Simulation and Projection of Snow Water Equivalent over the Eurasian Continent by CMIP5 Coupled Models
-
摘要: 基于美国冰雪资料中心(NSIDC)提供的卫星遥感雪水当量资料,评估了26个CMIP5(Coupled ModelInter-comparison Project)耦合模式对1981~2005年欧亚大陆冬季雪水当量的模拟能力,在此基础上应用多模式集合平均结果,预估了21世纪欧亚大陆雪水当量的变化情况。结果表明,CMIP5耦合模式对欧亚大陆冬季雪水当量空间分布具有一定的模拟能力,能够再现出欧亚大陆冬季雪水当量由南向北递增、青藏高原积雪多于同纬度其他地区的特征;就雪水当量的幅值而言,几乎所有模式均显著低估了西伯利亚中部雪水当量的大值中心,对中国东北地区雪水当量的模拟也显著偏低,但模式对乌拉尔山以西的东欧平原、我国北方及蒙古地区冬季雪水当量的模拟却比卫星遥感资料显著偏大,此外模式对堪察加半岛及以北的西伯利亚东北部地区的雪水当量也明显偏大。对于青藏高原地区,虽然部分模式可以模拟出青藏高原东部的雪水当量大值区,但大多数模式对青藏高原西部雪水当量的模拟却明显偏大,存在虚假的大值中心。对遥感反演资料的EOF(Empirical Orthogonal Function)分解表明,对于EOF第一个模态所对应欧亚大陆全区一致的年代际变化特征,仅有少数模式具有一定的模拟能力,大多数模式以及多模式集合的结果均未能予以反映;对应于欧亚大陆雪水当量年际变化的EOF第二模态而言,仅有少数模式(如俄罗斯的INMCM4)具有一定的再现能力,绝大多数模式对该模态及其时间演变的特征没有模拟能力。比较CMIP5多模式的集合预估结果与1981~2005年基准时段的雪水当量,可以发现在RCP4.5排放情景下,西伯利亚中东部地区的雪水当量相对于基准时段显著增加,区域平均的增加量在21世纪前、中、后期分别为4.1mm、5.4 mm和6.8 mm,且随时间增加得更显著;对90°E以西的欧洲大陆和青藏高原地区,其雪水当量则相对减少,减少的幅度和显著性也随时间而增大。就雪水当量的相对变化而言,在欧亚大陆东北部存在雪水当量相对变化的大值区,在21世纪后期相对变化显著区大都在5%~10%;但在青藏高原、斯堪的纳维亚半岛进和东欧平原,并没有发现雪水当量相对变化的髙值区,这是由于这些区域冬季雪水当量的幅值较大的缘故。RCP8.5情景下欧亚大陆雪水当量的变化特征与RCP4.5相类似,只是变化的幅度更大。Abstract: Based on the remote sensing data from National Snow and ICE Data Center (NSIDC), the performance of CMIP5 (Coupled Model Inter-comparison Project) models in reproducing the winter snow water equivalent (SWE) in the Eurasian continent during 1981-2005 was evaluated first, and the multi-model ensemble (MME) technique was then applied to project the SWE changes over Eurasian continent in the 21st century under the conditions of two different representative concentration pathways (RCP4.5 and RCP8.5) using eight good CMIP models out of total 26 models. The results show that the models were able to reproduce the spatial pattern of winter mean SWE in the Eurasia, i.e. the 25-year average of SWE increased from south to north and SWE in the Tibetan Plateau was much higher than those in other regions of the same latitude. However, some errors still existed in the models. For example, almost all models underestimated the maximum SWE in central Siberia, and SWE in northeastern China was also underestimated. It was found that SWE to the west of Ural Mountains and over northern part of China and Mongolia was overestimated when compared with observation. Meanwhile, only a subset of the models could produce the maximum SWE on the eastern Tibetan Plateau, and the spurious maximum SWE could be found on the western Tibetan Plateau in most CMIP5 models. The spatial and temporal characteristics of winter SWE from CMIP5 model simulations and observations were further analyzed using the Empirical Orthogonal Function (EOF) analysis, and the results suggested that only a small number of CMIP5 models could reproduce main features of the first eigenvector that reflects the decadal