基于广义线性模型和NCEP资料的降水随机发生器

天气发生器可以用来插补历史缺测气象数据或生成未来天气情境, 近年来被普遍应用于对气象变量的降尺度研究, 为陆面的水文、 生态模拟提供外强迫输入。广义线性模型 (GLM) 是近年来用于建立大尺度气象变量与地面气象因子之间的一种有效方法, 基于GLM的天气发生器具有一定的应用前景。本文以NCEP再分析资料中的单格点气温、 500 hPa位势高度、 位温、 相对湿度、 海平面气压等5个变量作为影响降水变化的大尺度因子建立模拟逐日降水量的广义线性模型。模型中对降水概率的描述采用Logistic模型模拟, 而对降水量则分别试用Gamma分布、 指数分布、 正态分布和对数正态分布来模拟, 试图比较和揭示这些基于不同理论分布的模型的能力。模型中待定参数的估计及对研究区逐日降水量的模拟采用了完全相同的实测逐日降水数据和同期NCEP再分析资料。参数的最大似然估计用遗传算法来实现, 对山东省临沂地区10个主要气象观测站降水资料的研究表明, Gamma分布模型的拟合效果最好, 对数正态分布次之, 指数分布再次, 正态分布最差; 参数估计分月获取的拟合效果略好于不分月的。模型逐日降水模拟表明, 对降水发生概率的模拟会低估各月的多年平均值, 基于指数分布的GLM会低估各月总降水量期望 (为月内每日降水量期望之和) 的多年平均值, 而基于对数正态分布的GLM则会在降水量较大时产生高估现象。由对应的天气发生器模型生成的随机模拟降水序列表明, 基于对数正态分布的模型会高估月降水量较大时的多年平均, 而基于指数分布及Gamma分布的模型则模拟效果较好。总体上看, 这种基于NCEP再分析资料和GLM的天气发生器对降水变率具有很强的解释和模拟能力。     

Uncertainty analysis of statistical downscaling models using Hadley Centre Coupled Model

Regression-based statistical downscaling is a method broadly used to resolve the coarse spatial resolution of general circulation models. Nevertheless, the assessment of uncertainties linked with climatic variables is essential to climate impact studies. This study presents a procedure to characterize the uncertainty in regression-based statistical downscaling of daily precipitation and temperature over a highly vulnerable area (semiarid catchment) in the west of Iran, based on two downscaling models: a statistical downscaling model (SDSM) and an artificial neural network (ANN) model. Biases in mean, variance, and wet/dry spells are estimated for downscaled data using vigorous statistical tests for 30 years of observed and downscaled daily precipitation and temperature data taken from the National Center for Environmental Prediction reanalysis predictors for the years of 1961 to 1990. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. In daily precipitation, downscaling uncertainties were evaluated from comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and modeled daily precipitation. Results showed that uncertainty in downscaled precipitation is high, but simulation of daily temperature can reproduce extreme events accurately. Finally, this study shows that the SDSM is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % confidence level, while the ANN model is the least capable in this respect. This study attempts to test uncertainties of regression-based statistical downscaling techniques in a semiarid area and therefore contributes to an improvement of the quality of predictions of climate change impact assessment in regions of this type.     

随机天气模型参数化方案的研究及其模拟能力评估

文中介绍了随机天气模型 WGEN的基本结构及其模拟原理 ,并针对其中随机过程的统计结构特征和 GCMs输出要素的不同时空尺度特点 ,利用动态数据的参数化分析方法等统计学技术 ,确定了该模型参数的估计方法。同时基于蒙特卡罗数值计算原理 ,给出了 WGEN的随机试验方法 ,并通过模拟基准气候 ,从时间分布和空间场两方面对模型在中国东北地区的模拟效果及其能力进行了评估。结果表明 ,模型对于最高气温、最低气温、降水和辐射等要素均具有较好的模拟效果 ,模拟序列与观测序列的取值分布有较一致的概率特性。由此可以结合 GCMs大尺度网格上输出的月和年要素值 ,通过调控随机过程的参数 ,生成具有不同气候变率的 2× CO2 逐日气候变化情景 ,实现气候预测模式与气候影响模式的嵌套 ,进一步研究气候变率变化的可能影响。     

