# Presentation: 573815

## Predictability of Seasonal Precipitation over Global Tropics 

**Predictability of Seasonal Precipitation over Global Tropics **
**in Coupled Climate Models**

**IPRC, University of Hawaii, USA**

**In-Sik Kang, Seoul National University, Korea**
**J. Shukla, George Mason University, USA**
**C.-K. Park and Saji Hameed, APCC, Korea**

**Notes:**
Good afternoon everyone (smile). On behalf of Bin, I’m gonna make this presentation on predictability of seasonal precipitation over global Tropics in coupled climate models. . 
This work is one part of a series of assessment efforts of current climate models’ one-month lead seasonal prediction in APCC/CliPAS project which Profs. Bin Wang, In-Sik Kang, and J. Shukla lead supported by APEC Climate Center. I’d like to acknowledge contributions from all APCC/CliPAS investigators. 

## CliPAS: Climate Prediction and Its

**Acknowledge contributions from the following CliPAS/APCC Investigators **

***CliPAS: Climate Prediction and Its***
***Application to Society***

**The international project, the CliPAS, in support of APCC is aimed at ****establishing well-validated multi-model ensemble (MME) prediction systems**** for climate prediction and developing economic and societal applications.**

** ****BMRC: O. Alves**
** ****CES/SNU: I.-S. Kang, J.-S. Kug **
** ****COLA/GMU:  J. Shukla, B. Kirtman, J. Kinter, K. Jin**
** ****FSU: T. Krishnamurti, S. Cocke, **
** ****FRCGC/JAMSTEC: J. Luo, T. Yamagata (UT) **
** ****IAP/CAS: T. Zhou, B. Wang **
** ****KMA: W.-T. Yun **
** ****NASA/GSFC: M. Suarez, S. Schubert, W. Lau **
** ****NOAA/GFDL: N.-C. Lau, T. Rosati,  W. Stern **
** ****NOAA/NCEP: J. Schemm, A. Kumar **
** ****UH/IPRC/ICCS: B. Wang, J.-Y. Lee, P. Liu, L. X. Fu **

**Notes:**
As In-Sik Mentioned this Monday, the Climate prediction and its application to society which the joint US-Korea project in support of APCC is aimed at establishing well-validated multi-model ensemble prediction systems for climate prediction and developing economic and societal application. Dr. Bin Wang is a PI and Dr. In-Sik Kang and Dr. Shukla are Co-PI’s. More than 20 researchers in climate prediction field have been involved in this project from 9 different institution in USA, Korea, and Japan.  Now the number of participating institutions and involved models are increasing. 

## Current Status of 

**Current Status of **
**     ****One-Month Lead MME Seasonal Prediction**

-  We now assured that **ENSO**, the major source of the Earth climate predictability, **can be predicted at a 6-month lead with correlation skill of 0.86**. But, forecast of transitional and decaying phases of ENSO remains far less reliable.
-  Prediction of **air temperature** is considerably **superior to the persistence skill in the warm pool oceans**, but not over the continental areas.
-  The **precipitation** prediction in Asian-Pacific monsoon region has **moderate skill in cold seasons but little skill over the continental summer monsoon regions**.
-  The seasonal march of the **thermal equator** seems to **add predictability to local summer hemisphere** and change of the westerly jet location can, to some extents, **provide prediction skill by influencing Rossby wave activity**.
-  The **CGCM MME** captures **the first two major modes of precipitation variability of the Asian-Australian monsoon system with high fidelity**, superior to the two reanalysis datasets.
**Sources of summer monsoon predictability** come primarily from **ENSO teleconnection**, **monsoon-warm pool interaction**, and possibly land surface-atmosphere interaction.

## Introduction

**Introduction**

**We propose one method to quantify predictability of global precipitation ****which relies on identification of the “predictable” leading modes of the observed interannual variations. The predictability is quantified by the fractional variance accounted for by these “predictable” leading modes.**

** ****Climate scientists have made tremendous advances in understanding and modeling the variability and predictability of the climate system. As a result, the prediction of seasonal-to-interannual climate variations and the associated uncertainties using multiple coupled models has become operational. However, *****how to determine the predictability in the coupled climate system, where no atmospheric lower boundary forcing given,***** remains an unresolved issue. **

**Notes:**
It is still open issue whether the conventional potential predictability analysis can be used for coupled model. 
The specific questions to be addressed are ....

