高级检索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

中国区域夏季地表气温与陆面过程耦合强度的分布特征

杨洋 林朝晖 骆利峰

杨洋, 林朝晖, 骆利峰. 2022. 中国区域夏季地表气温与陆面过程耦合强度的分布特征[J]. 气候与环境研究, 27(3): 333−350 doi: 10.3878/j.issn.1006-9585.2021.21060
引用本文: 杨洋, 林朝晖, 骆利峰. 2022. 中国区域夏季地表气温与陆面过程耦合强度的分布特征[J]. 气候与环境研究, 27(3): 333−350 doi: 10.3878/j.issn.1006-9585.2021.21060
YANG Yang, LIN Zhaohui, LUO Lifeng. 2022. Spatial Characteristics of Surface Air Temperature–Land Surface Coupling Strength during Summer in China [J]. Climatic and Environmental Research (in Chinese), 27 (3): 333−350 doi: 10.3878/j.issn.1006-9585.2021.21060
Citation: YANG Yang, LIN Zhaohui, LUO Lifeng. 2022. Spatial Characteristics of Surface Air Temperature–Land Surface Coupling Strength during Summer in China [J]. Climatic and Environmental Research (in Chinese), 27 (3): 333−350 doi: 10.3878/j.issn.1006-9585.2021.21060

中国区域夏季地表气温与陆面过程耦合强度的分布特征

doi: 10.3878/j.issn.1006-9585.2021.21060
基金项目: 国家重点研发计划项目2017YFA0604304
详细信息
    作者简介:

    杨洋,女,1992年出生,博士,主要从事陆气相互作用研究。E-mail: yangy@mail.iap.ac.cn

    通讯作者:

    林朝晖,E-mail: lzh@mail.iap.ac.cn

  • 中图分类号: P467

Spatial Characteristics of Surface Air Temperature–Land Surface Coupling Strength during Summer in China

Funds: National Key Research and Development Program of China (Grant 2017YFA0604304)
  • 摘要: 鉴于陆气相互作用对极端高温事件的重要作用,利用ERA5-Land陆面再分析资料,分析了基于敏感度的中国区域夏季地表气温与土壤湿度、土壤温度耦合强度的多时间尺度分布特征。研究结果表明,基于不同耦合过程指标定义的陆气耦合强度在中国区域呈不同的空间分布特征,其中基于潜热通量的陆面变量—地表气温耦合的“热点”区域主要分布在中国的西北地区和长江流域,基于感热通量的陆面变量—地表气温耦合的“热点”区域则主要分布在河套—内蒙古地区、新疆西南部地区,以及长江以南部分区域。这说明夏季地表气温对陆面变量异常的敏感度的区域差异,与不同区域陆面异常影响地表气温的主导过程密切相关。此外,陆气耦合强度的强弱还随时间尺度而变化,其中月—季节尺度的陆气耦合强度要明显弱于日、候及旬尺度的耦合强度;就日、候及旬时间尺度而言,基于潜热交换过程的陆面变量—地表气温的耦合强度在全国大部分地区随时间尺度的增加而减弱,基于感热通量的陆气耦合强度在南方大部分地区也随时间尺度的增加逐渐减弱,但在北方大部分地区则表现为随时间尺度增加逐渐增强。相比较于表层土壤湿度与地表气温的耦合强度,次表层土壤湿度与地表气温的耦合强度在中国西北地区明显减弱。
  • 图  1  中国区域1981~2013年夏季月尺度的表层土壤—地表气温耦合强度$ I $(单位:°C)的空间分布:(a)$ {I}_{\mathrm{S}\mathrm{M}-\mathrm{L}\mathrm{H}} $;(b)$ {I}_{\mathrm{S}\mathrm{M}-\mathrm{S}\mathrm{H}} $;(c)$ {I}_{\mathrm{S}\mathrm{T}-\mathrm{L}\mathrm{H}} $;(d)$ {I}_{\mathrm{S}\mathrm{T}-\mathrm{S}\mathrm{H}} $$ I $的绝对值越大代表耦合强度越强

    Figure  1.  Spatial distribution of the monthly surface soil–surface air temperature coupling strength (°C) in China during the summer of 1981–2013: (a) ${I}_{{\rm{SM}}-{\rm{LH}}}$; (b) ${I}_{{\rm{SM}}-{\rm{SH}}}$; (c) ${I}_{{\rm{ST}}-{\rm{LH}}}$; (d) ${I}_{{\rm{ST}}-{\rm{SH}}}$. The greater the absolute value, the stronger the coupling strength

