Analysis and modeling of the seasonal South China Sea temperature cycle using remote sensing
Abstract The present paper describes the analysis and modeling of the South China Sea (SCS) temperature cycle on a seasonal scale. It investigates the possibility to model this cycle in a consistent way while not taking into account tidal forcing and associated tidal mixing and exchange. This is mot...
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| Autor*in: |
Twigt, Daniel J. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2007 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag 2007 |
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| Übergeordnetes Werk: |
Enthalten in: Ocean dynamics - Springer-Verlag, 2001, 57(2007), 4-5 vom: Okt., Seite 467-484 |
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| Übergeordnetes Werk: |
volume:57 ; year:2007 ; number:4-5 ; month:10 ; pages:467-484 |
| Links: |
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| DOI / URN: |
10.1007/s10236-007-0123-4 |
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| Katalog-ID: |
OLC2070856925 |
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| 520 | |a Abstract The present paper describes the analysis and modeling of the South China Sea (SCS) temperature cycle on a seasonal scale. It investigates the possibility to model this cycle in a consistent way while not taking into account tidal forcing and associated tidal mixing and exchange. This is motivated by the possibility to significantly increase the model’s computational efficiency when neglecting tides. The goal is to develop a flexible and efficient tool for seasonal scenario analysis and to generate transport boundary forcing for local models. Given the significant spatial extent of the SCS basin and the focus on seasonal time scales, synoptic remote sensing is an ideal tool in this analysis. Remote sensing is used to assess the seasonal temperature cycle to identify the relevant driving forces and is a valuable source of input data for modeling. Model simulations are performed using a three-dimensional baroclinic-reduced depth model, driven by monthly mean sea surface anomaly boundary forcing, monthly mean lateral temperature, and salinity forcing obtained from the World Ocean Atlas 2001 climatology, six hourly meteorological forcing from the European Center for Medium range Weather Forecasting ERA-40 dataset, and remotely sensed sea surface temperature (SST) data. A sensitivity analysis of model forcing and coefficients is performed. The model results are quantitatively assessed against climatological temperature profiles using a goodness-of-fit norm. In the deep regions, the model results are in good agreement with this validation data. In the shallow regions, discrepancies are found. To improve the agreement there, we apply a SST nudging method at the free water surface. This considerably improves the model’s vertical temperature representation in the shallow regions. Based on the model validation against climatological in situ and SST data, we conclude that the seasonal temperature cycle for the deep SCS basin can be represented to a good degree. For shallow regions, the absence of tidal mixing and exchange has a clear impact on the model’s temperature representation. This effect on the large-scale temperature cycle can be compensated to a good degree by SST nudging for diagnostic applications. | ||
| 650 | 4 | |a South China Sea | |
| 650 | 4 | |a Baroclinic temperature model | |
| 650 | 4 | |a Reduced depth modeling | |
| 650 | 4 | |a Altimeter data | |
| 650 | 4 | |a Radiometer data | |
| 650 | 4 | |a Temperature nudging | |
| 700 | 1 | |a De Goede, Erik D. |4 aut | |
| 700 | 1 | |a Schrama, Ernst J. O. |4 aut | |
| 700 | 1 | |a Gerritsen, Herman |4 aut | |
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10.1007/s10236-007-0123-4 doi (DE-627)OLC2070856925 (DE-He213)s10236-007-0123-4-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn 38.90$jOzeanologie$jOzeanographie bkl Twigt, Daniel J. verfasserin aut Analysis and modeling of the seasonal South China Sea temperature cycle using remote sensing 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2007 Abstract The present paper describes the analysis and modeling of the South China Sea (SCS) temperature cycle on a seasonal scale. It investigates the possibility to model this cycle in a consistent way while not taking into account tidal forcing and associated tidal mixing and exchange. This is motivated by the possibility to significantly increase the model’s computational efficiency when neglecting tides. The goal is to develop a flexible and efficient tool for seasonal scenario analysis and to generate transport boundary forcing for local models. Given the significant spatial extent of the SCS basin and the focus on seasonal time scales, synoptic remote sensing is an