Long-Term Rainfall Trends in South West Asia—Saudi Arabia

In this study, rainfall data from 19 stations in Saudi Arabia (SA) for the period 1985-2019 was utilized to investigate interannual, monthly, and seasonal rainfall variations and trends. The magnitudes of these trends were characterized and tested using Mann-Kendall (MK) rank statistics at different significance levels. During this study period, the mean rainfall in SA showed a slight and significant decreasing trend by about 2 mm/35 years. Investigation of seasonal trends of rainfall revealed that Winter and Spring rainfall decreased significantly by 2.7 mm/35 years and 5.4 mm/35 years respectively. Three months showed very slight significant decreasing trends of rainfall. These were the months of February, March and April. Mann-Kendall analyses were carried out to investigate the annual trends of rainfall during three sub-periods, i.e., 1985-1996, 1997-2008, and 2009-2019. The results revealed that while rainfall increased by 5.3 mm/12 years and 7.8 mm/11 years for the first and the third periods respectively, it decreased by about 11 mm/12 years during the second period. While trends of rainfall in Saudi Arabia are affected by large scale circulations and local factors, the effect of extraterrestrial factors, such as solar activity and its consequent effects on the climate may, additionally, play a potential role in affecting the pattern of rainfall in Saudi Arabia.

The long-term trend of the sea surface wind speed and the wave height (wind wave, swell, mixed wave) in global ocean during the last 44 a

Utilizing the 45 a European Centre for Medium-Range Weather Forecasts （ECMWF） reanalysis wave da- ta （ERA-40）, the long-term trend of the sea surface wind speed and （wind wave, swell, mixed wave） wave height in the global ocean at grid point 1.5°× 1.5° during the last 44 a is analyzed. It is discovered that a ma- jority of global ocean swell wave height exhibits a significant linear increasing trend （2-8 cm/decade）, the distribution of annual linear trend of the significant wave height （SWH） has good consistency with that of the swell wave height. The sea surface wind speed shows an annually linear increasing trend mainly con- centrated in the most waters of Southern Hemisphere westerlies, high latitude of the North Pacific, Indian Ocean north of 30°S, the waters near the western equatorial Pacific and low latitudes of the Atlantic waters, and the annually linear decreasing mainly in central and eastern equator of the Pacific, Juan. Fernandez Archipelago, the waters near South Georgia Island in the Atlantic waters. The linear variational distribution characteristic of the wind wave height is similar to that of the sea surface wind speed. Another find is that the swell is dominant in the mixed wave, the swell index in the central ocean is generally greater than that in the offshore, and the swell index in the eastern ocean coast is greater than that in the western ocean inshore, and in year-round hemisphere westerlies the swell index is relatively low.

Long-Term Trends in Extreme Temperatures in Hong Kong and Southern China

The observed long-term trends in extreme temperatures in Hong Kong were studied based on the meteorological data recorded at the Hong Kong Observatory Headquarters from 1885-2008. Results show that, over the past 124 years, the extreme daily minimum and maximum temperatures, as well as the length of the warm spell in Hong Kong, exhibit statistically significant long-term rising trends, while the length of the cold spell shows a statistically significant decreasing trend. The time-dependent return period analysis also indicated that the return period for daily minimum temperature at 4°C or lower lengthened considerably from 6 years in 1900 to over 150 years in 2000, while the return periods for daily maximum temperature reaching 35°C or above shortened drastically from 32 years in 1900 to 4.5 years in 2000. Past trends in extreme temperatures from selected weather stations in southern China from 1951-2004 were also assessed. Over 70% of the stations studied yielded a statistically significant rising trend in extreme daily minimum temperature, while the trend for extreme maximum temperatures was found to vary, with no significant trend established for the majority of stations.

