共查询到20条相似文献，搜索用时 31 毫秒
1.
Young-Kwon Lim Steven Cocke D. W. Shin Justin T. Schoof Timothy E. LaRow James J. O’Brien 《Climate Dynamics》2010,35(2-3):449-471
Seasonally predicted precipitation at a resolution of 2.5° was statistically downscaled to a fine spatial scale of ~20 km over the southeastern United States. The downscaling was conducted for spring and summer, when the fine-scale prediction of precipitation is typically very challenging in this region. We obtained the global model precipitation for downscaling from the National Center for Environmental Prediction/Climate Forecast System (NCEP/CFS) retrospective forecasts. Ten member integration data with time-lagged initial conditions centered on mid- or late February each year were used for downscaling, covering the period from 1987 to 2005. The primary techniques involved in downscaling are Cyclostationary Empirical Orthogonal Function (CSEOF) analysis, multiple regression, and stochastic time series generation. Trained with observations and CFS data, CSEOF and multiple regression facilitated the identification of the statistical relationship between coarse-scale and fine-scale climate variability, leading to improved prediction of climate at a fine resolution. Downscaled precipitation produced seasonal and annual patterns that closely resemble the fine resolution observations. Prediction of long-term variation within two decades was improved by the downscaling in terms of variance, root mean square error, and correlation. Relative to the coarsely resolved unskillful CFS forecasts, the proposed downscaling drove a significant reduction in wet biases, and correlation increased by 0.1–0.5. Categorical predictability of seasonal precipitation and extremes (frequency of heavy rainfall days), measured with the Heidke skill score (HSS), was also improved by the downscaling. For instance, domain averaged HSS for two category predictability by the downscaling are at least 0.20, while the scores by the CFS are near zero and never exceed 0.1. On the other hand, prediction of the frequency of subseasonal dry spells showed limited improvement over half of the Georgia and Alabama region. 相似文献
2.
3.
Statistical downscaling is a technique widely used to overcome the spatial resolution problem of General Circulation Models (GCMs). Nevertheless, the evaluation of uncertainties linked with downscaled temperature and precipitation variables is essential to climate impact studies. This paper shows the potential of a statistical downscaling technique (in this case SDSM) using predictors from three different GCMs (GCGM3, GFDL and MRI) over a highly heterogeneous area in the central Andes. Biases in median and variance are estimated for downscaled temperature and precipitation using robust statistical tests, respectively Mann?CWhitney and Brown?CForsythe's tests. In addition, the ability of the downscaled variables to reproduce extreme events is tested using a frequency analysis. Results show that uncertainties in downscaled precipitations are high and that simulated precipitation variables failed to reproduce extreme events accurately. Nevertheless, a greater confidence remains in downscaled temperatures variables for the area. GCMs performed differently for temperature and precipitation as well as for the different test. In general, this study shows that statistical downscaling is able to simulate with accuracy temperature variables. More inhomogeneities are detected for precipitation variables. This first attempt to test uncertainties of statistical downscaling techniques in the heterogeneous arid central Andes contributes therefore to an improvement of the quality of predictions of climate impact studies in this area. 相似文献
4.
Statistical Downscaling Based on Dynamically DownscaledPredictors： Application to Monthly Precipitation in Sweden 总被引：3，自引：0，他引：3
A prerequisite of a successful statistical downscaling is that large-scale predictors simulated by the General Circulation Model (GCM) must be realistic. It is assumed here that features smaller than the GCM resolution are important in determining the realism of the large-scale predictors. It is tested whether a three-step method can improve conventional one-step statistical downscaling. The method uses predictors that are upscaled from a dynamical downscaling instead of predictors taken directly from a GCM simulation. The method is applied to downscaling of monthly precipitation in Sweden. The statistical model used is a multiple regression model that uses indices of large-scale atmospheric circulation and 850-hPa specific humidity as predictors. Data from two GCMs (HadCM2 and ECHAM4) and two RCM experiments of the Rossby Centre model (RCA1) driven by the GCMs are used. It is found that upscaled RCA1 predictors capture the seasonal cycle better than those from the GCMs, and hence increase the reliability of the downscaled precipitation. However, there are only slight improvements in the simulation of the seasonal cycle of downscaled precipitation. Due to the cost of the method and the limited improvements in the downscaling results, the three-step method is not justified to replace the one-step method for downscaling of Swedish precipitation. 相似文献
5.
