The Jet Driven Motions in the Narrow Line Region of NGC 1068

We have obtained HST FOC f/48 long-slit spectroscopy of the central 2 arcseconds of the Narrow Line Region of NGC 1068 between 3500-5400\OA with a spectral resolution of 1.78\OA/pixel. At a spatial scale of 0″.0287 per pixel these data provide an order of magnitude improvement in resolution over previous ground based spectra and allow us to trace the interaction between the radio jet and the gas in the NLR. Our results show that, within ±0″.5 of the radio-jet the emission lines are split into two components whose velocity separation is 1500 km s-1. The emission line structure is reminiscent of that seen previously around the jet of 3C120. Furthermore, this material enveloping the radio-jet is in a much higher ionization state than that of the surrounding NLR gas. The highest excitation is coincident with the jet axis where emission in the coronal line of [FeVII] λ3769\OA is detected but where [OII] λ3727 \OA is depressed. These results imply that we are witnessing a cocoon of hot gas in expansion around the radio-jet created by its interaction with the gas, and that these shocks are sufficiently fast, at least ± km s-1, that they are creating localized ionization effects. This revised version was published online in July 2006 with corrections to the Cover Date.     

Using groundwater noble gas measurements to confirm paleorecharge hypotheses at Pahute Mesa,Nevada National Security Site,Nevada, USA

Hydrogeology Journal - Pahute Mesa (Nevada, USA) was the site of 85 underground nuclear tests between 1965 and 1992 whose residual radiochemical inventory poses a contaminant threat to local...     

Consolidation properties and structural alteration of Old Alluvium

Acta Geotechnica - We present experimental observations and a conceptual model for understanding the compression and swelling characteristics of Old Alluvium (OA) from San Juan, Puerto Rico. Prior...     

Factors influencing public beliefs regarding the cause of induced earthquakes

Natural Hazards - The central USA has experienced an increase in the frequency and magnitude of human-induced earthquakes. The earthquakes are caused by the deep-well injection of water produced...     

Impacts of enhanced CCN on the organization of convection and recent reduced counts of monsoon depressions

Monsoon depressions, that form during the Indian summer monsoon season (June to September) are known to be baroclinic disturbances (horizontal scale 2,000–3,000 km) and are driven by deep convection that carries a very large vertical slope towards cold air aloft in the upper troposphere. Deep convection is nearly always organized around the scale of these depressions. In the maintenance of the monsoon depression the generation of eddy kinetic energy on the scale of the monsoon depression is largely governed by the “in scale” covariance of heating and temperature and of vertical velocity and temperature over the region of the monsoon depression. There are normally about 6–8 monsoon depressions during a summer monsoon season. Recent years 2009, 2010 and 2011 saw very few (around 1, 0 and 1 per season respectively). The best numerical models such as those from ECMWF and US (GFS) carried many false alarms in their 3–5 day forecasts, more like 6–8 disturbances. Even in recent years with fewer observed monsoon depressions a much larger number of depressions is noted in ECMWF forecasts. These are fairly comprehensive models that carry vast data sets (surface and satellite based), detailed data assimilation, and are run at very high resolutions. The monsoon depression is well resolved by these respective horizontal resolutions in these models (at 15 and 35 km). These models carry complete and detailed physical parameterizations. The false alarms in their forecasts leads us to suggest that some additional important ingredient may be missing in these current best state of the art models. This paper addresses the effects of pollution for the enhancement of cloud condensation nuclei and the resulting disruption of the organization of convection in monsoon depressions. Our specific studies make use of a high resolution mesoscale model (WRF/CHEM) to explore the impacts of the first and second aerosol indirect effects proposed by Twomey and Albrecht. We have conducted preliminary studies including examination of the evolution of radar reflectivity (computed inversely from the model hydrometeors) for normal and enhanced CCN effects (arising from enhanced monsoon pollution). The time lapse histories show a major disruption in the organization of convection of the monsoon depressions on the time scale of a week to 10 days in these enhanced CCN scenarios.     

Evolving Turbulence Realizations of Atmospheric Flow

A significant difference exists between estimates of contaminant atmospheric transport and dispersion calculated by an ensemble-averaged model and the turbulent details of any particular atmospheric transport and dispersion realization. In some cases, however, it is important to be able to make inferences of these realizations using ensemble-averaged models. It is possible to make such inferences if there are sensors in the field to report contaminant concentration observations. Any information determined about the atmospheric transport and dispersion realization can then be assimilated into a forecast model. This approach can enhance the accuracy of the atmospheric transport and dispersion forecast of a particular event. This work adopts that approach and reports on a genetic algorithm used to optimize the variational problem. Given contaminant sensor measurements and a transport and dispersion model, one can back-calculate unknown source and meteorological parameters. In this case, we demonstrate the dynamic recovery of unknown meteorological variables, including the transport variables that comprise the “outer variability” (wind speed and wind direction) and the dispersion variables that comprise the “inner variability” (contaminant spread). The optimization problem is set up in an Eulerian grid space, where the comparison of the concentration field variable between the predictions and the observations forms the cost function. The transport and dispersion parameters, which are determined from the optimization, are in Lagrangian space. This calculation is applied to continuous and instantaneous releases in a horizontally homogeneous wind field such as that observed during traditional transport and dispersion field experiments. The method proves to be successful at recovering the unknown transport and dispersion parameters for a numerical experiment.     

The challenge to detect and attribute effects of climate change on human and natural systems

Future scenarios of the energy system under greenhouse gas emission constraints depict dramatic growth in a range of energy technologies. Technological growth dynamics observed historically provide a useful comparator for these future trajectories. We find that historical time series data reveal a consistent relationship between how much a technology’s cumulative installed capacity grows, and how long this growth takes. This relationship between extent (how much) and duration (for how long) is consistent across both energy supply and end-use technologies, and both established and emerging technologies. We then develop and test an approach for using this historical relationship to assess technological trajectories in future scenarios. Our approach for “learning from the past” contributes to the assessment and verification of integrated assessment and energy-economic models used to generate quantitative scenarios. Using data on power generation technologies from two such models, we also find a consistent extent - duration relationship across both technologies and scenarios. This relationship describes future low carbon technological growth in the power sector which appears to be conservative relative to what has been evidenced historically. Specifically, future extents of capacity growth are comparatively low given the lengthy time duration of that growth. We treat this finding with caution due to the low number of data points. Yet it remains counter-intuitive given the extremely rapid growth rates of certain low carbon technologies under stringent emission constraints. We explore possible reasons for the apparent scenario conservatism, and find parametric or structural conservatism in the underlying models to be one possible explanation.