A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios

Coupled atmosphere-ocean general circulation models (GCMs) simulate different realizations of possible future climates at global scale under contrasting scenarios of land-use and greenhouse gas emissions. Such data require several additional processing steps before it can be used to drive impact models. Spatial downscaling, typically by regional climate models (RCM), and bias-correction are two such steps that have already been addressed for Europe. Yet, the errors in resulting daily meteorological variables may be too large for specific model applications. Crop simulation models are particularly sensitive to these inconsistencies and thus require further processing of GCM-RCM outputs. Moreover, crop models are often run in a stochastic manner by using various plausible weather time series (often generated using stochastic weather generators) to represent climate time scale for a period of interest (e.g. 2000 ± 15 years), while GCM simulations typically provide a single time series for a given emission scenario. To inform agricultural policy-making, data on near- and medium-term decadal time scale is mostly requested, e.g. 2020 or 2030. Taking a sample of multiple years from these unique time series to represent time horizons in the near future is particularly problematic because selecting overlapping years may lead to spurious trends, creating artefacts in the results of the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data for Europe that addresses these problems. Input data consist of daily temperature and precipitation from three dynamically downscaled and bias-corrected regional climate simulations of the IPCC A1B emission scenario created within the ENSEMBLES project. Solar radiation is estimated from temperature based on an auto-calibration procedure. Wind speed and relative air humidity are collected from historical series. From these variables, reference evapotranspiration and vapour pressure deficit are estimated ensuring consistency within daily records. The weather generator ClimGen is then used to create 30 synthetic years of all variables to characterize the time horizons of 2000, 2020 and 2030, which can readily be used for crop modelling studies.     

湖泊水位资料与模型模拟恢复的6000年前全球湿润状况的对比研究

本项研究是ＰＭＩＰ（ＰａｌａｅｏｃｌｉｍａｔｅＭｏｄｅｌｉｎｇＩｎｔｅｒｃｏｍｐａｒｉｓｏｎＰｒｏｊｅｃｔ）国际合作项目中有关模型模拟与观测资料对比的一部分。模型试验对象是６０００ｙｒ．ＢＰ的全球湿润状况。模拟试验以检测太阳辐射变化对全球大尺度气候系统的影响为主要目的。观测资料是利用地质证据恢复的古湖泊水位变化,实际上是某一地区的有效降水（降水减蒸发）的变化。通过两者的比较发现,所有模拟试验均能重现６０００ｙｒ．ＢＰ在亚洲南部与非洲北部的湿润环境,从而证实了因太阳辐射变化导致的亚洲与非洲季风的增强。但模拟的季风增强无论是强度还是范围均小于地质记录。原因很可能是模拟试验中下垫面特征用“现代”的来处理。模拟试验对北半球夏季辐射增加造成的西风带北移及由此引起的中纬度地区的气候变化不够成功。绝大多数模拟对受洋流与海温影响较大的地区是失败的。主要原因可能是所有ＰＭＩＰ中的模拟试验都未考虑海洋的作用。     

Prediction of Changes in Soil Moisture Associated with Climatic Changes and Their Implications for Vegetation Changes: Waves Model Simulation on Taihang Mountain, China

