Abstract
Seasonal ice cover is one of the important attributes of lakes at middle and high latitude regions. The annual freeze-up and break-up dates and the durations of ice cover (i.e., lake ice phenology) are sensitive to the weather and climate, and hence can be used as an indicator of climate variability and change. The Calibrated Enhanced Resolution Brightness Temperature (CETB) dataset available from the National Snow and Ice Data Center (NSIDC) provides an alternate source of passive microwave brightness temperature (TB) measurements for the determination of lake ice phenology on a 3.125 km grid. This study used Scanning Multi-channel Microwave Radiometer (SMMR), Special Sensor Microwave Image (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) data from the CETB dataset to extract the ice phenology for 56 lakes across the Northern Hemisphere from 1979 to 2019. According to the differences in TB between lake ice and open water, a threshold algorithm based on the moving t test method was applied to determine the lake ice status for grids located at least 6.25 km away from the lake shore, and the ice phenology dates for each lake were then extracted. When ice phenology could be extracted from more than one satellite over overlapping periods, results from the satellite offering the largest number of observations were prioritized. The lake ice phenology results showed strong agreement with an existing product derived from Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR-2) data (2002 to 2015), with mean absolute errors of ice dates ranging from 2 to 4 days. Compared to near-shore in-situ observations, the lake ice results, while different in terms of spatial coverage, still showed overall consistencies. The produced lake ice record also displayed significant consistencies when compared to a historical record of annual maximum ice cover of the Laurentian Great Lakes of North America. From 1979 to 2019, the average complete freezing duration and ice cover duration for lakes forming a complete ice cover on an annual basis were 153 and 161 days, respectively. The lake ice phenology dataset – a new climate data record (CDR) – will provide valuable information to the user community about the changing ice cover of lakes in the last four decades. The dataset is available at https://www.pangaea.de/tok/c8fc0eab3d30777fc38979ad514217b6b7e86a65 (Cai et al., 2021).
Highlights
According to the differences in TB between lake ice and open water, a threshold algorithm based on the moving t test method was applied to determine the lake ice status for grids located at least 6.25 km away from the lake shore, and the ice phenology dates for each lake were extracted
There are two main error sources of lake ice phenology derived from passive microwave data: 1) the periodically missing data caused by the polar orbit operation mode of passive microwave satellites; and 2) errors associated with the extraction process of lake ice phenology
The main error sources of the lake ice phenology extracted from Scanning Multi-channel Microwave Radiometer (SMMR), Sensor Microwave Image (SSM/I) & SMMIS data were attributed to the periodically missing data at middle and low latitudes caused by the polar orbit operation mode and the mixed pixels caused by the original coarse spatial resolution of the satellite acquisitions
Summary
Climate change is one of the major challenges facing humanity. New technologies and methods are urgently needed to monitor and quantify the rapid changes in climate at the regional and global scale. Lakes are closely tied to climate conditions and are characterized by many important parameters for long-term monitoring of climate change, including the coverage and duration of 1. Lake ice phenology describes the seasonal evolution of ice 40 cover, including the freeze-up and break-up dates, and ice cover duration (Duguay et al, 2015). The coverage and duration of lake ice affect human activities such as transportation, fishing, and winter recreational activities (Brown and Duguay, 2010; Prowse et al, 2011; Du et al, 2017; Sharma et al, 2019). Changing climate conditions in the cold season will alter the temporal and spatial characteristics of mass (such as precipitation and 45 suspended particles) and energy (such as solar radiation and atmospheric heat) input into the lake, affecting the freeze-thaw processes of lake ice (Mishra et al, 2011). Changes in the timing of freeze-up and break-up will cause sudden changes in lake surface properties (such as albedo and roughness) and affect the exchange between lakes and the atmosphere
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