黔西县油菜种植的气候条件分析

根据油菜生长的生物学特性,结合黔西县气候条件分析,总结了黔西县油菜高产稳产的有利气候因素及应当预防的气象灾害。     

青海省德令哈地区亚麻种植的气候适应性分析及研究

本文利用德令哈地区1973~2000年的气象观测资料,分析了德令哈地区的气候特征,研究了亚麻品种与产量形成之间的关系,对亚麻在德令哈地区种植的农业气象关键时段和关键因子作了分析,选取了最适宜德令哈地区种植的亚麻品种,建立了在不同气象条件下,亚麻在德令哈地区种植的田间管理模式。并通过亚麻的生物学特性与气象条件的关系,提出了科学种植亚麻的理论依据。     

甘肃省谷子气候生态适应性分析及适生种植区划

在分析谷子气候生态适应性的基础上,建立不同气候区谷子气候产量模型。确定影响产量形成关键时段的关键气象因素是≥10℃积温、8～9月平均气温、7月降水以及海拔高度和投入产出比等适生种植区划综合指标体系,据此划分5个谷子气候生态种植区。并提出提高气候生态资源的开发利用途径。     

修文县7种高产牧草种植气候适应性分析

该文利用修文县龙场镇1971-2010年40a气温、日照、雨量等气象要索资料,对修文县拟引进的象草、苏丹草、墨西哥玉米、皇竹草、牛鞭草、俄罗斯饲料菜、杂交狼尾草等7种牧草品种进行气候适用性分析,从而提出引种的可行性建议。     

崇信县甘薯气候适应性分析与种植技术

对崇信县甘薯气候适应性分析发现，在栽苗到封垄期温度偏低，而封垄前覆膜可解决热量不足问题；水分条件基本能满足甘薯生长需要。采用适当种植技术，可使甘薯获得较高产量。     

武汉市黄陂区芦笋种植的气候适应性分析

武汉市黄陂区的芦笋种植已有多年,但产量不理想。该文从气候的角度讨论分析了芦笋对当地气候的适应性,论证了发展芦笋种植的可行性,并提出了趋利避害的措施和具体的推广建议。     

米草在海南种植的气候适应性分析

米草是多年生禾本科植物，天然分布于英国南海岸，目前分布仅限于温带的欧、美、亚、澳四洲。在亚洲，我国首先于1963年引种米草成功，经过三十多年的艰苦努力，已取得了栽培和综合利用的成功经验。到1985年止，我国推广米草面积累计达到三万余公顷，跃居世界之首。海南省四周临海，却从未做过这方面的试验。为此，本文介绍米草的有关性能，从米草的抗逆性，来分析米草在海南种植的气候适应性。1米草的利用米草可利用于促淤保滩、改良土壤、航道减淤和作为鱼虾饵料和野生动物的栖息地等，其优越性是其他作物不可比拟的。1．1促淤保滩海滩上…     

子长县蚕桑种植适应性气候分析

根据子长县蚕桑中心统计多年蚕桑种植资料和经验,利用子长县气象站20a气象资料,南沟岔镇、玉家湾镇区域气象站5a气象资料,针对桑树露青期、鹊口期、五叶期等发育期的生长需求,对子长县蚕桑种植的温度、光照和水分条件进行分析,总结了桑树不同生育期适应的气候条件和主要气象灾害及其防御措施。     

子长县蚕桑种植适应性气候分析

根据子长县蚕桑中心统计多年蚕桑种植资料和经验,利用子长县气象站20a气象资料,南沟岔镇、玉家湾镇区域气象站5a气象资料,针对桑树露青期、鹊口期、五叶期等发育期的生长需求,对子长县蚕桑种植的温度、光照和水分条件进行分析,总结了桑树不同生育期适应的气候条件和主要气象灾害及其防御措施。     

