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기상청 기후예측시스템(GloSea6) - Part 2: 기후모의 평균 오차 특성 분석

The KMA Global Seasonal forecasting system (GloSea6) - Part 2: Climatological Mean Bias Characteristics

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초록

In this paper, the performance improvement for the new KMA’s Climate Prediction System (GloSea6), which has been built and tested in 2021, is presented by assessing the bias distribution of basic variables from 24 years of GloSea6 hindcasts. Along with the upgrade from GloSea5 to GloSea6, the performance of GloSea6 can be regarded as notable in many respects: improvements in (i) negative bias of geopotential height over the tropical and mid-latitude troposphere and over polar stratosphere in boreal summer; (ii) cold bias of tropospheric temperature; (iii) underestimation of mid-latitude jets; (iv) dry bias in the lower troposphere; (v) cold tongue bias in the equatorial SST and the warm bias of Southern Ocean, suggesting the potential of improvements to the major climate variability in GloSea6. The warm surface temperature in the northern hemisphere continent in summer is eliminated by using CDF-matched soil-moisture initials. However, the cold bias in high latitude snow-covered area in winter still needs to be improved in the future. The intensification of the westerly winds of the summer Asian monsoon and the weakening of the northwest Pacific high, which are considered to be major errors in the GloSea system, had not been significantly improved. However, both the use of increased number of ensembles and the initial conditions at the closest initial dates reveals possibility to improve these biases. It is also noted that the effect of ensemble expansion mainly contributes to the improvement of annual variability over high latitudes and polar regions.

Abstract

In this paper, the performance improvement for the new KMA’s Climate Prediction System (GloSea6), which has been built and tested in 2021, is presented by assessing the bias distribution of basic variables from 24 years of GloSea6 hindcasts. Along with the upgrade from GloSea5 to GloSea6, the performance of GloSea6 can be regarded as notable in many respects: improvements in (i) negative bias of geopotential height over the tropical and mid-latitude troposphere and over polar stratosphere in boreal summer; (ii) cold bias of tropospheric temperature; (iii) underestimation of mid-latitude jets; (iv) dry bias in the lower troposphere; (v) cold tongue bias in the equatorial SST and the warm bias of Southern Ocean, suggesting the potential of improvements to the major climate variability in GloSea6. The warm surface temperature in the northern hemisphere continent in summer is eliminated by using CDF-matched soil-moisture initials. However, the cold bias in high latitude snow-covered area in winter still needs to be improved in the future. The intensification of the westerly winds of the summer Asian monsoon and the weakening of the northwest Pacific high, which are considered to be major errors in the GloSea system, had not been significantly improved. However, both the use of increased number of ensembles and the initial conditions at the closest initial dates reveals possibility to improve these biases. It is also noted that the effect of ensemble expansion mainly contributes to the improvement of annual variability over high latitudes and polar regions.

참고문헌 (32)

