Extent and Causes of Chesapeake Bay Warming

Coastal environments such as the Chesapeake Bay have long been impacted by eutrophication stressors resulting from human activities, and these impacts are now being compounded by global warming trends. However, there are few studies documenting long-term estuarine temperature change and the relative contributions of rivers, the atmosphere, and the ocean. In this study, Chesapeake Bay warming, since 1985, is quantified using a combination of cruise observations and model outputs, and the relative contributions to that warming are estimated via numerical sensitivity experiments with a watershed–estuarine modeling system. Throughout the Bay’s main stem, similar warming rates are found at the surface and bottom between the late 1980s and late 2010s (0.02 +/- 0.02C/year, mean +/- 1 standard error), with elevated summer rates (0.04 +/- 0.01C/year) and lower rates of winter warming (0.01 +/- 0.01C/year). Most (~85%) of this estuarine warming is driven by atmospheric effects. The secondary influence of ocean warming increases with proximity to the Bay mouth, where it accounts for more than half of summer warming in bottom waters. Sea level rise has slightly reduced summer warming, and the influence of riverine warming has been limited to the heads of tidal tributaries. Future rates of warming in Chesapeake Bay will depend not only on global atmospheric trends, but also on regional circulation patterns in mid-Atlantic waters, which are currently warming faster than the atmosphere. Supporting model data available at: https://doi.org/10.25773/c774-a36

The redshift evolution of massive galaxy clusters in the MACSIS simulations

We present the MAssive ClusterS and Intercluster Structures (MACSIS) project, a suite of 390 clusters simulated with baryonic physics that yields realistic massive galaxy clusters capable of matching a wide range of observed properties. MACSIS extends the recent BAryons and HAloes of MAssive Systems simulation to higher masses, enabling robust predictions for the redshift evolution of cluster properties and an assessment of the effect of selecting only the hottest systems. We study the observable–mass scaling relations and the X-ray luminosity–temperature relation over the complete observed cluster mass range. As expected, we find that the slope of these scaling relations and the evolution of their normalization with redshift depart significantly from the self-similar predictions. However, for a sample of hot clusters with core-excised temperatures kBT ≥ 5 keV, the normalization and the slope of the observable–mass relations and their evolution are significantly closer to self-similar. The exception is the temperature–mass relation, for which the increased importance of non-thermal pressure support and biased X-ray temperatures leads to a greater departure from self-similarity in the hottest systems. As a consequence, these also affect the slope and evolution of the normalization in the luminosity–temperature relation. The median hot gas profiles show good agreement with observational data at z = 0 and z = 1, with their evolution again departing significantly from the self-similar prediction. However, selecting a hot sample of clusters yields profiles that evolve significantly closer to the self-similar prediction. In conclusion, our results show that understanding the selection function is vital for robust calibration of cluster properties with mass and redshift

Comparison of coarsening schemes for multilevel graph partitioning

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