天气尺度瞬变扰动的物理分解原理

大气变量可以在时空域内物理分解成四个部分.前两个是纬圈-时间平均的对称部分和时间平均的非对称部分,分别由太阳辐射和海陆分布热力调节的季节变化引起,并形成规则的逐日气候.第三部分是由年际和季节内的热带海洋或极地热力强迫引起的纬圈平均瞬变对称扰动,可形成大气变量的行星尺度指数循环.第四部分是一些复杂的天气尺度瞬变非对称扰动.大气变量中的逐日天气尺度瞬变扰动,可以用于指示区域持续性的干旱、暴雨、低温和热浪等极端天气事件.天气尺度瞬变扰动天气图能在极端天气事件的预报中发挥应有的作用.     

西南区域持续性干旱事件的行星尺度和天气尺度扰动信号

以2009-2010年发生在中国西南地区的持续性干旱事件为例,通过干旱和大气变量的物理分解得到了一些干旱事件发生的新认识.气象干旱多为年循环的气候干季与干旱扰动的叠加所致.一次干旱扰动大约为30-50天,而一次持续性干旱事件是由几次干旱扰动组成的.大气高度场和风场中存在三种时间尺度的扰动.一种是年际行星尺度的大气扰动,与ENSO冷暖事件有关,起源于赤道并传播到中高纬度地区需要2-4年.另一种是季节内行星尺度的大气扰动,与来自赤道地区的30-50天振荡有关.此外,大气中还存在天气尺度的扰动.利用行星尺度大气扰动向赤道外传播与天气尺度扰动的叠加,区域持续性干旱事件能够找到前期预报信号.     

北半球大气遥相关型与区域尺度大气扰动

北极涛动(AO)、北大西洋涛动(NAO)和太平洋-北美型(PNA)等北半球大气遥相关型,可以用大气位势高度的物理分解扰动分量解释.结果发现,AO反映的是北极地区行星尺度纬圈平均扰动分量的变化,PNA与持续性天气尺度扰动分量相联系,NAO是行星尺度纬圈平均扰动与天气尺度扰动共同作用的结果.对行星尺度纬圈平均扰动分量和天气尺度扰动分量用旋转经验正交函数(REOF)展开,不但可以证实人们已经命名的区域性大气涛动,还新发现了北极地区的两对偶极涛动、欧亚涛动(EAO)和"大西洋-欧亚型"(AEA)波列.这些涛动连接了相邻地区的异常天气和异常气候.     

如何提高天气预报和气候预测的技巧？

从理论上探讨如何提高天气预报和气候预测的技巧.气候包括以小时为基本单位的昼夜循环、以日为基本单位的年(季节)循环、年代际循环和世纪循环等时间尺度的变化.这些气候变化存在确定的外强迫,是可以被认识和预报的.相对气候昼夜循环和年(季节)循环的偏差是天气尺度扰动.天气尺度的瞬变大气扰动可引发极端天气事件.有技巧的天气预报正是要通过天气尺度大气扰动信号,提前几天甚至十几天,预报出极端天气事件的发生.相对气候年代际和世纪循环的偏差是气候异常,有技巧的气候预测正是要预报出这种异常.距平天气图会大大提高短期和中期—延伸期天气预报的技巧,距平数值预报模式的研制也会加快提高中期—延伸期天气预报和气候预测的技巧.     

中国热浪事件的大气扰动结构及其稳定性分析

利用对流层至平流层大气变量的物理分解,本文发现400 hPa上的天气尺度温度扰动正值和250 hPa上的天气尺度高度扰动正值能够指示地面上的高温和热浪事件.理论和观测分析发现,当扰动静止波中心出现在40°N-45°N时,准静止波波长较长,影响范围大,持续时间久,容易在中国形成区域持续性热浪事件.在江南-华南、长江沿江及河套-华北的区域,干热浪事件静止波的稳定时间在6.5天以上.对流层大气天气尺度扰动波动在垂直方向具有倾斜特征,250 hPa高度上,静止波稳定位置比地面热浪区域中心普遍偏北6~12个纬距.在低纬度(副热带高压带南侧),大气中向西北方向移动的天气尺度扰动,只有到达副高脊线以北的西风带后,才能形成静止波.     

