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1.
为了提高湖南极端降水的模拟能力,利用转移累计概率分布(CDF-t)统计降尺度方法及基于第5次国际耦合模式比较计划(CMIP5)中的24个耦合模式数据,结合3个极端降水指数,从空间特征和年际变率两方面评估降尺度前后CMIP5模式对湖南极端降水的模拟能力。结果表明,较低空间分辨率的CM IP5气候模式无法细致反映区域极端降水变化特征,且由于各模式结果差异较大,多模式集合的模拟效果差。CDF-t统计降尺度通过建立大尺度变量的CDF与区域尺度相同变量的CDF之间的函数关系,对CMIP5模拟湖南极端降水变化特征有一定的改善能力。就空间结构而言,该方法对于模式模拟大雨日数(R10)和连续5天最大降水量(R5d)的空间结构能力都有很大改善,且模式之间表现出较高的一致性,尤其是R10改善效果最显著,与观测相比,湖南地区空间平均绝对误差达到2. 18天,较降尺度前绝对误差降低了45. 46%。就时间变率而言,该方法对于模式模拟R90P和R5d的时间变率能力都有很大改善,降尺度后IVS值分别由降尺度前的2. 2和1. 5降低至0. 3和0. 6。 相似文献
2.
基于1961-2015年上海降水观测数据和8个全球气候模式GCMs模拟的日降水量数据,采用累计概率分布函数构建转换模型CDF-T建立了站点尺度日降水量的统计降尺度模型。结果表明,降尺度模型显著改善了GCMs对降水日数偏多、降水强度偏低和降水量偏少的模拟结果。与利用全年日降水序列建模结果相比,利用汛期日降水序列建模更好地刻画了汛期降水的累计概率分布曲线,同时提高了汛期总降水量、降水强度和年平均暴雨日数、暴雨量、暴雨强度的均值和变化趋势的降尺度效果。模型对较长年份的暴雨重现期订正效果更佳。与当代(2006-2015年)气候相比,2016-2095年上海降水呈现以下特征:全年和汛期总降水量和降水强度增加,降水日数减少,未来可能出现更多的旱涝年;汛期降水极端性增强,暴雨降水均值和极端值均增加;50年以上重现期的年最大日降水量未来呈前40年减少后40年增加的变化。CDF-T模型为站点尺度气候变化影响评估和未来预估提供降尺度技术和基础气候数据。 相似文献
3.
利用1961~2002年ERA-40逐日再分析资料和江淮流域56个台站逐日观测降水量资料,引入基于自组织映射神经网络(Self-Organizing Maps,简称SOM)的统计降尺度方法,对江淮流域夏季(6~8月)逐日降水量进行统计建模与验证,以考察SOM对中国东部季风降水和极端降水的统计降尺度模拟能力。结果表明,SOM通过建立主要天气型与局地降水的条件转换关系,能够再现与观测一致的日降水量概率分布特征,所有台站基于概率分布函数的Brier评分(Brier Score)均近似为0,显著性评分(Significance Score)全部在0.8以上;模拟的多年平均降水日数、中雨日数、夏季总降水量、日降水强度、极端降水阈值和极端降水贡献率区域平均的偏差都低于11%;并且能够在一定程度上模拟出江淮流域夏季降水的时间变率。进一步将SOM降尺度模型应用到BCCCSM1.1(m)模式当前气候情景下,评估其对耦合模式模拟结果的改善能力。发现降尺度显著改善了模式对极端降水模拟偏弱的缺陷,对不同降水指数的模拟较BCC-CSM1.1(m)模式显著提高,降尺度后所有台站6个降水指数的相对误差百分率基本在20%以内,偏差比降尺度前减小了40%~60%;降尺度后6个降水指数气候场的空间相关系数提高到0.9,相对标准差均接近1.0,并且均方根误差在0.5以下。表明SOM降尺度方法显著提高日降水概率分布,特别是概率分布曲线尾部特征的模拟能力,极大改善了模式对极端降水场的模拟能力,为提高未来预估能力提供了基础。 相似文献
4.