Long-Term Trends in Photosynthetically Active Radiation in Beijing

A long-term dataset of photosynthetically active radiation （Qp） is reconstructed from a broadband global solar radiation （Rs） dataset through an all-weather reconstruction model. This method is based on four years＇ worth of data collected in Beijing. Observation data of Rs and Qp from 2005-2008 are used to investigate the temporal variability of Qp and its dependence on the clearness index and solar zenith angle. A simple and effcient all-weather empirically derived reconstruction model is proposed to reconstruct Qp from Rs. This reconstruction method is found to estimate instantaneous Qp with high accuracy. The annual mean of the daily values of Qp during the period 1958-2005 period is 25.06 mol m-2 d-1. The magnitude of the long-term trend for the annual averaged Qp is presented （-0.19 mol m-2 yr-1 from 1958-1997 and -0.12 mol m-2 yr-1 from 1958-2005）. The trend in Qp exhibits sharp decreases in the spring and summer and more gentle decreases in the autumn and winter.

foF2 Long-Term Trend at a Station Located near the Crest of the Equatorial Ionization Anomaly

Critical frequency foF2 long-term trends at Dakar station (14.4°N, 342.74°E) located near the crest of the equatorial ionization anomaly EIA, are analysed taking into account geomagnetic activity, increasing greenhouse gases concentration and Earth’s magnetic field secular variation. After filtering solar activity effect using F10.7 as a solar activity proxy, we determined the relative residual trends slopes α values for three different levels of geomagnetic activity. For example, at 1200 LT, the value of α goes from -0.27%/year for very magnetically quiet days to -0.19%/year for magnetically quiet days and to -0.13%/year for all days. It appears from the slopes α obtained, that they increase with the level of geomagnetic activity and their negative values are qualitatively consistent with the expected decreasing trend due to the increase in greenhouse gases concentration but are greater than 0.003%/year which would result from a 20% increase in CO2 emissions which actually took place during the analysis period. Regarding Earth’s magnetic field magnitude, B secular variation and the dip equator secular movement, Dakar station is located near the crest of the equatorial ionization anomaly, Earth’s magnetic field magnitude, B decreases there and the trough approaches the position of Dakar during the period of analysis. These two phenomena induce a decrease in foF2 which is in agreement with the decreasing trend observed at this station.

Long-Term Visibility Trends in the Riyadh Megacity, Central Arabian Peninsula and Their Possible Link to Solar Activity

In this study, atmospheric visibility (AV) data from Riyadh, Saudi Arabia (24.91˚N, 46.41˚E, 760 m), for the period 1976-2011 were utilized to investigate the interannual, monthly, and seasonal AV variations and trends. The magnitudes of these trends were characterized and tested using mann-kendall (MK) rank statistics at different significance levels. No significant trend in AV was observed during the 36-year period. However, a significant increase in the annual mean AV by 0.24 km per year for the period between 1976 and 1999 was found. For the period 1999-2011, AV decreased significantly by 0.16 km per year. The potential effects of air temperature and relative humidity on AV were investigated. While these two variables could explain the observed trend of AV over some periods, they failed to do so for the whole study period. To search for extraterrestrial causes for long-term AV variations, correlation analyses between the time series of cosmic ray (CR) data (measured by NM and muon detector) and solar activity (represented by sunspot number) and AV were conducted and showed that these two variables are able to explain the AV variations for the whole study period. Additionally, power spectra analyses were conducted to investigate periodicities in the AV time series. Several significant periodicities, such as 9.8, 5.2, 2.2, 1.7, and 1.3 years were recognized. The obtained periodicities were similar to those reported by several investigators and found in solar, interplanetary, and CR parameters. The spectral and correlation results suggested that, with the expected effects of terrestrial and meteorological conditions on AV, long-term AV variations can also be related to the solar activity and associated CR modulations.

Analysis of Temperature Trends and Variations in the Arabian Peninsula’s Upper Atmosphere

In this study, the trends of upper-air temperatures are analysed by utilising radiosonde observations for the barometric levels at 700, 500, 300, 200, 150, 100 and 50 hPa from five meteorological stations within the Arabian Peninsula from January 1986 to August 2015. The mean monthly variations of the temperatures at these levels are characterised and established. The magnitudes of the annual trends of the mean temperatures for each site for the selected barometric levels are studied and statistically tested using Mann-Kendall rank statistics at different significance levels. The temperature trends at different pressure levels show that the upper troposphere and lower stratosphere are warming, while the middle troposphere is cooling which is consistent with the findings of other studies. The variations in upper air temperature observed in this study can be attributed to a range of factors, including increasing greenhouse gas concentrations, changes in atmospheric circulation patterns, variations in solar activity, aerosols and volcanic eruptions, and land use and land cover change.

