Global Ocean Surface Research Articles

Towards the construction of regional marine radiocarbon calibration curves: an unsupervised machine learning approach

Abstract. Radiocarbon may serve as a powerful dating tool in palaeoceanography, but its accuracy is limited by the need to calibrate radiocarbon dates to calendar ages. A key problem is that marine radiocarbon dates must be corrected for past offsets from either the contemporary atmosphere (i.e. “reservoir age” offsets) or a modelled estimate of the global average surface ocean (i.e. delta-R offsets). This presents a challenge because the spatial distribution of reservoir ages and delta-R offsets can vary significantly, particularly over periods of major marine hydrographic and/or carbon cycle change such as the last deglaciation. Modern reservoir age and delta-R estimates therefore have limited applicability. While forward modelling of past R-age variability has been proposed as a means of resolving this problem, this requires accurate a priori knowledge of past global radiocarbon budget closure (i.e. production, and cycling), which we currently lack. In this context, the construction of empirical regional marine calibration curves could provide a way forward. However, the spatial reach of such calibrations and their robustness subject to (uncertain) temporal changes in climate and ocean circulation would need to be tested. Here, we use unsupervised machine learning techniques to define distinct regions of the surface ocean that exhibit coherent behaviour in terms of their radiocarbon age offsets from the contemporary atmosphere (R ages), regardless of the causes of R-age variability. We apply multiple algorithms (k-means, k-medoids, and hierarchical clustering) to outputs from two different numerical models spanning a range of climate states, forcings, and timescales of adjustment. Comparisons between the cluster assignments across model runs confirm some robust regional patterns that likely stem from constraints imposed by large-scale ocean and atmospheric physics. At the coarsest scale, regions of coherent R-age variability correspond to the major ocean basins. By further dividing basin-scale shape-based clusters into amplitude-based subclusters, we recover regional associations, such as increased high-latitude R ages, or the propagation of R-age anomalies from regions of deep mixing in the Southern Ocean to upwelling sites in the eastern equatorial Pacific, which cohere with known modern oceanographic processes. We show that the medoids (i.e. the most representative locations) for these regional sub-clusters provide significantly better approximations of simulated local R-age variability than constant offsets from the global surface average. This remains true when cluster assignments obtained from one model simulation are applied to simulated R-age time series from another. Further, model-based clusters are found to be broadly consistent with existing reservoir age reconstructions that span the last ∼30 kyr. We therefore propose that machine learning provides a promising approach to the problem of defining regions for which empirical marine radiocarbon calibration curves may eventually be generated.

Satellite retrieval of oceanic particulate organic carbon: Towards an accurate and seamless dataset for the global ocean

Particulate organic carbon (POC) plays crucial roles in the global ocean carbon cycle and the oceanic biological pump. Satellite remote sensing has been demonstrated to be an effective technique for the retrieval of surface oceanic POC concentration. However, the complex spatiotemporal variations of the relationships between POC and oceanic optical properties across different waters posed challenges for accurate retrieval of POC concentration from satellite observations. Additionally, interference factors, such as cloud cover and sun glint, resulted in severe data missing problems and impeding daily coverage of the global ocean. With an attempt to generate accurate, seamless and readily available POC products for the global ocean, this study aimed to develop accurate satellite POC retrieval models for the Moderate Resolution Imaging Spectroradiometer (MODIS) data from both Terra and Aqua satellites, and to explore the possibility of using the empirical orthogonal function interpolation technique (DINEOF) to reconstruct satellite-retrieved POC data to generate gap-free global oceanic POC products. Results showed that the eXtreme Gradient Boosting (XGBoost) method could accurately retrieve POC with R2 approximately 0.80 and RMSE about 0.20 in log10 scale, obviously outperforming the operational blue-to-green band ratio algorithm and the hybrid polynomial algorithm based on two multi-band indices; and the DINEOF method, which could reconstruct approximately 88 % missing pixels for the global ocean, contributed to better revealing the global oceanic POC variations at a daily scale than the satellite-retrieved POC products. Based on the developed models, a suit of long time-series accurate and seamless POC products of the global surface ocean were generated, which is readily available for other applications and should be helpful to investigate the spatiotemporal variations of POC concentrations over global ocean and its roles in the global carbon cycle. The generated seamless products are openly accessible via the DOIs listed in the data availability section.

