热带海洋对大气进行非绝热加热的随机效应的进一步研究

本文考虑了热带海洋对大气进行非绝热加热过程中海温起伏的随机效应,建立了朗之万方程形式的描写大气热量变化的发展方程。由该随机微分方程的定态解,给出了加热势函数和概率密度函数。在确定的热力学参数下,利用实际海温起伏方差值,计算求得了加热势函数的分布及概率密度分布曲线。文中还进一步计算了概率密度流,得到了在两种稳定状态之间穿透势垒的弛豫时间。计算结果表明,海温异常的随机效应是一个长周期过程,时间尺度约为140天,可以用实际测得的拖曳系数C_D和海温起伏方差q2值对它进行预测。      

统计预报海温场驱动的CAM3.1模式预报试验

基于动力气候模式进行月一季尺度预报的“两步法”思想,提出一种新的预报海温场统计模型,并以该统计模型预报的海温场驱动NCAR CAM3.1模式对1981-2000年月时间尺度的东亚500 hPa高度距平场进行客观回报试验;在此基础上,提出了对预报结果的订正方法。结果表明:统计预报海温模型的预报海温场能够反映出全球海温空间分布的基本特征,并对表征ENSO事件的Ni?o3.4区海温变化的预报能力较强。该统计模型预报的海温场驱动的CAM3.1模式可以较好地预报出东亚500 hPa环流的主要分布特征,试验表明:适当的统计订正方法可以在一定程度上提高CAM3.1模式对东亚夏季500 hPa环流背景的预报技巧。     

给定参数条件下海温脉动随机模式的混沌行为

从Ｓａｌｔｚｍａｎ海气随机气候模式出发，得到了海温脉动θ＇的Ｌａｎｇｅｖｉｎ方程以及对应的Ｆｏｋｋｅｒ－ｐｌａｎｋ方程。在给定参数条件下求数值解，得到的概率密度曲线ｐ（ｘ，ｔ）具有多个极大值，并在ｐ（ｘ，ｔ）－ｐ（ｘ，ｔ＋τ）相空间中呈现Ｃａｎｔｏｒ集合图象，表明该随机系统在上述参数条件下出现了混沌行为。     

NCAR CAM3模式大气环流对日本东部附近海域海温异常的响应

使用NCAR CAM3.0大气环流模式,设计多组数值试验,研究了模式大气环流对初夏日本东部附近海域海温异常的响应。首先,在北太平洋中纬度选择异常海温区域,构造了假想的负海温异常,在5、6月份模式气候海温场上叠加该海温异常设计了敏感性试验。其次,在该试验的基础上,沿顺时针方向移动海温异常区域一个格点位置,其他条件不变,设计海温敏感区域变动后的系列试验;另外,通过改变加入模式中的海温异常强度,设计海温异常强度变化后的系列试验。最后,在把加入的海温异常变为正异常后,设计另一组试验。试验结果表明,模式能够较好地表现出初夏日本东部附近海域负海温异常引起的大气环流异常,欧亚中高纬纬向呈“＋、-”相间的波列结构。改变加入的负海温异常位置后,试验结果与对应的NCEP合成分析结果有较好的一致性,但是试验得到的大气环流异常分布并未随着加入的海温异常位置的有规律变化而发生同样的位置变动。模式中加入正的海温异常后,模式的试验结果不好。无论正负,模式中加入的海温异常增强后,试验得到的大气环流异常强度并未随着海温异常强度的增强而线性增强。     

