Under the background of global warming, a comprehensive understanding of the changing rules of the frost period will warnfrost damage earlier, protect the regional environment, and promote the rational development of climate resources in Gansu province.Ground 0 cm daily minimum temperature data collected at 61 meteorological stations combined with the linear propensity estimates method were used to obtainclimate tendency rate of frost date.Meanwhile, the Mann-Kendall and the sliding t-test methodwere used to detect the time of frost date which may change suddenly, thus building the impact range of frost in frost stations.Then stability of frost date was calculated by the standard deviation and predictions to future frost date were also made with the Hurst index method.Moreover, the correlation analysis method was used to analyze the influential factors of frost date.The following main results were obtained:(1) The mutation years of the first frost date, the last frost date, and the frost-free period were 2002, 1996, and 1999 respectively.(2)The change rate of frost period with a descending order is frost-free period, first frost date, last frost date.The change rate of Hexi was higher than that of Hedong, which had a greater contribution to the change of the frost period in whole province.(3)The descending order of the stability of frost date in Gansu province is first frost date, last frost date and frost-free period.The stability of frost date of Hexi was better than that of Hedong.(4) The spatial distribution patterns of the first frost date, the last frost date, and the frost-free period follow the rules as "Northern early and southern late, Western early and Eastern late", "Northern late and Southern early, Western late and Eastern early" and "Northern short and Southern long, Western Short and eastern long"respectively.5)The changes in the predicted frost period are roughly the delay of the first frost date, the advance of the last frost date, and the prolonged frost-free period.But there is a slight difference in the magnitude of the change with the descending order of frost-free period, last frost date and first frost date.The last frost date in Hexi may reach the average level of the province in advance, and the frost-free period of this area may exceed that of Hedong in the future.The conclusions can be drawn that the date of occurring, the length and the stability of the frost period are the results of the combined effects of the first and last frost date, altitude, latitude and longitude, in which the dominant factors were significantly different.Meanwhile, the prolonged frost-free period was caused by the deterioration of the stability of the first and last frost date.
[1]Augspurger C K, 2013.Reconstructing patterns of temperature, phenology, and frost damage over 124 years:spring damage risk is increasing[J].Ecology, 94(1):41-50.DOI:10.2307/23435667.
[2]Hong X W, Lu R Y, 2016.The meridional displacement of the summer Asian jet, silk road pattern, and tropical sst anomalies[J].Journal of Climate, 29(10):16-22.DOI:10.1175/JCLI-D-15-0541.1.
[3]Huang J B, Zhang X, Zhang Q, et al, 2017.Recently amplified arctic warming has contributed to a continual global warming trend[J].Nature Climate Change, 142(23):121-126.DOI:10.1038/s41558-017-0009-5.
[4]Hufkens K, Friedl M A, Keenan T F, et al, 2012.Ecological impacts of a widespread frost event following early spring leaf-out[J].Global Change Biology, 18(7):2365-2377.DOI:10.1111/j.1365-2486.2012.02712.x.
[5]Lenz A, Hoch G, Vitasse Y, et al, 2013.European deciduous trees exhibit similar safety margins against damage by spring freeze events along elevation gradients[J].New Phycologist, 200(4):1166-1175.DOI:10.1111/nph.12452.
[6]Liu Q, Piao S L, Janssens I A, et al, 2018.Extension of the growing season increases vegetation exposure to frost[J].Nature Communications, 9(1):426-430.DOI:10.1038/s41467-017-02690-y.
[7]Mensal A, Sparks T H, Estrella N, et al, 2006.European phonological response to climate change matches the warming pattern[J].Global Change Biology, 12(10):1969-1976.DOI:10.1053/jlts.2003.50055.
[8]Reid P C, Hari R E, Beaugrand G, et al, 2016.Global impacts of the 1980s regime shift[J].Global Change Biology, 22(2):682-683.DOI10.1111/gcb.13106.
[9]陈乾金, 夏洪星, 张永山, 1995.我国江淮流域近40年异常初终霜冻的分析[J].应用气象学报, 6(1):50-55.
[10]陈少勇, 郑延祥, 楼望萍, 等, 2013.中国西北地区初霜冻的气候变化特征[J].资源科学, 35(1):165-172.
[11]戴君虎, 王焕炯, 葛全胜, 2013.近50年中国温带季风区植物花期春季霜冻风险变化[J].地理学报, 68(5):593-601.DOI:10.11821/xb201305002.
