基于慢特征分析方法研究陆地表面气温变率的驱动力

慢特征分析（SFA）方法可以从非平稳时间序列中提取出慢变的外强迫信息。近年来,SFA方法被应用于气候变化研究领域,用于探究气候变化的潜在驱动力及相关的动力学机制。本文基于SFA方法,提取全球陆地表面气温（LSAT）的慢变外强迫信息,研究全球LSAT慢变驱动力的空间结构特征及低频变率的主要驱动因子。SFA方法提取的LSAT慢变驱动力与历史时期全球辐射强迫（GRF）和全球海表温度（SST）的主模态（大西洋多年代际振荡AMO、热带太平洋ENSO变率和太平洋年代际振荡PDO）有显著的相关关系,表明全球大部分地区LSAT的变率受到GRF和三个SST模态的显著影响。GRF对LSAT变率的影响有全球一致性的特征,而三个SST模态对LSAT变率的影响则呈现出明显的区域特点。此外,由于SFA方法可以有效降低原始LSAT序列中随机噪声的干扰,GRF和SST模态对LSAT变率的解释方差显著提高,进一步表明GRF和SST模态是全球LSAT低频变率主要的驱动因子。最后,利用历史海温驱动AGCM试验（即AMIP试验）的结果,验证了三个SST模态对区域LSAT变率的显著影响。     

The importance of precessional signals in the tropical climate

Past research on the climate response to orbital forcing has emphasized the glacial-interglacial variations in global ice volume, global-mean temperature, and the global hydrologic cycle. This emphasis may be inappropriate in the tropics, where the response to precessional forcing is likely to be somewhat independent of the glacial-interglacial variations, particularly in variables relating to the hydrologic cycle. To illustrate this point, we use an atmospheric general circulation model coupled to a slab ocean model, performing experiments that quantify the tropical climates response to (1) opposite phases of precessional forcing, and (2) Last Glacial Maximum boundary conditions. While the glacially-forced tropical temperature changes are typically more than an order of magnitude larger than those arising from precessional forcing, the hydrologic signals stemming from the two forcings are comparable in magnitude. The mechanisms behind these signals are investigated and shown to be quite distinct for the precessional and glacial forcing. Because of strong dynamical linkages in the tropics, the model results illustrate the impossibility of predicting the local hydrologic response to external forcing without understanding the response at much larger spatial scales. Examples from the paleoclimate record are presented as additional evidence for the importance of precessional signals in past variations of the tropical climate.     

Process, Forcing, and Signal Analysis of Global Mean TemperatureVariations by Means of a Three-Box Energy Balance Model

An analytic solution of an energy balance model (EBM) is presented which can beused as a recursive filter for time series analysis. It is shown that the EBM can reproduce the solution of a coupled atmosphere-ocean general circulation model (AOGCM) experiment. Contrary to the AOGCM, the EBM easily allows for variations in climate sensitivity to satisfy the full range of uncertainty concerned with this parameter. The recursive filter is applied to two natural and two anthropogenic forcing mechanisms which are expressed in terms of heating rate anomaly time series: volcanism, solar activity, greenhouse gases (GHG), and anthropogenic tropospheric aerosols. Thus, we obtain modelled global mean temperature variations as a response to the different forcings and with respect to the uncertainty in the forcing approximations and climate sensitivity. In addition, it is shown that the observed (ENSO-corrected) global mean temperature time series within the period from 1866 to 1997 can be explained by the external forcings which have been considered and an additional white noise forcing. In this way we are able to separate different signals and compare them. As a result, global anthropogenic climate change due to GHG forcing can be detected at a high level of significance without considering spatial patterns of climate change but including natural forcing, which is usually not done. Furthermore, it is shown that solar forcing alone does not lead to significantclimate change, whereas solar and volcanic forcing together lead to a significant natural climate change signal. Anthropogenic climate change due to GHG forcing may partly be masked by anthropogenic aerosol cooling.     

