Studies indicate greenhouse gas emissions following permafrost thaw will amplify current rates of atmospheric warming, a process referred to as the permafrost carbon feedback. However, large uncertainties exist regarding the timing and magnitude of the permafrost carbon feedback, in part due to uncertainties associated with subsurface permafrost parameterization and structure. Development of robust parameter estimation methods for permafrost-rich soils is becoming urgent under accelerated warming of the Arctic. Improved parameterization of the subsurface properties in land system models would lead to improved predictions and a reduction of modeling uncertainty. In this work we set the groundwork for future parameter estimation (PE) studies by developing and evaluating a joint PE algorithm that estimates soil porosities and thermal conductivities from time series of soil temperature and moisture measurements and discrete in-time electrical resistivity measurements. The algorithm utilizes the Model-Independent Parameter Estimation and Uncertainty Analysis toolbox and coupled hydrological-thermal-geophysical modeling. We test the PE algorithm against synthetic data, providing a proof of concept for the approach. We use specified subsurface porosities and thermal conductivities and coupled models to set up a synthetic state, perturb the parameters, and then verify that our PE method is able to recover the parameters and synthetic state. To evaluate the accuracy and robustness of the approach we perform multiple tests for a perturbed set of initial starting parameter combinations. In addition, we varied types and quantities of data to better understand the optimal dataset needed to improve the PE method. The results of the PE tests suggest that using multiple types of data improve the overall robustness of the method. Our numerical experiments indicate that special care needs to be taken during the field experiment setup so that (1) the vertical distance between adjacent measurement sensors allows the signal variability in space to be resolved and (2) the longer time interval between resistivity snapshots allows signal variability in time to be resolved.

Recent warming in the Arctic, which has been amplified during the winter^1-3, greatly enhances microbial decomposition of soil organic matter and subsequent release of carbon dioxide (CO₂)⁴. However, the amount of CO₂ released in winter is highly uncertain and has not been well represented by ecosystem models or by empirically-based estimates^5,6. Here we synthesize regional in situ observations of CO₂ flux from arctic and boreal soils to assess current and future winter carbon losses from the northern permafrost domain. We estimate a contemporary loss of 1662 Tg C yr^-1 from the permafrost region during the winter season (October through April). This loss is greater than the average growing season carbon uptake for this region estimated from process models (-1032 Tg C yr^-1). Extending model predictions to warmer conditions in 2100 indicates that winter CO₂ emissions will increase 17% under a moderate mitigation scenario-Representative Concentration Pathway (RCP) 4.5-and 41% under business-as-usual emissions scenario-RCP 8.5. Our results provide a new baseline for winter CO₂ emissions from northern terrestrial regions and indicate that enhanced soil CO₂ loss due to winter warming may offset growing season carbon uptake under future climatic conditions.