Stratospheric circulation in seasonal forecasting models: implications for seasonal prediction

Accurate seasonal forecasts rely on the presence of low frequency, predictable signals in the climate system which have a sufficiently well understood and significant impact on the atmospheric circulation. In the Northern European region, signals associated with seasonal scale variability such as ENSO, North Atlantic SST anomalies and the North Atlantic Oscillation have not yet proven sufficient to enable satisfactorily skilful dynamical seasonal forecasts. The winter-time circulations of the stratosphere and troposphere are highly coupled. It is therefore possible that additional seasonal forecasting skill may be gained by including a realistic stratosphere in models. In this study we assess the ability of five seasonal forecasting models to simulate the Northern Hemisphere extra-tropical winter-time stratospheric circulation. Our results show that all of the models have a polar night jet which is too weak and displaced southward compared to re-analysis data. It is shown that the models underestimate the number, magnitude and duration of periods of anomalous stratospheric circulation. Despite the poor representation of the general circulation of the stratosphere, the results indicate that there may be a detectable tropospheric response following anomalous circulation events in the stratosphere. However, the models fail to exhibit any predictability in their forecasts. These results highlight some of the deficiencies of current seasonal forecasting models with a poorly resolved stratosphere. The combination of these results with other recent studies which show a tropospheric response to stratospheric variability, demonstrates a real prospect for improving the skill of seasonal forecasts.     

The first coupled historical forecasting project (CHFP1)

Abstract

A set of retrospective multi‐seasonal ensemble predictions based on a coupled global atmosphere‐ocean model is described. These predictions, designated as the first coupled Historical Forecasting Project or CHFP1, are produced with the climate model CGCM3.1 of the Canadian Centre for Climate Modelling and Analysis using a very simple initialization procedure in which model sea surface temperatures (SSTs) are nudged toward the observed values during a multi‐year period preceding the beginning of a forecast. This procedure, in addition to constraining initial SSTs to be close to observations, initializes equatorial Pacific zonal wind stress and thermocline depth with some skill. The ability of the subsequent forecasts to predict the evolution of SSTs, particularly in the equatorial Pacific, and surface air temperatures globally and in Canada is assessed. The results are compared with those of the second Historical Forecasting Project or HFP2, which uses a two‐tier strategy in which model SSTs are externally specified. Skill of CHFP1 forecasts, though generally modest, exceeds those of HFP2 in some cases, despite the larger multi‐model ensemble used in HFP2. CHFP1 represents an initial step in development directed toward a coupled operational seasonal prediction system for Canada.     

The seasonal predictability of the Asian summer monsoon in a two-tiered forecast system

An extensive set of boreal summer seasonal hindcasts from a two tier system is compared with corresponding seasonal hindcasts from two other coupled ocean–atmosphere models for their seasonal prediction skill (for precipitation and surface temperature) of the Asian summer monsoon. The unique aspect of the two-tier system is that it is at relatively high resolution and the SST forcing is uniquely bias corrected from the multi-model averaged forecasted SST from the two coupled ocean–atmosphere models. Our analysis reveals: (a) The two-tier forecast system has seasonal prediction skill for precipitation that is comparable (over the Southeast Asian monsoon) or even higher (over the South Asian monsoon) than the coupled ocean–atmosphere. For seasonal anomalies of the surface temperature the results are more comparable across models, with all of them showing higher skill than that for precipitation. (b) Despite the improvement from the uncoupled AGCM all models in this study display a deterministic skill for seasonal precipitation anomalies over the Asian summer monsoon region to be weak. But there is useful probabilistic skill for tercile anomalies of precipitation and surface temperature that could be harvested from both the coupled and the uncoupled climate models. (c) Seasonal predictability of the South Asian summer monsoon (rainfall and temperature) does seem to stem from the remote ENSO forcing especially over the Indian monsoon region and the relatively weaker seasonal predictability in the Southeast Asian summer monsoon could be related to the comparatively weaker teleconnection with ENSO. The uncoupled AGCM with the bias corrected SST is able to leverage this teleconnection for improved seasonal prediction skill of the South Asian monsoon relative to the coupled models which display large systematic errors of the tropical SST’s.     

