南极涛动异常及其对冬春季北半球大气环流影响的数值模拟试验

利用IAP9L-AGCM模式考察了模式中与南极涛动异常相关的海温敏感区，发现南半球高纬海温异常能够强迫出南极涛动异常，而赤道东太平洋海温异常与太平洋南美型密切相关.研究了南极涛动异常对冬春季北半球大气环流及亚洲北部气温的影响，结果表明，南极涛动加强，能够引起北半球高纬环流异常和欧亚西风加强，以及亚洲北部地表气温和850 hPa气温显著增温.数值模拟支持了已有的诊断结果，也证实了冬春季节南极涛动异常下两半球高纬间的经向遥相关存在.     

北京沙尘频次的年际变化及其全球环流背景分析

本文采用相关和合成的分析方法，研究北京沙尘频次的年际变化及其冬、春季的全球环流背景.结果表明北京沙尘频次有年际变化的特点，并与全球范围内的环流异常相联系，特别是南、北半球的中高纬的环流异常.南半球环流异常与沙尘的联系在冬、春两季有很好的持续性和显著性，北半球中高纬环流异常与沙尘的联系冬季较春季显著.春季对流层高层的东亚西风急流增强能使低空的蒙古气旋加强和锋生，从而引起地面大风，为沙尘天气频次的增加提供动力条件.     

Weather and seasonal climate prediction for flood planning in the Yangtze River Basin

This paper describes the use of numerical weather and climate models for predicting severe rainfall anomalies over the Yangtze River Basin (YRB) from several days to several months in advance. Such predictions are extremely valuable, allowing time for proactive flood protection measures to be taken. Specifically, the dynamical climate prediction system (IAP DCP-II), developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP), is applied to YRB rainfall prediction and flood planning. IAP DCP-II employs ensemble prediction with dynamically conditioned perturbations to reduce the uncertainty associated with seasonal climate prediction. IAP DCP-II was shown to successfully predict seasonal YRB summer flooding events based on a 15-year (1980–1994) hindcast experiment and the real-time prediction of two summer flooding events (1999 and 2001). Finally, challenges and opportunities for applying seasonal dynamical forecasting to flood management problems in the YRB are discussed.     

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.     

春季Hadley环流异常对夏季西北太平洋热带气旋频数影响的数值模拟试验

观测事实揭示出春季Hadley环流与夏季西北太平洋热带气旋频数之间存在显著的负相关关系.由春季Hadley环流异常引起的西北太平洋地区夏季纬向风垂直切变、大气辐合辐散等的异常变化是这一关系存在的内在原因.本文通过数值试验对这一关系的真实性进行了验证,即利用中国科学院大气物理研究所发展的9层大气环流模式（IAP9L-AGCM）模拟了春季Hadley环流异常偏强情景,并分析了该情景下影响西北太平洋热带气旋生成的环境场的响应.结果表明,在春季Hadley环流偏强情景下,夏季西北太平洋地区纬向风垂直切变幅度加大,低空大气异常辐散,高空大气异常辐合,东亚夏季风减弱,这种环流背景不利于热带气旋生成和发展,因此,西北太平洋热带气旋频数异常偏少.数值模拟结果与已有的诊断结果相吻合,进而证实了春季Hadley环流与夏季西北太平洋热带气旋频数负相关关系的存在.因此,春季Hadley环流信号可以用于西北太平洋热带气旋活动的气候预测.     

Analysis on the decadal scale variation of the dust storm in North China

Dust storm is a disastrous weather that can cause serious environmental consequences and hazards. It causes excessive soil mass and nutrient loss in source areas, and air pollution in deposition area, which result in bad influence on agriculture, industry, traffic, and peoples’ daily life[1-4]. Dust storm can also affect the thermal balance of planetary radiation and then lead to meso- to macroscale climatic modification[5-7]. North- west China and North China are two high-frequency centers …     

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     

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.      

北半球大气遥相关型与区域尺度大气扰动

北极涛动(AO)、北大西洋涛动(NAO)和太平洋-北美型(PNA)等北半球大气遥相关型,可以用大气位势高度的物理分解扰动分量解释.结果发现,AO反映的是北极地区行星尺度纬圈平均扰动分量的变化,PNA与持续性天气尺度扰动分量相联系,NAO是行星尺度纬圈平均扰动与天气尺度扰动共同作用的结果.对行星尺度纬圈平均扰动分量和天气尺度扰动分量用旋转经验正交函数(REOF)展开,不但可以证实人们已经命名的区域性大气涛动,还新发现了北极地区的两对偶极涛动、欧亚涛动(EAO)和"大西洋-欧亚型"(AEA)波列.这些涛动连接了相邻地区的异常天气和异常气候.     

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.     

