Global trends in ocean phytoplankton: a new assessment using revised ocean colour data

Patterns in the temporal complexity of global chlorophyll concentration

Decades of research have relied on satellite-based estimates of chlorophyll-a concentration to identify oceanographic processes and plan in situ observational campaigns; however, the patterns of intrinsic temporal variation in chlorophyll-a concentration have not been investigated on a global scale. Here we develop a metric to quantify time series complexity (i.e., a measure of the ups and downs of sequential observations) in chlorophyll-a concentration and show that seemingly disparate regions (e.g., Atlantic vs Indian, equatorial vs subtropical) in the global ocean can be inherently similar. These patterns can be linked to the regularity of chlorophyll-a concentration change and the likelihood of anomalous events within the satellite record. Despite distinct spatial changes in decadal chlorophyll-a concentration, changes in time series complexity have been relatively consistent. This work provides different metrics for monitoring the global ocean and suggests that the complexity of chlorophyll-a time series can be independent of its magnitude.

Collapse of scallop Nodipecten nodosus production in the tropical Southeast Brazil as a possible consequence of global warming and water pollution

Mollusc rearing is a relevant global socioeconomic activity. However, this activity has faced severe problems in the last years in southeast Brazil. The mariculture scallop production dropped from 51,2 tons in 2016 to 10,2 tons in 2022 in the Baia da Ilha Grande (BIG; Rio de Janeiro). However, the possible causes of this collapse are unknown. This study aimed to analyze decadal trends of water quality in Nodipecten nodosus spat and adult production in BIG. We also performed physical-chemical and biological water quality analyses of three scallop farms and two nearby locations at BIG in 2022 to evaluate possible environmental stressors and risks. Scallop spat production dropped drastically in the last five years (2018-2022: mean ± stdev: 0.47 ± 0.45 million). Spat production was higher in colder waters and during peaks of Chlorophyll a in the last 13 years. Reduction of Chlorophyll a coincided with decreasing spat production in the last five years. Warmer periods (>27 °C) of the year may hamper scallop development. Counts of potentially pathogenic bacteria (Vibrios) and Escherichia coli were significantly higher in warmer periods which may further reduce scallop productivity. Shotgun metagenomics of seawater samples from the five studied corroborated these culture-based counts. Vibrios and fecal indicator bacteria metagenomic sequences were abundant across the entire study area throughout 2022. The results of this study suggest the collapse of scallop mariculture is the result of a synergistic negative effect of global warming and poor seawater quality.

Microbial plankton responses to multiple environmental drivers in marine ecosystems with different phosphorus limitation degrees

Multiple drivers are threatening the functioning of the microbial food webs and trophic interactions. Our understanding about how temperature, CO₂, nutrient inputs, and solar ultraviolet radiation (UVR) availability interact to alter ecosystem functioning is scarce because research has focused on single and double interactions. Moreover, the role that the degree of in situ nutrient limitation could play in the outcome of these interactions has been largely neglected, despite it is predominant in marine ecosystems. We address these uncertainties by combining remote-sensing analyses, and a collapsed experimental design with natural microbial communities from Mediterranean Sea and Atlantic Ocean exposed to temperature, nutrients, CO₂, and UVR interactions. At the decade scale, we found that more intense and frequent (and longer lasting) Saharan dust inputs (and marine heatwaves) were only coupled with reduced phytoplankton biomass production. When microbial communities were concurrently exposed to future temperature, CO₂, nutrient, and UVR conditions (i.e. the drivers studied over long-term scales), we found shifts from net autotrophy [primary production:respiration (PP:R) ratio > 1] towards a metabolic equilibrium (PP:R ratio ~ 1) or even a net heterotrophy (PP:R ratio < 1), as P-limitation degree was higher (i.e. Atlantic Ocean). These changes in the metabolic balance were coupled with a weakened phytoplankton-bacteria interaction (i.e. bacterial carbon demand exceeded phytoplankton carbon supply. Our work reveals that an accentuated in situ P limitation may promote reductions both in carbon uptake and fluxes between trophic levels in microbial plankton communities under global-change conditions. We show that considering long-term series can aid in identifying major local environmental drivers (i.e. temperature and nutrients in our case), easing the design of future global-change studies, but also that the abiotic environment to which microbial plankton communities are acclimated should be taken into account to avoid biased predictions concerning the effects of multiple interacting global-change drivers on marine ecosystems.

