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<p>The Korean Integrated Model (KIM) is a numerical weather prediction model developed by the Korea Institute of Atmospheric Prediction Systems (KIAPS) for operational medium-range forecasting. Recently, KIAPS has aimed to enhance the framework for seamless prediction extending to extended-range forecasts. In this context, the assessment of the climatological mean and variability of KIM is essential. To support this, we conducted a 36-year integration of KIM under Atmospheric Model Intercomparison Project (AMIP)-type conditions. To facilitate a more systematic assessment of the model, we compared the results with the CMIP6 archive of AMIP simulations. KIM shows comparable performance to CMIP6 models by reasonably simulating the atmospheric climatological mean state, although some noticeable biases are present due to physical uncertainties. In addition to the climatological mean fields, we also evaluated the modelğs representation of the dominant climate variabilities—specifically, the El Niño–Southern Oscillation (ENSO) and Arctic Oscillation (AO). The results show that KIM reproduces the large-scale ENSO and AO patterns, whereas its representation of the associated atmospheric responses over East Asia is comparatively limited. Possible processes underlying these limitations are also discussed in this study. This work represents the first systematic evaluation of KIM’s long-term climate simulations relative to established AMIP models. The results provide a benchmark for the development of a coupled model system based on KIM, aimed at improving extended-range prediction.</p>

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Evaluation of Climate Variability and Its Impact over East Asia in AMIP-type Simulation of the Korean Integrated Model

  • 期刊论文

  • 发表日期：

    2026年7月8日

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  • Sae-Rim Yeo,
  • Eun-Hee Lee,
  • Sang-Yoon Jun

摘要

The Korean Integrated Model (KIM) is a numerical weather prediction model developed by the Korea Institute of Atmospheric Prediction Systems (KIAPS) for operational medium-range forecasting. Recently, KIAPS has aimed to enhance the framework for seamless prediction extending to extended-range forecasts. In this context, the assessment of the climatological mean and variability of KIM is essential. To support this, we conducted a 36-year integration of KIM under Atmospheric Model Intercomparison Project (AMIP)-type conditions. To facilitate a more systematic assessment of the model, we compared the results with the CMIP6 archive of AMIP simulations. KIM shows comparable performance to CMIP6 models by reasonably simulating the atmospheric climatological mean state, although some noticeable biases are present due to physical uncertainties. In addition to the climatological mean fields, we also evaluated the modelğs representation of the dominant climate variabilities—specifically, the El Niño–Southern Oscillation (ENSO) and Arctic Oscillation (AO). The results show that KIM reproduces the large-scale ENSO and AO patterns, whereas its representation of the associated atmospheric responses over East Asia is comparatively limited. Possible processes underlying these limitations are also discussed in this study. This work represents the first systematic evaluation of KIM’s long-term climate simulations relative to established AMIP models. The results provide a benchmark for the development of a coupled model system based on KIM, aimed at improving extended-range prediction.