Estimation of mean and extreme waves in the East China Seas

Accurately estimating the mean and extreme wave statistics and better understanding their directional and seasonal variations are of great importance in the planning and designing of ocean and coastal engineering works. Due to the lack of long-term wave measurement data, the analysis of extreme waves is often based on the numerical wave hind-casting results. In this study, the wave climate in the East China Seas (including the Bohai Sea, the Yellow Sea and the East China Sea) for the past 35 years (1979–2013) is hind-casted using a third generation wave model – WAMC4 (Cycle 4 version of WAM model). Two sets of reanalysis wind data from NCEP (National Centers for Environmental Prediction, USA) and ECMWF (European Centre for Medium-range Weather Forecasts) are used to drive the wave model to generate the long-term wave climate. The hind-casted waves are then analysed to study the mean and extreme wave statistics in the study area. The results show that the mean wave heights decrease from south to north and from sea to land in general. The extreme wave heights with return periods of 50 and 100 years in the summer and autumn seasons are significantly higher than those in the other two seasons, mainly due to the effect of typhoon events. The mean wave heights in the winter season have the highest values, mainly due to the effect of winter monsoon winds. The comparison of extreme wave statistics from both wind fields with the field measurements at several nearshore wave observation stations shows that the extreme waves generated by the ECMWF winds are better than those generated by the NCEP winds. The comparison also shows the extreme waves in deep waters are better reproduced than those in shallow waters, which is partly attributed to the limitations of the wave model used. The results presented in this paper provide useful insight into the wave climate in the area of the East China Seas, as well as the effect of wind data resolution on the simulation of long-term waves.     

中国近海以及邻近海域海洋再分析产品(CORA)中季节内变化的评估

A regional reanalysis product—China Ocean Reanalysis(CORA)—has been developed for the China's seas and the adjacent areas. In this study, the intraseasonal variabilities(ISVs) in CORA are assessed by comparing with observations and two other reanalysis products(ECCO2 and SODA). CORA shows a better performance in capturing the intraseasonal sea surface temperatures(SSTs) and the intraseasonal sea surface heights(SSHs) than ECCO2 and SODA do, probably due to its high resolution, stronger response to the intraseasonal forcing in the atmosphere(especially the Madden-Julian Oscillation), and more available regional data for assimilation. But at the subsurface, the ISVs in CORA are likely to be weaker than reality, which is probably attributed to rare observational data for assimilation and weak diapycnal eddy diffusivity in the CORA model. According to the comparison results, CORA is a good choice for the study related to variabilities at the surface, but cares have to be taken for the study focusing on the subsurface processes.     

中国南部及邻区能源资源成矿区带数据集

能源矿产是国家安全和经济发展的战略资源,加强我国及周边地区油气和煤等能源资源的研究和开发利用意义重大。依据板块构造和大陆动力学以及油气与煤成藏地质学理论,以深部构造控制盆—山发育、盆—山耦合和热动力系统控制成矿的研究工作思路,应用70个油气田、471个煤田的基础地质资料,从沉积盆地发育的区域地质特征、成矿地质条件和成矿单元诸多方面,在地理信息系统（GIS）平台上提示了该区板块构造格局与能源资源的时空分布规律。中国南部及邻区能源资源可划分为3个成矿域,每个成矿域又可进一步划分为成矿省、含矿区（盆地）、矿田聚集区带、矿田（油气田）五个级别的成矿区域。数据集由构造带（断裂带和造山带）类型和时代、盆地类型和时代、能源矿产资源类型和丰度、成矿区带等元数据组成。该数据集是在区域大地构造研究的基础上,结合沉积盆地能源矿产（石油、天然气、煤）资源评价资料,综合分析成矿区带特征,利用地理信息系统（GIS）建立的一套完整的数据库,不仅是对中国南部及邻区区域地质认识和沉积盆地能源资源勘查工作成果的集成,而且为国家科学地引导地质找矿工作部署提供理论基础。     

Intercomparison of CRA-Interim Precipitation Products with ERA5 and JRA-55

By using the hourly data from surface meteorological stations in China, the 3-hour precipitation data from CRA-Interim (Chinese Reanalysis-Interim), ERA5 (ECMWF Reanalysis 5) and JRA-55 (Japanese Reanalysis-55) are compared, both on the spatial-temporal distributions and on bias with observation precipitation in China. The results show that: (1) The three sets of reanalysis datasets can all reflect the basic spatial distribution characteristics of annual average precipitation in China. The simulation of topographic forced precipitation in complex terrain by CRA-interim is more detailed, while CRA-interim has larger negative bias in central and East China, and larger positive bias in southwest China. (2) In terms of seasonal precipitation, the three sets of reanalysis datasets overestimate the precipitation in the heavy rainfall zone of spring and summer, especially in southwest China. CRA interim’s location of the rain belt in the First Rainy Season in South China is west by south, the summer precipitation has positive bias in southwest and South China. (3) All of the reanalysis datasets can basically reflect the distribution difference of inter-annual variation of drought and flood, but the overall the CRA-Interim generally shows negative bias, while the ERA5 and JRA-55 exhibit positive bias. (4) For the diurnal variation of precipitation in summer, all the reanalysis datasets perform better in simulating the daytime precipitation than in the night, and bias of CRA-interim is less in southeast and northeast than elsewhere. (5) ERA5 generally performs the best on the evaluation of quantitative precipitation forecast, the JRA-55 is the next, followed by the CRA-Interim. CRA-Interim has higher missing rate and lower threat score for heavy rains; however, at the level of downpour, the CRA-Interim performs slightly better.     

