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

• Main Authors: Carter, Jeremy, Leeson, Amber, Orr, Andrew, Kittel, Christoph, van Wessem, Jan Melchior
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2022
• Subjects: article; Verlagsveröffentlichung; Antarctic; Antarc*; Antarctica; Ice Sheet; Ice Shelves
• Online Access: https://doi.org/10.5194/egusphere-2022-86; https://noa.gwlb.de/receive/cop_mods_00060622; https://egusphere.copernicus.org/preprints/egusphere-2022-86/egusphere-2022-86.pdf

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.