海温异常对东亚夏季风及长江流域降水影响的分析及数值试验

对夏季的向外长波辐射(OLR)场及前期热带海温之间的奇异值分解(SVD)分析表明,当自前冬开始的热带海温呈La Nia 型异常分布,相应的对流的最佳耦合模态将是:从印度半岛经孟加拉湾直至南海以及西太平洋暖池都为强对流活动区,而在中国江淮流域地区则为弱区.当海温呈El Nio分布时,则相反.数值试验的结果是:在1998年实际海温强迫下,模式十分成功地模拟了该年夏季流场的基本特征,南海季风爆发的日期及强度皆与实际十分符合,印度季风也偏弱.特别是模式成功地模拟出了1998年长江流域6～7月的强降水,距平百分率达到100%以上.改变热带海温,分别用气候海温代替赤道东太平洋和西印度洋实际海温.结果发现,在用赤道东太平洋气候海温取代实际海温时,与1998年实况相比,印度季风与南海季风明显加强,长江流域降水趋于正常值.即如果1998年不出现El Nio现象,或者海温异常的强度减弱,则长江流域降水就会大大减弱.1998年热带西印度洋海温异常对东亚季风的影响与热带东太平洋十分相像.暖洋面将有利于长江流域的强降水,而不利于南海季风的加强.关键词:海面温度;长江流域;洪涝;南海季风;数值试验     

利用ARGO资料改进海洋资料同化和海洋模式中的物理过程

国际ARGO计划的实施每年将可提供多达 10万个剖面 (0～ 2 0 0 0m水深 )的海水温度和盐度资料 ,这些资料的获取无疑将会大大促进海洋和大气科学的发展 ,使人们加深对海洋过程的了解 ,揭示海 气相互作用的机理 ,为长期天气预报和短期气候预测提供模式初始场 ,提高长期天气预报和短期气候预测的能力。如何利用这些资料开展研究工作以及在实际业务中应用这些资料是目前大气和海洋科学界的一个前沿课题。本研究将ARGO浮标资料引入了国家气候中心的NCC GODAS同化系统 ,结果分析表明 ,同化ARGO资料后所得到的海温场在三大洋中不仅在温度数值的大小 ,而且在分布形式方面都与观测场具有较好的一致性 ,可以很好地反映出观测到的冬季和夏季海温的分布形式以及海温的季节变化特征和异常特征。本研究还应用最新的ARGO海洋观测资料 ,通过建立新的热带西太平洋次表层海温参数化方案 ,改进了Zebiak Cane(1987)海洋模式 (ZC模式 ) ,克服了ZC模式几乎没有模拟赤道西太平洋表层和次表层海温变化能力的缺陷。在ZC模式中引用新的次表层海温参数化方案后 ,在赤道西太平洋不仅次表层海温的模拟得到了改善 ,对海面温度异常的模拟也有了较大的改进 ,不仅模拟出了赤道西太平洋表层和次表层海温异常的年际变化特征 ,也模拟出了与观     

南亚地区季风与邻近海域海温相互影响的初步研究

文中利用简化的海 气耦合模式及低谱方法和多平衡态理论 ,讨论了南亚地区冬夏季风与邻近海域海温季节变化之间的相互影响。结果表明 :(1)冬季风较强时 ,冬季海温较低 ,翌年夏季海温也较低 ;反之亦然。夏季风较强时 ,夏季海温较高 ;反之亦然。夏季风强弱对冬季海温的影响不明显。 (2 )海 气相互作用使南亚冬季风和夏季风都加强。海温经向梯度使冬季风加强 ,而夏季风减弱。     

长期气候预测的一种两层耦合法

文章使用一个与大气环流模式串联耦合的预报热带太平洋海温（ＳＳＴ）的模式来制作全球长期气候预测。首先超前预报海温，然后将海洋预报用于强迫大气模式，从而超前于海温预报制作气候预测。在全球大部分地区该方法对７０年代至９０年代的７次重大气候事件的预测与观测结果很好吻合。     

