Stable isotope stacks from GRIP and GISP ice cores

Recent efforts to link the isotopic composition of snow in Greenland with meteorological and climatic parameters have indicated that relatively local information such as observed annual temperatures from coastal Greenland sites, as well as more synoptic scale features such as the North Atlantic Oscillation (NAO) and the temperature seesaw between Jakobshaven, Greenland, and Oslo, Norway, are significantly correlated with d18O and dD values from the past few hundred years measured in ice cores. In this study we review those efforts and then use a new record of isotope values from the Greenland Ice Sheet Project 2 and Greenland Ice Core Project sites at Summit, Greenland, to compare with meteorological and climatic parameters. This new record consists of six individual annually resolved isotopic records which have been average to produce a Summit stacked isotope record. The stacked record is significantly correlated with local Greenland temperatures over the past century (r=0.471), as well as a number of other records including temperatures and pressures from specific locations as well as temperature and pressure patterns such as the temperature seesaw and the North Atlantic Oscillation. A multiple linear regression of the stacked isotope record with a number of meteorological and climatic parameters in the North Atlantic region reveals that five variables contribute significantly to the variance in the isotope record: winter NAO, solar irradiance (as recorded by sunspot numbers), average Greenland coastal temperature, sea surface temperature in the moisture source region for Summit (30°-20°N), and the annual temperature seesaw between Jakobshaven and Oslo. Combined, these variables yield a correlation coefficient of r=0.71, explaining half of the variance in the stacked isotope record.

On the effects of circulation, sediment resuspension and biological incorporation by diatoms in an ocean model of aluminium, link to model data

The distribution of dissolved aluminium in the West Atlantic Ocean shows a mirror image with that of dissolved silicic acid, hinting at intricate interactions between the ocean cycling of Al and Si. The marine biogeochemistry of Al is of interest because of its potential impact on diatom opal remineralisation, hence Si availability. Furthermore, the dissolved Al concentration at the surface ocean has been used as a tracer for dust input, dust being the most important source of the bio-essential trace element iron to the ocean. Previously, the dissolved concentration of Al was simulated reasonably well with only a dust source, and scavenging by adsorption on settling biogenic debris as the only removal process. Here we explore the impacts of (i) a sediment source of Al in the Northern Hemisphere (especially north of ~ 40° N), (ii) the imposed velocity field, and (iii) biological incorporation of Al on the modelled Al distribution in the ocean. The sediment source clearly improves the model results, and using a different velocity field shows the importance of advection on the simulated Al distribution. Biological incorporation appears to be a potentially important removal process. However, conclusive independent data to constrain the Al / Si incorporation ratio by growing diatoms are missing. Therefore, this study does not provide a definitive answer to the question of the relative importance of Al removal by incorporation compared to removal by adsorptive scavenging.

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.