Satellites for long-term monitoring of inland U.S. lakes: The MERIS time series and application for chlorophyll-a

Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state. Here, we document the generation and distribution of the complete MEdium Resolution Imaging Spectrometer (MERIS; Appendix A provides a complete list of abbreviations) radiometric time series for over 2300 satellite resolvable inland bodies of water across the contiguous United States (CONUS) and more than 5,000 in Alaska. This contribution greatly increases the ease of use of satellite remote sensing data for inland water quality monitoring, as well as highlights new horizons in inland water remote sensing algorithm development. We evaluate the performance of satellite remote sensing Cyanobacteria Index (CI)-based chlorophyll algorithms, the retrievals for which provide surrogate estimates of phytoplankton concentrations in cyanobacteria dominated lakes. Our analysis quantifies the algorithms' abilities to assess lake trophic state across the CONUS. As a case study, we apply a bootstrapping approach to derive a new CI-to-chlorophyll relationship, Chl_BS, which performs relatively well with a multiplicative bias of 1.11 (11%) and mean absolute error of 1.60 (60%). While the primary contribution of this work is the distribution of the MERIS radiometric timeseries, we provide this case study as a roadmap for future stakeholders' algorithm development activities, as well as a tool to assess the strengths and weaknesses of applying a single algorithm across CONUS.

Complementary water quality observations from high and medium resolution Sentinel sensors by aligning chlorophyll-a and turbidity algorithms

High resolution imaging spectrometers are prerequisite to address significant data gaps in inland optical water quality monitoring. In this work, we provide a data-driven alignment of chlorophyll-a and turbidity derived from the Sentinel-2 MultiSpectral Imager (MSI) with corresponding Sentinel-3 Ocean and Land Colour Instrument (OLCI) products. For chlorophyll-a retrieval, empirical 'ocean colour' blue-green band ratios and a near infra-red (NIR) band ratio algorithm, as well as a semi-analytical three-band NIR-red ratio algorithm, were included in the analysis. Six million co-registrations with MSI and OLCI spanning 24 lakes across five continents were analysed. Following atmospheric correction with POLYMER, the reflectance distributions of the red and NIR bands showed close similarity between the two sensors, whereas the distribution for blue and green bands was positively skewed in the MSI results compared to OLCI. Whilst it is not possible from this analysis to determine the accuracy of reflectance retrieved with either MSI or OLCI results, optimizing water quality algorithms for MSI against those previously derived for the Envisat Medium Resolution Imaging Spectrometer (MERIS) and its follow-on OLCI, supports the wider use of MSI for aquatic applications. Chlorophyll-a algorithms were thus tuned for MSI against concurrent OLCI observations, resulting in significant improvements against the original algorithm coefficients. The mean absolute difference (MAD) for the blue-green band ratio algorithm decreased from 1.95 mg m^-3 to 1.11 mg m^-3, whilst the correlation coefficient increased from 0.61 to 0.80. For the NIR-red band ratio algorithms improvements were modest, with the MAD decreasing from 4.68 to 4.64 mg m^-3 for the empirical red band ratio algorithm, and 3.73 to 3.67 for the semi-analytical 3-band algorithm. Three implementations of the turbidity algorithm showed improvement after tuning with the resulting distributions having reduced bias. The MAD reduced from 0.85 to 0.72, 1.22 to 1.10 and 1.93 to 1.55 FNU for the 665, 708 and 778 nm implementations respectively. However, several sources of uncertainty remain: adjacent land showed high divergence between the sensors, suggesting that high product uncertainty near land continues to be an issue for small water bodies, while it cannot be stated at this point whether MSI or OLCI results are differentially affected. The effect of spectrally wider bands of the MSI on algorithm sensitivity to chlorophyll-a and turbidity cannot be fully established without further availability of in situ optical measurements.