21世纪前期长江中下游流域极端降水预估及不确定性分析 总被引：1，自引：0，他引：1
在全球变暖背景下,极端降水的频率、强度以及持续时间均在显著增加,尤其是对于气候变化敏感的长江中下游流域。由于模式本身、温室气体排放情景以及自然变率存在较大的不确定性,因此未来预估变化的不确定性一直备受关注。为了能够得到对于未来极端降水更为准确的预估结果,使用NEX-GDDP（NASA Earth Exchange Global Daily Downscaled Projections）提供的19个CMIP5降尺度高分辨率数据（0.25°×0.25°）,给出21世纪前期（2016—2035年）长江中下游流域极端降水的可能变化。根据长江中下游流域178个气象站1981—2005年的逐日降水量数据,计算了能够代表极端降水不同特征的指数,在评估模拟能力的基础上给出了21世纪前期RCP4.5情景下极端降水的变化。结果表明,降尺度结果对长江中下游流域极端降水有很好的模拟能力,除R90N外,所有模式模拟其余指数的空间结构与观测的相关系数均超过了0.6。其中所有模式模拟PRCPTOT和R10的相关系数均超过0.95。21世纪前期,长江中下游地区降水趋于极端化,尤其是在流域的西部地区。极端降水日数的变化在减少,表明对于极端降水的贡献主要来自于极端降水日的较大日降水量,而非极端降水日数。未来预估不确定性的大值区主要位于流域的南部地区,流域的西部地区不确定性较低,西部地区极端降水的增加应该受到更多的重视。 相似文献
5.
利用1986—2005年中国地面气象台站观测的格点化逐日降水数据(CN05.1)评估了NASA高分辨率降尺度逐日数据集NEX-GDDP中21个全球气候模式在0.25?(约25 km×25 km)分辨率下对中国极端降水的模拟能力.选取年最大日降水量(RX1D)、年最大5 d降水量(RX5D)、湿日总降水量(PRCPTOT... 相似文献
6.
CMIP6全球气候模式对中国极端降水模拟能力的评估及其与CMIP5的比较 总被引：8，自引：0，他引：8
对CMIP6全球气候模式在中国地区极端降水的模拟能力进行了综合评估。基于CN05.1观测数据集和32个CMIP6全球气候模式的降水数据,采用8个常用极端降水指数对极端降水进行了定量描述。研究结果表明,在极端降水的气候平均态方面,CMIP6多模式集合对1961—2005年中国地区区域平均的8个极端降水指数模拟的平均相对误差为29.94%,相较CMIP5降低了2.95个百分点。极端降水的气候变率方面,CMIP6多模式集合对区域平均的8个极端降水指数模拟的平均相对误差为10.10%,相较CMIP5降低5.45个百分点。此外,利用TS评分进行模式间比较,CMIP6的平均分（0.78）高于CMIP5（0.75）,且模拟能力排名前五的模式中CMIP6占4个。对比14个同源模式的TS评分可以发现,CMIP6（0.91）相对于CMIP5（0.68）的模拟能力显著提高。进一步研究发现,CMIP6相对于CMIP5对不同区域极端降水模拟能力的改进有所区别:CMIP6对干旱区平均的气候态和变率方面改进明显,而对于湿润区的改进主要表现在对极端降水空间相关模拟能力的提高。综上,在中国地区,CMIP6相较于CMIP5对极端降水的模拟能力总体上有提升。 相似文献
7.
杭州市降水特征及极端降水趋势预估 总被引：4，自引：4，他引：0
利用国家气候观象台杭州站百年降水观测数据和CMIP5模式模拟预估数据,分析了杭州市降水长期变化特征,采用累积概率分布函数转换方式(CDF-T),降尺度预估了未来气候情景下杭州极端降水发生趋势。结果表明:杭州市年降水量在百年时序(1907—2015年)上无显著性增加或减小趋势,1980年后春季降水明显下降,下降速率约32.1mm·(10a)~(-1),冬季降水显著增加,增加速率约35.4mm·(10a)~(-1)。1988—2015年的3和6h及日降水的各重现期降水量均较1961—1987年有所增大,1961—1987年的100年一遇日最大降水已演变为1988—2015年的50年一遇甚至是20年一遇。CMIP5模式降水的降尺度分析表明,2020—2039年杭州市日极端降水强度将可能会进一步加强,2020—2039年日降水的R95值和R99p值均较现气候期(1981—2010年)有所提高,超R95p和超R99p的极端降水发生日数分别为11.08和2.24d·a~(-1),分别较现气候期平均值增加了3.52和0.69d·a~(-1)。 相似文献
8.
