Reducing Uncertainty in Future Projections of CO2 and Net Ecosystem Exchange

Abstract:

Earth system models (ESMs) and land surface models (LSMs) show disagreement in projections of atmospheric CO2 levels and net ecosystem exchange (NEE) respectively. The objective of this work, which has been realized in the context of a master’s project, is to contribute to lowering the uncertainty in projections of atmospheric CO2 levels and NEE. We present parallel work in GHG sensor optimization and ESM/LSM development. Specifically, we [1] report on improvements in the limit of detection of low-cost, off-the-shelf, CO2 gas sensors achieved by calibrating them for gas concentration, temperature and humidity; [2] discuss new NEE model results obtained by running the CABLE land surface model for MsTMIP, a multi model inter-comparison project with the goal of isolating NEE uncertainty due to model structure by running sensitivity simulations on climate, LULCC, CO2 and nitrogen; and [3] present benchmark model results from running the new land model of the Climate Modeling Alliance (CliMA) ESM, which we developed. We make the link between sensor development and land modeling by discussing how in situ CO2 measurements can be used with ESMs and LSMs to reduce uncertainty in projections of atmospheric CO2 levels and NEE, and how remotely sensed data is used in CliMA's machine learning algorithms to improve land parameters.