Predictable and Unpredictable Components of the Summer East Asia–Pacific Teleconnection Pattern

The East Asia–Pacific(EAP) teleconnection pattern is the dominant mode of circulation variability during boreal summer over the western North Pacific and East Asia, extending from the tropics to high latitudes. However, much of this pattern is absent in multi-model ensemble mean forecasts, characterized by very weak circulation anomalies in the mid and high latitudes. This study focuses on the absence of the EAP pattern in the extratropics, using state-of-the-art coupled seasonal forecast systems. The results indicate that the extratropical circulation is much less predictable, and lies in the large spread among different ensemble members, implying a large contribution from atmospheric internal variability. However,the tropical–mid-latitude teleconnections are also relatively weaker in models than observations, which also contributes to the failure of prediction of the extratropical circulation. Further results indicate that the extratropical EAP pattern varies closely with the anomalous surface temperatures in eastern Russia, which also show low predictability. This unpredictable circulation–surface temperature connection associated with the EAP pattern can also modulate the East Asian rainband.     

Sensitivity of seasonal climate forecasts to persisted SST anomalies

Most estimates of the skill of atmospheric general circulation models (AGCMs) for forecasting seasonal climate anomalies have been based on simulations with actual observed sea surface temperatures (SSTs) as lower boundary forcing. Similarly estimates of the climatological response characteristics of AGCMs used for seasonal-to-interannual climate prediction generally rest on historical simulations using "perfect" SST forecasts. This work examines the errors and biases introduced into the seasonal precipitation response of an AGCM forced with persisted SST anomalies, which are generally considered to constitute a good prediction of SST in the first three-month season. The added uncertainty introduced by the persisted SST anomalies weakens, and in some cases nullifies, the skill of atmospheric predictions that is possible given perfect SST forcing. The use of persisted SST anomalies also leads to changes in local signal-to-noise characteristics. Thus, it is argued that seasonal-to-interannual forecasts using AGCMs should be interpreted relative to historical runs that were subject to the same strategy of boundary forcing used in the current forecast in order to properly account for errors and biases introduced by the particular SST prediction strategy. Two case studies are examined to illustrate how the sensitivity of the climate response to predicted SSTs may be used as a diagnostic to suggest improvements to the predicted SSTs.     

Impacts of the stratospheric quasi-biennial oscillation on the tropospheric circulation and climate in the Northeast Asia–North Pacific region in early summer

Previous studies have indicated that the stratospheric quasi-biennial oscillation (QBO) has a global impact on winter weather, but relatively less attention has been paid to its effect in summer. Using ERA5 data, this study reports that the QBO has a significant impact on the tropospheric circulation and surface air temperature (SAT) in the extratropics in Northeast Asia and the North Pacific in early summer. Specifically, a QBO-induced mean meridional circulation prevails from Northeast Asia to the North Pacific in the westerly QBO years, exhibiting westerly anomalies in 20°–35°N and easterly anomalies in 35°–65°N from the lower stratosphere to troposphere. This meridional pattern of zonal wind anomalies can excite positive vorticity and thus lead to anomalous low pressure and cyclonic circulation from Northeast Asia to the North Pacific, which in turn cause northerly wind anomalies and decreased SAT in Northeast Asia in June. Conversely, in the easterly QBO years, the QBO-related circulation and SAT anomalies are generally in an opposite polarity to those in the westerly QBO years. These findings provide new evidence of the impact of the QBO on the extratropical climate, and may benefit the prediction of SAT in Northeast Asia in early summer.摘要本文研究了平流层准两年振荡 (QBO) 对东北亚-北太平洋地区初夏对流层环流和地表气温的影响. 在QBO西风位相年, 东北亚至北太平洋地区存在一支由QBO引发的平均经向环流异常, 该经向环流异常可在东北亚至北太平洋地区激发正涡度, 并形成异常气旋式环流. 气旋左侧出现的异常偏北风导致6月东北亚地表气温下降. QBO东风位相年的结果与西风位相年大致相反. 这些结果为QBO对热带外地区天气,气候的影响提供了新的证据, 并为东北亚初夏地表气温的预测提供了新的线索.     

