Modes of interannual variability of Southern Hemisphere atmospheric circulation in CMIP3 models: assessment and projections

An assessment is made of the modes of interannual variability in the seasonal mean summer and winter Southern Hemisphere 500 hPa geopotential height in the twentieth century in models from the Coupled Model Intercomparison Project phase 3 (CMIP3) dataset. The analysis is done for both the intraseasonal and slow components of the geopotential height. When the CMIP3 models are assessed against reanalysis data, the spatial structure and variance of the leading modes in the intraseasonal component are generally well reproduced. There are systematic differences between the models in their reproduction of the leading modes in the slow component. An overall score using the leading modes in the slow component allows a categorisation of CMIP3 model performance. Using an ensemble from four models that suitably reproduce the twentieth century modes, modes of variability in the slow-internal and slow-external components are estimated. The leading mode of the slow-external component is shown to be related to observed changes in greenhouse gas concentrations. In this ensemble, there is little change in the leading modes in the intraseasonal component in the twenty-first century. Larger changes in variance, and subtle changes in regional-scale structure, are found for the leading modes in the slow-internal component. These are related to changes in the slowly varying dynamics of the Southern Annular Mode and the El Niño-Southern Oscillation. By far the biggest change is in the leading mode of the slow-external component. The spatial structure becomes uniform in the twenty-first century, and the variance increases with increasing greenhouse gas concentrations.     

Variability of seasonal-mean fields arising from intraseasonal variability. Part 3: Application to SH winter and summer circulations

A study has been made, using the National Centers for Environmental Prediction and National Center for Atmospheric Research re-analysis 500 hPa geopotential height data, to determine how intraseasonal variability influences, or can generate, coherent patterns of interannual variability in the extratropical summer and winter Southern Hemisphere atmospheric circulation. In addition, by separating this intraseasonal component of interannual variability, we also consider how slowly varying external forcings and slowly varying (interannual and longer) internal dynamics might influence the interannual variability of the Southern Hemisphere circulation. This slow component of interannual variation is more likely to be potentially predictable. How sea surface temperatures are related to the slow components is also considered. The four dominant intraseasonal modes of interannual variability have horizontal structures similar to those seen in both well-known intraseasonal dynamical modes and statistical modes of intraseasonal variability. In particular, they reflect intraseasonal variability in the high latitudes associated with the Southern Annular Mode, and wavenumber 4 (summer) and wavenumber 3 (winter) patterns associated with south Pacific regions of persistent anomalies and blocking, and possibly variability related to the Madden-Julian Oscillation (MJO). The four dominant slow components of interannual variability, in both seasons, are related to high latitude variability associated with the Southern Annular Mode, El Nino Southern Oscillation (ENSO) variability, and South Pacific Wave variability associated with Indian Ocean SSTs. In both seasons, there are strong linear trends in the first slow mode of high latitude variability and these are shown to be related to similar trends in the Indian Ocean. Once these are taken into account there is no significant sea surface temperature forcing of these high latitude modes. The second and third ENSO related slow modes, in each season, have high correlations with tropical sea surface temperature variability in the Pacific and Indian Oceans, both contemporaneously and at one season lag. The fourth slow mode has a characteristic South Pacific wave structure of either a wavenumber 4 (summer) or wavenumber 3 (winter) pattern, with strongest loadings in the South Pacific sector, and an association simultaneously with a dipole SST temperature gradient in the subtropical Indian Ocean.     

Variability of seasonal-mean fields arising from intraseasonal variability. part 2, application to nh winter circulations