不同降水方案对"03.7"一次暴雨过程模拟的影响 总被引：4，自引：0，他引：4
观测和数值模拟研究已经表明,潜热释放对中国东部夏季梅雨锋系统及其锋面降水的维持和发展发挥着非常重要的作用.然而,目前对于梅雨锋降水模拟中各种降水方案的相互协调和系统评估方面的工作仍不多见,为了增进对梅雨锋暴雨模拟中降水过程的认识,作者针对2003年7月4～5日一次梅雨锋暴雨过程,构造了四组试验,利用MM5模式考察了两种分辨率(36 km、12 km),各种隐/显式方案搭配下,对所生成的雨带、雨量和降水类型的配置进行了仔细的研究,得到了一些有意义的结论,为今后更好地使用模式、利用数值模式来认识中尺度降水过程中的气象问题打下基础.主要结论包括:模拟总降水的水平分布和强度,以及显式降水和隐式降水的划分对积云参数化方案的选择非常敏感.但对特定积云参数化方案而言,降水的模拟对36 km、12 km水平分辨率不敏感(除Betts-Miller方案外);在中尺度网格分辨率10～50 km范围内,不同积云参数化方案对梅雨锋降水分布和降水量模拟的影响比不同显式方案带来的变化大得多. 相似文献
6.
The role of horizontal resolution in simulating drivers of the global hydrological cycle 总被引：1，自引：1，他引：0
Marie-Estelle Demory Pier Luigi Vidale Malcolm J. Roberts Paul Berrisford Jane Strachan Reinhard Schiemann Matthew S. Mizielinski 《Climate Dynamics》2014,42(7-8):2201-2225
The role of atmospheric general circulation model (AGCM) horizontal resolution in representing the global energy budget and hydrological cycle is assessed, with the aim of improving the understanding of model uncertainties in simulating the hydrological cycle. We use two AGCMs from the UK Met Office Hadley Centre: HadGEM1-A at resolutions ranging from 270 to 60 km, and HadGEM3-A ranging from 135 to 25 km. The models exhibit a stable hydrological cycle, although too intense compared to reanalyses and observations. This over-intensity is explained by excess surface shortwave radiation, a common error in general circulation models (GCMs). This result is insensitive to resolution. However, as resolution is increased, precipitation decreases over the ocean and increases over the land. This is associated with an increase in atmospheric moisture transport from ocean to land, which changes the partitioning of moisture fluxes that contribute to precipitation over land from less local to more non-local moisture sources. The results start to converge at 60-km resolution, which underlines the excessive reliance of the mean hydrological cycle on physical parametrization (local unresolved processes) versus model dynamics (large-scale resolved processes) in coarser HadGEM1 and HadGEM3 GCMs. This finding may be valid for other GCMs, showing the necessity to analyze other chains of GCMs that may become available in the future with such a range of horizontal resolutions. Our finding supports the hypothesis that heterogeneity in model parametrization is one of the underlying causes of model disagreement in the Coupled Model Intercomparison Project (CMIP) exercises. 相似文献
7.