The impact of atmospheric initialisation on seasonal prediction of tropical Pacific SST

The impact of realistic atmospheric initialisation on the seasonal prediction of tropical Pacific sea surface temperatures is explored with the Predictive Ocean–Atmosphere Model for Australia (POAMA) dynamical seasonal forecast system. Previous versions of POAMA used data from an Atmospheric Model Intercomparison Project (AMIP)-style simulation to initialise the atmosphere for the hindcast simulations. The initial conditions for the hindcasts did not, therefore, capture the true intra-seasonal atmospheric state. The most recent version of POAMA has a new Atmosphere and Land Initialisation scheme (ALI), which captures the observed intra-seasonal atmospheric state. We present the ALI scheme and then compare the forecast skill of two hindcast datasets, one with AMIP-type initialisation and one with realistic initial conditions from ALI, focussing on the prediction of El Niño. For eastern Pacific (Niño3) sea surface temperature anomalies (SSTAs), both experiments beat persistence and have useful SSTA prediction skill (anomaly correlations above 0.6) at all lead times (forecasts are 9 months duration). However, the experiment with realistic atmospheric initial conditions from ALI is an improvement over the AMIP-type initialisation experiment out to about 6 months lead time. The improvements in skill are related to improved initial atmospheric anomalies rather than an improved initial mean state (the forecast drift is worse in the ALI hindcast dataset). Since we are dealing with a coupled system, initial atmospheric errors (or differences between experiments) are amplified though coupled processes which can then lead to long lasting errors (or differences).     

Dynamical seasonal prediction using the global environmental multiscale model with a variable resolution modeling approach

In this work we evaluate seasonal forecasts performed with the global environmental multiscale model (GEM) using a variable resolution approach and with a high-resolution region over different geographical locations. Therefore, using two grid positions, one over North America and the other over the tropical Pacific-eastern Indian Ocean, we compare the seasonal predictions performed with the variable resolution approach with seasonal forecast performed with the uniform grid GEM model. For each model configuration, a ten-member ensemble forecast of 4?months is performed starting from the first of December of selected ENSO winters between 1982 and 2000. The sea surface temperature anomaly of the month preceding the forecast (November) is persisted throughout the forecast period. There is not enough evidence to indicate that a Stretch-Grid configuration has a clear advantage in seasonal prediction compared to a Uniform-Grid configuration. Forecasts with highly resolved grids placed over North America have more accurate seasonal mean anomalies and more skill in representing near surface temperature over the North American continent. For 500-hPa geopotential height, however, no configuration stands out to be consistently superior in forecasting the ENSO related seasonal mean anomalies and skill score.     

Predictability of the western North Pacific summer climate demonstrated by the coupled models of ENSEMBLES

