Compound changes in temperature and snow depth lead to asymmetric and nonlinear responses in landscape freeze–thaw

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.

Cold region data accessibility portal for Québec (CRDAP-QC): An integrated, multi-variable and multi-scale data repository for studying cold-region hydrological processes in Québec

“…ERA-Interim [7] ), and/or other data variables (e.g. snow water equivalent) could have been included in the portal, the backbone data of CRDAP-QC are limited to the data support used in a number of recent contributions on the FT characteristics in Québec and their variations under changing climate conditions ( [2] , [3] , [4] , [5] ; see the information about the related data article in the specification table above).…”

“…Using the Comparison Tool, the influence of snow depth on FT characteristics can be revealed. Compound impacts on temperature and snow depth in FT characteristics are discussed in details in [5] . By integrating climate, snow and FT data over various spatial scales along with other relevant data such as elevation, as well as location of climate and hydrometric stations, CRDAP-QC can be used for land and water managers in Québec for supporting design, assessment and operational studies.…”

“…Terra and Aqua; and (c) satellite-based FT data, including Number of Frozen Days, Number of Thawed Days and Number of Transitional Days. These variables include the data support for a series of recent publications about Freeze-Thaw characteristics and their dependence with climate and snow variables [2] , [3] , [4] , [5] . The sources of raw data for each of these DATA variables are indicated in the Specification Table.…”

Long‐Term Simulation of Snow Cover and Its Potential Impacts on Seasonal Frost Dynamics in Croplands Across Southern Canada

“…It has been hypothesized that the frequency, duration, and intensity of soil freezing in some cold regions might intensify due to lesser snowfall induced by warming winter (Campbell et al, 2010;Groffman et al, 2001;Henry, 2007;Iwata et al, 2010;Kaczmarek et al, 2021;Zhang, 2005). Multiple types of approaches have been carried out on the topic, including modeling studies: Henry (2008), Campbell et al (2010), He et al (2019), Hatami and Nazemi (2022), Zhu et al (2022), andVenalainen et al (2001); observational studies: Qian et al (2011); experimental studies: Iwata et al (2010), Decker et al (2003), Friesen et al (2021), and Hardy et al (2001). Henry (2008) and Campbell et al (2010) reported increasing soil freeze-thaw cycles under future climates with decreasing snow depth (d snow ) through physical and data-driven modeling.…”

“…• air to soil (Henry, 2008). On the other hand, Hatami and Nazemi (2022) demonstrated that the response of freeze-thaw characteristics combined (of increasing air temperature and decreasing snow depth simultaneously) includes non-linearity. This indicates combined changes in temperature and snow depth to freeze-thaw characteristics could be more intense than only considering the individual change in temperature or snow depth.…”