  1. Robert Adler, "The Global Precipitation Climatology Project (GPCP) Monthly Analysis (New Version 2.3) and a Review of 2017 Global Precipitation", Atmosphere, 9(4), pp.138~ (2018), MDPI AG[DOI]
  2. Min‐Seop Ahn, "MJO Propagation Across the Maritime Continent: Are CMIP6 Models Better Than CMIP5 Models?", Geophysical Research Letters, 47(11) (2020), American Geophysical Union (AGU)[DOI]
  3. Alberto Arribas, "The GloSea4 Ensemble Prediction System for Seasonal Forecasting", Monthly Weather Review, 139(6), pp.1891~1910 (2011), American Meteorological Society[DOI]
  4. I. A. Boutle, "Spatial variability of liquid cloud and rain: observations and microphysical effects", Quarterly Journal of the Royal Meteorological Society, 140(679), pp.583~594 (2013), Wiley[DOI]
  5. 장필훈, "기상청 전지구 해양자료동화시스템(GODAPS): 개요 및 검증", 대기, 31(2), pp.229~240 (2021), 한국기상학회[DOI]
  6. Davis, P., "2020 : A large ensemble seasonal forecasting system : GloSea6", AGU Fall Meeting 2020, #A192-05 (2020), American Geophysical Union
  7. D. P. Dee, "The ERA-Interim reanalysis: configuration and performance of the data assimilation system", Quarterly Journal of the Royal Meteorological Society, 137(656), pp.553~597 (2011), Wiley[DOI]
  8. Alex O. Gonzalez, "Winter mean lower tropospheric moisture over the Maritime Continent as a climate model diagnostic metric for the propagation of the Madden‐Julian oscillation", Geophysical Research Letters, 44(5), pp.2588~2596 (2017), American Geophysical Union (AGU)[DOI]
  9. 현유경, "현 기후예측시스템에서의 기온과 강수 계절 확률 예측 신뢰도 평가", 대기, 30(2), pp.141~154 (2020), 한국기상학회[DOI]
  10. Daehyun Kang, "The Role of Background Meridional Moisture Gradient on the Propagation of the MJO over the Maritime Continent", Journal of Climate, pp.1~54 (2021), American Meteorological Society[DOI]
  11. Hyemi Kim, "MJO Propagation Processes and Mean Biases in the SubX and S2S Reforecasts", Journal of Geophysical Research: Atmospheres, 124(16), pp.9314~9331 (2019), American Geophysical Union (AGU)[DOI]
  12. 김혜리, "기상청 기후예측시스템(GloSea6) - Part 1: 운영 체계 및 개선 사항", 대기, 31(3), pp.341~359 (2021), 한국기상학회[DOI]
  13. 김지영, "Global Coupled (GC) 모델 개선에 따른 동아시아 여름 몬순 모의성능 평가", 대기, 31(5), pp.563~576 (2021), 한국기상학회[DOI]
  14. 김상욱, "GloSea5 모형의 계절내-계절(S2S) 예측성 검정: Part 1. 북반구 중위도 지위고도", 대기, 28(3), pp.233~245 (2018), 한국기상학회[DOI]
  15. "Long-range Forecast Work Manual" (2015)[URL]
  16. 이소정, "기상청 기후예측시스템(GloSea5)의 여름철 동아시아 몬순 지수 예측 성능 평가", 대기, 30(3), pp.293~309 (2020), 한국기상학회[DOI]
  17. 임소민, "현업 기후예측시스템에서의 지면초기화 적용에 따른 예측 민감도 분석", 대기, 31(3), pp.327~340 (2021), 한국기상학회[DOI]
  18. C. MacLachlan, "Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system", Quarterly Journal of the Royal Meteorological Society, 141(689), pp.1072~1084 (2014), Wiley[DOI]
  19. A. Megann, "GO5.0: the joint NERC–Met Office NEMO global ocean model for use in coupled and forced applications", Geoscientific Model Development, 7(3), pp.1069~1092 (2014), Copernicus GmbH[DOI]
  20. 박연희, "기상청 기후예측시스템(GloSea5)의 과거기후장 앙상블 확대에 따른 예측성능 평가", 대기, 31(5), pp.511~523 (2021), 한국기상학회[DOI]
  21. Rae, J. G. L., "Development of the Global Sea Ice 6.0 CICE configuration for the Met Office Global Coupled model", Geosci. Model Dev, 8, pp.2221~2230 (2015)[DOI]
  22. N. A. Rayner, "Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century", Journal of Geophysical Research, 108(D14) (2003), American Geophysical Union (AGU)[DOI]
  23. Jeff K. Ridley, "The sea ice model component of HadGEM3-GC3.1", Geoscientific Model Development, 11(2), pp.713~723 (2018), Copernicus GmbH[DOI]
  24. A. A. Scaife, "Skillful long‐range prediction of European and North American winters", Geophysical Research Letters, 41(7), pp.2514~2519 (2014), American Geophysical Union (AGU)[DOI]
  25. 서은교, "전지구 계절 예측 시스템의 토양수분 초기화 방법 개선", 대기, 26(1), pp.35~45 (2016), 한국기상학회
  26. Eunkyo Seo, "Impact of soil moisture initialization on boreal summer subseasonal forecasts: mid-latitude surface air temperature and heat wave events", Climate Dynamics, 52(3-4), pp.1695~1709 (2018), Springer Science and Business Media LLC[DOI]
  27. Storkey, D., "UK Global Ocean GO6and GO7 : a traceable hierarchy of model resolutions", Geosci. Model Dev, 11, pp.3187~3213 (2018)[DOI]
  28. Jennifer Waters, "Implementing a variational data assimilation system in an operational 1", 4 degree global ocean model, 2014, pp.141(687), 333~349[DOI]
  29. David Walters, "The Met Office Unified Model Global Atmosphere 6.0", 6.1 and JULES Global Land 6.0, Geoscientific Model Development, pp.2017, 10(4)[DOI]
  30. Walters, D., "The Met Office Unified Model Global Atmosphere 7.0", 7.1 and JULES Global Land 7.0 configurations, 2019, pp.12, 1909~1963
  31. K. D. Williams, "The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations", Journal of Advances in Modeling Earth Systems, 10(2), pp.357~380 (2018), American Geophysical Union (AGU)[DOI]
  32. Young-Min Yang, "Diagnosing ISO Forecast from GloSea5 Using Dynamic-Oriented ISO Theory", Atmosphere, 12(1), pp.114~ (2021), MDPI AG[DOI]

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