A new DRP-4DVar-based coupled data assimilation system for decadal predictions using a fast online localization technique

Climate Dynamics - A new coupled data assimilation (CDA) system based on dimension-reduced projection four-dimensional variational data assimilation (DRP-4DVar) for decadal predictions is developed...     

Improved methods for estimating equilibrium climate sensitivity from transient warming simulations

Climate Dynamics - Equilibrium climate sensitivity (ECS) refers to the total global warming caused by an instantaneous doubling of atmospheric CO2 from the pre-industrial level in a climate system....     

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。     

1960～2018年中国高温热浪的线性趋势分析方法与变化趋势

高温热浪直接影响人体健康和作物生长。研究全球变暖背景下我国高温热浪发生率的趋势是气候变化研究的基本问题之一,可为人们的生产生活等提供重要的科学信息。目前对于高温热浪趋势的研究大都使用最小二乘（Ordinary Least Squares,OLS）方法估计趋势,结合学生t检验判断趋势的统计显著性。本文审视了以往常用方法在研究我国高温热浪发生率的线性趋势时的适用性。首先,以2018年东北局部地区因当年高温日数异常多而形成离群值的例子展开,说明OLS方法估计趋势时对离群值非常敏感,造成虚假趋势。进一步,通过正态分布检验和自相关计算,发现1960~2018年中国至少有91.14%站点、90.06%格点的高温日数和92.18%站点、87.74%格点的热浪次数的序列不服从正态分布,而且多数存在自相关。采用一种不易受离群值影响并考虑自相关的非参数方法,本文对1960～2018年中国站点和格点、4个典型区域以及全国平均的高温日数和热浪次数的线性趋势做出了更为准确的估计。研究发现,高温日数显著增多的站点主要出现在华南和西北地区,热浪次数呈显著增多趋势的站点目前几乎仅限于华南地区和新疆的个别站点;区域平均而言,仅有华南区域和西北区域的高温日数和热浪次数是显著增多的,华北区域和东北区域趋势并不显著;全国平均的高温日数和热浪次数都是显著增多的。本文对高温热浪的趋势及其显著性估计、统计预测的方法选择上有重要参考价值。     

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.