Effect of Modeled Pre-industrial Greenland Ice Sheet Surface Mass Balance Bias on Uncertainty in Sea Level Rise Projections in 2100

Download or read book Effect of Modeled Pre-industrial Greenland Ice Sheet Surface Mass Balance Bias on Uncertainty in Sea Level Rise Projections in 2100 written by Gail Ruth Gutowski and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Changes to ice sheet surface mass balance (SMB) are going to play a significant role in future sea level rise (SLR), particularly for the Greenland ice sheet. The Coupled Model Intercomparison Project Phase 5 (CMIP5) found that Greenland ice sheet (GIS) response to changes in SMB is expected to contribute 9 ± 4 cm to sea level by 2100 (Fettweis et al 2013), though other estimates suggest the possibility of an even larger response. Modern ice sheet geometry and surface velocities are common metrics for determining a model's predictability of future climate. However, care must be taken to robustly quantify prediction uncertainty because errors in boundary conditions such as SMB can be compensated by (and therefore practically inseparable from) errors in other aspects of the model, complicating calculations of total uncertainty. We find that SMB calculated using the Community Earth System Model (CESM) differs from established standards due to errors in the CESM SMB boundary condition. During the long ice sheet initialization process, small SMB errors such as these have an opportunity to amplify into larger uncertainties in GIS sensitivity to climate change. These uncertainties manifest themselves in ice sheet surface geometry changes, ice mass loss, and subsequent SLR. While any bias in SMB is not desirable, it is not yet clear how sensitive SLR projections are to boundary condition forcing errors. We explore several levels of SMB forcing bias in order to analyze their influence on future SLR. We evaluate ensembles of ice sheets forced by 4 different levels of SMB forcing error, covering a range of errors similar to SMB biases between CESM and RACMO SMB. We find that GIS SMB biases on the order of 1 m/yr result in 7.8 ± 3.4 cm SLR between 1850 and 2100, corresponding to 100% uncertainty at the 2[sigma] level. However, we find unexpected feedbacks between SMB and surface geometry in the northern GIS. We propose that the use of elevation classes may be incorrectly altering the feedback mechanisms in that part of the ice sheet.