A method for studying patterns of interannual variability arising from intraseasonal variability has been applied to the extratropical Northern Hemisphere wintertime 500 hPa geopotential height, using data from the NCEP-NCAR. These patterns describe the effects predominantly of intraseasonal variability and blocking. Removing this component from the sample interannual covariance matrix, one can define a residual, or slow, component of interannual variability that is more closely related to external forcings and very slowly varying (interannual/supra-annual) internal dynamics. For the Northern Hemisphere NCEP-NCAR reanalysis data, there are considerable differences between the intraseasonal patterns and the total patterns. The intraseasonal patterns are more spatially localized and more closely related to known intraseasonal variability, especially blocking events and the Madden-Julian Oscillation. Although the slow patterns and the total patterns look similar, they have some important differences. The slow patterns are more closely related to the slowly varying external forcing and very low-frequency internal dynamics than those derived by the sample covariance matrix. This is evidenced by the fact that the principal component time series of the slow patterns have a larger proportion of variability related to these factors. Where tropical SST forcing is important, the slow patterns tended to be more highly correlated with the interannual variations in the forcing. Three slow modes, related to the Tropical Northern Hemisphere, East Atlantic and Western Pacific teleconnections, are all significantly related to tropical SST variability associated predominantly with the El Nino-Southern Oscillation, in the case of the first two, and Indian Ocean variability, in the third case. The derived slow patterns and intraseasonal patterns may help to better understand the long-range predictability, uncertainty, and forcing of climate variables, for the wintertime circulation.     

Teleconnections and predictive characteristics of Australian seasonal rainfall

A new methodology is proposed that allows patterns of interannual covariability, or teleconnections, between the intraseasonal and slow components of seasonal mean Australian rainfall and the corresponding components in the Southern Hemisphere atmospheric circulation to be estimated. In all seasons, the dominant rainfall–circulation teleconnections in the intraseasonal component are shown to have the characteristic features associated with well-known intraseasonal dynamical and statistical atmospheric modes and their relationship with rainfall. Thus, for example, there are patterns of interannual covariability that reflect rainfall relationships with the intraseasonal Southern Annular Mode, the Madden-Julian Oscillation and wavenumber 3 and 4 intraseasonal modes of variability. The predictive characteristics of the atmospheric circulation–rainfall relationship are shown to reside with the slow components. In all seasons, we find rainfall–circulation teleconnections in the slow components related to the El Niño-Southern Oscillation. Each season also has a coupled mode, with a statistically significant trend in the time series of the atmospheric component that appears to be related to recent observed trends in rainfall. The slow Southern Annular Mode also features in association with southern Australian rainfall, especially during austral winter and spring. There is also evidence of an influence of Indian Ocean sea surface temperature variability on rainfall in southeast Australia during austral winter and spring.     

CMIP模式对冬季北半球大气环流与青藏高原冬春季气温相关关系的模拟评估

本文评估了36个CMIP5模式和39个CMIP6模式对近期观测中揭示的北半球冬季大气环流与高原冬春气温之间的相关关系的模拟能力。利用最大协方差(MCA)分析方法,计算并比较了观测和模式中冬季北半球200 hPa位势高度场与同后期青藏高原近地面气温的耦合关系。整体而言,大部分CMIP模式能够模拟出显著的冬季北半球大气环流与青藏高原气温之间的相关关系,且CMIP6模式模拟相关特征和作用机制的能力较CMIP5均有所提升。与观测相比,历史情景下36个CMIP5模式中有26个能够模拟出显著的大气环流与同后期高原气温之间的相关关系,其中对于相关的位势高度场空间模态的模拟明显好于对高原气温异常场空间模态的模拟。同情景下39个CMIP6模式中有37个能模拟出显著相关关系,且CMIP6模式更能模拟出观测中MCA模态的位势高度场上北极涛动(AO)和西太平洋遥相关型(WP)反相位叠加的大气环流特征。在对MCA模态时间变率的模拟上,大部分模式都能重现青藏高原整体变暖的趋势,部分模式能够模拟出观测中位势高度场时间主成分的年际变率,并且CMIP6表现要优于CMIP5。对耦合环流型的动力诊断显示,相比CMIP5模式...     

