Cyanobacterial blooms in the Baltic Sea: Correlations with environmental factors.

Massive cyanobacteria blooms occur almost every summer in the Baltic Sea but the capability to quantitatively predict their extent and intensity is poorly developed. Here we analyse statistical relationships between multi-decadal satellite-derived time series of the frequency of cyanobacteria surface accumulations (FCA) in the central Baltic Sea Proper and a suite of environmental variables. Over the decadal scale (∼5-20 years) FCA was highly correlated (R2 ∼ 0.69) with a set of biogeochemical variables related to the amount of phosphorus and hypoxia in bottom layers. Water temperature in the surface layer was also positively correlated with FCA at the decadal scale. In contrast, the inter-annual variations in FCA had no correlation with the biogeochemical variables. Instead, significant correlations were found with the solar shortwave direct flux in July and the sea-surface temperature, also in July. It thus appears that it is not possible to predict inter-annual fluctuations in cyanobacteria blooms from water chemistry. Moreover, environmental variables could only explain about 45% of the inter-annual variability in FCA, probably because year-to-year variations in FCA are significantly influenced by biological interactions.

An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI).

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

Episodic organic carbon fluxes from surface ocean to abyssal depths during long-term monitoring in NE Pacific.

Growing evidence suggests substantial quantities of particulate organic carbon (POC) produced in surface waters reach abyssal depths within days during episodic flux events. A 29-year record of in situ observations was used to examine episodic peaks in POC fluxes and sediment community oxygen consumption (SCOC) at Station M (NE Pacific, 4,000-m depth). From 1989 to 2017, 19% of POC flux at 3,400 m arrived during high-magnitude episodic events (≥mean + 2 σ), and 43% from 2011 to 2017. From 2011 to 2017, when high-resolution SCOC data were available, time lags between changes in satellite-estimated export flux (EF), POC flux, and SCOC on the sea floor varied between six flux events from 0 to 70 days, suggesting variable remineralization rates and/or particle sinking speeds. Half of POC flux pulse events correlated with prior increases in EF and/or subsequent SCOC increases. Peaks in EF overlying Station M frequently translated to changes in POC flux at abyssal depths. A power-law model (Martin curve) was used to estimate abyssal fluxes from EF and midwater temperature variation. While the background POC flux at 3,400-m depth was described well by the model, the episodic events were significantly underestimated by ∼80% and total flux by almost 50%. Quantifying episodic pulses of organic carbon into the deep sea is critical in modeling the depth and intensity of POC sequestration and understanding the global carbon cycle.

Unexplained interannual oscillations of cyanobacterial blooms in the Baltic Sea.

Population oscillations in multi-species or even single species systems are well-known but have rarely been detected at the lower trophic levels in marine systems. Nitrogen fixing cyanobacteria are a major component of the Baltic Sea ecosystem and sometimes form huge surface accumulations covering most of the sea surface. By analysing a satellite-derived 39-year (1979-2017) data archive of surface cyanobacteria concentrations we have found evidence of strikingly regular interannual oscillations in cyanobacteria concentrations in the northern Baltic Sea. These oscillations have a period of ~3 years with a high-concentration year generally followed by one or two low-concentration years. Changes in abiotic factors known to influence the growth and survival of cyanobacteria could not provide an explanation for the oscillations. We therefore assume that these oscillations are intrinsic to the marine system, caused by an unknown, probably mainly biological mechanism that may be triggered by a combination of environmental factors. Interactions between different life cycle stages of cyanobacteria as well as between predator-prey or host-parasite are possible candidates for causing the oscillations.

Effects of sea ice cover on satellite-detected primary production in the Arctic Ocean.

The influence of decreasing Arctic sea ice on net primary production (NPP) in the Arctic Ocean has been considered in multiple publications but is not well constrained owing to the potentially large errors in satellite algorithms. In particular, the Arctic Ocean is rich in coloured dissolved organic matter (CDOM) that interferes in the detection of chlorophyll a concentration of the standard algorithm, which is the primary input to NPP models. We used the quasi-analytic algorithm (Lee et al 2002 Appl. Opti. 41, 5755-5772. (doi:10.1364/AO.41.005755)) that separates absorption by phytoplankton from absorption by CDOM and detrital matter. We merged satellite data from multiple satellite sensors and created a 19 year time series (1997-2015) of NPP. During this period, both the estimated annual total and the summer monthly maximum pan-Arctic NPP increased by about 47%. Positive monthly anomalies in NPP are highly correlated with positive anomalies in open water area during the summer months. Following the earlier ice retreat, the start of the high-productivity season has become earlier, e.g. at a mean rate of -3.0 d yr-1 in the northern Barents Sea, and the length of the high-productivity period has increased from 15 days in 1998 to 62 days in 2015. While in some areas, the termination of the productive season has been extended, owing to delayed ice formation, the termination has also become earlier in other areas, likely owing to limited nutrients.

