[보고서]CORDEX-동아시아 다중 지역기후모델 자료의 극한기후 미래전망 활용성 연구

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CORDEX-동아시아 다중 지역기후모델 자료의 극한기후 미래전망 활용성 연구 원문보기

보고서 정보
주관연구기관	포항공과대학교 Pohang University of Science and Technology
연구책임자	민승기
보고서유형	최종보고서
발행국가	대한민국
언어	한국어
발행년월	2016-03
과제시작연도	2015
주관부처	기상청 Korea Meteorological Administration(KMA)
등록번호	TRKO201800022700
과제고유번호	1365002070
사업명	기후변화감시·예측및국가정책지원강화
DB 구축일자	2018-06-23
키워드	기후변화.극한기후.동아시아.지역기후모델.Climate Change.Climate Extremes.East Asia.Regional Climate Model.CORDEX.

초록 ▼

○ CORDEX 다중 지역기후모델의 동아시아 극한기후 모의성능 평가
- 지역기후모델은 여름철 평균과 극한 모두에서 계통오차를 보임.
* 동아시아 해안을 따라 cold 바이어스가 우세, warm 바이어스는 중국 북부 우세
* 대부분 동아시아는 wet 바이어스, 우리나라는 상당한 dry 바이어스 (관측보다 장마전선이 북상하지 않음)

- 지역기후모델은 평균기온을 극한기온보다 잘 모의하며(높은 공간 상관성으로), 평균강수보다 극한강수를 잘 모의함(우수한 공간변동성 크기 모의).

○ CORDEX-동아시아 다중 지역기후모델에 나타나는 극한기후 미래변화 양상 분석
- 기온과 강수는 평균/극한 모두에서 동아시아 전반에 걸쳐 증가.
- 극한기온 증가는 평균강도(GEV 위치 매개변수)가, 극한강수의 증가는 경년변동성(GEV 규모 매개변수)이 중요한 요인으로 작용.
- 평균-극한 미래전망의 관련성은 지역 평균 및 격자 규모에서도 크게 나타남.
- 모델 성능과 미래 전망 간에 연관성이 나타남(성능이 좋은 모델이 미래 기온/강수 전망이 작은 경향).

○ CORDEX-동아시아 자료의 극한기후 미래전망 불확실성 평가
- CMIP5 모델을 이용한 동아시아 여름철 미래 전망에서 기온과 강수 모두 평균-극한 간의 양의 상관성을 보임.
- 평균/극한 기온의 미래 전망에 미치는 내부 변동성과 모델 변동성의 영향은 21세기 중반 이후 감소하지만 시나리오 불확실성은 크게 증가함.
- 평균강수의 미래전망에서 시나리오 불확실성 보다 내부 변동성의 영향이 더 중요하게 나타남.
- 평균 강수와 달리 극한강수에서는 2040년 이후 시나리오 불확실성의 기여가 커짐.

(출처 : 보고서 요약서 3p)

Abstract ▼

Purpose & Contents
- Evaluation of CORDEX-East Asia Multi-RCMs for climate extremes
- Future projections of climate extremes in CORDEX-East Asia Multi-RCMs
- Uncertainty assessment of future projections of climate extremes over East Asia

Results
○ Evaluation of CORDEX-East Asia Multi-RCMs for climate extremes
- RCMs have systematic biases in both means and extremes during JJA: cold bias along the coast, warm bias over northern China; overall wet bias; dry bias over South Korea (monsoonal front shifted to the south)

- Temperature (TAS) means are simulated more accurately than TAS extremes
(due to higher spatial correlation), whereas precipitation (PR) extremes are better simulated thatn PR means (due to less error in spatial variability).

○ Future projections of climate extremes in CORDEX-East Asia Multi-RCMs
- TAS and PR will increase across East Asia for both mean and extremes.
- Increase in TAS extreme is due mainly to increase in intensity (GEV location parameter) while increase in PR extreme is due to enhanced interannual variability (GEV scale parameter).

