THE LABORATORY OF ICE CORE AND COLD REGIONS ENVIRONMENT

Affiliation unit: Lanzhou Institute of Glaciology and Cold Regions Environment, CAS Brief history: The Laboratory of Ice Core and Cold Regions Environment (LICCRE) was formally approved to open domestically and internationally by Chinese Academy of Sciences in April 1997. It is attached to the Lanzhou Institute of Glaciology and Geocryology, CAS.

Winter sea ice albedo variations in the Bohai Sea of China

Sea ice conditions in the Bohai Sea of China are sensitive to large-scale climatic variations. On the basis of CLARA-A1-SAL data, the albedo variations are examined in space and time in the winter（December, January and February） from 1992 to 2008 in the Bohai Sea sea ice region. Time series data of the sea ice concentration（SIC）, the sea ice extent（SIE） and the sea surface temperature（SST） are used to analyze their relationship with the albedo. The sea ice albedo changed in volatility appears along with time, the trend is not obvious and increases very slightly during the study period at a rate of 0.388% per decade over the Bohai Sea sea ice region.The interannual variation is between 9.93% and 14.50%, and the average albedo is 11.79%. The sea ice albedo in years with heavy sea ice coverage, 1999, 2000 and 2005, is significantly higher than that in other years; in years with light sea ice coverage, 1994, 1998, 2001 and 2006, has low values. For the monthly albedo, the increasing trend（at a rate of 0.988% per decade） in December is distinctly higher than that in January and February. The mean albedo in January（12.90%） is also distinctly higher than that in the other two months. The albedo is significantly positively correlated with the SIC and is significantly negatively correlated with the SST（significance level 90%）.

Using a skillful statistical model to predict September sea ice covering Arctic shipping routes

The rapid decrease in Arctic sea ice cover and thickness not only has a linkage with extreme weather in the midlatitudes but also brings more opportunities for Arctic shipping routes and polar resource exploration,both of which motivate us to further understand causes of sea-ice variations and to obtain more accurate estimates of seaice cover in the future.Here,a novel data-driven method,the causal effect networks algorithm,is applied to identify the direct precursors of September sea-ice extent covering the Northern Sea Route and Transpolar Sea Route at different lead times so that statistical models can be constructed for sea-ice prediction.The whole study area was also divided into two parts:the northern region covered by multiyear ice and the southern region covered by seasonal ice.The forecast models of September sea-ice extent in the whole study area(TSIE)and southern region(SSIE)at lead times of 1–4 months can explain over 65%and 79%of the variances,respectively,but the forecast skill of sea-ice extent in the northern region(NSIE)is limited at a lead time of 1 month.At lead times of 1–4 months,local sea-ice concentration and sea-ice thickness have a larger influence on September TSIE and SSIE than other teleconnection factors.When the lead time is more than 4 months,the surface meridional wind anomaly from northern Europe in the preceding autumn or early winter is dominant for September TSIE variations but is comparable to thermodynamic factors for NSIE and SSIE.We suggest that this study provides a complementary approach for predicting regional sea ice and is helpful in evaluating and improving climate models.