Presentation is loading. Please wait.

Presentation is loading. Please wait.

Page 1 ECMWF decadal hindcasts Common ENSEMBLES experimental set-up  Stream 2 decadal hindcasts: 1960-2005, 1 start date every five years, three members.

Similar presentations


Presentation on theme: "Page 1 ECMWF decadal hindcasts Common ENSEMBLES experimental set-up  Stream 2 decadal hindcasts: 1960-2005, 1 start date every five years, three members."— Presentation transcript:

1 Page 1 ECMWF decadal hindcasts Common ENSEMBLES experimental set-up  Stream 2 decadal hindcasts: , 1 start date every five years, three members  IFS/HOPE, no sea-ice model, realistic initialization, ERA40/Int for atmosphere and land surface  Annual mean GHG concentration plus A1B, CMIP3 sulphate aerosols, mean seasonal cycle for CO2, O3, CH4 and sulphate, observed solar activity cycles, no volcanic aerosol

2 Page 2 experiment atmosphe ric model and resolution ocean model and resolution initialization comments atmosphere and land ocean Assim 33R1 IFS CY33R1; T159/L62 HOPE; 0.3º-1.4º/L29 ERA-40/oper. analysis, atmospheric singular vectors ORA-S3; wind stress perturbations to generate ensemble of ocean reanalyses; SST perturbations at initial time Bug in cloud/solar radiation NoOcObs 33R1 IFS CY33R1; T159/L62 HOPE; 0.3º-1.4º/L29 ERA-40/oper. analysis, atmospheric singular vectors ORA-Control (no subsurface assimilation); SST perturbations at initial time Bug in cloud/solar radiation XBT-C 33R1 IFS CY33R1; T159/L62 HOPE; 0.3º-1.4º/L29 ERA-40/oper. analysis, atmospheric singular vectors As ORA-S3 but with XBT correction; SST perturbations at initial time Bug in cloud/solar radiation Assim 35R3 IFS CY35R3; T159/L62 HOPE; 0.3º-1.4º/L29 ERA-40/oper. analysis, atmospheric singular vectors ORA-S3; wind stress perturbations to generate ensemble of ocean reanalyses; SST perturbations at initial time Modifications in effective radius size of cloud droplets NoOcObs 35R3 IFS CY35R3; T159/L62 HOPE; 0.3º-1.4º/L29 ERA-40/oper. analysis, atmospheric singular vectors ORA-Control (no subsurface assimilation); SST perturbations at initial time Modifications in effective radius size of cloud droplets NoOcObs 35R3 IFS CY35R3; T159/L62 HOPE; 0.3º-1.4º/L29 ERA-40/oper. analysis, atmospheric singular vectors As ORA-S3 but with XBT correction; SST perturbations at initial time Modifications in effective radius size of cloud droplets ECMWF decadal hindcasts

3 On representing model uncertainty in climate predictions T.N.Palmer ECMWF with thanks to Francisco Doblas-Reyes, Thomas Jung and Antje Weisheimer, ECMWF

4 Model uncertainty Scenario uncertainty Initial uncertainty Hawkins and Sutton, 2009

5 Standard Numerical Ansatz for Climate Model Deterministic local bulk-formula parametrisation Increasing scale Eg momentum“transport” by: Turbulent eddies in boundary layer Orographic gravity wave drag. Convective clouds Eg

6 Atmosphere Land surface Ocean & sea-ice Sulphate aerosol Sulphate aerosol Sulphate aerosol Non-sulphate aerosol Non-sulphate aerosol Carbon cycle Atmospheric chemistry Ocean & sea-ice model Sulphur cycle model Non-sulphate aerosols Carbon cycle model Land carbon cycle model Ocean carbon cycle model Atmospheric chemistry Atmospheric chemistry Off-line model development Strengthening colours denote improvements in models The Met.Office Hadley Centre Towards Comprehensive Earth System Models

7 Atmosphere Land surface Ocean & sea-ice Sulphate aerosol Sulphate aerosol Sulphate aerosol Non-sulphate aerosol Non-sulphate aerosol Carbon cycle Atmospheric chemistry Ocean & sea-ice model Sulphur cycle model Non-sulphate aerosols Carbon cycle model Land carbon cycle model Ocean carbon cycle model Atmospheric chemistry Atmospheric chemistry Off-line model development Strengthening colours denote improvements in models The Met.Office Hadley Centre Uncertainty A Missing Box