The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century

The boreal summer Asian monsoon has been evaluated in 25 Coupled Model Intercomparison Project-5 (CMIP5) and 22 CMIP3 GCM simulations of the late twentieth Century. Diagnostics and skill metrics have been calculated to assess the time-mean, climatological annual cycle, interannual variability, and intraseasonal variability. Progress has been made in modeling these aspects of the monsoon, though there is no single model that best represents all of these aspects of the monsoon. The CMIP5 multi-model mean (MMM) is more skillful than the CMIP3 MMM for all diagnostics in terms of the skill of simulating pattern correlations with respect to observations. Additionally, for rainfall/convection the MMM outperforms the individual models for the time mean, the interannual variability of the East Asian monsoon, and intraseasonal variability. The pattern correlation of the time (pentad) of monsoon peak and withdrawal is better simulated than that of monsoon onset. The onset of the monsoon over India is typically too late in the models. The extension of the monsoon over eastern China, Korea, and Japan is underestimated, while it is overestimated over the subtropical western/central Pacific Ocean. The anti-correlation between anomalies of all-India rainfall and Niño3.4 sea surface temperature is overly strong in CMIP3 and typically too weak in CMIP5. For both the ENSO-monsoon teleconnection and the East Asian zonal wind-rainfall teleconnection, the MMM interannual rainfall anomalies are weak compared to observations. Though simulation of intraseasonal variability remains problematic, several models show improved skill at representing the northward propagation of convection and the development of the tilted band of convection that extends from India to the equatorial west Pacific. The MMM also well represents the space–time evolution of intraseasonal outgoing longwave radiation anomalies. Caution is necessary when using GPCP and CMAP rainfall to validate (1) the time-mean rainfall, as there are systematic differences over ocean and land between these two data sets, and (2) the timing of monsoon withdrawal over India, where the smooth southward progression seen in India Meteorological Department data is better realized in CMAP data compared to GPCP data.     

Modes of variability of Southern Hemisphere atmospheric circulation estimated by AGCMs

The seasonal mean variability of the atmospheric circulation is affected by processes with time scales from less than seasonal to interannual or longer. Using monthly mean data from an ensemble of Atmospheric General Circulation Model (AGCM) realisations, the interannual variability of the seasonal mean is separated into intraseasonal, and slowly varying components. For the first time, using a recently developed method, the slowly varying component in multiple AGCM ensembles is further separated into internal and externally forced components. This is done for Southern Hemisphere 500?hPa geopotential height from five AGCMs in the CLIVAR International Climate of the Twentieth Century project for the summer and winter seasons. In both seasons, the intraseasonal and slow modes of variability are qualitatively well reproduced by the models when compared with reanalysis data, with a relative metric finding little overall difference between the models. The Southern Annular Mode (SAM) is by far the dominant mode of slowly varying internal atmospheric variability. Two slow-external modes of variability are related to El Ni?o-Southern Oscillation (ENSO) variability, and a third is the atmospheric response to trends in external forcing. An ENSO-SAM relationship is found in the model slow modes of variability, similar to that found by earlier studies using reanalysis data. There is a greater spread in the representation of model slow-external modes in winter than summer, particularly in the atmospheric response to external forcing trends. This may be attributable to weaker external forcing constraints on SH atmospheric circulation in winter.     

Daily modes of South Asian summer monsoon variability in the NCEP climate forecast system

The leading modes of daily variability of the Indian summer monsoon in the climate forecast system (CFS), a coupled general circulation model, of the National Centers for Environmental Predictions (NCEP) are examined. The space?Ctime structures of the daily modes are obtained by applying multi-channel singular spectrum analysis (MSSA) on the daily anomalies of rainfall. Relations of the daily modes to intraseasonal and interannual variability of the monsoon are investigated. The CFS has three intraseasonal oscillations with periods around 106, 57 and 30?days with a combined variance of 7%. The 106-day mode has spatial structure and propagation features similar to the northeastward propagating 45-day mode in the observations except for its longer period. The 57-day mode, despite being in the same time scale as of the observations has poor eastward propagation. The 30-day mode is northwestward propagating and is similar to its observational counterpart. The 106-day mode is specific to the model and should not be mistaken for a new scale of variability in observations. The dominant interannual signal is related to El Ni?o-Southern Oscillation (ENSO), and, unlike in the observations, has maximum variance in the eastern equatorial Indian Ocean. Although the Indian Ocean Dipole (IOD) mode was not obtained as a separate mode in the rainfall, the ENSO signal has good correlations with the dipole variability, which, therefore, indicates the dominance of ENSO in the model. The interannual variability is largely determined by the ENSO signal over the regions where it has maximum variance. The interannual variability of the intraseasonal oscillations is smaller in comparison.     

