Assimilation of sea surface temperature in the Northwest Pacific Ocean and its marginal seas using the ensemble Kalman filter

Seo, Gab-Su; Choi, Byoung-Ju; Cho, Yang Ki; Kim, Young Ho; Kim, Sangil

doi:10.1007/s12601-010-0021-4

Cited by 17 publications

(6 citation statements)

References 44 publications

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“…Several studies have investigated future change in temperature, transport, and circulation in this region using numerical models [Guo et al, 2003[Guo et al, , 2006Isobe and Beardsley, 2006;Lee and Takeshi, 2007;Yang et al, 2012;Cho et al, 2009Cho et al, , 2013Kim et al, 2013;Seo et al, 2010]. There are also a number of downscaling studies for other regions [Meier, 2006;Ådlandsvik and Bentsen, 2007;Sun et al, 2012].…”

Section: Introductionmentioning

confidence: 99%

“…Detailed simulations using a regional ocean circulation model may increase our understanding and predictability of climate change in the NWP marginal seas. Several studies have investigated future change in temperature, transport, and circulation in this region using numerical models [ Guo et al ., ; Isobe and Beardsley , ; Lee and Takeshi , ; Yang et al ., ; Cho et al ., ; Kim et al ., ; Seo et al ., ]. There are also a number of downscaling studies for other regions [ Meier , ; Ådlandsvik and Bentsen , ; Sun et al ., ].…”

Section: Introductionmentioning

confidence: 99%

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Climate change projection in the Northwest Pacific marginal seas through dynamic downscaling

et al. 2014

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This study presents future climate change projections in the Northwest Pacific (NWP) marginal seas using dynamic downscaling from global climate models (GCMs). A regional climate model (RCM) for the Northwest Pacific Ocean was setup and integrated over the period from 2001 to 2100. The model used forcing fields from three different GCM simulations to downscale the effect of global climate change. MIROC, ECHAM, and HADCM were selected to provide climate change signals for the RCM. These signals were calculated from the GCMs using Cyclostationary Empirical Orthogonal Function analysis and added to the present lateral open boundary and the surface forcing. The RCM was validated by comparing hindcast result with the observation. It was able to project detailed regional climate change processes that GCMs were not able to resolve. A relatively large increases of water temperature were found in the marginal seas. However, only a marginal change was found along the Kuroshio path. Heat supply to the atmosphere decreases in most study areas due to a slower warming of the sea surface compared to the atmosphere. The RCM projection suggests that the temperature of the Yellow Sea Bottom Cold Water will gradually increase by 2100. Volume transports through major straits except the Taiwan Strait in the marginal seas are projected to increase slightly in future. Increased northeasterly wind stress in the East China Sea may also result in the transport change.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Climate change projection in the Northwest Pacific marginal seas through dynamic downscaling

et al. 2014

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“…The simulation results of CRCM and ECCO2 are generally consistent with each other. Both are featured by high EKE values along the Kuroshio extension, although there is an acceptable northward displacement of the Kuroshio extension in CRCM that is a common problem for regional modeling (e.g., Kang 2008;Seo et al 2010). In addition, the EKE simulated by CRCM is slightly higher.…”

Section: Resultsmentioning

confidence: 98%

Impacts of ocean current–atmosphere interactions on mesoscale eddy energetics in the Kuroshio extension region

et al. 2020

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The surface ocean current's imprint on the wind stress (known as the current feedback) has a significant influence on the wind power input to the ocean. In this study, we investigate the effect of the current feedback on mesoscale eddy energetics in the Kuroshio extension region using a high-resolution (9 km) coupled regional climate model. We perform three sets of simulations: one calculates the wind stress without the surface current, one includes only the mesoscale eddy's current and another includes the entire current in the computation. In this way, the mesoscale eddy's current feedback can be isolated and its contribution to the entire current feedback can be assessed. The simulation results show that the mesoscale eddy's current feedback results in negative wind power input to mesoscale eddies and reduces the surface eddy kinetic energy (EKE) by ~ 20% in the Kuroshio extension region. This negative wind power is compensated primarily by the enhanced conversion from mean flow kinetic energy (MKE) and eddy potential energy (EPE) to EKE and secondarily by the reduced pressure flux divergence and horizontal dissipation. Compared to the mesoscale eddy's current feedback, the entire current feedback has a similar impact on EKE and the wind power on mesoscale eddies, while it affects the EKE budget differently. It weakens the MKE to EKE conversion partly due to the reduced kinetic energy input to background flows by wind. Correspondingly, the negative wind power on mesoscale eddies is primarily compensated by the enhanced EPE to EKE conversion.

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“…The error statistics is forecasted using a Monte Carlo method. The assimilation procedure consists of three steps to update the model state vector (Seo et al 2010(Seo et al , 2015. First, a numerical model integrates all the ensemble members forward in time with an ensemble of initial states to obtain the forecast estimates.…”

Section: Ensemble Kalman Filtermentioning

confidence: 99%

Effect of model error representation in the Yellow and East China Sea modeling system based on the ensemble Kalman filter

et al. 2016

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To increase the accuracy of ocean predictions in the Yellow and East China Sea (YES), the satellite-borne sea surface temperature (SST) data have been assimilated to an operational ocean modeling system by applying an ensemble Kalman filter (EnKF). As the observed SST was assimilated continuously into the model with time, the ensemble spread decreased and the efficiency of data assimilation degenerated. To increase liability of the system and the ensemble spread, model uncertainties were represented stochastically by perturbing the model tendency parameters such as eddy viscosity, bottom drag coefficient, light attenuation depth, as well as atmospheric forcing. Data assimilation experiments were performed with forcing from a regional atmospheric model from September 2011 to February 2012. The assimilation results with and without the stochastically perturbed model parameters and atmospheric forcing were compared. The ensemble with the perturbations has larger spread and smaller root-mean-square deviation (RMSD) in temperature compared with the ensemble without the perturbations. The SST RMSD relative to anther supplementary SST dataset was reduced from 0.91 to 0.81°C over the YES. The assimilation of the SST data improved the simulated SST compared with the observation at the ocean buoy stations, and also made the subsurface temperature profiles closer to the observed ones.The assimilation experiments showed that a stochastic representation of the model errors by the perturbations of the model parameters and atmospheric forcing increases the spread of the ensemble and improves the structure of background error covariance, which enhances the performance of the ensemble modeling system in the YES.

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Assimilation of sea surface temperature in the Northwest Pacific Ocean and its marginal seas using the ensemble Kalman filter

Cited by 17 publications

References 44 publications

Climate change projection in the Northwest Pacific marginal seas through dynamic downscaling

Climate change projection in the Northwest Pacific marginal seas through dynamic downscaling

Impacts of ocean current–atmosphere interactions on mesoscale eddy energetics in the Kuroshio extension region

Effect of model error representation in the Yellow and East China Sea modeling system based on the ensemble Kalman filter

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