Variability predictability and prediction of djf season climate in cfs
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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

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Variability, Predictability and Prediction of DJF season Climate in CFS

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Variability predictability and prediction of djf season climate in cfs

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


Why djf season

Why DJF season?

  • In NDJ, ENSOreachesits peak

  • In February, Atmospheric teleconnections are the strongest


Objectives

Objectives

  • Evaluate the performance of CFS in forecasting DJF climate

  • Understand the CFS performance

  • Estimate the potential predictability of DJF climate with CFS


Outline

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


Variability predictability and prediction of djf season climate in cfs

Data

  • Model: 23-year CFS hindcast dataset

    (1982-2004)

  • OBS:

    SST: OI SST

    Surface Temperature: CAMS data

    Z200: Reanalysis 2 (R2)


More for model data

More for Model Data

DJF

May

Jun

Jul

Aug

Sep

Oct

There are 15 runs from each month


Climatic state and its drift with lead time of forecast

Climatic state and its drift with lead time of forecast


Variability of djf mean

Variability of DJF mean

Total variance=

Variance of ensemble mean (signal) + Variance of spread (noise)


Eofs of z200

EOFs of Z200

CFS (total variability) vs OBS

EOFs of ensemble mean


Enso and its associated climate anomalies

ENSO and its associated climate anomalies

CFS vs OBS

El Nino vs La Nina (linearity)

Dependence on lead time


Variability predictability and prediction of djf season climate in cfs

obs

OCT_IC

Aug_IC

May_IC


Prediction skills

Prediction skills

Against obs

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


Summary

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.


Summary continued

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


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