Variability of seasonal-mean fields arising from intraseasonal variability: part 1, methodology

We propose a method for studying the influence of intraseasonal variability on the interannual variability of seasonal mean fields. The method, using monthly mean data, provides estimates of the interannual variance and covariance, in the seasonal mean field, associated with intraseasonal variability. These estimates can be used to derive patterns of interannual variability associated with meteorological phenomena that vary significantly within a season, such as atmospheric blocking, or intraseasonal oscillations. By removing this intraseasonal component from the total interannual variance/covariance, one can define a slow component of interannual variability that is closely related to very slowly varying (interannual/supra-annual) external forcings and internal dynamics. Together these patterns may help in our understanding of the source of climate predictive skill, and also the influence of intraseasonal variability on interannual variability. To show the efficacy of our methodology, we have tested it on synthetic data, using Monte Carlo simulations of the 500-hPa geopotential heights for boreal winter over the North Pacific/North American region. The synthetic data has been constructed in such a way that the intraseasonal and slow components of interannual variability are known a priori. It is demonstrated that our methodology can effectively separate the spatial patterns of both components of variability. The methodology is also applied to diagnose meteorological phenomena that play major roles in the variability and predictability of DJF New Zealand temperatures.     

Interannual variability of the sea surface salinity and its related freshwater flux in the tropical Pacific: A comparison of CMIP5 and CMIP6

This paper assesses the interannual variabilities of simulated sea surface salinity (SSS) and freshwater flux (FWF) in the tropical Pacific from phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). The authors focus on comparing the simulated SSS and FWF responses to El Niño–Southern Oscillation (ENSO) from two generations of models developed by the same group. The results show that CMIP5 and CMIP6 models can perform well in simulating the spatial distributions of the SSS and FWF responses associated with ENSO, as well as their relationship. It is found that most CMIP6 models have improved in simulating the geographical distribution of the SSS and FWF interannual variability in the tropical Pacific compared to CMIP5 models. In particular, CMIP6 models have corrected the underestimation of the spatial relationship of the FWF and SSS variability with ENSO in the central-western Pacific. In addition, CMIP6 models outperform CMIP5 models in simulating the FWF interannual variability (spatial distribution and intensity) in the tropical Pacific. However, as a whole, CMIP6 models do not show improved skill scores for SSS interannual variability, which is due to their overestimation of the intensity in some models. Large uncertainties exist in simulating the interannual variability of SSS among CMIP5 and CMIP6 models and some improvements with respect to physical processes are needed.摘要通过比较CMIP5和CMIP6来自同一个单位两代模式模拟, 表明CMIP5和CMIP6均能较好地模拟出热带太平洋的海表盐度 (SSS) 和淡水通量 (FWF) 对ENSO响应的分布及其响应间的关系. 与CMIP5模式相比, 大部份CMIP6模式模拟的SSS和FWF年际变化分布均呈现改进, 特别是纠正了较低的中西太平洋SSS和FWF变化的空间关系. 但是, 整体上, CMIP6模式模拟的SSS年际变化技巧没有提高, 与SSS年际变率的强度被高估有关. CMIP5和CMIP6模式模拟SSS的年际变化还存在较大的不确定性, 在物理方面需要改进.     

SST and ENSO variability and change simulated in historical experiments of CMIP5 models

This work documents the diversity in Coupled Model Inter-comparison Project Phase 5 (CMIP5) models in simulating different aspects of sea surface temperature (SST) variability, particularly those associated with the El Niño–Southern Oscillation (ENSO), as well as the impact of low-frequency variations on the ENSO variability and its global teleconnection. The historical simulations (1870–2005) include 10 models with ensemble member ranging from 3 to 10 that are forced with observed atmospheric composition changes reflecting both natural and anthropogenic forcings. It is shown that the majority of the CMIP5 models capture the relative large SST anomaly variance in the tropical central and eastern Pacific, as well as in North Pacific and North Atlantic. The frequency of ENSO is not well captured by almost all models, particularly for the period of 5–6 years. The low-frequency variations in SST caused by external forcings affect the SST variability and also modify the global teleconnection of ENSO. The models reproduce the global averaged SST low-frequency variations, particularly since 1970s. However, majority of the models are unable to correctly simulate the spatial pattern of the observed SST trends. These results suggest that it is still a challenge to reproduce the features of global historical SST variations with the state-of-the-art coupled general circulation model.     

