(Table 1) Active-layer thickness as measured by the Biocomplexity Experiment (BE) for the flooded, drained and control sections in Barrow, Alaska

A continuous time series of annual soil thaw records, extending from 1994 to 2009, is available for comparison with the records of thaw obtained from the Biocomplexity Experiment (BE) for the period 2006-2009. Discontinuous records of thaw at Barrow from wet tundra sites date back to the 1960s. Comparisons between the longer records with the BE observations reveal strong similarities. Records of permafrost temperature, reflecting changes in the annual surface energy exchange, are available from the 1950s for comparison with results from measurement programs begun in 2002. The long-term systematic geocryological investigations at Barrow indicate an increase in permafrost temperature, especially during the last several years. The increase in near-surface permafrost temperature is most pronounced in winter. Marked trends are not apparent in the active-layer record, although subsidence measurements on the North Slope indicate that penetration into the ice-rich layer at the top of permafrost has occurred over the past decade. Active-layer thickness values from the 1960s are generally higher than those from the 1990s, and are very similar to those of the 2000s. Analysis of spatial active-layer observations at representative locations demonstrates significant variations in active-layer thickness between different landscape types, reflecting the influence of vegetation, substrate, microtopography, and, especially, soil moisture. Landscape-specific differences exist in the response of active-layer thickness to climatic forcing. These differences are attributable to the existence of localized controls related to combinations of surface and subsurface characteristics. The geocryological records at Barrow illustrate the importance and effectiveness of sustained, well organized monitoring efforts to document long-term trends.

Spring snowmelt and midwinter thaw and refreeze north of 60°N based on Seawinds QuikScat 2000-2009

The scatterometer SeaWinds on QuikSCAT provided regular measurements at Ku-band from 1999 to 2009. Although it was designed for ocean applications, it has been frequently used for the assessment of seasonal snowmelt patterns aside from other terrestrial applications such as ice cap monitoring, phenology and urban mapping. This paper discusses general data characteristics of SeaWinds and reviews relevant change detection algorithms. Depending on the complexity of the method, parameters such as long-term noise and multiple event analyses were incorporated. Temporal averaging is a commonly accepted preprocessing step with consideration of diurnal, multi-day or seasonal averages.

Lithic grain and mineral composition of sand and sandstones from the North Pacific Ocean and the Bering Sea (Table 3)

Sand and sandstone compositions from different types of basins reflect provenance terranes governed by plate tectonics. One hundred and one thin sections of Upper Miocene to Holocene sand-sized material were examined from DSDP/IPOD Sites in the North Pacific Ocean and the Bering Sea. The Gazzi-Dickinson point-counting method was used to establish compositional characteristics of sands from different tectonic settings. Continental margin forearc sands from the western North America continental margin arc system are clearly different from backarc/marginal-sea sands from the Aleutian intraoceanic arc system. The forearc sands have average QFL percentages of 29-42-29, LmLvLst percentages of 32-34-34, 3 Fmwk%M and 0.82 P/F. Aleutian backarc sands have average QFL percentages of 8-22-69. LmLvLst percentages of 9-85-6, 0.5 Fmwk%M and 0.96 P/F. A trend of increasing QFL%Q and decreasing LmLvLst%Lv westward in the backarc region of the Aleutian Ridge reflects the influence of the Asiatic continental margin. Aleutian backarc sands without continental influence have average QFL percentages of 1-20-79, LmLvLst percentages of 1-98-1, 0 Fmwk%M and 0.99 P/F. Of the continental margin forearc samples, sands on the Astoria Fan (west of the Oregon-Washington trench) contain the highest LmLvLst%Lv and lowest P/F; sands from mixed transform-fault and trench settings (Delgada Fan and Gulf of Alaska samples) have slightly higher Qp/Q (0.03); and sands from the Pacific-Juan de Fuca-North America triple junction have the highest Fmwk%M. Delgada Fan and Gulf of Alaska sands have average QFL percentages of 27-38-35, LmLvLst percentages of 37-26-37, 2 Fmwk%M and 0.86 P/F. Astoria Fan sands have average QFL percentages of 35-41-24, LmLvLst percentages of 30-47-23, 3 Fmwk%M and 0.74 P/F. The triple-junction sands have average QFL percentages of 28-59-13, LmLvLst percentages of 25-26-49, 9 Fmwk%M and 0.87 P/F. The petrologic data from the modern ocean basins examined in this study can provide useful analogs for interpretation of ancient oceanic sequences. Our data suggest some refinements of, but generally substantiate, existing petrologic models relating sandstone composition to tectonic setting.

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.