Department Introduction

The Climate Prediction Division (CPD) of Beijing Climate Center undertakes the functions of:
- analysis and application of observation data of various spheres of earth system;
- dynamic diagnosis of climate anomalies;
- climate prediction of multiple time scales from extended to inter-annual range;
- prediction of climate phenomena, typical climate events and disasters plus sector-specific climatic impacts;
- developing standards and technical specifications related to climate diagnosis, short-term climate prediction and evaluation;
- reference products and technical supports for regional and provincial meteorological departments;
- WMO East Asian Monsoon Activity Centre (EAMAC) and WMO Global Producing Centre for long-range forecasts (GPC).

Operations and Techniques

Climate Prediction Division (CPD) makes multiple time scale prediction of temperature and precipitation in China, global ocean monitoring and prediction, and prediction of climatic influences on different industries on the basis of dynamic models and statistic methods. To adapt to changing service needs, CPD has shifted its focus to the prediction of typical climate events, climate disasters and environmental meteorology in recent years.
1. Extended, monthly, seasonal and inter-annual prediction of temperature and precipitation in China.
2. Prediction of climatic influences on different industries: prediction of climate in spring sowing period, monthly and seasonal typhoon prediction, prediction of the first frost in northern China, prediction of sand dust in northern China, and prediction of haze pollution processes.
3. Global climate monitoring and diagnostic analysis of climate anomalies.
4. Monitoring and prediction of global sea surface temperature, especially El Nino-Southern Oscillation (ENSO) monitoring and prediction.
5. Integration of climate event monitoring and prediction.
Typical climate events in the advancement of the East Asian summer monsoon include the first rainy season in South China, Meiyu, the rainy season in North China and autumn rain in West China. Atmospheric circulation anomalies have been diagnosed according to the latest monitoring standards in line with the occurrence years of each climate event. Evaluations have been made on the performance of the BCC_AGCM2.2 model in predicting key circulation factors that affect the characteristics of each climate event; and multi-time scale prediction products, developed.
Multiple East Asian monsoon indices were also evaluated and the indices that have significant indications for climate anomalies in China were selected for further analysis. Based on the BCC_AGCM2.2 model, the monitoring and prediction integration products of the South China Sea summer monsoon onset index, the East Asian summer monsoon intensity index, and the East Asian winter monsoon intensity index have been developed.
6. Climate disaster diagnosis and prediction.
The main climate disasters in different seasons of China include high temperature, heavy rainfall and tropical cyclone in summer, as well as low temperature in winter. Statistical methods were used to analyze the impacts of different phases and intensities of Madden Julian Oscillation (MJO) and ENSO events on high temperature and heavy rainfall anomalies in summer. Based on sub-seasonal dynamic models in BCC and in other countries, deterministic and probabilistic prediction products for each climate disaster in China and in some key regions have also been developed.
7. Operational system of environmental meteorology prediction.
Based on diagnostic analysis of regional heavy haze pollution events, some physical mechanism concerning the atmospheric circulation conditions has been revealed. The subseasonal prediction products for meteorological conditions that affect the diffusion of air pollution were developed based on the BCC_AGCM2.2 model and the NCEP version 2 coupled forecast system model (CFSv2).
The atmospheric chemistry model system (CFS-CUACE) for subseasonal prediction of haze pollution processes is now roughly in shape, to facilitate the development of subseasonal prediction of environmental meteorology.

Expert team
Undertake project

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