气候模式海洋低云模拟问题分析和改进

气候模式是我们理解过去气候变化和预估未来气候变化的有力工具，但云反馈严重影响气候模式对未来气候变化预估的不确定性。目前全球气候模式对海洋低云的模拟仍存在较大偏差，严重影响云反馈的强度。利用东南太平洋海洋低云外场试验得到的各种观测资料，借助云相关参数化试验平台，申请人计划对主流气候模式的海洋低云模拟问题进行深入的分析和归因。在此基础上，开发一个简单的、假定次网格水汽高斯分布、并把网格水汽变率和模式边界层和浅对流过程紧密联系起来的统计云量方案，改进海洋低云的模拟。最后通过和国内的模式中心研究人员合作，评估这一方案在我国气候模式中的效果。

Global climate model is the powerful tool for the understanding of the past climate change and the projection of future climate change, but cloud feedback significantly influences the uncertainties of the future projections. Large biases of marine low cloud remain in the simulations of current climate models, which strongly impact the magnitudes and signs of the cloud feedbacks. Utilizing various observations from the VOCALS experiment conducted over the Southeast Pacific Ocean, via CAPT (short numerical simulation using climate models) approach, we propose to investigate the causes of poor simulation of marine low clouds in major climate models. Furthermore, a simple statistical cloud model, which relates the subgrid scale moisture variance to the boundary layer turbulence and shallow convection, is proposed to improve low cloud simulations. Finally, the scheme will be further tested and evaluated in climate models in China.