• 대기
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• 제29권4호
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• pp.355-365
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• 2019

Based on in-situ monitoring data produced by National Institute of Meteorological Sciences, we evaluated the performance of Joint UK Land Environment Simulator (JULES) on the surface energy balance for rice-paddy and cropland in Korea with the operational ancillary data used for Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) (CTL) and the high-resolution ancillary data from external sources (EXP). For these experiments, we employed the one-year (March 2015~February 2016) observations of eddy-covariance fluxes and soil moisture contents from a double-cropping rice-paddy in BoSeong and a cropland in AnDong. On the rice-paddy site the model performed better in the CTL experiment except for the sensible heat flux, and the latent heat flux was underestimated in both of experiments which can be inferred that the model represents flood-irrigated surface poorly. On the cropland site the model performance of the EXP experiment was worse than that of CTL experiment related to unrealistic surface type fractions. The pattern of the modeled soil moisture was similar to the observation but more variable in time. Our results shed a light on that 1) the improvement of land scheme for the flood-irrigated rice-paddy and 2) the construction of appropriate high-resolution ancillary data should be considered in the future research.

• 한국농림기상학회:학술대회논문집
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• 한국농림기상학회 2017년도 추계 학술발표초록집
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• pp.75-76
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• 2017

• 한국농림기상학회지
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• 제12권2호
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• pp.107-121
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• 2010

본 연구에서는 한반도의 주요 생태계인 활엽수림과 농경지에서 지면 모형 JULES(Joint UK Land Environment Simulator)으로 모의한 총일차생산량 (Gross Primary Productivity: GPP)과 생태계 호흡량 (ecosystem respiration: RE)의 수치 모사 결과에 영향을 미치는 주요 모수를 파악하였으며, 민감한 모수에 대해 실측자료를 사용함에 따른 모형 예측력의 개선 정도를 평가하였다. 민감도 실험의 결과, 활엽수림과 농경지에서 모두 JULES로 모의한 GPP는 잎 내부의 질소농도와 리불로오스이인산(RuBP) 카르복실화의 최대 속도에 가장 민감하였다. RE는 활엽수림에서는 목질부 탄소량과 엽면적지수를 연결시켜주는 상수에 가장 민감하였다. 반면에 농경지에서 수치모사된 RE는 GPP와 같이 각각 잎 내부의 질소 농도와 RuBP 카르복실화의 최대 속도에 가장 민감하였다. JULES로부터 제공된 모수의 값으로 모의된 두 지역의 GPP와 RE는 모두 관측값에 비해 과대평가되었다. 특히 활엽수림에서 GPP가 가장 민감하게 반응했던 잎 질소 농도의 실제 관측값이 모형에서 사용하는 기존 설정값의 50% 이하임을 고려할 때 모형에서 설정된 모수의 값으로 탄소 순환을 수치 모사할 경우에 모수의 기존 설정값과 실제값의 차이가 모형의 과다모의에 상당한 영향을 미침을 확인할 수 있었다. 따라서 한반도 탄소순환의 현실적인 모의를 위해서는 모형에서 요구되는 생물리학적 정보가 한반도 다양한 식생 기능 형태를 현실적으로 잘 반영하는지를 확인해야 할 뿐 아니라 지속적인 현장 관측을 통해서 생물리학적 정보와 관련된 자료기반을 마련하는 것이 중요하다.

• 대기
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• 제31권3호
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• pp.327-340
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• 2021

In this study, the impact of soil moisture initialization in GloSea5, the operational climate prediction system of the Korea Meteorological Administration (KMA), has been investigated for the period of 1991~2010. To overcome the large uncertainties of soil moisture in the reanalysis, JRA55 reanalysis and CMAP precipitation were used as input of JULES land surface model and produced soil moisture initial field. Overall, both mean and variability were initialized drier and smaller than before, and the changes in the surface temperature and pressure in boreal summer and winter were examined using ensemble prediction data. More realistic soil moisture had a significant impact, especially within 2 months. The decreasing (increasing) soil moisture induced increases (decreases) of temperature and decreases (increases) of sea-level pressure in boreal summer and its impacts were maintained for 3~4 months. During the boreal winter, its effect was less significant than in boreal summer and maintained for about 2 months. On the other hand, the changes of surface temperature were more noticeable in the southern hemisphere, and the relationship between temperature and soil moisture was the same as the boreal summer. It has been noted that the impact of land initialization is more evident in the summer hemispheres, and this is expected to improve the simulation of summer heat wave in the KMA's operational climate prediction system.

• 대기
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• 제27권3호
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• pp.261-276
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• 2017

The main objectives of this study are to introduce Global Seasonal forecasting system version5 (GloSea5) of KMA and to evaluate the performance of ensemble prediction of system. KMA has performed an operational seasonal forecast system which is a joint system between KMA and UK Met office since 2014. GloSea5 is a fully coupled global climate model which consists of atmosphere (UM), ocean (NEMO), land surface (JULES) and sea ice (CICE) components through the coupler OASIS. The model resolution, used in GloSea5, is N216L85 (~60 km in mid-latitudes) in the atmosphere and ORCA0.25L75 ($0.25^{\circ}$ on a tri-polar grid) in the ocean. In this research, we evaluate the performance of this system using by RMSE, Correlation and MSSS for ensemble mean values. The forecast (FCST) and hindcast (HCST) are separately verified, and the operational data of GloSea5 are used from 2014 to 2015. The performance skills are similar to the past study. For example, the RMSE of h500 is increased from 22.30 gpm of 1 week forecast to 53.82 gpm of 7 week forecast but there is a similar error about 50~53 gpm after 3 week forecast. The Nino Index of SST shows a great correlation (higher than 0.9) up to 7 week forecast in Nino 3.4 area. It can be concluded that GloSea5 has a great performance for seasonal prediction.