SDSM统计降尺度方法对江淮地区地面气温模拟能力评估及其未来情景预估

运用SDSM统计降尺度对江淮地区五个代表站点进行统计降尺度研究,应用独立观测资料验证发现统计降尺度模式具有一定的可靠性.在A2排放情景下,对5个测站的未来情景研究发现,到21世纪中叶,各测站普遍增温1.5℃左右,到本世纪末,增温幅度在4℃左右.这一结论与LMDZ动力降尺度模式结果基本一致,但统计降尺度的模拟值要略高于动力降尺度.     

基于集合预报产品的降尺度降水预报试验

利用降水距平百分率的降尺度预报方法和1951-2008 NCEP资料及我国降水资料,建立了降水距平百分率的预报模型,基于T106L19模式的月动力延伸集合预报结果,进行了2007-2009年3 a的预报试验和效果检验.结果表明,基于集合预报产品的统计降尺度方法对降水距平百分率的预报技巧高于模式降水的预报技巧;500 hPa月平均高度场的预报技巧直接影响到降水距平百分率的预报技巧,平均环流的预报技巧越高,降水距平百分率的预报技巧越高;无论集合成员数为多少,集合预报的结果都明显优于控制预报,随着集合成员数的增多,预报技巧呈增大的趋势;我国降水具有显著的季节性和区域性,以江淮地区的降水距平百分率预报技巧最高,华南地区的预报技巧其次.     

江苏省汛期强降水过程的延伸期预报试验

针对汛期延伸期降水预报问题,根据大气低频振荡特性,运用低频天气图预报方法,通过分析关键区低频天气系统(低频气旋和低频反气旋)的活动特征,建立低频系统与强降水过程间的对应关系,通过低频系统的活动特征来预报降水过程。在2011年7—9月江苏省延伸期强降水过程预报试验中,低频天气图预报方法的预报效果较好,且预报时效为10~30 d,可以在延伸期业务预报中加以应用。此外,还运用模式统计降尺度方法预报降水落区,为强降水过程的发生提供背景依据和参考信息,具有一定的实用意义。     

Recursive cross-entropy downscaling model for spatially explicit future land uses: A case study of the Heihe River Basin

Downscaling methods assist decision makers in coping with the uncertainty regarding sustainable local area developments. In particular, they allow investigating local heterogeneities regarding water, food, energy, and environment consistently with global, national, and sub-national drivers and trends. In this paper, we develop a conceptual framework that integrates a partial equilibrium Global Biosphere Management Model (GLOBIOM) with a dynamic cross-entropy downscaling model to derive spatially explicit projections of land uses at 1-km spatial resolution from 2010 to 2050 relying on aggregate land demand projections. The fusion of the two models is applied in a case study in Heihe River Basin to analyze the extent of potential cropland, grassland, and unused land transformations, which may exacerbate already extensive water consumption caused by rapid expansion of irrigated agriculture in the case study region. The outcomes are illustrated for two Shared Socioeconomic Pathway scenarios. The kappa coefficients show that the downscaling results are in agreement with the land use and land cover map of the Heihe River Basin, which indicates that the proposed approach produces realistic local land use projections. The downscaling results show that under both SSP scenarios the cropland area is expected to increase from 2010 to 2050, while the grassland area is projected to increase sharply from 2010 to 2030 and then gradually come to a standstill after 2030. The results can be used as an input for planning sustainable land and water management in the study area, and the conceptual framework provides a general approach to creating high-resolution land-use datasets.     

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.     

统计降尺度方法和Delta方法建立黄河源区气候情景的比较分析

大气环流模型(GCMs)预测的气候变化情景,必须经降尺度处理得出小尺度上未来气候变化的时空分布资料,才能满足气候变化对资源、环境和社会经济等影响进行评估的需要。文中研究同时应用Delta方法和统计降尺度(SDS)方法对黄河源区的日降水量和日最高、最低气温进行降尺度处理,建立起未来3个时期(2006—2035、2036—2065和2066—2095年,简记为2020s、2050s和2080s)的气候变化情景,并比较分析两种方法的优缺点和适用性。结果表明,未来降水量有一定的增加趋势,但是增幅不大,而日最高、最低气温存在明显的上升趋势,且增幅较大。与基准期相比,Delta方法模拟的未来3个时期降水量将分别增加8.75%、19.70%和18.49%;日最高气温将分别升高1.41、2.42和3.44℃,同时,日最低气温将分别升高1.49、2.68和3.76℃,未来极值气温变幅减小。SDS法借助站点实测数据和NCEP再分析资料建立GCM强迫条件下的降尺度模型,模拟结果表明,未来3个时期降水量将分别增加3.47%、6.42%和8.67%,季节变化明显;气温随时间推移增幅明显,未来3个时期的日最高气温将分别升高1.34、2.60和3.90℃,最低气温增幅相对较小,3个时期将分别升高0.87、1.49和2.27℃,由此模拟的未来时期无霜期将延长。在降尺度方法的应用上,SDS方法存在明显的优势,但同时也存在不可避免的缺陷。因此,在实际的气候变化影响评估中,需要多种方法综合比较,以期为决策部门提供参考和依据。     

