Size-partitioned phytoplankton carbon concentrations retrieved from ocean color data, links to data in netCDF format

Owing to their important roles in biogeochemical cycles, phytoplankton functional types (PFTs) have been the aim of an increasing number of ocean color algorithms. Yet, none of the existing methods are based on phytoplankton carbon (C) biomass, which is a fundamental biogeochemical and ecological variable and the "unit of accounting" in Earth system models. We present a novel bio-optical algorithm to retrieve size-partitioned phytoplankton carbon from ocean color satellite data. The algorithm is based on existing methods to estimate particle volume from a power-law particle size distribution (PSD). Volume is converted to carbon concentrations using a compilation of allometric relationships. We quantify absolute and fractional biomass in three PFTs based on size - picophytoplankton (0.5-2 µm in diameter), nanophytoplankton (2-20 µm) and microphytoplankton (20-50 µm). The mean spatial distributions of total phytoplankton C biomass and individual PFTs, derived from global SeaWiFS monthly ocean color data, are consistent with current understanding of oceanic ecosystems, i.e., oligotrophic regions are characterized by low biomass and dominance of picoplankton, whereas eutrophic regions have high biomass to which nanoplankton and microplankton contribute relatively larger fractions. Global climatological, spatially integrated phytoplankton carbon biomass standing stock estimates using our PSD-based approach yield - 0.25 Gt of C, consistent with analogous estimates from two other ocean color algorithms and several state-of-the-art Earth system models. Satisfactory in situ closure observed between PSD and POC measurements lends support to the theoretical basis of the PSD-based algorithm. Uncertainty budget analyses indicate that absolute carbon concentration uncertainties are driven by the PSD parameter No which determines particle number concentration to first order, while uncertainties in PFTs' fractional contributions to total C biomass are mostly due to the allometric coefficients. The C algorithm presented here, which is not empirically constrained a priori, partitions biomass in size classes and introduces improvement over the assumptions of the other approaches. However, the range of phytoplankton C biomass spatial variability globally is larger than estimated by any other models considered here, which suggests an empirical correction to the No parameter is needed, based on PSD validation statistics. These corrected absolute carbon biomass concentrations validate well against in situ POC observations.

Stable isotope, UK'37, SST, chlorin, alkenone and TOC data of sediment cores GIK17927-2 and GIK17928-3

Upwelling intensity in the South China Sea has changed over glacial-interglacial cycles in response to orbital-scale changes in the East Asian Monsoon. Here, we evaluate new multi-proxy records of two sediment cores from the north-eastern South China Sea to uncover millennial-scale changes in winter monsoondriven upwelling over glacial Terminations I and II. On the basis of U/Th-based speleothem chronology, we compare these changes with sediment records of summer monsoondriven upwelling east of South Vietnam. Ocean upwelling is traced by reduced (UK'37-based) temperature and increased nutrient and productivity estimates of sea surface water (d13C on planktic foraminifera, accumulation rates of alkenones, chlorins, and total organic carbon). Accordingly, strong winter upwelling occurred north-west of Luzon (Philippines) during late Marine Isotope Stage 6.2, Heinrich (HS) and Greenland stadials (GS) HS-11, GS-26, GS-25, HS-1, and the Younger Dryas. During these stadials, summer upwelling decreased off South Vietnam and sea surface salinity reached a maximum suggesting a drop in monsoon rains, concurrent with speleothem records of aridity in China. In harmony with a stadial-to-interstadial see-saw pattern, winter upwelling off Luzon in turn was weak during interstadials, in particular those of glacial Terminations I and II, when summer upwelling culminated east of South Vietnam. Most likely, this upwelling terminated widespread deep-water stratification, coeval with the deglacial rise in atmospheric CO2. Yet, a synchronous maximum in precipitation fostered estuarine overturning circulation in the South China Sea, in particular as long as the Borneo Strait was closed when sea level dropped below -40 m.

Diatom oxygen and silicon isotopes data from IODP Hole 323-U1341B in the Bering Sea through the late Pliocene and over the onset of major Northern Hemisphere Glaciation

The Bering Sea is one of the most biologically productive regions in the marine system and plays a key role in regulating the flow of waters to the Arctic Ocean and into the subarctic North Pacific Ocean. Cores from Integrated Ocean Drilling Program (IODP) Expedition 323 to the Bering Sea provide the first opportunity to obtain reconstructions from the region that extend back to the Pliocene. Previous research at Bowers Ridge, south Bering Sea, has revealed stable levels of siliceous productivity over the onset of major Northern Hemisphere Glaciation (NHG) (circa 2.85-2.73 Ma). However, diatom silica isotope records of oxygen (d18Odiatom) and silicon (d30Sidiatom) presented here demonstrate that this interval was associated with a progressive increase in the supply of silicic acid to the region, superimposed on shift to a more dynamic environment characterized by colder temperatures and increased sea ice. This concluded at 2.58 Ma with a sharp increase in diatom productivity, further increases in photic zone nutrient availability and a permanent shift to colder sea surface conditions. These transitions are suggested to reflect a gradually more intense nutrient leakage from the subarctic northwest Pacific Ocean, with increases in productivity further aided by increased sea ice- and wind-driven mixing in the Bering Sea. In suggesting a linkage in biogeochemical cycling between the south Bering Sea and subarctic Northwest Pacific Ocean, mainly via the Kamchatka Strait, this work highlights the need to consider the interconnectivity of these two systems when future reconstructions are carried out in the region.

Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.