Abstract
Abstract. Existing Lagrangian particle dispersion models are capable of establishing source–receptor relationships by running either forward or backward in time. For receptor-oriented studies such as interpretation of "point" measurement data, backward simulations can be computationally more efficient by several orders of magnitude. However, to date, the backward modelling capabilities have been limited to atmospheric concentrations or mixing ratios. In this paper, we extend the backward modelling technique to substances deposited at the Earth's surface by wet scavenging and dry deposition. This facilitates efficient calculation of emission sensitivities for deposition quantities at individual sites, which opens new application fields such as the comprehensive analysis of measured deposition quantities, or of deposition recorded in snow samples or ice cores. This could also include inverse modelling of emission sources based on such measurements. We have tested the new scheme as implemented in the Lagrangian particle dispersion model FLEXPART v10.2 by comparing results from forward and backward calculations. We also present an example application for black carbon concentrations recorded in Arctic snow.
Highlights
Lagrangian particle dispersion models (LPDMs) are popular tools for simulating the dispersion of trace gases, aerosols or radionuclides in the atmosphere (e.g. Stohl et al, 1998; Lin et al, 2003; Witham et al, 2007; Stein et al, 2015)
We present an extension of the LPDM FLEXPART that allows such calculations and test its performance for both dry and wet deposition by performing thorough consistency tests between forward and backward runs.We present a representative application for black carbon concentrations recorded in high latitude snow samples
To test the implemented algorithm we modelled 24 h of dispersion, dry and wet deposition after an emission of black carbon (BC) in one grid cell over 1 h
Summary
Lagrangian particle dispersion models (LPDMs) are popular tools for simulating the dispersion of trace gases, aerosols or radionuclides in the atmosphere (e.g. Stohl et al, 1998; Lin et al, 2003; Witham et al, 2007; Stein et al, 2015). When M is known, the influence of changing the sources xi (e.g. using different emission scenarios) on the receptor values yl can be calculated without re-running the dispersion model It is not relevant whether M is obtained from forward or backward simulations and one can conveniently choose the computationally more efficient and/or more accurate option. The model output of a backward simulation can be multiplied directly with the emissions (in kg m−3 s−1) in order to obtain the desired concentration, mixing ratio or deposition quantity at the receptor All this is identical to the previous treatment in FLEXPART (Stohl et al, 2005), except for the addition of the deposition options
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