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MJO Prediction Evaluation and Ensemble Developments in Beijing

Climate Center (BCC)

REN Hongli WU Jie Zhao Chongbo WU Yujie

National Climate CenterLaboratory for Climate Studies

China Meteorological Administration

MJO Workshop

Outline

• 1 MJO prediction skill in BCC-AGCM2/CSM1.2

• 2 Initialization hindcast experiments

• 3 MJO operational products in BCC

• 4 Conclusion and discussion

Model versions Model components Resolutions

DERF2.0 BCC-AGCM2.1Atmos:T106L26,Top:2.19hPaOcn:weakly persistentSST

BCC-CSM1.2

BCC-AGCM2.2BCC-AVIM1MOM4-L40v2

SIS

Atmos:T106L40，Top:0.5hPaOcn:1/3o in30S-30Nand1/3-1o in

30-60N/S,and1oinhighlatitudes

BCCModelsforExtendRangeForecast

Atmosphere(BCC_AGCM)

Coupler

Sea Ice(SIS)

Ocean(MOM4_L40)

Land(BCC_AVIM)

Climate System Model (BCC_CSM) Daily initialization4 members

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Forecast Lead Time (Days)

BCC_AGCM2BCC_CSM1.2ENSEMBLEEMSEMBLE-Lag2

1.1 MJO prediction skill in BCC-AGCM2/CSM1.2

Based on bivariate RMM Index

(Hindcast period: 1994-2013)

Correlation Skill

BCC_AGCM2: 16 daysBCC_CSM1.2: 16 daysEnsemble (8 members): 19 daysEnsemble–lag 2 (24 members): 20 days

Composite lon-lead time sector of U850(contour) and OLR(shaded)

1）Eastward propagation is faster

Phase 2 Phase 4

OBS

FCS

1.2 MJO propagation characteristics in BCC-AGCM2

2）Encounter Maritime Continent barrier

1.3 Impacts of MJO on extratropical weather in BCC_AGCM2MJO-AO/NAO

（By ZUO Jingqing）

（Y）

（Y）

（Y）

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（N）

（Y）

（Y）

2 Initialization hindcastexperiments

Problem: 1)The direct replacing scheme may cause dynamic inconsistent2) Lack of moisture(q) initialization

Current Initialization of BCCCSM1.1m:Replacing by NCEP1/FNL data (U,V,T,Ps) on every step

Solution:Modify initialization scheme andincluding moisture variable (q)

The regression MJO moisture structure

MJO has a clear zonal asymmetry vertical moisture structure--Westward tilted

(Tim, 2014)

2.1 Modify initialization scheme (including Q)

OLD

Replacing（NDG.RPLC）

Implicit Nudging（NDG.UVT）

Implicit Nudging（NDG.UVTQ）

ENSEMBLE

NEW

The NDG.UVTQ scheme is slightly better !The ensemble of different initialization could extend the prediction skill for 3 days !

Experiment Design: 2001-2010 (10 years) 12 months (total 120 samples)

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b) RMSE

CTLNDG.UVTNDG.UVTQENSEMBLE

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a) COR

CTLNDG.UVTNDG.UVTQENSEMBLE

The regression MJO moisture structure during the initialization

More realistic moisture structure in NDG.UVTQ scheme !

Conclusion：Ø The new implicit nudging scheme ameliorate MJO moisture

structure during model initialization.Ø The ensemble of various initialization scheme can improve the

MJO prediction significantly.

MJO Prediction skill dependence on

Month Phase

3 MJO operational products in BCC

3.1 ISV/MJO Prediction System (IMPRESS)

ISV/MJO Monitoring & Prediction System (IMPRESS)

Real-time predictions of ISV/MJO indices & reconstructed fields

ISV/MJO-based extended-range forecasting & application

BCC-AGCM2.0prediction application

CMA T639 & FY-3B satellitereal-time data

BCC-CSM1.1mimproved version

prediction

ISV-based MJO STPM statistical

prediction

Real-time monitoring ISV/MJO indices & fields

OBS FCS

Operational products – RMM IndexDaily Real-time Update (latest result)：http://cmdp.ncc-cma.net/Monitoring/moni_mjo.php

BCC_AGCM2 BCCCSM1.2 T639 STPM

Operational products - Diagnose

U850-Time V850&OLR

3.2 Forecasting Case

Red solid line：ObservationColor dash line：every 5 day forecast

Large MJO event of Mar 2015

RMM Monitoring and forecasting U850 forecasting and re-construction(2nd Mar)

WWB

Successfully forecast the WWB 2 weeks ahead – Which has great impact on El Nino

• Conclusion:

a) The MJO prediction skill of BCC-AGCM2 and BCC_CSM1.2 both reach to 16days, and the ensemble could extend the skill to 20 days. The impacts of MJO on extratropical weather can be described to some extents.

b) The prediction skill has been extended for about 3 days by modifying initialization scheme and ensemble perturbations in the limited case hindcastexperiments.

c) The IMPRESS1.0（ISV/MJO Monitoring and Prediction System）is capable of providing reasonable MJO forecasting products at least 2 weeks ahead.

• Discussion:a) The initialization experiments samples are limited, need more experimentb) The BCCCSM model conceives the similar prediction skill with AGCM model

rather than extend the skill, we need check the air-sea coupling and other physics process.

4 Conclusion and Discussion

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MJO single-member and ensemble-mean Predictability Estimate

Ø The predictability limit is much longer

than the current prediction skill

Ø The model still has significant potential

room to improve its MJO prediction

skill if we develop better initialization

and ensemble strategies

Discussion – MJO predictability Estimate (BCC_AGCM2)

Error

Signal

(Neena, etal., 2014)

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single ensemble

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prediction skillPredictability Estimate

Reference:Wu J, Ren H L, Zhao C B, et al. MJO Prediction Skill, Predictability, and Teleconnection Impacts in the Beijing Climate Center Atmospheric General Circulation Model. Dynamics of Atmospheres and Oceans, 2016, doi:10.1016/j.dynatmoce.2016.06.001Ren H L, Wu J, Zhao C B, et al. MJO ensemble prediction in BCC-CSM1.1(m) using different initialization schemes. Atmospheric and Oceanic Science Letters, 2016, 9(1): 60-65.

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