Continuous observations of the surface energy budget and meteorology over the Arctic sea ice during MOSAiC

Cox, Christopher; Shupe, Matthew D.; Persson, P. Ola G.; Solomon, Amy; Fairall, C. W.; Ayers, Thomas E.; Blomquist, Byron; Brooks, Ian M.; Costa, Daniel P.; Grachev, A. A.; Gottas, Daniel J.; Hutchings, Jennifer K.; Kutchenreiter, Mark; Leach, J.; Morris, Sara; Morris, V. R.; Osborn, Jackson; Pezoa, Sergio; Preußer, Andreas; Riihimaki, Laura D.; Uttal, Taneil

doi:10.1038/s41597-023-02415-5

Cited by 25 publications

(33 citation statements)

References 81 publications

(119 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The conventional meteorological and turbulence data are collected from a meteorological tower of 10‐m height installed over the sea‐ice surface during the MOSAiC expedition (Cox et al., 2021, 2023). This tower was located 300–600 m from the research vessel Polarstern (Knust, 2017), which served as the base of operations for the expedition.…”

Section: Methodsmentioning

confidence: 99%

“…The interpolated sounding (Jensen et al., 2019) and precipitation (Wang et al., 2019) data are available from the US Department of Energy Atmospheric Radiation Measurement Program archive at http://dx.doi.org/10.5439/1095316 and http://dx.doi.org/10.5439/1779709, respectively. The MOSAiC surface flux and other meteorological data are available at the Arctic Data Center at http://dx.doi.org/10.18739/A2VM42Z5F (Cox et al., 2021) and http://dx.doi.org/10.18739/A2PV6B83F (Cox et al., 2023).…”

Section: Data Availability Statementmentioning

confidence: 99%

See 1 more Smart Citation

Atmospheric Turbulent Intermittency Over the Arctic Sea‐Ice Surface During the MOSAiC Expedition

et al. 2023

View full text Add to dashboard Cite

Turbulent motions in the Arctic stable boundary layer are characterized by intermittency, but they are rarely investigated due to limited observations, in particular over the sea‐ice surface. In the present study, we explore the characteristics of turbulent intermittency over the Arctic sea‐ice surface using data collected during the Multidisciplinary drifting Observation for the Study of Arctic Climate expedition from October 2019 to September 2020. We first develop a new algorithm, which performs well in identifying the spectral gap over the Arctic sea‐ice surface. Then the characteristics of intermittency are investigated. It is found that the strength of intermittency increases under the conditions of light surface wind speed, small surface wind speed gradient, and strong surface air temperature gradient. The momentum flux, sensible heat flux, and latent heat flux calculated by raw eddy‐covariance fluctuations are overestimated by 3%, 10%, and 24%, respectively, because submesoscale motions are included. Furthermore, the characteristics of the atmospheric boundary layer structure under various intermittency conditions reveal that strong low‐level jets are favorable to surface turbulent motions that result in weak intermittency, while strong temperature inversions above the surface layer suppress surface turbulent motions and lead to strong intermittency.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Data Availability Statementmentioning

confidence: 99%

Atmospheric Turbulent Intermittency Over the Arctic Sea‐Ice Surface During the MOSAiC Expedition

et al. 2023

View full text Add to dashboard Cite

show abstract

“…For the snow‐air interface temperature we use skin temperature calculated based on infrared thermometer data measured at the Met City location within the CO and from three additional stations in the DN (with average distances to the vessel between 10 and 23 km) (C. Cox et al., 2023a, 2023b, 2023c, 2023d; Herrmannsdörfer et al., 2023).…”

Section: Datamentioning

confidence: 99%

“…The meteorological observations used for WSP and ocean temperature data are available at PANGAEA via https://doi.pangaea.de/10.1594/PANGAEA.935264, https://doi.org/10.1594/PANGAEA.935263 and https://doi.org/10.1594/PANGAEA.935265 (Schmithüsen, Raeke, & Kieser, 2021; Schmithüsen, Rohleder, & Hausen, 2021; Schmithüsen, Schröter, & Wenzel, 2021). The infrared thermometer data is available at the Arctic Data Center via https://arcticdata.io/catalog/view/doi:10.18739/A2PV6B83F, https://arcticdata.io/catalog/view/doi:10.18739/A2FF3M18K, https://arcticdata.io/catalog/view/doi:10.18739/A25X25F0P, and https://arcticdata.io/catalog/view/doi:10.18739/A2XD0R00S (C. Cox et al., 2023a, 2023b, 2023c, 2023d). …”

