2010年国家级和自治区级业务预测产品对内蒙古短期气候预测能力的检验

对2010年1-12月和春季(3-5月)、夏季(6-8月)国家气候中心和内蒙古气候中心的短期气候预测业务质量进行评分,以检验国家级和自治区级气候预测业务对内蒙古的预测能力.通过对比分析表明:总体来看,2010年短期气候预测质量检验评分内蒙古气候中心91.7％的月份温度预测和75％的月份降水预测高于国家气候中心,其中,春季温度预测较差;夏季降水预测较差.平均而言,业务预测质量明显高于模式产品质量.因此,需要在内蒙古业务预测分析参考时注意,降水预测的指导能力相对较弱.     

From Conference to Conference

Jill Jäger was Head of the Climate Programme of the Stockholm Environment Institute during the time that she coordinated the AGGG ad hoc Working Groups and edited the Proceedings of the Second World Climate Conference. She is now Co-Director of the Climate Policy Division of the Wuppertal Institute for Climate, Environment and Energy in Germany.     

利马气候大会成果分析与展望

利马气候大会达成的《利马气候行动号令》明确了《联合国气候变化框架公约》所有原则,尤其是共同但有区别的责任原则适用于拟于2015年巴黎会议达成的新协议。会议还就“国家自主决定的贡献”等做出了决定。利马大会成果对2015年巴黎气候大会达成气候变化新协议具有重要推动作用。面对2020年后的新挑战,中国需尽快制定应对气候变化法,建立碳排放交易机制,促进对应对气候变化先进技术投资,以及加大参与治理国际气候变化事务力度。     

1995年热带太平洋海-气特征诊断分析

利用国家气候中心气候诊断资料和美国ＣＡＣ和澳大利亚ＮＣＣ资料，对１９９５年热带太平洋海域的海平面高度，南方涛动指数、高、低层纬向风，射出长波辐射，海温等物理量的分布及其演特征进行分析，为短期气候预测提供了大尺度的海－气环境背景。     

1995年热带太平洋海－气特征诊断分析

利用国家气候中心气候诊断资料和美国ＣＡＣ和澳大利亚ＮＣＣ资料，对１９９５年热带太平洋海域的海平面高度，南方涛动指数，高、低层纬向风，射出长波辐射，海温等物理量的分布及其演变特征进行了分析，为短期气候预测提供了大尺度的海－气环境背景。     

Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model

Climate Dynamics - The version of the Norwegian Climate Prediction Model (NorCPM) that only assimilates sea surface temperature (SST) with the Ensemble Kalman Filter has been used to investigate...     

Inter-annual variability and skill of tropical rainfall and SST in APCC seasonal forecast models

Climate Dynamics - The present study explored the performance of the current coupled models obtained from the Asia Pacific Economic Cooperation (APEC) Climate Centre (APCC) in representing the...     

基于卫星遥感的新疆地表太阳总辐射估算

为了实现地表太阳总辐射合理的精细化模拟,本文尝试将天文辐射分布式理论模型和总辐射气候学经验模型相结合,引入重采样后的FY-2G卫星遥感总云量资料,建立了基于卫星遥感数据的地表太阳总辐射估算模型,并以气象站点稀疏的新疆为例,完成年、季地表太阳总辐射的精细化空间模拟,同时对模拟结果进行分析和检验。结果表明:(1)新疆区域年天文辐射量由南向北递减,大致以天山为界,天山以南区域的年天文辐射量高于10 000 MJ·m^-2,天山以北低于9750 MJ·m^-2,三大山脉对天文辐射的影响非常明显;(2)基于条带状重采样后的FY-2G总云量建立的日照百分率模型,其模拟的新疆区域平均绝对误差14.4%,且空间分布更加客观;(3)新疆"单站单月式"地表太阳总辐射气候学估算模型中,相关系数在夏半年较高,冬半年略有下降,且a、b系数的互补关系较为稳定;(4)从地表太阳总辐射检验结果来看,全区地表太阳总辐射的均方根误差年平均3.08 MJ·m^-2,模拟结果夏半年好于冬半年,南疆好于北疆,其中乌鲁木齐误差最大;(5)新疆年地表太阳总辐射整体表现为由西北向东南逐渐增加的空间分布,南疆盆地的总辐射量高于北疆盆地,天山山区西部为低值中心,而春、夏季总辐射由西向东呈经向分布,秋、冬季则呈纬向分布。     

