Abstract
Abstract. The ability to predict the trajectory of climate change requires a clear understanding of the emissions and uptake (i.e., surface fluxes) of long-lived greenhouse gases (GHGs). Furthermore, the development of climate policies is driving a need to constrain the budgets of anthropogenic GHG emissions. Inverse problems that couple atmospheric observations of GHG concentrations with an atmospheric chemistry and transport model have increasingly been used to gain insights into surface fluxes. Given the inherent technical challenges associated with their solution, it is imperative that objective approaches exist for the evaluation of such inverse problems. Because direct observation of fluxes at compatible spatiotemporal scales is rarely possible, diagnostics tools must rely on indirect measures. Here we review diagnostics that have been implemented in recent studies and discuss their use in informing adjustments to model setup. We group the diagnostics along a continuum starting with those that are most closely related to the scientific question being targeted, and ending with those most closely tied to the statistical and computational setup of the inversion. We thus begin with diagnostics based on assessments against independent information (e.g., unused atmospheric observations, large-scale scientific constraints), followed by statistical diagnostics of inversion results, diagnostics based on sensitivity tests, and analyses of robustness (e.g., tests focusing on the chemistry and transport model, the atmospheric observations, or the statistical and computational framework), and close with the use of synthetic data experiments (i.e., observing system simulation experiments, OSSEs). We find that existing diagnostics provide a crucial toolbox for evaluating and improving flux estimates but, not surprisingly, cannot overcome the fundamental challenges associated with limited atmospheric observations or the lack of direct flux measurements at compatible scales. As atmospheric inversions are increasingly expected to contribute to national reporting of GHG emissions, the need for developing and implementing robust and transparent evaluation approaches will only grow.
Highlights
Introduction and the need for diagnosticsThe ability to predict the trajectory of climate change requires a clear understanding of the historical and current emissions and uptake of long-lived greenhouse gases (GHGs), and chief among them carbon dioxide (CO2) and methane (CH4), over the Earth’s land and ocean regions
The ability to constrain the anthropogenic components of greenhouse gas budget estimates, on the other hand, is becoming increasingly central to discussions aimed at setting emissions, or emissions reduction, targets at local to global scales (e.g., Pacala et al, 2010)
Michalak et al.: Diagnostic methods for atmospheric inversions of long-lived greenhouse gases while biospheric fluxes over land can be continuously monitored at plot scale using the eddy covariance technique (e.g., Baldocchi et al, 2001; Law et al, 2002), and ocean fluxes can be deduced locally from the difference between the partial pressure of CO2 measured in seawater and that in the overlying air (e.g., Takahashi et al, 1993, 2002)
Summary
The ability to predict the trajectory of climate change requires a clear understanding of the historical and current emissions and uptake (i.e., surface fluxes) of long-lived greenhouse gases (GHGs), and chief among them carbon dioxide (CO2) and methane (CH4), over the Earth’s land and ocean regions. The solution of atmospheric inverse problems invariably involves a series of decision points including, but not limited to, (1) the choice of the atmospheric observations to be used; (2) the choice of the atmospheric chemistry and transport model to be implemented; (3) the choice of a statistical framework for defining an objective function that captures the relative contribution of atmospheric observations, the chemistry and transport model, and any prior information in informing flux patterns; and (4) the choice of a numerical framework for the solution of the inverse problem Each of these choices will have a direct impact on estimates. At a minimum, the ultimate estimates must be consistent with the assumptions inherent to the specific modeling setup that was implemented
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