东亚地区小冰期气候的模拟

本文使用ECHO-G全球气候模式对1550～1850年的小冰期气候进行了300个模式年的模拟,着重分析了东亚地区小冰期的温度变化特征,并与目前所得到的小冰期气候重建结果进行了对比。结果表明,在考虑了太阳辐射、火山活动、CO2和CH4等主要气候影响驱动因子的条件下,较好地模拟出了东亚地区的小冰期气候特征,并与其它手段的气候重建结果相吻合,显示太阳活动和火山活动是小冰期气候形成的主要原因。     

The potential added value of Regional Climate Models in South America using a multiresolution approach

This paper aims to identify those regions within the South American continent where the Regional Climate Models (RCMs) have the potential to add value (PAV) compared to their coarser-resolution global forcing. For this, we used a spatial-scale filtering method based on the wavelet theory to distinguish the regional climatic signal present in atmospheric surface fields from observed data (CPC and TRMM) and 6 RCM simulations belonging to the CORDEX Project. The wavelet used for filtering was Haar wavelet, but a comparative analysis with Daubechies 4 wavelet indicated that meteorological fields or regional indices were not very sensitive to the wavelet selected. Once the longer wavelengths were filtered, we focused on analyzing the spatial variability of extreme rainfall and the spatiotemporal variability of maximum and minimum surface air temperature on a daily basis. The results obtained suggest essential differences in the spatial distribution of the small-scale signal of extreme precipitation between TRMM and regional models, together with a large dispersion between models. While TRMM and CPC register a large signal throughout the continent, the RCMs place it over the Andes Cordillera and some over tropical South America. PAV signal for surface air temperature was found over the Andes Cordillera and the Brazilian Highlands, which are regions characterized by complex topography, and also on the coasts of the continent. The signal came specially from the small-scale stationary component. The transient part is much smaller than the stationary one, except over la Plata Basin where they are of the same order of magnitude. Also, the RCMs and CPC showed a large spread between them in representing this transient variability. The results confirm that RCMs have the potential to add value in the representation of extreme precipitation and the mean surface temperature in South America. However, this condition is not applicable throughout the whole continent but is particularly relevant in those terrestrial regions where the surface forcing is strong, such as the Andes Cordillera or the coasts of the continent.     

基于慢特征分析对连续系统的外强迫提取

外强迫随时间的变化对于非平稳系统的影响十分重要,如何从该系统中重构或提取外强迫信息则成为研究其中动力学特征的关键所在。本文基于慢特征分析方法（Slow Feature Analysis,SFA）以连续系统（改变的Lorenz系统）为参考模型,分别讨论在周期型强迫、减弱的周期型强迫、指数衰减型强迫、伴随指数衰减的周期型强迫等条件下,SFA方法对模型中不同强迫信号的提取能力。结果显示,SFA方法能够提取作用于连续系统中的外强迫信息,其提取效果与外强迫的强度、噪声以及嵌入维数m有关:对于越弱的外强迫或者存在越强的噪声干扰,提取效果越差,提取信号中将出现虚假的高频波动;嵌入维数m的增大能在一定程度上提高外强迫信号的提取效果。试验还表明,作用在单一变量上的外强迫会将其驱动信息嵌入于系统中,因此,可以通过SFA分析方法从其他变量中提取其外强迫信号。     

欧亚大陆冬季地表温度南北反相变化的年代际特征及机理

对欧亚大陆冬季地表温度南北反相的时空分布特征及机理的分析结果表明,欧亚大陆冬季地表温度约以55°N为界存在南北反相变化特征。1961~2015年欧亚大陆冬季地表温度变化具有显著的年际和年代际变化特征;年代际尺度上,北半球行星波"冬三"分布型变化与欧亚大陆地表温度南北反相变化密切联系。冬季欧亚地表温度南北反相变化存在明显的季节内转变。滤除年代际信号和全球变暖趋势后,欧亚大陆冬季地表温度与秋季北极海冰面积之间存在显著相关;北极海冰面积减小是欧亚、尤其中亚地区冬季地表温度降低的主要外强迫因素之一;同期北大西洋"三极子"和欧亚大陆冬季地表温度南北反相变化在年际尺度上存在显著相关。     

