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2020 Vol.53, Issue 4 Preview Page

Research Article

Intercomparison of uncertainty to bias correction methods and GCM selection in precipitation projections

강수량예측에서 편이보정방법과 GCM 선택에 대한 불확실성 비교

Young Hoon Songa
,
Eun-Sung Chungb*
송 영훈a
,
정 은성b*
aPh. D. student, Department Civil Engineering, Seoul National University of Science and Technology
bProfessor, Department Civil Engineering, Seoul National University of Science and Technology
a서울과학기술대학교 건설시스템공학과 박사과정
b서울과학기술대학교 건설시스템공학과 교수
* Corresponding Author
30 April 2020. pp. 249-258
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Abstract

Many climate studies have used the general circulation models (GCMs) for climate change, which can be currently available more than sixty GCMs as part of the Assessment Report (AR5). There are several types of uncertainty in climate studies using GCMs. Various studies are currently being conducted to reduce the uncertainty associated with GCMs, and the bias correction method used to reduce the difference between the simulated and the observed rainfall. Therefore, this study mainly considered climate change scenarios from nine GCMs, and then quantile mapping methods were applied to correct biases in climate change scenarios for each station during the historical period (1970-2005). Moreover, the monthly rainfall for the future period (2011-2100) is obtained from the RCP 4.5 scenario. Based on the bias-corrected rainfall, the standard deviation and the inter-quartile range (IQR) from the first to third quartiles were estimated. For 2071-2100, the uncertainty for the selection of GCMs is larger than that for the selection of bias correction methods and vice versa for 2011-2040. Therefore, this study showed that the selection of GCMs and the bias correction methods can affect the result for the future climate projection.

Keywords
General circulation model
Uncertainty
Quantile mapping
Standard deviation
Inter-quartile range

많은 기후 연구에서는 General Circulation Model (GCM)을 사용하여 연구를 수행하고 있는데, 현재는 5th Assessment Report (AR5)를 기반으로 한 60개 이상의 GCM이 생성되어 있다. 다양한 GCM을 사용하여 기후 연구를 수행하는 데 있어서 여러 종류의 불확실성이 존재한다. 현재 GCM에 의해 발생되는 불확실성을 줄이기 위해 다양한 연구들이 수행되고 있는데, 그 중에서 GCM의 모의값과 관측값의 차이를 줄이기 위해 사용되는 통계학적 편이보정방법이 적용되는 과정에서 발생하는 불확실성도 중요한 요인으로 분류되고 있다. 따라서 본 연구에서는 과거기간(1970년-2005년)의 지점별로 9개의 GCM과 9개의 분위사상법을 사용하여 산정된 결과를 토대로 RCP 4.5를 사용하여 전망기간(2011-2100년)의 월 강수량을 산정하였다. 산정된 강수량을 토대로 표준 편차와 1분위와 3분위의 변위값(inter-quartile range, IQR)을 산정하여 GCM과 편이보정방법으로 기준을 나누어 변동성을 정량화하여 불확실성을 비교하였다. 분석 결과로 표준편차와 IQR은 전망 기간이 뒤로 갈수록 GCM을 기준으로 계산된 결과가 점차 크게 산정되었다. 이를 통해 GCM의 선정과 편이보정 방법 선택이 미래 기후예측에 어느 정도 영향을 미치는지 확인하였다.

