日本地球惑星科学連合2025年大会

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[E] 口頭発表

セッション記号 A (大気水圏科学) » A-CG 大気海洋・環境科学複合領域・一般

[A-CG40] 大気・海洋観測の気候・海洋予測へのインパクト評価

2025年5月27日(火) 09:00 〜 10:30 展示場特設会場 (6) (幕張メッセ国際展示場 7・8ホール)

コンビーナ:藤井 陽介(気象庁気象研究所)、木戸 晶一郎(海洋研究開発機構 付加価値情報創生部門 アプリケーションラボ)、Tseng Yu-heng(Institute of Oceanography, National Taiwan University)、Xie Jiping(Nansen Environmental and Remote Sensing Center, Norway)、座長:木戸 晶一郎(海洋研究開発機構 付加価値情報創生部門 アプリケーションラボ)、Jiping Xie(Nansen Environmental and Remote Sensing Center, Norway)


09:50 〜 10:10

[ACG40-04] Use cases of in-situ and satellite BGC data in CMEMS Arctic Ocean Biogeochemical forecast and reanalysis products.

★Invited Papers

*Tsuyoshi Wakamatsu1、Veli Caglar Yumruktepe1 (1.Nansen Environmental and Remote Sensing Center, and Bjerknes Centre for Climate Research )

キーワード:Arctic Ocean, Copernicus Marine Environment Monitoring Service , Biogeochemistry

The effective utilization of observational data is fundamental to the development of operational biogeochemical (BGC) products in the Arctic Ocean, where data availability is often limited. Given the region's accelerated environmental changes and ongoing warming trend, high-quality BGC forecast and reanalysis datasets are essential for advancing scientific understanding of ecosystem variability and supporting marine management. This study highlights the integration of BGC Argo float observations and satellite ocean color data in the Copernicus Marine Environment Monitoring Service (CMEMS) Arctic BGC forecast and reanalysis systems. In the CMEMS BGC forecasting framework, an empirical function, calibrated against BGC Argo chlorophyll-a profiles, is used to project satellite-derived chlorophyll-a concentrations onto the upper ocean, improving initialization. In the CMEMS BGC reanalysis system, BGC Argo chlorophyll-a profiles and satellite chlorophyll-a data are jointly assimilated into an ocean model through a multivariate ensemble covariance approach, enhancing model fidelity. These methodological advancements underscore the critical role of multiplatform BGC observations in addressing observational constraints and improving the predictive skill of Arctic Ocean operational BGC products.