季節内から季節予測（S2S予測）における気候・海洋予測では、数値モデルの初期状態を正確に推定するために、海洋や大気の観測データが不可欠である。観測ネットワークの継続的な運用・拡充を進めるには、これらのデータが予測システムに与える影響を定量的に評価することが求められる。
本セッションは、海洋観測システムが海洋・大気予測に果たす役割を評価することを主眼とした、国連の海洋10年プロジェクト「Synergistic Observing Network for Ocean Prediction（SynObs）」と連携し、各種観測システムが海洋・大気予測に果たす役割を評価するための手法や結果の議論を行うことで、観測と予測の連携強化を図る。具体的には、海洋・気候・地球システム予測における観測システムの影響評価研究を主な対象とし、知見の共有や議論、新たな機会の模索を行う。海洋観測のみならず、気象観測のインパクトを評価する研究も歓迎する。

In climate and ocean prediction, including subseasonal-to-seasonal (S2S) timescales, observations of the ocean, atmosphere and other components of the Earth system are crucial for accurately estimating the initial conditions that drive numerical models. These observation data not only enhance prediction accuracy but also serve as a foundation for the development of prediction systems with significant societal benefits. Evaluating the impact of these observations on prediction systems is essential to justify the continued operation and expansion of observing networks. The United Nations Ocean Decade Project "Synergistic Observing Network for Ocean Prediction" (SynObs) is advancing efforts to assess the importance of ocean observing systems in both oceanic and atmospheric predictions. This session, organized by SynObs, seeks to highlight the potential of these evaluation activities and foster collaboration between observation and prediction communities. The primary goal is to effectively use the evaluation results to design and optimize the global observing network for both oceanic and atmospheric predictions. The focus of the session is not limited to ocean observations but extended to observations throughout the whole earth system. We invite contributions from ongoing studies and proposals that assess the impact of various observing systems on ocean, climate and coupled earth system predictions at any time scale. This session provides a platform for sharing insights, discussing best practices, and identifying opportunities to enhance the global observing infrastructure in the ocean and climate prediction.