Abstract. Global distributions of atmospheric ammonia (NH 3 ) measured with satellite instruments such as the Infrared Atmospheric Sounding Interferometer (IASI) contain valuable information on NH 3 concentrations and variability in regions not yet covered by ground-based instruments. Due to their large spatial coverage and (bi-)daily overpasses, the satellite observations have the potential to increase our knowledge of the distribution of NH 3 emissions and associated seasonal cycles. However the observations remain poorly validated, with only a handful of available studies often using only surface measurements without any vertical information. In this study, we present the first validation of the IASI-NH 3 product using ground-based Fourier transform infrared spectroscopy (FTIR) observations. Using a recently developed consistent retrieval strategy, NH 3 concentration profiles have been retrieved using observations from nine Network for the Detection of Atmospheric Composition Change (NDACC) stations around the world between 2008 and 2015. We demonstrate the importance of strict spatiotemporal collocation criteria for the comparison. Large differences in the regression results are observed for changing intervals of spatial criteria, mostly due to terrain characteristics and the short lifetime of NH 3 in the atmosphere. The seasonal variations of both datasets are consistent for most sites. Correlations are found to be high at sites in areas with considerable NH 3 levels, whereas correlations are lower at sites with low atmospheric NH 3 levels close to the detection limit of the IASI instrument. A combination of the observations Published by Copernicus Publications on behalf of the European Geosciences Union. These results give an improved estimate of the IASI-NH3 product performance compared to the previous upper-bound estimates (−50 to +100 %).

Abstract. We investigate Arctic tropospheric composition using ground-based Fourier transform infrared (FTIR) solar absorption spectra, recorded at the Polar Environment Atmospheric Research Laboratory (PEARL, Eureka, Nunavut, Canada, 80 • 05 N, 86 • 42 W) and at Thule (Greenland, 76 • 53 N, −68 • 74 W) from 2008 to 2012. The target species, carbon monoxide (CO), hydrogen cyanide (HCN), ethane (C 2 H 6 ), acetylene (C 2 H 2 ), formic acid (HCOOH), and formaldehyde (H 2 CO) are emitted by biomass burning and can be transported from mid-latitudes to the Arctic.By detecting simultaneous enhancements of three biomass burning tracers (HCN, CO, and C 2 H 6 ), ten and eight fire events are identified at Eureka and Thule, respectively, within the 5-year FTIR time series. Analyses of Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model back-trajectories coupled with Moderate Resolution Imaging Spectroradiometer (MODIS) fire hotspot data, Stochastic Time-Inverted Lagrangian Transport (STILT) model footprints, and Ozone Monitoring Instrument (OMI) UV aerosol index maps, are used to attribute burning source regions and travel time durations of the plumes. By taking into account the effect of aging of the smoke plumes, measured FTIR enhancement ratios were corrected to obtain emission ratios and equivalent emission factors. The means of emission factors for extratropical forest estimated with the two FTIR data sets are 0.40 ± 0.21 g kg −1 for HCN, 1.24 ± 0.71 g kg −1 for C 2 H 6 , 0.34 ± 0.21 g kg −1 for C 2 H 2 , and 2.92 ± 1.30 g kg −1 for HCOOH. The emission factor for CH 3 OH estimated at Eureka is 3.44 ± 1.68 g kg −1 .To improve our knowledge concerning the dynamical and chemical processes associated with Arctic pollution from fires, the two sets of FTIR measurements were compared to the Model for OZone And Related chemical Tracers, version 4 (MOZART-4). Seasonal cycles and day-to-day variabilities were compared to assess the ability of the model to reproduce emissions from fires and their transport. Good agreement in winter confirms that transport is well implemented in the model. For C 2 H 6 , however, the lower wintertime concentration estimated by the model as compared to the FTIR observations highlights an underestimation of its emission. Results show that modeled and measured total columns are correlated (linear correlation coefficient r>0.6 for all gases except for H 2 CO at Eureka and HCOOH at Thule), but suggest a general underestimation of the concentrations in the model for all seven tropospheric species in the high Arctic.

