1971~2013年我国四季开始日期及生长期长度的变化特征分析

利用中国气象局国家气象信息中心提供的中国584个气象站点1971～2013年的逐日气温数据,采用线性倾向估计和经验模态分解（EMD）等方法,以地理信息系统为数据处理平台,分析我国43年来四季起始日以及生长期的变化特征。结果表明：新疆、云南和四川地区的四季起始日变化呈现明显的南北差异;全国大部分地区春、夏季起始日提前,春季比夏季提前趋势更明显,江苏、安徽、湖北大部和云南北部春季提前显著,提前率为4.1～7.2 d/10 a;夏季提前的区域更广,新疆东部、甘肃西部、华南大部和云南南部夏季提前显著,提前率为2.9～4.6 d/10 a;全国大部分地区秋、冬季起始日推迟,秋季比冬季推迟的范围更大,新疆南部和四川西部秋季推迟明显,推迟率为4.4～8.6 d/10 a;冬季推迟趋势更显著,新疆东南部和青海大部冬季推迟明显,推迟率为4.7～13.8 d/10 a;全国各地区生长期均有延长,最显著的是云川交界处和新疆东南部地区,延长率为20.1 d/10 a。EMD和线性倾向估计的结果基本一致,但EMD得到的春季起始日推迟地区的范围更大,夏、秋、冬季起始日以及生长期的变化趋势更显著。     

贵州玉屏四季气候季节的划分及特征分析

利用玉屏国家地面气象观测站1961—2016年逐日平均气温资料,采用《气候季节划分》(QX/T15—2012)方法,对玉屏县四季起始日期及长度进行分析。结果表明:(1)玉屏县常年四季起始日期:入春3月5日,入夏5月23日,入秋9月22日,入冬11月28日;四季长度:春季79 d,夏季122 d,秋季67 d,冬季97 d。(2)56 a来玉屏县春季起始日期呈提前趋势,长度呈增加趋势,两者均在20世纪90年代前后出现了转折,但未发生气候突变;夏季起始日期及长度趋势变化不明显;秋季起始日期呈推后趋势,长度变化不明显;冬季起始日期变化不明显,长度呈减少趋势;春季长度增加、冬季长度减少主要为春季起始日期提前所致。(3)玉屏县四季起始日期的年际变幅大,起始日期比常年偏早(晚)连续2候以上的异常年份,春季为23%,夏季为27%,秋季为32%,冬季为25%。(4)玉屏县春季开始后出现低于季节指标≥1候的概率达41%,表明玉屏县春季出现倒春寒天气的概率很大。(5)比较气象行标法与稳定通过法的四季起始日期及长度,气象行标法对玉屏县的四季划分更能满足于农业生产的需要。     

1960-2009年武汉城区与郊区气候季节变化

利用1960-2009年武汉城区与郊区气象站逐日平均气温资料,采用相同气候季节划分方法,系统分析武汉城区与郊区气候季节起始时间、季节长度的变化趋势及其差异。结果表明：1980-2009年,武汉城区入春、入夏时间比郊区分别提前10 d和5 d,入秋、入冬时间城区比郊区推迟;武汉夏季长度城区比郊区长12 d,冬季、春季长度城区比郊区短6 d和5 d。1960-2009年武汉四季平均起始时间城区与郊区差别较小,但四季最早、最晚出现时间年际差别较大;武汉入春、入夏时间城区与郊区均提前,入秋、入冬时间均推后,但城区四季变化较显著,郊区仅入秋变化显著;武汉城区夏季长度呈极显著延长,冬季长度呈较显著缩短,城区春季、秋季及郊区四季长度变化均不显著。2000-2009年武汉城区与郊区季节起始时间和季节长度的变化较大,这是因为近10 a武汉作为中部地区崛起的支点,城区发展迅速。     

