CNU-KOPRI-KMA activities for winter climate prediction March 10, 2014: WGSIP meeting CNU-KOPRI-KMA activities for winter climate prediction SAT anomalies (NCEP/NCAR R2) at Dec 2012 Predicted SAT anomalies at Dec 2012 Jee-Hoon Jeong Chonnam National University, Gwangju, South Korea

Winter climate prediction Z500 anomalies (NCEP/NCAR R) at Dec 2012 Predicted Z500 anomalies at Dec 2012 Recently East Asia has undergone extremely cold winters (except last winter) presumably affected by anomalous atmospheric & cryospheric conditions over the Arctic and high-latitudes. CNU-KOPRI-KMA team is developing a winter prediction system which utilizes Arctic sea ice and snow information.

Winter climate prediction system FNL/ERAInt (Atmosphere) HadISST/OISST Offline Land Surface Model Statistical Sea Ice Prediction Model JRA-25/CMC Snow Depth Boundary Condition Observation + Prediction SST, SIC (anomaly initialization) Initialization NCAR CAM3’ 2x2.5, 16 ens Modifications for resolving stratosphere, Arctic-extratropics interactions This system is an AGCM-based dynamical prediction system with a land-surface (snow) initialization and a statistical prediction model of the Arctic sea-ice concentration. Seasonal Prediction for winter season GloSea5 MetOffice/KMA comparison

Impact of snow initialization on SAT prediction [Left] Change in SAT predictability (r2 between obs and fcst) due to snow initialization, estimated from 10-year, 16 ensemble hindcasts with/without snow initialization (with daily CMC data). Significant (>10%) predictability increase over East Asia up to 2-month lead. One-time initialization, have a physical inconsistency between SM/ST and initialized snow We are testing a snow-nudging method (1-month before start) to offline LSM calculation for a better physical balance between soil-moisture/temperature and snow. Jeong et al. (2013, J Climate)

Forecasted SAT anomalies with/without snow nudging Oct 2009 1 month training applied; snow depth is nudged during offline LSM calculation for 1-month Without the training procedure; Snow depth is initialized once at the start date SAT anomalies (NCEP/NCAR R) Signal (to noise) increase, might have a more positive impact on following spring.

Statistical prediction of Arctic sea ice conc. Remote circulation response associated with Arctic sea ice variation is important for simulating East Asian winter climate accurately. For a prediction with AGCM, the SIC should be prescribed -> statistical prediction model is developed. [Upper] S-EOF (Season-reliant EOF) of Arctic sea ice concentration, [Lower] associated PC time-series S-EOFs modes indicate primary patterns of spatio-temporal change of Arctic SIC Based on the detected S-EOFs patterns and current state indices of SIC, we construct future projection of Arctic SIC with monthly resolution. [paper under revision]

Statistical prediction of Arctic sea ice Prediction from Sep 2007 Skill of the model to predict the Arctic sea ice extent (correlation) Good skills in the cold season (melting phase) vs. modest skills in the warm season (freezing phase)

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