吉安市芦笋种植的气候适应性分析和推广建议

从气候的角度讨论分析了在吉安市种植芦笋的可行性，并提出了具体的推广建议。     

适应性治理与气候变化：内蒙古草原案例分析与对策探讨

适应性治理通过边学边做,针对各地方的社会经济条件、自然生态系统、地方知识文化等基本特征,基于一个动态、自下而上和自组织的过程不断测试和修正制度安排与知识体系,形成一个旨在解决实际问题的循环过程。通过内蒙古3个地区案例的对比分析研究,基于对其气候变化风险和社会脆弱性的评估,发现其在气候变化影响下形成的不同程度的社会脆弱性正是源于不同的草原利用机制和基于此的社会合作机制。正是因为3个案例地的牧民有着不同的社会资本和社会记忆,所以他们面对极端天气导致的自然灾害时,采取了不同的应对方式,有的牧户可以依赖于社会资本移动牲畜来渡过难关,有的牧户则可以在嘎查范围内重启社会记忆,通过合理安排草场利用和移动牲畜提高自身的抗灾能力,而有的牧户则只能通过买草料独立抗灾。这样不同的结果有力证明了适应性治理在提升这些地区气候变化应对能力方面的必要性和可行性。在地区层面引入适应性治理,可以满足各利益相关方的需求,有利于自然、社会及管理的多学科协同,与“未来地球计划”的协同设计、协同实施和协同推广理念不谋而合,是“未来地球”思想在气候变化适应研究中的实践。     

Uncertainties in the regional climate models simulations of South-Asian summer monsoon and climate change

The uncertainties in the regional climate models (RCMs) are evaluated by analyzing the driving global data of ERA40 reanalysis and ECHAM5 general circulation models, and the downscaled data of two RCMs (RegCM4 and PRECIS) over South-Asia for the present day simulation (1971–2000) of South-Asian summer monsoon. The differences between the observational datasets over South-Asia are also analyzed. The spatial and the quantitative analysis over the selected climatic regions of South-Asia for the mean climate and the inter-annual variability of temperature, precipitation and circulation show that the RCMs have systematic biases which are independent from different driving datasets and seems to come from the physics parameterization of the RCMs. The spatial gradients and topographically-induced structure of climate are generally captured and simulated values are within a few degrees of the observed values. The biases in the RCMs are not consistent with the biases in the driving fields and the models show similar spatial patterns after downscaling different global datasets. The annual cycle of temperature and rainfall is well simulated by the RCMs, however the RCMs are not able to capture the inter-annual variability. ECHAM5 is also downscaled for the future (2071–2100) climate under A1B emission scenario. The climate change signal is consistent between ECHAM5 and RCMs. There is warming over all the regions of South-Asia associated with increasing greenhouse gas concentrations and the increase in summer mean surface air temperature by the end of the century ranges from 2.5 to 5 °C, with maximum warming over north western parts of the domain and 30 % increase in rainfall over north eastern India, Bangladesh and Myanmar.     

Tropical storms: representation and diagnosis in climate models and the impacts of climate change

Tropical storms are located and tracked in an experiment in which a high-resolution atmosphere only model is forced with observed sea surface temperatures (SSTs) and sea ice. The structure, geographic distribution and seasonal variability of the model tropical storms show some similarities with observations. The simulation of tropical storms is better in this high-resolution experiment than in a parallel standard resolution experiment. In an anomaly experiment, sea ice, SSTs and greenhouse-gas forcing are changed to mimic the changes that occur in a coupled model as greenhouse-gases are increased. There are more tropical storms in this experiment than in the control experiment in the Northeast Pacific and Indian Ocean basins and fewer in the North Atlantic, Northwest Pacific and Southwest Pacific region. The changes in the North Atlantic and Northwest Pacific can be linked to El Niño-like behaviour. A comparison of the tracking results with two empirically derived tropical storm genesis parameters is carried out. The tracking technique and a convective genesis parameter give similar results, both in the global distribution and in the changes in the individual basins. The convective genesis parameter is also applied to parallel coupled model experiments that have a lower horizontal resolution. The changes in the global distribution of tropical storms in the coupled model experiments are consistent with the changes seen at higher resolution. This indicates that the convective genesis parameter may still provide useful information about tropical storm changes in experiments carried out with models that cannot resolve tropical storms.     