丽水市四季起始日期的气候演变特征

根据丽水市国家气象观测站1953-2010年逐日气温资料,运用趋势分析、Morlet小波变化和Mann-Kendall检验对四季起始日期的气候变化特征、趋势演变规律和突变转折情况进行了研究。结果表明：四季起始日期,春季约在3月中旬,夏季在5月底,秋季在9月下旬,冬季在11月下旬,且春、秋季的长度较短,只有2个月左右,夏、冬季的长度较长,长达4个月。Morlet小波分析四季起始日期的周期变化特征,主要体现在年代际时间尺度上,且各周期强度有所差异,预测春、夏季起始日将按照提前趋势发展,秋、冬季起始日继续延后状态。Mann-Kendall检验得出,春、夏两季起始日期在21世纪初发生由推后转向提前的突变,而秋、冬两季起始日突变点都体现在20世纪60年代。     

青藏高原四季开始日期随海拔高度和纬度变化

利用中国气象局国家气象信息中心提供的青藏高原60个测站1961~2007年逐日气温资料, 分析了青藏高原近47年来四季开始日期随海拔高度和纬度的变化趋势。结果表明, 春季和夏季开始日期是整体提前, 而秋季和冬季开始日期是整体延迟的, 春季和冬季开始日期的变化相对夏季和秋季更为明显;四季开始日期随海拔高度变化分布明显不同, 海拔越高, 春夏季开始日期来临越晚, 秋冬季开始日期来临越早, 海拔越低, 春夏季开始日期来临越早, 秋冬季开始日期来临越晚;海拔越高, 春夏开始日期提前的天数越多, 秋冬开始日期推迟天数越多, 反之低海拔地区相对更小, 由此得知高海拔地区的季节开始日期对当地气温的增温更为敏感;春季开始日期在36°N以南基本随纬度递增而开始日期推后, 36°N以北地区春季相对偏早, 夏季、秋季、冬季开始日期随纬度的变化和春季变化基本相似;四季开始日期来临的早晚受到多种因素包括气温、海拔和纬度共同影响, 季节延迟率也受到气温和海拔的影响, 但是纬度对季节延迟率影响不大;四季开始日期的提前和延迟变化和当地气温的变化几乎一致, 秋冬季节的开始日期对气温变化更为敏感, 高海拔地区的季节开始日期对气温变化更为敏感。      

1961—2017年云南季节变化特征分析

参照《中华人民共和国气象行业标准-气候季节划分》（QX/T 152-2012）中关于气候季节的定义标准,利用1961-2017年云南122个气象站的气温资料,分析了云南的气候季节区域的空间分布和季节开始日期及长度的变化趋势。云南共有4种气候季节区域,分别是四季分明区、无夏区、无冬区和常春区。无夏区范围最广,无冬区其次。不同年代四种季节气候区域空间分布范围不尽相同,无夏区和无冬区空间范围变化最显著。2011年以后云南出现四季分明区范围明显增大的现象,这与近年来气候变暖背景下云南气温年较差增大的观测事实相一致。云南四季分明区春季和秋季较长,夏季和冬季较短。无夏区秋季最长、春季次之、冬季最短。无冬区夏季最长、春季和秋季长度接近。不同气候季节区域间春季和夏季开始日期的变化均呈提早趋势,秋季和冬季开始日期有推迟的趋势;在季节长度变化上,夏季增长,冬季变短,但春秋季长度的变化不尽相同。     

Latest update of the climatology and changes in the seasonal distribution of precipitation over China

Based on daily precipitation data from 524 meteorological stations in China during the period 1960–2009, the climatology and the temporal changes (trends, interannual, and decadal variations) in the proportion of seasonal precipitation to the total annual precipitation were analyzed on both national and regional scales. Results indicated that (1) for the whole country, the climatology in the seasonal distribution of precipitation showed that the proportion accounted for 55 % in summer (June–August), for around 20 % in both spring (March–May) and autumn (September–November), and around 5 % in winter (December–February). But the spatial features were region-dependent. The primary precipitation regime, “summer–autumn–spring–winter”, was located in central and eastern regions which were north of the Huaihe River, in eastern Tibet, and in western Southwest China. The secondary regime, “summer–spring–autumn–winter”, appeared in the regions south of the Huaihe River, except Jiangnan where spring precipitation dominated, and the southeastern Hainan Island where autumn precipitation prevailed. (2) For the temporal changes on the national scale, first, where the trends were concerned, the proportion of winter precipitation showed a significantly increasing trend, while that of the other three seasons did not show any significant trends. Second, for the interannual variation, the variability in summer was the largest among the four seasons and that in winter was the smallest. Then, on the decadal scale, China experienced a sharp decrease only in the proportion of summer precipitation in 2000. (3) For the temporal changes on the regional scale, all the concerned 11 geographic regions of China underwent increasing trends in the proportion of winter precipitation. For spring, it decreased over the regions south of the Yellow River but increased elsewhere. The trend in the proportion of summer precipitation was generally opposite to that of spring. For autumn, it decreased over the other ten regions except Inner Mongolia with no trend. It is noted that the interannual variability of precipitation seasonality is large over North China, Huanghuai, and Jianghuai; its decadal variability is large over the other regions, especially over those regions south of the Yangtze River.     

