1981-2008年中国陆地植被NPP对气候变化响应的敏感性试验

在验证CENTURY模型对中国陆地植被净初级生产力(Net Primary Productivity,NPP)模拟能力的基础上,利用该模型探讨了1981-2008年中国陆地植被NPP的年际变异和变化趋势对CO₂浓度、温度和降水变化的响应。结果表明,中国陆地植被NPP对不同气候因子的响应程度存在明显不同。其中,CO₂浓度变化对植被NPP年际变异的影响不显著,但能够引起中国大部分地区植被NPP趋势系数增大;温度对中国中高纬度地区植被NPP的年际变化影响显著,但就全国范围而言,植被NPP年际变异对温度变化的响应程度总体低于对降水变化的响应程度;降水变化是对中国植被NPP变化趋势起主导作用的气候因子。此外,综合考虑温度和降水变化的影响发现,植被NPP变化趋势的响应特征类似于降水单独变化时植被NPP变化趋势的响应特征。     

引发强降水的一次东移高原云团的能量演变特征研究

利用日本气象厅葵花-8卫星亮温资料、欧洲中心ERA5（the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis）再分析资料,根据时间尺度分解的局地能量诊断方法,本文从能量学多个角度研究了2016年6月5日00时（协调世界时,下同）至6日15时（持续40小时）一次东移并引发强降水的高原对流云团,得到了以下主要结论。本次事件中,高原东移对流云团在不同阶段的主要影响系统有所不同。移出高原前,其主要受高原涡和高原短波槽的共同影响,随着云团移出高原,高原涡消亡,而高原短波槽则随时间发展加强,成为东移云团的最主要影响系统。高原东移对流云团具有显著的深对流特征,自西向东引发了一系列的降水,移出高原后,其对流重心显著降低,降水达到最强。不同阶段高原东移对流云团的能量转换特征显著不同。云团位于高原上时（第一阶段）,背景场通过动能的降尺度能量级串为造成强降水的扰动流直接提供能量,这是此阶段扰动流动能维持的主要方式;云团移出高原过程中（第二阶段）,降水凝结潜热明显增强,由此制造的扰动有效位能也显著增强。在垂直运动配合下,扰动有效位能斜压释放所制造的动能是本阶段造成强降水扰动流动能维持的最主要能量来源;云团移出高原后（第三阶段）,背景场对造成强降水扰动流的影响再次增强,但是不同于第一阶段的直接影响方式,该阶段背景场的作用是以一种间接的影响方式出现。其首先通过有效位能的降尺度级串将背景场的有效位能转换为扰动流的有效位能,然后通过扰动有效位能的斜压能量释放为扰动流的动能维持不断地提供能量。此外,本阶段内还出现了扰动流向背景场动能的升尺度级串供给（即扰动流的反馈）,但其强度不足以对背景场的演变产生显著影响。     

大气物理过程方案对PM2.5预报的敏感性试验

利用WRF-Chem大气化学模式,选择2015年12月中旬发生在我国的大范围空气污染过程,在采用同样化学方案条件下,针对模式中不同物理过程及其参数化方案开展了地面PM_(2.5)预报的敏感性试验。结果表明:该模式能较好展示此次PM_(2.5)污染的演变过程,与实况也较接近,但对青海经宁夏至内蒙的PM_(2.5)高值区出现了漏报现象,这可能是模式外边界未对污染物做更新所致。对地面PM_(2.5)的预报,各物理过程的敏感度不同,边界层(含近地面层)过程的影响要明显大于积云对流及微物理过程的影响,不同的参数化方案会造成不同的预报误差。边界层过程QNSE和与其配套的近地面层方案的组合是预报的较佳组合;而TEMF和与其配套的组合以及ACM2和Pleim-X的组合则不佳。合理的物理过程参数化方案有助于提高PM_(2.5)预报质量。模式预报对排放源也有适应过程,其Spin-up时间较气象要素长。     

