Landfast sea-ice and platelet-layer thickness and conductivity of Atka Bay, Antarctica, December 2012

Ice shelves strongly impact coastal Antarctic sea-ice and the associated ecosystem through the formation of a sub-sea-ice platelet layer. Although progress has been made in determining and understanding its spatio-temporal variability based on point measurements, an investigation of this phenomenon on a larger scale remains a challenge due to logistical constraints and a lack of suitable methodology. In this study, we applied a laterally-constrained Marquardt-Levenberg inversion to a unique multi-frequency electromagnetic (EM) induction sounding dataset obtained on the landfast sea ice of Atka Bay, eastern Weddell Sea, in 2012. In addition to consistent fast-ice thickness and -conductivities along > 100 km transects; we present the first comprehensive, high resolution platelet-layer thickness and -conductivity dataset recorded on Antarctic sea ice. The reliability of the algorithm was confirmed by using synthetic data, and the inverted platelet-layer thicknesses agreed within the data uncertainty to drill-hole measurements. Ice-volume fractions were calculated from platelet-layer conductivities, revealing that an older and thicker platelet layer is denser and more compacted than a loosely attached, young platelet layer. The overall platelet-layer volume below Atka Bay fast ice suggests that the contribution of ocean/ice-shelf interaction to sea-ice volume in this region is even higher than previously thought. This study also implies that multi-frequency EM induction sounding is an effective approach in determining platelet layer volume on a larger scale than previously feasible. When applied to airborne multi-frequency EM, this method could provide a step towards an Antarctic-wide quantification of ocean/ice-shelf interaction.

(Table 3) Air temperatures, and snow and ice characteristics of fresh and brackish water lakes in the Northwest Territories, Canada

The algorithms designed to estimate snow water equivalent (SWE) using passive microwave measurements falter in lake-rich high-latitude environments due to the emission properties of ice covered lakes on low frequency measurements. Microwave emission models have been used to simulate brightness temperatures (Tbs) for snowpack characteristics in terrestrial environments but cannot be applied to snow on lakes because of the differing subsurface emissivities and scattering matrices present in ice. This paper examines the performance of a modified version of the Helsinki University of Technology (HUT) snow emission model that incorporates microwave emission from lake ice and sub-ice water. Inputs to the HUT model include measurements collected over brackish and freshwater lakes north of Inuvik, Northwest Territories, Canada in April 2008, consisting of snowpack (depth, density, and snow water equivalent) and lake ice (thickness and ice type). Coincident airborne radiometer measurements at a resolution of 80x100 m were used as ground-truth to evaluate the simulations. The results indicate that subsurface media are simulated best when utilizing a modeled effective grain size and a 1 mm RMS surface roughness at the ice/water interface compared to using measured grain size and a flat Fresnel reflective surface as input. Simulations at 37 GHz (vertical polarization) produce the best results compared to airborne Tbs, with a Root Mean Square Error (RMSE) of 6.2 K and 7.9 K, as well as Mean Bias Errors (MBEs) of -8.4 K and -8.8 K for brackish and freshwater sites respectively. Freshwater simulations at 6.9 and 19 GHz H exhibited low RMSE (10.53 and 6.15 K respectively) and MBE (-5.37 and 8.36 K respectively) but did not accurately simulate Tb variability (R= -0.15 and 0.01 respectively). Over brackish water, 6.9 GHz simulations had poor agreement with airborne Tbs, while 19 GHz V exhibited a low RMSE (6.15 K), MBE (-4.52 K) and improved relative agreement to airborne measurements (R = 0.47). Salinity considerations reduced 6.9 GHz errors substantially, with a drop in RMSE from 51.48 K and 57.18 K for H and V polarizations respectively, to 26.2 K and 31.6 K, although Tb variability was not well simulated. With best results at 37 GHz, HUT simulations exhibit the potential to track Tb evolution, and therefore SWE through the winter season.

Atmospheric, oceanic and hydrological polar motion excitation mechanisms based on a combination of geometric and gravimetric space observations