[12]冯秀藻, 1991.农业气象学原理[M].北京:气象出版社, 356-367.
[13]郭军, 任国玉, 任雨, 2011.近100年天津平均气温与极端气温变化[J].高原气象, 30(5):1399-1405.
[14]韩荣青, 李维京, 艾婉秀, 等, 2010.中国北方初霜冻日期变化及其对农业的影响[J].地理学报, 65(5):525-532.DOI:10.11821/xb201005002.
[15]贾艳青, 张勃, 张耀宗, 等, 2017.城市化对长三角地区极端气温影响的时空分异[J].自然资源学报, 32(5):814-828.DOI:10.11849/zrzyxb.20160575.
[16]拉巴次仁, 索朗加措, 白玛, 2014.1981-2010年西藏霜冻日数的变化特征[J].地理学报, 69(5):690-696.DOI:10.11821/dlxb201405012.
[17]李芬, 张建新, 武永利, 等, 2013.近50年山西终霜冻的时空分布及其影响因素[J].地理学报, 68(11):1472-1480.DOI:10.11821/dlxb201311003.
[18]梁进秋, 贾利芳, 何正梅, 等, 2010.大同市近50年霜冻及其异常事件变化特征分析[J].中国农业气象, 31(1):124-128.
[19]马彬, 张勃, 贾艳青, 等, 2017.1961-2014年中国内陆农业区异常初、终霜冻日期时空变化及其与环流因子的关系[J].气象学报, 75(4):661-671.DOI:10.11676/qxxb2017.048.
[20]马琼, 2016.1980-2014年黄土高原霜冻时空变化及冬小麦晚霜冻危险性研究[D].兰州: 西北师范大学, 10-14.
[21]马尚谦, 张勃, 唐敏, 等, 2018.1960-2015年淮河流域初终霜日时空变化分析[J].中国农业气象, 39(7):468-478.DOI:10.3969/j.issn.1000-6362.2018.07.005.
[22]马尚谦, 张勃, 唐敏, 等, 2019.淮河流域冬小麦晚霜冻时空演变分析[J].麦类作物学报, 39(1):105-113.DOI:10.7606/j.jssn.1009-1041.2019.01.14.
[23]马中华, 张勃, 张建香, 等, 2012.近30年甘肃省气温时空变异分析[J].高原气象, 31(3):760-767.
[24]马柱国, 2003.中国北方地区霜冻日的变化与区域增暖相互关系[J].地理学报, 58(1):31-37.
[25]宁晓菊, 张丽君, 杨群涛, 等, 2015.1951年以来中国无霜冻日期的变化趋势[J].地理学报, 70(11):1811-1822.
[26]申双和, 景元书, 2016.农业气象学原理[M].南京:气象出版社:127-140.
[27]王岱, 游庆龙, 江志红, 等, 2016.基于均一化资料的中国极端地面气温变化分析[J].高原气象, 35(5):1352-1363.DOI:10.7522/j.issn.1000-0534.2016.00019.
[28]王国强, 张勃, 张耀宗, 等, 2016.甘肃省近55年来积温变化趋势特征分析[J].水土保持研究, 23(5):193-198.
[29]王媛媛, 张勃, 2012.1971年至2010年陇东地区霜冻与积温变化特征[J].资源科学, 34(11):2181-2188.
[30]魏凤英, 2007.现代气候统计诊断与预测技术[M].北京:气象出版社, 58-63.
[31]温克刚, 2005.中国气象灾害大典, 甘肃卷[M].北京:气象出版社, 276-277.
[32]杨晓玲, 胡津革, 张宇林, 2016.河西走廊东部初、终霜冻日和无霜冻日期变化特征分析[J].中国农学通报, 32(11):149-155.
[33]张雪芬, 余卫东, 王春乙, 2012.基于作物模型灾损识别的黄淮区域冬小麦晚霜冻风险评估[J].高原气象, 31(1):277-284.
[34]张耀宗, 张勃, 刘艳艳, 等, 2016.乌鞘岭是甘肃气候转型变化的分界线吗[J].冰川冻土, 38(3):611-619.DOI:10.7522/j.issn.1000-0240.2016.0068.
[35]朱虹晖, 武永峰, 宋吉青, 等, 2018.基于多因子关联的冬小麦晚霜冻害分析-以河南省为例[J].中国农业气象, 39(1):59-68.DOI:10.3969/j.issn.1000-6362.2018.01.007.
