中国冬季积雪特征及欧亚大陆积雪对中国气候影响

该文首先回顾了有关中国冬季积雪的研究进展,包括中国冬季积雪的空间分布气候特征以及季节、年际和年代际变化,中国冬季降雪特征,气象因子对中国冬季积雪水量平衡的影响,外强迫和大气环流系统在积雪形成中的作用等。冬春季欧亚大陆积雪对同期和后期中国气候影响的相关研究说明与欧亚大陆积雪异常相关联的中国气候异常以及积雪通过改变土壤湿度、表面温度和辐射分布,引起大气环流异常,进而对中国气候产生影响的物理过程。应用美国环境预测中心 (NCEP) 第2版气候预测系统 (CFSv2) 的回报试验结果,对CFSv2在欧亚大陆积雪变化及其与中国气候关系的可预报性方面的分析表明,CFSv2能够较好地回报出春季欧亚积雪的年际和年代际变异及其与中国夏季降水之间的联系。文章最后提出了在积雪及其气候效应研究方面一些有待解决的问题。     

Relative contribution of soil moisture and snow mass to seasonal climate predictability: a pilot study

Land surface hydrology (LSH) is a potential source of long-range atmospheric predictability that has received less attention than sea surface temperature (SST). In this study, we carry out ensemble atmospheric simulations driven by observed or climatological SST in which the LSH is either interactive or nudged towards a global monthly re-analysis. The main objective is to evaluate the impact of soil moisture or snow mass anomalies on seasonal climate variability and predictability over the 1986–1995 period. We first analyse the annual cycle of zonal mean potential (perfect model approach) and effective (simulated vs. observed climate) predictability in order to identify the seasons and latitudes where land surface initialization is potentially relevant. Results highlight the influence of soil moisture boundary conditions in the summer mid-latitudes and the role of snow boundary conditions in the northern high latitudes. Then, we focus on the Eurasian continent and we contrast seasons with opposite land surface anomalies. In addition to the nudged experiments, we conduct ensembles of seasonal hindcasts in which the relaxation is switched off at the end of spring or winter in order to evaluate the impact of soil moisture or snow mass initialization. LSH appears as an effective source of surface air temperature and precipitation predictability over Eurasia (as well as North America), at least as important as SST in spring and summer. Cloud feedbacks and large-scale dynamics contribute to amplify the regional temperature response, which is however, mainly found at the lowest model levels and only represents a small fraction of the observed variability in the upper troposphere.     

Interpreting observed northern hemisphere snow trends with large ensembles of climate simulations

Simulated variability and trends in Northern Hemisphere seasonal snow cover are analyzed in large ensembles of climate integrations of the National Center for Atmospheric Research’s Community Earth System Model. Two 40-member ensembles driven by historical radiative forcings are generated, one coupled to a dynamical ocean and the other driven by observed sea surface temperatures (SSTs) over the period 1981–2010. The simulations reproduce many aspects of the observed climatology and variability of snow cover extent as characterized by the NOAA snow chart climate data record. Major features of the simulated snow water equivalent (SWE) also agree with observations (GlobSnow Northern Hemisphere SWE data record), although with a lesser degree of fidelity. Ensemble spread in the climate response quantifies the impact of natural climate variability in the presence and absence of coupling to the ocean. Both coupled and uncoupled ensembles indicate an overall decrease in springtime snow cover that is consistent with observations, although springtime trends in most climate realizations are weaker than observed. In the coupled ensemble, a tendency towards excessive warming in wintertime leads to a strong wintertime snow cover loss that is not found in observations. The wintertime warming bias and snow cover reduction trends are reduced in the uncoupled ensemble with observed SSTs. Natural climate variability generates widely different regional patterns of snow trends across realizations; these patterns are related in an intuitive way to temperature, precipitation and circulation trends in individual realizations. In particular, regional snow loss over North America in individual realizations is strongly influenced by North Pacific SST trends (manifested as Pacific Decadal Oscillation variability) and by sea level pressure trends in the North Pacific/North Atlantic sectors.     