Interannual variations of the boreal summer intraseasonal variability predicted by ten atmosphere–ocean coupled models

The reproducibility of boreal summer intraseasonal variability (ISV) and its interannual variation by dynamical models are assessed through diagnosing 21-year retrospective forecasts from ten state-of-the-art ocean–atmosphere coupled prediction models. To facilitate the assessment, we have defined the strength of ISV activity by the standard deviation of 20–90 days filtered precipitation during the boreal summer of each year. The observed climatological ISV activity exhibits its largest values over the western North Pacific and Indian monsoon regions. The notable interannual variation of ISV activity is found primarily over the western North Pacific in observation while most models have the largest variability over the central tropical Pacific and exhibit a wide range of variability in spatial patterns that are different from observation. Although the models have large systematic biases in spatial pattern of dominant variability, the leading EOF modes of the ISV activity in the models are closely linked to the models’ El Nino-Southern Oscillation (ENSO), which is a feature that resembles the observed ISV and ENSO relationship. The ENSO-induced easterly vertical shear anomalies in the western and central tropical Pacific, where the summer mean vertical wind shear is weak, result in ENSO-related changes of ISV activity in both observation and models. It is found that the principal components of the predicted dominant modes of ISV activity fluctuate in a very similar way with observed ones. The model biases in the dominant modes are systematic and related to the external SST forcing. Thus the statistical correction method of this study based on singular value decomposition is capable of removing a large portion of the systematic errors in the predicted spatial patterns. The 21-year-averaged pattern correlation skill increases from 0.25 to 0.65 over the entire Asian monsoon region after applying the bias correction method to the multi-model ensemble mean prediction.     

Intraseasonal variability in subtropical South America as depicted by precipitation data

Daily precipitation data from three stations in subtropical Argentina are used to describe intraseasonal variability (20–90 days) during the austral summer. This variability is compared locally and regionally with that present in outgoing longwave radiation (OLR) data, in order to evaluate the performance of this variable as a proxy for convection in the region. The influence of the intraseasonal activity of the South American Seesaw (SASS) leading convection pattern on precipitation is also explored. Results show that intraseasonal variability explains a significant portion of summer precipitation variance, with a clear maximum in the vicinity of the SASS subtropical center. Correlation analysis reveals that OLR can explain only a small portion of daily precipitation variability, implying that it does not constitute a proper proxy for precipitation on daily timescales. On intraseasonal timescales, though, OLR is able to reproduce the main features of precipitation variability. The dynamical conditions that promote the development of intraseasonal variability in the region are further analyzed for selected summers. Seasons associated with a strong intraseasonal signal in precipitation variability show distinctive wet/dry intraseasonal periods in daily raw data, and are associated with a well defined SASS-like spatial pattern of convection. During these summers, strong large-scale forcing (such as warm El Niño/Southern Oscillation (ENSO) events and/or tropical intraseasonal convective activity), and Rossby-wave-like circulation anomalies extending across the Pacific Ocean, are also observed.     

Evaluating the performance of CMIP3 and CMIP5 global climate models over the north-east Atlantic region

One of the main sources of uncertainty in estimating climate projections affected by global warming is the choice of the global climate model (GCM). The aim of this study is to evaluate the skill of GCMs from CMIP3 and CMIP5 databases in the north-east Atlantic Ocean region. It is well known that the seasonal and interannual variability of surface inland variables (e.g. precipitation and snow) and ocean variables (e.g. wave height and storm surge) are linked to the atmospheric circulation patterns. Thus, an automatic synoptic classification, based on weather types, has been used to assess whether GCMs are able to reproduce spatial patterns and climate variability. Three important factors have been analyzed: the skill of GCMs to reproduce the synoptic situations, the skill of GCMs to reproduce the historical inter-annual variability and the consistency of GCMs experiments during twenty-first century projections. The results of this analysis indicate that the most skilled GCMs in the study region are UKMO-HadGEM2, ECHAM5/MPI-OM and MIROC3.2(hires) for CMIP3 scenarios and ACCESS1.0, EC-EARTH, HadGEM2-CC, HadGEM2-ES and CMCC-CM for CMIP5 scenarios. These models are therefore recommended for the estimation of future regional multi-model projections of surface variables driven by the atmospheric circulation in the north-east Atlantic Ocean region.     

