一个基于耦合气候系统模式的气候预测系统的研制

基于美国通用气候系统模式CCSM4和自行设计的一套初始化方案,建立了一个全球气候预测系统(PCCSM4),并使用该预测系统对夏季气候进行了30年(1981~2010)系统性的超前一个月的集合回报试验.回报结果表明,PCCSM4基本可以把握观测中夏季(JJA)平均海表面温度(SST)、海平面气压(SLP)和降水的主要分布特征;PCCSM4对SST,尤其是赤道中东太平洋关键区SST具有较高的回报能力,30年的相关系数最高可达0.7;PCCSM4对500 hPa位势高度场、850hPa纬向风场和海平面气压场的回报性能高于降水;总的来看,热带地区的可预测性高于全球,更高于东亚地区;PCCSM4对于典型ENSO年的夏季气候和亚洲夏季风的年际变化具有较好的回报能力,经过进一步的检验和完善可以应用于全球和我国短期气候预测业务.     

应用九层全球大气格点模式进行跨季度短期气候预测系统性试验

利用中国科学院大气物理研究所9层大气环流模式（IAP9L AGCM）对夏季气候进行了30年（ 1970～1999年）集合回报试验,并采用统计学分析方法对跨季度夏季短期气候的可预测性问 题进行了初步探讨. 结果表明,该模式对对流层中、高层大气环流的预测能力强于低层,位 势高度场和表面气温的可预测性最大,而降水的可预测性则相对较小. 对流层中、高层位势 高度场的可预测性基本呈带状分布,越靠近赤道可预测性越高;而降水的可预测性基本局限 于赤道东太平洋及热带个别区域. 由此可见,降水的预测极为困难和复杂,订正系 统的研究和寻找新的预报物理因子非常重要.      

动力相似预报的策略和方法研究

为了在现有模式和资料条件下有效提高数值预报水平, 深入开展了动力相似预报(DAP)的策略和方法研究. 提出“利用历史相似信息对模式预报误差进行预报”的新思路, 从而将动力预报问题转化为预报误差的估计问题, 并发展了一种基于相似误差订正的预测新方法(FACE). 进一步将FACE应用于业务海气耦合模式的跨季度预测试验, 夏季平均环流和降水的预测结果表 明, FACE能在一定程度上减小预报误差、恢复预报方差、提高预报技巧. 此外, 敏感性试验显示, 相似的个数、选取变量和度量标准都对FACE预报有显著影响.     

1998年夏季全球大气环流异常的预测研究

应用日本东京大学气候系统研究中心(CCSR)发展起来的一个全球大气环流谱模式(T42L200版本),对l998年夏季气候异常和大气环流的预测问题进行了研究,定量地检查了该模式对夏季降水和大气环流异常的预测准确度.说明该模式对1998年的预测水平是比较高的;并证实大气环流在春季的初始异常对北半球夏季大气环流和降水异常起了很重要的作用,而对南半球的作用则小得多.就中国长江流域1998年的降水异常而言,初始环流的作用约占50%.     

未来百年东亚夏季降水和季风预测的研究

利用最新一代气候模式结果对政府间气候变化委员会0PCC）SRESA1B情景（中等排放情景）下的东亚夏季降水和季风环流未来演变特征进行了预测．结果表明,东亚地区的降水在未来将会增加,在21世纪40年代末（2040s年代末）出现阶段性变化,在此之前降水的增加量较小（～1％）,并有较明显的振荡特征,而在2040s年代末之后降水明显增加（～9％）,中国东部地区进入全面的多雨期．这种变化以华北最为明显,华南和长江中下游地区次之．而气候模式对未来中国东部夏季降水型预测的EOF分析表明,未来百年中国东部的雨型将以多雨型为主,相应的时间系数在2040s年代末后进入正位相的高值期,而其它降水型的方差贡献较小,无明显变化趋势．相应,未来东亚地区的夏季风环流将会加强,在低层这主要是由于西北太平洋地区的副热带反气旋西北侧西南气流加强的结果;而在高层主要是由于南亚上空异常反气旋东侧东北气流加强的结果．这一季风环流的加强在中国东部也呈现出阶段性的变化特征,在2040s年代末之后东亚夏季风得到全面加强．同时,未来东亚大气中的水汽含量将会逐渐增加,进入中国东部地区的西南水汽输送在2040s年代末也出现阶段性的增强．这说明,在全球气候变化的背景下,东亚地区的水循环和环流场对全球变暖的响应基本一致,即降水和水汽的增加对应着季风环流的加强,降水的变化是气候变暖条件下动力和热力学因子共同作用的结果．     