Satellite-Observed Multi-Scale Variability of Sea Surface Chlorophyll-a Concentration along the South Coast of the Sumatra-Java Islands

The southern coast of Java is known as one of the most productive fishing grounds for tuna, feeding by nutrient-rich water along the coast caused by the subsurface water upwelling. This primary productivity can be evidenced by the high sea surface chlorophyll-a concentration (SSC). Based on satellite remote sensing products, we investigate the multi-scale variability in SSC along the Sumatra-Java coast. The results show that seasonal variability of SSCs is primarily due to monsoon-driven upwelling and rainfall in the Indian Ocean and Indonesian seas sides of the Sumatra and Java Islands, respectively. Local Ekman pumping plays a secondary role, while rainfall input to the ocean has little effect. Coastally trapped Kelvin waves and mesoscale eddies are responsible for the intraseasonal SSC anomalies in regions along the south coast of Java and off the Sunda and Lombok Straits, respectively. The interannual variability in SSC is caused by the anomalous upwelling related to the Indian Ocean Dipole. There was a weak increasing trend of ~0.1–0.2 mg/m3 per decade, above the global averaged trend, which may be related to enhanced local Ekman pumping. These analyses provide an overall description of SSC variations based on satellite observations; however, further investigations based on in situ observations are needed to achieve better quantification.

Dynamic partitioning of tropical Indian Ocean surface waters using ocean colour data - management and modelling applications

Over the past few decades, partitioning of the surface ocean into ecologically-meaningful spatial domains has been approached using a range of data types, with the aim of improving our understanding of open ocean processes, supporting marine management decisions and constraining coupled ocean-biogeochemical models. The simplest partitioning method, which could provide low-latency information for managers at low cost, remains a purely optical classification based on ocean colour remote sensing. The question is whether such a simple approach has value. Here, the efficacy of optical classifications in constraining physical variables that modulate the epipelagic environment is tested for the tropical Indian Ocean, with a focus on the Chagos marine protected area (MPA). Using remote sensing data, it was found that optical classes corresponded to distinctive ranges of wind speed, wind stress curl, sea surface temperature, sea surface slope, sea surface height anomaly and geostrophic currents (Kruskal-Wallis and post-hoc Tukey honestly significantly different tests, α = 0.01). Between-class differences were significant for a set of sub-domains that resolved zonal and meridional gradients across the MPA and Seychelles-Chagos Thermocline Ridge, whereas between-domain differences were only significant for the north-south gradient (PERMANOVA, α = 0.01). A preliminary test of between-class differences in surface CO₂ concentrations from the Orbiting Carbon Observatory-2 demonstrated a small decrease in mean pCO₂ with increasing chlorophyll (chl), from 418 to 398 ppm. Simple optical class maps therefore provide an overview of growth conditions, the spatial distribution of resources - from which habitat fragmentation metrics can be calculated, and carbon sequestration potential. Within the 17 year study period, biotic variables were found to have decreased at up to 0.025%a^-1 for all optical classes, which is slower than reported elsewhere (Mann-Kendall-Sen regression, α = 0.01). Within the MPA, positive Indian Ocean Dipole conditions and negative Southern Oscillation Indices were weakly associated with decreasing chl, fluorescence line height (FLH), eddy kinetic energy, easterly wind stress and wind stress curl, and with increasing FLH/chl, sea surface temperature, SSH gradients and northerly wind stress, consistent with reduced surface mixing and increased stratification. The optical partitioning scheme described here can be applied in Google Earth Engine to support management decisions at daily or monthly scales, and potential applications are discussed.

