/bg-14-2113-2017 (DOI)000400007700001 ()
Available from: 2017-05-23 Created: 2017-05-23 Last updated: 2018-01-13Bibliographically approved
Axell, L. & Liu, Y. (2016). Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 1989-2013. Tellus. Series A, Dynamic meteorology and oceanography, 68, Article ID 24220.
Open this publication in new window or tab >>Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 1989-2013
2016 (English)In: Tellus. Series A, Dynamic meteorology and oceanography, ISSN 0280-6495, E-ISSN 1600-0870, Vol. 68, article id 24220Article in journal (Refereed) Published
Abstract [en]

A 3-D ensemble variational (3DEnVar) data assimilation method has been implemented and tested for oceanographic data assimilation of sea surface temperature (SST), sea surface salinity (SSS), sea ice concentration (SIC), and salinity and temperature profiles. To damp spurious long-range correlations in the ensemble statistics, horizontal and vertical localisation was implemented using empirical orthogonal functions. The results show that the 3DEnVar method is indeed possible to use in oceanographic data assimilation. So far, only a seasonally dependent ensemble has been used, based on historical model simulations. Near-surface experiments showed that the ensemble statistics gave inhomogeneous and anisotropic horizontal structure functions, and assimilation of real SST and SIC fields gave smooth, realistic increment fields. The implementation was multivariate, and results showed that the cross-correlations between variables work in an intuitive way, for example, decreasing SST where SIC was increased and vice versa. The profile data assimilation also gave good results. The results from a 25-year reanalysis showed that the vertical salinity and temperature structure were significantly improved, compared to both dependent and independent data.

National Category
Oceanography, Hydrology and Water Resources
Research subject
Oceanography
Identifiers
urn:nbn:se:smhi:diva-2031 (URN)10.3402/tellusa.v68.24220 (DOI)000372203600001 ()
Available from: 2016-05-03 Created: 2016-05-02 Last updated: 2018-01-10Bibliographically approved
Liu, Y., Meier, M. & Eilola, K. (2014). Improving the multiannual, high-resolution modelling of biogeochemical cycles in the Baltic Sea by using data assimilation. Tellus. Series A, Dynamic meteorology and oceanography, 66, Article ID 24908.
Open this publication in new window or tab >>Improving the multiannual, high-resolution modelling of biogeochemical cycles in the Baltic Sea by using data assimilation
2014 (English)In: Tellus. Series A, Dynamic meteorology and oceanography, ISSN 0280-6495, E-ISSN 1600-0870, Vol. 66, article id 24908Article in journal (Refereed) Published
Abstract [en]

The impact of assimilating temperature, salinity, oxygen, phosphate and nitrate observations on marine ecosystem modelling is assessed. For this purpose, two 10-yr (1970-1979) reanalyses of the Baltic Sea are carried out using the ensemble optimal interpolation (EnOI) method and a coupled physical-biogeochemical model of the Baltic Sea. To evaluate the reanalyses, climatological data and available biogeochemical and physical in situ observations at monitoring stations are compared with results from simulations with and without data assimilation. In the first reanalysis, only observed temperature and salinity profiles are assimilated, whereas biogeochemical observations are unused. Although simulated temperature and salinity improve considerably as expected, the quality of simulated biogeochemical variables does not improve and deep water nitrate concentrations even worsen. This unexpected behaviour is explained by a lowering of the halocline in the Baltic proper due to the assimilation causing increased oxygen concentrations in the deep water and consequently altered nutrient fluxes. In the second reanalysis, both physical and biogeochemical observations are assimilated and good quality in all variables is found. Hence, we conclude that if a data assimilation method like the EnOI is applied, all available observations should be used to perform reanalyses of high quality for the Baltic Sea biogeochemical state estimates.

Keywords
reanalysis, data assimilation, numerical modelling, Baltic Sea, biogeochemical simulation
National Category
Oceanography, Hydrology and Water Resources
Research subject
Oceanography
Identifiers
urn:nbn:se:smhi:diva-135 (URN)10.3402/tellusa.v66.24908 (DOI)000346299200001 ()
Available from: 2015-04-09 Created: 2015-03-26 Last updated: 2018-01-11Bibliographically approved
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