ideal tool in this analysis. Remote sensing is used to assess the seasonal temperature cycle to identify the relevant driving forces and is a valuable source of input data for modeling. Model simulations are performed using a three-dimensional baroclinic-reduced depth model, driven by monthly mean sea surface anomaly boundary forcing, monthly mean lateral temperature, and salinity forcing obtained from the World Ocean Atlas 2001 climatology, six hourly meteorological forcing from the European Center for Medium range Weather Forecasting ERA-40 dataset, and remotely sensed sea surface temperature (SST) data. A sensitivity analysis of model forcing and coefficients is performed. The model results are quantitatively assessed against climatological temperature profiles using a goodness-of-fit norm. In the deep regions, the model results are in good agreement with this validation data. In the shallow regions, discrepancies are found. To improve the agreement there, we apply a SST nudging method at the free water surface. This considerably improves the model’s vertical temperature representation in the shallow regions. Based on the model validation against climatological in situ and SST data, we conclude that the seasonal temperature cycle for the deep SCS basin can be represented to a good degree. For shallow regions, the absence of tidal mixing and exchange has a clear impact on the model’s temperature representation. This effect on the large-scale temperature cycle can be compensated to a good degree by SST nudging for diagnostic applications. South China Sea Baroclinic temperature model Reduced depth modeling Altimeter data Radiometer data Temperature nudging De Goede, Erik D. aut Schrama, Ernst J. O. aut Gerritsen, Herman aut Enthalten in Ocean dynamics Springer-Verlag, 2001 57(2007), 4-5 vom: Okt., Seite 467-484 (DE-627)335936091 (DE-600)2060148-7 (DE-576)096704470 1616-7341 nnns volume:57 year:2007 number:4-5 month:10 pages:467-484 https://doi.org/10.1007/s10236-007-0123-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OLC-GGO SSG-OPC-GGO GBV_ILN_21 GBV_ILN_22 GBV_ILN_40 GBV_ILN_62 GBV_ILN_70 GBV_ILN_154 GBV_ILN_183 GBV_ILN_600 GBV_ILN_602 GBV_ILN_608 GBV_ILN_2018 GBV_ILN_4277 GBV_ILN_4305 38.90$jOzeanologie$jOzeanographie VZ 106421921 (DE-625)106421921 AR 57 2007 4-5 10 467-484 |
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10.1007/s10236-007-0123-4 doi (DE-627)OLC2070856925 (DE-He213)s10236-007-0123-4-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn 38.90$jOzeanologie$jOzeanographie bkl Twigt, Daniel J. verfasserin aut Analysis and modeling of the seasonal South China Sea temperature cycle using remote sensing 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2007 Abstract The present paper describes the analysis and modeling of the South China Sea (SCS) temperature cycle on a seasonal scale. It investigates the possibility to model this cycle in a consistent way while not taking into account tidal forcing and associated tidal mixing and exchange. This is motivated by the possibility to significantly increase the model’s computational efficiency when neglecting tides. The goal is to develop a flexible and efficient tool for seasonal scenario analysis and to generate transport boundary forcing for local models. Given the significant spatial extent of the SCS basin and the focus on seasonal time scales, synoptic remote sensing is an ideal tool in this analysis. Remote sensing is used to assess the seasonal temperature cycle to identify the relevant driving forces and is a valuable source of input data for modeling. Model simulations are performed using a three-dimensional baroclinic-reduced depth model, driven by monthly mean sea surface anomaly boundary forcing, monthly mean lateral temperature, and salinity forcing obtained from the World Ocean Atlas 2001 climatology, six hourly meteorological forcing from the European Center for Medium range Weather Forecasting ERA-40 dataset, and remotely sensed sea surface temperature (SST) data. A sensitivity analysis of model forcing and coefficients is performed. The model results are quantitatively assessed against climatological temperature profiles using a goodness-of-fit norm. In the deep regions, the model results are in good agreement with this validation data. In the shallow regions, discrepancies are found. To improve the agreement there, we apply a SST nudging method at the free water surface. This considerably improves the model’s vertical temperature representation in the shallow regions. Based on the model validation against climatological in situ and SST data, we conclude that the seasonal temperature cycle for the deep SCS basin can be represented to a good degree. For shallow regions, the absence of tidal mixing and exchange has a clear impact on the model’s temperature representation. This effect on the large-scale