气候变化影响与风险评估方法的研究进展

气候变化影响是指气候变化背景下社会经济或资源环境的响应。气候变化风险是指由于气候变化所引起的社会经济或资源环境的可能损失。气候变化风险评估是对气候变化影响的定性和对风险的量化。针对气候变化风险评估方法的原理和技术体系,本文从风险指数、风险概率和脆弱性评估三个方面,对研究现状、热点问题和通常方法进行了评述,并对当前研究中存在的问题和未来需求进行了归纳和展望。     

多种土壤湿度资料在中国地区的对比分析

利用1992—2012年中国区域土壤湿度观测资料对目前使用较为广泛的5套土壤湿度资料(ERA-Interim、NCEP再分析资料、GLDAS同化资料、CPC模式资料和AMSR-E卫星反演资料)的适用性进行检验,分析5套资料对中国区域土壤湿度的时空分布特征和变化趋势的描述能力。结果表明:5套资料都能大体反映暖季中国区域土壤湿度东南湿、西北干,自东南向西北递减的分布格局以及西北干旱区和半干旱区变湿、长江流域部分地方变干的变化趋势,其中CPC资料最接近观测事实,并能较好地表现局地特征;在描述土壤湿度的季节变化和年际变化方面,GLDAS和NCEP资料与观测数据的相关性较好,能较好反映土壤湿度的时间演变特征。进一步利用检验效果较好的GLDAS、NCEP和CPC资料分别对中国区域土壤湿度时间和空间的长期变化趋势分析发现:中国区域标准化年平均土壤湿度在1948—1996年处在相对湿润期,而1996—2012年处在相对干旱期;中国区域年平均土壤湿度的空间变化特征是东部变干、西部变湿,自东北、华北至西南呈现一个干旱化带。     

不同水平分辨率BCC_CSM模式对中亚地面气温模拟能力评估

本研究基于IPCC AR5(Intergovernmental Panel on Climate Change:Fifth Assessment Report)中BCC_CSM1.1(Beijing Climate Center Climate System Model version 1.1)和BCC_CSM1.1(m)(Beijing Climate Center Climate System Model version 1.1 with a Moderate Resolution)气候模式的历史试验结果和CRU(Climatic Research Unit)资料, 采用趋势分析和滑动平均等方法检验了两个版本BCC_CSM模式对中亚地区1948～2011年平均地表气温、各热通量及其趋势的模拟性能, 并讨论了不同模式水平分辨率的影响, 结果显示:BCC_CSM1.1和BCC_CSM1.1(m)两个模式均能够模拟出中亚地区显著增温以及感热通量、长/短波净辐射等要素由南向北递减的总体趋势。其中, BCC_CSM1.1(m) 在对中亚地面年平均气温、感热通量和长/短波净辐射空间分布的模拟结果好于BCC_CSM1.1, 但对于气温标准差的模拟, BCC-CSM1.1模式略好于BCC-CSM1.1(m)。模式分辨率的提高, 能够更好地表现出地形的影响, 对气温和各热通量模拟性能改善较大, 在中亚地区年平均气温的模拟中表现出了一定的优势。     

