Barents and Kara Seas oceanographic data base (BarKode)

Oceanographic data collected by ocean research organisations in Russia, the USA, the United Kingdom, Germany, Norway, and Poland for the Barents, Kara and White Seas region are presented in this atlas. Recently declassified naval data from Norway, the USA, and the UK are also included. More than 1,000,000 oceanographic stations containing temperature and/or sea-water salinity data were originally selected. After correcting errors and eliminating duplicates, data from 206,300 checked stations were placed on CD-ROM, together with many figures describing the characteristics of both the single-input and combined data set. In addition, temperature and salinity measurements were interpolated to the following standard horizons: 0, 25, 50, 100, 150, 200, 250, 300 m, and bottom. This atlas covers the 100-year period 1898 to 1998 and is, to date, the most complete oceanographic data collection for these Arctic shelf seas. This data set is complemented by more than 9,000 measurements of sea surface temperature, which were recently digitized from ships' logbooks. They cover the same geographical area within the time period 1867-1912.

Part of the global DOC versus AOU (dissolved organic carbon/apparent oxygen utilization) data compilation, Southern Ocean

Recent evidence that dissolved organic carbon (DOC) is a significant component of the organic carbon flux below the photic layer of the ocean (1), together with verification of high respiration rates in the dark ocean (2), suggests that the downward flux of DOC may play a major role in supporting respiration there. Here we show, on the basis of examination of the relation between DOC and apparent oxygen utilization (AOU), that the DOC flux supports ~10% of the respiration in the dark ocean. The contribution of DOC to pelagic respiration below the surface mixed layer can be inferred from the relation between DOC and apparent oxygen utilization (AOU, µM O2), a variable quantifying the cumulative oxygen consumption since a water parcel was last in contact with the atmosphere. However, assessments of DOC/AOU relations have been limited to specific regions of the ocean (3, 4) and have not considered the global ocean. We assembled a large data set (N = 9824) of concurrent DOC and AOU observations collected in cruises conducted throughout the world's oceans (fig. S1, table S1) to examine the relative contribution of DOC to AOU and, therefore, respiration in the dark ocean. AOU increased from an average (±SE) 96.3 ± 2.0 µM at the base of the surface mixed layer (100 m) to 165.5 ± 4.3 µM at the bottom of the main thermocline (1000 m), with a parallel decline in the average DOC from 53.5 ± 0.2 to 43.4 ± 0.3 µM C (Fig. 1). In contrast, there is no significant decline in DOC with increasing depth beyond 1000 m depth (Fig. 1), indicating that DOC exported with overturning circulation plays a minor role in supporting respiration in the ocean interior (5). Assuming a molar respiratory quotient of 0.69, the decline in DOC accounts for 19.6 ± 0.4% of the AOU within the top 1000 m (Fig. 1). This estimate represents, however, an upper limit, because the correlation between DOC and AOU is partly due to mixing of DOC-rich warm surface waters with DOC-poor cold thermocline waters (6). Removal of this effect by regressing DOC against AOU and water temperature indicates that DOC supports only 8.4 ± 0.3% of the respiration in the mesopelagic waters.

Compilation of quality controlled nutrient profiles from the Mediterranean Sea

In the last decades, a striking amount of hydrographic data, covering the most part of Mediterranean basin, have been generated by the efforts made to characterize the oceanography and ecology of the basin. On the other side, the improvement in technologies, and the consequent perfecting of sampling and analytical techniques, provided data even more reliable than in the past. Nutrient data enter fully in this context, but suffer of the fact of having been produced by a large number of uncoordinated research programs and of being often deficient in quality control, with data bases lacking of intercalibration. In this study we present a computational procedure based on robust statistical parameters and on the physical dynamic properties of the Mediterranean sea and its morphological characteristics, to partially overcome the above limits in the existing data sets. Through a data pre filtering based on the outlier analysis, and thanks to the subsequent shape analysis, the procedure identifies the inconsistent data and for each basin area identifies a characteristic set of shapes (vertical profiles). Rejecting all the profiles that do not follow any of the spotted shapes, the procedure identifies all the reliable profiles and allows us to obtain a data set that can be considered more internally consistent than the existing ones.

