考虑GCM多时间尺度误差订正的动力降尺度方法及其在区域气候变化预估中的应用

本项目旨在发展一种新的动力降尺度方法，以显著提高区域气候变化的预估水平。众所周知，传统的动力降尺度方法采用全球模式（GCM）的输出直接驱动区域模式（RCM），两个模式的系统误差叠加往往给动力降尺度带来更大的不确定性。提高动力降尺度模拟的可信度，已成为气候变化影响评估和对策制定所面的关键科学问题。本项目拟发展一个新的动力降尺度方法，在综合以往GCM误差订正方法优势的基础上，进一步引入多GCM集合的非线性趋势，以减小单一GCM预估结果不确定性大的问题。同时，避免以往订正方法会不合理地改变未来趋势的问题。利用上述经过误差订正的GCM结果驱动RCM，在RCM内部考虑温室气体、气溶胶和土地利用等外强迫的时间变化。系统评估新动力降尺度方法与其它动力降尺度方法的差异，在此基础上预估未来东亚区域气候变化并研究其机理。项目的实施有望获得可信度更高的未来气候变化预估结果，为应对气候变化提供有力的科学支撑。

This project aims to develop a new dynamical downscaling method to significantly improve the quality of regional climate projection. It is known that the traditional dynamical downscaling method using GCM output as the initial and lateral boundary conditions directly. The dynamical downscaling simulation inherits the biases from both GCM and RCM, which likely cause greater uncertainty in the projection of future climate. It has become a key issue that affects the impact assessment and adaptation of climate change on how to improve the credibility of dynamical downscaling simulations. Based on our review of the dynamical downscaling methods developed over past decades, we propose a new dynamical downscaling method that incorporates the advantages of previous dynamical downscaling methods and further takes the nonlinear trend of the multi-GCM ensemble mean into account. In doing so, we expect that the new method can reduce the uncertainty of future climate projection and avoid the unrealistic correction of the long-term trend caused by a previous bias-correction method. Afterward, the bias-corrected GCM data is applied to RCM simulation. Meanwhile, in the inner domain of RCM, the temporal variations in anthropogenic forcing, e.g., greenhouse gas, aerosol, and land use and land cover changes, are considered. The performance of the new dynamical downscaling method will be evaluated and compared with other dynamical downscaling methods over the historical period. Finally, the new dynamical downscaling method will be applied to the future projection of the East Asian climate. We expect the outcome of the project can provide more accurate future climate projections and support climate adaptation and policy-makers.