The Atlantic Multidecadal Oscillation in twentieth century climate simulations: uneven progress from CMIP3 to CMIP5

Decadal variability in the climate system from the Atlantic Multidecadal Oscillation (AMO) is one of the major sources of variability at this temporal scale that climate models must properly incorporate because of its climate impact. The current analysis of historical simulations of the twentieth century climate from models participating in the CMIP3 and CMIP5 projects assesses how these models portray the observed spatiotemporal features of the sea surface temperature (SST) and precipitation anomalies associated with the AMO. A short sample of the models is analyzed in detail by using all ensembles available of the models CCSM3, GFDL-CM2.1, UKMO-HadCM3, and ECHAM5/MPI-OM from the CMIP3 project, and the models CCSM4, GFDL-CM3, UKMO-HadGEM2-ES, and MPI-ESM-LR from the CMIP5 project. The structure and evolution of the SST anomalies of the AMO have not progressed consistently from the CMIP3 to the CMIP5 models. While the characteristic period of the AMO (smoothed with a binomial filter applied fifty times) is underestimated by the three of the models, the e-folding time of the autocorrelations shows that all models underestimate the 44-year value from observations by almost 50 %. Variability of the AMO in the 10–20/70–80 year ranges is overestimated/underestimated in the models and the variability in the 10–20 year range increases in three of the models from the CMIP3 to the CMIP5 versions. Spatial variability and correlation of the AMO regressed precipitation and SST anomalies in summer and fall indicate that models are not up to the task of simulating the AMO impact on the hydroclimate over the neighboring continents. This is in spite of the fact that the spatial variability and correlations in the SST anomalies improve from CMIP3 to CMIP5 versions in two of the models. However, a multi-model mean from a sample of 14 models whose first ensemble was analyzed indicated there were no improvements in the structure of the SST anomalies of the AMO or associated regional precipitation anomalies in summer and fall from CMIP3 to CMIP5 projects.     

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 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.     

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.     

Evaluation of spring persistent rainfall over East Asia in CMIP3/CMIP5 AGCM simulations

The progress made fi＇om Phase 3 to Phase 5 of the Coupled Model Intercomparison Project （CMIP3 to CMIP5） in simulating spring persistent rainfall （SPR） over East Asia was examined from the outputs of nine atmospheric general circulation models （AGCMs）. The majority of the models overestimated the precipitation over the SPR domain, with the mean latitude of the SPR belt shifting to the north. The overestimation was about 1mm d-1 in the CMIP3 ensemble, and the northward displacement was about 3°, while in the CMIP5 ensemble the overestimation was suppressed to 0.7 mm d-i and the northward shift decreased to 2.5°. The SPR features a northeast-southwest extended rain belt with a slope of 0.4°N/°E. The CMIP5 ensemble yielded a smaller slope （0.2°N/°E）, whereas the CMIP3 ensemble featured an unre- alistic zonally-distributed slope. The CMIP5 models also showed better skill in simulating the interannual variability of SPR. Previous studies have suggested that the zonal land-sea thermal contrast and sensible heat flux over the southeastern Tibetan Plateau are important for the existence of SPR. These two ther- mal factors were captured well in the CMIP5 ensemble, but underestimated in the CMIP3 ensemble. The variability of zonal land-sea thermal contrast is positively correlated with the rainfall amount over the main SPR center, but it was found that an overestimated thermal contrast between East Asia and South China Sea is a common problem in most of the CMIP3 and CMIP5 models. Simulation of the meridional thermal contrast is therefore important for the future improvement of current AGCMs.     

CMIP5全球气候模式对青藏高原地区气候模拟能力评估

青藏高原是气候变化的敏感和脆弱区,全球气候模式对于这一地区气候态的模拟能力如何尚不清楚。为此,本文使用国际耦合模式比较计划第五阶段（CMIP5）的历史模拟试验数据,评估了44 个全球气候模式对1986~2005 年青藏高原地区地表气温和降水两个基本气象要素的模拟能力。结果表明,CMIP5 模式低估了青藏高原地区年和季节平均地表气温,年均平均偏低2.3℃,秋季和冬季冷偏差相对更大;模式可较好地模拟年和季节平均地表气温分布型,但模拟的空间变率总体偏大;地形效应校正能够有效订正地表气温结果。CMIP5 模式对青藏高原地区降水模拟能力较差。尽管它们能够模拟出年均降水自西北向东南渐增的分布型,但模拟的年和季节降水量普遍偏大,年均降水平均偏多1.3 mm d-1,这主要是源于春季和夏季降水被高估。同时,模式模拟的年和季节降水空间变率也普遍大于观测值,尤其表现在春季和冬季。相比较而言,44 个模式集合平均性能总体上要优于大多数单个模式;等权重集合平均方案要优于中位数平均;对择优挑选的模式进行集合平均能够提高总体的模拟能力,其中对降水模拟的改进更为显著。     

