[보고서]대기-해양 접합 모형을 이용한 계절 예측 및 예측성 연구

강인식

보고서 정보

주관연구기관

서울대학교
Seoul National University

연구책임자

강인식

참여연구자

양영민 , 함유근 , 박성빈 , 이종화 , 김대현 , 이동민 , 이은정 , 허소라 , 오봉현 , 홍수진 , 안민섭 , 장평화

보고서유형

2단계보고서

발행국가

대한민국

언어

한국어

발행년월

2012-02

과제시작연도

2011

주관부처

기상청

사업 관리 기관

기상청
Korea Meteorological Administration

등록번호

TRKO201200001075

과제고유번호

1365001131

DB 구축일자

2013-04-18

키워드

대기-해양 접합 모형,계절 예측,예측성,앙상블 칼만 필터,대기-해양 상호작용Coupled GCM,Seasonal Prediction,Predictability,Ensemble Kalman filter,Air-sea interaction

표 November-April mean precipitation(mm/day)(shaded) and 850-hPa zonal wind(m/s)(contoured) of (a) CMAP/NCEP/NCAR, (b) CAM3.5, (c) CAM3z, (d) CFS, (e) CM2.1, (f) ECHAM4/OPYC, (g) GEOS5, (h) SNU, and (i) SPCAM. Contours of mean 850-hPa zonal wind and plotted every 3 with the zero line represented by a thick solid line
표 Scatterplot of pattern correlation and normalized RMSE for November-April mean (a) precipitation, (b) outgoing longwave radiation, (c) 850-hPa and (d) 200-hPa zonal winds, and 20-100-day filtered variance map of (e) precipitation and (f) 850-hPa zonal wind.
표 As in Fig. 1 but for variance of 20-100-day bandpass filtered precipitation and 850-hPa zonal wind .
표 November-April wavenumber-frequency spectra of 10°S-10°N averaged precipitation(mm2day-2) (shaded) and 850-hPa zonal wind(m2s-2) (contoured) for the (a) CAMP/NCEP/NCAR, b) CAM3.5, (c) CAM3z, (d) CFS, (e) CM2.1, (f) ECHAM4/OPYC, (g) GEOS5, (h) SNU, and (i) SPCAM. Individual November-April spectra were calculated for each year and then averaged over all years of data. Only the climatological seasonal cycle and time mean for each November-April segment were removed before calculation of the spectra. The bandwidth is (180 days)-1.
표 Scatter plot of east/west ratio of power based on the data in Fig. 4.
표 First two CEOF modes of 20-100-day 15°S-15°N averaged 850-hPa and 200-hPa zonal wind and OLR for the (a) CAMP/NCEP/NCAR, (b) CAM3.5, (c) CAM3z, (d) CFS, (e) CM2.1, (f) ECHAM4/OPYC, (g) GEOS5, (h) SNU, and (i) SPCAM models.
표 The power spectrum of the unfiltered PC derived by projecting the CEOFs onto unfiltered data (seasonal cycle removed): first mode(blue) and second mode(green).
표 PC1 and PC2 phase space composite curves of the MJO index from the CEOF analysis [odd number (blue) and even number (green) initial phase].
표 Phase-longitude diagram of OLR [ contour plotted every 5 W m-2, positive (green) and negative (purple)] and surface latent heat flux (W m-2, shaded). Phases are from MJO life cycle composited and values averaged between 5°S and 5°N.
표 As in Fig. 9 but for 925-hPa moisture convergence
표 Scatterplot of mean coherence-squared PCs vs the pattern correlation of November-April time mean (a) precipitation, (b) 850-hPa zonal wind, (c) OLR, and (d) 200-hPa zonal wind.
표 Composite vertical profile of relative humidity based on precipitation rate: (a) ERA-40/GPCP, (b) CAM3.5, (c) CAM3z, (d) CM2.1, (e) ECAHM4/OPYC, (f) SNU, and (g) SNU without convective inhibition function.
$November-April mean stratiform rain fraction for the (a) TRMM 3A25 product, (b) CAM3.5, (c) CAM3z, (d) ECHAM4/OPYC, (e) SNU and (f) SNU (no trigger).$ 표 November-April mean stratiform rain fraction for the (a) TRMM 3A25 product, (b) CAM3.5, (c) CAM3z, (d) ECHAM4/OPYC, (e) SNU and (f) SNU (no trigger).
표 (a) SST and (b) PRCP climatological bias in the CNTL simulation, difference of (c) SST and (d) PRCP climatology in the DIUR simulation from that in the CNTL simulation. 850 hPa wind vector difference is also shown in panel (d).