variation of SWE over the whole Eurasia. The second mode reflects the annual variation of SWE over the Eurasia, and only a few models (e.g., INMCM4) could reproduce the spatial and temporal characteristics of the second mode to some extent. With respect to the reference period 1981-2005, projection of SWE by the MME under the RCP4.5 shows that SWE in the northeastern Eurasia continent would increase significantly with an increase of 4.1 mm for the 25-year averaged winter SWE in the early stage of the 21st century, followed by 5.4-mm and 6.8-mm increases in the middle and late 21st century, respectively. In contrast, there would exist a decrease of SWE in continental Europe to the west of 90°E and over the Tibetan Plateau and the decrease would become more severe with time. In terms of percentage change of SWE, the region with large magnitudes was found in the northeastern Eurasian continent, where the increase of SWE could be around 5%-10%. However, no maximum centers were found in the Tibetan Plateau, Scandinavian Peninsula and East European Plain possibly because of the large values of winter SWE in these regions. Projection of SWE changes by the MME under the high emission scenario RCP8.5 shows a similar pattern with results under the emission scenario RCP4.5, but with larger amplitudes of changes in snow water equivalence.
-
Key words:
- CMIP5 models /
- Snow water equivalent /
- Model evaluation /
- Climate projection
-
图 1 1981~2005年平均遥感反演(Obs)和26个CMIP5模式模拟的欧亚大陆冬季平均雪水当量空间分布(MME表示26个模式的集合平均结果)
Figure 1. Winter mean SWE (Snow Water Equivalent) from remote sensing data (Obs) and the 26 CMIP5 (Coupled Model Inter-comparison Project) models during 1981–2005 over the Eurasian continent (MME indicates multi-model ensemble)
图 4 1981~2005年遥感观测和CMIP5模式模拟的欧亚大陆冬季雪水当量EOF第一模态时间序列,左上角为其解释方差
Figure 4. The time series of first EOF mode of winter SWE from remote sensing data and CMIP5 models during 1981−2005 over the Eurasian continent. The variances explained by first EOF principal component are shown at the top left of each panel
图 5 1981~2005年遥感观测和CMIP5模式模拟的欧亚大陆冬季雪水当量EOF第二模态空间型,左上角为其解释方差
Figure 5. The second EOF mode of winter SWE from remote sensing data and the 26 CMIP5 models during 1981–2005 over the Eurasian continent. The variances explained by the second EOF principal component are shown at the top left of each panel
图 6 1981~2005年遥感观测和CMIP5模式模拟的欧亚大陆冬季雪水当量EOF第二模态时间序列,左上角为其解释方差
Figure 6. The time series of second EOF mode of winter SWE from remote sensing data and the 26 CMIP5 models during 1981–2005 over the Eurasian continent. The variances explained by the second EOF principal component are shown at the top left of each panel
图 7 不同样本的多模式集合对1981~2005年欧亚大陆冬季平均雪水当量模拟的相对偏差:(a)26个CMIP5模式的集合,记为MME(26);(b)6个优选CMIP5模式的集合平均,记为MME(6)
