# Presentation: 228443

## Predictability of Monthly Mean Temperature and Precipitation: Role of Initial Conditions

- Mingyue Chen, Wanqiu Wang, and Arun Kumar
- Climate Prediction Center/NCEP/NOAA
- Acknowledgments: Bhaskar Jha for providing the AMIP simulation data

- 33Rd Annual Climate Diagnostics & Prediction Workshop
- October 20-24, 2008, in Lincoln, Nebraska

## Monthly outlook is one of CPC’s official products

**All Stations   ****2.4    7.3****       +4.9 **
**Non-EC:       ****14.9  17.4****     +2.5 **
**% Cov:         16.0  42.2    +26.2     **

- Observation

- 0 lead (update)

- 0.5 Lead (Official)

- Temperature - Sep 2008

## How is current monthly outlook produced?(Ed O’ Lenic et al. 2008)

- 0.5-month lead 1-month outlook
- CCA, OCN, SMLR, and CFS
- 0-lead 1-month outlook
- CCA, OCN, SMLR, CFS, and GFS 1-14 day daily forecasts, etc.

- Sources of predictability

- Initial atmospheric and land conditions, and SSTs
- An initialized coupled atmosphere-land-ocean forecast system, such as CFS, is needed to harness this predictability

**Notes:**
CCA-the canonical correlation analysis,  uses linear statistical techniques to find optimum relationships among multivariate sets of independent and dependent variables.
OCN-the optimal climate normals
CAS-constructed analog on soil moisture
SMLR-screening multiple linear regression
CFS-40 members from last 10days

0.5-month lead is issued at 3rd Thursday of the month (Aug. 21). 
The 0-lead is issued at the last day of the month (Aug. 31). 

## Issues to be discussed

- What is the predictability (prediction skill) because of initialized observed conditions?
- What is the lead-time dependence?
- How does the predictability due to atmospheric/land initial conditions compare with that from SSTs?

- Analysis method

- Assess lead-time dependence of prediction skill of monthly means in CFS hindcasts
- Compare CFS with the simulation skill from the AMIP integrations to assess predictability due to SSTs, and to assess on what time scale influence of initial conditions decays

## Models and data

- Retrospective forecast
- CFS (5 member ensemble)
- AMIP simulations
- GFS (5 member ensemble)
- CCM3 (20 member ensemble)
- ECHAM (24 member ensemble)
- NSIPP (9 member ensemble)
- SFM (10 member ensemble)
- Variables to be analyzed
- T2m
- Precipitation
- The analysis is based on forecast and simulations for 1981-2006

## Assessment of CFS monthly mean forecast skills with different lead times

## Definition of forecast lead time

- Target month

- 1st day

- 11th day

- 21st day

- 1st day

**0-day-lead**

**10-day-lead**

**20-day-lead**

**30-day-lead**

## CFS T2m monthly correlation skill

- Dramatic skill decrease with lead time from 0-day lead to 10-day lead and more slow decrease afterwards
- Large spatial variation

_**CFS T2m monthly correlation skill**_

## CFS T2m monthly correlation skill (global mean)

- High CFS skill at 0-day lead time
- Dramatic skill decrease with lead time from 0-day lead to 10-day lead and more slow decrease afterwards

## CFS T2m monthly forecast skills with different lead time(zonal mean)

- _CFS T2m monthly forecast skills with different lead time__(zonal mean)_

**0**

**10**

**20**

**30**

**40**

**50**

- Little change with lead time over tropics
- Quick decrease in high latitudes

## CFS T2m monthly forecast skills with different lead time(zonal mean, DJF, MAM, JJA, & SON)

- CFS forecast skill decays vary seasonally
- Skills are higher in winter & spring over N. high latitudes
- Less changes over tropics

## CFS Prec monthly forecast skills with different lead time

- No useful skill at lead time long than 10 day for most regions
- Prec skill much lower than T2m skill

_**CFS Prec monthly forecast skills with different lead time**_

## Question:  What is the source of 	remaining skill for longer lead-time 	forecasts? 

- A comparison of CFS hindcasts with GFS AMIP simulations

## CFS T2m monthly correlation skill vs. GFS AMIP

- The AMIP skill in high-latitudes is low
- The GFS AMIP is similar to CFS in the tropics.

## CFS T2m monthly correlation skill vs. GFS AMIP(global mean)

- GFS AMIP

- CFS forecast

- Globally, the AMIP skill is comparable to CFS skill at 20-30-day lead

## T2m monthly correlation skill (CFS vs. GFS AMIP)(zonal mean)

_**T2m monthly correlation skill (CFS vs. GFS AMIP)**__**(zonal mean)**_

**0**

**10**

**20**

**30**

**40**

**50**

**GFS AMIP**

- Similar skills in CFS & GFS AMIP near the equator
- In N. lower latitudes (5N-35N), CFS skill higher at lead time shorter than 20 days
- Over N. high latitudes (35N-80N), CFS skill higher at lead time shorter than 20-30 days

## CFS T2m monthly forecast skills vs. AMIPs & MME

- The skills are different among 5 AMIPs
- GFS AMIP is comparable to 20-30 lead CFS
- The AMIP MME is almost comparable to 10-day lead CFS

- Similar to AMIP MME, coupled MME may have potential to improve.

## CFS T2m monthly forecast skills vs. AMIP GFS & MMEzonal mean

- _CFS T2m monthly forecast skills vs. AMIP GFS & MME__zonal mean_

**AMIP GFS**

**AMIP MME**

- The AMIP MME skills are better than the single GFS over all the latitudes.
- Similar to AMIP MME, coupled MME may have potential to improve.

## Conclusions

- For monthly forecasts, contribution from the observed land and atmospheric initial conditions does lead to improvements in skill.
- The improvement in skill, however, decays quickly, and within 20-30 days, skill of initialized runs asymptotes to that from SSTs.
- A simple average of multi-model AMIP runs shows a positive increase of the skill of monthly simulation, indicating room for further improvements with the MME coupled forecasts.

## Thanks!