We present a new technique for isolating climate signals in time series with a characteristic "red' noise background which arises from temporal persistence. This background is estimated by a 'robust' procedure that, unlike conventional techniques, is largely unbiased by the presence of signals immersed in the noise. Making use of multiple-taper spectral analysis methods, the technique further provides for a distinction between purely harmonic (periodic) signals, and broader-band ('quasiperiodic') signals. The effectiveness of our signal detection procedure is demonstrated with synthetic examples that simulate a variety of possible periodic and quasiperiodic signals immersed in red noise. We apply our methodology to historical climate and paleoclimate time series examples. Analysis of a ~ 3 million year sediment core reveals significant periodic components at known astronomical forcing periodicities and a significant quasiperiodic 100 year peak. Analysis of a roughly 1500 year tree-ring reconstruction of Scandinavian summer temperatures suggests significant quasiperiodic signals on a near-century timescale, an interdecadal 16-18 year timescale, within the interannual E1 Nifio/Southern Oscillation (ENSO) band, and on a quasibiennial timescale. Analysis of the 144 year record of Great Salt Lake monthly volume change reveals a significant broad band of significant interdecadal variability, ENSO-timescale peaks, an annual cycle and its harmonics. Focusing in detail on the historical estimated global-average surface temperature record, we find a highly significant secular trend relative to the estimated red noise background, and weakly significant quasiperiodic signals within the ENSO band. Decadal and quasibiennial signals are marginally significant in this series.

Augmenting heavy and power-hungry data collection equipment with lighter, smaller wireless sensor network nodes leads to faster, larger deployments. Arrays comprising dozens of wireless sensor nodes are now possible, allowing scientific studies that aren't feasible with traditional instrumentation. Designing sensor networks to support volcanic studies requires addressing the high data rates and high data fidelity these studies demand. The authors' sensor-network application for volcanic data collection relies on triggered event detection and reliable data retrieval to meet bandwidth and data-quality demands.

Most island-arc magmatism appears to result from the lowering of the melting point of peridotite within the wedge of mantle above subducting slabs owing to the introduction of fluids from the dehydration of subducting oceanic crust. Volcanic rocks interpreted to contain a component of melt (not just a fluid) from the subducting slab itself are uncommon, but possible examples have been recognized in the Aleutian islands, Baja California, Patagonia and elsewhere. The geochemically distinctive rocks from these areas, termed 'adakites, are often associated with subducting plates that are young and warm, and therefore thought to be more prone to melting. But the subducting lithosphere in some adakite locations (such as the Aleutian islands) appears to be too old and hence too cold to melt. This implies either that our interpretation of adakite geochemistry is incorrect, or that our understanding of the tectonic context of adakites is incomplete. Here we present geochemical data from the Kamchatka peninsula and the Aleutian islands that reaffirms the slab-melt interpretation of adakites, but in the tectonic context of the exposure to mantle flow around the edge of a torn subducting plate. We conclude that adakites are likely to form whenever the edge of a subducting plate is warmed or ablated by mantle flow. The use of adakites as tracers for such plate geometry may improve our understanding of magma genesis and thermal structure in a variety of subduction-zone environments.

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