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Seasonal-to-decadal forecasting with EC-Earth at IC3/BSC F.J. Doblas-Reyes ICREA, BSC and IC3, Barcelona, Spain.

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Presentation on theme: "Seasonal-to-decadal forecasting with EC-Earth at IC3/BSC F.J. Doblas-Reyes ICREA, BSC and IC3, Barcelona, Spain."— Presentation transcript:

1 Seasonal-to-decadal forecasting with EC-Earth at IC3/BSC F.J. Doblas-Reyes ICREA, BSC and IC3, Barcelona, Spain

2  Work on initialisation: initial conditions for all components (better ocean and sea ice), better ensemble generation, etc.  Model improvement: leverage knowledge and resources from modelling at other time scales, aim for a drift reduction, projections of volcanic and anthropogenic aerosols. More efficient codes.  Forecast quality assessment: scores closer to user, reliability as main target, process-based verification.  Attribution: new attribution approaches using predictions.  More sensitivity to the users’ needs: going beyond the assumption of needing downscaling, better documentation, demonstration of value and outreach. Our open fronts EC-Earth Meeting : S2d forecasting with EC-Earth at IC3/BSC, 6 May 2015 2

3 Assessment of full-field (red), anomaly in the ocean (blue), weighted anomaly in the ocean and the sea ice, with initialization of temperature and density instead of the usual temperature and salinity (green), and a weighted anomaly nudging in the ocean (pink). Decadal prediction experiments run with EC-Earth2.3. Comparison with historical ensemble simulation (orange). 5 ensemble members, one start date every 2 years. Reference data: ERSST for AMV and SST, sea-ice reconstruction from Guemas et al. 2013 for sea-ice area and volume. 3 D. Volpi (IC3) Initialization approaches AMV Arctic sea-ice volume EC-Earth Meeting : S2d forecasting with EC-Earth at IC3/BSC, 6 May 2015

4 Mean error of two variables from 360 decadal predictions performed with the Lorenz model with three compartments (ocean, tropical atmosphere and extra-tropical atmosphere). The configurations where AI outperforms FFI are associated with a strong initial shock and a larger bias. Model error “type #2” Model error “type #1” 4 Initialization approaches Carrassi et al. (2014, NPG ) EC-Earth Meeting : S2d forecasting with EC-Earth at IC3/BSC, 6 May 2015

5 PDFs of initial conditions (black and red) and of the model and “nature” climatologies (blue and green) for the Peña and Kalnay model with three compartments (ocean, tropical atmosphere and extra-tropical atmosphere). 5 Initialization approaches Weber et al. (2015, MWR) Model error “type #2” r m =42, AI better Model error “type #1” c m /c m z =0.8/0.9, FFI better EC-Earth Meeting : S2d forecasting with EC-Earth at IC3/BSC, 6 May 2015

6 Predictions Historical simulations Observations Atlantic multidecadal variability (AMV) Global mean surface air temperature (GMST) CMIP5 decadal predictions. Global-mean near-surface air temperature and AMV against GHCN/ERSST3b for forecast years 2-5. The initialized experiments reproduce the GMST trends and the AMV variability and suggest that initialization corrects the forced model response and phases in some of the internal variability. CMIP5 decadal predictions 6 Doblas-Reyes et al. (2013, Nat. Comms.) EC-Earth Meeting : S2d forecasting with EC-Earth at IC3/BSC, 6 May 2015

7 Service-driven predictions SST averaged over the subpolar gyre and MSLP averaged over the North Atlantic used to estimate basin-wide accumulated cyclone energy (ACE). Time series for 2-9 forecast year predictions with EC- Earth2.3 over 1969-2006. 7 Caron et al. (2015, GRL) EC-Earth Meeting : S2d forecasting with EC-Earth at IC3/BSC, 6 May 2015

8 Service-driven predictions Reliability diagrams of left) initialised and right) uninitialised MME simulations for basin-wide accumulated cyclone energy (ACE). The results are for 2-9 year averages above the climatological median over 1961-2009. Statistically significant values are in bold. Some of the added value of the predictions is their better management of uncertainty, which leads to increased credibility. 8 Caron et al. (2015, GRL) EC-Earth Meeting : S2d forecasting with EC-Earth at IC3/BSC, 6 May 2015

9 Land-surface initialization JJA near-surface temperature anomalies in 2010 from ERAInt (left) and odds ratio for anomalies above the upper quintile from experiments with a climatological (centre) and a realistic (right) land-surface initialisation. Results for EC-Earth2.3 started in May with initial conditions from ERAInt, ORAS4 and a sea-ice reconstruction over 1979-2010. Land-surface initialization is relevant to predict extreme events. 9 Prodhomme et al. (2015, Clim. Dyn.) EC-Earth Meeting : S2d forecasting with EC-Earth at IC3/BSC, 6 May 2015

