Model‐data comparison of MCI field campaign atmospheric CO₂ mole fractions

Abstract

Atmospheric transport model errors are a major contributor to uncertainty in CO 2 inverse flux estimates. Our study compares CO 2 mole fraction observations from the North American Carbon Program Mid-Continental Intensive (MCI) field campaign and modeled mole fractions from two atmospheric transport models: the global Transport Model 5 from NOAA's CarbonTracker system and the mesoscale Weather Research and Forecasting model. Both models are coupled to identical CO 2 fluxes and lateral boundary conditions from CarbonTracker (CT2009 release). Statistical analyses were performed for two periods of 2007 using observed daily daytime average mole fractions of CO 2 to test the ability of these models to reproduce the observations and to infer possible causes of the discrepancies. TM5-CT2009 overestimates midsummer planetary boundary layer CO 2 for sites in the U.S. corn belt by 10 ppm. Weather Research and Forecasting (WRF)-CT2009 estimates diverge from the observations with similar magnitudes, but the signs of the differences vary from site to site. The modeled mole fractions are highly correlated with the observed seasonal cycle (r ≥ 0.7) but less correlated in the growing season, where weather-related changes in CO 2 dominate the observed variability. Spatial correlations in residuals from TM5-CT2009 are higher than WRF-CT2009 perhaps due to TM5's coarse horizontal resolution and shallow vertical mixing. Vertical mixing appears to have influenced CO 2 residuals from both models. TM5-CT2009 has relatively weak vertical mixing near the surface limiting the connection between local CO 2 surface fluxes and boundary layer. WRF-CT2009 has stronger vertical mixing that may increase the connections between local surface fluxes and the boundary layer.

Joint inverse estimation of fossil fuel and biogenic CO2 fluxes in an urban environment: An observing system simulation experiment to assess the impact of multiple uncertainties

“…Multiple elements in the atmospheric transport model (e.g. parameterization schemes, boundary and initial conditions, and spatial resolution) complicate the assessment of transport errors (Isaac et al, 2014). Evaluation and minimization of transport errors can be achieved by improving model parameterizations (Sarmiento et al, 2017) and by assimilating site-specific meteorological observations .…”

“…Top-down methods infer quantitative information on surface CO 2 fluxes from variations in atmospheric CO 2 concentrations through inverse modeling with atmospheric tracer transport models (Ciais et al, 2011), and may include isotope composition measurements to identify fossil fuel sources (Levin et al, 2003;Miller et al, 2012;Turnbull et al, 2015;Basu et al, 2016). Uncertainties in atmospheric transport models (Peylin et al, 2002;Lauvaux et al, 2009;Peylin et al, 2011;Isaac et al, 2014), limited density of atmospheric measurements (Gerbig et al, 2009;Turner et al, 2016) and uncertainties in prior fluxes (Peylin et al, 2005;Carouge et al, 2010;Lauvaux et al, 2016) all constitute sources of error in this method (Engelen et al, 2002).…”

Diagnostic methods for atmospheric inversions of long-lived greenhouse gases

“…The spatial and temporal autocorrelation of posterior errors can also be used to inform model setup (Díaz Isaac et al, 2014) or to assess the identifiability of underlying fluxes .…”

“…More broadly, evaluation against all types of independent atmospheric observations provides an additional window into the degree to which estimated fluxes capture key features of the atmospheric signal, such as the seasonal cycle, latitudinal gradients, or regional patterns of concentrations (e.g., Zhang et al, 2014;Jiang et al, 2014;Díaz Isaac et al, 2014;Pandey et al, 2016;Liu and Bowman, 2016;Johnson et al, 2016).…”

“…The implementation of more than one transport model in a forward run can also shed light on consistent differences in the ability to represent observed atmospheric concentration signals, seasonal cycles of mixing ratios, or vertical profiles (e.g., Pillai et al, 2012;Díaz Isaac et al, 2014).…”