1. Model-data intercomparison for NACP Yiqi Luo and James Randerson

2. Need of data-model intercomparison Regional prediction of carbon sink in North America needs to use models Models without confrontation of data could not generate useful predictions Continuously confronting models against data can not only improve models but also improve data collection schemes.

3. Model improvement for IPCC assessment (for example) Currently models used by IPCC have incorporated many processes, including biogeochemical cycle, land, ocean, and climate. But precision of model prediction was not well evaluated. We need to use a variety of data sets to improve models

4. Benchmarking A common practice in the climate modeling community It helps improve model performance, diagnosis analysis, and prediction for future C dynamics. Need to select data sets for benchmarking Need methodological development for automated model improvement against benchmark data.

5. The comparison of data and model should be iterative. Model improvement not only benefits model prediction but also data collection quality. Handshakes between modelers and data persons. Need to evaluate data uncertainty and model uncertainty,

6. Techniques and data availability to improve model projections Techniques available for real-time forecasting and confront models with data, such as ensemble Kalman Filter. Tree ring data provide information on long- term processes. Soil carbon models have been improved using long-term experiments in agriculture.

7. Model projections for policy making Need well-characterized uncertainty of model projections Need to develop metrics of improvement of model projections after data assimilation.

8. Train students and post-docs to do both modeling and experiments Training

9. Financial support for the data-model intercomparison Most of data-model comparison activities have been done on the ad hoc basis and are usually not fully funded. Modeling and data groups are volunteering their times for the data-model intercomparison studies. To improve quality of data-model intercomparison, we need to evaluate how to financially support such activities.