Geosci. Model Dev., 6, 583-590, 2013
www.geosci-model-dev.net/6/583/2013/ doi:10.5194/gmd-6-583-2013 © Author(s) 2013. This work is distributed under the Creative Commons Attribution 3.0 License. |

03 May 2013

Department of Global Ecology, Carnegie Institution for Science, Stanford, California, 94305, USA

Received: 07 Sep 2012 – Published in Geosci. Model Dev. Discuss.: 19 Oct 2012

Revised: 07 Mar 2013 – Accepted: 26 Mar 2013 – Published: 03 May 2013

Abstract. Addressing a variety of questions within Earth science disciplines entails
the inference of the spatiotemporal distribution of parameters of interest
based on observations of related quantities. Such estimation problems often
represent inverse problems that are formulated as linear optimization
problems. Computational limitations arise when the number of observations
and/or the size of the discretized state space becomes large, especially if
the inverse problem is formulated in a probabilistic framework and therefore
aims to assess the uncertainty associated with the estimates. This work
proposes two approaches to lower the computational costs and memory
requirements for large linear space–time inverse problems, taking the
Bayesian approach for estimating carbon dioxide (CORevised: 07 Mar 2013 – Accepted: 26 Mar 2013 – Published: 03 May 2013