Predictability of Monthly Mean Temperature and Precipitation: Role of Initial Conditions Mingyue Chen, Wanqiu Wang, and Arun Kumar Climate Prediction Center/NCEP/NOAA.

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Presentation transcript:

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 October 20-24, 2008, in Lincoln, Nebraska

Off. Up. Change All Stations Non-EC: % Cov: Observation 0 lead (update) 0.5 Lead (Official) Temperature - Sep 2008 Monthly outlook is one of CPC’s official products

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

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

Assessment of CFS monthly mean forecast skills with different lead times

Definition of forecast lead time Target month 1 st day 11 th day21 st day 1 st 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 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) 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

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 skills 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 AMIP CFS forecast Globally, the AMIP skill is comparable to CFS skill at day lead

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

CFS T2m monthly forecast skills vs. AMIPs & MME The skills are different among 5 AMIPs GFS AMIP is comparable to 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 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 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!