키워드
General circulation model
불확실성
분위사상법
표준 편차
Inter-quartile range
References
1
Ahmed, K., Sachindra, D.A., Shahid, S., Demirel, M.C., and Chung, E.S. (2019). "Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics." Hydrology and Earth System Sciences, Vol. 23, pp. 4803-4824.
10.5194/hess-23-4803-2019
2
Andreas, M.J., Daniel, G.K., Nicolas, J. Cullen., and Josef, S. (2018). "Intercomparison of different uncertainty sources in hydrological climate change projections for an alpine catchment (upper Clutha River, New Zealand)." Hydrology and Earth System Sciences, Vol. 22, pp. 3125-3142.
10.5194/hess-22-3125-2018
3
Boe, J., Terray, L., Habets, F., and Martin, E. (2007). "Statistical and dynamical downscaling of the Seine basinclimate for hydro-meteorological studies." International Journal of Climatology, Vol. 27, pp. 1643-1655.
10.1002/joc.1602
4
Cannon, A.J. (2008). "Probabilistic multisite precipitation downscaling by an expanded Bernoulli-gamma density network." Journal of Hydrometeorology, Vol. 9, pp. 1284-1300.
10.1175/2008JHM960.1
5
Cho, J., Jung, I., Cho, W., and Hwang, S. (2018). "User-centered climate change scenarios technique development and application of Korean peninsula." Journal of Climate Change Research, Vol. 9, No. 1, pp. 13-29.
10.15531/ksccr.2018.9.1.13
6
Dosiom, A., and Paruolo, P. (2011). "Bias correction of the ENSEMBLES high resolution climatechange projections for use by impact models: Evaluation on the present climate." Journal of Geophysical research Atmospheres, Vol. 116, No. D16, pp. 1-22.
10.1029/2011JD015934
7
Eum, H.I., and Cannon, A.J. (2017). "Intercomparison of projected changes in climate extremes for South Korea: Application of trend preserving statistical downscaling methods to the CMIP5 ensemble." International Journal of Climatology, Vol. 37, No. 8, pp. 3381-3397.
10.1002/joc.4924
8
Grillakis, M.G., Koutroulis, A.G., and Tsanis, I.K. (2013). "Multisegment statistical bias correction of daily GCM precipitation output." Journal of Geophysical research Atmospheres, Vol. 118, No. 8, pp. 3150-3162.
10.1002/jgrd.50323
9
Gudmundsson, L., Bremnes, J.B., Haugen, J.E., and Engen-Skaugen, T. (2012). "Technical note: Downscaling RCM precipitation to the station scale using statistical transformations-a comparison of methods." Hydrology and Earth System Sciences, Vol. 16, pp. 3383-3390.
10.5194/hess-16-3383-2012
10
Hori, M.E., and Ueda, H. (2006). "Impact of global warming on the East Asian winter monsoon as revealed by nine coupled atmosphere-ocean GCMs." Geophysical Research Letters, Vol. 33, pp. 1-4.
10.1029/2005GL024961
11
Hwang, S.W. (2014). "Assessing the performance of CMIP5 GCMs for various climatic elements and indicators over the southeast US." Journal of Korea Water Resources Association, Vol. 47, No. 11, pp. 1039-1050.
10.3741/JKWRA.2014.47.11.1039
12
Im, E.S., Kwon, W.T., and Bae, D.H. (2006). "A study on the regional climate change scenario for impact assessment on water resources." Journal of Korea Water Resources Association, Vol. 39, No. 12, pp. 1043-1056.
10.3741/JKWRA.2006.39.12.1043
13
Ines, A.V.M., and Hansen, J.W. (2006). "Bias correction of daily GCM rainfall for crop simulation studies." Agricultural and Forest Meteorology, Vol. 138, No. 1-4, pp. 44-53.
10.1016/j.agrformet.2006.03.009
14
Lee, J.K., and Kim, Y.O. (2010). "A Study on selection of standard scenarios in Korea for climate change." Climate Change Research, Vol. 1, No. 1, pp. 59-73.
15
Mgangira, M.B. (2009). "Impact of global warming on the East Asian winter monsoon as revealed by nine coupled atmosphere-ocean GCMs." Geophysical Research Letters, Vol. 33, No. 3, pp. 1-4.
16
Moon, S.J, Kim, J.J., and Kang, B.S. (2013). "Bias correction for GCM long-term prediction using nonstationary quantile mapping." Journal of Korea Water Resources Association, Vol. 46, No. 8, pp. 833-842.
10.3741/JKWRA.2013.46.8.833
17
Pang, B., Yue, J., Zhao, G., and Xu, Z. (2017). "Statistical downscaling of temperature with the random forest model." Advances in Meteorology, Vol. 2017, pp. 1-11.
10.1155/2017/7265178
18
Patricia, J.A., and Chung, E.S. (2019). "Uncertainty assessment in drought severities for the Cheongmicheon watershed using multiple GCMs and the reliability ensemble averaging method." Sustainability, Vol. 11, No. 16, p. 4823.
10.3390/su11164283
19
Piani, C., Weedon, G.P., Best, M., Gomes, S.M., Viterbo, P., Hagemann, S., and Haerterd, J.O. (2010). "Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models." Journal of Hydrology, Vol. 395, No. 3-4, pp. 199-215.
10.1016/j.jhydrol.2010.10.024
20
Shiru, M.S., Shahid, S., Chung, E.S., and Alias, N. (2019a). "Changing characteristics of meteorological droughts in Nigeria during 1901-2010." Atmospheric Research, Vol. 223, pp. 60-73.
10.1016/j.atmosres.2019.03.010
21
Shiru, M.S., Shahid, S., Chung, E.S., Alias, N., and Scherer, L. (2019b). "A MCDM-based framework for projection of spatio-temporal rainfall changes using feature selection methods and random forest regression: A case study of Nigeria." Atmospheric Research, Vol. 225, pp. 1-16.
10.1016/j.atmosres.2019.03.033
22
Song, Y.H., Chung, E.S., and Sung, J.H. (2019). "Selection framework of representative general circulation models using the selected best bias correction method." Journal of Korea Water Resources Association, Vol. 52, No. 5, pp. 337-347.
23
Song, Y.H., and Chung, E.S. (2019). "Spatial prioritization of permeable pavement considering multiple general circulation models: Mokgamcheon watershed." Journal of Korea Water Resources Association, Vol. 52, No. 12, pp. 1011-1023.
24
Sung, J.H., Chung, E.S., and Shahid S. (2018). "Reliability-Resiliency-Vulnerability approach for drought analysis in South Korea sing 28 GCMs." Sustainability, Vol. 10, pp.1-16.
10.3390/su10093043
25
Tobler, W.R. (1970). "A computer movie simulating urbuan growth in the detroit region." Economic Geography, Vol. 46, pp. 234 -240.
10.2307/143141
26
Wang, Q.J. (2013). "Toward accurate and reliable forecasts of Australian seasonal rainfall by calibrating and merging multiple coupled GCMs." Monthly Weather Review, Vol. 141, pp. 4554-4563.
10.1175/MWR-D-12-00253.1
27
Wilby, R.L., and Harris, I. (2006). "A framework for assessing uncertainties in climate change impacts: Low-flow scenarios for the River Thames, UK." Water Resources Research, Vol. 42, pp. 1-10.
10.1029/2005WR004065
Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 53
  • No :4
  • Pages :249-258
  • Received Date : 2020-02-13
  • Revised Date : 2020-03-17
  • Accepted Date : 2020-03-17
  • DOI :https://doi.org/10.3741/JKWRA.2020.53.4.249
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