Strong natural variability has been thought to mask possible climate-change-driven trends in phytoplankton populations from Earth-observing satellites. More than 30 years of continuous data were thought to be needed to detect a trend driven by climate change¹. Here we show that climate-change trends emerge more rapidly in ocean colour (remote-sensing reflectance, R_rs), because R_rs is multivariate and some wavebands have low interannual variability. We analyse a 20-year R_rs time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite, and find significant trends in R_rs for 40% of the global surface ocean. The climate-change signal in R_rs emerges after 20 years in similar regions covering a similar fraction of the ocean in a state-of-the-art ecosystem model², which suggests that our observed trends indicate shifts in ocean colour—and, by extension, in surface-ocean ecosystems—that are driven by climate change. On the whole, low-latitude oceans have become greener in the past 20 years.

How well do we know the particulate backscattering coefficient (b_bp) in the global ocean? Satellite lidar b_bp has never been validated globally and few studies have compared lidar b_bp to b_bp derived from reflectances (via ocean color) or in situ observations. Here, we validate lidar b_bp with autonomous biogeochemical Argo floats using a decorrelation analysis to identify relevant spatiotemporal matchup scales inspired by geographical variability in the Rossby radius of deformation. We compare lidar, float, and ocean color b_bp at the same locations and times to assess performance. Lidar b_bp outperforms ocean color, with a median percent error of 18% compared to 24% in the best case and a relative bias of −11% compared to −21%, respectively. Phytoplankton carbon calculated from ocean color and lidar exhibits basin‐scale differences that can reach ±50%.

In this study, we identify a seasonal bias in the ocean color satellite-derived remote sensing reflectances ( R r s ( λ ) ; s r − 1 ) at the ocean color validation site, Marine Optical BuoY. The seasonal bias in R r s ( λ ) is present to varying degrees in all ocean color satellites examined, including the Visible Infrared Imaging Radiometer Suite, Sea-Viewing Wide Field-of-View Sensor, and Moderate Resolution Imaging Spectrometer. The relative bias in R r s has spectral dependence. Products derived from R r s ( λ ) are affected by the bias to varying degrees, with particulate backscattering varying up to 50% over a year, chlorophyll varying up to 25% over a year, and absorption from phytoplankton or dissolved material varying by up to 15%. The propagation of R r s ( λ ) bias into derived products is broadly confirmed on regional and global scales using Argo floats and data from the cloud-aerosol lidar with orthogonal polarization instrument aboard the cloud-aerosol lidar and infrared pathfinder satellite. The artifactual seasonality in ocean color is prominent in areas of low biomass (i.e., subtropical gyres) and is not easily discerned in areas of high biomass. While we have eliminated several candidates that could cause the biases in R r s ( λ ) , there are still outstanding questions regarding potential contributions from atmospheric corrections. Specifically, we provide evidence that the aquatic bidirectional reflectance distribution function may in part cause the observed seasonal bias, but this does not preclude an additional effect of the aerosol estimation. Our investigation highlights the contributions that atmospheric correction schemes can make in introducing biases in R r s ( λ ) , and we recommend more simulations to discern these influence R r s ( λ ) biases. Community efforts are needed to find the root cause of the seasonal bias because all past, present, and future data are, or will be, affected until a solution is implemented.

Satellite retrievals of particulate backscattering (b_bp) are widely used in studies of ocean ecology and biogeochemistry, but have been historically difficult to validate due to the paucity of available ship-based comparative field measurements. Here we present a comparison of satellite and in situ b_bp using observations from autonomous floats (n = 2,486 total matchups across three satellites), which provide b_bp at 700 nm. With these data, we quantify how well the three inversion products currently distributed by NASA ocean color retrieve b_bp. We find that the median ratio of satellite derived b_bp to float b_bp ranges from 0.77 to 1.60 and Spearman’s rank correlations vary from r = 0.06 to r = 0.79, depending on which algorithm and sensor is used. Model skill degrades with increased spatial variability in remote sensing reflectance, which suggests that more rigorous matchup criteria and factors contributing to sensor noisiness may be useful to address in future work, and/or that we have built in biases in the current widely distributed inversion algorithms.

Ocean biological processes mediate the transport of roughly 10 petagrams of carbon from the surface to the deep ocean each year and thus play an important role in the global carbon cycle. Even so, the globally integrated rate of carbon export out of the surface ocean remains highly uncertain. Quantifying the processes underlying this biological carbon export requires a synthesis between model predictions and available observations of particulate organic carbon (POC) flux; yet the scale dissimilarities between models and observations make this synthesis difficult. Here we compare carbon export predictions from a mechanistic model with observations of POC fluxes from several data sets compiled from the literature spanning different space, time, and depth scales as well as using different observational methodologies. We optimize model parameters to provide the best match between model‐predicted and observed POC fluxes, explicitly accounting for sources of error associated with each data set. Model‐predicted globally integrated values of POC flux at the base of the euphotic layer range from 3.8 to 5.5 Pg C/year, depending on the data set used to optimize the model. Modeled carbon export pathways also vary depending on the data set used to optimize the model, as well as the satellite net primary production data product used to drive the model. These findings highlight the importance of collecting field data that average over the substantial natural temporal and spatial variability in carbon export fluxes, and advancing satellite algorithms for ocean net primary production, in order to improve predictions of biological carbon export.

The depth-attenuation of sinking particulate organic carbon (POC) is of particular importance for the ocean's role in the global carbon cycle. Numerous idealized flux-vs.-depth relationships are available to parameterize this process in Earth System Models. Here we show that these relationships are statistically indistinguishable from available POC flux profile data. Despite their quantitative similarity, we also show these relationships have very different implications for the flux leaving the upper ocean, as well as for the mechanisms governing POC flux. We discuss how this tension might be addressed both observationally and in modeling studies.