Inversion of gravity anomalies over spreading oceanic ridges     

W.R. Jacoby  H. avak 《Journal of Geodynamics》2005,39(5):461

Models of spreading ocean ridges are derived by Bayesian gravity inversion with geophysical and geodynamic a priori information. The aim is to investigate the influence of spreading rate, plate dynamics and tectonic framework on crust and upper mantle structure by comparing the Mid Atlantic Ridge (MAR), the Indian Ocean Ridge (IND) and the East Pacific Rise (PAC). They differ in mean spreading rate, dynamic settings, as attached slabs, and plume interaction. Topography or bathymetry, gravity, isostasy, seismology and geology, etc. are averaged along the ridges and guide the construction of initial 2D models, including features as mean plumes, i.e. averaged along the ridge. This is a gross simplification, and the results are considered preliminary.Three model types are tested: (a) the temperature anomaly; (b) asthenospheric rise into thickening lithosphere; (c) a crustal root as had been anticipated before seafloor spreading was discovered. Additional model components are a mean plume, a non-compensated ridge uplift, an under-compensated asthenospheric rise, e.g. of partially molten material, and seismic velocity models for P and S waves. Model type (c), tends to permute to model type (b) from thick crust to thin axial lithosphere. Model type (a) renders ‘realistic’ values of the thermal expansivity, but is insufficient to fit the gravity data; partial melt may disturb the simple temperature effect. A combination of (a) and (b) is most adequate. Exclusive seismic velocity models of S or P waves do not lead to acceptable densities nor to adequate gravity fitting. The different ridges exhibit significant differences in the best models: ATL and IND show an axial mass excess fostering enhanced ridge push, and ATL, in addition, suggests a mean plume input, while PAC shows an axial mass deficit reducing ridge push, most probably due to dominance of slab pull in the force balance.Goodness of the gravity fit alone is no justifiable criterion for goodness of model, indeed minor modifications to each model within the uncertainties of the assumptions can make the fit arbitrarily good. Goodness of model is quantified exclusively by a priori information.  相似文献   

Integrated Global System Model for Climate Policy Assessment: Feedbacks and Sensitivity Studies   总被引：3，自引：3，他引：0  

R. Prinn  H. Jacoby  A. Sokolov  C. Wang  X. Xiao  Z. Yang  R. Eckhaus  P. Stone  D. Ellerman  J. Melillo  J. Fitzmaurice  D. Kicklighter  G. Holian  Y. Liu 《Climatic change》1999,41(3-4):469-546

Alternative policies to address global climate change are being debated in many nations and within the United Nations Framework Convention on Climate Change. To help provide objective and comprehensive analyses in support of this process, we have developed a model of the global climate system consisting of coupled sub-models of economic growth and associated emissions, natural fluxes, atmospheric chemistry, climate, and natural terrestrial ecosystems. The framework of this Integrated Global System Model is described and the results of sample runs and a sensitivity analysis are presented. This multi-component model addresses most of the major anthropogenic and natural processes involved in climate change and also is computationally efficient. As such, it can be used effectively to study parametric and structural uncertainty and to analyze the costs and impacts of many policy alternatives. Initial runs of the model have helped to define and quantify a number of feedbacks among the sub-models, and to elucidate the geographical variations in several variables that are relevant to climate science and policy. The effect of changes in climate and atmospheric carbon dioxide levels on the uptake of carbon and emissions of methane and nitrous oxide by land ecosystems is one potentially important feedback which has been identified. The sensitivity analysis has enabled preliminary assessment of the effects of uncertainty in the economic, atmospheric chemistry, and climate sub-models as they influence critical model results such as predictions of temperature, sea level, rainfall, and ecosystem productivity. We conclude that uncertainty regarding economic growth, technological change, deep oceanic circulation, aerosol radiative forcing, and cloud processes are important influences on these outputs.  相似文献