IOCAS-IR  > 海洋环流与波动重点实验室
热带海洋海温变率预报及可预报性研究
其他题名Prediction and Predictability of Temperature Variability in the Tropical Oceans
唐晓晖
学位类型博士
2008-12-25
学位授予单位中国科学院海洋研究所
学位授予地点海洋研究所
关键词热带海洋 气候预报 可预报性 Enso Tav Sst 耦合模式 大气噪声过滤器
摘要热带海洋是大尺度海气相互作用的关键区域,对全球气候变化有重要影响。厄尔尼诺与南方涛动(ENSO)及热带大西洋变率(TAV)是分别是热带太平洋、大西洋的最显著气候变率。对热带海洋海表温度(SST)的预报是预报ENSO和TAV的关键要素,对全球气候、生态环境及许多国家的防灾减灾、经济发展有非常重要的意义。 本文利用一个中等复杂程度的海-气耦合模式CCM3-RGO,对1980-2000年热带海洋SST变率进行回顾性预报。并创新性地在耦合模式中加入大气噪声过滤器,检验天气噪声等因素对预报的影响。 第一部分工作为ENSO预报。本文改进了初始化方案,并应用噪声过滤器减少天气噪声对耦合过程的影响,显著提高了ENSO预报技能,达到同类研究的先进水平。进一步分析表明,采用耦合同化方法产生的、与模式相容性和准确性皆优的初始条件,对2个季节以内的ENSO预报技巧的提高起主要作用;在适宜的初始条件下,过滤风应力中的天气噪声,可以增强海-气耦合过程的信噪比,改善模式对风-温跃层-SST相互作用的Bjerknes反馈机制的正确响应,对3-4季节预报技巧的改进起主要作用。 第二部分为热带大西洋SST预报。发现过滤热通量中的天气噪声可以加强局地热力学反馈,对以往预报水平较低的南热带大西洋SST预报有显著的改进作用。
其他摘要The tropical oceans are crucial to large scale ocean-atmosphere interactions, and have profound impact on global climate variability. El Niño-Southern Oscillation (ENSO) and Tropical Atlantic Variabiltiy (TAV) are the most notable phenomena among Tropical Pacific (TP) and Tropical Atlantic (TA) climate variations, respectively. Prediction of tropical sea surface temperatures (SSTs) is a key issue to seasonal-to-decadal climate prediction, and has important implication to global climate and marine ecosystem studies, as well as disaster relief and economic development of many countries. In this dissertation, an intermediate ocean-atmosphere coupled model is used for retrospective predictions of TP and TA SST variability. Factors that impact SST predictability are investigated. The coupled model consists of an atmospheric general circulation model (AGCM) (CCM3), and a Zebiak-Cane type of reduced gravity ocean model (RGO), and is equipped with a novel atmospheric noise filter in order to test impact of “weather noise” on SST prediction. Ensemble forecast experiments are initialized in four seasons, and carried out for 12 months for each ensemble member. In the first part of the dissertation, ENSO prediction is conducted with an improved initialization scheme and the atmospheric noise filter. The results show that these efforts lead to an improved ENSO forecast skill. Anomaly correlation of observed and predicted Nino3 index reaches 0.71 at 6-month lead time, and 0.43 at 12-month lead time. The value of of Nino3.4 index reaches 0.75 and 0.49, respectively. The overall forecast skill is comparable to some of the most advanced ENSO prediction models. The coupled model has higher predictability of warm and cold events than near-normal events. Spacial patterns and trends of the equatorial Pacific cold tongue variability can be well captured during prediction of El Niño and La Niña events. Impacts of initial condition, weather noise and model biases on ENSO prediction are investigated based on ensemble prediction experiments. Major findings can be summarized as follows: 1) Initial conditions are of significant importance to ENSO SST predictions. The initial conditions used in this dissertation were produced using the coupled model with a strong SST restoring to the observations, therefore can effectively reduce “initial shock” caused by mismatch between observations and model physics. Meanwhile, the noise filter is used in the initialization process to suppress impact of weather noise, leading to reduced initial noise in forecast. The resultant initial conditions, achieving both compatibility with model and accuracy, are shown to have major effect on improvement of short term ENSO forecast skill up to 2 seasons. 2) Weather noise plays a notable role in affecting ENSO prediction skill. With appropriate initial conditions, reducing weather noise can alleviate drop of forecast skill caused by the so called “spring predictability barrier” (SPB), and help improving forecast skill in 3-4 leading seasons. The noise filter mainly takes effect through boosting signal to noise ratio (SNR) of wind stress, and improving the model’s response to Bjerknes feedbacks between wind stress, thermocline and SST. 3) Atmospheric model biases have severe impact on simulation of thermocline depth, which in turn limits SST prediction skill at long lead times. Systematic model biases are a common problem in many coupled models, and need to be addressed before major improvement of model forecast skill at long lead time can be achieved. Attempts were made to correct wind stress bias in the atmospheric model using a model output statistics (MOS) technique. Primary results show improvement in short term forecast up to two seasons, and are promising in overcoming the spring predictability barrier. The second part of this dissertation investigates predictability of Tropical Atlantic SSTs and the role of weather noise in that region. With the utilization of the atmospheric noise filter, it is shown that filtering weather noise generally improves forecast skill. Improvement is particularly effective in the Southern Tropical Atlantic (STA), where previous models generally failed to show any useful prediction skill. Considerably high skill is achieved in forecasting STA SST 2-3 seasons in advance. This in turn leads to a useful forecast skill of spring rainfall over the Nordeste of Brazil. The improved skill is attributed to the reduction of weather noise in surface heat fluxes. Filtering the heat flux noise enhances feedbacks between surface heat fluxes and SST in the STA region, strengthening the SNR and resulting in an improved SST predictability. Ekman process in the upper ocean is found to be important as well in SST prediction in STA, while Bjerknes feedback mechanism is less significant in this region.
页数115
语种中文
文献类型学位论文
条目标识符http://ir.qdio.ac.cn/handle/337002/837
专题海洋环流与波动重点实验室
推荐引用方式
GB/T 7714
唐晓晖. 热带海洋海温变率预报及可预报性研究[D]. 海洋研究所. 中国科学院海洋研究所,2008.
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