Seasonal prediction skill of ECMWF System 4 and NCEP CFSv2 retrospective forecast for the Northern Hemisphere Winter

The seasonal prediction skill for the Northern Hemisphere winter is assessed using retrospective predictions (1982–2010) from the ECMWF System 4 (Sys4) and National Center for Environmental Prediction (NCEP) CFS version 2 (CFSv2) coupled atmosphere–ocean seasonal climate prediction systems. Sys4 shows a cold bias in the equatorial Pacific but a warm bias is found in the North Pacific and part of the North Atlantic. The CFSv2 has strong warm bias from the cold tongue region of the eastern Pacific to the equatorial central Pacific and cold bias in broad areas over the North Pacific and the North Atlantic. A cold bias in the Southern Hemisphere is common in both reforecasts. In addition, excessive precipitation is found in the equatorial Pacific, the equatorial Indian Ocean and the western Pacific in Sys4, and in the South Pacific, the southern Indian Ocean and the western Pacific in CFSv2. A dry bias is found for both modeling systems over South America and northern Australia. The mean prediction skill of 2 meter temperature (2mT) and precipitation anomalies are greater over the tropics than the extra-tropics and also greater over ocean than land. The prediction skill of tropical 2mT and precipitation is greater in strong El Nino Southern Oscillation (ENSO) winters than in weak ENSO winters. Both models predict the year-to-year ENSO variation quite accurately, although sea surface temperature trend bias in CFSv2 over the tropical Pacific results in lower prediction skill for the CFSv2 relative to the Sys4. Both models capture the main ENSO teleconnection pattern of strong anomalies over the tropics, the North Pacific and the North America. However, both models have difficulty in forecasting the year-to-year winter temperature variability over the US and northern Europe.     

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.     

Impact of land-atmospheric coupling in CFSv2 on drought prediction

Recent summers in the United States have been plagued by intense droughts that have caused significant damage to crops and have had a large impact on society. The ability to forecasts such events would allow for preparations that could help reduce the impact on society. Coupled land–atmosphere–ocean models were created to provide such forecasts but there are large uncertainties associated with their predictions. The predictive skill of these models is particularly low during the convective season due to the weaker connections with the oceans and an increase in the land–atmosphere interactions. To better understand the degradation of forecasts skill during the summer months and its connection to the land–atmosphere interactions we analyze National Centers for Environmental Prediction’s Climate Forecast System Version 2 (CFSv2) in terms of its climatological land–atmosphere interactions. To do this we use a recently developed classification of land–atmosphere interactions and other diagnostic variables to compare the reanalysis from the Climate Forecast System (CFSR) with CFSv2 re-forecasts (CFSRR) over the period 1982–2009. Coupling in the CFSRR tends toward the wet coupling regime for most areas east of the Rocky Mountains. Although the specific mechanism driving CFSRR to wet coupling state varies by region, the overall cause is enhanced vegetation rooting depth, originally implemented to address a near-surface warm bias in CFSR. The long-term tendency to wet coupling precludes the forecast model from consistently predicting and maintaining drought over the continental US.     

CFSv2对2018年浙江梅雨降水多尺度预测的性能评估

从梅雨预测的业务需求出发,系统开展了CFSv2模式对2018年浙江梅雨期降水预报能力的多时间尺度评估。结果发现3月1日—5月31日的起报结果整体上未能较准确地预测6月浙江大部降水偏少的趋势、仅5月31日的预测结果与实况相符;在延伸期尺度上,CFSv2预测的梅雨期总降水量较实况偏少30%左右;基于相关系数、均方根误差和新定义的综合预报技巧指数等指标分析模式的延伸期预报性能,发现对梅雨期总降水量、逐日区域平均降水量和逐日全省各站降水量的预报技巧有限,对浙江梅雨区的预报水平总体高于浙江全省。评估结果表明CFSv2预报产品表现出显著的系统性干偏差;在延伸期尺度上,随着预报时效的缩短,预报效果并非逐步提升、而是客观存在一个最佳预报时效,各起报日也分别对应着不同的最优预报时段,整体而言梅雨降水的延伸期预测可能对初值并不敏感。     

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.     

