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Dynamical downscaling of climate changes with an eddy-resolving OGCM

发布时间:2016/08/05     浏览次数:
时间:2016-8-8 (星期一) 10:00
地点:周隆泉楼
主讲人:张学斌研究员 Dr. Xu
来访单位:澳大利亚联邦科学工业研究组织 CSIRO Oceans and Atmosphere, Hobart, Australia
邀请人:胡建宇、刘志宇、严晓海
联系人:何志刚

Abstract:

In this seminar presentation, Australia CSIRO Ocean Downscaling Strategic Project, its design, progress and results are going to be discussed. The purpose of this strategic project is to provide high-resolution climate change and variability information in the global ocean over the past several decades and in the future, for better understanding, adaptation and mitigation purpose, because typical coarse-resolution climate models are only useful to provide some large-scale climate change information. To fulfil above purpose, we use dynamical downscaling by running an eddy-resolving (1/10 deg) near-global OGCM. Eddy-resolving global ocean models are highly desired for climate studies, but it is challenging because they require careful configuration and substantial computational resources. In the project, we have developed several strategies to run a near-global eddy-resolving OGCM for climate studies. The model is spun up for 20 years, with repeating year 1979 forcing from JRA-55 reanalysis and adaptive relaxation (Newtonian nudging) of temperature and salinity in the deep ocean to an observation-based seasonal climatology. The model is then integrated over the historical period (1979-2014) driven by the JRA-55 reanalysis, with a repeating non-adaptive relaxation of temperature and salinity derived from the last five years of the spin-up experiment. The model is further integrated from current to 2100, driven by merged atmospheric forcings which include high-frequency component from current-day JRA-55 reanalysis and long-term climate change signals from the ensemble of 17 CMIP5 models under RCP8.5. The differences between future and historical experiments are regarded as the “downscaled” changes in the ocean. The consistently designed model runs (historical + future) from 1979 to 2100 provide a powerful dataset for many applications, including providing high-resolution climate change projections for all popular ocean state variables, such as sea level.