The natural oscillation of two types of ENSO events based on analyses of CMIP5 model control runs

The eastern-and central-Pacific El Ni（n）o-Southem Oscillation （EP-and CP-ENSO） have been found to be dominant in the tropical Pacific Ocean,and are characterized by interannual and decadal oscillation,respectively.In the present study,we defined the EP-and CP-ENSO modes by singular value decomposition （SVD） between SST and sea level pressure （SLP） anomalous fields.We evaluated the natural features of these two types of ENSO modes as simulated by the pre-industrial control runs of 20 models involved in phase five of the Coupled Model Intercomparison Project （CMIP5）.The results suggested that all the models show good skill in simulating the SST and SLP anomaly dipolar structures for the EP-ENSO mode,but only 12 exhibit good performance in simulating the tripolar CP-ENSO modes.Wavelet analysis suggested that the ensemble principal components in these 12 models exhibit an interannual and multi-decadal oscillation related to the EP-and CP-ENSO,respectively.Since there are no changes in external forcing in the pre-industrial control runs,such a result implies that the decadal oscillation of CP-ENSO is possibly a result of natural climate variability rather than external forcing.     

Simulation of tropical cyclone activity over the western North Pacific based on CMIP5 models

Based on the Coupled Model Inter-comparison Project 5 (CMIP5) models, the tropical cyclone (TC) activity in the summers of 1965–2005 over the western North Pacific (WNP) is simulated by a TC dynamically downscaling system. In consideration of diversity among climate models, Bayesian model averaging (BMA) and equal-weighed model averaging (EMA) methods are applied to produce the ensemble large-scale environmental factors of the CMIP5 model outputs. The environmental factors generated by BMA and EMA methods are compared, as well as the corresponding TC simulations by the downscaling system. Results indicate that BMA method shows a significant advantage over the EMA. In addition, impacts of model selections on BMA method are examined. To each factor, ten models with better performance are selected from 30 CMIP5 models and then conduct BMA, respectively. As a consequence, the ensemble environmental factors and simulated TC activity are similar with the results from the 30 models’ BMA, which verifies the BMA method can afford corresponding weight for each model in the ensemble based on the model’s predictive skill. Thereby, the existence of poor performance models will not particularly affect the BMA effectiveness and the ensemble outcomes are improved. Finally, based upon the BMA method and downscaling system, we analyze the sensitivity of TC activity to three important environmental factors, i.e., sea surface temperature (SST), large-scale steering flow, and vertical wind shear. Among three factors, SST and large-scale steering flow greatly affect TC tracks, while average intensity distribution is sensitive to all three environmental factors. Moreover, SST and vertical wind shear jointly play a critical role in the inter-annual variability of TC lifetime maximum intensity and frequency of intense TCs.     

CMIP6全球气候模式对中国极端降水模拟能力的评估及其与CMIP5的比较

对CMIP6全球气候模式在中国地区极端降水的模拟能力进行了综合评估。基于CN05.1观测数据集和32个CMIP6全球气候模式的降水数据,采用8个常用极端降水指数对极端降水进行了定量描述。研究结果表明,在极端降水的气候平均态方面,CMIP6多模式集合对1961—2005年中国地区区域平均的8个极端降水指数模拟的平均相对误差为29.94%,相较CMIP5降低了2.95个百分点。极端降水的气候变率方面,CMIP6多模式集合对区域平均的8个极端降水指数模拟的平均相对误差为10.10%,相较CMIP5降低5.45个百分点。此外,利用TS评分进行模式间比较,CMIP6的平均分（0.78）高于CMIP5（0.75）,且模拟能力排名前五的模式中CMIP6占4个。对比14个同源模式的TS评分可以发现,CMIP6（0.91）相对于CMIP5（0.68）的模拟能力显著提高。进一步研究发现,CMIP6相对于CMIP5对不同区域极端降水模拟能力的改进有所区别:CMIP6对干旱区平均的气候态和变率方面改进明显,而对于湿润区的改进主要表现在对极端降水空间相关模拟能力的提高。综上,在中国地区,CMIP6相较于CMIP5对极端降水的模拟能力总体上有提升。      