    图  2  中国区域1981~2013年夏季月尺度的表层(a)土壤湿度—潜热通量相关系数($ {r}_{\mathrm{S}\mathrm{M}-\mathrm{L}\mathrm{H}} $)、(b)土壤湿度—感热通量相关系数($ {r}_{\mathrm{S}\mathrm{M}-\mathrm{S}\mathrm{H}} $)、(c)土壤温度—潜热通量相关系数($ {r}_{\mathrm{S}\mathrm{T}-\mathrm{L}\mathrm{H}} $)和(d)土壤温度—感热通量相关系数($ {r}_{\mathrm{S}\mathrm{T}-\mathrm{S}\mathrm{H}} $)的空间分布。打点区域代表相关系数通过了置信水平为95%的显著性检验

    Figure  2.  Spatial distribution of the monthly correlation coefficients between surface (a) soil moisture and latent heat flux ($ {r}_{\mathrm{S}\mathrm{M}-\mathrm{L}\mathrm{H}} $), (b) soil moisture and sensible heat flux ($ {r}_{\mathrm{S}\mathrm{M}-\mathrm{S}\mathrm{H}} $), (c) soil temperature and latent heat flux ($ {r}_{\mathrm{S}\mathrm{T}-\mathrm{L}\mathrm{H}} $), and (d) soil temperature and sensible heat flux ($ {r}_{\mathrm{S}\mathrm{T}-\mathrm{S}\mathrm{H}} $), in the summer of 1981–2013 over China. Areas with black dots indicate the correlation coefficients are significant at 95% confidence level

    图  3  中国区域1981~2013年夏季月尺度的(a)潜热通量—地表气温相关系数($ {r}_{\mathrm{L}\mathrm{H}-\mathrm{T}} $)、(b)感热通量—地表气温相关系数($ {r}_{\mathrm{S}\mathrm{H}-\mathrm{T}} $)和(c)地表气温标准差(单位:°C)的空间分布。(a、b)中打点区域代表相关系数通过了置信水平为95%的显著性检验

    Figure  3.  Spatial distribution of the monthly correlation coefficients between (a) latent heat flux and surface air temperature ($ {r}_{\mathrm{L}\mathrm{H}-\mathrm{T}} $), (b) sensible heat flux and surface air temperature ($ {r}_{\mathrm{S}\mathrm{H}-\mathrm{T}} $), as well as the (c) standard deviation of surface air temperature (°C) , in the summer of 1981–2013 over China. Areas with black dots in (a–b) indicate the correlation coefficients are significant at 95% confidence level

    图  4  中国区域1981~2013年夏季(a、d)日、(b、e)候、(c、f)旬尺度的表层土壤湿度—地表气温耦合强度$ I $(单位:°C)的气候态分布:(a–c) $ {I}_{\mathrm{S}\mathrm{M}-\mathrm{L}\mathrm{H}} $;(d–f)$ {I}_{\mathrm{S}\mathrm{M}-\mathrm{S}\mathrm{H}} $。耦合强度(g、h) $ {I}_{\mathrm{S}\mathrm{M}-\mathrm{L}\mathrm{H}} $、(i、j) $ {I}_{\mathrm{S}\mathrm{M}-\mathrm{S}\mathrm{H}} $在不同时间尺度上的差异:(g、i)日与候尺度的差异;(h、j)候与旬尺度的差异。蓝色调表示耦合强度减弱,红色调表示耦合强度增强

    Figure  4.  Spatial distribution of the multi-timescales (a, d) daily, (b, e) pentad, and (e, f) ten-day surface soil moisture–surface air temperature coupling strength (°C) in China during the summer of 1981–2013, including the metrics of (a–c) $ {I}_{\mathrm{S}\mathrm{M}-\mathrm{L}\mathrm{H}} $, and (d–f) $ {I}_{\mathrm{S}\mathrm{M}-\mathrm{S}\mathrm{H}} $, and the differences between three timescales for (g–h) $ {I}_{\mathrm{S}\mathrm{M}-\mathrm{L}\mathrm{H}} $ and (i–j) $ {I}_{\mathrm{S}\mathrm{M}-\mathrm{S}\mathrm{H}} $. The difference between daily and pentad scales is shown in (g, i); while the difference between pentad and ten-day scales is shown in (h, j). Blue and red tones indicate the decrease and increase in coupling strength, respectively