표 The covariance between diurnal variation of mixed layer depth and that of net heat flux divided by the squares of mean mixed layer depth ( unit: K/month )
표 (a) Monthly mean OLR anomalies regressed onto local SST anomalies in the CNTL simulation, (b) Difference of OLR climatology in the DIUR simulation from that in the CNTL simulation (DIUR-CNTL)
표 (a) Equatorial (5°S-5°N) ocean temperature bias in the CNTL simulation, and (b) difference of climatological ocean temperature in the DIUR simulation from that in the CNTL simulation
표 Standard deviation of monthly mean SST anomalies. (a) OISST, (b) CNTL, (c) DIUR
표 Net heat flux anomalies (atmospheric damping) regressed onto NINO3.4 index (unit: W/m2). (a) ERA40, (b) CNTL, (c) DIUR
표 Difference of (a) shortwave, (b) evaporation, (c) longwave radiation, and (d) sensible heat flux anomalies regressed onto NINO3.4 in the DIUR simulation from that in the CNTL simulation Unit of each regressed field is W/m2
표 Precipitation anomalies regressed onto NINO3.4 index (unit: mm/day/K). (a) CMAP, (b) CNTL, (c) DIUR
표 Classification of the Models Used in Sperber and Annamalai (2008) With Respect to the Existence of CMT in Them
표 June–September averaged climatological precipitation (mm day −1, shaded) for (a) TRMM, (b) CMT, and (c) CTL. Contour interval is 2 mm day−1.
표 (a) Zonal wind shear used in the present model. (b) Eigen solution of the zonal wind for the most unstable mode. Negative values are represented with dashed lines. (c) The first eigen mode (unstable mode) of zonal wind (solid), meridional wind (dashed), and diagnosed precipitation (dotted box) as a function of latitude
표 Schematic diagram of the three‐dimensional circulation and convection in the presence of the CMT mechanism under the mean condition of a large vertical shear in the zonal wind, represented by the large grey arrows.
표 Description of participating models
표 All season standard deviation of 20-100 day band pass filtered precipitation. a) GPCP, b) AM2 (tok=0.025), c) SNU (tok=0.0), d) CAM3.1R (evap=0.05), e) CAM3.0+NRJ08, f) GEOS5, g) AM2 (tok=0.1), h) SNU (tok=0.1), i) CAM3.1R (evap=0.6), j) CAM3.0+ZM05, and k) CAM3.0+SP.
표 November-April lag-longitude diagram of 10N-10S averaged intraseasonal 850 hPa zonal wind anomalies correlated against intraseasonal zonal wind anomalies at the Indian Ocean (70-95E, 5S-5N averaged) reference point.
표 Scatter plot of standard deviation averaged over 0-360E, 30S-30N and east/west ratio, defined as ratio of eastward propagating spectral power (summationover wavenumber 1-3, period 30-70 day) to that of westward propagating counterpart.
표 Same as Figure 1, except for May-October mean precipitation.
표 Same as Figure 4, except for November-April mean precipitation
표 Scatter plots of standard deviation averaged over 0-360E, 30S-30N and a) May-October mean precipitation index which is defined as averaged precipitation over WP1 (120-160E, 5S-20oN) minus IO (60-95E, 15S-5N), and b) NovemberApfil mean precipitation index defined as averaged precipitation over WP2 (140-180E, 20S-Eq.) minus EP (200-260E, Eq.-10oN).
표 Difference map of May-October precipitation a) AM2, b) SNU, and c) CAM3.1R. Difference map of May-October evaporation e) AM2, f) SNU, and g) CAM3.1R.
표 Difference map of May-October standard deviation of 20-100 day filtered evaporation a) AM2, b) SNU, and c) CAM3.1R.