Interdecadal variability of the ENSO teleconnection to the wintertime North Pacific

The El Niño/Southern Oscillation (ENSO) strongly influences the large-scale atmospheric circulation over the extratropical North Pacific during boreal winter, which has an important impact on North American winter climate. This study analyses the interdecadal variability of the ENSO teleconnection to the wintertime extratropical North Pacific, over the period 1900–2010, using a range of observationally derived datasets and an ensemble of atmospheric model simulations. The observed teleconnection strength is found to vary substantially over the 20th century. Specifically, 31-year periods in the early-century (1912–1942), mid-century (1946–1976) and the late-century (1980–2010) are identified in the observations when the ENSO teleconnection to the North Pacific circulation are found to be particularly strong, weak and strong respectively. The ENSO teleconnection to the North Pacific in the atmospheric model ensemble is weak in the mid-century period and substantially stronger in the late-century, closely following the variability in the observed ENSO-North Pacific teleconnection. In the early-century, however, the atmospheric model also exhibits a weak teleconnection to the North Pacific, unlike in observations. In a subset of the model realisations that exhibit similar ENSO-North Pacific teleconnection as in observations during the early-century period there are large differences in extratropical circulation but not in equatorial Pacific precipitation anomalies, in contrast to the late-century period. This suggests that the high correlation in the early century period is largely due to internal extratropical variability. The important implications of these results for seasonal predictability and the assessment of seasonal forecasting systems are discussed.

     

欧亚大陆积雪对我国春季气候可预报性的影响

利用大气环流模式IAP9L_CoLM,通过两组集合后报试验,考察了欧亚大陆积雪对我国春季气候可预报性的影响。一组试验为常规后报试验,积雪是由模式陆面过程预报得到的,另一组试验为积雪试验,模式积分过程中欧亚大陆雪水当量由微波遥感积雪资料替代,一天替换一次。通过分析两组试验后报结果的差异,来考察欧亚大陆积雪对我国春季（3~5月）气候可预报性的影响。分析表明:欧亚大陆积雪模拟水平的改善能提高春季欧亚大陆中高纬环流场（海平面气压场和中、高层位势高度场）的可预报性,模式对我国春季气温异常的年际变化和空间分布的可预报能力也有显著增强。对我国春季降水,虽然预报技巧较低,但引入较真实的欧亚积雪作用后,由于中高纬环流场预报技巧的改进导致降水的预测能力也有所改进。个例分析也表明,欧亚中高纬春季积雪异常模拟水平的改善导致了欧亚中高纬贝加尔湖及以南区域环流场可预报性的提高,最终使中国东部区域春季气候异常模拟技巧得以改善。以上结果也证实,欧亚大陆积雪是影响东亚区域春季气候的一个重要因子,要提高模式对中国春季气候的预报技巧,积雪模拟水平的改进是非常必要的。     

Assessing the Impacts of Eurasian Snow Conditions on Climate Predictability with a Global Climate Model

On the basis of two ensemble experiments conducted by a general atmospheric circulation model (Institute of Atmospheric Physics nine-level atmospheric general circulation model coupled with land surface model, hereinafter referred to as IAP9L_CoLM), the impacts of realistic Eurasian snow conditions on summer climate predictability were investigated. The predictive skill of sea level pressures (SLP) and middle and upper tropospheric geopotential heights at mid-high latitudes of Eurasia was enhanced when improved Eurasian snow conditions were introduced into the model. Furthermore, the model skill in reproducing the interannual variation and spatial distribution of the surface air temperature (SAT) anomalies over China was improved by applying realistic (prescribed) Eurasian snow conditions. The predictive skill of the summer precipitation in China was low; however, when realistic snow conditions were employed, the predictability increased, illustrating the effectiveness of the application of realistic Eurasian snow conditions. Overall, the results of the present study suggested that Eurasian snow conditions have a significant effect on dynamical seasonal prediction in China. When Eurasian snow conditions in the global climate model (GCM) can be more realistically represented, the predictability of summer climate over China increases.     

