Abstract

Abstract. A Lagrangian particle dispersion model, the FLEXible PARTicle dispersion chemical transport model (FLEXPART CTM), is used to simulate global three-dimensional fields of trace gas abundance. These fields are constrained with surface observation data through nudging, a data assimilation method, which relaxes model fields to observed values. Such fields are of interest to a variety of applications, such as inverse modelling, satellite retrievals, radiative forcing models and estimating global growth rates of greenhouse gases. Here, we apply this method to methane using 6 million model particles filling the global model domain. For each particle, methane mass tendencies due to emissions (based on several inventories) and loss by reaction with OH, Cl and O(1D), as well as observation data nudging were calculated. Model particles were transported by mean, turbulent and convective transport driven by 1∘×1∘ ERA-Interim meteorology. Nudging is applied at 79 surface stations, which are mostly included in the World Data Centre for Greenhouse Gases (WDCGG) database or the Japan–Russia Siberian Tall Tower Inland Observation Network (JR-STATION) in Siberia. For simulations of 1 year (2013), we perform a sensitivity analysis to show how nudging settings affect modelled concentration fields. These are evaluated with a set of independent surface observations and with vertical profiles in North America from the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL), and in Siberia from the Airborne Extensive Regional Observations in SIBeria (YAK-AEROSIB) and the National Institute for Environmental Studies (NIES). FLEXPART CTM results are also compared to simulations from the global Eulerian chemistry Transport Model version 5 (TM5) based on optimized fluxes. Results show that nudging strongly improves modelled methane near the surface, not only at the nudging locations but also at independent stations. Mean bias at all surface locations could be reduced from over 20 to less than 5 ppb through nudging. Near the surface, FLEXPART CTM, including nudging, appears better able to capture methane molar mixing ratios than TM5 with optimized fluxes, based on a larger bias of over 13 ppb in TM5 simulations. The vertical profiles indicate that nudging affects model methane at high altitudes, yet leads to little improvement in the model results there. Averaged from 19 aircraft profile locations in North America and Siberia, root mean square error (RMSE) changes only from 16.3 to 15.7 ppb through nudging, while the mean absolute bias increases from 5.3 to 8.2 ppb. The performance for vertical profiles is thereby similar to TM5 simulations based on TM5 optimized fluxes where we found a bias of 5 ppb and RMSE of 15.9 ppb. With this rather simple model setup, we thus provide three-dimensional methane fields suitable for use as boundary conditions in regional inverse modelling as a priori information for satellite retrievals and for more accurate estimation of mean mixing ratios and growth rates. The method is also applicable to other long-lived trace gases.

Highlights

  • Three-dimensional (3-D) global concentration fields of different trace gases, such as methane (CH4), carbon dioxide (CO2) or carbon monoxide (CO), are of interest for many applications

  • The performance for vertical profiles is thereby similar to Transport Model version 5 (TM5) simulations based on TM5 optimized fluxes where we found a bias of 5 ppb and root mean square error (RMSE) of 15.9 ppb

  • Methane starts to increase in the planetary boundary layer, because nudging occurs at surface stations, and over time the influence of nudging extends to higher altitudes

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Summary

Introduction

Three-dimensional (3-D) global concentration fields of different trace gases, such as methane (CH4), carbon dioxide (CO2) or carbon monoxide (CO), are of interest for many applications They can, for instance, inform regional air quality policies and verify climate policies via a comparison of global emissions and atmospheric concentration growth rates, and are required as input for many types of applications like radiation simulations or satellite retrievals. Regional inverse modelling of greenhouse gas emissions often requires global 3-D greenhouse gas concentrations as boundary conditions (Thompson and Stohl, 2014) These and many other applications explain the popularity of 3-D concentration fields produced with different tools (e.g. CarbonTracker; Peters et al, 2007). An average increase by 10 ppb in the methane background leads to a 20 % decrease in Swiss national methane emissions (Stephan Henne, unpublished results)

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