The importance of deep ocean observations has been recognized with regard to changes in the deep ocean such as global bottom-water warming. Therefore, sustainable deep ocean monitoring networks that use autonomous profiling floats have been widely proposed, and a number of deep-float deployment initiatives have begun around the world. Deployed floats promise to provide unprecedented deep ocean information. However, present deep-float data are known to have biases. In particular, a depth-dependent bias in salinity data is a major issue that prevents us from constructing global deep ocean monitoring networks. This paper proposes a new approach to utilize ongoing deep-float salinity data to reduce the bias in estimates of the global full-depth ocean state. It reports results from comparative experiments with and without deep-float data by using the proposed approach to examine the impact of data from currently operating deep floats on ocean state estimates. The results demonstrate that available float data possibly contribute local corrections to the modeled climate ocean state. Furthermore, we clarify how interannual basin-scale estimations are controlled by available deep-float salinity data in two specific regions of the Southern and Indian Oceans.

中文翻译：

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用于深海状态估算的深浮盐度数据综合：方法和影响

人们已经认识到深海观测对于诸如全球底水变暖等深海变化的重要性。因此，已经广泛提出了使用自主剖面浮标的可持续的深海监测网络，并且在世界范围内已经开始了许多深度浮标的部署计划。展开的浮标有望提供空前的深海信息。但是，目前的深浮数据已知存在偏差。特别是盐度数据中与深度有关的偏差是一个主要问题，使我们无法构建全球深海监测网络。本文提出了一种新的方法，以利用正在进行的深浮盐度数据来减少全球全深度海洋状态估算中的偏差。通过使用建议的方法来检查当前正在运行的深浮标数据对海洋状态估计值的影响，它报告了有或没有深浮标数据的比较实验的结果。结果表明，可用的浮标数据可能有助于对模拟气候海洋状态进行局部校正。此外，我们阐明了如何在南部和印度洋的两个特定区域中利用现有的深浮盐度数据来控制年际流域规模估算。