The Asian monsoon system, including the western North Pacific (WNP), East Asian, and Indian monsoons, dominates the climate of the Asia-Indian Ocean-Pacific region, and plays a significant role in the global hydrological and energy cycles. The prediction of monsoons and associated climate features is a major challenge in seasonal time scale climate forecast. In this study, a comprehensive assessment of the interannual predictability of the WNP summer climate has been performed using the 1-month lead retrospective forecasts (hindcasts) of five state-of-the-art coupled models from ENSEMBLES for the period of 1960–2005. Spatial distribution of the temporal correlation coefficients shows that the interannual variation of precipitation is well predicted around the Maritime Continent and east of the Philippines. The high skills for the lower-tropospheric circulation and sea surface temperature (SST) spread over almost the whole WNP. These results indicate that the models in general successfully predict the interannual variation of the WNP summer climate. Two typical indices, the WNP summer precipitation index and the WNP lower-tropospheric circulation index (WNPMI), have been used to quantify the forecast skill. The correlation coefficient between five models’ multi-model ensemble (MME) mean prediction and observations for the WNP summer precipitation index reaches 0.66 during 1979–2005 while it is 0.68 for the WNPMI during 1960–2005. The WNPMI-regressed anomalies of lower-tropospheric winds, SSTs and precipitation are similar between observations and MME. Further analysis suggests that prediction reliability of the WNP summer climate mainly arises from the atmosphere–ocean interaction over the tropical Indian and the tropical Pacific Ocean, implying that continuing improvement in the representation of the air–sea interaction over these regions in CGCMs is a key for long-lead seasonal forecast over the WNP and East Asia. On the other hand, the prediction of the WNP summer climate anomalies exhibits a remarkable spread resulted from uncertainty in initial conditions. The summer anomalies related to the prediction spread, including the lower-tropospheric circulation, SST and precipitation anomalies, show a Pacific-Japan or East Asia-Pacific pattern in the meridional direction over the WNP. Our further investigations suggest that the WNPMI prediction spread arises mainly from the internal dynamics in air–sea interaction over the WNP and Indian Ocean, since the local relationships among the anomalous SST, circulation, and precipitation associated with the spread are similar to those associated with the interannual variation of the WNPMI in both observations and MME. However, the magnitudes of these anomalies related to the spread are weaker, ranging from one third to a half of those anomalies associated with the interannual variation of the WNPMI in MME over the tropical Indian Ocean and subtropical WNP. These results further support that the improvement in the representation of the air–sea interaction over the tropical Indian Ocean and subtropical WNP in CGCMs is a key for reducing the prediction spread and for improving the long-lead seasonal forecast over the WNP and East Asia.     

CFSv2 ensemble prediction of the wintertime Arctic Oscillation

Lagged ensembles from the operational Climate Forecast System version 2 (CFSv2) seasonal hindcast dataset are used to assess skill in forecasting interannual variability of the December–February Arctic Oscillation (AO). We find that a small but statistically significant portion of the interannual variance (>20 %) of the wintertime AO can be predicted at leads up to 2 months using lagged ensemble averages. As far as we are aware, this is the first study to demonstrate that an operational model has discernible skill in predicting AO variability on seasonal timescales. We find that the CFS forecast skill is slightly higher when a weighted ensemble is used that rewards forecast runs with the most accurate representations of October Eurasian snow cover extent (SCE), hinting that a stratospheric pathway linking October Eurasian SCE with the AO may be responsible for the model skill. However, further analysis reveals that the CFS is unable to capture many important aspects of this stratospheric mechanism. Model deficiencies identified include: (1) the CFS significantly underestimates the observed variance in October Eurasian SCE, (2) the CFS fails to translate surface pressure anomalies associated with SCE anomalies into vertically propagating waves, and (3) stratospheric AO patterns in the CFS fail to propagate downward through the tropopause to the surface. Thus, alternate boundary forcings are likely contributing to model skill. Improving model deficiencies identified in this study may lead to even more skillful predictions of wintertime AO variability in future versions of the CFS.     

BCC二代气候系统模式的季节预测评估和可预报性分析

本文利用国家气候中心（BCC）第二代季节预测模式系统历史回报数据,从确定性预报和概率预报两个方面系统地评估了该模式对气温、降水和大气环流的季节预报性能,并与BCC一代气候预测模式的结果进行了对比,重点分析了二代模式的季节可预报性问题。结果显示,BCC二代模式对全球气温、降水和环流的预报性能整体上优于一代模式,特别在热带中东太平洋、印度洋和海洋大陆地区的温度和降水的预报效果改进尤为明显。这些热带地区降水预报的改进,可以通过激发太平洋—北美型（PNA）、东亚—太平洋型（EAP）等遥相关波列提升该模式在中高纬地区的季节预报技巧。分析表明,厄尔尼诺和南方涛动（ENSO）信号在热带和热带外地区均是模式季节可预报性的重要来源,BCC二代模式能够较好把握全球大气环流对ENSO信号的响应特征,从而通过对ENSO预报技巧的改进有效地提升了模式整体的预报性能。从概率预报来看,BCC二代模式对我国冬季气温和夏季降水具备一定的预报能力,特别是对我国东部大部分地区冬季气温正异常和负异常事件预报的可靠性和辨析度相对较高。因此,进一步提高模式对热带大尺度异常信号和大气主要模态的预报能力、加强概率预报产品释用对提高季节气候预测水平具有重要意义。     

