Seamless prediction Science drivers/ Model development

Transcription

1 Seamless prediction Science drivers/ Model development Wilco Hazeleger

2 Last winter. Anomalous geopotential height at 500 hpa (contour interval 3m) and 2-meter temperature (K)

3 Seamless prediction The science case: Same physical principles (but different processes acting on different scales) Prediction problem with different role initial and boundary conditions Decadal prediction as focus The modelling case: Convergence of NWP-Climate model development Seamless earth system prediction The application case: Calibration (weights) of climate predictions & projections Seamless connection to impact studies

4 Modelling climate changes: uncertainties Natural fluctuations GHG emission uncertainty Model uncertainty Hawkins and Sutton, 2009

5 Published decadal predictions, e.g.: Smith et al 2007 Keenlyside et al 2008 Pohlmann et al 2009 Challenge: initialisation, perturbation & verification

6 Global Ocean Heat Content 0-700m (10 22 J) Domingues et al. (2008) A. Koehl

7 No forecast without forecast skill (Tennekes) van Oldenborgh, Doblas-Reyes, Wouters and Hazeleger, GRL submitted

8 No forecast without forecast skill van Oldenborgh, Doblas-Reyes, Wouters and Hazeleger, GRL submitted

9 No forecast without forecast skill 2 meter temperature multi-model anomaly correlation 2-5 year lead time averaged, without trend. Some skill, but signal is small (no strong spectral peak at decadal time scale)

10 Convergence of NWP-ESM model development (EC-Earth, Arpege, UKMO Unified model) Denmark DMI The Netherlands KNMI, U Utrecht, WUR, VU Ireland MetEireann, UCD, ICHEC Portugal IM, U Lisbon Sweden SMHI, Lund U, Stockholm U, IRV Spain AEMET, BSC, IC3 Belgium UCL Italy ICTP Switzerland ETHZ, C2SM Norway MetNo Steering group: W. Hazeleger (KNMI), C. Jones (SMHI), J. Hesselbjerg, Christensen (DMI), R. McGrath (Met Eireann), observer E. Kallen (ECMWF), NEMO-representative

11 Atmosphere GCM: IFS Land: IFS H-tessel Vegetation: LPJ OASIS Ocean GCM: NEMO Sea-ice:LIM2/3 Marine ecosystem: PISCES For CMIP5: T159L62, 1 deg Ocean On 6 platforms Atmospheric Chemistry and aerosols: TM5 Joint EC-Earth and ECMWF seasonal forecast components New EC-Earth components Planned EC-Earth components

12 ECMWF EC-Earth EC-Earth Vx Atmospheric chemistry IFS Cycle 36 NEMO-3 Dynamic Vegetation ~3 yr EC-Earth V2 Snow Land use Aerosols EC-Earth V1 IFS Cycle 31 NEMO-2 Hazeleger et al, BAMS, submitted

13 Performance EC-Earth across scales: multidecadal Hazeleger et al, BAMS, submitted

14 Decadal predictions EC-Earth initialised from observed ocean state Observed global mean NEMOVAR Wouters, THOR project

15 Performance EC-Earth across scales: medium range

16 Model development: Earth system prediction Include: Chemistry Aerosol-cloud effect Biogeochemistry Cryosphere EC-Earth vs. ERA-Interim: SO4 T. van Noije, pers comm

17 Resolution effects ERA40/Interim T L 511 L62 T L 319 L62 T L 159 L62 T L 63 L31 Definition of blocking index follows Tibaldi and Molteni (1990) and Barriopedro et al. (2006) Shuting Yang (DMI) pers comm.

18 Calibration of climate model output Palmer et al, BAMS

19 WCC-3: Global Framework on Climate Services Provide sectoral users ranges of future changes, preferably to be used for risk-management strategies

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21 Selecting model output for scenario s (or weights), how to represent uncertainties? RMS monthly mean data in 20cm3 runs with respect to ADVICE data

22 Scenario development for users. How to represent uncertainties (KNMI 06, vd Hurk et al 2006)? IPCC4AR KNMI06

23 Development: propagation of uncertainties Changes in CATCHMOD simulated Q50 when uncertainties in CATCHMOD parameters are combined with the climateprediction.net ensemble. Challenge: Software (GIS) Bias corrections Uncertainty representation New M et al. Phil. Trans. R. Soc. A 2007;365:

24 Challenges Science Optimize complexity, ensembles and resolution. There is no formalism to decide on this and earth system components often semi-empirical. Critical processes: cumulus convection, aerosol-clouds, ocean variability, cryosphere-ocean, Prediction problem/limits: predictability from ocean, snow, sea ice, land Propagation of uncertainty Users perspective Signal of decadal variability is small. Users are already adapted to natural variability which is not likely to change over decadal time scales. Dutch users: Need for multivariate time series and case studies. Xynthia in a future climate setting (i.e. very high resolution). Flagship projects for very high resolution (Future Weather) Don t forget mitigation, including geo-engineering!

25 Seamless Same principles, prediction problem extended to longer time scales (nb truly probabilistic forecasts limited by verification; unclear whether model error or initialisation causes drift) Convergence NWP ESM developments New convection schemes (NWP ESM) New land surface schemes (ESM NWP) New aerosol schemes (ESM NWP) NB. Operationalize earth system prediction systems: Climate and earth system modelling is big science. We are entering an operational phase where data is used by many stakeholders: need for dedicated facilities. Learn from NWP.