The WAVES model was used to simulate the effect of global warming on soil moisture on the semi-arid Taihang Mountain in China. Parameters of the WAVES model were first adjusted according to soil moisture data from a field global warming experiment. Then, the reliability of WAVES in predicting soil moisture changes induced by climatic change was confirmed by comparing the simulated and observed soil moisture values under different climatic conditions and plant growth rates of another field treatment. Next, 10 climate change scenarios incorporating increases in temperature and changes in precipitation were designed. When a simulation was conducted using the leaf area index (LAI) growth pattern from a field experiment under the present climatic conditions, the results suggested that the combination of temperature increase and precipitation decrease would greatly decrease soil water content throughout the entire simulation period. On the other hand, only when precipitation increased by 20% and temperatureincreased by 2 °C, the effect of precipitation increase on soil moisture was obviously positive. Although soil moisture conditions in the T2P1 (temperature increase by 2 °C and precipitation increase by 10%) and T4P2 (temperature increase by 4 °C and precipitation increase by 20%) scenarios were slightly better during the rainy season and notmuch changed before the rainy season, the positive effect of 10%precipitation increase on soil moisture was totally offset by moisture decrease caused bya 4 °C temperature increase in the T4P1 scenario. At the same time, the trends of soil-moisture change were highly coincident with predicted changes in productivity. Finally, the predicted LAI values from other studies were combined with the climatic change scenarios and used in the simulation. The results showed that changes in LAI alleviated, at least to some extent, the effects of temperature and precipitation changes on soil moisture.     

Climate change,weather variability and corn yield at a higher latitude locale: Southwestern Quebec

Climate change has led to increased temperatures, and simulation models suggest that this should affect crop production in important agricultural regions of the world. Nations at higher latitudes, such as Canada, will be most affected. We studied the relationship between climate variability (temperature and precipitation) and corn yield trends over a period of 33 years for the Monteregie region of south-western Quebec using historical yield and climate records and statistical models. Growing season mean temperature has increased in Monterregie, mainly due to increased September temperature. Precipitation did not show any clear trend over the 33 year period. Yield increased about 118 kg ha⁻¹ year⁻¹ from 1973 to 2005 (under normal weather conditions) due mainly to changes in technology (genetics and management). Two climate variables were strongly associated with corn yield variability: July temperature and May precipitation. These two variables explain more than a half of yield variability associated with climate. In conclusion, July temperatures below normal and May precipitation above normal have negative effects on corn yield, and the growing seasons have warmed, largely due to increases in the September temperature.     

Evaluating historical simulations of CMIP5 GCMs for key climatic variables in Zhejiang Province,China

Assessing the regional impact of climate change on agriculture, hydrology, and forests is vital for sustainable management. Trustworthy projections of climate change are needed to support these assessments. In this paper, 18 global climate models (GCMs) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated for their ability to simulate regional climate change in Zhejiang Province, Southeast China. Simple graphical approaches and three indices are used to evaluate the performance of six key climatic variables during simulations from 1971 to 2000. These variables include maximum and minimum air temperature, precipitation, wind speed, solar radiation, and relative humidity. These variables are of great importance to researchers and decision makers in climate change impact studies and developing adaptation strategies. This study found that most GCMs failed to reproduce the observed spatial patterns, due to insufficient resolution. However, the seasonal variations of the six variables are simulated well by most GCMs. Maximum and minimum air temperatures are simulated well on monthly, seasonal, and yearly scales. Solar radiation is reasonably simulated on monthly, seasonal, and yearly scales. Compared to air temperature and solar radiation, it was found that precipitation, wind speed, and relative humidity can only be simulated well at seasonal and yearly scales. Wind speed was the variable with the poorest simulation results across all GCMs.     

13个IPCC AR4模式对中国区域近40a气候模拟能力的评估

利用中国区域550个站点逐日地面气温及降水资料,评估了参与政府间气候变化专门委员会第四次报告（the fourth assessment report of the intergovernmental panel on climate change,IPCCAR4）的13个新一代全球气候系统模式及多模式集合对中国近40 a（1961—2000年）地面气温和降水的模拟能力,结果表明：最新全球模式对中国地区地面气温年变化及空间分布的模拟结果均较好,但在整个模拟区域地面气温模拟值系统性偏低,东部地区模拟效果好于中西部;对于降水,大部分模式能模拟出中国降水的年变化及空间分布特征,但模拟的区域性差别较大,多数模式对中国东部季风区夏季雨带北抬的过程有一定的模拟能力,但模拟雨带位置偏北。新一代全球模式能模拟出温度的线性变化趋势,但对温度及降水的年际变率模拟能力较低。比较多种评估指标得出,模式集合对温度的模拟效果最好,模式UKMO-HadCM3对降水的模拟效果最好。     

