Estimates of the diffuse attenuation coefficient (Kd) at two different wavelengths and band-integrated (PAR) were obtained using different published algorithms developed for open ocean waters spanning in type from explicit-empirical, semi-analytical and implicit-empirical and applied to data from spectral radiometers on board six different satellites (MODIS-Aqua, MODIS-Terra, VIIRS–SNPP, VIIRS-JPSS, OLCI-Sentinel 3A and OLCI-Sentinel 3B). The resultant Kds were compared to those inferred from measurements of radiometry from sensors on board autonomous profiling floats (BGC-Argo). Advantages of BGC-Argo measurements compared to ship-based ones include: 1. uniform sampling in time throughout the year, 2. large spatial coverage, and 3. lack of shading by platform. Over 5000 quality-controlled matchups between Kds derived from float and from satellite sensors were found with values ranging from 0.01 to 0.67 m−1. Our results show that although all three algorithm types provided similarly ranging values of Kd to those of the floats, for most sensors, a given algorithm produced statistically different Kd distributions from the two others. Algorithm results diverged the most for low Kd (clearest waters). Algorithm biases were traced to the limitations of the datasets the algorithms were developed and trained with, as well as the neglect of sun angle in some algorithms. This study highlights: 1. the importance of using comprehensive field-based datasets (such as BGC-Argo) for algorithm development, 2. the limitation of using radiative-transfer model simulations only for algorithm development, and 3. the potential for improvement if sun angle is taken into account explicitly to improve empirical Kd algorithms. Recent augmentation of profiling floats with hyper-spectral radiometers should be encouraged as they will provide additional constraints to develop algorithms for upcoming missions such as NASA’s PACE and SBG and ESA’s CHIME, all of which will include a hyper-spectral radiometers.

Sudden shifts in marine plankton communities in response to environmental changes are of special concern because of their low predictability and high potential impacts on ocean ecosystems. We explored how anthropogenic climate change influences the spatial extent and frequency of changepoints in plankton populations by comparing the behavior of a plankton community in a coupled Earth System Model under pre-industrial, historical 20th-century, and projected 21st-century forcing. The ocean areas where surface ocean temperature, nutrient concentrations, and different plankton types exhibited changepoints expanded over time. In contrast, regional hotspots where changepoints occur frequently largely disappeared. Heterotrophy and larger organism sizes were associated with more changepoints. In the pre-industrial and 20th century, plankton changepoints were associated with shifts in physical fronts, and more often with changepoints for iron and silicate than for nitrate and phosphate. In the 21st century, climate change disrupts these interannual-variability-driven changepoint patterns. Together, our results suggest anthropogenic climate change may drive less frequent but more widespread changepoints simultaneously affecting several components of pelagic food webs.

We recently found a significant bias while validating frequently used ocean color algorithms retrieving the spectral diffuse attenuation coefficient (Kd(λ)) and the attenuation coefficient for photosynthetically available radiation (Kd(PAR)) [1]. Here we compute new coefficients for existing algorithms for Kd(λ), so as to remove the observed bias at Kd(490), and evaluate the impact on global and regional estimates of net primary production (NPP) using two different primary production models. The new parametrization results in improved retrieval of Kd(490) by both the empirical and the semi-analytical algorithms. Match-ups between BGC-Argo floats and Remote Sensing Reflectance (Rrs) for six different satellite sensors no longer have a small- value bias, and show a reduced RMSE and error ratio. The new coefficients are validated using measurements not included in the training dataset and are found to perform significantly better at a different wavelength (412nm) than the one used for the new parametrization (490nm) and perform reasonably well in Case-2 waters. Since the new coefficients presented here were developed with a dataset encompassing a larger proportion of the ocean’s variability, they are better suited to compute Kd(λ) in regions that were not present in the original algorithm’s dataset and are therefore appropriate for global Kd(λ) estimation. Using the new Kd parameterization results in a global increase of NPP of ≈ 37% in both models used, mostly driven by the previous overestimation of Kd(λ) (underestimation of light penetration) in the clear, subtropical gyres. Subtropical gyres show the largest increase (79%) in the VGPM model, with the presence of a strong seasonal cycle in the difference. High Kd(λ) areas are less affected by the new parameterization (no increase in NPP in VGPM, ≈ 20% in CbPMv2). Although the subtropical gyres are not very productive regions of the ocean, their large surface area and the magnitude of the bias in Kd(λ) between the old and new parameterization causes the observed significant difference in global NPP estimates. Our results suggest that the oceanic carbon uptake is larger than previously thought, which will be most relevant to the oceanic carbon dioxide budget once humanity slows the increase of atmospheric CO2.