• Atmosphere
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• v.24 no.3
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• pp.403-417
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• 2014

Future changes in seasonal mean temperature and precipitation over East Asia under anthropogenic global warming are investigated by comparing the historical run for 1979~2005 and the Representative Concentration Pathway (RCP) 4.5 run for 2006~2100 with 20 coupled models which participated in the phase five of Coupled Model Inter-comparison Project (CMIP5). Although an increase in future temperature over the East Asian monsoon region has been commonly accepted, the prediction of future precipitation under global warming still has considerable uncertainties with a large inter-model spread. Thus, we select best five models, based on the evaluation of models' performance in present climate for boreal summer and winter seasons, to reduce uncertainties in future projection. Overall, the CMIP5 models better simulate climatological temperature and precipitation over East Asia than the phase 3 of CMIP and the five best models' multi-model ensemble (B5MME) has better performance than all 20 models' multi-model ensemble (MME). Under anthropogenic global warming, significant increases are expected in both temperature and land-ocean thermal contrast over the entire East Asia region during both seasons for near and long term future. The contrast of future precipitation in winter between land and ocean will decrease over East Asia whereas that in summer particularly over the Korean Peninsula, associated with the Changma, will increase. Taking into account model validation and uncertainty estimation, this study has made an effort on providing a more reliable range of future change for temperature and precipitation particularly over the Korean Peninsula than previous studies.

• Atmosphere
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• v.18 no.4
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• pp.507-524
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• 2008

In this paper, future climate changes over East Asia($20^{\circ}{\sim}50^{\circ}N$, $100^{\circ}{\sim}150^{\circ}E$) are projected by anthropogenic forcing of greenhouse gases and aerosols using coupled atmosphere-ocean general circulation model (AOGCM) simulations based on Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) B1, A1B and A2 scenarios. Before projection future climate, model performance is assessed by the $20^{th}$ Century (20C3M) experiment with bias, root Mean Square Error (RMSE), ratio of standard deviation, Taylor diagram analysis. The result of examination of the seasonal uncertainty of T2m and PCP shows that cold bias, lowered than that of observation, of T2m and wet bias, larger than that of observation, of PCP are found over East Asia. The largest wet bias is found in winter and the largest cold bias is found in summer. The RMSE of temperature in the annual mean increases and this trend happens in winter, too. That is, higher resolution model shows generally better performances in simulation T2m and PCP. Based on IPCC SRES scenarios, East Asia will experience warmer and wetter climate in the coming $21^{st}$ century. It is predict the T2m increase in East Asia is larger than global mean temperature. As the latitude goes high, the warming over the continents of East Asia showed much more increase than that over the ocean. An enhanced land-sea contrast is proposed as a possible mechanism of the intensified Asian summer monsoon. But, the inter-model variability in PCP changes is large.

• Journal of Environmental Science International
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• v.16 no.3
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• pp.261-267
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• 2007

In this study, the Slab Ocean Model (SOM) is coupled with an Atmospheric General Circulation Model (AGCM) which developed in University of Kangnung based on the land surface model of Biosphere-Atmosphere Transfer Scheme (BATS). The purposes of this study are to understand radiative role of clouds considering of the atmospheric feedback, and to compare the Clouds Radiative Forcing (CRF) come from the analyses using the clear-cloud sky method and CGCM. The new CGCM was integrated by using two sets of the clouds with radiative role (EXP-A) and without radiative role (EXP-B). Clouds in this two cases show the negative effect $-26.0\;Wm^{-2}$ of difference of radiation budget at top of atmosphere (TOA). The annual global means radiation budget of this simulation at TOA is larger than the estimations ($-17.0 Wm^{-2}$) came from Earth Radiation Budget Experiment (ERBE). The work showed the surface negative effect with $-18.6 Wm^{-2}$ in the two different simulations of CRF. Otherwise, sensible heat flux in the simulation shows a great contribution with positive forcing of $+24.4 Wm^{-2}$. It is found that cooling effect to the surface temperature due to radiative role of clouds is about $7.5^{\circ}C$. From this study it could make an accurate of the different CRF estimation considering either feedback of EXP-B or not EXP-A under clear-sky and cloud-sky conditions respectively at TOA. This result clearly shows its difference of CRF $-11.1 Wm^{-2}$.

