Introduction

Beijing Climate Center (BCC) Climate Prediction System version 2 (BCC-CPS v2) is a climate prediction system used for S2S and seasonal forecast from China Meteorological Administration (CMA). This system consists of a fully-coupled BCC Climate System Model BCC-CSM2-HR (Wu et al. 2021), a coupled data assimilation system (Liu et al., 2021) and a forecast ensemble method using stochastically perturbed physical tendency (SPPT) scheme. The coupled data assimilation (CDA) system consisting of ocean, sea-ice, and atmosphere data assimilation components with the Beijing Climate Center (BCC) Climate System Model has been developed to provide reliable analyses of the atmosphere, ocean, and sea-ice states. It incorporates ocean temperature/salinity profiles, sea surface temperature, sea level height, and sea-ice concentration observations at a daily frequency, and atmosphere reanalysis at a 6-hourly frequency. The system is capable of realistically reproducing the climatology and variability of ocean, sea-ice, and atmosphere.The S2S forecasts are running on every Monday and Thursday with a 60-day integration. Hindcasts is set to on-the-fly type, containing past 15 year re-forecasts related to the real-time forecast date.

BCC-CPS v2 replaced BCC-CPS v1 in November 2019, when S2S phase II began. BCC-CPS v1, which served during S2S phase I, is based on lagged average forecasting (LAF) method using a fully-coupled BCC Climate System Model BCC-CSM1.2. The BCC-CPS v1 S2S forecasts are running on every day since 1 Jan 1994 and end with a 60-day integration. Each forecast consists of 4 LAF ensemble members, which are initialized at 00 UTC of the first forecast day and 18, 12 and 06 UTC of the previous day, respectively.

IAP-CAS model (ANSO)

Second generation Finite volume prediction system of Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land  system  version 1.3 (FGOALS-f2 v1.3) was developed at Institute of Atmospheric Physics (IAP) ,CAS, which is jointly funded by Alliance of International Science Organizations (ANSO) and the National Natural Science Foundation of China major research project of the earth-atmosphere coupling system on the Qinghai-Tibet Plateau and its global climate effect. The prediction model is CAS FGOALS-f2, which is a Climate System Model representing the interaction between the atmosphere, oceans, land and sea ice. The S2S Forecasts runs 16-ensemble members  each day for real-time forecast since 1st June 2019 and 4-ensemble members each day for re-forecast since 1st January 1999. This S2S prediction ends with a 65-day integration.

Institutions/Projects in the region working on S2S

National Climate Center (NCC), China Meteorological Administration (http://cmdp.ncc-cma.net/en)

National Meteorological Information Center (NMIC), China Meteorological Administration (http://data.cma.cn/en)

Center for Earth System Modeling and Prediction of CMA (CEMC), China Meteorological Administration 

•  How well are S2S forecasts capture high impact weather events in the China region during East-Asia Summer Monsoon period?

•  What are the potential influence of coupled data assimilation initialization on the S2S forecast?

•  What are the potential predictability of MJO, ENSO, QBO … in S2S forecasts, and how can we improve their prediction skills ?

Publications

Bo, Z., Liu, X., Gu, W., Huang, A., Fang, Y., Wu, T., ... & Li, Q. (2020). Impacts of atmospheric and oceanic initial conditions on boreal summer intraseasonal oscillation forecast in the BCC model. Theoretical and Applied Climatology, 1-14.

Chen, D., Qiao, S., Tang, S., Cheung, H. N., Liu, J., & Feng, G. (2020). Predictability of the Strong Ural blocking Event in January 2012 in the Subseasonal to Seasonal Models of Europe and Canada. Atmosphere, 11(5), 538.

Cui, J., Yang, S. & Li, T. Intraseasonal Variability of Summertime Surface Air Temperature over Mid-High-Latitude Eurasia and Its Prediction Skill in S2S Models. J Meteorol Res 35, 815–830 (2021).

Cui, J., Yang, S., & Li, T. (2021). How well do the S2S models predict intraseasonal wintertime surface air temperature over mid-high-latitude Eurasia?. Climate Dynamics, 57(1), 503-521.

Dai, G., Mu, M., Li, C., Han, Z., & Wang, L. (2021). Evaluation of the forecast performance for extreme cold events in East Asia with subseasonalto-seasonal data sets from ECMWF. Journal of Geophysical Research: Atmospheres, 126, 2020JD033860.

Gao, M., Wang, B., Yang, J., & Dong, W. (2018). Are peak summer sultry heat wave days over the Yangtze–Huaihe River basin predictable?. Journal of Climate, 31(6), 2185-2196.

