A method of analogue-based correction of errors in model prediction and its application to 2013

summer climate prediction

REN Hongli, LIU Ying, ZHENG Zhihai

BCC/CMA, Beijing, China 10081

April 8-10, 2013 Beijing

Systematic Error Correction

Developing statistical methods to utilize historical data information in predictions of climate model .

Statistical correction of model prediction errors

Analogue-based Correction of Errors (ACE)

.ranflowsys EEEE

Constant systematic errors

Flow-dependent errors

If is bounded and is small enough, then

Hypothesis

~~L

tL

t

ELt

~~~EL

t

Basic equation for ACE :

0 L

tNumerical model

Real model of atmosphere

Model satisfied by analogue

~D ~ ~EE

Analogue-based Correction of model prediction Errors (ACE)

Strategy: Improve model prediction through using

abundant historical analogue information.

（ Ren and Chou, 2005, 2007）

jj PPPP ~~~ˆSMSM0SM0SM

Application of the ACE in seasonal prediction

Using multi-analogues

m

jjj PPPP

100

~~~ˆ

Seasonal mean of the Eq.

To estimate prediction errors of the current seasonal-mean prediction and realize the error correction.

Based on the ACE method, the Analogue-Dynamical Seasonal Prediction System (ADSPS) has been developed.

（ Ren et al， 2006, 2007, 2009; Zheng et al, 2009）

Analogue-Dynamical Seasonal Prediction System（ ADSPS)

Basic structure of the ADSPS

Initial conditions Historical analogues

Model hindcasts

Model prediction

Corrected prediction Error characteristics

The schemes for the actual application are developed from this basic structure of the ADSPS.

Applications to spring-summer ENSO prediction

• DATA and model– OBS: monthly HadISST during 1983-2012

• Interpolated into the resolution of BCC model– Model: BCC Coupled GCM 1.0 (BCC_CGCM1) – Hindcasts: monthly SST during 1983-2012

• Initiate month: Feb. • Predict from Mar. to Aug., every year

• Method – BCC model prediction removing the systematic errors – Corrected prediction with the ACE

• Correction of SSTAs in Mar.―Aug. during 1993-2012

1993

1983―1992

1983―1993

1994

1983―2011

2012

…………

Illustrations of the designed scheme

Independent validation

Time series of Nino 3.4 Index

Mar. Apr. May Jun. Jul. Aug.

BCC 0.92 0.85 0.59 0.37 0.26 0.28

ACE 0.9 0.79 0.63 0.44 0.36 0.28

Temporal correlation coefficients of Nino 3.4 indices between OBS and BCC or Correction of BCC during 1993-2012

Mar. Apr. May Jun. Jul. Aug.

BCC 0.6 0.53 0.59 0.68 0.74 0.83

ACE 0.29 0.41 0.45 0.49 0.69 0.78

RMSE ( ) of Nino 3.4 indices (≥ 0.5 ) during 1993-2012℃ ℃

TCC

Predictions of Niño3.4 index and SSTA in 2013

May Jun. Junl. Aug.

Nino3.4I 0.089 0.277 0.284 0.426

A new Warm-Pool El Niño event may be emerging.

Applications to China summer PRCP prediction in recent 4 years 2009 2010

2011 2012

Year

BCC CGCM1

BCC OP Apr

ADSPS March

2009

70 76 72

2010

61 73 69

2011

60 70 75

2012

61 68 67

平均 63.0 71.8 70.8

PS scoresThe same level of skill with BCC operation

2013 China summer PRCP prediction

• Positive PRCP anomalies are located over North China, Northeast China and South China. • Negative PRCP anomalies are located over Northwest China and the Yangtze River basin.

The method of analogue-based correction of errors (ACE) has been introduced to improve seasonal-mean predictions produced by climate model;

This ACE method can reduce the flow-dependent prediction errors besides the constant systematic errors;

Applications of the method in correcting the predictions of ENSO and China precipitation anomalies this summer show encouraging performance;

The predictions for this summer are worthy of expecting.

Summary

Thank you！