Effects of climate warming on energetics and habitat of the world's largest marine ectotherm

Responses of organisms to climate warming are variable and complex. Effects on species distributions are already evident and mean global surface ocean temperatures are likely to warm by up to 4.1 °C by 2100, substantially impacting the physiology and distributions of ectotherms. The largest marine ectotherm, the whale shark Rhincodon typus, broadly prefers sea surface temperatures (SST) ranging from 23 to 30 °C. Whole-species distribution models have projected a poleward range shift under future scenarios of climate change, but these models do not consider intraspecific variation or phenotypic plasticity in thermal limits when modelling species responses, and the impact of climate warming on the energetic requirements of whale sharks is unknown. Using a dataset of 111 whale shark movement tracks from aggregation sites in five countries across the Indian Ocean and the latest Earth-system modelling produced from Coupled Model Intercomparison Project Phase 6 for the Intergovernmental Panel on Climate Change, we examined how SST and total zooplankton biomass, their main food source, may change in the future, and what this means for the energetic balance and extent of suitable habitat for whale sharks. Earth System Models, under three Shared Socioeconomic Pathways (SSPs; SSP1–2.6, SSP3–7.0 and SSP5–8.5), project that by 2100 mean SST in four regions where whale shark aggregations are found will increase by up to 4.9 °C relative to the present, while zooplankton biomass will decrease. This reduction in zooplankton is projected to be accompanied by an increase in the energetic requirements of whale sharks because warmer water temperatures will increase their metabolic rate. We found marked differences in projected changes in the extent of suitable habitat when comparing a whole-species distribution model to one including regional variation. This suggests that the conventional approach of combining data from different regions within a species' distribution could underestimate the amount of local adaptation in populations, although parameterising local models could also suffer from having insufficient data and lead to model mis-specification or highly uncertain estimates. Our study highlights the need for further research into whale shark thermal tolerances and energetics, the complexities involved in projecting species responses to climate change, and the potential importance of considering intraspecific variation when building species distribution models.

The importance of adding unbiased Argo observations to the ocean carbon observing system

The current coverage of direct, high-quality ship-based observations of surface ocean pCO2 includes large gaps in time and space, and has been declining since 2017. These ocean observations provide the basis for the data products that reconstruct surface ocean pCO2 and estimate ocean carbon uptake. Improved data coverage is needed to advance our understanding of the ocean carbon sink and air–sea CO2 exchange. Targeted sampling from autonomous platforms, such as biogeochemical floats, combined with traditional shipboard measurements represents a promising path forward to improve surface ocean pCO2 reconstructions. However, floats provide indirect pCO2 estimates derived from pH, and thus have higher uncertainty and are biased compared to direct shipboard measurements. Here, we use a Large Ensemble Testbed (LET) of Earth System Models and the pCO2-Residual method to reconstruct surface ocean pCO2 globally to test the impact of additional float observations, both with and without measurement uncertainties. Through comparison to the ‘model truth’, the LET allows for robust evaluation of the reconstructions. With only shipboard sampling, surface ocean pCO2 is overestimated, and the 2000–2016 global ocean carbon sink is underestimated by 0.1 Pg C year−1. Additional float observations significantly reduce this underestimation, and deviate from the ‘model truth’ by as little as 0.01 Pg C year−1, even when floats have random uncertainties of ± 11 μatm. However, systematic bias in the float observations significantly degrades the accuracy of pCO2 reconstructions, leading to an even stronger underestimation of the global ocean carbon sink of up to 0.32 Pg C year−1. We conclude that adding float-based observations to the global observing system can significantly improve reconstructions of global surface ocean pCO2, but only if these data are unbiased.

The global distribution and climate resilience of marine heterotrophic prokaryotes

Heterotrophic Bacteria and Archaea (prokaryotes) are a major component of marine food webs and global biogeochemical cycles. Yet, there is limited understanding about how prokaryotes vary across global environmental gradients, and how their global abundance and metabolic activity (production and respiration) may be affected by climate change. Using global datasets of prokaryotic abundance, cell carbon and metabolic activity we reveal that mean prokaryotic biomass varies by just under 3-fold across the global surface ocean, while total prokaryotic metabolic activity increases by more than one order of magnitude from polar to tropical coastal and upwelling regions. Under climate change, global prokaryotic biomass in surface waters is projected to decline ~1.5% per °C of warming, while prokaryotic respiration will increase ~3.5% ( ~ 0.85 Pg C yr−1). The rate of prokaryotic biomass decline is one-third that of zooplankton and fish, while the rate of increase in prokaryotic respiration is double. This suggests that future, warmer oceans could be increasingly dominated by prokaryotes, diverting a growing proportion of primary production into microbial food webs and away from higher trophic levels as well as reducing the capacity of the deep ocean to sequester carbon, all else being equal.