## (1) How to determine the major modes of variability for precipitation over global Tropics (30S-40N)?

**(1) How to determine the major modes of variability for precipitation over global Tropics (30S-40N)?**

**Season-Reliant EOF (SEOF)**
**(Wang and An 2005 (GRL), Wang et al 2007 (Clim Dyn) )**

** ****Physical consideration**

**Anomalous climate (ENSO, monsoon) is regulated by the ****seasonal march of the solar radiation forcing (annual cycle).**** ****Season-Reliant EOF (SEOF)**** analysis detects ****seasonal evolving major modes of climate variability.**

** ****Method**

**Construct a covariance matrix that consists of a sequence of seasonal anomalies within a “Monsoon year” (Meehl 1987, Yasunari 1991)**

-  **JJA (0)  |   SON (0)   |   DJF (0/1)   |   MAM (1)**

** ****IAV in each monsoon year **

**Notes:**
The specific questions to be addressed are ....

## Asian-Australian Monsoon Predictability (Wang et al. 2007 Clim Dyn)

**Asian-Australian Monsoon Predictability ****(Wang et al. 2007 Clim Dyn)**

**S-EOF of Seasonal Mean Precipitation Anomalies**

**The First Mode: 30%**

**The Second Mode: 13%**

- The **CGCM MME** captures **the first two major modes of precipitation variability of the Asian-Australian monsoon system with high fidelity**, superior to the two reanalysis datasets.

**Notes:**
And then, we found that these dominant modes are not just confined over Asian-Australian Monsoon domain. In fact, they are global precipitation modes over global Tropics. 

## 13 Coupled Climate Models

| Institute | AGCM | Resolution | OGCM | Resolution | Ensemble 
Member | Reference |
| --- | --- | --- | --- | --- | --- | --- |
| BMRC
POAMA1.5 | BAM 3.0d | T47 L17 | ACOM3 | 0.5-1.5olat x 2olon L32 | 10 | Zhong et al. (2005) |
| FRCGC | ECHAM4 | T106 L19 | OPA 8.2 | 2ocos(lat) x 2o lon L31 | 9 | Luo et al. (2005) |
| GFDL | AM2.1 | 2olat x 2.5olon L24 | MOM4 | 1/3olat x 1olon L50 | 10 | Delworth et al. (2006) |
| NCEP | GFS | T62 L64 | MOM3 | 1/3olat x 5/8olon L27 | 15 | Vintzileos et al. (2005)
Saha et al. (2006) |
| SNU | SNU | T42 L21 | MOM2.2 | 1/3olat x 1olon L40 | 6 | Kug et al. (2005) |
| UH | ECHAM4 | T31 L19 | UH Ocean | 1olat x 2olon L2 | 10 | Kug et al. (2005) |
| CERFACS | ARPEGE | T63 L31 | OPA8.2
 | 2.0o x 2.0o 
L31 | 9 |  Deque (2001)
Delecluse and Madec (1999) |
| ECMWF | IFS | T95 L 40 | HOPE-E
 | 1.4x0.3-1.4 L29 | 9 | Gregory et al. (2000)
Wolff et al. (1997)  |
| INGV | ECHAM-4 | T42 L19 | OPA 8.1 | 2.0x0.5-1.5
 L29 | 9 |  Roeckner (1996)
Madec et al. (1998) |
| LODYC | IFS | T95 L40 | OPA 8.2 | 2.0x2.0 
L29 | 9 | Gregory et al. (2000)
Delecluse and Madec (1999)  |
| Meteo-France | ARPEGE
 | T63 L31 | OPA 8.0 | 182GPx152GP 
L31 | 9 | Deque (2001)
Madec et al. (1997)  |
| MPI | ECHAM-5 | T42 L19 | MPI-OM1 | 2.5x0.5-2.5
L23 | 9 |  Pope et al. (2000)
Gordon et al. (2000) |
| UK Met Office | HadAM3 | 2.5x3.75 L19 | GloSea OGCM | 1.25x0.3-125
L40 | 9 |  Roeckner (1996)
Marsland et al. (2003) |

**13 Coupled Climate Models**

**Notes:**
Thus, we applied S-EOF analysis on 13 coupled climate models which include 6 from CliPAS and 7 from DEMETER models in order to answer the second question, how many modes are predictable.  
Season-reliant Empirical Orthogonal Function (S-EOF) analysis (Wang and An 2005; Wang et al 2007) was applied to seasonal precipitation over Tropics from 30oS to 30oN in order to identify the “predictable” leading modes of interannual variations of tropical precipitation. 

## (2) How many modes are predictable?

**Percentage Variance**

**SEOF Modes for Precipitation over Global Tropics  (1981-2001)**
**[0-360E, 30S-40N]**

**Prediction skill of each mode**

-  The first two SEOF modes are very well predicted. The third are also reasonably well predicted. But all other higher modes are not predictable as shown by the insignificant correlation skill in the spatial structures and temporal variation.
-  We defined **the first three modes are predictable part **of the interannual variation using the current coupled MME prediction system.

