Variability, Predictability and Prediction Variability, Predictability and Prediction of DJF season Climate in CFSof DJF season Climate in CFS

Peitao PengPeitao Peng11, Qin Zhang, Qin Zhang11, Arun Kumar, Arun Kumar11, , Huug van den DoolHuug van den Dool11, Wanqiu Wang, Wanqiu Wang11, ,

Suranjana SahaSuranjana Saha22 and Hualu Pan and Hualu Pan22

1 CPC/NCEP/NOAA1 CPC/NCEP/NOAA

2 EMC/NCEP/NOAA2 EMC/NCEP/NOAA

Why DJF season?Why DJF season? In NDJ, ENSOIn NDJ, ENSO reachesreaches its peakits peak In February, Atmospheric teleconnections In February, Atmospheric teleconnections

are the strongestare the strongest

ObjectivesObjectives

Evaluate the performance of CFS in Evaluate the performance of CFS in forecasting DJF climateforecasting DJF climate

Understand the CFS performanceUnderstand the CFS performance Estimate the potential predictability of DJF Estimate the potential predictability of DJF

climate with CFS climate with CFS

OutlineOutline1.1. Document the CFS forecasted climatic Document the CFS forecasted climatic

state and its drift with the lead time of state and its drift with the lead time of forecastforecast

2.2. Examine the variability of CFS forecasted Examine the variability of CFS forecasted climate and its dependence on the lead climate and its dependence on the lead time of forecasttime of forecast

3.3. Examine the CFS forecasted ENSO and Examine the CFS forecasted ENSO and its associated climate anomaliesits associated climate anomalies

4.4. Document the CFS prediction skill for DJF Document the CFS prediction skill for DJF climate and estimate the potential climate and estimate the potential predictability of CFSpredictability of CFS

DataData

Model: 23-year CFS hindcast dataset Model: 23-year CFS hindcast dataset (1982-2004)(1982-2004)

OBS: OBS: SST: OI SSTSST: OI SST Surface Temperature: CAMS dataSurface Temperature: CAMS data Z200: Reanalysis 2 (R2)Z200: Reanalysis 2 (R2)

More for Model DataMore for Model Data

MayMayJunJun

JulJulAugAug

SepSepOctOct

DJFDJF

There are 15 runs from each monthThere are 15 runs from each month

Climatic state and its drift with Climatic state and its drift with lead time of forecastlead time of forecast

Variability of DJF meanVariability of DJF mean

Total varianceTotal variance = = Variance of ensemble mean (Variance of ensemble mean (signalsignal) + ) + Variance of spread (Variance of spread (noisenoise))

EOFs of Z200EOFs of Z200

CFS (total variability) vs OBSCFS (total variability) vs OBSEOFs of ensemble meanEOFs of ensemble mean

ENSO and its associated ENSO and its associated climate anomaliesclimate anomalies

CFS vs OBS CFS vs OBS El Nino vs La Nina (El Nino vs La Nina (linearitylinearity))Dependence on lead timeDependence on lead time

obsobsOCT_ICOCT_ICAug_ICAug_ICMay_ICMay_IC

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

SummarySummary Part of the CFS climate drift in the extratropics Part of the CFS climate drift in the extratropics

is likely forced by the drift in the tropicsis likely forced by the drift in the tropics

Climate drift increases moderately as lead time Climate drift increases moderately as lead time of forecast increases from one to six monthsof forecast increases from one to six months

ENSO dominates the predictable component of ENSO dominates the predictable component of interannual climate variabilityinterannual climate variability

In the period of 1982-2004, ENSO-related mean In the period of 1982-2004, ENSO-related mean anomalies are pretty linear in both CFS and anomalies are pretty linear in both CFS and OBS.OBS.

Summary Summary continuedcontinued

CFS shows pretty high forecast skills for the CFS shows pretty high forecast skills for the tropics and appreciable skills for the extratropics tropics and appreciable skills for the extratropics with up to six-month lead timewith up to six-month lead time

The decrease of forecast skills in the extratropics The decrease of forecast skills in the extratropics for longer lead time is partially due to the for longer lead time is partially due to the westward shift of the ENSO teleconnection westward shift of the ENSO teleconnection patterns in forecast, which in turn is caused by patterns in forecast, which in turn is caused by the westward shift of tropical SST and the westward shift of tropical SST and precipitation patternsprecipitation patterns

““perfect model” skills show us brighter future perfect model” skills show us brighter future