Analysis of CO<sub>2</sub>, CH<sub>4</sub>, and CO surface and column concentrations observed at Réunion Island by assessing WRF-Chem simulations

Abstract

Abstract. Réunion Island is situated in the Indian Ocean and holds one of the very few atmospheric observatories in the tropical Southern Hemisphere. Moreover, it hosts experiments providing both ground-based surface and column observations of CO2, CH4, and CO atmospheric concentrations. This work presents a comprehensive study of these observations made in the capital Saint-Denis and at the high-altitude Maïdo Observatory. We used simulations of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), in its passive tracer option (WRF-GHG), to gain more insight to the factors that determine the observed concentrations. Additionally, this study provides an evaluation of the WRF-GHG performance in a region of the globe where it has not yet been applied. A comparison of the basic meteorological fields near the surface and along atmospheric profiles showed that WRF-GHG has decent skill in reproducing these meteorological measurements, especially temperature. Furthermore, a distinct diurnal CO2 cycle with values up to 450 ppm was found near the surface in Saint-Denis, driven by local anthropogenic emissions, boundary layer dynamics, and accumulation due to low wind speed at night. Due to an overestimation of local wind speed, WRF-GHG underestimates this nocturnal buildup. At Maïdo, a similar diurnal cycle is found but with much smaller amplitude. There, surface CO2 is essentially driven by the surrounding vegetation. The hourly column-averaged mole fractions of CO2 (XCO2) of WRF-GHG and the corresponding TCCON observations were highly correlated with a Pearson correlation coefficient of 0.90. These observations represent different air masses to those near the surface; they are influenced by processes from Madagascar, Africa, and further away. The model shows contributions from fires during the Southern Hemisphere biomass burning season but also biogenic enhancements associated with the dry season. Due to a seasonal bias in the boundary conditions, WRF-GHG fails to accurately reproduce the CH4 observations at Réunion Island. Furthermore, local anthropogenic fluxes are the largest source influencing the surface CH4 observations. However, these are likely overestimated. Furthermore, WRF-GHG is capable of simulating CO levels on Réunion Island with a high precision. As to the observed CO column (XCO), we confirmed that biomass burning plumes from Africa and elsewhere are important for explaining the observed variability. The in situ observations at the Maïdo Observatory can characterize both anthropogenic signals from the coastal regions and biomass burning enhancements from afar. Finally, we found that a high model resolution of 2 km is needed to accurately represent the surface observations. At Maïdo an even higher resolution might be needed because of the complex topography and local wind patterns. To simulate the column Fourier transform infrared (FTIR) observations on the other hand, a model resolution of 50 km might already be sufficient.

Mercury in the free troposphere and bidirectional atmosphere–vegetation exchanges – insights from Maïdo mountain observatory in the Southern Hemisphere tropics

“…the sum of vegetation and soil fluxes, with their relative contributions being unknown. In addition, our estimate assumes that GEM concentrations at Maïdo are not strongly affected by anthropogenic emissions downslope, similar to what has been reported for CO and CO 2 (mean anthropogenic contribution at noon < 7 ppbv and < 0.2 ppm, respectively; Callewaert et al, 2022).…”

“…5), a long-lived (Archer et al, 2009) greenhouse gas importantly taken up by vegetation, especially during daytime (Black and Clanton, 1973). Indeed, diurnal CO 2 variation at Maïdo is mostly driven by vegetation-gas exchange (Callewaert et al, 2022). Group 1 mean diurnal GEM variation also correlates significantly with the diurnal variation of isoprene (r = 0.95, p 0.01), a short-lived (∼ 1 h during daytime) VOC mostly emitted by terrestrial vegetation under sunlight and heat stress (Guenther et al, 1993;Pacifico et al, 2009).…”

“…In contrast to GEM, the application of the LFT filter had little effect on CO and O 3 concentrations, indicating that concentration differences between BL and LFT are much less pronounced for these compounds than for GEM. The strong CO enhancement between September and November is likely due to increased biomass burning in the SH during that time of the year (Edwards et al, 2006;Callewaert et al, 2022). While it is likely that LFT GEM concentrations in SON are biomass burning influenced, the seasonal variations of LFT GEM and LFT CO are clearly different, especially after February 2018, when CO remains low while GEM increases, again suggesting that the seasonal variation of Maïdo LFT GEM is likely dominated by other drivers than biomass burning.…”

Mercury in the Free Troposphere and Bidirectional Atmosphere-Vegetation Exchanges – Insights from Maïdo Mountain Observatory in the Southern Hemisphere Tropics

“…For data belonging to Group 1, mean diurnal GEM variation anticorrelates significantly (Pearson: r = -0.98, p << 500 0.01) with the diurnal variation of carbon dioxide (CO2; see Figure 5), a long-lived (Archer et al, 2009) greenhouse gas importantly taken up by vegetation, especially during daytime (Black and Clanton, 1973). Indeed, diurnal CO2 variation at Maïdo is mostly driven by vegetation gas exchange (Callewaert et al, 2022). Group 1 mean diurnal GEM variation also correlates significantly with the diurnal variation of isoprene (r = 0.95, p << 0.01), a shortlived (~1 hour during daytime) VOC mostly emitted by terrestrial vegetation under sunlight and heat stress 505 (Guenther et al, 1993;Pacifico et al, 2009).…”

Supplementary material to "Mercury in the Free Troposphere and Bidirectional Atmosphere-Vegetation Exchanges – Insights from Maïdo Mountain Observatory in the Southern Hemisphere Tropics"

“…we considered the position of FLEXPART-AROME particles up to 8 hours into the past), while we integrated the reemission-dependent part of Equation S4 over the last 3 hours before arrival (see Equation S5below). We only considered the last 3 hours before arrival for photo-reemission as the vegetated surfaces responsible for the observed isoprene and CO2 diurnal cycles appear to lie relatively close to the station (Verreyken et al, 2021;Callewaert et al, 2022)…”