    图  5  中国区域1981~2013年夏季(a、d)日、(b、e)候、(c、f)旬尺度的表层土壤温度—地表气温耦合强度$ I $(单位:°C)的气候态分布:(a–c)${I}_{{\rm{ST}}-{\rm{LH}}}$;(d–f)${I}_{{\rm{ST}}-{\rm{SH}}}$。耦合强度(g、h)${I}_{{\rm{ST}}-{\rm{LH}}}$、(i、j)${I}_{{\rm{ST}}-{\rm{SH}}}$在不同时间尺度上的差异:(g、i)日与候尺度的差异;(h、j)候与旬尺度的差异。蓝色调表示耦合强度减弱,红色调表示耦合强度增强

    Figure  5.  Spatial distribution of the multi-timescales (a, d) daily, (b, e) pentad, and (c, f) ten-day surface soil temperature-surface air temperature coupling strength (°C) in China during the summer of 1981–2013, including the metrics of (a–c) ${I}_{{\rm{ST}}-{\rm{LH}}}$, (d–f) ${I}_{{\rm{ST}}-{\rm{SH}}}$, and the differences between three timescales for (g–h) ${I}_{{\rm{ST}}-{\rm{LH}}}$ and (i–j) ${I}_{{\rm{ST}}-{\rm{SH}}}$. The difference between daily and pentad scales is shown in (g, i); while the difference between pentad and ten-day scales is shown in (h, j). Blue and red tones indicate the decrease and increase in coupling strength, respectively

    图  6  中国区域1981~2013年夏季日(左)—候(中)—旬(右)尺度的表层(a–c)土壤湿度与潜热通量相关系数(${r}_{{\rm{SM}}-{\rm{LH}}}$)、(d–f)土壤湿度与感热通量相关系数(${r}_{{\rm{SM}}-{\rm{SH}}}$)、(g–i)土壤温度与潜热通量相关系数(${r}_{{\rm{ST}}-{\rm{LH}}}$)和(j–l)土壤温度与感热通量相关系数(${r}_{{\rm{ST}}-{\rm{SH}}}$)的空间分布。打点区域代表相关系数通过了置信水平为95%的显著性检验

    Figure  6.  Spatial distribution of daily (left), pentad (middle), and ten-day (right) correlation coefficients between the surface (a–c) soil moisture and latent heat flux (${r}_{{\rm{SM}}-{\rm{LH}}}$), (d–f) soil moisture and sensible heat flux (${r}_{{\rm{SM}}-{\rm{SH}}}$), (g–i) soil temperature and latent heat flux (${r}_{{\rm{ST}}-{\rm{LH}}}$), and (j–l) soil temperature and sensible heat flux (${r}_{{\rm{ST}}-{\rm{SH}}}$), during the summer of 1981–2013 in China. Areas with black dots indicate that the correlation coefficients are significant at 95% confidence level

    图  7  中国区域1981~2013年夏季日(左)—候(中)—旬(右)尺度的(a–c)潜热通量与地表气温相关系数(${r}_{{\rm{LH}}-{\rm{T}}}$)、(d–f)感热通量与地表气温相关系数(${r}_{{\rm{SH}}-{\rm{T}}}$)和(g–i)地表气温标准差(单位:°C)的空间分布。(a–f)中打点区域代表相关系数通过了置信水平为95%的显著性检验

    Figure  7.  Spatial distribution of daily (left), pentad (middle), and ten-day (right) correlation coefficients between (a–c) latent heat flux and surface air temperature (${r}_{{\rm{LH}}-{\rm{T}}}$), (d–f) sensible heat flux and surface air temperature (${r}_{{\rm{SH}}-{\rm{T}}}$), and the (g–i) standard deviation of surface air temperature (°C), during the summer of 1981–2013 in China. Areas with black dots in (a–f) indicate the correlation coefficients are significant at 95% confidence level