Diagnosing southeast tropical Atlantic SST and ocean circulation biases in the CMIP5 ensemble

Warm sea-surface temperature (SST) biases in the southeastern tropical Atlantic (SETA), which is defined by a region from 5°E to the west coast of southern Africa and from 10°S to 30°S, are a common problem in many current and previous generation climate models. The Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble provides a useful framework to tackle the complex issues concerning causes of the SST bias. In this study, we tested a number of previously proposed mechanisms responsible for the SETA SST bias and found the following results. First, the multi-model ensemble mean shows a positive shortwave radiation bias of ~20 W m^?2, consistent with models’ deficiency in simulating low-level clouds. This shortwave radiation error, however, is overwhelmed by larger errors in the simulated surface turbulent heat and longwave radiation fluxes, resulting in excessive heat loss from the ocean. The result holds for atmosphere-only model simulations from the same multi-model ensemble, where the effect of SST biases on surface heat fluxes is removed, and is not sensitive to whether the analysis region is chosen to coincide with the maximum warm SST bias along the coast or with the main SETA stratocumulus deck away from the coast. This combined with the fact that there is no statistically significant relationship between simulated SST biases and surface heat flux biases among CMIP5 models suggests that the shortwave radiation bias caused by poorly simulated low-level clouds is not the leading cause of the warm SST bias. Second, the majority of CMIP5 models underestimate upwelling strength along the Benguela coast, which is linked to the unrealistically weak alongshore wind stress simulated by the models. However, a correlation analysis between the model simulated vertical velocities and SST biases does not reveal a statistically significant relationship between the two, suggesting that the deficient coastal upwelling in the models is not simply related to the warm SST bias via vertical heat advection. Third, SETA SST biases in CMIP5 models are correlated with surface and subsurface ocean temperature biases in the equatorial region, suggesting that the equatorial temperature bias remotely contributes to the SETA SST bias. Finally, we found that all CMIP5 models simulate a southward displaced Angola–Benguela front (ABF), which in many models is more than 10° south of its observed location. Furthermore, SETA SST biases are most significantly correlated with ABF latitude, which suggests that the inability of CMIP5 models to accurately simulate the ABF is a leading cause of the SETA SST bias. This is supported by simulations with the oceanic component of one of the CMIP5 models, which is forced with observationally derived surface fluxes. The results show that even with the observationally derived surface atmospheric forcing, the ocean model generates a significant warm SST bias near the ABF, underlining the important role of ocean dynamics in SETA SST bias problem. Further model simulations were conducted to address the impact of the SETA SST biases. The results indicate a significant remote influence of the SETA SST bias on global model simulations of tropical climate, underscoring the importance and urgency to reduce the SETA SST bias in global climate models.     

ENSO representation in climate models: from CMIP3 to CMIP5

We analyse the ability of CMIP3 and CMIP5 coupled ocean–atmosphere general circulation models (CGCMs) to simulate the tropical Pacific mean state and El Niño-Southern Oscillation (ENSO). The CMIP5 multi-model ensemble displays an encouraging 30 % reduction of the pervasive cold bias in the western Pacific, but no quantum leap in ENSO performance compared to CMIP3. CMIP3 and CMIP5 can thus be considered as one large ensemble (CMIP3 + CMIP5) for multi-model ENSO analysis. The too large diversity in CMIP3 ENSO amplitude is however reduced by a factor of two in CMIP5 and the ENSO life cycle (location of surface temperature anomalies, seasonal phase locking) is modestly improved. Other fundamental ENSO characteristics such as central Pacific precipitation anomalies however remain poorly represented. The sea surface temperature (SST)-latent heat flux feedback is slightly improved in the CMIP5 ensemble but the wind-SST feedback is still underestimated by 20–50 % and the shortwave-SST feedbacks remain underestimated by a factor of two. The improvement in ENSO amplitudes might therefore result from error compensations. The ability of CMIP models to simulate the SST-shortwave feedback, a major source of erroneous ENSO in CGCMs, is further detailed. In observations, this feedback is strongly nonlinear because the real atmosphere switches from subsident (positive feedback) to convective (negative feedback) regimes under the effect of seasonal and interannual variations. Only one-third of CMIP3 + CMIP5 models reproduce this regime shift, with the other models remaining locked in one of the two regimes. The modelled shortwave feedback nonlinearity increases with ENSO amplitude and the amplitude of this feedback in the spring strongly relates with the models ability to simulate ENSO phase locking. In a final stage, a subset of metrics is proposed in order to synthesize the ability of each CMIP3 and CMIP5 models to simulate ENSO main characteristics and key atmospheric feedbacks.     

