Current model capabilities for simulating black carbon and sulfate concentrations in the Arctic atmosphere: a multi-model evaluation using a comprehensive measurement data set

Abstract

International audienceThe concentrations of sulfate, black carbon (BC) and other aerosols in the Arctic are characterized by high values in late winter and spring (so-called Arctic Haze) and low values in summer. Models have long been struggling to capture this seasonality and especially the high concentrations associated with Arctic Haze. In this study, we evaluate sulfate and BC concentrations from eleven different models driven with the same emission inventory against a comprehensive pan-Arctic measurement data set over a time period of two years (2008–2009). The set of models consisted of one Lagrangian particle dispersion model, four chemistry-transport models (CTMs), one atmospheric chemistry-weather forecast model and five chemistry-climate models (CCMs), of which two were nudged to meteorological analyses and three were running freely. The measurement data set consisted of surface measurements of equivalent BC (eBC) from five stations (Alert, Barrow, Pallas, Tiksi and Zeppelin), elemental carbon (EC) from Station Nord and Alert and aircraft measurements of refractory BC (rBC) from six different campaigns. We find that the models generally captured the measured eBC/rBC and sulfate concentrations quite well, compared to past comparisons. However, the aerosol seasonality at the surface is still too weak in most models. Concentrations of eBC and sulfate averaged over three surface sites are underestimated in winter/spring in all but one model (model means for January-March underestimated by 59 and 37% for BC and sulfate, respectively), whereas concentrations in summer are overestimated in the model mean (by 88 and 44% for July–September), but with over- as well as underestimates present in individual models. The most pronounced eBC underestimates, not included in the above multi-site average, are found for the station Tiksi in Siberia where the measured annual mean eBC concentration is three times higher than the average annual mean for all other stations. This suggests an underestimate of BC sources in Russia in the emission inventory used. Based on the campaign data, biomass burning was identified as another cause of the modelling problems. For sulfate, very large differences were found in the model ensemble, with an apparent anti-correlation between modeled surface concentrations and total atmospheric columns. There is a strong correlation between observed sulfate and eBC concentrations with consistent sulfate/eBC slopes found for all Arctic stations, indicating that the sources contributing to sulfate and BC are similar throughout the Arctic and that the aerosols are internally mixed and undergo similar removal. However, only three models reproduced this finding, whereas sulfate and BC are weakly correlated in the other models. Overall, no class of models (e.g., CTMs, CCMs) performed better than the others and differences are independent of model resolution

Do contemporary (1980–2015) emissions determine the elemental carbon deposition trend at Holtedahlfonna glacier, Svalbard?

“…Note that the timing of observed spring peaks (Arctic haze) varies from year to year. This corroborates with several multi-model studies (Shindell et al, 2008;Koch et al, 2009;Eckhardt et al, 2015) showing that atmospheric models are usually not able to simulate the seasonality of BC in the Arctic precisely, typically underestimating the Arctic haze season occurring during the winter and early spring. A more detailed discussion on the uncertainties of the model and input driving the runs is presented in Sect.…”

“…Furthermore, the global bottom-up emission inventories are based on assumptions of emission factors (BC amount released from certain burned fuel using a given technology) and estimations of used fuel amounts (e.g. Bond et al, 2007), but recently the accuracy of the inventories on the quantity and spatial allocation of BC emissions has been questioned particularly for the Arctic (Eckhardt et al, 2015;Huang et al, 2015;Winiger et al, 2017). Possible underestimation of anthropogenic (e.g.…”

Effects of long-range aerosol transport on the microphysical properties of low-level liquid clouds in the Arctic

“…Studies that have used FLEXPART CO concentration fields (χ CO ) have found satisfactory agreement between model output and measurements (Stohl, 2006;Paris et al, 2009;Hirdman et al, 2010;Sodemann et al, 2011;Stohl et al, 2013Stohl et al, , 2015Eckhardt et al, 2015). In the Alaskan Arctic for the day of 18 April 2008, Warneke et al (2009) described a slope of 0.9 for a linear fit between FLEXPART model output of χ CO and airborne measurements of CO with a least-squares correlation coefficient of 0.63.…”

Global anthropogenic emissions of particulate matter including black carbon

“…unpaved roads. The ECLIPSE emission datasets have been used in several regional and global atmospheric transport and climate model simulations (AMAP, 2015;Eckhardt et al, 2015;Gadhavi et al, 2015;Lund et al, 2014;Quennehen et al, 2016;Stohl et al, 2013Stohl et al, , 2015Wobus et al, 2016;Yttri et al, 2014) where various aspects of several particulate matter species were addressed. The emissions developed during ECLIPSE also served as the basis for a recently published global particulate number estimate (Paasonen et al, 2016).…”