Section: Data Availability Statementmentioning

confidence: 99%

“…The infrared thermometer data is available at the Arctic Data Center via https://arcticdata.io/catalog/view/doi:10.18739/A2PV6B83F, https://arcticdata.io/catalog/view/doi:10.18739/A2FF3M18K, https://arcticdata.io/catalog/view/doi:10.18739/A25X25F0P, and https://arcticdata.io/catalog/view/doi:10.18739/A2XD0R00S (C. Cox et al., 2023a, 2023b, 2023c, 2023d).…”

Section: Data Availability Statementmentioning

confidence: 99%

See 1 more Smart Citation

Modeling Snow and Ice Microwave Emissions in the Arctic for a Multi‐Parameter Retrieval of Surface and Atmospheric Variables From Microwave Radiometer Satellite Data

Rückert,

Huntemann,

Tonboe

et al. 2023

Earth and Space Science

View full text Add to dashboard Cite

Monitoring surface and atmospheric parameters—like water vapor—is challenging in the Arctic, despite the daily Arctic‐wide coverage of spaceborne microwave radiometer data. This is mainly due to the difficulties in characterizing the sea ice surface emission: sea ice and snow microwave emission is high and highly variable. There are very few data sets combining relevant in situ measurements with co‐located remote sensing data, which further complicates the development of accurate retrieval algorithms. Here, we present a multi‐parameter retrieval based on the inversion of a forward model for both, atmosphere and surface, for non‐melting conditions. The model consists of a layered microwave emission model of snow and ice. Since snow scattering and emission effects, as well as temperature gradients, are taken into account, a high variability in brightness temperatures can be simulated. For ocean regions and the atmosphere existing parameterized forward models are used. By using optimal estimation, the forward model can be inverted allowing for the simultaneous and consistent retrieval of nine variables: integrated water vapor, liquid water path, sea ice concentration, multi‐year ice fraction, snow depth, snow‐ice interface temperature and snow‐air interface temperature as well as sea‐surface temperature and wind speed (over open ocean). In addition, the method provides retrieval uncertainty estimates for each retrieved parameter. To evaluate the forward model as well as the retrieval, we use the extensive data sets acquired during the year‐long Arctic expedition Multidisciplinary drifting Observatory for the Study of Arctic Climate (2019–2020) as a reference.

show abstract

Surface Turbulent Fluxes From the MOSAiC Campaign Predicted by Machine Learning

Cummins,

Guemas,

Cox

et al. 2023

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

Reliable boundary‐layer turbulence parametrizations for polar conditions are needed to reduce uncertainty in projections of Arctic sea ice melting rate and its potential global repercussions. Surface turbulent fluxes of sensible and latent heat are typically represented in weather/climate models using bulk formulae based on the Monin‐Obukhov Similarity Theory, sometimes finely tuned to high stability conditions and the potential presence of sea ice. In this study, we test the performance of new, machine‐learning (ML) flux parametrizations, using an advanced polar‐specific bulk algorithm as a baseline. Neural networks, trained on observations from previous Arctic campaigns, are used to predict surface turbulent fluxes measured over sea ice as part of the recent MOSAiC expedition. The ML parametrizations outperform the bulk at the MOSAiC sites, with RMSE reductions of up to 70 percent. We provide a plug‐in Fortran implementation of the neural networks for use in models.

show abstract

Continuous observations of the surface energy budget and meteorology over the Arctic sea ice during MOSAiC

Cited by 25 publications

References 81 publications

Atmospheric Turbulent Intermittency Over the Arctic Sea‐Ice Surface During the MOSAiC Expedition

Atmospheric Turbulent Intermittency Over the Arctic Sea‐Ice Surface During the MOSAiC Expedition

Modeling Snow and Ice Microwave Emissions in the Arctic for a Multi‐Parameter Retrieval of Surface and Atmospheric Variables From Microwave Radiometer Satellite Data

Surface Turbulent Fluxes From the MOSAiC Campaign Predicted by Machine Learning

Contact Info

Product

Resources

About