Long-Term Trends and Its Best Functional Form Estimation of Yearly Maximum and Minimum Temperatures at Cotonou City by Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Method

The understanding of the long-term trend in climatic variables is necessary for the climate change impacts studies and for modeling several processes in environmental engineering. However, for climatic variables, long-term trend is usually unknown whether there is a trend component and, if so, the functional form of this trend is also unknown. In this context, a conventional strategy consists to assume randomly the shape of the local trends in the time series. For example, the polynomial forms with random order are arbitrarily chosen as the shape of the trend without any previous justification. This study aims to 1) estimate the real long-term nonlinear trend and the changing rate of the yearly high temperature among the daily minimum (YHTaDMinT) and maximum temperatures (YHTaDMaxT) observed at Cotonou city, 2) find out for these real trend and trend increment, the best polynomial trend model among four trend models (linear, quadratic, third-order and fourth-order polynomial function). For both time series, the results show that YHTaDMinT and YHTaDMaxT time series are characterized by nonlinear and monotonically increasing trend. The trend increments present different phases in their nonmonotone variations. Among the four trend estimations models, the trend obtained by third-order and fourth-order polynomial functions exhibits a close pattern with the real long-term nonlinear trend given by the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). But, the fourth-order polynomial function is optimal, therefore, it can be used as the functional form of trend. In the trend increment case, for the YHTaDMaxT time series, the fourth-order fit is systematically the best among the four proposed trend models. Whereas for the YHTaDMinT time series, the third-order and fourth-order polynomial functions present the same performance. They can both be used as the functional form of trend increments. Overall, the fourth-order polynomial function presents a good performance in terms of trend and trend increments estimation.

The Long-term Variation of Extreme Heavy Precipitation and Its Link to Urbanization Effects in Shanghai during 1916–2014

Using the hourly precipitation records of meteorological stations in Shanghai, covering a period of almost a century （1916-2014）, the long-term variation of extreme heavy precipitation in Shanghai on multiple spatial and temporal scales is analyzed, and the effects of urbanization on hourly rainstorms studied. Results show that： （1） Over the last century, extreme hourly precipitation events enhanced significantly. During the recent urbanization period from 1981 to 2014, the frequency of heavy precipitation increased significantly, with a distinct localized and abrupt characteristic. （2） The spatial distribution of long-term trends for the occurrence frequency and total precipitation intensity of hourly heavy precipitation in Shanghai shows a distinct urban rain-island feature; namely, heavy precipitation was increasingly focused in urban and suburban areas. Attribution analysis shows that urbanization in Shanghai contributed greatly to the increase in both frequency and intensity of heavy rainfall events in the city, thus leading to an increasing total precipitation amount of heavy rainfall events. In addition, the diurnal variation of rainfall intensity also shows distinctive urban-rural differences, especially during late afternoon and early nighttime in the city area. （3） Regional warming, with subsequent enhancement of water vapor content, convergence of moisture flux and atmospheric instability, provided favorable physical backgrounds for the formation of extreme precipitation. This accounts for the consistent increase in hourly heavy precipitation over the whole Shanghai area during recent times.

Further-Adjusted Long-Term Temperature Series in China Based on MASH

A set of homogenized monthly mean surface air temperature （SAT） series at 32 stations in China back to the 19th century had previously been developed based on the RHtest method by Cao et al., but some inhomogeneities remained in the dataset. The present study produces a further-adjusted and updated dataset based on the Multiple Analysis of Series for Homogenization （MASH） method. The MASH procedure detects 33 monthly temperature records as erroneous outliers and 152 meaningful break points in the monthly SAT series since 1924 at 28 stations. The inhomogeneous parts are then adjusted relative to the latest homogeneous part of the series. The new data show significant warming trends during 1924-2016 at all the stations, ranging from 0.48 to 3.57℃ （100 yr）^-1, with a regional mean trend of 1.65℃ （100 yr）^-1 ; whereas, the previous results ranged from a slight cooling at two stations to considerable warming, up to 4.5℃ （100 yr）^-1. It is suggested that the further-adjusted data are a better representation of the large-scale pattern of climate change in the region for the past century. The new data axe available online at http：//www.dx.doi.org/10.11922/sciencedb.516.

