Abstract
Abstract. Geostatistical inverse modeling (GIM) has become a common approach to estimating greenhouse gas fluxes at the Earth's surface using atmospheric observations. GIMs are unique relative to other commonly used approaches because they do not require a single emissions inventory or a bottom–up model to serve as an initial guess of the fluxes. Instead, a modeler can incorporate a wide range of environmental, economic, and/or land use data to estimate the fluxes. Traditionally, GIMs have been paired with in situ observations that number in the thousands or tens of thousands. However, the number of available atmospheric greenhouse gas observations has been increasing enormously as the number of satellites, airborne measurement campaigns, and in situ monitoring stations continues to increase. This era of prolific greenhouse gas observations presents computational and statistical challenges for inverse modeling frameworks that have traditionally been paired with a limited number of in situ monitoring sites. In this article, we discuss the challenges of estimating greenhouse gas fluxes using large atmospheric datasets with a particular focus on GIMs. We subsequently discuss several strategies for estimating the fluxes and quantifying uncertainties, strategies that are adapted from hydrology, applied math, or other academic fields and are compatible with a wide variety of atmospheric models. We further evaluate the accuracy and computational burden of each strategy using a synthetic CO2 case study based upon NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. Specifically, we simultaneously estimate a full year of 3-hourly CO2 fluxes across North America in one case study – a total of 9.4×106 unknown fluxes using 9.9×104 synthetic observations. The strategies discussed here provide accurate estimates of CO2 fluxes that are comparable to fluxes calculated directly or analytically. We are also able to approximate posterior uncertainties in the fluxes, but these approximations are, typically, an over- or underestimate depending upon the strategy employed and the degree of approximation required to make the calculations manageable.
Highlights
Atmospheric observations of air pollutants and greenhouse gases have evolved dramatically over the past decade
We explore several approaches to geostatistical inverse modeling (GIM) (Eq 6) that are practical for very large datasets and do not necessarily require the dimension reduction strategies described above
We evaluate the inverse modeling algorithms described in this paper using two case studies based on NASA’s OCO2 satellite
Summary
Atmospheric observations of air pollutants and greenhouse gases have evolved dramatically over the past decade. Atmospheric monitoring of carbon dioxide (CO2) is a prime example. The number of in situ observation sites in the US, Canada, Europe, and elsewhere has greatly expanded since the early 2000s. A recent geostatistical inverse modeling (GIM) study of CO2 fluxes across North America used observations from 6 times as many continuous towerbased observation sites as a GIM study of the same region published 6 years earlier (Gourdji et al, 2012; Shiga et al, 2018). Aircraft-based observations have greatly expanded, including regular observations from civilian aircraft based in both Germany and Japan (Petzold et al, 2015; Machida et al, 2008)
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