Short‐term flood inundation prediction using hydrologic‐hydraulic models forced with downscaled rainfall from global NWP

A short‐term flood inundation prediction model has been formulated based on the combination of the super‐tank model, forced with downscaled rainfall from a global numerical weather prediction model, and a one‐dimensional (1D) hydraulic model. Different statistical methods for downscaled rainfall have been explored, taking into account the availability of historical data. It has been found that the full implementation of a statistical downscaling model considering physically‐based corrections to the numerical weather prediction model output for rainfall prediction performs better compared with an altitudinal correction method. The integration of the super‐tank model into the 1D hydraulic model demonstrates a minimal requirement for the calibration of rainfall–runoff and flood propagation models. Updating the model with antecedent rainfall and regular forecast renewal has enhanced the model's capabilities as a result of the data assimilation processes of the runoff and numerical weather prediction models. The results show that the predicted water levels demonstrate acceptable agreement with those measured by stream gauges and comparable to those reproduced using the actual rainfall. Moreover, the predicted flood inundation depth and extent exhibit reasonably similar tendencies to those observed in the field. However, large uncertainties are observed in the prediction results in lower, flat portions of the river basin where the hydraulic conditions are not properly analysed by the 1D flood propagation model. Copyright © 2013 John Wiley & Sons, Ltd.     

Dynamic characteristics of spring sandstorms in 2000

Systematical analyses of spring sandstorms in 2000 affecting Beijing area are carried out. Results revealed the key dynamic mechanisms of dust storm during its mobilization, lifting, horizontal advection and deposition processes. It turns out that in the processes of sandstorms influencing Beijing area in March–April 2000, the dynamic conditions for dust mobilization are significantly correlated with phenomena, such as cold air-related climatic activities, clay soils area of North China and surface friction velocity anomaly. The characteristics of sandstorm can be described by composite mode of dust particles mobilization-lifting-long-range transport. This paper will provide scientific evidences for further studies, prediction and harness of sandstorms in China.     

Intraseasonal predictability of the duration of flooding above National Weather Service flood warning levels across the U.S. Midwest

Unpreparedness is often the main cause of the economic and social damages caused by floods. To mitigate these impacts, short-term forecasting has been the focus of several studies during the past decades; however, less effort has been paid to flood predictions at longer lead times. Here, we use forecasts by six models from the North American Multi-Model Ensemble project with a lead time from 0.5 to 9.5 months to predict the seasonal duration of floods above four National Weather Service flood categories (“action,” “flood,” “moderate” and “major”). We focus on 202 U.S. Geological Survey gage stations across the U.S. Midwest and use a statistical framework which considers precipitation, temperature, and antecedent wetness conditions as predictors. We find that the prediction skill of the duration of floods for the “action” and “flood” categories is overall low, largely because of the low accuracy of the climate forecasts rather than of the errors introduced by the statistical models. The prediction skill slightly improves when considering the shortest lead times (i.e., from 0.5 to 2.5 months) during spring in the Northern Great Plains, where antecedent wetness conditions play an important role in influencing the generation of floods. It is very difficult to draw strong conclusions with respect to the “moderate” and “major” flood categories because of the limited number of available events.     

An intermediate coupled model for the tropical ocean-atmosphere system

An intermediate ocean-atmosphere coupled model is developed to simulate and predict the tropical interannual variability. Originating from the basic physical framework of the Zebiak-Cane(ZC) model, this tropical intermediate couple model(TICM) extends to the entire global tropics, with a surface heat flux parameterization and a surface wind bias correction added to improve model performance and inter-basin connections. The model well reproduces the variabilities in the tropical Pacific and Indian basins. The simulated El Ni?o-Southern Oscillation(ENSO) shows a period of 3–4 years and an amplitude of about 2°C, similar to those observed. The variabilities in the Indian Ocean, including the Indian Ocean basin mode(IOBM) and the Indian Ocean Dipole(IOD), are also reasonably captured with a realistic relationship to the Pacific. However, the tropical Atlantic variability in the TICM has a westward bias and is overly influenced by the tropical Pacific. A 47-year hindcast experiment using the TICM for the period of 1970–2016 indicates that ENSO is the most predictable mode in the tropics. Skillful predictions of ENSO can be made one year ahead, similar to the skill of the latest version of the ZC model, while a "spring predictability barrier" still exists as in other models. In the tropical Indian Ocean, the predictability seems much higher in the west than in the east. The correlation skill of IOD prediction reaches 0.5 at a 5-month lead, which is comparable to that of the state-of-the-art coupled general circulation models. The prediction of IOD shows a significant "winter-spring predictability barrier", implying combined influences from the tropical Pacific and the local sea-air interaction in the eastern Indian Ocean. The TICM has little predictive skill in the equatorial Atlantic for lead times longer than 3 months, which is a common problem of current climate models badly in need of further investigation.     

动力和统计预测信息融合预测方法及对我国夏季降水预测的检验

短期气候预测中如何将气候模式和统计方法的预测结果科学、客观的集成起来,一直是非常重要的问题.本文针对动力模式和统计方法预测结果相结合的问题,引入资料同化中信息融合的思想,采用最优内插同化方法,实现了动力模式和统计季节降水预测结果的融合.检验表明,对1982-2015年我国夏季降水百分率的回报,融合预测结果与观测的平均空间相关系数可达0.44,分别较统计预测和CFSv2模式统计降尺度订正的技巧提高了0.1左右,而均方根误差较两者可以降低5%~20%.可见,该方法可以进一步提升对我国夏季降水的预测技巧,具有显著的业务应用价值.     