Cut-off Lows in the South Africa region and their contribution to precipitation

The contribution of Cut-off Lows (CoLs) to precipitation and extreme rainfall frequency in South Africa has been quantified from 402 station records over the period 1979–2006. Firstly, 500 hPa CoL trajectories over Southern Africa and surrounding oceans were determined and their features thoroughly analyzed. In a second step, using daily precipitable water, outgoing long wave radiation data and station rainfall records, an area was defined where the occurrence of CoLs is associated with rainfall over South Africa. CoLs transiting in the 2.5°E–32.5°E/20°S–45°S are more likely to produce precipitation over the country. When 500 hPa CoLs are centered just off the west coast of the country (around 15°E/32.5°S) their impact is substantial in term of daily rainfall intensity and spatial coverage. CoL rainy days have been studied and it is shown that they significantly contribute to precipitation in South Africa, more strongly along the south and east coasts as well as inland, over the transition zone between the summer and winter rainfall domains where they contribute between 25 to more than 35 % of annual accumulation. At the country scale, CoL rainfall is more intense and widespread in spring than during other seasons. Over the analyzed period, a significant trend in annual CoLs’ frequency shows an increase of about 25 %. This increase is mainly realized in spring and in a lesser extent in summer. This trend is accompanied by a significant increase in the frequency of CoL rainy days specifically along the south coast and over the East of the country during the spring–summer period. In parallel, it is shown that from late spring until summer CoLs’ frequency varies significantly accordingly with large scale circulation modes of the Southern Hemisphere such as the Pacific South American pattern (PSA). This positive trend in CoLs’ frequency may be related with the positive trend in the PSA during the spring–summer period over the three last decades.     

On the role of domain size and resolution in the simulations with the HIRHAM region climate model

We investigate the simulated temperature and precipitation of the HIRHAM regional climate model using systematic variations in domain size, resolution and detailed location in a total of eight simulations. HIRHAM was forced by ERA-Interim boundary data and the simulations focused on higher resolutions in the range of 5.5–12 km. HIRHAM outputs of seasonal precipitation and temperature were assessed by calculating distributed model errors against a higher resolution data set covering Denmark and a 0.25° resolution data set covering Europe. Furthermore the simulations were statistically tested against the Danish data set using bootstrap statistics. The results from the distributed validation of precipitation showed lower errors for the winter (DJF) season compared to the spring (MAM), fall (SON) and, in particular, summer (JJA) seasons for both validation data sets. For temperature, the pattern was in the opposite direction, with the lowest errors occurring for the JJA season. These seasonal patterns between precipitation and temperature are seen in the bootstrap analysis. It also showed that using a 4,000 × 2,800 km simulation with an 11 km resolution produced the highest significance levels. Also, the temperature errors were more highly significant than precipitation. In similarly sized domains, 12 of 16 combinations of variables, observation validation data and seasons showed better results for the highest resolution domain, but generally the most significant improvements were seen when varying the domain size.     