Global seasonal climate predictability in a two tiered forecast system: part I: boreal summer and fall seasons

This paper shows demonstrable improvement in the global seasonal climate predictability of boreal summer (at zero lead) and fall (at one season lead) seasonal mean precipitation and surface temperature from a two-tiered seasonal hindcast forced with forecasted SST relative to two other contemporary operational coupled ocean–atmosphere climate models. The results from an extensive set of seasonal hindcasts are analyzed to come to this conclusion. This improvement is attributed to: (1) The multi-model bias corrected SST used to force the atmospheric model. (2) The global atmospheric model which is run at a relatively high resolution of 50 km grid resolution compared to the two other coupled ocean–atmosphere models. (3) The physics of the atmospheric model, especially that related to the convective parameterization scheme. The results of the seasonal hindcast are analyzed for both deterministic and probabilistic skill. The probabilistic skill analysis shows that significant forecast skill can be harvested from these seasonal hindcasts relative to the deterministic skill analysis. The paper concludes that the coupled ocean–atmosphere seasonal hindcasts have reached a reasonable fidelity to exploit their SST anomaly forecasts to force such relatively higher resolution two tier prediction experiments to glean further boreal summer and fall seasonal prediction skill.     

The seasonal footprinting mechanism in CFSv2: simulation and impact on ENSO prediction

The seasonal footprinting mechanism (SFM) is thought to be a pre-cursor to the El Nino Southern Oscillation (ENSO). Fluctuations in the North Pacific Oscillation (NPO) impact the ocean via surface heat fluxes during winter, leaving a sea-surface temperature (SST) “footprint” in the subtropics. This footprint persists through the spring, impacting the tropical Pacific atmosphere–ocean circulation throughout the following year. The simulation of the SFM in the National Centers for Environmental Prediction (NCEP)/Climate Forecast System, version 2 (CFSv2) is likely to have an impact on operational predictions of ENSO and potentially seasonal predictions in the United States associated with ENSO teleconnection patterns. The ability of the CFSv2 to simulate the SFM and the relationship between the SFM and ENSO prediction skill in the NCEP/CFSv2 are investigated. Results indicate that the CFSv2 is able to simulate the basic characteristics of the SFM and its relationship with ENSO, including extratropical sea level pressure anomalies associated with the NPO in the winter, corresponding wind and SST anomalies that impact the tropics, and the development of ENSO-related SST anomalies the following winter. Although the model is able to predict the correct sign of ENSO associated with the SFM in a composite sense, probabilistic predictions of ENSO following a positive or negative NPO event are generally less reliable than when the NPO is not active.     

不同区域海温异常对中国夏季旱涝影响的诊断研究和预测试验

利用季降水异常的典型集合相关预测模式, 研究了前期和同期不同季节全球海表温度距平场与中国夏季旱涝的遥相关分布特征以及这种相关型随季节的变化, 揭示了全球海温的异常变化在中国夏季旱涝中的信号特征.研究表明, 全球不同区域海温对我国夏季降水的影响存在着明显的季节差异.全球特定的海温分布可以作为中国夏季旱涝预报的信号因子.选取不同区域及不同时段的海温场作为因子场分别对1998、 1999年这两个典型年份的我国夏季降水进行了诊断研究和预测试验, 并通过不同区域海温的影响权重做集成预测.试验结果表明:不同区域海温的集成预测不仅可以有效地提高预测的准确性, 而且可以揭示不同时段不同区域海温的异常变化在夏季旱涝中的强信号现象.     

