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Synoptic variations of atmospheric CO2 produced by interactions between weather and surface fluxes are investigated mechanistically and quantitatively in midlatitude and tropical regions using continuous in-situ CO2 observations in North America, South America and Europe and forward chemical transport model simulations with the Parameterized Chemistry Transport Model. Frontal CO2 climatologies show consistently strong, characteristic frontal CO2 signals throughout the midlatitudes of North America and Europe. Transitions between synoptically identifiable CO2 air masses or transient spikes along the frontal boundary typically characterize these signals. One case study of a summer cold front shows CO2 gradients organizing with deformational flow along weather fronts, producing strong and spatially coherent variations. In order to differentiate physical and biological controls on synoptic variations in midlatitudes and a site in Amazonia, a boundary layer budget equation is constructed to break down boundary layer CO2 tendencies into components driven by advection, moist convection, and surface fluxes. This analysis suggests that, in midlatitudes, advection is dominant throughout the year and responsible for 60–70% of day-to-day variations on average, with moist convection contributing less than 5%. At a site in Amazonia, vertical mixing, in particular coupling between convective transport and surface CO2 flux, is most important, with advection responsible for 26% of variations, moist convection 32% and surface flux 42%. Transport model sensitivity experiments agree with budget analysis. These results imply the existence of a recharge-discharge mechanism in Amazonia important for controlling synoptic variations of boundary layer CO2, and that forward and inverse simulations should take care to represent moist convective transport. Due to the scarcity of tropical observations at the time of this study, results in Amazonia are not generalized for the tropics, and future work should extend analysis to additional tropical locations.

Earth system processes exhibit complex patterns across time, as do the models that seek to replicate these processes. Model output may or may not be significantly related to observations at different times and on different frequencies. Conventional model diagnostics provide an aggregate view of model-data agreement, but usually do not identify the time and frequency patterns of model misfit, leaving unclear the steps required to improve model response to environmental drivers that vary on characteristic frequencies. Wavelet coherence can quantify the times and frequencies at which models and measurements are significantly different. We applied wavelet coherence to interpret the predictions of twenty ecosystem models from the North American Carbon Program (NACP) Site-Level Interim Synthesis when confronted with eddy covariance-measured net ecosystem exchange (NEE) from ten ecosystems with multiple years of available data. Models were grouped into classes with similar approaches for incorporating phenology, the calculation of NEE, and the inclusion of foliar nitrogen (N). Models with prescribed, rather than prognostic, phenology often fit NEE observations better on annual to interannual time scales in grassland, wetland and agricultural ecosystems. Models that calculate NEE as net primary productivity (NPP) minus heterotrophic respiration (HR) rather than gross ecosystem productivity (GPP) minus ecosystem respiration (ER) fit better on annual time scales in grassland and wetland ecosystems, but models that calculate NEE as GPP – ER were superior on monthly to seasonal time scales in two coniferous forests. Models that incorporated foliar nitrogen (N) data were successful at capturing NEE variability on interannual (multiple year) time scales at Howland Forest, Maine. Combined with previous findings, our results suggest that the mechanisms driving daily and annual NEE variability tend to be correctly simulated, but the magnitude of these fluxes is often erroneous, suggesting that model parameterization must be improved. Few NACP models correctly predicted fluxes on seasonal and interannual time scales where spectral energy in NEE observations tends to be low, but where phenological events, multi-year oscillations in climatological drivers, and ecosystem succession are known to be important for determining ecosystem function. Mechanistic improvements to models must be made to replicate observed NEE variability on seasonal and interannual time scales.

Human conversion of natural ecosystems to croplands modifies not only the exchange of water and energy between the surface and the atmosphere, but also carbon fluxes. To investigate the impacts of crops on carbon fluxes and resulting atmospheric CO2 concentrations in the mid-continent region of the United States, we coupled a crop-specific phenology and physiology scheme for corn, soybean and wheat to the coupled ecosystem–atmosphere model SiB3–RAMS. Using SiBcrop–RAMS improved carbon fluxes at the local scale and had regional impacts, decreasing the spring uptake and increasing the summer uptake over the mid-continent. The altered fluxes changed the mid-continent atmospheric CO2 concentration field at 120 m compared to simulations without crops: concentrations increased in May and decreased >20 ppm during July and August, summer diurnal cycle amplitudes increased, synoptic variability correlations improved and the gradient across the mid-continent region increased. These effects combined to reduce the squared differences between the model and high-precision tower CO2 concentrations by 20%. Synoptic transport of the large-scale N–S gradient caused significant day-to-day variability in concentration differences measured between the towers. This simulation study shows that carbon exchange between crops and the atmosphere significantly impacts regional CO2 fluxes and concentrations.DOI: 10.1111/j.1600-0889.2010.00485.x

Conventionally, measurements of carbon isotopes in atmospheric CO2 (δ13CO2) have been used to partition fluxes between terrestrial and ocean carbon pools. However, novel analytical approaches combined with an increase in the spatial extent and frequency of δ13CO2 measurements allow us to conduct a global analysis of δ13CO2 variability to infer the isotopic composition of source CO2 to the atmosphere (δs). This global analysis yields coherent seasonal patterns of isotopic enrichment. Our results indicate that seasonal values of δs are more highly correlated with vapor pressure deficit (r=0.404) than relative humidity (r=0.149). We then evaluate two widely used stomatal conductance models and determine that Leuning Model, which is primarily driven by vapor pressure deficit is more effective globally at predicting δs (RMSE = 1.7 ‰) than the Ball-Berry model, which is driven by relative humidity (RMSE = 2.8) ‰. Thus stomatal conductance on a global scale may be more sensitive to changes in vapor pressure deficit than relative humidity. This approach highlights a new application of using δ13CO2 measurements to test global models.