CMIP6与CMIP5对历史大气层顶和地表辐射收支模拟的时空对比

基于云和地球辐射能量系统观测数据集（CERES),对比分析了耦合模式比较计划第五（CMIP5）和第六阶段（CMIP6）模拟的历史大气层顶和地表辐射收支的年际变化和空间分布,明确了多模式间不确定性大的关键区域。结果表明：在年际尺度上,除地表向上长波辐射外,CMIP6的辐射分量的集合均值较CMIP5更接近于CERES观测值,全球地表向下短波辐射的高估和大气逆辐射的低估在CMIP6中分别降低了1.9 W/m²和3.3 W/m²。除大气逆辐射外,CMIP6的辐射分量在多模式间的一致性较CMIP5提高。在北极,CMIP6对大气层顶反射短波、大气层顶出射长波和地表向下短波辐射的模拟偏差较CMIP5大。在南北纬60°,CMIP6对大气逆辐射的模拟偏差较CMIP5大。其他区域CMIP6的辐射分量更接近CERES观测值。CMIP6模拟的地表向下短波辐射和大气逆辐射的不确定性较大区域面积较CMIP5减小,但不确定性极大区域面积无变化。地表净辐射的不确定性空间分布在两代CMIP间变化甚小。青藏高原、赤道太平洋、热带雨林、阿拉伯半岛和南极洲沿海依然是地球系统模式模拟辐射收支不确定性极大的关键区域。     

CMIP5模式对我国西南地区干湿季降水的模拟和预估

利用降水观测资料, 评估了参加国际耦合模式比较计划第五阶段(CMIP5)的34个全球模式对1986～2005年我国西南地区干湿季降水的模拟能力。结果表明, 34个CMIP5模式中分别有30和25个模式模拟的干季和湿季降水偏多。34个模式对我国西南地区干湿季降水的模拟能力差异较大, 大约半数模式的模拟值与观测值的空间相关系数通过了99%的信度检验, 且标准差之比小于2。利用两个技巧评分标准, 分别挑选出了对干湿季降水模拟最优的9个模式。最优模式集合平均结果要优于34个模式的集合平均, 更要优于大多数单个模式。进一步利用最优的9个模式的集合平均对RCP4.5和RCP8.5两种典型浓度路径下我国西南地区干湿季降水的变化进行了预估。相对于1986～2005年气候平均态, 在21世纪初期(2016～2035年), 我国西南地区干季降水变化表现为川西高原降水增多, 而四川盆地及攀西地区、重庆、贵州和云南的大部分地区降水减少;湿季降水变化表现为川西高原、贵州和广西大部分地区降水增多, 而四川盆地及攀西地区和云南降水减少。在21世纪中期(2046～2065年)和末期(2080～2099年), 西南地区干湿季降水普遍增多。在RCP8.5情景下, 降水的变化幅度要强于RCP4.5情景。     

Present-day and future Amazonian precipitation in global climate models: CMIP5 versus CMIP3