표 Scatter plots of standard deviation averaged over 0-360E, 30S-30N and mean tropospheric temperature averaged over 0-360E, 30S-30N.
표 Composite vertical profile of relative humidity based on precipitation rate a) GPCP/ERAinterim, b) AM2 (Tok=0.025), c) AM2 (Tok=0.1), e) SNU (Tok=0.0), f) SNU (Tok=0.1), h) CAM3.1R (evap=0.05), and i) CAM3.1R (evap=0.6).
$Upper: precipitation composite based on saturation fraction. Middle: probability density function (PDF) of saturation fraction, Lower: PDF weighted precipitation.$ 표 Upper: precipitation composite based on saturation fraction. Middle: probability density function (PDF) of saturation fraction, Lower: PDF weighted precipitation.
표 Schematics of Ensemble Kalman Filter
표 Time series of NINO3.4 SST anomalies of 100-years CZ-SPEEDY simulations b). Power-spectrum of the index
표 True states(black lines), EnKF analysis values(Red line in the upper panel), and observations(Red line in the lower panel) of thermocline depth at 110°W, 0°N from year 15 to 34
표 EOF a). first and b). second eigen-vector of fastest thermocline depth perturbations.
표 Upper panels show difference of averaged SST RMS error of all ensemble members from a). that of small initial error ensembles(EAVE-EFAST_SMALL), b). that of large initial error ensembles among selected members(EAVE-EFAST_LARGE). Lower panels show difference of averaged SST RMS error of c). small initial error group from small initial error ensembles among selected ensemble members (EAVE_SMALL-EFAST_SMALL), d). large initial error group from large initial error ensembles among selected ensemble members (EAVE_LARGE-EFAST_LARGE)
표 a). Correlation skills and b). RMS errors of four ensemble member mean NINO3.4 SST anomalies of the selected prediction(red circle), the control prediction(black circle), and that of 16 ensemble -member mean NINO3.4 SST anomalies(whole ensemble member obtained by EnKF, blue squares)
표 Spatial patterns of correlation skill of the control prediction for SST anomalies(contour), and the difference between the control and the selected prediction is shown(shading).
표 Signal-to-noise (STN) ratio of SST anomalies with respect to the forecast lead month.
표 Scatter diagram between signal-to-noise ratio(x-axis) and correlation improvement(y-axis) over eastern Pacific regions(180°E-90°W, 10°S-10°N)
표 Reduction of RMS errors by selecting fast growing perturbation(thick bar), ensemble spread of 16 ensemble members(thin bar), and NINO3.4 SST anomalies(solid line) of five biggest El Nino events.
표 Time-averaged TIW activity (variance ) in SST on MAY 1st from the free integration of SNU coupled GCM and nudged initial conditions using GODAS.
표 Spatial pattern of strongest TIWs in SST on May 1stfrom free integration
표 SST evolution during seasonal forecasts of 1984 in CNTL and EDDY forecasts
표 20-year mean of area-averaged TIW variability (TIWV) in SST with respect to forecast days in CNTL and EDDY forecasts.
표 20-year averaged seasonal TIWV difference of EDDY forecasts from CNTL forecasts.
표 Correlation coefficient between nonlinear TIW advection and NINO3.4 index. RED(Black) color denotes the results from EDDY (CNTL) forecasts
표 The magnitude of horizontal (zonal plus meridional advection) and vertical TIWs advection over the eastern Pacific region (170°W-110°W) regressed onto the NINO3.4 index in EDDY forecasts
