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Combining Lagrangian trajectories and satellite observations provides a novel basis for monitoring changes in water properties with high temporal and spatial resolution. In this study, a prediction scheme was developed for synthesizing satellite observations and Lagrangian model data for better interpretation of harmful algal bloom (HAB) risk. The algorithm can not only predict variations in chlorophyll-a concentration but also changes in spectral properties of the water, which are important for discrimination of different algal species from satellite ocean color. The prediction scheme was applied to regions along the coast of England to verify its applicability. It was shown that the Lagrangian methodology can significantly improve the coverage of satellite products, and the unique animations are effective for interpretation of the development of HABs. A comparison between chlorophyll-a predictions and satellite observations further demonstrated the effectiveness of this approach: r2 = 0.81 and a low mean absolute percentage error of 36.9%. Although uncertainties from modeling and the methodology affect the accuracy of predictions, this approach offers a powerful tool for monitoring the marine ecosystem and for supporting the aquaculture industry with improved early warning of potential HABs.

The latest version of the TRACMASS trajectory code, version 7.0 will be presented. The latest version includes several new features, e.g. water tracing in the atmosphere, generalisation of the tracer handling, and improvements to the numerical scheme. The code has also become more user friendly and easier to get started with. Previous versions of TRACMASS only allowed temperature, salinity and potential density to be calculated along the trajectories, but the new version allows any tracer to be followed e.g. biogeochemical tracers or chemical compounds in the atmosphere. TRACMASS calculates Lagrangian trajectories offline for both the ocean and atmosphere by using already stored velocity fields, and optionally tracer fields. The code supports most vertical coordinate systems, e.g. z-star, z-tilde, sigma, and hybrid sigma-pressure coordinates. Hence, TRACMASS supports a range of atmosphere and ocean models such as ECMWF IFS, NEMO, ROMS, MOM, as well as reanalysis products (e.g. ERA-5) or observations (e.g. geostrophic currents from AVISO satellite altimetry). The fact that the numerical scheme in TRACMASS is mass conserving allows us to associate each trajectory with a mass transport and calculate the Lagrangian mass transport between different regions as well as construct Lagrangian stream functions. A short course on how to set up, configure and run the TRACMASS code will be given separately, SC5.17.

