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Presentation transcript:

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Beyond seasonal forecasting F. J. Doblas-Reyes, ICREA & IC3, Barcelona, Spain

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Outline Seasonal forecasting beyond a few months Dynamical seasonal forecasting: systematic errors Forecasting up to one year: global view and tropical indices Global-average temperature and trends Summary

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Simple empirical model: persistence Correlation (solid line p value) of a Niño3.4 persistence model based on lagged linear regression of HadISST over ; first regression model with data for L. Rodrigues (IC3)

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Experimental setup Two forecast systems: System 3 (IFS/HOPE) and EC- Earth (IFS/NEMO) Initial conditions: ERA40/ERAInt atmosphere and land, ORA-S3 and NEMOVAR-COMBINE ocean, DFS4.3 sea ice Five-member ensemble hindcasts up to 13 months Ensemble from five-member ocean analysis and atmospheric perturbations (singular vectors plus SST perturbations in System 3) added to each member Initial conditions valid for 0 GMT on the 1 st of a month Two start dates per year: May and November Forecast period

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Main systematic errors in dynamical climate forecasts: o Differences between the model climatological pdf (computed for a lead time from all start dates and ensemble members) and the reference climatological pdf (for the corresponding times of the reference dataset): systematic errors in mean and variability. o Conditional biases in the forecast pdf: errors in conditional probabilities implying that probability forecasts are not trustworthy. This type of systematic error is best assessed using the reliability diagram. Temperature Differences in climatological pdfs Reference pdfModel pdf Systematic errors in ensemble forecasts Threshold Forecast PDF t=1 t=2 t=3 Mean bias Different variabilities Actual occurrences

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Mean error: mean bias System 3 Bias of 8-10 month surface temperature re-forecasts wrt ERA40/Int over JJADJF EC-Earth

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Mean error: standard deviation System 3 Ratio of interannual standard deviation of 8-10 month near- surface temperature re-forecasts wrt ERA40/Int over JJADJF EC-Earth

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Global skill: lead time effect 7-month lead time Ensemble-mean correlation of EC-Earth near-surface air temperature re-forecasts wrt ERA40/Int over Dots for values statistically significant with 95% conf. JJADJF 1-month lead time

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA ENSO: Two examples System 3 annual Niño3.4 sea surface temperature forecasts (red lines) and observations (blue line). Nov 2008 start date Nov 2009 start date

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Global skill: theoretical predictability Against obs. Ensemble-mean correlation of EC-Earth 8-10 month near- surface air temperature re-forecasts over Dots for values statistically significant with 95% conf. JJADJF Against model

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Global skill: lead time effect 7-month lead time Ensemble-mean correlation of EC-Earth precipitation re- forecasts wrt GPCP over Dots for values statistically significant with 95% conf. JJADJF 1-month lead time

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Global skill: theoretical predictability Ensemble-mean correlation of EC-Earth 8-10 month precipitation re-forecasts over Dots for values statistically significant with 95% conf. JJADJF Against obs. Against model

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Global skill: annual predictions System 3 Ensemble-mean correlation of annual averages (months 2- 13, Nov start) from re-forecasts (wrt ERA40/Int and GPCP) over Dots for significant values with 95% conf. TemperaturePrecipitation EC-Earth

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA ENSO System 3 Ratio sd: 0.84 Corr: 0.87 RPSSd: 0.56 Niño3.4 time series for ERA40/Int (red dots), five-member ensemble (green box-and-whisker) and ensemble mean (blue dots) 8-10 month (DJF) re-forecasts over EC-Earth Ratio sd: 0.83 Corr: 0.80 RPSSd: 0.55

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA ENSO Niño3.4 ensemble-mean correlation (left) and debiased RPSS (right) for EC-Earth and System 3 five-member ensemble re-forecasts with May and November start dates over May start date, first to 13th month November start date, first to 13th month

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA ENSO Niño3.4 probabilistic scores (and 95% conf. intervals) for EC- Earth and System 3 ensemble re-forecasts over ROCSS RelSS Lower tercile threshold Upper tercile threshold

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Other tropical oceans Ensemble-mean correlation of different SST indices for EC- Earth and System 3 re-forecasts over Atlantic3 Tropical Atlantic meridional mode Tropical AtlanticIndian ocean dipole

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Regional skill Anomaly correlation coefficient (and 95% confidence intervals) for EC-Earth and System 3 five-member ensemble near-surface temperature re-forecasts wrt ERA40/Int over MayNov (1) (2-4) (8-10) (2-13) (1) (2-4) (8-10) (2-13) Tropics Southern South America MayNov (1) (2-4) (8-10) (2-13) (1) (2-4) (8-10) (2-13)

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Regional skill Anomaly correlation coefficient (and 95% confidence intervals) for EC-Earth and System 3 five-member ensemble near-surface temperature re-forecasts wrt GPCP over MayNov (1) (2-4) (8-10) (2-13) (1) (2-4) (8-10) (2-13) Tropics Central South America MayNov (1) (2-4) (8-10) (2-13) (1) (2-4) (8-10) (2-13)

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA MayNov Global mean Ensemble-mean correlation for EC-Earth and System 3 five- member ensemble near-surface temperature re-forecasts wrt ERA40/Int over Global mean Ocean onlyLand only

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Trends Normalized trends of near-surface temperature 8-10 month (DJF) re-forecasts over ERA40/Int EC-Earth System 3

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 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. Forecast calibration such as forecast assimilation is still needed. In a preliminary analysis, there is statistically significant skill in ENSO in dynamical models after the first few months beyond simple persistence. The annual time scale skill is linked to the good prediction of ENSO and other tropical SST modes, and the reproducibility of global warming. Many more processes to be analyzed: sea ice, anthropogenic aerosols, …