We investigate Arctic tropospheric composition using ground-based Fourier transform infrared (FTIR) solar absorption spectra, recorded at the Polar Environment Atmospheric Research Laboratory (PEARL, Eureka, Nunavut, Canada, 80 • 05 N, 86 • 42 W) and at Thule (Greenland, 76 • 53 N, −68 • 74 W) from 2008 to 2012. The target species, carbon monoxide (CO), hydrogen cyanide (HCN), ethane (C 2 H 6 ), acetylene (C 2 H 2 ), formic acid (HCOOH), and formaldehyde (H 2 CO) are emitted by biomass burning and can be transported from mid-latitudes to the Arctic.By detecting simultaneous enhancements of three biomass burning tracers (HCN, CO, and C 2 H 6 ), ten and eight fire events are identified at Eureka and Thule, respectively, within the 5-year FTIR time series. Analyses of Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model back-trajectories coupled with Moderate Resolution Imaging Spectroradiometer (MODIS) fire hotspot data, Stochastic Time-Inverted Lagrangian Transport (STILT) model footprints, and Ozone Monitoring Instrument (OMI) UV aerosol index maps, are used to attribute burning source regions and travel time durations of the plumes. By taking into account the effect of aging of the smoke plumes, measured FTIR enhancement ratios were corrected to obtain emission ratios and equivalent emission factors. The means of emission factors for extratropical forest estimated with the two FTIR data sets are 0.40 ± 0.21 g kg −1 for HCN, 1.24 ± 0.71 g kg −1 for C 2 H 6 , 0.34 ± 0.21 g kg −1 for C 2 H 2 , and 2.92 ± 1.30 g kg −1 for HCOOH. The emission factor for CH 3 OH estimated at Eureka is 3.44 ± 1.68 g kg −1 .To improve our knowledge concerning the dynamical and chemical processes associated with Arctic pollution from fires, the two sets of FTIR measurements were compared to the Model for OZone And Related chemical Tracers, version 4 (MOZART-4). Seasonal cycles and day-to-day variabilities were compared to assess the ability of the model to reproduce emissions from fires and their transport. Good agreement in winter confirms that transport is well implemented in the model. For C 2 H 6 , however, the lower wintertime concentration estimated by the model as compared to the FTIR observations highlights an underestimation of its emission. Results show that modeled and measured total columns are correlated (linear correlation coefficient r>0.6 for all gases except for H 2 CO at Eureka and HCOOH at Thule), but suggest a general underestimation of the concentrations in the model for all seven tropospheric species in the high Arctic.

[1] A novel approach for calculating downwelling surface longwave (DSLW) radiation under all sky conditions is presented. The DSLW model (hereafter, DSLW/UMD v2) similarly to its predecessor, DSLW/UMD v1, is driven with a combination of Moderate Resolution Imaging Spectroradiometer (MODIS) level-3 cloud parameters and information from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim model. To compute the clear sky component of DSLW a two layer feed-forward artificial neural network with sigmoid hidden neurons and linear output neurons is implemented; it is trained with simulations derived from runs of the Rapid Radiative Transfer Model (RRTM). When computing the cloud contribution to DSLW, the cloud base temperature is estimated by using an independent artificial neural network approach of similar architecture as previously mentioned, and parameterizations. The cloud base temperature neural network is trained using spatially and temporally co-

[1] Recent satellite and re-analysis model results on downwelling surface shortwave (DSSW) radiation allow the investigation of its role in the Arctic sea ice anomalies. Using satellite based information we revisit claims that reduced cloudiness and enhanced DSSW are associated with the significant loss of sea ice during 2007. We account for the fact that the Arctic Ocean is not homogenous in terms of the characteristics of the sea ice anomalies. We separate the Arctic Ocean region according to the spatial distribution of the sea ice anomalies and investigate the impact of DSSW on the ice conditions accordingly. The region which exhibits the strongest signal during the unprecedented 2007 reduction in sea ice is identified as 120 E to 210 E. Our analysis shows that the lowest cloud amount and the highest accumulated amount of DSSW prior to the ice melt season (June)