Preprints
https://doi.org/10.5194/egusphere-2022-86
https://doi.org/10.5194/egusphere-2022-86
 
12 Apr 2022
12 Apr 2022
Status: this preprint is open for discussion.

Variability in Antarctic Surface Climatology Across Regional Climate Models and Reanalysis Datasets

Jeremy Carter1, Amber Leeson2, Andrew Orr3, Christoph Kittel4, and Jan Melchior van Wessem5 Jeremy Carter et al.
  • 1Department of Mathematics and Statistics, Lancaster University, Lancaster, United Kingdom
  • 2Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom
  • 3British Antarctic Survey, High Cross, Madingley Road, Cambridge, UK
  • 4Laboratory of Climatology, Department of Geography, SPHERES, University of Liège, Liège, Belgium
  • 5Institute for Marine and Atmospheric Research Utrecht, Utrecht University, Utrecht, Netherlands

Abstract. Regional climate models (RCMs) and reanalysis datasets provide valuable information for assessing the vulnerability of ice shelves to collapse over Antarctica, which is important for 2100 global sea level rise estimates. Within this context, this paper examines variability in snowfall, near-surface air temperature and melt across products from the MetUM, RACMO and MAR RCMs, as well as the ERA-Interim and ERA5 reanalysis datasets. Seasonal and trend decomposition using Loess (STL) is applied to split the monthly time series at each model grid-cell into trend, seasonal and residual components. Significant, systematic differences between outputs are shown for all variables in the mean and seasonal/monthly standard deviations, occurring at both large and fine spatial scales across Antarctica. It is suggested that differences in the atmospheric dynamics, parametrisation, tuning and surface schemes between models together contribute more significantly to large-scale variability than differences in the driving data, resolution, domain specification, ice sheet mask, digital elevation model and boundary conditions. Despite significant systematic differences, high temporal correlations are found for snowfall and near-surface air temperature across all products at fine spatial scales. For melt, only moderate correlation exists at fine spatial scales between different RCMs and low correlation between RCM and reanalysis outputs. Root mean square deviations (RMSDs) between all outputs in the monthly time series for each variable are shown to be significant at fine spatial scales relative to the magnitude of annual deviations. Correcting for systematic differences results in significant reductions of RMSDs, suggesting the importance of observations and further development of bias-correction techniques.

Jeremy Carter et al.

Status: open (until 07 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Jeremy Carter et al.

Data sets

Variability in Antarctic Surface Climatology Across Regional Climate Models and Reanalysis Datasets Carter, Jeremy ; Leeson, Amber; Orr, Andrew; Kittel, Christoph; van Wessem, Melchior https://doi.org/10.5281/zenodo.6367850

Model code and software

Jez-Carter/Antarctica_Climate_Variability: 0.1.0 Carter, Jeremy https://doi.org/10.5281/zenodo.6375205

Jeremy Carter et al.

Viewed

Total article views: 167 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
113 49 5 167 2 2
  • HTML: 113
  • PDF: 49
  • XML: 5
  • Total: 167
  • BibTeX: 2
  • EndNote: 2
Views and downloads (calculated since 12 Apr 2022)
Cumulative views and downloads (calculated since 12 Apr 2022)

Viewed (geographical distribution)

Total article views: 159 (including HTML, PDF, and XML) Thereof 159 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 16 May 2022
Download
Short summary
Climate models provide valuable information for studying processes such as the collapse of ice shelves over Antarctica, which impacts estimates of sea level rise. This paper examines variability across climate simulations over Antarctica for fields including snowfall, temperature and melt. Significant, systematic differences between outputs are found, occurring at both large and fine spatial scales across Antarctica. Results are important for future impact assessments and model development.