外强迫因子变化在2003年夏季旱涝预测中的作用

利用中国科学院大气物理研究所大气科学和地球流体力学数值模拟实验室研发的全球海洋－大气－陆面过程气候系统耦合模式（IAP/LASG GOALS 40），对比分析了考虑和不考虑气候的外强迫因子（太阳活动、温室气体及硫酸盐气溶胶）变化对2003年夏季中国区域的短期气候预测的影响.结果发现，由于外强迫因子变化的影响，模式模拟的中国区域2003年夏季降水距平的分布比不考虑这种变化时更接近实况，它有效地改善了无外强迫变化时模式模拟预测的中国区域降水不真实偏大的缺点，使一些地区的模拟降水量值减小，范围扩大，位置北抬.更重要的是，由于考虑了外强迫的变化，GOALS耦合模式很好地模拟出了2003年夏季淮河流域较大的降水正距平区，同时相应的500 hPa环流场的模拟也有较大的改进.     

基于年代际突变分量的东亚夏季降水动力-统计预报方案研究

基于1983~2011年CMAP降水数据分析,揭示东亚及其近海地区(简称东亚)的夏季降水距平在1999年前后由"+-+"型分布(由北至南)调整为"-+-"型分布,且这种年代际变化主要体现在EOF第3模态中;而在国家气候中心海气耦合模式(BCC_CGCM)回报和预报资料中,1999年前后则由"+-+-"分布型(由北至南)调整为"-+-+"型,即BCC_CGCM对降水年代际分布的预报与实况有较大差别.同时,结合EOF年代际突变分量给出了全球海温场与东亚夏季降水年代际变化有显著联系的关键海区,分析了BCC_CGCM对关键区海温的预报能力及对应的东亚夏季降水预报效果的差异,验证了基于关键区海温指数对改进模式预报结果的可能性.在此基础上,给出了基于年代际突变分量的东亚夏季降水动力-统计预报方案研究,并进行独立样本回报.结果表明,基于V区海温指数订正结果的距平相关系数(ACC)达到了0.25,距平符号一致率(ACR)达到61%,7个区域订正结果平均的ACC为0.03和ACR为51%,较BCC_CGCM模式结果的-0.01和49%均有一定的改进;且订正后1999年前后两个时段降水距平的空间分布和纬圈平均演变均体现了降水型由"+-+"向"-+-"调整的特征.因此,基于海温关键区指数的动力-统计相结合的预报方案能够增加模式对东亚夏季降水的季节预报结果中的年代际变化信息.     

如何提高天气预报和气候预测的技巧？

从理论上探讨如何提高天气预报和气候预测的技巧.气候包括以小时为基本单位的昼夜循环、以日为基本单位的年(季节)循环、年代际循环和世纪循环等时间尺度的变化.这些气候变化存在确定的外强迫,是可以被认识和预报的.相对气候昼夜循环和年(季节)循环的偏差是天气尺度扰动.天气尺度的瞬变大气扰动可引发极端天气事件.有技巧的天气预报正是要通过天气尺度大气扰动信号,提前几天甚至十几天,预报出极端天气事件的发生.相对气候年代际和世纪循环的偏差是气候异常,有技巧的气候预测正是要预报出这种异常.距平天气图会大大提高短期和中期—延伸期天气预报的技巧,距平数值预报模式的研制也会加快提高中期—延伸期天气预报和气候预测的技巧.     