Figure 7. The percentage biases of winter mean SWE during 1981−2005 over the Eurasian continent: (a) Between all-model ensemble result [MME(26)] and remote sensing data; (b) between the ensemble of six good models [MME(6)] and remote sensing data
图 8 相对于1981~2005年基准期,RCP4.5(左列)和RCP8.5(右列)两种排放情景下,多模式集合预估的21世纪不同时期冬季雪水当量的变化:(a、b)21世纪早期(2016~2040年);(c、d)21世纪中期(2046~2070年);(e、f)21世纪后期(2076~2100年)
Figure 8. Changes in winter mean SWE over the Eurasian continent as projected by MME(6): (a, b) Early 21st century (2016–2040); (c, d) middle 21st century (2046–2070); (e, f) late 21st century (2076–2100). The left panels are for RCP4.5 scenario, and right panels are for RCP8.5 scenario, respectively. The reference period used in this study is 1981–2005
图 9 相对于1981~2005年基准期,RCP4.5(左列)和RCP8.5两种排放情景下,6个优选CMIP5模式集合预估的21世纪不同时期冬季雪水当量的相对变化:(a、b)21世纪早期(2016~2040年);(c、d)21世纪中期(2046~2070年);(e、f)21世纪后期(2076~2100年)
Figure 9. Percentage changes of winter mean SWE over the Eurasian continent as projected by MME(6): (a, b) Early 21st century (2016–2040); (c, d) middle 21st century (2046–2070); (e, f) late 21st century (2076–2100). Left panels are for RCP4.5 scenario, and right panels are for RCP8.5 scenario. The reference period used in this study is 1981–2005
表 1 26个CMIP5气候模式基本信息介绍
Table 1. Description of the 26 CMIP5 climate models used in this study
模式名称 空间分辨率(纬度×经度) 研发国家 ACCESS1.0 1.3°×1.9° 澳大利亚 ACCESS1.3 1.3°×1.9° 澳大利亚 BCC 2.8°×2.8° 中国 BCC-m 1.1°×1.1° 中国 CanESM2 2.8°×2.8° 加拿大 CCSM4 0.94°×1.3° 美国 CESM1-BGC 0.94°×1.3° 美国 CSIRO-Mk3.6.0 1.9°×1.9° 澳大利亚 FGOALS-g2 3.0°×2.8° 中国 FIO-ESM 2.8°×2.8° 中国 GFDL-CM3 2.0°×2.5° 美国 GFDL-ESM2G 2.0°×2.5° 美国 GFDL-ESM2M 2.0°×2.5° 美国 GISS-E2-H 2.0°×2.5° 美国 GISS-E2-R 2.0°×2.5° 美国 GISS-E2-H-CC 2.0°×2.5° 美国 GISS-E2-R-CC 2.0°×2.5° 美国 HadGEM2-AO 1.3°×1.9° 韩国 INMCM4 1.5°×2.0° 俄罗斯 MIROC5 1.4°×1.4° 日本 MIROC-ESM 2.8°×2.8° 日本 MIROC-ESM-CHEM 2.8°×2.8° 日本 MPI-ESM-LR 1.9°×1.9° 德国 MPI-ESM-MR 1.9°×1.9° 德国 MRI-CGCM3 1.1°×1.1° 日本 NorESM1-ME 1.9°×2.5° 挪威 表 2 CMIP5模式模拟与遥感反演的1981~2005年欧亚大陆冬季雪水当量间的偏差百分率、空间相关系数、空间标准差之比以及综合性能评估S指数
Table 2. Percentage biases and pattern correlation coefficients of winter mean SWE during 1981–2005 over the Eurasian continent between the 26 CMIP5 models and remote sensing data, and the ratio of the spatial standard deviations of the 26 CMIP5 models against that of remote sensing data, and the skill scores (S) of the CMIP models
模式名称 偏差百分率 空间相关系数 空间标准差之比 综合性能评估指数 ACCESS1.0 –30.76% 0.62 0.66 0.37 ACCESS1.3 –33.43% 0.61 0.72 0.38 BCC –7.75% 0.60 0.77 0.39 BCC-m 1.04% 0.64 1.00 0.45 CanESM2 –18.73% 0.66 0.67 0.41 CCSM4 –2.28% 0.65 1.00 0.46 CESM1-BGC –2.67% 0.66 1.02 0.47 CSIRO-Mk3.6.0 –35.18% 0.62 0.59 0.33 FGOALS-g2 37.3% 0.64 0.96 0.45 FIO-ESM 45.09% 0.56 0.72 0.33 GFDL-CM3 4.32% 0.26 1.34 0.15 GFDL-ESM2G –17.19% 0.40 1.21 0.23 GFDL-ESM2M –18.19% 0.42 