The impact of applying three-dimensional variational data assimilation (3D-Var DA) on convective-scale forecasts is investigated by using two mesoscale models, the Weather Research and Forecasting model (WRF-ARW) and the Hirlam and Aladin Research Model On Non-hydrostatic-forecast Inside Europe (HARMONIE-AROME). One month (1 to 30 December 2013) of numerical experiments were conducted with these two models at 2.5 km horizontal resolution, in order to partly resolve convective phenomena, on the same domain over a mountainous area in Iran and neighboring areas. Furthermore, in order to estimate the domain specific background error statistics (BES) in convective scales, two months (1 November to 30 December 2017) of numerical experiments were carried out with both models by downscaling operational ECMWF forecasts. For setting the numerical experiments in an operational scenario, ECMWF operational forecast data were used as initial and lateral boundary conditions (ICs/LBCs). In order to examine the impact of data assimilation, the 3D-Var method in cycling mode was adopted and the forecasts were verified every 6 hours up to 36 hours for selected meteorological variables. In addition, 24 h accumulated precipitation forecasts were verified separately. Generally, the WRF and HARMONIE-AROME exhibit similar verification statistics for the selected forecast variables. The impact of DA on the numerical forecast shows some evidence of improvement in both models, and this effect decreases severely at longer lead times. Results from verifying the 24 h convective-scale precipitation forecasts from both models with and without DA suggest the superiority of the WRF model in forecasting more accurately the occurred precipitation over the simulation domain, even for the downscaling run. 相似文献
8.
湿奇异向量（Moist Singular Vectors,简称MSVs）是包含了湿物理切线性过程计算得到的奇异向量。研究MSVs对最优化时间间隔（optimization time interval,简称OTI）及模式水平分辨率的敏感性对提高集合预报效果至关重要。本文基于中国气象局数值预报中心自主研发的全球/区域同化和预报系统（Global/Regional Assimilation and Prediction System,简称GRAPES）——全球集合预报系统（Global ensemble prediction system,简称GEPS）业务版本研究了4组不同时空尺度（不同OTI和水平分辨率）下的MSVs,从能量模、能量谱、空间剖面等方面分析热带外MSVs特征,并从等压面变量评分、降水评分、降水概率预报等方面评估不同初值的集合预报效果。结果表明:提高MSVs水平分辨率可使其扰动具有较大的增长率,缩短OTI后MSVs能量向上传播的趋势更明显,并可以在中尺度范围产生较大SVs扰动。不同OTI下初始MSVs相似性较低,结构差异较大。从集合预报的结果来看,OTI为24 h试验的集合扰动能量增长较大,集合离散度在预报的0～96 h有明显提升,特别是2 m温度,且近地面要素的outlier评分也有明显改进。进一步分析发现,提高水平分辨率和缩短OTI的MSVs能够提高降水概率预报,而降水评分显示,同一水平分辨率下,OTI越短评分越好,但是提高MSVs的水平分辨率并不一定会提升小雨到中雨量级的降水评分。 相似文献
9.
Emmanouil Flaounas Philippe Drobinski Mathieu Vrac Sophie Bastin Cindy Lebeaupin-Brossier Marc Stéfanon Marco Borga Jean-Christophe Calvet 《Climate Dynamics》2013,40(11-12):2687-2705
This study evaluates how statistical and dynamical downscaling models as well as combined approach perform in retrieving the space–time variability of near-surface temperature and rainfall, as well as their extremes, over the whole Mediterranean region. The dynamical downscaling model used in this study is the Weather Research and Forecasting (WRF) model with varying land-surface models and resolutions (20 and 50 km) and the statistical tool is the Cumulative Distribution Function-transform (CDF-t). To achieve a spatially resolved downscaling over the Mediterranean basin, the European Climate Assessment and Dataset (ECA&D) gridded dataset is used for calibration and evaluation of the downscaling models. In the frame of HyMeX and MED-CORDEX international programs, the downscaling is performed on ERA-I reanalysis over the 1989–2008 period. The results show that despite local calibration, CDF-t produces more accurate spatial variability of near-surface temperature and rainfall with respect to ECA&D than WRF which solves the three-dimensional equation of conservation. This first suggests that at 20–50 km resolutions, these three-dimensional processes only weakly contribute to the local value of temperature and precipitation with respect to local one-dimensional processes. Calibration of CDF-t at each individual grid point is thus sufficient to reproduce accurately the spatial pattern. A second explanation is the use of gridded data such as ECA&D which smoothes in part the horizontal variability after data interpolation and damps the added value of dynamical downscaling. This explains partly the absence of added-value of the 2-stage downscaling approach which combines statistical and dynamical downscaling models. The temporal variability of statistically downscaled temperature and rainfall is finally strongly driven by the temporal variability of its forcing (here ERA-Interim or WRF simulations). CDF-t is thus efficient as a bias correction tool but does not show any added-value regarding the time variability of the downscaled field. Finally, the quality of the reference observation dataset is a key issue. Comparison of CDF-t calibrated with ECA&D dataset and WRF simulations to local measurements from weather stations not assimilated in ECA&D, shows that the temporal variability of the downscaled data with respect to the local observations is closer to the local measurements than to ECA&D data. This highlights the strong added-value of dynamical downscaling which improves the temporal variability of the atmospheric dynamics with regard to the driving model. This article highlights the benefits and inconveniences emerging from the use of both downscaling techniques for climate research. Our goal is to contribute to the discussion on the use of downscaling tools to assess the impact of climate change on regional scales. 相似文献
10.
