EBMA를 이용한 CMIP 대기대순환모형의 일평균기온 앙상블 산출
Production of Daily Mean Temperature Ensemble from the CMIP GCM Using EBMA
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초록
Intergovernmental Panel on Climate Change (IPCC) provides various prospects of future climate change under the Representative Concentration Pathways (RCP) scenarios using General Circulation Models (GCMs) of Coupled Model Intercomparison Project (CMIP). This paper describes a modified application of Ensemble Bayesian Model Averaging (EBMA) to produce daily mean temperature ensembles using 19 GCMs provided by CMIP. We proposed two types of approach: (1) monthly weighting scheme for a whole area (EBMA.v1) and (2) monthly weighting for each grid point (EBMA.v2), which can take into account the spatially heterogeneous pattern of GCM. For the training period of 1979-2005 for East Asia, 9,855 sets of daily temperature ensembles (27 years × 365 days) were produced and compared to the ERA-Interim reanalysis data of European Centre for Medium-Range Weather Forecasts (ECMWF), which showed better validation statistics than the general mean and median ensembles. In particular, EBMA.v2 outperformed EBMA.v1 by diminishing the large errors of inland areas where the surface heterogeneity is larger than the ocean. The EBMA.v2 was able to handle the problem of spatial variability by employing monthly and spatially varying weighting scheme. We finally produced daily mean temperature ensembles for the period of 2006-2100 by using the EBMA.v2 under the RCP 6.0 scenario, which are going to be provided on the web.
Abstract
Intergovernmental Panel on Climate Change (IPCC) provides various prospects of future climate change under the Representative Concentration Pathways (RCP) scenarios using General Circulation Models (GCMs) of Coupled Model Intercomparison Project (CMIP). This paper describes a modified application of Ensemble Bayesian Model Averaging (EBMA) to produce daily mean temperature ensembles using 19 GCMs provided by CMIP. We proposed two types of approach: (1) monthly weighting scheme for a whole area (EBMA.v1) and (2) monthly weighting for each grid point (EBMA.v2), which can take into account the spatially heterogeneous pattern of GCM. For the training period of 1979-2005 for East Asia, 9,855 sets of daily temperature ensembles (27 years × 365 days) were produced and compared to the ERA-Interim reanalysis data of European Centre for Medium-Range Weather Forecasts (ECMWF), which showed better validation statistics than the general mean and median ensembles. In particular, EBMA.v2 outperformed EBMA.v1 by diminishing the large errors of inland areas where the surface heterogeneity is larger than the ocean. The EBMA.v2 was able to handle the problem of spatial variability by employing monthly and spatially varying weighting scheme. We finally produced daily mean temperature ensembles for the period of 2006-2100 by using the EBMA.v2 under the RCP 6.0 scenario, which are going to be provided on the web.
참고문헌 (16)
- 배덕효, "GCM과 수문모형의 불확실성을 고려한 기후변화에 따른 한반도 미래 수자원 전망", 한국수자원학회 논문집, 44(5), pp.389~406 (2011), 한국수자원학회
- Claeskens, G., "Model Selection and Model Averaging" (2008), Cambridge University Press
- The expectation maximization algorithm, "Dellaert, F.", Georgia Institute of Technology, ~, pp.Technical Report Number GIT-GVU-02-20 (2002)
- Dempster, A. P., "Maximum likelihood from incomplete data via the EM algorithm, Journal of the royal statistical society", Series B (methodological), 39(1), pp.1~38 (1977)
- Deser, C., "Uncertainty in climate change projections:the role of internal variability", Climate Dynamics, 38(3-4), pp.527~546 (2012)
- Duan, Q., "Multi-model ensemble hydrologic prediction using Bayesian model averaging", Advances in Water Resources, 30(5), pp.1371~1386 (2007)
- Giorgi, F., "Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the “Reliability Ensemble Averaging” (REA)method", Journal of Climate, 15(10), pp.1141~1158 (2002)
- Hoeting, J. A., "Bayesian model averaging: a tutorial", Statistical science, 14(4), pp.382~401 (1999)
- Climate Change 2014: Synthesis Report, Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, "IPCC", IPCC, 151~ (2014)
- Kim, K., "Blending of satellite SST products using ensemble Bayesian model averaging (EBMA)", Remote Sensing Letters, 7(9), pp.827~836 (2016)
- 김태정, "GCM Ensemble을 활용한 추계학적 강우자료 상세화 기법 개발", 한국수자원학회 논문집, 47(9), pp.825~838 (2014), 한국수자원학회
- Leith, C. E., "Theorectical skill of Monte-Carlo forecasts", Monthly Weather Review, 102(6), pp.409~418 (1974)
- Min, S. -K., "Probabilistic climate change predictions applying Bayesian model averaging, Philosophical Transactions of the Royal Society of London A:Mathematical", Physical and Engineering Sciences, 365(1857), pp.2103~2116 (2007)
- Raftery, A. E., "Using Bayesian model averaging to calibrate forecast ensembles", Monthly Weather Review, 133(5), pp.1155~1174 (2005)
- Sloughter, J. M., "Probabilistic wind vector forecasting using ensembles and Bayesian model averaging", Monthly Weather Review, 141(6), pp.2107~2119 (2013)
- Vrugt, J. A., "Ensemble Bayesian model averaging using Markov chain Monte Carlo sampling", Environmental fluid mechanics, 8(5-6), pp.579~595 (2008)