How are emergent constraints quantifying uncertainty and what do they leave behind?

The use of emergent constraints to quantify uncertainty for key policy relevant quantities such as Equilibrium Climate Sensitivity (ECS) has become increasingly widespread in recent years. Many researchers, however, claim that emergent constraints are inappropriate or even under-report uncertainty. In this paper we contribute to this discussion by examining the emergent constraints methodology in terms of its underpinning statistical assumptions. We argue that the existing frameworks are based on indefensible assumptions, then show how weakening them leads to a more transparent Bayesian framework wherein hitherto ignored sources of uncertainty, such as how reality might differ from models, can be quantified. We present a guided framework for the quantification of additional uncertainties that is linked to the confidence we can have in the underpinning physical arguments for using linear constraints. We provide a software tool for implementing our general framework for emergent constraints and use it to illustrate the framework on a number of recent emergent constraints for ECS. We find that the robustness of any constraint to additional uncertainties depends strongly on the confidence we can have in the underpinning physics, allowing a future framing of the debate over the validity of a particular constraint around the underlying physical arguments, rather than statistical assumptions

Targeted cortical reorganization using optogenetics in non-human primates.

Brain stimulation modulates the excitability of neural circuits and drives neuroplasticity. While the local effects of stimulation have been an active area of investigation, the effects on large-scale networks remain largely unexplored. We studied stimulation-induced changes in network dynamics in two macaques. A large-scale optogenetic interface enabled simultaneous stimulation of excitatory neurons and electrocorticographic recording across primary somatosensory (S1) and motor (M1) cortex (Yazdan-Shahmorad et al., 2016). We tracked two measures of network connectivity, the network response to focal stimulation and the baseline coherence between pairs of electrodes; these were strongly correlated before stimulation. Within minutes, stimulation in S1 or M1 significantly strengthened the gross functional connectivity between these areas. At a finer scale, stimulation led to heterogeneous connectivity changes across the network. These changes reflected the correlations introduced by stimulation-evoked activity, consistent with Hebbian plasticity models. This work extends Hebbian plasticity models to large-scale circuits, with significant implications for stimulation-based neurorehabilitation

USING MIXED-INTEGER PROGRAMMING TO DETERMINE THE POTENTIAL FOR FLOUR-MILLING INDUSTRY EXPANSION

As in most predominantly agricultural states, agricultural producers in Oklahoma have expressed an interest in value-added processing opportunities. While Oklahoma produces mostly hard red winter wheat, most Oklahoma bakers require predominantly soft wheat flour for their products, almost all of which is purchased from out-of-state suppliers. An economic engineering-based, mixed-integer programming model was used to determine the optimal number, size, and location of additional flour mills in Oklahoma to capture this excess flour demand. The results suggest that additional mills are potentially justified and that the potential for additional milling will increase if Oklahoma soft wheat production increases.Agribusiness,

Pre-operational baseline studies of selected nearshore marine biota at the Diablo Canyon power plant site: 1979-1982

This is the final report of the California Department of Fish and Games intertidal and subtidal surveys of plants and animals in the vicinity of the Diablo Canyon Power Plant. These studies cover the period from 1979 through 1982. Our previous report (Gotshall, et al. 1984) covered the period from 1973 through 1978. The report includes abundances and statistical analyses of comparisons of abundances between years and study areas for selected intertidal and subtidal plants and animals. A total of 556 random subtidal stations, 540 intertidal stations and 67 permanent abalone transects were completed during this report period. Trends in abundances of most species observed during our 1973 through 1978 studies continued, i.e. the population of giant red sea urchins remained at a very low level, bull kelp Nereocystis leutkeana densities continued to decline in Diablo Cove and North Control. These two trends are probably due to the effects of continued sea otter foraging in the study area. Our observations of the presence or absence of fishes at subtidal 30m stations indicate a continued decline in the abundances of lingcod, Ophiodon elongatus and a decline in the abundance of blue rockfish since the 1973 through 1978 study period. A new study was begun during this study period, the use of baited stations to obtain relative abundance indices for those species of fishes attracted to the bait. Black-and-yellow rockfish were the most frequently observed fishes at Diablo Cove stations, while blue rockfish were the most frequently observed fish at North control baited stations. (Document has 393 pages

Detection, attribution, and sensitivity of trends toward earlier streamflow in the Sierra Nevada

Observed changes in the timing of snowmelt dominated streamflow in the western United States are often linked to anthropogenic or other external causes. We assess whether observed streamflow timing changes can be statistically attributed to external forcing, or whether they still lie within the bounds of natural (internal) variability for four large Sierra Nevada (CA) basins, at inflow points to major reservoirs. Streamflow timing is measured by “center timing” (CT), the day when half the annual flow has passed a given point. We use a physically based hydrology model driven by meteorological input from a global climate model to quantify the natural variability in CT trends. Estimated 50-year trends in CT due to natural climate variability often exceed estimated actual CT trends from 1950 to 1999. Thus, although observed trends in CT to date may be statistically significant, they cannot yet be statistically attributed to external influences on climate. We estimate that projected CT changes at the four major reservoir inflows will, with 90% confidence, exceed those from natural variability within 1–4 decades or 4–8 decades, depending on rates of future greenhouse gas emissions. To identify areas most likely to exhibit CT changes in response to rising temperatures, we calculate changes in CT under temperature increases from 1 to 5°. We find that areas with average winter temperatures between −2°C and −4°C are most likely to respond with significant CT shifts. Correspondingly, elevations from 2000 to 2800 m are most sensitive to temperature increases, with CT changes exceeding 45 days (earlier) relative to 1961–1990