近50年深圳气候特点

本文概述了近50年来深圳气候变化情况.分析表明,深圳城市的快速发展对气候影响极显著,呈现出气温升高、湿度下降、日照减少的趋势,在80年代后期至90年代尤为明显,降水则呈波动变化,城市高楼群对风向风速也产生了一定的影响,同时简述了深圳主要灾害性天气的特点.     

1980-2008年黑龙江省气候变暖及其对大豆种植的影响

基于黑龙江省10个农业气象观测站1980-2008年地面观测田平行观测资料和逐日气象资料,运用气候倾向率、M-K检测和统计回归等方法,分析研究区表征气候变暖的主要气候要素的时空变化规律以及气候变暖对大豆的影响。结果表明：1980-2008年,研究区气候出现明显变暖趋势,年平均气温升高,热量增加,无霜期延长,且区域间存在差异,南部地区热量资源增加显著。大豆百粒重随9月平均气温的升高而增加,却随年平均气温的升高而下降,表明秋季增温对大豆产量有正效应,而年平均气温的升高对大豆产量存在负作用。气候变暖对大豆生产的影响是利弊兼存,在调整种植布局时应给予重点关注。     

近20年深圳城市气候环境研究的进展

近20余年来,深圳城市气候环境研究在气候变化、局地气候特征、大气灰霾等问题上取得了显著进展,研究发现:(1)受全球气候变暖和城市化进程的双重影响,深圳气候在过去30多年内发生了显著改变,体现为气温上升、极端气候事件增多、热岛效应明显、持续高温加剧以及汛期强降水显著增多;(2)在2004年以前,深圳的大气能见度总体上呈下降趋势,灰霾日数剧增且大气环境恶化,机动车尾气排放是大气环境恶化的重要原因,而自2004年以后,深圳的灰霾总体上呈好转趋势;(3)气候环境的变化已经对深圳的社会经济、生态环境乃至居民健康都产生了影响。     

深圳气候平均值的变化

按照WMO约定，进入2001年，气候多年平均值时段由原来的1961～1990年变为1971～2000年。本文对比分析了深圳主要气象要素滑动的气候平均值的演变，由于全球大气增温和深圳城市化的影响，深圳多年气候平均值出现了气温上升、雨量增加、湿度下降、日照减少等变化特点。     

桂林市气候变暖特征及其对柑桔物候期的影响

利用桂林市1984-2014年气候资料和同期柑桔物候资料,运用线性倾向估计、SPSS相关系数等方法研究了桂林市气候变暖的特征及其对柑桔物候期的影响。结果表明:31年中桂林市气候变暖趋势明显,其主要特点是年平均气温上升幅度约为0.4℃,20世纪90年代后期和21世纪初升温显著,在升温过程中以春季升温为主,冬季次之,夏季、秋季略升温,气候变暖春季气温升高贡献最大。气候变暖引起柑桔物候期的变化明显,春季物候期提前,秋季物候期推迟,生长季呈延长的趋势;气温是影响柑桔物候期最为显著的气象因子;2月平均最高气温升高1℃,柑桔芽开放期提前约3.4天,3月平均最高气温升高1℃,开花期提前约4.9天,年极端最高气温升高1℃,可采成熟期推迟6.8天。     

深、港等地雨量、气温分析及深圳市未来20～30年展望

本文对比分析了深圳、香港等地的雨量、气温变化趋势，得出年雨量、年平均气温升降趋势较为相似。又利用和澳门的年平均气温、年雨量较接近的情况，延长深圳气候资料，寻出5年、10年的变化规律并对深圳市2000－2029年各时段的年平均气温、雨量趋势进行展望。     

非对称性增温对农业生态系统影响研究进展

该文概述了北半球和我国气候变暖中增温的非对称性特征:北半球气候变暖存在明显的季节差异和昼夜不同步性, 大部分地区冬、春季升温高于夏、秋季, 日最低气温升幅是日最高气温升幅的2~3倍; 近50年我国近地表气温升高主要是最低气温明显上升的结果, 日最低气温升幅是日最气高温升幅的2~3倍, 与北半球基本一致; 升温最显著的季节为冬季和春季。在此基础上概述了非对称性增温对农业生态系统的影响, 论述了非对称性增温对农作物物候和农作物产量的影响, 得出最低气温升高促使整个生长季延长, 促使早春作物物候期提前, 但最低气温和最高气温对不同作物的物候以及同一作物的不同发育阶段影响不同。现有研究多采用模型或统计的方法研究气候变暖对作物生长的影响, 认为温度升高对作物有“强迫成熟”效应; 而现有的最低气温升高和最高气温升高对作物生长影响的研究结果并不一致。非对称性增温对农作物影响的实验研究极少, 且缺乏对模型模拟结果的实验验证。     