Annual and seasonal climate and climatic changes in the Canadian prairies simulated by the CCC GCM

Abstract

As part of a study on the effects of climatic variability and change on the sustainability of agriculture in Alberto, the modelling performance of the second‐generation Canadian Climate Centre GCM (general circulation model) is examined. For the region in general, the simulation of 1 × CO₂ mean temperature is generally better than that for mean precipitation, and summer is the season best modelled for each variable. At the scale of individual grid squares, DJF (December, January, February) (temperature) and JJA (June, July, August) (precipitation) are the seasons best modelled. The GCM‐simulated increases in mean annual temperature resulting from a doubling of CO₂ are of the order of 5 to 6°C in the Prairie region, with much of this increase resulting from substantial warming in the winter and spring. Increases in mean annual precipitation are of the order of 50 to 150 mm (changes of +5 to +15%), with the greatest changes again occurring in winter and spring. As far as the limited GCM run durations allow, temperature and precipitation variance generally show no significant changes from a 1 × CO₂ to a 2 × CO₂ climate. Increased precipitation in winter and spring does not result in greater snow accumulations owing to the magnitude of warming; and significant decreases in soil moisture content occur in summer and fall. The resulting effects on the growing season and moisture regime have the potential to affect agricultural practices in the area.     

The contribution of snow condition trends to future ground climate

Global climate models predict that terrestrial northern high-latitude snow conditions will change substantially over the twenty-first century. Results from a Community Climate System Model simulation of twentieth and twenty-first (SRES A1B scenario) century climate show increased winter snowfall (+10–40%), altered maximum snow depth (?5 ± 6 cm), and a shortened snow-season (?14 ± 7 days in spring, +20 ± 9 days in autumn). By conducting a series of prescribed snow experiments with the Community Land Model, we isolate how trends in snowfall, snow depth, and snow-season length affect soil temperature trends. Increasing snowfall, by countering the snowpack-shallowing influence of warmer winters and shorter snow seasons, is effectively a soil warming agent, accounting for 10–30% of total soil warming at 1 m depth and ~16% of the simulated twenty-first century decline in near-surface permafrost extent. A shortening snow season enhances soil warming due to increased solar absorption whereas a shallowing snowpack mitigates soil warming due to weaker winter insulation from cold atmospheric air. Snowpack deepening has comparatively less impact due to saturation of snow insulative capacity at deeper snow depths. Snow depth and snow-season length trends tend to be positively related, but their effects on soil temperature are opposing. Consequently, on the century timescale the net change in snow state can either amplify or mitigate soil warming. Snow state changes explain less than 25% of total soil temperature change by 2100. However, for the latter half of twentieth century, snow state variations account for as much as 50–100% of total soil temperature variations.     

Regional meteorological patterns for heavy pollution events in Beijing

The present study investigates meteorological conditions for the day-to-day changes of particulate matter (PM) concentration in Beijing city during the period 2008–2015. The local relationship of PM concentration to surface air temperature, pressure, wind speed, and relative humidity displays seasonal changes and year-to-year variations. The average correlation coefficient with PM₁₀ in spring, summer, fall, and winter is 0.45, 0.40, 0.38, and 0.30 for air temperature; –0.45, –0.05, –0.40, and –0.45 for pressure; 0.13, 0.04, 0.53, and 0.50 for relative humidity; and –0.18, –0.11, –0.45, and –0.33 for wind speed. A higher correlation with wind speed is obtained when wind speed leads by half a day. The heavily polluted and clean days, which are defined as the top and bottom 10% of the PM values, show obvious differences in the regional distribution of air temperature, pressure, and wind. Polluted days correspond to higher air temperature in all the four seasons, lower sea level pressure and anomalous southerly winds to the south and east of Beijing in spring, fall, and winter, and a northwest–southeast contrast in the pressure anomaly and anomalous southerly winds in summer. Higher relative humidity is observed on polluted days in fall and winter. The polluted days are preceded by an anomalous cyclone moving from the northwest, accompanied by lower pressure and higher air temperature, in all four seasons. This feature indicates the impacts of moving weather systems on local meteorological conditions for day-to-day air quality changes in Beijing.     

气候变化对我国小麦发育及产量可能影响的模拟研究

利用随机天气模型, 将气候模式对大气中CO₂倍增时预测的气候情景与CERES-小麦模式相连接, 研究了气候变化对我国冬小麦和春小麦生产的可能影响.结果表明, 气候变化后小麦发育将加快, 生育期缩短, 冬小麦平均缩短7.3天, 春小麦平均缩短10.5天, 春小麦生育期缩短的绝对数和相对数均大于冬小麦.籽粒产量呈下降趋势, 冬小麦平均减产7%~8%, 雨养条件下比水分适宜时减产幅度略大.春小麦的减产幅度大于冬小麦, 水分适宜时平均减产17.7%, 雨养时平均减产31.4%.     