    图  8  中国区域1981~2013年夏季月尺度的次表层(a–d)土壤—地表气温耦合强度的空间分布以及(e–h)对应指标的次表层与表层耦合强度的差异(单位:°C):(a、e)${I}_{{\rm{SM}}-{\rm{LH}}}$;(b、f)${I}_{{\rm{SM}}-{\rm{SH}}}$;(c、g)${I}_{{\rm{ST}}-{\rm{LH}}}$;(d、h)${I}_{{\rm{ST}}-{\rm{SH}}}$。(e–h)中蓝色调代表耦合强度减弱,红色调代表耦合强度增强

    Figure  8.  Spatial distribution of the monthly (a–d) subsurface soil–surface air temperature coupling strength (°C) and (e–h) the corresponding differences between subsurface and surface coupling strength for each metric in China during the summer of 1981–2013: (a, e) ${I}_{{\rm{SM}}-{\rm{LH}}}$, (b, f) ${I}_{{\rm{SM}}-{\rm{SH}}}$, (c, g) ${I}_{{\rm{ST}}-{\rm{LH}}}$, and (d, h) ${I}_{{\rm{ST}}-{\rm{SH}}}$. Blue and red tones in (e–h) indicate the decrease and increase in coupling strength, respectively

    图  9  中国区域1981~2013年夏季月尺度的次表层相关系数的空间分布:(a)${r}_{{\rm{SM}}-{\rm{LH}}}$;(b)${r}_{{\rm{SM}}-{\rm{SH}}}$;(c)${r}_{{\rm{ST}}-{\rm{LH}}}$;(d)${r}_{{\rm{ST}}-{\rm{SH}}}$。其中打点区域代表相关系数通过了置信水平为95%的显著性检验

    Figure  9.  Spatial distribution of the monthly correlation coefficients between subsurface (a) soil moisture and latent heat flux (${r}_{{\rm{SM}}-{\rm{LH}}}$), (b) soil moisture and sensible heat flux (${r}_{{\rm{SM}}-{\rm{SH}}}$), (c) soil temperature and latent heat flux (${r}_{{\rm{ST}}-{\rm{LH}}}$), and (d) soil temperature and sensible heat flux (${r}_{{\rm{ST}}-{\rm{SH}}}$), in the summer of 1981–2013 in China. Areas with black dots indicate the correlation coefficients are significant at 95% confidence level

    图  10  中国区域1981~2013年夏季平均的(a)表层和(b)次表层土壤湿度的空间分布

    Figure  10.  Spatial distribution of the summer-averaged (a) surface and (b) subsurface soil moisture in China during 1981–2013

    图  11  1981~2013年夏季中国西北部(36°N~48°N,72°E~107°E)区域平均的次表层和表层土壤湿度(单位:m3/m3)的年际变化曲线。其中实线对应次表层的土壤湿度,虚线则为表层结果

    Figure  11.  Time series of regional-averaged surface and subsurface soil moisture during the summer of 1981–2013 in Northwest China (NWC; 36°N–48°N and 72°E–107°E). Solid and dashed lines represent the soil moisture of subsurface and surface layers, respectively

  • [1] 戴永久, 曾庆存. 1996. 陆面过程研究 [J]. 水科学进展, 7(S1): 40−53.

    Dai Yongjiu, Zeng Qingcun. 1996. Study on land surface process [J]. Advances in Water Science (in Chinese), 7(S1): 40−53.
    [2] Diffenbaugh N S, Singh D, Mankin J S, et al. 2017. Quantifying the influence of global warming on unprecedented extreme climate events [J]. Proc. Natl. Acad. Sci. USA, 114(19): 4881−4886. doi: 10.1073/pnas.1618082114
    [3] Dirmeyer P A. 2011. The terrestrial segment of soil moisture-climate coupling [J]. Geophys. Res. Lett., 38(16): L16702. doi: 10.1029/2011GL048268
    [4] Dirmeyer P A, Wang Z Y, Mbuh M J, et al. 2014. Intensified land surface control on boundary layer growth in a changing climate [J]. Geophys. Res. Lett., 41(4): 1290−1294. doi: 10.1002/2013GL058826
    [5] 杜娟, 文莉娟, 苏东生. 2020. 青藏高原不同深度湖泊无冰期湖气温差及湖表辐射与能量平衡特征模拟分析 [J]. 高原气象, 39(6): 1181−1194. doi: 10.7522/j.issn.1000-0534.2019.00133

