四川地区近60 a大气可降水量分析

利用60a的NCEP2．5°×2．5°再分析资料,分析了四川地区大气可降水量的长期平均特征及其季节、年际与年代际变化。结果表明：四川地区年大气可降水量为181．30kg·m^-2;大气可降水量的空间分布明显不均匀,川西高原的大气可降水量明显低于四川盆地;大气可降水量季节变化显著,一年中夏季（6～8月）最多（74．33kg·m^-2）,秋季次之,冬季最少;大气可降水量1月最少（5．82kg·m^-2）,7月最多（25．77kg·m^-2）,2月开始逐月增加,8月开始又逐月减少。60a中大气可降水量年际变化小,丰年大气可降水量为枯年的1．15倍。60a来,大气可降水量在平均状态附近波动,略有减少。     

西藏近35年地表湿润指数变化特征及其影响因素

利用1971-2005年西藏25个气象站月平均最高气温、最低气温、风速、相对湿度、日照时数、降水量等资料,应用Penman-Monteith模犁计算了最大潜在蒸散、地表湿润指数,分析了其空间分布、年际变化特征及季节差异,并讨论了影响地表湿润指数变化的气象因子.研究表明:近35年,西藏年降水量表现为显著的增加趋势,增幅为15.0 mm/(10 a);年最大潜在蒸散呈不同程度的减小趋势,为-4.6--71.6 mm/(10 a).阿里地区西南部、聂拉木年地表湿润指数为不显著的减小趋势,其他各地均呈增大趋势,增幅为0.02-0.09.就西藏平均而言,年地表湿润指数以0.04/10 a的速率显著增大,尤其足近25年增幅更为明显.各季节地表湿润指数也表现为增大趋势,以夏季增幅最明显.20世纪70年代剑80年代主要表现为以低温低湿为主的年际变化特征,进入90年代后,气温持续升高,地表湿润指数明显增加,呈现山暖湿型的气候特征.降水量和相对湿度的明显增加,以及平均气温日较差的显著减小是地表湿润指数显著增加的主要原因,平均风速和日照时数的明显减少,在湿润指数增加趋势中也起着重要作用.     

阿拉山口1957—2007年气候变化特征

采用线性趋势方程、7 a滑动平均、气候趋势系数分析法对1957—2007年阿拉山口气象站的气温、降水、大风日数进行统计分析,得出51 a阿拉山口年平均气温呈上升趋势,增幅0.3℃/10 a;2、4—5、10—11月增暖较明显,增幅为0.4～0.7℃/10 a,3月增幅为0.1℃/10 a。年降水量呈增加趋势,增幅7.3 mm/10 a,5、7、12月的增幅1.6～2.9 mm/10 a,而8月降水量减少趋势较明显,减幅为-1.1 mm/10 a。年大风日数呈减少趋势,减少率7 d/10 a;秋季、夏季和春季大风日数减少率分别为3、2、2 d/10 a。     

重庆地区年气温与降水量变化特征及对NPP的影响

选取重庆34个测站1959—2001年共43年逐月平均气温和降水量资料, 利用Thornth-waite Memoriae模型, 即根据植物生物产量与年平均气温、年降水量之间的关系用实际蒸散量估算NPP (净第一性生产力), 采用EOF及MHF小波等方法分析重庆地区年平均气温、降水量及NPP的时空变化特征及相互关系, 最后采用Thornth-waite Memoriae模型分析气温、降水变化对NPP的影响, 并结合未来气候预测结果对NPP的变化进行了预估。结果表明:重庆区域的年平均气温、年总降水量及NPP空间变化均比较有规律, 在整个时间域内, 气温呈下降趋势, 而降水变化趋势不明显, NPP略有下降, 但它们都具有明显的阶段性变化特征, NPP与降水的变化趋势比较一致; 在不同时间尺度上, NPP的变化趋势与降水接近, 在10年时间尺度以下时, 它与气温变化关系不明显, NPP与降水的年际振动特征明显, 而气温的年代际振动特征较显著; 重庆地区“暖湿型”气候对NPP增加最有利, 而“冷干型”气候对NPP增加最不利, 未来50年内重庆地区气温及降水变化趋势将有利于NPP的增加, 2030年前后可能达到最大值。     