WRF模式对江西2006—2015年夏季气候模拟的性能分析

基于NCEP/FNL再分析资料,利用中尺度天气预报模式（WRF）对2006—2015年1月1日—8月31日的天气形势进行模拟,分析探讨了模式对江西省夏季(6—8月)气温和降水的模拟能力。结果表明：WRF模式能准确模拟出江西省气温和降水的空间分布气候特征,模拟结果与中国区域高时空分辨率地面气象要素驱动数据集（CMFD）接近。其中,降水的模拟精度低于气温模拟;模拟的气温在鄱阳湖地区出现低值,与CMFD的偏差最大。WRF模式模拟的地面反照率偏大导致气温模拟结果偏低。     

基于多状态Markov链模式的极端降水模拟试验

文中建立了基于多状态一阶Markov链的逐日降水量随机模式式结合广义帕雷托分布(GPD)产生夏季逐日极端降水量的模拟资料,结果所显示的各种气候特征表明,绝大多数站点(尤其是中国东部多雨地区)都达到较高的精度.分析表明,该模式对中国东部极端降水特征的模拟能力在某些方面优于两状态一阶Markov链模式.对东部6个代表站模拟试验结果表明,月降水均方差、日降水极大值、月半均降水日数、日降水均方差、日平均降水量等指标与实况比较,均证明该模式对逐日降水量的模拟效果较好,基本模拟出降水量的各种特征.对中同东部78个代表站采用的两种模式模拟结果对比发现,除日平均降水量以外,月半均降水日数、日降水平均极大值都与实际观测结果较为一致,总体上优于两状态模式,说明用该模式在全国范围内模拟逐日降水特征尤其是极端降水特征有较高的町行性.例如,由其中6个代表站模拟资料所拟合的极端降水GPD模式具有较高的拟合优度.无论从门限值或重现期值来看都可发现模拟与实测结果有较好的相似性,且两者门限值的误差越小,重现期极值的差距也越小.证明Markov链模式对极端降水的模拟有广泛的适用性.     

CMIP5全球气候模式对上海极端气温和降水的情景预估

基于国际耦合模式比较计划第五阶段(Coupled Model Intercomparison Project Phase 5,以下简称CMIP5)28个模式的数值模拟结果和1981~2010年华东和上海气温和降水观测数据,评估了该28个气候模式对华东和上海气温和降水的模拟能力,并预估了RCP4.5(Representative Concentration Pathway 4.5)情景下上海2021~2030年极端气温和降水气候的变化趋势和不确定性。结果表明:与观测值相比,模式对华东和上海年平均气温的模拟大多均值偏高、方差偏低;对年总降水量的模拟大多均值偏高,但方差以华东偏高、上海偏低为主;26个模式的气温变化趋势和12个模式的降水变化趋势与观测值相同。选出8个模式的预估结果表明:与2001~2010年相比,2021~2030年上海冬天极端低温的出现日数(冷夜日数)呈减少趋势,不确定性最小;夏天暖夜日数呈增加的趋势,不确定性较小;其他极端气温事件的变化趋势则存在较大的不确定性,冷夜指标的不确定性最大。强降水发生日数和强降水的强度都呈现增加的趋势,且不确定性较小。     

Possible Effects of Global Warming on Agriculture and Water Resources in Saudi Arabia: Impacts and Responses

This study assesses the possible impact of climatic change on Saudi Arabia's agriculture and water supplies using climatic change scenarios from GCMs (General Circulation Models) and related research. The resulting assessment indicates that an increase in temperature and decrease in precipitation could have a major negative impact on agriculture and water supplies in Saudi Arabia. To find signs of climatic change in Saudi Arabia a preliminaryassessment of systematic changes in temperature and precipitation was made, based on the records of four Saudi weather stations. The analysis of this data, which dates back to 1961, shows no discernable signs of climatic change during the study period. Such data is, however, limited both spatially and temporally and cannot provide conclusive evidence to confirm climatic changes projected by GCMs. Nevertheless, in the light of recent climatic conditions and rapid population growth, Saudi decision-makers are urged to adopt a `no regret' policy. Ideally, such a policy would include measures to avoid future environmental or socioeconomic problems that may occur in the event of significant climatic change.     

