Seamless prediction illustrated with EC-Earth
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1 Seamless prediction illustrated with EC-Earth Wilco Hazeleger G.J. van Oldenborgh (KNMI), T. Semmler (MetEireann), B. Wouters (KNMI), K. Wyser (SMHI), F. Doblas-Reyes (IC3) and EC-Earth consortium
2 EC-Earth consortium Denmark DMI, Univ Copenh Portugal IM, U Lisbon The Netherlands KNMI, U Utrecht, WUR, VU. SARA Sweden SMHI, Lund U, Stockholm U, IRV Germany IFM/GEOMAR Spain AEMET, BSC, IC3 Belgium UCL Ireland MetEireann, UCD, ICHEC Switzerland ETHZ, C2SM Norway NTNU Italy ICTP,CNR, ENEA Steering group: W. Hazeleger (KNMI, chair), C. Jones (SMHI), J. Hesselbjerg, Christensen (DMI), R. McGrath (Met Eireann), P. Viterbo (IM), E. C. Rodriguez (AEMET) observer E. Kallen (ECMWF), NEMOrepresentative
3 ECMWF EC-Earth Univ/institutes IFS Cycle 36 NEMO-3 EC-Earth V3 Atmospheric chemistry ~3 yr EC-Earth V2 Dynamic Vegetation EC-Earth V1 Snow Land use Aerosols IFS Cycle 31 NEMO-2
4 ECMWFs IFS c31r1+ atmosphere: T159 L62 (runs up to T799) NEMO V2 Ocean: 1 degree L42, with equatorial refinement and tripolar grid (runs up to 0.25 deg) Hazeleger et al BAMS, October, 2010
5 EC-Earth at a glance Hazeleger et al. Clim Dyn 2011 (minor rev)
6 Seasonal predictions 7 month, 5 member ensemble, start dates Feb, May, Aug, Nov., Initialization 1. ERA40/ERAInt atmosphere and land, NEMOVAR-ORAS4 ocean (3- D Var, XBTs, hydrography, SST, altimetry; 5 members), DFS4.3- NEMO/LIM sea ice. 2. Perturbations atmosphere: singular vectors 3. Perturbations ocean: 5 members of NEMOVAR (ORA-S4; representing observational error)
7 Seasonal predictions: bias after 1 month May Nov Bias of first month near-surface air temperature re-forecasts wrt ERA40/Int over Informs model development.
8 Annual predictions 13 month, 5 member ensemble, start dates May, Nov.,
9 Annual predictions: correlation skill (7 month lead time) JJA DJF Ensemble-mean correlation of EC-Earth near-surface air temperature re-forecasts wrt ERA40/Int over Dots for values statistically significant with 95% conf. IC3, Doblas Reyes
10 Decadal predictions 10 years, 10 member ensemble, start date Nov, 1960, 1965, 1970,,2005
11 EC-Earth hindcasts, drift corrected Wouters et al (KNMI) and Doblas Reyes et al (IC3) in prep
12 Decadal prediction skill: anomaly correlation SST (2-5 yr) CERFACS UKMO IFM-GEOMAR ECMWF DePreSys EC-Earth SST ensemble-mean correlation (lead: 2-5 year average; ) wrt ERSST.
13 Skill dominated by trend! Trend, defined by regression on global mean CO 2 concentrations removed (nb this is not the CMIP5 ensemble, this ensemble did not include volcanic forcing)
14 Historical and climate scenario simulations : 16 members with prescribed GHG and aerosol concentrations, volcanic aerosols and land use. Initialized from a range of start dates from a preindustrial spinup Preindust rial Ensemb le # Complet ed In progr ess Not started or unknow n Historical RCP RCP : RCP 2.6, 4.5, 8.5. Initialized from end historical simulations. AMIP # # # # Decadal (full field) Decadal (anom aly) Nb different HPC platforms by 10 partners
15 Historical and RCP 4.5 global mean 2-meter temperature EC-Earth V2.3 (7 members, another 9 to process) 12 CMIP5 models, including EC-Earth
16 Trends in EC-Earth and CMIP5 vs observations CMIP5 multimodel mean has slightly larger trend than observed. Idem dito for EC-Earth
17 Trend deviation in historical period vs future scenario No relation & clouds of model ensembles are found model uncertainty large
18 Developments 1) Higher resolution: T799 atmosphere 0.25 deg ocean, T799 and higher AMIP resolving synoptics relevant to society 2) New components: atmospheric chemistry (TM5), dynamic vegetation (LPJ-GUESS), ocean biogeochemistry (PISCES) 3) Couple to Integrated Assessment Model (IMAGE) 1) Land use scenarios 2) Emission policies (e.g. air quality vs GHG) 3) Coupled feedbacks (e.g. via crop damages)
19 Conclusions and outlook Seamless prediction strategy works (but not too strictly defined) Model development, e.g. via initial biases Near term prediction skill in EC-Earth: ENSO skill at seasonal time scales Skill in (externally forced) trend on multiannual time scales attention for radiative forcing needed (aerosols) Some skill in PDV and AMO up to 5 years Historical and future scenarios: EC-Earth similar to multi-model mean with slightly too high trends Good circulation characteristics More info:
20 Status Core CMIP5 runs (Oct 18, 2011) Ensemble size Completed In progress Not started or unknown Pre-industrial control Historical RCP RCP AMIP # # # # Decadal (full field) Decadal (anomaly) NB run at different HPC platforms by 10 different partners
21 Seamless prediction from seasons to decades Initialization and perturbation: 1. ERA40/ERAInt atmosphere and land, NEMOVAR-ORAS4 ocean (3-D Var, XBTs, hydrography, SST; 5 members), DFS4.3-NEMO/LIM sea ice. 2. Perturbations atmosphere: singular vectors 3. Perturbations ocean: 5 members of NEMOVAR (ORA-S4; representing observational error) Seasonal predictions: 7 month, 5 member ensemble, start dates Feb, May, Aug, Nov., Annual predictions: 13 month, 5 member ensemble, start dates May, Nov., Decadal predictions: 10 years, 10 member ensemble, start date Nov, 1960, 1965, 1970,,2005
22 EC-Earth hindcasts (full initialisation) 12 month running mean Global mean Ts anomaly relative to mean Wouters et al (KNMI) and Doblas Reyes et al (IC3) in prep
23 Seasonal predictions: ENSO EC-Earth Ratio sd: 1.34 Corr: 0.82 RPSSd: 0.48 ECMWF System 3 Ratio sd: 0.84 Corr: 0.86 RPSSd: 0.68 Niño3.4 time series for ERA40/Int (red dots), ensemble range (green box-and-whisker) and ensemble mean (blue dots) 2-4 month (JJA) re-forecasts over Doblas-Reyes, IC3-group
24 Predictions of Atlantic Multidecadal Oscillation R=0.82 R=0.89 R=0.46
25 Predictions of Pacific variability R=0.77 R=0.80 R=0.19
26 Extreme events in Europe 1 % percentile DJF from historical simulation and from E-OBS Tido Semmler MetEireann
27 Extreme events in Europe 99 % percentile JJA from mei2 industrial simulation and from E-OBS
28 CMIP5 historical and RCP simulations Ocean temperature Krakatoa El Chichon Agung Pinatubo
29 CMIP5 projections 21st century trends, RCP 4.5
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