Comparison of Passive Microwave-Derived Early Melt Onset Records on Arctic Sea Ice

• Published in: Remote Sensing
• Main Authors: Angela C. Bliss, Jeffrey A. Miller, Walter N. Meier
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2017
• Subjects: Arctic; sea ice; melt; melt onset; passive microwave; Science; Q; Arctic Ocean; Barents Sea; Chukchi; laptev; Sea ice
• Online Access: https://doi.org/10.3390/rs9030199; https://doaj.org/article/e0e2f74770cd43f8b3c75b8f9d05cc67

Two long records of melt onset (MO) on Arctic sea ice from passive microwave brightness temperatures (Tbs) obtained by a series of satellite-borne instruments are compared. The Passive Microwave (PMW) method and Advanced Horizontal Range Algorithm (AHRA) detect the increase in emissivity that occurs when liquid water develops around snow grains at the onset of early melting on sea ice. The timing of MO on Arctic sea ice influences the amount of solar radiation absorbed by the ice–ocean system throughout the melt season by reducing surface albedos in the early spring. This work presents a thorough comparison of these two methods for the time series of MO dates from 1979 through 2012. The methods are first compared using the published data as a baseline comparison of the publically available data products. A second comparison is performed on adjusted MO dates we produced to remove known differences in inter-sensor calibration of Tbs and masking techniques used to develop the original MO date products. These adjustments result in a more consistent set of input Tbs for the algorithms. Tests of significance indicate that the trends in the time series of annual mean MO dates for the PMW and AHRA are statistically different for the majority of the Arctic Ocean including the Laptev, E. Siberian, Chukchi, Beaufort, and central Arctic regions with mean differences as large as 38.3 days in the Barents Sea. Trend agreement improves for our more consistent MO dates for nearly all regions. Mean differences remain large, primarily due to differing sensitivity of in-algorithm thresholds and larger uncertainties in thin-ice regions.