- Future changes in means and extremes are closely related, even on grid-scale.
- Consistent relationships exist between model performances and future projections, with better models predicting less increase in both TAS and PR.

○ Uncertainty assessment of future projections of climate extremes over East Asia
- Analysis of CMIP5 models confirm positive mean–extreme relationships the in projections of both TAS and PR.

- For uncertainty in TAS mean and extreme projections, contribution of internal variability and model uncertainty declines after mid-21st century while role of scenario uncertainty grows rapidly.

- For uncertainty in PR mean projections, internal variability is more important than scenario unlike results for temperatures.

- Compared to PR mean, results for PR extreme uncertainty exhibits increasing contribution of the scenario uncertainty after 2040s.

Expected Contribution
- This study provides an important background for future studies by presenting the first Multi-RCM based results on climate extremes for East Asia.

- Multi-RCM evaluation results on temperature and precipitation extremes will contribute to the improvement of GCM/RCM performances for climate extremes for East Asia.

- This study also contributes to stimulating future studies on climate extremes using CORDEX-EAst Asia datasets through an improved understanding of future changes of climate extremes in regional climate models.

- Information on future changes in climate extremes obtained from CORDEX-East Asia datasets are expected to be useful for national climate change impacts and adaptation measures.

- This study will increase Korea's contribution to international activities on climate change downscaling by providing a basis for collaborative works with other CORDEX communities particularly in terms of climate extremes.

(출처 : SUMMARY 6p)

목차 Contents

표지 ... 1
제 출 문 ... 2
보고서 요약서 ... 3
국문 요약문 ... 5
SUMMARY ... 6
Contents ... 7
목차 ... 7
제1장. 연구개발과제의 개요 ... 8
1. 연구개발 목적 ... 8
2. 연구개발의 필요성 ... 8
3. 연구개발 범위 ... 9
제2장. 국내외 기술 개발 현황 ... 12
1. 세계적 수준 ... 12
2. 국내 수준 ... 13
제3장. 연구 수행 내용 및 성과 ... 15
제4장. 목표 달성도 및 관련 분야 기여도 ... 100
1. 목표 달성도 ... 100
2. 관련 분야 기여도 ... 100
제5장. 연구개발성과의 활용계획 ... 101
제6장. 연구 과정에서 수집한 해외 과학기술 정보 ... 102
제7장. 연구개발성과의 보안등급 ... 103
제8장. 국가과학기술종합정보시스템에 등록한 연구시설·장비 현황 ... 104
제9장. 연구개발과제 수행에 따른 연구실 등의 안전 조치 이행 실적 ... 105
제10장. 연구개발과제의 대표적 연구 실적 ... 106
제11장. 기타 사항 ... 107
제12장. 참고 문헌 ... 108
제13장. 부록 ... 114
끝페이지 ... 150

표/그림 (55)