Regional surface chlorophyll trends and uncertainties in the global ocean

Changes in marine primary productivity are key to determine how climate change might impact marine ecosystems and fisheries. Satellite ocean color sensors provide coverage of global ocean chlorophyll with a combined record length of ~ 20 years. Coupled physical–biogeochemical models can inform on expected changes and are used here to constrain observational trend estimates and their uncertainty. We produce estimates of ocean surface chlorophyll trends, by using Coupled Model Intercomparison Project (CMIP5) models to form priors as a “first guess”, which are then updated using satellite observations in a Bayesian spatio-temporal model. Regional chlorophyll trends are found to be significantly different from zero in 18/23 regions, in the range ± 1.8% year⁻¹. A global average of these regional trends shows a net positive trend of 0.08 ± 0.35% year⁻¹, highlighting the importance of considering chlorophyll changes at a regional level. We compare these results with estimates obtained with the commonly used “vague” prior, representing no independent knowledge; coupled model priors are shown to slightly reduce trend magnitude and uncertainties in most regions. The statistical model used here provides a robust framework for making best use of all available information and can be applied to improve understanding of global change.

Spatial Variability and Detection Levels for Chlorophyll-a Estimates in High Latitude Lakes Using Landsat Imagery

Monitoring lakes in high-latitude areas can provide a better understanding of freshwater systems sensitivity and accrete knowledge on climate change impacts. Phytoplankton are sensitive to various conditions: warmer temperatures, earlier ice-melt and changing nutrient sources. While satellite imagery can monitor phytoplankton biomass using chlorophyll a (Chl) as a proxy over large areas, detection of Chl in small lakes is hindered by the low spatial resolution of conventional ocean color satellites. The short time-series of the newest generation of space-borne sensors (e.g., Sentinel-2) is a bottleneck for assessing long-term trends. Although previous studies have evaluated the use of high-resolution sensors for assessing lakes’ Chl, it is still unclear how the spatial and temporal variability of Chl concentration affect the performance of satellite estimates. We discuss the suitability of Landsat (LT) 30 m resolution imagery to assess lakes’ Chl concentrations under varying trophic conditions, across extensive high-latitude areas in Finland. We use in situ data obtained from field campaigns in 19 lakes and generate remote sensing estimates of Chl, taking advantage of the long-time span of the LT-5 and LT-7 archives, from 1984 to 2017. Our results show that linear models based on LT data can explain approximately 50% of the Chl interannual variability. However, we demonstrate that the accuracy of the estimates is dependent on the lake’s trophic state, with models performing in average twice as better in lakes with higher Chl concentration (>20 µg/L) in comparison with less eutrophic lakes. Finally, we demonstrate that linear models based on LT data can achieve high accuracy (R2 = 0.9; p-value < 0.05) in determining lakes’ mean Chl concentration, allowing the mapping of the trophic state of lakes across large regions. Given the long time-series and high spatial resolution, LT-based estimates of Chl provide a tool for assessing the impacts of environmental change.

Assessing the Effect of Tandem Phase Sentinel-3 OLCI Sensor Uncertainty on the Estimation of Potential Ocean Chlorophyll-a Trends

The Sentinel-3 tandem project represents the first time that two ocean colour satellites have been flown in the same orbit with minimal temporal separation (~30 s), thus allowing them to have virtually identical views of the ocean. This offers an opportunity for understanding how differences in individual sensor uncertainty can affect conclusions drawn from the data. Here, we specifically focus on trend estimation. Observational chlorophyll-a uncertainty is assessed from the Sentinel-3A Ocean and Land Colour Imager (OLCI-A) and Sentinel-3B OLCI (OLCI-B) sensors using a bootstrapping approach. Realistic trends are then imposed on a synthetic chlorophyll-a time series to understand how sensor uncertainty could affect potential long-term trends in Sentinel-3 OLCI data. We find that OLCI-A and OLCI-B both show very similar trends, with the OLCI-B trend estimates tending to have a slightly wider distribution, although not statistically different from the OLCI-A distribution. The spatial pattern of trend estimates is also assessed, showing that the probability distributions of trend estimates in OLCI-A and OLCI-B are most similar in open ocean regions, and least similar in coastal regions and at high northern latitudes. This analysis shows that the two sensors should provide consistent trends between the two satellites, provided future ageing is well quantified and mitigated. The Sentinel-3 programme offers a strong baseline for estimating long-term chlorophyll-a trends by offering a series of satellites (starting with Sentinel-3A and Sentinel-3B) that use the same sensor design, reducing potential issues with cross-calibration between sensors. This analysis contributes an important understanding of the reliability of the two current Sentinel-3 OLCI sensors for future studies of climate change driven chlorophyll-a trends.

