Impact of the Intraseasonal Indo-west Pacific Convective Oscillation on Subseasonal-seasonal Atmospheric Predictability
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摘要: 利用非线性局部Lyapunov指数和条件非线性局部Lyapunov指数定量估计了季节内印度洋-西太平洋对流涛动(IPCO)和实时多变量Madden-Julian指数(RMM指数)可预报期限,量化了季节内IPCO对S2S尺度大气可预报性的贡献,深入研究了季节内IPCO演变下S2S尺度可预报期限空间分布的变化规律。结果表明:(1)与RMM指数相比,季节内IPCO指数可预报性更强,可预报期限达到31天左右,比RMM指数高出2周以上;(2)印度洋-西太平洋区域S2S尺度大气可预报性最强,可预报期限达到30天以上,其中季节内IPCO是该地区的主要可预报性来源之一,其贡献达到6天,占总可预报期限的25%以上;(3)随着季节内IPCO的演变,印度洋-西太平洋地区S2S尺度大气可预报性有空间结构变化,表现为可预报期限异常的传播和振荡。S2S尺度大气可预报期限正负异常沿季节内IPCO传播路径,一支以赤道中西印度洋为起点北传至印度半岛,一支向东传播,经过海洋性大陆到赤道西太平洋后向北传播,到达日本南部。同时,可预报性异常的传播在在东印度洋和西太平洋表现出反向变化的特征,形成东西两极振荡,当季节内IPCO向正位相发展时,东印度洋具有更强的可预报性,西太平洋具有更弱的可预报性,反之亦然。季节内IPCO的发展(衰退)可使东印度洋(西太平洋)S2S尺度大气可预报性更强,表明模式预报技巧对此具有更大的提升空间。
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关键词:
- 季节内印度洋-西太平洋对流涛动(IPCO) /
- S2S尺度可预报性 /
- 热带季节内振荡 /
- 非线性局部Lyapunov指数
Abstract: This work quantifies the contribution of seasonal IPCO to S2S atmospheric predictability by using the nonlinear local Lyapunov exponent and conditional nonlinear local Lyapunov exponent to estimate the forecastable period of intraseasonal IPCO and RMM index, and investigates the variation pattern of the spatial distribution of S2S predictability limit under the evolution of intraseasonal IPCO. The results show that: (1) Compared with the RMM index, the intraseasonal IPCO index is more predictable, with predictability limit of about 31 days, which is more than 2 weeks higher than the RMM index. (2) The S2S atmospheric predictability is the strongest in the Indo-West Pacific, with predictability limit of more than 30 days, in which the intraseasonal IPCO is one of the main predictability sources in this region, with its contribution reaching more than 6 days. (3) With the evolution of intraseasonal IPCO, the S2S atmospheric predictability of the Indian Ocean western Pacific region has a spatial structure change,manifesting as the propagation and oscillation of predictability period anomalies. The S2S atmospheric predictability anomalies propagate along the intraseasonal IPCO paths, one starting from the equatorial western and central Indian Ocean northward to the Indian Peninsula, and one propagating eastward through the oceanic continents to the equatorial western Pacific Ocean and then northward to southern Japan.Meanwhile, the propagation of the predictability anomaly shows reverse variability in the eastern Indian Ocean and the western Pacific Ocean, forming an east-west polar oscillation. When the intraseasonal IPCO develops toward the positive phase, the eastern Indian Ocean has stronger predictability and the western Pacific Ocean has weaker predictability, and vice versa. The development (decline) of intraseasonal IPCO can make the S2S atmosphere in the East Indian Ocean (West Pacific) more predictable, and the model forecast skill has more room for improvement. -
图 2 同图 1,但对逐日IPCO指数的情形。
图 4 同图 3,但为季节内IPCO条件下平均可预报期限(单位:天)
e、f分别为a、b中的条件可预报期限占各自总可预报期限的百分比(%)。
图 5 (a)、(b)分别同图 3(a)、(b),但对季节内IPCO演变的8个位相平均S2S尺度可预报期限的空间分布(单位:天)。(c)-(j)同(a),但分别对应季节内IPCO演变的8个位相的S2S尺度可预报期限异常的空间分布。P1到P8表示季节内IPCO的8个位相,此处的异常是分别相对于图(a)中季节内IPCO的8个位相平均的可预报期限。(k)~(r)同(c)~(j),但对位势高度场的情形以及相对于(b)的异常。图(c)中红色实线和绿色实线分别表示季节内IPCO沿(75 °E,2.5 °N)至(75 °E,20 °N)和(60 °E,2.5 °N)经(135 °E,2.5 °N)至(135°E,30°N)的传播路径,黑色矩形表示赤道东印度洋(EEIO;70~100 °E,5 °S~10 °N)和西北太平洋(WNP;110~160 °E,5~20 °N)区域。
图 7 同图 6,但为路径(60 °E,2.5 °N)经(135 °E,2.5 °N)至(135 °E,30 °N)的情形
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[1] LAU W K M, WALISER D E. Intraseasonal variability in the atmosphere-ocean climate system[M]. Springer Berlin Heidelberg, 2011. [2] VITART F, ARDILOUZE C, BONET A, et al. The Subseasonal to Seasonal (S2S) Prediction Project Database[J]. Bull Amer Meteor Soc, 2017, 98(1): 163-173. [3] PEGION K, KIRTMAN B P, BECKER E, et al. The Subseasonal Experiment (SubX): a multimodel subseasonal prediction experiment[J]. Bull Amer Meteor Soc, 2019, 100(10): 2 043-2 060. [4] 丑纪范, 郑志海, 孙树鹏. 10-30 d延伸期数值天气预报的策略思考: 直面混沌[J]. 气象科学, 2010, 30(5): 569-573. [5] 丁一汇, 梁萍. 基于MJO的延伸预报[J]. 气象学报, 2010, 36(7): 111-122. [6] 齐艳军, 容新尧. 次季节-季节预测的应用前景与展望——"次季节-季节预测(S2S)"会议评述[J]. 气象科技进展, 2014, 4(3): 74-75. [7] 章大全, 郑志海, 陈丽娟, 等. 10-30 d延伸期可预报性与预报方法研究进展[J]. 应用气象学报, 2019, 30(4): 416-430. [8] MADDEN R A, JULIAN P R. Detection of a 40-50 day oscillation in the zonal wind in the tropical Pacific[J]. J Atmos Sci, 1971, 28(5): 702-708. [9] MADDEN R A, JULIAN P R. Description of global-scale circulation cells in the tropics with a 40-50 day period[J]. J Atmos Sci, 1972, 29 (6): 1 109-1 123. [10] MADDEN R A, JULIAN P R. Observations of the 40-50 day tropical oscillation—A review[J]. Mon Wea Rev, 1994, 122(5): 814-837. [11] ZHANG, C D. Madden-Julian oscillation[J]. Rev Geophys, 2005, 43: RG2003 [12] ROBERTSON A W, KUMAR A, P\widetilde E\A M, et al. Improving and promoting subseasonal to seasonal prediction[J]. Bull Amer Meteor Soc, 2015, 96(3): 49-53. [13] FANG Y, LI B, LIU X. Predictability and prediction skill of the boreal summer intra-seasonal oscillation in BCC_CSM model[J]. J Meteorol Soc Japen, 2019, 97(1): 295-311. [14] LI T, WANG T. Impact of atmosphere-ocean interactions on propagation and initiation of boreal winter and summer intraseasonal oscillations[J]. Tropical and Extratropical Air-Sea Interactions, 2021: 17-60. [15] 梅双丽, 李勇, 马杰. 热带季节内振荡在延伸期预报中的应用进展[J]. 地球科学进展, 2020, 35(12): 1 222-1 231. [16] DING R, LI J P, SEO K H. Predictability of the Madden-Julian oscillation estimated using observational data[J]. Mon Wea Rev, 2010, 138 (3): 1 004-1 013. [17] DING R Q, LI J P, SEO K H. Estimate of the predictability of boreal summer and winter intraseasonal oscillations from observations[J]. Mon Wea Rev, 2011, 139(8): 2 421-2 438. [18] LU D Y, DING R Q, LI J P. The predictability limit of the amplitude and phase of the Madden‐Julian oscillation[J]. Atmos Sci Lett, 2020, 21 (6): e968. [19] HSU P C, LI T, YOU L, et al. A spatial-temporal projection model for 10-30 day rainfall forecast in South China[J]. Climate Dyn, 2015, 44 (5): 1 227-1 244. [20] JOHNSON N C, COLLINS D C, FELDSTEIN S B, et al. Skillful wintertime North American temperature forecasts out to 4 weeks based on the state of ENSO and the MJO[J]. Wea Forecasting, 2014, 29(1): 23-38. [21] HSU P C, QIAN Y, LIU Y, et al. Role of abnormally enhanced MJO over the Western Pacific in the formation and subseasonal predictability of the record-breaking Northeast Asian heatwave in the summer of 2018[J]. J Climate, 2020, 33(8): 3 333-3 349 [22] LEROY A, WHEELER M C. Statistical prediction of weekly tropical cyclone activity in the Southern Hemisphere[J]. Mon Wea Rev, 2008, 136(10): 3 637-3 654. [23] VIGAUD N, ROBERTSON A W, TIPPETT M K, et al. Subseasonal predictability of boreal summer monsoon rainfall from ensemble forecasts[J]. Front Environ Sci, 2017, 5: 67. [24] LI Y J, LI J P, FENG J. Boreal summer convection oscillation over the Indo‐Western Pacific and its relationship with the East Asian summer monsoon[J]. Atmos Sci Lett, 2013, 14(2): 66-71. [25] 张净雯, 李建平, 李艳杰. 印度洋—西太平洋对流涛动的季节内特征[J]. 