CCSM4.0模式及其驱动下RegCM4.4模式对中国夏季气候的回报分析

使用区域气候模式Reg CM4.4(Regional Climate Model version 4.4)单向嵌套CCSM4.0(Community Climate System Model version 4.0)气候系统模式输出结果,进行了2001~2010年逐年2月1日至9月1日共10年长度的季节尺度气候预测回报试验,针对平均气温和降水,分析了两个模式对中国地区夏季(6~8月)气候的回报能力。首先对气候态的分析表明,Reg CM4.4对气温和降水的回报/模拟效果均较CCSM4.0有所改进,特别是在提供更详细可靠的局地信息方面,其中降水回报与观测的空间相关系数,由CCSM4.0的0.39提高到Reg CM4.4的0.53,但同时Reg CM4.4对中国东部季风降水的回报表现出类似CCSM4.0北方偏多的偏差。对两个模式2001~2010年逐年气温和降水距平的回报能力,通过回报与观测空间和时间距平相关系数(ACCs和ACCt)、回报与观测空间和时间距平符号一致率(PCs和PCt)以及趋势异常综合评分(PS)进行了考察,结果表明两个模式的表现在整体分布上有一定相似的同时,Reg CM4.4能够提供更多的空间分布细节,并对降水的回报结果有一定的改善,如CCSM4.0和Reg CM4.4回报降水的ACCs多年平均分别为0.03和0.10,PS分别为70.4和71.4。同时给出了两个具体年份(2003年和2009年)的个例分析。     

混合集合预报法在华南暴雨短期预报中的试验

-WRF多模式集合3组试验,对比分析混合集合预报法与传统方法的降水预报效果。结果表明:ARPS模式集合改善了广东省南部局地强降水预报,该方法在中雨、大雨、暴雨量级改进效果显著。WRF模式集合对广东省北部强降水预报优于ARPS模式集合,但空报、漏报率较大,该方法有一定局限性。ARPS-WRF多模式集合在降水落区和量级预报上均优于传统方法。混合集合预报法利用低分辨率 (36 km) 集合预报和高分辨率 (12 km) 控制预报实现了高分辨率 (12 km) 集合预报,改善了降水预报效果,该方法可为业务高分辨率集合预报提供参考。     

Evaluation of a climate simulation in Europe based on the WRF–NOAH model system: precipitation in Germany

The Weather Research and Forecast (WRF) model with its land surface model NOAH was set up and applied as regional climate model over Europe. It was forced with the latest ERA-interim reanalysis data from 1989 to 2008 and operated with 0.33° and 0.11° resolution. This study focuses on the verification of monthly and seasonal mean precipitation over Germany, where a high quality precipitation dataset of the German Weather Service is available. In particular, the precipitation is studied in the orographic terrain of southwestern Germany and the dry lowlands of northeastern Germany. In both regions precipitation data is very important for end users such as hydrologists and farmers. Both WRF simulations show a systematic positive precipitation bias not apparent in ERA-interim and an overestimation of wet day frequency. The downscaling experiment improved the annual cycle of the precipitation intensity, which is underestimated by ERA-interim. Normalized Taylor diagrams, i.e., those discarding the systematic bias by normalizing the quantities, demonstrate that downscaling with WRF provides a better spatial distribution than the ERA interim precipitation analyses in southwestern Germany and most of the whole of Germany but degrades the results for northeastern Germany. At the applied model resolution of 0.11°, WRF shows typical systematic errors of RCMs in orographic terrain such as the windward–lee effect. A convection permitting case study set up for summer 2007 improved the precipitation simulations with respect to the location of precipitation maxima in the mountainous regions and the spatial correlation of precipitation. This result indicates the high value of regional climate simulations on the convection-permitting scale.     

基于4种高分辨率模式的辽宁省雷达回波预报邻域检验结果对比

选取2017—2018年6—9月辽宁省不同降水性质, 具有2种不同特征的20次天气过程个例, 应用模糊检验邻域法中的分数技巧评分(Fraction Skill Score, FSS), 评估华东模式、华北模式、GRAPES_3km模式和睿图东北模式对辽宁省中小尺度系统的预报能力。结果表明: 区域性降水过程和局地性降水过程雷达回波强度越小, 邻域半径越大, 高分辨率模式预报技巧越高。当雷达回波大于30 dBz时, 各高分辨率模式对局地性降水的雷达回波预报FSS评分均较高。当邻域半径为3 km时, 区域性降水过程中, 华北模式预报技巧在各级别雷达回波预报中均高于其他模式, 最大FSS差值为0.031。局地性降水过程中, 华东模式预报效果较好, 最大FSS评分为0.127, 表明华东模式预报中小尺度对流系统能力更强。局地性降水过程, 睿图东北模式在08—23时预报时次中, “中间”时次的预报效果优于“两头”时次的预报, 两个时次最大FSS差值为0.121。     