Changes in higher trophic level productivity, diversity and niche space in a rapidly warming continental shelf ecosystem

There is long-standing ecological and socioeconomic interest in what controls the diversity and productivity of ecosystems. That focus has intensified with shifting environmental conditions associated with accelerating climate change. The U.S. Northeast Shelf (NES) is a well-studied continental shelf marine ecosystem that is among the more rapidly warming marine systems worldwide. Furthermore, many constituent species have experienced significant distributional shifts. However, the system response of the NES to climate change goes beyond simple shifts in species distribution. The fish and macroinvertebrate communities of the NES have increased in species diversity and overall productivity in recent decades, despite no significant decline in fishing pressure. Species distribution models constructed using random forest classification and regression trees were fit for the dominant species in the system. Over time, the areal distribution of occupancy habitat has increased for approximately 80% of the modeled taxa, suggesting most species have significantly increased their range and niche space. These niche spaces were analyzed to determine the area of niche overlap between species pairs. For the vast majority of species pairs, interaction has increased over time suggesting greater niche overlap and the increased probability for more intense species interactions, such as between competitors or predators and prey. Furthermore, the species taxonomic composition and size structure indicate a potential tropicalization of the fish community. The system and community changes are consistent with the view that the NES may be transitioning from a cold temperate or boreal ecoregion to one more consistent with the composition of a warm temperate or Carolinian system.

Trends in tuna carbon isotopes suggest global changes in pelagic phytoplankton communities

Considerable uncertainty remains over how increasing atmospheric CO₂ and anthropogenic climate changes are affecting open-ocean marine ecosystems from phytoplankton to top predators. Biological time series data are thus urgently needed for the world's oceans. Here, we use the carbon stable isotope composition of tuna to provide a first insight into the existence of global trends in complex ecosystem dynamics and changes in the oceanic carbon cycle. From 2000 to 2015, considerable declines in δ¹³ C values of 0.8‰-2.5‰ were observed across three tuna species sampled globally, with more substantial changes in the Pacific Ocean compared to the Atlantic and Indian Oceans. Tuna recorded not only the Suess effect, that is, fossil fuel-derived and isotopically light carbon being incorporated into marine ecosystems, but also recorded profound changes at the base of marine food webs. We suggest a global shift in phytoplankton community structure, for example, a reduction in ¹³ C-rich phytoplankton such as diatoms, and/or a change in phytoplankton physiology during this period, although this does not rule out other concomitant changes at higher levels in the food webs. Our study establishes tuna δ¹³ C values as a candidate essential ocean variable to assess complex ecosystem responses to climate change at regional to global scales and over decadal timescales. Finally, this time series will be invaluable in calibrating and validating global earth system models to project changes in marine biota.

Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction

Developments in observing system technologies and ocean data assimilation (DA) are symbiotic. New observation types lead to new DA methods and new DA methods, such as coupled DA, can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners of DA are encouraged to make better use of observations that are already available, for example, taking advantage of strongly coupled DA so that ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate as well as the initialization of operational long-range prediction models. There are many remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean-observing system throughout its history, the presence of biases and drifts in models, and the simplifying assumptions made in DA solution methods. From a governance point of view, more support is needed to bring the ocean-observing and DA communities together. For prediction applications, there is wide agreement that protocols are needed for rapid communication of ocean-observing data on numerical weather prediction (NWP) timescales. There is potential for new observation types to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of NWP, covering hours to weeks, out to multiple decades. Better communication between DA and observation communities is encouraged in order to allow operational prediction centers the ability to provide guidance for the design of a sustained and adaptive observing network.