1. Hongli Ren, Changzheng Liu, Jianghua Wan, Ying Liu, Ben Tian
Fifteenth Session of the Forum on Regional Climate Monitoring, Assessment and Prediction for Asia (FOCRAII)
May 8-10, 2019, Nanning China
China Multi-Model Ensemble Prediction System Version 1.0 (CMMEv1.0) and its Application to Flood-Season Prediction in 2018 and 2019
Yujie WU
Hongli Ren, Changzheng Liu, Jianghua Wan, Ying Liu, Ben Tian
Beijing Climate Center, China Meteorological Administration

2. Introduction
The climate disasters have intensified over the world in the context of global warming. Short-term climate prediction is an important support for disaster prevention and mitigation.
Steadily raising the forecast level to meet the needs of national development is very necessary! However, the business model has a low prediction skill in China, and the forecast performance is unstable! The accuracy of prediction of major climate anomalies is lower!!!
OBS
ChinaBCCCSM
USA
CFS
EUP
ECMWF

3. Introduction
The multi-model ensemble (MME) prediction is superior to the predictions made by any single-model component (Krishnamurti et al. 1999, 2000; Palmer et al. 2000; Barnston et al. 2003; Krishnamurti et al. 2015).
Krishnamurti et al. 2015

4. Introduction
NMME
Various MME prediction systems are developed at several operational centers that routinely provide MME seasonal forecasts (e.g., the North American Multimodel Ensemble, the European Centre for Medium-Range Weather Forecasts and APCC from Korea).
ECMWF
APCC 4

5. Framework of CMME in BCC
In recent years, BCC is developing a MME prediction system called China Multi-Model ensemble System (CMME). In our conception, CMME will include both Chinese and international climate models, such as BCC model, IAP model,GFDL model et al. And the model data from ECMWF,CFS, JMA are also involved. Based on the retrospective and real-time forecast of these models and data, CMME will be used in the diagnostics, prediction and assessment of global climate variability and global and East Asian climate elements. So, CMME can provide climate data, climate prediction and climate service to scientist and decision-maker.

6. Atmospheric Resolution
CMME Version1.0
Currently, we are building the version 1 of CMME. We have 6 models including 4 Chinese models. And CMMEv1.0 has been applied in real-time forecast for 2018 and 2019 flood season.
Models
Institute
Atmospheric Resolution
Oceanic
Resolution
Ensemble members
Forecast months M1
FGOALS-f2
IAP
100 km, L32
1°×1°, L50 24 6 M2
FGOALS-s2
R42, L26
1°×1°, L30 4 M3
BCC-CSM1.1m
BCC
T106, L26
1°×1°, L40 13 M4
NZC-PCCSM4
2.5°×1.9°, L26
1°×1° 8 M5
ECMWF-S4
ECMWF
TL255, L91
1°×1°, L42 15 7 M6
NCEP-CFSv2
NCEP
T126, L64 9

7. Forecast Review from 2018 March: SST
CMME Skill
CMME: SSTA
OBS:SSTA
Good similarity between the real-time SSTA forecasts from March 2018 and their corresponding observations.
CMMEv1.0 accurately predicted that the cold SSTAs in the tropical central-eastern Pacific gradually warm up.
Firstly, we show the forecast review for 2018 flood season from 2018 March by CMMEv1.0. Fig. 2 shows the TCC skill of the CMMEv1.0 hindcast for the global SST anomalies (SSTAs) beginning from March. CMMEv1.0 exhibits significantly high prediction skills over most of the ocean region, especially in the mid- and low-latitudes.

8. Forecast Review from 2018 March: ENSO
ENSO indices are significantly skillful and scores of the MME for both Niño3.4 and Niño4 indices are larger than 0.8 at the 6-month lead, which is more skillful than any single model.
CMMEv1.0 prediction accurately captured the trend of the ENSO transition from its cold phase to the neutral phase and then the warm phase during spring–summer of 2018, where the MME prediction almost coincides with observations.
Fig. 3 Left column: prediction skills for Niño3.4, Niño4, Niño3 indices from March to August and for the JJA season from CMMEv1.0 starting from March; Right column: monthly Niño3.4, Niño4, Niño3 indices from OISSTv2 from March 2017 to August 2018, and their corresponding forecasts from March to August 2018 for individual models and the ensemble mean from CMMEv1.0