표 El Nino and La Nina composite of the area-averaged nonlinear TIWs temperature advection in CNTL and EDDY forecasts.
표 Asymmetricity of observed and simulated NINO3.4 index with respect to forecast lead month.
표 Scatter plot of observed and simulated NINO3.4 index in August, and reduction of RMS errors in EDDY forecasts compared to that in CNTL forecasts.
표 Correlation skill of a NINO4, b NINO3.4, and c NINO3 indices with respect to forecast lead month from 20-year hindcast experiments.
표 Correlation map between observed and simulated August SST in CNTL and EDDY forecasts Upper (Lower) panel shows correlation coefficients of CNTL (EDDY) forecasts
표 Covariance of SST ensemble perturbations between values at 140°W, 2°N and values over the Pacific during El Nino and La Nina season using 32 ensemble perturbations
표 Schematic diagram of the EnKF algorithm with growing-error correction (EnKF-GEC)
표 Time series of Pacific area-averaged anomalous magnitude of correction term (grey), and ratio of ensemble spread to model error (black) of thermocline depth.
표 RMS Error (RMSE) improvement for the SST and thermocline depth anomaly.
표 singular Value Decomposition (SVD) results between the analysis error in the convectional EnKF (upper panels) and the correction term (lower panels) corresponding to the thermocline depth.
표 (a) Correlation and (b) RMSE of NINO 3 SST anomalies in the CNTL (filled squares ) and GEC prediction (unfilled circles).
표 Spatial pattern of correlation skill improvement for the SST anomalies with respect to the forecast lead month.
표 Scatter diagram between correlation improvement (y-axis) and correlation skill of CNTL prediction (x-axis) for 3-month (closed circle), 6-month (cross), and 9-month (open square) lead forecast over the eastern Pacific region (190˚ E-90˚W, 10˚S-10˚N)
표 Correlation skill of multi-linear regression models for RMM1(solid line) and RMM2(dashed line) indices as a function of forecast lead time.
표 Distribution of correlation coefficients between predicted and observed OLR anomalies for lead times from (a) 5 to (d) 20 days. Contour interval is 0.1.
표 Correlation skill of the Wavelet based model. Plotted is the correlation coefficient between the predicted and observed RMM1 time series for individual wavelet bands as a function of forecast lead time.
표 Correlation coefficients of the wavelet based predicted and observed RMM1(solid line) and RMM2(dashed line) for the total unfiltered data(black) and 30-80day filtered data(gray).
표 Correlation skill of the SSA based model for RMM1 (dotted line).
표 Wave-number-frequency power spectra computed with the equatorial (10°S-10°N averaged) time-longitude velocity potential at 200hPa. (a) observation, (b) AGCM, (c) CGCM.
표 Correlation skills of AGCM (long-short-dashed line) and CGCM (dashed line) for RMM1 and RMM2 as a function of lead time.
표 Pattern correlation coefficient (for the domain of 15°S-15°N and 0°-300°E) between the predicted and verifying values of reconstructed OLR anomalies for the strong (dashed), moderate (solid), and weak (dotted) MJO cases.
표 Correlation skill of the multi-model ensemble (solid line). Also plotted are for the AGCM, CGCM, and the multi-linear regression model.
표 Pattern correlation skill (0°-300°E and 15°S-15°N) of the reconstructed OLR anomalies as functions of different phases of MJO and forecast lead time. (a) for the statistical multi-linear regression model, and (b) for the AGCM.
