[A-OS19] Sea level rise under global warming and its impact on coastal areas

[AOS19-P03] Developing a large ensemble simulation dataset of ocean future projection for the North Pacific using a high-resolution ocean model

*Shiro Nishikawa¹, Hideyuki Nakano², Toru Sugiyama¹, Masao Kurogi¹, Hiroyuki Tsujino², Yoichi Ishikawa¹ (1.JAMSTEC Japan Agency for Marine-Earth Science and Technology, 2.Meteorological Research Institute, Japan Meteorological Agency)

Keywords:Large ensemble simulation, Ocean future projection, High-resolution ocean model, North Pacific

We are developing a new simulation dataset of ocean historical and future projection for the North Pacific (NP), produced by multi-case and multi-scenario large ensemble simulations using a high-resolution (mesoscale-eddy resolving) ocean model. The simulations consist of five scenario experiments including historical run (1960–2020), historical run without anthropogenic global warming effect (1960–2020), and three future projection scenario runs (2021–2100) based on shared socioeconomic pathways (SSP: SSP1-2.6, SSP2-4.5, and SSP5-8.5) experiment products of coupled model intercomparison project phase 6 (CMIP6). We execute 36-member ensemble simulations in each experiment (except for the SSP5-8.5 case with 10-member simulations). As a result, the total size of the dataset (the total integration period of the simulations) is 10952 years. This is about eleven times as large as the data size of our previous FORP (future ocean regional projection) dataset, while the basic specifications of the main NP ocean model are common to those of the FORP. The surface driving forcing data for the ocean model simulations are obtained from outputs of global atmosphere climate projection experiments named “TSE-C” (time sequential experiments with coupled model) developed at the MRI/JMA, which incorporates ocean data assimilation process and uses the CMIP6 data for future climate projections. The use of this forcing data based on the TSE-C system has an advantage in reducing the model biases in comparison with the previous FORP data which directly used the CMIP data as the surface driving forcing. In this presentation, we will introduce the outline and some details of the ocean model and ensemble simulations with some preliminary results.