Determining TTOP model parameter importance and overall performance across northern Canada

Abstract. Modelling current permafrost distribution and response to a warming climate depends on understanding which factors most strongly control ground temperatures. The Temperature at the Top of Permafrost (TTOP) model provides a simple, widely used framework for estimating permafrost presence and thermal state, yet its sensitivity to key parameters remains poorly quantified across diverse northern environments. This study evaluates the relative influence of TTOP model parameters using ground and air temperature data from 330 sites across northern Canada. A leave – one – out cross-validation approach combined with random forest analysis was used to assess both model sensitivity and variable importance. Results show that TTOP performance is dominated by freezing-season conditions—particularly the freezing n-factor and freezing degree days—while thaw-season parameters exert less control. Sensitivity patterns vary by region, with thawing parameters becoming more influential where the duration of the freezing and thawing seasons is similar. Machine-learning results highlight the additional importance of thermal offset and mean surface temperatures, emphasizing the importance of substrate properties. While the model generally reproduces observed ground temperatures well, parameters derived from landcover classes were not transferable between sites, underscoring the importance of locally calibrated inputs. Overall, this study clarifies how different climatic and environmental factors shape the accuracy of permafrost temperature modelling and provides practical guidance for improving parameterization in regional and global permafrost models.