Impact of temperature and precipitation variability on crop model predictions

Future climate changes, as well as differences in climates from one location to another, may involve changes in climatic variability as well as changes in means. In this study, a synthetic weather generator is used to systematically change the within-year variability of temperature and precipitation (and therefore also the interannual variability), without altering long-term mean values. For precipitation, both the magnitude and the qualitative nature of the variability are manipulated. The synthetic daily weather series serve as input to four crop simulation models. Crop growth is simulated for two locations and three soil types. Results indicate that average predicted yield decreases with increasing temperature variability where growing-season temperatures are below the optimum specified in the crop model for photosynethsis or biomass accumulation. However, increasing within-year variability of temperature has little impact on year-to-year variability of yield. The influence of changed precipitation variability on yield was mediated by the nature of the soil. The response on a droughtier soil was greatest when precipitation amounts were altered while keeping occurrence patterns unchanged. When increasing variability of precipitation was achieved through fewer but larger rain events, average yield on a soil with a large plant-available water capacity was more affected. This second difference is attributed to the manner in which plant water uptake is simulated. Failure to account for within-season changes in temperature and precipitation variability may cause serious errors in predicting crop-yield responses to future climate change when air temperatures deviate from crop optima and when soil water is likely to be depleted at depth.     

两种积云对流参数化方案对1998年区域气候季节变化模拟的影响研究

利用区域气候模式RegCM3,选择Kuo-Anthes积云对流参数化方案和基于FC80假设的Grell积云对流参数化方案,对1998年东亚气候分别进行年尺度模拟,模拟结果对比分析表明:在春、夏季转换时期, 两者模拟的降水形势差别较大, 对江淮、中南和华南地区的夏季降水量模拟差别最为明显。对流层上层模式变量和模式大气质量对积云对流参数化方案的选择不敏感, 而对流层中、下层模式变量对积云对流参数化方案比较敏感。不同积云对流参数化方案对8天时间尺度的天气系统模拟差别比较大。在积云对流比较活跃的夏季,不同参数化方案会导致模式大气出现不同的系统性偏差。由于模式在陡峭地形处动力过程计算方案存在缺陷,在高原与盆地的交界处,模式误差会产生明显的突变。     

Intercomparison of Multiple Hydroclimatic Datasets across the Lower Nelson River Basin,Manitoba, Canada

ABSTRACT

This study evaluates the 1981–2010 spatiotemporal differences in six available climate datasets (daily total precipitation and mean air temperature) over the Lower Nelson River Basin (LNRB) in ten of its sub-watersheds at seasonal and annual time scales. We find that the Australian National University spline interpolation (ANUSPLIN), and inverse distance weighted (IDW) interpolated observations from 14 Environment and Climate Change Canada (ECCC) meteorological stations show dry biases, whereas reanalysis products tend to overestimate precipitation across most of the basin. All datasets exhibit prominent disagreement in precipitation trends whereby the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and European Union Water and Global Change (WATCH) Forcing Data ERA-Interim (WFDEI) show exceptional wetting trends, while the IDW and ANUSPLIN data manifest drying trends. Mean air temperature trends generally agree across most of the datasets; however, the North American Regional Reanalysis (NARR) and IDW show stronger warming relative to other datasets. Overall, analyses of the different climate datasets and their ensemble reveal that the choice of input dataset plays a crucial role in the accurate estimation of historical climatic conditions, particularly when assessing trends, for the LNRB. Using the ensemble has the distinct advantage of preserving the unique strengths of all datasets and affords the opportunity to estimate the uncertainty for hydrologic modelling and climate change impact studies.     

