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。     

IMPACT OF SUMMER WARMING ON DYNAMICS-STATISTICS-COMBINED METHOD TO PREDICT THE SUMMER TEMPERATURE IN CHINA

Based on NCEP/NCAR daily reanalysis data, climate trend rate and other methods are used to quantitatively analyze the change trend of China's summer observed temperature in 1983—2012. Moreover, a dynamics-statistics-combined seasonal forecast method with optimal multi-factor portfolio is applied to analyze the impact of this trend on summer temperature forecast. The results show that: in the three decades, the summer temperature shows a clear upward trend under the condition of global warming, especially over South China, East China, Northeast China and Xinjiang Region, and the trend rate of national average summer temperature was 0.27℃ per decade. However, it is found that the current business model forecast(Coupled Global Climate Model) of National Climate Centre is unable to forecast summer warming trends in China, so that the post-processing forecast effect of dynamics-statistics-combined method is relatively poor. In this study, observed temperatures are processed first by removing linear fitting trend, and then adding it after forecast to offset the deficiency of model forecast indirectly. After test, ACC average value in the latest decade was 0.44 through dynamics-statistics-combined independent sample return forecast. The temporal correlation(TCC) between forecast and observed temperature was significantly improved compared with direct forecast results in most regions, and effectively improved the skill of the dynamics-statistics-combined forecast method in seasonal temperature forecast.     

A study of atmosphere-ocean predictability on long time scales

 Two different methods, one diagnostic and the other prognostic, are used to investigate the predictability of the coupled atmosphere-ocean system on time scales of months to decades. The diagnostic approach analyzes the output of a 200 year coupled model simulation for evidence that the long time scale variability of annual, pentadal, and decadal means of surface air temperature are “potentially predictable”. The prognostic “perfect model” approach analyzes predictability of the system by calculating the rate of separation of a small set of coupled model simulations in terms of monthly and annual means of surface air temperature over the globe. Both approaches give reasonably coherent results. At the shorter of the time scales considered, there is little predictability over land but some evidence of predictability over the tropical Pacific, the Southern Ocean and to some extent the extratropical northern Pacific. At longer time scales there is some slight evidence of predictability over certain land areas and more definite evidence of predictability in the tropical Pacific and Southern Ocean. A new area of predictability emerges in the tropical Atlantic. There are plausible physical mechanisms which may explain these long time scale areas of predictability. Both approaches have the potential of shedding light on the long time scale predictability of the coupled system in more extensive predictability studies of this kind. Received: 24 September 1998 / Accepted: 8 October 1999     

Analysis of changes in air temperature in Russia and empirical forecast for the first quarter of the 21st century

Changes in air temperature in administrative districts of the Russian Federation are studied. The in-regional homogeneity of modern climate changes is estimated based on several criteria for the first half of the 20th century and for the next period. The relation between regional changes in mean annual and seasonal temperatures and global air temperature is considered, and an empirical forecast for one or two decades is suggested to be made on its basis. Empirical regional forecasts of a rate of changes in mean annual air temperature are compared with the calculation results from five general circulation models.     

Calibration and combination of dynamical seasonal forecasts to enhance the value of predicted probabilities for managing risk

Seasonal probability forecasts produced with numerical dynamics on supercomputers offer great potential value in managing risk and opportunity created by seasonal variability. The skill and reliability of contemporary forecast systems can be increased by calibration methods that use the historical performance of the forecast system to improve the ongoing real-time forecasts. Two calibration methods are applied to seasonal surface temperature forecasts of the US National Weather Service, the European Centre for Medium Range Weather Forecasts, and to a World Climate Service multi-model ensemble created by combining those two forecasts with Bayesian methods. As expected, the multi-model is somewhat more skillful and more reliable than the original models taken alone. The potential value of the multimodel in decision making is illustrated with the profits achieved in simulated trading of a weather derivative. In addition to examining the seasonal models, the article demonstrates that calibrated probability forecasts of weekly average temperatures for leads of 2–4 weeks are also skillful and reliable. The conversion of ensemble forecasts into probability distributions of impact variables is illustrated with degree days derived from the temperature forecasts. Some issues related to loss of stationarity owing to long-term warming are considered. The main conclusion of the article is that properly calibrated probabilistic forecasts possess sufficient skill and reliability to contribute to effective decisions in government and business activities that are sensitive to intraseasonal and seasonal climate variability.     

