An improved method for calculating the freezing/thawing index using monthly and annual temperature data

Abstract

Changes in soil thermal regimes in cold climates have widespread impacts on hydrology, ecology, and the carbon cycle. The annual freezing and thawing index, which is generally calculated using daily temperature, has been widely used to estimate the freezing depth, active layer thickness, and the distribution of permafrost. However, continuous and reliable daily temperature data are scarce in cold climates, while monthly and annual temperature data are more readily available. If daily temperature data are unavailable, these indices can be estimated based on monthly or annual temperature data. In this study, we developed a resampling method for estimating the annual freezing and thawing index and compared the results with those produced by the existing methods. Daily temperature data with a 0.5° resolution over the Northern Hemisphere during 1901–2012 were used to calculate the freezing/thawing index, and then the monthly and annual temperature were calculated and three different approaches were used to estimate the daily temperature and the freezing/thawing index. When the monthly data were used, the resampling method produced the smallest relative error (RE) and mean bias error (MBE), and the largest correlation in estimating the two indices, compared to the two other methods. Although the annual temperature data usually underestimate the freezing/thawing index, the RE is still <5% over most of the high‐latitude regions. The results suggest that if the daily temperature can be reliably estimated using the resampling method, the thermal regimes of permafrost can be reliably estimated using modelled monthly temperature and/or reconstructed past monthly/annual temperature. These estimations can also be used to validate modelled paleo‐permafrost and its variations. Additionally, our results indicate that after the 1970s the annual freezing index (DDF) increased substantially, while the frost index (FI) decreased substantially.

Predict Seasonal Maximum Freezing Depth Changes Using Machine Learning in China over the Last 50 Years

“…The freezing depth can be determined using direct observations [14], empirical calculations [15], and methods based on physical models with computer-aided simulations [16] or probability models [17,18]. Direct observations automatically collected by meteorological stations are the most direct and effective way to obtain the freezing depth [14].…”

Simulation of frozen ground distribution in northeast China based on a surface frost number model

“…Permafrost refers to rock or soil that has been frozen continuously for two or more years. The formation and development of frozen ground is affected by many factors, with temperature being one of the most important (Zhang et al, 2008;Farbrot et al, 2013;Guo and Wang, 2016;Liu et al, 2021a). The assessment report of the sixth meeting of the Intergovernmental Panel on Climate Change (IPCC) pointed out that the global average temperature has been rising continuously over the last 100 years and is 1℃ higher than 100 years ago (Zhou, 2021).…”