표 Domain region in this study (20°-50°N, 100°-150°E)
표 Observation datasets used in this study. Common analysis period is 1979-2005.
표 RCMs used in this study. Note that our analysis domain is a part of the simulation domain (entire CORDEX East Asia domain).
표 Spatial distributions of observed climatology
표 Spatial distributions of observed climatology (a,b), and RCM
표 Mean biases and RMSEs of TAS means and extremes from individual RCMs, MMEs, and GCM.
표 Mean biases and RMSEs of PR means and extremes from individual RCMs, MMEs, and GCM.
표 Scatter plots showing the inter-RCM relationship between means and extremes for biases (upper) and RMSEs (lower) of East Asian mean TAS (°C) and PR (mm d-1).
표 Taylor diagrams of means and extremes of TAS (left) and PR (right) for five ´RCMs and MMEs from the HIST (upper) and EVAL´ (lower)experiments.
표 The relationships of the Taylor skill score between means and extremes for TAS and PR (upper), and between TAS and PR (lower) obtained from five RCMs based on least square regression fitting.
표 Mean biases in JJA means and extremes of TAS (°C, upper) and PR (mm d-1, lower) over three sub-regions (SK, SC, and MN. Refer to Figure 1-2 for their domains) obtained from the HIST experiments of five RCMs and MME.
표 Frequency (%, left) and intensity (°C, right) distribution of daily TAS averaged over each sub-regions calculated from observations (OBS), MMEs of HIST experiments from five RCMs, and GCM. Intensity is measured based on percentiles with 10 bins of every 10% and also 10 bins of every 1% for the top 10%.
표 Same as Figure 1-12 except for frequency (%) and intensity (mm) distribution of daily PR averaged over three sub-regions.
표 Spatial patterns of MME future projections of means and extremes (rv20) for TAS and PR (a, b, c, and d) and the Clausius-Clapeyron relation (e) in JJA over East Asia.
표 Spatial patterns of HadGEM2-AO future projections of means and extremes (rv20) for TAS and PR (a, b, c, and d) and the Clausius-Clapeyron relation (e) in JJA over East Asia.
표 Spatial patterns of MME projections of extremes (a and b: rv20, c and d: location parameter) and difference rv20 and location parameter for TAS and PR.
표 MME spatial patterns of (a and b) SST (℃, shading) and JJA mean precipitation (6mm d-1) of the HIST (blue line) and the RCP8.5 (green line) experiments, respectively, (c) future change of SST (℃, shading) and JJA mean precipitation (6mm d-1) of the HIST and the RCP8.5 experiments, (d and e) geopotential height at 500hPa (m, shading) of the HIST and the RCP8.5 experiments, respectively, (f) its future change, (g and h) horizontal moisture flux (vector) and its magnitude(shading) at 850hPa (g kg-1 m s-1) of the HIST and the RCP8.5 experiments, respectively, (i) its future change, (j and k) horizontal moisture flux convergence at 850hPa (10-6 g kg-1 s-1) of the HIST and the RCP8.5 experiments, respectively, and (l) its future change in JJA over East Asia.
표 Scatter plot of the relationships of the future projections between mean and extreme (rv20) projections (a and b) over East Asia.
표 Scatter plot of the relationships between bias and future projection for mean and extreme (location parameter) in terms of TAS and PR over East Asia.
표 Same as figure 2-7 except for the relationships between RMSE and future projection.
표 Same as figure 2-7 except for the relationships between Taylor skill score and future projection.
표 Mean future projections in JJA means and extremes (rv20) of TAS (°C, upper) and PR (mm d-1, lower) over three sub-regions (SK, SC, and MN. Refer to figure. 2-1 for their domains) and East Asia obtained from five RCMs and MME.
표 Scatter plot of the relative changes (%) in PR extreme (rv20) projection corresponding to TAS mean projection for individual models in each sub-region.
표 MME spatial patterns of (a and c) future change of horizontal moisture flux (vector) and its magnitude (shading) at 850hPa (g kg-1 m s-1), (b and d) future change of horizontal moisture flux convergence at 850hPa (10-6 g kg-1 s-1) for SK (a and b) and SC (c and d) sub-regions.
표 Frequency (%, left) and intensity (°C, right) distribution of daily TAS averaged over each sub-region calculated from MMEs of HIST and RCP8.5 experiments from five RCMs and GCM HIST and RCP8.5 experiments.