豫东北主要农作物对气候变暖的响应

利用豫东北具有代表性的濮阳、南乐两个国家基本站1971-2000年的气象和农气观测资料,分析了气候变化趋势及其对农作物的影响,结果表明：濮阳和南乐30 a年平均气温呈逐年升高趋势,1993年后升高趋势明显。气候变暖使冬小麦播种期推延,返青提前,对冬小麦增产有利,也利于玉米生育期的延长和产量提高;终霜期明显提前,初霜期略推迟,霜期变短,使棉花平均生育期延长,霜后花率降低,对产量和质量提高有利。     

鲁西南木本植物生长季及物候期持续日数对气候变暖的响应

以1981-2010年中国鲁西南地区的植物为研究对象,采用统计分析法研究7种木本植物生长季与物候期持续日数对气候变暖的响应规律。结果表明：1981-2010年中国鲁西南地区气温呈极显著上升趋势（p<0.01）,随着气候变暖,木本植物春季物候期间隔缩短,秋季物候期间隔延长,全生长季延长,且全生长季变化趋势比各物候持续期变化趋势明显;展叶持续期主要与上月、当月和持续月平均气温呈负相关,其中梧桐展叶持续期与当月平均气温呈极显著负相关（p<0.01）;旱柳和刺槐开花持续期与持续月平均气温呈显著正相关（p<0.05）;叶变色持续期和落叶持续期与上月、当月、结束月和持续月平均气温呈正相关,其中旱柳与平均气温达到显著正相关（p<0.05）。木本植物全生长季与气温呈正相关,其中旱柳生长季变化对气候变暖的响应最敏感,其次是榆树、梧桐和楝树生长季变化对气候变暖的响应较敏感;随着年平均气温每升高1 ℃,植物生长季延长3.0-20.0 d;年平均最高气温每升高1℃,生长季延长5.0-14.0 d;年平均最低气温每升高1 ℃,生长季延长2.0-18.0 d。     

豫东北主要农作物对气候变暖的响应

利用豫东北具有代表性的濮阳、南乐两个国家基本站1971-2000年的气象和农气观测资料,分析了气候变化趋势及其对农作物的影响,结果表明:濮阳和南乐30 a年平均气温呈逐年升高趋势,1993年后升高趋势明显.气候变暖使冬小麦播种期推延,返青提前,对冬小麦增产有利,也利于玉米生育期的延长和产量提高;终霜期明显提前,初霜期略推迟,霜期变短,使棉花平均生育期延长,霜后花率降低,对产量和质量提高有利.     

基于自动气象站的深圳近10年城市气候变化特征研究:2011—2020年

利用广东省深圳市30个自动气象站观测数据对深圳市近10年的气候变化趋势进行了分析,结果表明:（1）深圳市2011—2020年的平均气温增长率约1.47 ℃/(10 a),比上一个10年显著增加,气候变暖并未停滞;（2）受城市化的影响,深圳市气温日较差在较大范围内呈减小趋势,但在少数地区却反常地呈现了上升趋势;（3）深圳的地面风速总体呈下降趋势,其中发达地区风速下降更快;（4）2011—2020年深圳市的平均相对湿度呈上升趋势,最高每年增长1.33%;（5）统计深圳各区不同时次的极端降雨量数据可以发现,未发展地区的极端降雨量增速较大,年总降水量的增长率也较高,增长率最高为42.41 mm/a,而其他地区的6 h以内的滑动降水量极大值均呈下降趋势;（6）利用多个自动气象站长时间序列数据,可以对深圳局地气候变化特征进行更加精细化的分析,这对国家基本站而言是一种有益的补充,有助于更加深刻地发掘城市化与气候变化的关系。      

