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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
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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
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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
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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
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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)
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More for Model DataMore for Model Data
MayMayJunJun
JulJulAugAug
SepSepOctOct
DJFDJF
There are 15 runs from each monthThere are 15 runs from each month
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Climatic state and its drift with Climatic state and its drift with lead time of forecastlead time of forecast
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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))
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EOFs of Z200EOFs of Z200
CFS (total variability) vs OBSCFS (total variability) vs OBSEOFs of ensemble meanEOFs of ensemble mean
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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
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obsobsOCT_ICOCT_ICAug_ICAug_ICMay_ICMay_IC
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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”)”)
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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.
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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