我国对流层臭氧增加对气温的影响

利用耦台的区域气候模式和大气化学模式模拟对流层臭氧的产生、分布和对辐射传输、地表温度、气温等的影响。通过对比模拟发现：对流层中臭氧的增加基本使大气顶晴空辐射强迫为正；对流层中的臭氧含量变化能影响云量且进一步影响温度。由于对流层臭氧增加导致的晴空辐射强迫在4月份最大、1月份最小。     

CLIMATE CHANGE: LONG-TERM TRENDS AND SHORT-TERM OSCILLATIONS

Identifying the Northern Hemisphere (NH) temperature reconstruction and instrumental data for the past 1000 years shows that climate change in the last millennium includes long-term trends and various oscillations. Two long-term trends and the quasi-70-year oscillation were detected in the global temperature series for the last 140 years and the NH millennium series. One important feature was emphasized that temperature decreases slowly but it increases rapidly based on the analysis of different series. Benefits can be obtained of climate change from understanding various long-term trends and oscillations. Millennial temperature proxies from the natural climate system and time series of nonlinear model system are used in understanding the natural climate change and recognizing potential benefits by using the method of wavelet transform analysis. The results from numerical modeling show that major oscillations contained in numerical solutions on the interdecadal timescale are consistent with that of natural proxies. It seems that these oscillations in the climate change are not directly linked with the solar radiation as an external forcing. This investigation may conclude that the climate variability at the interdecadal timescale strongly depends on the internal nonlinear effects in the climate system.     

Influence of local and remote SST on North Atlantic tropical cyclone potential intensity

We examine the role of local and remote sea surface temperature (SST) on the tropical cyclone potential intensity in the North Atlantic using a suite of model simulations, while separating the impact of anthropogenic (external) forcing and the internal influence of Atlantic Multidecadal Variability. To enable the separation by SST region of influence we use an ensemble of global atmospheric climate model simulations forced with historical, 1856–2006 full global SSTs, and compare the results to two other simulations with historical SSTs confined to the tropical Atlantic and to the tropical Indian Ocean and Pacific. The effects of anthropogenic plus other external forcing and that of internal variability are separated by using a linear, “signal-to-noise” maximizing EOF analysis and by projecting the three model ensemble outputs onto the respective external forcing and internal variability time series. Consistent with previous results indicating a tampering influence of global tropical warming on the Atlantic hurricane potential intensity, our results show that non-local SST tends to reduce potential intensity associated with locally forced warming through changing the upper level atmospheric temperatures. Our results further indicate that the late twentieth Century increase in North Atlantic potential intensity, may not have been dominated by anthropogenic influence but rather by internal variability.     

Hierarchical evaluation of IPCC AR4 coupled climate models with systematic consideration of model uncertainties

The capability of reproducing observed surface air temperature (SAT) changes for the twentieth century is assessed using 22 multi-models which contribute to the Intergovernmental Panel on Climate Change Fourth Assessment Report. A Bayesian method is utilized for model evaluation by which model uncertainties are considered systematically. We provide a hierarchical analysis for global to sub-continental regions with two settings. First, regions of different size are evaluated separately at global, hemispheric, continental, and sub-continental scales. Second, the global SAT trend patterns are evaluated with gradual refinement of horizontal scales (higher dimensional analysis). Results show that models with natural plus anthropogenic forcing (MME_ALL) generally exhibit better skill than models with anthropogenic only forcing (MME_ANTH) at all spatial scales for different trend periods (entire twentieth century and its first and second halves). This confirms previous studies that suggest the important role of natural forcing. For the second half of the century, we found that MME_ANTH performs well compared to MME_ALL except for a few models with overestimated warming. This indicates not only major contributions of anthropogenic forcing over that period but also the applicability of both MMEs to observationally-constrained future predictions of climate changes. In addition, the skill-weighted averages with the Bayes factors [Bayesian model averaging (BMA)] show a general superiority over other error-based weighted averaging methods, suggesting a potential advantage of BMA for climate change predictions.     

