Changing seasonality of the Baltic Sea

Changes in the phenology of physical and ecological variables associated with climate change are likely to have significant effect on many aspects of the Baltic ecosystem. We apply a set of phenological indicators to multiple environmental variables measured by satellite sensors for 17–36 years to detect possible changes in the seasonality in the Baltic Sea environment. We detect significant temporal changes, such as earlier start of the summer season and prolongation of the productive season, in several variables ranging from basic physical drivers to ecological status indicators. While increasing trends in the absolute values of variables like sea-surface temperature (SST), diffuse attenuation of light (Ked490) and satellite-detected chlorophyll concentration (CHL) are detectable, the corresponding changes in their seasonal cycles are more dramatic. For example, the cumulative sum of 30 000 W m−2 of surface incoming shortwave irradiance (SIS) was reached 23 days earlier in 2014 compared to the beginning of the time series in 1983. The period of the year with SST of at least 17 °C has almost doubled (from 29 days in 1982 to 56 days in 2014), and the period with Ked490 over 0.4 m−1 has increased from about 60 days in 1998 to 240 days in 2013 – i.e., quadrupled. The period with satellite-estimated CHL of at least 3 mg m−3 has doubled from approximately 110 days in 1998 to 220 days in 2013. While the timing of both the phytoplankton spring and summer blooms have advanced, the annual CHL maximum that in the 1980s corresponded to the spring diatom bloom in May has now shifted to the summer cyanobacteria bloom in July

An inverse relationship between production and export efficiency in the Southern Ocean

Author Posting. © American Geophysical Union, 2013. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 40 (2013): 1557–1561, doi:10.1002/grl.50219.In the past two decades, a number of studies have been carried out in the Southern Ocean to look at export production using drifting sediment traps and thorium-234 based measurements, which allows us to reexamine the validity of using the existing relationships between production, export efficiency, and temperature to derive satellite-based carbon export estimates in this region. Comparisons of in situ export rates with modeled rates indicate a two to fourfold overestimation of export production by existing models. Comprehensive analysis of in situ data indicates two major reasons for this difference: (i) in situ data indicate a trend of decreasing export efficiency with increasing production which is contrary to existing export models and (ii) the export efficiencies appear to be less sensitive to temperature in this region compared to the global estimates used in the existing models. The most important implication of these observations is that the simplest models of export, which predict increase in carbon flux with increasing surface productivity, may require additional parameters, different weighing of existing parameters, or separate algorithms for different oceanic regimes.This work was supported by NASA award number NNX08AB48G.2013-10-2

Satellite detection of increased cyanobacteria blooms in the Baltic Sea: Natural fluctuation or ecosystem change?

Using data from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA series of satellites, an increase in the area covered by cyanobacteria blooms in the Baltic Sea was detected. The time series of satellite data covers a period of 12 years from 1982 to 1993. The total area covered by surface-floating cyanobacteria (blue-green algae) has increased in the 1990s, reaching over 62 000 km in 1992. From 1992, visible accumulations appeared for the first time in the Gulf of Riga and reappeared, in the western Gulf of Finland, after being absent from 1984. Conspicuous surface blooms were also present in the early 1980s, coincident with a period of sunny and calm summers. However, when the influence of variable sunshine duration is taken into account, the increase in 1991-1993 is still distinct, indicating significant changes in the Baltic environment. The causal factors for the increased cyanobacteria blooms are still not clear

Optimized Merger of Ocean Chlorophyll Algorithms of MODIS-Aqua and VIIRS

Standard ocean chlorophyll-a (Chla) products from currently operational satellite sensors Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Visible Infrared Imager Radiometer Suite (VIIRS) underestimate medium and high in situ Chla concentrations and have approximately 9% bias between each other in the California Current. By using the regional optimization approach of Kahru et al., we minimized the differences between satellite estimates and in situ match-ups as well as between estimates of the two satellite sensors and created improved empirical algorithms for both sensors. The regionally optimized Chla estimates from MODIS-Aqua and VIIRS have no bias between each other, have improved retrievals at medium to high in situ Chla, and can be merged to improve temporal frequency and spatial coverage and to extend the merged time series

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

Satellite detection of dinoflagellate blooms off California by UV reflectance ratios

