Abstract

Abstract. Frozen soil processes are of great importance in controlling surface water and energy balances during the cold season and in cold regions. Over recent decades, considerable frozen soil degradation and surface soil warming have been reported over the Tibetan Plateau and North China, but most land surface models have difficulty in capturing the freeze–thaw cycle, and few validations focus on the effects of frozen soil processes on soil thermal characteristics in these regions. This paper addresses these issues by introducing a physically more realistic and computationally more stable and efficient frozen soil module (FSM) into a land surface model – the third-generation Simplified Simple Biosphere Model (SSiB3-FSM). To overcome the difficulties in achieving stable numerical solutions for frozen soil, a new semi-implicit scheme and a physics-based freezing–thawing scheme were applied to solve the governing equations. The performance of this model as well as the effects of frozen soil process on the soil temperature profile and soil thermal characteristics were investigated over the Tibetan Plateau and North China using observation sites from the China Meteorological Administration and models from 1981 to 2005. Results show that the SSiB3 model with the FSM produces a more realistic soil temperature profile and its seasonal variation than that without FSM during the freezing and thawing periods. The freezing process in soil delays the winter cooling, while the thawing process delays the summer warming. The time lag and amplitude damping of temperature become more pronounced with increasing depth. These processes are well simulated in SSiB3-FSM. The freeze–thaw processes could increase the simulated phase lag days and land memory at different soil depths as well as the soil memory change with the soil thickness. Furthermore, compared with observations, SSiB3-FSM produces a realistic change in maximum frozen soil depth at decadal scales. This study shows that the soil thermal characteristics at seasonal to decadal scales over frozen ground can be greatly improved in SSiB3-FSM, and SSiB3-FSM can be used as an effective model for TP and NC simulation during cold season. Overall, this study could help understand the vertical soil thermal characteristics over the frozen ground and provide an important scientific basis for land–atmosphere interactions.

Highlights

  • The freeze–thaw process affects the surface thermal characteristics of frozen soil

  • By using one host-biophysical model (SSiB3) with freeze–thaw processes in multi-layer soil (SSiB3-frozen soil module (FSM)) and comparing its simulated results with observation data as well as the SSiB3 results, this study focused on the soil temperature profile during freezing and thawing periods, the change in annual freeze soil depth, and its memory capability during the past decades, to investigate the effects of frozen soil process on these soil thermal characteristics

  • To improve the accuracy of soil temperature simulation in frozen ground, a multi-layer FSM was incorporated into SSiB3 to represent the freezing–thawing process and the heat and water transfer in a multi-layer frozen soil

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Summary

Introduction

The freeze–thaw process affects the surface thermal characteristics of frozen soil. At short timescales, the freeze–thaw process could delay the winter cooling/spring warming in the frozen soil because of the latent heat received/released through liquid–ice phase change, which affects surface hydrology (Poutou et al, 2004; Li et al, 2010; Bao et al, 2016). The SSiB3 model (Xue et al, 1991; Sun and Xue, 2001) substantially enhances the previous model’s ability to simulate cold season temperature dynamics (Xue et al, 2003). It only predicts temperatures of the near-surface soil layer (Tgs) and deep-soil layer (Td) based on the force-restore method (Deardorff, 1972; Xue et al, 1996). It is necessary to introduce a multi-layer frozen soil module based on physical processes into SSiB3 for more realistic cold season/region research under the climate change scenarios. A comprehensive multi-layer FSM (Zhang et al, 2007; Li et al, 2010), which takes into account the interactions be-

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