北太平洋海温距平经向差对付热带高压影响的若干事实

本文在系统分析1951—1975年北太平洋海温距平特征及夏季西太平洋付高强度的基础上,得到如下主要结果:整个北太平洋海温距平基本上可以分为正型和负型两种类型。正型是指海温距平经向差为正,即南正北负或南区的距平值大于北区,负型反之。正型和负型各有很长的持续性、周期性和经常在春、秋发生转换的阶段性。此外,北太平洋海温距平经向差与付高之间存在非常密切的正相关,它们有共同的周期,同时付高比海温的变化落后3—7个月。在付高与海温相关普查的基础上,给出了用海温距平经向差予报付高趋势的拟合曲线。     

北太平洋海温异常对我国降水影响的数值试验

利用全球大气环流模式 (OSU- AGCM) ,作中纬度北太平洋热源异常强迫的数值试验 ,分析中纬度北太平洋海温异常对降水的影响。结果表明 ,中纬度北太平洋大范围海温降低 ,将造成东北地区季内降水偏多 ,而我国 40°N以南大部分地区降水偏少 ;同时 ,北太平洋中低纬度地区降水将发生显著的变化。通过分析海温异常所造成的大气物理量场的变化 ,就海温异常对降水的影响机制进行了探讨 ,得出一些有益的结果。     

ON THE STOCHASTIC ANALYSIS OF A SIMPLE CLIMATE MODEL

By using the first thermodynamic law and stochastic function,a stochastic nonlinear climate model isproposed and the model analytical solution is obtained.The result indicates that the global temperature prob-ability density function has two peaks.If the solar radiation varies,a sudden change in the number of peakwill happen.In addition,we find that there are different sea temperature responses at different latitudes.     

广东气温变化与南方涛动关系的随机动力分析

本文推导出二维线性和非线性随机动力模式的Fokker—Planck方程,得到其稳定态的解析解和气候势函数,并应用于广东气温和南方涛动的随机分析。计算结果表明,线性模式能明确反映广东气温和南方涛动之间的反馈关系;非线性模式则表明非线性作用使广东气温和南方涛动之间的稳定平衡更趋于合理化。      

A nonlinear stochastic low-order energy balance climate model

The effects of stochastic forcing on a one-dimensional, energy balance climate model are considered. A linear, stochastic model is reviewed in analogy with the Brownian motion problem from classical statistical mechanics. An analogous nonlinear model is studied and shows different behavior from the linear model. The source of the nonlinearity is the dynamical heat transport. The role of nonlinearity in coupling different temporal and spatial scales of the atmosphere is examined. The Fokker-Planck equation from statistical mechanics is used to obtain a time evolution equation for the probability density function for the climate, and the climatic potential function is calculated. Analytical solutions to the steady-state Fokker-Planck equation are obtained, while the time-dependent solution is obtained numerically. The spread of the energy produced by a stochastic forcing element is found to be characterized by movement mainly from smaller to larger scales. Forced and free variations of climate are also explicitly considered.     

区域随机动力统计气候模式及其预报试验

本文从大气热力学与动力学原理出发,设计并推导了一个以高度场和海温场为主要变量的多网格点的海气耦合随机动力模式,用此模式作500hPa平均高度场的预报试验。预报以春季月份(2月)为初始场,取西太平洋副热带高压活动地区(10°N-40°N,110°E-170°W)6月500hPa高度场作为预报场。另外取赤道东太平洋地区5°S—10°S范围内海温场作为耦合场,把该模式中的微分方程组化为差分形式,以前期月平均高度场和海温场为初始场,以时间步长1天进行积分,积分系数是用依赖资料(1951—1980年)反演估计出来的,然后对独立样本(1981—1986年)作预报试验。方程中的随机项假设为白噪音,并对不同的白噪音量级作了一系列试验。结果表明,加上白噪音的效果均比未加时要好,说明次要因素的随机作用在海气相互作用中是不可忽视的。     

Does temperature contain a stochastic trend: linking statistical results to physical mechanisms

