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The absorption of atmospheric carbon dioxide (CO2) in the Southern Ocean represents a critical component of the global oceanic carbon budget. Previous assessments of air‐sea carbon flux variations and long‐term trends in polar regions during winter have faced limitations due to scarce field data and the lack of ocean color satellite imagery, causing uncertainties in estimating CO2 flux estimation. This study utilized the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation satellite to construct a continuous 16‐year (2006–2021) time series of sea surface partial pressure of CO2 (pCO2) in the Southern Ocean. Our findings revealed that the polar region in South Ocean acts as a carbon sink in winter, with CO2 flux of ∼30 TgC in high‐latitude areas (South of 50°S). This work highlights the efficacy of active remote sensing for monitoring sea surface pCO2 and contributes insights into the dynamic carbonate systems of the Southern Ocean. Plain Language Summary: Climate change data from recent decades have consistently shown an increase in atmospheric CO2 concentration. The Southern Ocean, a major carbon sink, is critical in this regard. However, limitations in ocean color remote sensing and infrequent sampling hinder a complete understanding of carbon uptake in high‐latitude regions during winter. Previous reconstructions inadequately considered the biological effect on the air‐sea CO2 exchange process in winter. This study used observations from an active remote sensing satellite to represent the biological effects of CO2 and construct a long‐term time series of sea surface CO2 level for the Southern Ocean. Additionally, the study reassessed the CO2 uptake capacity of the Southern Ocean in winter. These findings suggest that previous estimates may underestimate the CO2 uptake capacity in the high‐latitude regions during winter, potentially due to underestimations of biological effects. This research underscores the value of active remote sensing for obtaining critical biogeochemical parameters in high‐latitude oceans, providing an essential tool for monitoring carbonate systems. Key Points: A new method was proposed to reconstruct pressure of CO2 (pCO2) using CALIPSO‐derived bbp dataA 16‐year Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation observation‐based pCO2 product was constructedThe CO2 uptake capacity in the Southern Ocean during winter was estimated [ABSTRACT FROM AUTHOR]

Study on the upper ocean is of great significance to the global climate change and carbon cycle. Lidar can be used to effectively detect depth-resolved optical properties of the ocean. However, both theory and experiment of oceanic lidar are limited by complex multiple scattering. Several progresses by Zhejiang University will be illustrated in this paper: 1) a polarized lidar system was developed, and a Monte Carlo model and a radiative transfer model were established (Zhou, et al. remote sensing, 2019; Zhou, et al. Journal of remote sensing, 2019; Xu, et al. and Liu, et al. Journal of remote sensing, 2019); 2) Cross validations are demonstrated to verify the availability of the lidar system and models (Liu, et al. IEEE TGRS, 2019); 3) phase function effects on backscatter and attenuation are studied considering multiple scattering, respectively (Liu, et al. Optics Express, 2019). Oceanic lidar is proven to have great potential in marine studies.

Light scattering properties in such a highly turbid productive lake as Lake Taihu in China were examined through 118 samples collected during three cruises in November 2006, March 2007, and November 2007. The particulate scattering and backscattering coefficients were observed using WETLabs AC-S and ECO-BB9. A power model with a spectral exponent of -0.729 was used to simulate the particulate scattering coefficient (bp) spectra. It has a better performance than the linear model. Scattering parameters are more closely related to inorganic suspended matter (ISM) concentration than to other water components, such as total suspended matter (TSM), organic suspended matter (OSM), and chlorophyll a (Chla). This indicates that ISM dominates the scattering signal in the lake. Three discrepancies with oceanic/ coastal waters are observed: (a) the backscattering ratio ([[??].sub.bp]) decreases with an increase in the ISM concentration because of a highly strong contribution by ISM to [b.sub.p]; (b) the mass-specific scattering coefficient ([b.sub.p.sup.m]) exhibits a wider range of variability than that reported in previous studies, which can be attributed to considerable variation in the OSM and ISM distributions; (c) the particle size distribution slope ([xi]) is mostly larger than 4.0 in Lake Taihu, whereas it is usually within 3.54.0 for marine particles. In addition, the bulk refractive index ([[bar.n].sub.p]) calculated according to the Twardowski et al. model [J. Geophys. Res. 106, 14129 (2001)] indicates that some stations ([[bar.n].sub.p] < 1.07) can be regarded as organic-particle dominant. Other stations with high ISM concentrations have a very small [[bar.n].sub.p] value mostly within 1.10-1.17. Overall, the knowledge on the scattering properties gained in this study broadens our understanding of water optics in highly turbid productive water columns. OCIS codes: 290.0290, 280.1350.

The capacity of CO2 uptake in the Chukchi Sea is particularly sensitive to rapid physical and biological changes. However, scarce field observations pose a challenge in understanding the long‐term trend of CO2 uptake capacity on this continental shelf. We adopted a machine‐learning‐based approach to construct a 17‐years (2003–2019) long‐term time series of summer sea surface partial pressure of CO2 (pCO2) from remote sensing products. We show that the long‐term increase in CO2 uptake capacity can be attributed to strong and enhanced biological uptake. In addition, the intraseasonal variability of surface pCO2 in early summer confirms the crucial role of sea ice melt and the subsequently enhanced photosynthesis as soon as the surface ocean converts into an open system. Our results thus highlight the use of remote sensing data in interpolating/extrapolating the highly dynamic carbonate system in the continental shelf sea and shed light into future studies involving machine learning or algorithms. Plain Language Summary: The climate change in the recent decades has caused remarkable sea surface warming in the Arctic Ocean, with its warming rate twice higher than the global average. The Arctic warming could have some consequences such as shrinkage of sea ice and increase of freshwater storage which profoundly affect the carbon cycle in the Arctic Ocean. The Chukchi Sea serves as the largest oceanic CO2 sink in the Arctic Ocean, yet its long‐term variation and associated mechanism remain unclear due to undersampling. In this study, we adopted a machine‐learning‐based model to construct a 17‐years long‐term time series of sea surface partial pressure of CO2 (pCO2) from satellite observations. Results showed that the CO2 uptake capacity in the Chukchi Sea has increased in the recent decades, primarily owing to the increased primary productivity. This study demonstrated the great potential of ocean color remote sensing in retrieving key biogeochemical parameters in polar regions, and providing a powerful tool for monitoring carbonate system for the oceanographic community. Key Points: A machine learning approach was calibrated and validated to obtain sea surface pCO2 from satellite observations in the Chukchi SeaIncrease in CO2 uptake capacity is revealed by a 17‐years (2003–2019) long‐term time series of summer pCO2Strong and enhanced biological uptake contribute to the increase in CO2 uptake capacity [ABSTRACT FROM AUTHOR]