Tuesday, August 5, 2008: 10:10 AM
103 AB, Midwest Airlines Center
Jinyun Tang, Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA and Qian Lai Zhuang, Earth and Atmospheric Sciences, Purdue University, West Lafayette, IN
Background/Question/Methods A daily-time step snow model was coupled with a soil thermal model to simulate changes of daily soil thermal regime and growing season length for the boreal forest ecosystems in Alaska.
Results/Conclusions
We first used the coupled model to simulate the snow depth for a site in Canada based on daily air temperature and precipitation. We found that, for the period from July 1st 1993 to December 31, 1997, the simulated snow depth was compared well against observations (R2 =0.91; p<0.001). We then used the soil temperature data of 1995 from a black spruce forest ecosystem in Canada to calibrate the model and then used the rest of data from 1994 to 1997 to verify the model. We found that, for the period from 1994 to 1997, the daily soil temperature profile was well simulated from the soil surface down to the depth 50cm of soils (average R2 = 0.83, p < 0.001). Finally, we applied the model to Alaskan boreal forests from 1923 to 2000 to examine the changes of soil thermal regime and growing season length. Our simulation indicated that the length of growing season increased 0.3 days yr-1 (p<0.01) after 1962, with a dramatic decrease in 1992 due to the 1991 eruption of Mount Pinatubo. The soil temperature at top 20 cm depth increased 0.03oC yr-1 from 1963 to 2000 (R2 = 0.14, p < 0.015). The average active layer depth increased 10 cm from 1963 to 2000 over the region. Next, we will incorporate the updated soil thermal model to a biogeochemistry model to evaluate the effects of the daily changes of soil thermal regime and growth season length on carbon dynamics in this region.