Contributions of solar and greenhouse gases forcing during the present warm period

Due to the dramatic increase in the global mean surface temperature (GMST) during the twentieth century, the climate science community has endeavored to determine which mechanisms are responsible for global warming. By analyzing a millennium simulation (the period of 1000–1990 ad) of a global climate model and global climate proxy network dataset, we estimate the contribution of solar and greenhouse gas forcings on the increase in GMST during the present warm period (1891–1990 ad). Linear regression analysis reveals that both solar and greenhouse gas forcing considerably explain the increase in global mean temperature during the present warm period, respectively, in the global climate model. Using the global climate proxy network dataset, on the other hand, statistical approach suggests that the contribution of greenhouse gas forcing is slightly larger than that of solar forcing to the increase in global mean temperature during the present warm period. Overall, our result indicates that the solar forcing as well as the anthropogenic greenhouse gas forcing plays an important role to increase the global mean temperature during the present warm period.     

The influence of volcanic, solar and CO2 forcing on the temperatures in the Dalton Minimum (1790–1830): a model study

The Dalton Minimum (1790–1830) was a period with reduced solar irradiance and strong volcanic eruptions. Additionally, the atmospheric CO₂ concentrations started to rise from the background level of previous centuries. In this period most empirical climate reconstructions indicate a minimum in global or hemispheric temperatures. Here, we analyse several simulations starting in 1755 with the coupled atmosphere-ocean model ECHO-G driven by different forcing combinations to investigate which external forcing could have contributed most strongly to the reduced temperatures during the Dalton Minimum. Results indicate that on global and hemispheric scales, the volcanic forcing is largely responsible for the temperature drop in this period, especially during its second half, whereas changes in solar forcing and the increasing atmospheric CO₂ concentrations were of minor importance. At regional scales, especially the extratropical, the impact of volcanic forcing is much less discernible due to the large regional variability, a finding that agrees with empirical temperature reconstructions.     

季节内尺度热带-中纬相互作用机制初探

本文使用一个简单的全球二层大气环流模式作了强迫响应数值试验。模式中引入了代表热带低频偶极型对流的异常强迫.结果表明这一强迫不仅能激发显著的局地大气响应,也能引起北半球中纬显著的低频异常波列,中纬波列的发展与热带强迫变化之间存在四分之一位相差关系.这些结果与观测研究的结果一致.进一步的分析表明,大气响应的位相滞后与强迫产生的能量从内模向外模的非线性转换过程有关.最后提出了季节内尺度热带-中纬相互作用的可能机制.     

Solar signals in global climatic change

Based on the physical background that varying solar activity should lead to variations of the ‘solar constant’ and that the climate system may respond sensitively even to small solar variations, a correlation analysis is performed where hemispheric and global averages of the annual mean surface air temperature are compared with the variations of a variety of solar forcing parameters: sunspots, related hypotheses including variations of the quasi-eleven-year solar cycle length, solar diameter variations and gravitational effects. This analysis is based on the 1881–1988 period, for the northern hemisphere including proxy data 1671–1988. Cross correlations and correlations moving in time reveal some instability effects which are hard to interpret. The temperature variance components which may be hypothetically explained by solar forcing are small. Similarly, a seasonal and regional signal and signal-to-noise analysis based on a gridded temperature time series 1890–1985 reveals small signals which do not exceed roughly 1.5 K in the arctic winter (maximum) or 0.2-0.3 K on a global average.     

包含外强迫因子的平潭旬降水量预测

降水量是重要的预报要素之一,长期的降水预测更是能提前预测旱涝分布情况,为国民经济规划提供依据。但目前为止,长期的降水预测仍缺少客观的预报方法。为此,尝试利用非线性预测模型来预测旬降水量,并将该模型应用于福建平潭,分别用与原始数据的差值、与原始数据的相关系数、均方根误差,以及符号显著性检验方法,讨论了包含外强迫因子的平稳性模型与不包含外强迫因子的非线性模型的预测能力,结果表明:包含外强迫因子的模型第一步预测结果与原始观测数据的相关系数为0.73,不包含外强迫因子的模型第一步预测结果与原始观测数据的相关系数则为0.47。无论是从与原始数据的差值及相关系数,还是均方根误差等方面,外强迫模型都是优于平稳性模型,并且通过符号检验方法可看出两种模型存在差异性,这也说明加入外强迫因子可以有效地提高预测技巧,外强迫因子与状态变量在预测中扮演同等重要的角色。     

