The Canadian Seasonal to Interannual Prediction System (CanSIPS)

Transcription

1 The Canadian Seasonal to Interannual Prediction System (CanSIPS) Bill Merryfield, Woo-Sung Lee, Slava Kharin, George Boer, John Scinocca, Greg Flato Canadian Centre for Climate Modelling and Analysis (CCCma) Bertrand Denis, Juan-Sebastian Fontecilla, Jacques Hodgson, Benoit Archambault Canadian Meteorological Centre (CMC) WGSIP 25 Sep 2012

2 CanSIPS Models CanAM3 Atmospheric model - T63/L31 ( 2.8 spectral grid) - Deep convection scheme of Zhang & McFarlane (1995) - No shallow conv scheme - Simple radiative forcing CanOM4 Ocean model L40 - GM stirring, aniso visc - KPP+tidal mixing - Subsurface solar heating climatological chlorophyll CanAM4 Atmospheric model - T63/L35 ( 2.8 spectral grid) - Deep conv as in CanCM3 - Shallow conv as per von Salzen & McFarlane (2002) - Improved radiation, aerosols SST bias vs OISST CanCM3 CanCM4 C C

3 ENSO variability in models Monthly SSTA standard deviation HadISST CGCM3.1 IPCC AR4, CHFP1 CanCM3 CanSIPS CanCM4 CanSIPS *

4 GOAPP (Global Ocean- Atmosphere Prediction and Predictability) research network funded CanSIPS timeline CHFP1 pilot project (AR4 model, simple initialization) Initialization development CanCM3 development CanCM3 hindcasts CanCM4 development CanCM4 hindcasts Tech transfer & implementation

5 CanSIPS development & implementation Research & development Victoria CCCma Implementation & operations Dorval CMC

6 WMO Global Producing Centres for Long-Range Forecasts

7 CanSIPS initialization (off-line) *

8 Atmospheric assimilation Atmospheric (re)analysis T, winds, humidity assimilated every 6 hours using variant of incremental analysis update (IAU): IAU: 6h -1 analysis increment uncertainty Model equations CanSIPS: 24h -1 analysis increment (T21 truncated) weakening the assimilation increases ensemble spread to O(observational uncertainties) IAU CanSIPS τ dependence of T rmsd between pairs from assimilation run ensemble

9 Subsurface ocean assimilation Off-line variational assimilation of gridded T (Tang et al. JGR 2004; Derber & Rosati JPO 1989) S adjustment to preserve T-S relation, water column stability (Troccoli et al. MWR 2002) S background S assim T background T assim background T-S relationship bottom surface

10 Data Sources: Hindcasts vs Operational

11 ENSO prediction skill (Nino3.4 index) Anomaly correlation Mean-square skill score

12 Is there value at longest lead times? Long-lead skill for western Canada in winter/spring JFM FMA MAM Long-lead skill for eastern Canada in summer/fall JAS ASO SON Lead 9 month 2m temperature anomaly correlation

13 Lead 9 months NDJ issued 1 Feb 2010 NDJ skill DJF issued 1 Mar 2010 DJF skill JFM 2011 issued 1 Apr 2010 JFM skill FMA 2011 issued 1 May 2010 FMA skill Continuous Rank Probability Skill Score

14 Is there value at longest lead times? Seasonal precipitation forecasts at lead 9 months MJJ 2006 Issued 1 Aug 2005 ENSO neutral conditions predicted NDJ Issued 1 Feb 2010 La Niña predicted

15 Contributions to forecast compendia WMO GPC NMME APCC CanSIPS CanCM4 IRI ENSO Predictions UKMO Real-time Decadal Prediction Ensemble

16 IRI ENSO Prediction plume

17 IRI Nino3.4 forecasts from CanSIPS & other WMO GPCs Obs CMC-CanSIPS MetFRANCE UKMO AUS/POAMA ECMWF JMA

18 US National Multi-Model Ensemble (NMME) = CanCM3 = CanCM4 NMME models + ensemble mean T2m OND 2012 (from Sep 2012)

19 US National Multi-Model Ensemble (NMME) = CanCM3 = CanCM4 NMME models + ensemble mean precip DJF 2012 (from Sep 2012)

20 CanSIPS HFPs WRCP Climate-system historical forecast project (CHFP) CanCM3 CanCM4 YOTC MJO Diabatic Processes CanCM4 Global Land-Atmosphere Coupling Experiment 2 (GLACE-2) CanCM3 CanCM4 Ice Historical Forecast Project (IceHFP) CanSIPS CanCM4 CMIP5 Decadal Predictions CanAM3 CMAM Stratosphere-resolving Historical Forecast Project (Strat-HFP)