He, Z., Hsu, P., Liu, X. et al. (2019). Factors Limiting the Forecast Skill of the Boreal Summer Intraseasonal Oscillation in a Subseasonal-to-Seasonal Model. Adv. Atmos. Sci. (2019) 36: 104. https://doi.org/10.1007/s00376-018-7242-3

He, H., Yao, S., Huang, A., & Gong, K. (2020). Evaluation and Error Correction of the ECMWF Subseasonal Precipitation Forecast over Eastern China during Summer. Advances in Meteorology, 2020.

Li, Q., S. Yang, T. Wu and X. Liu, 2017: Subseasonal Dynamical Prediction of East Asian Cold Surges. Weather and forecasting, 32 (4), 1 August 2017, Pages 1675-1694

Li, X., & Tang, Y. (2021). Predictable mode of tropical intraseasonal variability in boreal summer. Journal of Climate, 34(9), 3355-3366.

Li, Y.;Wu, Z.; He, H.; Lu, G. Deterministic and Probabilistic Evaluation of Sub-Seasonal Precipitation Forecasts at Various Spatiotemporal Scales over China during the Boreal Summer Monsoon. Atmosphere 2021, 12, 1049.

Li, W., J. Chen, L. Li, H. Chen, B. Liu, C. Xu, and X. Li, (2019): Evaluation and Bias Correction of S2S Precipitation for Hydrological Extremes. J. Hydrometeor., 20, 1887–1906, https://doi.org/10.1175/JHM-D-19-0042.1

Liu, X., T. Wu, S. Yang, T. Li, W. Jie, L. Zhang, Z. Wang, X. Liang, Q. Li, Y. Cheng, H. Ren, Y. Fang, S. Nie, 2017: MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center, Clim. Dyn, 48, 3283-3307, DOI 10.1007/s00382-016-3264-7

Liu, X., Li, W., Wu, T., Li, T., Gu, W., Bo, Z., ... & Jie, W. (2019). Validity of parameter optimization in improving MJO simulation and prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center. Climate Dynamics, 52(7-8), 3823-3843.

Liu, X., Yao, J., Wu, T., Zhang, S., Xu, F., Zhang, L., ... & Cheng, Y. (2021). Development of Coupled Data Assimilation with the BCC Climate System Model: Highlighting the Role of Sea‐ice Assimilation for Global Analysis. Journal of Advances in Modeling Earth Systems, 13(4), e2020MS002368.

Ma, Y., Li, J., Zhang, S., & Zhao, H. (2021). A multi-model study of atmosphere predictability in coupled ocean–atmosphere systems. Climate Dynamics, 56(11), 3489-3509.

Miao, Q., Pan, B., Wang, H., Hsu, K., & Sorooshian, S. (2019). Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network. Water, 11(5), 977.

Jie, W., F. Vitart, T. Wu and X, Liu, 2017: Simulations of Asian Summer Monsoon in the Sub-seasonal to Seasonal Prediction Project (S2S) database, Quarterly J. of Royal Meter. Soc., DOI: 10.1002/qj.3085

Qiao, S., Chen, D., Wang, B., Cheung, H.-N., Liu, F., Cheng, J., et al. (2021). The longest 2020 Meiyu season over the past 60 years: Subseasonal perspective and its predictions. Geophysical Research Letters, 48, e2021GL093596.

Rao, J., Ren, R., Chen, H., Liu, X., Yu, Y., Hu, J., & Zhou, Y. ( 2019). Predictability of stratospheric sudden warmings in the Beijing Climate Center Forecast System with statistical error corrections. Journal of Geophysical Research: Atmospheres, 124, 8385– 8400. https://doi.org/10.1029/2019JD030900

Rao, J., Ren, R., Chen, H., Liu, X., Yu, Y., & Yang, Y. (2019). Sub-seasonal to seasonal hindcasts of stratospheric sudden warming by BCC_CSM1. 1 (m): A comparison with ECMWF. Advances in Atmospheric Sciences, 36(5), 479-494.

Rao, J., Garfinkel, C. I., White, I. P., & Schwartz, C. (2020). The Southern Hemisphere minor sudden stratospheric warming in September 2019 and its predictions in S2S models. Journal of Geophysical Research: Atmospheres, 125(14), e2020JD032723.

Rao, J., Garfinkel, C. I., & White, I. P. (2020). Predicting the downward and surface influence of the February 2018 and January 2019 sudden stratospheric warming events in subseasonal to seasonal (S2S) models. Journal of Geophysical Research: Atmospheres, 125(2), e2019JD031919.