年代际预测和耦合资料同化研究综述

年代际预测是近年来气候变化研究的一个迅速发展的新兴热点领域,其首要步骤是进行初始化,目的是为年代际预测提供包含观测变率信息的初值.发展效果好且省时的初始化方法是年代际预测的重大挑战之一,目前国际上主流的初始化方法是耦合资料同化,即在耦合模式框架下进行同化.在年代际预测时,由于模式偏差和初始化方法性能的限制会产生初始冲击问题.目前国际上的各模式机构普遍对北大西洋、热带东西太平洋和印度洋海表温度的年代际预测水平高,而对全球平均近地面气温和北太平洋海表温度的年代际预测水平相对较差.本文主要从初始化方法和年代际预测这两方面的研究现状进行全面回顾,指出存在的问题并讨论未来的发展趋势和挑战.     

初值协调性对模式数值积分结果的影响

利用国家气候中心新一代全球大气环流模式BCC_AGCM2.0.1,考虑了初值协调性对模式数值积分结果的影响,进行了两组数值回报试验(简称S1,S2),对27年(1980～2006年)的夏季基本气候态进行了对比分析,并考察了该模式对夏季气候的回报技巧。使用交叉检验的方法,计算了对模式结果的评估参数值,包括时间和空间距平相关系数,对该模式性能进行了评估和检验。结果表明,BCC_AGCM2.0.1对季节尺度的大气环流场具有良好的模拟性能,模式基本上再现了观测位势高度场、温度场、流场的分布特征以及大尺度降水分布特征。500 hPa位势高度、温度空间距平相关系数对比表明,平均而言,500 hPa位势高度、温度的空间距平相关性,热带区域(30°S～30°N)高于东亚区域(0°～60°N,60°E～150°E)和全球区域。回报与观测的降水距平百分率相关系数分布对比表明,试验S2在我国江淮地区及南方地区的回报技巧要明显优于S1。     

Statistical downscaling of extreme daily precipitation,evaporation, and temperature and construction of future scenarios

Generally, the statistical downscaling approaches work less perfectly in reproducing precipitation than temperatures, particularly for the extreme precipitation. This article aimed to testify the capability in downscaling the extreme temperature, evaporation, and precipitation in South China using the statistical downscaling method. Meanwhile, the linkages between the underlying driving forces and the incompetent skills in downscaling precipitation extremes over South China need to be extensively addressed. Toward this end, a statistical downscaling model (SDSM) was built up to construct future scenarios of extreme daily temperature, pan evaporation, and precipitation. The model was thereafter applied to project climate extremes in the Dongjiang River basin in the 21st century from the HadCM3 (Hadley Centre Coupled Model version 3) model under A2 and B2 emission scenarios. The results showed that: (1) The SDSM generally performed fairly well in reproducing the extreme temperature. For the extreme precipitation, the performance of the model was less satisfactory than temperature and evaporation. (2) Both A2 and B2 scenarios projected increases in temperature extremes in all seasons; however, the projections of change in precipitation and evaporation extremes were not consistent with temperature extremes. (3) Skills of SDSM to reproduce the extreme precipitation were very limited. This was partly due to the high randomicity and nonlinearity dominated in extreme precipitation process over the Dongjiang River basin. In pre‐flood seasons (April to June), the mixing of the dry and cold air originated from northern China and the moist warm air releases excessive rainstorms to this basin, while in post‐flood seasons (July to October), the intensive rainstorms are triggered by the tropical system dominated in South China. These unique characteristics collectively account for the incompetent skills of SDSM in reproducing precipitation extremes in South China. Copyright © 2011 John Wiley & Sons, Ltd.     

Forecasting the summer rainfall in North China using the year-to-year increment approach

A new approach to forecasting the year-to-year increment of rainfall in North China in July–August (JA) is proposed. DY is defined as the difference of a variable between the current year and the preceding year (year-to-year increment). NR denotes the seasonal mean precipitation rate over North China in JA. After analyzing the atmospheric circulation anomalies associated with the DY of NR, five key predictors for the DY of NR have been identified. The prediction model for the DY of NR is established by using multi-linear regression method and the NR is obtained (the current forecasted DY of NR added to the preceding observed NR). The prediction model shows a high correlation coefficient (0.8) between the simulated and the observed DY of NR throughout period 1965–1999, with an average relative root mean square error of 19% for the percentage of precipitation rate anomaly over North China. The prediction model makes a hindcast for 2000–2007, with an average relative root mean square error of 21% for the percentage of precipitation rate anomaly over North China. The model reproduces the downward trend of the percentage of precipitation rate anomaly over North China during 1965–2006. Because the current operational prediction models of the summer precipitation have average forecast scores of 60%–70%, it has been more difficult to forecast the summer rainfall over North China. Thus this new approach for predicting the year-to-year increment of the summer precipitation (and hence the summer precipitation itself) has the potential to significantly improve operational forecasting skill for summer precipitation. Supported by National Basic Research Program of China (Grant No. 2009CB421406), National Natural Science Foundation of China (Grant Nos. 40631005, 40775049) and Excellent Ph. D Dissertation in Chinese Academy of Sciences     

