Predictability and Variability of the Present Climate

摘要ATM-9321354 Shukla，Jagadish Schneider，Edwin K. 作者：James L. Straus，大卫M. 全球环境与社会研究所 当前气候的可预测性和变率 这项研究包括一些密切相关的项目，其目的是了解主要在目前气候状况下由大气层、水圈、陆地生物系统、海洋和冰组成的耦合气候系统的低频（每月至十年）变化。 目标包括用单个和耦合系统的复杂数值模型模拟这些低频变化的能力，以及估计这些变化可预测的程度的能力。 所采用的方法将涉及对现有的数值模式和将它们结合起来的方案进行广泛的试验，以及对来自现有记录和来自世界主要业务中心计划在不久的将来进行的大气数据回顾性同化的观测数据进行广泛的诊断和分析。 除了理解和预测气候系统低频变率的内在重要性之外， 这项工作将有助于定义当前和未来气候观测系统的准确性和覆盖范围要求，以便充分监测气候系统的当前行为。 1.海气耦合系统的可预报性。 将系统地探讨该系统在季节和年际时间尺度上的可预测性，前者通过大量的季节性大气模拟，后者通过完全耦合系统的积分。 2.陆面-气候相互作用 将探讨植被变化、干旱反馈和萨赫勒干旱全球边界条件的重要性。 将讨论人类活动通过（假设的）世界沙漠加倍、全球森林砍伐和咸海区域干旱对气候的潜在影响。 3.气候诊断。 对计划的大气数据再同化的诊断包括对水文循环、全球能量循环和关键的热带-热带外相互作用的详细检查。 这些努力将有助于一致的分歧，具体湿度和糖尿病发热的微妙数量的估计。 4.模拟目前的气候。 为了测试数值模式模拟观测到的低频异常的能力，PI计划使用观测到的边界条件对大气模式进行约100年的整合。 同样，他们将整合数百年的耦合气候系统模型，以测试其整体统计行为的现实性。 5.气候观测系统模拟。 PI将估计大气环流对边界条件的许多类型的变化的敏感性。 他们将使用类似于当前（和未来）真实的气候观测系统的方法“观测”数值模拟气候系统，以系统地探索其采样和精度特征。 这项研究是重要的，因为它旨在提高对气候过程的认识，并改善对气候变化和变化的预测。 这项研究的一部分由USGCRP CMAP项目资助。

Abstract ATM-9321354 Shukla, Jagadish Schneider, Edwin K. Kinter, James L. Straus, David M. Institute of Global Environment and Society, Inc. Title: Predictability and Variability of the Present Climate This research encompasses a number of closely related projects which are aimed at understanding the low frequency (monthly to decadal) variations of the coupled climate system consisting of the atmosphere, hydrosphere, land biosystems, oceans and ice, primarily in the present climatic regime. Goals include both the ability to simulate these low frequency variations with complex numerical models of the individual and coupled systems, and to estimate the degree to which these variations are predictable. The methods to be used will involve extensive experimentation with existing numerical models and schemes to couple them, and extensive diagnosis and analysis of observational data from both existing records and from the retrospective assimilation of atmospheric data planned by the major operational centers of the world for the near future. In addition to the intrinsic importance of understanding and predicting the low frequency variability of the climate system, this work will help to define the accuracy and coverage requirements for current and future climate observing systems in order to adequately monitor the current behavior of the climate system. 1. Predictability of the coupled ocean-land-atmosphere system. The predictability of this system on both the seasonal and interannual time scales will be systematically explored, the former by a large number of seasonal atmospheric simulations, and the latter from integrations of the fully coupled system. 2. Land-surface-climate interactions. The importance of vegetation change, albedo feedback and the global boundary conditions in the Sahel drought will be explored. The potential impacts of man's activity on climate through the (hypothetical) doubling of the world's deserts, global deforestati on and the desiccation of the Aral Sea region will be addressed. 3. Climate diagnostics. Diagnostics of the planned reassimilation of atmospheric data includes a detailed examination of the hydrological cycle, and global energy cycle and the critical tropical-extratropical interactions. These efforts will be aided by the availability of consistent estimates of the subtle quantities of divergence, specific humidity and diabetic heating. 4. Simulation of the present climate. To test the ability of numerical models to simulate the observed low frequency anomalies, the PIs plan an integration of the atmospheric model for about 100 years using observed boundary conditions. Similarly, they will integrate the coupled climate system model for several hundred years in order to test the realism of its overall statistical behavior. 5. Climate observing system simulation. The PIs will estimate the sensitivity of the atmospheric circulation to many types of changes in the boundary conditions. They will "observe" numerically modelled climate system using methods similar to the current (and future) observing systems for the real climate to explore their sampling and accuracy characteristics systematically. This research is important because it seeks to enhance knowledge about climate processes and improvements in predicting climate variation and change. Part of this research is funded under the USGCRP CMAP project.