基于多种集合的中国冬季气温逐月滚动预测

利用1979—2015年中国国家气候中心整编的160站月平均气温和NCEP/NCAR全球大气再分析资料,从1979/1980—2008/2009年冬季前期500 h Pa高度场、200 h Pa势函数和850 h Pa势函数场选择预测因子,考虑不同时效因子的组合及其独立性,综合应用多因子回归集合、交叉检验集合、逐月滚动集合,建立了针对中国冬季气温的逐月滚动预测模型,并利用该模型对2010/2011—2014/2015年冬季气温进行了独立预测试验和检验。结果表明,综合运用多种集合可提高短期气候客观定量预测的可行性和稳定性。多因子回归集合能增加可预测站点数,交叉检验集合可减少因统计关系不稳定而产生的对预报效果的影响,逐月滚动集合的应用不仅增加了可预测站点数,而且使预测效果更加稳定。本文建立的预测模型可对中国冬季气温进行长时效的预测,且有一定的预报技巧,对实际的季节预测业务有重要应用价值。     

Impact of initialization procedures on the predictive skill of a coupled ocean–atmosphere model

The sensitivity of the predictive skill of a decadal climate prediction system is investigated with respect to details of the initialization procedure. For this purpose, the coupled ocean–atmosphere UCLA/MITgcm climate model is initialized using the following three different initialization approaches: full state initialization (FSI), anomaly initialization (AI) and FSI employing heat flux and freshwater flux corrections (FC). The ocean initial conditions are provided by the German contribution to Estimating the Circulation and Climate of the Ocean state estimate (GECCO project), from which ensembles of decadal hindcasts are initialized every 5 years from 1961 to 2001. The predictive skill for sea surface temperature (SST), sea surface height (SSH) and the Atlantic meridional overturning circulation (AMOC) is assessed against the GECCO synthesis. In regions with a deep mixed layer the predictive skill for SST anomalies remains significant for up to a decade in the FC experiment. By contrast, FSI shows less persistent skill in the North Atlantic and AI does not show high skill in the extratropical Southern Hemisphere, but appears to be more skillful in the tropics. In the extratropics, the improved skill is related to the ability of the FC initialization method to better represent the mixed layer depth, and the highest skill occurs during wintertime. The correlation skill for the spatially averaged North Atlantic SSH hindcasts remains significant up to a decade only for FC. The North Atlantic MOC initialized hindcasts show high correlation values in the first pentad while correlation remains significant in the following pentad too for FSI and FC. Overall, for the current setup, the FC approach appears to lead to the best results, followed by the FSI and AI procedures.     

A Regression-based Assessment of the Predictability of New Zealand Climate Anomalies

Summary Estimates of the predictability of New Zealand monthly and seasonal temperature and rainfall anomalies are calculated using a cross-validated linear regression procedure. Predictors are indices of the large scale circulation, sea-surface temperatures, the Southern Oscillation Index and persistence. Statistical significance is estimated through a series of Monte Carlo trials. No significant forecast relationships are found for rainfall anomalies at either the monthly or seasonal time scale. Temperature forecasts are however considered to exhibit significant skill, with variance reductions of the order of 10–20% in independent trials. Temperature anomalies are most skilfully predicted over the North Island, and skill is greatest in Spring and Summer in most areas. At the monthly time scale, predictors local to the New Zealand region account for most of the forecast skill, while at the seasonal time scale, skill depends strongly upon “remote” predictors defined over regions of the southern hemisphere distant from New Zealand. Indices of meridional flow over the Tasman Sea/New Zealand region are found to be useful predictors, especially for monthly forecasts, perhaps as a proxy for atmospherically-forced sea surface temperature anomalies. Sea surface temperature anomalies to the west of New Zealand and in the tropical Indian Ocean are also useful, especially for seasonal predictions. Forecast skill is more reliably estimated at the monthly time scale than at the seasonal time scale, as a result of the larger sample size of monthly mean data. While long-term mean levels of skill may be estimated reliably over the whole data set, statistically significant decadal-scale variations are found in the predictability of temperature anomalies. Therefore, even if long-term forecast skill levels are reliably estimated, it may be impossible to predict the short-term skill of operational seasonal climate forecasts. Implications for operational climate predictions in mid-latitudes are discussed. Received July 18, 1997 Revised April 2, 1998     

Seasonal prediction of global sea level anomalies using an ocean–atmosphere dynamical model

