论文

基于动力降尺度方法预估的青藏高原降水变化

  • 张宏文 ,
  • 高艳红
展开
  • <sup>1.</sup>中国科学院西北生态环境资源研究院/中国科学院寒旱区陆面过程与气候变化重点实验室, 甘肃 兰州 730000;<sup>2.</sup>复旦大学 大气与海洋科学系/大气科学研究院, 上海 200438;<sup>3.</sup>中国科学院大学, 北京 100049

收稿日期: 2019-11-28

  网络出版日期: 2020-06-28

基金资助

中国科学院战略性先导科技专项(XDA2006010202);第二次青藏高原科考项目(2019QZKK010314);国家自然科学基金项目(91537105);中国科学院西北生态环境资源研究院青年基金项目(Y851D51001)

Projected Changes of Precipitation over the Qinghai-Tibetan Plateau Based on Dynamical Downscaling

  • Hongwen ZHANG ,
  • Yanhong GAO
Expand
  • <sup>1.</sup>Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Ecology and Environmental Resources, Chinese Academy of Sciences, Lanzhou 730000, Gansu, China;<sup>2.</sup>Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China;<sup>3.</sup>University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2019-11-28

  Online published: 2020-06-28

摘要

当前的全球气候模式分辨率较低, 难以合理再现青藏高原降水的时空分布特征, 动力降尺度方法成为一种有效的手段。本文利用全球气候模式CCSM4的输出结果驱动区域气候模式WRF进行动力降尺度模拟, 评估了动力降尺度对青藏高原湿季总降水率和对流降水比例的模拟能力, 对比了CCSM4和WRF模式预估的青藏高原湿季总降水、 层云降水和对流降水变化差异。结果表明, 相比于驱动数据, 动力降尺度模拟能更好地再现1998 -2005年青藏高原湿季总降水率和对流降水比例空间分布及随海拔分布的特征; WRF模式预估的高原未来(2070 -2099年)湿季总降水增加, 但空间上呈“北增南减”的变化特征, 对流降水的增加导致高原北部总降水增加, 而层云降水的减少导致高原南部总降水减少。整体而言, 对流降水的增加大于层云降水的减少, 且主要发生在海拔4000 m以下。

本文引用格式

张宏文 , 高艳红 . 基于动力降尺度方法预估的青藏高原降水变化[J]. 高原气象, 2020 , 39(3) : 477 -485 . DOI: 10.7522/j.issn.1000-0534.2019.00125.

Abstract

The General Circulation Models (GCMs) are not easy to reproduce the temporal-spatial patterns of precipitation in Qinghai-Tibetan Plateau (QTP) due to its current coarse resolution.To meet the requirement of high-resolution datasets for many applications, dynamical downscaling modeling (DDM) has been developed and proven to be an essential tool for achieving high-resolution climate data in study domain.DDM using a regional climate model WRF driven by a general circulation model CCSM4 has been adopted, the downscaling results for the historical period (1998 -2005) are evaluated for the wet season total precipitation rate and convective precipitation fraction over the QTP.The variations of total precipitation, stratiform precipitation and convective precipitation projected by CCSM4 and WRF are also analyzed.The results show that, compared with the coarse-resolution forcing, the DDM is able to better capture the spatial and elevation patterns of wet season total precipitation rate and convective precipitation fraction over the QTP in 1998 -2005.Compared with the uniform increase in CCSM, WRF also projects increasing precipitation for the future period 2070 -2099 under the two Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5, with an increase in the northern QTP and a decrease in the southern QTP, the increase of total precipitation in northern QTP basically resulted from the increased convective precipitation, while the reduction of stratiform precipitation was the main reason for the reduced total precipitation in the southern QTP.Overall, regarding the entire TP, the contribution of increased convective precipitation was far larger than reduced stratiform precipitation to the total precipitation, and which was predominantly seen at the altitude below 4000 m.

