Comment on amt-2022-268

Abstract

Arctic amplification, the phenomenon that the Arctic is warming faster than the global mean, is still not fully understood. The Transregional Collaborative Research Centre TR 172 – Arctic Amplification: Climate Relevant Atmospheric and Surface Processes (AC)3 funded by the DFG (German research foundation) contribute towards this research topic. For the purpose of measuring aerosol components, a Fourier-Transform InfraRed spectrometer (FTS) for measuring downwelling emission since 2019 and a Raman-Lidar are operated at the AWIPEV research base in Ny-Ålesund, Spitsbergen (79° N, 12° E). To do aerosol retrieval using measurements from the FTS, a retrieval algorithm based on Line-by-Line Radiative Transfer Model and DIScrete Ordinate Radiative Transfer model (LBLDIS), is modified for different aerosol types (dust, sea salt, black carbon, and sulfate), aerosol optical thickness (AOT) and effective radius (Reff). Using Lidar measurement, an aerosol and cloud classification method is developed for providing basic information about the distribution of aerosols or clouds in the atmosphere and used as an indicator to do aerosols or clouds retrieval in FTS. Therefore, a two-instruments joint observation scheme is designed and is performing on the data measured from 2019 to present. In order to show this measurement technique in details, two case studies are selected, one is an aerosol-only case on the 10th of June 2020 and the another is a cloud-only case on 11th of June 2020. In the aerosol-only case, the retrieval results show that sulfate (τ900cm−1 =0.007 ± 0.0027) is the dominant aerosol during the whole day, followed by dust (τ900cm−1 =0.0039 ± 0.0029) and black carbon (τ900cm−1 =0.0017 ± 0.0007). Sea salt (τ900cm−1 =0.0012 ± 0.0002) shows the lowest AOT value as its weakest emission ability in infrared waveband. Such proportions of sulfate, dust and BC also show good agreement with Merra-2 reanalysis data. Besides, comparing with sun-photometer (AERONET), the daily variation of aerosol AOT retrieved from FTS is similar with that in sun-photometer. In the cloud-only case study, Lidar distinguishes the cloud signal from aerosols accurately, giving a very good information on the state of the atmosphere. For showing the importance of Lidar measurement in the retrieval of FTS, two versions of retrieval algorithm, one for cloud retrieval and another for aerosols retrieval are applied for gaining cloud parameters and aerosol parameters respectively. The result shows that without information from Lidar measurement, the signal of cloud is misunderstood and retrieved as four aerosols in FTS, which indicates that the combination of both measurements is necessary and helpful in our aerosol retrieval.