Quantitative assessment of the climate components driving the pacific decadal oscillation in climate models

The Pacific decadal oscillation (PDO) is defined as the first empirical orthogonal function (EOF) mode of the North Pacific sea surface temperature anomalies. In this study, we reconstructed the PDO using the first-order autoregressive model from various climate indices representing the El Niño-Southern oscillation (ENSO), Aleutian Low (AL), sea surface height (SSH), and thermocline depth over the Kuroshio–Oyashio extension (KOE) region. The climate indices were obtained from observation and twentieth-century simulations of the eight coupled general circulation models (CGCMs) participating in the Climate Model Intercomparison Project Phase III (CMIP3). In this manner, we quantitatively assessed the major climate components generating the PDO using observation and models. Based on observations, the PDO pattern in the central to eastern North Pacific was accurately reconstructed by the AL and ENSO indices, and that in the western North Pacific was best reconstructed by the SSH and thermocline indices. In the CMIP3 CGCMs, the relative contribution of each component to the generation of the PDO varied greatly from model to model, and observations, although the PDO patterns from most of the models were similar to the pattern observed. In the models, the PDO pattern in the eastern and western North Pacific were well reconstructed using the AL and SSH indices, respectively. However, the PDO pattern reconstructed by the ENSO index was quite different from the observed pattern, which was possibly due to the model's common deficiency in simulating the amplitude and location of the ENSO. Furthermore, the differences in the contribution of the KOE thermocline index between the observed pattern and most of the models indicated that the PDO pattern associated with ocean wave dynamics is not properly simulated by most models. Therefore, the virtually well simulated PDO pattern by models is a result of physically inconsistent processes.     

A stochastic weather generator applied to hydrological models in climate impact analysis

Summary A pattern recognition methodology for estimating local climate variables such as regional precipitation and air temperature using local observation and scenario information provided by GCMs is presented. We have adopted a three step approach: (a) Feature information extraction of climate variables, where weather patterns are expanded by the Karhunen-Loeve (K-L) orthogonal functional series; (b) Grey associative clustering of the feature vectors; (3) Stochastic weather generation by a Monte Carlo simulation. The methods described in this paper were verified using the temperature and precipitation data set of Wuhan, Yangtze river basin and the Shun Tian catchment, Dongjiang River in China. The proposed method yields good stochastic simulations and also provides useful information on temporal or spatial downscaling and uncertainty.With 4 Figures     

Intercomparison of three urban climate models

An intercomparison of the surface energy budgets from three urban climate models was made to assess the comparability of results, and to evaluate the surface energy fluxes from each model. The three models selected spanned the continuum of approaches currently employed in the treatment of the effects of urban geometry. The first model was an urban canopy-layer model which explicitly examined urban canyon geometry. The second model treated the city as a warm, rough, moist plate but included greatly simplified parameterizations of urban geometry. Neither model included a dynamic link to the urban boundary-layer. The third model was a one-dimensional urban boundary-layer model which utilized a simple warm, rough, moist plate approach but included a dynamic coupling of the urban surface layer to the urban boundary-layer.Results showed considerable disagreement between the three models in regards to the individual energy fluxes. Average rankings of the energy fluxes in terms of comparability from high-to-low similarity were: (1) solar radiation, (2) sensible heat flux, (3) conduction, (4) latent heat flux, (5) longwave re-radiation, and (6) longwave radiation input. In general, the urban canopy-layer model provided more realistic results, although each model demonstrated strong and weak points. Results indicate that current urban boundary-layer models may produce surface energy budgets with lower sensible heat fluxes and substantially higher latent heat fluxes than is supported by field evidence from the literature.     

Internal variability of regional climate models

 Two regional climate models have been applied to the task of generating an ensemble of realizations of the year 1982 with observed boundary conditions in areas covering parts of the Mediterranean countries. These realizations were generated by applying boundary conditions from the ECMWF ERA reanalysis project consecutively, carrying over the soil variables from the regional models from one iteration to the next. Monthly mean fields for six iterations of each model have been used as statistical ensembles in order to investigate the internal variability of the regional model dynamics. This internal variability is a necessary consequence of the non-linear physical feedback mechanisms of the RCM being active. A small value of internal variability will give better statistics for climate sensitivity signals, but will make these results less credible. The internal variability is important for the quantitative assessment of a climate sensitivity signal. With the present choice of models and integration domains the internal variabilities of surface fields and precipitation do reach levels that are less than, but in summer of comparable order of magnitude to, corresponding atmospheric variabilities of an atmospheric general circulation model. Received: 26 October 1999 / Accepted: 18 December 2000