The Magnetic Helicity Budget of a cme-Prolific Active Region

Coronal mass ejections (CMEs) are thought to be the way by which the solar corona expels accumulated magnetic helicity which is injected into the corona via several methods. DeVore (2000) suggests that a significant quantity is injected by the action of differential rotation, however Démoulin et al. (2002b), based on the study of a simple bipolar active region, show that this may not be the case. This paper studies the magnetic helicity evolution in an active region (NOAA 8100) in which the main photospheric polarities rotate around each other during five Carrington rotations. As a result of this changing orientation of the bipole, the helicity injection by differential rotation is not a monotonic function of time. Instead, it experiences a maximum and even a change of sign. In this particular active region, both differential rotation and localized shearing motions are actually depleting the coronal helicity instead of building it. During this period of five solar rotations, a high number of CMEs (35 observed, 65 estimated) erupted from the active region and the helicity carried away has been calculated, assuming that each can be modeled by a twisted flux rope. It is found that the helicity injected by differential rotation (–7×10⁴² Mx²) into the active region cannot provide the amount of helicity ejected via CMEs, which is a factor 5 to 46 larger and of the opposite sign. Instead, it is proposed that the ejected helicity is provided by the twist in the sub-photospheric part of the magnetic flux tube forming the active region.     

Radio Emission from Three-dimensional Relativistic Hydrodynamic Jets: Observational Evidence of Jet Stratification

High-resolution, high signal-to-noise ratio, blue-violet spectra of three red giant branch tip stars in M15 have been obtained with the Keck I High-Resolution Echelle Spectrograph. These spectra have been analyzed to determine the abundances of several neutron-capture elements, including the radioactive chronometer element thorium. There are two principal results of this study. First, the abundances of the heavier (Z>/=56) elements for each of the three stars is well matched by a scaled solar system r-process abundance distribution. Second, a weighted mean-observed Th/Eu ratio for the stars implies an age for the neutron-capture material in M15 stars of 14+/-3 Gyr, in reasonable agreement with other recent age estimates for Galactic globular clusters.     

Solar rotation in the subphotospheric layers

The solar rotation rate at latitudes 0°, 15° and 30° has been inferred by averaging the results of 120 regions of 15°×15°, which have been studied over a total area of about 75° in latitude and 360° in longitude. A local helioseismology technique, the ring diagram analysis method, has been used to analyse the horizontal velocity vectors from about 0.95 R up to the surface. Our results are in very good agreement with those of other authors over most of the depth range. However, near the surface we find sharp local features which are not reported in other studies. The independent measurements of the rotation rate in the north and south hemispheres show asymmetries below 0.975 R . The data used are full-disc dopplergrams taken by Solar Oscillation Investigation/Michelson Doppler Imager (SOI/MDI) on board of the SOlar and Heliospheric Observatory (SOHO) during its first Dynamic Program, between 1996 May and June.     

A new method to detect transitory signatures and local time/space variability structures in the climate system: the scale-dependent correlation analysis

The study of transitory signals and local variability structures in both/either time and space and their role as sources of climatic memory, is an important but often neglected topic in climate research despite its obvious importance and extensive coverage in the literature. Transitory signals arise either from non-linearities, in the climate system, transitory atmosphere–ocean couplings, and other processes in the climate system evolving after a critical threshold is crossed. These temporary interactions that, though intense, may not last long, can be responsible for a large amount of unexplained variability but are normally considered of limited relevance and often, discarded. With most of the current techniques at hand these typology of signatures are difficult to isolate because the low signal-to-noise ratio in midlatitudes, the limited recurrence of the transitory signals during a customary interval of data considered. Also, there is often a serious problem arising from the smoothing of local or transitory processes if statistical techniques are applied, that consider all the length of data available, rather than taking into account the size of the specific variability structure under investigation. Scale-dependent correlation (SDC) analysis is a new statistical method capable of highlighting the presence of transitory processes, these former being understood as temporary significant lag-dependent autocovariance in a single series, or covariance structures between two series. This approach, therefore, complements other approaches such as those resulting from the families of wavelet analysis, singular-spectrum analysis and recurrence plots. A main feature of SDC is its high-performance for short time series, its ability to characterize phase-relationships and thresholds in the bivariate domain. Ultimately, SDC helps tracking short-lagged relationships among processes that locally or temporarily couple and uncouple. The use of SDC is illustrated in the present paper by means of some synthetic time-series examples of increasing complexity, and it is compared with wavelet analysis in order to provide a well-known reference of its capabilities. A comparison between SDC and companion techniques is also addressed and results are exemplified for the specific case of some relevant El Niño-Southern Oscillation teleconnections.     

Vulnerability assessment in a volcanic risk evaluation in Central Mexico through a multi-criteria-GIS approach

The Valley of Toluca is a major industrial and agricultural area in Central Mexico, especially the City of Toluca, the capital of The State of Mexico. The Nevado de Toluca volcano is located to the southwest of The Toluca Basin. Results obtained from the vulnerability assessment phase of the study area (5,040 km² and 42 municipalities) are presented here as a part of a comprehensive volcanic risk assessment of The Toluca Basin. Information has been gathered and processed at a municipal level including thematic maps at 1:250,000 scale. A database has been built, classified and analyzed within a GIS environment; additionally, a Multi-Criteria Evaluation (MCE) approach was applied as an aid for the decision-making process. Cartographic results were five vulnerability maps: (1) Total Population, (2) Land Use/Cover, (3) Infrastructure, (4) Economic Units and (5) Total Vulnerability. Our main results suggest that the Toluca and Tianguistenco urban and industrial areas, to the north and northeast of The Valley of Toluca, are the most vulnerable areas, for their high concentration of population, infrastructure, economic activity, and exposure to volcanic events.     

Validation of climate model output using Bayesian statistical methods

The growing interest in and emphasis on high spatial resolution estimates of future climate has demonstrated the need to apply regional climate models (RCMs) to that problem. As a consequence, the need for validation of these models, an assessment of how well an RCM reproduces a known climate, has also grown. Validation is often performed by comparing RCM output to gridded climate datasets and/or station data. The primary disadvantage of using gridded climate datasets is that the spatial resolution is almost always different and generally coarser than climate model output. We have used a Bayesian statistical model derived from observational data to validate RCM output. We used surface air temperature (SAT) data from 109 observational stations in California, all with records of approximately 50 years in length, and created a statistical model based on this data. The statistical model takes into account the elevation of the station, distance from coastline, and the NOAA climate region in which the station resides. Analysis indicates that the statistical model provides reliable estimates of the mean monthly SAT at any given station. In our method, the uncertainty in the estimates produced by the statistical model are directly determined by obtaining probability density functions for predicted SATs. This statistical model is then used to estimate average SATs corresponding to each of the climate model grid cells. These estimates are compared to the output of the RCM to assess how well the RCM matches the observed climate as defined by the statistical model. Overall, the match between the RCM output and the statistical model is good, with some deficiencies likely due in part to the representation of topography in the RCM.