temperature cycle can be compensated to a good degree by SST nudging for diagnostic applications. South China Sea Baroclinic temperature model Reduced depth modeling Altimeter data Radiometer data Temperature nudging De Goede, Erik D. aut Schrama, Ernst J. O. aut Gerritsen, Herman aut Enthalten in Ocean dynamics Springer-Verlag, 2001 57(2007), 4-5 vom: Okt., Seite 467-484 (DE-627)335936091 (DE-600)2060148-7 (DE-576)096704470 1616-7341 nnns volume:57 year:2007 number:4-5 month:10 pages:467-484 https://doi.org/10.1007/s10236-007-0123-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OLC-GGO SSG-OPC-GGO GBV_ILN_21 GBV_ILN_22 GBV_ILN_40 GBV_ILN_62 GBV_ILN_70 GBV_ILN_154 GBV_ILN_183 GBV_ILN_600 GBV_ILN_602 GBV_ILN_608 GBV_ILN_2018 GBV_ILN_4277 GBV_ILN_4305 38.90$jOzeanologie$jOzeanographie VZ 106421921 (DE-625)106421921 AR 57 2007 4-5 10 467-484 |
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10.1007/s10236-007-0123-4 doi (DE-627)OLC2070856925 (DE-He213)s10236-007-0123-4-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn 38.90$jOzeanologie$jOzeanographie bkl Twigt, Daniel J. verfasserin aut Analysis and modeling of the seasonal South China Sea temperature cycle using remote sensing 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2007 Abstract The present paper describes the analysis and modeling of the South China Sea (SCS) temperature cycle on a seasonal scale. It investigates the possibility to model this cycle in a consistent way while not taking into account tidal forcing and associated tidal mixing and exchange. This is motivated by the possibility to significantly increase the model’s computational efficiency when neglecting tides. The goal is to develop a flexible and efficient tool for seasonal scenario analysis and to generate transport boundary forcing for local models. Given the significant spatial extent of the SCS basin and the focus on seasonal time scales, synoptic remote sensing is an ideal tool in this analysis. Remote sensing is used to assess the seasonal temperature cycle to identify the relevant driving forces and is a valuable source of input data for modeling. Model simulations are performed using a three-dimensional baroclinic-reduced depth model, driven by monthly mean sea surface anomaly boundary forcing, monthly mean lateral temperature, and salinity forcing obtained from the World Ocean Atlas 2001 climatology, six hourly meteorological forcing from the European Center for Medium range Weather Forecasting ERA-40 dataset, and remotely sensed sea surface temperature (SST) data. A sensitivity analysis of model forcing and coefficients is performed. The model results are quantitatively assessed against climatological temperature profiles using a goodness-of-fit norm. In the deep regions, the model results are in good agreement with this validation data. In the shallow regions, discrepancies are found. To improve the agreement there, we apply a SST nudging method at the free water surface. This considerably improves the model’s vertical temperature representation in the shallow regions. Based on the model validation against climatological in situ and SST data, we conclude that the seasonal temperature cycle for the deep SCS basin can be represented to a good degree. For shallow regions, the absence of tidal mixing and exchange has a clear impact on the model’s temperature representation. This effect on the large-scale temperature cycle can be compensated to a good degree by SST nudging for diagnostic applications. South China Sea Baroclinic temperature model Reduced depth modeling Altimeter data Radiometer data Temperature nudging De Goede, Erik D. aut Schrama, Ernst J. O. aut Gerritsen, Herman aut Enthalten in Ocean dynamics Springer-Verlag, 2001 57(2007), 4-5 vom: Okt., Seite 467-484 (DE-627)335936091 (DE-600)2060148-7 (DE-576)096704470 1616-7341 nnns volume:57 year:2007 number:4-5 month:10 pages:467-484 https://doi.org/10.1007/s10236-007-0123-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OLC-GGO SSG-OPC-GGO GBV_ILN_21 GBV_ILN_22 GBV_ILN_40 GBV_ILN_62 GBV_ILN_70 GBV_ILN_154 GBV_ILN_183 GBV_ILN_600 GBV_ILN_602 GBV_ILN_608 GBV_ILN_2018 GBV_ILN_4277 GBV_ILN_4305 38.90$jOzeanologie$jOzeanographie VZ 106421921 (DE-625)106421921 AR 57 2007 4-5 10 467-484 |
