Page 33 - APEC CLIMATE CENTER 2025 Annual Report

APEC CLIMATE CENTER 2025 ANNUAL REPORT

Research were reflected, and the hindcast climatological period was extended to 1993–2016 (24 Research 3) Expected Implications

years) to better capture recent climate variability. - The gradual expansion of seasonal prediction information from extreme and physical
Projects - Automation of climate outlook production: Automation of the entire outlook produc- Projects perspectives will contribute to improved proactive decision-making and response ca-
tion process—including signal detection, text generation, and document assembly— pacities in sectors such as agriculture, water resources, and disaster management.
in 2025 was implemented to improve operational efficiency and consistency. in 2025 - Provision of information on prediction characteristics and limitations of major climate
- Improvement of the ENSO alert system: Alert criteria were refined by considering the
modes will enable forecasters and users to interpret and utilize MME seasonal forecasts
development and decay phases of ENSO events, enabling clearer communication of
more appropriately.
event evolution.
- Stable and efficient delivery of high-quality climate prediction information across the
- Improvement of the BSISO subseasonal prediction system: An in-house input data
Asia-Pacific region will be achieved through system enhancements, including expanded
processing system was established to respond to changes in external data acquisition
model participation, updated climatological periods, and increased automation.
environments, thereby enhancing the stability of subseasonal operational forecasting.
- Enhancement of the Fire and Haze Early Warning System (FHEWS): High-resolution
(1°×1° grid) fire risk prediction products were pilot-produced to supplement existing
Project 2. Development of Advanced Subseasonal-to-Seasonal Forecasting
region-based information and to evaluate operational applicability.
Approaches Enabling Seamless Prediction
- Domestic and international collaboration: Forecast products were provided in support
of the KMA’s three-month outlook, and global GPC datasets were standardized and
disseminated through the operation of the WMO Lead Centre for Seasonal Prediction ㉖ Dr. Suryun Ham (suryun01@apcc21.org)
Multi-Model Ensembles (LC-SPMME).
1) Background and Relevance
- There is an increasing need to develop a system that overcomes the limitations of con-
ventional seasonal prediction by providing weekly subseasonal information capable of
capturing rapid short-term variability. This is essential for the early detection and proac-
tive response to extreme climate events occurring on short timescales.
- The objective is to establish a system that collects subseasonal forecast information
on a weekly basis to produce reliable subseasonal predictions based on a multi-model
ensemble (MME) approach. Furthermore, the project aims to develop integrated sub-
seasonal-to-seasonal (S2S) utilization technologies to lay the foundation for advancing
toward seamless prediction.

2) Main Results

A. Development of integrated subseasonal-to-seasonal (S2S) forecasting approaches
Fig 20 Comparative Analysis of ENSO Alert History: Global Institutions vs. APCC (Pre- and Post-
- Establishment of an APCC S2S MME prediction system
System Revision) based on Niño 3.4 and SOI Indices
· Configuration of MME participating models and establishment of a visualization system
· Comparative evaluation and selection of optimal probabilistic forecasting techniques
for subseasonal prediction
· Development of an MME subseasonal prediction system and real-time pilot opera-
tion of subseasonal forecasts
· Identification of integrated S2S utilization prediction content based on predictability
and usability for seamless forecasting
- Production and provision of one- and three-month forecast information
·Production of KMA S2S MME forecast data to support the KMA one-month outlook (weekly)
·Production of East Asian extreme climate information for the APCC webpage (monthly)

B. Operation and improvement of the APCC in-house model (SCoPS) for subseasonal
forecasting
- Establishment of an operational SCoPS subseasonal prediction system and produc-
tion of real-time weekly prediction data
Fig 21 Monthly Prediction Skill of the Niño West Index: Comparison between MME Hindcast ·Establishment of a seasonal prediction data production system
(Left) and Forecast (Right) - Optimization of atmosphere-ocean and land surface initial conditions for SCoPS and
identification of improvement strategies
·Development of land surface initialization techniques and evaluation of their impacts
·Analysis of forecast error characteristics related to atmosphere-ocean initial conditions

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