Assessing Climate Change Effects on Soil and Vegetation Carbon Dynamics Using a Temperature and Precipitation Bias Correction Technique

The atmospheric CO2 concentration is projected to continue its increase over the next century, leading to changes in climate and photosynthesis that will both affect vegetation growth and soil respiration rates. This will consequently affect soil carbon levels and can further feedback onto the atmospheric CO2 concentration. Daycent, a plant and soil nutrient cycling model, can help assess future soil carbon levels based on weather data from climate models. Climate models often have a bias in their simulation of precipitation and temperature. Bias correction techniques are used to adjust the model output to make it more similar to the observations. In this presentation, we apply a bias correction technique to historic and future temperature and precipitation output data from NARCCAP's Canadian Regional Climate Model (CRCM) driven with the Canadian Global Climate Model (CGCM3) boundary conditions. We analyze how future vegetation growth and soil respiration rates are affected with and without the bias correction technique applied. Applying the bias correction technique to the temperature and precipitation data does have an effect on future vegetation growth and soil respiration rates. The bias correction technique increased precipitation for the Midwestern domain and resulted in an increase in plant vegetation and an increase in soil respiration rates. Vegetation growth and soil respiration were affected more than soil carbon stock however; because the two have opposing effects on the soil carbon stock.