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10.1007/s10236-007-0123-4 doi (DE-627)OLC2070856925 (DE-He213)s10236-007-0123-4-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn 38.90$jOzeanologie$jOzeanographie bkl Twigt, Daniel J. verfasserin aut Analysis and modeling of the seasonal South China Sea temperature cycle using remote sensing 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2007 Abstract The present paper describes the analysis and modeling of the South China Sea (SCS) temperature cycle on a seasonal scale. It investigates the possibility to model this cycle in a consistent way while not taking into account tidal forcing and associated tidal mixing and exchange. This is motivated by the possibility to significantly increase the model’s computational efficiency when neglecting tides. The goal is to develop a flexible and efficient tool for seasonal scenario analysis and to generate transport boundary forcing for local models. Given the significant spatial extent of the SCS basin and the focus on seasonal time scales, synoptic remote sensing is an ideal tool in this analysis. Remote sensing is used to assess the seasonal temperature cycle to identify the relevant driving forces and is a valuable source of input data for modeling. Model simulations are performed using a three-dimensional baroclinic-reduced depth model, driven by monthly mean sea surface anomaly boundary forcing, monthly mean lateral temperature, and salinity forcing obtained from the World Ocean Atlas 2001 climatology, six hourly meteorological forcing from the European Center for Medium range Weather Forecasting ERA-40 dataset, and remotely sensed sea surface temperature (SST) data. A sensitivity analysis of model forcing and coefficients is performed. The model results are quantitatively assessed against climatological temperature profiles using a goodness-of-fit norm. In the deep regions, the model results are in good agreement with this validation data. In the shallow regions, discrepancies are found. To improve the agreement there, we apply a SST nudging method at the free water surface. This considerably improves the model’s vertical temperature representation in the shallow regions. Based on the model validation against climatological in situ and SST data, we conclude that the seasonal temperature cycle for the deep SCS basin can be represented to a good degree. For shallow regions, the absence of tidal mixing and exchange has a clear impact on the model’s temperature representation. This effect on the large-scale temperature cycle can be compensated to a good degree by SST nudging for diagnostic applications. South China Sea Baroclinic temperature model Reduced depth modeling Altimeter data Radiometer data Temperature nudging De Goede, Erik D. aut Schrama, Ernst J. O. aut Gerritsen, Herman aut Enthalten in Ocean dynamics Springer-Verlag, 2001 57(2007), 4-5 vom: Okt., Seite 467-484 (DE-627)335936091 (DE-600)2060148-7 (DE-576)096704470 1616-7341 nnns volume:57 year:2007 number:4-5 month:10 pages:467-484 https://doi.org/10.1007/s10236-007-0123-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OLC-GGO SSG-OPC-GGO GBV_ILN_21 GBV_ILN_22 GBV_ILN_40 GBV_ILN_62 GBV_ILN_70 GBV_ILN_154 GBV_ILN_183 GBV_ILN_600 GBV_ILN_602 GBV_ILN_608 GBV_ILN_2018 GBV_ILN_4277 GBV_ILN_4305 38.90$jOzeanologie$jOzeanographie VZ 106421921 (DE-625)106421921 AR 57 2007 4-5 10 467-484 |
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10.1007/s10236-007-0123-4 doi (DE-627)OLC2070856925 (DE-He213)s10236-007-0123-4-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn 38.90$jOzeanologie$jOzeanographie bkl Twigt, Daniel J. verfasserin aut Analysis and modeling of the seasonal South China Sea temperature cycle using remote sensing 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2007 Abstract The present paper describes the analysis and modeling of the South China Sea (SCS) temperature cycle on a seasonal scale. It investigates the possibility to model this cycle in a consistent way while not taking into account tidal forcing and associated tidal mixing and exchange. This is motivated by the possibility to significantly increase the model’s computational efficiency when neglecting tides. The goal is to develop a flexible and efficient tool for seasonal scenario analysis and to generate transport boundary forcing for local models. Given the significant spatial extent of the SCS basin and the focus on seasonal time scales, synoptic remote sensing is an ideal tool in this analysis. Remote sensing is used to assess the seasonal temperature cycle to identify the relevant driving forces and is a valuable source of input data for modeling. Model simulations are performed using a three-dimensional baroclinic-reduced depth model, driven by monthly mean sea surface anomaly boundary forcing, monthly mean lateral temperature, and salinity forcing obtained from the World Ocean Atlas 2001 climatology, six hourly meteorological forcing from the European Center for Medium range Weather Forecasting ERA-40 dataset, and remotely sensed sea surface temperature (SST) data. A sensitivity analysis of model forcing and coefficients is performed. The model results are quantitatively assessed against climatological temperature profiles using a goodness-of-fit norm. In the deep regions, the model results are in good agreement with this validation data. In the shallow regions, discrepancies are found. To improve the agreement there, we apply a SST