1 / 32

Variability, Predictability and Prediction of DJF season Climate in CFS

Variability, Predictability and Prediction of DJF season Climate in CFS. Peitao Peng 1 , Qin Zhang 1 , Arun Kumar 1 , Huug van den Dool 1 , Wanqiu Wang 1 , Suranjana Saha 2 and Hualu Pan 2 1 CPC/NCEP/NOAA 2 EMC/NCEP/NOAA. Why DJF season?. In NDJ, ENSO reaches its peak

betty
Download Presentation

Variability, Predictability and Prediction of DJF season Climate in CFS

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. Variability, Predictability and Prediction of DJF season Climate in CFS Peitao Peng1, Qin Zhang1, Arun Kumar1, Huug van den Dool1, Wanqiu Wang1, Suranjana Saha2 and Hualu Pan2 1 CPC/NCEP/NOAA 2 EMC/NCEP/NOAA

  2. Why DJF season? • In NDJ, ENSOreachesits peak • In February, Atmospheric teleconnections are the strongest

  3. Objectives • Evaluate the performance of CFS in forecasting DJF climate • Understand the CFS performance • Estimate the potential predictability of DJF climate with CFS

  4. Outline • Document the CFS forecasted climatic state and its drift with the lead time of forecast • Examine the variability of CFS forecasted climate and its dependence on the lead time of forecast • Examine the CFS forecasted ENSO and its associated climate anomalies • Document the CFS prediction skill for DJF climate and estimate the potential predictability of CFS

  5. Data • Model: 23-year CFS hindcast dataset (1982-2004) • OBS: SST: OI SST Surface Temperature: CAMS data Z200: Reanalysis 2 (R2)

  6. More for Model Data DJF May Jun Jul Aug Sep Oct There are 15 runs from each month

  7. Climatic state and its drift with lead time of forecast

  8. Variability of DJF mean Total variance= Variance of ensemble mean (signal) + Variance of spread (noise)

  9. EOFs of Z200 CFS (total variability) vs OBS EOFs of ensemble mean

  10. ENSO and its associated climate anomalies CFS vs OBS El Nino vs La Nina (linearity) Dependence on lead time

  11. obs OCT_IC Aug_IC May_IC

  12. Prediction skills Against obs Against model itself: Taking one member as OBS and the average of other 14 members as forecast (“perfect model”)

  13. Summary • Part of the CFS climate drift in the extratropics is likely forced by the drift in the tropics • Climate drift increases moderately as lead time of forecast increases from one to six months • ENSO dominates the predictable component of interannual climate variability • In the period of 1982-2004, ENSO-related mean anomalies are pretty linear in both CFS and OBS.

  14. Summary continued • CFS shows pretty high forecast skills for the tropics and appreciable skills for the extratropics with up to six-month lead time • The decrease of forecast skills in the extratropics for longer lead time is partially due to the westward shift of the ENSO teleconnection patterns in forecast, which in turn is caused by the westward shift of tropical SST and precipitation patterns • “perfect model” skills show us brighter future

More Related