9. Forecast Review from 2018 March: IOD and NAST
IOD: Indian Ocean Dipole
NAST: North Atlantic SST Triple
TCC scores for IOD beginning from March were limited. This is presumably due to its winter prediction barrier.
CMMEv1.0 predicted a near-normal IOD state in spring and a weak positive anomaly in summer, which is consistent with the observation; however, the predicted intensity was significantly weakened.
CMMEv1.0 has a higher prediction skill for the NAST than for IOD.
NAST Prediction of CMMEv1.0 is quite consistent with observations with weaker NAST positive anomalies.
Fig. 4 (a) TCC skills for the IOD index from March to August and for the JJA season from CMMEv1.0 predictions that started from March; (b) the OISSTv2 IOD index from March 2017 to August 2018 and related forecasts during March to August 2018 by individual models and the ensemble mean from CMMEv1.0; (c) and (d) are the same as (a) and (b) but for the NAST index.

10. Forecast Review from 2018 March: WPSH and EASM
WPSH: western Pacific subtropical high
EASM: East-Asian summer monsoon
The WPSH area, intensity, and western ridge point in CMMEv1.0 are significantly skillful, especially for the JJA season.
Real-time forecasts of the WPSH indices by CMMEv1.0 are quite consistent with observations that both the area and intensity of the WPSH in the summer of 2018 tend to be near normal and the western ridge point is significantly eastward.
CMMEv1.0 has a quite high prediction skill of the EASM intensity index in JJA season.
The observed EASM was stronger than climatology, and the predictions of CMMEv1.0 are similar to the observations but tend to be slightly weaker.

11. Forecast Review from 2018 March: JJA Air Temperature
The summer SAT in most areas of China is skillful in CMMEv1.0 for a 3-month lead-time, though TCCs were relatively low for the Huang–Huai area and small parts of western China.
The SAT in most regions of China was warmer than the climatology with relatively larger anomalies in northern China and smaller anomalies in central and southern China; however, the predicted intensity was slightly weaker than the observations in most regions.

12. Forecast Review from 2018 March: JJA Precipitation
CMMEv1.0 has limited TCC skills for predicting summer precipitation in China for a lead-time of 3 months.
Observations showed that precipitation in the northern and southern China areas was more than climatology but less than climatology in the Yangtze River Basin region. This rainfall anomaly pattern in the summer of 2018 was well captured by the CMMEv1.0 prediction; however, the latter showed much more precipitation in the middle and lower reaches of the Yangtze River than the observation.

13. Forecast from 2019 March: SST and ENSO
Warm SSTAs in the tropical central-eastern Pacific gradually warm up.

14. Forecast from 2019 March: IOD and NAST
IOD: Development of a positive phase
NAST: Positive phase

15. Forecast from 2019 March: WPSH and EASM
WPSH Area: Larger than climatology
WPSH Intensity: Stronger than climatology
WPSH Ridge Point: Westward in spring and Eastward in summer
EASM: near normal

16. Forecast from 2019 March: JJA Air Temperature
Warmer than the climatology in most regions of China

17. Forecast from 2019 March: Precipitation
Wet in Southern China and Western China
Dry in Northern China

18. Summary
Based on the combination of localized operation outputs of Chinese climate models and imported forecast data of some international operational models, BCC has established the China Multi-Model Ensemble prediction system version 1.0 (CMMEv1.0) for monthly–seasonal prediction of primary climate variability modes and climate elements.
For real-time forecasts for March–August in 2018, CMMEv1.0 accurately predicted the transition of the ENSO state from cold to neutral phase in the tropical central-eastern Pacific and generally captured the time evolutions of the NAST and IOD.
CMMEv1.0 captured the main features of the summer WPSH and EASM in 2018, except that the prediction result for the latter is slightly weaker than observations.
CMMEv1.0 successfully predicted the spatial distribution of warmer air temperatures in northern China and captured the primary rainbelt positioned over northern China, except it predicted much more precipitation in the middle and lower reaches of the Yangtze River than observation.

19. Thanks for Your Attention