Stratospheric radiative feedback limited by the tropospheric influence in global warming

Whether the stratospheric radiative feedback amplifies the global warming remains under debate. The stratospheric water vapor (SWV), one of the primary feedbacks in the stratosphere, is argued to be an important contributor to the global warming. On the other hand, the overall stratospheric feedback, which consists of both the SWV feedback and the stratospheric temperature (ST) feedback, does not amount to a significant value. The key to reconciling these seemingly contradictory arguments is to understand the ST change. Here, we develop a method to decompose the ST change and to quantify the decomposed feedbacks. We find that the SWV feedback, which consists of a 0.04 W m⁻² K⁻¹ direct impact on the top-of-the-atmosphere radiation and 0.11 W m⁻² K⁻¹ indirect impact via ST cooling, is offset by a negative ST feedback of − 0.13 W m⁻² K⁻¹ that is radiatively driven by the tropospheric warming. This compensation results in an insignificant overall stratospheric feedback.

     

利用气温与云顶亮温差值检测云方法研究

利用2005年7-12月卫星资料、地面观测资料、探空资料和地形资料,统计分析了无云条件下气温与云顶亮温的差值,结果表明：无云情况下气温与云顶亮温差值平均为5.04℃,且具有较大的日变化性。由此提出了按时次分别求取阈值用以检测云。通过个例对比分析,初步得出该方法检测云具有以下特点：可用于全天候云检测;不受地形地貌等因素的影响;该云检测方法应用于反演计算,不仅反演的中、高云区域与实测的比较一致,而且反演的低云区域与实测结果也比较一致。     

基于MODIS-EVI数据的神农架林区植被指数变化特征研究

对植被覆盖动态变化监测可以提供生态系统状况有价值的信息,可以检测到人类或气候作用引起的变化.以2003-2012年MODIS的遥感数据为信息源,利用增强型植被指数(EVI),采用最大值合成法,对神农架林区植被覆盖动态变化进行监测,并与同期的气候因子进行相关性分析,结果表明:10 a来神农架林区植被覆盖整体呈增加趋势,且东部地区的EVI增幅大于西部增幅,特别是近5 a(2008-2012年),除2010年外,植被指数均为正距平.说明神农架林区近5 a来植被生长状况较好,生态环境得到了进一步改善.与气候因子进行相关性分析说明,气温是制约神农架植被生长的主要气候因子,而降水和日照是影响该地区植被生长的重要气候因子.     

2012年桂西北一次地面人工增雨作业天气条件的分析和捕捉

以百色市田林县一次降雨过程的人工增雨作业天气和作业实况作为研究对象,对相应天气形势和物理量场进行剖析并对增雨效果进行检验。得出:在短波槽、切变线和弱冷空气为主的中低层天气系统影响下,低层水汽充足,上升运动的加剧及弱冷空气的入侵可激发降雨的产生;在降雨初期对液水含量大,冰晶含量偏低的冷云及时进行人工催化作业,可收到明显的增雨效果。     

红壤旱地棉田间作小气候效应初步研究

为研究棉田间作对田间小气候的影响,共设置4个处理:棉花单作、棉花间作花生、棉花间作甘薯、棉花间作大豆,研究不同间作棉田群体对不同层次的光强、土壤温度以及棉花农艺性状及产量的影响。结果表明,与棉花单作系统相比,间作系统可通过改变受光结构、土壤温度、土壤含水量等促进棉花生长发育,利于棉花种植获得高产;棉花间作大豆模式是较适宜在红壤旱地推广的间作模式。     

Preliminary Calibration of GPS Signals and Its Effects on Soil Moisture Estimation

In recent years, Global Navigation Satellite Systems Reflectometry (GNSS-R) is developed to estimate soil moisture content (SMC) as a new remote sensing tool. Signal error of Global Positioning System (GPS) bistatic radar is an important factor that affects the accuracy of SMC estimation. In this paper, two methods of GPS signal calibration involving both the direct and reflected signals are introduced, and a detailed explanation of the theoretical basis for such methods is given. An improved SMC estimation model utilizing calibrated GPS L-band signals is proposed, and the estimation accuracy is validated using the airborne GPS data from the Soil Moisture Experiment in 2002 (SMEX02). We choose 21 sites with soybean and corn in the Walnut Creek region of the US for validation. The sites are divided into three categories according to their vegetation cover: bare soil, mid-vegetation cover (Mid-Veg), and high-vegetation cover (High-Veg). The accuracy of SMC estimation is 11.17% for bare soil and 8.12% for Mid-Veg sites, much better than that of the traditional model. For High-Veg sites, the effect of signal attenuation due to vegetation cover is preliminarily taken into consideration and a linear model related to Normalized Difference Vegetation Indices (NDVI) is adopted to obtain a factor for rectifying the "over-calibration", and the error for High-Veg sites is finally reduced to 3.81%.     

