Decadal Hindcasts and Forecasts at GFDL

Transcription

1 Decadal Hindcasts and Forecasts at GFDL Tony Rosati T. Delworth, R. Gudgel, F. Zang, S. Zhang

2 Key Ques;ons What seasonal decadal predictability exists in the climate system, and what are the mechanisms responsible for that predictability? To what degree is the iden;fied predictability (and associated clima;c impacts) dependent on model formula;on? Are current and planned ini;aliza;on and observing systems adequate to ini;alize models for decadal predic;on? Is the iden;fied decadal predictability of societal relevance? 2

3 GFDL Decadal Predic;on Research in support of IPCC AR5 Key goal: assess whether climate projec;ons for the next several decades can be enhanced when the models are ini;alized from observed state of the climate system. Use ECDA for ini;al condi;ons from observed state observed state Produce ocean reanalysis Use workhorse workhorse CM2.1 model from IPCC AR4 [2009] Decadal hindcasts from every year star;ng in JAN Decadal predic;ons star;ng from 2001 onwards Use experimental high resolu;on model (if scien;fically warranted) [2010] Decadal predic;ons star;ng from 2001 onwards Use CM3 model for IPCC AR5 [2010, tenta;ve] Decadal predic;ons star;ng from 2001 onwards 3

4 SIGNAL IDENTIFICATION What signals must be captured by an oceanic nowcasting system in order to monitor decadal variability (e.g. MOC, TAV, Gulf Stream path, sea ice cover, etc.)? How capable are the present observing networks and model based synthesis systems at resolving these signals? What research priorities should be set, including the development of tools to be used (e.g., analysis/reanalysis products, modeling strategies), in order to better identify, understand and resolve the key signals in the ocean-atmosphere-land-ice system?

5 Ocean observacons assimilated 1982 XBT s 60 s Satellite SST Moorings/Altimeter ARGO The ocean observing system has slowly been building up Its non-stationary nature is a challenge for the estimation of decadal variability

6 Number of Temperature ObservaCons per Month as a FuncCon of Depth 6

7 Ensemble Coupled Data Assimila;on (ECDA) is at the heart of GFDL predic;on efforts Provides ini;al condi;ons for Seasonal Decadal Predic;on Provides valida;on for predic;ons and model development Ocean Analysis kept current and available on GFDL website Ac;ve par;cipa;on in CLIVAR/GSOP intercomparisons

8 Pioneering development of coupled data assimila;on system Ensemble Coupled Data Assimila;on es;mates the temporally evolving probability distribu4on of climate states under observa;onal data constraint: MulC variate analysis maintains physical balances between state variables such as T S relaconship primarily geostrophic balance Ensemble filter maintains the nonlinearity of climate evolucon All coupled components adjusted by observed data through instantaneously exchanged fluxes OpCmal ensemble inicalizacon of coupled model with minimum inicalizacon shocks GHGNA forcings Atmosphere model u, v, t, q, ps Prior PDF obs PDF u o, v o, t o Land model x b y o Sea Ice model τ x,τ y (Q t,q q ) T obs,s obs Ocean model T,S,U,V Data Assim (Filtering) x a Analysis PDF S. Zhang, M. J. Harrison, A. Rosa3, and A. Wi6enberg MWR 2007

9 EXPERIMENTAL DESIGN Initialization- from Ensemble Coupled Data Simulation (ECDA) Reanalysis Atmosphere - NCEP Reanalysis2 (T,u,v,ps) Ocean - xbt,mbt,ctd,sst,ssh,argo Radiative Forcing - GHG, Solar, Volcano, Aerosol Hindcasts - 10 member ensembles, starting Jan every year from for 10 years (total of 3k years) Predictions - A1B scenario

10 All Hindcasts and PredicCons bias corrected and 3 year low pass filtered

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13 Hindcasts Persistance NoAssim 0.6 ACC

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16 ECDA ERSST HadSST NOASSIM

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18 RMS Tav300

19 RMS AtlanCc Temperature (0 1000m)

20 N.H. SST Predictions HadSST ECDA ERSST

21 Pentad Precip Anomalies

22 Pentad Precip Anomalies

23 Pentad SAT Anomalies

24 Pentad SAT Anomalies

25 Policy Relevance of the Predic;ons in the Presence of: Model Error Predic;on Uncertainty Projec;on Uncertainty Observa;onal Uncertainty

26 ECDA research ac;vi;es to improve ini;aliza;on Mul; model ECDA to help mi;gate bias Fully coupled model parameter es;ma;on within ECDA ECDA in high resolu;on CGCM Assess addi;onal predictability from full depth ARGO profilers and pseudo Salinity

27 Summary Development of new advanced assimila;on techniques using coupled climate models Apply these techniques to detec;ng climate change while providing es;mates of their uncertainty Improve our understanding of predictability at decadal ;me scales Provide a founda;on for the development of a NOAA capability for decadal predic;ons

28 Concluding Remarks Decadal climate variability: Crucial piece predictability may come from both forced component internal variability component and their interactions. Decadal predictions will require: Better characterization and mechanistic understanding (determines level of predictability) Sustained, global observations Advanced assimilation and initialization systems Advanced models (resolution, physics) Estimates of future changes in radiative forcing Decadal prediction is a major scientific challenge An equally large challenge is evaluating their utility