Ice-algal chlorophyll a and physical properties of multi-year and first-year sea ice in the Lincoln Sea during spring

With near-complete replacement of Arctic multi-year ice (MYI) by first-year ice (FYI) predicted to occur within this century, it remains uncertain how the loss of MYI will impact the abundance and distribution of sea ice associated algae. In this study we compare the chlorophyll a (chl a) concentrations and physical properties of MYI and FYI from the Lincoln Sea during 3 spring seasons (2010-2012). Cores were analysed for texture, salinity, and chl a. We identified annual growth layers for 7 of 11 MYI cores and found no significant differences in chl a concentration between the bottom first-year-ice portions of MYI, upper old-ice portions of MYI, and FYI cores. Overall, the maximum chl a concentrations were observed at the bottom of young FYI. However, there were no significant differences in chl a concentrations between MYI and FYI. This suggests little or no change in algal biomass with a shift from MYI to FYI and that the spatial extent and regional variability of refrozen leads and younger FYI will likely be key factors governing future changes in Arctic sea ice algal biomass. Bottom-integrated chl a concentrations showed negative logistic relationships with snow depth and bulk (snow plus ice) integrated extinction coefficients; indicating a strong influence of snow cover in controlling bottom ice algal biomass. The maximum bottom MYI chl a concentration was observed in a hummock, representing the thickest ice with lowest snow depth of this study. Hence, in this and other studies MYI chl a biomass may be under-estimated due to an under-representation of thick MYI (e.g., hummocks), which typically have a relatively thin snowpack allowing for increased light transmission. Therefore, we suggest the on-going loss of MYI in the Arctic Ocean may have a larger impact on ice-associated production than generally assumed.

Temperature, water level and bathymetry of thermokarst lakes in the continuous permafrost zone of northern Siberia - Lena River Delta, Siberia

Thermokarst lakes are typical features of the northern permafrost ecosystems, and play an important role in the thermal exchange between atmosphere and subsurface. The objective of this study is to describe the main thermal processes of the lakes and to quantify the heat exchange with the underlying sediments. The thermal regimes of five lakes located within the continuous permafrost zone of northern Siberia (Lena River Delta) were investigated using hourly water temperature and water level records covering a 3-year period (2009-2012), together with bathymetric survey data. The lakes included thermokarst lakes located on Holocene river terraces that may be connected to Lena River water during spring flooding, and a thermokarst lake located on deposits of the Pleistocene Ice Complex. Lakes were covered by ice up to 2 m thick that persisted for more than 7 months of the year, from October until about mid-June. Lake-bottom temperatures increased at the start of the ice-covered period due to upward-directed heat flux from the underlying thawed sediment. Prior to ice break-up, solar radiation effectively warmed the water beneath the ice cover and induced convective mixing. Ice break-up started at the beginning of June and lasted until the middle or end of June. Mixing occurred within the entire water column from the start of ice break-up and continued during the ice-free periods, as confirmed by the Wedderburn numbers, a quantitative measure of the balance between wind mixing and stratification that is important for describing the biogeochemical cycles of lakes. The lake thermal regime was modeled numerically using the FLake model. The model demonstrated good agreement with observations with regard to the mean lake temperature, with a good reproduction of the summer stratification during the ice-free period, but poor agreement during the ice-covered period. Modeled sensitivity to lake depth demonstrated that lakes in this climatic zone with mean depths > 5 m develop continuous stratification in summer for at least 1 month. The modeled vertical heat flux across the bottom sediment tends towards an annual mean of zero, with maximum downward fluxes of about 5 W/m**2 in summer and with heat released back into the water column at a rate of less than 1 W/m**2 during the ice-covered period. The lakes are shown to be efficient heat absorbers and effectively distribute the heat through mixing. Monthly bottom water temperatures during the ice-free period range up to 15 °C and are therefore higher than the associated monthly air or ground temperatures in the surrounding frozen permafrost landscape. The investigated lakes remain unfrozen at depth, with mean annual lake-bottom temperatures of between 2.7 and 4 °C.

Reference list of 269 sources used for exploratory search

Today's digital libraries (DLs) archive vast amounts of information in the form of text, videos, images, data measurements, etc. User access to DL content can rely on similarity between metadata elements, or similarity between the data itself (content-based similarity). We consider the problem of exploratory search in large DLs of time-oriented data. We propose a novel approach for overview-first exploration of data collections based on user-selected metadata properties. In a 2D layout representing entities of the selected property are laid out based on their similarity with respect to the underlying data content. The display is enhanced by compact summarizations of underlying data elements, and forms the basis for exploratory navigation of users in the data space. The approach is proposed as an interface for visual exploration, leading the user to discover interesting relationships between data items relying on content-based similarity between data items and their respective metadata labels. We apply the method on real data sets from the earth observation community, showing its applicability and usefulness.

Reference list of sources used for two experimental data files dataBSRN and dataMixed

Increasing amounts of data is collected in most areas of research and application. The degree to which this data can be accessed, analyzed, and retrieved, is a decisive in obtaining progress in fields such as scientific research or industrial production. We present a novel methodology supporting content-based retrieval and exploratory search in repositories of multivariate research data. In particular, our methods are able to describe two-dimensional functional dependencies in research data, e.g. the relationship between ination and unemployment in economics. Our basic idea is to use feature vectors based on the goodness-of-fit of a set of regression models to describe the data mathematically. We denote this approach Regressional Features and use it for content-based search and, since our approach motivates an intuitive definition of interestingness, for exploring the most interesting data. We apply our method on considerable real-world research datasets, showing the usefulness of our approach for user-centered access to research data in a Digital Library system.