天气尺度扰动流场对区域暴雨的指示能力

观测的大气流场可以物理分解为气候流场、行星尺度纬圈平均扰动流场和天气尺度扰动流场.低层大气的天气尺度扰动流场中的切变线、南方气旋、北方气旋、冷锋槽线、西南涡暖切变线、热带气旋、倒槽切变线、东风波切变线等对区域暴雨具有指示意义.扰动流场中的环流系统更适合天气分析的原理.中国的暴雨带多为扰动辐合线两侧气流对峙的结果.观测流场中,暴雨带出现在低空急流的左侧,是因为气候流场掩盖或削弱了天气尺度扰动流场的作用.对切变线暴雨,天气尺度扰动流场有99%的诊断能力,而原始流场只有66%的指示能力.     

用三维运动学模式研究太阳暗条消失与行星际扰动

本文用19个行星际扰动事件为样本,采用三维运动学模式研究暗条消失事件（简称FD事件）与行星际瞬变扰动间的关系．太阳上的扰动源取行星际扰动前2-5d以内的所有FD事件,用势场模式计算得到的源表面磁场为背景,模拟每个扰动在行星际中的传播及其相互作用过程,观察扰动到达地球的时间和扰动大小．从模拟的结果来看,FD事件与行星际扰动事件有较好的对应关系,说明FD事件是行星际瞬变事件的一类重要的太阳源．19个事件中有2个没有对应的FD事件,5个模拟结果与观测结果相差较大,其余12个模拟结果与实际观测符合很好．这与过去这方面的工作有所不同,我们具体模拟了每个扰动事件的传播过程,同时考虑了扰动间的相互作用,包括扰动与共转流之间的相互作用,为太阳-行星际-地磁链天气过程的预报奠定了基础．     

辽宁开原龙卷强对流过程的扰动天气环境

2019年7月3日下午辽宁部分地区出现短时强降水和冰雹等强对流天气,其中开原市金钩子镇遭遇了龙卷风,造成当地严重的生命财产损失.本文重点分析有利于这次龙卷和冰雹等强对流过程发生的大气变量扰动环境,包括数值模式预报产品中的扰动天气环境以及雷达回波和卫星云图演变对应的大气扰动风场变化特征.结果发现:开原单个龙卷与苏北多次单个龙卷发生的大气温压场扰动环境相似,发生地位于对流层上部下伸到地面的位势高度扰动槽线和近地面水平方向上冷暖温度扰动气团交界线附近,其冷暖扰动气团之间的强度对比较美国多龙卷爆发时的弱小.利用来自欧洲中期天气预报中心(ECMWF)模式产品和美国国家环境预报中心全球预报系统(NCEP/GFS)的实时分析资料进行扰动环流形势分析并与当地实况卫星云图和雷达回波相结合,可以从潜在趋势上定性地推断有利于强对流天气发展的位置.     

南方持续低温冻雨事件预测的前期信号

1960-2008年冬季期间,中国南方发生了23次低温冰冻(冻雨)天气事件,其中满足站日数大于10的事件有11次.2008年初中国南方发生了一场影响巨大的区域持续性低温冻雨天气事件,2011年初再次发生了类似的区域持续性低温冻雨事件.提前5天预报这类极端事件是国内外大气科学面临的难题.利用去逐日气候变化后的逐日850 hPa温度扰动,可以提前3~10天发现中国南方持续低温雨雪冰冻(冻雨)事件发生的信号.2008年初和2011初,影响中国南方的850 hPa冷空气扰动具有源地和路径相似性,它们都来自北非-中东并绕过青藏高原北侧到达中国南方,在对流层大气中形成"冷-暖-冷"的温度垂直结构.通过对欧洲中期天气预报模式产品中850 hPa温度扰动的相似性分析,成功地提前4~9天实际预报出了2011年初的中国南方低温冻雨天气过程.     

太阳风暴的日冕行星际过程三维数值研究进展

如何能够提前几个小时到几天的时间预报空间天气不仅是空间天气研究的重要课题,而且也是现代科技发展对空间天气预报的重大需求.基于物理的以强大计算能力为基础的太阳风暴日冕行星际过程三维数值研究已经成为灾害性空间天气预报建模的重要手段,并在近几年取得了很大的发展.本文聚焦于背景太阳风状态和太阳扰动事件在行星际空间传播的三维数值...     