The heaviest rainfall over 61 yr hit Beijing during 21-22 July 2012.Characterized by great rainfall amount and intensity,wide range,and high impact,this record-breaking heavy rainfall caused dozens of deaths and extensive damage.Despite favorable synoptic conditions,operational forecasts underestimated the precipitation amount and were late at predicting the rainfall start time.To gain a better understanding of the performance of mesoscale models,verification of high-resolution forecasts and analyses from the WRFbased BJ-RUCv2.0 model with a horizontal grid spacing of 3 km is carried out.The results show that water vapor is very rich and a quasi-linear precipitation system produces a rather concentrated rain area.Moreover,model forecasts are first verified statistically using equitable threat score and BIAS score.The BJ-RUCv2.0forecasts under-predict the rainfall with southwestward displacement error and time delay of the extreme precipitation.Further quantitative analysis based on the contiguous rain area method indicates that major errors for total precipitation( 5 mm h~(-1)) are due to inaccurate precipitation location and pattern,while forecast errors for heavy rainfall( 20 mm h~(-1)) mainly come from precipitation intensity.Finally,the possible causes for the poor model performance are discussed through diagnosing large-scale circulation and physical parameters(water vapor flux and instability conditions) of the BJ-RUCv2.0 model output. 相似文献
11.
This study addresses the predictability of rainfall variations over South America and the Amazon basin. A primary factor leading to model inaccuracy in precipitation forecasts is the coarse resolution data utilized by coupled models during the training phase. By using MERRA reanalysis and statistical downscaling along with the superensemble methodology, it is possible to obtain more precise forecast of rainfall anomalies over tropical South America during austral fall. Selective inclusion (and exclusion) of member models also allows for increased accuracy of superensemble forecasts. The use of coupled atmospheric–ocean numerical models to predict the rainfall anomalies has had mixed results. Improvement in individual member models is also possible on smaller spatial scales and in regions where substantial topographical changes were not handled well under original model initial conditions. The combination of downscaling and superensemble methodologies with other research methods presents the potential opportunity for increased accuracy not only in seasonal forecasts but on shorter temporal scales as well. 相似文献
12.
