Sensitivity Analysis of Arctic Sea Ice Extent Trends and Statistical Projections Using Satellite Data

Abstract

An ice-free Arctic summer would have pronounced impacts on global climate, coastal habitats, national security, and the shipping industry. Rapid and accelerated Arctic sea ice loss has placed the reality of an ice-free Arctic summer even closer to the present day. Accurate projection of the first Arctic ice-free summer year is extremely important for business planning and climate change mitigation, but the projection can be affected by many factors. Using an inter-calibrated satellite sea ice product, this article examines the sensitivity of decadal trends of Arctic sea ice extent and statistical projections of the first occurrence of an ice-free Arctic summer. The projection based on the linear trend of the last 20 years of data places the first Arctic ice-free summer year at 2036, 12 years earlier compared to that of the trend over the last 30 years. The results from a sensitivity analysis of six commonly used curve-fitting models show that the projected timings of the first Arctic ice-free summer year tend to be earlier for exponential, Gompertz, quadratic, and linear with lag fittings, and later for linear and log fittings. Projections of the first Arctic ice-free summer year by all six statistical models appear to converge to the 2037 ± 6 timeframe, with a spread of 17 years, and the earliest first ice-free Arctic summer year at 2031.

Sensitivity of Arctic Sea Ice Extent to Sea Ice Concentration Threshold Choice and Its Implication to Ice Coverage Decadal Trends and Statistical Projections

“…Dev. In comparison to the similar study done by Peng et al (2018), where a 15% threshold was used, the models calibrated with the full time series and the last 30 year time periods result in FIASY estimates that are slightly later and with smaller spreads in this analysis. The difference in this paper is that the full time series is 1979-2017, rather than 1979-2015, and the last 30 years is 1988-2017, rather than 1986-2015.…”

“…Similar to the analysis done in Peng et al (2018), where a threshold choice of 15% was used, we perform statistical model calibration and selection with data from SIE annual minimums derived from differing thresholds to determine the impact of threshold choice on optimal model selection along with the FIASY [19]. Using an updated version of the same long-term, consistent time series of sea ice data, we compare six commonly used statistical models for this type of application.…”

What Do Global Climate Models Tell Us about Future Arctic Sea Ice Coverage Changes?

“…Rapid and accelerated Arctic sea ice loss has brought the possibility of a nearly ice-free Arctic summer well into our near future (e.g., [1][2][3][4]). An Arctic ice-free state is defined as when the total Arctic sea ice extent (SIE) falls below one million square kilometers (10 6 km 2 ) [1].…”

“…By using sea ice extent predictions from six statistical models that were curve-fitted with satellite observational data from 1979-2015, Peng et al [4] demonstrated that the predicted FIASYs are converging around the year 2037, with a margin of error of seven years and with the earliest FIASY predicted to be in 2031. A similar conclusion was obtained by Wang and Overland [1,2], who used a subset of coupled climate models that participated in the Coupled Model Intercomparison Project (CMIP) Phase 3 and Phase 5 (CMIP3 and CMIP5, respectively).…”

“…In this paper, the performance of these 12 models in terms of simulating and projecting monthly Arctic sea ice extents is further evaluated by using a long-term, consistent satellite-based sea ice time series. A sensitivity analysis is then carried out by utilizing the same approach as in [4] but by using sea ice projections from the 12 CMIP5 models instead of the observed ice data. The goal in using only these 12 climate models that have simulated Arctic sea ice extent reasonably well is to reduce the uncertainty in projected future Arctic sea ice changes and therefore improve the quality of our analysis.…”