Browsing by Author "Platt, T."
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- Ocean surface partitioning strategies using Ocean Colour Remote Sensing: a reviewPublication . Krug, Lilian; Platt, T.; Sathyendranath, S.; Barbosa, Ana B.The ocean surface is organized into regions with distinct properties reflecting the complexity of interactions between environmental forcing and biological responses. The delineation of these functional units, each with unique, homogeneous properties and underlying ecosystem structure and dynamics, can be defined as ocean surface partitioning. The main purposes and applications of ocean partitioning include the evaluation of particular marine environments; generation of more accurate satellite ocean colour products; assimilation of data into biogeochemical and climate models; and establishment of ecosystem-based management practices. This paper reviews the diverse approaches implemented for ocean surface partition into functional units, using ocean colour remote sensing (OCRS) data, including their purposes, criteria, methods and scales. OCRS offers a synoptic, high spatial-temporal resolution, multi-decadal coverage of bio-optical properties, relevant to the applications and value of ocean surface partitioning. In combination with other biotic and/or abiotic data, OCRS-derived data (e.g., chlorophyll-a, optical properties) provide a broad and varied source of information that can be analysed using different delineation methods derived from subjective, expert-based to unsupervised learning approaches (e.g., cluster, fuzzy and empirical orthogonal function analyses). Partition schemes are applied at global to mesoscale spatial coverage, with static (time-invariant) or dynamic (time-varying) representations. A case study, the highly heterogeneous area off SW Iberian Peninsula (NE Atlantic), illustrates how the selection of spatial coverage and temporal representation affects the discrimination of distinct environmental drivers of phytoplankton variability. Advances in operational oceanography and in the subject area of satellite ocean colour, including development of new sensors, algorithms and products, are among the potential benefits from extended use, scope and applications of ocean surface partitioning using OCRS.
- Unravelling region-specific environmental drivers of phytoplankton across a complex marine domain (off SW Iberia)Publication . Krug, Lilian; Platt, T.; Sathyendranath, S.; Barbosa, Ana B.Phytoplankton, the dominant marine primary producers, are considered to be highly sensitive indicators of ecosystem condition and change. The southwest area off the Iberian Peninsula (SWIP, NE Atlantic) is located in a biogeographical transition zone between temperate and subtropical waters, and classified as being very vulnerable to climate change. SWIP includes a variety of oceanic and coastal domains, under the influence of topographic irregularities, coastal upwelling and continental freshwater outflows, that collectively challenge the understanding of phytoplankton dynamics and controls. This study aimed to evaluate patterns in seasonal and interannual variability in phytoplankton and underlying environmental determinants within specific regions of SWIP, during a 15-year period (1997–2012), and to assess whether climate variability affects the regions in different ways. Empirical Orthogonal Function (EOF) analysis of satellite-retrieved sea surface chlorophyll-a concentration (Chl-a), acquired from the Ocean Colour Climate Change Initiative (OC-CCI), 4-km, 16-day resolution, was used to regionalize the study area. Region-specific Chl-a variability patterns and their linkages with environmental determinants were explored using Generalized Additive Mixed Models (GAMM). A set of local physical-chemical variables, derived from satellite and model data, and large-scale climate indices, were used as environmental variables. EOF analysis of Chl-a variability over the heterogeneous SWIP area identified nine coherent regions, with distinctive variability patterns (4 coastals, 2 slopes and 3 open-ocean regions). Region specific GAMM models explained between 32% and 82% of Chl-a variance, with higher explanatory power (N61%) for open ocean regions and coastal regions under increased riverine influence. Chl-a model predictors, as well as their effects, varied markedly among SWIP regions. However, climate-sensitive local environmental variables (sea surface temperature – SST and photosynthetically available radiation) emerged as the most influential general predictors overall, and large-scale climate indices showed significant but minor effects. Over oceanic SWIP regions, Chl-a (0.08–1.50 μg L−1) showed a uni-modal annual cycle, with increases during the mixed layer deepening and late-winter maxima, reflecting seasonal changes in SST and ocean stratification, and probably related to increased nutrient availability and/or decreased mortality. Over coastal regions south of 37°N, Chl-a (0.23–10 μg L−1) also benefited from riverine discharges, mostly during winter, and upwelling induced by zonal westerly winds, stronger during summer. Over the Portuguese west coast region, Chl-a (0.26–2.20 μg L−1) showed a uni-modal annual cycle, with summer maxima, associated with the stimulatory effects of meridional northerly winds and coastal upwelling that partially extended into slope waters. Chl-a interannual variability showed zonal differences within SWIP, with significant interannual patterns only for regions south of 37°N. Nonetheless, contrasting trends were detected in coastal (decline) and oceanic (increase) regions, possibly a consequence of between-region differences in the relative roles of nutrient and light limitation, corresponding to significant interannual increases in wind speed and mixed layer depth. Our study used a biologically-relevant objective regionalization of a heterogeneous area, to elucidate phytoplankton dynamics and controls. The region-specific associations observed between phytoplankton and multiple climate-sensitive environmental drivers over the SWIP area reinforce the role of phytoplankton as a strategic element for evaluating ecosystem responses to climate variability and change.