    Du Juan, Wen Lijuan, Su Dongsheng. 2020. Analysis of simulated temperature difference between lake surface and air and energy balance of three alpine lakes with different depths on the Qinghai-Xizang plateau during the ice-free period [J]. Plateau Meteorology (in Chinese), 39(6): 1181−1194. doi: 10.7522/j.issn.1000-0534.2019.00133
    [6] Ford T W, Quiring S M. 2014. In situ soil moisture coupled with extreme temperatures: A study based on the Oklahoma Mesonet [J]. Geophys. Res. Lett., 41(13): 4727−4734. doi: 10.1002/2014GL060949
    [7] Gao C J, Chen H S, Sun S L, et al. 2018. Regional features and seasonality of land–atmosphere coupling over eastern China [J]. Adv. Atmos. Sci., 35(6): 689−701. doi: 10.1007/s00376-017-7140-0
    [8] Gevaert A I, Miralles D G, De Jeu R A M, et al. 2018. Soil moisture–temperature coupling in a set of land surface models [J]. J. Geophys. Res., 123(3): 1481−1498. doi: 10.1002/2017JD027346
    [9] 管晓丹, 石瑞, 孔祥宁, 等. 2018. 全球变化背景下半干旱区陆气机制研究综述 [J]. 地球科学进展, 33(10): 995−1004. doi: 10.11867/j.issn.1001-8166.2018.10.0995

    Guan Xiaodan, Shi Rui, Kong Xiangning, et al. 2018. An overview of researches on land–atmosphere interaction over semi-arid region under global changes [J]. Advances in Earth Science (in Chinese), 33(10): 995−1004. doi: 10.11867/j.issn.1001-8166.2018.10.0995
    [10] 郭东林, 杨梅学. 2010. SHAW模式对青藏高原中部季节冻土区土壤温、湿度的模拟 [J]. 高原气象, 29(6): 1369−1377.

    Guo Donglin, Yang Meixue. 2010. Simulation of soil temperature and moisture in seasonally frozen ground of central Tibetan Plateau by SHAW model [J]. Plateau Meteorology (in Chinese), 29(6): 1369−1377.
    [11] Hersbach H, Bell B, Berrisford P, et al. 2020. The ERA5 global reanalysis [J]. Quart. J. Roy. Meteor. Soc., 146(730): 1999−2049. doi: 10.1002/qj.3803
    [12] Jaeger E B, Seneviratne S I. 2011. Impact of soil moisture–atmosphere coupling on European climate extremes and trends in a regional climate model [J]. Climate Dyn., 36(9-10): 1919−1939. doi: 10.1007/s00382-010-0780-8
    [13] Koster R D, Dirmeyer P A, Guo Z C, et al. 2004. Regions of strong coupling between soil moisture and precipitation [J]. Science, 305(5687): 1138−1140. doi: 10.1126/science.1100217
    [14] 李军, 孙春宝, 刘咸德, 等. 2009. 气象因素对北京市大气颗粒物浓度影响的非参数分析 [J]. 环境科学研究, 22(6): 663−669. doi: 10.13198/j.res.2009.06.41.lij.007

    Li Jun, Sun Chunbao, Liu Xiande, et al. 2009. Non-parameter statistical analysis of impacts of meteorological conditions on PM concentration in Beijing [J]. Research of Environmental Sciences (in Chinese), 22(6): 663−669. doi: 10.13198/j.res.2009.06.41.lij.007
    [15] Li M X, Ma Z G, Gu H P, et al. 2017. Production of a combined land surface data set and its use to assess land–atmosphere coupling in China [J]. J. Geophys. Res., 122(2): 948−965. doi: 10.1002/2016jd025511
    [16] 林朝晖, 刘辉志, 谢正辉, 等. 2008. 陆面水文过程研究进展 [J]. 大气科学, 32(4): 935−949. doi: 10.3878/j.issn.1006-9895.2008.04.19

    Lin Zhaohui, Liu Huizhi, Xie Zhenghui, et al. 2008. Recent progress in the land–surface and hydrological process studies [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 32(4): 935−949. doi: 10.3878/j.issn.1006-9895.2008.04.19
    [17] 林朝晖, 杨小松, 郭裕福. 2001. 陆面过程模式对土壤含水量初值的敏感性研究 [J]. 气候与环境研究, 6(2): 240−248. doi: 10.3969/j.issn.1006-9585.2001.02.017

