APCC 다중 모형 자료 기반 계절 내 월 기온 및 강수 변동 예측성
Prediction Skill of Intraseasonal Monthly Temperature and Precipitation Variations for APCC Multi-Models
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초록
In this study, we investigate the predictability of intraseasonal monthly temperature and precipitation variations using hindcast datasets from eight global circulation models participating in the operational multi-model ensemble (MME) seasonal prediction system of the Asia- Pacific Economic Cooperation Climate Center for the 1983~2010 period. These intraseasonal monthly variations are defined by categorical deterministic analysis. The monthly temperature and precipitation are categorized into above normal (AN), near normal (NN), and below normal (BN) based on the -value ± 0.43 after standardization. The nine patterns of intraseasonal monthly variation are defined by considering the changing pattern of the monthly categories for the three consecutive months. A deterministic and a probabilistic analysis are used to define intraseasonal monthly variation for the multi-model consisting of numerous ensemble members. The results show that a pattern (pattern 7), which has the same monthly categories in three consecutive months, is the most frequently occurring pattern in observation regardless of the seasons and variables. Meanwhile, the patterns (e.g., patterns 8 and 9) that have consistently increasing or decreasing trends in three consecutive months, such as BN-NN-AN or AN-NNBN, occur rarely in observation. The MME and eight individual models generally capture pattern 7 well but rarely capture patterns 8 and 9.
Abstract
In this study, we investigate the predictability of intraseasonal monthly temperature and precipitation variations using hindcast datasets from eight global circulation models participating in the operational multi-model ensemble (MME) seasonal prediction system of the Asia- Pacific Economic Cooperation Climate Center for the 1983~2010 period. These intraseasonal monthly variations are defined by categorical deterministic analysis. The monthly temperature and precipitation are categorized into above normal (AN), near normal (NN), and below normal (BN) based on the -value ± 0.43 after standardization. The nine patterns of intraseasonal monthly variation are defined by considering the changing pattern of the monthly categories for the three consecutive months. A deterministic and a probabilistic analysis are used to define intraseasonal monthly variation for the multi-model consisting of numerous ensemble members. The results show that a pattern (pattern 7), which has the same monthly categories in three consecutive months, is the most frequently occurring pattern in observation regardless of the seasons and variables. Meanwhile, the patterns (e.g., patterns 8 and 9) that have consistently increasing or decreasing trends in three consecutive months, such as BN-NN-AN or AN-NNBN, occur rarely in observation. The MME and eight individual models generally capture pattern 7 well but rarely capture patterns 8 and 9.
참고문헌 (38)
- Ahn, J. -B., "A new multimodel ensemble method using nonlinear genetic algorithm: An application to boreal winter surface air temperature and precipitation prediction", J. Geophys. Res. Atmos, 121, pp.9263~9277 (2016)[DOI]
- 안중배, "PNU CGCM 앙상블 예보 시스템의 겨울철 남한 기온 예측 성능 평가", 대기, 28(4), pp.509~520 (2018), 한국기상학회[DOI]
- Alves, O., "Sensitivity of dynamical seasonal forecasts to ocean initial conditions", Q. J. R. Meteorol. Soc, 130, pp.647~667 (2004)[DOI]
- Balmaseda, M., "Impact of initialization strategies and observations on seasonal forecast skill", Geophys. Res. Lett, 36, pp.L01701~ (2009)[DOI]
- Bowler, N. E., "The MOGREPS short‐range ensemble prediction system", Q. J. R. Meteorol. Soc, 134, pp.703~722 (2008)[DOI]
- Ham, S., "A newly developed APCC SCoPS and its prediction of East Asia seasonal climate variability", Clim. Dyn, 52, pp.6391~6410 (2019)[DOI]
- Jeong, H. -I., "Assessment of the APCC coupled MME suite in predicting the distinctive climate impacts of two flavors of ENSO during boreal winter", Clim. Dyn, 39, pp.475~493 (2012)[DOI]
- Kanamitsu, M., "NCEPDOE AMIP-II Reanalysis(R-2)", Bull. Amer. Meteor. Soc, 83, pp.1631~1644 (2002)[DOI]
- Kang, D., "Prediction of the Arctic Oscillation in boreal winter by dynamical seasonal forecasting systems", Geophys. Res. Lett, 41, pp.3577~3585 (2014)[DOI]
