1. www.bsc.es Barcelona, 2015 Ocean prediction activites at BSC-IC3 http://ic3.cat/wikicfu Virginie Guemas and the Climate Forecasting Unit 9 February 2015

2. Climate timescales and climate prediction Meehl et al. (2009) Focus on sub-seasonal, seasonal, interannual and decadal timescales

3. Decadal climate prediction exercise Nov 2000 Nov 2001 Nov 2002 Nov 2003 Nov 2004 Nov 2005 Nov 2006 Forecast time 5 years Core Tier 1 Forecast time 1 year

4. Methodology Observations 1960 2005 … until 2009 5-member prediction started 1 Nov 1960 Experimental setup : 1 grid-point

5. Methodology Observations 1960 2005 5-member prediction started 1 Nov 1965 5-member prediction started 1 Nov 1960 Experimental setup : 1 grid-point

6. Methodology Observations 1960 2005 … until 2009 5-member prediction started 1 Nov 1970 5-member prediction started 1 Nov 1965 5-member prediction started 1 Nov 1960 Experimental setup : 1 grid-point

7. Methodology Observations 1960 2005 5-member prediction started 1 Nov 2005 … until 2009 5-member prediction started 1 Nov 1970 5-member prediction started 1 Nov 1965 5-member prediction started 1 Nov 1960 Experimental setup : 1 grid-point … every 5 years …

8. Methodology Observations 1960 2005 5-member prediction started 1 Nov 2005 … until 2009 5-member prediction started 1 Nov 1970 5-member prediction started 1 Nov 1965 5-member prediction started 1 Nov 1960 Experimental setup : 1 grid-point Focus on averages over forecast years 2 to 5 … every 5 years …

9. Methodology Observations 1960 2005 5-member prediction started 1 Nov 2005 … every 5 years … … until 2009 5-member prediction started 1 Nov 1970 5-member prediction started 1 Nov 1965 Experimental setup : 1 grid-point Focus on averages over forecast years 2 to 5 Ensemble-mean 5-member prediction started 1 Nov 1960

10. Methodology 1960 2005 … until 2009 Experimental setup : 1 grid-point As many values as hindcasts for both the model and the observations to compute skill scores. Ex : correlations

11. Typical decadal forecast skill – IPCC AR5 Doblas-Reyes et al. (2013) Nature Communications (Top row) Root mean square skill score (RMSSS) of the ensemble mean of the initialised predictions and (bottom row) ratio of the root mean square error (RMSE) of the initialised and uninitialised predictions for the near-surface temperature from the multi-model CMIP5 experiment (1960-2005) for (left) 2-5 and (right) 6-9 forecast years. Five-year start date interval. Added-value from initialisation Skill

12. Typical seasonal forecast skill Correlation of the ensemble mean for the ENSEMBLES multi-model (45 members) wrt ERA40-ERAInt (T2m over 1960-2005) and GPCP (precip over 1980-2005) with 1- month lead T2m JJA T2m DJF Prec JJAPrec DJF

13. Some open fronts Work on initialisation: generate initial conditions (e.g. for sea ice, ocean). Compare different initialisation techniques (e.g. full field versus anomaly initialisation) Improving model processes: Inclusion and/or testing of model components (biogeochemistry, vegetation, aerosols, sea ice) or new parameterizations, model parameter calibration, increase in resolution Calibration and combination: empirical prediction (better use of current benchmarks), local knowledge. Forecast quality assessment: scores closer to the user, reliability as a main target, process-based verification, attribution of climate events with successful predictions, diagnostics of model weaknesses with failing predictions More sensitivity to the users’ needs: going beyond downscaling, better documentation (e.g. use the IPCC language), demonstration of value and outreach.

14. The climate prediction drift issue Observed world Retrospective prediction ( hindcast ) affected by a strong drift, need for a-posteriori bias correction Time Predicted Variable (ex. Temperature) BIAS Biased model world Danila Volpi

15. Investigating processes driving the climate prediction drift Exarchou et al (2015) Climate Dynamics SST bias, May Forecasts from : 1 st November 1 st May Observational reference : HadISST SST bias, JuneSST bias, JulySST bias, August SST bias, NovemberSST bias, DecemberSST bias, JanuarySST bias, February

16. Developing a new bias correction method IC (Initial conditions) bias correction method (green) accounts for the dependence of the climate prediction drift on the observed initial conditions through a linear regression -> lower forecast error Fučkar et al (2014) Geophysical Research Letters Tools available in s2dverification R package

17. The climate prediction drift issue  Issue : Distinction between climate drift and climate signal  Hypothesis : If the model climate is stable (no drift), the simulated variability is independent of the model mean state within the range of current model biases and closer to the observed variability than when mixed with the drift  Testing the hypothesis : Allowing the climate model biases but constraining the phase of the simulated variability toward the contemporaneous observed one at the initialization time : Anomaly Initialization (AI) Danila Volpi

18. The climate prediction drift issue Observed world Biased model world Retrospective prediction ( hindcast ) affected by a strong drift, need for a-posteriori bias correction Time Predicted Variable (ex. Temperature) BIAS Retrospective prediction with anomaly initialization Danila Volpi

19. Anomaly versus full-field initialization EC-Earth2.3, 5 members, start dates every 2 years from 1960 to 2004 NOINI : historical simulation FFI : Full-field initialization from ORAS4 + ERA OSI-AI : Ocean and sea ice anomaly initialization with corrections to ensure consistency rho-OSI-wAI : Ocean and sea ice weighted anomaly initialization to account for the different model and observed amplitudes of variability + (density, temperature) Instead of (temperature, salinity) anomaly initialisation Volpi et al (2015) Climate Dynamics

20. Anomaly versus full-field initialisation Experiment with the minimum SST RMSE Forecast year 1Forecast years 2-5 Volpi et al (2015) Climate Dynamics

21. Impact of increasing the resolution Mean SST (K) systematic error versus ERAInt for JJA one-month lead five-member predictions of EC-Earth3 T255/ORCA1 and T511/ORCA025. May start dates over 1993- 2009 using ERA-Interim and GLORYS initial conditions. EC-Earth3 T255/ORCA1EC-Earth3 T511/ORCA025 Chloe Prodhomme High – Low resolution

22. Global mean Sea Surface Temperature Predictions of the XXI st century hiatus Forecast years 1 to 3 from climate predictions initialized from observations Observations (ERSST) Guemas et al (2013) Nature Climate Change EC-Earth2.3 CMIP5 decadal climate predictions capture the hiatus

23. Predictions of the XXI st century hiatus Observations EC-Earth historical simulations starting from 1850 preindustrial control simulations Forecast years 1 to 3 from EC-Earth climate predictions initialized from observations Crucial role of initialization from observations in capturing the plateau Guemas et al (2013) Nature Climate Change

24. Global Framework on Climate Services

25. Progress on open fronts Work on initialisation: more advanced data assimilation (ex: EnKF, coupled assimilation) to generate initial conditions, use of new observations and reanalyses, better ensemble generation. Improving model processes: Impact of aerosols, interactive vegetation, prediction of biogeochemistry, more efficient use of computing resources, drift reduction, leverage knowledge from modelling at other times scales Calibration and combination: estimation of uncertainty Forecast quality assessment: attribution of climate extremes (drought, sea ice minima and maxima), analysis of ocean, sea ice and land sources of predictability, role of external forcings More sensitivity to the users’ needs: going beyond downscaling, better documentation (e.g. use the IPCC language), demonstration of value and outreach.