A coccolithophore Emiliania huxleyi is the most abundant calcifying algal species throughout the world’s oceans. As it is capable of significantly affect the marine surface biogeochemistry and carbon cycling between the atmosphere and ocean, its importance has both climatic and aquatic ecology dimensions. Blooms of this alga exhibit remarkable spatiotemporal variations and proved to be aquatic environment specific. Here we present our hypothesis regarding the origination of the intense blooms of this alga that occurred in the Bering Sea during 1997-2001, and further on in 2018- 2019. Our hypothesis relies on (a) the salient transport anomalies in the Bering Sea Slope Current, and the Alaska Stream, and the Near Strait throughflow that were documented elsewhere for the above period, (b) the retrieved spaceborne time series of statistical occurrences of NE&E horizontal directions of the geostrophic current at the east passes in the Aleutian arc, and the timings of the two latest El Niño events.
Producing very extensive blooms in the world’s oceans in both hemispheres, a coccolithophore E. huxleyi affects both
marine ecology and carbon fluxes at the atmosphere-ocean interface. In turn, it is subject to impacts of multiple co-acting
environmental forcings responsible for spatio-temporal dynamics in E. huxleyi blooms.
To reveal the individual importance of each forcing factor (FF) that is known to significantly control the extent and
intensity of E. huxleyi blooms, the 1998-2016 spaceborne time series of sea surface temperature and salinity, incident
photosynthetically active radiation, and the Ekman depth relevant to the North, Norwegian, Greenland, Labrador,
Barents and Bering seas were employed.
The descriptive statistical approach showed that E. huxleyi phytoplankton blooms were capable of arising and developing
within wide but expressly sea-specific FFs ranges. Sea-specific FFs ranges, within which the blooms are particularly
extensive were identified.
The Random Forest Classifier (RFC) allowed to reliably rank the FFs in terms of their role in E. huxleyi bloom spatiotemporal
dynamics in each target sea. High prediction ability of RFC modelling (>70%) confirms the adequacy of the
developed FFs prioritization models.
Although the parameters of the carbon chemistry system per se were beyond consideration, however, over the twenty
years of observations, the prioritized FFs have not failed to explain the registered patterns of the spatial extent of and
particulate inorganic carbon content in E. huxleyi blooms. Also, several verifications (pastcasts) showed a high degree of
their consistency with the observations. Collectively, these results tell in favor of sufficiency of the FFs employed.
A coccolithophore E. huxleyi is one of the most significant sources of inorganic carbon in the world oceans. Forming vast bloom areas this species can affect the carbon balance in the atmosphere-ocean system, and thus interfere with climate and marine ecology. We obtained from 6 seas located at high latitudes a 19-year time series (1998-2016) of spaceborne data on this phenomenon as well as data on the phenomenon-affecting oceanographic and atmospheric variables. To efficiently concatenate and eventually analyze versatile data of huge size on the aforementioned blooms, a special GIS infrastructure (GISI) is developed. It is built on the principles of a service-based architecture with microservices. The GISI includes both a server application that controls information flows and automated data processing. Microservices with the RESTful architecture for data access and three types of interfaces for researchers are at the base of GISI. Researchers working with the GIS use both a dedicated web client for searching and downloading the required data, a desktop client developed as an extension for an open source desktop GIS QGIS and a Python library developed for the implementation of methods of interaction with the server. Another part of GISI is a virtual-machine based environment for user-side data processing. The use of such a system allows to improve the bloom identification, to map the variations in bloom location, extent, and its inherent properties as well as to perform time series analyses.
A satellite sensor nonspecific operational advanced algorithm is developed to simultaneously retrieve the concentrations of phytoplankton chlorophyll (chl), dissolved organics (doc) and suspended minerals (sm) in turbid and strongly absorbing natural waters (i.e., case II waters). Also, a new interpolation procedure is developed and used jointly with the advanced bio-optical and standard window-split algorithms to generate from SeaWiFS and AVHRR data the time series of spatial and temporal (seasonal and interannual) variations of chl, sm, doc and water surface temperature (TS) for the period 1998-2004 in Lake Ladoga, the largest European fresh water body. Obtained for the first time, the spaceborne fields of the above variables have revealed at an unprecedented time and space resolution some intrinsic features and interdependence of thermal and biogeochemical processes in the lake. Rates of thermobar displacement from the littoral zone to the central deep water area are quantified during periods of lake warming and cooling. From spring to mid-summer, the dynamics of phytoplankton biomass spatial distribution are evidenced to follow the retraction of the cold water zone bordered by the thermobar. Importantly, along with the thermobar dynamics, the zones of the most enhanced phytoplankton concentration are concurrently governed by the lake bathymetry, and thus gradually move from south to north along the eastern coast line. Brought with fluvial input, suspended minerals and allochthonous dissolved organics are not only restricted to the zones of major river deltas but also driven northward by coastal cyclonic currents prevailing in Lake Ladoga. The obtained space data allows the interplay of the above factors to be explicitly revealed and explains the observed interannual variations in the surficial expressions of biogeochemical processes inherent in Lake Ladoga.
Development of water quality retrieval algorithms is discussed in terms of causal dependence of the upwelling spectral radiance upon the water composition. Unlike clean marine/oceanic waters for which linear regression retrieval relationships are valid, inland and coastal zone water masses with high degree of optical complexity necessitate the development of nonparametric retrieval approaches. At the basis of these techniques are models considering the optical competitiveness of several coexisting aquatic components. Such models for Lakes Ladoga and Ontario are described and compared. Monte Carlo simulations have been performed to analyze the spectral and angular variations of the upwelling radiance scattered by the water column out into the atmosphere. Analysis of optical conditions for running remote sounding natural waters of various optical complexity is carried out. Relevant recommendations are formulated.
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