BCC_CSM1.1m对冬季典型环流系统的预测评估

基于国家气候中心气候系统模式1.1版本（BCC_CSM1.1m）的历史回报数据,利用时间相关系数和均方根误差等确定性技巧评分,对西伯利亚高压、阿留申低压、东亚冬季风3种东亚地区冬季典型环流系统的预报技巧进行检验评估,并通过时间序列分析和空间相关系数等方法,分析东亚地区冬季典型环流系统的可预报性来源。结果表明:由于模式对热带海洋和北太平洋海平面气压的预测偏差小、对欧亚大陆的预测偏差大,模式对阿留申低压、东亚冬季风的预测技巧高于西伯利亚高压。进一步分析表明:厄尔尼诺和南方涛动（ENSO）是阿留申低压和东亚冬季风的重要可预报性来源,而土壤温度是西伯利亚高压的重要可预报性来源,并受ENSO调制。此外,东亚冬季风的预报技巧也受到西伯利亚高压预报技巧的制约。     

Impact of snow initialization on sub-seasonal forecasts

The influence of the snowpack on wintertime atmospheric teleconnections has received renewed attention in recent years, partially for its potential impact on seasonal predictability. Many observational and model studies have indicated that the autumn Eurasian snow cover in particular, influences circulation patterns over the North Pacific and North Atlantic. We have performed a suite of coupled atmosphere-ocean simulations with the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecast system to investigate the impact of accurate snow initialisation. Pairs of 2-month ensemble forecasts were started every 15 days from the 15th of October through the 1st of December in the years 2004–2009, with either realistic initialization of snow variables based on re-analyses, or else with “scrambled” snow initial conditions from an alternate autumn date and year. Initially, in the first 15 days, the presence of a thicker snowpack cools surface temperature over the continental land masses of Eurasia and North America. At a longer lead of 30-day, it causes a warming over the Arctic and the high latitudes of Eurasia due to an intensification and westward expansion of the Siberian High. It also causes a cooling over the mid-latitudes of Eurasia, and lowers sea level pressures over the Arctic. This “warm Arctic—cold continent” difference means that the forecasts of near-surface temperature with the more realistic snow initialization are in closer agreement with re-analyses, reducing a cold model bias over the Arctic and a warm model bias over mid-latitudes. The impact of realistic snow initialization upon the forecast skill in snow depth and near-surface temperature is estimated for various lead times. Following a modest skill improvement in the first 15 days over snow-covered land, we also find a forecast skill improvement up to the 30-day lead time over parts of the Arctic and the Northern Pacific, which can be attributed to the realistic snow initialization over the land masses.     

Snow contribution to springtime atmospheric predictability over the second half of the twentieth century

A set of global atmospheric simulations has been performed with the ARPEGE-Climat model in order to quantify the contribution of realistic snow conditions to seasonal atmospheric predictability in addition to that of a perfect sea surface temperature (SST) forcing. The focus is on the springtime boreal hemisphere where the combination of a significant snow cover variability and an increasing solar radiation favour the potential snow influence on the surface energy budget. The study covers the whole 1950?C2000 period through the use of an original snow mass reanalysis based on an off-line land surface model and possibly constrained by satellite snow cover observations. Two ensembles of 10-member AMIP-type experiments have been first performed with relaxed versus free snow boundary conditions. The nudging towards the monthly snow mass reanalysis significantly improves both potential and actual predictability of springtime surface air temperature over Central Europe and North America. Yet, the impact is confined to the lower troposphere and there is no clear improvement in the predictability of the large-scale atmospheric circulation. Further constraining the prescribed snow boundary conditions with satellite observations does not change much the results. Finally, using the snow reanalysis only for initializing the model on March 1st also leads to a positive impact on predicted low-level temperatures but with a weaker amplitude and persistence. A conditional skill approach as well as some selected case studies provide some guidelines for interpreting these results and suggest that an underestimated snow cover variability and a misrepresentation of ENSO teleconnections may hamper the benefit of an improved snow initialization in the ARPEGE-Climat model.     

