Abstract
Cycles of freeze–thaw (FT) are among the key landscape processes in cold regions. Under current global warming, understanding the alterations in FT characteristics is of a great importance for advising land management strategies in northern latitudes. Using a generic statistical approach, we address the impacts of compound changes in air temperature and snow depth on FT responses across Québec, a Canadian province ~ 2.5 times larger than France. Our findings show significant and complex responses of landscape FT to compound changes in temperature and snow depth. We note a vivid spatial divide between northern and southern regions and point to the asymmetric and nonlinear nature of the FT response. In general, the response of FT characteristics is amplified under compound warming compared to cooling conditions. In addition, FT responses include nonlinearity, meaning that compounding changes in temperature and snow depth have more severe impacts compared to the cumulative response of each individually. These asymmetric and nonlinear responses have important implications for the future environment and socio-economic management in a thawing Québec and highlight the complexity of landscape responses to climatic changes in cold regions.
Highlights
GMFD dataset is constructed by blending the reanalysis data from the National Centers for Environmental Prediction, National Center for Atmospheric Research with a group of recent global observation-based data[37]
Snow depth data are obtained from the Canadian Meteorological Center (CMC; https://doi.org/10.5067/W9FOYWH0EQZ3)
CMC dataset is constructed by combining the information from in-situ snow depth measurements with optimal interpolation results of a simple physical snow accumulation and melt model[38]
Summary
We use the global landscape FT Earth System Data Record (FT-ESDR) from the National. This dataset includes the daily state of soil at the spatial resolution of 25 × 25 km[2] over the period of 1979–2017. Knowing the gridded daily states of FT, two critical FT characteristics, namely FDyear and FTDDJF, can be extracted at the grid scale or each ecozone, and paired with corresponding gridded temperature and snow depth data. We use the Global Meteorological Forcing Dataset (GMFD) provided by Princeton University available at https://hydrology.princeton.edu/data.pgf.php. GMFD provides daily maximum and minimum air temperature at 0.25° × 0.25° for the period of 1948–2016. Snow depth data are obtained from the Canadian Meteorological Center (CMC; https://doi.org/10.5067/W9FOYWH0EQZ3). The data is provided for the period of 1998–2020 at 24 × 24 km[2] across the northern hemisphere
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