Downscaling of precipitation for climate change scenarios: A support vector machine approach

The Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, general circulation models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling of precipitation at monthly time scale. The effectiveness of this approach is illustrated through its application to meteorological sub-divisions (MSDs) in India. First, climate variables affecting spatio-temporal variation of precipitation at each MSD in India are identified. Following this, the data pertaining to the identified climate variables (predictors) at each MSD are classified using cluster analysis to form two groups, representing wet and dry seasons. For each MSD, SVM- based downscaling model (DM) is developed for season(s) with significant rainfall using principal components extracted from the predictors as input and the contemporaneous precipitation observed at the MSD as an output. The proposed DM is shown to be superior to conventional downscaling using multi-layer back-propagation artificial neural networks. Subsequently, the SVM-based DM is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to obtain future projections of precipitation for the MSDs. The results are then analyzed to assess the impact of climate change on precipitation over India. It is shown that SVMs provide a promising alternative to conventional artificial neural networks for statistical downscaling, and are suitable for conducting climate impact studies.     

Statistical downscaling of extremes of precipitation and temperature and construction of their future scenarios in an elevated and cold zone

Reliable projections of extremes at finer spatial scales are important in assessing the potential impacts of climate change on societal and natural systems, particularly for elevated and cold regions in the Tibetan Plateau. This paper presents future projections of extremes of daily precipitation and temperature, under different future scenarios in the headwater catchment of Yellow River basin over the 21st century, using the statistical downscaling model (SDSM). The results indicate that: (1) although the mean temperature was simulated perfectly, followed by monthly pan evaporation, the skill scores in simulating extreme indices of precipitation are inadequate; (2) The inter-annual variabilities for most extreme indices were underestimated, although the model could reproduce the extreme temperatures well. In fact, the simulation of extreme indices for precipitation and evaporation were not satisfactory in many cases. (3) In future period from 2011 to 2100, increases in the temperature and evaporation indices are projected under a range of climate scenarios, although decreasing mean and maximum precipitation are found in summer during 2020s. The findings of this work will contribute toward a better understanding of future climate changes for this unique region.     

Design and testing of a global climate prediction system based on a coupled climate model

A global climate prediction system(PCCSM4) was developed based on the Community Climate System Model, version 4.0, developed by the National Center for Atmospheric Research(NCAR), and an initialization scheme was designed by our group. Thirty-year(1981–2010) one-month-lead retrospective summer climate ensemble predictions were carried out and analyzed. The results showed that PCCSM4 can efficiently capture the main characteristics of JJA mean sea surface temperature(SST), sea level pressure(SLP), and precipitation. The prediction skill for SST is high, especially over the central and eastern Pacific where the influence of El Ni?o-Southern Oscillation(ENSO) is dominant. Temporal correlation coefficients between the predicted Ni?o3.4 index and observed Ni?o3.4 index over the 30 years reach 0.7, exceeding the 99% statistical significance level. The prediction of 500-hPa geopotential height, 850-hPa zonal wind and SLP shows greater skill than for precipitation. Overall, the predictability in PCCSM4 is much higher in the tropics than in global terms, or over East Asia. Furthermore, PCCSM4 can simulate the summer climate in typical ENSO years and the interannual variability of the Asian summer monsoon well. These preliminary results suggest that PCCSM4 can be applied to real-time prediction after further testing and improvement.     

