Observing intermittent biological productivity and vertical carbon transports during the spring transition with BGC Argo floats in the western North Pacific

To investigate changes in ocean structure during the spring transition and responses of biological activity, two BGC-Argo floats equipped with oxygen, fluorescence (to estimate chlorophyll a concentration – Chl a), backscatter (to estimate particulate organic carbon concentration – [POC]), and nitrate sensors conducted daily vertical profiles of the water column from a depth of 2000 m to the sea surface in the western North Pacific from January to April of 2018. Data for calibrating each sensor were obtained via shipboard sampling that occurred when the floats were deployed and recovered. During the float-deployment periods, repeated meteorological disturbances passed over the study area and caused the mixed layer to deepen. After deep-mixing events, the upper layer restratified and nitrate concentrations decreased while Chl a and POC concentrations increased, suggesting that spring mixing events promote primary productivity through the temporary alleviation of nutrient and light limitation. At the end of March, POC accumulation rates and nitrate decrease rates within the euphotic zone (0–70 m) were the largest of the four events observed, ranging from +84 to +210 mmol C m−2 d−1 and –28 to –49 mmol N m−2 d−1, respectively. The subsurface consumption rate of oxygen (i.e., the degradation rate of organic matter) after the fourth event (the end of March) was estimated to be –0.62 micromol O2 kg−1 d−1. At depths of 300–400 m (below the mixed layer), the POC concentrations increased slightly throughout the observation period. The POC flux at a depth of 300 m was estimated to be 1.1 mmol C m−2 d−1. Our float observation has made it possible to observed biogeochemical parameters, which previously could only be estimated by shipboard observation and experiments, in the field and in real time.

Bridging the gaps between particulate backscattering measurements and modeled particulate organic carbon in the ocean

Oceanic particulate organic carbon (POC) is a relatively small (~4 Pg C) but dynamic component of the global carbon cycle with fast mean turnover rates compared to other oceanic, continental and atmospheric carbon stocks. Biogeochemical models historically focused on reproducing the sinking flux of POC driven by large fast-sinking particles (bPOC). However, suspended and slow-sinking particles (sPOC) typically represent 80–90 % of the POC stock, and can make important seasonal contributions to vertical fluxes through the mesopelagic layer (200–1000 m). Recent developments in the parameterization of POC reactivity in the PISCES model (PISCESv2_RC) have greatly improved its ability to capture sPOC dynamics. Here we evaluated this model by matching 3D and 1D simulations with BGC-Argo and satellite observations in globally representative ocean biomes, building on a refined scheme for converting particulate backscattering profiles measured by BGC-Argo floats to POC. We show that PISCES captures the major features of sPOC and bPOC as seen by BGC-Argo floats across a range of spatiotemporal scales, from highly resolved profile time series to biome-aggregated climatological profiles. Our results also illustrate how the comparison between the model and observations is hampered by (1) the uncertainties in empirical POC estimation, (2) the imperfect correspondence between modelled and observed variables, and (3) the bias between modelled and observed physics. Despite these limitations, we identified characteristic patterns of model-observations misfits in the mesopelagic layer of subpolar and subtropical gyres. These misfits likely result from both suboptimal model parameters and model equations themselves, pointing to the need to improve the model representation of processes with a critical influence on POC dynamics, such as sinking, remineralization, (dis)aggregation and zooplankton activity. Beyond model evaluation results, our analysis identified inconsistencies between current estimates of POC from satellite and BGC-Argo data, as well as POC partitioning into phytoplankton, heterotrophs and detritus deduced from in situ bio-optical data. Our approach can help constrain POC stocks, and ultimately budgets, in the epipelagic and mesopelagic ocean.

Observation system simulation experiments in the Atlantic Ocean for enhanced surface ocean <i>p</i>CO₂ reconstructions

To derive an optimal observation system for surface ocean pCO2 in the Atlantic Ocean and the Atlantic sector of the Southern Ocean, 11 observation system simulation experiments (OSSEs) were completed. Each OSSE is a feedforward neural network (FFNN) that is based on a different data distribution and provides ocean surface pCO2 for the period 2008–2010 with a 5 d time interval. Based on the geographical and time positions from three observational platforms, volunteering observing ships, Argo floats and OceanSITES moorings, pseudo-observations were constructed using the outputs from an online-coupled physical–biogeochemical global ocean model with 0.25∘ nominal resolution. The aim of this work was to find an optimal spatial distribution of observations to supplement the widely used Surface Ocean CO2 Atlas (SOCAT) and to improve the accuracy of ocean surface pCO2 reconstructions. OSSEs showed that the additional data from mooring stations and an improved coverage of the Southern Hemisphere with biogeochemical ARGO floats corresponding to least 25 % of the density of active floats (2008–2010) (OSSE 10) would significantly improve the pCO2 reconstruction and reduce the bias of derived estimates of sea–air CO2 fluxes by 74 % compared to ocean model outputs.

Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model

Sea surface salinity (SSS) satellite measurements are validated using in situ observations usually made by surfacing Argo floats. Validation statistics are computed using matched values of SSS from satellites and floats. This study explores how the matchup process is done using a high-resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values being greater than satellites. RMSD is computed using an “all salinity difference” method that averages level 2 satellite observations within a given time and space window for comparison with Argo floats. RMSD values range from 0.08 to 0.18 depending on the space–time window and the satellite. This range gives an estimate of the representation error inherent in comparing single point Argo floats to area-average satellite values. The study has implications for future SSS satellite missions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS values.