A Corrigendum on Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades.-- Special Issue Feature Paper Special Issue on Ocean Remote Sensing.-- 13 pages, 7 figures, 2 tables by Kulk, Gemma; Platt, Trevor; Dingle, James; Jackson, Thomas; Jönsson, Bror F.; Bouman, Heather A.; Babin, Marcel; Brewin, Robert; Doblin, Martina; Estrada, Marta; Figueiras, F. G.; Furuya, Ken; González, Natalia; Gudfinnsson, Hafsteinn G.; Gudmundsson, Kristinn; Huang, Bangqin; Isada, Tomonori; Kovač, Žarko; Lutz, Vivian A.; Marañón, Emilio; Raman, Mini; Richardson, Katherine; Rozema, Patrick D.; Poll, Willem H. van de; Segura, Valeria; Tilstone, Gavin H.; Uitz, Julia; Dongen-Vogels, Virginie van; Yoshikawa, Takashi; Sathyendranath, Shubha. (2021).Remote Sensing 12(5): 826 (2020). doi: 10.3390/rs12050826.-- pecial Issue Feature Paper Special Issue on Ocean Remote Sensing.-- 13 pages, 7 figures, 2 tables, Since the article “Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades” by Kulk et al. [1] was published, we discovered an error in the code of the primary production model, which crept in when the code was updated from the original version described by Platt and Sathyendranath (1988), Sathyendranath et al. (1995) and Longhurst et al. (1995) ([2,31,52] in [1]). The main error in the code led to a time interval for the integration of daily water-column primary production that was shorter than it should have been. As a consequence, daily surface irradiance and hence primary production were systematically underestimated by 20–25% for the entire time series. We also discovered that the Photosynthetic Active Radiation (PAR) products of the National Aeronautics and Space Administration (NASA) that were used to scale the daily light cycle were rounded down for 2003–2019 (MODIS years), which led to an additional but small underestimation of daily surface irradiance. In addition to addressing these errors, we have included a merged time series of the PAR product to remove inter-sensor biases (as described in the corrected text of Appendix B; see below). The main corrections increased our estimate of global annual primary production on average by +23.9% between 1998 and 2018, while the correction of the rounding error in the PAR products increased global annual primary production between 2003 and 2018 by +0.9%. Inclusion of the merged PAR product in the primary production model caused a −0.25% decrease in global annual primary production between 1998 and 2002 and a +0.08% increase between 2003 and 2010 (relative to the aforementioned +23.9% increase for the entire time series). Our estimate of global annual primary production between 1998 and 2018 now is 48.7 to 52.5 Gt C y−1 instead of the published estimate of 38.8 to 42.1 Gt C y−1. Although this is a substantial increase in the estimate of primary production, the results of the sensitivity analysis in which the photosynthesis versus irradiance parameters were varied by ±1 standard deviation and, importantly, the observed trends in regional and global annual primary production are largely unchanged. We therefore consider the outcomes of the study still valid after the corrections. We also note that our corrected estimate of global annual primary production is still within the range of earlier reports (32.0–70.7 Gt C y−1 [5,104] in [1]). The corrected paragraphs, tables and figures appear below. All references mentioned below can be found in the original article [1]. The corrections affect a number of results, but the nature of the corrections is largely the same: the magnitude of primary production has increased significantly everywhere, whereas the trends have been affected only marginally, and the major conclusions remain unchanged, except for the magnitude of marine primary production. The authors apologise for any inconvenience caused. The original article has been updated, This research was funded by the European Space Agency (ESA) Living Planet Fellowship programme (PICCOLO, G.K.), the Simons Foundation grant Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES, number 549947, S.S.) and the UK Natural Environment Research Council National Capability funding for the Atlantic Meridional Transect (AMT, G.H.T.). This paper is a contribution to the Ocean Colour Climate Change Initiative (OC-CCI) and Biological Pump and Carbon Exchange Processes (BICEP) projects of ESA. Additional support from the National Centre for Earth Observations (UK) is also gratefully acknowledge, With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

This study investigates sea surface salinity (SSS) and sea surface temperature (SST) variations in the tropical Atlantic east of the Lesser Antilles, a region where freshwater advection from the Amazon and Orinoco rivers, may potentially impact air-sea interaction. Observations are used to document later-summer variability and evaluate offshore riverine transport from 2010-2014. During the period 2010-2014, the largest difference in plume-affected areas, defined as the extent covered by SSS lower than 35.5 pss, is found between 2011 and 2014. Plume waters covered 92% of the study region in 2011 and 60% in 2014, with the average SSS in the study region being 2-pss lower in 2011. Lagrangian particle tracking based on satellite-derived ocean currents is used to diagnose the impact of the river plumes on SSS and SST from 2010 through 2014. Northward freshwater flux in summer 2014 was significantly weaker than fluxes in 2010-2013. This difference is not due to interannual discharge variability, but to significant changes in eddy-driven transport and cross-shore winds. In particular, the stronger cross-shore wind in May 2014 restricted offshore freshwater flow and lead to a smaller plume-affected area. Persistent SST gradients are often found near the plume edge, which may have implications for ocean-atmosphere coupling associated with atmospheric convection. SST in the study region was 1°C higher in 2010 than in other years, and is related to basin-scale ocean-atmosphere processes. Interannual variation in Amazon advective pathways and the associated SSS changes are also influenced by changes in the ITCZ position between 2011 and 2014.

