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Contents 5
요약문 13
SUMMARY 15
제1장 서론 18
제2장 기상연구소 3개월 예측시스템 운영 및 차세대 기후예측 시스템 기반 조성 21
제1절 기상연구소 3개월 예측시스템 운영 21
1. 개요 21
2. 현업 3개월 예보 지원 23
제2절 표준검증시스템 구축 및 3개월 예측성 평가 28
1. 3개월 예측의 표준검증시스템 구축 28
2. 기상연구소 기후모델의 3개월 예측성 평가 33
제3절 차세대 기후모델의 계절 예측 성능 평가 63
1. 서론 63
2. 실험 설계 65
3. 여름철 예측성 평가 70
제3장 동아시아 지역 계절 변동성 연구 85
제1절 동아시아 지역 여름 몬순에 대한 계절안 진동의 영향 85
1. 여름철 Madden-Julian Oscillation 지수의 정의 86
2. 여름철 MJO의 수십년 주기변동 93
3. 동아시아 여름몬순에 대한 MJO의 영향 96
4. 결론 100
제2절 계절안 진동 모의에 대한 모델 해상도 및 물리과정의 민감도 101
1. 연구배경 101
2. 실험 방법 및 자료 103
3. 모델 실험 결과 및 토의 105
4. 결론 133
제4장 기상연구소 기후 모델의 물리과정 개선 134
제1절 배경 134
제2절 물리과정 개선 결과 136
제5장 결론 138
References 143
2006 학술용역과제 150
기상연구소 기후모델의 물리과정 개선 (II) (Improvement in Physical Parameterization of METRI-AGCM (II)) 150
연구 보고서 151
목차 152
요약문 153
제1장 연구개발의 개요 157
제2장 연구개발 결과 158
제1절 새로운 복사 방안 및 지면 모형이 병합된 METRI AGCM의 개발과 계절 예측성 평가 158
1. 새로운 복사 모수화 방안의 개요 158
2. 새로운 지면 모델 CLM3의 개요 159
3. 현 기후 재현 실험 및 모의 결과 비교 검증 161
4. 하인드캐스트 실험의 수행과 계절 예측성 분석 227
제2절 지면 초기화 시스템 개발 및 계절 예측성 평가 253
1. 지면 과정의 개선 253
2. 지면 초기화 방안 (Land initialization method) 263
3. 지면 초기화 방안이 적용된 MAGCM-CLM3 접합 모델의 앙상블 계절 예측성 평가 277
제3절 모델 물리과정의 미세 조절 287
1. 모델 물리 과정의 튜닝 287
2. 미세 조절에 따른 민감도 분석 289
제3장 요약 및 결론 311
참고 문헌 313
Table 2.2.1. List of ENSO years with season. 44
Table 2.3.1. Compile options for MPI run of YOURS GSM. 68
Table 3.1.1. Correlation coefficients among the principal components of each mode and the variance ratio against total variance each MJO-related mode (Group1). 88
Table 3.2.1. Lists of sensitivity experiments. 104
Fig. 2.1.1. The schematic diagram of METRI three-month prediction system using the METRI AGCM. 22
Fig. 2.1.2. Snapshot from web page of METRI 3-month prediction system. 24
Fig. 2.1.3. Menu of 3-month prediction web page. 25
Fig. 2.1.4. Control panel for results of 3-month predictions. 26
Fig. 2.1.5. Control panel for hindcast verification. 26
Fig. 2.1.6. Control panel for forecast verification. 27
Fig. 2.1.7. Control panel for results of prediction from statistical model. 27
Fig. 2.2.1. Comparison of seasonal mean precipitation (mm day-¹) between (left panels) the observation and (right panels) the simulation from METRI AGCM. Shading interval is 2 mm day-¹(이미지참조) 35
Fig. 2.2.2. Zonal mean distribution of (solid line) the observation and (dashed line) the simulated precipitation (mm day-1)(이미지참조)) for each season. 36
Fig. 2.2.3. Seasonal mean difference of (a) 500 hPa geopotential height (m), (b) 200 hPa geopotential height (m), (c) Sea level pressure (hPa), and (d) 850 hPa temperature (K) taken from the simulation of METRI AGCM and the NCEP/DOE reanalysis II data. 38
Fig. 2.2.4. Statistics of (a)-(b) the simulated precipitation, (c)-(d) sea level pressure, (e)-(f) 850 hPa temperature, (g)-(h) 500 hPa geopotential height, and (i)-(j) 200 hPa geopotential height over the globe during the SMIP periods 41
Fig. 2.2.5. Warm-minus-cold composite seasonal-mean precipitation (mm day-¹(이미지참조)) for (left panels) the observation and (tight panels) the simulation. Panels from top to bottom indicate spring, summer, fall, and winter composite differences, respectively 46