An analysis of seasonal predictability in coupled model forecasts

In the recent decade, operational seasonal prediction systems based on initialized coupled models have been developed. An analysis of how the predictability of seasonal means in the initialized coupled predictions evolves with lead-time is presented. Because of the short lead-time, such an analysis for the temporal behavior of seasonal predictability involves a mix of both the predictability of the first and the second kind. The analysis focuses on the lead-time dependence of ensemble mean variance, and the forecast spread. Further, the analysis is for a fixed target season of December?CJanuary?CFebruary, and is for sea surface temperature, rainfall, and 200-mb height. The analysis is based on a large set of hindcasts from an initialized coupled seasonal prediction system. Various aspects of predictability of the first and the second kind are highlighted for variables with long (for example, SST), and fast (for example, atmospheric) adjustment time scale. An additional focus of the analysis is how the predictability in the initialized coupled seasonal predictions compares with estimates based on the AMIP simulations. The results indicate that differences in the set up of AMIP simulations and coupled predictions, for example, representation of air?Csea interactions, and evolution of forecast spread from initial conditions do not change fundamental conclusion about the seasonal predictability. A discussion of the analysis presented herein, and its implications for the use of AMIP simulations for climate attribution, and for time-slice experiments to provide regional information, is also included.     

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.     

中国区域月平均温度和降水的模式可预报性分析

基于中国台站降水和温度观测资料、中国气象局国家气候中心月动力延伸预报的回算和预测结果讨论了中国区域月平均温度和降水模式可预报性的时空变化特征。文中以持续性预报来表征中国区域月平均温度和降水受外强迫影响下的可预报性,持续性预报技巧存在明显的年际和年代际变化特征;春末夏初和秋季预报评分相对偏低;在中国区域气候变暖和平均降水强度极值增加的背景下,温度的持续性预报评分有明显提高,降水的持续性预报略有下降。月动力延伸预报对月降水和温度的预报能力也存在明显的年际和年代际变化特征;与持续性预报相比,月动力延伸温度预报总体优于持续性预报,降水预报在初春略差,温度预报在8月相对最低。近20余年,月动力延伸预报相对于持续性预报的温度和降水的均方根误差技巧均大于零,其年际变化表现为模式对降水的预测略有提高。两种预报评估结果的空间分布分析表明月动力延伸预报达到显著性水平的正相关区域总体上比持续性预报的范围大,并基本涵盖了持续性预报的高相关区。原因是可预测信息部分来源于外强迫异常的影响,部分来源于对大气内部动力过程的模拟。     

我国华东地区月尺度气候动力预测的研究:(Ⅱ) 嵌套模式的回报评估

采用数值模式方法对我国华东地区进行月尺度短期气候预测。预测框架由改进的低分辨率全球环流模式T63 L9嵌套并入了水文模型VXM的区域气候模式RegCM3构成,根据嵌套气候模式的积分结果,经剔除系统误差后制作短期气候预报。本文利用国家气候中心的评分方法对2003、2004两年的降水和地表气温回报结果作了评估;还将本系统的预报结果与CMAP降水资料、NMC温度资料及全国160站的观测资料进行了对比。结果表明,该系统可以比较稳定地对我国华东地区的降水和温度进行月尺度预测。     

广西电网电力负荷变化特征与气温的关系及其预测

通过分析1997-2003年5～10月广西电网电力负荷月、周和节假日的变化特征，及与气温的相关关系，发现：电力负荷基本呈逐年增加的趋势，工作日和非工作日有着不同的变化特征，电力负荷与气温有明显的相关关系。采用逐步回归方法，建立了广西电网逐日电力负荷预测模型，对2002-2003年模型的拟合结果及2004-2005年模型的预测结果分析表明：模型对广西逐日电力负荷具有较好的拟合和预测效果，对逐日电力负荷的季节变化具有较好的拟合效果，尤其是夏季预测与实况趋势基本吻合。     