nudging method at the free water surface. This considerably improves the model’s vertical temperature representation in the shallow regions. Based on the model validation against climatological in situ and SST data, we conclude that the seasonal temperature cycle for the deep SCS basin can be represented to a good degree. For shallow regions, the absence of tidal mixing and exchange has a clear impact on the model’s temperature representation. This effect on the large-scale temperature cycle can be compensated to a good degree by SST nudging for diagnostic applications. South China Sea Baroclinic temperature model Reduced depth modeling Altimeter data Radiometer data Temperature nudging De Goede, Erik D. aut Schrama, Ernst J. O. aut Gerritsen, Herman aut Enthalten in Ocean dynamics Springer-Verlag, 2001 57(2007), 4-5 vom: Okt., Seite 467-484 (DE-627)335936091 (DE-600)2060148-7 (DE-576)096704470 1616-7341 nnns volume:57 year:2007 number:4-5 month:10 pages:467-484 https://doi.org/10.1007/s10236-007-0123-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OLC-GGO SSG-OPC-GGO GBV_ILN_21 GBV_ILN_22 GBV_ILN_40 GBV_ILN_62 GBV_ILN_70 GBV_ILN_154 GBV_ILN_183 GBV_ILN_600 GBV_ILN_602 GBV_ILN_608 GBV_ILN_2018 GBV_ILN_4277 GBV_ILN_4305 38.90$jOzeanologie$jOzeanographie VZ 106421921 (DE-625)106421921 AR 57 2007 4-5 10 467-484 |
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It investigates the possibility to model this cycle in a consistent way while not taking into account tidal forcing and associated tidal mixing and exchange. This is motivated by the possibility to significantly increase the model’s computational efficiency when neglecting tides. The goal is to develop a flexible and efficient tool for seasonal scenario analysis and to generate transport boundary forcing for local models. Given the significant spatial extent of the SCS basin and the focus on seasonal time scales, synoptic remote sensing is an ideal tool in this analysis. Remote sensing is used to assess the seasonal temperature cycle to identify the relevant driving forces and is a valuable source of input data for modeling. 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550 VZ 14 ssgn 38.90$jOzeanologie$jOzeanographie bkl Analysis and modeling of the seasonal South China Sea temperature cycle using remote sensing South China Sea Baroclinic temperature model Reduced depth modeling Altimeter data Radiometer data Temperature nudging |
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analysis and modeling of the seasonal south china sea temperature cycle using remote sensing |
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Analysis and modeling of the seasonal South China Sea temperature cycle using remote sensing |
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Abstract The present paper describes the analysis and modeling of the South China Sea (SCS) temperature cycle on a seasonal scale. It investigates the possibility to model this cycle in a consistent way while not taking into account tidal forcing and associated tidal mixing and exchange. This is motivated by the possibility to significantly increase the model’s computational efficiency when neglecting tides. The goal is to develop a flexible and efficient tool for seasonal scenario analysis and to generate transport boundary forcing for local models. Given the significant spatial extent of the SCS basin and the focus on seasonal time scales, synoptic remote sensing is an ideal tool in this analysis. Remote sensing is used to assess the seasonal temperature cycle to identify the relevant driving forces and is a valuable source of input data for modeling. Model simulations are performed using a three-dimensional baroclinic-reduced depth model, driven by monthly mean sea surface anomaly boundary forcing, monthly mean lateral temperature, and salinity forcing obtained from the World Ocean Atlas 2001 climatology, six hourly meteorological forcing from the European Center for Medium range Weather Forecasting ERA-40 dataset, and remotely sensed sea surface temperature (SST) data. A sensitivity analysis of model forcing and coefficients is performed. The model results are quantitatively assessed against climatological temperature profiles using a goodness-of-fit norm. In the deep regions, the model results are in good agreement with this validation data. In the shallow regions, discrepancies are found. To improve the agreement there, we apply a SST nudging method at the free water surface. This considerably improves the model’s vertical temperature representation in the shallow regions. Based on the model validation against climatological in situ and SST data, we conclude that the seasonal temperature cycle for the deep SCS basin can be represented to a good degree. For shallow regions, the absence of tidal mixing and exchange has a clear impact on the model’s temperature representation. This effect on the large-scale temperature cycle can be compensated to a good degree by SST nudging for diagnostic applications. © Springer-Verlag 2007 |