杭州市典型雨转雪天气成因及预报模型

利用2008—2018年的NCEP(1°×1°)再分析资料、常规气象观测资料和降雪加密观测资料,选出杭州地区10次典型的雨转雪天气过程,从大尺度环流背景和动力、水汽以及热力因子等物理量场结构方面展开研究,最终得出杭州冬季典型雨转雪天气的预报模型:①大尺度环流配置需满足能为雨转雪天气的形成提供有利的水汽、动力抬升以及中低层上暖下冷的逆温或等温层结条件;②水汽和动力因子等物理量须满足产生纯雪的特定条件;③杭州温度层结须为T_(2m)≤1.5℃、T_(925)≤-4.0℃、T_(850)≤0℃、T_(700)≤-1.0℃和T_(500)≤-10.0℃。此外,进一步补充了杭州可能产生大雪甚至暴雪量级降雪的特定条件。最终选取2019年初的2次典型降水过程进行预报回报检验。     

Meteorology in Canada: A reaction to the rose report

The methods used in an earlier study focusing on the province of Ontario, Canada, were adapted for this current study to expand the study area over eastern Canada where the infrastructure is at risk of being impacted by freezing rain. To estimate possible impacts of climate change on future freezing rain events, a three-step process was used in the study: (1) statistical downscaling, (2) synoptic weather typing, and (3) future projections. A regression-based downscaling approach, constructed using different regression methods for different meteorological variables, was used to downscale the outputs of eight general circulation models to each of 42 hourly observing stations over eastern Canada. Using synoptic weather typing (principal components analysis, a clustering procedure, discriminant function analysis), the freezing rain-related weather types under historical climate (1958–2007) and future downscaled climate conditions (2016–2035, 2046–2065, 2081–2100) were identified for all selected stations. The potential changes in the frequency of future daily freezing rain events can be projected quantitatively by comparing future and historical frequencies of freezing rain-related weather types.

The modelled results show that eastern Canada could experience more freezing rain events late this century during the coldest months (i.e., December to February) than the averaged historical conditions. Conversely, during the warmest months of the study season (i.e., November and April in the southern regions, October in the northern regions), eastern Canada could experience less freezing rain events late this century. The increase in the number of daily freezing rain events in the future for the coldest months is projected to be progressively greater from south to north or from southwest to northeast across eastern Canada. The relative decrease in magnitude of future daily freezing rain events in the warmest months is projected to be much less than the relative increase in magnitude in the coldest months.     

C‐band SAR backscatter characteristics of Arctic sea and land ice during winter

Abstract

Synthetic Aperture Radar (SAR) data has become an important tool for studies of polar regions, due to high spatial resolution even during the polar night and under cloudy skies. We have studied the temporal variation of sea and land ice backscatter of twenty‐four SAR images from the European Remote Sensing satellite (ERS‐1) covering an area in Lady Ann Strait and Jones Sound, Nunavut, from January to March 1992. The presence of fast ice in Jones Sound and glaciers and ice caps on the surrounding islands provides an ideal setting for temporal backscatter studies of ice surfaces. Sample regions for eight different ice types were selected and the temporal backscatter variation was studied. The observed backscatter values for each ice type characterize the radar signatures of the ice surfaces. This time series of twenty‐four SAR images over a 3‐month period provides new insights into the degree of temporal variability of each surface. Ice caps exhibit the highest backscatter value of ‐3.9 dB with high temporal variability. Valley glacier ice backscatter values decrease with decreasing altitude, and are temporally the most stable, with standard deviations of 0.08–0.10 dB over the 90‐day period.

First‐year ice and lead ice show a negative trend in backscatter values in time and a positive correlation of up to 0.59 with air temperature over the 90‐day period. For first‐year ice and lead ice, episodes of large temperature fluctuations (±12°C) are associated with rapid changes in backscatter values (±2 dB). We attribute the backscatter increase to a temperature‐induced increase in brine volume at the base of the snow pack. Multi‐year ice, conglomerate ice and shore ice are relatively stable over the 3‐month period, with a backscatter variation of only a few dBs. An observed lag time of up to three days between backscatter increase/decrease and air temperature can be attributed to the insulation effect of the snow cover over sea ice. The net range of the backscatter values observed on the most temporally stable surface, valley glacier ice, of about 0.30 dB indicates that the ERS‐1 SAR instrument exceeds the 1 dB calibration accuracy specified for the Alaska SAR Facility processor for the three winter months.     

