CO2矿物捕获能力的研究进展

全球碳存储的研究表明, CO2最稳定的存储方式是 CO2的矿物捕获, 即将 CO2注入到地下,使其以方解石、菱铁矿、白云石及片钠铝石等碳酸盐矿物的形式存在.适合 CO2矿物捕获的岩石类型主要有火山岩、砂岩和火山碎屑岩.分析了3种岩石类型的金属元素含量、金属元素的释放能力、与 CO2反应生成的矿物类型及对 CO2的捕获量,并比较了3种岩石类型对 CO2矿物捕获能力的差异.其中玄武岩等火山岩的金属离子含量高,但其孔隙空间有限,制约了成岩反应,且 CO2注入后具有逸散的风险;砂岩分布广泛,有足够的孔隙利于流体注入,但是金属离子含量相对较低,对 CO2的矿物捕获所需时间相对较长;火山碎屑岩则结合了前2种岩石类型在矿物捕获方面的优势,是一种理想的 CO2矿物捕获的岩石类型.     

High-temperature emplacement and liquefaction of shallow-water caldera-forming eruption products: Early Miocene Tateyamazaki Dacite in the Oga Peninsula, NE Japan

The Early Miocene Tateyamazaki Dacite infills a 3.2 km diameter caldera. It comprises poorly sorted, massive, biotite-bearing dacite pumice lapilli tuff, in which huge blocks of densely welded dacite lapilli tuff, basaltic andesite lava, and other lithologies are commonly set. Dense blocks are variably cracked and intruded by the host lapilli tuff. Sparse blocks of bedded lapilli tuff and tuff are variably disaggregated to intermingle with the host rocks or are plastically deformed into irregular shapes. Rootless tuff veins millimeters to 30 cm thick are developed within the host rocks, mainly dipping at 10–30°, and are locally branched and mutually cut to form a network. Where thicker, they are stratified and locally carry accidental fragments. Accidental lapilli up to 2 or 3 cm wide and 30 cm long are locally set in near-vertical and variably sinuous arrays. Although poorly defined they are reminiscent of fluid escape structures. The host pumice lapilli tuff, however, retains in part a thermal remnant magnetization (TRM) vector stable at temperatures above 280 °C. Blocks in the caldera fill also retain TRM but the vectors are rotated significantly from those of the host pumice lapilli tuff and the adjacent volcanic rocks. Tateyamazaki Dacite is thus likely to have been emplaced at high temperatures, and intermingled with shattered basement rocks and ambient water to be partly liquefied within the caldera immediately after or during the caldera-forming eruption.     

Data-Assimilative Hindcast and Climate Forecast of Storm Surges with an ASGF Regression Model

ABSTRACT

This study demonstrates that long-term climate model solutions can be efficiently converted to storm surge time series at points of interest (POIs) for the future. The all-source Green's function (ASGF) regression model is used for this conversion. In addition to being data assimilative, the ASGF regression model can also simulate storm surges at a POI faster than the traditional modelling approach by orders of magnitude. This is demonstrated using the tidal gauge at Sept-Îles (Quebec, Canada) in the Gulf of St. Lawrence as the POI. First the ASGF regression model is used to assimilate 32 years of tidal gauge data, producing a continuous hindcast of storm surges and a set of best-estimate regression parameters. Second, the ASGF regression model with the best-estimate parameters is used to convert a Canadian Regional Climate Model solution (CRCM/AHJ) to an hourly time series of storm surges from 1961 to 2100. Gumbel's extreme value analysis (EVA) is then applied to the time series as a whole and also to tri-decadal segments. The tri-decadal approach is used to investigate whether there is any progressive shortening or lengthening of storm surge return periods as a result of future climate change. A method for correcting for bias due to the forcing field at the EVA level is also demonstrated.