江西省山地风场风速订正方法研究

利用气象站、探空及NASA再分析资料,对江西省4县山地风场的12座测风塔风速进行订正研究。研究结果表明:测风塔与气象站风速数据相关性较低,相关系数一般远小于0.45;测风塔与探空资料的风速相关系数可达到0.6以上,最高可达到0.8;NASA再分析资料可以作为江西山地风场风速订正参证数据,其与测风塔风速数据相关性较高,相关系数可达到0.54～0.77,大多数测风塔相关系数可达0.7左右。海拔高度小于1000 m的测风塔与NASA 50 m风速的相关系数明显高于其与NASA 850 hPa风速的相关系数,高度为1000—1200 m的测风塔与NASA 50 m风速和与NASA 850 hPa风速的相关系数相差不明显,高度大于1200 m的测风塔与NASA 850 hPa风速的相关系数明显大于其与NASA 50 m风速的相关系数。比值法订正效果略好于线性回归法的,订正后的风功率密度总体偏大。     

Comparison of products from ERA-40, NCEP-2, and CRU with station data for summer precipitation over China

Summer precipitation products from the 45-Year European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis (ERA-40), and NCEP-Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis (NCEP-2), and Climatic Research Unit (CRU) TS 2.1 dataset are compared with the corresponding observations over China in order to understand the quality and utility of the reanalysis datasets for the period 1979–2001. The results reveal that although the magnitude and location of the rainfall belts differ among the reanalysis, CRU, and station data over South and West China, the spatial distributions show good agreement over most areas of China. In comparison with the observations in most areas of China, CRU best matches the observed summer precipitation, while ERA-40 reports less precipitation and NCEP-2 reports more precipitation than the observations. With regard to the amplitude of the interannual variations, CRU is better than either of the reanalyses in representing the corresponding observations. The amplitude in NCEP-2 is stronger but that of ERA-40 is weaker than the observations in most study domains. NCEP-2 has a more obvious interannual variability than ERA-40 or CRU in most areas of East China. Through an Empirical orthogonal function (EOF) analysis, the main features of the rainfall belts produced by CRU agree better with the observations than with those produced by the reanalyses in the Yangtze-Huaihe River valley. In East of China, particularly in the Yangtze-Huaihe River valley, CRU can reveal the quasi-biennial oscillation of summer precipitation represented by the observations, but the signal of ERA-40 is comparatively weak and not very obvious, whereas that of NCEP-2 is also weak before 1990 but very strong after 1990. The results also suggest that the magnitude of the precipitation difference between ERA-40 and the observations is smaller than that between NCEP-2 and the observations, but the variations represented by NCEP-2 are more reasonable than those given by ERA-40 in most areas of East China to some extent.     

江淮流域极端降水时空变化特征:站点观测和再分析的对比

利用淮河流域1979—2011年260个站点观测、ERA-Interim和NCEP/DOE再分析资料的日降水量数据,选用8个极端降水指数,从空间分布、发展趋势、时间变化等方面对比分析了我国江淮流域极端降水的变化规律,研究了再分析数据的适用性,结果表明:1)持续湿润指数(CWD)、强降水日数(R10mm,R20mm)以及百分位指数(R95p,R99p)具有一致的北少南多的分布特征,而持续干燥指数(CDD)为北多南少,且强度指数(Rx1day,Rx5day)和百分位指数在浙江沿海均有极大值存在。2)大部分地区的强降水日数呈减少趋势,仅在江淮周边地区有弱上升趋势。3)区域平均的降水强度指数具有上升的趋势变化,逐月变化具有先增长后减少的结构特征,5—6月的增长量最大,峰值出现在7月,在夏末、冬季有较明显的随年代增加的趋势,在秋季则随年代减少。4)再分析资料ERA-Interim和NCEP/DOE对不同指数的再现能力有所不同,ERA-Interim对强降水日数(R10mm)、CDD、百分位指数的空间分布以及CDD的变化趋势再现能力较好,与强度指数和百分位指数年际变化的相关性较高,但对CWD变化趋势分布特点的再现能力较弱;NCEP/DOE更善于再现较强降水日数(R20mm)的空间分布以及强度指数和百分位指数的线性变化趋势。5)两种再分析资料能合理地再现强降水日数(R10mm,R20mm)和CDD年际变化特征和强度指数的季节变化特征。     

农户的非农就业如何影响中国的土地流转?