표 Composite maps of the reconstructed OLR anomalies starting from the phase 3 initial condition for lead times from day 5 to day 20.
표 Correlation coefficients between predicted and verifying values of RMM1(solid) and RMM2(dash) for CGCM(red), and AGCM(blue). Correlations are shown as a function of forecast lead time.
표 Correlation skill for the bivariate MJO index as functions of different start phase of MJO and forecast lead time.
표 The amplitude and the relative degree of phase evolution and forecast lead time.
표 Eperiments Used in This Paper and Their Purposes
표 Time series of annual mean and 11‐year filtered SST anomalies (with respect to the period 1961 to 1990)
표 Regression of the HadISST Atlantic mean SST index of Figure 1 (ATLM; red curve) onto SSTs from (a) HadISST, (b) ATL_CO2, (c) ATL, (d) NOATL_CO2 and difference in SST, (e) 2xCO2 ‐CTR, (f) 2xCO2_NA‐CTR_NA, (g) Figure 2e–Figure 2f, and (h) response of ocean model to central‐west Pacific wind stress forcing. Contours indicate anomalies that are significant at the 5% level.
표 Regression of the HadISST Atlantic mean SSTindex of Figure 1 (ATLM; red curve) onto 200 hPa velocity potential and 925 hPa winds from (a) ATL, (b) ATL_CO2, (c) ATL_AGCM. All shaded anomalies are significant at the 5% level.
표 Black solid line denotes globally averaged SST (K) and red solid line denotes the north Atlantic averaged SST (K).
표 Differences between the mean of 2000 to 2009 and the mean of 1990 to 1999 (a) in SST (K) from ERSST (v.3). (b) Same as in (a), but for zonal wind at 850hPa (m s-1) from NCEP-NCAR reanalysis. (c) Same as in (a), but for precipitation (mm day-1) from GPCP.
표 Atmospheric responses to the Atlantic heating. They show differences between the time means of simulations with SNU AGCM forced with Fig. 2a (ATL_FOR) only in the Atlantic region and control case (NO_FOR). (a) Anomalous zonal wind at 850hPa (m s-1). (b) Same as in (a), but for Precipitation (mm day-1).
표 (a) Basic zonal mean SST prescribed to Aqua planet simulations. (b) Spatial pattern of SST anomalies (K) for the idealized warm pool.
표 Performed experiments and the longitudinal (X. range) and latitudinal (Y. range) range for experiments with the warm pool that the longitudinal location of the maximum SST value (L.max) shifts from west to east.
표 Anomalous zonal wind at 850hPa (m s-1) which means the difference between time means of simulations ATL_NO WP and NO WP.
표 Mean zonal wind at 850hPa (m s-1) from No forcing experiments (WP EXPs) for (a) EXP1, (b) EXP2, (c) EXP3, (d) EXP4, and (e) EXP5. (f), (g), (h), (i), (j) Same as in (a), (b), (c), (d), (e), but for precipitation (mm day-1).
표 Anomalous zonal wind at 850hPa (m s-1) which means the differences between the Atlantic forcing (ATL_WP EXP) and the No forcing (WP EXP) experiments for (a) EXP1, (b) EXP2, (c) EXP3, (d) EXP4, and (e) EXP5. (f), (g), (h), (i), (j) Same as in (a), (b), (c), (d), (e), but for precipitation (mm day-1).
표 (a) Scatter plot of longitudinal center of equatorial averaged (5°S-5°N) mean precipitation (mm day-1) from WP EXPs versus that of equatorial averaged (10°S-10°N) anomalous zonal wind at 850hPa (m s-1), calculated from the differences between ATL_WP EXPs and WP EXPs. (b) Same as in (a), but for mean precipitation versus anomalous precipitation (mm day-1).
표 The standard deviation of monthly mean sea surface temperature (SST) (in ℃) in (a) observations and (b) free integration of SNU CGCM.