표 Same as figure 2-13 except for frequency (%) and intensity (mm) distribution of daily PR averaged over three sub-regions.
표 CMIP5 datasets used in this study.
표 Spatial patterns of periods projections for each CMIP5 model in terms of mean temperature for RCP4.5 experiment by water and ocean distributions.
표 Spatial patterns of periods projections for each CMIP5 model in terms of extreme temperature for RCP4.5 experiment by water and ocean distributions.
표 Spatial patterns of periods projections for each CMIP5 model in terms of mean precipitation for RCP4.5 experiment by water and ocean distributions.
표 Spatial patterns of periods projections for each CMIP5 model in terms of extreme precipitation for RCP4.5 experiment by water and ocean distributions.
표 Box and whisker plot of future projections of East Asian summer for each period of temperature and precipitation from 16 CMIP5 climate models. Dots denote HadGEM2-AO.
표 An uncertainty cascade of future projections of East Asian summer for temperature and precipitation by periods for the 16 CMIP5 models.
표 An uncertainty cascade of future projections of global in JJA for temperature and precipitation by periods for the 16 CMIP5 models.
표 Clausius-Clapeyron relationship cascade between temperature and precipitation for mean and extreme over East Asia
표 The relationships of future climate projections between mean and extreme for temperature and precipitation by periods.
표 (a) The changes of anomalies of sea surface temperature based on 1961-1990 in Nino3.4 region and (b) its standard deviations by four periods using HadGEM2-AO r3i1p1 dataset.
표 Spatial patterns of regression coefficient between JJA mean temperature and SST of Nino3.4 region (detrended) using HadGEM2-AO dataset by periods.
표 Spatial patterns of regression coefficient between JJA extreme temperature (non-stationary GEV) and SST of Nino3.4 region (detrended) using HadGEM2-AO dataset by periods.
표 Spatial patterns of regression coefficient between JJA mean precipitation and SST of Nino3.4 region (detrended) using HadGEM2-AO dataset by periods.
표 Spatial patterns of regression coefficient between JJA extreme precipitation (non-stationary GEV) and SST of Nino3.4 region (detrended) using HadGEM2-AO dataset by periods.
$[weighted] Temporal changes of (a) JJA mean temperature from 16 CMIP5 models under two different RCP scenarios from 2000 to 2099 over East Asia. All temperatures are shown as anomalies from 1971-2000 mean. (b) Accumulated total variance and (c) fraction of total variance from uncertainty factors of East Asian JJA mean temperature.$ 표 [weighted] Temporal changes of (a) JJA mean temperature from 16 CMIP5 models under two different RCP scenarios from 2000 to 2099 over East Asia. All temperatures are shown as anomalies from 1971-2000 mean. (b) Accumulated total variance and (c) fraction of total variance from uncertainty factors of East Asian JJA mean temperature.
$[weighted] Temporal changes of (a) JJA mean temperature from 16 CMIP5 models under two different RCP scenarios from 2000 to 2099 over global scale. All temperatures are shown as anomalies from 1971-2000 mean. (b) Accumulated total variance and (c) fraction of total variance from uncertainty factors of global JJA mean temperature.$ 표 [weighted] Temporal changes of (a) JJA mean temperature from 16 CMIP5 models under two different RCP scenarios from 2000 to 2099 over global scale. All temperatures are shown as anomalies from 1971-2000 mean. (b) Accumulated total variance and (c) fraction of total variance from uncertainty factors of global JJA mean temperature.
$[weighted] Temporal changes of (a) JJA TXx from 16 CMIP5 models under two different RCP scenarios from 2000 to 2099 over East Asia. All TXx are shown as anomalies from 1971-2000 mean. (b) Accumulated total variance and (c) fraction of total variance from uncertainty factors of East Asian JJA TXx.$ 표 [weighted] Temporal changes of (a) JJA TXx from 16 CMIP5 models under two different RCP scenarios from 2000 to 2099 over East Asia. All TXx are shown as anomalies from 1971-2000 mean. (b) Accumulated total variance and (c) fraction of total variance from uncertainty factors of East Asian JJA TXx.
