predictability of monthly mean temperature and precipitation: role of initial conditions
DESCRIPTION
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. 33 Rd Annual Climate Diagnostics & Prediction Workshop - PowerPoint PPT PresentationTRANSCRIPT
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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
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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
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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
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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
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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
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Assessment of CFS monthly mean forecast skills with different lead times
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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
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• 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
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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
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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
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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
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• 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
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Question:Question: What is the source of remaining skill for longer lead-time forecasts?
A comparison of CFS hindcasts with GFS AMIP simulations
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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.
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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
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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
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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.
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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.
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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.
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Thanks!