Predictability of Monthly Mean Predictability of Monthly Mean Temperature and Precipitation: Role of Temperature and Precipitation: Role of

Initial ConditionsInitial Conditions

Mingyue Chen, Wanqiu Wang, and Arun KumarClimate Prediction Center/NCEP/NOAA

Acknowledgments: Bhaskar Jha for providing the AMIP simulation data

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

Off. Up. ChangeAll 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

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

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

– 0.5-month lead 1-month outlookCCA, OCN, SMLR, and CFS

– 0-lead 1-month outlookCCA, OCN, SMLR, CFS, and GFS 1-14 day daily

forecasts, etc.

Sources of predictabilitySources 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

Issues to be discussedIssues 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 methodAnalysis 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 dataModels 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 timeDefinition of forecast lead time

Target month1st day11th day 21st day1st day

0-day-lead

10-day-lead

20-day-lead

30-day-lead

• 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

• Large spatial variation

CFS T2m monthly CFS T2m monthly correlation skillcorrelation skill

CFS T2m monthly correlation skill (global mean)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 timeCFS T2m monthly forecast skills with different lead time(zonal mean)(zonal mean)

010

20

304050

• Little change with lead time over tropics

•Quick decrease in high latitudes

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

• The monthly prec useful skills are at 0-day-lead forecast

• No useful skill at lead time long than 10 day for most regions

• Prec skill much lower than T2m skill

CFS Prec monthly forecast CFS Prec monthly forecast skills with different lead timeskills with different lead time

Question: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 AMIPCFS fo

recast

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

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

010

203040

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 & MMEzonal 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.

ConclusionsConclusions• 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!