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Abstract The present paper describes the analysis and modeling of the South China Sea (SCS) temperature cycle on a seasonal scale. It investigates the possibility to model this cycle in a consistent way while not taking into account tidal forcing and associated tidal mixing and exchange. This is motivated by the possibility to significantly increase the model’s computational efficiency when neglecting tides. The goal is to develop a flexible and efficient tool for seasonal scenario analysis and to generate transport boundary forcing for local models. Given the significant spatial extent of the SCS basin and the focus on seasonal time scales, synoptic remote sensing is an ideal tool in this analysis. Remote sensing is used to assess the seasonal temperature cycle to identify the relevant driving forces and is a valuable source of input data for modeling. Model simulations are performed using a three-dimensional baroclinic-reduced depth model, driven by monthly mean sea surface anomaly boundary forcing, monthly mean lateral temperature, and salinity forcing obtained from the World Ocean Atlas 2001 climatology, six hourly meteorological forcing from the European Center for Medium range Weather Forecasting ERA-40 dataset, and remotely sensed sea surface temperature (SST) data. A sensitivity analysis of model forcing and coefficients is performed. The model results are quantitatively assessed against climatological temperature profiles using a goodness-of-fit norm. In the deep regions, the model results are in good agreement with this validation data. In the shallow regions, discrepancies are found. To improve the agreement there, we apply a SST nudging method at the free water surface. This considerably improves the model’s vertical temperature representation in the shallow regions. Based on the model validation against climatological in situ and SST data, we conclude that the seasonal temperature cycle for the deep SCS basin can be represented to a good degree. For shallow regions, the absence of tidal mixing and exchange has a clear impact on the model’s temperature representation. This effect on the large-scale temperature cycle can be compensated to a good degree by SST nudging for diagnostic applications. © Springer-Verlag 2007 |
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Abstract The present paper describes the analysis and modeling of the South China Sea (SCS) temperature cycle on a seasonal scale. It investigates the possibility to model this cycle in a consistent way while not taking into account tidal forcing and associated tidal mixing and exchange. This is motivated by the possibility to significantly increase the model’s computational efficiency when neglecting tides. The goal is to develop a flexible and efficient tool for seasonal scenario analysis and to generate transport boundary forcing for local models. Given the significant spatial extent of the SCS basin and the focus on seasonal time scales, synoptic remote sensing is an ideal tool in this analysis. Remote sensing is used to assess the seasonal temperature cycle to identify the relevant driving forces and is a valuable source of input data for modeling. Model simulations are performed using a three-dimensional baroclinic-reduced depth model, driven by monthly mean sea surface anomaly boundary forcing, monthly mean lateral temperature, and salinity forcing obtained from the World Ocean Atlas 2001 climatology, six hourly meteorological forcing from the European Center for Medium range Weather Forecasting ERA-40 dataset, and remotely sensed sea surface temperature (SST) data. A sensitivity analysis of model forcing and coefficients is performed. The model results are quantitatively assessed against climatological temperature profiles using a goodness-of-fit norm. In the deep regions, the model results are in good agreement with this validation data. In the shallow regions, discrepancies are found. To improve the agreement there, we apply a SST nudging method at the free water surface. This considerably improves the model’s vertical temperature representation in the shallow regions. Based on the model validation against climatological in situ and SST data, we conclude that the seasonal temperature cycle for the deep SCS basin can be represented to a good degree. For shallow regions, the absence of tidal mixing and exchange has a clear impact on the model’s temperature representation. This effect on the large-scale temperature cycle can be compensated to a good degree by SST nudging for diagnostic applications. © Springer-Verlag 2007 |
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