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Submarine melting has been implicated in the accelerated retreat of marine‐terminating glaciers globally. Energetic ocean flows, such as subglacial discharge plumes, are known to enhance submarine melting in their immediate vicinity. Using observations and a large eddy simulation, we demonstrate that discharge plumes emit high‐frequency internal gravity waves that propagate along glacier termini and transfer energy to distant regions of the terminus. Our analysis of wave characteristics and their correlation with subglacial discharge forcing suggest that they derive their energy from turbulent motions within the discharge plume and its surface outflow. Accounting for the near‐terminus velocities associated with these waves increases predicted melt rates by up to 70%. This may help to explain known discrepancies between observed melt rates and theoretical predictions. Because the dynamical ingredients—a buoyant plume rising through a stratified ocean—are common to many tidewater glacier systems, such internal waves are likely to be widespread.

Turbulence in geophysical flows tends to organize itself so that the mean flow remains close to a stability boundary in parameter space. That characteristic suggests self-organized criticality (SOC), a statistical property that has been identified in a range of complex phenomena including earthquakes, forest fires and solar flares. This note explores the relationship between the properties of forced, sheared, stratified turbulence (as found in oceans, atmospheres and other geophysical fluids) and those of SOC. Self-organization to the critical state is demonstrated in a wide range of cases drawn mostly (but not entirely) fromin situobservations of ocean turbulence. Turbulent events in the ocean also exhibit a second characteristic associated with SOC: their sizes follow a power-law distribution indicating self-similarity. These results suggest SOC as a new conceptual foundation for the study of geophysical turbulence, an explanation for the mixing efficiency of ocean turbulence and a potential for cross-fertilization with other areas of geophysics.

A description is presented of the algorithms used to reconstruct energy deposited in the CMS hadron calorimeter during Run 2 (2015–2018) of the LHC. During Run 2, the characteristic bunch-crossing spacing for proton-proton collisions was 25 ns, which resulted in overlapping signals from adjacent crossings. The energy corresponding to a particular bunch crossing of interest is estimated using the known pulse shapes of energy depositions in the calorimeter, which are measured as functions of both energy and time. A variety of algorithms were developed to mitigate the effects of adjacent bunch crossings on local energy reconstruction in the hadron calorimeter in Run 2, and their performance is compared.