气候变化对宝鸡市作物气候生产力的影响

利用1960--2009年宝鸡市11个气象站年平均温度及降水资料,应用TuynthwhiteMemoral模式计算宝鸡地区作物气候生产力状况,同时分析影响因子的显著性。结果表明：降水量是影响宝鸡地区作物生产力的主要因子,气候变化对作物气候生产力影响显著,“暖湿型”气候对全区都十分有利,平均增产幅度约6．5％～7．2％;“冷干型”气候对全区都十分不利,减产幅度为7．3％～6．5％。“冷湿型”气候对川塬区较为有利,而对山区不利;“暖干型”气候对秦岭以北大部分地区较为不利。     

近50a金昌市气候逐渐转入相对暖湿分析

利用线性、多项式回归法对金昌市永昌县气象观测站(1959～2009年)月、季、年平均气温序列、降水量进行连续性变化趋势分析,确定该区域的气温、降水、干燥度变化趋势。应用Mann-Ken-dall法,滑动T-检验法和Yamamoto法检验气温序列变化的不连续性,确定突变的时间。结果表明:永昌县平均气温均呈升温趋势,进入1990年代至今气温持续走高,突变发生在1996年。50a的年降水量也呈波动上升趋势,从1983年以来降水呈相对增加态势,1992～2009年降水量比气候平均值年均多20.6mm,增幅相对较大,降水量序列突变时间是1984年。50a中86%的年份气候处于半干旱状态,而且具有准3a的波动周期,1960年代和1990年代初气候较干旱,历史上最干旱年份出现在这一时期,1970～1978年、1992～2009年气候相对湿润,1998年至今无霜期明显延长,气候有逐渐转入相对暖湿的趋势特点。     

基于空间化技术对中国近50年平均气温时空演变特征的研究

结合GIS空间化技术,对中国近50年平均气温数据进行定量化的分析,并分析其时空演变规律。采用空间化气候值 年际距平空间插值法生成中国1951—2001年分辨率为10 km×10 km的年、季平均气温空间化数据,通过对近50年空间化数据的分析,认为近50年中,年、季的全国平均气温都表现出显著增温趋势,冬季增幅最大,为0.313℃/10a,夏季增幅最小,为0.152℃/10a,年平均气温增幅为0.208℃/10a。1980年代中期以后增温趋势更加明显。从空间分布看,我国西南地区及西藏东南部和新疆西天山中段在不同季节都是主要的降温区域,而北方大部和西藏中西部地区在不同季节都表现为较强的增温趋势,我国东南地区在不同季节温度变化并不稳定,增温或降温的幅度都很小。暖冬事件在北方发生比较频繁,四川及云南部分地区较少,全国发生面积呈逐渐增大的趋势,增幅为国土面积的8.6%/10a。冷夏在新疆南部、西藏西北部和东南部以及江淮一带发生次数较多,辽宁南部、新疆沙漠地区、西藏中部、青海东部、四川大部分地区以及长江以南广大地区最少。全国发生冷夏的面积不断减小,50年平均减幅约为国土面积的5.8%/10a。     

气候变化对西藏雅鲁藏布江中游地区潜在蒸散量的影响分析

本文利用西藏雅江中游地区1961～2009年逐日气象资料和Penman–Monteith公式,计算并分析了PE（潜在蒸散量）的时空分布特征,运用多元回归方法定量计算各气候因子变化对PE变化的贡献率。研究表明：近49年来,拉萨年潜在蒸散量呈明显增加趋势,增幅为8.21mm/10a,日喀则和江孜呈不显著的减少趋势,而泽当减少趋势显著,减幅最大达-24.71mm/10a。PE变化趋势的季节差异较大,年潜在蒸散量在1993年发生突变,在全球气候变暖的背景下,平均风速明显减少从气候因子角度解释了潜在蒸散减少的原因。     