Development of climate change projections for small watersheds using multi-model ensemble simulation and stochastic weather generation

Regional climate models (RCMs) have been increasingly used for climate change studies at the watershed scale. However, their performance is strongly dependent upon their driving conditions, internal parameterizations and domain configurations. Also, the spatial resolution of RCMs often exceeds the scales of small watersheds. This study developed a two-step downscaling method to generate climate change projections for small watersheds through combining a weighted multi-RCM ensemble and a stochastic weather generator. The ensemble was built on a set of five model performance metrics and generated regional patterns of climate change as monthly shift terms. The stochastic weather generator then incorporated these shift terms into observed climate normals and produced synthetic future weather series at the watershed scale. This method was applied to the Assiniboia area in southern Saskatchewan, Canada. The ensemble led to reduced biases in temperature and precipitation projections through properly emphasizing models with good performance. Projection of precipitation occurrence was particularly improved through introducing a weight-based probability threshold. The ensemble-derived climate change scenario was well reproduced as local daily weather series by the stochastic weather generator. The proposed combination of dynamical downscaling and statistical downscaling can improve the reliability and resolution of future climate projection for small prairie watersheds. It is also an efficient solution to produce alternative series of daily weather conditions that are important inputs for examining watershed responses to climate change and associated uncertainties.     

Improved reliability of ENSO hindcasts with multi-ocean analyses ensemble initialization

Currently, ensemble seasonal forecasts using a single model with multiple perturbed initial conditions generally suffer from an “overconfidence” problem, i.e., the ensemble evolves such that the spread among members is small, compared to the magnitude of the mean error. This has motivated the use of a multi-model ensemble (MME), a technique that aims at sampling the structural uncertainty in the forecasting system. Here we investigate how the structural uncertainty in the ocean initial conditions impacts the reliability in seasonal forecasts, by using a new ensemble generation method to be referred to as the multiple-ocean analysis ensemble (MAE) initialization. In the MAE method, multiple ocean analyses are used to build an ensemble of ocean initial states, thus sampling structural uncertainties in oceanic initial conditions (OIC) originating from errors in the ocean model, the forcing flux, and the measurements, especially in areas and times of insufficient observations, as well as from the dependence on data assimilation methods. The merit of MAE initialization is demonstrated by the improved El Niño and the Southern Oscillation (ENSO) forecasting reliability. In particular, compared with the atmospheric perturbation or lagged ensemble approaches, the MAE initialization more effectively enhances ensemble dispersion in ENSO forecasting. A quantitative probabilistic measure of reliability also indicates that the MAE method performs better in forecasting all three (warm, neutral and cold) categories of ENSO events. In addition to improving seasonal forecasts, the MAE strategy may be used to identify the characteristics of the current structural uncertainty and as guidance for improving the observational network and assimilation strategy. Moreover, although the MAE method is not expected to totally correct the overconfidence of seasonal forecasts, our results demonstrate that OIC uncertainty is one of the major sources of forecast overconfidence, and suggest that the MAE is an essential component of an MME system.     

1961—2016年江西不同量级暴雨的时空分布特征

利用江西省83个站点逐日降水资料,采用滑动平均、小波分析和Mann Kendall检验等方法,分析了1961—2016年江西春季(3—5月)、汛期(5—7月)、秋季(9—11月)不同量级暴雨的时空分布特征。结果表明：不同量级的暴雨在不同季节的变化特征存在明显的差异,降水主要集中在汛期。暴雨量在春季和秋季变化趋势平稳,而汛期在1990年之前呈减少趋势,之后呈增加趋势;其空间分布均呈“南少北多、东多西少”特征;存在准30 a和准10 a两个变化周期。大暴雨量在春季和汛期呈“先下降、后上升”的趋势,空间分布较为平均。特大暴雨发生的概率很小,主要集中在汛期,其降水量变化趋势呈“先下降、后上升”特征,空间分布集中在赣北中部和赣中东部。     

热带气旋外眼墙形成机制研究综述

介绍了国内外关于热带气旋外眼墙形成和维持过程的相关研究进展,包括大尺度环境场和热带气旋涡旋内部动力学过程,如涡旋罗斯贝波理论、轴对称化过程、涡丝化作用、β-skirt轴对称化外眼墙形成假说和边界层非平衡动力过程等。随着对外眼墙形成机理研究的不断深入,当前存在多种外眼墙形成的机制理论,而这些机制均强调在外眼墙的形成阶段,热带气旋外围有大量对流及位势涡度扰动的发生发展。因此,热带气旋外眼墙的形成很有可能是多种机制相互作用导致的。最后,提出研究多种机制相互作用导致外眼墙处的对流和位势涡度扰动的发生发展过程具有重大意义。