1 / 20

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

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

rumer
Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. Monthly outlook is one of CPC’s official products Temperature - Sep 2008 0.5 Lead (Official) 0 lead (update) Observation Off. Up. Change All Stations 2.4 7.3 +4.9 Non-EC: 14.9 17.4 +2.5 % Cov: 16.0 42.2 +26.2

  3. 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

  4. 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

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

  6. Assessment of CFS monthly mean forecast skills with different lead times

  7. Definition of forecast lead time 30-day-lead 20-day-lead 10-day-lead 0-day-lead 1st day 1st day 11th day 21st day Target month

  8. CFS T2m monthly correlation skill • 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

  9. 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

  10. CFS T2m monthly forecast skills with different lead time(zonal mean) 20 10 50 40 30 0 • Little change with lead time over tropics • Quick decrease in high latitudes

  11. 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

  12. CFS Prec monthly forecast skills with different lead time • 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

  13. Question:What is the source of remaining skill for longer lead-time forecasts? A comparison of CFS hindcasts with GFS AMIP simulations

  14. 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.

  15. CFS T2m monthly correlation skill vs. GFS AMIP(global mean) CFS forecast GFS AMIP • Globally, the AMIP skill is comparable to CFS skill at 20-30-day lead

  16. T2m monthly correlation skill (CFS vs. GFS AMIP)(zonal mean) 0 10 20 40 30 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

  17. 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.

  18. CFS T2m monthly forecast skills vs. AMIP GFS & MMEzonal mean AMIP MME AMIP GFS • 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.

  19. 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.

  20. Thanks!

More Related