181 The DACAPO-PESO Field Experiment: Filling an Observational White Spot on the Globe By Longterm Ground-Based Remote Sensing Observations of Clouds and Aerosols in the Midlatitudes of South America

Wednesday, 11 July 2018
Regency A/B/C (Hyatt Regency Vancouver)
Patric Seifert, Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany; and H. Kalesse, M. Radenz, J. Bühl, A. Ansmann, H. Baars, R. Engelmann, B. Barja, and F. Zamorano

The southern midlatitudes are a region in which long-term ground-based combined radar-lidar observations of clouds and aerosols are sparse. Up to date, only about 15 months of ground-based supersite observations (containing at least lidar, cloud radar, and microwave radiometer) are available, all of them obtained in the Southern Ocean near Australia and New Zealand. We will thus conduct a one-year field experiment in Punta Arenas(53°S, 71°W), Chile to fill an observational gap for the midlatitudes of South America.

The field experiment Dynamics, Aerosol, Cloud And Precipitation Observations in the Pristine Environment of the Southern Ocean (DACAPO-PESO, https://dacapo.tropos.de) is a joint initiative of TROPOS, University of Magallanes (Punta Arenas, Chile), and University of Leipzig. The project is an endorsed member of the Year of Polar Prediction (YOPP) consortium. The experiment is dedicated to finding an explanation for the observed evidence of strong regional contrasts in heterogeneous ice formation efficiency that has been reported by independent studies (e.g., Kanitz et al., 2011; Zhang et al., 2017). Punta Arenas is located in the southern-hemispheric midlatitudes at the southernmost tip of the South-American continental land mass. Air masses from the closest upwind landmasses of Australia and New Zealand, which are located 10° further north, need to travel 8000 km over the Pacific Ocean before reaching Punta Arenas. The southern midlatitudes are a key region for the Earth’s climate and a source for uncertainties in climate modelling (Trenberth and Fasullo, 2010). Regarding aerosols it is one of the most pristine regions mostly influenced by marine emissions, as all major anthropogenic emission sources are far away (Hamilton et al., 2014). The low concentration of INP (Vergara-Temprado et al, 2017) is considered to diminish the percentage of ice containing clouds (Kanitz et al. 2011).

Core facility for the campaign will be the Leipzig Aerosol and Cloud Remote Observations System (LACROS) of the Leibniz Institute for Tropospheric Research (TROPOS). This instrument suite will be enhanced toward more detailed cloud and precipitation observations by additionally deploying a 24-GHz K-band micro rain radar (Metek MRR-Pro) and a 94-GHz FMCW Doppler Cloud Radar, the latter being provided by University of Leipzig. All instruments will be operated continuously for at least 12 months, beginning in November 2018, i.e., austral spring.

Lidar observations, including aerosol source attribution focusing on the analysis of profiles of aerosol optical and microphysical properties will be based on existing methodologies applied within PollyNet (Baars et al., 2016; Baars et al., 2017) which are currently extended toward the retrieval of INP and CCN concentrations (Mamouri and Ansmann, 2016).

While spaceborne lidar-radar synergistic measurements with CloudSat and CALIPSO provide fruitful data for multi-year coverage, their larger footprint and the fact that they cannot provide high-temporal continuous observation over a specific site, inhibits their usage for microphysical process studies which will be addressed by our ground-based remote-sensing. One major objective will be to derive detailed cloud thermodynamic state statistics over an entire seasonal cycle in the Southern midlatitudes which will be linked to the retrieved INP and CCN concentrations.

Specifically, we want to enhance the understanding of cold precipitation formation mechanisms - either through aggregation or riming - in mixed-phase clouds in the pristine Southern midlatitudes. This objective will be addressed by combining state-of-the-art multi-parameter cloud radar observations based on polarimetry, radar Doppler spectra features, and multiple wavelengths. Combining polarimetry with spectral techniques will likely improve the applicability of cloud radar observations to identify riming in clouds. Especially for the detection of small hydrometeors such as cloud droplets, cloud radars have enhanced sensitivity, thus helping to detect liquid layers which are a prerequisite for riming. For that purpose, we will make slanted linear depolarization (SLDR) polarimetric observations with a Ka-band radar a versatile technique for classification of hydrometeors in mixed-phase clouds. We will also develop methods for the identification of (supercooled) liquid layers based on Ka-band and W-band profiling cloud radar Doppler spectra analysis of reflectivity (see Kalesse et al., 2016) and linear depolarization ratio (LDR). The microphysical fingerprinting will then be assessed based on the observed evolution of dominant hydrometeor shape in the profile as derived from the SLDR observations, the existence/absence of supercooled liquid layers as characterized from the Doppler spectra analysis as well as the gradients of radar moments of each found Doppler peak.

The observed response of mixed-phase cloud processes to aerosol perturbations will be put in context to a model sensitivity study of simulations with a cloud-system-resolving (1 km) version of the ICON-NWP for the entire observation periods for regional domains around the observations sites in Cyprus and Chile that are accompanied by radar forward simulations.

References:

Baars, H., et al, 2016, Atm. Chem. and Phys., doi:10.5194/acp-16-5111-2016

Baars, H., et al, 2017, Atm. Meas. Tech., doi: 10.5194/amt-10-3175-2017

Hamilton, D.S., et al, 2014, PNAS, doi:10.1073/pnas.1415440111

Kalesse, H., et al., 2016, Atm. Chem. and Phys., doi: 10.5194/acp-16-2997-2016

Kanitz, T., et al., 2011, Geophys. Res. Lett, doi: 10.1029/2011GL048532

Mamouri, R. E., and Ansmann, A., 2016, Atm. Chem. and Phys., doi:10.5194/acp-16-5905-2016

Trenberth, K.E., and Fasullo, J.T., 2010, J. Climate, doi: 10.1175/2009JCLI3152.1

Vergara-Temprado, J., et al., 2017, Atmos. Chem. Phys., doi:10.5194/acp-17-3637-2017

Zhang, D., et al., 2017, Atmos. Chem. Phys. Discuss., doi: 10.5194/acp-2017-927

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