中低纬度高原山地气候变暖对海拔高度的依赖性

本文利用美国国家大气研究中心（NCAR）T85水平分辨率（～140km）的气候系统模式（CCSM3）在大气CO2含量每年增加1％情景下的模拟输出结果,分析了以青藏高原为代表的中低纬高原山地的温度变化特征。结果表明,随着大气CO2含量的增加,中低纬高原山地气候会显著变暖。地面温度的增加以最低气温最大,其次是平均气温,而最高气温最小。同时,寒冷季节的增温大于温暖季节。而且,气候变暖对海拔高度具有明显的依赖性,即增温幅度通常随海拔高度的增加而增大。青藏高原及其邻近地区（25°～42°N,70°110°E）1～1．5km,3～3．5km和5～5．5km三个高程范围内年平均（最低）气温的增温率分别为3．0（3．3）,3．5（3．7）和4．6（5．4）℃／100a,而冬半年平均（最低）气温的增温率分别达到3．1（3．7）,3．8（4．2）和4．9（5．9）℃／100a。     

贵州“3·23世界气象日”纪念活动丰富多彩

2009年“3·23世界气象日”的纪念主题是“天气、气候和我们呼吸的空气：保护人类健康和环境”。为了隆重纪念这一日子,贵州省气象局开展了丰富多彩的纪念活动：     

Strategies to adapt to an uncertain climate change

Many decisions concerning long-lived investments already need to take into account climate change. But doing so is not easy for at least two reasons. First, due to the rate of climate change, new infrastructure will have to be able to cope with a large range of changing climate conditions, which will make design more difficult and construction more expensive. Second, uncertainty in future climate makes it impossible to directly use the output of a single climate model as an input for infrastructure design, and there are good reasons to think that the needed climate information will not be available soon. Instead of optimizing based on the climate conditions projected by models, therefore, future infrastructure should be made more robust to possible changes in climate conditions. This aim implies that users of climate information must also change their practices and decision-making frameworks, for instance by adapting the uncertainty-management methods they currently apply to exchange rates or R&D outcomes. Five methods are examined: (i) selecting “no-regret” strategies that yield benefits even in absence of climate change; (ii) favouring reversible and flexible options; (iii) buying “safety margins” in new investments; (iv) promoting soft adaptation strategies, including long-term prospective; and (v) reducing decision time horizons. Moreover, it is essential to consider both negative and positive side-effects and externalities of adaptation measures. Adaptation–mitigation interactions also call for integrated design and assessment of adaptation and mitigation policies, which are often developed by distinct communities.     

How to attribute market leakage to CDM projects

Abstract

Economic studies suggest that market leakage rates of greenhouse gas abatement can reach the two-digit percentage range. Although the Marrakesh Accords require Clean Development Mechanism (CDM) projects to account for leakage, most projects neglect market leakage. Insufficient leakage accounting is facilitated by a lack of applicable methods regarding the quantification and attribution of project-related leakage effects. This article proposes a method for attributing CDM-related market leakage effects to individual projects. To this purpose, alternative attribution methods are analysed. We find that project-specific approaches fail to take account of market leakage effects. Consequently, we propose to estimate aggregate market leakage effects and attribute them proportionally to individual projects. We suggest that predetermined commodity-specific leakage factors are applied by project developers to any emission reductions that are associated with a project's leakage-relevant demand or supply changes. This approach is conservative, equitable, incentive-compatible and applicable at manageable costs.     

An approach to adaptation to climate changes in Poland

Adopted by COP 10 (Dec 1/CP.10) and approved by the MOP1, the Buenos Aires programme of adaptation and response measures opens doors to intensify preparations for expected climate change. By this decision the COP, requested the SBSTA to develop a structured 5-year programme of work of the SBSTA on the scientific, technical and socio-economic aspects of impacts of, and vulnerability and adaptation to, climate change. Consequently, the COP, by its decision 2/CP.11, adopted the “Five-year programme of work of the Subsidiary Body for Scientific and Technological Advice on impacts, vulnerability and adaptation to climate change” Finally during COP12 this programme was approved as “Nairobi Work Programme on impacts, vulnerability and adaptation to climate change”. This programme has fundamental significance not only for developing countries, but also for industrialized nations in which some sectors of the or social life are particularly vulnerable to climate change, specifically, inter alia EIT countries and new EU Member States. Further development of this adaptation programme economy should contain steps that provide optimum economic and social effectiveness, risk management, identification of vulnerable sectors and gaps in knowledge, preparation of a list of policy options, including an analysis of cost effectiveness, selection of the most effective policies, and a preparedness implementation plan. In Poland the preliminary adaptation programme covered agriculture, water management, and coastal zone management. For the time being, gaps in knowledge and preparedness measures have been identified. An estimation of possible impact on these areas was based on chosen GCMs, and sea level rise IPCC scenarios. In conclusion, it was stated that the results achieved should be seen as a first step forward and a more comprehensive study is necessary to update the results and cover other sectors of the economy, such as health protection, spatial planning, ecosystems and forestry, and to develop specific guidelines and recommendations for policy-makers.     

