Flood water has damaged an estimated 1,230 buildings in Fort McMurray, Alberta, including more than 1,000 homes and businesses in the city’s lower core fronting onto the Clearwater River, and 49 structures in the Taiga Nova industrial park bordering the Athabasca River.
Most estimates of global mean sea-level rise this century fall below 2 m. This quantity is comparable to the positive vertical bias of the principle digital elevation model (DEM) used to assess global and national population exposures to extreme coastal water levels, NASA’s SRTM. CoastalDEM is a new DEM utilizing neural networks to reduce SRTM error. Here we show – employing CoastalDEM—that 190 M people (150–250 M, 90% CI) currently occupy global land below projected high tide lines for 2100 under low carbon emissions, up from 110 M today, for a median increase of 80 M. These figures triple SRTM-based values. Under high emissions, CoastalDEM indicates up to 630 M people live on land below projected annual flood levels for 2100, and up to 340 M for mid-century, versus roughly 250 M at present. We estimate one billion people now occupy land less than 10 m above current high tide lines, including 230 M below 1 m.
The title of Eugene O’Neill’s 1939 noir epic on man’s need for self-deception could be the chyron for a recent article in the Proceedings of the National Academy of Sciences (PNAS) entitled “Ice Sheet Contributions to Future Sea Level Rise from Structured Expert Judgement” by J.L. Bamber, M. Oppenheimer, R.E. Kopp, W.P. Aspinall, and R.M. Cooke. The PNAS paper describes a structured expert judgment (SEJ) uncertainty quantification of ice sheets’ contribution to sea level rise (SLR) out to 2300 under +2°C and +5°C stabilization scenarios. Expanding on the methodology of Bamber and Aspinall’s groundbreaking 2013 study, the PNAS study again treats individual experts as testable statistical hypotheses, but this time, it targets upper-tail dependence between ice sheet processes. The result is higher median assessments and expanding uncertainties, especially in the upper tail, relative to the Fifth Assessment Report of the United Nations Intergovernmental Panel on Climate Change (IPCC AR5). Its potential for capturing a more nuanced and accurate picture of uncertainty, is precisely why Riskthinking.ai employs SEJ in the development of its scenarios tools.