A Long-Term Passive Microwave Snowoff Record for the Alaska Region 1988–2016

Caleb G Pan,Jinyang Du,John S Kimball,Peter B Kirchner

doi:10.3390/rs12010153

Caleb G Pan, Jinyang Du + Show 2 more

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https://doi.org/10.3390/rs12010153

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Abstract

Snowoff (SO) date—defined as the last day of observed seasonal snow cover—is an important governor of ecologic and hydrologic processes across Alaska and Arctic-Boreal landscapes; however, our understanding and capacity for the monitoring of spatial and temporal variability in the SO date is still lacking. In this study, we present a 6.25 km spatially gridded passive microwave (PMW) SO data record, complimenting current Alaskan SO records from Moderate Resolution Imaging Spectrometer (MODIS) and Landsat, but extending the SO record an additional 13 years. The PMW SO record was validated against in situ snow depth observations and showed favorable accuracy (0.66–0.92 mean correlations; 2–10 day mean absolute errors) for the major climate regions of Alaska. The PMW SO results were also within 10 days of finer spatial scale SO observational records, including Interactive Multisensor Snow and Ice Mapping System (IMS), MODIS, and Landsat, for a majority (75%) of Alaska. However, the PMW record showed a general SO delay at higher elevations and across the Alaska North Slope, and earlier SO in the Alaska interior and southwest regions relative to the other SO records. Overall, we assign an uncertainty +/−11 days to the PMW SO. The PMW SO record benefits from the near-daily temporal fidelity of underlying brightness temperature (Tb) observations and reveals a mean regional trend in earlier SO timing (−0.39 days yr−1), while significant (p < 0.1) SO trend areas encompassed 11% of the Alaska domain and ranged from −0.11 days yr−1 to −1.31 days yr−1 over the 29-year satellite record. The observed SO dates also showed anomalous early SO dates during markedly warm years. Our results clarify the pattern and rate of SO changes across Alaska, which are interactive with global warming and contributing to widespread permafrost degradation, changes in regional hydrology, ecosystems, and associated services. Our results also provide a robust means for SO monitoring from satellite PMW observations with similar precision as more traditional and finer scale observations.

Highlights

Snow is a defining regulator of the global energy budget and ecosystem function, governing both ecologic and hydrologic responses to climate variability
The mean absolute error (MAE) differences between the geospatial SO records and corresponding SO estimates defined from the in situ Snow Telemetry (SNOTEL) site measurements are presented in Figure 4, and stratified according to the corresponding fractional water inundation (FW), fractional forest cover (FF), and topographic complexity (TC) levels represented for each site location
The SO date is an important governor of ecologic and hydrologic processes across Alaska and Arctic-Boreal landscapes, yet a better understanding of regional patterns and trends in SO timing has been limited by a regionally sparse in situ observation network and uncertainties in geospatial SO records derived from available satellite observations and models

Summary

Introduction

Snow is a defining regulator of the global energy budget and ecosystem function, governing both ecologic and hydrologic responses to climate variability. Snow cover assessments can be obtained from models, in situ observations, remote sensing, or a combination of these. In Alaska, snow has been modeled using climate data products including the Parameter Elevation Relationships on Independent Slope Model (PRISM) and the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) [13,14]. These products are often limited by coarse spatial resolution (1–50 km) and/or limited spatial coverage. Remote sensing offers a remedy to the limitations of both modelled and in situ snow observations for regional assessment and monitoring of snow conditions. Each sensor type is sensitive to different snow properties, while providing variable sampling footprints, accuracy, and spatial and temporal coverage

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