Model exploration of microplastic effects on zooplankton grazing reveal potential impacts on the global carbon cycle

Amongst the increasing number of anthropogenic stress factors threatening ocean equilibrium, microplastics (MP; < 5 mm) have emerged as particularly worrisome. In situ observations have shown that MP accumulate in large areas at the surface ocean where it may threaten the functioning marine species. In particular, experimental evidence has shown that the grazing rates of several zooplankton species may be significantly altered by MP. These direct impacts on zooplankton may alter nutrient and carbon cycling. However, how these laboratory results may translate into impacts on the global ocean is yet unknown. Here, we use a global coupled physical-biogeochemical model including MP (NEMO/PISCES-PLASTIC) to investigate the impacts of MP exposure on zooplankton grazing rates. Drawing from experimental results, we use varying water contamination impact thresholds to explore the biogeochemical consequences of MP impacts on short (10 years) and long timescales (100 years). Our simulations show that the geographical extent of MP impacts on zooplankton remains restricted to about 10% of the global ocean surface, even after 100 years of constant MP contamination. However, in the most contaminated regions (e.g. the sub-tropical gyres), [MP] has surged from a few mg m−3 to > 50 mg m−3. Despite their oligotrophic nature and limited contribution to the overall ocean carbon cycle, MP impacts on zooplankton grazing could disrupt carbon cycling in these highly contaminated regions (up to 50% reduction in yearly primary production, carbon export fluxes and organic matter remineralisation after 100 years). Our research suggests that persistent MP pollution in the ocean could diminish primary production by 4%. In spite of the large sensitivity of our results to the water contamination impact threshold, we suggest MP impacts on zooplankton grazing may cause an annual loss of 1 Gt yr−1 of exported carbon after 100 years, if MP inputs remain constant globally.

Slow biological microplastics removal under ocean pollution phase-out trajectories

Microplastics are ubiquitous in marine environments and can be incorporated into biological aggregates including marine snows and faecal pellets. These aggregates are suspected to be a major removal mechanism for microplastics from the surface ocean, transporting them to deeper levels and the seafloor as they sink and remineralise. However, simple budget calculations, observations, and model parameter testing suggest that aggregation might also lead to retention of microplastics in the upper ocean, sustaining contamination in biologically-productive environments. The ability of the biological microplastic sink to reduce water column contamination has relevance to the setting of ocean plastics pollution reduction targets, as are currently under negotiation by the International Negotiating Committee of the United Nations Environment Assembly (UNEA). Here we apply eight idealised global pollution reduction trajectories, from 1%–100% per year, starting from the year 2026 and ending in the year 2100 to an Earth System Climate Model with a representation of ocean microplastics and their aggregation in biological particles. We find that the global ocean microplastic inventory and surface concentrations stabilize within this century for reduction rates exceeding 5% per year but the inventory does not substantially decrease under any trajectory. Furthermore, microplastics are retained by marine biology in the surface ocean, where concentrations stabilise to a non-zero value over decades. Lastly we find that irrespective of scenario, contamination of deeper ocean layers continues to increase for the duration of our simulations via the export of microplastics by biological aggregates. These results suggest that ambitious targets for pollution reduction exceeding 5% per year will be required to progress the resolution of the UNEA to ‘end plastic pollution’ in this century, and that ongoing microplastic contamination of the marine food web may be unavoidable.