The present study aims at evaluating and comparing precipitation over the Amazon in two sets of historical and future climate simulations based on phase 3 (CMIP3) and 5 (CMIP5) of the Coupled Model Intercomparison Project. Thirteen models have been selected in order to discuss (1) potential improvements in the simulation of present-day climate and (2) the potential reduction in the uncertainties of the model response to increasing concentrations of greenhouse gases. While several features of present-day precipitation—including annual cycle, spatial distribution and co variability with tropical sea surface temperature (SST)—have been improved, strong uncertainties remain in the climate projections. A closer comparison between CMIP5 and CMIP3 highlights a weaker consensus on increased precipitation during the wet season, but a stronger consensus on a drying and lengthening of the dry season. The latter response is related to a northward shift of the boreal summer intertropical convergence zone in CMIP5, in line with a more asymmetric warming between the northern and southern hemispheres. The large uncertainties that persist in the rainfall response arise from contrasted anomalies in both moisture convergence and evapotranspiration. They might be related to the diverse response of tropical SST and ENSO (El Niño Southern Oscillation) variability, as well as to spurious behaviours among the models that show the most extreme response. Model improvements of present-day climate do not necessarily translate into more reliable projections and further efforts are needed for constraining the pattern of the SST response and the soil moisture feedback in global climate scenarios.     

基于CMIP5资料的热带太平洋和印度洋海温变化与降水变化的关系及其成因分析

利用第五次耦合模式比较计划(Coupled Model Intercomparison Project Phase 5,简称CMIP5)月平均资料,从季节变化角度,对热带太平洋、印度洋海温变化与降水变化的关系及其成因进行了初步分析。20个模式集合平均结果表明:在全球增暖背景下,热带太平洋年平均的海温变化与降水变化符合"warmer-get-wetter"型特征,而季节平均与年平均存在明显的差异;冬季和春季,海温增暖最大区和降水增加区之间存在东西向和南北向的位置偏差;夏季和秋季,二者只存在明显的南北位置偏差,且与冬季和春季的情况相反。热带印度洋的冬季和春季海温变化与降水变化也存在位置偏差。两个热带大洋季节平均的降水变化均是"warmer-get-wetter"和"wet-get-wetter"两个机制共同作用的结果。     

CMIP5/AMIP GCM simulations of East Asian summer monsoon

The East Asian summer monsoon （EASM） is a distinctive component of the Asian climate system and critically influences the economy and society of the region.To understand the ability of AGCMs in capturing the major features of EASM,10 models that participated in Coupled Model Intercomparison Project/Atmospheric Model Intercomparison Project （CMIP5/AMIP）,which used observational SST and sea ice to drive AGCMs during the period 1979-2008,were evaluated by comparing with observations and AMIP Ⅱ simulations.The results indicated that the multi-model ensemble （MME） of CMIP5/AMIP captures the main characteristics of precipitation and monsoon circulation,and shows the best skill in EASM simulation,better than the AMIP Ⅱ MME.As for the Meiyu/Changma/Baiyu rainbelt,the intensity of rainfall is underestimated in all the models.The biases are caused by a weak western Pacific subtropical high （WPSH） and accompanying eastward southwesterly winds in group Ⅰ models,and by a too strong and west-extended WPSH as well as westerly winds in group Ⅱ models.Considerable systematic errors exist in the simulated seasonal migration of rainfall,and the notable northward jumps and rainfall persistence remain a challenge for all the models.However,the CMIP5/AMIP MME is skillful in simulating the western North Pacific monsoon index （WNPMI）.     

An objective analysis of the observed spatial structure of the tropical Indian Ocean SST variability

The observed interannual Indian Ocean sea surface temperature (SST) variability from 1950 to 2008 is analyzed in respect to the spatial structure of the variability. The analysis is based on an objective comparison of the leading empirical orthogonal function modes against the stochastic null hypothesis of spatial red noise (isotropic diffusion). Starting from this red noise assumption, the analysis searches for those structures that are most distinct from the red noise hypothesis. This objective approach will put previously well and less known modes of variability into the context of the multivariate SST variability. The Indian Ocean SST variability is marked by relatively weak SST variability, which is strongly dominated by a basin wide monopole pattern that is caused by different processes. The leading modes of variability are the El Nino Southern Oscillation (ENSO) variability and the warming trend, which both project onto the basin wide monopole structure. Other more characteristic spatial patterns of internal variability are much less dominant in the tropical Indian Ocean, which is quite different from all other ocean basin, where characteristic teleconnection patterns exist. The remaining, ENSO independent, detrended variability is dominated by multi-pole patterns from the southern Indian Ocean reaching into the tropical Indian Ocean, which are probably primarily caused by extra-tropical atmospheric forcings. The large scale tropical Indian Ocean internal variability itself has no dominant structure. The currently often used dipole mode index (DMI) does not appear to present a dominant teleconnection pattern of the Indian Ocean internal SST variability. In the context of the objective analysis presented here, the DMI partly reflects the ENSO variability and is also a representation of the multi-dimensional, chaotic spatial red noise (isotropic diffusion) process. As such the DMI cannot be interpreted as a coherent teleconnection between the two poles.     

