Atmospheric Methane and Carbon Dioxide Background Levels Computed by Modeling at Three WMO/GAW Stations in the Mediterranean Basin

There is an increasing concern over climate change and its environmental impacts. Effective greenhouse gases (GHG) monitoring and control strategies are of pivotal importance. Models such as STILT (Stochastic Time-Inverted Lagrangian Transport), statistical techniques, and experimental data analysis provide valuable tools for quantifying emissions and identifying greenhouse gas (GHG) tendencies. The Mediterranean basin is considered a global hotspot for air-quality and climate change: here, we combine experimental datasets of atmospheric methane (CH ₄ ) and carbon dioxide (CO ₂ ) with atmospheric transport models to present an atmospherically-based framework for monitoring GHG emissions. We applied methodologies, i.e., the Smoothed Minima (SM) and STILT, to extract background concentration data from the time series of atmospheric gases and identify measurements deemed representative of atmospheric background (GRD) levels. At the Lamezia Terme (Global Atmosphere Watch, GAW code: LMT), Capo Granitola (GAW code: CGR), and Lampedusa (GAW code: LMP) observation sites, GHG measurements were performed with specific calibration routines carried out using primary standards of calibration from the National Oceanic and Atmospheric Administration – Global Monitoring Laboratory (NOAA–GML), with secondary standards used to evaluate possible drifts and calibration factors stability. The first two are coastal stations and the third is an island station. At these sites, atmospheric CH ₄ and CO ₂ mole fractions can be evaluated at local and continental scales, in locations with specific Mediterranean climatic characteristics. This paper presents the variability of CH ₄ and CO ₂ in the central Mediterranean basin by analyzing hourly GHG concentrations over a 9-year period (2015–2023) for LMT, a 8-year period (2015–2022) for CGR, and a 19-year period (2006–2024) for CO ₂ and 5-year period (2020–2024) for CH ₄ at LMP. STILT provides 3-hourly results for methane and carbon dioxide concentrations that correlate well with surface measurements at LMT, CGR, and LMP. These analyses are aimed at relevant long-term datasets of GHG over southern Italy. This work would provide a useful contribution to comparing the observed concentrations of gases measured at three sites in the central Mediterranean with those predicted by models such as STILT. The results indicate good agreement between in situ measurements and modeling, and underline the importance of synergies between different institutions and methodologies. Compared to the CGR and LMP site, LMT has recorded higher levels of anthropogenic emissions in the area.

Graphical Abstract

Framework shows the variability of methane and carbon dioxide in the Mediterranean basin at three permanent World Meteorological Organization/Global Atmosphere Watch (WMO/GAW) stations in southern Italy: Lamezia Terme (GAW code: LMT), Capo Granitola (GAW code: CGR) and Lampedusa (GAW code: LMP). Accurate modeling of atmospheric transport is essential to address environmental concerns and to establish a quantitative link between observed gas distributions and surface emissions. In the present work, we used the Stochastic Time Inverted Lagrangian Transport (STILT) and the Smoothed Minima (SM) models. STILT simulates transport by following the time evolution of a particle ensemble, interpolating meteorological fields to the subgrid location of each particle. Both methods were used to extract background concentration data from the time series of atmospheric gases representative of the atmospheric background levels. The paper is an important contribution to the comparison between the observed and predicted by models concentrations of methane and carbon dioxide at three sites in the central Mediterranean. The STILT model datasets, validated at the three sites, show satisfactory results, with the exception of an overall underestimation in all comparisons (background and no-background). They demonstrate that the model can accurately estimate the CH ₄ and CO ₂ concentrations in the Mediterranean basin. Similar results are also obtained when comparing the SM and STILT background datasets. This last comparison indicates a good identification of the concentrations of the background gases by the models.