台风螺旋雨带云结构和降水形成机制研究

应用数值模式结果,选择台风登陆后两个不同时次螺旋雨带中两个强降水中心,对台风螺旋雨带的云结构和降水形成机制进行诊断分析.结果发现螺旋雨带云结构和降水形成机制有如下特点:在9～13 km高空范围内冰晶的非均质核化非常活跃,冰晶转化率高于台风眼壁暴雨数倍,但是冰晶通过贝吉龙过程生长为雪、雪通过凝华增长生长为霰的过程相对台风眼壁很弱,螺旋雨带雨水形成微物理机制以霰粒子融化成雨水(pgmlt)为主,冰相粒子转化率大值区位于垂直上升气流大值区,8 km高度霰收集雪(dgacs)干增长是最主要的冰相粒子生长过程,与北方层状云比较,螺旋雨带暴雨冷云中的凝华过程和撞冻过程非常活跃.螺旋雨带云水凝结过程呈双峰型,位于7～8 km高度冷云区的云水凝结峰值较大,暖云区0.5～1.5 km高度云水凝结峰值次之.     

影响北京的一例沙尘天气过程的起沙沉降及输送路径分析

本文选取2012年4月27-28日影响北京的一次沙尘天气过程进行分析,发现在本次过程中,沙源地区的沙尘暴主要发生在地面冷锋和500 hPa槽后,在近地层强烈的感热加热与中低层冷平流的共同作用下,形成了不稳定层结及深厚的混合层,最高可达600 hPa,是沙尘天气能够影响下游地区的重要条件。本文还通过分析等熵混合层厚度变化及其平流过程来追踪沙尘天气的输送路径和定位沉降地,发现本次过程有两种传输路径,一种是沿辽宁北部向东而后转向东北方向,主要沿等熵混合层平流向下游移动,之后随着等熵混合层的接地沉降到近地层,该路径影响高度较高;另一种是沿辽宁南部向东南方向移动,混合层平流较清楚,但沉降时与东北路径明显不同,在辽东半岛东部地面辐散气流及下沉运动的作用下发生沉降,并随冷锋后冷高压南侧的东风回流向西输送影响北京地区。另外,由于混合层之上的稳定层明显下压,使沙尘输送高度偏低。运用HYSPLIT模式模拟的前、后向轨迹证实了上述两种输送路径,表明北京的扬沙天气是由东风回流造成的。     

基于AMSR-E土壤湿度产品的LIS同化试验

由陆面信息系统 (Land Information System, 简称LIS) 通过NOAH陆面过程模型使用集合卡尔曼滤波开展AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System) 土壤湿度同化试验,得到2003年中国区域垂直深度为4层、水平空间分辨率为0.25°×0.25°的土壤湿度试验数据。使用农业气象观测站土壤相对湿度和国家生态系统野外科学观测研究站土壤体积含水量对试验结果进行检验,结果表明:同化过程整体上提高了陆面模型的模拟精度,草地生态系统模拟精度高于作物和森林生态系统;有效的同化过程依赖于AMSR-E土壤湿度的准确性;模拟出的土壤湿度空间分布特征与实际相符。同化试验得到的时空相对连续且精度相对准确的土壤湿度数据是气候变化和干旱监测的重要数据基础。     

基于ERSM涡轮盘径向变形的非线性动态概率分析

为了准确设计高压涡轮盘和叶尖间隙,从概率的角度进行了涡轮盘径向变形的分析。介绍了高精度高效率的非线性动态概率分析的极值响应面方法(Extremum Response Surface Method, ERSM),并建立了其数学模型。考虑材料属性和边界条件的非线性,以及热载荷和离心载荷的动态性,基于ERSM对涡轮盘径向变形进行了非线性动态概率分析,得到了输入输出参数的分布特征和影响涡轮盘径向动态变形的主要因素。最后,通过方法比较,验证了ERSM在保证计算精度的前提下能大大提高计算速度,节约计算时间,改善计算效率。为进行更有效的涡轮盘设计和优化,改善叶尖间隙设计和控制的合理性提供了有效依据。 