- Kim, G., "Global and regional skill of the seasonal predictions by WMO lead centre for long-range forecast multi-model ensemble", Int. J. Climatol, 36, pp.1657~1675 (2016)[DOI]
- Kim, H. -J., "Improvement in prediction of the arctic oscillation with a realistic ocean initial condition in a CGCM", J. Climate, 28, pp.8951~8967 (2015)[DOI]
- 김상욱, "GloSea5 모형의 계절내-계절(S2S) 예측성 검정: Part 1. 북반구 중위도 지위고도", 대기, 28(3), pp.233~245 (2018), 한국기상학회[DOI]
- Kirtman, B. P., "The North American multimodel ensemble : phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction", Bull. Amer. Meteor. Soc, 95, pp.585~601 (2014)[DOI]
- Krishnamurti, T. N., "Multimodel ensemble forecasts for weather and seasonal climate", J. Climate, 13, pp.4196~4216 (2000)[DOI]
- Lee, R. W. -K., "Predictability of two types of El Niño and their climate impacts in boreal spring to summer in coupled models", Clim. Dyn, 51, pp.4555~4571 (2018)[DOI]
- "Improvements in POAMA2 for the prediction of major climate drivers and south eastern Australian rainfall" (2012)[URL]
- Lin, H., "The Canadian Seasonal to Interannual Prediction System Version 2(CanSIPSv2)", Wea. Forecasting, 35, pp.1317~1343 (2020)[DOI]
- Lorenz, E. N., "The predictability of a flow which possesses many scales of motion", Tellus, 21, pp.289~307 (1969)[DOI]
- Merryfield, W. J., "The Canadian seasonal to interannual prediction system. Part I: Models and initialization", Mon. Wea. Rev, 141, pp.2910~2945 (2013)[DOI]
- Min, Y. -M., "Assessment of APCC multimodel ensemble prediction in seasonal climate forecasting : Retrospective(1983-2003)and real-time forecasts(2008-2013)", J. Geophys. Res. Atmos, 119, pp.12132~12150 (2014)[DOI]
- Min, Y. -M., "Skill of real-time operational forecasts with the APCC multimodel ensemble prediction system during the period 2008-2015", Clim. Dyn, 49, pp.4141~4156 (2017)[DOI]
- Molod, A., "GEOS‐S2S version 2:The GMAO high‐resolution coupled model and assimilation system for seasonal prediction", J. Geophys. Res. Atmos, 125 (2020)[DOI]
- Paek, H., "A source of AGCM bias in simulating the western Pacific subtropical high: Different sensitivities to the two types of ENSO", Mon. Wea. Rev, 143, pp.2348~2362 (2015)[DOI]
- Palmer, T. N., "Development of a European multimodel ensemble system for seasonalto-interannual prediction(DEMETER)", Bull. Amer. Meteor. Soc, 85, pp.853~872 (2004)[DOI]
- Park, H. -J., "One-monthlead predictability of asian summer monsoon indices based on the zonal winds by the APCC multimodel ensemble", J. Climate, 31, pp.8945~8960 (2018)[DOI]
- 박진경, "S2S 멀티 모델 앙상블을 이용한 북극 해빙 면적의 예측성", 대기, 28(1), pp.15~24 (2018), 한국기상학회[DOI]
- Richardson, D. S., "Skill and relative economic value of the ECMWF ensemble prediction system", Q. J. R. Meteorol. Soc, 126, pp.649~667 (2000)[DOI]
- Saha, S., "The NCEP climate forecast system version 2", J. Climate, 27, pp.2185~2208 (2014)[DOI]
- Sohn, S. -J., "Assessment of the long‐lead probabilistic prediction for the Asian summer monsoon precipitation (1983-2011)based on the APCC multimodel system and a statistical model", J. Geophys. Res. Atmos, 117, pp.D04102~ (2011)[DOI]
- 송강현, "GloSea5 모형의 계절내-계절 예측성 검정: Part 2. 성층권 돌연승온", 대기, 28(2), pp.123~139 (2018), 한국기상학회[DOI]
- Stensrud, D. J., "Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems", Mon. Wea. Rev, 128, pp.2077~2107 (2000)[DOI]
- Sun, J., "Dynamical seasonal predictability of the Arctic Oscillation using a CGCM", Int. J. Climatol, 35, pp.1342~1353 (2015)[DOI]
- Vitart, F., "The sub-seasonal to seasonal prediction project(S2S)and the prediction of extreme events", Clim. Atmos. Sci, 1, pp.3~ (2018)[DOI]
- Vitart, F., "Subseasonal to Seasonal Prediction Project : Bridging the gap between weather and climate", WMO Bull, 61, pp.23~28 (2012)
- Vitart, F., "The subseasonal to seasonal(S2S)prediction project database", Bull. Amer. Meteor. Soc, 98, pp.163~173 (2017)[DOI]
- Wei, M., "Initial perturbations based on the ensemble transform(ET)technique in the NCEP global operational forecast system", Tellus A, 60, pp.62~79 (2008)[DOI]
- Weigel, A. P., "Can multi‐model combination really enhance the prediction skill of probabilistic ensemble forecasts? Q", J. R. Meteorol. Soc, 134, pp.241~260 (2008)[DOI]
- Wilks, D. S., "Statistical methods in the atmospheric sciences", pp.676~ (2011), Academic Press