8 How can uncertainty be represented in ESMs? Multi-model ensembles Perturbed parameters Stochastic parametrisation

9 Seasonal multi- model ensemble

10 Seasonal Reforecasts (months 2-4) of El Niño with a comprehensive coupled model observations predictions

11 Multi-model seasonal reforecasts of El Niño

12 precipitation in DJF start dates: Nov hindcast period: lower tercile AmazonCentral AmericaNorthern Europe Multi-model Seasonal Forecast Reliability Failure of multi-model ensemlble

13 Slide 13 Surface Pressure Potential Vorticity on 315K Blocking Anticyclone As recognised in AR4, the current generation of climate models has difficulty simulating a number of internal modes of climate variability such as the persistent blocking anticyclone.

14 Blocking Index. DJFM ERA-40 T159 T1259 T1259 run on NSF Cray XT4 “Athena” (two months of dedicated usage) Similar results found by M.Matsueda MRI Japan

15 For all their pragmatic value, multi- model ensembles are ad hoc “ensembles of opportunity”. Component models have common shortcomings, eg due to limited resolution.

16 How can uncertainty be represented in ESMs? Multi-model ensembles Perturbed parameters Stochastic parametrisation

17 Deterministic local bulk-formula parametrisation Increasing scale Vary α Perturbed Parameters

18 How can uncertainty be represented in ESMs? Multi-model ensembles Perturbed parameters Stochastic parametrisation

19 A stochastic-dynamic paradigm for the Earth-System model Computationally-cheap nonlinear stochastic-dynamic models, providing specific possible realisations of sub-grid motions rather than sub-grid bulk effects Coupled over a range of scales Increasing scale ECMWF Tech Memo 598

20 SAC 2009 Spectral Stochastic Backscatter Scheme Origins: Leith (1990), Mason and Thomson (1992) Shutts, G.J. (2005). A kinetic energy backscatter algorithm for use in ensemble prediction systems. Q.J.R.Meteorol.Soc. 131, 3079 Berner, J. et al (2009). A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system. J. Atmos.Sci., 66,

21 Slide 21 SAC2009 Backscatter Algorithm Streamfunction forcing Pattern using spectral AR(1) processes as SPPT D tot is a smoothed total dissipation rate, normalized here by B tot and b R is the backscatter ratio

22 Realisations of stochastic pattern generator

23 In ENSEMBLES we have tested the relative ability of these different representations of uncertainty: Multi-model ensembles Perturbed parameters Stochastic physics to make skilful probabilistic seasonal climate predictions.

24 “Giorgi” Regions

25 lead times: 2-4 months Dry=lower tercile Wet=upper tercile Which is best? Brier Skill Score

26 lead times: 2-4 months Brier Skill Score Cold=lower tercile Warm=upper tercile

27 1055m007 corrected SP#7 2m temperature precipitation 44% 55% 52% 45% 30% 70% 60% 41% 31%36% 64% 69% Multi-model Stochastic physics Perturbed parameter Stochastic physics Perturbed parameter Stochastic physics Perturbed parameter Brier Skill Score Months 2-4. Upper and lower terciles, DJF, JJA. Giorgi regions.

28 precipitation over Northern Europe land (north of 48ºN) in DJF start dates: Nov 1 st. hindcast period: lower tercile stochastic physics #7 BSS(∞)=0.087BSS(∞)= perturbed physics multi-model BSS(∞)= Multi-model Seasonal Forecast Reliability

29 Conclusions Stochastic parametrisation and perturbed parameter methodologies are competitive with the traditional multi-model approach to representing model uncertainty Stochastic parametrisation “wins” overall for atmospheric variables, but needs to be extended to the ocean and the land surface. The ECMWF THOR integrations will be started next year using the latest stochastic parametrisation schemes.


Download ppt "Page 1 ECMWF decadal hindcasts Common ENSEMBLES experimental set-up  Stream 2 decadal hindcasts: 1960-2005, 1 start date every five years, three members."

Similar presentations


Ads by Google