CMG: Development of Spatio-Temporal and Multi-Resolution Methods for Detecting the Impact of Volcanic and Solar Forcings in Climate

这项调查的目的是发展数学方法，以确定火山爆发的证据和太阳辐射强度的变化，在高分辨率的代用气候数据在过去的千年。 这是一个具有挑战性的问题，因为记录是不完整和非平稳的，气候对太阳变化和火山活动的反应是未知的，可能是高度非线性的。 激发这项工作的问题是：（1）爆发性火山活动对气候的影响是什么？(2)太阳辐照度的变化是否有气候效应，如果有，影响的气候变率模式是什么？(3)气候代用指标、重建和预测中有多少不确定性可以用太阳和火山强迫来解释？ 该方法首先侧重于太阳和火山信号的时间方面，然后开发提取方法，用于分析具有不规则间隔数据的地球仪的空间特征，最后将这些联合收割机结合在一个时空模型中。 将采用的技术包括使用2-D小波来表征空间特征，并将卡尔曼滤波器估计的概念扩展到非高斯信号。 这些方法将通过使用NCAR社区气候模型的统计模拟进行测试。 这一研究将有助于把人类活动引起的气候变化与自然变率背景分开。 它由大气科学部和数学科学部通过NSF计划，数学地球科学合作（CMG）联合支持。

This investigation is aimed at developing mathematical methods to identify evidence of volcanic eruptions and variations in the intensity of solar radiation in high resolution proxy climate data of the past thousand years. This is a challenging problem because the records are incomplete and nonstationary, and the climatic response to solar variability and volcanic activity is unknown and probably highly nonlinear. Questions motivating the work are: (1) What has been the influence of explosive volcanism on climate? (2) Has there been a climatic effect of variations in solar irradiance, and if so what modes of climate variability are affected? (3) How much uncertainty in climate proxies, reconstructions, and predictions can be explained by solar and volcanic forcing? The approach is to focus first on the temporal aspects of solar and volcanic signals, then to develop extraction methods for analyzing spatial features on the globe with irregularly spaced data, and finally to combine these in a spatio-temporal model. Techniques to be employed include using 2-D wavelets to characterize spatial features and extending concepts of Kalman filter estimation to non-Gaussian signals. The methods will be tested through statistical simulations using the NCAR Community Climate Model. This research will help to separate the climatic changes caused by human activity from the background of natural variability. It is supported jointly by the Division of Atmospheric Sciences and the Division of Mathematical Sciences through the NSF program, Collaborations in Mathematical Geosciences (CMG).