10~30 d延伸期可预报性与预报方法研究进展

10~30 d延伸期的可预报性既依赖于初始条件,也与缓变的下垫面有关,寻找延伸期时段内可预报性较高的低频特征,识别延伸期的可预报性来源及影响的物理机制是提高延伸期预报水平的关键。近年延伸期可预报性来源、热带大气季节内振荡监测预测和影响等领域的研究取得较大进展,提出和应用了动力统计相结合以及大气低频信号释用等新的延伸期预报方法。对延伸期可预报性来源及其与初值和外强迫异常的关系分析表明,海气相互作用能提高亚洲和西太平洋区域延伸期时段大气环流和要素的可预报性。热带大气季节内振荡、平流层爆发性增温以及各种次季节尺度的海气、陆气耦合作用和大气响应均为延伸期预报提供了重要的可预报性来源。由于数值模式延伸期时段的预报性能与实际业务需求还存在一定距离,基于动力统计相结合和物理统计的延伸期预报方法被广泛应用于业务预报,表现出一定的预报技巧。     

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     

Assessing the Seasonal Predictability of Summer Precipitation over the Huaihe River Basin with Multiple APCC Models

Seasonal rainfall predictability over the Huaihe River basin is evaluated in this paper on the basis of 23-year(1981-2003) retrospective forecasts by 10 climate models from the Asia-Pacific Economic Cooperation(APEC) Climate Center(APCC) multi-model ensemble(MME) prediction system.It is found that the summer rainfall variance in this basin is largely internal,which leads to lower rainfall predictability for most individual climate models.By dividing the 10 models into three categories according to their sea surface temperature(SST) boundary conditions including observed,predicted,and persistent SSTs,the MME deterministic predictive skill of summer rainfall over Huaihe River basin is investigated.It is shown that the MME is effective for increasing the current seasonal forecast skill.Further analysis shows that the MME averaged over predicted SST models has the highest rainfall prediction skill,which is closely related to model’s capability in reproducing the observed dominant modes of the summer rainfall anomalies in Huaihe River basin.This result can be further ascribed to the fact that the predicted SST MME is the most effective model ensemble for capturing the relationship between the summer rainfall anomalies over Huaihe River basin and the SST anomalies(SSTAs) in equatorial oceans.     

Decadal predictability and forecast skill

The “potential predictability” of the climate system is the upper limit of available forecast skill and can be characterized by the ratio p of the predictable variance to the total variance. While the potential predictability of the actual climate system is unknown its analog q may be obtained for a model of the climate system. The usual correlation skill score r and the mean square skill score M are functions of p in the case of actual forecasts and potential correlation ρ and potential mean square skill score $\mathcal{M}$ are the same functions of q in the idealized model context. In the large ensemble limit the connection between model-based potential predictability and skill scores is particularly straightforward with $q=\rho^{2}=\mathcal{M}.$ Decadal predictions of annual mean temperature produced with the Canadian Centre for Climate Modelling and Analysis coupled climate model are analyzed for information on decadal climate predictability and actual forecast skill. Initialized forecast results are compared with the results of uninitialized climate simulations. Model-based values of potential predictability q and potential correlation skill ρ are obtained and ρ is compared with the actual forecast correlation skill r. The skill of externally forced and internally generated components of the variability are separately estimated. As expected, ρ > r and both decline with forecast range τ, at least for the first five years. The decline of skill is associated mainly with the decline of the skill of the internally generated component. The potential and actual skill of a forecast of time-averaged temperature depends on the averaging period. The skill of uninitialized simulations is low for short averaging times and increases as averaging time increases. By contrast, skill is high at short averaging times for forecasts initialized from observations and declines as averaging times increase to about three years, then increases somewhat at longer averaging times. The skills of the initialized forecasts and uninitialized simulations begin to converge for longer averaging times. The potential correlation skill ρ of the externally forced component of temperature is largest at tropical latitudes and the skill of the internally generated component is largest over the North Atlantic, parts of the Southern Ocean and to some extent the North Pacific. Potential skill over extratropical land is somewhat weaker than over oceans. The distribution of actual correlation skill r is broadly similar to that of potential skill for the externally forced component but less so for the internally generated component. Differences in potential and actual skill suggest where improvements in the forecast system might be found.     

