Global Climate Prediction: Between Weather Forecasting and Climate-Change Projections

1. 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. 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. Seamless prediction Illustration of the application of seamless climate and weather information. Example from the IRI-Red Cross collaboration. Courtesy IRI

4. Sources of climate predictability • Both internal and external • ENSO - large single signal • Other tropical ocean SST - difficult • Remote tropical atmospheric teleconnections • Climate change - largest for temperature • Local land surface conditions - soil moisture, snow • Atmospheric composition - difficult • Volcanic eruptions - important for large events • Mid-latitude ocean temperatures - longer time scales • Remote soil moisture/snow cover - not well established • Sea-ice anomalies - at least local effects • Stratospheric influences - various possibilities • Unknown or Unexpected

5. Methods for climate forecasting • Empirical/statistical forecasting • Use past observational record and statistical methods • Works with reality instead of error-prone numerical models  • Limited number of past cases  • A non-stationary climate is problematic  • Can be used as a benchmark  • GCM forecasts • Include comprehensive range of sources of predictability  • Predict joint evolution of ocean and atmosphere flow  • Includes a large range of physical processes  • Includes uncertainty sources, important for prob. Forecasts  • Systematic model error is an issue! 

6. To produce dynamical forecasts • Build a coupled model of the climate system • Prepare an ensemble of initial conditions to initial represent uncertainty • The 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 • Run 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. Assume an ensemble forecast system with coupled initialized GCMs Lead time = 3 Ensemble climate forecast systems May 87 Aug 87 Nov 87 Feb 88 May 88

8. Equatorial Atlantic: Taux anomalies Equatorial Atlantic upper heat content anomalies. No assimilation Equatorial Atlantic upper heat content anomalies. Assimilation Data assimilation for initial conditions • 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 ERA15/OPS ERA40

9. NO ? Consensus forecast: dry  YES Dry Rainy Should he/she? NO Why running several forecasts A farmer is planning to spray a crop tomorrow • 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! Let p denote the probability of rain from the forecast

10. Weather regimes and climate anomalies Mean frequencies: NAO+ : +62%,AR:+16%, NAO- : -75%,BL:-3% Observed anomalies for NDJFM 2007-8 Air Temp Precip Courtesy Ch. Cassou (CERFACS)

11. Weather regimes and the MJO Interaction between the Madden-Julian oscillation and the North Atlantic weather regimes OLR/STRF300 Cassou (2008)

12. Predicting the NAO and the MJO NAO ensemble-mean correlation from monthly forecasts with the Canadian forecast system (using observed SSTs). Lin et al. (2010)

13. ENSO: a seasonal prediction forcing Collins et al. (2010)

14. Extra-tropical links of ENSO 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. Frías et al. (2010)

15. ROC area for anomalies above the upper quartile ROC area for anomalies below the lower quartile Sources of predictability: snow cover Correlation of System 3 MAM temperature in 1981-2005 wrt to GHCN temperature (adapted from Shongwe et al., 2007).

16. How good are the seasonal forecasts? 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. JJA T2m DJF T2m JJA Prec DJF Prec

17. Decadal predictions 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

18. 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. 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. (FEB) (MAY) (NOV) Model drift: West African Monsoon 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 (NOV) ECMWF S4 - GPCP (FEB) ECMWF S4 - GPCP (MAY)

21. Multi-model benefits: Reliability Reliability for T2m>0, 1-month lead, May start, 1980-2001 System 1 System 2 System 3 System 4 System 5 System 7 System 6

22. control CASBS ERA40 Blocking frequency Error reduction: stochastic physics Precipitation bias (DJF, 1-month lead, 1991-2001, CY29R2) CASBS reduces the tropical and blocking frequency biases Control-GPCP Stochastic physics-GPCP Berner et al. (2008)

23. Skill improvement: model inadequacy 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. Weisheimer et al. (2011)

24. Increase of model resolution 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. Scaife et al. (2011)

25. 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, …