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1.
利用共同经验正交函数(EOF)分解和逐步线性回归相结合的统计降尺度方法,研究了1月和7月华东地区41个气象观测站2070—2100年未来月平均温度变化情景的集合预估。同时采用850 hPa温度场、850 hPa位势高度和温度的联合场以及海平面气压和850 hPa温度的联合场作为预报因子变量场,对于两个场联合的预报因子变量场,采用的是两个变量场空间联合的EOF分解的方法。同时通过改变统计降尺度过程中输入的预报因子变量场、预报因子变量取值的区域,以及输入逐步线性回归方程的主分量个数共建立27种统计降尺度模型,并把它们应用于2种全球气候模式(GCMs):Echam5和HadCM3 IPCC AR4 20C3M和A1b情景,从而每个站点均生成1950—2099年(HadCM3)或1951—2100年(Echam5)1月和7月共54个IPCC TR4 A1b温度变化情景,然后对54种预估情景进行集合分析。多个温度变化情景的集合预估采用它们的中位数来表示。结果表明:(1)当前气候条件下,多个统计降尺度结果的集合预报如采用箱线图的中位数能够在一定程度上提高统计降尺度方法的模拟性能;(2)2070—2100年1月和7月未来气温情景相比当前气候条件的增温约3～4℃,7月与1月相比不确定性增大。 相似文献
2.
典型相关分析方法对21世纪江淮流域极端降水的预估试验 总被引：6，自引：0，他引：6
利用1961—1990年江淮流域逐日降水资料、NCEP/NCAR再分析资料和HadCM3 SRES A1B情景下模式预估资料,采用典型相关分析统计降尺度方法,评估降尺度模型对当前极端降水指数的模拟能力,并对21世纪中期和末期的极端降水变化进行预估。结果表明:通过降尺度能够有效改善HadCM3对区域气候特征的模拟能力,极端降水指数气候平均态相对误差降低了30%～100%,但降尺度结果仍然在冬季存在湿偏差、夏季存在干偏差;在SRES A1B排放情景下,该区域大部分站点的极端强降水事件将增多,强度增大,极端强降水指数的变化幅度高于平均降水指数,且夏季增幅高于冬季;冬季极端降水贡献率(R95t)在21世纪中期和末期的平均增幅分别为14%和25%,夏季则分别增加24%和32%。 相似文献
3.
基于统计降尺度模型的江淮流域极端气候的模拟与预估 总被引：4，自引：0，他引：4
利用江淮流域29个代表站点1961--2000年逐日最高温度、最低温度和逐日降水资料,以及NCEP逐日大尺度环流场资料,引入基于多元线性回归与随机天气发生器相结合的统计降尺度模型SDSM（statistical downscalingmodel）,通过对每个站点建模,确立SDSM参数,并将该模型应用于SRESA2排放情景下HadCM3和cGcM3模式,得到了江淮流域各代表台站21世纪的逐日最高、最低温度和降水序列以及热浪、霜冻、强降水等极端气候指数。结果表明,当前气候下,统计降尺度方法模拟的极端温度指数与观测值有很好的一致性,能有效纠正耦合模式的“冷偏差”,如SDSM对江淮平均的冬季最高、最低温度的模拟偏差较CGCM3模式分别减少3℃和4．5℃。对于极端降水则能显著纠正耦合模式模拟的降水强度偏低的问题,如CGCM3对江淮流域夏季降水强度的模拟偏差为-60．6％,但降尺度后SDSM—CGCM3的偏差仅为-6％,说明降尺度模型SDSM的确有“增加值”的作用。21世纪末期在未来SRESA2情景下,对于极端温度,无论Had．CM3还是CGCM3模式驱动统计模型,江淮流域所有代表台站,各个季节的最高、最低温度都显著增加,且以夏季最为显著,增幅在2—4℃;与之相应霜冻天数将大幅减少,热浪天数大幅增多,各站点冬季霜冻天数减少幅度为5—25d,夏季热浪天数增加幅度为4～14d;对于极端降水指数,在两个不同耦合模式HadCM3和CGCM3驱动下的变化尤其是变化幅度的一致性比温度差,但大部分站点各个季节极端强降水事件将增多,强度增强,SDSM—HadCM3和SDSM-CGCM3预估的夏季极端降水贡献率将分别增加26％和27％。 相似文献
4.
利用5个全球气候模式和中国东北地区162个站点地面温度实测资料,评估全球气候模式和多模式集合平均对中国东北地区地面温度的模拟能力,并对SRES B1、A1B和A2排放情景下,中国东北地区未来地面温度变化进行预估。结果表明：全球气候模式能够较好地再现了东北地区地面温度的年变化和空间分布特征,但存在系统性冷偏差,模式对夏季地面温度模拟偏低1.16 ℃,优于冬季。预估结果表明,3种排放情景下21世纪中期和末期东北地区地面温度均将升高,末期增幅高于中期,冬季增幅高于其他季节, SRES A2排放情景下增幅最大,B1排放情景下最小;增温幅度自南向北逐渐增大,增温最显著地区位于黑龙江小兴安岭;21世纪末期3种情景下中国东北地区年平均地面温度将分别升高2.39 ℃(SRES B1)、3.62 ℃(SRES A1B)和4.43 ℃(SRES A2)。 相似文献
5.
利用中亚地区30个观测台站逐月降水资料及同期ERA-40再分析资料,结合8个CMIP5全球气候模式模拟与未来预估大尺度环流场,使用基于变形典型相关分析的统计降尺度方法（BP-CCA）建立降尺度模型,评估多个气候模式对当前气候下中亚地区春季降水的降尺度模拟能力,并对春季降水进行降尺度集合未来预估。结果表明,建立的降尺度模型能够很好地模拟出交叉检验期内春季降水的时间变化和空间结构：降尺度春季降水与相应观测序列的平均时间相关系数为0.35,最高为0.62,平均空间相关系数为0.87。气候模式对中亚春季降水的模拟能力通过降尺度方法得到了显著提高：8个模式降尺度后模拟的降水气候平均态相对误差绝对值降至0.2%—8%,相比降尺度前减小了10%—60%,模拟的降水量场与相应观测场的空间相关均超过0.77;对比降尺度前多模式集合结果,多模式降尺度集合模拟的相对误差绝对值由64%减小至4%,空间相关系数由0.47增大至0.81,标准化均方根误差降至0.59,且多模式降尺度集合结果优于大部分单个模式降尺度结果。多模式降尺度集合预估结果表明,在RCP4.5排放情景下,21世纪前期（2016—2035年）、中期（2046—2065年）和末期（2081—2100年）的全区平均降水变化率分别为-5.3%、3.0%和17.4%。21世纪前期中亚大部分地区降水呈减少趋势,降水呈增多趋势的站点主要分布在南部。21世纪中期整体降水变化率由减少变为增多趋势,21世纪末期中亚大部分台站降水增多较为明显。21世纪初期和末期可信度高的台站均主要位于中亚西部地区。 相似文献
6.
基于ASD（automated statistical downscaling）统计降尺度模型提供的多元线性回归和岭回归两种统计降尺度方法,采用RCP4.5（representative concentration pathways 4.5）和RCP8.5情景下全球气候模式MPI-ESM-LR输出的预报因子数据、NCEP/NCAR再分析数据和秦岭山地周边10个气象站观测数据,评估两种统计降尺度方法在秦岭山地的适用性及预估秦岭山地未来3个时期（2006-2040年、2041-2070年和2071-2100年）的平均气温和降水。结果表明:率定期和验证期内,两种统计降尺度方法均可以较好地模拟研究区域的平均气温和降水的变化特征,且多元线性回归的模拟效果优于岭回归。在未来气候情景下,两种统计降尺度方法预估的研究区域平均气温均呈明显上升趋势,气温增幅随辐射强迫增加而增大。降水方面,21世纪未来3个时期降水均呈不明显减少趋势,但季节分配发生变化。综合考虑两种统计降尺度方法在秦岭山地对平均气温和降水的模拟效果和情景预估结果,认为多元线性回归降尺度方法更适用于秦岭山地气候变化的降尺度预估研究。 相似文献
7.
在比较不同大尺度预报因子联合场、空间范围和数据预处理方法对统计降尺度模型预测能力影响的基础上,基于HadCM3和CCSM3两种分辨率大气环流模式(GCM)输出资料,采用主成分分析和多元线性回归相结合的降尺度方法,建立了渭河流域秋雨的统计降尺度模型,并根据分析两种GCM基准期内降尺度预测效果,对未来不同情景进行预估。结果表明,对降水量的3次方根正态化处理并不能改善模型的预测性能,而去趋势处理可提高模型对于9月的预测能力。850 hPa经向风和相对湿度组合是渭河流域秋雨的最佳预测因子。低分辨率的HadCM3模式降尺度预测效果优于高分辨率的CCSM3模式,具体表现在前者对整个流域的预测能力较高,后者只对中下游流域附近的预测效果较好。以基准期内可预报性较好的站点对渭河流域秋雨进行预测,两种模式的不同情景预估结果均表明,渭河流域2000—2099年9月降水量呈增加趋势,10月降水量呈下降趋势,降尺度模型成功预测了21世纪前10年的秋雨增加趋势。 相似文献
8.
利用澜沧江流域1951—2008年的降水和气温观测资料以及多模式集成的21世纪(2010—2099年)不同情景下(SRES A1B、SRES A2和SRES B1)气候变化模拟试验的预估结果,分析了该流域过去58年降水和气温的变化,并预估了未来90年的气候变化趋势。结果表明,在全球增暖的大背景下,过去58年澜沧江流域的年降水量下降了46.4mm,气温有所上升,升温率达到了0.15℃/10a。在未来的90年,无论在哪种排放情景下,降水都表现为明显的上升趋势,而且相对于过去58年的结果,3种不同情景下降水的年代际变率都有所增加,其中A2情景值最大,B1情景值最小。年平均气温无论是在过去的58年还是在未来的90年都以明显的上升趋势为主,3种情景下气温的升温率远远超过过去58的结果。 相似文献
9.
21世纪黄河流域上中游地区气候变化趋势分析 总被引：10，自引：0，他引：10
气候变化预估常用的全球气候模式（GCM）难以提供区域或更小尺度上可靠的逐日气候要素序列，针对这一问题，应用统计降尺度模型（statistical downscaling model，SDSM）将HadCM3的模拟数据（包括A2、B2两种情景）处理为具有较高可信度的逐日站点序列。以1961-1990年为基准期，分析了21世纪黄河流域上中游地区未来最高气温、最低气温与年降水量的变化。在A2、B2两种气候变化情景下，日最高气温、日最低气温均呈升高趋势；但A2的变化较显著，日最高气温的升高趋势在景泰站最明显，日最低气温的升高趋势在河曲站最显著。流域平均的年降水量变化范围为-18.2%～13.3%。A2情景下降水量增加和减少的面积基本相等，宝鸡站降水量增加最多；B2情景下大部分区域降水减少，西峰镇降水量减少最显著。 相似文献
10.
气候变化预估常用的全球气候模式（GCM）难以提供区域或更小尺度上可靠的逐日气候要素序列，针对这一问题，应用统计降尺度模型（statistical downscaling model，SDSM）将HadCM3的模拟数据（包括A2、B2两种情景）处理为具有较高可信度的逐日站点序列。以1961-1990年为基准期，分析了21世纪黄河流域上中游地区未来最高气温、最低气温与年降水量的变化。在A2、B2两种气候变化情景下，日最高气温、日最低气温均呈升高趋势；但A2的变化较显著，日最高气温的升高趋势在景泰站最明显，日最低气温的升高趋势在河曲站最显著。流域平均的年降水量变化范围为-18.2%～13.3%。A2情景下降水量增加和减少的面积基本相等，宝鸡站降水量增加最多；B2情景下大部分区域降水减少，西峰镇降水量减少最显著。 相似文献
11.
FAN Li-Jun 《大气和海洋科学快报》2009,2(4):208-213
Monthly mean temperatures at 562 stations in China are estimated using a statistical downscaling technique. The technique used is multiple linear regressions （MLRs） of principal components （PCs）. A stepwise screening procedure is used for selecting the skilful PCs as predictors used in the regression equation. The predictors include temperature at 850 hPa （7）, the combination of sea-level pressure and temperature at 850 hPa （P＋T） and the combination of geo-potential height and temperature at 850 hPa （H＋T）. The downscaling procedure is tested with the three predictors over three predictor domains. The optimum statistical model is obtained for each station and month by finding the predictor and predictor domain corresponding to the highest correlation. Finally, the optimum statistical downscaling models are applied to the Hadley Centre Coupled Model, version 3 （HadCM3） outputs under the Special Report on Emission Scenarios （SRES） A2 and B2 scenarios to construct local future temperature change scenarios for each station and month, The results show that （1） statistical downscaling produces less warming than the HadCM3 output itself; （2） the downscaled annual cycles of temperature differ from the HadCM3 output, but are similar to the observation; （3） the downscaled temperature scenarios show more warming in the north than in the south; （4） the downscaled temperature scenarios vary with emission scenarios, and the A2 scenario produces more warming than the B2, especially in the north of China. 相似文献
12.
Long-Term Trend of Temperature Derived by Statistical Downscaling Based on EOF Analysis 总被引：1，自引：0，他引：1 
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下载免费PDF全文 This study analyzes the ability of statistical downscaling models in simulating the long-term trend of temperature and associated
causes at 48 stations in northern China in January and July 1961–2006. The statistical downscaling models are established
through multiple stepwise regressions of predictor principal components (PCs). The predictors in this study include temperature
at 850 hPa (T850), and the combination of geopotential height and temperature at 850 hPa (H850+T850). For the combined predictors,
Empirical Orthogonal Function (EOF) analysis of the two combined fields is conducted. The modeling results from HadCM3 and
ECHAM5 under 20C3M and SERS A1B scenarios are applied to the statistical downscaling models to construct local present and
future climate change scenarios for each station, during which the projected EOF analysis and the common EOF analysis are
utilized to derive EOFs and PCs from the two general circulation models (GCMs). The results show that (1) the trend of temperature
in July is associated with the first EOF pattern of the two combined fields, not with the EOF pattern of the regional warming;
(2) although HadCM3 and ECHAM5 have simulated a false long-term trend of temperature, the statistical downscaling method is
able to well reproduce a correct long-term trend of temperature in northern China due to the successful simulation of the
trend of main PCs of the GCM predictors; (3) when the two-field combination and the projected EOF analysis are used, temperature
change scenarios have a similar seasonal variation to the observed one; and (4) compared with the results of the common EOF
analysis, those of the projected EOF analysis have been much more strongly determined by the observed large-scale atmospheric
circulation patterns. 相似文献
13.
哈萨克斯坦是世界最大的内陆国家,拥有典型的大陆性气候和多样的地理环境及生态系统,同时哈萨克斯坦的自然环境和人类社会对于气候变化这一全球性问题是敏感的、脆弱的,需要运用科学的研究方法应对气候变化的挑战。通常,区域或局地尺度的气候变化影响研究需要对气候模式输出或再分析资料进行降尺度以获得更细分辨率的气候资料。近年来,大量验证统计降尺度方法在各个地区能力的研究见诸文献,然而在哈萨克斯坦地区验证统计降尺度方法的研究非常少见。本文使用了岭回归的方法对哈萨克斯坦地区11个气象站点1960~2009年的月平均气温进行了统计降尺度研究。结果显示,使用前30年数据和岭回归模型建立大尺度预报因子和观测资料的统计关系可以较好地预测后20年的月平均气温,预测能力在各站各月均有不同程度的差异,地形复杂的站点预测效果较差,夏季预测结果好于冬季;此外,将哈萨克斯坦地区平均来看则与观测数据相吻合。 相似文献
14.
A multi-model ensemble approach for assessment of climate change impact on surface winds in France 总被引：2，自引：1，他引：1
Statistical downscaling of 14 coupled atmosphere-ocean general circulation models (AOGCM) is presented to assess potential
changes of the 10 m wind speeds in France. First, a statistical downscaling method is introduced to estimate daily mean 10 m
wind speed at specific sites using general circulation model output. Daily 850 hPa wind field has been selected as the large
scale circulation predictor. The method is based on a classification of the daily wind fields into a few synoptic weather
types and multiple linear regressions. Years are divided into an extended winter season from October to March and an extended
summer season from April to September, and the procedure is conducted separately for each season. ERA40 reanalysis and observed
station data have been used to build and validate the downscaling algorithm over France for the period 1974–2002. The method
is then applied to 14 AOGCMs of the coupled model intercomparison project phase 3 (CMIP3) multi-model dataset. Three time
periods are focused on: a historical period (1971–2000) from the climate of the twentieth century experiment and two climate
projection periods (2046–2065 and 2081–2100) from the IPCC SRES A1B experiment. Evolution of the 10 m wind speed in France
and associated uncertainties are discussed. Significant changes are depicted, in particular a decrease of the wind speed in
the Mediterranean area. Sources of those changes are investigated by quantifying the effects of changes in the weather type
occurrences, and modifications of the distribution of the days within the weather types. 相似文献
15.
R. Tomozeiu C. Cacciamani V. Pavan A. Morgillo A. Busuioc 《Theoretical and Applied Climatology》2007,90(1-2):25-47
Summary Possible changes of mean climate and the frequency of extreme temperature events in Emilia-Romagna, over the period 2070–2100
compared to 1960–1990, are assessed. A statistical downscaling technique, applied to HadAM3P experiments (control, A2 and
B2 scenarios) performed at the Hadley Centre, is used to achieve this objective. The method applied consists of a multivariate
regression based on Canonical Correlation Analysis (CCA), using as possible predictors mean sea level pressure (MSLP), geopotential
height at 500 hPa (Z500) and temperature at 850 hPa (T850), and as predictands the seasonal mean values of minimum and maximum
surface temperature (Tmin and Tmax), 90th percentile of maximum temperature (Tmax90), 10th percentile of minimum temperature (Tmin10), number of frost days (Tnfd) and heat wave duration (HWD) at the station level. First, the statistical model is optimised and calibrated using NCEP/NCAR
reanalysis to evaluate the large-scale predictors. The observational data at 32 stations uniformly distributed over Emilia-Romagna
are used to compute the local predictands. The results of the optimisation procedure reveal that T850 is the best predictor
in most cases, and in combination with MSLP, is an optimum predictor for winter Tmax90 and autumn Tmin10. Finally, MSLP is the best predictor for spring Tmin while Z500 is the best predictor for spring Tmax90 and heat wave duration index, except during autumn. The ability of HadAM3P to simulate the present day spatial and temporal
variability of the chosen predictors is tested using the control experiments. Finally, the downscaling model is applied to
all model output experiments to obtain simulated present day and A2 and B2 scenario results at the local scale. Results show
that significant increases can be expected to occur under scenario conditions in both maximum and minimum temperature, associated
with a decrease in the number of frost days and with an increase in the heat wave duration index. The magnitude of the change
is more significant for the A2 scenario than for the B2 scenario. 相似文献
16.
A statistical model to downscale local daily temperature extremes from synoptic-scale atmospheric circulation patterns in the Australian region 总被引：3，自引：0，他引：3
The study seeks to describe one method of deriving information about local daily temperature extremes from larger scale atmospheric
flow patterns using statistical tools. This is considered to be one step towards downscaling coarsely gridded climate data
from global climate models (GCMs) to finer spatial scales. Downscaling is necessary in order to bridge the spatial mismatch
between GCMs and climate impact models which need information on spatial scales that the GCMs cannot provide. The method of
statistical downscaling is based on physical interaction between atmospheric processes with different spatial scales, in this
case between synoptic scale mean sea level pressure (MSLP) fields and local temperature extremes at several stations in southeast
Australia. In this study it was found that most of the day-to-day spatial variability of the synoptic circulation over the
Australian region can be captured by six principal components. Using the scores of these PCs as multivariate indicators of
the circulation a substantial part of the local daily temperature variability could be explained. The inclusion of temperature
persistence noticeably improved the performance of the statistical model. The model established and tested with observations
is thought to be finally applied to GCM-simulated pressure fields in order to estimate pressure-related changes in local temperature
extremes under altered CO2 conditions.
Received: 26 March 1996 / Accepted: 20 September 1996 相似文献
17.
Several studies have been devoted to dynamic and statistical downscaling for both climate variability and climate change. This paper introduces an application of temporal neural networks for downscaling global climate model output and autocorrelation functions. This method is proposed for downscaling daily precipitation time series for a region in the Amazon Basin. The downscaling models were developed and validated using IPCC AR4 model output and observed daily precipitation. In this paper, five AOGCMs for the twentieth century (20C3M; 1970–1999) and three SRES scenarios (A2, A1B, and B1) were used. The performance in downscaling of the temporal neural network was compared to that of an autocorrelation statistical downscaling model with emphasis on its ability to reproduce the observed climate variability and tendency for the period 1970–1999. The model test results indicate that the neural network model significantly outperforms the statistical models for the downscaling of daily precipitation variability. 相似文献
18.
Constructing Site-Specific Climate Change Scenarios on a Monthly Scale Using Statistical Downscaling 总被引：1，自引：1，他引：0
Summary Monthly mean temperature and monthly precipitation totals in two small catchments in the Czech Republic are estimated from
large-scale 500 hPa height and 1000/500 hPa thickness fields using statistical downscaling. The method used is multiple linear
regression. Whereas precipitation can be determined from large-scale fields with some confidence in only a few months of the
year, temperature can be determined successfully. Principal components calculated separately from the height and thickness
anomalies are identified as the best predictor set. The method is most accurate if the regression is performed using seasons
based on three months. The test on an independent sample, consisting of warm seasons, confirms that the method successfully
reproduces the difference in mean temperature between two climatic states, which indicates that this downscaling method is
applicable for constructing scenarios of a future climate change. The ECHAM3 GCM is used for scenario construction. The GCM
is shown to simulate surface temperature and precipitation with low accuracy, whereas the large-scale atmospheric fields are
reproduced well; this justifies the downscaling approach. The observed regression equations are applied to 2xCO2 GCM output so that the model’s bias is eleminated. This procedure is then discussed and finally, temperature scenarios for
the 2xCO2 climate are constructed for the two catchments.
Received December 3, 1998 Revised December 4, 1999 相似文献
19.
This paper deals with different responses from various Atmosphere-Ocean Global Climate Models (AOGCMs) at the regional scale. What can be the best use of AOGCMs for assessing the climate change in a particular region? The question is complicated by the consideration of intra-year month-to-month variability of a particular climate variable such as precipitation or temperature in a specific region. A maximum entropy method (MEM), which combines limited information with empirical perspectives, is applied to assessing the probability-weighted multimodel ensemble average of a climate variable at the region scale. The method is compared to and coupled with other two methods: the root mean square error minimization method and the simple multimodel ensemble average method. A mechanism is developed to handle a comprehensive range of model uncertainties and to identify the best combination of AOGCMs based on a balance of two rules: depending equally on all models versus giving higher priority to models more strongly verified by the historical observation. As a case study, the method is applied to a central US region to compute the probability-based average changes in monthly precipitation and temperature projected for 2055, based on outputs from a set of AOGCMs. Using the AOGCM data prepared by international climate change study groups and local climate observation data, one can apply the MEM to precipitation or temperature for a particular region to generate an annual cycle, which includes the effects from both global climate change and local intra-year climate variability. 相似文献