The carbonate pump feedback on alkalinity and the carbon cycle in the 21st century and beyond

Abstract. Ocean acidification is likely to impact all stages of the ocean carbonate pump, i.e. the production, export, dissolution and burial of biogenic CaCO3. However, the associated feedback on anthropogenic carbon uptake and ocean acidification has received little attention. It has previously been shown that Earth system model (ESM) carbonate pump parameterizations can affect and drive biases in the representation of ocean alkalinity, which is critical to the uptake of atmospheric carbon and provides buffering capacity towards associated acidification. In the sixth phase of the Coupled Model Intercomparison Project (CMIP6), we show divergent responses of CaCO3 export at 100 m this century, with anomalies by 2100 ranging from −74 % to +23 % under a high-emission scenario. The greatest export declines are projected by ESMs that consider pelagic CaCO3 production to depend on the local calcite/aragonite saturation state. Despite the potential effects of other processes on alkalinity, there is a robust negative correlation between anomalies in CaCO3 export and salinity-normalized surface alkalinity across the CMIP6 ensemble. Motivated by this relationship and the uncertainty in CaCO3 export projections across ESMs, we perform idealized simulations with an ocean biogeochemical model and confirm a limited impact of carbonate pump anomalies on 21st century ocean carbon uptake and acidification. However, we highlight a potentially abrupt shift, between 2100 and 2300, in the dissolution of CaCO3 from deep to subsurface waters when the global-scale mean calcite saturation state reaches about 1.23 at 500 m (likely when atmospheric CO2 reaches 900–1100 ppm). During this shift, upper ocean acidification due to anthropogenic carbon uptake induces deep ocean acidification driven by a substantial reduction in CaCO3 deep dissolution following its decreased export at depth. Although the effect of a diminished carbonate pump on global ocean carbon uptake and surface ocean acidification remains limited until 2300, it can have a large impact on regional air–sea carbon fluxes, particularly in the Southern Ocean.

Global ocean surface and subsurface temperature forecast skill over subseasonal to seasonal timescales

Global ocean surface and subsurface temperature forecast skill over subseasonal to seasonal timescales

Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling

Abstract. The Southern Ocean plays an important role in the exchange of carbon between the atmosphere and oceans and is a critical region for the ocean uptake of anthropogenic CO2. However, estimates of the Southern Ocean air–sea CO2 flux are highly uncertain due to limited data coverage. Increased sampling in winter and across meridional gradients in the Southern Ocean may improve machine learning (ML) reconstructions of global surface ocean pCO2. Here, we use a large ensemble test bed (LET) of Earth system models and the “pCO2-Residual” reconstruction method to assess improvements in pCO2 reconstruction fidelity that could be achieved with additional autonomous sampling in the Southern Ocean added to existing Surface Ocean CO2 Atlas (SOCAT) observations. The LET allows for a robust evaluation of the skill of pCO2 reconstructions in space and time through comparison to “model truth”. With only SOCAT sampling, Southern Ocean and global pCO2 are overestimated, and thus the ocean carbon sink is underestimated. Incorporating uncrewed surface vehicle (USV) sampling increases the spatial and seasonal coverage of observations within the Southern Ocean, leading to a decrease in the overestimation of pCO2. A modest number of additional observations in Southern Hemisphere winter and across meridional gradients in the Southern Ocean leads to an improvement in reconstruction bias and root-mean-squared error (RMSE) of as much as 86 % and 16 %, respectively, as compared to SOCAT sampling alone. Lastly, the large decadal variability of air–sea CO2 fluxes shown by SOCAT-only sampling may be partially attributable to undersampling of the Southern Ocean.

Synthesis Product for Ocean Time Series (SPOTS) – a ship-based biogeochemical pilot