荔波菊苣引种试验

菊苣为多年生草本植物,主要用作畜牧饲料,有较高的经济价值,对发展畜牧业有重要意义,通过菊苣引种试验成功分析,提出菊苣适合在荔波县推广种植。     

Framing the way to relate climate extremes to climate change

The atmospheric and ocean environment has changed from human activities in ways that affect storms and extreme climate events. The main way climate change is perceived is through changes in extremes because those are outside the bounds of previous weather. The average anthropogenic climate change effect is not negligible, but nor is it large, although a small shift in the mean can lead to very large percentage changes in extremes. Anthropogenic global warming inherently has decadal time scales and can be readily masked by natural variability on short time scales. To the extent that interactions are linear, even places that feature below normal temperatures are still warmer than they otherwise would be. It is when natural variability and climate change develop in the same direction that records get broken. For instance, the rapid transition from El Ni?o prior to May 2010 to La Ni?a by July 2010 along with global warming contributed to the record high sea surface temperatures in the tropical Indian and Atlantic Oceans and in close proximity to places where record flooding subsequently occurred. A commentary is provided on recent climate extremes. The answer to the oft-asked question of whether an event is caused by climate change is that it is the wrong question. All weather events are affected by climate change because the environment in which they occur is warmer and moister than it used to be.     

如何将DOMINO邮件数据库移动到另外一个分区

随着用户的增多,MOMINO邮件服务器上的硬盘空间越来越少,如果不增加硬盘空间,将会严重影响到系统的运行,甚至死机。一个解决方案是:为服务器新增一个大硬盘,将数据库文件移到新硬盘上。如何以最小的代价、最快的速度、尽量不影响用户的正常使用来完成此项工作呢?通过查询相关资     

利用防火墙软件实现市、县局用户共享上网

介绍采用WinRoute Firewall软件实现市、县局用户共享上网的方法,使市县局用户既能共享网络资源,又能有效地保障网络安全.     

如何将DOMINO邮件数据库移动到另外一个分区

随着用户的增多,MOMINO邮件服务器上的硬盘空间越来越少,如果不增加硬盘空间,将会严重影响到系统的运行,甚至死机.一个解决方案是:为服务器新增一个大硬盘,将数据库文件移到新硬盘上.如何以最小的代价、最快的速度、尽量不影响用户的正常使用来完成此项工作呢?通过查询相关资料,并做了实验,发现有两种方法相对简单.     

利用防火墙软件实现市、县局用户共享上网

介绍采用W inRoute F irewall软件实现市、县局用户共享上网的方法,使市县局用户既能共享网络资源,又能有效地保障网络安全。     

Linear response functions to project contributions to future sea level

We propose linear response functions to separately estimate the sea-level contributions of thermal expansion and solid ice discharge from Greenland and Antarctica. The response function formalism introduces a time-dependence which allows for future rates of sea-level rise to be influenced by past climate variations. We find that this time-dependence is of the same functional type, R(t) ～ t ^α, for each of the three subsystems considered here. The validity of the approach is assessed by comparing the sea-level estimates obtained via the response functions to projections from comprehensive models. The pure vertical diffusion case in one dimension, corresponding to α =  ?0.5, is a valid approximation for thermal expansion within the ocean up to the middle of the twenty first century for all Representative Concentration Pathways. The approximation is significantly improved for α =  ? 0.7. For the solid ice discharge from Greenland we find an optimal value of α =  ?0.7. Different from earlier studies we conclude that solid ice discharge from Greenland due to dynamic thinning is bounded by 0.42 m sea-level equivalent. Ice discharge induced by surface warming on Antarctica is best captured by a positive value of α = 0.1 which reflects the fact that ice loss increases with the cumulative amount of heat available for softening the ice in our model.