Whether the stratospheric radiative feedback amplifies the global warming remains under debate. The stratospheric water vapor (SWV), one of the primary feedbacks in the stratosphere, is argued to be an important contributor to the global warming. On the other hand, the overall stratospheric feedback, which consists of both the SWV feedback and the stratospheric temperature (ST) feedback, does not amount to a significant value. The key to reconciling these seemingly contradictory arguments is to understand the ST change. Here, we develop a method to decompose the ST change and to quantify the decomposed feedbacks. We find that the SWV feedback, which consists of a 0.04 W m−2 K−1 direct impact on the top-of-the-atmosphere radiation and 0.11 W m−2 K−1 indirect impact via ST cooling, is offset by a negative ST feedback of − 0.13 W m−2 K−1 that is radiatively driven by the tropospheric warming. This compensation results in an insignificant overall stratospheric feedback.
我国远洋渔业已经走过30个年头,通过渔业生产、海区调查等方式积累了大量的远洋渔业数据。可以预见的是,随着时间的推移以及科学技术(如RS、GIS等技术)在远洋渔业方面的应用,远洋渔业数据必将具有海量特征。如何高效管理这些海量的远洋渔业数据是本研究要解决的关键。文章通过对远洋渔业中3种经济鱼(金枪鱼、竹荚鱼、鱿鱼)的生产、调查、地理等相关方面数据的分析,基于SQL Server 2000设计了远洋渔业调查数据库,基于Geodatebase设计了远洋渔业空间数据库。通过数据库的形式,实现了远洋渔业海量数据的高效管理。同时,针对所建立的远洋渔业数据库的空间数据建立了G树索引,为高效查询相关空间数据提供了支持。 相似文献
The decomposition problem—the assignment of sample observations to component populations—is studied in a spatial context. The observations are spatially located and the assignment to component populations takes into account the value of each observation as well as the values of neighboring observations. Both parameter estimation and assignment rules use a new method that integrates a standard multivariate decomposition algorithm with nonlinear regression. The method is illustrated and tested with artificial data. The distribution of the trace component Cr2O3in recent Lake Michigan sediments is, then, analyzed by the method. It yields a pattern of component populations that is correlated with the Lake's bottom structure and depositional environments.
Study carried out as visiting scholar at the Department of Geological Sciences, Northwestern University, Evanston, Illinois U.S.A. 相似文献
基于WRF/Chem(Weather Research Forecasting/Chemistry)模式对2015年11月25日至12月2日我国北方一次大范围PM2.5(空气动力学当量直径小于等于2.5 μm的颗粒物,即细颗粒物)重污染过程进行了模拟。与观测资料对比表明,模式能够较好地模拟出PM2.5浓度及气象因素的变化趋势,结果适用于此次污染事件的机理分析。动力、热力条件及化学转化等因素对此次强污染事件形成的机理分析表明,动力因子主要通过表面风和垂直风切变的减弱对此次污染事件造成影响,边界层逆温等热力因子促进了大气稳定性的增强,不利于污染物扩散。依据PM2.5组成成分变化分析可知,硝酸盐、硫酸盐和有机碳在此次事件中含量增加,说明机动车汽车尾气和燃煤排放所致的二次气溶胶生成对PM2.5污染加剧起重要贡献。多元线性回归分析和多因子相对贡献率量化解析结果表明,热力因子在此次污染过程中起主要作用,方差贡献率为52%,动力因子次之,方差贡献率为34%,而化学转化方差贡献率约为14%,说明气象条件,尤其是热力条件是引起此次污染事件的主要原因。 相似文献