Predictability of Short Term Climate Variations

Shukla, Jagadish Schneider, Edwin K. Kinter, James L. Straus, David M. George Mason University标题：当前气候的可预测性和变率本研究包括一些密切相关的项目，旨在了解由大气、水圈、陆地生物系统、海洋和冰组成的耦合气候系统的低频（月至年）变化，主要是在当前气候制度下。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。将要使用的方法将包括对现有数值模式和将它们结合起来的方案进行广泛的实验，以及对现有记录和世界主要业务中心计划在不久的将来对大气数据进行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，这项工作将有助于确定当前和未来气候观测系统的精度和覆盖要求，以便充分监测气候系统的当前行为。1. 海洋-陆地-大气耦合系统的可预测性。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大气模拟，后者通过完全耦合系统的整合。2. Land-surface-climate交互。探讨植被变化、反照率反馈和全球边界条件在萨赫勒干旱中的重要性。将讨论人类活动对气候的潜在影响，包括（假设的）世界沙漠增加一倍、全球森林砍伐和咸海地区干涸。3. 气候诊断。对计划的大气资料再模拟的诊断包括对水文循环、全球能量循环和关键的热带-温带相互作用的详细检查。这些努力将得到对细微差异、比湿度和糖尿病热的一致估计的帮助。4. 当前气候的模拟。为了测试数值模式模拟观测到的低频异常的能力，pi计划利用观测到的边界条件对大约100年的大气模式进行整合。同样，他们将整合几百年的耦合气候系统模型，以测试其总体统计行为的现实性。5. 气候观测系统模拟。pi将估算大气环流对多种边界条件变化的敏感性。他们将使用与当前（和未来）真实气候观测系统类似的方法“观测”数值模拟气候系统，系统地探索其采样和精度特征。这项研究很重要，因为它寻求加强对气候过程的认识，并改进对气候变化和变化的预测。该研究的一部分由USGCRP CMAP项目资助。

Abstract ATM-9321354 Shukla, Jagadish Schneider, Edwin K. Kinter, James L. Straus, David M. George Mason University 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 de forestati 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 dor 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.