Identifying source regions of air masses sampled at the tropical high-altitude site of Chacaltaya using WRF-FLEXPART and cluster analysis
- 1Institute for Atmospheric and Earth System Research /Physics, Faculty of Science, University of Helsinki, Helsinki, 00014, Finland
- 2Laboratory for Atmospheric Physics, Institute for Physics Research, Universidad Mayor de San Andrés, La Paz, Bolivia
- 3Department of Atmospheric and Oceanic Sciences, University of Maryland, College Park, MD, USA
- 4Institute of Physics, University of Sao Paulo, São Paulo, Brazil
- 5Federal University of Uberlândia, Agrarian Sciences Institute, Uberlândia, MG, Brazil
- 6Finnish Meteorological Institute, Helsinki, 00101, Finland
- 7Université Grenoble Alpes, CNRS, IRD, Grenoble INP, Institut des Géosciences de l’Environnement, Grenoble, France
- 8Leibniz Institute for Tropospheric Research, Permoserstr. 15, 04318 Leipzig, Germany
- 9Department of Environmental Science & Bolin Centre of Climate Research, Stockholm University, Stockholm 10691,Sweden
Abstract. Observations of aerosol and trace gases in the remote troposphere are vital to quantify background concentrations and identify long term trends in atmospheric composition on large spatial scales. Measurements made at high altitude are often used to study free tropospheric air however such high-altitude sites can be influenced by boundary layer air masses. Thus, accurate information on air mass origin and transport pathways to high altitude sites is required. Here we present a new method, based on the source-receptor relationship (SRR) obtained from backwards WRF-FLEXPART simulations and a k-means clustering approach, to identify source regions of air masses arriving at measurement sites. Our method is tailored to areas of complex terrain and to stations influenced by both local and long-range sources. We have applied this method to the Chacaltaya (CHC) GAW station (5240 m a.s.l.,16.35° S, 68.13° W) for the 6-month duration of the “Southern hemisphere high altitude experiment on particle nucleation and growth” (SALTENA) to identify where sampled air masses originate and to quantify the influence of the boundary layer and the free troposphere. A key aspect of our method is that it is probabilistic and for each observation time, more than one air mass (cluster) can influence the station and the percentage influence of each air mass can be quantified. This is in contrast to binary methods, which label each observation time as influenced either by boundary layer or free troposphere air masses. We find that on average, 9% of the air sampled at CHC, at any given observation time, has been in contact with the surface within 4 days prior to arriving at CHC, 24% of the air was located below 1.5 km above ground level and consequently, 76% of the measured air masses at CHC represent free tropospheric air. However, pure free-tropospheric influences are rare and often samples are concurrently influenced by both boundary-layer and free-tropospheric air masses. A clear diurnal cycle is present with very few air masses that have been in contact with the surface being detected at night. The 6-month analysis also shows that the most dominant air mass (cluster) originates in the Amazon and is responsible for 29% of the sampled air. Furthermore, short-range clusters (origins within 100 km of CHC) have high temporal frequency modulated by local meteorology driven by the diurnal cycle whereas the mid- and long-range clusters’ (>200 km) variability occurs on timescales governed by synoptic-scale dynamics. To verify the reliability of our method, in-situ sulfate observations from CHC are combined with the SRR clusters to correctly identify the (pre-known) source of the sulfate: the Sabancaya volcano located 400 km northwest from the station.
Diego Aliaga et al.
Diego Aliaga et al.
Source region clusters SRR timeseries https://doi.org/10.5281/zenodo.4539590
Diego Aliaga et al.
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