1.09 0.25 GISS-E2-H 39.08% 0.25 1.89 0.10 GISS-E2-R 16.74% 0.39 1.19 0.23 GISS-E2-H-CC 33.08% 0.26 1.72 0.12 GISS-E2-R-CC 17.93% 0.38 1.22 0.22 HadGEM2-AO –31.88% 0.64 0.66 0.39 INMCM4 –4.83% 0.66 0.82 0.46 MIROC5 –6.48% 0.63 0.77 0.41 MIROC-ESM 13.48% 0.58 0.89 0.39 MIROC-ESM-CHEM 12.63% 0.59 0.86 0.39 MPI-ESM-LR –27.76% 0.71 0.74 0.49 MPI-ESM-MR –30.34% 0.69 0.73 0.47 MRI-CGCM3 8.08% 0.54 1.12 0.35 NorESM1-ME –4.72% 0.63 1.07 0.44 MME(26) –4.39% 0.60 0.95 0.41 表 3 26个CMIP5模式与遥感资料1981~2005年欧亚大陆冬季平均雪水当量EOF前两个模态的空间和时间相关系数,ACC1代表第一模态空间相关系数;ACC2代表第二模态空间相关系数;TCC1代表第一模态时间序列的相关系数;TCC2代表第二模态时间序列的相关系数
Table 3. Spatial and temporal correlation coefficients for first two EOF principal components of winter mean SWE during 1981-2005 over the Eurasian continent between CMIP5 models and remote sensing data. ACC1 and ACC2 indicate the spatial correlation coefficients for the first and second EOF principal components, respectively. TCC1 and TCC2 labels the temporal correlation coefficients for the first and second principal components, respectively
模式名称 EOF1 EOF2 ACC1 TCC1 ACC2 TCC2 ACCESS1.0 0.07 –0.48** 0.20 –0.29 ACCESS1.3 0.32 0.12 0.15 –0.14 BCC 0.05 0.03 0.18 –0.04 BCC-m 0.05 0.08 0.05 –0.07 CanESM2 0.24 –0.08 0.10 0.05 CCSM4 0.06 –0.03 0.21 –0.05 CESM1-BGC 0.13 0.41** 0.21 –0.05 CSIRO-Mk3.6.0 0.35 0.41** 0.07 –0.19 FGOALS-g2 0.29 0.08 -0.10 –0.05 FIO-ESM 0.01 –0.29 0.05 0.32 GFDL-CM3 0.22 0.48** 0.02 –0.03 GFDL-ESM2G 0.15 0.00 0.08 0.23 GFDL-ESM2M 0.20 –0.41** 0.21 –0.22 GISS-E2-H 0.00 –0.75** 0.10 –0.08 GISS-E2-R 0.01 0.38* 0.15 0.15 GISS-E2-H-CC -0.04 –0.72** 0.19 0.17 GISS-E2-R-CC 0.03 –0.64** 0.14 0.00 HadGEM2-AO 0.19 -0.09 0.22 –0.10 INMCM4 0.22 0.02 0.26 0.10 MIROC5 0.02 -0.19 –0.06 0.21 MIROC-ESM 0.10 0.15 0.14 –0.22 MIROC-ESM-CHEM 0.07 –0.27 0.06 –0.10 MPI-ESM-LR 0.07 –0.14 0.10 0.07 MPI-ESM-MR 0.08 0.05 0.13 0.17 MRI-CGCM3 0.32 –0.09 0.15 0.01 NorESM1-ME 0.08 –0.14 –0.06 0.03 MME(26) 0.03 0.56** 0.01 0.15 *、**分别表示通过了90%、95%信度检验。 表 4 用于预估的6个优选模式基本信息
Table 4. Descriptions of the six selected climate models used for projection
模式名称 模式全名 分辨率(纬度×经度) 研发国家 BCC-m Beijing Climate Center Climate System Model version 1.1 running on a Moderate Resolution 1.1°×1.1° 中国 CCSM4 Community Climate System Model version 4 0.94°×1.3° 美国 CESM-BGC Community Earth System Model, version1 Biogeochemistry 0.94°×1.3° 美国 INMCM4 Institute for Numerical Mathematics Climate Model version 4 1.5°×2.0° 俄罗斯 MIROC5 Model for Interdisciplinary Research on Climate 5 1.4°×1.4° 日本 NorESM1-ME Norwegian Earth System Model 1 running on Medium Resolution with capability to be fully Emission riven 1.9°×2.5° 挪威 -
