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Climate Analysis and Modeling
Produce & Provide Custom Detailed Climate Information
The Global Climate Model (GCM) is the basic method for generating climate prediction information, helping to understand climate processes. It provides statistical information on the effects of climate change in the past, present, and future by considering the complicated interactions that produces the climate on a global-scale. The Climate Analysis Team strives to provide more accurate climate prediction information by developing their own climate model.
Another important role of the Climate Analysis Team is to increase the specification of data in spatial and temporal terms for climate application researchers to be able to effectively utilize the climate prediction information. This is done through downscaling climate prediction data. As GCM produces prediction data only for wide ranges, it generates issues when using GCM to generate more detailed regional climate predictions. The Climate Analysis Team addresses this issue by producing high-resolution climate prediction data after adding local features and fragmenting it spatially and timely.TOP
Irregular climate events (typhoons, heavy rain, heat waves, etc.) has begun to appear all over the world. This situation has raised the need for intraseasonal prediction, which provides short term (within 3 months) prediction information, and seasonal prediction, which provides a 3-6 month term prediction information. The intraseasonal prediction and seasonal prediction are able to effectively predict these irregular climate events. It has been fond recently that not only can the application of prediction information decrease potential economic losses, but when utilized fully, it can eve increase economic revenue. This type of utilization is becoming a global topic, and therefore has become a major research field at APCC.
The Climate Analysis Team performs downscaling on climate prediction data so that it can be used in climate application research (agriculture, water resources, etc.). The methods used to downscale climate data are the “dynamical downscaling technique” and the “statistical downscaling technique”. The “dynamical downscaling technique” uses a numerical model, and the “statistical downscaling technique” relates the information by finding statistical correlations between data points.
Even though the two techniques serve the same purpose of transforming data into high-resolution data, each technique has its limitations. In order to overcome these limitations, the Climate Analysis Team conducts research on these two techniques in hopes that they can refine the techniques to produce more accurate high-resolution data.TOP
The Climate Analysis Team plays an important role in APCC as they transform prediction data from climate models into data that can be used for research by other climate application researchers. The Team, therefore, must interact with researchers from other fields in determining the research direction, so that the data produced can be best fit to meet the application research’s needs.
There are difficulties when attempting to utilize the current downscaled data set into other application research efforts. In order to reduce these difficulties, the Climate Analysis Team will continue to improve the downscaling system to in order to provide even more customized data for application researchers.
Beginning from June 2015, APCC has begun using its own climate model instead of outside models. This has enabled APCC to produce data that is more accurate and high-resolution than ever before. The Climate Analysis Team will continuously develop the methods of producing customized climate prediction information, particularly now that APCC has a high-quality model that is able to compete with other global leading organizations.TOP
Multiscale processes in the genesis of a near-equatorial tropical cyclone during Dynamics of MJO: Results from partial lateral forcing experiments
|Hongwei Yang / Journal of Geophysical Research: Atmospheres||2018.05.04|
Observational estimation of radiative feedback to surface air temperature over Northern High Latitudes
|WonMoo Kim / Climate Dynamics||2018.01.01|
Long-term change of the atmospheric energy cycles and weather disturbances
|WonMoo Kim / Climate Dynamics||2017.11.01|
Revisiting the iris effect of tropical cirrus clouds with TRMM and A‐Train satellite data
|WonMoo Kim / Journal of Geophysical Research||2017.06.01|
Multiple aspects of northern hemispheric wintertime cold extremes as revealed by Markov Chain analysis
|WonMoo Kim / Asia-Pacific Journal of Atmospheric Science||2017.02.01|
The long-term variability of Changma in East Asian summer monsoon system: A review and revisit
|WonMoo Kim / Asia-Pacific Journal of Atmospheric Science||2017.05.30|
Subseasonal prediction of extreme precipitation over Asia: Boreal summer intraseasonal oscillation perspective
|Ja-Yeon Moon, Hae-Jeong Kim / Journal of Climate||2017.03.27|
Mean Bias in Seasonal Forecast Model and ENSO Prediction Error
|Seon Tae Kim / Scientific Reports||2017.07.20|
Feedback process responsible for intermodel diversity of ENSO variability
|Seon Tae Kim / Geophysical Research Letters||2017.05.13|
Attribution of the 2015 record high sea surface temperatures over the central equatorial Pacific and tropical Indian Ocean
|Seon Tae Kim / Environmental Research Letters||2017.04.21|