大气科学, 2015, 39(2): 221-234. [26] ZHENG J Y, LI Y J, LI J P, et al. The relationship between Indo-Pacific convection oscillation and summer surface air temperature in southern Asia[J]. SOLA, 2017, 13: 199-204. [27] WANG Q Y, LI J P, LI Y J, et al. Modulation of tropical cyclogenesis location and frequency over the Indo-western North Pacific by the intraseasonal Indo-western Pacific convection oscillation during the boreal extended summer[J]. J Climate, 2018, 31(4): 1 435-1 450. [28] WANG Q Y, LI J P, LI Y J, et al. Modulation of tropical cyclone tracks over the western North Pacific by intra-seasonal Indo-western Pacific convection oscillation during the boreal extended summer[J]. Climate Dyn, 2019, 52(1): 913-927. [29] 李建平, 吴国雄, 胡敦欣. 亚印太交汇区海气相互作用及其对我国短期气候的影响(上卷)[M]. 北京: 气象出版社, 2011. [30] ZHAO S, LI J P, LI Y J, et al. Interhemispheric influence of Indo-Pacific convection oscillation on Southern Hemisphere rainfall through southward propagation of Rossby waves[J]. Climate Dyn, 2019, 52(5): 3 203-3 221. [31] KALNAY E, KANAMITSU M, KISTLER R, et al. The NCEP/NCAR 40-year reanalysis project[J]. Bull Amer Meteor Soc, 1996, 77(3): 437-471. [32] KANAMITSU M, EBISUZAKI W, WOOLLEN J, et al. NCEP-DOE AMIP-Ⅱ Reanalysis (R-2)[J]. Bull Amer Meteor Soc, 2002, 83 (11): 1 631-1 644. [33] LEE H T. Climate Algorithm theoretical basis document (C-ATBD): Outgoing longwave radiation (OLR)-daily[R]. NOAA's Climate Data Record (CDR) Program, CDRP-ATBD-0526, 2014, 46pp. [34] WHEELER M C, HENDON H H. An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction [J]. Mon Wea Rev, 2004, 132(8): 1 917-1 932. [35] DING R Q, LI J P. Nonlinear finite-time Lyapunov exponent and predictability[J]. Phys Lett, 2007, 364(5): 396-400. [36] LI J P, WANG S. Some mathematical and numerical issues in geophysical fluid dynamics and climate dynamics[J]. Commun Comput Phys, 2008, 3: 759-793. [37] LI J P, DING R Q. Temporal-spatial distribution of atmospheric predictability limit by local dynamical analogs[J]. Mont Wea Rev, 2011, 139 (10): 3 265-3 283. [38] LI J P, DING R Q. Temporal-spatial distribution of the predictability limit of monthly sea surface temperature in the global oceans[J]. Int J Climatol, 2013, 33(8): 1 936-1 947. [39] LI J P,DING R Q. Seasonal and Interannual Weather Prediction[M]. Encyclopedia of atmospheric sciences,2nd edn. Academic Press and Elsevier. [40] HOU Z L, LI J P, DING R Q, et al. The application of nonlinear local Lyapunov vectors to the Zebiak-Cane model and their performance in ensemble prediction[J]. Climate Dyn, 2018, 51(1): 283-304. [41] HOU Z L, ZUO B, ZHANG S Q, et al. Model forecast error correction based on the local dynamical analog method: An example application to the ENSO forecast by an intermediate coupled model[J]. Geophys Res Lett, 2020, 47(19): e2020GL088986. [42] HOU Z L, LI J P, ZUO B. Correction of monthly SST forecasts in CFSv2 using the local dynamical analog method[J]. Wea Forecast, 2021, 36(3): 843-858. [43] FU X, WANG B, WALISER D E, et al. Impact of atmosphere-ocean coupling on the predictability of monsoon intraseasonal oscillations[J]. J Atmos Sci, 2007, 64(1): 157-174. [44] SEO K H, SCHEMM J K E, WANG W, et al. The boreal summer intraseasonal oscillation simulated in the NCEP Climate Forecast System: The effect of sea surface temperature[J]. Mon Wea Rev, 2007, 135(5): 1 807-1 827. [45] SEO K H, WANG W, GOTTSCHALCK J, et al. Evaluation of MJO forecast skill from several statistical and dynamical forecast models[J]. J Climate, 2009, 22(9): 2 372-2 388. [46] KIM H M, HOYOS C D, WEBSTER P J, et al. Sensitivity of MJO simulation and predictability to sea surface temperature variability[J]. J Climate, 2008, 21(20): 5 304-5 317. [47] REYNOLDS C A, WEBSTER P J, KALNAY E. Random error growth in NMC's global forecasts[J]. Mon Wea Rev, 1994, 122(6): 1 281- 1 305. [48] KALNEY E. Atmospheric modeling, data assimilation and predictability[M]. Cambridge: Cambridge University Press, 2003.