[1] Armstrong R L, Brodzik M J, Knowles K, et al. 2007. Global monthly EASE-Grid snow water equivalent climatology[R]. Boulder, Colorado USA:National Snow and Ice Data Center. [2] Brown R D, Robinson D A. 2011. Northern Hemisphere spring snow cover variability and change over 1922-2010 including an assessment of uncertainty[J]. The Cryosphere Discussions, 5:219-229, doi: 10.5194/tc-5-219-2011. [3] 陈烈庭, 阎志新. 1978. 青藏高原冬春季积雪对大气环流和我国南方汛期降水的影响[C]//水文气象预报讨论会文集(第一集). 北京: 水利电力出版社, 185-194.Chen Lieting, Yan Zhixin. 1978. The effects of snow depth in winter-spring over Qinghai-Xizang Plateau on precipitation of Yangzte River[C]//Proceeding of Medium and Long Term Hydrological and Meteorological Forecast (in Chinese). Beijing:Water-Power Press, 185-194. [4] Déry S J, Brown R D. 2007. Recent Northern Hemisphere snow cover extent trends and implications for the snow-albedo feedback[J]. Geophys. Res. Lett., 34:L22504, doi: 10.1029/2007GL031474. [5] 符淙斌. 1980.北半球冬春冰雪面积变化与我国东北地区夏季低温的关系[J].气象学报, 38(2):187-192. doi: 10.11676/qxxb1980.023Fu Congbin. 1980. The relationship between the changes of snow extent in boreal spring and summer and summer low temperature in Northeast China[J]. Acta Meteorologica Sinica (in Chinese), 38(2):187-192, doi: 10.11676/qxxb1980.023. [6] Hirota N, Takayabu Y N, Watanabe M, et al. 2011. Precipitation reproducibility over tropical oceans and its relationship to the double ITCZ problem in CMIP3 and MIROC5 climate models[J]. J. Climate, 24:4859-4873, doi: 10.1175/2011JCLI4156.1. [7] Hosaka M, Nohara D, Kitoh A. 2005. Changes in snow cover and snow water equivalent due to global warming simulated by a 20 km-mesh global atmospheric model[J]. SOLA, 1:93-96, doi: 10.2151/sola.2005-025. [8] IPCC. 2007. Climate Change 2007:the Physical Science Basis[M]. Cambridge:Cambridge University Press, 2007:343-346. [9] 李培基. 2001.新疆积雪对气候变暖的响应[J].气象学报, 59(4):491-501. doi: 10.3321/j.issn:0577-6619.2001.04.011Li P J. 2001. Response of Xinjiang snow cover to climate change[J]. Acta Meteorologica Sinica (in Chinese), 59(4):491-501, doi: 10.3321/j.issn:0577-6619.2001.04.011. [10] Lin Z H, Zeng Q C, Ouyang B. 1996. Sensitivity of the IAP two-level AGCM to surface albedo variations[J]. Theor. Appl. Climatol., 55:157-162, doi: 10.1007/BF00864711. [11] Liu J L, Li Z, Huang L, et al. 2014. Hemispheric-scale comparison of monthly passive microwave snow water equivalent products[J]. Journal of Applied Remote Sensing, 8:084688, doi: 10.1117/1.JRS.8.084688. [12] 刘俊峰, 陈仁升, 宋耀选. 2012.中国积雪时空变化分析[J].气候变化研究进展, 8(5):364-371. doi: 10.3969/j.issn.1673-1719.2012.05.008Liu Junfeng, Chen Rensheng, Song Yaoxuan. 2012. Distribution and variation of snow cover in China[J]. Progressus Inquisitiones de Mutatione Climatis (in Chinese), 8(5):364-371, doi: 10.3969/j.issn.1673-1719.2012.05.008. [13] 马丽娟, 罗勇, 秦大河. 2011. CMIP3模式对未来50 a欧亚大陆雪水当量的预估[J].