Collaborative Research: VOCALS--Structure and Mechanisms of Coupled General Circulation Models' Systematic Biases in Southeast Pacific

这个项目是对VAMOS海洋-云-大气-陆地研究的贡献。主要目的是通过耦合大气环流模式(CGCM)提高我们对东南太平洋(SEP)系统偏差的结构和机制的理解。CGCM和海洋GCM的模拟将与美国季风系统(VAMOS)、海洋-云-大气-陆地研究(VOALS)、区域实验(REX)和长期观测数据集的广泛测量结果进行比较。将审查参加政府间气候变化专门委员会(IPCC)第四次评估报告(AR4)的24个CGCM的模拟，以及使用一个海洋GCM进行的强迫实验。研究人员将诊断所有24个IPCC AR4 CGCM中层积云和表层通量中偏差的结构和机制，在这些模式中分析该地区的上层洋流、热结构和热量收支，检查该地区的海-气耦合过程，特别是海洋-大气反馈的模拟情况，并进行强迫海洋GCM实验，以检查上层海洋过程对与AGCM中的偏差相关的大气强迫的敏感性。这项工作的广泛影响在于其对改善全球气候模式的潜在影响。在全球气候模式中适当地表示层积云甲板区域的海洋-大气-陆地耦合过程是进一步改进这些模式的主要挑战。该项目将通过支持一名研究生来促进跨学科培训。

This project is a contribution to VOCALS (the VAMOS Ocean-Cloud-Atmosphere-Land Study).The primary goal is to improve our understanding of the structure and mechanisms of the systematic biases in the Southeast Pacific (SEP) in coupled general circulation models (CGCMs). Simulations by CGCMs and ocean GCMs will be compared with the extensive measurements from the Variability of the American MOnsoon Systems (VAMOS) Ocean-Cloud-Atmosphere-Land Study (VOCALS) Regional Experiment (REx) and long-term observational datasets. The simulations of 24 CGCMs participating in the Inter-governmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) will be examined, together with forced experiments using one ocean GCM. The investigators will diagnose the structure and mechanisms of the biases in stratocumulus clouds and surface fluxes in the SEP in all 24 IPCC AR4 CGCMs, analyze the upper ocean currents, thermal structures and heat budget in this region in these models, examine their air-sea coupling processes in this region, especially how well the ocean-atmosphere feedbacks are simulated, and conduct forced ocean GCM experiments to examine the sensitivity of upper ocean processes to atmospheric forcings relevant to the biases in the AGCMs.Broader impacts of this work are in its potential implications for improving global climate models. Proper representation of ocean-atmosphere-land coupled processes in the stratocumulus deck region in global climate models is a major challenge for their further improvement. The project will promote interdisciplinary training through support of a graduate student.