Bio-Optical Sensors on Argo Floats

The general objective of the IOCCG BIO-Argo working group is to elaborate recommendations for establishing a framework for the future development of a cost-effective, bio-optical float network corresponding to the needs and expectations of the scientific community. In this context, our recommendations will necessarily be broad; they range from the identification of key bio-optical measurements to be implemented on floats, to the real-time management of the data flux resulting from the deployment of a "fleet of floats". Each chapter of this report is dedicated to an essential brick leading towards the goal of implementing a bio-optical profiling float network. The following topics are discussed in the Chapters listed below: - Chapter 2 reviews the scientific objectives that could be tackled through the development of such networks, by allowing some of the gaps in the present spatio-temporal resolution of bio-optical variables to be progressively filled. - Chapter 3 identifies the optical and bio-optical properties that are now amenable to remote and autonomous measurement through the use of optical sensors mounted on floats. - Chapter 4 addresses the question of sensor requirements, in particular with respect to measurements performed from floats. - Chapter 5 proposes and argues for the development of dedicated float missions corresponding to specific scientific objectives and relying on specific optical sensor suites, as well as on specific modes of float operation. - Chapter 6 identifies technological issues that need to be addressed for the various bio-optical float missions to become even more cost-effective. - Chapter 7 covers all aspects of data treatment ranging from the development of various quality control procedures (from real-time to delayed mode) to the architecture required for favoring easy access to data. - Chapter 8 reviews the necessary steps and experience required before the operational implementation of different types of float networks can become a reality.JRC.H.5-Land Resources Managemen

An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O2 Data Using Bayesian Neural Networks

This work presents two new methods to estimate oceanic alkalinity (AT), dissolved inorganic carbon (CT), pH, and pCO2 from temperature, salinity, oxygen, and geolocation data. “CANYON-B” is a Bayesian neural network mapping that accurately reproduces GLODAPv2 bottle data and the biogeochemical relations contained therein. “CONTENT” combines and refines the four carbonate system variables to be consistent with carbonate chemistry. Both methods come with a robust uncertainty estimate that incorporates information from the local conditions. They are validated against independent GO-SHIP bottle and sensor data, and compare favorably to other state-of-the-art mapping methods. As “dynamic climatologies” they show comparable performance to classical climatologies on large scales but a much better representation on smaller scales (40–120 d, 500–1,500 km) compared to in situ data. The limits of these mappings are explored with pCO2 estimation in surface waters, i.e., at the edge of the domain with high intrinsic variability. In highly productive areas, there is a tendency for pCO2 overestimation due to decoupling of the O2 and C cycles by air-sea gas exchange, but global surface pCO2 estimates are unbiased compared to a monthly climatology. CANYON-B and CONTENT are highly useful as transfer functions between components of the ocean observing system (GO-SHIP repeat hydrography, BGC-Argo, underway observations) and permit the synergistic use of these highly complementary systems, both in spatial/temporal coverage and number of observations. Through easily and robotically-accessible observations they allow densification of more difficult-to-observe variables (e.g., 15 times denser AT and CT compared to direct measurements). At the same time, they give access to the complete carbonate system. This potential is demonstrated by an observation-based global analysis of the Revelle buffer factor, which shows a significant, high latitude-intensified increase between +0.1 and +0.4 units per decade. This shows the utility that such transfer functions with realistic uncertainty estimates provide to ocean biogeochemistry and global climate change research. In addition, CANYON-B provides robust and accurate estimates of nitrate, phosphate, and silicate. Matlab and R code are available at https://github.com/HCBScienceProducts/. Introductio

An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O2 Data Using Bayesian Neural Networks

This work presents two new methods to estimate oceanic alkalinity (AT), dissolved inorganic carbon (CT), pH, and pCO2 from temperature, salinity, oxygen, and geolocation data. “CANYON-B” is a Bayesian neural network mapping that accurately reproduces GLODAPv2 bottle data and the biogeochemical relations contained therein. “CONTENT” combines and refines the four carbonate system variables to be consistent with carbonate chemistry. Both methods come with a robust uncertainty estimate that incorporates information from the local conditions. They are validated against independent GO-SHIP bottle and sensor data, and compare favorably to other state-of-the-art mapping methods. As “dynamic climatologies” they show comparable performance to classical climatologies on large scales but a much better representation on smaller scales (40–120 d, 500–1,500 km) compared to in situ data. The limits of these mappings are explored with pCO2 estimation in surface waters, i.e., at the edge of the domain with high intrinsic variability. In highly productive areas, there is a tendency for pCO2 overestimation due to decoupling of the O2 and C cycles by air-sea gas exchange, but global surface pCO2 estimates are unbiased compared to a monthly climatology. CANYON-B and CONTENT are highly useful as transfer functions between components of the ocean observing system (GO-SHIP repeat hydrography, BGC-Argo, underway observations) and permit the synergistic use of these highly complementary systems, both in spatial/temporal coverage and number of observations. Through easily and robotically-accessible observations they allow densification of more difficult-to-observe variables (e.g., 15 times denser AT and CT compared to direct measurements). At the same time, they give access to the complete carbonate system. This potential is demonstrated by an observation-based global analysis of the Revelle buffer factor, which shows a significant, high latitude-intensified increase between +0.1 and +0.4 units per decade. This shows the utility that such transfer functions with realistic uncertainty estimates provide to ocean biogeochemistry and global climate change research. In addition, CANYON-B provides robust and accurate estimates of nitrate, phosphate, and silicate. Matlab and R code are available at https://github.com/HCBScienceProducts/