本文基于NOAA再分析逐日降水数据和22个CMIP6模式的降水模拟数据,选取了6个极端降水指数,从气候态和相对变率两个角度对CMIP6模式在中亚地区极端降水方面的模拟能力开展了评估。结果表明,在气候态方面,中亚地区降水的空间分布表现为由西南向东北递增,其东南部山地迎风侧降水偏多；多模式集合对SDII(简单降水强度)和CDD(最大无雨期)模拟的平均误差分别为-5.43%和0.45%,对PRCPTOT(年总降水量)、R1mm(有雨日数)、Rx5day(最大连续五日降水)和CWD(最大雨期)的模拟结果存在明显高估,且在中亚东南部高海拔地区误差偏高。在相对变率方面,多模式集合模拟的中亚极端降水的相对变率偏小,其中对CWD的模拟效果相对较好,平均误差为-4.78%；对R1mm的模拟效果最差,平均误差为-36.16%。模式间进行比较,TaiESM1、EC-Earth3-Veg-LR和GFDL-ESM为22个CMIP6模式中模拟能力最好的前3个模式。 相似文献
9.
CMIP3与CMIP5模式对中国西北干旱区气温和降水的模拟能力比较 总被引：2，自引：0，他引：2
通过对15组CMIP3和CMIP5两代模式集合平均对中国西北干旱区气温和降水的模拟能力比较,发现CMIP5模式对气温和降水的模拟更接近观测值。CMIP5模式模拟年、春季、夏季、秋季平均气温的相关系数比CMIP3模式分别提升了0.15、0.13、0.24和0.02,冬季下降了0.07。CMIP5模式对西北干旱区的平均气温变化趋势的模拟效果比CMIP3有所提高,对年、春季、夏季、秋季、冬季趋势的模拟偏差比CMIP3分别减少了0.03℃/10a、0.10℃/10a、0.01℃/10a、0.06℃/10a、0.14℃/10a。对西北干旱区平均气温年、季的模拟偏差分布上,CMIP5模式的偏差均比CMIP3低1~2℃。但是天山区年、季节平均气温的模拟与整体模拟偏低情况相反,CMIP3和CMIP5分别偏高3~6℃和1~4℃,对夏季的模拟偏高最严重,分别达到6℃和4℃。CMIP5模式整体对西北干旱区降水量的模拟结果与观测值的平均相关系数与CMIP3相差不大,均不超过0.1,而且偏差仍然较大。CMIP5模式对西北干旱区的降水量的变化趋势模拟效果比CMIP3有所降低,对年、春季、夏季、秋季、冬季趋势的模拟偏差比CMIP3增加了0.67 mm/10a、0.23 mm/10a、0.51 mm/10a、0.11 mm/10a、0.14 mm/10a。CMIP5模式对年、春季、夏季、秋季和冬季的降水量模拟的均方根误差相比CMIP3分别减少77.6 mm、25.5 mm、25.0 mm、18.8 mm和13.9 mm。在空间上,CMIP5模式对年、季节降水模拟仍然偏高,但是比CMIP3有明显缓解;CMIP3和CMIP5模式对夏季天山区年降水量和夏季降水量的模拟也与大部分区域偏高的趋势明显相反,两代模式对夏季天山区的降水模拟均偏低50 mm左右。 相似文献
10.
Future changes in precipitation extremes over China using the NEX-GDDP high-resolution daily downscaled data-set 
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下载免费PDF全文 《大气和海洋科学快报》2017,(6)
最近,NASA发布了一套基于CMIP5 21个耦合模式输出的高分辨率降尺度逐日数据集,简称NEX-GDDP。本文评估了NEX-GDDP对中国极端降水的模拟性能。研究发现:(1)相比CMIP5直接输出结果,NEX-GDDP能够更好刻画中国极端降水的空间分布;(2)未来中国极端降水事件明显增多、强度增强,NEX-GDDP在区域尺度上给出了更多的气候变化信息;(3)NEXGDDP预估的中国未来极端降水变化的不确定性范围相比CMIP5直接输出结果明显减少,使得预估结果更加可靠. 相似文献
11.
M. Gonçalves A. Barrera-Escoda D. Guerreiro J. M. Baldasano J. Cunillera 《Climatic change》2014,122(1-2):243-256
The complex topography and high climatic variability of the North Western Mediterranean Basin (NWMB) require a detailed assessment of climate change projections at high resolution. ECHAM5/MPIOM global climate projections for mid-21st century and three different emission scenarios are downscaled at 10 km resolution over the NWMB, using the WRF-ARW regional model. High resolution improves the spatial distribution of temperature and precipitation climatologies, with Pearson's correlation against observation being higher for WRF-ARW (0.98 for temperature and 0.81 for precipitation) when compared to the ERA40 reanalysis (0.69 and 0.53, respectively). However, downscaled results slightly underestimate mean temperature (≈1.3 K) and overestimate the precipitation field (≈400 mm/year). Temperature is expected to raise in the NWMB in all considered scenarios (up to 1.4 K for the annual mean), and particularly during summertime and at high altitude areas. Annual mean precipitation is likely to decrease (around ?5 % to ?13 % for the most extreme scenarios). The climate signal for seasonal precipitation is not so clear, as it is highly influenced by the driving GCM simulation. All scenarios suggest statistically significant decreases of precipitation for mountain ranges in winter and autumn. High resolution simulations of regional climate are potentially useful to decision makers. Nevertheless, uncertainties related to seasonal precipitation projections still persist and have to be addressed. 相似文献
12.
Uncertainty analysis of statistical downscaling models using Hadley Centre Coupled Model 总被引：2，自引：2，他引：0
S. Samadi Catherine A. M. E. Wilson Hamid Moradkhani 《Theoretical and Applied Climatology》2013,114(3-4):673-690
Regression-based statistical downscaling is a method broadly used to resolve the coarse spatial resolution of general circulation models. Nevertheless, the assessment of uncertainties linked with climatic variables is essential to climate impact studies. This study presents a procedure to characterize the uncertainty in regression-based statistical downscaling of daily precipitation and temperature over a highly vulnerable area (semiarid catchment) in the west of Iran, based on two downscaling models: a statistical downscaling model (SDSM) and an artificial neural network (ANN) model. Biases in mean, variance, and wet/dry spells are estimated for downscaled data using vigorous statistical tests for 30 years of observed and downscaled daily precipitation and temperature data taken from the National Center for Environmental Prediction reanalysis predictors for the years of 1961 to 1990. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. In daily precipitation, downscaling uncertainties were evaluated from comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and modeled daily precipitation. Results showed that uncertainty in downscaled precipitation is high, but simulation of daily temperature can reproduce extreme events accurately. Finally, this study shows that the SDSM is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % confidence level, while the ANN model is the least capable in this respect. This study attempts to test uncertainties of regression-based statistical downscaling techniques in a semiarid area and therefore contributes to an improvement of the quality of predictions of climate change impact assessment in regions of this type. 相似文献
13.
The current study examines the recently proposed “bias correction and stochastic analogues” (BCSA) statistical spatial downscaling technique and attempts to improve it by conditioning coarse resolution data when generating replicates. While the BCSA method reproduces the statistical features of the observed fine data, this existing model does not replicate the observed coarse spatial pattern, and subsequently, the cross-correlation between the observed coarse data and downscaled fine data with the model cannot be preserved. To address the dissimilarity between the BCSA downscaled data and observed fine data, a new statistical spatial downscaling method, “conditional stochastic simulation with bias correction” (BCCS), which employs the conditional multivariate distribution and principal component analysis, is proposed. Gridded observed climate data of mean daily precipitation (mm/day) covering a month at 1/8° for a fine resolution and at 1° for a coarse resolution over Florida for the current and future periods were used to verify and cross-validate the proposed technique. The observed coarse and fine data cover the 50-year period from 1950 to1999, and the future RCP4.5 and RCP8.5 climate scenarios cover the 100-year period from 2000 to 2099. The verification and cross-validation results show that the proposed BCCS downscaling method serves as an effective alternative means of downscaling monthly precipitation levels to assess climate change effects on hydrological variables. The RCP4.5 and RCP8.5 GCM scenarios are successfully downscaled. 相似文献
14.
Morten A. D. Larsen Peter Thejll Jens H. Christensen Jens C. Refsgaard Karsten H. Jensen 《Climate Dynamics》2013,40(11-12):2903-2918
We investigate the simulated temperature and precipitation of the HIRHAM regional climate model using systematic variations in domain size, resolution and detailed location in a total of eight simulations. HIRHAM was forced by ERA-Interim boundary data and the simulations focused on higher resolutions in the range of 5.5–12 km. HIRHAM outputs of seasonal precipitation and temperature were assessed by calculating distributed model errors against a higher resolution data set covering Denmark and a 0.25° resolution data set covering Europe. Furthermore the simulations were statistically tested against the Danish data set using bootstrap statistics. The results from the distributed validation of precipitation showed lower errors for the winter (DJF) season compared to the spring (MAM), fall (SON) and, in particular, summer (JJA) seasons for both validation data sets. For temperature, the pattern was in the opposite direction, with the lowest errors occurring for the JJA season. These seasonal patterns between precipitation and temperature are seen in the bootstrap analysis. It also showed that using a 4,000 × 2,800 km simulation with an 11 km resolution produced the highest significance levels. Also, the temperature errors were more highly significant than precipitation. In similarly sized domains, 12 of 16 combinations of variables, observation validation data and seasons showed better results for the highest resolution domain, but generally the most significant improvements were seen when varying the domain size. 相似文献
15.