基于机器学习的中国冬季气温影响因子分析及模型估算

使用1951—2021年160个中国国家级气象观测站冬季平均气温及多项大气环流及海温等指数,用机器学习方法研究影响中国冬季气温异常的大气环流及海温等外强迫因子,并建立估算拟合模型,评价筛选出的影响因子组合对中国冬季气温异常分布的贡献。使用最小绝对收缩和选择算子（LASSO）算法提取与冬季气温异常相关的影响因子。为体现特征因子之间非线性关系,使用泰勒公式对筛选后的特征进行多项式增广。使用最小二乘梯度提升决策树（LS-GBDT）算法对筛选出的特征因子与冬季气温异常之间的非线性关系进行估算拟合。结果表明,机器学习方法能够对影响冬季气温异常的特征因子进行合理筛选与重要性分析,建立的估算模型在一定程度上体现了气候系统特征因子与冬季气温距平的非线性联系。本研究为了解中国冬季气温异常分布的影响因素及其模拟与估算提供了新方法和途径。     

An attempt to dendroclimatic reconstruction of winter temperature based on multispecies tree-ring widths and extreme years chronologies (example of Upper Silesia, Southern Poland)

This study aims at investigating pre-instrumental tree-ring based winter thermal conditions from Upper Silesia, southern Poland. The Scots pine, pedunculate oak and sessile oak ring widths and the extreme index were used to reconstruct winter mean temperature back to A.D. 1770. The climate response analysis showed that the pine is the most sensitive to February (0.36) and March (0.41) temperature, the oaks were found to be sensitive to the previous December (0.27) and January (0.23) temperature. It was found out that the combination of temperature sensitive species and an additional extreme index in regression can improve the reconstruction, with an emphasis on more reliable reconstruction of extreme values. The elimination of variance reduction and precise reconstruction of actual values of temperature is possible by scaling. The obtained calibration/verification results suggest that, through the application of the long-term composite chronologies a detailed study of the climate variability in Upper Silesia in past centuries can be provided.     

利用WOFOST作物模型研究气候变暖对冬小麦产量的影响

全球气候变暖已经成为一个不争的事实,开展气候变化对冬小麦产量影响的数值模拟对制定农业政策以适应气候变化具有重要意义。本文使用荷兰瓦赫宁根大学开发的WOFOST模型,利用太谷2000年和2001年的数据对WOFOST模型进行验证,确定该模型在山西太谷地区的适用性。文章分析了太谷地区气温变化趋势,假定以1985年-2007年的变暖趋势增温,假设其它条件不变,从而构建了100年内每10a的时间间隔的气象情形。以这些气象情形驱动验证好的模型模拟100年内每10a的时间间隔气候变暖对冬小麦产量的影响。模拟结果表明,气温变化对冬小麦产量的影响不是单一的,未来冬小麦的产量是波动变化的。     

Winter crop sensitivity to inter-annual climate variability in central India

India is predicted to be one of the most vulnerable agricultural regions to future climate changes. Here, we examined the sensitivity of winter cropping systems to inter-annual climate variability in a local market and subsistence-based agricultural system in central India, a data-rich validation site, in order to identify the climate parameters to which winter crops – mainly wheat and pulses in this region – might be sensitive in the future. We used satellite time-series data to quantify inter-annual variability in multiple climate parameters and in winter crop cover, agricultural census data to quantify irrigation, and field observations to identify locations for specific crop types. We developed three mixed-effect models (250 m to 1 km scale) to identify correlations between crop cover (wheat and pulses) and twenty-two climate and environmental parameters for 2001-2013. We find that winter daytime mean temperature (November–January) is the most significant factor affecting winter crops, irrespective of crop type, and is negatively associated with winter crop cover. With pronounced winter warming projected in the coming decades, effective adaptation by smallholder farmers in similar landscapes would require additional strategies, such as access to fine-scale temperature forecasts and heat-tolerant winter crop varieties.     

