Preprints
https://doi.org/10.5194/tc-2022-192
https://doi.org/10.5194/tc-2022-192
 
19 Oct 2022
19 Oct 2022
Status: this preprint is currently under review for the journal TC.

Estimating surface melt in Antarctica from 1979 to 2022, using a statistically parameterized positive degree-day model

Yaowen Zheng1, Nicholas R. Golledge1, Alexandra Gossart1, Ghislain Picard2, and Marion Leduc-Leballeur3 Yaowen Zheng et al.
  • 1Antarctic Research Centre, Victoria University of Wellington, Wellington, New Zealand
  • 2Univ. Grenoble Alpes, CNRS, Institut des Géosciences de l’Environnement (IGE), UMR 5001, Grenoble, France
  • 3Institute of Applied Physics “Nello Carrara”, National Research Council, 50019 Sesto Fiorentino, Italy

Abstract. Surface melt is one of the primary drivers of ice shelf collapse in Antarctica. Surface melting is expected to increase in the future as the global climate continues to warm, because there is a statistically significant positive relationship between air temperature and melt. Enhanced surface melt will negatively impact the mass balance of the Antarctic Ice Sheet (AIS) and, through dynamic feedbacks, induce changes in global mean sea level (GMSL). However, current understanding of surface melt in Antarctica remains limited in past, present or future contexts. Continental-scale spaceborne observations of surface melt are limited to the satellite era (1979–present), meaning that current estimates of Antarctic surface melt are typically derived from surface energy balance (SEB) or positive degree-day (PDD) models. SEB models require diverse and detailed input data that are not always available and require considerable computational resources. The PDD model, by comparison, has fewer input and computational requirements and is therefor suited for exploring surface melt scenarios in the past and future. The use of PDD schemes for Antarctic melt has been less extensively explored than their application to surface melting of the Greenland Ice Sheet, particularly in terms of a spatially-varying parameterization. Here, we construct a PDD model, force it only with 2-m air temperature reanalysis data, and parameterize it by minimizing the error with respect to satellite observations and SEB model outputs over the period 1979 to 2022. We compare the spatial and temporal variability of surface melt from our PDD model over the last 43 years with that of satellite observations and SEB simulations. We find that the PDD model can generally capture the same spatial and temporal surface melt patterns. Although there were at most four years over/under- estimation on ice shelf regions in the epoch, these discrepancies reduce when considering the whole AIS. With the limitations discussed, we suggest that an appropriately parameterized PDD model can be a valuable tool for exploring Antarctic surface melt beyond the satellite era.

Yaowen Zheng et al.

Status: open (until 14 Dec 2022)

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Yaowen Zheng et al.

Yaowen Zheng et al.

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Short summary
Positive Degree Day (PDD) schemes are widely used in many Antarctic numerical ice sheet models. However, the PDD approach has not been systematically explored for its application to Antarctica. We make use of a PDD model, then we use this model to provide a new time series of surface melt amount covering the whole of Antarctica for the last four decades. We suggest that an appropriately parameterized PDD model can be a valuable tool for exploring Antarctic surface melt beyond the satellite era.