1. 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

2. 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.

3. 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

4. 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)

5. 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.

6. 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]

7. 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)

8. Thank you for your attention!