Evaluation of the performance of CMIP5 and CMIP6 models in simulating the South Pacific Quadrupole-ENSO relationship

先前的观测研究表明,南太平洋四极子海温模态(SPQ)可以有效地作为ENSO的前兆信号.本文利用20个CMIP6模式及其对应的20个先前的CMIP5模式的工业化前气候模拟试验数据,评估和比较了CMIP6以及CMIP5模式对SPQ与ENSO的关系的模拟能力.结果表明,大多数CMIP5和CMIP6模式可以合理地模拟SPQ的基本特征.与早期的CMIP5模式相比,CMIP6模式能够更加真实地模拟SPQ与ENSO之间的关系.进一步分析表明,CMIP6模式模拟SPQ与ENSO关系的能力提高,是因为CMIP6模式能够更好地模拟出在副热带/热带太平洋上与SPQ相关的表面海气热力耦合过程,以及在赤道太平洋上与SPQ相关的次表层海温的异常相应.     

An overview of the performance of CMIP6 models in the tropical Atlantic: mean state,variability, and remote impacts

General circulation models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) are examined with respect to their ability to simulate the mean state and variability of the tropical Atlantic and its linkage to the tropical Pacific. While, on average, mean state biases have improved little, relative to the previous intercomparison (CMIP5), there are now a few models with very small biases. In particular the equatorial Atlantic warm SST and westerly wind biases are mostly eliminated in these models. Furthermore, interannual variability in the equatorial and subtropical Atlantic is quite realistic in a number of CMIP6 models, which suggests that they should be useful tools for understanding and predicting variability patterns. The evolution of equatorial Atlantic biases follows the same pattern as in previous model generations, with westerly wind biases during boreal spring preceding warm sea-surface temperature (SST) biases in the east during boreal summer. A substantial portion of the westerly wind bias exists already in atmosphere-only simulations forced with observed SST, suggesting an atmospheric origin. While variability is relatively realistic in many models, SSTs seem less responsive to wind forcing than observed, both on the equator and in the subtropics, possibly due to an excessively deep mixed layer originating in the oceanic component. Thus models with realistic SST amplitude tend to have excessive wind amplitude. The models with the smallest mean state biases all have relatively high resolution but there are also a few low-resolution models that perform similarly well, indicating that resolution is not the only way toward reducing tropical Atlantic biases. The results also show a relatively weak link between mean state biases and the quality of the simulated variability. The linkage to the tropical Pacific shows a wide range of behaviors across models, indicating the need for further model improvement.

     

Understanding the sources of Caribbean precipitation biases in CMIP3 and CMIP5 simulations

We assess the ability of Global Climate Models participating in phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) to simulate observed annual precipitation cycles over the Caribbean. Compared to weather station records and gridded observations, we find that both CMIP3 and CMIP5 models can be grouped into three categories: (1) models that correctly simulate a bimodal distribution with two rainfall maxima in May–June and September–October, punctuated by a mid-summer drought (MSD) in July–August; (2) models that reproduce the MSD and the second precipitation maxima only; and (3) models that simulate only one precipitation maxima, beginning in early summer. These categories appear related to model simulation of the North Atlantic Subtropical High (NASH) and sea surface temperature (SST) in the Caribbean Sea and Gulf of Mexico. Specifically, models in category 2 tend to anticipate the westward expansion of the NASH into the Caribbean in early summer. Early onset of NASH results in strong moisture divergence and MSD-like conditions at the time of the May–June observed precipitation maxima. Models in category 3 tend to have cooler SST across the region, particularly over the central Caribbean and the Gulf of Mexico, as well as a weaker Caribbean low-level jet accompanying a weaker NASH. In these models, observed June-like patterns of moisture convergence in the central Caribbean and the Central America and divergence in the east Caribbean and the Gulf of Mexico persist through September. This analysis suggests systematic biases in model structure may be responsible for biases in observed precipitation variability over the Caribbean and more confidence may be placed in the precipitation simulated by the GCMs that are able to correctly simulate seasonal cycles of SST and NASH.