We present the latest version of the TRACMASS trajectory code, version 7.0. The new version includes new features such as water tracing in the atmosphere, parameterisation scheme for sub-grid scale turbulence, generalisation of the tracer handling, etc. The code has also become more user friendly and easier to get started with. Previous versions of TRACMASS only allowed temperature, salinity and potential density to be calculated along the trajectories, but the new version allows any tracer to be followed e.g. biogeochemical tracers or chemical compounds in the atmosphere. The new parameterisation of sub-grid turbulence will enhance the kinetic energy and dispersion of trajectories in the ocean so that results from eddy-permitting ocean models (dx ∼25km) resemble those from “eddy-resolving” models (dx ∼8km). We will demonstrate some use cases of these new capabilities for atmosphere and ocean sciences. TRACMASS calculates Lagrangian trajectories offline for both the ocean and atmosphere by using already stored velocity fields, and optionally tracer fields. The velocity fields may be taken from ocean or atmosphere circulation models (e.g. NEMO, OpenIFS), reanalysis products (e.g. ERA-5) or observations (e.g. geostrophic currents from satellite altimetry). The fact that the numerical scheme in TRACMASS is mass conserving allows us to associate each trajectory with a mass transport and calculate the Lagrangian mass transport between different regions as well as construct Lagrangian stream functions. A live demonstration on how to set up, configure and run the TRACMASS code will be given.

Special Issue Feature Paper Special Issue on Ocean Remote Sensing.-- 27 pages, 8 figures, 2 tables, 2 appendixes.-- Correction: Kulk et al. Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades. Remote Sens. 2020, 12, 826. doi: 10.3390/rs13173462, Primary production by marine phytoplankton is one of the largest fluxes of carbon on our planet. In the past few decades, considerable progress has been made in estimating global primary production at high spatial and temporal scales by combining in situ measurements of primary production with remote-sensing observations of phytoplankton biomass. One of the major challenges in this approach lies in the assignment of the appropriate model parameters that define the photosynthetic response of phytoplankton to the light field. In the present study, a global database of in situ measurements of photosynthesis versus irradiance (P-I) parameters and a 20-year record of climate quality satellite observations were used to assess global primary production and its variability with seasons and locations as well as between years. In addition, the sensitivity of the computed primary production to potential changes in the photosynthetic response of phytoplankton cells under changing environmental conditions was investigated. Global annual primary production varied from 38.8 to 42.1 Gt C yr −1 over the period of 1998–2018. Inter-annual changes in global primary production did not follow a linear trend, and regional differences in the magnitude and direction of change in primary production were observed. Trends in primary production followed directly from changes in chlorophyll-a and were related to changes in the physico-chemical conditions of the water column due to inter-annual and multidecadal climate oscillations. Moreover, the sensitivity analysis in which P-I parameters were adjusted by ±1 standard deviation showed the importance of accurately assigning photosynthetic parameters in global and regional calculations of primary production. The assimilation number of the P-I curve showed strong relationships with environmental variables such as temperature and had a practically one-to-one relationship with the magnitude of change in primary production. In the future, such empirical relationships could potentially be used for a more dynamic assignment of photosynthetic rates in the estimation of global primary production. Relationships between the initial slope of the P-I curve and environmental variables were more elusive, This research was funded by the European Space Agency (ESA) Living Planet Fellowship programme (PICCOLO, G.K.), the Simons Foundation grant Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES, number 549947, S.S.) and the UK Natural Environment Research Council National Capability funding for the Atlantic Meridional Transect (AMT, G.H.T.). This paper is a contribution to the Ocean Colour Climate Change Initiative (OC-CCI) and Biological Pump and Carbon Exchange Processes (BICEP) projects of ESA. Additional support from the National Centre for Earth Observations (UK) is also gratefully acknowledge, With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