21 Calibration of probability forecasts 3-category probabilities: Below - Near - Above normal Uncalibrated forecast Skill Uncalibrated: Compute anomalies for each ensemble member Count # of ensemble members in each category clim Calibrated: Fit Gaussian PDF to raw forecast anomalies From hindcasts, find optimal rescaling of Gaussian mean and σ that maximizes CRPSS Apply this calibration to forecast PDF Calibrated forecast Skill Reference: Kharin & Zwiers J. Clim 2003 White: no category exceeds 40%

22 Soil moisture in first forecast month: ERA vs CMC VFSM = volume fraction of soil moisture Global mean Canada mean

23 July lead 0 predicted temperature anomalies and verification July 2011 uncalibrated calibrated verification (GISS) July 2012 uncalibrated calibrated verification (GISS)

24 Journal publications References Merryfield, W. J., W.-S. Lee, G. J. Boer, V. V. Kharin, J. F. Scinocca, G. M. Flato, R. S. Ajayamohan, J. C. Fyfe, Y. Tang, and S. Polavarapu, The Canadian Seasonal to Interannual Prediction System. Part I: Models and Initialization, Mon. Wea. Rev., submitted The Canadian Seasonal to Interannual Prediction System. Part II: Hindcast performance, in preparation. Technical report The Canadian Seasonal to Interannual Prediction System (CanSIPS): An overview of its design and operational implementation. CMC technical note (web search CanSIPS CMC note): op_system/ doc_opchanges/technote_cansips_ _e.pdf Verification interface: username: cccmasf password: seasforum

25

26 Benefits of coupled atmospheric assimilation: Improved land initialization Correlation of assimilation run vs offline analysis SST nudging only SST nudging + atmospheric assim Soil temperature (top layer) Soil moisture (top layer)

27 Benefits of coupled atmospheric assimilation: Improved ocean initialization Correlations vs obs in equatorial Pacific (5S 5N) zonal wind stress thermocline depth zonal surface current sea surface temp SST nudging + atmos SST nudging only

28 CanSIPS sea ice predictions Anomaly correlation, Sep mean ice concentration CanCM3 CanCM4 Forecasts initialized End of July Forecasts initialized End of June

29 Assimilation runs: ocean ensemble spread rms potential temperature difference between two members of assimilation run ensemble 56 m depth 510 m depth rms ΔT ( C)

30 Operational Multi-Seasonal Forecast Ranges ECMWF System 4 NCEP CFSv2 CMC CanSIPS Lead (months) Example: 1 January start J F M F M A M A M A M J M J J 7 months J F M F M A M A M A M J M J J J J A J A S 9 months J F M F M A M A M A M J M J J J J A J A S A S O A O N O N D 12 months

31 Assimilation runs: ocean ensemble spread Potential temperature difference at 510m depth between two members of assimilation run ensemble ΔTemperature ( C)

32 ENSO prediction skill: model comparisons CanCM3, CanCM4 vs ENSEMBLES models CanCM3 CanCM4 CanCM3 CanCM4 Anomaly correlation MSSS Comparison of individual model forecasts (ensemble size 9 for ENSEMBLES forecasts, ensemble size 10 for CanCM3 & CanCM4). CanSIPS skills are shown for comparison

33 US National Multi-Model Ensemble (NMME) = CanCM3 = CanCM4 NMME models + ensemble mean anomaly correlation OND SST from Sep initialization

34 ENSO prediction skill: system comparisons CanSIPS vs CFSv1 CanSIPS CanSIPS vs CFSv2* CanSIPS * with recommended adjustment to hindcast climatology

35 Lead 9 months NDJ issued 1 Feb December 2010 DJF issued 1 Mar January 2011 JFM 2011 issued 1 Apr April 2011 FMA 2011 issued 1 May 2010

36 What s next? Increased ensemble size Increased horizontal resolution T63 T127? Assimilate CMC NEMOVAR ocean data Assimilate land surface analysis Sea ice thickness initialization GEM - NEMO coupled model + New products: ocean, climate indices, sea ice,.