1. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Global Climate Prediction: Between Weather Forecasting and Climate- Change Projections F. J. Doblas-Reyes ICREA & IC3, Barcelona, Spain in collaboration with V. Guémas, J. García-Serrano (IC3), Ch. Cassou (CERFACS), A. Weisheimer (ECMWF)

2. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Prediction on climate time scales Progression from initial-value problems with weather forecasting at one end and multi-decadal to century projections as a forced boundary condition problem at the other, with climate prediction (sub-seasonal, seasonal and decadal) in the middle. Prediction involves initialization and systematic simultaneous comparison with a reference. Meehl et al. (2009)

3. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Seamless prediction Courtesy IRI Illustration of the application of seamless climate and weather information. Example from the IRI-Red Cross collaboration.

4. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Sources of climate predictability Both internal and external oENSO- large single signal oOther tropical ocean SST- difficult oRemote tropical atmospheric teleconnections oClimate change- largest for temperature oLocal land surface conditions- soil moisture, snow oAtmospheric composition- difficult oVolcanic eruptions- important for large events oMid-latitude ocean temperatures- longer time scales oRemote soil moisture/snow cover- not well established oSea-ice anomalies- at least local effects oStratospheric influences- various possibilities Unknown or Unexpected

5. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Methods for climate forecasting Empirical/statistical forecasting oUse past observational record and statistical methods oWorks with reality instead of error-prone numerical models oLimited number of past cases  oA non-stationary climate is problematic  oCan be used as a benchmark GCM forecasts oInclude comprehensive range of sources of predictability oPredict joint evolution of ocean and atmosphere flow oIncludes a large range of physical processes oIncludes uncertainty sources, important for prob. Forecasts oSystematic model error is an issue! 

6. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA To produce dynamical forecasts Build a coupled model of the climate system Prepare an ensemble of initial conditions to initial represent uncertainty oThe aim is to start the system close to reality. Accurate SST is particularly important, plus ocean sub-surface. Usually, worry about “imbalances” a posteriori. Run an ensemble forecast oRun an ensemble on the e.g. 1st of a given month (the start date) for several weeks to years. Run a set of re-forecasts to deal with systematic error. Produce probability forecasts from the ensemble Apply calibration and combination if significant improvement is found

7. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA May 87 Aug 87 Nov 87 Feb 88 May 88 Assume an ensemble forecast system with coupled initialized GCMs Ensemble climate forecast systems Lead time = 3

8. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Equatorial Atlantic: Taux anomalies Equatorial Atlantic upper heat content anomalies. No assimilation Equatorial Atlantic upper heat content anomalies. Assimilation ERA15/OPS ERA40 Example for the ocean. Large uncertainty in wind products lead to large uncertainty in ocean subsurface. Possibility to use additional information from ocean data Assimilation of ocean data:  constrain the ocean state  improve the ocean estimate  improve the seasonal forecasts Data assimilation for initial conditions

9. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Dry Rainy A farmer is planning to spray a crop tomorrow Consensus forecast: dry  YES Should he/she? NO ? Let p denote the probability of rain from the forecast Why running several forecasts The farmer loses L if insecticide washes out. He/she has fixed (eg staff) costs If he/she doesn’t go ahead, there may be a penalty for late completion of the job. By delaying completion of the job, he/she will miss out on other jobs. These cost C Is Lp>C? If p > C/L don’t spray!

10. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Courtesy Ch. Cassou (CERFACS) Weather regimes and climate anomalies Observed anomalies for NDJFM 2007-8 Air TempPrecip Mean frequencies: NAO+ : +62%, AR:+16%, NAO- : -75%, BL:-3%

11. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Weather regimes and the MJO Cassou (2008) Interaction between the Madden-Julian oscillation and the North Atlantic weather regimes OLR/STRF300

12. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Predicting the NAO and the MJO Lin et al. (2010) NAO ensemble-mean correlation from monthly forecasts with the Canadian forecast system (using observed SSTs).

13. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Collins et al. (2010) ENSO: a seasonal prediction forcing

14. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA ROC area of the (left column) statistically downscaled and (right column) original DEMETER seasonal predictions for several events where the years verified are segregated depending on the ENSO phase. Only values statistically significant with 90% confidence level are shown. Extra-tropical links of ENSO Frías et al. (2010)

15. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Sources of predictability: snow cover Correlation of System 3 MAM temperature in 1981-2005 wrt to GHCN temperature (adapted from Shongwe et al., 2007). ROC area for anomalies above the upper quartile ROC area for anomalies below the lower quartile

16. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA DJF T2m JJA T2m JJA Prec DJF Prec Correlation of System 3 seasonal forecasts of temperature (top) and precipitation (bottom) wrt GHCN and GPCC over 1981-2005. Only values significant with 80% conf. plotted. How good are the seasonal forecasts?

17. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA EC-Earth 2-5 year near-surface air temperature ensemble-mean predictions started in November 2011. Anomalies are computed with respect to 1971-2000. Initialised Uninitialised Difference Decadal predictions

18. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA CMIP5 decadal predictions (Top) Near-surface temperature multi-model ensemble-mean correlation from CMIP5 decadal initialised predictions (1960-2005); (bottom) correlation difference with the uninitialised predictions of 2-5 year (left) and 6-9 year (right) wrt ERSST and GHCN. Init ensemble- mean correlation Init minus NoInit ensemble- mean correlation difference Doblas-Reyes et al. (2012)

19. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA But there are systematic errors Model drift is typically comparable to signal  Both SST and atmosphere fields Forecasts are made relative to past model integrations  Model climate estimated from e.g. 25 years of forecasts (1981-2005), all of which use a 11 member ensemble. Thus the climate has 275 members.  Model climate has both a mean and a distribution, allowing us to estimate eg tercile boundaries.  Model climate is a function of start date and forecast lead time. Implicit assumption of linearity  We implicitly assume that a shift in the model forecast relative to the model climate corresponds to the expected shift in a true forecast relative to the true climate, despite differences between model and true climate.  Most of the time, the assumption seems to work pretty well. But not always.

20. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Averaged precipitation over 10ºW-10ºE for the period 1982-2008 for GPCP (climatology) and ECMWF System 4 (systematic error) with start dates of November (6-month lead time), February (3) and May (0). GPCP climatology ECMWF S4 - GPCP (MAY)ECMWF S4 - GPCP (FEB)ECMWF S4 - GPCP (NOV) Model drift: West African Monsoon (NOV) (FEB) (MAY)

21. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Multi-model benefits: Reliability Reliability for T2m>0, 1-month lead, May start, 1980-2001 System 1 System 2 System 3System 4 System 5 System 6 System 7

22. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Control-GPCP Stochastic physics-GPCP Precipitation bias (DJF, 1-month lead, 1991-2001, CY29R2) CASBS reduces the tropical and blocking frequency biases Error reduction: stochastic physics controlCASBSERA40 Blocking frequency Berner et al. (2008)

23. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA System with the best skill score for one-month lead seasonal probability predictions (Brier skill score). The predictions have been formulated with a multi-model (MME), a system with stochastic physics (SPE) and a system with parameter perturbations (PPE) over 1991-2005. Skill improvement: model inadequacy Weisheimer et al. (2011)

24. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA SST (with North Atlantic Current path) and North Atlantic zonal wind bias, plus winter blocking frequency for HadGEM3 using the (left) standard (1º) and (right) high-resolution (0.25º) ocean components. Increase of model resolution Scaife et al. (2011)

25. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Summary ●Substantial systematic error, including lack of reliability, is still a fundamental problem in dynamical forecasting and forces a posteriori corrections to obtain useful predictions. Don’t take model probabilities as true probabilities. Initial conditions are still a very important issue. Estimating robust forecast quality is difficult, but there are windows of opportunity for reliable skilful predictions, and there is always the anthropogenic warming. There is potential in methods that deal with model inadequacy (multi-model ensembles, stochastic physics). Model development is a must. And many more processes to be included: sea ice, anthropogenic aerosols, chemistry, …

26. NLOA2012: Global Climate PredictionMadrid, 5 July 2012 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA