연구보고서
- 저자
- 함수련 박사
- 작성일
- 2018.04.24
- 조회
- 492
- 요약
- 목차
This study introduces the newly developed APEC Climate Center (APCC) in-house model (Seamless Coupled Prediction System; SCoPS) and its operation process for APCC multi-model ensemble (MME) prediction system for seasonal forecasts. The SCoPS is a state-of-the-art, global prediction system for seasonal time scales, based on a fully coupled climate model with integrated initialization processes of atmosphere, ocean and sea ice. The SCoPS initialized data for 10-member ensembles is assimilated by the NCEP CFS data and several subsurface profile data. Real-time forecast runs are started at the first and fifth day of each month with five perturbed ensemble members by a Gaussian spread function and are integrated up to 7 months. Having obtained the prediction data, we analyzed the temperature and precipitation prediction maps based on predicted the large-scale circulation patterns and an ENSO forecast on group discussion.
In a previous study, an evaluation of the prediction skill compared to the current operational model (APEC Climate Center Community Climate System Model version 3 - APCC CCSM3) using 32-year (1982-2013) ensemble hindcast runs. We found that SCoPS seasonal climate forecasts are useful for simulating climate variability, especially the East Asian monsoon system. Moreover, prediction results
from hindcast and forecast show reasonable performance over current MME individual models. Based on these studies, newly developed SCoPS seasonal forecast data has been provided to the APCC MME system as APCC operational model every month since November 2017.
Also, SCoPS has been updated with improved initial processes. We found that there were significant systematic biases in subsurface ocean temperature, and those biases lead to excessive or weak ENSO signals with long lead times. Currently, some processes related to the subsurface temperature in the ocean initialization have been fixed. The newly produced reforecast shows improved temperature and precipitation forecast ability over the East Asia region. Additionally, we suggest some processes for improving SCoPS.
Meanwhile, due to a recent increase in the occurrence of extreme weather and climate events, the prediction information on extreme events in various time frames is being requested. Therefore, through the development of SCoPS subseasonal forecast, the predictability is being evaluated and its application to prediction information. From discussions with the APCC Boreal Summer Intraseasonal Oscillation (BSISO) working group, the configuration for subseasonal prediction was set based on information provided by the NMME group discussion,S2S project. In the MJJAS 2014 case study, we found that the results from 1-3 weeks prediction produce a reasonable spatial distribution of large-scale circulation, as well as precipitation related to the East Asian monsoon system.
However, the temporal correlation coefficient of the predicted variables is lower than those from the 4-5 weeks prediction. Based on the analysis of this case study, we have expanded the reforecast periods from 2014 to 2016 and analyzed the prediction skill for the operational subseasonal forecast system. Moreover, real-time subseasonal forecast data will be provided to the APCC BSISO forecast
system after various validation processes are applied.