The Fourth SeaWiFS HPLC Analysis Round-Robin Experiment (SeaHARRE-4)

Ten international laboratories specializing in the determination of marine pigment concentrations using high performance liquid chromatography (HPLC) were intercompared using in situ samples and a mixed pigment sample. Although prior Sea-viewing Wide Field-of-view Sensor (SeaWiFS) High Performance Liquid Chromatography (HPLC) Round-Robin Experiment (SeaHARRE) activities conducted in open-ocean waters covered a wide dynamic range in productivity, and some of the samples were collected in the coastal zone, none of the activities involved exclusively coastal samples. Consequently, SeaHARRE-4 was organized and executed as a strictly coastal activity and the field samples were collected from primarily eutrophic waters within the coastal zone of Denmark. The more restrictive perspective limited the dynamic range in chlorophyll concentration to approximately one and a half orders of magnitude (previous activities covered more than two orders of magnitude). The method intercomparisons were used for the following objectives: a) estimate the uncertainties in quantitating individual pigments and higher-order variables formed from sums and ratios; b) confirm if the chlorophyll a accuracy requirements for ocean color validation activities (approximately 25%, although 15% would allow for algorithm refinement) can be met in coastal waters; c) establish the reduction in uncertainties as a result of applying QA procedures; d) show the importance of establishing a properly defined referencing system in the computation of uncertainties; e) quantify the analytical benefits of performance metrics, and f) demonstrate the utility of a laboratory mix in understanding method performance. In addition, the remote sensing requirements for the in situ determination of total chlorophyll a were investigated to determine whether or not the average uncertainty for this measurement is being satisfied

Estimates of Water-Column Nutrient Concentrations and Carbonate System Parameters in the Global Ocean: A Novel Approach Based on Neural Networks

A neural network-based method (CANYON: CArbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network) was developed to estimate water-column (i.e., from surface to 8,000 m depth) biogeochemically relevant variables in the Global Ocean. These are the concentrations of three nutrients [nitrate (NO3−), phosphate (PO43−), and silicate (Si(OH)4)] and four carbonate system parameters [total alkalinity (AT), dissolved inorganic carbon (CT), pH (pHT), and partial pressure of CO2 (pCO2)], which are estimated from concurrent in situ measurements of temperature, salinity, hydrostatic pressure, and oxygen (O2) together with sampling latitude, longitude, and date. Seven neural-networks were developed using the GLODAPv2 database, which is largely representative of the diversity of open-ocean conditions, hence making CANYON potentially applicable to most oceanic environments. For each variable, CANYON was trained using 80 % randomly chosen data from the whole database (after eight 10° × 10° zones removed providing an “independent data-set” for additional validation), the remaining 20 % data were used for the neural-network test of validation. Overall, CANYON retrieved the variables with high accuracies (RMSE): 1.04 μmol kg−1 (NO3−), 0.074 μmol kg−1 (PO43−), 3.2 μmol kg−1 (Si(OH)4), 0.020 (pHT), 9 μmol kg−1 (AT), 11 μmol kg−1 (CT) and 7.6 % (pCO2) (30 μatm at 400 μatm). This was confirmed for the eight independent zones not included in the training process. CANYON was also applied to the Hawaiian Time Series site to produce a 22 years long simulated time series for the above seven variables. Comparison of modeled and measured data was also very satisfactory (RMSE in the order of magnitude of RMSE from validation test). CANYON is thus a promising method to derive distributions of key biogeochemical variables. It could be used for a variety of global and regional applications ranging from data quality control to the production of datasets of variables required for initialization and validation of biogeochemical models that are difficult to obtain. In particular, combining the increased coverage of the global Biogeochemical-Argo program, where O2 is one of the core variables now very accurately measured, with the CANYON approach offers the fascinating perspective of obtaining large-scale estimates of key biogeochemical variables with unprecedented spatial and temporal resolutions. The Matlab and R codes of the proposed algorithms are provided as Supplementary Material

Development of a Multivariate Prediction Model for Early-Onset Bronchiolitis Obliterans Syndrome and Restrictive Allograft Syndrome in Lung Transplantation.