Nonlinear Differential Equation of Macroeconomic Dynamics for Long-Term Forecasting of Economic Development

In this article we derive a general differential equation that describes long-term economic growth in terms of cyclical and trend components. Equation is based on the model of non-linear accelerator of induced investment. A scheme is proposed for obtaining approximate solutions of nonlinear differential equation by splitting solution into the rapidly oscillating business cycles and slowly varying trend using Krylov-Bogoliubov-Mitropolsky averaging. Simplest modes of the economic system are described. Characteristics of the bifurcation point are found and bifurcation phenomenon is interpreted as loss of stability making the economic system available to structural change and accepting innovations. System being in a nonequilibrium state has a dynamics with self-sustained undamped oscillations. The model is verified with economic development of the US during the fifth Kondratieff cycle (1982-2010). Model adequately describes real process of economic growth in both quantitative and qualitative aspects. It is one of major results that the model gives a rough estimation of critical points of system stability loss and falling into a crisis recession. The model is used to forecast the macroeconomic dynamics of the US during the sixth Kondratieff cycle (2018-2050). For this forecast we use fixed production capital functional dependence on a long-term Kondratieff cycle and medium-term Juglar and Kuznets cycles. More accurate estimations of the time of crisis and recession are based on the model of accelerating log-periodic oscillations. The explosive growth of the prices of highly liquid commodities such as gold and oil is taken as real predictors of the global financial crisis. The second wave of crisis is expected to come in June 2011.

Long-term Prediction and Verification of Rainfall Based on the Seasonal Model

Using the seasonal cross-multiplication trend model, monthly precipitation of eight national basic weather stations of Shaanxi Province from 2005 to 2010 was predicted, and the forecast results were verified using the rainfall scoring rules of China Meteorological Administration. The verification results show that the average score of annual precipitation prediction in recent six years is higher than that made by a professional forecaster, so this model has a good prospect of application. Moreover, the level of making prediction is steady, and it can be widely used in long-term prediction of rainfall.

海洋悬浮物浓度的长期演变特征及机制

受气候变化和人类活动的影响,21世纪以来全球海洋的悬浮物浓度发生了显著变化。悬浮物浓度的剧烈变化不可避免地对海洋生态环境、地貌演化和生物地球化学循环产生重大影响。随着气候变化对人类活动的环境影响研究的深入,以及卫星遥感数据的不断积累,悬浮物浓度的长期变化趋势及其机制已成为地球科学研究的热点。本文概括性地总结了海洋悬浮物浓度长期变化的国内外研究进展,指出了中国近海相关研究的不足之处,并对未来发展方向作出了展望。

面向动态交通分配的交通需求深度学习预测方法

为满足动态交通分配对高精度、高时效性交通需求的要求,本文建立了一种交通需求深度学习预测方法。根据动态交通分配要求确定交通需求数据的时间间隔,构建对复杂交通需求预测性能较优的长短期记忆神经网络预测方法;针对动态交通分配中交通需求的周期性、随机性和非线性等特征,为减少数据噪声的干扰,引入局部加权回归周期趋势分解方法将交通需求数据分解,将其中的趋势分量和余项分量作为深度学习预测方法的输入量,周期分量采用周期估计进行预测;选用具有随机寻优能力强、寻优效率高等特点的布谷鸟寻优算法优化预测方法的隐藏层单元数量、学习速率和训练迭代次数等核心参数。应用西安市长安区的卡口车牌数据验证该方法。结果表明:本文模型的预测结果在高峰及平峰各连续4个时段内相比于自回归滑动平均模型、长短期记忆神经网络模型、支持向量回归模型,平均绝对误差降低了10.55%~19.80%,均方根误差降低了11.20%~17.99%,决定系数提升了8.62%~12.48%;相比遗传算法、粒子群算法优化的模型,平均绝对误差降低了7.36%~13.81%,均方根误差降低了4.23%~10.67%,决定系数提升了3.50%~7.01%,且本文模型运行时间最短。说明与对比模型相比,本文所建立的预测方法在面向动态交通分配的交通需求预测中具有更高的预测精度。

基于周期特征提取的DLnet预测模型研究

现有的预测方法很少独立分析能源消耗的周期性特征。本文提出了一个短期办公建筑能耗预测模型(DLnet),以解决周期性能耗数据利用效率低下的问题。首先,利用STL对能耗数据的周期成分进行分解,通过网格搜索算法寻找能耗数据的最优周期;然后,根据最优周期构建周期块;再根据周期块的数据形状构建时间序列块数据;之后,利用长短期记忆(LSTM)对时间序列块数据和周期块数据进行训练和学习;最后,通过线性回归将时间序列块数据和周期块数据的预测结果进行融合。事实证明,所提出的模型的4个预测精度指标分别比LSTM模型高7%,21%,25%和26%。

