1. 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

2.  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) Constant systematic errors Flow-dependent errors

3. If is bounded and is small enough, then Hypothesis Basic equation for ACE : Numerical model Real model of atmosphere Model satisfied by analogue Analogue-based Correction of model prediction Errors (ACE) Strategy Strategy: Improve model prediction through using abundant historical analogue information. （ Ren and Chou, 2005, 2007 ）

4. Application of the ACE in seasonal prediction Using multi-analogues 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. 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 ）

5. Analogue-Dynamical Seasonal Prediction System Analogue-Dynamical Seasonal Prediction System （ ADSPS) Basic structure of the ADSPS Initial conditions Historical analogues Model hindcastsModel prediction Corrected predictionError characteristics The schemes for the actual application are developed from this basic structure of the ADSPS.

6. 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

7. 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

8. Time series of Nino 3.4 Index

9. Mar.Apr.MayJun.Jul.Aug. BCC0.920.850.590.370.260.28 ACE0.90.790.630.440.360.28 Temporal correlation coefficients of Nino 3.4 indices between OBS and BCC or Correction of BCC during 1993-2012 Mar.Apr.MayJun.Jul.Aug. BCC0.60.530.590.680.740.83 ACE0.290.410.450.490.690.78 RMSE ( ℃ ) of Nino 3.4 indices (≥ 0.5 ℃ ) during 1993-2012 TCC

10. Predictions of Niño3.4 index and SSTA in 2013 MayJun.Junl.Aug. Nino3.4I0.0890.2770.2840.426 A new Warm-Pool El Niño event may be emerging.

11. Applications to China summer PRCP prediction in recent 4 years 20092010 20112012 YearBCC CGCM1 BCC OP Apr ADSPS March 2009707672 2010617369 2011607075 2012616867 平均 63.071.870.8 PS scores The same level of skill with BCC operation

12. 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.

13.  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