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Predictability of Northern Hemisphere Teleconnection Patterns in GloSea5 Hindcast Experiments up to 6 Weeks

GloSea5 북반구 대기 원격상관패턴의 1~6주 주별 예측성능 검증

  • Kim, Do-Kyoung (Department of Climate and Energy Systems Engineering, Ewha Womans University) ;
  • Kim, Young-Ha (Severe Storm Research Center, Ewha Womans University) ;
  • Yoo, Changhyun (Department of Climate and Energy Systems Engineering, Ewha Womans University)
  • 김도경 (이화여자대학교 기후.에너지시스템공학과) ;
  • 김영하 (이화여자대학교 국지재해기상예측기술센터) ;
  • 유창현 (이화여자대학교 기후.에너지시스템공학과)
  • Received : 2019.05.13
  • Accepted : 2019.07.21
  • Published : 2019.09.30

Abstract

Due to frequent occurrence of abnormal weather, the need to improve the accuracy of subseasonal prediction has increased. Here we analyze the performance of weekly predictions out to 6 weeks by GloSea5 climate model. The performance in circulation field from January 1991 to December 2010 is first analyzed at each grid point using the 500-hPa geopotential height. The anomaly correlation coefficient and mean-square skill score, calculated each week against the ECWMF ERA-Interim reanalysis data, illustrate better prediction skills regionally in the tropics and over the ocean and seasonally during winter. Secondly, we evaluate the predictability of 7 major teleconnection patterns in the Northern Hemisphere: North Atlantic Oscillation (NAO), East Atlantic (EA), East Atlantic/Western Russia (EAWR), Scandinavia (SCAND), Polar/Eurasia (PE), West Pacific (WP), Pacific-North American (PNA). Skillful predictability of the patterns turns out to be approximately 1~2 weeks. During summer, the EAWR and SCAND, which exhibit a wave pattern propagating over Eurasia, show a considerably lower skill than the other 5 patterns, while in winter, the WP and PNA, occurring in the Pacific region, maintain the skill up to 2 weeks. To account for the model's bias in reproducing the teleconnection patterns, we measure the similarity between the teleconnection patterns obtained in each lead time. In January, the model's teleconnection pattern remains similar until lead time 3, while a sharp decrease of similarity can be seen from lead time 2 in July.