Rao, J., & Garfinkel, C. I. (2021). The strong stratospheric polar vortex in March 2020 in sub-seasonal to seasonal models: Implications for empirical prediction of the low Arctic total ozone extreme. Journal of Geophysical Research: Atmospheres, 126, e2020JD034190.

Rao, J., Garfinkel, C. I., Wu, T., Lu, Y., Lu, Q., & Liang, Z. (2021). The January 2021 sudden stratospheric warming and its prediction in subseasonal to seasonal models. Journal of Geophysical Research: Atmospheres, 126, e2021JD035057.

Rao, J., & Garfinkel, C. I. (2021). The strong stratospheric polar vortex in March 2020 in sub-seasonal to seasonal models: Implications for empirical prediction of the low Arctic total ozone extreme. Journal of Geophysical Research: Atmospheres, 126, e2020JD034190.

Wang, L., & Robertson, A. W. (2019). Week 3–4 predictability over the United States assessed from two operational ensemble prediction systems. Climate Dynamics, 52(9-10), 5861-5875.

Wang, S., Liu, J., Cheng, X., Kerzenmacher, T., & Braesicke, P. (2020). Is enhanced predictability of the Amundsen Sea Low in subseasonal to seasonal hindcasts linked to stratosphere‐troposphere coupling?. Geophysical Research Letters, e2020GL089700.

Wang, Y., Ren, H. L., Zhou, F., Fu, J. X., Chen, Q. L., Wu, J., ... & Zhang, P. Q. (2020). Multi-Model Ensemble Sub-Seasonal Forecasting of Precipitation over the Maritime Continent in Boreal Summer. Atmosphere, 11(5), 515.

Wu, T., Yu, R., Lu, Y., Jie, W., Fang, Y., Zhang, J., ... & Hu, A. (2021). BCC-CSM2-HR: a high-resolution version of the Beijing Climate Center Climate System Model. Geoscientific Model Development, 14(5), 2977-3006.

Wu, J., Zhang, P., Li, L., Ren, H. L., Liu, X., Scaife, A. A., & Zhang, S. (2020). Representation and Predictability of the East Asia-Pacific Teleconnection in the Beijing Climate Center and UK Met Office Subseasonal Prediction Systems. Journal of Meteorological Research, 34(5), 941-964.

Wu, J., Zhang, P., Li, L., Ren, H. L., Liu, X., Scaife, A. A., & Zhang, S. (2020). Representation and Predictability of the East Asia-Pacific Teleconnection in the Beijing Climate Center and UK Met Office Subseasonal Prediction Systems. Journal of Meteorological Research, 34(5), 941-964.

Wu, J., Ren, H. L., Lu, B., Zhang, P., Zhao, C., & Liu, X. (2020). Effects of Moisture Initialization on MJO and its Teleconnection Prediction in BCC Subseasonal Coupled Model. Journal of Geophysical Research: Atmospheres, 125(1), e2019JD031537.

Wu, J., & Jin, F.-F. (2021). Improving the MJO forecast of S2S operation models by correcting their biases in linear dynamics. Geophysical Research Letters, 48.

Xie, J., Yu, J., Chen, H., & Hsu, P. C. (2020). Sources of Subseasonal Prediction Skill for Heatwaves over the Yangtze River Basin Revealed from Three S2S Models. Advances in Atmospheric Sciences, 1-16.

Yang, J., Zhu, T., Gao, M., Lin, H., Wang, B., & Bao, Q. (2018). Late‐July Barrier for Subseasonal Forecast of Summer Daily Maximum Temperature Over Yangtze River Basin. Geophysical Research Letters, 45(22), 12-610.

Yan, Y., Liu, B., Zhu, C. et al. Subseasonal forecast barrier of the North Atlantic oscillation in S2S models during the extreme mei-yu rainfall event in 2020. Clim Dyn (2021).

Yuan Li, Zhiyong Wu, Hai He, Quan J. Wang, Huating Xu, Guihua Lu, Post-processing sub-seasonal precipitation forecasts at various spatiotemporal scales across China during boreal summer monsoon, Journal of Hydrology, Volume 598, 2021, 125742, ISSN 0022-1694.

Zeng, D., & Yuan, X. (2018). Multiscale Land–Atmosphere Coupling and Its Application in Assessing Subseasonal Forecasts over East Asia. Journal of Hydrometeorology, 19(5), 745-760.

Zhang, K. Y., J. Li, Z. W. Zhu, and T. Li, 2021: Implications from subseasonal prediction skills of the prolonged heavy snow event over southern China in early 2008. Adv. Atmos. Sci., 38(11), 1873−1888.