Fig. 2.2.6. As in Fig. 2.2.5. except for the seasonal-mean geopotential height at the 500 hPa level with 15 m contour interval. 47
Fig. 2.2.7. Distribution of (left panel) signal variance, (middle panel) noise variance, and (right panel) signal-to-noise ratio from (a) simulated precipitation, (b) sea level pressure, (c) 850 hPa temperature, (d) 500 hPa geopotential height, and (e) 200 hPa geopotential height for boreal spring, respectively. 49
Fig. 2.2.8. As in Fig. 2.2.7. except for boreal summer. 50
Fig. 2.2.9. As in Fig. 2.2.7. except for boreal autumn. 51
Fig. 2.2.10. As in Fig. 2.2.7. but for boreal winter. 52
Fig. 2.2.11. Statistics of (a)-(b) precipitation, (c)-(d) sea level pressure, (e)-(f) 850 hPa temperature, (g)-(h) 500 hPa geopotential height, and (i)-(j) 200 hPa geopotential height over the globe (GL), northern hemisphere (NH), and East Asia (EA) with lead time. 55
Fig. 2.2.12. Change of the anomaly correlation of (a) precipitation, (b) sea level pressure, (c) 850 hPa temperature, (d) 500 hPa geopotential height, and (e) 200 hPa geopotential height with the lead time. 56
Fig. 2.2.13. Variation of monthly Heidke skill score of (a) precipitation, (b) 850 hPa temperature, and (c) 500 hPa geopotential height over the east Asian region (10˚N~60˚N, 80˚E ~ 180˚E). 58
Fig. 2.2.14. As in Fig. 2.2.13. except for mean squared skill score. 59
Fig. 2.2.15. Time-latitude change of the signal-to-noise ratio of (a) precipitation, (b) sea level pressure, (c) 850 hPa temperature, and (d) 500 hPa geopotential height over the east Asian region. 60
Fig. 2.2.16. Change of the signal-to-noise ratio of (a) precipitation, (b) sea level pressure, (c) 850 hPa temperature, (d) 500 hPa geopotential height, and (e) 200 hPa geopotential height with the lead time. 62
Fig. 2.3.1. Description of YOURS GSM structure. 67
Fig. 2.3.2. Comparison of summer mean precipitation (mm day-¹(이미지참조)), (a) means observation, (b)-(d) are the simulated precipitation by METRI AGCM. YOURS GSM with T62L28 resolution, and YOURS GSM with T126L28 resolution, respectively. 72
Fig. 2.3.3. Zonal mean distribution of summer precipitation (mm day-¹(이미지참조)) by observation (CMAP) and model simulation (T062, T126, METRI). 73
Fig. 2.3.4. Difference between the simulation and observation (RA2) for (a) 850 hPa temperature (K) and (b) 500 hPa geopotential height (m). 74
Fig. 2.3.5. Statistics of (top) the simulated precipitation, (middle) 850 hPa temperature, and (bottom) 500 hPa geopotential height over the globe during the SMIP periods. 76
Fig. 2.3.6. Warm-minus-cold composite seasonal-mean precipitation (mm day-¹(이미지참조)) for (a) the observation and (b)-(d) the simulations. 78
Fig. 2.3.7. As in Fig. 2.3.6. except for the summer mean geopotential height at the 500 hPa level with 15 m contour interval. 79
Fig. 2.3.8. Distribution of signal variance, noise variance, and signal-to-noise ratio from top to bottom panels of simulated precipitation for each experiment, respectively. 81
Fig. 2.3.9. As in Fig. 2.3.8. except for 850 hPa temperature in units of K² for signal and noise variances. 82
Fig. 2.3.10. As in Fig 2.3.8. except for 500 hPa geopotential height in units of 0.01 ㎡ for signal and noise variances. 84
