Abstract
Abstract. Due to its remote location and extreme weather conditions, atmospheric in situ measurements are rare in the Southern Ocean. As a result, aerosol–cloud interactions in this region are poorly understood and remain a major source of uncertainty in climate models. This, in turn, contributes substantially to persistent biases in climate model simulations such as the well-known positive shortwave radiation bias at the surface, as well as biases in numerical weather prediction models and reanalyses. It has been shown in previous studies that in situ and ground-based remote sensing measurements across the Southern Ocean are critical for complementing satellite data sets due to the importance of boundary layer and low-level cloud processes. These processes are poorly sampled by satellite-based measurements and are often obscured by multiple overlying cloud layers. Satellite measurements also do not constrain the aerosol–cloud processes very well with imprecise estimation of cloud condensation nuclei. In this work, we present a comprehensive set of ship-based aerosol and meteorological observations collected on the 6-week Southern Ocean Ross Sea Marine Ecosystem and Environment voyage (TAN1802) voyage of RV Tangaroa across the Southern Ocean, from Wellington, New Zealand, to the Ross Sea, Antarctica. The voyage was carried out from 8 February to 21 March 2018. Many distinct, but contemporaneous, data sets were collected throughout the voyage. The compiled data sets include measurements from a range of instruments, such as (i) meteorological conditions at the sea surface and profile measurements; (ii) the size and concentration of particles; (iii) trace gases dissolved in the ocean surface such as dimethyl sulfide and carbonyl sulfide; (iv) and remotely sensed observations of low clouds. Here, we describe the voyage, the instruments, and data processing, and provide a brief overview of some of the data products available. We encourage the scientific community to use these measurements for further analysis and model evaluation studies, in particular, for studies of Southern Ocean clouds, aerosol, and their interaction. The data sets presented in this study are publicly available at https://doi.org/10.5281/zenodo.4060237 (Kremser et al., 2020).
Highlights
The Southern Ocean is the cloudiest region on Earth and is distant from major anthropogenic sources of aerosol (Haynes et al, 2011)
We present a new data set of atmospheric and seawater measurements that were collected during the 6-week Southern Ocean Ross Sea Marine Ecosystem and Environment voyage (TAN1802) from Wellington, New Zealand, to the Ross Sea, Antarctica, in 2018
Incomplete understanding of aerosol–cloud interactions over the Southern Ocean leads to a misrepresentation of aerosol and clouds processes in climate models
Summary
The Southern Ocean is the cloudiest region on Earth and is distant from major anthropogenic sources of aerosol (Haynes et al, 2011). Hyder et al (2018) recently identified that 70 % of the sea surface temperature biases observed in model simulations, performed in support of the Coupled Model Intercomparison Project 5 (CMIP5), can be attributed to the models not representing clouds and their properties correctly These errors occur because climate models simulate too little cloud cover and contain biases in cloud albedo over the Southern Ocean (Bodas-Salcedo et al, 2012; Schuddeboom et al, 2019), resulting in projections that underestimate the reflected solar radiation at the top of the atmosphere (TOA; Haynes et al, 2011) and overestimate downwelling solar radiation at the ocean surface. Reducing the uncertainty in the simulation of aerosol– cloud interactions requires detailed observational data sets against which models can be evaluated This process is hindered over the Southern Ocean by the lack of ground-based and in situ measurements. As the TAN1802 voyage was only the second research cruise probing OCS in the Southern Ocean (the first one is described in Staubes and Georgii, 1993) and the first with sufficiently high temporal resolution to thoroughly test and improve the existing models, we include the OCS measurements in the data set accompanying this paper
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