Om Udskilning af Fløde fra Mælk ved Centrifugalkraft.

Om Udskilning af Fløde fra Mælk ved Centrifugalkraft

Priis-Courant paa de Agerdyrknings-Maskiner og Redskaber, som forfærdiges hos O. J. Winstrup.

Priis-Courant paa de Agerdyrknings-Maskiner og Redskaber, som forfærdiges hos O. J. Winstrup

Forsvar for den winstrupske Plov.

Forsvar for den winstrupske Plov

The WAIS Divide Deep Ice Core WD2014 Chronology – Part 2: Annual-Layer Counting (0–31 ka BP)

We present the WD2014 chronology for the upper part (0–2850 m; 31.2 ka BP) of the West Antarctic Ice Sheet (WAIS) Divide (WD) ice core. The chronology is based on counting of annual layers observed in the chemical, dust and electrical conductivity records. These layers are caused by seasonal changes in the source, transport, and deposition of aerosols. The measurements were interpreted manually and with the aid of two automated methods. We validated the chronology by comparing to two high-accuracy, absolutely dated chronologies. For the Holocene, the cosmogenic isotope records of Be from WAIS Divide and C for IntCal13 demonstrated that WD2014 was consistently accurate to better than 0.5 % of the age. For the glacial period, comparisons to the Hulu Cave chronology demonstrated that WD2014 had an accuracy of better than 1 % of the age at three abrupt climate change events between 27 and 31 ka. WD2014 has consistently younger ages than Greenland ice core chronologies during most of the Holocene. For the Younger Dryas–Preboreal transition (11.595 ka; 24 years younger) and the Bølling–Allerød Warming (14.621 ka; 7 years younger), WD2014 ages are within the combined uncertainties of the timescales. Given its high accuracy, WD2014 can become a reference chronology for the Southern Hemisphere, with synchronization to other chronologies feasible using high-quality proxies of volcanism, solar activity, atmospheric mineral dust, and atmospheric methane concentrations

An automated approach for annual layer counting in ice cores

A novel method for automated annual layer counting in seasonally-resolved paleoclimate records has been developed. It relies on algorithms from the statistical framework of hidden Markov models (HMMs), which originally was developed for use in machine speech recognition. The strength of the layer detection algorithm lies in the way it is able to imitate the manual procedures for annual layer counting, while being based on statistical criteria for annual layer identification. The most likely positions of multiple layer boundaries in a section of ice core data are determined simultaneously, and a probabilistic uncertainty estimate of the resulting layer count is provided, ensuring an objective treatment of ambiguous layers in the data. Furthermore, multiple data series can be incorporated and used simultaneously. In this study, the automated layer counting algorithm has been applied to two ice core records from Greenland: one displaying a distinct annual signal and one which is more challenging. The algorithm shows high skill in reproducing the results from manual layer counts, and the resulting timescale compares well to absolute-dated volcanic marker horizons where these exist

Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval

In this study, we compare eight recently developed snow depth products over Arctic sea ice, which use satellite observations, modeling, or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance observations and airborne measurements. Large mean snow depth discrepancies are observed over the Atlantic and Canadian Arctic sectors. The differences between climatology and the snow products early in winter could be in part a result of the delaying in Arctic ice formation that reduces early snow accumulation, leading to shallower snowpacks at the start of the freeze-up season. These differences persist through spring despite overall more winter snow accumulation in the reanalysis-based products than in the climatologies. Among the products evaluated, the University of Washington (UW) snow depth product produces the deepest spring (March-April) snowpacks, while the snow product from the Danish Meteorological Institute (DMI) provides the shallowest spring snow depths. Most snow products show significant correlation with snow depths retrieved from Operational IceBridge (OIB) while correlations are quite low against buoy measurements, with no correlation and very low variability from University of Bremen and DMI products. Inconsistencies in reconstructed snow depth among the products, as well as differences between these products and in situ and airborne observations, can be partially attributed to differences in effective footprint and spatial-temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in situ and remote sensing observations