R. S. J. Sparks

Bio: R. S. J. Sparks is an academic researcher from University of Bristol. The author has contributed to research in topics: Volcano & Magma. The author has an hindex of 99, co-authored 303 publications receiving 27906 citations. Previous affiliations of R. S. J. Sparks include Montserrat Volcano Observatory & Imperial College London.

The Genesis of Intermediate and Silicic Magmas in Deep Crustal Hot Zones

Abstract: A model for the generation of intermediate and silicic igneous rocks is presented, based on experimental data and numerical modelling. The model is directed at subduction-related magmatism, but has general applicability to magmas generated in other plate tectonic settings, including continental rift zones. In the model mantlederived hydrous basalts emplaced as a succession of sills into the lower crust generate a deep crustal hot zone. Numerical modelling of the hot zone shows that melts are generated from two distinct sources; partial crystallization of basalt sills to produce residual H2O-rich melts; and partial melting of pre-existing crustal rocks. Incubation times between the injection of the first sill and generation of residual melts from basalt crystallization are controlled by the initial geotherm, the magma input rate and the emplacement depth. After this incubation period, the melt fraction and composition of residual melts are controlled by the temperature of the crust into which the basalt is intruded. Heat and H2O transfer from the crystallizing basalt promote partial melting of the surrounding crust, which can include meta-sedimentary and meta-igneous basement rocks and earlier basalt intrusions. Mixing of residual and crustal partial melts leads to diversity in isotope and trace element chemistry. Hot zone melts are H2O-rich. Consequently, they have low viscosity and density, and can readily detach from their source and ascend rapidly. In the case of adiabatic ascent the magma attains a super-liquidus state, because of the relative slopes of the adiabat and the liquidus. This leads to resorption of any entrained crystals or country rock xenoliths. Crystallization begins only when the ascending magma intersects its H2O-saturated liquidus at shallow depths. Decompression and degassing are the driving forces behind crystallization, which takes place at shallow depth on timescales of decades or less. Degassing and crystallization at shallow depth lead to large increases in viscosity and stalling of the magma to form volcano-feeding magma chambers and shallow plutons. It is proposed that chemical diversity in arc magmas is largely acquired in the lower crust, whereas textural diversity is related to shallow-level crystallization.

Quantitative models of the fallout and dispersal of tephra from volcanic eruption columns

Abstract: A theoretical model of clast fallout from convective eruption columns has been developed which quantifies how the maximum clast size dispersal is determined by column height and wind strength. An eruption column consists of a buoyant convecting region which rises to a heightH B where the column density equals that of the atmosphere. AboveH B the column rises further to a heightH T due to excess momentum. BetweenH T andH B the column is forced laterally into the atmosphere to form an upper umbrella region. Within the eruption column, the vertical and horizontal velocity fields can be calculated from exprimental and theoretical studies and consideration of mass continuity. The centreline vertical velocity falls as a nearly linear function over most of the column's height and the velocity decreases as a gaussian function radially away from the centreline. Both column height and vertical velocity are strong functions of magma discharge rate. From calculations of the velocity field and the terminal fall velocity of clasts, a series of particle support envelopes has been constructed which represents positions where the column vertical velocity and terminal velocity are equal for a clast of specific size and density. The maximum range of a clast is determined in the absence of wind by the maximum width of the clast support envelope. The trajectories of clasts leaving their relevant support envelope at its maximum width have been modelled in columns from 6 to 43 km high with no wind and in a wind field. From these calculations the shapes and areas of maximum grain size contours of the air-fall deposit have been predicted. For the no wind case the theoretical isopleths show good agreement with the Fogo A plinian deposit in the Azores. A diagram has been constructed which plots, for a particular clast size, the maximum range normal to the dispersal axis against the downward range. From the diagram the column height (and hence magma discharge rate) and wind velocity can be determined. Historic plinian eruptions of Santa Maria (1902) and Mount St. Helens (1980) give maximum heights of 34 and 19 km respectively and maximum wind speeds at the tropopause of m/s and 30 m/s respectively. Both estimates are in good agreement with observations. The model has been applied to a number of other plinian deposits, including the ultraplinian phase of theA.D. 180 Taupo eruption in New Zealand which had an estimated column height of 51 km and wind velocity of 27 m/s.

Anthropogenic and Natural Radiative Forcing

Abstract: This chapter should be cited as: Myhre, G., D. Shindell, F.-M. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, J.-F. Lamarque, D. Lee, B. Mendoza, T. Nakajima, A. Robock, G. Stephens, T. Takemura and H. Zhang, 2013: Anthropogenic and Natural Radiative Forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Coordinating Lead Authors: Gunnar Myhre (Norway), Drew Shindell (USA)

Boundary Layer Theory

Abstract: The boundary layer equations for plane, incompressible, and steady flow are $$\matrix{ {u{{\partial u} \over {\partial x}} + v{{\partial u} \over {\partial y}} = - {1 \over \varrho }{{\partial p} \over {\partial x}} + v{{{\partial ^2}u} \over {\partial {y^2}}},} \cr {0 = {{\partial p} \over {\partial y}},} \cr {{{\partial u} \over {\partial x}} + {{\partial v} \over {\partial y}} = 0.} \cr }$$

Detecting the overlapping and hierarchical community structure in complex networks

Abstract: Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here, we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling different hierarchical levels of organization to be investigated. Tests on real and artificial networks give excellent results.

Intra-tumour heterogeneity: a looking glass for cancer?

Abstract: Populations of tumour cells display remarkable variability in almost every discernable phenotypic trait, including clinically important phenotypes such as ability to seed metastases and to survive therapy. This phenotypic diversity results from the integration of both genetic and non-genetic influences. Recent technological advances have improved the molecular understanding of cancers and the identification of targets for therapeutic interventions. However, it has become exceedingly apparent that the utility of profiles based on the analysis of tumours en masse is limited by intra-tumour genetic and epigenetic heterogeneity, as characteristics of the most abundant cell type might not necessarily predict the properties of mixed populations. In this Review, we discuss both genetic and non-genetic causes of phenotypic heterogeneity of tumour cells, with an emphasis on heritable phenotypes that serve as a substrate for clonal selection. We discuss the implications of intra-tumour heterogeneity in diagnostics and the development of therapeutic resistance.