非平稳时间序列的区域预测研究

基于重构状态空间理论和嵌入定理,给出一个新的非平稳场时间序列的区域预测方法。该方法将外强迫因子引入到预测模型中,并且将区域内预测相点的周围相点所对应的空间信息也引入到预测模型中。然后利用该方法对33模Lorenz系统得到的"理想"的非平稳场时间序列进行预测实验分析。结果表明,嵌入外强迫因子可以更好地重构出原来的动力系统,有效地提高非平稳时间序列的预测精度;同时引入空间和外强迫信息可以利用空间数据弥补时间序列长度的不足,从而进一步提高预测精度。     

大气对外强迫低频遥响应的数值模拟 II:对欧亚中高纬“寒潮”异常的响应

本文通过用IAP GCM所作的数值模拟研究了欧亚大陆中高纬度地区的外强迫在全球大气中激发的响应.结果清楚表明,同赤道地区的热源强迫一样,中高纬度地区的外强迫也可以在全球大气中产生低频遥响应;通过低频波列EAP,欧亚大陆中高纬度地区的寒潮异常可以对赤道中西太平洋地区的大气运动及全球大气环流有重要影响.对30—60天振荡的强迫激发来讲,地球大气的气候基本态是极为重要的,热带大气对于全球大气的低频振荡活动具有尤为突出的作用.     

Variation of surface temperature during the last millennium in a simulation with the FGOALS-gl climate system model

A reasonable past millennial climate simulation relies heavily on the specified external forcings, including both natural and anthropogenic forcing agents. In this paper, we examine the surface temperature responses to specified external forcing agents in a millennium-scale transient climate simulation with the fast version of LASG IAP Flexible Global Ocean-Atmosphere-Land System model (FGOALS-gl) developed in the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics (LASG/IAP). The model presents a reasonable performance in comparison with reconstructions of surface temperature. Differentiated from significant changes in the 20th century at the global scale, changes during the natural-forcing-dominant period are mainly manifested in the Northern Hemisphere. Seasonally, modeled significant changes are more pronounced during the wintertime at higher latitudes. This may be a manifestation of polar amplification associated with sea-ice-temperature positive feedback. The climate responses to total external forcings can explain about half of the climate variance during the whole millennium period, especially at decadal timescales. Surface temperature in the Antarctic shows heterogeneous and insignificant changes during the preindustrial period and the climate response to external forcings is undetectable due to the strong internal variability. The model response to specified external forcings is modulated by cloud radiative forcing (CRF). The CRF acts against the fluctuations of external forcings. Effects of clouds are manifested in shortwave radiation by changes in cloud water during the natural-forcing-dominant period, but mainly in longwave radiation by a decrease in cloud amount in the anthropogenic-forcing-dominant period.     

Seasonal characteristics and internnual variability of monthly scale low-frequency oscillation in a low-order global spectral model

Analysis is done of five-year low-pass filtered data by a Five-layer low-order global spectral model, indicating that although any non-seasonal external forcing is not considered in the model atmosphere, monthly-scale anomaly takes place which is of remarkable seasonality and interannual variability.Analysis also shows that for the same seasonal external forcing the model atmosphere can exhibit two climatic states, similar in the departure pattern but opposite in sign, indicating that the anomaly is but the manifestation of the adverse states, which supports the theory of multi-equilibria proposed by Charney and Devore (l979) once again.Finally, the source for the low-frequency oscillation of the global atmosphere is found to be the convective heat source / sink inside the tropical atmosphere as discussed before in our study.Therefore, the key approach to the exploration of atmospheric steady low-frequency oscillation and the associ-ated climatic effect lies in the examination of the distribution of convective heat sources / sinks and the variation in the tropical atmosphere.