    Lin Zhaohui, Yang Xiaosong, Guo Yufu. 2001. Sensitivity of land surface model to the initial condition of soil moisture [J]. Climatic and Environmental Research (in Chinese), 6(2): 240−248. doi: 10.3969/j.issn.1006-9585.2001.02.017
    [18] Liu D, Yu Z B, Zhang J Y. 2015. Diagnosing the strength of soil temperature in the land atmosphere interactions over Asia based on RegCM4 model [J]. Global Planet. Change, 130: 7−21. doi: 10.1016/j.gloplacha.2015.03.007
    [19] Liu J, Hagan D F T, Liu Y. 2021. Global land surface temperature change (2003–2017) and its relationship with climate drivers: AIRS, MODIS, and ERA5-land based analysis [J]. Remote Sens., 13(1): 44. doi: 10.3390/rs13010044
    [20] Lo M H, Wu W Y, Tang L I, et al. 2021. Temporal changes in land surface coupling strength: An example in a semi–arid region of australia [J]. J. Climate, 34(4): 1503−1513. doi: 10.1175/JCLI-D-20-0250.1
    [21] Lorenz R, Pitman A J, Hirsch A L, et al. 2015. Intraseasonal versus interannual measures of land–atmosphere coupling strength in a global climate model: GLACE-1 versus GLACE-CMIP5 experiments in ACCESS1.3b [J]. J. Hydrometeorol., 16(5): 2276−2295. doi: 10.1175/jhm-d-14-0206.1
    [22] Ma S M, Zhou T J, Stone D A, et al. 2017. Attribution of the July–August 2013 heat event in central and eastern China to anthropogenic greenhouse gas emissions [J]. Environ. Res. Lett., 12(5): 054020. doi: 10.1088/1748-9326/aa69d2
    [23] Mei R, Wang G L. 2011. Impact of sea surface temperature and soil moisture on summer precipitation in the United States based on observational data [J]. J. Hydrometeorol., 12(5): 1086−1099. doi: 10.1175/2011JHM1312.1
    [24] 孟宪贵, 郭俊建, 韩永清. 2018. ERA5再分析数据适用性初步评估 [J]. 海洋气象学报, 38(1): 91−99. doi: 10.19513/j.cnki.issn2096-3599.2018.01.011

    Meng Xiangui, Guo Junjian, Han Yongqing. 2018. Preliminarily assessment of ERA5 reanalysis data [J]. Journal of Marine Meteorology (in Chinese), 38(1): 91−99. doi: 10.19513/j.cnki.issn2096-3599.2018.01.011
    [25] Miralles D G, Van Den Berg M J, Teuling A J, et al. 2012. Soil moisture–temperature coupling: A multiscale observational analysis [J]. Geophys. Res. Lett., 39(21): L21707. doi: 10.1029/2012gl053703
    [26] Mora C, Dousset B, Caldwell I R, et al. 2017. Global risk of deadly heat [J]. Nat. Climate Change, 7(7): 501−506. doi: 10.1038/NCLIMATE3322
    [27] Muñoz-Sabater J. 2019. ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)[EB/OL].https://confluence.ecmwf.int/display/CUSF/Hourly+precipitation+out+of+range+-+ERA5-Land+hourly+data+from+1981+to+present.
    [28] Muñoz-Sabater J, Dutra E, Agustí-Panareda A, et al. 2021. ERA5-Land: A state-of-the-art global reanalysis dataset for land applications[EB/OL].https://doi.org/10.5194/essd-2021-82.
    [29] Pelosi A, Terribile F, D’Urso G, et al. 2020. Comparison of ERA5-land and UERRA MESCAN-SURFEX reanalysis data with spatially interpolated weather observations for the regional assessment of reference evapotranspiration [J]. Water, 12(6): 1669. doi: 10.3390/w12061669
    [30] Phillips T J, Klein S A, Ma H Y, et al. 2017. Using ARM observations to evaluate climate model simulations of land–atmosphere coupling on the U. S. Southern great plains [J]. J. Geophys. Res., 122(21): 11524−11548. doi: 10.1002/2017jd027141
    [31] Qi L, Wang Y Q. 2012. Changes in the observed trends in extreme temperatures over China around 1990 [J]. J. Climate, 25(15): 5208−5222. doi: 10.1175/jcli-d-11-00437.1
    [32] Seneviratne S I, Corti T, Davin E L, et al. 2010. Investigating soil moisture–climate interactions in a changing climate: A review [J]. Earth-Sci. Rev., 99(3-4): 125−161. doi: 10.1016/j.earscirev.2010.02.004
    [33] Seneviratne S I, Lüthi D, Litschi M, et al. 2006. Land–atmosphere coupling and climate change in Europe [J]. Nature, 443(7108): 205−209. doi: 10.1038/nature05095
    [34] 孙建奇, 王会军, 袁薇. 2011. 我国极端高温事件的年代际变化及其与大气环流的联系 [J]. 气候与环境研究, 16(2): 199−208. doi: 10.3878/j.issn.1006-9585.2011.02.09