The goal of our study is to determine accurate time series of geophysical Earth rotation excitations to learn more about global dynamic processes in the Earth system. For this purpose, we developed an adjustment model which allows to combine precise observations from space geodetic observation systems, such as Satellite Laser Ranging (SLR), Global Navigation Satellite Systems (GNSS), Very Long Baseline Interferometry (VLBI), Doppler Orbit determination and Radiopositioning Integrated on Satellite (DORIS), satellite altimetry and satellite gravimetry in order to separate geophysical excitation mechanisms of Earth rotation. Three polar motion time series are applied to derive the polar motion excitation functions (integral effect). Furthermore we use five time variable gravity field solutions from Gravity Recovery and Climate Experiment (GRACE) to determine not only the integral mass effect but also the oceanic and hydrological mass effects by applying suitable filter techniques and a land-ocean mask. For comparison the integral mass effect is also derived from degree 2 potential coefficients that are estimated from SLR observations. The oceanic mass effect is also determined from sea level anomalies observed by satellite altimetry by reducing the steric sea level anomalies derived from temperature and salinity fields of the oceans. Due to the combination of all geodetic estimated excitations the weaknesses of the individual processing strategies can be reduced and the technique-specific strengths can be accounted for. The formal errors of the adjusted geodetic solutions are smaller than the RMS differences of the geophysical model solutions. The improved excitation time series can be used to improve the geophysical modeling.

(Table 2) Production parameters of phytoplankton and some associated parameters in the area of the Titanic Polygon

Studies were carried out mostly in the area of RMS Titanic wreck site (41°44'N, 49°57'W) located above the continental slope and the south of the Grand Banks of Newfoundland. In a period from 18.06 to 24.09.2001 five surveys of production characteristics of surface phytoplankton were conducted over 5-9 days. Mean values of these characteristics obtained during the surveys were 9.2-11.7 mg C/m**3 per day for primary production (C_phs), 0.102-0.188 mg/m**3 for chlorophyll a (C_chls), and 4.44-7.42 mg C/mg chl. a per hour for assimilation number (AN). The main reason for low C_phs variability was a significant inverse relationship (R=-0.66) between AN and C_chls found over the research area. When cold shelf waters dominated in the area (27.07 to 19.08.2001), C_chls values for the slope region (0.125+/-0.031 µg/l) and for the outer shelf (0.130+/-0.040 µg/l) were similar. During strengthening of influence of warmer slope waters within area (from 29.08 to 13.09.2001), C_chls concentration within surface waters of the outer shelf was 0.152+/-0.039 µg/l and exceeded one for the slope region (0.094+/-0.004 µg/l) by factor 1.6. Against the background of low Cchls values, the High values of integral primary production in the water column (510-1010 mg C/m**2 per day) at low C_chls values measured within the area were determined both by high assimilation activity of phytoplankton and by the deep (30-40 m) maximum of primary production. Main reasons for formation of such a maximum were high chlorophyll concentration within the layer of the deep chlorophyll maximum (up to 0.5-2.5 µg/l) and in the relatively high solar irradiance within this layer varying from 1.4 to 8.6% of subsurface PAR.

Isotopic analysis of core gases at DSDP Leg 84 Holes

Methane carbon-isotopic compositions (d13C values relative to the PDB standard) at Sites 565, 566, 567, and 569 were lighter (enriched in 12C) than -60 per mil, indicating a biogenic origin. In the deeper sections at Sites 568 and 570, d13C values were heavier, approaching -40 per mil, and therefore suggest a thermogenic source. A significant thermogenic source was discounted, however, because the carbon dioxide d13C values in these sections were also anomalously heavy, suggesting that the methane may have formed biogenically by reduction of the heavy carbon dioxide. d13C values of ethane and higher hydrocarbons were measured in several sections from Sites 566 and 570 that contained sufficient C2-C4 hydrocarbon concentrations. Ethane values in six sections (245-395 m sub-bottom) from Site 570 were fairly uniform, ranging from -24 to -26 per mil. These values are among the heaviest ethane values reported for natural gases. The isobutane/ n-butane and isopentane/n-pentane ratios of the core gases suggested that the C2-C5 hydrocarbons are thermally produced by low-temperature chemical diagenesis of indigenous organic matter. This process apparently generates isotopically heavy C2-C5 hydrocarbons. High gas concentrations in the serpentinite basement rocks at Sites 566 and 570 appear to have resulted from migrated biogenic methane gas containing small amounts of immature C2-C5 hydrocarbons.

Global macrozoobenthos production and energy budget data base V150731, with link to database

I developed a new model for estimating annual production-to-biomass ratio P/B and production P of macrobenthic populations in marine and freshwater habitats. Self-learning artificial neural networks (ANN) were used to model the relationships between P/B and twenty easy-to-measure abiotic and biotic parameters in 1252 data sets of population production. Based on log-transformed data, the final predictive model estimates log(P/B) with reasonable accuracy and precision (r2 = 0.801; residual mean square RMS = 0.083). Body mass and water temperature contributed most to the explanatory power of the model. However, as with all least squares models using nonlinearly transformed data, back-transformation to natural scale introduces a bias in the model predictions, i.e., an underestimation of P/B (and P). When estimating production of assemblages of populations by adding up population estimates, accuracy decreases but precision increases with the number of populations in the assemblage.