Evaluation of a WRF dynamical downscaling simulation over California

This paper presents results from a 40 year Weather Research and Forecasting (WRF) based dynamical downscaling experiment performed at 12 km horizontal grid spacing, centered on the state of California, and forced by a 1° × 1.25° finite-volume current-climate Community Climate System Model ver. 3 (CCSM3) simulation. In-depth comparisons between modeled and observed regional-average precipitation, 2 m temperature, and snowpack are performed. The regional model reproduces the spatial distribution of precipitation quite well, but substantially overestimates rainfall along windward slopes. This is due to strong overprediction of precipitation intensity; precipitation frequency is actually underpredicted by the model. Moisture fluxes impinging on the coast seem to be well-represented over California, implying that precipitation bias is caused by processes internal to WRF. Positive-definite moisture advection and use of the Grell cumulus parameterization result in some decrease in precipitation bias, but other sources are needed to explain the full bias magnitude. Surface temperature is well simulated in all seasons except summer, when overly-dry soil moisture results in a several degree warm bias in both CCSM3 and WRF. Additionally, coastal temperatures appear to be too warm due to a coastal sea surface temperature bias inherited from CCSM3. Modeled snowfall/snowmelt agrees quite well with observations, but snow water equivalent is found to be much too low due to monthly reinitialization of all regional model fields from CCSM3 values.     

WRF模式对中国夏季降水的动力降尺度模拟研究

采用NCEP的FNL再分析资料驱动WRF模式,对中国10 a(2000—2009年)夏季降水进行双重动力降尺度(双重嵌套)模拟,将子、母区域模拟结果和观测进行对比,以检验双重动力降尺度对中国夏季降水模拟的"增值"能力。结果表明:单重动力降尺度(单重嵌套)方法能较好模拟出中国10 a夏季平均降水的空间分布,对季风雨带"北跳"特征模拟较好,但模拟降水具有系统性正偏差。在母区域的强迫下,双重动力降尺度模拟的降水分布与单重动力降尺度相比,没有发生根本性变化。但由于子区域的分辨率要高于母区域,双重动力降尺度比单重动力降尺度能提供更多有价值的降水细节。双重动力降尺度的这种"增值"能力存在地域依赖性,在华南地区和江淮地区,双重动力降尺度模拟出的降水分布、量值和逐日演变都要好于单重动力降尺度。但在华北地区,双重动力降尺度没有表现出明显的"增值"。     

2013年华中区域中尺度业务数值预报的客观检验

应用国家气象中心模式检验方法,对华中区域中尺度业务数值预报模式WRF和中国气象局下发的GRAPES模式2013年1-12月的预报产品（包括降水、2 m温度场、850 hPa温度场、850 hPa风场和500 hPa位势高度场预报）进行统计检验。检验结果表明：所有要素24 h预报均优于48 h;对于晴雨预报,GRAPES模式TS评分高于WRF,但随着降水量级增大,WRF的TS评分基本都高于GRAPES,同时WRF降水预报范围明显偏大;分析2 m温度场的均方根误差及预报准确率发现,WRF的2 m温度场预报优于GRAPES,且暖季预报优于冷季;形势场要素分析表明,WRF对850 hPa温度场和风场预报具有相当大的优势,全年误差变化较稳定,而对500 hPa位势高度场的预报误差存在一定的季节性特征,即夏半年WRF高度场预报优于冬半年,GRAPES模式则相反。总体上,华中区域中尺度业务数值模式产品对天气预报具有一定的参考价值。     