Initial skill assessment of the California Harmful Algae Risk Mapping (C-HARM) system.

Toxic algal events are an annual burden on aquaculture and coastal ecosystems of California. The threat of domoic acid (DA) toxicity to human and wildlife health is the dominant harmful algal bloom (HAB) concern for the region, leading to a strong focus on prediction and mitigation of these blooms and their toxic effects. This paper describes the initial development of the California Harmful Algae Risk Mapping (C-HARM) system that predicts the spatial likelihood of blooms and dangerous levels of DA using a unique blend of numerical models, ecological forecast models of the target group, Pseudo-nitzschia, and satellite ocean color imagery. Data interpolating empirical orthogonal functions (DINEOF) are applied to ocean color imagery to fill in missing data and then used in a multivariate mode with other modeled variables to forecast biogeochemical parameters. Daily predictions (nowcast and forecast maps) are run routinely at the Central and Northern California Ocean Observing System (CeNCOOS) and posted on its public website. Skill assessment of model output for the nowcast data is restricted to nearshore pixels that overlap with routine pier monitoring of HABs in California from 2014 to 2015. Model lead times are best correlated with DA measured with solid phase adsorption toxin tracking (SPATT) and marine mammal strandings from DA toxicosis, suggesting long-term benefits of the HAB predictions to decision-making. Over the next three years, the C-HARM application system will be incorporated into the NOAA operational HAB forecasting system and HAB Bulletin.

Human activities and climate variability drive fast-paced change across the world's estuarine-coastal ecosystems.

Time series of environmental measurements are essential for detecting, measuring and understanding changes in the Earth system and its biological communities. Observational series have accumulated over the past 2-5 decades from measurements across the world's estuaries, bays, lagoons, inland seas and shelf waters influenced by runoff. We synthesize information contained in these time series to develop a global view of changes occurring in marine systems influenced by connectivity to land. Our review is organized around four themes: (i) human activities as drivers of change; (ii) variability of the climate system as a driver of change; (iii) successes, disappointments and challenges of managing change at the sea-land interface; and (iv) discoveries made from observations over time. Multidecadal time series reveal that many of the world's estuarine-coastal ecosystems are in a continuing state of change, and the pace of change is faster than we could have imagined a decade ago. Some have been transformed into novel ecosystems with habitats, biogeochemistry and biological communities outside the natural range of variability. Change takes many forms including linear and nonlinear trends, abrupt state changes and oscillations. The challenge of managing change is daunting in the coastal zone where diverse human pressures are concentrated and intersect with different responses to climate variability over land and over ocean basins. The pace of change in estuarine-coastal ecosystems will likely accelerate as the human population and economies continue to grow and as global climate change accelerates. Wise stewardship of the resources upon which we depend is critically dependent upon a continuing flow of information from observations to measure, understand and anticipate future changes along the world's coastlines.

Deep ocean communities impacted by changing climate over 24 y in the abyssal northeast Pacific Ocean.

The deep ocean, covering a vast expanse of the globe, relies almost exclusively on a food supply originating from primary production in surface waters. With well-documented warming of oceanic surface waters and conflicting reports of increasing and decreasing primary production trends, questions persist about how such changes impact deep ocean communities. A 24-y time-series study of sinking particulate organic carbon (food) supply and its utilization by the benthic community was conducted in the abyssal northeast Pacific (~4,000-m depth). Here we show that previous findings of food deficits are now punctuated by large episodic surpluses of particulate organic carbon reaching the sea floor, which meet utilization. Changing surface ocean conditions are translated to the deep ocean, where decadal peaks in supply, remineralization, and sequestration of organic carbon have broad implications for global carbon budget projections.

Assessment of the ultraviolet radiation field in ocean waters from space-based measurements and full radiative-transfer calculations.

Quantitative assessment of the UV effects on aquatic ecosystems requires an estimate of the in-water radiation field. Actual ocean UV reflectances are needed for improving the total ozone retrievals from the total ozone mapping spectrometer (TOMS) and the ozone monitoring instrument (OMI) flown on NASA's Aura satellite. The estimate of underwater UV radiation can be done on the basis of measurements from the TOMS/OMI and full models of radiative transfer (RT) in the atmosphere-ocean system. The Hydrolight code, modified for extension to the UV, is used for the generation of look-up tables for in-water irradiances. A look-up table for surface radiances generated with a full RT code is input for the Hydrolight simulations. A model of seawater inherent optical properties (IOPs) is an extension of the Case 1 water model to the UV. A new element of the IOP model is parameterization of particulate matter absorption based on recent in situ data. A chlorophyll product from ocean color sensors is input for the IOP model. Verification of the in-water computational scheme shows that the calculated diffuse attenuation coefficient Kd is in good agreement with the measured Kd.