Tropical Intra-Seasonal Oscillations in the DEMETER Multi-Model System
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1 Tropical Intra-Seasonal Oscillations in the DEMETER Multi-Model System Francisco Doblas-Reyes Renate Hagedorn Tim Palmer European Centre for Medium-Range Weather Forecasts (ECMWF)
2 Outline Introduction to DEMETER Multi-model ensemble: a way to sample model uncertainty to increase seasonal skill Multi-model ensemble: a tool to diagnose model performance, the ISO case Summary
3 The idea behind DEMETER Growing demand for reliable seasonal forecasts Two main sources of uncertainty X error in initial conditions X error in model formulation Install a multi-model ensemble system Evaluate the skill and potential utility
4 Multi-model ensemble system DEMETER system: 7 coupled global circulation models Partner Atmosphere Ocean ECMWF IFS HOPE LODYC IFS OPA 8.3 CNRM ARPEGE OPA 8.1 CERFACS ARPEGE OPA 8.3 INGV ECHAM-4 OPA 8.2 MPI ECHAM-5 MPI-OM1 UKMO HadCM3 HadCM3 4 start dates per year 6 months hindcasts 9 member ensembles 3 ocean analyses, 4 ±SST pert ERA-40: ocean forcing and atmospheric initial conditions Hindcast production for: ( )
5 Verification Bias Indices Deterministic Scores Probabilistic Scores Single vs. multi-model 54-single vs. multi-model Ocean diagnostics
6 Outline Introduction to DEMETER Multi-model ensemble: a way to sample model uncertainty to increase seasonal skill Multi-model ensemble: a tool to diagnose model performance, the ISO case Summary
7 Multi-model forecast skill Niño-3.4 SST, 2-4 month seasonal hindcasts Multi- Model CERFACS CNRM ECMWF INGV LODYC MPI UKMO Correlation RPSS
8 Outline Introduction to DEMETER Multi-model ensemble: a way to sample model uncertainty to increase seasonal skill Multi-model ensemble: a tool to diagnose model performance, the ISO case Summary
9 Intraseasonal SD OLR (May to Oct.) Unfiltered data Météo-France Met Office ECMWF
10 IS SD OLR (Nov. to April) Unfiltered data Météo-France Met Office ECMWF
11 IS SD OLR (May to Oct.) ERA40 Difference wrt ERA40 Météo-France Met Office ECMWF
12 IS SD U 10m (May to Oct.) ERA40 Difference wrt ERA40 Météo-France Met Office ECMWF
13 Coupling (U 10m May to Oct.) Difference wrt ERA40 LODYC (IFS+OPA) ECMWF (IFS+HOPE) Met Office (coupled) Met Office (persisted SSTs)
14 SST-precip. feedback (May to Oct.) SST leading precip by 1 month Met Office (coupled) Precip leading SST by 1 month Met Office (persisted SSTs)
15 k-ω spectra (May to Oct.) Two-sided spectrum for OLR (15ºN-15ºS) Raw spectrum Background spectrum Symmetric component Westward Eastward
16 k-ω spectra (May to Oct.) Two-sided relative spectrum for OLR (15ºN-15ºS) Equatorial Rossby waves Symmetric component ERA40 Kelvin waves MJO
17 Comparison OLR ERA40-NOAA Two-sided spectra for OLR (5ºN-5ºS) Frequency NOAA ERA40 Raw spectrum Relative spectrum Wavenumber Courtesy from Oscar Alves (BMRC)
18 k-ω spectra (May to Oct.) Two-sided relative spectrum for OLR (15ºN-15ºS) Symmetric component ERA40 Météo- France Met Office ECMWF
19 k-ω spectra (May to Oct.) Two-sided relative spectrum for OLR (15ºN-15ºS) ERA40 Météo-France Symmetric component Met Office ECMWF
20 k-ω spectra (May to Oct.) Two-sided relative spectrum for U 10m (15ºN-15ºS) ERA40 Météo-France Symmetric component Met Office ECMWF