Reference list of 120 datasets from time series station Payerne used for exploratory search

The analysis of time-dependent data is an important problem in many application domains, and interactive visualization of time-series data can help in understanding patterns in large time series data. Many effective approaches already exist for visual analysis of univariate time series supporting tasks such as assessment of data quality, detection of outliers, or identification of periodically or frequently occurring patterns. However, much fewer approaches exist which support multivariate time series. The existence of multiple values per time stamp makes the analysis task per se harder, and existing visualization techniques often do not scale well. We introduce an approach for visual analysis of large multivariate time-dependent data, based on the idea of projecting multivariate measurements to a 2D display, visualizing the time dimension by trajectories. We use visual data aggregation metaphors based on grouping of similar data elements to scale with multivariate time series. Aggregation procedures can either be based on statistical properties of the data or on data clustering routines. Appropriately defined user controls allow to navigate and explore the data and interactively steer the parameters of the data aggregation to enhance data analysis. We present an implementation of our approach and apply it on a comprehensive data set from the field of earth bservation, demonstrating the applicability and usefulness of our approach.

Collection of data on particulate and dissolved soil nitrogen in the Jena Experiment (Main Experiment, time series since 2002)

This data set contains four time series of particulate and dissolved soil nitrogen measurements from the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. 1. Total nitrogen from solid phase: Stratified soil sampling was performed every two years since before sowing in April 2002 and was repeated in April 2004, 2006 and 2008 to a depth of 30 cm segmented to a depth resolution of 5 cm giving six depth subsamples per core. In 2002 five samples per plot were taken and analyzed independently. Averaged values per depth layer are reported. In later years, three samples per plot were taken, pooled in the field, and measured as a combined sample. Sampling locations were less than 30 cm apart from sampling locations in other years. All soil samples were passed through a sieve with a mesh size of 2 mm in 2002. In later years samples were further sieved to 1 mm. No additional mineral particles were removed by this procedure. Total nitrogen concentration was analyzed on ball-milled subsamples (time 4 min, frequency 30 s-1) by an elemental analyzer at 1150°C (Elementaranalysator vario Max CN; Elementar Analysensysteme GmbH, Hanau, Germany). 2. Total nitrogen from solid phase (high intensity sampling): In block 2 of the Jena Experiment, soil samples were taken to a depth of 1m (segmented to a depth resolution of 5 cm giving 20 depth subsamples per core) with three replicates per block ever 5 years starting before sowing in April 2002. Samples were processed as for the more frequent sampling but were always analyzed independently and never pooled. 3. Mineral nitrogen from KCl extractions: Five soil cores (diameter 0.01 m) were taken at a depth of 0 to 0.15 m (and between 2002 and 2004 also at a depth of 0.15 to 0.3 m) of the mineral soil from each of the experimental plots at various times over the years. In addition also plots of the management experiment, that altered mowing frequency and fertilized subplots (see further details below) were sampled in some later years. Samples of the soil cores per plot (subplots in case of the management experiment) were pooled during each sampling campaign. NO3-N and NH4-N concentrations were determined by extraction of soil samples with 1 M KCl solution and were measured in the soil extract with a Continuous Flow Analyzer (CFA, 2003-2005: Skalar, Breda, Netherlands; 2006-2007: AutoAnalyzer, Seal, Burgess Hill, United Kingdom). 4. Dissolved nitrogen in soil solution: Glass suction plates with a diameter of 12 cm, 1 cm thickness and a pore size of 1-1.6 µm (UMS GmbH, Munich, Germany) were installed in April 2002 in depths of 10, 20, 30 and 60 cm to collect soil solution. The sampling bottles were continuously evacuated to a negative pressure between 50 and 350 mbar, such that the suction pressure was about 50 mbar above the actual soil water tension. Thus, only the soil leachate was collected. Cumulative soil solution was sampled biweekly and analyzed for nitrate (NO3-), ammonium (NH4+) and total dissolved nitrogen concentrations with a continuous flow analyzer (CFA, Skalar, Breda, The Netherlands). Nitrate was analyzed photometrically after reduction to NO2- and reaction with sulfanilamide and naphthylethylenediamine-dihydrochloride to an azo-dye. Our NO3- concentrations contained an unknown contribution of NO2- that is expected to be small. Simultaneously to the NO3- analysis, NH4+ was determined photometrically as 5-aminosalicylate after a modified Berthelot reaction. The detection limits of NO3- and NH4+ were 0.02 and 0.03 mg N L-1, respectively. Total dissolved N in soil solution was analyzed by oxidation with K2S2O8 followed by reduction to NO2- as described above for NO3-. Dissolved organic N (DON) concentrations in soil solution were calculated as the difference between TDN and the sum of mineral N (NO3- + NH4+).

