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[1] Estimates of fossil fuel emissions on local to regional spatial scales and hourly to weekly temporal scales are increasingly useful in studies of the carbon cycle and mass and energy flow in cities. We used a tunable diode laser absorption spectrometer (TDL) to measure CO 2 mixing ratios and carbon isotope composition of CO 2 in order to estimate the contribution of gasoline versus natural gas combustion to atmospheric CO 2 in Salt Lake City. The results showed a pronounced diurnal pattern: the proportional contribution of natural gas combustion varied from 30-40% of total anthropogenic CO 2 during evening rush hour to 60-70% at pre-dawn. In addition, over a warming period of several days, the proportional contribution of natural gas combustion decreased with air temperature, likely related to decreased residential heating. These results show for the first time that atmospheric measurements may be used to infer patterns of energy and fuel usage on hourly to daily time scales. Citation: Pataki,

Nitrous oxide (N2O) emissions within the US Corn Belt have been previously estimated to be 200–900% larger than predictions from emission inventories, implying that one or more source categories in bottom‐up approaches are underestimated. Here we interpret hourly N2O concentrations measured during 2010 and 2011 at a tall tower using a time‐inverted transport model and a scale factor Bayesian inverse method to simultaneously constrain direct and indirect agricultural emissions. The optimization revealed that both agricultural source categories were underestimated by the Intergovernmental Panel on Climate Change (IPCC) inventory approach. However, the magnitude of the discrepancies differed substantially, ranging from 42 to 58% and from 200 to 525% for direct and indirect components, respectively. Optimized agricultural N2O budgets for the Corn Belt were 319 ± 184 (total), 188 ± 66 (direct), and 131 ± 118 Gg N yr−1 (indirect) in 2010, versus 471 ± 326, 198 ± 80, and 273 ± 246 Gg N yr−1 in 2011. We attribute the interannual differences to varying moisture conditions, with increased precipitation in 2011 amplifying emissions. We found that indirect emissions represented 41–58% of the total agricultural budget, a considerably larger portion than the 25–30% predicted in bottom‐up inventories, further highlighting the need for improved constraints on this source category. These findings further support the hypothesis that indirect emissions are presently underestimated in bottom‐up inventories. Based on our results, we suggest an indirect emission factor for runoff and leaching ranging from 0.014 to 0.035 for the Corn Belt, which represents an upward adjustment of 1.9–4.6 times relative to the IPCC and is in agreement with recent bottom‐up field studies.

Data assimilation, or the fusion of a mathematical model with ecological data, is rapidly expanding knowledge of ecological systems across multiple spatial and temporal scales. As the amount of ecological data available to a broader audience increases, quantitative proficiency with data assimilation tools and techniques will be an essential skill for ecological analysis in this data-rich era. We provide a data assimilation primer for the novice user by (1) reviewing data assimilation terminology and methodology, (2) showcasing a variety of data assimilation studies across the ecological, environmental, and atmospheric sciences with the aim of gaining an understanding of potential applications of data assimilation, and (3) applying data assimilation in specific ecological examples to determine the components of net ecosystem carbon uptake in a forest and also the population dynamics of the mayfly (Hexagenia limbata, Serville). The review and examples are then used to provide guiding principles to newly proficient data assimilation practitioners.

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