X-ray variability in a deep, flux-limited sample of QSOs

We present an analysis of X-ray variability in a flux-limited sample of quasi-stellar objects (QSOs). Selected from our deep ROSAT survey, these QSOs span a wide range in redshift (0.1< z <3.2) and are typically very faint, so we have developed a method to constrain the amplitude of variability in ensembles of low signal-to-noise ratio light curves. We find evidence for trends in this variability amplitude with both redshift and luminosity. The mean variability amplitude declines sharply with luminosity, as seen in local active galactic nuclei (AGN), but with some suggestion of an upturn for the most powerful sources. We find tentative evidence that this is caused by redshift evolution, since the high-redshift QSOs ( z >0.5) do not show the anticorrelation with luminosity seen in local AGN. We speculate on the implications of these results for physical models of AGN and their evolution. Finally, we find evidence for X-ray variability in an object classified as a narrow-emission-line galaxy, suggesting the presence of an AGN.     

Lateral and subsurface flows impact arctic coastal plain lake water budgets

Arctic thaw lakes are an important source of water for aquatic ecosystems, wildlife, and humans. Many recent studies have observed changes in Arctic surface waters related to climate warming and permafrost thaw; however, explaining the trends and predicting future responses to warming is difficult without a stronger fundamental understanding of Arctic lake water budgets. By measuring and simulating surface and subsurface hydrologic fluxes, this work quantified the water budgets of three lakes with varying levels of seasonal drainage, and tested the hypothesis that lateral and subsurface flows are a major component of the post‐snowmelt water budgets. A water budget focused only on post‐snowmelt surface water fluxes (stream discharge, precipitation, and evaporation) could not close the budget for two of three lakes, even when uncertainty in input parameters was rigorously considered using a Monte Carlo approach. The water budgets indicated large, positive residuals, consistent with up to 70% of mid‐summer inflows entering lakes from lateral fluxes. Lateral inflows and outflows were simulated based on three processes; supra‐permafrost subsurface inflows from basin‐edge polygonal ground, and exchange between seasonally drained lakes and their drained margins through runoff and evapotranspiration. Measurements and simulations indicate that rapid subsurface flow through highly conductive flowpaths in the polygonal ground can explain the majority of the inflow. Drained lakes were hydrologically connected to marshy areas on the lake margins, receiving water from runoff following precipitation and losing up to 38% of lake efflux to drained margin evapotranspiration. Lateral fluxes can be a major part of Arctic thaw lake water budgets and a major control on summertime lake water levels. Incorporating these dynamics into models will improve our ability to predict lake volume changes, solute fluxes, and habitat availability in the changing Arctic. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.     

Is your neighbor your friend? Scan methods for spatial social network hotspot detection

GIS analyses use moving window methods and hotspot detection to identify point patterns within a given area. Such methods can detect clusters of point events such as crime or disease incidences. Yet, these methods do not account for connections between entities, and thus, areas with relatively sparse event concentrations but high network connectivity may go undetected. We develop two scan methods (i.e., moving window or focal processes), EdgeScan and NDScan, for detecting local spatial-social connections. These methods capture edges and network density, respectively, for each node in a given focal area. We apply methods to a social network of Mafia members in New York City in the 1960s and to a 2019 spatial network of home-to-restaurant visits in Atlanta, Georgia. These methods successfully capture focal areas where Mafia members are highly connected and where restaurant visitors are highly local; these results differ from those derived using traditional spatial hotspot analysis using the Getis–Ord Gi* statistic. Finally, we describe how these methods can be adapted to weighted, directed, and bipartite networks and suggest future improvements.     

Impact of land-atmospheric coupling in CFSv2 on drought prediction

Recent summers in the United States have been plagued by intense droughts that have caused significant damage to crops and have had a large impact on society. The ability to forecasts such events would allow for preparations that could help reduce the impact on society. Coupled land–atmosphere–ocean models were created to provide such forecasts but there are large uncertainties associated with their predictions. The predictive skill of these models is particularly low during the convective season due to the weaker connections with the oceans and an increase in the land–atmosphere interactions. To better understand the degradation of forecasts skill during the summer months and its connection to the land–atmosphere interactions we analyze National Centers for Environmental Prediction’s Climate Forecast System Version 2 (CFSv2) in terms of its climatological land–atmosphere interactions. To do this we use a recently developed classification of land–atmosphere interactions and other diagnostic variables to compare the reanalysis from the Climate Forecast System (CFSR) with CFSv2 re-forecasts (CFSRR) over the period 1982–2009. Coupling in the CFSRR tends toward the wet coupling regime for most areas east of the Rocky Mountains. Although the specific mechanism driving CFSRR to wet coupling state varies by region, the overall cause is enhanced vegetation rooting depth, originally implemented to address a near-surface warm bias in CFSR. The long-term tendency to wet coupling precludes the forecast model from consistently predicting and maintaining drought over the continental US.     

