基于栅格的豫西山区地形起伏特征及其对人口和经济的影响

地形起伏是约束山区人口分布和经济发展的关键因素,尤其在山地平原过渡带其约束作用更为显著。以位于秦岭-黄淮平原过渡带的豫西山区为例,基于200 m分辨率DEM数据,采用均值变点法确定地形起伏度的最佳统计单元,并提取地形起伏度;以1∶10万土地利用、乡镇人口和县域各产业数据为基础,建立人口和经济空间化模型,同时生成200 m分辨率的栅格人口和经济密度图;在系统分析地形起伏度、人口和经济密度空间分布规律的基础上,借助统计分析方法基于栅格单元定量揭示了地形起伏度对人口和经济的影响,并与其他地形因子的影响进行对比分析。结果表明：① 豫西山区地形起伏度以低值为主,58.6%的区域地形起伏度小于等于0.5个基准山体的高度（相对高差≤ 250 m）;空间上呈西高东低、中间高南北低的格局,与海拔、坡度均呈较强的正相关,且与坡度的相关性更大。② 被选作验证的人口和经济数据与对应模拟值的线性拟合度分别为0.943和0.909,表明空间化结果能反映人口和经济分布的实际状况。③ 地形起伏度对人口和经济的影响强于其他地形因子的影响,与人口密度和经济密度呈较好的对数拟合关系,拟合度分别为0.911和0.874;豫西山区88.65%的人口居住在地形起伏度不超过0.5的地区,88.03%的生产总值分布在地形起伏度不超过0.3的地区。相对人口分布,研究区的经济发展向地形起伏度低值区的集聚态势更加明显。     

基于北极黄河站观测的日侧极光研究新进展

回顾了基于中国黄河站全天空极光观测对日侧弥散极光与喉区极光研究的最新进展。首先,利用黄河站极光观测,对日侧弥散极光展开系统性分类与统计研究,对这一重要空间物理现象取得新认识,指出日侧弥散极光对研究日侧外磁层冷等离子体的分布、形成及磁鞘粒子进入磁层都具有重要启示作用;同时发现并定义了一种新型分立极光结构——喉区极光,并推断其可能对应磁层顶的局地变形。喉区极光是指发生在电离层对流喉区附近、从极光卵赤道侧向低纬方向延伸出来的分立极光结构。全天空极光观测表明喉区极光走向大致与电离层对流方向一致。之后,观测验证了喉区极光对应磁层顶局地内陷式变形的推测;统计发现喉区极光是一种非常高发的现象,对应的磁层顶变形尺度可达2—3 Re,并指出这种变形最可能由磁鞘高速流冲击磁层顶产生;发现在喉区极光产生过程中还可能触发磁重联;证实伴随喉区极光的产生,磁鞘粒子会进入磁层并诱发产生一种新型弥散极光。通过喉区极光研究,可以将已有的磁鞘瞬态过程研究和触发式磁重联研究有机地结合在一起,对太阳风-磁层耦合过程形成一种新的认识,即:在磁鞘中局地产生(而不是存在于太阳风中)的瞬态过程可以在日下点附近频繁地导致磁层顶局地变形、触发重联、引发系列地球空间效应,可能对太阳风-磁层耦合具有不可忽略的重要性。以此为基础,讨论了日侧极光研究引出的新课题。     

Tracking embodied carbon flows in the Belt and Road regions

In the past few decades, economic globalization has driven rapid growth of cross-border trade and a new international division of labor, leading to increasing inter- country embodied carbon flows. Multi-region input-output (MRIO) analysis is used to identify embodied carbon flows between major world regions, including seven regions along the Belt and Road (BR), and the spatial distribution of production- and consumption-based carbon intensities. The results show that current embodied carbon flows are virtually all from BR regions to developed countries, with more than 95% of world net embodied carbon exports coming from BR regions. Consumption in the United States and European Union countries induce about 30% of the carbon emissions in most BR regions, indicating that the former bear a high proportion of consumers’ responsibility for the carbon emitted in the latter. For this reason, measuring environmental responsibilities from consumption rather than a production- based perspective is more equitable, while developing countries should be given a louder voice in the construction through dialogue and cooperation, in part in the context of the Belt and Road Initiative, of an inclusive global climate governance system.     

Multifractal process of runoff fluctuation of the Kaidu River in Xinjiang,China

Based on the hydrological data in the headwater region of the Kaidu River during 1972–2011, the multifractal process of runoff fluctuation was analyzed. Results indicated that, in the past 40 years, the overall runoff of the Kaidu River in Xinjiang has shown significant multifractal behavior. Its singular curve lnχ_q(ε)–ln(ε) verified a favorable scale invariance over the entire time scale. τ(q)–q proved that evolution of the runoff time series presented multifractal characteristics. Moreover,the multifractal spectrum f(α)–α curve was hooklike leftward which indicated that, compared to relatively large runoff events. And Δf0 indicated that these relatively small events took the leading role; B0 explained the Kaidu River's daily-runoff ascending tendency presented during 1972–2011. Besides that, the multifractal behavior of the Kaidu River's runoff variability over four decades was also analyzed. Generally speaking, by decades, their four corresponding spectrum variations were not noticeable. These Δα values showed larger runoff events occupied the leading position with some local values falling. During the 1970 s to the 1990 s, Δf0 illustrated the probability of the daily runoff at the lowest point is always larger than that of the highest during three continuous decades. At the beginning of the 21 st century, for Δf0 the trend presented was contrary from the 1970 s to the 1990 s. B values suggested an overall trend of increases during1972–2011. Until the 21 st century, the runoff with a slightly descending tendency on the whole explained these relatively large runoff events taking the leading role for the Kaidu River; but sometimes, some small events also played the dominant role.     