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
16.
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
17.
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
18.
Weidan Zhou Jianping Tang Xueyuan Wang Shuyu Wang Xiaorui Niu Yuan Wang 《Asia-Pacific Journal of Atmospheric Sciences》2016,52(2):107-127
The COSMO-CLM (CCLM) model is applied to perform regional climate simulation over the second phase of CORDEX-East Asia (CORDEX-EA-II) domain in this study. Driven by the ERAInterim reanalysis data, the model was integrated from 1988 to 2010 with a high resolution of 0.22°. The model’s ability to reproduce mean climatology and climatic extremes is evaluated based on various aspects. The CCLM model is capable of capturing the basic features of the East Asia climate, including the seasonal mean patterns, interannual variations, annual cycles and climate extreme indices for both surface air temperature and precipitation. Some biases are evident in certain areas and seasons. Warm and wet biases appear in the arid and semi-arid areas over the northwestern and northern parts of the domain. The simulated climate over the Tibetan Plateau is colder and wetter than the observations, while South China, East China, and India are drier. The model biases may be caused by the simulated anticyclonic and cyclonic biases in low-level circulations, the simulated water vapor content biases, and the inadequate physical parameterizations in the CCLM model. A parallel 0.44° simulation is conducted and the comparison results show some added value introduced by the higher resolution 0.22° simulation. As a result, the CCLM model could be an adequate member for the next stage of the CORDEX-EA project, while further studies should be encouraged. 相似文献
19.
This study was conducted using daily precipitation records gathered at 37 meteorological stations in northern Xinjiang, China, from 1961 to 2010. We used the extreme value theory model, generalized extreme value (GEV) and generalized Pareto distribution (GPD), statistical distribution function to fit outputs of precipitation extremes with different return periods to estimate risks of precipitation extremes and diagnose aridity–humidity environmental variation and corresponding spatial patterns in northern Xinjiang. Spatiotemporal patterns of daily maximum precipitation showed that aridity–humidity conditions of northern Xinjiang could be well represented by the return periods of the precipitation data. Indices of daily maximum precipitation were effective in the prediction of floods in the study area. By analyzing future projections of daily maximum precipitation (2, 5, 10, 30, 50, and 100 years), we conclude that the flood risk will gradually increase in northern Xinjiang. GEV extreme value modeling yielded the best results, proving to be extremely valuable. Through example analysis for extreme precipitation models, the GEV statistical model was superior in terms of favorable analog extreme precipitation. The GPD model calculation results reflect annual precipitation. For most of the estimated sites’ 2 and 5-year T for precipitation levels, GPD results were slightly greater than GEV results. The study found that extreme precipitation reaching a certain limit value level will cause a flood disaster. Therefore, predicting future extreme precipitation may aid warnings of flood disaster. A suitable policy concerning effective water resource management is thus urgently required. 相似文献
20.
Dynamical downscaling of ERA-40 in complex terrain using the WRF regional climate model 总被引：4，自引：0，他引：4
Results from a first-time employment of the WRF regional climate model to climatological simulations in Europe are presented. The ERA-40 reanalysis (resolution 1°) has been downscaled to a horizontal resolution of 30 and 10?km for the period of 1961?C1990. This model setup includes the whole North Atlantic in the 30?km domain and spectral nudging is used to keep the large scales consistent with the driving ERA-40 reanalysis. The model results are compared against an extensive observational network of surface variables in complex terrain in Norway. The comparison shows that the WRF model is able to add significant detail to the representation of precipitation and 2-m temperature of the ERA-40 reanalysis. Especially the geographical distribution, wet day frequency and extreme values of precipitation are highly improved due to the better representation of the orography. Refining the resolution from 30 to 10?km further increases the skill of the model, especially in case of precipitation. Our results indicate that the use of 10-km resolution is advantageous for producing regional future climate projections. Use of a large domain and spectral nudging seems to be useful in reproducing the extreme precipitation events due to the better resolved synoptic scale features over the North Atlantic, and also helps to reduce the large regional temperature biases over Norway. This study presents a high-resolution, high-quality climatological data set useful for reference climate impact studies. 相似文献