A simple carbon cycle representation for economic and policy analyses

Integrated Assessment Models (IAMs) that couple the climate system and the economy require a representation of ocean CO₂ uptake to translate human-produced emissions to atmospheric concentrations and in turn to climate change. The simple linear carbon cycle representations in most IAMs are not however physical at long timescales, since ocean carbonate chemistry makes CO₂ uptake highly nonlinear. No linearized representation can capture the ocean’s dual-mode behavior, with initial rapid uptake and then slow equilibration over ∽10,000 years. In a business-as-usual scenario followed by cessation of emissions, the carbon cycle in the 2007 version of the most widely used IAM, DICE (Dynamic Integrated model of Climate and the Economy), produces errors of ∽2^°C by the year 2300 and ∽6^°C by the year 3500. We suggest here a simple alternative representation that captures the relevant physics and show that it reproduces carbon uptake in several more complex models to within the inter-model spread. The scheme involves little additional complexity over the DICE model, making it a useful tool for economic and policy analyses.     

From Vegetable Box to Seafood Cooler: Applying the Community-Supported Agriculture Model to Fisheries

Community-supported fisheries (CSF) projects show signs of rapid growth. Modeled on community-supported agriculture (CSA) projects, CSFs share objectives of reducing social and physical distance between consumers and producers and re-embedding food systems in social and environmental contexts. This article offers a comparison of CSF and CSA, situated in the differences between seafood and agricultural products, and fishing and farming. We draw on economic and resource theory, past research on CSA, and a member survey from a case study CSF. Survey results show CSF members are interested in accessing high-quality, fresh, local seafood, and in supporting fishing communities, and they believe that participating in a CSF achieves both. They are less certain that a CSF can address environmental concerns, and few identify environmental motives as their primary reason for participating. The latter contrasts with CSA research results, and we contextualize these findings in our broader comparison.     

Chaoborus americanus predation influences Bosmina mucro lengths in fishless lakes

Size attributes of the cladoceran taxon Bosmina are known to vary in response to the intensity and dominant type of predation. Using a paleolimnological approach, we tested the hypothesis that Bosmina size structure differs between fishless lakes and those with presumed populations of planktivorous fish. We measured sedimentary remains of Bosmina longirostris and assessed assemblage structure of the gape-limited invertebrate predator Chaoborus within the surface sediments of 29 lakes in remote, northwestern Ontario, Canada. Mean B. longirostris mucro, antennule, and carapace lengths did not differ significantly between lake predation categories. However, within fishless lakes, a significant (r = 0.68, P = 0.01) positive correlation was apparent between B. longirostris mucro length and Chaoborus americanus percent abundance. We thus suggest that B. longirostris mucro length may be sensitive to C. americanus predation, however further information on the interactions with predators is needed.     

Mainstreaming local perceptions of hurricane risk into policymaking: A case study of community GIS in Mexico

This article suggests a framework for incorporating and communicating local perceptions of hurricane risk into policymaking through a case study conducted at El Zapotito commune in the State of Veracruz, Mexico. The authors constructed a geographical information system (GIS)-based model to quantify and spatially assess specific household-level vulnerabilities from information generated through interviews. This research developed a household vulnerability index applied to a participatory GIS to map vulnerability to hurricane hazard. The results indicate that infrastructural weaknesses are the most important factor contributing to vulnerability, explaining on their own 72.2% of the variation in the vulnerability patterns. These findings are corroborated by a vulnerability and capacity assessment (VCA), which shows that the community lacks strategies to cope with unsafe housing. It is suggested that linking community participation with modern techniques to analyse risk can empower communities and mobilise their capacities to address very specific vulnerabilities.