Climate and Forecast Mode Simulations for Antarctica: Implications for Temperature and Wind

Climate and forecast mode simulations with the regional climate model HIRlam-ECHAM(HIRHAM) are evaluated over a pan-Antarctic domain. The ability of the model to simulate temperature and wind profiles in the troposphere is quantified by comparing its results with radiosonde data acquired from the Davis station for January and July 2007. Compared to the climate mode, the forecast mode was found to deliver improved results for temperature and wind simulations at the near surface and in the lower troposphere. The main remaining model bias found was the under-representation of low-level wind jets. Based on ensemble simulations, it is shown that a distinct internal variability is inherent in the climate mode simulations, and associated areas of reduced predictability over Antarctica are identified.     

Statistical guidance on seasonal forecast of Korean dust days over South Korea in the springtime

This study aimed to develop the seasonal forecast models of Korean dust days over South Korea in the springtime. Forecast mode was a ternary forecast (below normal, normal, above normal) which was classified based on the mean and the standard deviation of Korean dust days for a period of 30 years (1981-2010). In this study, we used three kinds of monthly data: the Korean dust days observed in South Korea, the National Center for Environmental Prediction in National Center for Atmospheric Research (NCEP/NCAR) reanalysis data for meteorological factors over source regions of Asian dust, and the large-scale climate indices offered from the Climate Diagnostic Center and Climate Prediction Center in NOAA. Forecast guidance consisted of two components; ordinal logistic regression model to generate trinomial distributions, and conversion algorithm to generate ternary forecast by two thresholds. Forecast guidance was proposed for each month separately and its predictability was evaluated based on skill scores.     

Characteristics of tropical Pacific SST predictability in coupled GCM forecasts using the NCEP CFS

The limits of predictability of El Niño and the Southern Oscillation (ENSO) in coupled models are investigated based on retrospective forecasts of sea surface temperature (SST) made with the National Centers for Environmental Prediction (NCEP) coupled forecast system (CFS). The influence of initial uncertainties and model errors associated with coupled ENSO dynamics on forecast error growth are discussed. The total forecast error has maximum values in the equatorial Pacific and its growth is a strong function of season irrespective of lead time. The largest growth of systematic error of SST occurs mainly over the equatorial central and eastern Pacific and near the southeastern coast of the Americas associated with ENSO events. After subtracting the systematic error, the root-mean-square error of the retrospective forecast SST anomaly also shows a clear seasonal dependency associated with what is called spring barrier. The predictability with respect to ENSO phase shows that the phase locking of ENSO to the mean annual cycle has an influence on the seasonal dependence of skill, since the growth phase of ENSO events is more predictable than the decay phase. The overall characteristics of predictability in the coupled system are assessed by comparing the forecast error growth and the error growth between two model forecasts whose initial conditions are 1 month apart. For the ensemble mean, there is fast growth of error associated with initial uncertainties, becoming saturated within 2 months. The subsequent error growth follows the slow coupled mode related the model’s incorrect ENSO dynamics. As a result, the Lorenz curve of the ensemble mean NINO3 index does not grow, because the systematic error is identical to the same target month. In contrast, the errors of individual members grow as fast as forecast error due to the large instability of the coupled system. Because the model errors are so systematic, their influence on the forecast skill is investigated by analyzing the erroneous features in a long simulation. For the ENSO forecasts in CFS, a constant phase shift with respect to lead month is clear, using monthly forecast composite data. This feature is related to the typical ENSO behavior produced by the model that, unlike the observations, has a long life cycle with a JJA peak. Therefore, the systematic errors in the long run are reflected in the forecast skill as a major factor limiting predictability after the impact of initial uncertainties fades out.     