近60a来洞庭湖区气温的变化特征

以洞庭湖区24个气象站1952-2010年的平均气温资料为基础,利用气候倾向率、Mann-Kendall突变检验法和小波分析等方法,分析了洞庭湖区的气温变化特征.结果表明:洞庭湖区年平均、冬季、春季和秋季气温均呈显著上升趋势,增温速率尤以冬季和春季为甚.除夏季外,年平均和其他各季气温在1990s,先后发生增温性突变.高温日数呈上升趋势,但显著性不明显,低温日数下降趋势非常显著.除夏季外,年平均和各季异常冷年,基本出现在1950s至1970s,异常暖年,均出现在1998年以后.除夏季外,各季气温均存在准9a周期.     

北京1951—2008年升温趋势和季节变化

采用均一化订正的北京南郊地面日平均气温资料,分析了北京地区1951—2008年气温变化趋势。结果表明,年平均最高和最低气温的升高呈明显的不对称性,其中年平均最低气温升高较为明显,升温趋势为0.46℃/10a。根据1951—2008年日平均气温计算北京春、夏、秋、冬四季的季节长度和起始日期,发现北京地区冬季最长,秋季最短;夏季在逐渐延长,冬季在逐渐缩短,夏、冬两季长度变化的线性速率分别为4.4d/10a和-4.7d/10a。春、夏两季逐渐提前,趋势分别为3.0d/10a和2.5d/10a;而秋、冬两季在逐渐推迟,趋势分别为2.0d/10a和1.7d/10a。将季节起始日期与年平均气温进行相关性分析发现,春、夏两季的起始时间与年平均气温存在显著负相关,而秋、冬两季起始时间与年平均气温存在显著正相关。     

Changes in Canada's Climate: Trends in Indices Based on Daily Temperature and Precipitation Data

ABSTRACT

Trends in indices based on daily temperature and precipitation are examined for two periods: 1948–2016 for all stations in Canada and 1900–2016 for stations in the south of Canada. These indices, a number of which reflect extreme events, are considered to be impact relevant. The results show changes consistent with warming, with larger trends associated with cold temperatures. The number of summer days (when daily maximum temperature >25°C) has increased at most locations south of 65°N, and the number of hot days (daily maximum temperature >30°C) and hot nights (daily minimum temperature >22°C) have increased at a few stations in the most southerly regions. Very warm temperatures in both summer and winter (represented by the 95th percentile of their daily maximum and minimum temperatures, respectively) have increased across the country, with stronger trends in winter. Warming is more pronounced for cold temperatures. The frost-free season has become longer with fewer frost days, consecutive frost days, and ice days. Very cold temperatures in both winter and summer (represented by the 5th percentile of their daily maximum and minimum temperatures, respectively) have increased substantially across the country, again with stronger trends in the winter. Changes in other temperature indices are consistent with warming. The growing season is now longer, and the number of growing degree-days has increased. The number of heating degree-days has decreased across the country, while the number of cooling degree-days has increased at many stations south of 55°N. The frequency of annual and spring freeze–thaw days shows an increase in the interior provinces and a decrease in the remainder of the country. Changes in precipitation indices are less spatially coherent. An increase in the number of days with rainfall and heavy rainfall is found at several locations in the south. A decrease in the number of days with snowfall and heavy snowfall is observed in the western provinces, while an increase is found in the north. There is no evidence of significant changes in the annual highest 1-day rainfall and 1-day snowfall. The maximum number of consecutive dry days has decreased, mainly in the south.     