Effects of Climate Change on Evapotranspiration from Paddy Fields in Southern Taiwan

The major objective of this study was to investigate the effects of climate change on evapotranspiration from paddy fields. A sensitivity analysis of meteorological variables at the Kao-Hsiung station, one of meteorological stations in southern Taiwan, was carried out using the modified Penman formula. Forty-eight-year records of temperature, relative humidity, sunshine duration, wind speed, and precipitation depth comprised the database. Trend and persistence analyses of the data were performed using the Mann–Kendall test, the Cumulative Deviation test, Linear Regression, andthe Autocorrelation Coefficient. The results indicated that only temperature and relative humidity have significant long-term trends and persistence. Two climatic scenarios, viz. (1) linear extrapolation of climatic trends and (2) the predictions of General Circulation Models (GCMs), were assumed to investigate the effects of climate change on evapotranspiration. The study revealed that evapotranspiration from paddy fields increased under both climatic scenarios studied.     

Precipitation extremes and the impacts of climate change on stormwater infrastructure in Washington State

The design of stormwater infrastructure is based on an underlying assumption that the probability distribution of precipitation extremes is statistically stationary. This assumption is called into question by climate change, resulting in uncertainty about the future performance of systems constructed under this paradigm. We therefore examined both historical precipitation records and simulations of future rainfall to evaluate past and prospective changes in the probability distributions of precipitation extremes across Washington State. Our historical analyses were based on hourly precipitation records for the time period 1949–2007 from weather stations in and near the state’s three major metropolitan areas: the Puget Sound region, Vancouver (WA), and Spokane. Changes in future precipitation were evaluated using two runs of the Weather Research and Forecast (WRF) regional climate model (RCM) for the time periods 1970–2000 and 2020–2050, dynamically downscaled from the ECHAM5 and CCSM3 global climate models. Bias-corrected and statistically downscaled hourly precipitation sequences were then used as input to the HSPF hydrologic model to simulate streamflow in two urban watersheds in central Puget Sound. Few statistically significant changes were observed in the historical records, with the possible exception of the Puget Sound region. Although RCM simulations generally predict increases in extreme rainfall magnitudes, the range of these projections is too large at present to provide a basis for engineering design, and can only be narrowed through consideration of a larger sample of simulated climate data. Nonetheless, the evidence suggests that drainage infrastructure designed using mid-20th century rainfall records may be subject to a future rainfall regime that differs from current design standards.     

不同边界层参数化方案对东亚夏季风气候模拟的对比研究

用WRF v3.2.1中尺度预报模式和NCEP/NCAR再分析资料,对比研究了WRF模式中5个不同边界层参数化方案对东亚夏季风气候的模拟效果。结果表明:WRF模式对各边界层参数化方案均较为敏感,采用不同的边界层参数化方案对模拟区域内的夏季降水、气温、环流等气候要素均可产生明显影响。选取MYJ方案和QNSE方案对500 h Pa夏季平均环流的模拟效果较好,YSU方案和QNSE方案对夏季平均日降水量模拟与再分析资料更吻合,YSU方案和MYNN2.5方案对中国东部2 m气温的模拟结果较好。不同边界层参数化方案模拟结果都显示出由于副热带高压偏强,使副热带高压第2次北跳后停留时间过短,导致长江中下游降水偏少,华北地区降水增多。通过比较YSU和QNSE边界层方案,发现YSU方案相比QNSE方案的降水差异,是由于850 h Pa水汽输送造成的。在中国大部分地区YSU方案的2 m温度比QNSE方案高,并且地面2 m气温和降水存在一定对应关系。因此合理选取边界层参数化方案可以提高数值模拟的准确性。     

长江中下游夏季高温灾害机理及预测

利用我国1961-2003年夏季（6—8月）高温资料，建立长江中下游地区主要城市强高温及高温过程较完整的时间序列，探讨了该地区主要城市高温气候特征。分析该地区南京、杭州、南昌等城市夏季高温灾害机理，东亚副热带高压是造成长江中下游地区城市夏季高温的主要影响系统。在此基础上用均生函数-最佳子回归集构造预测模型，预测夏季月高温出现日数，通过42a高温资料预报检验，有较好的预测效果，值得在业务中应用。