Climate change projections for the Texas High Plains and Rolling Plains

Potential changes in future climate in the Texas Plains region were investigated in the context of agriculture by analyzing three climate model projections under the A2 climate scenario (medium–high emission scenario). Spatially downscaled historic (1971–2000) and future (2041–2070) climate datasets (rainfall and temperature) were downloaded from the North American Regional Climate Change Assessment Program (NARCCAP). Climate variables predicted by three regional climate models (RCMs) namely the Regional Climate Model Version3–Geophysical Fluid Dynamics Laboratory (RCM3-GFDL), Regional Climate Model Version3–Third Generation Coupled Global Climate Model (RCM3-CGCM3), and Canadian Regional Climate Model–Community Climate System Model (CRCM-CCSM) were evaluated in this study. Gaussian and Gamma distribution mapping techniques were employed to remove the bias in temperature and rainfall data, respectively. Both the minimum and maximum temperatures across the study region in the future showed an upward trend, with the temperatures increasing in the range of 1.9 to 2.9 °C and 2.0 to 3.2 °C, respectively. All three climate models predicted a decline in rainfall within a range of 30 to 127 mm in majority of counties across the study region. In addition, they predicted an increase in the intensity of extreme rainfall events in the future. The frost-free season as predicted by the three models showed an increase by 2.6–3.4 weeks across the region, and the number of frost days declined by 17.9 to 30 %. Overall, these projections indicate considerable changes to the climate in the Texas Plains region in the future, and these changes could potentially impact agriculture in this region.

     

Spuriously induced precipitation trends in the southeast United States

This study uses correlation and multiple regression techniques to document differences in annual and seasonal precipitation trends between the NCDC Climate Division database and the United States Historical Climate Network (USHCN) in the southeast United States. Findings indicate that the majority of climate divisions have different temporal patterns than those depicted by the USHCN. They did not, however, consistently possess statistically significant relationships between the ratio (CDD/USHCN) and changes in mean station location as noted in other studies. It appears that other influences cause the majority of the variance between the two datasets. The fact that the two datasets do not consistently agree, however, suggests that spuriously induced trends may be present in the NCDC Climate Division database.     

Brief Review of Some CLIVAR-Related Studies in China

The Climate Variability and Predictability （CLIVAR） program is one of the sub-programs of the World Climate Research Program （WCRP）. In this paper, CLIVAR related research in China （2003-2006） is briefly reviewed, including four major components, namely, low-frequency intraseasonal oscillations, interannual variability, decadal variations in East Asia, and global warming simulations.     

大兴安岭植被NDVI变化及其对气候的响应

利用S-G(Savitsky-Golay)滤波对2000—2016年呼伦贝尔市境内的大兴安岭植被NDVI序列进行逐栅格重构并剔除突变点,结合大兴安岭海拔、坡向、坡度等地理因子和气温、降水等气象因子,分析大兴安岭植被生长季NDVI的变化及其对气候的响应。结果表明:大兴安岭植被NDVI生长季均值呈上升趋势,平均增速为0. 029/10 a。植被NDVI随海拔的增加呈现先缓慢减小后迅速增加的趋势,随坡向变化不大,随坡度的增加而增加。植被NDVI的生长季均值与1 a前和2 a年前的降水呈现显著的正相关,具有明显的滞后性。在每年植被恢复生长初期各海拔植被均与气温呈现极显著的正相关,年内植被NDVI与降水的相关性在垂直方向上存在较大差异。     

大涡模式水平分辨率对边界层夹卷过程及示踪物垂直传输的影响

利用敦煌干旱区野外加密观测资料,结合大涡模式模拟研究模式水平分辨率对边界层对流、夹卷过程及示踪物垂直传输的影响。结果表明:模式水平分辨率越高,模拟的边界层对流泡个数越多,尺度越小,且对流强度越强;提高模式水平分辨率,夹卷层位温方差增大,水平速度方差减小,垂直速度方差增大,且上升冷气流对夹卷层热通量的贡献最大。模式水平分辨率越高,垂直速度、位温及示踪物绝对质量浓度概率密度函数分布变化范围相对越广,且模拟的细微变化特征越清晰。另外,提高模式水平分辨率,模拟的示踪物空间分布特征更加细致,示踪物传输高度也较高。综合考虑到分辨率越高在模拟过程中产生的噪音越大且计算时间越久等问题,认为采用200 m水平分辨率时,模式既能较好地模拟出边界层对流的平均结构,又能模拟出边界层湍流的较细微分布特征,是较为理想的选择。     

