Climate Forecasting Unit Arctic Sea Ice Predictability and Prediction on Seasonal-to- Decadal Timescale Virginie Guemas, Edward Blanchard-Wrigglesworth, Matthieu Chevallier, Michel Déqué, Francisco Doblas- Reyes, Neven S Fuckar, Francois Massonnet, Danila Volpi, David Salas y Mélia

Climate Forecasting Unit I - Arctic sea ice predictability sources Persistence Sea Ice Thickness Guemas et al (2014) QJRMS

Climate Forecasting Unit I - Arctic sea ice predictability sources Chevallier and Salas-Melia (2012) Journal of Climate Ice thickness preconditioning Thick ice = best predictor of September extent, thin ice good predictor for March extent Chevallier and Salas y Mélia, JCLIM, 2012 Total volume Total area Area of ice h > 0.9m Area of ice h > 1.5m Area of ice h < 1.5m Total volume Total area Area of ice h < 0.9m

Climate Forecasting Unit I - Arctic sea ice predictability sources Advection Guemas et al (2014) QJRMS

Climate Forecasting Unit I - Arctic sea ice predictability sources Ocean heat transport Guemas et al (2014) QJRMS Correlation detrended annual-mean ORAS4 60N heat transport and sea ice concentration Atlantic OHT lag -3Pacific OHT lag

Climate Forecasting Unit I - Arctic sea ice predictability sources Reemergence Blanchard-Wrigglesworth al (2011) Journal of Climate Melting season Growing season Local sea ice area anomalies associated with SST anomalies Local sea ice area anomalies with sea ice thickness anomalies SST persistence Local SST anomalies impact sea ice area anomalies Local sea ice thickness anomalies impact sea ice area anomalies SIT persistence

Climate Forecasting Unit II – Sea Ice Initialization Guemas et al (2014) Climate Dynamics  NEMO3.2 ocean model + LIM2 sea ice model  Forcings : DFS4.3 or ERA-interim  Nudging : T and S toward ORAS4, timescales = 360 days below 800m, and 10 days above except in the mixed layer, except at the equator (1°S-1°N), SST & SSS restoring (- 40W/m2, -150 mm/day/psu)  Wind perturbations + 5-member ORAS > 5 members for sea ice reconstruction 5 member sea ice reconstruction for 1958-present consistent with ocean and atmosphere states used for initialization

Climate Forecasting Unit Reconstruction IceSat Too much ice in central Arctic, too few in the Chukchi and East Siberian Seas October-November Arctic sea ice thickness II – Sea Ice Initialization Guemas et al (2014) Climate Dynamics

Climate Forecasting Unit March and September Arctic sea ice Sea ice area Sea ice volume DFS4.3 + Nudging ERAint + Nudging UCL HadISSTNSIDC DFS4.3 Free ERAint + Nudging DFS4.3 Free UCL PIOMAS DFS4.3 + Nudging Bias but reasonable agreement in terms of interannual variability Thousands km3 Millions km2 II – Sea Ice Initialization Guemas et al (2014) Climate Dynamics

Climate Forecasting Unit II – Sea Ice Initialization Anomaly versus Full Field Initialization EC-Earth2.3, 5 members, start dates every 2 years from 1960 to 2004 NOINI : historical simulation FFI : Full-field initialization from ORAS4 + ERA OSI-AI : Ocean and sea ice anomaly initialization with corrections to ensure consistency rho-OSI-wAI : Ocean and sea ice weighted anomaly initialization to account for the different model and observed amplitudes of variability + (density, temperature) Instead of (temperature, salinity) anomaly initialisation

Climate Forecasting Unit RMSE sea ice area. Ref: Guemas et al (2014) RMSE sea ice volume. Ref: Guemas et al (2014) NOINI FFI OSI-AI rho-OSI-wAI II – Sea Ice Initialization Anomaly versus Full Field Initialization Lower forecast error when using ocean and sea ice anomaly initialization, even lower with weighted anomalies

Climate Forecasting Unit Chevallier and Guemas (2014) in preparation III – Multi-model Sea Ice Prediction  CNRM-CM5.1 and EC-Earth2.3  Seasonal forecasts initialized every 1 st November and every 1 st May from 1990 to 2008  5 month long  Initialized from ERA-interim for the atmosphere + in-home sea ice reconstructions + ORAS4 (Ec-Earth2.3) or ocean reconstruction (CNRM-CM5.1) for the ocean  5 members using initial perturbations applied to all components for EC-Earth2.3, only the atmosphere for CNRM-CM5

Climate Forecasting Unit Chevallier and Guemas (2014) in preparation III – Multi-model Sea Ice Prediction Summer prediction Winter prediction Bering Okhotsk Greenland Barents Kara Baffin Hudson

Climate Forecasting Unit Chevallier and Guemas (2014) in preparation III – Multi-model Sea Ice Prediction Total Arctic Baffin Bay Linearly Detrended Anomaly Correlation for Sea Ice Extent (SIE) EC-Earth 2.3 CNRM-CM5.1 EC-Earth CNRM-CM 5.1 Predictability until March Similar performance between CNRM-CM5.1 and EC-Earth % confidence interval correlation

Climate Forecasting Unit Chevallier and Guemas (2014) in preparation III – Multi-model Sea Ice Prediction Greenland Sea Barents and Kara Seas Linearly Detrended Anomaly Correlation for Sea Ice Extent (SIE) EC-Earth 2.3 CNRM-CM5.1 EC-Earth CNRM-CM 5.1 Predictability for the first month only in Greenland, until March in Barents and Kara Better performance from CNRM-CM5.1 95% confidence interval correlation

Climate Forecasting Unit Chevallier and Guemas (2014) in preparation III – Multi-model Sea Ice Prediction Okhotsk Sea Bering Sea Linearly Detrended Anomaly Correlation for Sea Ice Extent (SIE) EC-Earth 2.3 CNRM-CM5.1 EC-Earth CNRM-CM 5.1 Better initialisation in Okhotsk Sea with CNRM-CM5: ocean ? Multi-model allows to make the most of individual performance 95% confidence interval correlation

Climate Forecasting Unit Conclusions  Sources of sea ice predictability: persistence, ice thickness preconditioning, advection, ocean heat transport, reemergence  Initialization: 1. Need to make the most of the growing sea ice observations (thickness, melt pond, ice drift) 2. Need for ensembles consistent with the atmospheric and oceanic state 3. Need for further investigation of anomaly initialisation benefits  Prediction: winter predictability until March in the Baffin Bay, Kara, Barents and Okhotsk seas