Climate change and soil freezing dynamics: historical trends and projected changes

Changes to soil freezing dynamics with climate change can modify ecosystem carbon and nutrient losses. Soil freezing is influenced strongly by both air temperature and insulation by the snowpack, and it has been hypothesized that winter climate warming may lead to increased soil freezing as a result of reduced snowpack thickness. I used weather station data to explore the relationships between winter air temperature, precipitation and soil freezing for 31 sites in Canada, ranging from the temperate zone to the high Arctic. Inter-annual climate variation and associated soil temperature variation over the last 40 years were examined and used to interpolate the effects of projected climate change on soil freezing dynamics within sites using linear regression models. Annual soil freezing days declined with increasing mean winter air temperature despite decreases in snow depth and cover, and reduced precipitation only increased annual soil freezing days in the warmest sites. Annual soil freeze–thaw cycles increased in both warm and dry winters, although the effects of precipitation were strongest in sites that experience low mean winter precipitation. Overall, it was projected that by 2050, changes in winter temperature will have a much stronger effect on annual soil freezing days and freeze–thaw cycles than changes in total precipitation, with sites close to but below freezing experiencing the largest changes in soil freezing days. These results reveal that experimental data relevant to the effects of climate changes on soil freezing dynamics and changes in associated soil physical and biological processes are lacking.     

The MGO climate model for Siberia

Validation results of the MGO regional climate model (RCM) with 50-km resolution for Siberia are discussed. For the specification of side boundary conditions, the reanalysis data are used. It is shown that the model satisfactorily simulates the sea-level pressure and temperature fields for all seasons and the year as a whole. The lowest computational errors in the simulation of regional surface temperature arise in the fall and winter; in spring and summer, the temperature errors are slightly higher. The model slightly underestimates the variability of daily mean temperature in winter relative to the reanalysis data. In summer, on the contrary, the RCM-simulated variability exceeds the variability in reanalysis. In winter, the space distribution of model precipitation is in qualitative agreement with the data of observational analysis; in summer, the space variability of model precipitation is significantly higher than that of precipitation in the reanalysis, especially in the mountains. Agreement between time changes in precipitation and temperature anomalies in RCM and in the reanalysis is better in the areas with a relatively large number of weather stations. The model can be used for estimation of future climate changes in the above-mentioned region.     

Numerical Simulation of Long-Term Climate Change in East Asia

A 10-yr regional climate simulation was performed using the fifth-generation PSU/NCAR Mesoscale Model Version 3 (MM5V3) driven by large-scale NCEP/NCAR reanalyses. Simulations of winter and summer mean regional climate features were examined against observations. The results showed that the model could well simulate the 10-yr winter and summer mean circulation, temperature, and moisture transport at middle and low levels. The simulated winter and summer mean sea level pressure agreed with the NCAR/NCEP reanalysis data. The model could well simulate the distribution and intensity of winter mean precipitation rates as well as the distribution of summer mean precipitation rates, but it overestimated the summer mean precipitation over North China. The model's ability to simulate the regional climate change in winter was superior to that in summer. In addition, the model could simulate the inter-annual variation of seasonal precipitation and surface air temperature. Geopotential heights and temperature at middle and high levels between simulations and observations exhibited high anomaly correlation coefficients. The model also showed large variability to simulate the regional climate change associated with the El Nino events. The MM5V3 well simulated the anomalies of summer mean precipitation in 1992 and 1995, while it demonstrated much less ability to simulate that in 1998. Generally speaking, the MM5V3 is capable of simulating the regional climate change, and could be used for long-term regional climate simulation.     

北太平洋冬季次表层热状况及其与中国东部夏季气候的关系

利用北太平洋次表层海温及热容量资料和我国160个台站1951-2002年夏季(6-8月)降水、气温资料,分析了北太平洋冬季次表层热状况的特征,研究了中国区域气候与热容量变化之间的关系,并用SVD方法研究了北太平洋热容量变化与中国东部夏季气候的耦合关系.结果表明:北太平洋冬季次表层海温按其特征可分为40 m和240 m两类,两者的年代际特征都很明显;北太平洋冬季热容量异常变化与其相似,并与中国东部夏季气候异常有较好的耦合关系,北太平洋中部偏北和东南部地区冬季热容量异常偏高时,中国华南和华北区夏季降水将会偏多,华南区夏季气温将会偏低;黑潮延续体及北太平洋中部偏东地区冬季热容量异常偏高和北美西海岸地区异常偏低时,中国东北和长江流域地区夏季降水将会偏少,东北和华北地区夏季气温将会偏低,其关系有着明显的年代际特征.