Radiometry on Argo Floats: From the Multispectral State-of-the-Art on the Step to Hyperspectral Technology

Over the past two decades, robotic technology such as Argo floats have revolutionized operational autonomous measurement of the oceans. Recently, Biogeochemical Argo floats (BGC-Argo floats) have measured optical and biogeochemical quantities down to a depth of 2,000 m. Among these parameters, are measurements of the underwater light field from which apparent optical properties (AOPs), such as the diffuse attenuation coefficient for downwelling irradiance Kd(λ), can be derived. Presently, multispectral observations are available on this platform at three wavelengths (with 10–20 nm bandwidths) in the ultraviolet and visible part of the spectrum plus the Photosynthetically Available Radiation (PAR; integrated radiation between 400 and 700 nm). This article reviews studies dealing with these radiometric observations and presents the current state-of-the-art in Argo radiometry. It focus on the successful portability of radiometers onboard Argo float platforms and covers applications of the obtained data for bio-optical modeling and ocean color remote sensing. Generating already high-quality datasets in the existing configuration, the BGC-Argo program must now investigate the potential to incorporate hyperspectral technology. The possibility to acquire hyperspectral information and the subsequent development of new algorithms that exploit these data will open new opportunities for bio-optical long-term studies of global ocean processes, but also present new challenges to handle and process increased amounts of data.

Matchup Characteristics of Sea Surface Salinity using a High-resolution Ocean Model

Sea surface salinity (SSS) satellite measurements are validated using in situ observations 8 usually made by surfacing Argo floats. Validation statistics are computed using matched values of 9 SSS from satellites and floats. This study explores how the matchup process is done using a high- 10 resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the 11 Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped 12 up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference 13 (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values 14 being greater than satellites. RMSD is computed using an “all salinity difference” method that av- 15 erages level 2 satellite observations within a given time and space window for comparison with 16 Argo floats. RMSD values range from 0.08 to 0.18 depending on the space-time window and the 17 satellite. This range gives an estimate of the representation error inherent in comparing single point 18 Argo floats to area-average satellite values. The study has implications for future SSS satellite mis- 19 sions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS 20 values.

Geophysical and biogeochemical observations using BGC Argo floats in the western North Pacific during late winter and early spring. Part 1: Restratification processes of the surface mixed layer

To understand oceanic restratification in the subtropical northwestern Pacific and its influence on biogeochemical (BGC) processes, we examined post-storm restratification events observed from February to April 2018 by BGC-Argo floats, the BGC data from which were stoichiometrically analyzed by Sukigara et al. (2021; this issue). We found that during these events, restratification of the mixed layer (ML) was driven by geostrophic adjustment or ML eddy formation related to surface cooling during February to March. At the end of March, high surface chlorophyll a concentrations were observed within submesoscale eddies and at the edge of a mesoscale cyclonic feature observed from satellite data. Our results indicate that primary production in the subtropical northwestern Pacific is enhanced by the combined effects of mesoscale upwelling, storm-driven formation of a deep ML, subsequent formation of ML eddies, weak cooling, and the length of intervals between storms.

Geophysical and biogeochemical observations using BGC Argo floats in the western North Pacific during late winter and early spring, Part 2: Biological processes during restratification periods in the euphotic and twilight layers

Two Argo floats equipped with oxygen, chlorophyll (Chl), backscatter, and nitrate sensors conducted daily vertical profiles of the water column from a depth of 2000 m to the sea surface in the western North Pacific from January to April of 2018. Data for calibrating each sensor were obtained via shipboard sampling that occurred when the floats were deployed and recovered. Float backscatter observations were converted to particulate organic carbon (POC) concentrations using an empirical relationship derived from contemporaneous float profiles of backscatter and shipboard observations of suspended organic carbon particles. During the float deployment periods, repeated meteorological disturbances (storms) passed over the study area and caused the mixed layer to deepen. During these events, nitrate was entrained from deeper layers into the surface mixed layer, while Chl and POC in the surface mixed layer were redistributed into deeper layers. After the storms, the upper layer gradually restratified, nitrate concentrations in the surface layer decreased, and Chl and POC concentrations increased. When the floats observed the same water mass, the net community production within the euphotic layer (0–70 m), determined from the increases in POC, was 126–664 mg C m−2 d−1 (10.5–55.3 mmol C m−2 d−1) close to the values reported from a nearby area. The C/N ratio of the increase in POC and the decrease in nitrate was closed to the Redfield ratio, which indicates that the sensors were able to observe the net biochemical processes in this area despite the relatively low concentrations of nitrate and POC. To determine the fate of particles transported from the surface ocean to the twilight layer, the ratio of oxygen consumption and nitrate regeneration rates were compared. This O2/N ratio approached the Redfield ratio when the floats followed the same water mass continuously, but the consumption rate of POC was significantly lower than what would be expected based on the oxygen consumption and nitrate release rates. This suggests that dissolved organic carbon was the main substrate for the respiration in the twilight layer.