Asian summer monsoon prediction in ECMWF System 4 and NCEP CFSv2 retrospective seasonal forecasts

The seasonal prediction skill of the Asian summer monsoon is assessed using retrospective predictions (1982–2009) from the ECMWF System 4 (SYS4) and NCEP CFS version 2 (CFSv2) seasonal prediction systems. In both SYS4 and CFSv2, a cold bias of sea-surface temperature (SST) is found over the equatorial Pacific, North Atlantic, Indian Oceans and over a broad region in the Southern Hemisphere relative to observations. In contrast, a warm bias is found over the northern part of North Pacific and North Atlantic. Excessive precipitation is found along the ITCZ, equatorial Atlantic, equatorial Indian Ocean and the maritime continent. The southwest monsoon flow and the Somali Jet are stronger in SYS4, while the south-easterly trade winds over the tropical Indian Ocean, the Somali Jet and the subtropical northwestern Pacific high are weaker in CFSv2 relative to the reanalysis. In both systems, the prediction of SST, precipitation and low-level zonal wind has greatest skill in the tropical belt, especially over the central and eastern Pacific where the influence of El Nino-Southern Oscillation (ENSO) is dominant. Both modeling systems capture the global monsoon and the large-scale monsoon wind variability well, while at the same time performing poorly in simulating monsoon precipitation. The Asian monsoon prediction skill increases with the ENSO amplitude, although the models simulate an overly strong impact of ENSO on the monsoon. Overall, the monsoon predictive skill is lower than the ENSO skill in both modeling systems but both systems show greater predictive skill compared to persistence.     

CFSv2模式产品在汛期海南热带气旋频数预测模型中的应用

利用1982—2014年汛期影响海南的热带气旋频数、NCEP/NCAR逐月再分析资料和CFSv2模式历史回报数据,分析了热带气旋频数特征及同期环流特征,并利用逐步回归构建基于模式有效预测信息的热带气旋频数预测模型。结果表明:汛期影响海南热带气旋频数的异常与同期大尺度环流变化密切相关,且CFSv2模式对其环流影响关键区具有较好的预测技巧,包括南海到热带太平洋的海平面气压、500 h Pa位势高度场、低层风及热带太平洋纬向风切变。据此,利用逐步回归构建热带气旋频数预测模型,其26 a交叉检验中实况与预测相关为0.88,距平同号率达88%;6 a预测试验仅2 a预测与观测反号,可见模型具有良好的稳定性和预测技巧,可为汛期热带气旋频数预测提供依据。     

Wintertime stratospheric anomalies— Part II: Sudden warmings

The process of stratospheric sudden warmings from development of planetary waves to the sudden cooling after reversal of mean zonal circulation will be studied with the primitive equations of heat and momentum balances. It will be explained that the sudden warmings may occur only in the polar regions of winter stratosphere where zonal mean temperature decreases poleward. The heating rate in the order of major warmings is produced by developed planetary waves in the stratospheric breaking layers. The particular perturbation structure characterized by large amplitude of wave 1 together with minimum of wave 2 discovered by Labitzke (1977) is crucial for initiation of major warmings. The cooling by the same mechanism can be produced in the regions with reversed mean temperature gradient.     

Decadal prediction skill for Eurasian surface air temperature in CMIP6 models

评估CMIP6年代际预测试验对季节平均SAT的预测技巧的结果表明:模式不能有效预测冬季和秋季SAT的年代际变率.IPSL-CM6A-LR和多模式集合平均对于春季SAT展现了预测技巧,其中对于变率的预测技巧好于振幅的结果.基于蒙古和我国华北地区的显著预测技巧,模式对于夏季SAT表现出最佳的预测水平.与外部强迫相比,模式对于SAT的预测技巧可能来自初始化.模式中的一个明显系统性误差值得注意,即模式中冬季SAT的变率可以持续到其他季节,而在观测中其他季节的SAT变化与冬季SAT相对独立.     

Predictions of Nino3.4 SST in CFSv1 and CFSv2: a diagnostic comparison

Diagnostic evaluations of the relative performances of CFSv1 and CFSv2 in prediction of monthly anomalies of the ENSO-related Nino3.4 SST index are conducted using the common hindcast period of 1982–2009 for lead times of up to 9 months. CFSv2 outperforms CFSv1 in temporal correlation skill for predictions at moderate to long lead times that traverse the northern spring ENSO predictability barrier (e.g., a forecast for July made in February). However, for predictions during less challenging times of the year (e.g., a forecast for January made in August), CFSv1 has higher correlations than CFSv2. This seeming retrogression is caused by a cold bias in CFSv2 predictions for Nino3.4 SST during 1982–1998, and a warm bias during 1999–2009. Work by others has related this time-conditional bias to changes in the observing system in late 1998 that affected the ocean reanalysis serving as initial conditions for CFSv2. A posteriori correction of these differing biases, and of a similar (but lesser) situation affecting CFSv1, allows for a more realistic evaluation of the relative performances of the two CFS versions. After the dual bias corrections, CFSv2 has slightly better correlation skill than CFSv1 for most months and lead times, with approximately equal skills for forecasts not traversing the ENSO predictability barrier and better skills for most (particularly long-lead) predictions traversing the barrier. The overall difference in correlation skill is not statistically field significant. However, CFSv2 has statistically significantly improved amplitude bias, and visibly better probabilistic reliability, and lacks target month slippage as compared with CFSv1. Together, all of the above improvements result in a highly significantly reduced overall RMSE—the metric most indicative of final accuracy.     