• Atmosphere
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• v.22 no.4
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• pp.455-464
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• 2012

This study investigates a 12 month-lead predictability of PNU Coupled General Circulation Model (CGCM) V1.1 hindcast, for which an oceanic data assimilated initialization is used to generate ocean initial condition. The CGCM, a participant model of APEC Climate Center (APCC) long-lead multi-model ensemble system, has been initialized at each and every month and performed 12-month-lead hindcast for each month during 1980 to 2011. The 12-month-lead hindcast consisted of 2-5 ensembles and this study verified the ensemble averaged hindcast. As for the sea-surface temperature concerns, it remained high level of confidence especially over the tropical Pacific and the mid-latitude central Pacific with slight declining of temporal correlation coefficients (TCC) as lead month increased. The CGCM revealed trustworthy ENSO prediction skills in most of hindcasts, in particular. For atmospheric variables, like air temperature, precipitation, and geopotential height at 500hPa, reliable prediction results have been shown during entire lead time in most of domain, particularly over the equatorial region. Though the TCCs of hindcasted precipitation are lower than other variables, a skillful precipitation forecasts is also shown over highly variable regions such as ITCZ. This study also revealed that there are seasonal and regional dependencies on predictability for each variable and lead.

• Atmosphere
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• v.25 no.2
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• pp.353-366
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• 2015

The impact of land and ocean initial condition on coupled general circulation model seasonal predictability is assessed in this study. The CGCM used here is Pusan National University Couple General Circulation Model (PNU CGCM). The seasonal predictability of the surface air temperature and ocean potential temperature for boreal winter are evaluated with 4 different experiments which are combinations of 2 types of land initial conditions (AMI and CMI) and 2 types of ocean initial conditions (DA and noDA). EXP1 is the experiment using climatological land initial condition and ocean initial condition to which the data assimilation technique is not applied. EXP2 is same with EXP1 but used ocean data assimilation applied ocean initial condition. EXP3 is same with EXP1 but AMIP-type land initial condition is used for this experiment. EXP4 is the experiment using the AMIP-type land initial condition and data assimilated ocean initial condition. By comparing these 4 experiments, it is revealed that the impact of data assimilated ocean initial is dominant compared to AMIP-type land initial condition for seasonal predictability of CGCM. The spatial and temporal patterns of EXP2 and EXP4 to which the data assimilation technique is applied were improved compared to the others (EXP1 and EXP3) in boreal winter 2m temperature and sea surface temperature prediction.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.138-142
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• 2005

The change of precipitation and temperature due to the global. warming eventually caused the variation of water availability in terms of potential evapotranspiration, soil moisture, and runoff. In this reason national long-term water resource planning should be considered the effect of climate change. Study of AOGCM-based scenario to proposed the plausible future states of the climate system has become increasingly important for hydrological impact assessment. Future climate changes over East Asia are projected from the coupled atmosphere-ocean general circulation model (AOGCM) simulations based on Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 and B2 scenarios using multi-model ensembles (MMEs) method (Min et al. 2004). MME method is used to reduce the uncertainty of individual models. However, the uncertainty increases are larger over the small area than the large area. It is demonstrated that the temperature increases is larger over continental area than oceanic area in the 21st century.

• Journal of the Korean Geographical Society
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• v.51 no.5
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• pp.633-649
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• 2016

Exposure to high temperatures during the reproductive period of crops decreases their productivity. The Intergovernmental Panel on Climate Change's (IPCC) fifth Assessment Report predicts that the frequency of high temperatures will continue to increase in the future, resulting in significant impacts on the world's food supply. This study evaluate climate change-induced heat stress on four major agricultural crops (rice, maize, soybean, and wheat) at a global level, using the coupled atmosphere-ocean model of Hadley Centre Global Environmental Model version 2 (HadGEM2-AO) and FAO/IIASA Global Agro-Ecological Zone (GAEZ) model data. The maximum temperature rise ($1.8-3.5^{\circ}C$) during the thermal-sensitive period (TSP) from the baseline (1961-1990) to the future (2070-2090) is expected to be larger under a Representative Concentration Pathway (RCP) 8.5 climate scenario than under a RCP2.6 climate scenario, with substantial heat stress-related damage to productivity. In particular, heat stress is expected to cause severe damage to crop production regions located between 30 and $50^{\circ}N$ in the Northern Hemisphere. According to the RCP8.5 scenario, approximately 20% of the total cultivation area for all crops will experience unprecedented, extreme heat stress in the future. Adverse effects on the productivity of rice and soybean are expected to be particularly severe in North America. In Korea, grain demands are heavily dependent on imports, with the share of imports from the U.S. at a particularly high level today. Hence, it is necessary to conduct continuous prediction on food security level following the climate change, as well as to develop adaptation strategy and proper agricultural policy.