近50a山东中部地区四季开始日期及长度变化特征

利用山东中部地区8个气象站1966—2015年逐日气温观测资料,用5日滑动平均气温作为划分依据,结合气候趋势法、Mann-Kendall法和经验正交分解法,对山东中部地区近50 a的四季开始日期及长度时空变化特征进行分析。结果表明:山东中部地区春季和夏季开始日期呈提前趋势,秋季和冬季呈推迟趋势,其中,夏季和冬季开始日期在1993年发生突变,四季开始日期的主要空间变化趋势一致,秋季变化强度中心在中北部平原,其他三季变化强度中心均出现在中部地区,四季开始日期空间变化规律在第二特征向量上呈现区域变化的不一致性。冬季日数最多,其次为夏季,春季日数最少,春季和冬季日数呈减少趋势,冬季减少趋势显著,气候倾向率为-2.98 d/10 a,夏季和秋季日数呈增加趋势,夏季日数增加显著,四季日数主要空间变化规律一致,强度中心在中部地区,四季日数空间变化规律在第二特征向量上存在不一致性,其中,夏季和秋季第二特征向量呈现南部山区与其他地区不同。     

Statistical and dynamical characteristics of the urban heat island intensity in Seoul

The statistical and dynamical characteristics of the urban heat island (UHI) intensity in Seoul are investigated for non-precipitation days and precipitation days using 4-year surface meteorological data with 1-h time intervals. Furthermore, the quantitative influence of synoptic pressure pattern on the UHI intensity is examined using a synoptic condition clustering method. The statistical analysis shows that the daily maximum UHI intensity in Seoul for non-precipitation days is strongest in autumn (4.8°C) and weakest in summer (3.5°C). The daily maximum UHI intensity is observed around midnight in all seasons except in winter when the maximum occurrence frequency is found around 08 LST. This implies that anthropogenic heating contributes to the UHI in the cold season. The occurrence frequency of the UHI intensity has a negatively skewed distribution for non-precipitation days but a positively skewed distribution for precipitation days. The amplitude of the heating/cooling rate and the difference in the heating/cooling rate between the urban and rural areas are smaller in all seasons for precipitation days than for non-precipitation days, resulting in weaker UHI intensities for precipitation days. The urban cool island occurs very often in the daytime, with an occurrence frequency being 77% of the total non-precipitation days in spring. The analysis of the impact of large-scale dynamical forcing shows that the daily maximum UHI intensity varies with synoptic pressure pattern, ranging from ?22% in spring to 28% in summer relative to the seasonal mean daily maximum UHI intensity. Comparison of the UHI intensity calculated using station-averaged temperatures to that based on the conventional two-station approach indicates that local effects on the UHI intensity are minimized by using multiple-station data. Accordingly, an estimation of the UHI intensity using station-averaged temperatures for both urban and rural areas is suggested.     

江苏省GEF项目区气候变暖的农业效应

基于江苏适应气候变化农业开发项目区1961-2008年逐日气温和降水资料,统计分析了48a来苏北项目区气温、降水的变化特征,以及在此背景下界限温度的积温、无霜期长短、四季时间和参考蒸散的变化特征.结果表明,48a来苏北≥0℃、≤ 10℃积温量和积温日数显著增加,无霜期延长了15-30d;春季时间延长最明显,夏季次之,冬...     

Impact of climate change on mid-twenty-first century growing seasons in Africa

Changes in growing seasons for 2041–2060 across Africa are projected using a regional climate model at 90-km resolution, and confidence in the predictions is evaluated. The response is highly regional over West Africa, with decreases in growing season days up to 20% in the western Guinean coast and some regions to the east experiencing 5–10% increases. A longer growing season up to 30% in the central and eastern Sahel is predicted, with shorter seasons in parts of the western Sahel. In East Africa, the short rains (boreal fall) growing season is extended as the Indian Ocean warms, but anomalous mid-tropospheric moisture divergence and a northward shift of Sahel rainfall severely curtails the long rains (boreal spring) season. Enhanced rainfall in January and February increases the growing season in the Congo basin by 5–15% in association with enhanced southwesterly moisture transport from the tropical Atlantic. In Angola and the southern Congo basin, 40–80% reductions in austral spring growing season days are associated with reduced precipitation and increased evapotranspiration. Large simulated reductions in growing season over southeastern Africa are judged to be inaccurate because they occur due to a reduction in rainfall in winter which is over-produced in the model. Only small decreases in the actual growing season are simulated when evapotranspiration increases in the warmer climate. The continent-wide changes in growing season are primarily the result of increased evapotranspiration over the warmed land, changes in the intensity and seasonal cycle of the thermal low, and warming of the Indian Ocean.