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Projected Sea Level Changes in the Marginal Seas near China Based on Dynamical Downscaling

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  • 1 a Department of Oceanography, College of Oceanic and Atmospheric Sciences, Ocean University of China, China
  • | 2 b Centre for Southern Hemisphere Oceans Research (CSHOR), CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia
  • | 3 c Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia
  • | 4 d Key Laboratory of Physical Oceanography, Ministry of Education, Ocean University of China, Qingdao, China
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Abstract

Projections of future sea level changes are usually based on global climate models (GCMs). However, the changes in shallow coastal regions, like the marginal seas near China, cannot be fully resolved in GCMs. To improve regional sea level simulations, a high-resolution (~8 km) regional ocean model is set up for the marginal seas near China for both the historical (1994–2015) and future (2079–2100) periods under representative concentration pathways (RCPs) 4.5 and 8.5. The historical ocean simulations are evaluated at different spatiotemporal scales, and the model is then integrated for the future period, driven by projected monthly climatological climate change signals from eight GCMs individually via both surface and open boundary conditions. The downscaled ocean changes derived by comparing historical and future experiments reveal greater spatial details than those from GCMs, such as a low dynamic sea level (DSL) center of −0.15 m in the middle of the South China Sea (SCS). As a novel test, the downscaled results driven by the ensemble mean forcings are almost identical with the ensemble average results from individually downscaled cases. Forcing of the DSL change and increased cyclonic circulation in the SCS are dominated by the climate change signals from the Pacific, while the DSL change in the East China marginal seas is caused by both local atmosphere forcing and signals from the Pacific. The method of downscaling developed in this study is a useful modeling protocol for adaptation and mitigation planning for future oceanic climate changes.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xuebin Zhang, xuebin.zhang@csiro.au

Abstract

Projections of future sea level changes are usually based on global climate models (GCMs). However, the changes in shallow coastal regions, like the marginal seas near China, cannot be fully resolved in GCMs. To improve regional sea level simulations, a high-resolution (~8 km) regional ocean model is set up for the marginal seas near China for both the historical (1994–2015) and future (2079–2100) periods under representative concentration pathways (RCPs) 4.5 and 8.5. The historical ocean simulations are evaluated at different spatiotemporal scales, and the model is then integrated for the future period, driven by projected monthly climatological climate change signals from eight GCMs individually via both surface and open boundary conditions. The downscaled ocean changes derived by comparing historical and future experiments reveal greater spatial details than those from GCMs, such as a low dynamic sea level (DSL) center of −0.15 m in the middle of the South China Sea (SCS). As a novel test, the downscaled results driven by the ensemble mean forcings are almost identical with the ensemble average results from individually downscaled cases. Forcing of the DSL change and increased cyclonic circulation in the SCS are dominated by the climate change signals from the Pacific, while the DSL change in the East China marginal seas is caused by both local atmosphere forcing and signals from the Pacific. The method of downscaling developed in this study is a useful modeling protocol for adaptation and mitigation planning for future oceanic climate changes.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xuebin Zhang, xuebin.zhang@csiro.au

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