Abstract. The presented pilot for the Synthesis Product for Ocean Time Series (SPOTS) includes data from 12 fixed ship-based time-series programs. The related stations represent unique open-ocean and coastal marine environments within the Atlantic Ocean, Pacific Ocean, Mediterranean Sea, Nordic Seas, and Caribbean Sea. The focus of the pilot has been placed on biogeochemical essential ocean variables: dissolved oxygen, dissolved inorganic nutrients, inorganic carbon (pH, total alkalinity, dissolved inorganic carbon, and partial pressure of CO2), particulate matter, and dissolved organic carbon. The time series used include a variety of temporal resolutions (monthly, seasonal, or irregular), time ranges (10–36 years), and bottom depths (80–6000 m), with the oldest samples dating back to 1983 and the most recent one corresponding to 2021. Besides having been harmonized into the same format (semantics, ancillary data, units), the data were subjected to a qualitative assessment in which the applied methods were evaluated and categorized. The most recently applied methods of the time-series programs usually follow the recommendations outlined by the Bermuda Time Series Workshop report (Lorenzoni and Benway, 2013), which is used as the main reference for “method recommendations by prevalent initiatives in the field”. However, measurements of dissolved oxygen and pH, in particular, still show room for improvement. Additional data quality descriptors include precision and accuracy estimates, indicators for data variability, and offsets compared to a reference and widely recognized data product for the global ocean: the GLobal Ocean Data Analysis Project (GLODAP). Generally, these descriptors indicate a high level of continuity in measurement quality within time-series programs and a good consistency with the GLODAP data product, even though robust comparisons to the latter are limited. The data are available as (i) a merged comma-separated file that is compliant with the World Ocean Circulation Experiment (WOCE) exchange format and (ii) a format dependent on user queries via the Environmental Research Division's Data Access Program (ERDDAP) server of the Global Ocean Observing System (GOOS). The pilot increases the data utility, findability, accessibility, interoperability, and reusability following the FAIR philosophy, enhancing the readiness of biogeochemical time series. It facilitates a variety of applications that benefit from the collective value of biogeochemical time-series observations and forms the basis for a sustained time-series living data product, SPOTS, complementing relevant products for the global interior ocean carbon data (GLobal Ocean Data Analysis Project), global surface ocean carbon data (Surface Ocean CO2 Atlas; SOCAT), and global interior and surface methane and nitrous oxide data (MarinE MethanE and NiTrous Oxide product). Aside from the actual data compilation, the pilot project produced suggestions for reporting metadata, implementing quality control measures, and making estimations about uncertainty. These recommendations aim to encourage the community to adopt more consistent and uniform practices for analysis and reporting and to update these practices regularly. The detailed recommendations, links to the original time-series programs, the original data, their documentation, and related efforts are available on the SPOTS website. This site also provides access to the data product (DOI: https://doi.org/10.26008/1912/bco-dmo.896862.2, Lange et al., 2024) and ancillary data.

Genome-scale community modelling reveals conserved metabolic cross-feedings in epipelagic bacterioplankton communities

Marine microorganisms form complex communities of interacting organisms that influence central ecosystem functions in the ocean such as primary production and nutrient cycling. Identifying the mechanisms controlling their assembly and activities is a major challenge in microbial ecology. Here, we integrated Tara Oceans meta-omics data to predict genome-scale community interactions within prokaryotic assemblages in the euphotic ocean. A global genome-resolved co-activity network revealed a significant number of inter-lineage associations across diverse phylogenetic distances. Identified co-active communities include species displaying smaller genomes but encoding a higher potential for quorum sensing, biofilm formation, and secondary metabolism. Community metabolic modelling reveals a higher potential for interaction within co-active communities and points towards conserved metabolic cross-feedings, in particular of specific amino acids and group B vitamins. Our integrated ecological and metabolic modelling approach suggests that genome streamlining and metabolic auxotrophies may act as joint mechanisms shaping bacterioplankton community assembly in the global ocean surface.

Retaining Short‐Term Variability Reduces Mean State Biases in Wind Stress Overriding Simulations

AbstractPositive feedbacks in climate processes can make it difficult to identify the primary drivers of climate phenomena. Some recent global climate model (GCM) studies address this issue by controlling the wind stress felt by the surface ocean such that the atmosphere and ocean become mechanically decoupled. Most mechanical decoupling studies have chosen to override wind stress with an annual climatology. In this study we introduce an alternative method of interannually varying overriding which maintains higher frequency momentum forcing of the surface ocean. Using a GCM (NCAR CESM1), we then assess the size of the biases associated with these two methods of overriding by comparing with a freely evolving control integration. We find that overriding with a climatology creates sea surface temperature (SST) biases throughout the global oceans on the order of ±1°C. This is substantially larger than the biases introduced by interannually varying overriding, especially in the tropical Pacific. We attribute the climatological overriding SST biases to a lack of synoptic and subseasonal variability, which causes the mixed layer to be too shallow throughout the global surface ocean. This shoaling of the mixed layer reduces the effective heat capacity of the surface ocean such that SST biases excite atmospheric feedbacks. These results have implications for the reinterpretation of past climatological wind stress overriding studies: past climate signals attributed to momentum coupling may in fact be spurious responses to SST biases.