$[weighted] Temporal changes of (a) JJA TXx from 16 CMIP5 models under two different RCP scenarios from 2000 to 2099 over global scale. All TXx are shown as anomalies from 1971-2000 mean. (b) Accumulated total variance and (c) fraction of total variance from uncertainty factors of global JJA TXx.$ 표 [weighted] Temporal changes of (a) JJA TXx from 16 CMIP5 models under two different RCP scenarios from 2000 to 2099 over global scale. All TXx are shown as anomalies from 1971-2000 mean. (b) Accumulated total variance and (c) fraction of total variance from uncertainty factors of global JJA TXx.
$Temporal changes of (a) JJA mean precipitation from 16 CMIP5 models under two different RCP scenarios from 2000 to 2099 over East Asia. All precipitations are shown as anomalies from 1971-2000 mean. (b) Accumulated total variance and (c) fraction of total variance from uncertainty factors of East Asian JJA mean precipitation.$ 표 Temporal changes of (a) JJA mean precipitation from 16 CMIP5 models under two different RCP scenarios from 2000 to 2099 over East Asia. All precipitations are shown as anomalies from 1971-2000 mean. (b) Accumulated total variance and (c) fraction of total variance from uncertainty factors of East Asian JJA mean precipitation.
$Temporal changes of (a) JJA mean precipitation from 16 CMIP5 models under two different RCP scenarios from 2000 to 2099 over global scale. All precipitations are shown as anomalies from 1971-2000 mean. (b) Accumulated total variance and (c) fraction of total variance from uncertainty factors of global JJA mean precipitation.$ 표 Temporal changes of (a) JJA mean precipitation from 16 CMIP5 models under two different RCP scenarios from 2000 to 2099 over global scale. All precipitations are shown as anomalies from 1971-2000 mean. (b) Accumulated total variance and (c) fraction of total variance from uncertainty factors of global JJA mean precipitation.
$Temporal changes of (a) JJA RX1day from 16 CMIP5 models under two different RCP scenarios from 2000 to 2099 over East Asia. All RX1day are shown as anomalies from 1971-2000 mean. (b) Accumulated total variance and (c) fraction of total variance from uncertainty factors of East Asian JJA mean RX1day.$ 표 Temporal changes of (a) JJA RX1day from 16 CMIP5 models under two different RCP scenarios from 2000 to 2099 over East Asia. All RX1day are shown as anomalies from 1971-2000 mean. (b) Accumulated total variance and (c) fraction of total variance from uncertainty factors of East Asian JJA mean RX1day.
$Temporal changes of (a) JJA RX1day from 16 CMIP5 models under two different RCP scenarios from 2000 to 2099 over global scale. All RX1day are shown as anomalies from 1971-2000 mean. (b) Accumulated total variance and (c) fraction of total variance from uncertainty factors of global JJA RX1day.$ 표 Temporal changes of (a) JJA RX1day from 16 CMIP5 models under two different RCP scenarios from 2000 to 2099 over global scale. All RX1day are shown as anomalies from 1971-2000 mean. (b) Accumulated total variance and (c) fraction of total variance from uncertainty factors of global JJA RX1day.
$[no weighted] (a) Accumulated total variance and (b) fraction of total variance from uncertainty factors of East Asian JJA mean temperature.$ 표 [no weighted] (a) Accumulated total variance and (b) fraction of total variance from uncertainty factors of East Asian JJA mean temperature.
$[no weighted] (a) Accumulated total variance and (b) fraction of total variance from uncertainty factors of East Asian JJA TXx.$ 표 [no weighted] (a) Accumulated total variance and (b) fraction of total variance from uncertainty factors of East Asian JJA TXx.
$Spatial distribution for fraction of total variance from uncertainty factors for JJA mean temperature$ 표 Spatial distribution for fraction of total variance from uncertainty factors for JJA mean temperature
$Spatial distribution for fraction of total variance from uncertainty factors for JJA TXx$ 표 Spatial distribution for fraction of total variance from uncertainty factors for JJA TXx
$Spatial distribution for fraction of total variance from uncertainty factors for JJA mean precipitation$ 표 Spatial distribution for fraction of total variance from uncertainty factors for JJA mean precipitation
$Spatial distribution for fraction of total variance from uncertainty factors for JJA RX1day$ 표 Spatial distribution for fraction of total variance from uncertainty factors for JJA RX1day

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CORDEX-동아시아 다중 지역기후모델 자료의 극한기후 미래전망 활용성 연구 원문보기