Chronic lung allograft dysfunction and its main phenotypes, bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS), are major causes of mortality after lung transplantation (LT). RAS and early-onset BOS, developing within 3 years after LT, are associated with particularly inferior clinical outcomes. Prediction models for early-onset BOS and RAS have not been previously described. LT recipients of the French and Swiss transplant cohorts were eligible for inclusion in the SysCLAD cohort if they were alive with at least 2 years of follow-up but less than 3 years, or if they died or were retransplanted at any time less than 3 years. These patients were assessed for early-onset BOS, RAS, or stable allograft function by an adjudication committee. Baseline characteristics, data on surgery, immunosuppression, and year-1 follow-up were collected. Prediction models for BOS and RAS were developed using multivariate logistic regression and multivariate multinomial analysis. Among patients fulfilling the eligibility criteria, we identified 149 stable, 51 BOS, and 30 RAS subjects. The best prediction model for early-onset BOS and RAS included the underlying diagnosis, induction treatment, immunosuppression, and year-1 class II donor-specific antibodies (DSAs). Within this model, class II DSAs were associated with BOS and RAS, whereas pre-LT diagnoses of interstitial lung disease and chronic obstructive pulmonary disease were associated with RAS. Although these findings need further validation, results indicate that specific baseline and year-1 parameters may serve as predictors of BOS or RAS by 3 years post-LT. Their identification may allow intervention or guide risk stratification, aiming for an individualized patient management approach

A Compilation of Global Bio-Optical In Situ Data for Ocean-Colour Satellite Applications

A compiled set of in situ data is important to evaluate the quality of ocean-colour satellite-data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO), span between 1997 and 2012, and have a global distribution. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties and spectral diffuse attenuation coefficients. The data were from multi-project archives acquired via the open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were preserved throughout the work and made available in the final table. Using all the data in a validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. By making available the metadata, it is also possible to analyse each set of data separately. The compiled data are available at doi:10.1594/PANGAEA.854832 (Valente et al., 2015)

Annual variations in the number of malaria cases related to two different patterns of Anopheles darlingi transmission potential in the Maroni area of French Guiana

<p>Abstract</p> <p>Background</p> <p>With an Annual Parasite Incidence (API) of 132.1, in the high and moderate risks zones, the Maroni area of French Guiana has the second highest malaria incidence of South-America after Guyana (API = 183.54) and far above Brazil (API = 28.25). Malaria transmission is occurring despite strong medical assistance and active vector control, based on general WHO recommendations. This situation is generated by two main factors that are the social and cultural characteristics of this border area, where several ethnic groups are living, and the lack of understanding of transmission dynamics of the main mosquito vector, <it>Anopheles darlingi.</it> In this context, entomological data collected in two villages belonging to two different ethnic groups of the French border of the Maroni River, were retrospectively analysed to find out how the mosquito bionomics are related to the malaria transmission patterns.</p> <p>Methods</p> <p>Data were provided by human landing catches of mosquitoes carried out each month for two years in two villages belonging to two ethnic groups, the Amerindians Wayanas and the Aloukous of African origin. The mosquitoes were sorted by species, sex, date, hour and place of collection and processed for <it>Plasmodium sp</it>. parasite detection. The data were compiled to provide the following variables: human biting rates (HBR), parity rates (PR), numbers of infective bites (IB), entomological inoculation rates (EIR) and numbers of infected mosquitoes surviving enough to transmit (IMT). Spatial and temporal differences of variables between locations and during the night were tested by the Kruskall-Wallis analysis of variance to find out significant variations.</p> <p>Results</p> <p>The populations of the main mosquito vector <it>An. darlingi </it>showed significant variations in the spatial and temporal HBR/person/night and HBR/person/hour, IB/person/month and IB/person/hour, and IMT/village/night and IMT/village/hour. In the village of Loca (Aloukous), the IMT peaked from June to August with a very low transmission during the other months. The risks were higher during the first part of the night and an EIR of 10 infective bites per person and per year was estimated. In the village of Twenke (Wayanas), high level of transmission was reported all year with small peaks in March and October. The risk was higher during the second part of the night and an EIR of 5 infective bites per person and per year was estimated.</p> <p>Conclusion</p> <p>For the first time in the past 40 years, the mosquito bionomics was related to the malaria transmission patterns in French Guiana. The peak of malaria cases reported from August to October in the Maroni region is concomitant with the significant peak of <it>An. darlingi </it>IMT, reported from the village of Loca where transmission is higher. However, the persistent number of cases reported all year long may also be related to the transmission in the Amerindian villages. The <it>An. darlingi </it>bionomics for these two close populations were found significantly different and may explain why a uniform vector control method is inadequate. Following these findings, malaria prevention measures adapted to the local conditions are needed. Finally, the question of the presence of <it>An. darlingi </it>sub-species is raised.</p