Wintertime stratospheric anomalies— Part I: Warm pools

The stratospheric worm pools, called the 4-day wave also, are mainly the temperature anomalies in the polar re-gions of winter hemisphere. It will be shown that their occurrence, propagation speed and specific structure can be explained by the lower frequency coherent heating resulting from the wave interaction in the breaking layers of the stratosphere. Although their vertical phase slope is negligibly small, the warm pools cannot be considered as a barotropic anomaly.     

Evaluation of summer temperature and precipitation predictions from NCEP CFSv2 retrospective forecast over China

National Centers for Environmental Prediction recently upgraded its operational seasonal forecast system to the fully coupled climate modeling system referred to as CFSv2. CFSv2 has been used to make seasonal climate forecast retrospectively between 1982 and 2009 before it became operational. In this study, we evaluate the model’s ability to predict the summer temperature and precipitation over China using the 120 9-month reforecast runs initialized between January 1 and May 26 during each year of the reforecast period. These 120 reforecast runs are evaluated as an ensemble forecast using both deterministic and probabilistic metrics. The overall forecast skill for summer temperature is high while that for summer precipitation is much lower. The ensemble mean reforecasts have reduced spatial variability of the climatology. For temperature, the reforecast bias is lead time-dependent, i.e., reforecast JJA temperature become warmer when lead time is shorter. The lead time dependent bias suggests that the initial condition of temperature is somehow biased towards a warmer condition. CFSv2 is able to predict the summer temperature anomaly in China, although there is an obvious upward trend in both the observation and the reforecast. Forecasts of summer precipitation with dynamical models like CFSv2 at the seasonal time scale and a catchment scale still remain challenge, so it is necessary to improve the model physics and parameterizations for better prediction of Asian monsoon rainfall. The probabilistic skills of temperature and precipitation are quite limited. Only the spatially averaged quantities such as averaged summer temperature over the Northeast China of CFSv2 show higher forecast skill, of which is able to discriminate between event and non-event for three categorical forecasts. The potential forecast skill shows that the above and below normal events can be better forecasted than normal events. Although the shorter the forecast lead time is, the higher deterministic prediction skill appears, the probabilistic prediction skill does not increase with decreased lead time. The ensemble size does not play a significant role in affecting the overall probabilistic forecast skill although adding more members improves the probabilistic forecast skill slightly.     

Prediction and monitoring of monsoon intraseasonal oscillations over Indian monsoon region in an ensemble prediction system using CFSv2

An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISO) of Indian summer monsoon (ISM) using National Centers for Environmental Prediction Climate Forecast System model version 2 at T126 horizontal resolution. The EPS is formulated by generating 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001–2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio of the forecasted rainfall becomes unity by about 18 days. The potential predictability error of the forecasted rainfall saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are found even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of large-scale MISO amplitude as well as the initial conditions related to the different phases of MISO. An analysis of categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.     

Effects of meridional sea surface temperature changes on stratospheric temperature and circulation

Using a state-of-the-art chemistry-climate model,we analyzed the atmospheric responses to increases in sea surface temperature （SST）.The results showed that increases in SST and the SST meridional gradient could intensify the subtropical westerly jets and significantly weaken the northern polar vortex.In the model runs,global uniform SST increases produced a more significant impact on the southern stratosphere than the northern stratosphere,while SST gradient increases produced a more significant impact on the northern stratosphere.The asymmetric responses of the northern and southern polar stratosphere to SST meridional gradient changes were found to be mainly due to different wave properties and transmissions in the northern and southern atmosphere.Although SST increases may give rise to stronger waves,the results showed that the effect of SST increases on the vertical propagation of tropospheric waves into the stratosphere will vary with height and latitude and be sensitive to SST meridional gradient changes.Both uniform and non-uniform SST increases accelerated the large-scale Brewer-Dobson circulation （BDC）,but the gradient increases of SST between 60°S and 60°N resulted in younger mean age-of-air in the stratosphere and a larger increase in tropical upwelling,with a much higher tropopause than from a global uniform 1.0 K SST increase.