基于IAP AGCM4的欧亚大陆春季雪水当量可预报性评估

欧亚大陆积雪是重要的气候预测因子,评估其在气候模式中的预测潜力可为季节气候预测和模式发展提供重要参考。本文利用IAP AGCM4的多年集合后报结果,分析了欧亚大陆春季雪水当量的可预报性。结果表明该模式对提前1月后报的欧亚大陆春季雪水当量的空间分布,主要模态及变化趋势具有较好的可预报能力。此外模式对欧亚中高纬积雪的年际异常也具有较高的预报技巧,特别是高纬度区域。可预报性来源分析则表明,大气初始异常对欧亚中高纬积雪可预报性的影响与海温异常相比显得更为重要。     

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.     

Probabilistic prediction of Indian summer monsoon rainfall using global climate models

Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982–2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low.     

北半球雪盖的气候特征及与印度季风降水的关系

利用卫星观测的1966年11月－2000年12月北半球雪盖资料，研究了北半球、欧亚、北美和青藏高原雪盖的气候学特征及其变化趋势。通过对雪盖与印度季风的分析，得出：（1）欧亚冬季（12月－翌年3月）雪盖面积与印度季风降水（6－9月）呈反相关，并指出印度季风降水不仅受欧亚雪盖的影响，可能与暖水年有一定的联系。（2）青藏高原10、11月雪盖面积与次年印度季风爆发及降水关系较好，并提出可能的影响机制。     

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.     

Seasonal prediction of African precipitation with ECHAM4–T42 ensemble simulations using a multivariate MOS re-calibration scheme

A 15 member ensemble of 20th century simulations using the ECHAM4–T42 atmospheric GCM is utilized to investigate the potential predictability of interannual variations of seasonal rainfall over Africa. Common boundary conditions are the global sea surface temperatures (SST) and sea ice extent. A canonical correlation analysis (CCA) between observed and ensemble mean ECHAM4 precipitation over Africa is applied in order to identify the most predictable anomaly patterns of precipitation and the related SST anomalies. The CCA is then used to formulate a re-calibration approach similar to model output statistics (MOS) and to derive precipitation forecasts over Africa. Predictand is the climate research unit (CRU) gridded precipitation over Africa. As predictor we use observed SST anomalies, ensemble mean precipitation over Africa and a combined vector of mean sea level pressure, streamfunction and velocity potential at 850 hPa. The different forecast approaches are compared. Most skill for African precipitation forecasts is provided by tropical Atlantic (Gulf of Guinea) SST anomalies which mainly affect rainfall over the Guinean coast and Sahel. The El Niño/Southern Oscillation (ENSO) influences southern and East Africa, however with a lower skill. Indian Ocean SST anomalies, partly independent from ENSO, have an impact particularly on East Africa. As suggested by the large agreement between the simulated and observed precipitation, the ECHAM4 rainfall provides a skillful predictor for CRU precipitation over Africa. However, MOS re-calibration is needed in order to provide skillful forecasts. Forecasts using MOS re-calibrated model precipitation are at least as skillful as forecast using dynamical variables from the model or instantaneous SST. In many cases, MOS re-calibrated precipitation forecasts provide more skill. However, differences are not systematic for all regions and seasons, and often small.     

东亚夏季风次季节变化研究进展

东亚夏季风次季节（10~90 d）变化是中国夏季持续性强降水、高温热浪等高影响天气事件的重要环流载体,处于天气预报上限和气候季节预测下限之间的预报过渡区。研究表明:东亚夏季风次季节变化是东亚夏季风的固有物理特征,它和季节进程之间的时间锁相关系是东亚夏季风次季节变化潜在可预报性的重要来源。东亚夏季风次季节变化与Madden-Julian振荡（MJO）存在显著差异,试图通过MJO来预测东亚夏季风次季节变化的不确定性较大。东亚夏季风次季节预测的另一重要来源是下垫面外强迫,包括欧亚大陆春季积雪、中国东部春季土壤湿度和厄尔尼诺-南方涛动（ENSO）事件。此外,去趋势偏-交叉相关分析统计方法能够分析东亚夏季风多因子和多时间尺度问题。目前,亟需解决的科学问题包括:东亚夏季风次季节模态的客观定量描述、造成东亚夏季风次季节模态年际变化的关键物理过程、不同外强迫因子对东亚夏季风次季节模态的共同影响。     