Dissolved Si isotope composition measured in water samples during METEOR cruises M77/3 and M77/4

Silicon isotopes are a powerful tool to investigate the cycling of dissolved silicon (Si). In this study the distribution of the Si isotope composition of dissolved silicic acid (d30Si(OH)4) was analyzed in the water column of the Eastern Equatorial Pacific (EEP) where one of the globally largest Oxygen Minimum Zones (OMZs) is located. Samples were collected at 7 stations along two meridional transects from the equator to 14°S at 85°50'W and 82°00'W off the Ecuadorian and Peruvian coast. Surface waters show a large range in isotope compositions d30Si(OH)4 (+2.2 per mil to +4.4 per mil) with the highest values found at the southernmost station at 14°S. This station also revealed the most depleted silicic acid concentrations (0.2 µmol/kg), which is a function of the high degree of Si utilization by diatoms and admixture with waters from highly productive areas. Samples within the upper water column and the OMZ at oxygen concentrations below 10 µmol/kg are characterized by a large range in d30Si(OH)4, which mainly reflects advection and mixing of different water masses, even though the highly dynamic hydrographic system of the upwelling area off Peru does not allow the identification of clear Si isotope signals for distinct water masses. Therefore we cannot rule out that also dissolution processes have an influence on the d30Si(OH)4 signature in the subsurface water column. Deep water masses (>2000 m) in the study area show a mean d30Si(OH)4 of +1.2±0.2 per mil, which is in agreement with previous studies from the eastern and central Pacific. Comparison of the new deep water data of this study and previously published data from the central Pacific and Southern Ocean reveal substantially higher d30Si(OH)4 values than deep water signatures from the North Pacific. As there is no clear correlation between d30Si(OH)4 and silicic acid concentrations in the entire data set the distribution of d30Si(OH)4 signatures in deep waters of the Pacific is considered to be mainly a consequence of the mixing of several end member water masses with distinct Si isotope signatures including Lower Circumpolar Deep Water (LCDW) and North Pacific Deep Water (NPDW).

Surface temperature measurements near Ny-Ålesund, Svalbard, Norway in 2008 with links to data files

The ground surface temperature is one of the key parameters that determine the thermal regime of permafrost soils in arctic regions. Due to remoteness of most permafrost areas, monitoring of the land surface temperature (LST) through remote sensing is desirable. However, suitable satellite platforms such as MODIS provide spatial resolutions, that cannot resolve the considerable small-scale heterogeneity of the surface conditions characteristic for many permafrost areas. This study investigates the spatial variability of summer surface temperatures of high-arctic tundra on Svalbard, Norway. A thermal imaging system mounted on a mast facilitates continuous monitoring of approximately 100 x 100 m of tundra with a wide variability of different surface covers and soil moisture conditions over the entire summer season from the snow melt until fall. The net radiation is found to be a control parameter for the differences in surface temperature between wet and dry areas. Under clear-sky conditions in July, the differences in surface temperature between wet and dry areas reach up to 10K. The spatial differences reduce strongly in weekly averages of the surface temperature, which are relevant for the soil temperature evolution of deeper layers. Nevertheless, a considerable variability remains, with maximum differences between wet and dry areas of 3 to 4K. Furthermore, the pattern of snow patches and snow-free areas during snow melt in July causes even greater differences of more than 10K in the weekly averages. Towards the end of the summer season, the differences in surface temperature gradually diminish. Due to the pronounced spatial variability in July, the accumulated degree-day totals of the snow-free period can differ by more than 60% throughout the study area. The terrestrial observations from the thermal imaging system are compared to measurements of the land surface temperature from the MODIS sensor. During periods with frequent clear-sky conditions and thus a high density of satellite data, weekly averages calculated from the thermal imaging system and from MODIS LST agree within less than 2K. Larger deviations occur when prolonged cloudy periods prevent satellite measurements. Futhermore, the employed MODIS L2 LST data set contains a number of strongly biased measurements, which suggest an admixing of cloud top temperatures. We conclude that a reliable gap filling procedure to moderate the impact of prolonged cloudy periods would be of high value for a future LST-based permafrost monitoring scheme. The occurrence of sustained subpixel variability of the summer surface temperature is a complicating factor, whose impact needs to be assessed further in conjunction with other spatially variable parameters such as the snow cover and soil properties.