A primer in gibbs energy minimization for geophysicists     

J. A. D. Connolly 《Petrology》2017,25(5):526-534

Gibbs energy minimization is the means by which the stable state of a system can be computed as a function of pressure, temperature and chemical composition from thermodynamic data. In this context, state implies knowledge of the identity, amount, and composition of the various phases of matter in heterogeneous systems. For seismic phenomena, which occur on time-scales that are short compared to the timescales of intra-phase equilibration, the Gibbs energy functions of the individual phases are equations of state that can be used to recover seismic wave speeds. Thermodynamic properties relevant to modelling of slower geodynamic processes are recovered by numeric differentiation of the Gibbs energy function of the system obtained by minimization. Gibbs energy minimization algorithms are categorized by whether they solve the non-linear optimization problem directly or solve a linearized formulation. The former express the objective function, the total Gibbs energy of the system, indirectly in terms of the partial molar Gibbs energies of phase species rather than directly in terms of the Gibbs energies of the possible phases. The indirect formulation of the objective function has the consequence that although these algorithms are capable of attaining high precision they have no generic means of treating phase separation and expertise is required to avoid local minima. In contrast, the solution of the fully linearized problem is completely robust, but offers limited resolution. Algorithms that iteratively refine linearized solutions offer a compromise between robustness and precision that is well suited to the demands of geophysical modeling.  相似文献   

Re-calibration of Arctic sea ice extent datasets using Arctic surface air temperature records     

Ronan Connolly  Michael Connolly  Willie Soon 《水文科学杂志》2017,62(8):1317-1340

A new seasonal and annual dataset describing Arctic sea ice extents for 1901–2015 was constructed by individually re-calibrating sea ice data sources from the three Arctic regions (North American, Nordic and Siberian) using the corresponding surface air temperature trends for the pre-satellite era (1901–1978), so that the strong relationship between seasonal sea ice extent and surface air temperature observed for the satellite era (1979-present) also applies to the pre-satellite era. According to this new dataset, the recent period of Arctic sea ice retreat since the 1970s followed a period of sea ice growth after the mid-1940s, which in turn followed a period of sea ice retreat after the 1910s. Arctic sea ice is a key component of the Arctic hydrological cycle, through both its freshwater storage role and its influence on oceanic and atmospheric circulation. Therefore, these new insights have significance for our understanding of Arctic hydrology.