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Water transparency can be measured with optical instruments and estimated with satellite sensors, but such measurements have been widely available for only a few decades. Estimates of water transparency using a white disk called a Secchi disk have been made for over a century and can be used to estimate long-term trends. However, historic in situ measurements of the Secchi depth (Z Sd ) were irregular in space and time and are difficult to interpret in regular time series due to biases introduced by changing locations and the timing of measurements. Satellite data time series, on the other hand, have consistent resolution in both space and time but cover too short a time to resolve climate-scale trends. We normalized historic Z Sd measurements in the Baltic Sea with a satellite-derived mean climatology at 5 d temporal and 4 km spatial resolutions and created a merged time series of Z Sd for the last century. The mean Z Sd in the Baltic Sea from 1927-2020 decreased by 4.2 ± 0.6 m at a rate of 0.045 ± 0.06 m yr -1 . Most of the change happened before 1987, and a further decrease was evident primarily in the satellite data during the 1998-2008 period. After 2008, no significant trend in Z Sd and or the coefficient of diffuse light attenuation was detected in the Baltic Sea. However, in some sub-basins of the Baltic Sea, the decrease in Z Sd continued even after that. The decrease in spectral water transparency in recent decades was highest in the 412 nm band, indicating an increase in the concentration of chromophoric dissolved organic matter. 

Background: Biogeochemical-Argo floats are collecting an unprecedented number of profiles of optical backscattering measurements in the global ocean. Backscattering (BBP) data are crucial to understanding ocean particle dynamics and the biological carbon pump. Yet, so far, no procedures have been agreed upon to quality control BBP data in real time. Methods: Here, we present a new suite of real-time quality-control tests and apply them to the current global BBP Argo dataset. The tests were developed by expert BBP users and Argo data managers and have been implemented on a snapshot of the entire Argo dataset. Results: The new tests are able to automatically flag most of the “bad” BBP profiles from the raw dataset. Conclusions: The proposed tests have been approved by the Biogeochemical-Argo Data Management Team and will be implemented by the Argo Data Assembly Centres to deliver real-time quality-controlled profiles of optical backscattering. Provided they reach a pressure of about 1000 dbar, these tests could also be applied to BBP profiles collected by other platforms.