Analysis of a Heavy Rainfall Event over Beijing During 21-22 July 2012 Based on High Resolution Model Analyses and Forecasts 
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下载免费PDF全文 JIANG Xiaoman YUAN Huiling XUE Ming CHEN Xi TAN Xiaoguang 《Acta Meteorologica Sinica》2014,28(2):199-212
The heaviest rainfall over 61 yr hit Beijing during 21-22 July 2012.Characterized by great rainfall amount and intensity,wide range,and high impact,this record-breaking heavy rainfall caused dozens of deaths and extensive damage.Despite favorable synoptic conditions,operational forecasts underestimated the precipitation amount and were late at predicting the rainfall start time.To gain a better understanding of the performance of mesoscale models,verification of high-resolution forecasts and analyses from the WRFbased BJ-RUCv2.0 model with a horizontal grid spacing of 3 km is carried out.The results show that water vapor is very rich and a quasi-linear precipitation system produces a rather concentrated rain area.Moreover,model forecasts are first verified statistically using equitable threat score and BIAS score.The BJ-RUCv2.0forecasts under-predict the rainfall with southwestward displacement error and time delay of the extreme precipitation.Further quantitative analysis based on the contiguous rain area method indicates that major errors for total precipitation（〉 5 mm h~（-1）） are due to inaccurate precipitation location and pattern,while forecast errors for heavy rainfall（〉 20 mm h~（-1）） mainly come from precipitation intensity.Finally,the possible causes for the poor model performance are discussed through diagnosing large-scale circulation and physical parameters（water vapor flux and instability conditions） of the BJ-RUCv2.0 model output. 相似文献
13.
A. Fernndez-Ferrero J. Senz G. Ibarra-Berastegi J. Fernndez 《Atmospheric Research》2009,94(3):448-461
The objective of this study is to compare several statistical downscaling methods for the development of an operational short-term forecast of precipitation in the area of Bilbao (Spain). The ability of statistical downscaling methods nested inside numerical simulations run by both coarse and regional model simulations is tested with several selections of predictors and domain sizes. The selection of predictors is performed both in terms of sound physical mechanisms and also by means of “blind” criteria, such as “give the statistical downscaling methods all the information they can process”.Results show that the use of statistical downscaling methods improves the ability of the mesoscale and coarse resolution models to provide quantitative precipitation forecasts. The selection of predictors in terms of sound physical principles does not necessarily improve the ability of the statistical downscaling method to select the most relevant inputs to feed the precipitation forecasting model, due to the fact that the numerical models do not always fulfil conservation laws or because precipitation events do not reflect simple phenomenological laws. Coarse resolution models are able to provide information usable in combination with a statistical downscaling method to achieve a quantitative precipitation forecast skill comparable to that obtained by other systems currently in use. 相似文献
14.
C. Cacciamani D. Cesari F. Grazzini T. Paccagnella M. Pantone 《Meteorology and Atmospheric Physics》2000,72(2-4):147-159
Summary In this paper we describe the results of several numerical experiments performed with the limited area model LAMBO, based
on a 1989 version of the NCEP (National Center for Environmental Prediction) ETA model, operational at ARPA-SMR since 1993.
The experiments have been designed to assess the impact of different horizontal resolutions and initial conditions on the
quality and detail of the forecast, especially as regards the precipitation field in the case of severe flood events. For
initial conditions we developed a mesoscale data assimilation scheme, based on the nudging technique. The scheme makes use
of upper air and surface meteorological observations to modify ECMWF (European Centre for Medium Range Weather Forecast) operational
analyses, used as first-guess fields, in order to better describe smaller scales features, mainly in the lower troposphere.
Three flood cases in the Alpine and Mediterranean regions have been simulated with LAMBO, using a horizontal grid spacing
of 15 and 5 km and starting either from ECMWF initialised analysis or from the result of our mesoscale analysis procedure.
The results show that increasing the resolution generally improves the forecast, bringing the precipitation peaks in the flooded
areas close to the observed values without producing many spurious precipitation patterns. The use of mesoscale analysis produces
a more realistic representation of precipitation patterns giving a further improvement to the forecast of precipitation. Furthermore,
when simulations are started from mesoscale analysis, some model-simulated thermodynamic indices show greater vertical instability
just in the regions where strongest precipitation occurred.
Received March 2, 1999/Revised May 30, 1999 相似文献
15.
