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5 期            顾思南等:不同主控天气型下湖泊效应对青藏高原中部秋季区域性极端降水的影响                                        1203
               48. 8% 和 42. 3% 的区域性极端降水事件的发生, 而                     013-0987-9.
                                                                 Gu H P, Jin J M, Wu Y H, et al, 2015. Calibration and validation of
               P3 型主要出现在 10 月, 其对秋季区域性极端降水
                                                                    lake surface temperature simulations with the coupled WRF-lake
               事件总发生频次的贡献为8. 8%。
                                                                    model[J]. Climatic Change, 129(3): 471-483. DOI: 10. 1007/
                   基于 WRF-Lake 湖-气耦合模式开展区域性极                        s10584-013-0978-y.
               端降水不同主控天气型下的多个例模拟, 对比有湖                           He J, Yang K, Tang W J, et al, 2020. The first high-resolution meteo‐

               和无湖试验结果间的差异发现, 三类主控天气型下                              rological forcing dataset for land process studies over China[J].
                                                                    Scientific Data, 7(1): 25. DOI: 10. 1038/s41597-020-0369-y.
               湖泊效应对降水影响的强度在空间分布上存在明
                                                                 Hersbach H, Bell B, Berrisford P, et al, 2020. The ERA5 global re‐
               显差异。P1 和 P2 型下, 湖泊效应降水在空间分布
                                                                    analysis[J]. Quarterly Journal of the Royal Meteorological Soci‐
               上非常零散且强度非常弱, 而 P3 型下湖泊效应导                            ety, 146(730): 1999-2049. DOI: 10. 1002/qj. 3803.
               致的降水差异显著的区域相比其它两型更为集中                             Huang A N, Lazhu, Wang J B, et al, 2019. Evaluating and improving
               且强度更大。在 P1、 P2 和 P3 型下湖泊群可分别导                        the Performance of three 1-D Lake models in a large deep Lake of
                                                                    the central Tibetan Plateau[J]. Journal of Geophysical Research:
               致 青 藏 高 原 中 部 平 均 降 水 量 减 少 2. 37%、 增 加
                                                                    Atmospheres,  124(6):  3143-3167. DOI:  10. 1029/2018JD02
               12. 11% 和增加 138. 37%。在三类天气型中, P3 型
                                                                    9610.
               下湖效应对降水的增强作用最为明显。机制分析                             Kourzeneva  E,  2010. External  data  for  lake  parameterization  in  Nu‐
               表明, 湖泊增暖、 增湿效应引起的湖泊及周边地区                             merical Weather Prediction and climate modeling[J]. Boreal En‐
               低层大气不稳定性和水汽辐合的增强在 P3 天气型                             vironment Research, 15(2): 165-177.
                                                                 Li L, Zhang R H, Wen M, 2014. Diurnal variation in the occurrence
               下区域性极端降水的形成中起着重要作用。
                                                                    frequency of the Tibetan Plateau vortices[J]. Meteorology and At‐
                   由于青藏高原地区数值模拟的复杂性以及计                              mospheric  Physics,  125(3):  135-144. DOI:  10. 1007/s00703-
               算资源和时间的限制, 每类天气型只能选取有限个                              014-0325-5.
               例进行试验, 可能会影响 P1 和 P2 型下湖效应降水                      Ma Y Y, Yang Y, Qiu C J, et al, 2019. Evaluation of the WRF-Lake
               的显著性水平。在未来的研究中需要增加试验个                                model  over  two  major  freshwater  lakes  in  China[J]. Journal  of
                                                                    Meteorological  Research,  33(2):  219-235. DOI:  10. 1007/
               例数量, 提高统计结果的稳健性。
                                                                    s13351-019-8070-9.
               参考文献 (References):                                MacKay M D, Neale P J, Arp C D, et al, 2009. Modeling lakes and
                                                                    reservoirs  in  the  climate  system[J]. Limnology  and  Oceanogra‐
               Cai Y, Ke C Q, Li X D, et al, 2019. Variations of lake ice phenology   phy,  54(2):  2315-2329. DOI:  10. 4319/lo. 2009. 54. 6_part_
                  on the Tibetan Plateau from 2001 to 2017 based on MODIS data  2. 2315.
                 [J]. Journal  of  Geophysical  Research: Atmospheres,  124(2):   Pei J, Wang L, Xu W J, et al, 2019. Recovered Tibetan antelope at
                  825-843. DOI: 10. 1029/2018JD028993.              risk again[J]. Science, 366(6462): 194. DOI: 10. 1126/science.
               Cui P, Jia Y, 2015. Mountain hazards in the Tibetan Plateau: research   aaz290.
                  status and prospects[J]. National Science Review, 2(4): 397-  Powers J G, Klemp J B, Skamarock W C, et al, 2017. The weather re‐
                  399. DOI: 10. 1093/nsr/nwv061.                    search and forecasting model: overview, system efforts, and fu‐
               Curio  J,  Scherer  D,  2016. Seasonality  and  spatial  variability  of  dy‐  ture directions[J]. Bulletin of the American Meteorological Soci‐
                  namic precipitation controls on the Tibetan Plateau[J]. Earth Sys‐  ety, 98(8): 1717-1737. DOI: 10. 1175/BAMS-D-15-00308. 1.
                  tem  Dynamics,  7(3):  767-782. DOI:  10. 5194/esd-7-767-  Shi Q, Xue P, 2019. Impact of lake surface temperature variations on
                  2016.                                             lake effect snow over the Great Lakes Region[J]. Journal of Geo‐
               Dai Y F, Yao T D, Wang L, et al, 2020. Contrasting roles of a large   physical Research: Atmospheres, 124(23): 12553-12567. DOI:
                  alpine  lake  on  Tibetan  Plateau  in  shaping  regional  precipitation   10. 1029/2019JD031261.
                  during  summer  and  autumn[J]. Frontiers  in  Earth  Science,  8  Su D S, Wen L J, Gao X Q, et al, 2020. Effects of the largest lake of
                 (358). DOI: 10. 3389/feart. 2020. 00358.           the Tibetan  Plateau  on  the  regional  climate[J]. Journal  of  Geo‐
               Friedl M A, Sulla-Menashe D, Tan B, et al, 2010. MODIS Collec‐  physical  Research:  Atmospheres,  125(22):  e2020JD033396.
                  tion 5 global land cover: Algorithm refinements and characteriza‐  DOI: 10. 1029/2020JD033396.
                  tion  of  new  datasets[J]. Remote  Sensing  of  Environment,  114  Subin Z M, Riley W J, Mironov D, 2012. An improved lake model
                 (1): 168-182. DOI: 10. 1016/j. rse. 2009. 08. 016.  for climate simulations: Model structure, evaluation, and sensi‐
               Gerken T, Biermann T, Babel W, et al, 2013. A modelling investiga‐  tivity  analyses  in  CESM1[J]. Journal  of Advances  in  Modeling
                  tion  into  lake-breeze  development  and  convection  triggering  in   Earth Systems, 4(1). DOI: 10. 1029/2011MS000072.
                  the Nam Co Lake basin, Tibetan Plateau[J]. Theoretical and Ap‐  Sun J, Yao X P, Deng G W, et al, 2021. Characteristics and synoptic
                  plied  Climatology,  117(1):  149-167. DOI:  10. 1007/s00704-  patterns of regional extreme rainfall over the central and eastern
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