四川盆地低涡的月际变化及其日降水分布统计特征

利用ERA-interim再分析资料和全国824个气象基准站的日降水资料,统计分析了1983年1月1日~2012年12月31日发生在四川盆地的低涡天气过程及其降水特征,结果表明:盆地涡初生位置主要位于盆地的西南部和东北部,盆地涡夏季出现最多,冬季出现最少,其中初生位置位于盆地西南部的低涡7月出现最多,12月和1月出现最少;位于东北部的低涡6月出现最多,1月出现最少;盆地涡具有明显的日变化,西南型盆地涡3~10月夜晚发生概率均大于白天,其他月份低涡夜发性不明显,而东北型盆地涡只在5~9月期间夜晚发生概率大于白天,其他月份低涡夜发性不明显;盆地涡生命史与对流程度具有相关性,对流发展有利于盆地涡长时间维持,然而,夏季西南型盆地涡即使对流没向上发展也能长时间维持;盆地涡夏季移出最多,尤其以7、8月最明显,冬季移出最少,7月前以偏东路径为主,7月后以东北路径为主;盆地涡频数的月际变化与川西高原西南涡源地的风场扰动移出有密切联系,九龙地区夏季风场扰动移出活跃,冬季移出不活跃。小金地区春季风场扰动移出活跃,冬季移出不活跃。九龙地区风场扰动移出对盆地涡频数的月际变化贡献明显,小金地区风场扰动移出对盆地涡频数的月际变化贡献不明显;夏半年(5~10月)西南型盆地涡和东北型盆地涡引起的日降水区域分布的月际变化特征不同,前者的日降水最大值中心随月份先由盆地东北部向西南部移动,之后再由盆地西南部向东北部折回,后者的日降水最大值中心会一直稳定维持在盆地的东北部达州地区。东北型盆地涡虽然出现频次低,但各月的日降水强度要远大于西南型盆地涡。     

From China’s Heavy Precipitation in 2020 to a “Glocal” Hydrometeorological Solution for Flood Risk Prediction

The prolonged mei-yu/baiu system with anomalous precipitation in the year 2020 has swollen many rivers and lakes,caused flash flooding,urban flooding and landslides,and consistently wreaked havoc across large swathes of China,particularly in the Yangtze River basin.Significant precipitation and flooding anomalies have already been seen in magnitude and extension so far this year,which have been exerting much higher pressure on emergency responses in flood control and mitigation than in other years,even though a rainy season with multiple ongoing serious flood events in different provinces is not that uncommon in China.Instead of delving into the causes of the uniqueness of this year’s extreme precipitation-flooding situation,which certainly warrants in-depth exploration,in this article we provide a short view toward a more general hydrometeorological solution to this annual nationwide problem.A“glocal”(global to local)hydrometeorological solution for floods(GHS-F)is considered to be critical for better preparedness,mitigation,and management of different types of significant precipitation-caused flooding,which happen extensively almost every year in many countries such as China,India and the United States.Such a GHS-F model is necessary from both scientific and operational perspectives,with the strength in providing spatially consistent flood definitions and spatially distributed flood risk classification considering the heterogeneity in vulnerability and resilience across the entire domain.Priorities in the development of such a GHS-F are suggested,emphasizing the user’s requirements and needs according to practical experiences with various flood response agencies.     