Intercomparison of multiple statistical downscaling methods: multi-criteria model selection for South Korea

A number of statistical downscaling methodologies have been introduced to bridge the gap in scale between outputs of climate models and climate information needed to assess potential impacts at local and regional scales. Four statistical downscaling methods [bias-correction/spatial disaggregation (BCSD), bias-correction/constructed analogue (BCCA), multivariate adaptive constructed analogs (MACA), and bias-correction/climate imprint (BCCI)] are applied to downscale the latest climate forecast system reanalysis (CFSR) data to stations for precipitation, maximum temperature, and minimum temperature over South Korea. All methods are calibrated with observational station data for 19 years from 1973 to 1991 and validated for the more recent 19-year period from 1992 to 2010. We construct a comprehensive suite of performance metrics to inter-compare methods, which is comprised of five criteria related to time-series, distribution, multi-day persistence, extremes, and spatial structure. Based on the performance metrics, we employ technique for order of preference by similarity to ideal solution (TOPSIS) and apply 10,000 different weighting combinations to the criteria of performance metrics to identify a robust statistical downscaling method and important criteria. The results show that MACA and BCSD have comparable skill in the time-series related criterion and BCSD outperforms other methods in distribution and extremes related criteria. In addition, MACA and BCCA, which incorporate spatial patterns, show higher skill in the multi-day persistence criterion for temperature, while BCSD shows the highest skill for precipitation. For the spatial structure related criterion, BCCA and MACA outperformed BCSD and BCCI. From the TOPSIS analysis, we found that MACA is the most robust method for all variables in South Korea, and BCCA and BCSD are the second for temperature and precipitation, respectively. We also found that the contribution of the multi-day persistence and spatial structure related criteria are crucial to ranking the skill of statistical downscaling methods.     

Semi-empirical prediction method for monthly precipitation prediction based on environmental factors and comparison with stochastic and machine learning models

ABSTRACT

Precipitation prediction is central in hydrology and water resources planning and management. This paper introduces a semi-empirical predictive model to predict monthly precipitation and compares its predictive skill with those of machine learning (ML) methods. The stochastic method presented herein estimates monthly precipitation with one-step-ahead prediction properties. The ML predictive skill of the algorithms is evaluated by predicting monthly precipitation relying on the statistical association between precipitation and environmental and topographic factors. The semi-empirical predictive model features non-negative matrix factorization (NMF) for investigating the influence of multiple predictor variables on precipitation. The semi-empirical predictive model’s parameters are optimized with the hybrid genetic algorithm (GA) and Levenberg-Marquardt algorithm (LM), or GALMA, yielding a validated model with high predictive skill. The methodologies are illustrated with data from Hubei Province, China, which comprise 27 meteorological station datasets from 1988–2017. The empirical results provide valuable insights for developing semi-empirical rainfall prediction models.     

Prediction of variability of precipitation in the Yangtze River Basin under the climate change conditions based on automated statistical downscaling

Many impact studies require climate change information at a finer resolution than that provided by general circulation models (GCMs). Therefore the outputs from GCMs have to be downscaled to obtain the finer resolution climate change scenarios. In this study, an automated statistical downscaling (ASD) regression-based approach is proposed for predicting the daily precipitation of 138 main meteorological stations in the Yangtze River basin for 2010–2099 by statistical downscaling of the outputs of general circulation model (HadCM3) under A2 and B2 scenarios. After that, the spatial–temporal changes of the amount and the extremes of predicted precipitation in the Yangtze River basin are investigated by Mann–Kendall trend test and spatial interpolation. The results showed that: (1) the amount and the change pattern of precipitation could be reasonably simulated by ASD; (2) the predicted annual precipitation will decrease in all sub-catchments during 2020s, while increase in all sub-catchments of the Yangtze River Basin during 2050s and during 2080s, respectively, under A2 scenario. However, they have mix-trend in each sub-catchment of Yangtze River basin during 2020s, but increase in all sub-catchments during 2050s and 2080s, except for Hanjiang River region during 2080s, as far as B2 scenario is concerned; and (3) the significant increasing trend of the precipitation intensity and maximum precipitation are mainly occurred in the northwest upper part and the middle part of the Yangtze River basin for the whole year and summer under both climate change scenarios and the middle of 2040–2060 can be regarded as the starting point for pattern change of precipitation maxima.