Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

Assessment of Near-Term Climate Prediction of DePreSys4 in East Asia

DePreSys4의 동아시아 근미래 기후예측 성능 평가

  • Jung Choi (School of Earth and Environmental Sciences, Seoul National University) ;
  • Seul-Hee Im (APEC Climate Center) ;
  • Seok-Woo Son (School of Earth and Environmental Sciences, Seoul National University) ;
  • Kyung-On Boo (Climate Research Department, National Institute of Meteorological Sciences) ;
  • Johan Lee (Climate Research Department, National Institute of Meteorological Sciences)
  • 최정 (서울대학교 지구환경과학부) ;
  • 임슬희 (APEC 기후센터) ;
  • 손석우 (서울대학교 지구환경과학부) ;
  • 부경온 (국립기상과학원 기후연구부) ;
  • 이조한 (국립기상과학원 기후연구부)
  • Received : 2023.04.04
  • Accepted : 2023.06.13
  • Published : 2023.08.31
⟨ Previous Next ⟩

Abstract

To proactively manage climate risk, near-term climate predictions on annual to decadal time scales are of great interest to various communities. This study evaluates the near-term climate prediction skills in East Asia with DePreSys4 retrospective decadal predictions. The model is initialized every November from 1960 to 2020, consisting of 61 initializations with ten ensemble members. The prediction skill is quantitatively evaluated using the deterministic and probabilistic metrics, particularly for annual mean near-surface temperature, land precipitation, and sea level pressure. The near-term climate predictions for May~September and November~March averages over the five years are also assessed. DePreSys4 successfully predicts the annual mean and the five-year mean near-surface temperatures in East Asia, as the long-term trend sourced from external radiative forcing is well reproduced. However, land precipitation predictions are statistically significant only in very limited sporadic regions. The sea level pressure predictions also show statistically significant skills only over the ocean due to the failure of predicting a long-term trend over the land.

Keywords

Acknowledgement

본 연구는 기상청 "가까운 미래 기후예측을 위한 검증 및 평가기술 개발(KMI2022-01114)"의 지원을 받아 수행되었습니다. 논문을 검토해주신 두 분의 심사위원께 감사드립니다.

