• Title/Summary/Keyword: Climate Research Unit (CRU)

Search Result 4, Processing Time 0.025 seconds

Uncertainty in the Estimation of Arctic Surface Temperature during Early 1900s Revealed by the Comparison between HadCRU4 and 20CR Reanalysis (HadCRU4 관측 온도자료와 20CR 재분석 자료 비교로부터 확인된 1900년대 초반 극지역 평균 온도 추정의 불확실성)

  • Kim, Baek-Min;Kim, Jin-Young
    • Journal of Climate Change Research
    • /
    • v.6 no.2
    • /
    • pp.95-104
    • /
    • 2015
  • To discuss whether we have credible estimations about historical surface temperature evolution since industrial revolution or not, present study investigates consistencies and differences of averaged surface air temperature since 1900 between the multiple data sources: Hadley Center Climate Research Unit (HadCRU4) surface air temperature data, ECMWF 20 Century Reanalysis data (ERA20CR), and NCEP 20 Century Reanalysis data (NCEP20CR). Averaged surface temperatures are obtained for the global, polar (90S~60S, 60N~0N), midlatitude (60S~30S, 30N~60N), tropical (30S~30N) region, separately. From the analysis, we show that: 1) spatio-temporal inhomogenity and scarcity of HadCRU4 data are not major obstacles in the reliable estimation of global surface air temperature. 2) Globally averaged temperature variability is largely contributed by those of tropical and midlatitude, which occupy more than 70% of earth surface in area. 3) Both data show consistent temperature variability in tropical region. 4) ERA20CR does not capture warm period over Arctic region in early 1900s, which is obvious feature in HadCRU4 data. Discrepancies among datasets suggest that high-level caution is needed especially in the interpretation of large Arctic warming in the early 1900s, which is often regarded as a natural variability in the Arctic region.

Analysis of historical drought in East Asia with CLM and CLM-VIC (CLM 및 CLM-VIC를 이용한 동아시아 지역의 과거 가뭄 분석)

  • Um, Myoung-Jin;Kim, Jeongbin;Kim, Mun Mo;Kim, Yeonjoo
    • Ecology and Resilient Infrastructure
    • /
    • v.5 no.3
    • /
    • pp.134-144
    • /
    • 2018
  • In this study, the historical drought in East Asia was analyzed with the Community Land Model (CLM) and CLM-Variable infiltration capacity (CLM-VIC). The observation dataset, Climate Research Unit (CRU), were also applied to check and estimate the historical drought for 1951 - 2010. The annual precipitation, temperature and evapotranspiration by CRU, CLM and CLM-VIC were investigated before estimating the meteorological drought index, which is the Standardized precipitation evapotranspiration index (SPEI). Three variables by observation and simulations have generally similar spatial pattern in East Asia even though there are some mere differences depending on the local area. These similar patterns are also founded in the results of SPEI by CRU, CLM and CLM-VIC. However, the similarity of SPEI becomes weaker as the drought severity goes severer from D1 to D4.

Changes in the Winter-Spring Center Timing over Upper Indus River Basin in Pakistan

  • Ali, Shahid;Kam, Jonghun
    • Proceedings of the Korea Water Resources Association Conference
    • /
    • 2021.06a
    • /
    • pp.372-372
    • /
    • 2021
  • The agriculture sector plays a vital role in the economy of Pakistan by contributing about 20% of the GDP and 42% of the labor force. Rivers from the top of Himalayas are the major water resources for this agriculture sector. Recent reports have found that Pakistan is one of the most vulnerable country to climate change that can cause water scarcity which is a big challenge to the communities. Previous studies have investigated the impact of climate change on the trend of streamflow, but the understanding of seasonal change in the regional hydrologic regimes remained limited. Therefore, a better understanding of the seasonal hydrologic change will help cope with the future water scarcity issue. In this study, we used the daily stream flow data for four major river basins of Pakistan (Chenab, Indus, Jhelum and Kabul) over 1962 - 2019. Utilizing these daily river discharge data, we calculated the winter-spring center time and the summer-autumn center times. In this study Winter-spring center time (WSCT) is defined as the day of the calendar year during which half of the total six months (Jan-Jun) discharge volume was exceeded. Results show that the four river basins experienced a statistically significant decreasing trend of WSCT, that is the center time keeps coming earlier compared to the past. We further used the Climate Research Unit (CRU) climate data comprising of the average temperature and precipitation for the four basins and found that the increasing average temperature value causes the early melting of the snow covers and glaciers that resulted in the decreasing of 1st center time value by 4 to 8 days. The findings of this study informs an alarming situation for the agriculture sector specifically.

  • PDF

An Uncertainty Assessment of AOGCM and Future Climate Projection over East Asia (동아시아 지역에서의 AOGCM 불확실성 평가 및 기후전망)

  • Kim, Min-Ji;Shin, Jin-Ho;Lee, Hyo-Shin;Kwon, Won-Tae
    • Proceedings of the Korea Water Resources Association Conference
    • /
    • 2008.05a
    • /
    • pp.1058-1062
    • /
    • 2008
  • 지구 온난화에 의한 대기 순환의 변화와 이에 따른 수증기 수송 및 강수량의 변화는 전지구 및 지역적인 수문환경의 변화를 초래하므로 장기적인 차원의 수자원 계획 수립에는 반드시 기후 변화에 따른 영향이 제대로 반영되어야 한다. 그러나 개별 모델이 사용하는 역학과정과 물리과정의 모수화 및 분해능이 다르고 이에 따른 모의 결과도 다르게 나타나는 등의 상당한 불확실성이 내재되어 있다. 따라서 본 연구에서는 기후변화에 관한 정부간 패널인 IPCC(Intergovernmental Panel on Climate Change)에 참여한 대기해양결합 대순환모델(AOGCMs)이 온실가스 배출 시나리오를 바탕으로 생산한 기온과 강수의 불확실성을 동아시아에 대해 평가하고 이를 바탕으로 미래 기후를 전망하였다. 국립기상연구소 ECHO-G/S 모델과 IPCC 23개 모델의 배출 시나리오(Special Report on Emissions Scenarios, SRES) 자료는 20세기(1900-1999년)와 21세기(2000-2099년)의 200년 동안이고, 관측자료는 영국 CRU(Climate Research Unit) 월평균 2m 기온의 30년(1961-1990년) 평균값과 CMAP 월 평균 강수량의 21년간(1979-1990년) 평균값을 이용하였다. 동아시아지역 기온과 강수의 불확실성을 평가하기 위해서 모델과 관측간 편이, 평균제곱근오차(RMSE) 등의 통계적인 방법을 사용하였다. 동아시아 지역의 연평균 기온은 대체로 모델의 기온이 관측보다 적게 모의되는 음의 편이를 나타내고, 강수는 모델이 관측보다 더 크게 모의 되는 양의 편이를 나타냈다. 계절적으로는 여름철 강수와 봄철 기온의 편이가 크게 나타났다. 연평균 및 겨울철 강수와 기온의 RMSE는 비례하는데 이는 기온 모의성능이 좋은 모델이 강수 모의성능도 좋게 나타나는 것을 의미한다.

  • PDF