Simulations and Prediction of Historical and Future Maximum Freeze Depth in the Northern Hemisphere
Academic Article
Overview
Research
Identity
Additional Document Info
Other
View All
Overview
abstract
AbstractThe maximum annual freeze depth (MFD) is a primary indicator of the thermal state of frozen ground, affecting ecosystems, hydrological processes, vegetation growth, infrastructure, and human activities in cold regions. It is thus important to quantify the past, present, and future spatial and temporal variability of MFD at the hemispheric scale. We develop a datadriven MFD simulation method within a machine learning framework, integrating MFD observations from meteorological stations and several environmental predictors, to analyze past and future scenarios in the Northern Hemisphere (NH). Based on ERA5 reanalysis estimates and historical to future CMIP6 scenarios, the NH MFD averaged 133cm (ERA5) and 131cm (CMIP6) during 19812010, and will vary 81112cm during 20152100 depending on the emission scenario. During 19502013, MFD decreased by 0.37cm/a (ERA5) versus 0.22cm/a (CMIP6), and is projected to decrease 0.160.69cm/a by 2100. During 19812010, MFD decreased by an average of 19.1% (ERA5) and 13.9% (CMIP6), with a net change of 17cm (ERA5) and 13cm (CMIP6). Depending on the emission scenario, MFD will decrease 11% (12cm) to 42% (19cm) between 2015 and 2099 relative to the 19812010. Warming, increased moisture, warmer cold seasons, warmer warm seasons, shallower snow depths, and increased vegetation cover all lead to a reduction in MFD. The results from this novel machine learning approach provide useful insights regarding the fate of future frozen ground changes.