By construction, the time series for radiative forcing that are used to run the 20c3m experiments, which are implemented by climate models, impart non-stationary movements (either stochastic or deterministic) to the simulated time series for global surface temperature. Here, we determine whether stochastic or deterministic trends are present in the simulated time series for global surface temperature by examining the time series for radiative forcing. Statistical tests indicate that the forcings contain a stochastic trend against the alternative hypothesis that the series are trend stationary with a one-time structural change. This result is consistent with the economic processes that impart a stochastic trend to anthropogenic emissions and the physical processes that integrate emissions in the atmosphere. Furthermore, the stochastic trend in the aggregate measure of radiative forcing also is present in the simulated time series for global surface temperature, which is consistent with the relation between these two variables that is represented by a zero dimensional energy balance model. Finally, we propose that internal weather variability imposed on the stochastic trend in radiative forcings is responsible for statistical results, which gives the impression that global surface temperature is trend stationary with a one-time structural change. We conclude that using the ideas of stochastic trends, cointegration, and error correction can generate reliable conclusions regarding the causes of changes in global surface temperature during the instrumental temperature record.     

A Lagrangian Stochastic Model for Sea-Spray Evaporation in the Atmospheric Marine Boundary Layer

The dispersion of heavy particles subjected to a turbulent forcing is often simulated with Lagrangian stochastic models. Although these models have been employed successfully over land, the implementation of traditional LS models in the marine boundary layer is significantly more challenging. We present an adaptation of traditional Lagrangian stochastic models to the atmospheric marine boundary layer with a particular focus on the representation of the scalar turbulence for temperature and humidity. In this new model, the atmosphere can be stratified and the bottom boundary is represented by a realistic wavy surface that moves and deforms. Hence, the correlation function for the turbulent flow following a particle is extended to the inhomogenous, anisotropic case. The results reproduce behaviour for scalar Lagrangian turbulence in a stratified airflow that departs only slightly from the expected behaviour in isotropic turbulence. When solving for the surface temperature and the radius of evaporating heavy water droplets in the airflow, the modelled turbulent forcing on the particle also behaves remarkably well. We anticipate that this model will prove especially useful in the context of sea-spray dispersion and its associated sensible heat, latent heat, and gas fluxes between spray droplets and the atmosphere.     

A new daily weather generator to preserve extremes and low-frequency variability

This paper addresses deficiencies of stochastic Weather Generators (WGs) in terms of reproduction of low-frequency variability and extremes, as well as the unanticipated effects of changes to precipitation occurrence under climate change scenarios on secondary variables. A new weather generator (named IWG) is developed in order to resolve such deficiencies and improve WGs performance. The proposed WG is composed of three major components, including a stochastic rainfall model able to reproduce realistic rainfall series containing extremes and inter-annual monthly variability, a multivariate daily temperature model conditioned to the rainfall occurrence, and a suitable multi-variate monthly generator to fit the low-frequency variability of daily maximum and minimum temperature series. The performance of IWG was tested by comparing statistical characteristics of the simulated and observed weather data, and by comparing statistical characteristics of the simulated runoff outputs by a daily rainfall-runoff model fed by the generated and observed weather data. Furthermore, IWG outputs are compared with those of the well-known LARS-WG weather generator. The tested characteristics are a variety of different daily statistics, low-frequency variability, and distribution of extremes. It is concluded that the performance of the IWG is acceptable, better than LARS-WG in the majority of tests, especially in reproduction of extremes and low-frequency variability of weather and runoff series.     

Inference about trends in global temperature data

Interpretation of the effects of increasing atmospheric carbon dioxide on temperature is made more difficult by the fact that it is unclear whether sufficient global warming has taken place to allow a statistically significant finding of any upward trend in the temperature series. We add to the few existing statistical results by reporting tests for both deterministic and stochastic non-stationarity (trends) in time series of global average temperature. We conclude that the statistical evidence is sufficient to reject the hypothesis of a stochastic trend; however, there is evidence of a trend which could be approximated by a deterministic linear model.The authors are grateful to the SSHRC (Green) and FCAR (Galbraith) for financial support under grants 10-89-0205 and NC-0047.