Improvement of seasonal forecasts with inclusion of tropical instability waves on initial conditions

Positive impacts of tropical instability waves (TIWs) in initial conditions on seasonal forecasts are investigated using a air-sea coupled GCM. Due to coarse observational networks and deficiencies in widely-used initialization methods (e.g. 3DVAR or OI methods), TIW variability in oceanic initial conditions is excessively suppressed. It ruins the interaction between TIWs and climate states, therefore, degrades the climate forecast skills. To settle this problem, TIW patterns obtained from free integration is added to the spatially-smoothed initial conditions to simulate realistic seasonal TIW variability (TIWV). Through 20-year ensemble forecast experiments, it is shown that seasonal TIWV with TIWs-seeded initial conditions is significantly stronger until 2-month lead time. In addition, enhanced TIWV amplifies nonlinear relationship between TIWs and ENSO, which leads realistic simulation of the El Ni?o-La Ni?a asymmetry. As a result of better ENSO simulation, correlation improvement of simulated NINO3 index with TIWs-seeded initial conditions is over 0.1 at 4-month lead time.     

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.     

An empirical model of decadal ENSO variability

This paper assesses potential predictability of decadal variations in the El Ni?o/Southern Oscillation (ENSO) characteristics by constructing and performing simulations using an empirical nonlinear stochastic model of an ENSO index. The model employs decomposition of global sea-surface temperature (SST) anomalies into the modes that maximize the ratio of interdecadal-to-subdecadal SST variance to define low-frequency predictors called the canonical variates (CVs). When the whole available SST time series is so processed, the leading canonical variate (CV-1) is found to be well correlated with the area-averaged SST time series which exhibits a non-uniform warming trend, while the next two (CV-2 and CV-3) describe secular variability arguably associated with a combination of Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO) signals. The corresponding ENSO model that uses either all three (CVs 1–3) or only AMO/PDO-related (CVs 2 and 3) predictors captures well the observed autocorrelation function, probability density function, seasonal dependence of ENSO, and, most importantly, the observed interdecadal modulation of ENSO variance. The latter modulation, and its dependence on CVs, is shown to be inconsistent with the null hypothesis of random decadal ENSO variations simulated by multivariate linear inverse models. Cross-validated hindcasts of ENSO variance suggest a potential useful skill at decadal lead times. These findings thus argue that decadal modulations of ENSO variability may be predictable subject to our ability to forecast AMO/PDO-type climate modes; the latter forecasts may need to be based on simulations of dynamical models, rather than on a purely statistical scheme as in the present paper.     

南京信息工程大学气候预测系统1.0版简介

南京信息工程大学气候预测系统1.0版(NUIST CFS1.0)是基于日本海洋科学技术开发机构(JAMSTEC)的SINTEX-F模式发展而来,可以实现对全球气候异常的季节-年际预测。对过去近40 a的集合历史回报预测试验结果的评估发现,该预测系统对热带太平洋和印度洋海温异常具有良好的预测技巧,并且该系统能提前1.5~2 a对ENSO(Nino3.4指数)做出有技巧的预测(即相关系数达0.5),同时也可以提前1~2个季节对印度洋偶极子(IOD)做出有较高技巧的预测,展现了对主要热带气候信号的良好预测技巧。但是与国内外所有动力模式预测系统类似,该系统对东亚地区的气候异常预测还存在较大的不足。考虑到ENSO对东亚地区气候异常的强烈影响,本文尝试去除与ENSO预测相关的系统偏差来初步订正东亚地区夏季温度异常和降水距平百分率的预测结果。对比订正前后的结果表明,这一简单的订正方法有助于提高我国气候异常的预测准确率。同时选取2019年夏季气温异常和降水距平百分率的实时预测结果作为个例进行分析,发现订正能够提供一定的技巧改善,但与观测结果相比仍存在较大偏差,需要在今后的工作中不断改进完善。此外,本文也初步评估了NUIST CFS1.0对我国冬春季的气候预测技巧,并提供了经简单订正后的2019/2020年冬季和2020年春季的实时预测结果。