1961—2010年中国气候生产潜力时空格局变化及其潜在可承载人口分析

以气温、降水格点数据为基础,采用Thornthwaite Memorial模型计算了中国气候生产潜力(CPP),并从气候的角度估算了耕地上气候资源潜在可承载的人口数,以便增强了解气候变化的影响及气候资源最大人口支撑能力。结果表明:1961—2010年中国CPP总体呈突变性增加趋势,1987年为突变点,年最低、最高及平均值分别为689. 18、814. 56和744. 05 g·m^-2·a^-1。空间上呈现出从西北向东南逐渐递增的带状分布,其中高值区主要分布在华南大部,最高值达2103. 56 g·m^-2·a^-1;低值区主要分布在西、北部地区,最低值为39. 28 g·m^-2·a^-1。2001—2010年中国大部分地区CPP年平均值相对于1961—2010年多年平均值变化幅度不大,变化比例高的地区基本上分布于中国西、北部,其中增加的区域达82%,主要分布在华东地区、新疆西部、西藏北部及青海大部,远大于缩减的区域(17%)。1995—2010年,基于公里网格的耕地气候潜在可承载人口为46—2180人·km^-2,全国平均值最低的年份为1130人·km^-2,对应的实际人口为0—49729人·km^-2,全国平均值均不高于137人·km^-2;全国实际总人口为11. 43—13. 04亿,耕地气候潜在可承载总人口为19. 72—20. 22亿,前后比值为58%—65%。这表明,中国耕地生产力未达到气候生产潜力,尚有一定的开发潜力;实际人口在中国大部分地区均没有超出气候资源潜在可承载的最大人口,然而在少数省市(如生态环境脆弱的青海省以及经济发达的大城市及沿海地区)已超出。     

中国区域气候模式对1991年江淮流域特大暴雨过程的模拟

文章引进了美国NCAR的区域气候模式，并对该模式进行了改造和改进，建立了能适合中国区域性气候特征，模拟中国区域性气候的模式。在此基础上运用该模式对1991年夏季长江流域强降水过程进行了模拟计算，结果表明，该模式具有模拟长江流域强降水过程的能力。     

Stochastic Post-Processing of CFSR Daily Precipitation across Canada

Reanalyses, based on numerical weather prediction methods assimilating past observations, provide continuous precipitation datasets and represent interesting options for assessing the climatology of regions with sparse station networks (e.g., northern Canada). However, reanalysis series cannot be used directly because of possible biases and mismatch between their spatial and temporal resolutions with that needed for local applications. To address these issues, a Stochastic Model Output Statistics (SMOS) approach was selected to post-process precipitation series simulated by the Climate Forecast System Reanalysis (CFSR) across Canada. This approach uses CFSR precipitation as a covariate and is based on two regression models: the first one is a logistic regression that deals with precipitation occurrence, and the second is a vector generalized linear model for precipitation intensity. At-site post-processed daily precipitation series are randomly generated using the SMOS approach, and selected climate indicators from the Expert Team on Climate Change Detection and Indices, which is jointly sponsored by the Commission for Climatology of the World Meteorological Organization's (WMO) World Climate Data and Monitoring Programme, the Climate Variability and Predictability Programme of the World Climate Research Programme, and the Joint WMO-IOC Technical Commission for Oceanography and Marine Meteorology (CCI/CLIVAR/JCOMM) are estimated and compared with corresponding observed and CFSR values. The two models in the SMOS approach, in addition to adequately correcting systematic biases, produced better predictions than the climatology of the wet and dry and intensity sequences. Additionally, the SMOS generally yields consistent climate indices when compared with those from CFSR without post-processing, though there is still room for improvement for specific indices (e.g., annual maximum of cumulative wet days).