Dynamic responses of atmospheric carbon dioxide concentration to global temperature changes between 1850 and 2010

Changes in Earth's temperature have significant impacts on the global carbon cycle that vary at different time scales, yet to quantify such impacts with a simple scheme is traditionally deemed difficult. Here, we show that, by incorporating a temperature sensitivity parameter(1.64 ppm yr~(-1) ?C~(-1)) into a simple linear carbon-cycle model, we can accurately characterize the dynamic responses of atmospheric carbon dioxide(CO_2) concentration to anthropogenic carbon emissions and global temperature changes between 1850 and 2010(r~2 0.96 and the root-mean-square error 1 ppm for the period from 1960onward). Analytical analysis also indicates that the multiplication of the parameter with the response time of the atmospheric carbon reservoir(～12 year) approximates the long-term temperature sensitivity of global atmospheric CO_2concentration(～15 ppm?C~(-1)), generally consistent with previous estimates based on reconstructed CO_2 and climate records over the Little Ice Age. Our results suggest that recent increases in global surface temperatures, which accelerate the release of carbon from the surface reservoirs into the atmosphere, have partially offset surface carbon uptakes enhanced by the elevated atmospheric CO_2 concentration and slowed the net rate of atmospheric CO_2 sequestration by global land and oceans by ～30%since the 1960 s. The linear modeling framework outlined in this paper thus provides a useful tool to diagnose the observed atmospheric CO_2 dynamics and monitor their future changes.     

一种裸露土壤湿度反演方法

针对目前土壤湿度反演方法研究较少且缺少实时性的现状,该文提出一种土壤湿度反演方法——最小二乘支持向量机技术。以积分方程模型为正向算法,数值模拟不同雷达参数(频率、入射角及极化)下后向散射系数随土壤含水量和地表粗糙度的变化情况。经过数据敏感性分析,选取C-波段和X-波段、小入射角下的同极化后向散射系数作为支持向量回归的训练样本信息;经过适当的训练,利用支持向量回归技术对土壤含水量进行了反演研究;并考虑通过多频率、多极化、多入射角数据的组合,消除地表粗糙度的影响,提高反演精度。模拟结果表明,该方法反演土壤湿度具有较高的精度和较好的实时性;同时,与人工神经网络方法的结果比较,证明了该方法的有效性,为土壤湿度的反演研究提供了一种方法。