参考文献

[1]Cook K H, Meehl G A, Arblaster J M, 2012.Monsoon regimes and processes in CCSM4.Part II: African and American monsoon systems[J].Journal of Climate, 25(8): 2609-2621.DOI: 10.1175/JCLI-D-11-00185.1.
[2]Cuo L, Zhang Y, 2017.Spatial patterns of wet season precipitation vertical gradients on the Tibetan Plateau and the surroundings[J].Scientific Reports, 7: 5057.DOI: 10.1038/s41598-017-05345-6.
[3]El-Samra R, Bou-Zeid E, Bangalath H K, al et, 2017.Future intensification of hydro-meteorological extremes: Downscaling using the weather research and forecasting model[J].Climate Dynamics, 49: 3765-3785, DOI: 10.1007/s00382-017-3542-z.
[4]Gao G, Chen Q, Cai H, al et, 2019.Comprehensive characteristics of summer deep convection over Tibetan Plateau and its south slope from the global precipitation measurement core observatory[J].Atmosphere, 10(1): 9.DOI: 10.3390/atmos10010009.
[5]Gao Y, Li X, Leung L R, al et, 2015a.Aridity changes in the Tibetan Plateau in a warming climate[J].Environmental Research Letters, 10: 034013.DOI: 10.1088/1748-9326/10/3/034013.
[6]Gao Y, Xu J, Chen D, 2015b.Evaluation of WRF mesoscale climate simulations over the Tibetan Plateau during 1979-2011[J].Journal of Climate, 28: 2823-2841.DOI: 10.1175/JCLI-D-14-00300.1.
[7]Gao Y, Xiao L, Chen D, al et, 2017.Comparison between past and future extreme precipitations simulated by global and regional climate models over the Tibetan Plateau[J].International Journal of Climatology, 38: 1285-1297.DOI: 10.1002/joc.5243.
[8]Immerzeel W W, Van Beek L P H, Bierkens M F P, 2010.Climate change will affect the Asian water towers[J].Science, 328 (5984): 1382-1385.DOI: 10.1126/science.1183188.
[9]Maussion F, Scherer D, Thomas M, al et, 2013.Precipitation seasonality and variability over the Tibetan Plateau as resolved by the High Asia Reanalysis[J].Journal of Climate, 27(5): 1910-1927.DOI: 10.1175/JCLI-D-13-00282.1.
[10]Sato T, Yoshikane T, Satoh M, al et, 2008.Resolution dependency of the diurnal cycle of convective clouds over the Tibetan Plateau in a mesoscale model[J].Journal of the Meteorological Society of Japan, 86: 17-31.
[11]Skamarock W C, Klemp J B, Dudhia J, al et, 2008.A description of the advanced research WRF version 3, NCAR Technical Note[M].Boulder, CO, USA: National Center for Atmospheric Research.
[12]Sugimoto S, Ueno K, 2010.Formation of mesoscale convective systems over the eastern Tibetan Plateau affected by plateau-scale heating contrasts[J].Journal of Geophysical Research: Atmospheres, 115: D16105.DOI: 10.1029/2009JD013609.
[13]Su F, Duan X, Chen D, al et, 2013.Evaluation of the global climate models in the CMIP5 over the Tibetan Plateau[J].Journal of Climate, 26(10): 3187-3208.DOI: 10.1175/JCLI-D-12-00321.1.