21 k-ω spectra (May to Oct.) Two-sided relative spectrum for U 10m (15ºN-15ºS) Symmetric component Met Office (coupled) Met Office (persisted SSTs)
22 EOFs U 10m (May to Oct.) day filtered data (20ºN-20ºS) ERA40 EOF1 ERA40 EOF2 Météo-France EOF1 Météo-France EOF2
23 EOFs U 10m (May to Oct.) day filtered data (20ºN-20ºS) ERA40 EOF1 ERA40 EOF2 ECMWF EOF1 ECMWF EOF2
24 Local mode analysis Complex principal component analysis of a bandpass filtered field over a region on a 90-day running time window (a new analysis every 5 days) Separate analysis for each year and ensemble member Provides, for each member and year, an estimate of the IS signal features (date, spatial pattern, activity, ) as part of the most coherent patterns in a spatio-temporal sense Collaboration with J. Ph. Duvel (LMD, Paris); preliminary results for OLR during boreal winter
25 Local mode analysis (Nov. to April) Percentage of the variance captured by the LM (results without ERA40 influence in the filtering) Average for all members and years NOAA Météo-France Lack of organization of the modes Met Office ECMWF
26 Local mode analysis (Nov. to April) Example of Météo-France hindcast 20 Mode in the NOAA dataset % Period: 49±14.2 std Max: Memb: Mode in ensemble member Mode in ensemble member % Period: 34.5±12.6 std Max: Memb: % Period: 48±18.8 std Max: Memb:
27 Local mode analysis (Nov. to April) Results for Météo-France (Indian Basin) Variance (Wm -2 ) 2 1/01/81 1/01/82 1/01/83 1/01/84 1/01/85 1/01/86 1/01/87 1/01/88 1/01/89 1/01/90 1/01/91 1/01/92 1/01/93 1/01/94 1/01/95 1/01/96 1/01/97 1/01/98 1/01/99 1/01/00 1/01/01 1/01/ day window filtered variance First mode variance Variance (Wm -2 ) 2 1/01/81 1/01/82 1/01/83 1/01/84 1/01/85 1/01/86 1/01/87 1/01/88 1/01/89 1/01/90 1/01/91 1/01/92 1/01/93 1/01/94 1/01/95 1/01/96 1/01/97 1/01/98 1/01/99 1/01/00 1/01/01 1/01/02
28 Predictability (Nov. to April) Correlation of the variance (ensemble mean) Correlation for the filtered variance Met Office 1.0 CORRELATION CNRM Météo-France 1/09/00 1/10/00 1/11/00 1/12/00 1/01/01 1/02/01 1/03/ NOAA - ERA VFIL NOAA - ERA VMODE NOAA - CNRM VFIL NOAA - CNRM VMODE dates_clef_y Correlation for the mode activity Correlation between ERA40 and NOAA ECMWF CORRELATION CORRELATION UKMO NOAA - ERA VFIL NOAA - ERA VMODE NOAA - UKMO VFIL NOAA - UKMO VMODE dates_clef_y SCWC NOAA - ERA VFIL NOAA - ERA VMODE NOAA - SCWC VFIL NOAA - SCWC VMODE dates_clef_y 1/09/00 1/10/00 1/11/00 1/12/00 1/01/01 1/02/01 1/03/01 1/09/00 1/10/00 1/11/00 1/12/00 1/01/01 1/02/01 1/03/01
29 Summary Different biases in the intraseasonal variability: ECMWF and MetOffice tend to underestimate Météo-France tends to overestimate MJO amplitude and frequency are misrepresented, especially by ECMWF and MetOffice and for OLR Beneficial impact of the coupling: increase of variability, representation of feedbacks Weak spatial coherence of the large-scale perturbations; link to mean state and variability errors No clear signs of long-range predictability of the IS activity (excess of ensemble spread)
30 Period # Years ECMWF CNRM UKMO MPI INGV LODYC CERFACS Retrieve NetCDF
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