References

  1. Borchert, L. F., H. Pohlmann, J. Baehr, N.-C. Neddermann, L. Suarez-Gutierrez, and W. A. Muller, 2019: Decadal predictions of the probability of occurrence for warm summer temperature extremes. Geophy. Res. Lett., 46, 14042-14051, doi:10.1029/2019GL085385.
  2. 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.
  3. Choi, J., S.-W. Son, Y.-G. Ham, J.-Y. Lee, and H.-M. Kim, 2022: Seasonal-to-decadal prediction of El Nino-Southern Oscillation and Pacific Decadal Oscillation. npj Climate and Atmos. Sci., 5, 29, doi:10.1038/s41612-022-00251-9.
  4. Corti, S., A. Weisheimer, T. N. Palmer, F. J. Doblas-Reyes, and L. Magnusson, 2012: Reliability of decadal predictions. Geophys. Res. Lett., 39, L21712, doi:10.1029/2012GL053354.
  5. Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553-597, doi:10.1002/qj.828.
  6. Delgado-Torres, C., M. G. Donat, A. Soret, N. Gonzalez-Reviriego, P.-A. Bretonniere, A.-C. Ho, N. Perez-Zanon, M. S. Cabre, and F. J. Doblas-Reyes, 2023: Multi-annual predictions of the frequency and intensity of daily temperature and precipitation extremes. Environ. Res., Lett., 18, 034031, doi:10.1088/1748-9326/acbbe1.
  7. Dunstone, N., D. Smith, A. Scaife, L. Hermanson, R. Eade, N. Robinson, M. Andrews, and J. Knight, 2016: Skilful predictions of the winter North Atlantic Oscillation one year ahead. Nat. Geosci., 9, 809-814, doi: 10.1038/ngeo2824.
  8. Dunstone, N., D. Smith, A. Scaife, L. Hermanson, R. Eade, N. Robinson, M. Andrews, and J. Knight, and Coauthors, 2022: Towards Useful Decadal Climate Services. Bull. Amer. Meteor. Soc., 103, E1705-E1719, doi:10.1175/BAMS-D-21-0190.1.
  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.
  10. Gordon, C., C. Cooper, C. A. Senior, H. Banks, J. M. Greg ory, T. C. Johns, J. F. B. Mitchell, and R. A. Wood, 2000: The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Climate Dyn., 16, 147-168, doi:10.1007/s003820050010.
  11. Hermanson, L., and Coauthors, 2022: WMO global annual to decadal climate update: A prediction for 2021~25. Bull. Amer. Meteor. Soc., 103, E1117-E1129, doi:10.1175/BAMS-D-20-0311.1.
  12. Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 1999-2049, doi:10.1002/qj.3803.
  13. Hewitt, H. T., D. Copsey, I. D. Culverwell, C. M. Harris, R. S. R. Hill, A. B. Keen, A. J. McLaren, and E. C. Hunke, 2011: Design and implementation of the infrastructure of HadGEM3: the next-generation Met Office climate modelling system. Geosci. Model Dev., 4, 223-253, doi:10.5194/gmd-4-223-2011.
  14. Hyun, Y.-K., and Coauthors, 2022: The KMA Global Seasonal forecasting system (GloSea6) - Part 2: Climatological mean bias characteristics. Atmosphere, 32, 87-101, doi:10.14191/Atmos.2022.32.2.087.
  15. ICPO, 2011: Data and Bias Correction for Decadal Climate Predictions. CMIP-WGCMWGSIP Decadal Climate Prediction Panel. International CLIVAR Project Office Publication Series No. 150, 5 pp.
  16. Kim, H., J. Lee, Y.-K. Hyun, and S.-O. Hwang, 2021: The KMA Global Seasonal forecasting system (GloSea6) - Part 1: Operational system and improvements. Atmosphere, 31, 341-359, doi:10.14191/Atmos.2021.31.3.341.
  17. Knight, J. R., and Coauthors, 2014: Predictions of climate several years ahead using an improved decadal prediction system. J. Climate., 27, 7550-7567, doi:10.1175/JCLI-D-14-00069.1.
  18. Li, M., C. Li, Z. Jiang, X. Zhang, and F. W. Zwiers, 2022: Deciphering China's complex pattern of summer precipitation trends. Earth's Future, 10, e2022EF002797, doi:10.1029/2022EF002797.
  19. Liu, B., Y. Fang, S. Sun, G. Yang, Y. Duan, and C. Tana, 2022: Increasing precipitation in early winter over the southern China during the past 40 years. Geophy. Res. Lett., 49, e2022GL101134, doi:10.1029/2022GL101134.
  20. Meehl, G. A., and Coauthors, 2021: Initialized Earth system prediction from subseasonal to decadal timescales. Nat. Rev. Earth Environ., 2, 340-357, doi:10.1038/s43017-021-00155-x.
  21. Previdi, M., K. L. Smith, and L. M. Polvani, 2021: Arctic amplification of climate change: a review of underlying mechanisms. Environ. Res. Lett., 16, 093003, doi:10.1088/1748-9326/ac1c29.
  22. Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice and night marine air temperature since the late nineteenth century. J. Geophys. Res. Atmos., 108, 4407, doi:10.1029/2002JD002670.
  23. Schneider, U., P. Finger, A. Meyer-Christoffer, E. Rustemeier, M. Ziese, and A. Becker, 2017: Evaluating the hydrological cycle over land using the newly-corrected precipitation climatology from the Global precipitation Climatology Centre (GPCC). Atmosphere, 8, 52, doi:10.3390/atmos8030052
  24. Smith, D. M., and J. M. Murphy, 2007: An objective ocean temperature and salinity analysis using covariances from a global climate model. J. Geophys. Res. Oceans, 112, C02022, doi:10.1029/2005JC003172.
  25. Smith, D. M., S. Cusack, A. W. Colman, C. K. Folland, G. R. Harris, and J. M. Murphy, 2007: Improved surface temperature prediction for the coming decade from a global climate model. Science, 317, 796-799, doi: 10.1126/science.1139540.
  26. Smith, D. M., R. Eade, and H. Pohlmann, 2013: A comparison of full-field and anomaly initialization for seasonal to decadal climate prediction. Climate Dyn., 41, 3325-3338, doi:10.1007/s00382-013-1683-2.
  27. Son, S.-W., H. Kim, K. Song, S.-W. Kim, P. Martineau, Y.-K. Hyun, and Y. Kim, 2020: Extratropical prediction skill of the subseasonal-to-seasonal (S2S) prediction models. J. Geophy. Res. - Atmospheres, 125, e2019-JD031273, doi:10.1029/2019JD031273.
  28. Tippett, M. K., M. Ranganathan, M. L'Heureux, A. G. Barnston, and T. DelSole, 2019: Assessing probabilistic predictions of ENSO phase and intensity from the North American Multimodel Ensemble. Climate Dyn., 53, 7497-7518, doi:10.1007/s00382-017-3721-y.
  29. Vicente-Serrano, and Coauthors, 2022: Do CMIP models capture long-term observed annual precipitation trends? Climate Dyn., 58, 2825-2842, doi:10.1007/s00382-021-06034-x.
  30. Williams, K. D., C. M. Harris, A. Bodas-Salcedo, and Coauthors, 2015: The Met Office Global Coupled model 2.0 (GC2) configuration. Geosci. Model Dev., 8, 1509-1524, doi:10.5194/gmd-8-1509-2015.
  31. Williams, K. D., C. M. Harris, A. Bodas-Salcedo, and Coauthors, 2017: The Met Office Global Coupled model 3.0 and 3.1 (GC3.0 and GC3.1) configurations. J. Adv. Model. Earth Sys., 10, 357-380, doi: 10.1002/2017MS001115.
  32. Wills, R. C. J., Y. Dong, C. Proistosecu, K. C. Armour, and D. S. Battisti, 2022: Systematic climate model biases in the large-scale patterns of recent sea-surface temperature and sea-level pressure change. Geophys. Res. Lett., 49, e2022GL100011, doi:10.1029/2022GL100011.