Major modes of short-term climate variability in the newly developed NUIST Earth System Model (NESM)

A coupled earth system model(ESM) has been developed at the Nanjing University of Information Science and Technology(NUIST) by using version 5.3 of the European Centre Hamburg Model(ECHAM), version 3.4 of the Nucleus for European Modelling of the Ocean(NEMO), and version 4.1 of the Los Alamos sea ice model(CICE). The model is referred to as NUIST ESM1(NESM1). Comprehensive and quantitative metrics are used to assess the model's major modes of climate variability most relevant to subseasonal-to-interannual climate prediction. The model's assessment is placed in a multi-model framework. The model yields a realistic annual mean and annual cycle of equatorial SST, and a reasonably realistic precipitation climatology, but has difficulty in capturing the spring–fall asymmetry and monsoon precipitation domains. The ENSO mode is reproduced well with respect to its spatial structure, power spectrum, phase locking to the annual cycle, and spatial structures of the central Pacific(CP)-ENSO and eastern Pacific(EP)-ENSO; however, the equatorial SST variability,biennial component of ENSO, and the amplitude of CP-ENSO are overestimated. The model captures realistic intraseasonal variability patterns, the vertical-zonal structures of the first two leading predictable modes of Madden–Julian Oscillation(MJO), and its eastward propagation; but the simulated MJO speed is significantly slower than observed. Compared with the T42 version, the high resolution version(T159) demonstrates improved simulation with respect to the climatology, interannual variance, monsoon–ENSO lead–lag correlation, spatial structures of the leading mode of the Asian–Australian monsoon rainfall variability, and the eastward propagation of the MJO.     

CMIP5模式对冬季北极涛动的模拟和预估

基于NCEP/NCAR再分析资料和CMIP5的19个模式结果,从异常模态、年代际趋势和周期特征等方面评估了CMIP5耦合模式对冬季北极涛动(Arctic Oscillation,AO)的模拟能力,并对未来RCP4.5、RCP8.5两种浓度路径下AO的可能变化趋势给出了定性的预估。CMIP5模式历史试验结果显示,大多数模式都能够模拟出AO模态的基本结构,但是对中心位置、强度的模拟存在较大的偏差,其中MPI-ESM-LR和Had GEM2-AO能较好地模拟出AO整体模态来。在历史演变和周期特征的刻画方面,模式的冬季海平面气压经验正交函数分解第一模态时间序列(Principal Component,PC1)基本能够反映出1950~1970年以来的减弱趋势,但对1970年以后的增长趋势模拟并不明显,而北半球环状模指数(Zonal Index,ZI)序列对两个阶段的趋势均可模拟出来,模式的PC1和ZI序列总体表现为正的变化趋势。有一半以上的模式对2~3 a高频周期模拟较好,但对20 a左右的周期模拟较差,其中仅有Can ESM2、CNRM-CM5、GFDL-ESM2G这3个模式对ZI指数的两个周期变化模拟较好。在RCP4.5和RCP8.5两种浓度路径下,ZI序列有显著的上升趋势,从长期趋势系数看RCP4.5路径下有14个模式呈现正的变化趋势,其中有10个模式通过了检验。RCP8.5浓度路径下,16个模式为正变化趋势,有11个模式通过了检验,集合平均结果正变化趋势较为显著。两种浓度路径下不同时段的海平面气压变化趋势表明,ZI序列的年代际变化明显,存在3个不同的变化阶段——2006~2039年、2070~2100年为两个上升阶段,2040~2069年为缓慢下降阶段。     

Quantitative analysis of the feedback induced by the freshwater flux in the tropical Pacific using CMIP5

Freshwater flux (FWF) directly affects sea surface salinity (SSS) and hence modulates sea surface temperature (SST) in the tropical Pacific. This paper quantifies a positive correlation between FWF and SST using observations and simulations of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) to analyze the interannual variability in the tropical Pacific. Comparisons among the displacements of FWF, SSS and SST interannual variabilities illustrate that a large FWF variability is located in the west-central equatorial Pacific, covarying with a large SSS variability, whereas a large SST variability is located in the eastern equatorial Pacific. Most CMIP5 models can reproduce the fact that FWF leads to positive feedback to SST through an SSS anomaly as observed. However, the difference in each model's performance results from different simulation capabilities of the CMIP5 models in the magnitudes and positions of the interannual variabilities, including the mixed layer depth and the buoyancy flux in the equatorial Pacific. SSS anomalies simulated from the CMIP5 multi-model are sensitive to FWF interannual anomalies, which can lead to differences in feedback to interannual SST variabilities. The relationships among the FWF, SSS and SST interannual variabilities can be derived using linear quantitative measures from observations and the CMIP5 multi-model simulations. A 1 mm d-1 FWF anomaly corresponds to an SSS anomaly of nearly 0.12 psu in the western tropical Pacific and a 0.11°C SST anomaly in the eastern tropical Pacific.     