青藏高原1981-2015年暖季降水变化趋势:受控于大尺度环流型变化

利用最新的高时空分辨率融合降水数据集,研究青藏高原的长期降水变化趋势。结果表明,在1981—2015年的暖季(5—9月),青藏高原降水量显著增加(格点平均趋势为0.9 mm/a),其东北部、中部和西部最为显著。采用T模态倾斜旋转主成分分析法(PCT),将青藏高原500 hPa位势高度场分为9个典型环流型,发现第2和第4环流型(T2和T4)是暖季降水量增加的主导环流型(DT)。DT位势高度场为西低东高的“槽脊对峙”分布,青藏高原暖季降水量增加体现在DT主导降水日数和日均降水量的共同增加。除DT环流型数量的增加导致青藏高原中西部降水增加外,降水增加机制还包括降水条件的优化,即T2以“槽脊对峙”加强为驱动的动力条件优化为主导,T4以“水汽滞增”加强为驱动的热力条件优化为主导。

中国海风能和波浪能的长期趋势与演变规律分析

海上风能和波浪能是极具吸引力的可再生能源。在气候变化的背景下,基于ERA5再分析数据产品研究并对比了1979~2020年的中国海风能和波浪能的长期历史趋势和多尺度演化规律;采用Mann-Kendall检验和Theil-Sen方法分别量化了趋势显著性、趋势幅度和突变点。结果表明,海上风能和波浪能在气候特征和长期趋势上存在显著差异。风能的增长趋势在统计上不显著,平均增长率为每10年0.55%;而波浪能呈现强劲的增长趋势,以每10年4.8%的正向速率上升。海浪特性普遍向大波高、长周期和高能量的海况转移。不同季节和地区的长期趋势区别明显,其中冬季是风与波资源储量和趋势增长的优势季节、东海等开阔海域显示了风能和波浪能持续开发的潜力。基于这些分析结果,进一步探究了风浪能和涌浪能的时空多样性,揭示了海浪场的强化以及风波系统在能量场中的差异主要由涌浪的资源量所贡献,并受涌浪变化的调制。这些结果为海上资源评估、风和海浪气候预测以及海洋工程等众多领域提供了科学参考。

1961一2020年中国复合湿热的变化特征

随着全球变暖加剧,复合湿热天气在世界各地呈现显著加剧趋势,中国东部也是极端湿热事件的高发区。为更好了解中国复合湿热事件的变化特征,基于1961—2020年中国日最高湿球温度观测数据,利用趋势分析、小波功率谱分析和广义极值分布分析等方法,对中国日最高湿球温度的时空变化特征进行了深入分析。结果表明:1)1961—2020年中国日最高湿球温度平均值和最大值主要呈“南高北低”的分布特点,最大值高值区集中在中国南部和四川盆地。全国日最高湿球温度平均值呈增强趋势,最大值无明显的变化趋势。全国平均值有2~6 a尺度的周期震荡,全国最大值在多个时间段和时间尺度有短周期。2)全国极端湿热阈值分布与日最高湿球温度最大值比较类似,极端湿热强度呈现增强趋势,全国极端湿热频次也以0.098 d/a的速率增多。西北东部地区极端湿热强度增强幅度最大,但南方地区呈减弱趋势;西北东部、南方和东北地区极端湿热频次持续增多。3)多年一遇事件的阈值分布同样与最大值分布类似,多年一遇事件频次呈现显著的区域特征,多年一遇事件主要发生在四川盆地,其中西北东部地区显著增多,南方地区有减少趋势。

Climatology and Trends of High Temperature Extremes across China in Summer

Based on the daily maximum air temperature data from 300 stations in China from 1958 to 2008, the climatological distribution of the number of days with high temperature extremes （HTEs, maximum temperatures higher than 35℃） are studied with a focus on the long-term trends. Although the number of HTE days display well-defined sandwich spatial structures with significant decreasing trends in central China and increasing trends in northern China and southern China, the authors show that the decrease of HTE days in central China occurs mainly in the early period before the 1980s, and a significant increase of HTE days dominates most of the stations after the 1980s. The authors also reveal that there is a jump-like acceleration in the number of HTE days at most stations across China since the mid 1990s, especially in South China, East China, North China, and northwest China.