一种基于粗糙度分布的大风风速降尺度方法初步研究——以上海市域为例

精细准确预报大风风速对于风功率预报和风灾防御具有重要意义。本文以上海市域为研究区域,基于形态学方法估算上海市域空气动力学粗糙度分布,并通过近地层分层风速插值方法推算上海市域10 m高度精细风速分布。针对2016年1月23日20时—24日20时一次大风过程开展实证检验,对比分析降尺度模拟风速、地面实测风速、ECMWF预报10 m风速的空间分布、时间变化以及垂直廓线特征。结果表明,该方法可以较好地模拟上海市域风速大小的精细空间分布,模拟精度总体上表现为中心城区精度高、郊区精度稍低,但相比于ECMWF预报10 m风速分布有了明显提高,可以为风速的精细化预报提供技术支撑。     

区域尺度近地表气候要素驱动数据研制的研究综述

区域和流域尺度的陆面、水文和生态模拟与同化,都需要高分辨率的大气近地表层风、温、压、湿、辐射、降水等资料作为驱动。介绍了流域尺度驱动数据的重要性,对现阶段大气驱动数据的制备方法进行评论,总结了降水、辐射、气温和其他近地表要素的若干产品,探讨了北美陆面数据同化系统、全球陆面数据同化系统、欧洲陆面数据同化系统和中国西部陆面数据同化系统中大气驱动数据的研究思路和研究成果,最后提出了现阶段制备流域尺度驱动数据存在的问题,并针对中国西部复杂地形的高时空分辨率驱动数据制备提出一些看法。     

Future changes in rainfall,temperature and reference evapotranspiration in the central India by least square support vector machine

Climate change affects the environment and natural resources immensely. Rainfall, temperature and evapotranspiration are major parameters of climate affecting changes in the environment. Evapotranspiration plays a key role in crop production and water balance of a region, one of the major parameters affected by climate change. The reference evapotranspiration or ET₀ is a calculated parameter used in this research. In the present study, changes in the future rainfall, minimum and maximum temperature, and ET₀ have been shown by downscaling the HadCM3 (Hadley Centre Coupled Model version 3) model data. The selected study area is located in a part of the Narmada river basin area in Madhya Pradesh in central India. The downscaled outputs of projected rainfall, ET₀ and temperatures have been shown for the 21st century with the HADCM3 data of A2 scenario by the Least Square Support Vector Machine (LS-SVM) model. The efficiency of the LS-SVM model was measured by different statistical methods. The selected predictors show considerable correlation with the rainfall and temperature and the application of this model has been done in a basin area which is an agriculture based region and is sensitive to the change of rainfall and temperature. Results showed an increase in the future rainfall, temperatures and ET₀. The temperature increase is projected in the high rise of minimum temperature in winter time and the highest increase in maximum temperature is projected in the pre-monsoon season or from March to May. Highest increase is projected in the 2080s in 2081–2091 and 2091–2099 in maximum temperature and 2091–2099 in minimum temperature in all the stations. Winter maximum temperature has been observed to have increased in the future. High rainfall is also observed with higher ET₀ in some decades. Two peaks of the increase are observed in ET₀ in the April–May and in the October. Variation in these parameters due to climate change might have an impact on the future water resource of the study area, which is mainly an agricultural based region, and will help in proper planning and management.     

Simulated changes due to global warming in the variability of precipitation, and their interpretation using a gamma-distributed stochastic model

The interannual variability of monthly mean January and July precipitation and its possible change due to global warming are assessed using a five-member ensemble of climate for the period 1871–2100, simulated by the CSIRO Mark 2 global coupled atmosphere–ocean model. In the 1961–1990 climate, for much of the middle to high latitudes the standard deviation of precipitation for both months is roughly proportional to the mean, with the coefficient of variation (C) typically 0.3–0.5. The variability there is shown to be largely consistent with that from a first-order Markov chain model of the daily rainfall occurrence, with the distribution of wet-day amounts approximated by a gamma distribution. Global distributions of Mark 2-based parameters of this stochastic model, commonly used in weather generators, are presented. In low latitudes, however, the variability from the coupled model is typically double that anticipated by the stochastic model, as quantified by an ‘overdispersion ratio’. C often exceeds one at subtropical locations, where rain is less frequent, but sometimes relatively heavy.The standard deviation of monthly mean precipitation S generally increases as the global model warms, with the global mean S in 2071–2100 in January (July) being 9.0% (11.5%) larger than in 1961–1990. Decreases in some subtropical locations occur, particularly where mean precipitation decreases. The global pattern of overdispersion is largely unchanged, however, and the changes in S can be related to those in the stochastic model parameters. Much of the increase in S is associated with increases in the scale parameter of the gamma distribution of wet-day amounts. Changes in C, which is unaffected by this parameter, are generally small. Increases in C in several subtropical bands and over northern midlatitude land in July are related to a decreased frequency of precipitation, and (to a lesser degree) changes in the gamma shape parameter. Some potential applications of the results to downscaling are discussed, and illustrated using observed rainfall from southeast Australia.