ASSESSMENT OF CLIMATE CHANGE AND FRESHWATER ECOSYSTEMS OF THE ROCKY MOUNTAINS,USA AND CANADA

The Rocky Mountains in the USA and Canada encompass the interior cordillera of western North America, from the southern Yukon to northern New Mexico. Annual weather patterns are cold in winter and mild in summer. Precipitation has high seasonal and interannual variation and may differ by an order of magnitude between geographically close locales, depending on slope, aspect and local climatic and orographic conditions. The region's hydrology is characterized by the accumulation of winter snow, spring snowmelt and autumnal baseflows. During the 2–3-month ‘spring runoff’ period, rivers frequently discharge > 70% of their annual water budget and have instantaneous discharges 10–100 times mean low flow. Complex weather patterns characterized by high spatial and temporal variability make predictions of future conditions tenuous. However, general patterns are identifiable; northern and western portions of the region are dominated by maritime weather patterns from the North Pacific, central areas and eastern slopes are dominated by continental air masses and southern portions receive seasonally variable atmospheric circulation from the Pacific and the Gulf of Mexico. Significant interannual variations occur in these general patterns, possibly related to ENSO (El Niño–Southern Oscillation) forcing. Changes in precipitation and temperature regimes or patterns have significant potential effects on the distribution and abundance of plants and animals. For example, elevation of the timber-line is principally a function of temperature. Palaeolimnological investigations have shown significant shifts in phyto- and zoo-plankton populations as alpine lakes shift between being above or below the timber-line. Likewise, streamside vegetation has a significant effect on stream ecosystem structure and function. Changes in stream temperature regimes result in significant changes in community composition as a consequence of bioenergetic factors. Stenothermic species could be extirpated as appropriate thermal criteria disappear. Warming temperatures may geographically isolate cold water stream fishes in increasingly confined headwaters. The heat budgets of large lakes may be affected resulting in a change of state between dimictic and warm monomictic character. Uncertainties associated with prediction are increased by the planting of fish in historically fishless, high mountain lakes and the introduction of non-native species of fishes and invertebrates into often previously simple food-webs of large valley bottom lakes and streams. Many of the streams and rivers suffer from the anthropogenic effects of abstraction and regulation. Likewise, many of the large lakes receive nutrient loads from a growing human population. We concluded that: (1) regional climate models are required to resolve adequately the complexities of the high gradient landscapes; (2) extensive wilderness preserves and national park lands, so prevalent in the Rocky Mountain Region, provide sensitive areas for differentiation of anthropogenic effects from climate effects; and (3) future research should encompass both short-term intensive studies and long-term monitoring studies developed within comprehensive experimental arrays of streams and lakes specifically designed to address the issue of anthropogenic versus climatic effects. © 1997 John Wiley & Sons, Ltd.     

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.     

格点大气环流模式GAMIL2参数不确定性的量化分析与优化

物理过程参数化方案的不确定性是目前气候系统模式不确定性的重要来源之一.随着模式内在复杂度攀升,模拟场景多样化,参数化方案中基于先验的和人工的物理参数选取方法已经逐步成为限制模式模拟能力的瓶颈之一.为此,本文设计并提出了初选与寻优相结合的两步法参数优化方案.初选阶段用全因子采样方法对不确定参数空间进行初始敏感性分析,估计最优解所在区域;寻优步采用单纯型下山法,基于初选阶段确定的参数组合快速寻优.将两步法应用于中国科学院大气物理研究所(英文缩写:IAP)大气科学和地球流体力学数值模拟国家重点实验室(英文缩写:LASG)格点大气模式第2版:GAMIL2,选取其深对流方案和云量方案中的3个重要参数开展寻优,优化以综合减小模式降水、风场、温度、湿度、位势高度以及辐射通量的误差为目标.这些变量用GAMIL2标准版本标准化后形成单一的目标.结果显示,优化后的目标函数值比GAMIL2标准版本改进了7.5%.机理分析表明,调优后的参数优化了大气中的水汽凝结作用,进而减少模式的湿度偏差,改进云量的模拟效果;同时水汽凝结作用的变化通过大气内部动力和热力相互作用及响应影响温度、位势高度和风场的模拟.     

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.     

Data mining methods for hydroclimatic forecasting

Skillful streamflow forecasts at seasonal lead times may be useful to water managers seeking to provide reliable water supplies and maximize hydrosystem benefits. In this study, a class of data mining techniques, known as tree-structured models, is investigated to address the nonlinear dynamics of climate teleconnections and screen promising probabilistic streamflow forecast models for river–reservoir systems. In a case study of the Lower Colorado River system in central Texas, a number of potential predictors are evaluated for forecasting seasonal streamflow, including large-scale climate indices related to the El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and others. Results show that the tree-structured models can effectively capture the nonlinear features hidden in the data. Skill scores of probabilistic forecasts generated by both classification trees and logistic regression trees indicate that seasonal inflows throughout the system can be predicted with sufficient accuracy to improve water management, especially in the winter and spring seasons in central Texas.