Assessment of the impact of climate change on the flow regime of the Han River basin using indicators of hydrologic alteration

Most natural disasters are caused by water‐/climate‐related hazards, such as floods, droughts, typhoons, and landslides. In the last few years, great attention has been paid to climate change, and especially the impact of climate change on water resources and the natural disasters that have been an important issue in many countries. As climate change increases the frequency and intensity of extreme rainfall, the number of water‐related disasters is expected to rise. In this regard, this study intends to analyse the changes in extreme weather events and the associated flow regime in both the past and the future. Given trend analysis, spatially coherent and statistically significant changes in the extreme events of temperature and rainfall were identified. A weather generator based on the non‐stationary Markov chain model was applied to produce a daily climate change scenario for the Han River basin for a period of 2001–2090. The weather generator mainly utilizes the climate change SRES A2 scenario driven by input from the regional climate model. Following this, the SLURP model, which is a semi‐distributed hydrological model, was applied to produce a long‐term daily runoff ensemble series. Finally, the indicator of hydrologic alteration was applied to carry out a quantitative analysis and assessment of the impact of climate change on runoff, the river flow regime, and the aquatic ecosystem. It was found that the runoff is expected to decrease in May and July, while no significant changes occur in June. In comparison with historical evidence, the runoff is expected to increase from August to April. A remarkable increase, which is about 40%, in runoff was identified in September. The amount of the minimum discharge over various durations tended to increase when compared to the present hydrological condition. A detailed comparison for discharge and its associated characteristics was discussed. Copyright © 2010 John Wiley & Sons, Ltd.     

Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed

Future climate projections of Global Climate Models (GCMs) under different emission scenarios are usually used for developing climate change mitigation and adaptation strategies. However, the existing GCMs have only limited ability to simulate the complex and local climate features, such as precipitation. Furthermore, the outputs provided by GCMs are too coarse to be useful in hydrologic impact assessment models, as these models require information at much finer scales. Therefore, downscaling of GCM outputs is usually employed to provide fine-resolution information required for impact models. Among the downscaling techniques based on statistical principles, multiple regression and weather generator are considered to be more popular, as they are computationally less demanding than the other downscaling techniques. In the present study, the performances of a multiple regression model (called SDSM) and a weather generator (called LARS-WG) are evaluated in terms of their ability to simulate the frequency of extreme precipitation events of current climate and downscaling of future extreme events. Areal average daily precipitation data of the Clutha watershed located in South Island, New Zealand, are used as baseline data in the analysis. Precipitation frequency analysis is performed by fitting the Generalized Extreme Value (GEV) distribution to the observed, the SDSM simulated/downscaled, and the LARS-WG simulated/downscaled annual maximum (AM) series. The computations are performed for five return periods: 10-, 20-, 40-, 50- and 100-year. The present results illustrate that both models have similar and good ability to simulate the extreme precipitation events and, thus, can be adopted with confidence for climate change impact studies of this nature.     

IMAGE: a multivariate multi-site stochastic weather generator for European weather and climate

Capturing the spatial and temporal correlation of multiple variables in a weather generator is challenging. A new massively multi-site, multivariate daily stochastic weather generator called IMAGE is presented here. It models temperature and precipitation variables as latent Gaussian variables with temporal behaviour governed by an auto-regressive model whose residuals and parameters are correlated through resampling of principle component time series of empirical orthogonal function modes. A case study using European climate data demonstrates the model’s ability to reproduce extreme events of temperature and precipitation. The ability to capture the spatial and temporal extent of extremes using a modified Climate Extremes Index is demonstrated. Importantly, the model generates events covering not observed temporal and spatial scales giving new insights for risk management purposes.     

The impact of extreme weather events on the seagrass Zostera noltii and related Hydrobia ulvae population

Coastal areas are typically subjected to a range of stressors, but they now face the additional stressor of climate change, manifested in part by an increased intensity and frequency of extreme weather events. Thus, the Mondego estuary (Portugal) has experienced organic enrichment (eutrophication) issues and these are potentially exacerbated by extreme weather events (floods, droughts and heat waves). In this paper, we explore the impact of interactions of these different stressors on the ecology of the system, specifically on the two key components, the seagrass Zostera noltii and the mud snail Hydrobia ulvae. Extreme events affected different components of the estuarine ecosystem (primary producers and macrofauna) differently. Whilst the floods directly impacted on H. ulvae, by wiping out part of its population, they did not directly affect the biomass of Z. noltii. In contrast, drought events, through their effects on salinity, directly impacted the biomass of Zostera, which had knock-on effects on the dynamics of H. ulvae. We conclude that over the period when the estuary experienced eutrophication, extreme weather events contributed to the overall degradation of the estuary, while during the recovery phase following the introduction of a management programme, those extreme weather episodes delayed the recovery process significantly.