Advanced warning of extreme sea level events is an invaluable tool for coastal communities, allowing the implementation of management policies and strategies to minimise loss of life and infrastructure damage. This study is an initial attempt to apply a dynamical coupled ocean–atmosphere model to the prediction of seasonal sea level anomalies (SLA) globally for up to 7 months in advance. We assess the ability of the Australian Bureau of Meteorology’s operational seasonal dynamical forecast system, the Predictive Ocean Atmosphere Model for Australia (POAMA), to predict seasonal SLA, using gridded satellite altimeter observation-based analyses over the period 1993–2010 and model reanalysis over 1981–2010. Hindcasts from POAMA are based on a 33-member ensemble of seasonal forecasts that are initialised once per month for the period 1981–2010. Our results show POAMA demonstrates high skill in the equatorial Pacific basin and consistently exhibits more skill globally than a forecast based on persistence. Model predictability estimates indicate there is scope for improvement in the higher latitudes and in the Atlantic and Southern Oceans. Most characteristics of the asymmetric SLA fields generated by El-Nino/La Nina events are well represented by POAMA, although the forecast amplitude weakens with increasing lead-time.     

Connection of stratospheric QBO with global atmospheric general circulation and tropical SST. Part I: methodology and composite life cycle

The stratospheric quasi-biennial oscillation (QBO) and its association with the interannual variability in the stratosphere and troposphere, as well as in tropical sea surface temperature anomalies (SSTA), are examined in the context of a QBO life cycle. The analysis is based on the ERA40 and NCEP/NCAR reanalyses, radiosonde observations at Singapore, and other observation-based datasets. Both reanalyses reproduce the QBO life cycle and its associated variability in the stratosphere reasonably well, except that some long-term changes are detected only in the NCEP/NCAR reanalysis. In order to separate QBO from variability on other time scales and to eliminate the long-term changes, a scale separation technique [Ensemble Empirical Mode Decomposition (EEMD)] is applied to the raw data. The QBO component of zonal wind anomalies at 30?hPa, extracted using the EEMD method, is defined as a QBO index. Using this index, the QBO life cycle composites of stratosphere and troposphere variables, as well as SSTA, are constructed and examined. The composite features in the stratosphere are generally consistent with previous investigations. The correlations between the QBO and tropical Pacific SSTA depend on the phase in a QBO life cycle. On average, cold (warm) SSTA peaks about half a year after the maximum westerlies (easterlies) at 30?hPa. The connection of the QBO with the troposphere seems to be associated with the differences of temperature anomalies between the stratosphere and troposphere. While the anomalies in the stratosphere propagate downward systematically, some anomalies in the troposphere develop and expand vertically. Therefore, it is possible that the temperature difference between the troposphere and stratosphere may alter the atmospheric stability and tropical deep convection, which modulates the Walker circulation and SSTA in the equatorial Pacific Ocean.     

Interdecadal change in the Northern Hemisphere seasonal climate prediction skill: part II. predictability and prediction skill

The interdecadal change in seasonal predictability and numerical models’ seasonal forecast skill in the Northern Hemisphere are examined using both observations and the seasonal hindcast from six coupled atmosphere-ocean climate models from the 21 period of 1960–1980 (P1) to that of 1981–2001 (P2). It is shown that the one-month lead seasonal forecast skill of the six models’ multi-model ensemble is significantly increased from P1 to P2 for all four seasons. We identify four possible reasons accounting for the interdecadal change of the seasonal forecast skill. Firstly, the numerical model’s ability to simulate the mean state, the time variability and the spatial structures of the sea surface temperature and precipitation over the tropical Pacific is improved in P2 compared to P1. Secondly, an examination of the potential predictability of the atmosphere, estimated by the ratio of the total variance to the variance due to the internal dynamics of the model atmosphere, reveals that the atmospheric potential predictability is significantly increased after 1980s which is mainly due to an increased influence of El Niño-Southern Oscillation signal over the North Pacific and North American regions. Thirdly, the long-term climate trends in the atmosphere are found to contribute, to some extent, to the increased seasonal forecast skill especially over the Eurasian regions. Finally, the improved ocean observations in P2 may provide better initial conditions for the coupled models’ seasonal forecast.