CMEMS-LSCE: a global, 0.25°, monthly reconstruction of the surface ocean carbonate system

Abstract. Observation-based data reconstructions of global surface ocean carbonate system variables play an essential role in monitoring the recent status of ocean carbon uptake and ocean acidification, as well as their impacts on marine organisms and ecosystems. So far, ongoing efforts are directed towards exploring new approaches to describe the complete marine carbonate system and to better recover its fine-scale features. In this respect, our research activities within the Copernicus Marine Environment Monitoring Service (CMEMS) aim to develop a sustainable production chain of observation-derived global ocean carbonate system datasets at high space–time resolutions. As the start of the long-term objective, this study introduces a new global 0.25∘ monthly reconstruction, namely CMEMS-LSCE (Laboratoire des Sciences du Climat et de l'Environnement) for the period 1985–2021. The CMEMS-LSCE reconstruction derives datasets of six carbonate system variables, including surface ocean partial pressure of CO2 (pCO2), total alkalinity (AT), total dissolved inorganic carbon (CT), surface ocean pH, and saturation states with respect to aragonite (Ωar) and calcite (Ωca). Reconstructing pCO2 relies on an ensemble of neural network models mapping gridded observation-based data provided by the Surface Ocean CO2 ATlas (SOCAT). Surface ocean AT is estimated with a multiple-linear-regression approach, and the remaining carbonate variables are resolved by CO2 system speciation given the reconstructed pCO2 and AT; 1σ uncertainty associated with these estimates is also provided. Here, σ stands for either the ensemble standard deviation of pCO2 estimates or the total uncertainty for each of the five other variables propagated through the processing chain with input data uncertainty. We demonstrate that the 0.25∘ resolution pCO2 product outperforms a coarser spatial resolution (1∘) thanks to higher data coverage nearshore and a better description of horizontal and temporal variations in pCO2 across diverse ocean basins, particularly in the coastal–open-ocean continuum. Product qualification with observation-based data confirms reliable reconstructions with root-mean-square deviation from observations of less than 8 %, 4 %, and 1 % relative to the global mean of pCO2, AT (CT), and pH. The global average 1σ uncertainty is below 5 % and 8 % for pCO2 and Ωar (Ωca), 2 % for AT and CT, and 0.4 % for pH relative to their global mean values. Both model–observation misfit and model uncertainty indicate that coastal data reproduction still needs further improvement, wherein high temporal and horizontal gradients of carbonate variables and representative uncertainty from data sampling would be taken into account as a priority. This study also presents a potential use case of the CMEMS-LSCE carbonate data product in tracking the recent state of ocean acidification. The data associated with this study are available at https://doi.org/10.14768/a2f0891b-763a-49e9-af1b-78ed78b16982 (Chau et al., 2023).

Iron limitation of heterotrophic bacteria in the California Current System tracks relative availability of organic carbon and iron.

Iron is an essential nutrient for all microorganisms of the marine environment. Iron limitation of primary production has been well documented across a significant portion of the global surface ocean, but much less is known regarding the potential for iron limitation of the marine heterotrophic microbial community. In this work, we characterize the transcriptomic response of the heterotrophic bacterial community to iron additions in the California Current System, an eastern boundary upwelling system, to detect in situ iron stress of heterotrophic bacteria. Changes in gene expression in response to iron availability by heterotrophic bacteria were detected under conditions of high productivity when carbon limitation was relieved but when iron availability remained low. The ratio of particulate organic carbon to dissolved iron emerged as a biogeochemical proxy for iron limitation of heterotrophic bacteria in this system. Iron stress was characterized by high expression levels of iron transport pathways and decreased expression of iron-containing enzymes involved in carbon metabolism, where a majority of the heterotrophic bacterial iron requirement resides. Expression of iron stress biomarkers, as identified in the iron-addition experiments, was also detected insitu. These results suggest iron availability will impact the processing of organic matter by heterotrophic bacteria with potential consequences for the marine biological carbon pump.