冰川冻土, 33(4):707-720. http://www.cnki.com.cn/Article/CJFDTOTAL-BCDT201104002.htmMa Lijuan, Luo Yong, Qin Dahe. 2011. Snow water equivalent over Eurasia in next 50 Years projected by CMIP3 models[J]. Journal of Glaciology and Geocryology(in Chinese), 33(4):707-720. http://www.cnki.com.cn/Article/CJFDTOTAL-BCDT201104002.htm [14] Meleshko V P, Kattsov V M, Govorkova V A, et al. 2005. Anthropogenic climate changes in the twenty-first century in northern Eurasia[J]. Russian Meteorology and Hydrology, 8(7):1-17. https://www.researchgate.net/publication/289133918_Anthropogenic_Climate_changes_in_the_twenty-first_century_in_northern_Eurasia [15] Qin D H, Liu S Y, Li P J. 2006. Snow cover distribution, variability, and response to climate change in western China[J]. J. Climate, 19:1820-1833, doi: 10.1175/JCLI3694.1. [16] 孙燕华, 黄晓东, 王玮, 等. 2014. 2003-2010年青藏高原积雪及雪水当量的时空变化[J].冰川冻土, 36(6):1337-1344. doi: 10.7522/j.issn.1000-0240.2014.0160Sun Yanhua, Huang Xiaodong, Wang Wei, et al. 2014. Spatio-temporal changes of snow cover and snow water equivalent in the Tibetan Plateau during 2003-2010[J]. Journal of Glaciology and Geocryology (in Chinese), 36(6):1337-1344, doi: 10.7522/j.issn.1000-0240.2014.0160. [17] Taylor K E, Stouffer R J, Meehl G A. 2011. An overview of CMIP5 and the experiment design[J]. Bull. Amer. Meteor. Soc., 93:485-498, doi: 10.1175/BAMS-D-11-00094.1. [18] Vavrus S. 2007. The role of terrestrial snow cover in the climate system[J]. Climate Dyn., 29:73-88, doi: 10.1007/s00382-007-0226-0. [19] Walker M D, Ingersoll R C, Webber P J. 1995. Effects of interannual climate variation on phenology and growth of two alpine forbs[J]. Ecology, 76:1067-1083, doi: 10.2307/1940916. [20] 王澄海, 王芝兰, 沈永平. 2010.新疆北部地区积雪深度变化特征及未来50a的预估[J].冰川冻土, 32(6):1059-1065. http://www.cnki.com.cn/Article/CJFDTOTAL-BCDT201006000.htmWang Chenghai, Wang Zhilan, Shen Yongping. 2010. A prediction of snow cover depth in the northern Xinjiang in the next 50 years[J]. Journal of Glaciology and Geocryology (in Chinese), 32(6):1059-1065. http://www.cnki.com.cn/Article/CJFDTOTAL-BCDT201006000.htm [21] 王秋香, 张春良, 刘静, 等. 2009.北疆积雪深度和积雪日数的变化趋势[J].气候变化研究进展, 5(1):39-43. doi: 10.3969/j.issn.1673-1719.2009.01.008Wang Qiuxiang, Zhang Chunliang, Liu Jing, et al. 2009. The changing tendency on the depth and days of snow cover in northern Xinjiang[J]. Advances in Climate Change Research (in Chinese), 5(1):39-43, doi:10.3969/j.issn.1673-1719.2009. 01.008. [22] 王芝兰, 王澄海. 2012. IPCC AR4多模式对中国地区未来40 a雪水当量的预估[J].冰川冻土, 34(6):1273-1283. http://www.cnki.com.cn/Article/CJFDTOTAL-BCDT201206002.htmWang Zhilan, Wang Chenghai. 2012. Predicting the snow water equivalent over China in the next 40 Years based on climate models from IPCC AR4[J]. Journal of Glaciology and Geocryology (in Chinese), 34(6):1273-1283. http://www.cnki.com.cn/Article/CJFDTOTAL-BCDT201206002.htm [23] 韦志刚, 罗四维, 董文杰, 等. 1998.青藏高原积雪资料分析及其与我国夏季降水的关系[J].