首页|Findings from Lanzhou University Broaden Understanding of Machine Learning (Simulations and Prediction of Historical and Future Maximum Freeze Depth In the Northern Hemisphere)
Findings from Lanzhou University Broaden Understanding of Machine Learning (Simulations and Prediction of Historical and Future Maximum Freeze Depth In the Northern Hemisphere)
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NETL
NSTL
Amer Geophysical Union
A new study on Machine Learning is now available. According to news originating from Lanzhou, People’s Republic of China, by NewsRx correspondents, research stated, “The 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.” Funders for this research include National Natural Science Foundation of China (NSFC), Fundamental Research Funds for the Central Universities. Our news journalists obtained a quote from the research from Lanzhou University, “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 133 cm (ERA5) and 131 cm (CMIP6) during 1981-2010, and will vary 81-112 cm during 2015-2100 depending on the emission scenario. During 1950-2013, MFD decreased by 0.37 cm/a (ERA5) versus 0.22 cm/a (CMIP6), and is projected to decrease 0.16-0.69 cm/a by 2100. During 1981-2010, MFD decreased by an average of 19.1% (ERA5) and 13.9% (CMIP6), with a net change of -17 cm (ERA5) and -13 cm (CMIP6). Depending on the emission scenario, MFD will decrease 11% (-12 cm) to 42% (-19 cm) between 2015 and 2099 relative to the 1981-2010. 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. Seasonally frozen ground covers approximately half of the exposed ground surface in the Northern Hemisphere and is found in areas of intense human activity. There, the moisture retention and occurrence of freezing and thawing significantly impact agricultural production and infrastructure. Maximum freeze depth is a key indicator of the status of seasonally frozen ground. We simulate and predict the spatial distribution of maximum freeze depth at the hemispheric scale and quantify the variability of maximum freeze depth over past and future periods. Depending on the choice of future emission scenario, average maximum freeze depth in the Northern Hemisphere will decrease by 11% (-12 cm) to 42% (-19 cm) between 2015 and 2099, relative to the base period (1981-2010).”
LanzhouPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningLanzhou University
NETL
NSTL
Amer Geophysical Union