WSPTGAN for Global Ocean Surface Wind Speed Generation With High Temporal Resolution and Spatial Coverage

WSPTGAN for Global Ocean Surface Wind Speed Generation With High Temporal Resolution and Spatial Coverage

Spatiotemporal reconstruction of global ocean surface pCO2 based on optimized random forest

The partial pressure of ocean surface CO2 (pCO2) plays an important role in quantifying the carbon budget and assessing ocean acidification. For such a vast and complex ocean system as the global ocean, most current research practices tend to study the ocean into regions. In order to reveal the overall characteristics of the global ocean and avoid mutual influence between zones, a holistic research method was used to detect the correlation of twelve predictive factors, including chlorophyll concentration (Chlor_a), diffuse attenuation coefficient at 490 nm (Kd_490), density ocean mixed layer thickness (Mlotst), eastward velocity (East), northward velocity (North), salinity (Sal), temperature (Temp), dissolved iron (Fe), dissolved silicate (Si), nitrate (NO3), potential of hydrogen (pH), phosphate (PO4), at the global ocean scale. Based on measured data from the Global Surface pCO2 (LDEO) database, combined with National Aeronautics and Space Administration (NASA) Ocean Color satellite data and Copernicus Ocean reanalysis data, an improved optimized random forest (ORF) method is proposed for the overall reconstruction of global ocean surface pCO2, and compared with various machine learning methods. The results indicate that the ORF method is the most accurate in overall modeling at the global ocean scale (mean absolute error of 6.27μatm, root mean square error of 15.34μatm, R2 of 0.92). Based on independent observations from the LDEO dataset and time series observation stations, the ORF model was further validated, and the global ocean surface pCO2 distribution map of 0.25° × 0.25° for 2010 to 2019 was reconstructed, which is of significance for the global ocean carbon cycle and carbon assessment.

Global Patterns of Surface Ocean Dissolved Organic Matter Stoichiometry

AbstractSurface ocean marine dissolved organic matter (DOM) serves as an important reservoir of carbon (C), nitrogen (N), and phosphorus (P) in the global ocean, and is produced and consumed by both autotrophic and heterotrophic communities. While prior work has described distributions of dissolved organic carbon (DOC) and nitrogen (DON) concentrations, our understanding of DOC:DON:DOP stoichiometry in the global surface ocean has been limited by the availability of DOP concentration measurements. Here, we estimate mean surface ocean bulk and semi‐labile DOC:DON:DOP stoichiometry in biogeochemically and geographically defined regions using newly available marine DOM concentration databases. Global mean surface ocean bulk (C:N:P = 387:26:1) and semi‐labile (C:N:P = 179:20:1) DOM stoichiometries are higher than Redfield stoichiometry, with semi‐labile DOM stoichiometry similar to that of global mean surface ocean particulate organic matter (C:N:P = 160:21:1) reported in a recent compilation. DOM stoichiometry varies across ocean basins, ranging from 251:17:1 to 638:43:1 for bulk and 83:15:1 to 414:49:1 for semi‐labile DOM C:N:P, respectively. Surface ocean DOP concentration exhibits larger relative changes than DOC and DON, driving surface ocean gradients in DOC:DON:DOP stoichiometry. Inferred autotrophic consumption of DOP helps explain intra‐ and inter‐basin patterns of marine DOM C:N:P stoichiometry, with regional patterns of water column denitrification and iron supply influencing the biogeochemical conditions favoring DOP use as an organic nutrient. Specifically, surface ocean marine DOM exhibits increasingly P‐depleted stoichiometries from east to west in the Pacific and from south to north in the Atlantic, consistent with patterns of increasing P stress and alleviated iron stress.

A 37-year record of ocean acidification in the Southern California current

Long-term ocean time series have proven to be the most robust approach for direct observation of climate change processes such as Ocean Acidification. The California Cooperative Oceanic Fisheries Investigations (CalCOFI) program has collected quarterly samples for seawater inorganic carbon since 1983. The longest time series is at CalCOFI line 90 station 90 from 1984–present, with a gap from 2002 to 2008. Here we present the first analysis of this 37- year time series, the oldest in the Pacific. Station 90.90 exhibits an unambiguous acidification signal in agreement with the global surface ocean (decrease in pH of −0.0015 ± 0.0001 yr−1), with a distinct seasonal cycle driven by temperature and total dissolved inorganic carbon. This provides direct evidence that the unique carbon chemistry signature (compared to other long standing time series) results in a reduced uptake rate of carbon dioxide (CO2) due to proximity to a mid-latitude eastern boundary current upwelling zone. Comparison to an independent empirical model estimate and climatology at the same location reveals regional differences not captured in the existing models.

Global climate-change trends detected in indicators of ocean ecology

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, Rrs), because Rrs is multivariate and some wavebands have low interannual variability. We analyse a 20-year Rrs time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite, and find significant trends in Rrs for 56% of the global surface ocean, mainly equatorward of 40°. The climate-change signal in Rrs 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.