Zhou, Y., Yang, B., Chen, H., Zhang, Y., Huang, A., & La, M. (2018). Effects of the Madden–Julian Oscillation on 2-m air temperature prediction over China during boreal winter in the S2S database. Climate Dynamics, 1-19.

Zhou, Y., Yang, B., Chen, H., Zhang, Y., Huang, A., & La, M. (2019). Effects of the Madden–Julian Oscillation on 2-m air temperature prediction over China during boreal winter in the S2S database. Climate dynamics, 52(11), 6671-6689.

Zhou Y and Wang Y (2021) Influence of the Madden–Julian Oscillation on the Arctic Oscillation Prediction in S2S Operational Models. Front. Earth Sci. 9:787680. doi: 10.3389/feart.2021.787680

ZHU, Hanchen and Haishan CHEN and Yang ZHOU and Xuan DONG, (2019). Evaluation of the subseasonal forecast skill of surface soil moisture in the S2S database. Atmospheric and Oceanic Science Letters, 12, 6, 467-474, https://doi.org/10.1080/16742834.2019.1663123

Zhu, S., Zhi, X., Ge, F., Fan, Y., Zhang, L., & Gao, J. (2020). Subseasonal Forecast of Surface Air Temperature Using Superensemble Approaches: Experiments over Northeast Asia for 2018. Weather and Forecasting, 1-44.

Bao, Q., X. F. Wu, J. X. Li, L. Wang, B. He, X. C. Wang, Y. M. Liu, and G. X. Wu. 2019. “Outlook for El Nino and the Indian Ocean Dipole in Autumn-winter 2018–2019.” Chinese Science Bulletin (In Chinese) 64 (1): 73–78. doi:10.1360/N972018-00913.

Li, J., Bao, Q., Liu, Y., Wu, G., Wang, L., He, B., Wang, X., Yang, J., Wu, X., Shen, Z., 2021a. Dynamical seasonal prediction of tropical cyclone activity using the FGOALS- f2 ensemble prediction system. Weather and Forecasting. doi: 10.1175/WAF-D-20-0189.1

Bao, Q., and J. Li. 2020. “Progress in Climate Modeling of Precipitation over the Tibetan Plateau.” National Science Review. 7(3): 486–487. doi:10.1093/nsr/nwaa006.

He, B., Bao, Q., Wang, X., Zhou, L., Wu, X., Liu, Y., ... & Zhang, X. (2019). CAS FGOALS-f3-L model datasets for CMIP6 historical atmospheric model Intercomparison project simulation. Advances in Atmospheric Sciences, 36(8), 771-778.

Lei WANG, Qing BAO, Jinxiao LI, Dongxiao WANG, Yimin LIU, Guoxiong WU & Xiaofei WU (2019) Comparisons of the temperature and humidity profiles of reanalysis products with shipboard GPS sounding measurements obtained during the 2018 Eastern Indian Ocean Open Cruise, Atmospheric and Oceanic Science Letters, 12:3, 177-183, DOI: 10.1080/16742834.2019.1588065

Bao, Q., Y. Liu, G. Wu et al.,(2020): CAS FGOALS-f3-H and CAS FGOALS-f3-L outputs for the high-resolution model intercomparison project simulation of CMIP6, Atmospheric and Oceanic Science Letters, DOI: 10.1080/16742834.2020.1814675

Li, J., Bao, Q., Liu, Y., Wang, L., Yang, J., Wu, G., ... & Shen, Z. (2021). Effect of Horizontal Resolution on the Simulation of Tropical Cyclones in the Chinese Academy of Sciences FGOALS-f3 Climate System Model. Geoscientific Model Development, https://doi.org/10.5194/gmd-2021-19,1-42.

Li, J., Bao, Q., Liu, Y., Wu, G., Wang, L., He, B., ... & Li, J. (2019). Evaluation of FAMIL2 in simulating the climatology and seasonal‐to‐interannual variability of tropical cyclone characteristics. Journal of Advances in Modeling Earth Systems, 11(4), 1117-1136.

Li, J., Bao, Q., Liu, Y., & Wu, G. (2017). Evaluation of the computational performance of the finite-volume atmospheric model of the IAP/LASG (FAMIL) on a high-performance computer. Atmospheric and Oceanic Science Letters, 10(4), 329-336.

Zhou, L., Bao, Q., Liu, Y., Wu, G., Wang, W. C., Wang, X., ... & Li, J. (2015). Global energy and water balance: Characteristics from F inite‐volume A tmospheric M odel of the IAP/LASG (FAMIL 1). Journal of Advances in Modeling Earth Systems, 7(1), 1-20.