10 High resolution High resolution has been thoroughly tested in climate prediction mode. The same applies to the stochastic physics. RMSE and spread of Niño3.4 SST (versus ERSST) from EC-Earth3.0.1 simulations: standard resolution (SR, T255/ORCA1), high resolution (HR, T511/ORCA025) without and with stochastic physics (SPPT3). 10.member ensembles for May start dates over 1993-2009 using ERA-Interim and GLORYS. 10 Batté and Doblas-Reyes (2015, Clim. Dyn.) EC-Earth Meeting : S2d forecasting with EC-Earth at IC3/BSC, 6 May 2015

11 Volcanic aerosol Global-mean surface temperature before and after the Pinatubo eruption simulated by EC-Earth 2.3 with five-member ensemble hindcasts. Observational data is a mix between GHCN, ERSST and GISS. Both the initialization and the volcanic forcing specification improve the simulations. M. Ménégoz (IC3) 11 EC-Earth Meeting : S2d forecasting with EC-Earth at IC3/BSC, 6 May 2015

12 The multi-model real-time decadal prediction exchange is a research exercise that guarantees equal ownership to the contributors. WMO-CCl plans to sponsor this activity. http://www.metoffice.gov.uk/research/climate/seasonal-to-decadal/long-range/decadal-multimodel Real-time decadal prediction 12 EC-Earth Meeting : S2d forecasting with EC-Earth at IC3/BSC, 6 May 2015

13 13  Automatization: Preparing and running, postprocessing and output transfer, all managed by Autosubmit. No user intervention.  Provenance: Assigns unique identifiers to each experiment and stores information about model version, configuration options, etc  Failure tolerance: Automatic retrials and ability to repeat tasks in case of corrupted or missing data.  Versatility: Currently runs EC-Earth, NEMO and NMMB models on several platforms. Infrastructure: Autosubmit 13 D. Manubens, J. Vegas (IC3) Workflow of an experiment monitored with Autosubmit (yellow = completed, green = running, red = failed, … ) Currently version 3, with flexible workflow definition EC-Earth Meeting : S2d forecasting with EC-Earth at IC3/BSC, 6 May 2015

14 14 The EC-Earth data produced in SPECS are now visible from the Climate4impact portal http://climate4impact.eu.http://climate4impact.eu Lots of work still missing: e.g. use cases and processing demonstration video for climate predictions, etc Downstream services 14 EC-Earth Meeting : S2d forecasting with EC-Earth at IC3/BSC, 6 May 2015

15 15 Common tools for verification S2dverification is an R package to verify seasonal to decadal forecasts by comparing experiments with observational data. It allows analyzing data available either locally or remotely. It can also be used online as the model runs. Available from CRAN. LOCAL STORAGE ESGF NODE or OPeNDAP SERVER BASIC STATISTICS SCORES Correlation, RMSSS, CRPS,... PLOTS Anomaly Correlation Coefficient. 10M Wind Speed ECMWF S4 1 month lead with start dates once a year on first of November and Era-Interim in DJF from 1981 to 2011. Simple bias correction with cross-validation. PLOTS Supports datasets stored locally or in ESGF (OPeNDAP) servers. Exploits multi-core capabilities Collects observational and experimental datasets stored in multiple conventions: NetCDF3, NetCDF4 File per member, file per starting date, single file, … Supports specific folder and file naming conventions. N. Manubens (IC3) EC-Earth Meeting : S2d forecasting with EC-Earth at IC3/BSC, 6 May 2015

16  MIPs (ensemble size in brackets) DECK: AOGCM standard resolution, historical (5) and AMIP (1) Decadal prediction (DCPP): components A (10), B (10) and C, possibly HR ScenarioMIP: one scenario (5) HiResMIP: coupled T511-ORCA025 (5) VolMIP: initialized simulations with Pinatubo and Tambora forcing SIMIP: sea-ice diagnostics  Data Contribution to CMOR3 for climate predictions (e.g. double time axis) Testing CMOR3 on different platforms Preparation of tools for standardizing (CMORizing) the EC-Earth output Implementation of a Spanish ESGF node, link to Big Data and downstream service developments  Infrastructure Coordinate PRACE proposals Release an Autosubmit version for EC-Earth partners Increase model performance BSC-ES CMIP6 contributions EC-Earth Meeting : S2d forecasting with EC-Earth at IC3/BSC, 6 May 2015 16

17  Work on initialisation: initial conditions for all components (better ocean and sea ice), better ensemble generation, etc.  Model improvement: leverage knowledge and resources from modelling at other time scales, aim for a drift reduction, projections of volcanic and anthropogenic aerosols. More efficient codes.  Forecast quality assessment: scores closer to user, reliability as main target, process-based verification.  Attribution: new attribution approaches using predictions.  More sensitivity to the users’ needs: going beyond the assumption of needing downscaling, better documentation, demonstration of value and outreach. Our open fronts 17 EC-Earth Meeting : S2d forecasting with EC-Earth at IC3/BSC, 6 May 2015

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