CCSM4.0的长期积分试验及其对东亚和中国气候模拟的评估

本文利用通用气候系统模式CCSM4.0的低分辨率 (T31, 约3.75° × 3.75°) 版本进行了700年的长期积分试验, 将中国地表气温、降水及东亚海平面气压、500 hPa和100 hPa位势高度、850 hPa风场的最后100年模拟结果与观测和再分析资料进行了定性比较, 并对前三个要素的不同统计量值进行了定量计算, 系统评估了CCSM4.0对东亚及我国气候的模拟能力。结果表明, 模式能够合理模拟各变量的基本分布形态, 但幅度与观测有所差别, 其中地表气温的模拟效果最好, 降水的相对最差。具体而言, 地表气温空间分布型与观测一致, 但全年青藏高原地表气温模拟值偏高, 位于塔里木盆地的暖中心未能模拟出来; 降水空间分布型模拟较差, 除冬季不明显之外, 我国中南部全年都存在一个虚假降水中心, 并在夏季达到最强; 冬季东亚地区海陆热力对比大于观测, 夏季海平面气压场整体模拟效果不如冬季; 模式对冬、夏季500 hPa东亚大槽和西北太平洋副热带高压的主要特征刻画较好, 但模拟结果整体比观测偏强; 夏季100 hPa南亚高压强度与观测接近, 但高压范围及中心位置存在偏差; 850 hPa东亚冬季风和夏季风环流模拟较好, 但冬季西北气流偏强, 夏季索马里越赤道气流偏弱、我国东部西南气流偏强。总的来说, CCSM4.0对东亚和我国大尺度气候特征具备合理的模拟能力, 尽管在定量上还存在着不足。     

Assessing the Impacts of Initial Snow Conditions over the Tibetan Plateau on China Precipitation Prediction Using a Global Climate Model

Two ensemble experiments were conducted using a general atmospheric circulation model. These experiments were used to investigate the impacts of initial snow anomalies over the Tibetan Plateau（TP） on China precipitation prediction. In one of the experiments, the initial snow conditions over the TP were climatological values; while in the other experiment, the initial snow anomalies were snow depth estimates derived from the passive microwave remote-sensing data. In the current study, the difference between these two experiments was assessed to evaluate the impact of initial snow anomalies over the TP on simulated precipitation. The results indicated that the model simulation for precipitation over eastern China had certain improvements while applying a more realistic initial snow anomaly, especially for spring precipitation over Northeast China and North China and for summer precipitation over North China and Southeast China. The results suggest that seasonal prediction could be enhanced by using more realistic initial snow conditions over TP, and microwave remote-sensing snow data could be used to initialize climate models and improve the simulation of eastern China precipitation during spring and summer. Further analyses showed that higher snow anomalies over TP cooled the surface, resulting in lower near- surface air temperature over the TP in spring and summer. The surface cooling over TP weakened the Asian summer monsoon and brought more precipitation in South China in spring and more precipitation to Southeast China during summer.     

Validating a regional climate model’s downscaling ability for East Asian summer monsoonal interannual variability

Performance of a regional climate model (RCM), WRF, for downscaling East Asian summer season climate is investigated based on 11-summer integrations associated with different climate conditions with reanalysis data as the lateral boundary conditions. It is found that while the RCM is essentially unable to improve large-scale circulation patterns in the upper troposphere for most years, it is able to simulate better lower-level meridional moisture transport in the East Asian summer monsoon. For precipitation downscaling, the RCM produces more realistic magnitude of the interannual variation in most areas of East Asia than that in the reanalysis. Furthermore, the RCM significantly improves the spatial pattern of summer rainfall over dry inland areas and mountainous areas, such as Mongolia and the Tibetan Plateau. Meanwhile, it reduces the wet bias over southeast China. Over Mongolia, however, the performance of precipitation downscaling strongly depends on the year: the WRF is skillful for normal and wet years, but not for dry years, which suggests that land surface processes play an important role in downscaling ability. Over the dry area of North China, the WRF shows the worst performance. Additional sensitivity experiments testing land effects in downscaling suggest the initial soil moisture condition and representation of land surface processes with different schemes are sources of uncertainty for precipitation downscaling. Correction of initial soil moisture using the climatology dataset from GSWP-2 is a useful approach to robustly reducing wet bias in inland areas as well as to improve spatial distribution of precipitation. Despite the improvement on RCM downscaling, regional analyses reveal that accurate simulation of precipitation over East China, where the precipitation pattern is strongly influenced by the activity of the Meiyu/Baiu rainfall band, is difficult. Since the location of the rainfall band is closely associated with both lower-level meridional moisture transport and upper-level circulation structures, it is necessary to have realistic upper-air circulation patterns in the RCM as well as lower-level moisture transport in order to improve the circulation-associated convective rainfall band in East Asia.     