参加CMIP5的四个中国气候模式模拟的东亚冬季风年际变率

本文比较了中国参加“国际耦合模式比较计划”（CMIP5）的四个大气环流模式（即FGOALS-g2、FGOALS-s2、BCC-CSM1-1、BNU-ESM大气模式）在观测海温驱动下,对东亚冬季风（EAWM）气候态和年际变率的模拟能力。结果表明,在气候态上,四个模式均合理再现了EAWM高低层环流系统（包括低层西伯利亚高压（SH）、阿留申低压、异常偏北风、和中高层东亚大槽、西风急流）,其中对2 m气温和500 hPa高度场的模拟技巧最高,四个模式模拟的结果与再分析资料的空间相关系数都达到0.99。在年际变率上,分别对东亚北部地区（30°N～60°N,100°E～140°E）和东亚南部地区（0°～30°N,100°E～140°E）的2 m气温进行经验正交函数分解（EOF）,提取变率主导模态。结果表明,在东亚北部地区,四个模式对2 m气温第一模态（简称“北部型”）的空间分布均有很高的模拟技巧,但只有BNU-ESM能够较好再现其对应的年际变率,其模拟的时间序列与观测的相关系数为0.69。四个模式均能模拟出观测中的3.1 a主导周期,但只有FGOALS-s2和BNU-ESM能模拟出观测中的2.5 a主导周期。在东亚南部地区,模式模拟的前两个主模态共同解释观测中第一模态（简称“南部型”）的特征,其中FGOALS-g2、FGOALS-s2和BNU-ESM的综合模拟技巧较高,但只有BNU-ESM成功再现了观测中2.5 a和3.1 a的主导周期。机理分析表明,FGOALS-g2、FGOALS-s2、BNU-ESM三个模式能合理再现菲律宾海反气旋,同时对南部型有较高的模拟能力,而BCC-CSM1-1则未能有效再现菲律宾海反气旋,使得 BCC-CSM1-1对南部型模拟技巧较低。观测和四个模式模拟的结果一致表现出北极涛动（AO）与北部型PC1呈显著相关,影响大于SH。     

CMIP5模式对青藏高原中东部夏季降水双极型模拟能力的评估

青藏高原中东部夏季降水主要表现为东北和东南反位相变化的双极型特征。采用经验正交函数(empirical orthogonal function,EOF)分解方法,系统性地评估参与第五次耦合模式比较计划 (Coupled Model Intercomparison Project Phase 5,CMIP5)历史模拟试验的 47 个模式对青藏高原中东部夏季降水双极型变化特征的模拟能力。结果表明,大多数模式基本可以反映青藏高原中东部夏季降水东北部和东南部反位相的变化特征。模式间 EOF 分析结果表明在35°N 以南的东西向模拟偏差是 CMIP5 模式模拟降水空间型态的主要偏差,且大多数模式对时间系数的模拟效果差于空间型态。文中定义了一个综合评估指标 Snew 来定量描述模式对空间型态、时间系数以及方差贡献的综合模拟效果。由定量评估结果来看,MIROC-ESM、HadGEM2-CC 和 ACCESS1-0 (FIO-ESM、 HadGEM2-AO 和 MIROC-ESM-CHEM)模式对观测降水的 EOF1(EOF2)模态的综合模拟能力相对较好,而 GISS 系列模式、CESM1-CAM5 和 MPI-ESM-LR (CMCC-CESM、MPI-ESM-MR 和 GFDL- CM3)模式对观测降水的 EOF1(EOF2)模态的综合模拟效果较差。由 EOF1 和 EOF2 的综合评估结果来看,MIROC-ESM-CHEM模式对观测降水的 EOF1 和 EOF2 模态的综合模拟效果最好。