Inverse Methods for Climate Model Parameter Estimation and Climate Sensitivity

拟议的研究将探讨应用系统的技术参数估计的逆方法的气候模式具有现实的动力学复杂性，但与理想的物理参数化。这些技术试图找到参数和经验得出的强迫修正，最大限度地减少偏差的气候模式的解决方案，从分析大气相空间轨迹的NCEP/NCAR再分析数据。这一方法可被视为利用大气环流数据，以改进随后的预报和气候模型模拟。拟议研究的目标是（i）评估这些技术在这种理想化环境中应用的潜力，着眼于应用于更现实的气候模式，（ii）研究和理解在大气边界条件强迫的AMIP式模拟中经验物理参数化气候模式的技能和技能丧失的原因，和（iii）使用这些技术来归因于理解中纬度大气动力过程在气候对强迫的响应中的催化作用的问题的因果关系。这项工作很重要，因为它将通过更现实的气候模型模拟来改善气候预测。

The proposed research will explore the application of systematic techniques for parameter estimation based on inverse methods to climate models having realistic dynamical complexity but with ideal physical parameterizations. These techniques seek to find parameters and empirically derived forcing corrections that minimize the deviation of the climate model solution from analyzed atmospheric phase space trajectories in the NCEP/NCAR reanalysis data. This approach may be viewed as using atmospheric circulation data in order to improve subsequent forecasts and climate model simulations. The goals of the proposed research are (i) to assess the potential for the application of these techniques in this idealized setting with an eye toward application to more realistic climate models, (ii) to study and understand the skill and reasons for loss of skill for climate models with empirical physical parameterizations in AMIP-style simulations forced by atmospheric boundary conditions, and (iii) to use these techniques to attribute cause and effect in the problem of understanding the catalytic role of midlatitude atmospheric dynamical processes in the response of the climate to imposed forcing. The work is important because it will yield insights into improving climate forecasts through more realistic climate model simulation.