• Atmosphere
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• v.28 no.1
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• pp.99-112
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• 2018

In this study, the accuracy of ocean analysis data, which are produced from the Korea Meteorological Administration (KMA) Nucleus for European Modelling of the Ocean/Variational Data Assimilation (NEMO/NEMOVAR, hereafter NEMO) system and the HYbrid Coordinate Ocean Model/Navy Coupled Ocean Data Assimilation (HYCOM/NCODA, hereafter HYCOM) system, was evaluated using various oceanic observation data from March 2015 to February 2016. The evaluation was made for oceanic thermal environments in the tropical Pacific, the western North Pacific, and the Korean peninsula. NEMO generally outperformed HYCOM in the three regions. Particularly, in the tropical Pacific, the RMSEs (Root Mean Square Errors) of NEMO for both the sea surface temperature and vertical water temperature profile were about 50% smaller than those of HYCOM. In the western North Pacific, in which the observational data were not used for data assimilation, the RMSE of NEMO profiles up to 1000 m ($0.49^{\circ}C$) was much lower than that of HYCOM ($0.73^{\circ}C$). Around the Korean peninsula, the difference in RMSE between the two models was small (NEMO, $0.61^{\circ}C$; HYCOM, $0.72^{\circ}C$), in which their errors show relatively big in the winter and small in the summer. The differences reported here in the accuracy between NEMO and HYCOM for the thermal environments may be attributed to horizontal and vertical resolutions of the models, vertical coordinate and mixing scheme, data quality control system, data used for data assimilation, and atmosphere forcing. The present results can be used as a basic data to evaluate the accuracy of NEMO, before it becomes the operational model of the KMA providing real-time ocean analysis and prediction data.

• Atmosphere
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• v.31 no.5
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• pp.563-576
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• 2021

The performance of East Asian summer monsoon is assessed for GC2 and GC3.1, which are climate change models of the current and next climate prediction system in the Korea Meteorological Administration (KMA), GloSea5 and GloSea6. The most pronounced characteristics of GC models are strong monsoon trough and the weakening of the Western North Pacific Subtropical High (WNPSH). These are related to the weakening of the southwesterly wind and resulting weak monsoon band toward the Korean Peninsula. The GC3.1 is known to have improved the model configuration version compared to GC2, such as cloud physics and ocean parameters. We can confirm that the overall improvements of GC3.1 against GC2, especially in pressure, 850 hPa wind fields, and vertical wind shear. Also, the precipitation band stagnant in the south of 30°N in late spring is improved, therefore the biases of rainy onset and withdrawal on the Korean Peninsula are reduced by 2~4 pentad. We also investigate the impact of initialization in comparison with GloSea5 hindcast. Compared with GCs, hindcast results show better simulation within 1 month lead time, especially in pressure and 850 hPa wind fields, which can be expected to the improvement of WNPSH. Therefore, it is expected that the simulation performance of WNPSH will be improved in the result of applying the initialization of GloSea6.

• Atmosphere
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• v.26 no.1
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• pp.59-72
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• 2016

The comparison of prediction errors in geopotential height, temperature, and precipitation forecasts is made quantitatively to evaluate medium-range forecast skills between Global Seasonal Forecasting System version 5 (GloSea5) and Unified Model (UM) in operation by Korea Meteorological Administration during 2014. In addition, the performances in prediction of sea surface temperature anomaly in NINO3.4 region, Madden and Julian Oscillation (MJO) index, and tropical storms in western north Pacific are evaluated. The result of evaluations appears that the forecast skill of UM with lower values of root-mean square error is generally superior to GloSea5 during forecast periods (0 to 12 days). The forecast error tends to increase rapidly in GloSea5 during the first half of the forecast period, and then it shows down so that the skill difference between UM and GloSea5 becomes negligible as the forecast time increases. Precipitation forecast of GloSea5 is not as bad as expected and the skill is comparable to that of UM during 10-day forecasts. Especially, in predictions of sea surface temperature in NINO3.4 region, MJO index, and tropical storms in western Pacific, GloSea5 shows similar or better performance than UM. Throughout comparison of forecast skills for main meteorological elements and weather extremes during medium-range, the effects of initial and model errors in atmosphere-ocean coupled model are verified and it is suggested that GloSea5 is useful system for not only seasonal forecasts but also short- and medium-range forecasts.