Uncertainties in Spatio-Temporal Prediction for Carbon Cycle Science: From Satellite Data to Surface Fluxes

• Activity Number: 631
• Type: Invited
• Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
• Sponsor: ASA Advisory Committee on Climate Change Policy
• Author(s): Noel Cressie* and Andrew Zammit-Mangion and Amy Braverman and Jonathan Hobbs
• Companies: University of Wollongong and University of Wollongong and Jet Propulsion Laboratory and Jet Propulsion Laboratory
• Keywords: Remote sensing ; Inverse problem ; Surface CO2 fluxes ; Carbon cycle ; Uncertainty quantification ; Climate change

Abstract

An important compartment of the carbon cycle is atmospheric carbon dioxide (CO2), where it (and other gases) contribute to climate change through a greenhouse effect. There are a number of CO2 observational programs, some of which are land-based or aircraft-based: Their measurements are made around the globe at a small number of locations at regular time intervals. Satellite-based programs solve this problem of having spatially sparse data while giving up some of their temporal richness. The most recent satellite launched to measure CO2 was NASA's Orbiting Carbon Observatory-2. The ultimate goal for a carbon-cycle scientist is to use remote sensing data to solve an inverse problem, resulting in estimated surface CO2 fluxes. This is key, since state-of-the-art climate models (or Earth system models) incorporate the impact of surface CO2 fluxes on the climate system. Uncertainty quantification is also key and starts with inferring the spatio-temporal CO2 data from retrieved spectra, progresses to dynamical spatial modelling of column-averaged CO2, and arrives at inference on spatially resolved flux fields.