Arctic Sea Ice Volume decline uncertainties reduced using Passive Microwave-based Sea Ice Thickness

Abstract Arctic sea ice volume (SIV) is a key climate indicator and memory source in sea ice predictions and projections, yet suffering from large observational and model uncertainty. Here, we test whether passive-microwave (PMW) data constrain the long-term evolution of Arctic SIV, as recently hypothesized. We find many commonalities in Arctic SIV changes from a PMW sea ice thickness (SIT) 1992-2020 time series reconstructed with a machine-learning algorithm trained on lidar altimetry, and the reference PIOMAS reanalysis: relatively low differences in SIV mean (45%), SIV trends (17%), and phased variability (r2=0.55). Key to reduced differences is the consistent evolution of many SIV contributors: seasonal and perennial ice coverage, their SIT contrast, whereas perennial SIT provides the largest remaining uncertainty source. We argue that PMW includes useful SIT information, reducing SIV uncertainty. We foresee progress from sea ice reanalyses combining dynamical models and data assimilation of PMW SIT, in addition to the already assimilated PWM sea ice concentration.