Polar Climate Prediction Seasonal-to-Decadal Prediction at the Climate Forecasting Unit (Catalan Institute of Climate Sciences) F.J. Doblas-Reyes Seasonal Forecasting Decadal Climate Services Forecast System Development Research Groups Research Lines Data Assimilation Polar Climate Prediction

Focus: seasonal-to-decadal prediction Francisco J Doblas-Reyes Isabel Andreu-Burillo: ocean processes Muhammad Asif: EC-Earth Lauriane Batté (V): stochastic parameterizations Pierre-Antoine Bretonnière: SPECS data Louis-Philippe Caron (V): tropical cyclones Alberto Carrassi: data assimilation Melanie Davis: climate services Neven Fuckar: Arctic sea ice Virginie Guémas: sea ice, XXIst century hiatus Fabian Lienert: North Pacific, climate services Domingo Manubens : autosubmit developer Oriol Mula-Valls: system administrator Aida Pintó: prediction of extremes Mar Rodríguez: SPECS manager Luis Rodrigues: seasonal climate predictability Gabriela Tarabanoff: secretary, climate services Verónica Torralba: climate services Danila Volpi: initialisation, decadal prediction Robin Weber: initialisation in simple models http://www.ic3.cat http://ic3.cat/wikicfu Objectives: Development of s2d prediction capability Forecast quality assessment Downscaling of probabilistic forecasts Climate services We share on request: Autosubmit Sea-ice restarts R diagnostic functions We run on: Marenostrum (Spain) ECMWF Lindgren (Sweden) HECTOR (UK) Our local cluster

Autosubmit Autosubmit acts as a wrapper to run a climate experiment on a HPC. The experiment is a sequence of jobs that it submits, manages and monitors. When a job is complete, the next one can be executed. Divided in 3 phases: ExpID assign, experiment creation, run. Separation experiment/autosubmit codes. Config files for autosubmit and experiment. Database to store experiment information. Common templates for all platforms. Recovery after crashes. Dealing with a list of schedulers and communication protocols. Each job has a colour in the monitoring tool: yellow=completed, green=running, blue=pending, etc.

Initial conditions: sea-ice reconstructions Sea ice simulation constrained by ocean and atmosphere observational data Arctic sea ice area March and September reconstruction Root Mean Square Error of predictions Observation 1 Sensitivity experiment from sea-ice reconstruction CMIP5 predictions Reconstruction 1 Reconstruction 2 Observation 2 Sea ice reanalysis Guemas et al. (2014)

Seasonal prediction: Increase in resolution Correlation of the ensemble mean and ratio RMSE/spread of Niño3.4 SST (versus ERSST) from four-month EC-Earth3 simulations: m019 (T255/ORCA1), m01n (T511/ORCA025). May start dates over 1993-2009 using ERA-Interim and GLORYS initial conditions and five-member ensembles. ECMWF S4 added for reference. M. Asif, I. Andreu-Burillo (IC3)

Init RMSSS of ensemble mean Ratio RMSE Init/NoInit Impact of initialisation in CMIP5 (Top row) Root mean square skill score (RMSSS) of the ensemble mean of the initialised predictions and (bottom row) ratio of the root mean square error (RMSE) of the initialised and uninitialised predictions for the near- surface temperature from the multi-model CMIP5 experiment (1960-2005) for (left) 2-5 and (right) 6-9 forecast years. Five-year start date interval. Forecast year 2-5 Forecast year 6-9 Init RMSSS of ensemble mean Ratio RMSE Init/NoInit Doblas-Reyes et al. (2013)

Attribution of the XXIst century hiatus Predictions of the recent global-temperature slow down with EC-Earth 2.3. Global-mean SST from observations (ERSST) and simulations, three-year averages. The experiments suggest an important role of the internal variability, especially increased capture of heat in the ocean, in the hiatus. Forecast years 1 to 3 from initialised climate predictions Historical simulations starting from 1850 preindustrial control simulations Observations Guemas et al. (2013)

Attribution of the XXIst century hiatus Predictions of the recent global-temperature slow down with EC-Earth 2.3. OHC for the top 800 m (109 J, excluding the mixed layer) for the ORAS4 reanalysis and the initialised hindcasts, three-year average at the onset of the hiatus. The hiatus is associated with an increased ocean heat absortion, especially in the Pacific. This is captured by the Init experiment. ORAS4 Init

Attribution of the XXIst century hiatus Predictions of the recent global-temperature slow down with EC-Earth 2.3. OHC for the top 800 m (109 J, excluding the mixed layer) for the ORAS4 reanalysis and the initialised hindcasts, three-year average at the onset of the hiatus. The hiatus is associated with an increased ocean heat absortion, especially in the Pacific. This is captured by the Init experiment. ORAS4 NoInit

AI/FFI in a simple model Mean error of two variables from 360 decadal predictions performed with the Lorenz model with three compartments (ocean, tropical atmosphere and extratropical atmosphere). The configurations where AI outperforms FFI are associated with a strong initial shock and a larger bias. Carrassi et al. (2014)

Calibration and combination (Left) Multi-model seasonal predictions of Sahel precipitation, including its intraseasonal variability from June to October, started in April. (Right) Correlation of the ensemble mean prediction for Guinean and Sahel precipitation. Reliability is fundamental for climate services. Rodrigues et al. (2014)

Climate services: renewable energy 28.04.11 Climate services: renewable energy Some initial climate analyses relevant to the RE sector have been made by the climate scientists at IC3. Here I will show the results for temperature and then downward solar radiation. These top 2 plots show observed and forecast temperature variation from the mean across the Mediterranean for 2005-2010. Although the forecast represents temperatures slightly different to the observed, the climate model has accurately forecast the overall trend which shows a warmer eastern mediterranean compared to the west. The probability plot is an example of how climate services within the CLIMRUN project can present climate information in a format that can help stakeholders to incorporate it within their decision making processes. Plot C shows that most areas across the Mediterannean demonstrate an above normal temperature between 2005 and 2010, with the majority of the areas showing a 50-70% probability of this above normal temperature trend. Doblas-Reyes et al. (2013) 12

Climate services: Users should also adapt Impact surfaces of a simple wind-energy model over the North Sea for DJF as a function of the mean seasonal wind and the wind intraseasonal variability. Power density estimates obtained using the XXth Century Reanalysis, a Rayleigh function to estimate high-frequency winds from mean daily values and a wind profile power law to obtain 100 m winds from 10 m winds. D. MacLeod (Univ. Oxford)

Expected progress in climate prediction Extratropical SSTs: the North Atlantic, North and South Pacific Model response: representation of forced response to fast and slow forcings Model inadequacy Model improvement: all components Initialisation: soil moisture, snow, sea ice, ensemble generation Stratospheric processes, including ozone and aerosol Calibration and combination Documentation (follow the IPCC calibrated language), demonstration of value and outreach