To clarify the impact of non-agricultural employment on rural land circulation in China,we built logit models using the Chinese Household Income Project 2013 dataset,which includes 18,948 household samples over 15 provinces,126 cities and 234 counties of China in 2013.We use the proportion of non-agricultural income,the proportion of non-agricultural laborers and non-agricultural fixed operating assets to reflect the degree of the households’dependence on agriculture,the degree of the households’laborers committed to non-agricultural employment and the stability of non-agricultural employment,respectively.The results show that the stability of non-agricultural employment is an important reason for farmers to transfer out their land,and an increase in non-agricultural income is the fundamental reason.The proportion of non-agricultural assets has the greatest impact on the decision to transfer land,followed by the proportion of non-agricultural income.Per unit increase in the non-agricultural income ratio has a stronger effect on the transfer-out decision than it does on the transfer-in decision,which is a 0.09 increase of the probability of transfer-out the land and a 0.07 decrease of the probability of transfer-in the land.In terms of regional differences,when considering the impact of non-agricultural employment on the land transfer-out decision,the impacts of non-agricultural income and labor force are the greatest in the Central region.The impact of non-agricultural assets is the greatest in the Eastern region.For the Eastern region,the decision to transfer out land is mainly affected by non-agricultural assets and the non-agricultural labor force,and the decision to transfer in land is mainly affected by non-agricultural assets.In the Central and Western regions,the decision to transfer out land is mainly affected by non-agricultural assets,non-agricultural income and the non-agricultural labor force,in that order.The decision to transfer in land in the Central region is not significantly affected by non-agricultural employment.The decision to transfer in land in the Western region is mainly affected by non-agricultural assets,non-agricultural labor force and non-agricultural income,in that order.We note that non-agricultural assets have a prominent impact on land transfer,which shows that the stability of non-agricultural employment has an important impact on land transfer decision-making.Vocational training for rural labor forces may be an effective means to promote stable non-agricultural employment and simultaneously facilitate rural land circulation,especially in Central and Western China.     

基于高分辨率风场的海洋近惯性能通量计算——时空特征及其影响因素

基于高分辨率CFSR(climate forecast system reanalysis)风场资料、气候态海洋混合层厚度资料和卫星高度计海面高度异常资料,本文估计了大气风场向全球海洋混合层的近惯性能通量和近惯性能量输入功率,并探究了混合层厚度、风场时间分辨率、经验衰减系数和中尺度涡旋涡度对近惯性能通量和能量输入功率的影响。浮标实测风场和流速表明,本文所用的风场和阻尼平板模型可用于估计风场向全球海洋的近惯性能通量。本文计算得到的大气向全球海洋输入近惯性能量的功率为0.56TW(1TW=10~(12)W),其中北半球贡献0.22TW,南半球贡献0.34TW。在时间上,风场的近惯性能通量呈现各个半球冬季最强、夏季最弱的特征,这和西风带风场的季节变化有关。在空间上,近惯性能通量的高值海域为南、北半球西风带海洋,尤其是南大洋。混合层厚度和风场空间不均匀性使得西风带近惯性能通量呈现纬向变化,即海盆西部强于海盆东部。风场时间分辨率对近惯性能通量的估计至关重要,低时间分辨率风场对近惯性能通量的低估达到13%—30%。阻尼平板模型中的经验衰减系数对近惯性能通量估计的影响不超过5%。中尺度涡旋涡度仅改变近惯性能通量的空间分布,而对全球近惯性能量输入功率的影响可以忽略。     

An assessment of Arctic cloud water paths in atmospheric reanalyses

The role of Arctic clouds in the recent rapid Arctic warming has attracted much attention. However, Arctic cloud water paths(CWPs) from reanalysis datasets have not been well evaluated. This study evaluated the CWPs as well as LWPs(cloud liquid water paths) and IWPs(cloud ice water paths) from five reanalysis datasets(MERRA-2,MERRA, ERA-Interim, JRA-55, and ERA5) against the COSP(Cloud Feedback Model Intercomparison Project Observations Simulator Package) output for MODIS from the MERRA-2 CSP(COSP satellite simulator) collection(defined as M2 Modis in short). Averaged over 1980–2015 and over the Arctic region(north of 60°N), the mean CWPs of these five datasets range from 49.5 g/m~2(MERRA) to 82.7 g/m~2(ERA-Interim), much smaller than that from M2 Modis(140.0 g/m~2). However, the spatial distributions of CWPs, show similar patterns among these reanalyses, with relatively small values over Greenland and large values over the North Atlantic. Consistent with M2 Modis, these reanalyses show larger LWPs than IWPs, except for ERA-Interim. However, MERRA-2 and MERRA underestimate the ratio of IWPs to CWPs over the entire Arctic, while ERA-Interim and JRA-55 overestimate this ratio. ERA5 shows the best performance in terms of the ratio of IWPs to CWPs. All datasets exhibit larger CWPs and LWPs in summer than in winter. For M2 Modis, IWPs hold seasonal variation similar with LWPs over the land but opposite over the ocean. Following the Arctic warming, the trends in LWPs and IWPs during 1980～2015 show that LWPs increase and IWPs decrease across all datasets, although not statistically significant. Correlation analysis suggests that all datasets have similar interannual variability. The study further found that the inclusion of re-evaporation processes increases the humidity in the atmosphere over the land and that a more realistic liquid/ice phase can be obtained by independently treating the liquid and ice water contents.