We estimated mixed layer gross and net community production on a total of 20 crossings in the Australian sector of the Southern Ocean during the summer half-years (October–March) of 2007–2010. These estimates were calculated from measurements of O 2 /Ar ratios and triple isotope compositions of O 2 in ~250 seawater samples collected underway. For comparison purposes, we also measured the seasonal drawdown of mixed layer NO 3 - and SiO 2 concentrations during 2006–2007 and 2007–2008. Across all samples, average values of gross and net O 2 production (measured by O 2 /Ar and O 2 isotopes), were about 86±90 and 18±17 mmol O 2 m −2 day −1 , respectively. Gross production was highest at the Subtropical Front (up to ~230 mmol O 2 m −2 day −1 ), and decreased southward (to ~10 near the southern boundary of the Antarctic Circumpolar Current). In contrast, net community production showed little meridional variation. Net and gross O 2 production increased throughout the spring-to-fall period, although most SiO 2 drawdown occurred in December. Consistent with satellite chlorophyll estimates, we saw no evidence for an intense spring bloom (e.g. as has been observed in the North Atlantic). Volumetric net and gross O 2 production in the mixed layer, normalized to chlorophyll, increased (with considerable scatter) with average irradiance in the mixed layer. These relationships provide a basis for estimating production from Argo float data and properties observed by satellite.

Three different trajectory schemes for oceanic and atmospheric general circulation models are compared in two different experiments. The theories of the trajectory schemes are presented showing the differential equations they solve and why they are mass conserving. One scheme assumes that the velocity fields are stationary for set intervals of time between saved model outputs and solves the trajectory path from a differential equation only as a function of space, i.e. stepwise stationary. The second scheme is a special case of the stepwise-stationary scheme, where velocities are assumed constant between general circulation model (GCM) outputs; it uses hence a fixed GCM time step. The third scheme uses a continuous linear interpolation of the fields in time and solves the trajectory path from a differential equation as a function of both space and time, i.e. a time-dependent scheme. The trajectory schemes are tested offline, i.e. using the already integrated and stored velocity fields from a GCM. The first comparison of the schemes uses trajectories calculated using the velocity fields from a high-resolution ocean general circulation model in the Agulhas region. The second comparison uses trajectories calculated using the wind fields from an atmospheric reanalysis. The study shows that using the time-dependent scheme over the stepwise-stationary scheme greatly improves accuracy with only a small increase in computational time. It is also found that with decreasing time steps the stepwise-stationary scheme becomes increasingly more accurate but at increased computational cost. The time-dependent scheme is therefore preferred over the stepwise-stationary scheme. However, when averaging over large ensembles of trajectories, the two schemes are comparable, as intrinsic variability dominates over numerical errors. The fixed GCM time step scheme is found to be less accurate than the stepwise-stationary scheme, even when considering averages over large ensembles.

We present a novel method for continuous and automated shipboard measurements of dissolved inorganic carbon concentration ([DIC]) in surface water. The method is based on dual isotope dilution and cavity ring-down spectroscopy (DID-CRDS). In this method, seawater is continuously sampled and mixed with a flow of NaH(13)CO3 solution that is also enriched in deuterated water (the spike). The isotopic composition of CO2 (δ(13)C(spiked_sample)) derived from the DIC in the mixture, and the D/H ratio of the mixed water (δD(spiked_sample)), are measured by CRDS analyzers. The D/H of the water in the mixture allows accurate estimates of the mixing ratio of the sample and the spike. [DIC] of the sample is then calculated from the mixing ratio, [DI(13)C] of the spike, and δ(13)C(spiked_sample). In the laboratory, the precision of the method is0.02% (±0.4 μmol kg(-1) when [DIC] = 2000 μmol kg(-1)). A shipboard test was conducted in the Delaware Bay and Estuary. For 2 min average [DIC], a precision of0.03% was achieved. Measurements from the DID-CRDS showed good agreement with independent measurements of discrete samples using the well-established coulometric method (mean difference = -1.14 ± 1.68 μmol kg(-1)), and the nondispersive infrared(NDIR)-based methods (mean difference = -0.9 ± 4.73 μmol kg(-1)).