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Kahru, M., Anderson, C., Barton, A. D., Carter, M. L., Catlett, D., Send, U., Sosik, H. M., Weiss, E. L., & Mitchell, B. G. Satellite detection of dinoflagellate blooms off California by UV reflectance ratios. Elementa: Science of the Anthropocene, 9(1), (2021): 00157, https://doi.org/10.1525/elementa.2020.00157.As harmful algae blooms are increasing in frequency and magnitude, one goal of a new generation of higher spectral resolution satellite missions is to improve the potential of satellite optical data to monitor these events. A satellite-based algorithm proposed over two decades ago was used for the first time to monitor the extent and temporal evolution of a massive bloom of the dinoflagellate Lingulodinium polyedra off Southern California during April and May 2020. The algorithm uses ultraviolet (UV) data that have only recently become available from the single ocean color sensor on the Japanese GCOM-C satellite. Dinoflagellates contain high concentrations of mycosporine-like amino acids and release colored dissolved organic matter, both of which absorb strongly in the UV part of the spectrum. Ratios 1, consistent with historical observations showing a sharp transition from dinoflagellate- to diatom-dominated waters in these areas. UV bands are thus potentially useful in the remote sensing of phytoplankton blooms but are currently available only from a single ocean color sensor. As several new satellites such as the NASA Plankton, Aerosol, Cloud, and marine Ecosystem mission will include UV bands, new algorithms using these bands are needed to enable better monitoring of blooms, especially potentially harmful algal blooms, across large spatiotemporal scales.Part of this work was funded by National Science Foundation (NSF) grants to the CCE-LTER Program, most recently OCE-1637632. Processing of Second-Generation Global Imager satellite data was funded by Japan Aerospace Exploration Agency. Data shown in Figure 1 were collected by BGM and MK with support from the NASA SIMBIOS project. DC was supported by the NASA Biodiversity and Ecological Forecasting Program (Grant NNX14AR62A), the Bureau of Ocean and Energy Management Ecosystem Studies Program (BOEM award MC15AC00006), and the NOAA through the Santa Barbara Channel Marine Biodiversity Observation Network. HMS was supported by NSF (Grant OCE-1810927) and the Simons Foundation (Grant 561126). ELW was supported by NSF GRFP (Grant DGE-1650112). Funding for Scripps and Santa Monica Piers sampling was through the Southern California Coastal Ocean Observing Harmful Algal Bloom Monitoring Program by NOAA NA16NOS0120022

Phytoplankton absorption, photosynthetic parameters, and primary production off Baja California: summer and autumn

Abstract To estimate ocean primary production at large space and time scales, it is necessary to use models combined with ocean-color satellite data. Detailed estimates of primary production are typically done at only a few representative stations. To get survey-scale estimates of primary production, one must introduce routinely measured Chlorophyll-a (Chl-a) into models. For best precision, models should be based on accurate parameterizations developed from optical and photosynthesis data collected in the region of interest. To develop regional model parameterizations 14 Cbicarbonate was used to estimate in situ primary production and photosynthetic parameters ða à ; P à m , and E k ) derived from photosynthesis-irradiance (P-E) experiments from IMECOCAL cruises to the southern California Current during July and October 1998. The P-E experiments were done for samples collected from the 50% surface light depth for which we also determined particle and phytoplankton absorption coefficients (a p , a f , and a à f Þ. Physical data collected during both surveys indicated that the 1997-1998 El Nin˜o was abating during the summer of 1998, with a subsequent transition to the typical California Current circulation and coastal upwelling conditions. Phytoplankton chl-a and in situ primary production were elevated at coastal stations for both surveys, with the highest values during summer. Phytoplankton specific absorption coefficients in the blue peak ða à f (440) ) ranged from 0.02 to 0.11 m 2 (mg Chl-a) À1 with largest values in offshore surface waters. In general a à f was lower at depth compared to the surface. P-E samples were collected at the 50% light level that was usually in the surface mixed layer. Using a à and spectral absorption, we estimated maximum photosynthetic quantum yields (f max ; mol C/mol quanta). f max values were lowest in offshore surface waters, with a total range of 0.01-0.07. Mean values of f max for July and October were 0.011 and 0.022, respectively. In July P à m was approximately double and a à was about 1.4 times the values for October. Since the P-E samples were generally within the upper mixed layer, these tendencies in the photosynthetic parameters are attributed to deeper mixing of this layer during October when the mean mixed layer for the photosynthesis stations was 35 m compared to a mean of 10 m in July. Application of a semi-analytical model using mean values of P-E parameters determined at the 50% light depth provided good agreement with 14 C in situ estimates at the discrete 50% light depth and for the water-column integrated primary production.

Autonomous Ocean Measurements in the California Current Ecosystem

Event-scale phenomena, of limited temporal duration or restricted spatial extent, often play a disproportionately large role in ecological processes occurring in the ocean water column. Nutrient and gas fluxes, upwelling and downwelling, transport of biogeochemically important elements, predator-prey interactions, and other processes may be markedly influenced by such events, which are inadequately resolved from infrequent ship surveys. The advent of autonomous instrumentation, including underwater gliders, profiling floats, surface drifters, enhanced moorings, coastal high-frequency radars, and satellite remote sensing, now provides the capability to resolve such phenomena and assess their role in structuring pelagic ecosystems. These methods are especially valuable when integrated together, and with shipboard calibration measurements and experimental programs

An ocean-colour time series for use in climate studies: the experience of the ocean-colour climate change initiate (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 viewingWide-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