    Sun Jianqi, Wang Huijun, Yuan Wei. 2011. Decadal variability of the extreme hot event in China and its association with atmospheric circulations [J]. Climatic and Environmental Research (in Chinese), 16(2): 199−208. doi: 10.3878/j.issn.1006-9585.2011.02.09
    [35] Sun Y, Zhang X B, Ren G Y, et al. 2016. Contribution of urbanization to warming in China [J]. Nat. Climate Change, 6(7): 706−709. doi: 10.1038/nclimate2956
    [36] Tarek M, Brissette F P, Arsenault R. 2020. Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America [J]. Hydrol. Earth Syst. Sci., 24(5): 2527−2544. doi: 10.5194/hess-24-2527-2020
    [37] 王万秋. 1991. 土壤温湿异常对短期气候影响的数值模拟试验 [J]. 大气科学, 15(5): 115−123. doi: 10.3878/j.issn.1006-9895.1991.05.14

    Wang Wanqiu. 1991. Numerical experiments of the soil temperature and moisture anomalies' effects on the short term climate [J]. Chinese Journal of Atmospheric Sciences (Scientia Atmospherica Sinica) (in Chinese), 15(5): 115−123. doi: 10.3878/j.issn.1006-9895.1991.05.14
    [38] Williams C J R, Allan R P, Kniveton D R. 2012. Diagnosing atmosphere–land feedbacks in CMIP5 climate models [J]. Environ. Res. Lett., 7(4): 044003. doi: 10.1088/1748-9326/7/4/044003
    [39] Xu B, Chen H S, Gao C J, et al. 2019. Decadal intensification of local thermal feedback of summer soil moisture over North China [J]. Theor. Appl. Climatol., 138(3): 1563−1571. doi: 10.1007/s00704-019-02918-5
    [40] Zeng D W, Yuan X. 2018. Multiscale land–atmosphere coupling and its application in assessing subseasonal forecasts over East Asia [J]. J. Hydrometeorol., 19(5): 745−760. doi: 10.1175/JHM-D-17-0215.1
    [41] Zeng X B, Barlage M, Castro C, et al. 2010. Comparison of land–precipitation coupling strength using observations and models [J]. J. Hydrometeorol., 11(4): 979−994. doi: 10.1175/2010jhm1226.1
    [42] 曾毓金, 谢正辉. 2015. 基于CMIP5模拟的中国区域陆气耦合强度评估及未来情景预估 [J]. 气候与环境研究, 20(3): 337−346. doi: 10.3878/j.issn.1006-9585.2015.14242

    Zeng Yujin, Xie Zhenghui. 2015. Projection and evaluation of the land−atmosphere coupling strength over China by CMIP5 models [J]. Climatic and Environmental Research (in Chinese), 20(3): 337−346. doi: 10.3878/j.issn.1006-9585.2015.14242
    [43] Zhang H L, Wang Y G, Hu J L, et al. 2015. Relationships between meteorological parameters and criteria air pollutants in three megacities in China [J]. Environ. Res., 140: 242−254. doi: 10.1016/j.envres.2015.04.004
    [44] Zhang J Y, Wu L Y, Dong W J. 2011. Land-atmosphere coupling and summer climate variability over East Asia [J]. J. Geophys. Res., 116(D5): D05117. doi: 10.1029/2010JD014714
  • 加载中
图(11)
计量
  • 文章访问数:  167
  • HTML全文浏览量:  29
  • PDF下载量:  41
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-03-24
  • 网络出版日期:  2021-07-08
  • 刊出日期:  2022-06-02

目录

    /

    返回文章
    返回