A centrifugal ice microtome for measurements of atmospheric CO₂ on air trapped in polar ice cores

For atmospheric CO2 reconstructions using ice cores, the technique to release the trapped air from the ice samples is essential for the precision and accuracy of the measurements. We present here a new dry extraction technique in combination with a new gas analytical system that together show significant improvements with respect to current systems. Ice samples (3–15 g) are pulverised using a novel centrifugal ice microtome (CIM) by shaving the ice in a cooled vacuum chamber (−27 °C) in which no friction occurs due to the use of magnetic bearings. Both, the shaving principle of the CIM and the use of magnetic bearings have not been applied so far in this field. Shaving the ice samples produces finer ice powder and releases a minimum of 90% of the trapped air compared to 50%–70% when needle crushing is employed. In addition, the friction-free motion with an optimized design to reduce contaminations of the inner surfaces of the device result in a reduced system offset of about 2.0 ppmv compared to 4.9 ppmv. The gas analytical part shows a higher precision than the corresponding part of our previous system by a factor of two, and all processes except the loading and cleaning of the CIM now run automatically. Compared to our previous system, the complete system shows a 3 times better measurement reproducibility of about 1.1 ppmv (1 σ) which is similar to the best reproducibility of other systems applied in this field. With this high reproducibility, no replicate measurements are required anymore for most future measurement campaigns resulting in a possible output of 12–20 measurements per day compared to a maximum of 6 with other systems.

Brief Announcement: Node Sampling Using Centrifugal Random Walks.

We propose distributed algorithms for sampling networks based on a new class of random walks that we call Centrifugal Random Walks (CRW). A CRW is a random walk that starts at a source and always moves away from it. We propose CRW algorithms for connected networks with arbitrary probability distributions, and for grids and networks with regular concentric connectivity with distance based distributions. All CRW sampling algorithms select a node with the exact probability distribution, do not need warm-up, and end in a number of hops bounded by the network diameter.

Node sampling using random centrifugal walks

Sampling a network with a given probability distribution has been identified as a useful operation. In this paper we propose distributed algorithms for sampling networks, so that nodes are selected by a special node, called the source, with a given probability distribution. All these algorithms are based on a new class of random walks, that we call Random Centrifugal Walks (RCW). A RCW is a random walk that starts at the source and always moves away from it. Firstly, an algorithm to sample any connected network using RCW is proposed. The algorithm assumes that each node has a weight, so that the sampling process must select a node with a probability proportional to its weight. This algorithm requires a preprocessing phase before the sampling of nodes. In particular, a minimum diameter spanning tree (MDST) is created in the network, and then nodes weights are efficiently aggregated using the tree. The good news are that the preprocessing is done only once, regardless of the number of sources and the number of samples taken from the network. After that, every sample is done with a RCW whose length is bounded by the network diameter. Secondly, RCW algorithms that do not require preprocessing are proposed for grids and networks with regular concentric connectivity, for the case when the probability of selecting a node is a function of its distance to the source. The key features of the RCW algorithms (unlike previous Markovian approaches) are that (1) they do not need to warm-up (stabilize), (2) the sampling always finishes in a number of hops bounded by the network diameter, and (3) it selects a node with the exact probability distribution.