利用区域气候模式对我国南方百年气温和降水的动力降尺度模拟

本文采用NCAR的WRF3.5.1模式,以NOAA的20世纪再分析资料作为区域气候模式的初始场和侧边界场,对东亚地区进行了百年以上(1900~2010年)尺度、水平分辨率为50 km的动力降尺度数值模拟试验。通过与观测气候资料的对比,分析了驱动场(20世纪再分析资料)和区域气候模式对我国南方地区近50年(1961~2010年)气温和降水的气候平均态的模拟能力。结果表明:经过动力降尺度的区域气候模式试验结果能更好地模拟我国南方地区气温气候平均态和季节循环。WRF模式模拟的气温与观测的气温的空间相关系数均在0.97以上。年平均和夏季,WRF模式模拟的气温与观测的气温的偏差大多介于-1°C到+1°C之间。对于降水,WRF模式显著提高了我国南方降水的模拟能力。和驱动场相比,WRF模式模拟的降水与观测的偏差明显减小。夏季,WRF模式模拟的降水空间相关系数在0.5以上。由此延伸至对近百年我国南方地区三个子区域(华南地区、江淮地区和西南地区)四个时段(1914~1942年、1943~1971年、1972~2000年和2001~2010年)的分析,结果表明区域气候模式动力降尺度的结果在区域平均的气温和降水的模拟数值上与观测比较接近,夏季模拟能力有明显的提高,冬季存在气温模拟偏低的误差。对气温趋势分析表明,在20世纪40年代以后的两个时间段,区域气候模式明显提高了气温变化线性趋势的模拟性能。     

持续性强降水的区域模式动力中期预报研究

持续性强降水及其次生灾害给人民的生产和生活造成严重影响, 延伸其模式动力预报能力对防灾、减灾具有重要意义。随着对持续性强降水过程形成机理及模式动力中期预报认识的不断提高, 以减小模式初始条件误差、边界条件误差以及内场预报误差为目标提出了一系列动力中期预报技术方法, 主要包括:针对边界条件提出低通滤波技术方案, 改进了5 d以上的环流及降水预报; 针对模式预报内场进行谱逼近技术试验, 对提前3—7 d的小雨以上量级的降水预报改进明显; 针对初始条件进行多尺度混合更新初值技术预报试验, 融合全球预报的大尺度场及区域模式预报的中小尺度场进行15 d预报, 明显提高了50及100 mm以上的持续性累积降水预报时效。      

华北及周边地区夏季分区客观降水预报

在降水客观分区的基础上,对华北及周边地区进行夏季降水预报。利用2006—2008年的6—8月T213资料和相应时段的实况资料,通过概率回归降水等级方案建模,对2009年和2010年6—8月进行了试报。结果表明:分区建模的降水预报与单站建模预报相比,TS评分在不同时效、不同量级上均有提高,并且在空报和漏报上有较大改善,特别是大量级降水预报改善明显。从因子分析上看,分区建模较单站建模所选因子更丰富,利用了模式产品的有用信息,因此做出了更好的预报。分区建模与模式降水预报的对比分析表明:分区建模的降水预报效果好于模式直接降水预报,空报现象改善明显。     

不同分辨率CCSM4对东亚和中国气候模拟能力分析

本文利用通用气候系统模式CCSM4在三种水平分辨率下的工业化革命前期气候模拟试验,结合观测和再分析资料,比较了各分辨率下模式对中国温度和降水、东亚海平面气压和850 hPa风场的模拟能力,综合评价了模式分辨率对东亚和中国气候模拟的影响.结果表明,三种分辨率对中国温度均具有很好的模拟能力,除春季外,低分辨率(T31,约3.75°×3.75°)对全年温度的模拟能力均要稍好于中(f19,约1.9°×2.5°)、高(f09,约0.9°×1.25°)分辨率;各分辨率对中国降水的模拟能力远不如温度,除冬季外全年都出现的中部地区虚假降水并未因为模式分辨率提高而得到本质改善;对于东亚海平面气压场,低分辨率在冬季模拟能力相对最好,中等分辨率在夏季相对较好,而高分辨率的模拟能力均表现最差;低分辨率对850 hPa东亚冬季风和夏季风的模拟能力均要好于中、高分辨率,而两种较高分辨率的模拟能力则比较接近.总的来说,低分辨率CCSM4在东亚和中国气候模拟中表现出了较大优势,加之其计算代价小,适合进行需要较长时间积分的气候模拟研究.     

Seasonal Predictions of Summer Precipitation in the Middle-lower Reaches of the Yangtze River with Global and Regional Models Based on NUIST-CFS1.0

Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River (MLYR) is of urgent demand for the local economic and societal development. This study assesses the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology (NUIST-CFS1.0, previously SINTEX-F). The results show that the model can provide moderate skill in predicting the interannual variations of the MLYR rainbands, initialized from 1 March. In addition, the nine-member ensemble mean can realistically reproduce the links between the MLYR precipitation and tropical sea surface temperature (SST) anomalies, but the individual members show great discrepancies, indicating large uncertainty in the forecasts. Furthermore, the NUIST-CFS1.0 can predict five of the seven extreme summer precipitation anomalies over the MLYR during 1982–2020, albeit with underestimated magnitudes. The Weather Forecast and Research (WRF) downscaling hindcast experiments with a finer resolution of 30 km, which are forced by the large-scale information of the NUIST-CFS1.0 predictions with a spectral nudging method, display improved predictions of the extreme summer precipitation anomalies to some extent. However, the performance of the downscaling predictions is highly dependent on the global model forecast skill, suggesting that further improvements on both the global and regional climate models are needed.     

一次粤西南暴雨过程的预报误差来源分析

本文基于WRF模式研究了2015年5月16～17日广东西南地区的一次暴雨过程的预报误差来源。首先比较了以NCEP_FNL为初始资料的WRF模式的模拟预报（记为WRF_FNL）和ECMWF（European Centre for Medium-Range Weather Forecasts）关于该次暴雨过程的确定性预报。结果表明,ECMWF具有较高的预报技巧,因此,认为ECMWF的模式和初始场都较为准确。进一步,以ECMWF的初值作为初始场,选用相同的物理参数化方案,再次用WRF模式进行预报（预报结果记为WRF_EC）。结果表明相对WRF_FNL,WRF_EC的预报结果有明显改善。这表明,初始场的改进对预报有较大的影响,初始误差是预报误差的重要来源。进一步,分析了初始误差的主要来源区域和来源变量。结果表明,南海北部湾至广西西南区域为本次暴雨预报初始误差的主要来源区域,而初始温度场和初始湿度场则为此次暴雨预报初始误差的主要来源变量。同时改进初始温度场和湿度场可以较大程度提高本次暴雨过程的预报技巧。     

Dynamical downscaling of ERA-40 in complex terrain using the WRF regional climate model

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.     

A Quick Report on a Dynamical Downscaling Simulation over China Using the Nested Model

This paper describes a dynamical downscaling simulation over China using the nested model system, which consists of the modified Weather Research and Forecasting Model (WRF) nested with the NCAR Community Atmosphere Model (CAM). Results show that dynamical downscaling is of great value in improving the model simulation of regional climatic characteristics. WRF simulates regional detailed temperature features better than CAM. With the spatial correlation coefficient between the observation and the simulation increasing from 0.54 for CAM to 0.79 for WRF, the improvement in precipitation simulation is more perceptible with WRF. Furthermore, the WRF simulation corrects the spatial bias of the precipitation in the CAM simulation.     

On the role of domain size and resolution in the simulations with the HIRHAM region climate model

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.     

A regional climate model downscaling projection of China future climate change

Climate changes over China from the present (1990–1999) to future (2046–2055) under the A1FI (fossil fuel intensive) and A1B (balanced) emission scenarios are projected using the Regional Climate Model version 3 (RegCM3) nests with the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM). For the present climate, RegCM3 downscaling corrects several major deficiencies in the driving CCSM, especially the wet and cold biases over the Sichuan Basin. As compared with CCSM, RegCM3 produces systematic higher spatial pattern correlation coefficients with observations for precipitation and surface air temperature except during winter. The projected future precipitation changes differ largely between CCSM and RegCM3, with strong regional and seasonal dependence. The RegCM3 downscaling produces larger regional precipitation trends (both decreases and increases) than the driving CCSM. Contrast to substantial trend differences projected by CCSM, RegCM3 produces similar precipitation spatial patterns under different scenarios except autumn. Surface air temperature is projected to consistently increase by both CCSM and RegCM3, with greater warming under A1FI than A1B. The result demonstrates that different scenarios can induce large uncertainties even with the same RCM-GCM nesting system. Largest temperature increases are projected in the Tibetan Plateau during winter and high-latitude areas in the northern China during summer under both scenarios. This indicates that high elevation and northern regions are more vulnerable to climate change. Notable discrepancies for precipitation and surface air temperature simulated by RegCM3 with the driving conditions of CCSM versus the model for interdisciplinary research on climate under the same A1B scenario further complicated the uncertainty issue. The geographic distributions for precipitation difference among various simulations are very similar between the present and future climate with very high spatial pattern correlation coefficients. The result suggests that the model present climate biases are systematically propagate into the future climate projections. The impacts of the model present biases on projected future trends are, however, highly nonlinear and regional specific, and thus cannot be simply removed by a linear method. A model with more realistic present climate simulations is anticipated to yield future climate projections with higher credibility.