Surface energy balance on the Antarctic plateau as measured with an automatic weather station during 2014

AWS data during 2014 collected at PANDA-N station, on the East Antarctica Plateau, are analysed. Net Short Wave Radiation ( Q SWR ), net Long Wave Radiation ( Q LWR ), sensible ( Q H ), latent ( Q L ) and subsurface or ground ( Q G ) heat fluxes are computed. Annual averages for Q SWR , Q LWR , Q H...

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Bibliographic Details
Main Authors: Mingu, D, Agrawal, A, Heil, P, Diyi, Y
Format: Article in Journal/Newspaper
Language:English
Published: Zhongguo Jidi Yanjiusuo,Polar Research Institute of China 2019
Subjects:
Earth Sciences
Atmospheric Sciences
Atmospheric Dynamics
Antarctic
The Antarctic
East Antarctica
Advances in Polar Science
Antarc*
Antarctica
Polar Science
Online Access:https://doi.org/10.13679/j.advps.2018.0050
http://ecite.utas.edu.au/137629
Description
Summary:AWS data during 2014 collected at PANDA-N station, on the East Antarctica Plateau, are analysed. Net Short Wave Radiation ( Q SWR ), net Long Wave Radiation ( Q LWR ), sensible ( Q H ), latent ( Q L ) and subsurface or ground ( Q G ) heat fluxes are computed. Annual averages for Q SWR , Q LWR , Q H , Q L and Q G of 19.65, −49.16, 26.40, −0.77 and 3.86 Wm −2 were derived based on an albedo value of 0.8. Q SWR and Q H are the major sources of heat gain to the surface and Q LWR is the major component of heat loss from the surface. An iterative method is used to estimate surface temperature in this paper; surface temperature of snow/ice is gradually increased or decreased, thereby changing longwave radiation, sensible, latent and subsurface heat fluxes, so that the net energy balance becomes zero. Mass loss due to sublimation at PANDA-N station for 2014 is estimated to be 12.18 mm w.e.a −1 and mass gain due to water vapour deposition is estimated to be 3.58 mm w.e.a −1 . Thus the net mass loss due to sublimation/deposition is 8.6 mm w.e.a −1 . This study computes surface energy fluxes using a model, instead of direct measurements. Also there are missing data especially for wind speed, though 2 m air temperature data is almost continuously available throughout the year. The uncertainties of albedo, wind speed and turbulent fluxes cause the most probable error in monthly values of Q LWR , Q H , Q L , Q G and surface temperature of about 4%, 20%, 50%, 11% and 0.74 K respectively.

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