표 First leading singular mode of initial (a) perturbation of SST and (b) thermocline depth and final (c) perturbation of SST and (d) thermocline depth.
표 First empirical orthogonal function mode of thermocline depth in October for (a) observations and (b) free integration of SNU CGCM. Note that the integration period for SNU CGCM is 40 years.
표 Ratio of SST ensemble spread between initial perturbation and monthly mean forecast over tropical Pacific SST (170°W–.90°W and 5°S–.5°N) in control (CNTL) (black) and ESV method (ESVM) (red).
표 Spatial pattern of correlation skill for September, October, and November (SON) SST in (a) CNTL and (b) ESVM.
표 (a) Correlation and (b) RMS errors of NINO3 SST in CNTL (black) and ESVM (red).
표 (a) Time series of NINO3 index in observations (black), CNTL forecast (blue), and ESVM forecast (red).
표 Noise‐to‐error ratio for SON SST forecast in (a) CNTL, (b) ESVM, and (c) ESVM‐.CNTL
표 계절예측을 위해서 수행된 실험설계
표 너징 자료에 하루씩 래그를 주어 만든 6개 대조군의 평균 예측 상 관계수. 예측 기간은 5월1일부터 6개월 간.
표 브리딩 기간을 3달로 한 초기조건을 이용해 5월 1일부터 6개월 간 적분한 해수면 온도의 상관계수
표 브리딩 기간을 3달로 한 초기조건을 이용해 5월 1일부터 6개월 간 적분한 해수면 온도의 상관계수
표 브리딩 기간을 6달로 한 초기조건을 이용해 5월 1일부터 6개월 간 적분한 해 수면 온도의 상관계수
표 브리딩 기간 1개월과 6개월로 5월1일부터 6개월 간 적분한 해수면 온도의평균 상관계수
표 브리딩 기간 1, 3, 6개월로 5월1일부터 6개월 간 적분한 해수면 온도의 평균 상관계수
표 ESV 초기조건으로 5월1일부터 6개월 간 적분한 해수면 온도의 상관계수
표 5월1일부터 7개월 간 적분한 니노 인덱스의 상관계수.
표 5월1일부터 7개월 간 적분한 니노 인덱스의 RMS error.
표 5월부터 시작한 각 실험 별 적분 첫째 달과 5월 관측 해수면 온도의 표준편차
표 5월부터 시작한 각 실험 별 적분 셋째 달과 7월 관측 해수면 온도의 표준편차
표 5월부터 시작한 각 실험 별 적분 여섯째 달과 10월 관측 해수면 온도의 표준편차
표 니노4, 니노3.4, 니노3 지역 평균 표준편차
표 The summary for experiment settings.
표 the eigen vector of the perturbation of bred vector in boreal summer, velocity potential at 200 hPa, the contour interval is 10e+6 m2/s.
표 space-time power spectra of velocity potential at 200 hPa.
표 hovmouller diagram of velocity potential at 200 hPa.
표 Time series of variance of perturbation.
표 여름철 ISO 예측을 위한 일련 예측 실험 세팅의 도식
표 correlation skill along lead day for RMM index using bred method
표 Correlation skill for the bivariate MJO index as functions of different start phase of MJO and forecast lead time, bred method
표 same as fig 3.60 except, using successive manifold scheme
표 same as fig 3.61 except using successive manifold scheme
표 same as fig 3.62 The red line indicates
표 5월 1일부터 적분한 BV3의 9월 서울 강수 아노말리(얇 은 검은 선), 서울의 9월 관측 강수 아노말리(굵은 검은 선) 그리고 BV3를 다운 스케일한 아노말리(빨간선)
표 5월 1일부터 적분한 BV3와 그 다운 스케일 값의 관측치와의 월별 패턴상관관계 계수 값.
표 The monthly data for CMAP and monthly predicted in SNUCGCM are expressed on upper and lower panel, respectively.
표 same as 그림 3.67 except for the daily data for GPCP and daily predicted in SNUCGCM, respectively.
표 forecast and observation time series of the daily precipitation anomaly in seoul.

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연구책임자(Manager) :	-
과제기간(DetailSeriesProject) :	-
총연구비 (DetailSeriesProject) :	-
키워드(keyword) :	-
과제수행기간(LeadAgency) :	-
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대기-해양 접합 모형을 이용한 계절 예측 및 예측성 연구
Seasonal Prediction and Predictability with a Coupled GCM 원문보기

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대기-해양 접합 모형을 이용한 계절 예측 및 예측성 연구 Seasonal Prediction and Predictability with a Coupled GCM 원문보기

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대기-해양 접합 모형을 이용한 계절 예측 및 예측성 연구
Seasonal Prediction and Predictability with a Coupled GCM 원문보기

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