应用气象学报, 9(S1):39-46. http://www.cnki.com.cn/Article/CJFDTOTAL-YYQX8S1.005.htmWei Zhigang, Luo Siwei, Dong Wenjie, et al. 1998. Snow cover data on Qinghai-Xizang Plateau and its correlation with summer rainfall in China[J]. Quarterly Journal of Applied Meteorology (in Chinese), 9(S1):39-46. http://www.cnki.com.cn/Article/CJFDTOTAL-YYQX8S1.005.htm [24] 韦志刚, 黄荣辉, 陈文, 等. 2002.青藏高原地面站积雪的空间分布和年代际变化特征[J].大气科学, 26(4):496-508. doi: 10.3878/j.issn.1006-9895.2002.04.07Wei Zhigang, Huang Ronghui, Chen Wen, et al. 2002. Spatial distributions and interdecadal variations of the snow at the Tibetan Plateau weather stations[J]. Chinese Journal of Atmospheric Sciences (in Chinese), 26(4):496-508, doi: 10.3878/j.issn.1006-9895.2002.04.07. [25] Wu R G, Kirtman B P. 2007. Observed relationship of spring and summer East Asian rainfall with winter and spring Eurasian snow[J]. J. Climate, 20:1285-1304, doi: 10.1175/JCLI4068.1. [26] 吴统文, 钱正安. 2000.青藏高原冬春积雪异常与中国东部地区夏季降水关系的进一步分析[J].气象学报, 58(5):570-581. doi: 10.3321/j.issn:0577-6619.2000.05.006Wu Tongwen, Qian Zheng'an. 2000. Further analyses of the linkage between winter and spring snow depth anomaly over Qinghai-Xizang Plateau and summer rainfall of eastern China[J]. Acta Meteorologica Sinica (in Chinese), 58(5):570-581, doi: 10.3321/j.issn:0577-6619.2000.05.006. [27] Wu T W, Qian Z G. 2003. The relation between the Tibetan winter snow and the Asian summer monsoon and rainfall:An observational investigation[J]. J. Climate, 16:2038-2051, doi:10.1175/1520-0442(2003)016<2038:TRBTTW>2.0.CO;2. [28] 张若楠, 张人禾, 左志燕. 2014.中国冬季多种积雪参数的时空特征及差异性[J].气候与环境研究, 19(5):572-586. doi: 10.3878/j.issn.1006-9585.2013.13063Zhang Ruonan, Zhang Renhe, Zuo Zhiyan. 2014. Characteristics and differences of multi-snow data in winter over China[J]. Climatic and Environmental Research (in Chinese), 19(5):572-586, doi: 10.3878/j.issn.1006-9585.2013.13063. [29] Zhao P, Zhou Z J, Liu J P. 2007. Variability of Tibetan spring snow and its associations with the hemispheric extratropical circulation and East Asian summer monsoon rainfall:An observational investigation[J]. J. Climate, 20:3942-3955, doi: 10.1175/JCLI4205.1. [30] 朱献, 董文杰. 2013. CMIP5耦合模式对北半球3-4月积雪面积的历史模拟和未来预估[J].气候变化研究进展, 9(3):173-180. doi: 10.3969/j.issn.1673-1719.2013.03.003Zhu Xian, Dong Wenjie. 2013. Evaluation and projection of northern hemisphere March-April snow covered Area simulated by CMIP5 coupled climate models[J]. Progressus Inquisitiones de Mutatione Climatis (in Chinese), 9(3):173-180, doi: 10.3969/j.issn.1673-1719.2013.03.003. [31] Zuo Z Y, Zhang R H, Wu B Y, et al. 2011. Decadal variability in springtime snow over Eurasia:Relation with circulation and possible influence on springtime rainfall over China[J]. International Journal of Climatology, 32:1336-1345, doi: 10.1002/joc.2355. -