Keywords

S2S;weekly prediction;GloSea5 hindcast experiment;teleconnection patterns

Acknowledgement

Supported by : 한국연구재단

References

  1. Baldwin, M. P., and T. J. Dunkerton, 2001: Stratospheric harbingers of anomalous weather regimes. Science, 294, 581-584. https://doi.org/10.1126/science.1063315
  2. Barnston, A. G., and R. E. Livezey, 1987: Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Wea. Rev., 115, 1083-1126. https://doi.org/10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2
  3. Best, M. J., and Coauthors, 2011: The Joint UK Land Environment Simulator (JULES), model description-Part 1: energy and water fluxes. Geosci. Model Dev., 4, 677-699, doi:10.5194/gmd-4-677-2011. https://doi.org/10.5194/gmd-4-677-2011
  4. Brown, A., S. Milton, M. Cullen, B. Golding, J. Mitchell, and A. Shelly, 2012: Unified modeling and prediction of weather and climate: A 25-year journey. Bull. Amer. Meteor. Soc., 93, 1865-1877, doi:10.1175/BAMS-D-12-00018.1. https://doi.org/10.1175/BAMS-D-12-00018.1
  5. Choi, J., S.-W. Son, Y.-G. Ham, J.-Y. Lee, and H.-M. Kim, 2016: Seasonal-to-interannual prediction skills of near-surface air temperature in the CMIP5 decadal hindcast experiments. J. Climate, 29, 1511-1527, doi:10.1175/JCLI-D-15-0182.1. https://doi.org/10.1175/JCLI-D-15-0182.1
  6. Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553-597, doi:10.1002/qj.828. https://doi.org/10.1002/qj.828
  7. Eady, E. T., 1949: Long Waves and Cyclone Waves. Tellus, 1, 33-52.
  8. Feldstein, S. B., 2000: The timescale, power spectra, and climate noise properties of teleconnection patterns. J. Climate, 13, 4430-4440. https://doi.org/10.1175/1520-0442(2000)013<4430:TTPSAC>2.0.CO;2
  9. Goddard, L., and Coauthors, 2013: A verification framework for interannual-to-decadal predictions experiments. Climate Dyn., 40, 245-272, doi:10.1007/s00382-012-1481-2. https://doi.org/10.1007/s00382-012-1481-2
  10. Gupta, A. S., N. C. Jourdain, J. N. Brown, and D. Monselesan, 2013: Climate drift in the CMIP5 models. J. Climate, 26, 8597-8615, doi:10.1175/JCLI-D-12-00521.1. https://doi.org/10.1175/JCLI-D-12-00521.1
  11. Hakkinen, S., P. B. Rhines, and D. L. Worthen, 2011: Atmospheric blocking and Atlantic multidecadal ocean variability. Science, 334, 655-659, doi:10.1126/science. 1205683. https://doi.org/10.1126/science.1205683
  12. Hoskins, B. J., and D. J. Karoly, 1981: The steady linear response of a spherical atmosphere to thermal and orographic forcing. J. Atmos. Sci., 38, 1179-1196. https://doi.org/10.1175/1520-0469(1981)038<1179:TSLROA>2.0.CO;2
  13. Hunke, E. C., and W. H. Lipscomb, 2010: CICE: The Los Alamos sea ice model documentation and software user's manual Version 4.1 LA-CC-06-012. Technical report, Los Alamos National Laboratory NM87545, 76 pp.
  14. Jin, F., and B. J. Hoskins, 1995: The direct response to tropical heating in a baroclinic atmosphere. J. Atmos. Sci., 52, 307-319. https://doi.org/10.1175/1520-0469(1995)052<0307:TDRTTH>2.0.CO;2
  15. Johansson, A., 2007: Prediction Skill of the NAO and PNA from Daily to Seasonal Time Scales. J. Climate, 20, 1957-1975. https://doi.org/10.1175/JCLI4072.1
  16. Jung, M.-I., S.-W. Son, J. Choi, and H.-S. Kang, 2015: Assessment of 6-month lead prediction skill of the GloSea5 hindcast experiment, Atmosphere, 25, 323-337, doi:10.14191/Atmos.2015.25.2.323 (in Korean with English abstract). https://doi.org/10.14191/Atmos.2015.25.2.323
  17. Kushnir, Y., W. A. Robinson, P. Chang, and A. W. Robertson, 2006: The physical basis for predicting atlantic sector seasonal-to-interannual climate variability. J. Climate, 19, 5949-5970. https://doi.org/10.1175/JCLI3943.1
  18. Lorenz, E. N., 1963: Deterministic Nonperiodic Flow. J. Atmos. Sci., 20, 130-141. https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2
  19. MacLachlan, C., and Coauthors, 2015: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Q. J. R. Meteorol. Soc., 141, 1072-1084, doi:10.1002/qj.2396. https://doi.org/10.1002/qj.2396
  20. Madden, R. A., and P. R. Julian, 1971: Detection of a 40-50 day oscillation in the zonal wind in the tropical pacific. J. Atmos. Sci., 28, 702-708. https://doi.org/10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2
  21. Madec, G., 2008: NEMO ocean engine. Note du Po?le de modelisation No.27 Institut Pierre-Simon Laplace (IPSL) France, 300 pp.
  22. Mills, C. M., and J. E. Walsh, 2013: Seasonal variation and spatial patterns of the atmospheric component of the pacific decadal oscillation. J. Climate, 26, 1575-1594, doi:10.1175/JCLI-D-12-00264.1. https://doi.org/10.1175/JCLI-D-12-00264.1
  23. Murphy, A. H., 1988: Skill scores based on the mean square error and their relationships to the correlation coefficient. Mon. Wea. Rev., 116, 2417-2424. https://doi.org/10.1175/1520-0493(1988)116<2417:SSBOTM>2.0.CO;2
  24. Linkin, M. E., and S. Nigam, 2008: The North Pacific Oscillation-West Pacific teleconnection pattern: mature-phase structure and winter impacts. J. Climate, 21, 1979-1997. https://doi.org/10.1175/2007JCLI2048.1
  25. Owens, R. G., and T. D. Hewson, 2018: ECMWF Forecast User Guide. Reading, ECMWF, doi:10.21957/m1cs7h.
  26. Park, H.-J., and J.-B. Ahn, 2016: Combined effect of the Arctic Oscillation and the Western Pacific pattern on East Asia winter temperature. Climate Dyn., 46, 3205-3221, doi:10.1007/s00382-015-2763-2. https://doi.org/10.1007/s00382-015-2763-2
  27. Park, W.-S., and M.-S. Suh, 2011: Characteristics and trends of tropical night occurrence in South Korea for recent 50 years (1958-2007). Atmosphere, 21, 361-371 (in Korean with English abstract).
  28. Seo, K.-H., and S.-W. Son, 2012: The global atmospheric circulation response to tropical diabatic heating associated with the Madden-Julian oscillation during northern winter. J. Atmos. Sci., 69, 79-96, doi:10.1175/2011JAS3686.1. https://doi.org/10.1175/2011JAS3686.1
  29. Seo, K.-H., H.-J. Lee, and D. M. W. Frierson, 2016: Unraveling the teleconnection mechanisms that induce win tertime temperature anomalies over the Northern Hemisphere continents in response to the MJO. J. Atmos. Sci., 73, 3557-3571, doi:10.1175/JAS-D-16-0036.1. https://doi.org/10.1175/JAS-D-16-0036.1
  30. Vitart, F., 2014: Evolution of ECMWF sub-seasonal forecast skill scores. Q. J. R. Meteorol. Soc., 140, 1889-1899, doi:10.1002/qj.2256. https://doi.org/10.1002/qj.2256
  31. Walters, D. N., and Coauthors, 2011: The Met Office Unified Model global atmosphere 3.0/3.1 and JULES global land 3.0/3.1 configurations. Geosci. Model Dev., 4, 919-941, doi: 10.5194/gmd-4-919-2011. https://doi.org/10.5194/gmd-4-919-2011
  32. Wilks, D. S., 2011: Statistical methods in the atmospheric sciences. Vol. 100, Academic press, 676 pp.
  33. White, C. J., and Coauthors, 2017: Potential applications of subseasonal-to-seasonal (S2S) predictions. Meteor. Appl., 24, 315-325, doi:10.1002/met.1654. https://doi.org/10.1002/met.1654
  34. WMO, 2012: Subseasonal to seasonal prediction - Research implementation plan. World Meteorological Organization, 66 pp [Available online at https://library.wmo.int/pmb_ged/subseasonal_to_seasonal_prediction-research_implementation_plan_2012.pdf ].
  35. Yiou, P., and M. Nogaj, 2004: Extreme climatic events and weather regimes over the North Atlantic: When and where? Geophys. Res. Lett., 31, L07202.
  36. Yoo, C., N. C. Johnson, C.-H. Chang, S. B. Feldstein, and Y.-H. Kim, 2018: Subseasonal prediction of wintertime East Asian temperature based on atmospheric teleconnections. J. Climate, 31, 9351-9366, doi:10.1175/JCLI-D-17-0811.1. https://doi.org/10.1175/JCLI-D-17-0811.1
  37. Zhu, H., M. C. Wheeler, A. H. Sobel, and D. Hudson, 2014: Seamless precipitation prediction skill in the tropics and extratropics from a global model. Mon. Wea. Rev., 142, 1556-1569, doi:10.1175/MWR-D-13-00222.1. https://doi.org/10.1175/MWR-D-13-00222.1