Space-based observations offer unique capabilities for studying spatial and temporal dynamics of the upper ocean inorganic carbon cycle and, in turn, supporting research tied to ocean acidification (OA). Satellite sensors measuring sea surface temperature, color, salinity, wind, waves, currents, and sea level enable a fuller understanding of a range of physical, chemical, and biological phenomena that drive regional OA dynamics as well as the potentially varied impacts of carbon cycle change on a broad range of ecosystems. Here, we update and expand on previous work that addresses the benefits of space-based assets for OA and carbonate system studies. Carbonate chemistry and the key processes controlling surface ocean OA variability are reviewed. Synthesis of present satellite data streams and their utility in this arena are discussed, as are opportunities on the horizon for using new satellite sensors with increased spectral, temporal, and/or spatial resolution. We outline applications that include the ability to track the biochemically dynamic nature of water masses, to map coral reefs at higher resolution, to discern functional phytoplankton groups and their relationships to acid perturbations, and to track processes that contribute to acid variation near the land-ocean interface.

Villarino, Ernesto ... et al.-- 13 pages, 5 figures, 5 tables, supplementary material https://dx.doi.org/10.1038/s41467-017-02535-8, Global patterns of planktonic diversity are mainly determined by the dispersal of propagules with ocean currents. However, the role that abundance and body size play in determining spatial patterns of diversity remains unclear. Here we analyse spatial community structure - β-diversity - for several planktonic and nektonic organisms from prokaryotes to small mesopelagic fishes collected during the Malaspina 2010 Expedition. β-diversity was compared to surface ocean transit times derived from a global circulation model, revealing a significant negative relationship that is stronger than environmental differences. Estimated dispersal scales for different groups show a negative correlation with body size, where less abundant large-bodied communities have significantly shorter dispersal scales and larger species spatial turnover rates than more abundant small-bodied plankton. Our results confirm that the dispersal scale of planktonic and micro-nektonic organisms is determined by local abundance, which scales with body size, ultimately setting global spatial patterns of diversity, This research was funded by the project Malaspina 2010 Circumnavigation Expedition (Consolider-Ingenio 2010, CSD2008-00077) and cofounded by the Basque Government (Department Deputy of Agriculture, Fishing and Food Policy). [...] E.V. was supported by a PhD Scholarship granted by the Iñaki Goenaga−Technology Centres Foundation

We assess the ability of ocean biogeochemical models to represent seasonal structures in biomass and net community production (NCP) in the Southern Ocean. Two models are compared to observations on daily to seasonal timescales in four different sections of the region. We use daily satellite fields of chlorophyll (Chl) as a proxy for biomass and in situ observations of O2 and Ar supersaturation (ΔO2 / Ar) to estimate NCP. ΔO2 / Ar is converted to the flux of biologically generated O2 from sea to air (O2 bioflux). All data are aggregated to a climatological year with a daily resolution. To account for potential regional differences within the Southern Ocean, we conduct separate analyses of sections south of South Africa, around the Drake Passage, south of Australia, and south of New Zealand. We find that the models simulate the upper range of Chl concentrations well, underestimate spring levels significantly, and show differences in skill between early and late parts of the growing season. While there is a great deal of scatter in the bioflux observations in general, the four sectors each have distinct patterns that the models pick up. Neither model exhibits a significant distinction between the Australian and New Zealand sectors and between the Drake Passage and African sectors. South of 60° S, the models fail to predict the observed extent of biological O2 undersaturation. We suggest that this shortcoming may be due either to problems with the ecosystem dynamics or problems with the vertical transport of oxygen.

Planktonic communities are shaped through a balance of local evolutionary adaptation and ecological succession driven in large part by migration. The timescales over which these processes operate are still largely unresolved. Here we use Lagrangian particle tracking and network theory to quantify the timescale over which surface currents connect different regions of the global ocean. We find that the fastest path between two patches—each randomly located anywhere in the surface ocean—is, on average, less than a decade. These results suggest that marine planktonic communities may keep pace with climate change—increasing temperatures, ocean acidification and changes in stratification over decadal timescales—through the advection of resilient types., The adaptive capabilities of planktonic communities to climate change remain uncertain. Here, using Lagrangian particle tracking and network theory, the authors show that surface ocean currents can navigate the globe within 10 years, suggesting that marine plankton may keep pace with climate change.