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We present a new compilation and analysis of broad-band ocean bottom seismometer noise properties from 15 yr of seismic deployments. We compile a comprehensive data set of representative four-component (seismometer and pressure gauge) noise spectra and cross-spectral properties (coherence, phase and admittance) for 551 unique stations spanning 18 U.S.-led experiments. This is matched with a comprehensive compilation of metadata parameters related to instrumentation and environmental properties for each station. We systematically investigate the similarity of noise spectra by grouping them according to these metadata parameters to determine which factors are the most important in determining noise characteristics. We find evidence for improvements in similarity of noise properties when grouped across parameters, with groupings by seismometer type and deployment water depth yielding the most significant and interpretable results. Instrument design, that is the entire deployed package, also plays an important role, although it strongly covaries with seismometer and water depth. We assess the presence of traditional sources of tilt, compliance, and microseismic noise to characterize their relative role across a variety of commonly used seismic frequency bands. We find that the presence of tilt noise is primarily dependent on the type of seismometer used (covariant with a particular subset of instrument design), that compliance noise follows anticipated relationships with water depth, and that shallow, oceanic shelf environments have systematically different microseism noise properties (which are, in turn, different from instruments deployed in shallow lake environments). These observations have important implications for the viability of commonly used seismic analysis techniques. Finally, we compare spectra and coherences before and after vertical channel tilt and compliance noise removal to evaluate the efficacy and limitations of these now standard processing techniques. These findings may assist in future experiment planning and instrument development, and our newly compiled noise data set serves as a building block for more targeted future investigations by the marine seismology community.

 

Abstract Machine-learning-based methods that identify drought in three-dimensional space–time are applied to climate model simulations and tree-ring-based reconstructions of hydroclimate over the Northern Hemisphere extratropics for the past 1000 years, as well as twenty-first-century projections. Analyzing reconstructed and simulated drought in this context provides a paleoclimate constraint on the spatiotemporal characteristics of simulated droughts. Climate models project that there will be large increases in the persistence and severity of droughts over the coming century, but with little change in their spatial extent. Nevertheless, climate models exhibit biases in the spatiotemporal characteristics of persistent and severe droughts over parts of the Northern Hemisphere. We use the paleoclimate record and results from a linear inverse modeling-based framework to conclude that climate models underestimate the range of potential future hydroclimate states. Complicating this picture, however, are divergent changes in the characteristics of persistent and severe droughts when quantified using different hydroclimate metrics. Collectively our results imply that these divergent responses and the aforementioned biases must be better understood if we are to increase confidence in future hydroclimate projections. Importantly, the novel framework presented herein can be applied to other climate features to robustly describe their spatiotemporal characteristics and provide constraints on future changes to those characteristics.