Greenhouse gas network design using backward Lagrangian particle dispersion modelling − Part 1: Methodology and Australian test case

Abstract

Abstract. This paper describes the generation of optimal atmospheric measurement networks for determining carbon dioxide fluxes over Australia using inverse methods. A Lagrangian particle dispersion model is used in reverse mode together with a Bayesian inverse modelling framework to calculate the relationship between weekly surface fluxes, comprising contributions from the biosphere and fossil fuel combustion, and hourly concentration observations for the Australian continent. Meteorological driving fields are provided by the regional version of the Australian Community Climate and Earth System Simulator (ACCESS) at 12 km resolution at an hourly timescale. Prior uncertainties are derived on a weekly timescale for biosphere fluxes and fossil fuel emissions from high-resolution model runs using the Community Atmosphere Biosphere Land Exchange (CA-BLE) model and the Fossil Fuel Data Assimilation System (FFDAS) respectively. The influence from outside the modelled domain is investigated, but proves to be negligible for the network design. Existing ground-based measurement stations in Australia are assessed in terms of their ability to constrain local flux estimates from the land. We find that the six stations that are currently operational are already able to reduce the uncertainties on surface flux estimates by about 30 %. A candidate list of 59 stations is generated based on logistic constraints and an incremental optimisation scheme is used to extend the network of existing stations. In order to achieve an uncertainty reduction of about 50 %, we need to double the number of measurement stations in Australia. Assuming equal data uncertainties for all sites, new stations would be mainly located in the northern and eastern part of the continent.

Reviews and syntheses: guiding the evolution of the observing system for the carbon cycle through quantitative network design

“…Due to the limited domain, fluxes on the boundary had to be included in the control vector. Recent examples for QND studies addressing rather practical design questions with a regional model are provided by Ziehn et al (2014) for Australia and Nickless et al (2015) for South Africa.…”

Estimates of CO<sub>2</sub> fluxes over the City of Cape Town, South Africa, through Bayesian inverse modelling

“…The particle counts were used to calculate the source-receptor (s-r) relationship, or influence functions, which form the sensitivity matrix T. Here, we followed Seibert and Frank (2004) to derive the elements of that matrix. As described in Ziehn et al (2014), we modified the 20 approach of Seibert and Frank (2004) to account for the particle counts which were produced by our LPDM as opposed to the mass concentrations which were outputted by the atmospheric transport model in their study. The resulting s-r relationship between the measurement site and source i at time interval n, which provide the elements of the matrix H, is:…”

“…In the case of the boundary sources (or contributions from outside of the domain) which are given as concentrations, their contributions to the concentration at the 15 measurement site are expressed as a proportion of their concentration, dependent on their influence at the receptor site. Ziehn et al (2014) shows that by calculating the Jacobian which provides the sensitivities of observed concentrations to boundary concentrations, the boundary contribution can then be written as: 2.9 A priori covariance matrix -C s0…”

“…The cumulus convection scheme uses a mass-flux closure (McGregor, 2003), and includes downdrafts, entrainment and detrainment. Gravity wave drag time, in receptor-orientated mode, the particle counts can be used to generate H for a given receptor site, as described in Ziehn et al (2014) and Nickless et al (2015b) following Seibert and Frank (2004).…”

Designing optimal greenhouse gas observing networks that consider performance and cost

“…Quantitative methods for designing "optimal" observing networks have been described for inferring carbon dioxide (CO 2 ) emissions, improving weather forecasts, collecting oceanographic data, and monitoring air quality and climate change (e.g., Barth and Wunsch, 1990;Morss et al, 2001;Patra and Maksyutov, 2002;Gloor et al, 2000;Carmichael et al, 2008;Stuart et al, 2007;Mauger et al, 2013). Ziehn et al (2014) and Nickless et al (2015) illustrate recent applications of using optimization methods to design GHG observing networks. Without requiring actual observations, so-called observing system simulation experiments (e.g., Masutani et al, 2010) can be used to create synthetic observations and assess the scientific value of adding new observations at various locations and times.…”