Journal of Oceanology and Limnology - The 22-year (1998–2019) surface seawater dimethylsulfide (DMS) concentrations in the Yellow Sea (YS) were hindcasted based on satellite sea surface... 相似文献
Abrupt climate change has an important impact on sustainable economic and social development, as well as ecosystem. However, it is very difficult to predict abrupt climate changes because the climate system is a complex and nonlinear system. In the present paper, the nonlinear local Lyapunov exponent (NLLE) is proposed as a new early warning signal for an abrupt climate change. The performance of NLLE as an early warning signal is first verified by those simulated abrupt changes based on four folding models. That is, NLLE in all experiments showed an almost monotonous increasing trend as a dynamic system approached its tipping point. For a well-studied abrupt climate change in North Pacific in 1976/1977, it is also found that NLLE shows an almost monotonous increasing trend since 1970 which give up to 6 years warning before the abrupt climate change. The limit of the predictability for a nonlinear dynamic system can be quantitatively estimated by NLLE, and lager NLLE of the system means less predictability. Therefore, the decreasing predictability may be an effective precursor indicator for abrupt climate change.
Of great importance for guiding numerical weather and climate predictions, understanding predictability of the atmosphere in the ocean − atmosphere coupled system is the first and critical step to understand predictability of the Earth system. However, previous predictability studies based on prefect model assumption usually depend on a certain model. Here we apply the predictability study with the Nonlinear Local Lyapunov Exponent and Attractor Radius to the products of multiple re-analyses and forecast models in several operational centers to realize general predictability of the atmosphere in the Earth system. We first investigated the predictability characteristics of the atmosphere in NCEP, ECMWF and UKMO coupled systems and some of their uncoupled counterparts and other uncoupled systems. Although the ECMWF Integrated Forecast System shows higher skills in geopotential height over the tropics, there is no certain model providing the most precise forecast for all variables on all levels and the multi-model ensemble not always outperforms a single model. Improved low-frequency signals from the air − sea and stratosphere − troposphere interactions that extend predictability of the atmosphere in coupled system suggests the significance of air − sea coupling and stratosphere simulation in practical forecast development, although uncertainties exist in the model representation for physical processes in air − sea interactions and upper troposphere. These inspire further exploration on predictability of ocean and stratosphere as well as sea − ice and land processes to advance our understanding of interactions of Earth system components, thus enhancing weather − climate prediction skills.
The aim of this study was to understand the cause of Madden–Julian oscillation (MJO) bias in the High Resolution Atmospheric Model (HiRAM) driven by observed SST through process-oriented diagnosis. Wavenumber-frequency power spectrum and composite analyses indicate that HiRAM underestimates the spectral amplitude over the MJO band and mainly produces non-propagating rather than eastward-propagating intraseasonal rainfall anomalies, as observed. Column-integrated moist static energy (MSE) budget analysis is conducted to understand the MJO propagation bias in the simulation. It is found that the bias is due to the lack of a zonally asymmetric distribution of the MSE tendency anomaly in respect to the MJO convective center, which is mainly attributable to the bias in vertical MSE advection and surface turbulent flux. Further analysis suggests that it is the unrealistic simulation of MJO vertical circulation anomalies in the upper troposphere as well as overestimation of the Rossby wave response that results in the bias.摘要本研究评估了高分辨率大气环流模式HiRAM模拟的MJO. 结果表明, HiRAM模拟的MJO东传很弱. 我们通过计算整层积分的湿静力能 (MSE) 收支来诊断MJO东传模拟偏差的原因. 结果发现, MSE倾向相对于MJO对流中心的纬向非对称分布很弱是导致东传模拟偏弱的原因, 这主要是由MSE垂直平流和地表湍流通量的模拟偏差造成的. 进一步研究表明, 对流层上层MJO垂直环流结构的模拟偏差和MJO对流西侧的Rossby波环流偏强共同导致了模式的偏差. 本研究中指出的MJO传播模拟偏差的原因与之前基于多模式结果的结论不同, 这意味着要想了解特定模式的模拟偏差, 有必要对该模式进行具体分析. 相似文献
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