No abstract

For over a decade, several research groups have been developing air-sea heat flux information over the global ocean, including latent (LHF) and sensible (SHF) heat fluxes over the global ocean. This paper aims to provide new insight into the quality and error characteristics of turbulent heat flux estimates at various spatial and temporal scales (from daily upwards). The study is performed within the European Space Agency (ESA) Ocean Heat Flux (OHF) project. One of the main objectives of the OHF project is to meet the recommendations and requirements expressed by various international programs such as the World Research Climate Program (WCRP) and Climate and Ocean Variability, Predictability, and Change (CLIVAR), recognizing the need for better characterization of existing flux errors with respect to the input bulk variables (e.g. surface wind, air and sea surface temperatures, air and surface specific humidities), and to the atmospheric and oceanic conditions (e.g. wind conditions and sea state). The analysis is based on the use of daily averaged LHF and SHF and the associated bulk variables derived from major satellite-based and atmospheric reanalysis products. Inter-comparisons of heat flux products indicate that all of them exhibit similar space and time patterns. However, they also reveal significant differences in magnitude in some specific regions such as the western ocean boundaries during the Please note that this is an author-produced PDF of an article accepted for publication following peer review. The definitive publisher-authenticated version is available on the publisher Web site. Northern Hemisphere winter season, and the high southern latitudes. The differences tend to be closely related to large differences in surface wind speed and/or specific air humidity (for LHF) and to air and sea temperature differences (for SHF). Further quality investigations are performed through comprehensive comparisons with daily-averaged LHF and SHF estimated from moorings. The resulting statistics are used to assess the error of each OHF product. Consideration of error correlation between products and observations (e.g., by their assimilation) is also given. This reveals generally high noise variance in all products and a weak signal in common with in situ observations, with some products only slightly better than others. The OHF LHF and SHF products, and their associated error characteristics, are used to compute daily OHF multiproduct-ensemble (OHF/MPE) estimates of LHF and SHF over the ice-free global ocean on a 0.25° × 0.25° grid. The accuracy of this heat multiproduct, determined from comparisons with mooring data, is greater than for any individual product. It is used as a reference for the anomaly characterization of each individual OHF product. Highlights ► Establishing reference input dataset maximizing the use of remotely sensed data ► Performing a cross-comparison of different heat flux algorithms and approaches ► Generating an ensemble of turbulent fluxes, including multiple approaches ► Evaluating the q...

Acquisition of high resolution time series of water column and bottom dissolved oxygen (DO) concentrations inform the dynamics of oxygen depletion in St Helena Bay in the southern Benguela upwelling system at several scales of variability. The bay is characterized by seasonally recurrent hypoxia (<1.42 ml l 21 ) associated with a deep pool of oxygen-depleted water and episodic anoxia (<0.02 ml l 21 ) driven by the nearshore (<20 m isobath) decay of red tide. Coastal wind forcing influences DO concentrations in the nearshore through its influence on bay productivity and the development of red tides; through shoreward advection of the bottom pool of oxygen-depleted water as determined by the upwelling-downwelling cycle; and through its control of water column stratification and mixing. A seasonal decline in bottom DO concentrations of 1.2 ml l 21 occurs with a concurrent expansion of the bottom pool of oxygen depleted water in St Helena Bay. Upwelling of this water into the nearshore causes severe drops in DO concentration (<0.2 ml l 21 ), particularly during end-of-season upwelling, resulting in a significant narrowing of the habitable zone. Episodic anoxia through the entire water column is caused by localized degradation of red tides within the confines of the shallow nearshore environment. Oxygenation of the nearshore is achieved by ventilation of the water column particularly with the onset of winter mixing. No notable changes are evident in comparing recent measures of bottom DO concentrations in St Helena Bay to data collected in the late 1950s and early 1960s.

Particulate Organic Carbon (POC) plays a vital role in the ocean carbon cycle. Though relatively small compared with other carbon pools, the POC pool is responsible for large fluxes and is linked to many important ocean biogeochemical processes. The satellite ocean-color signal is influenced by particle composition, size, and concentration and provides a way to observe variability in the POC pool at a range of temporal and spatial scales. To provide accurate estimates of POC concentration from satellite ocean color data requires algorithms that are well validated, with uncertainties characterized. Here, a number of algorithms to derive POC using different optical variables are applied to merged satellite ocean color data provided by the Ocean Color Climate Change Initiative (OC-CCI) and validated against the largest database of in situ POC measurements currently available. The results of this validation exercise indicate satisfactory levels of performance from several algorithms (highest performance was observed from the algorithms of Loisel et al., 2002;Stramski et al., 2008) and uncertainties that are within the requirements of the user community. Estimates of the standing stock of the POC can be made by applying these algorithms, and yield an estimated mixed-layer integrated global stock of POC between 0.77 and 1.3 Pg C of carbon. Performance of the algorithms vary regionally, suggesting that blending of region-specific algorithms may provide the best way forward for generating global POC products.