岩溶泉补给地表溪流二氧化碳脱气作用研究

碳酸盐岩风化作用(即岩溶作用)能够吸收大气二氧化碳(CO_2),形成溶解无机碳(DIC,dissolved inorganic carbon),被认为是一种重要的陆地碳汇,其在全球碳收支平衡和未来陆地增汇中可能会有重要贡献。然而,目前对岩溶碳汇的稳定性还存在争议,一些学者认为岩溶地下水出露地表后会发生CO_2脱气,对岩溶碳汇通量估算带来不确定性。本文以广西桂林长流水表层岩溶泉补给的溪流(约2.7km长)为例,利用水化学和同位素质谱仪测试技术,研究了溪水水化学指标和溶解无机碳同位素(δ~(13)C_(DIC))沿流程变化,探讨了溪流CO_2脱气过程、通量及其影响因素,以更好地了解岩溶碳汇的稳定性。结果表明:从泉口向下游,在陡坡地段(C1～C14段,长约270m,坡度约10°),溪水pH值、方解石饱和指数和δ~(13)C_(DIC)沿流程分别升高了0.9、0.9和1.8‰,而CO_2分压、电导率、Ca~(2+)浓度和DIC浓度分别下降了85%、34μS/cm、0.2mmol/L和0.7mmol/L,说明溪水发生了显著的CO_2脱气,并伴随碳酸钙沉淀。而在平缓地段(C18～C26段,长约2.1km,坡度1°),溪水各水化学指标和δ~(13)C_(DIC)变化较小,指示CO_2脱气作用较弱。这些发现表明溪流CO_2脱气受到了地形决定的水动力条件控制。另外,在下游渠段,受支流汇入影响,溪水pH值和方解石饱和指数有所降低,在一定程度上抑制了CO_2脱气。溪流CO_2脱气能够抵消部分岩溶作用固定的大气CO_2量,但是在长流水这一高地势、低流量且有碳酸钙沉积的环境下,其抵消的量也仅占29%。对于在低缓地区受流量很大的岩溶泉/地下河补给的河流,其CO_2脱气作用对岩溶碳汇的影响有限,加之受可能增强的水生光合生物固碳效应影响,岩溶碳汇应具有很高的稳定性。     

Implementation of a surface runoff model with Horton and Dunne mechanisms into the regional climate model RegCM_NCC

A surface runoff parameterization scheme that dynamically represents both Horton and Dunne runoff generation mechanisms within a model grid cell together with a consideration of the subgrid-scaie soil heterogeneity, is implemented into the National Climate Center regional climate model （RegCM_NCC）. The effects of the modified surface runoff scheme on RegCMANCC performance are tested with an abnormal heavy rainfall process which occurred in summer 1998. Simulated results show that the model with the original surface runoff scheme （noted as CTL） basically captures the spatial pattern of precipitation, circulation and land surface variables, but generally overestimates rainfall compared to observations. The model with the new surface runoff scheme （noted as NRM） reasonably reproduces the distribution pattern of various variables and effectively diminishes the excessive precipitation in the CTL. The processes involved in the improvement of NRM-simulated rainfall may be as follows： with the new surface runoff scheme, simulated surface runoff is larger, soil moisture and evaporation （latent heat flux） are decreased, the available water into the atmosphere is decreased; correspondingly, the atmosphere is drier and rainfall is decreased through various processes. Therefore, the implementation of the new runoff scheme into the RegCMANCC has a significant effect on results at not only the land surface, but also the overlying atmosphere.     

武汉市10个主要极端天气气候指数变化趋势分析

根据武汉市1951—2007年逐日气温、降水量计算分析了10个极端天气气候指数的变化特征。结果表明:1)4个气温指数中,年及四季高、低温阈值均为上升趋势,并造成最长热浪天数的延长和霜冻日数的减少;低温阈值升速明显快于高温阈值,高温阈值仅在春季变化显著,最长热浪天数仅在冬季变化显著;低温阈值则为极显著上升趋势,尤其是年和冬季,造成热春、暖冬频繁;暖夜、闷热、傍晚至夜间的强对流等显著增多,暖日、高温热浪增加,霜冻日大幅减少。2)6个极端降水指数以增趋势为主,其中强降水阈值、比例、日数以及最大5日降水量在冬季增趋势最明显,仅夏季强降水阈值、比例略有减小,冬季日降水强度的增大趋势、夏季持续干期的缩短趋势显著性水平分别可达0.1、0.01。3)一些气温指数在1980—1990年代发生突变,而降水指数未现突变。     