**54.3% (****CMAP)**
**83% (MME)**

**Notes:**
The first two SEOF modes are very well predicted. The third are also reasonably well predicted. But all other higher modes are not predictable as shown by the insignificant correlation skill in the spatial structures 
Thus, we consider the first four major modes as predictable part of the interannual variations. 

## PC Spectra

**PC Spectra**

**PC time series**

**Lead/lag correlation with Nino3.4 SSTA**

**SEOF2 leads ENSO 1 year**

**S-EOF1 concurs with ENSO**

- MMEs are  highly correlated with CMAP

- MMEs underestimate QB Peak and total variances

- MME capture ENSO-MNS relation

**0.98**

**0.93**

**0.74**

- It is found that those predictable modes are significantly related with ENSO variability with different lead-lag relationship, especially in the 1st and 2nd modes.

## Eigenvectors of SEOF/ Precipitation

**Eigenvectors of SEOF/ Precipitation**

**S-EOF 1 of CMAP: 33%**
**Quasi-Biennial (QB)**

**S-EOF 2 of CMAP: 15%**
**Quasi-Quadrennial (QQ)**

## (3) How large fractional variance can be accounted for by the “predictable” leading modes?

**(3) How large fractional variance can be accounted for by the “predictable” leading modes?**

**Fractional variance accounted for by the “predictable” part**

-  **The fractional variance is obtained from the ratio of the variance associated with a single SEOF mode to the total variance** (Wang and An 2005).
-  If we take these three predictable modes together, about 53% of the total variance can be captured by those observational modes.
-  In observation, the fractional variance accounted for by the “predictable part” exhibits large spatial and seasonal variance. The MME prediction exaggerates the fractional variance of predictable modes, suggesting that the MME does not capture the higher modes.

## (4) How good is the prediction skill of the MME in terms of the “predictable” part?

**(4) How good is the prediction skill of the MME in terms of the “predictable” part?**

-  Figure b shows the correlation skill for the reconstructed precipitation just by using the three predictable modes of MME prediction. The similarity between (a) and (b) indicates that **the MME prediction skill basically comes from the first three leading modes of seasonal precipitation.**

**(a)**

**(b)**

**(c)**

**JJA**

**SON**

**DJF**

**MAM**

**Notes:**
we keep using all modes for observation to calculate correlation skill. 

## Predictability in Coupled Models

**Predictability in Coupled Models**

**Upper limit of predictability if there is no other prediction source in MME system**

**We can quantify the “predictability” by the fractional variance that is accounted for by the “predictable” leading modes in the observations. Such “predictable” modes can be determined by examining models’ hindcast results**

**Notes:**
If you look at this figure, other variability in MME add some prediction skill. However, that is very marginal. 

## Summary

**1**

**The three modes together account for about 53% of the total interannual variance averaged over the tropics in observations. This portion of the variation may be considered as practically predictable part of the precipitation variability, because the MME can capture these three major modes reasonably well but cannot capture the rest higher modes **

**2**

**3**

**This result leads to a new approach to estimate the practical predictability of the tropical seasonal precipitation in the coupled climate models; i.e., we can quantify the “predictability” by the fractional variance that is accounted for by the “predictable” leading modes in the observations. Such “predictable” modes can be determined by examining models’ hindcast results **

**How to measure the predictability in coupled climate system, where no atmospheric lower boundary forcing given, is an open issue. We have shown that the prediction skill of the coupled model MME basically comes from the skill in prediction of the first three major modes of interannual variations in the global tropical precipitation **

**Notes:**
To summerize, 

## Thank You !

## Predictability

- The sum of the fractional variances of the first four leading S-EOF modes in observation : the second kind potential predictability
- The temporal correlation skill of 10-model MME prediction

**Contour: 1**

**Contour: 50%**

**Contour: 0.6**

**Predictability**

## Summary

**Upper limit for total field if there is no other prediction source**

**Upper limit for predictable part if there is no other prediction source**

## Fractional variance and correlation skill in predictable modes

-  Sum of the fractional variances of first three and fifth modes in MME prediction and correlation skill between both predictable modes in observation and MME prediction.

**Fractional variance and correlation skill in predictable modes**

## Summary

**Upper limit if **

## Variance/ Precipitation

- Variance of observed precipitation for 21 years
- Mean of variances of each individual model ensemble predictions: The models intrinsically underestimate the precipitation variability.
- Variance of MME prediction
- Inter-model variance from multi-model ensemble mean (model spread): It is shown that the intermodel variances vary with season

**Variance/ Precipitation**