The mesoscale moist adjoint sensitivities related to the initiation of
mesoscale convective systems (MCSs) are evaluated for a mei-yu heavy
rainfall event. The sensitivities were calculated on a realistic background
gained from a four-dimensional variational data assimilation of
precipitation experiment to make the sensitivity computation possible and
reasonable within a strong moist convective event at the mesoscale. The
results show that the computed sensitivities at the mesoscale were capable
of capturing the factors affecting MCS initiation. The sensitivities to the
initial temperature and moisture are enhanced greatly by diabatic processes,
especially at lower levels, and these sensitivities are much larger than
those stemming from the horizontal winds, which implies that initiation of
MCSs is more sensitive to low-level temperature and moisture perturbations
rather than the horizontal winds. Moreover, concentration of sensitivities
at low levels reflects the characteristics of the mei-yu front. The results
provide some hints about how to improve quantitative precipitation forecasts
of mei-yu heavy rainfall, such as by conducting mesoscale targetted
observations via the adjoint-based method to reduce the low-level errors in
the initial temperature and moisture. 相似文献
16.
利用TIGGE资料中欧洲中期天气预报中心(ECMWF,the European Centre for Medium-Range Weather Forecasts)、日本气象厅(JMA,the Japan Meteorological Agency)、美国国家环境预报中心(NCEP,the National Centers for Environmental Prediction)以及英国气象局(UKMO,the UK Met Office)4个中心1~7 d预报的日降水量集合预报资料,并以中国降水融合产品作为"观测值",对我国地面降水量预报进行统计降尺度处理。采用空间滑动窗口增加中雨和大雨雨量样本,建立分级雨量的回归方程,并与未分级雨量的统计降尺度预报进行对比。结果表明,对于不同模式、不同预报时效以及不同降水量级,统计降尺度的预报技巧改进程度不尽相同。统计降尺度的预报技巧依赖于模式本身的预报效果。相比雨量未分级回归,雨量分级回归的统计降尺度预报与观测值的距平相关系数更高,均方根误差更小,不同量级降水的ETS评分明显提高。对雨量分级回归统计降尺度预报结果进行二次订正,可大大减少小雨的空报。 相似文献
17.
Michelle T. H. van Vliet Stephen Blenkinsop Aidan Burton Colin Harpham Hans Peter Broers Hayley J. Fowler 《Climatic change》2012,111(2):249-277
Regional or local scale hydrological impact studies require high resolution climate change scenarios which should incorporate
some assessment of uncertainties in future climate projections. This paper describes a method used to produce a multi-model
ensemble of multivariate weather simulations including spatial–temporal rainfall scenarios and single-site temperature and
potential evapotranspiration scenarios for hydrological impact assessment in the Dommel catchment (1,350 km2) in The Netherlands and Belgium. A multi-site stochastic rainfall model combined with a rainfall conditioned weather generator
have been used for the first time with the change factor approach to downscale projections of change derived from eight Regional
Climate Model (RCM) experiments for the SRES A2 emission scenario for the period 2071–2100. For winter, all downscaled scenarios
show an increase in mean daily precipitation (catchment average change of +9% to +40%) and typically an increase in the proportion
of wet days, while for summer a decrease in mean daily precipitation (−16% to −57%) and proportion of wet days is projected.
The range of projected mean temperature is 7.7°C to 9.1°C for winter and 19.9°C to 23.3°C for summer, relative to means for
the control period (1961–1990) of 3.8°C and 16.8°C, respectively. Mean annual potential evapotranspiration is projected to
increase by between +17% and +36%. The magnitude and seasonal distribution of changes in the downscaled climate change projections
are strongly influenced by the General Circulation Model (GCM) providing boundary conditions for the RCM experiments. Therefore,
a multi-model ensemble of climate change scenarios based on different RCMs and GCMs provides more robust estimates of precipitation,
temperature and evapotranspiration for hydrological impact assessments, at both regional and local scale. 相似文献
18.