[14]Wang C, Shi H, Hu H, al et, 2015.Properties of cloud and precipitation over the Tibetan Plateau[J].Advances in Atmospheric Sciences, 32(11): 1504-1516.DOI: 10.1007/s00376-015-4254-0.
[15]Xu J, Gao Y, Chen D, al et, 2017.Evaluation of global climate models for downscaling applications centred over the Tibetan Plateau[J].International Journal of Climatology, 37: 657-671, DOI: 10.1002/joc.4731.
[16]Yao T, Thompson L, Yang W, al et, 2012.Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings[J].Nature Climate Change, 2(9): 663-667.DOI: 10.1038/nclimate1580.
[17]蔡英, 李栋梁, 汤懋苍, 等, 2003.青藏高原近50年来气温的年代际变化[J].高原气象, 22(5): 464-470.
[18]杜振彩, 黄荣辉, 黄刚, 等, 2011.亚洲季风区积云降水和层云降水时空分布特征及其可能成因分析[J].大气科学, 35(6): 993-1008.
[19]傅云飞, 李宏图, 自勇, 2007.TRMM卫星探测青藏高原谷地的降水云结构个例分析[J].高原气象, 26(1): 98-106.
[20]高学杰, 石英, Giorgi F, 2010.中国区域气候变化的一个高分辨率数值模拟[J].中国科学(地球科学), 40(7): 911-922.DOI: 10.1007/s11430-010-4035-7.
[21]郝振纯, 童凯, 张磊磊, 等, 2011.TRMM降水资料在青藏高原的适用性分析[J].水文, 31(5): 18-23.
[22]胡亮, 李耀东, 杨松, 等, 2011.东亚热带与副热带季风区对流降水和层云降水季节变化特征对比分析研究[J].中国科学(地球科学), 41(8): 1182-1191.
[23]李振朝, 韦志刚, 吕世华, 等, 2013.CMIP5部分模式气温和降水模拟结果在北半球及青藏高原的检验[J].高原气象, 32(4): 921-928.DOI: 10.7522/j.issn.1000-0534.2012.00088.
[24]刘奇, 傅云飞, 2007.基于TRMM/TMI的亚洲夏季降水研究[J].中国科学(地球科学), 37(1): 111-122.
[25]罗小青, 杨梅学, 王学佳, 等, 2014.两种积云参数化方案对青藏高原夏季降水影响的模拟[J].高原气象, 33(2): 313-322.DOI: 10.7522/j.issn.1000-0534.2013.00177.
[26]齐文文, 张百平, 庞宇, 等, 2013.基于TRMM数据的青藏高原降水的空间和季节分布特征[J].地理科学, 33(8): 999-1005.DOI: 1000-0690(2013)08-0999-07.
[27]孙礼璐, 王瑞, 谭瑞婷, 等, 2019.基于TRMM PR和VIRS探测的青藏高原夏季横切变线云降水个例分析[J].高原气象, 38(6): 1194-1207.DOI:10.7522/j.issn.1000-0534.2018.00160.
[28]田芝平, 姜大膀, 张冉, 等, 2012.CCSM4.0的长期积分试验及其对东亚和中国气候模拟的评估[J].大气科学, 36(3): 619-632.DOI: 10.3878/j.issn.1006-9895.2011.11092.
[29]王梦晓, 王瑞, 傅云飞, 2019.利用TRMM PR和IGRA探测分析的拉萨降水云内大气温湿廓线特征[J].高原气象, 38(3): 539-551.DOI: 10.7522 /j.issn.1000-0534.2019.00011.
[30]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.
[31]吴国雄, 段安民, 张雪芹, 等, 2013.青藏高原极端天气气候变化及其环境效应[J].自然杂志, 35(3): 167-171.DOI: 10.3969/j.issn.0253-9608.2013.03.002.
[32]王晓聪, 包庆, 刘琨, 等, 2012.两种对流参数化方案下降水和潜热加热空间结构的模拟及其影响[J].中国科学(地球科学), 42(4): 587-598.DOI: 10.1007/s11430-011-4282-2.
[33]徐丽娇, 胡泽勇, 赵亚楠,等, 2019.1961-2010年青藏高原气候变化特征分析[J].高原气象, 38(5): 911-919.DOI: 10.7522 /j.issn.1000-0534.2018.00137.
[34]徐忠峰, 韩瑛, 杨宗良, 2019.区域气候动力降尺度方法研究综述[J].中国科学(地球科学), 49(3): 487-498.DOI: 10.1360/N072018-00075.
[35]姚隽琛, 周天军, 邹立维, 2018.基于气候系统模式 FGOALS-g2 的热带气旋活动及其影响的动力降尺度模拟[J].大气科学, 42(1): 150-163.DOI: 10.3878/j.issn.1006-9895.1704.17129.
[36]张人禾, 苏凤阁, 江志红, 等, 2015.青藏高原21世纪气候和环境变化预估研究进展[J].科学通报, 60(32): 3036-3047.DOI: 10.1360/N972014-01296.
文章导航

/