Mesoscale observations of temperature and salinity in the Arctic Transpolar Drift: a high-resolution dataset from the MOSAiC Distributed Network

• Published in: Earth System Science Data
• Main Authors: Hoppmann, Mario, Kuznetsov, Ivan, Fang, Ying-Chih, Rabe, Benjamin
• Format: Text
• Language: English
• Published: 2022
• Subjects: Arctic; Arctic Ocean; Sea ice
• Online Access: https://doi.org/10.5194/essd-14-4901-2022; https://essd.copernicus.org/articles/14/4901/2022/

Measurements targeting mesoscale and smaller-scale processes in the ice-covered part of the Arctic Ocean are sparse in all seasons. As a result, there are significant knowledge gaps with respect to these processes, particularly related to the role of eddies and fronts in the coupled ocean–atmosphere–sea ice system. Here we present a unique observational dataset of upper ocean temperature and salinity collected by a set of buoys installed on ice floes as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network. The multi-sensor systems, each equipped with five temperature and salinity recorders on a 100 m long inductive modem tether, drifted together with the main MOSAiC ice camp through the Arctic Transpolar Drift between October 2019 and August 2020. They transmitted hydrographic in situ data via the iridium satellite network at 10 min intervals. While three buoys failed early due to ice dynamics, five of them recorded data continuously for 10 months. A total of four units were successfully recovered in early August 2020, additionally yielding internally stored instrument data at 2 min intervals. The raw data were merged, processed, quality controlled, and validated using independent measurements also obtained during MOSAiC. Compilations of the raw and processed datasets are publicly available at https://doi.org/10.1594/PANGAEA.937271 ( Hoppmann et al. , 2021 i ) and https://doi.org/10.1594/PANGAEA.940320 ( Hoppmann et al. , 2022 i ) , respectively. As an important part of the MOSAiC physical oceanography program, this unique dataset has many synergies with the manifold co-located observational datasets and is expected to yield significant insights into ocean processes and to contribute to the validation of high-resolution numerical simulations. While this dataset has the potential to contribute to submesoscale process studies, this paper mainly highlights selected preliminary findings on mesoscale processes.