Analysis of temporal and spatial variability of atmospheric CO<sub>2</sub> concentration within Paris from the GreenLITE™ laser imaging experiment

Abstract

Abstract. In 2015, the Greenhouse gas Laser Imaging Tomography Experiment (GreenLITE™) measurement system was deployed for a long-duration experiment in the center of Paris, France. The system measures near-surface atmospheric CO2 concentrations integrated along 30 horizontal chords ranging in length from 2.3 to 5.2 km and covering an area of 25 km2 over the complex urban environment. In this study, we use this observing system together with six conventional in situ point measurements and the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and two urban canopy schemes (Urban Canopy Model – UCM; Building Effect Parameterization – BEP) at a horizontal resolution of 1 km to analyze the temporal and spatial variations in CO2 concentrations within the city of Paris and its vicinity for the 1-year period spanning December 2015 to November 2016. Such an analysis aims at supporting the development of CO2 atmospheric inversion systems at the city scale. Results show that both urban canopy schemes in the WRF-Chem model are capable of reproducing the seasonal cycle and most of the synoptic variations in the atmospheric CO2 point measurements over the suburban areas as well as the general corresponding spatial differences in CO2 concentration that span the urban area. However, within the city, there are larger discrepancies between the observations and the model results with very distinct features during winter and summer. During winter, the GreenLITE™ measurements clearly demonstrate that one urban canopy scheme (BEP) provides a much better description of temporal variations and horizontal differences in CO2 concentrations than the other (UCM) does. During summer, much larger CO2 horizontal differences are indicated by the GreenLITE™ system than both the in situ measurements and the model results, with systematic east–west variations.

Sensitivity to the sources of uncertainties in the modeling of atmospheric CO<sub>2</sub> concentration within and in the vicinity of Paris

“…We also used the surface analysis nudging and observation nudging options to assimilate the National Centers for Environmental Prediction (NCEP) operational global upper-air (ds351.0) and surface (ds461.0) observation weather station data (https://rda.ucar.edu/datasets/ ds351.0/; https://rda.ucar.edu/datasets/ds461.0/, last access: 5 July 2021), which are described in more detail in Lian et al (2018). The biogenic CO 2 fluxes were calculated online in WRF-Chem by the diagnostic biosphere Vegetation Photosynthesis and Respiration Model (VPRM) (Mahadevan et al, 2008;Ahmadov et al, 2007Ahmadov et al, , 2009. The values of the four parameters (α, β, λ, and PAR0) for each vegetation category used by VPRM have been optimized against eddy covariance flux measurements over Europe collected during the Integrated Project CarboEurope-IP (http: //www.carboeurope.org/, last access: 5 July 2021).…”

Standoff Chemical Detection Using Laser Absorption Spectroscopy: A Review

“…Field tests show that an accuracy of source localization is better than 11 m on a 0.2-km 2 grid, while in the presence of ambient CO 2 concentrations and prevailing local wind. Field deployments demonstrate GreenLITE's capability for many applications ranging from continuous remote monitoring of ground carbon storage/sequestration facilities to the real-time measurement and assessment of subscale GHG events within complex open-air environments [61][62][63].…”

Performance of an open-path near-infrared measurement system for measurements of CO<sub>2</sub> and CH<sub>4</sub> during extended field trials

“…Desservettaz et al, 2019;Phillips et al, 2019) in an urban environment. More typically they have been applied on 100 m scales for quantification of area sources coupled with a tracer gas, micrometerological techniques or a backward lagrangian stochastic model (WINDTRAX) (Bai et al, 2018;Flesch et al, 2016;Jones et al, 2011;Laubach et al, 2013;Loh et al, 2009;Naylor et al, 2016;Phillips et al, 2016). OP-MIR has also been used to detect and quantify a point CO 2 and CH 4 source during a controlled release experiment (Cartwright et al, 2019;Feitz et al, 2018).…”