Sustained global ocean observations are needed to recognise, understand, and manage changes in marine biodiversity, resources and habitats, and to implement wise conservation and sustainable development strategies. To meet this need, the Global Ocean Observing System (GOOS), a network of observing systems distributed around the world and coordinated by the Intergovernmental Oceanographic Commission (IOC) has proposed Essential Ocean Variables (EOVs) that are relevant to both the scientific and the broader community, including resource managers. Building a network that is truly global requires expanding participation beyond scientists from well-resourced countries to a far broader representation of the global community. New approaches are required to provide appropriate training, and resources and technology should follow to enable the application of this training to engage meaningfully in global observing networks and in the use of the data. Investments in technical capacity fulfil international reporting obligations under the UN Sustainable Development Goal 14A. Important opportunities are emerging now for countries to develop research partnerships with the IOC and GOOS to address these obligations. Implementing these partnerships requires new funding models and initiatives that support a sustained research capacity and marine technology transfer.

The differences among phytoplankton carbon (C phy ) predictions from six ocean color algorithms are investigated by comparison with in situ estimates of phytoplankton carbon. The common satellite data used as input for the algorithms is the Ocean Color Climate Change Initiative merged product. The matching in situ data are derived from flow cytometric cell counts and per-cell carbon estimates for different types of pico-phytoplankton. This combination of satellite and in situ data provides a relatively large matching dataset (N > 500), which is independent from most of the algorithms tested and spans almost two orders of magnitude in C phy . Results show that not a single algorithm outperforms any of the other when using all matching data. Concentrating on the oligotrophic regions (Chlorophyll-a concentration, B, less than 0.15 mg Chl m −3 ), where flow cytometric analysis captures most of the phytoplankton biomass, reveals significant differences in algorithm performance. The bias ranges from −35 to +150% and unbiased root mean squared difference from 5 to 10 mg C m −3 among algorithms, with chlorophyll-based algorithms performing better than the rest. The backscatteringbased algorithms produce different results at the clearest waters and these differences are discussed in terms of the different algorithms used for optical particle backscattering coefficient (b bp ) retrieval.

There is a pressing need for improved bio-optical models of high biomass waters as eutrophication of coastal and inland waters becomes an increasing problem. Seasonal boom conditions in the Southern Benguela and persistent harmful algal production in various inland waters in Southern Africa present valuable opportunities for the development of such modelling capabilities. The phytoplankton-dominated signal of these waters additionally addresses an increased interest in Phytoplankton Functional Type (PFT) analysis. To these ends, an initial validation of a new model of Equivalent Algal Populations (EAP) is presented here. This paper makes a first order comparison of two prominent phytoplankton Inherent Optical Property (IOP) models with the EAP model, which places emphasis on explicit bio-physical modelling of the phytoplankton population as a holistic determinant of inherent optical properties. This emphasis is shown to have an impact on the ability to retrieve the detailed phytoplankton spectral scattering information necessary for PFT applications and to successfully simulate reflectance across wide ranges of physical environments, biomass, and assemblage characteristics.

Synoptic scale knowledge of the size structure of phytoplankton communities can offer insight in to primary ecosystem diversity and biogeochemical variability from operational to the decadal scales. Accordingly, obtaining estimates of size and other phytoplankton functional type descriptors within known confidence limits from remotely sensed data has become a major objective to extend the use of ocean colour data beyond chlorophyll a retrievals. Here, a new forward and inverse modelling structure is proposed to determine information about the cell size of phytoplankton communities using Standard size distributions of two layered spheres to derive a full suite of algal inherent optical properties for a coupled radiative transfer model. This new capability allows explicit quantification of the remote sensing reflectance signal attributable to changes in phytoplankton cell size. Inversion of this model reveals regions within the parameter space where ambiguity may limit potential of inversion algorithms. Validation of the algorithm within the Benguela upwelling system using independent data shows promise for ecosystem applications and further investigation of the interaction between phytoplankton functional types and optical signals. The results here suggest that the utility of assemblage related signals in spectral reflectance is highly sensitive to algal biomass, the presence of other absorbing and scattering constituents and the resultant constituent-specific inherent optical property budget. As such, optimal methods for determining phytoplankton size from (in situ or satellite) ocean colour data will likely rely on appropriately spectrally dense and optimised sensors, well characterised measurement errors including those from atmospheric correction, and an ability to appropriately limit ambiguity within the context of regional inherent optical properties.