季节预测模式对东亚夏季环流的预测能力及其对热带海洋的响应分析

东亚夏季环流变化对中国夏季降水的年际变化有重要影响,因此需要进一步理解季节预测模式对东亚夏季环流的预测能力。利用1991~2013年美国国家环境预测中心（NCEP）、中国气象局国家气候中心（NCC）和日本东京气候中心（TCC）的三个季节预测模式（CFS V2、BCC_CSM V2和MRI-CGCM）以及NCEP/NCAR再分析资料,定量评估了模式对东亚夏季风（EASM）和夏季西太平洋副热带高压（WPSH）强度的预测能力。在此基础上,分析了模式预测的EASM和WPSH对热带海温异常的响应能力,以及ENSO事件对EASM和WPSH预测的影响,阐述了预测误差产生的原因。结果表明:整体而言,三个模式对EASM和WPSH的预测技巧较高,但TCC模式对WPSH的预测技巧相对较低。三个模式预测的850 hPa风场在西北太平洋存在一个异常气旋,使得预测的EASM偏强和WPSH偏弱。同时,二者的年际变率整体比观测小。三个模式预测的EASM和WPSH对热带海洋海温异常的响应随季节演变特征与观测比较接近,但NCEP模式和TCC模式预测的EASM对前期热带太平洋和前期、同期热带印度洋的海温异常响应要强于观测,NCC模式预测的EASM对前期和同期的热带太平洋的海温异常响应明显比观测强。此外,三个模式预测的WPSH对前期和同期的热带太平洋、热带印度洋和热带大西洋的海温异常响应明显强于观测。三个模式预测的EASM和WPSH在ENSO年的平均绝对误差（MAE）整体而言要比正常年的小很多,NCEP模式和NCC模式预测的EASM和WPSH的MAE在La Ni?a年和El Ni?o年差别不大,而TCC模式预测的EASM和WPSH的MAE在El Ni?o年比在La Ni?a年大很多,表明ENSO事件是东亚夏季环流重要的可预报源。     

Progress in Predictability Studies in China （2003-2006）

Since the last International Union of Geodesy and Geophysics General Assembly(2003),predictability studies in China have made significant progress.For dynamic forecasts,two novel approaches of conditional nonlinear optimal perturbation and nonlinear local Lyapunov exponents were proposed to cope with the predictability problems of weather and climate,which are superior to the corresponding linear theory.A possible mechanism for the"spring predictability barrier"phenomenon for the El Ni(?)o-Southern Oscillation (ENSO)was provided based on a theoretical model.To improve the forecast skill of an intermediate coupled ENSO model,a new initialization scheme was developed,and its applicability was illustrated by hindcast experiments.Using the reconstruction phase space theory and the spatio-temporal series predictive method, Chinese scientists also proposed a new approach to improve dynamical extended range(monthly)prediction and successfully applied it to the monthly-scale predictability of short-term climate variations.In statistical forecasts,it was found that the effects of sea surface temperature on precipitation in China have obvious spatial and temporal distribution features,and that summer precipitation patterns over east China are closely related to the northern atmospheric circulation.For ensemble forecasts,a new initial perturbation method was used to forecast heavy rain in Guangdong and Fujian Provinces on 8 June 1998.Additionally, the ensemble forecast approach was also used for the prediction of a tropical typhoons.A new downscaling model consisting of dynamical and statistical methods was provided to improve the prediction of the monthly mean precipitation.This new downsealing model showed a relatively higher score than the issued operational forecast.     

The influence of oceanic conditions on the hot European summer of 2003

The summer of 2003 was the hottest on record throughout much of Europe. Understanding how the event developed and the factors that contributed to it may help us improve seasonal forecasting models and assess the risk of such events in the future. This study uses atmosphere-only model integrations and observed data to investigate the potential predictability of the climate anomalies, and in particular the impact that the warming in the Indian Ocean and Mediterranean Sea had on the development of the temperature anomalies. The model results suggest that the temperature anomalies were potentially predictable and that both Indian Ocean and Mediterranean sea surface temperature anomalies contributed to the development of the observed warm and dry anomalies over Europe. Furthermore, it was found that, in the model, the Mediterranean anomalies contributed most strongly to the warming in June and July and the Indian Ocean anomalies enabled the positive temperature anomalies to persist into August. Previously published work has described the role of the Indian monsoon in modulating the seasonal cycle in rainfall over Europe. Comparison with this work suggests a mechanism by which warming in the Indian Ocean may have contributed to the persistence of the temperature and precipitation anomalies into August.