Household and community responses to impacts of climate change in the rural hills of Nepal

The research was designed to answer how households and local communities in rural Nepal are responding to the impacts of climate change. Using four villages as case study units, a mixed method approach was adopted in a multi-scaled process carried out at community, district and national levels. The research found that adaptation practices being adopted differ according to household well-being and are largely governed by access to education, information and resources within the community. Responses such as livelihood and income diversification, internal migration, share cropping, taking consumption loans, use of alternative energy and use of bio-pesticides were found to mostly vary according to well-being status of the interviewees. Development of adaptation plans, strategies and support mechanisms should take account of the different adaptation practices and needs of households. If such individual situations are not considered, adaptation responses may be ineffective or even be maladaptive and increase vulnerability. The research also found that the autonomous, unplanned and reactive nature of adaptation practices chosen by rural communities can contribute to further inequity and unequal power relations. The knowledge generated from this research contributes to understanding of how climate change contributes to vulnerability, but also how local practices and lack of an effective climate policy or response measures may magnify the effects of many existing drivers of vulnerability in terms of maladaptation and increasing social inequalities.     

Synoptic characteristics of heavy snowfalls at Busan of Korea caused by polar lows over the East/Japan Sea

Theoretical and Applied Climatology - The results of the present study prove that snowfall occurred due to the polar low (PL) in the Korean Peninsula and six cases of snowfall exceeding a snow...     

SWAN2.0系统的设计与实现

强对流天气短时临近预报系统（Severe Weather Automatic Nowcasting,SWAN）是面向短时临近监测、分析、预报、预警制作等功能为一体的业务平台。SWAN2.0基于MICAPS4（Meteorological Information Comprehensive Analysis and Processing System Version 4.0,人机交互气象信息处理和天气预报制作系统）二次开发框架,采用C/S架构,服务器部署在省级,负责收集数据,运算SWAN产品;客户端部署在气象台站,实现具体的预报业务,并形成算法二次开发接口。SWAN2.0新增了三维变分风场反演、基于分雨团技术的雷达降水估测、冰雹识别等方法,实现了算法管理、产品生成、分析处理、资料检索显示、实时监控报警、预警产品制作等功能。SWAN2.0业务系统已在全国试用,在强对流天气监测、分析和短时临近预报预警中发挥了重要作用。     

The global impacts of US climate policy: a model simulation using GCAM-TU and MAGICC

To assess the potential impacts of the US withdrawal from the Paris Agreement, this study applied GCAM-TU (an updated version of the Global Change Assessment Model) to simulate global and regional emission pathways of energy-related CO₂, which show that US emissions in 2100 would reduce to ?2.4?Gt, ?0.7?Gt and ?0.2?Gt under scenarios of RCP2.6, RCP3.7 and RCP4.5, respectively. Two unfavourable policy scenarios were designed, assuming a temporary delay and a complete stop for US mitigation actions after 2015. Simulations by the Model for the Assessment of Greenhouse-gas Induced Climate Change (MAGICC) indicate that the temperature increase by 2100 would rise by 0.081°C–0.161°C compared to the three original RCPs (Representative Concentration Pathways) if US emissions were kept at their 2015 levels until 2100. The probability of staying below 2°C would decrease by 6–9% even if the US resumes mitigation efforts for achieving its Nationally Determined Contribution (NDC) target after 2025. It is estimated by GCAM-TU that, without US participation, increased reduction efforts are required for the rest of the world, including developing countries, in order to achieve the 2°C goal, resulting in 18% higher global cumulative mitigation costs from 2015 to 2100.

Key policy insights

• President Trump’s climate policies, including planned withdrawal from the Paris Agreement, cast a shadow on international climate actions, and would lower the likelihood of achieving the 2°C target.

• To meet the 2°C target without the US means increased reduction efforts and mitigation costs for the rest of the world, and considerable economic burdens for major developing areas.

• Active state-, city- and enterprise-level powers should be supported to keep the emission reduction gap from further widening even with reduced mitigation efforts from the US federal government.

     

A Climate Prediction Method Based on EMD and Ensemble Prediction Technique

Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.     

Real Data Assimilation Using the Local Ensemble Transform Kalman Filter (LETKF) System for a Global Non-hydrostatic NWP model on the Cubed-sphere

An ensemble data assimilation system using the 4-dimensional Local Ensemble Transform Kalman Filter is implemented to a global non-hydrostatic Numerical Weather Prediction model on the cubed-sphere. The ensemble data assimilation system is coupled to the Korea Institute of Atmospheric Prediction Systems Package for Observation Processing, for real observation data from diverse resources, including satellites. For computational efficiency in a parallel computing environment, we employ some advanced software engineering techniques in the handling of a large number of files. The ensemble data assimilation system is tested in a semi-operational mode, and its performance is verified using the Integrated Forecast System analysis from the European Centre for Medium-Range Weather Forecasts. It is found that the system can be stabilized effectively by additive inflation to account for sampling errors, especially when radiance satellite data are additionally used.