北京“7.21”特大暴雨高分辨率模式分析场及预报分析 总被引：1，自引：0，他引：1
2012年7月21-22日,61年以来最强降水袭击北京,北京大部分地区出现大暴雨,局部特大暴雨,过程雨量大、雨势强、范围广,造成了严重影响。此次强降水配置较为典型,业务预报提前指示出了此次过程,但预报结果存在强度偏弱,峰值偏晚等偏差。在对此次大暴雨进行综合分析的基础上,利用中国自动气象站与NOAA气候预测中心卫星反演降水资料CMORPH(Climate Prediction Center Morphing Technique)产品融合的逐时降水量网格数据资料作为观测,着重对北京市气象局新的快速更新循环同化和预报系统(BJ-RUC v2.0)的3 km高分辨率模式分析场和预报场进行了检验与分析,以期通过对中尺度模式预报性能的了解,为暴雨可预报性问题提供进一步的参考。研究结果表明,此次特大暴雨过程水汽条件极佳,降水区域较为集中,呈现西南一东北走向的中尺度雨带特征。利用常规检验评分对预报降水的时间序列进行检验发现,预报降水在时间上滞后,降水强度偏弱,存在偏西南的位置误差,并且未能反映降水系统的线状特征。进一步利用检验连续降水区域定量降水预报的CRA(contiguous rain area)方法,对预报误差进行分解表明,整体降水(5 mm/h)的主要误差来自于位置和形状误差;而在暴雨(20 mm/h)的预报中,降水强度的偏差占误差的主要部分。最后结合对预报场大尺度环流和物理量的诊断(水汽条件和不稳定条件),分析探讨了此次极端暴雨预报不佳的原因。 相似文献
19.
The spatial resolution gap between global or regional climate models and the requirements for local impact studies motivates the need for climate downscaling. For impact studies that involve glacier modelling, the sparsity or complete absence of climate monitoring activities within the regions of interest presents a substantial additional challenge. Downscaling methods for this application must be independent of climate observations and cannot rely on tuning to station data. We present new, computationally-efficient methods for downscaling precipitation and temperature to the high spatial resolutions required to force mountain glacier models. Our precipitation downscaling is based on an existing linear theory for orographic precipitation, which we modify for large study regions by including moist air tracking. Temperature is downscaled using an interpolation scheme that reconstructs the vertical temperature structure to estimate surface temperatures from upper air data. Both methods are able to produce output on km to sub-km spatial resolution, yet do not require tuning to station measurements. By comparing our downscaled precipitation (1 km resolution) and temperature (200 m resolution) fields to station measurements in southern British Columbia, we evaluate their performance regionally and through the annual cycle. Precipitation is improved by as much as 30% (median relative error) over the input reanalysis data and temperature is reconstructed with a mean bias of 0.5°C at locations with high vertical relief. Both methods perform best in mountainous terrain, where glaciers tend to be concentrated. 相似文献
20.
A comparison of statistical downscaling and climate change factor methods: impacts on low flows in the River Thames, United Kingdom 总被引：13，自引：0，他引：13
Strategic-scale assessments of climate change impacts are often undertaken using the change factor (CF) methodology whereby future changes in climate projected by General Circulation Models (GCMs) are applied to a baseline climatology. Alternatively, statistical downscaling (SD) methods apply climate variables from GCMs to statistical transfer functions to estimate point-scale meteorological series. This paper explores the relative merits of the CF and SD methods using a case study of low flows in the River Thames under baseline (1961–1990) and climate change conditions (centred on the 2020s, 2050s and 2080s). Archived model outputs for the UK Climate Impacts Programme (UKCIP02) scenarios are used to generate daily precipitation and potential evaporation (PE) for two climate change scenarios via the CF and SD methods. Both signal substantial reductions in summer precipitation accompanied by increased PE throughout the year, leading to reduced flows in the Thames in late summer and autumn. However, changes in flow associated with the SD scenarios are generally more conservative and complex than that arising from CFs. These departures are explained in terms of the different treatment of multidecadal natural variability, temporal structuring of daily climate variables and large-scale forcing of local precipitation and PE by the two downscaling methods. 相似文献