Fig. 3.1.1. EOF analysis of pentad OLR anomalies meridional-averaged over the 10˚N~10˚S for 1979~2004. OLR data are filtered by high-pass and low-pass filters in 25~75days band. 87
Fig. 3.1.2. Hovmoller(이미지참조) of total tropical intraseasonal oscillation (a) and that of Group1 decomposition (b). 90
Fig. 3.1.3. Classification of key MJO regions. Boxes denote the Indo-west Pacific region(40˚E~160˚W), east Indian Ocean(70˚E~100˚E) and west Pacific region(110˚E~ 200˚E), respectively. 92
Fig. 3.1.4. Summertime(JJA) MJO amplitude in the Indo-west Pacific region for 1979~2004. 92
Fig. 3.1.5. Summertime(JJA) MJO amplitude in the east Indian region (black bars) and the west Pacific region(grey bars) for 1979~2004. 93
Fig. 3.1.6. The zonal wavenumber-frequency power spectra of the filtered pentad OLR anomaly data in the tropics (10˚N~ 10˚S). a) indicates the raw power spectra for the former term (1979~1992) and b) is for the latter term (1992~2004). 95
Fig. 3.1.7. Summertime 5880 line in the East Asian region each year for 1992~2004 (a), mean 5880 line in the extreme MJO case (thick solid line, 2000, 2002, 2004) and in normal MJO summer (thin solid line). 97
Fig. 3.1.8. Composite of summertime precipitation anomalies in the extreme MJO case for 1992~2004 (a), and precipitation anomalies each extreme case (b)~(d). 98
Fig. 3.1.9. Composite of korean summertime precipitation (a) and temperature (b) in the extreme MJO cases. 99
Fig. 3.2.1. The 8 years-mean summertime (MJJAS, 1997-2004) climate of precipitation from observation and experiments. 106
Fig. 3.2.2. Same as in Fig 3.2.1 but for 850 hPa wind (vector) and its zonal components (contours). 107
Fig. 3.2.3. Global distributions of the 20-70-day variance of precipitation from observation and experiments. 109
Fig. 3.2.4. Wavenumber-frequency power spectra for precipitation over 15˚S~15˚N from observation and experiments. 111
Fig. 3.2.5. Power spectra for timeseries of the first CEOF mode with 200 hPa velocity potential from observation and experiments. 113
Fig. 3.2.6. Composite 200 hPa velocity potential anomalies (contours) and precipitation anomalies (shaded) for observation at phases 1, 4, 7, 10, respectively. 116
Fig. 3.2.7. Same As in Fig. 3.2.6. except for CTRL experiment. 117
Fig. 3.2.8. Same As in Fig. 3.2.6. except for EXP-cnv experiment. 118
Fig. 3.2.9. Same As in Fig. 3.2.6. except for EXP-pbl experiment. 119
Fig. 3.2.10. Same As in Fig. 3.2.6. except for EXP-res experiment. 120
Fig. 3.2.11. Longitude-phase cross sections of the composite precipitation anomalies averaged over 5˚S~5˚N for observation and experiments. 122
Fig. 3.2.12. Same as in Fig. 3.2.11. except for 850 hPa zonal wind anomalies. 124
Fig. 3.2.13. As in Fig. 3.2.11. but for 200 hPa zonal wind anomalies. 125
Fig. 3.2.14. Phase-height cross sections of the velocity potential for observation and experiments. 126
Fig. 3.2.15. Same as in Fig. 3.2.14. except for zonal wind anomalies. 127
Fig. 3.2.16. Same As in Fig. 3.2.14. except for specific humidity anomalies. 128
Fig. 3.2.17. Longitude-height cross sections of velocity potential, wind, and specific humidity for observation and experiments at phase 4. 130
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