2021年夏季中国大陆涝渍灾害时空分布分析

涝渍害是中国除干旱以外对农作物影响最严重的气象灾害,涝渍害的发生和发展对人民的生命和财产安全以及农作物的生长发育产生巨大的影响。2021年7月至8月间,中国北方多地降水达到历史观测极值,而相应时期地面的涝渍害发生和发展状况及其时空特征并未得到有效研究。因此,本研究首先利用中国大陆土壤水分站点日数据和被动微波遥感卫星SMAP反演土壤水分日产品获取中国高精度表层土壤湿度数据（0—10 cm）;随后,结合田间持水量数据计算土壤表层相对含水量。在此基础上,以连续10 d土壤相对含水量大于等于90%为标准,分析中国大陆2021年7月1日—8月25日的涝渍害时空分布情况。结果表明：（1）融合后的土壤水分产品较原始的SMAP微波土壤水分产品精度具有明显的提高;（2）中国东北地区水稻田种植区的土壤相对含水量大于等于90%的最长持续天数均为56 d,土壤的水分长期处于饱和状态,说明了本文方法能够较为准确的反应出土壤涝渍害的情况;（3）中国东北及华北地区受灾较为严重,其中黑龙江的西部和河北、河南、山东发生的涝渍面积最大。中国大陆耕地部分受涝渍灾害区域占到总耕地面积的1/2左右,重灾区面积为1.940×10⁵ km²;（4）黑龙江的西部及河北、河南、山东等地较往年降水偏多,这与涝渍害受灾区基本吻合。     

厄尔尼诺对长江中下游地区夏季持续性降水结构的影响及其可能机理

根据中国国家气象信息中心提供的1961-2016年2400多站的逐日降水观测数据,分析了厄尔尼诺对长江中下游地区夏季持续性降水结构的影响。发现长江中下游地区的降水主要以5 d及以内的降水事件为主,其强度主要分布在4-24 mm/d;5 d以上降水所占比例相对较小,而其强度主要分布在12-24 mm/d。其中,长江中下游地区夏季大于5 d的降水事件（长持续性降水事件）所占比例和强度在长江以南地区要大于长江以北地区。同时,在厄尔尼诺的影响下,长江以南地区的降水结构从2-5 d持续性降水事件（短持续性降水事件）和1 d的降水事件（非持续性降水事件）向长持续性降水事件转变,且其强度增加。而长江以北地区,以湖北为主,降水结构存在从非持续性向短持续性降水事件转变的现象,短持续性降水事件的强度也略有增强。因此,厄尔尼诺使得长江中下游地区的降水事件更多地以持续性降水为主,不同持续性降水事件的强度加强。进一步分析发现厄尔尼诺次年西太平洋副热带高压西伸加强,与其相关的东南季风所输送的水汽也有所加强。同时,中高纬度阻塞高压环流形势稳定维持。受这些因子的共同作用,最终导致长江中下游地区夏季降水持续性延长和降水强度加强。而这将给长江中下游地区的农作物种植和经济发展等带来较严重的影响,使防洪、防涝工作面临严峻的挑战。      

Diagnostic study on seasonality and interannual variability of wind field

l.Intr0ductionThoughseasonalvariationoftheatmosphericgeneralcirculationismainlycausedbythatofthesolarradiation,itsdistributionsareinhomogeneousovertheglobe,forinstance,itismoresignificantinmonsoonregionthaninanyotherregions.Inatraditionalsense,mon-soonsummarisesalldrasticseasonalvariationsinthetropicsandsubtropics(e.g.,IndiaandEastAsia).Besidestheclassicmonsoonregions,thereexistsomeotherregionsovertheglobe,wheretheseasonalvariationisclearorevendrastic.Inordertodescribequantitativelysea-sonal…     

气溶胶光学厚度的时空变化

在大气中气溶胶微粒是一种重要的大气微量成分。气溶胶光学厚度也是大气校正所需的重要大气参数，同时也是海洋水色卫星主要的数据产品。由于气溶胶光学厚度的时空变化较大，所以如何准确获取大气校正和卫星数据产品真实性检验所需的气溶胶光学厚度则是至关重要的。在简述气溶胶光学性质的基础上，并结合2002年6月HY—1南海实验数据来阐述现场气溶胶光学厚度的准确获取。     

Prediction of UT1–UTC,LOD and AAM χ<Subscript>3</Subscript> by combination of least-squares and multivariate stochastic methods

This article presents the application of a multivariate prediction technique for predicting universal time (UT1–UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ ₃). The multivariate predictions of LOD and UT1–UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ ₃ time-series as the explanatory variable for the computation of LOD or UT1–UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ ₃ data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ ₃ predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1–UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1–UTC based on LS extrapolation or on LS + AR. In particular, the UT1–UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ ₃ time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ ₃ reveal the best accuracy for LS + AR.